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<front>
<journal-meta>
<journal-id journal-id-type="publisher-id">Front. Nutr.</journal-id>
<journal-title>Frontiers in Nutrition</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Nutr.</abbrev-journal-title>
<issn pub-type="epub">2296-861X</issn>
<publisher>
<publisher-name>Frontiers Media S.A.</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="doi">10.3389/fnut.2024.1345570</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Nutrition</subject>
<subj-group>
<subject>Original Research</subject>
</subj-group>
</subj-group>
</article-categories>
<title-group>
<article-title>A novel nomogram integrating body composition and inflammatory-nutritional markers for predicting postoperative complications in patients with adhesive small bowel obstruction</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name><surname>Wang</surname> <given-names>Zhibo</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<uri xlink:href="https://loop.frontiersin.org/people/2589412/overview"/>
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</contrib>
<contrib contrib-type="author">
<name><surname>Sun</surname> <given-names>Baoying</given-names></name>
<xref ref-type="aff" rid="aff2"><sup>2</sup></xref>
<role content-type="https://credit.niso.org/contributor-roles/formal-analysis/"/>
<role content-type="https://credit.niso.org/contributor-roles/methodology/"/>
<role content-type="https://credit.niso.org/contributor-roles/writing-original-draft/"/>
</contrib>
<contrib contrib-type="author">
<name><surname>Yu</surname> <given-names>Yimiao</given-names></name>
<xref ref-type="aff" rid="aff3"><sup>3</sup></xref>
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<contrib contrib-type="author">
<name><surname>Liu</surname> <given-names>Jingnong</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<role content-type="https://credit.niso.org/contributor-roles/validation/"/>
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</contrib>
<contrib contrib-type="author">
<name><surname>Li</surname> <given-names>Duo</given-names></name>
<xref ref-type="aff" rid="aff4"><sup>4</sup></xref>
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<contrib contrib-type="author" corresp="yes">
<name><surname>Lu</surname> <given-names>Yun</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="corresp" rid="c001"><sup>&#x002A;</sup></xref>
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<contrib contrib-type="author" corresp="yes">
<name><surname>Liu</surname> <given-names>Ruiqing</given-names></name>
<xref ref-type="aff" rid="aff1"><sup>1</sup></xref>
<xref ref-type="aff" rid="aff4"><sup>4</sup></xref>
<xref ref-type="corresp" rid="c002"><sup>&#x002A;</sup></xref>
<uri xlink:href="https://loop.frontiersin.org/people/2589438/overview"/>
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<aff id="aff1"><sup>1</sup><institution>Department of Gastroenterological Surgery, The Affiliated Hospital of Qingdao University</institution>, <addr-line>Qingdao</addr-line>, <country>China</country></aff>
<aff id="aff2"><sup>2</sup><institution>Neurology Department, Central Hospital Affiliated to Shandong First Medical University</institution>, <addr-line>Jinan</addr-line>, <country>China</country></aff>
<aff id="aff3"><sup>3</sup><institution>Department of Radiation Oncology, The Affiliated Hospital of Qingdao University</institution>, <addr-line>Qingdao</addr-line>, <country>China</country></aff>
<aff id="aff4"><sup>4</sup><institution>Institute of Nutrition and Health, College of Public Health, Qingdao University</institution>, <addr-line>Qingdao</addr-line>, <country>China</country></aff>
<author-notes>
<fn fn-type="edited-by" id="fn0003">
<p>Edited by: Gabriela Villa&#x00E7;a Chaves, National Cancer Institute (INCA), Brazil</p>
</fn>
<fn fn-type="edited-by" id="fn0004">
<p>Reviewed by: Xiaobin Gu, First Affiliated Hospital of Zhengzhou University, China</p>
<p>Mingkun Zhao, Fudan University, China</p>
<p>Zhongheng Zhang, Sir Run Run Shaw Hospital, China</p>
</fn>
<corresp id="c001">&#x002A;Correspondence: Yun Lu, <email>luyun@qdu.edu.cn</email></corresp>
<corresp id="c002">Ruiqing Liu, <email>liuruiqing@qdu.edu.cn</email></corresp>
</author-notes>
<pub-date pub-type="epub">
<day>19</day>
<month>04</month>
<year>2024</year>
</pub-date>
<pub-date pub-type="collection">
<year>2024</year>
</pub-date>
<volume>11</volume>
<elocation-id>1345570</elocation-id>
<history>
<date date-type="received">
<day>28</day>
<month>11</month>
<year>2023</year>
</date>
<date date-type="accepted">
<day>08</day>
<month>04</month>
<year>2024</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#x00A9; 2024 Wang, Sun, Yu, Liu, Li, Lu and Liu.</copyright-statement>
<copyright-year>2024</copyright-year>
<copyright-holder>Wang, Sun, Yu, Liu, Li, Lu and Liu</copyright-holder>
<license xlink:href="http://creativecommons.org/licenses/by/4.0/">
<p>This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</p>
</license>
</permissions>
<abstract>
<sec id="sec1">
<title>Background</title>
<p>Postoperative complications in adhesive small bowel obstruction (ASBO) significantly escalate healthcare costs and prolong hospital stays. This study endeavors to construct a nomogram that synergizes computed tomography (CT) body composition data with inflammatory-nutritional markers to forecast postoperative complications in ASBO.</p>
</sec>
<sec id="sec2">
<title>Methods</title>
<p>The study&#x2019;s internal cohort consisted of 190 ASBO patients recruited from October 2017 to November 2021, subsequently partitioned into training (<italic>n</italic>&#x2009;=&#x2009;133) and internal validation (<italic>n</italic>&#x2009;=&#x2009;57) groups at a 7:3 ratio. An additional external cohort comprised 52 patients. Body composition assessments were conducted at the third lumbar vertebral level utilizing CT images. Baseline characteristics alongside systemic inflammatory responses were meticulously documented. Through univariable and multivariable regression analyses, risk factors pertinent to postoperative complications were identified, culminating in the creation of a predictive nomogram. The nomogram&#x2019;s precision was appraised using the concordance index (C-index) and the area under the receiver operating characteristic (ROC) curve.</p>
</sec>
<sec id="sec3">
<title>Results</title>
<p>Postoperative complications were observed in 65 (48.87%), 26 (45.61%), and 22 (42.31%) patients across the three cohorts, respectively. Multivariate analysis revealed that nutrition risk score (NRS), intestinal strangulation, skeletal muscle index (SMI), subcutaneous fat index (SFI), neutrophil-lymphocyte ratio (NLR), and lymphocyte-monocyte ratio (LMR) were independently predictive of postoperative complications. These preoperative indicators were integral to the nomogram&#x2019;s formulation. The model, amalgamating body composition and inflammatory-nutritional indices, demonstrated superior performance: the internal training set exhibited a 0.878 AUC (95% CI, 0.802&#x2013;0.954), 0.755 accuracy, and 0.625 sensitivity; the internal validation set displayed a 0.831 AUC (95% CI, 0.675&#x2013;0.986), 0.818 accuracy, and 0.812 sensitivity. In the external cohort, the model yielded an AUC of 0.886 (95% CI, 0.799&#x2013;0.974), 0.808 accuracy, and 0.909 sensitivity. Calibration curves affirmed a strong concordance between predicted outcomes and actual events. Decision curve analysis substantiated that the model could confer benefits on patients with ASBO.</p>
</sec>
<sec id="sec4">
<title>Conclusion</title>
<p>A rigorously developed and validated nomogram that incorporates body composition and inflammatory-nutritional indices proves to be a valuable tool for anticipating postoperative complications in ASBO patients, thus facilitating enhanced clinical decision-making.</p>
</sec>
</abstract>
<kwd-group>
<kwd>body composition</kwd>
<kwd>inflammatory-nutritional markers</kwd>
<kwd>adhesive small bowel obstruction</kwd>
<kwd>postoperative complications</kwd>
<kwd>prediction</kwd>
</kwd-group>
<contract-num rid="cn1">82000482</contract-num>
<contract-num rid="cn2">Y-NESTLE2022QN-0230</contract-num>
<contract-sponsor id="cn1">National Natural Science Foundation of China<named-content content-type="fundref-id">10.13039/501100001809</named-content></contract-sponsor>
<contract-sponsor id="cn2">Beijing Xisike Clinical Oncology Research Foundation</contract-sponsor>
<counts>
<fig-count count="5"/>
<table-count count="4"/>
<equation-count count="0"/>
<ref-count count="54"/>
<page-count count="12"/>
<word-count count="6939"/>
</counts>
<custom-meta-wrap>
<custom-meta>
<meta-name>section-at-acceptance</meta-name>
<meta-value>Clinical Nutrition</meta-value>
</custom-meta>
</custom-meta-wrap>
</article-meta>
</front>
<body>
<sec sec-type="intro" id="sec5">
<title>Introduction</title>
<p>Adhesive small bowel obstruction (ASBO) ranks as a leading cause of emergency hospital admissions and surgeries (<xref ref-type="bibr" rid="ref1">1</xref>). Despite advancements in surgical methods, treatment for ASBO patients may lead to extended hospital stays, increased healthcare costs, and notably high morbidity (48%) and mortality rates (5%) (<xref ref-type="bibr" rid="ref2">2</xref>, <xref ref-type="bibr" rid="ref3">3</xref>). The incidence of postoperative complications significantly impacts patients&#x2019; postoperative quality of life, an essential metric in evaluating therapeutic effectiveness. Clavien et al. introduced a surgical complications classification system that aids in accurately assessing outcomes across different treatment approaches (<xref ref-type="bibr" rid="ref4">4</xref>). While the American College of Surgeons National Surgical Quality Improvement Project (NSQIP) is a prevalent risk prediction tool, its complexity and potential inaccuracies render it less suitable for specific patient populations (<xref ref-type="bibr" rid="ref5">5</xref>).</p>
<p>To date, no universally accepted risk prediction system exists for ASBO patients. Developing a model to forecast postoperative complications and identify risk factors is crucial. Growing evidence suggests that the prognosis and progression of bowel obstruction are linked not only to bowel dysfunction but also to systemic inflammatory responses (<xref ref-type="bibr" rid="ref6">6</xref>&#x2013;<xref ref-type="bibr" rid="ref8">8</xref>). The persistent obstruction leads to digestive tract dilation, intestinal barrier compromise, microbial translocation, and infiltration of inflammatory cells like neutrophils, lymphocytes, platelets, and monocytes, indicative of inflammatory responses in clinical settings (<xref ref-type="bibr" rid="ref9">9</xref>, <xref ref-type="bibr" rid="ref10">10</xref>). Recent research has explored the connection between patient outcomes and various inflammatory-nutritional scores in small bowel obstruction cases (<xref ref-type="bibr" rid="ref11">11</xref>, <xref ref-type="bibr" rid="ref12">12</xref>). These studies have examined scores such as the neutrophil&#x2013;lymphocyte ratio (NLR), platelet&#x2013;lymphocyte ratio (PLR), monocyte&#x2013;lymphocyte ratio (MLR), the albumin&#x2013;alkaline phosphatase ratio (ALP), systemic immune-inflammation index (SII), and prognostic nutritional index (PNI). These calculated indicators, including NLR and PLR, have proven more sensitive than singular hematological markers like C-reactive protein (CRP) or lymphocyte count in reflecting the inflammatory response and predicting disease progression (<xref ref-type="bibr" rid="ref13">13</xref>).</p>
<p>Nutritional status is a critical determinant for ASBO, influencing disease progression and patient prognosis (<xref ref-type="bibr" rid="ref14">14</xref>, <xref ref-type="bibr" rid="ref15">15</xref>). Lee et al. demonstrated that nutritional data, such as body mass index (BMI) and weight loss, are correlated with an increased risk of major complications in ASBO patients (<xref ref-type="bibr" rid="ref16">16</xref>). However, this study did not provide detailed quantitative insights into nutritional status. The widespread adoption of computed tomography (CT) has advanced body composition research, offering more granular insights than traditional metrics like BMI and weight fluctuation (<xref ref-type="bibr" rid="ref17">17</xref>). CT-based multiple body composition parameters are usually obtained from the images at the level of third lumbar vertebra (L3), which focus on skeletal muscle and adipose tissue (<xref ref-type="bibr" rid="ref18">18</xref>). These parameters offer superior informativeness in defining nutrition related disorders such as sarcopenia, visceral obesity and sarcopenic obesity, and are associated with adverse outcomes in several gastrointestinal diseases (<xref ref-type="bibr" rid="ref19">19</xref>, <xref ref-type="bibr" rid="ref20">20</xref>).</p>
<p>Despite numerous studies exploring the links between individual nutritional and inflammatory markers and surgical outcomes, comprehensive research integrating laboratory and CT-derived body composition data for ASBO patients is scant. We conducted a systematic and thorough collection of body composition and systemic inflammatory markers (NLR, PLR, LMR, SII, PNI) data to explore their associations. Furthermore, a nomogram was developed to ascertain their predictive capacity for postoperative complications in ASBO patients.</p>
</sec>
<sec sec-type="materials|methods" id="sec6">
<title>Materials and methods</title>
<sec id="sec7">
<title>Patients</title>
<p>This study adheres to the principles of the Declaration of Helsinki. We retrospectively reviewed cases of ASBO from October 2017 to November 2021, utilizing our center&#x2019;s clinicopathologic database. The inclusion criteria were: (1) diagnosis of ASBO based on clinical or radiological evidence; (2) undergoing emergent surgery due to ASBO; (3) availability of abdominal CT scans and comprehensive hematological indices during hospitalization preoperatively. Exclusion criteria included: (1) conditions that could affect peripheral blood cell counts, such as autoimmune diseases, leukemia, and other hematological malignancies; (2) small bowel obstruction due to primary tumors, hernias, or inflammatory bowel disease; (3) lack of complete clinical data; and (4) age under 18&#x2009;years. The participants were subsequently divided into training (<italic>n</italic>&#x2009;=&#x2009;133) and internal validation (<italic>n</italic>&#x2009;=&#x2009;57) cohorts at a 7:3 ratio. A total of 52 patients were enrolled in the external validation cohort from the Central Hospital Affiliated to Shandong First Medical University between January 2022 and January 2023. <xref ref-type="fig" rid="fig1">Figure 1</xref> illustrates the flowchart of patient selection.</p>
<fig position="float" id="fig1">
<label>Figure 1</label>
<caption>
<p>Flowchart for selecting the study population.</p>
</caption>
<graphic xlink:href="fnut-11-1345570-g001.tif"/>
</fig>
</sec>
<sec id="sec8">
<title>Date collection</title>
<p>This retrospective study extracted basic clinical data, including age, sex, BMI, symptoms, comorbidities, nutritional risk score (NRS), American Society of Anesthesiologists (ASA) score, intraoperative findings, and related laboratory indicators from the de-identified database and electronic medical record system. Inflammatory-nutritional markers were determined as follows: NLR&#x2009;=&#x2009;N/L, PLR&#x2009;=&#x2009;P/L, LMR&#x2009;=&#x2009;L/M; SII=P&#x2009;&#x00D7;&#x2009;N/L; PNI&#x2009;=&#x2009;albumin (g/L) +5&#x2009;&#x00D7;&#x2009;L (109/L), where N: neutrophil count, L: lymphocyte [109]/L; P: platelet count, M: monocyte count (<xref ref-type="bibr" rid="ref21">21</xref>, <xref ref-type="bibr" rid="ref22">22</xref>).</p>
</sec>
<sec id="sec9">
<title>Evaluation of CT-based body composition</title>
<p>Using the institutional PACS (Picture Archiving and Communication System), postoperative L3 CT images were obtained for each patient. Slicer O Matic software (version 5.0)<xref ref-type="fn" rid="fn0001"><sup>1</sup></xref> was used for assessing body composition. The CT Hounsfield units (HU) thresholds were set at &#x2212;190 to &#x2212;30 for intermuscular and subcutaneous adipose tissue, &#x2212;150 to &#x2212;50 for visceral adipose tissue, and &#x2212;29 to +150 for skeletal muscle area (<xref ref-type="bibr" rid="ref23">23</xref>). The evaluation areas were delineated by two experienced radiologists who were blinded to the clinical characteristics of the patients. The body composition indexes (cm<sup>2</sup>/m<sup>2</sup>), including skeletal muscle index (SMI), subcutaneous fat index (SFI), visceral fat index (VFI), and intermuscular adipose tissue index (IFI), were defined as the body composition area (cm<sup>2</sup>) by height squared (m<sup>2</sup>). <xref ref-type="fig" rid="fig2">Figure 2</xref> presents a schematic diagram of the study workflow.</p>
<fig position="float" id="fig2">
<label>Figure 2</label>
<caption>
<p>The process of analyzing inflammatory-nutritional markers, from data collection to model creation, involves: <bold>(A)</bold> blood test, <bold>(B)</bold> body composition, <bold>(C)</bold> risk assessment, and <bold>(D)</bold> model evaluation.</p>
</caption>
<graphic xlink:href="fnut-11-1345570-g002.tif"/>
</fig>
</sec>
<sec id="sec10">
<title>Definitions of postoperative complications</title>
<p>Postoperative complications were classified according to the Clavien&#x2013;Dindo classification (<xref ref-type="bibr" rid="ref4">4</xref>). Our analysis focused on complications that occurred within 1 month after the surgical procedure. In cases where a patient experienced multiple complications either simultaneously or sequentially, the most severe complication was selected as the primary outcome for this study.</p>
</sec>
<sec id="sec11">
<title>Statistical analysis</title>
<p>Statistical analysis was conducted using R software version 3.6.3<xref ref-type="fn" rid="fn0002"><sup>2</sup></xref> and SPSS version 25.0. We utilized the Kolmogorov&#x2013;Smirnov test to assess the normal distribution of texture features. Intergroup categorical variables were examined using Fisher&#x2019;s exact tests and Chi-square tests, while independent-sample <italic>t</italic>-tests were applied for continuous variables. The &#x201C;rms&#x201D; R package facilitated the generation of ROC curves, areas under the curves (AUCs), a nomogram, and corresponding calibration curves (<xref ref-type="bibr" rid="ref24">24</xref>, <xref ref-type="bibr" rid="ref25">25</xref>). The &#x201C;rmda&#x201D; package was employed for decision curve analysis (DCA) (<xref ref-type="bibr" rid="ref26">26</xref>). A <italic>p</italic>-value of less than 0.05 was considered statistically significant.</p>
</sec>
</sec>
<sec sec-type="results" id="sec12">
<title>Results</title>
<sec id="sec13">
<title>Characteristics of enrolled patients</title>
<p>In total, 190 patients with ASBO were included in the study (96 men and 94 women; average age 62.48&#x2009;&#x00B1;&#x2009;13.50&#x2009;years). They were randomized into training (<italic>n</italic>&#x2009;=&#x2009;133) and internal validation (<italic>n</italic> =&#x2009;57) cohorts at a 7:3 ratio. An external validation cohort comprised 52 patients from another center. Basic characteristics of the three cohorts are presented in <xref ref-type="table" rid="tab1">Table 1</xref>. In the training cohort, 65 patients (48.87%; 35 men and 30 women; average age 63.20&#x2009;&#x00B1;&#x2009;13.73&#x2009;years) experienced complications, compared to 26 patients (45.61%; 15 men and 11 women; average age 67.46&#x2009;&#x00B1;&#x2009;15.85&#x2009;years) in the internal validation cohort. The external validation cohort included 22 patients (42.31%; 12 men and 10 women; average age 61.23&#x2009;&#x00B1;&#x2009;10.70&#x2009;years) with complications. Factors such as preoperative infection, ASA score, NRS, intestinal strangulation, CRP, NLR, PLR, LMR, SII, PNI, SMI, and SFI showed a significant correlation with postoperative complications in the training set (<italic>p</italic>&#x2009;&#x003C;&#x2009;0.05).</p>
<table-wrap position="float" id="tab1">
<label>Table 1</label>
<caption>
<p>Clinical characteristics of patients in this study.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th/>
<th align="center" valign="top" colspan="2">Training set</th>
<th align="center" valign="top"><italic>p</italic> value</th>
<th align="center" valign="top" colspan="2">Internal validation set</th>
<th align="center" valign="top"><italic>p</italic> value</th>
<th align="center" valign="top" colspan="2">External validation set</th>
<th align="center" valign="top"><italic>p</italic> value</th>
</tr>
<tr>
<th/>
<th align="center" valign="top">Complications group (<italic>n</italic>&#x2009;=&#x2009;65)</th>
<th align="center" valign="top">Non-complications group (<italic>n</italic>&#x2009;=&#x2009;68)</th>
<th/>
<th align="center" valign="top">Complications group (<italic>n</italic>&#x2009;=&#x2009;26)</th>
<th align="center" valign="top">Non-complications group (<italic>n</italic>&#x2009;=&#x2009;31)</th>
<th/>
<th align="center" valign="top">Complications group (<italic>n</italic>&#x2009;=&#x2009;22)</th>
<th align="center" valign="top">Non-complications group (<italic>n</italic>&#x2009;=&#x2009;30)</th>
<th/>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="top">Age (years), mean (SD)</td>
<td align="char" valign="top" char="(">63.20 (13.73)</td>
<td align="char" valign="top" char="(">60.93 (12.48)</td>
<td align="char" valign="top" char=".">0.319</td>
<td align="char" valign="top" char="(">67.46 (15.85)</td>
<td align="char" valign="top" char="(">60.23 (12.46)</td>
<td align="char" valign="top" char=".">0.059</td>
<td align="char" valign="top" char="(">61.23 (10.70)</td>
<td align="char" valign="top" char="(">59.37 (11.61)</td>
<td align="char" valign="top" char=".">0.558</td>
</tr>
<tr>
<td align="left" valign="top">Gender, <italic>n</italic> (%)</td>
<td/>
<td/>
<td align="char" valign="top" char=".">0.300</td>
<td/>
<td/>
<td align="char" valign="top" char=".">0.790</td>
<td/>
<td/>
<td align="char" valign="top" char=".">0.575</td>
</tr>
<tr>
<td align="left" valign="top">Male</td>
<td align="char" valign="top" char="(">35 (53.85%)</td>
<td align="char" valign="top" char="(">30 (44.12%)</td>
<td/>
<td align="char" valign="top" char="(">15 (57.69%)</td>
<td align="char" valign="top" char="(">16 (51.61%)</td>
<td/>
<td align="char" valign="top" char="(">12 (54.55%)</td>
<td align="char" valign="top" char="(">13 (43.33%)</td>
<td/>
</tr>
<tr>
<td align="left" valign="top">Female</td>
<td align="char" valign="top" char="(">30 (46.15%)</td>
<td align="char" valign="top" char="(">38 (55.88%)</td>
<td/>
<td align="char" valign="top" char="(">11 (42.31%)</td>
<td align="char" valign="top" char="(">15 (48.39%)</td>
<td/>
<td align="char" valign="top" char="(">10 (45.45%)</td>
<td align="char" valign="top" char="(">17 (56.67%)</td>
<td/>
</tr>
<tr>
<td align="left" valign="top">BMI (kg/m<sup>2</sup>), mean (SD)</td>
<td align="char" valign="top" char="(">20.97 (3.48)</td>
<td align="char" valign="top" char="(">22.01 (3.39)</td>
<td align="char" valign="top" char=".">0.122</td>
<td align="char" valign="top" char="(">21.86 (3.69)</td>
<td align="char" valign="top" char="(">21.54 (3.25)</td>
<td align="char" valign="top" char=".">0.745</td>
<td align="char" valign="top" char="(">21.81 (3.83)</td>
<td align="char" valign="top" char="(">22.11 (2.95)</td>
<td align="char" valign="top" char=".">0.779</td>
</tr>
<tr>
<td align="left" valign="top" char="." colspan="10"><bold>Manifestations</bold></td>
</tr>
<tr>
<td align="left" valign="top">Obstruction time (d), mean (SD)</td>
<td align="char" valign="top" char="(">7.32 (9.67)</td>
<td align="char" valign="top" char="(">9.33 (12.95)</td>
<td align="char" valign="top" char=".">0.313</td>
<td align="char" valign="top" char="(">5.77 (6.25)</td>
<td align="char" valign="top" char="(">6.39 (6.66)</td>
<td align="char" valign="top" char=".">0.724</td>
<td align="char" valign="top" char="(">5.68 (6.64)</td>
<td align="char" valign="top" char="(">5.13 (5.79)</td>
<td align="char" valign="top" char=".">0.752</td>
</tr>
<tr>
<td align="left" valign="top">Vomit, <italic>n</italic> (%)</td>
<td align="char" valign="top" char="(">40 (61.54%)</td>
<td align="char" valign="top" char="(">47 (69.12%)</td>
<td align="char" valign="top" char=".">0.369</td>
<td align="char" valign="top" char="(">14 (53.85%)</td>
<td align="char" valign="top" char="(">20 (64.52%)</td>
<td align="char" valign="top" char=".">0.432</td>
<td align="char" valign="top" char="(">18 (81.82%)</td>
<td align="char" valign="top" char="(">20 (66.67%)</td>
<td align="char" valign="top" char=".">0.344</td>
</tr>
<tr>
<td align="left" valign="top">Abdominal pain, <italic>n</italic> (%)</td>
<td align="char" valign="top" char="(">58 (89.23%)</td>
<td align="char" valign="top" char="(">66 (97.06%)</td>
<td align="char" valign="top" char=".">0.092</td>
<td align="char" valign="top" char="(">23 (88.46%)</td>
<td align="char" valign="top" char="(">30 (96.77%)</td>
<td align="char" valign="top" char=".">0.322</td>
<td align="char" valign="top" char="(">22 (100%)</td>
<td align="char" valign="top" char="(">28 (93.33%)</td>
<td align="char" valign="top" char=".">0.502</td>
</tr>
<tr>
<td align="left" valign="top">Abdominal distention, <italic>n</italic> (%)</td>
<td align="char" valign="top" char="(">53 (81.54%)</td>
<td align="char" valign="top" char="(">50 (73.53%)</td>
<td align="char" valign="top" char=".">0.304</td>
<td align="char" valign="top" char="(">20 (76.92%)</td>
<td align="char" valign="top" char="(">25 (80.65%)</td>
<td align="char" valign="top" char=".">0.755</td>
<td align="char" valign="top" char="(">16 (72.73%)</td>
<td align="char" valign="top" char="(">18 (60%)</td>
<td align="char" valign="top" char=".">0.390</td>
</tr>
<tr>
<td align="left" valign="top">No exhaust or defecation, <italic>n</italic> (%)</td>
<td align="char" valign="top" char="(">36 (55.38%)</td>
<td align="char" valign="top" char="(">33 (48.53%)</td>
<td align="char" valign="top" char=".">0.489</td>
<td align="char" valign="top" char="(">18 (69.23%)</td>
<td align="char" valign="top" char="(">16 (51.61%)</td>
<td align="char" valign="top" char=".">0.278</td>
<td align="char" valign="top" char="(">12 (54.55%)</td>
<td align="char" valign="top" char="(">11 (36.67%)</td>
<td align="char" valign="top" char=".">0.262</td>
</tr>
<tr>
<td align="left" valign="top" char="." colspan="10"><bold>Commodities</bold></td>
</tr>
<tr>
<td align="left" valign="top">Hypertension, <italic>n</italic> (%)</td>
<td align="char" valign="top" char="(">13 (20.00%)</td>
<td align="char" valign="top" char="(">18 (26.47%)</td>
<td align="char" valign="top" char=".">0.417</td>
<td align="char" valign="top" char="(">7 (26.92%)</td>
<td align="char" valign="top" char="(">6 (19.35%)</td>
<td align="char" valign="top" char=".">0.541</td>
<td align="char" valign="top" char="(">7 (31.82%)</td>
<td align="char" valign="top" char="(">8 (26.67%)</td>
<td align="char" valign="top" char=".">0.762</td>
</tr>
<tr>
<td align="left" valign="top">Diabetes mellitus, <italic>n</italic> (%)</td>
<td align="char" valign="top" char="(">7 (10.77%)</td>
<td align="char" valign="top" char="(">4 (5.88%)</td>
<td align="char" valign="top" char=".">0.358</td>
<td align="char" valign="top" char="(">5 (19.23%)</td>
<td align="char" valign="top" char="(">3 (9.68%)</td>
<td align="char" valign="top" char=".">0.448</td>
<td align="char" valign="top" char="(">1 (4.55)</td>
<td align="char" valign="top" char="(">2 (6.67%)</td>
<td align="char" valign="top" char=".">0.999</td>
</tr>
<tr>
<td align="left" valign="top">Coronary disease, <italic>n</italic> (%)</td>
<td align="char" valign="top" char="(">4 (6.15%)</td>
<td align="char" valign="top" char="(">4 (5.88%)</td>
<td align="char" valign="top" char=".">0.999</td>
<td align="char" valign="top" char="(">5 (19.23%)</td>
<td align="char" valign="top" char="(">2 (6.45%)</td>
<td align="char" valign="top" char=".">0.228</td>
<td align="char" valign="top" char="(">3 (13.64%)</td>
<td align="char" valign="top" char="(">2 (6.67%)</td>
<td align="char" valign="top" char=".">0.639</td>
</tr>
<tr>
<td align="left" valign="top">Preoperative infection, <italic>n</italic> (%)</td>
<td align="char" valign="top" char="(">10 (15.38%)</td>
<td align="char" valign="top" char="(">2 (2.94%)</td>
<td align="char" valign="top" char=".">0.015<sup>&#x002A;</sup></td>
<td align="char" valign="top" char="(">6 (23.08%)</td>
<td align="char" valign="top" char="(">1 (3.23%)</td>
<td align="char" valign="top" char=".">0.045<sup>&#x002A;</sup></td>
<td align="char" valign="top" char="(">4 (18.18%)</td>
<td align="char" valign="top" char="(">2 (6.67%)</td>
<td align="char" valign="top" char=".">0.382</td>
</tr>
<tr>
<td align="left" valign="top">ASA score, mean (SD)</td>
<td align="char" valign="top" char="(">2.97 (0.59)</td>
<td align="char" valign="top" char="(">2.72 (0.54)</td>
<td align="char" valign="top" char=".">0.012<sup>&#x002A;</sup></td>
<td align="char" valign="top" char="(">3.15 (0.73)</td>
<td align="char" valign="top" char="(">2.74 (0.51)</td>
<td align="char" valign="top" char=".">0.016<sup>&#x002A;</sup></td>
<td align="char" valign="top" char="(">3.00 (0.44)</td>
<td align="char" valign="top" char="(">2.77 (0.43)</td>
<td align="char" valign="top" char=".">0.062</td>
</tr>
<tr>
<td align="left" valign="top">NRS, mean (SD)</td>
<td align="char" valign="top" char="(">4.08 (1.80)</td>
<td align="char" valign="top" char="(">2.13 (1.33)</td>
<td align="char" valign="top" char=".">0.001<sup>&#x002A;</sup></td>
<td align="char" valign="top" char="(">3.56 (1.67)</td>
<td align="char" valign="top" char="(">2.59 (1.04)</td>
<td align="char" valign="top" char=".">0.050<sup>&#x002A;</sup></td>
<td align="char" valign="top" char="(">4.09 (1.27)</td>
<td align="char" valign="top" char="(">3.17 (0.70)</td>
<td align="char" valign="top" char=".">0.004<sup>&#x002A;</sup></td>
</tr>
<tr>
<td align="left" valign="top" char="." colspan="10"><bold>Intraoperative findings</bold></td>
</tr>
<tr>
<td align="left" valign="top">Enterotomy, <italic>n</italic> (%)</td>
<td align="char" valign="top" char="(">35 (53.85%)</td>
<td align="char" valign="top" char="(">25 (36.76%)</td>
<td align="char" valign="top" char=".">0.056</td>
<td align="char" valign="top" char="(">17 (65.38%)</td>
<td align="char" valign="top" char="(">15 (48.39%)</td>
<td align="char" valign="top" char=".">0.284</td>
<td align="char" valign="top" char="(">11 (50%)</td>
<td align="char" valign="top" char="(">8 (26.67%)</td>
<td align="char" valign="top" char=".">0.144</td>
</tr>
<tr>
<td align="left" valign="top">Intestinal strangulation, <italic>n</italic> (%)</td>
<td align="char" valign="top" char="(">21 (32.31%)</td>
<td align="char" valign="top" char="(">11 (16.18%)</td>
<td align="char" valign="top" char=".">0.042<sup>&#x002A;</sup></td>
<td align="char" valign="top" char="(">12 (46.15%)</td>
<td align="char" valign="top" char="(">6 (19.35%)</td>
<td align="char" valign="top" char=".">0.045&#x002A;</td>
<td align="char" valign="top" char="(">7 (31.82%)</td>
<td align="char" valign="top" char="(">4 (13.33%)</td>
<td align="char" valign="top" char=".">0.169</td>
</tr>
<tr>
<td align="left" valign="top">HB (g/L), mean (SD)</td>
<td align="char" valign="top" char="(">116.83 (20.68)</td>
<td align="char" valign="top" char="(">116.04 (24.41)</td>
<td align="char" valign="top" char=".">0.841</td>
<td align="char" valign="top" char="(">121.15 (17.93)</td>
<td align="char" valign="top" char="(">124.65 (20.87)</td>
<td align="char" valign="top" char=".">0.505</td>
<td align="char" valign="top" char="(">117.91 (24.86)</td>
<td align="char" valign="top" char="(">125.70 (23.32)</td>
<td align="char" valign="top" char=".">0.253</td>
</tr>
<tr>
<td align="left" valign="top">CRP (mg/L), mean (SD)</td>
<td align="char" valign="top" char="(">45.50 (47.83)</td>
<td align="char" valign="top" char="(">22.36 (34.62)</td>
<td align="char" valign="top" char=".">0.003<sup>&#x002A;</sup></td>
<td align="char" valign="top" char="(">47.45 (53.73)</td>
<td align="char" valign="top" char="(">34.71 (57.33)</td>
<td align="char" valign="top" char=".">0.405</td>
<td align="char" valign="top" char="(">86.04 (102.24)</td>
<td align="char" valign="top" char="(">10.63 (18.05)</td>
<td align="char" valign="top" char=".">0.002<sup>&#x002A;</sup></td>
</tr>
<tr>
<td align="left" valign="top">NLR, mean (SD)</td>
<td align="char" valign="top" char="(">6.83 (5.83)</td>
<td align="char" valign="top" char="(">4.78 (4.27)</td>
<td align="char" valign="top" char=".">0.022<sup>&#x002A;</sup></td>
<td align="char" valign="top" char="(">8.41 (7.21)</td>
<td align="char" valign="top" char="(">5.09 (3.28)</td>
<td align="char" valign="top" char=".">0.037<sup>&#x002A;</sup></td>
<td align="char" valign="top" char="(">9.79 (8.64)</td>
<td align="char" valign="top" char="(">3.34 (4.23)</td>
<td align="char" valign="top" char=".">0.001<sup>&#x002A;</sup></td>
</tr>
<tr>
<td align="left" valign="top">PLR, mean (SD)</td>
<td align="char" valign="top" char="(">318.43 (226.02)</td>
<td align="char" valign="top" char="(">231.24 (111.59)</td>
<td align="char" valign="top" char=".">0.006<sup>&#x002A;</sup></td>
<td align="char" valign="top" char="(">296.09 (191.32)</td>
<td align="char" valign="top" char="(">224.65 (108.02)</td>
<td align="char" valign="top" char=".">0.099</td>
<td align="char" valign="top" char="(">321.80 (315.97)</td>
<td align="char" valign="top" char="(">181.19 (117.61)</td>
<td align="char" valign="top" char=".">0.058</td>
</tr>
<tr>
<td align="left" valign="top">LMR, mean (SD)</td>
<td align="char" valign="top" char="(">2.19 (1.43)</td>
<td align="char" valign="top" char="(">2.83 (1.45)</td>
<td align="char" valign="top" char=".">0.011<sup>&#x002A;</sup></td>
<td align="char" valign="top" char="(">2.27 (1.93)</td>
<td align="char" valign="top" char="(">2.75 (2.04)</td>
<td align="char" valign="top" char=".">0.367</td>
<td align="char" valign="top" char="(">2.44 (2.22)</td>
<td align="char" valign="top" char="(">4.30 (2.32)</td>
<td align="char" valign="top" char=".">0.005<sup>&#x002A;</sup></td>
</tr>
<tr>
<td align="left" valign="top">SII, mean (SD)</td>
<td align="char" valign="top" char="(">1734.83 (1692.46)</td>
<td align="char" valign="top" char="(">1126.24 (1060.17)</td>
<td align="char" valign="top" char=".">0.015<sup>&#x002A;</sup></td>
<td align="char" valign="top" char="(">2228.30 (2185.85)</td>
<td align="char" valign="top" char="(">1101.10 (729.05)</td>
<td align="char" valign="top" char=".">0.018<sup>&#x002A;</sup></td>
<td align="char" valign="top" char="(">2018.78 (1623.55)</td>
<td align="char" valign="top" char="(">839.09 (1494.27)</td>
<td align="char" valign="top" char=".">0.010<sup>&#x002A;</sup></td>
</tr>
<tr>
<td align="left" valign="top">PNI, mean (SD)</td>
<td align="char" valign="top" char="(">39.12 (6.48)</td>
<td align="char" valign="top" char="(">41.73 (8.17)</td>
<td align="char" valign="top" char=".">0.044<sup>&#x002A;</sup></td>
<td align="char" valign="top" char="(">40.31 (9.40)</td>
<td align="char" valign="top" char="(">40.44 (7.48)</td>
<td align="char" valign="top" char=".">0.953</td>
<td align="char" valign="top" char="(">40.16 (12.06)</td>
<td align="char" valign="top" char="(">45.59 (7.66)</td>
<td align="char" valign="top" char=".">0.053</td>
</tr>
<tr>
<td align="left" valign="top">SMI (cm<sup>2</sup>/m<sup>2</sup>), mean (SD)</td>
<td align="char" valign="top" char="(">33.58 (8.76)</td>
<td align="char" valign="top" char="(">38.86 (10.07)</td>
<td align="char" valign="top" char=".">0.002&#x002A;</td>
<td align="char" valign="top" char="(">32.40 (8.64)</td>
<td align="char" valign="top" char="(">39.30 (9.59)</td>
<td align="char" valign="top" char=".">0.007<sup>&#x002A;</sup></td>
<td align="char" valign="top" char="(">32.87 (7.25)</td>
<td align="char" valign="top" char="(">40.58 (9.01)</td>
<td align="char" valign="top" char=".">0.002<sup>&#x002A;</sup></td>
</tr>
<tr>
<td align="left" valign="top">IFI (cm<sup>2</sup>/m<sup>2</sup>), mean (SD)</td>
<td align="char" valign="top" char="(">3.03 (2.37)</td>
<td align="char" valign="top" char="(">2.75 (2.31)</td>
<td align="char" valign="top" char=".">0.500</td>
<td align="char" valign="top" char="(">3.02 (2.88)</td>
<td align="char" valign="top" char="(">2.48 (2.39)</td>
<td align="char" valign="top" char=".">0.442</td>
<td align="char" valign="top" char="(">3.29 (3.25)</td>
<td align="char" valign="top" char="(">2.54 (2.15)</td>
<td align="char" valign="top" char=".">0.353</td>
</tr>
<tr>
<td align="left" valign="top">SFI (cm<sup>2</sup>/m<sup>2</sup>), mean (SD)</td>
<td align="char" valign="top" char="(">39.72 (25.02)</td>
<td align="char" valign="top" char="(">31.51 (18.59)</td>
<td align="char" valign="top" char=".">0.034<sup>&#x002A;</sup></td>
<td align="char" valign="top" char="(">42.44 (19.43)</td>
<td align="char" valign="top" char="(">28.06 (16.51)</td>
<td align="char" valign="top" char=".">0.004<sup>&#x002A;</sup></td>
<td align="char" valign="top" char="(">39.59 (16.98)</td>
<td align="char" valign="top" char="(">30.11 (14.54)</td>
<td align="char" valign="top" char=".">0.035<sup>&#x002A;</sup></td>
</tr>
<tr>
<td align="left" valign="top">VFI (cm<sup>2</sup>/m<sup>2</sup>), mean (SD)</td>
<td align="char" valign="top" char="(">23.16 (19.98)</td>
<td align="char" valign="top" char="(">23.29 (17.75)</td>
<td align="char" valign="top" char=".">0.968</td>
<td align="char" valign="top" char="(">26.12 (23.44)</td>
<td align="char" valign="top" char="(">24.17 (18.24)</td>
<td align="char" valign="top" char=".">0.726</td>
<td align="char" valign="top" char="(">26.44 (19.66)</td>
<td align="char" valign="top" char="(">18.63 (13.53)</td>
<td align="char" valign="top" char=".">0.095</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<p>&#x002A;<italic>p</italic>&#x2009;&#x003C;&#x2009;0.05.</p>
<p>BMI, body mass index; ASA, American Society of Anesthesiologists; NRS, nutritional risk score; HB, hemoglobin; CRP, C-reactive protein; NLR, neutrophil-lymphocyte ratio; PLR, platelet-lymphocyte ratio; LMR, lymphocyte-monocyte ratio; SII, systemic immune inflammation index; PNI, prognostic nutritional index; SMI, skeletal muscle index; IFI, intermuscle fat index; SFI, subcutaneous fat index; VFI, visceral fat index.</p>
</table-wrap-foot>
</table-wrap>
</sec>
<sec id="sec14">
<title>Overview of complications</title>
<p>The incidence of complications across different grades did not significantly differ among the three cohorts (<italic>p</italic>&#x2009;&#x003E;&#x2009;0.05) (<xref ref-type="table" rid="tab2">Table 2</xref>). There were 37 (27.82%), 14 (24.56%), and 13 (25%) patients who experienced severe complications (Grade III or higher) in training, internal validation, and external validation cohorts, demonstrating comparable rates of severe complications.</p>
<table-wrap position="float" id="tab2">
<label>Table 2</label>
<caption>
<p>Thirty-day postoperative complications.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th/>
<th align="center" valign="top">Training set (<italic>n</italic>&#x2009;=&#x2009;133)</th>
<th align="center" valign="top">Internal validation set (<italic>n</italic>&#x2009;=&#x2009;57)</th>
<th align="center" valign="top">External validation set (<italic>n</italic>&#x2009;=&#x2009;52)</th>
<th align="center" valign="top"><italic>p</italic> value</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="top">Overall postoperative complication</td>
<td align="center" valign="top">65 (48.87%)</td>
<td align="center" valign="top">26 (45.61%)</td>
<td align="center" valign="top">22 (42.31%)</td>
<td align="center" valign="top">0.711</td>
</tr>
<tr>
<td align="left" valign="top">Grade I</td>
<td align="center" valign="top">11 (8.27%)</td>
<td align="center" valign="top">5 (8.77%)</td>
<td align="center" valign="top">4 (7.69%)</td>
<td align="center" valign="top">0.979</td>
</tr>
<tr>
<td align="left" valign="top">Superficial wound infection</td>
<td align="center" valign="top">3</td>
<td align="center" valign="top">1</td>
<td align="center" valign="top">0</td>
<td/>
</tr>
<tr>
<td align="left" valign="top">Electrolyte imbalance</td>
<td align="center" valign="top">8</td>
<td align="center" valign="top">4</td>
<td align="center" valign="top">4</td>
<td/>
</tr>
<tr>
<td align="left" valign="top">Grade II</td>
<td align="center" valign="top">17 (12.78%)</td>
<td align="center" valign="top">7 (12.28%)</td>
<td align="center" valign="top">5 (9.62%)</td>
<td align="center" valign="top">0.835</td>
</tr>
<tr>
<td align="left" valign="top">Ileus (treated conservatively)</td>
<td align="center" valign="top">4</td>
<td align="center" valign="top">0</td>
<td align="center" valign="top">0</td>
<td/>
</tr>
<tr>
<td align="left" valign="top">Intraperitoneal hemorrhage (necessitating transfusion)</td>
<td align="center" valign="top">2</td>
<td align="center" valign="top">2</td>
<td align="center" valign="top">1</td>
<td/>
</tr>
<tr>
<td align="left" valign="top">Fever with antibiotics</td>
<td align="center" valign="top">4</td>
<td align="center" valign="top">2</td>
<td align="center" valign="top">3</td>
<td/>
</tr>
<tr>
<td align="left" valign="top">Respiratory infection</td>
<td align="center" valign="top">6</td>
<td align="center" valign="top">2</td>
<td align="center" valign="top">1</td>
<td/>
</tr>
<tr>
<td align="left" valign="top">Urinary infection</td>
<td align="center" valign="top">1</td>
<td align="center" valign="top">1</td>
<td align="center" valign="top">0</td>
<td/>
</tr>
<tr>
<td align="left" valign="top">Grade III</td>
<td align="center" valign="top">24 (18.05%)</td>
<td align="center" valign="top">6 (10.53%)</td>
<td align="center" valign="top">5 (9.62%)</td>
<td align="center" valign="top">0.214</td>
</tr>
<tr>
<td align="left" valign="top">Wound infection (necessitating reoperation)</td>
<td align="center" valign="top">7</td>
<td align="center" valign="top">1</td>
<td align="center" valign="top">3</td>
<td/>
</tr>
<tr>
<td align="left" valign="top">Intraperitoneal hemorrhage (necessitating reoperation)</td>
<td align="center" valign="top">2</td>
<td align="center" valign="top">2</td>
<td align="center" valign="top">0</td>
<td/>
</tr>
<tr>
<td align="left" valign="top">Intraperitoneal infection (necessitating reoperation)</td>
<td align="center" valign="top">8</td>
<td align="center" valign="top">1</td>
<td align="center" valign="top">0</td>
<td/>
</tr>
<tr>
<td align="left" valign="top">Ileus (necessitating reoperation)</td>
<td align="center" valign="top">3</td>
<td align="center" valign="top">2</td>
<td align="center" valign="top">1</td>
<td/>
</tr>
<tr>
<td align="left" valign="top">Intestinal fistula (necessitating reoperation)</td>
<td align="center" valign="top">4</td>
<td align="center" valign="top">0</td>
<td align="center" valign="top">1</td>
<td/>
</tr>
<tr>
<td align="left" valign="top">Grade IV</td>
<td align="center" valign="top">11 (8.27%)</td>
<td align="center" valign="top">7 (12.28%)</td>
<td align="center" valign="top">8 (15.38%)</td>
<td align="center" valign="top">0.340</td>
</tr>
<tr>
<td align="left" valign="top">Respiratory failure</td>
<td align="center" valign="top">1</td>
<td align="center" valign="top">1</td>
<td align="center" valign="top">2</td>
<td/>
</tr>
<tr>
<td align="left" valign="top">Cardiac failure</td>
<td align="center" valign="top">2</td>
<td align="center" valign="top">1</td>
<td align="center" valign="top">1</td>
<td/>
</tr>
<tr>
<td align="left" valign="top">Renal failure</td>
<td align="center" valign="top">0</td>
<td align="center" valign="top">1</td>
<td align="center" valign="top">1</td>
<td/>
</tr>
<tr>
<td align="left" valign="top">Multiple organ dysfunction syndrome</td>
<td align="center" valign="top">8</td>
<td align="center" valign="top">4</td>
<td align="center" valign="top">4</td>
<td/>
</tr>
<tr>
<td align="left" valign="top">Grade V</td>
<td align="center" valign="top">2 (1.50%)</td>
<td align="center" valign="top">1 (1.75%)</td>
<td align="center" valign="top">0</td>
<td align="center" valign="top">0.594</td>
</tr>
<tr>
<td align="left" valign="top">Death</td>
<td align="center" valign="top">2</td>
<td align="center" valign="top">1</td>
<td align="center" valign="top">0</td>
<td/>
</tr>
</tbody>
</table>
<table-wrap-foot>
<p>The definition of complications was given in the article.</p>
</table-wrap-foot>
</table-wrap>
</sec>
<sec id="sec15">
<title>Univariate and multivariate analysis of inflammatory-nutritional markers</title>
<p><xref ref-type="table" rid="tab3">Table 3</xref> indicates significant differences between the two groups in preoperative infection, ASA score, NRS, intestinal strangulation, CRP, NLR, PLR, LMR, SII, PNI, SMI, and SFI, as identified by univariate regression in the training cohort (<italic>p</italic> &#x003C;&#x2009;0.05). These variables with <italic>p</italic>-values less than 0.05 were subsequently included in the multivariate regression analysis. Our findings reveal that NRS (OR&#x2009;=&#x2009;21.731, <italic>p</italic> =&#x2009;0.002), intestinal strangulation (OR&#x2009;=&#x2009;401.665, <italic>p</italic> =&#x2009;0.008), NLR (OR&#x2009;=&#x2009;4.264, <italic>p</italic> =&#x2009;0.029), LMR (OR&#x2009;=&#x2009;0.183, <italic>p</italic> =&#x2009;0.034), SMI (OR&#x2009;=&#x2009;0.708, <italic>p</italic> =&#x2009;0.008), and SFI (OR&#x2009;=&#x2009;1.115, <italic>p</italic> =&#x2009;0.014) are independent predictors of postoperative complications.</p>
<table-wrap position="float" id="tab3">
<label>Table 3</label>
<caption>
<p>Univariate and multivariate analysis of patients with complications versus those without in the training set.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th/>
<th align="center" valign="top" colspan="4">Univariate analysis</th>
<th align="center" valign="top" colspan="4">Multivariate analysis</th>
</tr>
<tr>
<th/>
<th align="center" valign="top">SE</th>
<th align="center" valign="top">Exp(B)</th>
<th align="center" valign="top"><italic>p</italic> value</th>
<th align="center" valign="top">95%CI</th>
<th align="center" valign="top">SE</th>
<th align="center" valign="top">Exp(B)</th>
<th align="center" valign="top"><italic>p</italic> value</th>
<th align="center" valign="top">95%CI</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="top">Preoperative infection, <italic>n</italic> (%)</td>
<td align="char" valign="top" char=".">0.796</td>
<td align="char" valign="top" char=".">6.000</td>
<td align="char" valign="top" char=".">0.024</td>
<td align="char" valign="top" char="&#x2013;">1.261&#x2013;28.547</td>
<td align="char" valign="top" char=".">2.360</td>
<td align="char" valign="top" char=".">69.815</td>
<td align="char" valign="top" char=".">0.072</td>
<td align="char" valign="top" char="&#x2013;">0.684&#x2013;7122.731</td>
</tr>
<tr>
<td align="left" valign="top">ASA score, mean (SD)</td>
<td align="char" valign="top" char=".">0.340</td>
<td align="char" valign="top" char=".">2.268</td>
<td align="char" valign="top" char=".">0.016</td>
<td align="char" valign="top" char="&#x2013;">1.166&#x2013;4.414</td>
<td align="char" valign="top" char=".">1.363</td>
<td align="char" valign="top" char=".">2.077</td>
<td align="char" valign="top" char=".">0.592</td>
<td align="char" valign="top" char="&#x2013;">0.144&#x2013;30.020</td>
</tr>
<tr>
<td align="left" valign="top">NRS, mean (SD)</td>
<td align="char" valign="top" char=".">0.219</td>
<td align="char" valign="top" char=".">2.278</td>
<td align="char" valign="top" char=".">0.001</td>
<td align="char" valign="top" char="&#x2013;">1.484&#x2013;3.497</td>
<td align="char" valign="top" char=".">0.995</td>
<td align="char" valign="top" char=".">21.731</td>
<td align="char" valign="top" char=".">0.002</td>
<td align="char" valign="top" char="&#x2013;">3.092&#x2013;152.746</td>
</tr>
<tr>
<td align="left" valign="top">Intestinal strangulation, <italic>n</italic> (%)</td>
<td align="char" valign="top" char=".">0.423</td>
<td align="char" valign="top" char=".">2.473</td>
<td align="char" valign="top" char=".">0.032</td>
<td align="char" valign="top" char="&#x2013;">1.080&#x2013;5.665</td>
<td align="char" valign="top" char=".">2.262</td>
<td align="char" valign="top" char=".">401.665</td>
<td align="char" valign="top" char=".">0.008</td>
<td align="char" valign="top" char="&#x2013;">4.774&#x2013;33797.868</td>
</tr>
<tr>
<td align="left" valign="top">CRP (mg/L), mean (SD)</td>
<td align="char" valign="top" char=".">0.005</td>
<td align="char" valign="top" char=".">1.014</td>
<td align="char" valign="top" char=".">0.005</td>
<td align="char" valign="top" char="&#x2013;">1.004&#x2013;1.024</td>
<td align="char" valign="top" char=".">0.019</td>
<td align="char" valign="top" char=".">1.036</td>
<td align="char" valign="top" char=".">0.065</td>
<td align="char" valign="top" char="&#x2013;">0.998&#x2013;1.075</td>
</tr>
<tr>
<td align="left" valign="top">NLR, mean (SD)</td>
<td align="char" valign="top" char=".">0.038</td>
<td align="char" valign="top" char=".">1.086</td>
<td align="char" valign="top" char=".">0.028</td>
<td align="char" valign="top" char="&#x2013;">1.009&#x2013;1.169</td>
<td align="char" valign="top" char=".">0.663</td>
<td align="char" valign="top" char=".">4.264</td>
<td align="char" valign="top" char=".">0.029</td>
<td align="char" valign="top" char="&#x2013;">1.162&#x2013;15.648</td>
</tr>
<tr>
<td align="left" valign="top">PLR, mean (SD)</td>
<td align="char" valign="top" char=".">0.001</td>
<td align="char" valign="top" char=".">1.004</td>
<td align="char" valign="top" char=".">0.008</td>
<td align="char" valign="top" char="&#x2013;">1.001&#x2013;1.006</td>
<td align="char" valign="top" char=".">0.005</td>
<td align="char" valign="top" char=".">1.004</td>
<td align="char" valign="top" char=".">0.388</td>
<td align="char" valign="top" char="&#x2013;">0.994&#x2013;1.015</td>
</tr>
<tr>
<td align="left" valign="top">LMR, mean (SD)</td>
<td align="char" valign="top" char=".">0.129</td>
<td align="char" valign="top" char=".">0.728</td>
<td align="char" valign="top" char=".">0.014</td>
<td align="char" valign="top" char="&#x2013;">0.566&#x2013;0.938</td>
<td align="char" valign="top" char=".">0.800</td>
<td align="char" valign="top" char=".">0.183</td>
<td align="char" valign="top" char=".">0.034</td>
<td align="char" valign="top" char="&#x2013;">0.038&#x2013;0.879</td>
</tr>
<tr>
<td align="left" valign="top">SII, mean (SD)</td>
<td align="char" valign="top" char=".">0.000</td>
<td align="char" valign="top" char=".">1.000</td>
<td align="char" valign="top" char=".">0.020</td>
<td align="char" valign="top" char="&#x2013;">0.999&#x2013;1.000</td>
<td align="char" valign="top" char=".">0.004</td>
<td align="char" valign="top" char=".">1.004</td>
<td align="char" valign="top" char=".">0.074</td>
<td align="char" valign="top" char="&#x2013;">1.000&#x2013;1.009</td>
</tr>
<tr>
<td align="left" valign="top">PNI, mean (SD)</td>
<td align="char" valign="top" char=".">0.025</td>
<td align="char" valign="top" char=".">0.952</td>
<td align="char" valign="top" char=".">0.049</td>
<td align="char" valign="top" char="&#x2013;">0.906&#x2013;1.000</td>
<td align="char" valign="top" char=".">0.112</td>
<td align="char" valign="top" char=".">0.834</td>
<td align="char" valign="top" char=".">0.105</td>
<td align="char" valign="top" char="&#x2013;">0.670&#x2013;1.039</td>
</tr>
<tr>
<td align="left" valign="top">SMI (cm<sup>2</sup>/m<sup>2</sup>), mean (SD)</td>
<td align="char" valign="top" char=".">0.020</td>
<td align="char" valign="top" char=".">0.940</td>
<td align="char" valign="top" char=".">0.003</td>
<td align="char" valign="top" char="&#x2013;">0.903&#x2013;0.979</td>
<td align="char" valign="top" char=".">0.131</td>
<td align="char" valign="top" char=".">0.708</td>
<td align="char" valign="top" char=".">0.008</td>
<td align="char" valign="top" char="&#x2013;">0.547&#x2013;0.915</td>
</tr>
<tr>
<td align="left" valign="top">SFI (cm<sup>2</sup>/m<sup>2</sup>), mean (SD)</td>
<td align="char" valign="top" char=".">0.008</td>
<td align="char" valign="top" char=".">1.018</td>
<td align="char" valign="top" char=".">0.037</td>
<td align="char" valign="top" char="&#x2013;">1.001&#x2013;1.035</td>
<td align="char" valign="top" char=".">0.044</td>
<td align="char" valign="top" char=".">1.115</td>
<td align="char" valign="top" char=".">0.014</td>
<td align="char" valign="top" char="&#x2013;">1.022&#x2013;1.217</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<p>ASA, American Society of Anesthesiologists; NRS, nutritional risk score; CRP, C-reactive protein; NLR, neutrophil-lymphocyte ratio; PLR, platelet-lymphocyte ratio; LMR, lymphocyte-monocyte ratio; SII, systemic immune inflammation index; PNI, prognostic nutritional index; SMI, skeletal muscle index; SFI, subcutaneous fat index.</p>
</table-wrap-foot>
</table-wrap>
</sec>
<sec id="sec16">
<title>Inflammatory-nutritional model construction and verification</title>
<p><xref ref-type="fig" rid="fig3">Figure 3</xref> presents a correlation matrix of inflammatory-nutritional biomarkers, with correlation coefficients ranging from &#x2212;1 (red) to 1 (blue) in training and validation sets. Hemoglobin (HB) was found to be correlated with SMI in both the training [Pearson Correlation Coefficient (PCC)&#x2009;=&#x2009;0.196, <italic>p</italic> =&#x2009;0.023] and internal validation sets (PCC&#x2009;=&#x2009;0.348, <italic>p</italic> =&#x2009;0.008), as shown in <xref rid="SM1" ref-type="supplementary-material">Supplementary Tables S1&#x2013;S3</xref>. To avoid multicollinearity, the inflammatory-nutritional model was constructed using indicators with a PCC below 0.7 (<xref ref-type="bibr" rid="ref27">27</xref>).</p>
<fig position="float" id="fig3">
<label>Figure 3</label>
<caption>
<p>Correlation matrix of nutritional inflammatory biomarkers in the training <bold>(A)</bold>, internal validation <bold>(B)</bold>, and external validation <bold>(C)</bold> sets.</p>
</caption>
<graphic xlink:href="fnut-11-1345570-g003.tif"/>
</fig>
<p>A nomogram derived from the multivariate analysis was developed, incorporating NRS, NLR, PLR, SMI, and SFI. Each patient&#x2019;s total score was calculated by summing the scores of these five predictive factors, which were then used to evaluate the risk of postoperative complications. A higher total score correlated positively with an increased probability of postoperative complications (<xref ref-type="fig" rid="fig4">Figure 4A</xref>). The inflammatory-nutritional score (INS) was calculated as 1.103&#x2013;0.102&#x002A;SMI&#x2009;+&#x2009;0.037&#x002A;SFI&#x2009;+&#x2009;0.034&#x002A;NLR-0.649&#x002A;LMR&#x2009;+ 1.044&#x002A;NRS. Calibration curves demonstrated good agreement in three cohorts (<xref ref-type="fig" rid="fig4">Figures 4B</xref>&#x2013;<xref ref-type="fig" rid="fig4">D</xref>). Validation was conducted using the bootstrap method, and model performance was assessed over 1,000 iterations.</p>
<fig position="float" id="fig4">
<label>Figure 4</label>
<caption>
<p>The inflammatory-nutrition nomogram and its calibration curves. <bold>(A)</bold> Development of the inflammatory-nutrition nomogram in the training set, incorporating SMI, SFI, NLR, PLR, and NRS. Calibration curves for the nomogram in the training <bold>(B)</bold>, internal validation <bold>(C)</bold>, and external validation <bold>(D)</bold> sets.</p>
</caption>
<graphic xlink:href="fnut-11-1345570-g004.tif"/>
</fig>
</sec>
<sec id="sec17">
<title>Evaluating predictive performance of the three models</title>
<p>Based on inflammatory-nutritional markers, we established an inflammatory model (NLR, PLR, LMR, SII, PNI) and nutritional model (SMI, IFI, SFI, VFI). ROC analysis revealed that the nomogram achieved AUCs of 0.878 (95% CI, 0.802&#x2013;0.954) in the training set, 0.831 (95% CI, 0.675&#x2013;0.986) in the internal validation set, and 0.886 (95% CI, 0.799&#x2013;0.974) in the external validation set. These results surpassed those of the inflammatory model (0.648, 95% CI 0.554&#x2013;0.742) and the nutritional model (0.674, 95% CI 0.583&#x2013;0.766) in the training set, 0.655 (95% CI 0.508&#x2013;0.802) and 0.766 (95% CI 0.642&#x2013;0.889) in the internal validation set, and 0.814 (95% CI 0.695&#x2013;0.933) and 0.811 (95% CI 0.689&#x2013;0.932) respectively (<xref ref-type="fig" rid="fig5">Figures 5A</xref>&#x2013;<xref ref-type="fig" rid="fig5">C</xref>) in the external validation set. Decision curve analysis (DCA) indicated that our nomogram achieved greater net benefits at optimal threshold probabilities in predicting complications in ASBO cases (<xref ref-type="fig" rid="fig5">Figures 5D</xref>&#x2013;<xref ref-type="fig" rid="fig5">F</xref>). <xref ref-type="table" rid="tab4">Table 4</xref> details the predictive performance of the inflammatory model, nutritional model, and nomogram in three cohorts.</p>
<fig position="float" id="fig5">
<label>Figure 5</label>
<caption>
<p>Receiver operating characteristic curves of the inflammatory-nutrition model, the inflammatory model, and the nutritional model in the training <bold>(A)</bold>, internal validation <bold>(B)</bold>, and external validation <bold>(C)</bold> sets. Decision curve analysis for the inflammatory-nutrition model in the training <bold>(D)</bold>, internal validation <bold>(E)</bold>, and external validation <bold>(F)</bold> sets.</p>
</caption>
<graphic xlink:href="fnut-11-1345570-g005.tif"/>
</fig>
<table-wrap position="float" id="tab4">
<label>Table 4</label>
<caption>
<p>Predictive performance of the three models in the training and validation sets.</p>
</caption>
<table frame="hsides" rules="groups">
<thead>
<tr>
<th/>
<th align="center" valign="top" colspan="6">Training set (<italic>n</italic>&#x2009;=&#x2009;133)</th>
<th align="center" valign="top" colspan="6">Internal validation set (<italic>n</italic>&#x2009;=&#x2009;57)</th>
<th align="center" valign="top" colspan="6">External validation set (<italic>n</italic>&#x2009;=&#x2009;52)</th>
</tr>
<tr>
<th/>
<th align="center" valign="top">AUC (95%CI)</th>
<th align="center" valign="top">ACC</th>
<th align="center" valign="top">SEN</th>
<th align="center" valign="top">SPE</th>
<th align="center" valign="top">PPV</th>
<th align="center" valign="top">NPV</th>
<th align="center" valign="top">AUC (95%CI)</th>
<th align="center" valign="top">ACC</th>
<th align="center" valign="top">SEN</th>
<th align="center" valign="top">SPE</th>
<th align="center" valign="top">PPV</th>
<th align="center" valign="top">NPV</th>
<th align="center" valign="top">AUC (95%CI)</th>
<th align="center" valign="top">ACC</th>
<th align="center" valign="top">SEN</th>
<th align="center" valign="top">SPE</th>
<th align="center" valign="top">PPV</th>
<th align="center" valign="top">NPV</th>
</tr>
</thead>
<tbody>
<tr>
<td align="left" valign="top">Inflammatory model</td>
<td align="char" valign="top" char="(">0.648 (95%CI 0.554&#x2013;0.742)</td>
<td align="char" valign="top" char=".">0.639</td>
<td align="char" valign="top" char=".">0.431</td>
<td align="char" valign="top" char=".">0.838</td>
<td align="char" valign="top" char=".">0.718</td>
<td align="char" valign="top" char=".">0.606</td>
<td align="char" valign="top" char="(">0.655 (95%CI 0.508&#x2013;0.802)</td>
<td align="char" valign="top" char=".">0.702</td>
<td align="char" valign="top" char=".">0.385</td>
<td align="char" valign="top" char=".">0.968</td>
<td align="char" valign="top" char=".">0.909</td>
<td align="char" valign="top" char=".">0.652</td>
<td align="char" valign="top" char="(">0.814 (95%CI 0.695&#x2013;0.933)</td>
<td align="char" valign="top" char=".">0.788</td>
<td align="char" valign="top" char=".">0.591</td>
<td align="char" valign="top" char=".">0.933</td>
<td align="char" valign="top" char=".">0.867</td>
<td align="char" valign="top" char=".">0.757</td>
</tr>
<tr>
<td align="left" valign="top">Nutritional model</td>
<td align="char" valign="top" char="(">0.674 (95%CI 0.583&#x2013;0.766)</td>
<td align="char" valign="top" char=".">0.654</td>
<td align="char" valign="top" char=".">0.508</td>
<td align="char" valign="top" char=".">0.794</td>
<td align="char" valign="top" char=".">0.702</td>
<td align="char" valign="top" char=".">0.628</td>
<td align="char" valign="top" char="(">0.766 (95%CI 0.642&#x2013;0.889)</td>
<td align="char" valign="top" char=".">0.737</td>
<td align="char" valign="top" char=".">0.423</td>
<td align="char" valign="top" char=".">0.999</td>
<td align="char" valign="top" char=".">0.999</td>
<td align="char" valign="top" char=".">0.674</td>
<td align="char" valign="top" char="(">0.811 (95%CI 0.689&#x2013;0.932)</td>
<td align="char" valign="top" char=".">0.788</td>
<td align="char" valign="top" char=".">0.727</td>
<td align="char" valign="top" char=".">0.833</td>
<td align="char" valign="top" char=".">0.762</td>
<td align="char" valign="top" char=".">0.806</td>
</tr>
<tr>
<td align="left" valign="top">Inflammatory-nutritional model</td>
<td align="char" valign="top" char="(">0.878 (95%CI 0.802&#x2013;0.954)</td>
<td align="char" valign="top" char=".">0.755</td>
<td align="char" valign="top" char=".">0.625</td>
<td align="char" valign="top" char=".">0.968</td>
<td align="char" valign="top" char=".">0.962</td>
<td align="char" valign="top" char=".">0.667</td>
<td align="char" valign="top" char="(">0.831 (95%CI 0.675&#x2013;0.986)</td>
<td align="char" valign="top" char=".">0.818</td>
<td align="char" valign="top" char=".">0.812</td>
<td align="char" valign="top" char=".">0.824</td>
<td align="char" valign="top" char=".">0.812</td>
<td align="char" valign="top" char=".">0.824</td>
<td align="char" valign="top" char="(">0.886 (95%CI 0.799&#x2013;0.974)</td>
<td align="char" valign="top" char=".">0.808</td>
<td align="char" valign="top" char=".">0.909</td>
<td align="char" valign="top" char=".">0.733</td>
<td align="char" valign="top" char=".">0.714</td>
<td align="char" valign="top" char=".">0.917</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<p>AUC, area under the curve; CI, confidence interval; ACC, accuracy; SEN, sensitivity; SPE, specificity; PPV, positive predictive value; NPV, negative predictive value.</p>
</table-wrap-foot>
</table-wrap>
</sec>
</sec>
<sec sec-type="discussion" id="sec18">
<title>Discussion</title>
<p>The choice between conservative management and surgical intervention for ASBO patients continues to be debated. Previous research indicated that the method of treatment correlates with postoperative complications in ASBO patients (<xref ref-type="bibr" rid="ref28">28</xref>, <xref ref-type="bibr" rid="ref29">29</xref>). In our study, 113 of the 242 ASBO patients (46.69%) encountered complications (<xref ref-type="bibr" rid="ref30">30</xref>). This significant rate of postoperative complications adversely affects ASBO patient outcomes, underscoring the necessity for a predictive model to foresee these complications and assist in clinical decision-making (<xref ref-type="bibr" rid="ref31">31</xref>). Our current research, involving 242 ASBO patients, validated the efficacy of a nomogram that incorporates CT-based body composition and inflammatory-nutritional markers. This model, easy to compute, holds broad applicability in clinical practice.</p>
<p>Increasing evidence suggests that poor nutritional status is a prognostic risk factor for various gastrointestinal disorders, encompassing both malignancies and benign conditions (<xref ref-type="bibr" rid="ref14">14</xref>, <xref ref-type="bibr" rid="ref32">32</xref>, <xref ref-type="bibr" rid="ref33">33</xref>). In ASBO cases, impaired intestinal function curtails the efficacy of standard enteral interventions in swiftly rectifying malnutrition, elevating the risk of severe malnutrition and impeding potential enhancements in nutritional status due to acute gastrointestinal failure (<xref ref-type="bibr" rid="ref34">34</xref>). Consequently, clinicians are in pursuit of reliable indicators to accurately evaluate the nutritional status of ASBO patients (<xref ref-type="bibr" rid="ref35">35</xref>). Although traditional nutritional assessment tools like body weight and BMI offer a general insight into an individual&#x2019;s nutritional status, they do not provide specific details on body composition, such as muscle mass or regional fat distribution. Recent research has increasingly acknowledged the pivotal role of body composition in determining a patient&#x2019;s nutritional state and postoperative outcomes (<xref ref-type="bibr" rid="ref36">36</xref>, <xref ref-type="bibr" rid="ref37">37</xref>). Our study also showed that the SMIs of ASBO patients with complications were significantly lower than those of patients without complications (<italic>p</italic> &#x003C;&#x2009;0.05), and multivariable analysis confirmed this protective factor (OR&#x2009;=&#x2009;0.708). This association indicated the importance of maintaining skeletal muscle mass quality for postoperative recovery of ASBO patients (<xref ref-type="bibr" rid="ref38">38</xref>). Patients at a heightened risk of sarcopenia may undergo a persistent inflammatory response that disrupts normal nitrogen metabolism, increasing the likelihood of postoperative complications. This is consistent with prior findings that surgical patients with reduced skeletal muscle mass have poorer prognoses, encounter more postoperative complications, require more intensive care, and exhibit higher mortality rates (<xref ref-type="bibr" rid="ref39">39</xref>, <xref ref-type="bibr" rid="ref40">40</xref>). Furthermore, our observations indicate that a high SFI correlates with postoperative complications in ASBO patients, adding to the discourse on the &#x201C;obesity paradox&#x201D; and supporting the notion that sarcopenic obesity is as indicative of surgical outcomes as sarcopenia alone, as several studies have previously reported (<xref ref-type="bibr" rid="ref41">41</xref>, <xref ref-type="bibr" rid="ref42">42</xref>).</p>
<p>ASBO is often associated with acute inflammatory process, a key marker of disease progression. Mueller et al. discovered that as obstruction advances and intestinal barrier dysfunction ensues, luminal flora can penetrate the mucosal layer, triggering host immune responses (<xref ref-type="bibr" rid="ref43">43</xref>). This uncontrolled inflammatory response is characterized by immune cells infiltration and the release of inflammatory mediators. Numerous systemic inflammatory response indicators, derived from serum biomarkers, have been developed to assess the extent of this response. In our study, systemic inflammatory response indicators, including CRP, PLR, NLR, LMR, PNI and SII, were examined. We found that patients with higher NLR and lower LMR values were more likely to experience postoperative complications, corroborating previous findings that NLR and LMR are crucial indicators for predicting disease severity and prognosis in cancer patients (<xref ref-type="bibr" rid="ref12">12</xref>, <xref ref-type="bibr" rid="ref44">44</xref>, <xref ref-type="bibr" rid="ref45">45</xref>). NLR and LMR calculations involve neutrophils, lymphocytes, and monocytes. Changes in NLR and LMR values generally reflect disturbances in these immune cell types and their prognostic significance, linked to the effects of such cells. Neutrophil and monocyte activation, a response to infection signals, is a fundamental component of the innate immune response phase and is implicated in the pathogenesis of various diseases (<xref ref-type="bibr" rid="ref46">46</xref>). A decrease in lymphocyte count, indicating impaired immune function, hampers the body&#x2019;s ability to combat persistent infections (<xref ref-type="bibr" rid="ref11">11</xref>, <xref ref-type="bibr" rid="ref47">47</xref>, <xref ref-type="bibr" rid="ref48">48</xref>). In recent years, the interplay between inflammation and nutrition has gained significant attention. The volume of research examining the impact of nutrition on the immune system is continuously expanding (<xref ref-type="bibr" rid="ref49">49</xref>, <xref ref-type="bibr" rid="ref50">50</xref>). On correlation analysis, we also found HB and SMI were positively correlated (<xref rid="SM1" ref-type="supplementary-material">Supplementary Table S1</xref>) which implied the underlying mechanistic association between anemia and sarcopenia in ASBO settings. Consistent with the findings of Hirani&#x2019;s study, lower HB levels might contribute to a decrease in skeletal muscle volume, through biological pathways generally involving decreased oxygenation of skeletal muscle tissues (<xref ref-type="bibr" rid="ref51">51</xref>). Anemia caused by ASBO may impair oxygen delivery and expenditure within muscle tissues, creating hypoxia within the local microenvironment that undermines the function of skeletal muscle cells, ultimately leading to skeletal muscle loss and sarcopenia.</p>
<p>The findings from our multivariate and correlation analyses have paved the way for the development of a practical predictive model for postoperative complications in ASBO patients. This model aims to identify those at heightened risk for such complications. While most previous studies have focused on the predictive power of single inflammatory&#x2013;nutritional scores or CT composition indices on prognosis (<xref ref-type="bibr" rid="ref45">45</xref>, <xref ref-type="bibr" rid="ref52">52</xref>), these singular measures often fail to provide a comprehensive and accurate representation of a patient&#x2019;s entire inflammatory-nutritional status, thereby limiting their practical accuracy (<xref ref-type="bibr" rid="ref21">21</xref>). Recently, there has been a trend toward developing prognostic scores based on multiple inflammatory-nutrition indices. For instance, Wang et al. created a prognostic score incorporating LMR, NLR, and PLR to predict outcomes for gastrectomy patients&#x2019; post-chemotherapy, with their nomogram demonstrating superior predictive performance (C-index 0.707) compared to single-index models (<xref ref-type="bibr" rid="ref53">53</xref>). Our previous research utilized multiple inflammatory-nutritional scores to construct a model for predicting postoperative quality of life in gastric cancer patients (<xref ref-type="bibr" rid="ref22">22</xref>). In this study, we initially attempted to create an inflammation-based model (Inflam-model) using inflammatory scores (Inflam-scores) and a radiography-based model (Radio-model) using body composition parameters. However, both models exhibited suboptimal performance. Consequently, we explored whether combining various inflammatory factors with nutrition-related indicators could enhance the predictive accuracy for ASBO patients. We selected significant inflammatory-nutritional and radiographic indicators from the multivariable analysis to construct a combined predictive model. Our risk predictive model, integrating two Inflam-scores, two Radio-scores, and NRS, showed improved performance in both cohorts. These Radio-scores and Inflam-scores, derived from routine clinical practice, make this multiparametric model practical, particularly in preoperative settings. Identifying patients with a high inflammatory state and low nutritional status preoperatively is crucial in clinical practice. Accordingly, prognosis may be improved through prompt and effective therapeutic interventions.</p>
<p>This study has several limitations. Firstly, the sample size was comparatively small, indicating the necessity for subsequent multicenter studies with expanded sample sizes. Secondly, the expansion of the intestine lumen within the abdominal cavity may affect the accuracy of visceral adipose tissue detection on CT images. Relying solely on measurements of visceral adipose tissue in single CT slides may not sufficiently predict postoperative outcomes. Dynamic and comprehensive assessments of whole-body composition warrant further study. Thirdly, although the correlation between the inflammatory and nutritional factors identified in ultimate model was investigated initially, the causal relationship of these factors is unknown, which might have impact on clinical management. Some promising new statistical methods could assist in quantifying robustness of causal inferences in future research (<xref ref-type="bibr" rid="ref54">54</xref>).</p>
</sec>
<sec sec-type="conclusions" id="sec19">
<title>Conclusion</title>
<p>In summary, we have developed and validated a nomogram that incorporates CT body composition data and inflammatory&#x2013;nutritional scores to predict postoperative complications in patients with ASBO. Given its usability and the positive results achieved in our initial cohort, this model demonstrates potential as an effective tool for guiding nutritional treatment and decision-making in ASBO cases in future clinical settings.</p>
</sec>
<sec sec-type="data-availability" id="sec20">
<title>Data availability statement</title>
<p>The original contributions presented in the study are included in the article/<xref rid="SM1" ref-type="supplementary-material">Supplementary material</xref>, further inquiries can be directed to the corresponding authors.</p>
</sec>
<sec sec-type="author-contributions" id="sec21">
<title>Author contributions</title>
<p>ZW: Data curation, Writing &#x2013; original draft. BS: Formal analysis, Methodology, Writing &#x2013; original draft. YY: Formal analysis, Writing &#x2013; original draft. JL: Validation, Writing &#x2013; original draft. DL: Formal analysis, Writing &#x2013; review &#x0026; editing. YL: Writing &#x2013; review &#x0026; editing. RL: Funding acquisition, Writing &#x2013; review &#x0026; editing.</p>
</sec>
<sec sec-type="ethics-statement" id="sec22">
<title>Ethics statement</title>
<p>The studies involving humans were approved by The Affiliated Hospital of Qingdao University. The studies were conducted in accordance with the local legislation and institutional requirements. Written informed consent for participation was not required from the participants or the participants&#x2019; legal guardians/next of kin in accordance with the national legislation and institutional requirements.</p>
</sec>
</body>
<back>
<sec sec-type="funding-information" id="sec24">
<title>Funding</title>
<p>The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This research was supported by the National Natural Science Foundation of China (82000482) and Beijing Xisike Clinical Oncology Research Foundation (Y-NESTLE2022QN-0230).</p>
</sec>
<sec sec-type="COI-statement" id="sec25">
<title>Conflict of interest</title>
<p>The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p>
</sec>
<sec id="sec100" sec-type="disclaimer">
<title>Publisher&#x2019;s note</title>
<p>All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.</p>
</sec>
<sec sec-type="supplementary-material" id="sec26">
<title>Supplementary material</title>
<p>The Supplementary material for this article can be found online at: <ext-link xlink:href="https://www.frontiersin.org/articles/10.3389/fnut.2024.1345570/full#supplementary-material" ext-link-type="uri">https://www.frontiersin.org/articles/10.3389/fnut.2024.1345570/full#supplementary-material</ext-link></p>
<supplementary-material xlink:href="Table_1.docx" id="SM1" mimetype="application/vnd.openxmlformats-officedocument.wordprocessingml.document" xmlns:xlink="http://www.w3.org/1999/xlink"/>
</sec>
<fn-group>
<fn id="fn0001">
<p><sup>1</sup><ext-link xlink:href="http://www.tomovision.com" ext-link-type="uri">http://www.tomovision.com</ext-link>
</p>
</fn>
<fn id="fn0002">
<p><sup>2</sup><ext-link xlink:href="https://www.r-project.org" ext-link-type="uri">https://www.r-project.org</ext-link>
</p>
</fn>
</fn-group>
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</ref-list>
<glossary>
<def-list>
<title>Glossary</title>
<def-item><term>ASBO</term><def><p>Adhesive small bowel obstruction</p></def></def-item>
<def-item><term>NLR</term><def><p>Neutrophil-lymphocyte ratio</p></def></def-item>
<def-item><term>PLR</term><def><p>Platelet&#x2013;lymphocyte ratio</p></def></def-item>
<def-item><term>LMR</term><def><p>Lymphocyte-monocyte ratio</p></def></def-item>
<def-item><term>SII</term><def><p>Systemic immune-inflammation index</p></def></def-item>
<def-item><term>PNI</term><def><p>Prognostic nutritional index</p></def></def-item>
<def-item><term>CRP</term><def><p>C-reactive protein</p></def></def-item>
<def-item><term>BMI</term><def><p>Body mass index</p></def></def-item>
<def-item><term>CT</term><def><p>Computed tomography</p></def></def-item>
<def-item><term>ASA</term><def><p>American society of anesthesiologists</p></def></def-item>
<def-item><term>NRS</term><def><p>Nutritional risk score</p></def></def-item>
<def-item><term>SMI</term><def><p>Skeletal muscle index</p></def></def-item>
<def-item><term>SFI</term><def><p>Subcutaneous fat index</p></def></def-item>
<def-item><term>IFI</term><def><p>Intermuscular fat index</p></def></def-item>
<def-item><term>VFI</term><def><p>Visceral fat index</p></def></def-item>
<def-item><term>INS</term><def><p>Inflammatory-nutritional score</p></def></def-item>
<def-item><term>ROC</term><def><p>Receiver-operating characteristic</p></def></def-item>
<def-item><term>DCA</term><def><p>Decision curve analysis</p></def></def-item>
<def-item><term>OR</term><def><p>Odds ratio</p></def></def-item>
</def-list>
</glossary>
</back>
</article>