Aneurysmal subarachnoid hemorrhage (aSAH) is an acute cerebrovascular injury with high morbidity and mortality worldwide (1). Roughly one-third of survivors have severe disability and functional dependence, some of which may be predicted and treated early (2, 3). Therefore, a practical prediction model for treating and evaluating these patients with aSAH is urgently needed.

Several studies have shown that a series of serum biochemical and immunological indices could predict the prognosis of patients with aSAH after surgery, including white blood cell (WBC) count, neutrophil-to-lymphocyte ratio (NLR), C-reactive protein level, and blood glucose level on admission (4–6). Meanwhile, predictive models combining multiple independent predictors have been used to predict prognosis. However, these predictive models lack calibration tests, model external validation, or evaluation of their clinical usefulness; therefore, the accuracy and practicability of the models are difficult to assess (7, 8). High-quality predictive models are necessary to guide clinical decision-making, personal care, and patient management (9). Therefore, a more practical and accurate predictive model is urgently needed in this field. A web-based dynamic nomogram that precisely calculates the probability of the disease is a more precise and practical tool than standard nomograms and some predictive models (10, 11). However, no study has reported a dynamic nomogram to predict the clinical prognosis after aSAH.

Thus, this study aimed to develop a dynamic web-based nomogram that incorporated blood laboratory tests, clinical state at admission, and postoperative complications for the prognosis of patients with aSAH. The predictive model was validated using internal and independent external validation cohorts.

This study constructed and validated a dynamic nomogram model to predict unfavorable prognosis at 3 months after aSAH. The nomogram incorporated the NLR, admission WFNS grade, and DCI. To our knowledge, this study is the first to develop a web-based dynamic nomogram application to calculate the risk of poor prognosis (mRS, 3–6 points). In addition, external validation using data from another center was conducted for the first time. Precise calculation of the probability of the disease makes the dynamic nomogram a more accurate and practical tool compared to ordinary predictive models.

In a previous study, Zhang et al. developed an ordinary nomogram combined with a systemic inflammation response index and other conventional factors to predict the prognostic outcome in patients with aSAH (8). These factors were selected using univariate and multivariate logistic analyses. Lai et al. conducted a predicting model for aSAH prognosis, which included the GCS score, surgical clipping, and NLR (7). These independent prognostic variables were also selected by multivariate logistic regression. Compared with the study by Zhang et al., the model by Lai et al. was only internally validated rather than externally validated. Although a large-scale retrospective study has been conducted on poor recovery in patients with aSAH, the study only contained an internal validation (31). Our study included a total of 84 patients retrospectively collected from September 2019 to September 2020 from another center for external validation of the model. The validation AUC value was nearly identical to the original AUC value in the training cohort. The calibration curve showed good calibration of the model in the external validation cohort.

Contrary to the conventional multivariate logistic method, this study applied the LASSO regression analysis, which performed well in reducing the data dimensionality and decreasing multicollinearity between variables, and it was used to select variables for multivariable logistic regression by minimizing coefficients and reducing variance (32). As a result, an optimal λ of 0.1356, with log(λ) = −1.997, was adapted. Three factors with non-zero coefficients were finally selected by 5-fold cross-validation to prevent overfitting. A logistic regression model was then used to establish a nomogram. The original AUC was 0.88, while the bias-corrected C-index with 1,000 bootstraps was 0.82, suggesting that the model had excellent discrimination. Compared with a single independent prognostic factor, the nomogram model better predicted the occurrence of DCI. The calibration capacity was also internally validated; the Hosmer–Lemeshow p-value in the training cohort suggested an excellent fit of the nomogram. By contrast, a Brier score and calibration curve with 1,000 bootstrap resamples showed that the model had a good calibration ability, demonstrating no significant deviation between the actual and predicted probabilities. In addition, DCA showed a superior overall net benefit for a threshold probability of 0–1. The CIC also demonstrated a good performance over the entire range of threshold probabilities.

We observed no significant difference in NLR between the training and external validation cohorts. However, the NLR in patients with an unfavorable prognosis was higher than that in patients with a favorable prognosis in the training cohort (p = 0.005). Our study identified NLR on admission as an independent risk factor for unfavorable prognosis, similar to previous findings (4, 7, 33, 34). Lai et al. reported that a high NLR on admission could increase the risk of poor prognosis 3 months postoperatively. Giede-Jeppe et al. suggested that higher NLR was associated with more significant neurological injury, as reflected by higher subarachnoid and intraventricular blood. Therefore, increased NLR resulted in worse functional outcomes beyond these associations. The following possible mechanisms may explain why high NLR on admission increased the risk of unfavorable prognosis at 3 months after aSAH. First, a high NLR could promote the synthesis and secretion of matrix metalloproteinase, which could degrade components of the extracellular matrix and play an essential role in the blood–brain barrier, resulting in secondary brain injury (35, 36). Second, increased NLR at admission may include excessive neuroinflammation caused by elevated neutrophil granulocyte counts and lymphocyte depletion leading to immunodepression. Physiological stress responses lead to an increased postictal release of endogenous catecholamines and cortisol, resulting in secondary brain injury and possibly inflammatory complications (37, 38).

The WFNS grade or GCS have gradually become essential validated scales to evaluate clinical characteristics and neurological status on admission. A good grade was defined as I–III on WFNS, while IV–V on the WFNS was considered poor (39, 40). In our study, 40 (32%) and 11 (13%) patients in the training and external validation cohorts, respectively, had WFNS grades of IV–V. WFNS grade (p = 0.002) on admission independently predicted unfavorable prognosis and patients with WFNS grades IV–V had a higher risk of developing a poor prognosis compared with patients with WFNS grades I–III. This finding is similar to those of previous studies showing that WFNS grades IV–V was correlated with a poor outcome and was a good predictor of unfavorable prognosis after aSAH (41, 42).

Furthermore, the DCI occurrence in the two cohorts was 35 and 15%, respectively. The onset of aSAH was concentrated during the COVID-19 period, which resulted in patients not receiving timely anti-vasospasm. It may be interpreted that the training cohort had a larger percentage of DCI. DCI was identified as another crucial prognostic factor in this study. The results of a large-scale retrospective study also suggested that DCI plays an important role in short-term and long-term prognosis (43). Cerebrovascular disorders, microthrombosis, cortical diffuse depolarization, and neuroinflammation all reportedly play potential roles in DCI pathogenesis (17, 44–46).

Contrary to nomograms or other models calculated as an approximation in previous studies, the web-based dynamic nomogram application developed in the present study (https://hu-ping.shinyapps.io/DynNomapp/) can be used to calculate an exact value for developing poor outcomes at 3 months after aSAH. This tool will help in the individualized treatment and management of patients, including neuro-intensive care, blood pressure control, nimodipine anti-vasospasm, anti-inflammatory drugs, and rehabilitation treatment, and help neurosurgeons make better clinical decisions.

However, this study has several limitations. First, this was a retrospective study; therefore, prospective studies are needed to verify our results. Second, owing to the low effective sample size in the training cohort, to reduce the data dimensionality and decrease multicollinearity between variables, we used LASSO regression analysis to select predictors. Third, future studies should collect complete clinical treatment information.

This study identified independent predictors for an unfavorable prognosis in patients with aSAH, including NLR on admission, the WFNS grade, and DCI. On this basis, a web-based dynamic nomogram application, after externally validated, was developed to calculate the precise probability of a poor outcome in patients 3 months after aSAH. This will benefit personalized treatment and patient management and help neurosurgeons make better clinical decisions.

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

The studies involving human participants were reviewed and approved by the Medical Ethics Committee of Renmin Hospital of Wuhan University. (approval number WDRM2021-K022). Written informed consent from the patients/participants or patients/participants legal guardian/next of kin was not required to participate in this study in accordance with the national legislation and the institutional requirements.

PH, YX, GD, and QC: Study design. PH, SZ, XZ, QS, and YW: Literature search. PH, YLiu, YLi, and LY: Data acquisition, data analysis, and statistical analysis. PH, YX, YQ, HZ, GD, and QC: Article preparation, editing, and review. All authors read and approved the final article.

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.

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