Demand Ischemia as a Predictor of Mortality in Older Patients With Delirium

Introduction

Cardiovascular disease (CVD) is the leading cause of death in the United States and worldwide, inflicting a significant societal burden in terms of morbidity, mortality, disability, and healthcare costs (1). Coronary artery disease (CAD) is one of the most common CVDs seen in older adults. The incidence and prevalence of CAD increase with age (2). CAD often presents as an acute coronary syndrome (ACS) wherein a plaque rupture or erosion causes total or subtotal coronary artery obstruction and subsequent myocardial ischemia/necrosis (Type 1 myocardial infarction, T1MI). This myocardial injury causes elevation of cardiac biomarkers such as cardiac troponin T and troponin I (3). Troponin assays detect minor myocardial injury with high sensitivity and specificity and have become the cornerstone for the diagnosis of myocardial injury. These cardiac biomarkers, however, do not define the cause of myocardial injury and can be elevated in the absence of coronary artery obstruction and non-coronary diseases as well (4). This non-coronary release of troponin occurs due to a mismatch between myocardial oxygen demand and supply and is known as demand ischemia or Type II myocardial infarction (T2MI) (3, 4). The hallmark of type II MI is the absence of a plaque rupture or erosion as the primary cause of this release and can occur both in the presence or absence of obstructive CAD. This may happen in several non-thrombotic coronary and systemic diseases such as sepsis, hypoxia, anemia, arrhythmia, congestive heart failure, pulmonary embolism, myocarditis, and renal failure (4).

One such condition that may elicit an imbalance between myocardial oxygen demand and supply is delirium. Delirium is a neuropsychiatric syndrome frequently encountered in hospitalized old patients. The reported prevalence of delirium varies by patient group and setting and ranges from 23% in medical inpatients to 83% in the intensive care unit (5, 6). Delirium results in an altered state of arousal ranging from hypoactive response to hypervigilance, psychosis, and severe agitation. The neuropsychiatric symptoms cause severe physiological distress with subsequent catecholamine release, which in turn, can invoke demand ischemia (7, 8). Of note, both ischemia and delirium not only coexist in hospitalized older patients but also share similar precipitating factors and pathophysiological mechanisms (4, 5). Thus, it is of clinical importance to evaluate the impact of myocardial infarction on outcomes in the setting of delirium. In patients with T1MI, the occurrence of delirium is associated with increased mortality (9, 10). However, the impact of T2MI, and delirium on clinical outcomes is largely unexplored. In this study, we aim to evaluate the effects of demand ischemia in hospitalized older patients with delirium on in-hospital mortality and length of stay (LOS) using the largest US inpatient care database, National Inpatient Sample (NIS).

Methods

Data Source

We obtained data from the year 2010 to 2014 from NIS, which is the largest publicly available all-payer inpatient care database in the United States (11). It is developed as a part of the Healthcare Cost and Utilization Project (HCUP) and is sponsored by the Agency for Healthcare Research and Quality. The NIS dataset includes data from all non-federal, short-term, general, and other specialty hospitals in the United States (excluding rehabilitation and long-term acute care hospitals) in the form of de-identified patient information containing demographics, discharge diagnoses, comorbidities, procedures, outcomes, and hospitalization costs. All the states participating in HCUP provide data to the NIS, covering >95% of the U.S. population. The database was designed to include data from a 20% sample of discharges from all participating hospitals. This large sample size of the NIS reduces the margin of error for estimates and delivers more stable and precise estimations (11). The study was exempt from an Institutional Review Board approval because HCUP-NIS is a publicly available database containing only de-identified patient information.

Study Population

We used the International Classification of Diseases-Ninth Edition-Clinical Modification (ICD-9-CM) diagnosis codes 290.3, 290.41, 293.0, 293.1, 348.31, and 780 to identify all patients who are 65 years of age or older, hospitalized with a primary or secondary diagnosis of delirium. We then used ICD-9-CM diagnosis code 411.89 to identify patients with T2MI or demand ischemia. We excluded patients who were below the age of 65, and had a diagnosis of acute myocardial infarction (MI) or cardiogenic shock. Also, in order to reduce the possibility of data duplication, patients with an indicator of transfer to another acute care facility were excluded.

Outcomes Measured

We studied the impact of demand ischemia in older patients with delirium during inpatient hospitalization. All-cause in-hospital mortality, which was defined as “died” during the hospitalization encounter in the NIS database, was the primary outcome of interest and median length of stay (LOS) was used as the secondary outcome.

Patient Characteristics

Baseline patient characteristics included demographics (age, sex, race/ethnicity), and other clinically relevant characteristics (non-cardiogenic shock, arrhythmias, mechanical ventilation, post procedure bleeding, sepsis, acute kidney injury, fluid and electrolyte disorders, hypertension, dyslipidemia, smoking). Clinical Classification Software codes were used to identify the comorbidities described above.

Statistical Analysis

A descriptive analysis was conducted to summarize variables for patients with demand ischemia and those without the condition, separately. The two groups of patients were compared with respect to each variable using a Fisher's exact test. A multivariate logistic regression model was used to associate DI with in-hospital mortality adjusting for all demographic and clinically relevant characteristics. Subgroup analysis was performed excluding critically ill patients with non-cardiogenic shock or mechanical ventilation. Length of stay was analyzed in the same manner restricted to survivors using a Poisson regression model. p-values smaller than 5% were considered statistically significant. All the statistical analyses were carried out in R 4.1.0.

Results

Baseline Characteristics

We included a total of 232,137 admission records from patients older than 65 years of age, who had delirium during the inpatient hospitalization from the year 2010 to 2014 NIS dataset. Out of these, 1,079 (0.49%) patients denoted the diagnosis of demand ischemia. Patients with demand ischemia were slightly older as compared to those without demand ischemia (79.43 ±8.20 vs. 78.16 ± 8.03 years, p < 0.001), as shown in Table 1. The incidence of demand ischemia increased significantly with aging as evident by a higher incidence in the >80-year-old age groups than that in the younger groups. The frequency of coded diagnoses of demand ischemia increased gradually with each year from 0.13% in 2010 to 0.90% in 2014. Patients with demand ischemia were associated with higher incidence of non-cardiogenic shock (18 vs. 4%, p < 0.001), mechanical ventilation (19 vs. 6%, p < 0.001), sepsis (37 vs. 14%, p < 0.001), acute kidney injury (41 vs. 24%, p < 0.001), chronic pulmonary disease (28.6 vs. 24.7%, p = 0.003), gastrointestinal bleed (2.4 vs. 1%, p < 0.001), and peripheral vascular disease (13.7 vs. 9.2%, p < 0.001) (Table 1).

TABLE 1

Table 1. Baseline demographics and comorbidities of patients with and without demand ischemia.

In-hospital Mortality

The overall mortality of patients with delirium was 12%. Patients with demand ischemia were noted to have higher unadjusted in-hospital mortality than those without demand ischemia (28 vs. 12%, p < 0.001), as shown in Table 1. However, there was no significant association between demand ischemia and in-hospital mortality after adjusting for demographics and other clinical variables (Table 2; adjusted OR: 1.14; 95% CI: 0.96–1.35; p = 0.14). As a significant number of deaths were from critically ill patients with either non-cardiogenic shock or mechanical ventilation (11,552 of 19,879, 58%), further analysis to exclude the aforementioned critically ill patients was performed. When patients with either non-cardiogenic shock or mechanical ventilation were excluded, demand ischemia was noted to have a significant association with in-hospital mortality (Table 3; adjusted OR: 1.39; 95% CI: 1.13–1.71; p = 0.002).

TABLE 2

Table 2. Association between demand ischemia and in hospital mortality adjusting for demographics and clinically relevant characteristics.

TABLE 3

Table 3. Association Between demand ischemia and in hospital mortality adjusting for demographics and clinically relevant characteristics in records without either non-cardiogenic shock or mechanical ventilation.

Length of Stay

Among the survivors excluding those with non-cardiogenic shock or mechanical ventilation, patients with demand ischemia (median 4 days, range: 0–85) had a longer median LOS than those without demand ischemia (median 4 days, range: 0–354) (p < 0.001) but the LOS rate ratio (RR) comparing demand ischemia patients to non-demand ischemia patients after adjusting for demographic and clinically relevant characteristics was not statistically significant (adjusted RR 1.03; 95% CI: 1.00–1.06; p = 0.096) (Table 4).

TABLE 4

Table 4. Association between demand ischemia and length of stay in survivors without either non-cardiogenic shock or mechanical ventilation adjusting for demographics and clinically relevant characteristics.

Discussion

The results of our study demonstrated that in older patients with delirium, demand ischemia did not have a significant impact on mortality in critically ill patients, such as those with non-cardiogenic shock or respiratory failure requiring mechanical ventilation. However, excluding patients with the aforementioned two conditions, demand ischemia was associated with significantly higher in-hospital mortality, implicating its value as an independent predictor of mortality.

The number of patients diagnosed with demand ischemia increased gradually from the year 2010 to 2014 in the NIS database. This might be attributed to improved definition, more precise profiling, and increased awareness of this condition among clinicians (12). Demand ischemia also became more frequent with aging and was highest in the age group older than 90 years. Increased prevalence and severity of CAD with aging resulting in lower thresholds for invoking demand ischemia may explain the increasing trend of demand ischemia with aging.

The reported in-hospital mortality rates with septic shock and demand ischemia range from 26.9 to 56% (13–17). The mortality rate in our cohort of critically sick patients with non-cardiogenic shock or respiratory failure requiring mechanical ventilation was 59.2%. This slightly higher mortality rate in our study was likely due to older age, a sicker population, and the presence of delirium. Our results, nevertheless, are consistent with prior studies showing no impact of non-coronary troponin elevation on mortality in patients with septic shock (13, 14).

However, overall data related to the impact of demand ischemia on mortality in critically sick patients are conflicting, with some of the studies showing higher in-hospital mortality (15, 16).

Demand ischemia remained an independent predictor of mortality in delirium patients without non-cardiogenic shock or mechanical ventilation. Previous studies evaluating the impact of delirium in older patients with acute myocardial infarction have reported in-hospital mortality ranging from 10.5% in all comers to 42.7% in patients with ST-elevation MI (9, 10). In-hospital mortality with demand ischemia ranges from 12 to 26.9% and is driven primarily by acute systemic illness and comorbidities (13, 18, 19). The overall mortality with demand ischemia in our study was 28 and 15.5% in patients without mechanical ventilation and/or non-cardiogenic shock. Our results are consistent with the previous analysis, even though we included older patients with concurrent delirium.

Demand ischemia and delirium are highly prevalent in older hospitalized adults. Both of these entities often have shared predisposing and precipitating factors, complex multifactorial etiology, and are associated with poor outcomes (20). The interplay between the two is not well understood; therefore, prevention, timely diagnosis and management are necessary to reduce short- and long-term poor outcomes. Once delirium is diagnosed, it is necessary to identify and treat underlying triggers to reduce downstream complications. Once a myocardial injury is detected, the first step is to determine if the myocardial injury is due to a plaque disruption (type I MI) or supply demand mismatch (type II MI). Timely differentiation of type I and type II MI is often challenging, however, critical for optimal care, as the treatment widely differs (21). A combination of symptoms, electrocardiogram (EKG) changes, cardiac biomarkers, invasive and non-invasive imaging can be utilized to guide further management. However, the presence of delirium adds complexity to assessment and decision-making as patients may have marked inattention and/or behavioral disturbances that may preclude appropriate cardiac evaluation. If based on symptoms, EKG changes, pre-test probability of type I MI is low, and troponin elevation occurs in the context of acute illness such as infection, anemia, respiratory failure, and arrhythmias, and multiple comorbidities, type II MI is likely, even though acute plaque ruptures can be triggered by systemic inflammation in the setting of acute illness (22). In cases of diagnostic uncertainty, coronary angiography or non-invasive imaging modalities such as coronary CTA, echocardiography and cardiac MRI may be more suitable to aid in the differentiation of type I and type II MI (23).

Treatment strategies for type II MI are directed at the specific underlying etiology. Demand ischemia is associated with higher short- and long-term all-cause mortality as well as cardiovascular mortality (18, 21, 24–26). The higher early mortality is driven primarily by non-cardiovascular acute illness. There are no treatment options to reduce in-hospital mortality. Beta-blockers can be used in hemodynamically stable patients to reduce the heart rate and subsequent myocardial oxygen demand. However, whether it reduces mortality in the short term is not known. Long-term mortality is driven by both non-cardiovascular and cardiovascular causes, which emphasizes the importance of cardiac evaluation to improve long term outcomes (26, 27). Once patients are discharged from the hospital, they may benefit from a further cardiac work up for underlying cardiovascular disease such as atherosclerosis, structural heart disease or heart failure (23).

However, following discharge, delirium may be protracted, and is strongly associated with several short- and long-term complications such as increased risk of institutionalization, falls, persistent cognitive dysfunction, poor quality of life, long term disability, and mortality (5, 20, 28, 29). Therefore, further cardiac work up may not be feasible or desired depending upon frailty, functional status, cognitive impairment, the likelihood of recovery, severity of psychiatric and non-cardiac comorbidities, polypharmacy, and most importantly patient's preference and goals of care. And often, depending upon a patient's preference, treatments aimed at improving function and reducing symptoms may be more suitable than those focused on prolonging survival (30).

Our study has certain limitations. First, because NIS is an administrative database, the accuracy of the data depends highly on the training and expertise of the coders. Hence, there is the potential of unrecognized miscoding of diagnosis codes, leading to underestimation of demand ischemia or delirium based on ICD-9-CM coding (31). Second, we were not able to determine the time of onset, duration, type or severity of delirium as well as demand ischemia occurring during the hospitalization. We were also unable to examine the temporal relation between demand ischemia and the time of occurrence of delirium. Third, all cases coded as T2MI may not only be due to demand supply mismatch, and a plaque rupture or erosion cannot be completely ruled out without a coronary angiogram. Fourth, data on laboratory values, use of adjunctive medications and cause of death were not available. Fifth, this is a retrospective observational study and an unobserved confound might have occurred, even after accounting for a relatively large number of covariates. Lastly, data in the NIS are limited to in-hospital events, and information on long-term outcomes is not available.

Despite the limitations, our study has significant value. First, we used a large dataset that is representative of a large national population, leading to a higher power as well as generalizability. Second, our results emphasize the utmost need for finding appropriate novel diagnostic and intervention strategies for delirium as well as demand ischemia to reduce short- and long-term poor outcomes. In critically sick delirious patients with non-cardiogenic shock or mechanical ventilation, demand ischemia appears to be an innocent bystander without significant impact on in-hospital mortality. Given a >55% mortality rate in these patients, depending upon clinical circumstances, goals of care, including end-of life discussions may be appropriate. Patients with delirium who are not critically sick may benefit from further cardiac intervention after an individualized risk-benefit assessment (23).

Conclusion

Delirium and demand ischemia is common in hospitalized older individuals and represents end organ damage as a result of acute illness. Demand ischemia did not affect mortality in critically ill patients, where aggressive cardiac intervention may not be warranted. In other non-critical patients. However, demand ischemia was significantly associated with mortality after adjusting for demographics and clinical characteristics, likely paralleling severity of the underlying acute illness. Measures aimed at the prevention and management delirium and demand ischemia are of paramount importance.

Contributions

Troponin assays are frequently used to diagnose myocardial injury, and are one of the cornerstones for the diagnosis of acute coronary syndrome (ACS). Troponin assays, however, are not specific and can occur due to non-coronary causes as well. This is termed as demand ischemia and occurs due to myocardial supply demand mismatch. Delirium is another such condition that occurs due to demand supply mismatch in brain. Delirium often shares the similar precipitating factors and pathophysiological mechanisms. Our results indicate that demand ischemia did not have a significant impact on mortality in critically ill older patients with delirium. However, in patients who are not critically sick, demand ischemia was associated with significant higher in-hospital mortality, implicating its value as an independent predictor. These results have important clinical implications, such as, in critically sick patients with delirium and demand ischemia, aggressive cardiac intervention may not be warranted. And perhaps, goals of care, including end-of life discussions may be appropriate as per the clinical condition given >50% mortality in these patients. Patients with delirium who are not critically sick, may benefit from further cardiac intervention after an individualized risk-benefit assessment.

Data Availability Statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

Ethics Statement

Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.

Author Contributions

MK: conceptualization, writing, editing, and reviewing. SP, GK, and KC: conceptualization, editing, and reviewing. LG, CK, and HS: statistical analysis, editing, and reviewing. All authors contributed to the article and approved the submitted version.

Conflict of Interest

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.

Publisher's Note

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.

References

1. Tsao CW, Aday AW, Almarzooq ZI, Alonso A, Beaton AZ, Bittencourt MS, et al. Heart disease and stroke statistics-2022 update: a report from the American heart association. Circulation. (2022) 145:e153–639. doi: 10.1161/CIR.0000000000001052

PubMed Abstract | CrossRef Full Text | Google Scholar

2. Yazdanyar A, Newman AB. The burden of cardiovascular disease in the elderly: morbidity, mortality, and costs. Clin Geriatr Med. (2009) 25:563–77. doi: 10.1016/j.cger.2009.07.007

PubMed Abstract | CrossRef Full Text | Google Scholar

3. Muzyk P, Twerenbold R, Morawiec B, Ayala PL, Boeddinghaus J, Nestelberger T, et al. Use of cardiac troponin in the early diagnosis of acute myocardial infarction. Kardiol Pol. (2020) 78:1099–106. doi: 10.33963/KP.15585

PubMed Abstract | CrossRef Full Text | Google Scholar

4. Agewall S, Giannitsis E, Jernberg T, Katus H. Troponin elevation in coronary vs. non-coronary disease. Eur Heart J. (2011) 32:404–11. doi: 10.1093/eurheartj/ehq456

PubMed Abstract | CrossRef Full Text | Google Scholar

5. Wilson JE, Mart MF, Cunningham C, Shehabi Y, Girard TD, MacLullich AMJ, et al. Delirium. Nat Rev Dis Primers. (2020) 6:90. doi: 10.1038/s41572-020-00223-4

PubMed Abstract | CrossRef Full Text | Google Scholar

6. Fuchs S, Bode L, Ernst J, Marquetand J, von Kanel R, Bottger S. Delirium in elderly patients: prospective prevalence across hospital services. Gen Hosp Psychiatry. (2020) 67:19–25. doi: 10.1016/j.genhosppsych.2020.08.010

PubMed Abstract | CrossRef Full Text | Google Scholar

7. Schwartzberg D, Shiroff A. Repetitive myocardial infarctions secondary to delirium tremens. Case Rep Crit Care. (2014) 2014:638493. doi: 10.1155/2014/638493

PubMed Abstract | CrossRef Full Text | Google Scholar

8. Maclullich AM, Ferguson KJ, Miller T, de Rooij SE, Cunningham C. Unravelling the pathophysiology of delirium: a focus on the role of aberrant stress responses. J Psychosom Res. (2008) 65:229–38. doi: 10.1016/j.jpsychores.2008.05.019

PubMed Abstract | CrossRef Full Text | Google Scholar

9. Patil S, Gonuguntala K, Rojulpote C, Kumar M, Corradi JP, Chen K. Delirium is an important predictor of mortality in elderly patients with ST-elevation myocardial infarction: insight from National Inpatient Sample database. Coron Artery Dis. (2020) 31:665–70. doi: 10.1097/MCA.0000000000000978

PubMed Abstract | CrossRef Full Text | Google Scholar

10. Abdullah A, Eigbire G, Salama A, Wahab A, Awadalla M, Hoefen R, et al. Impact of delirium on patients hospitalized for myocardial infarction: a propensity score analysis of the National Inpatient Sample. Clin Cardiol. (2018) 41:910–5. doi: 10.1002/clc.22972

PubMed Abstract | CrossRef Full Text | Google Scholar

11. Healthcare Cost and Utilization Project (HCUP). Overview of the National (Nationwide) Inpatient Sample (NIS)(Agency for Healthcare Research and Quality, Rockville, MD) Available at: https://www.hcup-us.ahrq.gov/overview.jsp (accessed October 01, 2020).

12. Thygesen K, Alpert JS, Jaffe AS, Chaitman BR, Bax JJ, Morrow DA, et al. Fourth universal definition of myocardial infarction. J Am Coll Cardiol. (2018) 72:2231–64. doi: 10.1016/j.jacc.2018.08.1038

PubMed Abstract | CrossRef Full Text | Google Scholar

13. Shah M, Patnaik S, Maludum O, Patel B, Tripathi B, Agarwal M, et al. Mortality in sepsis: comparison of outcomes between patients with demand ischemia, acute myocardial infarction, and neither demand ischemia nor acute myocardial infarction. Clin Cardiol. (2018) 41:936–44. doi: 10.1002/clc.22978

PubMed Abstract | CrossRef Full Text | Google Scholar

14. Mehta S, Granton J, Gordon AC, Cook DJ, Lapinsky S, Newton G, et al. Cardiac ischemia in patients with septic shock randomized to vasopressin or norepinephrine. Crit Care. (2013) 17:R117. doi: 10.1186/cc12789

PubMed Abstract | CrossRef Full Text | Google Scholar

15. Choon-ngarm T, Partpisanu P. Serum cardiac troponin-T as a prognostic marker in septic shock. J Med Assoc Thai. (2008) 91:1818–21.

PubMed Abstract | Google Scholar

16. Mehta NJ, Khan IA, Gupta V, Jani K, Gowda RM, Smith PR. Cardiac troponin I predicts myocardial dysfunction and adverse outcome in septic shock. Int J Cardiol. (2004) 95:13–7. doi: 10.1016/j.ijcard.2003.02.005

PubMed Abstract | CrossRef Full Text | Google Scholar

17. Alatassi A, Habbal M, Tamim H, Sadat M, Al Qasim E, Arabi YM. Association between troponin-I levels and outcome in critically ill patients admitted to non-cardiac intensive care unit with high prevalence of cardiovascular risk factors. BMC Anesthesiol. (2018) 18:54. doi: 10.1186/s12871-018-0515-7

PubMed Abstract | CrossRef Full Text | Google Scholar

18. Saaby L, Poulsen TS, Diederichsen AC, Hosbond S, Larsen TB, Schmidt H, et al. Mortality rate in type 2 myocardial infarction: observations from an unselected hospital cohort. Am J Med. (2014) 127:295–302. doi: 10.1016/j.amjmed.2013.12.020

PubMed Abstract | CrossRef Full Text | Google Scholar

19. Meigher S, Thode HC, Peacock WF, Bock JL, Gruberg L, Singer AJ. Causes of elevated cardiac troponins in the emergency department and their associated mortality. Acad Emerg Med. (2016) 23:1267–73. doi: 10.1111/acem.13033

PubMed Abstract | CrossRef Full Text | Google Scholar

20. Kalish VB, Gillham JE, Unwin BK. Delirium in older persons: evaluation and management. Am Fam Physician. (2014) 90:150–8.

PubMed Abstract | Google Scholar

21. Lopez-Cuenca A, Gomez-Molina M, Flores-Blanco PJ, Sanchez-Martinez M, Garcia-Narbon A, De Las Heras-Gomez I, et al. Comparison between type-2 and type-1 myocardial infarction: clinical features, treatment strategies and outcomes. J Geriatr Cardiol. (2016) 13:15–22.

PubMed Abstract | Google Scholar

22. Musher DM, Abers MS, Corrales-Medina VF. Acute infection and myocardial infarction. N Engl J Med. (2019) 380:171–6. doi: 10.1056/NEJMra1808137

PubMed Abstract | CrossRef Full Text | Google Scholar

23. DeFilippis AP, Chapman AR, Mills NL, de Lemos JA, Arbab-Zadeh A, Newby LK, et al. Assessment and treatment of patients with type 2 myocardial infarction and acute nonischemic myocardial injury. Circulation. (2019) 140:1661–78. doi: 10.1161/CIRCULATIONAHA.119.040631

PubMed Abstract | CrossRef Full Text | Google Scholar

24. Sandoval Y, Thordsen SE, Smith SW, Schulz KM, Murakami MM, Pearce LA, et al. Cardiac troponin changes to distinguish type 1 and type 2 myocardial infarction and 180-day mortality risk. Eur Heart J Acute Cardiovasc Care. (2014) 3:317–25. doi: 10.1177/2048872614538411

PubMed Abstract | CrossRef Full Text | Google Scholar

25. Sandoval Y, Smith SW, Sexter A, Thordsen SE, Bruen CA, Carlson MD, et al. Type 1 and 2 myocardial infarction and myocardial injury: clinical transition to high-sensitivity cardiac troponin I. Am J Med. (2017) 130:1431–9. doi: 10.1016/j.amjmed.2017.05.049

PubMed Abstract | CrossRef Full Text | Google Scholar

26. Sandoval Y, Jaffe AS. Type 2 Myocardial infarction: JACC review topic of the week. J Am Coll Cardiol. (2019) 73:1846–60. doi: 10.1016/j.jacc.2019.02.018

PubMed Abstract | CrossRef Full Text | Google Scholar

27. Raphael CE, Roger VL, Sandoval Y, Singh M, Bell M, Lerman A, et al. Incidence, trends, and outcomes of type 2 myocardial infarction in a community cohort. Circulation. (2020) 141:454–63. doi: 10.1161/CIRCULATIONAHA.119.043100

PubMed Abstract | CrossRef Full Text | Google Scholar

28. Witlox J, Eurelings LS, de Jonghe JF, Kalisvaart KJ, Eikelenboom P, van Gool WA. Delirium in elderly patients and the risk of postdischarge mortality, institutionalization, and dementia: a meta-analysis. JAMA. (2010) 304:443–51. doi: 10.1001/jama.2010.1013

PubMed Abstract | CrossRef Full Text | Google Scholar

29. McCusker J, Cole MG, Dendukuri N, Belzile E. Does delirium increase hospital stay? J Am Geriatr Soc. (2003) 51:1539–46. doi: 10.1046/j.1532-5415.2003.51509.x

PubMed Abstract | CrossRef Full Text | Google Scholar

30. Nanna MG, Peterson ED, Wu A, Harding T, Galanos AN, Wruck L, et al. Age, knowledge, preferences, and risk tolerance for invasive cardiac care. Am Heart J. (2020) 219:99–108. doi: 10.1016/j.ahj.2019.09.008

PubMed Abstract | CrossRef Full Text | Google Scholar

31. Kim DH, Lee J, Kim CA, Huybrechts KF, Bateman BT, Patorno E, et al. Evaluation of algorithms to identify delirium in administrative claims and drug utilization database. Pharmacoepidemiol Drug Saf. (2017) 26:945–53. doi: 10.1002/pds.4226

PubMed Abstract | CrossRef Full Text | Google Scholar