Mortality in sepsis: Comparison of outcomes between patients with demand ischemia, acute myocardial infarction, and neither demand ischemia nor acute myocardial infarction

Shah, Mahek; Patnaik, Soumya; Maludum, Obiora; Patel, Brijesh; Tripathi, Byomesh; Agarwal, Manyoo; Garg, Lohit; Agrawal, Sahil; Jorde, Ulrich P.; Martinez, Matthew W.

doi:10.1002/clc.22978

Cited by 12 publications

(10 citation statements)

References 40 publications

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“…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 ).…”

Section: Discussionsupporting

confidence: 93%

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

Kumar

Patil

Godoy

et al. 2022

Front. Cardiovasc. Med.

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IntroductionThe impact of demand ischemia on clinical outcomes in patients with delirium remains largely unexplored. This study aims to evaluate the effects of demand ischemia in older patients with delirium on in-hospital mortality and length of stay (LOS) using the largest US inpatient care database, National Inpatient Sample (NIS).MethodsWe obtained data from the year 2010 to 2014 National Inpatient Sample (NIS). We used the International Classification of Diseases-Ninth Edition-Clinical Modification (ICD-9-CM) diagnosis codes to identify all the records with a primary or secondary diagnosis of delirium with or without demand ischemia and other clinical characteristics. We then compared in-hospital mortality and length of stay (LOS) in patients with and without demand ischemia.ResultsWe analyzed 232,137 records. Patients with demand ischemia had higher overall in-hospital mortality than those without demand ischemia (28 vs. 12%, p < 0.001). After adjusting for clinical comorbidities and complications, demand ischemia was no longer associated with increased in-hospital mortality (OR: 1.14; 95% CI: 0.96–1.35; p = 0.141). However, further analysis with the exclusion of critically ill patients with non-cardiogenic shock or mechanical ventilation showed a significant association of demand ischemia with increased in-hospital mortality (adjusted OR: 1.39; 95% CI: 1.13–1.71; p = 0.002). Among non-critically ill survivors, patients with demand ischemia had a longer median LOS [4, (3–7) days] than those without demand ischemia [4, (2–6) days] (p < 0.001). However, the difference was not statistically significant after adjustment for covariates.Conclusion/RelevanceDemand ischemia did not affect mortality in critically sick patients. In non-critically ill patients, however, demand ischemia was significantly associated with increased in-hospital mortality, likely due to the severity of the underlying acute illness. Measures aimed at mitigating risk factors that contribute to delirium and/or demand ischemia need to be explored.

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Section: Discussionsupporting

confidence: 93%

“…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%.…”

Section: Discussionmentioning

confidence: 99%

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

Kumar

Patil

Godoy

et al. 2022

Front. Cardiovasc. Med.

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“…Out of 16 patients on ECMO, 15 died with 94% mortality [12][13][14]. Additionally, the in-hospital mortality is high for patients with sepsis in addition to acute myocardial infarction as compared to patients without infarction (48.3% vs. 17.7%) regardless of COVID-19 infection [15].…”

Section: Discussionmentioning

confidence: 99%

COVID-19 with non-obstructive coronary artery disease in a young adult

Sheikh

Ijaz

Javed

et al. 2021

Journal of Community Hospital Internal Medicine Perspectives

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Systemic inflammatory response due to cytokine storm in severe COVID-19 cases can lead to acute myocardial infarction, also affecting the younger population, without significant risk factors. We present the case of a 36-year-old male with morbid obesity and well-controlled asthma who had developed COVID-19-induced acute respiratory distress syndrome requiring mechanical ventilation and, subsequently, extracorporeal membrane oxygenation (ECMO) who developed myocardial infarction on Day 10 of admission and died on Day 15 of admission due to sequelae of severe COVID-19 infection. In young patients with COVID-19induced respiratory infection, severe inflammatory response can lead to acute coronary syndrome in absence of obstructive lesions or plaque ruptures.

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“…When its level increases, it can reflect the increased severity of myocardial injury, with high specificity. [13][14][15] Previous studies have suggested that myocardial injury, as reflected by elevated cardiac troponin levels in plasma, is common in patients with community-acquired pneumonia. [16] The main reason for poor prognosis in patients with severe sepsis is that patients often have cardiac dysfunction.…”

Section: Discussionmentioning

confidence: 99%

Prediction of fatal adverse prognosis in patients with fever-related diseases based on machine learning: A retrospective study

Zhao

Che

et al. 2020

Chinese Medical Journal

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Background: Fever is the most common chief complaint of emergency patients. Early identification of patients at an increasing risk of death may avert adverse outcomes. The aim of this study was to establish an early prediction model of fatal adverse prognosis of fever patients by extracting key indicators using big data technology. Methods: A retrospective study of patients' data was conducted using the Emergency Rescue Database of Chinese People's Liberation Army General Hospital. Patients were divided into the fatal adverse prognosis group and the good prognosis group. The commonly used clinical indicators were compared. Recursive feature elimination method was used to determine the optimal number of the included variables. In the training model, logistic regression, random forest, adaboost, and bagging were selected. We also collected the emergency room data from December 2018 to December 2019 with the same inclusion and exclusion criterion. The performance of the model was evaluated by accuracy, F1-score, precision, sensitivity, and the areas under receiver operator characteristic curves (ROC-AUC). Results: The accuracy of logistic regression, decision tree, adaboost and bagging was 0.951, 0.928, 0.924, and 0.924, F1-scores were 0.938, 0.933, 0.930, and 0.930, the precision was 0.943, 0.938, 0.937, and 0.937, ROC-AUC were 0.808, 0.738, 0.736, and 0.885, respectively. ROC-AUC of ten-fold cross-validation in logistic and bagging models were 0.80 and 0.87, respectively. The top six coefficients and odds ratio (OR) values of the variables in the logistic regression were cardiac troponin T (CTnT) (coefficient = 0.346, OR = 1.413), temperature (T) (coefficient = 0.235, OR = 1.265), respiratory rate (RR) (coefficient= -0.206, OR = 0.814), serum kalium (K) (coefficient = 0.137, OR = 1.146), pulse oxygen saturation (SPO 2 ) (coefficient = -0.101, OR = 0.904), and albumin (ALB) (coefficient = -0.043, OR = 0.958). The weights of the top six variables in the bagging model were: CTnT, RR, lactate dehydrogenase, serum amylase, heart rate, and systolic blood pressure. Conclusions: The main clinical indicators of concern included CTnT, RR, SPO 2 , T, ALB, and K. The bagging model and logistic regression model had better diagnostic performance comprehesively. Those may be conducive to the early identification of critical patients with fever by physicians.

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Mortality in sepsis: Comparison of outcomes between patients with demand ischemia, acute myocardial infarction, and neither demand ischemia nor acute myocardial infarction

Abstract: Among patients with sepsis, those with DI had similar adjusted in-hospital mortality compared to those with neither DI nor AMI. Patients with AMI had the highest in-hospital mortality among all groups.

Cited by 12 publications

References 40 publications

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

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

COVID-19 with non-obstructive coronary artery disease in a young adult

Prediction of fatal adverse prognosis in patients with fever-related diseases based on machine learning: A retrospective study

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