Mortality during inpatient admissions for Takotsubo cardiomyopathy: can mortality be predicted with a scoring system?

Author:

Loomba Rohit S.ORCID

Abstract

AbstractIntroductionThis study aims to identify which comorbidities and demographic characteristics increase the inpatient mortality rate of patients with TCM and to establish a simple scoring system to help identify those at high risk for mortality.MethodsA retrospective cross-sectional study using the Nationwide Inpatient Sample was conducted. Admissions with and without inpatient mortality were then compared. Univariate analyses were conducted to compare admission characteristics with and without inpatient mortality. A logistic regression analysis was conducted with inpatient mortality as the dependent variability and various admission characteristics as the independent variables in order to develop a model to predict inpatient mortality. The model was then used to calculate a score, the TCM mortality score, for each admission. A receiver operator curve analysis was conducted to determine the sensitivity, specificity, negative predictive value, and positive predictive value of the TCM mortality score in prediction of inpatient mortality.ResultsA total of 19,454 admissions were included in the final analyses. Inpatient mortality was greater in patients that were older, males, Hispanics or Asian Pacific Islanders, and those with Medicare/Medicaid. In regards to comorbidities, inpatient mortality was greater in those with heart failure, arrhythmia, and acute kidney injury. Those with mortality were also more likely to have had an intra-aortic balloon pump or having had to be placed on extracorporeal membrane oxygenation. A mortality score was found to be 4.4 in the mortality group as compared to 1.6 for those in the non-mortality group.ConclusionInpatient mortality with Takotsubo cardiomyopathy is approximately 4.0% and those presenting with certain demographic characteristics and comorbidities are at an increased risk for mortality. A simple scoring system can help identify those at high risk for mortality.

Publisher

Cold Spring Harbor Laboratory

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