Machine learning risk-prediction model for in-hospital mortality in Takotsubo cardiomyopathy

Abstract

Background

Takotsubo cardiomyopathy (TC) is an acute heart failure syndrome characterized by transient left ventricular dysfunction, often triggered by stress. Data on risk scores predicting mortality in TC is sparse. We developed a machine-learning risk score model to predict in-hospital mortality in patients with TC.

Methods

The National Inpatient Sample (NIS) database 2016–2020 was queried to identify adult patients (≥18 years) with TC using ICD-10 code I51.81. The primary outcome was in-hospital mortality. The dataset was randomly split into training (70 %), validation (20 %), and testing (10 %) dataset. Model performance was assessed using the area under the curve (AUC) with 95 % confidence intervals (95 % CI).

Results

Amongst 38,662 TC patients identified [mean age 67.15 ± 14.17 years, female 32,089 (83 %)], 2499 (6.5 %) died. A novel risk score (0–127) was developed on age, race, Elixhauser comorbidity burden, history of hypertension, history of cardiac arrhythmia, presentation of cardiac arrest, cardiogenic shock, and acute kidney injury. Model AUCs (95 % CI) in the training, validation, and testing datasets were 0.809 (0.781–0.838), 0.809 (0.780–0.837), and 0.838 (0.820–0.856), respectively.

Conclusion

TC carries high morbidity and mortality. Our novel machine learning-based risk score is an important tool for risk stratification. External validation is needed to confirm these findings.