A nomogram for predicting in‐hospital death in a multinational cohort of patients with takotsubo syndrome

BACKGROUND: An effective risk stratification model on hospitalized patients with takotsubo syndrome (TTS) helps guide treatment to mitigate adverse events and improve prognosis. We aimed to develop a nomogram for predicting in-hospital death in a multinational cohort of TTS patients. METHODS: We enrolled 829 TTS patients from AmSC Research Network, InterTAK registry and ChiTTS registry, classified into the training (n = 578), test (n = 145) and external validation (n = 106) cohorts. RESULTS: /L) were independent positive predictors, while chest pain was an independent negative predictor of in-hospital death. A nomogram was constructed to predict in-hospital death in TTS patients based on these seven independent variables, which showed that the area under the curves (AUCs) in the training and test cohorts were .854 (95% CI: .805-.904, p < .001) and .836 (95% CI: .737-.934, p < .001), respectively. The calibration curves showed good consistency between the prediction of the nomogram and the actual observation in both the training and test cohorts. Decision curve analyses indicated that the use of the nomogram to predict in-hospital death in TTS patients could provide better net benefit than the 'treat all' or 'treat none' strategies when the threshold probability ranged from 2% to 75% in the training cohort and from 2% to 72% in the test cohort. The nomogram was further validated, with AUC of .838 (95% CI: .663-1.000, p = .003) in the external validation cohort. CONCLUSIONS: The nomogram, composed of BMI, CKD, neurologic disorders, chest pain, cardiogenic shock, low SBP and abnormal WBC, helps predict in-hospital death in TTS patients.