Development and validation of a prognostic nomogram for Takotsubo syndrome patients in the intensive care units: a retrospective cohort study

• Published in: Scientific reports Vol. 13; no. 1; pp. 477 - 10
• Main Authors: Chen, Jun, Wang, Yimin, Shou, Xinyang, Liu, Qiang, Mei, Ziwei
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 10.01.2023; Nature Publishing Group; Nature Portfolio
• Subjects: 692/308; 692/4019; 692/499; 692/53; 692/699; Atrophy; Calibration; Cohort analysis; Death; Hematocrit; Hospital Mortality; Hospitals; Humanities and Social Sciences; Humans; Intensive care; Intensive Care Units; Mortality; multidisciplinary; Myocardial infarction; Nomograms; Patients; Prognosis; Prothrombin; Regression analysis; Retrospective Studies; Risk factors; Science; Science (multidisciplinary); Takotsubo Cardiomyopathy - diagnosis
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-022-27224-5

Patients with Takotsubo syndrome (TTS) admitted to the intensive care unit (ICU) always confront a higher risk of in-hospital death than those hospitalized in the cardiology unit. The prognosis of the latter was analyzed by a large number of studies. However, there was no utility model to predict the risk of in-hospital death for patients with TTS in the ICU. This study aimed to establish a model predicting in-hospital death in patients with TTS admitted to ICU. We retrospectively included ICU patients with TTS from the MIMIC-IV database. The outcome of the nomogram was in-hospital death. Least Absolute Shrinkage Selection Operator (LASSO) analysis selected predictors preliminarily. The model was developed by multivariable logistic regression analysis. Calibration, decision curve analysis (DCA), and receiver operating characteristic (ROC) measured the performance of the nomogram on the accuracy, clinical utility, and discrimination, respectively. Eventually, 368 ICU patients with TTS were enrolled in this research. The in-hospital mortality was 13.04%. LASSO regression and multivariate logistic regression analysis verified risk factors significantly associated with in-hospital mortality. They were potassium, prothrombin time (PT), age, myocardial infarction, white cell count (WBC), hematocrit, anion gap, and sequential organ failure assessment (SOFA) score. This nomogram excellently discriminated against patients with a risk of in-hospital death. The area under curve (AUC) was 0.779 (95%CI: 0.732–0.826) in training set and 0.775 (95%CI: 0.711–0.839) in test set. The calibration plot and DCA showed good clinical benefits for this nomogram. We developed a nomogram that predicts the probability of in-hospital death for ICU patients with TTS. This nomogram was able to discriminate patients with a high risk of in-hospital death and performed clinical utility.