Prediction model of ICU readmission in Chinese patients with acute type A aortic dissection: a retrospective study.

Abstract

Background: Readmission to the intensive care unit (ICU) remains a severe challenge, leading to higher rates of death and a greater financial burden. This study aimed to develop a nomogram-based prediction model for individuals with acute type A aortic dissection (ATAAD).

Methods: A total of 846 ATAAD patients were retrospectively enrolled between May 2014 and October 2021. Logistic regression was employed to identify the independent risk factors. The prediction model was evaluated using the Hosmer-Lemeshow (H-L) test, the calibration curve, and the area under the receiver operating characteristic curve (AUC). Decision curve analysis (DCA) was used to assess the clinical utility.

Results: 57 (6.7%) ATAAD patients were readmitted to ICU following their release from the ICU. ICU readmission was predicted with age ≥ 65 years old, body mass index (BMI) ≥ 28 kg/m², tracheotomy, continuous renal replacement therapy (CRRT), and the length of initial ICU stay were predictors of ICU readmission. The AUC was 0.837 (95%CI: 0.789-0.884) and the model fit the data well (H-L test, P = 0.519). DCA also demonstrated good clinical practicability.

Conclusions: This prediction model may be helpful for clinicians to assess the risk of ICU readmission, and facilitate the early identification of ATAAD patients at high risk.