Predicting the risk of acute respiratory failure among asthma patients—the A2-BEST2 risk score: a retrospective study

Abstract

Objectives Acute respiratory failure (ARF) is a common complication of bronchial asthma (BA). ARF onset increases the risk of patient death. This study aims to develop a predictive model for ARF in BA patients during hospitalization. Methods This was a retrospective cohort study carried out at two large tertiary hospitals. Three models were developed using three different ways: (1) the statistics-driven model, (2) the clinical knowledge-driven model, and (3) the decision tree model. The simplest and most efficient model was obtained by comparing their predictive power, stability, and practicability. Results This study included 398 patients, with 298 constituting the modeling group and 100 constituting the validation group. Models A, B, and C yielded seven, seven, and eleven predictors, respectively. Finally, we chose the clinical knowledge-driven model, whose C-statistics and Brier scores were 0.862 (0.820–0.904) and 0.1320, respectively. The Hosmer-Lemeshow test revealed that this model had good calibration. The clinical knowledge-driven model demonstrated satisfactory C-statistics during external and internal validation, with values of 0.890 (0.815–0.965) and 0.854 (0.820–0.900), respectively. A risk score for ARF incidence was created: The A2-BEST2 Risk Score (A2 (area of pulmonary infection, albumin), BMI, Economic condition, Smoking, and T2(hormone initiation Time and long-term regular medication Treatment)). ARF incidence increased gradually from 1.37% (The A2-BEST2 Risk Score ≤ 4) to 90.32% (A2-BEST2 Risk Score ≥ 11.5). Conclusion We constructed a predictive model of seven predictors to predict ARF in BA patients. This predictor’s model is simple, practical, and supported by existing clinical knowledge.