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

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 A(2)-BEST(2) Risk Score (A(2) (area of pulmonary infection, albumin), BMI, Economic condition, Smoking, and T(2)(hormone initiation Time and long-term regular medication Treatment)). ARF incidence increased gradually from 1.37% (The A(2)-BEST(2) Risk Score ≤ 4) to 90.32% (A(2)-BEST(2) 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.