ArticlePeerJ2023
Predicting the risk of acute respiratory failure among asthma patients-the A2-BEST2 risk score: a retrospective study.
Article in PeerJ, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Developing a rapid predictive model for falls in older hospitalized patients.Frontiers in public health · 2024Observational
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
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 A 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.
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