Development and internal validation of a diagnostic prediction model for life-threatening events in callers with shortness of breath: a cross-sectional study in out-of-hours primary care.

Abstract

Aim: To develop and internally validate a model predicting life-threatening events for out-of-hours primary care callers with shortness of breath.

Method: This cross-sectional study includes data from 1,952 patients with shortness of breath who called out-of-hours primary care between September 2020 and August 2021. Four logistic regression models were developed with life-threatening events as the outcome. We started with a model of age and gender (model 1) and successively added call characteristics (calling at night and someone else calling on behalf of the patient; model 2), symptoms (cough, fever, inability to speak full sentences and wheezing; model 3), and medical history and medication use (cardiovascular and/or pulmonary; model 4). The models were internally validated using optimism correction via bootstrap with 1000 repetitions. Performance measures of discrimination (c-statistic) and calibration (calibration intercept and slope) were determined.

Results: Approximately 17% of callers with shortness of breath had a life-threatening event. Model 3 performed best. This model exhibited good discriminative ability (internal validation c-statistic of 0.764 (95% CI: 0.739 to 0.792)) and was well calibrated. All models had a high net benefit compared to using no model. Models 3 and 4 had a higher net benefit compared with models 1 and 2. As models 3 and 4 were similar in terms of net benefit, the model with fewer parameters (model 3) is preferred.

Conclusion: A prediction model consisting of age, gender, call characteristics, and symptoms holds promise for improving telephone triage of callers to out-of-hours primary care with shortness of breath.