The British journal of general practice : the journal of the Royal College of General Practitioners · 2025
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.
СтатьяВалидационное исследованиеНазвание и аннотация: Перевод в очереди
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Spek Michelle, Venekamp Roderick P, de Hond Anne Ah, de Groot Esther, Geersing Geert-Jan, Dobbe Anna Sm, Delissen Mathé, Rutten Frans H, van Smeden Maarten, Zwart Dorien L
Аннотация
BACKGROUND: Shortness of breath often prompts calls to out-of-hours primary care and is the prime reason for home visits by GPs. However, a diagnostic prediction model in this setting is lacking.
AIM: To develop and internally validate a model predicting life-threatening events for out-of-hours primary care callers with shortness of breath.
DESIGN AND SETTING: This cross-sectional study includes data from 1952 patients with shortness of breath who called Dutch out-of-hours primary care between September 2020 and August 2021. The trial was registered with the Netherlands Trial Register, number: NL9682.
METHOD: Four logistic regression models were developed with life-threatening events as the outcome. The first model included age and gender (model 1) and then, successively, 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) were added. 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% (329/1952) 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 0.76, 95% confidence interval = 0.74 to 0.79) and was well calibrated. All models had a high net benefit compared with 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. However, as the study period coincided with the COVID-19 pandemic, temporal validation is necessary before widespread implementation.
AIM: To develop and internally validate a model predicting life-threatening events for out-of-hours primary care callers with shortness of breath.
DESIGN AND SETTING: This cross-sectional study includes data from 1952 patients with shortness of breath who called Dutch out-of-hours primary care between September 2020 and August 2021. The trial was registered with the Netherlands Trial Register, number: NL9682.
METHOD: Four logistic regression models were developed with life-threatening events as the outcome. The first model included age and gender (model 1) and then, successively, 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) were added. 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% (329/1952) 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 0.76, 95% confidence interval = 0.74 to 0.79) and was well calibrated. All models had a high net benefit compared with 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. However, as the study period coincided with the COVID-19 pandemic, temporal validation is necessary before widespread implementation.