• Lukas De Clercq, Martijn C Schut, BossuytPatrick M MPMM0000-0003-4427-0128Department of Public Health and Clinical Epidemiology, Amsterdam UMC, Location Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands., van WeertHenk C P MHCPM0000-0001-6370-4724Department of General Practice, Amsterdam UMC, Location Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands., M Louis Handoko, and Ralf E Harskamp.
• Department of General Practice, Amsterdam UMC, Location Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands.
• Fam Pract. 2023 Feb 9; 40 (1): 188194188-194.

BackgroundTimely diagnosis of heart failure (HF) is essential to optimize treatment opportunities that improve symptoms, quality of life, and survival. While most patients consult their general practitioner (GP) prior to HF, the early stages of HF may be difficult to identify. An integrated clinical support tool may aid in identifying patients at high risk of HF. We therefore constructed a prediction model using routine health care data.MethodsOur study involved a dynamic cohort of patients (≥35 years) who consulted their GP with either dyspnoea and/or peripheral oedema within the Amsterdam metropolitan area from 2011 to 2020. The outcome of interest was incident HF, verified by an expert panel. We developed a regularized, cause-specific multivariable proportional hazards model (TARGET-HF). The model was evaluated with bootstrapping on an isolated validation set and compared to an existing model developed with hospital insurance data as well as patient age as a sole predictor.ResultsData from 31,905 patients were included (40% male, median age 60 years) of whom 1,301 (4.1%) were diagnosed with HF over 124,676 person-years of follow-up. Data were allocated to a development (n = 25,524) and validation (n = 6,381) set. TARGET-HF attained a C-statistic of 0.853 (95% CI, 0.834 to 0.872) on the validation set, which proved to provide a better discrimination than C = 0.822 for age alone (95% CI, 0.801 to 0.842, P < 0.001) and C = 0.824 for the hospital-based model (95% CI, 0.802 to 0.843, P < 0.001).ConclusionThe TARGET-HF model illustrates that routine consultation codes can be used to build a performant model to identify patients at risk for HF at the time of GP consultation.© The Author(s) 2022. Published by Oxford University Press.

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