TARGET-HF: Developing a model for detecting incident heart failure among symptomatic patients in general practice using routine health care data
Abstract Background Timely 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, 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.Methods Our study involved a dynamic cohort of patients (≥35 years) who consulted their GP with either dyspnea and/or peripheral edema within the Amsterdam metropolitan area in 2011-2020. The outcome of interest was incident HF, verified by an expert panel. We developed a regularized 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.Results Data from 31,905 patients were included (40% male, median age 60) 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:0.834-0.872) on the validation set, which proved to provide a better discrimination than C=0.822 for age alone (95% CI:0.801-0.842, p<0.001) and C=0.824 for the hospital-based model (95% CI:0.802-0.843, p<0.001).Conclusion The TARGET-HF model illustrates that routine consultation codes can be used to build a performant model to identify patients at risk for HF at time of GP consultation..
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Preprint| Erscheinungsjahr: |
2022 |
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| Erschienen: |
2022 |
| Enthalten in: |
bioRxiv.org - (2022) vom: 21. März Zur Gesamtaufnahme - year:2022 |
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| Sprache: |
Englisch |
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| Beteiligte Personen: |
De Clercq, Lukas [VerfasserIn] |
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| Links: |
Volltext [kostenfrei] |
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| doi: |
10.1101/2022.03.17.22270808 |
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| funding: |
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| PPN (Katalog-ID): |
XBI035533137 |
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| 520 | |a Abstract Background Timely 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, 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.Methods Our study involved a dynamic cohort of patients (≥35 years) who consulted their GP with either dyspnea and/or peripheral edema within the Amsterdam metropolitan area in 2011-2020. The outcome of interest was incident HF, verified by an expert panel. We developed a regularized 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.Results Data from 31,905 patients were included (40% male, median age 60) 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:0.834-0.872) on the validation set, which proved to provide a better discrimination than C=0.822 for age alone (95% CI:0.801-0.842, p<0.001) and C=0.824 for the hospital-based model (95% CI:0.802-0.843, p<0.001).Conclusion The TARGET-HF model illustrates that routine consultation codes can be used to build a performant model to identify patients at risk for HF at time of GP consultation. | ||
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