Predicting control of cardiovascular disease risk factors in South Asia using machine learning
A substantial share of patients at risk of developing cardiovascular disease (CVD) fail to achieve control of CVD risk factors, but clinicians lack a structured approach to identify these patients. We applied machine learning to longitudinal data from two completed randomized controlled trials among...
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| Main Authors: | , , , , , , , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
2024
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| In: |
npj digital medicine
Year: 2024, Volume: 7, Pages: 1-10 |
| ISSN: | 2398-6352 |
| DOI: | 10.1038/s41746-024-01353-9 |
| Online Access: | Verlag, kostenfrei, Volltext: https://doi.org/10.1038/s41746-024-01353-9 Verlag, kostenfrei, Volltext: https://www.nature.com/articles/s41746-024-01353-9 
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| Author Notes: | Anna Reuter, Mohammed K. Ali, Viswanathan Mohan, Lydia Chwastiak, Kavita Singh, K.M. Venkat Narayan, Dorairaj Prabhakaran, Nikhil Tandon & Nikkil Sudharsanan |
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| 520 | |a A substantial share of patients at risk of developing cardiovascular disease (CVD) fail to achieve control of CVD risk factors, but clinicians lack a structured approach to identify these patients. We applied machine learning to longitudinal data from two completed randomized controlled trials among 1502 individuals with diabetes in urban India and Pakistan. Using commonly available clinical data, we predict each individual’s risk of failing to achieve CVD risk factor control goals or meaningful improvements in risk factors at one year after baseline. When classifying those in the top quartile of predicted risk scores as at risk of failing to achieve goals or meaningful improvements, the precision for not achieving goals was 73% for HbA1c, 30% for SBP, and 24% for LDL, and for not achieving meaningful improvements 88% for HbA1c, 87% for SBP, and 85% for LDL. Such models could be integrated into routine care and enable efficient and targeted delivery of health resources in resource-constrained settings. | ||
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