Performance and clinical utility of supervised machine-learning approaches in detecting familial hypercholesterolaemia in primary care

Abstract Familial hypercholesterolaemia (FH) is a common inherited disorder, causing lifelong elevated low-density lipoprotein cholesterol (LDL-C). Most individuals with FH remain undiagnosed, precluding opportunities to prevent premature heart disease and death. Some machine-learning approaches imp...

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Autores principales:	Ralph K. Akyea, Nadeem Qureshi, Joe Kai, Stephen F. Weng
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/1a6aee63d7fb4492b12b4d6a4b286e1b
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spelling	oai:doaj.org-article:1a6aee63d7fb4492b12b4d6a4b286e1b2021-12-02T16:23:11ZPerformance and clinical utility of supervised machine-learning approaches in detecting familial hypercholesterolaemia in primary care10.1038/s41746-020-00349-52398-6352https://doaj.org/article/1a6aee63d7fb4492b12b4d6a4b286e1b2020-10-01T00:00:00Zhttps://doi.org/10.1038/s41746-020-00349-5https://doaj.org/toc/2398-6352Abstract Familial hypercholesterolaemia (FH) is a common inherited disorder, causing lifelong elevated low-density lipoprotein cholesterol (LDL-C). Most individuals with FH remain undiagnosed, precluding opportunities to prevent premature heart disease and death. Some machine-learning approaches improve detection of FH in electronic health records, though clinical impact is under-explored. We assessed performance of an array of machine-learning approaches for enhancing detection of FH, and their clinical utility, within a large primary care population. A retrospective cohort study was done using routine primary care clinical records of 4,027,775 individuals from the United Kingdom with total cholesterol measured from 1 January 1999 to 25 June 2019. Predictive accuracy of five common machine-learning algorithms (logistic regression, random forest, gradient boosting machines, neural networks and ensemble learning) were assessed for detecting FH. Predictive accuracy was assessed by area under the receiver operating curves (AUC) and expected vs observed calibration slope; with clinical utility assessed by expected case-review workload and likelihood ratios. There were 7928 incident diagnoses of FH. In addition to known clinical features of FH (raised total cholesterol or LDL-C and family history of premature coronary heart disease), machine-learning (ML) algorithms identified features such as raised triglycerides which reduced the likelihood of FH. Apart from logistic regression (AUC, 0.81), all four other ML approaches had similarly high predictive accuracy (AUC > 0.89). Calibration slope ranged from 0.997 for gradient boosting machines to 1.857 for logistic regression. Among those screened, high probability cases requiring clinical review varied from 0.73% using ensemble learning to 10.16% using deep learning, but with positive predictive values of 15.5% and 2.8% respectively. Ensemble learning exhibited a dominant positive likelihood ratio (45.5) compared to all other ML models (7.0–14.4). Machine-learning models show similar high accuracy in detecting FH, offering opportunities to increase diagnosis. However, the clinical case-finding workload required for yield of cases will differ substantially between models.Ralph K. AkyeaNadeem QureshiJoe KaiStephen F. WengNature PortfolioarticleComputer applications to medicine. Medical informaticsR858-859.7ENnpj Digital Medicine, Vol 3, Iss 1, Pp 1-9 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 Ralph K. Akyea Nadeem Qureshi Joe Kai Stephen F. Weng Performance and clinical utility of supervised machine-learning approaches in detecting familial hypercholesterolaemia in primary care
description	Abstract Familial hypercholesterolaemia (FH) is a common inherited disorder, causing lifelong elevated low-density lipoprotein cholesterol (LDL-C). Most individuals with FH remain undiagnosed, precluding opportunities to prevent premature heart disease and death. Some machine-learning approaches improve detection of FH in electronic health records, though clinical impact is under-explored. We assessed performance of an array of machine-learning approaches for enhancing detection of FH, and their clinical utility, within a large primary care population. A retrospective cohort study was done using routine primary care clinical records of 4,027,775 individuals from the United Kingdom with total cholesterol measured from 1 January 1999 to 25 June 2019. Predictive accuracy of five common machine-learning algorithms (logistic regression, random forest, gradient boosting machines, neural networks and ensemble learning) were assessed for detecting FH. Predictive accuracy was assessed by area under the receiver operating curves (AUC) and expected vs observed calibration slope; with clinical utility assessed by expected case-review workload and likelihood ratios. There were 7928 incident diagnoses of FH. In addition to known clinical features of FH (raised total cholesterol or LDL-C and family history of premature coronary heart disease), machine-learning (ML) algorithms identified features such as raised triglycerides which reduced the likelihood of FH. Apart from logistic regression (AUC, 0.81), all four other ML approaches had similarly high predictive accuracy (AUC > 0.89). Calibration slope ranged from 0.997 for gradient boosting machines to 1.857 for logistic regression. Among those screened, high probability cases requiring clinical review varied from 0.73% using ensemble learning to 10.16% using deep learning, but with positive predictive values of 15.5% and 2.8% respectively. Ensemble learning exhibited a dominant positive likelihood ratio (45.5) compared to all other ML models (7.0–14.4). Machine-learning models show similar high accuracy in detecting FH, offering opportunities to increase diagnosis. However, the clinical case-finding workload required for yield of cases will differ substantially between models.
format	article
author	Ralph K. Akyea Nadeem Qureshi Joe Kai Stephen F. Weng
author_facet	Ralph K. Akyea Nadeem Qureshi Joe Kai Stephen F. Weng
author_sort	Ralph K. Akyea
title	Performance and clinical utility of supervised machine-learning approaches in detecting familial hypercholesterolaemia in primary care
title_short	Performance and clinical utility of supervised machine-learning approaches in detecting familial hypercholesterolaemia in primary care
title_full	Performance and clinical utility of supervised machine-learning approaches in detecting familial hypercholesterolaemia in primary care
title_fullStr	Performance and clinical utility of supervised machine-learning approaches in detecting familial hypercholesterolaemia in primary care
title_full_unstemmed	Performance and clinical utility of supervised machine-learning approaches in detecting familial hypercholesterolaemia in primary care
title_sort	performance and clinical utility of supervised machine-learning approaches in detecting familial hypercholesterolaemia in primary care
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/1a6aee63d7fb4492b12b4d6a4b286e1b
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Performance and clinical utility of supervised machine-learning approaches in detecting familial hypercholesterolaemia in primary care

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