Examining the impact of data quality and completeness of electronic health records on predictions of patients' risks of cardiovascular diseaseCitation formats

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@article{7c8bea771de44e68b6001250af8b16ce,
title = "Examining the impact of data quality and completeness of electronic health records on predictions of patients' risks of cardiovascular disease",
abstract = "OBJECTIVE: To assess the extent of variation of data quality and completeness of electronic health records and impact on the robustness of risk predictions of incident cardiovascular disease (CVD) using a risk prediction tool that is based on routinely collected data (QRISK3).DESIGN: Longitudinal cohort study.SETTINGS: 392 general practices (including 3.6 million patients) linked to hospital admission data.METHODS: Variation in data quality was assessed using S{\'a}ez's stability metrics quantifying outlyingness of each practice. Statistical frailty models evaluated whether accuracy of QRISK3 predictions on individual predictions and effects of overall risk factors (linear predictor) varied between practices.RESULTS: There was substantial heterogeneity between practices in CVD incidence unaccounted for by QRISK3. In the lowest quintile of statistical frailty, a QRISK3 predicted risk of 10 % for female was in a range between 7.1 % and 9.0 % when incorporating practice variability into the statistical frailty models; for the highest quintile, this was 10.9%-16.4%. Data quality (using Saez metrics) and completeness were comparable across different levels of statistical frailty. For example, recording of missing information on ethnicity was 55.7 %, 62.7 %, 57.8 %, 64.8 % and 62.1 % for practices from lowest to highest quintiles of statistical frailty respectively. The effects of risk factors did not vary between practices with little statistical variation of beta coefficients.CONCLUSIONS: The considerable unmeasured heterogeneity in CVD incidence between practices was not explained by variations in data quality or effects of risk factors. QRISK3 risk prediction should be supplemented with clinical judgement and evidence of additional risk factors.",
author = "Yan Li and Matthew Sperrin and Martin, {Glen P} and Ashcroft, {Darren M} and {van Staa}, {Tjeerd Pieter}",
note = "Copyright {\textcopyright} 2019 Elsevier B.V. All rights reserved.",
year = "2020",
month = jan,
doi = "10.1016/j.ijmedinf.2019.104033",
language = "English",
volume = "133",
pages = "104033",
journal = "International journal of medical informatics",
issn = "1386-5056",
publisher = "Elsevier BV",

}

RIS

TY - JOUR

T1 - Examining the impact of data quality and completeness of electronic health records on predictions of patients' risks of cardiovascular disease

AU - Li, Yan

AU - Sperrin, Matthew

AU - Martin, Glen P

AU - Ashcroft, Darren M

AU - van Staa, Tjeerd Pieter

N1 - Copyright © 2019 Elsevier B.V. All rights reserved.

PY - 2020/1

Y1 - 2020/1

N2 - OBJECTIVE: To assess the extent of variation of data quality and completeness of electronic health records and impact on the robustness of risk predictions of incident cardiovascular disease (CVD) using a risk prediction tool that is based on routinely collected data (QRISK3).DESIGN: Longitudinal cohort study.SETTINGS: 392 general practices (including 3.6 million patients) linked to hospital admission data.METHODS: Variation in data quality was assessed using Sáez's stability metrics quantifying outlyingness of each practice. Statistical frailty models evaluated whether accuracy of QRISK3 predictions on individual predictions and effects of overall risk factors (linear predictor) varied between practices.RESULTS: There was substantial heterogeneity between practices in CVD incidence unaccounted for by QRISK3. In the lowest quintile of statistical frailty, a QRISK3 predicted risk of 10 % for female was in a range between 7.1 % and 9.0 % when incorporating practice variability into the statistical frailty models; for the highest quintile, this was 10.9%-16.4%. Data quality (using Saez metrics) and completeness were comparable across different levels of statistical frailty. For example, recording of missing information on ethnicity was 55.7 %, 62.7 %, 57.8 %, 64.8 % and 62.1 % for practices from lowest to highest quintiles of statistical frailty respectively. The effects of risk factors did not vary between practices with little statistical variation of beta coefficients.CONCLUSIONS: The considerable unmeasured heterogeneity in CVD incidence between practices was not explained by variations in data quality or effects of risk factors. QRISK3 risk prediction should be supplemented with clinical judgement and evidence of additional risk factors.

AB - OBJECTIVE: To assess the extent of variation of data quality and completeness of electronic health records and impact on the robustness of risk predictions of incident cardiovascular disease (CVD) using a risk prediction tool that is based on routinely collected data (QRISK3).DESIGN: Longitudinal cohort study.SETTINGS: 392 general practices (including 3.6 million patients) linked to hospital admission data.METHODS: Variation in data quality was assessed using Sáez's stability metrics quantifying outlyingness of each practice. Statistical frailty models evaluated whether accuracy of QRISK3 predictions on individual predictions and effects of overall risk factors (linear predictor) varied between practices.RESULTS: There was substantial heterogeneity between practices in CVD incidence unaccounted for by QRISK3. In the lowest quintile of statistical frailty, a QRISK3 predicted risk of 10 % for female was in a range between 7.1 % and 9.0 % when incorporating practice variability into the statistical frailty models; for the highest quintile, this was 10.9%-16.4%. Data quality (using Saez metrics) and completeness were comparable across different levels of statistical frailty. For example, recording of missing information on ethnicity was 55.7 %, 62.7 %, 57.8 %, 64.8 % and 62.1 % for practices from lowest to highest quintiles of statistical frailty respectively. The effects of risk factors did not vary between practices with little statistical variation of beta coefficients.CONCLUSIONS: The considerable unmeasured heterogeneity in CVD incidence between practices was not explained by variations in data quality or effects of risk factors. QRISK3 risk prediction should be supplemented with clinical judgement and evidence of additional risk factors.

U2 - 10.1016/j.ijmedinf.2019.104033

DO - 10.1016/j.ijmedinf.2019.104033

M3 - Article

C2 - 31785526

VL - 133

SP - 104033

JO - International journal of medical informatics

JF - International journal of medical informatics

SN - 1386-5056

ER -