TY - JOUR
T1 - The value of parental medical records for the prediction of diabetes and cardiovascular disease
T2 - a novel method for generating and incorporating family histories
AU - Barak-Corren, Yuval
AU - Tsurel, David
AU - Keidar, Daphna
AU - Gofer, Ilan
AU - Shahaf, Dafna
AU - Leventer-Roberts, Maya
AU - Barda, Noam
AU - Reis, Ben Y.
N1 - Publisher Copyright:
© The Author(s) 2023. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved.
PY - 2023/12/1
Y1 - 2023/12/1
N2 - Objective: To determine whether data-driven family histories (DDFH) derived from linked EHRs of patients and their parents can improve prediction of patients’ 10-year risk of diabetes and atherosclerotic cardiovascular disease (ASCVD). Materials and Methods: A retrospective cohort study using data from Israel’s largest healthcare organization. A random sample of 200 000 subjects aged 40–60 years on the index date (January 1, 2010) was included. Subjects with insufficient history (<1 year) or insufficient follow-up (<10 years) were excluded. Two separate XGBoost models were developed—1 for diabetes and 1 for ASCVD—to predict the 10-year risk for each outcome based on data available prior to the index date of January 1, 2010. Results: Overall, the study included 110 734 subject-father-mother triplets. There were 22 153 cases of diabetes (20%) and 11 715 cases of ASCVD (10.6%). The addition of parental information significantly improved prediction of diabetes risk (P < .001), but not ASCVD risk. For both outcomes, maternal medical history was more predictive than paternal medical history. A binary variable summarizing parental disease state delivered similar predictive results to the full parental EHR. Discussion: The increasing availability of EHRs for multiple family generations makes DDFH possible and can assist in delivering more personalized and precise medicine to patients. Consent frameworks must be established to enable sharing of information across generations, and the results suggest that sharing the full records may not be necessary. Conclusion: DDFH can address limitations of patient self-reported family history, and it improves clinical predictions for some conditions, but not for all, and particularly among younger adults.
AB - Objective: To determine whether data-driven family histories (DDFH) derived from linked EHRs of patients and their parents can improve prediction of patients’ 10-year risk of diabetes and atherosclerotic cardiovascular disease (ASCVD). Materials and Methods: A retrospective cohort study using data from Israel’s largest healthcare organization. A random sample of 200 000 subjects aged 40–60 years on the index date (January 1, 2010) was included. Subjects with insufficient history (<1 year) or insufficient follow-up (<10 years) were excluded. Two separate XGBoost models were developed—1 for diabetes and 1 for ASCVD—to predict the 10-year risk for each outcome based on data available prior to the index date of January 1, 2010. Results: Overall, the study included 110 734 subject-father-mother triplets. There were 22 153 cases of diabetes (20%) and 11 715 cases of ASCVD (10.6%). The addition of parental information significantly improved prediction of diabetes risk (P < .001), but not ASCVD risk. For both outcomes, maternal medical history was more predictive than paternal medical history. A binary variable summarizing parental disease state delivered similar predictive results to the full parental EHR. Discussion: The increasing availability of EHRs for multiple family generations makes DDFH possible and can assist in delivering more personalized and precise medicine to patients. Consent frameworks must be established to enable sharing of information across generations, and the results suggest that sharing the full records may not be necessary. Conclusion: DDFH can address limitations of patient self-reported family history, and it improves clinical predictions for some conditions, but not for all, and particularly among younger adults.
KW - cardiovascular disease
KW - clinical prediction
KW - diabetes
KW - family history
KW - health informatics
UR - http://www.scopus.com/inward/record.url?scp=85177103425&partnerID=8YFLogxK
U2 - 10.1093/jamia/ocad154
DO - 10.1093/jamia/ocad154
M3 - Article
C2 - 37535812
AN - SCOPUS:85177103425
SN - 1067-5027
VL - 30
SP - 1915
EP - 1924
JO - Journal of the American Medical Informatics Association
JF - Journal of the American Medical Informatics Association
IS - 12
ER -