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. 2022 Apr 8;29(1):e100460. doi: 10.1136/bmjhci-2021-100460

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© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.

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Visual abstract. Data from the six considered data sets: ARIC (Atherosclerosis Risk in Communities Study), CARDIA (Coronary Artery Risk Development in Young Adults Study), CHS (Cardiovascular Health Study), FHS OS (Framingham Heart Study Offspring Cohort), MESA (Multi-Ethnic Study of Atherosclerosis) and JHS (Jackson Heart Study), is extracted using the cohort definition used in the original pooled cohort equations (PCEs), and divided into train (80%), validation (10%) and test (10%) sets. Equalised odds and unconstrained (UC) models are derived directly from the training set. The recalibrated model is derived from the UC model using a recalibration procedure, which uses the validation data set (not seen during training). Finally, predictions on the test set are generated for all models—including the PCEs and the revised PCEs (rPCE), derived in past work—and evaluated.