TY  - JOUR
A1  - Meid, Andreas
A1  - González-González, Ana I.
A1  - Dinh, Truc Sophia
A1  - Blom, Jeanette Wilhelmina
A1  - Akker, Marjan van den
A1  - Elders, Petra
A1  - Thiem, Ulrich
A1  - Küllenberg de Gaudry, Daniela
A1  - Swart, Karin M. A.
A1  - Rudolf, Henrik
A1  - Bosch-Lenders, Donna
A1  - Trampisch, Hans Joachim
A1  - Meerpohl, Jörg J.
A1  - Gerlach, Ferdinand M.
A1  - Flaig, Benno
A1  - Kom, Ghainsom D.
A1  - Snell, Kym I. E.
A1  - Perera, Rafael
A1  - Haefeli, Walter E.
A1  - Glasziou, Paul
A1  - Muth, Christiane
T1  - Predicting hospital admissions from individual patient data (IPD): an applied example to explore key elements driving external validity
T2  - BMJ open
N2  - Objective To explore factors that potentially impact external validation performance while developing and validating a prognostic model for hospital admissions (HAs) in complex older general practice patients.
Study design and setting Using individual participant data from four cluster-randomised trials conducted in the Netherlands and Germany, we used logistic regression to develop a prognostic model to predict all-cause HAs within a 6-month follow-up period. A stratified intercept was used to account for heterogeneity in baseline risk between the studies. The model was validated both internally and by using internal-external cross-validation (IECV).
Results Prior HAs, physical components of the health-related quality of life comorbidity index, and medication-related variables were used in the final model. While achieving moderate discriminatory performance, internal bootstrap validation revealed a pronounced risk of overfitting. The results of the IECV, in which calibration was highly variable even after accounting for between-study heterogeneity, agreed with this finding. Heterogeneity was equally reflected in differing baseline risk, predictor effects and absolute risk predictions.
Conclusions Predictor effect heterogeneity and differing baseline risk can explain the limited external performance of HA prediction models. With such drivers known, model adjustments in external validation settings (eg, intercept recalibration, complete updating) can be applied more purposefully.
Trial registration number PROSPERO id: CRD42018088129.
Y1  - 2021
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/69744
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-697440
SN  - 2044-6055
N1  - Data availability statement
All data relevant to the study are included in the article or uploaded as online supplemental information. Source data originate from separate primary studies and can potentially be requested for anonymous use from the PROPERmed IPD-MA database.
N1  - This work was supported by the German Innovation Fund in accordance with § 92a (2) Volume V of the Social Insurance Code (§ 92a Abs. 2, SGB V - Fünftes Buch Sozialgesetzbuch), grant number: 01VSF16018.
VL  - 11
IS  - 8, art. e045572
SP  - 1
EP  - 11
PB  - BMJ Publishing Group
CY  - London
ER  -