Publication
Cohort-derived machine learning models for individual prediction of chronic kidney disease in people living with HIV: a prospective multicentre cohort study
Journal Paper/Review - May 9, 2020
Roth Jan A, Bogojeska Jasmina, Bernasconi Enos, Kahlert Christian, Cavassini Matthias, Calmy Alexandra, Scherrer Alexandra U, Fux Christoph A, Kouyos Roger D, Günthard Huldrych F, Rauch Andri, Marzolini Catia, Radevski Gorjan, Battegay Manuel
Units
PubMed
Doi
Citation
Type
Journal
Publication Date
Issn Electronic
Brief description/objective
BACKGROUND
It is unclear whether data-driven machine learning models, which are trained on large epidemiological cohorts, may improve prediction of co-morbidities in people living with HIV.
METHODS
In this proof-of-concept study, we included people living with HIV of the prospective Swiss HIV Cohort Study with a first estimated glomerular filtration rate (eGFR) >60 ml/min/1.73 m2 after January 1, 2002. Our primary outcome was chronic kidney disease (CKD) ─ defined as confirmed decrease in eGFR ≤60 ml/min/1.73 m2 over three months apart. We split the cohort data into a training set (80%), validation set (10%), and test set (10%) ─ stratified for CKD status and follow-up length.
RESULTS
Of 12,761 eligible individuals (median baseline eGFR, 103 ml/min/1.73 m2), 1,192 (9%) developed a CKD after a median of eight years. We used 64 static and 502 time-changing variables: Across prediction horizons and algorithms and in contrast to expert-based standard models, most machine learning models achieved state-of-the-art predictive performances with areas under the receiver operating characteristic curve and precision recall curve ranging from 0.926 to 0.996 and from 0.631 to 0.956, respectively.
CONCLUSIONS
In people living with HIV, we observed state-of-the-art performances in forecasting individual CKD onsets with different machine learning algorithms.