Cohort-derived machine learning models for individual prediction of chronic kidney disease in people living with HIV: a prospective multicentre cohort study.

Roth, Jan A; Radevski, Gorjan; Marzolini, Catia; Rauch, Andri; Günthard, Huldrych F; Kouyos, Roger D; Fux, Christoph A; Scherrer, Alexandra U; Calmy, Alexandra; Cavassini, Matthias; Kahlert, Christian R; Bernasconi, Enos; Bogojeska, Jasmina; Battegay, Manuel (2020). Cohort-derived machine learning models for individual prediction of chronic kidney disease in people living with HIV: a prospective multicentre cohort study. (In Press). The journal of infectious diseases Oxford University Press 10.1093/infdis/jiaa236

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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.

Item Type:

Journal Article (Original Article)

Division/Institute:

04 Faculty of Medicine > Department of Haematology, Oncology, Infectious Diseases, Laboratory Medicine and Hospital Pharmacy (DOLS) > Clinic of Infectiology

UniBE Contributor:

Rauch, Andri

Subjects:

600 Technology > 610 Medicine & health

ISSN:

1537-6613

Publisher:

Oxford University Press

Language:

English

Submitter:

Annelies Luginbühl

Date Deposited:

16 Jun 2020 09:21

Last Modified:

16 Jun 2020 09:21

Publisher DOI:

10.1093/infdis/jiaa236

PubMed ID:

32386061

Uncontrolled Keywords:

HIV chronic kidney disease digital epidemiology machine learning prediction

BORIS DOI:

10.7892/boris.144192

URI:

https://boris.unibe.ch/id/eprint/144192

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