A machine-learning model for reducing misdiagnosis in heparin-induced thrombocytopenia: A prospective, multicenter, observational study.

Nilius, Henning; Cuker, Adam; Haug, Sigve; Nakas, Christos; Studt, Jan-Dirk; Tsakiris, Dimitrios A; Greinacher, Andreas; Mendez, Adriana; Schmidt, Adrian; Wuillemin, Walter A; Gerber, Bernhard; Kremer, Johanna A; Vishnu, Prakash; Graf, Lukas; Kashev, Alexander; Sznitman, Raphael; Bakchoul, Tamam; Nagler, Michael (2023). A machine-learning model for reducing misdiagnosis in heparin-induced thrombocytopenia: A prospective, multicenter, observational study. EClinicalMedicine, 55, p. 101745. Elsevier 10.1016/j.eclinm.2022.101745

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BACKGROUND

Diagnosing heparin-induced thrombocytopenia (HIT) at the bedside remains challenging, exposing a significant number of patients at risk of delayed diagnosis or overtreatment. We hypothesized that machine-learning algorithms could be utilized to develop a more accurate and user-friendly diagnostic tool that integrates diverse clinical and laboratory information and accounts for complex interactions.

METHODS

We conducted a prospective cohort study including 1393 patients with suspected HIT between 2018 and 2021 from 10 study centers. Detailed clinical information and laboratory data were collected, and various immunoassays were conducted. The washed platelet heparin-induced platelet activation assay (HIPA) served as the reference standard.

FINDINGS

HIPA diagnosed HIT in 119 patients (prevalence 8.5%). The feature selection process in the training dataset (75% of patients) yielded the following predictor variables: (1) immunoassay test result, (2) platelet nadir, (3) unfractionated heparin use, (4) CRP, (5) timing of thrombocytopenia, and (6) other causes of thrombocytopenia. The best performing models were a support vector machine in case of the chemiluminescent immunoassay (CLIA) and the ELISA, as well as a gradient boosting machine in particle-gel immunoassay (PaGIA). In the validation dataset (25% of patients), the AUROC of all models was 0.99 (95% CI: 0.97, 1.00). Compared to the currently recommended diagnostic algorithm (4Ts score, immunoassay), the numbers of false-negative patients were reduced from 12 to 6 (-50.0%; ELISA), 9 to 3 (-66.7%, PaGIA) and 14 to 5 (-64.3%; CLIA). The numbers of false-positive individuals were reduced from 87 to 61 (-29.8%; ELISA), 200 to 63 (-68.5%; PaGIA) and increased from 50 to 63 (+29.0%) for the CLIA.

INTERPRETATION

Our user-friendly machine-learning algorithm for the diagnosis of HIT (https://toradi-hit.org) was substantially more accurate than the currently recommended diagnostic algorithm. It has the potential to reduce delayed diagnosis and overtreatment in clinical practice. Future studies shall validate this model in wider settings.

FUNDING

Swiss National Science Foundation (SNSF), and International Society on Thrombosis and Haemostasis (ISTH).

Item Type:

Journal Article (Original Article)

Division/Institute:

10 Strategic Research Centers > ARTORG Center for Biomedical Engineering Research
10 Strategic Research Centers > ARTORG Center for Biomedical Engineering Research > ARTORG Center - AI in Medical Imaging Laboratory
08 Faculty of Science > Department of Mathematics and Statistics > Institute of Mathematics
10 Strategic Research Centers > Albert Einstein Center for Fundamental Physics (AEC)
08 Faculty of Science > Physics Institute > Laboratory for High Energy Physics (LHEP)
04 Faculty of Medicine > Department of Haematology, Oncology, Infectious Diseases, Laboratory Medicine and Hospital Pharmacy (DOLS) > Clinic of Haematology and Central Haematological Laboratory
04 Faculty of Medicine > Department of Haematology, Oncology, Infectious Diseases, Laboratory Medicine and Hospital Pharmacy (DOLS) > Institute of Clinical Chemistry

UniBE Contributor:

Nilius, Henning Jürgen Jean, Haug, Sigve, Nakas, Christos T., Kremer Hovinga Strebel, Johanna Anna, Kashev, Alexander, Sznitman, Raphael, Nagler, Michael

Subjects:

500 Science > 570 Life sciences; biology
600 Technology > 610 Medicine & health
500 Science > 510 Mathematics
500 Science > 530 Physics

ISSN:

2589-5370

Publisher:

Elsevier

Language:

English

Submitter:

Karin Balmer

Date Deposited:

02 Dec 2022 16:46

Last Modified:

05 Dec 2022 16:29

Publisher DOI:

10.1016/j.eclinm.2022.101745

PubMed ID:

36457646

Uncontrolled Keywords:

Anticoagulants Diagnosis Heparin Heparin-induced thrombocytopenia Low-molecular-weight Platelet count Thrombocytopenia

BORIS DOI:

10.48350/175460

URI:

https://boris.unibe.ch/id/eprint/175460

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