Wearable based monitoring and self-supervised contrastive learning detect clinical complications during treatment of Hematologic malignancies

NPJ Digital Medicine · 17 authors, 10 centres

AI SUMMARY

FIDELITY 100%

This summary was generated by AI from a single paper. It has not been reviewed by a clinician and is not clinical advice. Verify against the source before acting on it.

AGENT 01 · STRUCTURED SUMMARY

TRACE TO SOURCE

“

TL;DR

This study used wearable devices to continuously monitor vital signs and physical activity in patients with hematologic malignancies, and a deep learning model to detect and predict serious clinical complications. The model could predict complications up to 48 hours before clinical diagnosis.

Full summary

907 CHARS

This observational cohort study evaluated wearable-based remote patient monitoring (RPM) in patients receiving intensive treatment for hematologic malignancies. A self-supervised deep learning model was applied to longitudinal vital sign and activity data to calculate a clinical complications score (SCC-Score). The primary finding was that the SCC-Score could detect and predict serious clinical complications (SCC), including infections and cardiac events, up to 48 hours before clinical diagnosis. Performance was assessed using AUROC analysis. Limitations include the exploratory nature of the study, limited sample size for a clinical trial, and the fact that only CTCAE grade ≥3 complications were analyzed. The findings provide proof-of-principle that RPM with a deep learning model may enable early detection of complications in this high-risk population, potentially improving clinical management.

STUDY TYPE

cohort

SPECIALTY

hematology

SUMMARISED BY

AI pipeline

FIDELITY CHECK

100% · A

ORIGINAL PAPER

PMC10238496

Wearable based monitoring and self-supervised contrastive learning detect clinical complications during treatment of Hematologic malignancies

NPJ Digital Medicine

READ THE FULL PAPER

ABSTRACT

Abstract Serious clinical complications (SCC; CTCAE grade ≥ 3) occur frequently in patients treated for hematological malignancies. Early diagnosis and treatment of SCC are essential to improve outcomes. Here we report a deep learning model-derived SCC-Score to detect and predict SCC from time-series data recorded continuously by a medical wearable. In this single-arm, single-center, observational cohort study, vital signs and physical activity were recorded with a wearable for 31,234 h in 79 patients (54 Inpatient Cohort (IC)/25 Outpatient Cohort (OC)). Hours with normal physical functioning without evidence of SCC (regular hours) were presented to a deep neural network that was trained by a self-supervised contrastive learning objective to extract features from the time series that are typical in regular periods. The model was used to calculate a SCC-Score that measures the dissimilarity to regular features. Detection and prediction performance of the SCC-Score was compared to clinical documentation of SCC (AUROC ± SD). In total 124 clinically documented SCC occurred in the IC, 16 in the OC. Detection of SCC was achieved in the IC with a sensitivity of 79.7% and specificity of 87.9%, with AUROC of 0.91 ± 0.01 (OC sensitivity 77.4%, specificity 81.8%, AUROC 0.87 ± 0.02). Prediction of infectious SCC was possible up to 2 days before clinical diagnosis (AUROC 0.90 at −24 h and 0.88 at −48 h). We provide proof of principle for the detection and prediction of SCC in patients treated for hematological malignancies using wearable data and a deep learning model. As a consequence, remote patient monitoring may enable pre-emptive complication management. Subject terms: Medical research, Diagnosis

CITATION EXPORT

Malte Jacobsen, Rahil Gholamipoor, Till A Dembek et al.. Wearable based monitoring and self-supervised contrastive learning detect clinical complications during treatment of Hematologic malignancies. NPJ Digital Medicine. (2023). https://doi.org/10.1038/s41746-023-00847-2

AUTHORS

17 · 10 CENTRES

Malte Jacobsen, Rahil Gholamipoor, Till A Dembek, Pauline Rottmann, Marlo Verket, Julia Brandts, Paul Jäger, Ben-Niklas Baermann, Mustafa Kondakci, Lutz Heinemann, Anna L Gerke, Nikolaus Marx, Dirk Müller-Wieland, Kathrin Möllenhoff, Melchior Seyfarth, Markus Kollmann, Guido Kobbe

1Faculty of Health, University Witten/Herdecke, 58448 Witten, Germany
2Department of Internal Medicine I, University Hospital Aachen, RWTH Aachen University, 52074 Aachen, Germany
3Department of Computer Science, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany
4Department of Neurology, Faculty of Medicine, University of Cologne, 50937 Cologne, Germany
5Department of Hematology, Oncology, and Clinical Immunology, University Hospital Düsseldorf, Medical Faculty, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany
6Department of Oncology and Hematology, St. Lukas Hospital Solingen, 42697 Solingen, Germany
7Science-Consulting in Diabetes, 41564 Kaarst, Germany
8Mathematical Institute, Heinrich Heine University Düsseldorf, 40225 Düsseldorf, Germany
9Department of Cardiology, Helios University Hospital Wuppertal, 42117 Wuppertal, Germany
10Department of Biology, Heinrich Heine University Düsseldorf, Düsseldorf, 40225 Germany