Digital remote monitoring for screening and early detection of urinary tract infections.
Journal
NPJ digital medicine
ISSN: 2398-6352
Titre abrégé: NPJ Digit Med
Pays: England
ID NLM: 101731738
Informations de publication
Date de publication:
13 Jan 2024
13 Jan 2024
Historique:
received:
18
08
2023
accepted:
11
12
2023
medline:
14
1
2024
pubmed:
14
1
2024
entrez:
13
1
2024
Statut:
epublish
Résumé
Urinary Tract Infections (UTIs) are one of the most prevalent bacterial infections in older adults and a significant contributor to unplanned hospital admissions in People Living with Dementia (PLWD), with early detection being crucial due to the predicament of reporting symptoms and limited help-seeking behaviour. The most common diagnostic tool is urine sample analysis, which can be time-consuming and is only employed where UTI clinical suspicion exists. In this method development and proof-of-concept study, participants living with dementia were monitored via low-cost devices in the home that passively measure activity, sleep, and nocturnal physiology. Using 27828 person-days of remote monitoring data (from 117 participants), we engineered features representing symptoms used for diagnosing a UTI. We then evaluate explainable machine learning techniques in passively calculating UTI risk and perform stratification on scores to support clinical translation and allow control over the balance between alert rate and sensitivity and specificity. The proposed UTI algorithm achieves a sensitivity of 65.3% (95% Confidence Interval (CI) = 64.3-66.2) and specificity of 70.9% (68.6-73.1) when predicting UTIs on unseen participants and after risk stratification, a sensitivity of 74.7% (67.9-81.5) and specificity of 87.9% (85.0-90.9). In addition, feature importance methods reveal that the largest contributions to the predictions were bathroom visit statistics, night-time respiratory rate, and the number of previous UTI events, aligning with the literature. Our machine learning method alerts clinicians of UTI risk in subjects, enabling earlier detection and enhanced screening when considering treatment.
Identifiants
pubmed: 38218738
doi: 10.1038/s41746-023-00995-5
pii: 10.1038/s41746-023-00995-5
doi:
Types de publication
Journal Article
Langues
eng
Pagination
11Subventions
Organisme : Alzheimer's Society
ID : UKDRI-7002
Pays : United Kingdom
Organisme : Alzheimer's Society
ID : UKDRI-7002
Pays : United Kingdom
Organisme : Alzheimer's Society
ID : UKDRI-7002
Pays : United Kingdom
Organisme : Alzheimer's Society
ID : UKDRI-7002
Pays : United Kingdom
Organisme : Alzheimer's Society
ID : UKDRI-7002
Pays : United Kingdom
Organisme : Alzheimer's Society
ID : UKDRI-7002
Pays : United Kingdom
Organisme : Alzheimer's Society
ID : UKDRI-7002
Pays : United Kingdom
Organisme : Alzheimer's Society
ID : UKDRI-7002
Pays : United Kingdom
Organisme : Alzheimer's Society
ID : UKDRI-7002
Pays : United Kingdom
Organisme : Alzheimer's Society
ID : UKDRI-7002
Pays : United Kingdom
Organisme : Alzheimer's Society
ID : UKDRI-7002
Pays : United Kingdom
Organisme : Alzheimer's Society
ID : UKDRI-7002
Pays : United Kingdom
Organisme : Alzheimer's Society
ID : UKDRI-7002
Pays : United Kingdom
Organisme : Alzheimer's Society
ID : UKDRI-7002
Pays : United Kingdom
Organisme : Alzheimer's Society
ID : UKDRI-7002
Pays : United Kingdom
Organisme : Alzheimer's Society
ID : UKDRI-7002
Pays : United Kingdom
Organisme : Alzheimer's Society
ID : UKDRI-7002
Pays : United Kingdom
Organisme : Alzheimer's Society
ID : UKDRI-7002
Pays : United Kingdom
Organisme : Alzheimer's Society
ID : UKDRI-7002
Pays : United Kingdom
Informations de copyright
© 2024. The Author(s).
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