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Capstick A, Palermo F, Zakka K, Fletcher-Lloyd N, Walsh C, Cui T, Kouchaki S, Jackson R, Tran M, Crone M, Jensen K, Freemont P, Vaidyanathan R, Kolanko M, True J, Daniels S, Wingfield D, Nilforooshan R, Barnaghi P. Digital remote monitoring for screening and early detection of urinary tract infections. NPJ Digit Med 2024; 7:11. [PMID: 38218738 PMCID: PMC10787784 DOI: 10.1038/s41746-023-00995-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Accepted: 12/11/2023] [Indexed: 01/15/2024] Open
Abstract
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.
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Affiliation(s)
- Alexander Capstick
- Imperial College London, London, UK.
- UK Dementia Research Institute, Care Research and Technology Centre, London, UK.
| | - Francesca Palermo
- Imperial College London, London, UK
- UK Dementia Research Institute, Care Research and Technology Centre, London, UK
| | - Kimberley Zakka
- University College London, London, UK
- Great Ormond Street Hospital NHS Foundation Trust, London, UK
| | - Nan Fletcher-Lloyd
- Imperial College London, London, UK
- UK Dementia Research Institute, Care Research and Technology Centre, London, UK
| | - Chloe Walsh
- Imperial College London, London, UK
- UK Dementia Research Institute, Care Research and Technology Centre, London, UK
| | - Tianyu Cui
- Imperial College London, London, UK
- UK Dementia Research Institute, Care Research and Technology Centre, London, UK
| | - Samaneh Kouchaki
- Imperial College London, London, UK
- UK Dementia Research Institute, Care Research and Technology Centre, London, UK
- University of Surrey, London, UK
| | - Raphaella Jackson
- Imperial College London, London, UK
- UK Dementia Research Institute, Care Research and Technology Centre, London, UK
| | - Martin Tran
- Imperial College London, London, UK
- UK Dementia Research Institute, Care Research and Technology Centre, London, UK
| | - Michael Crone
- Imperial College London, London, UK
- UK Dementia Research Institute, Care Research and Technology Centre, London, UK
| | - Kirsten Jensen
- Imperial College London, London, UK
- UK Dementia Research Institute, Care Research and Technology Centre, London, UK
| | - Paul Freemont
- Imperial College London, London, UK
- UK Dementia Research Institute, Care Research and Technology Centre, London, UK
| | - Ravi Vaidyanathan
- Imperial College London, London, UK
- UK Dementia Research Institute, Care Research and Technology Centre, London, UK
| | - Magdalena Kolanko
- Imperial College London, London, UK
- UK Dementia Research Institute, Care Research and Technology Centre, London, UK
| | - Jessica True
- UK Dementia Research Institute, Care Research and Technology Centre, London, UK
- Surrey and Borders Partnership NHS Foundation Trust, Leatherhead, UK
| | - Sarah Daniels
- Imperial College London, London, UK
- UK Dementia Research Institute, Care Research and Technology Centre, London, UK
| | - David Wingfield
- Imperial College London, London, UK
- UK Dementia Research Institute, Care Research and Technology Centre, London, UK
| | - Ramin Nilforooshan
- Imperial College London, London, UK
- UK Dementia Research Institute, Care Research and Technology Centre, London, UK
- University of Surrey, London, UK
- Surrey and Borders Partnership NHS Foundation Trust, Leatherhead, UK
| | - Payam Barnaghi
- Imperial College London, London, UK.
- UK Dementia Research Institute, Care Research and Technology Centre, London, UK.
- University College London, London, UK.
- Great Ormond Street Hospital NHS Foundation Trust, London, UK.
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