Key result
Post-cardiac surgery remote monitoring achieves ~99% completion alongside feasible AI wound triage.
Why the study?
Unplanned readmission within 30 days affects 12–20% of patients after cardiac surgery, largely through complications developing at home. This study evaluated the feasibility of a 30-day post-discharge remote monitoring programme and the accuracy of an investigational AI-based classifier for triaging wound photographs.
Is a 30-day post-discharge remote monitoring programme after cardiac surgery feasible and acceptable, and how accurately does an investigational AI-based classifier triage wound photographs compared to clinicians?
Cohort (n=76)
Blinded clinicians for retrospective wound photograph labelling
No
Is a 30-day post-discharge remote monitoring programme after cardiac surgery feasible and acceptable, and how accurately does an investigational AI-based classifier triage wound photographs compared to clinicians?
Intensive 30-day remote monitoring with daily clinician review is highly feasible and well-adhered to after cardiac surgery, and an ensemble CNN model shows proof-of-concept capability for automated triage of post-operative wound photographs.
High completion supports remote monitoring feasibility in this cohort; hypothesis-generating and requires prospective trials before clinical adoption.
Background/Objectives: Unplanned readmission within 30 days affects 12–20% of patients after cardiac surgery, largely through complications developing at home. We evaluated the feasibility, acceptability, and adherence of a 30-day post-discharge remote monitoring programme. In a separate retrospective analysis that did not inform patient care, we also assessed how accurately an investigational AI-based classifier triages the programme’s wound photographs. Methods: In this prospective single-centre study, 76 of 85 screened patients were monitored for 30 days with twice-daily blood pressure and pulse, daily weight, electrocardiography (ECG), and alternate-day wound photographs, all reviewed daily by a study clinician. Retrospectively, 735 photographs of 88 wounds from 71 patients, labelled by two blinded clinicians, were used to train an ensemble of five convolutional neural networks, tested on a patient-disjoint test set of 147 images from 15 patients. Results: All 76 eligible patients enrolled and 75 (99%) completed the programme, with 82% adherence to the core measurements and an acceptability score of 6.3 (SD 0.5) of 7. Daily review produced 494 teleconsultations, 10 emergency referrals, and 6 admissions (7.9%), and atrial fibrillation was confirmed in 18 episodes in 11 patients. Agreement between the two wound labelling clinicians was κ 0.89. On the test set, the classifier achieved an area under the precision–recall curve of 0.747 (95% CI 0.402–1.000), image-level sensitivity 73.3% (11/15; 95% CI 33.3–100%), precision 73.3%, and wound-level sensitivity 5/6; the one confirmed wound complication in the test set was flagged. Conclusions: Intensive remote monitoring with daily clinician review was feasible after cardiac surgery and was sustained by patients. Daily review identified arrhythmias and wound problems, most of which were managed remotely or in the outpatient clinic. The classifier is a proof of concept for automated triage of such photographs by reproducing clinicians’ visual judgment; given the wide confidence intervals, larger multicentre validation is needed.
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Bis et al. (2026) conducted a cohort in Post-cardiac surgery (n=76). 30-day post-discharge remote monitoring programme with daily clinician review and retrospective AI-based wound image classifier vs. Two blinded clinicians' visual judgment (for retrospective AI wound classification); no comparator for the prospective monitoring cohort was evaluated on Feasibility (completion rate), adherence to core measurements, acceptability, and AI wound classifier performance (area under the precision-recall curve and sensitivity) (AUPRC 0.747, 95% CI 0.402-1.000). Post-cardiac surgery remote monitoring had 99% completion and 82% adherence, while the AI wound classifier achieved an AUPRC of 0.747 (95% CI 0.402-1.000) and 73.3% sensitivity.
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