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Proposing a novel Seriously Deteriorated Patient Indicator (SDPI) for hospitalised ward patients

van der Vegt, A. H.; Campbell, V.; Mitchell, I.; Malycha, J.; Scott, I.; Flabouris, A.; Mehta, N.; Schnetler, R. J.; Anderson, C.; Jones, D.

2025-07-10 health informatics
10.1101/2025.07.09.25331163 medRxiv
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ObjectiveCurrent deteriorated patient outcome measures, death and unplanned ICU admission (UPICU), dont consider patients who deteriorate and recover on the ward, nor correctly identify the time that significant deterioration occurs. This limits fair comparative evaluation of Early Warning Tool (EWT) performance and may degrade AI deterioration prediction algorithm accuracy. A Seriously Deteriorated Patient Indicator (SDPI) is required to overcome these limitations. Materials and methodsUsing a multi-hospital, retrospective dataset and supported by a clinician committee, we developed the SDPI by (i) identifying a well-embedded baseline EWT with superior identification of patients who would transfer to the ICU in the setting of severe illness, (ii) testing additions/variations in scoring elements to improve that tools accuracy, and (iii) selecting an SDPI threshold above which patients are labelled as seriously deteriorated. Results957,445 ward episodes were included (UPICU prevalence 0.4%). The superior baseline tool (AUPRC 0.0752), was successfully augmented by 13.1% (AUPRC 0.085) through 11 adjustments. The final SDPI identified 12,323 seriously deteriorated patients (1.3% of the cohort), of which 8,701 (0.9% of cohort) recovered on the ward, identifying deteriorating patients 7.2 and 71 hours earlier than UPICU or death outcomes respectively. DiscussionThis physiologically derived, reproducible SDPI identifies deteriorated patients significantly earlier than UPICU or death and includes a significant cohort of seriously deteriorated patients who would have previously been mislabelled as not deteriorated. ConclusionWe propose this novel SDPI as part of a composite outcome measure for fairer evaluation of EWT performance and better training of AI prediction models.

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