Background Current procedures for effective personal protective equipment (PPE) usage rely on the availability of trained observers or ‘buddies’ who, during the COVID-19 pandemic, are not always available. The application of artificial intelligence (AI) has the potential to overcome this limitation by assisting in complex task analysis. To date, AI use for PPE protocols has not been studied. In this paper we validate the performance of an AI PPE system in a hospital setting. Methods A clinical cohort study of 74 healthcare workers (HCW) at a 144-bed University teaching hospital. Participants were recruited to use the AI system for PPE donning and doffing. Performance was validated by the current gold standard double-buddy system across seven donning and ten doffing steps based on local infection control guidelines. Results The AI-PPE platform was 98.9% sensitive on doffing and 85.3% sensitive on donning, when compared to remediated double buddy. On average, buddy correction of PPE was required 3.8 ± 1.5% of the time. The average time taken to don was 240 ± 51.5 seconds and doff was 241 ± 35.3 seconds. Conclusion This study demonstrates the ability of an AI model to analyse PPE donning and doffing with real-time feedback for remediation. The AI platform can identify complex multi-task PPE donning and doffing in a single validated system. This AI system can be employed to train, audit, and thereby improve compliance whilst reducing reliance on limited HCW resources. Further studies may permit the development of this educational tool into a medical device with other industry uses for safety.
Background and Aims: Infections are common in hospitals, and if mismanaged can develop into sepsis, a leading cause of death and disability worldwide. This study aimed to examine whether combining C-reactive protein (CRP) with the quick sequential organ failure assessment (qSOFA) improves its accuracy for predicting mortality and sepsis in adult inpatients.
ObjectiveTo examine the prognostic performance of combining the biomarker c-reactive protein (CRP) with the quick sequential organ failure assessment (qSOFA) bedside tool on mortality prediction in adult inpatients.MethodsWe searched PubMed, MEDLINE, EMBASE, Scopus, Web of Science, Science Direct, CINAHL, Open Grey, Grey Literature Report, and the Clinical Trials registry. Title, abstract, and full text screening were performed by two independent reviewers using pre-determined eligibility criteria. The eligibility criteria were (i) original research, (ii) adult populations, (iii) a comparison between qSOFA and qSOFA combined with CRP, and (iv) set in a hospital environment. The same two reviewers independently extracted data into a pre-designed form and performed a risk of bias assessment using the Quality Assessment tool for Diagnostic Accuracy Studies version 2 (QUADAS-2). Disagreements were settled through discussion and a third reviewer was consulted if necessary. Our primary outcome is mortality.ResultsThree retrospective studies with a total of 1521 patients were included in the review. Adding CRP to qSOFA improved the Area Under the Receiver Operating Characteristic Curve (AUROC) value in all three studies by 3-10%. In the two studies with available data, the addition of CRP improved the sensitivity of qSOFA for mortality risk stratification by 43% and 71%, while decreasing the specificity by 12% and 7% respectively. The cut-off values of CRP ranged from 60 to 128.8mg/L across the three studies. A meta-analysis was not performed due to the heterogeneity across studies and the small sample size.ConclusionsThis comprehensive review provides initial evidence that combining CRP with qSOFA could improve the prognostic performance of qSOFA alone in identifying patients at risk of dying in hospital. The combined tool demonstrates potential to improve patient outcomes, especially in low resource settings. The review also reveals research gaps in this area.RegistrationPROSPERO registration No. CRD42020190973
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