Combining C-reactive protein and quick sequential organ failure assessment (qSOFA) to improve prognostic accuracy for sepsis and mortality in adult inpatients: A systematic review.

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. Methods: PubMed, MEDLINE, EMBASE, Scopus, Web of Science, Science Direct, CINAHL, Open Grey, Grey Literature Report, and the Clinical Trials registry were searched using CRP and qSOFA search terms. Title, abstract, and full-text screening were performed by two independent reviewers using pre-determined eligibility criteria, followed by data extraction and a risk of bias assessment using the Quality Assessment tool for Diagnostic Accuracy Studies 2 (QUADAS-2). Disagreements were settled through discussion and consultation with a third reviewer. Results: Four retrospective studies with a total of 2070 patients were included in this review. Adding CRP to qSOFA improved the Area Under the Receiver Operating Characteristic Curve up to 9.7% for predicting mortality and by 14.9% for identifying sepsis. The sensitivity and specificity of the combined score for mortality prediction were available in two studies. CRP improved the sensitivity of qSOFA by 43% and 71% while only decreasing the specificity by 12% and 7%, respectively. A meta-analysis was not performed due to study heterogeneity. Conclusion: This comprehensive review provided initial evidence that combining CRP with qSOFA may improve the accuracy of qSOFA alone in identifying sepsis or patients at risk of dying in hospital. The combined tool demonstrated the potential to improve patient outcomes, with implications for low-resource settings given its simplicity and low-cost.

Using artificial intelligence for personal protective equipment guidance for healthcare workers in the COVID-19 pandemic and beyond.

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.