Stroke quality of care improvement & SARS-CoV-2 disease severity

Abstract:

Part I: Applying machine learning for stroke quality care improvement

Machine learning was implemented for risk prediction of 30-day mortality after stroke using data from the Sentinel Stroke National Audit Programme (SSNAP) which is the national registry of stroke care in England, Wales and Northern Ireland. The ML model developed more accurately predicted 30-day mortality (AUC 0.896) compared to the previously developed model used in SSNAP (0.854) and was reasonably well calibrated, thus could potentially be used as benchmarking model for quality improvement in stroke care in SSNAP

Part II: Multivariable analysis of the association of the Alpha variant (B.1.1.7 lineage) of SARS-CoV-2 with disease severity in inner London

Through a descriptive comparison of admission characteristics between pandemic waves and multivariable analysis of the association of the Alpha variant (B.1.1.7 lineage) of SARS-CoV-2 with disease severity in inner London, we discovered that increased severity of disease associated with the Alpha variant and the number of nosocomial cases was similar in both waves despite the introduction of many infection control interventions before wave 2.

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