Objective To collect and review data from consecutive patients admitted to Queen’s Hospital, Burton on Trent for treatment of Covid‐19 infection, with the aim of developing a predictive algorithm that can help identify those patients likely to survive. Design Consecutive patient data were collected from all admissions to hospital for treatment of Covid‐19. Data were manually extracted from the electronic patient record for statistical analysis. Results Data, including outcome data (discharged alive/died), were extracted for 487 consecutive patients, admitted for treatment. Overall, patients who died were older, had very significantly lower Oxygen saturation (SpO2) on admission, required a higher inspired Oxygen concentration (IpO2) and higher CRP as evidenced by a Bonferroni‐corrected ( P < 0.0056). Evaluated individually, platelets and lymphocyte count were not statistically significant but when used in a logistic regression to develop a predictive score, platelet count did add predictive value. The 5‐parameter prediction algorithm we developed was: Conclusion Age, IpO2 on admission, CRP, platelets and number of lungs consolidated were effective marker combinations that helped identify patients who would be likely to survive. The AUC under the ROC Plot was 0.8129 (95% confidence interval 0.0.773 ‐ 0.853; P < .001).

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OBJECTIVE: To collect and review data from consecutive patients admitted to Queen's Hospital, Burton on Trent for treatment of Covid-19 infection, with the aim of developing a predictive algorithm that can help identify those patients likely to survive. DESIGN: Consecutive patient data was collected from all admissions to hospital for treatment of Covid-19. Data was manually extracted from the electronic patient record for statistical analysis. RESULTS: Data, including outcome data (discharged alive / died) was extracted for 487 consecutive patients, admitted for treatment. Overall, patients who died were older, had very significantly lower Oxygen saturation (SpO2) on admission, and higher CRP as evidenced by a Bonferroni-corrected P<0.0056). Evaluated individually, platelets and lymphocyte count were not statistically significant but when used in a logistic regression to develop a predictive score, platelet count did add predictive value. The prediction algorithm we developed was: P(survival) = 1 1+e-1(-16.7104-3.3810LN(age)+6.5592LN(SpO2)-0.4584LN(CRP)+0.7183LN(Plt)) CONCLUSION: Age, SpO2 on Admmission, CRP and platelets were an effective marker combination that helped identify patients who would be likely to survive. The AUC under the ROC Plot was 0.737 (95% Conf.

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Multiple environmental risk factors contribute towards atopic dermatitis (AD) prevalence and persistence. Maternal alcohol consumption during pregnancy may have pro-inflammatory effects leading to AD in their offspring. Moreover, AD is associated with chronic sleep disturbance, psychosocial distress, stigma, social isolation, anxiety and depression, which might lead to increased alcohol consumption in children and adolescents. We sought to understand the association between 1. maternal alcohol consumption during pregnancy and childhood AD; 2. AD and alcohol use in adolescents. We used data from the Fragile Families and Child Wellbeing Study, a longitudinal US birth cohort study of 4898 urban children.