ABSTRACT
Introduction Hospital and social care suffered major alterations during the SARS-CoV-2 pandemic in the UK. Older adults were disproportionally affected by routine care disruption. To our knowledge, no data has been published so far on the impact of service disruption on 30-day readmission. Methods We performed a retrospective observational study of all patients admitted to a single east London hospital with laboratory-confirmed or clinical diagnosis of COVID-19 between 16th March and 6th April 2020. Older patients were defined as aged 80 years and over. Readmission was captured within 30 days of discharge. Comparator defined as the same period in 2019. Descriptive statistics were used. Results Three hundred and ninety-three patients were included. The majority survived to discharge (69.7%). Positive laboratory testing was similar between older and younger patients (85.7% vs 84.7%, p = NS). Mortality was significantly higher for older patients on index presentation (60.2% vs 20.3%, p < 0.001). Length of stay was also significantly longer for these patients (median 9 vs 7 days, p = 0.00694). The readmission rate for the 274 individuals discharged after index admission was 11.3% (n = 31). Amongst older patients, readmission rate during the study period was slightly higher than the same period in 2019 (17.9% vs 14.8%, p = 0.36). The median time interval between discharge and re-attendance was 8 [1–29]days. All re-attending older patients were re-admitted, whereas 54.2% of younger patients were sent home directly from the emergency department. Only 1 of the 31 patients re-attended because of insufficient social care. Conclusions Our data shows that readmission rates in the older population of East London during the SARS-CoV-2 pandemic were largely similar to non-pandemic periods. During this period, readmission rates appear to have been driven by clinical rather than social imperatives. This suggests that adapted social care services performed well and should be reinforced for future surges.
ABSTRACT
BACKGROUND: Early coronavirus disease 2019 (COVID-19) diagnosis prior to laboratory testing results is crucial for infection control in hospitals. Models exist predicting COVID-19 diagnosis, but significant concerns exist regarding methodology and generalizability. AIM: To generate the first COVID-19 diagnosis risk score for use at the time of hospital admission using the TRIPOD (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) checklist. DESIGN: A multivariable diagnostic prediction model for COVID-19 using the TRIPOD checklist applied to a large single-centre retrospective observational study of patients with suspected COVID-19. METHODS: 581 individuals were admitted with suspected COVID-19; the majority had laboratory-confirmed COVID-19 (420/581, 72.2%). Retrospective collection was performed of electronic clinical records and pathology data. RESULTS: The final multivariable model demonstrated AUC 0.8535 (95% confidence interval 0.8121-0.8950). The final model used six clinical variables that are routinely available in most low and high-resource settings. Using a cut-off of 2, the derived risk score has a sensitivity of 78.1% and specificity of 86.8%. At COVID-19 prevalence of 10% the model has a negative predictive value (NPV) of 96.5%. CONCLUSIONS: Our risk score is intended for diagnosis of COVID-19 in individuals admitted to hospital with suspected COVID-19. The score is the first developed for COVID-19 diagnosis using the TRIPOD checklist. It may be effective as a tool to rule out COVID-19 and function at different pandemic phases of variable COVID-19 prevalence. The simple score could be used by any healthcare worker to support hospital infection control prior to laboratory testing results.