RESUMO
The SARS-CoV2 pandemic and high hospitalization rates placed a tremendous strain on hospital resources necessitating models to predict hospital volumes and the associated resource requirements. Complex epidemiologic models have been developed and published, but many require continued adjustment of input parameters. We developed a simplified model for short-term bed need predictions that self-adjusts to changing patterns of disease in the community and admission rates. The model utilizes public health data on community new case counts for SARS-CoV2 and projects anticipated hospitalization rates. The model was retrospectively evaluated after the second wave of SARS-CoV2 2 in New York (October 2020-April 2021) for its accuracy in predicting number of COVID-19 admissions at three, five, seven and 10 days into the future comparing predicted admissions with actual admissions for each day at a large integrated healthcare delivery network. Mean absolute percent error of the model was found to be low when evaluated across the entire health system, for a single region of the health system or for a single large hospital (6.1%-7.6% for 3-day predictions, 9.2%-10.4% for five-day predictions, 12.4%-13.2% for seven-day predictions, and 17.1-17.8% for 10-day predictions).
RESUMO
We performed a prospective study of 501 patients, regardless of symptoms, admitted to the hospital, to estimate the predictive value of a negative nasopharyngeal swab for severe acute respiratory coronavirus virus 2 (SARS-CoV-2). At a positivity rate of 10.2%, the estimated negative predictive value (NPV) was 97.2% and the NPV rose as prevalence decreased during the study.
Assuntos
COVID-19 , Técnicas de Laboratório Clínico , Hospitalização , Humanos , Estudos Prospectivos , SARS-CoV-2RESUMO
BACKGROUND: Immunosuppressive therapies proposed for Coronavirus disease 2019 (COVID-19) management may predispose to secondary infections. We evaluated the association of immunosuppressive therapies with bloodstream-infections (BSIs) in hospitalized COVID-19 patients. METHODS: This was an institutional review board-approved retrospective, multicenter, cohort study of adults hospitalized with COVID-19 over a 5-month period. We obtained clinical, microbiologic and laboratory data from electronic medical records. Propensity-score-matching helped create balanced exposure groups. Demographic characteristics were compared across outcome groups (BSI/no BSI) using two-sample t-test and Chi-Square test for continuous and categorical variables respectively, while immunosuppressive therapy use was compared using McNemar's test. Conditional logistic regression helped assess the association between immunosuppressive therapies and BSIs. RESULTS: 13,007 patients were originally included, with propensity-score-matching producing a sample of 6,520 patients. 3.74% and 3.97% were diagnosed with clinically significant BSIs in the original and propensity-score-matched populations respectively. COVID-19 patients with BSIs had significantly longer hospitalizations, higher intensive care unit admission and mortality rates compared to those without BSIs. On univariable analysis, combinations of corticosteroids/anakinra [odds-ratio (OR) 2.00, 95% confidence intervals (C.I.) 1.05-3.80, P value.0342] and corticosteroids/tocilizumab [OR 2.13, 95% C.I. 1.16-3.94, P value .0155] were significantly associated with BSIs. On multivariable analysis (adjusting for confounders), combination corticosteroids/tocilizumab were significantly associated with any BSI [OR 1.97, 95% C.I. 1.04-3.73, P value.0386] and with bacterial BSIs [OR 2.13, 95% C.I. 1.12-4.05, p-value 0.0217]. CONCLUSIONS: Combination immunosuppressive therapies were significantly associated with BSI occurrence in COVID-19 patients; their use warrants increased BSI surveillance. Further studies are needed to establish their causative role.