ABSTRACT
BACKGROUND: The early months of the COVID-19 pandemic were fraught with much uncertainty and some resource constraint. We assessed the change in survival to hospital discharge over time for intensive care unit patients with COVID-19 during the first 3 months of the pandemic and the presence of any surge effects on patient outcomes. METHODS: Retrospective cohort study using electronic medical record data for all patients with laboratory-confirmed COVID-19 admitted to intensive care units from February 25, 2020, to May 15, 2020, at one of 26 hospitals within an integrated delivery system in the Western USA. Patient demographics, comorbidities, and severity of illness were measured along with medical therapies and hospital outcomes over time. Multivariable logistic regression models were constructed to assess temporal changes in survival to hospital discharge during the study period. RESULTS: Of 620 patients with COVID-19 admitted to the ICU [mean age 63.5 years (SD 15.7) and 69% male], 403 (65%) survived to hospital discharge and 217 (35%) died in the hospital. Survival to hospital discharge increased over time, from 60.0% in the first 2 weeks of the study period to 67.6% in the last 2 weeks. In a multivariable logistic regression analysis, the risk-adjusted odds of survival to hospital discharge increased over time (biweekly change, adjusted odds ratio [aOR] 1.22, 95% CI 1.04-1.40, P = 0.02). Additionally, an a priori-defined explanatory model showed that after adjusting for both hospital occupancy and percent hospital capacity by COVID-19-positive individuals and persons under investigation (PUI), the temporal trend in risk-adjusted patient survival to hospital discharge remained the same (biweekly change, aOR 1.18, 95% CI 1.00-1.38, P = 0.04). The presence of greater rates of COVID-19 positive/PUI as a percentage of hospital capacity was, however, significantly and inversely associated with survival to hospital discharge (aOR 0.95, 95% CI 0.92-0.98, P < 0.01). CONCLUSIONS: During the early COVID-19 pandemic, risk-adjusted survival to hospital discharge increased over time for critical care patients. An association was also seen between a greater COVID-19-positive/PUI percentage of hospital capacity and a lower survival rate to hospital discharge.
Subject(s)
COVID-19/epidemiology , COVID-19/therapy , Pandemics , Patient Discharge/statistics & numerical data , Aged , COVID-19/mortality , Critical Care , Female , Hospital Mortality , Humans , Intensive Care Units , Male , Middle Aged , Retrospective Studies , SARS-CoV-2/isolation & purification , Survival Analysis , United States/epidemiologyABSTRACT
Bit error rate (BER) versus optical signal to noise ratio (OSNR) characteristics determines the transmission performance for coherent optical transponder. We have developed a model to predict BER versus OSNR at various receiver optical power (ROP). The model has three parameters, which are related to BER noise floor, filter mismatching, and OSNR value without noise loading. The model is applied to high baud rate and quadrature amplitude modulation (QAM) transponders. By considering the influence of baud rate on the fitting parameters, accurate prediction of performance for coherent transponder can be achieved over various baud rates. Novel applications enabled by this model include in-field measurement of BER versus OSNR, simple abstraction of coherent transponder, accurate OSNR monitor and coherent optical channel monitor.