Outcomes of non-COVID-19 critically ill patients during the COVID-19 pandemic

Abstract

Summary Background Coronavirus disease 2019 (COVID-19) disrupts routine care and alters treatment pathways in every medical specialty, including intensive care medicine, which has been at the core of the pandemic response. The impact of the pandemic is inevitably not limited to patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and their outcomes; however, the impact of COVID-19 on intensive care has not yet been analyzed. Methods The objective of this propensity score-matched study was to compare the clinical outcomes of non-COVID-19 critically ill patients with the outcomes of prepandemic patients. Critically ill, non-COVID-19 patients admitted to the intensive care unit (ICU) during the first wave of the pandemic were matched with patients admitted in the previous year. Mortality, length of stay, and rate of readmission were compared between the two groups after matching. Results A total of 211 critically ill SARS-CoV‑2 negative patients admitted between 13 March 2020 and 16 May 2020 were matched to 211 controls, selected from a matching pool of 1421 eligible patients admitted to the ICU in 2019. After matching, the outcomes were not significantly different between the two groups: ICU mortality was 5.2% in 2019 and 8.5% in 2020, p = 0.248, while intrahospital mortality was 10.9% in 2019 and 14.2% in 2020, p = 0.378. The median ICU length of stay was similar in 2019: 4 days (IQR 2–6) compared to 2020: 4 days (IQR 2–7), p = 0.196. The rate of ICU readmission was 15.6% in 2019 and 10.9% in 2020, p = 0.344. Conclusion In this retrospective single center study, mortality, ICU length of stay, and rate of ICU readmission did not differ significantly between patients admitted to the ICU during the implementation of hospital-wide COVID-19 contingency planning and patients admitted to the ICU before the pandemic.

The association of the COVID-19 pandemic and short-term outcomes of non-COVID-19 critically ill patients: an observational cohort study in Brazilian ICUs

“…Results of the logistic regression model are shown in Fig. 3 (numeric raw values are shown in sTable 2 and subgroup analysis are shown in sFigures [6][7][8][9][10][11][12]. A similar pattern to the main analysis was seen for most subgroups.…”

“…The coronavirus disease 2019 (COVID-19) pandemic has stressed the ICUs in several ways, including an abrupt increase in the need for ICU beds, a rise in the proportion of mechanically ventilated patients, the adaptation of biosafety protocols to a new disease, among others [6][7][8]. While much attention has been given to the outcomes of critically ill COVID-19 patients [9], it is uncertain whether the sudden changes in case-mix and the burden imposed by the pandemic had an impact on the outcomes of non-COVID-19 critically ill patients [10].…”

Machine Learning and Deep Learning Based Time Series Prediction and Forecasting of Ten Nations’ COVID-19 Pandemic

“…Keras is a user interface for the Tensor Flow library [ 26 ]. StatsModel is a Python package that includes classes and methods for estimating various statistical models, running statistical tests, and exploring statistical data [ 27 ]. PmdarimaMath is a statistical library created to cover a gap in Python's time series capabilities.…”

Characteristics and Outcomes of ICU Patients Without COVID-19 Infection—Pandemic Versus Nonpandemic Times: A Population-Based Cohort Study

“…Historically, pandemics have negatively impacted healthcare access, leading to worsened patient outcomes (5)(6)(7). Outcomes for COVID-19 patients are well-studied (8); however, the pandemic's impact on critically ill patients without COVID-19 remains less clear due to conflicting data from small cohort studies (9)(10)(11). While social and physical distancing have been paramount in reducing COVID infections (12), they have created barriers in healthcare access (6,13).…”