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Background: Days alive without life support (DAWOLS) and similar outcomes that seek to summarise mortality and non-mortality experiences are increasingly used in critical care research. The use of these outcomes is challenged by different definitions and non-normal outcome distributions that complicate statistical analysis decisions. Methods: We scrutinized the central methodological considerations when using DAWOLS and similar outcomes and provide a description and overview of the pros and cons of various statistical methods for analysis supplemented with a comparison of these methods using data from the COVID STEROID 2 randomised clinical trial. We focused on readily available regression models of increasing complexity (linear, hurdle-negative binomial, zero–one-inflated beta, and cumulative logistic regression models) that allow comparison of multiple treatment arms, adjustment for covariates and interaction terms to assess treatment effect heterogeneity. Results: In general, the simpler models adequately estimated group means despite not fitting the data well enough to mimic the input data. The more complex models better fitted and thus better replicated the input data, although this came with increased complexity and uncertainty of estimates. While the more complex models can model separate components of the outcome distributions (i.e., the probability of having zero DAWOLS), this complexity means that the specification of interpretable priors in a Bayesian setting is difficult. Finally, we present multiple examples of how these outcomes may be visualised to aid assessment and interpretation. Conclusions: This summary of central methodological considerations when using, defining, and analysing DAWOLS and similar outcomes may help researchers choose the definition and analysis method that best fits their planned studies. Trial registration: COVID STEROID 2 trial, ClinicalTrials.gov: NCT04509973, ctri.nic.in: CTRI/2020/10/028731. [ABSTRACT FROM AUTHOR]

Background: Patients ≥80 years of age are increasingly being admitted to the intensive care unit (ICU). The impact of relevant variables, such comorbidities and performance status, on short-term outcomes in the very elderly is largely unknown. Few studies address the calibration of illness severity scores (SAPS3 score) within this population. We investigated the risk factors for hospital mortality in critically ill patients ≥80 years old, emphasizing performance status and comorbidities, and assessed the calibration of SAPS3 scores in this population., Methods: 1129 very elderly patients admitted to a tertiary ICU in Brazil during a two-year period were retrospectively included in this study. Demographic features, reasons for admission, illness severity, comorbidities (including the Charlson Comorbidity Index) and a simplified performance status measurement were obtained. After univariate analysis, a multivariate model was created to evaluate the factors that were associated with hospital mortality. Alternatively, a conditional inference tree with recursive partitioning was constructed. Calibration of the SAPS3 scores and the multivariate model were evaluated using the Hosmer-Lemeshow test and a calibration plot. Discrimination was assessed using a receiver operating characteristics curve., Results: On multivariate analysis after stepwise regression, only the SAPS3 score (OR 1.08, 95% CI 1.06-1.10), Charlson Index (OR 1.16, 95% CI 1.07-1.27), performance status (OR 1.61, 95% CI 1.05-2.64 for partially dependent patients and OR 2.39, 95% CI 1.38-4.13 for fully dependent patients) and a non-full code status (OR 11.74, 95% CI 6.22-22.160) were associated with increased hospital mortality. Conditional inference tree showed that performance status and Charlson Index had the greatest influence on patients with less severe disease, whereas a non-full code status was prominent in patients with higher illness severity (SAPS3 score >61). The model obtained after logistic regression that included the before mentioned variables demonstrated better calibration and greater discrimination capability (AUC 0.86, 95% CI 0.83-0.89 versus AUC 0.81, 95% CI 0.78-0.84, respectively; p < 0.001) than the SAPS3 score alone., Conclusions: Performance status and comorbidities are important determinants of short-term outcome in critically ill elderly patients ≥80 years old. The addition of simple background information may increase the calibration of the SAPS3 score in this population.