Limitations and Mortality in Intensive Care
Condition: Critical Illness
MeSH terms: Critical Illness
Study type: Observational

Summary: 
      This is an observational prospective pilot-study that investigates which patient-related
      variables that predict a decision to limit life sustaining treatments. Some of the variables
      we were interested in cannot be accessed in registry data, such as frailty scale score and
      living at home or not. Furthermore we wanted to investigate if the variables that were
      independently associated with a decision to limit LST were different for critically ill
      patients with COVID-19 than for critically ill patients with other diagnoses in a Swedish
      intensive care unit during the pandemic.
    

Detailed description: 
      Previous publications have demonstrated variability in end-of-life decision-making (ELDM) for
      critically ill patients (1-3).

      Factors commonly described as underlying the variability in ELDM in northern Europe are
      patient-related factors such as age, sex, the severity of illness, frailty, chronic poor
      health, and diagnosis (4-6). Variability in ELDM is also caused by other less readily
      identifiable factors, such as preferences and experiences of the attending physicians (7-8).

      When the pandemic of the coronavirus disease 2019 (COVID-19) struck worldwide, intensive care
      units (ICUs) were put under immense pressure, and resources were strained to their limit.
      More than 550 million cases and 6.3 million deaths have been reported worldwide (9). In
      Sweden, more than 9000 patients with COVID-19 were admitted for intensive care, dramatically
      increasing the demand for intensive care resources (10).

      At the beginning of the pandemic, this disease was primarily unknown to physicians in the
      ICU, and its course and plausible patient outcomes were difficult to predict. Factors that
      have been demonstrated to predict poor long-term outcomes in critically ill COVID-19 patients
      are extensively studied. In a Swedish setting, they were found to be male sex, high age, high
      simplified acute physiology score (SAPS 3) (11), the month of admission, and preexisting
      conditions such as obesity, asthma, chronic obstructive pulmonary disease, malignancy, and
      immune deficiency (12).

      End-of-life decisions in critically ill patients with and without COVID-19 are closely
      associated with short-term mortality (13-14). There are surprisingly few studies on EDLM in
      COVID-19 patients and whether these decisions were handled differently compared to other
      critically ill patients during the pandemic (15-16). It could be postulated that the process
      of ELDM changed during the pandemic as increased demands for intensive care caused an
      explicit lack of resources (17).

      We hypothesised that ELDM was challenging for COVID-19 patients due to several uncertainties
      concerning prognostication. Therefore, we stipulated that end-of-life decisions in COVID-19
      patients were made for older patients with higher frailty scores than the non-COVID-19 cohort
      and that end-of-life decisions were postponed to later in the course of the COVID-19 disease.
      We also hypothesised that a more significant proportion of patients in the COVID-19 cohort
      died without any end-of-life decision being made before death compared to the non-COVID-19
      cohort.

      In this study, we primarily aimed to investigate which independent factors predicted a
      decision to withdraw or withhold life-sustaining treatments (LST) in critically ill patients
      if these decisions were based on different variables for critically ill patients with
      COVID-19 compared to critically ill patients with other diagnoses in a Swedish ICU. Secondly,
      we aimed to investigate if ELDM was made later in the course of the disease for COVID-19
      patients and if a higher proportion of COVID-19 patients died without any decision to limit
      LST.
    

Intervention name(s): No intervention
Intervention type(s): Other
Intervention description(s): No intervention
Observational model: Cohort
Time perspective: Prospective
Sampling method: Probability Sample
Study population: 
        I cohort with critically ill COVID-19 patients, 1 cohort with critically ill patients with
        other diagnosis than COVID-19.