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This trial randomly assigned patients with coma after out-of-hospital cardiac arrest to undergo targeted hypothermia at 33°C or normothermia with treatment of fever. At 6 months, there were no significant between-group differences regarding death or functional outcomes.

Background Early identification of predictors for a poor long-term outcome in patients who survive the initial phase of out-of-hospital cardiac arrest (OHCA) may facilitate future clinical research, the process of care and information provided to relatives. The aim of this study was to determine the association between variables available from the patient’s history and status at intensive care admission with outcome in unconscious survivors of OHCA. Methods Using the cohort of the Target Temperature Management trial, we performed a post hoc analysis of 933 unconscious patients with OHCA of presumed cardiac cause who had a complete 6-month follow-up. Outcomes were survival and neurological function as defined by the Cerebral Performance Category (CPC) scale at 6 months after OHCA. After multiple imputations to compensate for missing data, backward stepwise multivariable logistic regression was applied to identify factors independently predictive of a poor outcome (CPC 3–5). On the basis of these factors, a risk score for poor outcome was constructed. Results We identified ten independent predictors of a poor outcome: older age, cardiac arrest occurring at home, initial rhythm other than ventricular fibrillation/tachycardia, longer duration of no flow, longer duration of low flow, administration of adrenaline, bilateral absence of corneal and pupillary reflexes, Glasgow Coma Scale motor response 1, lower pH and a partial pressure of carbon dioxide in arterial blood value lower than 4.5 kPa at hospital admission. A risk score based on the impact of each of these variables in the model yielded a median (range) AUC of 0.842 (0.840–0.845) and good calibration. Internal validation of the score using bootstrapping yielded a median (range) AUC corrected for optimism of 0.818 (0.816–0.821). Conclusions Among variables available at admission to intensive care, we identified ten independent predictors of a poor outcome at 6 months for initial survivors of OHCA. They reflected pre-hospital circumstances (six variables) and patient status on hospital admission (four variables). By using a simple and easy-to-use risk scoring system based on these variables, patients at high risk for a poor outcome after OHCA may be identified early.

Less than 500 participants have been included in randomized trials comparing hypothermia with regular care for out-of-hospital cardiac arrest patients, and many of these trials were small and at a high risk of bias. Consequently, the accrued data on this potentially beneficial intervention resembles that of a drug following small phase II trials. A large confirmatory trial is therefore warranted. The TTM2-trial is an international, multicenter, parallel group, investigator-initiated, randomized, superiority trial in which a target temperature of 33°C after cardiac arrest will be compared with a strategy to maintain normothermia and early treatment of fever (≥37.8°C). Participants will be randomized within 3 hours of return of spontaneous circulation with the intervention period lasting 40 hours in both groups. Sedation will be mandatory for all patients throughout the intervention period. The clinical team involved with direct patient care will not be blinded to allocation group due to the inherent difficulty in blinding the intervention. Prognosticators, outcome-assessors, the steering group, the trial coordinating team, and trial statistician will be blinded. The primary outcome will be all-cause mortality at 180 days after randomization. We estimate a 55% mortality in the control group. To detect an absolute risk reduction of 7.5% with an alpha of 0.05 and 90% power, 1900 participants will be enrolled. The main secondary neurological outcome will be poor functional outcome (modified Rankin Scale 4–6) at 180 days after arrest. The TTM2-trial will compare hypothermia to 33°C with normothermia and early treatment of fever (≥37.8°C) after out-of-hospital cardiac arrest.

Previous studies have shown that a high body mass index (BMI) is a risk factor for severe COVID-19. The aim of the present study was to assess whether a high BMI affects the risk of death or prolonged length of stay (LOS) in patients with COVID-19 during intensive care in Sweden. In this observational, register-based study, we included patients with COVID-19 from the Swedish Intensive Care Registry admitted to intensive care units (ICUs) in Sweden. Outcomes assessed were death during intensive care and ICU LOS [greater than or equal to]14 days. We used logistic regression models to evaluate the association (odds ratio [OR] and 95% confidence interval [CI]) between BMI and the outcomes. Valid weight and height information could be retrieved in 1,649 patients (1,227 (74.4%) males) with COVID-19. We found a significant association between BMI and the risk of the composite outcome death or LOS [greater than or equal to]14 days in survivors (OR per standard deviation [SD] increase 1.30, 95%CI 1.16-1.44, adjusted for sex, age and comorbidities), and this association remained after further adjustment for severity of illness (simplified acute physiology score; SAPS3) at ICU admission (OR 1.30 per SD, 95%CI 1.17-1.45). Individuals with a BMI [greater than or equal to] 35 kg/m.sup.2 had a doubled risk of the composite outcome. A high BMI was also associated with death during intensive care and a prolonged LOS in survivors assessed as separate outcomes. In this large cohort of Swedish ICU patients with COVID-19, a high BMI was associated with increasing risk of death and prolonged length of stay in the ICU. Based on our findings, we suggest that individuals with obesity should be more closely monitored when hospitalized for COVID-19.

Background Intensive care unit-to-unit transfer due to temporary shortage of beds is increasing in Sweden. Transportation induces practical hazards, and the change of health care provider may prolong the length of stay in intensive care. We previously showed that the risk of death at 90 days did not differ between patients transferred due to a shortage of beds and non-transferred patients with a similar burden of illness in a tertiary intensive care unit. The aim of this study was to widen the analysis to a nation-wide cohort of critically ill patients transferred to another intensive care unit in Sweden due to shortage of intensive care beds. Methods Retrospective comparison between capacity transferred and non-transferred patients, based on data from the Swedish Intensive Care Registry during a 5-year period before the COVID-19 pandemic. Patients with insufficient data entries or a recurring capacity transfer within 90 days were excluded. To assess the association between capacity transfer and death as well as intensive care stay within 90 days after ICU admission, logistic regression models with step-wise adjustment for SAPS3 score, primary ICD-10 ICU diagnosis and the number of days in the intensive care unit before transfer were applied. Results From 161,140 eligible intensive care admissions, 2912 capacity transfers were compared to 135,641 discharges or deaths in the intensive care unit. Ninety days after ICU admission, 28% of transferred and 21% of non-transferred patients were deceased. In the fully adjusted model, capacity transfer was associated with a lower risk of death within 90 days than no transfer; OR (95% CI) 0.71 (0.65–0.69) and the number of days spent in intensive care was longer: 12.4 [95% CI 12.2–12.5] vs 3.3 [3.3–3.3]. Conclusions Intensive care unit-to-unit transfer due to shortage of bed capacity as compared to no transfer during a 5-year period preceding the COVID-19 pandemic in Sweden was associated with lower risk of death within 90 days but with longer stay in intensive care.

Objective30-day survival after cardiac arrest is low, 12.4% and 36% for out-of-hospital and in-hospital cardiac arrest, respectively. Heart failure is a known risk condition for cardiac arrest. Improving our ability to identify patients at high risk of cardiac arrest would enable prevention. We aimed to develop a prediction model for cardiac arrest to be used in patients newly diagnosed with heart failure.DesignA nationwide registry-based observational study.SettingData were sourced from the Swedish Heart Failure Registry (1 January 2005 to 31 December 2021).ParticipantsThis cohort included 45 068 patients discharged from hospital after first hospitalisation for newly diagnosed heart failure. Patients discharged from hospital with palliative care and/or implantable defibrillators were excluded.Outcome measure and analysisThe primary outcome was defined as cardiac arrest registered in the Swedish Registry for Cardiopulmonary Resuscitation until final follow-up (15 November 2022). Patients who died without resuscitation were treated as competing events. A Random Survival Forest model for competing risk was developed using predictors from the heart failure registry. The model was evaluated with Brier score, observed versus predicted cumulative incidence, Concordance-index (C-index) and time-dependent area under the curve of a receiver operating characteristics graph (AUC-ROC).ResultsIn this cohort, 2399 (5%) patients had received cardiopulmonary resuscitation (CPR) (5%), and 31 989 (71%) patients died without resuscitation. Our model with 82 predictors had a low Brier score indicating a capacity to accurately predict cumulative incidence of cardiac arrest on a group level. However, the model also had a low C-index 0.52 and low AUC-ROC 0.63–0.65.ConclusionOur Random Survival Forest model for competing risk could not accurately predict cardiac arrest in individual patients newly diagnosed with heart failure, because the event death without attempted resuscitation was treated as a competing event. The lack of information on transitions to palliative care and Do-Not-Attempt-CPR-orders limits the clinical relevance of any cardiac arrest prediction model.

Background ICU survivorship includes a diverse burden of disease. Current questionnaires used for collecting information about health-related problems and their relation to quality of life lack detailed questions in several areas relevant to ICU survivors. Our aim was to construct a provisional questionnaire on health-related issues based on interviews with ICU survivors and to test if this questionnaire was able to show differences between ICU survivors and a control group. Methods Thirty-two ICU survivors were identified at a post-ICU clinic and interviewed at least six months after ICU discharge. Using an established qualitative methodology from oncology, all dysfunctions and disabilities were extracted, rephrased as questions and compiled into a provisional questionnaire. In a second part, this questionnaire was tested on ICU survivors and controls. Inclusion criteria for the ICU survivors were ICU stay at least 72 h with ICU discharge six months to three years prior to the study. A non-ICU-treated control group was obtained from the Swedish Population Register, matched for age and sex. Eligible participants received an invitation letter and were contacted by phone. If willing to participate, they were sent the questionnaire. Descriptive statistics were applied. Results Analysis of the interviews yielded 238 questions in 13 domains: cognition, fatigue, physical health, pain, psychological health, activities of daily living, sleep, appetite and alcohol, sexual health, sensory functions, gastrointestinal functions, urinary functions and work life. In the second part, 395 of 518 ICU survivors and 197 of 231 controls returned a completed questionnaire, the response rates being 76.2% and 85.3%, respectively. The two groups differed significantly in 13 of 22 comorbidities. ICU survivors differed in a majority of questions (p ≤ 0.05) distributed over all 13 domains compared with controls. Conclusions This study describes the development of a provisional questionnaire to identify health-related quality of life issues and long-term burden of disease after intensive care. The questionnaire was answered by 395 ICU survivors. The questionnaire could identify that they experience severe difficulties in a wide range of domains compared with a control group. Trial registry ClinicalTrials.gov Ref# NCT 02767180

Background Unit-to-unit transfer of critically ill patients infers hazards that may cause adverse events. Circumstantial factors associated with mortality after intensive care include days in the ICU, night-time or weekend discharge and capacity transfer as compared to other reasons for transfer. Distance travelled may also constitute an indirect risk. The aim of this study was to assess potential associations between these circumstantial factors and the risk of death 30 days after transfer. Methods Data from 2015 to 2019 was retrieved from the Swedish Intensive Care Registry. Logistic regression was used for risk analysis. Results Among 4,327 patients, 965 (22%) were deceased 30 days after transfer. 1351 patients undergoing capacity transfer had a higher morbidity than patients transferred for other reasons. Using univariable logistic regression, days spent in the referring ICU before transfer, capacity transfer as compared to clinical transfer and repatriation as well as SAPS3 in the receiving ICU were associated with a higher risk of death at 30 days. However, after multivariable regression with adjustment for ICD-10 diagnosis and Standardised Mortality Rate in the receiving ICU, these associations were lost. Conclusion Our results suggest that inter-hospital transfer is safe to carry out at any time of day and over shorter as well as longer distances.

Background Cognitive impairment is common following out-of-hospital cardiac arrest (OHCA), but the nature of the impairment is poorly understood. Our objective was to describe cognitive impairment in OHCA survivors, with the hypothesis that OHCA survivors would perform significantly worse on neuropsychological tests of cognition than controls with acute myocardial infarction (MI). Another aim was to investigate the relationship between cognitive performance and the associated factors of emotional problems, fatigue, insomnia, and cardiovascular risk factors following OHCA. Methods This was a prospective case–control sub-study of The Targeted Hypothermia versus Targeted Normothermia after Out-of-Hospital Cardiac Arrest (TTM2) trial. Eight of 61 TTM2-sites in Sweden, Denmark, and the United Kingdom included adults with OHCA of presumed cardiac or unknown cause. A matched non-arrest control group with acute MI was recruited. At approximately 7 months post-event, we administered an extensive neuropsychological test battery and questionnaires on anxiety, depression, fatigue, and insomnia, and collected information on the cardiovascular risk factors hypertension and diabetes. Results Of 184 eligible OHCA survivors, 108 were included, with 92 MI controls enrolled. Amongst OHCA survivors, 29% performed z -score ≤ − 1 (at least borderline–mild impairment) in ≥ 2 cognitive domains, 14% performed z -score ≤ − 2 (major impairment) in ≥ 1 cognitive domain while 54% performed without impairment in any domain. Impairment was most pronounced in episodic memory, executive functions, and processing speed. OHCA survivors performed significantly worse than MI controls in episodic memory (mean difference, MD = − 0.37, 95% confidence intervals [− 0.61, − 0.12]), verbal (MD = − 0.34 [− 0.62, − 0.07]), and visual/constructive functions (MD = − 0.26 [− 0.47, − 0.04]) on linear regressions adjusted for educational attainment and sex. When additionally adjusting for anxiety, depression, fatigue, insomnia, hypertension, and diabetes, executive functions (MD = − 0.44 [− 0.82, − 0.06]) were also worse following OHCA. Diabetes, symptoms of anxiety, depression, and fatigue were significantly associated with worse cognitive performance. Conclusions In our study population, cognitive impairment was generally mild following OHCA. OHCA survivors performed worse than MI controls in 3 of 6 domains. These results support current guidelines that a post-OHCA follow-up service should screen for cognitive impairment, emotional problems, and fatigue. Trial registration ClinicalTrials.gov, NCT03543371. Registered 1 June 2018.

Background Patients in the intensive care unit (ICU) are increasingly being transferred between ICUs due to a shortage of ICU beds, although this practice is potentially harmful. However, in tertiary units, the transfer of patients who are not in need of highly specialized care is often necessary. The aim of this study was to assess the association between a 90-day mortality and inter-hospital transfer due to a shortage of ICU beds in a tertiary centre. Methods Data were retrieved from the local ICU database from December 2011 to September 2019. The primary analysis was a risk-adjusted logistic regression model. Secondary analyses comprised case/control (transfer/non-transfer) matching. Results A total of 573 patients were transferred due to a shortage of ICU beds, and 8106 patients were not transferred. Crude 90-day mortality was higher in patients transferred due to a shortage of beds (189 patients (33%) vs 2188 patients (27%), p = 0.002). In the primary, risk-adjusted analysis, the risk of death at 90 days was similar between the groups (odds ratio 0.923, 95% confidence interval 0.75–1.14, p = 0.461). In the secondary analyses, a 90-day mortality was similar in transferred and non-transferred patients matched according to SAPS 3-score, age, days in the ICU and ICU diagnosis ( p = 0.407); SOFA score on the day of discharge, ICU diagnosis and age ( p = 0.634); or in a propensity score model ( p = 0.229). Conclusion Mortality at 90 days in critically ill patients treated in a tertiary centre was not affected by transfer to another intensive care units due to a shortage of beds. We found this conclusion to be valid under the assumption that patients are carefully selected and that the transports are safely performed.