Impact of High Dose Early Mobilization on Outcomes for Patients with Diabetes: A Secondary Analysis of the TEAM Trial.

RATIONALE: Patients with diabetes represent almost 20% of all ICU admissions and might respond differently to high dose early active mobilization. OBJECTIVES: To assess whether diabetes modified the relationship between the dose of early mobilization on clinical outcomes in the TEAM trial. METHODS: All TEAM trial patients were included. The primary outcome was days alive and out of hospital at day 180. Secondary outcomes included 180-day mortality and long-term functional outcomes at day 180. Logistic and median regression models were used to explore the effect of high dose early mobilization on outcomes by diabetes status. MEASUREMENTS AND MAIN RESULTS: All 741 patients from the original trial were included. Of these, 159 patients (21.4%) had diabetes. Patients with diabetes had a lower number of days alive and out of hospital at day 180 (124 [0-153] vs. 147 [82-164], p = 0.013), and higher 180-day mortality (30% vs. 18%, p = 0.044). In patients receiving high dose early mobilization, days alive and out of hospital at day 180 was 73.0 (0.0 - 144.5) in patients with diabetes and 146.5 (95.8 - 163.0) in patients without diabetes (p for interaction = 0.108). However, in patients with diabetes, high dose early mobilization increased the odds of mortality at 180 days (adjusted odds ratio 3.47; 95% confidence interval [CI], 1.67-7.61, p value for interaction, 0.001). CONCLUSIONS: In this secondary analysis of the TEAM trial, in patients with diabetes, a high dose early mobilization strategy did not significantly decrease the number of days alive and out of hospital at day 180 but it increased 180-day mortality.

Association of active mobilisation variables with adverse events and mortality in patients requiring mechanical ventilation in the intensive care unit: a systematic review and meta-analysis.

BACKGROUND: Mobilisation during critical illness is now included in multiple clinical practice guidelines. However, a large, randomised trial and systematic review have recently identified an increased probability of adverse events and mortality in patients who received early active mobilisation in the intensive care unit (ICU). We aimed to determine the effects of mobilisation compared with usual care on adverse events and mortality in an acute ICU setting. In subgroup analyses, we specifically aimed to investigate possible sources of harm, including the timing and duration of mobilisation achieved, ventilation status, and admission diagnosis. METHODS: In this systematic review with frequentist and Bayesian analyses, we searched MEDLINE, Embase, Cochrane Central Register of Controlled Trials, CINAHL, SPORTDiscus, SCOPUS, Web of Science, and PEDro electronic databases, as well as clinical trial registries (ICTRP and ClinicalTrials.gov), from inception to March 16, 2023, without language restrictions. Eligible studies were randomised controlled trials that examined active mobilisation compared with either no mobilisation or mobilisation commencing later, or at a lower frequency or intensity, in adults who were critically ill during or after a period of mechanical ventilation in an acute ICU setting. Two authors independently screened reports, extracted data, and assessed the risk of bias using the Cochrane risk-of-bias tool (version 1). The primary outcome was the number of adverse events that occurred during the implementation of mobilisation, with the effect of mobilisation on mortality being the secondary outcome. Risk ratios (RRs) with 95% CIs were calculated in R (version 4.0.3) using random-effects modelling, with Bayesian analysis completed to calculate the probability of treatment harm (ie, RR >1). Subgroup analyses were completed to investigate the association of various factors of mobilisation on adverse events and mortality: duration of mobilisation (longer [≥20 min per day] vs shorter [<20 min per day]), timing of commencement (early [≤72 h from ICU admission] vs late [>72 h from ICU admission]), ventilation status at commencement (all patients mechanically ventilated vs all patients extubated), and ICU admission diagnosis (surgical vs medical). This study was registered with PROSPERO, CRD42022369272. FINDINGS: After title and abstract screening of 14 440 studies and review of 466 full texts, 67 trials with 7004 participants met inclusion criteria, with 59 trials contributing to the meta-analysis. Of the 67 included studies, 15 (22%) did not mention adverse events and 13 (19%) reported no adverse events occurring across the trial period. Overall, we found no effect of mobilisation compared with usual care on the occurrence of adverse events (RR 1·09 [95% CI 0·69-1·74], p=0·71; I2 91%; 32 731 events, 20 studies; very low certainty), with a 2·96% occurrence rate (693 events in 23 395 intervention sessions; 25 studies). Mobilisation did not have any effect on mortality (RR 0·98 [95% CI 0·87-1·12], p=0·81; I2 0%; n=6218, 58 studies; moderate certainty). Subgroup analysis was hindered by the large amount of data that could not be allocated and analysed, making the results hypothesis generating only. INTERPRETATION: Implementation of mobilisation in the ICU was associated with a less than 3% chance of an adverse event occurring and was not found to increase adverse events or mortality overall, providing reassurance for clinicians about the safety of performing this intervention. Subgroup analyses did not clearly identify any specific variable of mobilisation implementation that increased harm. FUNDING: None.

Physical rehabilitation, mobilization and patient-centred outcomes: what is new?

PURPOSE OF REVIEW: Physical rehabilitation and mobilization interventions aim to reduce the incidence of intensive care unit (ICU)-acquired weakness and subsequently reduce morbidity in critically ill patients. This chapter will explore the evidence for physical rehabilitation and mobilization with an emphasis on patient-centred outcomes selected in randomized controlled trials. This is particularly pertinent at a time when clinicians are deciding how to implement physical rehabilitation and mobilization into the treatment of critically ill patients. RECENT FINDINGS: Multiple trials of physical rehabilitation and mobilization were published in 2022 and 2023 with conflicting results. Analysing the complexities of physical rehabilitation research provides an insight into these results and will aid in the interpretation of trials of physical rehabilitation and mobilization. SUMMARY: Patient-centred outcomes are often utilized in physical rehabilitation and mobilization research, but this does not automatically correspond to an increase in research quality. Improving consistency in trials of physical rehabilitation will aid in the interpretation and translation of physical rehabilitation research.

Characteristics and outcomes of an international cohort of 600 000 hospitalized patients with COVID-19.

BACKGROUND: We describe demographic features, treatments and clinical outcomes in the International Severe Acute Respiratory and emerging Infection Consortium (ISARIC) COVID-19 cohort, one of the world's largest international, standardized data sets concerning hospitalized patients. METHODS: The data set analysed includes COVID-19 patients hospitalized between January 2020 and January 2022 in 52 countries. We investigated how symptoms on admission, co-morbidities, risk factors and treatments varied by age, sex and other characteristics. We used Cox regression models to investigate associations between demographics, symptoms, co-morbidities and other factors with risk of death, admission to an intensive care unit (ICU) and invasive mechanical ventilation (IMV). RESULTS: Data were available for 689 572 patients with laboratory-confirmed (91.1%) or clinically diagnosed (8.9%) SARS-CoV-2 infection from 52 countries. Age [adjusted hazard ratio per 10 years 1.49 (95% CI 1.48, 1.49)] and male sex [1.23 (1.21, 1.24)] were associated with a higher risk of death. Rates of admission to an ICU and use of IMV increased with age up to age 60 years then dropped. Symptoms, co-morbidities and treatments varied by age and had varied associations with clinical outcomes. The case-fatality ratio varied by country partly due to differences in the clinical characteristics of recruited patients and was on average 21.5%. CONCLUSIONS: Age was the strongest determinant of risk of death, with a ∼30-fold difference between the oldest and youngest groups; each of the co-morbidities included was associated with up to an almost 2-fold increase in risk. Smoking and obesity were also associated with a higher risk of death. The size of our international database and the standardized data collection method make this study a comprehensive international description of COVID-19 clinical features. Our findings may inform strategies that involve prioritization of patients hospitalized with COVID-19 who have a higher risk of death.

Diabetes mellitus, glycaemic control, and severe COVID-19 in the Australian critical care setting: A nested cohort study.

BACKGROUND: Internationally, diabetes mellitus is recognised as a risk factor for severe COVID-19. The relationship between diabetes mellitus and severe COVID-19 has not been reported in the Australian population. OBJECTIVE: The objective of this study was to determine the prevalence of and outcomes for patients with diabetes admitted to Australian intensive care units (ICUs) with COVID-19. METHODS: This is a nested cohort study of four ICUs in Melbourne participating in the Short Period Incidence Study of Severe Acute Respiratory Infection (SPRINT-SARI) Australia project. All adult patients admitted to the ICU with COVID-19 from 20 February 2020 to 27 February 2021 were included. Blood glucose and glycated haemoglobin (HbA1c) data were retrospectively collected. Diabetes was diagnosed from medical history or an HbA1c ≥6.5% (48 mmol/mol). Hospital mortality was assessed using logistic regression. RESULTS: There were 136 patients with median age 58 years [48-68] and median Acute Physiology and Chronic Health Evaluation II (APACHE II) score of 14 [11-19]. Fifty-eight patients had diabetes (43%), 46 patients had stress-induced hyperglycaemia (34%), and 32 patients had normoglycaemia (23%). Patients with diabetes were older, were with higher APACHE II scores, had greater glycaemic variability than patients with normoglycaemia, and had longer hospital length of stay. Overall hospital mortality was 16% (22/136), including nine patients with diabetes, nine patients with stress-induced hyperglycaemia, and two patients with normoglycaemia. CONCLUSION: Diabetes is prevalent in patients admitted to Australian ICUs with severe COVID-19, highlighting the need for prevention strategies in this vulnerable population.

The Effect of Mobilization at 6 Months after Critical Illness - Meta-Analysis.

BACKGROUND: The comparative efficacy and safety of early active mobilization compared with usual care regarding long-term outcomes for adult critically ill survivors remain uncertain. METHODS: We systematically reviewed randomized clinical trials comparing early active mobilization versus usual care in critically ill adults. The primary outcome was days alive and out of hospital to day 180 after pooling data using random effects modeling. We also performed a Bayesian meta-analysis to describe the treatment effect in probability terms. Secondary outcomes were mortality, physical function, strength, health-related quality of life at 6 months, and adverse events. RESULTS: Fifteen trials from 11 countries were included with data from 2703 participants. From six trials (1121 participants) reporting the primary outcome, the pooled mean difference was an increase of 4.28 days alive and out of hospital to day 180 in those patients who received early active mobilization (95% confidence interval, −4.46 to 13.03; I2=41%). Using Bayesian analyses with vague priors, the probability that the intervention increased days alive and out of hospital was 75.1%. In survivors, there was a 95.1% probability that the intervention improved physical function measured through a patient-reported outcome measure at 6 months (standardized mean difference, 0.2; 95% confidence interval, 0.09 to 0.32; I2=0%). Although no treatment effect was identified on any other secondary outcome, there was a 66.4% possibility of increased adverse events with the implementation of early active mobilization and a 72.2% chance it increased 6-month mortality. CONCLUSIONS: Use of early active mobilization for critically ill adults did not significantly affect days alive and out of hospital to day 180. Early active mobilization was associated with improved physical function in survivors at 6 months; however, the possibility that it might increase mortality and adverse events needs to be considered when interpreting this finding. (PROSPERO number, CRD42022309650.)

Early Active Mobilization during Mechanical Ventilation in the ICU.

BACKGROUND: Intensive care unit (ICU)-acquired weakness often develops in patients who are undergoing invasive mechanical ventilation. Early active mobilization may mitigate ICU-acquired weakness, increase survival, and reduce disability. METHODS: We randomly assigned 750 adult patients in the ICU who were undergoing invasive mechanical ventilation to receive increased early mobilization (sedation minimization and daily physiotherapy) or usual care (the level of mobilization that was normally provided in each ICU). The primary outcome was the number of days that the patients were alive and out of the hospital at 180 days after randomization. RESULTS: The median number of days that patients were alive and out of the hospital was 143 (interquartile range, 21 to 161) in the early-mobilization group and 145 days (interquartile range, 51 to 164) in the usual-care group (absolute difference, -2.0 days; 95% confidence interval [CI], -10 to 6; P = 0.62). The mean (±SD) daily duration of active mobilization was 20.8±14.6 minutes and 8.8±9.0 minutes in the two groups, respectively (difference, 12.0 minutes per day; 95% CI, 10.4 to 13.6). A total of 77% of the patients in both groups were able to stand by a median interval of 3 days and 5 days, respectively (difference, -2 days; 95% CI, -3.4 to -0.6). By day 180, death had occurred in 22.5% of the patients in the early-mobilization group and in 19.5% of those in the usual-care group (odds ratio, 1.15; 95% CI, 0.81 to 1.65). Among survivors, quality of life, activities of daily living, disability, cognitive function, and psychological function were similar in the two groups. Serious adverse events were reported in 7 patients in the early-mobilization group and in 1 patient in the usual-care group. Adverse events that were potentially due to mobilization (arrhythmias, altered blood pressure, and desaturation) were reported in 34 of 371 patients (9.2%) in the early-mobilization group and in 15 of 370 patients (4.1%) in the usual-care group (P = 0.005). CONCLUSIONS: Among adults undergoing mechanical ventilation in the ICU, an increase in early active mobilization did not result in a significantly greater number of days that patients were alive and out of the hospital than did the usual level of mobilization in the ICU. The intervention was associated with increased adverse events. (Funded by the National Health and Medical Research Council of Australia and the Health Research Council of New Zealand; TEAM ClinicalTrials.gov number, NCT03133377.).

Machine learning predicts the short-term requirement for invasive ventilation among Australian critically ill COVID-19 patients.

OBJECTIVE(S): To use machine learning (ML) to predict short-term requirements for invasive ventilation in patients with COVID-19 admitted to Australian intensive care units (ICUs). DESIGN: A machine learning study within a national ICU COVID-19 registry in Australia. PARTICIPANTS: Adult patients who were spontaneously breathing and admitted to participating ICUs with laboratory-confirmed COVID-19 from 20 February 2020 to 7 March 2021. Patients intubated on day one of their ICU admission were excluded. MAIN OUTCOME MEASURES: Six machine learning models predicted the requirement for invasive ventilation by day three of ICU admission from variables recorded on the first calendar day of ICU admission; (1) random forest classifier (RF), (2) decision tree classifier (DT), (3) logistic regression (LR), (4) K neighbours classifier (KNN), (5) support vector machine (SVM), and (6) gradient boosted machine (GBM). Cross-validation was used to assess the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity of machine learning models. RESULTS: 300 ICU admissions collected from 53 ICUs across Australia were included. The median [IQR] age of patients was 59 [50-69] years, 109 (36%) were female and 60 (20%) required invasive ventilation on day two or three. Random forest and Gradient boosted machine were the best performing algorithms, achieving mean (SD) AUCs of 0.69 (0.06) and 0.68 (0.07), and mean sensitivities of 77 (19%) and 81 (17%), respectively. CONCLUSION: Machine learning can be used to predict subsequent ventilation in patients with COVID-19 who were spontaneously breathing and admitted to Australian ICUs.

People in intensive care with COVID-19: demographic and clinical features during the first, second, and third pandemic waves in Australia.

OBJECTIVE: To compare the demographic and clinical features, management, and outcomes for patients admitted with COVID-19 to intensive care units (ICUs) during the first, second, and third waves of the pandemic in Australia. DESIGN, SETTING, AND PARTICIPANTS: People aged 16 years or more admitted with polymerase chain reaction-confirmed COVID-19 to the 78 Australian ICUs participating in the Short Period Incidence Study of Severe Acute Respiratory Infection (SPRINT-SARI) Australia project during the first (27 February - 30 June 2020), second (1 July 2020 - 25 June 2021), and third COVID-19 waves (26 June - 1 November 2021). MAIN OUTCOME MEASURES: Primary outcome: in-hospital mortality. SECONDARY OUTCOMES: ICU mortality; ICU and hospital lengths of stay; supportive and disease-specific therapies. RESULTS: 2493 people (1535 men, 62%) were admitted to 59 ICUs: 214 during the first (9%), 296 during the second (12%), and 1983 during the third wave (80%). The median age was 64 (IQR, 54-72) years during the first wave, 58 (IQR, 49-68) years during the second, and 54 (IQR, 41-65) years during the third. The proportion without co-existing illnesses was largest during the third wave (41%; first wave, 32%; second wave, 29%). The proportion of ICU beds occupied by patients with COVID-19 was 2.8% (95% CI, 2.7-2.9%) during the first, 4.6% (95% CI, 4.3-5.1%) during the second, and 19.1% (95% CI, 17.9-20.2%) during the third wave. Non-invasive (42% v 15%) and prone ventilation strategies (63% v 15%) were used more frequently during the third wave than during the first two waves. Thirty patients (14%) died in hospital during the first wave, 35 (12%) during the second, and 281 (17%) during the third. After adjusting for age, illness severity, and other covariates, the risk of in-hospital mortality was similar for the first and second waves, but 9.60 (95% CI, 3.52-16.7) percentage points higher during the third than the first wave. CONCLUSION: The demographic characteristics of patients in intensive care with COVID-19 and the treatments they received during the third pandemic wave differed from those of the first two waves. Adjusted in-hospital mortality was highest during the third wave.

Outcomes for patients with COVID-19 admitted to Australian intensive care units during the first four months of the pandemic.

OBJECTIVES: To describe the characteristics and outcomes of patients with COVID-19 admitted to intensive care units (ICUs) during the initial months of the pandemic in Australia. DESIGN, SETTING: Prospective, observational cohort study in 77 ICUs across Australia. PARTICIPANTS: Patients admitted to participating ICUs with laboratory-confirmed COVID-19 during 27 February - 30 June 2020. MAIN OUTCOME MEASURES: ICU mortality and resource use (ICU length of stay, peak bed occupancy). RESULTS: The median age of the 204 patients with COVID-19 admitted to intensive care was 63.5 years (IQR, 53-72 years); 140 were men (69%). The most frequent comorbid conditions were obesity (40% of patients), diabetes (28%), hypertension treated with angiotensin-converting enzyme inhibitors or angiotensin II receptor blockers (24%), and chronic cardiac disease (20%); 73 patients (36%) reported no comorbidity. The most frequent source of infection was overseas travel (114 patients, 56%). Median peak ICU bed occupancy was 14% (IQR, 9-16%). Invasive ventilation was provided for 119 patients (58%). Median length of ICU stay was greater for invasively ventilated patients than for non-ventilated patients (16 days; IQR, 9-28 days v 3 days; IQR, 2-5 days), as was ICU mortality (26 deaths, 22%; 95% CI, 15-31% v four deaths, 5%; 95% CI, 1-12%). Higher Acute Physiology and Chronic Health Evaluation II (APACHE-II) scores on ICU day 1 (adjusted hazard ratio [aHR], 1.15; 95% CI, 1.09-1.21) and chronic cardiac disease (aHR, 3.38; 95% CI, 1.46-7.83) were each associated with higher ICU mortality. CONCLUSION: Until the end of June 2020, mortality among patients with COVID-19 who required invasive ventilation in Australian ICUs was lower and their ICU stay longer than reported overseas. Our findings highlight the importance of ensuring adequate local ICU capacity, particularly as the pandemic has not yet ended.

Comparison of Critical Care Occupancy and Outcomes of Critically Ill Patients during the 2020 COVID-19 Winter Surge and 2009 H1N1 Influenza Pandemic in Australia.

Rationale: Both 2009 pandemic influenza A (H1N1) and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are transmitted by respiratory secretions and in severe cases result in a viral pneumonitis, requiring intensive care unit (ICU) admission. However, no studies have compared the clinical characteristics and outcomes of such patients. Objectives: To report and compare the demographic characteristics, treatments, use of critical care resources, and outcomes of patients admitted to an Australian ICU with H1N1 influenza during the winter of 2009, and SARS-CoV-2 during the winter of 2020. Methods: This was a multicenter project, using national data from previous and ongoing epidemiological studies concerning severe acute respiratory infections in Australia. All ICUs admitting patients with H1N1 or coronavirus disease (COVID-19) were included and contributed data. We compared clinical characteristics and outcomes of patients with H1N1 admitted to ICU in the winter of 2009 versus patients with COVID-19 admitted to ICU in the winter of 2020. The primary outcome was in-hospital mortality. Potential years of life lost (PYLL) were calculated according to sex-adjusted life expectancy in Australia. Results: Across the two epochs, 861 patients were admitted to ICUs; 236 (27.4%) with COVID-19 and 625 (72.6%) with H1N1 influenza. The number of ICU admissions and bed-days occupied were higher with 2009 H1N1 influenza. Patients with COVID-19 were older, more often male and overweight, and had lower Acute Physiology and Chronic Health Evaluation II scores at ICU admission. The highest age-specific incidence of ICU admission was among infants (0-1 yr of age) for H1N1, and among the elderly (≥65 yr) for COVID-19. Unadjusted in-hospital mortality was similar (11.5% in COVID-19 vs. 16.1% in H1N1; odds ratio, 0.68 [95% confidence interval (95% CI), 0.42-1.06]; P = 0.10). The PYLL was greater with H1N1 influenza than with COVID-19 at 154.1 (95% CI, 148.7-159.4) versus 13.6 (95% CI, 12.2-15.1) PYLL per million inhabitants. Conclusions: In comparison with 2009 H1N1 influenza, COVID-19 admissions overwinter in Australia resulted in fewer ICU admissions, and lower bed-day occupancy. Crude in-hospital mortality was similar, but because of demographic differences in affected patients, deaths due to 2009 H1N1 influenza led to an 11-fold increase in the number of PYLL in critically ill patients.

Nosocomial infections amongst critically ill COVID-19 patients in Australia.

Purpose: To determine the frequency of nosocomial infections including hospital-acquired pneumonia (HAP) and bloodstream infection (BSI), amongst critically ill patients with COVID-19 infection in Australian ICUs and to evaluate associations with mortality and length of stay (LOS). Methods: The effect of nosocomial infections on hospital mortality was evaluated using hierarchical logistic regression models to adjust for illness severity and mechanical ventilation. Results: There were 490 patients admitted to 55 ICUs during the study period. Adjusted odds ratio (OR) for hospital mortality was 1.61 (95% confidence interval (CI) 0.61-4.27, p = 0.3) when considering BSI, and 1.76 (95% CI 0.73-4.21, p = 0.2) for HAP. The average adjusted ICU LOS was significantly longer for patients with BSI (geometric mean 9.0 days vs 6.3 days, p = 0.04) and HAP (geometric mean 13.9 days vs 6.0 days p<0.001). Conclusion: Nosocomial infection rates amongst patients with COVID-19 were low and their development was associated with a significantly longer ICU LOS.

Comparison of baseline characteristics, treatment and celinical outcomes of critically ill COVID-19 patients admitted in the first and second waves in Australia.

Objective: To report longitudinal differences in baseline characteristics, treatment, and outcomes in patients with coronavirus disease 2019 (COVID-19) admitted to intensive care units (ICUs) between the first and second waves of COVID-19 in Australia. Design, setting and participants: SPRINT-SARI Australia is a multicentre, inception cohort study enrolling adult patients with COVID-19 admitted to participating ICUs. The first wave of COVID-19 was from 27 February to 30 June 2020, and the second wave was from 1 July to 22 October 2020. Results: A total of 461 patients were recruited in 53 ICUs across Australia; a higher number were admitted to the ICU during the second wave compared with the first: 255 (55.3%) versus 206 (44.7%). Patients admitted to the ICU in the second wave were younger (58.0 v 64.0 years; P = 0.001) and less commonly male (68.9% v 60.0%; P = 0.045), although Acute Physiology and Chronic Health Evaluation (APACHE) II scores were similar (14 v 14; P = 0.998). High flow oxygen use (75.2% v 43.4%; P < 0.001) and non-invasive ventilation (16.5% v 7.1%; P = 0.002) were more common in the second wave, as was steroid use (95.0% v 30.3%; P < 0.001). ICU length of stay was shorter (6.0 v 8.4 days; P = 0.003). In-hospital mortality was similar (12.2% v 14.6%; P = 0.452), but observed mortality decreased over time and patients were more likely to be discharged alive earlier in their ICU admission (hazard ratio, 1.43; 95% CI, 1.13-1.79; P = 0.002). Conclusion: During the second wave of COVID-19 in Australia, ICU length of stay and observed mortality decreased over time. Multiple factors were associated with this, including changes in clinical management, the adoption of new evidence-based treatments, and changes in patient demographic characteristics but not illness severity.