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.

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.

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.

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.

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.