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.

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.

Optimising a targeted test reduction intervention for patients admitted to the intensive care unit: The Targeted Intensive Care Test Ordering Cluster Trial intervention

BACKGROUND

Approaches to routine diagnostic testing in the intensive care unit include time-scheduled testing and targeted testing. Blood tests and chest radiographs requested on a routine, time-scheduled basis may reduce the risk of missing important findings. Targeted testing, considering individual patient needs, may reduce unnecessary testing, wasted clinician time, and costs. However, existing evidence of targeted testing interventions is generally of low quality, and the optimal testing approach is uncertain.

OBJECTIVES

The aim of the study was to describe the development of an intervention to reduce unnecessary diagnostic test ordering by clinicians working in intensive care, with the aim of informing the design of a pivotal clinical trial.

METHODS

The Capability, Opportunity, Motivation-Behaviour model was used as a theoretical framework for change. The intervention components were informed by systematically identifying, assessing, and classifying targeted testing interventions in behavioural terms. Feedback from intensive care clinicians and patients was sought using surveys and a consumer reference group.

RESULTS

The mean percentage of routine tests considered unnecessary by 201 intensive care clinicians was 33 (standard deviation = 16). When presented with a statement of the pros and cons for targeted versus liberal testing (n = 154), 93 (60%) consumer survey respondents preferred a more liberal approach, 33 (21%) preferred a more restrictive approach, and 28 (18%) were unsure. There were 24 behavioural interventions identified and incorporated into the final intervention. This had five major components: (i) a management committee to acquire, disseminate, and coordinate intervention-related information, (ii) a targeted testing guideline for sites, (iii) educational material for sites, (iv) site medical and nursing champions, and (v) site audit and feedback.

CONCLUSIONS

Although surveyed intensive care clinicians report substantial unnecessary routine diagnostic testing, on the basis of currently available evidence, consumers prefer a more liberal approach. This feedback, and a framework to identify behavioural interventions, has been used to inform the design of a proposed targeted testing clinical trial.

Incident haemodialysis and outcomes in the Top End of Australia

Objective The Northern Territory has the highest incidence of haemodialysis care for end-stage kidney disease in Australia. Although acute kidney injury (AKI) is a recognised risk for chronic kidney disease (CKD), the effect of AKI causing incident haemodialysis (iHD) is unknown. Audits identifying antecedents of iHD may inform health service planning. Thus, the aims of this study were to describe: (1) the development of an iHD recording system involving patients with AKI and CKD; and (2) the incidence, patient characteristics and mortality for patients with dialysis-requiring AKI. Methods A retrospective data linkage study was conducted using eight clinical and administrative datasets of adults receiving iHD during the period from July 2011 to December 2012 within a major northern Australian hospital for AKI without CKD (AKI), AKI in people with pre-existing CKD (AKI/CKD) and CKD (without AKI). The time to death was identified by the Northern Territory Register of deaths. Results In all, 121 iHD treatments were provided for the cohort, whose mean age was 51.5 years with 53.7% female, 68.6% Aboriginal ethnicity and 46.3% with diabetes. iHD was provided for AKI (23.1%), AKI/CKD (47.1%) and CKD (29.8%). The 90-day mortality rate was 25.6% (AKI 39.3%, AKI/CKD 22.8%, CKD 19.4%). The 3-year mortality rate was 45.5% (AKI 53.6%, AKI/CKD 22.8%, CKD 19.4%). The time between requesting data from custodians and receipt of data ranged from 15 to 1046 days. Conclusion AKI in people with pre-existing CKD was a common cause of iHD. Health service planning and community health may benefit from AKI prevention strategies and the implementation of sustainable and permanent linkages with the datasets used to monitor prospective incident haemodialysis. What is known about the topic? AKI is a risk factor for CKD. The Northern Territory has the highest national incidence rates of dialysis-dependent end-stage kidney disease, but has no audit tool describing outcomes of dialysis-requiring AKI. What does this paper add? We audited all iHD and showed 25.6% mortality within the first 90 days of iHD and 45.5% overall mortality at 3 years. AKI in people with pre-existing CKD caused 47.1% of iHD. What are the implications for practitioners? Health service planning and community health may benefit from AKI prevention strategies and the implementation of sustainable and permanent linkages with the datasets used to monitor prospective incident haemodialysis.

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.

Frequency modulated free electron laser

It is shown that the output frequency of a free electron laser may be modulated to generate a series of modes that span a bandwidth of at least an order of magnitude greater than the normal FEL bandwidth. This new method of frequency modulated FEL operation has close analogies to frequency modulation in conventional cavity lasers. The FM-FEL is analysed and described in the linear regime by a summation over the exponentially amplified frequency modes. Simulations using a 3D, broad bandwidth, numerical code also demonstrate FM-FEL operation for parameters typical of FEL facilities currently under construction. Harmonic bunching methods are used to seed the FM-FEL modes to generate a temporally correlated frequency modulated output over a large bandwidth. This new, FM-FEL mode of operation scales well for X-ray generation, offering users a significantly new form of high-power, short wavelength FEL output.