Impact of Early COVID-19 Waves on Cardiac Rehabilitation Delivery in Australia: A National Survey.

BACKGROUND: The novel coronavirus disease of 2019 (COVID-19) pandemic significantly disrupted health care, especially outpatient services such as cardiac rehabilitation (CR). We investigated the impact of early COVID-19 waves on the delivery of Australian CR programs, comparing this time period with usual practice prior to the pandemic (2019) and current practice (2021) once the early waves had subsided. Specifically, we aimed to understand how the delivery of programs during COVID-19 compared to usual practice. METHODS: An anonymous online cross-sectional survey of Australian CR program staff was conducted, comprising three sections: program and respondent characteristics, COVID-19 impact on program delivery, and barriers to, and enablers of, program delivery. Respondents were asked to consider three key timepoints: 1) Pre-COVID-19 (i.e. usual practice in 2019), 2) Early COVID-19 waves (March-December 2020), and 3) Currently, at time of survey completion post early COVID-19 waves (May-July 2021). RESULTS: Of the 314 Australian CR programs, 115 responses were received, of which 105 had complete data, representing a 33% response rate. All states and territories were represented. During early COVID-19 waves programs had periods of closure (40%) or reduced delivery (70%). The majority of programs reported decreased CR referrals (51.5%) and decreased participation (77.5%). The two core components of CR-exercise and education-were significantly impacted during early COVID-19 waves, affecting both the number and duration of sessions provided. Exercise session duration did not return to pre-pandemic levels (53.5 min compared to 57.7 min, p=0.02). The majority of respondents (77%) reported their CR program was inferior in quality to pre-pandemic and more organisational support was required across information technology, staffing, administration and staff emotional and social support. CONCLUSION: Australian CR programs underwent significant change during the early COVID-19 waves, consistent with international CR reports. Fewer patients were referred and attended CR and those who did attend received a lower dose of exercise and education. It will be important to continue to monitor the long-term impacts of the COVID-19 pandemic to ensure CR programs return to pre-pandemic functioning and continue to deliver services in line with best practice and evidence-based recommendations.

Conducting a supportive oncology clinical trial during the COVID-19 pandemic: challenges and strategies.

The coronavirus disease 2019 (COVID-19) pandemic resulted in severe interruptions to clinical research worldwide. This global public health crisis required investigators and researchers to rapidly develop and implement new strategies and solutions to mitigate its negative impact on the progress of clinical trials. In this paper, we describe the challenges, strategies, and lessons learned regarding the continuation of a supportive oncology clinical trial during the pandemic. We hope to provide insight into the implementation of clinical trials during a public health emergency to be better prepared for future instances.Trial registration: ClinicalTrials.gov, a service of the US National Institute of Health (NCT03030859). Registered on 22 January 2017.

AKI in Hospitalized Patients with COVID-19.

BACKGROUND: Early reports indicate that AKI is common among patients with coronavirus disease 2019 (COVID-19) and associated with worse outcomes. However, AKI among hospitalized patients with COVID-19 in the United States is not well described. METHODS: This retrospective, observational study involved a review of data from electronic health records of patients aged ≥18 years with laboratory-confirmed COVID-19 admitted to the Mount Sinai Health System from February 27 to May 30, 2020. We describe the frequency of AKI and dialysis requirement, AKI recovery, and adjusted odds ratios (aORs) with mortality. RESULTS: Of 3993 hospitalized patients with COVID-19, AKI occurred in 1835 (46%) patients; 347 (19%) of the patients with AKI required dialysis. The proportions with stages 1, 2, or 3 AKI were 39%, 19%, and 42%, respectively. A total of 976 (24%) patients were admitted to intensive care, and 745 (76%) experienced AKI. Of the 435 patients with AKI and urine studies, 84% had proteinuria, 81% had hematuria, and 60% had leukocyturia. Independent predictors of severe AKI were CKD, men, and higher serum potassium at admission. In-hospital mortality was 50% among patients with AKI versus 8% among those without AKI (aOR, 9.2; 95% confidence interval, 7.5 to 11.3). Of survivors with AKI who were discharged, 35% had not recovered to baseline kidney function by the time of discharge. An additional 28 of 77 (36%) patients who had not recovered kidney function at discharge did so on posthospital follow-up. CONCLUSIONS: AKI is common among patients hospitalized with COVID-19 and is associated with high mortality. Of all patients with AKI, only 30% survived with recovery of kidney function by the time of discharge.

Machine Learning to Predict Mortality and Critical Events in a Cohort of Patients With COVID-19 in New York City: Model Development and Validation.

BACKGROUND: COVID-19 has infected millions of people worldwide and is responsible for several hundred thousand fatalities. The COVID-19 pandemic has necessitated thoughtful resource allocation and early identification of high-risk patients. However, effective methods to meet these needs are lacking. OBJECTIVE: The aims of this study were to analyze the electronic health records (EHRs) of patients who tested positive for COVID-19 and were admitted to hospitals in the Mount Sinai Health System in New York City; to develop machine learning models for making predictions about the hospital course of the patients over clinically meaningful time horizons based on patient characteristics at admission; and to assess the performance of these models at multiple hospitals and time points. METHODS: We used Extreme Gradient Boosting (XGBoost) and baseline comparator models to predict in-hospital mortality and critical events at time windows of 3, 5, 7, and 10 days from admission. Our study population included harmonized EHR data from five hospitals in New York City for 4098 COVID-19-positive patients admitted from March 15 to May 22, 2020. The models were first trained on patients from a single hospital (n=1514) before or on May 1, externally validated on patients from four other hospitals (n=2201) before or on May 1, and prospectively validated on all patients after May 1 (n=383). Finally, we established model interpretability to identify and rank variables that drive model predictions. RESULTS: Upon cross-validation, the XGBoost classifier outperformed baseline models, with an area under the receiver operating characteristic curve (AUC-ROC) for mortality of 0.89 at 3 days, 0.85 at 5 and 7 days, and 0.84 at 10 days. XGBoost also performed well for critical event prediction, with an AUC-ROC of 0.80 at 3 days, 0.79 at 5 days, 0.80 at 7 days, and 0.81 at 10 days. In external validation, XGBoost achieved an AUC-ROC of 0.88 at 3 days, 0.86 at 5 days, 0.86 at 7 days, and 0.84 at 10 days for mortality prediction. Similarly, the unimputed XGBoost model achieved an AUC-ROC of 0.78 at 3 days, 0.79 at 5 days, 0.80 at 7 days, and 0.81 at 10 days. Trends in performance on prospective validation sets were similar. At 7 days, acute kidney injury on admission, elevated LDH, tachypnea, and hyperglycemia were the strongest drivers of critical event prediction, while higher age, anion gap, and C-reactive protein were the strongest drivers of mortality prediction. CONCLUSIONS: We externally and prospectively trained and validated machine learning models for mortality and critical events for patients with COVID-19 at different time horizons. These models identified at-risk patients and uncovered underlying relationships that predicted outcomes.

Retrospective cohort study of clinical characteristics of 2199 hospitalised patients with COVID-19 in New York City.

OBJECTIVE: The COVID-19 pandemic is a global public health crisis, with over 33 million cases and 999 000 deaths worldwide. Data are needed regarding the clinical course of hospitalised patients, particularly in the USA. We aimed to compare clinical characteristic of patients with COVID-19 who had in-hospital mortality with those who were discharged alive. DESIGN: Demographic, clinical and outcomes data for patients admitted to five Mount Sinai Health System hospitals with confirmed COVID-19 between 27 February and 2 April 2020 were identified through institutional electronic health records. We performed a retrospective comparative analysis of patients who had in-hospital mortality or were discharged alive. SETTING: All patients were admitted to the Mount Sinai Health System, a large quaternary care urban hospital system. PARTICIPANTS: Participants over the age of 18 years were included. PRIMARY OUTCOMES: We investigated in-hospital mortality during the study period. RESULTS: A total of 2199 patients with COVID-19 were hospitalised during the study period. As of 2 April, 1121 (51%) patients remained hospitalised, and 1078 (49%) completed their hospital course. Of the latter, the overall mortality was 29%, and 36% required intensive care. The median age was 65 years overall and 75 years in those who died. Pre-existing conditions were present in 65% of those who died and 46% of those discharged. In those who died, the admission median lymphocyte percentage was 11.7%, D-dimer was 2.4 µg/mL, C reactive protein was 162 mg/L and procalcitonin was 0.44 ng/mL. In those discharged, the admission median lymphocyte percentage was 16.6%, D-dimer was 0.93 µg/mL, C reactive protein was 79 mg/L and procalcitonin was 0.09 ng/mL. CONCLUSIONS: In our cohort of hospitalised patients, requirement of intensive care and mortality were high. Patients who died typically had more pre-existing conditions and greater perturbations in inflammatory markers as compared with those who were discharged.

COVID-19 and kidney transplantation: Results from the TANGO International Transplant Consortium.

Kidney transplant recipients may be at a high risk of developing critical coronavirus disease 2019 (COVID-19) illness due to chronic immunosuppression and comorbidities. We identified hospitalized adult kidney transplant recipients at 12 transplant centers in the United States, Italy, and Spain who tested positive for COVID-19. Clinical presentation, laboratory values, immunosuppression, and treatment strategies were reviewed, and predictors of poor clinical outcomes were determined through multivariable analyses. Among 9845 kidney transplant recipients across centers, 144 were hospitalized due to COVID-19 during the 9-week study period. Of the 144 patients, 66% were male with a mean age of 60 (±12) years, and 40% were Hispanic and 25% were African American. Prevalent comorbidities included hypertension (95%), diabetes (52%), obesity (49%), and heart (28%) and lung (19%) disease. Therapeutic management included antimetabolite withdrawal (68%), calcineurin inhibitor withdrawal (23%), hydroxychloroquine (71%), antibiotics (74%), tocilizumab (13%), and antivirals (14%). During a median follow-up period of 52 days (IQR: 16-66 days), acute kidney injury occurred in 52% cases, with respiratory failure requiring intubation in 29%, and the mortality rate was 32%. The 46 patients who died were older, had lower lymphocyte counts and estimated glomerular filtration rate levels, and had higher serum lactate dehydrogenase, procalcitonin, and interleukin-6 levels. In sum, hospitalized kidney transplant recipients with COVID-19 have higher rates of acute kidney injury and mortality.