Outcomes of Patients with Active Cancers and Pre-Existing Cardiovascular Diseases Infected with SARS-CoV-2 (preprint)

Objective To determine the impact of acute SARS-CoV-2 infection on patient with concomitant active cancer and CVD.Methods The researchers extracted and analyzed data from the National COVID Cohort Collaborative (N3C) database between January 1, 2020, and July 22, 2022. They included only patients with acute SARS-CoV-2 infection, defined as a positive test by PCR 21 days before and 5 days after the day of index hospitalization. Active cancers were defined as last cancer drug administered within 30 days of index admission. The “Cardioonc” group consisted of patients with CVD and active cancers. The cohort was divided into four groups: (1) CVD (-), (2) CVD (+), (3) Cardioonc (-), and (4) Cardioonc (+), where (-) or (+) denotes acute SARS-CoV-2 infection status. The primary outcome of the study was major adverse cardiovascular events (MACE), including acute stroke, acute heart failure, myocardial infarction, or all-cause mortality. The researchers analyzed the outcomes by different phases of the pandemic and performed competing-risk analysis for other MACE components and death as a competing event.Results The study analyzed 418,306 patients, of which 74%, 10%, 15.7%, and 0.3% had CVD (-), CVD (+), Cardioonc (-), and Cardioonc (+), respectively. The Cardioonc (+) group had the highest MACE events in all four phases of the pandemic. Compared to CVD (-), the Cardioonc (+) group had an odds ratio of 1.66 for MACE. However, during the Omicron era, there was a statistically significant increased risk for MACE in the Cardioonc (+) group compared to CVD (-). Competing risk analysis showed that all-cause mortality was significantly higher in the Cardioonc (+) group and limited other MACE events from occurring. When the researchers identified specific cancer types, patients with colon cancer had higher MACE.Conclusion In conclusion, the study found that patients with both CVD and active cancer suffered relatively worse outcomes when they had acute SARS-CoV-2 infection during early and alpha surges in the United States. These findings highlight the need for improved management strategies and further research to better understand the impact of the virus on vulnerable populations during the COVID-19 pandemic.

The effects of sleep disturbance on dyspnoea and impaired lung function following COVID-19 hospitalisation: a prospective multi-centre cohort study (preprint)

Background Sleep disturbance is common following hospitalisation both for COVID-19 and other causes. The clinical associations are poorly understood, despite it altering pathophysiology in other scenarios. We, therefore, investigated whether sleep disturbance is associated with dyspnoea along with relevant mediation pathways. Methods Sleep parameters were assessed in a prospective cohort of patients (n=2,468) hospitalised for COVID-19 in the United Kingdom in 39 centres using both subjective and device-based measures. Results were compared to a matched UK biobank cohort and associations were evaluated using multivariable linear regression. Findings 64% (456/714) of participants reported poor sleep quality; 56% felt their sleep quality had deteriorated for at least 1-year following hospitalisation. Compared to the matched cohort, both sleep regularity (44.5 vs 59.2, p<0.001) and sleep efficiency (85.4% vs 88.5%, p<0.001) were lower whilst sleep period duration was longer (8.25h vs 7.32h, p<0.001). Overall sleep quality (effect estimate 4.2 (3.0-5.5)), deterioration in sleep quality following hospitalisation (effect estimate 3.2 (2.0-4.5)), and sleep regularity (effect estimate 5.9 (3.7-8.1)) were associated with both dyspnoea and impaired lung function (FEV1 and FVC). Depending on the sleep metric, anxiety mediated 13-42% of the effect of sleep disturbance on dyspnoea and muscle weakness mediated 29-43% of this effect. Interpretation Sleep disturbance is associated with dyspnoea, anxiety and muscle weakness following COVID-19 hospitalisation. It could have similar effects for other causes of hospitalisation where sleep disturbance is prevalent.

A comparison of long term outcomes in patients managed with VV-ECMO in the first and second waves of the COVID-19 pandemic in the UK (preprint)

BackgroundEarly studies of veno-venous extracorporeal membrane oxygenation (VV-ECMO) in COVID-19 have revealed similar outcomes to historical cohorts. Changes in the disease and treatments has led to differences in the patients supported on VV-ECMO in the 1st and 2nd waves. We aimed to compare these two groups in both the acute and follow-up phase.MethodsIn this retrospective study, we identified the differences between patients supported on ECMO for COVID-19 between wave 1 (17/03/2020-31/08/2020) and wave 2 (01/09/2020-25/05/2021). We examined mortality at censoring date (30/11/2021) and decannulation, patient characteristics, complications and lung function and quality of life (QOL – by EQ5D3L) at first follow-up.FindingsOne-hundred and twenty-three patients were included in our analysis. Survival at censoring date [Chi-sqaured 6.35, p=0.012] and decannulation [90.4% vs 70.0%, p<0.001], was significantly lower in the 2nd wave, whilst duration of ECMO run was longer [12.0(18.0-30.0) days vs. 29.5(15.5-58.3)] days (p=0.005)). Wave 2 patients had longer application of non-invasive ventilation (NIV) prior to ECMO and a higher incidence of barotrauma. Patient age and NIV use were independently associated with increased mortality [OR 1.07(1.01-1.14), p=0.025 and 3.37(1.12–12.60), p=0.043 respectively]. QOL and lung function, apart from KCOc was similar at follow up across the waves.ConclusionMost patients with COVID-19 supported on ECMO in both waves survived in the short and longer term. At follow-up patients had similar lung function and QOL across the 2 waves. This suggests that ECMO has an ongoing role in the management of a carefully selected group of patients with COVID-19.Trial RegistrationResearch Ethics Committee (20/EM/0204)

Decision support system to evaluate VENTilation in the Acute Respiratory Distress Syndrome (DeVENT study) – Trial Protocol (preprint)

Background The Acute Respiratory Distress Syndrome (ARDS) occurs in response to a variety of insults, and mechanical ventilation is life-saving in this setting, but ventilator induced lung injury can also contribute to the morbidity and mortality in the condition. The Beacon Caresystem is a model-based bedside decision support system using mathematical models tuned to the individual patient’s physiology to advise on appropriate ventilator settings. Personalised approaches using individual patient description may be particularly advantageous in complex patients, including those who are difficult to mechanically ventilate and wean, in particular ARDS. Methods We will conduct a multi-centre international randomised, controlled, allocation concealed, open, pragmatic clinical trial to compare mechanical ventilation in ARDS patients following application of the Beacon Caresystem to that of standard routine care to investigate whether use of the system results in a reduction in driving pressure across all severities and phases of ARDS. Discussion Despite 20 years of clinical trial data showing significant improvements in ARDS mortality through mitigation of ventilator induced lung injury, there remains a gap in its personalised application at the bedside. Importantly, the protective effects of higher positive end-expiratory pressure (PEEP) were noted only when there were associated decreases in driving pressure. Hence, the pressures set on the ventilator should be determined by the diseased lungs’ pressure-volume relationship which is often unknown or difficult to determine. Knowledge of extent of recruitable lung could improve the ventilator driving pressure. Hence, personalised management demands the application of mechanical ventilation according to the physiological state of the diseased lung at that time. Hence, there is significant rationale for the development of point-of-care clinical decision support systems which help personalise ventilatory strategy according to the current physiology. Furthermore, the potential for the application of the Beacon Caresystem to facilitate local and remote management of large numbers of ventilated patients (as seen during this COVID-19 pandemic), could change the outcome of mechanically ventilated patients during the course of this and future pandemics. Trial registration ClinicalTrials.gov identifier ( NCT number): NCT04115709 Administrative information Note: the numbers in curly brackets in this protocol refer to SPIRIT checklist item numbers. The order of the items has been modified to group similar items (see http://www.equator-network.org/reporting-guidelines/spirit-2013-statement-defining-standard-protocol-items-for-clinical-trials/ ).

A Vital Sign-based Prediction Algorithm for Differentiating COVID-19 Versus Seasonal Influenza in Hospitalized Patients (preprint)

Patients with influenza and SARS-CoV2/Coronavirus disease 2019 (COVID-19) infections have different clinical course and outcomes. We developed and validated a supervised machine learning pipeline to distinguish the two viral infections using the available vital signs and demographic dataset from the first hospital/emergency room encounters of 3,883 patients who had confirmed diagnoses of influenza A/B, COVID-19 or negative laboratory test results. The models were able to achieve an area under the receiver operating characteristic curve (ROC AUC) of at least 97% using our multiclass classifier. The predictive models were externally validated on 15,697 encounters in 3,125 patients available on TrinetX database that contains patient-level data from different healthcare organizations. The influenza vs. COVID-19-positive model had an AUC of 98%, and 92% on the internal and external test sets, respectively. Our study illustrates the potentials of machine-learning models for accurately distinguishing the two viral infections. The code is made available at https://github.com/ynaveena/COVID-19-vs-Influenza and may be have utility as a frontline diagnostic tool to aid healthcare workers in triaging patients once the two viral infections start cocirculating in the communities.