Evolution of Clinical Phenotypes of COVID-19 Patients During Intensive Care Treatment: An Unsupervised Machine Learning Analysis (preprint)

Background: Identification of distinct clinical phenotypes in critically ill COVID-19 patients could improve understanding of the disease heterogeneity and enable prognostic and predictive enrichment facilitating more personalized treatment. However, previous attempts did not take into account temporal dynamics of the disease. By including the dimension of time, we aim to gain further insights into the heterogeneity of COVID-19.Methods: We used highly granular data from 3202 adult critically ill COVID patients in the multicenter Dutch Data Warehouse that were admitted to one of 25 Dutch ICUs between February 2020 and March 2021. Parameters including demographics, clinical observations, medications, laboratory values, vital signs, and data from life support devices were selected based on relevance and availability. Twenty-one consecutive datasets were created that each covered 24 hours of ICU data for each day of ICU treatment up until day 21. After aggregation and multiple imputation of the temporal data, clinical phenotypes in each dataset were identified by performing multiple cluster analyses. Clinical phenotypes were identified by aggregating values from all patients per cluster. Both evolution of the clinical phenotypes over time and patient allocation to these clusters over time were tracked.Results: The final patient cohort consisted of 2438 critically ill COVID-19 patients with a registered ICU mortality outcome. Forty-one parameters were chosen for the cluster analysis. On admission, both a mild and a more severe clinical phenotype were found. After day 4, the severe phenotype split into an intermediate and a severe phenotype for 11 consecutive days. Heterogeneity between phenotypes appears to be strongly driven by inflammation and dead space ventilation. During the 21-day period only 8.2% and 4.6% of patients in the initial mild and severe clusters remained assigned to the same phenotype respectively. The clinical phenotype half-life was between 5 and 6 days for the mild and severe phenotypes, and about 3 days for the medium severe phenotype.Conclusions: Patients typically do not remain in the same cluster throughout intensive care treatment. This may have important implications for prognostic or predictive enrichment. Prominent dissimilarities between clinical phenotypes are predominantly driven by inflammation and dead space ventilation.

Lung ultrasound findings in patients with novel SARS-CoV2 (preprint)

Background: Over 2 million people worldwide have been infected with Severe Acute Respiratory Distress Syndrome Corona Virus 2 (SARS CoV2). Lung ultrasound has been proposed to diagnose and it. However, little is known about ultrasound findings in these patients. Our aim is to present an overview of lung ultrasound characteristics in critically ill patients with SARS CoV2 pneumonia overall and in relation to the duration of symptoms and clinical parameters. Methods: On the Intensive Care Unit of two academic hospitals, adult patients who tested positive for SARS-CoV2 were included. Images were analyzed for pleural line characteristics, number and appearance of B-lines, BLUE-profiles (Bedside Lung Ultrasound in Emergency), pathology in the PLAPS (Postero Lateral Alveolar and Pleural Syndrome) point and a LUS-score (lung ultrasound). The primary outcomes were frequencies, percentages and differences in lung ultrasound findings overall and between short ([≤]14 days) and long (>14 days) duration of symptoms and their correlation with clinical parameters. Results: In this pilot observational study, 61 patients were included with 75 examinations for analysis. The most prevalent ultrasound findings were decreased lung sliding (36%), thickening of the pleural line (42%) and a C-profile per view (37%). Patients with ''long'' duration of symptoms presented more frequently with a thickened and irregular pleural line (21% (32) vs 9% (11), p=.01), C-profile per patient (47% (18) vs. 25% (8),p=.01) and pleural effusion (19% (14) vs 5% (3),p=.02) compared to patients with short duration of symptoms. Lung ultrasound findings did not correlate with P/F ratio, fluid balance or dynamic compliance, with the exception of the LUS-score and dynamic compliance (R2=0.27, p=.02). Conclusion: SARS CoV2 results in significant ultrasound changes, with decreased lung sliding, thickening of the pleural line and a C-profile being the most observed. With time, a thickened and irregular pleural line, C-profile and pleural effusion become more common findings.