Developing a COVID-19 WHO Clinical Progression Scale inpatient database from electronic health record data

Public Health Informatics and the Perioperative Physician: Looking to the Future

The role of informatics in public health has increased over the past few decades, and the coronavirus disease 2019 (COVID-19) pandemic has underscored the critical importance of aggregated, multicenter, high-quality, near-real-time data to inform decision-making by physicians, hospital systems, and governments. Given the impact of the pandemic on perioperative and critical care services (eg, elective procedure delays; information sharing related to interventions in critically ill patients; regional bed-management under crisis conditions), anesthesiologists must recognize and advocate for improved informatic frameworks in their local environments. Most anesthesiologists receive little formal training in public health informatics (PHI) during clinical residency or through continuing medical education. The COVID-19 pandemic demonstrated that this knowledge gap represents a missed opportunity for our specialty to participate in informatics-related, public health-oriented clinical care and policy decision-making. This article briefly outlines the background of PHI, its relevance to perioperative care, and conceives intersections with PHI that could evolve over the next quarter century.

Baricitinib versus tocilizumab in critically ill COVID-19 patients: A retrospective cohort study

OBJECTIVES

The immunomodulators tocilizumab and baricitinib improve outcomes in severely ill patients with coronavirus disease 2019 (COVID-19); however, comparative analyses of clinical outcomes related to these agents are lacking. A tocilizumab national shortage shifted treatment to baricitinib in critically ill patients, allowing for an outcome comparison in a similar population. The purpose of this study is to compare clinical outcomes in critically ill COVID-19 patients who received tocilizumab and those who received baricitinib.

DESIGN

Retrospective, observational cohort study using generalized estimating equation models, accounting for clustering by hospital and known confounders, to estimate the proportional odds of the ordinal World Health Organization Clinical Progression Scale (WHO-CPS) score at day 14, the primary outcome. Secondary outcomes included WHO-CPS score at day 7.

SETTING

Multiple hospitals within the Cleveland Clinic Health System.

PATIENTS

Adult patients admitted for COVID-19 between January 2021 and November 2021.

INTERVENTIONS

Receipt of tocilizumab, before its shortage, or baricitinib, during shortage.

MEASUREMENTS AND MAIN RESULTS

In total, 507 patients were included; 217 received tocilizumab and 290 received baricitinib. Over 96% of patients required ICU admission and 98% received concomitant dexamethasone. Tocilizumab recipients had higher (worse) baseline WHO-CPS scores. After adjustment, tocilizumab use was associated with higher odds of a worse day 14 WHO-CPS score compared with baricitinib (adjusted odds ratio [OR] 1.65 [95% confidence interval (CI) 1.10-2.48]). Similarly, after adjustment, tocilizumab use was associated with higher odds of a worse day 7 WHO-CPS score (adjusted OR 1.65 [95% CI 1.22-2.24]).

CONCLUSIONS

Baricitinib use was associated with better WHO-CPS scores at day 14 and day 7 compared with tocilizumab in a cohort of critically ill patients with COVID-19. The odds of having a one unit increase in WHO-CPS score at day 14 was 71% higher with tocilizumab than baricitinib. No difference in mortality or adverse effects was noted.

A Multiclass Radiomics Method-Based WHO Severity Scale for Improving COVID-19 Patient Assessment and Disease Characterization From CT Scans

OBJECTIVES

The aim of this study was to evaluate the severity of COVID-19 patients' disease by comparing a multiclass lung lesion model to a single-class lung lesion model and radiologists' assessments in chest computed tomography scans.

MATERIALS AND METHODS

The proposed method, AssessNet-19, was developed in 2 stages in this retrospective study. Four COVID-19-induced tissue lesions were manually segmented to train a 2D-U-Net network for a multiclass segmentation task followed by extensive extraction of radiomic features from the lung lesions. LASSO regression was used to reduce the feature set, and the XGBoost algorithm was trained to classify disease severity based on the World Health Organization Clinical Progression Scale. The model was evaluated using 2 multicenter cohorts: a development cohort of 145 COVID-19-positive patients from 3 centers to train and test the severity prediction model using manually segmented lung lesions. In addition, an evaluation set of 90 COVID-19-positive patients was collected from 2 centers to evaluate AssessNet-19 in a fully automated fashion.

RESULTS

AssessNet-19 achieved an F1-score of 0.76 ± 0.02 for severity classification in the evaluation set, which was superior to the 3 expert thoracic radiologists (F1 = 0.63 ± 0.02) and the single-class lesion segmentation model (F1 = 0.64 ± 0.02). In addition, AssessNet-19 automated multiclass lesion segmentation obtained a mean Dice score of 0.70 for ground-glass opacity, 0.68 for consolidation, 0.65 for pleural effusion, and 0.30 for band-like structures compared with ground truth. Moreover, it achieved a high agreement with radiologists for quantifying disease extent with Cohen κ of 0.94, 0.92, and 0.95.

CONCLUSIONS

A novel artificial intelligence multiclass radiomics model including 4 lung lesions to assess disease severity based on the World Health Organization Clinical Progression Scale more accurately determines the severity of COVID-19 patients than a single-class model and radiologists' assessment.

Use of convalescent plasma therapy in hospitalised adult patients with non-critical COVID-19: a focus on the elderly from Hungary

Convalescent plasma therapy might be a feasible option for treatment of novel infections. During the early phases of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) pandemic, several promising results were published with convalescent plasma therapy, followed by more disappointing findings of randomised controlled trials. In our single-centre, open-label, prospective, cohort study, we assessed the findings of 180 patients treated with convalescent plasma during the first four waves of the pandemic in Hungary. The primary outcome was all-cause mortality; secondary outcomes were clinical improvement and need for intensive care unit admission by day 28. Subgroup analysis comparing elderly and non-elderly (less than 65 years of age) was performed. Twenty (11.4%) patients died by day 28, at significantly higher rates in the elderly subgroup (3 vs. 17, p < 0.01). One hundred twenty-eight (72.7%) patients showed clinical improvement, and 15 (8.5%) were transferred to the intensive care unit until day 28. Non-elderly patients showed clinical improvement by day 28 in significantly higher rates (improvement 74 vs. 54, no improvement 15 vs. 11, worsening or death 4 vs. 18 patients, p < 0.01). In conclusion, we found similar clinical outcome results as randomised controlled trials, and the impact of risk factors for unfavourable clinical outcomes among patients in the elderly population.