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

Ramaswamy, Priya; Gong, Jen J.; Saleh, Sameh N; McDonald, Samuel; Blumberg, Seth; Medford, Richard J; Liu, Xinran

doi:10.1093/jamia/ocac041

Cited by 9 publications

(6 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…16 Although this scale has been used in several studies, to our knowledge, no study has applied AI and chest imaging to assess the severity proposed by the WHO. Ramaswamy et al 26 detailed the calculation of the WHO score from electronic medical records but did not use AI techniques or imaging. Bennett et al 27 used the WHO scale and grouped severity categories to predict clinical severity and found that demographics and comorbidities were correlated with disease severity.…”

Section: Discussionmentioning

confidence: 99%

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

Henao,

Depotter,

Bower

et al. 2023

Invest Radiol

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

confidence: 99%

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

Henao,

Depotter,

Bower

et al. 2023

Invest Radiol

View full text Add to dashboard Cite

show abstract

“…26 Secondary clinical outcomes included WHO-CPS score at day 7 and day 28, in-hospital mortality, ICU, hospital, and mechanical ventilation-free days at day 28. [26][27][28] WHO-CPS score was selected as it highlights patient progression throughout their disease course, highlighting key markers for critically ill patients such as requirement for mechanical ventilation, extracorporeal membrane oxygenation (ECMO), and vasopressor use.…”

Section: Discussionmentioning

confidence: 99%

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

Conroy,

Bauer,

Pallotta

et al. 2023

Pharmacotherapy

View full text Add to dashboard Cite

BackgroundTocilizumab and baricitinib improve outcomes in severely ill patients with coronavirus disease 2019 (COVID‐19), however, comparative analyses of clinical outcomes related to these agents is 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.MethodsThis retrospective, multicenter cohort study of adult patients with severe COVID‐19 compared the use of tocilizumab, before its shortage, to baricitinib use during the shortage. Both drugs were formulary restricted at the study sites with identical patient criteria for use. The primary outcome was World Health Organization Clinical Progression Scale (WHO‐CPS) score at day 14. Secondary outcomes included WHO‐CPS score at day 7. Generalized estimating equation models were utilized, accounting for clustering by hospital and known confounders, to estimate the proportional odds of the ordinal WHO‐CPS score at day 14.ResultsIn 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]).ConclusionsBaricitinib 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.

show abstract

“…48 In a bi-institutional study, health care systems automated data acquisition and clinical decision aid support to calculate the severity score for COVID-19 patients. 49 Daily, the system queried data from the institution's EHR and included demographic data, hospital length of stay, vital settings, ventilator settings, oxygen utilization, and dialysis use to produce a daily score. A time span of 13,386 patient days was analyzed and authors demonstrated successful implementation of such scoring system in 100% of inpatient days.…”

Section: Relevance Of Public Health Informatics To Perioperative Info...mentioning

confidence: 99%

“…A time span of 13,386 patient days was analyzed and authors demonstrated successful implementation of such scoring system in 100% of inpatient days. 49 While the mortality and morbidity benefit of these systems is still pending, this initiative demonstrates the ability of perioperative physicians to study the clinical characteristics of COVID-19 to contribute data and to recruit patients to trials across informatics platforms. Reeves et al 50 describe the implementation of an Incidence Command Center that helped identify EHR-based tools to support clinical care during a COVID-19 surge.…”

Section: Relevance Of Public Health Informatics To Perioperative Info...mentioning

confidence: 99%

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

Mudumbai,

Gabriel,

Howell

et al. 2024

Anesthesia &Amp; Analgesia

View full text Add to dashboard Cite

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.

show abstract

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

Cited by 9 publications

References 11 publications

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

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

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

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

Contact Info

Product

Resources

About