Tri-light Warning System for Hospitalized COVID-19 Patients]{A Tri-light Warning System for Hospitalized COVID-19 Patients: Credibility-based Risk Stratification under Data Shift (preprint)

Early warning of the novel coronavirus pneumonia (COVID-19) during the evolving pandemic waves is crucial for the timely treatment of patients and optimization of medical resource allocation. However, prior AI-based models often lack the reliability and performance validation under data distribution drifts, and are therefore problematic to be reliably utilized in real-world clinical practice. To address this challenge, we developed a tri-light warning system based on conformal prediction for rapidly stratification of COVID-19 inpatients. This system can automatically extract radiomic features from CT images and integrate clinical record information to output a prediction probability, as well as a credibility of each prediction. This system classifies patients in the general ward into red label (high risk) indicating a possible admission to ICU care, yellow label (uncertain risk) indicating closer monitoring, and green label (low risk) indicating a stable condition. The subsequent health policies can be further designed based on this system according to the specific needs of different hospitals. Extensive experiment from a multi-center cohort (n= 8,721) shows that our method is applicable to both the original strain and the variant strains of COVID-19. Given the rapid mutation rate of COVID-19, the proposed system demonstrates its potential to identify epidemiological risks early to improve patient stratification performance under data shift.

CT-based Rapid Triage of COVID-19 Patients: Risk Prediction and Progression Estimation of ICU Admission, Mechanical Ventilation, and Death of Hospitalized Patients (preprint)

The wave of COVID-19 continues to overwhelm the medical resources, especially the stressed intensive care unit (ICU) capacity and the shortage of mechanical ventilation (MV). Here we performed CT-based analysis combined with electronic health records and clinical laboratory results on Cohort 1 (n = 1662 from 17 hospitals) with prognostic estimation for the rapid stratification of PCR confirmed COVID-19 patients. These models, validated on Cohort 2 (n = 700) and Cohort 3 (n = 662) constructed from 9 external hospitals, achieved satisfying performance for predicting ICU, MV and death of COVID-19 patients (AUROC 0.916, 0.919 and 0.853), even on events happened two days later after admission (AUROC 0.919, 0.943 and 0.856). Both clinical and image features showed complementary roles in events prediction and provided accurate estimates to the time of progression (p

Psychological morbidities and fatigue in patients with confirmed COVID-19 during disease outbreak: prevalence and associated biopsychosocial risk factors (preprint)

Objective: The coronavirus disease 2019 (COVID-19) - a novel and highly infectious pneumonia - has now spread across China and beyond for over four months. However, its psychological impact on patients is unclear. We aim to examine the prevalence and associated risk factors for psychological morbidities and fatigue in patients with confirmed COVID-19 infection. Methods: Amidst the disease outbreak, 41 out of 105 COVID-19 patients in a local designated hospital in China were successfully assessed using a constellation of psychometric questionnaires to determine their psychological morbidities and fatigue. Several potential biopsychosocial risk factors (including pre-existing disabilities, CT severity score of pneumonia, social support, coping strategies) were assessed through multivariable logistic regression analyses to clarify their association with mental health in patients. Results: 43.9% of 41 patients presented with impaired general mental health, 12.2% had post-traumatic stress disorder (PTSD) symptoms, 26.8% had anxiety and/or depression symptoms, and 53.6% had fatigue. We did not find any association between pneumonia severity and psychological morbidities or fatigue in COVID-19 patients. However, high perceived stigmatization was associated with an increased risk of impaired general mental health and high perceived social support was associated with decreased risk. Besides, negative coping inclination was associated with an increased risk of PTSD symptoms; high perceived social support was associated with a decreased risk of anxiety and/or depression symptoms. Conclusions: Psychological morbidities and chronic fatigue are common among COVID-19 patients. Negative coping inclination and being stigmatized are primary risk factors while perceived social support is the main protective factor.