Incorporation of Chest Computed Tomography Quantification to Predict Outcomes for Patients on Hemodialysis with COVID-19

Abstract

Introduction: Patients undergoing maintenance hemodialysis are vulnerable to coronavirus disease 2019 (COVID-19), exhibiting a high risk of hospitalization and mortality. Thus, early identification and intervention are important to prevent disease progression in these patients. Methods: This was a two-center retrospective observational study of patients on hemodialysis diagnosed with COVID-19 at the Lingang and Xuhui campuses of Shanghai Sixth People’s Hospital. Patients were randomized into the training (130) and validation cohorts (54), while 59 additional patients served as an independent external validation cohort. Artificial intelligence-based parameters of chest computed tomography (CT) were quantified, and a nomogram for patient outcomes at 14 and 28 days was created by screening quantitative CT measures, clinical data, and laboratory examination items, using univariate and multivariate Cox regression models. Results: The median dialysis duration was 48 (interquartile range, 24–96) months. Age, diabetes mellitus, serum phosphorus level, lymphocyte count, and chest CT score were identified as independent prognostic indicators and included in the nomogram. The concordance index values were 0.865, 0.914, and 0.885 in the training, internal validation, and external validation cohorts, respectively. Calibration plots showed good agreement between the expected and actual outcomes. Conclusion: This is the first study in which a reliable nomogram was developed to predict short-term outcomes and survival probabilities in patients with COVID-19 on hemodialysis. This model may be helpful to clinicians in treating COVID-19, managing serum phosphorus, and adjusting the dialysis strategies for these vulnerable patients to prevent disease progression in the context of COVID-19 and continuous emergence of novel viruses.