Derivation and validation of novel integrated inpatient mortality prediction score for COVID-19 (IMPACT) using clinical, laboratory, and AI—processed radiological parameter upon admission: a multicentre study

Abstract Limited studies explore the use of AI for COVID-19 prognostication. This study investigates the relationship between AI-aided radiographic parameters, clinical and laboratory data, and mortality in hospitalized COVID-19 patients. We conducted a multicentre retrospective study. The derivation and validation cohort comprised of 512 and 137 confirmed COVID-19 patients, respectively. Variable selection for constructing an in-hospital mortality scoring model was performed using the least absolute shrinkage and selection operator, followed by logistic regression. The accuracy of the scoring model was assessed using the area under the receiver operating characteristic curve. The final model included eight variables: anosmia (OR: 0.280; 95%CI 0.095–0.826), dyspnoea (OR: 1.684; 95%CI 1.049–2.705), loss of consciousness (OR: 4.593; 95%CI 1.702–12.396), mean arterial pressure (OR: 0.928; 95%CI 0.900–0.957), peripheral oxygen saturation (OR: 0.981; 95%CI 0.967–0.996), neutrophil % (OR: 1.034; 95%CI 1.013–1.055), serum urea (OR: 1.018; 95%CI 1.010–1.026), affected lung area score (OR: 1.026; 95%CI 1.014–1.038). The Integrated Inpatient Mortality Prediction Score for COVID-19 (IMPACT) demonstrated a predictive value of 0.815 (95% CI 0.774–0.856) in the derivation cohort. Internal validation resulted in an AUROC of 0.770 (95% CI 0.661–0.879). Our study provides valuable evidence of the real-world application of AI in clinical settings. However, it is imperative to conduct prospective validation of our findings, preferably utilizing a control group and extending the application to broader populations

Markers of Renal Complications in Beta Thalassemia Patients with Iron Overload Receiving Chelation Agent Therapy: A Systematic Review

Objective: The emerging renal complications in beta-thalassemia patients have raised the global exchange of views. Despite better survival due to blood transfusion and iron chelation therapy, the previously unrecognized renal complication remain a burden of disease affecting this population —the primary concern on how iron overload and chelation therapy correlated with renal impairment is still controversial. Early detection and diagnosis is crucial in preventing further kidney damage. Therefore, a systematic review was performed to identify markers of kidney complications in beta thalassemia patients with iron overload receiving chelation therapy. Methods: Searches of PubMed, Scopus, Science Direct, and Web of Science were conducted according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) to identify studies of literature reporting renal outcome in β-TM patients with iron overload and receiving chelation therapy. The eligible 17 studies were obtained. Results: uNGAL/NGAL, uNAG/NAG, uKIM-1 are markers that can be used as predictor of renal tubular damage in early renal complications, while Cystatin C and uβ2MG showed further damage at the glomerular level. Discussion and Conclusion: The renal complication in beta-thalassemia patients with iron overload receiving chelating agent therapy may progress to kidney disease. Early detection using accurate biological markers is a substantial issue that deserves further evaluation to determine prevention and management. © The Authors