Abstract
PURPOSE: SARS-CoV-2 is extremely infectious, and the incidence of nosocomial infection is conceivably high. We aimed to develop and validate a nomogram to assist monitoring nosocomial SARS-CoV-2 infection in hospitalized patients. PATIENTS AND METHODS: There were 437 COVID-19 hospitalized cases and 420 negative inpatients enrolled from two hospitals in Hubei province, China. We compared the demographic and clinical characteristics of participants between the two groups. Then, LASSO regression and logistic regression were applied to build a nomogram for SARS-CoV-2 infection prediction in the development cohort. Our nomogram was assessed by area under the curve (AUC), calibration curve, decision curve (DCA) and clinical impact curve analysis (CICA). RESULTS: After LASSO regression filtration, eleven laboratory indicators were correlated with SARS-CoV-2 infection. Then, we integrated these features and constructed a nomogram, which showed a high AUC 0.863 (95% CI: 0.834-0.892) in the development cohort with a sensitivity of 80.41% and specificity of 77.38% and 0.813 (95% CI: 0.760-0.866) in validation cohort with a sensitivity of 82.98% and specificity of 70.43%. The calibration plot displayed that the predicted outcomes were in good concordance with the actual observations. DCA and CICA further showed a larger clinical net benefit. CONCLUSION: We constructed and validated a nomogram that integrated eleven laboratory indexes to assist monitoring of nosocomial SARS-CoV-2 infection in hospitalized patients. Our nomogram is remarkably informative for clinical practice, which will be helpful for preventing SARS-CoV-2 further transmission in hospital and avoiding nosocomial infection.