Development and validation of a nomogram for predicting severity in patients with hemorrhagic fever with renal syndrome: A retrospective study

Abstract

Background Hemorrhagic fever with renal syndrome (HFRS) is a zoonotic disease caused by hantavirus infection. Patients with severe HFRS may develop multiple organ failure or even death, which makes HFRS a serious public health problem. Methods In this retrospective study, we included a total of 155 consecutive patients who were diagnosed with HFRS, of whom 109 patients served as a training cohort and 46 patients as an independent verification cohort. In the training set, the least absolute shrinkage and selection operator (LASSO) regression was used to screen the characteristic variables of the risk model. Multivariate logistic regression analysis was used to construct a nomogram containing the characteristic variables selected in the LASSO regression model. Results The area under the receiver operating characteristic curve (AUC) of the nomogram indicated that the model had good discrimination. The calibration curve exhibited that the nomogram was in good agreement between the prediction and the actual observation. Decision curve analysis and clinical impact curve suggested that the predictive nomogram had clinical utility. Conclusion In this study, we established a simple and feasible model to predict severity in patients with HFRS, with which HFRS would be better identified and patients can be treated early.

Analysis of Infertility Factors Caused by Gynecological Chronic Pelvic Inflammation Disease Based on Multivariate Regression Analysis of Logistic

“…Yang et al believed that the transmission route of pelvic inflammation usually included the following four forms: lymphatic system, blood circulation, spread along the genital mucosa, and direct spread. At present, it was believed that PID episodes were mostly related to the following factors: infection after intrauterine operation, sexual activity and age, lower reproductive tract infection, poor sexual hygiene, and inflammation of adjacent organs [ 11 ]. Lemly and Gupta summarized the following 10 inducing factors of PID: female genital anatomy characteristics, female genital natural defense function vulnerable, female life period of physiological characteristics, sexual activity, sexually transmitted diseases, contraception, abortion or vaginitis, vaginal flushing, iatrogenic infection, and other diseases such as tuberculosis and appendicitis [ 12 ].…”

Early Warning Models for Predicting Severity in Febrile and Nonfebrile Stages of Hemorrhagic Fever with Renal Syndrome

“…progression. Z Yang et al(15) established a model to predict severity in patients with HFRS by 6 laboratoty and cilical parameters.But they did not perform staging analysis. According to the situation of the patients at admission, we divided them into febrile and non-febrile period, and established predicting models respectively, which can better predict for HFRS patients in different stage.…”