Establishment and validation of a nomogram for predicting severe fever with thrombocytopenia syndrome complicated by invasive pulmonary aspergillosis

Severe fever with thrombocytopenia syndrome (SFTS) complicated by invasive pulmonary aspergillosis (IPA) is associated with high incidence and mortality. This study aimed to develop a clinically practical predictive model for assessing the risk of IPA in SFTS patients, thereby enabling early identification of high-risk patients and improving treatment strategies and prognosis. A retrospective analysis was conducted on patients diagnosed with SFTS at Nanjing Drum Tower Hospital, The Affiliated Hospital of Nanjing University Medical School, from January 2016 to June 2024. These patients were classified into IPA and non-IPA groups and randomly divided into a training set and a validation set at a 7:3 ratio. Variables with statistical significance were incorporated into a multivariate logistic regression to construct a predictive model and develop a nomogram. The discrimination, calibration, and clinical applicability of the predictive model were evaluated using receiver operating characteristic (ROC) curve analysis, calibration curve assessment, and decision curve analysis (DCA), respectively. Internal validation was performed using the Bootstrap method. A total of 360 SFTS patients were enrolled, among whom 72 (20%) were diagnosed with IPA. Univariate analysis initially identified 16 variables (P < 0.05). Subsequent least absolute shrinkage and selection operator (LASSO) regression with 10-fold cross-validation reduced these to eight variables. Multivariate logistic regression further identified four independent risk factors: maximum viral load, total white blood cell (WBC) count, blood urea nitrogen (BUN), and activated partial thromboplastin time (APTT). A nomogram was constructed with an area under the receiver operating characteristic (ROC) curve (AUC) of 0.76 (95% CI: 0.70–0.83) in the training set and 0.75 (95% CI: 0.63–0.87) in the validation set. The optimal cut-off value of the total nomogram score for each patient, determined using X-Tile software, enabled stratification of patients into three risk groups. The incidence of IPA was significantly higher in the high-risk group compared with the low-risk group (RR = 6.97, 95% CI: 3.60–13.48, P < 0.001). Maximum viral load, total WBC count, BUN, and APTT are independent risk factors for the early identification of IPA in SFTS patients. The predictive model demonstrated good predictive performance.