Shiyu Hu's research while affiliated with Zhejiang Chinese Medical University and other places

Background This study aims to construct a model predicting the probability of RF in AECOPD patients upon hospital admission. Methods This study retrospectively extracted data from MIMIC-IV database, ultimately including 3776 AECOPD patients. The patients were randomly divided into a training set (n = 2643) and a validation set (n = 1133) in a 7:3 ratio. First, LASSO regression analysis was used to optimize variable selection by running a tenfold k-cyclic coordinate descent. Subsequently, a multifactorial Cox regression analysis was employed to establish a predictive model. Thirdly, the model was validated using ROC curves, Harrell’s C-index, calibration plots, DCA, and K-M curve. Result Eight predictive indicators were selected, including blood urea nitrogen, prothrombin time, white blood cell count, heart rate, the presence of comorbid interstitial lung disease, heart failure, and the use of antibiotics and bronchodilators. The model constructed with these 8 predictors demonstrated good predictive capabilities, with ROC curve areas under the curve (AUC) of 0.858 (0.836–0.881), 0.773 (0.746–0.799), 0.736 (0.701–0.771) within 3, 7, and 14 days in the training set, respectively and the C-index was 0.743 (0.723–0.763). Additionally, calibration plots indicated strong consistency between predicted and observed values. DCA analysis demonstrated favorable clinical utility. The K-M curve indicated the model’s good reliability, revealed a significantly higher RF occurrence probability in the high-risk group than that in the low-risk group (P < 0.0001). Conclusion The nomogram can provide valuable guidance for clinical practitioners to early predict the probability of RF occurrence in AECOPD patients, take relevant measures, prevent RF, and improve patient outcomes.

Purpose Chronic obstructive pulmonary disease (COPD) concurrent with respiratory failure (RF) is devastating, and may result in death and disability. Systemic immune-inflammation index (SII) is a new prognostic biomarker linked to unfavorable outcomes of acute coronary syndrome, ischemic stroke, and heart failure. Nonetheless, its role in COPD is rarely investigated. Consequently, this study intends to assess the accuracy of SII in predicting the prognosis of COPD. Patients and Methods The clinical information was retrospectively acquired from the Medical Information Mart for Intensive Care-IV database. The outcomes encompassed the incidence of RF and mortality. The relationship between different SII and outcomes was examined utilizing the Cox proportional-hazards model and restricted cubic splines. Kaplan-Meier analysis was employed for all-cause mortality. Results The present study incorporated 1653 patients. During hospitalization, 697 patients (42.2%) developed RF, and 169 patients (10.2%) died. And 637 patients (38.5%) died during long-term follow-up. Higher SII increased the risk of RF (RF: HR: 1.19, 95% CI 1.12–1.28, P<0.001), in-hospital mortality (HR: 1.22, 95% CI 1.07–1.39, P=0.003), and long-term follow-up mortality (HR: 1.12, 95% CI 1.05–1.19, P<0.001). Kaplan-Meier analysis suggested a significantly elevated risk of all-cause death (log-rank P<0.001) in patients with higher SII, especially during the short-term follow-up period of 21 days. Conclusion SII is closely linked to an elevated risk of RF and death in COPD patients. It appears to be a potential predictor of the prognosis of COPD patients, which is helpful for the risk stratification of this population. However, more prospective studies are warranted to consolidate our conclusion.