Objective:To identify independent risk factors for chronic critical illness (CCI) secondary to severe pneumonia and to develop and validate a clinical prediction model.Methods:A retrospective cohort study was conducted using electronic medical records from 415 patients with severe pneumonia admitted between January 2023 and March 2024. Patients were randomly divided into a training set ( n = 290) and a validation set ( n = 125) at a 7:3 ratio. Univariate and multivariate logistic regression analyses were used to identify independent risk factors, and a nomogram was constructed. The model’s discriminative ability, calibration, and clinical utility were assessed using receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis (DCA). Results:The overall incidence of CCI was 23.13% (96/415). Multivariate analysis identified five independent predictors: virus infection ( OR = 13.00, 95% CI: 5.07–33.35, P < 0.001), mechanical ventilation ≥72 hours ( OR = 8.06, 95% CI: 3.68–20.09, P < 0.001), neutrophil-to-albumin ratio (NAR) ( OR = 27848, 95% CI: 193.93–5542274.11, P < 0.001), oxygenation index ( OR =1.09, 95% CI: 1.01–1.09, P < 0.001), and age ( OR = 0.94, 95% CI: 0.91–0.97, P < 0.001). The model demonstrated excellent performance in both sets: training set AUC = 0.96 (95% CI: 0.94–0.98), sensitivity 0.93, specificity 0.89, Brier score 0.09; validation set AUC = 0.93 (95% CI: 0.88–0.98), sensitivity 0.89, specificity 0.64, Brier score 0.13. Calibration curves showed high consistency between predicted and observed risks (mean absolute error < 3%), and DCA indicated significant net clinical benefit within the threshold probability range of 15–60%. Conclusions:The developed prediction model integrates etiological, inflammatory, metabolic, and respiratory support parameters and demonstrates outstanding predictive performance (AUC > 0.90). It may serve as a quantitative tool for early risk stratification and intervention in patients with severe pneumonia. Further multicenter external validation and exploration of integrating dynamic biomarker monitoring are recommended.

Objective:This study aimed to analyze and summarize the clinicopathologic characteristics and treatment protocols of large cell lung carcinoma (LCLC). Methods:Clinicopathologic data of 83 cases with LCLC confirmed by pathology in 2012 were retrospectively reviewed. Results:Exactly 83 cases of LCLC accounted for 5.4%of lung cancer in 2012. Sixty-three cases were male and twenty were female. The average age was 60.4 years old. The average maximum diameter of the tumor was 4.6 cm. The common manifestations in imageology were peripheral type. Only four cases were correctly diagnosed by sputum exfoliocytology, biopsy of bronchofibroscope, and paracentesis before surgery. Sixty-three cases (76%) underwent surgical resection, and pulmonary lobectomy was mainly selected. Postoperative pathology diagnosis indicated that 39 cases were classic large cell carcinoma, 31 were large cell neu-roendocrine carcinoma, 2 were combined large cell neuroendocrine carcinoma, 8 were basaloid carcinoma, 2 were clear cell carcinoma, and 1 was lymphoepithelioma-like carcinoma. Each subtype of LCLC had respective characteristics of pathomorphology and immuno-histochemistry. Lymph node metastasis occurred in 62 cases (75%). Conclusion:The incidence rate of LCLC, which is a highly aggres-sive malignancy, is low. The clinical manifestation and imageology characteristics of LCLC do not have specificity, and its final diagno-sis depends on pathology diagnosis. Operation is the main treatment method. Improving the diagnosis rate of LCLC and further subdi-viding the pathological subtypes are important for a normalized comprehensive treatment of LCLC.