ObjectiveTo explore the clinical value of three early predictive scale of lung injury (ALI) in patients with high risk of acute lung injury (ALI) after lung cancer surgery.MethodsA convenient sampling method was used in this study. A retrospective analysis was performed on patients with lung cancer underwent lung surgery. The patients were divided into an ALI group and a non-ALI group according to ALI diagnostic criteria. Three kinds of lung injury predictive scoring methods were used, including lung injury prediction score (LIPS), surgical lung injury prediction (SLIP) and SLIP-2. The differences in the scores of the two groups were compared. The correlation between the three scoring methods was also analyzed. The diagnostic value was analyzed by drawing receiver operating characteristic (ROC) curves.ResultsA total of 400 patients underwent lung cancer surgery, and 38 patients (9.5%) developed ALI after operation. Among them, 2 cases progressed to acute respiratory distress syndrome and were treated in intensive care unit. There were no deaths. The predictive scores of the patients in the ALI group were higher than those in the non-ALI group, and the difference was statistically significant (all P<0.001). There was a good correlation between the three scoring methods (allP<0.001). The three scoring methods had better diagnostic value for early prediction of high risk ALI patients after lung cancer surgery and their area under ROC curve (AUC) were larger than 0.8. LIPS score performed better than others, with an AUC of 0.833, 95%CI (0.79, 0.87).ConclusionThree predictive scoring methods may be applied to early prediction of high risk ALI patients after lung cancer surgery, in which LIPS performs better than others.
ObjectiveTo discuss the risk factors of acute respiratory distress syndrome (ARDS) in patients with severe pneumonia.MethodsData of 80 patients with severe pneumonia admitted in our ICU were analyzed retrospectively, and they were divided into two groups according to development of ARDS, which was defined according to the Berlin new definition. The age, gender, weight, Acute Physiology and Chronic Health EvaluationⅡscore, lactate, PSI score and LIPS score, etc. were collected. Statistical significance results were evaluated by multivariate logistic regression analysis after univariate analysis. Receiver operating characteristic (ROC) curve was plotted to analyze the predictive value of the parameter for ARDS after severe pneumonia.ResultsForty patients with severe pneumonia progressed to ARDS, there were 4 moderate cases and 36 severe cases according to diagnostic criteria. Univariate analysis showed that procalcitonin (t=4.08, P<0.001), PSI score (t=10.67, P<0.001), LIPS score (t=5.14, P<0.001), shock (χ2=11.11, P<0.001), albumin level (t=3.34, P=0.001) were related to ARDS. Multivariate logistic regression analysis showed that LIPS ［odds ratio (OR) 0.226, 95%CI=4.62-5.53, P=0.013］ and PSI (OR=0.854, 95%CI=132.2-145.5, P=0.014) were independent risk factors for ARDS. The predictive value of LIPS and PSI in ARDS occurrence was significant. The area under ROC curve (AUC) of LIPS was 0.901, the cut-off value was 7.2, when LIPS ≥7.2, the sensitivity and specificity were both 85.0%. AUC of PSI was 0.947, the cut-off value was 150.5, when PSI score ≥150.5, the sensitivity and specificity were 87.5% and 90.0% respectively.ConclusionsPSI and LIPS are independent risk factors of ARDS in patients with severe pneumonia, which may be references for guiding clinicians to make an early diagnosis and treatment plan.
Objective To develop a risk prediction model for venous thromboembolism (VTE) in lung cancer patients receiving immune checkpoint inhibitors (ICIs). Methods A retrospective analysis was conducted on lung cancer patients treated with ICIs at West China Hospital of Sichuan University between January 2018 and March 2022. The primary outcome was newly diagnosed VTE within 12 months. Independent risk factors for VTE were identified using multivariable logistic regression analysis in the training cohort and used to construct a risk prediction model, which was subsequently validated in a validation cohort. The performance of the model was evaluated using receiver operating characteristic (ROC) curve. Results During the 12-month follow-up period, a total of 105 patients developed VTE, with an incidence rate of 9.4% (105/1115). Multivariable logistic regression analysis identified an Eastern Cooperative Oncology Group performance status (ECOG PS) ≥2, severe pulmonary disease, respiratory failure, abnormal pulmonary function, a history of VTE, prior chemotherapy, and corticosteroid therapy as independent predictors of VTE. The area under the ROC curve (AUC) of the prediction model was 0.822 (95%CI 0.762-0.882) in the training cohort and 0.779 (95%CI 0.687-0.871) in the validation cohort. Based on the model score, the patients were stratified into three risk categories for VTE: low risk (0-1 points), intermediate risk (2-3 points), and high risk (≥4 points). Conclusion The VTE risk prediction model developed in this study demonstrated good predictive performance in lung cancer patients receiving ICIs, and may serve as a practical tool for guiding individualized thromboprophylaxis strategies in clinical practice.