To establish a risk prediction model for the occurrence of hypoxemia during painless bronchoscopy.

The present study was focused on evaluating the clinical predictors of hypoxemia and establishing a multivariable, predictive model for hypoxemia in painless bronchoscopy. A total of 244 patients were enrolled in the study, and data were collected using a self-designed data collection. The retrospective data collected in this study included the relevant data of patients undergoing the painless bronchoscopy, and we used univariate analysis to deal with these influencing factors. Multivariate logistic regression analysis was used to establish the prediction equation, and receiver operating characteristic curve analysis was carried out. Receiver operating characteristic curves and the Hosmer-Lemeshow test were used to evaluate the model performance. P < .05 was considered to indicate statistical significance. Multivariate logistic regression indicated that body mass index (BMI) (odds ratio [OR]: 1.169; 95% confidence interval [CI]: 1.070-1.277), arterial partial pressure of oxygen (PaO2) (OR: 4.279; 95% CI: 2.378-7.699), alcohol consumption (OR: 2.021; 95% CI: 1.063-3.840), and whether the bronchoscope operation time exceeds 30 minutes (OR: 2.486; 95% CI: 1.174-5.267) were closely related to the occurrence of hypoxemia. The prediction model developed by the logistic regression equation was -4.911 + 1.454 (PaO2) + 0.156 (BMI) + 0.703 (Alcohol consumption) + 0.911 (time > 30th minutes). The prediction model showed that the area under the receiver operating characteristic curve was 0.687. The predictive model was well calibrated with a Hosmer-Lemeshow x2 statistic of 4.869 (P = .772), indicating that our prediction model fit well. The accuracy (number of correct predictions divided by the number of total predictions) was 75%. The prediction model, consisting of BMI, PaO2, alcohol consumption, and whether the bronchoscope operation time exceeds 30 minutes. It is an effective predictor of hypoxemia during sedation for painless bronchoscopy.

Construction of the model for predicting prognosis by key genes regulating EGFR-TKI resistance.

Background: Previous studies have suggested that patients with lung adenocarcinoma (LUAD) will significantly benefit from epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKI). However, many LUAD patients will develop resistance to EGFR-TKI. Thus, our study aims to develop models to predict EGFR-TKI resistance and the LUAD prognosis. Methods: Two Gene Expression Omnibus (GEO) datasets (GSE31625 and GSE34228) were used as the discovery datasets to find the common differentially expressed genes (DEGs) in EGFR-TKI resistant LUAD profiles. The association of these common DEGs with LUAD prognosis was investigated in The Cancer Genome Atlas (TCGA) database. Moreover, we constructed the risk score for prognosis prediction of LUAD by LASSO analysis. The performance of the risk score for predicting LUAD prognosis was calculated using an independent dataset (GSE37745). A random forest model by risk score genes was trained in the training dataset, and the diagnostic ability for distinguishing sensitive and EGFR-TKI resistant samples was validated in the internal testing dataset and external testing datasets (GSE122005, GSE80344, and GSE123066). Results: From the discovery datasets, 267 common upregulated genes and 374 common downregulated genes were identified. Among these common DEGs, there were 59 genes negatively associated with prognosis, while 21 genes exhibited positive correlations with prognosis. Eight genes (ABCC2, ARL2BP, DKK1, FUT1, LRFN4, PYGL, SMNDC1, and SNAI2) were selected to construct the risk score signature. In both the discovery and independent validation datasets, LUAD patients with the higher risk score had a poorer prognosis. The nomogram based on risk score showed good performance in prognosis prediction with a C-index of 0.77. The expression levels of ABCC2, ARL2BP, DKK1, LRFN4, PYGL, SMNDC1, and SNAI2 were positively related to the resistance of EGFR-TKI. However, the expression level of FUT1 was favorably correlated with EGFR-TKI responsiveness. The RF model worked wonderfully for distinguishing sensitive and resistant EGFR-TKI samples in the internal and external testing datasets, with predictive area under the curves (AUC) of 0.973 and 0.817, respectively. Conclusion: Our investigation revealed eight genes associated with EGFR-TKI resistance and provided models for EGFR-TKI resistance and prognosis prediction in LUAD patients.