Development and validation of a prospective study to predict the risk of readmission within 365 days of respiratory failure: based on a random survival forest algorithm combined with COX regression modeling.

BACKGROUND: There is a need to develop and validate a widely applicable nomogram for predicting readmission of respiratory failure patients within 365 days. METHODS: We recruited patients with respiratory failure at the First People's Hospital of Yancheng and the People's Hospital of Jiangsu. We used the least absolute shrinkage and selection operator regression to select significant features for multivariate Cox proportional hazard analysis. The Random Survival Forest algorithm was employed to construct a model for the variables that obtained a coefficient of 0 following LASSO regression, and subsequently determine the prediction score. Independent risk factors and the score were used to develop a multivariate COX regression for creating the line graph. We used the Harrell concordance index to quantify the predictive accuracy and the receiver operating characteristic curve to evaluate model performance. Additionally, we used decision curve analysiso assess clinical usefulness. RESULTS: The LASSO regression and multivariate Cox regression were used to screen hemoglobin, diabetes and pneumonia as risk variables combined with Score to develop a column chart model. The C index is 0.927 in the development queue, 0.924 in the internal validation queue, and 0.922 in the external validation queue. At the same time, the predictive model also showed excellent calibration and higher clinical value. CONCLUSIONS: A nomogram predicting readmission of patients with respiratory failure within 365 days based on three independent risk factors and a jointly developed random survival forest algorithm has been developed and validated. This improves the accuracy of predicting patient readmission and provides practical information for individualized treatment decisions.

A novel microRNA miR-4433a-3p as a potential diagnostic biomarker for lung adenocarcinoma.

Background: Lung adenocarcinoma is one of the leading causes of cancer-related deaths because of the lack of early specific clinical indicators. MicroRNAs (miRNAs) have become the focus in lung cancer diagnosis. Further studies are required to explore miRNA expression in the serum of lung adenocarcinoma patients and their correlation with therapy and analyse specific messenger RNA targets to improve the specificity and sensitivity of early diagnosis. Methods: The Toray 3D-Gene miRNA array was used to compare the expression levels of various miRNAs in the sera of patients with lung adenocarcinoma and healthy volunteers. Highly expressed miRNAs were selected for further analysis. To verify the screening results, serum and pleural fluid samples were analysed using qRT-PCR. Serum levels of the miRNAs and their correlation with the clinical information of patients with lung adenocarcinoma were analysed. The functions of miRNAs were further analysed using the Kyoto Encyclopedia of Gene and Genomes and Gene Ontology databases. Results: Microarray analysis identified 60 and 50 miRNAs with upregulated and downregulated expressions, respectively, in the serum of patients with lung adenocarcinoma compared to those in healthy individuals. Using qRT-qPCR to detection of miRNAs expression in the serum or pleural effusion of patients with early and advanced lung adenocarcinoma, we found that miR-4433a-3p could be used as a diagnostic marker and therapeutic evaluation indicator for lung adenocarcinoma. Serum of miR-4433a-3p levels significantly correlated with the clinical stage. miR-4433a-3p may be more suitable than other tumour markers for the early diagnosis and evaluation of therapeutic effects in lung adenocarcinoma. miR-4433a-3p may affect tumour growth and metastasis by acting on target genes (PIK3CD, UBE2J2, ICMT, PRDM16 and others) and regulating tumour-related signalling pathways (MAPK signal pathway, Ras signalling pathway and others). Conclusion: miR-4433a-3p may serve as a biomarker for the early diagnosis of lung adenocarcinoma and monitoring of therapeutic effects.

Associations between short-term exposure to fine particulate matter and acute exacerbation of asthma in Yancheng, China.

Scarce evidence existed on the association between short-term exposure to fine particulate matter (PM2.5) and asthma in China. In this study, we aimed to explore the relationship of PM2.5 with acute asthma exacerbation in a coastal city of China. Cases of acute asthma exacerbation were identified from hospital outpatient visits in Yancheng, China, from 2015 to 2018. We utilized the generalized additive model linked by a quasi-Poisson distribution to assess the association between PM2.5 and daily acute asthma exacerbation. Different lag structures were built, and we conducted stratification analyses by gender, age, and season. Two-pollutant models were fitted, and concentration-response (C-R) curves were pooled. A total of 3,520 cases of acute asthma exacerbation were recorded, with a daily average of 3. We observed positive and significant associations of PM2.5 on lag 1, 2, lag 02, and lag 03 day. For each 10-µg/m3 increase in PM2.5 (lag 02), the associated increment in asthma was 3.15% (95% CI: 0.99%, 5.31%). The association remained after adjusting for gaseous co-pollutants. We observed significant PM2.5-asthma associations in males, patients ≤64 years, and during cold seasons. The C-R curves were positive and almost linear for total and strata-specific associations. In conclusion, this study provided robust evidence on the association of PM2.5 with acute asthma exacerbation, which may benefit future prevention strategy and policy making.