RESUMO
Cardiovascular diseases have been identified as one of the top three causes of death worldwide, with onset and deaths mostly due to heart failure (HF). In ICU, where patients with HF are at increased risk of death and consume significant medical resources, early and accurate prediction of the time of death for patients at high risk of death would enable them to receive appropriate and timely medical care. The data for this study were obtained from the MIMIC-III database, where we collected vital signs and tests for 6699 HF patient during the first 24 h of their first ICU admission. In order to predict the mortality of HF patients in ICUs more precisely, an integrated stacking model is proposed and applied in this paper. In the first stage of dataset classification, the datasets were subjected to first-level classifiers using RF, SVC, KNN, LGBM, Bagging, and Adaboost. Then, the fusion of these six classifier decisions was used to construct and optimize the stacked set of second-level classifiers. The results indicate that our model obtained an accuracy of 95.25% and AUROC of 82.55% in predicting the mortality rate of HF patients, which demonstrates the outstanding capability and efficiency of our method. In addition, the results of this study also revealed that platelets, glucose, and blood urea nitrogen were the clinical features that had the greatest impact on model prediction. The results of this analysis not only improve the understanding of patients' conditions by healthcare professionals but allow for a more optimal use of healthcare resources.
RESUMO
BACKGROUND: This meta-analysis was to explore the clinical significance of circulating tumor cells (CTCs) in predicting the tumor response to chemotherapy and prognosis of patients with lung cancer. METHODS: We searched PubMed, Embase, Cochrane Database, Web of Science and reference lists of relevant articles. Our meta-analysis was performed by Stata software, version 12.0, with a random effects model. Risk ratio (RR), hazard ratio (HR) and 95% confidence intervals (CI) were used as effect measures. RESULTS: 8 studies, including 453 patients, were eligible for analyses. We showed that the disease control rate (DCR) in CTCs-negative patients was significantly higher than CTCs-positive patients at baseline (RR = 2.56, 95%CI [1.36, 4.82], p < 0.05) and during chemotherapy (RR = 9.08, CI [3.44, 23.98], p < 0.001). Patients who converted form CTC-negative to positive or persistently positive during chemotherapy had a worse disease progression than those with CTC-positive to negative or persistently negative (RR = 8.52, CI [1.66, 43.83], p < 0.05). Detection of CTCs at baseline and during chemotherapy also indicated poor overall survival (OS) (baseline: HR = 3.43, CI [2.21, 5.33], p<0.001; during chemotherapy: HR = 3.16, CI [2.23, 4.48], p < 0.001) and progression-free survival (PFS) (baseline: HR = 3.16, 95%CI [2.23, 4.48], p < 0.001; during chemotherapy: HR = 3.78, CI [2.33, 6.13], p < 0.001). CONCLUSIONS: Detection of CTCs in peripheral blood indicates poor tumor response to chemotherapy and poor prognosis in patients with lung cancer.