Currently, monitoring system of awareness of the depth of anesthesia has been more and more widely used in clinical practices. The intelligent evaluation algorithm is the key technology of this type of equipment. On the basis of studies about changes of electroencephalography (EEG) features during anesthesia, a discussion about how to select reasonable EEG parameters and classification algorithm to monitor the depth of anesthesia has taken place. A scheme which combines time domain analysis, frequency domain analysis and the variability of EEG and decision tree as classifier and least squares to compute Depth of anesthesia Index (DOAI) is proposed in this paper. Using the EEG of 40 patients who underwent general anesthesia with propofol, and the classification and the score of the EEG annotated by anesthesiologist, we verified this scheme with experiments. Classification and scoring was based on a combination of modified observer assessment of alertness/sedation (MOAA/S), and the changes of EEG parameters of patients during anesthesia. Then we used the BIS index to testify the validation of the DOAI. Results showed that Pearson's correlation coefficient between the DOAI and the BIS over the test set was 0.89. It is demonstrated that the method is feasible and has good accuracy.
ObjectiveTo explore the value of a decision tree (DT) model based on CT for predicting pathological complete response (pCR) after neoadjuvant chemotherapy therapy (NACT) in patients with locally advanced rectal cancer (LARC).MethodsThe clinical data and DICOM images of CT examination of 244 patients who underwent radical surgery after the NACT from October 2016 to March 2019 in the Database from Colorectal Cancer (DACCA) in the West China Hospital were retrospectively analyzed. The ITK-SNAP software was used to select the largest level of tumor and sketch the region of interest. By using a random allocation software, 200 patients were allocated into the training set and 44 patients were allocated into the test set. The MATLAB software was used to read the CT images in DICOM format and extract and select radiomics features. Then these reduced-dimensions features were used to construct the prediction model. Finally, the receiver operating characteristic (ROC) curve, area under the ROC curve (AUC), sensitivity, and specificity values were used to evaluate the prediction model.ResultsAccording to the postoperative pathological tumor regression grade (TRG) classification, there were 28 cases in the pCR group (TRG0) and 216 cases in the non-pCR group (TRG1–TRG3). The outcomes of patients with LARC after NACT were highly correlated with 13 radiomics features based on CT (6 grayscale features: mean, variance, deviation, skewness, kurtosis, energy; 3 texture features: contrast, correlation, homogeneity; 4 shape features: perimeter, diameter, area, shape). The AUC value of DT model based on CT was 0.772 [95% CI (0.656, 0.888)] for predicting pCR after the NACT in the patients with LARC. The accuracy of prediction was higher for the non-PCR patients (97.2%), but lower for the pCR patients (57.1%).ConclusionsIn this preliminary study, the DT model based on CT shows a lower prediction efficiency in judging pCR patient with LARC before operation as compared with homogeneity researches, so a more accurate prediction model of pCR patient will be optimized through advancing algorithm, expanding data set, and digging up more radiomics features.
Objective To explore the subdivision method of diagnosis-related group (DRG) by case-mix payment, and provide reference for reasonable imbursement mechanism and standard for DRG grouping, as well as disease cost accounting and performance assessment for hospitals. Methods The first page data of medical records of 17010 inpatients with uterine fibroids in Obstetrics and Gynecology Hospital of Fudan University from 2019 to 2021 were included. Based on the disease and treatment, combined with the length of hospital stay, other diagnosis and other factors, nonparametric test and generalized linear model were used to explore the factors affecting hospitalization expenses. Decision tree model was performed to yield case-mix related groups and predict the cost. Results The inpatients with uterine fibroids were classified into 13 groups in decision tree model based on the main surgical methods, other surgical types, and length of hospital stay. The reduction in variance was 0.34, and the coefficient of variation was 0.19-0.88. Conclusions The case-mix payment approach based on the decision tree model as the grouping method is more consistent with the actual clinical diagnosis and treatment of uterine fibroids, and can be used as method reference for the subdivision of DRG. Under the background of DRG, subdivision of DRG can provide decision-making basis for refined hospital management, including in-hospital cost accounting and performance allocation.
Objective To analyze the influencing factors of hospitalization costs of obstructive hydronephrosis and explore the optimal grouping of diagnosis-intervention packet (DIP), so as to provide a basis for hospitals to strengthen the cost control of diseases, improve the level of refined management, and improve the compensation mechanism of DIP expenses by medical insurance departments. Methods The homepage data of medical records of Pingshan District People’s Hospital of Shenzhen City from January 2019 to December 2021 were collected, and the information of the discharged patients with the International Classification of Diseases-10th revision code as N13.2 was selected. The factors affecting hospitalization costs were analyzed by single factor analyses and multiple stepwise linear regression, the main surgical methods, number of other operations, and influencing factors of expenses were used as classification nodes, and the decision tree model was used to group and predict costs. Results A total of 1319 patients were included, the median inpatient expense was 10889.59 yuan, and the interquartile range was 10943.89 yuan. The case classification, days of hospitalization, condition of admission, whether it was hospitalized for the first time, whether clinical pathway was implemented, the way of discharge, the number of other diagnoses, and admission path were important factors affecting the inpatient expenses, and 12 groups of case mixes and corresponding expense standards were formed. The reduction in variance was 86.10%, the maximum coefficient of variation was 0.33, and the cost analysis ratio was 96.25%. Conclusions Combining the DIP grouping principle and the multi-factor grouping strategy of diagnosis-related groups, the grouping of obstructive hydronephrosis cases constructed by decision tree model is reasonable and the cost standard is close to reality. The case mixes and cost criteria can provide data support and decision-making reference for hospitals and medical insurance institutions.
Objective To compare the clinical characteristics of chronic cough, and to establish the Modified Cough Assessment Test and the simple decision tree to improve the efficacy of etiologic diagnosis. Methods Patients with chronic cough consulted in Tongji Hospital between October 2021 and August 2023 were enrolled in our study. The patients with identified single cause were divided into 3 groups accordingly: corticosteroid-responsive cough (CRC), upper airway cough syndrome (UACS) and gastroesophageal reflux-related cough (GERC). And the characteristics of chronic cough in different causes were assessed and compared by cough questionnaires. Independent predictors of various causes were identified by multivariate logistic regression analysis and used to establish the Modified Cough Assessment Test (MCET) and to construct the simple decision tree. Results A total of 358 patients were enrolled, including 201 with CRC (56.1%), 125 with UACS (34.9%) and 32 with GERC (8.94%). "Cough with wheezing or chest tightness" (OR=3.222, 95%CI 2.144 - 4.843, P<0.001), "Cough with daytime heaviness and nighttime lightness" (OR=1.755, 95%CI 1.264 - 2.435, P<0.001), and "Cough with acid reflux, heartburn or indigestion" (OR=15.580, 95%CI 5.894 - 41.184, P<0.001) were independent factors for each group, respectively. The area under ROC curve for classification of CRC, UACS and GERC were 0.871, 0.840 and 0.988 for MCET, which were better than those of Leicester Cough Questionnaire (LCQ) (0.792, 0.766 and 0.913) and Cough Evaluation Test (CET) (0.649, 0.691 and 0.580). The accuracy of the simple decision tree for the differential diagnosis of chronic cough was 77.4%. Conclusion The simple decision tree based on the Modified Cough Evaluation Test is a simple and effective method of etiologic diagnosis of chronic cough, which can be used as a tool to improve the efficacy of clinical diagnosis in outpatient settings.