Development of mortality prediction model for critically ill patients based on multidimensional and dynamic clinical characteristics / 中华危重病急救医学

OBJECTIVE@#To develop a mortality prediction model for critically ill patients based on multidimensional and dynamic clinical data collected by the hospital information system (HIS) using random forest algorithm, and to compare the prediction efficiency of the model with acute physiology and chronic health evaluation II (APACHE II) model.@*METHODS@#The clinical data of 10 925 critically ill patients aged over 14 years old admitted to the Third Xiangya Hospital of Central South University from January 2014 to June 2020 were extracted from the HIS system, and APACHE II scores of the critically ill patients were extracted. Expected mortality of patients was calculated according to the death risk calculation formula of APACHE II scoring system. A total of 689 samples with APACHE II score records were used as the test set, and the other 10 236 samples were used to establish the random forest model, of which 10% (n = 1 024) were randomly selected as the validation set and 90% (n = 9 212) were selected as the training set. According to the time series of 3 days before the end of critical illness, the clinical characteristics of patients such as general information, vital signs data, biochemical test results and intravenous drug doses were selected to develope a random forest model for predicting the mortality of critically ill patients. Using the APACHE II model as a reference, receiver operator characteristic curve (ROC curve) was drawn, and the discrimination performance of the model was evaluated through the area under the ROC curve (AUROC). According to the precision and recall, Precision-Recall curve (PR curve) was drawn, and the calibration performance of the model was evaluated through the area under the PR curve (AUPRC). Calibration curve was drawn, and the consistency between the predicted event occurrence probability of the model and the actual occurrence probability was evaluated through the calibration index Brier score.@*RESULTS@#Among the 10 925 patients, there were 7 797 males (71.4%) and 3 128 females (28.6%). The average age was (58.9±16.3) years old. The median length of hospital stay was 12 (7, 20) days. Most patients (n = 8 538, 78.2%) were admitted to intensive care unit (ICU), and the median length of ICU stay was 66 (13, 151) hours. The hospitalized mortality was 19.0% (2 077/10 925). Compared with the survival group (n = 8 848), the patients in the death group (n = 2 077) were older (years old: 60.1±16.5 vs. 58.5±16.4, P < 0.01), the ratio of ICU admission was higher [82.8% (1 719/2 077) vs. 77.1% (6 819/8 848), P < 0.01], and the proportion of patients with hypertension, diabetes and stroke history was also higher [44.7% (928/2 077) vs. 36.3% (3 212/8 848), 20.0% (415/2 077) vs. 16.9% (1 495/8 848), 15.5% (322/2 077) vs. 10.0% (885/8 848), all P < 0.01]. In the test set data, the prediction value of random forest model for the risk of death during hospitalization of critically ill patients was greater than that of APACHE II model, which showed by that the AUROC and AUPRC of random forest model were higher than those of APACHE II model [AUROC: 0.856 (95% confidence interval was 0.812-0.896) vs. 0.783 (95% confidence interval was 0.737-0.826), AUPRC: 0.650 (95% confidence interval was 0.604-0.762) vs. 0.524 (95% confidence interval was 0.439-0.609)], and Brier score was lower than that of APACHE II model [0.104 (95% confidence interval was 0.085-0.113) vs. 0.124 (95% confidence interval was 0.107-0.141)].@*CONCLUSIONS@#The random forest model based on multidimensional dynamic characteristics has great application value in predicting hospital mortality risk for critically ill patients, and it is superior to the traditional APACHE II scoring system.

Influencing factors of early activity in patients with acute ischemic stroke based on social ecological model: A qualitative study / 中南大学学报(医学版)

OBJECTIVES@#Acute ischemic stroke (AIS) is one of the main causes of disability in middle-aged and elderly people, and early activity plays an important role in functional recovery. This study aims to understand the factors that affect the implementation of early activity in patients with AIS and to provide reference for promoting early activity implementation and developing intervention strategies for AIS patients.@*METHODS@#Using purposive sampling, 19 AIS patients and their caregivers who visited at Stroke Center in the Third Xiangya Hospital of Central South University and the Third Hospital of Changsha from June to December 2021, as well as 19 medical staff, hospital administrators, or community workers providing medical health services to stroke patients, were selected as interviewes. A semi-structured interview was conducted based on the social ecological theory model, and the Colaizzi seven-step method was used to analyze the interview data.@*RESULTS@#According to qualitative interview results, the factors affecting early activity in AIS patients were summarized into 4 themes and 12 sub-themes: medical staff factors (insufficient knowledge and skills, insufficient knowledge of early activity, unclear division of responsibilities), patient factors (severity of the disease, lack of knowledge, psychological pressure, fear of falling), social environmental factors (lack of social support, shortage of human resources and rehabilitation equipment, insufficient medical insurance support), and evidence and norms (the evidence for early activity needs improvement, lack of standardized early activity procedures).@*CONCLUSIONS@#Early activity in AIS patients is impacted by factors at multiple levels, including medical staff, patients, social environment, and evidence and norms. Developing comprehensive intervention strategies to address these factors can promote early activity implementation in AIS patients.

Current situation and demands for diabetes knowledge in prediabetes patients at different self-management levels / 中南大学学报(医学版)

To understand the current situation regarding the knowledge and demand for patients with prediabetes at different self-management levels, and to provide guide for improving their knowledge.
 Methods: A total of 312 prediabetes patients from 3 hospitals in Changsha were enrolled in this survey. The questionnaires covered diabetes self-management behavior scale and prediabetes knowledge status and demand questionnaire. Diabetes knowledge acquisition and demand were analyzed among patients with different levels of self-management.
 Results: The score of self-management behavior for patients with prediabetes was 39.1±13.9. The rate of knowledge acquisition was low and the rate of demand was high. The knowledge acquisition rate was high and the knowledge demand rate was low in patients with high levels of self-management. As for the contents of health education, the dietary collocation and method for glucose detection were highly needed by all self-management levels of patients.
 Conclusion: Prediabetes patients' self-management level are low. Health education to patients with prediabetes should be based on individualized demands.