| Summary: | 碩士 === 中臺科技大學 === 醫療暨健康產業管理系碩士班 === 103 === Background: Readmission denotes the event that a patient has been hospitalized again after being discharged in a certain period. Readmission is caused by failed treatment or a new or worsening comorbid illness. and manifests two significant problems: deteriorated health care quality and increased health care cost. Hence, readmission is treated as an indicator for evaluating the overall health care quality. Decrease in pneumonia-related readmission has been recognized as a benchmark of quality care.
Objectives: This study aims to find the demographic factors, readmission rate, disease factors, treatment factors, comorbidities, and the other risk factors to predict readmissions and to design a model to predicting readmission for pneumonia patients within 30 days after discharge.
Methods: The patients with age greater than 18 years old, who had been hospitalized from January 2013 to December 2013, were recruited for this study. The patients were divided into 2 groups. The study group included patients who had been readmitted within 30days after discharge, while patient who hadn’t been readmitted within 30days after discharge were assigned in the control group. Data contain a total of 66 variables, including demographic information, disease factors, treatment factors, and comorbidities, were retrieved from the hospital information system of a regional teaching hospital. After single-variate inferential analyses (Student’s t-test and Pearson χ2) and logistic regression analysis, significant variables which reached significant level (p<0.05) were selected for construct the predictive model. The performance was evaluated with predictive accuracy (or misclassification rate) and receiver operating characteristic (ROC) curve.
Result: It was observed that a total of 17,222 patients were discharged from the hospital during the study period; among them, 781 patients with principal diagnosis of pneumonia and age greater 18 years old. After excluding patients who died before discharge (n=15), transferred to other hospitals (n=21), and discharged against medical advice (n=22), data of 723 cases were used for analysis. Of these patients, men accounted for 66.9% with mean age of 72.2 (SD 16.5), and 82 cases were followed by a 30-day readmission (readmission rate 11.3%). The top five primary diagnoses were pneumonia, urinary tract infection, pneumonitis due to inhalation of food or vomitus, chronic airway obstruction, acute respiratory failure, and heart failure. The age (P<0.001), age group (χ2=31.314, P<0.001), length of admission (P<0.05), BUN (P<0.05), Neutrophil (P<0.05), number of medication (P<0.01), number of treatment process (P<0.001), oxygen use (χ2=4.584, P<0.05), inhalation treatment (χ2=2.974, P<0.05), airway suction (χ2=13.973, P<0.001), nasal gastric tube feeding (χ2= 14.604, P<0.001), use of indwelling catheters (χ2=19.961, P<0.001), congestive heart failure (χ2=3.424, P<0.05), and cardiac arrhythmia (χ2 = 8.121, P<0.05) were found to be significantly different between 2 groups.
After logistic regression analysis, only 10 variables, including age, length of admission, number of medication, number of treatment procedure, oxygen use, use of indwelling catheter, nasal gastric tube feeding, BUN, cardiac arrhythmia, and valvular disease were significant and selected for constructing the prediction model with a Cox-Snell R2 of 0.75, a predictive accuracy of 87.7%, and an area under ROC curve (AUC) of 0.747.
Conclusions: The model constructed using 10 variables for predicting readmission achieved an accuracy of 87.7%. Among these 10 variables, age, indwelling catheters use, and cardiac arrhythmia were the most important predictive factors for readmission prediction. These 3 factors can be evaluated at the time of discharge and are can be used to predict patients who have higher probability to be readmitted. A post-discharge heath care plan can be conducted to prevent patient readmission, thereby increasing the patient safety and reducing the healthcare cost.
|
|---|
