Classifying Patients With Chronic Heart Failure Into Levels Using Latent Profile Analysis Of Patient Reported Outcome Scores

Objective:In view of the serious harm of chronic heart failure to patients,family members and society,the different reflections of different groups of patients with different severity of heart failure on treatment plans,and the defects of existing classification methods of patients,this topic identifies the heterogeneous subgroups existing in the group of patients with chronic heart failure by using latent profile analysis method,so as to realize the selection of individualized diagnosis and treatment schemes for patients.Choose to provide theoretical basis to achieve precise treatment.According to the distribution of the scores of each group,the classification boundaries of the dimensions in the report outcome scale for patients with chronic heart failure were identified,which could provide a quantitative basis for the clinical application of the scale,further optimize the scoring system of the report outcome scale for patients with chronic heart failure,and provide a reasonable choice of the diagnosis and treatment methods,evaluation of the therapeutic effect and recognition of the changes of patients' state.Method:A questionnaire survey was conducted among patients with chronic heart failure diagnosed in three hospitals and communities in Shanxi Province from May 3,2017 to December 31,2018.The CHF-PRO manual was filled in.After finishing the questionnaire,latent profile analysis was used to analyze the scores of patients reported in the manual.The explicit variables of the model were the final scores of each dimension.In the section model,the parameters are estimated by the maximum likelihood method,and the EM algorithm realizes the iteration process,identifies the best number of categories and names them.Finally,the patients are divided into the groups with the highest probability and verified.Secondly,the probability density curve is drawn according to the scores of each group in each dimension,and the intersection points of each group curve are the classification boundaries of each dimension.Result:In this study,592 questionnaires were sent out and 565 were recovered.The recovery rate of the questionnaires was 95.44%,and 558 valid questionnaires were sent out.The validity rate of the questionnaires was 98.76%.In this study,318 males and 240 females with an average age of 67.66±14.66 years were found to have chronic heart failure.In thefield of physiology,patients are divided into two categories: low physiological function group,two categories: high physiological function group,the critical value between physical symptoms and sleep,appetite and sleep,the critical value between the two categories is 61.60,and independence is 66.32;in the field of psychology,patients are divided into two categories: 1 category: low psychological function group,2 categories:high psychological function group and anxiety group.The critical values of depression,fear and paranoia were 60.40,72.50,76.38 and 92.91 respectively.In the social field,patients were divided into two categories: low social function group,high social function group,64.97 and 46.24 respectively.In each dimension,the lower critical value was the low functional group,and the higher critical value was the high functional group.It is proved that the incidence of complications of high-function combination in all fields is lower than that of low-function group,and the classification results are reasonable.Conclusion:In this study,according to the scores of the report outcome scale for chronic heart failure patients,patients in different fields have been preliminarily classified objectively and effectively.The higher the scores,the better the function,the higher the quality of life,and vice versa,the lower the quality of life.On the other hand,this study determined the critical value of CHF-PRO scale for identifying different groups of patients with chronic heart failure,improved the CHF-PRO scale scoring system,and considered that the scale can effectively identify different severity groups of patients with chronic heart failure,and provide new basis for rational selection of treatment options,thereby improving the quality of life of patients with chronic heart failure and reducing reentry and mortality rate,improve the utilization rate of hospital resources.When it is applied to the treatment or follow-up of discharged patients,the change of patient's state can be found in time according to the change of patient's score.On the one hand,it can be used for the evaluation of clinical therapeutic effect.On the other hand,it can also providecorresponding preventive and intervention measures to the high-risk population close to the critical value in time,to realize early prevention,early diagnosis and early treatment,to reduce the medical burden of families and hospitals and reduce the occurrence of diseases.Rate,readmission rate.