Developing A Risk Prediction Model For Frality Among Coronary Heart Disease Patients After Discharge

ObjectiveThrough evidence-based methodology,this study summarized and classified the influencing factors causing frality in patients with coronary heart disease,and designed a survey questionnaire based on the findings.The research investigated the incidence of frality in patients with coronary heart disease three months after discharge,analyzed its influencing factors,and used these factors to build a risk prediction model.Finally,the model results were visually presented using a nomogram to provide reference for predicting and developing intervention plans for frality occurring after discharge in patients with coronary heart disease.MethodsA literature review was conducted by searching eight databases including the Cochrane library,Pub Med,Web of science,Embase,CNKI,Wanfang database,China Biology Medicine disc,and VIP,using a combination of controlled vocabulary and free-text terms to identify relevant studies about the factors associated with frality in patients with coronary heart disease.Then the relevant factors identified by literature review were extracted and used to create a survey questionnaire.Using convenience sampling,the medical records and related information of coronary heart disease patients who met the study criteria were collected from a tertiary hospital in Henan Province from November 2021 to September 2022.The patient information was collected through electronic medical record systems and questionnaires,and a three-month follow-up was conducted on the patients after their discharge to detect any occurrence of frailty development.A total of 434 people were investigated and 412were included.The data was inputted into Epidata 3.1 software,while the statistical analysis was performed using IBM SPSS Statistics 26.0 and R(R4.1.3)software.Mean±standard deviation was used for statistical description of continuous data,while frequency and percentage were used for categorical data.Two-sample mean comparisons were analyzed using t-test,and two or more sample comparisons of categorical data were analyzed using the chi-square test or Fisher’s exact probability method.After identifying significant influencing factors in the single-factor analysis,Lasso regression was employed to further screen and predict the risk of frailty in patients after discharge.The selected prediction factors were then regressed using logistic regression to establish a risk prediction model,which was presented visually in a nomogram.The model was internally validated using the bootstrap method and evaluated using C-index and receiver operating characteristic(ROC)curves to assess its discriminative ability,and Hosmer-Lemeshow goodness-of-fit test and calibration curves were used to evaluate its calibration.To assess the clinical utility of the model,clinical decision curve analysis(DCA)and clinical impact curve(CIC)were plotted.Statistical analysis was performed using two-tailed tests with a significance level of alpha=0.05.Results1.Through literature screening based on inclusion and exclusion criteria,18 articles were selected,including a total of 36 influencing factors from various aspects such as general demographic information,measurement indicators,disease-related information,blood biochemical indicators,cardiac imaging examination indicators,psychological health,and social support.These factors served as the basis for constructing a risk prediction model for post-discharge frailty in patients with coronary heart disease.2.A total of 434 patients with coronary heart disease were collected in this study,of which 22 patients(5.1%)were lost to follow-up.Finally,412 patients(94.9%)were included in the study,among which 91(22.1%)experienced frailty while 321(77.9%)did not.The lowest score for frailty assessment was 0,and the highest score was 9.Single-factor analysis showed that gender,age,education level,current address,height,weight,BMI,Geriatric Nutritional Risk Index(GNRI),hypertension,comorbidity,smoking,alcohol consumption,activities of daily living assessment,fall risk assessment,creatinine(CREA),brain natriuretic peptide(BNP),glucose(GLU),left ventricular ejection fraction(LVEF),stroke volume(SV),anxiety score,depression score,and social support score had statistical significance(P<0.05)and were related to the occurrence of post-discharge frailty in patients with coronary heart disease.3.After Lasso regression screening,the logistic regression analysis showed that the risk prediction model for post-discharge frailty in patients with coronary heart disease was composed of eight variables,including age,current address,anxiety,hypertension,alcohol consumption,LVEF,GNRI and GLU.The regression equation was:Logit(P)=1.692+0.056×age-0.779×current address+0.235×anxiety+1.137×hypertension-1.053×alcohol consumption-0.041×LVEF-0.052×GNRI+0.594×GLU,.and a nomogram was drawn according to the logistic regression results.The initial C-index of the model was 0.826(95%CI:0.777-0.874),the specificity was 0.735,the sensitivity was 0.802,and the optimal cut-off value was 0.206.The internal validation showed that the C-index of the model was 0.806.The Homser-Lemeshow goodness-of-fit test result of the model wasX~2=2.238,df=2,P=0.318(P>0.05),indicating a good fit of the model.The calibration plot also showed good calibration of the model.In addition,the DCA and CIC indicated that the model had good clinical utility.ConclusionsThis study determined the assessment contents for frailty in patients discharged with coronary heart disease using evidence-based literature.The study then developed a risk prediction model in the form of a nomogram,which included eight predictive factors:age,current residence,anxiety,hypertension,alcohol consumption,LVEF,GNRI,and GLU.The model can effectively predict the risk of frailty in patients after discharge with coronary heart disease,which provides a valuable reference for clinical development of relevant prevention and intervention measures.