PREDICTIVE MODEL BASED ON HEALTH DATA ANALYSIS FOR RISK OF READMISSION IN DISEASE-SPECIFIC COHORTS.
Background: Intervention planning to reduce 30-day readmission post-acute myocardial infarction (AMI) in an environment of resource scarcity can be improved by readmission prediction score. The aim of study is to derive and validate a prediction model based on routinely collected hospital data for i...
| Publicado en: | Perspectives in Health Information Management pp. 1 - 12 |
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| Autores principales: | , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
American Health Information Management Association
Spring2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=149624838&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 149624838 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 15594122 2QEL jtl: Perspectives in Health Information Management issn: 15594122 maglogo: N pubinfo: dt: Spring2021 pid: 6825 pub: American Health Information Management Association place: Chicago, Illinois artinfo: ui: 149624838 149624838 149624838 149624838 ppf: 1 ppct: 11 formats: fmt: @attributes: type: P tig: atl: PREDICTIVE MODEL BASED ON HEALTH DATA ANALYSIS FOR RISK OF READMISSION IN DISEASE-SPECIFIC COHORTS. aug: au: Ansari, Md. Shahid Alok, Abhay Kumar Jain, Dinesh Rana, Santu Gupta, Sunil Salwan, Roopa Venkatesh, Svetha affil: Deputy manager of clinical data analytics at Max Super Specialty Hospital, New Delhi, India sug: subj: Acute Disease Myocardial Infarction Therapy Readmission Risk Assessment Prediction Models Human Retrospective Design Tertiary Health Care International Classification of Diseases Logistic Regression Ethnic Groups Hospital Information Systems Confidence Intervals Descriptive Statistics ab: Background: Intervention planning to reduce 30-day readmission post-acute myocardial infarction (AMI) in an environment of resource scarcity can be improved by readmission prediction score. The aim of study is to derive and validate a prediction model based on routinely collected hospital data for identification of risk factors for all-cause readmission within zero to 30 days post discharge from AMI. Methods: Our study includes 2,849 AMI patient records (January 2005 to December 2014) from a tertiary care facility in India. EMR with ICD-10 diagnosis, admission, pathological, procedural and medication data is used for model building. Model performance is analyzed for different combination of feature groups and diabetes sub-cohort. The derived models are evaluated to identify risk factors for readmissions. Results: The derived model using all features has the highest discrimination in predicting readmission, with AUC as 0.62; (95 percent confidence interval ) in internal validation with 70/30 split for derivation and validation. For the sub-cohort of diabetes patients (1359) the discrimination is slightly better with AUC 0.66; (95 percent CI; ). Some of the positively associated predictive variables, include age group 80-90, medicine class administered during index admission (Anti-ischemic drugs, Alpha 1 blocker, Xanthine oxidase inhibitors), additional procedure in index admission (Dialysis). While some of the negatively associated predictive variables, include patient demography (Male gender), medicine class administered during index admission (Betablocker, Anticoagulant, Platelet inhibitors, Anti-arrhythmic). Conclusions: Routinely collected data in the hospital's clinical and administrative data repository can identify patients at high risk of readmission following AMI, potentially improving AMI readmission rate. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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