title: Predicting readmission risk following percutaneous coronary intervention at the time of admission.
abstract: OBJECTIVE
  To investigate whether a prediction model based on data available early in percutaneous coronary intervention (PCI) admission can predict the risk of readmission.
  
  
  BACKGROUND
  Reducing readmissions following hospitalization is a national priority. Identifying patients at high risk for readmission after PCI early in a hospitalization would enable hospitals to enhance discharge planning.
  
  
  METHODS
  We developed 3 different models to predict 30-day inpatient readmission to our institution for patients who underwent PCI between January 2010 and April 2013. These models used data available: 1) at admission, 2) at discharge 3) from CathPCI Registry data. We used logistic regression and assessed the discrimination of each model using the c-index. The models were validated with testing on a different patient cohort who underwent PCI between May 2013 and September 2015.
  
  
  RESULTS
  Our cohort included 6717 PCI patients; 3739 in the derivation cohort and 2978 in the validation cohort. The discriminative ability of the admission model was good (C-index of 0.727). The c-indices for the discharge and cath PCI models were slightly better. (C-index of 0.751 and 0.752 respectively). Internal validation of the models showed a reasonable discriminative admission model with slight improvement with adding discharge and registry data (C-index of 0.720, 0.739 and 0.741 respectively). Similarly validation of the models on the validation cohort showed similar results (C-index of 0.703, 0.725 and 0.719 respectively).
  
  
  CONCLUSIONS
  Simple models based on available demographic and clinical data may be sufficient to identify patients at highest risk of readmission following PCI early in their hospitalization.
authors.affiliation: Section of Cardiology, Christiana Care Health System, Newark, DE; Division of Cardiology, University of Kansas School of Medicine. Electronic address: zfanari@gmail.com.
authors.forename: Zaher
authors.initials: Z
authors.lastname: Fanari
authors.affiliation: Department of Medicine, Christiana Care Health System, Newark, DE; Value Institute, Christiana Care Health System, Newark, DE.
authors.forename: Daniel
authors.initials: D
authors.lastname: Elliott
authors.affiliation: Value Institute, Christiana Care Health System, Newark, DE.
authors.forename: Carla A
authors.initials: CA
authors.lastname: Russo
authors.affiliation: Value Institute, Christiana Care Health System, Newark, DE.
authors.forename: Paul
authors.initials: P
authors.lastname: Kolm
authors.affiliation: Section of Cardiology, Christiana Care Health System, Newark, DE; Value Institute, Christiana Care Health System, Newark, DE.
authors.forename: William S
authors.initials: WS
authors.lastname: Weintraub
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D010351:Patient Discharge / Q000706:statistics & numerical data; D010359:Patient Readmission / Q000706:statistics & numerical data*; D062645:Percutaneous Coronary Intervention / Q000009:adverse effects* / Q000706:statistics & numerical data*; D012042:Registries; D018570:Risk Assessment; D012307:Risk Factors; D013997:Time Factors; D016896:Treatment Outcome
keywords: Percutaneous coronary intervention; Readmission; Risk prediction