Derivation and external validation of a simple risk tool to predict 30-day hospital readmissions after transcatheter aortic valve replacement

Sahil Khera, Dhaval Kolte, Salil Deo, Ankur Kalra, Tanush Gupta, Dawn Abbott, Neal Kleiman, Deepak L. Bhatt, Gregg C. Fonarow, Omar Khalique, Susheel Kodali, Martin B. Leon, Sammy Elmariah

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

Aims: Patients undergoing transcatheter aortic valve replacement (TAVR) possess a higher risk of recurrent healthcare resource utilisation due to multiple comorbidities, frailty, and advanced age. We sought to devise a simple tool to identify TAVR patients at increased risk of 30-day readmission. Methods and results: We used the Nationwide Readmissions Database from January 2013 to September 2015. Complex survey methods and hierarchical regression in R were implemented to create a prediction tool to determine probability of 30-day readmission. Boot-strapped internal validation and cross-validation were performed to assess model accuracy. External validation was performed using a single-centre data set. Of 39,305 patients who underwent endovascular TAVR, 6,380 (16.2%) were readmitted within 30 days. The final 30-day readmission risk prediction tool included the following variables: Chronic kidney disease, endstage renal disease on dialysis (ESRD), anaemia, chronic lung disease, chronic liver disease, atrial fibrillation, length of stay, acute kidney injury, and discharge disposition. ESRD (OR 2.11, 95% CI: 1.7-2.63), length of stay ≥5 days (OR 1.64, 95% CI: 1.50-1.79), and short-term hospital discharge disposition (OR 1.81, 95% CI: 1.2-2.7) were the strongest predictors. The c-statistic of the prediction model was 0.63. The c-statistic in the external validation cohort was 0.69. On internal calibration, the tool was extremely accurate in predicting readmissions up to 25%. Conclusions: A simple and easy-to-use risk prediction tool utilising standard clinical parameters identifies TAVR patients at increased risk of 30-day readmission. The tool may consequently inform hospital discharge planning, optimise transitions of care, and reduce resource utilisation.

Original languageEnglish (US)
Pages (from-to)155-163
Number of pages9
JournalEuroIntervention
Volume15
Issue number2
DOIs
StatePublished - Jun 2019

Keywords

  • Miscellaneous
  • Risk stratification
  • TAVI

ASJC Scopus subject areas

  • Cardiology and Cardiovascular Medicine

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