Author + information
- Shilpkumar Arora,
- Prashant Patel,
- Harshil Shah,
- Varun Kumar,
- Chirag Savani,
- Kosha Thakore,
- Sidakpal Panaich,
- Rajvee Patel,
- Ambarish Pathak and
- Apurva Badheka
Background: Understanding of predictors of readmissions and mortality associated with PCI though vital has sparse real time data.
Methods: We used National Readmission Database (NRD) for the year 2013. The NRD is one of the largest all-payer national inpatient care database. ICD 9 codes 36.06 and 36.07 in either primary or secondary procedural field were used to extract PCI data. Patients with missing information on age, gender and mortality were removed. 90-day readmission was primary and In hospital morality was secondary outcome of our study. Hierarchical multivariate models were built to assess study outcomes.
Results: Of 136,399(weighted 309,032) index admissions for PCI, 26,107(19.14%) were readmitted and 2,520(1.84%) died. Female gender, increased burden of comorbidities, medium/large hospital size, discharge disposition to facility and higher length of stay on primary admissions were predictors of increased readmission. Increasing age, female gender and increased burden of comorbidities were among the significant predictors of increased mortality whereas private insurance, teaching hospital status and elective admissions were predictor of decreased mortality (Table 1). Interestingly higher income category of patient's zip code was a significant predictor of reduced readmissions but not mortality.
Conclusions: Our study identifies high risk population undergoing PCI; additional interventions in such population might help to reduce readmissions and mortality.
Poster Hall, Hall C
Friday, March 17, 2017, 3:45 p.m.-4:30 p.m.
Session Title: PCI for NSTEMI and Complex Patients With Multiple Co-Morbidities
Abstract Category: 19. Interventional Cardiology: Complex Patients/Comorbidities
Presentation Number: 1154-138
- 2017 American College of Cardiology Foundation