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RISS 인기검색어
- 검색키워드
- 저자 : Aksharananda Rambachan (검색결과 2 건)
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Predictors of Readmission after Inpatient Plastic Surgery
Jain, Umang,Salgado, Christopher,Mioton, Lauren,Rambachan, Aksharananda,Kim, John Y.S. Korean Society of Plastic and Reconstructive Surge 2014 Archives of Plastic Surgery Vol.41 No.2
Background Understanding risk factors that increase readmission rates may help enhance patient education and set system-wide expectations. We aimed to provide benchmark data on causes and predictors of readmission following inpatient plastic surgery. Methods The 2011 National Surgical Quality Improvement Program dataset was reviewed for patients with both "Plastics" as their recorded surgical specialty and inpatient status. Readmission was tracked through the "Unplanned Readmission" variable. Patient characteristics and outcomes were compared using chi-squared analysis and Student's t-tests for categorical and continuous variables, respectively. Multivariate regression analysis was used for identifying predictors of readmission. Results A total of 3,671 inpatient plastic surgery patients were included. The unplanned readmission rate was 7.11%. Multivariate regression analysis revealed a history of chronic obstructive pulmonary disease (COPD) (odds ratio [OR], 2.01; confidence interval [CI], 1.12- 3.60; P=0.020), previous percutaneous coronary intervention (PCI) (OR, 2.69; CI, 1.21-5.97; P=0.015), hypertension requiring medication (OR, 1.65; CI, 1.22-2.24; P<0.001), bleeding disorders (OR, 1.70; CI, 1.01-2.87; P=0.046), American Society of Anesthesiologists (ASA) class 3 or 4 (OR, 1.57; CI, 1.15-2.15; P=0.004), and obesity (body mass index ${\geq}30$) (OR, 1.43; CI, 1.09-1.88, P=0.011) to be significant predictors of readmission. Conclusions Inpatient plastic surgery has an associated 7.11% unplanned readmission rate. History of COPD, previous PCI, hypertension, ASA class 3 or 4, bleeding disorders, and obesity all proved to be significant risk factors for readmission. These findings will help to benchmark inpatient readmission rates and manage patient and hospital system expectations.
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Predictors of Readmission after Inpatient Plastic Surgery
Umang Jain,Christopher Salgado,Lauren Mioton,Aksharananda Rambachan,John YS Kim 대한성형외과학회 2014 Archives of Plastic Surgery Vol.41 No.2
Background: Understanding risk factors that increase readmission rates may help enhance patient education and set system-wide expectations. We aimed to provide benchmark data on causes and predictors of readmission following inpatient plastic surgery. Methods: The 2011 National Surgical Quality Improvement Program dataset was reviewed forpatients with both “Plastics” as their recorded surgical specialty and inpatient status. Readmissionwas tracked through the “Unplanned Readmission” variable. Patient characteristics and outcomes were compared using chi-squared analysis and Student’s t-tests for categorical and continuous variables, respectively. Multivariate regression analysis was used for identifying predictors of readmission. Results: A total of 3,671 inpatient plastic surgery patients were included. The unplanned readmission rate was 7.11%. Multivariate regression analysis revealed a history of chronic obstructive pulmonary disease (COPD) (odds ratio [OR], 2.01; confidence interval [CI], 1.12–3.60; P=0.020), previous percutaneous coronary intervention (PCI) (OR, 2.69; CI, 1.21–5.97; P=0.015), hypertension requiring medication (OR, 1.65; CI, 1.22–2.24; P<0.001), bleeding disorders (OR, 1.70; CI, 1.01–2.87; P=0.046), American Society of Anesthesiologists (ASA) class 3 or 4 (OR, 1.57; CI, 1.15–2.15; P=0.004), and obesity (body mass index ≥30) (OR, 1.43; CI, 1.09–1.88, P=0.011) to be significant predictors of readmission. Conclusions: Inpatient plastic surgery has an associated 7.11% unplanned readmission rate. History of COPD, previous PCI, hypertension, ASA class 3 or 4, bleeding disorders, and obesity all proved to be significant risk factors for readmission. These findings will help to benchmark inpatient readmission rates and manage patient and hospital system expectations.
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