Traditional Risk Factors and Logistic Regression Failed to Reliably Predict a "Bundle Buster" After Total Joint
Yale A Fillingham1, Chad A Krueger1, Alexander J Rondon1
1Rothman Orthopaedic Institute at Thomas Jefferson University, Philadelphia, PA.
Insights
Identifying high-cost patients after joint replacement surgery is complex. Risk factors differ significantly between Medicare and privately insured individuals, necessitating further research into cost drivers.
Area of Science:
- Orthopedic Surgery
- Health Economics
- Health Services Research
Background:
- Understanding patient factors predicting high healthcare costs after joint arthroplasty is crucial for resource allocation.
- Previous research has not clearly defined cost predictors specific to different insurance groups.
Purpose of the Study:
- To identify patient characteristics associated with high costs in Medicare and privately insured populations undergoing total joint arthroplasty.
Main Methods:
- Analysis of 90-day episode-of-care payments, demographics, comorbidities, and readmissions for primary total joint arthroplasty patients (2015-2016).
- Utilized univariate analysis, logistic regression, and receiver operating characteristic (ROC) curves to identify cost predictors.
- Defined high-cost patients based on nonparametric cost data cutoffs.
Main Results:
- Privately insured high-cost patients were older, more likely to be readmitted, and less likely to be discharged home.
- For Medicare patients, diabetes, congestive heart failure, cerebrovascular events, and rheumatic disease predicted higher costs.
- Logistic regression identified no predictive factors for high costs in privately insured patients.
Conclusions:
- Traditional risk factors for complications do not reliably predict increased patient costs post-arthroplasty.
- Cost-predicting factors diverge significantly between Medicare and privately insured patients.
- Further investigation is needed to pinpoint specific cost drivers and inform cost-prevention strategies.
Background:
The purpose of this study was to determine if we could identify patient factors that were predictive of Medicare and privately insured patients being "high-cost."
Methods:
Ninety-day episode-of-care insurance company payments along with collected demographics, comorbidities, and readmissions were reviewed for a consecutive series of primary total joint arthroplasty patients from 2015 to 2016 at our institution. High-cost patients were identified by determining those patients above the cutoff, where the cost data became demonstrably nonparametric and both univariate analysis and logistical regressions were performed to identify risk factors that lead to increased costs. Receiver operator curves were created to determine the predictive nature of these risk factors.
Results:
Univariate analysis showed that high-cost privately insured patients were significantly older, more likely to be readmitted and less likely to be discharged to home (P < .001) whereas high-cost Medicare total knee/total hip arthroplasty patients were more likely to have many of the comorbidities analyzed. Logistical regression did not find any predictive factors for privately insured patients and found that diabetes (OR 1.47 and 1.75, respectively), congestive heart failure (OR 1.94 and 3.46, respectively), cerebrovascular event (OR 2.20 and 2.20, respectively) and rheumatic disease (OR 1.78 and 1.78, respectively) were all predictive of being a high-cost Medicare patient.
Conclusion:
Traditional risk factors for postoperative complications are not reliably associated with increased patient costs after total hip and total knee arthroplasty. Furthermore, the risk factors associated with increased costs vary greatly between privately insured and Medicare-insured patients. Further investigation is necessary to identify cost drivers in this patient subset to preventive higher costs.
Related Concept Videos
01:12Relative Risk
01:26Factors Affecting the Risk of Infection
The integrity and count of the white blood cells help the body resist pathogens and fight infection. When impaired, it reduces the body's resistance to pathogens. The acidic pH levels of the gastrointestinal, genitourinary tracts, and skin...
01:09Truncation in Survival Analysis
Left truncation occurs when individuals who experienced the event of interest before a certain time are not included in the study. This is often due to a "delayed entry" into the study where only those who survive until a certain entry point are...
01:15Assumptions of Survival Analysis
01:09Odds Ratio
01:17Clearance Models: Noncompartmental Models
The noncompartmental approach capitalizes on extensive sampling data, correlating the volume of distribution to systemic exposure and the administered dosage. This method enables...