Current diagnosis-related group-based bundling for upper-extremity arthroplasty: a case of insufficient risk adjustment and misaligned incentives

DRGKB: a knowledgebase of worldwide diagnosis-related groups' practices for comparison, evaluation and knowledge-guided application

As a prospective payment method, diagnosis-related groups (DRGs)'s implementation has varying effects on different regions and adopt different case classification systems. Our goal is to build a structured public online knowledgebase describing the worldwide practice of DRGs, which includes systematic indicators for DRGs' performance assessment. Therefore, we manually collected the qualified literature from PUBMED and constructed DRGKB website. We divided the evaluation indicators into four categories, including (i) medical service quality; (ii) medical service efficiency; (iii) profitability and sustainability; (iv) case grouping ability. Then we carried out descriptive analysis and comprehensive scoring on outcome measurements performance, improvement strategy and specialty performance. At last, the DRGKB finally contains 297 entries. It was found that DRGs generally have a considerable impact on hospital operations, including average length of stay, medical quality and use of medical resources. At the same time, the current DRGs also have many deficiencies, including insufficient reimbursement rates and the ability to classify complex cases. We analyzed these underperforming parts by domain. In conclusion, this research innovatively constructed a knowledgebase to quantify the practice effects of DRGs, analyzed and visualized the development trends and area performance from a comprehensive perspective. This study provides a data-driven research paradigm for following DRGs-related work along with a proposed DRGs evolution model. Availability and implementation: DRGKB is freely available at http://www.sysbio.org.cn/drgkb/. Database URL: http://www.sysbio.org.cn/drgkb/.

The 5-Item Modified Frailty Index for Risk Stratification of Patients Undergoing Total Elbow Arthroplasty

BACKGROUND

Frailty, quantified using the 5-item modified frailty index (mFI-5), has been shown to predict adverse outcomes in orthopaedic surgery. The utility in total elbow arthroplasty (TEA) patients is unclear. We evaluated if increasing frailty would correlate with worse postoperative outcomes.

METHODS

A retrospective assessment of patients in the American College of Surgeons National Surgery Quality Improvement Program undergoing primary TEA was performed. The mFI-5 was calculated by assigning 1 point for each comorbidity (diabetes, hypertension, congestive heart failure, chronic obstructive pulmonary disease, and functionally dependent health status). Poisson regression was used to evaluate mFI-5 scores on complications, length of stay (LOS), and adverse discharge. A significance threshold was at P < .05.

RESULTS

In total, 609 patients were included; 34.5% (n = 210) were not frail (mFI = 0), 44.0% (n = 268) were slightly frail (mFI = 1), and 21.5% (n = 131) were frail (mFI ≥ 2). As mFI score increased from 0 to ≥ 2, the following rates increased: any complication (9.0%-19.8%), major complication (11.0%-20.6%), cardiac complication (0.0%-2.3%), hematologic complication (3.3%-9.2%), adverse discharge (2.9%-22.9%), and LOS from 2.08 to 3.97 days (all P < .048). Following adjustment, Poisson regression demonstrated patients with a mFI ≥ 2 had increased risk of major complication (risk ratio [RR]: 2.13; P = .029), any complication (RR: 2.49; P = .032), Clavien-Dindo IV complication (RR: 5.53; P = .041), and adverse discharge (RR: 5.72; P < .001).

CONCLUSIONS

Frailty is not only associated with longer hospitalizations, but more major complications and non-home discharge. The mFI-5 is a useful risk stratification that may assist in decision-making for TEA.

Anemia Severity and the Risks of Postoperative Complications and Extended Length of Stay Following Primary Total Elbow Arthroplasty

BACKGROUND

Anemia is a modifiable risk factor that may influence postoperative complications following orthopedic surgical procedures. The objective was to determine the influence of preoperative anemia severity on postoperative complications and length of stay (LOS) following total elbow arthroplasty (TEA).

METHODS

The American College of Surgeons National Surgical Quality Improvement Program registry was queried from 2006 to 2019 for patients undergoing primary TEA. Using the World Health Organization definitions of anemia, patients undergoing TEA were stratified into 3 cohorts: nonanemia (hematocrit >36% for women, >39% for men), mild anemia (hematocrit 33%-36% for women, 33%-39% for men), and moderate-to-severe anemia (hematocrit <33% for both women and men). Patient demographics, surgical time, LOS, and postoperative complications were compared between the groups. A P value <.004 was considered significant.

RESULTS

After exclusion, 589 patients, of whom 369 (62.6%) did not have anemia, 129 (21.9%) had mild anemia, and 91 (15.5%) had moderate/severe anemia, were included. Increasing severity of anemia was associated with an increased average hospital LOS (2.30 vs 2.81 vs 4.91 days, P < .001). There was a statistically significant increase in blood transfusions (1.08% vs 7.75% vs 17.58%, P < .001), major complications (9.21% vs 17.83% vs 34.07%, P < .001), any complications (11.11% vs 23.26% vs 36.26%, P < .001), and extended LOS ≥6 days (6.23% vs 6.98% vs 31.87%, P < .001) with increasing severity of anemia. Multivariate analysis identified moderate-to-severe anemia was significantly associated with major complications and extended LOS (P < .001).

CONCLUSIONS

Preoperative anemia is a modifiable risk factor for medical and surgical complications within 30 days of TEA.

Prediction of total healthcare cost following total shoulder arthroplasty utilizing machine learning

BACKGROUND

Given the increase in demand in treatment of glenohumeral arthritis with anatomic total (aTSA) and reverse shoulder arthroplasty (RTSA), it is imperative to improve quality of patient care while controlling costs as private and federal insurers continue its gradual transition toward bundled payment models. Big data analytics with machine learning shows promise in predicting health care costs. This is significant as cost prediction may help control cost by enabling health care systems to appropriately allocate resources that help mitigate the cause of increased cost.

METHODS

The Nationwide Readmissions Database (NRD) was accessed in 2018. The database was queried for all primary aTSA and RTSA by International Classification of Diseases, Tenth Revision (ICD-10) procedure codes: 0RRJ0JZ and 0RRK0JZ for aTSA and 0RRK00Z and 0RRJ00Z for RTSA. Procedures were categorized by diagnoses: osteoarthritis (OA), rheumatoid arthritis (RA), avascular necrosis (AVN), fracture, and rotator cuff arthropathy (RCA). Costs were calculated by utilizing the total hospital charge and each hospital's cost-to-charge ratio. Hospital characteristics were included, such as volume of procedures performed by the respective hospital for the calendar year and wage index, which represents the relative average hospital wage for the respective geographic area. Unplanned readmissions within 90 days were calculated using unique patient identifiers, and cost of readmissions was added to the total admission cost to represent the short-term perioperative health care cost. Machine learning algorithms were used to predict patients with immediate postoperative admission costs greater than 1 standard deviation from the mean, and readmissions.

RESULTS

A total of 49,354 patients were isolated for analysis, with an average patient age of 69.9 ± 9.6 years. The average perioperative cost of care was $18,843 ± $10,165. In total, there were 4279 all-cause readmissions, resulting in an average cost of $13,871.00 ± $14,301.06 per readmission. Wage index, hospital volume, patient age, readmissions, and diagnosis-related group severity were the factors most correlated with the total cost of care. The logistic regression and random forest algorithms were equivalent in predicting the total cost of care (area under the receiver operating characteristic curve = 0.83).

CONCLUSION

After shoulder arthroplasty, there is significant variability in cumulative hospital costs, and this is largely affected by readmissions. Hospital characteristics, such as geographic area and volume, are key determinants of overall health care cost. When accounting for this, machine learning algorithms may predict cases with high likelihood of increased resource utilization and/or readmission.

Influence of medical comorbidity and surgical indication on total elbow arthroplasty cost of care

BACKGROUND

Movement toward providing value-based musculoskeletal care requires understanding the cost associated with surgical care as well as the drivers of these costs. The aim of this study was to investigate the effect of common medical comorbidities and specific total elbow arthroplasty (TEA) indications on reimbursement costs throughout the 90-day TEA episode of care. The secondary aim was to identify the drivers of these costs.

METHODS

Administrative health claims for patients who underwent orthopedic intervention between 2010 and 2020 were queried using specific disease classification and procedural terminology codes from a commercially available national database of 53 million patients. Patients with commercial insurance were divided into various cohorts determined by different surgical indications and medical comorbidities. The reimbursement costs of the surgical encounter, 89-day postoperative period, and total 90-day period in each cohort were evaluated. The cost drivers for the 89-day postoperative period were also determined. Analyses were performed using descriptive statistics and the Kruskal-Wallis test for comparison.

RESULTS

A total of 378 patients who underwent TEA were identified. The mean reimbursement cost of the surgical encounter ($13,393 ± $8314) did not differ significantly based on patient factors. The mean reimbursement cost of the 89-day postoperative period ($4232 ± $2343) differed significantly when stratified by surgical indication (P < .0001) or by medical comorbidity (P < .0001). The indication of rheumatoid arthritis ($4864 ± $1136) and the comorbidity of chronic kidney disease ($5873 ± $1165) had the most expensive postoperative period. In addition, the total 90-day reimbursement cost ($16,982 ± $4132) differed significantly when stratified by surgical indication (P = .00083) or by medical comorbidity (P < .0001), with the indication of acute fracture ($18,870 ± $3971) and the comorbidity of chronic pulmonary disease ($19,194 ± $3829) showing the highest total 90-day cost. Inpatient costs related to readmissions represented 38% of the total reimbursement cost. The overall readmission rate was 5.0%, and the mean readmission cost was $16,296.

CONCLUSION

TEA reimbursements are significantly influenced by surgical indications and medical comorbidities during the postoperative period and the total 90-day episode of care. As the United States transitions to delivering value-based health care, the need for surgeons and policy makers to understand treatment costs associated with different patient-level factors will expand.

Modifiable risk factors increase length of stay and 90-day cost of care after shoulder arthroplasty

BACKGROUND

Baseline health conditions can negatively impact cost of care and risk of complications after joint replacement, necessitating additional care and incurring higher costs. Bundled payments have been used for hip and knee replacement and the Centers for Medicare & Medicaid Services (CMS) is testing bundled payments for upper extremity arthroplasty. The purpose of this study was to determine the impact of predefined modifiable risk factors (MRFs) on total encounter charges, hospital length of stay (LOS), related emergency department (ED) visits and charges, and related hospital readmissions within 90 days after shoulder arthroplasty.

METHODS

We queried the electronic medical record (EPIC) for all shoulder arthroplasty cases under DRG 483 within a regional 7-hospital system between October 2015 and December 2019. Data was used to calculate mean LOS, total 90-day charges, related emergency department (ED) visits and charges, and related hospital readmissions after shoulder arthroplasty. Data for patients who had 1 or more MRFs, defined as anemia (hemoglobin < 10 g/dL), malnutrition (albumin < 3.4 g/dL), obesity (BMI > 40), uncontrolled diabetes (random glucose > 180 mg/dL or glycated hemoglobin > 8.0%), tobacco use (International Classification of Diseases, Tenth Revision, code indicating patient is a smoker), and opioid use (opioid prescription within 90 days of surgery), were evaluated as potential covariates to assess the relationship between MRFs and total encounter charges, LOS, ED visits, ED charges, and hospital readmissions.

RESULTS

A total of 1317 shoulder arthroplasty patients were identified. Multivariable analysis demonstrated that anemia (+$19,847, confidence interval [CI] $15,743, $23,951; P < .001), malnutrition (+$5850, CI $3712, $7988; P < .001), and obesity (+$2762, CI $766, $4758, P = .007) independently contributed to higher charges after shoulder arthroplasty. Mean LOS was higher in patients with anemia (5.0 ± 4.0 days vs. 2.2 ± 1.6 days, P < .001), malnutrition (3.7 ± 2.8 days vs. 2.2 ± 1.5 days, P < .001), and uncontrolled diabetes (2.8 ± 2.8 days vs. 2.3 ± 1.7 days, P = .019). Univariate risk factors associated with a significant increase in total 90-day encounter charges included anemia (+$19,345, n = 37, P < .001), malnutrition (+$6971, n = 116, P < .001), obesity (+$2615, n = 184, P = .011), and uncontrolled diabetes (+$4377, n = 66, P = .011). Univariate risk for readmission within 90 days was higher in patients with malnutrition (odds ratio 3.0, CI 1.8, 4.9; P < .001).

CONCLUSION

Malnutrition, obesity, and anemia contribute to significantly higher costs after shoulder arthroplasty. Medical strategies to optimize patients before shoulder arthroplasty are warranted to reduce total 90-day encounter charges, length of stay, and risk of readmission within 90 days of surgery. Optimizing patient health before shoulder surgery will positively impact outcomes and cost containment for patients, institutions, and payors after shoulder arthroplasty.