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Saudi Journal of Kidney Diseases and Transplantation
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Table of Contents   
ORIGINAL ARTICLE  
Year : 2011  |  Volume : 22  |  Issue : 1  |  Page : 24-39
Three-year post-transplant medicare payments in kidney transplant recipients: Associations with pre-transplant comorbidities


1 Center for Outcomes Research, Saint Louis University School of Medicine, Saint Louis, USA
2 Department of Surgery, University of Washington, Seattle, WA, USA
3 Department of Medicine, Washington University, Saint Louis, MO, USA

Click here for correspondence address and email

Date of Web Publication30-Dec-2010
 

   Abstract 

Little is known about the influence of pre-transplant comorbidities on post-transplant expenditures. We estimated the associations between pre-transplant comorbidities and post-transplant Medicare costs, using several comorbidity classification systems. We included recipients of first-kidney deceased donor transplants from 1995 through 2002 for whom Medicare was the primary payer for at least one year pre-transplant (N = 25,175). We examined pre-transplant comorbidities as classified by International Classification of Diseases (ICD-9-CM) codes from Medicare claims with the Clinical Cla­ssifications Software (CCS) and Charlson and Elixhauser algorithms. Post-transplant costs were calcu­lated from payments on Medicare claims. We developed models considering Organ Procurement and Transplantation Network (OPTN) variables plus: 1) CCS categories, 2) Charlson, 3) Elixhauser, 4) num­ber of Charlson and 5) number of Elixhauser comorbidities, independently. We applied a novel regression methodology to account for censoring. Costs were estimated at individual and population levels. The comorbidities with the largest impact on mean Medicare payments included cardiovascular disease, ma­lignancies, cerebrovascular disease, mental conditions and functional limitations. Skin ulcers and infec­tions, rheumatic and other connective tissue disease and liver disease also contributed to payments and have not been considered or described previously. A positive graded relationship was found between costs and the number of pre-transplant comorbidities. In conclusion, we showed that expansion beyond the usually considered pre-transplant comorbidities with inclusion of CCS and Charlson or Elixhauser comorbidities increased the knowledge about comorbidities related to augmented Medicare payments. Our expanded methodology can be used by others to assess more accurately the financial implications of renal transplantation to Medicare and individual transplant centers.

How to cite this article:
Machnicki G, Lentine KL, Salvalaggio PR, Burroughs TE, Brennan DC, Schniztler MA. Three-year post-transplant medicare payments in kidney transplant recipients: Associations with pre-transplant comorbidities. Saudi J Kidney Dis Transpl 2011;22:24-39

How to cite this URL:
Machnicki G, Lentine KL, Salvalaggio PR, Burroughs TE, Brennan DC, Schniztler MA. Three-year post-transplant medicare payments in kidney transplant recipients: Associations with pre-transplant comorbidities. Saudi J Kidney Dis Transpl [serial online] 2011 [cited 2014 Aug 20];22:24-39. Available from: http://www.sjkdt.org/text.asp?2011/22/1/24/74337

   Introduction Top


Kidney transplantation is the preferred renal replacement therapy due to improved survival and quality of life and reduced costs compared to dialysis. [1],[2],[3] However, a changing donor and recipient patient mix, the increasing organ shor­tage and a stringent economic environment create new challenges to maximize the achievements of kidney transplantation.

In particular, the impact of pre-transplant co­morbidities is of interest. End-stage renal di­sease (ESRD) patients have many comorbidi­ties [4] and transplant recipients carry these co­morbidity loads. Comorbidities are increasing given the aging transplant recipient population. [5] Additionally, growing organ shortages reinforce the need for organ allocation to maximize bene­fit while maintaining equality of access. [6] Life years from transplant (LYFT) is one principle proposed to increase the benefit derived from kidney transplantation. Whether to include co­morbidities and which ones to include in such system is an area of recent scrutiny. [5],[7],[8] Comorbidities also impact the likelihood of access to transplantation. [9]

Pre-transplant comorbidities are also relevant for resource allocation and healthcare adminis­tration. The assessment of cost effectiveness of medications or procedures is a continuous pro­cess that must be reassessed with changes in clinical practices and patient mix. The cost ef­fectiveness of kidney transplantation has been started to be studied further in specific popula­tions with comorbid disease. [10]

Insurers and individual transplant programs need to understand the determinants of post­transplant costs and reimbursement [11] to identify modifiable factors to reduce costs without com­promising quality, aid in predicting future costs and in the implementation of new payment schemes.

In this study, we focused on Medicare trans­plant payments. By December 2004, 55% of the transplants were covered by Medicare, [12] making this institution the biggest payer for transplant services in the United States (US). The Medi­care ESRD program spends approximately $23 billion per year, which accounts for 6.4% of the annual Medicare budget for 1.1% of the Medi­care population. [4] Although total payments for transplant procedures and subsequent care re­present less than 15% of total ESRD Medicare expenses, payments for patients with functio­ning grafts have increased 3.4 times since 1991. [4]

Comorbidities are a relevant topic also in regards to reimbursement. A small segment of the Medicare transplant population is covered by private insurers that receive capitated payments (Medicare Advantage Plans). [13] In 2003, the ESRD capitated payment scheme implemented adjust­ments for comorbid disease, using the Hierar­chical Condition Categories (HCC) comorbidity classification system. [13] However, regression esti­mates were generated only for dialysis patients and were assumed to be the same for transplant recipients. Other comorbidity classification sys­tems, such as the Clinical Classifications Soft­ware (CCS), Charlson and Elixhauser are used in research to predict healthcare costs. [14],[15]

The Organ Procurement and Transplantation Network (OPTN) records pre-transplant comor­bidities such as diabetes, cardiovascular disease, peripheral vascular disease and hepatitis C. These have been associated with increased post-transplant Medicare costs. [16],[17] Other patient and transplant factors that have been associated with higher Medicare payments include HLA matching, [16],[18] cold ischemia time, [16] patient age and race, [16] deceased donor [19] and expanded criteria donor types. [20]

The United States Renal Data System (US­RDS) includes information from the OPTN and Medicare enrolment and billing claims collec­ted by the Center for Medicare and Medicaid Services (CMS). [4] The USRDS captures primary and secondary diagnoses submitted on Medicare billing claims for hospitalizations and medical visits and offers the opportunity to explore a diverse spectrum of pre-transplant comorbidities. Only a limited number of comorbidities have been studied in the USRDS database and in international research. Additionally, most of the previous research provided unadjusted, bivariate estimates and not the multivariable associations between pre-transplant comorbidities and cost.

Current cost estimations should be enriched with clinical factors such as additional pre-trans­plant comorbidities. We sought to estimate the associations between 3-year post-transplant me­dicare payments and a broad array of pretrans­plant comorbidities in the Medicare population, controlling for other patient, donor and trans­plant factors recorded not only by the OPTN but also through other measures such as the CCS and Charlson-Elixhauser methodologies.


   Subjects and Methods Top


This was a retrospective cohort study of all adult (>18 years) recipients of deceased-donor, first renal only transplants in 1995-2002 for whom Medicare was the primary insurer at the time of transplant. Patients were reported to the OPTN and included in the USRDS registry.

Comorbidity and payments information from claims

Comorbidity information from claims was iden­tified using the Medicare part-A and part-B claims files. Any primary or secondary diagnosis by the International Classification of Diseases, 9th revision, clinical modification (ICD-9-CM) code for each hospitalization or physician visit du­ring the last pre-transplant year was considered. To identify pre-transplant comorbidities using a comparable time frame, we limited the study sample to patients with at least 1 year of con­tinuous Medicare coverage in the year before transplant. Three-year Medicare payments were aggregated from the post-transplant part-A and part-B claims. All payments were adjusted for inflation with the medical component of the consumer price index using the year 2000 as the base year.

Comorbidity summary measures

Three different systems that reduce the ICD-9­CM diagnosis to manageable categories for data analysis were used. First, we applied the CCS. [21],[22] The CCS was developed by the Agency for Healthcare Research and Quality (AHRQ). [23],[24] The single-level CCS classifies ICD-9-CM diagnoses into 260 mutually exclusive catego­ries. The CCS has been used to describe post­transplant hospitalizations in the USRDS [25] as well as associations between comorbidities and graft outcomes. [8] To avoid the risk of inappro­priate inclusion, we eliminated CCS variables with prevalence <1 case per 100 patients. This yielded 129 CCS variables.

The Charlson [26] and Elixhauser [27] comorbidities classification algorithms were also examined. These algorithms have been previously studied in dialysis [28] and transplant populations [8],[29] and are widely used measures of comorbidity. [30] The Charlson index considers 19 conditions; while the Elixhauser index accounts for 31 comorbi­dities. Adaptations of both algorithms for claims databases were employed. [31]

Other study variables

All OPTN variables describing recipient, do­nor and transplant factors were included in the regression models. These included age, race, body mass index (BMI), cause of ESRD, dia­lysis duration, peak panel reactive antibodies (PRA) ≥ 50% and OPTN-reported comorbidities; donor type [expanded criteria donor (ECD), do­nation after cardiac death (DCD)], donor age, race, BMI, cause of death; number of donor-re­cipient ABDR Human leukocyte antigen (HLA) mismatches (categorized into none, AB mis­matches, DR mismatches), donor-recipient cyto­megalovirus (CMV) sero-pairing, cold ischemia time, immunosuppressant therapy at discharge, induction therapy and year of transplant. De­layed graft function (DGF) was considered a post-transplant variable rather than a pre-trans­plant or baseline transplant procedure event. Continuous variables (recipient and donor age, BMI and time on dialysis) were not categorized.


   Statistical Analyses Top


In the three-year payments estimation, patients were censored at lost to follow-up or end of study observation because the possible follow­up was less than three years. We used multiple linear regression models to account for censo­ring. [32] This methodology has been shown to be better than restriction to cases with complete observation through the time period of interest, a method previously applied in the transplant literature. [16],[17]

Five statistical models were built: 1) CCS co­morbidities, 2) Charlson comorbidities, 3) Elix­hauser comorbidities, 4) number of Charlson comorbidities (0 to ≥6) and 5) number of Elix­hauser comorbidities (0 to ≥11). Costs were divided into 36 monthly intervals (covering three years of post-transplant costs). Patients who died or who reached the end of a given monthly pe­riod with observed costs were considered to have completely known costs for that monthly period. Payments for patients with complete in­formation at the end of each period were con­sidered in monthly regressions for each of the 36 post-transplant months. To account for cen­soring, payments at the end of each period where weighted by the probability of observing pa­tients at that day. [32] The probability of censoring was estimated from a Kaplan-Meier curve that used censoring as the endpoint. Once regression estimates for each monthly period were obtained, these were aggregated over time to obtain a to­tal estimate for three-year payments. We ob­tained 95% confidence intervals of the regre­ssion coefficients by using 200 bootstrap repli­cations. The mean of those 200 bootstrap repli­cations was used as the point-estimate. Estimates represented adjusted costs for each comorbidity accounting for other demographic and transplant factors.

After applying our inclusion and exclusion cri­teria, 27,177 patients were eligible. Missing do­nor age data for 3,261 patients (12.9%) were imputed with the mean donor age. Missing data for recipient BMI (n = 849, 3.36%) and donor BMI (n = 894, 3.54%) were imputed with the median BMI. Extreme values in BMI (smaller than 15 and greater than 45) were imputed to 15 and 45. No other data imputation was performed.

For comparative purposes, we also derived cost curves, dividing the cohort into two groups, based on the presence versus absence of any given comorbidity. Cost curves are an adapta­tion of the Kaplan-Meier methodology for cost analyses and had been previously used in renal transplantation. [16],[17],[18],[20] Given that an individual patient could have more than one comorbidity, cost curves across comorbidities do not repre­sent mutually exclusive groups. To adjust for this, cost curves with mutually exclusive cohorts were only produced for the patient-groups cate­gorized by increasing the number of Charlson and Elixhauser comorbidities. Cost curves for the mutually exclusive Charlson and Elixhauser groups are also presented graphically. This me­thod provides an unadjusted estimate of pay­ments because it does not take other variables into account.

Regression estimates for the OPTN variables are only presented for the CCS regression, gi­ven the general similarity of those estimates with the other models. The top 10 CCS, Charlson and Elixhauser categories that increased indivi­dual mean payments and population costs are summarized. Total population payments were obtained multiplying the prevalence of each co­morbidity by the difference in mean payments obtained from the regression equation applied to two cohorts, one with and another without the comorbidity of interest. Population payments attributable to each comorbidity were calculated multiplying the comorbidity prevalence by the regression coefficient associated with that co­morbidity. Payment estimations derived from cost curves and regression models were com­pared for the models with sums of Charlson and Elixhauser comorbidities. All analyses were per­formed using SAS 9.1 (Cary, NC, USA).


   Results Top


Patient population and comorbidities

After excluding patients with non-imputable missing data, 25,175 patients remained. Most of the patients were between 45 and 59 years of age and were males. The mean number of CCS, Charlson and Elixhauser comorbidities were 9.5, 1.6 and 3.2, respectively [Table 1]. Additional recipient, donor and transplant information is presented in [Table 1].
Table 1: Patient, donor and transplant characteristics of the study population.

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Regression results

OPTN and CCS variables


In the multivariable regression model with OPTN variables and CCS comorbidities, the fac­tors with largest associations with 3-year post­transplant Medicare payments included African­American recipient, type I diabetes as the cause of ESRD, arrhythmia and cerebrovascular di­sease as reported to the OPTN, ECD, DCD and the interaction of these variables and African American or other donor race, transplant cold ischemia time > 36 hours, unknown CMV sero­pairing and immunosuppressant regimen diffe­rent from calcineurin inhibitor + mycophenolate mofetil or calcineurin inhibitor + azathioprine [Table 2]. Other factors associated with increased payments included number of HLA mismatches, donor+/recipient- CMV sero-pairing and reci­pient age, peripheral vascular disease and phy­sical functional limitations. Medicare payments were lower for patients transplanted in later years. Payments were also lower for recipients with polycystic kidney disease and race other than white or African-American, Hispanic donor ethnicity and use of induction.
Table 2: OPTN + CCS regression model for 3-year Medicare payments.

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CCS, Charlson and Elixhauser comorbidities regression models

Recipient comorbidities associated with the lar­gest increases in individual payments in the CCS regression model included chronic ulcers of the skin, affective disorders, pancreatic disor­ders (no diabetes), hyperplasia of the prostate, superficial injury, acute cerebrovascular disease, diseases of the vein and lymphatics, acute bron­chitis, acquired foot deformities and skin and subcutaneous tissue infections [Table 2]. Comor­bidities associated with highest reductions in payments included benign neoplasm (including breast conditions), senility and organic mental disorders, and a set of bone, gynecologic and other conditions. The full set of CCS regression coefficients is available from the authors.

Charlson comorbidities conferring the highest increases in terms of individual payments in­cluded liver disease, paraplegia and hemiplegia, metastatic carcinoma, peptic ulcer disease, peri­pheral vascular disease, cerebrovascular disease, congestive heart failure, connective tissue and rheumatic disease, diabetes without complica­tions and chronic pulmonary disease [Table 3]. Only dementia was associated with a decrease in mean payments.
Table 3: OPTN + Charlson and OPTN + Elixhauser regression models for 3-year Medicare payments (comorbidity results).

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Models were also estimated using the Elix­hauser comorbidity classification system, which includes more comorbidities than the Charlson algorithm. Regression models with Elixhauser comorbidities identified the following most im­portant contributors to individual payments: pa­ralysis, drug abuse, metastatic cancer, peptic ulcer disease excluding bleeding, obesity, alco­hol abuse, peripheral vascular disorders, valvular disease, other neurological disorders and rheu­matoid arthritis/collagen disorders [Table 3]. Two comorbidities (psychoses and hypothyroi­dism) were associated with reduced costs.

The top ten comorbidities increasing popula­tion payments were similar across all comor­bidity classification algorithms. All classifica­tions included renal failure and diabetes. Other commonly identified comorbidities included hypertension, coronary atherosclerosis and dif­ferent types of heart disease. Comorbidities more unique to a specific classification included skin and subcutaneous tissue infection (CCS), liver disease and peptic ulcer disease (Charlson) and other neurological disease (Elixhauser). Total population payments were higher than popu­lation attributable payments, as the latter only consider the association that can be attributed to the comorbidity of interest after controlling for other variables, which is generally smaller than the unadjusted payments [Table 4].
Table 4: Top 10 CCS, Charlson and Elixhauser comorbidities by population cost.

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Regression models with number of Charlson and Elixhauser comorbidities and comparison with cost curves

The number of Charlson comorbidities resu­ted in seven mutually exclusive, ranked groups for regression analysis. A statistically significant gradient of increasing payments was observed with each additional number of Charlson-comor­bidity [Table 5]. This gradient was also observed at all time points using cost curve estimates [Figure 1]. The cost curves generally yielded higher (but comparable) mean and population payments estimates compared to regression methods.
Table 5: OPTN + Charlson and OPTN + Elixhauser regression models for 3-year Medicare payments (comorbidity results).

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The number of Elixhauser comorbidities resul­ted in 12 mutually exclusive, ranked groups for inclusion in a regression model controlling for other variables. A positive graded relationship was observed between higher number of comor­bidities and increased payments. This gradient was statistically significant for all groups. In general, similar relationships were observed for the cost curves [Figure 1].
Figure 1: Cost curves.

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   Discussion Top


Renal replacement therapies are expensive and understanding their costs is important, given the limited resources for healthcare. We studied fac­tors associated with 3-year post-transplant Me­dicare payments. We found that the OPTN do­nor, recipient and transplant factors associated with increased in Medicare payments were con­sistent with previous research. Indeed, higher costs with older recipient age, African-Ame­rican recipient race, longer cold ischemia time, and increased HLA mismatches [16],[17],[18],[19],[20] were replicated. The reduction in Medicare payments in current years is consistent with recent similar observations. [33],[34]

In this study, we expanded the description of prevalent comorbidities at transplant beyond the limited set of OPTN variables. Using three diffe­rent classification systems to identify clinically diagnosed comorbid conditions from billing claims, we explored the associations between a diverse array of pre-transplant comorbid disea­ses and 3-year post-transplant Medicare pay­ments. These systems yielded coincident, also divergent findings.

The OPTN reported comorbidities, cardiovas­cular disease, cerebrovascular disease, diabetes, hypertension, chronic pulmonary disease and physical functional limitations appeared as im­portant contributors when captured in claims. However, some comorbidities not routinely re­ported to OPTN were associated with either high individual or population cost contributions. These included skin infections, malignancies, liver disease, rheumatoid arthritis and other co­nnective tissue disease, affective disorders and peptic ulcer disease. We also found that the number of Charlson and Elixhauser comorbi­dities had a positive and graded relationship with post-transplant Medicare payments.

This study has two important strengths. First, it adds to current knowledge of costs after trans­plant by examining a larger number of comorbi­dities. Second, we applied a regression tech­nique that accounts for censoring and thus uses information more appropriately than restriction to cases with complete observation.

Comorbidity-associated costs after transplant in our models may reflect the costs of condi­tions in patients with functioning grafts as well as the impact of disease on graft loss. Some of the comorbidities among the top ten associated with higher population costs have been asso­ciated with higher risk of all-cause graft-failure, death with function or death including death after graft failure. These include acute cerebro­vascular disease, chronic ulcer of the skin, heart valve disorders, liver disease, hypertension with complications and diabetes mellitus with com­plications. [8] These comorbidities may be impor­tant directly or may be markers of disease se­verity. For example, chronic ulcer of the skin may be an indicator of previous diabetic com­plications or peripheral vascular disease. How­ever, chronic ulcer of the skin was shown to be an independent factor for increased costs out­side of diabetes and peripheral vascular disease. [8] Some pre-transplant comorbidities (such as dia­betes) are risk factors for cardiovascular events that may trigger higher costs (like myocardial infarction). [35],[36]

These estimates could be used to project expec­ted 3-year Medicare costs by patient groups with specific comorbidity-loads. Together with additional data, these results could be used to determine the cost effectiveness of kidney trans­plantation according to patient comorbid pro­file. Specific policies stimulating transplantation in the most cost-effective groups would have the potential to increase the efficiency of health­care expenditure in transplantation.

Our findings may not be generalizable to other populations, such as privately insured patients. However, as approximately half of ESRD pa­tients are insured by Medicare for some period of dialysis and transplantation, our findings re­present an important segment of the US renal transplant population. Additional research using these comordibity systems in other countries would also be of high interest.

Other factors may have influenced the results. The comorbidities were derived from medical claims, which are known to have limitations. [37] Claims perform well to detect disease but their ability to rule out disease is less powerful. [38],[39],[40],[41],[42],[43] In transplantation, claims are useful to charac­terize immunosuppressant medications, [44] diabetes and hypertension. [45]

The identification of pre-transplant comorbi­dities relied on the incident claims of the last year on dialysis before transplantation, which may underestimate the prevalence of pre-trans­plant comorbidities. Such misclassification, how­ever, would produce conservative estimates in our models.

In summary, several pre-transplant comorbi­dities reported in the OPTN are associated with post-transplant costs. Our analysis is the first to consider an expanded number of comorbidities in the estimation of post-transplant cost to medi­care and individual transplant centers. Expansion beyond OPTN recorded pre-transplant comorbi­dities with CCS and Charlson and Elixhauser comorbidities yielded useful information regar­ding increased Medicare payments. Our metho­dology can be used by others to assess more accurately the financial implications of renal transplantation to Medicare and individual trans­plant centers in the US and other countries.

 
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39.Katz JN, Barrett J, Liang MH, et al. Sensitivity and positive predictive value of Medicare Part B physician claims for rheumatologic diagnoses and procedures. Arthritis Rheum 1997;40:1594-600.  Back to cited text no. 39
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44.Stirnemann PM, Takemoto SK, Schnitzler MA, et al. Agreement of immunosuppression regimens described in Medicare pharmacy claims with the Organ Procurement and Transplantation Net-work survey. J Am Soc Nephrol 2006;17:2299-306.  Back to cited text no. 44
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Correspondence Address:
Mark A Schniztler
Saint Louis University Center for Outcomes Research, 3545 Lafayette Avenue, Salus Center, 2nd Floor, Room 2825, St. Louis, MO 63104
USA
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PMID: 21196610

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    Abstract
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    Statistical Analyses
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