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  • Research article
  • Open Access
  • Open Peer Review

Geographic variation of parathyroidectomy in patients receiving hemodialysis: a retrospective cohort analysis

  • 1, 2, 3Email author,
  • 1,
  • 4,
  • 1, 3, 5,
  • 6 and
  • 1, 3
BMC SurgeryBMC series – open, inclusive and trusted201616:77

https://doi.org/10.1186/s12893-016-0193-7

  • Received: 14 June 2016
  • Accepted: 24 November 2016
  • Published:
Open Peer Review reports

Abstract

Background

Secondary hyperparathyroidism (SHPT) is associated with adverse outcomes in patients receiving maintenance dialysis. Parathyroidectomy is a treatment for SHPT; whether parathyroidectomy utilization varies geographically in the US is unknown.

Methods

A retrospective cohort analysis was undertaken to identify all patients aged 18 years or older who were receiving in-center hemodialysis between 2007 and 2009, were covered by Medicare Parts A and B, and had been receiving hemodialysis for at least 1 year. Parathyroidectomy was identified from inpatient claims using relevant International Classification of Diseases, Ninth Revision, Clinical Modification procedure codes. Patient characteristics and End-Stage Renal Disease Network (a proxy for geography) were ascertained. Adjusted odds ratios for parathyroidectomy were estimated from a logistic model.

Results

A total of 286,569 patients satisfied inclusion criteria, of whom 4435 (1.5%) underwent PTX. After adjustment for a variety of patient characteristics, there was a 2-fold difference in adjusted odds of parathyroidectomy between the most- and least-frequently performing regions. Adjusted odds ratios were more than 20% higher than average in four networks, and more than 20% lower in four networks.

Conclusions

Parathyroidectomy use varies substantially by geography in the US; the factors responsible should be further investigated.

Keywords

  • End-stage renal disease
  • Dialysis
  • Mineral metabolism
  • Parathyroidectomy
  • Secondary hyperparathyroidism

Background

Secondary hyperparathyroidism (SHPT) is associated with adverse outcomes in patients receiving maintenance dialysis [1, 2]. Anecdotally, physicians appear to have widely variable criteria regarding which patients they choose to refer for parathyroidectomy, at least in the US. Perhaps reflecting uncertainty over its role, rates of parathyroidectomy have changed substantially over time in recent decades [3]. While guidelines recommend parathyroidectomy in patients with severe SHPT [4], how it might be used most optimally is uncertain. Parathyroidectomy has been shown to be associated with improved outcomes in some studies [5, 6]; however, it has also been shown to be associated with mortality, protracted hypocalcemia, and over-suppression of parathyroid hormone (PTH) [7], and its results with regard to mineral metabolic control are often suboptimal [8]. Thus, understanding the differences between hemodialysis patients who do and do not undergo parathyroidectomy may be important. However, the effect of geographic variation, which is associated with a variety of outcomes and care differences in the dialysis population [9, 10] has not been examined in the context of parathyroidectomy. We therefore conducted a retrospective cohort study to examine whether parathyroidectomy use varies geographically in the United States.

Methods

Using the United States Renal Data System end-stage renal disease database, we identified patients aged 18 years or older who were receiving in-center hemodialysis between 2007 and 2009, were covered by Medicare Part A (inpatient, outpatient, skilled nursing facility, hospice, or home health agency) and Part B (physician/supplier) as primary payer, and had been receiving hemodialysis for at least 1 year. Parathyroidectomy was identified from inpatient claims using International Classification of Diseases, Ninth Revision, Clinical Modification procedure codes 06.81 (complete parathyroidectomy), 06.89 (partial parathyroidectomy and parathyroidectomy not otherwise specified), and 06.95 (parathyroid tissue reimplantation).

Patient characteristics, derived from the end-stage renal disease database Medical Evidence Report and Medicare claims, were assessed on the parathyroidectomy date and on January 1 for non-parathyroidectomy patients. Characteristics included age, sex, race, body mass index, cause of renal disease, dialysis duration, and common comorbid conditions, as have been used previously [11]. Our proxy for geography was US End-Stage Renal Disease Network (n = 18, Table 1), geographically based regions designed to facilitate care and monitor quality on a regional level. Adjusted odds ratios (ORs) and 95% confidence intervals (CIs) for parathyroidectomy were estimated from a logistic model adjusting for the factors described above. The adjusted ORs for the renal networks were calculated using the whole nation as the reference. All statistical analyses were conducted using SAS software, Version 9.2, SAS Institute Inc., Cary, NC, USA.
Table 1

End-stage renal disease networks and associate US states

Network number

States and territories

1

Connecticut, Maine, Massachusetts, New Hampshire, Rhode Island, Vermont

2

New York

3

New Jersey, Puerto Rico, Virgin Islands

4

Delaware, Pennsylvania

5

District of Columbia, Maryland, Virginia, West Virginia

6

Georgia, North Carolina, South Carolina

7

Florida

8

Alabama, Mississippi, Tennessee

9

Indiana, Kentucky, Ohio

10

Illinois

11

Michigan, Minnesota, North Dakota, South Dakota, Wisconsin

12

Iowa, Kansas, Missouri, Nebraska

13

Arkansas, Louisiana, Oklahoma

14

Texas

15

Arizona, Colorado, Nevada, New Mexico, Utah, Wyoming

16

Alaska, Idaho, Montana, Oregon, Washington

17

American Samoa, Guam, Mariana Islands, Hawaii, Northern California

18

Southern California

Results

We identified 286,569 patients who satisfied our inclusion criteria, of whom 4435 (1.5%) underwent parathyroidectomy (Table 2). Parathyroidectomy frequency was 2.3 fold greater, in unadjusted terms, for the least-frequently performing region (0.97% of patients, Network 18) compared with the most-frequently performing region (2.20% of patients, Network 6).
Table 2

Characteristics of patients who did and did not undergo parathyroidectomy

 

PTX

Non-PTX

n

%

n

%

Total

4435

100

282,134

100

Age at PTX, years

 19–44

1764

39.8

38,830

13.8

 45–64

2154

48.6

110,788

39.3

 65–74

410

9.2

69,550

24.7

 ≥ 75

107

2.4

62,966

22.3

Race

 White

1685

38.0

156,638

55.5

 Black

2551

57.5

108,246

38.4

 Other

199

4.5

17,250

6.1

Sex

 Male

2298

51.8

155,257

55.0

 Female

2137

48.2

126,877

45.0

ESRD primary cause

 Diabetes

1013

22.8

128,202

45.4

 Hypertension

1462

33.0

81,231

28.8

 Glomerulonephritis

934

21.1

27,250

9.7

 Other/unknown/missing

1026

23.1

45,451

16.1

BMI, kg/m2

 < 18

151

3.4

8074

2.9

 18– < 25

1217

27.4

88,838

31.5

 25– < 30

1026

23.1

78,938

28.0

 30– < 35

772

17.4

49,310

17.5

 35– < 40

517

11.7

25,987

9.2

 ≥ 40

536

12.1

23,383

8.3

 Missing

216

4.9

7604

2.7

Dialysis duration, years

 1– < 3

538

12.1

151,778

53.8

 3– < 5

970

21.9

58,341

20.7

 > 5

2927

66.0

72,015

25.5

Comorbidities

 Diabetes

1954

44.1

185,029

65.6

 ASHD

1542

34.8

131,678

46.7

 CHF

1973

44.5

144,792

51.3

 CVA/TIA

522

11.8

57,532

20.4

 PVD

1389

31.3

112,972

40.0

 Dysrhythmia

1057

23.8

77,320

27.4

 Other cardiac disease

1623

36.6

91,955

32.6

Network

 1

151

3.4

9407

3.3

 2

192

4.3

16,808

6.0

 3

156

3.5

11,887

4.2

 4

118

2.7

11,689

4.1

 5

260

5.9

17,080

6.1

 6

667

15.0

29,663

10.5

 7

253

5.7

16,076

5.7

 8

364

8.2

17,060

6.1

 9

293

6.6

21,152

7.5

 10

154

3.5

11,977

4.3

 11

252

5.7

18,814

6.7

 12

199

4.5

11,188

4.0

 13

254

5.7

12,073

4.3

 14

487

11.0

27,892

9.9

 15

182

4.1

12,016

4.3

 16

139

3.1

7254

2.6

 17

136

3.1

11,860

4.2

 18

178

4.0

18,238

6.5

ASHD atherosclerotic heart disease, BMI body mass index, CHF congestive heart failure, CVA/TIA cerebrovascular accident/transient ischemic attack, ESRD end-stage renal disease, PTX parathyroidectomy, PVD peripheral vascular disease

Network was associated with substantial variability in likelihood of parathyroidectomy (Fig. 1). Even after adjustment for all characteristics in Table 2, adjusted ORs for parathyroidectomy varied from 0.67 (95% CIs 0.58–0.78) to 1.37 (1.17–1.60) between the least- and most-frequently performing regions. Adjusted ORs were more than 20% higher than the national level in four networks and more than 20% lower in four networks.
Fig. 1
Fig. 1

Odds ratios for factors associated with parathyroidectomy. ASHD, atherosclerotic heart disease; CHF, congestive heart failure; CVA, cerebrovascular accident; ESRD, end-stage renal disease; PVD, peripheral vascular disease

In addition, younger age (adjusted OR 1.95, 95% CI 1.83–2.08, vs. age 45–64 years), female sex (1.23, 1.16–1.30), black race (1.29, 1.21–1.37 vs. white), dialysis duration > 5 years (3.70, 3.27–4.05 vs. 1- < 3 years), and atherosclerotic heart disease (1.15, 1.07–1.23) were associated with parathyroidectomy (P < 0.001). Diabetes (0.82, 0.76–0.89) and history of stroke (0.82, 0.74–0.89) were inversely associated with parathyroidectomy.

Results for the multivariable model for factors associated with parathyroidectomy are shown in Table 3.
Table 3

Multivariable model for factors associated with parathyroidectomy

Factors

HR (95% CI)

P

Age at PTX, years

 19–44

1.95 (1.83–2.08)

<0.001

 45–64

1 (Referent)

 

 65–74

0.37 (0.34–0.42)

< 0.001

 ≥ 75

0.11 (0.09–0.13)

< 0.001

Race

 White

1 (Referent)

 

 Black

1.29 (1.21–1.37)

< 0.001

 Other

0.89 (0.77–1.03)

0.11

Sex

 Male

0.82 (0.77–0.87)

< 0.001

 Female

1 (Referent)

 

ESRD primary cause

 Diabetes

0.58 (0.53–0.64)

< 0.001

 Hypertension

1 (Referent)

 

 Glomerulonephritis

1.11 (1.02–1.21)

0.011

 Other/unknown/missing

1.04 (0.96–1.13)

0.30

BMI, kg/m2

 < 18

0.96 (0.82–1.13)

0.62

 18– < 25

1 (Referent)

 

 25– < 30

1.10 (1.02–1.20)

0.016

 30– < 35

1.32 (1.21–1.44)

< 0.001

 35– < 40

1.48 (1.34–1.64)

< 0.001

 ≥ 40

1.53 (1.38–1.69)

< 0.001

 Missing

1.08 (0.94–1.24)

0.29

Dialysis duration, years

 1– < 3

1 (Referent)

 

 3– < 5

2.23 (2.02–2.47)

< 0.001

 ≥ 5

3.70 (3.37–4.05)

< 0.001

Comorbid conditions

 Diabetes

0.82 (0.76–0.89)

< 0.001

 ASHD

1.15 (1.07–1.23)

< 0.001

 CHF

1.08 (1.01–1.15)

0.019

 CVA/TIA

0.82 (0.74–0.89)

< 0.001

 PVD

0.97 (0.91–1.04)

0.42

 Dysrhythmia

1.08 (1.00–1.16)

0.058

 Other cardiac disease

1.37 (1.29–1.47)

< 0.001

ESRD Network

 16

1.37 (1.17–1.60)

< 0.001

 1

1.35 (1.17–1.57)

< 0.001

 12

1.24 (1.09–1.40)

0.001

 13

1.24 (1.08–1.37)

0.001

 6

1.18 (1.09–1.28)

< 0.001

 8

1.17 (1.06–1.29)

0.002

 15

1.14 (0.99–1.31)

0.067

 14

1.13 (1.03–1.23)

0.008

 11

1.03 (0.92–1.16)

0.60

 7

1.01 (0.90–1.13)

0.91

 3

0.97 (0.84–1.12)

0.69

 9

0.92 (0.83–1.02)

0.12

 5

0.88 (0.79–0.99)

0.032

 10

0.88 (0.76–1.01)

0.070

 17

0.80 (0.68–0.93)

0.005

 2

0.78 (0.68–0.88)

< 0.001

 4

0.69 (0.59–0.82)

< 0.001

 18

0.67 (0.58–0.78)

< 0.001

Year

 2007

1 (Referent)

 

 2008

0.89 (0.83–0.95)

0.001

 2009

0.83 (0.78–0.89)

< 0.001

ASHD atherosclerotic heart disease, BMI body mass index, CHF congestive heart failure, CI confidence interval, CVA/TIA cerebrovascular accident/transient ischemic attack, ESRD end-stage renal disease, HR hazard ratio, PTX parathyroidectomy, PVD peripheral vascular disease

Discussion

SHPT treatment presents a complex clinical challenge. Practice guidelines provide direction [4] but suffer from lack of randomized clinical trial data, resulting in uncertainty about the benefits and risks of parathyroidectomy. Understanding use of parathyroidectomy is important, given widely varying recent data demonstrating both clinical benefits [5, 6], as well as high rates of adverse events and suboptimal mineral metabolic outcomes [7, 8]. Our large retrospective analysis demonstrated substantial geographic variation in parathyroidectomy use. This difference was not driven solely by outliers at the extremes; AORs were 20% higher or lower than unity in eight Networks. This could reflect regional differences in many potential factors, including provider-related ones such as particular treatment approaches instilled during training, access to qualified parathyroid surgeons, or local “cultures” of treatment, all of which might play substantial roles in how care is differentially rendered [12].

Certain demographic factors, specifically younger age and black race, were also associated with likelihood of parathyroidectomy; this was not unexpected given that both of these factors have been previously reported to be associated with more severe SHPT [2, 13]. Dialysis duration was also associated with parathyroidectomy, possibly because the changes that characterize severe parathyroid gland dysregulation may take many years to develop [14]; alternatively, providers may be resorting to parathyroidectomy only after prolonged attempts at other interventions prove fruitless. The inverse associations between older age and history of stroke and parathyroidectomy may reflect poor surgical candidacy in the provider’s estimation.

Our study was limited by lack of patient-level data about degree of PTH control, SHPT therapies employed, or other SHPT markers such as serum calcium and phosphorus, which likely predict the parathyroidectomy decision. For example, use of cinacalcet, which has been shown to reduce rates of parathyroidectomy [15], might vary widely by region, although we have no a priori reason to posit this and it seems unlikely to account for a more than 2-fold variation in parathyroidectomy rates. Additionally, we lack information about geographic variation in renal transplant; fewer individuals in areas in which early transplant occurs more commonly might be at risk of developing severe SHPT and subsequently undergoing parathyroidectomy. Again, given the magnitude of variation between the most- and least-frequently parathyroidectomy performing regions, case mix alone is unlikely to fully account for it.

Conclusion

Even after adjustment of a variety of case-mix variables, use of parathyroidectomy varies substantially by geography in the US; the factors responsible should be further investigated. Given recent information about the potential risks associated with parathyroidectomy [7, 8], the factors responsible for shaping the decision to undertake it should also be the subject of future investigation.

Abbreviations

CI: 

Confidence interval

OR: 

Odds ratio

PTH: 

Parathyroid hormone

SHPT: 

Secondary hyperparathyroidism

Declarations

Acknowledgments

The data reported here have been supplied by the United States Renal Data System. The interpretation and reporting of these data are the responsibility of the authors and in no way should be seen as an official policy or interpretation of the US Government. The authors thank Chronic Disease Research Group colleagues Delaney Berrini, BS, for manuscript preparation and figure design and Nan Booth, MSW, MPH, ELS, for manuscript editing.

Funding

This study was supported by a research contract from Amgen Inc., Thousand Oaks, California. The contract provides for the Minneapolis Medical Research Foundation authors to have final determination of manuscript content.

Availability of data and materials

Data were obtained from the United States Renal Data System (USRDS), which is publically available and free of charge from the USRDS Coordinating Center.

Authors’ contributions

Substantial contributions to conception and design, or acquisition of data, or analysis and interpretation of data: JBW, JL, PJD, AI, GAB, AJC; drafting the manuscript or revising it critically for important intellectual content: JBW, JL, PJD, AI, GAB, AJC; final approval of the version to be published: JBW, JL, PJD, AI, GAB, AJC; sufficient participation in the work to take public responsibility for appropriate portions of the content: JBW, JL, PJD, AI, GAB, AJC; agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved: JBW, JL, PJD, AI, GAB, AJC. All authors read and approved the final manuscript.

Competing interests

James B. Wetmore, Jiannong Liu, Areef Ishani, and Allan J. Collins are employed by the Chronic Disease Research Group, which receives research support from Amgen. Dr. Liu has provided consultation to Daiichi Sankyo. Dr. Dluzniewski is employed by Amgen and owns Amgen stock. Dr. Collins has provided consultation to Amgen, Relypsa, DaVita Clinical Research, NxStage, Keryx, and ZS Pharma. Geoffrey A. Block is employed by Denver Nephrologists, and has provided consultation to, and received research support from, Amgen.

Consent for publication

Not applicable.

Ethics approval and consent to participate

We applied to and received approval from the Human Subjects Research Committee of the Hennepin County Medical Center/Hennepin Healthcare System, Inc., Minneapolis, Minnesota.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Chronic Disease Research Group, Minneapolis Medical Research Foundation, 914 South 8th Street, Suite S4.100, Minneapolis, MN 55404, USA
(2)
Division of Nephrology, Hennepin County Medical Center, Minneapolis, MN, USA
(3)
Department of Medicine, University of Minnesota, Minneapolis, MN, USA
(4)
Center for Observational Research, Amgen Inc, Thousand Oaks, CA, USA
(5)
Section of Renal Diseases and Hypertension, Minneapolis Veterans Administration Health Care System, Minneapolis, MN, USA
(6)
Denver Nephrology Clinical Research Division, Denver, CO, USA

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