Clinical Therapeutics/Volume 31, Number 6, 2009

Identification of Patients Receiving Peritoneal DialysisUsing Health Insurance Claims Data

Ariel Berger, MPH1;John Edelsberg, MD, MPH1; Gary Inglese, RN, MBA2;Samir Bhattacharyya, PhD2; and Gerry Oster, PhD1

lpolicy Analysis) lnc., Brookline) Massachusetts; and2Baxter Healthcare Corporation) McGraw Park) l!Iinois

ABSTRACTObjective: The aim of this analysis was to assess

alternative methods of identification of patients treat­ed with peritoneal dialysis (PD) in health care claimsdatabases for possible use in future analyses of costsof this treatment modality.

Methods: Using a US health insurance claims data­base spanning January 1,2004, to December 31,2006,we identified all patients with renal failure who satis­fied a case-finding algorithm for PD anticipated to behighly specific, but not necessarily sensitive-namely,~2 claims for PD-related physician services (algorithm 1).All claims from these patients were assessed to iden­tify additional PD-related codes, from which 6 addi­tional algorithms were developed, each of which focusedon specific categories of billing codes (eg, diagnostic,procedural/service, equipment). Patient selection wasthen reimplemented using these alternative algorithms.Concordance between the various algorithms and theextent to which resulting samples were similar interms of patient characteristics, health care resourceutilization, and costs were assessed.

Results: We identified a total of 132,274 patientsin the database with ~1 claim for renal failure andvalid enrollment data. Among these patients, a total of2329 satisfied case-selection criteria for algorithm 1,and 4031 patients met criteria for at least 1 of the7 algorithms for PD. The most sensitive algorithmidentified 2859 patients who might have received PD;the least sensitive, 211. Concordance between algo­rithms was relatively poor. Patients identified usingeach algorithm were similar, however, with respect tomean age (45-50 years), sex (54%-56% male), andthe prevalence of selected comorbidities. Annualizedmedian health care costs were similar across the vari­ous algorithms (range, US $80,967-$118,668).

Conclusions: Based on the results from this analy­sis, it seems that health care providers bill insurers forPD-related care using a variety of codes. Investigators

June 2009

using health insurance claims data for analyses ofpatients treated with PD need to take this into ac­count. (Clin Ther. 2009;31:1321-1334) © 2009 Ex­cerpta Medica Inc.

Key words: peritoneal dialysis, health insuranceclaims database, health care resource utilization, costanalysis.

INTRODUCTIONThe cost of dialysis in the United States is largelyborne by the Medicare End-Stage Renal Disease(ESRD) Program, which accepts all patients previ­ously enrolled in Medicare on initiation of dialysis(principally, those aged ~65 years), and those ineligi­ble for Medicare after the first 3 months of dialysis(although for these latter patients, there is an addi­tional 30-month period of coordination of benefits.during which Medicare is a secondary payer and theprivate insurer is the primary payer ).1 Persons aged~65 years who are still employed or have a spousewho is still employed also may have their costs borneby private health insurers. The proportion of patientswith ESRD among whom private health insurance isthe primary payer has been estimated to be ~25%.2

Hemodialysis (HD) is the most common type ofdialysis used for the treatment of ESRD. In HD, bloodis removed from the patient, screened through a filterto remove waste, and then returned to the body. HDis typically performed 3 times weekly at a dialysis cen­ter. Alternatively, patients may receive peritoneal dial­ysis (PD), in which the abdomen is filled with dialysissolution a number of times (typically about 4 times)each day and drained several hours later (a process

Accepted for publtcation April 24, 2009dotl 0.1016/J.c1lnthera.2009.06.0130149-2918/$ - see front matter

© 2009 Excerpta Medica Inc. All rights reserved.

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Clinical Therapeutics

known as exchange). As opposed to HD, PD does notrequire visits to a specialized center, but rather can beincorporated into aspects of patients' daily lives (eg, athome, at work). In 2005, 6.6% of patients began di­alysis with PD; the remaining patients' initial dialysiswas HD.3 The reasons for this imbalance are unknown.Compared with patients with ESRD whose initial di­alysis was HD, those who began with PD have beenreported to be healthier on averagev- and have lowertotal health care costs after adjustment for differencesin baseline characteristics such as age, sex, race, co­morbidities, and etiology of ESRD.6-13

Much of what is known about patients treated withdialysis has come from analyses of Medicare data;comparatively little is known about patients withESRD who are privately insured. Because substantialdifferences in health care costs have been reportedbetween Medicare recipients who receive PD versusHD (1 recent study reported annual per-patient Medi­care expenditures to be $56,807 in PD patients vs$68,253 in HD patients; P < 0.001),12 an obviousquestion is whether similar differences exist in pri­vately insured patients. While data on health care re­source utilization and costs among these patients arereadily available-in principle-from private insur­ance databases, the best way to identify these patientsin such databases is unclear due to variations in thecodes used to indicate dialysis for third-party billingand reimbursement. For example, there are 2 specificCurrent Procedural Terminology. 4th edition (CPT-4)!4procedural/service codes used for billing for PD ser­vices: 90945 (PD, single evaluation) and 90947 (PD,repeat evaluation). However, other codes also may beused to bill for PD-related care, including so-calledrevenue codes (eg, 0802 [inpatient PD, non--continuousambulatory PD (CAPD)], 0803 [inpatient CAPD]),International Classification of Diseases. Ninth Revi­sion. Clinical Modification (ICD-9-CM)!5 diagnosticcodes (eg, 996.68 [exit-site infection or inflammationdue to PD catheterizationj), and CPT-4 and HealthCare Financing Adrninstration Common ProcedureCoding System (HCPCS) procedural/service codes (eg,E1594 [cycler dialysis machine for PD]).!6

How should patients treated with PD be identifiedusing health insurance claims data? Identification ofall patients with mention of any code suggestive ofreceipt of PD might be sensitive (ie, it would identifyall patients who received PD) but also relatively non­specific (ie, many patients who did not receive PD also

1322

might be identified). Alternatively, if a more stringentcriterion were used, specificity would improve, but thedecrease in sensitivity might lead to the exclusion of alarge number of persons who received PD. This issueis not trivial, as the quality of PD information gleanedfrom analyses of health care claims data may dependsubstantially on the accuracy of the case-selection al­gorithm used.

The purpose of this analysis was to assess differentalgorithms used for the identification of patients treatedwith PD in health care claims databases for possibleuse in future analyses of the costs of this treatmentmodality.

MATERIALS AND METHODSData were obtained from the PharMetrics Patient­Centric Database, Watertown, Massachusetts, whichcomprised facility, professional-service, and retail (ie,outpatient) pharmacy claims from >85 US health plans.The plans provided health care coverage to ~11 mil­lion persons each year throughout the United States(Midwest, 35%; South, 31 %; Northeast, 21 %; andWest, 13%). All patient identifiers in the databasewere fully encrypted, and the database was fully com­pliant with the Health Insurance Portability and Ac­countability Act of 1996.

Information available from each facility andprofessional-service claim included the date and placeof service, ICD-9-CM diagnostic codes, ICD-9-CM(selected plans only), CPT-4, and HCPCS procedural!service codes, provider specialty, and charged and paidamounts. Data available from each retail pharmacyclaim included the drug dispensed (in National DrugCode format!"], dispensation date, quantity dispensed,and number of days' supply dispensed (selected plansonly). All claims included a charged amount; the data­base also provided paid amounts (ie, total reimbursed,including patient deductible, copayrnent, and/orcoinsurance).

Selected demographic and eligibility information,including age, sex, geographic region, coverage type,and the dates of insurance coverage, was also avail­able. All patient-level data was arrayed in chronologicorder to provide a detailed, longitudinal profile of allmedical and pharmacy services used by each insuredperson. Because this assessment was retrospective,used completely anonymized data, and did not involvepatient contact, institutional review board approvalwas neither required nor sought.

Volume 31 Number 6

Using this database, we identified all patients with~1 medical encounter for renal failure (any [ie, pri­mary or secondary] listing of ICD-9-CM diagnosticcodes 403.x1, 404.X2, 404.x3, 585, 585.X, or 586)between January 1, 2004, and December 31, 2006(study period).

Among these patients, we identified all who satis­fied a case-selection algorithm for receipt of PD thatwas anticipated to be highly specific but not necessarilysensitive-namely, ~2 physician encounters with CPT-4codes 90945 (dialysis other than HD leg, PD hemofil­tration or other continuous renal-replacement thera­pies] with single physician evaluation) and/or 90947(dialysis other than HD leg, PD, hernofiltration, orother continuous renal-replacement therapies] requir­ing repeated physician evaluations, with or without sub­stantial revision of dialysis prescription) (algorithm 1).All health care claims from these selected patients wereassessed to identify additional PD-related services thatthey received and the codes (eg, diagnostic, procedural/service, equipment) associated with such services (ie,that appeared on their paid claims). Drawing on theadditional PD-related codes that appeared with highfrequency in these patients, 6 alternative algorithmswere developed, each of which focused on varioustypes of PD-specific codes (diagnostic [algorithm 3],procedural/services [algorithms 1, 2, 4, and 5], andequipment [algorithms 6 and 7]). A detailed descrip­tion of each algorithm is shown in Table I. Patientselection was then reimplemented using each alterna­tive PD algorithm. Concordance between the resultingpatient samples (ie, the extent to which the same pa­tients were identified with the different algorithms)was assessed, together with the prevalence of the fol­lowing (medically attended) comorbidities for patientsin each of the resulting samples: diabetes (ICD-9-CMcode 250.xX; receipt of a-glucosidase inhibitors,insulin, metformin, nonsulfonylurea insulin secreta­gogues, sulfonylurea, or thiazolidinedione], coronaryartery disease (410.XX-414.xX), congestive heartfailure (428.xX), anemia (280.XX-285.XX and/orreceipt of darbepoetin alfa or epoetin alfa}, renal osteo­dystrophy (588.0), sleep disorders (307.4X, 780.5X,and/or \'69.4), amyloidosis (277.3), and hyperten­sion (401.XX-405.XX, 459.10, 459.30, 459.31,459.32,459.33, and/or 459.39 and/or receipt of anti­hypertensives). Patients were deemed to have theseconditions if they had at least 2 outpatient claims (ie,for visits and/or prescriptions, as appropriate) on dif-

June 2009

A. Berger et al.

ferent days, or at least 1 inpatient claim, that con­tained the relevant diagnostic codes.

The use and cost of the following health care ser­vices were also assessed: erythropoietin-stimulatingagents (ie, darbepoetin alfa or epoetin alfa], all otherprescription medications, physician's office visits, otheroutpatient visits, emergency department visits, hospi­talizations, and inpatient days. Health care resourceutilization was examined in terms of the proportionsof patients receiving each service, as well as the num­ber of times each service was rendered. Total reimbursedamount (ie, the amount paid by the insurer plus pa­tient liability leg, copayment, deductible]) was used asa proxy for cost. All estimates of health care resourceutilization and cost were tallied during the 1-year pe­riod beginning on the date of the first-noted dialysis­related claim (irrespective of whether it was includedin the algorithm that was used to identify the patient)and ending 365 days thereafter.

All analyses were conducted using SAS version 9.1(SAS Institute Inc., Cary, North Carolina).

RESULTSWe identified a total of 132,274 patients in the data­base with ~1 claim for renal failure and valid enroll­ment data. A total of 4031 patients met the criteria forat least 1 of the 7 case-selection algorithms for PD. Thenumbers of patients identified using algorithms 1 to 7were 2329, 2859 (most sensitive), 1047, 1170, 1519,211 (least sensitive), and 251, respectively. Most pa­tients (90%) were identified using procedural/servicecodes (algorithms 1,2,4, and 5); very few (~6%) wereidentified using PD-specific equipment codes only(algorithms 6 and 7). Most patients with 1 claim witha given type of code had ~2 such claims (ie, 81 % ofpatients with ~1 claim with CPT-4 codes 90945 or90947 [algorithm 2] had ~2 such claims [algorithm 1],77% of patients who satisfied criteria for algorithm 5also satisfied criteria for algorithm 4, and 84% of pa­tients who satisfied criteria for algorithm 7 also satis­fied criteria for algorithm 6).

Concordance between the various algorithms wasrelatively poor. The proportions of patients identifiedusing algorithm 1 who also would have been identi­fied with algorithms 3 to 7 were 23.8%, 20.5%, 26.8%,5.2%, and 5.5%, respectively (Table II). More thanhalf of patients identified using PD-specific diagnosticcodes (algorithm 3) or equipment codes (algorithms 6and 7) also met the criteria for algorithm 1.

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Table I. Alternative for peritoneal dialysis (PD) case-se lection a lgorithms.

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Algorithm

Init ia l, restri ctive definit ion

Algo rithm 1

Algo rith m 2

Diagn osis ba sed

Algo rith m 3

Proced ure/service based

Algorithm 4

Algorithm 5

Defi nition

At lea st 2 cla ims fo r 909 45 (PD, sing le eva luat io n) a nd/o r 909 47 (PD, repeat eva lua t io n)

At lea st 1 cla im fo r 90945 or 909 47

At lea st 2 o ut patie nt cla ims on d ifferent days o r any 1 inpatient c la im fo r:996 .68 (infectio n d ue to PD cathete rization : exit-site infec t io n or inf lam ma tio n),996 .56 (infection d ue to PD cathete rization: excludes mech a nical co mplica t io n of a rterioveno us dia lysis

ca t he te rization) ,V56 .8 (othe r d ialysis-PD),V56. 2 (fitting a nd adj ustment of PD cat hete rizatio n), a nd/orV56 .32 (enco unte r for adeq uacy testi ng for PD)

At lea st 2 cla ims o n differe nt days fo r:080 2 (inpatie nt PD), 08 03 (inpat ient co nti nuo us a mb ulatory PD),080 4 (inpatient co nti nuo us cycling PD),0830 (PD, o utpatie nt or ho me, genera l c lass ificat ion),0831 (PD, ou t pa tie nt or ho me, per iton ea l/com posite or other ra te),0839 (PD, o ut patient o r hom e, othe r PD),08 40 (CAPD, o utpatient or home, ge nera l classification) ,0841 (CAPD, o ut patie nt o r home, CAPD/co mposite o r other rate),0845 (CAPD, o utpatie nt o r ho me, sup po rt services),08 49 (CAPD, o utpatient o r hom e, othe r CAPD d ia lysis),0851 (CCPD, o ut pat ien t o r hom e, CCPD/ co mposite or o th er rate),085 4 (CCPD, o utpatient o r ho me , main tenan ce 100 %),0855 (CCPD, o utpatient or ho me, support servic es), and/or0859 (CCPD, o utpat ient o r ho me, o t he r CCPD dialysis)

At lea st 1 clai m with a proced ura l/service co de listed in a lgorit hm 4

(co nti nued)

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Table I (continued).

Algorithm

Equipment based

Algori t hm 6

Algori t hm 7

Oefini tion

At least 2 cla ims on different days for:08 32 (PO, out patient o r ho me, ho me sup plies),A4 653 (PO cat heter a ncho ring device, be lt, each) ,A4 671 (d isposa ble cycle r set used with cycler d ialysis machin e, each),A4 672 (drainage exte nsion line, st eri le, for d ialysis, eac h),A4 67 3 (exte nsion line with easy lock con necto rs, used wit h dialysis),A471 9 (Y-set tu bing for PO),A472 0 (d ialysate so lut ion, any concent ration of dextrose, f lu id vo lum e >249-999 mL, for PO),A4721 (dialysa te so lut io n, a ny con centratio n of dextrose, f luid volu me >999-1999 mL, fo r PO),A472 2 (d ialysate so lut ion, any concent ration of dextrose, fluid volume >1999- 2999 mL, for PO),A472 3 (d ia lysate so lut ion, a ny co ncentration of dextrose, fluid volume >2999-3999 mL, fo r PO),A4725 (d ialysate so lut ion, a ny concent rati on of dextrose, fluid volume >4449-5999 mL, fo r PO),A4726 (d ialysate solu tio n, any co ncentrat io n of dextrose, f luid vo lume >5999 mL),A4860 (d isposable cathete r tips for PO, per 10),E1594 (cycler dialysis machi ne fo r PO), a nd/orE1634 (PO clam ps, each)

At least 1 claim with a procedu ra l/service code listed in a lgo rit hm 6

CAPO - co nt inuo us a mbulatory PO; CCP D - co nt inuo us cycl ing PD.

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Tab le II. Co nco rdan ce bet ween periton eal dialysis (PD ) case-selectio n algo rith ms. Values are no. (%) of pati ent s.

Algorithm

1* 2 3 4 5 6 7 AnyAlgorithm (n = 2329) (n = 2859) (n = 1047) (n = 1170) (n = 1519) (n = 211) (n = 251) (N = 40 31)

1 2329 (100) 2329 (81.5) 554 (52 .9) 477 (40 .8) 624 (41.1 ) 120 (56.9) 128 (51.0) 2329 (57.8)

2 2329 (100) 28 59 (100) 615 (58 .7) 565 (48 .3) 742 (48 .8) 132 (62.6) 142 (56.6) 2859 (70 .9)

3 554 (23.8) 615 (21.5) 1047 (100) 317 (27.1 ) 388 (25 .5) 59 (28 .0) 66 (26.3) 1047 (26.0 )

4 477 (20.5) 565 (19.8) 317 (30 .3) 1170 (100 ) 1170 (77.0) 72 (34.1) 79 (31.5) 11 70 (29.0)

5 624 (26.8) 742 (26.0) 388 (37.1) 1170 (100 ) 1519 (100) 85 (40 .3) 94 (37.5) 1519 (37.7)

6 120 (5.2) 132 (4 .6) 59 (5.6) 72 (6.2) 85 (5.6) 211 (100 ) 211 (84 .1 ) 211 (5.2)

7 128 (5.5) 142 (5.0) 66 (6.3) 79 (6.8) 94 (6.2) 211 (100 ) 251 (100 ) 251 (6.2)

Any 2329 (100) 28 59 (100) 1047 (100) 1170 (100 ) 1519 (100 ) 211 (100) 251 (100) 40 31 (100)

*Original restrictive definition of PD.

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When analyses were limited to algorithms 2, 3, 5,and 7-the 4 most inclusive (ie, sensitive) algorithmsbased on diagnostic, procedural/service, and equip­ment codes-68% of the 4031 patients identified us­ing any of the 7 algorithms were identified with a singlealgorithm only, as follows: 43 % with algorithm 2;16% with algorithm 4; 8% with algorithm 3; and2% with algorithm 6 (Figure 1). Among the 32% ofpatients whose characteristics satisfied the criteria ofmultiple algorithms, 23 % had characteristics that sat­isfied criteria from 2 algorithms; 8%, 3 algorithms;and 1%, 4 algorithms.

Patients' age and sex were similar across the 7 al­gorithms (mean age, 45-50 years; male sex 54%-56%),as were the prevalences of selected comorbidities(Table III) and median levels of health care resourceutilization (Table IV). Values for most measures ofutilization in patients identified using algorithms 2 to7 were within 20% of those observed in patients iden­tified using algorithm 1.

Mean and median 1-year total health care costs arereported in Figure 2. Median (interquartile range) an-

A

A. Berger et al.

nualized total health care costs were: algorithm 1,$86,666 ($27,688-$188,788); algorithm 2, $80,967($21,659-$181,592);algorithm3,$118,668 ($56,965­$215,691); algorithm 4, $108,949 ($58,694-$194,328);algorithm 5, $112,240 ($56,341-$209,971); algorithm 6,$114,213 ($60,123-$173,516); and algorithm 7,$105,396 ($51,671-$171,916).

DISCUSSIONIdeally, the sensitivity and specificity of any proposedcase-selection algorithm used for the identification ofpatients treated with PD in health care claims datashould be assessed against a "gold standard" (eg, medi­cal record review). Because medical records were un­available, we were unable to estimate the accuracy ofvarious algorithms for identifying these patients andfocused instead on concordance across the differentsamples so constituted.

Algorithms 3, 4, and 6 were developed based onadditional codes found in the claims histories of pa­tients identified with algorithm 1; algorithms 2,5, and7 were developed to gauge the impact of increasing

• >1 Algorithmo 1 Algorithmo Algorithm 2 only13 Algorithm 3 only• Algorith m 5 onlyf2l Algorithm 7 only

B42 .7%

31.6% 68.4%7.7%

16 .0%

Figure 1. Pat ient s t reated with peritoneal dia lysis identified using 1 versus multip le case-selecti o n a lgorithms.(A) Overall pattern of 1 or >1 a lgorithm used and (B) breakdown of individ ual a lgorithms used in1-a lgor ithm selections.

June 2009 1327

.....

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N Tab le III. Demographic and clinical characteristics of patients defined as receiving peritoneal dialysis (PO), based on various selection algo- :::l00 ;:;.

rit hms. Values are no. (%) of pati ents unless otherwise noted. ~

-lAlgor it hm I

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Character istic 1* (n = 2329) 2 (n = 2859) 3 (n = 1047 ) 4 (n = 1170) 5 (n = 1519) 6(n =211) 7(n =251) Any (N = 4031)

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Age, y III

<18 176 (7.6) 20 (7.0) 89 (8.5) 71 (6.1) 101 (6.6) 24(11 .4) 27 (10.8) 243 (6.0)18-<30 115 (4.9) 131 (4.6) 61 (5.8) 67 (5.7) 89 (5.9) 15 (7.1) 17 (6.8) 204 (5.1)30- <40 211 (9.1) 257 (9.0) 112 (10.7) 133 (11.4) 163 (10.7) 20 (9.5) 25 (10.0) 395 (9.8)40 - <50 436 (18.7) 522 (18.3) 231 (22 .1) 234 (20.0) 291 (19.2) 37 (17.5) 48 (19.1) 766 (19.0)50- <60 826 (35.5) 1011 (35.4) 34 7 (33 .1) 430 (36 .8) 553 (36.4) 84 (39.8) 94 (37.5) 1417 (35 .2)60- <65 373 (16.0) 460 (16.1) 129 (12.3) 167 (14.3) 228 (15.0) 28 (13.3) 35 (13.9) 645 (16.0)65 - <75 76 (3.3) 103 (3.6) 36 (3.4) 26 (2.2) 41 (2.7) 0 1 (0.4) 136 (3.4)75- <85 99 (4 .3) 151 (5.3) 30 (2.9) 37 (3.2) 47(3.1) 3 (1.4) 3 (1.2) 189 (4.7 )<':85 17 (0.7) 23 (0.8) 12 (1.1) 5 (0.4) 6 (0.4) 0 1 (0.4) 36 (0.9)Mean (SO) 49.1 (17.1) 49.9 (17.0) 47.3 (17.2) 48.4 (15.6) 48.4 (16.1) 44.9 (16.7) 45.3 (16.8) 49.8 (16.5)Median (IQ R) 53 (42- 59) 53 (43-60) 51 (39- 58) 52 (41- 58) 52 (41- 59) 51 (36-57) SO (37-57) 53 (42-59)Range 0-98 0-98 0-98 0-98 0-98 0-78 0-98 0-98

SexFemale 1302 (55.9) 1584 (55.4) 562 (53.7) 633 (54 .1) 83 5 (55.0) 119 (56.4) 138 (55.0) 2218 (55.0)Male 1027 (44.1) 1275 (44 .6) 485 (46 .3) 537 (45 .9) 684 (45 .0) 92 (43.6) 113 (45.0) 1813 (45 .0)

Com or bid it yAnem ia 2046 (87.9) 2498 (87.4) 1010 (96 .5) 1093 (93.4) 1383 (91.1) 196 (92.9) 228 (90.8) 3563 (88 .4)Hypertension 2013 (86.4) 2480 (86 .7) 100 2 (95.7) 1097 (93.8) 1398 (92 .0) 190 (90.1) 219 (87.3) 3552 (88 .1)Diab etes 1252 (53 .8) 1559 (54 .5) 615 (58.7) 688 (58 .8) 866 (57.0) 119 (56.4) 138 (55.0) 2210 (54.8)Con gestive hear t

failure 1151 (49.4 ) 1422 (49.7) 473 (45.2) 421 (36 .0) 578 (38 .1) 63 (29.9) 76 (30.3) 83 5 (20.7)Coron ar y arte ry

disease 1111 (47.7) 1386 (48.5) 488 (46 .6) 493 (42.1) 645 (42.5) 88 (41.7) 101 (40.2) 18 65 (46 .3)~ Sleep disorders 326 (14 .0) 407 (14 .2) 133 (12.7) 146 (12.5) 196 (12.9) 29 (13.7) 35 (13.9) 554 (13.7)i: Renal3I'D osteodyst ro phy 190 (8.2) 244 (8.5) 134 (12.8) 147 (12.6) 172 (11.3) 19 (9.0) 22 (8.8) 368 (9.1)(JJ

Amylo idosis 17 (0.7) 22 (0.8) 3 (0.3) 13 (1.1) 16 (1.1) 1 (0.5) 1 (0.4) 32 (0.8).....Zc3

IIQR = inte rq uart ile ra ng e.

c:r "T he o rig ina l restri c tive d efin iti on of PD.I'D....0\

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ITab le IV. Pat t ern s of ut ilizat ion and costs of health care services during the 1-year period subsequent to f irst-noted dialysis -related claim amo ng:::l

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N patients defi ned as having received peritoneal dia lysis, based on various case-selection algorith ms .0010 I Algorit hm

1 2 3 4 5 6 7 AnyUse a nd Services (n = 2329) (n = 28 59) (n = 1047 ) (n = 1170) (n = 1519) (n =211) (n = 251) (N = 4031)

Duration ofenro llment, d"

Mean (SO) 234.94 (132.5) 227.8 (134 .6) 271.5 (118.2) 295 .6 (99.9) 273 .1 (117.1) 307.2 (99.0) 297.4 (107.0) 246.4 (130 .1)Median (IQR) 274 (91-365) 243 (91-365) 365 (152-365) 365 (243 -365 ) 365 (183-3 65) 365 (243 -3 65) 365 (213-365 ) 304 (122-3 65)

Prescription sEryt hrop o ietin -sti mulat ingagents

No. (%) 877 (37.7) 1008 (35.3) 469 (44 .8) 295 (25.2) 403 (26.5) 115 (54 .5) 125 (49.8) 1286 (31.9)Mean (SO) 12.2 (21.9) 12.7 (23 .3) 15.7 (26 .9) 11 .7 (29.3) 11.5 (26.4) 8.1 (9.5) 9.3 (13.1) 13.6 (26 .7)Med ian (IQR) 7 (3-11) 6 (3- 11) 7 (3-13) 4 (2-11) 4 (2-11) 5 (3-1 0) 6 (3- 10) 6 (3-12)

All ot herNo. (%) 1440 (61.8) 1720 (60.2) 810 (77.4) 881 (75.3) 1078 (71.0) 172 (81.5) 199 (79.3) 2551 (63 .3)Mean (SO) 48 .5 (39.8) 48.4 (39.9) 52.7 (40 .7) 49 .2 (38 .9) 48.4 (39.3) 51.2 (48.6) 50 .3 (47.3) 47.2 (39.8)Median (IQR) 39 (18-71) 39 (18-71) 44 (20 -78) 42 (19-70) 40 (18-69) 38 (22-67) 37 (20 -68) 37 (16-69)

Any of t he a boveNo. (%) 1453 (62.4) 1736 (60.7) 816 (77.9) 884 (75.6) 1083 (71.3) 173 (82.0) 200 (79.7) 2571 (63.8)Mean (SO) 48 .9 (40 .2) 48 .8 (40.3) 53.4 (41.2) 49.7 (39.2) 48 .9 (39.6) 52.4 (49.2 ) 51.4 (47.8) 47.7 (40.2)Median (IQR) 40 (17-72) 39 (17-71) 45 (21-79) 42 (19-70) 41 (18-69) 39 (22-68) 38 (21-69) 38 (16-69)

Out pa tient visit sPhysician 's off icevisits

No. (%) with ~1 1900 (81.6) 2288 (80 .0) 992 (94 .8) 11 17 (95.5) 1364 (89.8) 201 (95.3) 238 (94 .8) 3327 (82 .5)Mean (SO) 49 .7 (74.4 ) 45 .9 (70.3) 49 .2 (64.7) 31.4 (46.4) 32.3 (49.4) 32 .3 (32.4) 34.0 (33 .6) 40.4 (62.5)Median (IQR) 24 (8-52) 22 (6-48) 27 (14-50) 20 (11-33) 19 (1 0-34) 24 (12-41) 25 (12- 45) 21 (7-42)

Ot her o ut patientvisits

No. (%) wit h ~1 1716 (73.7) 2082 (72 .8) 938 (89.6) 1164 (99.5) 14 25 (93.8) 195 (92.4) 229 (91.2) 3153 (78 .2) ?>Mean (SO) 27.9 (55 .1) 26.7 (52.6) 32.4 (63 .1) 52 .8 (74.4) 46 .5 (69.9) 17.3 (18.0) 16.9 (18.2) 30 .4 (57.3) C:l

I'D

Median (IQR) 11 (0-27) 11 (0-27) 17 (6-33) 26 (17-49) 24 (14-44) 12 (5-24) 12 (4-24) 14 (2-3 1) ~I'D.........

II'D

(JJ (continued) ...N

~10

..... Q(JJ(JJ :::l0 ;:;.

~

-l::rI'D

P:l"'0I'Dc...;:;.III

Table IV(continued) .

Algorith m

1 2 3 4 5 6 7 AnyUse a nd Services (n = 2329) (n = 2859) (n = 1047 ) (n = 1170) (n = 1519) (n = 211 ) (n = 251) (N = 4031)

Any of th e a boveNo. (%) with z l 1931 (82 .9) 2334 (81.6) 100 9 (96 .4) 11 67 (99.7) 1443 (95.0) 209 (99.1) 248 (98 .8) 3447 (85.5)Mea n (SO) 77.6 (98 .1 ) 72 .6 (93.8) 81.6 (95.0) 84 .2 (90.4) 78.7 (89.5) 49.6 (38 .9) 50 .9 (40.8) 70.8 (89.8)Med ia n (IQR) 43 (15-92) 41 (12-86) 52 (30 -93) 51 (33-90) 48 (29-88) 42 (24 - 59) 42 (24- 61) 42 (17-83)

EO visitsNo. (%) wit h ;:::1 892 (38 .3) 1080 (37.8) 580 (55.4) 574 (49.1) 673 (44 .3) 98 (46.4) 11 6 (46.2) 1555 (38 .6)Mea n (SO) 1.2 (3.3) 1.2 (3.5) 1.9 (5.2) 1.5 (4.1 ) 1.3 (3.8) 1.5 (3 .5) 1.5 (3.4) 1.2 (3.2)Median (IQR) o(0-2) o (0-2) 1 (0-2) o (0-2) o(0-2) o(0-2) o (0-2) o(0-2)

Hospi talizationsNo. (%) with z l 1236 (53 .1) 1496 (52.3) 796 (76.0) 650 (55.6) 89 1 (58.7) 126 (59.7) 147 (58 .6) 209 1 (51.9)Mean (SO) 1.8 (2.7) 1.9 (2.8) 2.8 (3.8) 1.6 (2.3) 1.9 (2.6) 1.5 (1.9) 1.5 (2.0) 1.9(3.1 )Media n (IQR) 1 (0-3 ) 1 (0- 3) 2 (1- 4) 1 (0- 2) 1 (0- 3) 1 (0-2) 1 (0-2) 1 (0-3)

Length of st ay, dMea n (SO) 10.9 (28.3) 10.3 (27.4) 6.3 (20.0) 2.9 (11.9) 3.2 (12.6) 0.8 (8 .4) 0.7 (7.8) 13.2 (29.2)Median (IQR) o(0-9 ) 0 (0- 8) o(0- 5) o (0-0) o(0-0) o(0- 0) o(0-0) 2 (0-13)

~i:3I'D(JJ.....Zc3c:rI'D....0\

IQR = int erq uar ti le ran ge; ED = emerge ncy department .*During th e l -year pe riod subse q uent to first-noted d ialysis-related cla im.

A. Berger et al.

• Meano Medi an

,...... 200 ,000

""V1 175 ,000::J

'-'VI0 150,000UQ)....rn 125 ,000U

...c~rn 100,000Q)

I(ij 75 ,000'-'

~-0 50 ,000Q)

. ~

rn:J 25 ,000cc-c

01

(n ~ 23 29 )2

(n ~ 2859 )3

( n ~ 1047)

4( n ~ 1 1 70)

Algori thm

5(n ~ 151 9)

6( n ~2 1 1 )

7( n ~25 1)

Figure 2. Annualized mean and median total healt h care costs in patients ide ntified a s having received peri­toneal d ialysis, based on vario us ca se-se lect io n a lgorithms.

sensitivity (and presumably decreasing specificity) byrequiring 1 "qualifying" claim, rather than 2. Algo­rithm 3 was diagnosis based and did not have a cor­responding more-inclusive algorithm that would in­crease sensitivity because of concerns about the validityof patient identification based on a single diagnosticcode (which may not be required for reimbursement)versus procedural/service and equipment codes (whichare required for third-party payment).

Among the 4031 patients with PD who met the cri­teria for at least 1 of the 7 algorithms, 58% were identi­fied using algorithms based on procedural/service codesalone. Only 6% were identified using algorithms basedon codes for PD-related equipment (eg, cyclers, di­alysate solution). When attention was focused on themost sensitive variants of each algorithm (ie, algo­rithms 2 [service based], 3 [diagnosis based], 5 [proce­dure based], and 7 [equipment basedj), most patients(68 %) were identified by only 1 of the 4 algorithms.

We identified new (ie, incident) PD cases and fol­lowed them from their first claim for PD until disen­rollment from the health plan or the end of the studyperiod. Most PD cases (~80%) identified by algo-

June 2009

rithms requiring >1 claim were also identified by algo­rithms requiring ::::2 claims. The shorter median dura­tion of follow-up for the cohorts with z 1 claim almostcertainly reflects the ~20% difference in the numberof cases identified using algorithms based on ::::1 PD­related claim versus those based on ::::2 such claims(ie, loss to follow-up). Because patients with ::::2 PD­related claims are likely to be followed for longer pe­riods of time than those with only 1 such claim, weexamined patterns of utilization and costs on an an­nualized basis (and why such patterns were reportedover the I-year period beginning on the date of thefirst-noted claim for dialysis). While it would be inap­propriate to compare unadjusted patterns of utiliza­tion and cost of patients identified with a particularalgorithm with those of another, we believe that limit­ing our focus to the I-year period subsequent to thefirst-noted dialysis claim allowed us to perform suchcomparisons.

Therefore, our findings suggest that the codes usedby providers to bill for PD-related care-and by insur­ers to pay for such care-appeared to be quite varied.Given the large numbers of patients identified using

1331

Clinical Therapeutics

only 1 PD-related algorithm, it appeared that someindividual providers used the same codes repetitivelyand consistently (eg, procedural/service codes only,equipment codes only) to the exclusion of others.Many health plans did not appear to record CPT-4codes for physicians' services. Because relatively fewpatients were identified with equipment codes (eg,cyclers, dialysate solution), the findings from the pres­ent study suggest that commercial payers may be us­ing nonspecific codes for reimbursement purposes.

Algorithm 1 was developed based on 2 proceduralcodes used by physicians who prescribe PD (CPT-4codes 90945 and 90947); all subsequent algorithmswere developed based on diagnostic, procedural/service, and equipment codes observed among pa­tients identified using algorithm 1. These 2 codes, how­ever, also may be used for various continuous renal­replacement treatments (CRRT), such as continuousvenovenous hernofiltration, continuous venovenousHD, and sustained low-efficiency dialysis. While weassumed that the specificity of the algorithms basedon these procedural/service codes was high, withoutaccess to patients' medical records, it is unknownwhether patients who received CRRT were inadver­tently included, and how any resulting misclassifica­tion may have affected the findings.

For the most part, patients' demographic and clini­cal characteristics were similar across the 7 algorithms,as were levels of health care resource utilization andcosts over the I-year period following the date of thefirst-noted dialysis-related claim.

In the present study, mean health care costs overthis I-year period in patients identified with any of the7 algorithms were $135,782, which is somewhat lessthan the $180,000 estimated annual costs of dialysis(all modalities) reported in 2006 by the US RenalData System (USRDS) in patients whose health carewas covered by private health insurance ("EmployerGroup Health Plans").3

A variety of factors may account for the difference inthese estimates. First, attention was focused on patientswhose initial dialysis was PD, while the USRDS assessedall patients treated with dialysis, irrespective of modality(PD has been reported to be substantially less costlythan HD6-13). Data from patients who were switchedfrom PD to HD were included in our study. Althoughboth PD and HD were captured in the present study,findings from previously published studies suggest thatpatients who switch from HD to PD are rare compared

1332

with the reverse (~3%-5% vs 25%-33%, respective­ly).12 Regardless, the purpose of estimating costs in thepresent study was to compare algorithms, and not toestimate the costs of PD in the private sector.

Second, our analyses were limited to total costs ofcare during the year immediately following the first­noted dialysis-related claim, while the USRDS reportedaverage annual costs in all patients who received dialy­sis (ie, prevalent as opposed to incident cohort). In ananalysis of 2114 managed care recipients commencingdialysis (all modalities), Robbins et al18 estimated totalmean charges in the month before dialysis initiation(including the day of initiation) and for the subsequent3-month period. Unfortunately, differences in method­ology (Robbins et al reported charges, and informationwas limited to the first 3 months only) render compari­sons of our estimates with theirs problematic at best.Furthermore, because we used an intent-to-treat ap­proach to analyze the data, total health care costs dur­ing the I-year period of interest in patients whose ini­tial dialysis was PD and who were switched to HDwithin 12 months would have been included (ie, count­ed as patients treated with PD). Therefore, it would beincorrect to interpret our findings as indicative of thetrue costs of PD in the private sector.

Given our findings, and the apparent tendency ofdifferent health plans to rely on different codes forreimbursing providers and patients for dialysis-relatedcare, a case-selection methodology that combines thevaried types of PD-related codes (eg, diagnostic,procedural/service, equipment) may best capture pa­tients receiving PD. Ideally, this methodology shouldinclude a minimum of 2 claims to avoid data entryerrors (in the present study, algorithms 1,3,4, and 6),which are not uncommon in electronic health careclaims databases. However, the balance between sensi­tivity and specificity of criteria for identifying patientstreated with PD in any future analyses should depend,to a large extent, on the purpose of the analysis.

LimitationsEnrollment in any of the health plans that composed

the study database was independent of the incidence ofrenal failure (ie, the occurrence of renal failure does notlead to enrollment). The analyses were focused on theincidence of PD-related codes during the 3-year periodbeginning on January 1,2004, and ending on Decem­ber 31, 2006, and because renal failure is likely to haveoccurred prior to dialysis initiation, it is also likely that

Volume 31 Number 6

the first such code fell outside of the 3-year period ofinterest. Given the relatively short time span of thestudy, it could not be ascertained whether the first diag­nosis of renal failure noted during the study period wasnot preceded by similar diagnoses prior to the studyperiod. As medical records were unavailable, the accu­racy of PD-selection algorithms developed for thisstudy could not be verified. Health care claims data­bases are composed of records of third-party paymentfor service rendered, and lack the clinical richness anddetail of medical records, which limited our ability toexamine certain clinical characteristics common todialysis. For example, the prevalence of amyloidosis­a common comorbidity among persons undergoingdialysis-was relatively low. The ICD-9-CM diagnosticcode for amyloidosis (the method by which we ascer­tained prevalence-at any time during the study periodnot necessarily prior to the initiation of PD) does notdistinguish between primary and secondary amyloido­sis. It is possible that low levels of amyloidosis are morereflective of limitations in the data source we employedthan is the actual prevalence of this condition amongpatients with renal failure. Diagnoses of medical condi­tions secondary to renal failure, such as amyloidosis,are likely underreported in claims data.

CONCLUSIONSBased on the findings from this study, health care pro­viders appear to bill insurers for PD-related care usinga variety of diagnostic, procedural/service, and equip­ment codes. Investigators using health insuranceclaims data to identify patients with ESRD treatedwith PD should take these coding practices intoaccount.

ACKNOWLEDGM ENTSFunding for this research was provided by BaxterHealthcare Corporation. Mr. Inglese and Dr. Bhat­tacharyya are employed by Baxter Healthcare Corpo­ration. Mr. Berger, Dr. Edelsberg, and Dr. Oster areemployed by Policy Analysis Inc., an independent con­tract research organization with previous and ongoingengagements with Baxter Healthcare Corporation aswell as other pharmaceutical companies.

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Address correspondence to: Gerry Oster, PhD, Policy Analysis, Inc.,4 Davis Court, Brookline, MA 02445. E-mail: goster@pai2.com

Volume 31 Number 6