Open Access
Open Peer Review

This article has Open Peer Review reports available.

How does Open Peer Review work?

Sensitivity of International Classification of Diseases codes for hyponatremia among commercially insured outpatients in the United States

  • Alisa M Shea1,
  • Lesley H Curtis1, 3Email author,
  • Lynda A Szczech2, 4 and
  • Kevin A Schulman1, 3
BMC Nephrology20089:5

DOI: 10.1186/1471-2369-9-5

Received: 30 August 2007

Accepted: 18 June 2008

Published: 18 June 2008

Abstract

Background

Administrative claims are a rich source of information for epidemiological and health services research; however, the ability to accurately capture specific diseases or complications using claims data has been debated. In this study, the authors examined the validity of International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) diagnosis codes for the identification of hyponatremia in an outpatient managed care population.

Methods

We analyzed outpatient laboratory and professional claims for patients aged 18 years and older in the National Managed Care Benchmark Database from Integrated Healthcare Information Services. We obtained all claims for outpatient serum sodium laboratory tests performed in 2004 and 2005, and all outpatient professional claims with a primary or secondary ICD-9-CM diagnosis code of hyponatremia (276.1).

Results

A total of 40,668 outpatient serum sodium laboratory results were identified as hyponatremic (serum sodium < 136 mmol/L). The sensitivity of ICD-9-CM codes for hyponatremia in outpatient professional claims within 15 days before or after the laboratory date was 3.5%. Even for severe cases (serum sodium ≤ 125 mmol/L), sensitivity was < 30%. Specificity was > 99% for all cutoff points.

Conclusion

ICD-9-CM codes in administrative data are insufficient to identify hyponatremia in an outpatient population.

Background

Hyponatremia, defined as an abnormally low level of serum sodium, is the most frequently observed electrolyte disorder in the United States and is associated with significant morbidity and mortality in patients with heart failure [1, 2], myocardial infarction [3, 4], and liver cirrhosis [5, 6], as well as in the hospitalized elderly population at large [7]. Among general acute care patients, the prevalence of hyponatremia is estimated to be approximately 1% [8, 9]. However, much higher rates–ranging from 18% to 30%–have been observed among elderly nursing home residents [10] and in intensive care settings [11]. Little is known about the prevalence of hyponatremia in outpatient settings or in the general population.

Administrative claims data are a rich source of information for epidemiological and health services research. With increasing frequency, researchers are turning to administrative claims data to ascertain information about patient outcomes and hospital quality [1219]. However, the ability to accurately capture specific diseases or complications using claims data has been a subject of considerable debate [2024].

Most validation studies of diagnosis and procedure codes have relied on retrospective chart review as the source of comparative information. Using medical record review as the gold standard, Quan et al [25] found that the validity of International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) codes for invasive or major surgical procedures in inpatient discharge claims was high; however, codes for routine procedures were often inaccurate or incomplete. Geraci et al [26] used the same method to assess the validity of 30 ICD-9-CM codes for common in-hospital complications as observed in patient discharge records from nine hospitals. They found an overall sensitivity of 34% and a positive predictive value of 32%. A handful of studies have used laboratory information to validate administrative claims codes. Wei and Walsh [27] compared managed care claims data with laboratory results and found that less than 25% of female beneficiaries with a positive test for chlamydia were coded as such. In another study, researchers used clinical, radiological, and laboratory data to assess the validity of ICD-9 codes for the diagnosis of gout in an ambulatory managed care population and found a positive predictive value of 61% [28].

To our knowledge, there has been only one published study of the validity of ICD-9-CM codes for the diagnosis of hyponatremia. Movig et al [29] compared inpatient hospital discharge records with inpatient laboratory data and reported a sensitivity of 30% for even the strictest definition of hyponatremia (≤ 115 mmol/L). Positive predictive value for laboratory results showing serum sodium ≤ 135 mmol/L was 91.7%. The study did not address the validity of coding for hyponatremia outside the inpatient setting. Therefore, we sought to examine the validity of ICD-9-CM diagnosis codes for the identification of hyponatremia in an outpatient managed care population.

Methods

Data source

We used data from the National Managed Care Benchmark Database from Integrated Healthcare Information Services (IHCIS; Waltham, MA). The database includes complete medical and eligibility data from over 30 health plans covering more than 25 million lives in the United States. Outpatient laboratory data are available for approximately 10% of members, and outpatient pharmacy information is available for 90% of members. Laboratory tests performed during inpatient hospitalizations are not collected in the database. To protect member confidentiality, IHCIS removed all direct identifiers.

Claim identification

We limited the analysis to claims filed in 2004 and 2005 for members aged 18 years and older. We obtained all claims for outpatient serum sodium laboratory tests performed between January 1, 2004, and December 31, 2005. Serum sodium values < 8 mmol/L were excluded (n = 1854) because these were considered data errors. All remaining values were ≥ 100 mmol/L. When multiple serum sodium tests were performed on the same day, we retained the highest value for the analysis. We also obtained all outpatient professional claims incurred in 2004 and 2005 with a primary or secondary ICD-9-CM diagnosis code of hyponatremia (276.1).

For each claim, members were required to have had continuous eligibility for at least 60 days before and 15 days after the claim date of service. Because this database represents a transient managed care population with a great deal of movement into and out of plans, we chose a 60-day period of observation prior to the serum sodium laboratory test so that we might still observe comorbid claims but not reduce the study population substantially by requiring a lengthy period of pretest eligibility. Multiple successive periods of eligibility, defined as an observed coverage end date followed immediately by a new coverage start date, were considered switches in insurance product and not a discontinuation of coverage; therefore, such changes were not considered an interruption in coverage but rather a single, continuous period of eligibility.

We excluded members with a professional claim for dialysis in 2004 or 2005. We also excluded members with serum/plasma triglycerides > 400 mg/dL as measured 15 days before or after the reference serum sodium date, due to the possibility that any observed changes in serum sodium were related to pseudohyponatremia. If blood glucose was > 300 mg/dL as measured 15 days before or after the reference serum sodium lab date, we adjusted the serum sodium laboratory result by a factor of 1.6 (adjusted value = original value + ([glucose – 100]/100) × 1.6) [30].

We reviewed inpatient, outpatient, and professional claims for evidence of underlying comorbid conditions within the 60-day period before through 15 days after the reference serum sodium date. Specifically, we searched for evidence of liver cirrhosis (ICD-9-CM code 572.4); congestive heart failure (428.0); nephritis, nephrotic syndrome, and nephrosis (580–589); and syndrome of inappropriate secretion of antidiuretic hormone (253.6). In addition, we identified comorbid conditions using the coding algorithms described by Birman-Deych et al [31] and Quan et al [32]. We searched all inpatient, outpatient, and professional claims for 60 days before through 15 days after the reference serum sodium date for evidence of cerebrovascular disease (362.34, 430.x-438.x), chronic obstructive pulmonary disease (416.8, 416.9, 490.x-505.x, 506.4, 508.1, 508.8), coronary heart disease (410.x-414.x, 429.2, V45.81), dementia (290.x, 294.1, 331.2), diabetes mellitus (250.x), hypertension (401.x-405.x, 437.2), kidney disease (403.01, 403.11, 403.91, 404.02, 404.036, 404.12, 404.13, 404.92, 404.93, 582.x, 583.0–583.7, 585.x, 586.x, 588.0, V42.0, V45.1, V56.x), metastatic carcinoma (196.x-199.x), peripheral vascular disease (093.0 437.3, 440.x, 441.x, 443.1–443.9, 47.1, 557.1, 557.9, V43.4), and rheumatic disease (446.5, 710.0–710.4, 714.0–714.2, 714.8, 725.x).

We also identified outpatient pharmacy claims for medications known to cause hyponatremia. We used National Drug Codes to identify claims for phenothiazines, selective serotonin reuptake inhibitors, thiazide diuretics, tricyclic antidepressants, prostaglandin synthesis inhibitors, desmopressin, oxytocin, opiate derivatives, chlorpropamide, clofibrate, carbamazepine, cyclophosphamide, or vincristine incurred 60 days before through 15 days after each outpatient serum sodium laboratory date [33].

Finally, for each member with an eligible outpatient serum sodium laboratory claim, we obtained all outpatient professional claims from 2004 or 2005 that did not include an ICD-9-CM diagnosis code for hyponatremia.

Statistical analysis

We defined hyponatremia as serum sodium < 136 mmol/L [1, 2, 4, 3335]; however, we also performed validity analyses on three additional strata: serum sodium ≤ 133 mmol/L; ≤ 130 mmol/L; and ≤ 125 mmol/L. We used basic descriptive statistics to summarize demographic characteristics, comorbidities, and prescription drug claim information for members with serum sodium values indicating hyponatremia both with and without a corresponding outpatient professional claim for hyponatremia. We assessed differences between groups using χ2 tests for categorical variables and Wilcoxon rank sum tests for continuous variables.

For all measurements of validity, we considered the laboratory result to be the gold standard of diagnosis and the outpatient professional claim to be the test. We defined sensitivity as the probability of a positive test–an outpatient professional claim with a primary or secondary ICD-9-CM diagnosis code of hyponatremia observed within 15 days before or after a serum sodium laboratory result indicating hyponatremia. We defined specificity as the probability of a negative test–an outpatient professional claim that did not include an ICD-9-CM code for hyponatremia or the absence of any outpatient professional claim within 15 days before or after a serum sodium laboratory result not indicating hyponatremia. Positive predictive value was the probability of a laboratory result indicating hyponatremia among positive outpatient professional claims; negative predictive value was the probability of a laboratory result not indicating hyponatremia among negative outpatient professional claims or in the absence of any outpatient claim.

Using all serum sodium laboratory claims indicating hyponatremia, we performed logistic regression analysis to explore the relationships between undocumented hyponatremia (serum sodium laboratory claim indicating hyponatremia, but no professional ICD-9-CM claim for hyponatremia) and members' demographic characteristics and comorbidities as observed within 60 days before through 15 days after the laboratory encounter.

We used SAS version 9.1.5 for all analyses (SAS Institute Inc, Cary, NC). The institutional review board of the Duke University Health System approved this study.

Results

There were 1,901,254 eligible serum sodium laboratory claims in the study sample. Of these, 40,668 (2.1%) indicated hyponatremia (serum sodium < 136 mmol/L). Outpatient professional claims with an ICD-9-CM diagnosis code for hyponatremia were observed within 15 days before or after the hyponatremic serum sodium lab date for 1407 of these claims (3.5%) (Table 1).
Table 1

Sample characteristics*

Characteristic

Laboratory Claims Indicating Hyponatremia†

p||

 

All Claims (n = 40,668)

Negative Test‡ (n = 39,261)

Positive Test§(n = 1407)

 

Age, mean (SD), y

59 (15.7)

59 (15.7)

67 (12.3)

< 0.001

Female sex

24,694 (60.7)

23,769 (60.5)

925 (65.7)

< 0.001

Underlying conditions and comorbidities

    

   Cerebrovascular disease

1874 (4.6)

1721 (4.4)

153 (10.9)

< 0.001

   Chronic obstructive pulmonary disease

4279 (10.5)

4020 (10.2)

259 (18.4)

< 0.001

   Congestive heart failure

2742 (6.7)

2605 (6.6)

137 (9.7)

< 0.001

   Coronary heart disease

5857 (14.4)

5560 (14.2)

297 (21.1)

< 0.001

   Dementia

164 (4.0)

141 (3.5)

23 (1.6)

< 0.001

   Diabetes mellitus

8342 (20.5)

8102 (20.6)

240 (17.1)

0.001

   Hypertension

14,825 (36.5)

13,997 (35.7)

828 (58.8)

< 0.001

   Kidney disease

1491 (3.7)

1405 (3.6)

86 (6.1)

< 0.001

   Liver cirrhosis

26 (0.1)

24 (0.1)

2 (0.1)

0.24

   Metastatic carcinoma

2325 (5.7)

2246 (5.7)

79 (5.6)

0.87

   Nephritis, nephrotic syndrome, and nephrosis

1918 (4.7)

1800 (4.6)

118 (8.4)

< 0.001

   Peripheral vascular disease

1613 (4.0)

1546 (3.9)

67 (4.8)

0.12

   Rheumatic disease

1291 (3.2)

1254 (3.2)

37 (2.6)

0.24

   Syndrome of inappropriate antidiuretic hormone

157 (3.9)

91 (2.3)

66 (4.7)

< 0.001

Medications known to cause hyponatremia

10,492 (25.8)

10,144 (25.8)

348 (24.7)

0.35

* Values are expressed as number (percentage) unless otherwise indicated.

† Hyponatremia was defined as serum sodium < 136 mmol/L.

‡ A negative test was defined as the presence of an outpatient professional claim with no ICD-9-CM code for hyponatremia or the absence of any outpatient professional claims within 15 days before or after the laboratory claim indicating hyponatremia.

§ A positive test was defined as the presence of an outpatient professional claim with a primary or secondary ICD-9-CM diagnosis code of hyponatremia observed within 15 days before or after the laboratory claim indicating hyponatremia.

|| p-Values for the comparison between the positive and negative test groups.

Mean age of members at the time of the laboratory claim indicating hyponatremia was 59 years, and over half of all claims were for women (61%). Hypertension was the most commonly identified comorbidity in this sample. Evidence of hypertension during the 60-day period preceding the laboratory claim date was present for 36.5% of claims. Diabetes (20.5%), coronary heart disease (14.4%), and chronic obstructive pulmonary disease (10.5%) were also observed more frequently than other conditions. Outpatient pharmacy claims for medications known to cause hyponatremia were observed within the -60/+15-day period before or after the laboratory date in 1 of every 4 claims (Table 1).

Compared to claims without a corresponding diagnosis code, mean age was greater among outpatient laboratory claims indicating hyponatremia with an outpatient professional diagnosis code for hyponatremia observed within 15 days (positive test, 67 versus 59 years; p < .001), and claims were observed significantly more often for women (61% versus 66%; p = < .001). Evidence of hypertension was observed nearly twice as often during the period preceding a laboratory claim with a positive test than for claims with a negative test or with no temporally adjacent outpatient professional claims (59% versus 36%; p < .001). Compared to claims that did not have a diagnosis code for hyponatremia, claims with a positive test were also significantly more likely to be observed for members diagnosed with kidney disease, cardiovascular conditions, and/or chronic obstructive pulmonary disease; however, claims with a positive test were significantly less likely to be observed among members with diabetes or dementia (Table 1).

Sensitivity for hyponatremia defined as serum sodium < 136 mmol/L was 3.5%; specificity was greater than 99%. Positive predictive value was 63%, and negative predictive value was 98% (Table 2). Sensitivity values for the ≤ 133 mmol/L, ≤ 130 mmol/L, and ≤ 125 mmol/L strata were 7.5%, 13.9%, and 29.6%, respectively. Specificity was greater than 99% for all of the alternative cutoff points (Table 3).
Table 2

Relationship between ICD-9-CM code documentation and laboratory serum sodium < 136 mmol/L

 

Hyponatremia

 

+

-

ICD-9 = 276.1

+

1407

839

 

-

39,261

1,859,747

Sensitivity = 3.46%

Specificity = 99.95%

Positive predictive value = 62.64%

Negative predictive value = 97.93%

Table 3

Validity measures by laboratory serum sodium values

 

Serum Sodium (mmol/L)

 

< 136

≤ 133

≤ 130

≤ 125

Sensitivity

3.46

7.50

13.85

29.57

   False-negative rate

96.54

92.50

86.15

70.43

Specificity

99.95

99.94

99.92

99.90

   False-positive rate

0.05

0.06

0.08

0.10

Positive predictive value

62.64

48.74

30.00

10.42

Negative predictive value

97.93

99.28

99.78

99.97

In the multivariable analysis exploring the relationships between undocumented hyponatremia and member demographic characteristics and comorbidities, laboratory results indicating hyponatremia among older members were significantly more likely to have a corresponding outpatient professional claim than those observed for younger members. Controlling for other comorbid diagnoses, medications known to cause hyponatremia, and sex, an increase of 10 years in age was associated with an almost 30% drop in the likelihood of a negative claim or no claim at all (odds ratio: 0.74; 95% confidence interval: 0.66, 0.74; data not shown). Laboratory claims preceded by a comorbid diagnosis of cerebrovascular disease, chronic obstructive pulmonary disease, hypertension, dementia, nephritis/nephrosis, or syndrome of inappropriate antidiuretic hormone were also less likely to be undocumented by a diagnosis code on an outpatient professional claim. Laboratory claims preceded by a comorbid diagnosis of diabetes or peripheral vascular disease were significantly more likely to be undocumented by a diagnosis code. Claims for medications known to cause hyponatremia did not have a significant impact on ICD-9-CM documentation of laboratory-identified hyponatremia (Table 4).
Table 4

Likelihood of a negative test for claims indicating hyponatremia*

 

OR

95% CI

Age in years

0.97

0.96, 0.97

Female

0.75

0.67, 0.85

Underlying conditions and comorbidities

  

   Cerebrovascular disease

0.61

0.50, 0.73

   Chronic obstructive pulmonary disease

0.62

0.54, 0.72

   Congestive heart failure

1.09

0.89, 1.34

   Coronary heart disease

0.98

0.84, 1.14

   Dementia

0.44

0.27, 0.70

   Diabetes mellitus

1.47

1.27, 1.70

   Hypertension

0.53

0.47, 0.59

   Kidney disease

1.24

0.84, 1.83

   Liver cirrhosis†

0.31

0.07, 1.38

   Metastatic carcinoma

1.11

0.87, 1.41

   Nephritis, nephrotic syndrome, and nephrosis

0.49

0.35, 0.69

   Peripheral vascular disease

1.40

1.08, 1.82

   Rheumatic disease

1.27

0.91, 1.77

   Syndrome of inappropriate antidiuretic hormone

0.06

0.04, 0.08

Medications known to cause hyponatremia

1.08

0.95, 1.22

* Hyponatremia was defined as serum sodium < 136 mmol/L. A negative test was defined as the presence of an outpatient professional claim with no ICD-9-CM code for hyponatremia or the absence of any outpatient professional claims within 15 days before or after the laboratory claim indicating hyponatremia.

† Indicates that the condition was present in < 1% of the population.

Abbreviations: OR indicates odds ratio; and CI, confidence interval.

Discussion

We examined the validity of ICD-9-CM diagnosis codes for the identification of hyponatremia in an outpatient managed care population using data from the IHCIS National Managed Care Benchmark Database. Our results show that while the ICD-9-CM code for hyponatremia is highly specific in outpatient claims, its sensitivity is extremely low. Even for the most severe cases (serum sodium ≤ 125 mmol/L), we found sensitivity to be less than 30%. Similarly, the positive predictive value of an outpatient professional claim for hyponatremia was only 63% using the least strict serum sodium measurement (< 136 mmol/L). These findings are consistent with an earlier study by Movig et al [29], which showed low sensitivity for the coding of hyponatremia in inpatient settings, although the positive predictive value of ICD-9-CM codes in outpatient claims in our study was significantly less than the corresponding inpatient rates found in the previous study.

These low rates of coding for hyponatremia may be largely due to the ICD-9-CM diagnostic coding and reporting guidelines for outpatient services. According to these guidelines, only conditions that "require or affect patient care treatment or management" should be documented. Moreover, "related signs and symptoms" should not be coded when a more definitive diagnosis is known [36]. Thus, at mildly decreased levels requiring no medical intervention and/or in the presence of causal underlying disease such as syndrome of inappropriate antidiuretic hormone or liver cirrhosis, it may be inappropriate to code for hyponatremia on an outpatient claim. Similarly, there may be limited space available for diagnoses on the outpatient claim form. The IHCIS database, for example, allows for a maximum of 3 diagnosis codes on each professional claim record. As shown in Table 1, hyponatremia is often seen in the presence of other significant comorbidities. Given that ICD-9-CM coding guidelines also specify that one should "list first the ICD-9-CM code for the diagnosis, condition, problem, or other reason for encounter/visit shown in the medical record to be chiefly responsible for the services provided" [36], other conditions may have taken precedence on the claim form.

The clinical consequences of even mild hyponatremia are well-documented [3, 4, 6, 34, 35, 3739]. In a cohort of elderly patients, hyponatremia at hospital admission was a significant independent predictor of mortality after adjustment for age, sex, length of stay, and several clinical factors [7]. Hyponatremia (serum sodium ≤ 135 mmol/L) is independently associated with major complications, greater length of stay, higher hospital costs, and greater inpatient mortality in patients with suspected congestive heart failure [1] and with greater in-hospital and 60-day mortality in patients with heart failure [2]. It is an independent predictor of 30-day mortality in patients with acute ST-segment elevation myocardial infarction [3]. Given the preponderance of evidence suggesting that hyponatremia is an independent predictor of poorer outcomes, our findings suggest that the prognostic value of even severe hyponatremia may be underappreciated.

Another potential explanation for the low observed rates of coding is that hyponatremia, as identified by a single laboratory value, may have been a transient condition for some members. We analyzed single laboratory values and did not consider the results of prior or subsequent testing. Thus, it is possible that some members received follow-up testing that showed the condition to be resolved, thereby eliminating the need for documentation on the outpatient professional claim. Also, because the sample included commercially insured adults with relatively low amounts of comorbid illness, mildly decreased levels of serum sodium may not be clinically significant or require medical intervention. Nevertheless, we found sensitivity to be less than 30%, even in the case of serum sodium ≤ 125 mmol/L. Finally, because documentation of hyponatremia is unlikely to generate additional reimbursement for outpatient services, there may be little incentive for physicians to include it on the claim.

In contrast, the positive predictive value of outpatient claims for hyponatremia was also low (63% for serum sodium < 136 mmol/L). This finding may reflect the fact that hyponatremia is a chronic condition for some patients and is therefore likely to be noted on outpatient professional claims despite the fact that there are no observable laboratory claims within +/-15 days to support the diagnosis. The data used here, however, do not include sufficient detail to validate this hypothesis.

Although the impact of medications known to cause hyponatremia was not significant in the multivariable model, there was a trend toward less ICD-9-CM documentation of laboratory-identified hyponatremia in the presence of this factor. Outpatient claims for medications known to cause hyponatremia were observed within 60 days before through 15 days after 25% of all laboratory claims indicating hyponatremia. This finding suggests that some clinicians may choose not to code hyponatremia when it may be the result of drug therapy. Other results of the multivariable model also suggest that ICD-9-CM coding of laboratory-identified hyponatremia may be especially poor for patients with peripheral vascular disease or diabetes. Claims for members with these conditions were more than 40% less likely to have an ICD-9-CM code for hyponatremia.

This study has some limitations. First, outpatient laboratory data are only available for approximately 10% of members in the IHCIS database. Second, the IHCIS database consists of managed care claims only and is therefore representative of an employer-based, commercially insured population. We expect that the elderly are significantly underrepresented in the database. Because the risk of hyponatremia is highest among the elderly and our results show that the frequency of coding for hyponatremia increases with age, our findings may not be generalizable to older populations. Inconsistent and inaccurate coding and the absence of clinical data regarding disease severity may have also affected our estimates. We required just 60 days of continuous coverage before and 15 days of coverage after the reference serum sodium date and searched for evidence of comorbidity claims within that time period only. Although this approach maximized our sample size, it also limited the available time frame in which we could observe comorbid illnesses, which may have led to an underestimation of these conditions in our analyses. We also chose to search for outpatient professional claims that were temporally adjacent to serum sodium laboratory tests (+/-15 days). As a result, the analysis is unable to capture instances where abnormal test results were addressed by providers without generation of an outpatient claim (eg, by phone or e-mail) or when a follow-up visit was performed outside of this time window. Finally, the study used outpatient clinical and laboratory information only. The results do not account for the possibility that a laboratory-identified diagnosis of hyponatremia, for example, may have been documented by an ICD-9-CM code on an inpatient claim just before or after the outpatient laboratory date.

Conclusion

Our results suggest that the use of ICD-9-CM codes in administrative data alone is insufficient to identify hyponatremia in outpatient populations. Whenever possible, supplementary laboratory information should be used to help overcome this limitation of administrative claims.

Abbreviations

CI: 

confidence interval

ICD-9-CM: 

International Classification of Diseases, Ninth Revision, Clinical Modification

IHCIS: 

Integrated Healthcare Information Services

OR: 

odds ratio.

Declarations

Acknowledgements

Funding/support

This study was supported by Sanofi-Aventis. The sponsor had no role in the design and conduct of the study; the collection, management, analysis, or interpretation of the data; or the preparation of the manuscript. According to the terms of the research agreement, the sponsor had an opportunity to review a draft of the manuscript. The authors had full control over the preparation and approval of the manuscript and the decision to submit the manuscript for publication.

Additional contributions

We thank Damon Seils of Duke University for editorial assistance and manuscript preparation. Mr Seils did not receive compensation for his assistance apart from his employment at the institution where the study was conducted.

Authors’ Affiliations

(1)
Center for Clinical and Genetic Economics, Duke Clinical Research Institute
(2)
Duke Clinical Research Institute
(3)
Division of General Internal Medicine, Department of Medicine, Duke University School of Medicine
(4)
Division of Nephrology, Department of Medicine, Duke University School of Medicine

References

  1. Chin MH, Goldman L: Correlates of major complications or death in patients admitted to the hospital with congestive heart failure. Arch Intern Med. 1996, 156: 1814-1820. 10.1001/archinte.156.16.1814.View ArticlePubMedGoogle Scholar
  2. Klein L, O'Connor CM, Leimberger JD, Gattis-Stough W, Pina IL, Felker GM, Adams KF, Califf RM, Gheorghiade M: Lower serum sodium is associated with increased short-term mortality in hospitalized patients with worsening heart failure: results from the Outcomes of a Prospective Trial of Intravenous Milrinone for Exacerbations of Chronic Heart Failure (OPTIME-CHF) study. Circulation. 2005, 111: 2454-2460. 10.1161/01.CIR.0000165065.82609.3D.View ArticlePubMedGoogle Scholar
  3. Goldberg A, Hammerman H, Petcherski S, Zdorovyak A, Yalonetsky S, Kapeliovich M, Agmon Y, Markiewicz W, Aronson D: Prognostic importance of hyponatremia in acute ST-elevation myocardial infarction. Am J Med. 2004, 117: 242-248. 10.1016/j.amjmed.2004.03.022.View ArticlePubMedGoogle Scholar
  4. Goldberg A, Hammerman H, Petcherski S, Nassar M, Zdorovyak A, Yalonetsky S, Kapeliovich M, Agmon Y, Beyar R, Markiewicz W, Aronson D: Hyponatremia and long-term mortality in survivors of acute ST-elevation myocardial infarction. Arch Intern Med. 2006, 166: 781-786. 10.1001/archinte.166.7.781.View ArticlePubMedGoogle Scholar
  5. Borroni G, Maggi A, Sangiovanni A, Cazzaniga M, Salerno F: Clinical relevance of hyponatraemia for the hospital outcome of cirrhotic patients. Dig Liver Dis. 2000, 32: 605-610. 10.1016/S1590-8658(00)80844-0.View ArticlePubMedGoogle Scholar
  6. Londono MC, Guevara M, Rimola A, Navasa M, Taura P, Mas A, Garcia-Valdecasas JC, Arroyo V, Gines P: Hyponatremia impairs early posttransplantation outcome in patients with cirrhosis undergoing liver transplantation. Gastroenterology. 2006, 130: 1135-1143. 10.1053/j.gastro.2006.02.017.View ArticlePubMedGoogle Scholar
  7. Terzian C, Frye EB, Piotrowski ZH: Admission hyponatremia in the elderly: factors influencing prognosis. J Gen Intern Med. 1994, 9: 89-91. 10.1007/BF02600208.View ArticlePubMedGoogle Scholar
  8. Anderson RJ, Chung HM, Kluge R, Schrier RW: Hyponatremia: a prospective analysis of its epidemiology and the pathogenetic role of vasopressin. Ann Intern Med. 1985, 102: 164-168.View ArticlePubMedGoogle Scholar
  9. Gross P: Correction of hyponatremia. Semin Nephrol. 2001, 21: 269-272. 10.1053/snep.2001.21655.View ArticlePubMedGoogle Scholar
  10. Miller M, Morley JE, Rubenstein LZ: Hyponatremia in a nursing home population. J Am Geriatr Soc. 1995, 43: 1410-1413.View ArticlePubMedGoogle Scholar
  11. DeVita MV, Gardenswartz MH, Konecky A, Zabetakis PM: Incidence and etiology of hyponatremia in an intensive care unit. Clin Nephrol. 1990, 34: 163-166.PubMedGoogle Scholar
  12. Asch SM, Sloss EM, Hogan C, Brook RH, Kravitz RL: Measuring underuse of necessary care among elderly Medicare beneficiaries using inpatient and outpatient claims. JAMA. 2000, 284: 2325-2333. 10.1001/jama.284.18.2325.View ArticlePubMedGoogle Scholar
  13. Bradley EH, Herrin J, Elbel B, McNamara RL, Magid DJ, Nallamothu BK, Wang Y, Normand SL, Spertus JA, Krumholz HM: Hospital quality for acute myocardial infarction: correlation among process measures and relationship with short-term mortality. JAMA. 2006, 296: 72-78. 10.1001/jama.296.1.72.View ArticlePubMedGoogle Scholar
  14. Encinosa WE, Bernard DM, Chen CC, Steiner CA: Healthcare utilization and outcomes after bariatric surgery. Med Care. 2006, 44: 706-712. 10.1097/01.mlr.0000220833.89050.ed.View ArticlePubMedGoogle Scholar
  15. Hassett MJ, O'Malley AJ, Pakes JR, Newhouse JP, Earle CC: Frequency and cost of chemotherapy-related serious adverse effects in a population sample of women with breast cancer. J Natl Cancer Inst. 2006, 98: 1108-1117.View ArticlePubMedGoogle Scholar
  16. Krumholz HM, Wang Y, Mattera JA, Wang Y, Han LF, Ingber MJ, Roman S, Normand SL: An administrative claims model suitable for profiling hospital performance based on 30-day mortality rates among patients with heart failure. Circulation. 2006, 113: 1693-1701. 10.1161/CIRCULATIONAHA.105.611194.View ArticlePubMedGoogle Scholar
  17. Pine M, Jordan HS, Elixhauser A, Fry DE, Hoaglin DC, Jones B, Meimban R, Warner D, Gonzales J: Enhancement of claims data to improve risk adjustment of hospital mortality. JAMA. 2007, 297: 71-76. 10.1001/jama.297.1.71.View ArticlePubMedGoogle Scholar
  18. Tang PC, Ralston M, Arrigotti MF, Qureshi L, Graham J: Comparison of methodologies for calculating quality measures based on administrative data versus clinical data from an electronic health record system: implications for performance measures. J Am Med Inform Assoc. 2007, 14: 10-15. 10.1197/jamia.M2198.View ArticlePubMedPubMed CentralGoogle Scholar
  19. Werner RM, Bradlow ET: Relationship between Medicare's hospital compare performance measures and mortality rates. JAMA. 2006, 296: 2694-2702. 10.1001/jama.296.22.2694.View ArticlePubMedGoogle Scholar
  20. Jollis JG, Ancukiewicz M, DeLong ER, Pryor DB, Muhlbaier LH, Mark DB: Discordance of databases designed for claims payment versus clinical information systems. Implications for outcomes research. Ann Intern Med. 1993, 119: 844-850.View ArticlePubMedGoogle Scholar
  21. McCarthy EP, Iezzoni LI, Davis RB, Palmer RH, Cahalane M, Hamel MB, Mukamal K, Phillips RS, Davies DT: Does clinical evidence support ICD-9-CM diagnosis coding of complications?. Med Care. 2000, 38: 868-876. 10.1097/00005650-200008000-00010.View ArticlePubMedGoogle Scholar
  22. Romano PS, Chan BK, Schembri ME, Rainwater JA: Can administrative data be used to compare postoperative complication rates across hospitals?. Med Care. 2002, 40: 856-867. 10.1097/00005650-200210000-00004.View ArticlePubMedGoogle Scholar
  23. Romano PS, Schembri ME, Rainwater JA: Can administrative data be used to ascertain clinically significant postoperative complications?. Am J Med Qual. 2002, 17: 145-154. 10.1177/106286060201700404.View ArticlePubMedGoogle Scholar
  24. Solberg LI, Engebretson KI, Sperl-Hillen JM, Hroscikoski MC, O'Connor PJ: Are claims data accurate enough to identify patients for performance measures or quality improvement? The case of diabetes, heart disease, and depression. Am J Med Qual. 2006, 21: 238-245. 10.1177/1062860606288243.View ArticlePubMedGoogle Scholar
  25. Quan H, Parsons GA, Ghali WA: Validity of procedure codes in International Classification of Diseases, 9th revision, clinical modification administrative data. Med Care. 2004, 42: 801-809. 10.1097/01.mlr.0000132391.59713.0d.View ArticlePubMedGoogle Scholar
  26. Geraci JM, Ashton CM, Kuykendall DH, Johnson ML, Wu L: International Classification of Diseases, 9th Revision, Clinical Modification codes in discharge abstracts are poor measures of complication occurrence in medical inpatients. Med Care. 1997, 35: 589-602. 10.1097/00005650-199706000-00005.View ArticlePubMedGoogle Scholar
  27. Wei F, Walsh CM: Validation of data collection for the HEDIS performance measure on chlamydia screening in an MCO. Am J Manag Care. 2003, 9: 585-593.PubMedGoogle Scholar
  28. Harrold LR, Saag KG, Yood RA, Mikuls TR, Andrade SE, Fouayzi H, Davis J, Chan KA, Raebel MA, Von Worley A, Platt R: Validity of gout diagnoses in administrative data. Arthritis Rheum. 2007, 57: 103-108. 10.1002/art.22474.View ArticlePubMedGoogle Scholar
  29. Movig KL, Leufkens HG, Lenderink AW, Egberts AC: Validity of hospital discharge International Classification of Diseases (ICD) codes for identifying patients with hyponatremia. J Clin Epidemiol. 2003, 56: 530-535. 10.1016/S0895-4356(03)00006-4.View ArticlePubMedGoogle Scholar
  30. Katz MA: Hyperglycemia-induced hyponatremia--calculation of expected serum sodium depression. N Engl J Med. 1973, 289: 843-844.View ArticlePubMedGoogle Scholar
  31. Birman-Deych E, Waterman AD, Yan Y, Nilasena DS, Radford MJ, Gage BF: Accuracy of ICD-9-CM codes for identifying cardiovascular and stroke risk factors. Med Care. 2005, 43: 480-485. 10.1097/01.mlr.0000160417.39497.a9.View ArticlePubMedGoogle Scholar
  32. Quan H, Sundararajan V, Halfon P, Fong A, Burnand B, Luthi JC, Saunders LD, Beck CA, Feasby TE, Ghali WA: Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data. Med Care. 2005, 43: 1130-1139. 10.1097/01.mlr.0000182534.19832.83.View ArticlePubMedGoogle Scholar
  33. Adrogue HJ: Consequences of inadequate management of hyponatremia. Am J Nephrol. 2005, 25: 240-249. 10.1159/000086019.View ArticlePubMedGoogle Scholar
  34. De LL, Klein L, Udelson JE, Orlandi C, Sardella G, Fedele F, Gheorghiade M: Hyponatremia in patients with heart failure. Am J Cardiol. 2005, 96: 19L-23L.Google Scholar
  35. Adrogue HJ, Madias NE: Hyponatremia. N Engl J Med. 2000, 342: 1581-1589. 10.1056/NEJM200005253422107.View ArticlePubMedGoogle Scholar
  36. 2006 Professional ICD-9-CM for Hospitals--Volumes 1, 2, and 3. Edited by: Hart AC, Hopkins CA and Ford B. 2005, Salt Lake City, Utah, Ingenix, 6Google Scholar
  37. Fraser JF, Stieg PE: Hyponatremia in the neurosurgical patient: epidemiology, pathophysiology, diagnosis, and management. Neurosurgery. 2006, 59: 222-229. 10.1227/01.NEU.0000223440.35642.6E.View ArticlePubMedGoogle Scholar
  38. Renneboog B, Musch W, Vandemergel X, Manto MU, Decaux G: Mild chronic hyponatremia is associated with falls, unsteadiness, and attention deficits. Am J Med. 2006, 119: 71-78. 10.1016/j.amjmed.2005.09.026.View ArticlePubMedGoogle Scholar
  39. Wu CC, Yeung LK, Tsai WS, Tseng CF, Chu P, Huang TY, Lin YF, Lu KC: Incidence and factors predictive of acute renal failure in patients with advanced liver cirrhosis. Clin Nephrol. 2006, 65: 28-33.View ArticlePubMedGoogle Scholar
  40. Pre-publication history

    1. The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2369/9/5/prepub

Copyright

© Shea et al; licensee BioMed Central Ltd. 2008

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Advertisement