Risk adjustment performance of Charlson and Elixhauser comorbidities in ICD-9 and ICD-10 administrative databases

Li, Bing; Evans, Dewey; Faris, Peter; Dean, Stafford; Quan, Hude

doi:10.1186/1472-6963-8-12

Cited by 277 publications

(185 citation statements)

References 15 publications

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“…Despite the likely underreporting of weight loss and obesity in administrative data noted in this study and others, 10,24,38,40,41 the replacement of administrative data with clinical weight data for obesity and weight-loss Elixhauser comorbidities did not improve the predictive performance of the method. Two conditions significantly related to survival (congestive heart failure and renal failure) are well-known causes of cachexia and wasting, 27 and it may be that the addition of weight loss data adds little to an analysis in which these conditions are already accounted for.…”

Section: Discussioncontrasting

confidence: 66%

“…37 Overall, the c-statistics for all-cause mortality for the different comorbidity measures were similar to or slightly higher than values reported in other populations. 11,12,24,38 It will also be of interest to determine how the 2 approaches compare with other cancer types. It is important to do this analysis by cancer stage because stage by itself already exerts substantial survival discrimination.…”

Section: Discussionmentioning

confidence: 99%

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A comparison of charlson and elixhauser comorbidity measures to predict colorectal cancer survival using administrative health data

MSc

Baracos

Winget

et al. 2010

Cancer

136

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BACKGROUND: Cancer survival is related to features of the primary malignancy and concurrent presence of nonmalignant diseases (comorbidities), including weight-related conditions (obesity, weight loss). The Charlson and Elixhauser methods are 2 well-known methods that take comorbidities into account when explaining survival. They differ in both the number and categorization of comorbidities. METHODS: Cancer, comorbidity, and survival data were acquired from inpatient administrative hospital records in 574 colorectal cancer patients. Robust Poisson regression was used to analyze 2-and 3-year survival according to cancer features and comorbidities classified by the Charlson and Elixhauser methods. Data for weight-related conditions (body mass index, weight loss) and performance status were acquired upon a new patient visit to the regional cancer center. Discrimination was assessed with the concordance Overall, the Elixhauser comorbidities outperformed the Charlson comorbidities (P < .05). The use of self-reported weight and performance status data significantly increased discrimination by the Elixhauser method in 2-year but not 3-year survival. CONCLUSIONS: The Elixhauser method is a superior comorbidity risk-adjustment model for colorectal cancer survival prediction.

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Section: Discussioncontrasting

confidence: 66%

Section: Discussionmentioning

confidence: 99%

A comparison of charlson and elixhauser comorbidity measures to predict colorectal cancer survival using administrative health data

MSc

Baracos

Winget

et al. 2010

Cancer

136

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“…[38][39][40][41][42] Additionally, ICD-9 codes have been widely used to ascertain outcomes of Bdiabetes mellitus^and Bdiabetes mellitus with complications,â s these two outcomes are essential components in calculating the Charlson comorbidity index using Dyeo's method 17 and the Elixhauser comorbidity score 43 from administrative data, and both of these scores are widely used. 44 The sensitivity and specificity of ICD-9 codes for diagnosing diabetes without complications were 77.7 and 98.4 %, and for diabetes with complications were 63.6 and 98.9 %, respectively. 45 In a study using the VA database, the sensitivity and specificity of ICD-9 codes in comparison to chart review to identify diabetes were 78.3 and 95.7 %, respectively.…”

Section: Discussionmentioning

confidence: 94%

Statins and New-Onset Diabetes Mellitus and Diabetic Complications: A Retrospective Cohort Study of US Healthy Adults

et al. 2015

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BACKGROUND: Statin use is associated with increased incidence of diabetes and possibly with increased body weight and reduced exercise capacity. Data on the longterm effects of these associations in healthy adults, however, are very limited. In addition, the relationship between these effects and diabetic complications has not been adequately studied. OBJECTIVE: To examine the association between statin use and new-onset diabetes, diabetic complications, and overweight/obesity in a cohort of healthy adults. RESEARCH DESIGN: This was a retrospective cohort study. PARTICIPANTS: Subjects were Tricare beneficiaries who were evaluated between October 1, 2003 and March 1, 2012. Patients were divided into statin users and nonusers. INTERVENTION: We excluded patients who, at baseline, had a preexisting disease indicative of cardiovascular diseases, any positive element of the Charlson comorbidity index (including diabetes mellitus), or life-limiting chronic diseases. Using 42 baseline characteristics, we generated a propensity score to match statin users and nonusers. MAIN MEASURES: Outcomes assessed included newonset diabetes, diabetic complications, and overweight/ obesity. KEY RESULTS: A total of 25,970 patients (3982 statin users and 21,988 nonusers) were identified as healthy adults at baseline. Of these, 3351 statins users and 3351 nonusers were propensity score-matched. Statin users had higher odds of new-onset diabetes (odds ratio [OR] 1.87; 95 % confidence interval [95 % CI] 1.67-2.01), diabetes with complications (OR 2.50; 95 % CI 1.88-3.32), and overweight/obesity (OR 1.14; 95 % CI 1.04-1.25). Secondary and sensitivity analyses demonstrated similar findings. CONCLUSIONS: Diabetes, diabetic complications, and overweight/obesity were more commonly diagnosed among statin-users than similar nonusers in a healthy cohort of adults. This study demonstrates that short-term clinical trials might not fully describe the risk/benefit of long-term statin use for primary prevention.

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“…Fourth, given the similarities of this data set to claims data, we lacked access to all granular data and clinical information within an individual patient's chart that could significantly affect mortality like blood pressure, ejection fraction and fluid and weight loss. We relied on diagnostic codes incorporated in the Elixhauser Comorbidity Index which has been validated in congestive heart failure 31. Finally, as this analysis was restricted to metolazone, these results may not be generalizable to all thiazide diuretics.…”

Section: Discussionmentioning

confidence: 99%

Outcomes Associated With a Strategy of Adjuvant Metolazone or High‐Dose Loop Diuretics in Acute Decompensated Heart Failure: A Propensity Analysis

Brisco‐Bacik

Maaten

Houser

et al. 2018

JAHA

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BackgroundIn acute decompensated heart failure, guidelines recommend increasing loop diuretic dose or adding a thiazide diuretic when diuresis is inadequate. We set out to determine the adverse events associated with a diuretic strategy relying on metolazone or high‐dose loop diuretics.Methods and ResultsPatients admitted to 3 hospitals using a common electronic medical record with a heart failure discharge diagnosis who received intravenous loop diuretics were studied in a propensity‐adjusted analysis of all‐cause mortality. Secondary outcomes included hyponatremia (sodium <135 mEq/L), hypokalemia (potassium <3.5 mEq/L) and worsening renal function (a ≥20% decrease in estimated glomerular filtration rate). Of 13 898 admissions, 1048 (7.5%) used adjuvant metolazone. Metolazone was strongly associated with hyponatremia, hypokalemia, and worsening renal function (P<0.0001 for all) with minimal effect attenuation following covariate and propensity adjustment. Metolazone remained associated with increased mortality after multivariate and propensity adjustment (hazard ratio=1.20, 95% confidence interval 1.04–1.39, P=0.01). High‐dose loop diuretics were associated with hypokalemia and hyponatremia (P<0.002) but only worsening renal function retained significance (P<0.001) after propensity adjustment. High‐dose loop diuretics were not associated with reduced survival after multivariate and propensity adjustment (hazard ratio=0.97 per 100 mg of IV furosemide, 95% confidence interval 0.90–1.06, P=0.52).ConclusionsDuring acute decompensated heart failure, metolazone was independently associated with hypokalemia, hyponatremia, worsening renal function and increased mortality after controlling for the propensity to receive metolazone and baseline characteristics. However, under the same experimental conditions, high‐dose loop diuretics were not associated with hypokalemia, hyponatremia, or reduced survival. The current findings suggest that until randomized control trial data prove otherwise, uptitration of loop diuretics may be a preferred strategy over routine early addition of thiazide type diuretics when diuresis is inadequate.

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Risk adjustment performance of Charlson and Elixhauser comorbidities in ICD-9 and ICD-10 administrative databases

Cited by 277 publications

References 15 publications

A comparison of charlson and elixhauser comorbidity measures to predict colorectal cancer survival using administrative health data

A comparison of charlson and elixhauser comorbidity measures to predict colorectal cancer survival using administrative health data

Statins and New-Onset Diabetes Mellitus and Diabetic Complications: A Retrospective Cohort Study of US Healthy Adults

Outcomes Associated With a Strategy of Adjuvant Metolazone or High‐Dose Loop Diuretics in Acute Decompensated Heart Failure: A Propensity Analysis

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