Dataset Information

Validation of a hierarchical algorithm to define chronic liver disease and cirrhosis etiology in administrative healthcare data.

ABSTRACT: BACKGROUND AND AIMS:Chronic liver disease (CLD) and cirrhosis are leading causes of death globally with the burden of disease rising significantly over the past several decades. Defining the etiology of liver disease is important for understanding liver disease epidemiology, healthcare planning, and outcomes. The aim of this study was to validate a hierarchical algorithm for CLD and cirrhosis etiology in administrative healthcare data. METHODS:Consecutive patients with CLD or cirrhosis attending an outpatient hepatology clinic in Ontario, Canada from 05/01/2013-08/31/2013 underwent detailed chart abstraction. Gold standard liver disease etiology was determined by an attending hepatologist as hepatitis C (HCV), hepatitis B (HBV), alcohol-related, non-alcoholic fatty liver disease (NAFLD)/cryptogenic, autoimmune or hemochromatosis. Individual data was linked to routinely collected administrative healthcare data at ICES. Diagnostic accuracy of a hierarchical algorithm incorporating both laboratory and administrative codes to define etiology was evaluated by calculating sensitivity, specificity, positive (PPV) and negative predictive values (NPV), and kappa's agreement. RESULTS:442 individuals underwent chart abstraction (median age 53 years, 53% cirrhosis, 45% HCV, 26% NAFLD, 10% alcohol-related). In patients with cirrhosis, the algorithm had adequate sensitivity/PPV (>75%) and excellent specificity/NPV (>90%) for all etiologies. In those without cirrhosis, the algorithm was excellent for all etiologies except for hemochromatosis and autoimmune diseases. CONCLUSIONS:A hierarchical algorithm incorporating laboratory and administrative coding can accurately define cirrhosis etiology in routinely collected healthcare data. These results should facilitate health services research in this growing patient population.

SUBMITTER: Philip G

PROVIDER: S-EPMC7028265 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Validation of a hierarchical algorithm to define chronic liver disease and cirrhosis etiology in administrative healthcare data.

Philip George G Djerboua Maya M Carlone David D Flemming Jennifer A JA

PloS one 20200218 2

<h4>Background and aims</h4>Chronic liver disease (CLD) and cirrhosis are leading causes of death globally with the burden of disease rising significantly over the past several decades. Defining the etiology of liver disease is important for understanding liver disease epidemiology, healthcare planning, and outcomes. The aim of this study was to validate a hierarchical algorithm for CLD and cirrhosis etiology in administrative healthcare data.<h4>Methods</h4>Consecutive patients with CLD or cirr ...[more]

PMID: 32069337

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Project description:STUDY DESIGNRetrospective analysis of Medicare claims linked to a multicenter clinical trial.OBJECTIVEThe Spine Patient Outcomes Research Trial (SPORT) provided a unique opportunity to examine the validity of a claims-based algorithm for grouping patients by surgical indication. SPORT enrolled patients for lumbar disc herniation, spinal stenosis, and degenerative spondylolisthesis. We compared the surgical indication derived from Medicare claims with that provided by SPORT surgeons, the "gold standard."SUMMARY OF BACKGROUND DATAAdministrative data are frequently used to report procedure rates, surgical safety outcomes, and costs in the management of spinal surgery. However, the accuracy of using diagnosis codes to classify patients by surgical indication has not been examined.METHODSMedicare claims were link to beneficiaries enrolled in SPORT. The sensitivity and specificity of 3 claims-based approaches to group patients on the basis of surgical indications were examined: (1) using the first listed diagnosis; (2) using all diagnoses independently; and (3) using a diagnosis hierarchy on the basis of the support for fusion surgery.RESULTSMedicare claims were obtained from 376 SPORT participants, including 21 with disc herniation, 183 with spinal stenosis, and 172 with degenerative spondylolisthesis. The hierarchical coding algorithm was the most accurate approach for classifying patients by surgical indication, with sensitivities of 76.2%, 88.1%, and 84.3% for disc herniation, spinal stenosis, and degenerative spondylolisthesis cohorts, respectively. The specificity was 98.3% for disc herniation, 83.2% for spinal stenosis, and 90.7% for degenerative spondylolisthesis. Misclassifications were primarily due to codes attributing more complex pathology to the case.CONCLUSIONStandardized approaches for using claims data to group patients accurately by surgical indications have widespread interest. We found that a hierarchical coding approach correctly classified more than 90% of spine patients into their respective SPORT cohorts. Therefore, claims data seem to be a reasonably valid approach to classifying patients by surgical indication.LEVEL OF EVIDENCE3.

Project description:Objective: We developed and internally validated an algorithm for disability status in multiple sclerosis (MS) using administrative data. Methods: We linked administrative data from Manitoba, Canada to a clinical dataset with Expanded Disability Status Scale (EDSS) scores for people with MS. Clinical EDSS scores constituted the reference standard. We created candidate indicators using the administrative data. These included indicators based on use of particular health care services (home care, long-term care, rehabilitation admission), use of specific diagnostic codes (such as spasticity, quadriplegia), and codes based on use of Employment and Income Insurance. We developed algorithms to predict severe disability (EDSS ≥6.0), and to predict disability as a continuous measure. We manually developed algorithms, and also employed regression approaches. After we selected our preferred algorithms for disability, we tested their association with health care use due to any cause and infection after potential confounders. Results: We linked clinical and administrative data for 1,767 persons with MS, most of whom were women living in urban areas. All individual indicators tested had specificities >90% for severe disability, and all but a diagnosis of visual disturbance had positive predictive values (PPV) >70%. The combination of home care or long-term care use or rehabilitation admission had a sensitivity of 61.9%, specificity of 90.76%, PPV of 70.06% and negative predictive of 87.21%. Based on regression modeling, the best-performing algorithm for predicting the EDSS as a continuous variable included age, home care use, long-term care admission, admission for rehabilitation, visual disturbance, other paralytic syndromes and spasticity. The mean difference between observed and predicted values of the EDSS was -0.0644 (95%CI -0.1632, 0.0304). Greater disability, whether measured using the clinical EDSS or either of the administrative data algorithms was similarly associated with increased hospitalization rates due to any cause and infection. Conclusion: We developed and internally validated an algorithm for disability in MS using administrative data that may support population-based studies that wish to account for disability status but do not have access to clinical data sources with this information. We also found that more severe disability is associated with increased health care use, including due to infection.

Dataset Information

Validation of a hierarchical algorithm to define chronic liver disease and cirrhosis etiology in administrative healthcare data.

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Validation of a hierarchical algorithm to define chronic liver disease and cirrhosis etiology in administrative healthcare data.

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