Literature DB >> 18625948

Identification of patients with diabetic macular edema from claims data: a validation study.

Srilaxmi Bearelly1, Prithvi Mruthyunjaya, Janice P Tzeng, Ivan J Suñer, Alisa M Shea, Jeffrey T Lee, Jonathan W Kowalski, Lesley H Curtis, Kevin A Schulman, Paul P Lee.   

Abstract

OBJECTIVE: To assess the validity of an algorithm for identifying patients with diabetic macular edema (DME) using International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) diagnosis codes in administrative billing data from a convenience sample of physician offices.
METHODS: A convenience sample of 12 general ophthalmologists and 10 retina specialists applied prespecified algorithms based on ICD-9-CM diagnosis codes to the billing claims of their practices and selected the associated medical records. Four ophthalmologists abstracted data from the medical records, which were then compared with the coded diagnoses. Main outcome measures were sensitivity, specificity, and the kappa statistic for the DME algorithm (a combination of codes 250.xx and 362.53), treating medical record documentation of DME as the standard criterion.
RESULTS: The DME algorithm had a sensitivity of 0.88 and a specificity of 0.96 for identifying DME. Excellent agreement was noted between the algorithm and the medical records (kappa = 0.84). The algorithm performed less well in identifying patients with a diagnosis of clinically significant DME (sensitivity, 0.86; specificity, 0.84; kappa = 0.64).
CONCLUSIONS: The results of this pilot study suggest that patients with DME can be identified accurately in claims data using ICD-9-CM diagnosis codes. Application of this algorithm could improve investigations of disease prevalence and disease burden and provide an efficient means of assessing care and interventions.

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Mesh:

Year:  2008        PMID: 18625948     DOI: 10.1001/archopht.126.7.986

Source DB:  PubMed          Journal:  Arch Ophthalmol        ISSN: 0003-9950


  22 in total

1.  Risk of Noninfectious Uveitis with Female Hormonal Therapy in a Large Healthcare Claims Database.

Authors:  Lucia Sobrin; Yinxi Yu; Gayatri Susarla; Weilin Chan; Tian Xia; John H Kempen; Rebecca A Hubbard; Brian L VanderBeek
Journal:  Ophthalmology       Date:  2020-04-27       Impact factor: 12.079

2.  Determinants in Initial Treatment Choice for Diabetic Macular Edema.

Authors:  Brian L VanderBeek; Kurt Scavelli; Yinxi Yu
Journal:  Ophthalmol Retina       Date:  2019-05-25

3.  Techniques for improving ophthalmic studies performed on administrative databases.

Authors:  Durga S Borkar; Lucia Sobrin; Rebecca A Hubbard; John H Kempen; Brian L VanderBeek
Journal:  Ophthalmic Epidemiol       Date:  2018-12-06       Impact factor: 1.648

4.  Chronic medical conditions as risk factors for herpes zoster.

Authors:  Riduan M Joesoef; Rafael Harpaz; Jessica Leung; Stephanie R Bialek
Journal:  Mayo Clin Proc       Date:  2012-10       Impact factor: 7.616

Review 5.  Use of health care claims data to study patients with ophthalmologic conditions.

Authors:  Joshua D Stein; Flora Lum; Paul P Lee; William L Rich; Anne L Coleman
Journal:  Ophthalmology       Date:  2014-01-14       Impact factor: 12.079

6.  Exploration of Temporal ICD Coding Bias Related to Acute Diabetic Conditions.

Authors:  Mollie McKillop; Fernanda Polubriaginof; Chunhua Weng
Journal:  AMIA Annu Symp Proc       Date:  2015-11-05

7.  Resource use and costs associated with diabetic macular edema in elderly persons.

Authors:  Alisa M Shea; Lesley H Curtis; Bradley G Hammill; Jonathan W Kowalski; Arliene Ravelo; Paul P Lee; Frank A Sloan; Kevin A Schulman
Journal:  Arch Ophthalmol       Date:  2008-12

8.  Accuracy of international classification of diseases, ninth revision, clinical modification billing codes for common ophthalmic conditions.

Authors:  Kelly W Muir; Chirag Gupta; Prakriti Gill; Joshua D Stein
Journal:  JAMA Ophthalmol       Date:  2013-01       Impact factor: 7.389

9.  Accuracy of Billing Codes Used in the Therapeutic Care of Diabetic Retinopathy.

Authors:  Marisa Lau; Jonathan L Prenner; Alexander J Brucker; Brian L VanderBeek
Journal:  JAMA Ophthalmol       Date:  2017-07-01       Impact factor: 7.389

10.  Validity of autism diagnoses using administrative health data.

Authors:  L Dodds; A Spencer; S Shea; D Fell; B A Armson; A C Allen; S Bryson
Journal:  Chronic Dis Can       Date:  2009
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