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Accuracy of diagnosis codes to identify febrile young infants using administrative data.

ABSTRACT: Administrative data can be used to determine optimal management of febrile infants and aid clinical practice guideline development.Determine the most accurate International Classification of Diseases, Ninth Revision (ICD-9) diagnosis coding strategies for identification of febrile infants.Retrospective cross-sectional study.Eight emergency departments in the Pediatric Health Information System.Infants aged <90 days evaluated between July 1, 2012 and June 30, 2013 were randomly selected for medical record review from 1 of 4 ICD-9 diagnosis code groups: (1) discharge diagnosis of fever, (2) admission diagnosis of fever without discharge diagnosis of fever, (3) discharge diagnosis of serious infection without diagnosis of fever, and (4) no diagnosis of fever or serious infection.The ICD-9 diagnosis code groups were compared in 4 case-identification algorithms to a reference standard of fever ?100.4°F documented in the medical record.Algorithm predictive accuracy was measured using sensitivity, specificity, and negative and positive predictive values.Among 1790 medical records reviewed, 766 (42.8%) infants had fever. Discharge diagnosis of fever demonstrated high specificity (98.2%, 95% confidence interval [CI]: 97.8-98.6) but low sensitivity (53.2%, 95% CI: 50.0-56.4). A case-identification algorithm of admission or discharge diagnosis of fever exhibited higher sensitivity (71.1%, 95% CI: 68.2-74.0), similar specificity (97.7%, 95% CI: 97.3-98.1), and the highest positive predictive value (86.9%, 95% CI: 84.5-89.3).A case-identification strategy that includes admission or discharge diagnosis of fever should be considered for febrile infant studies using administrative data, though underclassification of patients is a potential limitation.

SUBMITTER: Aronson PL

PROVIDER: S-EPMC4715646 | biostudies-literature | 2015 Dec

REPOSITORIES: biostudies-literature

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Accuracy of diagnosis codes to identify febrile young infants using administrative data.

Aronson Paul L PL Williams Derek J DJ Thurm Cary C Tieder Joel S JS Alpern Elizabeth R ER Nigrovic Lise E LE Schondelmeyer Amanda C AC Balamuth Fran F Myers Angela L AL McCulloh Russell J RJ Alessandrini Evaline A EA Shah Samir S SS Browning Whitney L WL Hayes Katie L KL Feldman Elana A EA Neuman Mark I MI

Journal of hospital medicine 20150806 12

<h4>Background</h4>Administrative data can be used to determine optimal management of febrile infants and aid clinical practice guideline development.<h4>Objective</h4>Determine the most accurate International Classification of Diseases, Ninth Revision (ICD-9) diagnosis coding strategies for identification of febrile infants.<h4>Design</h4>Retrospective cross-sectional study.<h4>Setting</h4>Eight emergency departments in the Pediatric Health Information System.<h4>Patients</h4>Infants aged <90 d ...[more]

PMID: 26248691

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Project description:Community-acquired pneumonia (CAP) remains one of the most common indications for pediatric hospitalization in the United States, and it is frequently the focus of research and quality studies. Use of administrative data is increasingly common for these purposes, although proper validation is required to ensure valid study conclusions.To validate administrative billing data for hospitalizations owing to childhood CAP.Case-control study of 4 tertiary care, freestanding children’s hospitals in the United States.A total of 998 medical records of a 25% random sample of 3646 children discharged in 2010 with at least 1 International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM) code representing possible pneumonia were reviewed. Discharges (matched on date of admission) without a pneumonia-related discharge code were also examined to identify potential missed pneumonia cases. Two reference standards, based on provider diagnosis alone (provider confirmed) or in combination with consistent clinical and radiographic evidence of pneumonia (definite), were used to identify CAP.Twelve ICD-9-CM–based coding strategies, each using a combination of primary or secondary codes representing pneumonia or pneumonia-related complications. Six algorithms excluded children with complex chronic conditions.Sensitivity, specificity, and negative and positive predictive values (NPV and PPV, respectively) of the 12 identification strategies.For provider-confirmed CAP (n?=?680), sensitivity ranged from 60.7% to 99.7%; specificity, 75.7% to 96.4%; PPV, 67.9% to 89.6%; and NPV, 82.6% to 99.8%. For definite CAP (n?=?547), sensitivity ranged from 65.6% to 99.6%; specificity, 68.7% to 93.0%; PPV, 54.6% to 77.9%; and NPV, 87.8% to 99.8%. Unrestricted use of the pneumonia-related codes was inaccurate, although several strategies improved specificity to more than 90% with a variable effect on sensitivity. Excluding children with complex chronic conditions demonstrated the most favorable performance characteristics. Performance of the algorithms was similar across institutions.Administrative data are valuable for studying pediatric CAP hospitalizations. The strategies presented here will aid in the accurate identification of relevant and comparable patient populations for research and performance improvement studies.

Project description:BackgroundCapturing frailty within administrative claims data may help to identify high-risk patients and inform population health management strategies. Although it is common to ascertain frailty status utilizing claims-based surrogates (e.g. diagnosis and health service codes) selected according to clinical knowledge, the accuracy of this approach has not yet been examined. We evaluated the accuracy of claims-based surrogates against two clinical definitions of frailty.MethodsThis cross-sectional study was conducted in a Health and Retirement Study subsample of 3097 participants, aged 65 years or older and with at least 12-months of continuous fee-for-service Medicare enrollment. We defined 18 previously utilized claims-based surrogates of frailty from Medicare data and evaluated each against clinical reference standards, ascertained from a direct examination: a deficit accumulation frailty index (FI) (range: 0-1) and frailty phenotype. We also compared the accuracy of the total count of 18 claims-based surrogates with that of a validated claims-based FI model, comprised of 93 claims-based variables.Results19% of participants met clinical criteria for the clinical frailty phenotype. The mean clinical FI for our sample was 0.20 (standard deviation 0.13). Hospital Beds and associated supplies was the claims-based surrogate associated with the highest clinical FI (mean FI 0.49). Claims-based surrogates had low sensitivity ranging from 0.01 (cachexia, adult failure to thrive, anorexia) to 0.38 (malaise and fatigue) and high specificity ranging from 0.79 (malaise and fatigue) to 0.99 (cachexia, adult failure to thrive, anorexia) in discriminating the clinical frailty phenotype. Compared with a validated claims-based FI, the total count of claims-based surrogates demonstrated lower Spearman correlation with the clinical FI (0.41 [95% CI 0.38-0.44] versus 0.59 [95% CI, 0.56-0.61]) and poorer discrimination of the frailty phenotype (C-statistics 0.68 [95% CI, 0.66-0.70] versus 0.75 [95% CI, 0.73-0.77]).ConclusionsClaims-based surrogates, selected according to clinical knowledge, do not accurately capture frailty in Medicare claims data. A simple count of claims-based surrogates improves accuracy but remains inferior to a claims-based FI model.

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Accuracy of diagnosis codes to identify febrile young infants using administrative data.

Publications

Accuracy of diagnosis codes to identify febrile young infants using administrative data.

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