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Accuracy of diagnosis and health service codes in identifying frailty in Medicare data.

ABSTRACT: BACKGROUND:Capturing 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. METHODS:This 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. RESULTS:19% 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]). CONCLUSIONS:Claims-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.

SUBMITTER: Festa N

PROVIDER: S-EPMC7487915 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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Accuracy of diagnosis and health service codes in identifying frailty in Medicare data.

Festa Natalia N Shi Sandra M SM Kim Dae Hyun DH

BMC geriatrics 20200907 1

<h4>Background</h4>Capturing 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.<h4>Methods</h4 ...[more]

PMID: 32894057

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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:ObjectivesTo assess the reliability of codes relevant to critically ill patients in administrative data.DesignRetrospective cohort study linking data from Acute Physiology and Chronic Health Evaluation Outcomes, a clinical database of ICU patients with data from Medicare Provider Analysis and Review. We linked data based on matching for sex, date of birth, hospital, and date of admission to hospital.SettingForty-six hospitals in the United States participating in Acute Physiology and Chronic Health Evaluation Outcomes.PatientsAll patients in Acute Physiology and Chronic Health Evaluation Outcomes greater than or equal to 65 years old who could be linked with hospitalization records in Medicare Provider Analysis and Review from January 1, 2009, through September 30, 2012.Measurements and main resultsOf 62,451 patients in the Acute Physiology and Chronic Health Evaluation Outcomes dataset, 80.1% were matched with data in Medicare Provider Analysis and Review. All but 2.7% of Acute Physiology and Chronic Health Evaluation Outcomes ICU patients had either an ICU or coronary care unit charge in Medicare Provider Analysis and Review. In Acute Physiology and Chronic Health Evaluation Outcomes, 37.0% received mechanical ventilation during the ICU stay versus 24.1% in Medicare Provider Analysis and Review. The Medicare Provider Analysis and Review procedure codes for mechanical ventilation had high specificity (96.0%; 95% CI, 95.8-96.2), but only moderate sensitivity (58.4%; 95% CI, 57.7-59.1), with a positive predictive value of 89.6% (95% CI, 89.1-90.1) and negative predictive value of 79.7% (95% CI, 79.4-80.1). For patients with mechanical ventilation codes, Medicare Provider Analysis and Review overestimated the percentage with a duration greater than 96 hours (36.6% vs 27.3% in Acute Physiology and Chronic Health Evaluation Outcomes). There was discordance in the hospital discharge status (alive or dead) for only 0.47% of all linked records (κ = 1.00).ConclusionsMedicare Provider Analysis and Review data contain robust information on hospital mortality for patients admitted to the ICU but have limited ability to identify all patients who received mechanical ventilation during a critical illness. Estimates of use of mechanical ventilation in the United States should likely be revised upward.

Dataset Information

Accuracy of diagnosis and health service codes in identifying frailty in Medicare data.

Publications

Accuracy of diagnosis and health service codes in identifying frailty in Medicare data.

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