Dataset Information

Evaluation of algorithms to identify delirium in administrative claims and drug utilization database.

ABSTRACT: To evaluate the performance of delirium-identification algorithms in administrative claims and drug utilization data.We used data from a prospective study of 184 older adults who underwent aortic valve replacement at a single academic medical center to evaluate the following delirium-identification algorithms: (1) International Classification of Diseases (ICD) diagnosis codes for delirium; (2) antipsychotics use; (3) either ICD diagnosis codes or antipsychotics use; and (4) both ICD diagnosis codes and antipsychotics use. These algorithms were evaluated against a validated bedside assessment, the Confusion Assessment Method, and a validated delirium severity scale, the CAM-S.Delirium occurred in 66 patients (36%), of which 14 (21%) had hyperactive or mixed features and 15 (23%) had severe delirium. ICD diagnosis codes for delirium were present in 15 patients (8%). Antipsychotics were used in 13 patients (7%). ICD diagnosis codes alone and antipsychotics use alone had comparable sensitivity (18% vs. 18%) and specificity (98% vs. 99%). Defining delirium using either ICD diagnosis codes or antipsychotics use, sensitivity improved to 30% with little change in specificity (97%). This algorithm showed higher sensitivity for hyperactive or mixed delirium (64%) and severe delirium (73%). Requiring both ICD diagnosis codes and antipsychotics use resulted in perfect specificity but low sensitivity (6%).Delirium-identification algorithms in claims data have low sensitivity and high specificity. Defining delirium using ICD diagnosis codes or antipsychotics use performs better than considering either type of information alone. This information should inform the design and interpretation of claims-based comparative effectiveness and safety research. Copyright © 2017 John Wiley & Sons, Ltd.

SUBMITTER: Kim DH

PROVIDER: S-EPMC5583076 | biostudies-literature | 2017 Aug

REPOSITORIES: biostudies-literature

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Evaluation of algorithms to identify delirium in administrative claims and drug utilization database.

Kim Dae Hyun DH Lee Jung J Kim Caroline A CA Huybrechts Krista F KF Bateman Brian T BT Patorno Elisabetta E Marcantonio Edward R ER

Pharmacoepidemiology and drug safety 20170509 8

<h4>Purpose</h4>To evaluate the performance of delirium-identification algorithms in administrative claims and drug utilization data.<h4>Methods</h4>We used data from a prospective study of 184 older adults who underwent aortic valve replacement at a single academic medical center to evaluate the following delirium-identification algorithms: (1) International Classification of Diseases (ICD) diagnosis codes for delirium; (2) antipsychotics use; (3) either ICD diagnosis codes or antipsychotics us ...[more]

PMID: 28485014

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Project description:BackgroundLack of using a validated algorithm to select patients is a source of selection bias in oncology studies using administrative claims. The objective of this study to evaluate published algorithms to identify patients with soft tissue sarcoma (STS) in administrative claims and to evaluate new algorithms to improved performance.MethodsTwo cancer populations including STS cases and non-STS controls were selected from the MarketScan Explorys Linked Claims-Electronic Medical Record (EMR) Database between January 1, 2000 and July 31, 2018. Eligible cases had a diagnosis on a clinical record for STS in the EMR while controls had no evidence of STS on any EMR records. Both cases and controls were enrolled in administrative claims during a period of observation and were aged???18 years. A split sample was used to test and validate algorithms using data from administrative claims. Values for sensitivity, specificity, and positive predictive value (PPV) were calculated for 14 algorithms. Prior literature validating algorithms in administrative claims across other cancer types report both sensitivity and specificity ranging from as low as 73% to as high as 95%. This was used as a benchmark for defining algorithm success.ResultsThere were 784 STS cases and 249,062 non-STS cancer controls eligible for analysis. Requiring at least two claims with an ICD-CM diagnosis code for STS achieved a sensitivity of 67% but had a specificity of 72%. Algorithms that required NCCN-recommended systemic treatment for STS improved the specificity to over 90% but dropped the sensitivity to below 20%. Other combinations of diagnostic tests, symptoms, and procedures did not improve performance.ConclusionsThe algorithms tested in this study sample did not achieve sufficient performance and suggest the ability to accurately identify the STS population in administrative data is problematic. Difficulties are likely due to the origin of STS in a variety of locations, the non-specific symptoms of STS, and the common diagnostic tests recommended to diagnose the disease. Future research applying machine learning to examine timing and patterns of variables that comprise the diagnostic process may further investigate the ability to accurately identify STS cases in claims databases.

Project description:INTRODUCTION:In addition to the considerable patient and societal burdens, the financial burdens of ankylosing spondylitis (AS) are substantial. Understanding both all-cause and AS-specific direct costs in patients with AS is important if we are to understand the financial impact on patients with AS and payers in the United States. This study assessed both all-cause and AS-specific healthcare utilization and direct costs in US patients with AS using administrative claims data. METHODS:Adults aged ≥ 18 years enrolled in the MarketScan® Commercial and Medicare databases with ≥ 1 inpatient or ≥ 2 non-rule-out outpatient diagnoses of AS between January 1, 2013, and December 31, 2013, were included. Patients had continuous enrollment with medical and pharmacy benefits for ≥ 12 months before and after the index date (first diagnosis). Non-AS controls were matched up to 5:1 to patients with AS on age, geographic region, index calendar year, and sex. All-cause and AS-specific healthcare utilization and direct costs were measured during the follow-up period and reported as per patient per year. RESULTS:Patients with AS (N = 6679) had significantly higher rates of total all-cause inpatient admission (12% vs 6%), emergency department visits (23% vs 15%), nonhospital-based outpatient visits (100% vs 84%), hospital-based outpatient visits (68% vs 46%), other outpatient services (97% vs 81%), and medication use (97% vs 82%) compared with matched controls (N = 19,951). Patients with AS had approximately tenfold higher median total healthcare costs than matched controls ($24,978 vs $2139 per patient per year), largely driven by increased outpatient and pharmacy costs; P < 0.05 for all comparisons. The median (IQR) total AS-specific healthcare costs were $10,250 ($774, $28,824). CONCLUSION:In this analysis of claims data, increased outpatient and pharmacy costs were key contributors to higher all-cause total healthcare costs in US patients with AS. FUNDING:Novartis Pharmaceuticals Corporation, East Hanover, NJ.

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Evaluation of algorithms to identify delirium in administrative claims and drug utilization database.

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Evaluation of algorithms to identify delirium in administrative claims and drug utilization database.

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