Dataset Information

Development and Validation of an Algorithm to Identify Patients with Multiple Myeloma Using Administrative Claims Data.

ABSTRACT: PURPOSE:The objective was to expand on prior work by developing and validating a new algorithm to identify multiple myeloma (MM) patients in administrative claims. METHODS:Two files were constructed to select MM cases from MarketScan Oncology Electronic Medical Records (EMR) and controls from the MarketScan Primary Care EMR during January 1, 2000-March 31, 2014. Patients were linked to MarketScan claims databases, and files were merged. Eligible cases were age ?18, had a diagnosis and visit for MM in the Oncology EMR, and were continuously enrolled in claims for ?90?days preceding and ?30?days after diagnosis. Controls were age ?18, had ?12?months of overlap in claims enrollment (observation period) in the Primary Care EMR and ?1 claim with an ICD-9-CM diagnosis code of MM (203.0×) during that time. Controls were excluded if they had chemotherapy; stem cell transplant; or text documentation of MM in the EMR during the observation period. A split sample was used to develop and validate algorithms. A maximum of 180?days prior to and following each MM diagnosis was used to identify events in the diagnostic process. Of 20 algorithms explored, the baseline algorithm of 2 MM diagnoses and the 3 best performing were validated. Values for sensitivity, specificity, and positive predictive value (PPV) were calculated. CONCLUSION:Three claims-based algorithms were validated with ~10% improvement in PPV (87-94%) over prior work (81%) and the baseline algorithm (76%) and can be considered for future research. Consistent with prior work, it was found that MM diagnoses before and after tests were needed.

SUBMITTER: Princic N

PROVIDER: S-EPMC5081355 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Development and Validation of an Algorithm to Identify Patients with Multiple Myeloma Using Administrative Claims Data.

Princic Nicole N Gregory Chris C Willson Tina T Mahue Maya M Felici Diana D Werther Winifred W Lenhart Gregory G Foley Kathleen A KA

Frontiers in oncology 20161027

<h4>Purpose</h4>The objective was to expand on prior work by developing and validating a new algorithm to identify multiple myeloma (MM) patients in administrative claims.<h4>Methods</h4>Two files were constructed to select MM <i>cases</i> from MarketScan Oncology Electronic Medical Records (EMR) and <i>controls</i> from the MarketScan Primary Care EMR during January 1, 2000-March 31, 2014. Patients were linked to MarketScan claims databases, and files were merged. Eligible cases were age ≥18, h ...[more]

PMID: 27833899

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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.

Project description:ObjectivePressure ulcer development is a quality of care indicator, as pressure ulcers are potentially preventable. Yet pressure ulcer is a leading cause of morbidity, discomfort and additional healthcare costs for inpatients. Methods are lacking for accurate surveillance of pressure ulcer in hospitals to track occurrences and evaluate care improvement strategies. The main study aim was to validate hospital discharge abstract database (DAD) in recording pressure ulcers against nursing consult reports, and to calculate prevalence of pressure ulcers in Alberta, Canada in DAD. We hypothesised that a more inclusive case definition for pressure ulcers would enhance validity of cases identified in administrative data for research and quality improvement purposes.SettingA cohort of patients with pressure ulcers were identified from enterostomal (ET) nursing consult documents at a large university hospital in 2011.ParticipantsThere were 1217 patients with pressure ulcers in ET nursing documentation that were linked to a corresponding record in DAD to validate DAD for correct and accurate identification of pressure ulcer occurrence, using two case definitions for pressure ulcer.ResultsUsing pressure ulcer definition 1 (7 codes), prevalence was 1.4%, and using definition 2 (29 codes), prevalence was 4.2% after adjusting for misclassifications. The results were lower than expected. Definition 1 sensitivity was 27.7% and specificity was 98.8%, while definition 2 sensitivity was 32.8% and specificity was 95.9%. Pressure ulcer in both DAD and ET consultation increased with age, number of comorbidities and length of stay.ConclusionDAD underestimate pressure ulcer prevalence. Since various codes are used to record pressure ulcers in DAD, the case definition with more codes captures more pressure ulcer cases, and may be useful for monitoring facility trends. However, low sensitivity suggests that this data source may not be accurate for determining overall prevalence, and should be cautiously compared with other prevalence studies.

Dataset Information

Development and Validation of an Algorithm to Identify Patients with Multiple Myeloma Using Administrative Claims Data.

Publications

Development and Validation of an Algorithm to Identify Patients with Multiple Myeloma Using Administrative Claims Data.

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