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Validation of Algorithms to Identify Pancreatic Cancer and Thyroid Neoplasms from Health Insurance Claims Data in a 10-Year Foll

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Validation of Algorithms to Identify Pancreatic Cancer and Thyroid Neoplasms from Health Insurance Claims Data in a 10-Year Foll Validation of Algorithms to Identify Pancreatic Cancer and Thyroid Neoplasms From Health Insurance Claims Data in a 10-Year Follow-Up Study Caihua Liang, MD, PhD1*; Monica L Bertoia, MPH, PhD1; C Robin Clifford, MS1; Yan Ding, MSc1; Qing Qiao, MD, PhD2; Joshua J Gagne, PharmD, ScD1,3; David D Dore, PharmD, PhD1 1Optum Epidemiology, Boston, MA/Ann Arbor, MI, USA; 2Global Medical Affair AstraZeneca, Gothenburg, Sweden; 3Division of Pharmacoepidemiology, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, USA. *Corresponding author: [email protected]. This study was funded through a research contract between Optum Epidemiology and AstraZeneca. Background Methods (cont.) Identification of cancer events in health insurance claims data can be Figure 1. Pancreatic cancer and thyroid neoplasm relaxed and restricted challenging and subject to misclassification. Given the low incidence of algorithms certain cancers, robust performance evaluation requires a large sample size PANCREATIC CANCER THYROID NEOPLASMS with long-term follow-up. ICD-9 Codes ICD-9 Codes (Malignant neoplasm of …) 193 Malignant neoplasm of thyroid gland 157.X … pancreas 226 Benign neoplasm of thyroid gland Objective 157.0 … head of pancreas 157.1 … body of pancreas Thyroid Cancer Benign Thyroid Neoplasm To validate algorithms for potential incident pancreatic cancer and thyroid 157.2 … tail of pancreas neoplasms in a 10-year follow-up study using a health insurance claims 157.3 … pancreatic duct Restricted Algorithm: Restricted Algorithm: 157.4 … islets of Langerhans a+b+c a+b+c database. 157.8 … other specified sites of Relaxed Algorithm: Relaxed Algorithm: pancreas a+b or a+c a+b or a+c 157.9 … pancreas, part unspecified Data Source a. Any in- or out-patient a. Any in- or out-patient thyroid cancer diagnosis benign thyroid neoplasm Optum Research Database (ORD) Restricted Algorithm: a+b+c+d code codes b. Without a diagnosis of b. Without a diagnosis of . Relaxed Algorithm: a+b or a+c Contains eligibility, pharmacy and medical claims data from a large US benign thyroid neoplasm thyroid cancer within 60 health insurer. It is geographically diverse and represents ~4% of the US a. Any in- or out-patient diagnosis within 60 days after the days after the diagnosis of diagnosis of thyroid cancer benign thyroid neoplasm population. codes of pancreatic cancer b. Without a diagnosis of benign c. With one or more of thyroid c. With biopsy claims within pancreatic neoplasm within 60 surgery, chemotherapy, 90 days before the days after the diagnosis of radioiodine therapy or diagnosis of benign thyroid Methods pancreatic cancer radiation therapy within 180 neoplasm c. With one or more of pancreas days after the diagnosis of . Study Design and Study Population surgery, chemotherapy or thyroid cancer radiation therapy within 180 days d. With one or more claims – This validation study was performed in the context of a cohort study of after the diagnosis of pancreatic evidence of serum antidiabetic drug use and incidence of pancreatic cancer and thyroid cancer calcitonin levels within 180 neoplasms (including both malignant and benign neoplasms). d. Without a diagnosis of other days after thyroid surgery cancers within 60 days before or or thyroid cancer diagnosis – Study population included type 2 diabetic patients who initiated a new after the diagnosis of pancreatic cancer Medullary Thyroid Cancer antidiabetic drug between 01 June 2005 and 30 June 2015 with ≥9 (MTC) months continuous enrollment in the health plan (baseline period) prior Relaxed = Restricted to cohort entry. Algorithm: a+b+d or a+c+d – Patients with pancreatic or thyroid neoplasms during the baseline period were excluded. – Potential cases of pancreatic cancer and thyroid neoplasms during ICD-9: International Classification of Diseases, 9th Revision follow-up were identified using both relaxed and restricted algorithms Note: the date of diagnosis refers to the date of first claim for the diagnosis listed in Figure1. The restricted algorithms are always a subset of the relaxed algorithms. Results . Approvals . With the ‘relaxed’ algorithms, we identified 558 potential cases and received – Institutional Review Board and affiliated Privacy Board. medical records for 422 (173 for pancreatic cancer, 144 for thyroid cancer and 105 for benign thyroid neoplasm), for an abstraction proportion of 76%. Medical Record Abstraction . With the ‘restricted’ algorithms, we identified 97 potential pancreatic cancers, – Facilities or providers were selected with the following preference 103 potential thyroid cancers, and 32 potential benign thyroid neoplasms. order: . PPVs and conditional sensitivities were presented in Table 1. The hospital where the patient was diagnosed or treated for pancreatic cancer or thyroid neoplasms Table 1. Positive Predictive Value (PPV) and sensitivity of algorithm- . The surgeon associated with pancreatectomy or thyroidectomy identified cases . Medical specialists (e.g., endocrinologist, oncologist) who treated Chart- Conditional the patient for pancreatic cancer or thyroid neoplasms Algorithm-identified confirmed PPV sensitivity cancer cases (95% CI) . Other (e.g., consultation, primary care physician) (95% CI) Yes No − Medical record abstraction included information relevant to case Pancreatic cancer1 adjudication from 9 months prior to through 6 months after the claims- Yes 76 21 0.78 (0.69 - 0.86) 0.68 (0.58 - 0.76) identified diagnosis date. No 36 40 − − . Adjudication Thyroid cancer (all)1 − Review panels included one pancreatic oncologist and one general Yes 91 12 0.88 (0.81 - 0.94) 0.81 (0.73 - 0.88) oncologist for pancreatic cancer; one thyroid oncologist and one No 21 20 − − 1 general oncologist for thyroid cancer. Non-medullary thyroid cancer Yes 91 12 0.88 (0.81 - 0.94) 0.86 (0.78 - 0.92) − Adjudicators reviewed the medical records and determined the case No 15 19 − − status, assigning the certainty level (i.e., definite, probable, possible, 2 non-case). Discrepancies between adjudicators were resolved by Medullary thyroid cancer mutual consensus. Yes 6 1 0.86 (0.42 - 1.00) − No 0 137 − − − All the reviews were blinded to exposure status and protected health Benign thyroid neoplasm1 information. Yes 31 1 0.97 (0.84 - 1.00) 0.36 (0.26 - 0.48) . Data Analysis No 54 19 − − 1 − Positive predictive values (PPVs): number of confirmed cases (definite Application of the restricted algorithm 2 cases and probable cases) divided by the number of algorithm- Application of the relaxed algorithm identified cases for which charts were received − Conditional sensitivity was estimated in relation to potential cases Discussion identified from a more inclusive ‘relaxed’ algorithm that required only . The restricted algorithms accurately identified cases of pancreatic cancer the presence of ICD-9 diagnosis codes. and thyroid neoplasms from health insurance claims with high PPV. − The fraction of cases identified by the restricted algorithms among the . The conditional sensitivity was lower for benign thyroid neoplasm than for medical record-confirmed cases identified by the relaxed algorithms. pancreatic and thyroid cancer. − We computed the exact binomial 95% confidence intervals (CIs). There is a tradeoff between maximizing the specificity versus sensitivity of To download a PDF copy of the poster, an algorithm. please scan the QR code: Acknowledgements: We thank Kwame Appenteng and Stephen P Motsko for their valuable contributions on the algorithm development. Presented at the 34th International Conference on Pharmacoepidemiology and Therapeutic Risk Management, Prague, Czech Republic; August 22-26, 2018, Abstract #2349 .
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