Purpose: Large numbers of multiple myeloma patients can be studied in real-world clinical settings using administrative databases. The validity of these studies is contingent upon accurate case identification. Our objective was to develop and evaluate algorithms to use with administrative data to identify multiple myeloma cases.
Methods: Patients aged ≥18 years with ≥1 International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM) code for multiple myeloma (203.0x) were identified at two study sites. At site 1, several algorithms were developed and validated by comparing results to tumor registry cases. An algorithm with a reasonable positive predictive value (PPV) (0.81) and sensitivity (0.73) was selected and then validated at site 2 where results were compared with medical chart data. The algorithm required that ICD-9-CM codes 203.0x occur before and after the diagnostic procedure codes for multiple myeloma.
Results: At site 1, we identified 1432 patients. The PPVs of algorithms tested ranged from 0.54 to 0.88. Sensitivities ranged from 0.30 to 0.88. At site 2, a random sample (n = 400) was selected from 3866 patients, and medical charts were reviewed by a clinician for 105 patients. Algorithm PPV was 0.86 (95% CI, 0.79-0.92).
Conclusions: We identified cases of multiple myeloma with adequate validity for claims database analyses. At least two ICD-9-CM diagnosis codes 203.0x preceding diagnostic procedure codes for multiple myeloma followed by ICD-9-CM codes within a specific time window after diagnostic procedure codes were required to achieve reasonable algorithm performance.
Keywords: ICD-9 codes; administrative data; algorithm; multiple myeloma; pharmacoepidemiology; positive predictive value; sensitivity.
© 2019 John Wiley & Sons, Ltd.