Predictive values of diagnostic codes for identifying serious hypocalcemia and dermatologic adverse events among women with postmenopausal osteoporosis in a commercial health plan database

Abstract Background Post-marketing safety studies of medicines often rely on administrative claims databases to identify adverse outcomes following drug exposure. Valid ascertainment of outcomes is essential for accurate results. We aim to quantify the validity of diagnostic codes for serious hypocalcemia and dermatologic adverse events from insurance claims data among women with postmenopausal osteoporosis (PMO). Methods We identified potential cases of serious hypocalcemia and dermatologic events through ICD-9 diagnosis codes among women with PMO within claims from a large US healthcare insurer (June 2005-May 2010). A physician adjudicated potential hypocalcemic and dermatologic events identified from the primary position on emergency department (ED) or inpatient claims through medical record review. Positive predictive values (PPVs) and 95% confidence intervals (CIs) quantified the fraction of potential cases that were confirmed. Results Among 165,729 patients with PMO, medical charts were obtained for 40 of 55 (73%) potential hypocalcemia cases; 16 were confirmed (PPV 40%, 95% CI 25â 57%). The PPV was higher for ED than inpatient claims (82 vs. 24%). Among 265 potential dermatologic events (primarily urticaria or rash), we obtained 184 (69%) charts and confirmed 128 (PPV 70%, 95% CI 62â 76%). The PPV was higher for ED than inpatient claims (77 vs. 39%). Conclusion Diagnostic codes for hypocalcemia and dermatologic events may be sufficient to identify events giving rise to emergency care, but are less accurate for identifying events within hospitalizations.

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PID https://www.doi.org/10.6084/m9.figshare.c.4064015.v1
PID https://www.doi.org/10.6084/m9.figshare.c.4064015
URL https://dx.doi.org/10.6084/m9.figshare.c.4064015.v1
URL https://dx.doi.org/10.6084/m9.figshare.c.4064015
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Author Wang, Florence
Author Xue, Fei
Author Ding, Yan
Author Ng, Eva
Author Critchlow, Cathy
Author Dore, David
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Publication Date 2018-04-10
Publisher Figshare
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keyword FOS: Sociology
keyword FOS: Computer and information sciences
keyword FOS: Mathematics
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Source https://science-innovation-policy.openaire.eu/search/dataset?datasetId=dedup_wf_001::e639189f50c9859fc20b105abd14c816
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Last Updated 15 December 2020, 19:08 (CET)
Created 15 December 2020, 19:08 (CET)