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Phenotyping Adverse Drug Reactions: Statin-Related Myotoxicity.

Wiley LK, Moretz JD, Denny JC, Peterson JF, Bush WS - AMIA Jt Summits Transl Sci Proc (2015)

Bottom Line: We compared multiple phenotyping algorithms using administrative codes, laboratory measurements, and full-text keyword matching to identify statin-related myopathy from EMRs.Unlike in most disease phenotyping algorithms, addition of ICD9 codes or laboratory data did not appreciably increase algorithm accuracy.We conclude that phenotype algorithms for adverse drug events should consider text based approaches.

View Article: PubMed Central - PubMed

Affiliation: Department of Biomedical Informatics, Vanderbilt University, Nashville, TN.

ABSTRACT
It is unclear the extent to which best practices for phenotyping disease states from electronic medical records (EMRs) translate to phenotyping adverse drug events. Here we use statin-induced myotoxicity as a case study to identify best practices in this area. We compared multiple phenotyping algorithms using administrative codes, laboratory measurements, and full-text keyword matching to identify statin-related myopathy from EMRs. Manual review of 300 deidentified EMRs with exposure to at least one statin, created a gold standard set of 124 cases and 176 controls. We tested algorithms using ICD-9 billing codes, laboratory measurements of creatine kinase (CK) and keyword searches of clinical notes and allergy lists. The combined keyword algorithms produced were the most accurate (PPV=86%, NPV=91%). Unlike in most disease phenotyping algorithms, addition of ICD9 codes or laboratory data did not appreciably increase algorithm accuracy. We conclude that phenotype algorithms for adverse drug events should consider text based approaches.

No MeSH data available.


Related in: MedlinePlus

Receiver Operator Characteristic Graph Comparison of Phenotyping Algorithms Comparison of true positive (TPR) vs false positive rate (FPR). Points in upper left are better classifiers.
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f1-2092127: Receiver Operator Characteristic Graph Comparison of Phenotyping Algorithms Comparison of true positive (TPR) vs false positive rate (FPR). Points in upper left are better classifiers.

Mentions: Combined Algorithms and Algorithm Comparison: The most sensitive algorithm (sens. = 0.99, spec. = 0.32, PPV = 0.51) included: all corrected allergy, all corrected keyword, the second CK (any value of CK, no troponin measurement) and the ICD9 algorithms and identified all but one case in our data set. The second most sensitive algorithm (sens. = 0.94, spec. = 0.84, PPV = 0.81) contained: all corrected allergy algorithms, all corrected keyword algorithms, and all ICD9 codes. This algorithm identified 116 of our 124 cases. The algorithm with the best balance of sensitivity and specificity (sensitivity = 0.88, specificity = 0.90, PPV = 0.86) included only the corrected allergy algorithms and the corrected keyword algorithms and identified 109 of our cases. A comparison of all algorithms in ROC (receiver operator characteristic) space is presented in Figure 1.


Phenotyping Adverse Drug Reactions: Statin-Related Myotoxicity.

Wiley LK, Moretz JD, Denny JC, Peterson JF, Bush WS - AMIA Jt Summits Transl Sci Proc (2015)

Receiver Operator Characteristic Graph Comparison of Phenotyping Algorithms Comparison of true positive (TPR) vs false positive rate (FPR). Points in upper left are better classifiers.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4525276&req=5

f1-2092127: Receiver Operator Characteristic Graph Comparison of Phenotyping Algorithms Comparison of true positive (TPR) vs false positive rate (FPR). Points in upper left are better classifiers.
Mentions: Combined Algorithms and Algorithm Comparison: The most sensitive algorithm (sens. = 0.99, spec. = 0.32, PPV = 0.51) included: all corrected allergy, all corrected keyword, the second CK (any value of CK, no troponin measurement) and the ICD9 algorithms and identified all but one case in our data set. The second most sensitive algorithm (sens. = 0.94, spec. = 0.84, PPV = 0.81) contained: all corrected allergy algorithms, all corrected keyword algorithms, and all ICD9 codes. This algorithm identified 116 of our 124 cases. The algorithm with the best balance of sensitivity and specificity (sensitivity = 0.88, specificity = 0.90, PPV = 0.86) included only the corrected allergy algorithms and the corrected keyword algorithms and identified 109 of our cases. A comparison of all algorithms in ROC (receiver operator characteristic) space is presented in Figure 1.

Bottom Line: We compared multiple phenotyping algorithms using administrative codes, laboratory measurements, and full-text keyword matching to identify statin-related myopathy from EMRs.Unlike in most disease phenotyping algorithms, addition of ICD9 codes or laboratory data did not appreciably increase algorithm accuracy.We conclude that phenotype algorithms for adverse drug events should consider text based approaches.

View Article: PubMed Central - PubMed

Affiliation: Department of Biomedical Informatics, Vanderbilt University, Nashville, TN.

ABSTRACT
It is unclear the extent to which best practices for phenotyping disease states from electronic medical records (EMRs) translate to phenotyping adverse drug events. Here we use statin-induced myotoxicity as a case study to identify best practices in this area. We compared multiple phenotyping algorithms using administrative codes, laboratory measurements, and full-text keyword matching to identify statin-related myopathy from EMRs. Manual review of 300 deidentified EMRs with exposure to at least one statin, created a gold standard set of 124 cases and 176 controls. We tested algorithms using ICD-9 billing codes, laboratory measurements of creatine kinase (CK) and keyword searches of clinical notes and allergy lists. The combined keyword algorithms produced were the most accurate (PPV=86%, NPV=91%). Unlike in most disease phenotyping algorithms, addition of ICD9 codes or laboratory data did not appreciably increase algorithm accuracy. We conclude that phenotype algorithms for adverse drug events should consider text based approaches.

No MeSH data available.


Related in: MedlinePlus