No consistency in ICD-10 codes for adverse drug events

As more older adults take more medications, the drugs may have unintended consequences. Researchers whose work was published online this week in the Journal of the American Medical Informatics Association found wild variability in medical research when they set out to create a comprehensive set of ICD-10 codes used to identify these adverse drug events from administrative data.

The codes, they said, could be used in population health studies to look at factors such as prescribing methods or care settings associated with these adverse events and ways to prevent them.

"However, no consensus presently exists among health researchers on how to identify adverse drug events reliably within such data sources, leading to substantial variability in the methods used for their identification," they added.

The research focused on 41 published studies that used administrative data to ascertain the prevalence of adverse drug events in certain populations. Only 13 were conducted in North America, since the U.S. has yet to fully implement ICD-10.

They found 827 ICD-10 codes that have been used for this purpose, including 175 citing external injury and 652 based on disease manifestation. Only one published guideline recommended the use of algorithms to identify external injury cause codes clustered with disease manifestation codes. Of the reviewed studies, two provided estimates of the code set's sensitivity and specificity.

Also, because of the multiple ways in which adverse drug events may be coded, researchers must develop methods to avoid double counting, the researchers said. Much work remains to be done to reach consensus on appropriate ICD-10 coding for drug events, the authors concluded.

Researchers at Microsoft Research Labs, in conjunction with Stanford University, have found that mining web search data can help the FDA and pharmaceutical companies discover previously unknown dangerous drug interactions.

Stanford University researchers also are claiming success in using analysis of free-text notes in electronic health records for surveillance of drug interactions in near real time.

To learn more:
- find the research

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