PS: I usually used my own programs to extract comorbidities. And in the documentation, the user should be encouraged to run validate() before running comorbidity() or any other function.

However, I think having a function in comorbidity (and icd and medicalrisk) package called validate() which just checks your data to see if it contains invalid ICD codes is a great idea. I generally am a fan of ellessenne's approach: the fact that a data set might contain invalid ICD codes is the data curator's fault and should not be addressed by comorbidity package. We also need to discuss a higher level question: if ICD codes change over time (which they do) but algorithms like Charlson and Elixhauser are not updated periodically, who is accountable for making those algorithms 'current'? Programmers or researchers? was 498.82 ever a valid ICD code? in this case, no) and specifically determine if any ICD9/ICD10 codes that have been added or removed over time were part of any of the categories for Charlson or Elixhauser. To that end, we need to first analyze how ICD codes change over time (eg. We need to standardize our approaches across all these packages. I'm open to considering other ways for us to collaborate. One way for us all to work together might be for you to continue to implement comorbidities how you wish, and consider importing the 'icd' package for validation and explanation of actual codes, which I've put a lot of time into. I see you generated test data by sampling only valid codes. You can see I do this in the icd source code. I didn't look into the cancer side, but there were many more discrepancies which I suspect to be of the same origin.Ĭan I suggest randomly generating strings for testing. In 'icd' I took the view that I would count non-existent descendents of extant codes, but I would exclude codes which had no parent with any association with a comorbidity. It is an open question what we should all be doing if potentially valid, but utterly non-existent codes appear, particularly as different annual revisions may gain or lose codes, and we would probably want to sweep them all up when looking for comorbidities. I dug into your source code and noticed you grep for descendents of a non-existent top-level code (498), giving a false positive for chronic lung disease with a random test code 498.82. I was distressed to see we all differed from each other, particularly in the COPD/chronic lung disease and cancer/tumor categories. I took some time this morning to compare the comorbidity computations between our packages both in speed and content. Also, glad to see you live in my home country!

Hi, I'm the author of the R package 'icd', and I'm glad to see that several of us have worked on solving the comorbidity computation problem.

Spss code elixhauser comorbid score