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Derivation and validation of automated electronic search strategies to extract Charlson comorbidities from electronic medical records
Singh B, Singh A, Ahmed A, Wilson GA, Pickering BW, Herasevich V, Gajic O, Li G.. Mayo Clinic proceedings, 2012-09-01
Topic: Risk, prevention and care in population studies
Objective To develop and validate automated electronic note search strategies (automated digital algorithm) to identify Charlson comorbidities. Patients and methods The automated digital algorithm was built by a series of programmatic queries applied to an institutional electronic medical record database. The automated digital algorithm was derived from secondary analysis of an observational cohort study of 1447 patients admitted to the intensive care unit from January 1 through December 31, 2006, and validated in an independent cohort of 240 patients. The sensitivity, specificity, and positive and negative predictive values of the automated digital algorithm and International Classification of Diseases, Ninth Revision (ICD-9) codes were compared with comprehensive medical record review (reference standard) for the Charlson comorbidities. Results In the derivation cohort, the automated digital algorithm achieved a median sensitivity of 100% (range, 99%-100%) and a median specificity of 99.7% (range, 99%-100%). In the validation cohort, the sensitivity of the automated digital algorithm ranged from 91% to 100%, and the specificity ranged from 98% to 100%. The sensitivity of the ICD-9 codes ranged from 8% for dementia to 100% for leukemia, whereas specificity ranged from 86% for congestive heart failure to 100% for leukemia, dementia, and AIDS. Conclusion Our results suggest that search strategies that use automated electronic search strategies to extract Charlson comorbidities from the clinical notes contained within the electronic medical record are feasible and reliable. Automated digital algorithm outperformed ICD-9 codes in all the Charlson variables except leukemia, with greater sensitivity, specificity, and positive and negative predictive values.
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
APA 7th
Singh, B., Singh, A., Ahmed, A., Wilson, G. A., Pickering, B. W., Herasevich, V., et al. (2012). Derivation and validation of automated electronic search strategies to extract Charlson comorbidities from electronic medical records. Mayo Clinic proceedings. https://doi.org/10.1016/j.mayocp.2012.04.015
Vancouver
Singh B, Singh A, Ahmed A, Wilson GA, Pickering BW, Herasevich V, et al. Derivation and validation of automated electronic search strategies to extract Charlson comorbidities from electronic medical records. Mayo Clinic proceedings. 2012. doi:10.1016/j.mayocp.2012.04.015. PMID: 22958988.
BibTeX
@article{singh2012-22958988,
author = {Singh, B. and Singh, A. and Ahmed, A. and Wilson, G. A. and Pickering, B. W. and Herasevich, V. and Gajic, O. and Li, G.},
title = {{Derivation and validation of automated electronic search strategies to extract Charlson comorbidities from electronic medical records}},
journal = {Mayo Clinic proceedings},
year = {2012},
doi = {10.1016/j.mayocp.2012.04.015},
pmid = {22958988},
url = {https://doi.org/10.1016/j.mayocp.2012.04.015}
}
Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. The record names 8 authors; APA and Vancouver show the first six, BibTeX and the downloads carry all of them. Check the citation against the original before you use it.
Key findings
- The sensitivity, specificity, and positive and negative predictive values of the automated digital algorithm and International Classification of Diseases, Ninth Revision (ICD-9) codes were compared with comprehensive medical record review (reference standard) for the Charlson comorbidities.
- Results In the derivation cohort, the automated digital algorithm achieved a median sensitivity of 100% (range, 99%-100%) and a median specificity of 99.7% (range, 99%-100%).
- Conclusion Our results suggest that search strategies that use automated electronic search strategies to extract Charlson comorbidities from the clinical notes contained within the electronic medical record are feasible and reliable.
Why it matters
This study developed and validated automated electronic search strategies to identify Charlson comorbidities from medical records. It is about extracting health record data, not about treating, curing or preventing Alzheimer's disease.
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Study details
- Study designcohort study
- Participants1,447
- Effect size99.7%
- Evidence strengthModerate
How this study scored 61/100
Published in a peer-reviewed journal, and it is a cohort study following people over time. Scored zero for randomized / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Mayo Clinic proceedings) |
| Journal's citation record | 3 / 4 | Mayo Clinic Proceedings: journal h-index 293 (OpenAlex, 2026-10-29) |
| Study design | 7 / 12 | A cohort study following people over time |
| Strength of evidence | 3 / 5 | Rated “moderate” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 1 / 5 | 2012 — within 20 year(s) of the newest study here |
| Sample size | 3 / 4 | 1,447 participants |
| Effect reported | 2 / 3 | Effect reported as a number, without an interval or test |
| Studied in people | 3 / 3 | Studied in people (participant count stated) |
| Randomized / blinded | 0 / 2 | No randomization or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 5 / 7 | Cited 211 times (OpenAlex, 2026-10-10) |
| Citations per year | 3 / 5 | 13.2 citations a year over 16 year(s) since 2012 |
| Impact for its field and year | 4 / 6 | Cited 16.9× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 1 / 6 | Cited by 2 other studies in this library |
| Still being cited | 2 / 4 | Cited 21 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 2 / 4 | Last-listed author Guangxi Li: h-index 37 (OpenAlex, whole career) |
| Institution's track record | 2 / 3 | Mayo Clinic: institution h-index 1,606 (OpenAlex) |
| Multicenter collaboration | 1 / 3 | OpenAlex lists 3 institution(s) in 2 countries |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 22958988) |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Total | 61 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
- DOI: 10.1016/j.mayocp.2012.04.015
- PubMed: 22958988
- Original source →
- Free full text: Mayo Clinic Proceedings →
Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).
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