Development of algorithms to identify individuals with Neurofibromatosis type 1 within administrative data and electronic medical records in Ontario, Canada
Abstract Background There is limited population-based data on Neurofibromatosis type 1 (NF1) in North America. We aimed to develop and validate algorithms using administrative health data and electronic medical records (EMRs) to identify individuals with NF1 in Ontario, Canada. Methods We conducted...
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| Format: | Article |
| Language: | English |
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BMC
2022-08-01
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| Series: | Orphanet Journal of Rare Diseases |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13023-022-02493-5 |
| _version_ | 1811285855211356160 |
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| author | Carolina Barnett Elisa Candido Branson Chen Priscila Pequeno Patricia C. Parkin Karen Tu |
| author_facet | Carolina Barnett Elisa Candido Branson Chen Priscila Pequeno Patricia C. Parkin Karen Tu |
| author_sort | Carolina Barnett |
| collection | DOAJ |
| description | Abstract Background There is limited population-based data on Neurofibromatosis type 1 (NF1) in North America. We aimed to develop and validate algorithms using administrative health data and electronic medical records (EMRs) to identify individuals with NF1 in Ontario, Canada. Methods We conducted an electronic free-text search of 15 commonly-used terms related to NF1 in the Electronic Medical Records Primary Care Database. Records were reviewed by two trained abstractors who classified them as confirmed, possible, and not NF1. An investigator with clinical expertise performed final NF1 classification. Patients were classified as confirmed if there was a documented diagnosis, meeting NIH criteria. Patients were classified as possible if (1) NF1 was recorded in the cumulative patient profile, but no clinical information to support the diagnosis; (2) only one criterion for diagnosis (e.g. child of confirmed case) but no further data to confirm or rule out. We tested different combinations of outpatient and inpatient billing codes, and applied a free-text search algorithm to identify NF1 cases in administrative data and EMRs, respectively. Results Of 273,440 eligible patients, 2,058 had one or more NF1 terms in their medical records. The terms “NF”, “café-au-lait”, or “sheath tumour” were constrained to appear in combination with another NF1 term. This resulted in 837 patients: 37 with possible and 71 with confirmed NF1. The population prevalence ranged from 1 in 3851 (confirmed NF1) to 1 in 2532 (possible and confirmed NF1). Billing code algorithms had poor performance, with overall low PPV (highest being 71%). The accuracy of the free-text EMR algorithm in identifying patients with NF1 was: sensitivity 85% (95% CI 74–92%), specificity 100% (95% CI 100–100%), positive predictive value 80% (95% CI 69–88%), negative predictive value 100% (95% CI 100–100%), and false positive rate 20% (95% CI 11–33%). Of false positives, 53% were possible NF1. Conclusions A free-text search algorithm within the EMR had high sensitivity, specificity and predictive values. Algorithms using billing codes had poor performance, likely due to the lack of NF-specific codes for outpatient visits. While NF1 ICD-9 and 10 codes are used for hospital admissions, only ~ 30% of confirmed NF1 cases had a hospitalization associated with an NF1 code. |
| first_indexed | 2024-04-13T02:50:19Z |
| format | Article |
| id | doaj.art-3cee17bb5ad24204ba49ef33d5d2b376 |
| institution | Directory Open Access Journal |
| issn | 1750-1172 |
| language | English |
| last_indexed | 2024-04-13T02:50:19Z |
| publishDate | 2022-08-01 |
| publisher | BMC |
| record_format | Article |
| series | Orphanet Journal of Rare Diseases |
| spelling | doaj.art-3cee17bb5ad24204ba49ef33d5d2b3762022-12-22T03:05:51ZengBMCOrphanet Journal of Rare Diseases1750-11722022-08-011711810.1186/s13023-022-02493-5Development of algorithms to identify individuals with Neurofibromatosis type 1 within administrative data and electronic medical records in Ontario, CanadaCarolina Barnett0Elisa Candido1Branson Chen2Priscila Pequeno3Patricia C. Parkin4Karen Tu5Elisabeth Raab Neurofibromatosis Clinic, Toronto General Hospital, University of TorontoICESICESICESInstitute of Health Policy, Management and Evaluation, University of TorontoInstitute of Health Policy, Management and Evaluation, University of TorontoAbstract Background There is limited population-based data on Neurofibromatosis type 1 (NF1) in North America. We aimed to develop and validate algorithms using administrative health data and electronic medical records (EMRs) to identify individuals with NF1 in Ontario, Canada. Methods We conducted an electronic free-text search of 15 commonly-used terms related to NF1 in the Electronic Medical Records Primary Care Database. Records were reviewed by two trained abstractors who classified them as confirmed, possible, and not NF1. An investigator with clinical expertise performed final NF1 classification. Patients were classified as confirmed if there was a documented diagnosis, meeting NIH criteria. Patients were classified as possible if (1) NF1 was recorded in the cumulative patient profile, but no clinical information to support the diagnosis; (2) only one criterion for diagnosis (e.g. child of confirmed case) but no further data to confirm or rule out. We tested different combinations of outpatient and inpatient billing codes, and applied a free-text search algorithm to identify NF1 cases in administrative data and EMRs, respectively. Results Of 273,440 eligible patients, 2,058 had one or more NF1 terms in their medical records. The terms “NF”, “café-au-lait”, or “sheath tumour” were constrained to appear in combination with another NF1 term. This resulted in 837 patients: 37 with possible and 71 with confirmed NF1. The population prevalence ranged from 1 in 3851 (confirmed NF1) to 1 in 2532 (possible and confirmed NF1). Billing code algorithms had poor performance, with overall low PPV (highest being 71%). The accuracy of the free-text EMR algorithm in identifying patients with NF1 was: sensitivity 85% (95% CI 74–92%), specificity 100% (95% CI 100–100%), positive predictive value 80% (95% CI 69–88%), negative predictive value 100% (95% CI 100–100%), and false positive rate 20% (95% CI 11–33%). Of false positives, 53% were possible NF1. Conclusions A free-text search algorithm within the EMR had high sensitivity, specificity and predictive values. Algorithms using billing codes had poor performance, likely due to the lack of NF-specific codes for outpatient visits. While NF1 ICD-9 and 10 codes are used for hospital admissions, only ~ 30% of confirmed NF1 cases had a hospitalization associated with an NF1 code.https://doi.org/10.1186/s13023-022-02493-5NF1EMRAdministrative databaseAlgorithm |
| spellingShingle | Carolina Barnett Elisa Candido Branson Chen Priscila Pequeno Patricia C. Parkin Karen Tu Development of algorithms to identify individuals with Neurofibromatosis type 1 within administrative data and electronic medical records in Ontario, Canada Orphanet Journal of Rare Diseases NF1 EMR Administrative database Algorithm |
| title | Development of algorithms to identify individuals with Neurofibromatosis type 1 within administrative data and electronic medical records in Ontario, Canada |
| title_full | Development of algorithms to identify individuals with Neurofibromatosis type 1 within administrative data and electronic medical records in Ontario, Canada |
| title_fullStr | Development of algorithms to identify individuals with Neurofibromatosis type 1 within administrative data and electronic medical records in Ontario, Canada |
| title_full_unstemmed | Development of algorithms to identify individuals with Neurofibromatosis type 1 within administrative data and electronic medical records in Ontario, Canada |
| title_short | Development of algorithms to identify individuals with Neurofibromatosis type 1 within administrative data and electronic medical records in Ontario, Canada |
| title_sort | development of algorithms to identify individuals with neurofibromatosis type 1 within administrative data and electronic medical records in ontario canada |
| topic | NF1 EMR Administrative database Algorithm |
| url | https://doi.org/10.1186/s13023-022-02493-5 |
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