Predictive models of disease burden at diagnosis in persons with adult-onset ulcerative colitis using health administrative data
Abstract Background Health administrative data is increasingly used to conduct population-based health services research. A major limitation of these data for the study of inflammatory bowel diseases is the absence of detailed clinical information relating to disease burden. We used Ontario health a...
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doaj-ac1b410edd6b4853bc239fd36c5b62942020-11-25T03:45:54ZengBMCBMC Gastroenterology1471-230X2019-01-011911810.1186/s12876-018-0924-6Predictive models of disease burden at diagnosis in persons with adult-onset ulcerative colitis using health administrative dataSanjay K. Murthy0Tushar Shukla1Lilia Antonova2Marc-Andre Belair3Tim Ramsay4Zane Gallinger5Geoffrey C. Nguyen6Eric I. Benchimol7University of Ottawa, The Ottawa Hospital, Ottawa Hospital Research Institute, IC/ESThe Ottawa Hospital, University of OttawaOttawa Hospital Research InstituteIC/ES, Ottawa Hospital Research InstituteUniversity of Ottawa, Ottawa Hospital Research InstituteSinai Health System, University of TorontoSinai Health System, University of Toronto, Lunenfeld-Tanenbaum Research Institute, IC/ES Mount Sinai HospitalUniversity of Ottawa, IC/ES, Children’s Hospital of Eastern Ontario (CHEO), CHEO Research Institute, Children’s Hospital of Eastern OntarioAbstract Background Health administrative data is increasingly used to conduct population-based health services research. A major limitation of these data for the study of inflammatory bowel diseases is the absence of detailed clinical information relating to disease burden. We used Ontario health administrative data to develop predictive models of disease burden at diagnosis in ulcerative colitis (UC) patients for future use in population-based studies of incident UC cohorts. Methods Through chart review, we characterized macroscopic colitis activity and extent at diagnosis in consecutive adult-onset UC patients diagnosed at The Ottawa Hospital between 2001 and 2012. We linked this cohort to Ontario health administrative data to test the capacity of administrative variables to discriminate different levels of disease activity, disease extent and the disease burden (a composite of disease extent and activity). We modelled outcomes as binary (using logistic regression) and ordinal (using proportional odds regression) variables and performed bootstrap validation of our final models. Results We tested 20 administrative variables in 587 eligible patients. The logistic model of total disease burden (severe and extensive colitis vs. all other phenotypes) showed moderate discriminatory capacity (optimism-corrected c-statistic value 0.729). Individual models of disease extent and disease activity showed poorer discriminatory capacity (c-statistic value < 0.7 for 3 of 4 models). Conclusions Ontario health administrative data may reasonably discriminate levels of total disease burden at diagnosis in adult-onset UC patients. Our models should be externally validated before their widespread application in future population-based studies of incident UC cohorts to adjust for the confounding effects of differences in disease burden.http://link.springer.com/article/10.1186/s12876-018-0924-6Ulcerative colitisPredictive modellingDisease burdenHealth administrative data |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Sanjay K. Murthy Tushar Shukla Lilia Antonova Marc-Andre Belair Tim Ramsay Zane Gallinger Geoffrey C. Nguyen Eric I. Benchimol |
spellingShingle |
Sanjay K. Murthy Tushar Shukla Lilia Antonova Marc-Andre Belair Tim Ramsay Zane Gallinger Geoffrey C. Nguyen Eric I. Benchimol Predictive models of disease burden at diagnosis in persons with adult-onset ulcerative colitis using health administrative data BMC Gastroenterology Ulcerative colitis Predictive modelling Disease burden Health administrative data |
author_facet |
Sanjay K. Murthy Tushar Shukla Lilia Antonova Marc-Andre Belair Tim Ramsay Zane Gallinger Geoffrey C. Nguyen Eric I. Benchimol |
author_sort |
Sanjay K. Murthy |
title |
Predictive models of disease burden at diagnosis in persons with adult-onset ulcerative colitis using health administrative data |
title_short |
Predictive models of disease burden at diagnosis in persons with adult-onset ulcerative colitis using health administrative data |
title_full |
Predictive models of disease burden at diagnosis in persons with adult-onset ulcerative colitis using health administrative data |
title_fullStr |
Predictive models of disease burden at diagnosis in persons with adult-onset ulcerative colitis using health administrative data |
title_full_unstemmed |
Predictive models of disease burden at diagnosis in persons with adult-onset ulcerative colitis using health administrative data |
title_sort |
predictive models of disease burden at diagnosis in persons with adult-onset ulcerative colitis using health administrative data |
publisher |
BMC |
series |
BMC Gastroenterology |
issn |
1471-230X |
publishDate |
2019-01-01 |
description |
Abstract Background Health administrative data is increasingly used to conduct population-based health services research. A major limitation of these data for the study of inflammatory bowel diseases is the absence of detailed clinical information relating to disease burden. We used Ontario health administrative data to develop predictive models of disease burden at diagnosis in ulcerative colitis (UC) patients for future use in population-based studies of incident UC cohorts. Methods Through chart review, we characterized macroscopic colitis activity and extent at diagnosis in consecutive adult-onset UC patients diagnosed at The Ottawa Hospital between 2001 and 2012. We linked this cohort to Ontario health administrative data to test the capacity of administrative variables to discriminate different levels of disease activity, disease extent and the disease burden (a composite of disease extent and activity). We modelled outcomes as binary (using logistic regression) and ordinal (using proportional odds regression) variables and performed bootstrap validation of our final models. Results We tested 20 administrative variables in 587 eligible patients. The logistic model of total disease burden (severe and extensive colitis vs. all other phenotypes) showed moderate discriminatory capacity (optimism-corrected c-statistic value 0.729). Individual models of disease extent and disease activity showed poorer discriminatory capacity (c-statistic value < 0.7 for 3 of 4 models). Conclusions Ontario health administrative data may reasonably discriminate levels of total disease burden at diagnosis in adult-onset UC patients. Our models should be externally validated before their widespread application in future population-based studies of incident UC cohorts to adjust for the confounding effects of differences in disease burden. |
topic |
Ulcerative colitis Predictive modelling Disease burden Health administrative data |
url |
http://link.springer.com/article/10.1186/s12876-018-0924-6 |
work_keys_str_mv |
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