Abstract: Accuracy of Algorithms from Administrative Data to Identify Persons with Cord Injury Undergoing Surgical Closure of Stage IV Pelvic Pressure Ulcers in Ontario, Canada (Plastic Surgery The Meeting (September 28

Monday, October 1, 2018: 2:50 PM

Laura Teague, MN, NP-Adult , Faculty of Health Sciences, School of Nursing, McMaster University, Hamilton, ON, Canada

James L Mahoney, MD , Plastic Surgery, University of Toronto, Toronto, ON, Canada

Maya Deeb, BSc , Faculty of Medicine, University of Toronto, Toronto, ON, Canada

Susan Jaglal, PHD , Institute for Clinical Evaluative Sciences, Toronto, ON, Canada

Andrew Calzavara, MSc , Institute for Clinical Evaluative Sciences, toronto, ON, Canada

Jennifer Voth, PhD , Institute for Clinical Evaluative Sciences, Toronto, ON, Canada

Lehana Thabane, PhD , Department of Clinical Epidemiology and Biostatistics, McMaster Universty, Hamilton, Ontario, ON, Canada

Stephen Birch, PhD , Centre for Health Economics and Policy Analysis, Mcmaster Universty, Hamilton, ON, Canada

Gina Browne, RN, PhD , Faculty of Heatlh Sciences, School of Nursing and Department of Clinical Epidemiology & Biostatistics., McMaster University, Hamiton, ON, Canada

Objective:

The objective of this study was to assess the accuracy of procedure, diagnosis and physician billing code algorithms to identify cases of spinal cord injured persons having undergone surgical flap closure of pelvic pressure injuries in a provincial administrative data base. Hospital medical records with confirmed cases (true positive) and controls (true negative) were used as reference standard.

Methods:

Following research ethics approval, 111 patients were included with 136 cases of pressure injury (PI) reconstruction procedures from the billing codes of one plastic/reconstructive surgeon from the years 2002-2015, at one tertiary care hospital in Toronto, Ontario. 38 controls were further identified through medical record review. Ontario Health Insurance Plan (OHIP) billing codes, ICD-10-CA and Canadian Classification of Health Interventions codes (CCI) were recorded for each of the cases. Spinal cord diagnosis, the index surgery codes and billing codes were use to build several algorithms tested for sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV).

Results:

The final and best algorithm displayed a sensitivity of 69.1%, specificity of 97.37%; PPV of 98.95% and NPV of 46.84%. In other words, 30.9% of the true positives in this cohort were missed using the algorithm while 97.37% of the true negatives were identified using the same algorithm.

Conclusion:

Use of retrospective observational study employing administrative algorithms to identify SCI patients who have undergoing pressure injury reconstruction is currently insufficient to proceed with population based study in Ontario. This study emphasizes the importance of evaluating accuracy and completeness of codes in administrative databases in order to reduce the risk of misclassification and subsequent reduction of power and generalizability.

References

Chuback, J.,Pocobelli, G., & Weiss, N.S (2012). Tradeoffs between accuracy measures for electronic health care data algorithms. Journal of Clinical Epidemilogy, 65(3), 343-349.e2. doi: 10.1016/j.jclinepi.2011.09.002. Epub 2011 Dec 23.

De Coster C, Quan H, Finlayson A, Gao M, Halfon P, Humphries KH, et al. Identifying priorities in methodological research using ICD-9-CM and ICD-10 administrative data: report from an international consortium. BMC Health Serv Res. 2006;6:77. doi:10.1186/1472-6963-6-77. Epub 2006/06/17. PubMed PMID: 16776836

Schneeweiss, S. & Avorn, J. (2005). A review of uses of health care utilization databases for epidemiological research on therapeutics. Journal of Clinical Epidemiology. 58, 323-337.

35450 Accuracy of Algorithms from Administrative Data to Identify Persons with Cord Injury Undergoing Surgical Closure of Stage IV Pelvic Pressure Ulcers in Ontario, Canada