Abstract
Purpose
Approximately, one out of five patients hospitalized following injury will develop at least one hospital complication, more than three times that observed for general admissions. We currently lack actionable Quality Indicators (QI) targeting specific complications in this population. We aimed to derive and validate QI targeting hospital complications for injury admissions and develop algorithms to identify patient charts to review.
Methods
We conducted a retrospective cohort study including patients with major trauma admitted to any level I or II adult trauma center an integrated Canadian trauma system (2014–2019). We used the trauma registry to develop five QI targeting deep vein thrombosis/pulmonary embolism (DVT/PE), decubitus ulcers, delirium, pneumonia and urinary tract infection (UTI). We developed algorithms to identify patient charts to revise on consultation with a group of clinical experts.
Results
The study population included 14,592 patients of whom 5.3% developed DVT or PE, 2.7% developed a decubitus ulcer, 8.6% developed delirium, 14.7% developed pneumonia and 7.3% developed UTI. The indicators demonstrated excellent predictive performance (Area Under the Curve 0.81–0.87). We identified 4 hospitals with a higher than average incidence of at least one of the targeted complications. The algorithms identified on average 50 and 20 charts to be reviewed per year for level I and II centers, respectively.
Conclusion
In line with initiatives to improve the quality of trauma care, we propose QI targeting reductions in hospital complications for injury admissions and algorithms to generate case lists to facilitate the review of patient charts.
Similar content being viewed by others
References
The American Association for the Surgery of Trauma. Trauma Facts [AAST web site]. August 09, 2012. https://www.aast.org/trauma-facts.
Parachute. The cost of injury in Canada Report [Parachute web site]. June, 2015. http://www.parachutecanada.org/downloads/research/Cost_of_Injury-2015.pdf.
Centers for Disease Control and Prevention. Cost of Injuries and Violence in the United States [CDC web site]. December 04, 2019. https://www.cdc.gov/injury/wisqars/overview/cost_of_injury.html.
Moore L, Lauzier F, Stelfox HT, Kortbeek J, Simons R, Bourgeois G, et al. Validation of complications selected by consensus to evaluate the acute phase of adult trauma care: a multicenter cohort study. Ann Surg. 2015;262(6):1123–9. https://doi.org/10.1097/sla.0000000000000963.
Moore L, Stelfox HT, Turgeon AF, Nathens AB, Le Sage N, Émond M, et al. Rates, patterns, and determinants of unplanned readmission after traumatic injury: a multicenter cohort study. Ann Surg. 2014;259(2):374–80. https://doi.org/10.1097/SLA.0b013e31828b0fae.
Shafi S, Barnes S, Nicewander D, Ballard D, Nathens AB, Ingraham AM, et al. Health care reform at trauma centers–mortality, complications, and length of stay. J Trauma. 2010;69(6):1367–71. https://doi.org/10.1097/TA.0b013e3181fb785d.
Osler T, Glance LG, Hosmer DW. Complication-associated mortality following trauma: a population-based observational study. Arch Surg. 2012;147(2):152–8. https://doi.org/10.1001/archsurg.2011.888.
Hemmila MR, Jakubus JL, Maggio PM, Wahl WL, Dimick JB, Campbell DA Jr, et al. Real money: complications and hospital costs in trauma patients. Surgery. 2008;144(2):307–16. https://doi.org/10.1016/j.surg.2008.05.003.
Moore L, Lavoie A, Bourgeois G, Lapointe J. Donabedian’s structure-process-outcome quality of care model: validation in an integrated trauma system. J Trauma Acute Care Surg. 2015;78(6):1168–75. https://doi.org/10.1097/ta.0000000000000663.
International Society of Surgery and International Association for Trauma Surgery and Intensive Care. Guidelines for trauma quality improvement programmes [WHO web site]. 2009. https://apps.who.int/iris/rest/bitstreams/52394/retrieve.
Moore L, Lauzier F, Stelfox HT, Kortbeek J, Simons R, Berthelot S, et al. Derivation and validation of a quality indicator to benchmark in-hospital complications among injury admissions. JAMA Surg. 2016;151(7):622–30. https://doi.org/10.1001/jamasurg.2015.5484.
Abajas Bustillo R, Amo Setién FJ, Ortego Mate MDC, Seguí Gómez M, Durá Ros MJ, Leal CC. Predictive capability of the injury severity score versus the new injury severity score in the categorization of the severity of trauma patients: a cross-sectional observational study. Eur J Trauma Emerg Surg. 2020;46(4):903–11. https://doi.org/10.1007/s00068-018-1057-x.
Palmer C. Major trauma and the injury severity score–where should we set the bar? Annu Proc Assoc Adv Automot Med. 2007;51:13–29.
Palmer CS, Gabbe BJ, Cameron PA. Defining major trauma using the 2008 Abbreviated Injury Scale. Injury. 2016;47(1):109–15. https://doi.org/10.1016/j.injury.2015.07.003.
Ministère de la Santé et des Services sociaux. Cadre normatif du système d'information du Registre des traumatismes du Québec (SIRTQ) [MSSS web site] https://publications.msss.gouv.qc.ca/msss/document-001684/?&date=DESC&titre=ASC&type=cadre-de-reference&critere=type.
Canadian Institute for Health Information. Canadian Coding Standards for Version 2018 ICD-10-CA and CCI [CIHI web site]. 2018. https://secure.cihi.ca/free_products/CodingStandards_v2018_EN.pdf.
Teasdale G, Jennett B. Assessment of coma and impaired consciousness. A practical scale. Lancet. 1974;2(7872):81–4. https://doi.org/10.1016/s0140-6736(74)91639-0.
Lavoie A, Moore L, LeSage N, Liberman M, Sampalis JS. The New Injury Severity Score: a more accurate predictor of in-hospital mortality than the Injury Severity Score. J Trauma. 2004;56(6):1312–20. https://doi.org/10.1097/01.ta.0000075342.36072.ef.
Moore L, Lavoie A, Le Sage N, Bergeron E, Emond M, Liberman M, et al. Using information on preexisting conditions to predict mortality from traumatic injury. Ann Emerg Med. 2008;52(4):356-64.e2. https://doi.org/10.1016/j.annemergmed.2007.09.007.
Moons KG, Altman DG, Reitsma JB, Ioannidis JP, Macaskill P, Steyerberg EW, et al. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): explanation and elaboration. Ann Intern Med. 2015;162(1):W1-73. https://doi.org/10.7326/m14-0698.
Harrell FE Jr, Lee KL, Mark DB. Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat Med. 1996;15(4):361–87. https://doi.org/10.1002/(sici)1097-0258(19960229)15:4%3c361::aid-sim168%3e3.0.co;2-4.
Snell KI, Ensor J, Debray TP, Moons KG, Riley RD. Meta-analysis of prediction model performance across multiple studies: which scale helps ensure between-study normality for the C-statistic and calibration measures? Stat Methods Med Res. 2018;27(11):3505–22. https://doi.org/10.1177/0962280217705678.
Fenlon C, O’Grady L, Doherty ML, Dunnion J. A discussion of calibration techniques for evaluating binary and categorical predictive models. Prev Vet Med. 2018;149:107–14. https://doi.org/10.1016/j.prevetmed.2017.11.018.
Njd N. A note on a general definition of the coefficient of determination. Biometrika. 1991;78(3):691–2.
Steyerberg EW, Harrell FE Jr, Borsboom GJ, Eijkemans MJ, Vergouwe Y, Habbema JD. Internal validation of predictive models: efficiency of some procedures for logistic regression analysis. J Clin Epidemiol. 2001;54(8):774–81. https://doi.org/10.1016/s0895-4356(01)00341-9.
Steyerberg EWGM, Krickeberg K, Samet J, Tsiatis A, Wong W. Clinical prediction models: a practical approach to development, validation, and updating. New York: Springer; 2009. p. 497.
Miao Y CS, Kirby KA, Boscardin WJ. Estimating Harrell’s optimism on predictive indices using bootstrap samples. SAS Global Forum. 2013; p. 504.
Campbell MK, Fayers PM, Grimshaw JM. Determinants of the intracluster correlation coefficient in cluster randomized trials: the case of implementation research. Clin Trials. 2005;2(2):99–107. https://doi.org/10.1191/1740774505cn071oa.
Huseynova K, Xiong W, Ray JG, Ahmed N, Nathens AB. Venous thromboembolism as a marker of quality of care in trauma. J Am Coll Surg. 2009;208(4):547–52. https://doi.org/10.1016/j.jamcollsurg.2009.01.002.
Freeman EA, Moisen G. A comparison of the performance of threshold criteria for binary classification in terms of predicted prevalence and kappa. Ecol Modell. 2008;217:48–58.
O’Reilly GM, Jolley DJ, Cameron PA, Gabbe B. Missing in action: a case study of the application of methods for dealing with missing data to trauma system benchmarking. Acad Emerg Med. 2010;17(10):1122–9. https://doi.org/10.1111/j.1553-2712.2010.00887.x.
Moore L, Evans D, Yanchar NL, Thakore J, Stelfox HT, Hameed M, et al. Canadian benchmarks for acute injury care. Can J Surg. 2017;60(6):380–7. https://doi.org/10.1503/cjs.002817.
Moore L, Hanley JA, Lavoie A, Turgeon A. Evaluating the validity of multiple imputation for missing physiological data in the national trauma data bank. J Emerg Trauma Shock. 2009;2(2):73–9. https://doi.org/10.4103/0974-2700.44774.
Rubin DB. Multiple imputation for nonresponse in surveys. New York: Wiley; 2004.
Sterne JAC, White IR, Carlin JB, Spratt M, Royston P, Kenward MG, et al. Multiple imputation for missing data in epidemiological and clinical research: potential and pitfalls. BMJ. 2009;338:b2393. https://doi.org/10.1136/bmj.b2393.
Little RJA, Rubin DB. Statistical analysis with missing data. 2nd ed. New York, NY: Wiley; 2002.
Wyatt JC, Altman DG. Commentary: prognostic models: clinically useful or quickly forgotten? BMJ. 1995;311(7019):1539–41. https://doi.org/10.1136/bmj.311.7019.1539.
Cox J. Predictors of pressure ulcers in adult critical care patients. Am J Crit Care. 2011;20(5):364–75. https://doi.org/10.4037/ajcc2011934.
De Jongh MA, Bosma E, Leenen LP, Verhofstad MH. Increased consumption of hospital resources due to complications: an assessment of costs in a level I trauma center. J Trauma. 2011;71(5):E102–9. https://doi.org/10.1097/TA.0b013e31820e351f.
Holbrook TL, Hoyt DB, Anderson JP. The impact of major in-hospital complications on functional outcome and quality of life after trauma. J Trauma Acute Care Surg. 2001;50(1):91–5.
De Jongh MA, Bosma E, Verhofstad MH, Leenen LP. Prediction models for complications in trauma patients. Br J Surg. 2011;98(6):790–6. https://doi.org/10.1002/bjs.7436.
Victorian State Trauma Outcomes Registry and Monitoring Group (VSTORM web site). https://www.monash.edu/medicine/sphpm/vstorm/home.
Performance Comparison: Information For Hospitals: survival rates of major injury for patients who have been admitted to hospital (TARN web site). https://www.tarn.ac.uk/Content.aspx?ca=16.
The American College of Surgeons Trauma Quality Improvement Program (ACS TQIP). https://www.facs.org/quality-programs/trauma/tqp/center-programs/tqip.
Tardif PA, Moore L, Boutin A, Dufresne P, Omar M, Bourgeois G, et al. Hospital length of stay following admission for traumatic brain injury in a Canadian integrated trauma system: a retrospective multicenter cohort study. Injury. 2017;48(1):94–100. https://doi.org/10.1016/j.injury.2016.10.042.
Omar M, Moore L, Lauzier F, Tardif PA, Dufresne P, Boutin A, et al. Complications following hospital admission for traumatic brain injury: a multicenter cohort study. J Crit Care. 2017;41:1–8. https://doi.org/10.1016/j.jcrc.2017.04.031.
Hemmila MR, Jakubus JL, Wahl WL, Arbabi S, Henderson WG, Khuri SF et al. Detecting the blind spot: complications in the trauma registry and trauma quality improvement. Surgery. 2007;142(4):439–48; discussion 48–9. https://doi.org/10.1016/j.surg.2007.07.002.
Drake JM, Singhal A, Kulkarni AV, DeVeber G, Cochrane DD. Consensus definitions of complications for accurate recording and comparisons of surgical outcomes in pediatric neurosurgery. J Neurosurg Pediatr. 2012;10(2):89–95. https://doi.org/10.3171/2012.3.peds11233.
Moore L, Lavoie A, Camden S, Le Sage N, Sampalis JS, Bergeron E et al. Statistical validation of the Glasgow Coma Score. J Trauma. 2006;60(6):1238–43; discussion 43–4. https://doi.org/10.1097/01.ta.0000195593.60245.80.
Turgeon AF, Lauzier F, Zarychanski R, Fergusson DA, Léger C, McIntyre LA, et al. Prognostication in critically ill patients with severe traumatic brain injury: the TBI-prognosis multicentre feasibility study. BMJ Open. 2017;7(4):e013779. https://doi.org/10.1136/bmjopen-2016-013779.
Leeies M, Flynn E, Turgeon AF, Paunovic B, Loewen H, Rabbani R, et al. High-flow oxygen via nasal cannulae in patients with acute hypoxemic respiratory failure: a systematic review and meta-analysis. Syst Rev. 2017;6(1):202. https://doi.org/10.1186/s13643-017-0593-5.
Stelfox HT, Straus SE. Measuring quality of care: considering measurement frameworks and needs assessment to guide quality indicator development. J Clin Epidemiol. 2013;66(12):1320–7. https://doi.org/10.1016/j.jclinepi.2013.05.018.
Funding
No funding was received in support of this work.
Author information
Authors and Affiliations
Contributions
AIH, LM, MB and AB contributed to the conception and design of the study. AIH, LM, MB, AB and XN contributed to the acquisition of data. AIH drafted the manuscript. All authors contributed to the analysis and interpretation of the data, critically revised and approved the manuscript.
Corresponding author
Ethics declarations
Conflict of interest
None of the authors have any conflicts of interest to declare.
Ethics approval
This project was approved by the research ethics committee of the CHU-de-Québec, Université Laval.
Supplementary Information
Below is the link to the electronic supplementary material.
Rights and permissions
About this article
Cite this article
Idriss-Hassan, A., Bérubé, M., Belcaïd, A. et al. Derivation and validation of actionable quality indicators targeting reductions in complications for injury admissions. Eur J Trauma Emerg Surg 48, 1351–1361 (2022). https://doi.org/10.1007/s00068-021-01681-5
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00068-021-01681-5