Derivation and validation of a national multicenter mortality risk stratification model - the ExCare model: a study protocol

Sávio Cavalcante Passos, Adriene Stahlschmidt, João Blanco, Mariana Lunardi Spader, Rodrigo Borges Brandão, Stela Maris de Jezus Castro, Claudia de Souza Gutierrez, Paulo Corrêa da Silva Neto, Luciana Paula Cadore Stefani, Sávio Cavalcante Passos, Adriene Stahlschmidt, João Blanco, Mariana Lunardi Spader, Rodrigo Borges Brandão, Stela Maris de Jezus Castro, Claudia de Souza Gutierrez, Paulo Corrêa da Silva Neto, Luciana Paula Cadore Stefani

Abstract

Introduction: Surgical care is essential for proper management of various diseases. However, it can result in unfavorable outcomes. In order to identify patients at higher risk of complications, several risk stratification models have been developed. Ideally, these tools should be simple, reproducible, accurate, and externally validated. Unfortunately, none of the best-known risk stratification instruments have been validated in Brazil. In this sense, the Ex-Care model was developed by retrospective data analysis of surgical patients in a major Brazilian university hospital. It consists of four independent predictors easily collected in the preoperative evaluation, showing high accuracy in predicting death within 30 days after surgery.

Objectives: To update and validate a Brazilian national-based model of postoperative death probability within 30 days based on the Ex-Care model. Also, to develop an application for smartphones that allows preoperative risk stratification by Ex-Care model.

Methods: Ten participating centers will collect retrospective data from digital databases. Variables age, American Society of Anesthesiologists (ASA) physical status, surgical severity (major or non-major) and nature (elective or urgent) will be evaluated as predictors for in-hospital mortality within 30 postoperative days, considered the primary outcome.

Expected results: We believe that the Ex-Care model will present discriminative capacity similar to other classically used scores validated for surgical mortality prediction. Furthermore, the mobile application to be developed will provide a practical and easy-to-use tool to the professionals enrolled in perioperative care.

Keywords: Hospital mortality; Mobile health application; Risk assessment; Surgical procedures; Validation studies.

Copyright © 2021. Published by Elsevier Editora Ltda.

Figures

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Figure 1
Model development methodology.

References

    1. Yu P.C., Calderaro D., Gualandro D.M., et al. Non-cardiac surgery in developing countries: epidemiological aspects and economical opportunities — the case of Brazil. PLoS One. 2010;5:e10607.
    1. Weiser T.G., Haynes A.B., Molina G., et al. Size and distribution of the global volume of surgery in 2012. Bull World Heal Organ. 2016;94:201F–209F.
    1. Kable A.K., Gibberd R.W., Spigelman A.D. Adverse events in surgical patients in Australia. Int J Qual Heal Care. 2002;14:269–276.
    1. Grocott M.P.W., Browne J.P., Van der Meulen J., et al. The Postoperative Morbidity Survey was validated and used to describe morbidity after major surgery. J Clin Epidemiol. 2007;60:919–928.
    1. Liao L., Mark D.B. Clinical prediction models: are we building better mousetraps? J Am Coll Cardiol. 2003;42:851–853.
    1. Minto G., Biccard B. Assessment of the high-risk perioperative patient. Contin Educ Anaesthesia, Crit Care Pain. 2014;14:12–17.
    1. Shah N., Hamilton M. Clinical review: can we predict which patients are at risk of complications following surgery? Crit Care. 2013;17:226.
    1. Stefani L., Gutierrez C., Castro S., et al. Derivation and validation of a preoperative risk model for postoperative mortality (SAMPE model): an approach to care stratification. PLoS One. 2017;12
    1. Gutierrez C.S., Passos S.C., Castro S.M.J., et al. Few and feasible preoperative variables can identify high-risk surgical patients: derivation and validation of the Ex-Care risk model. Br J Anaesth. 2021;126:525–532.
    1. Duceppe E., Parlow J., MacDonald P., et al. Canadian Cardiovascular Society guidelines on perioperative cardiac risk assessment and management for patients who undergo noncardiac surgery. Can J Cardiol. 2017;33:17–32.
    1. Collins G.S., Reitsma J.B., Altman D.G., et al. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. BMJ. 2015;350:g7594.
    1. Moonesinghe S., Mythen M., Das P., et al. Risk stratification tools for predicting morbidity and mortality in adult patients undergoing major surgery: qualitative systematic review. Anesthesiology. 2013;119:959–981.
    1. Glance L.G., Lustik S.J., Hannan E.L., et al. The surgical mortality probability model. Ann Surg. 2012;255:696–702.
    1. Steyerberg E.W., Vickers A.J., Cook N.R., et al. Assessing the performance of prediction models: a framework for traditional and novel measures. Epidemiology. 2010;21:128–138.
    1. Steyerberg E.W., Vergouwe Y. Towards better clinical prediction models: seven steps for development and an ABCD for validation. Eur Hear J. 2014;35:1925–1931.
    1. Covre E.R., De Melo W.A., Tostes M.F.D.P., et al. Trend of hospitalizations and mortality from surgical causes in brazil, 2008 to 2016. Rev Col Bras Cir. 2019;46:e1979.
    1. Desquilbet L., Mariotti F. Dose-response analyses using restricted cubic spline functions in public health research. Stat Med. 2010;29:1037–1057.
    1. Pearse R.M., Moreno R.P., Bauer P., et al. Mortality after surgery in Europe : a 7 day cohort study. Lancet. 2011;380:1059–1065.
    1. Bainbridge D., Martin J., Arango M., et al. Perioperative and anaesthetic-related mortality in developed and developing countries: a systematic review and meta-analysis. Lancet. 2012;380:1075–1081.
    1. International Surgical Outcomes Study group Global patient outcomes after elective surgery: prospective cohort study in 27 low-, middle- and high-income countries. Br J Anaesth. 2016;117:601–609.
    1. Collaborative G Mortality of emergency abdominal surgery in high-, middle- and low-income countries. Br J Surg. 2016;103:971–988.
    1. Massenburg B.B., Saluja S., Jenny H.E., et al. Assessing the Brazilian surgical system with six surgical indicators: a descriptive and modelling study. BMJ Glob Health. 2017;2
    1. Protopapa K.L., Simpson J.C., Smith N.C.E., et al. Development and validation of the Surgical Outcome Risk Tool (SORT) Br J Surg. 2014;101:1774–1783.
    1. Campbell D., Boyle L., Hider P., et al. National risk prediction model for perioperative mortality in non-cardiac surgery. Br J Surg. 2019;106:1549–1557.

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