Brazilian Journal of Anesthesiology
https://bjan-sba.org/article/doi/10.1016/j.bjane.2021.07.003
Brazilian Journal of Anesthesiology
Original Investigation

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

Derivação e validação de um modelo nacional de estratificação de risco de mortalidade multicêntrico – o modelo ExCare: um protocolo de estudo

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

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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 Anesthesiology (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

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

Resumo

Introdução: Os cuidados cirúrgicos são essenciais para o manejo adequado de diversas doenças. No entanto, pode resultar em resultados desfavoráveis. Para identificar pacientes com maior risco de complicações, vários modelos de estratificação de risco foram desenvolvidos. Idealmente, essas ferramentas devem ser simples, reprodutíveis, precisas e validadas externamente. Infelizmente, nenhum dos instrumentos de estratificação de risco mais conhecidos foi validado no Brasil. Nesse sentido, o modelo Ex-Care foi desenvolvido por meio de análise retrospectiva de dados de pacientes cirúrgicos em um grande hospital universitário brasileiro. Consiste em quatro preditores independentes facilmente coletados na avaliação pré-operatória, apresentando alta precisão ao predizer óbito em até 30 dias após a cirurgia. Objetivos: Atualizar e validar um modelo nacional brasileiro de probabilidade de morte pós- -operatória em 30 dias com base no modelo Ex-Care. Além disso, desenvolver um aplicativo para smartphones que permita a estratificação de risco pré-operatório pelo modelo Ex-Care. Métodos: Dez centros participantes coletarão dados retrospectivos de bancos de dados digitais. As variáveis idade, estado físico da Sociedade Americana de Anestesiologia (ASA), gravidade cirúrgica (maior ou não maior) e natureza (eletiva ou urgente) serão avaliadas como preditores de mortalidade intra-hospitalar em até 30 dias de pós-operatório, considerado o desfecho primário. Resultados esperados: Acreditamos que o modelo Ex-Care apresentará capacidade discriminativa semelhante a outros escores classicamente utilizados e validados para predição de mortalidade cirúrgica. Além disso, o aplicativo móvel a ser desenvolvido será uma ferramenta prática e de fácil utilização para os profissionais inscritos na assistência perioperatória.

Palavras-chave

Procedimentos cirúrgicos; Avaliação de risco; Mortalidade hospitalar; Estudos de validação; Aplicativo móvel de saúde

References

1 P.C. Yu, D. Calderaro, D.M. Gualandro, et al. Non-cardiac surgery in developing countries: epidemiological aspects and economical opportunities — the case of Brazil PLoS One, 5 (2010), p. e10607

2 T.G. Weiser, A.B. Haynes, G. Molina, et al. Size and distribution of the global volume of surgery in 2012 Bull World Heal Organ, 94 (2016), pp. 201F-209F

3 A.K. Kable, R.W. Gibberd, A.D. Spigelman Adverse events in surgical patients in Australia Int J Qual Heal Care, 14 (2002), pp. 269-276

4 M.P.W. Grocott, J.P. Browne, J. Van der Meulen, et al. The Postoperative Morbidity Survey was validated and used to describe morbidity after major surgery J Clin Epidemiol, 60 (2007), pp. 919-928

5 L. Liao, D.B. Mark Clinical prediction models: are we building better mousetraps? J Am Coll Cardiol, 42 (2003), pp. 851-853

6 G. Minto, B. Biccard Assessment of the high-risk perioperative patient Contin Educ Anaesthesia, Crit Care Pain, 14 (2014), pp. 12-17

7 N. Shah, M. Hamilton Clinical review: can we predict which patients are at risk of complications following surgery? Crit Care, 17 (2013), p. 226

8 L. Stefani, C. Gutierrez, S. Castro, et al. Derivation and validation of a preoperative risk model for postoperative mortality (SAMPE model): an approach to care stratification PLoS One, 12 (2017), Article e0187122

9 C.S. Gutierrez, S.C. Passos, S.M.J. Castro, 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, 126 (2021), pp. 525-532

10 E. Duceppe, J. Parlow, P. MacDonald, et al. Canadian Cardiovascular Society guidelines on perioperative cardiac risk assessment and management for patients who undergo noncardiac surgery Can J Cardiol, 33 (2017), pp. 17-32

11 G.S. Collins, J.B. Reitsma, D.G. Altman, et al. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement BMJ, 350 (2015), p. g7594

12 S. Moonesinghe, M. Mythen, P. Das, et al. Risk stratification tools for predicting morbidity and mortality in adult patients undergoing major surgery: qualitative systematic review Anesthesiology, 119 (2013), pp. 959-981

13 L.G. Glance, S.J. Lustik, E.L. Hannan, et al. The surgical mortality probability model Ann Surg, 255 (2012), pp. 696-702

14 E.W. Steyerberg, A.J. Vickers, N.R. Cook, et al. Assessing the performance of prediction models: a framework for traditional and novel measures Epidemiology, 21 (2010), pp. 128-138

15 E.W. Steyerberg, Y. Vergouwe Towards better clinical prediction models: seven steps for development and an ABCD for validation Eur Hear J, 35 (2014), pp. 1925-1931

16 E.R. Covre, W.A. De Melo, M.F.D.P. Tostes, et al. Trend of hospitalizations and mortality from surgical causes in brazil, 2008 to 2016 Rev Col Bras Cir, 46 (2019), p. e1979

17 L. Desquilbet, F. Mariotti Dose-response analyses using restricted cubic spline functions in public health research Stat Med, 29 (2010), pp. 1037-1057

18 R.M. Pearse, R.P. Moreno, P. Bauer, et al. Mortality after surgery in Europe : a 7 day cohort study Lancet, 380 (2011), pp. 1059-1065

19 D. Bainbridge, J. Martin, M. Arango, et al. Perioperative and anaesthetic-related mortality in developed and developing countries: a systematic review and meta-analysis Lancet, 380 (2012), pp. 1075-1081

20 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, 117 (2016), pp. 601-609

21 Collaborative G Mortality of emergency abdominal surgery in high-, middle- and low-income countries Br J Surg, 103 (2016), pp. 971-988

22 B.B. Massenburg, S. Saluja, H.E. Jenny, et al. Assessing the Brazilian surgical system with six surgical indicators: a descriptive and modelling study BMJ Glob Health, 2 (2017), Article e000226

23 K.L. Protopapa, J.C. Simpson, N.C.E. Smith, et al. Development and validation of the Surgical Outcome Risk Tool (SORT) Br J Surg, 101 (2014), pp. 1774-1783

24 D. Campbell, L. Boyle, P. Hider, et al. National risk prediction model for perioperative mortality in non-cardiac surgery Br J Surg, 106 (2019), pp. 1549-1557

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