ORIGINAL
Introdução: Pacientes pediátricos submetidos a procedimentos neurocirúrgicos representam uma população vulnerável com riscos significativos de mortalidade, sobretudo em cenários com recursos limitados e escassez de ferramentas prognósticas estruturadas. Objetivo: Desenvolver e validar internamente um modelo de predição clínica para mortalidade hospitalar em pacientes pediátricos de neurocirurgia em um grande centro de trauma brasileiro. Métodos: Estudo de coorte retrospectivo analisando 188 pacientes pediátricos submetidos a neurocirurgia craniana no Hospital João XXIII (janeiro de 2020 a junho de 2025). Variáveis preditoras associadas estatisticamente à mortalidade (p < 0,05) foram selecionadas para análise discriminante. O desempenho estatístico foi avaliado por meio de acurácia, sensibilidade, especificidade e AUC-ROC. Resultados: Escala de Coma de Glasgow reduzida na admissão, trauma torácico, trauma abdominal e etiologia congênita correlacionaram-se significativamente com o desfecho de óbito. O modelo preditivo alcançou acurácia de 93,1% e especificidade de 97,0%, exibindo excelente capacidade discriminatória (AUC = 0,910; IC 95%: 0,846–0,975; p < 0,001). Conclusão: O modelo demonstra excelente poder discriminatório para predição de mortalidade em neurocirurgia pediátrica. A elevada especificidade confere confiabilidade na identificação de pacientes de baixo risco; todavia, a sensibilidade limitada demanda refinamento e validação externa antes de sua incorporação definitiva na prática clínica
Introduction: Pediatric neurosurgery patients represent a highly vulnerable population with significant mortality risks, particularly in resource-constrained environments lacking tailored prognostic tools. Objective: To develop and internally validate a clinical prediction model for in-hospital mortality in pediatric neurosurgical patients at a major Brazilian trauma center. Methods: A retrospective cohort study was conducted with 188 pediatric patients undergoing cranial neurosurgery at Hospital João XXIII between January 2020 and June 2025. Predictor variables significantly associated with mortality (p < 0.05) were included in a discriminant analysis. Model performance was thoroughly evaluated using accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC-ROC). Results: Lower Glasgow Coma Scale scores at admission, thoracic trauma, abdominal trauma, and congenital etiology were significantly associated with mortality. The developed model achieved 93.1% accuracy and 97.0% specificity, demonstrating excellent discriminatory ability (AUC = 0.910; 95% CI: 0.846–0.975; p < 0.001). Conclusion: The model shows robust discriminatory power for predicting mortality in pediatric neurosurgery. While high specificity reliably identifies low-risk patients, its lower sensitivity highlights the need for further refinement and external validation before widespread clinical implementation.
1. Nguyen T, Mueller S, Malbari F. Review: neurological complications from therapies for pediatric brain tumors. Front Oncol. 2022;12:853034. https://doi.org/10.3389/fonc.2022.853034. PMid:35480100.
2. Henriksen KA, Brix N, Jakubauskaite R, et al. Thirty-day surgical morbidity and risk factors in pediatric brain tumor surgery: a 10-year nationwide retrospective study. J Neurosurg Pediatr. 2023;33(2):165-73. https://doi.org/10.3171/2023.9.PEDS23351. PMid:37976503.
3. Lima LC, Cumino DO, Vieira AM, et al. Recommendations from the Brazilian Society of Anesthesiology (SBA) for difficult airway management in pediatric care. Braz J Anesthesiol. 2024;74(1):744478. https://doi.org/10.1016/j.bjane.2023.12.002. PMid:38147975.
4. Campbell E, Beez T, Todd L. Prospective review of 30-day morbidity and mortality in a paediatric neurosurgical unit. Childs Nerv Syst. 2017;33(3):483-9. https://doi.org/10.1007/s00381-017-3358-5. PMid:28247111.
5. Sese LVC, Guillermo MCL. Risk factors for unplanned readmissions in paediatric neurosurgery: a systematic review. BMJ Open. 2025;15(7):e093459. https://doi.org/10.1136/bmjopen-2024-093459. PMid:40669921.
6. Drake JM, Riva-Cambrin J, Jea A, Auguste K, Tamber M, LambertiPasculli M. Prospective surveillance of complications in a pediatric neurosurgery unit. J Neurosurg Pediatr. 2010;5(6):544-8. https://doi.
org/10.3171/2010.1.PEDS09305. PMid:20515324.
7. Madan MM, Alshereiqi AM, Abdulla NM, Albreiki M, Al-Saadi T. Quality improvement in neurosurgery: A systematic review. Med Int. 2025;5(3):23. https://doi.org/10.3892/mi.2025.222. PMid:40093580.
8. Macêdo LJM Fo, Mansouri A, Otamendi-Lopez A, et al. Congenital pediatric hydrocephalus in the Brazilian public health system: the reality of a middle-income country in the past 13 years. World Neurosurg. 2024;181:e8018. https://doi.org/10.1016/j.wneu.2023.10.137. PMid:37923015.
9. Roach JT, Qaddoumi I, Baticulon RE, et al. Pediatric neurosurgical capacity for the care of children with CNS tumors worldwide: a cross-sectional assessment. JCO Glob Oncol. 2023;9(9):e2200402. https://doi.org/10.1200/GO.22.00402. PMid:36763918.
10. Krishnaswami S, Nwomeh BC, Ameh EA. The pediatric surgery workforce in low- and middle-income countries: problems and priorities.
Semin Pediatr Surg. 2016;25(1):32-42. https://doi.org/10.1053/j. sempedsurg.2015.09.007. PMid:26831136.
11. Prithula J, Chowdhury MEH, Khan MS, et al. Improved pediatric ICU mortality prediction for respiratory diseases: machine learning and data subdivision insights. Respir Res. 2024;25(1):216. https://doi. org/10.1186/s12931-024-02753-x. PMid:38783298.
12. Rogers MP, DeSantis AJ, Kuo PC, Janjua HM. Predictive modeling of in-hospital mortality following elective surgery. Am J Surg. 2022;223(3):544-8. https://doi.org/10.1016/j.amjsurg.2021.11.037. PMid:34895894.
13. Dewan MC, Rattani A, Fieggen G, et al. Global neurosurgery: the current capacity and deficit in the provision of essential neurosurgical care. Executive Summary of the Global Neurosurgery Initiative at the Program in Global Surgery and Social Change. J Neurosurg. 2019;130(4):1055-64. https://doi.org/10.3171/2017.11.JNS171500. PMid:29701548.
14. Dakhil ZA, Cader FA, Banerjee A. Challenges in clinical research in low and middle income countries: early career cardiologists’ perspective. Glob Heart. 2024;19(1):13. https://doi.org/10.5334/gh.1293. PMid:38273996.
15. Franzen SRP, Chandler C, Lang T. Health research capacity development in low and middle income countries: reality or rhetoric? A systematic meta-narrative review of the qualitative literature. BMJ Open. 2017;7(1):e012332. https://doi.org/10.1136/bmjopen-2016-012332. PMid:28131997.
16. Efthimiou O, Seo M, Chalkou K, Debray T, Egger M, Salanti G. Developing clinical prediction models: a step-by-step guide. BMJ. 2024;386:e078276. https://doi.org/10.1136/bmj-2023-078276. PMid:39227063.
17. Ting HW, Chen MS, Hsieh YC, Chan CL. Good mortality prediction by Glasgow Coma Scale for neurosurgical patients. J Chin Med Assoc. 2010;73(3):139-43. https://doi.org/10.1016/S1726-4901(10)70028-9. PMid:20230998.
18. Steyerberg EW, Mushkudiani N, Perel P, et al. Predicting outcome after traumatic brain injury: development and international validation of prognostic scores based on admission characteristics. PLoS Med. 2008;5(8):e165. https://doi.org/10.1371/journal.pmed.0050165. PMid:18684008.
19. Watanabe T, Kawai Y, Iwamura A, Maegawa N, Fukushima H, Okuchi K. Outcomes after traumatic brain injury with concomitant severe extracranial injuries. Neurol Med Chir. 2018;58(9):393-9. https:// doi.org/10.2176/nmc.oa.2018-0116. PMid:30101808.
20. McDowell MM, Blatt JE, Deibert CP, Zwagerman NT, Tempel ZJ, Greene S. Predictors of mortality in children with myelomeningocele and symptomatic Chiari type II malformation. J Neurosurg Pediatr. 2018;21(6):587-96. https://doi.org/10.3171/2018.1.PEDS17496.
PMid:29570035.
21. Isikay AI, Gurses ME, Gecici NN, et al. Congenital brain tumors: surgical outcomes and long-term prognostic factors. World Neurosurg. 2024;191:e66473. https://doi.org/10.1016/j.wneu.2024.09.021. PMid:39265942.
22. Addisu Y, Wassie GT. Patterns and short term neurosurgical treatment outcomes of neonates with neural tube defects admitted to Felege Hiwot Specialized Hospital, Bahir Dar, Ethiopia. BMC Pediatr. 2024;24(1):350. https://doi.org/10.1186/s12887-024-04837-5. PMid:38773409.
23. Glinianaia SV, Morris JK, Best KE, et al. Long-term survival of children born with congenital anomalies: a systematic review and metaanalysis of population-based studies. PLoS Med. 2020;17(9):e1003356. https://doi.org/10.1371/journal.pmed.1003356. PMid:32986711.
1Universidade Federal de Minas Gerais – UFMG, Belo Horizonte, MG, Brazil.
2Hospital João XXIII, Fundação Hospitalar do Estado de Minas Gerais – FHEMIG, Belo Horizonte, MG, Brazil.
Received May 3, 2026
Corrected May 13, 2026
Accepted May 13, 2026
