Comparative Analysis of Two Methods for Assessing Complications in Pediatric Surgery for Extravisceral Neck Tumors: A Cohort Study



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Abstract

BACKGROUND: The lack of a unified approach to assessing postoperative complications in children with extravisceral neck tumors hinders risk stratification and selection of the optimal management strategy.

AIM: The study aimed to compare the prognostic accuracy of the Clavien–Dindo classification (CDC) and Comprehensive Complication Index (CCI) for predicting prolonged hospitalization (>8 days) and adverse oncologic outcomes.

METHODS: Data from 153 patients (≤17 years) who underwent extravisceral neck tumor resection at the Dmitry Rogachev National Medical Research Center between 2012 and 2022 were analyzed. Postoperative complications were graded using CDC and CCI. Univariable and multivariable logistic regression, Cox regression, survival analysis, and ROC analysis were performed, and model comparison techniques were applied.

RESULTS: Of the operated children, the median age was 2.9 years (1.2–8.3; 0.1–17.9), and 49% were boys. Severe complications (CDC ≥IIIa) were conventionally reported in 13.1% of patients (20/153), whereas minor complications were determined in 24.8% (38/153). The median CCI for the entire cohort was 0 (0–20.9) and 21.8 (8.7–32.4) among those with recorded complications. Using CCI, 22.2% (34/153) of patients were identified to have severe complications (CCI ≥26.2), versus 13.1% (20/153) using CDC ≥ IIIa. Prognostic models using either CCI or CDC showed comparable discrimination (AUC >0.84), sensitivity of 62.7%–68.7%, and specificity of 80.2%–83.7%. The model including CCI retained advantages by information criteria, whereas the CDC-based model may be clinically useful when complication classification needs to be considered. Complications were not associated with local recurrence or with overall survival.

CONCLUSION: Combined CCI and CDC enable more precise stratification by complication severity and, when incorporated into a prognostic model, accurately predicts prolonged hospitalization in approximately 8 of 10 patients.

About the authors

Georgy A. Polev

Dmitry Rogachev National Medical Research Center for Children's Hematology, Oncology and Immunology; Ilyinskaya hospital

Author for correspondence.
Email: dr.polev@gmail.com
ORCID iD: 0000-0002-7175-6417
SPIN-code: 7778-3356

MD, Cand. Sci. (Medicine), Senior researcher of the Department of Head and Neck Surgery and Reconstructive Plastic Surgery; Director of the Head and Neck Surgery Center

Russian Federation, Moscow; Krasnogorsk

Nikolai S. Grachev

Dmitry Rogachev National Medical Research Center for Children's Hematology, Oncology and Immunology

Email: nick-grachev@yandex.ru
ORCID iD: 0000-0002-4451-3233
SPIN-code: 2836-2349

MD, Dr. Sci. (Medicine), Professor, General Director

Russian Federation, Moscow

Raisa S. Oganesyan

Dmitry Rogachev National Medical Research Center for Children's Hematology, Oncology and Immunology

Email: raisaoganesyan@gmail.com
ORCID iD: 0000-0002-1698-2956
SPIN-code: 3617-0340

Pediatric Surgeon, Depart. of Oncology, Head and Neck Surgery and Neurosurgery

Russian Federation, Moscow

Ekaterina Iu. Iaremenko

Dmitry Rogachev National Medical Research Center for Children's Hematology, Oncology and Immunology

Email: selvaggio@yandex.ru
ORCID iD: 0000-0003-1196-5070
SPIN-code: 3203-9151
Scopus Author ID: 57202806377

Laboratory assistant, Depart. of Head and Neck Surgery and Reconstructive Plastic Surgery

Russian Federation, Moscow

References

  1. Abramov AA, Avanesov VM, Adamyan AA, et al. Tumors of the Head and Neck Organs: Technologies for Treatment and Rehabilitation of Patients: Tissue Reconstruction. Moscow, 2016. (In Russ.) EDN: ZHATPD
  2. Asakage T. Epidemiology and treatment of head and neck malignancies in the AYA generation. Int J Clin Oncol. 2022;27(3):465–472. doi: 10.1007/s10147-021-02093-6 EDN: PHSZML
  3. Shadmani G, Don S. What is this bump in my neck? Ultrasonographic evaluation of pediatric neck masses. J Clin Ultrasound. 2023;51(5):919–930. doi: 10.1002/jcu.23400 EDN: HSKYPP
  4. Meier JD, Grimmer JF. Evaluation and management of neck masses in children. Am Fam Physician. 2014;89(5):353–358.
  5. Park YW. Evaluation of neck masses in children. Am Fam Physician. 1995;51(8):1904–1912. EDN: BZCXEJ
  6. Gov-Ari E, Leann Hopewell B. Correlation between pre-operative diagnosis and post-operative pathology reading in pediatric neck masses-a review of 281 cases. Int J Pediatr Otorhinolaryngol. 2015;79(1):2–7. doi: 10.1016/j.ijporl.2014.11.011
  7. Zorzela L, Loke YK, Ioannidis JP, et al. PRISMA harms checklist: improving harms reporting in systematic reviews. BMJ. 2016;352:i157. doi: 10.1136/bmj.i157
  8. Qaisi M, Eid I. Pediatric Head and Neck Malignancies. Oral Maxillofac Surg Clin North Am. 2016;28(1):11–19. doi: 10.1016/j.coms.2015.07.008
  9. Dombrowski ND, Wolter NE, Robson CD, et al. Role of Surgery in Rhabdomyosarcoma of the Head and Neck in Children. Laryngoscope. 2021;131(3):E984–E992. doi: 10.1002/lary.28785 EDN: OWYBCN
  10. Fang X, Wang S, Zhao J, et al. A population-based analysis of clinical features and lymph node dissection in head and neck malignant neurogenic tumors. BMC Cancer. 2021;21(1):598. doi: 10.1186/s12885-021-08307-4 EDN: BEUPHJ
  11. Embrechts JLA, Hiddinga S, Bot JC, et al. Surgery versus sclerotherapy versus combined therapy in head and neck lymphatic malformations in the pediatric population: systematic review and meta-analysis. Eur Arch Otorhinolaryngol. 2024;281(9):4529–4539. doi: 10.1007/s00405-024-08661-6 EDN: BLXHEV
  12. Micangeli G, Menghi M, Profeta G, et al. Malignant and Benign Head and Neck Tumors of the Pediatric Age: A Narrative Review. Curr Pediatr Rev. 2025;21(2):118–132. doi: 10.2174/0115733963258575231123043807 EDN: UINGOV
  13. Sarma A, Gadde JA. Post-treatment Evaluation of Pediatric Head and Neck. Semin Roentgenol. 2023;58(3):363–373. doi: 10.1053/j.ro.2023.03.005 EDN: ICQYOX
  14. Khanwalkar A, Carter J, Bhushan B, et al. Thirty-day perioperative outcomes in resection of cervical lymphatic malformations. Int J Pediatr Otorhinolaryngol. 2018;106:31–34. doi: 10.1016/j.ijporl.2017.12.034
  15. Vallur S, Dutta A, Arjun AP. Use of Clavien-Dindo Classification System in Assessing Head and Neck Surgery Complications. Indian J Otolaryngol head neck Surg Off Publ Assoc Otolaryngol India. 2020;72(1):24–29. doi: 10.1007/s12070-019-01718-7 EDN: JTIALW
  16. Hyvönen H, Salminen P, Kyrklund K. Long-term outcomes of lymphatic malformations in children: An 11-year experience from a tertiary referral center. J Pediatr Surg. 2022;57(12):1005–1010. doi: 10.1016/j.jpedsurg.2022.07.024 EDN: TQVUCX
  17. Dindo D, Demartines N, Clavien PA. Classification of surgical complications: a new proposal with evaluation in a cohort of 6336 patients and results of a survey. Ann Surg. 2004;240(2):205–213. doi: 10.1097/01.sla.0000133083.54934.ae
  18. Clavien PA, Vetter D, Staiger RD, et al. The Comprehensive Complication Index (CCI®): Added Value and Clinical Perspectives 3 Years "Down the Line". Ann Surg. 2017;265(6):1045–1050. doi: 10.1097/SLA.0000000000002132 EDN: YGXECC
  19. Madadi-Sanjani O, Zoeller C, Kuebler JF, et al. Severity grading of unexpected events in paediatric surgery: evaluation of five classification systems and the Comprehensive Complication Index (CCI®). BJS open. 2021;5(6). doi: 10.1093/bjsopen/zrab138 EDN: ZGMFAX
  20. Li D, Niu Q, Wang C, et al. Comprehensive complication index: A new reporting standard for postoperative complications of free-flap reconstruction in head and neck cancer patients. Oral Surg Oral Med Oral Pathol Oral Radiol. 2023;135(1):33–41. doi: 10.1016/j.oooo.2022.05.007 EDN: EWBWZZ
  21. Ruspi L, Cananzi FCM, Aymerito F, et al. Measuring the impact of complications after surgery for retroperitoneal sarcoma: Is comprehensive complication index better than Clavien-Dindo Classification? Eur J Surg Oncol J Eur Soc Surg Oncol Br Assoc Surg Oncol. 2022;48(5):978–984. doi: 10.1016/j.ejso.2021.12.010 EDN: GTPFZM
  22. Ricci C, Ingaldi C, Grego DG, et al. The use of comprehensive complication Index® in pancreatic surgery: a comparison with the Clavien-Dindo system in a high volume center. HPB Off J Int Hepato Pancreato Biliary Assoc. 2021;23(4):618–624. doi: 10.1016/j.hpb.2020.09.002 EDN: HXNSYR
  23. Giani A, Cipriani F, Famularo S, et al. Performance of Comprehensive Complication Index and Clavien-Dindo Complication Scoring System in Liver Surgery for Hepatocellular Carcinoma. Cancers. 2020;12(12):3868. doi: 10.3390/cancers12123868 EDN: AOIACM
  24. Smeyers KMCI, Slankamenac K, Houben B, Sergeant G. Comparison of the Clavien-Dindo and Comprehensive Complication Index systems for grading of surgical complications after colorectal resections. Acta Chir Belg. 2022;122(6):403–410. doi: 10.1080/00015458.2021.1920682 EDN: GRIEIA
  25. Tirotta F, Parente A, Hodson J, et al. Cumulative Burden of Postoperative Complications in Patients Undergoing Surgery for Primary Retroperitoneal Sarcoma. Ann Surg Oncol. 2021;28(12):7939–7949. doi: 10.1245/s10434-021-10059-1 EDN: JFWYGK
  26. Abe T, Yamada S, Kikuchi H, et al. Impact of postoperative complications on long-term survival in bladder cancer patients. Jpn J Clin Oncol. 2023;53(10):966–976. doi: 10.1093/jjco/hyad079 EDN: FCVSGR
  27. Yilmaz H, Cinar NB, Avci IE, et al. Evaluation of comprehensive complication index versus Clavien-Dindo classification in prediction of overall survival after radical cystectomy. Int Urol Nephrol. 2023;55(6):1459–1465. doi: 10.1007/s11255-023-03564-7 EDN: ZOSHOO
  28. Birrer DL, Golcher H, Casadei R, et al. Neoadjuvant Therapy for Resectable Pancreatic Cancer: A New Standard of Care. Pooled Data From 3 Randomized Controlled Trials. Ann Surg. 2021;274(5):713–720. doi: 10.1097/SLA.0000000000005126 EDN: AUZVYU
  29. Triemstra L, de Jongh C, Tedone F, et al. The Comprehensive Complication Index versus Clavien-Dindo grading after laparoscopic and open D2-gastrectomy in the multicenter randomized LOGICA-trial. Eur J Surg Oncol J Eur Soc Surg Oncol Br Assoc Surg Oncol. 2023;49(12):107095. doi: 10.1016/j.ejso.2023.107095 EDN: ZQELSG
  30. Katsimperis S, Bellos T, Manolitsis I, et al. Reporting and Grading of Complications in Urological Surgery: Current Trends and Future Perspectives. Urol Res Pract. 2024;50(3):154–159. doi: 10.5152/tud.2024.24050 EDN: LEBVFV
  31. Slankamenac K, Graf R, Barkun J, et al. The comprehensive complication index: a novel continuous scale to measure surgical morbidity. Ann Surg. 2013;258(1):1–7. doi: 10.1097/SLA.0b013e318296c732
  32. Habre W, Disma N, Virag K, et al. Incidence of severe critical events in paediatric anaesthesia (APRICOT): a prospective multicentre observational study in 261 hospitals in Europe. Lancet Respir Med. 2017;5(5):412–425. doi: 10.1016/S2213-2600(17)30116-9
  33. Dell-Kuster S, Gomes NV, Gawria L, et al. Prospective validation of classification of intraoperative adverse events (ClassIntra): international, multicentre cohort study. BMJ. 2020;370:m2917. doi: 10.1136/bmj.m2917 EDN: RTAXLS
  34. Temple WC, Vo KT, Matthay KK, et al. Association of image-defined risk factors with clinical features, histopathology, and outcomes in neuroblastoma. Cancer Med. 2021;10(7):2232–2241. doi: 10.1002/cam4.3663 EDN: FRBOFK
  35. Steyerberg EW. Clinical Prediction Models: A Practical Approach to Development, Validation, and Updating. Springer International Publishing; 2019. Available from: https://books.google.lk/books?id=oKCkDwAAQBAJ
  36. Vittinghoff E, McCulloch CE. Relaxing the rule of ten events per variable in logistic and Cox regression. Am J Epidemiol. 2007;165(6):710–718. doi: 10.1093/aje/kwk052 EDN: IKOECB
  37. Baeza-Delgado C, Cerdá Alberich L, Carot-Sierra JM, et al. A practical solution to estimate the sample size required for clinical prediction models generated from observational research on data. Eur Radiol Exp. 2022;6(1):22. doi: 10.1186/s41747-022-00276-y EDN: XZKVLZ
  38. Ogundimu EO, Altman DG, Collins GS. Adequate sample size for developing prediction models is not simply related to events per variable. J Clin Epidemiol. 2016;76:175–182. doi: 10.1016/j.jclinepi.2016.02.031
  39. van Smeden M, Moons KG, de Groot JA, et al. Sample size for binary logistic prediction models: Beyond events per variable criteria. Stat Methods Med Res. 2019;28(8):2455–2474. doi: 10.1177/0962280218784726
  40. Riley RD, Ensor J, Snell KIE, et al. Calculating the sample size required for developing a clinical prediction model. BMJ. 2020;368:m441. doi: 10.1136/bmj.m441
  41. Pavlou M, Ambler G, Qu C, et al. An evaluation of sample size requirements for developing risk prediction models with binary outcomes. BMC Med Res Methodol. 2024;24(1):146. doi: 10.1186/s12874-024-02268-5 EDN: MTWYQN
  42. Sebök M, Blum P, Sarnthein J, et al. Validation of the Clavien-Dindo grading system of complications for microsurgical treatment of unruptured intracranial aneurysms. Neurosurg Focus. 2021;51(5):E10. doi: 10.3171/2021.8.FOCUS20892 EDN: BLDKJA
  43. Yamamichi T, Ichinose J, Omura K, et al. Impact of postoperative complications on the long-term outcome in lung cancer surgery. Surg Today. 2022;52(9):1254–1261. doi: 10.1007/s00595-022-02452-4 EDN: RQXBGT
  44. Long VD, Thong DQ, Dat TQ, et al. Risk factors of postoperative complications and their effect on survival after laparoscopic gastrectomy for gastric cancer. Ann Gastroenterol Surg. 2024;8(4):580–594. doi: 10.1002/ags3.12780 EDN: EJXSTY

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