Machine Learning-Based Clinical Adjusted Selection of Predicting Risk Factors for Shunt Infection in Children
International Clinical Neuroscience Journal,
Vol. 8 No. 3 (2021),
30 Tir 2021
,
Page 135-143
Abstract
Background: Shunt Infection is a common complication of shunt insertion in children which can lead to bad neuro-developmental conditions and impose a considerable economic burden for the health care system. So, identifying predictive factors of shunt infection could help us in the proper improvement of this deteriorating condition.
Methods: In this study, related risk factors of 68 patients with history of shunt infection and 80 matched controls without any history of shunt infection, who were all operated in a single referral hospital were assessed. Three machine learning (ML)-based measures including sparsity, correlation, and redundancy along with specialist’s score were applied to select the most important predictive risk factors for shunt infection. ML was determined by summation of sparsity, correlation and redundancy measures, and the final total score was considered as normalization (ML-based score + specialist score).
Results: According to the total score, prematurity, first ventriculoperitoneal shunting (VPS) age, intraventricular hemorrhage (IVH), myelomeningocele (MMC) and low birth weight had higher weights as shunt infection risk factors. icterus, trauma, co-infection and tumor had the lowest weights and history of meningitis and number of shunt revisions were defined as intermediate risk factors.
Conclusion: The “ML-based clinical adjusted” method may be used as a complementary tool to help neurosurgeons in better patient selection and more accurate follow-up of children with higher risk of shunt infection.
- Hydrocephalus; Shunt infection; Sparsity; Correlation; Redundancy.
How to Cite
References
Cinalli G, Maixner WJ, Sainte-Rose C. Pediatric Hydrocephalus. Springer Science & Business Media; 2012.
Gutierrez-Murgas Y, Snowden JN. Ventricular shunt infections: immunopathogenesis and clinical management. J Neuroimmunol. 2014;276(1-2):1-8. doi: 10.1016/j. jneuroim.2014.08.006.
Kestle JR, Holubkov R, Douglas Cochrane D, Kulkarni AV, Limbrick DD Jr, Luerssen TG, et al. A new Hydrocephalus Clinical Research Network protocol to reduce cerebrospinal fluid shunt infection. J Neurosurg Pediatr. 2016;17(4):391- 6. doi: 10.3171/2015.8.peds15253.
Lee JK, Seok JY, Lee JH, Choi EH, Phi JH, Kim SK, et al. Incidence and risk factors of ventriculoperitoneal shunt infections in children: a study of 333 consecutive shunts in 6 years. J Korean Med Sci. 2012;27(12):1563-8. doi: 10.3346/jkms.2012.27.12.1563.
Habibi Z, Ertiaei A, Nikdad MS, Mirmohseni AS, Afarideh M, Heidari V, et al. Predicting ventriculoperitoneal shunt infection in children with hydrocephalus using artificial neural network. Childs Nerv Syst. 2016;32(11):2143-51. doi: 10.1007/s00381-016-3248-2.
Sabeti M, Boostani R, Moradi E, Habibi Z, Nejat F. Predicting shunt infection in children with hydrocephalus. Intell Based Med. 2021;5:100029. doi: 10.1016/j. ibmed.2021.100029.
Tunthanathip T, Sae-Heng S, Oearsakul T, Sakarunchai I, Kaewborisutsakul A, Taweesomboonyat C. Machine learning applications for the prediction of surgical site infection in neurological operations. Neurosurg Focus. 2019;47(2):E7. doi: 10.3171/2019.5.focus19241.
Luz CF, Vollmer M, Decruyenaere J, Nijsten MW, Glasner C, Sinha B. Machine learning in infection management using routine electronic health records: tools, techniques, and reporting of future technologies. Clin Microbiol Infect. 2020;26(10):1291-9. doi: 10.1016/j.cmi.2020.02.003.
Muscas G, Matteuzzi T, Becattini E, Orlandini S, Battista F, Laiso A, et al. Development of machine learning models to prognosticate chronic shunt-dependent hydrocephalus after aneurysmal subarachnoid hemorrhage. Acta Neurochir (Wien). 2020;162(12):3093-105. doi: 10.1007/ s00701-020-04484-6.
Hospital SCs. Seattle Children’s Hospital 2021. Available from: https://www.seattlechildrens.org/conditions/ hydrocephalus.
Remeseiro B, Bolon-Canedo V. A review of feature selection methods in medical applications. Comput Biol Med. 2019;112:103375. doi: 10.1016/j.compbiomed.2019.103375.
Shilaskar S, Ghatol A. Feature selection for medical diagnosis: evaluation for cardiovascular diseases. Expert Syst Appl. 2013;40(10):4146-53. doi: 10.1016/j. eswa.2013.01.032.
Liu Y, Wu JM, Avdeev M, Shi SQ. Multi-layer feature selection incorporating weighted score-based expert knowledge toward modeling materials with targeted properties. Adv Theory Simul. 2020;3(2):1900215. doi: 10.1002/adts.201900215.
Awaysheh A, Wilcke J, Elvinger F, Rees L, Fan W, Zimmerman KL. Review of medical decision support and machine-learning methods. Vet Pathol. 2019;56(4):512-25. doi: 10.1177/0300985819829524.
Sidey-Gibbons JAM, Sidey-Gibbons CJ. Machine learning in medicine: a practical introduction. BMC Med Res Methodol. 2019;19(1):64. doi: 10.1186/s12874-019-0681-4.
Baskin II, Marcou G, Horvath D, Varnek A. Bagging and boosting of classification models. In: Varnek A, ed. Tutorials in Chemoinformatics. Wiley; 2017. p. 241-7. doi: 10.1002/9781119161110.ch15.
Witten IH, Frank E, Hall MA, Pal CJ. The WEKA Workbench. Online Appendix for “Data Mining: Practical Machine Learning Tools and Techniques”. Morgan Kaufmann; 2016.
Kulkarni AV, Drake JM, Lamberti-Pasculli M. Cerebrospinal fluid shunt infection: a prospective study of risk factors. J Neurosurg. 2001;94(2):195-201. doi: 10.3171/ jns.2001.94.2.0195.
Moussa WM, Mohamed MA. Efficacy of postoperative antibiotic injection in and around ventriculoperitoneal shunt in reduction of shunt infection: a randomized controlled trial. Clin Neurol Neurosurg. 2016;143:144-9. doi: 10.1016/j.clineuro.2016.02.034.
Spader HS, Hertzler DA, Kestle JR, Riva-Cambrin J. Risk factors for infection and the effect of an institutional shunt protocol on the incidence of ventricular access device infections in preterm infants. J Neurosurg Pediatr. 2015;15(2):156-60. doi: 10.3171/2014.9.peds14215.
Braga MH, Carvalho GT, Brandão RA, Lima FB, Costa BS. Early shunt complications in 46 children with hydrocephalus. Arq Neuropsiquiatr. 2009;67(2a):273-7. doi: 10.1590/s0004-282x2009000200019.
Choux M, Genitori L, Lang D, Lena G. Shunt implantation: reducing the incidence of shunt infection. J Neurosurg. 1992;77(6):875-80. doi: 10.3171/jns.1992.77.6.0875.
Winn H. Youmans Neurological Surgery. Vol 4. New York: Elsevier; 2011.
Bruinsma N, Stobberingh EE, Herpers MJ, Vles JS, Weber BJ, Gavilanes DA. Subcutaneous ventricular catheter reservoir and ventriculoperitoneal drain-related infections in preterm infants and young children. Clin Microbiol Infect. 2000;6(4):202-6. doi: 10.1046/j.1469- 0691.2000.00052.x.
Dallacasa P, Dappozzo A, Galassi E, Sandri F, Cocchi G, Masi M. Cerebrospinal fluid shunt infections in infants. Childs Nerv Syst. 1995;11(11):643-8. doi: 10.1007/ bf00300722.
Simon TD, Whitlock KB, Riva-Cambrin J, Kestle JR, Rosenfeld M, Dean JM, et al. Revision surgeries are associated with significant increased risk of subsequent cerebrospinal fluid shunt infection. Pediatr Infect Dis J. 2012;31(6):551-6. doi: 10.1097/INF.0b013e31824da5bd.
Wells DL, Allen JM. Ventriculoperitoneal shunt infections in adult patients. AACN Adv Crit Care. 2013;24(1):6-12. doi: 10.1097/NCI.0b013e31827be1d1.
Rogers EA, Kimia A, Madsen JR, Nigrovic LE, Neuman MI. Predictors of ventricular shunt infection among children presenting to a pediatric emergency department. Pediatr Emerg Care. 2012;28(5):405-9. doi: 10.1097/ PEC.0b013e318252c23c.
Simon TD, Butler J, Whitlock KB, Browd SR, Holubkov R, Kestle JR, et al. Risk factors for first cerebrospinal fluid shunt infection: findings from a multi-center prospective cohort study. J Pediatr. 2014;164(6):1462-8.e2. doi: 10.1016/j.jpeds.2014.02.013.
Sacar S, Turgut H, Toprak S, Cirak B, Coskun E, Yilmaz O, et al. A retrospective study of central nervous system shunt infections diagnosed in a university hospital
during a 4-year period. BMC Infect Dis. 2006;6:43. doi: 10.1186/1471-2334-6-43.
Vafaee Shahi M, Noorbakhsh S, Ashouri S, Tahernia L, Raghami Derakhshani M. The complication for ventricular shunt based on different etiologies: a prospective study in Tehran, Iran. Open Neurol J. 2018;12(1):57-63. doi: 10.2174/1874205x01812010057.
Reddy GK, Bollam P, Caldito G. Ventriculoperitoneal shunt surgery and the risk of shunt infection in patients with hydrocephalus: long-term single institution experience. World Neurosurg. 2012;78(1-2):155-63. doi: 10.1016/j. wneu.2011.10.034.
Vinchon M, Dhellemmes P. Cerebrospinal fluid shunt infection: risk factors and long-term follow-up. Childs Nerv Syst. 2006;22(7):692-7. doi: 10.1007/s00381-005- 0037-8.
Vinchon M, Lemaitre MP, Vallée L, Dhellemmes P. Late shunt infection: incidence, pathogenesis, and therapeutic implications. Neuropediatrics. 2002;33(4):169-73. doi: 10.1055/s-2002-34490.
Gassas A, Kennedy J, Green G, Connolly B, Cohen J, Dag-Ellams U, et al. Risk of ventriculoperitoneal shunt infections due to gastrostomy feeding tube insertion in pediatric patients with brain tumors. Pediatr Neurosurg. 2006;42(2):95-9. doi: 10.1159/000090462.
Hong B, Polemikos M, Heissler HE, Hartmann C, Nakamura M, Krauss JK. Challenges in cerebrospinal fluid shunting in patients with glioblastoma. Fluids Barriers CNS. 2018;15(1):16. doi: 10.1186/s12987-018-0101-x.
Kim HS, Lee SU, Cha JH, Heo W, Song JS, Kim SJ. Clinical analysis of results of shunt operation for hydrocephalus following traumatic brain injury. Korean J Neurotrauma. 2015;11(2):58-62. doi: 10.13004/kjnt.2015.11.2.58.
Meng F, Wu H, Yang S. Clinical application of ventriculoperitoneal shunting in treating traumatic brain injury. Exp Ther Med. 2019;18(4):2497-502. doi: 10.3892/etm.2019.7860.
- Abstract Viewed: 175 times
- PDF Downloaded: 208 times