Izvestiya of Saratov University.

Mathematics. Mechanics. Informatics

ISSN 1816-9791 (Print)
ISSN 2541-9005 (Online)


For citation:

Dmitriev P. O., Kharlamov A. V., Kazhanov I. V., Kirillova I. V., Kossovich L. Y., Falkovich A. S., Mikityuk S. I., Petrov A. V. Specification of prognostic models and software implementation of a calculator for predicting a fatal outcome in a combined pelvic injury. Izvestiya of Saratov University. Mathematics. Mechanics. Informatics, 2022, vol. 22, iss. 3, pp. 376-392. DOI: 10.18500/1816-9791-2022-22-3-376-392, EDN: CQUHLI

This is an open access article distributed under the terms of Creative Commons Attribution 4.0 International License (CC-BY 4.0).
Published online: 
31.08.2022
Full text:
(downloads: 1287)
Language: 
Russian
Heading: 
Article type: 
Article
UDC: 
004.94:519.23:617-089
EDN: 
CQUHLI

Specification of prognostic models and software implementation of a calculator for predicting a fatal outcome in a combined pelvic injury

Autors: 
Dmitriev Pavel O., Saratov State University
Kharlamov Alexander Vladimirovich, Saratov State University
Kazhanov Igor V., S. M. Kirov Military Medical Academy of the Ministry of Defense of the Russian Federation
Kirillova Irina V., Saratov State University
Kossovich Leonid Yurevich, Saratov State University
Falkovich Alexander Savelievich, Saratov State University
Mikityuk Sergey I., S. M. Kirov Military Medical Academy of the Ministry of Defense of the Russian Federation
Petrov Artem V., Saint-Petersburg Institute of Emergency Care n.a. I. I. Dzhanelidze
Abstract: 

Based on the regression, factor and discriminant analysis of the depersonalized data of 1082 patients with combined pelvic injuries, prognostic logit models were developed, including such factors as age, the variant of the mechanism of pelvic injury, results of the assessment of the degree of impaired consciousness and coma on the Glasgow scale, and total quantitative scores of damage severity for each of the three commonly used scales (Yu. N. Tsibin's, VPH-P (MT), ISS). The resulting three models for each of the three scales of injury severity have almost equal prediction efficiency for the dependent variable “outcome”. The revealed regularities and the coefficient-formalized models for the predicting of the fatal outcome of  pelvic injury treatment formed the base for the software implementations in the forms of tables and the calculator. During the testing, the usability as well as the satisfactory prediction accuracy were confirmed.

Acknowledgments: 
The work was supported by the Russian Foundation for Advanced Research (contract No. 6/130/2018-2021 of 01.06.2018).
References: 
  1. Kossovich L. Yu., Kharlamov A. V., Lysunkina Yu. V., Shulga A. E. Mathematical modeling and prediction of the effectiveness of surgical treatment in surgery of the pelvic complex. Journal of Samara State Technical University, Ser. Physical and Mathematical Sciences, 2019, vol. 23, iss. 4, pp. 744–755. https://doi.org/10.14498/vsgtu1702
  2. Dreizin D., Bodanapally U., Boscak A., Tirada N., Issa G., Nascone J. W., Bivona L., Mascarenhas D., O’Toole R. V., Nixon E., Chen R., Siegel E. CT prediction model for major arterial injury after blunt pelvic ring disruption. Radiology, 2018, vol. 287, iss. 3, pp. 1061–1069. https://doi.org/10.1148/radiol.2018170997
  3. Gumanenko E. K., Scherbuk Yu. A., Silyuk M. G., Golovko K. P., Maday O. D., Udaltsova N. A., Gorshkov E. A., Bumay A. O., Afinogenova A. G., Afinogenov G. E., Maday D. Yu. Biometric aspects in treatment of combined trauma. Grekov’s Bulletin of Surgery, 2018, vol. 177, iss. 3, pp. 25—30 (in Russian). https://doi.org/10.24884/0042-4625-2018-177-3-25-30
  4. Aksekili M. A. F., Asilturk M., Akcaalan S., Aksekili H., Alkan H., Demir P. Radiological evaluation of normal sagittal vertebral, pelvis and global spinopelvic parameters in a young adult Turkish population. Journal of Turkish Spinal Surgery, 2021, vol. 32, iss. 1, pp. 20–25. https://doi.org/10.4274/jtss.galenos.2021.314
  5. De Munter L., Polinder S., Lansink K. W. W., Cnossen M. C., Steyerberg E. W., de Jongh M. A. C. Mortality prediction models in the general trauma population: A systematic review. Injury, 2017, vol. 48, iss. 2, pp. 221–229. https://doi.org/10.1016/j. injury.2016.12.009
  6. Pencina M. J., D’Agostino R. B. Sr., Song L. Quantifying discrimination of Framingham risk functions with different survival C statistics. Statistics in Medicine, 2012, vol. 10, iss. 31 (15), pp. 1543–1553. https://doi.org/10.1002/sim.4508
  7. Wolbers M., Blanche P., Koller M. T., Witteman J. C., Gerds T. A. Concordance for prognostic models with competing risks. Biostatistics, 2014, vol. 15, iss. 3, pp. 526–539. https://doi.org/10.1093/biostatistics/kxt059
  8. Jang H. D., Bang C., Lee J. C., Soh J. W., Choi S. W., Cho H. K., Shin B. J. Corrigendum to ‘Risk factor analysis for predicting vertebral body re-collapse after posterior instrumented fusion in thoracolumbar burst fracture’ [The Spine Journal 18/2 (2018) 285–293]. The Spine Journal, 2021, vol. 21, iss. 11, pp. 1961–1962. https://doi.org/10.1016/j.spinee.2021.07.001
  9. Berne J. D., Cook A., Rowe S. A., Norwood S. H. A multivariate logistic regression analysis of risk factors for blunt cerebrovascular injury. Journal of Vascular Surgery, 2010, vol. 51, iss. 1, pp. 57–64. https://doi.org/10.1016/j.jvs.2009.08.071
  10. Zhang B., Li S., Miao D., Zhao C., Wang L. Risk factors of cage subsidence in patients with ossification of posterior longitudinal ligament (OPLL) after anterior cervical discectomy and fusion. Medical Science Monitor, 2018, vol. 24, pp. 4753–4759. https://doi.org/10.12659/MSM.910964
  11. Ovcharenko S. I. Prediction of the Volume and Outcome of Surgical Intervention in Lumbar Osteochondrosis. Thesis Diss. Cand. Sci. (Med.). St. Petersburg, 2007. 21 p. (in Russian). EDN: NJALUV
  12. Antipko A. L. Prediction of Recurrences of Herniated Discs of the Lumbar Spine on the Basis of Magnetic Resonance Imaging and Mathematical Modeling. Thesis Diss. Cand. Sci. (Med.). Voronezh, 2009. 18 p. (in Russian). EDN: NLAQIV
  13. Krutko A. V., Baykov E. S. Prognozirovanie rezul’tatov khirurgicheskogo lecheniya patsientov s gryzhami poiasnichnykh mezhpozvonochnykh diskov (M51.0, M51.2, M51.3, M51.8, M51.9): klinicheskie rekomendatsii [Prediction of the Results of Surgical Treatment of Patients with Herniated Lumbar Intervertebral Discs (M51.0, M51.2, M51.3, M51.8, M51.9): Clinical Guidelines]. Novosibirsk, NNIITO, 2016. 13 p. (in Russian). EDN: YLEDML
  14. Mofidi R., Duff M. D., Madhavan K. K., Garden O. J., Parks R. W. Identification of severe acute pancreatitis using an artificial neural network. Surgery, 2007, vol. 141, iss. 1, pp. 59–66. https://doi.org/10.1016/j.surg.2006.07.022
  15. Andersson B., Andersson R., Ohlsson M., Nilsson J. Prediction of severe acute pancreatitis at admission to hospital using artificial neural networks. Pancreatology, 2011, vol. 11, iss. 3, pp. 328–335. https://doi.org/10.1159/000327903
  16. Sergeeva N. S., Skachkova T. E., Marshutina N. V., Nyushko K. M., Shevchuk I. M., Nazirov M. R., Alekseev B. Ya., Pirogov S. A., Yurkov E. F., Gitis V. G., Kaprin A. D. The validation of threshold decision ruls and calculator for APhiG algoritm for clarification of prostate cancer staging before treatment. Cancer Urology, 2020, vol. 16, iss. 1, pp. 43–53 (in Russian). https://doi.org/10.17650/1726-9776-2020-16-1-43-53, EDN: DBYFPV
  17. Vasin A. B., Malashenko V. N., Sgonnik A. V. Predicting complications during minimally invasive biliary tract decompression. Creative Surgery and Oncology, 2020, vol. 10, iss. 1, pp. 28–32 (in Russian). https://doi.org/10.24060/2076-3093-2020-10-1-28-32, EDN: UYPGNC
  18. Zhuravlev Yu. I., Nazarenko G. I., Cherkashov A. M., Ryazanov V. V., Nazarenko A. G. Predicting of outcomes of surgical treatment of degenerative lumbar disk disease. Burdenko’s Journal of Neurosurgery, 2009, no. 1, pp. 42–47 (in Russian). EDN: KCKTIF
  19. Supilnikov A. A., Pribytkov D. L., Starostina A. A. Optimal surgical method for the treatment of patients with acute ascending thrombophlebitis of superficial veins of the lower extremities. Bulletin of the Medical Institute “REAVIZ” (REHABILITATION, DOCTOR AND HEALTH), 2017, no. 5 (29), pp. 65–68 (in Russian). EDN: ZVFAJT
  20. Pribytkov D. L., Supilnikov A. A., Minaev Yu. L. Prognosis of treatment of patients with obliterating atherosclerosis of the arteries of the lower extremities based on the results of computer capillaroscopy: No. 2019612630. Certificate of state registration of the computer program No. 2019613961 Russian Federation. EDN: ZMLLZQ
  21. Lee J. B., Kim I. S., Lee J. J., Park J. H., Cho C. B., Yang S. H., Sung J. H., Hong J. T. Validity of a Smartphone Application (Sagittalmeter Pro) for the Measurement of Sagittal Balance Parameters. World Neurosurg, 2019, vol. 126, pp. e8–e15. https://doi.org/10.1016/j.wneu.2018.11.242
  22. Ivanov D. V., Kirillova I. V., Kossovich L. Yu., Likhachev S. V., Polienko A. V., Kharlamov A. V., Shulga A. E. Comparative analysis of the SpinoMeter mobile application and Surgimap. Genij Ortopedii, 2021, vol. 27, no. 1, pp. 74–79 (in Russian). https://doi.org/10.18019/1028-4427-2021-27-1-74-79, EDN: MXWDWV
  23. Bloodless A. S., Bessonov L. V., Dol A. V. [et al.]. Mobile application for measuring and calculating the parameters of the sagittal balance of the vertebral-pelvic complex “SpinoMetr”: No. 2019664415. Certificate of state registration of the computer program No. 2019665169 Russian Federation. EDN: CMSXOY
  24. Burgess A. R., Eastridge B. J., Young J. W., Ellison T. S., Ellison P. S. Jr., Poka A., Bathon G. H., Brumback R. J. Pelvic ring disruptions: effective classification system and treatment protocols. The Journal of Trauma, 1990, vol. 30, iss. 7, pp. 848–856. https://doi.org/10.1097/00005373-199007000-00015
  25. Muller M. E., Allgover M., Schneider R., Willinegger H. Rukovodstvo po vnutrennemu osteosintezu: Metodika, rekomendovannaia gruppoy AO (Shveytsariya) [Manual of Internal Osteosynthesis: Methodology Recommendation JSC Group (Switzerland): Transl. A. V. Korolev]. 3rd ed. expanded. and completely reworked. Moscow, Ad Marginem, 1996. 750 p. (in Russian).
  26. Meinberg E. G., Agel J., Roberts C. S., Karam M. D., Kellam J. F. Fracture and dislocation classification Compendium-2018. Journal of Orthopaedic Trauma, 2018, vol. 32, pp. S1–S170. https://doi.org/10.1097/BOT.0000000000001063
  27. Kasimov R. R., Makhnovsky A. I., Loginov V. I., Tutaev O. I., Neganov I. M., Smorka[1]lov A. Yu., Kukoz G. V., Elfimov D. A. Ob"ektivnaia otsenka tiazhesti travmy v voiskovom zvene, garnizonnykh i bazovykh voennykh gospitaliakh (metodicheskie rekomendatsii) [Objective Assessment of the Severity of Injury in the Military Unit, Garrison and Base Military Hospitals (Methodological Recommendations)]. Nizhny Novgorod, LLC “Stimul-ST”, 2017. 133 p. (in Russian). EDN: ZFLLIX
  28. SmartPlan Ortho2D preoperative planning system. Entry No. 10490 dated 06.05.2021 in the Unified Register of Russian Programs for Electronic Computers and Databases. https://reestr.digital.gov.ru/reestr/339480/?sphrase_id=465723
  29. Beskrovny A. S., Bessonov L. V., Golyadkina A. A., Dol A. V., Ivanov D. V., Kirillova I. V., Kossovich L. Yu., Sidorenko D. A. Development of a decision support system in traumatology and orthopedics. Biomechanics as a tool for preoperative planning. Russian Journal of Biomechanics, 2021, vol. 25, iss. 2, pp. 118–133 (in Russian). https://doi.org/10.15593/RZhBiomeh/2021.2.01, EDN: IEGOHC
  30. Kossovich L. Yu., Kirillova I. V., Falkovich A. S. [et al.] Database “Medical” for a prototype of a medical decision support system, personal virtual operating room mode: No. 2020621719. Certificate of state registration of the database No. 2020622181 Russian Federation. EDN: QOKAVZ
Received: 
17.10.2021
Accepted: 
10.02.2022
Published: 
31.08.2022