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

P. O. Dmitriev ; A. V. Kharlamov ; I. V. Kazhanov ; I. V. Kirillova ; L. Yu. Kossovich ; A. S. Falkovich ; S. I. Mikityuk ; A. V. Petrov

Izvestiya of Saratov University. Mathematics. Mechanics. Informatics, Tome 22 (2022) no. 3, pp. 376-392.

Résumé

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.

Bibliographie

[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, 23:4 (2019), 744–755 | DOI | MR | Zbl

[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, 287:3 (2018), 1061–1069 | DOI

[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, 177:3 (2018), 25–30 (in Russian) | DOI

[4] Aksekili M. A. F., Asiltürk 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, 32:1 (2021), 20–25 | DOI

[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, 48:2 (2017), 221–229 | DOI

[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, 10:31 (15) (2012), 1543–1553 | DOI | MR

[7] Wolbers M., Blanche P., Koller M. T., Witteman J. C., Gerds T. A., “Concordance for prognostic models with competing risks”, Biostatistics, 15:3 (2014), 526–539 | DOI

[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, 21:11 (2021), 1961–1962 | DOI

[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, 51:1 (2010), 57–64 | DOI

[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, 24 (2018), 4753–4759 | DOI

[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 pp. (in Russian) (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 pp. (in Russian) (NLAQIV)

[13] Krutko A. V., Baykov E. S., 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, NNIITO, Novosibirsk, 2016, 13 pp. (in Russian) (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, 141:1 (2007), 59–66 | DOI

[15] Andersson B., Andersson R., Ohlsson M., Nilsson J., “Prediction of severe acute pancreatitis at admission to hospital using artificial neural networks”, Pancreatology, 11:3 (2011), 328–335 | DOI

[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, 16:1 (2020), 43–53 (in Russian) (DBYFPV) | DOI | MR

[17] Vasin A. B., Malashenko V. N., Sgonnik A. V., “Predicting complications during minimally invasive biliary tract decompression”, Creative Surgery and Oncology, 10:1 (2020), 28–32 (in Russian) (UYPGNC) | DOI

[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, 42–47 (in Russian) (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), 65–68 (in Russian) (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 (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, 126 (2019), e8–e15 | DOI

[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, 27:1 (2021), 74–79 (in Russian) (MXWDWV) | DOI

[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 (CMSXOY) | Zbl

[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, 30:7 (1990), 848–856 | DOI

[25] Muller M. E., Allgover M., Schneider R., Willinegger H., Manual of Internal Osteosynthesis: Methodology Recommendation JSC Group (Switzerland), Transl. A. V. Korolev, 3rd ed. expanded. and completely reworked, Ad Marginem, M., 1996, 750 pp. (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, 32 (2018), S1–S170 | DOI

[27] Kasimov R. R., Makhnovsky A. I., Loginov V. I., Tutaev O. I., Neganov I. M., Smorkalov A. Yu., Kukoz G. V., Elfimov D. A., Objective Assessment of the Severity of Injury in the Military Unit, Garrison and Base Military Hospitals (Methodological Recommendations), LLC “Stimul-ST”, Nizhny Novgorod, 2017, 133 pp. (in Russian) (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

[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, 25:2 (2021), 118–133 (in Russian) (IEGOHC) | DOI

[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 (QOKAVZ)