The site of the Media Sphera Publishers contains materials intended solely for healthcare professionals.
By closing this message, you confirm that you are a certified medical professional or a student of a medical educational institution.

Login
EN
+7 (495) 482-4118
+7 (495) 482-4329
+8 800 250-18-12
  • (toll-free number for subscriptions)
    Mon-Fri from 10 a.m. to 6 p.m.

  • © 1998-2026
+7 (495) 482-43-29
EN
All journals
Journal
Year
Year
Search result: 0

Kolesov D.L.

Federal Research Clinical Center of Intensive Care Medicine and Rehabilitology

Berikashvili L.B.

Federal Research Clinical Center of Intensive Care and Rehabilitology

Polyakov P.A.

Federal Research Clinical Center for Intensive Care and Rehabilitation

Yadgarov M.Ya.

Federal Research Clinical Center of Intensive Care and Rehabilitology

Kuzovlev A.N.

Negovsky Research Institute of Intensive Care

Yakovlev A.A.

Federal Research Clinical Center of Intensive Care Medicine and Rehabilitology

Likhvantsev V.V.

Federal Research Clinical Center of Intensive Care and Rehabilitology

Model for predicting mortality in patients with traumatic brain injury

Authors:

Kolesov D.L., Berikashvili L.B., Polyakov P.A., Yadgarov M.Ya., Kuzovlev A.N., Yakovlev A.A., Likhvantsev V.V.

More about the authors

Journal: Russian Journal of Anesthesiology and Reanimatology. 2025;(5): 66‑73

DOI: 10.17116/anaesthesiology202505166

Read: 424 times

Buy for 300₽
Contact the author
Table of contents

MODEL FOR PREDICTING MORTALITY IN PATIENTS WITH TRAUMATIC BRAIN INJURY
MODEL FOR PREDICTING MORTALITY IN PATIENTS WITH TRAUMATIC BRAIN INJURY

To cite this article:

Kolesov DL, Berikashvili LB, Polyakov PA, Yadgarov MYa, Kuzovlev AN, Yakovlev AA, Likhvantsev VV. Model for predicting mortality in patients with traumatic brain injury. Russian Journal of Anesthesiology and Reanimatology. 2025;(5):66‑73. (In Russ.)
https://doi.org/10.17116/anaesthesiology202505166

МСФ на ЯМ
Использование инфографики авторами научных работ
Recommended articles:
Dete­rmination of formation age of acute traumatic intracranial subdural hema­tomas using computed tomo­graphy. Fore­nsic Medi­cal Expe­rtise. 2025;(1):17-21
On the challenges and shortfalls of expert asse­ssment of maxi­llofacial inju­ries with concussions acco­rding to medi­cal docu­ments only. Fore­nsic Medi­cal Expe­rtise. 2025;(1):54-58
Factors dete­rmining postoperative mortality in patients with hype­rtensive intracerebral hema­tomas. S.S. Korsakov Journal of Neurology and Psychiatry. 2025;(3-2):35-43
Risk factors of unfa­vorable outcome after deco­mpressive craniectomy in children with severe traumatic brain injury. Burdenko's Journal of Neurosurgery. 2025;(3):42-47
Reorganization of the corticospinal tract and thalamocortical projections in children with sequelae of severe traumatic brain injury. S.S. Korsakov Journal of Neurology and Psychiatry. 2025;(5):56-61
Effi­cacy of Reco­gnan in the therapy of neurological diso­rders after traumatic brain injury. S.S. Korsakov Journal of Neurology and Psychiatry. 2025;(7):41-45
Predictors of adve­rse outcomes after severe traumatic brain injury. Burdenko's Journal of Neurosurgery. 2025;(5):71-78
Prediction of respiratory complications after exte­nsive chest wall rese­ctions. Piro­gov Russian Journal of Surgery. 2025;(12):14-22
Abstract:

Chronic critical illness is characterized by extremely severe course of disease and significantly increases the burden on healthcare and social security systems. In these conditions, prediction of clinical outcomes becomes a priority task for specialists.

OBJECTIVE

To develop a nomogram predicting the probability of death within 14, 21, 28, 42 days in chronic critical illness after traumatic brain injury.

MATERIAL AND METHODS

A retrospective single-center cohort study was performed. The Russian Intensive Care Dataset (RICD) was used to develop the nomogram. This database includes information on hospitalizations to the Federal Research Clinical Center of Intensive Care Medicine and Rehabilitology between 2017 and 2023. We extracted clinical data of patients for the first 48 hours after admission (anamnestic data, laboratory parameters, scores), demographic data and outcomes (in-hospital mortality, length of hospital-stay, mechanical ventilation, infusion of vasoactive drugs).

RESULTS

The study included 324 patients (23 (7.1%) fatal cases). According to univariate and multivariate analysis, we identified and included 3 parameters into the nomogram predicting the probability of mortality: absolute neutrophil count, albumin concentration and chronic kidney disease. AUC was 0.696 (95% CI 0.590; 0.796).

CONCLUSION

Original nomogram involving absolute neutrophil count, albumin concentration and chronic kidney disease allows for mid-term prediction of mortality in chronic critical illness following traumatic brain injury.

Keywords:

traumatic brain injury
chronic critical illness
prediction of fatal outcome
post-intensive care syndrome
nomogram

Authors:

Kolesov D.L.

Federal Research Clinical Center of Intensive Care Medicine and Rehabilitology

Berikashvili L.B.

Federal Research Clinical Center of Intensive Care and Rehabilitology

Polyakov P.A.

Federal Research Clinical Center for Intensive Care and Rehabilitation

Yadgarov M.Ya.

Federal Research Clinical Center of Intensive Care and Rehabilitology

Kuzovlev A.N.

Negovsky Research Institute of Intensive Care

Yakovlev A.A.

Federal Research Clinical Center of Intensive Care Medicine and Rehabilitology

Likhvantsev V.V.

Federal Research Clinical Center of Intensive Care and Rehabilitology

Received:

31.01.2025

Accepted:

07.04.2025

Close metadata

References:

  1. Sychev AA, Savin IA, Baranich AI, Danilov GV, Strunina YuV, Oshorov AV, Polupan AA. Hemodynamic profile in acute period of severe traumatic brain injury. Russian Journal of Anesthesiology and Reanimatology. 2023;(3):32-36. (In Russ.). https://doi.org/10.17116/anaesthesiology202303132
  2. Syrovatsky AA, Simutis IS, Svetlikov AV, Lebedinsky KM, Shcheglov AN, Ratnikov VA, Reznichek DE, Khaldina EV. Perioperative Neuroprotection with Systemic Hypothermia During Carotid Endarterectomy. General Reanimatology. 2025;21(1):28-37. (In Russ.). https://doi.org/10.15360/1813-9779-2025-1-28-37
  3. Lamas D. Chronic Critical Illness. New England Journal of Medicine. 2014; 370(2):175-177.  https://doi.org/10.1056/nejmms1310675
  4. Nelson JE, Cox CE, Hope AA, Carson SS. Chronic critical illness. American Journal of Respiratory Critical Care Medicine. 2010;182(4):446-454.  https://doi.org/10.1164/rccm.201002-0210CI
  5. Rosenthal MD, Vanzant EL, Moore FA. Chronic critical illness and pics nutritional strategies. Journal of Clinical Medicine. 2021;10(11):2294. https://doi.org/10.3390/jcm10112294
  6. Rousseau AF, Prescott HC, Brett SJ, Weiss B, Azoulay E, Creteur J, Latronico N, Hough CL, Weber-Carstens S, Vincent JL, Preiser JC. Long-term outcomes after critical illness: recent insights. Critical Care. 2021;25(1):108.  https://doi.org/10.1186/s13054-021-03535-3
  7. Hawkins RB, Raymond SL, Stortz JA, Horiguchi H, Brakenridge SC, Gardner A, Efron PA, Bihorac A, Segal M, Moore FA, Moldawer LL. Chronic Critical Illness and the Persistent Inflammation, Immunosuppression, and Catabolism Syndrome. Frontiers in Immunology. 2018;9:1511. https://doi.org/10.3389/fimmu.2018.01511
  8. Voiriot G, Oualha M, Pierre A, Salmon-Gandonnière C, Gaudet A, Jouan Y, Kallel H, Radermacher P, Vodovar D, Sarton B, Stiel L, Bréchot N, Préau S, Joffre J; la CRT de la SRLF. Chronic critical illness and post-intensive care syndrome: from pathophysiology to clinical challenges. Annals of Intensive Care. 2022;12(1):58.  https://doi.org/10.1186/s13613-022-01038-0
  9. Wiencek C, Winkelman C. Chronic critical illness: Prevalence, profile, and pathophysiology. AACN Advanced Critical Care. 2010;21(1):44-61. quiz 63. PMID: 20118704. https://doi.org/10.1097/NCI.0b013e3181c6a162
  10. Madrid RA, McGee W. Value, Chronic Critical Illness, and Choosing Wisely. Journal of Intensive Care Medicine. 2019;34(8):609-614.  https://doi.org/10.1177/0885066618790942
  11. Marchioni A, Fantini R, Antenora F, Clini E, Fabbri L. Chronic critical illness: The price of survival. European Journal of Clinical Investigation. 2015; 45(12):1341-1349. https://doi.org/10.1111/eci.12547
  12. Rosenthal MD, Kamel AY, Rosenthal CM, Brakenridge S, Croft CA, Moore FA. Chronic Critical Illness: Application of What We Know. Nutrition in Clinical Practice. 2018;33(1):39-45.  https://doi.org/10.1002/ncp.10024
  13. Likhvantsev VV, Berikashvili LB, Yadgarov MY, Yakovlev AA, Kuzovlev AN. The Tri-Steps Model of Critical Conditions in Intensive Care: Introducing a New Paradigm for Chronic Critical Illness. Journal of Clinical Medicine. 2024;13(13):3683. https://doi.org/10.3390/jcm13133683
  14. Nelson JE, Kinjo K, Meier DE, Ahmad K, Morrison RS. When critical illness becomes chronic: Informational needs of patients and families. Journal of Critical Care. 2005;20(1):79-89.  https://doi.org/10.1016/j.jcrc.2004.11.003
  15. Fried TR, Bradley EH, Towle VR, Allore H. Understanding the Treatment Preferences of Seriously Ill Patients. New England Journal of Medicine. 2002;346(14):1061-1066. https://doi.org/10.1056/nejmsa012528
  16. de Cezar AG, Del Castanhel F, Grosseman S. Needs of family members of patients in intensive care and their perception of medical communication. Critical Care Science. 2023;35(1):73-83.  https://doi.org/10.5935/2965-2774.20230374-en
  17. Lee K, Hong SB, Lim CM, Koh Y. Sequential Organ Failure Assessment score and comorbidity: Valuable prognostic indicators in chronically critically ill patients. Anaesthesia and Intensive Care. 2008;36(4):528-534.  https://doi.org/10.1177/0310057×0803600422
  18. Nelson JE, Tandon N, Mercado AF, Camhi SL, Ely EW, Morrison RS. Brain dysfunction: Another burden for the chronically critically ill. Archives of Internal Medicine. 2006;166(18):1993-1999. https://doi.org/10.1001/archinte.166.18.1993
  19. Grechko AV, Yadgarov MY, Yakovlev A., Berikashvili LB, Kuzovlev AN, Polyakov PA, Kuznetsov IV, Likhvantsev VV. Russian Intensive Care Dataset — RICD. Obshchaya reanimatologiya. 2024;20(3):22-31. (In Russ.). https://doi.org/10.15360/1813-9779-2024-3-22-31
  20. Zlotnik A, Abraira V. A general-purpose nomogram generator for predictive logistic regression models. Stata Journal. 2015;15(2):537-546.  https://doi.org/10.1177/1536867X1501500212
  21. Ohbe H, Satoh K, Totoki T, Tanikawa A, Shirasaki K, Kuribayashi Y, Tamura M, Takatani Y, Ishikura H, Nakamura K; J-STAD (JAPAN Sepsis Treatment and Diagnosis) Study Group. Definitions, epidemiology, and outcomes of persistent/chronic critical illness: a scoping review for translation to clinical practice. Critical Care. 2024;28(1):435.  https://doi.org/10.1186/s13054-024-05215-4
Contact the author
Email
Message
The publishing house "Media Sphere" does not provide treatment services, does not give recommendations on contacting medical institutions and specific specialists, on the prescription of medicines and dietary supplements.

Email Confirmation

An email was sent to test@gmail.com with a confirmation link. Follow the link from the letter to complete the registration on the site.

Email Confirmation

Contact us
If you have any questions, complaints or suggestions, please use this feedback form.
Email
Message
The publishing house "Media Sphere" does not provide treatment services, does not give recommendations on contacting medical institutions and specific specialists, on the prescription of medicines and dietary supplements.
Submit Application

You can become an author of one of our magazines, send a request and we will consider it in during the day.

Name
Phone
E-mail
Message
Login
Registration
E-mail
Password
Forgot Password?

Log in to the site using your account in one of the services

Password recovery
E-mail
Login
Register

We use cооkies to improve the performance of the site. By staying on our site, you agree to the terms of use of cооkies. To view our Privacy and Cookie Policy, please. click here.