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Machine Learning to Predict Three Types of Outcomes After Traumatic Brain Injury Using Data at Admission: A Multi-Center Study for Development and Validation

Matsuo, Kazuya Aihara, Hideo Hara, Yoshie Morishita, Akitsugu Sakagami, Yoshio Miyake, Shigeru Tatsumi, Shotaro Ishihara, Satoshi Tohma, Yoshiki Yamashita, Haruo Sasayama, Takashi 神戸大学

2023.08.16

概要

The difficulty of accurately identifying patients who would benefit from promising treatments makes it challenging to prove the efficacy of novel treatments for traumatic brain injury (TBI). Although machine learning is being increasingly applied to this task, existing binary outcome prediction models are insufficient for the effective stratification of TBI patients. The aim of this study was to develop an accurate 3-class outcome prediction model to enable appropriate patient stratification. To this end, retrospective balanced data of 1200 blunt TBI patients admitted to six Japanese hospitals from January 2018 onwards (200 consecutive cases at each institution) were used for model training and validation. We incorporated 21 predictors obtained in the emergency department, including age, sex, six clinical findings, four laboratory parameters, eight computed tomography findings, and an emergency craniotomy. We developed two machine learning models (XGBoost and dense neural network) and logistic regression models to predict 3-class outcomes based on the Glasgow Outcome Scale-Extended (GOSE) at discharge. The prediction models were developed using a training dataset with n = 1000, and their prediction performances were evaluated over two validation rounds on a validation dataset (n = 80) and a test dataset (n = 120) using the bootstrap method. Of the 1200 patients in aggregate, the median patient age was 71 years, 199 (16.7%) exhibited severe TBI, and emergency craniotomy was performed on 104 patients (8.7%). The median length of stay was 13.0 days. The 3-class outcomes were good recovery/moderate disability for 709 patients (59.1%), severe disability/vegetative state in 416 patients (34.7%), and death in 75 patients (6.2%). XGBoost model performed well with 69.5% sensitivity, 82.5% accuracy, and an area under the receiver operating characteristic curve of 0.901 in the final validation. In terms of the receiver operating characteristic curve analysis, the XGBoost outperformed the neural network-based and logistic regression models slightly. In particular, XGBoost outperformed the logistic regression model significantly in predicting severe disability/vegetative state. Although each model predicted favorable outcomes accurately, they tended to miss the mortality prediction. The proposed machine learning model was demonstrated to be capable of accurate prediction of in-hospital outcomes following TBI, even with the three GOSE-based categories. As a result, it is expected to be more impactful in the development of appropriate patient stratification methods in future TBI studies than conventional binary prognostic models. Further, outcomes were predicted based on only clinical data obtained from the emergency department. However, developing a robust model with consistent performance in diverse scenarios remains challenging, and further efforts are needed to improve generalization performance.

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参考文献

1. Maas AIR, Menon DK, Adelson PD, et al. Traumatic brain injury: integrated

approaches to improve prevention, clinical care, and research. Lancet

Neurol 2017;16(12):987–1048; doi: 10.1016/S1474-4422(17)30371-X

2. Kato Y, Narisawa A, Karibe H, et al. The age distribution of severe head

injury in Japan Neurotrauma Data Bank: the changes among Project

1998, 2004, 2009, and 2015. Neurotraumatology 2019;42(2):160–167.

3. Rosenfeld JV, Maas AI, Bragge P, et al. Early management of severe

traumatic brain injury. Lancet 2012;380(9847):1088–1098; doi: 10.1016/

s0140-6736(12)60864-2

4. Basso A, Previgliano I, Servadei F. Traumatic brain injuries. In: Neurological

Disorders: Public Health Challenges. (World Health Organization eds.)

WHO Press: Geneva, Switzerland; 2006; pp. 164–173.

5. GBD 2016 Traumatic Brain Injury and Spinal Cord Injury Collaborators.

Global, regional, and national burden of traumatic brain injury and

spinal cord injury, 1990–2016: a systematic analysis for the Global

Burden of Disease Study 2016. Lancet Neurol 2019;18(1):56–87; doi: 10

.1016/s1474-4422(18)30415-0

6. Maas AI, Stocchetti N, Bullock R. Moderate and severe traumatic brain

injury in adults. Lancet Neurol 2008;7(8):728–741; doi: 10.1016/s14744422(08)70164-9

7. CRASH-3 trial collaborators. Effects of tranexamic acid on death, disability,

vascular occlusive events and other morbidities in patients with acute

traumatic brain injury (CRASH-3): a randomised, placebo-controlled trial.

Lancet 2019;394(10210):1713–1723; doi: 10.1016/s0140-6736(19)32233-0

8. Matsuo K, Aihara H, Nakai T, et al. Machine learning to predict in-hospital

morbidity and mortality after traumatic brain injury. J Neurotrauma

2020;37(1):202–210; doi: 10.1089/neu.2018.6276

9. Amorim RL, Oliveira LM, Malbouisson LM, et al. Prediction of early TBI

mortality using a machine learning approach in a LMIC population.

Front Neurol 2020; 10:1366; doi: 10.3389/fneur.2019.01366

10. Abujaber A, Fadlalla A, Gammoh D, et al. Prediction of in-hospital mortality in patients with post traumatic brain injury using national trauma

registry and machine learning approach. Scand J Trauma Resusc Emerg

Med 2020;28(1):44; doi: 10.1186/s13049-020-00738-5

11. Warman PI, Seas A, Satyadev N, et al. Machine learning for predicting inhospital mortality after traumatic brain injury in both high-income and

low- and middle-income countries. Neurosurgery 2022;90(5):605–612;

doi: 10.1227/neu.0000000000001898

12. Gravesteijn BY, Nieboer D, Ercole A, et al. Machine learning algorithms

performed no better than regression models for prognostication in

traumatic brain injury. J Clin Epidemiol 2020;122:95–107; doi: 10.1016/j

.jclinepi.2020.03.005

13. Wang R, Wang L, Zhang J, et al. XGBoost machine learning algorism

performed better than regression models in predicting mortality of

moderate-to-severe traumatic brain injury. World Neurosurg

2022;163:e617–e622; doi: 10.1016/j.wneu.2022.04.044

14. Adil SM, Elahi C, Patel DN, et al. Deep learning to predict traumatic brain

injury outcomes in the low-resource setting. World Neurosurg

2022;164:e8–e16; doi: 10.1016/j.wneu.2022.02.097

15. Servia L, Montserrat N, Badia M, et al. Machine learning techniques for

mortality prediction in critical traumatic patients: anatomic and physiologic variables from the RETRAUCI study. BMC Med Res Methodol

2020;20(1):262; doi: 10.1186/s12874-020-01151-3

16. Moons KG, Altman DG, Reitsma JB, et al. Transparent Reporting of a

multivariable prediction model for Individual Prognosis or Diagnosis

(TRIPOD): explanation and elaboration. Ann Intern Med

2015;162(1):W1–W73; doi: 10.7326/M14-0698

17. Hawryluk GWJ, Rubiano AM, Totten AM, et al. Guidelines for the Management of Severe Traumatic Brain Injury: 2020 Update of the

Decompressive Craniectomy Recommendations. Neurosurgery

2020;87(3):427–434; doi: 10.1093/neuros/nyaa278

18. Hawryluk GWJ, Aguilera S, Buki A, et al. A management algorithm for

patients with intracranial pressure monitoring: the Seattle International

Severe Traumatic Brain Injury Consensus Conference (SIBICC). Intensive

Care Med 2019;45(12):1783–1794; doi: 10.1007/s00134-019-05805-9

19. Maas AI, Hukkelhoven CW, Marshall LF, et al. Prediction of outcome in

traumatic brain injury with computed tomographic characteristics: a

comparison between the computed tomographic classiﬁcation and

1706

20.

21.

22.

23.

24.

25.

26.

27.

28.

29.

30.

31.

32.

33.

34.

35.

36.

combinations of computed tomographic predictors. Neurosurgery

2005;57(6):1173–1182; doi: 10.1227/01.neu.0000186013.63046.6b

Wilson JT, Pettigrew LE, Teasdale GM. Structured interviews for the

Glasgow Outcome Scale and the extended Glasgow Outcome Scale:

guidelines for their use. J Neurotrauma 1998;15(8):573–585; doi: 10

.1089/neu.1998.15.5730

Wilson L, Boase K, Nelson LD, et al. A manual for the Glasgow Outcome

Scale-Extended interview. J Neurotrauma 2021; 38(17):2435–2446; doi:

10.1089/neu.2020.7527

Troyanskaya O, Cantor M, Sherlock G, et al. Missing value estimation

methods for DNA microarrays. Bioinformatics 2001;17(6):520–525; doi:

10.1093/bioinformatics/17.6.520

Chen T, Guestrin C. XGBoost: a scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge

Discovery and Data Mining; 2016; doi: 10.1145/2939672.2939785

Farre P, Heurteau A, Cuvier O, et al. Dense neural networks for predicting

chromatin conformation. BMC Bioinformatics 2018;19(1):372; doi: 10

.1186/s12859-018-2286-z

Bergstra J, Komer B, Eliasmith C, et al. Hyperopt: a Python library for

model selection and hyperparameter optimization. Comput Sci Discov

2015;8(1):014008; doi: 10.1088/1749–4699/8/1/014008

Hand DJ, Till RJ. A simple generalisation of the area under the ROC curve

for multiple class classiﬁcation problems. Machine Learning

2001;45:171–186; doi: 10.1023/A:1010920819831

Petrucci CJ. A primer for social worker researchers on how to conduct a

multinomial logistic Rregression. J Soc Serv Res 2009;35(2):193–205.

Hosmer Jr DW, Lemeshow S, Sturdivant RX. Applied logistic regression

(Vol. 398). John Wiley & Sons: Hoboken, N.J.; 2013.

Lemeshow S, Hosmer Jr DW. A review of goodness of ﬁt statistics for use

in the development of logistic regression models. Am J Epidemiol

1982;115(1):92–106; doi: 10.1093/oxfordjournals.aje.a113284

DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under

two or more correlated receiver operating characteristic curves: a

nonparametric approach. Biometrics 1988;44(3):837–845.

Robin X, Turck N, Hainard A, et al. pROC: an open-source package for R

and S+ to analyze and compare ROC curves. BMC Bioinformatics

2011;12:77; doi: 10.1186/1471-2105-12-77.

Satyadev N, Warman PI, Seas A, et al. Machine learning for predicting

discharge disposition after traumatic brain injury. Neurosurgery

2022;90(6):768–774; doi: 10.1227/neu.0000000000001911

Rau CS, Kuo PJ, Chien PC, et al. Mortality prediction in patients with isolated moderate and severe traumatic brain injury using machine

learning models. PLoS One 2018;13(11):e0207192; doi: 10.1371/journal

.pone.0207192

Shi HY, Hwang SL, Lee KT, et al. In-hospital mortality after traumatic brain

injury surgery: a nationwide population-based comparison of mortality

predictors used in artiﬁcial neural network and logistic regression

models. J Neurosurg 2013;118(4):746–752; doi: 10.3171/2013.1

.JNS121130

Rughani AI, Dumont TM, Lu Z, et al. Use of an artiﬁcial neural network to

predict head injury outcome. J Neurosurg 2010;113(3):585–590; doi: 10

.3171/2009.11.JNS09857

Tu KC, Eric Nyam TT, Wang CC, et al. A computer-assisted system for early

mortality risk prediction in patients with traumatic brain injury using

MATSUO ET AL.

37.

38.

39.

40.

41.

42.

43.

44.

45.

46.

47.

48.

49.

50.

51.

artiﬁcial intelligence algorithms in emergency room triage. Brain Sci

2022;12(5):612; doi: 10.3390/brainsci12050612

Hernandes Rocha TA, Elahi C, Cristina da Silva N, et al. A traumatic brain

injury prognostic model to support in-hospital triage in a low-income

country: a machine learning-based approach. J Neurosurg

2019;132(6):1961–1969; doi: 10.3171/2019.2.JNS182098

Pease M, Arefan D, Barber J, et al. Outcome prediction in patients with

severe traumatic brain injury using deep learning from head CT scans.

Radiology 2022;304(2):385–394; doi: 10.1148/radiol.212181

Christodoulou E, Ma J, Collins GS, et al. A systematic review shows no

performance beneﬁt of machine learning over logistic regression for

clinical prediction models. J Clin Epidemiol 2019;110:12–22; doi: 10

.1016/j.jclinepi.2019.02.004

van der Ploeg T, Nieboer D, Steyerberg EW. Modern modeling techniques

had limited external validity in predicting mortality from traumatic

brain injury. J Clin Epidemiol 2016;78:83–89; doi: 10.1016/j.jclinepi.2016

.03.002

Maegele M, Scho¨chl H, Menovsky T, et al. Coagulopathy and haemorrhagic progression in traumatic brain injury: advances in mechanisms,

diagnosis, and management. Lancet Neurol 2017;16(8):630–647; doi: 10

.1016/S1474-4422(17)30197-7

Epstein DS, Mitra B, O’Reilly G, et al. Acute traumatic coagulopathy in the

setting of isolated traumatic brain injury: a systematic review and metaanalysis. Injury 2014;45(5): 819–824; doi: 10.1016/j.injury.2014.01.011

Haas T, Fries D, Tanaka KA, et al. Usefulness of standard plasma coagulation tests in the management of perioperative coagulopathic bleeding: is there any evidence? Br J Anaesth 2015;114(2):217–224; doi: 10

.1093/bja/aeu303

Saggar V, Mittal RS, Vyas MC. Hemostatic abnormalities in patients with

closed head injuries and their role in predicting early mortality.

J Neurotrauma 2009;26(10): 1665–1668; doi: 10.1089/neu.2008.0799

Maas AIR, Menon DK, Manley GT, et al. Traumatic brain injury: progress

and challenges in prevention, clinical care, and research. Lancet Neurol

2022;21(11):1004–1060; doi: 10.1016/s1474-4422(22)00309-x

McCrea MA, Giacino JT, Barber J, et al. Functional outcomes over the ﬁrst

year after moderate to severe traumatic brain injury in the prospective,

longitudinal TRACK-TBI study. JAMA Neurol 2021;78(8):982–992; doi: 10

.1001/jamaneurol.2021.2043

Suehiro E, Kiyohira M, Haji K, et al. Changes in outcomes after discharge

from an acute hospital in severe traumatic brain injury. Neurol Med Chir

(Tokyo) 2022;62(3):111–117; doi: 10.2176/nmc.oa.2021-0217

Lu J, Marmarou A, Lapane K, et al. A method for reducing misclassiﬁcation

in the extended Glasgow Outcome Score. J Neurotrauma

2010;27(5):843–852; doi: 10.1089/neu.2010.1293

Ritchie H, Roser M. OurWorldInData.org. Age Structure; 2019. Available

from: https://ourworldindata.org/age-structure. [Last accessed: March

3, 2023].

Okada Y, Kiguchi T, Iiduka R, et al. Association between the Japan Coma

Scale scores at the scene of injury and in-hospital outcomes in trauma

patients: an analysis from the nationwide trauma database in Japan.

BMJ open 2019;9(7):e029706; doi: 10.1136/bmjopen-2019-029706

Obermeyer Z, Powers B, Vogeli C, et al. Dissecting racial bias in an algorithm used to manage the health of populations. Science

2019;366(6464):447–453; doi: 10.1126/science.aax234

...

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Machine Learning to Predict Three Types of Outcomes After Traumatic Brain Injury Using Data at Admission: A Multi-Center Study for Development and Validation

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Machine Learning to Predict Three Types of Outcomes After Traumatic Brain Injury Using Data at Admission: A Multi-Center Study for Development and Validation

概要

この論文で使われている画像

関連論文

Development and validation of early prediction for neurological outcome at 90 days after return of spontaneous circulation in out-of-hospital cardiac arrest

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参考文献