Clinical nomogram predicting intracranial injury in pediatric traumatic brain injury

Tunthanathip, Thara; Duangsuwan, Jarunee; Wattanakitrungroj, Niwan; Tongman, Sasiporn; Phuenpathom, Nakornchai

doi:10.4103/jpn.jpn_11_20

Cited by 11 publications

(18 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This is a retrospective cohort study of 964 children with TBI who were younger than 15 years of age who presented with TBI from January 2009 to December 2019. The study population was divided into two cohorts; 900 children composed the first cohort, which was used in a prior study by Tunthanathip et al, 11 and 64 children, who were newly admitted from January to December 2019, were added in this study. Therefore, children who died before arrival (within the first 24 hours following TBI) and those patients who did not undergo CT of the brain were excluded.…”

Section: Methods Study Design and Populationmentioning

confidence: 99%

“…8 Nomograms built from a model of logistic or Cox regression have been used in neurosurgery as diagnostic or prediction tools. [9][10][11] Tunthanatip et al developed a nomogram for predicting positive findings following CT of the brain in pediatric patients with TBI and reported an area under the receiver operating characteristic (ROC) curve (AUC) of 0.71. 11 After conducting a literature review, we found a lack of evidence comparing predictive performance between ML and the nomogram with the same clinical outcome.…”

mentioning

confidence: 99%

“…[9][10][11] Tunthanatip et al developed a nomogram for predicting positive findings following CT of the brain in pediatric patients with TBI and reported an area under the receiver operating characteristic (ROC) curve (AUC) of 0.71. 11 After conducting a literature review, we found a lack of evidence comparing predictive performance between ML and the nomogram with the same clinical outcome. Therefore, the objective of this study was to compare the predictive performance of ML with the nomogram for intracranial injury after cranial CT in children with TBI.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Comparison of intracranial injury predictability between machine learning algorithms and the nomogram in pediatric traumatic brain injury

Tunthanathip

Duangsuwan

Wattanakitrungroj

et al. 2021

Neurosurgical Focus

Self Cite

View full text Add to dashboard Cite

OBJECTIVE The overuse of head CT examinations has been much discussed, especially those for minor traumatic brain injury (TBI). In the disruptive era, machine learning (ML) is one of the prediction tools that has been used and applied in various fields of neurosurgery. The objective of this study was to compare the predictive performance between ML and a nomogram, which is the other prediction tool for intracranial injury following cranial CT in children with TBI. METHODS Data from 964 pediatric patients with TBI were randomly divided into a training data set (75%) for hyperparameter tuning and supervised learning from 14 clinical parameters, while the remaining data (25%) were used for validation purposes. Moreover, a nomogram was developed from the training data set with similar parameters. Therefore, models from various ML algorithms and the nomogram were built and deployed via web-based application. RESULTS A random forest classifier (RFC) algorithm established the best performance for predicting intracranial injury following cranial CT of the brain. The area under the receiver operating characteristic curve for the performance of RFC algorithms was 0.80, with 0.34 sensitivity, 0.95 specificity, 0.73 positive predictive value, 0.80 negative predictive value, and 0.79 accuracy. CONCLUSIONS The ML algorithms, particularly the RFC, indicated relatively excellent predictive performance that would have the ability to support physicians in balancing the overuse of head CT scans and reducing the treatment costs of pediatric TBI in general practice.

show abstract

Section: Methods Study Design and Populationmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

Comparison of intracranial injury predictability between machine learning algorithms and the nomogram in pediatric traumatic brain injury

Tunthanathip

Duangsuwan

Wattanakitrungroj

et al. 2021

Neurosurgical Focus

Self Cite

View full text Add to dashboard Cite

show abstract

“…Additionally, internal validation was conducted by the bootstrap validation with 200-time resampling, and the result of the validation was reported as the area under the ROC curves (AUC). 19 The statistical analysis was performed using the R program version 3.4.0 software (R Foundation, Vienna, Austria). Moreover, a web-based nomogram was developed through https://www.shinyapps.io/.…”

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Prognostic Impact of the Combination of MGMT Methylation and TERT Promoter Mutation in Glioblastoma

Tunthanathip

Sangkhathat

Tanvejsilp

et al. 2021

JNRP

Self Cite

View full text Add to dashboard Cite

Background The concept of combinational analysis between the methylation of O6-methylguanine-DNA methyltransferase (MGMT) and telomerase reverse transcriptase promoter (pTERT) mutation in glioblastoma (GBM) has been reported. The main study objective was to determine the prognosis of patients with GBM based on MGMT/pTERT classification, while the secondary objective was to estimate the temozolomide effect on the survival time of GBM with MGMT/pTERT classification. Methods A total of 50 GBM specimens were collected after tumor resection and were selected for investigating MGMT methylation and pTERT mutation. Clinical imaging and pathological characteristics were retrospectively analyzed. Patients with MGMT/pTERT classification were analyzed using survival analysis to develop the nomogram for forecasting and individual prognosis. Results All patients underwent resection (total resection: 28%, partial resection: 64%, biopsy: 8%). Thirty-two percent of all cases received adjuvant temozolomide with radiotherapy. Sixty-four percent of the case was found methylated MGMT, and 56% of the present cohort found pTERT mutation. Following combinational analysis of biomarkers, results showed that the GBMs with methylated MGMT and wild-type pTERT had a superior prognosis compared with other subtypes. Using Cox regression analysis with multivariable analysis, the extent of resection, postoperative chemoradiotherapy, MGMT/pTERT classification were associated with a favorable prognosis. Hence, a web-based nomogram was developed for deploying individual prognostication. Conclusions The interaction of MGMT methylation and pTERT mutation was confirmed for predicting prognosis. The results from the present study could help physicians create treatment strategies for GBM patients in real-world situations.

show abstract

Prognostic factors and clinical nomogram for in-hospital mortality in traumatic brain injury

Tunthanathip,

Phuenpathom,

Jongjit

2024

The American Journal of Emergency Medicine

View full text Add to dashboard Cite

Clinical nomogram predicting intracranial injury in pediatric traumatic brain injury

Cited by 11 publications

References 25 publications

Comparison of intracranial injury predictability between machine learning algorithms and the nomogram in pediatric traumatic brain injury

Comparison of intracranial injury predictability between machine learning algorithms and the nomogram in pediatric traumatic brain injury

Prognostic Impact of the Combination of MGMT Methylation and TERT Promoter Mutation in Glioblastoma

Prognostic factors and clinical nomogram for in-hospital mortality in traumatic brain injury

Contact Info

Product

Resources

About