Predicting Neurological Deterioration after Moderate Traumatic Brain Injury: Development and Validation of a Prediction Model Based on Data Collected on Admission

Chen, Mingsheng; Li, Zhihong; Yan, Zhengzheng; Ge, Shunnan; Zhang, Yongbing; Yang, Haigui; Zhao, Lanfu; Liu, Lingyu; Zhang, Xingye; Cai, Yanli; Qu, Yan

doi:10.1089/neu.2021.0360

Cited by 14 publications

(19 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, this study developed a prediction model for the individualized risk of behavioral problems in children with TD, and a nomogram was plotted for the prediction model. To date, the application of a nomogram to predict the risk of TD with behavioral problems is lacking, although nomograms have been widely used as a reliable clinical tool to create a simple intuitive graph to quantify the risk of a clinical event of interest in other diseases [ 19 , 20 ]. In the present study, the model based on age, abnormal birth history, parenting pattern, family history, TD type and tic severity had a significant predictive performance for behavioral problems in children with TD.…”

Section: Discussionmentioning

confidence: 99%

“…Knowledge of the impact of sociodemographic and clinical characteristics on behavioral problems would be helpful to clinicians in tailoring treatment interventions, and ultimately improve the quality of care for children with TD. Nomograms have been used to provide individualized evaluation of the clinical event incidence on many occasions and as a reliable statistical tool to create a simple intuitive graph to quantify the risk of a clinical event [ 18 – 20 ]. It is typically constructed based on multivariate regression analysis and transforms complex regression equations into visual graphs, to exhibit the combined impact of variables in the prediction model.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Sociodemographic and clinical characteristics of children with tic disorders and behavioral problems: A real-world study and development of a prediction model

Liu

Wang²,

Ye³

et al. 2023

BMC Pediatr

View full text Add to dashboard Cite

Background Tic disorders (TD) are complex neuropsychiatric disorders frequently associated with a variety of comorbid problems, whose negative effects may exceed those of the tics themselves. In this study, we aimed to explore the sociodemographic and clinical characteristics of children with TD and behavioral problems, and develop a prediction model of behavioral problems based on the predictors under real-world conditions. Methods A hospital-based cross-sectional study was conducted on children with TD. Behavioral problems were surveyed using the Achenbach Child Behavior Checklist (CBCL). Sociodemographic information was collected from face-to-face interviews using an electronic questionnaire administered during the initial ambulatory visit. Clinical data were collected from medical records, and quality control was performed. The sociodemographic and clinical characteristics of patients with and without behavioral problems were statistically compared, and a nomogram prediction model was developed based on multivariate logistic regression analysis. The discriminatory ability and clinical utility of the nomogram were assessed by concordance index (C-index), receiver operating characteristic (ROC) curve, decision curve analysis (DCA) and clinical impact curve (CIC). Results A total of 343 TD cases were included in the final analysis, of which 30.32% had behavioral problems. The prediction model showed age 12–16 years, abnormal birth history, parenting pattern of indulgence, parent/close relatives with psychiatric disorders, chronic motor or vocal tic disorder (CTD)/Tourette syndrome (TS) and moderate/severe tic severity were associated with behavioral problems in children with TD. The C-index of the prediction model (nomogram) was 0.763 (95% confidence interval, 0.710 ~ 0.816). The nomogram was feasible for making beneficial clinical decisions, according to the satisfactory results of the DCA and CIC. Conclusions A nomogram prediction model for comorbid behavioral problems in children with TD was established. The prediction model demonstrated a good discriminative ability and predictive performance for beneficial clinical decisions. This model further provides a comprehensive understanding of associated sociodemographic and clinical characteristics by visual graphs and allows clinicians to rapidly identify patients with a higher risk of behavioral problems and tailor necessary interventions to improve clinical outcomes.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Sociodemographic and clinical characteristics of children with tic disorders and behavioral problems: A real-world study and development of a prediction model

Liu

Wang²,

Ye³

et al. 2023

BMC Pediatr

View full text Add to dashboard Cite

show abstract

“…In a retrospective study, Chen et al 10 also developed a predictive model of SND in patients with moTBI. They identified 8 parameters, including clinical characteristics (history of hypertension, GCS score on admission, and ISS score), CT scan findings (Marshall score and localization of contusion), and laboratory parameters (D-dimer and platelets counts).…”

Section: Discussionmentioning

confidence: 99%

“…Recently, Chen et al 10 developed and validated a predictive model of SND with 8 prognostic factors, including clinical parameters, neuroimaging, and laboratory. However, they did not report a simple scoring system, and some of these factors highlighted in this study may be challenging to assess routinely in a daily clinical practice.…”

mentioning

confidence: 99%

Secondary Neurologic Deterioration After Moderate Traumatic Brain Injury: Development of a Multivariable Prediction Model and Proposition of a Simple Triage Score

Ariès

Ognard

Cadieu

et al. 2023

Anesthesia &Amp; Analgesia

View full text Add to dashboard Cite

BACKGROUND: Identifying patients at risk of secondary neurologic deterioration (SND) after moderate traumatic brain injury (moTBI) is a challenge, as such patients will need specific care. No simple scoring system has been evaluated to date. This study aimed to determine clinical and radiological factors associated with SND after moTBI and to propose a triage score. METHODS: All adults admitted in our academic trauma center between January 2016 and January 2019 for moTBI (Glasgow Coma Scale [GCS] score, 9–13) were eligible. SND during the first week was defined either by a decrease in GCS score of >2 points from the admission GCS in the absence of pharmacologic sedation or by a deterioration in neurologic status associated with an intervention, such as mechanical ventilation, sedation, osmotherapy, transfer to the intensive care unit (ICU), or neurosurgical intervention (for intracranial mass lesions or depressed skull fracture). Clinical, biological, and radiological independent predictors of SND were identified by logistic regression (LR). An internal validation was performed using a bootstrap technique. A weighted score was defined based on beta (β) coefficients of the LR. RESULTS: A total of 142 patients were included. Forty-six patients (32%) showed SND, and 14-day mortality rate was 18.4%. Independent variables associated with SND were age above 60 years (odds ratio [OR], 3.45 [95% confidence interval {CI}, 1.45–8.48]; P = .005), brain frontal contusion (OR, 3.22 [95% CI, 1.31–8.49]; P = .01), prehospital or admission arterial hypotension (OR, 4.86 [95% CI, 2.03–12.60]; P = .006), and a Marshall computed tomography (CT) score of 6 (OR, 3.25 [95% CI, 1.31–8.20]; P = .01). The SND score was defined with a range from 0 to 10. The score included the following variables: age >60 years (3 points), prehospital or admission arterial hypotension (3 points), frontal contusion (2 points), and Marshall CT score of 6 (2 points). The score was able to detect patients at risk of SND, with an area under the receiver operating characteristic curve (AUC) of 0.73 (95% CI, 0.65–0.82). A score of 3 had a sensitivity of 85%, a specificity of 50%, a VPN of 87%, and a VPP of 44 % to predict SND. CONCLUSIONS: In this study, we demonstrate that moTBI patients have a significant risk of SND. A simple weighted score at hospital admission could be able to detect patients at risk of SND. The use of the score may enable optimization of care resources for these patients.

show abstract

“…The model accuracy and fit were assessed using ROC and calibration curves, and decision curve analysis (DCA) [24] was conducted to assess the rate of the benefit of the model to patients. Clinical impact curve (CIC) was used to stratify risk proportions for each threshold probability in the model [25]. P < 0.05 was considered to indicate a statistically significant difference.…”

Section: Discussionmentioning

confidence: 99%

Nomogram model predicts the risk of visual impairment in diabetic retinopathy: a retrospective study

Zhao

Sun

et al. 2022

BMC Ophthalmol

View full text Add to dashboard Cite

Background To develop a model for predicting the risk of visual impairment in diabetic retinopathy (DR) by a nomogram. Methods Patients with DR who underwent both optical coherence tomography angiography (OCTA) and fundus fluorescein angiography (FFA) were retrospectively enrolled. FFA was conducted for DR staging, swept-source optical coherence tomography (SS-OCT) of the macula and 3*3-mm blood flow imaging by OCTA to observe retinal structure and blood flow parameters. We defined a logarithm of the minimum angle of resolution visual acuity (LogMAR VA) ≥0.5 as visual impairment, and the characteristics correlated with VA were screened using binary logistic regression. The selected factors were then entered into a multivariate binary stepwise regression, and a nomogram was developed to predict visual impairment risk. Finally, the model was validated using the area under the receiver operating characteristic (ROC) curve (AUC), calibration plots, decision curve analysis (DCA), and clinical impact curve (CIC). Results A total of 29 parameters were included in the analysis, and 13 characteristics were used to develop a nomogram model. Finally, diabetic macular ischaemia (DMI) grading, disorganization of the retinal inner layers (DRIL), outer layer disruption, and the vessel density of choriocapillaris layer inferior (SubVD) were found to be statistically significant (P < 0.05). The model was found to have good accuracy based on the ROC (AUC = 0.931) and calibration curves (C-index = 0.930). The DCA showed that risk threshold probabilities in the (3–91%) interval models can be used to guide clinical practice, and the proportion of people at risk at each threshold probability is illustrated by the CIC. Conclusion The nomogram model for predicting visual impairment in DR patients demonstrated good accuracy and utility, and it can be used to guide clinical practice. Trial registration Chinese Clinical Trial Registry, ChiCTR2200059835. Registered 12 May 2022, https://www.chictr.org.cn/edit.aspx?pid=169290&htm=4

show abstract

Predicting Neurological Deterioration after Moderate Traumatic Brain Injury: Development and Validation of a Prediction Model Based on Data Collected on Admission

Cited by 14 publications

References 28 publications

Sociodemographic and clinical characteristics of children with tic disorders and behavioral problems: A real-world study and development of a prediction model

Sociodemographic and clinical characteristics of children with tic disorders and behavioral problems: A real-world study and development of a prediction model

Secondary Neurologic Deterioration After Moderate Traumatic Brain Injury: Development of a Multivariable Prediction Model and Proposition of a Simple Triage Score

Nomogram model predicts the risk of visual impairment in diabetic retinopathy: a retrospective study

Contact Info

Product

Resources

About