Traumatic Head Injury and the Diagnosis of Abuse: A Cluster Analysis

PEDIATRICS ◽  
2021 ◽  
Author(s):  
Stephen C. Boos ◽  
Ming Wang ◽  
Wouter A. Karst ◽  
Kent P. Hymel
Keyword(s):  
Cluster Analysis ◽  
Head Injury ◽  
Diagnostic Criteria ◽  
Skull Fracture ◽  
Fluid Collection ◽  
Young Patients ◽  
Subdural Hemorrhage ◽  
Data Set ◽  
Cluster Algorithms ◽  
Clinical Difference

OBJECTIVES: Data guiding abusive head trauma (AHT) diagnosis rest on case-control studies that have been criticized for circularity. We wished to sort children with neurologic injury using mathematical algorithms, without reference to physicians’ diagnoses or predetermined diagnostic criteria, and to compare the results to existing AHT data, physicians’ diagnoses, and a proposed triad of findings. METHODS: Unsupervised cluster analysis of an existing data set regarding 500 young patients with acute head injury hospitalized for intensive care. Three cluster algorithms were used to sort (partition) patients into subpopulations (clusters) on the basis of 32 reliable (κ > 0.6) clinical and radiologic variables. P values and odds ratios (ORs) identified variables most predictive of partitioning. RESULTS: The full cohort partitioned into 2 clusters. Variables substantially (P < .001 and OR > 10 in all 3 cluster algorithms) more prevalent in cluster 1 were imaging indications of brain hypoxemia, ischemia, and/or swelling; acute encephalopathy, particularly when lasting >24 hours; respiratory compromise; subdural hemorrhage or fluid collection; and ophthalmologist-confirmed retinoschisis. Variables substantially (P < .001 and OR < 0.10 in any cluster algorithm) more prevalent in cluster 2 were linear parietal skull fracture and epidural hematoma. Postpartitioning analysis revealed that cluster 1 had a high prevalence of physician-diagnosed abuse. CONCLUSIONS: Three cluster algorithms partitioned the population into 2 clusters without reference to predetermined diagnostic criteria or clinical opinion about the nature of AHT. Clinical difference between clusters replicated differences previously described in comparisons of AHT with non-AHT. Algorithmic partition was predictive of physician diagnosis and of the triad of findings heavily discussed in AHT literature.

scholarly journals CT scan Positive Finding Pattern of Head Injury at the University of Gondar Hospital; North West Ethiopia

2014 ◽  
Vol 1 (2) ◽  
pp. 65-72
Author(s):  
Zerubabel Tegegne Desita ◽  
Wossen Mulugeta
Keyword(s):  
Head Injury ◽  
Ct Scan ◽  
Computerized Tomography ◽  
Head Injuries ◽  
Skull Fracture ◽  
Subdural Hemorrhage ◽  
Positive Finding ◽  
Cross Sectional ◽  
North West ◽  
The University

Background: Head injuries rank high among morbidities due to trauma. Computerized tomography is an important modality in the investigation of these cases. However, there is no literature on the importance of computerized tomography in the diagnosis of head injury in Ethiopia. This study therefore is aimed to document the computerized tomographic features of patients with head injury managed at the University of Gondar Teaching Hospital. Materials and Methods: A cross sectional study involving 96 patients with head injury who had CT scan of the head in the UOG hospital over a 12-month period. Results: Most of the patients were male (74%).  Majority (58%) were in the age range of 20 to 40 years with a mean age of 31yrs. The most common abnormal findings were skull fracture (52%) and intracerebral hemorrhage and contusions (51%). It is followed by subdural hemorrhage (33%) soft tissue swelling 32% and epidural hemorrhage 10%. Conclusion:  Skull fracture and intra cerebral hemorrhage were the most common abnormal findings. This study has demonstrated the importance of CT scan in the evaluation of head injury by giving visibility of intracranial post traumatic injuries in a high proportion of patients which would be difficult to reach in to diagnosis clinically or using skull radiography alone. This obviously will have a significant role in improving patient management. Taking this in to account expansion of CT scan service for moderate to severe head injury patients is recommended in Ethiopia.   

scholarly journals Characterisation of MS phenotypes across the age span using a novel data set integrating 34 clinical trials (NO.MS cohort): Age is a key contributor to presentation

2021 ◽  
pp. 135245852098863
Author(s):  
Frank Dahlke ◽  
Douglas L Arnold ◽  
Piet Aarden ◽  
Habib Ganjgahi ◽  
Dieter A Häring ◽  
...  
Keyword(s):  
Disease Activity ◽  
Brain Volume ◽  
Young Patients ◽  
Clinical Trial Data ◽  
Study Data ◽  
Phase Iii ◽  
Brain Images ◽  
Data Set ◽  
Brain Volume Loss ◽  
Phase Iii Study

Background: The Oxford Big Data Institute, multiple sclerosis (MS) physicians and Novartis aim to address unresolved questions in MS with a novel comprehensive clinical trial data set. Objective: The objective of this study is to describe the Novartis–Oxford MS (NO.MS) data set and to explore the relationships between age, disease activity and disease worsening across MS phenotypes. Methods: We report key characteristics of NO.MS. We modelled MS lesion formation, relapse frequency, brain volume change and disability worsening cross-sectionally, as a function of patients’ baseline age, using phase III study data (≈8000 patients). Results: NO.MS contains data of ≈35,000 patients (>200,000 brain images from ≈10,000 patients), with >10 years follow-up. (1) Focal disease activity is highest in paediatric patients and decreases with age, (2) brain volume loss is similar across age and phenotypes and (3) the youngest patients have the lowest likelihood (<25%) of disability worsening over 2 years while risk is higher (25%–75%) in older, disabled or progressive MS patients. Young patients benefit most from treatment. Conclusion: NO.MS will illuminate questions related to MS characterisation, progression and prognosis. Age modulates relapse frequency and, thus, the phenotypic presentation of MS. Disease worsening across all phenotypes is mediated by age and appears to some extent be independent from new focal inflammatory activity.

scholarly journals Epidemiology and Treatment Outcomes of Head Injury in Bangladesh: Perspective from the Largest Tertiary Care Hospital

2021 ◽  
Author(s):  
Sukriti Das ◽  
Bipin Chaurasia ◽  
Dipankar Ghosh ◽  
Asit Chandra Sarker
Keyword(s):  
Head Injury ◽  
Road Traffic ◽  
Head Injuries ◽  
Skull Fracture ◽  
Tertiary Care ◽  
Acute Subdural Hematoma ◽  
Care Hospital ◽  
Female Ratio ◽  
Mortality And Morbidity ◽  
The Government

Abstract Background Traumatic brain injury (TBI) is one of the leading causes of mortality and morbidity. Economic impact is much worse in developing countries like Bangladesh, as victims are frequently male, productive, and breadwinners of the families. Objectives The objective of our study was to highlight the etiological pattern and distribution of varieties of head injuries in Bangladesh and give recommendations regarding how this problem can be solved or reduce to some extent at least. Methods From January 2017 to December 2019, a total of 14,552 patients presenting with head injury at emergency got admitted in Neurosurgery department of Dhaka Medical College and Hospital and were included in this study. Results The most common age group was 21 to 30 years (36%: 5,239) with a male-to-female ratio of 2.6:1. Injury was mostly caused by road traffic accident (RTA [58.3%: 8,484]), followed by fall (25%: 3,638) and history of assault (15.3%: 2,226). The common varieties of head injury were: acute extradural hematoma (AEDH [42.30%: 1,987]), skull fracture either linear or depressed (28.86%: 1,347), acute subdural hematoma (ASDH [12.30%: 574]), brain contusion (10.2%: 476), and others (6.04%: 282). Conclusion RTA is the commonest cause of TBI, and among them motor bike accident is the severe most form of TBI. AEDH is the commonest variety of head injuries. Proper steps taken by the Government, vehicle owners, and drivers, and proper referral system and prompt management in the hospital can reduce the mortality and morbidity from TBI in Bangladesh.

Application of multivariable statistical techniques in plant-wide WWTP control strategies analysis

2007 ◽  
Vol 56 (6) ◽  
pp. 75-83 ◽  
Author(s):  
X. Flores ◽  
J. Comas ◽  
I.R. Roda ◽  
L. Jiménez ◽  
K.V. Gernaey
Keyword(s):  
Cluster Analysis ◽  
Control Strategies ◽  
Treatment Plant ◽  
Principal Component ◽  
Statistical Techniques ◽  
Data Sets ◽  
Data Set ◽  
Casual Relation ◽  
Evaluation Matrix ◽  
Natural Groups

The main objective of this paper is to present the application of selected multivariable statistical techniques in plant-wide wastewater treatment plant (WWTP) control strategies analysis. In this study, cluster analysis (CA), principal component analysis/factor analysis (PCA/FA) and discriminant analysis (DA) are applied to the evaluation matrix data set obtained by simulation of several control strategies applied to the plant-wide IWA Benchmark Simulation Model No 2 (BSM2). These techniques allow i) to determine natural groups or clusters of control strategies with a similar behaviour, ii) to find and interpret hidden, complex and casual relation features in the data set and iii) to identify important discriminant variables within the groups found by the cluster analysis. This study illustrates the usefulness of multivariable statistical techniques for both analysis and interpretation of the complex multicriteria data sets and allows an improved use of information for effective evaluation of control strategies.

Empirical Evaluation of Genetic Clustering Methods Using Multilocus Genotypes From 20 Chicken Breeds

Genetics ◽  
2001 ◽  
Vol 159 (2) ◽  
pp. 699-713
Author(s):  
Noah A Rosenberg ◽  
Terry Burke ◽  
Kari Elo ◽  
Marcus W Feldman ◽  
Paul J Freidlin ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Population Structure ◽  
Clustering Algorithm ◽  
Empirical Evaluation ◽  
Unknown Origin ◽  
Clustering Methods ◽  
Genetic Cluster ◽  
Data Set ◽  
Multilocus Genotypes ◽  
Chicken Breeds

Abstract We tested the utility of genetic cluster analysis in ascertaining population structure of a large data set for which population structure was previously known. Each of 600 individuals representing 20 distinct chicken breeds was genotyped for 27 microsatellite loci, and individual multilocus genotypes were used to infer genetic clusters. Individuals from each breed were inferred to belong mostly to the same cluster. The clustering success rate, measuring the fraction of individuals that were properly inferred to belong to their correct breeds, was consistently ~98%. When markers of highest expected heterozygosity were used, genotypes that included at least 8–10 highly variable markers from among the 27 markers genotyped also achieved &gt;95% clustering success. When 12–15 highly variable markers and only 15–20 of the 30 individuals per breed were used, clustering success was at least 90%. We suggest that in species for which population structure is of interest, databases of multilocus genotypes at highly variable markers should be compiled. These genotypes could then be used as training samples for genetic cluster analysis and to facilitate assignments of individuals of unknown origin to populations. The clustering algorithm has potential applications in defining the within-species genetic units that are useful in problems of conservation.

A case of contrecoup skull fracture caused by mild head injury

2014 ◽  
Vol 125 ◽  
pp. 106-108 ◽  
Author(s):  
Shoko Merrit Yamada ◽  
Yoshiro Takaoka ◽  
Hiroshi Matsuura
Keyword(s):  
Head Injury ◽  
Skull Fracture ◽  
Mild Head Injury

scholarly journals Cluster analysis of the results of intraoperative optical spectroscopic diagnostics In brain glioma neurosurgery

2019 ◽  
Vol 7 (4) ◽  
pp. 23-34
Author(s):  
I. A. Osmakov ◽  
T. A. Savelieva ◽  
V. B. Loschenov ◽  
S. A. Goryajnov ◽  
A. A. Potapov
Keyword(s):  
Cluster Analysis ◽  
Optical Spectroscopy ◽  
Protoporphyrin Ix ◽  
Spectroscopy Data ◽  
Clustering Methods ◽  
Agglomerative Clustering ◽  
Data Set ◽  
Broadband Radiation ◽  
Degree Of Malignancy ◽  
Normal White Matter

The paper presents the results of a comparative study of methods of cluster analysis of optical intraoperative spectroscopy data during surgery of glial tumors with varying degree of malignancy. The analysis was carried out both for individual patients and for the entire dataset. The data were obtained using combined optical spectroscopy technique, which allowed simultaneous registration of diffuse reflectance spectra of broadband radiation in the 500–600 nm spectral range (for the analysis of tissue blood supply and the degree of hemoglobin oxygenation), fluorescence spectra of 5‑ALA induced protoporphyrin IX (Pp IX) (for analysis of the malignancy degree) and signal of diffusely reflected laser light used to excite Pp IX fluorescence (to take into account the scattering properties of tissues). To determine the threshold values of these parameters for the tumor, the infltration zone and the normal white matter, we searched for the natural clusters in the available intraoperative optical spectroscopy data and compared them with the results of the pathomorphology. It was shown that, among the considered clustering methods, EM‑algorithm and k‑means methods are optimal for the considered data set and can be used to build a decision support system (DSS) for spectroscopic intraoperative navigation in neurosurgery. Results of clustering relevant to thepathological studies were also obtained using the methods of spectral and agglomerative clustering. These methods can be used to postprocess combined spectroscopy data.

scholarly journals PENGARUH PEMBERIAN OKSIGEN DAN ELEVASI KEPALA 30º TERHADAP TINGKAT KESADARAN PADA PASIEN CEDERA KEPALA SEDANG

2020 ◽  
Vol 2 (2) ◽  
pp. 102-112
Author(s):  
Luci Riani Ginting ◽  
Kuat Sitepu ◽  
Renni Ariana Ginting
Keyword(s):  
Head Injury ◽  
Skull Fracture ◽  
P Value ◽  
Average Value ◽  
Level Of Consciousness ◽  
Before And After ◽  
Quasi Experimental ◽  
Oxygen Perfusion ◽  
The Brain ◽  
Adequate Oxygenation

Head injury is directly or indirectly mechanical injuries that resulted wound in the scalp, skull fracture, tear the lining of the brain, and brain damage, and neurological disorders. The basic method for brain protection of head injury patients are freeing the airway and giving adequate oxygenation. Giving oxygen and headv elevation 30° of head are the appropriate action for the moderate head injury classification to launch the cerebral oxygen perfusion and to increase consciousness level. The purpose of this research were to determine the GCS before and after giving oxygenation with and position 30 ° of head and to analyze the effect of giving oxygen and headv elevation30 °of head to change the levels of consciousness of moderate head injury patients. This research was an Quasi-Experimental study with 10 respondents. The test were used Paired Sample T-test Test. The results showed that there was an effect of giving oxygen and headv elevation 30 °of head toward to change the level of consciousness of moderate head injury patients. GCS average value before was 10.10 and GCS average after 12.90 value was with p value was 0.000. Keywords : Levels of Consciousness GCS, Moderate Head Injury, Position 30° of the Head

scholarly journals Traumatic Brain Injury Patterns on Ct Scan: A Comparison of Motorcyclist Versus Passenger Vehicle Patients Involved in Road Traffic Accidents

2017 ◽  
Vol 16 (1) ◽  
Author(s):  
Radhiana Hassan ◽  
Muniruddin Mohamad ◽  
Muhamad Zaim Azami ◽  
Husin Ali ◽  
Hafizah Pasi
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Traffic Accidents ◽  
Road Traffic ◽  
Skull Fracture ◽  
Common Finding ◽  
Subdural Hemorrhage ◽  
Road Traffic Accidents ◽  
Passenger Vehicle ◽  
Injury Patterns

Introduction: Traumatic brain injury following road traffic accidents is a common cause of morbidity and mortality in Malaysia. We aim to determine the differences of traumatic brain injury patterns based on CT findings among motorcyclist versus passenger vehicle patients involved in road traffic accidents. Materials and method: This retrospective study was conducted in Hospital Tengku Ampuan Afzan (HTAA), Kuantan, Pahang. A total of 100 CT scan brains of patients who were involved in road traffic accidents were retrieved and reviewed, 50 of them were motorcyclists and the other 50 were passenger vehicles. Results: Fifty percent of the motorcyclists had an abnormal CT brain finding while only 24% of the passenger vehicle showed abnormal finding. Among motorcyclist, skull fracture was the most common finding (30%) followed by subdural hemorrhage (28%). Among passenger vehicle, the most common finding was subdural hemorrhage (10%) followed by subarachnoid hemorrhage, intraparenchymal haemorrhage and skull fracture (8% each). The motorcyclist had significantly higher rate of subdural haemorrhage, extradural haemorrhage, intraparenchymal contusion and skull fracture compared to passenger vehicle patients with p value of 0.02, 0.03, 0.007 and 0.005 respectively. Conclusion: The occurrence of traumatic brain injury was significantly higher among the motorcyclist compared to passenger vehicle patients involved in road traffic accidents. The findings of this study highlighted the need for taking further measures to increase safety among the motorcyclists.

Exploring the Unknown Nature of Data

2010 ◽  
pp. 1-27
Author(s):  
Rui Xu ◽  
Donald C. Wunsch II
Keyword(s):  
Neural Networks ◽  
Cluster Analysis ◽  
Data Structures ◽  
Clustering Algorithms ◽  
Ground Truth ◽  
Human Beings ◽  
Data Set ◽  
Learning Capabilities ◽  
Good Learning ◽  
Hidden Data

To classify objects based on their features and characteristics is one of the most important and primitive activities of human beings. The task becomes even more challenging when there is no ground truth available. Cluster analysis allows new opportunities in exploring the unknown nature of data through its aim to separate a finite data set, with little or no prior information, into a finite and discrete set of “natural,” hidden data structures. Here, the authors introduce and discuss clustering algorithms that are related to machine learning and computational intelligence, particularly those based on neural networks. Neural networks are well known for their good learning capabilities, adaptation, ease of implementation, parallelization, speed, and flexibility, and they have demonstrated many successful applications in cluster analysis. The applications of cluster analysis in real world problems are also illustrated. Portions of the chapter are taken from Xu and Wunsch (2008).

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