Predictive values of early head computed tomography for survival outcome after cardiac arrest in childhood: a pilot study.
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ABSTRACT:
SUBMITTER: Tetsuhara K
PROVIDER: S-EPMC8187472 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundThis study aimed to develop an automated method to measure the gray-white matter ratio (GWR) from brain computed tomography (CT) scans of patients with out-of-hospital cardiac arrest (OHCA) and assess its significance in predicting early-stage neurological outcomes.MethodsPatients with OHCA who underwent brain CT imaging within 12 h of return of spontaneous circulation were enrolled in this retrospective study. The primary outcome endpoint measure was a favorable neurological outcome, defined as cerebral performance category 1 or 2 at hospital discharge. We proposed an automated method comprising image registration, K-means segmentation, segmentation refinement, and GWR calculation to measure the GWR for each CT scan. The K-means segmentation and segmentation refinement was employed to refine the segmentations within regions of interest (ROIs), consequently enhancing GWR calculation accuracy through more precise segmentations.ResultsOverall, 443 patients were divided into derivation N=265, 60% and validation N=178, 40% sets, based on age and sex. The ROI Hounsfield unit values derived from the automated method showed a strong correlation with those obtained from the manual method. Regarding outcome prediction, the automated method significantly outperformed the manual method in GWR calculation (AUC 0.79 vs. 0.70) across the entire dataset. The automated method also demonstrated superior performance across sensitivity, specificity, and positive and negative predictive values using the cutoff value determined from the derivation set. Moreover, GWR was an independent predictor of outcomes in logistic regression analysis. Incorporating the GWR with other clinical and resuscitation variables significantly enhanced the performance of prediction models compared to those without the GWR.ConclusionsAutomated measurement of the GWR from non-contrast brain CT images offers valuable insights for predicting neurological outcomes during the early post-cardiac arrest period.
| S-EPMC11005205 | biostudies-literature
Project description:BackgroundHead computed tomography (CT) is a guideline recommended method to predict functional outcome after cardiac arrest (CA), but standardized criteria for evaluation are lacking. To date, no prospective trial has systematically validated methods for diagnosing hypoxic-ischaemic encephalopathy (HIE) on CT after CA. We present a protocol for validation of pre-specified radiological criteria for assessment of HIE on CT for neuroprognostication after CA.Methods/designThis is a prospective observational international multicentre substudy of the Targeted Hypothermia versus Targeted Normothermia after out-of-hospital cardiac arrest (TTM2) trial. Patients still unconscious 48 hours post-arrest at 13 participating hospitals were routinely examined with CT. Original images will be evaluated by examiners blinded to clinical data using a standardized protocol. Qualitative assessment will include evaluation of absence/presence of "severe HIE". Radiodensities will be quantified in pre-specified regions of interest for calculation of grey-white matter ratios (GWR) at the basal ganglia level. Functional outcome will be dichotomized into good (modified Rankin Scale 0-3) and poor (modified Rankin Scale 4-6) at six months post-arrest. Prognostic accuracies for good and poor outcome will be presented as sensitivities and specificities with 95% confidence intervals (using pre-specified cut-offs for quantitative analysis), descriptive statistics (Area Under the Receiver Operating Characteristics Curve), inter- and intra-rater reliabilities according to STARD guidelines.ConclusionsThe results from this prospective trial will validate a standardized approach to radiological evaluations of HIE on CT for prediction of functional outcome in comatose CA patients.The TTM2 trial and the TTM2 CT substudy are registered at ClinicalTrials.gov NCT02908308 and NCT03913065.
| S-EPMC9576971 | biostudies-literature
Project description:PURPOSE:To assess the performance of neuroprognostic guidelines proposed by the American Academy of Neurology (AAN), European Resuscitation Council/European Society of Intensive Care Medicine (ERC/ESICM), and American Heart Association (AHA) in predicting outcomes of patients who remain unconscious after cardiac arrest. METHODS:We retrospectively identified a cohort of unconscious post-cardiac arrest patients at a single tertiary care centre from 2011 to 2017 and reviewed hospital records for clinical, radiographic, electrophysiologic, and biochemical findings. Outcomes at discharge and 6 months post-arrest were abstracted and dichotomized as good (Cerebral Performance Category (CPC) scores of 1-2) versus poor (CPC 3-5). Outcomes predicted by current guidelines were compared to actual outcomes, with false positive rate (FPR) used as a measure of predictive value. RESULTS:Of 226 patients, 36% survived to discharge, including 24 with good outcomes; 52% had withdrawal of life-sustaining therapies (WLST) during hospitalization. The AAN guideline yielded discharge and 6-month FPR of 8% and 15%, respectively. In contrast, the ERC/ESICM had a FPR of 0% at both discharge and 6 months. The AHA predictors had variable specificities, with diffuse hypoxic-ischaemic injury on MRI performing especially poorly (FPR 12%) at both discharge and 6 months. CONCLUSIONS:Though each guideline had components that performed well, only the ERC/ESICM guideline yielded a 0% FPR. Amongst the AAN and AHA guidelines, false positives emerged more readily at 6 months, reflective of continuing recovery after discharge, even in a cohort inevitably biased by WLST. Further assessment of predictive modalities is needed to improve neuroprognostic accuracy.
| S-EPMC6589093 | biostudies-literature
Project description:Materials and methodsThe PubMed, ScienceDirect, Web of Science, and China National Knowledge Infrastructure databases were searched for all relevant articles published before March 31, 2020, without any language restrictions. The pooled odds ratios (ORs) and 95% confidence intervals (CIs) were calculated with a random-effects model using Stata 14.0 software.ResultA total of 24 eligible studies with 2812 CA patients were recruited in the meta-analysis. The pooled result showed that decreased GWR was correlated with poor neurological outcomes after CA (OR = 11.28, 95% CI: 6.29-20.21, and P < 0.001) with moderate heterogeneity (I 2 = 71.5%, P < 0.001). The pooled sensitivity and specificity were 0.58 (95% CI: 0.47-0.68) and 0.95 (95% CI: 0.87-0.98), respectively. The area under the curve (AUC) of GWR was 0.84 (95% CI: 0.80-0.87). Compared with GWR (cerebrum) and GWR (average), GWR using the basal ganglion level of brain CT had the highest AUC of 0.87 (0.84-0.90). Subgroup analysis indicated that heterogeneity may be derived from the time of CT measurement, preset specificity, targeted temperature management, or proportion of cardiac etiology. Sensitivity analysis indicated that the result was stable, and Deeks' plot showed no possible publication bias (P = 0 .64).ConclusionCurrent research suggests that GWR, especially using the basal ganglion level of brain CT, is a useful parameter for determining neurological outcomes after CA.
| S-EPMC7803139 | biostudies-literature
Project description:Background Patients resuscitated from out-of-hospital circulatory arrest (OHCA) frequently have cardiopulmonary resuscitation injuries identifiable by computed tomography, although the prevalence, types of injury, and effects on clinical outcomes are poorly characterized. Methods and Results We assessed the prevalence of resuscitation-associated injuries in a prospective, observational study of a head-to-pelvis sudden-death computed tomography scan within 6 hours of successful OHCA resuscitation. Primary outcomes included total injuries and time-critical injuries (such as organ laceration). Exploratory outcomes were injury associations with mechanical cardiopulmonary resuscitation and survival to discharge. Among 104 patients with OHCA (age 56±15 years, 30% women), 58% had bystander cardiopulmonary resuscitation, and total cardiopulmonary resuscitation time was 15±11 minutes. The prevalence of resuscitation-associated injury was high (81%), including 15 patients (14%) with time-critical findings. Patients with resuscitation injury were older (58±15 versus 46±13 years; P<0.001), but had otherwise similar baseline characteristics and survival compared with those without. Mechanical chest compression systems (27%) had more frequent sternal fractures (36% versus 12%; P=0.009), including displaced fractures (18% versus 1%; P=0.005), but no difference in survival (46% versus 41%; P=0.66). Conclusions In patients resuscitated from OHCA, head-to-pelvis sudden-death computed tomography identified resuscitation injuries in most patients, with nearly 1 in 7 with time-critical complications, and one-half with extensive rib-cage injuries. These data suggest that sudden-death computed tomography may have additional diagnostic utility and treatment implications beyond evaluating causes of OHCA. These important findings need to also be taken in context of the certain fatal outcome without resuscitation efforts. Registration URL: https://www.clinicaltrials.gov; Unique identifier: NCT03111043.
| S-EPMC9238478 | biostudies-literature
Project description:BackgroundMortality after out-of-hospital cardiac arrest (OHCA) with return of spontaneous circulation (ROSC) remains high despite numerous efforts to improve outcome. For patients with suspected coronary cause of arrest, coronary angiography is crucial. However, there are other causes and potentially life-threatening injuries related to cardiopulmonary resuscitation (CPR), which can be detected by routine computed tomography (CT).Materials and methodsAt Hannover Medical School, rapid coronary angiography and CT are performed in successfully resuscitated OHCA patients as a standard of care prior to admission to intensive care. We analyzed all patients who received CT following OHCA with ROSC over a three-year period.ResultsThere were 225 consecutive patients with return of spontaneous circulation following out-of-hospital cardiac arrest. Mean age was 64 ± 13 years, 75% were male. Of them, 174 (77%) had witnessed arrest, 145 (64%) received bystander CPR, and 123 (55%) had a primary shockable rhythm. Mean time to ROSC was 24 ± 20 min. There were no significant differences in CT pathologies in patients with or without ST-segment elevations in the initial ECG. Critical CT findings qualifying as a potential cause for cardiac arrest were intracranial bleeding (N = 6), aortic dissection (N = 5), pulmonary embolism (N = 17), pericardial tamponade (N = 3), and tension pneumothorax (N = 11). Other pathologies were regarded as consequences of CPR and relevant for further treatment: aspiration (N = 62), rib fractures (N = 161), sternal fractures (N = 50), spinal fractures (N = 11), hepatic bleeding (N = 12), and intra-abdominal air (N = 3).ConclusionEarly CT fallowing OHCA uncovers a high number of causes and consequences of OHCA and CPR. Those are relevant for post-arrest care and are frequently life-threatening, suggesting that CT can contribute to improving prognosis following OHCA.
| S-EPMC8850697 | biostudies-literature
Project description:BackgroundHead computed tomography (CT) is used to predict neurological outcome after cardiac arrest (CA). The current reference standard includes quantitative image analysis by a neuroradiologist to determine the Gray-White-Matter Ratio (GWR) which is calculated via the manual measurement of radiodensity in different brain regions. Recently, automated analysis methods have been introduced. There is limited data on the Inter-rater agreement of both methods.MethodsThree blinded human raters (neuroradiologist, neurologist, student) with different levels of clinical experience retrospectively assessed the Gray-White-Matter Ratio (GWR) in head CTs of 95 CA patients. GWR was also quantified by a recently published computer algorithm that uses coregistration with standardized brain spaces to identify regions of interest (ROIs). We calculated intraclass correlation (ICC) for inter-rater agreement between human and computer raters as well as area under the curve (AUC) and sensitivity/specificity for poor outcome prognostication.ResultsInter-rater agreement on GWR was very good (ICC 0.82-0.84) between all three human raters across different levels of expertise and between the computer algorithm and neuroradiologist (ICC 0.83; 95% CI 0.78-0.88). Despite high overall agreement, we observed considerable, clinically relevant deviations of GWR measurements (up to 0.24) in individual patients. In our cohort, at a GWR threshold of 1.10, this did not lead to any false poor neurological outcome prediction.ConclusionHuman and computer raters demonstrated high overall agreement in GWR determination in head CTs after CA. The clinically relevant deviations of GWR measurement in individual patients underscore the necessity of additional qualitative evaluation and integration of head CT findings into a multimodal approach to prognostication of neurological outcome after CA.
| S-EPMC9606648 | biostudies-literature
Project description:BackgroundStructural changes in the left atrium (LA) modestly predict outcomes in patients undergoing catheter ablation for atrial fibrillation (AF). Machine learning (ML) is a promising approach to personalize AF management strategies and improve predictive risk models after catheter ablation by integrating atrial geometry from cardiac computed tomography (CT) scans and patient-specific clinical data. We hypothesized that ML approaches based on a patient's specific data can identify responders to AF ablation.MethodsConsecutive patients undergoing AF ablation, who had preprocedural CT scans, demographics, and 1-year follow-up data, were included in the study for a retrospective analysis. The inputs of models were CT-derived morphological features from left atrial segmentation (including the shape, volume of the LA, LA appendage, and pulmonary vein ostia) along with deep features learned directly from raw CT images, and clinical data. These were merged intelligently in a framework to learn their individual importance and produce the optimal classification.ResultsThree hundred twenty-one patients (64.2 ± 10.6 years, 69% male, 40% paroxysmal AF) were analyzed. Post 10-fold nested cross-validation, the model trained to intelligently merge and learn appropriate weights for clinical, morphological, and imaging data (AUC 0.821) outperformed those trained solely on clinical data (AUC 0.626), morphological (AUC 0.659), or imaging data (AUC 0.764).ConclusionOur ML approach provides an end-to-end automated technique to predict AF ablation outcomes using deep learning from CT images, derived structural properties of LA, augmented by incorporation of clinical data in a merged ML framework. This can help develop personalized strategies for patient selection in invasive management of AF.
| S-EPMC10857794 | biostudies-literature
Project description:This study compared the predictive utility of Marshall, Rotterdam, Stockholm, Helsinki, and NeuroImaging Radiological Interpretation System (NIRIS) scorings based on early non-contrast brain computed tomography (CT) scans in patients with traumatic brain injury (TBI). The area under a receiver operating characteristic curve (AUROC) was used to determine the predictive utility of scoring systems. Subgroup analyses were performed among patients with head AIS scores > 1. A total of 996 patients were included, of whom 786 (78.9%) were males. In-hospital mortality, ICU admission, neurosurgical intervention, and prolonged total hospital length of stay (THLOS) were recorded for 27 (2.7%), 207 (20.8%), 82 (8.2%), and 205 (20.6%) patients, respectively. For predicting in-hospital mortality, all scoring systems had AUROC point estimates above 0.9 and 0.75 among all included patients and patients with head AIS > 1, respectively, without any significant differences. The Marshall and NIRIS scoring systems had higher AUROCs for predicting ICU admission and neurosurgery than the other scoring systems. For predicting THLOS ≥ seven days, although the NIRIS and Marshall scoring systems seemed to have higher AUROC point estimates when all patients were analyzed, five scoring systems performed roughly the same in the head AIS > 1 subgroup.
| S-EPMC10452909 | biostudies-literature
Project description:Legal age estimation of living individuals is a critically important issue, and radiomics is an emerging research field that extracts quantitative data from medical images. However, no reports have proposed age-related radiomics features of the condylar head or an age classification model using those features. This study aimed to introduce a radiomics approach for various classifications of legal age (18, 19, 20, and 21 years old) based on cone-beam computed tomography (CBCT) images of the mandibular condylar head, and to evaluate the usefulness of the radiomics features selected by machine learning models as imaging biomarkers. CBCT images from 85 subjects were divided into eight age groups for four legal age classifications: ≤17 and ≥18 years old groups (18-year age classification), ≤18 and ≥19 years old groups (19-year age classification), ≤19 and ≥20 years old groups (20-year age classification) and ≤20 and ≥21 years old groups (21-year age classification). The condylar heads were manually segmented by an expert. In total, 127 radiomics features were extracted from the segmented area of each condylar head. The random forest (RF) method was utilized to select features and develop the age classification model for four legal ages. After sorting features in descending order of importance, the top 10 extracted features were used. The 21-year age classification model showed the best performance, with an accuracy of 91.18%, sensitivity of 80%, and specificity of 95.83%. Radiomics features of the condylar head using CBCT showed the possibility of age estimation, and the selected features were useful as imaging biomarkers.
| S-EPMC9851527 | biostudies-literature