Probability Cutoff Research Articles

How to implement magnetic resonance imaging before prostate biopsy in clinical practice: nomograms for saving biopsies.

To combine multiparametric MRI (mpMRI) findings and clinical parameters to provide nomograms for diagnosing different scenarios of aggressiveness of prostate cancer (PCa). A cohort of 346 patients with suspicion of PCa because of abnormal finding in digital rectal examination (DRE) and/or high prostate specific antigen (PSA) level received mpMRI prior to prostate biopsy (PBx). A conventional 12-core transrectal PBx with two extra cores from suspicious areas in mpMRI was performed by cognitive fusion. Multivariate logistic regression analysis was performed combining age, PSA density (PSAD), DRE, number of previous PBx, and mpMRI findings to predict three different scenarios: PCa, significant PCa (ISUP-group ≥ 2), or aggressive PCa (ISUP-group ≥ 3). We validate models by ROC curves, calibration plots, probability density functions (PDF), and clinical utility curves (CUC). Cut-off probabilities were estimated for helping decision-making in clinical practice. Our cohort showed 39.6% incidence of PCa, 32.6% of significant PCa, and 23.4% of aggressive PCa. The AUC of predictive models were 0.856, 0.883, and 0.911, respectively. The PDF and CUC showed 11% missed diagnoses of significant PCa (35 cases of 326 significant PCa expected in 1000 proposed Bx) when choosing < 18% as the cutoff of probability for not performing PBx; the percentage of saved PBx was 47% (474 avoided PBx in 1000 proposed). We developed clinical and mpMRI-based nomograms with a high discrimination ability for three different scenarios of PCa aggressiveness (https://urostatisticalsolutions.shinyapps.io/MRIfusionPCPrediction/). Specific clinical cutoff points allow us to save a high number of PBx with a minimum of missed diagnoses.

Urban Flood Detection with Sentinel-1 Multi-Temporal Synthetic Aperture Radar (SAR) Observations in a Bayesian Framework: A Case Study for Hurricane Matthew

In this study we explored the application of synthetic aperture radar (SAR) intensity time series for urban flood detection. Our test case was the flood in Lumberton, North Carolina, USA, caused by the landfall of Hurricane Matthew on 8 October 2016, for which airborne imagery—taken on the same day as the SAR overpass—is available for validation of our technique. To map the flood, we first carried out normalization of the SAR intensity observations, based on the statistics from the time series, and then construct a Bayesian probability function for intensity decrease (due to specular reflection of the signal) and intensity increase (due to double bounce) cases separately. We then formed a flood probability map, which we used to create our preferred flood extent map using a global cutoff probability of 0.5. Our flood map in the urban area showed a complicated mosaicking pattern of pixels showing SAR intensity decrease, pixels showing intensity increase, and pixels without significant intensity changes. Our approach shows improved performance when compared with global thresholding on log intensity ratios, as the time series-based normalization has accounted for a certain level of spatial variation by considering the different history for each pixel. This resulted in improved performance for urban and vegetated regions. We identified smooth surfaces, like asphalt roads, and SAR shadows as the major sources of underprediction, and aquatic plants and soil moisture changes were the major sources of overprediction.

Screening of urine identifies PLA2G16 as a field defect methylation biomarker for prostate cancer detection.

BackgroundProstate cancer (PC) is a multifocal disease. DNA methylation alterations are not restricted to the immediate peritumor environment, but spatially widespread in the adjacent and distant histologically normal prostate tissues. In the current study, we utilized high-throughput methylation arrays to identify epigenetic changes in the urine from men with and without cancer.Design, setting, and participantsDNA urine samples were enriched for methylated fragments using MBD methyl-binding antibodies and applied to high density CytoScanHD arrays. Significant loci were validated using quantitative pyrosequencing and binary logistic regression modeling applied to urine sample analyses in a training (n = 83) and validation approach (n = 84). Methylation alterations in prostate tissues using pyrosequencing at the PLA2G16 locus were examined in 38 histologically normal specimens from men with (TA, n = 26) and without (NTA, n = 12) cancer and correlated to gene expression.ResultsMethylation microarrays identified 3,986 loci showing significantly altered methylation in the urine samples from patients with PC compared to those without (TA vs NTA; p<0.01). These loci were then compared against subjects with their prostates removed to exclude non-prostate cell markers yielding 196 significant regions. Multiple CpGs adjacent to PLA2G16 CpG island showed increased methylation in TA compared to NTA (p<0.01) in a large validation study of urine samples. The predictive accuracy of PLA2G16 methylation at CG2 showed the highest predictive value at 0.8 (odds ratio, 1.37; 95% confidence interval, 1.16–1.62; p<0.001). Using a probability cutoff of 0.065, the sensitivity and specificity of the multivariate model was 92% and 35%. When histologically normal prostate tissues/biopsies from patients with PC (TA) were compared to subjects without cancer, significant hypermethylation of PLA2G16 was noted (odds ratio, 1.35; 95% confidence interval, 1.07–1.71; p = 0.01).ConclusionPLA2G16 methylation defines an extensive field defect in histologically normal prostate tissue associated with PC. PLA2G16 methylation in urine and prostate tissues can detect the presence of PC.

Validating Syndromic Data for Opioid Overdose Surveillance in Florida

Validating Syndromic Data for Opioid Overdose Surveillance in Florida

Validation of a Model Predicting De Novo Stress Urinary Incontinence in Women Undergoing Pelvic Organ Prolapse Surgery.

To validate a previously developed prediction model for de novo stress urinary incontinence (SUI) after undergoing vaginal surgery for pelvic organ prolapse (POP). Model performance was determined using a cohort of women who participated in two, 14-center randomized trials in the Netherlands that evaluated whether postoperative SUI 1 year after surgery was reduced with or without concomitant midurethral sling at the time of surgery for symptomatic women who had at least stage 2 POP. Age, number of previous vaginal births, urine leakage associated with urgency, history of diabetes, body mass index, preoperative stress test result, and placement of a midurethral sling were used to calculate the predicted probability of an individual developing de novo SUI. Predicted probabilities were compared with outcomes and quantitated using the concordance index and calibration curves. Model accuracy was compared with and without the preoperative stress test, and net reclassification improvement was measured using probability cutoffs of 0.2, 0.3, and 0.4. Of 239 participants who did not report preoperative SUI and underwent surgery, 152 were eligible for analysis with complete baseline and outcome data. Model discrimination was acceptable and consistent with performance in the original development cohort when the preoperative stress test result was included (concordance index 0.63; 95% CI 0.52-0.74) and had lower discrimination than when the stress test variable was not included (concordance index 0.57; 95% CI 0.46-0.67, P=.048). The model that included the stress test variable was most accurate when predicted probabilities of de novo SUI were between 0 and 50%, and it correctly reclassified upward 5.9% (95% CI -14.8 to 26.8) of participants with de novo SUI and correctly reclassified downward 16.9% (95% CI 6.6-27.7) of participants without de novo SUI. On external validation, the model was predictive of de novo SUI after vaginal prolapse surgery and may facilitate decision making regarding concomitant sling placement. Nederlands Trial Register, NTRR 1197 en 1070.

A prediction model for good neurological outcome in successfully resuscitated out-of-hospital cardiac arrest patients

BackgroundIn the initial hours after out-of-hospital cardiac arrest (OHCA), it remains difficult to estimate whether the degree of post-ischemic brain damage will be compatible with long-term good neurological outcome. We aimed to construct prognostic models able to predict good neurological outcome of OHCA patients within 48 h after CCU admission using variables that are bedside available.MethodsBased on prospectively gathered data, a retrospective data analysis was performed on 107 successfully resuscitated OHCA patients with a presumed cardiac cause of arrest. Targeted temperature management at 33 °C was initiated at CCU admission. Prediction models for good neurological outcome (CPC1–2) at 180 days post-CA were constructed at hour 1, 12, 24 and 48 after CCU admission. Following multiple imputation, variables were selected using the elastic-net method. Each imputed dataset was divided into training and validation sets (80% and 20% of patients, respectively). Logistic regression was fitted on training sets and prediction performance was evaluated on validation sets using misclassification rates.ResultsThe prediction model at hour 24 predicted good neurological outcome with the lowest misclassification rate (21.5%), using a cut-off probability of 0.55 (sensitivity = 75%; specificity = 82%). This model contained sex, age, diabetes status, initial rhythm, percutaneous coronary intervention, presence of a BIS 0 value, mean BIS value and lactate as predictive variables for good neurological outcome.DiscussionThis study shows that good neurological outcome after OHCA can be reasonably predicted as early as 24 h following ICU admission using parameters that are bedside available. These prediction models could identify patients who would benefit the most from intensive care.

Acrosome reaction and chromatin integrity as additional parameters of semen analysis to predict fertilization and blastocyst rates

BackgroundTraditional semen parameters have shown little to none predictive value for fertilization and blastocyst viability for a successful pregnancy. Therefore, the purpose of this study was to explore the usefulness of incorporating the acrosome reaction (AR) and chromatin integrity to conventional semen analysis to individually predict the fertile potential of sperm samples.MethodsA cross-sectional study was conducted in 69 participants undergoing IVF using oocyte donation. Semen samples were collected and evaluated for: AR [spontaneous (sAR) and induced (iAR)] by flow cytometry using anti-CD46-FITC, Acrosome Response to an Ionophore Challenge (ARIC), chromatin integrity by Sperm Chromatin Structure Assay (DNA Fragmentation Index-%DFI and High DNA Stainability-%HDS), WHO semen analysis, fertilization and blastocyst rates.ResultsThe participant age was 40.0 ± 6.1 years (66% were normozoospermic). Sperm morphology, sAR, iAR, and ARIC were associated with the fertilization (β = 3.56, R2 = 0.054; β = − 5.92, R2 = 0.276; β = 1.83, R2 = 0.150; and β = 2.10, R2 = 0.270, respectively, p < 0.05). A logit model was developed to calculate the probability of fertilization (≥ 60%) for each participant, using the sperm morphology and ARIC as independent variables, followed by ROC analysis to determine a cutoff probability of 0.65 (specificity = 80.6%, sensitivity = 63.2%). %DFI was inversely associated with the viable blastocyst rate (β = − 1.77, R2 = 0.057, p = 0.003), by the logit model and ROC analysis, a cutoff probability of 0.70 (specificity = 80.6%, sensitivity = 72.3%) was obtained to predict blastocyst viability (≥ 40%). There was no difference in the results with normozoospermic samples (n = 46).ConclusionsThe incorporation of ARIC and %DFI allowed to obtain predictive models for high fertilization and blastocyst rates in an individualized way, being promising tools to improve the diagnosis of male fertility potential for research or assisted reproduction, even in men with unknown infertility.

Prediction of prostate cancer by deep learning with multilayer artificial neural network.

To predict the rate of prostate cancer detection on prostate biopsy more accurately, the performance of deep learning using a multilayer artificial neural network was investigated. A total of 334 patients who underwent multiparametric magnetic resonance imaging before ultrasonography guided transrectal 12-core prostate biopsy were enrolled in the analysis. Twenty-two non-selected variables, as well as selected ones by least absolute shrinkage and selection operator (Lasso) regression analysis and by stepwise logistic regression analysis, were input into the constructed multilayer artificial neural network (ANN) programs; 232 patients were used as training cases of ANN programs and the remaining 102 patients were for the test to output the probability of prostate cancer existence, accuracy of prostate cancer prediction, and area under the receiver operating characteristic (ROC) curve with the learned model. With any prostate cancer objective variable, Lasso and stepwise regression analyses selected 12 and nine explanatory variables, respectively, from 22. Using trained ANNs with multiple hidden layers, the accuracy of predicting any prostate cancer in test samples was about 5-10% higher compared to that with logistic regression analysis (LR). The area under the curves (AUC) with multilayer ANN were significantly larger on inputting variables that were selected by the stepwise LR compared with the AUC with LR. The ANN had a higher net benefit than LR between prostate cancer probability cutoff values of 0.38 and 0.6. ANN accurately predicted prostate cancer without biopsy marginally better than LR. However, for clinical application, ANN performance may still need improvement.

A smart algorithm for the prevention and risk management of QTc prolongation based on the optimized RISQ-PATH model.

QTc prolongation is a complex problem linked with multiple risk factors. The RISQ-PATH score was previously developed to identify high-risk patients for QTc prolongation. The aim of this study was to optimize and validate this risk score in a large patient cohort, and to propose an algorithm to generate smart QT signals in the electronic medical record. A retrospective study was performed in the Nexus hospital network (n=17) in Belgium. All electrocardiograms performed in 2015 in both ambulatory and hospitalized patients were collected together with risk factors for QTc prolongation (training database). Multiple logistic regression was performed to obtain the optimal prediction (RISQ-PATH) model. The model was tested in a validation database (electrocardiograms between January and April 2016). In total, 60 208 patients (52.8% males, mean age 63±18 years) were included; 3543 patients (5.9%) had a QTc≥450(♂)/470(♀)ms and 453 (0.8%) a QTc≥500ms. The optimized RISQ-PATH model has an area under the ROC-curve of 0.772 [95% CI 0.763-0.780] to predict QTc≥450(♂)/470(♀)ms. A predicted probability of ≥0.035 was set as cutoff for a high risk of QTc prolongation. This cutoff resulted in a sensitivity of 87.4% [95% CI 86.2-88.5] and a specificity of 46.2% [95% CI 45.8-46.6]. These results could be confirmed for QTc≥500ms and in the validation database (n=28 400). The RISQ-PATH model, with a cutoff probability of 0.035, predicted a prolonged QTc interval ≥ 450/470ms or ≥500ms with a sensitivity of ±87% and a specificity of ±45%. This RISQ-PATH model can be used in clinical decision support systems to create smart QT alerts.

Improving risk prediction for pancreatic cancer in symptomatic patients: a Saudi Arabian study.

BackgroundImaging tests used in our center are usually inadequate to confirm the high risk for pancreatic cancer. We aimed to use a combination of potential predictors including imaging tests to quantify the risk of pancreatic cancer and evaluate its utility.MethodsThis was a retrospective cohort study of patients who were suspected as having pancreatic cancer and underwent biopsy examination of pancreatic mass at King Abdulaziz Medical City, Riyadh, Saudi Arabia, between January 1, 2013, and December 31, 2016. We retrieved data on demographics, clinical history, imaging tests, and final pancreatic diagnosis from medical records.ResultsOf the 206 who underwent pancreatic biopsies, the mean age was 63.6 years; 54.4% were male. Of all the biopsies, 57.8% were malignant and 42.2% were benign masses. Nine factors contributed significantly to the risk of pancreatic cancer and were noted: older age (adjusted odds ratio [aOR] =1.048; P=0.010), male gender (aOR =4.670; P=0.008), weight loss (aOR =14.810; P=0.001), abdominal pain (aOR =7.053; P=0.0.001), blood clots (aOR =20.787; P=0.014), pancreatitis (aOR =4.473; P=0.021), jaundice (aOR =7.446; P=0.003), persistent fatigue (aOR =22.015; P=0.015), and abnormal imaging tests (aOR =67.124; P=0.001). The model yielded powerful calibration (P=0.953), excellent predictive utility (area under the receiver operating characteristic curve 96.3%; 95% CI =94.1, 98.6), with optimism-corrected area under the curve bootstrap resampling of 94.9%. An optimal cut-off risk probability of 0.513 yielded a sensitivity of 94% and specificity of 84.7% for risk classification.ConclusionThe study developed and validated a risk model for quantifying the risk of pancreatic cancer. Nine characteristics were associated with increased risk of pancreatic cancer. This risk assessment model is feasible and highly sensitive and could be useful to improve screening performance and the decision-making process in clinical settings in Saudi Arabia.

A machine learning approach to estimating preterm infants survival: development of the Preterm Infants Survival Assessment (PISA) predictor

Estimation of mortality risk of very preterm neonates is carried out in clinical and research settings. We aimed at elaborating a prediction tool using machine learning methods. We developed models on a cohort of 23747 neonates <30 weeks gestational age, or <1501 g birth weight, enrolled in the Italian Neonatal Network in 2008–2014 (development set), using 12 easily collected perinatal variables. We used a cohort from 2015–2016 (N = 5810) as a test set. Among several machine learning methods we chose artificial Neural Networks (NN). The resulting predictor was compared with logistic regression models. In the test cohort, NN had a slightly better discrimination than logistic regression (P < 0.002). The differences were greater in subgroups of neonates (at various gestational age or birth weight intervals, singletons). Using a cutoff of death probability of 0.5, logistic regression misclassified 67/5810 neonates (1.2 percent) more than NN. In conclusion our study – the largest published so far – shows that even in this very simplified scenario, using only limited information available up to 5 minutes after birth, a NN approach had a small but significant advantage over current approaches. The software implementing the predictor is made freely available to the community.

Self-reported oral health predicts tooth loss after five and ten years in a population-based study.

To assess the suitability of self-reported oral health status to predict 5- and 10-year tooth loss without involvement of any clinical measures. Within the population Study of Health in Pomerania (SHIP), 2,776 subjects with 5-year follow-up data and 2,016 subjects with 10-year follow-up examination data were studied. Self-rated oral health was assessed at baseline and related to 5- and 10-year tooth loss. Odds ratios and probability of loss were estimated. Five- and 10-year tooth loss was significantly associated with baseline self-rated oral health. The predictive value of oral health ratings for tooth loss was comparable to that of the CDC/AAP case definition or caries and periodontitis diagnostics. In regression analyses including age, sex, smoking, education, income, and further items collected from questionnaires, odds ratios for dichotomous tooth loss associated with rating of oral health as poor were 3.04 (95% CI: 2.43-3.82) and 2.80 (2.11-3.71) after 5 and 10years, respectively. Prediction with cut-off probability at 0.25 resulted in sensitivity 85.8% and specificity 44.2% for 5 years, and that with cut-off probability at 0.40 resulted in sensitivity 83.5% and specificity 46.5% for 10years. Self-reported oral health provides reliable predictions of tooth loss comparable to those assessed by clinical diagnostics.

Cholesteatoma vs granulation tissue: a differential diagnosis by DWI-MRI apparent diffusion coefficient.

To diagnose cholesteatoma when it is not visible through tympanic perforation, imaging techniques are necessary. Recently, the combination of computed tomography and magnetic resonance imaging has proven effective to diagnose middle ear cholesteatoma. In particular, diffusion weighted images have integrated the conventional imaging for the qualitative assessment of cholesteatoma. Accordingly, the aim of this study was to obtain a quantitative analysis of cholesteatoma calculating the apparent diffusion coefficient value. So, we investigated whether it could differentiate cholesteatoma from other inflammatory tissues both in a preoperative and in a postoperative study. This study included 109 patients with clinical suspicion of primary or residual/recurrent cholesteatoma. All patients underwent preoperative computed tomography and magnetic resonance imaging with diffusion sequences before primary or second-look surgery to calculate the apparent diffusion coefficient value. We found that the apparent diffusion coefficient values of cholesteatoma were significantly lower than those of non cholesteatoma. In particular, the apparent diffusion coefficient median value of the cholesteatoma group (0.84 × 10- 3mm2/s) differed from the inflammatory granulation tissue (2.21 × 10- 3mm2/s) group (p < 2.2 × 10- 16). Furthermore, we modeled the probability of cholesteatoma by means of a logistic regression and we determined an optimal cut-off probability value of ~ 0.86 (specificity = 1.0, sensitivity = 0.97), corresponding to an apparent diffusion coefficient cut-off value of 1.37 × 10- 3mm2/s. Our study has demonstrated that apparent diffusion coefficient values constitute a valuable quantitative parameter for preoperative differentiation of cholesteatomas from other middle ear inflammatory diseases and for postoperative diagnosis of recurrent/residual cholesteatomas.

Global temporal lobe asymmetry as a semi-quantitative imaging biomarker for temporal lobe epilepsy lateralization: A machine learning classification study.

The purpose of this study was to evaluate the utility of global semi-quantitative analysis via fluorine-18-flurodeoxyglucose positron emission tomography (18F-FDG PET) at lateralizing seizure foci and diagnosing patients with unilateral temporal lobe epilepsy (TLE). Seventeen patients with unilateral TLE (11 right TLE and 6 left TLE) were retrospectively selected for semi-quantitative 18F-FDG PET analysis. Twenty-three control subjects with a Mini Mental State Examination (MMSE) score of 29 or greater were selected for comparison. Globally averaged standardized uptake value (gSUVmean) was computed for each temporal lobe. Lateralization indices (LI) and the absolute value of lateralization indices (|LI|) were calculated to assess the degree of asymmetry in each subject. Logistic regression analyses were performed at a probability cutoff of 0.5 to classify TLE patients as left or right TLE and to discriminate patients from control subjects. Receiver operating characteristic (ROC) curves were generated to evaluate the utility of LI and |LI| as classification predictors. The Bland Altman test was used to evaluate the reproducibility of the measurements. There was a statistically significant difference in gSUVmean computed LI between left and right TLE patients (P<0.01). There was no statistically significant difference in |LI| between the patient and control groups (P=0.22). Logistic regression revealed that 82% of TLE patients were lateralized correctly using LI as the sole predictor. The area under the ROC curve (AUC) was 0.80. Logistic regression using |LI| on the combined patient/control population showed a diagnostic accuracy of 65% and an AUC of 0.44. Bland Altman analysis revealed an intra-observer reproducibility of 96% and an inter-observer reproducibility of 96% and 91% on successive trials. We conclude that gSUVmean computed LI is a reliable and reproducible measure for predicting seizure lateralization in unilateral TLE patients. However, gSUVmean computed |LI| does not appear to be particularly effective at diagnosing TLE patients from control subjects. Further studies with more patients should investigate other machine learning techniques that combine gSUVmean with other diagnostic predictors.

Common Determinants of the Likelihood of Currency Crises in BRICS

We consider a panel of quarterly data for five BRICS countries for the period 1996: Q1 through 2015: Q4 to examine the currency crises. In this article, we construct an exchange market pressure index using Kaminsky, Lizondo and Reinhart (KLR) methodology to identify the crisis periods. Upon identifying the crisis periods, we examine the common determinants of the currency crises in these five counties using a panel probit model with random effects. Among the 22 identified periods across five BRICS countries, Russia has seen a currency stress quite often followed by Brazil, South Africa, China and India. Our main finding is that variables such as base money to broad money, broad money growth, current account balance, exports, imports, inflation, real interest rate and real effective exchange rate (REER) work as leading indicators of the crises and major contributing factors in a country’s vulnerability to crisis. We also find that the prediction of crises is robust at cut-off probability of 50 per cent.

Identification of surgical site infections using electronic health record data

The objective of this study was to develop an algorithm for identifying surgical site infections (SSIs) using independent variables from electronic health record data and outcomes from the American College of Surgeons National Surgical Quality Improvement Program to supplement manual chart review. We fit 3 models to data from patients undergoing operations at the University of Colorado Hospital between 2013 and 2015: a similar model reported previously in the literature, a comprehensive model with 136 possible predictors, and a combination of those. All models used a generalized linear model with a lasso penalty. Several techniques for handling imbalance in the outcome were also used: Youden's J statistic to optimize the probability cutoff and sampling techniques combined with Youden's J. The models were then tested on data from patients undergoing operations during 2016. Two hundred thirty of 6,840 patients (3.4%) had an SSI. The comprehensive model fit to the full set of training data performed the best, achieving 90% specificity, 80% sensitivity, and an area under the receiver operating characteristic curve of 0.89. We identified a model that accurately identified SSIs. The framework presented can be easily implemented by other American College of Surgeons National Surgical Quality Improvement Program-participating hospitals to develop models for enhancing surveillance of SSIs.

An Improved Facet-Based TSM for Electromagnetic Scattering From Ocean Surface

An improved facet-based model derived from a two-scale model (TSM) is proposed to evaluate the scattering strength of an electrically large ocean surface. In this model, a slope cutoff probability distribution function is introduced into geometrical optics to deal with the specular scattering of an individual facet. This modification allows us to approximate the rough surface with a large facet size without disrupting the accuracy of scattering evaluation. In addition, considering the sensitivity of the classical TSM to the cutoff wavenumber, the cutoff-invariant TSM is used into the facet-based model. Finally, our numerical results show that the facet-based model can obtain the comparable accuracy with the statistical models regardless of facet size and cutoff wavenumber. Due to the advantages of simplicity and efficiency, this model is of high applicable value for the synthetic aperture radar imaging.

Probability of Heat Intolerance: Standardized Interpretation of Heat-Tolerance Testing Results Versus Specialist Judgment.

The heat-tolerance test (HTT) is a screening tool for secondary prevention of exertional heat illness by the Israel Defense Forces. To discern participant tolerance, recruits are exposed to intermediate environmental and exercise stresses, and their physiological responses, core temperature, and heart rate are monitored. When their physiological measures rise at a higher rate or exceed the upper levels of absolute values compared with other participants, heat intolerance (HI) is diagnosed. To develop a mathematical model to interpret HTT results and provide a quantitative estimate of the probability of heat tolerance (PHT). Cross-sectional study. Warrior Health Research Institute. The HTT results of 175 random individuals tested after an episode of exertional heat illness were classified qualitatively and then divided into training (n = 112) and testing (n = 63) datasets. All individuals were male soldiers (age range = 18-22 years) who had sustained an episode of definitive or suspected exertional heat stroke. Based on the decision algorithm used by the Israel Defense Forces for manual interpretation of the HTT, we designed a logistic regression model to predict the heat-tolerance state. The model used a time series of physiological measures (core temperature and heart rate) of individuals to predict the manually assigned diagnosis of HT or HI. It was initially fitted and then tested on 2 separate, random datasets. The model produced a single value, the PHT, and its predictive ability was demonstrated by prediction-density plots, receiver operating characteristic curve, contingency tables, and conventional screening test evaluation measures. According to prediction-density plots of the testing set, all HT patients had a PHT of 0.7 to 1. The receiver operating characteristic curve plot showed that PHT was an excellent predictor of the manual HT interpretations (area under the curve = 0.973). Using a cutoff probability of 0.5 for the diagnosis of HI, we found that PHT had sensitivity, specificity, and accuracy of 100%, 90%, and 92.06%, respectively. The PHT has the potential to be substituted for manual interpretation of the HTT and to serve in a variety of clinical and research applications.

CT Prediction Model for Major Arterial Injury after Blunt Pelvic Ring Disruption.

Purpose To develop and test a computed tomography (CT)-based predictive model for major arterial injury after blunt pelvic ring disruptions that incorporates semiautomated pelvic hematoma volume quantification. Materials and Methods A multivariable logistic regression model was developed in patients with blunt pelvic ring disruptions who underwent arterial phase abdominopelvic CT before angiography from 2008 to 2013. Arterial injury at angiography requiring transarterial embolization (TAE) served as the outcome. Areas under the receiver operating characteristic (ROC) curve (AUCs) for the model and for two trauma radiologists were compared in a validation cohort of 36 patients from 2013 to 2015 by using the Hanley-McNeil method. Hematoma volume cutoffs for predicting the need for TAE and probability cutoffs for the secondary outcome of mortality not resulting from closed head injuries were determined by using ROC analysis. Correlation between hematoma volume and transfusion was assessed by using the Pearson coefficient. Results Independent predictor variables included hematoma volume, intravenous contrast material extravasation, atherosclerosis, rotational instability, and obturator ring fracture. In the validation cohort, the model (AUC, 0.78) had similar performance to reviewers (AUC, 0.69-0.72; P = .40-.80). A hematoma volume cutoff of 433 mL had a positive predictive value of 87%-100% for predicting major arterial injury requiring TAE. Hematoma volumes correlated with units of packed red blood cells transfused (r = 0.34-0.57; P = .0002-.0003). Predicted probabilities of 0.64 or less had a negative predictive value of 100% for excluding mortality not resulting from closed head injuries. Conclusion A logistic regression model incorporating semiautomated hematoma volume segmentation produced objective probability estimates of major arterial injury. Hematoma volumes correlated with 48-hour transfusion requirement, and low predicted probabilities excluded mortality from causes other than closed head injury. © RSNA, 2018 Online supplemental material is available for this article.

BIOIMPEDANCE: NEW APPROACH TO NON-INVASIVE DETECTION OF LIVER FIBROSIS - A PILOT STUDY.

Fibrosis are common structural hepatic change in patients with chronic hepatitis. Liver biopsy is the gold standard for determining the extent of liver fibrosis. Considering the technical difficulties and cost, improvements in non-invasive screening tools are greatly needed. Bioimpedance have been shown to be safe to evaluate tissue fibrosis. To assess the utility of using monofrequential bipolar bioimpedance for the detection of severity of liver fibrosis consistent with chronic viral hepatitis C infections. One hundred and ten patients were studied prospectively and formed two groups according to the lab tests results for the detection of HCV, ALT and AST: Group 1 Control (n=50 healthy patients with HCV negative and with ALT and AST values within the normal clinical range) and Group 2 Positive (n=60 patients positive for anti-HCV positive) which were biopsied. All patients underwent an examination with an Electro Sensor Complex, bioimpedance technology. To compare the groups 1 and 2, the ROC curves was used to determine the specificity and sensitivity of the bioimpedance to detect liver fibrosis. To identify liver fibrosis severity the Group 2 Positive was subdivided according to the liver biopsy results (Metavir fibrosis score) into: Sub Group 2A (F0-F1 n=25) - patients without or with minimal portal fibrosis and Sub Group 2B (F3-F4 n=20) patients with numerous septa/cirrhosis. A statistical analysis was conducted to analyze the bioimpedance data differences in delta of the conductance. From the comparison between Groups 1 and 2: 1) The delta value for conductance in the pathway representing the right foot-left hand minus left hand-right foot demonstrated a sensitivity of 85% and a specificity of 78% with a cutoff value ≤5 and P=0.0001. 2) For the comparison between Sub Group 2A (Metavir F0+F1) and Sub Group 2B (Metavir F3+F4), the neural network for the Electro Sensor Complex data demonstrated a sensitivity of 85% and a specificity of 72% with a cutoff probability >50% and P=0.001. AUCROC=0.81. Bioimpedance technology had good level sensitivity and acceptable specificity for detecting liver fibrosis using delta of the conductance. There is a potential for the use of bioimpedance technology as non-invasive approaches for screening of liver fibrosis.