scholarly journals Machine and Deep Learning Applied to Predict Metabolic Syndrome Without a Blood Screening

2021 ◽  
Vol 11 (10) ◽  
pp. 4334
Author(s):  
Guadalupe O. Gutiérrez-Esparza ◽  
Tania A. Ramírez-delReal ◽  
Mireya Martínez-García ◽  
Oscar Infante Infante Vázquez ◽  
Maite Vallejo ◽  
...  
Keyword(s):  
Metabolic Syndrome ◽  
Deep Learning ◽  
Positive Predictive Value ◽  
Predictive Value ◽  
Dietary Habits ◽  
Smoking Habit ◽  
Performance Model ◽  
Whole Milk ◽  
Corn Tortilla ◽  
Blood Screening

The exponential increase of metabolic syndrome and its association with the risk impact of morbidity and mortality has propitiated the development of tools to diagnose this syndrome early. This work presents a model that is based on prognostic variables to classify Mexicans with metabolic syndrome without blood screening applying machine and deep learning. The data that were used in this study contain health parameters related to anthropometric measurements, dietary information, smoking habit, alcohol consumption, quality of sleep, and physical activity from 2289 participants of the Mexico City Tlalpan 2020 cohort. We use accuracy, balanced accuracy, positive predictive value, and negative predictive value criteria to evaluate the performance and validate different models. The models were separated by gender due to the shared features and different habits. Finally, the highest performance model in women found that the most relevant features were: waist circumference, age, body mass index, waist to height ratio, height, sleepy manner that is associated with snoring, dietary habits related with coffee, cola soda, whole milk, and Oaxaca cheese and diastolic and systolic blood pressure. Men’s features were similar to women’s; the variations were in dietary habits, especially in relation to coffee, cola soda, flavored sweetened water, and corn tortilla consumption. The positive predictive value obtained was 84.7% for women and 92.29% for men. With these models, we offer a tool that supports Mexicans to prevent metabolic syndrome by gender; it also lays the foundation for monitoring the patient and recommending change habits.

scholarly journals Simulated clinical deployment of fully automatic deep learning for clinical prostate MRI assessment

2020 ◽  
Vol 31 (1) ◽  
pp. 302-313
Author(s):  
Patrick Schelb ◽  
Xianfeng Wang ◽  
Jan Philipp Radtke ◽  
Manuel Wiesenfarth ◽  
Philipp Kickingereder ◽  
...  
Keyword(s):  
Quality Assurance ◽  
Deep Learning ◽  
Positive Predictive Value ◽  
Predictive Value ◽  
Learning Algorithm ◽  
Simultaneous Detection ◽  
Calibration Method ◽  
Radiological Assessment ◽  
Grade Group ◽  
Predictive Values

Abstract Objectives To simulate clinical deployment, evaluate performance, and establish quality assurance of a deep learning algorithm (U-Net) for detection, localization, and segmentation of clinically significant prostate cancer (sPC), ISUP grade group ≥ 2, using bi-parametric MRI. Methods In 2017, 284 consecutive men in active surveillance, biopsy-naïve or pre-biopsied, received targeted and extended systematic MRI/transrectal US-fusion biopsy, after examination on a single MRI scanner (3 T). A prospective adjustment scheme was evaluated comparing the performance of the Prostate Imaging Reporting and Data System (PI-RADS) and U-Net using sensitivity, specificity, predictive values, and the Dice coefficient. Results In the 259 eligible men (median 64 [IQR 61–72] years), PI-RADS had a sensitivity of 98% [106/108]/84% [91/108] with a specificity of 17% [25/151]/58% [88/151], for thresholds at ≥ 3/≥ 4 respectively. U-Net using dynamic threshold adjustment had a sensitivity of 99% [107/108]/83% [90/108] (p > 0.99/> 0.99) with a specificity of 24% [36/151]/55% [83/151] (p > 0.99/> 0.99) for probability thresholds d3 and d4 emulating PI-RADS ≥ 3 and ≥ 4 decisions respectively, not statistically different from PI-RADS. Co-occurrence of a radiological PI-RADS ≥ 4 examination and U-Net ≥ d3 assessment significantly improved the positive predictive value from 59 to 63% (p = 0.03), on a per-patient basis. Conclusions U-Net has similar performance to PI-RADS in simulated continued clinical use. Regular quality assurance should be implemented to ensure desired performance. Key Points • U-Net maintained similar diagnostic performance compared to radiological assessment of PI-RADS ≥ 4 when applied in a simulated clinical deployment. • Application of our proposed prospective dynamic calibration method successfully adjusted U-Net performance within acceptable limits of the PI-RADS reference over time, while not being limited to PI-RADS as a reference. • Simultaneous detection by U-Net and radiological assessment significantly improved the positive predictive value on a per-patient and per-lesion basis, while the negative predictive value remained unchanged.

scholarly journals New Simplified Diagnostic Decision Trees for the Detention of Metabolic Syndrome in the Elderly

2020 ◽  
Vol 17 (14) ◽  
pp. 5191
Author(s):  
Enrique Rodríguez-Guerrero ◽  
Manuel Romero-Saldaña ◽  
Azahara Fernández-Carbonell ◽  
Rafael Molina-Luque ◽  
Guillermo Molina-Recio
Keyword(s):  
Metabolic Syndrome ◽  
Positive Predictive Value ◽  
Older People ◽  
Decision Trees ◽  
Older Women ◽  
Predictive Value ◽  
Final Decision ◽  
Fat Percentage ◽  
Validity Index ◽  
Cutting Point

Background: A new simplified method for the detention of metabolic syndrome (MetS) is proposed using two variables (anthropometric and minimally invasive). Methods: A study of MetS prevalence was made on a sample of 361 older people. The anthropometric variables analyzed were: blood pressure, body mass index, waist circumference (WC), waist–height ratio, body fat percentage, and waist–hip ratio. A crude and adjusted binary logistic regression was performed, and receiver operating characteristic curves were obtained for determining the predictive capacity of those variables. For the new detection method, decision trees were employed using automatic detection by interaction through Chi-square. Results: The prevalence of the MetS was of 43.7%. The final decision trees uses WC and basal glucose (BG), whose cutoff values were: for men, WC ≥ 102.5 cm and BG > 98 mg/dL (sensitivity = 67.1%, specificity = 90.3%, positive predictive value = 85%, validity index = 79.9%); and for women, WC ≥ 92.5 cm and BG ≥ 97 mg/dL (sensitivity = 65.9%, specificity = 92.7%, positive predictive value = 87.1%, validity index = 81.3%). In older women the best predictive value of MetS was a WC of 92.5 cm. Conclusions: It is possible to make a simplified diagnosis of MetS in older people using the WC and basal capillary glucose, with a high diagnostic accuracy and whose use could be recommended in the resource-poor health areas. A new cutting point in older women for the WC should be valued.

scholarly journals Diagnostic performance of deep learning-based vascular extraction and stenosis detection technique for coronary artery disease

2020 ◽  
Vol 93 (1113) ◽  
pp. 20191028 ◽  
Author(s):  
Meng Chen ◽  
Ximing Wang ◽  
Guangyu Hao ◽  
Xujie Cheng ◽  
Chune Ma ◽  
...  
Keyword(s):  
Coronary Artery Disease ◽  
Deep Learning ◽  
Coronary Artery ◽  
Positive Predictive Value ◽  
Negative Predictive Value ◽  
Predictive Value ◽  
Diagnostic Performance ◽  
Operating Characteristics ◽  
Stenosis Detection ◽  
Artery Disease

Objective: To investigate the diagnostic performance of deep learning (DL)-based vascular extraction and stenosis detection technology in assessing coronary artery disease (CAD). Methods: The diagnostic performance of DL technology was evaluated by retrospective analysis of coronary computed tomography angiography in 124 suspected CAD patients, using invasive coronary angiography as reference standard. Lumen diameter stenosis ≥50% was considered obstructive, and the diagnostic performances were evaluated at per-patient, per-vessel and per-segment levels. The diagnostic performances between DL model and reader model were compared by the areas under the receiver operating characteristics curves (AUCs). Results: In patient-based analysis, AUC of 0.78 was obtained by DL model to detect obstructive CAD [sensitivity of 94%, specificity of 63%, positive predictive value of 94%, and negative predictive value of 59%], While AUC by reader model was 0.74 (sensitivity of 97%, specificity of 50%, positive predictive value of 93%, negative predictive value of 73%). In vessel-based analysis, the AUCs of DL model and reader model were 0.87 and 0.89 respectively. In segment-based analysis, the AUCs of 0.84 and 0.89 were obtained by DL model and reader model respectively. It took 0.47 min to analyze all segments per patient by DL model, which is significantly less than reader model (29.65 min) (p < 0.001). Conclusion: The DL technology can accurately and effectively identify obstructive CAD, with less time-consuming, and it could be a reliable diagnostic tool to detect CAD. Advances in knowledge: The DL technology has valuable prospect with the diagnostic ability to detect CAD.

scholarly journals Deep Learning Based Software to Identify Large Vessel Occlusion on Noncontrast Computed Tomography

Stroke ◽  
2020 ◽  
Vol 51 (10) ◽  
pp. 3133-3137
Author(s):  
Marta Olive-Gadea ◽  
Carlos Crespo ◽  
Cristina Granes ◽  
Maria Hernandez-Perez ◽  
Natalia Pérez de la Ossa ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Deep Learning ◽  
Positive Predictive Value ◽  
Acute Stroke ◽  
Negative Predictive Value ◽  
Predictive Value ◽  
Learning Algorithm ◽  
Large Vessel Occlusion ◽  
Vessel Occlusion ◽  
Noncontrast Computed Tomography

Background and Purpose: Reliable recognition of large vessel occlusion (LVO) on noncontrast computed tomography (NCCT) may accelerate identification of endovascular treatment candidates. We aim to validate a machine learning algorithm (MethinksLVO) to identify LVO on NCCT. Methods: Patients with suspected acute stroke who underwent NCCT and computed tomography angiography (CTA) were included. Software detection of LVO (MethinksLVO) on NCCT was tested against the CTA readings of 2 experienced radiologists (NR-CTA). We used a deep learning algorithm to identify clot signs on NCCT. The software image output trained a binary classifier to determine LVO on NCCT. We studied software accuracy when adding National Institutes of Health Stroke Scale and time from onset to the model (MethinksLVO+). Results: From 1453 patients, 823 (57%) had LVO by NR-CTA. The area under the curve for the identification of LVO with MethinksLVO was 0.87 (sensitivity: 83%, specificity: 71%, positive predictive value: 79%, negative predictive value: 76%) and improved to 0.91 with MethinksLVO+ (sensitivity: 83%, specificity: 85%, positive predictive value: 88%, negative predictive value: 79%). Conclusions: In patients with suspected acute stroke, MethinksLVO software can rapidly and reliably predict LVO. MethinksLVO could reduce the need to perform CTA, generate alarms, and increase the efficiency of patient transfers in stroke networks.

scholarly journals OP0062 PREDICTIVE VALUE OF BONE TEXTURE FEATURES EXTRACTED BY DEEP LEARNING MODELS FOR THE DETECTION OF OSTEOARTHRITIS: DATA FROM THE OSTEOARTHRITIS INITIATIVE

2020 ◽  
Vol 79 (Suppl 1) ◽  
pp. 41.2-42
Author(s):  
C. F. Kuo ◽  
K. Zheng ◽  
S. Miao ◽  
L. Lu ◽  
C. I. Hsieh ◽  
...  
Keyword(s):  
Deep Learning ◽  
Positive Predictive Value ◽  
Correlation Coefficient ◽  
Predictive Value ◽  
Texture Features ◽  
Learning Models ◽  
Training Set ◽  
Radiographic Osteoarthritis ◽  
Osteoarthritis Initiative ◽  
Spearman’S Correlation

Background:Osteoarthritis is a degenerative disorder characterized by radiographic features of asymmetric loss of joint space, subchondral sclerosis, and osteophyte formation. Conventional plain films are essential to detect structural changes in osteoarthritis. Recent evidence suggests that fractal- and entropy-based bone texture parameters may improve the prediction of radiographic osteoarthritis.1In contrast to the fixed texture features, deep learning models allow the comprehensive texture feature extraction and recognition relevant to osteoarthritis.Objectives:To assess the predictive value of deep learning-extracted bone texture features in the detection of radiographic osteoarthritis.Methods:We used data from the Osteoarthritis Initiative, which is a longitudinal study with 4,796 patients followed up and assessed for osteoarthritis. We used a training set of 25,978 images from 3,086 patients to develop the textual model. We use the BoneFinder software2to do the segmentation of distal femur and proximal tibia. We used the Deep Texture Encoding Network (Deep-TEN)3to encode the bone texture features into a vector, which is fed to a 5-way linear classifier for Kellgren and Lawrence grading for osteoarthritis classification. We also developed a Residual Network with 18 layers (ResNet18) for comparison since it deals with contours as well. Spearman’s correlation coefficient was used to assess the correlation between predicted and reference KL grades. We also test the performance of the model to identify osteoarthritis (KL grade≥2).Results:We obtained 6,490 knee radiographs from 446 female and 326 male patients who were not in the training sets to validate the performance of the models. The distribution of the KL grades in the training and testing sets were shown in Table 1. The Spearman’s correlation coefficient was 0.60 for the Deep-TEN and 0.67 for the ResNet18 model. Table 2 shows the performance of the models to detect osteoarthritis. The positive predictive value for Deep-TEN and ResNet18 model classification for OA was 81.37% and 87.46%, respectively.Table 1Distribution of KL grades in the training and testing sets.KL grades01234TotalTraining set1089341.9%458218.7%611423.5%332012.8%7993.1%25,978Testing set247238.1%135320.8%169626.1%77511.9%1943.0%6,490Table 2Performance matrices of the Deep-Ten and ResNet18 models to detect osteoarthritisDeep-TENResNet18Sensitivity62.29%(95% CI, 60.42%–64.13%)59.14%(95% CI, 57.24%–61.01%)Specificity90.07%(95% CI, 89.07%–91.00%)94.09%(95% CI, 93.30%–94.82%)Positive predictive value81.37%(95% CI, 79.81%–82.84%)87.46%(95% CI, 85.96%–88.82%)Negative predictive value77.42%(95% CI, 77.64%–79.65%)76.77%(95% CI, 75.93%–77.59%)Conclusion:This study demonstrates that the bone texture model performs reasonably well to detect radiographic osteoarthritis with a similar performance to the bone contour model.References:[1]Bertalan Z, Ljuhar R, Norman B, et al. Combining fractal- and entropy-based bone texture analysis for the prediction of osteoarthritis: data from the multicenter osteoarthritis study (MOST). Osteoarthritis Cartilage 2018;26:S49.[2]Lindner C, Wang CW, Huang CT, et al. Fully Automatic System for Accurate Localisation and Analysis of Cephalometric Landmarks in Lateral Cephalograms. Sci Rep 2016;6:33581.[3]Zhang H, Xue J, Dana K. Deep TEN: Texture Encoding Network. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017:708-17.Disclosure of Interests:None declared

scholarly journals The diagnostic performance of deep-learning-based CT severity score to identify COVID-19 pneumonia

2022 ◽  
Vol 95 (1129) ◽  
Author(s):  
Anna Sára Kardos ◽  
Judit Simon ◽  
Chiara Nardocci ◽  
István Viktor Szabó ◽  
Norbert Nagy ◽  
...  
Keyword(s):  
Deep Learning ◽  
Positive Predictive Value ◽  
Predictive Value ◽  
Severity Score ◽  
Clinical Decision Making ◽  
Rapid Diagnosis ◽  
Youden Index ◽  
Rt Pcr ◽  
Data Set ◽  
Pcr Test

Objective: To determine the diagnostic accuracy of a deep-learning (DL)-based algorithm using chest computed tomography (CT) scans for the rapid diagnosis of coronavirus disease 2019 (COVID-19), as compared to the reference standard reverse-transcription polymerase chain reaction (RT-PCR) test. Methods: In this retrospective analysis, data of COVID-19 suspected patients who underwent RT-PCR and chest CT examination for the diagnosis of COVID-19 were assessed. By quantifying the affected area of the lung parenchyma, severity score was evaluated for each lobe of the lung with the DL-based algorithm. The diagnosis was based on the total lung severity score ranging from 0 to 25. The data were randomly split into a 40% training set and a 60% test set. Optimal cut-off value was determined using Youden-index method on the training cohort. Results: A total of 1259 patients were enrolled in this study. The prevalence of RT-PCR positivity in the overall investigated period was 51.5%. As compared to RT-PCR, sensitivity, specificity, positive predictive value, negative predictive value and accuracy on the test cohort were 39.0%, 80.2%, 68.0%, 55.0% and 58.9%, respectively. Regarding the whole data set, when adding those with positive RT-PCR test at any time during hospital stay or “COVID-19 without virus detection”, as final diagnosis to the true positive cases, specificity increased from 80.3% to 88.1% and the positive predictive value increased from 68.4% to 81.7%. Conclusion: DL-based CT severity score was found to have a good specificity and positive predictive value, as compared to RT-PCR. This standardized scoring system can aid rapid diagnosis and clinical decision making. Advances in knowledge: DL-based CT severity score can detect COVID-19-related lung alterations even at early stages, when RT-PCR is not yet positive.

scholarly journals OP0301 PREDICTION OF LOW BONE MINERAL DENSITY AND FRAX SCORE BY ASSESSING HIP BONE TEXTURE WITH DEEP LEARNING

2020 ◽  
Vol 79 (Suppl 1) ◽  
pp. 187.2-187
Author(s):  
C. F. Kuo ◽  
S. Miao ◽  
K. Zheng ◽  
L. Lu ◽  
C. I. Hsieh ◽  
...  
Keyword(s):  
Bone Mineral Density ◽  
Deep Learning ◽  
Positive Predictive Value ◽  
Correlation Coefficient ◽  
Predictive Value ◽  
Mineral Density ◽  
T Score ◽  
Texture Model ◽  
The Mean ◽  
Frax Score

Background:Osteoporosis is a widespread health concern associated with an increased risk of fractures in individuals with low bone mineral density (BMD). Dual-energy x-ray absorptiometry (DXA) is the gold standard to measure BMD, but methods based on the assessment of plain films, such as the digital radiogrammetry,1are also available. We describe a novel approach based on the assessment of hip texture with deep learning to estimate BMD.Objectives:To compare the BMD estimated by assessing hip texture using a deep learning model and that measured by DXA.Methods:In this study, we identified 1,203 patients who underwent DXA of left hip and hip plain film within six months. The dataset was split into a training set with 1,024 patients and a testing set with 179 patients. Hip images were obtained and regions of interest (ROI) around left hips were segmented using a tool based on the curve Graph Convolutional Network. The ROIs are processed using a Deep Texture Encoding Network (Deep-TEN) model,2which comprises the first 3 blocks of Residual Network with 18 layers (ResNet-18) model followed by a dictionary encoding operator (Figure 1). The encoded features are processed using a fully connected layer to estimate BMD. Five-fold cross-validation was conducted. Pearson’s correlation coefficient was used to assess the correlation between predicted and reference BMD. We also test the performance of the model to identify osteoporosis (T-score ≤ -2.5)Figure 1.Schematic representation of deep learning models to extract and encode texture features for estimation of hip bone density.Results:We included 151 women and 18 men in the testing dataset (mean age, 66.1 ± 1.7 years). The mean predicted BMD was 0.724 g/cm2compared with the mean BMD measured by DXA of 0.725 g/cm2(p = 0.51). Pearson’s correlation coefficient between predicted and true BMD was 0.88. The performance of the model to detect osteoporosis/osteopenia was shown in Table 1. The positive predictive value was 87.46% for a T-score ≤ -1 and 83.3% for a T-score ≤ -2.5. Furthermore, the mean FRAX® 10-year major fracture risk did not differ significantly between scores based on predicted (6.86%) and measured BMD (7.67%, p=0.52). The 10-year probability of hip fracture was lower in the predicted score (1.79%) than the measured score (2.43%, p = 0.01).Table 1.Performance matrices of the deep texture model to detect osteoporosis/osteopeniaT-score ≤ -1T-score ≤ -2.5Sensitivity91.11%(95% CI, 83.23% to 96.08%)33.33%(95% CI, 17.29% to 52.81%)Specificity86.08%(95% CI, 76.45% to 92.84%)98.56%(95% CI, 94.90% to 99.83%)Positive predictive value88.17%(95% CI, 81.10% to 92.83%)83.33%(95% CI, 53.58% to 95.59%)Negative predictive value89.47%(95% CI, 81.35% to 94.31%)87.26%(95% CI, 84.16% to 89.83%)Conclusion:This study demonstrates the potential of the bone texture model to detect osteoporosis and to predict the FRAX score using plain hip radiographs.References:[1]Zandieh S, Haller J, Bernt R, et al. Fractal analysis of subchondral bone changes of the hand in rheumatoid arthritis. Medicine (Baltimore) 2017;96(11):e6344.[2]Zhang H, Xue J, Dana K. Deep TEN: Texture Encoding Network. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2017:708-17.Disclosure of Interests:None declared

scholarly journals Using deep learning to assist readers during the arbitration process: a lesion-based retrospective evaluation of breast cancer screening performance

2021 ◽  
Author(s):  
Laura Kerschke ◽  
Stefanie Weigel ◽  
Alejandro Rodriguez-Ruiz ◽  
Nico Karssemeijer ◽  
Walter Heindel
Keyword(s):  
Breast Cancer ◽  
Artificial Intelligence ◽  
Deep Learning ◽  
Positive Predictive Value ◽  
Predictive Value ◽  
False Positive ◽  
False Positives ◽  
Arbitration Process ◽  
Screening Performance ◽  
Malignant Lesions

Abstract Objectives To evaluate if artificial intelligence (AI) can discriminate recalled benign from recalled malignant mammographic screening abnormalities to improve screening performance. Methods A total of 2257 full-field digital mammography screening examinations, obtained 2011–2013, of women aged 50–69 years which were recalled for further assessment of 295 malignant out of 305 truly malignant lesions and 2289 benign lesions after independent double-reading with arbitration, were included in this retrospective study. A deep learning AI system was used to obtain a score (0–95) for each recalled lesion, representing the likelihood of breast cancer. The sensitivity on the lesion level and the proportion of women without false-positive ratings (non-FPR) resulting under AI were estimated as a function of the classification cutoff and compared to that of human readers. Results Using a cutoff of 1, AI decreased the proportion of women with false-positives from 89.9 to 62.0%, non-FPR 11.1% vs. 38.0% (difference 26.9%, 95% confidence interval 25.1–28.8%; p < .001), preventing 30.1% of reader-induced false-positive recalls, while reducing sensitivity from 96.7 to 91.1% (5.6%, 3.1–8.0%) as compared to human reading. The positive predictive value of recall (PPV-1) increased from 12.8 to 16.5% (3.7%, 3.5–4.0%). In women with mass-related lesions (n = 900), the non-FPR was 14.2% for humans vs. 36.7% for AI (22.4%, 19.8–25.3%) at a sensitivity of 98.5% vs. 97.1% (1.5%, 0–3.5%). Conclusion The application of AI during consensus conference might especially help readers to reduce false-positive recalls of masses at the expense of a small sensitivity reduction. Prospective studies are needed to further evaluate the screening benefit of AI in practice. Key Points • Integrating the use of artificial intelligence in the arbitration process reduces benign recalls and increases the positive predictive value of recall at the expense of some sensitivity loss. • Application of the artificial intelligence system to aid the decision to recall a woman seems particularly beneficial for masses, where the system reaches comparable sensitivity to that of the readers, but with considerably reduced false-positives. • About one-fourth of all recalled malignant lesions are not automatically marked by the system such that their evaluation (AI score) must be retrieved manually by the reader. A thorough reading of screening mammograms by readers to identify suspicious lesions therefore remains mandatory.

scholarly journals Usefulness of the metabolic syndrome criteria as predictors of insulin resistance among obese Korean women

2009 ◽  
Vol 13 (2) ◽  
pp. 181-186 ◽  
Author(s):  
Kayoung Lee
Keyword(s):  
Insulin Resistance ◽  
Metabolic Syndrome ◽  
Positive Predictive Value ◽  
Likelihood Ratio ◽  
Predictive Value ◽  
Positive Likelihood Ratio ◽  
Model Assessment ◽  
Korean Women ◽  
Obese Women ◽  
The Metabolic Syndrome

AbstractObjectiveTo investigate the ability of each metabolic syndrome (MetS) criterion, defined by the International Diabetes Federation, to predict insulin resistance (IR).DesignA cross-sectional study. IR was defined as homeostasis model assessment of IR (HOMA-IR) ≥3·04. The MetS criteria considered were TAG ≥ 1·69 mmol/l, HDL cholesterol (HDL-C) <1·29 mmol/l, blood pressure (BP) ≥130/85 mmHg and fasting glucose (FG) ≥5·6 mmol/l.SettingBusan, South Korea.SubjectsNinety-six apparently healthy Korean women (mean age 42 (sd 10·6) years) with abdominal obesity (waist circumference (WC) ≥80 cm) were studied.ResultsOf the ninety-six obese women, 11 % were insulin-resistant and 33 % fulfilled the criteria for IDF-defined MetS. Glucose and TAG were more likely to predict IR than BP and HDL-C when assessed using receiver-operating characteristic curves, multiple regression and multiple logistic regression analyses. Of the variation in HOMA-IR, TAG, FG, WC and age explained 42 %. High FG was independently associated with the presence of IR (OR = 8·6, 95 % CI 1·8, 41·8) even after adjusting for other components of MetS. The positive predictive value and positive likelihood ratio to detect IR were the highest for the FG criterion (33 % and 3·9, respectively), followed by TAG (28 %, 3·0), BP (19 %, 1·8) and HDL-C criteria (18 %, 1·7). The IDF definition of MetS exhibited a positive predictive value of 29 % and a positive likelihood ratio of 3·1.ConclusionsOf the MetS criteria, high FG and high TAG seem to be more suitable for identifying obese women with IR than high BP and low HDL-C.

Diagnosis of cerebral hemorrhage before the era of computed tomography

2013 ◽  
Vol 154 (44) ◽  
pp. 1743-1746
Author(s):  
Gergely Hofgárt ◽  
Rita Szepesi ◽  
Bertalan Vámosi ◽  
László Csiba
Keyword(s):  
Computed Tomography ◽  
Cerebrospinal Fluid ◽  
Positive Predictive Value ◽  
Predictive Value ◽  
Hemorrhagic Stroke ◽  
Cranial Computed Tomography ◽  
Cerebral Stroke ◽  
Daily Routine ◽  
Stroke Patients ◽  
The Difference

Introduction: During the past decades there has been a great progress in neuroimaging methods. Cranial computed tomography is part of the daily routine now and its use allows a fast diagnosis of parenchymal hemorrhage. However, before the availability of computed tomography the differentiation between ischemic and hemorrhagic stroke was based on patient history, physical examination, percutan angiography and cerebrospinal fluid sampling, and the clinical utility could be evaluated by autopsy of deceased patients. Aim: The authors explored the diagnostic performance of cerebrospinal fluid examination for the diagnosis of ischemic and hemorrhagic stroke. Method: Data of 200 deceased stroke patients were retrospectively evaluated. All patients had liquor sampling at admission and all of them had brain autopsy. Results: Bloody or yellowish cerebrospinal fluid at admission had a positive predictive value of 87.5% for hemorrhagic stroke confirmed by autopsy, while clear cerebrospinal fluid had positive predictive value of 90.7% for ischemic stroke. Patients who had clear liquor, but autopsy revealed hemorrhagic stroke had higher protein level in the cerebrospinal fluid, but the difference was not statistically significant (p = 0.09). Conclusions: The results confirm the importance of pathological evaluation of the brain in cases deceased from cerebral stroke. With this article the authors wanted to salute for those who contributed to the development of the Hungarian neuropathology. In this year we remember the 110th anniversary of the birth, and the 60th anniversary of the death of professor Kálmán Sántha. Professor László Molnár would be 90 years old in 2013. Orv. Hetil., 154 (44), 1743–1746.

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