scholarly journals Musculoskeletal ultrasound features-based scoring system can evaluate the severity of gout and asymptomatic hyperuricaemia

2021 ◽  
Vol 13 ◽  
pp. 1759720X2110069
Author(s):  
Fengjing Liu ◽  
Si Chen ◽  
Zhumeng Hu ◽  
Jie Chen ◽  
Lixin Jiang ◽  
...  
Keyword(s):  
Scoring System ◽  
Bone Erosion ◽  
Characteristic Curve ◽  
Binary Logistic Regression ◽  
Binary Logistic Regression Analysis ◽  
Joint Injury ◽  
Synovial Hypertrophy ◽  
Synovial Effusion ◽  
Ultrasound Score ◽  
Ultrasound Features

Objectives: To develop a tool which can evaluate the severity of the joint injury in individuals with gout and asymptomatic hyperuricaemia. Methods: This retrospective study included 616 male patients: 245 with asymptomatic hyperuricaemia and 371 with intercritical gout. All patients underwent ultrasonography of the knee, ankle and first metatarsophalangeal (MTP) joints. Ultrasound features that were significantly different between groups were entered into a binary logistic regression analysis to identify discriminative factors. The ultrasound signs were scored based on their odds ratios, which were then used to evaluate the severity of gout and asymptomatic hyperuricaemia. The performance of the ultrasound score was validated in an additional population including 163 patients with asymptomatic hyperuricaemia and 196 patients with gout. Results: Ultrasound signs were scored as follows: knee joint: synovial effusion, 2 points, tophus, 5 points; ankle joint: synovial effusion, 2 points, synovial hypertrophy, 5 points, tophus, 3 points, bone erosion, 7 points; and first MTP joint: double contour sign, 2 points, synovial hypertrophy, 3 points, tophus, 9 points, bone erosion, 4 points. The maximum possible total score was 42. The optimal cut-off score for gout was 6.5. The sensitivity, specificity and area under the receiver operating characteristic curve (AUC) were 86.9%, 75.2% and 0.878, respectively. The patients were stratified according to their ultrasound score (range 0–42). The prevalence of intercritical gout, tophi, and bone erosion increased with the increase of the score. In the validation population, 83.20% of 193 patients with gout had ultrasound scores above 6.5; 76.10% of 163 patients with asymptomatic hyperuricaemia had ultrasound scores under 6.5. Conclusion: The scoring system based on the differential ultrasound signs can effectively evaluate the severity of joint injury in individuals with gout and asymptomatic hyperuricaemia.

scholarly journals Quad test for fetal aneuploidy screening as a predictor of small-for-gestational age fetuses: a population-based study

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Rakchanok Boonpiam ◽  
Chanane Wanapirak ◽  
Supatra Sirichotiyakul ◽  
Ratanaporn Sekararithi ◽  
Kuntharee Traisrisilp ◽  
...  
Keyword(s):  
Gestational Age ◽  
Small For Gestational Age ◽  
Characteristic Curve ◽  
Binary Logistic Regression ◽  
Normal Growth ◽  
Predictive Performance ◽  
Population Based ◽  
Binary Logistic Regression Analysis ◽  
Aneuploidy Screening ◽  
The Individual

Abstract Background To identify the relationship between quadruple test for aneuploidy screening (alpha-fetoprotein: AFP; free beta-human chorionic gonadotropin: b-hCG; unconjugated estriol: uE3 and inhibin-A: IHA) and fetal growth restriction and to construct predictive models for small-for-gestational-age (SGA) fetuses. Methods Women who underwent quadruple test for aneuploidy were followed-up for final outcomes. The multiples of the median (MoMs) of the four biochemical markers for the SGA group and those of normal fetuses were compared. The models for predicting SGA by the individual biomarkers and their combination were constructed using binary logistic regression analysis, and their diagnostic performances in predicting SGA were determined. Results Of 10,155 eligible pregnant women, 578 (5.7%) and 9577 (94.3%) had SGA and normal growth, respectively. High levels of AFP, b-hCG and IHA but low levels of uE3 significantly increased the risk of SGA. The constructed predictive equations had predictive performance for SGA, with areas under the receiver-operated characteristic curve of 0.724, 0.655, 0.597, 0.664 and 0.754 for AFP, b-hCG, uE3, IHA, and the combination, respectively. Conclusion The quad test for aneuploidy screening could also be used as a predictor of SGA, without extra-effort and extra-cost.

scholarly journals Prior Infarcts, Reactivity, and Angiography in Moyamoya Disease (PIRAMD): a scoring system for moyamoya severity based on multimodal hemodynamic imaging

2017 ◽  
Vol 126 (2) ◽  
pp. 495-503 ◽  
Author(s):  
Travis R. Ladner ◽  
Manus J. Donahue ◽  
Daniel F. Arteaga ◽  
Carlos C. Faraco ◽  
Brent A. Roach ◽  
...  
Keyword(s):  
Moyamoya Disease ◽  
Scoring System ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Binary Logistic Regression ◽  
Cerebrovascular Reactivity ◽  
Management Decisions ◽  
Healthy Control ◽  
Grade 3 ◽  
Ischemic Attack

OBJECTIVE Quantification of the severity of vasculopathy and its impact on parenchymal hemodynamics is a necessary prerequisite for informing management decisions and evaluating intervention response in patients with moyamoya. The authors performed digital subtraction angiography and noninvasive structural and hemodynamic MRI, and they outline a new classification system for patients with moyamoya that they have named Prior Infarcts, Reactivity, and Angiography in Moyamoya Disease (PIRAMD). METHODS Healthy control volunteers (n = 11; age 46 ± 12 years [mean ± SD]) and patients (n = 25; 42 ± 13.5 years) with angiographically confirmed moyamoya provided informed consent and underwent structural (T1-weighted, T2-weighted, FLAIR, MR angiography) and hemodynamic (T2*- and cerebral blood flow–weighted) 3-T MRI. Cerebrovascular reactivity (CVR) in the internal carotid artery territory was assessed using susceptibility-weighted MRI during a hypercapnic stimulus. Only hemispheres without prior revascularization were assessed. Each hemisphere was considered symptomatic if localizing signs were present on neurological examination and/or there was a history of transient ischemic attack with symptoms referable to that hemisphere. The PIRAMD factor weighting versus symptomatology was optimized using binary logistic regression and receiver operating characteristic curve analysis with bootstrapping. The PIRAMD finding was scored from 0 to 10. For each hemisphere, 1 point was assigned for prior infarct, 3 points for reduced CVR, 3 points for a modified Suzuki Score ≥ Grade II, and 3 points for flow impairment in ≥ 2 of 7 predefined vascular territories. Hemispheres were divided into 3 severity grades based on total PIRAMD score, as follows: Grade 1, 0–5 points; Grade 2, 6–9 points; and Grade 3, 10 points. RESULTS In 28 of 46 (60.9%) hemispheres the findings met clinical symptomatic criteria. With decreased CVR, the odds ratio of having a symptomatic hemisphere was 13 (95% CI 1.1–22.6, p = 0.002). The area under the curve for individual PIRAMD factors was 0.67–0.72, and for the PIRAMD grade it was 0.845. There were 0/8 (0%), 10/18 (55.6%), and 18/20 (90%) symptomatic PIRAMD Grade 1, 2, and 3 hemispheres, respectively. CONCLUSIONS A scoring system for total impairment is proposed that uses noninvasive MRI parameters. This scoring system correlates with symptomatology and may provide a measure of hemodynamic severity in moyamoya, which could be used for guiding management decisions and evaluating intervention response.

scholarly journals Quad test for fetal aneuploidy screening as a predictor of small-for-gestational age fetuses: A population-based study

2020 ◽  
Author(s):  
Rakchanok Boonpiam ◽  
Chanane Wanapirak ◽  
Supatra Sirichotiyakul ◽  
Ratanaporn Sekararithi ◽  
Kuntharee Traisrisilp ◽  
...  
Keyword(s):  
Gestational Age ◽  
Small For Gestational Age ◽  
Characteristic Curve ◽  
Binary Logistic Regression ◽  
Normal Growth ◽  
Predictive Performance ◽  
Population Based ◽  
Binary Logistic Regression Analysis ◽  
Aneuploidy Screening ◽  
The Individual

Abstract Background: To identify the relationship between quadruple test for aneuploidy screening (alpha-fetoprotein: AFP; free beta-human chorionic gonadotropin: b-hCG; unconjugated estriol: uE3 and inhibin-A: IHA) and fetal growth restriction and to construct predictive models for small-for-gestational-age (SGA) fetuses.Methods: Women who underwent quadruple test for aneuploidy were followed-up for final outcomes. The multiples of the median (MoMs) of the four biochemical markers for the SGA group and those of normal fetuses were compared. The models for predicting SGA by the individual biomarkers and their combination were constructed using binary logistic regression analysis, and their diagnostic performances in predicting SGA were determined.Results: Of 10,155 eligible pregnant women, 578 (5.7%) and 9,577 (94.3%) had SGA and normal growth, respectively. High levels of AFP, b-hCG and IHA but low levels of uE3 significantly increased the risk of SGA. The constructed predictive equations had predictive performance for SGA, with areas under the receiver-operated characteristic curve of 0.724, 0.655, 0.597, 0.664 and 0.754 for AFP, b-hCG, uE3, IHA, and the combination, respectively.Conclusion: The quad test for aneuploidy screening could also be used as a predictor of SGA, without extra-effort and extra-cost.

scholarly journals Quad test for fetal aneuploidy screening as a predictor of fetal growth restriction: A population-based study

2020 ◽  
Author(s):  
Rakchanok Boonpiam ◽  
Chanane Wanapirak ◽  
Supatra Sirichotiyakul ◽  
Ratanaporn Sekararithi ◽  
Kuntharee Traisrisilp ◽  
...  
Keyword(s):  
Fetal Growth ◽  
Fetal Growth Restriction ◽  
Characteristic Curve ◽  
Binary Logistic Regression ◽  
Normal Growth ◽  
Predictive Performance ◽  
Population Based ◽  
Growth Restriction ◽  
Binary Logistic Regression Analysis ◽  
Aneuploidy Screening

Abstract Background: To identify the relationship between quadruple test for aneuploidy screening (alpha-fetoprotein: AFP; free beta-human chorionic gonadotropin: b-hCG; unconjugated estriol: uE3 and inhibin-A: IHA) and fetal growth restriction and to construct predictive models for fetal growth restriction (FGR).Methods: Women who underwent quadruple test for aneuploidy were followed-up for final outcomes. The multiple of median (MoMs) of the four biochemical markers for the FGR group and those of normal fetuses were compared. The models for predicting FGR by the individual biomarkers and their combination were constructed using binary logistic regression analysis, and their diagnostic performances in predicting FGR were determined.Results: Of 10,155 eligible pregnant women, 578 (5.7%) and 9577 (94.3%) had FGR and normal growth, respectively. High levels of AFP, b-hCG and IHA but low levels of uE3 significantly increased the risk of FGR. The constructed predictive equations had predictive performance for FGR, with areas under the receiver-operated characteristic curve of 0.724, 0.655, 0.597, 0.664 and 0.754 for AFP, b-hCG, uE3, IHA, and the combination, respectively.Conclusion: The quad test for aneuploidy screening could also be used as a predictor of FGR, without extra-effort and extra-cost.

scholarly journals Prediction of Prehypertenison and Hypertension Based on Anthropometry, Blood Parameters, and Spirometry

2018 ◽  
Vol 15 (11) ◽  
pp. 2571 ◽  
Author(s):  
Byeong Mun Heo ◽  
Keun Ho Ryu
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Prediction Model ◽  
Large Scale ◽  
Prediction Models ◽  
Statistical Significance ◽  
Characteristic Curve ◽  
Binary Logistic Regression ◽  
Blood Parameters ◽  
Binary Logistic Regression Analysis

Hypertension and prehypertension are risk factors for cardiovascular diseases. However, the associations of both prehypertension and hypertension with anthropometry, blood parameters, and spirometry have not been investigated. The purpose of this study was to identify the risk factors for prehypertension and hypertension in middle-aged Korean adults and to study prediction models of prehypertension and hypertension combined with anthropometry, blood parameters, and spirometry. Binary logistic regression analysis was performed to assess the statistical significance of prehypertension and hypertension, and prediction models were developed using logistic regression, naïve Bayes, and decision trees. Among all risk factors for prehypertension, body mass index (BMI) was identified as the best indicator in both men [odds ratio (OR) = 1.429, 95% confidence interval (CI) = 1.304–1.462)] and women (OR = 1.428, 95% CI = 1.204–1.453). In contrast, among all risk factors for hypertension, BMI (OR = 1.993, 95% CI = 1.818–2.186) was found to be the best indicator in men, whereas the waist-to-height ratio (OR = 2.071, 95% CI = 1.884–2.276) was the best indicator in women. In the prehypertension prediction model, men exhibited an area under the receiver operating characteristic curve (AUC) of 0.635, and women exhibited a predictive power with an AUC of 0.777. In the hypertension prediction model, men exhibited an AUC of 0.700, and women exhibited an AUC of 0.845. This study proposes various risk factors for prehypertension and hypertension, and our findings can be used as a large-scale screening tool for controlling and managing hypertension.

Corporate Default Prediction Model: Evidence from the Indian Industrial Sector

2021 ◽  
pp. 097226292110362
Author(s):  
Shilpa Shetty H. ◽  
Theresa Nithila Vincent
Keyword(s):  
Prediction Model ◽  
Financial Distress ◽  
Default Risk ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Binary Logistic Regression ◽  
Industrial Sector ◽  
Binary Logistic Regression Analysis ◽  
Default Prediction ◽  
Credit Default

The unprecedented pandemic COVID-19 has impacted businesses across the globe. A significant jump in the credit default risk is expected. Credit default is an indicator of financial distress experienced by the business. Credit default often leads to bankruptcy filing against the defaulting company. In India, the Insolvency and Bankruptcy Code (IBC) is the law that governs insolvency and bankruptcy. As reported by the Insolvency and Bankruptcy Board of India (IBBI), the number of companies filing for bankruptcy under IBC is on a rise, and the industrial sector has witnessed the maximum number of bankruptcy filings. The present article attempts to develop a credit default prediction model for the Indian industrial sector based on a sample of 164 companies comprising an equal number of defaulting and nondefaulting companies. A total of 120 companies are used as training samples and 44 companies as the testing samples. Binary logistic regression analysis is employed to develop the model. The diagnostic ability of the model is tested using receiver operating characteristic curve, area under the curve and annual accuracy. According to the study, return on assets, current ratio, debt to total assets ratio, sales to working capital ratio and cash flow to total assets ratio is statistically significant in predicting default. The findings of the study have significant implications in lending and investment decisions.

Front-line thrombectomy for acute large-vessel occlusion with underlying severe intracranial stenosis: stent retriever versus contact aspiration

2020 ◽  
Vol 132 (4) ◽  
pp. 1202-1208 ◽  
Author(s):  
Dong-Hun Kang ◽  
Woong Yoon ◽  
Byung Hyun Baek ◽  
Seul Kee Kim ◽  
Yun Young Lee ◽  
...  
Keyword(s):  
Binary Logistic Regression ◽  
Stent Retriever ◽  
Large Vessel ◽  
Binary Logistic Regression Analysis ◽  
Large Vessel Occlusion ◽  
Front Line ◽  
Vessel Occlusion ◽  
Aspiration Thrombectomy ◽  
Atherosclerotic Stenosis ◽  
Antegrade Flow

OBJECTIVEThe optimal front-line thrombectomy choice for primary recanalization of a target artery remains unknown for patients with acute large-vessel occlusion (LVO) and an underlying intracranial atherosclerotic stenosis (ICAS). The authors aimed to compare procedural characteristics and outcomes between patients who received a stent-retriever thrombectomy (SRT) and patients who received a contact aspiration thrombectomy (CAT), as the front-line approach for treating LVO due to severe underlying ICAS.METHODSOne hundred thirty patients who presented with acute LVO and underlying severe ICAS at the occlusion site were included. Procedural characteristics and treatment outcomes were compared between patients treated with front-line SRT (n = 70) and those treated with front-line CAT (n = 60). The primary outcomes were the rate of switching to an alternative thrombectomy technique, time from groin puncture to initial reperfusion, and duration of the procedure. Initial reperfusion was defined as revealing the underlying culprit stenosis with an antegrade flow after thrombectomy.RESULTSThe rate of switching to an alternative thrombectomy after failure of the front-line technique was significantly higher in the CAT group than in the SRT group (40% vs 4.3%; OR 2.543, 95% CI 1.893–3.417, p < 0.001). The median time from puncture to initial reperfusion (17 vs 31 minutes, p < 0.001) and procedure duration (39 vs 75.5 minutes, p < 0.001) were significantly shorter in the SRT group than in the CAT group. In the binary logistic regression analysis, a longer time from puncture to initial reperfusion was an independent predictor of a 90-day poor (modified Rankin Scale score 3–6) functional outcome (per 1-minute increase; OR 1.029, 95% CI 1.008–1.050, p = 0.006).CONCLUSIONSThe authors’ results suggest that SRT may be more effective than CAT for identifying underlying culprit stenosis and therefore considered the optimal front-line thrombectomy technique in acute stroke patients with LVO and severe underlying ICAS.

scholarly journals Development of a practical prediction score for acute renal injury after surgery for Stanford type A aortic dissection

2020 ◽  
Vol 30 (5) ◽  
pp. 746-753
Author(s):  
Ning Dong ◽  
Hulin Piao ◽  
Yu Du ◽  
Bo Li ◽  
Jian Xu ◽  
...  
Keyword(s):  
Aortic Dissection ◽  
Risk Score ◽  
Scoring System ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Kidney Injury ◽  
Type A ◽  
Postoperative Management ◽  
Prediction Score ◽  
Improve Patient

Abstract OBJECTIVES Acute kidney injury (AKI) is a common complication of cardiovascular surgery that is associated with increased mortality, especially after surgeries involving the aorta. Early detection and prevention of AKI in patients with aortic dissection may help improve outcomes. The objective of this study was to develop a practical prediction score for AKI after surgery for Stanford type A acute aortic dissection (TAAAD). METHODS This was a retrospective cohort study that included 2 independent hospitals. A larger cohort of 326 patients from The Second Hospital of Jilin University was used to identify the risk factors for AKI and to develop a risk score. The derived risk score was externally validated in a separate cohort of 102 patients from the other hospital. RESULTS The scoring system included the following variables: (i) age &gt;45 years; (ii) body mass index &gt;25 kg/m2; (iii) white blood cell count &gt;13.5 × 109/l; and (iv) lowest perioperative haemoglobin &lt;100 g/l, cardiopulmonary bypass duration &gt;150 min and renal malperfusion. On receiver operating characteristic curve analysis, the score predicted AKI with fair accuracy in both the derivation [area under the curve 0.778, 95% confidence interval (CI) 0.726–0.83] and the validation (area under the curve 0.747, 95% CI 0.657–0.838) cohorts. CONCLUSIONS We developed a convenient scoring system to identify patients at high risk of developing AKI after surgery for TAAAD. This scoring system may help identify patients who require more intensive postoperative management and facilitate appropriate interventions to prevent AKI and improve patient outcomes.

scholarly journals Development and Verification of a Deep Learning Algorithm to Evaluate Small-Bowel Preparation Quality

Diagnostics ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1127
Author(s):  
Ji Hyung Nam ◽  
Dong Jun Oh ◽  
Sumin Lee ◽  
Hyun Joo Song ◽  
Yun Jeong Lim
Keyword(s):  
Deep Learning ◽  
Small Bowel ◽  
Scoring System ◽  
Operating Characteristic ◽  
Clinical Evidence ◽  
Learning Algorithm ◽  
Characteristic Curve ◽  
External Validation ◽  
Test Results ◽  
Deep Learning Algorithm

Capsule endoscopy (CE) quality control requires an objective scoring system to evaluate the preparation of the small bowel (SB). We propose a deep learning algorithm to calculate SB cleansing scores and verify the algorithm’s performance. A 5-point scoring system based on clarity of mucosal visualization was used to develop the deep learning algorithm (400,000 frames; 280,000 for training and 120,000 for testing). External validation was performed using additional CE cases (n = 50), and average cleansing scores (1.0 to 5.0) calculated using the algorithm were compared to clinical grades (A to C) assigned by clinicians. Test results obtained using 120,000 frames exhibited 93% accuracy. The separate CE case exhibited substantial agreement between the deep learning algorithm scores and clinicians’ assessments (Cohen’s kappa: 0.672). In the external validation, the cleansing score decreased with worsening clinical grade (scores of 3.9, 3.2, and 2.5 for grades A, B, and C, respectively, p < 0.001). Receiver operating characteristic curve analysis revealed that a cleansing score cut-off of 2.95 indicated clinically adequate preparation. This algorithm provides an objective and automated cleansing score for evaluating SB preparation for CE. The results of this study will serve as clinical evidence supporting the practical use of deep learning algorithms for evaluating SB preparation quality.

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