scholarly journals Intermediate Likelihood of Choledocholithiasis: Do All Need EUS or MRCP?

2021 ◽  
Vol 12 (01) ◽  
pp. 019-023
Author(s):  
Nitin Jagtap ◽  
Arun Karyampudi ◽  
HS Yashavanth ◽  
Mohan Ramchandani ◽  
Sundeep Lakhtakia ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Sensitivity And Specificity ◽  
Logistic Regression Analysis ◽  
Validation Cohort ◽  
Magnetic Resonance Cholangiopancreatography ◽  
Composite Score ◽  
Published Data ◽  
Derivation Cohort ◽  
Intermediate Likelihood

Abstract Background Recently updated guidelines for choledocholithiasis stratify suspected patients into high, intermediate, and low likelihood, with the aim to reduce risk of diagnostic endoscopic retrograde cholangiopancreatography. This approach has increased proportion of patients in intermediate likelihood making it heterogenous. We aim to substratify intermediate group so that diagnostic tests (endoscopic ultrasound/magnetic resonance cholangiopancreatography) are judicially used. Methods This is a single-center retrospective analysis of prospectively maintained data. We used subset of patients who met intermediate likelihood of American Society of Gastrointestinal Endoscopy (ASGE) criteria from previously published data (PMID:32106321) as derivation cohort. Binominal logistic regression analysis was used to define independent predictors of choledocholithiasis. A composite score was derived by allotting 1 point for presence of each independent predictor. The diagnostic performance of a composite score of ≥ 1 was evaluated in validation cohort. Results A total of 678 (mean age [standard deviation]: 47.0 [15.9] years; 48.1% men) and 162 (mean age 47.8 [14.8] years; 47.4% men) patients in ASGE intermediate-likelihood group were included as derivation cohort and validation cohort, respectively. Binominal logistic regression analysis showed that male gender (p = 0.024; odds ratio [OR] = 1.92), raised bilirubin (p = 0.001; OR = 2.40), and acute calculus cholecystitis (p = 0.010; OR = 2.04) were independent predictors for choledocholithiasis. A composite score was derived by allotting 1 point for presence of independent predictors Using ≥ 1 as cutoff, sensitivity and specificity for detection of choledocholithiasis were 80% (95% confidence interval [CI]: 68.2–88.9) and 36.2% (95% CI: 32.2–40.0), respectively, in derivation cohort. Applying composite score in independent validation cohort showed sensitivity and specificity of 73.3% (95% CI: 44.9–92.2) and 40.1% (95% CI: 30.1–48.5), respectively. Conclusion Substratification of intermediate-likelihood group of ASGE criteria is feasible. It may be useful in deciding in whom confirmatory tests should be performed with priority and in whom watchful waiting may be sufficient.

Multivariable Logistic Regression Equation to Predict Prostate Cancer

2021 ◽  
Author(s):  
Lu Ma ◽  
Dong Cheng ◽  
Qinghua Li ◽  
Jingbo Zhu ◽  
Yu Wang ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Prediction Model ◽  
Prostate Specific Antigen ◽  
Sensitivity And Specificity ◽  
Logistic Regression Analysis ◽  
Roc Curve ◽  
Specific Antigen ◽  
Clinical Feature

Abstract Objective: To explore the predictive value of white blood cell (WBC), monocyte (M), neutrophil-to-lymphocyte ratio (NLR), fibrinogen (FIB), free prostate-specific antigen (fPSA) and free prostate-specific antigen/prostate-specific antigen (f/tPSA) in prostate cancer (PCa).Materials and methods: Retrospective analysis of 200 cases of prostate biopsy and collection of patients' systemic inflammation indicators, biochemical indicators, PSA and fPSA. First, the dimensionality of the clinical feature parameters is reduced by the Lass0 algorithm. Then, the logistic regression prediction model was constructed using the reduced parameters. The cut-off value, sensitivity and specificity of PCa are predicted by the ROC curve analysis and calculation model. Finally, based on Logistic regression analysis, a Nomogram for predicting PCa is obtained.Results: The six clinical indicators of WBC, M, NLR, FIB, fPSA, and f/tPSA were obtained after dimensionality reduction by Lass0 algorithm to improve the accuracy of model prediction. According to the regression coefficient value of each influencing factor, a logistic regression prediction model of PCa was established: logit P=-0.018-0.010×WBC+2.759×M-0.095×NLR-0.160×FIB-0.306×fPSA-2.910×f/tPSA. The area under the ROC curve is 0.816. When the logit P intercept value is -0.784, the sensitivity and specificity are 72.5% and 77.8%, respectively.Conclusion: The establishment of a predictive model through Logistic regression analysis can provide more adequate indications for the diagnosis of PCa. When the logit P cut-off value of the model is greater than -0.784, the model will be predicted to be PCa.

scholarly journals Performance of cytokine models in predicting SLE activity

2019 ◽  
Vol 21 (1) ◽  
Author(s):  
Nopparat Ruchakorn ◽  
Pintip Ngamjanyaporn ◽  
Thanitta Suangtamai ◽  
Thanuchporn Kafaksom ◽  
Charin Polpanumas ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Disease Activity ◽  
Sensitivity And Specificity ◽  
Logistic Regression Analysis ◽  
Lupus Erythematosus ◽  
Activity Index ◽  
Multiple Logistic Regression Analysis ◽  
Negative Relationship ◽  
Complement C3

Abstract Background Identification of universal biomarkers to predict systemic lupus erythematosus (SLE) flares is challenging due to the heterogeneity of the disease. Several biomarkers have been reported. However, the data of validated biomarkers to use as a predictor for lupus flares show variation. This study aimed to identify the biomarkers that are sensitive and specific to predict lupus flares. Methods One hundred and twenty-four SLE patients enrolled in this study and were prospectively followed up. The evaluation of disease activity achieved by the SLE disease activity index (SLEDAI-2K) and clinical SLEDAI (modified SLEDAI). Patients with active SLE were categorized into renal or non-renal flares. Serum cytokines were measured by multiplex bead-based flow cytometry. The correlation and logistic regression analysis were performed. Results Levels of IFN-α, MCP-1, IL-6, IL-8, and IL-18 significantly increased in active SLE and correlated with clinical SLEDAI. Complement C3 showed a weakly negative relationship with IFN-α and IL-18. IL-18 showed the highest positive likelihood ratios for active SLE. Multiple logistic regression analysis showed that IL-6, IL-8, and IL-18 significantly increased odds ratio (OR) for active SLE at baseline while complement C3 and IL-18 increased OR for active SLE at 12 weeks. IL-18 and IL-6 yielded higher sensitivity and specificity than anti-dsDNA and C3 to predict active renal and active non-renal, respectively. Conclusion The heterogeneity of SLE pathogenesis leads to different signaling mechanisms and mediates through several cytokines. The monitoring of cytokines increases the sensitivity and specificity to determine SLE disease activity. IL-18 predicts the risk of active renal SLE while IL-6 and IL-8 predict the risk of active non-renal. The sensitivity and specificity of these cytokines are higher than the anti-dsDNA or C3. We propose to use the serum level of IL-18, IL-6, and IL-8 to monitor SLE disease activity in clinical practice.

scholarly journals Accuracy of Topometric Indices for Distinguishing between Keratoconic and Normal Corneas

2013 ◽  
Vol 2 (3) ◽  
pp. 108-112 ◽  
Author(s):  
Bernardo Lopes ◽  
Isaac C Ramos ◽  
Bruno F Valbon ◽  
Marcella Q Salomao ◽  
Frederico P Guerra ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Sensitivity And Specificity ◽  
Logistic Regression Analysis ◽  
False Negative ◽  
Roc Curves ◽  
False Positives ◽  
Corneal Topography ◽  
Calculated Parameter ◽  
The Individual

ABSTRACT Purpose To evaluate the sensitivity and specificity of the Pentacam topometric indices derived from the corneal surface curvature to distinguish between normal and keratoconic corneas. Methods The study consisted of 226 normal corneas from 113 patients and 88 keratoconic eyes from 44 patients. Eyes were defined as keratoconus based on comprehensive ocular examination, including Placido-disk-based corneal topography (Atlas Corneal Topography System; Humphrey, San Leandro, California) and rotating Scheimpflug corneal tomography (Pentacam HR; Oculus, Wetzlar, Germany). Corneal Topometric indices ISV, IVA, KI, CKI, IHA and IHD, along with the TKC (Topometric Keratoconus Classification) score were calculated from the Pentacam HR exam. Statistical analysis were accomplished using BioEstat 5.0 (Instituto Mamiraua, Amazonas, Brazil) and MedCalc 12.0 (MedCalc Software, Mariakerke, Belgium) using unpaired nonparametric Mann Whitney test (Wilcoxon ranked-sum). ROC curves were calculated for each topometric parameter to determine the best cut off values from the significantly different parameters. A logistic regression analysis was performed to provide a combined parameter for optimizing accuracy. Results Statistical significant differences were found between keratoconic and normal corneas for all topometric indices (Mann Whitney, p < 0.05). There were four false negative cases among the keratoconic cases on the TKC classification (4.54%) and 16 false positive cases among normal (7.08%), so that the sensitivity and specificity of the TKC were 95.54 and 92.92% respectively. The areas under the ROC curves (AUC) for the individual topometric indices varied from 0.843 (CKI) and 0.992 (ISV). The sensitivity and specificity of the most accurate ISV were 97.7 and 96.5% respectively. The calculated parameter from logistic regression had AUC of 0.996, with sensitivity of 97.7% and specificity of 98.7%. Conclusion Pentacam topometric indices were useful for distinguishing between normal and keratoconic corneas. The TKC classification should be expected to have false positives and negatives and should not be considered alone. TKC had more false positives and false negatives than some individual topometric parameters. A novel combined parameter based on logistic regression analysis may improve accuracy for the diagnosis of keratoconus. Further studies are necessary to evaluate if adding other curvature derived indices is beneficial for the regression analysis, as well as for testing the sensitivity of such parameters for the diagnosis of milder forms of ectasia and for testing correlations with severity of the disease. How to cite this article Salomao MQ, Guerra FP, Ramos IC, Jordao LF, Canedo ALC, Valbon BF, Luz A, Correa R, Lopes B, Ambrósio Jr R. Accuracy of Topometric Indices for Distinguishing between Keratoconic and Normal Corneas. J Kerat Ect Cor Dis 2013;2(3):108-112.

scholarly journals Identification of Patients Requiring Adrenalectomy for Unilateral Adrenal Tumors Through a Dual Approach: ceCT and [18F]FDG-PET

2021 ◽  
Author(s):  
Andrea Farolfi ◽  
Elisa Maietti ◽  
Federica Piperno ◽  
Pietro Coppolino ◽  
Guido Di Dalmazi ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Sensitivity And Specificity ◽  
Logistic Regression Analysis ◽  
Fdg Pet ◽  
Flow Chart ◽  
Adrenal Tumors ◽  
Optimal Management ◽  
Diagnostic Score

Abstract PurposeHormonal assessment (HA) and contrast-enhanced CT (ceCT) show insufficient sensitivity and specificity when staging unilateral adrenal lesions (ALs). We aimed at: 1) developing an imaging-based, i.e. ceCT and FDG-PET, diagnostic score able to discriminate adrenal tumors entailing adrenalectomy from those who need clinical monitoring, and 2) identifying a diagnostic flow-chart that allows clinicians to avoid unneeded diagnostic procedures and to address patients to the optimal management.MethodsRetrospective single-center study assessing patients with unilateral AL and the following inclusion criteria: a) a four-phase ceCT; b) FDG-PET within one month of the ceCT; c) histopathology or a clinical follow-up of at least 24 months. Firstly, multivariate logistic regression analysis was employed to identify the predictors of adrenal tumors to treat surgically (AL-to-treat) and regression-based coefficients were used to develop a diagnostic score. Secondly, using cut-offs of predictor variables, a diagnostic flow-chart was defined.ResultsForty-eight patients were enrolled (mean age 61 years), of whom 21/48 (44%) had AL-to-treat. The remaining 27/48 (56%) presented with AL to follow-up only (i.e. benign). Maximum and minimum lesion diameter, Hounsfield units (HU) before contrast media injection and HU at the delayed phase (HUdelayed), relative and absolute washout, AL SUVmax, AL SUVmean, ratio SUVmax AL/SUVmax liver (adrenal-liver ratio) were associated with the presence of AL-to-treat (all p<0.05). In multiple logistic regression analysis, SUVmax and HUdelayed showed to be significant predictors of AL-to-treat and were used to create a diagnostic score. ceCT parameters’ cut-offs discriminating AL-to-treat surgically from AL-to-follow-up with 100% PPV and NPV were first identified, finding 4/48 AL-to-treat and 15/48 ALs to follow-up. Applying the adrenal-liver ratio cut-off of 1.7 to the 29/48 remaining patients with uncertain AL management, for adrenal tumors we found an overall accuracy, sensitivity and specificity of 83%, 76% and 89%, respectively, and a diagnostic flow-chart based on these results was proposed. ConclusionWe developed a two-parameter imaging-based score that may be applied to predict adrenal tumors requiring adrenalectomy. Furthermore, a diagnostic flow-chart to stratify patients on the basis of the optimal management was proposed, thus guiding undefined unilateral adrenal lesions to FDG-PET imaging.

Diagnostic performance of T2-weighted imaging and intravoxel incoherent motion diffusion-weighted MRI for predicting metastatic axillary lymph nodes in T1 and T2 stage breast cancer

2021 ◽  
pp. 028418512110028
Author(s):  
Yuanyuan Liu ◽  
Hongbing Luo ◽  
Chunhua Wang ◽  
Xiaoyu Chen ◽  
Min Wang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Sensitivity And Specificity ◽  
Logistic Regression Analysis ◽  
Signal Intensity ◽  
Intravoxel Incoherent Motion ◽  
Axillary Lymph ◽  
Abnormal Signal ◽  
Abnormal Signal Intensity

Background Non-invasive modalities for assessing axillary lymph node (ALN) are needed in clinical practice. Purpose To investigate the suspicious ALN on unenhanced T2-weighted (T2W) imaging and intravoxel incoherent motion diffusion-weighted imaging (IVIM DWI) for predicting ALN metastases (ALNM) in patients with T1–T2 stage breast cancer and clinically negative ALN. Material and Methods Two radiologists identified the most suspicious ALN or the largest ALN in negative axilla by T2W imaging features, including short axis (Size-S), long axis (Size-L)/S ratio, fatty hilum, margin, and signal intensity on T2W imaging. The IVIM parameters of these selected ALNs were also obtained. The Mann–Whitney U test or t-test was used to compare the metastatic and non-metastatic ALN groups. Finally, logistic regression analysis with T2W imaging and IVIM features for predicting ALNM was conducted. Results This study included 49 patients with metastatic ALNs and 50 patients with non-metastatic ALNs. Using the above conventional features on T2W imaging, the sensitivity and specificity in predicting ALNM were not high. Compared with non-metastatic ALNs, metastatic ALNs had lower pseudo-diffusion coefficient (D*) ( P = 0.043). Logistic regression analysis showed that the most useful features for predicting ALNM were signal intensity and D*. The sensitivity and specificity predicting ALNM that satisfied abnormal signal intensity and lower D* were 73.5% and 84%, respectively. Conclusions The abnormal signal intensity on T2W imaging and one IVIM feature (D*) were significantly associated with ALNM, with sensitivity of 73.5% and specificity of 84%.

scholarly journals Distinguish Coronavirus Disease 2019 Patients in General Surgery Emergency by CIAAD Scale: Development and Validation of a Prediction Model Based on 822 Cases in China

2020 ◽  
Author(s):  
Bangbo Zhao ◽  
Yingxin Wei ◽  
Wenwu Sun ◽  
Cheng Qin ◽  
Xingtong Zhou ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Prediction Model ◽  
Acute Abdomen ◽  
Logistic Regression Analysis ◽  
Validation Cohort ◽  
Curve Analysis ◽  
Multivariable Logistic Regression Analysis ◽  
Decision Curve Analysis ◽  
Multivariable Logistic Regression

ABATRACTIMPORTANCEIn the epidemic, surgeons cannot distinguish infectious acute abdomen patients suspected COVID-19 quickly and effectively.OBJECTIVETo develop and validate a predication model, presented as nomogram and scale, to distinguish infectious acute abdomen patients suspected coronavirus disease 2019 (COVID-19).DESIGNDiagnostic model based on retrospective case series.SETTINGTwo hospitals in Wuhan and Beijing, China.PTRTICIPANTS584 patients admitted to hospital with laboratory confirmed SARS-CoV-2 from 2 Jan 2020 to15 Feb 2020 and 238 infectious acute abdomen patients receiving emergency operation from 28 Feb 2019 to 3 Apr 2020.METHODSLASSO regression and multivariable logistic regression analysis were conducted to develop the prediction model in training cohort. The performance of the nomogram was evaluated by calibration curves, receiver operating characteristic (ROC) curves, decision curve analysis (DCA) and clinical impact curves in training and validation cohort. A simplified screening scale and managing algorithm was generated according to the nomogram.RESULTSSix potential COVID-19 prediction variables were selected and the variable abdominal pain was excluded for overmuch weight. The five potential predictors, including fever, chest computed tomography (CT), leukocytes (white blood cells, WBC), C-reactive protein (CRP) and procalcitonin (PCT), were all independent predictors in multivariable logistic regression analysis (p ≤0.001) and the nomogram, named COVID-19 Infectious Acute Abdomen Distinguishment (CIAAD) nomogram, was generated. The CIAAD nomogram showed good discrimination and calibration (C-index of 0.981 (95% CI, 0.963 to 0.999) and AUC of 0.970 (95% CI, 0.961 to 0.982)), which was validated in the validation cohort (C-index of 0.966 (95% CI, 0.960 to 0.972) and AUC of 0.966 (95% CI, 0.957 to 0.975)). Decision curve analysis revealed that the CIAAD nomogram was clinically useful. The nomogram was further simplified into the CIAAD scale.CONCLUSIONSWe established an easy and effective screening model and scale for surgeons in emergency department to distinguish COVID-19 patients from infectious acute abdomen patients. The algorithm based on CIAAD scale will help surgeons manage infectious acute abdomen patients suspected COVID-19 more efficiently.

Abstract T P2: A Clinical Score to Predict Major Arterial Occlusions Eligible for Endovascular Recanalization in Acute Stroke based on the ASTRAL registry

Stroke ◽  
2015 ◽  
Vol 46 (suppl_1) ◽  
Author(s):  
Peter Vanacker ◽  
Mirjam Heldner ◽  
Michael Amiguet ◽  
Mohamed Faouzi ◽  
Patrick Cras ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Acute Stroke ◽  
Logistic Regression Analysis ◽  
Validation Cohort ◽  
External Validation ◽  
Clinical Score ◽  
Rank Test ◽  
Stroke Patients ◽  
Item Score

Aims: Endovascular treatment (EVT) for acute ischemic stroke (AIS) has the potential to improve clinical outcome in well selected patients with a major intracranial occlusion. This study aimed to identify easily available pre-hospital predictors and to develop a clinical score to detect AIS patients with such occlusions potentially eligible for acute EVT. Methods: Consecutive AIS enrolled in the ASTRAL registry with good quality CT-angiography within 12h after onset were selected (1’645 patients, Lausanne) and categorized according to occlusion site. Easily accessible demographic and clinical information were used in a logistic regression analysis to derive predictors of major intracranial occlusions (intracranial carotid, basilar and M1-artery). A The score was created developed using the beta-logistic regression coefficients of the logistic regression analysis, and was validated internally and externally (848 patients, Bern). Results: Major intracranial occlusions were present in 316 (21%) acute stroke patients in the derivation and 566 (28%) in the internal validation cohort. From all 12 significant predictors of major intracranial occlusions, a 7- item score was developed to obtain the best diagnostic performance; it was termed ASTRAL-O score according to its constituents: Admission NIHSS (A): (NIHSS 0-4: 0 points; 5-9: 4; 10-14: 8; ≥15: 12), absence of pre-STroke handicap (mRS≤2) (ST: 2 points), absence of previous STroke (ST: 1 point), Right-sided stroke (R: 3 points), Atrial fibrillation (A: 1 point), Level of consciousness decreased (L: 1 point) and wOmen (O:2 points). Diagnostic accuracy of the score in both the internal and external validation cohort was good at different cut-off levels (AUC 0.84 and 0.76 respectively). A Wilcoxon signed rank test showed that the ASTRAL-0 score performed better than NIHSS alone in term of predicted probabilities. Conclusions: The ASTRAL-O score accurately predicts the major intracranial occlusions eligible for EVT. It improves predicted probabilities over the NIHSS alone. This tool could help to rapidly triage stroke patients in the hyper-acute phase

scholarly journals The PREdictor of MAlnutrition in Systemic Sclerosis (PREMASS) Score: A Combined Index to Predict 12 Months Onset of Malnutrition in Systemic Sclerosis

2021 ◽  
Vol 8 ◽  
Author(s):  
Gianluca Bagnato ◽  
Erika Pigatto ◽  
Alessandra Bitto ◽  
Gabriele Pizzino ◽  
Natasha Irrera ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Systemic Sclerosis ◽  
Logistic Regression Analysis ◽  
Predictive Value ◽  
Univariate Analysis ◽  
Predictive Index ◽  
Derivation Cohort ◽  
Malnutrition Universal Screening Tool ◽  
Significant Mortality

Objective: Malnutrition is a severe complication in Systemic Sclerosis (SSc) and it is associated with significant mortality. Notwithstanding, there is no defined screening or clinical pathway for patients, which is hampering effective management and limiting the opportunity for early intervention. Here we aim to identify a combined index predictive of malnutrition at 12 months using clinical data and specific serum adipokines.Methods: This was an international, multicentre observational study involving 159 SSc patients in two independent discovery (n = 98) and validation (n = 61) cohorts. Besides routine clinical and serum data at baseline and 12 months, Malnutrition Universal Screening Tool (MUST) score and serum concentration of leptin and adiponectin were measured for each participant at baseline. The endpoint of malnutrition was defined according to European Society of Clinical Nutrition and Metabolism (ESPEN) recommendation. Significant parameters from univariate analysis were tested in logistic regression analysis to identify the predictive index of malnutrition in the derivation cohort.Results: The onset of malnutrition at 12 months correlated with adiponectin, leptin and their ratio (A/L), MUST, clinical subset, disease duration, Scl70 and Forced Vital Capaciy (FVC). Logistic regression analysis defined the formula: −2.13 + (A/L*0.45) + (Scl70*0.28) as the best PREdictor of MAlnutrition in SSc (PREMASS) (AUC = 0.96; 95% CI 0.93, 0.99). PREMASS &lt; −1.46 had a positive predictive value (PPV) &gt; 62% and negative predictive value (NPV) &gt; 97% for malnutrition at 12 months.Conclusion: PREMASS is a feasible index which has shown very good performance in two independent cohorts for predicting malnutrition at 12 months in SSc. The implementation of PREMASS could aid both in clinical management and clinical trial stratification/enrichment to target malnutrition in SSc.

scholarly journals Cerebrovascular risk factors are the predictive factor for ischemic cerebrovascular diseases in patients with Philadelphia chromosome (Ph)–negative myeloproliferative neoplasms: a case control study

2021 ◽  
Author(s):  
Shirong Wen ◽  
Wenxiao Zhang ◽  
Yiping Fei ◽  
Ke Guan ◽  
Hui Zhao ◽  
...  
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Logistic Regression Analysis ◽  
Myeloproliferative Neoplasms ◽  
Philadelphia Chromosome ◽  
Jak2v617f Mutation ◽  
Published Data ◽  
Relative Role ◽  
Calr Mutations

Abstract Background: The ischemic cerebrovascular disease (ICVD) is major thrombotic complication of Philadelphia chromosome (Ph)-negative myeloproliferative neoplasms (Ph-negative MPNs) which included essential thrombocythemia (ET), polycythemia vera (PV) and primary myelofibrosis (PMF) leading to high disability and mortality rates. However, risk factors attributable to ICVD in Ph-negative MPNs patients are still not understood. This study aimed to identify risk factors for ICVD in Ph-negative MPNs. Methods: Patients with Ph-negative MPNs were divided into ICVD and non-ICVD groups. The demographic, biochemical, genetic parameters (JAK2V617F and CALR mutations) were assessed. The association between these factors and ICVD was assessed using logistic regression analysis.Results: One hundred eighty five Ph-negative MPNs patients (82 ET, 78 PV, and 25 PMF) were recruited，and 57 (30.8%) had ICVD which was higher than recently published data. The higher prevalence of hypertension(59.6% vs 32.0%), smoking(22.8% vs 6.3%), drinking(22.8% vs 6.3%), JAK2V617F mutation(78.9% vs 63.3%), the percentage of neutrophils( 78.76% vs 71.73%)， the lower prevalence of CALR mutation(3.2% vs 22.2%) and the percentage of basophils(0.82% vs 1.40%) were found in the ICVD group comparing with the non-ICVD group. The frequency of ICVD events was significantly higher in patients with JAK2V617F mutation than in those without (35.7% and 20.3%, P=0.034). Multivariate logistic regression analysis showed that hypertension (OR=2.464, 95%CI 1.218-4.983, p=0.012) and smoking (OR=5.426, 95%CI 1.919-15.340, p=0.001) were significantly positively associated with ICVD events. For ET patients, both smoking (OR=4.414, 95%CI 1.079-15.685, p=0.038) and increased percentage of neutrophils (OR=1.080, 95%CI 1.019-1.144, p=0.009) were independently associated with ICVD incidence. Hypertension (OR=4.647, 95%CI 1.215-17.781, p=0.025), smoking (OR=6.065, 95%CI 1.083-33.951, p=0.040), and increased percentage of lymphocytes (OR=1.039, 95%CI 1.002-1.078, p=0.039) were all positively correlated with ICVD risk in PV patients. Conclusions: Our data suggest that hypertension, smoking, higher percentage of neutrophils and lymphocytes rather than JAK2V617F and CALR mutations may be associated with an elevated risk of ICVD in Ph-negative MPNs patients, although the relative role of each factor may vary in the individual subgroup. Additional studies of large patient cohorts will be essential to validate these findings.

scholarly journals Diagnostic Utility of Combined 2D Ultrasonography and Contrast-Enhanced Ultrasonography in Evaluation of Carotid Plaque Vulnerability for Predicting Recurrent Ischemic Strokes

2021 ◽  
Author(s):  
Fuyong Ye ◽  
Yuwen Yang ◽  
Yinting Liang ◽  
Jianhua Liu
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Regression Analysis ◽  
Sensitivity And Specificity ◽  
Logistic Regression Analysis ◽  
Multivariate Logistic Regression Analysis ◽  
Multivariate Logistic Regression ◽  
Contrast Enhanced ◽  
Contrast Enhanced Ultrasonography ◽  
Independent Risk Factors

Abstract Objective: To evaluate the sensitivity and specificity of combined 2D ultrasonography (USG) and contrast-enhanced ultrasonography (CEUS) in analyzing the carotid plaque vulnerability for predicting the recurrent ischemic strokes (IS). Methods: One hundred and fifteen patients with first IS were studied by 2D USG and CEUS. The carotid plaques were then classified on the basis of echogenicity (2D USG) and neovascularization (CEUS). The presence or absence of recurrent IS was considered as the dependent variable. Age, gender, body mass index (BMI), hypertension, hyperglycemia, hyperlipidemia, history of smoking and drinking, type of plaque echogenicity, and grade of plaque neovascularization were considered as independent variables. The risk factors of recurrent IS were analyzed by both univariate and multivariate logistic regression analysis. Finally, the sensitivity and specificity of combined 2D USG and CEUS in the diagnosis of recurrent IS was evaluated by receiver operating characteristic curve. Results: Univariate logistic regression analysis revealed that hypertension, echogenicity type, and grade of plaque neovascularization were predictors of recurrent IS. Further, multivariate logistic regression analysis revealed that the echogenicity type (OR=0.282, P=0.012) and grade of plaque neovascularization (OR=7.408, P<0.0001) were independent risk factors for recurrent IS. The sensitivity, specificity, and area under the curve of combined method were 0.865, 0.769, and 0.817, respectively (95%CI: 0.733-0.902, P<0.0001), which were higher than both 2D USG and CEUS.Conclusions: The echogenicity type and grade of plaque neovascularization are independent risk factors for recurrent IS. The combination of two methods has high sensitivity and specificity in predicting the recurrent IS.

Sign in / Sign up

Export Citation Format

Share Document