Maximum Area Under The Curve Research Articles

Simple tuberculosis screening tool using signs, symptoms, and risk factors to reduce the missed opportunity in the older population

BackgroundThere is a growing concern on how to increase tuberculosis (TB) case detection in resource-poor settings. The healthcare facilities routinely providing services to the elderly for chronic diseases often failed to detect TB cases, causing a missed opportunity. This study aimed to develop a simple and sensitive screening tool using signs, symptoms, and risk factors for TB case detection in the elderly.MethodsThis was a cross-sectional study conducted from August to December 2020. A random sample of 302 subjects was taken from the elderly aged ≥ 60 years attending the outpatient polyclinic at Mangunharjo hospital, Madiun, East Java, Indonesia, for a chronic disease problem. The test was developed using 16 TB signs, symptoms, and risk factors. Test performance was assessed by comparing it against the GeneXpert MTB/RIF.ResultsMarginal analysis resulted in the optimal cut-point of ≥ 7 for the test, which gave an area under the curve (AUC) of 0.62, with the maximum marginal AUC of 0.04 (p < 0.001). The sensitivity and specificity were 60.26% and 64.29%, meaning 60 TB cases for every 100 elderly with TB would be otherwise left undetected if this screening test did not take place.ConclusionA simple TB screening tool with moderate sensitivity and AUC has been developed using TB signs, symptoms, and risk factors. It can be used as an initial step of the systematic TB screening in the elderly visiting healthcare facilities for routine chronic disease examination, with the additional utility of reducing the missed opportunity.

Effect of metritis on in-vitro uterine contractility in cows during the puerperium

Aim of this study was to determine the effect of metritis on in-vitro uterine contractility. Uteri obtained from 16 euthanized Holstein-Friesian cows were divided into two groups depending on whether metritis was absent (M-, n = 6) or present (M+, n = 10). Four longitudinal and four circular myometrial strips of all uteri were incubated in an organ bath. Spontaneous contractility was recorded in five consecutive 30-minute periods (T1-T5). This was followed by stimulation of one longitudinal and one circular strip with increasing concentrations of oxytocin, prostaglandinF2α (PGF2α), and calcium chloride (each during four 30-minute periods [T6-T9]). Strips in group M+ had higher minimum amplitude (minA) values at T1 and higher minA, mean amplitude (meanA), and area under the curve (AUC) values at T2 than strips in group M- (P ≤ 0.05). In the M+ group, the maximum amplitude (maxA), meanA, and AUC values of circular strips were higher than those of longitudinal strips during spontaneous contractility (T1, T4, and T5; P ≤ 0.05). The minA, meanA, and AUC values for strips in group M+ were higher than those in group M- when exposed to the highest concentration of PGF2α (P ≤ 0.05). During stimulation with PGF2α (T9), longitudinal strips had higher maxA values than the circular strips in group M+ (P ≤ 0.05). Spontaneous and stimulated contractility were temporarily increased in uteri with metritis compared to healthy uteri. Both myometrial layers, especially in uteri with metritis, reacted differently during spontaneous contractility and to stimulation with PGF2α.

Recalibrating the Non-Communicable Diseases risk prediction tools for the rural population of Western India

BackgroundThe aim of the present study was to recalibrate the effectiveness of Indian Diabetes Risk Score (IDRS) and Community-Based Assessment Checklist (CBAC) by opportunistic screening of Diabetes Mellitus (DM) and Hypertension (HT) among the people attending health centres, and estimating the risk of fatal and non-fatal Cardio-Vascular Diseases (CVDs) among them using WHO/ISH charts.MethodsAll the people aged ≥ 30 years attending the health centers were screened for DM and HT. Weight, height, waist circumference, and hip circumferences were measured, and BMI and Waist-Hip Ratio (WHR) were calculated. Risk categorization of all participants was done using IDRS, CBAC, and WHO/ISH risk prediction charts. Individuals diagnosed with DM or HT were started on treatment. The data was recorded using Epicollect5 and was analyzed using SPSS v.23 and MedCalc v.19.8. ROC curves were plotted for DM and HT with the IDRS, CBAC score, and anthropometric parameters. Sensitivity (SN), specificity (SP), Positive Predictive Value (PPV), Negative Predictive Value (NPV), Accuracy and Youden’s index were calculated for different cut-offs of IDRS and CBAC scores.ResultsA total of 942 participants were included for the screening, out of them, 9.2% (95% CI: 7.45–11.31) were diagnosed with DM for the first time. Hypertension was detected among 25.7% (95% CI: 22.9–28.5) of the participants. A total of 447 (47.3%) participants were found with IDRS score ≥ 60, and 276 (29.3%) with CBAC score > 4. As much as 26.1% were at moderate to higher risk (≥ 10%) of developing CVDs. Area Under the Curve (AUC) for IDRS in predicting DM was 0.64 (0.58–0.70), with 67.1% SN and 55.2% SP (Youden’s Index 0.22). While the AUC for CBAC was 0.59 (0.53–0.65). For hypertension both the AUCs were 0.66 (0.62–0.71) and 0.63 (0.59–0.67), respectively.ConclusionsIDRS was found to have the maximum AUC and sensitivity thereby demonstrating its usefulness as compared to other tools for screening of both diabetes and hypertension. It thus has the potential to expose the hidden NCD iceberg. Hence, we propose IDRS as a useful tool in screening of Diabetes and Hypertension in rural India.

Integrating microRNA expression, miRNA-mRNA regulation network and signal pathway: a novel strategy for lung cancer biomarker discovery.

BackgroundSince there are inextricably connections among molecules in the biological networks, it would be a more efficient and accurate research strategy to screen microRNA (miRNA) markers combining with miRNA-mRNA regulatory networks. The independent regulation mode is more “fragile” and “influential” than the co-regulation mode. miRNAs can be used as biomarkers if they can independently regulate hub genes with important roles in the PPI network, simultaneously the expression products of the regulated hub genes play important roles in the signaling pathways of related tissue diseases.MethodsWe collected miRNA expression of non-small cell lung cancer (NSCLC) from The Cancer Genome Atlas (TCGA) database and the Gene Expression Omnibus (GEO) database. Volcano plot and signal-to-noise ratio (SNR) methods were used to obtain significant differentially expressed (SDE) miRNAs from the TCGA database and GEO database, respectively. A human miRNA-mRNA regulatory network was constructed and the number of genes uniquely targeted (NOG) by a certain miRNA was calculated. The area under the curve (AUC) values were used to screen for clinical sensitivity and specificity. The candidate markers were obtained using the criteria of the top five maximum AUC values and NOG ≥ 3. The protein–protein interaction (PPI) network was constructed and independently regulated hub genes were obtained. Gene Ontology (GO) analysis and KEGG pathway analysis were used to identify genes involved in cancer-related pathways. Finally, the miRNA which can independently regulate a hub gene and the hub gene can participate in an important cancer-related pathway was considered as a biomarker. The AUC values and gene expression profile analysis from two external GEO datasets as well as literature validation were used to verify the screening capability and reliability of this marker.ResultsFifteen SDE miRNAs in lung cancer were obtained from the intersection of volcano plot and SNR based on the GEO database and the TCGA database. Five miRNAs with the top five maximum AUC values and NOG ≥ 3 were screened out. A total of 61 hub genes were obtained from the PPI network. It was found that the hub gene GTF2F2 was independently regulated by miR-708-5p. Further pathway analysis indicated that GTF2F2 participates in protein expression by binding with polymerase II, and it can regulate transcription and accelerate tumor growth. Hence, miR-708-5p could be used as a biomarker. The good screening capability and reliability of miR-708-5p as a lung cancer marker were confirmed by AUC values and gene expression profiling of external datasets, and experimental literature. The potential mechanism of miR-708-5p was proposed.ConclusionsThis study proposes a new idea for lung cancer marker screening by integrating microRNA expression, regulation network and signal pathway. miR-708-5p was identified as a biomarker using this novel strategy. This study may provide some help for cancer marker screening.

Discerning Mouse Trajectory Features With the Drift Diffusion Model.

Mouse tracking, a new action-based measure of behavior, has advanced theories of decision making with the notion that cognitive and social decision making is fundamentally dynamic. Implicit in this theory is that people's decision strategies, such as discounting delayed rewards, are stable over task design and that mouse trajectory features correspond to specific segments of decision making. By applying the hierarchical drift diffusion model and the Bayesian delay discounting model, we tested these assumptions. Specifically, we investigated the extent to which the "mouse-tracking" design of decision-making tasks (delay discounting task, DDT and stop-signal task, SST) deviate from the standard "keypress" design of decision making tasks. We found remarkable agreement in delay discounting rates (intertemporal impatience) obtained in the keypress and mouse-tracking versions of DDT (ρ = 0.90) even though these tasks were given about 1 week apart. Rates of evidence accumulation converged well in the two versions (DDT, ρ = .86; SST, ρ = .55). Omission/commission error in SST showed high agreement (ρ = .42, ρ = .53). Mouse-motion features such as maximum velocity and AUC (area under the curve) correlated well with nondecision time (ρ = -.42) and boundary separation (ρ = .44)-the amount of information needed to accumulate prior to making a response. These results indicate that the response time (RT) and motion-based decision tasks converge well at a fundamental level, and that mouse-tracking features such as AUC and maximum velocity do indicate the degree of decision conflict and impulsivity.

Near-infrared fluorescence imaging with indocyanine green for quantification of changes in tissue perfusion following revascularization.

Current diagnostic modalities for patients with peripheral artery disease (PAD) mainly focus on the macrovascular level. For assessment of tissue perfusion, near-infrared (NIR) fluorescence imaging using indocyanine green (ICG) seems promising. In this prospective cohort study, ICG NIR fluorescence imaging was performed pre- and post-revascularization to assess changes in foot perfusion. ICG NIR fluorescence imaging was performed in 36 patients with PAD pre- and post-intervention. After intravenous bolus injection of 0.1mg/kg ICG, the camera registered the NIR fluorescence intensity over time on the dorsum of the feet for 15min using the Quest Spectrum Platform®. Time-intensity curves were plotted for three regions of interest (ROI): (1) the dorsum of the foot, (2) the forefoot, and (3) the hallux. Time-intensity curves were normalized for maximum fluorescence intensity. Extracted parameters were the maximum slope, area under the curve (AUC) for the ingress, and the AUC for the egress. The non-treated contralateral leg was used as a control group. Successful revascularization was performed in 32 patients. There was a significant increase for the maximum slope and AUC egress in all three ROIs. The most significant difference was seen for the maximum slope in ROI 3 (3.7%/s to 6.6%/s, p < 0.001). In the control group, no significant differences were seen for the maximum slope and AUC egress in all ROIs. This study shows the potential of ICG NIR fluorescence imaging in assessing the effect of revascularization procedures on foot perfusion. Future studies should focus on the use of this technique in predicting favorable outcome of revascularization procedures.

Analysis of Periodontal Conditions in the Provinces of Vietnam: Results From the National Dental Survey.

Nationwide dental health surveys are crucial for providing essential information on dental health and dental condition-related problems in the community. However, the relationship between periodontal conditions and sociodemographic data has not been well investigated in Vietnam. With data from the National Oral Health Survey in 2019, we performed several machine learning methods on this dataset to investigate the impacts of sociodemographic features on gingival bleeding, periodontal pockets, and Community Periodontal Index. From the experiments, LightGBM produced a maximum AUC (area under the curve) value of 0.744. The other models in descending order were logistic regression (0.705), logiboost (0.704), and random forest (0.684). All methods resulted in significantly high overall accuracies, all exceeding 90%. The results show that the gradient boosting model can predict well the relationship between periodontal conditions and sociodemographic data. The investigated model also reveals that the geographic region has the most significant influence on dental health, while the consumption of sweet foods/drinks is the second most crucial. These findings advocate for a region-specific approach for the dental care program and the implementation of a sugar-risk food reduction program.

Influence of type of starch and feeding management on glycaemic control in diabetic dogs.

The present study evaluated the effects of two diets with different starch sources and two feeding methods on the glycaemic control in dogs with diabetes mellitus. The diets had similar nutrient contents (40% starch and 16% dietary fibre), one formulated with 46% of broken rice and the other with 42% sorghum and 10% lentils (as-fed). Ten client-owned diabetic dogs were fed with each diet for 2months, in a crossover design. Five dogs received NPH human insulin and food every 12h (feeding method 1), and the other five received insulin every 12h but were fed three times a day (feeding method 2). In feeding method 2, morning insulin was higher than the evening dose and dogs received the second meal after 4 to 5h of the morning insulin and meal. Parameters evaluated included insulin dosage, 12- and 8-h glycaemic curves, complete blood count, biochemical profile and urinalysis. Glycaemic curves were analysed by ANOVA with repeated measures. Glycaemic control parameters (fasting, mean, minimum and maximum glycaemia and serum fructosamine) and glucose area under the curve (AUC) were calculated and analysed by paired t test (p<0.05). In feeding method 1, dogs fed the sorghum-based diet presented lower mean (p=0.04) and minimum blood glucose concentrations (p=0.03), and a tendency to lower maximum blood glucose (p=0.06) and glucose AUC (p=0.08) than when fed the rice-based diet. When food was provided twice a day, the ingestion of the rice-based diet resulted in higher post-prandial glucose response than the diet with sorghum and lentil. In feeding method 2, there was no effect of diet on the assessed parameters (p>0.05). No differences in insulin dosage were observed between groups or feeding methods (p>0.05). Providing two meals a day followed by insulin administration associated with the sorghum- and lentil-based diet improved glycaemic control in diabetic dogs.

Machine learning algorithm for characterizing risks of hypertension, at an early stage in Bangladesh

Background and aimsHypertension has become a major public health issue as the prevalence and risk of premature death and disability among adults due to hypertension has increased globally. The main objective is to characterize the risk factors of hypertension among adults in Bangladesh using machine learning (ML) algorithms. Materials and methodsThe hypertension data was derived from Bangladesh demographic and health survey, 2017–18, which included 6965 people aged 35 and above. Two most promising risk factor identification methods, namely least absolute shrinkage operator (LASSO) and support vector machine recursive feature elimination (SVMRFE) are implemented to detect the critical risk factors of hypertension. Additionally, four well-known ML algorithms as artificial neural network, decision tree, random forest, and gradient boosting (GB) have been used to predict hypertension. Performance scores of these algorithms were evaluated by accuracy, precision, recall, F-measure, and area under the curve (AUC). ResultsThe results clarify that age, BMI, wealth index, working status, and marital status for LASSO and age, BMI, marital status, diabetes and region for SVMRFE appear to be the top-most five significant risk factors for hypertension. Our findings reveal that the combination of SVMRFE-GB gives the maximum accuracy (66.98%), recall (97.92%), F-measure (78.99%), and AUC (0.669) compared to others. ConclusionGB-based algorithm confirms the best performer for prediction of hypertension, at an early stage in Bangladesh. Therefore, this study highly suggests that the policymakers make proper judgments for controlling hypertension using SVMRFE-GB-based combination to save time and reduce cost for Bangladeshi adults.

Applying deep learning and benchmark machine learning algorithms for landslide susceptibility modelling in Rorachu river basin of Sikkim Himalaya, India

Landslide is considered as one of the most severe threats to human life and property in the hilly areas of the world. The number of landslides and the level of damage across the globe has been increasing over time. Therefore, landslide management is essential to maintain the natural and socio-economic dynamics of the hilly region. Rorachu river basin is one of the most landslide-prone areas of the Sikkim selected for the present study. The prime goal of the study is to prepare landslide susceptibility maps (LSMs) using computer-based advanced machine learning techniques and compare the performance of the models. To properly understand the existing spatial relation with the landslide, twenty factors, including triggering and causative factors, were selected. A deep learning algorithm viz. convolutional neural network model (CNN) and three popular machine learning techniques, i.e., random forest model (RF), artificial neural network model (ANN), and bagging model, were employed to prepare the LSMs. Two separate datasets including training and validation were designed by randomly taken landslide and non-landslide points. A ratio of 70:30 was considered for the selection of both training and validation points. Multicollinearity was assessed by tolerance and variance inflation factor, and the role of individual conditioning factors was estimated using information gain ratio. The result reveals that there is no severe multicollinearity among the landslide conditioning factors, and the triggering factor rainfall appeared as the leading cause of the landslide. Based on the final prediction values of each model, LSM was constructed and successfully portioned into five distinct classes, like very low, low, moderate, high, and very high susceptibility. The susceptibility class-wise distribution of landslides shows that more than 90% of the landslide area falls under higher landslide susceptibility grades. The precision of models was examined using the area under the curve (AUC) of the receiver operating characteristics (ROC) curve and statistical methods like root mean square error (RMSE) and mean absolute error (MAE). In both datasets (training and validation), the CNN model achieved the maximum AUC value of 0.903 and 0.939, respectively. The lowest value of RMSE and MAE also reveals the better performance of the CNN model. So, it can be concluded that all the models have performed well, but the CNN model has outperformed the other models in terms of precision.

Validation of two predictive models designed to aid clinicians in identifying Stevens-Johnson syndrome/toxic epidermal necrolysis: A single institution retrospective review

Validation of two predictive models designed to aid clinicians in identifying Stevens-Johnson syndrome/toxic epidermal necrolysis: A single institution retrospective review

High diagnostic value of miRNAs for NSCLC: quantitative analysis for both single and combined miRNAs in lung cancer

Background MicroRNAs (miRNAs) are good candidates as biomarkers for Lung cancer (LC). The aim of this article is to figure out the diagnostic value of both single and combined miRNAs in LC. Methods Normative meta-analysis was conducted based on PRISMA. We assessed the diagnostic value by calculating the combined sensitivity (Sen), specificity (Spe), positive likelihood ratio (PLR), negative likelihood ratio (NLR) and diagnostic odds ratio (DOR) and the area under the curve (AUC) of single and combined miRNAs for LC and specific subgroups. Results A total of 80 qualified studies with a total of 8971 patients and 10758 controls were included. In non-small cell lung carcinoma (NSCLC), we involved 20 single-miRNAs and found their Sen, Spe and AUC ranged from 0.52-0.81, 0.66-0.88, and 0.68-0.90, respectively, specially, miR-19 with the maximum Sen, miR-20 and miR-10 with the highest Spe as well as miR-17 with the maximum AUC. Additionally, we detected miR-21 with the maximum Sen of 0.74 [95%CI: 0.62-0.83], miR-146 with the maximum Spe and AUC of 0.93 [95%CI: 0.79-0.98] and 0.89 [95%CI: 0.86-0.92] for early-stage NSCLC. We also identified the diagnostic power of available panel (miR-210, miR-31 and miR-21) for NSCLC with satisfying Sen, Spe and AUC of 0.82 [95%CI: 0.78-0.84], 0.87 [95%CI: 0.84-0.89] and 0.91 [95%CI: 0.88-0.93], and furtherly constructed 2 models for better diagnosis. Conclusions We identified several single miRNAs and combined groups with high diagnostic power for NSCLC through pooled quantitative analysis, which shows that specific miRNAs are good biomarker candidates for NSCLC and further researches needed.

Peripheral Inflammatory Blood Markers in Diagnosis of Glioma and IDH Status.

Objective Gliomas are the most common intracranial tumors. Histopathology and neuroimaging are the main modalities used for diagnosis and treatment response monitoring. However, both are expensive and insensitive methods and can cause neurological deterioration. This study aimed to develop a minimally invasive peripheral inflammatory biomarker for diagnosis of glioma, its grade, and isocitrate dehydrogenase (IDH) status. Materials and Methods Patients undergoing surgery for glioma, acoustic neuroma, and meningioma between January 2019 and December 2019 were included. Preoperative neutrophil/lymphocyte ratio (NLR), derived NLR (dNLR), platelet/lymphocyte ratio (PLR), lymphocyte/monocyte ratio (LMR), eosinophil/lymphocyte ratio (ELR), and prognostic nutritional index (PNI) were calculated. Histopathology and immunohistochemistry (IHC) staining were done postoperatively. Results A total of 154 patients of glioma, 36 patients of acoustic neuroma, 58 patients of meningioma, and 107 healthy controls were included. dNLR showed the maximum area under the curve (AUC) (0.656639) for diagnosis of glioma from other tumors and among combinations. dNLR +NLR showed the maximum AUC (0.647865). Maximum AUC for glioblastoma multiforme (GBM) versus other grades and among combinations was shown by NLR (0.83926). NLR + dNLR had the maximum AUC (0.764794). NLR showed significant p value in differentiating IDH wild from IDH mutant GBM. Conclusion dNLR has the maximum diagnostic value in diagnosing glioma from other tumors. NLR (AUC = 0.83926) showed the highest accuracy for GBM diagnosis and may be a parameter in predicting the grade of glioma; also, it has maximum diagnostic value in differentiating IDH wild GBM from IDH mutant GBM. These peripheral inflammatory parameters may prove to be sensitive and cost-effective markers for glioma diagnosis, predicting grade of glioma, monitoring of treatment response, and in predicting recurrence.

Diagnostic accuracy of respiratory impedance parameters to detect airflow obstruction in adults

OBJECTIVE: The present study was aimed to evaluate the diagnostic accuracy of impedance parameters to detect airflow obstruction and the severity of airflow obstruction in adults. METHODS: The spirometry parameters (forced expiratory volume in 1 s [FEV1], forced vital capacity [FVC], FEV1/FVC, FVC3/FVC, and FEF25-75) and respiratory impedance parameters (R5, R19, R5-19, and X5) measured by forced oscillation technique of consecutive 299 adults (male: 186) were included in the present analysis. The Spearman correlation coefficient (ρ) was used to assess the correlations of impedance parameters with spirometry indices. The area under the curve (AUC) was used to assess respiratory impedance parameters' diagnostic accuracy. RESULTS: The mean age of the study population was 54.1 ± 12.3 years, and 99 individuals (33%) had airflow obstruction (FEV1/FVC &lt; 0.70) in spirometry. All spirometry indices (% of predicted) showed a weak negative correlation with R5, R19, R5-19, and weak positive correlation with X5. The R5 &gt;142% predicted and X5 &gt;136% predicted had the maximum AUC (0.75) with a sensitivity of up to 56% and specificity up to 86% to identify airflow obstruction. The impedance parameters showed low concordance with the severity of airflow obstruction. CONCLUSION: Respiratory impedance parameters had insufficient sensitivity to diagnose airflow obstruction and the severity of obstruction in adults. Thus, impedance parameters cannot a substitute for spirometry in diagnosing obstructive lung function.

Cost per cumulative clinical benefit of biologic therapies for patients with plaque psoriasis: a systematic review.

BACKGROUND: Measuring cumulative clinical treatment benefit over time captures speed and magnitude of effects. Assessing the cost of biologics relative to their cumulative clinical benefits versus a single time point represents an alternative to evaluate the value of a given biologic used to treat psoriasis. OBJECTIVE: To compare cumulative benefit and cost per cumulative benefit of biologics in treatment of moderate to severe psoriasis from a network meta-analysis (NMA). METHODS: Biologics included in the analysis were ixekizumab, adalimumab, guselkumab, ustekinumab, secukinumab, risankizumab, and certolizumab pegol. Psoriasis Area and Severity Index (PASI) responses over the initial 16-week treatment period were obtained from 31 articles. Cumulative benefits for PASI 75, PASI 90, and PASI 100 responses were measured as area under the curve (AUC) using the trapezoidal method. Bayesian-based NMA modeled percent maximum AUC through week 16 (%Max_AUCW16). The AUC estimates over 16 weeks were converted to total skin clearance threshold days achieved for PASI 75, PASI 90, and PASI 100 with each biologic. Cost per cumulative benefit was estimated by multiplying number of doses (per FDA label) by nationally representative discounted wholesale acquisition costs (WACs) for 16 weeks of treatment divided by %Max_AUCW16. The primary cost analysis used WACs, including week 16 doses. Co-primary cost analysis used discounted WACs, including week 16 doses. Sensitivity analysis was conducted using WACs and discounted WACs, excluding doses administered at week 16. RESULTS: Among biologics with available week 16 AUC data for PASI 90 and PASI 100, cumulative benefits over the initial 16-week treatment period ranged from 20.2% (certolizumab pegol) to 47.0% (ixekizumab) for PASI 90 and from 7.4% (adalimumab) to 22.2% (ixekizumab) for PASI 100. The total number of estimated PASI 90 and PASI 100 days achieved over the first 16 weeks of treatment was highest with ixekizumab (53 days and 25 days, respectively). In the primary analysis, guselkumab had the lowest cost per cumulative benefit (95% credible interval [CrI]; $99,742 [$89,941-$111,653]), followed by ixekizumab ($108,906 [$95,928-$126,093]) and adalimumab ($111,233 [$97,549-$129,022]) for PASI 90, and ixekizumab had the lowest cost per cumulative benefit ($230,884 [$191,611-$291,115]), followed by secukinumab ($238,945 [$204,029-$288,072]) and risankizumab ($279,968 [$250,683-$316,872]) for PASI 100 responses. In the co-primary analysis, ixekizumab had the lowest discounted cost per AUC (95% CrI; $60,988 [$53,719-$70,612]), followed by guselkumab ($66,827 [$60,260-$74,807]) and secukinumab ($69,622 [$61,783-$79,786]) for PASI 90, and ixekizumab had the lowest cost per cumulative benefit ($129,295 [$107,302-$163,024]), followed by secukinumab ($148,146 [$126,498-$178,605]) and guselkumab ($188,190 [$166,791-$215,969]) for PASI 100 responses. Conclusions: Among biologics studied, ixekizumab demonstrated the greatest cumulative clinical benefit, maintaining the lowest cost per cumulative benefit for PASI 100 responses and lowest discounted cost per cumulative benefit for PASI 90 and PASI 100 responses for moderate to severe psoriasis over the initial 16-week treatment period. DISCLOSURES: This study was funded by Eli Lilly and Company (Indianapolis, IN). Blauvelt has served as a scientific adviser and/or clinical study investigator for AbbVie, Aclaris, Almirall, Arena, Athenex, Boehringer Ingelheim, Bristol-Myers Squibb, Dermavant, Dermira, Eli Lilly and Company, Forte, Galderma, Incyte, Janssen, Leo, Novartis, Ortho, Pfizer, Rapt, Regeneron, Sandoz, Sanofi Genzyme, Sun Pharma, and UCB Pharma and as a paid speaker for AbbVie. Burge, Zhu, Malatestinic, Brnabic, Guo, and Janardhanan are employees and shareholder of Eli Lilly and Company.

D-dimer can help differentiate suspected pulmonary embolism patients that require anti-coagulation.

ObjectivesDetermine whether D-dimer concentration in the absence of imaging can differentiate patients that require anti-coagulation from patients who do not require anti-coagulation.MethodsData was obtained retrospectively from 366 hemodynamically stable adult ED patients with suspected pulmonary embolism (PE).Patients were categorized by largest occluded artery and aggregated into: ‘Require anti-coagulation’ (main, lobar, and segmental PE), ‘Does not require anti-coagulation’ (sub-segmental and No PE), ‘High risk of deterioration’ (main and lobar PE), and ‘Not high risk of deterioration’ (segmental, sub-segmental, and No PE) groups.Wilcoxon rank-sum test was used for 2 sample comparisons of median D-dimer concentrations. Receiver operating characteristic (ROC) curve analysis was utilized to determine a D-dimer cut-off that could differentiate ‘Require anti-coagulation’ from ‘Does not require anti-coagulation’ and ‘High risk of deterioration’ from ‘Low risk of deterioration’ groups.ResultsThe ‘Require anti-coagulation’ group had a maximum area under the curve (AUC) of 0.92 at an age-adjusted D-dimer cut-off of 1540 with a specificity of 86% (95% CI, 81–91%), and sensitivity of 84% (79–90%). The ‘High risk of deterioration’ group had a maximum AUC of 0.93 at an age-adjusted D-dimer cut-off of 2500 with a specificity of 90% (85–93%) and sensitivity of 83% (77–90%).ConclusionsAn age-adjusted D-dimer cut-off of 1540 ng/mL differentiates suspected PE patients requiring anti-coagulation from those not requiring anti-coagulation. A cut-off of 2500 differentiates those with high risk of clinical deterioration from those not at high risk of deterioration. When correlated with clinical outcomes, these cut-offs can provide an objective method for clinical decision making when imaging is unavailable.

Pharmacokinetic and Pharmacodynamic Equivalence of Pegfilgrastim-cbqv and Pegfilgrastim in Healthy Subjects.

Pegfilgrastim-cbqv was developed as a biosimilar of pegfilgrastim, a pegylated form of recombinant human granulocyte colony-stimulating factor approved for decreasing febrile neutropenia-associated infection in patients receiving myelosuppressive drugs. This multicenter, randomized, single-blind, partial-reference-replicated, three-sequence crossover study assessed pharmacokinetic and pharmacodynamic bioequivalence of pegfilgrastim-cbqv and pegfilgrastim in healthy subjects. One hundred twenty-two subjects were randomized to one of three treatment sequences; each included one dose of pegfilgrastim-cbqv and two doses of pegfilgrastim separated by ≥ 28days. The primary pharmacokinetic end points were area under the curve (AUC) from 0 to infinity (AUC0-∞) and maximum concentration (Cmax). The primary pharmacodynamic end points were maximum absolute neutrophil count (ANCmax) and ANC AUC from time 0 to the last measurable observation (ANC AUC0-last). Pharmacokinetic and pharmacodynamic bioequivalences were demonstrated if the 90% CI for the geometric mean ratio (GMR) of pegfilgrastim-cbqv to pegfilgrastim was within 80-125% for the primary end points. Pharmacokinetic bioequivalence criteria were met for Cmax (GMR 105.0; 90% CI 95.5-115.4) and AUC0-∞ (GMR 97.5; 90% CI 88.6-107.2). Pharmacodynamic bioequivalence criteria were met for ANCmax (GMR 99.6; 90% CI 96.2-103.2) and ANC AUC0-last (GMR 96.7; 90% CI 92.2-101.4). Adverse events occurred in 76.0%, 76.6%, and 73.1% of subjects for pegfilgrastim-cbqv, first pegfilgrastim, and second pegfilgrastim dosing periods across treatment sequences, respectively. Investigators found no drug-related serious adverse events. This study established pharmacokinetic and pharmacodynamic bioequivalence of pegfilgrastim-cbqv to pegfilgrastim. The treatments displayed similar safety profiles, including immunogenicity, with no unexpected safety findings. ClinicalTrials.gov, NCT02650973, February 2016.

The diagnostic value of non-invasive serum liver fibrosis indices in the prediction of portal hypertension in cirrhotic patients: a cross-sectional study

Introduction: Considering that portal hypertension is principally caused by hepatic fibrosis, some studies postulated the predictive value of serum liver fibrosis indices in the diagnosis of portal hypertension. In this study we assessed the prognostic value of serum indices in the diagnosis of portal hypertension in cirrhotic patients. Methods: One hundred two cirrhotic patients were selected according to inclusion/exclusion criteria. Cirrhosis and the Child-Pugh score was determined by the gastroenterologist. Portal hypertension was diagnosed by the radiologist using the color Doppler method. The fasting blood sample was drawn and different serum indices were determined. The following indices were calculated: FIB4, Fibroindex, APRI, FORNS, LOK. Results: The patients mean age was 54.39±6.60 years. About 52.94% of the patients were women. The mean liver enzymes level was higher in patients with portal hypertension. In 50.98% of patients, the etiology of cirrhosis was viral hepatitis. Significant differences was observed between the two groups regarding the studied indices (P&lt;0.05). According to the results of the ROC curve, the FORNS index was a better predictive marker of portal hypertension in cirrhotic patients. The maximum AUC (area under the curve) was for the FORNS index. According to the results of diagnostic tests, the FORNS index had adequate sensitivity and specificity in the diagnosis of portal hypertension with the cut-off value of 8.51. Conclusion: The results of the present study showed that the FORNS index is a better predictor of portal hypertension in cirrhotic patients.

Temporal Lobe Necrosis in Head and Neck Cancer Patients after Proton Therapy to the Skull Base.

Purpose:To demonstrate temporal lobe necrosis (TLN) rate and clinical/dose-volume factors associated with TLN in radiation-naïve patients with head and neck cancer treated with proton therapy where the field of radiation involved the skull base.Materials and Methods:Medical records and dosimetric data for radiation-naïve patients with head and neck cancer receiving proton therapy to the skull base were retrospectively reviewed. Patients with <3 months of follow-up, receiving <45 GyRBE or nonconventional fractionation, and/or no follow-up magnetic resonance imaging (MRI) were excluded. TLN was determined using MRI and graded using Common Terminology Criteria for Adverse Events (CTCAE) v5.0. Clinical (gender, age, comorbidities, concurrent chemotherapy, smoking, radiation techniques) and dose-volume parameters were analyzed for TLN correlation. The receiver operating characteristic curve and area under the curve (AUC) were performed to determine the cutoff points of significant dose-volume parameters.Results:Between 2013 and 2019, 234 patients were included. The median follow-up time was 22.5 months (range = 3.2–69.3). Overall TLN rates of any grade, ≥ grade 2, and ≥ grade 3 were 5.6% (N = 13), 2.1%, and 0.9%, respectively. The estimated 2-year TLN rate was 4.6%, and the 2-year rate of any brain necrosis was 6.8%. The median time to TLN was 20.9 months from proton completion. Absolute volume receiving 40, 50, 60, and 70 GyRBE (absolute volume [aV]); mean and maximum dose received by the temporal lobe; and dose to the 0.5, 1, and 2 cm3 volume receiving the maximum dose (D0.5cm3, D1cm3, and D2cm3, respectively) of the temporal lobe were associated with greater TLN risk while clinical parameters showed no correlation. Among volume parameters, aV50 gave maximum AUC (0.921), and D2cm3 gave the highest AUC (0.935) among dose parameters. The 11-cm3 cutoff value for aV50 and 62 GyRBE for D2cm3 showed maximum specificity and sensitivity.Conclusion:The estimated 2-year TLN rate was 4.6% with a low rate of toxicities ≥grade 3; aV50 ≤11 cm3, D2cm3 ≤62 GyRBE and other cutoff values are suggested as constraints in proton therapy planning to minimize the risk of any grade TLN. Patients whose temporal lobe(s) unavoidably receive higher doses than these thresholds should be carefully followed with MRI after proton therapy.

The relation of skin conductance and pupillary fluctuations assessed by phase-rectified signal averaging.

The reaction of pupil diameter and skin conductance are widely studied marker of the autonomic response to arousing stimuli. However, little is known about their relation at rest. In this study, we used bivariate phase-rectified signal averaging to investigate the relationship of skin conductance fluctuations (SCF) and pupillary fluctuations in 83 healthy volunteers. The onset of each SCF was detected by a pattern matching algorithm. Those time points were corrected for a different neurotransmission delay. Cross-correlation of temporal derivatives of pupil diameter and skin conductance revealed an average time lag of 2.5s. Thus, anchor points 2.5s before an SCF were defined. The pupillary signal following these anchors (7s) were extracted and normalized to baseline 1s prior to the anchor point. Aligned segments were averaged to determine the amplitude and area under the curve (AUC) of characteristic pupil diameter fluctuations (PDF) concurrent to SCF. The same procedure was applied to random time points (non-SCF) with at least 1s distance to actually detected SCF which served as control condition. SCF were accompanied by increases of pupil diameter with a maximum dilation of 9% on average. The one sample t-test indicated an AUC of PDF (70 ± 110 n.u., p<; 0.001). The maximum change and AUC of pupillary reactions were significantly higher when PDF extraction was triggered by actual SCF (both p<; 0.001). Both measures of pupillary dilations contributed significantly to pupillary unrest. Our results suggest that sympathetic arousal as detected by skin conductance fluctuations is accompanied by pupil dilation.