Optimal Agreement Research Articles

A MEA is a MEA is a MEA? Sequential decision making and the impact of different managed entry agreements at the manufacturer and payer level, using a case study for an oncology drug in England.

In a typical single-payer setting that uses an explicit cost-effectiveness (CE) threshold in its decision-making, the payer aims to maximize the net-monetary-benefit (NMB) given the CE threshold, whilst the manufacturer aims to maximize the expected discounted-cash-flow (DCF) resulting from the sales of that technology. Managed entry agreements (MEAs) are tools that are used to improve access to expensive technologies that would otherwise not be deemed to be cost-effective to payers. While simple discount on the list price is the most commonly applied MEA type, there are different forms, each having a different impact on the cost-effectiveness of the technology, on the lifetime DCF-per-patient and on the decision uncertainty. We aim to analyze the sequential decision-making (SDM) of different MEAs (i.e. simple discount, free treatment initiation, lifetime treatment acquisition cost-capping [LTTACC], performance-based money-back guarantee [MBG]) at the manufacturer and at the payer level, respectively. We first model the SDM of the manufacturer and the payer as a sequential game and explain the challenges to find an equilibrium analytically. Then we propose a heuristic computational method to follow for each of the MEA types, based on practice. To demonstrate this SDM on a case study, a UK-based cost-utility analysis using a three-state, partitioned-survival-model was constructed to determine the cost-effectiveness of regorafenib versus best-supportive-care for the second-line treatment of hepatocellular carcinoma. The optimal agreement terms that would maximise the lifetime DCF-per-patient for each MEA, whilst remaining below the CE-threshold (£50,000/QALY gained) were obtained in the deterministic base-case. Robustness for each optimized MEA was then assessed using probabilistic sensitivity and scenario analyses, the value of information (VoI), and HTA-risk analyses. As expected, the introduction of all MEAs improved the probabilistic ICER and NMB values to (almost) acceptable levels, compared to the "no-MEA" case (ICER ~ £78,000/QALY-gained). The expected DCFs across the explored MEAs were all similar, whilst the payer strategy & uncertainty burden (PSUB) for regorafenib decreased in all MEAs explored. VoI analyses revealed that regorafenib mean-dose-intensity and time-on-treatment (ToT) parameters attributed most to the decision uncertainty. LTTACC provided the smallest PSUB and the most robust NMB estimates under parametric uncertainty. For scenarios assuming increased regorafenib ToT or mean-dose-intensity, LTACC again provided acceptable cost-effectiveness outcomes, whereas for scenarios assuming decreased regorafenib progression-free/overall survival effectiveness, only MBG resulted in plausible ICER values. In scenarios, where the source of uncertainty was not targeted by MEA parameters (e.g. the scenario assuming higher progressed disease resource utilization), all investigated MEA types resulted in unacceptable cost-effectiveness outcomes. Each MEA type has a different implication. The impact of different MEAs on the NMB is more noteworthy than on the DCF, in relative terms, hence payers will benefit from the early participation of the MEA design rather than leaving this up to the prerogative of the manufacturer. While simple discount might be practical for implementation purposes, other MEAs can provide additional benefits to the payer in terms of increased NMB, reduced decision risk and reduced uncertainty. MEA performance should be investigated not only under parametric uncertainty, but also under-identified structural uncertainty, and the barriers of implementation should be considered thoroughly before choosing the most appropriate MEA type.

Nomograms for prediction of overall and cancer-specific survival in young breast cancer.

To assess the prognostic risk factors and establish prognostic nomograms based on lymph node ratio (LNR) to predict the survival of young patients with breast cancer (BC). Patients aged < 40years and diagnosed with BC between 2010 and 2016 from the Surveillance, Epidemiology and End Results database were assessed. Nomograms incorporating LNR were constructed to predict overall survival (OS) and breast cancer-specific survival (BCSS) based on Cox proportional hazards model. The performance of the nomograms was assessed by C-index, calibration curves, receiver operating characteristic (ROC) curves, decision curve analysis (DCA), and risk group stratification and compared with the TNM staging system. Based on the univariate and multivariate Cox regression analysis, significant prognostic factors were identified and integrated to create the nomograms for OS and BCSS. The calibration curves indicated optimal agreement between model predictions and actual observations. The nomograms showed favorable sensitivity with a C-index of 0.8351 (95% CI 0.8234-0.8469) for OS and 0.8474 (95% CI 0.8355-0.8594) for BCSS. The ROC curves of the nomograms showed better predictive ability than those of the TNM staging system for OS (AUC: 0.8503 vs. 0.7819) and BSCC (AUC: 0.8607 vs. 0.8081). Significant differences in Kaplan-Meier curves were observed in patients stratified into different risk groups (p < 0.001). These nomograms provided more accurate individualized risk prediction of OS and BCSS and may assist clinicians in making decisions for young patients with BC.

Biomarkers of the Response to Immune Checkpoint Inhibitors in Metastatic Urothelial Carcinoma.

The mechanisms underlying the resistance to immune checkpoint inhibitors (ICIs) therapy in metastatic urothelial carcinoma (mUC) patients are not clear. It is of great significance to discern mUC patients who could benefit from ICI therapy in clinical practice. In this study, we performed machine learning method and selected 10 prognostic genes for constructing the immunotherapy response nomogram for mUC patients. The calibration plot suggested that the nomogram had an optimal agreement with actual observations when predicting the 1- and 1.5-year survival probabilities. The prognostic nomogram had a favorable discrimination of overall survival of mUC patients, with area under the curve values of 0.815, 0.752, and 0.805 for ICI response (ICIR) prediction in the training cohort, testing cohort, and combined cohort, respectively. A further decision curve analysis showed that the prognostic nomogram was superior to either mutation burden or neoantigen burden for overall survival prediction when the threshold probability was >0.35. The immune infiltrate analysis indicated that the low ICIR-Score values in mUC patients were significantly related to CD8+ T cell infiltration and immune checkpoint-associated signatures. We also identified differentially mutated genes, which could act as driver genes and regulate the response to ICI therapy. In conclusion, we developed and validated an immunotherapy-responsive nomogram for mUC patients, which could be conveniently used for the estimate of ICI response and the prediction of overall survival probability for mUC patients.

Prediction Model of HBsAg Seroclearance in Patients with Chronic HBV Infection.

Background Prediction of HBsAg seroclearance, defined as the loss of circulating HBsAg with or without development of antibodies for HBsAg in patients with chronic hepatitis B (CHB), is highly difficult and challenging due to its low incidence. This study is aimed at developing and validating a nomogram for prediction of HBsAg loss in CHB patients. Methods We analyzed a total of 1398 patients with CHB. Two-thirds of the patients were randomly assigned to the training set (n = 918), and one-third were assigned to the validation set (n = 480). Univariate and multivariate analysis by Cox regression analysis was performed using the training set, and the nomogram was constructed. Discrimination and calibration were performed using the training set and validation set. Results On multivariate analysis of the training set, independent factors for HBsAg loss including BMI, HBeAg status, HBsAg titer (quantitative HBsAg), and baseline hepatitis B virus (HBV) DNA level were incorporated into the nomogram. The HBsAg seroclearance calibration curve showed an optimal agreement between predictions by the nomogram and actual observation. The concordance index (C-index) of nomogram was 0.913, with confirmation in the validation set where the C-index was 0.886. Conclusions We established and validated a novel nomogram that can individually predict HBsAg seroclearance and non-seroclearance for CHB patients, which is clinically unprecedented. This practical prognostic model may help clinicians in decision-making and design of clinical studies.

Nomogram for individualized prediction of incident multidrug-resistant tuberculosis after completing pulmonary tuberculosis treatment

The purposes of this study were to construct a comprehensive nomogram for providing a simple, precise and personalized prediction of incident multidrug-resistant tuberculosis (MDR-TB) after completing pulmonary tuberculosis treatment (CPTBT). A matched case–control study (1:2 ratios) was performed between 2005 and 2018. A multivariable Cox regression analysis was used to evaluate independent predictors of incident MDR-TB after the CPTBT. A comprehensive nomogram was developed based on the multivariable Cox model. Overall, 1, 836 participants were included in this study. We developed and validated a simple-to-use nomogram that predicted the individualized risk of incident MDR-TB by using 10 parameters after the CPTBT. The concordance index of this nomogram was 0.833 [95% confidence interval (CI) 0.807–0.859] and 0.871 (95% CI 0.773–0.969) for the training and validation sets, respectively, which indicated adequate discriminatory power. The calibration curves for the risk of incident MDR-TB showed an optimal agreement between nomogram prediction and actual observation in the training and validation sets, respectively. The high sensitivity and specificity of nomogram was indicated by using a receiver operating characteristic curve analysis. Through this clinic tool, TB control executives could more precisely monitor, estimate and intervene the risk of incident MDR-TB among individuals with CPTBT.

Structure/activity virtual screening and in vitro testing of small molecule inhibitors of 8-hydroxy-5-deazaflavin:NADPH oxidoreductase from gut methanogenic bacteria

Virtual screening techniques and in vitro binding/inhibitory assays were used to search within a set of more than 8,000 naturally occurring small ligands for candidate inhibitors of 8-hydroxy-5-deazaflavin:NADPH oxidoreductase (FNO) from Methanobrevibacter smithii, the enzyme that catalyses the bidirectional electron transfer between NADP+ and F420H2 during the intestinal production of CH4 from CO2. In silico screening using molecular docking classified the ligand-enzyme complexes in the range between − 4.9 and − 10.5 kcal/mol. Molecular flexibility, the number of H-bond acceptors and donors, the extent of hydrophobic interactions, and the exposure to the solvent were the major discriminants in determining the affinity of the ligands for FNO. In vitro studies on a group of these ligands selected from the most populated/representative clusters provided quantitative kinetic, equilibrium, and structural information on ligands’ behaviour, in optimal agreement with the predictive computational results.

Energy Performance Contracting and Public-Private Partnership: How to Share Risks and Balance Benefits

Public private partnerships (PPPs) are a well-known instrument used worldwide by public administration (PA) to build public infrastructure using private knowhow and financial resources, and sharing risks. In recent years, PPPs have been widely adopted to develop energy efficiency projects between public and private sectors. In this context, a successful project requires a contractual arrangement based on energy performance contracting (EPC) that balances the interests of the two parties. This paper aims to answer two questions: how to share the benefits between the contractual parties and reach an optimal long-term contractual agreement; and which type of contract ensures a consistent risk transfer to the private partner, allowing the PA an “off balance” accounting treatment. The research questions are answered through the development of a mathematical equation able to calculate the optimal percentage of benefits sharing between partners in a long-term contractual agreement. The results are tested with a simulation based on a case study about the energy efficiency project of an Italian hospital. The paper is innovative because it provides suggestions to improve the EPC-PPP contractual structure and realize a balanced agreement between the public and private partners. Moreover, it analyzes the different allocation of risks in EPC contracts to identify the implication for the PA in terms of on-off balance accounting treatment in energy efficiency investment. We show how a successful long-term EPC-PPP can benefit from a mixed contractual structure in which profit-sharing percentage changes during the contract’s life to ensure the same net present value (NPV) to both public and private partners. This paper supports public decision making in order to choose contracts that are able to transfer energy and management risks. Moreover, it helps to understand the balance between public and private interests in a long-term EPC-PPP contract.

A Novel Prediction Model for Significant Liver Fibrosis in Patients with Chronic Hepatitis B.

Background Preventing liver fibrosis from progressing to cirrhosis and even liver cancer is a key step in the treatment of chronic hepatitis B (CHB). This study is aimed at constructing and validating a new nomogram for predicting significant liver fibrosis (S ≥ 2) in CHB patients. Methods The nomogram was based on a retrospective study of 252 CHB patients. The predictive accuracy and discriminative ability of the nomogram were evaluated by the area under receiver operating characteristic curve (AUROC), decision curves, and calibration curve compared with the fibrosis 4 score (FIB-4) and aspartate aminotransferase-to-platelet ratio index (APRI). The results were validated using bootstrap resampling and an external set of 168 CHB patients. Results A total of 420 CHB patients were enrolled based on liver biopsy results. Independent factors predicting significant liver fibrosis were laminin (LN), procollagen type III N-terminal peptide (PIIINP), and blood platelet count (PLT) in a multivariate analysis, and these factors were selected to construct the nomogram. The calibration curve for the probability of significant liver fibrosis showed optimal agreement between the prediction from the nomogram and actual observation. The prediction from the nomogram was more consistent with the results of liver biopsy than FIB-4 and APRI. The AUROC of the nomogram was higher than that of FIB-4 and APRI for predicting significant liver fibrosis. These results were confirmed in the validation set. Furthermore, the decision curve analysis suggested that the most net benefits were provided by the nomogram. Conclusions We found the proposed nomogram resulted in a more accurate prediction of significant liver fibrosis in CHB patients and could provide the most net benefits. We recommend this noninvasive assessment for patients with liver fibrosis to avoid the risk of liver biopsy and earlier intervention to prevent the development of cirrhosis or liver cancer.

Relationship of Tumor Radiation-absorbed Dose to Survival and Response in Hepatocellular Carcinoma Treated with Transarterial Radioembolization with 90Y in the SARAH Study.

Background Little is known about factors that influence the efficacy of transarterial radioembolization (TARE). Purpose To determine the relationship between tumor radiation-absorbed dose and survival and tumor response in locally advanced inoperable hepatocellular carcinoma treated with TARE. Materials and Methods This was a secondary analysis of prospectively acquired data (between December 2011 and March 2015) from participants who received TARE in the Sorafenib versus Radioembolization in Advanced Hepatocellular Carcinoma (SARAH) trial (ClinicalTrials.gov identifier: NCT01482442). Tumor-absorbed dose was computed using technetium 99m (99mTc) macroaggregated human albumin (MAA) SPECT/CT. Visual agreement among CT, 99mTc-MAA SPECT/CT, and yttrium 90 (90Y) SPECT/CT or PET/CT was scored as optimal, suboptimal, or not optimal. Overall survival (OS) and tumor response at 6-month follow-up CT (Response Evaluation Criteria in Solid Tumors, version 1.1) were assessed. OS was evaluated using Kaplan-Meier tests. A propensity score comparing participants receiving a tumor dose greater than or equal to 100 Gy (best cut-off according to the receiver operating characteristic curve and median tumor radiation-absorbed dose values in the study groups) with those receiving sorafenib was calculated. Results One hundred twenty-one participants (median age, 67 years; interquartile range [IQR]: 61-73 years; 110 men) were evaluated in the dose-survival group, and 109 (median age, 66 years; IQR: 61-71 years; 100 men) were evaluated in the dose-tumor response group. In the dose-survival group, median OS was 9.3 months (95% confidence interval [CI]: 6.7 months, 10.7 months), and median tumor radiation-absorbed dose was 112 Gy (IQR: 68-220 Gy). Participants who received at least 100 Gy (n = 67) had longer survival than those who received less than 100 Gy (median, 14.1 months [95% CI: 9.6 months, 18.6 months] vs 6.1 months [95% CI: 4.9 months, 6.8 months], respectively; P < .001), and those with optimal agreement (n = 24) had the longest median OS (24.9 months; 95% CI: 9.6 months, 33.9 months). In the dose-tumor response group, tumor radiation-absorbed dose was higher in participants with disease control versus those with progressive disease (median, 121 Gy [IQR: 86-190 Gy] vs 85 Gy [IQR: 58-164 Gy]; P = .02). The highest disease control rate was observed in 31 of 40 participants (78%) with a tumor radiation-absorbed dose greater than or equal to 100 Gy and optimal agreement. Conclusion Higher tumor radiation-absorbed dose computed at technetium 99m macroaggregated human albumin SPECT/CT was associated with better overall survival and disease control in hepatocellular carcinoma treated with transarterial radioembolization with yttrium 90 in the Sorafenib versus Radioembolization in Advanced Hepatocellular Carcinoma trial. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Sofocleous and Kamarinos in this issue.

Development and validation of a nomogram for assessing survival in patients with hepatocellular carcinoma after hepatectomy.

Background and aim: Assessing the average survival rate of patients with hepatocellular carcinoma (HCC) after hepatectomy is important for making critical decisions in everyday clinical practice. The present study aims to develop and validate a nomogram for assessing the overall survival probability for such patients.Methods: The putative prognostic indicators for constructing the nomogram were identified using multivariable Cox regression and model selection based on the Akaike information criterion. The nomogram was subjected to internal and external validation. The nomogram endpoints were death within 1, 3, and 5 years.Results: A consecutive sample of 522 HCC patients who underwent potentially curative hepatectomy was retrospectively analyzed. Age, Barcelona clinic liver cancer (BCLC) stage, tumor size, alanine transaminase, alpha fetal protein, and serum prealbumin were included in the final model. The nomogram's discriminative ability was good in the training set (C-index was 0.74 for 1 year, 0.73 for 3 years, 0.70 for 5 years) and was validated using both an internal bootstrap method (C-index was 0.73 for 1 year, 0.72 for 3 years, 0.69 for 5 years) and an external validating set (C-index was 0.72 for 1 year, 0.72 for 3 years, 0.69 for 5 years). The calibration plots for the endpoints showed optimal agreement between the nomogram's assessment and actual observations.Conclusions: The nomogram (an Excel-based tool) can be useful for assessing the probability of survival at 1, 3, and 5 years in patients with HCC after hepatectomy.

Early Detection of Non-Small Cell Lung Cancer by Using a 12-microRNA Panel and a Nomogram for Assistant Diagnosis.

Background: We previously identified a 12-microRNA (miRNA) panel (miRNA-17, miRNA-146a, miRNA-200b, miRNA-182, miRNA-155, miRNA-221, miRNA-205, miRNA-126, miRNA-7, miRNA-21, miRNA-145, and miRNA-210) that aided in the early diagnosis of non-small cell lung cancer (NSCLC). We validated the diagnostic value of this miRNA panel and compared it with that of traditional tumor markers and radiological diagnosis. We constructed a nomogram based on the miRNA panel's results to predict the risk of NSCLC.Methods: Eighty-two participants with pulmonary nodules on a CT scan and who underwent a pathological examination and surgical treatment were enrolled in our study. Patients were randomly divided into a training group or a validation group. The miRNA concentrations were quantified by RT-PCR and log-transformed for analysis. The cutoff value was determined in the training group and then applied in the validation group. A comparison between the miRNAs and traditional tumor markers [CEA, NSE, and cytokeratin 19 fragment 21-1 (Cyfra21-1)] and radiological diagnosis was performed. A nomogram based on the miRNA panel's results to predict the risk of NSCLC was constructed.Results: The expression level of these 12 miRNAs was significantly higher in NSCLC patients than in benign patients. In the validation group, the specificity and positive predictive value were 96.4 and 95.8%, respectively, which were significantly higher than those using traditional tumor markers or radiological diagnosis. The sensitivity was 42.6%, which was also higher than that using tumor markers. Moreover, the sensitivity increased to 63.6% when the nodule diameters were larger than 2 cm. The miRNAs and seven clinical factors were integrated into the nomogram, and the calibration curves showed optimal agreement between the predicted and actual probabilities.Conclusions: Our miRNA panel has clinical value for the early detection of NSCLC. A nomogram was constructed and internally validated, and the results indicate that it can assist clinicians in making treatment recommendations in the clinic.

Physical characterization of a novel wireless DRX Plus 3543C using both a carbon nano tube (CNT) mobile x-ray system and a traditional x-ray system

This work aims to characterize the novel DRX Plus 3543C detector in terms of detective quantum efficiency (DQE) using both a mobile x-ray system called Carestream DRX Revolution Nano and a traditional x-ray system (Carestream DRX Evolution). We used the commercial system DRX Revolution Nano, equipped with a new x-ray source based on CNT technology and field emission (FE) as the electron emitter (cathode). An innovative aspect of this device is its intrinsic selection of the focal spot size. We tested the system using three IEC-specified beam qualities (RQA3, 5 and 7) in terms of modulation transfer function (MTF), normalized noise power spectra (NNPS) and DQE as defined in the IEC 62220-1-1:2015. We compared the results obtained using DRX Revolution Nano and DRX Evolution with correlation and with Bland–Altman plots to study their agreement. RQA3 MTF is slightly lower than the RQA5 and 7 curves between 0.5 and 2.5 cycles mm−1. We measured MTF values of about 0.6 at 1 lp mm−1 and about 0.28 lp mm−1 at 2 lp mm−1. The NNPS curves show a decreasing trend with the energy regarding the DRX Revolution Nano. On the other hand, the DRX Evolution NNPS curve at RQA3 is greater than the one at RQA5, but the one at RQA5 is less than the one at RQA7. The DQE(0) ranged between about 0.82 (DRX Evolution at RQA3) and 0.54 (DRX Evolution at RQA7). As expected, the squared Pearson’s correlation coefficients between the two x-ray tubes were always in an optimal agreement, and Bland–Altman plots confirmed a substantial equivalence between the two physical characterizations of the wireless detector. In conclusion, we can show that the dynamic focal selection of the system equipped with CNT does not play a substantial role in image quality compared to a traditional system in terms of physical characterisation of the detector in our measurement conditions.

Improving Coronal Magnetic Field Models Using Image Optimization

Abstract We have reported previously on our development and testing of a new method for using coronal images to improve coronal magnetic field models. In this technique, which we call image-optimization, coronal magnetic field models are extrapolated from synoptic photospheric magnetograms. The resulting models are then compared to morphological constraints derived from images of the solar corona, and the photospheric magnetograms are perturbed iteratively via an optimization algorithm to achieve optimal agreement with the image-based constraints. Here we present results from the first application of this technique using Mauna Loa Solar Observatory K-Coronagraph images and Global Oscillation Network Group synoptic magnetograms to create optimized models for two time periods, 2014 November 16–29 and 2016 May 16–29. We find that for both time periods the optimization algorithm converges well and results in better agreement between the model and the images, relatively small changes to the synoptic magnetogram, and an overall increase in the amount of open magnetic flux.

Model simulations of atmospheric methane (1997–2016) and their evaluation using NOAA and AGAGE surface and IAGOS-CARIBIC aircraft observations

Abstract. Methane (CH4) is an important greenhouse gas, and its atmospheric budget is determined by interacting sources and sinks in a dynamic global environment. Methane observations indicate that after almost a decade of stagnation, from 2006, a sudden and continuing global mixing ratio increase took place. We applied a general circulation model to simulate the global atmospheric budget, variability, and trends of methane for the period 1997–2016. Using interannually constant CH4 a priori emissions from 11 biogenic and fossil source categories, the model results are compared with observations from 17 Advanced Global Atmospheric Gases Experiment (AGAGE) and National Oceanic and Atmospheric Administration (NOAA) surface stations and intercontinental Civil Aircraft for the Regular observation of the atmosphere Based on an Instrumented Container (CARIBIC) flights, with &gt; 4800 CH4 samples, gathered on &gt; 320 flights in the upper troposphere and lowermost stratosphere. Based on a simple optimization procedure, methane emission categories have been scaled to reduce discrepancies with the observational data for the period 1997–2006. With this approach, the all-station mean dry air mole fraction of 1780 nmol mol−1 could be improved from an a priori root mean square deviation (RMSD) of 1.31 % to just 0.61 %, associated with a coefficient of determination (R2) of 0.79. The simulated a priori interhemispheric difference of 143.12 nmol mol−1 was improved to 131.28 nmol mol−1, which matched the observations quite well (130.82 nmol mol−1). Analogously, aircraft measurements were reproduced well, with a global RMSD of 1.1 % for the measurements before 2007, with even better results on a regional level (e.g., over India, with an RMSD of 0.98 % and R2=0.65). With regard to emission optimization, this implied a 30.2 Tg CH4 yr−1 reduction in predominantly fossil-fuel-related emissions and a 28.7 Tg CH4 yr−1 increase of biogenic sources. With the same methodology, the CH4 growth that started in 2007 and continued almost linearly through 2013 was investigated, exploring the contributions by four potential causes, namely biogenic emissions from tropical wetlands, from agriculture including ruminant animals, and from rice cultivation, and anthropogenic emissions (fossil fuel sources, e.g., shale gas fracking) in North America. The optimization procedure adopted in this work showed that an increase in emissions from shale gas (7.67 Tg yr−1), rice cultivation (7.15 Tg yr−1), and tropical wetlands (0.58 Tg yr−1) for the period 2006–2013 leads to an optimal agreement (i.e., lowest RMSD) between model results and observations.

A Tool for Early Prediction of Severe Coronavirus Disease 2019 (COVID-19): A Multicenter Study Using the Risk Nomogram in Wuhan and Guangdong, China

BackgroundDue to no reliable risk stratification tool for severe coronavirus disease 2019 (COVID-19) patients at admission, we aimed to construct an effective model for early identification of cases at high risk of progression to severe COVID-19.MethodsIn this retrospective three-centers study, 372 non-severe COVID-19 patients during hospitalization were followed for more than 15 days after admission. Patients who deteriorated to severe or critical COVID-19 and patients who kept non-severe state were assigned to the severe and non-severe group, respectively. Based on baseline data of the two groups, we constructed a risk prediction nomogram for severe COVID-19 and evaluated its performance.ResultsThe training cohort consisted of 189 patients, while the two independent validation cohorts consisted of 165 and 18 patients. Among all cases, 72 (19.35%) patients developed severe COVID-19. We found that old age, and higher serum lactate dehydrogenase, C-reactive protein, the coefficient of variation of red blood cell distribution width, blood urea nitrogen, direct bilirubin, lower albumin, are associated with severe COVID-19. We generated the nomogram for early identifying severe COVID-19 in the training cohort (AUC 0.912 [95% CI 0.846-0.978], sensitivity 85.71%, specificity 87.58%); in validation cohort (0.853 [0.790-0.916], 77.5%, 78.4%). The calibration curve for probability of severe COVID-19 showed optimal agreement between prediction by nomogram and actual observation. Decision curve and clinical impact curve analysis indicated that nomogram conferred high clinical net benefit.ConclusionOur nomogram could help clinicians to early identify patients who will exacerbate to severe COVID-19, which will enable better centralized management and early treatment of severe patients.

Development and validation of nomograms for predicting overall and cancer-specific survival in young patients with non-small cell lung cancer.

BackgroundYoung patients with non-small cell lung cancer (NSCLC) represent a distinct subgroup of patients with this disease. This study aimed to construct nomograms to predict the overall survival (OS) and cancer-specific survival (CSS) of young patients with NSCLC.MethodsNSCLC patients under 50 years old diagnosed between 2010 and 2016 were selected from the Surveillance, Epidemiology, and End Results (SEER) database and randomly divided into training (n=1,357) and validation (n=678) cohorts at a ratio of 2:1. Independent prognostic factors for OS or CSS were identified through the log-rank test, Cox proportional hazards models or competing risk model and further integrated to construct nomograms. The predictive capability of the nomogram was assessed by Harrell’s concordance index (C-index), the calibration curve and risk group stratification.ResultsA total of 2,035 patients were enrolled. In the training cohort, insurance, marital status, histological type, grade, T stage, N stage and surgery were identified as independent prognostic for OS and CSS. The C-index value were 0.759 [95% confidence interval (CI): 0.731–0.787] for OS and 0.810 (95% CI: 0.803–0.818) for BCSS in the training cohort and 0.751 (95% CI: 0.711–0.790) for OS and 0.807 (95% CI: 0.795–0.819) for CSS in the validation cohort. The calibration curves showed optimal agreement between the predicted and actual survival both in internal and external validation. In addition, patients in the validation cohort within different risk groups exhibited significantly different survival even in each TNM stage.ConclusionsNomograms were developed and validated to predict OS and CSS of young patients with NSCLC in our study. A prospective study with more potential prognostic factors and the latest TNM classification is required to ameliorate this model.

A BOA-based adaptive strategy with multi-party perspective for automated multilateral negotiations

Determining an effective strategy for intelligent agents in multilateral negotiations is a more complicated problem than in bilateral negotiations. In order to achieve an optimal and beneficial agreement the agent needs to consider the behavior and desired utility of more than one opponent, determine a concession tactic based on a smaller agreement space, and use a computationally efficient mechanism for generating optimal offers. However, a mere extension of bilateral negotiation strategies cannot be effective in multilateral negotiations because the nature of most bilateral negotiation strategies is based on interaction with only one opponent and tracking a single behavior during the negotiation process. In this paper, we propose an adaptive approach based on a multi-party perspective to determine multilateral negotiation strategy. The proposed approach applies the BOA framework (Bidding, Opponent model, and Acceptance) and dynamically models the opponents’ preference profiles. In order to estimate the obtainable utility from opponents and help find a good offer, the agent uses an ensemble model made by individual frequency-based opponent models and a different level of attention to each party’s behavior. The proposed approach also implements a bidding strategy which applies the opponents’ desirable utility to adapt the agent’s concession tactic and produce appropriate offers. The results of experimental evaluations on various negotiation scenarios against the state of the art multilateral negotiation strategies show that our proposed strategy can provide superior performance in both individual utility and social welfare and lead to more optimal and fairer agreements.

Testing different thresholds for patient global assessment in defining remission for rheumatoid arthritis: are the current ACR/EULAR Boolean criteria optimal?

ObjectivesThis study aimed to evaluate different patient global assessment (PGA) cut-offs required in the American College of Rheumatology/European League Against Rheumatism (ACR/EULAR) Boolean remission definition for their utility in rheumatoid...

Performance of TDDFT Vertical Excitation Energies of Core-Substituted Naphthalene Diimides.

We have evaluated the performance of various density functionals, covering generalized gradient approximation (GGA), global hybrid (GH) and range‐separated hybrid (RSH), using time dependent density functional theory (TDDFT) for computing vertical excitation energies against experimental absorption maximum (λmax) for a set of 10 different core‐substituted naphthalene diimides (cNDI) recorded in dichloromethane. The computed excitation in case of GH PBE0 is most accurate while the trend is most systematic with RSH LCY‐BLYP compared to λmax. We highlight the importance of including solvent effects for optimal agreement with the λmax. Increasing the basis set size from TZ2P to QZ4P has a negligible influence on the computed excitation energies. Notably, RSH CAMY‐B3LYP gave the least error for charge‐transfer excitation. The poorest agreement with λmax is obtained with semi‐local GGA functionals. Use of the optimally‐tuned RSH LCY‐BLYP* is not recommended because of the high computational cost and marginal improvement in results.

A methodology for obtaining primary and secondary creep characteristics from indentation experiments, using a recess

A procedure is described for Indentation Creep Plastometery (using a spherical indenter), which is analogous to that developed previously for Indentation Plastometry. As in that case, it is based on iterative numerical simulation of the indentation process, with repeated comparison between an experimental outcome and the corresponding model prediction, systematically varying the values of parameters in a constitutive law until optimal agreement is achieved. The constitutive law used here is the Miller–Norton relationship, which covers both primary and secondary creep regimes (although the transition between them is not well-defined). The experimental outcome is the penetration depth as a function of time, under a constant applied load. An important feature of the procedure is the prior creation of a spherical recess in the sample, having a pre-selected depth and a curvature radius equal to that of the indenter. This allows control over the stress levels created during the indentation creep testing and can be used to ensure that no (time-independent) plastic deformation is stimulated during the test. In the absence of such a recess, this is virtually unavoidable, since the stress levels created during initial contact between a spherical indenter and a flat surface tend to be very high. Such plasticity introduces unwanted complications into creep testing. Confirmation of the viability of the procedure is provided via comparisons between the creep characteristics of pure nickel samples at 750 °C, obtained in this way and via conventional uniaxial tensile testing.