Greater Net Benefit Research Articles

Selecting among land sparing, sharing and Triad in a temperate rainforest depends on biodiversity and timber production targets

Abstract As demand for wood products increases in step with global population growth, balancing the potentially competing values of biodiversity conservation, carbon storage and timber production is a major challenge. Land sparing involves conserving forest while growing timber in intensively managed areas. On the other hand, land sharing utilizes ecological forestry approaches, but with a larger management footprint due to lower yields. While the sparing‐sharing framework has been widely tested and debated in agricultural settings to balance competing values, such land‐allocation strategies have been rarely studied in forestry. We examined whether a sparing, sharing or Triad strategy best achieves multiple forest objectives simultaneously. In Triad, management units (stands) in forest landscapes are allocated to one of three treatments: reserve (where conservation is the sole objective), intensive (timber production is the sole objective) and ecological (both objectives are combined). To our knowledge, ours is the first Triad study from the temperate zone to quantify direct measures of biodiversity (e.g. species' abundance). Using a commonly utilized forest planning tool parameterized with empirical data, we modelled the capacity of a temperate rainforest to provide multiple ecosystem services (biodiversity, carbon storage, timber production and old‐growth forest structure) over 125 years based on 43 different allocation scenarios. We then quantified trade‐offs between scenarios, taking into account the landscape structure, and determined which strategies most consistently balanced ecosystem services. Sparing strategies were best when the services provided by both old‐growth and early seral (young) forests were prioritized, but at a cost to species associated with mid‐seral stages, which benefitted most from Triad and sharing strategies. Therefore, sparing provides the greatest net benefit, particularly given that old‐growth‐associated species and ecosystem services are currently of the greatest conservation concern. Synthesis and applications. We found that maximizing multiple elements of biodiversity and ecosystem services simultaneously with a single management strategy was elusive. The strategy that maximized each service and species varied greatly by both the service and the level of timber production. Fortunately, a diversity of management options can produce the same wood supply, providing ample decision space for establishing priorities and evaluating trade‐offs.

Perioperative parameters-based prediction model for acute kidney injury in Chinese population following valvular surgery.

Acute kidney injury (AKI) is a relevant complication after cardiac surgery and is associated with significant morbidity and mortality. Existing risk prediction tools have certain limitations and perform poorly in the Chinese population. We aimed to develop prediction models for AKI after valvular cardiac surgery in the Chinese population. Models were developed from a retrospective cohort of patients undergoing valve surgery from December 2013 to November 2018. Three models were developed to predict all-stage, or moderate to severe AKI, as diagnosed according to Kidney Disease: Improving Global Outcomes (KDIGO) based on patient characteristics and perioperative variables. Models were developed based on lasso logistics regression (LLR), random forest (RF), and extreme gradient boosting (XGboost). The accuracy was compared among three models and against the previously published reference AKICS score. A total of 3,392 patients (mean [SD] age, 50.1 [11.3] years; 1787 [52.7%] male) were identified during the study period. The development of AKI was recorded in 50.5% of patients undergoing valve surgery. In the internal validation testing set, the LLR model marginally improved discrimination (C statistic, 0.7; 95% CI, 0.66-0.73) compared with two machine learning models, RF (C statistic, 0.69; 95% CI, 0.65-0.72) and XGBoost (C statistic, 0.66; 95% CI, 0.63-0.70). A better calibration was also found in the LLR, with a greater net benefit, especially for the higher probabilities as indicated in the decision curve analysis. All three newly developed models outperformed the reference AKICS score. Among the Chinese population undergoing CPB-assisted valvular cardiac surgery, prediction models based on perioperative variables were developed. The LLR model demonstrated the best predictive performance was selected for predicting all-stage AKI after surgery. Trial registration: Clinicaltrials.gov, NCT04237636.

Transit Benefit Index: A Comprehensive Index for Capturing Externalities in Transit Planning

This research provides a methodology for estimating the total societal benefit generated from substituting private vehicle trips with public transportation trips. The external costs of private and public transportation were estimated using a base case travel demand model and then a mode shift was simulated to calculate the effects of shifting one full transit unit (e.g., bus) of demand from a private to public mode. This shift was performed for all origin–destination (O-D) pairs in a region to find the O-D pairs that resulted in the greatest net benefit. These benefits were then normalized using the total automobile vehicle kilometers traveled removed from the network to generate a “transit benefit index.” This methodology was applied to a case study of the city of Bogotá, Colombia. A total of 102 scenarios were simulated: a 2 in base case, and 10 total sensitivity analyses, each including two transit provision alternatives. The results were contrasted with the cost of a new transit unit—a new bus in this case—revealing that the total economic benefit derived from 1 year of increased transit ridership was larger than the financial cost of a new bus to the transit operator. These results suggest that the City of Bogotá should consider further subsidies to transit fares to increase ridership and mitigate externalities.

Development and validation of a nomogram for predicting survival in patients with pancreatic ductal adenocarcinoma after radical pancreatoduodenectomy

Abstract Objectives Hypercoagulation and malnutrition are the characteristic pathophysiological changes associated with pancreatic ductal adenocarcinoma (PDAC), which are intimately related to cancer progression and prognosis. We aimed to integrate related indicators to build a nomogram model to predict the overall survival (OS) of PDAC patients underwent radical pancreatoduodenectomy (PD). Methods Clinicopathological and survival data of 138 patients were retrospectively analyzed according to inclusion and exclusion criteria. A nomogram was built based on the multivariate Cox regression analysis. The receiver operating characteristic curve (ROC) and calibration curves were performed based on the bootstrap method to evaluate the predictive performance of the nomogram. Decision curve analysis (DCA) was performed to assess the clinical usefulness of the nomogram. Results High-grade tumor (Hazard ratio [HR]: 3.70; 95% confidence interval [CI]: 1.51–3.82; p<0.001), vessel carcinoma embolus (HR: 2.69; 95% CI: 1.30–5.31, p=0.007), N2 (HR: 2.90; 95% CI: 1.47–7.37; p=0.004), anemia (HR: 1.98; 95% CI: 1.01–2.70; p=0.047), PLR>244.8 (HR: 2.13; 95% CI: 1.05–3.45; p=0.033), FBG>3.50 g/L (HR: 2.10; 95% CI: 1.04–3.09, p=0.008), and DRR>1.1 (HR: 2.69; 95% CI: 1.56–4.27; p<0.001) served as independent risk factors for poor OS of patients with PDAC underwent radical PD and were implemented to construct a nomogram. The area under curve (AUCs) for the first, second, and third years were 0.713, 0.777, and 0.845, respectively. Besides, calibration curves fitted well to the ideal line. DCA shows that the nomogram has greater net benefit than the existing TNM staging system, suggesting that this model is a more practical clinical tool for predicting the prognosis of PDAC patients. Conclusions The nomogram we established based on the characteristic pathophysiological alterations of PDAC for predicting OS in patients who underwent radical pancreatoduodenectomy presented considerable predictive power. It may facilitate prognostic risk stratification and optimize therapeutic decision-making.

Nomogram based on spectral CT quantitative parameters and typical radiological features for distinguishing benign from malignant thyroid micro-nodules

PurposeTo analyse the predictive effect of a nomogram combining dual-layer spectral computed tomography (DSCT) quantitative parameters with typical radiological features in distinguishing benign micro-nodule from thyroid microcarcinoma (TMC).MethodsData from 342 instances with thyroid micro-nodules (≤1 cm) who underwent DSCT (benign group: n = 170; malignant group: n = 172) were reviewed. Typical radiological features including micro-calcification and enhanced blurring, and DSCT quantitative parameters including attenuation on virtual monoenergetic images (40 keV, 70 keV and 100 keV), the slope of the spectral HU curve (λHU), normalized iodine concentration (NIC), and normalized effective atomic number (NZeff) in the arterial phase (AP) and venous phase (VP), were measured and compared between the benign and malignant groups. The receiver operating characteristic (ROC) curve was used to assess the diagnostic performance of significant quantitative DSCT parameters or the models combining DSCT parameters respectively and typical radiological features based on multivariate logistic regression (LR) analysis. A nomogram was developed using predictors with the highest diagnostic performance in the above model, as determined by multivariate LR analysis.ResultsThe DSCT parameter APλHU showed the greatest diagnostic efficiency in identifying patients with TMC, with an area under the ROC curve (AUC) of 0.829, a sensitivity and specificity of 0.738 and 0.753, respectively. Then, APλHU was combined with the two radiological features to construct the DSCT-Radiological nomogram, which had an AUC of 0.858, a sensitivity of 0.791 and a specificity of 0.800. The calibration curve of the nomogram demonstrated that the prediction result was in good agreement with the actual observation. The decision curve revealed that the nomogram can result in a greater net benefit than the all/none-intervention strategy for all threshold probabilities.ConclusionAs a valid and visual noninvasive prediction tool, the DSCT-Radiological nomogram incorporating DSCT quantitative parameters and radiological features shows favourable predictive efficiency for identifying benign and malignant thyroid micro-nodules.

A Clinical-Radiomics Model of Preoperative Prediction IMN Status of Breast Cancer Patients

<h3>Purpose/Objective(s)</h3> To establish a prediction model based on clinicopathological factors and dynamic contrast enhanced (DCE)-MRI radiomics features, and to make a non-invasive prediction of IMN metastasis risk before operation. <h3>Materials/Methods</h3> The DCE-MR images and clinicopathological data of 124 breast cancer patients who had undergone internal mammary sentinel lymph node biopsy and / or internal mammary lymph node (IMN)dissection with IMN negative on preoperative images from January 2013 to December 2019 were obtained. All patients received plain breast scan and dynamic contrast-enhanced MR in Shandong Cancer Hospital before operation. In this study, three types of predictive models were established, which are based on clinicopathological risk factors, DCE-MRI imaging features and the combination of the two: 1) Clinical model: a logistic regression model based on clinicopathological factors was developed to predict ALN status. 2) Radiomics model: radiomics model based on radiomics score (RS) score is developed. 3) Clinical-radiomics model: combining the imaging features with the independent risk factors for independent prediction of IMN status, the multiple logistic regression method was used to establish a nomogram for predicting the risk of IMN metastasis. All models were evaluated by ROC and AUC, and clinical decision analysis curves was used to evaluate the clinical application value of the model. <h3>Results</h3> Three variables including primary tumor location, PR status and axillary lymph node metastasis status were included in the clinical model, and the AUC was 0.913. 150 imaging features were extracted from DCE-MRI images of primary tumors. Finally, 4 imaging features were selected to participate in the development of RS. Finally, the AUC of the imaging model combined with DCE-MRI imaging features of IMN was 0.831. The clinical-radiomics model with an AUC of 0.964. Delong test showed that, there was no significant difference in ROC curve between clinical model and radiomics model (p=0.1337) , but there were significant differences in ROC curve between clinical model and clinical-radiomics model, and between clinical model and clinical-radiomics model (p<0.05). DCA curve analysis shows that the application of clinical-radiomics model has the greatest net benefit, optimal predictive efficiency and better distinguishing ability, which is better than clinical model and radiomics model. <h3>Conclusion</h3> Clinical-radiomics model is helpful to evaluate the IMN metastasis status of breast cancer patients before operation.

Joint Planning of Offshore Wind Power Storage and Transmission Considering Carbon Emission Reduction Benefits

There are two situations of transmission redundancy and transmission congestion when large-scale offshore wind farms send power out. The energy storage system can store the power blocked by wind power due to insufficient transmission capacity and release it in the period when the wind power output level is low. In this paper, a full-life-cycle cost model is established for energy storage, and a joint planning model for offshore wind power storage and transmission considering carbon emission reduction benefits is established, which integrates power grid transmission benefits, carbon emission reduction benefits, energy storage construction costs, transmission project construction costs and wind abandonment penalty costs. The channel construction and energy storage configuration scheme with the greatest net benefit can be obtained. The relationship between the transmission channel capacity setting and the energy storage parameter configuration under this model is studied, and the combined effect of transmission channel and energy storage system in improving the level of wind power transmission is analyzed. The sensitivity analysis of the influence of the optimal storage and transmission planning scheme of the offshore wind farms is carried out from the perspectives of transmission line engineering cost and transmission channel curve type.

Development and validation of an early warning model for hospitalized COVID-19 patients: a multi-center retrospective cohort study

BackgroundTimely identification of deteriorating COVID-19 patients is needed to guide changes in clinical management and admission to intensive care units (ICUs). There is significant concern that widely used Early warning scores (EWSs) underestimate illness severity in COVID-19 patients and therefore, we developed an early warning model specifically for COVID-19 patients.MethodsWe retrospectively collected electronic medical record data to extract predictors and used these to fit a random forest model. To simulate the situation in which the model would have been developed after the first and implemented during the second COVID-19 ‘wave’ in the Netherlands, we performed a temporal validation by splitting all included patients into groups admitted before and after August 1, 2020. Furthermore, we propose a method for dynamic model updating to retain model performance over time. We evaluated model discrimination and calibration, performed a decision curve analysis, and quantified the importance of predictors using SHapley Additive exPlanations values.ResultsWe included 3514 COVID-19 patient admissions from six Dutch hospitals between February 2020 and May 2021, and included a total of 18 predictors for model fitting. The model showed a higher discriminative performance in terms of partial area under the receiver operating characteristic curve (0.82 [0.80–0.84]) compared to the National early warning score (0.72 [0.69–0.74]) and the Modified early warning score (0.67 [0.65–0.69]), a greater net benefit over a range of clinically relevant model thresholds, and relatively good calibration (intercept = 0.03 [− 0.09 to 0.14], slope = 0.79 [0.73–0.86]).ConclusionsThis study shows the potential benefit of moving from early warning models for the general inpatient population to models for specific patient groups. Further (independent) validation of the model is needed.

Post-RFS supports for cellulosic ethanol: Evaluation of economic and environmental impacts of alternative policies

Cellulosic ethanol is a sustainable and low-emission transportation fuel, but production has been low in the past years. Existing policies, including the Renewable Fuel Standard (RFS) and producers’ tax credit, have been driving the market for cellulosic ethanol. But they are expected to expire at the end of 2022 and their efficacy is compromised by multiple waivers and exemptions. This study models potential regulatory and financial policy instruments and evaluates their cost-effectiveness in supporting cellulosic ethanol in the post-RFS era using the National Energy Modeling System (NEMS), a hybrid computational general equilibrium model of the U.S. energy market. The analysis suggests that the high volumetric mandate and financial subsidy will increase cellulosic ethanol production from 20.78 million liters (ML) to 57.84–65.94 ML in 2050. When policies are bundled, they will boost production reaching 63.29–65.94 ML more quickly than a singular policy. Finally, cost-benefit analysis and sensitivity analysis were employed to study the tradeoff of economic cost and environmental benefits of the policies. The result finds that the high mandate scenario has the greatest net benefit, which is more cost-effective than other policy instruments.

Development and validation of a nomogram to predict the 30-day mortality risk of patients with intracerebral hemorrhage

BackgroundIntracerebral hemorrhage (ICH) is a stroke syndrome with an unfavorable prognosis. Currently, there is no comprehensive clinical indicator for mortality prediction of ICH patients. The purpose of our study was to construct and evaluate a nomogram for predicting the 30-day mortality risk of ICH patients.MethodsICH patients were extracted from the MIMIC-III database according to the ICD-9 code and randomly divided into training and verification cohorts. The least absolute shrinkage and selection operator (LASSO) method and multivariate logistic regression were applied to determine independent risk factors. These risk factors were used to construct a nomogram model for predicting the 30-day mortality risk of ICH patients. The nomogram was verified by the area under the receiver operating characteristic curve (AUC), integrated discrimination improvement (IDI), net reclassification improvement (NRI), and decision curve analysis (DCA).ResultsA total of 890 ICH patients were included in the study. Logistic regression analysis revealed that age (OR = 1.05, P < 0.001), Glasgow Coma Scale score (OR = 0.91, P < 0.001), creatinine (OR = 1.30, P < 0.001), white blood cell count (OR = 1.10, P < 0.001), temperature (OR = 1.73, P < 0.001), glucose (OR = 1.01, P < 0.001), urine output (OR = 1.00, P = 0.020), and bleeding volume (OR = 1.02, P < 0.001) were independent risk factors for 30-day mortality of ICH patients. The calibration curve indicated that the nomogram was well calibrated. When predicting the 30-day mortality risk, the nomogram exhibited good discrimination in the training and validation cohorts (C-index: 0.782 and 0.778, respectively). The AUCs were 0.778, 0.733, and 0.728 for the nomogram, Simplified Acute Physiology Score II (SAPSII), and Oxford Acute Severity of Illness Score (OASIS), respectively, in the validation cohort. The IDI and NRI calculations and DCA analysis revealed that the nomogram model had a greater net benefit than the SAPSII and OASIS scoring systems.ConclusionThis study identified independent risk factors for 30-day mortality of ICH patients and constructed a predictive nomogram model, which may help to improve the prognosis of ICH patients.

Predicting complications of spine surgery: external validation of three models

BACKGROUND CONTEXTNumerous prediction tools are available for estimating postoperative risk following spine surgery. External validation and comparison of these tools is critical prior to clinical use. No model for adverse events after spine surgery has undergone decision curve analysis. PURPOSEExternal validation, comparison, and decision curve analysis of 3 previously described models [SpineSage, Risk Assessment Tool (RAT), National Surgical Quality Improvement Program Risk Calculator (NSQIP)] for predicting 30-day postoperative complications after spine surgery STUDY DESIGNRetrospective cohort study. PATIENT SAMPLEThree hundred fifteen patients who underwent spine surgery at a tertiary academic surgical center in New Zealand between January 2019 and April 2020. OUTCOME MEASURESAs defined by each risk prediction tool and objectively using the Comprehensive Complication Index. METHODSWe retrospectively reviewed risk of postoperative complication was calculated for each patient according to the 3 models. Overall model fit, calibration, discrimination, and decision curve analysis for each model were assessed in line with the transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) guidelines. RESULTS100 (35%) patients experienced complications. SpineSage and RAT were well calibrated, NSQIP systematically underestimated risk. Area under the curve was greatest for SpineSage (0.75) compared with the NSQIP (0.72) and the RAT (0.69). Decision curve analysis showed SpineSage resulted in greatest net benefit across all risk thresholds. CONCLUSIONSOf the models studied, SpineSage most accurately predicted risk and can be expected to perform better than a strategy of treating all patients if patient or surgeon deem complication risk >10% significant. NSQIP may not be suitable for the clinical use in our local population.

A nomogram to predict left atrial appendage thrombus and spontaneous echo contrast in non-valvular atrial fibrillation patients

BackgroundNon-valvular atrial fibrillation (NVAF) significantly increases the risk of stroke. Although there is availability of prediction models, their ability to predict the risk of stroke in NVAF patients remains suboptimal. Therefore, there is need to improve prediction of high-risk individuals, which is critical for efficient management of patients with NVAF.ObjectiveThe objective of our paper is to develop a nomogram for predicting the risk of left atrial appendage thrombus (LAAT) and spontaneous echo contrast (SEC), thereby replacing the risk of stroke in NVAF patients.DesignThis was a retrospective cohort study that analyzed clinical data and echocardiographic indices of 387 patients with NVAF from October 2018 to June 2021. Multivariable logistic regression was used to evaluate independent factors that were used to construct the prediction nomogram.AnalysisThe discriminative ability and calibration of the nomogram to predict LAAT/SEC were tested using C-statistic and calibration plot. The performance of the nomogram was assessed against the CHA2DS2 score, CHA2DS2-VASc score and ATRIA score using the receiver operating characteristic curve (ROC), decision curve analysis (DCA), integrated discrimination index (IDI) and net reclassification index (NRI).ResultOut of the total 387 patients enrolled in this study, 232 had LAAT/SEC. Multivariable analyses demonstrated that N-terminal pro-B-type natriuretic peptide (NT-proBNP), albumin (ALB), LAA ejection fraction (LAAEF) and LAA global peak longitudinal strain (LAA GPLS) were independent predictors of LAAT/SEC. The constructed nomogram had good discriminative (C = 0.886) and calibration (0.876) abilities after bias correction by the C-index. Compared with other models, the decision curve analyses demonstrated that the nomogram had greater net benefits. Besides, the nomogram had significant improvement in predictive performance, sensitivity and reclassification for LAAT/SEC compared with the CHA2DS2 [(c-index: 0.886 vs. 0.576, p < 0.05), (NRI: 0.539, p < 0.05), (IDI: 0.432, p < 0.05)], CHA2DS2-VASc [(c-index: 0.886 vs0.579, p < 0.05), (NRI: 0.513, p < 0.05), (IDI: 0.432, p < 0.05)] or ATRIA [(c-index: 0.886 vs0.583, p < 0.05), (NRI: 0.546, p < 0.05), (IDI: 0.432, p < 0.05)].ConclusionTaken together, our data demonstrated that the developed nomogram was effective and had potential clinical application in the prediction of LAAT/SEC in patients with NVAF.

Evaluating future risk of NAFLD in adolescents: a prediction and decision curve analysis

BackgroundNon-alcoholic fatty liver disease (NAFLD) is the commonest liver condition in the western world and is directly linked to obesity and the metabolic syndrome. Elevated body mass index is regarded as a major risk factor of NAFL (steatosis) and NAFLD fibrosis. Using data from the Avon Longitudinal Study of Parents and Children (ALSPAC), we sought to investigate whether other variables from adolescence could improve prediction of future NAFL and NAFLD fibrosis risk at 24 years, above BMI and sex.MethodsAged 24 years, 4018 ALSPAC participants had transient elastography (TE) and controlled attenuation parameter (CAP) measurement using Echosens 502 Touch. 513 participants with harmful alcohol consumption were excluded. Logistic regression models examined which variables measured at 17 years were predictive of NAFL and NAFLD fibrosis in young adults. Predictors included sex, BMI, central adiposity, lipid profile, blood pressure, liver function tests, homeostatic model assessment for insulin resistance (HOMA-IR), and ultrasound defined NAFL at 17 years (when examining fibrosis outcomes). A model including all these variables was termed “routine clinical measures”. Models were compared using area under the receiver operator curve (AUROC) and Bayesian Information Criterion (BIC), analysis, which penalises model complexity. Models were tested in all participants and those with overweight or obese standardised BMIs (BMI SDS) centiles at the 17-year time point. A decision curve analysis (DCA) was performed to evaluate the clinical utility of models in overweight and obese adolescents predicting NAFLD fibrosis at a threshold probability of 0.1.ResultsThe “routine clinical measures” model had the highest AUROC for predicting NAFL in all adolescent participants (AUROC 0.79 [SD 0.00]) and those with an overweight/obese BMI SDS centile (AUROC 0.77 [SD 0.01]). According to BIC analysis, insulin resistance was the best predictor of NAFL in all adolescents, whilst central adiposity was the best predictor in those with an overweight/obese BMI SDS centile. The “routine clinical measures” model also had the highest AUROC for predicting NAFLD fibrosis in all adolescent participants (AUROC 0.78 [SD 0.02]) and participants with an overweight/obese BMI SDS centile (AUROC 0.84 [SD 0.03]). However, following BIC analysis, BMI was the best predictor of NAFLD fibrosis in all adolescents including those with an overweight/obese BMI SDS centile. A decision curve analysis examining overweight/obese adolescent participants showed the model that had the greatest net benefit for increased NAFLD fibrosis detection, above a treat all overweight and obese adolescents’ assumption, was the “routine clinical measures” model. However, the net benefit was marginal (0.0054 [0.0034–0.0075]).ConclusionIn adolescents, routine clinical measures were not superior to central adiposity and BMI at predicting NAFL and NAFLD fibrosis respectively in young adulthood. Additional routine clinical measurements do provide incremental benefit in detecting true positive fibrosis cases, but the benefit is small. Thus, to reduce morbidity and mortality associated with NASH cirrhosis in adults, the ultimate end point of NAFLD, the focus must be on obesity management at a population level.

Safety and Efficacy of Selective, Clopidogrel-Based Strategies in Acute Coronary Syndrome: A Study-Level Meta-analysis.

To investigate outcomes with selective, clopidogrel-based therapies versus conventional treatment in patients undergoing percutaneous coronary intervention (PCI), especially for acute coronary syndrome. Safety and efficacy of alternative, selective, clopidogrel-based therapies after PCI are not robustly established. We performed a study-level meta-analysis on six randomized trials investigating selective clopidogrel-based therapies (three on unguided de-escalation, N = 3,473; three on guided clopidogrel therapy, N = 7,533). Control groups received ticagrelor or prasugrel treatment. Main endpoints were major bleeding, any bleeding, major adverse cardiovascular events (MACE), and net clinical endpoint. The incidence of major bleeding and MACE was similar in the selective, clopidogrel-based therapy versus the conventional treatment arm (odds ratio [OR]: 0.72, 95% confidence interval [CI]: 0.51-1.01, p = 0.06; OR: 0.93, 0.72-1.20, p = 0.58; respectively). The rates of any bleeding were lower in the selective, clopidogrel-based therapy versus conventional treatment group (OR: 0.57, 95% CI: 0.40-0.80, p = 0.001); this greater safety was significant for unguided de-escalation (OR: 0.43, 95% CI: 0.32-0.58, p = 0.00001) and nonsignificant for guided clopidogrel therapy (OR: 0.72, 95% CI: 0.51-1.02, p = 0.07; p for interaction: 0.03). The incidence of the net clinical endpoint was fewer in the selective, clopidogrel-based therapy versus the conventional treatment arm (OR: 0.59, 95% CI: 0.41-0.85, p = 0.004); this benefit was significant for unguided de-escalation (OR: 0.50, 95% CI: 0.39-0.64, p < 0.00001) and nonsignificant for guided clopidogrel therapy (OR 0.85, 95% CI: 0.62-1.16, p = 0.30; p for interaction: 0.01). As compared with prasugrel/ticagrelor treatment, alternative, selective, clopidogrel-based approaches provide a similar protection from cardiovascular events, reduce the risk of any bleeding, and are associated with a greater net benefit. These beneficial effects were prevalent with unguided de-escalation to clopidogrel.

Utility of pre-operative haemoglobin concentration to guide peri-operative blood tests for hip and knee arthroplasty: A decision curve analysis.

ObjectiveAssess the prognostic value of pre‐operative haemoglobin concentration (Hb) for identifying patients who develop severe post‐operative anaemia or require blood transfusion following primary total hip or knee, or unicompartmental knee arthroplasty (THA, TKA, UKA).BackgroundPre‐operative group and save (G&S), and post‐operative Hb measurement may be unnecessary for many patients undergoing hip and knee arthroplasty provided individuals at greatest risk of severe post‐operative anaemia can be identified.Methods and MaterialsPatients undergoing THA, TKA, or UKA between 2011 and 2018 were included. Outcomes were post‐operative Hb below 70 and 80 g/L, and peri‐operative blood transfusion. Logistic regression assessed the association between pre‐operative Hb and each outcome. Decision curve analysis compared strategies for selecting patients for G&S and post‐operative Hb measurement.Results10 015 THA, TKA and UKA procedures were performed in 8582 patients. The incidence of blood transfusion (4.5%) decreased during the study. Using procedure specific Hb thresholds to select patients for pre‐operative G&S and post‐operative Hb testing had a greater net benefit than selecting all patients, no patients, or patients with pre‐operative anaemia.ConclusionsPre‐operative G&S and post‐operative Hb measurement may not be indicated for UKA or TKA when adopting restrictive transfusion thresholds, provided clinicians accept a 0.1% risk of patients developing severe undiagnosed post‐operative anaemia (Hb < 70 g/L). The decision to perform these blood tests for THA patients should be based on local institutional data and selection of acceptable risk thresholds.

PD57-02 ASSESSMENT OF CLINICAL UTILITY OF PSA DENSITY IN RISK STRATIFICATION OF PI-RADS V2.1 CATEGORIES ON PROSTATE MRI

You have accessJournal of UrologyCME1 May 2022PD57-02 ASSESSMENT OF CLINICAL UTILITY OF PSA DENSITY IN RISK STRATIFICATION OF PI-RADS V2.1 CATEGORIES ON PROSTATE MRI Gianluca Giannarini, Rossano Girometti, Valeria Panebianco, Silvio Maresca, Lorenzo Cereser, Maria De Martino, Stefano Pizzolitto, Vincenzo Ficarra, Chiara Zuiani, and Claudio Valotto Gianluca GiannariniGianluca Giannarini More articles by this author , Rossano GiromettiRossano Girometti More articles by this author , Valeria PanebiancoValeria Panebianco More articles by this author , Silvio MarescaSilvio Maresca More articles by this author , Lorenzo CereserLorenzo Cereser More articles by this author , Maria De MartinoMaria De Martino More articles by this author , Stefano PizzolittoStefano Pizzolitto More articles by this author , Vincenzo FicarraVincenzo Ficarra More articles by this author , Chiara ZuianiChiara Zuiani More articles by this author , and Claudio ValottoClaudio Valotto More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000002637.02AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: With the advent of the novel MRI-based pathway for prostate cancer (PCa) detection, there is intense research to implement risk-adjusted strategies for the proper selection of biopsy patients and the reduction of false-negative results. In this study we investigated the clinical utility of different thresholds of PSA density (PSAD) in improving the accuracy of Prostate Imaging – Reporting And Data System version 2.1 (PI-RADS v2.1) categories for detecting clinically significant PCa on multiparametric MRI (mpMRI) and mpMRI-targeted and systematic biopsy. METHODS: A total of 123 consecutive biopsy-naïve men referred to mpMRI and prostate biopsy between April 2019 and October 2020 were retrospectively analysed. mpMRI was performed on a 3.0T magnet with a 32-channel surface coil, using a PI-RADS v2.1-compliant protocol. Two readers (>1500 and >500 mpMRI examinations) were included. All patients underwent transperineal mpMRI-targeted and systematic prostate biopsy. Clinically significant PCa was defined as International Society of Urogenital Pathology grading group ≥2 on combined biopsy. Analysis was performed on a per-index lesion basis. Receiver operating characteristic analysis was used to calculate sensitivity, specificity, and area under the curve (AUC) of PI-RADS v2.1 categories after adjusting for different PSAD thresholds (≥0.10, ≥0.15, and ≥0.20 ng/mL/mL). Cancer detection rate (CDR) was calculated on a per-adjusted category basis, and decision analysis was performed to assess the net benefit of different PSAD-adjusted PI-RADS v2.1 categories as a biopsy trigger. RESULTS: Clinically significant PCa was found in 54/123 men (43.9%; 95%CI 40.5-70.4). PSAD-adjustment increased the CDR of PI-RADS v2.1 category 4 regardless of the threshold. Sensitivity/specificity/AUC were 92.6%/53.6%/0.82 for unadjusted PI-RADS, and 85.2%/72.4%/0.84, 62.9%/85.5%/0.83, and 92.4%/53.6%/0.82 when adjusting PIRADS categories for a 0.10, 0.15, and 0.20 ng/ml/ml PSAD threshold, respectively. The greatest net benefit at 30% and 40% risk probability of clinically significant PCa (0.307 and 0.271, respectively) was observed assuming biopsy patients with a PI-RADS 4 observation and PSAD ≥0.10 ng/mL/mL. CONCLUSIONS: Within the range of expected prevalence for clinically significant PCa, decision analysis showed that PI-RADS v2.1 category 4 with high PSAD at the 0.10 ng/mL/mL threshold was the biopsy-triggering cut-off with the highest net benefit. If confirmed in larger prospective studies, our results might emphasize the concept that the combination of lower PSAD threshold and higher PI-RADS v2.1 cut-off may allow for better risk stratification of men referred to prostate biopsy. Source of Funding: None © 2022 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 207Issue Supplement 5May 2022Page: e959 Advertisement Copyright & Permissions© 2022 by American Urological Association Education and Research, Inc.MetricsAuthor Information Gianluca Giannarini More articles by this author Rossano Girometti More articles by this author Valeria Panebianco More articles by this author Silvio Maresca More articles by this author Lorenzo Cereser More articles by this author Maria De Martino More articles by this author Stefano Pizzolitto More articles by this author Vincenzo Ficarra More articles by this author Chiara Zuiani More articles by this author Claudio Valotto More articles by this author Expand All Advertisement PDF DownloadLoading ...

Identification of Tumor Microenvironment and DNA Methylation-Related Prognostic Signature for Predicting Clinical Outcomes and Therapeutic Responses in Cervical Cancer.

Background: Tumor microenvironment (TME) has been reported to have a strong association with tumor progression and therapeutic outcome, and epigenetic modifications such as DNA methylation can affect TMB and play an indispensable role in tumorigenesis. However, the potential mechanisms of TME and DNA methylation remain unclear in cervical cancer (CC). Methods: The immune and stromal scores of TME were generated by the ESTIMATE algorithm for CC patients in The Cancer Genome Atlas (TCGA) database. The TME and DNA methylation-related genes were identified by the integrative analysis of DNA promoter methylation and gene expression. The least absolute shrinkage and selection operator (LASSO) Cox regression was performed 1,000 times to further identify a nine-gene TME and DNA methylation-related prognostic signature. The signature was further validated in Gene Expression Omnibus (GEO) dataset. Then, the identified signature was integrated with the Federation International of Gynecology and Obstetrics (FIGO) stage to establish a composite prognostic nomogram. Results: CC patients with high immunity levels have better survival than those with low immunity levels. Both in the training and validation datasets, the risk score of the signature was an independent prognosis factor. The composite nomogram showed higher accuracy of prognosis and greater net benefits than the FIGO stage and the signature. The high-risk group had a significantly higher fraction of genome altered than the low-risk group. Eleven genes were significantly different in mutation frequencies between the high- and low-risk groups. Interestingly, patients with mutant TTN had better overall survival (OS) than those with wild type. Patients in the low-risk group had significantly higher tumor mutational burden (TMB) than those in the high-risk group. Taken together, the results of TMB, immunophenoscore (IPS), and tumor immune dysfunction and exclusion (TIDE) score suggested that patients in the low-risk group may have greater immunotherapy benefits. Finally, four drugs (panobinostat, lenvatinib, everolimus, and temsirolimus) were found to have potential therapeutic implications for patients with a high-risk score. Conclusions: Our findings highlight that the TME and DNA methylation-related prognostic signature can accurately predict the prognosis of CC and may be important for stratified management of patients and precision targeted therapy.

Rigidity with partial elasticity: Local government adaptation under the centralized land quota system in China

Under the rigid centralized land quota system in China, local governments actively adopt various strategies to achieve economic growth. This paper describes and evaluates a new local decentralized strategy in China called the flexible quota system, which is used to satisfy the land use requirements of key and uncertain projects. Using Zhejiang Province as an example, we find that the innovative aspect of this flexible quota system is that the upper-level government reserves a share of quotas for overall decision-making, and sub-level governments competitively apply for quotas based on project importance. Through the above mechanism, the flexible quota system smoothly guarantees unforeseeable land use arrangements in key projects and improves the overall land quota allocation performance. Furthermore, data from scenario simulations show that compared with the original system, this flexible quota system has a greater net benefit in land development and is almost twice as successful in dealing with land use for uncertain key projects. The paper may provide new knowledge for local innovation in land planning under the traditional centralized system.

A Nomogram-Based Model to Predict Respiratory Dysfunction at 6 Months in Non-Critical COVID-19 Survivors.

ObjectiveTo assess the prevalence of respiratory sequelae of Coronavirus disease 2019 (COVID-19) survivors at 6 months after hospital discharge and develop a model to identify at-risk patients.Patients and MethodsIn this prospective cohort study, hospitalized, non-critical COVID-19 patients evaluated at 6-month follow-up between 26 August, 2020 and 16 December, 2020 were included. Primary outcome was respiratory dysfunction at 6 months, defined as at least one among tachypnea at rest, percent predicted 6-min walking distance at 6-min walking test (6MWT) ≤ 70%, pre-post 6MWT difference in Borg score ≥ 1 or a difference between pre- and post-6MWT oxygen saturation ≥ 5%. A nomogram-based multivariable logistic regression model was built to predict primary outcome. Validation relied on 2000-resample bootstrap. The model was compared to one based uniquely on degree of hypoxemia at admission.ResultsOverall, 316 patients were included, of whom 118 (37.3%) showed respiratory dysfunction at 6 months. The nomogram relied on sex, obesity, chronic obstructive pulmonary disease, degree of hypoxemia at admission, and non-invasive ventilation. It was 73.0% (95% confidence interval 67.3–78.4%) accurate in predicting primary outcome and exhibited minimal departure from ideal prediction. Compared to the model including only hypoxemia at admission, the nomogram showed higher accuracy (73.0 vs 59.1%, P < 0.001) and greater net-benefit in decision curve analyses. When the model included also respiratory data at 1 month, it yielded better accuracy (78.2 vs. 73.2%) and more favorable net-benefit than the original model.ConclusionThe newly developed nomograms accurately identify patients at risk of persistent respiratory dysfunction and may help inform clinical priorities.

Radiofrequency Ablation in Cooled Monopolar or Conventional Bipolar Modality Yields More Beneficial Short-Term Clinical Outcomes Versus Other Treatments for Knee Osteoarthritis: A Systematic Review and Network Meta-Analysis of Randomized Controlled Trials

To evaluate various radiofrequency ablation (RFA) treatments for knee osteoarthritis (OA) and determine the best modality, target, number of electrodes, and image guidance for improving knee pain and function. Electronic databases were searched for randomized controlled trials (RCTs) comparing the efficacy of RFA treatments for knee OA from inception up to September 30, 2021. The primary outcome was the visual analog scale (VAS), and the secondary outcome was the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC). Bayesian network meta-analysis was performed to synthesize the mean difference (MD) and rank the comparative effectiveness of treatments. A total of 21 eligible RCTs (from 2011 to 2021, involving 1,818 patients) with 8 RFA treatments, 6 intra-articular injections, NSAIDS, exercise, and placebo were assessed. Conventional bipolar genicular nerve RFA (GNRFA) had the greatest net benefit on the VAS at 6 months (MD,-5.5; 95% confidence interval [CI],-4.3 to-6.7; SUCRA, .98). And cooled monopolar GNRFA had the greatest net benefit on the WOMAC at 6 months (MD,-33; 95% CI,-37 to-29; SUCRA, .99). In conventional and pulsed modalities, bipolar RFA was associated with a significant decrease in VAS and WOMAC than monopolar RFA. Combining pulsed intra-articular RFA and platelet-rich plasma injection had no additional positive effects on VAS or WOMAC at 3 months. RFA is effective in improving both knee pain and function in patients with OA, at least in the short term (6 months). Patients respond better to the cooled modality than the conventional and pulsed modalities. Bipolar is more effective than monopolar for improving pain and function in conventional and pulsed modalities. Fluoroscopy and ultrasound guidance showed no differences in improving pain and function. The effectiveness of RFA in cooled modality using bipolar or in combination with various intra-articular injections remains to be compared. II, systematic review and meta-analysis of Level I and II studies.