Similar Discrimination Research Articles

Machine learning analysis of bleeding status in venous thromboembolism patients

BackgroundAnticoagulation therapy is the mainstay of therapy for patients with venous thromboembolism (VTE). However, continuing or stopping anticoagulants after the first 3 to 6 months is a difficult decision that requires ascertainment of the risk of bleeding and recurrent VTE. Despite the development of several statistical models to predict bleeding, the benefit of machine learning (ML) models has not been investigated in depth. ObjectivesTo assess the benefits of ML algorithms in bleeding risk evaluation in VTE patients and gain insight into their baseline information. MethodsThe baseline clinical, demographic, and genotype information was collected for 2542 patients with VTE who were on extended anticoagulation therapy. Six unsupervised dimensionality reduction and clustering ML algorithms were used to visualize and cluster the data for patients with major bleeding (118 patients) and nonbleeders. Eight supervised ML algorithms were trained and compared with the previously derived clinical models using a 5-fold nested cross-validation scheme. ResultsThe baseline dataset for bleeders and nonbleeders showed a high degree of similarity. Two novel clusters were discovered within the dataset for bleeders based on the presence of isolated pulmonary embolism or isolated deep vein thrombosis, though the difference in bleeding risks was not statistically significant (P = .32). The supervised analysis showed that the ML and clinical models have similar discrimination (c-statistics, ∼62%) and calibration performance (Brier score, ∼0.045). ConclusionThe clinical variables recorded at baseline are not distinctive enough to improve bleeding prediction beyond the performance of the existing models, and other strategies or data modalities should be considered.

A dynamic nomogram to predict invasive fungal super-infection during healthcare-associated bacterial infection in intensive care unit patients: an ambispective cohort study in China.

Invasive fungal super-infection (IFSI) is an added diagnostic and therapeutic dilemma. We aimed to develop and assess a nomogram of IFSI in patients with healthcare-associated bacterial infection (HABI). An ambispective cohort study was conducted in ICU patients with HABI from a tertiary hospital of China. Predictors of IFSI were selected by both the least absolute shrinkage and selection operator (LASSO) method and the two-way stepwise method. The predictive performance of two models built by logistic regression was internal-validated and compared. Then external validity was assessed and a web-based nomogram was deployed. Between Jan 1, 2019 and June 30, 2023, 12,305 patients with HABI were screened in 14 ICUs, of whom 372 (3.0%) developed IFSI. Among the fungal strains causing IFSI, the most common was C.albicans (34.7%) with a decreasing proportion, followed by C.tropicalis (30.9%), A.fumigatus (13.9%) and C.glabrata (10.1%) with increasing proportions year by year. Compared with LASSO-model that included five predictors (combination of priority antimicrobials, immunosuppressant, MDRO, aCCI and S.aureus), the discriminability of stepwise-model was improved by 6.8% after adding two more predictors of COVID-19 and microbiological test before antibiotics use (P<0.01).And the stepwise-model showed similar discriminability in the derivation (the area under curve, AUC=0.87) and external validation cohorts (AUC=0.84, P=0.46). No significant gaps existed between the proportion of actual diagnosed IFSI and the frequency of IFSI predicted by both two models in derivation cohort and by stepwise-model in external validation cohort (P=0.16, 0.30 and 0.35, respectively). The incidence of IFSI in ICU patients with HABI appeared to be a temporal rising, and our externally validated nomogram will facilitate the development of targeted and timely prevention and control measures based on specific risks of IFSI.

The Ankle Ligament Reconstruction-Return to Sport after Injury (ALR-RSI) is a valid and reliable measure to assess psychological readiness before returning to sport following peroneal tendon pathology surgery.

The objective of this study was to validate a scale that could help surgeons evaluate patients' psychological readiness to return to sport (RTS) after peroneal tendon pathology surgery. The Ankle Ligament Reconstruction-Return to Sport after Injury (ALR-RSI) scale, which had previously been validated in ankle ligament reconstruction patients, was adapted to evaluate the psychological preparedness for RTS in athletic patients who underwent peroneal tendinopathy surgery. The Foot and Ankle Outcome Score (FAOS) and Foot Ankle Ability Measurement (FAAM) scores were employed as patient-related outcome measurement (PROM) instruments. This study included 57 patients. There was a strong correlation between ALR-RSI and both FAOS and FAAM (r = -0.68 and 0.74, respectively). ALR-RSI was considerably higher in patients who returned to sports than in those who did not. The mean score was 72.9 ± 19.0 in patients who returned to the same preinjury level, 48.5 ± 24.0 in those who returned to a lower leveland 53.6 ± 31.1 in patients who changed their athletic activity (p < 0.0001). Furthermore, ALR-RSI showed at least a similar discrimination ability when compared to FAOS and FAAM. The test-retest intraclass correlation coefficient was 0.95. The Cronbach's α statistic used to measure the internal consistency was high (0.95). A Youden index of 0.65 was observed for a cut-off score of 68 points. ALR-RSI is a valid instrument for assessing psychological readiness to RTS in an athletic population following peroneal tendon surgery. When compared to the most commonly used PROMs, it was strongly correlated and demonstrated at least similar discrimination capacity. This could assist surgeons in identifying athletes who will have poor postoperative results and advising them on their capability to RTS. Level III.

Prediction model for haematoma after tissue expander placement: A retrospective cohort study of 7080 cases over 20 years

Haematoma is an early complication of tissue expander placement and can lead to infection, capsule contracture and various complications, hindering successful reconstruction. However, no scientific models can accurately predict the risk of haematoma following tissue expansion. Therefore, this study aimed to develop and validate a prediction model for haematoma following tissue expander placement. The medical records of patients who underwent expander placement between 2001 and 2021 were obtained from the clinical database of the Department of Plastic Surgery at the Xijing Hospital. A total of 4579 consecutive patients with 7080 expanders and 179 expanded pocket haematomas were analysed. Multivariate logistic regression analysis identified adult age (P = 0.006), male sex (P < 0.001), scar reconstruction (P = 0.019), perioperative hypertension (P < 0.001), face and neck location (P = 0.002) and activated partial thromboplastin time above the normal range (P < 0.001) as risk factors for haematoma. Therefore, these were included in the prediction model, and a nomogram was constructed. The discrimination of the nomogram was robust (area under the curve: 0.78; 95% confidence interval: 0.72–0.83). Further, the prediction model had a strong fit (Hosmer–Lemeshow test, P = 0.066) and maintained similar discrimination after considering performance optimism (bootstrapped area under the curve: 0.79; 95% confidence interval: 0.73–0.84). This clinical prediction model was created using a generalisable dataset and can be utilised to obtain valid haematoma predictions after expander placement, assisting surgeons in implementing preventive measures or interventions to reduce the occurrence of haematoma.

Combining Charlson comorbidity and VACS indices improves prognostic accuracy for all-cause mortality for patients with and without HIV in the Veterans Health Administration.

As people age with HIV (PWH), many comorbid diseases are more common than among age matched comparators without HIV (PWoH). While the Veterans Aging Cohort (VACS) Index 2.0 accurately predicts mortality in PWH using age and clinical biomarkers, the only included comorbidity is hepatitis C. We asked whether adding comorbid disease groupings from the Charlson Comorbidity Index (CCI) improves the accuracy of VACS Index. To maximize our ability to model mortality among older age groups, we began with PWoH in Veterans Health Administration (VA) from 2007-2017, divided into development and validation samples. Baseline predictors included age, and components of CCI and VACS Index (excluding CD4 count and HIV RNA). Patients were followed until December 31, 2021. We used Cox models to develop the VACS-CCI score and estimated mortality using a parametric (gamma) survival model. We compared accuracy using C-statistics and calibration curves in validation overall and within subgroups (gender, age </≥65 years, race/ethnicity, and CCI score). We then applied VACS-CCI in PWH and compared its accuracy to age, VACS Index 2.0, CCI and VACS-CCI with CD4 and HIV RNA added. The analytic sample consisted of 6,588,688 PWoH and 30,539 PWH. Among PWoH/PWH, median age was 65/55 years; 6%/3% were women; 15%/48% were Black and 5%/7% Hispanic. VACS-CCI provided the best discrimination (C-statistic = 0.81) with excellent calibration (predicted and observed mortality largely overlapped) overall and within subgroups. When VACS-CCI was applied to PWH it demonstrated similar discrimination as VACS Index 2.0 (C-statistic = 0.77 for both) but superior calibration among those with CD4 < 200. Discrimination was improved when CD4 and HIV RNA were added VACS-CCI (C-statistic = 0.79). Liver and kidney disease, congestive heart failure, malignancy, and dementia were negatively associated with CD4 (p-trends all <0.0001). Among PWH and PWoH in VA care, age alone weakly discriminates risk of mortality. VACS Index 2.0, CCI, and VACS-CCI all provide better discrimination, but VACS-CCI is more consistently calibrated. The association of comorbid diseases with lower CD4 underscores the likely role of HIV in non-AIDS conditions. Future work will include adding CD4 and HIV RNA to VACS-CCI and validating it in independent data.

Comparison of Two Methods for Determining Item Characteristic Functions and Latent Variable Time-Course for Pharmacometric Item Response Models

There are examples in the literature demonstrating different approaches to defining the item characteristic functions (ICF) and characterizing the latent variable time-course within a pharmacometrics item response theory (IRT) framework. One such method estimates both the ICF and latent variable time-course simultaneously, and another method establishes the ICF first then models the latent variable directly. To date, a direct comparison of the “simultaneous” and “sequential” methodologies described in this work has not yet been systematically investigated. Item parameters from a graded response IRT model developed from Parkinson’s Progression Marker Initiative (PPMI) study data were used as simulation parameters. Each method was evaluated under the following conditions: (i) with and without drug effect and (ii) slow progression rate with smaller sample size and rapid progression rate with larger sample size. Overall, the methods performed similarly, with low bias and good precision for key parameters and hypothesis testing for drug effect. The ICF parameters were well determined when the model was correctly specified, with an increase in precision in the scenario with rapid progression. In terms of drug effect, both methods had large estimation bias for the slow progression rate; however, this bias can be considered small relative to overall progression rate. Both methods demonstrated type 1 error control and similar discrimination between model with and without drug effect. The simultaneous method was slightly more precise than the sequential method while the sequential method was more robust towards longitudinal model misspecification and offers practical advantages in model building.

Merging machine learning and patient preference: a novel tool for risk prediction of percutaneous coronary interventions.

Predicting personalized risk for adverse events following percutaneous coronary intervention (PCI) remains critical in weighing treatment options, employing risk mitigation strategies, and enhancing shared decision-making. This study aimed to employ machine learning models using pre-procedural variables to accurately predict common post-PCI complications. A group of 66 adults underwent a semiquantitative survey assessing a preferred list of outcomes and model display. The machine learning cohort included 107 793 patients undergoing PCI procedures performed at 48 hospitals in Michigan between 1 April 2018 and 31 December 2021 in the Blue Cross Blue Shield of Michigan Cardiovascular Consortium (BMC2) registry separated into training and validation cohorts. External validation was conducted in the Cardiac Care Outcomes Assessment Program database of 56 583 procedures in 33 hospitals in Washington. Overall rate of in-hospital mortality was 1.85% (n = 1999), acute kidney injury 2.51% (n = 2519), new-onset dialysis 0.44% (n = 462), stroke 0.41% (n = 447), major bleeding 0.89% (n = 942), and transfusion 2.41% (n = 2592). The model demonstrated robust discrimination and calibration for mortality {area under the receiver-operating characteristic curve [AUC]: 0.930 [95% confidence interval (CI) 0.920-0.940]}, acute kidney injury [AUC: 0.893 (95% CI 0.883-0.903)], dialysis [AUC: 0.951 (95% CI 0.939-0.964)], stroke [AUC: 0.751 (95%CI 0.714-0.787)], transfusion [AUC: 0.917 (95% CI 0.907-0.925)], and major bleeding [AUC: 0.887 (95% CI 0.870-0.905)]. Similar discrimination was noted in the external validation population. Survey subjects preferred a comprehensive list of individually reported post-procedure outcomes. Using common pre-procedural risk factors, the BMC2 machine learning models accurately predict post-PCI outcomes. Utilizing patient feedback, the BMC2 models employ a patient-centred tool to clearly display risks to patients and providers (https://shiny.bmc2.org/pci-prediction/). Enhanced risk prediction prior to PCI could help inform treatment selection and shared decision-making discussions.

Scrutinizing the Bathroom Binary: Equal Protection Theories for Nonbinary Students

Over the past decade, transgender students have challenged discriminatory school bathroom policies under the Equal Protection Clause with varying success. But another group of students, facing similar discrimination, has yet to see its day in court. Like their transgender peers, nonbinary students often lack access to gender-appropriate restrooms at school. Many K–12 schools offer only “boys” and “girls” restrooms, ignoring the needs of students who identify as neither boys nor girls, as both of those genders, or as something else entirely. Forced to use sex-segregated bathrooms (or no bathroom at all), nonbinary students suffer adverse health, safety, and educational outcomes. To illustrate pathways to relief, this Note provides an inventory of equal protection claims nonbinary students can make to challenge discriminatory bathroom policies. Each theory—alleging unconstitutional discrimination on the basis of sex, transgender status, or nonbinary status—is promising and limited in its own respect. Independently or together, these theories can support a viable equal protection challenge that triggers heightened scrutiny.

Association of Clinical Characteristics With Familial Hypercholesterolaemia Variants in a Lipid Clinic Setting: A Case-Control Study.

Familial hypercholesterolaemia (FH) variant positive subjects have over double the cardiovascular risk of low-density-lipoprotein-cholesterol (LDL-C) matched controls. It is desirable to optimise FH variant detection. We identified 213 subjects with FH gene panel reports (LDLR, APOB, PCSK9, and APOE) based on total cholesterol >310 mg/dL; excluding triglycerides >400 mg/dL, cascade screening, and patients without pre-treatment LDL-C recorded. Demographic, clinical and lipid parameters were recorded. A 31/213 (14.6%) patients had pathogenic or likely pathogenic FH variants. 10/213 (4.7%) had variants of uncertain significance. Compared with patients without FH variants, patients with FH variants were younger (median age, 39 years vs. 48 years), had more tendon xanthomata (25.0% vs. 11.4%), greater proportion of first degree relatives with total cholesterol >95th percentile (40.6% vs. 16.5%), higher LDL-C (median, 271 mg/dL vs. 236 mg/dL), and lower triglycerides (median, 115 mg/dL vs. 159 mg/dL). The Besseling et al. model (c-statistic 0.798) improved FH variant discrimination over Friedewald LDL-C (c-statistic 0.724), however, Dutch Lipid Clinic Network Score (DLCNS) did not (c-statistic 0.665). Sampson LDL-C (c-statistic 0.734) had similar discrimination to Friedewald. Although tendon xanthomata and first degree relatives with high total cholesterol >95th percentile were associated with FH variants, DLCNS or Simon Broome criteria did not improve FH detection over LDL-C. Sampson LDL-C did not significantly improve discrimination over Friedewald. Although lower triglycerides and younger age of presentation are positively associated with presence of FH variants, this information is not commonly used in FH detection algorithms apart from Besseling et al.

Development and internal validation of a multivariable model for the prediction of the probability of 1-year readmission to the emergency department for acute alcohol intoxication.

To develop and internally validate a multivariable logistic regression model (LRM) for the prediction of the probability of 1-year readmission to the emergency department (ED) in patients with acute alcohol intoxication (AAI). We developed and internally validated the LRM on a previously analyzed retrospective cohort of 3304 patients with AAI admitted to the ED of the Sant'Orsola-Malpighi Hospital (Bologna, Italy). The benchmark LRM employed readmission to the same ED for AAI within 1year as the binary outcome, age as a continuous predictor, and sex, alcohol use disorder, substance use disorder, at least one previous admission for trauma, mental or behavioral disease, and homelessness as the binary predictors. Optimism correction was performed using the bootstrap on 1000 samples without replacement. The benchmark LRM was gradually simplified to get the most parsimonious LRM with similar optimism-corrected overall fit, discrimination and calibration. The 1-year readmission rate was 15.7% (95% CI 14.4-16.9%). A reduced LRM based on sex, age, at least one previous admission for trauma, mental or behavioral disease, and homelessness, performed nearly as well as the benchmark LRM. The reduced LRM had the following optimism-corrected metrics:scaled Brier score 17.0%,C-statistic 0.799 (95% CI 0.778 to 0.821),calibration in the large 0.000 (95% CI -0.099 to 0.099),calibration slope 0.985 (95% CI 0.893 to 1.088),and an acceptably accurate calibration plot. An LRM based on sex, age, at least one previous admission for trauma, mental or behavioral disease, and homelessness can be used to estimate the probability of 1-year readmission to ED for AAI. To begin proving its clinical utility, this LRM should be validated in external cohorts.

Development and validation of delirium prediction models for noncardiac surgery patients

Study objectivePostoperative delirium is associated with morbidity and mortality, and its incidence varies widely. Using known predisposing and precipitating factors, we sought to develop postoperative delirium prediction models for noncardiac surgical patients. DesignRetrospective prediction model study. SettingMajor quaternary medical center. PatientsOur January 2016 to June 2020 training dataset included 51,677 patients of whom 2795 patients had delirium. Our July 2020 to January 2022 validation dataset included 14,438 patients of whom 912 patients had delirium. InterventionsNone. MeasurementsWe trained and validated two static prediction models and one dynamic delirium prediction model. For the static models, we used random survival forests and traditional Cox proportional hazard models to predict postoperative delirium from preoperative variables, or from a combination of preoperative and intraoperative variables. We also used landmark modeling to dynamically predict postoperative delirium using preoperative, intraoperative, and postoperative variables before onset of delirium. Main resultsIn the validation analyses, the static random forest model had a c-statistic of 0.81 (95% CI: 0.79, 0.82) and a Brier score of 0.04 with preoperative variables only, and a c-statistic of 0.86 (95% CI: 0.84, 0.87) and a Brier score of 0.04 when preoperative and intraoperative variables were combined. The corresponding Cox models had similar discrimination metrics with slightly better calibration. The dynamic model - using all available data, i.e., preoperative, intraoperative and postoperative data - had an overall c-index of 0.84 (95% CI: 0.83, 0.85). ConclusionsUsing preoperative and intraoperative variables, simple static models performed as well as a dynamic delirium prediction model that also included postoperative variables. Baseline predisposing factors thus appear to contribute far more to delirium after noncardiac surgery than intraoperative or postoperative variables. Improved postoperative data capture may help improve delirium prediction and should be evaluated in future studies.

Development and validation of a quantitative Proximity Extension Assay instrument with 21 proteins associated with cardiovascular risk (CVD-21).

Treatment of cardiovascular diseases (CVD) is a substantial burden to healthcare systems worldwide. New tools are needed to improve precision of treatment by optimizing the balance between efficacy, safety, and cost. We developed a high-throughput multi-marker decision support instrument which simultaneously quantifies proteins associated with CVD. Candidate proteins independently associated with different clinical outcomes were selected from clinical studies by the screening of 368 circulating biomarkers. We then custom-designed a quantitative PEA-panel with 21 proteins (CVD-21) by including recombinant antigens as calibrator samples for normalization and absolute quantification of the proteins. The utility of the CVD-21 tool was evaluated in plasma samples from a case-control cohort of 4224 patients with chronic coronary syndrome (CCS) using multivariable Cox regression analyses and machine learning techniques. The assays in the CVD-21 tool gave good precision and high sensitivity with lower level of determination (LOD) between 0.03-0.7 pg/ml for five of the biomarkers. The dynamic range for the assays was sufficient to accurately quantify the biomarkers in the validation study except for troponin I, which in the modeling was replaced by high-sensitive cardiac troponin T (hs-TnT). We created seven different multimarker models, including a reference model with NT-proBNP, hs-TnT, GDF-15, IL-6, and cystatin C and one model with only clinical variables, for the comparison of the discriminative value of the CVD-21 tool. All models with biomarkers including hs-TnT provided similar discrimination for all outcomes, e.g. c-index between 0.68-0.86 and outperformed models using only clinical variables. Most important prognostic biomarkers were MMP-12, U-PAR, REN, VEGF-D, FGF-23, TFF3, ADM, and SCF. The CVD-21 tool is the very first instrument which with PEA simultaneously quantifies 21 proteins with associations to different CVD. Novel pathophysiologic and prognostic information beyond that of established biomarkers were identified by a number of proteins.

Taking Stock of Solidarity between People of Color: A Mini Meta-Analysis of Five Experiments

Recent work suggests that solidarity between people of color (PoC) is triggered when a minoritized ingroup believes they are discriminated similarly to another outgroup based on their alleged foreignness or inferiority. Heightened solidarity then boosts support for policies that benefit minoritized outgroups who are not one’s own. Available experiments on this pathway vary by participants (e.g., Asian, Black, Middle Eastern, and Latino adults), manipulations (similar discrimination as foreign vs. inferior), and pro-outgroup outcomes (support for undocumented immigrants, Black Lives Matter). We report a pre-registered mini meta-analysis of this solidarity mechanism. Across five experiments (N = 3,252), similar discrimination as foreign or inferior reliably triggers solidarity between PoC, which then substantially increases support for pro-outgroup policies. This mediated pathway is robust to possible confounding and emerges across studies and planned contrasts of them. We discuss what the viability of this mechanism implies for further theoretic and empirical innovations in a racially diversifying polity.

Abstract 14312: Comparison of Machine Learning and Conventional Statistical Modeling for Predicting Readmissions Following Acute Heart Failure Hospitalization

Introduction: Developing accurate models for predicting risk of 30-day readmission has been a major healthcare interest. Evidence suggests that models developed using machine learning (ML) may have better discrimination than conventional statistical models (CSM), but the calibration of such models is unclear. Objectives: To compare models developed using CSM or ML to predict 30-day readmission for cardiovascular and non-cardiovascular causes in HF patients. Methods: We studied 10,919 patients with HF (&gt; 18 years) discharged alive from a hospital or emergency department (2004-2007) in Ontario, Canada, linked to administrative databases for hospitalization and vital status resulting in complete follow-up. The study sample was randomly divided into training and validation sets in a 2:1 ratio. CSMs to predict 30-day readmission were developed using Fine-Gray subdistribution hazards regression (treating death as a competing risk), and the ML algorithm employed random survival forests. Models were evaluated in the validation set using both discrimination and calibration metrics. Results: In the validation sample of 3602 patients (median age 76 [IQR, 67-82] years, 46.6% females), Random Survival Forests (c-statistic = 0.620) showed similar discrimination to the Fine-Gray competing risk model (c-statistic= 0.621) for 30-day cardiovascular readmission. In contrast, for 30-day non-cardiovascular readmission, the Fine-Gray model (c-statistic= 0.641) slightly outperformed the random survival forests model (c-statistic = 0.632). For both outcomes, The Fine-Gray model displayed better calibration than random survival forests when deciles of observed vs. predicted risks were compared (Panels A-D). Conclusions: In HF patients, time-to-event analysis of outcomes using Fine-Gray models had similar discrimination but superior calibration to ML model, highlighting the importance of reporting calibration metrics for ML-based prediction models.

Abstract 17712: The Discrimination Ability of the CHA2DS2-VASc Score for Predicting Ischemic Stroke in Patients With and Without Atrial Fibrillation: An Updated Meta-Analysis

BACKGROUND: The CHA2DS2-VASc score guides anticoagulation therapy for stroke prevention in non-valvular atrial fibrillation (NVAF) patients. A previous meta-analysis showed a moderate discrimination ability of CHA2DS2-VASc score to detect stroke in patients with &amp; without NVAF. In the presence of newly published data over the past three years, there is a need to recalibrate the existing statistical evidence. METHODS: A comprehensive literature search was conducted across PubMed, Embase, CENTRAL (the Cochrane Library), &amp; Google Scholar from inception till May 2023. All studies evaluating the discrimination ability of the CHA2DS2-VASc score, as measured by the C statistic, for short (&lt;2 years), intermediate (2-5 years), &amp; long-term (&gt;5 years) follow-ups in patients with &amp; without NVAF were included. The generic inverse variance weighted random effects method was used to pool C-statistic &amp; corresponding standard errors (SEs) from each study. RESULTS: A total of 28 studies with a population of 639450 patients were included. Seventeen studies (n=500555) reported the discrimination ability of CHA2DS2-VASc score for NVAF patients, &amp; 11 studies (n=138895) for patients without NVAF. The summary C statistic for short-term follow up was 0.66 (0.62-0.70) for NVAF subgroup. For mid-term follow-up, the pooled C statistic was 0.65 (0.63-0.67), while being 0.63 (0.60-0.66) &amp; 0.68 (0.66-0.70) for the subgroup with NVAF &amp; without NVAF respectively. The CHA2DS2-VASC score showed moderate discrimination [0.66 (0.62-0.70)] at long-term follow up (NVAF [0.58 (0.55-0.61)]; without NVAF [0.67 (0.64-0.70]). CONCLUSION: CHA2DS2-VASc score has moderate &amp; similar discrimination ability for detecting stroke in both NVAF &amp; non-NVAF patients.

"Cat Ladies" and "Mama's Boys": A Mixed-Methods Analysis of the Gendered Discrimination and Stereotypes of Single Women and Single Men.

Do single women and single men differ in their experiences of "singlism"? This mixed-methods research examined whether single women and single men report quantitative differences in amounts of singlehood-based discrimination and explored qualitative reports of stereotypic traits associated with single women and single men. We recruited Canadian and American single adults across two Prolific studies (total N = 286). The results demonstrated that single female and male participants did not differ in their personal discrimination, but female participants perceived single women to experience more discrimination than single men. Furthermore, qualitative analyses revealed four overlapping "archetypes" of single women and men including: Professional ("independent," "hard-working"), Carefree ("free," "fun"), Heartless ("selfish," "promiscuous"), and Loner ("lonely," "antisocial"). Overall, single women and men may experience similar stereotypes and discrimination, but there are also important nuances that highlight the need for more research at the intersection of gender and singlehood.

The impact of feedback training on prediction of cancer clinical trial results.

Funders must make difficult decisions about which squared treatments to prioritize for randomized trials. Earlier research suggests that experts have no ability to predict which treatments will vindicate their promise. We tested whether a brief training module could improve experts' trial predictions. We randomized a sample of breast cancer and hematology-oncology experts to the presence or absence of a feedback training module where experts predicted outcomes for five recently completed randomized controlled trials and received feedback on accuracy. Experts then predicted primary outcome attainment for a sample of ongoing randomized controlled trials. Prediction skill was assessed by Brier scores, which measure the average deviation between their predictions and actual outcomes. Secondary outcomes were discrimination (ability to distinguish between positive and non-positive trials) and calibration (higher predictions reflecting higher probability of trials being positive). A total of 148 experts (46 for breast cancer, 54 for leukemia, and 48 for lymphoma) were randomized between May and December 2017 and included in the analysis (1217 forecasts for 25 trials). Feedback did not improve prediction skill (mean Brier score for control: 0.22, 95% confidence interval = 0.20-0.24 vs feedback arm: 0.21, 95% confidence interval = 0.20-0.23; p = 0.51). Control and feedback arms showed similar discrimination (area under the curve = 0.70 vs 0.73, p = 0.24) and calibration (calibration index = 0.01 vs 0.01, p = 0.81). However, experts in both arms offered predictions that were significantly more accurate than uninformative forecasts of 50% (Brier score = 0.25). A short training module did not improve predictions for cancer trial results. However, expert communities showed unexpected ability to anticipate positive trials.Pre-registration record: https://aspredicted.org/4ka6r.pdf.

Algorithms of Electrocardiographic Changes for Quantitative and Localization Analysis of Thrombus Burden in Patients with Acute Pulmonary Thromboembolism.

Various electrocardiographic (ECG) abnormalities are associated with the severity of pulmonary thromboembolism (PTE). The utility of evaluating the clot burden of PTE based on ECG findings alone has yet to be thoroughly investigated in Chinese patients. The aim of this study was therefore to use ECG signs to establish novel models for quantitative and localization analysis of clot burden in patients with acute PTE. Acute PTE patients from three centers were enrolled between 2015 and 2019 in a retrospective cohort study (NCT03802929). We analyzed the 12-lead ECGs at admission and studied computed tomography pulmonary angiography (CTPA) features to obtain the Qanadli score of clot burden and location of thrombus. Novel risk prediction models were developed and validated using derivation and external validation cohorts, respectively. A total of 341 acute PTE patients were screened, of whom 246 (72.1%) were from Shanghai Tenth People's Hospital, 71 (20.8%) were from Shanghai Pulmonary Hospital and 24 (7.0%) were from Qidong People's Hospital. In the derivation cohort, predictors included in the final models were congestive heart failure, chronic obstructive pulmonary disease, hypertension, coronary heart disease, atrial fibrillation and ECG abnormalities. The CHARIS (COPD/CHF/CHD, HTN, Atrial arrhythmias/AF, RBBB/RAD, Inverted T wave and S1Q3T3/ Sinus tachycardia) I model was established for quantitatively assessing Qanadli score. It had moderate discrimination in both the derivation cohort (concordance index (c-index) of 0.720, 95% CI 0.655-0.780) and the validation cohort (c-index of 0.663, 95% CI 0.559-0.757). The CHARIS II model was used to predict the probability of trunk obstruction. It showed similar discrimination in the derivation cohort (c-index of 0.753, 95% CI 0.691-0.811) and in the validation cohort (c-index of 0.741, 95% CI 0.641-0.827). Calibration curves and Hosmer-Lemeshow test confirmed the accuracy of the risk prediction equations in the external validation dataset. Decision curve analysis showed the CHARIS I and CHARIS II algorithms had positive net benefits in both the derivation and validation cohorts. From quantitative and localization perspectives, the CHARIS algorithms can identify acute PTE patients with heavy thrombus burdens prior to imaging diagnosis. NCT03802929, https://www.clinicaltrials.gov/study/NCT03802929.

Smartphone-assisted sensor array constructed by copper-based laccase-like nanozymes for specific identification and discrimination of organophosphorus pesticides.

Accurate pesticide identification is of great importance for regulating food safety. However, the discrimination between organophosphorus pesticides (OPs) and carbamate pesticides (CPs) is still a challenge for existing analytical methods based on cholinesterase inhibition. It mainly because of the similar inhibitory effect of OPs and CPs on cholinesterase. Herein, we found that OPs and CPs differentially affected nanozymes with laccase-like activity, which would be interfered by OPs in different degrees rather than CPs. Thus, we fabricated a nanozyme sensor array and successfully achieved the OPs identification and similar individual discrimination, ignoring the interference from CPs or other potential interferents (antibiotics, ions, other pesticides). On the basis of nanozyme sensor array, a portable method using smartphone was constructed and utilized to determine OPs in fruits and vegetables. This work would contribute to the development of portable sensors and the highly selective identification and discrimination of OPs in complex samples.

Research on linguistic multi-attribute decision making method for normal cloud similarity

Aiming at the uncertainty linguistic transformation problem in multi-attribute decision making, a decision-making method based on normal cloud similarity was proposed. Firstly, starting from the normal cloud characteristic curves, a normal cloud similarity measurement method based on Wasserstein distance is proposed by combing with the normal cloud entropy-containing expectation curve, which is using the Wasserstein distance to characterize the similarity characteristics of probability distribution. The properties of the proposed similarity measure are discussed in the paper. Secondly, the performance of the proposed method is compared and analyzed with the existed methods by numerical simulation experiment and time series data classification experiment. The experimental results show that the proposed method has good similarity discrimination ability, high classification accuracy and low CPU time cost. Finally, the method was successfully applied into linguistic multi-attribute decision making, and TOPSIS thought is used to compare and rank the schemes, so as to realize the final decision.