Search

Background & Aims: Colonoscopic surveillance is recommended in patients with colonic inflammatory bowel disease (IBD) given their increased risk of colorectal cancer (CRC). We aimed to develop and validate a dynamic prediction model for the occurrence of advanced colorectal neoplasia (aCRN, including high-grade dysplasia and CRC) in IBD., Methods: We pooled data from 6 existing cohort studies from Canada, The Netherlands, the United Kingdom, and the United States. Patients with IBD and an indication for CRC surveillance were included if they underwent at least 1 follow-up procedure. Exclusion criteria included prior aCRN, prior colectomy, or an unclear indication for surveillance. Predictor variables were selected based on the literature. A dynamic prediction model was developed using a landmarking approach based on Cox proportional hazard modeling. Model performance was assessed with Harrell's concordance-statistic (discrimination) and by calibration curves. Generalizability across surveillance cohorts was evaluated by internal-external cross-validation., Results: The surveillance cohorts comprised 3731 patients, enrolled and followed-up in the time period from 1973 to 2021, with a median follow-up period of 5.7 years (26,336 patient-years of follow-up evaluation); 146 individuals were diagnosed with aCRN. The model contained 8 predictors, with a cross-validation median concordance statistic of 0.74 and 0.75 for a 5- and 10-year prediction window, respectively. Calibration plots showed good calibration. Internal-external cross-validation results showed medium discrimination and reasonable to good calibration., Conclusions: The new prediction model showed good discrimination and calibration, however, generalizability results varied. Future research should focus on formal external validation and relate predicted aCRN risks to surveillance intervals before clinical application., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Accurate prediction of response to neoadjuvant chemotherapy (NAC) can help tailor treatment to individual patients' needs. Little is known about the combination of liquid biopsies and computer extracted features from multiparametric magnetic resonance imaging (MRI) for the prediction of NAC response in breast cancer. Here, we report on a prospective study with the aim to explore the predictive potential of this combination in adjunct to standard clinical and pathological information before, during and after NAC. The study was performed in four Dutch hospitals. Patients without metastases treated with NAC underwent 3 T multiparametric MRI scans before, during and after NAC. Liquid biopsies were obtained before every chemotherapy cycle and before surgery. Prediction models were developed using penalized linear regression to forecast residual cancer burden after NAC and evaluated for pathologic complete response (pCR) using leave-one-out-cross-validation (LOOCV). Sixty-one patients were included. Twenty-three patients (38%) achieved pCR. Most prediction models yielded the highest estimated LOOCV area under the curve (AUC) at the post-treatment timepoint. A clinical-only model including tumor grade, nodal status and receptor subtype yielded an estimated LOOCV AUC for pCR of 0.76, which increased to 0.82 by incorporating post-treatment radiological MRI assessment (i.e., the "clinical-radiological" model). The estimated LOOCV AUC was 0.84 after incorporation of computer-extracted MRI features, and 0.85 when liquid biopsy information was added instead of the radiological MRI assessment. Adding liquid biopsy information to the clinical-radiological resulted in an estimated LOOCV AUC of 0.86. In conclusion, inclusion of liquid biopsy-derived markers in clinical-radiological prediction models may have potential to improve prediction of pCR after NAC in breast cancer., (© 2024. The Author(s).)

Results of simulation studies evaluating the performance of statistical methods can have a major impact on the way empirical research is implemented. However, so far there is limited evidence of the replicability of simulation studies. Eight highly cited statistical simulation studies were selected, and their replicability was assessed by teams of replicators with formal training in quantitative methodology. The teams used information in the original publications to write simulation code with the aim of replicating the results. The primary outcome was to determine the feasibility of replicability based on reported information in the original publications and supplementary materials. Replicasility varied greatly: some original studies provided detailed information leading to almost perfect replication of results, whereas other studies did not provide enough information to implement any of the reported simulations. Factors facilitating replication included availability of code, detailed reporting or visualization of data-generating procedures and methods, and replicator expertise. Replicability of statistical simulation studies was mainly impeded by lack of information and sustainability of information sources. We encourage researchers publishing simulation studies to transparently report all relevant implementation details either in the research paper itself or in easily accessible supplementary material and to make their simulation code publicly available using permanent links., Competing Interests: We declare we have no competing interests., (© 2024 The Authors.)

Background and Aims: Prior studies on the effect of smoking on the risk of colitis-associated colorectal neoplasia (CRN) have reported conflicting results. We aimed to further elucidate the association between smoking, including possible dose-effects, and the development of colorectal neoplasia in patients with inflammatory bowel disease (IBD)., Methods: We performed a prospective multicenter cohort study including patients with colonic IBD enrolled in a surveillance program in four academic hospitals between 2011 and 2021. The effects of smoking status and pack-years at study entry on subsequent recurrent events of CRN (including indefinite, low- and high-grade dysplasia, and colorectal cancer [CRC]) were evaluated using uni- and multivariable Prentice, Williams, and Peterson total-time Cox proportional hazard models. Adjustment was performed for extensive disease, prior/index dysplasia, sex, age, first-degree relative with CRC, primary sclerosing cholangitis, and endoscopic inflammation., Results: In 501 of the enrolled 576 patients, at least one follow-up surveillance was performed after the study index (median follow-up 5 years). CRN occurred at least once in 105 patients. Ever smoking was not associated with recurrent CRN risk (adjusted hazard ratio [aHR] 1.04, 95% confidence interval [CI] 0.75-1.44), but an increasing number of pack-years was associated with an increased risk of recurrent CRN (aHR per 10 pack-years 1.17, 95% CI 1.03-1.32; p < 0.05). Separate analyses per IBD type did not reveal differences., Conclusions: This study found that an increase in pack-years is associated with a higher risk of recurrent CRN in patients with IBD, independent of established CRN risk factors (NCT01464151)., (© 2023 The Authors. United European Gastroenterology Journal published by Wiley Periodicals LLC on behalf of United European Gastroenterology.)

Aims: Postoperative atrial fibrillation (POAF) is a common complication of cardiac surgery, yet difficult to detect in ambulatory patients. The primary aim of this study is to investigate the effect of a mobile health (mHealth) intervention on POAF detection after cardiac surgery., Methods and Results: We performed an observational cohort study among 730 adult patients who underwent cardiac surgery at a tertiary care hospital in The Netherlands. Of these patients, 365 patients received standard care and were included as a historical control group, undergoing surgery between December 2017 and September 2018, and 365 patients were prospectively included from November 2018 and November 2020, undergoing an mHealth intervention which consisted of blood pressure, temperature, weight, and electrocardiogram (ECG) monitoring. One physical outpatient follow-up moment was replaced by an electronic visit. All patients were requested to fill out a satisfaction and quality of life questionnaire. Mean age in the intervention group was 62 years, 275 (70.4%) patients were males. A total of 4136 12-lead ECGs were registered. In the intervention group, 61 (16.7%) patients were diagnosed with POAF vs. 25 (6.8%) patients in the control group [adjusted risk ratio (RR) of POAF detection: 2.15; 95% confidence interval (CI): 1.55-3.97]. De novo atrial fibrillation was found in 13 patients using mHealth (6.5%) vs. 4 control group patients (1.8%; adjusted RR 3.94, 95% CI: 1.50-11.27)., Conclusion: Scheduled self-measurements with mHealth devices could increase the probability of detecting POAF within 3 months after cardiac surgery. The effect of an increase in POAF detection on clinical outcomes needs to be addressed in future research., Competing Interests: Conflict of interest: None declared., (© The Author(s) 2022. Published by Oxford University Press on behalf of the European Society of Cardiology.)

Aims: Multiple risk scores to predict ischaemic stroke (IS) in patients with atrial fibrillation (AF) have been developed. This study aims to systematically review these scores, their validations and updates, assess their methodological quality, and calculate pooled estimates of the predictive performance., Methods and Results: We searched PubMed and Web of Science for studies developing, validating, or updating risk scores for IS in AF patients. Methodological quality was assessed using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). To assess discrimination, pooled c-statistics were calculated using random-effects meta-analysis. We identified 19 scores, which were validated and updated once or more in 70 and 40 studies, respectively, including 329 validations and 76 updates-nearly all on the CHA2DS2-VASc and CHADS2. Pooled c-statistics were calculated among 6 267 728 patients and 359 373 events of IS. For the CHA2DS2-VASc and CHADS2, pooled c-statistics were 0.644 [95% confidence interval (CI) 0.635-0.653] and 0.658 (0.644-0.672), respectively. Better discriminatory abilities were found in the newer risk scores, with the modified-CHADS2 demonstrating the best discrimination [c-statistic 0.715 (0.674-0.754)]. Updates were found for the CHA2DS2-VASc and CHADS2 only, showing improved discrimination. Calibration was reasonable but available for only 17 studies. The PROBAST indicated a risk of methodological bias in all studies., Conclusion: Nineteen risk scores and 76 updates are available to predict IS in patients with AF. The guideline-endorsed CHA2DS2-VASc shows inferior discriminative abilities compared with newer scores. Additional external validations and data on calibration are required before considering the newer scores in clinical practice., Clinical Trial Registration: ID CRD4202161247 (PROSPERO)., Competing Interests: Conflicts of interest: None declared., (© The Author(s) 2022. Published by Oxford University Press on behalf of the European Society of Cardiology.)

Purpose: In studies of effects of time-varying drug exposures, adequate adjustment for time-varying covariates is often necessary to properly control for confounding. However, the granularity of the available covariate data may not be sufficiently fine, for example when covariates are measured for participants only when their exposure levels change., Methods: To illustrate the impact of choices regarding the frequency of measuring time-varying covariates, we simulated data for a large target trial and for large observational studies, varying in covariate measurement design. Covariates were measured never, on a fixed-interval basis, or each time the exposure level switched. For the analysis, it was assumed that covariates remain constant in periods of no measurement. Cumulative survival probabilities for continuous exposure and non-exposure were estimated using inverse probability weighting to adjust for time-varying confounding, with special emphasis on the difference between 5-year event risks., Results: With monthly covariate measurements, estimates based on observational data coincided with trial-based estimates, with 5-year risk differences being zero. Without measurement of baseline or post-baseline covariates, this risk difference was estimated to be 49% based on the available observational data. With measurements on a fixed-interval basis only, 5-year risk differences deviated from the null, to 29% for 6-monthly measurements, and with magnitude increasing up to 35% as the interval length increased. Risk difference estimates diverged from the null to as low as -18% when covariates were measured depending on exposure level switching., Conclusion: Our simulations highlight the need for careful consideration of time-varying covariates in designing studies on time-varying exposures. We caution against implementing designs with long intervals between measurements. The maximum length required will depend on the rates at which treatments and covariates change, with higher rates requiring shorter measurement intervals., (© 2021 The Authors. Pharmacoepidemiology and Drug Safety published by John Wiley & Sons Ltd.)

Objectives: Epidemiologic studies often suffer from incomplete data, measurement error (or misclassification), and confounding. Each of these can cause bias and imprecision in estimates of exposure-outcome relations. We describe and compare statistical approaches that aim to control all three sources of bias simultaneously., Study Design and Setting: We illustrate four statistical approaches that address all three sources of bias, namely, multiple imputation for missing data and measurement error, multiple imputation combined with regression calibration, full information maximum likelihood within a structural equation modeling framework, and a Bayesian model. In a simulation study, we assess the performance of the four approaches compared with more commonly used approaches that do not account for measurement error, missing values, or confounding., Results: The results demonstrate that the four approaches consistently outperform the alternative approaches on all performance metrics (bias, mean squared error, and confidence interval coverage). Even in simulated data of 100 subjects, these approaches perform well., Conclusion: There can be a large benefit of addressing measurement error, missing values, and confounding to improve the estimation of exposure-outcome relations, even when the available sample size is relatively small., (Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved.)

Objective: Article full texts are often inaccessible via the standard search engines of biomedical literature, such as PubMed and Embase, which are commonly used for systematic reviews. Excluding the full-text bodies from a literature search may result in a small or selective subset of articles being included in the review because of the limited information that is available in only title, abstract, and keywords. This article describes a comparison of search strategies based on a systematic literature review of all articles published in 5 top-ranked epidemiology journals between 2000 and 2017., Study Design and Setting: Based on a text-mining approach, we studied how nine different methodological topics were mentioned across text fields (title, abstract, keywords, and text body). The following methodological topics were studied: propensity score methods, inverse probability weighting, marginal structural modeling, multiple imputation, Kaplan-Meier estimation, number needed to treat, measurement error, randomized controlled trial, and latent class analysis., Results: In total, 31,641 Hypertext Markup Language (HTML) files were downloaded from the journals' websites. For all methodological topics and journals, at most 50% of articles with a mention of a topic in the text body also mentioned the topic in the title, abstract, or keywords. For several topics, a gradual decrease over calendar time was observed of reporting in the title, abstract, or keywords., Conclusion: Literature searches based on title, abstract, and keywords alone may not be sufficiently sensitive for studies of epidemiological research practice. This study also illustrates the potential value of full-text literature searches, provided there is accessibility of full-text bodies for literature searches., (Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved.)

This article gives a review of the limitations of propensity score matching as a tool for confounding control in the presence of censoring. Using an illustrative simulation study, we emphasize the importance of explicit adjustment for selective loss to follow-up and explain how this may be achieved.

Background: Associations between atmospheric pressure and abdominal aortic aneurysm (AAA) rupture risk have been reported, but empirical evidence is inconclusive and largely derived from studies that did not account for possible nonlinearity, seasonality, and confounding by temperature., Methods: Associations between atmospheric pressure and AAA rupture risk were investigated using local meteorological data and a case series of 358 patients admitted to hospital for ruptured AAA during the study period, January 2002 to December 2012. Two analyses were performed-a time series analysis and a case-crossover study., Results: Results from the 2 analyses were similar; neither the time series analysis nor the case-crossover study showed a significant association between atmospheric pressure ( P = .627 and P = .625, respectively, for mean daily atmospheric pressure) or atmospheric pressure variation ( P = .464 and P = .816, respectively, for 24-hour change in mean daily atmospheric pressure) and AAA rupture risk., Conclusion: This study failed to support claims that atmospheric pressure causally affects AAA rupture risk. In interpreting our results, one should be aware that the range of atmospheric pressure observed in this study is not representative of the atmospheric pressure to which patients with AAA may be exposed, for example, during air travel or travel to high altitudes in the mountains. Making firm claims regarding these conditions in relation to AAA rupture risk is difficult at best. Furthermore, despite the fact that we used one of the largest case series to date to investigate the effect of atmospheric pressure on AAA rupture risk, it is possible that this study is simply too small to demonstrate a causal link.

Background: There is no effective way to accurately predict paroxysmal and persistent atrial fibrillation (AF) subtypes unless electrocardiogram (ECG) observation is obtained. We aim to develop a predictive model using a machine learning algorithm for identification of paroxysmal and persistent AF, and investigate the influencing factors. Methods: We collected demographic data, medication use, serological indicators, and baseline cardiac ultrasound data of all included subjects, totaling 50 variables. The diagnosis of AF subtypes is confirmed by ECG observation for at least more than 7 days. Variable selection was performed by spearman correlation analysis, recursive feature elimination, and least absolute shrinkage and selection operator regression. We built a prediction model for AF using three machine learning methods. Finally, the significance of each variable was analyzed by Shapley additive explanations method. Results: After screening, we found the optimal variable set consisting of 10 variables. The model we built achieved good predictive performance (AUC = 0.870, 95%CI 0.858 to 0.882), and had specificity of 0.851 (95%CI 0.844 to 0.858) and sensitivity of 0.716 (95%CI 0.676 to 0.755). Good predictive performance was stably achieved in different age subgroups and different gender subgroups. LA and NT-proBNP were the two most important variables for predicting paroxysmal and persistent AF in all models, except for the female subgroup aged less than 60 years. Conclusions: Our model makes it possible to predict paroxysmal and persistent AF based on baseline data at admission. Early and individualized intervention strategies based on our model may help to improve clinical outcomes in AF patients. [ABSTRACT FROM AUTHOR]

Background: Surgical site infections (SSIs) lead to increased mortality and morbidity, as well as increased healthcare costs. Multiple models for the prediction of this serious surgical complication have been developed, with an increasing use of machine learning (ML) tools. Objective: The aim of this systematic review was to assess the performance as well as the methodological quality of validated ML models for the prediction of SSIs. Methods: A systematic search in PubMed, Embase and the Cochrane library was performed from inception until July 2023. Exclusion criteria were the absence of reported model validation, SSIs as part of a composite adverse outcome, and pediatric populations. ML performance measures were evaluated, and ML performances were compared to regression-based methods for studies that reported both methods. Risk of bias (ROB) of the studies was assessed using the Prediction model Risk of Bias Assessment Tool. Results: Of the 4,377 studies screened, 24 were included in this review, describing 85 ML models. Most models were only internally validated (81%). The C-statistic was the most used performance measure (reported in 96% of the studies) and only two studies reported calibration metrics. A total of 116 different predictors were described, of which age, steroid use, sex, diabetes, and smoking were most frequently (100% to 75%) incorporated. Thirteen studies compared ML models to regression-based models and showed a similar performance of both modelling methods. For all included studies, the overall ROB was high or unclear. Conclusions: A multitude of ML models for the prediction of SSIs are available, with large variability in performance. However, most models lacked external validation, performance was reported limitedly, and the risk of bias was high. In studies describing both ML models and regression-based models, one modelling method did not outperform the other. [ABSTRACT FROM AUTHOR]

Background: In addition to the great challenge of early diagnosis and prognosis in breast cancer (BC), the role of gene promoters in BC remains largely unexplored. This study aimed to evaluate aldo-keto reductase family 1 member B1 (AKR1B1) methylation as noninvasive biomarker for early BC diagnosis. Methods: A total of 200 (120 with BC, 40 with benign breast diseases, 40 healthy) Egyptian women were enrolled. AKR1B1 methylation level was determined using EpiTect Methyl II QPCR assay quantitative polymerase chain reaction. Results: Findings revealed that hypermethylation AKR1B1 was reported to be associated (P <.0001) with BC cases (93.2 [75.4-98.6]) compared with benign (23.9 [22.6-48.3]) or healthy (15.5 [10.6-16]) controls. It had a great diagnostic power (area under the curve [AUC] = 0.909) that was superior to cancer antigen (CA) 15-3 (AUC = 0.681) and carcinoembryonic antigen (CEA) (AUC = 0.539). Interestingly, AKR1B1 hypermethylation was reported to be significant in identifying BC early stages (AUC = 0.899) and grades (AUC = 0.903). Independent to hormonal status and HER2neu expression, AKR1B1 hypermethylation was related to some tumor severity features, including advanced stages, high histological grades, and lymph node invasion. Also, AKR1B1 high degrees of methylation were significantly correlated with the increase in CEA (r =.195; P =.027), CA-15.3 (r =.351; P =.0001) and tumor stages (r =.274; P =.014), grades (r =.253; P =.024), and lymph node invasion (r =.275; P =.014). Conclusions: This study revealed that aberrant AKR1B1 methylation could facilitate early BC detection from benign br0east disorders. Hypermethylated AKR1B1 was related to BC aggressiveness suggesting its potential role as diagnostic and prognostic BC biomarker. [ABSTRACT FROM AUTHOR]

Background: Missing data are common in observational studies and often occur in several of the variables required when estimating a causal effect, i.e. the exposure, outcome and/or variables used to control for confounding. Analyses involving multiple incomplete variables are not as straightforward as analyses with a single incomplete variable. For example, in the context of multivariable missingness, the standard missing data assumptions ("missing completely at random", "missing at random" [MAR], "missing not at random") are difficult to interpret and assess. It is not clear how the complexities that arise due to multivariable missingness are being addressed in practice. The aim of this study was to review how missing data are managed and reported in observational studies that use multiple imputation (MI) for causal effect estimation, with a particular focus on missing data summaries, missing data assumptions, primary and sensitivity analyses, and MI implementation. Methods: We searched five top general epidemiology journals for observational studies that aimed to answer a causal research question and used MI, published between January 2019 and December 2021. Article screening and data extraction were performed systematically. Results: Of the 130 studies included in this review, 108 (83%) derived an analysis sample by excluding individuals with missing data in specific variables (e.g., outcome) and 114 (88%) had multivariable missingness within the analysis sample. Forty-four (34%) studies provided a statement about missing data assumptions, 35 of which stated the MAR assumption, but only 11/44 (25%) studies provided a justification for these assumptions. The number of imputations, MI method and MI software were generally well-reported (71%, 75% and 88% of studies, respectively), while aspects of the imputation model specification were not clear for more than half of the studies. A secondary analysis that used a different approach to handle the missing data was conducted in 69/130 (53%) studies. Of these 69 studies, 68 (99%) lacked a clear justification for the secondary analysis. Conclusion: Effort is needed to clarify the rationale for and improve the reporting of MI for estimation of causal effects from observational data. We encourage greater transparency in making and reporting analytical decisions related to missing data. [ABSTRACT FROM AUTHOR]

Antithrombotic therapies (ATT) play a pivotal role in the management of cardiovascular diseases, aiming to prevent ischemic events while maintaining a delicate balance with the patient’s bleeding risk. Typically, ATT can be classified into antiplatelet and anticoagulant therapies. Their application spans a broad spectrum of cardiovascular conditions, ranging from ischemic heart disease to atrial fibrillation, encompassing venous thromboembolisms and innovative structural interventional cardiology procedures. The global burden of cardiovascular diseases is steadily increasing, often giving rise to overlapping clinical presentations. Accordingly, the adoption of combined pharmacological approaches becomes imperative, potentially disrupting the delicate equilibrium between ischemic and bleeding risk, thus leading to nuanced pharmacotherapeutic pathways. In this context, contemporary investigations strive to identify a convergence point that optimizes the duration of medical therapy while addressing the need for antithrombotic effects, especially in the context of ischemic heart disease. This review aims to comprehensively revisit the main antithrombotic strategies in cardiovascular diseases, with the intention of enhancing a systematic approach which is key for the effective clinical management of these patients. Also, the review will examine the most impactful studies that have established the groundwork for current scientific evidence, with acknowledgement of special populations. Finally, we will cast a gaze into the future of this dynamic and evolving research field, exploring forthcoming perspectives and advancements. [ABSTRACT FROM AUTHOR]

Background: Atrial fibrillation and atrial flutter represent the most prevalent clinically significant cardiac arrhythmias. While the CHA2DS2-VASc score is commonly used to inform anticoagulation therapy decisions for patients with these conditions, its predictive power is limited. Therefore, we sought to improve risk prediction for left atrial appendage thrombus (LAAT), a known risk factor for stroke in these patients., Methods: We developed and validated an explainable machine learning model using the eXtreme Gradient Boosting algorithm with 5 × 5 nested cross-validation. The primary outcome was to predict the probability of LAAT in patients with atrial fibrillation and atrial flutter who underwent transesophageal echocardiogram prior to cardioversion. Our algorithm used 37 demographic, comorbid, and transthoracic echocardiographic variables., Results: A total of 795 patients were included in our analysis. LAAT was present in 11.3% of the patients. The average age of patients was 63.3 years and 34.7% were women. Patients with LAAT had significantly lower left ventricular ejection fraction (29.9% vs 43.5%; p < 0.001), lower E' lateral velocity (5.7 cm vs. 7.9 cm; p < 0.001) and higher E/A ratio (2.6 vs 1.8; p = 0.002). Our machine learning model achieved a high AUC of 0.79, with a high specificity of 0.82, and modest sensitivity of 0.57. Left ventricular ejection fraction was the most important variable in predicting LAAT. Patients were split into 10 buckets based on the percentile of their predicted probability of having thrombus. The lower the percentile (e.g., 10%), the lower the probability of having thrombus. Using a cutoff point of 0.16 which includes 10.0% of the patients, we can rule out thrombus with 100% confidence., Conclusion: Using machine learning, we refined the predictive power of predicting LAAT and explained the model. These results show promise in providing better guidance for anticoagulation therapy and cardioversion in AF and AFL patients., Competing Interests: Declarations. Ethical approval: The study was approved by the IRB office at the University of Florida. Informed consent: was not required as the study was retrospective. Conflict of interest: Authors declare no relevant conflicts of interest., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Dynamic preload parameters are used to guide perioperative fluid management. However, reported cut-off values vary and the presence of a gray zone complicates clinical decision making. Measurement error, intrinsic to the calculation of pulse pressure variation (PPV) has not been studied but could contribute to this level of uncertainty. The purpose of this study was to quantify and compare measurement errors associated with PPV calculations. Hemodynamic data of patients undergoing liver transplantation were extracted from the open-access VitalDatabase. Three algorithms were applied to calculate PPV based on 1 min observation periods. For each method, different durations of sampling periods were assessed. Best Linear Unbiased Prediction was determined as the reference PPV-value for each observation period. A Bayesian model was used to determine bias and precision of each method and to simulate the uncertainty of measured PPV-values. All methods were associated with measurement error. The range of differential and proportional bias were [− 0.04%, 1.64%] and [0.92%, 1.17%] respectively. Heteroscedasticity influenced by sampling period was detected in all methods. This resulted in a predicted range of reference PPV-values for a measured PPV of 12% of [10.2%, 13.9%] and [10.3%, 15.1%] for two selected methods. The predicted range in reference PPV-value changes for a measured absolute change of 1% was [− 1.3%, 3.3%] and [− 1.9%, 4%] for these two methods. We showed that all methods that calculate PPV come with varying degrees of uncertainty. Accounting for bias and precision may have important implications for the interpretation of measured PPV-values or PPV-changes. [ABSTRACT FROM AUTHOR]

Background: Atrial fibrillation (AF) is strongly associated with an increased risk of ischemic events. Anticoagulation focuses on reducing the risk of embolism. Guideline recommended CHA2DS2-VASc scoring system is most widely used; however, different scoring systems do exist. Thus, we sought to assess the impact of anticoagulant treatment and different scoring systems on the development of stroke, myocardial infarction, and allcause mortality in patients with nonvalvular AF. Methods: The present study was designed as a prospective cohort study. The enrollment of the patients was conducted between August 1, 2015, and January 1, 2016. The followup period was defined as the time from enrollment to the end of April 1, 2017, which also provided at least 12 months of prospective follow-up for each patient. Results: A total of 1807 patients with AF were enrolled. During the follow-up, 2.7% (48) of patients had stroke, 0.8% (14) had myocardial infarction, and 7.5% (136) died. The anticoagulation and risk factors in AF (ATRIA) score had a better accuracy for the prediction of stroke compared to other scoring systems (0.729, 95% CI, 0.708-0.750, P < .05). Patients under low-dose rivaroxaban treatment had significantly worse survival (logrank P < .001). Age, CHA2DS2-VASc score, R2CHADS2 score, ATRIA score, chronic heart failure, prior stroke, and being under low-dose rivaroxaban treatment were independent predictors of clinical endpoint (P < .001). Conclusion: Low-dose rivaroxaban treatment was independently and strongly associated with the combined clinical endpoint. Furthermore, the ATRIA score proved to be a stronger predictor of stroke in the Turkish population. [ABSTRACT FROM AUTHOR]

This article discusses the need for new cardiovascular tools to reclassify stroke risk. It highlights that a significant proportion of strokes are of undetermined origin and that current risk factors do not fully explain the risk of stroke. The article explores the potential use of novel assessment tools such as circadian rhythm of blood pressure, arterial stiffness, and atrial cardiopathy in improving stroke risk assessment. It also suggests that reclassifying strokes into different types based on factors like carotid plaque characteristics can help guide treatment decisions. Overall, the article emphasizes the importance of understanding the underlying causes of strokes to improve prevention and treatment strategies. [Extracted from the article]

Background: Time‐varying drug treatments are common in studies using routinely collected health data (RCD) for assessing treatment effects. This study aimed to examine how these studies reported, handled, and interpreted time‐varying drug treatments. Methods: A systematic search was conducted on PubMed from 2018 to 2020. Eligible studies were those used RCD to explore drug treatment effects. We summarized the reporting characteristics and methods employed for handling time‐varying treatments. Logistic regressions were performed to investigate the association between study characteristics and the reporting of time‐varying treatments. Results: Two hundred and fifty‐six studies were included, and 225 (87.9%) studies involved time‐varying treatments. Of these, 24 (10.7%) reported the proportion of time‐varying treatments and 105 (46.7%) reported methods used to handle time‐varying treatments. Multivariable logistic regression showed that medical studies, prespecified protocol, and involvement of methodologists were associated with a higher likelihood of reporting the methods applied to handle time‐varying treatments. Among the 105 studies that reported methods, as‐treated analyses were the most commonly used analysis sets, which were employed in 73.9%, 75.3% and 88.2% of studies that reported approaches for treatment discontinuation, treatment switching and treatment add‐on. Among the 225 studies involved time‐varying treatments, 27 (12.0%) acknowledged the potential bias introduced by treatment change, of which 14 (51.9%) suggested that potential biases may impact acceptance or rejection of the null hypothesis. Conclusions: Among observational studies using RCD, the underreporting about the presence and methods for handling time‐varying treatments was largely common. The potential biases due to time‐varying treatments have frequently been disregarded. Collaborative endeavors are strongly needed to enhance the prevailing practices. [ABSTRACT FROM AUTHOR]

Background: Thrombosis is a unique complication of coronavirus disease 2019 (COVID-19). Although antiphospholipid antibodies (aPL) are detected in COVID-19 patients, their clinical significance remains elusive. We evaluated the prevalence of aPL and serum concentrations of beta-2 glycoprotein I (b2GPI), a major selfantigen for aPL, in Japanese COVID-19 patients with and without thrombosis. Methods: This retrospective single-center nested case-control study included 594 hospitalized patients with COVID-19 between January 2020 and August 2021. Thrombotic complications were collected from medical records. Propensity score-matching method (PSM) (1:2 matching including age, sex, severity on admission, and prior history of thrombosis) was performed to compare the prevalence and titer of aPL (anti-cardiolipin (aCL) IgG/IgM, antib2GPI IgG/IgM/IgA, and anti-phosphatidylserine/prothrombin antibody (aPS/PT) IgG/IgM) and serum b2GPI concentration. In addition, PSM (1:1 matching including age and sex) was performed to compare the serum b2GPI concentration between COVID-19 patients and healthy donors. Results: Among the patients, 31 patients with thrombosis and 62 patients without were compared. The prevalence of any aPLs was indifferent regardless of the thrombosis (41.9% in those with thrombosis vs. 38.7% in those without, p =0.82). The positive rates of individual aPL were as follows: anti-CL IgG (9.7% vs. 1.6%, p =0.11)/IgM (0% vs. 3.2%, p =0.55), anti-b2GP1 IgG (22.6% vs. 9.7%, p =0.12)/IgA (9.7% vs. 9.7%, p =1.0)/IgM (0% vs. 0%, p =1.0), and anti-PS/PT IgG (0% vs. 1.6%, p =1.0)/IgM (12.9% vs. 21.0%, p =0.41), respectively. The aPL titers were also similar regardless of thrombosis. The levels of b2GPI in COVID-19 patients were lower than those in the healthy donors. Conclusion: Although aPLs were frequently detected in Japanese COVID-19 patients, their prevalence and titer were irrelevant to thrombotic complications. While COVID-19 patients have lower levels of serum b2GPI than healthy blood donors, b2GPI levels were indifferent regardless of thrombosis. Although most of the titers were below cut-offs, positive correlations were observed among aPLs, suggesting that the immune reactions against aPL antigens were induced by COVID-19. We should focus on the long-term thromboembolic risk and the development of APS in the aPL-positive patients with high titer or multiple aPLs. [ABSTRACT FROM AUTHOR]

Aims: The present guidelines advise replacing the aortic valve for individuals with severe aortic stenosis (AS) based on various echocardiographic parameters. Accurate measurements are essential to avoid misclassification and unnecessary interventions. The objective of this study was to evaluate the influence of measurement error on the echocardiographic evaluation of the severity of AS. Methods and results: A systematic review was performed to examine whether measurement errors are reported in studies focusing on the prognostic value of peak aortic jet velocity (Vmax), mean pressure gradient (MPG), and effective orifice area (EOA) in asymptomatic patients with AS. Out of the 37 studies reviewed, 17 (46%) acknowledged the existence of measurement errors, but none of them utilized methods to address them. Secondly, the magnitude of potential errors was collected from available literature for use in clinical simulations. Interobserver variability ranged between 0.9% and 8.3% for Vmax and MPG but was higher for EOA (range 7.7%‐12.7%), indicating lower reliability. Assuming a circular left ventricular outflow tract area led to a median underestimation of EOA by 23% compared to planimetry by other modalities. A clinical simulation resulted in the reclassification of 42% of patients, shifting them from a diagnosis of severe AS to moderate AS. Conclusions: Measurement errors are underreported in studies on echocardiographic assessment of AS severity. These errors can lead to misclassification and misdiagnosis. Clinicians and scientists should be aware of the implications for accurate clinical decision‐making and assuring research validity. [ABSTRACT FROM AUTHOR]

Aim: CHA2DS2‐VASc and modified‐CHADS2 score can easily estimate the risk of stroke in atrial fibrillation. Study's purpose was to evaluate these in haemodialysis patients, and assess the effect of diabetes mellitus (DM). Methods: The scores calculated in 237 haemodialysis patients, 121 diabetics (58 females) and 116 non‐diabetics (41 females). Results correlated to cardiovascular events (acute myocardial infarction, atrial fibrillation, heart failure, peripheral arterial disease, stroke, mortality). Results: CHA2DS2‐VASc score correlated with the occurrence of stroke and heart failure (p <.01, p <.01), (p <.01, p <.01), respectively in diabetics and non‐diabetics. CHA2DS2‐VASc score could predict the risk of all‐cause mortality in both groups, p =.03, p <.01, respectively, however, the risk of cardiovascular death could be predicted in non‐diabetics, p <.01. Modified‐CHADS2 score associated with heart failure (p =.04), cardiovascular (p <.01) and all‐cause mortality (p <.01) only on non‐diabetics. C statistics indicated that the first score showed modest discrimination in patients with and without DM, for stroke and all‐cause mortality. The second score performed modestly only on patients without DM for all‐cause mortality. Both scores showed poor calibration. Stroke was a common cause of cardiovascular death (OR = 3.52, 95% CI = 1.92–6.47, p <.01) and associated with central venous catheter (OR = 2.19, 95% CI = 1.12–4.27, p =.02) and pre‐existing atrial fibrillation (OR = 1.94, 95% CI = 1.06–3.58, p =.03). Conclusion: CHA2DS2‐VASc score correlated with stroke, heart failure and all‐cause mortality in haemodialysis patients with and without DM. The risk of cardiovascular death could be predicted only in non‐diabetics patients. Modified‐CHADS2 score correlated with heart failure, cardiovascular and all‐cause mortality only on non‐diabetics. Both had modest discrimination and poor calibration. [ABSTRACT FROM AUTHOR]

Aims Atrial fibrillation (AF) is one of the major causes of ischaemic stroke. In addition to clinical risk evaluated by the CHA2DS2-VASC score, the impact of genetic factors on the risk of AF-related thromboembolic stroke has been largely unknown. We found several copy number variations (CNVs) in novel genes that were associated with thromboembolic stroke risk in our AF patients by genome-wide approach. Among them, the gasdermin D (GSDMD) gene was related to inflammation. We aimed to test whether GSDMD deletion was associated with AF-related stroke. Methods and results A total of 400 patients with documented non-familial AF were selected, of which 100 patients were diagnosed with ischaemic stroke. The baseline characteristics of age, sex, valvular heart disease, coronary artery disease, heart failure, and CHA2DS2-VASc scores were not statistically different between cases and controls. We found that individuals who carried GSDMD homozygous deletion genotype had a higher risk for ischaemic stroke (odds ratio 2.195; 95% confidence interval, 1.24–3.90; P = 0.007), even adjusted by CHA2DS2-VASc scores. We also validated the association of GSDMD with AF stroke in a large Caucasian population (UK Biobank). Conclusion We found a link between the homozygous deletion of the GSDMD gene and an increased risk of stroke in patients with AF. This may implicate the use of therapy targeting GSDMD in the prevention of ischaemic stroke for AF patients. [ABSTRACT FROM AUTHOR]

Objective: Time-varying treatments are common in observational studies. However, when assessing treatment effects, the methodological framework has not been systematically established for handling time-varying treatments. This study aimed to examine the current methods for dealing with time-varying treatments in observational studies and developed practical recommendations., Methods: We searched PubMed from 2000 to 2021 for methodological articles about time-varying treatments, and qualitatively summarized the current methods for handling time-varying treatments. Subsequently, we developed practical recommendations through interactive internal group discussions and consensus by a panel of external experts., Results: Of the 36 eligible reports (22 methodological reviews, 10 original studies, 2 tutorials and 2 commentaries), most examined statistical methods for time-varying treatments, and only a few discussed the overarching methodological process. Generally, there were three methodological components to handle time-varying treatments. These included the specification of treatment which may be categorized as three scenarios (i.e., time-independent treatment, static treatment regime, or dynamic treatment regime); definition of treatment status which could involve three approaches (i.e., intention-to-treat, per-protocol, or as-treated approach); and selection of analytic methods. Based on the review results, a methodological workflow and a set of practical recommendations were proposed through two consensus meetings., Conclusions: There is no consensus process for assessing treatment effects in observational studies with time-varying treatments. Previous efforts were dedicated to developing statistical methods. Our study proposed a stepwise workflow with practical recommendations to assist the practice., (© 2024 Chinese Cochrane Center, West China Hospital of Sichuan University and John Wiley & Sons Australia, Ltd.)

Purpose: Beyond its direct effects on physical health the COVID-19 pandemic has been shown to have negative effects on the living situation of people with severe mental illness (SMI). To date, there has been little research on resilience factors preventing people with SMI from experiencing negative effects of the COVID-19 pandemic. The objective of this study was to investigate the role of perceived empowerment (PE) as a resilience factor, preventing people with SMI from experiencing negative effects of the COVID-19 pandemic on daily living. Methods: We investigated negative effects of the COVID-19 pandemic on daily living in 931 persons with SMI at two times within six month between June 2020 and Mai 2021. To take into account the longitudinal structure of the data we applied mixed effects regression analyses and longitudinal path models. Results: A majority of participants experienced negative effects of the COVID-19 pandemic on several dimensions of daily living. Negative effects increased with rising levels of illness-related impairment but decreased as the level of PE rose. While negative effects of the COVID-19 pandemic at follow-up were negatively associated with overall subjective quality of life baseline, PE was negatively associated with the negative impact of the pandemic and positively with quality of life. Conclusion: Patients with SMI need support to reduce negative effects of the COVID-19 pandemic on their quality of life. The promotion of PE could help strengthen resilience in this target group. Trial registration: German Clinical Trial Register, DRKS00019086, registered on 3 January 2020. (https://www.drks.de/drks_web/navigate.do?navigationId=trial.HTML&TRIAL_ID=DRKS00019086). [ABSTRACT FROM AUTHOR]

Background: Traumatic aortic injuries (TAIs) are rare but are associated with a high mortality. Prior studies have shown skiers and pilots, whose injuries occur at high altitudes, are at an increased risk for a TAI. The purpose of this study was to examine the effect of altitude on the incidence of TAIs across all causes of injury. Methods: This retrospective cohort study at six Level I trauma centers (8/1/2016–1/1/2020) included adult blunt trauma patients with a chest or abdomen injury. High altitude injuries (> 5000 ft.) were compared to low altitude injuries (≤ 5000 ft.). The primary outcome was incidence of TAI. Results: There were 8562 patients, 37% were at high altitude and 63% at low altitude. High altitude patients were older (p < 0.01), more often Caucasian (p < 0.01) and had a higher ISS (p < 0.01). There was a significantly greater incidence of TAI at high altitude than low altitude (1.5% vs. 1.1%, p = 0.01). The median altitude was significantly higher for patients with a TAI than for patients without a TAI (5100 ft. vs. 1400 ft., p = 0.01). After adjustment, high altitude patients had 2-fold [OR: 2.4 (1.6, 3.7)] greater odds of having a TAI than low altitude patients. Conclusion: TAIs were more prevalent among high altitude injuries. Providers should be aware of the increased incidence of TAIs at high altitudes particularly when there is a delay in diagnosis and transfer to a trauma center with appropriate resources to manage these critical injuries. TAI screening at high altitude trauma centers may improve survival rates. [ABSTRACT FROM AUTHOR]

Purpose: Propensity score-weighting for confounder control and multiple imputation to counter missing data are both widely used methods in epidemiological research. Combination of the two is not trivial and requires a number of decisions to produce valid inference. In this tutorial, we outline the assumptions underlying each of the methods, present our considerations in combining the two, discuss the methodological and practical implications of our choices and briefly point to alternatives. Throughout we apply the theory to a research project about post-traumatic stress disorder in Syrian refugees. Patients and Methods: We detail how we used logistic regression-based propensity scores to produce "standardized mortality ratio"-weights and Substantive Model Compatible-Full Conditional Specification for multiple imputation of missing data to get the estimate of association. Finally, a percentile confidence interval was produced by bootstrapping. Results: A simple propensity score model with weight truncation at 1st and 99th percentile obtained acceptable balance on all covariates and was chosen as our model. Due to computational issues in the multiple imputation, two levels of one of the substantive model covariates and two levels of one of the auxiliary covariates were collapsed. This slightly modified propensity score model was the substantive model in the SMC-FCS multiple imputation, and regression models were set up for all partially observed covariates. We set the number of imputations to 10 and number of iterations to 40. We produced 999 bootstrap estimates to compute the 95-percentile confidence interval. Conclusion: Combining propensity score-weighting and multiple imputation is not a trivial task. We present considerations necessary to do so, realizing it is demanding in terms of both workload and computational time; however, we do not consider the former a drawback: it makes some of the underlying assumptions explicit and the latter may be a nuisance that will diminish with faster computers and better implementations. [ABSTRACT FROM AUTHOR]

Copyright of American Journal of Drug & Alcohol Abuse is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The search for new strategies for better understanding cardiovascular (CV) disease is a constant one, spanning multitudinous types of observations and studies. A comprehensive characterization of each disease state and its biomolecular underpinnings relies upon insights gleaned from extensive information collection of various types of data. Researchers and clinicians in CV biomedicine repeatedly face questions regarding which types of data may best answer their questions, how to integrate information from multiple datasets of various types, and how to adapt emerging advances in machine learning and/or artificial intelligence to their needs in data processing. Frequently lauded as a field with great practical and translational potential, the interface between biomedical informatics and CV medicine is challenged with staggeringly massive datasets. Successful application of computational approaches to decode these complex and gigantic amounts of information becomes an essential step toward realizing the desired benefits. In this review, we examine recent efforts to adapt informatics strategies to CV biomedical research: automated information extraction and unification of multifaceted -omics data. We discuss how and why this interdisciplinary space of CV Informatics is particularly relevant to and supportive of current experimental and clinical research. We describe in detail how open data sources and methods can drive discovery while demanding few initial resources, an advantage afforded by widespread availability of cloud computing-driven platforms. Subsequently, we provide examples of how interoperable computational systems facilitate exploration of data from multiple sources, including both consistently formatted structured data and unstructured data. Taken together, these approaches for achieving data harmony enable molecular phenotyping of CV diseases and unification of CV knowledge. [ABSTRACT FROM AUTHOR]

As developments in artificial intelligence and machine learning become more widespread in healthcare, their potential to transform clinical outcomes also increases. Peripartum cardiomyopathy is a rare and poorly-characterised condition that presents as heart failure in the last trimester prior to delivery or within 5–6 months postpartum. The lack of a definitive understanding of the molecular causes and clinical progress of this condition suggests that bibliometrics will be well-suited to creating new insights into this serious clinical problem. We examine similarities and differences between peripartum and its closely related familial dilated cardiomyopathy and idiopathic dilated cardiomyopathy. Using PubMed as the source of bibliometric data, we apply artificial intelligence–supported natural language processing to compare extracted data and genes association with these cardiomyopathies. Gene data were enhanced with additional metadata from third-party datasets and then analysed for their impact and specificity for peripartum cardiomyopathy. Artificial intelligence identified 14 genes that distinguished peripartum from both dilated and familial dilated cardiomyopathy. They are as follows: CTSD, RLN2, MMP23B*, SLC17A5, ST2*, PTHLH, CFH*, CFI, GPT, MR1, Rln1, SRI, STAT5A* and THBD. We then used the Human Protein Atlas website that uses affinity-purified rabbit polyclonal antibodies to identify genes that are expressed at the protein level (bold), or as RNA transcripts (*) in healthy human left ventricles. Additional analysis focussed on the full set of peripartum genes on linkage and specificity to cardiomyopathy yielded a different set of thirteen genes (bold font indicates those expressed in cardiomyocytes: PRL, RLN2, PLN, ST2, CTSD, F2, ACE, STAT3, TTN, SPP1, LGALS3, miR-146a, GNB3, SRI). This type of analysis can highlight new avenues for research, aimed at improving genomics-driven peripartum cardiomyopathy diagnosis as well as potential pathological and clinical sub-classification. We expect that this will allow for future improvements in identification, treatment and management of this condition. The first step in the application of these bibliometric-based artificial intelligence methods is to understand the current knowledge, and it is the aim of this paper to show how this might be achieved. [ABSTRACT FROM AUTHOR]

Background: In recent years, studies that used routinely collected data (RCD), such as electronic medical records and administrative claims, for exploring drug treatment effects, including effectiveness and safety, have been increasingly published. Abstracts of such studies represent a highly attended source for busy clinicians or policy-makers, and are important for indexing by literature database. If less clearly presented, they may mislead decisions or indexing. We thus conducted a cross-sectional survey to systematically examine how the abstracts of such studies were reported.Methods: We searched PubMed to identify all observational studies published in 2018 that used RCD for assessing drug treatment effects. Teams of methods-trained collected data from eligible studies using pilot-tested, standardized forms that were developed and expanded from "The reporting of studies conducted using observational routinely collected health data statement for pharmacoepidemiology" (RECORD-PE) statement. We used descriptive analyses to examine how authors reported data source, study design, data analysis, and interpretation of findings.Results: A total of 222 studies were included, of which 118 (53.2%) reported type of database used, 17 (7.7%) clearly reported database linkage, and 140 (63.1%) reported coverage of data source. Only 44 (19.8%) studies stated a predefined hypothesis, 127 (57.2%) reported study design, 140 (63.1%) reported statistical models used, 142 (77.6%) reported adjusted estimates, 33 (14.9%) mentioned sensitivity analyses, and 39 (17.6%) made a strong claim about treatment effect. Studies published in top 5 general medicine journals were more likely to report the name of data source (94.7% vs. 67.0%) and study design (100% vs. 53.2%) than those in other journals.Conclusions: The under-reporting of key methodological features in abstracts of RCD studies was common, which would substantially compromise the indexing of this type of literature and prevent the effective use of study findings. Substantial efforts to improve the reporting of abstracts in these studies are highly warranted. [ABSTRACT FROM AUTHOR]

Aim: CHA 2 DS 2 -VASc and modified-CHADS 2 score can easily estimate the risk of stroke in atrial fibrillation. Study's purpose was to evaluate these in haemodialysis patients, and assess the effect of diabetes mellitus (DM)., Methods: The scores calculated in 237 haemodialysis patients, 121 diabetics (58 females) and 116 non-diabetics (41 females). Results correlated to cardiovascular events (acute myocardial infarction, atrial fibrillation, heart failure, peripheral arterial disease, stroke, mortality)., Results: CHA 2 DS 2 -VASc score correlated with the occurrence of stroke and heart failure (p < .01, p < .01), (p < .01, p < .01), respectively in diabetics and non-diabetics. CHA 2 DS 2 -VASc score could predict the risk of all-cause mortality in both groups, p = .03, p < .01, respectively, however, the risk of cardiovascular death could be predicted in non-diabetics, p < .01. Modified-CHADS 2 score associated with heart failure (p = .04), cardiovascular (p < .01) and all-cause mortality (p < .01) only on non-diabetics. C statistics indicated that the first score showed modest discrimination in patients with and without DM, for stroke and all-cause mortality. The second score performed modestly only on patients without DM for all-cause mortality. Both scores showed poor calibration. Stroke was a common cause of cardiovascular death (OR = 3.52, 95% CI = 1.92-6.47, p < .01) and associated with central venous catheter (OR = 2.19, 95% CI = 1.12-4.27, p = .02) and pre-existing atrial fibrillation (OR = 1.94, 95% CI = 1.06-3.58, p = .03)., Conclusion: CHA 2 DS 2 -VASc score correlated with stroke, heart failure and all-cause mortality in haemodialysis patients with and without DM. The risk of cardiovascular death could be predicted only in non-diabetics patients. Modified-CHADS 2 score correlated with heart failure, cardiovascular and all-cause mortality only on non-diabetics. Both had modest discrimination and poor calibration., (© 2023 Asian Pacific Society of Nephrology.)

Background and Aims: In recent years, the best treatment method for pancreatic cancer in elderly patients has remained controversial. Surgery is the main treatment modality for pancreatic cancer. This study aimed to determine whether elderly patients with pancreatic cancer can gain survival advantages through more active and radical surgical treatment and evaluate the best treatment method and potential prognostic factors. Methods: From the Surveillance, Epidemiology, and End Results program (SEER) database, 10,557 elderly patients (aged ≥65 years) with pancreatic cancer were included as Cohort 1, and Propensity Score Matching (PSM) evaluation was performed to generate Cohort 2 (424 pairs). Overall Survival (OS) and Cause-Specific Survival (CSS) were determined using Kaplan–Meier survival curves, and differences were assessed using the Log-rank test. Multivariate logistic regression analysis and the forest plot of hazard ratio (HR) was made to assess the association between potential prognostic factors, including surgery and different surgical methods, and survival in elderly patients. Results: We identified 10,557 eligible patients with pancreatic cancer, who formed Cohort 1. The total OS and CSS in the surgery group were significantly higher than those in the non-surgery group (P < 0.001). Age, stage (AJCC 8th), grade, lymph node metastasis, radiation, chemotherapy, and surgical methods were independent factors affecting the prognosis of elderly patients. In Cohort 2, Total pancreatectomy (Total PT) had the lowest risk ratio (HR = 0.31, P < 0.001) and longest median CSS (18.000 months), while Extension Total pancreatectomy (Ex-Total PT, HR = 0.34, P < 0.001) showed the lower median CSS (17.000 months) and median OS (14.000 months). Partial pancreatectomy (Partial PT, HR = 0.46, P < 0.001) showed the lowest median CSS (13.000 months) and median OS (12.000 months), although they were still higher than the median CSS (6.000 months) and median OS (5.000 months) in the non-surgery group. Conclusions: Based on the SEER database, surgical treatment is an independent prognostic factor in elderly patients with pancreatic cancer. Compared with other surgical methods, Total PT can offer elderly patients the best survival advantages. However, Ex-Total PT, a more radical method, does not seem to be the best treatment option for the survival and benefit of elderly patients. [ABSTRACT FROM AUTHOR]

Our aim is to examine the effects of climatic conditions on the incidence of ruptured abdominal aortic aneurysms (rAAA) in Catalonia, Spain. We combined clinical data from the Public Health official registries in Catalonia, Spain (HD-MBDS) of all rAAA with local climatic data obtained from the closest meteorological station (69 stations, National Meteorological Service: MeteoCat) from 2008 to 2017. We analyzed the median, maximum, minimum, and variability of atmospheric pressure (hPa) and air temperature (°C), solar irradiation (MJ/m2), humidity (%), accumulated precipitation (mm), median wind, and maximum flaw direction and velocity (°, m/s), recorded on the days of events, the previous day, and mean results for 3, 7, and 30 days before, as well as seasonality. Seventy-five control days were randomly selected in a 1-year period around every rAAA day at the same meteorological station, and compared. A total of 717 days and locations with rAAA were identified, and 53,775 controls were randomly selected. For the rAAA days, there were significantly lower temperatures, lower solar global irradiation, and higher mean humidity levels in all time periods (p < 0.001, p < 0.001, p < 0.05); higher atmospheric pressure variability during 1 week and 1 month before (p = 0.011, p = 0.007); and they often occurred during autumn/winter (57.6%, p < 0.001). Logistic regression identified low mean temperatures on the days of ruptures and high mean humidity the week before as independent rupture predictors. In conclusion, low median temperatures the same day and high humidity during 1 week before were identified as independent predictors of rAAA occurrence. The role of climate on pathophysiologic mechanisms may require further investigation. [ABSTRACT FROM AUTHOR]

Background: Cardiovascular diseases (CVD) combined with Type 2 diabetes mellitus (T2DM) frequently occurred. In this study, we aimed at exploring the prognostic significance of blood neutrophil-lymphocyte ratio (NLR) in these types of patients.Patients and Methods: Between June 30, 2010 and August 30, 2017, 1454 patients with CVD were enrolled in this study. Kaplan and Meier methodology was used for survival analysis. We also used propensity score matching (PSM) to further compare survival in patients with or without T2DM.Results: Among all patients, we applied ROC curve analysis to stratify all patients into two different groups including NLR >2.5 (n=432) and NLR≤ 2.5 (n=1022) groups. After that, we further performed survival analysis between different groups. We found that patients with NLR ≤2.5 had significantly favorable OS compared with the overall survival in patients with NLR >2.5. We further built the PSM using 242 pairs of patients who have CVD and with or without T2DM. After adjusting for competing risk factors, we performed Cox proportional hazards models to identify the independent prognostic factors in multivariable adjustment. We found that NLR ≤2.5 (HR: 2.576, 95% CI: 1.241-4.583, P =0.001) and extent of coronary artery disease (HR: 2.432, 95% CI: 1.189-4.392, P =0.005) remained independent predictors of OS.Conclusion: In conclusion, we have established an PSM model and found that a high NLR value was an independent prognostic factor for survival, predicting in patients with both CAD and T2DM. The NLR value would be a valuable biomarker to evaluate the outcomes of patients and give them opportunities for choosing alternative therapies. [ABSTRACT FROM AUTHOR]

Overcoming bias due to confounding and missing data is challenging when analyzing observational data. Propensity scores are commonly used to account for the first problem and multiple imputation for the latter. Unfortunately, it is not known how best to proceed when both techniques are required. We investigate whether two different approaches to combining propensity scores and multiple imputation (Across and Within) lead to differences in the accuracy or precision of exposure effect estimates. Both approaches start by imputing missing values multiple times. Propensity scores are then estimated for each resulting dataset. Using the Across approach, the mean propensity score across imputations for each subject is used in a single subsequent analysis. Alternatively, the Within approach uses propensity scores individually to obtain exposure effect estimates in each imputation, which are combined to produce an overall estimate. These approaches were compared in a series of Monte Carlo simulations and applied to data from the British Society for Rheumatology Biologics Register. Results indicated that the Within approach produced unbiased estimates with appropriate confidence intervals, whereas the Across approach produced biased results and unrealistic confidence intervals. Researchers are encouraged to implement the Within approach when conducting propensity score analyses with incomplete data. [ABSTRACT FROM AUTHOR]

Aims: According to cardiovascular outcome trials, some sodium‐glucose contransporter‐2 inhibitors (SGLT2i) and glucagon‐like peptide‐1 receptor agonists (GLP‐1RA) are recommended for secondary cardiovascular prevention in type 2 diabetes (T2D). In this real‐world study, we compared the simultaneous reductions in HbA1c, body weight and systolic blood pressure after initiation of dapagliflozin or GLP‐1RA as second or a more advanced line of therapy. Materials and methods: DARWIN‐T2D was a retrospective multi‐centre study conducted at diabetes specialist clinics in Italy that compared T2D patients who initiated dapagliflozin or GLP‐1RA (exenatide once weekly or liraglutide). Data were collected at baseline and at the first follow‐up visit after 3 to 12 months. The primary endpoint was the proportion of patients achieving a simultaneous reduction in HbA1c, body weight and systolic blood pressure. To reduce confounding, we used multivariable adjustment (MVA) or propensity score matching (PSM). Results: Totals of 473 patients initiating dapagliflozin and 336 patients initiating GLP‐1RA were included. The two groups differed in age, diabetes duration, HbA1c, weight and concomitant medications. The median follow‐up was 6 months in both groups. Using MVA or PSM, the primary endpoint was observed in 30% to 32% of patients, with no difference between groups. Simultaneous reduction of HbA1c, BP and SBP by specific threshold, as well as achievement of final goals, did not differ between groups. GLP‐1RA reduced HbA1c by 0.3% more than the reduction achieved with dapagliflozin. Conclusion: In routine specialist care, initiation of dapagliflozin can be as effective as initiation of a GLP‐1RA for attainment of combined risk factor goals. [ABSTRACT FROM AUTHOR]