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This candid perspective written by scholars from diverse disciplinary backgrounds is intended to advance conversations about the realities of peer review and its inherent limitations. Trust in a process or institution is built slowly and can be destroyed quickly. Trust in the peer review process for scholarly outputs (i.e., journal articles) is being eroded by high-profile scandals, exaggerated news stories, exposés, corrections, retractions, and anecdotes about poor practices. Diminished trust in the peer review process has real-world consequences and threatens the uptake of critical scientific advances. The literature on “crises of trust” tells us that rebuilding diminished trust takes time and requires frank admission and discussion of problems, creative thinking that addresses rather than dismisses criticisms, and planning and enacting short- and long-term reforms to address the root causes of problems. This article takes steps in this direction by presenting eight peer review reality checks and summarizing efforts to address their weaknesses using a harm reduction approach, though we recognize that reforms take time and some problems may never be fully rectified. While some forms of harm reduction will require structural and procedural changes, we emphasize the vital role that training editors, reviewers, and authors has in harm reduction. Additionally, consumers of science need training about how the peer review process works and how to critically evaluate research findings. No amount of self-policing, transparency, or reform to peer review will eliminate all bad actors, unscrupulous publishers, perverse incentives that reward cutting corners, intentional deception, or bias. However, the scientific community can act to minimize the harms from these activities, while simultaneously (re)building the peer review process. A peer review system is needed, even if it is imperfect.

Sediment barriers are used on construction sites to protect downstream waterbodies from the impacts of sediment-laden stormwater runoff. Although ubiquitous on construction sites, many sediment barrier practices lack performance-based testing to determine effectiveness and treatment mechanisms, with previous evaluations being limited to conditions local to the Southeastern U.S., with conditions in other regions remaining untested. Testing was conducted to determine the effectiveness of woven silt fence barriers and provide structural improvements to common installation methods. Testing was conducted using a large-scale sediment barrier testing apparatus at the Auburn University—Stormwater Research Facility. The results from testing indicate that Nebraska DOT standard silt fence installations can be improved to reduce the risk of structural failures such as undermining, complete installation failure, slow dewatering, and overtopping. To improve structural performance, four modifications (a 15.2 cm [6 in.] offset trench, wooden posts, a dewatering board with an overflow weir, and a dewatering board with an overflow weir with adjusted post spacing) were tested. On average, 83% of introduced sediment was retained behind the tested barriers. The water quality results across the testing of standard and modified installations indicated that stormwater treatment was due to sedimentation within the impoundment formed by silt fence installations and not filtration through geotextile fabric.

Different techniques and devices can achieve haemostasis after endovascular procedures to reduce time to haemostasis and facilitate percutaneous large-bore access. One such device is the Angio-Seal, which accomplishes haemostasis by compressing a collagen plug against the arteriotomy. Multiple complications related to Angio-Seal exist, most commonly acute artery occlusions and rarely infections and endarteritis. We present a case of an adult male (aged 80-85) who developed a groin haematoma associated with Angio-Seal use. Arterial bypass for arterial reconstruction and intravenous antibiotics were required. This case addresses the associated pathologies of Angio-Seal failure and Angio-Seal infection, patient- and device-specific risk factors and measures to treat and prevent these complications (ANGIO-SEAL VIP Vascular Closure Device-6 Fr Terumo Medical, NJ, USA)., Competing Interests: Competing interests: None declared., (© BMJ Publishing Group Limited 2024. No commercial re-use. See rights and permissions. Published by BMJ.)

Objective: Inferior vena cava (IVC) filter placement is associated with important long-term complications. Predictive models for filter-related complications may help guide clinical decision-making but remain limited. We developed machine learning (ML) algorithms that predict 1-year IVC filter complications using preoperative data., Methods: The Vascular Quality Initiative database was used to identify patients who underwent IVC filter placement between 2013 and 2024. We identified 77 preoperative demographic and clinical features from the index hospitalization when the filter was placed. The primary outcome was 1-year filter-related complications (composite of filter thrombosis, migration, angulation, fracture, and embolization or fragmentation, vein perforation, new caval or iliac vein thrombosis, new pulmonary embolism, access site thrombosis, or failed retrieval). The data were divided into training (70%) and test (30%) sets. Six ML models were trained using preoperative features with 10-fold cross-validation (Extreme Gradient Boosting, random forest, Naïve Bayes classifier, support vector machine, artificial neural network, and logistic regression). The primary model evaluation metric was area under the receiver operating characteristic curve (AUROC). Model robustness was assessed using calibration plot and Brier score. Performance was evaluated across subgroups based on age, sex, race, ethnicity, rurality, median Area Deprivation Index, planned duration of filter, landing site of filter, and presence of prior IVC filter placement., Results: Overall, 14,476 patients underwent IVC filter placement and 584 (4.0%) experienced 1-year filter-related complications. Patients with a primary outcome were younger (59.3 ± 16.7 years vs 63.8 ± 16.0 years; P < .001) and more likely to have thrombotic risk factors including thrombophilia, prior venous thromboembolism (VTE), and family history of VTE. The best prediction model was Extreme Gradient Boosting, achieving an AUROC of 0.93 (95% confidence interval, 0.92-0.94). In comparison, logistic regression had an AUROC of 0.63 (95% confidence interval, 0.61-0.65). Calibration plot showed good agreement between predicted/observed event probabilities with a Brier score of 0.07. The top 10 predictors of 1-year filter-related complications were (1) thrombophilia, (2) prior VTE, (3) antiphospholipid antibodies, (4) factor V Leiden mutation, (5) family history of VTE, (6) planned duration of IVC filter (temporary), (7) unable to maintain therapeutic anticoagulation, (8) malignancy, (9) recent or active bleeding, and (10) age. Model performance remained robust across all subgroups., Conclusions: We developed ML models that can accurately predict 1-year IVC filter complications, performing better than logistic regression. These algorithms have potential to guide patient selection for filter placement, counselling, perioperative management, and follow-up to mitigate filter-related complications and improve outcomes., Competing Interests: Disclosures None., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Background: Transfemoral carotid artery stenting (TFCAS) carries important perioperative risks. Outcome prediction tools may help guide clinical decision-making but remain limited. We developed machine learning algorithms that predict 1-year stroke or death following TFCAS., Methods and Results: The VQI (Vascular Quality Initiative) database was used to identify patients who underwent TFCAS for carotid artery stenosis between 2005 and 2024. We identified 112 features from the index hospitalization (82 preoperative [demographic/clinical], 13 intraoperative [procedural], and 17 postoperative [in-hospital course/complications]). The primary outcome was 1-year postprocedural stroke or death. The data were divided into training (70%) and test (30%) sets. Six machine learning models were trained using preoperative features with 10-fold cross-validation. The primary model evaluation metric was area under the receiver operating characteristic curve. The algorithm with the best performance was further trained using intra- and postoperative features. Model robustness was assessed using calibration plots and Brier scores. Overall, 35 214 patients underwent TFCAS during the study period and 3257 (9.2%) developed 1-year stroke or death. The best preoperative prediction model was extreme gradient boosting, achieving an area under the receiver operating characteristic curve of 0.94 (95% CI, 0.93-0.95). In comparison, logistic regression had an AUROC of 0.65 (95% CI, 0.63-0.67). The extreme gradient boosting model maintained excellent performance at the intra- and postoperative stages, with area under the receiver operating characteristic curve values of 0.94 (95% CI, 0.93-0.95) and 0.98 (95% CI, 0.97-0.99), respectively. Calibration plots showed good agreement between predicted/observed event probabilities with Brier scores of 0.11 (preoperative), 0.11 (intraoperative), and 0.09 (postoperative)., Conclusions: Machine learning can accurately predict 1-year stroke or death following TFCAS, performing better than logistic regression.

Background: The Vascular Outcomes Study of aspirin (ASA) Along with Rivaroxaban in Endovascular or Surgical Limb Revascularization for peripheral artery disease (PAD) trial demonstrated the superiority of ASA and low-dose rivaroxaban (Cardiovascular Outcomes for People Using Anticoagulation Strategies (COMPASS) trial dosing) compared with ASA alone in reducing major adverse cardiovascular events and major adverse limb events. We studied the COMPASS discharge prescription patterns in patients with symptomatic PAD who have undergone revascularization in our institution, since the time of publication of the Vascular Outcomes Study of ASA Along with Rivaroxaban in Endovascular or Surgical Limb Revascularization for PAD trial., Methods: All patients included in this study had documented lower-extremity atherosclerotic PAD and were eligible for COMPASS dosing. Revascularization strategies included endovascular (n = 299), suprainguinal bypass (n = 18), and infrainguinal bypass (n = 36)., Results: COMPASS prescription patterns for the composite of endovascular and surgical strategies demonstrated a consistently low rate over time, without a trend toward increasing use. COMPASS dosing was prescribed as often as antiplatelet monotherapy (33.4% COMPASS vs. 34.6% antiplatelet monotherapy). This low COMPASS prescription rate was driven by significantly lower COMPASS prescriptions following endovascular therapy compared to surgical bypass (28.8% endovascular vs. 59.3% surgical bypass). COMPASS prescriptions following surgical bypass showed better trends; half of suprainguinal bypass patients (50.0%) and two-thirds of infrainguinal bypass patients (63.9%) were discharged on COMPASS. Despite patients with chronic limb-threatening ischemia (CLTI) representing a high-risk limb presentation, COMPASS prescriptions were low (29.8%), as opposed to patients without CLTI, and did not show a trend toward increasing use. In patients who underwent reinterventions throughout the observation period, there was a low conversion rate from ASA alone to COMPASS (3/26, 11.5%)., Conclusions: In this observational study, one-third of patients were undertreated by prescription of antiplatelet monotherapy, indicating that there is significant room for medical optimization. This is especially true of patients undergoing endovascular treatment, including the high-risk subgroup of patients with CLTI. We highlight the importance of dual pathway antithrombotic therapy in patients eligible for COMPASS dosing to optimize best current evidence medical therapy., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Aim: Patients with chronic non-cancer pain are referred to pain centres to improve their pain treatment. The discontinuation of pain medications in case of poor efficacy can be difficult to accept for patients, particularly opioid analgesics. Previous research has described that from the patients' perspective, the psychological relief of a negative effect of chronic pain and withdrawal symptoms of prescription opioids represent drivers of persistent use and first stage of opioid use disorder, despite insufficient pain relief. There is no validated tool to investigate this psychological dependence. This study aimed to assess discordance between patients and pain specialists in their perception of dependence on pain medication and investigate associations with characteristics of patients, type of pain and iatrogenic pharmacodependence., Methods: Self-administered questionnaires (patients and physicians) were administered in six pain centres in France. A question on perceived dependence on pain medications was addressed to the patient and the physician in a matched pair. Discordance between them was evaluated by the Cohen kappa coefficient. Demographics, pain, anxiety and depression, pain medication withdrawal symptoms, diverted use, and craving represented variables studied in a multivariate model as potentially associated with patient-physician discordance., Results: According to the 212 pairs of completed questionnaires, a perceived dependence was reported by the majority of patients (65.6%) and physicians (68.4%). However, the concordance was fair (kappa=0.38; CI [95%]: 0.25-0.51). Almost all patients (89.3%) were treated with an opioid analgesic. A higher likelihood of discordance was observed when patients suffered from nociplastic pain (odds ratio [OR]: 2.72, 95% [CI]: 1.29-5.84)., Conclusion: Medical shared-decision for changing pain treatment could be improved by taking into account the perception of patient dependence on medications for pain relief and or psychoactive effects, particularly in nociplastic pain for which the treatment is challenging., (Copyright © 2024 The Author(s). Published by Elsevier Masson SAS.. All rights reserved.)

Background: Although renal artery aneurysms (RAAs) are rare and often asymptomatic with slow growth, their natural progression and optimal management are not well understood. Treatment recommendations for RAAs do exist; however, they are supported by limited data., Methods: A retrospective cohort study was conducted to explore the management of patients diagnosed with an RAA at our institution from January 1st, 2013, to December 31st, 2020. Patients were identified through a search of our radiological database, followed by a comprehensive chart review for further assessment. Data collection encompassed patient and aneurysm characteristics, the rationale for initial imaging, treatment, surveillance, and all-cause mortality., Results: One hundred eighty-five patients were diagnosed with or treated for RAAs at our center during this timeframe, with most aneurysms having been discovered incidentally. Average aneurysm size was 1.40 cm (±0.05). Of those treated, the mean size was 2.38 cm (±0.24). Among aneurysms larger than 3 cm in size, comprising 3.24% of the total cases, 83.3% underwent treatment procedures. Only 20% of women of childbearing age received treatment for their aneurysms. There was one instance of aneurysm rupture, with no associated mortality or significant morbidity., Conclusions: Our institution's management of RAAs over the period of the study generally aligned with guidelines. One potential area of improvement is more proactive intervention for women of childbearing age., Competing Interests: Declaration of conflicting interestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Background: The association between the intestinal microbiota and psychiatric disorders is becoming increasingly apparent. The gut microbiota contributes to colorectal carcinogenesis (CRC), as demonstrated with colibactin-producing Escherichia coli (CoPEC)., Aim: To evaluate the association between CoPEC prevalence and anxiety- and depressive-like behaviors with both preclinical and clinical approaches., Methods: Patients followed after a CRC surgery and for whom the prevalence of CoPEC has been investigated underwent a psychiatric interview. Results were compared according to the CoPEC colonization. In parallel C57BL6/J wild type mice and mice with a CRC susceptibility were chronically infected with a CoPEC strain. Their behavior was assessed using the Elevated Plus Maze test, the Forced Swimming Test and the Behavior recognition system PhenoTyper ® ., Results: In a limited cohort, all patients with CoPEC colonization presented with psychiatric disorders several years before cancer diagnosis, whereas only one patient (17%) without CoPEC did. This result was confirmed in C57BL6/J wild-type mice and in a CRC susceptibility mouse model (adenomatous polyposis coli multiple intestinal neoplasia/+ ). Mice exhibited a significant increase in anxiety- and depressive-like behaviors after chronic infection with a CoPEC strain., Conclusion: This finding provides the first evidence that CoPEC infection can induce microbiota-gut-brain axis disturbances in addition to its procarcinogenic properties., Competing Interests: Conflict-of-interest statement: Pr. Bonnet has nothing to disclose., (©The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.)

Background: Thrombolysis for arteriovenous grafts (AVG) yields high technical success rates, however, long-term outcomes are unclear. We conducted a multicenter retrospective cohort study to analyze 5-year patency rates following AVG thrombolysis., Methods: All patients who underwent AVG thrombolysis between 2005 and 2015 at three academic hospitals were included. Prospectively maintained institutional nephrology and radiology databases were used to record demographic, clinical, and AVG characteristics. The primary outcome was primary patency, defined as AVG access survival without re-intervention including angioplasty ± stent with/without re-thrombolysis. Secondary outcomes were assisted primary patency and cumulative patency, defined as AVG access survival until re-thrombosis requiring re-thrombolysis or abandonment, respectively. Technical success was defined as restoration of flow with <30% residual stenosis. Patients were followed until 2017. Patency rates were assessed using Kaplan-Meier survival analysis and Cox proportional hazards were calculated to determine associations between covariates and patency loss., Results: Seventy-four patients underwent AVG thrombolysis during the study period with a median follow-up period of 21.4 (IQR 8.3-42.8) months. The average age was 58.6 years with a high rate of comorbidities, including hypertension (82.4%) and diabetes (54.1%). Thrombolysis technical success was 96%. There were 147 re-interventions in 46 patients, of which 98 were re-thrombolysis (mean re-intervention rate of 1.27/patient/year). Primary patency at 1, 3, and 5 years were 43.2%, 20.2%, and 7.7%. Assisted primary patency at 1, 3, and 5 years were 47.5%, 20.2%, and 7.7%. Cumulative patency at 1, 3, and 5 years were 75.0%, 38.8%, and 22.6%. Cox proportional hazards analysis demonstrated no associations between demographic, clinical, and procedural characteristics and patency rates., Conclusions: Despite a high technical success rate, thrombolysis for AVG dysfunction is associated with poor long-term patency. Future studies are needed to determine risk factors for re-thrombosis to identify patients who will benefit from AVG thrombolysis in the long-term., Competing Interests: Declaration of conflicting interestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Objective: To develop machine learning (ML) algorithms that predict outcomes after infrainguinal bypass., Background: Infrainguinal bypass for peripheral artery disease carries significant surgical risks; however, outcome prediction tools remain limited., Methods: The Vascular Quality Initiative database was used to identify patients who underwent infrainguinal bypass for peripheral artery disease between 2003 and 2023. We identified 97 potential predictor variables from the index hospitalization [68 preoperative (demographic/clinical), 13 intraoperative (procedural), and 16 postoperative (in-hospital course/complications)]. The primary outcome was 1-year major adverse limb event (composite of surgical revision, thrombectomy/thrombolysis, or major amputation) or death. Our data were split into training (70%) and test (30%) sets. Using 10-fold cross-validation, we trained 6 ML models using preoperative features. The primary model evaluation metric was the area under the receiver operating characteristic curve (AUROC). The top-performing algorithm was further trained using intraoperative and postoperative features. Model robustness was evaluated using calibration plots and Brier scores., Results: Overall, 59,784 patients underwent infrainguinal bypass, and 15,942 (26.7%) developed 1-year major adverse limb event/death. The best preoperative prediction model was XGBoost, achieving an AUROC (95% CI) of 0.94 (0.93-0.95). In comparison, logistic regression had an AUROC (95% CI) of 0.61 (0.59-0.63). Our XGBoost model maintained excellent performance at the intraoperative and postoperative stages, with AUROCs (95% CI's) of 0.94 (0.93-0.95) and 0.96 (0.95-0.97), respectively. Calibration plots showed good agreement between predicted and observed event probabilities with Brier scores of 0.08 (preoperative), 0.07 (intraoperative), and 0.05 (postoperative)., Conclusions: ML models can accurately predict outcomes after infrainguinal bypass, outperforming logistic regression., Competing Interests: The authors report no conflicts of interest., (Copyright © 2023 Wolters Kluwer Health, Inc. All rights reserved.)

Importance: Endovascular intervention for peripheral artery disease (PAD) carries nonnegligible perioperative risks; however, outcome prediction tools are limited., Objective: To develop machine learning (ML) algorithms that can predict outcomes following endovascular intervention for PAD., Design, Setting, and Participants: This prognostic study included patients who underwent endovascular intervention for PAD between January 1, 2004, and July 5, 2023, with 1 year of follow-up. Data were obtained from the Vascular Quality Initiative (VQI), a multicenter registry containing data from vascular surgeons and interventionalists at more than 1000 academic and community hospitals. From an initial cohort of 262 242 patients, 26 565 were excluded due to treatment for acute limb ischemia (n = 14 642) or aneurysmal disease (n = 3456), unreported symptom status (n = 4401) or procedure type (n = 2319), or concurrent bypass (n = 1747). Data were split into training (70%) and test (30%) sets., Exposures: A total of 112 predictive features (75 preoperative [demographic and clinical], 24 intraoperative [procedural], and 13 postoperative [in-hospital course and complications]) from the index hospitalization were identified., Main Outcomes and Measures: Using 10-fold cross-validation, 6 ML models were trained using preoperative features to predict 1-year major adverse limb event (MALE; composite of thrombectomy or thrombolysis, surgical reintervention, or major amputation) or death. The primary model evaluation metric was area under the receiver operating characteristic curve (AUROC). After selecting the best performing algorithm, additional models were built using intraoperative and postoperative data., Results: Overall, 235 677 patients who underwent endovascular intervention for PAD were included (mean [SD] age, 68.4 [11.1] years; 94 979 [40.3%] female) and 71 683 (30.4%) developed 1-year MALE or death. The best preoperative prediction model was extreme gradient boosting (XGBoost), achieving the following performance metrics: AUROC, 0.94 (95% CI, 0.93-0.95); accuracy, 0.86 (95% CI, 0.85-0.87); sensitivity, 0.87; specificity, 0.85; positive predictive value, 0.85; and negative predictive value, 0.87. In comparison, logistic regression had an AUROC of 0.67 (95% CI, 0.65-0.69). The XGBoost model maintained excellent performance at the intraoperative and postoperative stages, with AUROCs of 0.94 (95% CI, 0.93-0.95) and 0.98 (95% CI, 0.97-0.99), respectively., Conclusions and Relevance: In this prognostic study, ML models were developed that accurately predicted outcomes following endovascular intervention for PAD, which performed better than logistic regression. These algorithms have potential for important utility in guiding perioperative risk-mitigation strategies to prevent adverse outcomes following endovascular intervention for PAD.

Objective: Suprainguinal bypass for peripheral artery disease (PAD) carries important surgical risks; however, outcome prediction tools remain limited. We developed machine learning (ML) algorithms that predict outcomes following suprainguinal bypass., Methods: The Vascular Quality Initiative database was used to identify patients who underwent suprainguinal bypass for PAD between 2003 and 2023. We identified 100 potential predictor variables from the index hospitalization (68 preoperative [demographic/clinical], 13 intraoperative [procedural], and 19 postoperative [in-hospital course/complications]). The primary outcomes were major adverse limb events (MALE; composite of untreated loss of patency, thrombectomy/thrombolysis, surgical revision, or major amputation) or death at 1 year following suprainguinal bypass. Our data were split into training (70%) and test (30%) sets. Using 10-fold cross-validation, we trained six ML models using preoperative features (Extreme Gradient Boosting [XGBoost], random forest, Naïve Bayes classifier, support vector machine, artificial neural network, and logistic regression). The primary model evaluation metric was area under the receiver operating characteristic curve (AUROC). The best performing algorithm was further trained using intra- and postoperative data. Model robustness was evaluated using calibration plots and Brier scores. Performance was assessed on subgroups based on age, sex, race, ethnicity, rurality, median Area Deprivation Index, symptom status, procedure type, prior intervention for PAD, concurrent interventions, and urgency., Results: Overall, 16,832 patients underwent suprainguinal bypass, and 3136 (18.6%) developed 1-year MALE or death. Patients with 1-year MALE or death were older (mean age, 64.9 vs 63.5 years; P < .001) with more comorbidities, had poorer functional status (65.7% vs 80.9% independent at baseline; P < .001), and were more likely to have chronic limb-threatening ischemia (67.4% vs 47.6%; P < .001) than those without an outcome. Despite being at higher cardiovascular risk, they were less likely to receive acetylsalicylic acid or statins preoperatively and at discharge. Our best performing prediction model at the preoperative stage was XGBoost, achieving an AUROC of 0.92 (95% confidence interval [CI], 0.91-0.93). In comparison, logistic regression had an AUROC of 0.67 (95% CI, 0.65-0.69). Our XGBoost model maintained excellent performance at the intra- and postoperative stages, with AUROCs of 0.93 (95% CI, 0.92-0.94) and 0.98 (95% CI, 0.97-0.99), respectively. Calibration plots showed good agreement between predicted and observed event probabilities with Brier scores of 0.12 (preoperative), 0.11 (intraoperative), and 0.10 (postoperative). Of the top 10 predictors, nine were preoperative features including chronic limb-threatening ischemia, previous procedures, comorbidities, and functional status. Model performance remained robust on all subgroup analyses., Conclusions: We developed ML models that accurately predict outcomes following suprainguinal bypass, performing better than logistic regression. Our algorithms have potential for important utility in guiding perioperative risk mitigation strategies to prevent adverse outcomes following suprainguinal bypass., Competing Interests: Disclosures None., (Copyright © 2023 Society for Vascular Surgery. Published by Elsevier Inc. All rights reserved.)