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Introduction Mechanical thrombectomy (MT) is the gold standard for acute ischemic stroke (AIS) due to large vessel occlusion (LVO). Although >70% of patients in the randomized clinical trials (RCTs) assessing the efficacy of MT for AIS‐LVO had successful recanalization, only up to 27% were free of disability according to the 90‐day modified Rankin scale (mRS) score. It is suggested that microcirculation disruption distal to the LVO might contribute to futile recanalization. Hence, combining intra‐arterial (IA) tissue plasminogen activator (tPA) with MT was investigated in a few studies in an attempt to reduce the burden of distal microthrombi and microcirculation disruption. We conducted this meta‐analysis to provide collective evidence in this regard. Methods We performed this meta‐analysis following the Preferred Reporting Items for Systematic Review and Meta‐Analyses statement recommendations. We aimed to include all original studies investigating the benefits of IA tPA in AIS patients undergoing MT.All screening and extraction stages were conducted by two authors, with a third author resolving any conflicts. Using R software, we calculated pooled odds ratios (ORs) and their corresponding 95% confidence intervals (CI). A fixed‐effect model was adopted to pool all data due to the absence of significant heterogeneity among the included studies. Heterogeneity was assessed using Q statistics and the I2test, where I2> 50% or P‐value < 0.05 were considered significant. Results Following the database search, a manual search of references, and different screening phases, we included 5 studies (2,686 patients) satisfying the predefined inclusion criteria. Successful recanalization rates were comparable between both groups. The 90‐day functional independence was reported in 4 studies (2,474 patients), with comparable rates between both groups (OR = 1.25; 95%CI = 0.92‐1.70; P‐value = 0.154). Mortality was reported in 3 studies (368 patients), with comparable rates between both groups (OR = 0.73; 95%CI = 0.44‐1.23; P‐value = 0.240). Symptomatic intracranial hemorrhage (sICH) was reported in 5 studies (2,678 patients) with no significant difference between the two groups (OR = 0.66; 95%CI = 0.34‐1.26; P‐value = 0.304). Conclusions The current evidence does not show any significant differences between MT alone versus MT plus IA tPA in terms of 90‐day functional independence, mortality, and sICH. With the limited number of studies and included patients, more studies are needed to investigate the benefits and safety of the combined MT and IA tPA.

HCC recurrence following liver transplantation (LT) is highly morbid and occurs despite strict patient selection criteria. Individualized prediction of post-LT HCC recurrence risk remains an important need. Clinico-radiologic and pathologic data of 4981 patients with HCC undergoing LT from the US Multicenter HCC Transplant Consortium (UMHTC) were analyzed to develop a REcurrent Liver cAncer Prediction ScorE (RELAPSE). Multivariable Fine and Gray competing risk analysis and machine learning algorithms (Random Survival Forest and Classification and Regression Tree models) identified variables to model HCC recurrence. RELAPSE was externally validated in 1160 HCC LT recipients from the European Hepatocellular Cancer Liver Transplant study group. Of 4981 UMHTC patients with HCC undergoing LT, 71.9% were within Milan criteria, 16.1% were initially beyond Milan criteria with 9.4% downstaged before LT, and 12.0% had incidental HCC on explant pathology. Overall and recurrence-free survival at 1, 3, and 5 years was 89.7%, 78.6%, and 69.8% and 86.8%, 74.9%, and 66.7%, respectively, with a 5-year incidence of HCC recurrence of 12.5% (median 16 months) and non-HCC mortality of 20.8%. A multivariable model identified maximum alpha-fetoprotein (HR = 1.35 per-log SD, 95% CI,1.22–1.50, p < 0.001), neutrophil-lymphocyte ratio (HR = 1.16 per-log SD, 95% CI,1.04–1.28, p < 0.006), pathologic maximum tumor diameter (HR = 1.53 per-log SD, 95% CI, 1.35–1.73, p < 0.001), microvascular (HR = 2.37, 95%–CI, 1.87–2.99, p < 0.001) and macrovascular (HR = 3.38, 95% CI, 2.41–4.75, p < 0.001) invasion, and tumor differentiation (moderate HR = 1.75, 95% CI, 1.29–2.37, p < 0.001; poor HR = 2.62, 95% CI, 1.54–3.32, p < 0.001) as independent variables predicting post-LT HCC recurrence (C-statistic = 0.78). Machine learning algorithms incorporating additional covariates improved prediction of recurrence (Random Survival Forest C-statistic = 0.81). Despite significant differences in European Hepatocellular Cancer Liver Transplant recipient radiologic, treatment, and pathologic characteristics, external validation of RELAPSE demonstrated consistent 2- and 5-year recurrence risk discrimination (AUCs 0.77 and 0.75, respectively). We developed and externally validated a RELAPSE score that accurately discriminates post-LT HCC recurrence risk and may allow for individualized post-LT surveillance, immunosuppression modification, and selection of high-risk patients for adjuvant therapies.

A 35-year-old male greenhouse worker presented with myalgia, fatigue, and fever. Initially, he was thought to have an unspecified viral infection and was treated with conservative therapy. However, the patient’s symptoms persisted, and he reported additional symptoms of mild abdominal pain and headaches. Laboratory evaluation was significant for elevated liver enzymes. Due to concern for acute hepatitis and persistent fever the patient was hospitalized. During his hospital course, no infectious etiology was found to explain his symptoms. After discharge from the hospital, additional testing showed positive serology for Q fever IgG phase II antibody (1:8192) and phase II antibody IgM (> 1:2048). He was treated with doxycycline and had a good clinical response. Upon follow-up, he had worsening Phase I IgG serologies. Transesophageal echo demonstrated vegetations consistent with endocarditis.

ImportanceA safe and effective treatment for recurrent Clostridioides difficile infection (CDI) is urgently needed. Antibiotics kill toxin-producing bacteria but do not repair the disrupted microbiome, which promotes spore germination and infection recurrence.ObjectivesTo evaluate the safety and rate of CDI recurrence after administration of investigational microbiome therapeutic SER-109 through 24 weeks.Design, Setting, and ParticipantsThis phase 3, single-arm, open-label trial (ECOSPOR IV) was conducted at 72 US and Canadian outpatient sites from October 2017 to April 2022. Adults aged 18 years or older with recurrent CDI were enrolled in 2 cohorts: (1) rollover patients from the ECOSPOR III trial who had CDI recurrence diagnosed by toxin enzyme immunoassay (EIA) and (2) patients with at least 1 CDI recurrence (diagnosed by polymerase chain reaction [PCR] or toxin EIA), inclusive of their acute infection at study entry.InterventionsSER-109 given orally as 4 capsules daily for 3 days following symptom resolution after antibiotic treatment for CDI.Main Outcomes and MeasuresThe main outcomes were safety, measured as the rate of treatment-emergent adverse events (TEAEs) in all patients receiving any amount of SER-109, and cumulative rates of recurrent CDI (toxin-positive diarrhea requiring treatment) through week 24 in the intent-to-treat population.ResultsOf 351 patients screened, 263 were enrolled (180 [68.4%] female; mean [SD] age, 64.0 [15.7] years); 29 were in cohort 1 and 234 in cohort 2. Seventy-seven patients (29.3%) were enrolled with their first CDI recurrence. Overall, 141 patients (53.6%) had TEAEs, which were mostly mild to moderate and gastrointestinal. There were 8 deaths (3.0%) and 33 patients (12.5%) with serious TEAEs; none were considered treatment related by the investigators. Overall, 23 patients (8.7%; 95% CI, 5.6%-12.8%) had recurrent CDI at week 8 (4 of 29 [13.8%; 95% CI, 3.9%-31.7%] in cohort 1 and 19 of 234 [8.1%; 95% CI, 5.0%-12.4%] in cohort 2), and recurrent CDI rates remained low through 24 weeks (36 patients [13.7%; 95% CI, 9.8%-18.4%]). At week 8, recurrent CDI rates in patients with a first recurrence were similarly low (5 of 77 [6.5%; 95% CI, 2.1%-14.5%]) as in patients with 2 or more recurrences (18 of 186 [9.7%; 95% CI, 5.8%-14.9%]). Analyses by select baseline characteristics showed consistently low recurrent CDI rates in patients younger than 65 years vs 65 years or older (5 of 126 [4.0%; 95% CI, 1.3%-9.0%] vs 18 of 137 [13.1%; 95% CI, 8.0%-20.0%]) and patients enrolled based on positive PCR results (3 of 69 [4.3%; 95% CI, 0.9%-12.2%]) vs those with positive toxin EIA results (20 of 192 [10.4%; 95% CI, 6.5%-15.6%]).Conclusions and RelevanceIn this trial, oral SER-109 was well tolerated in a patient population with recurrent CDI and prevalent comorbidities. The rate of recurrent CDI was low regardless of the number of prior recurrences, demographics, or diagnostic approach, supporting the beneficial impact of SER-109 for patients with CDI.Trial RegistrationClinicalTrials.gov identifier: NCT03183141

Objective: The aim of this study was to analyze overall survival (OS) of robotic-assisted lobectomy (RL), video-assisted thoracoscopic lobectomy (VATS), and open lobectomy (OL) performed by experienced thoracic surgeons across multiple institutions. Summary Background Data: Surgeons have increasingly adopted RL for resection of early-stage lung cancer. Comparative survival data following these approaches is largely from single-institution case series or administrative data sets. Methods: Retrospective data was collected from 21 institutions from 2013 to 2019. Consecutive cases performed for clinical stage IA–IIIA lung cancer were included. Induction therapy patients were excluded. The propensity-score method of inverse-probability of treatment weighting was used to balance baseline characteristics. OS was estimated using the Kaplan-Meier method. Multivariable Cox proportional hazard models were used to evaluate association among OS and relevant risk factors. Results: A total of 2789 RL, 2661 VATS, and 1196 OL cases were included. The unadjusted 5-year OS rate was highest for OL (84%) followed by RL (81%) and VATS (74%); P=0.008. Similar trends were also observed after inverse-probability of treatment weighting adjustment (RL 81%; VATS 73%, OL 85%, P=0.001). Multivariable Cox regression analyses revealed that OL and RL were associated with significantly higher OS compared with VATS (OL vs. VATS: hazard ratio=0.64, P<0.001 and RL vs. VATS: hazard ratio=0.79; P=0.007). Conclusions: Our finding from this large multicenter study suggests that patients undergoing RL and OL have statistically similar OS, while the VATS group was associated with shorter OS. Further studies with longer follow-up are necessary to help evaluate these observations.

Abstract Aims Current non-invasive screening methods for cardiac allograft rejection have shown limited discrimination and are yet to be broadly integrated into heart transplant care. Given electrocardiogram (ECG) changes have been reported with severe cardiac allograft rejection, this study aimed to develop a deep-learning model, a form of artificial intelligence, to detect allograft rejection using the 12-lead ECG (AI-ECG). Methods and results Heart transplant recipients were identified across three Mayo Clinic sites between 1998 and 2021. Twelve-lead digital ECG data and endomyocardial biopsy results were extracted from medical records. Allograft rejection was defined as moderate or severe acute cellular rejection (ACR) based on International Society for Heart and Lung Transplantation guidelines. The extracted data (7590 unique ECG-biopsy pairs, belonging to 1427 patients) was partitioned into training (80%), validation (10%), and test sets (10%) such that each patient was included in only one partition. Model performance metrics were based on the test set (n = 140 patients; 758 ECG-biopsy pairs). The AI-ECG detected ACR with an area under the receiver operating curve (AUC) of 0.84 [95% confidence interval (CI): 0.78–0.90] and 95% (19/20; 95% CI: 75–100%) sensitivity. A prospective proof-of-concept screening study (n = 56; 97 ECG-biopsy pairs) showed the AI-ECG detected ACR with AUC = 0.78 (95% CI: 0.61–0.96) and 100% (2/2; 95% CI: 16–100%) sensitivity. Conclusion An AI-ECG model is effective for detection of moderate-to-severe ACR in heart transplant recipients. Our findings could improve transplant care by providing a rapid, non-invasive, and potentially remote screening option for cardiac allograft function.

NAFLD will soon be the most common indication for liver transplantation (LT). In NAFLD, HCC may occur at earlier stages of fibrosis and present with more advanced tumor stage, raising concern for aggressive disease. Thus, adult LT recipients with HCC from 20 US centers transplanted between 2002 and 2013 were analyzed to determine whether NAFLD impacts recurrence-free post-LT survival. Five hundred and thirty-eight (10.8%) of 4981 total patients had NAFLD. Patients with NAFLD were significantly older (63 vs. 58, p<0.001), had higher body mass index (30.5 vs. 27.4, p<0.001), and were more likely to have diabetes (57.3% vs. 28.8%, p<0.001). Patients with NAFLD were less likely to receive pre-LT locoregional therapy (63.6% vs. 72.9%, p<0.001), had higher median lab MELD (15 vs. 13, p<0.001) and neutrophil-lymphocyte ratio (3.8 vs. 2.9, p<0.001), and were more likely to have their maximum pre-LT alpha fetoprotein at time of LT (44.1% vs. 36.1%, p<0.001). NAFLD patients were more likely to have an incidental HCC on explant (19.4% vs. 10.4%, p<0.001); however, explant characteristics including tumor differentiation and vascular invasion were not different between groups. Comparing NAFLD and non-NAFLD patients, the 1, 3, and 5-year cumulative incidence of recurrence (3.1%, 9.1%, 11.5% vs. 4.9%, 10.1%, 12.6%, p=0.36) and recurrence-free survival rates (87%, 76%, and 67% vs. 87%, 75%, and 67%, p=0.97) were not different. In competing risks analysis, NAFLD did not significantly impact recurrence in univariable (HR: 0.88, p=0.36) nor in adjusted analysis (HR: 0.91, p=0.49). With NAFLD among the most common causes of HCC and poised to become the leading indication for LT, a better understanding of disease-specific models to predict recurrence is needed. In this NAFLD cohort, incidental HCCs were common, raising concerns about early detection. However, despite less locoregional therapy and high neutrophil-lymphocyte ratio, explant tumor characteristics and post-transplant recurrence-free survival were not different compared to non-NAFLD patients.

Purpose To develop a predictive machine learning model to identify prognostic factors for continued opioid prescriptions after arthroscopic meniscus surgery. Methods Patients undergoing arthroscopic meniscal surgery, such as meniscus debridement, repair, or revision at a single institution from 2013 to 2017 were retrospectively followed up to 1 year postoperatively. Procedural details were recorded, including concomitant procedures, primary versus revision, and whether a partial debridement or a repair was performed. Intraoperative arthritis severity was measured using the Outerbridge Classification. The number of opioid prescriptions in each month was recorded. Primary analysis used was the multivariate Cox‐Regression model. We then created a naïve Bayesian model, a machine learning classifier that uses Bayes’ theorem with an assumption of independence between variables. Results A total of 581 patients were reviewed. Postoperative opioid refills occurred in 98 patients (16.9%). Multivariate logistic modeling was used; independent risk factors for opioid refills included male sex, larger body mass index, and chronic preoperative opioid use, while meniscus resection demonstrated decreased likelihood of refills. Concomitant procedures, revision procedures, and presence of arthritis graded by the Outerbridge classification were not significant predictors of postoperative opioid refills. The naïve Bayesian model for extended postoperative opioid use demonstrated good fit with our cohort with an area under the curve of 0.79, sensitivity of 94.5%, positive predictive value (PPV) of 83%, and a detection rate of 78.2%. The two most important features in the model were preoperative opioid use and male sex. Conclusion After arthroscopic meniscus surgery, preoperative opioid consumption and male sex were the most significant predictors for sustained opioid use beyond 1 month postoperatively. Intraoperative arthritis was not an independent risk factor for continued refills. A machine learning algorithm performed with high accuracy, although with a high false positive rate, to function as a screening tool to identify patients filling additional narcotic prescriptions after surgery. Level of Evidence III, retrospective comparative study.