Citations

AimThis study aimed to evaluate the impact of intraoperative direct sonication on the yield of traditional culture and the time to positivity (TTP) of cultures obtained for periprosthetic joint infection (PJI), thereby assessing its potential to improve diagnostic efficiency and reduce contamination risk.MethodA prospective cohort study was conducted at a tertiary care center, involving 190 patients undergoing revision surgery for PJI from August 2021 to January 2024. Patients were included based on the 2018 International Consensus Meeting definition of PJI. The study utilized a novel sonication protocol, which involved direct intraoperative sonication of the implant and tissue, followed by incubation in a BACT/ALERT 3D system. The primary outcomes measured were the number and percentage of positive culture samples, identified microorganisms, and the TTP of each culture. Statistical analysis was performed using R software, with various tests applied to assess the significance of findings.ResultsThe study included 510 positive cultures from 190 patients, demonstrating that sonication significantly improved the positivity rate for both tissue and prosthesis specimens (p < 0.05). The median TTP for all samples was 3.13 days, with sonicated samples showing a significantly shorter TTP compared to non-sonicated samples (p < 0.05). Specifically, the shortest median TTP was observed in prosthesis post-sonication samples. Furthermore, the study found that Gram-positive organisms had a shorter TTP than gram-negative organisms, and specific microorganisms like Staphylococcus aureus and MRSE showed the fastest TTP. The analysis also revealed higher positivity rates in chronic PJIs compared to acute PJIs for sonicated tissue samples.ConclusionsThe study demonstrates that intraoperative direct sonication combined with the BACT/ALERT 3D system can significantly enhance the diagnostic yield of cultures and reduce the TTP for common PJI pathogens. This novel technique not only improves pathogen detection, facilitating the tailoring of antibiotic therapy, but also potentially reduces the risk of contamination associated with sonication. These findings suggest that direct intraoperative sonication could be a valuable addition to the current diagnostic protocols for PJI, contributing to more effective management and treatment of this complex condition. Further research is necessary to explore the clinical significance of TTP and its correlation with patient outcomes in PJI.

Background: Conventional neoadjuvant chemoradiotherapy (nCRT) yields a pathologic complete response (pCR) rate of 15–30% for locally advanced rectal cancer (LARC). This study ventures to shift this paradigm by incorporating short-course nCRT with immunotherapy, specifically Envafolimab, to achieve improved treatment efficacy and possibly redefine the standard of care for LARC. Materials and methods: The PRECAM study is a prospective, single-arm, phase 2 clinical trial for LARC in patients with microsatellite stable (MSS) tumors. Participants received short-course radiotherapy (25Gy/5f), followed by two cycles of CAPEOX chemotherapy and six weekly doses of Envafolimab, a PD-L1 antibody, before total mesorectal excision surgery. The primary endpoint was the pCR rate. Results: From April to December 2022, 34 patients were enrolled, of whom 32 completed the study, each diagnosed with an MSS rectal adenocarcinoma. All patients underwent preoperative CRT combined with Envafolimab. Remarkably, a pCR rate of 62.5% (20/32) was attained, and a significant pathologic response rate of 75% (24/32) was achieved. Additionally, 21 of 32 participants achieved a neoadjuvant rectal (NAR) score below 8, suggesting an effective treatment response. Common adverse events included tenesmus (78.1%), diarrhea (62.5%), and leukocyte decrease (40.6%). Two Grade 3 adverse events were noted, one related to liver function abnormality and the other to a decrease in platelet count. Surgical procedures were performed in all cases, with minor complications, including ileus, infections, and anastomotic leakage. As of this report, there have been no reported cases of recurrence or death during the follow-up period, ranging from 12 to 20 months. Conclusion: In LARC patients exhibiting MSS tumors, combining short-course nCRT with Envafolimab demonstrated favorable efficacy, leading to a significant pCR rate. Minor adverse effects and surgical complications were observed. These preliminary but promising results underscore the potential of this approach and call for further exploration and validation through a randomized controlled trial.

3614 Background: Conventional neoadjuvant chemoradiotherapy (nCRT) yields a pathologic complete response (pCR) rate of about 15%–- 30% for locally advanced rectal cancer (LARC). This PRECAM study aimed to investigate the efficacy and safety of neoadjuvant short-course radiotherapy combined with CAPEOX plus Envafolimab in Patients with Microsatellite Stable (MSS) LARC, redefining the standard of care (SOC) for LARC. Methods: The study is an open-label, prospective, single-arm, phase II clinical study . Eligible patients with MSS LARC were consecutively enrolled. Participants received short-course radiotherapy (25Gy/5f) with subsequent two cycles of CAPEOX (capecitabine and oxaliplatin) and 6 cycles of Envafolimab (subcutaneous injection, 150 mg, QW ) followed by total mesorectal excision surgery. The primary endpoint was pathological complete response (pCR) rate, and the secondary endpoint was major pathologic (MPR) rate and safety. Results: From April to December 2022, 34 patients were enrolled, of whom 32 completed the study. Remarkably, The pCR rate was62.5% (20/32), and the (MPR) rate was 75% (24/32). Furthermore, 65.6% (21/32) patients achieved a neoadjuvant rectal (NAR) score below 8, suggesting an effective treatment response. Common adverse events included tenesmus (78.1%), diarrhea (62.5%), and leukocyte decrease (40.6%), with two cases of Grade 3 adverse events. Surgical procedures were performed in all 32 cases, with minor complications observed. During the follow-up period of up to 20 months, no recurrence or death were reported. Conclusions: Short-course radiotherapy combined with CAPEOX plus Envafolimab showed favorable pCR rate with manageable adverse effects and surgical complications in LARC, underscoring the potential of the combination therapy for MSS LARC. A Randomized controlled trial is warranted for further exploration and validation. Clinical trial information: NCT05216653 .

Abstract Objectives To develop and validate a standards-based phenotyping tool to author electronic health record (EHR)-based phenotype definitions and demonstrate execution of the definitions against heterogeneous clinical research data platforms. Materials and Methods We developed an open-source, standards-compliant phenotyping tool known as the PhEMA Workbench that enables a phenotype representation using the Fast Healthcare Interoperability Resources (FHIR) and Clinical Quality Language (CQL) standards. We then demonstrated how this tool can be used to conduct EHR-based phenotyping, including phenotype authoring, execution, and validation. We validated the performance of the tool by executing a thrombotic event phenotype definition at 3 sites, Mayo Clinic (MC), Northwestern Medicine (NM), and Weill Cornell Medicine (WCM), and used manual review to determine precision and recall. Results An initial version of the PhEMA Workbench has been released, which supports phenotype authoring, execution, and publishing to a shared phenotype definition repository. The resulting thrombotic event phenotype definition consisted of 11 CQL statements, and 24 value sets containing a total of 834 codes. Technical validation showed satisfactory performance (both NM and MC had 100% precision and recall and WCM had a precision of 95% and a recall of 84%). Conclusions We demonstrate that the PhEMA Workbench can facilitate EHR-driven phenotype definition, execution, and phenotype sharing in heterogeneous clinical research data environments. A phenotype definition that integrates with existing standards-compliant systems, and the use of a formal representation facilitates automation and can decrease potential for human error.

R47H- TREM2 mutation enhances AKT signaling in human AD microglia and mediates proinflammatory and synaptic toxicity in a tauopathy mouse model.

Abstract Standard treatment of catheter-associated upper extremity deep vein thrombosis (UE-DVT) is anticoagulation, although catheters are often removed for this indication. The optimal time for catheter removal and whether the act and/or timing of catheter removal is associated with pulmonary embolism (PE) remain unknown. A retrospective cohort study was performed at 8 participating institutions through the Venous thromboEmbolism Network US. Patients with hematologic malignancies and central venous catheter (CVC)–associated UE-DVT were included from 1 January 2010 through 31 December 2016. The primary outcome was objectively confirmed PE within 7 days of UE-DVT diagnosis in anticoagulated patients comparing early (≤48 hours) vs delayed (&gt;48 hours) catheter removal. A total of 626 patients were included, among whom 480 were treated with anticoagulation. Among anticoagulated patients, 255 underwent early CVC removal, while 225 had delayed or no CVC removal; 146 patients received no anticoagulation, among whom 116 underwent CVC removal alone. PE within 7 days occurred in 2 patients (0.78%) with early removal compared with 1 patient (0.44%) with delayed or no CVC removal (P &gt; .9). PE or any cause of death within 7 days occurred in 3 patients in both the early removal (1.18%) and delayed/no removal (1.33%) groups (P &gt; .9). In patients treated with CVC removal only (no anticoagulation), there were no PEs but 3 deaths within 7 days. In patients with hematological malignancy and CVC-associated UE-DVT, early removal of CVCs was not associated with an increased risk of PE compared with delayed or no removal.

AbstractIntroductionWe sought to assess longitudinal electronic health records (EHRs) using machine learning (ML) methods to computationally derive probable Alzheimer's Disease (AD) and related dementia subphenotypes.MethodsA retrospective analysis of EHR data from a cohort of 7587 patients seen at a large, multi‐specialty urban academic medical center in New York was conducted. Subphenotypes were derived using hierarchical clustering from 792 probable AD patients (cases) who had received at least one diagnosis of AD using their clinical data. The other 6795 patients, labeled as controls, were matched on age and gender with the cases and randomly selected in the ratio of 9:1. Prediction models with multiple ML algorithms were trained on this cohort using 5‐fold cross‐validation. XGBoost was used to rank the variable importance.ResultsFour subphenotypes were computationally derived. Subphenotype A (n = 273; 28.2%) had more patients with cardiovascular diseases; subphenotype B (n = 221; 27.9%) had more patients with mental health illnesses, such as depression and anxiety; patients in subphenotype C (n = 183; 23.1%) were overall older (mean (SD) age, 79.5 (5.4) years) and had the most comorbidities including diabetes, cardiovascular diseases, and mental health disorders; and subphenotype D (n = 115; 14.5%) included patients who took anti‐dementia drugs and had sensory problems, such as deafness and hearing impairment.The 0‐year prediction model for AD risk achieved an area under the receiver operating curve (AUC) of 0.764 (SD: 0.02); the 6‐month model, 0.751 (SD: 0.02); the 1‐year model, 0.752 (SD: 0.02); the 2‐year model, 0.749 (SD: 0.03); and the 3‐year model, 0.735 (SD: 0.03), respectively. Based on variable importance, the top‐ranked comorbidities included depression, stroke/transient ischemic attack, hypertension, anxiety, mobility impairments, and atrial fibrillation. The top‐ranked medications included anti‐dementia drugs, antipsychotics, antiepileptics, and antidepressants.ConclusionsFour subphenotypes were computationally derived that correlated with cardiovascular diseases and mental health illnesses. ML algorithms based on patient demographics, diagnosis, and treatment demonstrated promising results in predicting the risk of developing AD at different time points across an individual's lifespan.

AbstractObjectiveTo identify depression subphenotypes from Electronic Health Records (EHRs) using machine learning methods, and analyze their characteristics with respect to patient demographics, comorbidities, and medications.Materials and MethodsUsing EHRs from the INSIGHT Clinical Research Network (CRN) database, multiple machine learning (ML) algorithms were applied to analyze 11 275 patients with depression to discern depression subphenotypes with distinct characteristics.ResultsUsing the computational approaches, we derived three depression subphenotypes: Phenotype_A (n = 2791; 31.35%) included patients who were the oldest (mean (SD) age, 72.55 (14.93) years), had the most comorbidities, and took the most medications. The most common comorbidities in this cluster of patients were hyperlipidemia, hypertension, and diabetes. Phenotype_B (mean (SD) age, 68.44 (19.09) years) was the largest cluster (n = 4687; 52.65%), and included patients suffering from moderate loss of body function. Asthma, fibromyalgia, and Chronic Pain and Fatigue (CPF) were common comorbidities in this subphenotype. Phenotype_C (n = 1452; 16.31%) included patients who were younger (mean (SD) age, 63.47 (18.81) years), had the fewest comorbidities, and took fewer medications. Anxiety and tobacco use were common comorbidities in this subphenotype.ConclusionComputationally deriving depression subtypes can provide meaningful insights and improve understanding of depression as a heterogeneous disorder. Further investigation is needed to assess the utility of these derived phenotypes to inform clinical trial design and interpretation in routine patient care.

AbstractIntroductionElectronic health record (EHR)‐driven phenotyping is a critical first step in generating biomedical knowledge from EHR data. Despite recent progress, current phenotyping approaches are manual, time‐consuming, error‐prone, and platform‐specific. This results in duplication of effort and highly variable results across systems and institutions, and is not scalable or portable. In this work, we investigate how the nascent Clinical Quality Language (CQL) can address these issues and enable high‐throughput, cross‐platform phenotyping.MethodsWe selected a clinically validated heart failure (HF) phenotype definition and translated it into CQL, then developed a CQL execution engine to integrate with the Observational Health Data Sciences and Informatics (OHDSI) platform. We executed the phenotype definition at two large academic medical centers, Northwestern Medicine and Weill Cornell Medicine, and conducted results verification (n = 100) to determine precision and recall. We additionally executed the same phenotype definition against two different data platforms, OHDSI and Fast Healthcare Interoperability Resources (FHIR), using the same underlying dataset and compared the results.ResultsCQL is expressive enough to represent the HF phenotype definition, including Boolean and aggregate operators, and temporal relationships between data elements. The language design also enabled the implementation of a custom execution engine with relative ease, and results verification at both sites revealed that precision and recall were both 100%. Cross‐platform execution resulted in identical patient cohorts generated by both data platforms.ConclusionsCQL supports the representation of arbitrarily complex phenotype definitions, and our execution engine implementation demonstrated cross‐platform execution against two widely used clinical data platforms. The language thus has the potential to help address current limitations with portability in EHR‐driven phenotyping and scale in learning health systems.