Publications by authors named "Victor H van Berkel"

There are reports of successful lung transplants using SARS-CoV-2+ donors, but the data on their overall outcome is limited. We used the United Network for Organ Sharing Database (UNOS) to identify all lung transplant patients who received lungs from SARS-CoV-2+ donors between 2020 and 2023. There was no difference in survival between those who received lungs from SARS-CoV-2- and SARS-CoV-2+ donors (P = .

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Article Synopsis
  • - The study investigates how being ambulatory while on extracorporeal membrane oxygenation (ECMO) affects survival rates for patients on the lung transplant waitlist and after the transplant.
  • - Using the UNOS database, researchers classified patients on ECMO into ambulatory (AMB) and non-ambulatory (nAMB) groups, finding that both AMB groups had better waitlist survival rates than nAMB groups.
  • - After lung transplantation, the one-year survival rates for non-ECMO patients and AMB patients on veno-venous ECMO were similar, suggesting that physical activity during ECMO may enhance transplant outcomes.
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Background: Pathophysiological conditions underlying pulmonary fibrosis remain poorly understood. Exhaled breath volatile organic compounds (VOCs) have shown promise for lung disease diagnosis and classification. In particular, carbonyls are a byproduct of oxidative stress, associated with fibrosis in the lungs.

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The standard of care for intermediate (Stage II) and advanced (Stages III and IV) non-small cell lung cancer (NSCLC) involves chemotherapy with taxane/platinum derivatives, with or without radiation. Ideally, patients would be screened a priori to allow non-responders to be initially treated with second-line therapies. This evaluation is non-trivial, however, since tumors behave as complex multiscale systems.

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Introduction: While prediction of short versus long term survival from lung cancer is clinically relevant in the context of patient management and therapy selection, it has proven difficult to identify reliable biomarkers of survival. Metabolomic markers from tumor core biopsies have been shown to reflect cancer metabolic dysregulation and hold prognostic value.

Objectives: Implement and validate a novel ensemble machine learning approach to evaluate survival based on metabolomic biomarkers from tumor core biopsies.

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Introduction: Metabolomics has emerged as a powerful method to provide insight into cancer progression, including separating patients into low- and high-risk groups for overall (OS) and progression-free survival (PFS). However, survival prediction based mainly on metabolites obtained from biofluids remains elusive.

Objectives: This proof-of-concept study evaluates metabolites as biomarkers obtained directly from tumor core biopsies along with covariates age, sex, pathological stage at diagnosis (I/II vs.

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Objectives: Despite extensive effort, the search for clinically-relevant metabolite biomarkers for early detection, disease monitoring, and outcome prediction in lung cancer remains unfulfilled. Although biofluid evaluation has been explored, the complexity inherent in metabolite data and the dynamic discrepancy between metabolites in biofluids vs. tumor tissue have prevented conclusive results.

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Background: National guidelines suggest routine intraoperative esophagogastroduodenoscopy (EGD) during laparoscopic Heller myotomy (LHM) to assess for mucosal perforation and myotomy adequacy, but the utility of this is unknown. This study aimed to evaluate the effect of intraoperative EGD on outcomes after LHM.

Methods: Patients who underwent LHM in a single center were retrospectively identified.

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Background: Patients with clinically/pathologically diagnosed stage IIIa non-small cell lung cancer (NSCLC) considered for surgery are recommended to undergo neoadjuvant chemotherapy with or without radiation. The timing of an operation after therapy is not standardized; therefore, we investigated the timing of intervention after neoadjuvant therapy and the impact on outcomes in this demographic.

Methods: The National Cancer Database was queried between 2010 and 2015 for patients with clinical/pathologic stage IIIa NSCLC.

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The objective of the study is to evaluate the impact of the Affordable Care Act (ACA) on accessibility to solid organ transplant and outcomes. Data source registry: United Network of Organ Sharing database. Patients aged ≥18 years listed for kidney, liver, heart, and lung transplant between years 2010 and 2016 were classified by insurance and status of Medicaid adoption under ACA to evaluate insurance distribution.

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Background: There is no objective method to estimate post-lung transplant survival solely on the basis of cumulative donor risk factors.

Methods: The United Network Organ Sharing thoracic transplant database was queried to identify patients who underwent lung transplantation between 2005 and 2015. A Cox proportional hazard model was generated using a training set to identify donor risk factors significantly associated with posttransplant survival.

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Chemotherapy for non-small cell lung cancer (NSCLC) typically involves a doublet regimen for a number of cycles. For any particular patient, a course of treatment is usually chosen from a large number of combinational protocols with drugs in concomitant or sequential administration. In spite of newer drugs and protocols, half of patients with early disease will live less than five years and 95% of those with advanced disease survive for less than one year.

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Outcomes for cancer patients have been previously estimated by applying various machine learning techniques to large datasets such as the Surveillance, Epidemiology, and End Results (SEER) program database. In particular for lung cancer, it is not well understood which types of techniques would yield more predictive information, and which data attributes should be used in order to determine this information. In this study, a number of supervised learning techniques is applied to the SEER database to classify lung cancer patients in terms of survival, including linear regression, Decision Trees, Gradient Boosting Machines (GBM), Support Vector Machines (SVM), and a custom ensemble.

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Background: In an effort to expand the donor pool for lung transplants, numerous studies have examined the use of advanced age donors with mixed results, including decreased survival among younger recipients. We evaluated the impact of the use of advanced age donors and single versus double lung transplantation on posttransplant survival.

Methods: The United Network for Organ Sharing database was retrospectively queried between January 2005 and June 2014 to identify lung transplant patients aged at least 18 years.

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This study applies unsupervised machine learning techniques for classification and clustering to a collection of descriptive variables from 10,442 lung cancer patient records in the Surveillance, Epidemiology, and End Results (SEER) program database. The goal is to automatically classify lung cancer patients into groups based on clinically measurable disease-specific variables in order to estimate survival. Variables selected as inputs for machine learning include Number of Primaries, Age, Grade, Tumor Size, Stage, and TNM, which are numeric or can readily be converted to numeric type.

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Background: The use of left ventricular assist devices (LVAD) has increased significantly in the last decade. However, right heart dysfunction remains a problem despite the improved outcomes with continuous-flow LVADs. Surgical options for bridge to transplantation (BTT) in patients with biventricular failure are total artificial heart (TAH) or biventricular support (BiVAD).

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Background: Donor heart availability has limited the number of heart transplants performed in the United States, while the number of patients waiting for a transplant continues to increase. Optimizing the use of all available donor hearts is important to reduce waiting list deaths and to increase the number of patients who can ultimately undergo a successful heart transplant. Donor cardiopulmonary resuscitation (CPR) time has been proposed to be a selection criterion to consider in donor selection.

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Background: Ex vivo lung perfusion (EVLP) has the potential to increase the donor pool for lung transplantation by facilitating resuscitation and extended evaluation of marginal organs. Current EVLP methodology employs continuous flow (CF) pumps that produce non-pulsatile EVLP hemodynamics. In this feasibility study, we tested the hypothesis that a pulsatile flow (PF) pump will provide better EVLP support than a CF pump through delivery of physiologic hemodynamics.

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