Effectiveness of sleeve gastrectomy plus fundoplication versus sleeve gastrectomy alone for treatment of patients with severe obesity: a systematic review and meta-analysis.

BACKGROUND: Laparoscopic sleeve gastrectomy (SG) is a widely performed bariatric surgery, but it is associated with an increased risk of gastroesophageal reflux (GERD) in the long term. The addition of fundoplication to laparoscopic SG may improve lower oesophageal sphincter function and reduce postoperative GERD. OBJECTIVES: This systematic review and meta-analysis aims to compare the efficacy and safety of SG plus fundoplication (SG + F) versus SG alone for the treatment of patients with severe obesity (≥35 kg/m2). SETTING: Meta-analysis. METHODS: Three electronic databases were searched from inception until January 2023. Studies were included if they compared outcomes of SG + F versus SG in patients with severe obesity (≥35 kg/m2). The primary outcome was remission of GERD postoperatively. Secondary outcomes were the percentage of excess weight loss, percentage of total weight loss, postoperative complication rate, operative time, and length of stay. RESULTS: A total of 5 studies with 539 subjects (212 SG + F and 327 SG alone) were included. The mean preoperative body mass index was 42.6 kg/m2. SG + F achieved higher remission of GERD compared with laparoscopic SG (odds ratio [OR] = 13.13; 95% CI, 3.54-48.73; I2 = 0%). However, the percentage of total weight loss was lower in the SG + F group (mean difference [MD] = -2.75, 95% CI, -4.28 to -1.23; I2 = 0%), whereas there was no difference in the percentage of excess weight loss (MD = -0.64; 95% CI, -20.62-19.34; I2 = 83%). There were higher postoperative complications in SG + F (OR = 2.56; 95% CI, 1.12-5.87; I2 = 0%) as well. There was no difference in operative time or length of stay between the 2 groups. CONCLUSION: SG + F achieved better GERD remission but is associated with lesser weight loss and increased postoperative complications compared with SG alone. Further studies are required to ascertain the overall clinical benefit of SG + F for patients with severe obesity.

Rationale and Trial Protocol for a Double-Blinded Randomized Controlled Trial to assess the Impact of a Concomitant Crural Repair during Laparoscopic Sleeve Gastrectomy in Patients with a Lax Gastroesophageal Junction without Frank Hiatal Hernia (REPAIR trial protocol).

INTRODUCTION: Laparoscopic sleeve gastrectomy (LSG) is associated with postoperative gastroesophageal reflux disease (GERD) and erosive esophagitis (EE). The role of crural repair during LSG is still controversial. The preoperative laxity of the gastroesophageal junction (GEJ), graded by the Hill's classification, is more predictive for postoperative GERD and EE after LSG than the presence of a hiatal hernia seen on endoscopy. Thus, the authors hypothesize that a concomitant crural repair in a specific subgroup of patients with a lax GEJ (Hill's III) may reduce the incidence of postoperative GERD and EE. METHODS: A double-blinded, randomized controlled trial of patients with Hill's III GEJ undergoing LSG will be randomized to a concomitant crural repair (experimental) versus LSG alone (control). Primary outcome measures will be presence of EE at 1-year. Secondary outcome measures will include proton pump inhibitor use, postoperative complications, operative time, blood loss, quality of life, GERD and gastrointestinal symptoms. CONCLUSION: Conflicting crural repair results may be explained by differences in preoperative GEJ laxity. Patients with a frank hiatal hernia and patulous GEJ (Hill's IV) have a very high, while patients with an apposed GEJ (Hill's I, Hill's II) have a low incidence of postoperative GERD and EE respectively. Thus, the authors hypothesize that patients with a lax GEJ without frank hiatal hernia (Hill's III), might benefit from a crural repair. This study results can potentially highlight the clinical importance of preoperative endoscopic evaluation of the GEJ in all patients planned for LSG, to determine which subgroup patients may benefit from a crural repair. (Clinicaltrials.gov: NCT05330910, Registered 15-April-2022).

Investigating healthcare worker mobility and patient contacts within a UK hospital during the COVID-19 pandemic.

BACKGROUND: Insights into behaviours relevant to the transmission of infections are extremely valuable for epidemiological investigations. Healthcare worker (HCW) mobility and patient contacts within the hospital can contribute to nosocomial outbreaks, yet data on these behaviours are often limited. METHODS: Using electronic medical records and door access logs from a London teaching hospital during the COVID-19 pandemic, we derive indicators for HCW mobility and patient contacts at an aggregate level. We assess the spatial-temporal variations in HCW behaviour and, to demonstrate the utility of these behavioural markers, investigate changes in the indirect connectivity of patients (resulting from shared contacts with HCWs) and spatial connectivity of floors (owing to the movements of HCWs). RESULTS: Fluctuations in HCW mobility and patient contacts were identified during the pandemic, with the most prominent changes in behaviour on floors handling the majority of COVID-19 patients. The connectivity between floors was disrupted by the pandemic and, while this stabilised after the first wave, the interconnectivity of COVID-19 and non-COVID-19 wards always featured. Daily rates of indirect contact between patients provided evidence for reactive staff cohorting in response to the number of COVID-19 patients in the hospital. CONCLUSIONS: Routinely collected electronic records in the healthcare environment provide a means to rapidly assess and investigate behaviour change in the HCW population, and can support evidence based infection prevention and control activities. Integrating frameworks like ours into routine practice will empower decision makers and improve pandemic preparedness by providing tools to help curtail nosocomial outbreaks of communicable diseases.

Movement of healthcare workers and their patient contacts can contribute to outbreaks of infection in the healthcare environment. We use electronic medical records and door access logs from a London hospital to derive indicators for staff behaviour during the COVID-19 pandemic. Changes in staff behaviour were most prominent on floors handling the majority of COVID-19 patients. We also show how the flow of staff between COVID-19 and non-COVID-19 wards continued throughout the pandemic, but find evidence that indirect contact between COVID-19 positive and negative patients reduced as COVID-19 prevalence increased. We suggest these routinely collected data on HCW behaviour should be used to support decision makers in activities to help curtail disease outbreaks in healthcare settings.

Characterization of patients with idiopathic normal pressure hydrocephalus using natural language processing within an electronic healthcare record system.

OBJECTIVE: Idiopathic normal pressure hydrocephalus (iNPH) is an underdiagnosed, progressive, and disabling condition. Early treatment is associated with better outcomes and improved quality of life. In this paper, the authors aimed to identify features associated with patients with iNPH using natural language processing (NLP) to characterize this cohort, with the intention to later target the development of artificial intelligence-driven tools for early detection. METHODS: The electronic health records of patients with shunt-responsive iNPH were retrospectively reviewed using an NLP algorithm. Participants were selected from a prospectively maintained single-center database of patients undergoing CSF diversion for probable iNPH (March 2008-July 2020). Analysis was conducted on preoperative health records including clinic letters, referrals, and radiology reports accessed through CogStack. Clinical features were extracted from these records as SNOMED CT (Systematized Nomenclature of Medicine Clinical Terms) concepts using a named entity recognition machine learning model. In the first phase, a base model was generated using unsupervised training on 1 million electronic health records and supervised training with 500 double-annotated documents. The model was fine-tuned to improve accuracy using 300 records from patients with iNPH double annotated by two blinded assessors. Thematic analysis of the concepts identified by the machine learning algorithm was performed, and the frequency and timing of terms were analyzed to describe this patient group. RESULTS: In total, 293 eligible patients responsive to CSF diversion were identified. The median age at CSF diversion was 75 years, with a male predominance (69% male). The algorithm performed with a high degree of precision and recall (F1 score 0.92). Thematic analysis revealed the most frequently documented symptoms related to mobility, cognitive impairment, and falls or balance. The most frequent comorbidities were related to cardiovascular and hematological problems. CONCLUSIONS: This model demonstrates accurate, automated recognition of iNPH features from medical records. Opportunities for translation include detecting patients with undiagnosed iNPH from primary care records, with the aim to ultimately improve outcomes for these patients through artificial intelligence-driven early detection of iNPH and prompt treatment.

Deployment of a Free-Text Analytics Platform at a UK National Health Service Research Hospital: CogStack at University College London Hospitals.

BACKGROUND: As more health care organizations transition to using electronic health record (EHR) systems, it is important for these organizations to maximize the secondary use of their data to support service improvement and clinical research. These organizations will find it challenging to have systems capable of harnessing the unstructured data fields in the record (clinical notes, letters, etc) and more practically have such systems interact with all of the hospital data systems (legacy and current). OBJECTIVE: We describe the deployment of the EHR interfacing information extraction and retrieval platform CogStack at University College London Hospitals (UCLH). METHODS: At UCLH, we have deployed the CogStack platform, an information retrieval platform with natural language processing capabilities. The platform addresses the problem of data ingestion and harmonization from multiple data sources using the Apache NiFi module for managing complex data flows. The platform also facilitates the extraction of structured data from free-text records through use of the MedCAT natural language processing library. Finally, data science tools are made available to support data scientists and the development of downstream applications dependent upon data ingested and analyzed by CogStack. RESULTS: The platform has been deployed at the hospital, and in particular, it has facilitated a number of research and service evaluation projects. To date, we have processed over 30 million records, and the insights produced from CogStack have informed a number of clinical research use cases at the hospital. CONCLUSIONS: The CogStack platform can be configured to handle the data ingestion and harmonization challenges faced by a hospital. More importantly, the platform enables the hospital to unlock important clinical information from the unstructured portion of the record using natural language processing technology.

The Hill's Classification Is Useful to Predict the Development of Postoperative Gastroesophageal Reflux Disease and Erosive Esophagitis After Laparoscopic Sleeve Gastrectomy.

BACKGROUND: Laparoscopic sleeve gastrectomy (LSG) is one of the commonest bariatric procedures. However, it is associated with postoperative gastroesophageal reflux disease (GERD) and erosive esophagitis (EE). This study aims to assess the impact of various preoperative clinical and endoscopic characteristics on the development of postoperative GERD and EE. METHODS: This study is a single-institution retrospective cohort study involving all patients who underwent LSG. A univariate and multivariate analysis was performed to identify preoperative parameters that were significantly associated with the development of postoperative GERD and EE, at up to 1-year follow-up. RESULTS: At up to 1-year follow-up, out of 127 patients, only preoperative endoscopic presence of a hiatal hernia noted on axial length (p=0.024) and the Hill's classification of the gastroesophageal junction (p<0.001) were significantly associated with the development of postoperative GERD. Similarly, at 1-year follow-up endoscopy, the presence of a hiatal hernia (p=0.041) and the Hill's classification (p=0.001) were associated with postoperative EE. On the multivariate analysis, compared to patients with a Hill's I flap valve, Hill's II patients were more likely to develop postoperative GERD (OR 7.13, 95% CI: 1.69-29.98, p=0.007), and Hill's III patients were more likely to develop postoperative GERD (OR 20.84, 95% CI: 3.98-109.13, p<0.001) and EE (OR 34.49, 95% CI: 1.08-1105.36, p=0.045). All patients with Hill's IV developed postoperative GERD and EE in this study. CONCLUSION: Postoperative GERD and EE remain an important limitation following LSG. Proper preoperative assessment using the Hill's classification can help to accurately predict patients at risk of postoperative GERD and EE.

Multi-domain clinical natural language processing with MedCAT: The Medical Concept Annotation Toolkit.

Electronic health records (EHR) contain large volumes of unstructured text, requiring the application of information extraction (IE) technologies to enable clinical analysis. We present the open source Medical Concept Annotation Toolkit (MedCAT) that provides: (a) a novel self-supervised machine learning algorithm for extracting concepts using any concept vocabulary including UMLS/SNOMED-CT; (b) a feature-rich annotation interface for customizing and training IE models; and (c) integrations to the broader CogStack ecosystem for vendor-agnostic health system deployment. We show improved performance in extracting UMLS concepts from open datasets (F1:0.448-0.738 vs 0.429-0.650). Further real-world validation demonstrates SNOMED-CT extraction at 3 large London hospitals with self-supervised training over ∼8.8B words from ∼17M clinical records and further fine-tuning with ∼6K clinician annotated examples. We show strong transferability (F1 > 0.94) between hospitals, datasets and concept types indicating cross-domain EHR-agnostic utility for accelerated clinical and research use cases.

Excess deaths in people with cardiovascular diseases during the COVID-19 pandemic.

AIMS: Cardiovascular diseases (CVDs) increase mortality risk from coronavirus infection (COVID-19). There are also concerns that the pandemic has affected supply and demand of acute cardiovascular care. We estimated excess mortality in specific CVDs, both 'direct', through infection, and 'indirect', through changes in healthcare. METHODS AND RESULTS: We used (i) national mortality data for England and Wales to investigate trends in non-COVID-19 and CVD excess deaths; (ii) routine data from hospitals in England (n = 2), Italy (n = 1), and China (n = 5) to assess indirect pandemic effects on referral, diagnosis, and treatment services for CVD; and (iii) population-based electronic health records from 3 862 012 individuals in England to investigate pre- and post-COVID-19 mortality for people with incident and prevalent CVD. We incorporated pre-COVID-19 risk (by age, sex, and comorbidities), estimated population COVID-19 prevalence, and estimated relative risk (RR) of mortality in those with CVD and COVID-19 compared with CVD and non-infected (RR: 1.2, 1.5, 2.0, and 3.0).Mortality data suggest indirect effects on CVD will be delayed rather than contemporaneous (peak RR 1.14). CVD service activity decreased by 60-100% compared with pre-pandemic levels in eight hospitals across China, Italy, and England. In China, activity remained below pre-COVID-19 levels for 2-3 months even after easing lockdown and is still reduced in Italy and England. For total CVD (incident and prevalent), at 10% COVID-19 prevalence, we estimated direct impact of 31 205 and 62 410 excess deaths in England (RR 1.5 and 2.0, respectively), and indirect effect of 49 932 to 99 865 deaths. CONCLUSION: Supply and demand for CVD services have dramatically reduced across countries with potential for substantial, but avoidable, excess mortality during and after the pandemic.

Systematic evaluation and external validation of 22 prognostic models among hospitalised adults with COVID-19: an observational cohort study.

The number of proposed prognostic models for coronavirus disease 2019 (COVID-19) is growing rapidly, but it is unknown whether any are suitable for widespread clinical implementation.We independently externally validated the performance of candidate prognostic models, identified through a living systematic review, among consecutive adults admitted to hospital with a final diagnosis of COVID-19. We reconstructed candidate models as per original descriptions and evaluated performance for their original intended outcomes using predictors measured at the time of admission. We assessed discrimination, calibration and net benefit, compared to the default strategies of treating all and no patients, and against the most discriminating predictors in univariable analyses.We tested 22 candidate prognostic models among 411 participants with COVID-19, of whom 180 (43.8%) and 115 (28.0%) met the endpoints of clinical deterioration and mortality, respectively. Highest areas under receiver operating characteristic (AUROC) curves were achieved by the NEWS2 score for prediction of deterioration over 24 h (0.78, 95% CI 0.73-0.83), and a novel model for prediction of deterioration <14 days from admission (0.78, 95% CI 0.74-0.82). The most discriminating univariable predictors were admission oxygen saturation on room air for in-hospital deterioration (AUROC 0.76, 95% CI 0.71-0.81), and age for in-hospital mortality (AUROC 0.76, 95% CI 0.71-0.81). No prognostic model demonstrated consistently higher net benefit than these univariable predictors, across a range of threshold probabilities.Admission oxygen saturation on room air and patient age are strong predictors of deterioration and mortality among hospitalised adults with COVID-19, respectively. None of the prognostic models evaluated here offered incremental value for patient stratification to these univariable predictors.

Estimating excess 1-year mortality associated with the COVID-19 pandemic according to underlying conditions and age: a population-based cohort study.

BACKGROUND: The medical, societal, and economic impact of the coronavirus disease 2019 (COVID-19) pandemic has unknown effects on overall population mortality. Previous models of population mortality are based on death over days among infected people, nearly all of whom thus far have underlying conditions. Models have not incorporated information on high-risk conditions or their longer-term baseline (pre-COVID-19) mortality. We estimated the excess number of deaths over 1 year under different COVID-19 incidence scenarios based on varying levels of transmission suppression and differing mortality impacts based on different relative risks for the disease. METHODS: In this population-based cohort study, we used linked primary and secondary care electronic health records from England (Health Data Research UK-CALIBER). We report prevalence of underlying conditions defined by Public Health England guidelines (from March 16, 2020) in individuals aged 30 years or older registered with a practice between 1997 and 2017, using validated, openly available phenotypes for each condition. We estimated 1-year mortality in each condition, developing simple models (and a tool for calculation) of excess COVID-19-related deaths, assuming relative impact (as relative risks [RRs]) of the COVID-19 pandemic (compared with background mortality) of 1·5, 2·0, and 3·0 at differing infection rate scenarios, including full suppression (0·001%), partial suppression (1%), mitigation (10%), and do nothing (80%). We also developed an online, public, prototype risk calculator for excess death estimation. FINDINGS: We included 3 862 012 individuals (1 957 935 [50·7%] women and 1 904 077 [49·3%] men). We estimated that more than 20% of the study population are in the high-risk category, of whom 13·7% were older than 70 years and 6·3% were aged 70 years or younger with at least one underlying condition. 1-year mortality in the high-risk population was estimated to be 4·46% (95% CI 4·41-4·51). Age and underlying conditions combined to influence background risk, varying markedly across conditions. In a full suppression scenario in the UK population, we estimated that there would be two excess deaths (vs baseline deaths) with an RR of 1·5, four with an RR of 2·0, and seven with an RR of 3·0. In a mitigation scenario, we estimated 18 374 excess deaths with an RR of 1·5, 36 749 with an RR of 2·0, and 73 498 with an RR of 3·0. In a do nothing scenario, we estimated 146 996 excess deaths with an RR of 1·5, 293 991 with an RR of 2·0, and 587 982 with an RR of 3·0. INTERPRETATION: We provide policy makers, researchers, and the public a simple model and an online tool for understanding excess mortality over 1 year from the COVID-19 pandemic, based on age, sex, and underlying condition-specific estimates. These results signal the need for sustained stringent suppression measures as well as sustained efforts to target those at highest risk because of underlying conditions with a range of preventive interventions. Countries should assess the overall (direct and indirect) effects of the pandemic on excess mortality. FUNDING: National Institute for Health Research University College London Hospitals Biomedical Research Centre, Health Data Research UK.

Randomized control trial comparing an Alvarado Score-based management algorithm and current best practice in the evaluation of suspected appendicitis.

BACKGROUND: An objective algorithm for the management of suspected appendicitis guided by the Alvarado Score had previously been proposed. This algorithm was expected to reduce computed tomography (CT) utilization without compromising the negative appendectomy rate. This study attempts to validate the proposed algorithm in a randomized control trial. METHODS: A randomized control trial comparing the management of suspected acute appendicitis using the proposed algorithm compared to current best practice, with the rate of CT utilization as the primary outcome of interest. Secondary outcomes included the percentage of missed diagnosis, negative appendectomies, length of stay in days, and overall cost of stay in dollars. RESULTS: One hundred sixty patients were randomized. Characteristics such as age, ethnic group, American Society of Anesthesiologist score, white cell count, and symptom duration were similar between the two groups. The overall CT utilization rate of the intervention arm and the usual care arm were similar (93.7% vs 92.5%, p = 0.999). There were no differences in terms of negative appendectomy rate, length of stay, and cost of stay between the intervention arm as compared to the usual care arm (p = 0.926, p = 0.705, and p = 0.886, respectively). Among patients evaluated with CT, 75% (112 out of 149) revealed diagnoses for the presenting symptoms. CONCLUSION: The proposed AS-based management algorithm did not reduce the CT utilization rate. Outcomes such as missed diagnoses, negative appendectomy rates, length of stay, and cost of stay were also largely similar. CT utilization was prevalent as 93% of the study cohort was evaluated by CT scan. TRIAL REGISTRATION: The study has been registered at ClinicalTrials.gov (NCT03324165, Registered October 27 2017).

A functional network of gastric-cancer-associated splicing events controlled by dysregulated splicing factors.

Comprehensive understanding of aberrant splicing in gastric cancer is lacking. We RNA-sequenced 19 gastric tumor-normal pairs and identified 118 high-confidence tumor-associated (TA) alternative splicing events (ASEs) based on high-coverage sequencing and stringent filtering, and also identified 8 differentially expressed splicing factors (SFs). The TA ASEs occurred in genes primarily involved in cytoskeletal organization. We constructed a correlative network between TA ASE splicing ratios and SF expression, replicated it in independent gastric cancer data from The Cancer Genome Atlas and experimentally validated it by knockdown of the nodal SFs (PTBP1, ESRP2 and MBNL1). Each SF knockdown drove splicing alterations in several corresponding TA ASEs and led to alterations in cellular migration consistent with the role of TA ASEs in cytoskeletal organization. We have therefore established a robust network of dysregulated splicing associated with tumor invasion in gastric cancer. Our work is a resource for identifying oncogenic splice forms, SFs and splicing-generated tumor antigens as biomarkers and therapeutic targets.

Epigenomic Promoter Alterations Amplify Gene Isoform and Immunogenic Diversity in Gastric Adenocarcinoma.

Promoter elements play important roles in isoform and cell type-specific expression. We surveyed the epigenomic promoter landscape of gastric adenocarcinoma, analyzing 110 chromatin profiles (H3K4me3, H3K4me1, H3K27ac) of primary gastric cancers, gastric cancer lines, and nonmalignant gastric tissues. We identified nearly 2,000 promoter alterations (somatic promoters), many deregulated in various epithelial malignancies and mapping frequently to alternative promoters within the same gene, generating potential pro-oncogenic isoforms (RASA3). Somatic promoter-associated N-terminal peptides displaying relative depletion in tumors exhibited high-affinity MHC binding predictions and elicited potent T-cell responses in vitro, suggesting a mechanism for reducing tumor antigenicity. In multiple patient cohorts, gastric cancers with high somatic promoter usage also displayed reduced T-cell cytolytic marker expression. Somatic promoters are enriched in PRC2 occupancy, display sensitivity to EZH2 therapeutic inhibition, and are associated with novel cancer-associated transcripts. By generating tumor-specific isoforms and decreasing tumor antigenicity, epigenomic promoter alterations may thus drive intrinsic tumorigenesis and also allow nascent cancers to evade host immunity.Significance: We apply epigenomic profiling to demarcate the promoter landscape of gastric cancer. Many tumor-specific promoters activate different promoters in the same gene, some generating pro-oncogenic isoforms. Tumor-specific promoters also reduce tumor antigenicity by causing relative depletion of immunogenic peptides, contributing to cancer immunoediting and allowing tumors to evade host immune attack. Cancer Discov; 7(6); 630-51. ©2017 AACR.This article is highlighted in the In This Issue feature, p. 539.

Prognostic significance of KIT exon 11 deletion mutation in intermediate-risk gastrointestinal stromal tumor.

AIM: Benefit of adjuvant imatinib therapy following curative resection in patients with intermediate-risk gastrointestinal stromal tumor (GIST) is unclear. GIST-specific exon mutations, in particular exon 11 deletions, have been shown to be prognostic. We hypothesize that specific KIT mutations may improve risk stratification in patients with intermediate-risk GIST, identifying a subgroup of patients who may benefit from adjuvant therapy. METHODS: In total, 142 GIST patients with complete clinicopathologic and mutational data from two sites were included. Risk classification was based on the modified National Institute of Health (NIH) criteria. RESULTS: In this cohort, 74% (n = 105) of patients harbored a KIT mutation; 61% (n = 86) were found in exon 11 of which nearly 70% were KIT exon 11 deletions (n = 60). A total of 18% (n = 25) of cases were classified as having intermediate-risk disease. Univariate analysis confirmed tumor size, mitotic index, nongastric origin, presence of tumor rupture and modified NIH criteria were adversely prognostic for relapse-free survival (RFS). Among KIT/PDGFRA mutants, KIT exon 11 deletions had a significantly worse prognosis (hazard ratio 2.31; 95% confidence interval, 1.30-4.10; P = 0.003). Multivariate analysis confirmed KIT exon 11 deletion (P = 0.003) and clinical risk classification (P < 0.001) as independent adverse prognostic factors for RFS. Intermediate-risk patients harboring KIT exon 11 deletions had RFS outcomes similar to high-risk patients. CONCLUSION: The presence of KIT exon 11 deletion mutation in patients with intermediate-risk GIST is associated with an inferior clinical outcome with RFS similar to high-risk patients.

Laparoscopic wedge resection for suspected large (≥5 cm) gastric gastrointestinal stromal tumors.

BACKGROUND: Laparoscopic wedge resection (LWR) for small gastric gastrointestinal stromal tumors (GIST) is now widely accepted, but its application for large GISTs remains controversial. This study aims to evaluate the feasibility and safety of LWR for suspected large (≥5 cm) gastric GISTs. METHODS: Retrospective review of 82 consecutive patients who underwent attempted LWR for suspected gastric GIST. LWR for large (≥5 cm) (n = 23) tumors was compared with LWR for small (<5 cm) tumors (n = 59). The 23 patients with LWR for large tumors were also compared to 36 consecutive patients who underwent open wedge resection (OWR) for large tumors. RESULTS: Comparison between patients who underwent LWR for large versus small tumors demonstrated that resection of large tumors was associated with a longer operating time. There was no difference in other perioperative outcomes, and oncological outcomes such as frequency of close margins (≤1 mm) and recurrence-free survival. Comparison between patients who underwent LWR versus OWR for large tumors showed that LWR was associated with decreased median time to fluid or solid diet, shorter postoperative stay but longer operating times. There was no difference in oncological outcomes. CONCLUSION: LWR for suspected large gastric GIST is feasible and safe. It is associated with similar short-term outcomes with LWR for small tumors and favorable short-term outcomes over OWR for large tumors without compromising on oncological outcomes.