Patient Data Research Articles

Centralized vs. Decentralized Cloud Computing in Healthcare

Healthcare is one of the industries that seeks to deliver medical services to patients on time. One of the issues it currently grapples with is real-time patient data exchange between various healthcare organizations. This challenge was solved by both centralized and decentralized cloud computing architecture solutions. In this paper, we review the current state of these two cloud computing architectures in the health sector with regard to the effect on the efficiency of Health Information Exchange (HIE) systems. Our study seeks to determine the relevance of these cloud computing approaches in assisting healthcare facilities in the decision-making process to adopt HIE systems. This paper considers the system performance, patient data privacy, and cost and identifies research directions in each of the architectures. This study shows that there are some benefits in both cloud architectures, but there are also some drawbacks. The prominent characteristic of centralized cloud computing is that all data and information are stored together at one location, known as a single data center. This offers many services, such as integration, effectiveness, simplicity, and rapid information access. However, it entails providing data privacy and confidentiality aspects because it will face the hazard of a single point of failure. On the other hand, decentralized cloud computing is built to safeguard data privacy and security whereby data are distributed to several nodes as a way of forming mini-data centers. This increases the system’s ability to cope with a node failure. Thus, continuity and less latency are achieved. Nevertheless, it poses integration issues because managing data from several sites could be a problem, and the costs of operating several data centers are higher and complex. This paper also pays attention to the differences in aspects like efficiency, capacity, and cost. This paper assists healthcare organizations in determining the most suitable cloud architecture strategy for deploying secure and effective HIE systems.

Asthma management in the digital age

Asthma affects 25 million people in the United States, and its prevalence is increasing. Access to care and adherence to prescribed asthma-treatment programs remain the principal formidable challenges for asthma management. Telemedicine offers substantial opportunities for improved asthma care of patients across the full range of socioeconomic strata. Ever-improving digital tools for asthma assessment and treatment are key components of telemedicine platforms for asthma management. These include a variety of remote patient-monitoring devices, digital inhaler systems, and mobile-health applications that facilitate ongoing assessment and adherence to treatment protocols. Digital tools for monitoring treatment focus on tracking medication use, inhalation technique, and physiological markers such as peak-flow rate and pulse-oximetry. Telemedicine visits allow for elements of assessment via video, approximating or duplicating many aspects of in-person visits, such as evaluating a patient's general appearance, breathing effort, and cough. Challenges remain in ensuring equitable access to these technologies, especially in rural and low-income areas, and in maintaining patient privacy and data security in digital platforms.

Generalizing Treatment Effect to a Target Population Without Individual Patient Data in a Real-World Setting.

The innovative use of real-world data (RWD) can answer questions that cannot be addressed using data from randomized clinical trials (RCTs). While the sponsors of RCTs have a central database containing all individual patient data (IPD) collected from trials, analysts of RWD face a challenge: regulations on patient privacy make access to IPD from all regions logistically prohibitive. In this research, we propose a double inverse probability weighting (DIPW) approach for the analysis sponsor to estimate the population average treatment effect (PATE) for a target population without the need to access IPD. One probability weighting is for achieving comparable distributions in confounders across treatment groups; another probability weighting is for generalizing the result from a subpopulation of patients who have data on the endpoint to the whole target population. The likelihood expressions for propensity scores and the DIPW estimator of the PATE can be written to only rely on regional summary statistics that do not require IPD. Our approach hinges upon the positivity and conditional independency assumptions, prerequisites to most RWD analysis approaches. Simulations are conducted to compare the performances of the proposed method against a modified meta-analysis and a regular meta-analysis.

A Clinician and Electronic Health Record Wearable Device Intervention to Increase Physical Activity in Patients With Obesity: Formative Qualitative Study.

The number of individuals using digital health devices has grown in recent years. A higher rate of use in patients suggests that primary care providers (PCPs) may be able to leverage these tools to effectively guide and monitor physical activity (PA) for their patients. Despite evidence that remote patient monitoring (RPM) may enhance obesity interventions, few primary care practices have implemented programs that use commercial digital health tools to promote health or reduce complications of the disease. This formative study aimed to assess the perceptions, needs, and challenges of implementation of an electronic health record (EHR)-integrated RPM program using wearable devices to promote patient PA at a large urban primary care practice to prepare for future intervention. Our team identified existing workflows to upload wearable data to the EHR (Epic Systems), which included direct Fitbit (Google) integration that allowed for patient PA data to be uploaded to the EHR. We identified pictorial job aids describing the clinical workflow to PCPs. We then performed semistructured interviews with PCPs (n=10) and patients with obesity (n=8) at a large urban primary care clinic regarding their preferences and barriers to the program. We presented previously developed pictorial aids with instructions for (1) providers to complete an order set, set step-count goals, and receive feedback and (2) patients to set up their wearable devices and connect them to their patient portal account. We used rapid qualitative analysis during and after the interviews to code and develop key themes for both patients and providers that addressed our research objective. In total, 3 themes were identified from provider interviews: (1) providers' knowledge of PA prescription is focused on general guidelines with limited knowledge on how to tailor guidance to patients, (2) providers were open to receiving PA data but were worried about being overburdened by additional patient data, and (3) providers were concerned about patients being able to equitably access and participate in digital health interventions. In addition, 3 themes were also identified from patient interviews: (1) patients received limited or nonspecific guidance regarding PA from providers and other resources, (2) patients want to share exercise metrics with the health care team and receive tailored PA guidance at regular intervals, and (3) patients need written resources to support setting up an RPM program with access to live assistance on an as-needed basis. Implementation of an EHR-based RPM program and associated workflow is acceptable to PCPs and patients but will require attention to provider concerns of added burdensome patient data and patient concerns of receiving tailored PA guidance. Our ongoing work will pilot the RPM program and evaluate feasibility and acceptability within a primary care setting.

Risk Factors for Nonunion After Nonoperative Treatment for Pediatric Lumbar Spondylolysis: A Retrospective Case-Control Study.

Pediatric lumbar spondylolysis, a stress fracture of the lumbar spine, frequently affects young athletes, and nonoperative treatment is often the first choice of management. Because the union rate in lumbar spondylolysis is lower than that in general fatigue fractures, identifying risk factors for nonunion is essential for optimizing treatment. To determine the risk factors for nonunion after nonoperative treatment of acute pediatric lumbar spondylolysis through multivariate analysis. Case-control study; Level of evidence, 3. We analyzed 574 pediatric patients (mean age, 14.3 ± 1.9 years) with lumbar spondylolysis who underwent nonoperative treatment between 2015 and 2022. Nonoperative treatment included the elimination of sports activities, bracing, and weekly athletic rehabilitation, with follow-up computed tomography. Patient data, lesion characteristics, sports history, presence of spina bifida occulta at the lamina with a lesion or at the lumbosacral spine excluding the lesion level, and lumbosacral parameters were examined. Differences between the union and nonunion groups were investigated using multivariate analysis to determine the risk factors for nonunion. Of the 574 patients, 81.7% achieved bone union. Multivariate analysis revealed that an L5 lesion and the progression of the main and contralateral lesion stages were significant independent risk factors for nonunion. An L5 lesion had a lower union rate than non-L5 lesions. As the main lesion progressed, the likelihood of nonunion increased significantly, and the progression of the contralateral lesion also showed a similar trend. Spina bifida occulta and lumbosacral parameters were not significant predictors of nonunion in this study. We identified the L5 lesion level and the progression of the main and contralateral lesion stages as independent risk factors for nonunion in pediatric lumbar spondylolysis after nonoperative treatment. These findings aid in treatment decision-making. When bone union cannot be expected with nonoperative treatment, symptomatic treatment is required without prolonged external fixation and rest, and without aiming for bone union. Individualized treatment plans are crucial based on identified risk factors.

Characteristics and Outcomes of Operatively Treated Discoid Lateral Meniscus in Pediatric and Young Adult Patients: A Multicenter Study.

Discoid lateral meniscus (DLM) is the most common congenital abnormality of the meniscus. Tears are common; treatment is frequently not definitive, often requiring reoperation. To report the clinical manifestations, physical characteristics, operative treatments and findings, complications, and reoperations of DLM in pediatric patients from multiple centers across North America. Case series; Level of evidence, 4. Consecutive patients who underwent treatment for symptomatic DLM at 9 institutions between 2000 and 2020 were included. Patient data, presenting symptoms and signs, surgical findings, treatments rendered, and postoperative complications, including reoperation rates, were collected. Means with ranges and counts with proportions are reported for continuous and categorical variables, respectively, and comparisons were made using either the chi-square or Fisher exact test. In total, 784 patients (867 knees) were included with a mean age at diagnosis of 12 years (range, 1-22 years) and a mean follow-up of 22.6 months (range, 0-154 months). Common preoperative symptoms were locking (33%) and snapping (30%). At surgery, tears in the DLM were present in 647 knees (594 patients [76%]); 95 knees (11%) had multiple tears; and in 140 knees, tears extended into >1 zone. Tears, when present, were more common within the posterior horn (41%) or body (34%) than the anterior horn (25%). Peripheral rim instability was reported in 241 knees (28%). Significantly more knees had instability posteriorly (15%; P = .0004) and anteriorly (9%; P = .0013) than along the body (3%). Tear type was most commonly complex (38%) or horizontal (34%). A total of 358 knees in 333 patients with tears (42% of all patients) underwent repair (55% of knees with tears). A total of 175 complications were reported, occurring in 139 knees in 134 patients (17%); 116 of these knees with complications (83%) had a single complication, while 23 (17%) had >1. Of the 784 patients, 105 (13%) underwent reoperation, undergoing 135 additional procedures related to their DLM. Of those, 60 (44%) were repeat arthroscopy and meniscal trim; 40 (30%), arthroscopy and meniscal repair; and 17 (13%), an articular cartilage procedure. Locking and snapping were common presenting symptoms. Over three-quarters of patients had meniscal tears, which were most often complex and located posteriorly. Seventeen percent of patients experienced complications, and a sixth of patients with complications had >1. Reoperation was typically for persistent symptoms or meniscal retear.

Data Mining Applications in the Health Care Sector

Data mining, which uses sophisticated analytics to glean insightful information from massiveand complicated healthcare data, is essential to the healthcare industry. Applications for managinghealthcare, finding new drugs, monitoring diseases, and diagnosing and treating patients are all included.Data mining algorithms may find patterns and trends in patient data, clinical databases, and genetic datathat help in early illness identification, individualized treatment recommendations, and efficient resourceallocation. Data mining also aids in pharmaceutical research, streamlines the medication developmentprocess, and enhances healthcare operations, eventually improving patient outcomes and loweringcosts.

Retrospective analysis of the resuscitation room management of nontraumatic critically ill children in a university emergency department (OBSERvE-DUS-PED study)

The establishment of a resuscitation room management for nontraumatic critically ill children appears to make sense. This study collected data of pediatric patients suffering from nontraumatic critically ill conditions treated in aresuscitation room. The retrospective OBSERvE-DUS-PED study (November 2019-October 2022) recorded pediatric patients (age < 18years) who were admitted to the emergency department (ED) for resuscitation room care. The routinely documented data on treatment were taken from the hospital information system MEDICO® and the patient data management system COPRA® in accordance with the OBSERvE dataset. The study was approved by the Ethics Committee of the Medical Faculty of the Heinrich Heine University (2023-2377). The study included 52pediatric resuscitation room patients. Adolescents aged 14-17years were the most frequent in the cohort representing 37% of the total and neonates/infants (0-1year) were lowest at 8%. The most common symptoms categorized according to ABCDE problems were disturbance of consciousness(D) at 61%, cardiovascular failure(C) at 25%, respiratory insufficiency(B) at 6%, airway obstruction(A) and exposure/environment (E) problems each at 4%. The out-of-hospital and in-hospital emergency procedures were performed with the following frequencies: venous (58%vs. 65%), intraosseous (14%vs. 2%) and central venous access (0%vs. 12%), invasive airway management (35% vs.8%), cardiopulmonary resuscitation (21%vs. 10%), vasopressors (15%vs. 17%), and intra-arterial pressure measurement (0%vs. 17%). The mean duration of resuscitation room management was 70 ± 43 min. The 30-day mortality was 17%. The OBSERvE-DUS-PED study demonstrates the major challenges in the care of critically ill nontraumatic pediatric patients, both in out-of-hospital and in-hospital management. The variety and complexity of the referral diagnoses as well as the immediate vital threat to the patients make it appear sensible to treat such patients primarily in aresuscitation room of the ED due to the available material, infrastructural and personnel resources.

Maintaining Security of Patient Data by Employing Private Blockchain and Fog Computing Technologies based on Internet of Medical Things

Maintaining Security of Patient Data by Employing Private Blockchain and Fog Computing Technologies based on Internet of Medical Things

Extracorporeal life support for severe leptospirosis: Case series and narrative review

Introduction: Leptospirosis can be associated with multi-system organ failure (MSOF) and significant morbidity and mortality. Extracorporeal life support (ECLS) has been used as salvage therapy for severe leptospirosis complicated by acute respiratory distress syndrome (ARDS). Current knowledge in this field is limited, with no standardized treatment approaches. We aim to describe the literature to date on the use of ECLS in patients with leptospirosis, highlighting associations, outcomes, and complications. Methods: We report on the successful use of ECLS in two cases of severe leptospirosis and conduct a narrative review of the literature. Using a search strategy developed in consultation with a medical librarian and validated across pre-selected articles, several databases were searched. We included case reports, case series, cohort studies, and prospective studies of adult patients with confirmed leptospirosis undergoing ECLS. Editorials, surveys, or opinion articles without primary patient data were excluded. Overall mortality was our primary outcome. Results: Two cases of previously healthy males presenting with ARDS due to leptospiral infection are described. Literature review identified 25 articles containing 43 reported cases of patients treated with ECLS for severe leptospirosis. Patients were mostly young and male. Overall mortality was 16%. The most common complication recognized was acute renal failure requiring renal replacement therapy. Additional complications included diffuse intravascular coagulation, necrotizing pancreatitis, and limb ischemia. Conclusion: Leptospirosis should be considered in patients with epidemiologic exposure(s) presenting with critical illness, including ARDS and MSOF. ECLS is a viable rescue strategy in severe leptospirosis, even with established MSOF.

Lipid-Lowering Treatment Gaps in Patients after Acute Myocardial Infarction: Using Global Database TriNetX

Background and Objectives: Patients with previous acute myocardial infarction are at significantly higher risk of recurrent events. Early and intensive lipid-lowering therapy targeting low-density lipoprotein cholesterol is a key strategy for reducing cardiovascular risk in post-acute myocardial infarction patients worldwide. This study aimed to assess patients’ real-life lipid-lowering treatment gaps after acute myocardial infarction using a global network, TriNetX, of anonymous, real-time patient data. The uniqueness of the study was the use of the novel, evolving, and constantly improving TriNetX platform and the evaluation of its feasibility for clinical research. Materials and Methods: A retrospective study was conducted on global repository patients in 2020, diagnosed with acute myocardial infarction, with a three-year follow-up. Results: After acute myocardial infarction, the prescribing rate of lipid-lowering medication (statins, ezetimibe and PCSK9I) was insufficient to reach target LDL-C values. The mean LDL-C level decreased from 2.7 mmol/L (103 mg/dL) as measured on the day of AMI to 1.97 mmol/L (76 mg/dL) between 31D and 3M. During the second and third years, the mean LDL-C value was stable (around 2.0 mmol/L (78 mg/dL)). LDL-C goals were not sufficiently reached, as only 7–12% of patients were reported to have LDL-C values &lt; 55 mg/dL (1.4 mmol/L) and 13–20% of patients were reported to have LDL-C values &lt; 70 mg/dL (1.8 mmol/L) during the follow-up periods. This means that a substantial number of patients remain at a very high risk for CV complications and mortality. Most cardiovascular complications happen within three months after acute myocardial infarction. Conclusions: Gaps remain between the recommendations for managing LDL-C in guidelines and what occurs in real life. The TriNetX platform is an innovative platform with significant potential and should be further developed for clinical research, as it enables the use of valuable interinstitutional data.

Multimodal Disease Classification and Severity analysis Approaches using Machine and Deep Learning

Chest X-rays are a common diagnostic tool for pulmonary and cardiac conditions in hospitals because they provide a clear picture of the patient's thorax. With the use of image-to-text radiology report production, medical imaging results may be automatically described in radiology reports. There are a lot of different pieces of patient data that radiologists may access, but most current systems only use the picture data. the objective of developing AI systems with a focus on humans, with the ability to learn radiologists' search habits via their eye movements, with the hope of enhancing DL system categorisation. The goal of this research is to evaluate several multimodal DL architectures in collaboration with trained radiologists to see which ones work best. In particular, this study aims to build strong DL models for medical picture analysis by investigating the integration of several data modalities, such as eye tracking data and patients' clinical data. A multimodal DL model integrating clinical data and chest X-rays (CXRs) was suggested by us. Findings demonstrated that baseline performance was unaffected by directly supplying fixation masks of radiologists' gaze patterns as input. Confine Areas Using R-CNN (Recurrent Neural Networks).

Bibliometric Analysis of Compliance and Completeness in Electronic Medical Record Filling: Trends and Insights from Hospital Documentation

ABSTRACT The introduction of Electronic Medical Records (EMR) has revolutionized healthcare documentation, emphasizing the importance of compliance and completeness in medical data recording. This study aims to analyze the bibliometric data and visualize the publication trends related to compliance and completeness in EMR using VOSviewer. We employed a bibliometric approach to identify key clusters and relationships among terms in the literature from various journals and countries over recent years. The results highlight three main clusters: documentation and adherence, data completeness and quality, and accuracy and compliance in hospital settings. The analysis shows a significant global interest, with the USA, Germany, and Indonesia being major contributors. The study also reveals a rise in publications from 2018 to 2022, predominantly consisting of research articles. The most cited article, "Tools and Technologies for Registry Interoperability" by V. Ehrenstein et al., underscores the influence of registry interoperability on patient outcomes. Our conclusions indicate that while substantial progress has been made, further research is needed to explore factors affecting compliance and completeness across different healthcare systems, the long-term impact on patient outcomes, the integration of AI technologies, and real-time monitoring tools for data accuracy and compliance. Keywords: Electronic Medical Records, Compliance, Data Completeness, Bibliometric Analysis, Healthcare Documentation

Transparent precision: Explainable AI empowered breast cancer recommendations for personalized treatment

&lt;p&gt;Breast cancer stands as a prevalent global concern, prompting extensive research into its origins and personalized treatment through Artificial Intelligence (AI)-driven precision medicine. However, AI's black box nature hinders result acceptance. This study delves into Explainable AI (XAI) integration for breast cancer precision medicine recommendations. Transparent AI models, fuelled by patient data, enable personalized treatment recommendations. Techniques like feature analysis and decision trees enhance transparency, fostering trust between medical practitioners and patients. This harmonizes AI's potential with the imperative for clear medical decisions, propelling breast cancer care within the precision medicine era. This research work is dedicated to leveraging clinical and genomic data from samples of metastatic breast cancer. The primary aim is to develop a machine learning (ML) model capable of predicting optimal treatment approaches, including but not limited to hormonal therapy, chemotherapy, and anti-HER2 therapy. The objective is to enhance treatment selection by harnessing advanced computational techniques and comprehensive data analysis. A decision tree model developed here for the prediction of suitable personalized treatment for breast cancer patients achieves 99.87% overall prediction accuracy. Thus, the use of XAI in healthcare will build trust in doctors as well as patients.&lt;/p&gt;

The Association of Socio-Environmental Inequality and Outcomes Among Patients Undergoing Major Surgery

IntroductionEnvironmental hazards may influence health outcomes and be a driver of health inequalities. We sought to characterize the extent to which social–environmental inequalities were associated with surgical outcomes following a complex operation. MethodsIn this cross-sectional study, patients who underwent abdominal aortic aneurysm repair, coronary artery bypass grafting, colectomy, pneumonectomy, or pancreatectomy between 2016 and 2021 were identified from Medicare claims data. Patient data were linked with social–environmental data sourced from Centers for Disease Control and Agency for Toxic Substances and Disease Registry data based on county of residence. The Environmental Justice Index social–environmental ranking (SER) was used as a measure of environmental injustice. Multivariable regression analysis was performed to assess the relationship between SER and surgical outcomes. ResultsAmong 1,052,040 Medicare beneficiaries, 346,410 (32.9%) individuals lived in counties with low SER, while 357,564 (33.9%) lived in counties with high SER. Patients experiencing greater social–environmental injustice were less likely to achieve textbook outcome (odds ratio 0.95, 95% confidence interval 0.94-0.96, P < 0.001) and to be discharged to an intermediate care facility or home with a health agency (odds ratio 0.97, 95% confidence interval 0.96-0.98, P < 0.001). ConclusionsCumulative social and environmental inequalities, as captured by the Environmental Justice Index SER, were associated with postoperative outcomes among Medicare beneficiaries undergoing a range of surgical procedures. Policy makers should focus on environmental, as well as socioeconomic injustice to address preventable health disparities.

474. SELF EXPANDABLE METAL STENT AS A BRIDGE TO RESECTION IN ESOPHAGEAL CANCER – A NATIONAL REGISTER ANALYSIS ON MORTALITY

Abstract Background Patients with esophageal cancer commonly present with malnutrition and weight loss due to dysphagia. Self Expandable Metal Stent (SEMS) have been used to relieve dysphagia and optimize nutrition prior to surgery and during neoadjuvant treatment. However, SEMS is associated with various complications and studies have further found a decreased survival in patients preoperatively treated with SEMS. The aim of the study was to investigate whether SEMS as a bridge to resection has an impact on mortality. Methods All patients with esophageal cancer in the Swedish National Register for Esophageal and Gastric cancer (NREV) between 2006 and 2018 were included. Patient data, tumor and treatment characteristics were assessed. Resected patients who received SEMS preoperatively were compared to patients who did not receive SEMS with regard to patient, tumor and treatment characteristics. Overall survival was explored by multivariable Cox regression models and adjusted for age, ASA score, preoperative BMI, clinical T-stage and neoadjuvant chemo- or chemoradiotherapy. Primary outcome was mortality. Results In total, 1841 resected patients with esophageal cancer and curative intent were identified. Of these, 83 patients received SEMS as a bridge to resection. T-stage was more advanced in patients with SEMS than those without SEMS, but there was no difference in N-stage, ASA-score or preoperative BMI. Early mortality (&amp;lt;90 days) and total mortality was higher in the SEMS group (12.4% vs 5.3%, p=0.002 and 77.1 % vs 58.2%, p&amp;lt;0.001 respectively. Mean follow-up time was 45 months for the all included patients. SEMS was an independent predictor of earlier death in multivariable analysis (Hazard Ratio 1,710; CI 95% 1,27-2,23; p=&amp;lt;0,001). Conclusion SEMS as a bridge to resection in the treatment of esophageal cancer seem to be associated with poor outcome with regard to both early and late mortality.

Blood Pressure and Cardiovascular Risk in Women With Breast Cancer: The Pathways Heart Study

BackgroundHypertension is an important contributor to cardiovascular disease (CVD) in breast cancer (BC) survivors; however, research on blood pressure (BP) and CVD outcomes in BC survivors is limited. ObjectivesThe purpose of this study was to better characterize the association between BP and CVD in a large, longitudinal cohort of BC patients. MethodsWomen with invasive BC diagnosed from 2005 to 2013 at Kaiser Permanente Northern California were matched 1:5 to women without BC. Patient data were obtained from electronic health records. Multivariable Cox regression and penalized spline models were used to explore the linear and nonlinear relationship of systolic blood pressure (SBP) and diastolic blood pressure (DBP) on CVD outcomes. ResultsBC cases (n = 12,713) and controls (n = 55,886) had median follow-up of 9.6 years (IQR: 5.0-11.9 years). Women with BC had a mean age of 60.6 years; 64.8% were non-Hispanic White. For ischemic heart disease (IHD), every 10 mmHg increase in SBP and DBP was associated with 1.23 (95% CI: 1.14-1.33) and 1.10 (95% CI: 0.98-1.24) risk, respectively, in women with BC. For stroke, every 10 mmHg increase in SBP and DBP was associated with a 1.45 (95% CI: 1.34-1.58) and 1.91 (95% CI: 1.68-2.18) risk, respectively. A U-shaped relationship was observed between heart failure/cardiomyopathy and BP. The associations between BP and risk of IHD, stroke, and any primary CVD were not statistically different comparing women with BC to controls, but risks varied by BC status for heart failure/cardiomyopathy (P for interaction = 0.01). ConclusionsWomen with and without BC showed similar risks for IHD, stroke, and any primary CVD suggesting similar BP targets should be pursued regardless of BC survivorship status.

252. RIGHT GASTROEPIPLOIC ARTERY LENGTH DETERMINED ANASTOMOTIC LEAKRIGHT GASTROEPIPLOIC ARTERY LENGTH DETERMINED ANASTOMOTIC LEAKAGE AFTER MINIMALLY INVASIVE ESOPHAGECTOMY FOR ESOPHAGEAL CANCER-A PROSPECTIVE COHORT STUDY

Abstract Background This prospective cohort study, conducted at a high-volume esophageal cancer center from July 2019 to July 2022, aimed to investigate the link between the right gastroepiploic artery (RGEA) length and anastomotic leakage (AL) rates following minimally invasive esophagectomy (MIE). Real-world data on stomach blood supply in the Chinese population were examined. Materials and methods A total of 516 cases were enrolled, categorized into two groups based on the Youden index-determined optimal cut-off value for the relative length of RGEA (length of RGEA/length of gastric conduit, 64.69%) through ROC analysis: Group SR (short RGEA) and Group LR (long RGEA). The primary observation parameter was the relationship between AL incidence and the ratio of direct blood supply from RGEA. Secondary parameters included the mean length of the right gastroepiploic artery, greater curvature, and the connection type between right and left gastroepiploic vessels. Patient data were prospectively recorded in electronic case report forms. Results The study revealed median lengths of 43.60 cm for greater curvature, 43.16 cm for the gastric conduit, and 26.75 cm for RGEA. AL, the most common postoperative complication, showed a significant difference between groups (16.88% vs. 8.84%, p = 0.01). Multivariable binary logistic regression identified Group SR and LR (Odds ratio: 2.651, 95% CI: 1.124–6.250, p = 0.03) and Neoadjuvant therapy (Odds ratio: 2.479, 95% CI: 1.374–4.473, p = 0.00) as independent predictors of AL. Conclusions The study emphasizes the crucial role of RGEA length in determining AL incidence in MIE for esophageal cancer. Preserving RGEA and fostering capillary arches between RGEA and LGEA are recommended strategies to mitigate AL risk.

457. EVALUATING THE ACCURACY OF CLINICAL SIEWERT CLASSIFICATION, A RETROSPECTIVE REVIEW OF 807 PATIENTS

Abstract Background Adenocarcinoma of the gastro-esophageal junction (GEJ) is increasingly common in Western societies. GEJ tumours are typically categorized using the Siewert classification, which divides the tumours in to 3 types based on location relative to the GEJ. Studies have shown that there is improved survival with a proximal surgical resection such as esophagectomy for type 1 tumours while type 3 tumours have improved survival when managed as other gastric cancers with a total or extended total gastrectomy. This requires accurate clinical classification. This study aims to evaluate the accuracy of commonly used staging modalities in the clinical classification of GEJ tumours. Methods A review of 807 consecutive patients undergoing curative resection for GEJ cancer was performed. Patient data was obtained from a prospectively maintained database between February 2010 and April 2023. Siewert type was established clinically as part of the staging process. The standard staging modalities used for every patient included computerized tomography (CT) and endoscopy, with most patients also undergoing positron emission tomography (PET). Endoscopic ultra-sonography (EUS) and staging laparoscopy was also used selectively. Accuracy was determined by comparing clinical classification to pathological classification following surgery. Results 807 patients were included [319 type 1, 401 type 2, 87 type 3]. Clinical classification was the same as pathological classification in 59.5% of cases [69.6% for type 1, 48.6% for type 2, 71.3% for type 3 (p&amp;lt;0.001). The use of different staging modalities for each sub-type is presented in table 1. For patients that underwent EUS, Siewert type was accurate in 59.4% of cases overall. The failure rate for EUS was 2.9%. For patients undergoing staging laparoscopy Siewert type was accurate in 69.3% and for patients that underwent PET, EUS and staging lap the accuracy was 74.4%. Conclusion Accurately determining Siewert type clinically is challenging. This is particularly relevant for type 2 tumours which had the least accurate comparison of clinical to pathological type. The use of additional modalities including EUS, and staging laparoscopy improves accuracy but may not be feasible in all patients.

Evaluating performance: a scoping review on video-based assessments of non-technical skills in the operating room

Background: Patient safety remains a critical concern in the high-stakes environment of the operating room (OR). Human factors and non-technical skills (NTS) play pivotal roles in surgical performance and in preventing errors. Various assessment tools and methodologies have been developed to evaluate NTS among OR personnel, reflecting the growing recognition of their importance. Objectives: To map and analyze NTS among OR personnel during real-life surgeries using medical video recording systems and to evaluate NTS assessment tools described in existing literature. Methods: Four databases, PubMed, Web of Science, Medline and Embase, were searched for relevant studies. The Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) protocol was followed. Eligibility criteria included studies focusing on video recordings providing an overview of the entire OR with a focus on NTS. Results: Twenty-four articles were included in this scoping review. A total of eight NTS were assessed, most frequently communication (79%), leadership (54%), teamwork (50%) and situational awareness (50%). Observational methods varied, ranging from one or more cameras to more sophisticated medical data recorders, such as the OR Black Box (ORBB). Additionally, a wide variety of assessment methods were used, mostly validated tools (NOTSS, SPLINTS, ANTS), but also broader systems-based frameworks (SEIPS) or self-developed tools. Conclusions: This scoping review highlights the diverse methodologies used to measure NTS in the OR using medical video recordings, indicating a need for further research to address challenges and standardize practices. Despite the proven potential benefits of video recording in the OR, legal, ethical and logistical challenges may serve as possible barriers to actual implementation.