Use of hospital big data to optimize and personalize laboratory test interpretation with an application.

BACKGROUND AND AIMS: In laboratory medicine, test results are generally interpreted with 95% reference intervals but correlations between laboratory tests are usually ignored. We aimed to use hospital big data to optimize and personalize laboratory data interpretation, focusing on platelet count. MATERIAL AND METHODS: Laboratory tests were extracted from the hospital database and exploited by an algorithmic stepwise procedure. For any given laboratory test Y, an "optimized and personalized reference population" was defined by keeping only patients whose laboratory values for all Y-correlated tests fell within their own usual reference intervals, and by partitioning groups by individual-specific variables like sex and age category. The method was applied to platelet count. RESULTS: Laboratory data were recorded for 28,082 individuals. At the end of the algorithmic process, seven correlated laboratory tests were chosen, resulting in a reference sample of 159 platelet counts. A new 95 % reference interval was constructed [152-334 × 109/L], notably reduced (27.2 %) compared to conventional reference values [150-400 × 109/L]. The reference interval was validated on a sample of 2,129 patients from another downtown laboratory, emphasizing the potential transference of the hospital-derived reference limits. CONCLUSION: This method offers new perspectives in laboratory data interpretation, especially in patient screening and longitudinal follow-up.

Editor's Choice -- European Society for Vascular Surgery (ESVS) 2024 Clinical Practice Guidelines on the Management of Abdominal Aorto-Iliac Artery Aneurysms.

OBJECTIVE: The European Society for Vascular Surgery (ESVS) has developed clinical practice guidelines for the care of patients with aneurysms of the abdominal aorta and iliac arteries in succession to the 2011 and 2019 versions, with the aim of assisting physicians and patients in selecting the best management strategy. METHODS: The guideline is based on scientific evidence completed with expert opinion on the matter. By summarising and evaluating the best available evidence, recommendations for the evaluation and treatment of patients have been formulated. The recommendations are graded according to a modified European Society of Cardiology grading system, where the strength (class) of each recommendation is graded from I to III and the letters A to C mark the level of evidence. RESULTS: A total of 160 recommendations have been issued on the following topics: Service standards, including surgical volume and training; Epidemiology, diagnosis, and screening; Management of patients with small abdominal aortic aneurysm (AAA), including surveillance, cardiovascular risk reduction, and indication for repair; Elective AAA repair, including operative risk assessment, open and endovascular repair, and early complications; Ruptured and symptomatic AAA, including peri-operative management, such as permissive hypotension and use of aortic occlusion balloon, open and endovascular repair, and early complications, such as abdominal compartment syndrome and colonic ischaemia; Long term outcome and follow up after AAA repair, including graft infection, endoleaks and follow up routines; Management of complex AAA, including open and endovascular repair; Management of iliac artery aneurysm, including indication for repair and open and endovascular repair; and Miscellaneous aortic problems, including mycotic, inflammatory, and saccular aortic aneurysm. In addition, Shared decision making is being addressed, with supporting information for patients, and Unresolved issues are discussed. CONCLUSION: The ESVS Clinical Practice Guidelines provide the most comprehensive, up to date, and unbiased advice to clinicians and patients on the management of abdominal aorto-iliac artery aneurysms.

Breast cancer survival analysis agents for clinical decision support.

Personalized support and assistance are essential for cancer survivors, given the physical and psychological consequences they have to suffer after all the treatments and conditions associated with this illness. Digital assistive technologies have proved to be effective in enhancing the quality of life of cancer survivors, for instance, through physical exercise monitoring and recommendation or emotional support and prediction. To maximize the efficacy of these techniques, it is challenging to develop accurate models of patient trajectories, which are typically fed with information acquired from retrospective datasets. This paper presents a Machine Learning-based survival model embedded in a clinical decision system architecture for predicting cancer survivors' trajectories. The proposed architecture of the system, named PERSIST, integrates the enrichment and pre-processing of clinical datasets coming from different sources and the development of clinical decision support modules. Moreover, the model includes detecting high-risk markers, which have been evaluated in terms of performance using both a third-party dataset of breast cancer patients and a retrospective dataset collected in the context of the PERSIST clinical study.

AI-Based Chest CT Analysis for Rapid COVID-19 Diagnosis and Prognosis: A Practical Tool to Flag High-Risk Patients and Lower Healthcare Costs.

Early diagnosis of COVID-19 is required to provide the best treatment to our patients, to prevent the epidemic from spreading in the community, and to reduce costs associated with the aggravation of the disease. We developed a decision tree model to evaluate the impact of using an artificial intelligence-based chest computed tomography (CT) analysis software (icolung, icometrix) to analyze CT scans for the detection and prognosis of COVID-19 cases. The model compared routine practice where patients receiving a chest CT scan were not screened for COVID-19, with a scenario where icolung was introduced to enable COVID-19 diagnosis. The primary outcome was to evaluate the impact of icolung on the transmission of COVID-19 infection, and the secondary outcome was the in-hospital length of stay. Using EUR 20000 as a willingness-to-pay threshold, icolung is cost-effective in reducing the risk of transmission, with a low prevalence of COVID-19 infections. Concerning the hospitalization cost, icolung is cost-effective at a higher value of COVID-19 prevalence and risk of hospitalization. This model provides a framework for the evaluation of AI-based tools for the early detection of COVID-19 cases. It allows for making decisions regarding their implementation in routine practice, considering both costs and effects.

Editor's Choice - European Society for Vascular Surgery Clinical Practice Guideline Development Scheme: An Overview of Evidence Quality Assessment Methods, Evidence to Decision Frameworks, and Reporting Standards in Guideline Development.

OBJECTIVE: A structured and transparent approach is instrumental in translating research evidence to health recommendations and evidence informed clinical decisions. The aim was to conduct an overview and analysis of principles and methodologies for health guideline development. METHODS: A literature review on methodologies, strategies, and fundamental steps in the process of guideline development was performed. The clinical practice guideline development process and methodology adopted by the European Society for Vascular Surgery are also presented. RESULTS: Sophisticated methodologies for health guideline development are being applied increasingly by national and international organisations. Their overarching principle is a systematic, structured, transparent, and iterative process that is aimed at making well informed healthcare choices. Critical steps in guideline development include the assessment of the certainty of the body of evidence; evidence to decision frameworks; and guideline reporting. The goal of strength of evidence assessments is to provide well reasoned judgements about the guideline developers' confidence in study findings, and several evidence hierarchy schemes and evidence rating systems have been described for this purpose. Evidence to decision frameworks help guideline developers and users conceptualise and interpret the construct of the quality of the body of evidence. The most widely used evidence to decision frameworks are those developed by the GRADE Working Group and the WHO-INTEGRATE, and are structured into three distinct components: background; assessment; and conclusions. Health guideline reporting tools are employed to ensure methodological rigour and transparency in guideline development. Such reporting instruments include the AGREE II and RIGHT, with the former being used for guideline development and appraisal, as well as reporting. CONCLUSION: This guide will help guideline developers/expert panels enhance their methodology, and patients/clinicians/policymakers interpret guideline recommendations and put them in context. This document may be a useful methodological summary for health guideline development by other societies and organisations.

Predictive factors for the presence and long-term persistence of SARS-CoV-2 antibodies in healthcare and university workers.

While patient groups at risk for severe COVID-19 infections are now well identified, the risk factors associated with SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) transmission and immunization are still poorly understood. In a cohort of staff members of a Belgian tertiary academic hospital tested for SARS-CoV-2 antibodies during the early phase of the pandemic and followed-up after 6 weeks, 3 months and 10 months, we collected personal, occupational and medical data, as well as symptoms based on which we constructed a COVID-19 score. Seroprevalence was higher among participants in contact with patients or with COVID-19 confirmed subjects or, to a lesser extent, among those handling respiratory specimens, as well as among participants reporting an immunodeficiency or a previous or active hematological malignancy, and correlated with several symptoms. In multivariate analysis, variables associated with seropositivity were: contact with COVID-19 patients, immunodeficiency, previous or active hematological malignancy, anosmia, cough, nasal symptoms, myalgia, and fever. At 10 months, participants in contact with patients and those with higher initial COVID-19 scores were more likely to have sustained antibodies, whereas those with solid tumors or taking chronic medications were at higher risk to become seronegative.

Establishing an Expert Consensus on Key Indicators of the Quality of Life among Breast Cancer Survivors: A Modified Delphi Study.

(1) Background: The needs of cancer survivors are often not reflected in practice. One of the main barriers of the use of patient-reported outcomes is associated with data collection and the interpretation of patient-reported outcomes (PROs) due to a multitude of instruments and measuring approaches. The aim of the study was to establish an expert consensus on the relevance and key indicators of quality of life in the clinical practice of breast cancer survivors. (2) Methods: Potential indicators of the quality of life of breast cancer survivors were extracted from the established quality of life models, depicting survivors' perspectives. The specific domains and subdomains of quality of life were evaluated in a two-stage online Delphi process, including an international and multidisciplinary panel of experts. (3) Results: The first round of the Delphi process was completed by 57 and the second by 37 participants. A consensus was reached for the Physical and Psychological domains, and on eleven subdomains of quality of life. The results were further supported by the additional ranking of importance of the subdomains in the second round. (4) Conclusions: The current findings can serve to optimize the use of instruments and address the challenges related to data collection and interpretation as the facilitators of the adaption in routine practice.

Long-term longitudinal evaluation of the prevalence of SARS-CoV-2 antibodies in healthcare and university workers.

Asymptomatic and pauci-symptomatic cases contribute to underestimating the prevalence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections. Moreover, we have few studies available on the longitudinal follow-up of SARS-CoV-2 antibodies after natural infection. We tested staff members of a Belgian tertiary academic hospital for SARS-CoV-2 IgG, IgM, and IgA antibodies. We analyzed the evolution of IgM and IgG after 6 weeks, and the persistence of IgG after 3 and 10 months. At the first evaluation, 409/3776 (10.8%) participants had a positive SARS-CoV-2 serology. Among initially seropositive participants who completed phases 2 and 3, IgM were still detected after 6 weeks in 53.1% and IgG persisted at 12 weeks in 82.0% (97.5% of those with more than borderline titers). IgG levels were higher and increased over time in symptomatic but were lower and stable in asymptomatic participants. After 10 months, 88.5% of participants had sustained IgG levels (97.0% of those with more than borderline titers).