Hamostaseologie最新文献

The use of machine-learning (ML) algorithms in medicine has sparked a heated discussion. It is considered one of the most disruptive general-purpose technologies in decades. It has already permeated many areas of our daily lives and produced applications that we can no longer do without, such as navigation apps or translation software. However, many people are still unsure if ML algorithms should be used in medicine in their current form. Doctors are doubtful to what extent they can trust the predictions of algorithms. Shortcomings in development and unclear regulatory oversight can lead to bias, inequality, applicability concerns, and nontransparent assessments. Past mistakes, however, have led to a better understanding of what is needed to develop effective models for clinical use. Physicians and clinical researchers must participate in all development phases and understand their pitfalls. In this review, we explain the basic concepts of ML, present examples in the field of thrombosis and hemostasis, discuss common pitfalls, and present a methodological framework that can be used to develop effective algorithms.

The high incidence of venous thromboembolism (VTE) globally and the morbidity and mortality burden associated with the disease make it a pressing issue. Machine learning (ML) can improve VTE prevention, detection, and treatment. The ability of this novel technology to process large amounts of high-dimensional data can help identify new risk factors and better risk stratify patients for thromboprophylaxis. Applications of ML for VTE include systems that interpret medical imaging, assess the severity of the VTE, tailor treatment according to individual patient needs, and identify VTE cases to facilitate surveillance. Generative artificial intelligence may be leveraged to design new molecules such as new anticoagulants, generate synthetic data to expand datasets, and reduce clinical burden by assisting in generating clinical notes. Potential challenges in the applications of these novel technologies include the availability of multidimensional large datasets, prospective studies and clinical trials to ensure safety and efficacy, continuous quality assessment to maintain algorithm accuracy, mitigation of unwanted bias, and regulatory and legal guardrails to protect patients and providers. We propose a practical approach for clinicians to integrate ML into research, from choosing appropriate problems to integrating ML into clinical workflows. ML offers much promise and opportunity for clinicians and researchers in VTE to translate this technology into the clinic and directly benefit the patients.

Background:  This systematic review aims to comprehensively survey digital technologies used in the prevention, diagnosis, and treatment of hereditary blood coagulation disorders.

Methods:  The systematic review was performed according to the PRISMA guidelines. A systematic search was conducted on PubMed on January 29, 2024. Articles were excluded if they were reviews, meta-analyses, or systematic reviews. Articles were included if they were published from January 1, 2014, onward, written in English, described an actual application of digital tools, were in the context of hereditary coagulation disorders, and involved studies or trials on humans or human data with at least three subjects.

Results:  The initial PubMed search on January 29, 2024, identified 2,843 articles, with 672 from January 1, 2014, onward. After screening, 21 articles met the exclusion and inclusion criteria. Among these, 12 focused on artificial intelligence (AI) technologies and 9 on digital applications. AI was predominantly used for diagnosis (five studies) and treatment (four studies), while digital applications were mainly used for treatment (eight studies). Most studies addressed hemophilia A, with a smaller number including hemophilia B or von Willebrand disease.

Discussion:  The findings reveal a lack of intervention studies in the prevention, diagnosis, and treatment. However, digital tools, including AI and digital applications, are increasingly used in managing hereditary coagulation disorders. AI enhances diagnostic accuracy and personalizes treatment, while digital applications improve patient care and engagement. Despite these advancements, study biases and design limitations indicate the need for further research to fully harness the potential of these technologies.

Venous thromboembolism (VTE) represents a worldwide health challenge, impacting millions of people each year. The genesis of venous thrombosis is influenced in part by genetic components. Hereditary thrombosis is described as a genetically determined susceptibility to VTE. In the present study, a male patient was referred to our department presenting with multiple venous thrombosis events in different locations. Given a lack of identifiable risk factors, we aimed to investigate the possible genetic factor underlying venous thrombosis. Whole-exome sequencing was employed to examine genes linked to inherited thrombophilia in the proband. Putative variants were subsequently confirmed through Sanger sequencing within the family. The proband was identified as carrying two genetic mutations. One is the novel c.400G > C (p.E134Q) mutation affecting the final nucleotide of exon 5 in the PROC gene, potentially impacting splicing. The other is a previously reported heterozygous nonsense variant c.1016G > A (p.W339X) in the SERPINC1 gene. The proband inherited the former from her mother and the latter from her father. The presence of digenic inheritance in the patient reflects the complex phenotype of venous thrombosis and demonstrates the significance of an unbiased approach to detect pathogenic variants, especially in patients with a high risk of hereditary thrombosis.

Prophylaxis is the standard of care for patients with severe hemophilia, patients with moderate hemophilia, or those with another congenital bleeding disorder that is associated with a severe bleeding phenotype and/or a high risk of spontaneous life-threatening bleeding. Patients with nonsevere hemophilia (factor VIII [FVIII] ≥ 1%) may also have a bleeding phenotype that requires prophylaxis. To date, however, there are no clear criteria as to when prophylaxis is indicated in these patients. Also, the term "severe bleeding phenotype (SBPT)" is neither included in the definitions of the International Society on Thrombosis and Haemostasis (ISTH) nor specified in the World Federation of Hemophilia (WFH) guidelines. Based on our personal experience and available evidence, we propose the criteria we use to define an SBPT and when we consider offering prophylaxis in patients with nonsevere hemophilia. Our proposals can be the basis for discussions in the community about the assessment of SBPT and the initiation of prophylaxis in patients with nonsevere hemophilia without inhibitors.