Hamostaseologie最新文献

Since the 1970s, specialized hemophilia centers have been established to optimize the complex and costly treatment of patients with severe bleeding disorders. In 2019, the first GTH guidelines on the structural and process quality of hemophilia centers were published. On this basis, a procedure for the certification of hemophilia centers has been established under the technical leadership of the GTH. These GTH guidelines are essentially based on the European Guidelines for the Certification of Haemophilia Centers published in 2013, created by the European Haemophilia Network (EUHANET). In 2023, this European guideline was revised by the EAHAD Accreditation and Audit of Haemophilia Centers Working Group. On this background, the GTH guidelines have now been revised to take relevant updates to the European guidelines into account.

Background:  Acquired hemophilia A (AHA) is a severe bleeding disorder caused by autoantibodies against coagulation factor VIII (FVIII). Standard treatment consists of bleeding control with bypassing agents and immunosuppressive therapy. Emicizumab is a bispecific antibody that mimics the function of activated FVIII irrespective of the presence of neutralizing antibodies. Recently, the GTH-AHA-EMI study demonstrated that emicizumab prevents bleeds and allows to postpone immunosuppression, which may influence future treatment strategies.

Aim:  To provide clinical practice recommendations on the use of emicizumab in AHA.

Methods:  A Delphi procedure was conducted among 33 experts from 16 German and Austrian hemophilia care centers. Statements were scored on a scale of 1 to 9, and agreement was defined as a score of ≥7. Consensus was defined as ≥75% agreement among participants, and strong consensus as ≥95% agreement.

Results:  Strong consensus was reached that emicizumab is effective for bleed prophylaxis and should be considered from the time of diagnosis (100% consensus). A fast-loading regimen of 6 mg/kg on day 1 and 3 mg/kg on day 2 should be used if rapid bleeding prophylaxis is required (94%). Maintenance doses of 1.5 mg/kg once weekly should be given (91%). Immunosuppression should be offered to patients on emicizumab if they are eligible based on physical status (97%). Emicizumab should be discontinued when remission of AHA is achieved (97%).

Conclusion:  These GTH consensus recommendations provide guidance to physicians on the use of emicizumab in AHA and follow the results of clinical trials that have shown emicizumab is effective in preventing bleeding in AHA.

In spite of my personal belief in the benefits of artificial intelligence (AI), reading Cathy O'Neil's book "Weapons of Math Destruction" left me feeling unsettled.1 She describes how flawed and unchecked algorithms are widely applied in areas that affect us all: hiring, credit scoring, access to education, and insurance pricing. In one example, a fixed percentage of teachers in a U.S. region was dismissed every year based on biased and opaque algorithms. The authors concluded that such algorithms act as "weapons of math destruction," perpetuate and amplify societal biases, act unethically, and harm vulnerable populations. The question arises as to what happens when we apply these algorithms to medicine? How do we know whether we are giving our patients the correct diagnosis or prognosis? Are we still sure that patients are receiving the appropriate treatment? Would we notice if the algorithms were geared more toward the needs of companies (make a lot of money) or health insurance companies (spend as little as possible)? In fact, evidence of bias and inequality of algorithms in medicine is already available.2 Due to these risks, some of my colleagues suggest that AI should be completely banned from medicine.

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.