Digital Technologies in Hereditary Coagulation Disorders: A Systematic Review

Abstract

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.

Keywords

coagulation - digital technologies - artificial intelligence - systematic review - E-diary

Authors' Contributions

F.K., M.K., and S.M.J. conceived and designed the analysis. M.K. extracted titles and abstracts from PubMed and applied the automatic filtering by date range and language. All authors equally participated in the double-checked screening of titles and abstracts for inclusion. F.K., M.K., and L.N. handled conflicts in titles and abstract inclusion. During the full-text screening, F.K. reviewed all the articles, while the other authors screened the articles by equally sharing among them, ensuring double-checking. Information extraction from the full articles was performed, with F.K. handling all articles and the remaining workload was equally shared among the others, and also double-checked. F.K. wrote the manuscript, which was critically revised by all authors. They provided final approval of the version to be published and agreed to be accountable for all aspects of the work.

Declaration of Generative AI and AI-Assisted Technologies in the Writing Process

During the preparation of this work, we used generative AI to proofread the text and eliminate typos and grammatical flaws. After that, the authors reviewed and edited the content as needed and took full responsibility for the publication's content.

Publication History

Received: 01 August 2024

Accepted: 14 September 2024

Article published online:
10 December 2024

• References

• 1 Lippi G, Franchini M, Montagnana M, Favaloro EJ. Inherited disorders of blood coagulation. Ann Med 2012; 44 (05) 405-418
• 2 Hoyer LW. Hemophilia A. N Engl J Med 1994; 330 (01) 38-47
• 3 Castaman G, Matino D. Hemophilia A and B: molecular and clinical similarities and differences. Haematologica 2019; 104 (09) 1702-1709
• 4 Rodeghiero F, Castaman G, Dini E. Epidemiological investigation of the prevalence of von Willebrand's disease. Blood 1987; 69 (02) 454-459
• 5 Senbekov M, Saliev T, Bukeyeva Z. et al. The recent progress and applications of digital technologies in healthcare: a review. Int J Telemed Appl 2020; 2020 (01) 8830200
• 6 Stoumpos AI, Kitsios F, Talias MA. Digital transformation in healthcare: technology acceptance and its applications. Int J Environ Res Public Health 2023; 20 (04) 3407
• 7 Page MJ, McKenzie JE, Bossuyt PM. et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 2021; 372: n71
• 8 Evans MS, Donaldson KJ, Eyster ME. Development of a novel automated screening method for detection of FVIII Inhibitors. Int J Lab Hematol 2017; 39 (02) 185-190
• 9 Barrera JAM, Rocha Guzmán S, Hierro Cascajares E. et al. Who's your data? Primary immune deficiency differential diagnosis prediction via machine learning and data mining of the USIDNET registry. Clin Immunol 2023; 255: 109759
• 10 Valls R, Wagg J, Paz-Priel I. et al. Application of systems biology to identify pharmacological mechanisms of thrombotic microangiopathy evoked by combined activated prothrombin complex concentrate and emicizumab. Sci Rep 2023; 13 (01) 10078
• 11 Hay CRM, Xiang H, Scott M. et al. The haemtrack home therapy reporting system: design, implementation, strengths and weaknesses: a report from UK Haemophilia Centre Doctors Organisation. Haemophilia 2017; 23 (05) 728-735
• 12 Lyons J, Desai V, Xu Y. et al. Development and validation of an algorithm for identifying patients with hemophilia A in an administrative claims database. Value Health 2018; 21 (09) 1098-1103
• 13 Chelle P, Yeung CHT, Bonanad S. et al. Routine clinical care data for population pharmacokinetic modeling: the case for Fanhdi/Alphanate in hemophilia A patients. J Pharmacokinet Pharmacodyn 2019; 46 (05) 427-438
• 14 Singh VK, Maurya NS, Mani A, Yadav RS. Machine learning method using position-specific mutation based classification outperforms one hot coding for disease severity prediction in haemophilia ‘A’. Genomics 2020; 112 (06) 5122-5128
• 15 Ai D, Cui C, Tang Y. et al. Machine learning model for predicting physical activity related bleeding risk in Chinese boys with haemophilia A. Thromb Res 2023; 232: 43-53
• 16 Li Z, Sun J, Li Z. et al. Low-dose immune tolerance induction therapy in severe hemophilia a children in China: starting earlier resulted in better inhibitor eradication outcomes. Thromb Res 2023; 225: 33-38
• 17 van Velzen AS, Eckhardt CL, Peters M. et al; INSIGHT Consortium. Product type and the risk of inhibitor development in nonsevere haemophilia A patients: a case-control study. Br J Haematol 2020; 189 (06) 1182-1191
• 18 Shepherd AJ, Skelton S, Sansom CE, Gomez K, Moss DS, Hart DP. A large-scale computational study of inhibitor risk in non-severe haemophilia A. Br J Haematol 2015; 168 (03) 413-420
• 19 Janssen A, Leebeek FWG, Cnossen MH, Mathôt RAA. OPTI-CLOT Study Group and SYMPHONY Consortium. Deep compartment models: a deep learning approach for the reliable prediction of time-series data in pharmacokinetic modeling. CPT Pharmacometrics Syst Pharmacol 2022; 11 (07) 934-945
• 20 Janssen A, Hoogendoorn M, Cnossen MH, Mathôt RAA. OPTI-CLOT Study Group and SYMPHONY Consortium. Application of SHAP values for inferring the optimal functional form of covariates in pharmacokinetic modeling. CPT Pharmacometrics Syst Pharmacol 2022; 11 (08) 1100-1110
• 21 van Velzen AS, Eckhardt CL, Peters M. et al. Intensity of factor VIII treatment and the development of inhibitors in non-severe hemophilia A patients: results of the INSIGHT case-control study. J Thromb Haemost 2017; 15 (07) 1422-1429
• 22 Chowdary P, Hampton K, Jiménez-Yuste V. et al. Predictive modeling identifies total bleeds at 12-weeks Postswitch to N8-GP prophylaxis as a predictor of treatment response. Thromb Haemost 2022; 122 (06) 913-925
• 23 Cuesta-Barriuso R, López-Pina JA, Nieto-Munuera J, Sagarra-Valls G, Panisello-Royo JM, Torres-Ortuño A. Effectiveness of the Medtep Hemophilia online platform for adherence to prophylactic treatment in haemophilia patients: results from a 1-year observational study. Haemophilia 2018; 24 (03) 452-459
• 24 Banchev A, Goldmann G, Marquardt N. et al. Impact of telemedicine tools on record keeping and compliance in haemophilia care. Hamostaseologie 2019; 39 (04) 347-354
• 25 Mondorf W, Eichler H, Fischer R. et al. Smart Medication™, an electronic diary for surveillance of haemophilia home care and optimization of resource distribution. Hamostaseologie 2019; 39 (04) 339-346
• 26 Russell S, Whitehart S, Mason J, Window P. Does the method of telehealth delivery affect the physiotherapy management of adults with bleeding disorders? A comparison of audioconferencing and videoconferencing. Haemophilia 2023; 29 (06) 1589-1596
• 27 Tiede A, Bonanad S, Santamaria A. et al. Quality of electronic treatment records and adherence to prophylaxis in haemophilia and von Willebrand disease: Systematic assessments from an electronic diary. Haemophilia 2020; 26 (06) 999-1008
• 28 Jacobson K, Hooke MC. Telehealth videoconferencing for children with hemophilia and their families: a clinical project. J Pediatr Oncol Nurs 2016; 33 (04) 282-288
• 29 Germini F, Borg Debono V, Page D. et al; PROBE Investigators. User-centered development and testing of the online patient-reported outcomes, burdens, and experiences (PROBE) survey and the myPROBE app and integration with the Canadian bleeding disorder registry: mixed methods study. JMIR Hum Factors 2022; 9 (01) e30797
• 30 Babington-Ashaye A, de Moerloose P, Diop S, Geissbuhler A. Design, development and usability of an educational AI chatbot for people with haemophilia in Senegal. Haemophilia 2023; 29 (04) 1063-1073
• 31 Cruz-Montecinos C, Maas H, Cerda M, Pérez-Alenda S. Altered neural control of gait and its association with pain and joint impairment in adults with haemophilic arthropathy: clinical and methodological implications. Haemophilia 2022; 28 (03) 497-504
• 32 Starnecker F, Reimer LM, Nissen L. et al. Guideline-based cardiovascular risk assessment delivered by an mhealth app: development study. JMIR Cardio 2023; 7 (01) e50813
• 33 Neubeck L, Lowres N, Benjamin EJ, Freedman SB, Coorey G, Redfern J. The mobile revolution – using smartphone apps to prevent cardiovascular disease. Nat Rev Cardiol 2015; 12 (06) 350-360
• 34 Ana FA, Loreto MS, José LMM, Pablo SM, María Pilar MJ, Myriam SA. Mobile applications in oncology: a systematic review of health science databases. Int J Med Inform 2020; 133: 104001
• 35 Zhao P, Yoo I, Lancey R, Varghese E. Mobile applications for pain management: an app analysis for clinical usage. BMC Med Inform Decis Mak 2019; 19 (01) 106
• 36 Pierce GF, Haffar A, Ampartzidis G. et al. First-year results of an expanded humanitarian aid programme for haemophilia in resource-constrained countries. Haemophilia 2018; 24 (02) 229-235