Prediction of The Patients at Risk for Development Hematoma After Percutaneous Coronary Angiography: A Nursing Decision Support Model Pilot Study

Year 2024, Volume: 13 Issue: 2, 571 - 578, 29.06.2024

İlknur Buçan Kıkrbir , Yeter Kurt

https://doi.org/10.37989/gumussagbil.1366514

Abstract

The study aimed to develop a nursing clinical decision support model using the machine learning method, which is one of the important fields today, to identify patients with risk of hematoma development after Percutaneous Coronary Intervention and to help plan appropriate nursing interventions. In this study, the data of 100 patients with myocardial infarction was used in the development of the decision support model. R open-source programming language was used for statistical analysis of the data and the random forest method, one of the machine learning methods was used for the development of the model. The result of this pilot study, a nursing decision support model with a sensitivity of 69% and a specificity of 64% was developed with the Random forest method using 24 features regarding the demographic, laboratory, and percutaneous coronary intervention procedures of the patients.

Keywords

Coronary angiography, decision support systems, machine learning, nursing informatics, artificial intelligence

Perkütan Koroner Anjiyografi Sonrası Hematom Gelişimi Riski Olan Hastaların Tahmini: Hemşirelik Karar Destek Modeli Pilot Çalışması

Abstract

Çalışmada, günümüzün önemli alanlarından biri olan makine öğrenmesi yöntemini kullanarak, Perkütan Koroner Girişim sonrası hematom gelişme riski taşıyan hastaların belirlenmesi ve uygun hemşirelik girişimlerinin planlanmasına yardımcı olacak bir hemşirelik klinik karar destek modelinin geliştirilmesi amaçlandı. Bu çalışmada karar destek modelinin geliştirilmesinde 100 miyokard enfarktüsü hastasının verileri kullanıldı. Verilerin istatistiksel analizinde R açık kaynak programlama dili kullanılmış olup, modelin geliştirilmesinde makine öğrenmesi yöntemlerinden biri olan rastgele orman yöntemi kullanılmıştır. Bu pilot çalışmanın sonucunda hastaların demografik, laboratuvar ve perkütan koroner girişim işlemlerine ilişkin 24 özelliği kullanarak Rastgele orman yöntemiyle %69 duyarlılığa ve %64 özgüllüğe sahip bir hemşirelik karar destek modeli geliştirildi.

Keywords

Hemşirelik bilişimi, karar destek sistemi, koroner anjiyografi, makine öğrenmesi, yapay zeka

References

• 1. Zhang, Z, Bai, J, Huang, Y. and Wang, L. (2020). “Implementation of A Clinical Nursing Pathway For Percutaneous Coronary İntervention: A Randomized Controlled Trial Protocol”. Medicine, 99 (43), e22866.
• 2. Boland, JE. and Muller, DWM. (2019). “Interventional Cardiology and Cardiac Catheterisation: The Essential Guide”. Boca Raton/USA: CRC Press.
• 3. Levine, G.N, Bates, E.R, Blankenship, J.C, Bailey, S.R, Bittl, J.A, Cercek, B, et al. (2011). “ACCF/AHA/SCAI Guideline for Percutaneous Coronary Intervention”. A Report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines and the Society for Cardiovascular Angiography and Interventions, 58 (24), 44-122.
• 4. Kwok, C.S, Khan, M.A, Rao, S.V, Kinnaird, T, Sperrin, M, Buchan, I, Belder, M.A, Ludman, P.F, Nolan, J, Loke, Y.K. and Mamas, A. (2015). “Access and Non-Access Site Bleeding After Percutaneous Coronary Intervention and Risk Of Subsequent Mortality and Major Adverse Cardiovascular Events: Systematic Review and Meta-Analysis”. Circ Cardiovasc Interv, 8 (4), e001645.
• 5. Chhatriwalla, A.K, Amin, A.P, Kennedy, K.F, House, J.A, Cohen, D.J, Rao, S.V, John, C. and Marso, S.P. (2013). “Association Between Bleeding Events and In-hospital Mortality After Percutaneous Coronary Intervention”. JAMA, 309 (10), 1022-9.
• 6. Ibanez, B. and James, S. (2018). “The 2017 ESC STEMI Guidelines”. Eur Heart J Open, 39 (2), 79-82.
• 7. Sorajja, P. Holmes, D. (2015). “Periprocedural bleeding in patients undergoing percutaneous coronary intervention. UpToDate:https://www.uptodate.com/contents/periprocedural-bleeding-in-patients-undergoing-percutaneous-coronary-intervention (Accessed Date: 19. 08.2022).
• 8. Rolley, J.X, Salamonson, Y, Wensley, C, Dennison, C.R. and Davidson, P.M. (2011). “Nursing Clinical Practice Guidelines To Improve Care For People Undergoing Percutaneous Coronary Interventions”. Aust Crit Care, 24 (1), 18-38.
• 9. Odom, B.S. (2008). “Management of Paients After Percutaneous Coronary Interventions”. Crit Care Nurse, 28 (5), 26-41.
• 10. Murali, S, Vogrin, S, Noaman, S, Dinh, D.T, Brennan, A.L, Lefkovits, J, Reid, C.M, Cox, N. and Chan, W. (2020). “Bleeding Severity in Percutaneous Coronary Intervention (PCI) and Its Impact on Short-Term Clinical Outcomes”. J Clin Med, 9 (5), 1426.
• 11. Olson, N.C. (2016). “Comparison of Head Elevation Protocols Following Femoral Artery Sheath Removal After Coronary Angiography”. Crit Care Nurse, 36 (3), 20-34.
• 12. Cosman, T.L, Arthur, H.M, Natarajan, M.K. (2011). “Prevalence of Bruising At The Vascular Access Site One Week After Elective Cardiac Catheterisation Or Percutaneous Coronary Intervention”. J Clin Nurs, 20 (9‐10), 1349-56.
• 13. Kurt, Y. and Kaşıkçı, M. (2019). “The Effect Of The Application Of Cold On Hematoma, Ecchymosis, and Pain At The Catheter Site in Patients Undergoing Percutaneous Coronary Intervention”. Int J Nurs Sci, 6 (4), 378-84.
• 14. Sindberg, B, Schou, M, Hansen L, Christiansen KJ, Jørgensen KS, Søltoft M, Holm, N.R, Maeng, M, Kristensen, S.B. and Lassen, J.F. (2014). “Pain and Discomfort in Closure Of Femoral Access Coronary Angiography. The Closuredevices Used in Everyday Practice (CLOSE-UP) Pain Sub Study”. Eur J Cardiovasc Nurs, 13 (3), 221-6.
• 15. Mert Boğa, S. and Öztekin, S.D. (2018). “The Effect Of Position Change On Vital Signs, Back Pain and Vascular Complications Following Percutaneous Coronary Intervention”. J Clin Nurs, 28 (7-8), 1135-47.
• 16. Wentworth, L.J, Bechtum, E.L, Hoffman, J.G, Kramer, R.R, Bartel, D.C, Slusser, J.P. and Tilbury, R.T. (2018). “Decreased Bed Rest Post-Percutaneous Coronary Intervention With A 7-French Arterial Sheath and Its Effects On Vascular Complications”. J Clin Nurs, 27 (1-2), e109-e15.
• 17. Lantz, B. (2015). “Machine Learning With R”. Packt Publishing Ltd.
• 18. Li, T. and Zhou, M. (2016). “ECG Classification Using Wavelet Packet Entropy and Random Forests”. Entropy, 18 (8), 285.
• 19. Yadav, S. and Shukla, S, editors. ( 2016). “Analysis of K-Fold Cross-Validation Over Hold-Out Validation On Colossal D Yadav, S. Shukla, S.(2016). “Analysis of k-fold cross-validation over hold-out validation on colossal datasets for quality classification”. 27-28 February, 6th International conference on advanced computing (78-83). India/ Bhimavaram.
• 20. Dauwan, M, Van der Zande, J.J, Van Dellen, E, Sommer, I.E.C, Scheltens, P, Lemstra, A.W. et al. (2016). “Random Forest To Differentiate Dementia With Lewy Bodies From Alzheimer's Disease”. Alzheimers Dement (Amst), 4, 99-106.
• 21. Abouzari, M, Rashidi, A, Zandi-Toghani, M, Behzadi, M. and Asadollahi, M. (2009). “Chronic Subdural Hematoma Outcome Prediction Using Logistic Regression and An Artificial Neural Network”. Neurosurg Rev, 32 (4), 479-84.
• 22. Groselj, C, Kukar, M, Fettich, J.J. and Kononenko, I. (1997). “Machine Learning Improves The Accuracy Of Coronary Artery Disease Diagnostic Methods”. Comput Cardiol, 7-10 Sept. 1997.
• 23. Ross, E.G, Shah, N.H, Dalman, R.L, Nead, K.T, Cooke, J.P. and Leeper, N.J. (2016). “The Use Of Machine Learning For The Identification Of Peripheral Artery Disease and Future Mortality Risk”. J Vasc Surg, 64 (5), 1515-22.e3.
• 24. O'neill, E.S, Dluhy, N.M, Hansen, A.S, Ryan, J.R. (2006). “Coupling The N-CODES System With Actual Nurse Decision-making”. Comput Inform Nurs, 24 (1), 28-34.
• 25. Weber S. (2011). “Impacts Of Clinical Decision Support Technology On Nursing and Medical Practice in US Critical Care”. CJNI, 5 (4).
• 26. Anderson, J.A. and Willson, P. (2008). “Clinical Decision Support Systems in Nursing: Synthesis of the Science for Evidence-Based Practice”. Comput Inform Nurs, 26 (3), 151-8.
• 27. Van Oostveen, C.J, Braaksma, A. and Vermeulen, H. (2014). “Developing and Testing a Computerized Decision Support System for Nurse-to-Patient Assignment: A Multimethod Study”. Comput Inform Nurs, 32 (6), 276-85.