Cardiovascular Journal of Africa: Vol 35 No 2 (MAY/AUGUST 2024)

CARDIOVASCULAR JOURNAL OF AFRICA • Volume 35, No 2, May – August 2024 AFRICA 101 with different sizes of mechanical valves. Cardiovasc J Afr 2018; 29(6): 338–343. 10. Elefteriades JA, Ziganshin BA. Reply: Effective orifice area of prosthetic heart valves-not perfect, but still valuable. J Thorac Cardiovasc Surg 2020; 159(6): e330–e332. 11. Fischlein T, Meuris B, Hakim-Meibodi K, Misfeld M, Carrel T, Zembala M, et al. The sutureless aortic valve at 1 year: A large multicenter cohort study. J Thorac Cardiovasc Surg 2016; 151(6): 1617–1626. 12. Young C, Laufer G, Kocher A, Solinas M, Alamanni F, Polvani G, et al. One-year outcomes after rapid-deployment aortic valve replacement. J Thorac Cardiovasc Surg 2018; 155(2): 575–585. 13. Badiani S, van Zalen J, Treibel TA, Bhattacharyya S, Moon JC, Lloyd G. Aortic stenosis, a left ventricular disease: insights from advanced imaging. Curr Cardiol Rep 2016; 18(8): 80. 14. Koyama T, Okura H, Kume T, Fukuhara K, Imai K, Hayashida A, et al. Impact of energy loss index on left ventricular mass regression after aortic valve replacement. J Echocardiogr. 2014; 12(2): 51–58. 15. Suri RM, Zehr KJ, Sundt TM, 3rd, Dearani JA, Daly RC, Oh JK, et al. Left ventricular mass regression after porcine versus bovine aortic valve replacement: a randomized comparison. Ann Thorac Surg 2009; 88(4): 1232–1237. 16. Rubens FD, Gee Y-Y, Ngu JM, Chen L, Burwash IG. Effect of aortic pericardial valve choice on outcomes and left ventricular mass regression in patients with left ventricular hypertrophy. J Thorac Cardiovasc Surg 2016; 152(5): 1291–1298. 17. Concistrè G, Chiaramonti F, Santarpino G, Pfeiffer S, Marchi F, Vogt F, et al. Left ventricular mass regression after two alternative sutureless aortic bioprostheses. Innovations (Phila) 2015; 10(2): 114–119. 18. Santarpino G, Pfeiffer S, Pollari F, Concistrè G, Vogt F, Fischlein T. Left ventricular mass regression after sutureless implantation of the Perceval S aortic valve bioprosthesis: preliminary results. Interact Cardiovasc Thorac Surg 2014; 18(1): 38–42. 19. DayanV, VignoloG, SocaG, Paganini JJ, BrusichD, Pibarot P. Predictors and outcomes of prosthesis-patient mismatch after aortic valve replacement. J Am Coll Cardiol Cardiovasc Imaging 2016; 9(8): 924–933. 20. Blais C, Dumesnil JG, Baillot R, Simard S, Doyle D, Pibarot P. Impact of valve prosthesis–patient mismatch on short-term mortality after aortic valve replacement. Circulation 2003; 108(8): 983–988. 21. Kato Y, Suehiro S, Shibata T, Sasaki Y, Hirai H. Impact of valve prosthesis–patient mismatch on long-term survival and left ventricular mass regression after aortic valve replacement for aortic stenosis. J Card Surg 2007; 22(4): 314–319. 22. Mohty D, Malouf JF, Girard SE, Schaff HV, Grill DE, EnriquezSarano ME, et al. Impact of prosthesis–patient mismatch on long-term survival in patients with small St Jude Medical mechanical prostheses in the aortic position. Circulation 2006; 113(3): 420–426. 23. Ruel M, Rubens FD, Masters RG, Pipe AL, Bédard P, Hendry PJ, et al. Late incidence and predictors of persistent or recurrent heart failure in patients with aortic prosthetic valves. J Thorac Cardiovasc Surg 2004; 127(1): 149–159. 24. Tasca G, Mhagna Z, Perotti S, Centurini PB, Sabatini T, Amaducci A, et al. Impact of prosthesis–patient mismatch on cardiac events and midterm mortality after aortic valve replacement in patients with pure aortic stenosis. Circulation 2006; 113(4): 570–576. 25. Kim SJ, Samad Z, Bloomfield GS, Douglas PS. A critical review of hemodynamic changes and left ventricular remodeling after surgical aortic valve replacement and percutaneous aortic valve replacement. Am Heart J 2014; 168(2): 150–159. 26. Flameng W, Meuris B, Herijgers P, Herregods MC. Prosthesis–patient mismatch is not clinically relevant in aortic valve replacement using the Carpentier–Edwards Perimount valve. Ann Thorac Surg 2006; 82(2): 530–536. 27. Howell NJ, Keogh BE, Ray D, Bonser RS, Graham TR, Mascaro J, et al. Patient–prosthesis mismatch in patients with aortic stenosis undergoing isolated aortic valve replacement does not affect survival. Ann Thorac Surg 2010; 89(1): 60–64. New ‘more reliable’ NHLS LDL cholesterol calculation method Clinicians rely heavily on low-density lipoprotein cholesterol (LDL-C) levels, the main target for lipid-lowering therapies and preventing cardiovascular disease, and in randomised, control trials and meta-analyses, statin and non-statin therapy for this has proved to reduce the relative risk of atherosclerotic cardiovascular disease by 20 to 25%. Writing in the SA Medical Journal, T Pillay, HMRossouw, N Steyn and A Carelse say accurate LDL-C measurement to guide therapy is crucial, and for this reason, the National Health Laboratory Service (NHLS) chemical expert committee has introduced newer, more accurate equations to replace the older, less reliable Friedewald method, generally used to calculate LDL-C levels. If LDL-C is a primary target in dyslipidaemia, they point out, accurate, precise and affordable assessment is vital. ApoB100 is a test that is more specific and precise, however, only private-sector laboratories offer the apoB100 assay. LDL-C is generally calculated (c-LDL-C) using the Friedewald equation, while some labs routinely measure LDL-C (d-LDL-C) with a direct assay. Calculating LDL-C is a simple cost-free way to determine routine LDL-C by subtracting high-density lipoprotein cholesterol (HDL-C) and triglycerides (TG) from total cholesterol when TG is < 4.5 mol/l. Yet the Friedewald equation is inaccurate when TG is > 4.5 mol/l or with new lipid-lowering therapies that lower the LDL-C levels to < 1.8 mmol/l, and when performed poorly in large varied South African cohorts, including children and patients with diabetes, particularly in samples with hypertriglyceridaemia (TG > 4.5 mmol/l). Thus far, this has been the only equation used in SA laboratories for routine calculation of LDL-C with TG < 4.5 mmol/l. Hypertriglyceridaemic samples are referred for direct assay, and this increases turnaround time and has logistical obstacles. In SA, using big data analysis, differences in instrument performance are also of concern. continued on page 105…

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