METHODS: This is a retrospective, single-centre study comprising 105 living kidney donor candidates from the year 2007 to 2020. By comparing against 51-Chromium ethylenediamine-tetraacetic acid (51Cr-EDTA), we analysed creatinine clearance for correlation, bias, precision and accuracy.
RESULTS: The study group had a mean age of 45.68 ± 10.97 years with a mean serum creatinine of 64.43 ± 17.68 µmol/L and a urine volume of 2.06 ± 0.83 L. Mean measured GFR from 51Cr-EDTA was 124.37 ± 26.83 ml/min/1.73m2 whereas mean creatinine clearance was 132.35 ± 38.18 ml/min/1.73m2. Creatinine clearance overestimated 51Cr-EDTA significantly with a correlation coefficient of 0.48 (p
METHOD: A multi-centre cross-sectional survey was conducted in person among 409 kidney transplant recipients in six public hospitals in the Klang Valley, Malaysia. Catastrophic health expenditure is considered at 10% out-of-pocket payment from household income used for healthcare expenditure. The association of socioeconomic status with catastrophic health expenditure is determined via multiple logistic regression analysis.
RESULTS: 93 kidney transplant recipients (23.6%) incurred catastrophic health expenditures. Kidney transplant recipients in the Middle 40% (RM 4360 to RM 9619 or USD 1085.39 -USD 2394.57) and Bottom 40% (RM 9619 or > USD 2394.57) income group. Kidney transplant recipients in the Bottom 40% and Middle 40% income groups were more susceptible to catastrophic health expenditure at 2.8 times and 3.1 times compared to higher-income groups, even under the care of the Ministry of Health.
CONCLUSION: Universal health coverage in Malaysia cannot address the burden of out-of-pocket healthcare expenditure on low-income Kidney transplant recipients for long-term post-transplantation care. Policymakers must reexamine the healthcare system to protect vulnerable households from catastrophic health expenditures.
METHODS: Retrospective study of 236 patients with CID from the region were enrolled from 2004 to 2022.
RESULTS: 236 patients were included with a majority being profound CID. Among patients with a family history of CID, the ages at onset and diagnosis, and the delay in diagnosis were lower compared to those with no family history of CID, but this did not affect time to transplant. HSCT was performed for 51.27% of the patients with median time from diagnosis to HSCT of 6.36 months. On multivariate analysis, patients who underwent early transplant had increased odds of having CD3 count ≤1000 cell/μl, diagnosed by screening or erythroderma.
CONCLUSION: There is a delay in diagnosis and treatment of CID in our region. Establishing newborn screening programs and HSCT units in our region are the urgent need.
METHODS: A retrospective audit of heart transplant recipients (n = 87) treated with tacrolimus was performed. Relevant data were collected from the time of transplant to discharge. The concordance of tacrolimus dosing and monitoring according to hospital guidelines was assessed. The observed and software-predicted tacrolimus concentrations (n = 931) were compared for the first 3 weeks of oral immediate-release tacrolimus (Prograf) therapy, and the predictive performance (bias and imprecision) of the software was evaluated.
RESULTS: The majority (96%) of initial oral tacrolimus doses were guideline concordant. Most initial intravenous doses (93%) were lower than the guideline recommendations. Overall, 36% of initial tacrolimus doses were administered to transplant recipients with an estimated glomerular filtration rate of <60 mL/min/1.73 m despite recommendations to delay the commencement of therapy. Of the tacrolimus concentrations collected during oral therapy (n = 1498), 25% were trough concentrations obtained at steady-state. The software displayed acceptable predictions of tacrolimus concentration from day 12 (bias: -6%; 95%confidence interval, -11.8 to 2.5; imprecision: 16%; 95% confidence interval, 8.7-24.3) of therapy.
CONCLUSIONS: Tacrolimus dosing and monitoring were discordant with the guidelines. The Bayesian forecasting software was suitable for guiding tacrolimus dosing after 11 days of therapy in heart transplant recipients. Understanding the factors contributing to the variability in tacrolimus pharmacokinetics immediately after transplant may help improve software predictions.