Forecasting the Incidence and Prevalence of Patients with End-Stage Renal Disease in Malaysia up to the Year 2040 (original) (raw)
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2021
Background: The effect of dialysis modality on the survival of end-stage renal disease patients is a major public health interest. Methods: In this retrospective cohort study, all adult end-stage renal disease patients receiving dialysis treatment in Sabah between January 1, 2007 and December 31, 2017 as identified from the Malaysian Dialysis and Transplant Registry were evaluated and followed up through December 31, 2018. The endpoint was all-cause mortality. The observation time was defined as the time from the date of dialysis initiation after the onset of end-stage renal disease to whichever of the following that came first: date of death, date of transplantation, date of last follow-up, date of recovered kidney function, or December 31, 2018. Weighted Cox regression was used to estimate the effect of dialysis modality. Analyses were restricted to patients with complete data on all variables. Results: 1,837 patients began hemodialysis and 156 patients started with peritoneal dia...
F1000Research
Background: An interrupted time series (ITS) analysis is a powerful tool for policy evaluation. In Thailand, chronic kidney disease (CKD) is a public health problem that requires a long recovery time and has a high treatment cost. The universal coverage policy for renal replacement therapy (universal dialysis policy), is used to treat this disease but policy evaluation using ITS analysis has rarely been conducted. This study applied ITS analysis to test the effect of such a policy between 2006 and 2016. Methods: Data were retrieved from the electronic database of the health data center in Roi Et Province for the period between January 1, 2006 and December 31, 2016. 15,681 CKD stage 5 patients were included. The intervention under assessment was the universal health coverage system, which has been implemented since 2008. Results: Results showed that before implementation of the universal dialysis policy, the overall trend of access to renal replacement therapy (RRT) slightly increase...
Forecasting incidence of hemorrhagic fever with renal syndrome in China using ARIMA model
BMC infectious diseases, 2011
Background: China is a country that is most seriously affected by hemorrhagic fever with renal syndrome (HFRS) with 90% of HFRS cases reported globally. At present, HFRS is getting worse with increasing cases and natural foci in China. Therefore, there is an urgent need for monitoring and predicting HFRS incidence to make the control of HFRS more effective. In this study, we applied a stochastic autoregressive integrated moving average (ARIMA) model with the objective of monitoring and short-term forecasting HFRS incidence in China. Methods: Chinese HFRS data from 1975 to 2008 were used to fit ARIMA model. Akaike Information Criterion (AIC) and Ljung-Box test were used to evaluate the constructed models. Subsequently, the fitted ARIMA model was applied to obtain the fitted HFRS incidence from 1978 to 2008 and contrast with corresponding observed values. To assess the validity of the proposed model, the mean absolute percentage error (MAPE) between the observed and fitted HFRS incidence was calculated. Finally, the fitted ARIMA model was used to forecast the incidence of HFRS of the years 2009 to 2011. All analyses were performed using SAS9.1 with a significant level of p < 0.05. Results: The goodness-of-fit test of the optimum ARIMA (0,3,1) model showed non-significant autocorrelations in the residuals of the model (Ljung-Box Q statistic = 5.95,P = 0.3113). The fitted values made by ARIMA (0,3,1) model for years 1978-2008 closely followed the observed values for the same years, with a mean absolute percentage error (MAPE) of 12.20%. The forecast values from 2009 to 2011 were 0.69, 0.86, and 1.21per 100,000 population, respectively. Conclusion: ARIMA models applied to historical HFRS incidence data are an important tool for HFRS surveillance in China. This study shows that accurate forecasting of the HFRS incidence is possible using an ARIMA model. If predicted values from this study are accurate, China can expect a rise in HFRS incidence.
Validation of the kidney failure risk equation for end-stage kidney disease in Southeast Asia
BMC Nephrology
Background Patients with chronic kidney disease (CKD) are at high risk of end-stage kidney disease (ESKD). The Kidney Failure Risk Equation (KFRE), which predicts ESKD risk among patients with CKD, has not been validated in primary care clinics in Southeast Asia (SEA). Therefore, we aimed to (1) evaluate the performance of existing KFRE equations, (2) recalibrate KFRE for better predictive precision, and (3) identify optimally feasible KFRE thresholds for nephrologist referral and dialysis planning in SEA. Methods All patients with CKD visiting nine primary care clinics from 2010 to 2013 in Singapore were included and applied 4-variable KFRE equations incorporating age, sex, estimated glomerular filtration rate (eGFR), and albumin-to-creatinine ratio (ACR). ESKD onset within two and five years were acquired via linkage to the Singapore Renal Registry. A weighted Brier score (the squared difference between observed vs predicted ESKD risks), bias (the median difference between observe...
Multivariable prognostic model for dialysis patients with end stage renal disease
Saudi Medical Journal, 2021
Objectives: To develop an externally validated multivariable prognostic model for an underprivileged dialysis population. Methods: This was a multicenter retrospective cohort study of 5 years duration from January 2013 to December 2017. A total of 758 patients (37.5% female; mean±SD age, 44.26±14.77 years) were enrolled for construction of the prognostic model. The data were analyzed using a proportional hazards model to identify predictors of survival. Three risk groups were identified at the 25th and 75th percentiles of the resultant prognostic index. The model was externally validated with another dataset of 622 dialysis patients. Original Article Results: The prognostic index included 5 predictor variables: hemoglobin, serum potassium, interdialytic weight gain, serum albumin, and duration of dialysis, which had good predictive performance on the calibration and discrimination aspects of the model (Harrell's c statistic: 0.748, Gonen and Heller k statistic: 0.647, Somers' D statistic: 0.496, calibration slope: 1.156). There were significant interaction effects between weight and hemoglobin, weight and albumin, albumin and potassium, and albumin and hemoglobin. Conclusions: We developed an externally validated model that contained 5 routinely collected prognosticators and confirmed its calibration and discrimination abilities in obtaining reliable prognostic estimates in developing countries. The model will assist clinicians in deciding the prognosis of dialysis patients. The application of this model in different clinical settings of developing countries can indicate interesting findings regarding public health.
Clinical Epidemiology
Objective: In medicine, many more prediction models have been developed than are implemented or used in clinical practice. These models cannot be recommended for clinical use before external validity is established. Though various models to predict mortality in dialysis patients have been published, very few have been validated and none are used in routine clinical practice. The aim of the current study was to identify existing models for predicting mortality in dialysis patients through a review and subsequently to externally validate these models in the same large independent patient cohort, in order to assess and compare their predictive capacities. Methods: A systematic review was performed following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. To account for missing data, multiple imputation was performed. The original prediction formulae were extracted from selected studies. The probability of death per model was calculated for each individual within the Netherlands Cooperative Study on the Adequacy of Dialysis (NECOSAD). The predictive performance of the models was assessed based on their discrimination and calibration. Results: In total, 16 articles were included in the systematic review. External validation was performed in 1,943 dialysis patients from NECOSAD for a total of seven models. The models performed moderately to well in terms of discrimination, with C-statistics ranging from 0.710 (interquartile range 0.708-0.711) to 0.752 (interquartile range 0.750-0.753) for a time frame of 1 year. According to the calibration, most models overestimated the probability of death. Conclusion: Overall, the performance of the models was poorer in the external validation than in the original population, affirming the importance of external validation. Floege et al's models showed the highest predictive performance. The present study is a step forward in the use of a prediction model as a useful tool for nephrologists, using evidence-based medicine that combines individual clinical expertise, patients' choices, and the best available external evidence.
Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association, 2018
An easy-to-use prediction model for long-term renal patient survival based on only four predictors [age, primary renal disease, sex and therapy at 90 days after the start of renal replacement therapy (RRT)] has been developed in The Netherlands. To assess the usability of this model for use in Europe, we externally validated the model in 10 European countries. Data from the European Renal Association-European Dialysis and Transplant Association (ERA-EDTA) Registry were used. Ten countries that reported individual patient data to the registry on patients starting RRT in the period 1995-2005 were included. Patients <16 years of age and/or with missing predictor variable data were excluded. The external validation of the prediction model was evaluated for the 10- (primary endpoint), 5- and 3-year survival predictions by assessing the calibration and discrimination outcomes. We used a data set of 136 304 patients from 10 countries. The calibration in the large and calibration plots ...
American Journal of Tropical Medicine and Hygiene, 2012
The Box-Jenkins approach was used to fit an autoregressive integrated moving average (ARIMA) model to the incidence of hemorrhagic fever with renal Syndrome (HFRS) in China during 1986-2009. The ARIMA (0, 1, 1) + (2, 1, 0) 12 models fitted exactly with the number of cases during January 1986-December 2009. The fitted model was then used to predict HFRS incidence during 2010, and the number of cases during January-December 2010 fell within the model's confidence interval for the predicted number of cases in 2010. This finding suggests that the ARIMA model fits the fluctuations in HFRS frequency and it can be used for future forecasting when applied to HFRS prevention and control.
Majalah Kedokteran Bandung
The prevalence of chronic kidney disease on dialysis or CKD5D is increasing with a significant impact on disease burden in many countries. Patients are usually listed in the national renal registries, which report demographic data, incidence, prevalence, and outcome. The survival rate is an important outcome measure to characterize the impact of treatment in the CKD5 patient population in the national and international renal registries. Indonesian Society of Nephrology (InaSN) has the Indonesian Renal Registry program to collect data that was endorsed to monitor dialysis treatment quality in Indonesia. IRR releases an annual report, but there is no survival analysis yet. This study aimed to discover the five-year survival rate of CKD5D patients in West Java between 2007–2018 and its factor based on the IRR database. A retrospective cohort study was performed by gaining all patients' data from the IRR database, then data on all of the patients from West Java province who complete...