Dialysis Outcomes in a Middle-Income Country: An Updated Comparison of Patient Mortality between Hemodialysis and Peritoneal Dialysis

<b><i>Introduction:</i></b> Comparisons of survival between dialysis modalities is of great importance to patients with kidney failure, their families, and healthcare systems. <b><i>Objective:</i></b> This study’s objective was to compare mortality of patients on chronic hemodialysis (HD) or peritoneal dialysis (PD) and identify variables associated with mortality. <b><i>Methods:</i></b> This retrospective cohort study included adult incident patients with kidney failure treated with HD or PD by the Baxter Renal Care Services network in Colombia. The study was conducted between January 1, 2008, and December 31, 2013 (recruitment period), with follow-up until December 31, 2018. The outcome was the cumulative mortality rate at 1, 2, 3, 4, and 5 years. Propensity score matching (PSM) and the Gompertz parametric survival model were used to compare mortality in HD versus PD. <b><i>Results:</i></b> The analysis included 12,499 patients, of whom 57.4% were on PD at inception. The overall mortality rate was 14.0 events per 100 patient-years (95% confidence interval [CI], 13.61–14.42). Using an intention-to-treat approach, crude mortality rates were significantly lower in patients receiving HD (HD: 12.3 deaths per 100 patient-years [95% CI, 11.7–12.8] vs. PD: 15.5 [14.9–16.1], <i>p</i> &#x3c; 0.01). Using a Gompertz parametric survival model, dialysis modality was not significantly associated with mortality (hazard ratio HD vs. PD 1.0, 95% CI, 0.9–1.1). After PSM, the mortality cumulative incidence functions between HD and PD were not statistically significantly different (<i>p</i> = 0.88). <b><i>Conclusions:</i></b> The present study in a large cohort of incident dialysis patients with at least 5 years follow-up and using PSM methods showed no differences in cumulative mortality between HD and PD patients. This evidence from a middle-income country may facilitate the process of dialysis modality selection globally.

Comparison of survival models and assessment of risk factors for survival of cardiovascular patients at Addis Ababa Cardiac Center, Ethiopia: a retrospective study

Background: Cardiovascular diseases (CVDs) is disorders of heart and blood vessels. It is a major health problem across the world,and 82% of CVD deaths is contributed by countries with low and middle income. The aim of this study was to choose appropriate model for the survival of cardiovascular patients data and identify the factors that affect the survival of cardiovascular patients at Addis Ababa Cardiac Center. Method: A Retrospective study was conducted on patients under follow-up at Addis Ababa Cardiac Center between Sep- tember 2010 to December 2018. The patients included have made either post operation or pre-operation. Out of 1042 car- diac patients, a sample of 332 were selected for the current study using simple random sampling technique. Non-parametric, semi-parametric and parametric survival models were used and comparisons were made to select the appropriate predicting model. Results: Among the sample of 332 cardiac patients, only 67(20.2%) experienced CVD and the remaining 265(79.8%) were censored. The median and the maximum survival time of cardiac patients was 1925 and 1403 days respectively.The estimated hazard ratio of male patients to female patients is 1.926214 (95%CI: 1.111917-3.336847; p = 0.019) implying that the risk of death of male patients is 1.926214 times higher than female cardiac patients keeping the other covariates constant in the model. Even if, all semi parametric and parametric survival models fitted to the current data well, various model comparison criteria showed that parametric/weibull AFT survival model is better than the other. Conclusions: The governmental and non-governmental stakeholders should pay attention to give training on the risk fac- tors identified on the current study to optimize individual’s knowledge and awareness so that death due to CVDs can be minimized. Keywords: Cardiovascular patient; survival analysis; non-parametric; semi-parametric; parametric.

1506Flexible parametric survival models investigating factors associated with diabetes-related foot ulcer time-to-healing

Background Diabetes-related foot ulcers (DFU) take months to heal, reduce patient’s quality-of-life, and induce large healthcare expenditure. Various factors have been identified to influence DFU healing at fixed periods, however, data on factors associated with time-to-healing is scarce. Methods Patients presenting with DFU to Diabetic Foot Services across Queensland, Australia between July 2011 and December 2017 were included and had their demographics, disease history and treatments examined at baseline. Outcome of interest was healing of all ulcers within two-year follow-up time. Time-to-healing and associated factors were examined using flexible parametric survival models, which easily enabled including time-varying coefficients and predicting proportions healed. Results Of 4,709 included patients (median age 63 years, 69.5% male, 10.5% Indigenous), median time-to-healing was 112 days, and 68% healed within two years. Younger age (&lt;60 years), geographical remoteness, smoking, neuropathy, deep ulcers, infection, not receiving offloading, and no recent podiatry treatment were independently associated with longer time-to-healing. Time-varying effects of peripheral artery disease and ulcer size were identified for the first time: both had a negative influence on healing with effects diminishing after six months. The predicted proportions healed, for example, within six months is 65.0% (63.3-66.7) for people residing in a major city, 54.6% (52.6-56.8) in regional area, and 40.3% (34.6-47.1) in remote area. Conclusions This study identified novel and confirmatory factors influencing time-to-healing over 24 months in a large real-world cohort of people with diabetes-related foot ulcers. Visualizing the adjusted predicted proportion healed revealed the influence each factor had on healing rates over time. Key messages Flexible parametric survival model provided flexibility in investigating time-varying effects and outcome prediction in those with diabetes-related foot ulcer healing.

Choice of HbA1c threshold for identifying individuals at high risk of type 2 diabetes and implications for diabetes prevention programmes: a cohort study

Background Type 2 diabetes (T2D) is common and increasing in prevalence. It is possible to prevent or delay T2D using lifestyle intervention programmes. Entry to these programmes is usually determined by a measure of glycaemia in the ‘intermediate’ range. This paper investigated the relationship between HbA1c and future diabetes risk and determined the impact of varying thresholds to identify those at high risk of developing T2D. Methods We studied 4227 participants without diabetes aged ≥ 40 years recruited to the Exeter 10,000 population cohort in South West England. HbA1c was measured at study recruitment with repeat HbA1c available as part of usual care. Absolute risk of developing diabetes within 5 years, defined by HbA1c ≥ 48 mmol/mol (6.5%), according to baseline HbA1c, was assessed by a flexible parametric survival model. Results The overall absolute 5-year risk (95% CI) of developing T2D in the cohort was 4.2% (3.6, 4.8%). This rose to 7.1% (6.1, 8.2%) in the 56% (n = 2358/4224) of participants classified ‘high-risk’ with HbA1c ≥ 39 mmol/mol (5.7%; ADA criteria). Under IEC criteria, HbA1c ≥ 42 mmol/mol (6.0%), 22% (n = 929/4277) of the cohort was classified high-risk with 5-year risk 14.9% (12.6, 17.2%). Those with the highest HbA1c values (44–47 mmol/mol [6.2–6.4%]) had much higher 5-year risk, 26.4% (22.0, 30.5%) compared with 2.1% (1.5, 2.6%) for 39–41 mmol/mol (5.7–5.9%) and 7.0% (5.4, 8.6%) for 42–43 mmol/mol (6.0–6.1%). Changing the entry criterion to prevention programmes from 39 to 42 mmol/mol (5.7–6.0%) reduced the proportion classified high-risk by 61%, and increased the positive predictive value (PPV) from 5.8 to 12.4% with negligible impact on the negative predictive value (NPV), 99.6% to 99.1%. Increasing the threshold further, to 44 mmol/mol (6.2%), reduced those classified high-risk by 59%, and markedly increased the PPV from 12.4 to 23.2% and had little impact on the NPV (99.1% to 98.5%). Conclusions A large proportion of people are identified as high-risk using current thresholds. Increasing the risk threshold markedly reduces the number of people that would be classified as high-risk and entered into prevention programmes, although this must be balanced against cases missed. Raising the entry threshold would allow limited intervention opportunities to be focused on those most likely to develop T2D.

Evaluation of factors related to the survival of hospitalized patients with COVID-19: Survival analysis with frailty approach

Introduction: Considering that covid-19 is an emerging disease and results in very different outcomes-from complete recovery to death, it is important to determine the factors affecting the survival of patients. Given the lack of knowledge about effective factors and the existence of differences in the outcome of individuals with similar values of the observed covariates, this study aimed to investigate the factors affecting the survival of patients with COVID-19 by the parametric survival model with the frailty approach. Methods: The data of 139 patients with COVID-19 hospitalized in Imam Reza Hospital in Tabriz were analyzed by the Gompertz survival model with gamma frailty effect. At first, variables with p-value<.1 in univariable analysis were included in the multivariable analysis, and then the stepwise method was used for variable selection. Results: Diabetes mellitus (p-value =.021) was significantly related to the survival of hospitalized patients. The rest of the investigated variables were not significant. The frailty effect was significant (p-value=.019). Conclusion: In the investigated sample of patients with covid19, diabetes was an important variable related to patient survival. Also, the significant frailty effect indicates the existence of unobserved heterogeneity that cause individuals with a similar value of the observed covariates to have different survival distributions.