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.

752. Costs Attributable to Clostridioides difficile Infection in the Presence of Differential Mortality

Background CDI imposes a major burden on the U.S. healthcare system. Obtaining accurate estimates of economic costs is critical to determining the cost-effectiveness of preventive measures. This task is complicated by differences in epidemiology, mortality, and baseline health status of infected and uninfected individuals, and by the statistical properties of costs data (e.g., right-skewed, excess of zeros costs). Methods Incident CDI cases were identified from Medicare 5% fee-for-service data between 2011 and 2017 and classified into standard surveillance definitions: hospital-onset (HO); other healthcare facility-onset (OHFO); community-onset, healthcare-associated (CO-HCFA); or community-associated (CA). Cases were frequency matched 1:4 to uninfected controls based on age, sex, and year of CDI. Controls were assigned to surveillance definitions based on location at index dates. Medicare allowed costs were summed in 30-day intervals up to 3 years following index. One- and 3-year cumulative costs attributable to CDI were computed using a 3-part estimator consisting of a parametric survival model and a pair of 2-part models predicting costs separately in intervals where death did and did not occur, adjusting for underlying acute and chronic conditions. Results 60,492 CDI cases (Figure 1) were matched to 241,968 controls. Three-year mortality was higher among CDI cases compared to matched controls for HO (45% vs 26%) and OHFO (42% vs 36%), whereas mortality was slightly lower for CDI cases compared to controls for those with community onset (CO-HCFA: 28% vs 32%; CA: 10% vs 11%). One- and 3-year attributable costs due to CDI are shown in Figure 2. Adjusted 1-year attributable costs amounted to &26,954 (95% CI: &26,154–&27,939) for HO; &10,539 (&9,564–&11,518) for OHFO; &6,525 (&5,012–&8,171) for CO-HCFA; and &3,171 (&1,841–&4,200) for CA. Adjusted 3-year attributable costs were &44,736 (&43,063–&46,483) for HO; &13,994 (&12,529–&15,975) for OHFO; &7,349 (&4,738–&10,246) for CO-HCFA; and &2,377 (&166–&4,722) for CA. Figure 1. Proportion of Cases by CDI Surveillance Definitions Abbreviations: HO: hospital-onset; OHFO: other healthcare facility-onset; CO-HCFA: community-onset, healthcare-associated; CA: community-associated. Figure 2. Estimates of Costs Attributable to CDI by CDI Surveillance Definitions at One and Three Years after Onset Top panels: One-year cost estimates. Bottom panels: Three-year cost estimates. Abbreviations: HO: hospital-onset; OHFO: other healthcare facility-onset; CO-HCFA:community-onset, healthcare-associated; CA:community-associated. Conclusion CDI was associated with increased healthcare costs across surveillance definitions in Medicare fee-for-service patients after adjusting for survival and underlying conditions. Disclosures Dustin Stwalley, MA, AbbVie Inc (Shareholder)Bristol-Myers Squibb (Shareholder) Margaret A. Olsen, PhD, MPH, Pfizer (Consultant, Research Grant or Support) Holly Yu, MSPH, Pfizer (Employee) Erik R. Dubberke, MD, MSPH, Ferring (Grant/Research Support)Merck (Consultant)Pfizer (Consultant, Grant/Research Support)Seres (Consultant)Summit (Consultant)

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.

Development and validation of a life expectancy estimator for multimorbid older adults: a cohort study protocol

BackgroundOlder multimorbid adults have a high risk of mortality and a short life expectancy (LE). Providing high-value care and avoiding care overuse, including of preventive care, is a serious challenge among multimorbid patients. While guidelines recommend to tailor preventive care according to the estimated LE, there is no tool to estimate LE in this specific population. Our objective is therefore to develop an LE estimator for older multimorbid adults by transforming a mortality prognostic index, which will be developed and internally validated in a prospective cohort.Methods and analysisWe will analyse data of the Optimising Therapy to Prevent Avoidable Hospital Admissions in Multimorbid Older People cohort study in Bern, Switzerland. 822 participants were included at hospitalisation with age of 70 years or older, multimorbidity (three or more chronic medical conditions) and polypharmacy (use of five drugs or more for >30 days). All-cause mortality will be assessed during 3 years of follow-up. We will apply a flexible parametric survival model with backward stepwise selection to identify the mortality risk predictors. The model will be internally validated using bootstrapping techniques. We will derive a point-based risk score from the regression coefficients. We will transform the 3-year mortality prognostic index into an LE estimator using the Gompertz survival function. We will perform a qualitative assessment of the clinical usability of the LE estimator and its application. We will conduct the development and validation of the mortality prognostic index following the Prognosis Research Strategy (PROGRESS) framework and report it following the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) statement.Ethics and disseminationWritten informed consent by patients themselves or, in the case of cognitive impairment, by a legal representative, was required before enrolment. The local ethics committee (Kantonale Ethikkommission Bern) has approved the study. We plan to publish the results in peer-reviewed journals and present them at national and international conferences.

Evidence for improved prognosis of colorectal cancer diagnosed following the detection of iron deficiency anaemia

Iron deficiency anaemia (IDA) is common in colorectal cancer (CRC), especially, in right-sided CRC which is known to have an overall worse prognosis. The associations between diagnostic pathway (Bowel Cancer Screening Programme (BCSP), IDA, symptomatic) and tumour side/stage was assessed using logistic regression models in 1138 CRC cases presenting during 2010–2016 at a single secondary-care centre in the UK. In the IDA sub-group, the relationship between CRC stage and the event of having a blood count prior to CRC diagnosis was examined using Bayesian parametric survival model. IDA was found as the only significant predictor of right-sided CRC (OR 10.61, 95% CI 7.02–16.52). Early-stage CRC was associated with both the IDA (OR 1.65, 95% CI 1.18–2.29) and BCSP pathway (OR 2.42, 95% CI 1.75–3.37). At any age, the risk of detecting CRC at late-stage was higher in those without a previous blood count check (hazard ratio 1.53, 95% credibility interval 1.08–2.14). The findings of this retrospective observational study suggest a benefit from diagnosing CRC through the detection of IDA, and warrant further research into the prognosis benefit of systematic approach to blood count monitoring of the at-risk population.

Different approaches to quantify years of life lost from COVID-19

The burden of an epidemic is often characterized by death counts, but this can be misleading as it fails to acknowledge the age of the deceased patients. Years of life lost is therefore widely used as a more relevant metric, however, such calculations in the context of COVID-19 are all biased upwards: patients dying from COVID-19 are typically multimorbid, having far worse life expectation than the general population. These questions are quantitatively investigated using a unique Hungarian dataset that contains individual patient level data on comorbidities for all COVID-19 deaths in the country. To account for the comorbidities of the patients, a parametric survival model using 11 important long-term conditions was used to estimate a more realistic years of life lost. As of 12 May, 2021, Hungary reported a total of 27,837 deaths from COVID-19 in patients above 50 years of age. The usual calculation indicates 10.5 years of life lost for each death, which decreases to 9.2 years per death after adjusting for 11 comorbidities. The expected number of years lost implied by the life table, reflecting the mortality of a developed country just before the pandemic is 11.1 years. The years of life lost due to COVID-19 in Hungary is therefore 12% or 1.3 years per death lower when accounting for the comorbidities and is below its expected value, but how this should be interpreted is still a matter of debate. Further research is warranted on how to optimally integrate this information into epidemiologic risk assessments during a pandemic.

Distractive Tasks and the Influence of Driver Attributes

Driver distraction is a major problem nowadays, contributing to many deaths, injuries, and economic losses. Despite the effort that has been made to minimize these impacts, considering the technological evolution, distraction at the wheel has tended to increase. Not only tech-related tasks but every task that captures a driver’s attention has impacts on road safety. Moreover, driver behavior and characteristics are known to be heterogeneous, leading to a distinct driving performance, which is a challenge in the road safety perspective. This study aimed to capture the effects of drivers’ personal aspects and habits on their distraction behavior. Following a within-subjects approach, a convenience sample of 50 drivers was exposed to three unexpected events reproduced in a driving simulator. Drivers’ reactions were evaluated through three distinct models: a Lognormal Model to make analyze the visual distraction, a Binary Logit Model to explore the adopted type of reaction, and a Parametric Survival Model to study the reaction times. The research outcomes revealed that drivers’ behavior and perceived workload were distinct when they were engaged in specific secondary tasks and for distinct drivers’ personal attributes and habits. Age and type of distraction showed statistical significance regarding the visual behavior. Moreover, reaction times were consistently related to gender, BMI, sleep patterns, speed, habits while driving, and type of distraction. The habit of engaging in secondary tasks while driving resulted in a cumulative better performance.

Factors Determining Birth Intervals: A Multilevel Mixed Effect Parametric Survival Approach

Interval between births plays an important role in maternal health as well as child health. This study applies the methodology of Flexible parametric survival models to data on successive births among Nigeria women using the dataset from 2018 National Demographic Health survey. The flexible parametric survival model with Weibull baseline distribution was found to be the best among other fitted baseline distributions. The factors, zone of residence, educational qualification, religion, economic status and age at first birth were found to be significant in predicting the birth intervals. It was found that random effect parameter indicates that the interval between successive births is similar from the same woman. Keywords: Birth intervals, Baseline hazard, Mixed effect, Flexible parametric model, AIC.