Comparison of 2021 CKD-EPI Equations for Estimating Racial Differences in Preemptive Waitlisting for Kidney Transplantation

Periodontitis and the risk of all-cause and cause-specific mortality among US adults with diabetes: A population-based cohort study

AIM

To evaluate the association between periodontitis, all-cause and cause-specific mortality, and its prognostic utility among adults with diabetes.

MATERIALS AND METHODS

Periodontal health records were retrieved from the NHANES database for 4297 participants with diabetes aged >30 years at baseline during 1988-1994, 1999-2004 and 2009-2014. Multivariable Cox proportional hazards regression model was applied to calculate the hazard ratios (HRs) and 95% confidence intervals (CIs) for moderate/severe periodontitis with all-cause and cause-specific mortality in participants with diabetes. Area under the curve (AUC) was used to assess predictive value.

RESULTS

During a median follow-up of 15.41 years, 1701 deaths occurred. After multivariate adjustments, moderate/severe periodontitis was significantly associated with increased risk of all-cause (HR: 1.27; 95% CI: 1.07-1.50; p = .005) and cardiovascular disease (CVD)-related (HR: 1.35, 95% CI: 1.03-1.76, p = .031) mortality in participants with diabetes. The absolute risk difference based on the cumulative incidence information was 0.022 (5-year, 95% CI: 0.021-0.023) and 0.044 (10-year, 95% CI: 0.041-0.048). Periodontitis improved the prediction of all-cause (AUC: 0.652; 95% CI: 0.627-0.676) and CVD-related (AUC: 0.649; 95% CI: 0.624-0.676) mortality over standard risk factors (all-cause: AUC: 0.631; 95% CI: 0.606-0.656; CVD-related: AUC: 0.629; 95% CI: 0.604-0.655).

CONCLUSIONS

Moderate/severe periodontitis is associated with an increased risk of all-cause and CVD-related mortality in adults with diabetes. Periodontitis might represent a marker for residual risk.

Strategies to Guide Preemptive Waitlisting and Equity in Waittime Accrual by Race/Ethnicity

BACKGROUND

Use of eGFR to determine preemptive waitlisting eligibility may contribute to racial/ethnic disparities in access to waitlisting, which can only occur when the eGFR falls to ≤20 ml/min per 1.73 m 2 . Use of an alternative risk-based strategy for waitlisting may reduce these inequities ( e.g. , a kidney failure risk equation [KFRE] estimated 2-year risk of kidney failure) rather than the standard eGFR threshold for determining waitlist eligibility. Our objective was to model the amount of preemptive waittime that could be accrued by race and ethnicity, applying two different strategies to determine waitlist eligibility.

METHODS

Using electronic health record data, linear mixed models were used to compare racial/ethnic differences in preemptive waittime that could be accrued using two strategies: estimating the time between an eGFR ≤20 and 5 ml/min per 1.73 m 2 versus time between a 25% 2-year predicted risk of kidney failure (using the KFRE, which incorporates age, sex, albuminuria, and eGFR to provide kidney failure risk estimation) and eGFR of 5 ml/min per 1.73 m 2 .

RESULTS

Among 1290 adults with CKD stages 4-5, using the Chronic Kidney Disease Epidemiology Collaboration equation yielded shorter preemptive waittime between an eGFR of 20 and 5 ml/min per 1.73 m 2 in Black (-6.8 months; 95% confidence interval [CI], -11.7 to -1.9), Hispanic (-10.2 months; -15.3 to -5.1), and Asian/Pacific Islander (-10.3 months; 95% CI, -15.3 to -5.4) patients compared with non-Hispanic White patients. Use of a KFRE threshold to determine waittime yielded smaller differences by race and ethnicity than observed when using a single eGFR threshold, with shorter time still noted for Black (-2.5 months; 95% CI, -7.8 to 2.7), Hispanic (-4.8 months; 95% CI, -10.3 to 0.6), and Asian/Pacific Islander (-5.4 months; -10.7 to -0.1) individuals compared with non-Hispanic White individuals, but findings only met statistical significance criteria in Asian/Pacific Islander individuals. When we compared potential waittime availability using a KFRE versus eGFR threshold, use of the KFRE yielded more equity in waittime for Black ( P = 0.02), Hispanic ( P = 0.002), and Asian/Pacific Islander ( P = 0.002) patients.

CONCLUSIONS

Use of a risk-based strategy was associated with greater racial equity in waittime accrual compared with use of a standard single eGFR threshold to determine eligibility for preemptive waitlisting.

Differences in the Evolution of Clinical, Biochemical, and Hematological Indicators in Hospitalized Patients with COVID-19 According to Their Vaccination Scheme: A Cohort Study in One of the World's Highest Hospital Mortality Populations

COVID-19 vaccines primarily prevent severe illnesses or hospitalization, but there is limited data on their impact during hospitalization for seriously ill patients. In a Mexican cohort with high COVID-19 mortality, a study assessed vaccination's effects. From 2021 to 2022, 462 patients with 4455 hospital days were analyzed. The generalized multivariate linear mixed model (GENLINMIXED) with binary logistic regression link, survival analysis and ROC curves were used to identify risk factors for death. The results showed that the vaccinated individuals were almost half as likely to die (adRR = 0.54, 95% CI = 0.30-0.97, p = 0.041). When stratifying by vaccine, the Pfizer group (BNT162b2) had a 2.4-times lower risk of death (adRR = 0.41, 95% CI = 0.2-0.8, p = 0.008), while the AstraZeneca group (ChAdOx1-S) group did not significantly differ from the non-vaccinated (adRR = 1.04, 95% CI = 0.5-2.3, p = 0.915). The Pfizer group exhibited a higher survival, the unvaccinated showed increasing mortality, and the AstraZeneca group remained intermediate (p = 0.003, multigroup log-rank test). Additionally, BNT162b2-vaccinated individuals had lower values for markers, such as ferritin and D-dimer. Biochemical and hematological indicators suggested a protective effect of both types of vaccines, possibly linked to higher lymphocyte counts and lower platelet-to-lymphocyte ratio (PLR). It is imperative to highlight that these results reinforce the efficacy of COVID-19 vaccines. However, further studies are warranted for a comprehensive understanding of these findings.

Mitigating Health Disparities in Transplantation Requires Equity, Not Equality

Despite decades of research and evidence-based mitigation strategies, disparities in access to transplantation persist for all organ types and in all stages of the transplant process. Although some strategies have shown promise for alleviating disparities, others have fallen short of the equity goal by providing the same tools and resources to all rather than tailoring the tools and resources to one's circumstances. Innovative solutions that engage all stakeholders are needed to achieve equity regardless of race, sex, age, socioeconomic status, or geography. Mitigation of disparities is paramount to ensure fair and equitable access for those with end-stage disease and to preserve the trust of the public, upon whom we rely for their willingness to donate organs. In this overview, we present a summary of recent literature demonstrating persistent disparities by stage in the transplant process, along with policies and interventions that have been implemented to combat these disparities and hypotheses for why some strategies have been more effective than others. We conclude with future directions that have been proposed by experts in the field and how these suggested strategies may help us finally arrive at equity in transplantation.

Comparing Decentralized Learning Methods for Health Data Models to Nondecentralized Alternatives: Protocol for a Systematic Review

BACKGROUND

Considering the soaring health-related costs directed toward a growing, aging, and comorbid population, the health sector needs effective data-driven interventions while managing rising care costs. While health interventions using data mining have become more robust and adopted, they often demand high-quality big data. However, growing privacy concerns have hindered large-scale data sharing. In parallel, recently introduced legal instruments require complex implementations, especially when it comes to biomedical data. New privacy-preserving technologies, such as decentralized learning, make it possible to create health models without mobilizing data sets by using distributed computation principles. Several multinational partnerships, including a recent agreement between the United States and the European Union, are adopting these techniques for next-generation data science. While these approaches are promising, there is no clear and robust evidence synthesis of health care applications.

OBJECTIVE

The main aim is to compare the performance among health data models (eg, automated diagnosis and mortality prediction) developed using decentralized learning approaches (eg, federated and blockchain) to those using centralized or local methods. Secondary aims are comparing the privacy compromise and resource use among model architectures.

METHODS

We will conduct a systematic review using the first-ever registered research protocol for this topic following a robust search methodology, including several biomedical and computational databases. This work will compare health data models differing in development architecture, grouping them according to their clinical applications. For reporting purposes, a PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 flow diagram will be presented. CHARMS (Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies)-based forms will be used for data extraction and to assess the risk of bias, alongside PROBAST (Prediction Model Risk of Bias Assessment Tool). All effect measures in the original studies will be reported.

RESULTS

The queries and data extractions are expected to start on February 28, 2023, and end by July 31, 2023. The research protocol was registered with PROSPERO, under the number 393126, on February 3, 2023. With this protocol, we detail how we will conduct the systematic review. With that study, we aim to summarize the progress and findings from state-of-the-art decentralized learning models in health care in comparison to their local and centralized counterparts. Results are expected to clarify the consensuses and heterogeneities reported and help guide the research and development of new robust and sustainable applications to address the health data privacy problem, with applicability in real-world settings.

CONCLUSIONS

We expect to clearly present the status quo of these privacy-preserving technologies in health care. With this robust synthesis of the currently available scientific evidence, the review will inform health technology assessment and evidence-based decisions, from health professionals, data scientists, and policy makers alike. Importantly, it should also guide the development and application of new tools in service of patients' privacy and future research.

TRIAL REGISTRATION

PROSPERO 393126; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=393126.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

PRR1-10.2196/45823.