Balancing the Needs of Acute and Maintenance Dialysis Patients during the COVID-19 Pandemic: A Proposed Ethical Framework for Dialysis Allocation

Impact of COVID-19 pandemic on care of maintenance hemodialysis patients: a multicenter study

BACKGROUND

The COVID-19 pandemic posed a challenge to hemodialysis (HD) patients. While most outpatient and elective medical services stopped during lockdown, HD patients continued to visit their dialysis centers. We aimed to assess how the initial phase of the pandemic affected patient care by comparing dialysis adequacy and other parameters of patient care before and during the first 10 months of the COVID-19 pandemic.

METHODS

In a retrospective multi-center observational study, all adult dialysis patients in five dialysis centers in Alexandria, Egypt were included. Dialysis adequacy, missed sessions, laboratory parameters and hospitalization were recorded. Data of the 10 months before and the 10 months after the pandemic were compared and predictors of adequacy were determined.

RESULTS

In the 388 HD patients included in the study, the number of missed sessions was higher during the pandemic with peaks during the first and second wave of the pandemic. The ratio of patients to nurses, phosphorus and parathormone levels were significantly higher during the pandemic, while urea reduction ratio, Kt/V, hemoglobin, calcium and albumin levels were significantly lower. In patients who reported difficult accessibility, missed HD sessions were higher during lockdown. Hospital admissions doubled during the pandemic, with COVID-19 infection being the main cause (45.5%). Number of patients per nurse and interdialytic weight gain were predictors of inadequate dialysis.

CONCLUSION

The COVID-19 pandemic and its related lockdown negatively affected multiple aspects of dialysis patient care. Continued access of optimum care in dialysis patients should be a priority in any future mass events.

Challenges in the Care of Patients with AKI Receiving Outpatient Dialysis: AKINow Recovery Workgroup Report

BACKGROUND

Up to one third of survivors of AKI that required dialysis (AKI-D) during hospitalization remain dialysis dependent at hospital discharge. Of these, 20%-60%, depending on the clinical setting, eventually recover enough kidney function to stop dialysis, and the remainder progress to ESKD.

METHODS

To describe the challenges facing those still receiving dialysis on discharge, the AKINow Committee conducted a group discussion comprising 59 participants, including physicians, advanced practitioners, nurses, pharmacists, and patients. The discussion was framed by a patient who described gaps in care delivery at different transition points and miscommunication between care team members and the patient.

RESULTS

Group discussions collected patient perspectives of ( 1 ) being often scared and uncertain about what is happening to and around them and ( 2 ) the importance of effective and timely communication, a comfortable physical setting, and attentive and caring health care providers for a quality health care experience. Provider perspectives included ( 1 ) the recognition of the lack of evidence-based practices and quality indicators, the significant variability in current care models, and the uncertain reimbursement incentives focused on kidney recovery and ( 2 ) the urgency to address communication barriers among hospital providers and outpatient facilities.

CONCLUSIONS

The workgroup identified key areas for future research and policy change to ( 1 ) improve communication among hospital providers, dialysis units, and patients/care partners; ( 2 ) develop tools for risk classification, subphenotyping, and augmented clinical decision support; ( 3 ) improve education to providers, staff, and patients/care partners; ( 4 ) identify best practices to improve relevant outcomes; ( 5 ) validate quality indicators; and ( 6 ) assess the effect of social determinants of health on outcomes. We urge all stakeholders involved in the process of AKI-D care to align goals and work together to fill knowledge gaps and optimize the care to this highly vulnerable patient population.

Dialysis resource allocation in critical care: the impact of the COVID-19 pandemic and the promise of big data analytics

The COVID-19 pandemic resulted in an unprecedented burden on intensive care units (ICUs). With increased demands and limited supply, critical care resources, including dialysis machines, became scarce, leading to the undertaking of value-based cost-effectiveness analyses and the rationing of resources to deliver patient care of the highest quality. A high proportion of COVID-19 patients admitted to the ICU required dialysis, resulting in a major burden on resources such as dialysis machines, nursing staff, technicians, and consumables such as dialysis filters and solutions and anticoagulation medications. Artificial intelligence (AI)-based big data analytics are now being utilized in multiple data-driven healthcare services, including the optimization of healthcare system utilization. Numerous factors can impact dialysis resource allocation to critically ill patients, especially during public health emergencies, but currently, resource allocation is determined using a small number of traditional factors. Smart analytics that take into account all the relevant healthcare information in the hospital system and patient outcomes can lead to improved resource allocation, cost-effectiveness, and quality of care. In this review, we discuss dialysis resource utilization in critical care, the impact of the COVID-19 pandemic, and how AI can improve resource utilization in future public health emergencies. Research in this area should be an important priority.

Multidisciplinary considerations of fairness in medical AI: A scoping review

INTRODUCTION

Artificial Intelligence (AI) technology has been developed significantly in recent years. The fairness of medical AI is of great concern due to its direct relation to human life and health. This review aims to analyze the existing research literature on fairness in medical AI from the perspectives of computer science, medical science, and social science (including law and ethics). The objective of the review is to examine the similarities and differences in the understanding of fairness, explore influencing factors, and investigate potential measures to implement fairness in medical AI across English and Chinese literature.

METHODS

This study employed a scoping review methodology and selected the following databases: Web of Science, MEDLINE, Pubmed, OVID, CNKI, WANFANG Data, etc., for the fairness issues in medical AI through February 2023. The search was conducted using various keywords such as "artificial intelligence," "machine learning," "medical," "algorithm," "fairness," "decision-making," and "bias." The collected data were charted, synthesized, and subjected to descriptive and thematic analysis.

RESULTS

After reviewing 468 English papers and 356 Chinese papers, 53 and 42 were included in the final analysis. Our results show the three different disciplines all show significant differences in the research on the core issues. Data is the foundation that affects medical AI fairness in addition to algorithmic bias and human bias. Legal, ethical, and technological measures all promote the implementation of medical AI fairness.

CONCLUSIONS

Our review indicates a consensus regarding the importance of data fairness as the foundation for achieving fairness in medical AI across multidisciplinary perspectives. However, there are substantial discrepancies in core aspects such as the concept, influencing factors, and implementation measures of fairness in medical AI. Consequently, future research should facilitate interdisciplinary discussions to bridge the cognitive gaps between different fields and enhance the practical implementation of fairness in medical AI.

The impact of resource limitations on care delivery and outcomes: routine variation, the coronavirus disease 2019 pandemic, and persistent shortage

PURPOSE OF REVIEW

Resource limitation, or capacity strain, has been associated with changes in care delivery, and in some cases, poorer outcomes among critically ill patients. This may result from normal variation in strain on available resources, chronic strain in persistently under-resourced settings, and less commonly because of acute surges in demand, as seen during the coronavirus disease 2019 (COVID-19) pandemic.

RECENT FINDINGS

Recent studies confirmed existing evidence that high ICU strain is associated with ICU triage decisions, and that ICU strain may be associated with ICU patient mortality. Studies also demonstrated earlier discharge of ICU patients during high strain, suggesting that strain may promote patient flow efficiency. Several studies of strain resulting from the COVID-19 pandemic provided support for the concept of adaptability - that the surge not only caused detrimental strain but also provided experience with a novel disease entity such that outcomes improved over time. Chronically resource-limited settings faced even more challenging circumstances because of acute-on-chronic strain during the pandemic.

SUMMARY

The interaction between resource limitation and care delivery and outcomes is complex and incompletely understood. The COVID-19 pandemic provides a learning opportunity for strain response during both pandemic and nonpandemic times.

Priority Setting as an Ethical Imperative in Managing Global Dialysis Access and Improving Kidney Care

Priority-setting dilemmas arise when trade-offs must be made regarding the kinds of services that should be provided and to whom, thereby withholding other services from individuals or groups that could benefit from them. Currently, it is practically impossible for lower-income countries to provide dialysis for all patients with kidney failure; however, the fundamental premise of the human right to health, while acknowledging the current resource constraints, is the progressive realization of access to care for all. In this article we outline the rationale for priority setting, starting with the global goal of achieving universal health coverage, the prerequisites for fair and transparent priority setting, and discuss how these may apply to expensive care such as dialysis. Priority is inherently a value-laden process, and cannot be whittled down to technical considerations of clinical or cost effectiveness alone. Fair and transparent priority setting should originate from population health needs, be based on evidence, and be associated with ethical values or principles. This requires effective engagement with relevant stakeholders. Once policies are developed and implemented, good oversight is crucial to ensure accountability and to provide iterative feedback such that the goals of universal health coverage may be progressively realized.