Analysis of clinical characteristics of dialysis patients at the University Hospital.

OBJECTIVES: The incidence and prevalence of Chronic Kidney Disease (CKD) patients treated with dialysis has continued to increase worldwide. These patients are at the highest risk of developing cardiovascular complications which contribute to their demise. The objective of this study was to describe the clinical and demographic characteristics of the patients on maintenance hemodialysis at the University Hospital, in San Juan Puerto Rico. METHODS: We reviewed a total of 55 active patients. Data was collected to identify the demographic and clinical characteristics. Co-morbid conditions such as diabetes mellitus, hypertension, and coronary artery disease (CAD) were noted as well as age, dialysis vintage and social and educational profiles. RESULTS: Patients were divided into two groups according to age (group 1 were less than 60 years and group 2 were more than 60 years). Average age was 42 years in group 1 with 17 females and 24 males and 78 years in group 2 with 6 females and 8 males. Diabetes mellitus was present in 17 (41%) of group 1 and 5 patients (35%) of group 2. Likewise hypertension was present in 26 (63%) of group 1 patients and 11(78%) of group 2 patients. CAD was present in 15 (36%) of group 1 patients and 7 (50%) of group 2 patients. There was an association between low annual income in both groups but not in the level of education. CONCLUSION: A relatively young population was identified in our descriptive analysis of hemodialysis patients at the University Hospital. The expected high incidence of diabetes mellitus observed in the general population of hemodialysis patients in our country was not present. Hypertension and CAD were major risk factors in these patients. In view of these findings we recommend better control of blood pressure, prevention and early detection of CAD in CKD patients before initiating dialysis.

Knowing what we do and doing what we should: quality assurance in hemodialysis.

An international group of around 50 nephrologists and scientists, including representatives from large dialysis provider organisations, formulated recommendations on how to develop and implement quality assurance measures to improve individual hemodialysis patient care, population health and cost effectiveness. Discussed were methods thought to be of highest priority, those clinical indicators which might be most related to meaningful patient outcomes, tools to control treatment delivery and the role of facilitating computerized expert systems. Emphasis was given to the use of new technologies such as measurement of online dialysance and ways of assessing fluid status. The current evidence linking achievement of quality criteria with patient outcomes was reviewed. This paper summarizes useful processes and quality measures supporting quality assurance that have been agreed across the expert panel. It also notes areas where more understanding is required.

Deep Learning in Kidney Ultrasound: Overview, Frontiers, and Challenges.

Ultrasonography is a practical imaging technique used in numerous health care settings. It is relatively inexpensive, portable, and safe, and it has dynamic capabilities that make it an invaluable tool for a wide variety of diagnostic and interventional studies. Recently, there has been a revolution in medical imaging using artificial intelligence (AI). A particularly potent form of AI is deep learning, in which the computer learns to recognize pixel or written data on its own without the selection of predetermined features, usually through a specific neural network architecture. Neural networks vary in architecture depending on their task, and key design considerations include the number of layers and complexity, data available, technical requirements, and domain knowledge. Deep learning models offer the potential for promising innovations to workflow, image quality, and vision tasks in sonography. However, there are key limitations and challenges in creating reliable and safe AI models for patients and clinicians.

Assessment of dry weight by monitoring changes in blood volume during hemodialysis using Crit-Line.

BACKGROUND: Routine assessment of dry weight in chronic hemodialysis patients relies primarily on clinical evaluation of patient fluid status. We evaluated whether measurement of postdialytic vascular refill could assist in the assessment of dry weight. METHODS: Twenty-eight chronic, stable hemodialysis patients were studied during routine treatment sessions using constant dialysate temperature and dialysate sodium concentration, and relative changes in blood volume were monitored using Crit-Line III monitors throughout this study. The study was divided into three phases. Phase 1 studies evaluated the time-dependence of vascular compartment refill after completion of hemodialysis. Phase 2 studies evaluated the relationships in patient subgroups between intradialytic changes in blood volume and the presence of postdialytic vascular compartment refill during that last 10 minutes of hemodialysis after stopping ultrafiltration. Phase 3 studies evaluated the extent of dry weight changes following the application of a protocol for blood volume reduction, postdialytic vascular compartment refill, and correlation with clinical evidence of intradialytic hypovolemia and/or postdialytic fatigue. Phase 3 included anywhere from three to five treatments. RESULTS: Phase 1 studies demonstrated that despite interpatient variability in the magnitude of postdialytic vascular compartment refill, when significant refill was evident, it always continued for at least 30 minutes. However, the majority of refill took place within 10 minutes postdialysis. Phase 2 studies identified 3 groups of patients: those who exhibited intradialytic reductions in blood volume but not postdialytic vascular compartment refill (group 1), those who exhibited intradialytic reductions in blood volume and postdialytic vascular compartment refill (group 2), and those whose blood volume did not change substantially during hemodialysis treatment (group 3). In phase 3 studies, use of an ultrafiltration protocol for blood volume reduction and monitoring of postdialytic vascular compartment refill combined with clinical assessment of hypovolemia and postdialytic fatigue demonstrated that patients often had a clinical dry weight assessment which was too low or too high. In all 28 patients studied, dry weight was either increased or decreased following use of this protocol. CONCLUSION: Determination of the extent of both intradialytic decreases in blood volume and postdialytic vascular compartment refill, combined with clinical assessment of intradialytic hypovolemia and postdialytic fatigue, can help assess patient dry weight and optimize volume status while reducing dialysis associated morbidity. The number of hospital admissions due to fluid overload may be reduced.