Background Access-related infections are a major cause of morbidity and mortality in haemodialysis patients. Our goal was to decrease the rate of these infections by implementing an intervention and surveillance program. Methods This intervention took place in two haemodialysis units (Units A and B) and was a joint effort by the haemodialysis staff and the unit for infection prevention and control. It included reviewing the work methods and work space, observations on compliance with standard precautions and handling of the vascular access, creating a checklist and a designated kit for handling the vascular access and prospective surveillance of access-related infections. Results During a nine-year period, the haemodialysis units A and B treated 4471 and 7547 patients (mean number of patients per year: 497 (range 435–556) and 839 (range 777–1055), respectively). For most patients, the procedure was done through an arteriovenous fistula (66.7%, range 50.3–81.5%). The access-related infection rate decreased significantly in both haemodialysis units: from 3 to 0.9% (trend: p < 0.05, linear regression: p < 0.001) in Unit A and from 0.9 to 0.2% (trend: p < 0.05, linear regression: p = 0.01) in Unit B. Conclusions An intervention which included introduction of a checklist and designated kit, together with ongoing surveillance and feedback, resulted in a significant decrease in the access-related infection rates in both haemodialysis units.

No abstract

Aims This study aimed to assess the ability of a voice analysis application to discriminate between wet and dry states in chronic heart failure (CHF) patients undergoing regular scheduled haemodialysis treatment due to volume overload as a result of their chronic renal failure. Methods and resultsIn this single-centre, observational study, five patients with CHF, peripheral oedema of ≥2, and pulmonary congestion-related dyspnoea, undergoing haemodialysis three times per week, recorded five sentences into a standard smartphone/tablet before and after haemodialysis. Recordings were provided that same noon/early evening and the next morning and evening. Patient weight was measured at the hospital before and after each haemodialysis session. Recordings were analysed by a smartphone application (app) algorithm, to compare speech measures (SMs) of utterances collected over time. On average, patients provided recordings throughout 25.8 ± 3.9 dialysis treatment cycles, resulting in a total of 472 recordings. Weight changes of 1.95 ± 0.64 kg were documented during cycles. Median baseline SM prior to dialysis was 0.87 ± 0.17, and rose to 1.07 ± 0.15 following the end of the dialysis session, at noon (P = 0.0355), and remained at a similar level until the following morning (P = 0.007). By the evening of the day following dialysis, SMs returned to baseline levels (0.88 ± 0.19). Changes in patient weight immediately after dialysis positively correlated with SM changes, with the strongest correlation measured the evening of the dialysis day [slope: À0.40 ± 0.15 (95% confidence interval: À0.71 to À0.10), P = 0.0096]. Conclusions The fluid-controlled haemodialysis model demonstrated the ability of the app algorithm to identify cyclic changes in SMs, which reflected bodily fluid levels. The voice analysis platform bears considerable potential as a harbinger of impending fluid overload in a range of clinical scenarios, which will enhance monitoring and triage efforts, ultimately optimizing remote CHF management.

No abstract