Abnormal nocturnal heart rate variability response among chronic kidney disease and dialysis patients during wakefulness and sleep

Background. Dialysis patients and patients with chronic kidney disease (CKD) experience a substantial risk for abnormal autonomic function and abnormal heart rate variability (HRV). It remains unknown whether HRV changes across sleep stages in patients with different severity of CKD or dialysis dependency. We hypothesized that high-frequency (HF) HRV (vagal tone) will be attenuated from wakefulness to non-rapid eye movement (NREM) and then to rapid eye movement (REM) sleep in dialysis patients as compared to patients with CKD

Administrative applications

Nurse managers play a critical role in ensuring an appropriate number and mix of staff are available to ensure safe patient care is provided. When leadership decisions are effective, we see improved patient care outcomes, better staff performance, increased job satisfaction and staff retention. However, when decision making is less effective both patients and staff can be negatively impacted. The impact is particularly noticeable for patients who may experience increased adverse events, including greater risk of dying. Making evidence-based staffing decisions can be challenging for nurse managers given the complexity of today’s workplace and importantly, a lack of access to real-time data. Many factors impact on their decisions including nursing shortages; challenges to skills mix (human capital such as experience and qualifications); staff stress, burnout and fatigue; changes to the complexity of patient care needs; an aging workforce and communication inefficiencies. There are many workload measurement tools used internationally, but most are not based on real-time data showing patient acuity, bed occupancy rates and the quality and availability of staffing resources, all factors which are necessary to make cost-effective staffing decisions. Instead, nurse managers are left with many static and disparate reporting systems that do not meet managerial requirements for decision-making. This can result in increased workloads and stress for nurse managers, which also ultimately impact clinical staff. Hospitals need to develop and use software systems which will harness existing data, allowing nurse managers to extract, analyze and interpret data in a timely manner to support appropriate and safe nurse staffing decisions