Design and implementation of the canadian kidney disease cohort study (CKDCS): A prospective observational study of incident hemodialysis patients

<p>Abstract</p> <p>Background</p> <p>Many nephrology observational studies use renal registries, which have well known limitations. The Canadian Kidney Disease Cohort Study (CKDCS) is a large prospective observational study of patients commencing hemodialysis in five Canadian centers. This study focuses on delineating potentially reversible determinants of adverse outcomes that occur in patients receiving dialysis for end-stage renal disease (ESRD).</p> <p>Methods/Design</p> <p>The CKDCS collects information on risk factors and outcomes, and stores specimens (blood, dialysate, hair and fingernails) at baseline and in long-term follow-up. Such specimens will permit measurements of biochemical markers, proteomic and genetic parameters (proteins and DNA) not measured in routine care. To avoid selection bias, all consenting incident hemodialysis patients at participating centers are enrolled, the large sample size (target of 1500 patients), large number of exposures, and high event rates will permit the exploration of multiple potential research questions.</p> <p>Preliminary Results</p> <p>Data on the baseline characteristics from the first 1074 subjects showed that the average age of patients was 62 (range; 50-73) years. The leading cause of ESRD was diabetic nephropathy (41.9%), and the majority of the patients were white (80.0%). Only 18.7% of the subjects received dialysis in a satellite unit, and over 80% lived within a 50 km radius of the nearest nephrologist's practice.</p> <p>Discussion</p> <p>The prospective design, detailed clinical information, and stored biological specimens provide a wealth of information with potential to greatly enhance our understanding of risk factors for adverse outcomes in dialysis patients. The scientific value of the stored patient tissue will grow as new genetic and biochemical markers are discovered in the future.</p

Indicators of intensive care unit capacity strain: a systematic review

Abstract Background Strained intensive care unit (ICU) capacity represents a fundamental supply-demand mismatch in ICU resources. Strain is likely to be influenced by a range of factors; however, there has been no systematic evaluation of the spectrum of measures that may indicate strain on ICU capacity. Methods We performed a systematic review to identify indicators of strained capacity. A comprehensive peer-reviewed search of MEDLINE, EMBASE, CINAHL, Cochrane Library, and Web of Science Core Collection was performed along with selected grey literature sources. We included studies published in English after 1990. We included studies that: (1) focused on ICU settings; (2) included description of a quality or performance measure; and (3) described strained capacity. Retrieved studies were screened, selected and extracted in duplicate. Quality was assessed using the Newcastle-Ottawa Quality Assessment Scale (NOS). Analysis was descriptive. Results Of 5297 studies identified in our search; 51 fulfilled eligibility. Most were cohort studies (n = 39; 76.5%), five (9.8%) were case-control, three (5.8%) were cross-sectional, two (3.9%) were modeling studies, one (2%) was a correlational study, and one (2%) was a quality improvement project. Most observational studies were high quality. Sixteen measures designed to indicate strain were identified 110 times, and classified as structure (n = 4, 25%), process (n = 7, 44%) and outcome (n = 5, 31%) indicators, respectively. The most commonly identified indicators of strain were ICU acuity (n = 21; 19.1% [process]), ICU readmission (n = 18; 16.4% [outcome]), after-hours discharge (n = 15; 13.6% [process]) and ICU census (n = 13; 11.8% [structure]). There was substantial heterogeneity in the operational definitions used to define strain indicators across studies. Conclusions We identified and characterized 16 indicators of strained ICU capacity across the spectrum of healthcare quality domains. Future work should aim to evaluate their implementation into practice and assess their value for evaluating strategies to mitigate strain. Systematic review registration This systematic review was registered at PROSPERO (March 27, 2015; CRD42015017931)

Additional file 3: of Indicators of intensive care unit capacity strain: a systematic review

Grey literature sources. (DOCX 45 kb

Additional File 2:

Proposed example of a peer reviewed search strategy for measures of ICU capacity strain for OVID Medline (version 2, August 11, 2015). (DOCX 21 KB

LIBERATE: a study protocol for midodrine for the early liberation from vasopressor support in the intensive care unit (LIBERATE): protocol for a randomized controlled trial

Abstract Background Intravenous (IV) vasopressors to support hemodynamics are a primary indication for intensive care unit (ICU) admission. Utilization of oral vasopressor therapy may offer an alternative to IV vasopressor therapy in the ICU, thus decreasing the need for ICU admission. Oral vasopressors, such as midodrine, have been used for hemodynamic support in non-critically ill patients, but their evaluation in critically ill patients to potentially spare IV vasopressor therapy has been limited. Methods The LIBERATE study will be a multicenter, parallel-group, blinded, randomized placebo-controlled trial. It will recruit adult (i.e., age ≥ 18 years) critically ill patients receiving stable or decreasing doses of IV vasopressors. Eligible patients will be randomized to receive either midodrine 10 mg administered enterally every 8 h or placebo until 24 h post-discontinuation of IV vasopressors. The primary outcome will be ICU length of stay. Secondary outcomes include all-cause mortality at 90 days, hospital length of stay, length of IV vasopressor support, re-initiation of IV vasopressors, rates of ICU readmission, and occurrence of AEs. Health economic outcomes including ICU, hospital and healthcare costs, and cost-effectiveness will be evaluated. Pre-planned subgroup analyses include age, sex, frailty, severity of illness, etiology of shock, and comorbid conditions. Discussion LIBERATE will rigorously evaluate the effect of oral midodrine on duration of ICU stay and IV vasopressor support in critically ill patients. Trial registration ClinicalTrials.gov NCT05058612 . Registered on September 28, 202

Additional File 1:

PRISMA-P checklist for the systematic review protocol. (DOCX 17 KB

Perspectives on strained intensive care unit capacity: A survey of critical care professionals.

BACKGROUND:Strained intensive care unit (ICU) capacity represents a supply-demand mismatch in ICU care. Limited data have explored health care worker (HCW) perceptions of strain. METHODS:Cross-sectional survey of HCW across 16 Alberta ICUs. A web-based questionnaire captured data on demographics, strain definition, and sources, impact and strategies for management. RESULTS:658 HCW responded (33%; 95%CI, 32-36%), of which 452 were nurses (69%), 128 allied health (19%), 45 physicians (7%) and 33 administrators (5%). Participants (agreed/strongly agreed: 94%) reported that strain was best defined as "a time-varying imbalance between the supply of available beds, staff and/or resources and the demand to provide high-quality care for patients who may become or who are critically ill"; while some recommended defining "high-quality care", integrating "safety", and families in the definition. Participants reported significant contributors to strain were: "inability to discharge ICU patients due to lack of available ward beds" (97%); "increases in the volume" (89%); and "acuity and complexity of patients requiring ICU support" (88%). Strain was perceived to "increase stress levels in health care providers" (98%); and "burnout in health care providers" (96%). The highest ranked strategies were: "have more consistent and better goals-of-care conversations with patients/families outside of ICU" (95%); and "increase non-acute care beds" (92%). INTERPRETATION:Strain is perceived as common. HCW believe precipitants represent a mix of patient-related and operational factors. Strain is thought to have negative implications for quality of care, HCW well-being and workplace environment. Most indicated strategies "outside" of ICU settings were priorities for managing strain

Needs Assessment Survey Identifying Research Processes Which may be Improved by Automation or Artificial Intelligence: ICU Community Modeling and Artificial Intelligence to Improve Efficiency (ICU-Comma)

Background: Critical care research in Canada is conducted primarily in academically-affiliated intensive care units with established research infrastructure, including research coordinators (RCs). Recently, efforts have been made to engage community hospital ICUs in research albeit with barriers. Automation or artificial intelligence (AI) could aid the performance of routine research tasks. It is unclear which research study processes might be improved through AI automation. Methods: We conducted a cross-sectional survey of Canadian ICU research personnel. The survey contained items characterizing opinions regarding research processes that may be amenable to AI automation. We distributed the questionnaire via email distribution lists of 3 Canadian research societies. Open-ended questions were analyzed using a thematic content analysis approach. Results: A total of 49 survey responses were received (response rate: 8%). Tasks that respondents felt were time-consuming/tedious/tiresome included: screening for potentially eligible patients (74%), inputting data into case report forms (65%), and preparing internal tracking logs (53%). Tasks that respondents felt could be performed by AI automation included: screening for eligible patients (59%), inputting data into case report forms (55%), preparing internal tracking logs (51%), and randomizing patients into studies (45%). Open-ended questions identified enthusiasm for AI automation to improve information accuracy and efficiency while freeing up RCs to perform tasks that require human interaction. This enthusiasm was tempered by the need for proper AI education and oversight. Conclusions: There were balanced supportive (increased efficiency and re-allocation of tasks) and challenges (informational accuracy and oversight) with regards to AI automation in ICU research