Arterial line pressure control enhanced extracorporeal blood flow prescription in hemodialysis patients

<p>Abstract</p> <p>Background</p> <p>In hemodialysis, extracorporeal blood flow (Qb) recommendation is 300–500 mL/min. To achieve the best Qb, we based our prescription on dynamic arterial line pressure (DALP).</p> <p>Methods</p> <p>This prospective study included 72 patients with catheter Group 1 (G1), 1877 treatments and 35 arterio-venous (AV) fistulae Group 2 (G2), 1868 treatments. The dialysis staff was trained to prescribe Qb sufficient to obtain DALP between -200 to -250 mmHg. We measured ionic clearance (IK: mL/min), access recirculation, DALP (mmHg) and Qb (mL/min). Six prescription zones were identified: from an optimal A zone (Qb > 400, DALP -200 to -250) to zones with lower Qb E (Qb < 300, DALP -200 to -250) and F (Qb < 300, DALP > -199).</p> <p>Results</p> <p>Treatments distribution in A was 695 (37%) in G1 vs. 704 (37.7%) in G2 (<it>P </it>= 0.7). In B 150 (8%) in G1 vs. 458 (24.5%) in G2 (<it>P </it>< 0.0001). Recirculation in A was 10.0% (Inter quartile rank, IQR 6.5, 14.2) in G1 vs. 9.8% (IQR 7.5, 14.1) in G2 (<it>P </it>= 0.62). IK in A was 214 ± 34 (G1) vs. 213 ± 35 (G2) (<it>P </it>= 0.65). IK Anova between G2 zones was: A vs. C and D (<it>P </it>< 0.000001). Staff prescription adherence was 81.3% (G1) vs. 84.1% (G2) (<it>P </it>= 0.02).</p> <p>Conclusion</p> <p>In conclusion, an optimal Qb can de prescribed with DALP of -200 mmHg. Staff adherence to DLAP treatment prescription could be reached up to 81.3% in catheters and 84.1% in AV fistulae.</p

Management of a Large Qualitative Data Set: Establishing Trustworthiness of the Data

Health services research is multifaceted and impacted by the multiple contexts and stakeholders involved. Hence, large data sets are necessary to fully understand the complex phenomena (e.g., scope of nursing practice) being studied. The management of these large data sets can lead to numerous challenges in establishing trustworthiness of the study. This article reports on strategies utilized in data collection and analysis of a large qualitative study to establish trustworthiness. Specific strategies undertaken by the research team included training of interviewers and coders, variation in participant recruitment, consistency in data collection, completion of data cleaning, development of a conceptual framework for analysis, consistency in coding through regular communication and meetings between coders and key research team members, use of N6™ software to organize data, and creation of a comprehensive audit trail with internal and external audits. Finally, we make eight recommendations that will help ensure rigour for studies with large qualitative data sets: organization of the study by a single person; thorough documentation of the data collection and analysis process; attention to timelines; the use of an iterative process for data collection and analysis; internal and external audits; regular communication among the research team; adequate resources for timely completion; and time for reflection and diversion. Following these steps will enable researchers to complete a rigorous, qualitative research study when faced with large data sets to answer complex health services research questions