Biomedical System Dynamics to Improve Anemia Control With Darbepoetin Alfa in Long-Term Hemodialysis Patients

McCarthy, James T.; Hocum, Craig; Albright, Robert C.; Rogers, James R.; Gallaher, Edward J.; Steensma, David P.; Gudgell, Stephen F.; Bergstralh, Eric J.; Dillon, John; Hickson, La Tonya J.; Williams, Amy W.; Dingli, David

doi:10.1016/j.mayocp.2013.10.022

Cited by 25 publications

(25 citation statements)

References 29 publications

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“…This is suggestive that we experienced an increase in the number of seriously ill inpatients - who might require more RBC units per patient - during this time. As we decreased the desired ranges for Hb values in our outpatient HD units, our data confirmed the expected decreases in average outpatient Hb values and a reduction in darbepoetin doses because a less number of ESAs would be needed to maintain the lower Hb values [23,24]. …”

Section: Discussionsupporting

confidence: 61%

“…Beginning 30 days after the first HD treatment, all RBC units transfused to such patients were included that occurred in either the outpatient MCDS unit or the inpatient setting. The initial 30 days were excluded to allow the Hb level to become more dependent on MCDS anemia-management protocols [23,24]. Even when a patient reentered the hospital after the initial 3 outpatient treatments, the patient was included in our analysis.…”

Section: Methodsmentioning

confidence: 99%

“…This system is a proprietary software program that simulates the dynamics of erythropoiesis and its response to ESA for an iron-replete HD patient [23,24]. …”

Section: Methodsmentioning

confidence: 99%

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Total Red Blood Cell Transfusions for Chronic Hemodialysis Patients in a Single Center, 2009-2013

Albright

Dillon

Hocum

et al. 2016

Nephron

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Background: Anemia management in chronic hemodialysis (HD) has been affected by the implementation of the prospective payment system (PPS) and changes in clinical guidelines. These factors could impact red blood cell (RBC) transfusion in HD patients. Our distinctive care system contains complete records for all RBC transfusions among our HD patients. Aims: To determine RBC transfusions in patients with prevalent chronic HD, site of administration (inpatient or outpatient), and ordering physician specialty for inpatients; compare pre- and post-PPS RBC transfusions; and compare RBC transfusions during changes in desired outpatient hemoglobin (Hb) range for patients with chronic HD. Methods: Retrospective analysis of medical and blood bank records for patients with prevalent chronic HD July 2009 through June 2013. Results: In total, 310-356 patients were studied. Mean (SD) units of RBCs per 100 patients per month for the study's 48 months were outpatient, 2.6 (1.5), and inpatient, 9.4 (4.6). Outpatient pre-PPS RBC units transfused were 2.1 (0.6) vs. post-PPS of 2.6 (1.5; p = 0.22, t test); for inpatients pre-PPS, 7.9 (4.5) RBC units per month vs. post-PPS, 11.5 (5.1; p = 0.11, t test). Inpatient RBC transfusions accounted for 75.2% (14.2%) of all RBC transfusions; 67.3% (16.3%) of inpatient transfusions were ordered by nonnephrologists. Changes in desired Hb range for outpatient HD patients did not lead to changes in RBC transfusions. Conclusions: No changes in RBC transfusions occurred among our patients with chronic HD with PPS implementation and in desired Hb range during the study period. Most transfusions were given in inpatient settings by nonnephrologists.

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Section: Discussionsupporting

confidence: 61%

Section: Methodsmentioning

confidence: 99%

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Total Red Blood Cell Transfusions for Chronic Hemodialysis Patients in a Single Center, 2009-2013

Albright

Dillon

Hocum

et al. 2016

Nephron

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“…Some of these algorithms have also been tested in a clinical setting, albeit often in relatively small cohorts of patients. [20][21][22][23] In a previous work, we built our anemia modeling approach and evaluated its reliability and predictive value in a retrospective study involving a large number of ESKD patients treated in the Fresenius Medical Care clinical network. 24 Motivated by our encouraging results, we decided to deploy our decision support system, the Anemia Control Model (ACM), in 3 pilot clinics as part of the daily care routine of a large population of unselected patients.…”

mentioning

confidence: 99%

An international observational study suggests that artificial intelligence for clinical decision support optimizes anemia management in hemodialysis patients

Barbieri

Molina

Ponce³

et al. 2016

Kidney International

110

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Managing anemia in hemodialysis patients can be challenging because of competing therapeutic targets and individual variability. Because therapy recommendations provided by a decision support system can benefit both patients and doctors, we evaluated the impact of an artificial intelligence decision support system, the Anemia Control Model (ACM), on anemia outcomes. Based on patient profiles, the ACM was built to recommend suitable erythropoietic-stimulating agent doses. Our retrospective study consisted of a 12-month control phase (standard anemia care), followed by a 12-month observation phase (ACM-guided care) encompassing 752 patients undergoing hemodialysis therapy in 3 NephroCare clinics located in separate countries. The percentage of hemoglobin values on target, the median darbepoetin dose, and individual hemoglobin fluctuation (estimated from the intrapatient hemoglobin standard deviation) were deemed primary outcomes. In the observation phase, median darbepoetin consumption significantly decreased from 0.63 to 0.46 μg/kg/month, whereas on-target hemoglobin values significantly increased from 70.6% to 76.6%, reaching 83.2% when the ACM suggestions were implemented. Moreover, ACM introduction led to a significant decrease in hemoglobin fluctuation (intrapatient standard deviation decreased from 0.95 g/dl to 0.83 g/dl). Thus, ACM support helped improve anemia outcomes of hemodialysis patients, minimizing erythropoietic-stimulating agent use with the potential to reduce the cost of treatment.

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“…The literature presents several studies using SD as a tool for modeling subjects as: technology innovation (Swinerd and McNaught 2014), improvement of anemia control (McCarthy et al 2014), adult obesity trends (Fallah-Fini et al 2014), infection screening among young women (Teng et al 2015), information sharing, psychological safety performance effects (Bendoly 2014), and diffusion of technological innovation (Swinerd and McNaught 2014).…”

Section: Systems Dynamicsmentioning

confidence: 99%

Knowledge Management Patterns Model for a Flight Test Environment

Follador¹,

Trabasso²

2016

J.Aerosp. Technol. Manag.

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This paper investigates how Knowledge Management patterns in a Brazilian Air Force flight test environment can be simulated using a System Dynamics approach. The research has been conducted initially by a literature review on the main Knowledge Management and System Dynamics theories. Data for this research has been collected in a previous study consisted of documental research regarding the flight test environment Knowledge Management and a questionnaire-based survey which identified both a low Knowledge Management maturity level and the flight test core competence as the capability of performing flight test campaigns. The issued problem was the tradeoff between actions focused on performing flight test campaigns versus Knowledge Management to transfer the core competence inside organization in order to keep it in a high level. A system dynamics quantitative model has been developed as a result of this research. Fluxes and stokes were identified within the model and the relation between them emerged by identifying systemic feedback loops that may compromise the Knowledge Management and the core competence transferring. These features enable a holistic visualization and better understanding of the problem as well as the possibilities of identifying ways of improvement. KEYWORDS: 264Follador RC, Trabasso LG of the core competence inside the organization preventing an ideal sharing of important knowledge. The objective of this paper is to simulate and analyze the KM patterns, in a BAF FTO, in order to apprehend the relationship between the activities of performing flight test campaigns and those of KM within the organization. This problem has been addressed using a system dynamics approach, where a quantitative model is proposed to identify the main stakeholders of the FTO KM system, pointing out fluxes and stocks as well as the relationships between them.This article is organized as follows: the second section presents the literature review and describes the contribution of this paper. The third section describes the methodology used to construct the model. The following section presents the results and discussions about FT KM model, regarding the commitment between performing flight test campaigns and KM activities necessary to maintain the core competence in the organization. The final section summarizes the contribution of this research and discusses future studies. Sterman (2002) suggested that, in a world of accelerating complexity and change, thoughtful leaders increasingly recognize that the management tools that had been used have not only failed to solve the persistent problems encountered, but may, in fact, be causing them. Examples of organizational policy resistance are: well-intentioned efforts to solve pressing problems create unanticipated side effects. "Learning about complex systems when you also live in them is difficult. We are all passengers on an aircraft, we must not only fly but redesign in flight" (Sterman 2002, p. 4). LITERATURE REVIEWTo understand the system a literature revi...

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Biomedical System Dynamics to Improve Anemia Control With Darbepoetin Alfa in Long-Term Hemodialysis Patients

Cited by 25 publications

References 29 publications

Total Red Blood Cell Transfusions for Chronic Hemodialysis Patients in a Single Center, 2009-2013

Total Red Blood Cell Transfusions for Chronic Hemodialysis Patients in a Single Center, 2009-2013

An international observational study suggests that artificial intelligence for clinical decision support optimizes anemia management in hemodialysis patients

Knowledge Management Patterns Model for a Flight Test Environment

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