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NDT Advance Access originally published online on March 27, 2008
Nephrology Dialysis Transplantation 2008 23(9):2943-2947; doi:10.1093/ndt/gfn116
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© The Author [2008]. Published by Oxford University Press on behalf of ERA-EDTA. All rights reserved. For Permissions, please e-mail: journals.permissions@oxfordjournals.org


Effect of a quality improvement strategy on several haemodialysis outcomes

Eduardo Parra1, Rosa Ramos2, Angels Betriu3, José Paniagua4, Montse Belart3 and Tomás Martínez5

1 Hospital Reina Sofía of Tudela (Navarra) 2 Vilanova y Geltrú Haemodialysis Centre (Barcelona) 3 Sistemes Renals Haemodialysis Centre (Lérida) 4 Hospital Ponferrada (León) 5 Biostatistic Department, Medicine University of Zaragoza, Spain

Correspondence and offprint requests to: Eduardo Parra Moncasi, Unidad de Nefrología, Hospital Reina Sofía de Tudela, Carretera de Tarazona Km 3, 31500 Tudela, Spain. Tel: +848-434000; Fax: +848-434111; E-mail: eparramo{at}cfnavarra.es



   Abstract
Top
 Abstract
Introduction
 Results
 Discussion
 References
 
Background. Intermediate outcomes are associated with the survival of long-term haemodialysis patients; however, outcome variability across centres may result in heterogeneous quality of care. The study aim was to evaluate a multifaceted quality improvement activity (QIA) targeting several haemodialysis clinical performance measures.

Methods. A total (prevalent and incident) of 313 patients from four dialysis units were included. The QIA was based on a multifaceted strategy involving collection of haemodialysis clinical performance measures every 6–8 months, feedback about results, improvement plans and benchmarking, and it was tested in a 3-year prospective interventional study. Two timepoints of clinical performance measures were considered for evaluating the QIA: baseline (February 2003, pre-QIA) and final (February 2006, post-QIA).

Results. Centres showed significant improvement in percentage of patients with haemoglobin <11 g/dl, mean haemoglobin; percentage of patients with Kt/v <1.2, mean Kt/v; percentage of patients with phosphorous >5.5 mg/dl, mean phosphorous; percentage of patients with calcium phosphate product >55, mean calcium phosphate product; and percentage of patients with ferritin <200 ng/ml, mean ferritin. No change was observed in percentage of patients with haemoglobin between 11 and 13 g/dl, erythropoietin consumed; percentage of patients with ferritin <100 ng/ml; percentage of patients with ferritin >800 ng/ml; percentage of patients with albumin <3.5 g/dl, mean albumin; or percentage of native arteriovenous fistula. The percentage of patients with haemoglobin >13 g/dl was increased.

Conclusions. Quality-improvement strategies can help improve haemodialysis performance for anaemia, dialysis dose and bone metabolism. The importance of assessing patients with high haemoglobin level should be stressed.

Keywords: benchmarking; feedback; haemodialysis; quality of care; quality strategy



   Introduction
Top
 Abstract
 Introduction
Results
 Discussion
 References
 
In haemodialysis, some intermediate indicators are well correlated with morbidity and mortality. Outcomes such as anaemia, dialysis doses, calcium phosphate metabolism, albumin and vascular access type are associated with the survival of long-term dialysis patients [1,2]. Furthermore, it has been observed that the annual mortality rate and hospitalization are progressively increased for any additional target not achieved [3,4]. However, there is evidence of variability across centres in achieving therapeutic goals, which cannot be explained based on patient factors such as the severity of illness and are better related to the heterogeneous quality of care [5–9]. This variability suggests an improvement opportunity for quality of care.

The effects of some quality improvement interventions have been tested with variable results. These interventions have included educational network meetings, case management, qualified staff visits, audits, use of reminders, feedback about performance data (centres know their own results) [10–12] and benchmarking (the process of evaluating outcomes against those achieved using the gold standard or best practices) [13–14]. However, the most effective strategy is a multifaceted intervention, with strategies that combine different approaches and target different barriers to improve care) [10].

Few studies have been designed to test strategies for improving haemodialysis intermediate outcomes [15–17]. Our aim was to evaluate a structured multifaceted quality-improvement strategy by testing a battery of haemodialysis clinical performance measures.

Subjects and methods
We designed a prospective study carried out in four haemodialysis volunteer Spanish centres that included every admitted patient (prevalent and incident) at these units from February 2003 to February 2006. At those centres, we initiated the quality-improvement activities (QIAs) based on a consensus of haemodialysis indicators (as of January 2003), recollection and evaluation of performance data (from February 2003), establishment of objective outcomes, feedback about results, improvement plans and benchmarking. Indicators were related to haemoglobin, haemodialysis dose, bone metabolism, albumin level and vascular access.

The structured multifaceted intervention consisted of several features targeting different barriers to improving care (Table 1). The first feature was establishing a consensus and assumption of relevant haemodialysis indicators for the four centres. This basis would give nephrologists awareness and knowledge of clinical indicators and standards, the concrete description of desirable levels of clinical performance measures (barrier 1 to improved care). These indicators had to fulfil the following criteria: (a) exhibit an association with morbidity and risk of death; (b) be associated with the availability of adequate therapeutic tools and (c) be part of simple and routine procedures at the renal units [1–3]. The clinical performance measures used to assess quality improvement are shown in Table 2. We did not consider indicators related to mortality and morbidity, such as cardiovascular events and hospitalization, because the number of centres and patients in the investigation did not yield adequate statistical power to elucidate variations.


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  		Table 1 Main barriers and features of the multifaceted intervention

 

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  		Table 2 Selected quality-improvement fields, clinical indicators and clinical performance measures

 
The second feature was that data of indicators for every relevant patient at the unit were collected every 6–8 months from February 2003 to February 2006 and were e-mailed to a data management centre (DMC). Performance data corresponding to February 2003 were considered pre-QIA and those from February 2006 were considered post-QIA. Outcomes were monitored independently of patient outcome (transplantation or death). For the third feature, we established realistic aims for all performance data, which varied depending on the unit and the baseline results. Therefore, the centre-specific objectives were not rigidly defined but suitable and feasible according to their performance data, overcoming the lack of achievable objectives (barrier 2).

As the fourth feature, indicators were processed at the DMC, and every unit received updated feedback and benchmarking information (barrier 3). Therefore, nephrologists periodically knew their own results (feedback) and their relationship with the remaining three units (benchmarking). Openness about performance may encourage nephrologists to change behaviour. At this point, nephrologists were stimulated to design a centre-specific management policy (CSMP) for their results that were below average. The CSMP was local adaptation of guidelines designed specifically for each unit that had failed to achieve the average result and was implemented exclusively by that unit. For instance, when the percentage of patients with Kt/v <1.2 was above the target in a specific unit, that unit developed and implemented a tailored plan to improve the value. The fifth feature of the strategy involved a meeting among the units a month after every benchmarking result was calculated. At the meeting, units’ members analysed clinical performance measures, reviewed published guidelines, explained their CSMP and proposed an aim for the next meeting (these proposed activities were considered the improvement plan). The improvement plan would give nephrologists knowledge, trust and familiarity with their CSMP (barrier 4). CSMP was designed as a pragmatic way to assess clinics, involving a brief statement of the diagnostic and therapeutic approach and using an effective decision aid. This presentation would give nephrologists guidelines in straightforward formats that promote their use (barrier 5). Periodic meetings may additionally help physicians to overcome the inertia of previous practices (barrier 6).

Statistical analysis
For the statistical analysis, we considered the four centres together. Centres completed a database, which included demographic data, associated morbidity and indicators, specifically designed using Excel for Windows. The data were e-mailed to the DMC with an encrypted code for every patient to preserve legal confidentiality. After indicator processing, information about benchmarking was e-mailed back to the centres.

Demographic and associated morbidity data from February 2003 were collected. Associated morbidity included number of months on haemodialysis, delayed referral to a nephrology unit (<1 month), associated diseases and renal disease.

The clinical indicators used for the study analysis were the pre-QIA (February 2003) and post-QIA (February 2006) data. All statistical analysis was performed using SPSS 11.5. Descriptive analyses were conducted depending on whether the characteristics were categorical or continuous. Normality was determined using the Kolmogorov–Smirnov test with Lilliefors correction. Comparison between initial (pre-QIA) and follow-up (post-QIA) outcomes was made using student's t-tests for normally distributed data and Wilcoxon's non-parametric test for data that were not normally distributed. P < 0.05 was considered to indicate significance.



   Results
Top
 Abstract
 Introduction
 Results
Discussion
 References
 
In January 2003, there was a meeting to identify and achieve consensus about clinical indicators representing key components of haemodialysis care. Selected quality-improvement indicators focused on measures of anaemia, dialysis doses, bone metabolism, nutrition and vascular access (Table 2).

There were mainly four CSMPs developed during the 3-year follow-up period. The first was the ‘Anaemia CSMP’, which was implemented in two of the four centres. This approach concisely considered the anaemia aetiology (ferrous metabolism, inflammation, dialysis doses, vitamin deficit, occult blood losses and other less-common causes) and the treatment, including ferrous treatment, erythropoietin, darbepoietin (one centre), vitamin supplementation and other less-common treatments. The route of erythropoietin administration was 78% IV at the pre-QIA and 100% IV at the post-QIA.

The second plan was the ‘Dialysis Doses CSMP’, which was implemented in three of four centres. It basically focused on longer dialysis time (three centres), blood and dialysate flow rates (three centres), access review (three centres) and a shift to more biocompatible dialysis membranes (one centre). Table 3 shows a dialysis dose decision aid example included in the ‘Dialysis Doses CSMP’.


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  		Table 3 Therapeutic approach of dialysis dose using an effective decision aid

 
The third approach was the ‘Osteodystrophy CSMP’. This plan was implemented in three of four centres and encompassed dialysis dose review, diet modifications, therapeutic compliance review (either medication or dialysis prescription), dialysate calcium concentration and phosphate binder, and vitamin D administration. During the study follow-up, neither paricalcitol nor cinacalcet was used at any centre; the exception was paricalcitol at one centre, where it has been used for five patients from February 2005.

The fourth approach was the ‘Nutritional CSMP’, which was implemented in one of four centres. This plan entailed checking the dialysis dose as well as haemoglobin, ruling out any underlying inflammatory disease and also involved the administration of oral supplemental nutrition (parenteral nutrition was not considered for the plan).

From the four centres, there were 313 patients (54, 47, 97 and 115 patients). The clinical indicators that we examined were collected from every attending patient at the study time. Table 4 provides the demographic characteristics of the patients and their clinical features.


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  		Table 4 Demographic characteristics and clinical features of patients

 
Table 5 shows the results corresponding to the timepoints of February 2003 (baseline, pre-QIA) and February 2006 (follow-up, post-QIA). The percentage of patients with haemoglobin (Hb) < 11 g/dl declined between the pre-QIA and post-QIA (29.2 versus 11.4; P < 0.0001) and the percentage of patients with Hb > 13 g/dl increased (19.3 versus 31.2; P < 0.0001). No differences were observed between pre-QIA and post-QIA for the percentage of patients with Hb between 11–13 g/dl, erythropoietin doses, percentage of patients with ferritin < 100 ng/ml, percentage of patients with ferritin > 800 ng/ml, albumin or vascular access type. All variables were normally distributed except for the erythropoietin, Kt/v and ferritin values.


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  		Table 5 Outcomes corresponding to February 2003 (baseline, pre-QIA) and February 2006 (final, post-QIA)

 


   Discussion
Top
 Abstract
 Introduction
 Results
 Discussion
References
 
Our study reinforces the idea that implementation of a multifaceted quality strategy may be useful in improving several haemodialysis outcomes such as anaemia, dialysis dose and calcium phosphate metabolism.

Centres showed improvement in the mean haemoglobin and the percentage of patients with haemoglobin <11 g/dl. This achievement was not associated with an increase in the erythropoietin used. In our study, several factors may have contributed to raising haemoglobin levels. First, the iron stores may play some role in patients with ferritin <200 ng/ml; second, the haemodialysis dose may have been increased, and inadequate dialysis doses are associated with a suboptimal response to erythropoietin therapy. We have reviewed other possible factors, such as blood transfusions or darbepoietin use, but excluded them. Use of darbepoietin showed changes only in one unit without a haemoglobin increase at this centre. On the other hand, during the study we did not set a standard for high haemoglobin levels but we encouraged the nephrologists to an individualized target. We subsequently reviewed that outcome, noting an increase in the percentage of patients with haemoglobin >13 g/dl. The clinical guidelines of the Kidney-Disease Outcomes Quality Initiative (K-DOQI) have recommended recently that the haemoglobin target should not be greater than that level, and our results underline the importance of its assessment [18]. Nevertheless, because the increase in values >13 g/dl is almost certainly a consequence of the effort to reduce the percentage of patients <11 g/dl, the outcome becomes almost inevitable; therefore, the National Kidney Foundation needs to fully discuss this recommendation publicly.

An important modifiable factor related to morbidity and mortality in haemodialysis patients is the dialysis dose (Kt/v) [19]. In our study, almost 90% of the patients had attained the Kt/v objective at the post-QIA, which may be considered a standard.

In terms of calcium–phosphorous metabolism, performance improved among the centres. The role of non-calcium-containing oral phosphate binders is not clear; we did not analyse the use through the follow-up period, but our plans involve encouraging use of non-calcium binders if calcium binders fail, as is recommended. Other new treatments—such as calcimimetics, which were not used during the study, or vitamin D analogues, which were only rarely used—might be an additional help in the future.

Although albumin is clearly related to mortality on haemodialysis, we could not identify any changes between timepoints in the centres. This outcome could reflect a difficult-to-modify patient factor, strongly associated with patient morbidity rather than with poor quality of care. The percentage of native arteriovenous fistulas was unaffected, probably because this performance data was already successful at baseline (80% of native fistulas), suggesting that this is a standard we ought to achieve.

Our study results suggest that some indicators are mainly related to quality of care (anaemia, dialysis dose, calcium phosphate metabolism and vascular access), but that others are less modifiable and probably strongly related to patient characteristics (e.g. albumin). Rocco et al. studied the relationship between annual mortality rates and four intermediate indicators in haemodialysis patients (they did not include calcium phosphate metabolism) [3]. The authors found that the failure to meet targets related to these outcomes was associated with an increase in mortality and hospitalization rates for every additional failure indicator. However, we should be cautious because the study did not determine whether the outcome resulted from patient factors or from poor quality of care. Other studies have not identified an improvement in albumin levels after an intervention [12,20]

The gap between performance based on the best available research evidence and performance based on current practice deserves careful attention. Our management strategy focussed mainly on barriers operating at the professional level through several tactical solutions, although others are still working across different levels (patient, organizational, structural, social, economic and political) [21,22]. Identifying the barriers and designing strategies to overcome them are essential steps in closing the evidence–practice gap.

Our study has several limitations. The first is its size. The number of centres participating was small, especially considering the great variability not only among patient conditions but also among centres in terms of abilities and other intangible aspects. Although the number of centres was low, the results are satisfactory, and we may consider testing the strategy at a larger number of centres. Second, this study did not have a control group. We did not randomize centre selection but included volunteer units; thus, there could be a bias because all centres were motivated to improve quality. Third, we did not consider confounding factors, such as changes in morbidity conditions. Finally, workers at participating centres were aware of the study hypothesis, and their data entry could have been affected by bias. For these types of studies, we suggest an independent or automated data entry system.

In conclusion, our data suggest that implementation of a multifaceted quality-improvement strategy can be useful in improving haemodialysis performance in terms of anaemia, dialysis dose and calcium phosphate metabolism. The importance of checking patients with a high haemoglobin level should be stressed. Nutritional status outcome, such as albumin, did not improve, possibly representing an only partially modifiable factor strongly dependent on patient morbidity rather than on quality of care. New QIAs should be designed and tested and possibly extended beyond such investigations and to general clinical practice.



   Acknowledgments
 
This study was fully funded by the Spanish grant (‘Fondo de Investigación Sanitaria’, identification number PI021581).

Conflict of interest statement. None declared.



   References
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 Abstract
 Introduction
 Results
 Discussion
 References
 

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Received for publication: 27. 7.07
Accepted in revised form: 11. 2.08


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