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1 The effect of optimising clinical performance measures on outcomes in haemodialysis patients Amy Bagatto 1, Nalayeni Errakiah 1, Mathan Munusamy 1, Rory McQuillan 2, Peter Conlon 2 1 RCSI medical student 2 Department of Nephrology, Beaumont Hospital, Dublin Abstract Background: Chronic kidney disease is a worldwide public health problem that is increasing in incidence and prevalence. In 1997, the Dialysis Outcomes Quality Initiative (DOQI) of the National Kidney Foundation (NKF) advised that certain performance measures be met to improve end stage renal failure patient survival. Aims: To examine whether attainment of the performance measures was associated with decreased mortality and hospitalisation in an Irish setting. The effect of comorbidities on survival and target attainment was also analysed. Methods: A one-year retrospective analysis of 304 haemodialysis patients attending Beaumont Hospital. The Charlson Comorbidity Index (CCI) was calculated to determine the effect of comorbidity on survival and attainment of the targets. Results: The number of targets met was positively correlated with survival rates (P=0.0028), and negatively correlated with both the percentage of patients hospitalised (P=0.0036) and the number of comorbidities (P=0.020). Conclusions: Our findings highlight the importance of quality of care in haemodialysis patients, and the need to implement strategies to ensure attainment of NKF-DOQI guidelines targets. Royal College of Surgeons in Ireland Student Medical Journal 2008; 1: Page 22 Volume 1: Number

2 Introduction Chronic kidney disease (CKD) is a worldwide public health problem that is increasing in incidence and prevalence, with poor outcomes and high costs. 1 Among Caucasians in the UK, around 100 patients per million population commence renal replacement therapy (RRT) each year. 3 The corresponding figure in African and Asian patients is three to four times higher, largely owing to diabetic and hypertensive nephropathy. 3 Haemodialysis (HD) has allowed longer survival of patients with end stage renal failure (ESRF). 4 The number of people commencing RRT is projected to increase from 340,000 in 1999 to 651,000 in Over the last two decades, mortality rates among ESRD patients on HD have declined; 4 however, they remain relatively high. In 2004, the mortality rate of HD patients in Europe was 14.8%, and in the United States it was 22.3%. 4 The Dialysis Outcomes Quality Initiative (DOQI) of the National Kidney Foundation (NKF) was started in an effort to improve ESRF patient survival, reduce patient morbidity, increase efficiency of care, and improve quality of life (QoL). 2 Their guidelines suggest that attaining clinical performance markers may increase survival in these patients. These markers, which are predictive of overall survival, include biochemical serum levels such as albumin and haemoglobin (Hb), 6,7 vascular access in the form of arteriovenous fistulas (AVF), and a measurement of dialysis adequacy using Kt/V, where K is treatment clearance, t is treatment time and V is the urea distribution volume. Because a major cause of deterioration in patients with ESRF is the accumulation of waste products normally excreted by healthy kidneys, 8 providing optimal HD has a direct effect on patients outcomes. Numerous studies have shown the beneficial effects of correcting anaemia on cognitive function, patient activity, QoL and left ventricular hypertrophy (LVH). 2 A high serum albumin is a marker for good nutrition, and this has been associated with better prognosis in HD patients. 7 Many studies have shown albumin to be the single most important risk predictor of mortality in patients with ESRF on RRT. 7,9,10,11 Lastly, AVF use in HD patients is associated with the lowest infection and thrombosis rates and the longest patency rates of any access modality. 12 Overall, attainment of these markers has been associated with improved QoL in patients with ESRF. 11,13 Most studies have examined the association between a single clinical performance measure of ESRF patients on dialysis, and subsequent morbidity and mortality rates. This approach fails to investigate whether attainment of multiple clinical measures is associated with better outcomes. Our study examined this hypothesis in an Irish context for the first time. We aimed to determine the association between hospitalisation, mortality rates, and the attainment of multiple guidelines in patients receiving HD. The Charlson Comorbidity Index (CCI) was also used to examine the association between ESRD patients comorbid conditions, and their effect on HD and overall survival. This study was conducted with the aim of improving clinical standards, quality of care, and overall survival in patients receiving RRT. TABLE 1: Charlson Comorbidity Index for HD patients. Score Condition 1 Myocardial infarction Congestive cardiac failure Peripheral vascular disease Cerebrovascular accident Dementia Chronic pulmonary disease Connective tissue disease Peptic ulcer disease Mild liver disease Diabetes mellitus without end organ damage Hospitalisations (if > than 8 days) 2 Hemiplegia Diabetes mellitus with end organ damage Tumour without metastasis Leukaemia Lymphoma 3 Severe liver disease 6 Metastatic solid tumour AIDS All patients were on HD and therefore have a minimum score of 2. For each decade >40 years of age, a score of 1 is added to the above score. Methods In a retrospective one-year analysis of 304 HD patients attending Beaumont Hospital, we examined whether attainment of multiple clinical performance measures was associated with better outcomes. Patients were included in the study if they were receiving in-centre HD for longer than six months prior to January 1, The NKF- DOQI guidelines recommend the following targets: mean serum albumin 37g/l; mean haemoglobin 11g/dl; mean Kt/V urea 1.2; and, the presence of a functioning AVF six months after beginning dialysis. 14 Study endpoints were defined as mortality and hospitalisations. We obtained these clinical data from medical records and from the computer-based data systems Clinical Vision and Proton at Beaumont hospital. Data on vascular access type was obtained through a review of discharge summaries, dialysis clinic progress notes, and dialysis flow sheets. We used the number of comorbidities present in each patient at the start of dialysis to calculate the CCI (Table 1). 15 Subsequently, we linked dates of death and hospitalisation to the clinical information in the database for analysis. Volume 1: Number Page 23

3 3 months 6 months Time 9 months 12 months 0-1 guidelines 2 guidelines 3-4 guidelines 3 months 0 guidelines 1 guidelines 6 months Time 2 guidelines 3 guidelines 9 months 12 months 4 guidelines FIGURE 1a: Survival curve based on the number of clinical guidelines met. Y axis = Proportion of patients surviving; X axis = Time FIGURE 1b: Hospitalisation curve based on the number of clinical guidelines met. Y axis = Proportion of patients not hospitalised; X axis = Time Statistical methods In calculating mean values for albumin, haemoglobin, and Kt/V, all the available data in the one-year time period was used. The data available varied between patients, as it was dependent on hospital visits over the one-year period. If only one value was found, the available data became the mean value for that patient. A log-rank test was used to examine the relationship between one-year survival and hospitalisations, and the number of guidelines attained. A Kruskal-Wallis one-way analysis of variance was used to examine the relationship between clinical guidelines attained, and both hospitalisations and comorbidities. Finally, to assess the effects of comorbidity alone on survival, we performed a log-rank test. Results Patient grouping Patients were grouped into categories depending on the number of clinical performance targets met over the one-year observation period. Of these patients, 8% did not meet any of the clinical criteria, 23% met one clinical target, 33% met two clinical targets, 27% met three of the clinical targets, and only 10% met all four targets. Survival curves for clinical targets Mortality rates for patients obtaining from 0 to 4 clinical indicators were 73%, 77%, 85%, 95%, and 95%, respectively. Patients were categorised into one of the following groups: 0-1 targets met; 2 targets met; and, 3-4 targets met. Figure 1a shows Kaplan-Meier survival curves based on the number of clinical performance targets attained. There is clearly a positive relationship between the number of guidelines attained, and length of patient survival. Hospitalisation rates A hospitalisation curve for clinical guidelines attained was plotted and showed an increase in hospitalisations for each additional target not attained (P=0.0036) (Figure 1b). The percentage of patients hospitalised during the one-year observation period in each of the five groups was 83%, 83%, 76%, 68%, and 52%, respectively. Length of stay A positive relationship was found between length of hospitalisation and the number of clinical targets attained. Inpatient length of stay for hospitalisation in days during the one-year period for patients obtaining 0 to 4 clinical indicators were 30, 32, 19, 22, and 13, respectively (P=0.0004) (Figure 2). Comorbid conditions Using the CCI, patients were grouped into the following four categories: 3-4, 5-6, 7-8, and >8, according to the number of comorbid conditions present at the start of HD (Table 1). A survival curve was then calculated, and showed a progressive reduction in survival with increasing CCI score (Figure 3). A score of 3 was recorded as a baseline score free of comorbidities because all patients have ESRF and were hospitalised for more than eight days. The one-year survival rates for the CCI score groups were 96%, 98%, 86%, and 85%, respectively (P=0.02). Figure 4 shows that patients with higher comorbidity scores attained fewer clinical performance measures. The mean CCI scores for patients attaining 0 to 4 targets were 8.3, 8.1, 8.0, 6.6, and 5.7, respectively (P=0.002). Discussion This study is the first in an Irish population to show, in HD patients, that there is a relationship between attaining multiple clinical performance measures, recommended by the NFK-DOQI Practical Clinical Guidelines, and mortality. Attainment of even one clinical performance target was shown to be associated with decreased mortality, fewer hospitalisations, and overall better outcomes. Using the CCI, our results show that comorbid conditions in ESRF patients can affect adequacy of dialysis, attainment of clinical targets and, subsequently, patient survival. This underlines the importance of providing high-quality patient care for those with ESRF. Page 24 Volume 1: Number

4 LOS months 6 months 9 months 12 months comorbidity 3-4 comorbidity 5-6 comorbidity 7-8 comorbidity >8 FIGURE 2: Inpatient length of stay based on number of clinical guidelines met. Y axis = Length of stay (days); X axis = number of guidelines met FIGURE 3: Survival curve based on Charlson Comorbidity Index. Y axis = Proportion surviving; X axis = Time Previous studies There have been several similarly designed studies of ESRF patients in the US and the UK. In a recent national US cohort study of 15,287 dialysis patients, Rocco et al showed a cumulative relationship between attaining clinical measures, reaching guideline recommendations, and improved outcomes of care. 16 The authors found that more than two-thirds of patients met two or fewer targets, and consequently had greater risk of both hospitalisation and death. A UK study of 523 ESRF patients showed that attainment of recommended standards for albumin and haemoglobin had a significant positive effect on patient survival. 17 In a prospective cohort study conducted by Plantinga et al, patients attaining a greater number of targets at six months had lower comorbidity and were more compliant with dialysis. 7 Lower mortality rates were found in patients attaining the albumin target and also in those attaining multiple targets; again highlighting the importance of albumin levels on survival. Effect of comorbid conditions In this study we demonstrate the major effect of comorbid illness on the survival of patients treated with RRT. This is important because, in addition to the devastating effects of the comorbid illness itself, how it interferes with the patient s ESRF is crucial in predicting patient outcome. For example, peptic ulcer disease, liver disease and peripheral vascular disease can affect patients haemoglobin, serum albumin, and vascular access, respectively. 17 We found that increased comorbidity was associated with poorer outcomes. Patients who scored eight or more on the CCI had a one-year survival rate of 86%, compared with 96% in those with no comorbidities (P=0.002). Secondly, we found that patients with more comorbidity were likely to have attained fewer clinical targets. This highlights not only the importance of comorbidity in ESRF patients, but also the necessity of finding a practical method of recording it. This information becomes important for healthcare providers when discussing realistic goals and survival with RRT patients. Charleston co-morbidity score FIGURE 4: Association between Charlson Comorbidity Index and guidelines met. Y axis = CCI; X axis = number of guidelines met Lack of adherence Our results are in accordance with similar studies demonstrating a link between attaining clinical targets and improved health outcomes. Patients who met none of the clinical performance measures had a greater number of hospitalisations and a higher mortality rate. This demonstrates the importance of determining why targets were not attained, as well as implementing new standards to optimise patient care, and in particular to ensure that optimal haemoglobin and albumin levels are present prior to dialysis. How to determine whether quality of care is in fact suboptimal for these patients remains a challenge; however, many methods have been used and are being attempted to increase performance levels. For example, providing performance results to patients using a reward system, and evidence of its link to outcomes, may stimulate improvement. 18,19 Factors other than patient quality of care must be considered as part of the explanation for fewer clinical targets met. Patient Volume 1: Number Page 25

5 characteristics including noncompliance, socioeconomic factors, and the presence of comorbid conditions make attaining multiple clinical targets more problematic and unlikely. These factors may not allow for attainment of the targets with exposure to the same quality of care, or may need more than six months to start seeing improvements. 7 Psychiatric problems also affect patient outcomes. ESRF patients suffering with depression have higher morbidity and mortality. 20 Limitations This was a retrospective study and therefore no causal relationships can be determined, only associations drawn between attaining clinical performance measures and medical outcomes. The power of our study is reduced due to the relatively small sample size from one hospital. Our small sample size precluded us from performing multivariate analysis to determine which clinical marker had the greatest impact on survival. Also, the severity of comorbid conditions was not assessed, and so their true effects on survival could not be established. Conclusion This study strongly suggests that clinical performance target attainment is associated with better outcomes. Our results show that in an Irish patient population, there is a cumulative effect in attaining multiple clinical performance measures on patients morbidity and mortality. We have also demonstrated the effects of comorbid illness on patient survival, highlighting the need to adopt a universal method for recording these illnesses to adequately assess HD patients. Our findings emphasise that special care must be given to patients with comorbidities, and that further time and resources should be directed at ensuring attainment of the NKF-DOQI guidelines. References 1. Levey AS, Coresh J, Balk E, Kausz AT, Levin A, Steffes MW et al. National Kidney Foundation practice guidelines for chronic kidney disease: evaluation, classification, and stratification. Ann Intern Med 2003; 139 (2): Levin N, Eknoyan G, Pipp M, Steinberg E. National Kidney Foundation: Dialysis Outcome Quality Initiative development of methodology for clinical practice guidelines. Nephrol Dial Transplant 1997; 12 (10): Kumar P, Clark M. Clinical Medicine, Philadelphia, Elsevier Limited, Morsch CM, Goncalves LF, Barros E. Health-related quality of life among haemodialysis patients relationship with clinical indicators, morbidity and mortality. J Clin Nurs 2006; 15 (4): United States Renal Systems Data, Excerpts from the 2000 US Renal Data System Annual Data Report: Atlas of End Stage Renal Disease in the United States. Am J Kid Dis; 36: Aujesky D, Auble TE, Yealy DM, Stone RA, Obrosky DS, Meehan TP et al. Prospective comparison of three validated prediction rules for prognosis in community-acquired pneumonia. Am J Med 2005; 118 (4): Plantinga LC, Fink NE, Jaar BG, Sadler JH, Levin NW, Coresh J et al. Attainment of clinical performance targets and improvement in clinical outcomes and resource use in hemodialysis care: a prospective cohort study. BMC Health Serv Res 2007; 7 (1): Vanholder R, De Smet R. Pathophysiologic effects of uremic retention solutes. J Am Soc Nephrol 1999; 10 (8): Goldwasser P, Mittman N, Antignani A, Burrell D, Michel MA, Collier J et al. Predictors of mortality in hemodialysis patients. J Am Soc Nephrol 1993; 3 (9): Owen WF Jr, Lew NL, Liu Y, Lowrie EG, Lazarus JM. The urea reduction ratio and serum albumin concentration as predictors of mortality in patients undergoing hemodialysis. N Engl J Med 1993; 329 (14): Goldman RS. Improving serum albumin levels in hemodialysis patients by a continuous quality improvement project. Adv Ren Replace Ther 2001; 8 (2): Anel RL, Yevzlin AS, Ivanovich P. Vascular access and patient outcomes in hemodialysis: questions answered in recent literature. Artif Organs 2003; 27 (3): Leon JB, Albert JM, Gilchrist G, Kushner I, Lerner E, Mach S et al. Improving albumin levels among hemodialysis patients: a communitybased randomised controlled trial. Am J Kidney Dis 2006; 48 (1): NFK-K/DOQI guidelines Anaemia of chronic kidney disease: II. Anemia work-up: Guidelines 1, 2, 3. Online at doqi/13449 [cited January 15, 2007]. 15. Di Iorio B, Cillo N, Cirillo M, De Santo NG. Charlson Comorbidity Index is a predictor of outcomes in incident hemodialysis patients and correlates with phase angle and hospitalisation. Int J Artif Organs 2004; 27 (4): Rocco MV, Frankenfield DL, Hopson SD, McClellan WM. Relationship between clinical performance measures and outcomes among patients receiving long-term hemodialysis. Ann Intern Med 2006; 145 (7): Metcalfe W, Khan IH, Prescott GJ, Simpson K, Macleod AM. End-stage renal disease in Scotland: outcomes and standards of care. Kidney Int 2003; 64 (5): Longo DR, Land G, Schramm W, Fraas J, Hoskins B, Howell V. Consumer reports in health care. Do they make a difference in patient care? JAMA 1997; 278 (19): Longo DR, Everet KD. Health care consumer reports: an evaluation of consumer perspectives. J Health Care Finance 2003; 30 (1): Wuerth D, Finkelstein SH, Finkelstein FO. The identification and treatment of depression in patients maintained on dialysis. Semin Dial 2005; 18 (2): Page 26 Volume 1: Number