Stewardship or clinical freedom? Variations in dialysis decision making

Frank Kee1,, Chris C. Patterson1, E. Ann Wilson2, Janice M. McConnell3, Seana M. Wheeler3 and John D. Watson3

1 Department of Epidemiology and Public Health, the Queen's University of Belfast, 2 Department of Public Health Medicine, Eastern Health and Social Services Board, Belfast, 3 Department of Public Health Medicine, Northern Health and Social Services Board, Ballymena, UK,



   Abstract
 Top
 Abstract
 Introduction
 Subjects and methods
 Results
 Discussion
 References
 
Background. It is generally agreed that acceptance criteria for dialysis have varied and changed over time and that implicit rationing, to some extent forced on clinicians by limited capacity, has been widely practised. Our objective was to study the basis and extent of variation in dialysis decision making among nephrologists in one NHS region.

Design and methods. In a clinical judgement analysis, linear regression models were employed to reflect the impact of clinical and non-clinical cues on nephrologists’ decisions to offer dialysis to 60 ‘paper patients’ under current capacity constraints and under an assumption of no capacity limit. A short questionnaire was also completed by eight nephrologists to elicit their expressed decision drivers, which were subsequently compared with those tacitly derived from the appraisal of the 60 clinical vignettes.

Results. Doctors showed substantial variation in their propensity to offer dialysis and in their perceptions of the benefits of dialysis. Even for the five patients where the discordance in propensity to offer dialysis was least, the range in perceived gain in life expectancy was from 24 to 264 months (mean 91 months). The decision models had relatively good explanatory power with an average r2 of 0.67 (0.39–0.90) and 0.70 (0.47–0.95) for decisions made under current capacity constraints and under an assumption of no limit capacity respectively. Surprisingly, for most doctors, the patient's age had very little impact on dialysis decisions but the magnitude of the beta-coefficients for the patient's mental state (mean -30.7) was of a similar order of magnitude to the coefficient for the principal ‘renal’ drivers (e.g. the mean coefficient for uraemic symptomatology under current capacity constraints was 47.7). The influence of other non-renal factors on the doctor's likelihood to offer dialysis was largely independent of the capacity assumption. A comparison of the doctor's stated decision drivers with those tacitly derived from their decision models showed only modest correlation.

Conclusions. The extent to which doctors vary in their propensity to offer dialysis is substantial. Very few non-clinical cues appear to influence the decision to offer dialysis. The most important non-renal factor in determining dialysis decisions was the patient's mental state.

Keywords: analysis; clinical; decision making; dialysis; judgement



   Introduction
 Top
 Abstract
 Introduction
 Subjects and methods
 Results
 Discussion
 References
 
In 1993 the Department of Health, in its Health of the Nation strategy, accorded a relatively high priority to services for end-stage renal failure by setting a target acceptance rate for renal replacement therapy (RRT) of 80 new patients per million. Though the UK rate in the early 1980s was barely 30 per million, the challenge to achieve the new target varied across the country. It is generally agreed that acceptance criteria also varied and have changed over time and that implicit rationing [1,2], to some extent forced on clinicians by limited capacity, has been widely practised. This can only get worse, as RRT is expensive and the number of patients receiving such treatment in England is predicted to rise by 50–100% over the next 15 years [3]. However, despite the regional and international variation in the preferred treatment modality (haemodialysis (HD) vs continuous ambulatory peritoneal dialysis (CAPD)), a recent systematic review concluded that available data did not permit reliable conclusions to be drawn about their relative effectiveness or efficiency [4].

The Renal Association has recognized the dilemma and urged nephrologists to agree local guidelines to ensure that all patients are offered treatment appropriate to their needs. The Association has not specified how local consensus might be secured, but a Health Technology Assessment report has recently examined the advantages and disadvantages of a number of options [5]. In it a call is made for more explicit methods of quantitative judgement analysis. As a first step in developing consensus in one region serving a population of 1.6 million, we have used clinical judgement analysis to better describe the basis of local dialysis decision making, to establish whether differences between clinicians arise out of differences in how they attend to non-renal and demographic factors and to highlight how the important determinants might be affected by the perceived resource constraints.



   Subjects and methods
 Top
 Abstract
 Introduction
 Subjects and methods
 Results
 Discussion
 References
 
Patients and population
Northern Ireland is served by a team of six nephrologists based at the Regional centre in the Belfast City Hospital (with access to 34 stations providing three shifts per day) which networks closely with three other consultant-led sub-regional facilities (with access to a combined total of the equivalent of 19 stations operating three shifts per day) based in provincial towns between 20 and 60 miles away. Over a 10-year period between 1987/88 and 1997/98 the annual acceptance rate for renal replacement therapy in Northern Ireland rose from 40 per million to 109 per million. Over this period a detailed clinical database, which meets and surpasses all the requirements of the European Dialysis and Transplant Association, has been held by the regional unit on all patients entering the RRT programme. From this sampling frame we drew a random sample of 100 sets of case-notes from patients registered over the last 5 years. From those with sufficient information we devised a series of 60 ‘paper cases’ or clinical vignettes. Ten of these were duplicates. Two consultant nephrologists advised on the design of the vignettes. We deliberately included six cases who had been referred but not accepted onto the RRT programme. For each case, a short series of questions was posed on which eight doctors were to give a view, including their perception of the benefits of RRT for the quality and duration of life of that patient and the likelihood (on a visual analogue scale) of their offering dialysis to that individual under current capacity restraints and under an assumption of unlimited capacity. One of the nephrologists pre-piloted the exercise to assess the clarity of the task and the length of time required for its completion. An example of one of the ‘paper’ cases is given in Figure 1Go. Prior to their assessment of the vignettes, each nephrologist was asked, in a short questionnaire, to attempt to specify the relative impact that various clinical cues had on their decisions to offer dialysis.



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Fig. 1. One of the ‘paper’ cases.

 

Statistical methods
Multiple regression analysis was used to express the relationship between judgements about the likelihood of offering dialysis and the demographic and clinical cues describing the cases. Stepwise (backwards) elimination of variables was used to select these for the decision making model. To minimize the risk of rejecting cues inappropriately, we set a relatively conservative P value of 0.10. The contribution of each cue to the model is represented by its contribution to r2, which is assessed by dropping each variable in turn from the model (the change in the type II sum of squares, cr2). We also compared equations from different judges in terms of the cr2 relative to that of all the other cues in the equation (rcr2)—a method that standardizes for variation in the models explanatory power. Though neither method overcomes entirely the problem of collinearity, the rank order of importance of the cues in the decision models was not changed. The regression coefficients represent the strength of the effect on the dependent variable. Categorical variables (with n categories) were fitted when appropriate, using n–1 dummy variables.



   Results
 Top
 Abstract
 Introduction
 Subjects and methods
 Results
 Discussion
 References
 
There was a substantial variation in the doctors' propensity to offer dialysis to these patients. Figure 2aGo illustrates this for the five patients where the discordance among the doctors was greatest. Figure 2bGo shows a similarly broad range in their perception of the capacity of dialysis to affect the patient's quality of life. Interestingly, of the six patients included in the case series who had been referred for assessment but not actually offered dialysis, five would have been offered dialysis by at least one of the eight participating doctors. Despite the wide between-doctor variation, the intra-class correlation coefficients for the duplicate cases were all quite high (mean 0.87).



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Fig. 2a. Most discordant cases concerning doctors’ propensity to offer dialysis under current capacity constraints.

 


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Fig. 2b. Doctors' perception of enhanced quality of life for five cases with least discordance concerning doctors' propensity to offer dialysis.

 
There was a similarly wide range in the perceptions of the benefits of dialysis for such patients. For example, even for the five patients where the discordance in propensity to offer dialysis was least, the range in perceived gain in life expectancy was from 24 to 264 months (mean 91 months). Two of our participants felt that they might have contributed disproportionately to the variation across doctors by being more inclined to interpret the judgement as the patients’ immediate need to commence dialysis on the same day. This impression was not borne out by comparing the distribution of their responses with those of the other six doctors (data not shown).

A model was then derived in respect of each nephrologist's decisions to offer dialysis, firstly under current capacity constraints and then under an assumption of no capacity limit.

Table 1Go provides the results for each of the eight doctors while Figure 3Go shows the range in the magnitude of the beta coefficients for the clinical cues. While the range in coefficients is apparent from the Figure, comparing individual doctors under the two capacity assumptions is difficult. Table 2Go thus shows how the change in assumed capacity affected individual doctors for the main non-renal patient cues (mental state, independence in daily living, distance from the dialysis centre and co-morbidity). What is immediately apparent from this table is that the impact of the patient's mental state vastly outweighs that of the other non-renal factors. Also, for several doctors, for whom one of these non-renal factors had a significant bearing on the likelihood of offering dialysis, the effect disappeared under the ‘no capacity limit’ assumption.


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Table 1. Contribution to regression variance

 


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Fig. 3. Beta-coefficients for clinical cues in decision models. Age per decade of age; Indep: independence, Needs outside help or assistance from family vs functioning on own; Mental, mental state, temporarily on permanently confused vs normal for age; Travel, travel time >60 vs <=60 min; Ursympt, uraemic symptomatology yes vs no; Haemgl, haemoglobin, per g/dl; Creatin, serum creatinine, per 100 µmol/l; Refrahtn, refractory hypertension, yes vs no; Diabetes, history of diabetes, yes vs no; Heartdis, history of heart disease/coronary artery disease/PVD, yes vs no; Hepb, hepatitis B and HIV, yes vs no.

 

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Table 2. Unstandardized beta-coefficients for non-renal cues

 
Finally, in Figure 4Go we have compared the doctors stated or expressed decision drivers, under current capacity constraints, with those tacitly derived from their decision model. Ranking the cues in order of importance (in terms of their contribution to that doctor's decisions), we then calculated the Spearman's rank order correlation coefficient between the stated and tacit models. In this latter case we felt it more logical to use a fully saturated model (and the respective contribution to the total sum of squares) as the questionnaire to obtain the expressed weights had asked the doctors to ensure summation to 100. As the figure and the coefficients in Table 3Go show, the degree of concordance is modest.




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Fig. 4. Comparison of doctors' expressed and derived decision drivers under current capacity constraints.

 

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Table 3. Spearman's rank order correlation coefficients between stated and tacit decision models

Current capacity constraints

 



   Discussion
 Top
 Abstract
 Introduction
 Subjects and methods
 Results
 Discussion
 References
 
We have shown that even in a single region (where weekly team meetings and postgraduate seminars are the norm) the degree of variation in dialysis decision making is considerable. A reduction in interpersonal variation in judgement is an essential pre-requisite to co-operative decision making and the use of clinical decision analysis to reveal the systematic element of these variations, as described in the recent HTA report [5], seems to provide an avenue for reaching agreed policies. While some might criticize the ostensibly ‘artificial’ method of appraising paper patients, several studies have demonstrated that judgements made in response to paper cases resemble those made with actual patients and that ‘process’ or ‘cognitive’ feedback (i.e. revealing cue weights) can surpass the agreement between clinicians that mere discussion and exchange of ideas might achieve [6].

It might be argued that a shortcoming of our study is that we have only investigated clinical behaviour among consultant nephrologists in one region but it would be very difficult to argue that the variation in decision making is likely to be less in a larger national sample. All but one of the consultants in this study had undertaken some of their postgraduate training in centres outside Northern Ireland, both in Britain and North America and this is not uncharacteristic of many of today's specialists.

Several previous attempts have been made to describe and explain variations in decisions to initiate or withdraw dialysis. One recent study has described the area-level variations in uptake across England for 1991/92 and related this to the characteristics of the populations served [7]. The authors showed that females and particularly elderly females were significantly under-represented and that rates of uptake were lower among populations living further from dialysis centres. The methodology adopted, however, was not designed to study the nephrologists’ decision making for individual cases and it is possible that some of the variation observed reflects on referral practice rather than decision making in the dialysis centres.

In one form or another many other studies have used written case scenarios to assess differences in decision making. Most have surveyed large numbers of clinicians (often several hundred) but, probably for logistical reasons, have used only a limited number of scenarios with a limited number of clinical descriptors or cues [1,810]. The use of only a few case scenarios does not permit the impact of various decision drivers to be studied nor the characteristic decision-making style of the clinicians to be compared. The recent Technology Assessment Report on consensus development has stressed the importance of the representativeness of cue selection to the outcome of the process, but the problem with asking doctors directly is that even specialists are poor at identifying the key influences on their decisions [11,12]. While most can name their perceived decision drivers, our results bear out the fact that few can accurately judge their relative impact and frequently the number of key influences is over-estimated.

Though the Renal Association in the UK has to date eschewed explicit guidelines or standards, in favour of a broader agreement on ‘principles’, there have been previous attempts at guideline development, notably in the US (well reviewed by Moss [13]) by Hirsch et al. [14] and by Lowance [15]. In those suggested by Lowance, the doctor is required to estimate the degree to which the life expectancy of the subject might be reduced by other co-morbidity. Even for the cases where discordance among our doctors was least there was substantial variation in the estimates of the patients’ life expectancy. This bears out a growing literature which points to the rather modest and variable prognostic abilities of both specialists and generalists alike and the generally low correlation between the confidence in and accuracy of clinical prediction, irrespective of experience [16]. Interestingly, the contribution of co-morbidity to the decision-making models of our doctors was very small and was generally over-estimated by most of them. This contrasts markedly with an early 1980 attempt to describe the factors underpinning dialysis rejection decisions both in Belfast and London [17].

There has been recent debate in the pages of the British Medical Journal on the critical part played by the patients’ expected survival probabilities and their effect on the extent of or the need for dialysis ‘rationing’ [18,19]. In practice, in Northern Ireland at least where the acceptance rate is now >100 patients per million, the patients’ mental state seemed to have the most significant bearing on decision making. It may be that in other regions where capacity differs, other clinical cues might predominate. For the five patients in our sample with the most compromised mental state, several of the clinicians opted to offer twice-weekly rather than three times weekly dialysis. One might suppose that judgements were being made that had at least something to do with collective as well as individual welfare maximization. Part of the problem may not be one of ethical conflict but an ‘informational’ shortcoming of the system. This has been borne out by a more recent decision analysis [20], which also demonstrated that a key influence on the maximization of graft quality-adjusted survival was the perceptions of quality of life while on the various types of chronic renal replacement. Our results demonstrate how variable were the doctors’ views of the quality-of-life enhancement offered by dialysis. Though we are not aware of any material evidence on the matter, one wonders whether a more collective and co-operative model of clinical decision making, such as that proposed for cancer therapy, would be either feasible or effective in renal medicine.



   Acknowledgments
 
The Northern Health and Social Services Board provided funding for a research nurse who abstracted the data for this study.



   Notes
 
Correspondence and offprint requests to: Professor Frank Kee, Department of Epidemiology and Public Health, Mulhouse Building, Royal Victoria Hospital, Belfast BT12 6BJ, UK. Back

On behalf of the Northern Ireland Nephrology Forum. The participating members are: Dr Henry Brown, Dr C. Doherty, Dr P. Garrett, Dr C. Harron, Dr J. Harty and Dr A. P. Maxwell. Back



   References
 Top
 Abstract
 Introduction
 Subjects and methods
 Results
 Discussion
 References
 

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Received for publication: 13. 9.99
Revision received 16.12.99.



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