Determinación de nuevas rutas

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6.1

Introduction

The aim of this research was to investigate the use of primary care data to support targeted cardiovascular risk reduction. The more specific objective of the e-Nudge trial was to test the effect of electronic reminders designed for the same purpose in the routine general practice environment. Some of the methodological details discussed

here are adapted from the e-Nudge study protocol published in the journal Trials (1),

and others from the final trial report (2). The first of these publications (describing the design and methods) was aligned where possible with the revised CONSORT statement of 2001 (3). The final report was also guided by a more recent extension of the statement specifically for pragmatic trials published in November 2008 (4).

6.2

Outline of the e-Nudge trial design

6.2.1 Hypotheses

I hypothesised that an automated system of electronic reminders drawing on routinely collected primary care data would improve the visibility of the population at risk of cardiovascular disease (CVD), the adequacy of cardiovascular risk factor data, and ultimately cardiovascular event rates.

6.2.2 Setting

The trial was set in the routine environment of primary care in the United Kingdom (UK).

6.2.3 Participants

The over 50 year old population registered with 19 general practices in the West Midlands of the UK.

6.2.4 Intervention

The ‘e-Nudge’ software tool was designed to extract data from practice systems and generate regularly updated lists of patients in six groups. These were based on estimated risk of cardiovascular disease, adequacy of data to support risk estimation, the need for clarification of diabetes status, and persistently raised blood pressure in patients over 75 years. Intervention patients currently identified in any of the lists received a screen reminder whenever their electronic record was accessed. Practice teams could examine the lists of identified patients if they wished and were reminded about them every eight weeks by an email sent to a nominated team member. Responses to the notification mechanisms (lists and screen reminders) were entirely optional.

6.2.5 Control condition

The control arm would receive usual care without the assistance of e-Nudge software, but practitioners would employ their usual means of assessing cardiovascular risk, including the use of currently available software tools. The use of such tools in UK practice during the trial was not very commonplace. All practitioners involved in the trial were in receipt of the British National Formulary (updated every six months), which contains risk charts recommended by the Joint British Societies for calculating cardiovascular risk.

6.2.6 Outcomes

2. Proportion of the trial population identified in each of the e-Nudge Groups at the end of the study. The mean of the proportions in the final three eight-weekly data captures would be used to define this outcome.

6.2.7 Duration of the study

Twenty-four months.

6.2.8 Analysis

By intention to treat, using Poisson inference techniques for cardiovascular event rates (primary outcome), and Chi-squared tests for Group differences (secondary outcomes).

6.2.9 Quality assurance

A sub-study of the trial population was used to confirm the validity of the primary outcome search techniques.

6.3

The e-Nudge algorithm

The e-Nudge algorithm was only part of the e-Nudge intervention. A flow diagram defining the e-Nudge Groups 1-6 is given in Figure 1. The code sets were wherever possible the same as for the quality and outcomes framework (QOF). The inputs were based on the most recent values of the risk variables (or in the case of systolic blood pressure, an average of up to three recent values). I chose this option over alternatives (e.g. pre-treatment levels, where available, or highest ever level) for two main reasons. First of all, there was an increasing move to base risk estimates on current data rather than ‘pre-treatment’ data, recognising that for many patients it will not be clear enough which category an electronically retrieved measurement fits. In principle it is possible for a general practice computer to identify the date of onset of anti-hypertensive or lipid lowering drug therapy within that practice, but the patient might have already been

taking such therapy when registering with the practice. In other cases, the pre-treatment levels may pre-date the establishment of electronic records, making retrieval of the original levels impossible. This is an example of compromise (as both JBS2 and the later NICE CG67 stated a preference for pre-treatment values if available) due to the limitations of the source data. Its main implication was that a patient currently on treatment for either blood pressure or cholesterol might have their true risk under- estimated by e-Nudge. For this reason, I was careful to emphasise to e-Nudge users that the software was a case-finding tool, not a definitive risk scoring algorithm. Estimated risk should be confirmed by practitioners if they considered it necessary. This might also include the use of other factors not included in Framingham but recommended in JBS2 for correcting the estimate. These factors include male Asian ethnicity, family history of CHD, impaired glucose regulation, hypertriglyceridaemia, low physical activity, and impaired renal function. The e-Nudge would be unlikely to actually over- estimate risk, as most corrective factors inflate rather than reduce the initial estimate, and this was one of its strengths. A patient identified as at high risk would be unlikely to have a risk estimate less than 20% following the inclusion of corrective factors, and the use of most recent blood pressure and cholesterol data made this even less likely if these values were taken on treatment.

The second reason for using most recent levels as a basis for risk estimation was related to the trial design itself. The impact of the e-Nudge reminders on the Group proportions would be evident through the trial by determining the change in estimated risk based on these most recently recorded data. Action taken on the basis of a reminder (e.g. measurement of blood pressure in a patient requiring this to complete the risk profile, or correction of raised blood pressure by drug therapy) would only be evident if the most recent measurements were used.

A related issue is the existence of patients with diagnoses that have later resolved. The only disease diagnoses relevant to e-Nudge were those of ischaemic heart disease, cerebrovascular disease, and diabetes. In the first two cases, a patient is always

considered to be eligible for indefinite follow up if the diagnosis is confirmed and will remain on the register. This also applies to most cases of diabetes, but there are occasions when diabetes may arise temporarily (e.g. during high dose corticosteroid use) and later be considered to have resolved. The QOF recognises a code for ‘Diabetes resolved’ and this is included in the definition of QOF Diabetes disease registers. The e- Nudge was therefore able to incorporate this as well as it drew on QOF registers for this and the vascular diagnoses.

6.3.1 Framingham algorithm

The Framingham CVD algorithm as defined by Anderson et al (5) was used to identify patients for Groups 2 and 3 (later termed B and A). It has the following structure:

μ = Σ βixi

σ = exp(θ0+θ1μ)

u = (ln(t)-μ)/σ

p= 1 - exp(-exp(u))

where

βi = the coefficient for each risk variable xi

xi = the value of the corresponding risk variable

θ0 and θ1 = constants

e = the base of the natural logarithm

t = timescale for the risk estimate (10 years for this trial)

p = the probability of a cardiovascular event within this

timescale

(not CHD, Acute MI, CHD death, stroke, or CVD death functions, which have different coefficient values). The risk threshold used to identify patients in this study is >20% in 10 years, which was the threshold recommended by the BHS Guidelines (6) and the JBS2 report (7).

6.3.2 Groups identified by e-Nudge

Six groups 1-6 (later just four, relabelled A-D) were identified automatically using patient data and were updated every 24 hours to take account of new information. The search protocol is described in Figures 6.1-6.3. The original six groups can be summarised as:

Group 1: Patients over 50 years with existing cardiovascular disease or diabetes, whose