Read Core Topics in General & Emergency Surgery: Companion to Specialist Surgical Practice Online
Authors: Simon Paterson-Brown MBBS MPhil MS FRCS
Core Topics in General & Emergency Surgery: Companion to Specialist Surgical Practice (10 page)
Pain is a common and important symptom of many medical conditions and deserves special consideration. While many generic and condition-specific instruments dedicate specific dimensions to the measurement of pain, there are also a number of instruments designed specifically to assess levels of pain. The measurement of pain cannot be directly assessed through clinical measures (e.g. blood samples) and, in the absence of such objective approaches, it is necessary to assess pain subjectively through the patient's own perceptions.
Subjective measures allow for reproducible results provided that the tools used to assess pain are measured appropriately. While subjective measures for the measurement of pain have many advantages over other instruments, there are problems in assessing subjects for whom communication is difficult. Examples include very young children and patients who are incapable of expressing how they feel. Those patients who are unconscious or who are terminally ill will continue to present particular difficulties for those attempting to assess levels of pain.
As with other outcome measures, options exist to use binary, categorical or visual analogue scales. Within this area, there are both pain-relief scales and pain-intensity scales to be considered. There are often occasions when it will be necessary to measure the state of the pain rather than the effect of a particular intervention or therapy, and in these circumstances pain-intensity scales will be appropriate. Pain-relief scales may well encompass more than an intensity scale, as any side-effects resulting from an intervention, such as dizziness, might be included in such measurements. Pain-relief scales also require the patient to make a judgment as to how the current pain compares with remembered pain, before the intervention. This might make such scales more complicated for patients to grasp compared with intensity scales and this may raise doubts about validity. In situations where both pain-relief and pain-intensity scales are appropriate, a decision is required regarding which one should be used.
Pain can manifest itself in a variety of qualities, and one of the most widely used tools is the McGill Pain Questionnaire.
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This is a generic instrument, designed primarily for adults, which was developed to specify the qualities and intensities of pain; as such, it is intended to provide a quantifiable profile of pain. The questionnaire can be completed by the patient or administered in an interview. Completion takes roughly 15–20 minutes but becomes quicker on subsequent applications. The instrument contains 78 pain descriptor words, which are grouped into 20 subclasses, each of which contains two to five words that describe pain on an ordinal scale. These words are arranged to reflect three dimensions of pain: sensory, affective and evaluative. The questionnaire can yield three indices of pain: a pain-rating index based on the scale values of the words chosen by the patient; a rank score using the rank values within each subgroup chosen; and the total number of descriptions chosen by the patient.
The McGill Pain Questionnaire has been thoroughly investigated in recent years and the instrument has proved to be the most reliable in applications to patients with moderate-to-severe chronic or acute pain. It has been shown to be particularly useful in disaggregating explained pain from unexplained pain. When the instrument was used in a cohort of cancer patients with lymphoedema, it proved to be more sensitive than categorical or analogue scales.
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In those patients who are critically ill or unconscious, Behavioural Pain Score (BPS) was developed to assess response to pain. BPS is based on three items: facial expression, movement of upper limbs and compliance with mechanical ventilation. Each of these items is graded with values from 1 to 4, 1 being no response and 4 being full response, giving a minimal score of 3 and maximal score of 12.
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Over the last decade BPS has been validated in a number of studies.
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Recent years have witnessed quite an upsurge in interest in the measurement of health-related quality of life, much of which has no doubt been stimulated by the analytical demands of researchers and the need for outcome information as a basis for policy decisions.
Such health status measures are standardised questionnaires, or instruments, which are used to evaluate patient health across a broad range of areas. These areas include symptoms, physical functioning, mental well-being, work and social activities. Measures can be either generic or condition specific, and such measures can generate a profile of scores or a single index. These scores can be based upon people's preferences (e.g. EQ-5D) or, more usually, arbitrary scoring procedures (e.g. SF-36, assumes equal weighting for most items).
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Four of the most frequently used instruments are discussed in more detail below.
Of those instruments that generate a single index, EQ-5D has been rapidly adopted and widely used.
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The measure was developed by a group of researchers from seven centres across five countries.
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The present measure has five dimensions, whereas the original version had six. Patients are classified by the completion of a five-item questionnaire. The EuroQol is a brief, easy-to-use questionnaire of two pages. It can be made even simpler by using the one-page descriptive classification. Self-completion, or interview, usually only takes a matter of minutes and response rates tend to be extremely high.
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The five dimensions of the EQ-5D comprise mobility, self-care, usual activities, pain/discomfort and anxiety/ depression. Each of the five questions has three levels; therefore, combining the five questions defines 243 health states. In addition, the instrument contains a visual analogue scale: a thermometer scale calibrated from 0 (representing ‘worst possible health state’) to 100 (representing ‘best imaginable health state’).
The EQ-5D is now one of the most widely adopted methods for measuring health-related quality of life and is the method favoured by the National Institute for Health and Clinical Excellence (NICE) for deriving utility weightings for cost-effectiveness analysis (see below). It has been used in a wide variety of clinical areas, many pharmaceutical companies now include it as a standard outcome measure in clinical research, and the questionnaire has been translated into many different languages and validated in a number of different countries.
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The SF-36 is a self-administered questionnaire composed of 36 items. It measures health across eight multi-item dimensions, covering functional status, well-being and overall evaluation of health. Responses within each of the dimensions are combined in order to generate a score from 0 to 100, where 0 represents ‘worst health’ and 100 indicates ‘best health’. Dimension scores should not be aggregated into a single index score. The questionnaire takes about 5 minutes to complete, attains good response rates and is suitable for completion by the patients themselves or for administration by trained interviewers on a face-to-face or telephone basis.
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The questionnaire has been used in a variety of settings, administered on differing populations and exhibits good psychometric properties. Detailed information regarding the scoring process and computation are readily available, as are published data for comparative norms. The question complexity can be a problem in situations where the sample comprises individuals with low education levels, but otherwise the instrument is suitable for administration in a wide range of settings.
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This is a newer health measure, which is derived from various items of SF-36 or SF-12 and provides a single index measure.
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It comprises six multi-level dimensions and describes 18 000 health states. The SF-6D measure can be derived for any individual patient completing SF-36 or SF-12. A standard gamble technique has been used to obtain parametric and non-parametric preference weights from a sample of the general population who were asked to rate a variety of health states. These weights are modelled to predict health states obtained from SF-6D. This measure, however, may not be readily used in all countries as preferences vary between people living in different countries. Currently, SF-6D preference weights are available for the UK, Australia, Brazil, Hong Kong, Japan, Portugal and Singapore.
The Nottingham Health Profile (NHP) measures levels of self-reported distress. The instrument consists of two parts, each of which can be used independent of the other. Part 1, the most frequently used component, comprises 38 statements that are grouped into six sections: physical mobility, pain, sleep, social isolation, emotional reaction, and energy. The number of statements in each of these sections varies from three for the energy dimension to nine for emotional reaction. The second part of the instrument asks respondents to indicate whether or not their state of health influences activity in seven areas of everyday life: work, looking after the home, social life, home life, sex life, interests and hobbies, and holidays. Responses for both parts 1 and 2 are yes/no responses.
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Scoring is straightforward, with 0 assigned to a ‘no’ response and 1 to a ‘yes’ response. Scores for each of the sections range between 0 (‘worst health’) and 100 (‘best health’). The NHP was designed for self-completion and can readily be used in postal surveys, although it can also be administered by an interviewer. Generally, the instrument takes around 5 minutes to complete.
QALYs were developed as an outcome measure to incorporate effects on both the quality (morbidity) and quantity (mortality) of life. Each year of life is multiplied by a weighting factor reflecting quality of life.
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An alternative to QALYs is healthy years equivalent (HYE), which is discussed below.
QALYs are estimated by assigning every life-year a weight between 0 and 1, where a weight of 0 reflects a health status that is valued as equal to being dead and a weight of 1 represents full health. For example, consider a patient with a colon cancer who has a health state of 0.9. Without surgery, he will die in 2 years. With surgery, his health state deteriorates slightly to 0.7, but he lives 5 more years (in total 7 years). Therefore, the QALY gained with surgery = [(0.7 × 7) − (0.9 × 2)] = 4.9 − 1.8 = 3.1. Another way of expressing QALY gained is from QALY charts. For example, a patient with a particular disease is expected to deteriorate and die at an estimate point 1. With intervention the patient would deteriorate slowly and die at an estimate point 2. The area between the two curves is the QALY gained by the intervention (
Fig. 2.1
).
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Figure 2.1
An example QALY chart showing the QALY gained with intervention.
The QALY is frequently used by decision-makers or researchers to draw comparisons between differing types of health programme or intervention. The QALY can be used in economic evaluation: the number of additional QALYs that a new surgical intervention might yield can be compared with the costs of the new procedure, thus enabling cost per QALY ratios to be generated.
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Such ratios have been used in so-called QALY league tables, whereby procedures or interventions are ranked on this basis. These tables might be seen as useful within the decision-making process, but caution should be exercised in making quick decisions regarding the allocation of resources based upon these comparative tables (see below).
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HYEs were developed as an alternative in order to address some of the suggested shortcomings of QALYs.
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HYEs produce a hypothetical combination of the number of years in full health that equates to the individual's utility of living a number of years at a health state rated at less than full health. In effect, this can be considered as a lifetime profile of health and is often referred to as a stream of health states. In order to produce the values for the HYEs, a two-stage gamble procedure is used. There has, however, been criticism of the use of HYEs and at present the use of QALYs remains the most common method of performing cost-effectiveness analysis.
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For the QALY to be a useful measure of outcome, it should reflect patient or individual preferences, which means that if an individual has a choice between two or more treatments or interventions then they should choose the treatment option that yields the most QALYs.
Quality weights for use in QALYs can be obtained directly using one of three main methods – visual analogue scales (VAS), standard gamble and time trade-off – which are described in turn below. In addition to these direct methods of measurement, it is possible to obtain utilities through the mapping of health states derived from other quality-of-life measures onto valuations obtained from members of the general population.
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The method for obtaining utility weightings is currently the subject of considerable research, and there is evidence that different values are obtained depending on the method of evaluation and framing of questions. In addition, there is considerable controversy as to whether the most appropriate utility values for economic evaluation are those of the general population or of patients with the particular conditions.
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Rating scale or visual analogue scales:
In this approach respondents are asked to rate their preference of outcomes or their present health state on a straight-line chart, often a thermometer with fixed points, where the bottom of the thermometer is represented by 0, which is a health state equivalent to being dead, and the top of the thermometer is 1, which indicates full health. If thermometers ranging between 0 and 100 are used, these values are equated to 0–1, and the same assumptions regarding the health states hold. If, by way of an example, we wanted to measure the quality weight for being an amputee, and the respondent selects the health state at 55 on the thermometer scale of 0–100, then for that health state their quality weight is equivalent to 0.55. Advantages of VAS include their simplicity, but this can be counterbalanced by the fact that there is no choice to be made between health states and therefore it is not possible to observe any trade-offs that the individual might have between health states. Another problem with VAS is that it is uncertain whether the responses lie on a linear interval scale: it is questionable whether moving from 20 to 30 is equivalent to moving from 70 to 80. Further, there is scope for end-of-scale bias, where respondents tend to avoid the extremes of scales, and spacing out bias, where respondents tend to space out the outcomes irrespective of the significance.
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Given these reservations, it is difficult to use VAS methods to determine quality weights in a way that is consistent with the theoretical basis of utility measurement.
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Standard gamble:
The second method routinely used to estimate quality weights is standard gamble. Under this approach, the quality weight of a health state (patients' feeling of being well or unwell) can be constructed by comparing a specific number of years in the health state to a gamble with a probability (
P
) of achieving full health for the same number of years and a complementary probability (1 −
P
) of immediate death. The probability of full health is varied until the individual is indifferent between the alternatives, and the quality weight of the assessed health state is therefore equal to
P
. As an example, let us consider that we are comparing 10 years with heart disease with the gamble of full health for 10 years (probability
P
) or immediate death, with a complementary probability (1 −
P
). Here, let us assume that the individual is indifferent at a probability of 0.6 of full health. In practical terms, this means that the individual would consider a certainty of living 10 years with the heart disease to be equivalent to taking a gamble that gives a 60% chance of living 10 years in full health and a 40% chance of immediate death. The quality weight here would be 0.6 for the heart disease health state. The advantages of standard gamble include the fact that it is based on expected utility theory;
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however, one of the major disadvantages is that respondents might find the concept difficult to understand because of the probabilities associated with the method. Another drawback is that the hypothetical choices used in such an approach are not representative of ‘real’ life, since choices between large improvements in health status and large mortality risks are seldom encountered, especially with the assumption that we will live for a certain number of years for sure.
Time trade-off:
A third approach to estimating quality weights is time trade-off.
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This method compares
D
years duration in the health state with
X
years in full health. The number of years in full health is varied until the individual is indifferent between their options; at this point, the quality weight of the health state is calculated by
X
/
D
. As an example, consider that we wish to calculate the quality weight for heart disease and that we assess the measurement for 10 years of heart disease. If the individual considers that 10 years of living with heart disease (
D
= 10) is equivalent to 6 years of full health (
X
= 6), then the quality weight is equal to 0.6 (6/10).
Obtaining utilities from health-related quality of life:
The direct methods of obtaining utilities have a number of drawbacks. In particular, the VAS has a poor theoretical basis, and standard gamble and time trade-off methods may be complex to administer.
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An alternative approach is to derive single index measures from generic or disease-specific health-related quality-of-life measures.
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One of the most widely adopted methods is derived from the EQ-5D, as described above.
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The questionnaire generates a limited number of discrete health states depending upon the grading of the responses to each of the questions and previous research has used direct measurement methods to obtain a utility tariff associated with each of these states.
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These tariffs have been widely used for cost-effectiveness analysis and alternative tariffs for different populations have also been developed.
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Another generic measure that has been used to generate utilities is the SF-6D, a measure derived from the SF-36 questionnaire.
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This has been extensively validated and compared with EQ-5D in a number of studies.
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In cases where information from generic scores is not available, methods have been developed to derive utility weightings from disease-specific measures such as scoring systems for arthritis
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or even from laboratory measures such as haemoglobin levels, used as a proxy for anaemia-related symptoms.
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Disease-specific scores may be more sensitive to changes in symptoms for the condition in question, but suffer from the disadvantage that they may fail to capture adverse events or other outcomes of treatment that are outside the main domains that are of interest in the specific disease so that comparability with other diseases may be questionable. There remains considerable controversy about the relative benefits of the use of generic and disease-specific measures to generate utilities.
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