Evaluación del Impacto de Ciclo de Vida

Aim 1b: evaluation of the extended MRC Dyspnoea Scale with specific reference to correlation with survival, quality of life, readmission rates length of stay and frequency of hospital readmission.

Aim 1c: evaluation of Malnutrition Universal Screening Tool with specific reference to correlation with survival, quality of life, readmission rates length of stay and frequency of hospital readmission.

The severity of dyspnoea during a stable state, measured by the traditional MRCD scale (Table 1.1) is a strong predictor of mortality in AECOPD. A previous study in our hospital [367] has suggested that subdividing individuals with traditional MRCD 5 in to two levels, depending on their ability to independently perform washing or dressing, more accurately predicted the risk of hospital admission following discharge than the

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traditional scale. This novel modification of the MRCD is termed the Extended MRCD (eMRCD) and is detailed in Table 5.1:

Table 5.1 The Extended MRC Dyspnoea scale

Grade Degree of breathlessness related to exercise

1 Not troubled by breathlessness except on strenuous exercise 2 Short of breath when hurrying or walking up a slight hill

3 Walks slower than contemporaries on level ground because of breathlessness, or has to stop for breath when walking at own pace

4 Stops for breath when walking about 100m or after a few minutes on level ground 5a Too breathless to leave the house unaided but independent in washing and / or dressing 5b Too breathless to leave the house unaided and requires assistance in washing and dressing

The relationship between the extended MRCD scale (Table 5.1) and mortality has not previously been investigated, and the suggestion that eMRCD may be a better

discriminator for hospital readmission [367] requires further investigation. We therefore wished to clarify the association between MRCD and outcome in AECOPD, and investigate whether eMRCD is a stronger predictor of mortality and readmission than MRCD.

Poor nutritional status is an important prognostic index in AECOPD (section 2.2.4.1), but malnutrition can be measured in a variety of ways and no single, easy to measure index has been found to accurately predict both short and long-term mortality, and readmission. The Malnutrition Universal Screening Tool (MUST) has been shown to be a useful prognostic tool in elderly acute general medical admissions [39, 41] but its utility in patients with AECOPD has not been investigated.

We therefore wished: to report the estimated risk of malnutrition in our population according to MUST; and to assess the prognostic strength of MUST compared to BMI and weight loss in AECOPD.

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Table 5.2 The 'Malnutrition Universal Screening Tool' ('MUST')

Nutritional measurement Score

Body mass index (BMI), >20 18.5-20 <18.5 0 1 2 Unplanned weight loss in past 6 months,

<5% 5-10% >10% 0 1 2 If patient acutely ill and there has been, or is likely to

be, little nutritional intake for >5 days 2 Total MUST score

Low risk of malnutrition Moderate risk of malnutrition

High risk of malnutrition

/6 0 1 ≥ 2

MUST is reproduced here with the kind permission of BAPEN (British Association for Parenteral and Enteral Nutrition).[368]

5.3 PREDICTING OUTCOME FOLLOWING HOSPITALISATION FOR AECOPD

Aim 1d: identify independent predictors of in-hospital mortality following

hospitalisation for AECOPD and develop a clinical prediction tool to accurately predict the risk of in-hospital mortality.

Aim 1e: identify independent predictors of in-hospital mortality in patients receiving assisted ventilation following hospitalisation for AECOPD.

Aim 1f: identify independent predictors of twelve-month mortality following hospitalisation for AECOPD.

Aim 1g: identify independent predictors of early and frequent readmission, and develop a clinical prediction tool, in patients surviving to discharge following hospitalisation with AECOPD.

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Chapter 2 and Chapter 3 detail the current difficulties that clinicians face when

attempting to predict outcome in AECOPD. Therefore, we aimed to identify, in a broad population of patients with AECOPD, independent predictors of mortality and

readmission. Furthermore, we aimed to develop clinical prediction tools to assist clinicians in the prediction of in-hospital mortality and early readmission following discharge in a population hospitalised for AECOPD.

The planned prediction tools should be easily memorised and simple to use, and would therefore contain a limited number of variables, with predictor variables consisting of, ideally, two or three categories. The prediction tools would be internally validated during this study, but external validation would require subsequent studies. Predicting short and long-term survival has been more extensively researched in patients with acidaemic respiratory failure requiring assisted ventilation than in general patients with AECOPD (Table 2.8) yet many of these studies have only been performed in the intensive care setting or have strict entry criteria. We aimed to identify independent predictors of short-term survival in this population. Comparisons with previously published data would be problematic and were therefore not

undertaken. In most previous research, prognostic data (particularly APACHE and CAPS) were collected at the time of clinical deterioration, for example at the time of admission to ICU or commencement of ventilation. In the present study, most physiological data in patients requiring ventilation were collected at admission to hospital. Therefore, comparing our predictive model with the performance of APACHE and CAPS in our study is flawed because APACHE and CAPS were designed to be calculated at the time of clinical decline.

5.4 LONGITUDINAL ASSESSMENT OF QUALITY OF LIFE AND HEALTH