Rationale – the need for prognostic models to predict mortality post-CAP in older

(either directly or indirectly due to CAP) in the 28 days after a CAP diagnosis declined. Thus there is a growing population of older patients who survive hospitalisation after CAP and are released back into their GPs’ care. In this Chapter I examine predictors of subsequent mortality for these individuals, in order to try to aid GP decision making about plans for future care and support.

I first provide a brief rationale for this study, and review the literature on risk factors associated with increased mortality after adults with CAP are discharged from hospital. This is followed by an outline of the methodology behind prognostic modelling, a statistical technique that enables the development of risk scores to aid and inform clinical decision making. I then apply these methods to the patients in the linked CPRD- HES cohort who were hospitalised for CAP and survived the hospitalisation. The mortality rate and cause of death of these hospitalised CAP patients in the year after their discharge is examined, and I create a series of prognostic models to try to help GPs identify patients with a high predicted mortality risk over this period. Finally, I discuss the limitations of my approach and alternative strategies which could be used to tackle this aim.

7.1 Rationale – the need for prognostic models to predict mortality post-CAP in older adults

7.1.1 Increasing GP interaction with older adults and those at high risk of hospital admission

As outlined in section 1.3.2.4, patients hospitalised for CAP have a higher mortality risk for at least a year post-discharge compared with both the general population and with patients hospitalised for other conditions.[88, 92] The combination of several trends has resulted in an increasing number of older adults belonging to this higher-risk group

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of individuals who survive a CAP hospitalisation: an expanding older population, the rising incidence of CAP, the rising number of hospitalisations for CAP and decreasing in- hospital mortality following CAP.

These patients are returned to the care of their GP, who may learn about the CAP episode for the first time via a hospital discharge summary and who then need to decide what future care the patient requires. This has recently become a particular focus for a specific group of patients, as changes to the GP contract (2014/15) in England have resulted in ‘at-risk’ patients being placed on the Enhanced Service (ES) register. Patients placed on this list (a minimum of 2% of the practice list) have been identified as at risk of an unplanned admission to hospital, and their general practice is required to contact them within three days of discharge should admission occur.[170] Given the increased mortality risk after a CAP hospitalisation, a prognostic model to assess a patient’s risk of death in the year after CAP hospital discharge would be useful for GPs at the time of this post-discharge contact, in order to inform decisions on the kind of support to be offered to the patient by the practice. While not all patients on the ES register will be older adults, patients are chosen using a risk stratification tool or clinical judgement, making it highly likely that many will be aged ≥65 years. Additional changes have resulted in all patients aged ≥75 years being assigned a named GP who is responsible for general oversight of their care.[171] A simple to use prognostic model for mortality that requires minimal clinical input could also prove useful in health care planning for this group after a CAP hospitalisation.

7.1.2 Limitations of currently available models

Prognostic models are available to predict mortality risk post-CAP over short risk periods, however commonly used tools such as CURB-65 and the PSI were designed to be used at the point of CAP diagnosis, which may make them less suitable for use post- discharge.[81, 80] Patients who die during a CAP hospitalisation are likely to have different health profiles to those who survive, and therefore these populations need to be considered separately. For example those with cardiovascular disease may have high in-hospital mortality (where it would thus be an important predictor of mortality), and so these patients would be less well represented in the post-discharge population (where it may be a less important predictor). Fitting prognostic models to CAP survivors’

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characteristics would result in more accurate future risk predictions than those obtained from a model used at the point of hospital admission. Additionally, models such as CURB-65 and PSI include clinical signs and symptoms present at the point of diagnosis such as respiratory rate, which are unlikely to be available in a primary care setting when the GP takes over a patient’s care post-discharge.

As described in section 1.3.2.3, only three scores have been specifically developed for use in older patients in an outpatient or primary care setting.[85-87] Unfortunately these three models were not limited to patients with pneumonia but also included patients with other LRTI, and all used a combined end point of hospitalisation or death. This combined outcome is problematical, as some of the risk factors for hospitalisation are likely to differ from those for death, or the direction of effect may not be the same. None of the previous primary care models were optimised for patients who survive a CAP hospitalisation, or to predict mortality over a period longer than 30 days after diagnosis.

A wealth of information regarding patients’ medical histories, such as their co-morbidity profile, vaccination status and lifestyle factors is available to GPs. A model including readily accessible factors such as these would therefore be appropriate in the CAP post- discharge setting.

7.1.3 Aim

The objective of this study was to develop easy to use prognostic models to predict the risk of death in the year after a patient’s discharge from a CAP hospitalisation. The results generated by the score could be used in addition to the GP’s own knowledge of the patient to aid in planning the patient’s future care. An important feature of a good prognostic model is ease of use. Some of the software used for clinical management by GPs already has clinical risk scores built in, such as QRISK, a score to assess cardiovascular risk.[172] The prognostic models developed in this study were designed to similarly utilise patients pre-existing electronic health records, and so require minimal clinical input.

Before discussing the methodology behind developing a prognostic model, I looked at the existing literature around risk factors for mortality post CAP-discharge, to see what

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models were currently available, and to inform my choice of potential prognostic factors.

7.2 Literature review of factors associated with long-term mortality after a CAP