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40 Individualising the patient’s life expectancy is essential for optimal prescribing in clinical practice
  1. Vije Kumar Rajput1,
  2. Jack Dowie2,3,
  3. Mette Kjer Kaltoft3
  1. 1Stonydelph Health Centre, Tamworth, UK
  2. 2London School of Hygiene and Tropical Medicine, London, UK
  3. 3University of Southern Denmark, Odense, Denmark


One possible cause of overprescribing (or insufficient deprescribing) is the failure to explicitly address the individual’s life expectancy (LE). For example, if a LE estimate shows the person has six months to live, this should influence the prescribing of a medication that offers benefits only over a much longer LE. Predicting exactly the number of years a person can be expected to live is impossible, but probabilistic forecasting is possible and arguably essential, both for the selection of the optimal intervention and for meeting the ‘reasonable patient’ standard of information about the harms and benefits of alternative options. One side-effect of the COVID-19 pandemic has been to bring mortality into greater prominence, perhaps facilitating its discussion in the clinic as part of the ‘new normal’. This paper outlines the case for introducing LE in prescribing decisions as a way of making more individualised prescribing decisions and potentially reducing overprescribing. However, it concentrates on how the clinical task of arriving at individualised estimates of LE might be tackled, especially in the case of the growing number of older patients with heterogeneous sociodemographic characteristics who are experiencing multiple long term conditions of varying severity and are frequently subject to ‘polypharmacy’. This means that the bulk of prognostic studies and scoring systems, which are for single conditions - such as diabetes, heart failure and COPD - have only indirect relevance.

We outline different approaches to the task of estimating the LE for an individual patient of the above type. They all assume that the final central point estimate will draw on both analytical sources (e.g. Network Meta-Analyses, Prognostic Scoring Systems) and intuitive sources (e.g. Expert surveys, Clinical experience) perceived as relevant. Two overall strategies are:

‘Intuition-Informed Analysis’ - the final LE estimate favours the analytical sources

‘Analysis-Informed Intuition’ - the final LE estimate favours the intuitive sources

Two subsidiary strategies are:

‘Population Average Anchored’ - progressively modifies, on the basis of the individual’s LE-related attributes, the most individualised average estimate obtainable from the most relevant life table (e.g. one based on the UK Office of National Statistics age/sex calculator).

‘Population Average Unanchored’ - adds years of life to the person’s current age on the basis of the persons individual LE-related attributes, without reference to such a population average.

We compare applications of two different strategies in an illustrative case and suggest how decision support might be provided for this challenging task. We confirm the expectation that different strategies can result in different central point estimates of LE. Whether the different approaches will lead to different prescribing is an empirical question, involving consideration of the possible interventions effects on criteria other than LE. Nevertheless, the decision following from either strategy can be claimed to be more informed compared with one not incorporating any explicit estimate of LE. Assuming that failure to consider LE is a major source of overprescribing and overtreatment – more research is clearly needed on this - there are implications for the patient, clinician and effective utilisation of health resources.

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