Multi-Morbidity

Guidelines are built in a solipsistic way around individual diseases and do not take into account the fact that most patients have more than one disease? Well this is not quite true; statins trials have been tested over a range and mix of conditions. Nevertheless, most trials are based on a rather ‘clean’ population. This creates three theoretical problems:

  1. Drugs prescribed for one disease may interact with those prescribed for another. For example, non-steroidals agents administered for osteoarthritis may interact with warfarin given for atrial fibrillation.
  2. Drugs prescribed for one disease may interact directly with another disease. Beta-blockers prescribed for hypertension may aggravate asthma, for example.
  3. Drugs may be less effective in treating the target disease when lots of diseases are present.

The first problem is well known – polypharmacy, apart from being bothersome, can be dangerous, as in the example cited. However, e-prescribing can reduce this risk, not just theoretically, but empirically.[1]

The second problem is also well documented, and it too can be tackled by e-prescribing, given a sufficiently data-rich IT set.[2]

The third problem, that multi-morbidity may affect the effectiveness of treatment for the index condition, has received less attention. There are theoretical reasons why multi-morbidity may attenuate effectiveness – for example, by reducing adherence to treatment. There are also theoretical arguments for an augmented effect when diseases share a common pathway targeted by the drug – inflammatory mechanisms for instance. This issue has now been investigated empirically in a recent paper [3] and editorial [4] in the BMJ. Tinetti and colleagues studied nine drugs shown in RCTs to decrease risk of death in people with cardiovascular disease. They used a database of 8,578 Americans who all had at least one condition in addition to the index cardiovascular disease. The hypothesis that effectiveness is affected by the presence of multi-morbidity was testable because many (nearly half) of the patients in the database had not received the drug that had been shown to be effective for their condition in RCTs. So the outcomes of those where the drug was indicated and prescribed could be compared to those where it was indicated but not prescribed. The results anticipated from the RCTs were replicated among the database patients – in other words, multi-morbidity did not modify treatment effectiveness in terms of adjusted hazard ratios.

Of course, such an observational study is beset with selection biases, including ‘healthy user bias’, whereby those who take medicines have a better prognosis a priori, and ‘immortal time bias,’ whereby some of those who would have received the intervention in an RCT are just not there to be counted in the database because they have died. In other words, receiving or not receiving the indicated drug may not be a good instrumental variable. Nevertheless, the results were adjusted for as many confounders as possible and this provides a measure of assurance that drugs produce anticipated effects, notwithstanding multi-morbidity. CLAHRC WM has an active theme of work in tailoring care according to patient preference, and this study is certainly highly relevant to this project. The CLAHRC WM Director makes the further point that if the relative risk reduction is the same in patients with multi-morbidity and those with single diseases, and if the multi-morbidity is associated with higher baseline morbidity/mortality, then the absolute risk reduction will be higher in multi-morbid than in uni-morbid patients

— Richard Lilford, CLAHRC WM Director

References:

  1. Nuckols TK, Smith-Spangler C, Morton SC, et al. The effectiveness of computerized order entry at reducing preventable adverse drug events and medication errors in hospital settings: a systematic review and meta-analysis. Syst Rev. 2014; 3: 56.
  2. Avery AJ, Rodgers S, Cantrill JA, et al. A pharmacist-led information technology intervention for medication errors (PINCER): a multicentre, cluster randomised, controlled trial and cost-effectiveness analysis. Lancet. 2012; 379: 1310-9.
  3. Tinetti ME, McAvay G, Trentalange M, Cohen AB, Allore HG. Association between guideline recommended drugs and death in older adults with multiple chronic conditions: population based cohort study. BMJ. 2015; 351: h4984.
  4. Muth C & Glasziou PP. Guideline recommended treatments in patients with multimorbidity. BMJ. 2015; 351: h5145.
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