LAUSR

Emanuel Bäckryd′s letter points out a fundamental methodological problem in clinical pain trials: Analgesics, offered both regularly and as rescue during the study period are major confounders as they can completely obscure the measured analgesic effect of the study medication. Several attempts have been made in order to solve this problem. One solution is to use opioid consumption as marker of pain. However, opioid analgesic consumption is a surrogate pain marker that is not affected by pain alone, but by numerous other factors like anxiety, preoperative pain, preoperative analgesic usage, side effects like nausea, sedation, route of administration, the size of the bolus dose and the great inter‐individual variation in opioid sensitivity (5‐ to 10‐fold). Ideally, pain intensity should be main outcome in pain studies. A method to achieve this despite rescue analgesic usage is to exclude pain observations from the moment rescue analgesics is given to a patient. A standard method used to handle these missing observations is “Last‐Observation‐Carried‐Forward (LOCF)”. However, in most trials rescue analgesics are given early to a significant proportion of study patients, and the number of “true” pain observations decline rapidly. Thus, the LOCF method will soon be based on very few observations and therefore has reduced validity. To solve this problem, it is possible to calculate composite scores based on all actual pain observations and rescue analgesic consumption. Emmanuel Bäckryd proposes a novel composite score called “The Opioid and Pain Intensity Index (OPI)”. This is a 0‐10 metric that combines 0‐10 Numeric Rating Scale (NRS) data with the dose of opioids expressed as mg per day of Oral Morphine Equivalents (OME), which is converted to OME category (OMEC) 0‐10, where OMEC 0 = 0‐99 mg, and OMEC 10 is ≥1000 mg. We have used Silvermann′s integrated approach (SIA) as follows: Rank all subjects according to their sum of actual pain intensities and rescue analgesic use, express the difference of each treated subject′s rank as a percentage of the mean rank, and for each subject, the sum of the two percentage differences calculated for actual pain rank and rescue analgesic usage rank constitutes the composite score. Permutation tests have proved the SIA score to be accurate and efficient, and has substantially more statistically power to detect differences in analgesic efficacy when it exists, compared with analyses of pain or opioid analgesic consumption separately. However, the method appears complicated and is not intuitive for all. Some find Mercadante`s Effective Analgesic Score (EAS) more intuitive. Here, pain intensity and consumption of analgesics is integrated to a composite score by using the formula: (NRS + 1) x (1 – opioid dose/10). The original formula has been modulated by Krohg et al as NRS +1 replaces pain intensity to avoid zero product in case of NRS = 0. Composite pain scores do have limitations. They are not directly sampled data but calculated by the use of multistep formulas that are more or less mathematically and statistically valid. Calculation of equianalgesic doses of different opioids and non‐opioids into standard opioid units are sources of error. A major problem with all composite scores is their lack of intuitive clinical relevance. We propose that if a composite score is used as primary outcome in a study, both pain intensity and opioid use should be reported as well. These outcomes are needed for clinicians in order to translate the results to a clinical setting. Composite pain intensity and analgesic usage scores are needed for increasing the sensitivity in pain studies. The most intuitive methods seem to be Bäckryd′s OPI, and Mercadante′s EAS. Due to the familiar 0‐10 metric, the OPI score may be a candidate for a standard pain and analgesic composite score. Thus, Bäckryd's OPI score is promising. However, it remains to be evaluated and validated in comparison with the different composite pain intensity and analgesic usage scores available. The overall aim should be to improve the composite pain scores or develop a new score with higher sensitivity, validity and reliability than the existing composite scores.