CANCER PAIN & PALLIATIVE CARE SECTION

bs_bs_banner Pain Medicine 2014; 15: 225–232 Wiley Periodicals, Inc. CANCER PAIN & PALLIATIVE CARE SECTION Original Research Article Assessing Analg...
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Pain Medicine 2014; 15: 225–232 Wiley Periodicals, Inc.

CANCER PAIN & PALLIATIVE CARE SECTION Original Research Article Assessing Analgesic Use in Patients with Advanced Cancer: Development of a New Scale—The Analgesic Quantification Algorithm Karen C. Chung, PharmD, MS,* Arie Barlev, PharmD, MS,* Ada H. Braun MD, PhD,† Yi Qian, PhD,‡ and Martin Zagari, MD* *Global Health Economics, †

Global Development Hematology/Oncology and



Global Biostatistics and Epidemiology, Amgen Inc., Thousand Oaks, California, USA Reprint requests to: Karen C. Chung, PharmD, MS, Global Health Economics, Amgen Inc., One Amgen Center Drive, MS: 28-3-A, Thousand Oaks, CA 91320-1799, USA. Tel: 805-447-9083; Fax: 805-376-1816; E-mail: [email protected]. Financial Support: Funding for this study and technical writing of this manuscript was provided by Amgen Inc.

Methods. An expanded equianalgesic potency conversion table was developed to establish oral morphine equivalents for use in the AQA. Categories of opioid use were selected to increase sensitivity within the higher dose range of opioids and to better capture increases in analgesic dose intensity. The resulting 8-point AQA scale corresponds to no analgesic use, non-opioid analgesics, weak opioids only, ≤75 mg, >75–150 mg, >150–300 mg, >300– 600 mg, and >600 mg oral morphine equivalents per day. Baseline and 6-month analgesic data from a clinical trial of cancer patients were compared for each instrument. Results. At both time points, the 4-point WHO-AL demonstrated a ceiling effect with a clustering of patients in the strong opioid category, whereas the AQA resulted in a distribution of scores throughout the eight categories, including the five strong opioid categories.

Disclosure: All authors are employees of and hold stock in Amgen Inc. Authors have full access to the primary data and agree to make the data available to the journal upon request.

Conclusions. The AQA represents a more sensitive measure of analgesic use than the WHO-AL, and may better determine whether changes in pain assessments in clinical trials are due to the intervention or changes in analgesic use.

Abstract

Key Words. WHO Analgesic Treatment Ladder; Analgesics; Cancer Pain; Pain Assessment; Analgesic Quantification Algorithm; AQA

Objective. Many patients with advanced cancer frequently use analgesic medications for their pain. Systematically assessing and quantifying changes in analgesic use remains challenging in the clinical trials setting. Currently, there is no sensitive scale for categorizing the intensity of analgesic medications to understand the reasons for changes in patient-reported pain. We assessed whether the Analgesic Quantification Algorithm (AQA) is more sensitive than the World Health Organization Analgesic Treatment Ladder (WHO-AL) for quantifying analgesic medication use among patients with advanced cancer. © 2014 Amgen Inc. Pain Medicine © 2014 Wiley Periodicals, Inc.

Introduction Pain is commonly experienced by patients with cancer, particularly those with advanced disease. Approximately two thirds of patients with advanced cancer experience pain and, of these, more than one third rate their pain as moderate to severe [1]. Cancer pain can reduce patients’ health-related quality of life and can interfere with a broad array of functional activities including mood, walking, working, relationships, sleep, enjoyment of life, and general activity [2]. Alleviating pain is an important goal of 225

Chung et al. cancer treatment, and assessing patient pain scores has become an integral component in the study of cancer treatments. Pain assessment instruments have historically focused on multiple aspects of pain, but studies using factor analysis of different measures have found that the pain measures can largely be reduced to a single rating of pain intensity [3,4]. Pain intensity is commonly measured using an 11-point scale anchored at 0 to indicate “no pain” and 10 for “pain as bad as you can imagine,” similar to that found in the Brief Pain Inventory (BPI) [5]. A patient’s rating of pain reflects more than just the underlying injury. A growing body of research has identified multiple contributing factors to pain, such as mood states, perceptions of control, social contingencies, and concurrent analgesic use [6]. While the physiological mechanisms of most of these contributing factors are not well understood, analgesic medication use represents an important contributing factor to pain that does have well-understood physiological mechanisms. Research in cancer patients has found that measures to assess the adequacy of pain treatment represent a distinct aspect of pain assessment separate from measures of pain intensity or pain relief [7], and are predictive of functional impairments [8]. Analgesic scores can help assess the appropriateness of cancer patients’ current analgesic treatment, given the patients’ ratings of pain intensity [7,8] on measures such as the World Health Organization’s three-step Analgesic Treatment Ladder (WHO-AL) [9,10]. In the context of clinical trials, pain intensity is frequently assessed as an outcome measure for cancer treatments. In order for these data to be interpretable, results should be adjusted to reflect the intensity of patients’ concurrent analgesic medication use. For example, an effective cancer treatment could mitigate pain by affecting the underlying disease progression; however, this finding could be hidden because of increased levels of analgesic medication use in the less effective treatment arm. Due to ethical concerns, analgesic medication use cannot be limited in clinical trials, nor can the amount of analgesic medication be fixed experimentally. Additionally, in the absence of a universally accepted classification system for cancer pain which incorporates pain severity and analgesic use [11], there is a need for a systematic and sensitive method of categorizing analgesic medication use and capturing changes in the dosing, frequency, and/or type of analgesic medications administered. Such an assessment is particularly important in order to control for differential changes in analgesic medication use between treatment groups over the course of clinical trials. The most common method of categorizing analgesics and adjuvant medications for the treatment of cancer pain is the WHO-AL [9,10]. These clinical guidelines were originally developed in 1986 by the cancer unit of the WHO to provide recommendations for analgesic medication selection in the treatment of cancer pain. The steps of the 226

Figure 1 World Health Organization (WHO) pain relief ladder. Source: Reprinted with permission from the World Health Organization [36] WHO has developed a three-step “ladder” for the treatment of cancer pain. If pain is present, analgesia should be initiated starting with non-opioid analgesics (step 1). As pain increases or persists, the level of analgesia is increased in steps from mild opioids to strong opioids, until the patient is free of pain.

ladder are: 1) no analgesia, 2) non-opioids (e.g., salicylates and other nonsteroidal anti-inflammatory drugs [NSAIDs]), 3) weak opioids (e.g., codeine), and 4) strong opioids (e.g., morphine, oxycodone, hydromorphone, or methadone). Figure 1 graphically depicts this extensively validated treatment algorithm that is commonly used in the management of cancer pain [12–14]. Although the WHO-AL has been used as a measure of analgesic medication use in clinical studies [15–18], it was originally designed to improve the management of cancer pain. For measuring analgesic medication use in cancer patients with advanced disease, the WHO-AL demonstrates a ceiling effect where most patients (62%) are classified on the top rung regardless of level of strong opioid use, because the WHO-AL is not sufficiently sensitive at higher levels of analgesic use [14]. In the treatment of cancer pain in particular, where addiction is not a primary concern, a wide range of opioid dose levels is used. For example, many patients can achieve pain control with 240 mg of oral morphine per day or less, but some patients may require as much as 1,000 to 4,500 mg of parenteral morphine per day [19]. While many

Analgesic Quantification Algorithm (AQA) physicians may be reluctant to lower the dose of opioids for patients with cancer pain, they are more likely to increase the dose of opioids in cases of insufficient pain control. The failure of the WHO-AL to differentiate among strong opioid usage could potentially mask meaningful differences in opioid use and produce misleading findings. Therefore, it has been suggested that the WHO-AL could be expanded to include dose as a measure of analgesic medication use [7]. In order to address this methodological limitation, the Analgesic Quantification Algorithm (AQA), a modification of the WHO ladder that includes additional specificity for patients receiving strong opioids, was developed. This algorithm facilitates differentiation between the full range of doses of strong opioids used by patients and thus, more accurately enables researchers to determine and control for changes in levels of analgesic medication use over time in clinical trials. It is expected that this modification will be most relevant in cancer treatment trials, where the use of a wide range of opioid doses is common. This article describes the development of the AQA and examines its distributional properties in a relevant population of cancer patients.

Table 1

Equianalgesic potency conversions Equianalgesic Dose (mg)

Name

IV

SC

IM

PO

OME

Morphine Fentanyl* Hydromorphone Methadone Oxycodone Hydrocodone Codeine Tramadol Buprenorphine Butorphanol Nalbuphine Pentazocine

10 0.1 1 10 — — 120 100 0.3 — 10 60

10 0.1 1 10 — — 120 100 0.3 — 10 60

10 0.1 1 10 — — 120 100 0.3 2 10 60

30 2.4 5 20 20 40 200 100 0.2 — — 60

30 30 20 30 30 40 30 30 30 30 30 20

* Fentanyl, 0.1 mg transdermal has an OME of 30 mg. IV = intravenous; SC = subcutaneous; IM = intramuscular; OME = oral morphine equivalent; PO = oral.

Methods

it is the drug of choice for managing moderate to severe cancer pain, with the oral route being the simplest and most accepted for administering morphine [9,21,22].

Development of an Expanded Equianalgesic Potency Conversion Table

Selection of Categories of Strong Opioid Use for the AQA

Equianalgesic dose tables, which are broad guidelines for analgesic dose conversions, have previously been developed to help determine an appropriate analgesic dose when switching between opioids and/or when changing the route of administration in clinical practice. The first step in modifying the WHO-AL was to compile a comprehensive list of published equianalgesic potency conversions that would permit the establishment of oral morphine equivalence (OME) among various opioid medications for use in the AQA.

An analgesic scoring system based on four categories of daily morphine use was developed by Bercovitch et al. using the Edmonton Staging System [23] and applied in a retrospective chart review of analgesic use in cancer patients [24]. The categories were as follows: low (≤59 mg/day), moderate (60–299 mg/day), high (300– 599 mg/day), and very high (≥600 mg/day), with low and moderate doses considered “regular doses” and high and very high doses considered “elevated doses.”

A list of equianalgesic potency conversions was assembled to reflect the analgesic agents used in a large international, randomized, clinical trial comparing the effects of denosumab and zoledronic acid in patients with bone metastases from solid tumors other than breast or prostate or patients with multiple myeloma [20]. Published equianalgesic potency conversion tables were consolidated into a comprehensive list of several thousand entries according to analgesic medication name (verbatim term based on verbatim medication listed and preferred term based on verbatim medication name using the WHO Drug Dictionary version 08.4), dose, unit, route of administration, and dosing frequency. Equianalgesic potency conversions by term and route of administration are listed in Table 1. Each analgesic was standardized to its OME dose. Weak opioids were also included in order to estimate a total oral morphine equivalent dose, as they may be used in combination with strong opioids. The use of oral morphine as the reference standard is proposed since

The AQA increases the number of analgesic categories used by Bercovitch et al. from four to eight and modifies the cut-points to ensure equal spacing (Table 2). The 60 mg OME cut-point was raised to 75 mg OME, and an additional cut-point at 150 mg OME was added to improve sensitivity within the lower dose range and to capture dose increases as a change in analgesic medication use. Each cut-point, from ≤75 mg OME per day to >600 mg OME per day, is twice as high as the previous one, reflecting the observation that frequently, dose adjustments are calculated based on a percentage of the current opioid dose to provide continued pain relief [25,26]. A lower cut-point (i.e., 75–150 mg OME per day Strong opioids >150–300 mg OME per day Strong opioids >300–600 mg OME per day Strong opioids >600 mg OME per day

* For example, codeine and tramadol. AQA = Analgesic Quantification Algorithm; OME = oral morphine equivalent.

of mild to moderate cancer pain. The equianalgesic dose ratio of codeine with oral morphine is approximately 7:1 (i.e., 200 mg codeine [oral] = 30 mg morphine [oral]). Therefore, using the proposed equianalgesic conversion table (Table 1) to convert the typical daily dose of codeine (120 mg to 360 mg per day) to oral morphine, based on the equianalgesic dose of 200 mg oral codeine to 30 mg oral morphine, results in a typical daily dose of oral morphine ranging from 18 mg to 54 mg. Including a lower cut-point of 37.5 OME per day in the AQA could have potentially blurred the boundaries between weak opioids and the lowest levels of the strong opioids, and was therefore not included in the AQA score categories. A higher cut-point (e.g., >1,200 mg) was not employed since only a minority of patients use such a high daily dose [24]. These new categories were combined with the 4-point analgesic score for type of medication (derived from the WHO ladder), which resulted in the 8-point AQA for determining an analgesic score (Table 2). Implementation To determine the AQA score, each patient was initially scored by the WHO-AL levels of analgesic medication use: no analgesic use, non-opioid analgesic use only, weak opioid analgesic use (e.g., codeine, tramadol) with or without non-opioid analgesic use, and opioid analgesic use. Next, the dosage of all opioid analgesic use was converted into daily OMEs using Table 1, and the cumulative daily OME for each patient was calculated. Finally, using Table 2, the OME was translated into the AQA score. Validation In order to empirically compare the AQA with the WHOAL, data from an international, randomized, clinical trial comparing the effects of denosumab and zoledronic acid in patients with bone metastases from solid tumors other than breast or prostate or patients with multiple myeloma were used [20]. Pooled analgesic use data were scored using both the AQA and the WHO-AL at baseline and week 25. Shift in analgesic use from baseline among 228

patients with AQA score ≤2 was also assessed post hoc by treatment group using the AQA. Results A total of 1,776 patients were randomized in the clinical trial. Baseline characteristics and demographics have been reported previously and were shown to be balanced between treatment groups [20,27]. The mean (SD) age was 60 [11] years, and 64% were male. Non-small cell lung cancer was the most common tumor type (40%). Previous cancer treatments included chemotherapy (87%), surgery (46%), radiotherapy (38%), and other therapies (2%). Approximately 80% of patients in each group discontinued before the primary analysis cut-off date, most commonly due to death, withdrawal of consent, or disease progression. Baseline worst pain and health-related quality of life (FACT-G) were also balanced between treatment groups at baseline. Approximately, 38% reported no or mild pain and 55% reported moderate or severe pain (BPI-SF). Among the enrolled patients, 624 (35%) did not report using any analgesics at baseline. In the remaining patients who reported analgesic use at baseline, the WHO-AL demonstrated a ceiling effect that was not evident on the AQA (Figure 2A). Approximately, 37% of patients were represented in the highest WHO-AL category (category 3). In comparison, implementation of the AQA resulted in a distribution of scores across the five strong opioid categories (12%, 5.4%, 5.2%, 5.2%, and 9.3% in categories 3, 4, 5, 6, and 7, respectively). More than 25% of patients were represented in AQA categories 4–7 of strong opioid categories representing >75 mg OME daily. After 6 months (25 weeks) of treatment, analgesic use was again scored on the AQA and WHO-AL (Figure 2B). Similar to baseline scores, the WHO-AL demonstrated a ceiling effect with a clustering of 37% of patients in the strong opioid category. In comparison, the AQA demonstrated a distribution of scores across the five strong opioid categories at 6 months, with more than 20% of patients reporting analgesic use in AQA categories 4–7 of strong opioid categories representing >75 mg OME per day. When assessing shift from baseline in analgesic use among patients with no or weak opioid analgesics at baseline by treatment group, we were able to observe a greater proportion of zoledronic acid-treated patients shifted to AQA category >2 (i.e., strong opioid analgesic use). In addition, the AQA also demonstrated that increasingly stronger opioid analgesics are used by a greater proportion of zoledronic acid-treated patients than denosumab-treated patients (Figure 3). Discussion Several clinical trials of bisphosphonates have evaluated analgesic medication use for alleviating pain in patients with bone metastases secondary to advanced cancers [28–30]. A measure that is capable of differentiating

Analgesic Quantification Algorithm (AQA)

Figure 2 Distribution of AQA and WHO-AL scores in patients with solid tumors and bone metastases (except breast or prostate cancer) or multiple myeloma at (A) Baseline and (B) week 25. (A) At baseline, the AQA resulted in a distribution of scores throughout eight categories including the five strong opioid categories. However, scores on the WHO-AL were clustered in the strong opioid category. (B) After 6 months (25 weeks) of follow-up, the AQA again resulted in additional specificity with regard to the use of strong opioids compared with the WHO-AL, which demonstrated a ceiling effect with a clustering of patients in the strong opioid category. AQA = Analgesic Quantification Algorithm; WHO-AL = World Health Organization-Analgesic Ladder.

analgesic medication use at high levels is needed in order to better assess and understand the reasons underlying changes in patient-reported pain. The WHO-AL, which was issued by the WHO Expert Committee in 1986, is the most widely cited guideline for the management of pain. However, this three-step “ladder” was developed to improve the management of cancer pain (establishing the basic tenants of pain control wherein the treatment of cancer pain progresses through various steps of the ladder until relief from cancer pain is achieved) rather than to assess the use of analgesics. Given the frequent use of high-dose opioids to manage cancer pain, its use as an index for analgesic dose intensity appears to lack sensitivity, indicating a lack of differentiation between different dose levels of strong opioids.

The results of this study indicate that the AQA may be a more sensitive measure of changes in analgesic medication use compared with the WHO-AL as it further quantifies doses of strong opioids. Thus, the AQA provides additional specificity relative to the 3-step WHO-AL for assessing changes in the use of strong opioids. When used in patients with cancer pain, the AQA overcomes the problematic ceiling effect seen with the WHO-AL in which approximately two thirds of patients receive the top score [14]. Such ceiling effects have been found to attenuate the reliability and validity of measures [31,32]. Using clinical trial data for validation, the AQA was not found to be associated with ceiling effects and, in fact, provided greater detail for assessing differences between treatments, resulting in a greater ability to observe changes in standardized scores with the AQA relative to the WHO-AL.

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Figure 3 Proportion of patients with baseline AQA scores of ≤2 who shifted to >2. Using the AQA to assess shift in analgesic use, we were able to observe with greater granularity, the level of strong opioid analgesic use in patients who shifted from no, non-opioid or weak-opioid analgesia to strong opioid analgesia, and also observe between-treatment differences in strong opioid use that would otherwise not be detected with the WHO-AL. AQA = Analgesic Quantification Algorithm; WHO-AL = World Health Organization-Analgesic Ladder.

Specifically, the granularity of the AQA delineates the decreased analgesic use associated with denosumab versus zoledronic acid among patients with bone metastases from solid tumors other than breast or prostate or patients with multiple myeloma. Perhaps most notable, however, was the decreased proportion of denosumabtreated patients with AQA score of 7 compared with zoledronic acid treated subjects. This observation further supports the previously published data demonstrating a benefit with denosumab in preventing pain worsening and pain interference relative to zoledronic acid in this patient population [27]. Morphine equivalents are typically used to facilitate conversion between opioid analgesics based on relative analgesic potencies. While morphine equivalents, which are represented on a continuous scale, can be used as a measure of analgesic use, the AQA allows for the categorization of levels of narcotic use via discrete categories that can facilitate analyses (e.g., clinically meaningful shift from one category to another) and subsequent communication of the degree of change in analgesic use in clinical trial settings, which may better reflect the nonlinear upward titration of opioid analgesics in patients with cancer pain. Also, the AQA could be applied to the recent recommendation by the European Association for Palliative Care [33] that weak opioid analgesics may be substituted with lower doses of strong opioids, because AQA scores are first converted to OMEs. Development of the AQA required multiple scoring decisions. In order to equalize doses, therapies were classified by potency expressed in terms of their OME doses. 230

Assuming a precise linear relationship between dose and potency across different molecular compounds used by different individuals may be a simplification since there is wide variability in individuals’ responses to different opioids. Pereira and colleagues note that this variability may be due to factors such as the administration route, drug half-life or bioavailability, drug interactions, accumulation of opioid metabolites, or others [34]. The equianalgesic dosing conversions were consolidated from available published literature to account for differences in administration routes, unit, dose, and dose frequency for available formulations of strong and weak opioids. Although this list of conversions was employed successfully in recent multinational pivotal phase 3 clinical trials, it may need to be expanded when new opioids become available or if novel analgesic formulations are used that are not currently captured in the table. Still, the categorization of OME doses into five groups may offset potential misspecifications in the equianalgesic dosing table. The scale for the dosing categories (Table 2), where each step represents a doubling of OME dose, makes each category conceptually different and reflects a heuristic for dosing increases [25]. When applied to a clinical oncology trial, the AQA dosing categories adequately captured the range of doses of strong opioids observed in the trial. The AQA was developed for analyzing analgesic medication data from clinical trials. In order to ensure appropriate assessment of analgesic use, medication data should be collected using standardized methodology (i.e., case report form) that includes the drug name, narcotic

Analgesic Quantification Algorithm (AQA) component, narcotic dose, frequency, and route of administration, as well as the duration of treatment. The AQA score can then be calculated at baseline and at subsequent time points when analgesic medication use is assessed, in particular at time points when pain is also evaluated. Changes in pain scores can thus be evaluated before and after controlling for changes in AQA scores. If significant differences in AQA scores exist between treatment groups, then the AQA score should be included as a covariate when evaluating between-group differences in pain. While minor changes in analgesic medication use may not be clinically relevant and would not likely impact the AQA score, significant changes, likely indicative of meaningful changes in the amount of pain, would be captured. In addition to the limitations inherent in the OME dose conversions [34], another important limitation of our proposed methodology is that it covers only analgesic medications and does not include the effects of other adjuvant treatments or non-pharmaceutical interventions that are often used for cancer pain such as antidepressants and/or anticonvulsants [35]. In conclusion, the AQA may prove to be a more functional measure of analgesic medication use than the WHO-AL given its increased granularity. The AQA differentiates analgesic medication use based on dosing to a greater extent than the WHO-AL, particularly at higher dosing levels. Despite the inherently limited accuracy of equianalgesic dose conversions to OMEs, the expanded cut-points employed on the AQA scale represent an improvement in the assessment and analysis of analgesic medication use in controlled, comparative clinical trials. In clinical trials of cancer treatments, the AQA may assist in differentiating whether changes in individual patientreported pain outcomes are due to the investigational therapy versus changes in the level of analgesic medication use. More work on the psychometric properties and validation of the utility of the AQA for its proposed use is needed. Additionally, future research should quantify the increased precision achieved in estimating changes in pain scores when statistically controlling for analgesic use as measured by the AQA. Acknowledgments The authors wish to thank Vidya S. Beckman, MPH, MBA and Lori Gorton, PhD of Amgen Inc. for editing assistance with this article. References 1 van den Beuken-van Everdingen MH, de Rijke JM, Kessels AG, et al. Prevalence of pain in patients with cancer: A systematic review of the past 40 years. Ann Oncol 2007;18:1437–49. 2 Serlin RC, Mendoza TR, Nakamura Y, Edwards KR, Cleeland CS. When is cancer pain mild, moderate or severe? Grading pain severity by its interference with function. Pain 1995;61:277–84.

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