Variability among Online Opioid Conversion Calculators Performing Common Palliative Care Conversions

Abstract

Introduction:

Online opioid conversion calculators (OOCCs) are commonly used to aid conversion between opioids to overcome tolerance, reduce adverse effects, or challenges related to administration. The purpose of this study was to describe and characterize variability among OOCC used by health care practitioners when converting common opioids and doses encountered in the hospice and palliative care setting.

Methods:

We collected 58 quantitative surveys and performed sentiment analysis on 62 qualitative responses from adult learners primarily practicing in the palliative care setting and enrolled in an online palliative care Master of Science program through the University of Maryland, Baltimore, who were asked to perform opioid conversion calculations using realistic patient cases.

Results:

OOCC have substantial variability leading to a wide range of outputs, which may put patients at risk for opioid-related harm. Assessing participant sentiment toward OOCC showed most participants held a “Negative Sentiment” toward these calculators after the activity.

Conclusion:

Overall, findings reveal that given the same information, clinicians can come to widely different opioid doses and these differences can be amplified by OOCC. These differences can be particularly dangerous given the higher opioid doses commonly used in the palliative care setting. Considering the significant harm that can arise from an error when converting between opioids, clinicians should avoid the routine use of OOCC in real-world patient care settings. If an OOCC is used, organizations should endorse a specific calculator, provide training and education about the algorithm that supports the calculations, and encourage clinicians to use it only after their own manual calculation, which should be documented in the medical record.

Introduction

Calculating conversions between opioid regimens is a common intervention performed by pharmacists, physicians, and other health care practitioners (HCPs). Opioid conversions are commonly calculated to overcome tolerance, reduce adverse effects, and overcome administration or formulary barriers.^1,2 Furthermore, there is a strong therapeutic rationale for the conversion to a different opioid as an estimated 80% of patients experience a positive response after switching agents.³ In the inpatient setting, this may be a necessary intervention to facilitate continuous pain control during acute illness or improved safety if organ function is impaired or route of administration must be changed. Despite the relative frequency of this intervention, clinicians remain apprehensive because of the potential safety risks associated with inaccurate dose calculations when converting between agents.⁴

Equianalgesic tables and online opioid conversion calculators (OOCCs) have been developed to simplify the process of opioid conversion, which would otherwise require a manual mathematical calculation. The operator inputs the total daily dose (TDD) of the patient's current opioid regimen and selects the desired alternative. The calculator then proceeds to provide an output of the TDD of the alternate opioid. This usually leaves the interpretation, the validity, and potential clinical utilization of this value to the discretion of the operator.

There is no doubt that OOCCs are increasingly attractive to HCPs, given they are widely available, are free to access, and decrease the mental effort in the calculation process.⁵ However, there is a significant concern for the accuracy and reliability of such resources.⁶ OOCCs rely on opioid conversion factors that are used to convert between different opioids. One of the most common approaches used to standardize the assessment of an opioid dose is to convert all opioids to morphine (i.e., morphine equivalent dose [MED]). However, the evidence supporting conversion tables and equianalgesic doses continues to be a topic of debate leading to variability in the referenced opioid conversion chart used by each OOCC, which can contribute to large differences in the OOCC output.⁷

OOCCs often fail to provide a reference table making it difficult to validate or cross-check a calculated dose. Differences also exist in the doses generated by different calculators because some automatically account for cross-tolerance, while others provide a raw conversion to enable providers to reduce the dose by a specific amount based on a patient's current pain control. Without knowledge of the calculated method, two harms could be committed: (1) one may assume a dose reduction has automatically been built in and expose a patient to a 25–50% higher than intended dose, resulting in unacceptable toxicity, or (2) patients may be at risk of being undertreated if the dose is excessively reduced, leaving them with intolerable uncontrolled pain. Perhaps more importantly, essential steps for safe conversions are skipped if the result of OOCC is blindly applied to patients without adding clinical judgment.

OOCCs often fail to account for patient-specific factors that influence dose selection, including, but not limited to, organ function, age, and current pain severity. In the hospice and palliative care setting, the potential for error with the use of OOCCs may be amplified by the larger doses needed to control intractable pain and the common use of methadone, which has no standard conversion method.⁸ The margin for error in dose calculations in this population is extremely low, considering the consequences if a person's pain is under or over treated. This investigation aims to describe and characterize the variability among OOCCs used by HCP to convert common opioids and doses encountered in the hospice and palliative care setting as applied to realistic patient cases in an educational environment.

Methods

Setting

This study was conducted among a cohort of students enrolled during the summer of 2018 and 2019 in an advanced pain management and opioid dosing course within an online graduate certificate and Master of Science in Palliative Care program at the University of Maryland, Baltimore. Students were adult learners primarily practicing in hospice and palliative care, each with a varying degree of work experience. Data were extracted from an online discussion board and entered into an online database using Qualtrics^XM®. The Institutional Review Board of the University of Maryland, Baltimore approved the protocol (HP-00087799).

Scenarios

Participants were asked to identify three OOCCs and complete conversions for three scenarios described in Table 1. Following this exercise, participants were asked to reflect on their sentiments regarding using OOCCs in practice after obtaining their results with each tool used.

Table 1.

Description of Opioid Conversion Scenarios

Assignment
Identify three OOCCs and complete the following conversions (below). Which online calculators did you select? Are the answers the same? Different? Does the website tell you what algorithm supports their calculations? If you can find these data, how is it different from what is explained in your reading (chapter 1)? Which of the 5 steps explained in chapter 1 would you hypothesize are missing by using these online apps? What does your gut tell you about these calculations? Would you advise a colleague to use an OOCC? Why or why not? Reference your work.

Scenario A	Scenario B	Scenario C
78-year-old woman receiving transdermal fentanyl 75 mcg/h. Patient does not seem to be responding, despite dose increases. She is 5′4′′ and weighs 82 pounds. Convert to long-acting oral morphine and determine a dose of short-acting oral morphine for breakthrough pain.	58-year-old man with end-stage lung cancer receiving IV hydromorphone, 6 mg/hour with 3 mg bolus every 10 minutes (uses about 3 doses/hour). The pharmacy just called and you just got the last of the IV hydromorphone they have in stock. The patient can swallow tablets and capsules. Calculate an equivalent oral morphine regimen (oral long acting and short acting for breakthrough pain).	42-year-old man with low-back pain, receiving MS Contin 45 mg po q12h, with MSIR 15 mg po q4h as needed (takes 2 doses per day on average). His pain seems to have a neuropathic component. Convert to oral methadone.

OOCC, online opioid conversion calculator.

Statistical analysis

Participant professions were summarized using frequencies and percentages. While 62 participants completed the assignment, 4 were excluded from the quantitative analysis, having only provided qualitative responses. Based on quantitative responses across 3 scenarios, 17 different calculators were identified across 58 students. Participants could choose a different calculator per scenario and were asked to perform the conversion only once per calculator, not allowing for a test-retest reliability assessment per calculator. This ensured independence between calculators for purposes of statistical comparisons. Within each scenario, the calculator choices are summarized by frequencies and percentages.

Due to small sample sizes for some of the calculator choices, statistical analysis was focused on the top 5 calculators (1:Practical Pain, 3:GlobalRPh, 6:ClinCalc, 7:Oregon.gov, and 8:Agency Medical Director's Group) chosen across the three scenarios. Calculator MED outputs were tested for normality with Shapiro-Wilks and by visual assessment. The MED measures of the top 5 calculators are summarized by mean, standard deviation, median, and range. Calculations that resulted in missing measurements because the values were out of range or not calculated by the selected calculator were removed from the sample. A general linear model was utilized to assess for MED differences between the top 5 calculators, with a Dunnett's test applied to compare each calculator to the most frequently chosen calculator. The Practical Pain calculator was the most frequently used across all three scenarios. The calculated MED for each calculator was compared to the Practical Pain Management value.⁹ A separate referent value was determined by consensus between three pharmacists performed manual opioid conversion calculations.¹⁰ See Supplementary Appendix SA1 for referent calculation explanations.

Each calculator was compared to this referent value using a Wilcoxon Sign test for location. Indicated breakthrough opioid dose percentages, dosing frequencies, and whether the user believed that a cross-tolerance adjustment should occur were reported by scenario. A general best practice based on expert opinion is to reduce the equianalgesic dose of an opioid after switching by 25–50% to account for incomplete cross-tolerance, except in the case of methadone or fentanyl, which requires special consideration.¹¹ The medians and ranges of the calculated MEDs within the top 5 calculators by scenario were plotted against the referent values in each scenario. All statistical testing was two sided with a significance level of α = 0.05. All analyses were performed using SAS^® 9.4.

Two coders independently reviewed the written sentiments of the students toward the use of OOCC in practice and classified the sentiments as positive, negative, or neutral. The two coders then met to identify any difference in classifications and discussed the differences until consensus was achieved regarding differing classifications.

Results

The sample consisted of 58 participants, who were primarily physicians (25.86%), nurses (24.14%), and pharmacists (18.97%). The professions of the participants are described in Table 2. In all three scenarios, the most popular choices were the Practical Pain Management (27%), ClinCalc (25%), GlobalRPh (14%), Agency Medical Director's Group (8%), and Oregon.gov (8%) calculators as summarized in Table 3.

Table 2.

Description of Participant Professions, n = 58

Profession	Frequency	Percentage
Physician	15	25.86
Nurse	14	24.14
Pharmacist	11	18.97
Advanced practice nurse	7	12.07
Pharmacy student	4	6.9
Physician assistant	2	3.45
Hospice Liaison	1	1.72
Missionary	1	1.72
Other	1	1.72
Student	1	1.72
Veterinarian	1	1.72

Table 3.

Frequency and Percentages of Chosen Calculators by Scenario

Calculator	Scenario A		Scenario B		Scenario C
Calculator	Frequency	Percent	Frequency	Percent	Frequency	Percent
Practical pain management	47	27.01	48	27.59	47	27.01
ClinCalc	45	25.86	44	25.29	45	25.86
Global RPh	25	14.37	25	14.37	25	14.37
Agency medical director's group	15	8.62	15	8.62	15	8.62
Oregon.gov	14	8.05	14	8.05	14	8.05
MedCalc	6	3.45	6	3.45	6	3.45
HOPWEB	6	3.45	4	2.3	6	3.45
Other	4	2.3	2	1.15	4	2.3
Oregon pain guidance	3	1.72	3	1.72	3	1.72
Ohio PMP	0	0.00	3	1.72	0	0.00
AAFP	2	1.15	4	2.3	2	1.15
LexiComp	2	1.15	1	0.57	2	1.15
eOpioid	1	0.57	1	0.57	1	0.57
FPM ANZCA	1	0.57	1	0.57	1	0.57
Fast facts	1	0.57	1	0.57	1	0.57
Clinical pharmacology	1	0.57	1	0.57	1	0.57
Johns Hopkins	1	0.57	1	0.57	1	0.57
Total	152		152		152
Missing values	12		37		29

In scenario A, 140 calculations were collected from the 5 most popular calculators. The most frequently used calculator was the Practical Pain Management calculator (30.9%), which produced a mean of 117.14 MED, a median of 130 MED, and a range of 60 to 315 MED. When comparing the mean calculated MEDs of the four most frequently used calculators to the Practical Pain Management calculator, the mean differences of the Global RPh (185.3 vs. 117.1, p = 0.02) and ClinCalc (311.8 vs. 117.1, p ≤ 0.0001) were significantly different. The mean difference of MED between the Oregon.gov and Principal Pain calculator was marginally significant (185.1 vs. 117.1, p = 0.055). The median of the determined referent value for Scenario A was 60 MED and the range was 40 to 80 MED. In comparisons between the referent value to the other calculators in scenario A, all calculations were significantly different from the referent value as illustrated in Figure 1A.

FIG. 1.

Graph of medians and ranges of MEDD for referent values and the most frequently used calculators. MEDD, morphine equivalent daily doses.

In scenario B, 115 calculations were performed by the same 5 calculators. The Practical Pain Management calculations produced a mean of 4631.25 MED, a median of 5040 MED, and a range of 150 to 7300. The referent value was determined to have a median of 2250 MED, and a range of 1500 to 3000 MED. When compared to the Practical Pain Management calculator, the mean of the Global RPh calculator was determined to be not significantly different and almost equal to the Practical Pain Management calculated MEDs (4114.28 vs. 4631.2, p = 0.63). All other calculations were found to be significantly different from the Practical Pain Management calculation. The Practical Pain Management Calculator (p < 0.01), Global RPh (p < 0.01), and ClinCalc (p < 0.01) calculations were significantly different from the referent value. Calculations from Oregon.gov (p = 0.29) and Agency Medical Director's Group (p = 0.57) were not significantly different from the referent value. This is illustrated in Figure 1B.

In scenario C, 123 calculations were performed with all calculators, except the Agency Medical Director's group due to the fact it only had a single measurement. The Practical Pain Management calculations produced a mean of 15.65 MED, a median of 15 MED, with a range of 9.3 to 27.3 MED. When compared to the other three calculators, there were no differences in their mean MED calculations. The referent value was determined to have a median of 12 MED, and a range of 10 to 15 MED. Calculations from Oregon.gov were the only calculations that were not significantly different from the Practical Pain Management (p = 0.45) calculations or from the referent value (p = 0.13). This is illustrated in Figure 1C. Comparisons could not be performed with the Agency Medical Director's Group calculations in Scenario C due to the small sample size.

The means, standard deviations, medians, and ranges of the 5 most frequently used calculators and referent values, as well as the p-values from the Dunnett's tests for all scenarios are summarized in Table 4. The range when converting fentanyl to morphine in Scenario A was 60–3125 mg, when converting hydromorphone to morphine in Scenario B was 90–8306 mg, and when converting morphine to methadone in Scenario C was 7.5–120 mg

Table 4.

Mean, Standard Deviations, Median, Interquartile Range, Minimum, Maximum, and p Values

		Referent value	Practical pain management	Global RPh	ClinCalc	Oregon.gov	Agency medical director's group
Scenario A	N	3	47	20	42	13	14
	Mean (SD)	—	117.14 (43.39)	332.33 (671.57)	311.9 (114.1)	185.08 (37.62)	167.07 (25.82)
	Median [IQR]	60	130 [94.5, 135]	150 [87.75, 245]	340 [220, 340]	180 [180, 180]	180 [180, 180]
	Min, Max	40, 80	60, 315	75, 3125	120, 770	126, 300	113, 180
	p Value^*	—	Ref.	0.019	<0.0001	0.055	0.0218
	p Value^†	Ref.	<0.01	<0.01	<0.01	<0.01	<0.01
Scenario B	N	3	48	25	10	14	13
	Mean (SD)	—	4631.25 (2010.12)	4114.28 (2076.8)	465.2 (395)	2058.86 (2186.1)	1955.54 (1543.54)
	Median [IQR]	2250	5040 [3600, 5880]	3600 [3600, 5400]	275 [210, 1001]	1420 [860, 1440]	1440 [1440, 3600]
	Min, Max	1500, 3000	150, 7300	120, 8306	100, 1080	432, 8640	90, 5040
	p Value^*	—	Ref.	0.705	<0.0001	<0.0001	<0.0001
	p Value^†	Ref.	<0.01	<0.01	<0.01	0.29	0.57
Scenario C	N	3	47	21	45	4	1
	Mean (SD)	—	15.65 (3.87)	17.54 (3.88)	14.51 (16.55)	23.5 (11.27)	17.2 (.)
	Median [IQR]	12	15 [13.9, 18.6]	15 [15, 22.5]	11 [11, 15]	19.5 [16.5, 30.5]	17.2 [17.2, 17.2]
	Min, Max	10, 15	9.3, 27.3	11.2, 22.5	7.5, 120	15, 40	17.2, 17.2
	p Value^*	—	Ref.	0.936	0.976	0.507	—
	p Value^†	Ref.	<0.01	<0.01	<0.01	0.13	—

Dunnett's test, p value for comparison to a single control with the Practical Pain Management calculator as a reference.

†

p Value generated by the Wilcoxon Sign test for location with the referent value.

IQR, interquartile range; SD, standard deviation.

For scenario A, the most frequently indicated breakthrough pain percentage was less than 10% (31%). Around 22.41% of the students did not opt to provide a dose for breakthrough pain. The most frequent dosing frequency for breakthrough pain was Q4h (50.00%) or was not stated by the calculator (27.59%). The majority of participants (60.34%) indicated that reduction for cross-tolerance would be needed when converting from transdermal fentanyl to long-acting morphine. Within scenario B, most participants did not provide a dose or frequency for breakthrough pain (34.48% and 39.66%, respectively). Finally, 68.97% of participants indicated that there should be a dose reduction for cross-tolerance. For scenario C, participants were not asked to provide dosing or frequencies for breakthrough pain. A majority of participants (58.62%) did, however, indicate that a dose reduction for cross-tolerance should occur. The breakthrough dose ranges, dosing frequencies, and cross-tolerance reduction indicators for each calculator are summarized in Table 5.

Table 5.

Dosing and Reduced Tolerance Indicator for All Scenarios

	Scenario A		Scenario B		Scenario C
	N	Frequency	N	Frequency	N	Frequency
Break
<10%	18	31.03	13	22.41	—	—
10–15%	17	29.31	17	29.31	—	—
>15%	10	17.24	8	13.79	—	—
Did not provide	13	22.41	20	34.48	—	—
Dosing
Q1h	4	6.90	2	3.45	—	—
Q2h	4	6.90	10	17.24	—	—
Q3h	2	3.45	1	1.72	—	—
Q4h	29	50.00	21	36.21	—	—
>Q4h	3	5.17	1	1.72	—	—
Not stated	16	27.59	23	39.66	—	—
Reduce cross-tolerance
Yes	35	60.34	40	68.97	34	58.62
No	23	39.66	18	31.03	24	41.38

In the sentiment analysis, out of 62 students, 47 (75.81%) students expressed negative sentiments regarding the opioid conversion calculators. Two (3.23%) were positive, and 13 (20.97%) were neutral.

Discussion

Opioid calculators are convenient, accessible, and can reduce the amount of time clinicians need to spend performing a manual calculation. However, the manual process of performing an opioid conversion calculation typically involves a five-step process.¹² The steps include a thorough assessment of the patient's pain (Step 1), accurate calculation of the patient's total daily use of current opioid (Step 2), mathematical conversion to the new opioid (Step 3), adjustment of the calculated dose based on patient-specific variables (Step 4), and monitoring the patient's response to the new opioid regimen (Step 5). At best, OOCCs only perform Steps 2 and 3 of the five steps leaving OOCCs prone to substantial errors. Our study found significant variability in manual calculation using OOCCs leading to subtherapeutic or supratherapeutic dosing regimens with inadequate breakthrough medications. Subtherapeutic dosing regimens can lead to unrelieved pain, which has consequences beyond the perception of pain. Particularly, in patients living with serious illness, unrelieved pain can worsen depression, decrease quality of life, and impact a person's ability to function.^13,14 Supratherapeutic dosing regimens may increase a person's risk for an adverse reaction or harm, such as increased somnolence, respiratory depression, or death.¹⁵ For example, one of the five steps requires a clinician to individualize the dose and ensure adequate access to breakthrough medication, yet this aspect of conversion was often missing when participants provided their answers for Scenario A and B. Furthermore, Scenario A and C involve conversions with fentanyl and methadone. While HCPs will typically reduce the dose of a new opioid by 25–50% to account for incomplete cross-tolerance, this step is generally not performed for fentanyl and methadone as incomplete cross-tolerance is already factored in the calculation. These complexities are not made clear to the operator using the calculator and provide another point of failure when using an OOCC. Many states also have limits on oral morphine equivalents, which are not reflected in these tools and may lead to patient mismanagement.¹⁶ These examples along with the clinical reasoning provided in Supplementary Appendix SA1 demonstrate the critical thinking that must be applied by any user of an OOCC.

The outcomes of this dataset also suggest substantial variability among the calculators available. Whether the calculators were compared to the reference value calculated manually or to the Practical Pain Management calculator, the results were variable to a statistically and clinically significant degree. Among the 58 participants included in the analysis, 20 different OOCCs were identified. Previous evaluations have demonstrated that not all OOCCs are created equal, but the average user of an OOCC may not be aware of these differences or have a way to judge the quality of the tool.¹⁷ The utility of educational interventions to improve opioid conversions demonstrated mixed results when it comes to improving overall knowledge and competence performing conversion calculations.¹⁸ Nevertheless, education on the strengths, weaknesses, and appropriate use of OOCC appears to raise awareness of some of their limitations as demonstrated by the negative sentiment a majority of the participants in our study expressed at the conclusion of the activity.

Recognizing the differences in calculators, participants recommended having single calculator established within health systems or hospice organizations. While this may reduce some of the variance, it is unlikely to fully resolve the safety concerns, given patient-specific factors such as pharmacogenomics that science continues to elucidate. The data demonstrate that even when clinicians use the same calculator, the proposed dosages can still vary. Even if the results are not statistically different, there can still be a relevant safety concern related to some of the results. For example, in Scenario C, the Oregon.gov calculator provided users recommendations for a methadone dose that ranged between 15.5 and 30.05 mg/methadone/day. This was almost double the dose that was calculated manually and could place a patient at significant risk for harm if they are in a setting without vigilant monitoring and assessment. These results were consistent with a previous study by Rennick et al., which found substantial variability in OOCCs, especially as it relates to fentanyl and methadone where they found conversions could vary by up to 100% and 242%, respectively.⁸

The variability described represents differences in calculator output based on operator interaction with the tool. A common feature of all OOCCs is the disclaimer, terms, and conditions that state their calculator is no substitute for clinical judgment. While this statement may absolve them from legal repercussions, it does not remove the moral obligation of those who create them to ensure they adhere to best practices. The preferred approach for an opioid conversion is to engage a clinician with competency in pain management to manually perform the opioid conversion and optimize the dose based on patient-specific factors. An alternate approach would be for OOCCs to incorporate the five steps of an OOCC, transparently report conversion factors, methodologies, and provide specific monitoring instructions to ensure the user is well informed.

OOCC should also identify or name the health care professionals who were involved in the creation and maintenance of the calculators. For example, the Practical Pain Management Calculator provides details about the authors of their calculator, which was developed with a physician and two pharmacists.¹⁹ Palliative care professionals should continue to raise awareness among trainees regarding the limitations of opioid calculators in patients with serious illnesses. Future studies could use a study design that supports both a inter-rater reliability and intra-rater reliability assessment.

Due to sample size and the large number of calculators available, certain calculators did not have enough responses to enable comparison and had to be dropped from the statistical analysis. Responses in this analysis may not be fully representative of the care delivered to people in palliative care or hospice settings, given that they were provided by students in an online Master of Science in Palliative Care program. However, given that many of the students are licensed clinicians actively practicing in hospice and palliative care, these responses provide useful insights to answer the research question.

Conclusion

This study highlights the variability within and between OOCCs. This can likely be attributed to differences in both calculator methodology and human factors. It also highlights the limitations these tools have when performing calculations with opioids at doses commonly seen in the palliative and hospice setting. Considering the significant harm that can arise from an error when converting to an alternative opioid, clinicians should avoid the routine use of OOCCs in real-world patient care settings. If an OOCC is used, organizations should endorse a specific calculator, provide training and education about the algorithm that supports the calculations, and recommend clinicians only use it after their own manual calculation, which should be documented in the medical record.

Footnotes

Disclaimer

The views expressed are solely those of the authors and do not reflect the official policy or position of the Uniformed Services University, U.S. Army, the Department of Defense, or the U.S. Government.

Funding Information

No funding was received for this project.

Author Disclosure Statement

No competing financial interests exist.

Supplementary Material

References

Mercadante

, Bruera

: Opioid switching: A systematic and critical review. Cancer Treat Rev, 2006; 32:304–315.

Mercadante

, Ferrera

, Villari

, et al.: Frequency, indications, outcomes, and predictive factors of opioid switching in an acute palliative care unit. J Pain Symptom Manage, 2009; 37:632–641.

Webster

, Fine

: Review and critique of opioid rotation practices and associated risks of toxicity. Pain Med, 2012; 13:562–570.

Buss

, Lessen

, Sullivan

, et al.: Hematology/oncology fellows' training in palliative care: Results of a national survey. Cancer, 2011; 117:4304–4311.

Plagge

, Ruppen

, Ott

, et al.: Dose calculation in opioid rotation: Electronic calculator vs manual calculation. Int J Clin Pharm, 2011; 33:25–32.

Lynn

, Webster

, Fine

: Review and critique of opioid rotation practices and associated risks of toxicity. Pain Med, 2012; 13:562–570.

Vissers

, Besse

, Hans

, et al.: Opioid rotation in the management of chronic pain: Where is the evidence?. Pain Pract, 2010; 10:85–93.

Rennick

, Atkinson

, Cimino

, et al.: Variability in opioid equivalence calculations. Pain Med, 2016; 17:892–898.

Opioid Calculator—Conversion Data and Methods. Practical Pain Management. https://opioidcalculator.practicalpainmanagement.com/methods.php. (Last accessed April 25, 2021).

10.

McPherson

: Demystifying Opioid Conversion Calculations: A Guide for Effective Dosing. Bethesda, MD: American Society of Health Systems Pharmacists, 2018.

11.

Fine

, Portenoy

: Establishing “best practices” for opioid rotation: Conclusions of an expert panel. J Pain Symptom Manage, 2009; 38:418–425.

12.

Gammaitoni

, Fine

, Alvarez

, et al.: Clinical application of opioid equianalgesic data. Clin J Pain, 2003; 19:286–297.

13.

Sinatra

: Causes and consequences of inadequate management of acute pain. Pain Med, 2010; 11:1859–1871.

14.

Strassels

, McNicol

, Wagner

, et al.: Persistent postoperative pain, health-related quality of life, and functioning 1 month after hospital discharge. Acute Pain, 2004; 6:95–104.

15.

Dowell

, Haegerich

, Chou

: CDC Guideline for Prescribing Opioids for Chronic Pain—United States, 2016. MMWR Recomm Rep, 2016; 65(No. RR-1):1–49.

16.

Chua

, Brummett

, Waljee

: Opioid prescribing limits for acute pain: Potential problems with design and implementation. JAMA, 2019; 321:643–644.

17.

Shaw

, Fudin

: Evaluation and Comparison of Online Equianalgesic Opioid Dose Conversion Calculators. https://www.practicalpainmanagement.com/treatments/pharmacological/opioids/evaluation-comparison-online-equianalgesic-opioid-dose-conversion. (Last accessed September 26, 2020).

18.

Scott

, Borate

, Heitner

, et al.: Pain management practices by internal medicine residents—A comparison before and after educational and institutional interventions. Am J Hosp Palliat Care, 2009; 25:431–439.

19.

Opioid Calculator—About the Authors. Practical Pain Management. https://opioidcalculator.practicalpainmanagement.com/authors.php. (Last accessed March 1, 2020).

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.02 MB