Background & Aims

When developing treatment guidelines authors rely on the highest level of evidence available (1, 2). In the medical fields, where randomized clinical trials are missing, recommendations are often based on experts’ consensus. However, the commonly applied consensus methods are prone to bias due to the negotiating skills of the participants included in the panel (3). Furthermore, for complex clinical situations with multiple criteria, the application of guidelines in clinical practice may be challenging.
Here we present a novel approach to understand decision-making among experts in orofacial pain. We decided to focus primarily on the therapy algorithms for post-traumatic trigeminal neuropathic pain (PTNP), a diagnosis with no specific guidelines yet. Current therapy is typically based on general guidelines for neuropathic pain (4). Moreover, the literature on treatment outcomes for PTNP is scares, leaving plenty of leeway and limited guidance in the clinical practice.

Methods

The objective consensus method aims to transform recommendations from multiple experts into a summary decision-tree, which graphically presents the clinical decision process. The method was initially developed and applied in the field of radio-oncology. It has provided insight into otherwise hidden decision-making parameters and was able to identify areas of consensus and discrepancies (5, 6). To our knowledge, the method has not yet been employed in the area of pain treatment.
Experts in orofacial pain were asked to describe their first-line treatment strategy for patients with diagnosed PTNP. From the provided descriptions, all the decision criteria were identified and transformed into standardized decision trees based on a predefined methodology (7). The decision trees were then compared and the most common (mode) treatment recommendations for each possible combination of parameters (criteria) were identified.

Results

As the result of the comparison of the individual decision trees, a common (mode) tree was created. At the end of each branch, there is a treatment recommendation for the specific combination of criteria as well as the percentage of congruence.

Conclusions

The objective consensus method offers a possibility to quantitatively describe the clinical reasoning process and identify majorities for recommendations from multiple experts. The strengths of the method are its transparency as well as the completeness of the final tree. The graphical representation of the criteria and the resulting therapeutic choice is explicit and may assist in clinical decision-making. Potentially information provided by this method may motivate further efforts to come up with standardized recommendations in PTNP. We hope that the presented method will be of interest for the future guidelines’ authors and the IASP community in general.

References

1. Sackett DL, Rosenberg WM, Gray JA, Haynes RB, Richardson WS. Evidence based medicine: what it is and what it isn’t. BMJ. 1996 Jan 13;312(7023):71-2. doi: 10.1136/bmj.312.7023.71 . PMID: 8555924; PMCID: PMC2349778.

2. Hollon SD, Areán PA, Craske MG, Crawford KA, Kivlahan DR, Magnavita JJ, Ollendick TH, Sexton TL, Spring B, Bufka LF, Galper DI, Kurtzman H. Development of clinical practice guidelines. Annu Rev Clin Psychol. 2014; 10:213-41. doi: 10.1146/annurev-clinpsy-050212-185529. PMID: 24679179.

3. Jones J, Hunter D. Consensus methods for medical and health services research. BMJ. 1995 Aug 5;311(7001):376-80. doi: 10.1136/bmj.311.7001.376 . PMID: 7640549 ; PMCID: PMC2550437.

4. Finnerup NB, Attal N, Haroutounian S, McNicol E, Baron R, Dworkin RH, Gilron I, Haanpää M, Hansson P, Jensen TS, Kamerman PR, Lund K, Moore A, Raja SN, Rice AS, Rowbotham M, Sena E, Siddall P, Smith BH, Wallace M. Pharmacotherapy for neuropathic pain in adults: a systematic review and meta-analysis. Lancet Neurol. 2015 Feb;14(2):162-73. doi: 10.1016/S1474-4422(14)70251-0. Epub 2015 Jan 7. PMID: 25575710; PMCID: PMC4493167.

5. Putora PM, Panje CM, Papachristofilou A, Dal Pra A, Hundsberger T, Plasswilm L. Objective consensus from decision trees. Radiat Oncol. 2014 Dec 5;9:270. doi: 10.1186/s13014-014-0270-y. PMID: 25476988; PMCID: PMC4269842.

6. Rothermundt C, Bailey A, Cerbone L, Eisen T, Escudier B, Gillessen S, Grünwald V, Larkin J, McDermott D, Oldenburg J, Porta C, Rini B, Schmidinger M, Sternberg C, Putora PM. Algorithms in the First-Line Treatment of Metastatic Clear Cell Renal Cell Carcinoma–Analysis Using Diagnostic Nodes. Oncologist. 2015 Sep;20(9):1028-35. doi: 10.1634/theoncologist.2015-0145. Epub 2015 Aug 3. PMID: 26240132; PMCID: PMC4571803.

7. Panje CM, Glatzer M, von Rappard J, Rothermundt C, Hundsberger T, Zumstein V, Plasswilm L, Putora PM. Applied Swarm-based medicine: collecting decision trees for patterns of algorithms analysis. BMC Med Res Methodol. 2017 Aug 16;17(1):123. doi: 10.1186/s12874-017-0400-y. PMID: 28814269; PMCID: PMC5559810.

Presenting Author

Alexandra Lübber

Poster Authors

Aleksandra Zumbrunn Wojczynska

DMD

Univeristy of Zurich, Center of Dental Medicine

Lead Author

Alexandra Lüber

Universität Zürich ZZM

Lead Author

Paul Martin Putora

Prof. Dr. med. PhD MA MHI

Department of Radiation Oncology, Kantonsspital St. Gallen, Switzerland

Lead Author

Topics

  • Specific Pain Conditions/Pain in Specific Populations: Orofacial Pain