Stereotactic Radiosurgery for Type 1 versus Type 2 Trigeminal Neuralgias

Treatment Outcomes in Trigeminal Neuralgia-A Systematic Review of Domains, Dimensions and Measures

BACKGROUND

Trigeminal neuralgia (TN) is a painful disorder characterized by sudden electric shock-like pain. It is a rare condition for which multiple treatments are available, including medical and surgical. The best treatment option is yet to be defined, and this is related to the lack of definition in the treatment outcomes and outcome measures. The aim of this systematic review was to summarize all the outcomes and outcomes measures that have been published to date and highlight variability in their use.

METHODS

We have conducted a literature search using a wide range of databases (1946-2019 for medical and 2008-2019 for surgical treatment), for all intervention studies in TN. Four hundred and sixty-seven studies were selected for data extraction on TN classification, data collection method, intervention, and treatment outcomes mapped to the Initiative on Methods, Measurement, and Pain Assessment in Clinical Trials (IMMPACT guidelines).

RESULTS

Most studies collected data on pain (n = 459) and side effects (n = 386) domains; however, very few collected data on the impact of treatment on physical (n = 46) and emotional functioning (n = 17) and on patient satisfaction (n = 35). There was high variability on outcome measures used for pain relief (n = 10), pain intensity (n = 9), and frequency of pain episodes (n = 3).

CONCLUSIONS

A clear definition of what are the important outcomes for patients with TN is essential. The choice of standardized outcome measures allowing for consistent reporting in TN treatment will allow for comparison of studies and facilitate treatment choice for patients and clinicians thus, improving health outcomes and reducing health care cost.

Application of an artificial neural network model for early outcome prediction of gamma knife radiosurgery in patients with trigeminal neuralgia and determining the relative importance of risk factors

OBJECTIVES

Stereotactic radiosurgery (SRS) is a minimally invasive modality for the treatment of trigeminal neuralgia (TN). Outcome prediction of this modality is very important for proper case selection. The aim of this study was to create artificial neural networks (ANN) to predict the clinical outcomes after gamma knife radiosurgery (GKRS) in patients with TN, based on preoperative clinical factors.

PATIENTS AND METHODS

We used the clinical findings of 155 patients who were underwent GKRS (from March 2000 to march 2015) at Iran Gamma Knife center, Teheran, Iran. Univariate analysis was performed for a long list of risk factors, and those with P-Value < 0.2 were used to create back-propagation ANN models to predict pain reduction and hypoesthesia after GKRS. Pain reduction was defined as BNI score 3a or lower and hypoesthesia was defined as BNI score 3 or 4.

RESULTS

Typical trigeminal neuralgia (TTN) (P-Value = 0.018) and age>65 (P-Value = 0.040) were significantly associated with successful pain reduction and three other variables including radiation dosage >85 (P-Value = 0.098), negative history of diabetes mellitus (P-Value = 0.133) and depression (P-Value = 0.190). On the other hand, radio dosage>85 (P-Value = 0.008) was significantly associated with hypoesthesia, other related risk factors (with p-Value<0.2), were history of multiple sclerosis (P-Value = 0.106), pain duration more than 10 years before GKRS (P-Value = 0.115), history of depression (P-Value = 0.139), history of percutaneous ablative procedures (P-Value = 0.148) and history of diabetes mellitus (P-Value = 0.169).ANN models could predict pain reduction and hypoesthesia with the accuracy of 84.5% and 91.5% respectively. By mutual elimination of each factor in this model we could also evaluate the contribution of each factor in the predictive performance of ANN.

CONCLUSIONS

The findings show that artificial neural networks can predict post operative outcomes in patients who underwent GKRS with a high level of accuracy. Also the contribution of each factor in the prediction of outcomes can be determined using the trained network.