Determining the Minimal Clinical Important Difference for Medication Quantification Scale III and Morphine Milligram Equivalents in Patients with Failed Back Surgery Syndrome.
Lisa GoudmanAnn De SmedtPatrice ForgetMaartens MoensPublished in: Journal of clinical medicine (2020)
The Medication Quantification Scale III (MQS) is a tool to represent the negative impact of medication. A reduction in medication can serve as an indicator to evaluate treatment success. However, no cut-off value has yet been determined to evaluate whether a decrease in medication is clinically relevant. Therefore, the objective is to estimate the thresholds for the MQS and morphine milligram equivalents (MMEs) that best identify a clinically relevant important improvement for patients. Data from the Discover registry, in which patients with failed back surgery syndrome were treated with high-dose spinal cord stimulation, were used. Patient satisfaction was utilized to evaluate a clinically important outcome 12 months after stimulation. Anchor-based and distribution-based methods were applied to determine the minimal clinical important difference (MCID). Distribution-based methods revealed a value of 4.28 for the MQS and 33.61 for the MME as MCID. Anchor-based methods indicated a percentage change score of 41.2% for the MQS and 28.2% for the MME or an absolute change score of 4.72 for the MQS and 22.65 for the MME. For assessing a treatment outcome, we recommend using the percentage change score, which better reflects a clinically important outcome and is not severely influenced by high medication intake at baseline.
Keyphrases
- healthcare
- adverse drug
- spinal cord
- high dose
- minimally invasive
- patient satisfaction
- end stage renal disease
- newly diagnosed
- coronary artery bypass
- chronic kidney disease
- low dose
- case report
- ejection fraction
- spinal cord injury
- prognostic factors
- single cell
- coronary artery disease
- machine learning
- atrial fibrillation
- stem cell transplantation
- combination therapy
- replacement therapy
- artificial intelligence
- patient reported