Visualization of Individual Data: A Powerful Tool in Personalized Medicine
Authors: Kenneth Shulman, Amparo Casanova, & Marina Vives-Mestres
In clinical care, the primary goal is to determine treatment effectiveness for the individual patient. Patients want to know: which treatment is likely to work better for me?
Although data from randomized, parallel group clinical trials provide the gold-standard results used to practice evidence-based medicine, such results support only average treatment effects in a population, which may not apply to a given individual. Consequently, many patients may derive less than average benefit from a particular treatment. Therefore, informed clinical decisions are reliant on patient reported outcomes to assess individual burden of disease. Without a way to systematically assess clinical outcomes on an individual level, it is easy for both patient and clinician to be misled about the true effects of a particular therapy. With the help of a platform designed to capture individual patient reported outcomes, practicing clinicians can take an evidence-based approach to personalized medicine in the daily care of their patients.
Real World Evidence: Case studies from a migraine registry
Three cases of individuals with migraine are presented below to show how applying N-of-1 analytics and visualization of high-resolution data collected using a digital platform offers an objective and practical way of examining individual response to different therapies. Because migraine is a heterogeneous neurologic disorder with significant individual variation in headache frequency, severity and disability, response to migraine medication can vary between patients but also within the same patient over time. These individuals used the N1-Headache®app to enter details about their headache and migraine days, migraine-related disability and medications used to treat their migraine attacks. At baseline, the Migraine Disability Assessment (MIDAS) questionnaire established a self-reported retrospective, estimation of disability over the past three months in each of three domains (school/work, household work, and family, social and leisure). After 90 days of data collection, monthly disability scores were calculated from data captured prospectively by each individual on headache days using questions in the same domains.
Patient 1 had a baseline of 18 headaches per month, severe disability and eletriptan overuse. They were administered Botox for migraine prevention and taken off gabapentin. The effect of Botox is expected to last up to 3 months. After 3 months of improvement, gabapentin was reintroduced followed by 3 months of progressive regression to baseline headache frequency and increased disability. Acute treatment with eletriptan was discontinued and headache frequency gradually improved to 8 per month with moderate disability, eliminating the need for further Botox in the following year.
Patient 2 had episodic migraine and was started on Botox after worsening to 17 headaches per month. Despite eletriptan overuse, rapid improvement was seen following the first Botox treatment and sustained for 4 months up to the second treatment. Both migraine and headache frequency doubled following a switch to naratriptan for acute therapy, requiring steroids and a third Botox treatment.
Patient 3 had chronic migraine and severe disability (MIDAS score greater than 20), and had an initial response from 16 headaches/month to 13 headaches/month after Botox administration. Three months after a second course of Botox, headache frequency improved significantly to 4 per month, while disability improved yet remained severe.
Visualization of these results clearly illustrates inter- and intra-individual differences in migraine treatment outcomes over time, providing a more accurate reflection of the true heterogeneity of migraine, compared to results based on the standard approach of aggregating data and analyzing average patterns. Collecting and visualizing individual N-of-1 data using a digital platform is an effective way to monitor migraine treatment outcomes remotely, providing clinical decision support for the use of acute and preventative medications. Recognition of the clinical differences that can occur across individuals and migraine episodes offers the opportunity for better patient care and reduced healthcare costs.
Footnote: Figures reproduced from Prieto P, de la Torre ER, Shulman KJ, Vives-Mestres M, Lipton RB. Real world longitudinal analysis of response with an electronic platform. Cephalalgia 2018; 38 (1S): 14-15
About the authors
Ken Shulman, DO, former VP of Medical Affairs at Curelator Inc, is a clinical neurologist and headache specialist with over two decades of experience in patient care, medical affairs and clinical research, bringing innovative therapies and disease management tools to people with migraine.
Amparo Casanova is the VP Clinical Statistics at Curelator Inc. Amparo is a physician and a statistician. For the past twenty years, Amparo has contributed to enhancing clinical research methodology, with a particular emphasis on the issue of sample size for complex designs.
Marina Vives Mestres is a visiting professor at the University of Girona and the Analytics Lead at Curelator Inc. Marina is a statistician and an Industrial Engineer with special interest in solving real-world problems crossing several disciplines and communicating to all types of audiences.