Personalized Depression Treatment

Traditional therapies and medications don't work for a majority of people who are depressed. A customized treatment could be the solution.

Cue is an intervention platform that converts passively acquired sensor data from smartphones into personalised micro-interventions for improving mental health. We analyzed the best-fitting personalized ML models to each subject, using Shapley values to discover their characteristic predictors. The results revealed distinct characteristics that deterministically changed mood over time.

Predictors of Mood

Depression is one of the world's leading causes of mental illness.1 However, only half of people suffering from the disorder receive treatment1. To improve the outcomes, doctors must be able to recognize and treat patients who are the most likely to benefit from certain treatments.

Personalized depression treatment is one method to achieve this. Researchers at the University of Illinois Chicago are developing new methods to predict which patients will benefit the most from certain treatments. They are using mobile phone sensors, a voice assistant with artificial intelligence as well as other digital tools. Two grants totaling more than $10 million will be used to determine biological and behavior indicators of response.

To date, the majority of research on factors that predict depression treatment effectiveness has centered on sociodemographic and clinical characteristics. These include demographic variables like age, sex and education, clinical characteristics including the severity of symptoms and comorbidities and biological markers such as neuroimaging and genetic variation.

While many of these aspects can be predicted by the information in medical records, very few studies have utilized longitudinal data to explore predictors of mood in individuals. Many studies do not take into consideration the fact that mood can vary significantly between individuals. Therefore, it is crucial to develop methods that permit the identification of different mood predictors for each person and the effects of treatment.

The team's new approach uses daily, in-person evaluations of mood and lifestyle variables using a smartphone app called AWARE, a cognitive evaluation with the BiAffect app and electroencephalography -- an imaging technique that monitors brain activity. The team is able to develop algorithms to identify patterns of behavior and emotions that are unique to each person.

In addition to these modalities the team created a machine learning algorithm to model the changing factors that determine a person's depressed mood. The algorithm combines these individual variations into a distinct "digital phenotype" for each participant.

This digital phenotype was correlated with CAT-DI scores, which is a psychometrically validated symptom severity scale. However, the correlation was weak (Pearson's r = 0.08, BH-adjusted P-value of 3.55 x 10-03) and varied widely among individuals.

Predictors of Symptoms

Depression is the most common reason for disability across the world1, however, it is often untrtempts, and the frequency with which they consumed alcohol. The CAT-DI was used to rate the severity of depression-related symptoms on a scale of zero to 100. CAT-DI assessments were conducted each week for those who received online support and weekly for those receiving in-person care.

Predictors of Treatment Response

The development of a personalized depression treatment is currently a major research area, and many studies aim to identify predictors that help clinicians determine the most effective medications for each individual. In particular, pharmacogenetics identifies genetic variants that influence how the body's metabolism reacts to antidepressants. This allows doctors to select drugs that are likely to work best for each patient, reducing the time and effort in trial-and-error treatments and avoiding side effects that might otherwise hinder the progress of the patient.

Another promising approach is to build predictive models that incorporate the clinical data with neural imaging data. These models can then be used to determine the best combination of variables that is predictors of a specific outcome, like whether or not a drug is likely to improve symptoms and mood. These models can be used to determine the patient's response to an existing treatment, allowing doctors to maximize the effectiveness of the treatment currently being administered.

A new generation of machines employs machine learning methods such as supervised and classification algorithms, regularized logistic regression and tree-based methods to integrate the effects from multiple variables and increase the accuracy of predictions. These models have shown to be useful for the prediction of treatment outcomes like the response to antidepressants. These approaches are gaining popularity in psychiatry, and it is likely meds that treat depression and anxiety they will become the norm for the future of clinical practice.

Research into depression's underlying mechanisms continues, in addition to predictive models based on ML. Recent findings suggest that depression is connected to the dysfunctions of specific neural networks. This suggests that an the treatment for depression will be individualized built around targeted treatments that target these circuits in order to restore normal function.

One way to do this is through internet-delivered interventions that offer a more individualized and tailored experience for patients. A study showed that an internet-based program helped improve symptoms and led to a better quality life for MDD patients. In addition, a controlled randomized study of a personalised approach to treating depression showed steady improvement and decreased adverse effects in a large number of participants.

Predictors of Side Effects

A major challenge in personalized depression treatment resistant depression treatment involves identifying and predicting which antidepressant medications will cause the least amount of side effects or none at all. Many patients are prescribed various medications before settling on a treatment that is safe and effective. Pharmacogenetics is an exciting new method for an efficient and specific approach to choosing antidepressant medications.

Several predictors may be used to determine which antidepressant is best to prescribe, including genetic variants, phenotypes of patients (e.g. gender, sex or ethnicity) and the presence of comorbidities. To identify the most reliable and accurate predictors for a particular treatment, randomized controlled trials with larger numbers of participants will be required. This is because it may be more difficult to determine interactions or moderators in trials that contain only one episode per person instead of multiple episodes spread over time.

Additionally the prediction of a patient's response to a specific medication to treat anxiety and depression is likely to require information on symptoms and comorbidities and the patient's personal experience of its tolerability and effectiveness. At present, only a few easily identifiable sociodemographic and clinical variables appear to be reliable in predicting the severity of MDD factors, including gender, age race/ethnicity, BMI and the presence of alexithymia and the severity of depressive symptoms.

The application of pharmacogenetics to depression treatment is still in its beginning stages, and many challenges remain. First it is necessary to have a clear understanding of the underlying genetic mechanisms is needed, as is an understanding of what is a reliable indicator of treatment response. Ethics, such as privacy, and the ethical use of genetic information must also be considered. In the long-term, pharmacogenetics may offer a chance to lessen the stigma that surrounds mental health treatment and to improve treatment outcomes for those struggling with morning depression treatment. As with any psychiatric approach it is crucial to give careful consideration and implement the plan. For now, the best option is to offer patients a variety of effective medications for depression and encourage them to talk with their physicians about their experiences and concerns.