Finding treatment-resistant depression in real-world data: How a data-driven approach compares with expert-based heuristics

Clinical associations with treatment resistance in depression: An electronic health record study

Treatment resistance is common in major depressive disorder (MDD), yet clinical risk factors are not well understood. Using a discovery-replication design, we conducted phenome-wide association studies (PheWASs) of MDD treatment resistance in two electronic health record (EHR)-linked biobanks. The PheWAS included participants with an MDD diagnosis in the EHR and at least one antidepressant (AD) prescription. Participant lifetime diagnoses were mapped to phecodes. PheWASs were conducted for three treatment resistance outcomes based on AD prescription data: number of unique ADs prescribed, ≥1 and ≥2 CE switches. Of the 180 phecodes significantly associated with these outcomes in the discovery cohort (n = 12,558), 71 replicated (n = 8,206). In addition to identifying known clinical factors for treatment resistance in MDD, the total unique AD prescriptions was associated with additional clinical variables including irritable bowel syndrome, gastroesophageal reflux disease, symptomatic menopause, and spondylosis. We calculated polygenic risk of specific-associated conditions and tested their association with AD outcomes revealing that genetic risk for many of these conditions is also associated with the total unique AD prescriptions. The number of unique ADs prescribed, which is easily assessed in EHRs, provides a more nuanced measure of treatment resistance, and may facilitate future research and clinical application in this area.

The complexity of treatment-resistant depression: A data-driven approach

BACKGROUND

Recent systematic reviews highlight great variability in defining and assessing treatment-resistant depression (TRD). A key problem is that definitions are consensus rather than data-led. This study seeks to offer a comprehensive socio-demographic and clinical description of a relevant sample.

METHODS

As part of a pragmatic randomized controlled trial, patients (N = 129) were managed in primary care for persistent depression and diagnosed with TRD. Data included previous treatment attempts, characteristics of the depressive illness, functioning, quality of life, co-occurring problems including suicidality, psychiatric and personality disorders, physical health conditions, and adverse events.

RESULTS

Findings show a severe and chronic course of depression with a duration of illness of 25+ years. Overall, 82.9 % had at least one other psychiatric diagnosis and 82.2 % at least one personality disorder; 69.8 % had significant musculoskeletal, gastrointestinal, genitourinary, or cardiovascular and respiratory physical health problems. All but 14 had severe difficulties in social and occupational functioning and reported severely impaired quality of life. Suicidal ideation was high: 44.9 % had made at least one serious suicide attempt and several reported multiple attempts with 17.8 % reporting a suicide attempt during childhood or adolescence. Of the patients, 79.8 % reported at least one adverse childhood experience.

LIMITATIONS

Potential for recall bias, not examining possible interactions, and absence of a control group.

CONCLUSIONS

Our findings reveal a complex and multifaceted condition and call for an urgent reconceptualization of TRD, which encompasses many interdependent variables and experiences. Individuals with TRD may be at a serious disadvantage in terms of receiving adequate treatment.

Facts and myths about use of esketamine for treatment-resistant depression: a narrative clinical review

Introduction and aims

Treatment-resistant depression (TRD) occurs when at least two different antidepressants, taken at the right dosage, for adequate period of time and with continuity, fail to give positive clinical effects. Esketamine, the S-enantiomer of ketamine, was recently approved for TRD treatment from U.S. Food and Drug Administration and European Medicine Agency. Despite proved clinical efficacy, many misconceptions by clinicians and patients accompany this medication. We aimed to review the most common "false myths" regarding TRD and esketemine, counterarguing with evidence-based facts.

Methods

The keywords "esketamine", "treatment resistance depression", "depression", "myth", "mythology", "pharmacological treatment", and "misunderstanding" were entered in the main databases and combined through Boolean operators.

Results

Misconceptions regarding the TRD prevalence, clinical features and predictors have been found. With respect of esketamine, criteria to start treatment, dissociative symptoms, potential addiction and aspects of administration and monitoring, were found to be affected by false beliefs by clinicians and patients.

Discussion and conclusion

TRD represents a challenging condition, requiring precise diagnosis in order to achieve patient's full recovery. Esketamine has been proved as an effective medication to treat TRD, although it requires precautions. Evidence can inform clinical practice, in order to offer this innovative treatment to all patients with TRD.

Explainable multimodal prediction of treatment-resistance in patients with depression leveraging brain morphometry and natural language processing

Although 20 % of patients with depression receiving treatment do not achieve remission, predicting treatment-resistant depression (TRD) remains challenging. In this study, we aimed to develop an explainable multimodal prediction model for TRD using structured electronic medical record data, brain morphometry, and natural language processing. In total, 247 patients with a new depressive episode were included. TRD-predictive models were developed based on the combination of following parameters: selected tabular dataset features, independent components-map weightings from brain T1-weighted magnetic resonance imaging (MRI), and topic probabilities from clinical notes. All models applied the extreme gradient boosting (XGBoost) algorithm via five-fold cross-validation. The model using all data sources showed the highest area under the receiver operating characteristic of 0.794, followed by models that used combined brain MRI and structured data, brain MRI and clinical notes, clinical notes and structured data, brain MRI only, structured data only, and clinical notes only (0.770, 0.762, 0.728, 0.703, 0.684, and 0.569, respectively). Classifications of TRD were driven by several predictors, such as previous exposure to antidepressants and antihypertensive medications, sensorimotor network, default mode network, and somatic symptoms. Our findings suggest that a combination of clinical data with neuroimaging and natural language processing variables improves the prediction of TRD.

Irremediability in psychiatric euthanasia: examining the objective standard

BACKGROUND

Irremediability is a key requirement for euthanasia and assisted suicide for psychiatric disorders (psychiatric EAS). Countries like the Netherlands and Belgium ask clinicians to assess irremediability in light of the patient's diagnosis and prognosis and 'according to current medical understanding'. Clarifying the relevance of a default objective standard for irremediability when applied to psychiatric EAS is crucial for solid policymaking. Yet so far, a thorough examination of this standard is lacking.

METHODS

Using treatment-resistant depression (TRD) as a test case, through a scoping review in PubMed, we analyzed the state-of-the-art evidence for whether clinicians can accurately predict individual long-term outcome and single out irremediable cases, by examining the following questions: (1) What is the definition of TRD; (2) What are group-level long-term outcomes of TRD; and (3) Can clinicians make accurate individual outcome predictions in TRD?

RESULTS

A uniform definition of TRD is lacking, with over 150 existing definitions, mostly focused on psychopharmacological research. Available yet limited studies about long-term outcomes indicate that a majority of patients with long-term TRD show significant improvement over time. Finally, evidence about individual predictions in TRD using precision medicine is growing, but methodological shortcomings and varying predictive accuracies pose important challenges for its implementation in clinical practice.

CONCLUSION

Our findings support the claim that, as per available evidence, clinicians cannot accurately predict long-term chances of recovery in a particular patient with TRD. This means that the objective standard for irremediability cannot be met, with implications for policy and practice of psychiatric EAS.

Suicide-specific mortality among patients with treatment-resistant major depressive disorder, major depressive disorder with prior suicidal ideation or suicide attempts, or major depressive disorder alone

BACKGROUND

The impact of treatment-resistant depression (TRD) or prior suicidal ideation/suicide attempt (SI/SA) on mortality by suicide among patients with major depressive disorder (MDD) is not well known. This retrospective, observational, descriptive cohort study characterized real-world rates of suicide-specific mortality among patients with MDD with or without TRD or SI/SA.

METHODS

Adult patients with MDD among commercially insured and Medicare enrollees in Optum Research Database were included and assigned to three cohorts: those with treatment-resistant MDD (TRD), those with MDD and SI/SA (MDD+SI/SA), and those with MDD without TRD or SI/SA (MDD alone). Suicide-specific mortality was obtained from the National Death Index. The effects of demographic characteristics and SI/SA in the year prior to the end of observation on suicide-specific mortality were assessed.

RESULTS

For the 139,753 TRD, 85,602 MDD+SI/SA, and 572,098 MDD alone cohort patients, mean age ranged from 55 to 59 years and the majority were female. At baseline, anxiety disorders were present in 53.92%, 44.11%, and 21.72% of patients with TRD, MDD+SI/SA, and MDD alone, respectively. Suicide-mortality rates in the three cohorts were 0.14/100 person-years for TRD, 0.27/100 person-years for MDD+SI/SA, and 0.04/100 person-years for MDD alone. SI/SA during the year prior to the end of observation, younger age, and male sex were associated with increased suicide risk.

CONCLUSIONS

Patients with TRD and MDD+SI/SA have a heightened risk of mortality by suicide compared with patients with MDD alone. Suicide rates were higher in patients with recent history versus older or no history of SI/SA, men versus women, and those of young age versus older age.

Incidence of treatment-resistant depression during the first mood depressive episode

INTRODUCTION

In spite of several approaches and therapeutic measures, treatment-resistant depression (TRD) continues to inflict serious, individual and collective consequences. Therefore, there is a persistent need to scrutinize the concept of TRD in order to adapt the therapeutic strategies.

AIM

To estimate the incidence of TRD in patients with a first major depressive episode (MDD), and study factors associated with resistance.

METHODS

A descriptive prospective longitudinal study of outpatients with a first MDD, was conducted. Patients with a history of subthreshold hypomania were excluded. Eligible patients were put on a selective serotonin reuptake inhibitor (SSRI), either fluoxetine or sertraline. Participants were followed regularly until they had a therapeutic response or they met the criteria for TRD.

RESULTS

The study involved 82 adults. The incidence of treatment-resistant depression was 19.4% CI95%=[5.5-33.3]. Among the sociodemographic and clinical factors, family history of psychosis (p=0.038) and chronic respiratory comorbidities (p=0.016) were associated with TRD. The small size of the sample is a potential limitation of this study. Besides, the use of only two SSRIs could influence the results.

CONCLUSION

In this study, the incidence of TRD was at the lower limit of the rates reported in clinical studies. Clinical factors associated with TRD suggest the relevance of genotype analysis to identify patients with TRD. Furthermore, our results highlight the importance of heeding comorbidities to optimize care. Larger multicenter studies are needed to generalize.

Observational study to characterize treatment-resistant depression in Germany, France and the United Kingdom: analysis of real-world data collected through a survey of healthcare professionals

OBJECTIVE

This study describes treatment patterns, productivity, healthcare resource utilization and previous episodes of depression for patients with treatment-resistant depression (TRD).

METHODS

In this cross-sectional study, a quantitative survey was administered to 225 healthcare providers (HCPs) distributed evenly across Germany, France and the UK from July to August 2021. Each HCP was asked to answer based on medical records of five patients with TRD, defined as patients failing to respond to two or more treatments of adequate dose and duration in the same episode of major depressive disorder (MDD), which provided a sample size of 1125 patients.

RESULTS

Of the 1125 patients with TRD, 73.2% had two or more previous episodes of MDD, 46.3% had a history of suicidal ideation and 24.8% had attempted suicide. Only 26.8% of patients were employed either full-time or part-time. During the most recent/current TRD episode, 45.5% of patients received five or more lines of treatment, and 46.0% remained on monotherapy. For multiple pharmacological treatments, too many distinct combinations were used to discern trends. Overall, 60.6% of patients had at least one mental health-related hospitalization in the last 12 months; 35.0% had two or more hospitalizations. Half of TRD patients saw a doctor five or more times per year for their depression.

CONCLUSIONS

This study addresses the knowledge gap about treatment patterns and healthcare utilization in real-world practice for TRD patients in three European countries. It provides data that potentially could inform treatment guideline development and optimize patient-perceived benefits from the treatment of TRD.

Differences between men and women in response to antiseizure medication use and the likelihood of developing treatment resistant epilepsy

OBJECTIVE

The prevalence of epilepsy is slightly higher in women than in men and sensitivity to seizure stimuli differs between sexes. Some evidence suggests sex differences in response to antiseizure medications exist mainly due to inconsistent pharmacokinetic differences; however, there is a lack of real-world evidence examining differences in response to antiseizure medications between men and women.

METHODS

This was a retrospective population-based cohort study in five large US healthcare databases. The population included adult patients with epilepsy, newly exposed to levetiracetam, and naive to antiseizure medication. The first exposure to levetiracetam was the index date. The requirement that all patients received the same medication was done to avoid potential confounding due to differences in index treatment. The outcome was the development of treatment resistant epilepsy (TRE), defined as having at least three distinct antiseizure medications in 1 year. The proportion of patients who developed TRE within 1 year following the index date was calculated. To compare the risk of developing TRE between sexes, relative risks (RR) and 95% confidence intervals (CI) were calculated, and estimates were pooled using meta-analytic techniques stratified by gender and age.

RESULTS

A total of 147 334 subjects were included in the databases, 50.8% were women, and 4.27% developed TRE. The comorbid profile differed greatly between men and women; however, the types of epilepsy syndromes observed during baseline were similar between the two groups. Across all databases, women were more likely to develop TRE than men (pooled RR 1.27, 95% CI 1.17-1.38). Results remained similar when stratified by age.

SIGNIFICANCE

This study assessed sex differences in response to antiseizure medications using the development of TRE as a proxy for effectiveness. Women newly exposed to levetiracetam were 27% more likely to develop TRE than men, independent of age.

One-year incidence rate of Treatment Resistant Depression (TRD) and treatment characteristics in China

BACKGROUND

Little is known about the characteristics of Treatment-Resistant Depression (TRD) in China. In previous studies various identification approaches have led to a wide range of results, and it is unclear how Chinese patients compare to those in other studies.

METHODS

This is a retrospective cohort study using electronic health records (EHR) from two major psychiatric hospitals in China. Adult major depressive disorder (MDD) patients who initiated pharmaceutical treatment during 2010-2018 were enrolled and follow-up was 1 year. TRD was primarily identified by consensus definition of two failures of adequate (≥4 weeks) regimens. Alternative regimens of 2-weeks and 6-weeks duration, and a data-driven definition were also applied.

RESULTS

In the two hospitals, 12,257 (mean age: 40.8y, 63.6% female) and 8314 (mean age: 42.4y, 68.4% female) eligible patients were included. The 1-year incidence rate of TRD was estimated to be 5.2%-7.7% using the primary definition. TRD patients had mean treatment duration of 302.5 days and 285.7 days; had 3.6 and 3.7 treatment steps on average; 94.0% and 72.6% were prescribed polypharmacy regimens, which were all marginally greater than that of non-TRD patients. Alternative definitions resulted in a wide range of incidence estimates (0.5%-20.0%).

LIMITATIONS

Medications were assumed to be consumed as prescribed and lack of rating scales from EHRs may limit our TRD identification.

CONCLUSIONS

The incidence of TRD among Chinese MDD patients was comparable to other countries under similar settings and more complex treatment characteristics were observed among TRD patients. Alternative TRD definitions revealed the need for better treatment management in practices.

Management of Psychotic Symptoms in a Patient With Parkinson’s Disease Maintained on Levodopa-Carbidopa Intestinal Gel by Paliperidone Long-Acting Injection: A Case Report

This case presents a 47-year-old man, without known past psychiatric history who developed psychotic symptoms including delusions of infidelity and had homicidal plans against his wife after 10 months of Levodopa-Carbidopa intestinal gel insertion (LCIG). The patient was diagnosed with Parkinson’s disease at age 34, which is being managed with LCIG. Patient Parkinson's symptoms were not well controlled with other pharmacological and surgical interventions tried previously. Despite the current guidelines in treating Parkinson’s disease psychosis, the treating teams have faced many difficulties with the management of this patient’s psychotic symptoms. After trying Risperidone Consta on August 24, 2018, the patient improved gradually, then he was shifted to Paliperidone long-acting injection (LAI) on September 12, 2018. One month later, the patient was seen in the outpatient department with much improvement in Paliperidone LAI. Reporting this case as the patient was seen on November 29, 2021, the patient is stable and doing well overall in terms of absent psychotic symptoms with minimal resting tremors. The success story of using LAIs such as our patient’s response to Paliperidone LAI can help other psychiatrists expand their treatment options when facing such difficulties.

Machine learning in the prediction of depression treatment outcomes: a systematic review and meta-analysis

BACKGROUND

Multiple treatments are effective for major depressive disorder (MDD), but the outcomes of each treatment vary broadly among individuals. Accurate prediction of outcomes is needed to help select a treatment that is likely to work for a given person. We aim to examine the performance of machine learning methods in delivering replicable predictions of treatment outcomes.

METHODS

Of 7732 non-duplicate records identified through literature search, we retained 59 eligible reports and extracted data on sample, treatment, predictors, machine learning method, and treatment outcome prediction. A minimum sample size of 100 and an adequate validation method were used to identify adequate-quality studies. The effects of study features on prediction accuracy were tested with mixed-effects models. Fifty-four of the studies provided accuracy estimates or other estimates that allowed calculation of balanced accuracy of predicting outcomes of treatment.

RESULTS

Eight adequate-quality studies reported a mean accuracy of 0.63 [95% confidence interval (CI) 0.56-0.71], which was significantly lower than a mean accuracy of 0.75 (95% CI 0.72-0.78) in the other 46 studies. Among the adequate-quality studies, accuracies were higher when predicting treatment resistance (0.69) and lower when predicting remission (0.60) or response (0.56). The choice of machine learning method, feature selection, and the ratio of features to individuals were not associated with reported accuracy.

CONCLUSIONS

The negative relationship between study quality and prediction accuracy, combined with a lack of independent replication, invites caution when evaluating the potential of machine learning applications for personalizing the treatment of depression.

Novel drug developmental strategies for treatment-resistant depression

Major depressive disorder is a leading cause of disability worldwide. Because conventional therapies are ineffective in many patients, novel strategies are needed to overcome treatment‐resistant depression (TRD). Limiting factors of successful drug development in the last decades were the lack of (1) knowledge of pathophysiology, (2) translational animal models and (3) objective diagnostic biomarkers. Here, we review novel drug targets and drug candidates currently investigated in Phase I–III clinical trials. The most promising approaches are inhibition of glutamatergic neurotransmission by NMDA and mGlu₅ receptor antagonists, modulation of the opioidergic system by κ receptor antagonists, and hallucinogenic tryptamine derivates. The only registered drug for TRD is the NMDA receptor antagonist, S‐ketamine, but add‐on therapies with second‐generation antipsychotics, certain nutritive, anti‐inflammatory and neuroprotective agents seem to be effective. Currently, there is an intense research focus on large‐scale, high‐throughput omics and neuroimaging studies. These results might provide new insights into molecular mechanisms and potential novel therapeutic strategies.

Individuals who develop drug-resistant epilepsy within a year after initial diagnosis have higher burden of mental and physical diseases one-year prior to epilepsy diagnosis as compared to those whose seizures were controlled during the same interval

INTRODUCTION

Epilepsy is a neurological disease characterized by recurrent, unprovoked seizures and its impact on biological, cognitive, psychological, and social outcomes. An unmet need for finding effective treatment options exists. Identifying medical diagnoses present prior to a diagnosis of epilepsy is an important step in increasing our understanding of how people with epilepsy may respond to therapy, help guide clinicians in managing associated comorbid conditions, and inform future research.

METHODS

A population-based retrospective comparative cohort study was conducted using administrative claims data to explore differences in medical diagnoses prior to an initial diagnosis of epilepsy between patients with and without drug-resistant epilepsy (DRE) identified within one-year post diagnosis by evaluating standardized mean differences between the groups.

RESULTS

A total of 205,183 patients with newly diagnosed epilepsy were identified. Of those, 4.1% (n = 8340) were considered drug resistant one-year post diagnosis. Pain and mood disorders were the common physical and psychiatric diagnoses in both cohorts. Differences between the newly diagnosed epilepsy and DRE cohorts were observed. Patients in the DRE cohort were younger, had more encounters with the healthcare system, and higher burden of disease for both physical (e.g., headache, neuropathy, muscular-skeletal disorders, and traumatic brain injury) and psychiatric diagnoses (e.g., depression, anxiety, bipolar disorder, suicidal thoughts, drug dependency, and sleep disorders).

CONCLUSION

Physical and psychiatric diagnoses are common one year prior to first diagnosis of epilepsy in administrative claims data. Compared to patients without DRE, those who develop DRE within one-year post initial diagnosis demonstrated a higher burden of disease.

Healthcare Resource Consumption and Related Costs of Patients Estimated with Treatment-Resistant Depression in Italy

Purpose

To analyse the healthcare resource consumption and related costs for the Italian National Health System of patients estimated to be affected by treatment-resistant depression (TRD) in Italy.

Patients and Methods

This was an observational retrospective study based on administrative databases, including those related to residential/semiresidential structures, of Veneto Region and the Local Health Unit of Bergamo in Italy (for a total of around 6 million health-assisted subjects). Between July 2011 and December 2017, all adult patients with a third antidepressant (AD) after ≥2 AD (each one with at least ≥4 weeks duration, ≥1 prescription at maximum dosage reported in datasheets, a grace period ≤30 days when switching AD and treatment maintained ≥9 months) were included. Overall and psychiatry-related healthcare resources consumption and related costs were estimated on a 12-months based analysis. Data were re-proportioned to the Italian population.

Results

We have previously estimated a total of 101,455 patients with TRD in Italy (130,049 considering the mean maximum dosage of AD). Of them, 44.2% had at least a psychiatric hospitalization/visit or accessed a residential/semiresidential structure, and 31% added another AD or a mood stabilizer/antipsychotic drug. Patients with at least one psychiatry-related hospitalization increased over the number of antidepressant lines from 12.0% during first line up to 24.5% during fourth line. Direct healthcare costs increased from €4,405 for first line to €9,251 from fifth line onwards. Psychiatry-related costs went from €1,817 (first line) to €4,606 (fifth line onwards) and were mainly driven by residential/semiresidential structures and hospitalizations.

Conclusion

An upward trend with number of AD lines was observed for all healthcare resource utilization and consequently for all direct costs, thus indicating an increasing burden for patients as they move forward AD lines.

Exploring real-world evidence to uncover unknown drug benefits and support the discovery of new treatment targets for depressive and bipolar disorders

BACKGROUND

Major depressive and bipolar disorders are associated with impaired quality of life and high economic burden. Although progress has been made in our understanding of the underlying pathophysiology and the development of novel pharmacological treatments, a large unmet need remains for finding effective treatment options. The purpose of this study was to identify potential new mechanisms of actions or treatment targets that could inform future research and development opportunities for major depressive and bipolar disorders.

METHODS

A self-controlled cohort study was conducted to examine associations between 1933 medications and incidence of major depressive and bipolar disorders across four US insurance claims databases. Presence of incident depressive or bipolar disorders were captured for each patient prior to or after drug exposure and incident rate ratios were calculated. Medications that demonstrated ≥50% reduction in risk for both depressive and bipolar disorders within two or more databases were evaluated as potential treatment targets.

RESULTS

Eight medications met our inclusion criteria, which fell into three treatment groups: drugs used in substance use disorders; drugs that affect the cholinergic system; and drugs used for the management of cardiovascular-related conditions.

LIMITATIONS

This study was not designed to confirm a causal association nor inform current clinical practice. Instead, this research and the methods employed intended to be hypothesis generating and help uncover potential treatment pathways that could warrant further investigation.

CONCLUSIONS

Several potential drug targets that could aid further research and discovery into novel treatments for depressive and bipolar disorders were identified.

At baseline patients treated with esketamine have higher burden of disease than other patients with treatment resistant depression: Learnings from a population based study

INTRODUCTION

It is critical to assess who is being treated with a new marketed drug like esketamine to understand how it is used in the real-world setting and the effects of the medication.

METHODS

Retrospective analysis using two large U.S. health care databases that included commercially insured and Medicaid patients. Patients treated with esketamine were identified and their baseline characteristics described and compared with the baseline characteristics of patients with treatment resistant depression (TRD) and with patients undergoing transcranial magnetic stimulation (TMS). To quantify the differences, standardized mean differences were calculated.

RESULTS

In the commercially insured database, 418 patients were treated with esketamine and 830,047 patients were in the TRD group. Large differences in baseline characteristics were observed. Patients in the esketamine group were more likely to have severe depression, suicidal thoughts, and prior treatments with TMS or electroconvulsive therapy than the TRD control group. Patients in the esketamine group had more comorbid psychiatric conditions (anxiety disorder, posttraumatic stress disorders, substance use disorders) and higher exposure to antipsychotics, antiepileptics, hypnotics and sedatives. In terms of general health, patients in the esketamine group had many more outpatient visits, were more likely to have chronic pain and higher Charlson comorbidity scores, a predicator of mortality. Results were similar for both the Medicaid and TMS populations.

CONCLUSION

Patients treated with esketamine have a higher burden of disease than other patients with TRD. In any real-world comparative effectiveness or safety study these differences need to be understood and accounted for to produce valid results.

Clinical characteristics of treatment-resistant depression in adults in Hungary: Real-world evidence from a 7-year-long retrospective data analysis

Purpose

Treatment-resistant depression (TRD) is associated with a poor quality of life and high economic burden. This observational retrospective epidemiological study aimed to estimate the proportion of patients with TRD within a cohort of patients with major depressive disorder (MDD) in Hungary and examine the mortality and comorbidities of patients with and without TRD.

Patients and methods

This study included patients with MDD who experienced onset of a new depressive episode between 01 January 2009 and 31 August 2015, using data from a nationwide, longitudinal database.

Results

Overall, 99,531 patients were included in the MDD cohort, of which 8,268 (8.3%) also met the criteria for TRD. The overall survival of non-TRD patients was longer than in TRD patients; the risk of mortality for TRD patients was significantly higher than of non-TRD patients (HR [CI] 1.381 [1.212–1.571]; p<0.001). Patients with TRD had a significantly higher probability of having “Neurotic, stress-related and somatoform disordersˮ, autoimmune conditions, cardio- or cerebrovascular diseases, thyroid gland diseases and self-harming behaviour not resulting in death than non-TRD patients (for all comparisons, p values were less than 0.005).

Discussion

To our best knowledge, this is the first study to assess the frequency of TRD in Hungary. In a cohort of Hungarian MDD patients, we have found that the proportion of TRD (~8.3%) is comparable to those reported in previous studies with similar methodology from other countries. The majority of our other main findings (e.g. more frequent self-harming behaviour, increased risk of “Neurotic, stress-related and somatoform disordersˮ and higher overall mortality in TRD subjects) are also in line with previous results from other countries. Taking the substantial proportion of patients with TRD into consideration, a more comprehensive and targeted treatment strategy would be required for these individuals.

Real-world data analysis of the clinical and economic burden and risk factors in patients with major depressive disorder with an inadequate response to initial antidepressants

AIM

We aimed to determine the incidence of and identify the factors associated with treatment-resistant depression (TRD), psychiatric conditions, hospitalization, and cost in patients with major depressive disorder (MDD) who were treated using second-line strategies after an inadequate response to initial antidepressants (AD).

MATERIALS AND METHODS

Using South Korean National Health Insurance claims data (1 January 2013 to 30 June 2018), we conducted a retrospective cohort analysis in newly treated patients with MDD who subsequently switched or added AD, or added atypical antipsychotics (AAPs) as a second-line treatment. We assessed the incidence of treatment-resistant depression (TRD), psychiatric conditions, and hospitalization for the first 2 years and costs in the third year. Odds ratios (ORs) or relative ratios were estimated using logistic and linear regression models to identify the risk factors for clinical and economic outcomes.

RESULTS

In 15,887 patients, the TRD was 16.81% during the 24-month follow-up period (14.14% in switching AD, 19.65% in adding AD, and 19.91% in adding AAP; p < 0.0001). When adding AD or AAP, the OR of TRD was 1.43 (95% confidence interval (CI): 1.30-1.56) and 1.42 (95% CI: 1.23-1.65), respectively, compared to switching AD. However, these factors were not associated with the incidence of psychiatric conditions. Adding AAP increased hospitalization (OR = 1.25, 95% CI: 1.11-1.41), the number of inpatient days by 2.57-fold (95% CI: 1.75-3.76), and cost by 1.20-fold (95% CI: 1.02-1.40), compared to switching AD; adding AD did not show a significant association with these outcomes.

CONCLUSIONS

In patients with MDD with inadequate responses to initial AD, TRD still occurred after subsequent treatments according to clinical guidelines. Since the effectiveness of second treatment strategies can differ in reality, further analysis of the clinical and economic evidence regarding second treatment strategies, such as adding AD or AAP, is needed using real-world data.

Treatment-Resistant Depression in a Real-World Setting: First Interim Analysis of Characteristics, Healthcare Resource Use, and Utility Values of the FondaMental Cohort

Background: Major depressive disorder (MDD) is among the most common psychiatric disorders. One-third of patients are usually unresponsive to several lines of treatment. This study aimed to describe the FondaMental French cohort of patients with treatment-resistant depression (TRD) and to estimate utility and healthcare resource use outcomes. Methods: Patients with TRD were evaluated prospectively over four years (baseline, 6, 12, 18, 24, 36 and 48 months) in a real-world clinical setting. Interim analyses focused on the first two consecutive years. Four MDD-related states (major depressive episode (MDE), response, remission, recovery) were defined based on the MADRS (Montgomery–Åsberg depression rating scale) and other clinical events. Health status was assessed with the EuroQol 5 Dimensions 5 Level (EQ-5D-5L) questionnaire. Utility values were estimated as preference measures that the patients assigned to their overall health status. Results: This study was based on 252 patients with TRD. The mean utility value by health state was 0.41, 0.63, 0.80, and 0.90, for MDE, response, remission, and recovery, respectively. At baseline, 59% of patients had an MADRS score of at least 28. Their baseline average utility value was lower compared to the other patients (0.43 versus 0.58, p < 0.001). This significant difference persisted at the following visits. The rate of patients in MDEs having at least one hospitalisation for depression or other reasons than depression was generally higher than that in the other health states. Conclusion: This study documented patterns in healthcare resource consumption, quality of life, and other characteristics in patients with TRD, both globally and by health state and depression severity.

A two-site, open-label, non-randomized trial comparing Focal Electrically-Administered Seizure Therapy (FEAST) and right unilateral ultrabrief pulse electroconvulsive therapy (RUL-UBP ECT)

BACKGROUND

Focal Electrically-Administered Seizure Therapy (FEAST) is a form of electroconvulsive therapy (ECT) that spatially focuses the electrical stimulus to initiate seizure activity in right prefrontal cortex. Two open-label non-comparative studies suggested that FEAST has reduced cognitive side effects when compared to historical data from other forms of ECT. In two different ECT clinics, we compared the efficacy and cognitive side effects of FEAST and Right Unilateral Ultrabrief Pulse (RUL-UBP) ECT.

METHODS

Using a non-randomized, open-label design, 39 depressed adults were recruited after referral for ECT. Twenty patients received FEAST (14 women; age 45.2 ± 12.7), and 19 received RUL-UBP ECT (16 women; age 43.2 ± 16.4). Key cognitive outcome measures were the postictal time to reorientation and the Columbia University Autobiographical Memory Interview: Short-Form (CUAMI-SF). Antidepressant effects were assessed using the Hamilton Rating Scale for Depression (HRSD24).

RESULTS

In the Intent-to-treat sample, a repeated measures mixed model suggested no between group difference in HRSD24 score over time (F1,35 = 0.82, p = 0.37), while the response rate favored FEAST (FEAST: 65%; RUL-UBP ECT: 57.9%), and the remission rate favored RUL-UBP ECT (FEAST: 35%; RUL-UBP ECT: 47.4%). The FEAST group had numeric superiority in average time to reorientation (FEAST: 6.6 ± 5.0 min; RUL-UBP ECT: 8.8 ± 5.8 min; Cohens d = 0.41), and CUAMI-SF consistency score (FEAST: 69.2 ± 14.2%; RUL-UBP ECT: 63.9 ± 9.9%; Cohens d = 0.43); findings that failed to meet statistical significance.

CONCLUSIONS

FEAST exerts similar efficacy relative to an optimal form of conventional ECT and may have milder cognitive side effects. A blinded, randomized, non-inferiority trial is needed.

Buprenorphine: prospective novel therapy for depression and PTSD

BACKGROUND

Depression and post-traumatic stress disorder (PTSD) are leading causes of disability and loss of life by suicide. Currently, there are less than satisfactory medical solutions to treat these mental disorders. Here, we explore recent preclinical and clinical studies demonstrating the potential of using buprenorphine to treat major depressive disorder, treatment-resistant depression, and PTSD.

METHOD

Bibliographic databases were searched to include preclinical and clinical studies demonstrating the therapeutic potential of buprenorphine and the involvement of the kappa opioid receptor (KOR) in mediating these effects.

RESULTS

Original clinical studies examining the effectiveness of buprenorphine to treat depression were mixed. The majority of participants in the PTSD studies were males and suffer from chronic pain and/or substance use disorders. Nonetheless, these recent studies and analyses established proof of concept warranting farther investigations. Additionally, KOR likely mediates the antidepressant and some of the anxiolytic effects of buprenorphine. Still, it appears that the full spectrum of buprenorphine's beneficial effects might be due to activity at other opioid receptors as well.

CONCLUSIONS

Pharmaceuticals' abilities to treat medical conditions directly relates to their ability to act upon the endogenous biological systems related to the conditions. Thus, these recent findings are likely a reflection of the central role that the endogenous opioid system has in these mental illnesses. Further studies are necessary to study the involvement of endogenous opioid systems, and specifically KOR, in mediating buprenorphine's beneficial effects and the ability to treat these medical conditions while minimizing risks for misuse and diversion.

Can Machine Learning help us in dealing with treatment resistant depression? A review

BACKGROUND

About one third of patients treated with antidepressant do not show sufficient symptoms relief and up to 15% of patients remain symptomatic even after multiple trials are applied, configuring a state called treatment resistant depression (TRD). A clear definition of this state and the understanding of underlying mechanisms contributing to chronic disability caused by major depressive disorder is still unknown. Therefore, Machine Learning (ML) techniques emerged in the last years as interesting approaches to deal with such complex problems.

METHODS

We performed a bibliographic search on Pubmed, Google Scholar and Medline of clinical, imaging, genetic and EEG ML classification studies on treatment-responding depression and TRD as well as studies trying to predict response to a specific treatment in already established TRD. The inclusion criteria were met by eleven studies. Seven focused on the definition of predictors of TRD onset while four attempted to predict the response to specific treatments in TRD.

RESULTS

The results showed that it seems possible to classify between responders MDD and TRD with good accuracies based on clinical variables. Moreover, some studies reported the possibility of using EEG measures to predict response to different pharmacological and non-pharmacological treatments in established TRD.

LIMITATIONS

The definition of TRD, the selection of variables together with ML algorithms and pipelines varies across the studies, ultimately determining the unfeasibility to implement these models in clinical practice.

CONCLUSIONS

The findings suggest that ML could be a valid approach to increase our understanding of TRD and to better classify and stratify this disorder, which may ultimately help clinicians in the assessment of major depressive disorders.

Burden of treatment-resistant depression in Medicare: A retrospective claims database analysis

Background

Previous studies have assessed the incremental economic burden of treatment-resistant depression (TRD) versus non-treatment-resistant major depressive disorder (i.e., non-TRD MDD) in commercially-insured and Medicaid-insured patients, but none have focused on Medicare-insured patients.

Objective

To assess healthcare resource utilization (HRU) and costs of patients with TRD versus non-TRD MDD or without major depressive disorder (MDD; i.e., non-MDD) in a Medicare-insured population.

Methods

Adult patients were retrospectively identified from the Chronic Condition Warehouse de-identified 100% Medicare database (01/2010-12/2016). MDD was defined as ≥1 MDD diagnosis and ≥1 claim for an antidepressant. Patients initiated on a third antidepressant following two antidepressant treatment regimens of adequate dose and duration were considered to have TRD. The index date was defined as the date of the first antidepressant claim for the TRD and non-TRD MDD cohorts, and as a randomly imputed date for the non-MDD cohort. Patients with TRD were matched 1:1 to non-TRD MDD patients and randomly selected non-MDD patients based on propensity scores. Analyses were also performed for a subset of patients aged ≥65.

Results

Of 29,543 patients with MDD, 3,225 (10.9%) met the study definition of TRD; 157,611 were included in the non-MDD cohort. Matched patients with TRD and non-TRD MDD were, on average, 58.9 and 59.0 years old, respectively. The TRD cohort had higher per-patient-per-year (PPPY) HRU than the non-TRD MDD (e.g., inpatient visits: incidence rate ratio [IRR] = 1.36) and non-MDD cohorts (e.g., inpatient visits: IRR = 1.84, all P<0.001). The TRD cohort had significantly higher total PPPY healthcare costs than the non-TRD MDD cohort ($25,517 vs. $20,425, adjusted cost difference = $3,385) and non-MDD cohort ($25,517 vs. $14,542, adjusted cost difference = $4,015, all P<0.001). Similar results were found for the subset of patients ≥65.

Conclusion

Among Medicare-insured patients, those with TRD had higher HRU and costs compared to those with non-TRD MDD and non-MDD.

Treatment resistant depression in women with peripartum depression

BACKGROUND

Peripartum depression is a leading cause of disease burden for women and yet there is little evidence as to how often peripartum depression does not respond to treatment and becomes treatment resistant depression. We sought to determine the incidence of treatment resistant depression (TRD) in women with peripartum depression.

METHODS

Population based retrospective cohort study using a large US claims database. Peripartum depression was defined as having a depression diagnosis during pregnancy or up to 6 months after the end of pregnancy. We included women with prevalent or incident depression. The outcome was the development of TRD within 1 year after the diagnosis of peripartum depression. TRD was defined as having 3 distinct antidepressants or 1 antidepressant and 1 antipsychotic in 1 year. Women with peripartum depression may not be exposed to pharmacological treatments early in pregnancy, therefore we created two groups: 1. women with peripartum depression, and 2. women with peripartum depression diagnosed 3 months before a live birth delivery or within 6 months after that delivery.

RESULTS

There were 3,207,684 pregnant women, of whom 2.5% had peripartum depression. Of these women half had incident depression during pregnancy. Five percent of women with peripartum depression developed TRD within 1 year of the depression diagnosis. The risk of developing TRD was 50% higher in women with prevalent depression than in women with incident peripartum depression (P < 0.0001). Results were similar in women with peripartum depression diagnosed later in their pregnancy. Women who went on to develop TRD had more substance use disorders, anxiety, insomnia and painful conditions.

CONCLUSIONS

TRD occurs in approximately 5% of women with peripartum depression. The risk of TRD is higher in pregnant women with a history of depression. Women who went on to develop TRD had more psychiatric comorbidities and painful conditions than women who did not.

Antidepressant discontinuation in treatment resistant depression

Treatment-resistant depression (TRD) is a growing problem in psychiatric practice with some 15–20% of depressed patients becoming chronically depressed and perhaps as many as 40% in tertiary settings. Several groups have championed the idea that TRD may be attributed to the long-term treatment with antidepressant drugs (AD). Subjects with the short form of the serotonin transporter gene (both heterozygotes and homozygotes) have an increased risk for depression in the setting of adversity compared to people with the long form. Moreover, these same individuals have a reduced likelihood of responding well to antidepressants, with reports of no response, delayed response, and increased side effects. This hypothesis needs to be examined in a randomized clinical trial. The study will examine the effect of discontinuation versus continuation of serotonergic antidepressants on disease progression in patients with treatment resistant depression. We will recruit 30 subjects and assess the depressive symptoms and disease progression. Genetic testing will be performed to optimize clinical outcome in both groups, but will also be used to evaluate if the short form of the serotonin transporter predicts disease progression and long-term antidepressant treatment response.

The assessment of resistance to antidepressant treatment: Rationale for the Antidepressant Treatment History Form: Short Form (ATHF-SF)

There is considerable diversity in how treatment-resistant depression (TRD) is defined. However, every definition incorporates the concept that patients with TRD have not benefited sufficiently from one or more adequate trials of antidepressant treatment. This review examines the issues fundamental to the systematic evaluation of antidepressant treatment adequacy and resistance. These issues include the domains of interventions deemed effective in treatment of major depressive episodes (e.g., pharmacotherapy, brain stimulation, and psychotherapy), the subgroups of patients for whom distinct adequacy criteria are needed (e.g., bipolar vs. unipolar depression, psychotic vs. nonpsychotic depression), whether trials should be rated dichotomously as adequate or inadequate or on a potency continuum, whether combination and augmentation strategies require specific consideration, and the criteria used to evaluate the adequacy of treatment delivery (e.g., dose, duration), trial adherence, and clinical outcome. This review also presents the Antidepressant Treatment History Form: Short-Form (ATHF-SF), a completely revised version of an earlier instrument, and details how these fundamental issues were addressed in the ATHF-SF.

Medicaid spending burden among beneficiaries with treatment-resistant depression

Aim: To evaluate Medicaid spending and healthcare resource utilization (HRU) in treatment-resistant depression (TRD). Materials & methods: TRD beneficiaries were identified from Medicaid claims databases (January 2010–March 2017) and matched 1:1 with major depressive disorder (MDD) beneficiaries without TRD (non-TRD-MDD) and randomly selected patients without MDD (non-MDD). Differences in HRU and per-patient-per-year costs were reported in incidence rate ratios (IRRs) and cost differences (CDs), respectively. Results: TRD beneficiaries had higher HRU than 1:1 matched non-TRD-MDD (e.g., inpatient visits: IRR = 1.41) and non-MDD beneficiaries (N = 14,710 per cohort; e.g., inpatient visits: IRR = 3.42, p < 0.01). TRD beneficiaries incurred greater costs versus non-TRD-MDD (CD = US$4382) and non-MDD beneficiaries (CD = US$8294; p < 0.05). Conclusion: TRD is associated with higher HRU and costs versus non-TRD-MDD and non-MDD. TRD poses a significant burden to Medicaid.

Digital Interventions for Mental Disorders: Key Features, Efficacy, and Potential for Artificial Intelligence Applications

Mental disorders are highly prevalent and often remain untreated. Many limitations of conventional face-to-face psychological interventions could potentially be overcome through Internet-based and mobile-based interventions (IMIs). This chapter introduces core features of IMIs, describes areas of application, presents evidence on the efficacy of IMIs as well as potential effect mechanisms, and delineates how Artificial Intelligence combined with IMIs may improve current practices in the prevention and treatment of mental disorders in adults. Meta-analyses of randomized controlled trials clearly show that therapist-guided IMIs can be highly effective for a broad range of mental health problems. Whether the effects of unguided IMIs are also clinically relevant, particularly under routine care conditions, is less clear. First studies on IMIs for the prevention of mental disorders have shown promising results. Despite limitations and challenges, IMIs are increasingly implemented into routine care worldwide. IMIs are also well suited for applications of Artificial Intelligence and Machine Learning, which provides ample opportunities to improve the identification and treatment of mental disorders. Together with methodological innovations, these approaches may also deepen our understanding of how psychological interventions work, and why. Ethical and professional restraints as well as potential contraindications of IMIs, however, should also be considered. In sum, IMIs have a high potential for improving the prevention and treatment of mental health disorders across various indications, settings, and populations. Therefore, implementing IMIs into routine care as both adjunct and alternative to face-to-face treatment is highly desirable. Technological advancements may further enhance the variability and flexibility of IMIs, and thus even further increase their impact in people's lives in the future.

Treatment resistant depression incidence estimates from studies of health insurance databases depend strongly on the details of the operating definition

Background

Health services databases provide population-based data that have been used to describe the epidemiology and costs of treatment resistant depression (TRD). This retrospective cohort study estimated TRD incidence and, via sensitivity analyses, assessed the variation of TRD incidence within the range of implementation choices.

Methods

In three US databases widely used for observational studies, we defined TRD as failure of two medications as evidenced by their replacement or supplementation by other medications, and set maximum durations (caps) for how long a medication regimen could remain in use and still be eligible to fail.

Results

TRD incidence estimates varied approximately 2-fold between the two databases (CCAE, Medicaid) that described socioeconomically different non-elderly populations; for a given cap varied 2-fold to 4-fold within each database across the other implementation choices; and if the cap was also allowed to vary, varied 6-fold or 7-fold within each database.

Limitations

The main limitations were typical of studies from health services databases and included the lack of complete -rather than recent - medical histories, the limited amount of clinical information, and the assumption that medication dispensed was consumed as directed.

Conclusion

In retrospective cohort studies from health services databases, TRD incidence estimates vary widely depending on the implementation choices. Unless a firm basis for narrowing the range of these choices can be found, or a different analytic approach not dependent on such choices is adopted, TRD incidence and prevalence estimates from such databases will be difficult to compare or interpret.

Finding factors that predict treatment-resistant depression: Results of a cohort study

BACKGROUND

Treatment for depressive disorders often requires subsequent interventions. Patients who do not respond to antidepressants have treatment-resistant depression (TRD). Predicting who will develop TRD may help healthcare providers make more effective treatment decisions. We sought to identify factors that predict TRD in a real-world setting using claims databases.

METHODS

A retrospective cohort study was conducted in a US claims database of adult subjects with newly diagnosed and treated depression with no mania, dementia, and psychosis. The index date was the date of antidepressant dispensing. The outcome was TRD, defined as having at least three distinct antidepressants or one antidepressant and one antipsychotic within 1 year after the index date. Predictors were age, gender, medical conditions, medications, and procedures 1 year before the index date.

RESULTS

Of 230,801 included patients, 10.4% developed TRD within 1 year. TRD patients at baseline were younger; 10.87% were between 18 and 19 years old versus 7.64% in the no-TRD group, risk ratio (RR) = 1.42 (95% confidence interval [CI] 1.37-1.48). TRD patients were more likely to have an anxiety disorder at baseline than non-TRD patients, RR = 1.38 (95% CI 1.35-1.14). At 3.68, fatigue had the highest RR (95% CI 3.18-4.25). TRD patients had substance use disorders, psychiatric conditions, insomnia, and pain more often at baseline than non-TRD patients.

CONCLUSION

Ten percent of subjects newly diagnosed and treated for depression developed TRD within a year. They were younger and suffered more frequently from fatigue, substance use disorders, anxiety, psychiatric conditions, insomnia, and pain than non-TRD patients.

Finding treatment-resistant depression in real-world data: How a data-driven approach compares with expert-based heuristics

BACKGROUND

Depression that does not respond to antidepressants is treatment-resistant depression (TRD). TRD definitions include assessments of treatment response, dose and duration, and implementing these definitions in claims databases can be challenging. We built a data-driven TRD definition and evaluated its performance.

METHODS

We included adults with depression, ≥1 antidepressant, and no diagnosis of mania, dementia, or psychosis. Subjects were stratified into those with and without proxy for TRD. Proxies for TRD were electroconvulsive therapy, deep brain, or vagus nerve stimulation. The index date for subjects with proxy for TRD was the procedure date, and for subjects without, the date of a randomly selected visit. We used three databases. We fit decision tree predictive models. We included number of distinct antidepressants, with and without adequate doses and duration, number of antipsychotics and psychotherapies, and expert-based definitions, 3, 6, and 12 months before index date. To assess performance, we calculated area under the curve (AUC) and transportability.

RESULTS

We analyzed 33,336 subjects with no proxy for TRD, and 3,566 with the proxy. Number of antidepressants and antipsychotics were selected in all periods. The best model was at 12 months with an AUC = 0.81. The rule transported well and states that a subject with ≥1 antipsychotic or ≥3 antidepressants in the last year has TRD. Applying this rule, 15.8% of subjects treated for depression had TRD.

CONCLUSION

The definition that best discriminates between subjects with and without TRD considers number of distinct antidepressants (≥3) or antipsychotics (≥1) in the last year.