Managing Esketamine Treatment Frequency Toward Successful Outcomes: Analysis of Phase 3 Data.

BACKGROUND: Esketamine nasal spray was recently approved for treatment-resistant depression. The current analysis evaluated the impact of symptom-based treatment frequency changes during esketamine treatment on clinical outcomes. METHODS: This is a post-hoc analysis of an open-label, long-term (up to 1 year) study of esketamine in patients with treatment-resistant depression (SUSTAIN 2). During a 4-week induction phase, 778 patients self-administered esketamine twice weekly plus a new oral antidepressant daily. In responders (≥50% reduction in Montgomery-Åsberg Depression Rating Scale total score from baseline), esketamine treatment frequency was thereafter decreased during an optimization/maintenance phase to weekly for 4 weeks and then adjusted to the lowest frequency (weekly or every other week) that maintained remission (Montgomery-Åsberg Depression Rating Scale ≤ 12) based on a study-defined algorithm. The relationship between treatment frequency and symptom response, based on clinically meaningful change in Clinical Global Impression-Severity score, was subsequently evaluated 4 weeks after treatment frequency adjustments in the optimization/maintenance phase. RESULTS: Among 580 responders treated with weekly esketamine for the first 4 weeks in the optimization/maintenance phase (per protocol), 26% continued to improve, 50% maintained clinical benefit, and 24% worsened. Thereafter, when treatment frequency could be reduced from weekly to every other week, 19% further improved, 49% maintained benefit, and 32% worsened. For patients no longer in remission after treatment frequency reduction, an increase (every other week to weekly) resulted in 47% improved, 43% remained unchanged, and 10% worsened. CONCLUSIONS: These findings support individualization of esketamine nasal spray treatment frequency to optimize treatment response in real-world clinical practice. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT02497287.

Patient-reported outcomes in major depressive disorder with suicidal ideation: a real-world data analysis using PatientsLikeMe platform.

BACKGROUND: The current analysis utilized data collected via an online patient community platform, PatientsLikeMe (PLM) to compare patient-reported experiences in patients with major depressive disorder (MDD) with suicidal ideation (MDSI) to those with MDD but without suicidal ideation. METHODS: PLM members who joined PLM between May-2007 and February-2018 and reported a diagnosis of MDD were included. The MDSI cohort included patients with MDD who reported at least one suicide-related symptom at a severity greater than "none". Demographics, comorbidities, symptoms, and side-effects were compared between MDSI and MDD cohorts. Factors correlated with suicidal ideation (SI) were determined by a random forest procedure. RESULTS: Patients in the MDSI cohort (n = 266) were younger (median age, 36 vs 44 years) with an earlier disease onset (before 30 years, 83% vs 71%), and a longer diagnosis latency (median, 4 vs 2 years) vs patients in the MDD cohort (n = 11,963). Majority of patients were women in both cohorts (73% vs 83%). Median number of psychiatric comorbidities was higher in the MDSI cohort (4 vs 3). Unprompted symptoms (e.g., loneliness, feeling of hopelessness, social anxiety, impulsivity, and self-hating thoughts) were more frequent in the MDSI cohort. Hopelessness, loneliness, anhedonia, social anxiety, and younger age were highly correlated with suicidal ideation. CONCLUSIONS: This analysis utilized patient-reported data to better understand symptoms, experiences, and characteristics of patients with MDSI compared to patients with MDD. The results identified various risk factors correlated with suicidal ideation that may help guide clinical judgement for patients with MDD who may not voluntarily report suicidal ideation.

Risk and risk factors for disability pension among patients with treatment resistant depression- a matched cohort study.

BACKGROUND: Treatment resistant depression (TRD) is common among patients with depression, and is associated with clinical and functional disability. However, the risk and risk factors for being granted disability pension (DP) among patients with TRD have not been investigated. METHODS: All antidepressant initiators in Sweden with a diagnosis of depression in specialized care were identified in nationwide registers 2006-2013 and followed regarding treatment trials. TRD was defined as the start of a third sequential trial. Patients with TRD who were not on DP (N = 3204) were matched by age, sex, history of depression, calendar year, and time for treatment start with 3204 comparators with depression and ongoing antidepressant treatment. A proportional Cox Regression was performed with DP as outcome, adjusted for various sociodemographic and clinical covariates. RESULTS: Compared to the comparison cohort, TRD was associated with a doubled risk for all-cause DP (aHR 2.07; 95%CI 1.83-2.35), DP due to depression (2.28; 1.82-2.85) and to any mental disorder (2.24; 1.95-2.57) but not due to somatic diagnoses (1.25; 0.84-1.86). Among significant risk factors for DP in TRD were female sex, being > 29 years of age, unemployment and a diagnosis of comorbid personality disorder (ICD-10 codes F60.0-9). CONCLUSION: TRD is associated with an elevated risk for DP compared to other patients with depression, with large potential costs for the affected patients and for society. Clinical and therapeutic implications for patients with TRD who are granted DP should be further investigated. LIMITATION: No clinical data, e.g. type of depression or reason for treatment switch, was available for this study.

All-cause mortality in patients with treatment-resistant depression: a cohort study in the US population.

BACKGROUND: Treatment-resistant depression (TRD) may represent a substantial proportion of major depressive disorder (MDD); however, the risk of mortality in TRD is still incompletely assessed. METHODS: Data were obtained from Optum Clinformatics™ Extended, a US claims database. Date of the first antidepressant (AD) dispensing was designated as the index date for study entry and 6 months prior to that was considered the baseline period. Patients with MDD aged ≥ 18 years, index date between January 1, 2008 and September 30, 2015, no AD claims during baseline, and continuous enrollment in the database during baseline were included. Patients who started a third AD regimen after two regimens of appropriate duration were included in the TRD cohort. All-cause mortality was compared between patients with TRD and non-TRD MDD using a proportional hazards model and Kaplan-Meier estimate with TRD status being treated as a time-varying covariate. The model was adjusted for study year, age, gender, depression diagnosis, substance use disorder, psychiatric comorbidities, and Charlson comorbidity index. RESULTS: Out of 355,942 patients with MDD, 34,176 (9.6%) met the criterion for TRD. TRD was associated with a significantly higher mortality compared with non-TRD MDD (adjusted HR: 1.29; 95% CI 1.22-1.38; p < 0.0001). Survival time was significantly shorter in the TRD cohort compared with the non-TRD MDD cohort (p < 0.0001). CONCLUSIONS: Patients with TRD had a higher all-cause mortality compared with non-TRD MDD patients.

Humanistic outcomes in treatment resistant depression: a secondary analysis of the STAR*D study.

BACKGROUND: In the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study, a third of patients did not achieve remission or adequate response after two treatment trials, fulfilling requirements for treatment resistant depression (TRD). The present study is a secondary analysis of the STAR*D data conducted to compare the humanistic outcomes in patients with TRD and non-TRD MDD. METHODS: Patients with major depressive disorder who entered level 3 of the STAR*D were included in the TRD group, while patients who responded to treatment and entered follow-up from level 1 or 2 were included in the non-TRD group. The first visit in level 1 was used for baseline assessments. The time-point of assessments for comparison was the first visit in level 3 for TRD patients (median day: 141), and the visit closest to 141 ± 60 days from baseline for non-TRD patients. Outcomes were assessed by the 12-item Short Form Health Survey (SF12), 16-item Quality of Life Enjoyment and Satisfaction Questionnaire (Q-LES-Q), Work and Social Adjustment Scale (WSAS), and Work Productivity and Activity Impairment scale (WPAI). Scores were compared in a linear model with adjustment for covariates including age, gender, and depression severity measured by the 17-item Hamilton Rating Scale for Depression (HDRS17) and Quick Inventory of Depressive Symptomatology (QIDS). RESULTS: A total of 2467 (TRD: 377; non-TRD: 2090) patients were studied. TRD patients were slightly older (mean age 44 vs 42 years), had a higher proportion of men (49% vs 37%, p < .0001), and baseline depression severity (HDRS17: 24.4 vs 22.0, p < .0001) vs non-TRD patients. During follow-up, TRD patients had lower health-related quality of life (HRQOL) scores on mental (30 vs 45.7) and physical components (47.7 vs 48.9) of the SF12, and lower Q-LES-Q scores (43.6 vs 63.7), greater functional and work impairments and productivity loss vs non-TRD patients (all p < 0.05). CONCLUSION: Patients with TRD had worse HRQOL, work productivity, and social functioning than the non-TRD patients.

Relationship between brain volume loss and cognitive outcomes among patients with multiple sclerosis: a systematic literature review.

Patients with multiple sclerosis (MS) experience varying rates of brain volume (BV) loss ranging from 0.5 to 1.5 % per year. In addition, 66 % of patients with MS experience cognitive impairment, resulting in impact on daily activities. A systematic literature review (2003-2013) was conducted to identify all studies reporting a relationship between whole BV measures and selected patient outcomes measuring cognition, including the Symbol Digit Modalities Test (SDMT), Paced Auditory Serial Addition Test (PASAT) and MS Functional Composite (MSFC) scores. We identified 18 studies reporting associations between whole BV and cognitive outcomes. Six studies (33 %) examined the association between BV and SDMT; all six studies reported that BV loss (BVL) was significantly associated with a decline in SDMT scores (all p < 0.05). Among 14 studies (78 %) that examined the association between BV and PASAT scores, 12 (86 %) found a significant relationship between BVL and lower PASAT scores (all p < 0.05). Of the seven studies (39 %) that looked at BV and MSFC, six studies (86 %) found BVL significantly associated with lower MSFC scores (all p < 0.05). Our study demonstrated that BVL is associated with declines in cognition in MS patients across several cognition measures. The results of this study suggest that BV is a critical component of disease activity and progression in MS and has implications for treatment decisions to minimize BVL and preserve cognitive functioning.

Robotic measurement of arm movements after stroke establishes biomarkers of motor recovery.

BACKGROUND AND PURPOSE: Because robotic devices record the kinematics and kinetics of human movements with high resolution, we hypothesized that robotic measures collected longitudinally in patients after stroke would bear a significant relationship to standard clinical outcome measures and, therefore, might provide superior biomarkers. METHODS: In patients with moderate-to-severe acute ischemic stroke, we used clinical scales and robotic devices to measure arm movement 7, 14, 21, 30, and 90 days after the event at 2 clinical sites. The robots are interactive devices that measure speed, position, and force so that calculated kinematic and kinetic parameters could be compared with clinical assessments. RESULTS: Among 208 patients, robotic measures predicted well the clinical measures (cross-validated R(2) of modified Rankin scale=0.60; National Institutes of Health Stroke Scale=0.63; Fugl-Meyer=0.73; Motor Power=0.75). When suitably scaled and combined by an artificial neural network, the robotic measures demonstrated greater sensitivity in measuring the recovery of patients from day 7 to day 90 (increased standardized effect=1.47). CONCLUSIONS: These results demonstrate that robotic measures of motor performance will more than adequately capture outcome, and the altered effect size will reduce the required sample size. Reducing sample size will likely improve study efficiency.

Efficacy and safety of carisbamate in patients with diabetic neuropathy or postherpetic neuralgia: results from 3 randomized, double-blind placebo-controlled trials.

The results of 3 proof-of-concept studies to evaluate carisbamate's efficacy and safety in treating neuropathic pain are presented. In studies 1 (postherpetic neuralgia, n = 91) and 2 (diabetic neuropathy, n = 137), patients received carisbamate 400 mg/day or placebo for 4 weeks and then crossed over to the other treatment for 4 weeks. In study 3 (diabetic neuropathy, higher carisbamate doses), patients (n = 386) were randomized (1:1:1:1) to receive either carisbamate 800 mg/day, 1200 mg/day, pregabalin 300 mg/day or placebo for 15 weeks. Primary efficacy end point was the mean of the last 7 average daily pain scores obtained on days the study drug was taken, for all 3 studies. Least square mean (95% CI) differences between carisbamate and placebo groups on the primary end point were as follows: study 1: -0.512 (-1.32, 0.29) carisbamate 400 mg/day; study 2: -0.307 (-0.94, 0.33) carisbamate 400 mg/day; and study 3: -0.51 (-1.10, 0.08), carisbamate 800 mg/day; -0.55 (-1.13, 0.04), carisbamate 1200 mg/day; and -0.43 (-1.01, 0.15), pregabalin 300 mg/day. Neither carisbamate (all 3 studies) nor pregabalin (study 3) significantly differed from placebo, although multiple secondary end points showed significant improvement in efficacy with carisbamate in studies 1 and 2. Dizziness was the only treatment-emergent adverse event occurring at ≥10% difference in carisbamate groups versus placebo (study 1: 12% vs. 1%; study 3: 14% vs. 4%; study 2: 1% vs. 2%). Carisbamate, although well tolerated, did not demonstrate efficacy in neuropathic pain across these studies, nor did the active comparator pregabalin (study 3).

Efficacy and Safety of Ponesimod Compared with Teriflunomide in Female Patients with Relapsing Multiple Sclerosis: Findings from the Pivotal OPTIMUM Study.

Background: Multiple sclerosis (MS) is threefold more prevalent in women than men. However, sex-specific efficacy analysis for MS disease-modifying therapies is not typically performed. Methods: Post hoc analyses of data from female patients enrolled in the phase 3, double-blind OPTIMUM study of relapsing MS were carried out. Eligible adults were randomized to ponesimod 20 mg or teriflunomide 14 mg once daily for up to 108 weeks. The primary endpoint was annualized relapse rate (ARR); secondary endpoints included change in symptom domain of Fatigue Symptom and Impact Questionnaire-Relapsing Multiple Sclerosis (FSIQ-RMS) at week 108, number of combined unique active lesions (CUALs) per year on magnetic resonance imaging, and time to 12- and 24-week confirmed disability accumulation (CDA). Results: A total of 735 female patients (581 of childbearing potential) were randomized to ponesimod (n = 363, 49.4%) or teriflunomide (n = 372, 50.6%). Relative risk reduction in the ARR for ponesimod versus teriflunomide was 33.1% (mean, 0.192 vs. 0.286, respectively; p < 0.002). Mean difference in FSIQ-RMS for ponesimod versus teriflunomide was -4.34 (0.12 vs. 4.46; p = 0.002); rate ratio in CUALs per year, 0.601 (1.45 vs. 2.41; p < 0.0001), and hazard ratio for time to 12- and 24-week CDA risk estimates, 0.83 (10.7% vs. 12.9%; p = 0.38) and 0.91 (8.8% vs. 9.7%; p = 0.69), respectively. Incidence of treatment-emergent adverse events was similar between treatment groups (89.0% and 90.1%). Conclusions: Analyses demonstrate the efficacy and safety of ponesimod, versus active comparator, for women with relapsing MS, supporting data-informed decision-making for women with MS. Clinical Trial Registration Number: NCT02425644.

Disease progression model in subjects with mild cognitive impairment from the Alzheimer's disease neuroimaging initiative: CSF biomarkers predict population subtypes.

AIM: The objective is to develop a semi-mechanistic disease progression model for mild cognitive impairment (MCI) subjects. The model aims to describe the longitudinal progression of ADAS-cog scores from the Alzheimer's disease neuroimaging initiative trial that had data from 198 MCI subjects with cerebrospinal fluid (CSF) information who were followed for 3 years. METHOD: Various covariates were tested on disease progression parameters and these variables fell into six categories: imaging volumetrics, biochemical, genetic, demographic, cognitive tests and CSF biomarkers. RESULTS: CSF biomarkers were associated with both baseline disease score and disease progression rate in subjects with MCI. Baseline disease score was also correlated with atrophy measured using hippocampal volume. Progression rate was also predicted by executive functioning as measured by the Trail B-test. CONCLUSION: CSF biomarkers have the ability to discriminate MCI subjects into sub-populations that exhibit markedly different rates of disease progression on the ADAS-cog scale. These biomarkers can therefore be utilized for designing clinical trials enriched with subjects that carry the underlying disease pathology.

The ADAS-Cog revisited: novel composite scales based on ADAS-Cog to improve efficiency in MCI and early AD trials.

BACKGROUND: The Alzheimer's Disease Assessment Scale-Cognitive (ADAS-Cog) has been used widely as a cognitive end point in Alzheimer's Disease (AD) clinical trials. Efforts to treat AD pathology at earlier stages have also used ADAS-Cog, but failure in these trials can be difficult to interpret because the scale has well-known ceiling effects that limit its use in mild cognitive impairment (MCI) and early AD. A wealth of data exists in ADAS-Cog from both historical trials and contemporary longitudinal natural history studies that can provide insights about parts of the scale that may be better suited for MCI and early AD trials. METHODS: Using Alzheimer's Disease Neuroimaging Initiative study data, we identified the most informative cognitive measures from the ADAS-Cog and other available scales. We used cross-sectional analyses to characterize trajectories of ADAS-Cog and its individual subscales, as well as other cognitive, functional, or global measures across disease stages. Informative measures were identified based on standardized mean of 2-year change from baseline and were combined into novel composite endpoints. We assessed performance of the novel endpoints based on sample size requirements for a 2-year clinical trial. A bootstrap validation procedure was also undertaken to assess the reproducibility of the standardized mean changes of the selected measures and the corresponding composites. RESULTS: All proposed novel endpoints have improved standardized mean changes and thus improved statistical power compared with the ADAS-Cog 11. Further improvements were achieved by using cognitive-functional composites. Combining the novel composites with an enrichment strategy based on cerebral spinal fluid beta-amyloid (Aß(1-42)) in a 2-year trial yielded gains in power of 20% to 40% over ADAS-Cog 11, regardless of the novel measure considered. CONCLUSION: An empirical, data-driven approach with existing instruments was used to derive novel composite scales based on ADAS-Cog 11 with improved performance characteristics for MCI and early AD clinical trials. Together with patient enrichment based on Aß(1-42) pathology, these modified endpoints may allow more efficient clinical trials in these populations and can be assessed without modifying current test administration procedures in ongoing trials.

Sparse learning and stability selection for predicting MCI to AD conversion using baseline ADNI data.

BACKGROUND: Patients with Mild Cognitive Impairment (MCI) are at high risk of progression to Alzheimer's dementia. Identifying MCI individuals with high likelihood of conversion to dementia and the associated biosignatures has recently received increasing attention in AD research. Different biosignatures for AD (neuroimaging, demographic, genetic and cognitive measures) may contain complementary information for diagnosis and prognosis of AD. METHODS: We have conducted a comprehensive study using a large number of samples from the Alzheimer's Disease Neuroimaging Initiative (ADNI) to test the power of integrating various baseline data for predicting the conversion from MCI to probable AD and identifying a small subset of biosignatures for the prediction and assess the relative importance of different modalities in predicting MCI to AD conversion. We have employed sparse logistic regression with stability selection for the integration and selection of potential predictors. Our study differs from many of the other ones in three important respects: (1) we use a large cohort of MCI samples that are unbiased with respect to age or education status between case and controls (2) we integrate and test various types of baseline data available in ADNI including MRI, demographic, genetic and cognitive measures and (3) we apply sparse logistic regression with stability selection to ADNI data for robust feature selection. RESULTS: We have used 319 MCI subjects from ADNI that had MRI measurements at the baseline and passed quality control, including 177 MCI Non-converters and 142 MCI Converters. Conversion was considered over the course of a 4-year follow-up period. A combination of 15 features (predictors) including those from MRI scans, APOE genotyping, and cognitive measures achieves the best prediction with an AUC score of 0.8587. CONCLUSIONS: Our results demonstrate the power of integrating various baseline data for prediction of the conversion from MCI to probable AD. Our results also demonstrate the effectiveness of stability selection for feature selection in the context of sparse logistic regression.

Montgomery-Åsberg Depression Rating Scale factors in treatment-resistant depression at onset of treatment: Derivation, replication, and change over time during treatment with esketamine.

OBJECTIVE: Derive and confirm factor structure of the Montgomery-Åsberg Depression Rating Scale (MADRS) in patients with treatment-resistant depression (TRD) and evaluate how the factors evident at baseline change over 4 weeks of esketamine treatment. METHODS: Two similarly-designed, short-term TRANSFORM trials randomized adults to esketamine or matching placebo nasal spray, each with a newly-initiated oral antidepressant, for 4 weeks (TRANSFORM-1: N = 342 patients; TRANSFORM-2: N = 223 patients). The factor structure of MADRS item scores at baseline was determined by exploratory factor analysis in TRANSFORM-2 and corroborated by confirmatory factor analysis in TRANSFORM-1. Change in MADRS factor scores from baseline (day 1) to the end of the 28-day double-blind treatment phase of TRANSFORM-2 was analyzed using a mixed-effects model for repeated measures (MMRM). RESULTS: Three factors were identified based on analysis of MADRS items: Factor 1 labeled affective and anhedonic symptoms (apparent sadness, reported sadness, lassitude, inability to feel), Factor 2 labeled anxiety and vegetative symptoms (inner tension, reduced sleep, reduced appetite, concentration difficulties), and Factor 3 labeled hopelessness (pessimistic thoughts, suicidal thoughts). The three-factor structure observed in TRANSFORM-2 was verified in TRANSFORM-1. Treatment benefit at 24 h with esketamine versus placebo was observed on all 3 factors and continued throughout the 4-week double-blind treatment period. CONCLUSIONS: A three-factor structure for MADRS appears to generalize to TRD. All three factors improved over 4 weeks of treatment with esketamine nasal spray.

Risk Factors for Suicide and Suicide Attempts Among Patients With Treatment-Resistant Depression: Nested Case-Control Study.

The risk of suicide is elevated among patients with treatment-resistant depression (TRD). Risk factors for suicide and suicide attempts among cases and controls with TRD were investigated using data from nationwide Swedish registers. Among 119,407 antidepressant initiators with a diagnosis of depression, 15,631 patients who started a third sequential treatment trial during the same depressive episode were classified with TRD. A nested case-control study compared cases with suicide and suicide attempts with up to three closely matched controls. Sociodemographic and clinical risk factors were assessed using conditional logistic regression analyses. In all, 178 patients died by suicide and 1,242 experienced a suicide attempt during follow-up. History of suicide attempts, especially if <1 year after the attempt, was a significant independent risk factor for suicide (adjusted odds ratio [aOR], 8.9; 95% confidence interval [CI], 5.1-15.7) as were 10 to 12 years of education compared to lower education (aOR, 1.69; 95% CI, 1.02-2.81). For attempted suicide, the strongest independent risk factors were history of suicide attempts (<1 year aOR, 5.1; 95% CI, 4.0-6.5), substance abuse (aOR, 2.6; 95% CI, 2.2-3.1), personality disorders (aOR, 1.9; 95% CI, 1.5-2.3), and somatic comorbidity (aOR, 2.0; 95% CI, 1.04-3.9). Suicide attempts, especially if recent, are strong risk factors for completed suicide among patients with TRD. Established risk factors for suicide attempts were confirmed for patients with TRD.

Accurate prediction of clinical stroke scales and improved biomarkers of motor impairment from robotic measurements.

OBJECTIVE: One of the greatest challenges in clinical trial design is dealing with the subjectivity and variability introduced by human raters when measuring clinical end-points. We hypothesized that robotic measures that capture the kinematics of human movements collected longitudinally in patients after stroke would bear a significant relationship to the ordinal clinical scales and potentially lead to the development of more sensitive motor biomarkers that could improve the efficiency and cost of clinical trials. MATERIALS AND METHODS: We used clinical scales and a robotic assay to measure arm movement in 208 patients 7, 14, 21, 30 and 90 days after acute ischemic stroke at two separate clinical sites. The robots are low impedance and low friction interactive devices that precisely measure speed, position and force, so that even a hemiparetic patient can generate a complete measurement profile. These profiles were used to develop predictive models of the clinical assessments employing a combination of artificial ant colonies and neural network ensembles. RESULTS: The resulting models replicated commonly used clinical scales to a cross-validated R2 of 0.73, 0.75, 0.63 and 0.60 for the Fugl-Meyer, Motor Power, NIH stroke and modified Rankin scales, respectively. Moreover, when suitably scaled and combined, the robotic measures demonstrated a significant increase in effect size from day 7 to 90 over historical data (1.47 versus 0.67). DISCUSSION AND CONCLUSION: These results suggest that it is possible to derive surrogate biomarkers that can significantly reduce the sample size required to power future stroke clinical trials.

A phase I, pharmacokinetic, dosage escalation study of creatine monohydrate in subjects with amyotrophic lateral sclerosis.

Creatine monohydrate (creatine) has potential neuroprotective properties and is a commonly used supplement in amyotrophic lateral sclerosis (ALS) and other neurodegenerative disorders. Minimum therapeutic and maximum tolerated dosages of creatine are not yet known, nor is it known what systemic plasma concentrations result from specific dosage regimens. The objectives of this study were to establish steady-state plasma pharmacokinetics of creatine at several dosages, and to evaluate the effects of creatine on brain metabolites using proton magnetic resonance spectroscopy ((1)H-MRS). Six participants with ALS received creatine at three weekly escalating oral dosages (5, 10, and 15 g b.i.d.). Plasma creatine levels and MR spectra were obtained at baseline and with each dosage increase. Mean pre-dose steady-state creatine plasma concentrations were 20.3, 39.3, and 61.5 ug/ml at 5, 10, and 15 g b.i.d., respectively. Creatine spectra increased by 8% (p = 0.06) and glutamate + glutamine signals decreased by 17% (p = 0.039) at higher dosages. There were no safety concerns at any of the dosages. In conclusion, creatine plasma concentrations increased in a dose-dependent manner. Creatine appears to cross the blood-brain barrier, and oral administration of 15 g b.i.d. is associated with increased in vivo brain creatine concentrations and decreased glutamate concentrations.

Appraising esketamine nasal spray for the management of treatment-resistant depression in adults: Number needed to treat, number needed to harm, and likelihood to be helped or harmed.

INTRODUCTION: This post hoc study assessed the evidence-base for esketamine nasal spray for management of treatment-resistant depression (TRD) using number needed to treat (NNT), number needed to harm (NNH), and likelihood to be helped or harmed (LHH). METHODS: Data sources were four phase III randomized, double-blind studies including two positive studies (acute flexible-dose; maintenance) in patients with TRD. Key efficacy study outcomes: acute response (≥50% decrease from baseline on Montgomery-Asberg Depression Rating Scale [MADRS] total score), acute remission (MADRS scores ≤12). NNT, NNH were calculated for esketamine nasal spray+newly initiated oral antidepressant (esketamine+AD) vs. placebo+AD. RESULTS: In the pivotal acute flexible-dose study, MADRS response (63.4% vs. 49.5%) and remission (48.2% vs. 30.3%) at 4 weeks resulted in NNT of 8 and 6 for esketamine+AD vs. placebo+AD. NNH values <10 included dissociation (26.1% vs. 3.7%), vertigo (26.1% vs. 2.8%), nausea (26.1% vs. 6.4%), dizziness (20.9% vs. 4.6%), and dysgeusia (24.3% vs. 11.9%). Discontinuation rates due to adverse events (AE) (7.0% vs. 0.9%) yielded NNH=17. LHH comparing MADRS remission vs. discontinuation due to AE was 17 vs. 6. Maintenance use of esketamine+AD demonstrated NNT values<10 for relapse and/or maintenance of remission. In maintenance study, discontinuation due to AE (2.6% vs. 2.1%) yielded NNH=178 (non-significant). LIMITATIONS: Only dichotomous outcomes were included. CONCLUSION: NNT<10 for efficacy outcomes suggests potential benefit of esketamine+AD for both acute and maintenance use. LHH was favorable: esketamine+AD was 3 times likely to result in acute remission vs. discontinuations due to AE.

A register-based approach to identifying treatment-resistant depression-Comparison with clinical definitions.

BACKGROUND: Several definitions of treatment-resistant depression (TRD) are used for clinical research, but no verified model for use in register data exists. We aimed to compare a novel model created for use in register data-the Karolinska Institutet Model (KIM)-to the clinical definitions regarding the proportion of patients identified with TRD, their characteristics and clinical outcomes. METHODS: All patients in Sweden initiating antidepressant treatment with a diagnosis of depression in specialized healthcare 2006-2014 were identified and followed in national registers. In KIM, patients who initiated a third sequential, >28-day antidepressant treatment trial were defined as having TRD. Proportion of TRD and patient characteristics were compared with register adaptations of the European Staging Model (ESM), Massachusetts General Hospital Staging Method (MGH-s), and Maudsley Staging Model (MSM). Differences in patient characteristics were assessed with Chi-square tests and one-way ANOVAs. Hazard ratios for psychiatric hospitalization and for death from external causes were estimated by Cox proportional hazard regressions. RESULTS: Out of 127,108 antidepressant initiators with depression, the highest proportion of TRD was found using the MGH-s (19.0%), followed by MSM (15.3%), KIM (12.9%), and ESM (9.5%). Clinical characteristics were similar across the models. Compared with TRD patients identified by KIM, those identified by ESM had a marginally higher risk for psychiatric hospitalization (adjusted hazard ratio [aHR] 1.03, 95%CI 1.00-1.05), whereas those identified by MGH-s (aHR 0.92; 0.90-0.94) and MSM (aHR 0.95; 0.94-0.97) had a slightly reduced risk. Patients identified by MGH-s showed a reduced mortality compared with KIM (aHR 0.84; 0.72-0.98). CONCLUSIONS: This study provides insight into the differing characteristics of patients captured by various TRD models when used for register research. Models yielding lower proportions of TRD seemed to identify patients with greater morbidity. The KIM may be useful for register based research in TRD.

Substance use disorders and risk for treatment resistant depression: a population-based, nested case-control study.

BACKGROUND AND AIMS: Treatment-resistant depression (TRD), defined as inadequate treatment response after at least two adequate treatment trials, is common among patients initiating antidepressant treatment. Current or previous substance use disorders (SUD) are common among patients with depression and often lead to worse treatment outcomes. However, in clinical studies, SUD have not been found to increase the risk for TRD. The aim of this study was to investigate the association between SUD and TRD. DESIGN: Nested case-control study. SETTING: Nation-wide governmental health-care registers in Sweden. CASES AND CONTROLS: Data on prescribed drugs and diagnoses from specialized health care were used to establish a prospectively followed cohort of antidepressant initiators with depression (n = 121 669) from 2006 to 2014. Of these, 15 631 patients (13%) were defined as TRD cases, with at least three treatment trials within a single depressive episode. Each case with TRD was matched on socio-demographic data with five controls with depression. MEASUREMENTS: Crude and adjusted odds ratios (aOR) with 95% confidence intervals (CI) estimated the association between TRD and SUD diagnosis and/or treatment in five different time intervals until the time for fulfillment of TRD definition for the case. The analysis was adjusted for clinical and socio-demographic covariates. FINDINGS: Having any SUD during, or ≤ 180 days before start of, antidepressant treatment was associated with almost double the risk for TRD [≤ 180 days before: adjusted OR (aOR) = 1.86, CI = 1.70-2.05]. Increased risks for TRD were found ≤ 180 days before treatment start for the subcategories of sedative use (aOR = 2.37; 1.88-2.99), opioids (aOR = 2.02; 1.48-2.75), alcohol (aOR = 1.77; CI = 1.59-1.98) and combined substance use (aOR = 2.31; 1.87-2.99). CONCLUSIONS: Recent or current substance use disorders is positively associated with treatment resistance among patients initiating treatment for depression.

A retrospective analysis to estimate the healthcare resource utilization and cost associated with treatment-resistant depression in commercially insured US patients.

OBJECTIVE: The economic burden of commercially insured patients in the United States with treatment-resistant depression and patients with non-treatment-resistant major depressive disorder was compared using data from the Optum Clinformatics™ claims database. METHODS: Patients 18-63 years on antidepressant treatment between 1/1/13 and 9/30/13, who had no treatment claims for depression 6 months before the index date (first antidepressant dispensing), and who had a major depressive disorder or depression diagnosis within 30 days of the index date, were included. Treatment-resistant depression was defined as receiving 3 antidepressant regimens during 1 major depressive disorder episode. Patients with treatment-resistant depression were matched with patients with non-treatment-resistant major depressive disorder at a 1:4 ratio using propensity score matching. The study consisted of 1-year baseline (pre-index) and 2-year follow-up (post index) periods. Cost outcomes were compared using a generalized linear model. RESULTS: 2,370 treatment-resistant depression and 9,289 non-treatment-resistant major depressive disorder patients were included. In year 1 of the follow-up period, compared with non-treatment-resistant major depressive disorder, patients with treatment-resistant depression had: more emergency department visits (odds ratio = 1.39, 95% confidence interval = 1.24-1.56); more inpatient hospitalizations (odds ratio = 1.73, 95% confidence interval = 1.46-2.05); longer hospital stays (mean difference vs non-treatment-resistant major depressive disorder = 2.86, 95% confidence interval = 0.86-4.86 days); and more total healthcare costs (mean difference vs non-treatment-resistant major depressive disorder = US$3,846, 95% confidence interval = $2,855-$4,928). These patterns remained consistent in year 2 of the follow-up period. CONCLUSION: Treatment-resistant depression was associated with higher healthcare resource utilization and costs versus non-treatment-resistant major depressive disorder in this commercially insured cohort of patients in the United States.