Patrice Verpillat

Efficacy of escitalopram in patients with severe depression: a pooled analysis

Escitalopram is an effective, well‐tolerated treatment for major depressive disorder in both primary and specialist settings. This analysis compared the efficacy of escitalopram with citalopram in the treatment of patients with severe depression [defined as a score of ≥30 Montgomery–Åsberg Depression Rating Scale (MADRS)]. Data from three clinical trials were used for this pooled analysis. A total of 506 severely depressed patients were included (169 received escitalopram, 171 citalopram and 166 placebo). Mean change from baseline in MADRS total scores (primary efficacy parameter) was significantly higher in the escitalopram‐treated group compared with the citalopram‐treated group (p = 0.003). There was a significant difference in response between escitalopram and citalopram (56 vs. 41%, respectively, p = 0.007). Results from secondary efficacy parameters (Hamilton rating for depression and Clinical Global Impression of Improvement and Severity scales) were consistent with previous results. The benefits in severe depression of escitalopram vs. citalopram were so demonstrated.

Traitement des épisodes dépressifs sévères: escitalopram est plus efficace que citalopram

Resume La prise en charge therapeutique des patients atteints de depression severe reste encore un challenge pour les medecins. Par rapport a la forme legere ou moderee, la depression severe se caracterise souvent par une duree plus longue, une co-morbidite superieure, une plus faible probabilite de remission spontanee et un taux de rechute plus important. Par ailleurs, si l’efficacite des nouveaux antidepresseurs est bien etablie dans la depression legere a moderee, beaucoup moins d’etudes ont ete realisees dans la depression severe. Probablement parce que la depression severe n’est pas percue comme une entite a part, mais comme un continuum dans la clinique de cette pathologie, et aussi peut-etre en raison d’une absence de consensus clair sur la definition meme de cette severite. Apres avoir utilise les definitions basees sur la clinique et sur le mode de prise en charge, les auteurs semblent desormais privilegier l’utilisation des echelles cliniques avec un seuil specifique pour cette definition. En utilisant l’echelle MADRS (Montgomery-Asberg Depression Rating Scale) et la valeur seuil de 30 pour definir la severite de la depression, nous avons realise une analyse poolee avec les donnees des trois essais cliniques comparant l’efficacite du escitalopram a celle du citalopram. Les resultats montrent de facon constante une meilleure efficacite du escitalopram par rapport au citalopram, dans cette indication particuliere qu’est la depression severe.

Prescription patterns of antidepressants: findings from a US claims database.

Abstract Background: Introduction of serotonin reuptake inhibitors in the 1990s has increased the use of antidepressants and modified their prescription patterns. Objective: To identify reasons for prescriptions of antidepressants and factors associated with absence of a labelled indication on the prescription patterns of antidepressants and healthcare costs in a claims database. Methods: Antidepressant users with a new treatment episode with bupropion, citalopram, duloxetine, escitalopram, fluoxetine, fluvoxamine, paroxetine, sertraline or venlafaxine in 2003 and 2004 were identified in the PharMetrics database. Any ICD-9 code for an approved or clinically-accepted diagnosis for antidepressant treatments (‘diagnosis of interest’) occurring within the month before or after the index claim was considered as a reason for prescription. Socio-demographic and medical characteristics were described between users with and without a diagnosis of interest and analysed using logistic regression. Results: A total of 392 409 antidepressant users were identified. Diagnoses of interest were recorded for 46.7% of users, the most frequent diagnosis being depressive disorders (29% of the patients), anxiety disorders (17%) and abuse and dependence (5%). There were no major differences in patterns of diagnoses of interest between the antidepressants except for fluvoxamine and bupropion. Users without a diagnosis of interest had similar somatic comorbidities and overall baseline costs to users with a diagnosis of interest. However, they used specialised care less often (4.3 vs. 17.8%, OR = 0.50 [0.48; 0.51]), received psychotherapies less frequently (2.7 vs. 26.6%, OR = 0.12 [0.12; 0.12]), and had a shorter duration of use of antidepressants more often (36.9 vs. 28.5%, OR = 1.18 [1.17; 1.20]). Conclusions: The reason for prescribing antidepressants was often not reported in claims databases, and although antidepressant users with or without a diagnosis of interest can have similar somatic medical profiles and overall costs, they do not follow the same trajectory in the mental healthcare system. Depending on the research question to be answered, it is therefore important to specify which users are being targeted.

A review of accessibility of administrative healthcare databases in the Asia-Pacific region

Objective We describe and compare the availability and accessibility of administrative healthcare databases (AHDB) in several Asia-Pacific countries: Australia, Japan, South Korea, Taiwan, Singapore, China, Thailand, and Malaysia. Methods The study included hospital records, reimbursement databases, prescription databases, and data linkages. Databases were first identified through PubMed, Google Scholar, and the ISPOR database register. Database custodians were contacted. Six criteria were used to assess the databases and provided the basis for a tool to categorise databases into seven levels ranging from least accessible (Level 1) to most accessible (Level 7). We also categorised overall data accessibility for each country as high, medium, or low based on accessibility of databases as well as the number of academic articles published using the databases. Results Fifty-four administrative databases were identified. Only a limited number of databases allowed access to raw data and were at Level 7 [Medical Data Vision EBM Provider, Japan Medical Data Centre (JMDC) Claims database and Nihon-Chouzai Pharmacy Claims database in Japan, and Medicare, Pharmaceutical Benefits Scheme (PBS), Centre for Health Record Linkage (CHeReL), HealthLinQ, Victorian Data Linkages (VDL), SA-NT DataLink in Australia]. At Levels 3–6 were several databases from Japan [Hamamatsu Medical University Database, Medi-Trend, Nihon University School of Medicine Clinical Data Warehouse (NUSM)], Australia [Western Australia Data Linkage (WADL)], Taiwan [National Health Insurance Research Database (NHIRD)], South Korea [Health Insurance Review and Assessment Service (HIRA)], and Malaysia [United Nations University (UNU)-Casemix]. Countries were categorised as having a high level of data accessibility (Australia, Taiwan, and Japan), medium level of accessibility (South Korea), or a low level of accessibility (Thailand, China, Malaysia, and Singapore). In some countries, data may be available but accessibility was restricted based on requirements by data custodians. Conclusions Compared with previous research, this study describes the landscape of databases in the selected countries with more granularity using an assessment tool developed for this purpose. A high number of databases were identified but most had restricted access, preventing their potential use to support research. We hope that this study helps to improve the understanding of the AHDB landscape, increase data sharing and database research in Asia-Pacific countries.

Clobazam and clonazepam use in epilepsy: Results from a UK database incident user cohort study

OBJECTIVE To compare patient characteristics and treatment patterns among clobazam (CLB) and clonazepam (CZP)-treated patients with epilepsy in a longitudinal primary care database. METHODS In this pharmacoepidemiological study, real-life usage data from the Clinical Practice Research Database (CPRD) were evaluated. The CPRD collects data from approximately 690 primary care practices throughout the UK. Data included were from patients with ≥1 incident CLB or CZP prescription from 1995 to 2011 and were present in the database for ≥182 days prior to the index date (date patient was first prescribed CLB or CZP within the study period). RESULTS Of 21,099 patients who met inclusion criteria, 18.4% were receiving CLB and 81.6% were receiving CZP. More patients used CLB for epilepsy than CZP (76.1% vs 8.7%). CLB-treated adults (≤18years) were younger than those treated with CZP (41.0 vs 48.2 years; p<0.001), while CLB-treated children (≤18 years) were older than those treated with CZP (8.8 vs 7.3 years, p<0.001). The median CLB dosage did not change from baseline to last follow-up, while median CZP dosage increased 25% in adults and 50% in children. Median treatment duration, as well as retention rate up to 10 years, was similar between CLB and CZP in each age group. CONCLUSIONS Among adult and pediatric patients in the UK, CLB is more often prescribed for epilepsy than CZP. The median CLB dosage used by both adults and children remained stable over the 16-year study period, while the median CZP dosage increased in both adults and children.

Escitalopram in major depressive disorder: clinical benefits and cost effectiveness versus citalopram

Objective. Escitalopram is the most selective of the serotonin reuptake inhibitors. Methods. We review all the clinical trials (three pivotal placebo-controlled trials with citalopram as an active reference, one long-term non-inferiority study and one head-to-head superiority study) that include citalopram as an active reference in major depressive disorder (MDD), and studies that evaluate the cost-effectiveness of the two drugs. Results. In two of the pivotal studies and in the long-term study, escitalopram was numerically better than citalopram in reducing Montgomery-Åsberg Depression Rating Scale (MADRS) scores from baseline, with comparative tolerability. Meta-analyses of these studies showed statistically significant differences in favour of escitalopram in terms of reducing MADRS and increasing response. This effect was particularly apparent in patients with higher baseline MADRS scores. These trends were confirmed in a head-to-head study, which clearly demonstrated the superiority of escitalopram compared with citalopram on primary and secondary endpoints. The difference between treatments was clinically relevant. Cost-effectiveness analyses demonstrated that although escitalopram has a slightly higher unit cost than generic citalopram, expected direct medical and productivity- related costs were lower with escitalopram than citalopram. Conclusion. On the basis of these results, escitalopram was concluded to be more clinically effective and more cost-effective than citalopram for the treatment of MDD, with a similar tolerability profile.

Creating an index to measure health state of depressed patients in automated healthcare databases: the methodology

ABSTRACT Background and objective: Automated healthcare databases (AHDB) are an important data source for real life drug and healthcare use. In the filed of depression, lack of detailed clinical data requires the use of binary proxies with important limitations. The study objective was to create a Depressive Health State Index (DHSI) as a continuous health state measure for depressed patients using available data in an AHDB. Methods: The study was based on historical cohort design using the UK Clinical Practice Research Datalink (CPRD). Depressive episodes (depression diagnosis with an antidepressant prescription) were used to create the DHSI through 6 successive steps: (1) Defining study design; (2) Identifying constituent parameters; (3) Assigning relative weights to the parameters; (4) Ranking based on the presence of parameters; (5) Standardizing the rank of the DHSI; (6) Developing a regression model to derive the DHSI in any other sample. Results: The DHSI ranged from 0 (worst) to 100 (best health state) comprising 29 parameters. The proportion of depressive episodes with a remission proxy increased with DHSI quartiles. Conclusion: A continuous outcome for depressed patients treated by antidepressants was created in an AHDB using several different variables and allowed more granularity than currently used proxies.

A chart review of management of ischemic stroke patients in Germany

Background Ischemic stroke (IS) poses physical, emotional, and economic burdens on both patients and the healthcare system in Germany. However, the management of IS is not well described, especially after hospital discharge. In this study, we aim to describe the management of IS at onset, admission, and during follow-up. Methods German general practitioners (GPs) (n=40) extracted data on patient characteristics, hospitalizations, discharge, and ambulatory care from both GPs patient databases and hospital letters. Descriptive analyses were conducted. Results The sample included 185 patients with a mean age of 70 years [standard deviation (SD)=11.7]. Most patients (63%) contacted the Emergency Medical Services, while 36% contacted their GPs. The majority of patients were hospitalized within 1 h from onset, and the length of stay was on average 14 days. Half of the patients (50%) were admitted to the stroke unit, and 16% of patients received thrombolysis treatment with 2 h (SD=2.6) of time to treatment. Of the admitted patients, 32% were discharged to their homes, while the remaining patients were discharged to nursing homes (16.2%) and rehabilitation centers (47.6%). During the 12 months follow-up, 22% of patients were re-hospitalized and patients visited their GP (11.7 times), psychologist or psychiatrist (9.5 times), and neurologist (2.2 times). Death rate after stroke event was 13%. Conclusion The rate of patients who received thrombolysis is lower than the optimal rate in Germany. More research is needed to determine the factors that could predict the utilization of thrombolysis treatment.

Ischemic stroke management in West Scotland: a chart review

Background Little information is available about the long-term management of ischemic stroke (IS) in West Scotland. In this study we aim to describe the management of IS at onset, admission, and during follow-up among patients who survived an IS event. Methods General practitioners (GPs) (n=20) were randomly selected to recruit IS patients and extract data about patient characteristics, hospitalizations, discharge, and ambulatory care from GP databases, hospital letters, and direct contact with patients and their relatives. Descriptive analyses were conducted. Results One hundred and one patients were included, with a mean age of 65.6±13.4. About half of the patients contacted their GPs at the time of onset (45.4%). Cardiovascular history was prevalent in 29.7% of cases, and 14% of all cases were recurrences. Of the patients, 89 (88%) were hospitalized with mean length of stay (LOS) 11.8 days. Treatment was administered on average within 12.9 hours of admission and 23.6% of the admitted patients received thrombolytic treatment. During the 1-year follow-up period, 33.6% of patients were rehospitalized and the mean LOS was 15.1±29.5 days. Further, patients on average sought nursing care (10.9%), physical therapy (45.5%), occupational therapy (27.7%), speech therapy (12.9%), and professional caregivers (12%). Conclusion The health-care resource utilization of IS patients is a major driver of economic burden.

Assessing Balance in Baseline Characteristics Using Different Propensity Adjusted Methods for Bipolar I Mixed Disorder Patients Initiating Asenapine Versus Other Oral Atypical Antipsychotics.

OBJECTIVES: Asenapine, an oral atypical antipsychotic (AA), was initially used for more severe bipolar I mixed disorder. Different propensity score (PS) methods were investigated to achieve balanced baseline characteristics between asenapine and four oral AA cohorts for eventual outcomes analyses. METHODS: Adults with ≥1 asenapine, aripiprazole, olanzapine, quetiapine, or risperidone prescription fill (Aug 2009 to Dec 2010) and diagnosis of bipolar I mixed disorder (ICD-9-CM: 296.6x) from MarketScan® claims databases, yielded 230 asenapine, 2726 aripiprazole, 984 olanzapine, 3056 quetiapine, and 1623 risperidone patients. PS were derived using logistic regression models for asenapine and each AA with baseline demographic and clinical characteristics as covariates. PS, inverse probability treatment weight (IPTW: 1/PS asenapine; 1/(1-PS) AA), and standard mortality ratio weight (SMR: 1 asenapine; PS/ (1-PS) AA) distributions were evaluated. Asenapine-AA un-weighted, IPTW, and SMR baseline characteristics were compared using standardized differences, chi-squares, and t-tests. RESULTS: Un-weighted asenapine patients had pre-index greater bipolar I episodes rates, psychiatric drug use, dyslipidemia and obesity (all comparators). PS distributions for asenapine-olanzapine overlapped to some degree, while PS of asenapine and the other comparators overlapped little to not at all. Comparing IPTW baseline characteristics, asenapine more resembled the AA cohorts. Demographic imbalance increased between asenapine and each AA. IPTW improved clinical characteristic balance for asenapine versus olanzapine and risperidone, but only slightly improved imbalance versus aripiprazole and quetiapine. However some clinical characteristics not previously balanced in the un-weighted analyses for asenapine versus each AA were now unbalanced. Applying SMR, AA cohorts more resembled the asenapine cohort and all baseline demographic and clinical characteristics were finally balanced. CONCLUSIONS: SMR, a less common PS method, resulted in balanced baseline characteristics. SMR should be considered when IPTW leaves imbalance and the cohort of primary interest differs significantly from the broader underlying population to which it’s being compared. Baseline Sample Description • The final sample consisted of 230 asenapine, 2,726 aripiprazole, 984 olanzapine, 3,056 quetiapine, and 1,623 risperidone bipolar I mixed patients. • Patients initiating asenapine had clinical histories indicating more severe mental health disease in the 6-month pre-index period than patients initiating any of the other AAs. A significantly greater proportion of asenapine patients had: (Table 1) – ≥1 prior bipolar episode (bipolar I or bipolar II). – ≥1 prescription fill for each psychotropic medication evaluated. – Visited a psychiatrist immediately prior to the index prescription fill. Lundbeck SAS, Global Analytics, Issy-les-Moulineaux, France Lundbeck SAS, Global Epidemiology, Issy-les-Moulineaux, France IPTWand SMRWeighted Baseline Characteristics • Using the IPTW method, many bipolar I mixed asenapine patients have weights (1/PS) >1.0, whereas the weights of the nonasenapine patients are <1.0 (1/[1-PS]). This resulted in weighted sample sizes of 1,457 for asenapine patients (rather than the 230 identified patients), 623 for olanzapine (rather than the 984 identified patients), and 1,700 for quetiapine (rather than the 3,056 identified patients). (Table 2 and Table 3) – Demographic imbalance remained between asenapine and each AA cohort. (Figure 2 and Figure 3) (Table 2 and Table 3) – Some clinical characteristics previously balanced in the un-weighted analyses were now unbalanced. – IPTW improved baseline characteristics’ balance with asenapine vs olanzapine and risperidone, but only slightly improved balance vs aripiprazole and quetiapine. • With SMR weighting, asenapine patients are assigned a weight = 1.0 and the non-asenapine patients are assigned weights much less than 1.0 (PS/1-PS). This resulted in weighted sample sizes of 230 for asenapine (sample size unchanged), 232 for olanzapine (rather than the 984 identified patients), and 230 for quetiapine (rather than the 3,056 identified patients). (Table 2 and Table 3) – All baseline demographic and clinical characteristics were balanced post-SMR weighting. (Figure 2 and Figure 4) (Table 2 and Table 3) • Observational studies are increasingly being used to estimate the effect of treatments on outcomes. • Treatment allocation in these studies is non-random, and therefore cohorts of interest may differ significantly in their pretreatment characteristics. • If the pre-treatment characteristics are unbalanced, and are associated with the study outcomes, the study suffers from confounding by indication bias. Similarly, the study suffers from selection bias if the unbalanced pre-treatment characteristics are associated with treatment allocation. • Different PS methods are used to achieve balanced treatment cohorts prior to outcomes comparison in observational studies. Introduction In a recent historical cohort study, asenapine, an oral AA, was found to be used among patients with more severe bipolar I mixed disorder when compared to aripiprazole, olanzapine, quetiapine, and risperidone users. In order to achieve balance in the baseline characteristics between asenapine and each AA cohort, different PS methods (IPTW and SMR), were evaluated before the comparison of outcomes. Objectives • Data source: Truven Health MarketScan® Commercial, Medicare Supplemental, and Multi-State Medicaid claims databases, from August 1, 2008 to December 31, 2011. Medical claims are linked to outpatient prescription drug claims and person-level enrollment information using blinded patient identifiers (which are Health Insurance and Portability Act [HIPAA] compliant). • Target Population: Adults (≥18 years old) with ≥1 prescription fill for any of the 5 aforementioned AAs of interest during the enrollment window (August 1, 2009 to December 31, 2010). The first chronologically occurring AA was deemed the index AA if no claims for that AA were identified in the 6-month pre-index period. The first prescription fill for the index AA was the index date. Patients then had to be continuously enrolled from 6 months pre-index through 12 months post-index in medical and pharmacy benefits, have ≥1 medical claim with a diagnosis of bipolar I disorder mixed (International Classification of Diseases, Ninth Revision, Clinical Modification [ICD-9-CM] 296.6x, in any position), no prescription for a depot antipsychotic ±2 months of the index date, and no concomitant schizophrenia. • Time frame: The 6-month pre-index period was used to measure and evaluate the balance of baseline demographic and clinical characteristics among the cohorts. Statistical Analysis • Student’s t-test was used to compare continuous measures and chi-squares for categorical measures between the asenapine and other AA cohorts. Based on the distributional properties of each measure under study, non-parametric tests were also utilized when appropriate. • PS were derived using logistic regression models for asenapine and each AA cohort with baseline demographic and clinical characteristics as covariates. The following variables were used to compute PS: – Age, gender, plan type, prescribing physician specialty*, cardiovascular and metabolic conditions, prior bipolar I and II episodes, and prescriptions for specific somatic and psychotropic medications. • IPTW and SMR weighting were assessed to achieve balance among the treatment cohorts.1,2,3 – IPTW: 1/PS for the asenapine cohort and 1/(1–PS) for the comparator AA cohort. – SMR: 1 (or PS/PS) for asenapine cohort and PS/(1–PS) for the comparator cohort. • Un-weighted, and IPTWand SMR-weighted baseline characteristics were compared using standardized differences, chisquares, and t-tests. • Tables and figures are presented for asenapine compared to olanzapine and quetiapine. Data for all other comparators have not been provided due to space constraints. *Prescribing physician specialty was assigned by proxy using the specialty of the provider noted on the medical claim associated with the index diagnosis. Methods