Marcia G. Toprac

The Inventory of Depressive Symptomatology, Clinician Rating (IDS-C) and Self-Report (IDS-SR), and the Quick Inventory of Depressive Symptomatology, Clinician Rating (QIDS-C) and Self-Report (QIDS-SR) in public sector patients with mood disorders: a psychometric evaluation.

BACKGROUNDnThe present study provides additional data on the psychometric properties of the 30-item Inventory of Depressive Symptomatology (IDS) and of the recently developed Quick Inventory of Depressive Symptomatology (QIDS), a brief 16-item symptom severity rating scale that was derived from the longer form. Both the IDS and QIDS are available in matched clinician-rated (IDS-C30; QIDS-C16) and self-report (IDS-SR30; QIDS-SR16) formats.nnnMETHODnThe patient samples included 544 out-patients with major depressive disorder (MDD) and 402 out-patients with bipolar disorder (BD) drawn from 19 regionally and ethnicically diverse clinics as part of the Texas Medication Algorithm Project (TMAP). Psychometric analyses including sensitivity to change with treatment were conducted.nnnRESULTSnInternal consistencies (Cronbachs alpha) ranged from 0.81 to 0.94 for all four scales (QIDS-C16, QIDS-SR16, IDS-C30 and IDS-SR30) in both MDD and BD patients. Sad mood, involvement, energy, concentration and self-outlook had the highest item-total correlations among patients with MDD and BD across all four scales. QIDS-SR16 and IDS-SR30 total scores were highly correlated among patients with MDD at exit (c = 0.83). QIDS-C16 and IDS-C30 total scores were also highly correlated among patients with MDD (c = 0.82) and patients with BD (c = 0.81). The IDS-SR30, IDS-C30, QIDS-SR16, and QIDS-C16 were equivalently sensitive to symptom change, indicating high concurrent validity for all four scales. High concurrent validity was also documented based on the SF-12 Mental Health Summary score for the population divided in quintiles based on their IDS or QIDS score.nnnCONCLUSIONnThe QIDS-SR16 and QIDS-C16, as well as the longer 30-item versions, have highly acceptable psychometric properties and are treatment sensitive measures of symptom severity in depression.

The Texas Children's Medication Algorithm Project : Report of the Texas Consensus Conference Panel on Medication Treatment of Childhood Attention-Deficit/Hyperactivity Disorder. Part II : Tactics

OBJECTIVESnExpert consensus methodology was used to develop a medication treatment algorithm for attention-deficit/hyperactivity disorder (ADHD). The algorithm broadly outlined the choice of medication for ADHD and some of its most common comorbid conditions. Specific tactical recommendations were developed with regard to medication dosage, assessment of drug response, management of side effects, and long-term medication management.nnnMETHODnThe consensus conference of academic clinicians and researchers, practicing clinicians, administrators, consumers, and families developed evidence-based tactics for the pharmacotherapy of childhood ADHD and its common comorbid disorders. The panel discussed specifics of treatment of ADHD and its comorbid conditions with stimulants, antidepressants, mood stabilizers, alpha-agonists, and (when appropriate) antipsychotics.nnnRESULTSnSpecific tactics for the use of each of the above agents are outlined. The tactics are designed to be practical for implementation in the public mental health sector, but they may have utility in many practice settings, including the private practice environment.nnnCONCLUSIONSnTactics for psychopharmacological management of ADHD can be developed with consensus.

The Texas children's medication algorithm project: Report of the Texas consensus conference panel on medication treatment of childhood major depressive disorder

OBJECTIVESnTo develop consensus guidelines for medication treatment algorithms for childhood major depressive disorder (MDD) based on scientific evidence and clinical opinion when science is lacking. The ultimate goal of this approach is to synthesize research and clinical experience for the practitioner and to increase the uniformity of preferred treatment for childhood MDD. A final goal is to develop an approach that can be tested as to whether it improves clinical outcomes for children and adolescents with MDD.nnnMETHODnA consensus conference was held. Participants included academic clinicians and researchers, practicing clinicians, administrators, consumers, and families. The focus was to review and use clinical evidence to recommend specific pharmacological approaches for treatment of MDD in children and adolescents. After a series of presentations of current research evidence and panel discussion, the consensus panel met, agreed on assumptions, and drafted the algorithms. The process initially addressed strategies of treatment and then tactics to implement the strategies.nnnRESULTSnConsensually agreed-upon algorithms for major depressions (with and without psychosis) and comorbid attention deficit disorders were developed. Treatment strategies emphasized the use of selective serotonin reuptake inhibitors. The algorithm consists of systematic strategies for treatment interventions and recommended tactics for implementation of the strategies, including medication augmentation and medication combinations. Participants recommended prospective evaluation of the algorithms in various public sector settings, and many volunteered as sites for such an evaluation.nnnCONCLUSIONSnUsing scientific and clinical experience, consensus-derived algorithms for children and adolescents with MDD can be developed.

Clinical factors associated with employment among people with severe mental illness: Findings from the employment intervention demonstration program

Research has shown that supported employment programs are effective in helping psychiatric outpatients achieve vocational outcomes, yet not all program participants are able to realize their employment goals. This study used 24 months of longitudinal data from a multisite study of supported employment interventions to examine the relationship of patient clinical factors to employment outcomes. Multivariate random regression analysis indicated that, even when controlling for an extensive series of demographic, study condition (experimental versus control), and work history covariates, clinical factors were associated with individuals’ ability to achieve competitive jobs and to work 40 or more hours per month. Poor self-rated functioning, negative psychiatric symptoms, and recent hospitalizations were most consistently associated with failure to achieve these employment outcomes. These findings suggest ways that providers can tailor supported employment programs to achieve success with a diverse array of clinical subpopulations.

Brief Psychiatric Rating Scale Expanded Version: How do new items affect factor structure?

Our goal was to suggest a factor structure for the Brief Psychiatric Rating Scale Expanded Version (BPRS-E) based upon a large and diverse sample and to determine which of the new items improved the factors derived from the 18-item version of the scale that have been used in clinical research for decades. We investigated the consistency of our proposed model over time and across demographic groups. As part of the Texas Medication Algorithm Project, the BPRS-E was administered to a total of 1440 psychiatric outpatients in three different diagnostic groups on multiple occasions. The sample was randomly split so that exploratory factor analysis could be done with the first half, and the model could be confirmed on the second half. A four-factor structure including factors assessing depression/anxiety, psychosis, negative symptoms, and activation was found. For each factor, we specify items in the expanded version that added to the breadth of the commonly used clinical factors while improving or maintaining goodness of fit and reliability. The final model proposed was consistent over time and across diagnosis, phase of illness, age, gender, ethnicity, and level of education. The BPRS-E has a stable four-factor structure, making it useful as a clinical outcome measure.

SPECIAL COMMUNICATIONThe Texas Children's Medication Algorithm Project: Report of the Texas Consensus Conference Panel on Medication Treatment of Childhood Attention-Deficit/Hyperactivity Disorder. Part I

OBJECTIVESnExpert consensus methodology was used to develop evidence-based, consensually agreed-upon medication treatment algorithms for attention-deficit/hyperactivity disorder (ADHD) in the public mental health sector. Although treatment algorithms for adult mental disorders have been developed, this represents one of the first attempts to develop similar algorithms for childhood mental disorders. Although these algorithms were developed initially for the public sector, the goals of this approach are to increase the uniformity of treatment and improve the clinical outcomes of children and adolescents with ADHD in a variety of treatment settings.nnnMETHODnA consensus conference of academic clinicians and researchers, practicing clinicians, administrators, consumers, and families was convened to develop evidence-based consensus algorithms for the pharmacotherapy of childhood ADHD. After a series of presentations of current research evidence and panel discussion, the consensus panel met and drafted the algorithms along with guidelines for implementation.nnnRESULTSnThe panel developed consensually agreed-upon algorithms for ADHD with and without specific comorbid disorders. The algorithms consist of systematic strategies for psychopharmacological interventions and tactics to ensure successful implementation of the strategies. While the algorithms focused on the medication management of ADHD, the conference emphasized that psychosocial treatments are often a critical component of the overall management of ADHD.nnnCONCLUSIONSnMedication algorithms for ADHD can be developed with consensus. A companion article will discuss the implementation of these algorithms.

A feasibility study of the childhood depression medication algorithm: The Texas Children's Medication Algorithm Project (CMAP)

OBJECTIVEnTo evaluate the feasibility and impact on clinical response and function associated with the use of an algorithm-driven disease management program (ALGO) for children and adolescents treated for depression with or without attention-deficit/hyperactivity disorder (ADHD) in community mental health centers.nnnMETHODnInterventions included (1). medication algorithms, (2). clinical and technical support for the physician, (3). uniform chart documentation of outcomes, and (4). a patient/family psychoeducation program. Children eligible for entry into the study were referred to the child psychiatrist for initiation or change in medicine. Outcomes of treatment with the ALGO for up to 4 months are presented. Measures of change included clinical symptoms, functioning, and global improvement (Clinical Global Impression Scale). A historical chart cohort from the same clinics was used as a quasi-control.nnnRESULTSnThirty-nine individuals (depression = 24; comorbid depression with ADHD = 15) were enrolled for treatment with ALGO. One hundred fourteen children were in the control cohort (74 depressed, 40 comorbid). For the ALGO groups, Childrens Depression Rating Scale-Revised depression severity scores decreased from 48.2 to 32.5 and Child Adolescent Functioning Assessment Scale function scores improved from 70.3 to 40.9 (all p < or =.0005). Clinical Global Impression Scale severity scores decreased from 5.7 to 3.7 in ALGO compared to only 5.8 to 4.8 in the control (p <.003).nnnCONCLUSIONSnThere was clear improvement in clinical symptoms, functioning, and global response with ALGO treatment. The magnitude of the improvement was greater in children and adolescents treated with the ALGO program compared with a historical cohort. These data support the need for controlled studies in larger populations examining the effects of algorithm-driven disease management programs on the clinical outcomes of children with mental illness.

A Feasibility Study of the Children's Medication Algorithm Project (CMAP) Algorithm for the Treatment of ADHD

OBJECTIVEnTo determine whether an algorithm for the treatment of attention-deficit/hyperactivity disorder (ADHD) can be implemented in a community mental health center.nnnMETHODnFifty child and adolescent patients at Texas community mental health centers who met criteria for ADHD were treated according to an algorithm-based disease management program for ADHD. Psychiatrists were trained in the use of the algorithm, and each subject underwent a baseline assessment consisting of a structured interview and standardized rating scales. Subjects were monitored for 4 months. At the end of treatment, the psychiatrists completed the Clinical Global Impression Scale (CGI) and the baseline rating scales were repeated. The primary variables of interest were psychiatrist and family adherence to the algorithm. To examine impact on treatment outcome, the CGI of the algorithm subjects was compared with CGIs based on chart reviews of 118 historical controls.nnnRESULTSnPsychiatrists implemented the major aspects of the algorithm, but the detailed tactics of the algorithm (use of fixed titration of stimulants) were less well adhered to.nnnCONCLUSIONSnAn algorithm for the treatment of ADHD can be implemented in a community mental health center.

A comparison of alternative assessments of depressive symptom severity: a pilot study

This study compared the performance of an itemized symptom self-report (Inventory of Depressive Symptomatology - Self-Report; IDS-SR), patient global ratings, and clinician global ratings with an itemized clinician-rated symptom severity measure (Inventory of Depressive Symptomatology - Clinician-Rated; IDS-C) in detecting treatment effects in patients with major depressive disorder (MDD). A total of 28 inpatients (30.8% psychotic) and 34 outpatients (17.9% psychotic) with MDD began treatment that followed the Texas medication algorithm. The clinicians completed the IDS-C and a Physician Global Rating Scale (PhGRS) at each assessment visit, while the patients completed the IDS-SR and a Patient Global Rating Scale (PtGRS). Change scores from the baseline to subsequent weeks were computed for all subjects, utilizing all four measures. The IDS-SR was a significant independent predictor of the response to treatment as compared to the two global ratings. The IDS-SR was as sensitive to change as the IDS-C. While the clinician-rated itemized symptom severity rating scale remains the standard to assess the symptomatic outcome of the treatment of MDD, a self-report of identical symptomatology may be a reasonable alternative for many patients.

Catching Up on Health Outcomes: The Texas Medication Algorithm Project

OBJECTIVEnTo develop a statistic measuring the impact of algorithm-driven disease management programs on outcomes for patients with chronic mental illness that allowed for treatment-as-usual controls to catch up to early gains of treated patients.nnnDATA SOURCES/STUDY SETTINGnStatistical power was estimated from simulated samples representing effect sizes that grew, remained constant, or declined following an initial improvement. Estimates were based on the Texas Medication Algorithm Project on adult patients (age > or = 18) with bipolar disorder (n = 267) who received care between 1998 and 2000 at 1 of 11 clinics across Texas.nnnSTUDY DESIGNnStudy patients were assessed at baseline and three-month follow-up for a minimum of one year. Program tracks were assigned by clinic.nnnDATA COLLECTION/EXTRACTION METHODSnHierarchical linear modeling was modified to account for declining-effects. Outcomes were based on 30-item Inventory for Depression Symptomatology-Clinician Version.nnnPRINCIPAL FINDINGSnDeclining-effect analyses had significantly greater power detecting program differences than traditional growth models in constant and declining-effects cases. Bipolar patients with severe depressive symptoms in an algorithm-driven, disease management program reported fewer symptoms after three months, with treatment-as-usual controls catching up within one year.nnnCONCLUSIONSnIn addition to psychometric properties, data collection design, and power, investigators should consider how outcomes unfold over time when selecting an appropriate statistic to evaluate service interventions. Declining-effect analyses may be applicable to a wide range of treatment and intervention trials.