Psychometrics of the Concise Health Risk Tracking Self-Report (CHRT-SR16) Assessment of Suicidality in a Sample of Adults with Moderate to Severe Methamphetamine Use Disorder: Findings from the ADAPT-2 Randomized Trial.

Background: The co-occurrence of suicidality and substance use disorders has been well established, but rating scales to examine suicidal behavior and risk are sparse among participants with substance use disorders. We examined the psychometric properties of the 16-item Concise Health Risk Tracking Scale - Self Report (CHRT-SR16) to measure suicidality among adults with moderate-to-severe methamphetamine use disorder. Methods: Participants (n = 403) with moderate-to-severe methamphetamine use disorder completed the CHRT-SR16 as part of a randomized, double-blind, placebo-controlled pharmacotherapy trial. The CHRT-SR16 factor structure was assessed using confirmatory factor analysis (CFA). Internal consistency was estimated with coefficients alpha (α) and omega (ω), test-retest reliability with intraclass correlation coefficient (ICC) and standard error of measurement, and convergent validity using Spearman's ρ rank order correlation coefficient test between CHRT-SR16 factors and the Patient Health Questionnaire (PHQ-9). The analyses utilized baseline and week 1 data (for test-retest reliability only). Results: CFA revealed a seven-factor model of Pessimism, Helplessness, Social Support, Despair, Impulsivity, Irritability, and Suicidal Thoughts as the best-fitting model. The CHRT-SR16 also exhibited strong internal consistency (α = 0.89; ω = 0.89), test-retest reliability (ICC = 0.78) and convergent validity with the PHQ-9 total score (ρ = 0.62). Conclusion: The CHRT-SR16 showed strong psychometric properties in a sample of participants with primary methamphetamine use disorder. Clinicaltrialsgov Identifier: NCT03078075.

Psychometric properties of the Treatment Effectiveness Assessment in methamphetamine use disorder.

INTRODUCTION: The ability for people living with stimulant use disorder to live meaningful lives requires not only abstinence from addictive substances, but also healthy engagement with their community, lifestyle practices, and overall health. The Treatment Effectiveness Assessment (TEA) assesses components of recovery consisting of four functional domains: substance use, health, lifestyle, and community. This secondary data analysis of 403 participants with severe methamphetamine use disorder tested the reliability and validity of the TEA. METHODS: Participants were enrolled in the Accelerated Development of Additive Pharmacotherapy Treatment (ADAPT-2) for methamphetamine use disorder. The study used total TEA and domain scores at baseline to assess factor structure and internal consistency, as well as construct validity related to substance cravings (visual analog scale [VAS]), quality of life (quality-of-life assessment [QoL]), mental health (Patient Health Questionnaire-9 [PHQ-9], Concise Health Risk Tracking Scale Self-Report [CHRT-SR16]), and social support (CHRT-SR16). RESULTS: Individual TEA items showed moderate to large correlations with each other (r = 0.27-0.51; p < .001), and strong correlations to the total score (r = 0.69-0.78; p < .001). Internal consistency was strong (coefficient α = 0.73 [0.68-0.77]; coefficient ω = 0.73 [0.69-0.78]). Construct validity was acceptable, with the strongest correlation between the TEA Health item and the general health status item on the QoL (r = 0.53, p < .001). CONCLUSIONS: TEA has acceptable levels of reliability and validity supporting prior similar findings in a sample of participants with moderate to severe methamphetamine use disorder. Results from this study provide support for its use in assessing clinically meaningful changes beyond simply reduced substance use.

The Concise Health Risk Tracking Self-Report (CHRT-SR) assessment of suicidality in depressed outpatients: A psychometric evaluation.

BACKGROUND: The 12-item Concise Health Risk Tracking Self-Report (CHRT-SR12 ) is a brief, self-report measure that systematically assesses both suicidal thinking and associated thoughts that may indicate the propensity for suicidal acts. It can be used as a tool to both assess risk and guide treatment interventions targeting associated cognitions. METHODS: This report used acute treatment data from a clinically representative sample of outpatients with nonpsychotic major depressive disorder (N = 665) participating in the Combining Medications to Enhance Depression Outcomes trial, who received up to 12 weeks of escitalopram, escitalopram plus bupropion SR, or venlafaxine XR plus mirtazapine. Outcome assessors and patients were masked to treatment. RESULTS: Factor analysis of CHRT-SR12 confirmed that the 12 items have higher order structure with two subscales (Propensity, Suicidal Thoughts) and a total score. Internal consistencies were acceptable for both subscales and total score. All three scales were modestly correlated with overall depression severity (r = 0.54 to r = 0.21) and highly discriminating among patients grouped by suicide item ratings on three different depressive symptom ratings. The three scales also distinguished change over the acute phase treatment for those with different levels of baseline suicidal ideation (measured by 30-item Inventory of Depressive Symptomatology (item 18) and for those with change in suicidal ideation (baseline to last visit). CONCLUSIONS: The CHRT-SR12 has good to excellent psychometric properties and is sensitive to change in suicidal thinking and propensity toward suicidal behavior in outpatients with major depressive disorder. It allows for the monitoring of thoughts and feelings associated with increased suicidal risk as well as levels of thoughts about suicide.

Prognostic Utility of a Self-Reported Depression Questionnaire versus Clinician-Based Assessment on Renal Outcomes.

BACKGROUND: The prognostic utility of self-administered depression scales in chronic kidney disease (CKD) independent of a clinician-based major depressive disorder (MDD) diagnosis is neither clearly established nor are the optimal cutoff scores for predicting outcomes. The overlap between symptoms of depression and chronic disease raises the question of whether a cutoff score on a depression scale can be substituted for a time-consuming diagnostic interview to prognosticate risk. METHODS: The 16-item Quick Inventory of Depression Symptomatology-Self Report scale (QIDS-SR16) was administered to 266 consecutive outpatients with non-dialysis CKD, followed prospectively for 12 months for an apriori composite outcome of death or dialysis or hospitalization. Association of QIDS-SR16 best cutoff score, determined by receiver/responder operating characteristics curves, with outcomes was investigated using survival analysis. The effect modification of an interview-based clinician MDD diagnosis on this association was ascertained. RESULTS: There were 126 composite events. A QIDS-SR16 cutoff ≥8 had the best prognostic accuracy, hazards ratio (HR) = 1.77, 95% CI 1.24-2.53, p = 0.002. This cutoff remained significantly associated with outcomes even after controlling for comorbidities, estimated glomerular filtration rate, hemoglobin and serum albumin, adjusted HR (aHR) = 1.80, 95% CI 1.23-2.62, p = 0.002, and performed similarly to a clinician-based MDD diagnosis (aHR = 1.72, 95% CI 1.14-2.68). Adjustment for MDD conferred the association of QIDS-SR16 with outcomes no longer significant. CONCLUSIONS: QIDS-SR16 cutoff ≥8 adds to the prognostic information available to practicing nephrologists during routine clinic visits from comorbidities and laboratory data. This cutoff score performs similar to a clinician diagnosis of MDD and provides a feasible and time-saving alternative to an interview-based MDD diagnosis for determining prognosis in CKD patients.

Men and women from the STRIDE clinical trial: An assessment of stimulant abstinence symptom severity at residential treatment entry.

BACKGROUND AND OBJECTIVES: Gender-specific factors associated with stimulant abstinence severity were examined in a stimulant abusing or dependent residential treatment sample (N = 302). METHOD: Bivariate statistics tested gender differences in stimulant abstinence symptoms, measured by participant-reported experiences of early withdrawal. Multivariate linear regression examined gender and other predictors of stimulant abstinence symptom severity. RESULTS: Women compared to men reported greater stimulant abstinence symptom severity. Anxiety disorders and individual anxiety-related abstinence symptoms accounted for this difference. African American race/ethnicity was predictive of lower stimulant abstinence severity. DISCUSSION AND CONCLUSIONS: Women were more sensitive to anxiety-related stimulant withdrawal symptoms. SCIENTIFIC SIGNIFICANCE: Clinics that address anxiety-related abstinence symptoms, which more commonly occur in women, may improve treatment outcome.

Self-rated measure of pain frequency, intensity, and burden: psychometric properties of a new instrument for the assessment of pain.

BACKGROUND: A brief, self-administered measurement of pain frequency, intensity, and burden is desirable in both research and clinical settings. We describe the development and initial psychometric properties of a new instrument, the Pain Frequency, Intensity, and Burden Scale (P-FIBS). METHODS: The P-FIBS was administered to all participants (N = 302) with psychostimulant use disorders in the National Institute on Drug Abuse Clinical Trials Network's STRIDE (Stimulant Reduction Intervention using Dose Exercise) multisite trial. RESULTS: The four items on the P-FIBS demonstrate high item-total correlations (range 0.70-0.85) with a high Cronbach's alpha (0.90). The P-FIBS demonstrated a strong negative correlation with the bodily pain sub-score of the Short Form Health Survey (r = -0.76, p < 0.0001) and did not correlate with a measure of cocaine (r = 0.09, p = 0.12) or methamphetamine (r = -0.06, p = 0.33) craving. CONCLUSIONS: The P-FIBS demonstrates good psychometric properties. This brief measure can be used to assess pain in research settings or as a screen in clinical settings. Further research is needed to assess the measure's sensitivity to change with treatment.

A significant relationship between mercury exposure from dental amalgams and urinary porphyrins: a further assessment of the Casa Pia children's dental amalgam trial.

Previous studies noted specific changes in urinary porphyrin excretion patterns associated with exposure to mercury (Hg) in animals and humans. In our study, urinary porphyrin concentrations were examined in normal children 8-18 years-old from a reanalysis of data provided from a randomized, prospective clinical trial that was designed to evaluate the potential health consequences of prolonged exposure to Hg from dental amalgam fillings (the parent study). Our analysis examined dose-dependent correlations between increasing Hg exposure from dental amalgams and urinary porphyrins utilizing statistical models with adjustments for the baseline level (i.e. study year 1) of the following variables: urinary Hg, each urinary porphyrin measure, gender, race, and the level of lead (Pb) in each subject's blood. Significant dose-dependent correlations between cumulative exposure to Hg from dental amalgams and urinary porphyrins associated with Hg body-burden (pentacarboxyporphyrin, precoproporphyrin, and coproporphyrin) were observed. Overall, 5-10% increases in Hg-associated porphyrins for subjects receiving an average number of dental amalgam fillings in comparison to subjects receiving only composite fillings were observed over the 8-year course of the study. In contrast, no significant correlations were observed between cumulative exposure to Hg from dental amalgams and urinary porphyrins not associated with Hg body-burden (uroporphyrin, heptacarboxyporphyrin, and hexacarboxyporphyrin). In conclusion, our study, in contrast to the no-effect results published from the parent study, further establishes the sensitivity and specificity of specific urinary porphyrins as a biomarker for low-level Hg body-burden, and also reveals that dental amalgams are a significant chronic contributor to Hg body-burden.

Assessing physicians' use of treatment algorithms: Project IMPACTS study design and rationale.

Effective treatments for major depressive disorder have been available for 35 years, yet inadequate pharmacotherapy continues to be widespread leading to suboptimal outcomes. Evidence-based medication algorithms have the potential to bring much-needed improvement in effectiveness of antidepressant treatment in "real-world" clinical settings. Project IMPACTS (Implementation of Algorithms using Computerized Treatment Systems) addresses the critical question of how best to facilitate integration of depression treatment algorithms into routine care. It tests an algorithm implemented through a computerized decision support system using a measurement-based care approach for depression against a paper-and-pencil version of the same algorithm and non-algorithm-based, specialist-delivered usual care. This paper reviews issues related to the Project IMPACTS study rationale, design, and procedures. Patient outcomes include symptom severity, social and work function, and quality of life. The economic impact of treatment is assessed in terms of health care utilization and cost. Data collected on physician behavior include degree of adherence to guidelines and physician attitudes about the perceived utility, ease of use, and self-reported effect of the use of algorithms on workload. Novel features of the design include a two-tiered study enrollment procedure, which initially enroll physicians as subjects, and then following recruitment of physicians, enrollment of subjects takes place based initially on an independent assessment by study staff to determine study eligibility. The study utilizes brief, easy-to-use symptom severity measures that facilitate physician decision making, and it employs a validated, phone-based, follow-up assessment protocol in order to minimize missing data, a problem common in public sector and longitudinal mental health studies. IMPACTS will assess the success of algorithm implementation and subsequent physician adherence using study-developed criteria and related statistical approaches. These new procedures and data points will also allow a more refined assessment of algorithm-driven treatment in the future.

An empirical analysis of cost outcomes of the Texas Medication Algorithm Project.

OBJECTIVE: Disease management systems that incorporate medication algorithms have been proposed as cost-effective means to offer optimal treatment for patients with severe and chronic mental illnesses. The Texas Medication Algorithm Project was designed to compare health care costs and clinical outcomes between patients who received algorithm-guided medication management or usual care in 19 public mental health clinics. METHODS: This longitudinal cohort study for patients with major depression (N=350), bipolar disorder (N=267), and schizophrenia (N=309) applied a multi-part declining-effects cost model. Outcomes were assessed by the Inventory of Depressive Symptomatology and the Brief Psychiatric Rating Scale. RESULTS: Compared with patients in usual care, patients in algorithm-based care incurred higher medication costs and had more frequent physician visits, although these differences often became smaller with time. For major depression, algorithm-based care achieved better outcomes sustainable with time but at higher agency and non-agency costs (mixed cost-effective). For bipolar disorder, patients in algorithm-based management achieved better outcomes at lower agency costs (cost-effective). For schizophrenia, patients in algorithm-based care achieved better outcomes that diminished with time, with no detectable difference in health care costs (cost-effective). CONCLUSIONS: Cost outcomes of algorithm-based care and usual care varied by disorder and over time. For bipolar disorder and schizophrenia, algorithm-based care improved outcomes without higher costs for health care services. For major depression, substantively better and sustained outcomes were obtained but at greater costs.

An analysis of the effect of funding source in randomized clinical trials of second generation antipsychotics for the treatment of schizophrenia.

OBJECTIVE: The effect of funding source on the outcome of randomized controlled trials has been investigated in several medical disciplines; however, psychiatry has been largely excluded from such analyses. In this article, randomized controlled trials of second generation antipsychotics in schizophrenia are reviewed and analyzed with respect to funding source (industry vs. non-industry funding). METHOD: A literature search was conducted for randomized, double-blind trials in which at least one of the tested treatments was a second generation antipsychotic. In each study, design quality and study outcome were assessed quantitatively according to rating scales. Mean quality and outcome scores were compared in the industry-funded studies and non-industry-funded studies. An analysis of the primary author's affiliation with industry was similarly performed. RESULTS: Results of industry-funded studies significantly favored second generation over first generation antipsychotics when compared to non-industry-funded studies. Non-industry-funded studies showed a trend toward higher quality than industry-funded studies; however, the difference between the two was not significant. Also, within the industry-funded studies, outcomes of trials involving first authors employed by industry sponsors demonstrated a trend toward second generation over first generation antipsychotics to a greater degree than did trials involving first authors employed outside the industry (p=0.05). CONCLUSIONS: While the retrospective design of the study limits the strength of the findings, the data suggest that industry bias may occur in randomized controlled trials in schizophrenia. There appears to be several sources by which bias may enter clinical research, including trial design, control of data analysis and multiplicity/redundancy of trials.

Clinical results for patients with major depressive disorder in the Texas Medication Algorithm Project.

CONTEXT: The Texas Medication Algorithm Project is an evaluation of an algorithm-based disease management program for the treatment of the self-declared persistently and seriously mentally ill in the public mental health sector. OBJECTIVE: To present clinical outcomes for patients with major depressive disorder (MDD) during 12-month algorithm-guided treatment (ALGO) compared with treatment as usual (TAU). DESIGN: Effectiveness, intent-to-treat, prospective trial comparing patient outcomes in clinics offering ALGO with matched clinics offering TAU. SETTING: Four ALGO clinics, 6 TAU clinics, and 4 clinics that offer TAU to patients with MDD but provide ALGO for schizophrenia or bipolar disorder. Patients Male and female outpatients with a clinical diagnosis of MDD (psychotic or nonpsychotic) were divided into ALGO and TAU groups. The ALGO group included patients who required an antidepressant medication change or were starting antidepressant therapy. The TAU group initially met the same criteria, but because medication changes were made less frequently in the TAU group, patients were also recruited if their Brief Psychiatric Rating Scale total score was higher than the median for that clinic's routine quarterly evaluation of each patient. MAIN OUTCOME MEASURES: Primary outcomes included (1) symptoms measured by the 30-item Inventory of Depressive Symptomatology-Clinician-Rated scale (IDS-C(30)) and (2) function measured by the Mental Health Summary score of the Medical Outcomes Study 12-item Short-Form Health Survey (SF-12) obtained every 3 months. A secondary outcome was the 30-item Inventory of Depressive Symptomatology-Self-Report scale (IDS-SR(30)). RESULTS: All patients improved during the study (P<.001), but ALGO patients had significantly greater symptom reduction on both the IDS-C(30) and IDS-SR(30) compared with TAU. ALGO was also associated with significantly greater improvement in the SF-12 mental health score (P =.046) than TAU. CONCLUSION: The ALGO intervention package during 1 year was superior to TAU for patients with MDD based on clinician-rated and self-reported symptoms and overall mental functioning.

The Texas medication algorithm project: clinical results for schizophrenia.

In the Texas Medication Algorithm Project (TMAP), patients were given algorithm-guided treatment (ALGO) or treatment as usual (TAU). The ALGO intervention included a clinical coordinator to assist the physicians and administer a patient and family education program. The primary comparison in the schizophrenia module of TMAP was between patients seen in clinics in which ALGO was used (n = 165) and patients seen in clinics in which no algorithms were used (n = 144). A third group of patients, seen in clinics using an algorithm for bipolar or major depressive disorder but not for schizophrenia, was also studied (n = 156). The ALGO group had modestly greater improvement in symptoms (Brief Psychiatric Rating Scale) during the first quarter of treatment. The TAU group caught up by the end of 12 months. Cognitive functions were more improved in ALGO than in TAU at 3 months, and this difference was greater at 9 months (the final cognitive assessment). In secondary comparisons of ALGO with the second TAU group, the greater improvement in cognitive functioning was again noted, but the initial symptom difference was not significant.

Texas Medication Algorithm Project, phase 3 (TMAP-3): clinical results for patients with a history of mania.

BACKGROUND: The Texas Medication Algorithm Project (TMAP) assessed the clinical and economic impact of algorithm-driven treatment (ALGO) as compared with treatment-as-usual (TAU) in patients served in public mental health centers. This report presents clinical outcomes in patients with a history of mania (BD), including bipolar I and schizoaffective disorder, bipolar type, during 12 months of treatment beginning March 1998 and ending with the final active patient visit in April 2000. METHOD: Patients were diagnosed with bipolar I disorder or schizoaffective disorder, bipolar type, according to DSM-IV criteria. ALGO was comprised of a medication algorithm and manual to guide treatment decisions. Physicians and clinical coordinators received training and expert consultation throughout the project. ALGO also provided a disorder-specific patient and family education package. TAU clinics had no exposure to the medication algorithms. Quarterly outcome evaluations were obtained by independent raters. Hierarchical linear modeling, based on a declining effects model, was used to assess clinical outcome of ALGO versus TAU. RESULTS: ALGO and TAU patients showed significant initial decreases in symptoms (p =.03 and p <.001, respectively) measured by the 24-item Brief Psychiatric Rating Scale (BPRS-24) at the 3-month assessment interval, with significantly greater effects for the ALGO group. Limited catch-up by TAU was observed over the remaining 3 quarters. Differences were also observed in measures of mania and psychosis but not in depression, side-effect burden, or functioning. CONCLUSION: For patients with a history of mania, relative to TAU, the ALGO intervention package was associated with greater initial and sustained improvement on the primary clinical outcome measure, the BPRS-24, and the secondary outcome measure, the Clinician-Administered Rating Scale for Mania (CARS-M). Further research is planned to clarify which elements of the ALGO package contributed to this between-group difference.

Texas Medication Algorithm Project, phase 3 (TMAP-3): rationale and study design.

BACKGROUND: Medication treatment algorithms may improve clinical outcomes, uniformity of treatment, quality of care, and efficiency. However, such benefits have never been evaluated for patients with severe, persistent mental illnesses. This study compared clinical and economic outcomes of an algorithm-driven disease management program (ALGO) with treatment-as-usual (TAU) for adults with DSM-IV schizophrenia (SCZ), bipolar disorder (BD), and major depressive disorder (MDD) treated in public mental health outpatient clinics in Texas. DISCUSSION: The disorder-specific intervention ALGO included a consensually derived and feasibility-tested medication algorithm, a patient/family educational program, ongoing physician training and consultation, a uniform medical documentation system with routine assessment of symptoms and side effects at each clinic visit to guide ALGO implementation, and prompting by on-site clinical coordinators. A total of 19 clinics from 7 local authorities were matched by authority and urban status, such that 4 clinics each offered ALGO for only 1 disorder (SCZ, BD, or MDD). The remaining 7 TAU clinics offered no ALGO and thus served as controls (TAUnonALGO). To determine if ALGO for one disorder impacted care for another disorder within the same clinic ("culture effect"), additional TAU subjects were selected from 4 of the ALGO clinics offering ALGO for another disorder (TAUinALGO). Patient entry occurred over 13 months, beginning March 1998 and concluding with the final active patient visit in April 2000. Research outcomes assessed at baseline and periodically for at least 1 year included (1) symptoms, (2) functioning, (3) cognitive functioning (for SCZ), (4) medication side effects, (5) patient satisfaction, (6) physician satisfaction, (7) quality of life, (8) frequency of contacts with criminal justice and state welfare system, (9) mental health and medical service utilization and cost, and (10) alcohol and substance abuse and supplemental substance use information. Analyses were based on hierarchical linear models designed to test for initial changes and growth in differences between ALGO and TAU patients over time in this matched clinic design.