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

Model-Informed Precision Dosing Using Machine Learning for Levothyroxine in General Practice: Development, Validation and Clinical Simulation Trial

Levothyroxine is one of the most prescribed drugs in the western world. Dosing is challenging due to high-interindividual differences in effective dosage and the narrow therapeutic window. Model-informed precision dosing (MIPD) using machine learning could assist general practitioners (GPs), but no such models exist for primary care. Furthermore, introduction of decision-support algorithms in healthcare is limited due to the substantial gap between developers and clinicians' perspectives. We report the development, validation, and a clinical simulation trial of the first MIPD application for primary care. Stable maintenance dosage of levothyroxine was the model target. The multiclass model generates predictions for individual patients, for different dosing classes. Random forest was trained and tested on a national primary care database (n = 19,004) with a final weighted AUC across dosing options of 0.71, even in subclinical hypothyroidism. TSH, fT4, weight, and age were most predictive. To assess the safety, feasibility, and clinical impact of MIPD for levothyroxine, we performed clinical simulation studies in GPs and compared MIPD to traditional prescription. Fifty-one GPs selected starting dosages for 20 primary hypothyroidism cases without and then with MIPD 2 weeks later. Overdosage and underdosage were defined as higher and lower than 12.5 μg relative to stable maintenance dosage. MIPD decreased overdosage in number (30.5 to 23.9%, P < 0.01) and magnitude (median 50 to 37.5 μg, P < 0.01) and increased optimal starting dosages (18.3 to 30.2%, P < 0.01). GPs considered lab results more often with MIPD and most would use the model frequently. This study demonstrates the clinical relevance, safety, and effectiveness of MIPD for levothyroxine in primary care.

Optimizing the Management and Outcomes of Failed Back Surgery Syndrome: A Proposal of a Standardized Multidisciplinary Team Care Pathway

Failed back surgery syndrome (FBSS) is a major, worldwide health problem that generates considerable expense for healthcare systems. A number of controversial issues concerning the management of FBSS are regularly debated, but no clear consensus has been reached. This pitfall is the result of lack of a standardized care pathway due to insufficient characterization of underlying pathophysiological mechanisms, which are essential to identify in order to offer appropriate treatment, and the paucity of evidence of treatment outcomes. In an attempt to address the challenges and barriers in the clinical management of FBSS, an international panel of physicians with a special interest in FBSS established the Chronic Back and Leg Pain (CBLP) Network with the primary intention to provide recommendations through consensus on how to optimize outcomes. In the first of a series of two papers, a definition of FBSS was delineated with specification of criteria for patient assessment and identification of appropriate evaluation tools in order to choose the right treatment options. In this second paper, we present a proposal of a standardized care pathway aiming to guide clinicians in their decision-making on how to optimize their management of FBSS patients. The utilization of a multidisciplinary approach is emphasized to ensure that care is provided in a uniform manner to reduce variation in practice and improve patient outcomes.

A model for digital mental healthcare: Its usefulness and potential for service delivery in low- and middle-income countries

BACKGROUND

Using digital technology to deliver mental health care can possibly serve as a viable adjunct or alternative to mainstream services in lessening the mental health gap in a large number of resource deficient and LAMI countries. Conventional models of telepsychiatric services available so far, however, have been inadequate and ineffective, as these address only a small component of care, and rely on engagement of specialists who are grossly insufficient in numbers.

AIM

To describe an innovative digital model of mental health care, enabling and empowering the non-specialists to deliver high quality mental health care in remote areas.

METHODS

The model is powered by an online, fully automated clinical decision support system (CDSS), with interlinked modules for diagnosis, management and follow-up, usable by non-specialists after brief training and minimal supervision by psychiatrist, to deliver mental health care at remote sites.

RESULTS

The CDSS has been found to be highly reliable, feasible, with sufficient sensitivity and specificity. This paper describes the model and initial experience with the digital mental health care system deployed in three geographically difficult and remote areas in northern hill states in India. The online system was found to be reasonably comprehensive, brief, feasible, user-friendly, with high levels of patient satisfaction. 2594 patients assessed at the three remote sites and the nodal center represented varied diagnoses.

CONCLUSIONS

The digital model described here has the potential to serve as an effective alternative or adjunct for delivering comprehensive and high quality mental health care in LAMI countries like India in the primary and secondary care settings.

Diagnostic Evaluation of Children with Autism Spectrum Disorders: Clinician Compliance with Published Guidelines

OBJECTIVE

To assess to what extent child neurologists (CNs) and developmental-behavioral pediatricians (DBPs) order diagnostic tests that are not recommended/indicated and/or fail to order tests that are recommended/indicated when evaluating children with an autism spectrum disorder (ASD).

METHOD

CNs and DBPs in the United States were asked which laboratory tests they would "routinely order" for a preschool child with ASD and IQ = 58 (ASD + Intellectual Disability (ID)), and a preschool child with ASD and IQ = 85 (ASD-ID). Chi-square tests were performed to identify differences (CNs vs DBPs) in laboratory testing.

RESULTS

The sample consisted of 267 respondents (127 CN's; 140 DBPs). When evaluating ASD + ID or ASD - ID, inappropriate tests (≥1) were ordered by 76.8% and 76.4% of MDs, respectively. There was no significant difference between specialties in compliance with evaluation guidelines for ASD + ID (CN = 20.5% vs DBP = 16.4%; χ = 0.73). No significant differences were noted (DBP vs CN) regarding the percent ordering inappropriate tests for either clinical case or within each specialty when comparing testing for ASD + ID versus ASD - ID. Relative to DBPs, CNs were more likely to order EEGs and MRIs when evaluating children with ASD + ID or ASD - ID. 10% and 40% of respondents did not order any recommended genetic tests when evaluating ASD + ID and ASD - ID, respectively.

CONCLUSION

When evaluating children with ASD, many CNs and DBPs fail to order tests that should be routinely performed and often order tests that are not routinely indicated yet are neither benign nor inexpensive. Recommended molecular genetic tests are often not ordered. Clinical guidelines must be updated and better promulgated.

A meta-analysis on the efficacy and safety of St John's wort extract in depression therapy in comparison with selective serotonin reuptake inhibitors in adults

OBJECTIVE

The aim of the study was to investigate the efficacy and safety of St John's wort extract and selective serotonin reuptake inhibitors (SSRIs) in the treatment of depression.

METHODS

Databases were searched for studies comparing efficacy and/or safety of St John's wort extract with SSRIs in depression from 1966 to April 2015. Stata software was used for statistical analysis.

RESULTS

Twenty-seven studies met the study entry criteria. A total of 3,126 patients with depression were included. St John's wort extract did not differ from SSRIs in clinical response, remission, and mean reduction in Hamilton Rating Scale for Depression score. St John's wort extract had a significantly lower rate of adverse events compared to SSRIs (summary relative risk: 0.77; 95% confidence interval: 0.70, 0.84, P=0.00) and had fewer withdrawals due to adverse events. St John's wort extract had superior safety in the management of patients with depression.

CONCLUSION

Both St John's wort extract and SSRIs are effective in treating mild-to-moderate depression. St John's wort extract is safer than SSRIs.

Evaluation of anhedonia with the Snaith-Hamilton Pleasure Scale (SHAPS) in adult outpatients with major depressive disorder

Anhedonia or inability to experience pleasure not only is a core symptom of major depressive disorder (MDD), but also is identified as an important component of the positive valence system in the NIMH Research Domain Criteria. The Snaith-Hamilton Pleasure Scale (SHAPS) has been developed for the assessment of hedonic experience or positive valence, but has not been well-studied in depressed outpatient populations. The current study examined the reliability and validity of the SHAPS using a sample of adult outpatients with treatment resistant MDD. Data for the current study were obtained from 122 adult outpatients with a diagnosis of MDD and non-response to adequate treatment with an SSRI and who participated in Project TReatment with Exercise Augmentation for Depression (TREAD). A Principal Components Analysis was used to define the dimensionality of the SHAPS. Convergent and discriminant validity were evaluated via correlations of the SHAPS total score with "gold standard" measures of depression severity and quality of life. The SHAPS was found to have high internal consistency (Cronbach's coefficient α = .82). A Principal Components Analysis suggests that the SHAPS is mainly "unidimensional" and limited to hedonic experience among adult outpatients with MDD. Convergent and discriminant validity were assessed by examining the Spearman rank-order correlation coefficient between the SHAPS total score and the HRSD17 (rs = 0.22, p < .03), IDS-C30 (rs = 0.26, p < .01), IDS-SR30 (rs = 0.23, p < .02), QIDS-C16 (rs = 0.22, p < .03), QIDS-SR16 (rs = 0.17, p < .10), QLES-Q (rs = -0.32, p < .002), and the pleasure/enjoyment item (sub-item 21) of the IDS-C (rs = 0.44, p < .0001) and IDS-SR (rs = 0.38, p < .0002). The self-administered SHAPS showed modest sensitivity (76%) and specificity (54%) with the self-administered pleasure/enjoyment single item (sub-item 21) of IDS-SR30. The current study shows that the SHAPS is a reliable and valid instrument to assess hedonic experience or positive valence in adult outpatients with MDD and provides a broader assessment of this important domain.

Evaluating the promise of health IT to enhance/expand the reach of mental health services

OBJECTIVE

A growing number of health information technologies (HIT) are being developed and tested to address mental health conditions. HIT includes Internet and smartphone programs or apps, text messaging protocols and telepsychiatry. We reviewed the promise and evidence that HIT can expand access to mental health care and reduce disparities in use of services across groups in need.

CONCLUSIONS

Limited reach of mental health services is a pervasive problem in the United States, and solving it will require innovations that enable us to extend our clinical reach into underserved populations without significantly expanding our workforce. In theory, HIT can extend access to mental health care in several ways: by enhancing the reach to priority populations, addressing system capacity issues, supporting training, improving clinical decision making, lowering the "consumer's threshold" for treatment, delivering preventive mental health services, speeding innovation and adoption and reducing cost barriers to treatment. At present, evidence is limited, and research is needed, focusing on consumer engagement strategies, the benefits and harms of HIT for the therapeutic relationship and the comparative effectiveness of various HIT alternatives.

Feasible evidence-based strategies to manage depression in primary care

According to the World Health Organization, major depressive disorder (MDD) is a leading cause of disability-adjusted life years worldwide. However, recent evidence suggests depression remains undertreated in primary care settings. Measurement-based care (MBC) is an evidence-based strategy that can feasibly assist primary care physicians in managing patients with MDD. Utilizing health information technology tools, such as computer decision support software, can improve adherence to evidence-based treatment guidelines and MBC at the point of care.

A computerized decision support system for depression in primary care

OBJECTIVE

In 2004, results from The Texas Medication Algorithm Project (TMAP) showed better clinical outcomes for patients whose physicians adhered to a paper-and-pencil algorithm compared to patients who received standard clinical treatment for major depressive disorder (MDD). However, implementation of and fidelity to the treatment algorithm among various providers was observed to be inadequate. A computerized decision support system (CDSS) for the implementation of the TMAP algorithm for depression has since been developed to improve fidelity and adherence to the algorithm.

METHOD

This was a 2-group, parallel design, clinical trial (one patient group receiving MDD treatment from physicians using the CDSS and the other patient group receiving usual care) conducted at 2 separate primary care clinics in Texas from March 2005 through June 2006. Fifty-five patients with MDD (DSM-IV criteria) with no significant difference in disease characteristics were enrolled, 32 of whom were treated by physicians using CDSS and 23 were treated by physicians using usual care. The study's objective was to evaluate the feasibility and efficacy of implementing a CDSS to assist physicians acutely treating patients with MDD compared to usual care in primary care. Primary efficacy outcomes for depression symptom severity were based on the 17-item Hamilton Depression Rating Scale (HDRS(17)) evaluated by an independent rater.

RESULTS

Patients treated by physicians employing CDSS had significantly greater symptom reduction, based on the HDRS(17), than patients treated with usual care (P < .001).

CONCLUSIONS

The CDSS algorithm, utilizing measurement-based care, was superior to usual care for patients with MDD in primary care settings. Larger randomized controlled trials are needed to confirm these findings.

TRIAL REGISTRATION

clinicaltrials.gov Identifier: NCT00551083.

Psychometric evaluation of the Snaith-Hamilton pleasure scale in adult outpatients with major depressive disorder

The inability to experience pleasure, anhedonia, is recognized as a hallmark symptom of depression. An instrument developed for the assessment of hedonic capacity is the 14-item, self-report, Snaith-Hamilton Pleasure Scale (SHAPS), but its psychometric properties have not been adequately evaluated. This study examined the reliability and validity of the SHAPS using a large sample of adult outpatients with major depressive disorder (MDD). Data for this study were obtained from 461 adult outpatients with a diagnosis of MDD who participated in Implementation of Algorithms using Computerized Treatment Systems Project. Internal consistency of the SHAPS was assessed using the Cronbach's coefficient α. A principal factor analysis was used to define the dimensionality of the SHAPS. Convergent and discriminant validity was assessed by evaluating the Pearson correlations between the SHAPS total score and the pleasure/enjoyment item of the 30-item Inventory of Depressive Symptomatology - Clinician-rating (IDS-C30); Quality of Life, Enjoyment, and Satisfaction Questionnaire; 17-item Hamilton Rating Scale for Depression; IDS-C30; 16-item Quick Inventory of Depressive Symptomatology; and 10-item clinician-rated Montgomery-Asberg Depression Rating Scale, respectively. The internal consistency of the SHAPS was 0.91. A one-factor solution emerged for the SHAPS (eigen-values of the first two initial factors were 5.95 and 0.43, respectively). Pearson correlations revealed a positive linear relationship between the SHAPS total score and the total scores on the 17-item Hamilton Rating Scale for Depression (r=0.49, P<0.0001), IDS-C30 (r=0.56, P<0.0001), 16-item Quick Inventory of Depressive Symptomatology (r=0.55, P<0.0001), and 10-item clinician-rated Montgomery-Asberg Depression Rating Scale (r=0.53, P<0.0001). The SHAPS total score was negatively correlated with the Quality of Life, Enjoyment, and Satisfaction Questionnaire (r=-0.65, P<0.0001). This study shows that the SHAPS is a reliable, valid, and unidimensional instrument used to assess the hedonic capacity in adult outpatients with MDD.

Telephone-based assessments to minimize missing data in longitudinal depression trials: a project IMPACTS study report

PURPOSE

Missing data in clinical efficacy and effectiveness trials continue to be a major threat to the validity of study findings. The purpose of this report is to describe methods developed to ensure completion of outcome assessments with public mental health sector subjects participating in a longitudinal, repeated measures study for the treatment of major depressive disorder. We developed longitudinal assessment procedures that included telephone-based clinician interviews in order to minimize missing data commonly encountered with face-to-face assessment procedures.

METHODS

A pre-planned, multi-step strategy was developed to ensure completeness of data collection. The procedure included obtaining multiple pieces of patient contact information at baseline, careful education of both staff and patients concerning the purpose of assessments, establishing good patient rapport, and finally being flexible and persistent with phone appointments to ensure the completion of telephone-based follow-up assessments. A well-developed administrative and organizational structure was also put in place prior to study implementation.

RESULTS

The assessment completion rate for the primary outcome for 310 of 504 subjects who enrolled and completed 52 weeks (at the time of manuscript) of telephone-based follow-up assessments was 96.8%.

CONCLUSION

By utilizing telephone-based follow-up procedures and adapting our easy-to-use pre-defined multi-step approach, researchers can maximize patient data retention in longitudinal studies.

What do general medical guidelines say about depression care? Depression treatment recommendations in general medical practice guidelines

PURPOSE OF REVIEW

Depression is one of the most costly and prevalent mental disorders, and it often co-occurs in patients with general medical conditions. This review identifies the extent to which depression treatment is being addressed in guidelines for general medical illness treatment.

RECENT FINDINGS

We conducted a review of the literature published within the past year pertaining to depression treatment guidelines in medical care. We also conducted a systematic review of available practice guidelines listed by the Agency for Research and Quality to assess the extent to which depression treatment is being addressed in current general medical treatment guidelines. Overall, only a handful of articles addressed treatment of depression within the context of general medical care, and most only addressed screening. Among 199 available practice guidelines for general medical conditions, only 83 mentioned depression treatment, and the majority of these only addressed depression screening.

SUMMARY

To improve outcomes in patients with co-occurring general medical conditions, general medical guidelines should include specific recommendations for treating and co-managing depression. Additional strategies that facilitate the translation of guidelines into routine practice, address the management of multiple chronic illnesses, and reduce the stigma of mental illness are also needed.

Measurement-based care for refractory depression: a clinical decision support model for clinical research and practice

Despite years of antidepressant drug development and patient and provider education, suboptimal medication dosing and duration of exposure resulting in incomplete remission of symptoms remains the norm in the treatment of depression. Additionally, since no one treatment is effective for all patients, optimal implementation focusing on the measurement of symptoms, side effects, and function is essential to determine effective sequential treatment approaches. There is a need for a paradigm shift in how clinical decision making is incorporated into clinical practice and for a move away from the trial-and-error approach that currently determines the "next best" treatment. This paper describes how our experience with the Texas Medication Algorithm Project (TMAP) and the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) trial has confirmed the need for easy-to-use clinical support systems to ensure fidelity to guidelines. To further enhance guideline fidelity, we have developed an electronic decision support system that provides critical feedback and guidance at the point of patient care. We believe that a measurement-based care (MBC) approach is essential to any decision support system, allowing physicians to individualize and adapt decisions about patient care based on symptom progress, tolerability of medication, and dose optimization. We also believe that successful integration of sequential algorithms with MBC into real-world clinics will facilitate change that will endure and improve patient outcomes. Although we use major depression to illustrate our approach, the issues addressed are applicable to other chronic psychiatric conditions including comorbid depression and substance use disorder as well as other medical illnesses.

Improving depression care for ethnic and racial minorities: a concept for an intervention that integrates CME planning with improvement strategies

Depression is one of the most common reasons that individuals seek treatment in the primary care setting. Research in the past 15 years has shown that dramatic improvement in the management of patients with depression is possible. Advances in pharmacotherapy and delivery of depression care have been reported, but few currently benefit members of ethnic and racial minorities. Educating physicians and other health professionals has been suggested as one approach to address the issues related to disparities in depression care. There is little evidence, however, that education alone is effective. The authors of this article believe that incorporating physician learning activities that are planned using approaches that have been shown to be effective in interventions currently demonstrating some success in improving depression care provided to ethnic and racial minorities will enhance the impact and sustainability of these interventions. This article--the conclusion of this supplement--will describe an intervention concept that integrates a quality improvement model (the Institute for Health Improvement's Breakthrough Series Collaborative model) with an evidence-based approach to planning CME and supports the integration by using action inquiry technologies and community-based participatory research methods. Relevant approaches from implementation research are discussed, and suggestions for testing the intervention concept are provided.