Patients' opinions about knowing their risk for depression and what to do about it. The predictD-qualitative study

A personalized intervention to prevent depression in primary care based on risk predictive algorithms and decision support systems: protocol of the e-predictD study

The predictD is an intervention implemented by general practitioners (GPs) to prevent depression, which reduced the incidence of depression-anxiety and was cost-effective. The e-predictD study aims to design, develop, and evaluate an evolved predictD intervention to prevent the onset of major depression in primary care based on Information and Communication Technologies, predictive risk algorithms, decision support systems (DSSs), and personalized prevention plans (PPPs). A multicenter cluster randomized trial with GPs randomly assigned to the e-predictD intervention + care-as-usual (CAU) group or the active-control + CAU group and 1-year follow-up is being conducted. The required sample size is 720 non-depressed patients (aged 18-55 years), with moderate-to-high depression risk, under the care of 72 GPs in six Spanish cities. The GPs assigned to the e-predictD-intervention group receive brief training, and those assigned to the control group do not. Recruited patients of the GPs allocated to the e-predictD group download the e-predictD app, which incorporates validated risk algorithms to predict depression, monitoring systems, and DSSs. Integrating all inputs, the DSS automatically proposes to the patients a PPP for depression based on eight intervention modules: physical exercise, social relationships, improving sleep, problem-solving, communication skills, decision-making, assertiveness, and working with thoughts. This PPP is discussed in a 15-min semi-structured GP-patient interview. Patients then choose one or more of the intervention modules proposed by the DSS to be self-implemented over the next 3 months. This process will be reformulated at 3, 6, and 9 months but without the GP-patient interview. Recruited patients of the GPs allocated to the control-group+CAU download another version of the e-predictD app, but the only intervention that they receive via the app is weekly brief psychoeducational messages (active-control group). The primary outcome is the cumulative incidence of major depression measured by the Composite International Diagnostic Interview at 6 and 12 months. Other outcomes include depressive symptoms (PHQ-9) and anxiety symptoms (GAD-7), depression risk (predictD risk algorithm), mental and physical quality of life (SF-12), and acceptability and satisfaction ('e-Health Impact' questionnaire) with the intervention. Patients are evaluated at baseline and 3, 6, 9, and 12 months. An economic evaluation will also be performed (cost-effectiveness and cost-utility analysis) from two perspectives, societal and health systems.

Trial registration

ClinicalTrials.gov, identifier: NCT03990792.

Does providing personalized depression risk information lead to increased psychological distress and functional impairment? Results from a mixed-methods randomized controlled trial

BACKGROUND

Multivariable risk algorithms (MVRP) predicting the personal risk of depression will form an important component of personalized preventive interventions. However, it is unknown whether providing personalized depression risk will lead to unintended psychological harms. The objectives of this study were to evaluate the impact of providing personalized depression risk on non-specific psychological distress and functional impairment over 12 months.

METHODS

A mixed-methods randomized controlled trial was conducted in 358 males and 354 females who were at high risk of having a major depressive episode according to sex-specific MVRPs, and who were randomly recruited across Canada. Participants were assessed at baseline, 6 and 12 months.

RESULTS

Over 93% of participants were interested in knowing their depression risk. The intervention group had a greater reduction in K10 score over 12 months than the control group; complete-case analysis found a significant between-group difference in mean K10 change score (d = 1.17, 95% CI 0.12-2.23) at 12 months. Participants in the intervention group also reported significantly less functional impairment in the domains of home and work/school activities, than did those in the control group. A majority of the qualitative interviewees commented that personalized depression risk information does not have a negative impact on physical and mental health.

CONCLUSIONS

This study found no evidence that providing personalized depression risk information will lead to worsening psychological distress, functional impairment, and absenteeism. Provision of personalized depression risk information may have positive impacts on non-specific psychological distress and functioning.

TRIAL REGISTRATION

ClinicalTrials.gov NCT02943876.

Real-World Implementation of Precision Psychiatry: A Systematic Review of Barriers and Facilitators

Background: Despite significant research progress surrounding precision medicine in psychiatry, there has been little tangible impact upon real-world clinical care. Objective: To identify barriers and facilitators affecting the real-world implementation of precision psychiatry. Method: A PRISMA-compliant systematic literature search of primary research studies, conducted in the Web of Science, Cochrane Central Register of Controlled Trials, PsycINFO and OpenGrey databases. We included a qualitative data synthesis structured according to the ‘Consolidated Framework for Implementation Research’ (CFIR) key constructs. Results: Of 93,886 records screened, 28 studies were suitable for inclusion. The included studies reported 38 barriers and facilitators attributed to the CFIR constructs. Commonly reported barriers included: potential psychological harm to the service user (n = 11), cost and time investments (n = 9), potential economic and occupational harm to the service user (n = 8), poor accuracy and utility of the model (n = 8), and poor perceived competence in precision medicine amongst staff (n = 7). The most highly reported facilitator was the availability of adequate competence and skills training for staff (n = 7). Conclusions: Psychiatry faces widespread challenges in the implementation of precision medicine methods. Innovative solutions are required at the level of the individual and the wider system to fulfil the translational gap and impact real-world care.

Users' perceptions about receiving personalized depression risk information: findings from a qualitative study

OBJECTIVES

To understand users' perceptions about receiving their personalized depression risk score and to gain an understanding about how to improve the efficiency of risk communication from the user perspective.

METHODS

A qualitative study embedded in a randomized controlled trial (RCT) on evaluating the impact of providing personalized depression risk information on psychological harms and benefits. The participants (20 males and 20 females) were randomly selected from the intervention arm of the RCT after the 12-month assessment. The qualitative interviews were conducted through telephone, audio recorded and transcribed verbatim. We conducted a content analysis to describe the content and contextual meaning of data collected from participants.

RESULTS

The first theme explained the motivation for receiving a risk score. Most participants chose to receive their personalised depression risk score with the goal of improving their self-awareness. The results revealed three sub-themes surrounding perceptions and implication of receiving their risk score: positive, negative, and neutral. Most participants found that receiving their score was positive because it improved their awareness of their mental health, but some participants could see that some people would have negative feelings when getting the score causing them to be more likely to get depression. The final theme focussed on improvements including: the best delivery methods, having resources and strategies, and targeting younger people.

CONCLUSION

The most significant motivation for, and benefit of receiving one's personalized depression risk score was improved awareness of one's mental health. A comprehensive risk communication program may improve the uptake and maximize the impact on behavior changes and risk reduction.

The impact of communicating personal mental ill-health risk: A randomized controlled non-inferiority trial

AIM

Risk algorithms predicting personal mental ill-health will form an important component of digital and personalized preventive interventions, yet it is unknown whether informing people of personal risk may cause unintended harm. This trial evaluated the comparative effect of communicating personal mental ill-health risk profiles on psychological distress.

METHODS

Australian participants using a mood-monitoring app were randomly allocated to receiving their current personal mental ill-health risk profile (n = 119), their achievable personal risk profile (n = 118) or to a control group (n = 118) in which no risk information was communicated, in a non-inferiority trial design. The primary outcome was psychological distress at four-weeks as assessed on the Kessler Psychological Distress Scale.

RESULTS

There was high attrition in the trial with 64% of data missing at follow up. Per-protocol (completer) analysis found that the lower bounds of the confidence intervals of the estimated mean change of the current risk (m = 0.19, 95% CI: -2.59- 2.98) and achievable risk (m = -0.09, 95% CI: -2.84 to 2.66) groups were within the non-inferiority margin of the control group's mean at follow up. Supplementary intention-to-treat analysis using Multivariate Imputation by Chained Equations (MICE) found that 98/100 imputed datasets of the current risk profile group, and all imputed datasets of the achievable risk profile group showed non-inferiority to the control group.

CONCLUSIONS

This study provides preliminary support that providing personal mental health risk profiles does not lead to unacceptable worsening of distress compared to no risk feedback, although this needs to be replicated in a fully powered RCT.

Use of a personalised depression intervention in primary care to prevent anxiety: a secondary study of a cluster randomised trial

Background

In the predictD-intervention, GPs used a personalised biopsychosocial programme to prevent depression. This reduced the incidence of major depression by 21.0%, although the results were not statistically significant.

Aim

To determine whether the predictD-intervention is effective at preventing anxiety in primary care patients without depression or anxiety.

Design and setting

Secondary study of a cluster randomised trial with practices randomly assigned to either the predictD-intervention or usual care. This study was conducted in seven Spanish cities from October 2010 to July 2012.

Method

In each city, 10 practices and two GPs per practice, as well as four to six patients every recruiting day, were randomly selected until there were 26–27 eligible patients for each GP. The endpoint was cumulative incidence of anxiety as measured by the PRIME-MD screening tool over 18 months.

Results

A total of 3326 patients without depression and 140 GPs from 70 practices consented and were eligible to participate; 328 of these patients were removed because they had an anxiety syndrome at baseline. Of the 2998 valid patients, 2597 (86.6%) were evaluated at the end of the study. At 18 months, 10.4% (95% CI = 8.7% to 12.1%) of the patients in the predictD-intervention group developed anxiety compared with 13.1% (95% CI = 11.4% to 14.8%) in the usual-care group (absolute difference = −2.7% [95% CI = −5.1% to −0.3%]; P = 0.029).

Conclusion

A personalised intervention delivered by GPs for the prevention of depression provided a modest but statistically significant reduction in the incidence of anxiety.

Development of Digitally Obtainable 10-Year Risk Scores for Depression and Anxiety in the General Population

The burden of depression and anxiety in the world is rising. Identification of individuals at increased risk of developing these conditions would help to target them for prevention and ultimately reduce the healthcare burden. We developed a 10-year predictive algorithm for depression and anxiety using the full cohort of over 400,000 UK Biobank (UKB) participants without pre-existing depression or anxiety using digitally obtainable information. From the initial 167 variables selected from UKB, processed into 429 features, iterative backward elimination using Cox proportional hazards model was performed to select predictors which account for the majority of its predictive capability. Baseline and reduced models were then trained for depression and anxiety using both Cox and DeepSurv, a deep neural network approach to survival analysis. The baseline Cox model achieved concordance of 0.7772 and 0.7720 on the validation dataset for depression and anxiety, respectively. For the DeepSurv model, respective concordance indices were 0.7810 and 0.7728. After feature selection, the depression model contained 39 predictors and the concordance index was 0.7769 for Cox and 0.7772 for DeepSurv. The reduced anxiety model, with 53 predictors, achieved concordance of 0.7699 for Cox and 0.7710 for DeepSurv. The final models showed good discrimination and calibration in the test datasets. We developed predictive risk scores with high discrimination for depression and anxiety using the UKB cohort, incorporating predictors which are easily obtainable via smartphone. If deployed in a digital solution, it would allow individuals to track their risk, as well as provide some pointers to how to decrease it through lifestyle changes.

Dynamic ElecTronic hEalth reCord deTection (DETECT) of individuals at risk of a first episode of psychosis: a case-control development and validation study

BACKGROUND

Many individuals who will experience a first episode of psychosis (FEP) are not detected before occurrence, limiting the effect of preventive interventions. The combination of machine-learning methods and electronic health records (EHRs) could help address this gap.

METHODS

This case-control development and validation study is based on EHR data from IBM Explorys. The IBM Explorys Platform holds standardised, longitudinal, de-identified, patient-level EHR data pooled from different health-care systems with distinct EHRs. The present EHR-based studies were retrospective, matched (1:1), case-control studies compliant with RECORD, STROBE, and TRIPOD statements. The study included individuals in the IBM Explorys database who at some point between 1990 and 2018 had a diagnosis of FEP followed by schizophrenia, and psychosis-free matched control individuals from a random subsample of the full cohort. For every individual in the FEP cohort, the individual in the control cohort was matched to have a similar date for inclusion in the database and a similar total observation time. Individuals in the FEP cohort had their index date defined as the first diagnosis of psychosis or the first prescription of antipsychotic medication. Individuals in the control cohort had their index date defined to occur the same number of days after inclusion in the database as their matching FEP individual. The FEP and control cohorts were both randomly split into development and validation datasets in a ratio of 7:3. The subset of individuals in the validation dataset who had all their health-care encounters at providers that were not seen in the development dataset made up the external validation subset. A novel recurrent neural network model was developed to predict the risk of FEP 1 year before the index date by employing demographics and medical events (in the categories diagnoses, prescriptions, procedures, encounters and admissions, observations, and laboratory test results) dynamically collected in the EHR as part of clinical routine. We named the recurrent neural network Dynamic ElecTronic hEalth reCord deTection (DETECT). The main outcomes were accuracy and area under receiver operating characteristic curve (AUROC). Decision-curve analyses and dynamic patient journey plots were used to evaluate clinical usefulness.

FINDINGS

The FEP and control cohorts each comprised 72 860 individuals. 102 030 individuals (51 015 matching pairs) were randomly allocated to the development dataset and the remaining 43 690 to the validation dataset. In the validation dataset, 4770 individuals had all their encounters outside of the 118 790 health-care providers that were encountered in the development dataset. The data from these individuals made up the external validation subset. The median follow-up (observation time before index date) was 6·0 years (IQR 3·0-10·4). In the development dataset, DETECT's prognostic accuracy was 0·787 and AUROC was 0·868. In the validation dataset, DETECT's prognostic accuracy was 0·774 and AUROC was 0·856. In the external test subset, DETECT's balanced prognostic accuracy was 0·724 and AUROC was 0·799. Prevalence-adjusted decision-curve analyses suggested that DETECT was associated with a positive net benefit in two different scenarios for FEP detection.

INTERPRETATION

DETECT showed adequate prognostic accuracy to detect individuals at risk of developing a FEP in primary and secondary care. Replication and refinement in a population-based setting are needed to consolidate these findings.

FUNDING

Lundbeck.

A Primary Care Prevention System for Behavioral Health: The Behavioral Health Annual Wellness Checkup

Behavioral health problems are involved in the majority of primary care visits. These behavior disorders (e.g., depression, anxiety, smoking, insomnia, etc.) are costly, burdensome to both the patient and the healthcare system, and result in greater medical utilization/cost and poorer future health outcomes. Integrated behavioral healthcare has been proposed as a model for more efficiently addressing the burden of behavioral health problems. While this model has demonstrated some promise in the treatment of behavioral health problems, as well as in the reduction in costs and improvement in healthcare outcomes, the primary prevention of behavioral health problems in this delivery model has been relatively neglected. The present paper discusses the potential value of incorporating the prevention of behavioral health problems into the annual physical/wellness checkup and proposes a detailed system for how this might be accomplished. Limitations, future research, and costs associated with increased prevention in a primary care context are discussed.

Family physicians' views on participating in prevention of major depression. The predictD-EVAL qualitative study

BACKGROUND

The predictD intervention, a multicomponent intervention delivered by family physicians (FPs), reduced the incidence of major depression by 21% versus the control group and was cost-effective. A qualitative methodology was proposed to identify the mechanisms of action of these complex interventions.

PURPOSE

To seek the opinions of these FPs on the potential successful components of the predictD intervention for the primary prevention of depression in primary care and to identify areas for improvement.

METHOD

Qualitative study with FPs who delivered the predictD intervention at 35 urban primary care centres in seven Spanish cities. Face-to-face semi-structured interviews adopting a phenomenological approach. The data was triangulated by three investigators using thematic analysis and respondent validation was carried out.

RESULTS

Sixty-seven FPs were interviewed and they indicated strategies used to perform the predictD intervention, including specific communication skills such as empathy and the activation of patient resources. They perceived barriers such as lack of time and facilitators such as prior acquaintance with patients. FPs recognized the positive consequences of the intervention for FPs, patients and the doctor-patient relationship. They also identified strategies for future versions and implementations of the predictD intervention.

CONCLUSIONS

The FPs who carried out the predictD intervention identified factors potentially associated with successful prevention using this program and others that could be improved. Their opinions about the predictD intervention will enable development of a more effective and acceptable version and its implementation in different primary health care settings.

Qualitative evaluation of a complex intervention to implement health promotion activities according to healthcare attendees and health professionals: EIRA study (phase II)

OBJECTIVE

To evaluate the implementation and development of a complex intervention on health promotion and changes in health-promoting behaviours in primary healthcare according to healthcare attendees and health professionals.

DESIGN

Descriptive qualitative evaluation research conducted with 94 informants. Data collection techniques consisted of 14 semistructured individual interviews, 9 discussion groups, 1 triangular group and 6 documents. Three analysts carried out a thematic content analysis with the support of Atlas.ti software. This evaluation was modelled on Proctor and colleagues' concept of outcomes for implementation research.

SETTING

7 primary care centres from seven Spanish regions: Andalusia, Aragon, Balearic Islands, Basque Country, Castilla-La Mancha, Castilla-Leon and Catalonia.

PARTICIPANTS

The study population were healthcare attendees (theoretical sampling) and health professionals (opportunistic sampling) who had participated in the exploratory trial of the EIRA intervention (2015).

RESULTS

Healthcare attendees and professionals had a positive perception of the study. Healthcare attendees even reported that they would recommend participation to family and friends. Health professionals became aware of the significance of the motivational interview, especially for health promotion, and emphasised social prescribing of physical activity. They also put forward recommendations to improve recruitment, screening and retention of participants. Healthcare attendees modified behaviours and health professionals modified working practices. To achieve sustainability, health professionals believe that it is crucial to adapt agendas and involve all the staff.

CONCLUSIONS

The discourses of all stakeholders on the intervention must be taken into consideration for the successful, setting-specific implementation of adequate, acceptable, equitable and sustainable strategies aimed at health promotion and well-being.

Primary care patients' perspectives of barriers and enablers of primary prevention and health promotion-a meta-ethnographic synthesis

Background

Primary care (PC) patients have difficulties in committing to and incorporating primary prevention and health promotion (PP&HP) activities into their long-term care. We aimed to re-interpret, for the first time, qualitative findings regarding factors affecting PC patients' acceptance of PP&HP activities.

Methods and Findings

A meta-ethnographic synthesis was generated following electronic and manual searches that retrieved 29 articles. Papers were reviewed and translated to produce a re-interpretation of the extracted concepts. The factors affecting PC patients' receptiveness to PP&HP activities were framed in a four-level ecological model (intrapersonal, interpersonal, institutional and environment and society). Intrapersonal factors (patients' beliefs/attitudes, knowledge, skills, self-concept, motivation and resources) were the most numerous, with almost 25 different factors. Public health education to modify erroneous beliefs and values regarding PP&HP could encourage a transition to healthier lifestyles. Health care professionals' abilities to communicate and involve patients in the decision-making process can act as facilitators. Biopsychosocial training (with emphasis on communication skills) for health professionals must start with undergraduates. Increased consultation time, the use of reminders, follow-up visits and tools for communicating risk and motivating patients could be applied at the intrapersonal level. Collaborative care involving other health professionals (nutritionists or psychotherapists) and family and community stakeholders (teachers or gym trainers) was important in developing healthier habits. Patients also cited barriers related to the built environment and socioeconomic difficulties that highlighted the need for policies promoting social justice and equity. Encouraging PP&HP using social marketing strategies and regulating media to control its impact on health were also cited. Only the perspectives of PC patients in the context of chronic conditions were considered thus limiting extrapolation to other contexts.

Conclusions

Several factors affect PP&HP. This must be taken into account when designing PP&HP activities if they are to be successfully implemented and maintained in routine practice.