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1.
J Affect Disord ; 295: 410-415, 2021 Aug 31.
Artigo em Inglês | MEDLINE | ID: mdl-34507220

RESUMO

BACKGROUND: People with bipolar disorder have elevated suicide risk. We estimated the ability of the Patient Health Questionnaire (PHQ9) to predict suicide outcomes for outpatients with bipolar disorder. METHODS: Visits by adults with bipolar disorder who completed a PHQ9 were identified using electronic health record (EHR) data. Bipolar diagnoses and suicide attempts were ascertained from EHR and claims data, and suicide deaths from state and federal records. Depression severity was assessed via the first eight items of the PHQ9, while suicidal ideation was assessed by the ninth item. RESULTS: 37,243 patients made 126,483 visits. Patients reported at least moderate symptoms of depression in 49% and suicidal ideation in 30% of visits. Risk of suicide attempt was 4.21% in the subsequent 90 days for those reporting nearly daily suicidal ideation compared to 0.74% in those reporting none. Patients with nearly daily suicidal ideation were 3.85 (95% CI 3.32-4.47) times more likely to attempt suicide and 13.78 (95% CI 6.56-28.94) times more likely to die by suicide in the subsequent 90 days than patients reporting none. Patients with self-harm in the last year were 8.86 (95% 7.84-10.02) times more likely to attempt suicide in the subsequent 90 days than those without. LIMITATIONS: Our sample was limited to patients completing the PHQ9 and did not include data on some important social risk or protective factors. CONCLUSIONS: The PHQ9 was a robust predictor of suicide. Suicidal ideation reported on the PHQ9 should be considered a strong indicator of suicide risk and prompt further evaluation.

2.
Appl Clin Inform ; 12(4): 778-787, 2021 08.
Artigo em Inglês | MEDLINE | ID: mdl-34407559

RESUMO

BACKGROUND: Suicide risk prediction models have been developed by using information from patients' electronic health records (EHR), but the time elapsed between model development and health system implementation is often substantial. Temporal changes in health systems and EHR coding practices necessitate the evaluation of such models in more contemporary data. OBJECTIVES: A set of published suicide risk prediction models developed by using EHR data from 2009 to 2015 across seven health systems reported c-statistics of 0.85 for suicide attempt and 0.83 to 0.86 for suicide death. Our objective was to evaluate these models' performance with contemporary data (2014-2017) from these systems. METHODS: We evaluated performance using mental health visits (6,832,439 to mental health specialty providers and 3,987,078 to general medical providers) from 2014 to 2017 made by 1,799,765 patients aged 13+ across the health systems. No visits in our evaluation were used in the previous model development. Outcomes were suicide attempt (health system records) and suicide death (state death certificates) within 90 days following a visit. We assessed calibration and computed c-statistics with 95% confidence intervals (CI) and cut-point specific estimates of sensitivity, specificity, and positive/negative predictive value. RESULTS: Models were well calibrated; 46% of suicide attempts and 35% of suicide deaths in the mental health specialty sample were preceded by a visit (within 90 days) with a risk score in the top 5%. In the general medical sample, 53% of attempts and 35% of deaths were preceded by such a visit. Among these two samples, respectively, c-statistics were 0.862 (95% CI: 0.860-0.864) and 0.864 (95% CI: 0.860-0.869) for suicide attempt, and 0.806 (95% CI: 0.790-0.822) and 0.804 (95% CI: 0.782-0.829) for suicide death. CONCLUSION: Performance of the risk prediction models in this contemporary sample was similar to historical estimates for suicide attempt but modestly lower for suicide death. These published models can inform clinical practice and patient care today.

3.
Trials ; 22(1): 541, 2021 Aug 17.
Artigo em Inglês | MEDLINE | ID: mdl-34404466

RESUMO

BACKGROUND: In 1979, Marvin Zelen proposed a new design for randomized clinical trials intended to facilitate clinicians' and patients' participation. The defining innovation of Zelen's proposal was random assignment of treatment prior to patient or participant consent. Following randomization, a participant would receive information and asked to consent to the assigned treatment. METHODS: This narrative review examined recent examples of Zelen design trials evaluating clinical and public health interventions. RESULTS: Zelen designs have often been applied to questions regarding real-world treatment or intervention effects under conditions of incomplete adherence. Examples include evaluating outreach or engagement interventions (especially for stigmatized conditions), evaluating treatments for which benefit may vary according to participant motivation, and situations when assignment to a control or usual care condition might prompt a disappointment effect. Specific practical considerations determine whether a Zelen design is scientifically appropriate or practicable. Zelen design trials usually depend on identifying participants automatically from existing records rather than by advertising, referral, or active recruitment. Assessments of baseline or prognostic characteristics usually depend on available records data rather than research-specific assessments. Because investigators must consider how exposure to treatments or interventions might bias ascertainment of outcomes, assessment of outcomes from routinely created records is often necessary. A Zelen design requires a waiver of the usual requirement for informed consent prior to random assignment of treatment. The Revised Common Rule includes specific criteria for such a waiver, and those criteria are most often met for evaluation of a low-risk and potentially beneficial intervention added to usual care. Investigators and Institutional Review Boards must also consider whether the scientific or public health benefit of a Zelen design trial outweighs the autonomy interests of potential participants. Analysis of Zelen trials compares outcomes according to original assignment, regardless of any refusal to accept or participate in the assigned treatment. CONCLUSIONS: A Zelen design trial assesses the real-world consequences of a specific strategy to prompt or promote uptake of a specific treatment. While such trials are poorly suited to address explanatory or efficacy questions, they are often preferred for addressing pragmatic or policy questions.


Assuntos
Consentimento Livre e Esclarecido , Projetos de Pesquisa , Comitês de Ética em Pesquisa , Humanos
4.
J Affect Disord ; 294: 39-47, 2021 Nov 01.
Artigo em Inglês | MEDLINE | ID: mdl-34265670

RESUMO

BACKGROUND: Few studies report on machine learning models for suicide risk prediction in adolescents and their utility in identifying those in need of further evaluation. This study examined whether a model trained and validated using data from all age groups works as well for adolescents or whether it could be improved. METHODS: We used healthcare data for 1.4 million specialty mental health and primary care outpatient visits among 256,823 adolescents across 7 health systems. The prediction target was 90-day risk of suicide attempt following a visit. We used logistic regression with least absolute shrinkage and selection operator (LASSO) and generalized estimating equations (GEE) to predict risk. We compared performance of three models: an existing model, a recalibrated version of that model, and a newly-learned model. Models were compared using area under the receiver operating curve (AUC), sensitivity, specificity, positive predictive value and negative predictive value. RESULTS: The AUC produced by the existing model for specialty mental health visits estimated in adolescents alone (0.796; [0.789, 0.802]) was not significantly different than the AUC of the recalibrated existing model (0.794; [0.787, 0.80]) or the newly-learned model (0.795; [0.789, 0.801]). Predicted risk following primary care visits was also similar: existing (0.855; [0.844, 0.866]), recalibrated (0.85 [0.839, 0.862]), newly-learned (0.842, [0.829, 0.854]). LIMITATIONS: The models did not incorporate non-healthcare risk factors. The models relied on ICD9-CM codes for diagnoses and outcome measurement. CONCLUSIONS: Prediction models already in operational use by health systems can be reliably employed for identifying adolescents in need of further evaluation.

5.
Artigo em Inglês | MEDLINE | ID: mdl-34256967

RESUMO

OBJECTIVE: To develop a new approach to prescribing guidelines as part of a pragmatic trial, Targeted and Safer Use of Antipsychotics in Youth (SUAY; ClinicalTrials.gov Identifier: NCT03448575), which supports prescribers in delivering high-quality mental health care to youths. METHOD: A nominal group technique was used to identify first- to nth-line treatments for target symptoms and potential diagnoses. The panel included US pediatricians, child and adolescent psychiatrists, and psychopharmacology experts. Meeting materials included information about Medicaid review programs, systematic reviews, prescribing guidelines, and a description of the pragmatic trial. Afterward, a series of 4 webinar discussions were held to achieve consensus on recommendations. RESULTS: The panel unanimously agreed that the guideline should focus on target symptoms rather than diagnoses. Guidance included recommendations for first- to nth-line treatment of target mental health symptoms, environmental factors to be addressed, possible underlying diagnoses that should first be considered and ruled out, and general considerations for pharmacological and therapeutic treatments. CONCLUSION: Prescribing guidelines are often ignored because they do not incorporate the real-world availability of first-line psychosocial treatments, comorbid conditions, and clinical complexity. Our approach addresses some of these concerns. If the approach proves successful in our ongoing pragmatic trial, Targeted and Safer Use of Antipsychotics in Youth (SUAY), it may serve as a model to state Medicaid programs and health systems to support clinicians in delivering high-quality mental health care to youths. CLINICAL TRIAL REGISTRATION INFORMATION: Targeted and Safer Use of Antipsychotics in Youth; http://clinicaltrials.gov/; NCT03448575.

6.
Artigo em Inglês | MEDLINE | ID: mdl-34331466

RESUMO

INTRODUCTION: Previous studies report that item 9 of the Patient Health Questionnaire (PHQ9) is useful for stratifying risk of suicide attempt in adults. This study re-produced the utility of item 9 of PHQ9 in assessing risk of suicide attempt in adolescents. MATERIALS AND METHODS: Individuals aged 13 to 17 years in 4 health systems with a diagnosis of depression and history of treatment were included. We estimated time to first observed fatal or non-fatal suicide attempt in the 2 years following completion of a PHQ9, stratified by response to item 9. RESULTS: There were 51,807 PHQ9 questionnaires for 20,363 youth and 861 instances of suicide attempt. Cumulative probability of suicide attempt ranged from approximately 3.3% (95% CI, 3.0 to 3.5%) for those responding "not at all" on item 9 to 10.8% (95% CI, 9.2 to 12.4%) for those responding "nearly every day". These probabilities are more than 3 times higher than previously reported in adults. CONCLUSION: PHQ item 9 is useful for stratifying risk of suicide attempt in the 2 years following completion of the questionnaire. Monitoring PHQ item 9 over time for patients in treatment for depression can be useful for population health management of adolescents with depression.

8.
Biom J ; 63(7): 1375-1388, 2021 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-34031916

RESUMO

Clinical visit data are clustered within people, which complicates prediction modeling. Cluster size is often informative because people receiving more care are less healthy and at higher risk of poor outcomes. We used data from seven health systems on 1,518,968 outpatient mental health visits from January 1, 2012 to June 30, 2015 to predict suicide attempt within 90 days. We evaluated true performance of prediction models using a prospective validation set of 4,286,495 visits from October 1, 2015 to September 30, 2017. We examined dividing clustered data on the person or visit level for model training and cross-validation and considered a within cluster resampling approach for model estimation. We evaluated optimism by comparing estimated performance from a left-out testing dataset to performance in the prospective dataset. We used two prediction methods, logistic regression with least absolute shrinkage and selection operator (LASSO) and random forest. The random forest model using a visit-level split for model training and testing was optimistic; it overestimated discrimination (area under the curve, AUC = 0.95 in testing versus 0.84 in prospective validation) and classification accuracy (sensitivity = 0.48 in testing versus 0.19 in prospective validation, 95th percentile cut-off). Logistic regression and random forest models using a person-level split performed well, accurately estimating prospective discrimination and classification: estimated AUCs ranged from 0.85 to 0.87 in testing versus 0.85 in prospective validation, and sensitivity ranged from 0.15 to 0.20 in testing versus 0.17 to 0.19 in prospective validation. Within cluster resampling did not improve performance. We recommend dividing clustered data on the person level, rather than visit level, to ensure strong performance in prospective use and accurate estimation of future performance at the time of model development.

9.
Jt Comm J Qual Patient Saf ; 47(7): 452-457, 2021 07.
Artigo em Inglês | MEDLINE | ID: mdl-33994334

RESUMO

BACKGROUND: The COVID-19 pandemic prompted a rapid shift to virtual (video and telephone) delivery of mental health care, disrupting established processes for identifying people at increased risk of suicidal behavior. METHODS: Following the shift to virtual care, Kaiser Permanente Washington implemented a series of workflow changes to administer standard screening and monitoring questionnaires at virtual visits and to complete structured suicide risk assessments for patients reporting frequent suicidal ideation. These new workflows included automated distribution of questionnaires via the electronic health record (EHR) patient portal and automated alerts to clinicians regarding indicators of high risk. RESULTS: In March 2020, in-person mental health visits were rapidly and completely replaced by video and telephone visits. The proportion of mental health visits with completed screening and monitoring questionnaires fell from approximately 80% in early 2020 to approximately 30% in late March, then gradually recovered to approximately 60% by the end of 2020. Among patients reporting frequent suicidal ideation on monitoring questionnaires, the proportion with a recorded suicide risk assessment fell from over 90% in early 2020 to approximately 40% in late March, then gradually recovered to nearly 100% by the end of 2020. CONCLUSION: Use of EHR patient portal messaging capabilities can facilitate systematic identification and assessment of suicide risk for patients receiving mental health care by telephone or video visit.


Assuntos
COVID-19 , Ideação Suicida , Registros Eletrônicos de Saúde , Humanos , Pandemias , SARS-CoV-2
10.
Clin Pharmacol Ther ; 2021 May 01.
Artigo em Inglês | MEDLINE | ID: mdl-33932030

RESUMO

Concerns regarding both the limited generalizability and the slow pace of traditional randomized trials have led to calls for greater use of real-world evidence (RWE) in the evaluation of new treatments or products. RWE studies often rely on real-world data (RWD), including data extracted from healthcare records or data captured by mobile phones or other consumer devices. Global assessments of RWD sources are not helpful in assessing whether any specific RWD element is fit for any specific purpose. Instead, evidence generators and evidence consumers should clearly identify the specific health state or clinical phenomenon of interest and then consider each step between that clinical phenomenon and its representation in a research database. We propose specific questions regarding potential error or bias affecting each of those steps: Would a person experiencing this clinical phenomenon present for care in this setting or interact with this recording device? Would this clinical phenomenon be accurately recognized or assessed? How might the recording environment or tools affect accurate and consistent recording of this clinical phenomenon? Can data elements from different sources be harmonized, both technically (same format) and semantically (same meaning)? Can the original data elements be consistently reduced to a useful clinical phenotype? Addressing these questions requires a range of clinical, organizational, and technical expertise. Transparency regarding each step in the creation of RWD is essential if evidence consumers are to rely on RWE studies.

11.
Psychiatr Serv ; 72(8): 920-925, 2021 08 01.
Artigo em Inglês | MEDLINE | ID: mdl-33882679

RESUMO

OBJECTIVE: Suicide rates continue to rise, necessitating the identification of risk factors. Obesity and suicide mortality rates have been examined, but associations among weight change, death by suicide, and depression among adults in the United States remain unclear. METHODS: Data from 387 people who died by suicide in 2000-2015 with a recorded body mass index (BMI) in the first and second 6 months preceding their death ("index date") were extracted from the Mental Health Research Network. Each person was matched with five people in a control group (comprising individuals who did not die by suicide) by age, sex, index year, and health care site (N=1,935). RESULTS: People who died by suicide were predominantly male (71%), White (69%), and middle aged (mean age=57 years) and had a depression diagnosis (55%) and chronic health issues (57%) (corresponding results for the control group: 71% male, 66% White, 14% with depression diagnosis, and 43% with chronic health issues; mean age=56 years). Change in BMI within the year before the index date statistically significantly differed between those who died by suicide (mean change=-0.72±2.42 kg/m2) and the control group (mean change=0.06±4.99 kg/m2) (p<0.001, Cohen's d=0.17). A one-unit BMI decrease was associated with increased risk for suicide after adjustment for demographic characteristics, mental disorders, and Charlson comorbidity score (adjusted odds ratio=1.11, 95% confidence interval=1.05-1.18, p<0.001). For those without depression, a BMI change was significantly associated with suicide (p<0.001). CONCLUSIONS: An increased suicide mortality rate was associated with weight loss in the year before a suicide after analyses accounted for general and mental health indicators.


Assuntos
Suicídio , Adulto , Índice de Massa Corporal , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Obesidade/epidemiologia , Fatores de Risco , Estados Unidos/epidemiologia , Perda de Peso
12.
Clin Pharmacol Ther ; 2021 Apr 07.
Artigo em Inglês | MEDLINE | ID: mdl-33826756

RESUMO

The randomized controlled trial (RCT) is the gold standard for evaluating the causal effects of medications. Limitations of RCTs have led to increasing interest in using real-world evidence (RWE) to augment RCT evidence and inform decision making on medications. Although RWE can be either randomized or nonrandomized, nonrandomized RWE can capitalize on the recent proliferation of large healthcare databases and can often answer questions that cannot be answered in randomized studies due to resource constraints. However, the results of nonrandomized studies are much more likely to be impacted by confounding bias, and the existence of unmeasured confounders can never be completely ruled out. Furthermore, nonrandomized studies require more complex design considerations which can sometimes result in design-related biases. We discuss questions that can help investigators or evidence consumers evaluate the potential impact of confounding or other biases on their findings: Does the design emulate a hypothetical randomized trial design? Is the comparator or control condition appropriate? Does the primary analysis adjust for measured confounders? Do sensitivity analyses quantify the potential impact of residual confounding? Are methods open to inspection and (if possible) replication? Designing a high-quality nonrandomized study of medications remains challenging and requires broad expertise across a range of disciplines, including relevant clinical areas, epidemiology, and biostatistics. The questions posed in this paper provide a guiding framework for assessing the credibility of nonrandomized RWE and could be applied across many clinical questions.

13.
JAMA Psychiatry ; 78(7): 726-734, 2021 Jul 01.
Artigo em Inglês | MEDLINE | ID: mdl-33909019

RESUMO

Importance: Clinical prediction models estimated with health records data may perpetuate inequities. Objective: To evaluate racial/ethnic differences in the performance of statistical models that predict suicide. Design, Setting, and Participants: In this diagnostic/prognostic study, performed from January 1, 2009, to September 30, 2017, with follow-up through December 31, 2017, all outpatient mental health visits to 7 large integrated health care systems by patients 13 years or older were evaluated. Prediction models were estimated using logistic regression with LASSO variable selection and random forest in a training set that contained all visits from a 50% random sample of patients (6 984 184 visits). Performance was evaluated in the remaining 6 996 386 visits, including visits from White (4 031 135 visits), Hispanic (1 664 166 visits), Black (578 508 visits), Asian (313 011 visits), and American Indian/Alaskan Native (48 025 visits) patients and patients without race/ethnicity recorded (274 702 visits). Data analysis was performed from January 1, 2019, to February 1, 2021. Exposures: Demographic, diagnosis, prescription, and utilization variables and Patient Health Questionnaire 9 responses. Main Outcomes and Measures: Suicide death in the 90 days after a visit. Results: This study included 13 980 570 visits by 1 433 543 patients (64% female; mean [SD] age, 42 [18] years. A total of 768 suicide deaths were observed within 90 days after 3143 visits. Suicide rates were highest for visits by patients with no race/ethnicity recorded (n = 313 visits followed by suicide within 90 days, rate = 5.71 per 10 000 visits), followed by visits by Asian (n = 187 visits followed by suicide within 90 days, rate = 2.99 per 10 000 visits), White (n = 2134 visits followed by suicide within 90 days, rate = 2.65 per 10 000 visits), American Indian/Alaskan Native (n = 21 visits followed by suicide within 90 days, rate = 2.18 per 10 000 visits), Hispanic (n = 392 visits followed by suicide within 90 days, rate = 1.18 per 10 000 visits), and Black (n = 65 visits followed by suicide within 90 days, rate = 0.56 per 10 000 visits) patients. The area under the curve (AUC) and sensitivity of both models were high for White, Hispanic, and Asian patients and poor for Black and American Indian/Alaskan Native patients and patients without race/ethnicity recorded. For example, the AUC for the logistic regression model was 0.828 (95% CI, 0.815-0.840) for White patients compared with 0.640 (95% CI, 0.598-0.681) for patients with unrecorded race/ethnicity and 0.599 (95% CI, 0.513-0.686) for American Indian/Alaskan Native patients. Sensitivity at the 90th percentile was 62.2% (95% CI, 59.2%-65.0%) for White patients compared with 27.5% (95% CI, 21.0%-34.7%) for patients with unrecorded race/ethnicity and 10.0% (95% CI, 0%-23.0%) for Black patients. Results were similar for random forest models, with an AUC of 0.812 (95% CI, 0.800-0.826) for White patients compared with 0.676 (95% CI, 0.638-0.714) for patients with unrecorded race/ethnicity and 0.642 (95% CI, 0.579-0.710) for American Indian/Alaskan Native patients and sensitivities at the 90th percentile of 52.8% (95% CI, 50.0%-55.8%) for White patients, 29.3% (95% CI, 22.8%-36.5%) for patients with unrecorded race/ethnicity, and 6.7% (95% CI, 0%-16.7%) for Black patients. Conclusions and Relevance: These suicide prediction models may provide fewer benefits and more potential harms to American Indian/Alaskan Native or Black patients or those with undrecorded race/ethnicity compared with White, Hispanic, and Asian patients. Improving predictive performance in disadvantaged populations should be prioritized to improve, rather than exacerbate, health disparities.

14.
Clin Pharmacol Ther ; 2021 Apr 25.
Artigo em Inglês | MEDLINE | ID: mdl-33895994

RESUMO

Concerns regarding both the limited generalizability and the slow pace of traditional randomized trials have led to calls for greater use of real-world evidence (RWE) in the evaluation of new treatments or products. The RWE label has been used to refer to a variety of departures from the methods of traditional randomized controlled trials. Recognizing this complexity and potential confusion, the National Academies of Science, Engineering, and Medicine convened a series of workshops to clarify and address questions regarding the use of RWE to evaluate new medical treatments. Those workshops identified three specific dimensions in which RWE studies might differ from traditional clinical trials: use of real-world data (data extracted from health system records or data captured by mobile devices), delivery of real-world treatment (open-label treatments delivered in community settings by community practitioners), and real-world treatment assignment (including nonrandomized comparisons and variations on random assignment such as before-after or stepped-wedge designs). For any RWE study, decisions regarding each of these dimensions depends on the specific research question, characteristics of the potential study settings, and characteristics of the settings where study results would be applied.

15.
JMIR Form Res ; 5(4): e21127, 2021 Apr 12.
Artigo em Inglês | MEDLINE | ID: mdl-33843599

RESUMO

BACKGROUND: New opportunities to create and evaluate population-based selective prevention programs for suicidal behavior are emerging in health care settings. Standard depression severity measures recorded in electronic medical records (EMRs) can be used to identify patients at risk for suicide and suicide attempt, and promising interventions for reducing the risk of suicide attempt in at-risk populations can be adapted for web-based delivery in health care. OBJECTIVE: This study aims to evaluate a pilot of a psychoeducational program, focused on developing emotion regulation techniques via a web-based dialectical behavior therapy (DBT) skills site, including four DBT skills, and supported by secure message coaching, including elements of caring messages. METHODS: Patients were eligible based on the EMR-documented responses to the Patient Health Questionnaire indicating suicidal thoughts. We measured feasibility via the proportion of invitees who opened program invitations, visited the web-based consent form page, and consented; acceptability via qualitative feedback from participants about the DBT program; and engagement via the proportion of invitees who began DBT skills as well as the number of website visits for DBT skills and the degree of site engagement. RESULTS: A total of 60 patients were invited to participate. Overall, 93% (56/60) of the patients opened the invitation and 43% (26/60) consented to participate. DBT skills website users visited the home page on an average of 5.3 times (SD 6.0). Procedures resulted in no complaints and some participant feedback emphasizing the usefulness of DBT skills. CONCLUSIONS: This study supports the potential of using responses to patient health questionnaires in EMRs to identify a high-risk population and offer key elements of caring messages and DBT adapted for a low-intensity intervention. A randomized trial evaluating the effectiveness of this program is now underway (ClinicalTrials.gov: NCT02326883).

16.
Clin Pharmacol Ther ; 2021 Apr 08.
Artigo em Inglês | MEDLINE | ID: mdl-33829639

RESUMO

Concerns regarding both the limited generalizability and the slow pace of traditional randomized trials have led to calls for greater use of real-world evidence in the evaluation of new treatments or products. Real-world clinical trials or pragmatic trials often differ from traditional clinical trials in the use of open-label or nonblinded treatments delivered by real-world clinicians in community practice settings. Blinding and standardization of treatment may sometimes be necessary for internal validity, but they may also obscure or distort meaningful differences between treatments. When investigators consider whether blinding of clinicians, patients, or assessors is necessary, we suggest they consider several specific questions: Will clinicians, patients, and assessors have expectations or preferences regarding benefits or adverse effects? How might those expectations affect treatment uptake, treatment adherence, or assessment of outcomes? Will expectations differ in the settings where trial results will be applied? How would blinding of treatment reduce biases? How would blinding obscure true differences between treatments? How would procedures necessary for blinding reduce acceptability or increase risk of trial participation? When investigators consider how strictly treatments should be standardized, we suggest they consider several specific questions: How would treatment effectiveness or safety vary according to clinician experience or expertise? What level of experience or expertise is available in potential trial settings and settings where trial results would be applied? Is some level of standardization necessary for valid inference? Considering any special vulnerabilities of the study population, is some level of standardization necessary to assure participant safety?

17.
Psychiatr Serv ; 72(5): 555-562, 2021 05 01.
Artigo em Inglês | MEDLINE | ID: mdl-33691491

RESUMO

Statistical models, including those based on electronic health records, can accurately identify patients at high risk for a suicide attempt or death, leading to implementation of risk prediction models for population-based suicide prevention in health systems. However, some have questioned whether statistical predictions can really inform clinical decisions. Appropriately reconciling statistical algorithms with traditional clinician assessment depends on whether predictions from these two methods are competing, complementary, or merely duplicative. In June 2019, the National Institute of Mental Health convened a meeting, "Identifying Research Priorities for Risk Algorithms Applications in Healthcare Settings to Improve Suicide Prevention." Here, participants of this meeting summarize key issues regarding the potential clinical application of suicide prediction models. The authors attempt to clarify the key conceptual and technical differences between traditional risk prediction by clinicians and predictions from statistical models, review the limited evidence regarding both the accuracy of and the concordance between these alternative methods of prediction, present a conceptual framework for understanding agreement and disagreement between statistical and clinician predictions, identify priorities for improving data regarding suicide risk, and propose priority questions for future research. Future suicide risk assessment will likely combine statistical prediction with traditional clinician assessment, but research is needed to determine the optimal combination of these two methods.


Assuntos
Modelos Estatísticos , Tentativa de Suicídio , Algoritmos , Registros Eletrônicos de Saúde , Previsões , Humanos , Medição de Risco
18.
J Addict Med ; 15(1): 55-60, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-32657957

RESUMO

OBJECTIVES: Individuals with substance use disorders (SUD) are at risk for suicide, but no studies have assessed whether routinely administered screeners for suicidal ideation accurately identify outpatients with SUD who are at risk for suicide attempt or death. METHODS: Data from more than 186,000 visits by over 55,000 patients with mental health and SUD diagnoses receiving care in 7 health systems were analyzed to determine whether responses to item 9 of the 9-item Patient Health Questionnaire, which assesses frequency of thoughts of death and self-harm, are associated with suicide outcomes after an outpatient visit. Odds of suicide attempt or death were computed using generalized estimating equations. RESULTS: In bivariate analyses, a nearly 5-fold risk was observed for patients answering "nearly every day" relative to "not at all" among individuals who made a suicide attempt within 90 days (4.9% vs 1.1%; χ2 = 1151, P < 0.0001). At nearly half of visits (46%) followed by a suicide attempt within 90 days, patients responded "not at all." In logistic models, compared to "not at all," all other responses were associated with higher odds of suicide attempt or death within 90 days. Fully adjusted models attenuated results but odds of suicide attempt (AOR = 3.24, CI: 2.69-3.91) and suicide death (AOR = 5.67, CI: 2.0-16.1) remained high for those reporting "nearly every day." CONCLUSIONS: In people with SUD, increasing Patient Health Questionnaire item 9 response predicts increased risk of subsequent suicidal behavior and should prompt intervention. However, clinicians should realize that those reporting "not at all" are not immune from subsequent suicide risk.


Assuntos
Transtornos Relacionados ao Uso de Substâncias , Ideação Suicida , Humanos , Pacientes Ambulatoriais , Questionário de Saúde do Paciente , Fatores de Risco , Transtornos Relacionados ao Uso de Substâncias/epidemiologia , Tentativa de Suicídio
19.
Crisis ; : 1-8, 2020 Nov 05.
Artigo em Inglês | MEDLINE | ID: mdl-33151092

RESUMO

Background: In the US, more than one million people attempt suicide each year. History of suicide attempt is a significant risk factor for death by suicide; however, there is a paucity of data from the US general population on this relationship. Aim: The objective of this study was to examine suicide attempts needing medical attention as a risk for suicide death. Method: We conducted a case-control study involving eight US healthcare systems. A total of 2,674 individuals who died by suicide from 2000 to 2013 were matched to 267,400 individuals by year and location. Results: Prior suicide attempt associated with a medical visit increases risk for suicide death by 39.1 times, particularly for women (OR = 79.2). However, only 11.3% of suicide deaths were associated with an attempt that required medical attention. The association was the strongest for children 10-14 years old (OR = 98.0). Most suicide attempts were recorded during the 20-week period prior to death. Limitations: Our study is limited to suicide attempts for which individuals sought medical care. Conclusion: In the US, prior suicide attempt is associated with an increased risk of suicide death; the risk is high especially during the period immediately following a nonlethal attempt.

20.
Contemp Clin Trials ; 99: 106184, 2020 12.
Artigo em Inglês | MEDLINE | ID: mdl-33091587

RESUMO

BACKGROUND: Programs such as the Pediatric Access Line in Washington state have shown decreases in antipsychotic medication use by youth with non-psychotic disorders. Program outcomes have been studied with observational designs. This manuscript describes the protocol for Targeted and Safer Use of Antipsychotics in Youth (SUAY), a randomized controlled trial of psychiatrist review of prescriptions and facilitated access to psychosocial care. The aim of the intervention is to reduce the number of person-days of antipsychotic use among participants. METHODS: Recruitment occurs at 4 health systems. Targeted enrollment is 800 youth aged 3-17 years. Clinicians are block randomized to intervention versus usual care prior to the study. Youth are nested within the arm of the prescribing clinician. Clinicians in the intervention group receive an EHR-based best practice alert with options to expedite access to psychosocial care and all medication orders are reviewed by a child and adolescent psychiatrist with feedback provided to the prescriber. The primary outcome is person-days of antipsychotic medication use in the 6 months following the initial order. All randomized individuals contribute data regardless of their level of participation (including declining all services). DISCUSSION: The trial has been approved by the institutional review boards at each of the 4 sites. The intervention has 4 novel design features including automated recruitment using a best practice alert, psychiatrist medication order review and consultation, telephone navigation to psychosocial care, and telemental health visits. Recruitment began in March of 2018 and will be completed in June 2020. Follow-up will be completed December 31, 2020. TRIAL REGISTRATION: Clinicaltrials.gov, NCT03448575.

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