Developing an individualized treatment rule for Veterans with major depressive disorder using electronic health records.

Efforts to develop an individualized treatment rule (ITR) to optimize major depressive disorder (MDD) treatment with antidepressant medication (ADM), psychotherapy, or combined ADM-psychotherapy have been hampered by small samples, small predictor sets, and suboptimal analysis methods. Analyses of large administrative databases designed to approximate experiments followed iteratively by pragmatic trials hold promise for resolving these problems. The current report presents a proof-of-concept study using electronic health records (EHR) of n = 43,470 outpatients beginning MDD treatment in Veterans Health Administration Primary Care Mental Health Integration (PC-MHI) clinics, which offer access not only to ADMs but also psychotherapy and combined ADM-psychotherapy. EHR and geospatial databases were used to generate an extensive baseline predictor set (5,865 variables). The outcome was a composite measure of at least one serious negative event (suicide attempt, psychiatric emergency department visit, psychiatric hospitalization, suicide death) over the next 12 months. Best-practices methods were used to adjust for nonrandom treatment assignment and to estimate a preliminary ITR in a 70% training sample and to evaluate the ITR in the 30% test sample. Statistically significant aggregate variation was found in overall probability of the outcome related to baseline predictors (AU-ROC = 0.68, S.E. = 0.01), with test sample outcome prevalence of 32.6% among the 5% of patients having highest predicted risk compared to 7.1% in the remainder of the test sample. The ITR found that psychotherapy-only was the optimal treatment for 56.0% of patients (roughly 20% lower risk of the outcome than if receiving one of the other treatments) and that treatment type was unrelated to outcome risk among other patients. Change in aggregate treatment costs of implementing this ITR would be negligible, as 16.1% fewer patients would be prescribed ADMs and 2.9% more would receive psychotherapy. A pragmatic trial would be needed to confirm the accuracy of the ITR.

Development of a model to predict psychotherapy response for depression among Veterans.

BACKGROUND: Fewer than half of patients with major depressive disorder (MDD) respond to psychotherapy. Pre-emptively informing patients of their likelihood of responding could be useful as part of a patient-centered treatment decision-support plan. METHODS: This prospective observational study examined a national sample of 807 patients beginning psychotherapy for MDD at the Veterans Health Administration. Patients completed a self-report survey at baseline and 3-months follow-up (data collected 2018-2020). We developed a machine learning (ML) model to predict psychotherapy response at 3 months using baseline survey, administrative, and geospatial variables in a 70% training sample. Model performance was then evaluated in the 30% test sample. RESULTS: 32.0% of patients responded to treatment after 3 months. The best ML model had an AUC (SE) of 0.652 (0.038) in the test sample. Among the one-third of patients ranked by the model as most likely to respond, 50.0% in the test sample responded to psychotherapy. In comparison, among the remaining two-thirds of patients, <25% responded to psychotherapy. The model selected 43 predictors, of which nearly all were self-report variables. CONCLUSIONS: Patients with MDD could pre-emptively be informed of their likelihood of responding to psychotherapy using a prediction tool based on self-report data. This tool could meaningfully help patients and providers in shared decision-making, although parallel information about the likelihood of responding to alternative treatments would be needed to inform decision-making across multiple treatments.

Development of a model to predict antidepressant treatment response for depression among Veterans.

BACKGROUND: Only a limited number of patients with major depressive disorder (MDD) respond to a first course of antidepressant medication (ADM). We investigated the feasibility of creating a baseline model to determine which of these would be among patients beginning ADM treatment in the US Veterans Health Administration (VHA). METHODS: A 2018-2020 national sample of n = 660 VHA patients receiving ADM treatment for MDD completed an extensive baseline self-report assessment near the beginning of treatment and a 3-month self-report follow-up assessment. Using baseline self-report data along with administrative and geospatial data, an ensemble machine learning method was used to develop a model for 3-month treatment response defined by the Quick Inventory of Depression Symptomatology Self-Report and a modified Sheehan Disability Scale. The model was developed in a 70% training sample and tested in the remaining 30% test sample. RESULTS: In total, 35.7% of patients responded to treatment. The prediction model had an area under the ROC curve (s.e.) of 0.66 (0.04) in the test sample. A strong gradient in probability (s.e.) of treatment response was found across three subsamples of the test sample using training sample thresholds for high [45.6% (5.5)], intermediate [34.5% (7.6)], and low [11.1% (4.9)] probabilities of response. Baseline symptom severity, comorbidity, treatment characteristics (expectations, history, and aspects of current treatment), and protective/resilience factors were the most important predictors. CONCLUSIONS: Although these results are promising, parallel models to predict response to alternative treatments based on data collected before initiating treatment would be needed for such models to help guide treatment selection.

The Cost-Effectiveness of Cognitive Behavioral Therapy Versus Second-Generation Antidepressants for Initial Treatment of Major Depressive Disorder in the United States: A Decision Analytic Model.

Background: Most guidelines for major depressive disorder recommend initial treatment with either a second-generation antidepressant (SGA) or cognitive behavioral therapy (CBT). Although most trials suggest that these treatments have similar efficacy, their health economic implications are uncertain. Objective: To quantify the cost-effectiveness of CBT versus SGA for initial treatment of depression. Design: Decision analytic model. Data Sources: Relative effectiveness data from a meta-analysis of randomized controlled trials; additional clinical and economic data from other publications. Target Population: Adults with newly diagnosed major depressive disorder in the United States. Time Horizon: 1 to 5 years. Perspectives: Health care sector and societal. Intervention: Initial treatment with either an SGA or group and individual CBT. Outcome Measures: Costs in 2014 U.S. dollars, quality-adjusted life-years (QALYs), and incremental cost-effectiveness ratios. Results of Base-Case Analysis: In model projections, CBT produced higher QALYs (3 days more at 1 year and 20 days more at 5 years) with higher costs at 1 year (health care sector, $900; societal, $1500) but lower costs at 5 years (health care sector, -$1800; societal, -$2500). Results of Sensitivity Analysis: In probabilistic sensitivity analyses, SGA had a 64% to 77% likelihood of having an incremental cost-effectiveness ratio of $100 000 or less per QALY at 1 year; CBT had a 73% to 77% likelihood at 5 years. Uncertainty in the relative risk for relapse of depression contributed the most to overall uncertainty in the optimal treatment. Limitation: Long-term trials comparing CBT and SGA are lacking. Conclusion: Neither SGAs nor CBT provides consistently superior cost-effectiveness relative to the other. Given many patients' preference for psychotherapy over pharmacotherapy, increasing patient access to CBT may be warranted. Primary Funding Source: Department of Veterans Affairs, National Institute of Mental Health.

Cost-effectiveness of HIV treatment as prevention in serodiscordant couples.

BACKGROUND: The cost-effectiveness of early antiretroviral therapy (ART) in persons infected with human immunodeficiency virus (HIV) in serodiscordant couples is not known. Using a computer simulation of the progression of HIV infection and data from the HIV Prevention Trials Network 052 study, we projected the cost-effectiveness of early ART for such persons. METHODS: For HIV-infected partners in serodiscordant couples in South Africa and India, we compared the early initiation of ART with delayed ART. Five-year and lifetime outcomes included cumulative HIV transmissions, life-years, costs, and cost-effectiveness. We classified early ART as very cost-effective if its incremental cost-effectiveness ratio was less than the annual per capita gross domestic product (GDP; $8,100 in South Africa and $1,500 in India), as cost-effective if the ratio was less than three times the GDP, and as cost-saving if it resulted in a decrease in total costs and an increase in life-years, as compared with delayed ART. RESULTS: In South Africa, early ART prevented opportunistic diseases and was cost-saving over a 5-year period; over a lifetime, it was very cost-effective ($590 per life-year saved). In India, early ART was cost-effective ($1,800 per life-year saved) over a 5-year period and very cost-effective ($530 per life-year saved) over a lifetime. In both countries, early ART prevented HIV transmission over short periods, but longer survival attenuated this effect; the main driver of life-years saved was a clinical benefit for treated patients. Early ART remained very cost-effective over a lifetime under most modeled assumptions in the two countries. CONCLUSIONS: In South Africa, early ART was cost-saving over a 5-year period. In both South Africa and India, early ART was projected to be very cost-effective over a lifetime. With individual, public health, and economic benefits, there is a compelling case for early ART for serodiscordant couples in resource-limited settings. (Funded by the National Institute of Allergy and Infectious Diseases and others.).

The clinical role and cost-effectiveness of long-acting antiretroviral therapy.

BACKGROUND: Long-acting antiretroviral therapy (LA-ART) is currently under development and could improve outcomes for human immunodeficiency virus (HIV)-infected individuals with poor daily ART adherence. METHODS: We used a computer simulation model to evaluate the cost-effectiveness of 3 LA-ART strategies vs daily oral ART for all: (1) LA-ART for patients with multiple ART failures; (2) second-line LA-ART for those failing first-line therapy; and (3) first-line LA-ART for ART-naive patients. We calculated the maximum annual cost of LA-ART at which each strategy would be cost-effective at a willingness to pay of $100 000 per quality-adjusted life-year. We assumed HIV RNA suppression on daily ART ranged from 0% to 91% depending on adherence, vs 91% suppression on LA-ART regardless of daily ART adherence. In sensitivity analyses, we varied adherence, efficacy of LA-ART and daily ART, and loss to follow-up. RESULTS: Relative to daily ART, LA-ART increased overall life expectancy by 0.15-0.24 years, and by 0.51-0.89 years among poorly adherent patients, depending on the LA-ART strategy. LA-ART after multiple failures became cost-effective at an annual drug cost of $48 000; in sensitivity analysis, this threshold varied from $40 000-$70 000. Second-line LA-ART and first-line LA-ART became cost-effective at an annual drug cost of $26 000-$31 000 and $24 000-$27 000, vs $28 000 and $25 000 for current second-line and first-line regimens. CONCLUSIONS: LA-ART could improve survival of HIV patients, especially those with poor daily ART adherence. At an annual cost of $40 000-$70 000, LA-ART will offer good value for patients with multiple prior failures. To be a viable option for first- or second-line therapy, however, its cost must approach that of currently available regimens.

Estimated Average Treatment Effect of Psychiatric Hospitalization in Patients With Suicidal Behaviors: A Precision Treatment Analysis.

Importance: Psychiatric hospitalization is the standard of care for patients presenting to an emergency department (ED) or urgent care (UC) with high suicide risk. However, the effect of hospitalization in reducing subsequent suicidal behaviors is poorly understood and likely heterogeneous. Objectives: To estimate the association of psychiatric hospitalization with subsequent suicidal behaviors using observational data and develop a preliminary predictive analytics individualized treatment rule accounting for heterogeneity in this association across patients. Design, Setting, and Participants: A machine learning analysis of retrospective data was conducted. All veterans presenting with suicidal ideation (SI) or suicide attempt (SA) from January 1, 2010, to December 31, 2015, were included. Data were analyzed from September 1, 2022, to March 10, 2023. Subgroups were defined by primary psychiatric diagnosis (nonaffective psychosis, bipolar disorder, major depressive disorder, and other) and suicidality (SI only, SA in past 2-7 days, and SA in past day). Models were trained in 70.0% of the training samples and tested in the remaining 30.0%. Exposures: Psychiatric hospitalization vs nonhospitalization. Main Outcomes and Measures: Fatal and nonfatal SAs within 12 months of ED/UC visits were identified in administrative records and the National Death Index. Baseline covariates were drawn from electronic health records and geospatial databases. Results: Of 196 610 visits (90.3% men; median [IQR] age, 53 [41-59] years), 71.5% resulted in hospitalization. The 12-month SA risk was 11.9% with hospitalization and 12.0% with nonhospitalization (difference, -0.1%; 95% CI, -0.4% to 0.2%). In patients with SI only or SA in the past 2 to 7 days, most hospitalization was not associated with subsequent SAs. For patients with SA in the past day, hospitalization was associated with risk reductions ranging from -6.9% to -9.6% across diagnoses. Accounting for heterogeneity, hospitalization was associated with reduced risk of subsequent SAs in 28.1% of the patients and increased risk in 24.0%. An individualized treatment rule based on these associations may reduce SAs by 16.0% and hospitalizations by 13.0% compared with current rates. Conclusions and Relevance: The findings of this study suggest that psychiatric hospitalization is associated with reduced average SA risk in the immediate aftermath of an SA but not after other recent SAs or SI only. Substantial heterogeneity exists in these associations across patients. An individualized treatment rule accounting for this heterogeneity could both reduce SAs and avert hospitalizations.

Development of a model to predict combined antidepressant medication and psychotherapy treatment response for depression among veterans.

BACKGROUND: Although research shows that more depressed patients respond to combined antidepressants (ADM) and psychotherapy than either alone, many patients do not respond even to combined treatment. A reliable prediction model for this could help treatment decision-making. We attempted to create such a model using machine learning methods among patients in the US Veterans Health Administration (VHA). METHODS: A 2018-2020 national sample of VHA patients beginning combined depression treatment completed self-report assessments at baseline and 3 months (n = 658). A learning model was developed using baseline self-report, administrative, and geospatial data to predict 3-month treatment response defined by reductions in the Quick Inventory of Depression Symptomatology Self-Report and/or in the Sheehan Disability Scale. The model was developed in a 70 % training sample and tested in the remaining 30 % test sample. RESULTS: 30.0 % of patients responded to treatment. The prediction model had a test sample AUC-ROC of 0.657. A strong gradient was found in probability of treatment response from 52.7 % in the highest predicted quintile to 14.4 % in the lowest predicted quintile. The most important predictors were episode characteristics (symptoms, comorbidities, history), personality/psychological resilience, recent stressors, and treatment characteristics. LIMITATIONS: Restrictions in sample definition, a low recruitment rate, and reliance on patient self-report rather than clinician assessments to determine treatment response limited the generalizability of results. CONCLUSIONS: A machine learning model could help depressed patients and providers predict likely response to combined ADM-psychotherapy. Parallel information about potential harms and costs of alternative treatments would be needed, though, to inform optimal treatment selection.

Cost-effectiveness of Aducanumab and Donanemab for Early Alzheimer Disease in the US.

Importance: Several anti-amyloid monoclonal antibodies have been developed for slowing the progression of Alzheimer disease (AD). Among the furthest developed are aducanumab, which received accelerated approval from the US Food and Drug Administration in 2021, and donanemab, which is currently undergoing phase 3 trials. The cost-effectiveness of these treatments has not been established. Objectives: To estimate the cost-effectiveness of aducanumab and donanemab relative to standard care for early AD in the US. Design, Setting, and Participants: A decision analytic model was used to estimate the lifetime health and economic outcomes of adults with early AD, from US healthcare sector and societal perspectives. Simulated patients had a mean (SD) age of 75.2 (5.5) years; 65% had mild cognitive impairment and 35% had mild dementia. Analyses were conducted from April 6, 2021, to January 20, 2022. Interventions: Standard care, aducanumab (selected inputs including disease progression hazard ratio [HR] of 0.89 [95% CI, 0.63-1.15], annual price of $28 000, and twice-yearly monitoring with magnetic resonance imaging [MRI] of the brain), or donanemab (selected inputs including disease progression HR of 0.68 [95% CI, 0.44-0.99], annual price of $28 000, and twice-yearly monitoring with MRI of the brain and amyloid positron emission tomography [PET] monitoring). Donanemab was switched to placebo after substantial amyloid reduction on PET imaging, which occurred in 27% of patients at 6 months and 55% of patients at 12 months. Main Outcomes and Measures: Quality-adjusted life-years (QALYs); costs, in 2020 US dollars; incremental cost-effectiveness ratios (ICERs); and value-based prices, defined as the maximum price at which a treatment would be cost-effective given a cost-effectiveness threshold of ICER of $150 000/QALY. Results: Lifetime QALYs increased by 0.133 with aducanumab and 0.408 with donanemab. Total health care sector and societal costs increased by $130 100 and $127 800, respectively, with aducanumab and by $78 700 and $71 600, respectively, with donanemab, driven largely by drug costs ($119 000 for aducanumab and $44 600 for donanemab). Health care sector and societal ICERs relative to standard care were $981 000/QALY and $964 000/QALY, respectively, for aducanumab and $193 000/QALY and $176 000/QALY, respectively, for donanemab. In sensitivity analysis, aducanumab's value-based price remained less than $50 000/y, even when assuming a 90% reduction in disease progression. Donanemab's value-based price surpassed $50 000/y once its efficacy exceeded 50%. Conclusions and Relevance: These findings suggest that at current expected prices, neither aducanumab nor donanemab would be cost-effective for early AD in the US. Donanemab's dosing scheme, in which patients suspend treatment on achieving substantial amyloid reductions, may provide a rubric by which sufficiently effective anti-amyloid antibody treatments could be cost-effective even when priced comparably to other biologics.

Does Screening for Depressive Symptoms Help Optimize Duloxetine Use in Knee Osteoarthritis Patients With Moderate Pain? A Cost-Effectiveness Analysis.

OBJECTIVE: Duloxetine is a treatment approved by the US Food and Drug Administration for both osteoarthritis (OA) pain and depression, though uptake of duloxetine in knee OA management varies. We examined the cost-effectiveness of adding duloxetine to knee OA care in the absence or presence of depression screening. METHODS: We used the Osteoarthritis Policy Model, a validated computer microsimulation of knee OA, to examine the value of duloxetine for patients with knee OA who have moderate pain by comparing 3 strategies: 1) usual care, 2) usual care plus duloxetine for patients who screen positive for depression on the Patient Health Questionnaire 9 (PHQ-9), and 3) usual care plus universal duloxetine. Outcome measures included quality-adjusted life years (QALYs), lifetime direct medical costs, and incremental cost-effectiveness ratios (ICERs), discounted at 3% annually. Model inputs, drawn from the published literature and national databases, included annual cost of duloxetine ($721-937); average pain reduction for duloxetine (17.5 points on the Western Ontario and McMaster Universities Osteoarthritis Index pain scale [0-100]), and likelihood of depression remission with duloxetine (27.4%). We considered 2 willingness-to-pay (WTP) thresholds of $50,000/QALY and $100,000/QALY. We varied parameters related to the PHQ-9 and the cost of duloxetine, efficacy, and toxicities to address uncertainty in model inputs. RESULTS: The screening strategy led to an additional 17 QALYs per 1,000 subjects and increased costs by $289/subject (ICER = $17,000/QALY). Universal duloxetine led to an additional 31 QALYs per 1,000 subjects and $1,205 per subject (ICER = $39,300/QALY). Under the majority of sensitivity analyses, universal duloxetine was cost-effective at the $100,000/QALY threshold. CONCLUSION: The addition of duloxetine to usual care for knee OA patients with moderate pain, regardless of depressive symptoms, is cost-effective at frequently used WTP thresholds.

Prediction of Suicide Attempts Using Clinician Assessment, Patient Self-report, and Electronic Health Records.

Importance: Half of the people who die by suicide make a health care visit within 1 month of their death. However, clinicians lack the tools to identify these patients. Objective: To predict suicide attempts within 1 and 6 months of presentation at an emergency department (ED) for psychiatric problems. Design, Setting, and Participants: This prognostic study assessed the 1-month and 6-month risk of suicide attempts among 1818 patients presenting to an ED between February 4, 2015, and March 13, 2017, with psychiatric problems. Data analysis was performed from May 1, 2020, to November 19, 2021. Main Outcomes and Measures: Suicide attempts 1 and 6 months after presentation to the ED were defined by combining data from electronic health records (EHRs) with patient 1-month (n = 1102) and 6-month (n = 1220) follow-up surveys. Ensemble machine learning was used to develop predictive models and a risk score for suicide. Results: A total of 1818 patients participated in this study (1016 men [55.9%]; median age, 33 years [IQR, 24-46 years]; 266 Hispanic patients [14.6%]; 1221 non-Hispanic White patients [67.2%], 142 non-Hispanic Black patients [7.8%], 64 non-Hispanic Asian patients [3.5%], and 125 non-Hispanic patients of other race and ethnicity [6.9%]). A total of 137 of 1102 patients (12.9%; weighted prevalence) attempted suicide within 1 month, and a total of 268 of 1220 patients (22.0%; weighted prevalence) attempted suicide within 6 months. Clinicians' assessment alone was little better than chance at predicting suicide attempts, with externally validated area under the receiver operating characteristic curve (AUC) of 0.67 for the 1-month model and 0.60 for the 6-month model. Prediction accuracy was slightly higher for models based on EHR data (1-month model: AUC, 0.71; 6 month model: AUC, 0.65) and was best using patient self-reports (1-month model: AUC, 0.76; 6-month model: AUC, 0.77), especially when patient self-reports were combined with EHR and/or clinician data (1-month model: AUC, 0.77; and 6 month model: AUC, 0.79). A model that used only 20 patient self-report questions and an EHR-based risk score performed similarly well (1-month model: AUC, 0.77; 6 month model: AUC, 0.78). In the best 1-month model, 30.7% (positive predicted value) of the patients classified as having highest risk (top 25% of the sample) made a suicide attempt within 1 month of their ED visit, accounting for 64.8% (sensitivity) of all 1-month attempts. In the best 6-month model, 46.0% (positive predicted value) of the patients classified at highest risk made a suicide attempt within 6 months of their ED visit, accounting for 50.2% (sensitivity) of all 6-month attempts. Conclusions and Relevance: This prognostic study suggests that the ability to identify patients at high risk of suicide attempt after an ED visit for psychiatric problems improved using a combination of patient self-reports and EHR data.

Accuracy Requirements for Cost-effective Suicide Risk Prediction Among Primary Care Patients in the US.

Importance: Several statistical models for predicting suicide risk have been developed, but how accurate such models must be to warrant implementation in clinical practice is not known. Objective: To identify threshold values of sensitivity, specificity, and positive predictive value that a suicide risk prediction method must attain to cost-effectively target a suicide risk reduction intervention to high-risk individuals. Design, Setting, and Participants: This economic evaluation incorporated published data on suicide epidemiology, the health care and societal costs of suicide, and the costs and efficacy of suicide risk reduction interventions into a novel decision analytic model. The model projected suicide-related health economic outcomes over a lifetime horizon among a population of US adults with a primary care physician. Data analysis was performed from September 19, 2019, to July 5, 2020. Interventions: Two possible interventions were delivered to individuals at high predicted risk: active contact and follow-up (ACF; relative risk of suicide attempt, 0.83; annual health care cost, $96) and cognitive behavioral therapy (CBT; relative risk of suicide attempt, 0.47; annual health care cost, $1088). Main Outcomes and Measures: Fatal and nonfatal suicide attempts, quality-adjusted life-years (QALYs), health care sector costs and societal costs (in 2016 US dollars), and incremental cost-effectiveness ratios (ICERs) (with ICERs ≤$150 000 per QALY designated cost-effective). Results: With a specificity of 95% and a sensitivity of 25%, primary care-based suicide risk prediction could reduce suicide death rates by 0.5 per 100 000 person-years (if used to target ACF) or 1.6 per 100 000 person-years (if used to target CBT) from a baseline of 15.3 per 100 000 person-years. To be cost-effective from a health care sector perspective at a specificity of 95%, a risk prediction method would need to have a sensitivity of 17.0% or greater (95% CI, 7.4%-37.3%) if used to target ACF and 35.7% or greater (95% CI, 23.1%-60.3%) if used to target CBT. To achieve cost-effectiveness, ACF required positive predictive values of 0.8% for predicting suicide attempt and 0.07% for predicting suicide death; CBT required values of 1.7% for suicide attempt and 0.2% for suicide death. Conclusions and Relevance: These findings suggest that with sufficient accuracy, statistical suicide risk prediction models can provide good health economic value in the US. Several existing suicide risk prediction models exceed the accuracy thresholds identified in this analysis and thus may warrant pilot implementation in US health care systems.

Cost-Effectiveness of Esketamine Nasal Spray for Patients With Treatment-Resistant Depression in the United States.

OBJECTIVE: This study aimed to estimate the cost-effectiveness of esketamine, a novel intranasally dosed antidepressant, for patients in the United States with treatment-resistant depression. METHODS: A decision-analytic model parameterized with efficacy data from phase 3 randomized trials of esketamine was used to simulate the effects of treatment with esketamine versus oral antidepressants over a 5-year horizon, from both societal and health care sector perspectives. Outcomes included remission and response of depression, quality-adjusted life-years (QALYs), costs, and incremental cost-effectiveness ratios (ICERs) for esketamine. Value-based prices were calculated, defined as the per-dose price at which esketamine would become cost-effective given cost-effectiveness thresholds of $50,000/QALY, $100,000/QALY, and $150,000/QALY. Uncertainty in these outcomes was assessed with probabilistic sensitivity analyses. Key model parameters included the efficacy of esketamine versus oral antidepressants (relative risk of 1.39 for remission; 1.32 for response) and the monthly cost of esketamine ($5,572 for month 1; $1,699-$2,244 thereafter). RESULTS: Over 5 years, esketamine was projected to increase time in remission from 25.3% to 31.1% of life-years, resulting in a gain of 0.07 QALYs. Esketamine increased societal costs by $16,617 and health care sector costs by $16,995. Base case ICERs were $237,111/QALY (societal) and $242,496/QALY (health care sector). Probabilistic sensitivity analysis showed a greater than 95% likelihood that esketamine's ICER would be above $150,000/QALY. At a cost-effectiveness threshold of $150,000/QALY, esketamine's value-based price was approximately $140/dose (versus a current price of $240/dose). CONCLUSIONS: Esketamine is unlikely to be cost-effective for management of treatment-resistant depression in the United States unless its price falls by more than 40%.

Cost-Effectiveness of Routine Screening for Autoimmune Encephalitis in Patients With First-Episode Psychosis in the United States.

OBJECTIVE: Autoimmune encephalitis (AE) is a highly treatable neurologic condition that can cause psychosis. Screening for AE is not currently recommended in routine workup for first-episode psychosis (FEP), owing partly to the high cost of testing for AE-associated neuronal autoantibodies. METHODS: This study used a decision-analytic model to estimate the cost-effectiveness of routine serum screening for AE compared with clinically targeted screening in patients with FEP. Model parameters drawn from prior published literature included the prevalence of neuronal autoantibodies in FEP (4.5%), serum autoantibody panel cost (US $291), remission probability with antipsychotics (0.58), and remission probability with immunotherapy for patients diagnosed with AE (0.85). Outcomes included quality-adjusted life-years (QALYs), costs, and incremental cost-effectiveness ratios (ICERs), assessed over a 5-year horizon from the US health care sector and societal perspectives. ICER thresholds of $50,000/QALY to $150,000/QALY were used to define cost-effectiveness. The analysis was conducted between June 2018 and January 2020. RESULTS: Routine screening led to mean QALY gains of 0.008 among all patients and 0.174 among the subgroup of patients with neuronal autoantibodies. Mean costs increased by $780 from a societal perspective and $1,150 from a health care sector perspective, resulting in ICERs of $99,330/QALY and $147,460/QALY, respectively. Incorporating joint input data uncertainty, the likelihood routine screening has an ICER ≤ $150,000/QALY was 55% from a societal perspective and 37% from a health care sector perspective. The model parameter with the greatest contribution to overall uncertainty was the effectiveness of immunotherapy relative to antipsychotics. CONCLUSIONS: Routine screening for AE in patients with FEP may be cost-effective in the United States. As further immunotherapy effectiveness data become available, a more definitive recommendation to perform routine screening could be warranted.

Cost-effectiveness of Electroconvulsive Therapy vs Pharmacotherapy/Psychotherapy for Treatment-Resistant Depression in the United States.

Importance: Electroconvulsive therapy (ECT) is a highly effective treatment for depression but is infrequently used owing to stigma, uncertainty about indications, adverse effects, and perceived high cost. Objective: To assess the cost-effectiveness of ECT compared with pharmacotherapy/psychotherapy for treatment-resistant major depressive disorder in the United States. Design, Setting, and Participants: A decision analytic model integrating data on clinical efficacy, costs, and quality-of-life effects of ECT compared with pharmacotherapy/psychotherapy was used to simulate depression treatment during a 4-year horizon from a US health care sector perspective. Model input data were drawn from multiple meta-analyses, randomized trials, and observational studies of patients with depression. Where possible, data sources were restricted to US-based studies of nonpsychotic major depression. Data were analyzed between June 2017 and January 2018. Interventions: Six alternative strategies for incorporating ECT into depression treatment (after failure of 0-5 lines of pharmacotherapy/psychotherapy) compared with no ECT. Main Outcomes and Measures: Remission, response, and nonresponse of depression; quality-adjusted life-years; costs in 2013 US dollars; and incremental cost-effectiveness ratios. Strategies with incremental cost-effectiveness ratios of $100 000 per quality-adjusted life-year or less were designated cost-effective. Results: Based on the Sequenced Treatment Alternatives to Relieve Depression trial, we simulated a population with a mean (SD) age of 40.7 (13.2) years, and 62.2% women. Over 4 years, ECT was projected to reduce time with uncontrolled depression from 50% of life-years to 33% to 37% of life-years, with greater improvements when ECT is offered earlier. Mean health care costs were increased by $7300 to $12 000, with greater incremental costs when ECT was offered earlier. In the base case, third-line ECT was cost-effective, with an ICER of $54 000 per quality-adjusted life-year. Third-line ECT remained cost-effective in a range of univariate, scenario, and probabilistic sensitivity analyses. Incorporating all input data uncertainty, we estimate a 74% to 78% likelihood that at least 1 of the ECT strategies is cost-effective and a 56% to 58% likelihood that third-line ECT is the optimal strategy. Conclusions and Relevance: For US patients with treatment-resistant depression, ECT may be an effective and cost-effective treatment option. Although many factors influence the decision to proceed with ECT, these data suggest that, from a health-economic standpoint, ECT should be considered after failure of 2 or more lines of pharmacotherapy/psychotherapy.

Clinical Benefits and Cost-Effectiveness of Laboratory Monitoring Strategies to Guide Antiretroviral Treatment Switching in India.

Current Indian guidelines recommend twice-annual CD4 testing to monitor first-line antiretroviral therapy (ART), with a plasma HIV RNA test to confirm failure if CD4 declines, which would prompt a switch to second-line ART. We used a mathematical model to assess the clinical benefits and cost-effectiveness of alternative laboratory monitoring strategies in India. We simulated a cohort of HIV-infected patients initiating first-line ART and compared 11 strategies with combinations of CD4 and HIV RNA testing at varying frequencies. We included adaptive strategies that reduce the frequency of tests after 1 year from 6 to 12 months for virologically suppressed patients. We projected life expectancy, time on failed first-line ART, cumulative 10-year HIV transmissions, lifetime cost (2014 US dollars), and incremental cost-effectiveness ratios (ICERs). We defined strategies as cost-effective if their ICER was <1 × the Indian per capita gross domestic product (GDP, $1,600). We found that the current Indian guidelines resulted in a per person life expectancy (from mean age 37) of 150.2 months and a per person cost of $2,680. Adding annual HIV RNA testing increased survival by ∼8 months; adaptive strategies were less expensive than similar nonadaptive strategies with similar life expectancy. The most effective strategy with an ICER <1 × GDP was the adaptive HIV RNA strategy (ICER $840/year). Cumulative 10-year transmissions decreased from 27.2/1,000 person-years with standard-of-care to 20.9/1,000 person-years with adaptive HIV RNA testing. In India, routine HIV RNA monitoring of patients on first-line ART would increase life expectancy, decrease transmissions, be cost-effective, and should be implemented.

Implementation and Operational Research: A Cost-Effective, Clinically Actionable Strategy for Targeting HIV Preexposure Prophylaxis to High-Risk Men Who Have Sex With Men.

BACKGROUND: Preexposure prophylaxis (PrEP) is effective at preventing HIV infection among men who have sex with men (MSM), but there is uncertainty about how to identify high-risk MSM who should receive PrEP. METHODS: We used a mathematical model to assess the cost-effectiveness of using the HIV Incidence Risk Index for MSM (HIRI-MSM) questionnaire to target PrEP to high-risk MSM. We simulated strategies of no PrEP, PrEP available to all MSM, and eligibility thresholds set to HIRI-MSM scores between 5 and 45, in increments of 5 (where a higher score predicts greater HIV risk). Based on the iPrEx, IPERGAY, and PROUD trials, we evaluated PrEP efficacies from 44% to 86% and annual costs from $5900 to 8700. We designate strategies with incremental cost-effectiveness ratio (ICER) ≤$100,000/quality-adjusted life-year (QALY) as "cost-effective." RESULTS: Over 20 years, making PrEP available to all MSM is projected to prevent 33.5% of new HIV infections, with an ICER of $1,474,000/QALY. Increasing the HIRI-MSM score threshold reduces the prevented infections, but improves cost-effectiveness. A threshold score of 25 is projected to be optimal (most QALYs gained while still being cost-effective) over a wide range of realistic PrEP efficacies and costs. At low cost and high efficacy (IPERGAY), thresholds of 15 or 20 are optimal across a range of other input assumptions; at high cost and low efficacy (iPrEx), 25 or 30 are generally optimal. CONCLUSIONS: The HIRI-MSM provides a clinically actionable means of guiding PrEP use. Using a score of 25 to determine PrEP eligibility could facilitate cost-effective use of PrEP among high-risk MSM who will benefit from it most.

Cost-effectiveness of Aflibercept, Bevacizumab, and Ranibizumab for Diabetic Macular Edema Treatment: Analysis From the Diabetic Retinopathy Clinical Research Network Comparative Effectiveness Trial.

IMPORTANCE: Anti-vascular endothelial growth factor (VEGF) medicines have revolutionized diabetic macular edema (DME) treatment. A recent randomized clinical trial comparing anti-VEGF agents for patients with decreased vision from DME found that at 1 year aflibercept (2.0 mg) achieved better visual outcomes than repackaged (compounded) bevacizumab (1.25 mg) or ranibizumab (0.3 mg); the worse the starting vision, the greater the treatment benefit with aflibercept. However, aflibercept and ranibizumab, respectively, are approximately 31 and 20 times more expensive than bevacizumab. OBJECTIVE: To examine the incremental cost-effectiveness ratios (ICERs) of aflibercept, bevacizumab, and ranibizumab for the treatment of DME. DESIGN, SETTING, AND PARTICIPANTS: Post hoc analysis of efficacy, safety, and resource utilization data at 1-year follow-up from the Diabetic Retinopathy Clinical Research Network Comparative Effectiveness Trial. Patients were enrolled from August 22, 2012, through August 28, 2013, and analysis was performed from August 21, 2014, through November 7, 2015. MAIN OUTCOMES AND MEASURES: The ICERs for all trial participants and subgroups with baseline vision of approximate Snellen equivalent 20/32 to 20/40 (better vision) and baseline vision of approximate Snellen equivalent 20/50 or worse (worse vision). One-year trial data were used to calculate cost-effectiveness for 1 year for the 3 anti-VEGF agents; mathematical modeling was then used to project 10-year cost-effectiveness results. RESULTS: The study included 624 participants (mean [SD] age, 60.6 [10.5] years; 45.7% female; 65.5% white), 209 in the aflibercept group, 207 in the bevacizumab group, and 208 in the ranibizumab group. For all participants, during 1 year, the ICERs of aflibercept and ranibizumab compared with bevacizumab were $1 110 000 per quality-adjusted life-year (QALY) and $1 730 000 per QALY, respectively. During 10 years, they were $349 000 per QALY and $603 000 per QALY, respectively. Compared with ranibizumab, aflibercept's ICER was $648 000 per QALY at 1 year and $203 000 per QALY at 10 years. For the subgroup with worse baseline vision, the 10-year ICERs of aflibercept and ranibizumab compared with bevacizumab were $287 000 per QALY and $817 000 per QALY, respectively. In eyes with decreased vision from DME, treatment costs of aflibercept and ranibizumab would need to decrease by 69% and 80%, respectively, to reach a cost-effectiveness threshold of $100 000 per QALY compared with bevacizumab during a 10-year horizon; for the subgroup with worse baseline vision, the costs would need to decrease by 62% and 84%, respectively. CONCLUSIONS AND RELEVANCE: Aflibercept (2.0 mg) and ranibizumab (0.3 mg) are not cost-effective relative to bevacizumab for treatment of DME unless their prices decrease substantially. These results highlight the challenges that physicians, patients, and policymakers face when safety and efficacy results are at odds with cost-effectiveness results.

Clinical impact and cost-effectiveness of making third-line antiretroviral therapy available in sub-Saharan Africa: a model-based analysis in Côte d'Ivoire.

OBJECTIVE: In sub-Saharan Africa, HIV-infected adults who fail second-line antiretroviral therapy (ART) often do not have access to third-line ART. We examined the clinical impact and cost-effectiveness of making third-line ART available in Côte d'Ivoire. METHODS: We used a simulation model to compare 4 strategies after second-line ART failure: continue second-line ART (C-ART2), continue second-line ART with an adherence reinforcement intervention (AR-ART2), immediate switch to third-line ART (IS-ART3), and continue second-line ART with adherence reinforcement, switching patients with persistent failure to third-line ART (AR-ART3). Third-line ART consisted of a boosted-darunavir plus raltegravir-based regimen. Primary outcomes were 10-year survival and lifetime incremental cost-effectiveness ratios (ICERs), in $/year of life saved (YLS). ICERs below $3585 (3 times the country per capita gross domestic product) were considered cost-effective. RESULTS: Ten-year survival was 6.0% with C-ART2, 17.0% with AR-ART2, 35.4% with IS-ART3, and 37.2% with AR-ART3. AR-ART2 was cost-effective ($1100/YLS). AR-ART3 had an ICER of $3600/YLS and became cost-effective if the cost of third-line ART decreased by <1%. IS-ART3 was less effective and more costly than AR-ART3. Results were robust to wide variations in the efficacy of third-line ART and of the adherence reinforcement, as well as in the cost of second-line ART. CONCLUSIONS: Access to third-line ART combined with an intense adherence reinforcement phase, used as a tool to distinguish between patients who can still benefit from their current second-line regimen and those who truly need third-line ART would provide substantial survival benefits. With minor decreases in drug costs, this strategy would be cost-effective.