Trends in SARS-CoV-2 cycle threshold (Ct) values from nucleic acid testing predict the trajectory of COVID-19 waves.

Forecasting COVID-19 waves helps with public health planning and resource allocation. Cycle threshold (Ct) values obtained from positive SARS-CoV-2 nucleic acid amplification test (NAAT) results offer limited value for individual patient management, but real-time analysis of temporal trends of aggregated Ct values may provide helpful information to predict the trajectories of COVID-19 waves in the community. Ct value trends on 59,609 SARS-CoV-2 NAAT-positive results from 574,403 tests using a single testing assay system, between September 2021 and January 2023, were examined to monitor the trend of the proportion of positive NAAT with lower Ct values (≤28) in relation to changing COVID-19 case numbers over time. We applied regression with autoregressive integrated moving average errors modelling approach to study the relation between Ct values and case counts. We also developed an insight product to monitor the temporal trends with Ct values obtained from SARS-CoV-2 NAAT-positive results. In this study, the proportion of lower Ct values preceded by a range of 7-32 days the rising population COVID-19 testing rate reflecting onset of a COVID-19 wave. Monitoring population Ct values may assist in predicting increased disease activity.

Frequency and costs of low-value preoperative tests for patients undergoing low-risk procedures in the veterans health administration.

BACKGROUND: Clinical practice guidelines discourage routine preoperative screening tests for patients undergoing low-risk procedures. This study sought to determine the frequency and costs of potentially low-value preoperative screening tests in Veterans Health Administration (VA) patients undergoing low-risk procedures. METHODS: Using the VA Corporate Data Warehouse, we identified Operative Stress Score class 1 procedures ("very minor") performed without general anesthesia in VA during fiscal year 2019 and calculated the overall national and facility-level rates and costs of nine common tests received in the 30 preoperative days. Patient factors associated with receiving at least one screening test, and the number of tests received, were examined. RESULTS: Eighty-six thousand three hundred twenty-seven of 178,775 low-risk procedures (49.3%) were preceded by 321,917 potentially low-value screening tests representing $11,505,170 using Medicare average costs. Complete blood count was the most common (33.2% of procedures), followed by basic metabolic profile (32.0%), urinalysis (26.3%), electrocardiography (18.9%), and pulmonary function test (12.4%). Older age, female sex, Black race, and having more comorbidities were associated with higher odds of low-value testing. Transthoracic echocardiogram occurred prior to only 4.5% of the procedures but accounted for 47.8% of the total costs ($5,499,860). In 129 VA facilities, the facility-level proportion of procedures preceded by at least one test ranged from 0 to 81.2% and facility-level costs ranged from $0 to $388,476. CONCLUSIONS: Routine preoperative screening tests for very low-risk procedures are common and costly in some VA facilities. These results highlight a potential target to improve quality and value by reducing unnecessary care. Measures of low-value perioperative care could be integrated into VA's extensive quality monitoring and improvement infrastructure.

Can Machine Learning Methods Produce Accurate and Easy-to-Use Preoperative Prediction Models of One-Year Improvements in Pain and Functioning After Knee Arthroplasty?

BACKGROUND: Approximately 15%-20% of total knee arthroplasty (TKA) patients do not experience clinically meaningful improvements. We sought to compare the accuracy and parsimony of several machine learning strategies for developing predictive models of failing to experience minimal clinically important differences in patient-reported outcome measures (PROMs) 1 year after TKA. METHODS: Patients (N = 587) in 3 large Veteran Health Administration facilities completed PROMs before and 1 year after TKA (92% follow-up). Preoperative PROMs and electronic health record data were used to develop and validate models to predict failing to experience at least a minimal clinically important difference in Knee Injury and Osteoarthritis Outcome Score (KOOS) Total, KOOS JR, and KOOS subscales (Pain, Symptoms, Activities of Daily Living, Quality of Life, and recreation). Several machine learning strategies were used for model development. Ten-fold cross-validation and bootstrapping were used to produce measures of overall accuracy (C-statistic, Brier Score). The sensitivity and specificity of various predicted probability cut-points were examined. RESULTS: The most accurate models produced were for the Activities of Daily Living, Pain, Symptoms, and Quality of Life subscales of the KOOS (C-statistics 0.76, 0.72, 0.72, and 0.71, respectively). Strategies varied substantially in terms of the numbers of inputs required to achieve similar accuracy, with none being superior for all outcomes. CONCLUSION: Models produced in this project provide estimates of patient-specific improvements in major outcomes 1 year after TKA. Integrating these models into clinical decision support, informed consent and shared decision making could improve patient selection, education, and satisfaction. LEVEL OF EVIDENCE: Level III, diagnostic study.

Associations between stopping prescriptions for opioids, length of opioid treatment, and overdose or suicide deaths in US veterans: observational evaluation.

OBJECTIVE: To examine the associations between stopping treatment with opioids, length of treatment, and death from overdose or suicide in the Veterans Health Administration. DESIGN: Observational evaluation. SETTING: Veterans Health Administration. PARTICIPANTS: 1 394 102 patients in the Veterans Health Administration with an outpatient prescription for an opioid analgesic from fiscal year 2013 to the end of fiscal year 2014 (1 October 2012 to 30 September 2014). MAIN OUTCOME MEASURES: A multivariable Cox non-proportional hazards regression model examined death from overdose or suicide, with the interaction of time varying opioid cessation by length of treatment (≤30, 31-90, 91-400, and >400 days) as the main covariates. Stopping treatment with opioids was measured as the time when a patient was estimated to have no prescription for opioids, up to the end of the next fiscal year (2014) or the patient's death. RESULTS: 2887 deaths from overdose or suicide were found. The incidence of stopping opioid treatment was 57.4% (n=799 668) overall, and based on length of opioid treatment was 32.0% (≤30 days), 8.7% (31-90 days), 22.7% (91-400 days), and 36.6% (>400 days). The interaction between stopping treatment with opioids and length of treatment was significant (P<0.001); stopping treatment was associated with an increased risk of death from overdose or suicide regardless of the length of treatment, with the risk increasing the longer patients were treated. Hazard ratios for patients who stopped opioid treatment (with reference values for all other covariates) were 1.67 (≤30 days), 2.80 (31-90 days), 3.95 (91-400 days), and 6.77 (>400 days). Descriptive life table data suggested that death rates for overdose or suicide increased immediately after starting or stopping treatment with opioids, with the incidence decreasing over about three to 12 months. CONCLUSIONS: Patients were at greater risk of death from overdose or suicide after stopping opioid treatment, with an increase in the risk the longer patients had been treated before stopping. Descriptive data suggested that starting treatment with opioids was also a risk period. Strategies to mitigate the risk in these periods are not currently a focus of guidelines for long term use of opioids. The associations observed cannot be assumed to be causal; the context in which opioid prescriptions were started and stopped might contribute to risk and was not investigated. Safer prescribing of opioids should take a broader view on patient safety and mitigate the risk from the patient's perspective. Factors to address are those that place patients at risk for overdose or suicide after beginning and stopping opioid treatment, especially in the first three months.

Comparing Methods to Determine the Minimal Clinically Important Differences in Patient-Reported Outcome Measures for Veterans Undergoing Elective Total Hip or Knee Arthroplasty in Veterans Health Administration Hospitals.

Importance: The minimal clinically important difference (MCID) in a patient-reported outcome measure (PROM) is the smallest change that patients perceive as beneficial. Accurate MCIDs are required when PROMs are used to evaluate the value of surgical interventions. Objective: To use well-defined distribution-based and anchor-based methods to calculate MCIDs in the Hip Disability and Osteoarthritis Outcome Score (HOOS) and in the Knee Injury and Osteoarthritis Outcome Score (KOOS) for veterans undergoing primary total hip arthroplasty or total knee arthroplasty. Design, Setting, and Participants: A prospective cohort study was conducted of 858 patients undergoing total joint replacement between March 16, 2015, and March 9, 2017, at 3 high-complexity Veterans Affairs Medical Centers. Interventions: Patients undergoing total hip arthroplasty or total knee arthroplasty were administered HOOS or KOOS PROMs prior to and 1 year after surgery. The Self-Administered Patient Satisfaction Scale (SAPS) for primary hip or knee arthroplasty was administered at 1-year follow-up as an anchor PROM. Main Outcomes and Measures: The HOOS and KOOS before and 1 year after surgery, change scores (difference between postoperative and preoperative PROM scores), and MCIDs for each measure. For anchor-based methods, receiver operating characteristic curve analysis was performed, including calculation of the area under the curve. Results: The mean (SD) age of the 271 patients who underwent hip arthroplasty was 65.6 (8.3) years, and the mean (SD) age of the 587 patients who underwent knee arthroplasty was 66.1 (8.2) years. There were 547 men in the knee arthroplasty cohort and 256 men in the hip arthroplasty cohort (total, 803 men). There were significant improvements in the mean values of every PROM, with mean (SD) differences greater than 39 for HOOS Joint Replacement (JR) and every hip subscale (HOOS JR, 39.7 [20.2]; pain, 47.6 [20.5]; symptoms, 45.1 [21.5]; activities of daily living, 43.7 [22.1]; recreation, 49.2 [33.5]; quality of life, 50.3 [27.8]) and mean (SD) differences greater than 29 for KOOS JR and every knee subscale (KOOS JR, 30.4 [17.5]; pain, 38.0 [20.4]; symptoms, 29.5 [22.1]; activities of daily living, 34.8 [20.5]; recreation, 34.6 [31.1]; quality of life, 35.2 [26.8]). Different calculation methods yielded a wide range of MCIDs. Distribution-based approaches tended to give lower values than the anchor-based approaches, which gave similar values for most PROMs. Area under the curve values demonstrated good to excellent discrimination for SAPS for nearly all PROMs. Conclusions and Relevance: Minimal clinically important difference estimates can be highly variable depending on the method used. Patient satisfaction measured by SAPS is a suitable anchor for the HOOS and KOOS. This study suggests that the SAPS-anchored MCID values presented here be used in future studies of total hip arthroplasty and total knee arthroplasty for veterans.

Development and validation of a predictive model for American Society of Anesthesiologists Physical Status.

BACKGROUND: The American Society of Anesthesiologists Physical Status (ASA-PS) classification system was developed to categorize the fitness of patients before surgery. Increasingly, the ASA-PS has been applied to other uses including justification of inpatient admission. Our objectives were to develop and cross-validate a statistical model for predicting ASA-PS; and 2) assess the concurrent and predictive validity of the model by assessing associations between model-derived ASA-PS, observed ASA-PS, and a diverse set of 30-day outcomes. METHODS: Using the 2014 American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) Participant Use Data File, we developed and internally cross-validated multinomial regression models to predict ASA-PS using preoperative NSQIP data. Accuracy was assessed with C-Statistics and calibration plots. We assessed both concurrent and predictive validity of model-derived ASA-PS relative to observed ASA-PS and 30-day outcomes. To aid further research and use of the ASA-PS model, we implemented it into an online calculator. RESULTS: Of the 566,797 elective procedures in the final analytic dataset, 8.9% were ASA-PS 1, 48.9% were ASA-PS 2, 39.1% were ASA-PS 3, and 3.2% were ASA-PS 4. The accuracy of the 21-variable model to predict ASA-PS was C = 0.77 +/- 0.0025. The model-derived ASA-PS had stronger association with key indicators of preoperative status including comorbidities and higher BMI (concurrent validity) compared to observed ASA-PS, but less strong associations with postoperative complications (predictive validity). The online ASA-PS calculator may be accessed at https://s-spire-clintools.shinyapps.io/ASA_PS_Estimator/ CONCLUSIONS: Model-derived ASA-PS better tracked key indicators of preoperative status compared to observed ASA-PS. The ability to have an electronically derived measure of ASA-PS can potentially be useful in research, quality measurement, and clinical applications.

Can Machine Learning Methods Produce Accurate and Easy-to-use Prediction Models of 30-day Complications and Mortality After Knee or Hip Arthroplasty?

BACKGROUND: Existing universal and procedure-specific surgical risk prediction models of death and major complications after elective total joint arthroplasty (TJA) have limitations including poor transparency, poor to modest accuracy, and insufficient validation to establish performance across diverse settings. Thus, the need remains for accurate and validated prediction models for use in preoperative management, informed consent, shared decision-making, and risk adjustment for reimbursement. QUESTIONS/PURPOSES: The purpose of this study was to use machine learning methods and large national databases to develop and validate (both internally and externally) parsimonious risk-prediction models for mortality and complications after TJA. METHODS: Preoperative demographic and clinical variables from all 107,792 nonemergent primary THAs and TKAs in the 2013 to 2014 American College of Surgeons-National Surgical Quality Improvement Program (ACS-NSQIP) were evaluated as predictors of 30-day death and major complications. The NSQIP database was chosen for its high-quality data on important outcomes and rich characterization of preoperative demographic and clinical predictors for demographically and geographically diverse patients. Least absolute shrinkage and selection operator (LASSO) regression, a type of machine learning that optimizes accuracy and parsimony, was used for model development. Tenfold validation was used to produce C-statistics, a measure of how well models discriminate patients who experience an outcome from those who do not. External validation, which evaluates the generalizability of the models to new data sources and patient groups, was accomplished using data from the Veterans Affairs Surgical Quality Improvement Program (VASQIP). Models previously developed from VASQIP data were also externally validated using NSQIP data to examine the generalizability of their performance with a different group of patients outside the VASQIP context. RESULTS: The models, developed using LASSO regression with diverse clinical (for example, American Society of Anesthesiologists classification, comorbidities) and demographic (for example, age, gender) inputs, had good accuracy in terms of discriminating the likelihood a patient would experience, within 30 days of arthroplasty, a renal complication (C-statistic, 0.78; 95% confidence interval [CI], 0.76-0.80), death (0.73; 95% CI, 0.70-0.76), or a cardiac complication (0.73; 95% CI, 0.71-0.75) from one who would not. By contrast, the models demonstrated poor accuracy for venous thromboembolism (C-statistic, 0.61; 95% CI, 0.60-0.62) and any complication (C-statistic, 0.64; 95% CI, 0.63-0.65). External validation of the NSQIP- derived models using VASQIP data found them to be robust in terms of predictions about mortality and cardiac complications, but not for predicting renal complications. Models previously developed with VASQIP data had poor accuracy when externally validated with NSQIP data, suggesting they should not be used outside the context of the Veterans Health Administration. CONCLUSIONS: Moderately accurate predictive models of 30-day mortality and cardiac complications after elective primary TJA were developed as well as internally and externally validated. To our knowledge, these are the most accurate and rigorously validated TJA-specific prediction models currently available (http://med.stanford.edu/s-spire/Resources/clinical-tools-.html). Methods to improve these models, including the addition of nonstandard inputs such as natural language processing of preoperative clinical progress notes or radiographs, should be pursued as should the development and validation of models to predict longer term improvements in pain and function. LEVEL OF EVIDENCE: Level III, diagnostic study.

The impact of substance use disorders on treatment engagement among justice-involved veterans with posttraumatic stress disorder.

Veterans involved with the criminal justice system represent a particularly vulnerable population who experience high rates of both posttraumatic stress disorder (PTSD) and substance use disorders (SUD). This study sought to investigate whether having co-occurring SUD is a barrier to PTSD treatment. This is a retrospective observational study of a national sample of justice-involved veterans served by the Veterans Health Administration Veterans Justice Outreach program who had a diagnosis of PTSD (N = 27,857). Mixed effects logistic regression models with a random effect for facility (N = 141 medical centers) were utilized to estimate the odds of receiving each type of PTSD treatment as a function of having a SUD diagnosis. Results indicate that a majority of veterans with PTSD served by the Veterans Justice Outreach program have an SUD diagnosis (73%), and having a co-occurring SUD was associated with higher odds of receiving PTSD treatment, after adjusting for demographic differences. Although not without limitations, these results suggest that among justice-involved veterans enrolled in the Veterans Health Administration with PTSD, having an SUD comorbidity is not a barrier to PTSD treatment and may in fact facilitate access to PTSD treatment. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Barriers and Facilitators to Implementation of Pharmacotherapy for Opioid Use Disorders in VHA Residential Treatment Programs.

OBJECTIVE: Despite evidence of effectiveness, pharmacotherapy-methadone, buprenorphine, or naltrexone-is prescribed to less than 35% of Veterans Health Administration (VHA) patients diagnosed with opioid use disorder (OUD). Among veterans whose OUD treatment is provided in VHA residential programs, factors influencing pharmacotherapy implementation are unknown. We examined barriers to and facilitators of pharmacotherapy for OUD among patients diagnosed with OUD in VHA residential programs to inform the development of implementation strategies to improve medication receipt. METHOD: VHA electronic health records and program survey data were used to describe pharmacotherapy provided to a national cohort of VHA patients with OUD in residential treatment programs (N = 4,323, 6% female). Staff members (N = 63, 57% women) from 44 residential programs (response rate = 32%) participated in interviews. Barriers to and facilitators of pharmacotherapy for OUD were identified from transcripts using thematic analysis. RESULTS: Across all 97 residential treatment programs, the average rate of pharmacotherapy for OUD was 21% (range: 0%-67%). Reported barriers included provider or program philosophy against pharmacotherapy, a lack of care coordination with nonresidential treatment settings, and provider perceptions of low patient interest or need. Facilitators included having a prescriber on staff, education and training for patients and staff, and support from leadership. CONCLUSIONS: Contrary to our hypothesis, barriers to and facilitators of pharmacotherapy for OUD in VHA residential treatment programs were consistent with prior research in outpatient settings. Intensive educational programs, such as academic detailing, and policy changes such as mandating buprenorphine waiver training for VHA providers, may help improve receipt of pharmacotherapy for OUD.

American Joint Replacement Registry Risk Calculator Does Not Predict 90-day Mortality in Veterans Undergoing Total Joint Replacement.

BACKGROUND: The American Joint Replacement Registry (AJRR) Total Joint Risk Calculator uses demographic and clinical parameters to provide risk estimates for 90-day mortality and 2-year periprosthetic joint infection (PJI). The tool is intended to help surgeons counsel their Medicare-eligible patients about their risk of death and PJI after total joint arthroplasty (TJA). However, for a predictive risk model to be useful, it must be accurate when applied to new patients; this has yet to be established for this calculator. QUESTIONS/PURPOSES: To produce accuracy metrics (ie, discrimination, calibration) for the AJRR mortality calculator using data from Medicare-eligible patients undergoing TJA in the Veterans Health Administration (VHA), the largest integrated healthcare system in the United States, where more than 10,000 TJAs are performed annually. METHODS: We used the AJRR calculator to predict risk of death within 90 days of surgery among 31,214 VHA patients older than 64 years of age who underwent primary TJA; data was drawn from the Veterans Affairs Surgical Quality Improvement Project (VASQIP) and VA Corporate Data Warehouse (CDW). We then used VHA mortality data to evaluate the extent to which the AJRR calculator estimates distinguished individuals who died compared with those who did not (C-statistic), and graphically depicted the relationship between estimated risk and observed mortality (calibration). As a secondary evaluation of the calculator, a sample of 39,300 patients younger than 65 years old was assigned to the youngest age group available to the user (65-69 years) as might be done in real-world practice. RESULTS: C-statistics for 90-day mortality for the older samples were 0.62 (95% CI, 0.60-0.64) and for the younger samples they were 0.46 (95% CI, 0.43-0.49), suggesting poor discrimination. Calibration analysis revealed poor correspondence between deciles of predicted risk and observed mortality rates. Poor discrimination and calibration mean that patients who died will frequently have a lower estimated risk of death than surviving patients. CONCLUSIONS: For Medicare-eligible patients receiving TJA in the VA, the AJRR risk calculator had a poor performance in the prediction of 90-day mortality. There are several possible reasons for the model's poor performance. Veterans Health Administration patients, 97% of whom were men, represent only a subset of the broader Medicare population. However, applying the calculator to a subset of the target population should not affect its accuracy. Other reasons for poor performance include a lack of an underlying statistical model in the calculator's implementation and simply the challenge of predicting rare events. External validation in a more representative sample of Medicare patients should be conducted to before assuming this tool is accurate for its intended use. LEVEL OF EVIDENCE: Level I, diagnostic study.

Risk Reduction Compared with Access to Care: Quantifying the Trade-Off of Enforcing a Body Mass Index Eligibility Criterion for Joint Replacement.

BACKGROUND: Morbidly obese patients with severe osteoarthritis benefit from successful total joint arthroplasty. However, morbid obesity increases the risk of complications. Because of this, some surgeons enforce a body mass index (BMI) eligibility criterion above which total joint arthroplasty is denied. In this study, we investigate the trade-off between avoiding complications and restricting access to care when enforcing BMI-based eligibility criteria for total joint arthroplasty. METHODS: In this retrospective cohort study, the Veterans Health Administration (VHA) Corporate Data Warehouse (CDW) and Veterans Affairs Surgical Quality Improvement Program (VASQIP) databases were reviewed for patients undergoing total joint arthroplasty from October 2011 through September 2014. We determined, if various BMI eligibility criteria had been enforced over that period of time, how many short-term complications would have been avoided, how many complication-free surgical procedures would have been denied, and the positive predictive value of BMI eligibility criteria as tests for major complications. To provide a frame of reference, we also determined what would have happened if eligibility for total joint arthroplasty were arbitrarily determined by flipping a coin. RESULTS: In this study, 27,671 total joint arthroplasties were reviewed. With a BMI criterion of ≥40 kg/m, 1,148 patients would have been denied a surgical procedure free of major complications, and 83 patients would have avoided a major complication. The positive predictive value of a complication using a BMI of ≥40 kg/m as a test for major complications was 6.74% (95% confidence interval [CI], 5.44% to 8.33%). The positive predictive value of a complication using a BMI criterion of 30 kg/m was 5.33% (95% CI, 4.99% to 5.71%). Flipping a coin had a positive predictive value of 5.05%. CONCLUSIONS: A 30 kg/m criterion for total joint arthroplasty eligibility is marginally better than flipping a coin and should not determine surgical eligibility. With a BMI criterion of ≥40 kg/m, the number of patients denied a complication-free surgical procedure is about 14 times larger than those spared a complication. Although the acceptable balance between avoiding complications and providing access to care can be debated, such a quantitative assessment helps to inform decisions regarding the advisability of enforcing a BMI criterion for total joint arthroplasty. LEVEL OF EVIDENCE: Therapeutic Level III. See Instructions for Authors for a complete description of levels of evidence.

Prediction Models for 30-Day Mortality and Complications After Total Knee and Hip Arthroplasties for Veteran Health Administration Patients With Osteoarthritis.

BACKGROUND: Statistical models to preoperatively predict patients' risk of death and major complications after total joint arthroplasty (TJA) could improve the quality of preoperative management and informed consent. Although risk models for TJA exist, they have limitations including poor transparency and/or unknown or poor performance. Thus, it is currently impossible to know how well currently available models predict short-term complications after TJA, or if newly developed models are more accurate. We sought to develop and conduct cross-validation of predictive risk models, and report details and performance metrics as benchmarks. METHODS: Over 90 preoperative variables were used as candidate predictors of death and major complications within 30 days for Veterans Health Administration patients with osteoarthritis who underwent TJA. Data were split into 3 samples-for selection of model tuning parameters, model development, and cross-validation. C-indexes (discrimination) and calibration plots were produced. RESULTS: A total of 70,569 patients diagnosed with osteoarthritis who received primary TJA were included. C-statistics and bootstrapped confidence intervals for the cross-validation of the boosted regression models were highest for cardiac complications (0.75; 0.71-0.79) and 30-day mortality (0.73; 0.66-0.79) and lowest for deep vein thrombosis (0.59; 0.55-0.64) and return to the operating room (0.60; 0.57-0.63). CONCLUSIONS: Moderately accurate predictive models of 30-day mortality and cardiac complications after TJA in Veterans Health Administration patients were developed and internally cross-validated. By reporting model coefficients and performance metrics, other model developers can test these models on new samples and have a procedure and indication-specific benchmark to surpass.

Outpatient continuing care after residential substance use disorder treatment in the US Veterans Health Administration: Facilitators and challenges.

BACKGROUND: In the U.S. Veterans Health Administration (VHA), residential treatment programs are an important part of the continuum of care for patients with substance use disorders (SUDs). Outpatient continuing care after residential treatment helps maintain early recovery and treatment gains. Knowing more about the drivers of variation in continuing care practices and performance across residential programs might inform quality improvement efforts. METHODS: Metrics of continuing care were operationalized and calculated for each of VHA's 63 SUD Residential Rehabilitation Treatment Programs (SUD RRTPs) and 34 Mental Health Residential Rehabilitation Treatment Programs (MH RRTPs) with a SUD track in fiscal year 2012. Management and frontline staff of these programs were then interviewed to learn what factors might contribute to high or low program performance on the metrics compared with national averages. RESULTS: Among SUD RRTPs, the mean rate of outpatient SUD/MH continuing care was 59% within 7 days and 80% within 30 days, and the mean rate of SUD continuing care was 63% within 30 days. Among MH RRTPs with a SUD track, these rates were 56%, 75%, and 36%, respectively. There was substantial variability in continuing care rates across the 97 programs: 21%-93% for SUD/MH care within 7 days, 36%-100% for SUD/MH care within 30 days, and 4%-91% for SUD care within 30 days. Interviews with representatives of 44 programs revealed key facilitators of continuing care: accountability of program staff, predischarge scheduling, predischarge introductions to continuing care providers, strong patient relationships, accessibility, and persistent emphasis. Key challenges included inadequate program staffing, lack of program staff accountability, and poor accessibility. CONCLUSIONS: Wide variation in continuing care rates across programs and identification of common facilitators at high-performing programs suggest substantial opportunity for improvement for programs with lower performance.

Barriers to and facilitators of pharmacotherapy for alcohol use disorder in VA residential treatment programs.

Among US military veterans, alcohol use disorder (AUD) is prevalent and in severe cases patients need intensive AUD treatment beyond outpatient care. The Department of Veterans Affairs (VA) delivers intensive, highly structured addiction and psychosocial treatment through residential programs. Despite the evidence supporting pharmacotherapy among the effective treatments for AUD, receipt of these medications (e.g., naltrexone, acamprosate) among patients in residential treatment programs varies widely. In order to better understand this variation, the current study examined barriers and facilitators to use of pharmacotherapy for AUD among patients in VA residential treatment programs. Semi-structured qualitative interviews with residential program management and staff were conducted and the Consolidated Framework for Implementation Research was used to guide coding and analysis of interview transcripts. Barriers to use of pharmacotherapy for AUD included cultural norms or philosophy against prescribing, lack of access to willing prescribers, lack of interest from leadership, and perceived lack of patient interest or need. Facilitators included cultural norms of openness or active promotion of pharmacotherapy; education for patients, program staff and prescribers; having prescribers on staff, and care coordination within residential treatment and with other clinic settings in and outside VA. Developing and testing improvement strategies to increase care coordination and consistent support from leadership may also yield increases in the use of pharmacotherapy for AUD among residential patients.

VA residential substance use disorder treatment program providers' perceptions of facilitators and barriers to performance on pre-admission processes.

BACKGROUND: In the U.S. Department of Veterans Affairs (VA), residential treatment programs are an important part of the continuum of care for patients with a substance use disorder (SUD). However, a limited number of program-specific measures to identify quality gaps in SUD residential programs exist. This study aimed to: (1) Develop metrics for two pre-admission processes: Wait Time and Engagement While Waiting, and (2) Interview program management and staff about program structures and processes that may contribute to performance on these metrics. The first aim sought to supplement the VA's existing facility-level performance metrics with SUD program-level metrics in order to identify high-value targets for quality improvement. The second aim recognized that not all key processes are reflected in the administrative data, and even when they are, new insight may be gained from viewing these data in the context of day-to-day clinical practice. METHODS: VA administrative data from fiscal year 2012 were used to calculate pre-admission metrics for 97 programs (63 SUD Residential Rehabilitation Treatment Programs (SUD RRTPs); 34 Mental Health Residential Rehabilitation Treatment Programs (MH RRTPs) with a SUD track). Interviews were then conducted with management and front-line staff to learn what factors may have contributed to high or low performance, relative to the national average for their program type. We hypothesized that speaking directly to residential program staff may reveal innovative practices, areas for improvement, and factors that may explain system-wide variability in performance. RESULTS: Average wait time for admission was 16 days (SUD RRTPs: 17 days; MH RRTPs with a SUD track: 11 days), with 60% of Veterans waiting longer than 7 days. For these Veterans, engagement while waiting occurred in an average of 54% of the waiting weeks (range 3-100% across programs). Fifty-nine interviews representing 44 programs revealed factors perceived to potentially impact performance in these domains. Efficient screening processes, effective patient flow, and available beds were perceived to facilitate shorter wait times, while lack of beds, poor staffing levels, and lengths of stay of existing patients were thought to lengthen wait times. Accessible outpatient services, strong patient outreach, and strong encouragement of pre-admission outpatient treatment emerged as facilitators of engagement while waiting; poor staffing levels, socioeconomic barriers, and low patient motivation were viewed as barriers. CONCLUSIONS: Metrics for pre-admission processes can be helpful for monitoring residential SUD treatment programs. Interviewing program management and staff about drivers of performance metrics can play a complementary role by identifying innovative and other strong practices, as well as high-value targets for quality improvement. Key facilitators of high-performing facilities may offer programs with lower performance useful strategies to improve specific pre-admission processes.

Predictive Validity of Outpatient Follow-up After Detoxification as a Quality Measure.

OBJECTIVES: Care coordination for substance use disorder (SUD) treatment is a persistent challenge. Timely outpatient follow-up after detoxification from alcohol and opiates is associated with improved outcomes, leading some care systems to attempt to measure and incentivize this practice. This study evaluated the predictive validity of a 7-day outpatient follow-up after detoxification quality measure used by the Veterans Health Administration (VHA). METHODS: A national sample of patients who received detoxification from alcohol or opiates (N = 25,354) was identified in VHA administrative data. Propensity score-weighted mixed-effects regressions modeled associations between receiving an outpatient follow-up visit within 7 days of completing detoxification and patient outcomes, controlling for facility-level performance and clustering of patients within facilities. RESULTS: Baseline differences between patients who did (39.6%) and did not (60.4%) receive the follow-up visit were reduced or eliminated with propensity score weighting. Meeting the quality measure was associated with significantly more outpatient treatment for SUD (b = 1.07 visits) and other mental health conditions (b = 0.58 visits), and higher inpatient utilization for SUD (b = 0.75 admissions) and other mental health conditions (b = 0.76 admissions). Notably, meeting the quality measure was associated with 53.3% lower odds of 2-year mortality (P < 0.001 for all). CONCLUSIONS: These findings support the predictive validity of 7-day follow-up after detoxification as a care coordination measure. Well-coordinated care may be associated with higher outpatient and inpatient utilization, and such engagement in care may be protective against mortality in people who receive detoxification from alcohol or opiates.

Development and applications of the Veterans Health Administration's Stratification Tool for Opioid Risk Mitigation (STORM) to improve opioid safety and prevent overdose and suicide.

Concerns about opioid-related adverse events, including overdose, prompted the Veterans Health Administration (VHA) to launch an Opioid Safety Initiative and Overdose Education and Naloxone Distribution program. To mitigate risks associated with opioid prescribing, a holistic approach that takes into consideration both risk factors (e.g., dose, substance use disorders) and risk mitigation interventions (e.g., urine drug screening, psychosocial treatment) is needed. This article describes the Stratification Tool for Opioid Risk Mitigation (STORM), a tool developed in VHA that reflects this holistic approach and facilitates patient identification and monitoring. STORM prioritizes patients for review and intervention according to their modeled risk for overdose/suicide-related events and displays risk factors and risk mitigation interventions obtained from VHA electronic medical record (EMR)-data extracts. Patients' estimated risk is based on a predictive risk model developed using fiscal year 2010 (FY2010: 10/1/2009-9/30/2010) EMR-data extracts and mortality data among 1,135,601 VHA patients prescribed opioid analgesics to predict risk for an overdose/suicide-related event in FY2011 (2.1% experienced an event). Cross-validation was used to validate the model, with receiver operating characteristic curves for the training and test data sets performing well (>.80 area under the curve). The predictive risk model distinguished patients based on risk for overdose/suicide-related adverse events, allowing for identification of high-risk patients and enrichment of target populations of patients with greater safety concerns for proactive monitoring and application of risk mitigation interventions. Results suggest that clinical informatics can leverage EMR-extracted data to identify patients at-risk for overdose/suicide-related events and provide clinicians with actionable information to mitigate risk. (PsycINFO Database Record

An RCT of Effects of Telephone Care Management on Treatment Adherence and Clinical Outcomes Among Veterans With PTSD.

OBJECTIVE: This study assessed whether adding telephone care management to usual outpatient mental health care improved treatment attendance, medication compliance, and clinical outcomes of veterans with posttraumatic stress disorder (PTSD). METHODS: In a multisite randomized controlled trial, 358 veterans were assigned to either usual outpatient mental health treatment (N=165) or usual care plus twice-a-month telephone care management (TCM) and support in the first three months of treatment (N=193). Treatment utilization and medication refills were determined from U.S. Department of Veterans Affairs administrative data. PTSD, depression, quality of life, aggressive behavior, and substance use were assessed with self-report questionnaires at intake, four months, and 12 months. RESULTS: Telephone care managers reached 95% of TCM participants (N=182), completing an average 5.1 of 6.0 planned telephone calls. During the three-month intervention period, TCM participants completed 43% more mental health visits (M±SD=5.9±6.8) than did those in usual care (4.1±4.2) (incident rate ratio=1.36, χ2=6.56, df=1, p<.01). Treatment visits in the nine-month follow-up period and medication refills did not differ by condition. Only 9% of participants were scheduled to receive evidence-based psychotherapy. Slopes of improvement in PTSD, depression, alcohol misuse, drug problems, aggressive behavior, and quality of life did not differ by condition or treatment attendance. CONCLUSIONS: TCM improved PTSD patients' treatment attendance but not their outcomes. TCM can enhance treatment engagement, but outcomes depend on the effectiveness of the treatments that patients receive.

Predictive validity of a quality measure for intensive substance use disorder treatment.

BACKGROUND: Measures of substance use disorder (SUD) treatment quality are essential tools for performance improvement. The Veterans Health Administration (VHA) developed a measure of access to and engagement in intensive outpatient programs (IOPs) for SUD. However, predictive validity, or associations between this measure and treatment outcomes, has not been examined. METHODS: Data on veterans with SUD came from 3 samples: the Outcomes Monitoring Project (N = 5436), a national evaluation of VHA mental health services (N = 339,887), and patients receiving detoxification services (N = 23,572). Propensity score-weighted mixed-effects regressions modeled associations between receiving at least 1 week of IOP treatment and patient outcomes, controlling for facility-level performance and a random effect for facility. RESULTS: Propensity score weighting reduced or eliminated observable baseline differences between patient groups. Patients who accessed IOPs versus those who did not reported significantly reduced alcohol- and drug-related symptom severity, with significantly fewer past-month days drinking alcohol (b = 1.83, P < .001) and fewer past-month days intoxicated (b = 1.55, P < .001). Patients who received IOP after detoxification services had higher 6-month utilization of SUD outpatient visits (b = 2.09, P < .001), more subsequent detoxification episodes (b = 0.25, P < .001), and lower odds of 2-year mortality (odds ratio [OR] = 0.68, 95% confidence interval [CI]: 0.61-0.75; P < .001). CONCLUSIONS: Receiving at least 1 week of SUD treatment in an IOP was associated with higher follow-up utilization, improved health outcomes, and reduced mortality. These associations lend support to the predictive validity of VHA's IOP quality measure. Future research should focus on measure feasibility and validity outside of VHA, and whether predictive validity is maintained once this quality measure is tied to performance incentives.

Multifaceted academic detailing program to increase pharmacotherapy for alcohol use disorder: interrupted time series evaluation of effectiveness.

BACKGROUND: Active consideration of effective medications to treat alcohol use disorder (AUD) is a consensus standard of care, yet knowledge and use of these medications are very low across diverse settings. This study evaluated the overall effectiveness a multifaceted academic detailing program to address this persistent quality problem in the US Veterans Health Administration (VHA), as well as the context and process factors that explained variation in effectiveness across sites. METHODS: An interrupted time series design, analyzed with mixed-effects segmented logistic regression, was used to evaluate changes in level and rate of change in the monthly percent of patients with a clinically documented AUD who received naltrexone, acamprosate, disulfiram, or topiramate. Using data from a 20 month post-implementation period, intervention sites (n = 37) were compared to their own 16 month pre-implementation performance and separately to the rest of VHA. RESULTS: From immediately pre-intervention to the end of the observation period, the percent of patients in the intervention sites with AUD who received medication increased over 3.4 % in absolute terms and 68 % in relative terms (i.e., 4.9-8.3 %). This change was significant compared to the pre-implementation period in the intervention sites and secular trends in control sites. Sites with lower pre-implementation adoption, more person hours of detailing, but fewer people detailed, had larger immediate increases in medication receipt after implementation. The average number of detailing encounters per person was associated with steeper increases in slope over time. CONCLUSIONS: This study found empirical support for a multifaceted quality improvement strategy aimed at increasing access to and utilization of pharmacotherapy for AUD. Future studies should focus on determining how to enhance the programs effects, especially in non-responsive locations.