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Few performance measures assess presurgical value (quality and utilization).Using carpal tunnel syndrome (CTS) as a case study: (1) develop a model to evaluate presurgical quality and utilization and (2) identify opportunities for value improvement.A retrospective cohort study utilizing Veterans Affairs (VA) national administrative data.Patients who were evaluated in a VA primary care clinic on at least 1 occasion for CTS and received carpal tunnel release over a 7-year period.We modeled facility-level performance on 2 outcomes: surgical delay (marker of quality) and number of presurgical encounters (utilization) for CTS, and examined association between patient, facility, and care process variables and performance.Among 41,912 Veterans undergoing carpal tunnel release at 127 VA medical centers, the median facility-level predicted probability of surgical delay was 48%, with 16 (13%) facilities having significantly less delay than the median and 13 (10%) facilities having greater delay. The median facility-level predicted number of presurgical encounters was 8.8 visits, with 22 (17%) facilities having significantly fewer encounters and 22 (17%) facilities having more. Care processes had a stronger association with both outcomes than structural variables included in the models. Processes associated with the greatest deviations in predicted delay and utilization included receipt of repeat electrodiagnostic testing, use of 2 or more nonoperative treatments, and community referral outside of VA.Using CTS as a test case, this study demonstrates the potential to assess presurgical value and identify modifiable care processes associated with presurgical delay and utilization performance.

Preventing chronic diseases is an essential aspect of medical care. To prevent chronic diseases, physicians focus on monitoring their risk factors and prescribing the necessary medication. The optimal monitoring policy depends on the patient's risk factors and demographics. Monitoring too frequently may be unnecessary and costly; on the other hand, monitoring the patient infrequently means the patient may forgo needed treatment and experience adverse events related to the disease. We propose a finite horizon and finite-state Markov decision process to define monitoring policies. To build our Markov decision process, we estimate stochastic models based on longitudinal observational data from electronic health records for a large cohort of patients seen in the national U.S. Veterans Affairs health system. We use our model to study policies for whether or when to assess the need for cholesterol-lowering medications. We further use our model to investigate the role of gender and race on optimal monitoring policies.

Background: Ethnic differences in cognitive decline have been reported. Whether they can be explained by differences in systolic blood pressure (SBP) is uncertain. Objective: Determine whether cumulative mean SBP levels explain differences in cognitive decline between Hispanic and White individuals. Methods: Pooled cohort study of individual participant data from six cohorts (1971–2017). The present study reports results on SBP and cognition among Hispanic and White individuals. Outcomes were changes in global cognition (GC) (primary), executive function (EF) (secondary), and memory standardized as t-scores (mean [SD], 50 [10]); a 1-point difference represents a 0.1 SD difference in cognition. Median follow-up was 7.7 (Q1–Q3, 5.2–20.1) years. Results: We included 24,570 participants free of stroke and dementia: 2,475 Hispanic individuals (median age, cumulative mean SBP at first cognitive assessment, 67 years, 132.5 mmHg; 40.8% men) and 22,095 White individuals (60 years,134 mmHg; 47.3% men). Hispanic individuals had slower declines in GC, EF, and memory than White individuals when all six cohorts were examined. Two cohorts recruited Hispanic individuals by design. In a sensitivity analysis, Hispanic individuals in these cohorts had faster decline in GC, similar decline in EF, and slower decline in memory than White individuals. Higher time-varying cumulative mean SBP was associated with faster declines in GC, EF, and memory in all analyses. After adjusting for time-varying cumulative mean SBP, differences in cognitive slopes between Hispanic and White individuals did not change. Conclusion: We found no evidence that cumulative mean SBP differences explained differences in cognitive decline between Hispanic and White individuals.

Purpose The U.S. Department of Veterans Affairs (VA) health care system monitors time from referral to specialist visit. We compared wait times for carpal tunnel release (CTR) at a VA hospital and its academic affiliate. Methods We selected patients who underwent CTR at a VA hospital and its academic affiliate (AA) (2010–2015). We analyzed time from primary care physician (PCP) referral to CTR, which was subdivided into PCP referral to surgical consultation and surgical consultation to CTR. Electrodiagnostic testing (EDS) was categorized in relation to surgical consultation (prereferral vs postreferral). Multivariable Cox proportional hazard models were used to examine associations between clinical variables and surgical location. Results Between 2010 and 2015, VA patients had a shorter median time from PCP referral to CTR (VA: 168 days; AA: 410 days), shorter time from PCP referral to surgical consultation (VA: 43 days; AA: 191 days), but longer time from surgical consultation to CTR (VA: 98 days; AA: 55 days). Using multivariable models, the VA was associated with a 35% shorter time to CTR (AA hazard ratio [HR], 0.65; 95% confidence interval [CI], 0.52–0.82) and 75% shorter time to surgical consultation (AA HR, 0.25; 95% CI, 0.20–0.03). Receiving both prereferral and postreferral EDS was associated with almost a 2-fold prolonged time to CTR (AA HR, 0.49; 95% CI, 0.36–0.67). Conclusions The VA was associated with shorter overall time to CTR compared with its AA. However, the VA policy of prioritizing time from referral to surgical consultation may not optimally incentivize time to surgery. Repeat EDS was associated with longer wait times in both systems. Clinical Relevance Given differences in where delays occur in each health care system, initiatives to improve efficiency will require targeting the appropriate sources of preoperative delay. Judicious use of EDS may be one avenue to decrease wait times in both systems.

OBJECTIVE The objective of this study was to assess the long-term role of intensive glycemic control (INT) compared with standard glycemic control in accumulated eye procedures in patients with advanced diabetes. RESEARCH DESIGN AND METHODS We compared the effect of treatment assignment on the accumulated number of eye procedures during the intervention period of the Veteran Affairs Diabetes Trial (VADT) (2000–2008) (median follow-up 5.6 years), the interim VADT follow-up study (2000–2013), and the full 17 years of VADT follow-up (2000–2017). We further analyzed data using various cardiovascular markers in two models. Model I included total cholesterol, HDL cholesterol, LDL cholesterol, triglycerides, systolic and diastolic blood pressure, and BMI. Model II included these covariates plus age and diabetic retinopathy (DR) severity score at baseline of the original trial. RESULTS The final analysis of the data showed a mild but nonsignificant increase in number of procedures and in retinal or retinal plus cataract surgery during the three periods of the study. CONCLUSIONS We found no significant benefit of INT during the original trial period in eye-related procedures, such as various procedures for DR, or during the 17 years of follow-up in cataract surgery. However, after adjusting data for some known vascular markers, the increase in the number of eye procedures with INT becomes more prevalent. This finding indicates that INT might not have a protective role in events requiring surgery in individuals with advanced diabetes.

ImportanceIncident stroke is associated with accelerated cognitive decline. Whether poststroke vascular risk factor levels are associated with faster cognitive decline is uncertain.ObjectiveTo evaluate associations of poststroke systolic blood pressure (SBP), glucose, and low-density lipoprotein (LDL) cholesterol levels with cognitive decline.Design, Setting, and ParticipantsIndividual participant data meta-analysis of 4 US cohort studies (conducted 1971-2019). Linear mixed-effects models estimated changes in cognition after incident stroke. Median (IQR) follow-up was 4.7 (2.6-7.9) years. Analysis began August 2021 and was completed March 2023.ExposuresTime-dependent cumulative mean poststroke SBP, glucose, and LDL cholesterol levels.Main Outcomes and MeasuresThe primary outcome was change in global cognition. Secondary outcomes were change in executive function and memory. Outcomes were standardized as t scores (mean [SD], 50 [10]); a 1-point difference represents a 0.1-SD difference in cognition.ResultsA total of 1120 eligible dementia-free individuals with incident stroke were identified; 982 (87.7%) had available covariate data and 138 (12.3%) were excluded for missing covariate data. Of the 982, 480 (48.9%) were female individuals, and 289 (29.4%) were Black individuals. The median age at incident stroke was 74.6 (IQR, 69.1-79.8; range, 44.1-96.4) years. Cumulative mean poststroke SBP and LDL cholesterol levels were not associated with any cognitive outcome. However, after accounting for cumulative mean poststroke SBP and LDL cholesterol levels, higher cumulative mean poststroke glucose level was associated with faster decline in global cognition (−0.04 points/y faster per each 10–mg/dL increase [95% CI, −0.08 to −0.001 points/y]; P = .046) but not executive function or memory. After restricting to 798 participants with apolipoprotein E4 (APOE4) data and controlling for APOE4 and APOE4 × time, higher cumulative mean poststroke glucose level was associated with a faster decline in global cognition in models without and with adjustment for cumulative mean poststroke SBP and LDL cholesterol levels (−0.05 points/y faster per 10–mg/dL increase [95% CI, −0.09 to −0.01 points/y]; P = .01; −0.07 points/y faster per 10–mg/dL increase [95% CI, −0.11 to −0.03 points/y]; P = .002) but not executive function or memory declines.Conclusions and RelevanceIn this cohort study, higher poststroke glucose levels were associated with faster global cognitive decline. We found no evidence that poststroke LDL cholesterol and SBP levels were associated with cognitive decline.

Background Self-reported tobacco pack-year history plays a large role in decisions about low-dose CT screening for lung cancer, yet is challenging to measure accurately. Research Question To what extent does random measurement error in pack-year information impact screening decisions and screening effectiveness? Study Design and Methods Retrospective cohort study of 10,449 patients with pack-year history documented at least twice between October 2013 and July 2017 across 8 academic Veterans Affairs sites. Outcome measures included (1) observed reliability of pack-year information based on all repeat measures for the study population and (2) each person’s statistically “true” pack-year information based on best linear unbiased predictor from a multilevel linear random effects model. To examine how unreliability leads to misclassification of screening eligibility and inaccuracy in estimating lung cancer risk, we simulated pack-year observations for each person, first comparing simulated pack-year and lung cancer risk values with true values, then comparing outcomes when basing screening decisions on unreliable pack-year information vs true information. Results Reliability of assessing pack-year information in routine practice varied across sites. Thus, we examined the clinical impact of two different levels of reliability, based on the range of intraclass correlation coefficients observed. Using a ≥ 30-pack-year threshold led to a high rate of eligibility misclassifications (48.1% misclassified with higher reliability pack-year information and 60.7% misclassified with lower reliability information). However, using a lung cancer risk threshold leads to fewer misclassifications (47.3%-49.7% misclassified when using lower reliability pack-year information) and maintains screening effectiveness better when using unreliable pack-year information. Interpretation Random error in real-world pack-year assessments leads to a substantial rate of misclassifying who should be offered CT screening if a ≥ 30-pack-year criterion is used. However, using a lung cancer risk threshold mitigates the impact of unreliable pack-year information. Decision-makers concerned about the impact of unreliable pack-year information should consider using risk-based approaches to CT screening.

Policymakers and researchers are strongly encouraging clinicians to support patient autonomy through shared decision-making (SDM). In setting policies for clinical care, decision-makers need to understand that current models of SDM have tended to focus on major decisions (e.g., surgeries and chemotherapy) and focused less on everyday primary care decisions. Most decisions in primary care are substantive everyday decisions: intermediate-stakes decisions that occur dozens of times every day, yet are non-trivial for patients, such as whether routine mammography should start at age 40, 45, or 50. Expectations that busy clinicians use current models of SDM (here referred to as “detailed” SDM) for these decisions can feel overwhelming to clinicians. Evidence indicates that detailed SDM is simply not realistic for most of these decisions and without a feasible alternative, clinicians usually default to a decision-making approach with little to no personalization. We propose, for discussion and refinement, a compromise approach to personalizing these decisions (everyday SDM). Everyday SDM is based on a feasible process for supporting patient autonomy that also allows clinicians to continue being respectful health advocates for their patients. We propose that alternatives to detailed SDM are needed to make progress toward more patient-centered care. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s11606-020-06043-2) contains supplementary material, which is available to authorized users.

Strategies to prevent or delay Alzheimer’s disease and Alzheimer’s disease-related dementias (AD/ADRD) are urgently needed. Blood pressure (BP) management is a promising strategy for AD/ADRD prevention and the key element in the primary and secondary prevention of atherosclerotic cardiovascular disease (ASCVD), yet the effects of different population level BP control strategies across the life course on AD/ADRD are not known.Large-scale randomized controlled trials are the least biased approach to identifying the effect of BP control on AD/ADRD, yet trials may be infeasible due to the need for prolonged follow-up and very large sample sizes. Thus, simulation analyses leveraging the best available observational data may be the best and most practical approach to answering these questions.In this manuscript, we describe the design principles, implementation details, and population-level validation of a novel population health microsimulation framework, the MIchigan ChROnic Disease SIMulation (MICROSIM), for The Effect of Lower Blood Pressure over the Life Course on Late-life Cognition in Blacks, Hispanics, and Whites (BP COG) study of the effect of BP levels over the life course on cognitive decline and dementia.MICROSIM was designed by applying computer programming best practices to create a novel simulation model. The initial purpose of this extensible, open-source framework is to explore a series of questions related to the impact of different blood pressure management strategies on late-life cognition and all-cause dementia, as well as the effects on race differences in all-cause dementia incidence. Ultimately, though, the framework is designed to be extensible such that a variety of different clinical conditions could be added to the framework.

BACKGROUND: Ethnic differences in cognitive decline have been reported. Whether they can be explained by differences in systolic blood pressure (SBP) is uncertain. OBJECTIVE: Determine whether cumulative mean SBP levels explain differences in cognitive decline between Hispanic and White individuals. METHODS: Pooled cohort study of individual participant data from six cohorts (1971–2017). The present study reports results on SBP and cognition among Hispanic and White individuals. Outcomes were changes in global cognition (GC) (primary), executive function (EF) (secondary), and memory standardized as t-scores (mean [SD], 50 [10]); a 1-point difference represents a 0.1 SD difference in cognition. Median follow-up was 7.7 (Q1–Q3, 5.2–20.1) years. RESULTS: We included 24,570 participants free of stroke and dementia: 2,475 Hispanic individuals (median age, cumulative mean SBP at first cognitive assessment, 67 years, 132.5 mmHg; 40.8% men) and 22,095 White individuals (60 years,134 mmHg; 47.3% men). Hispanic individuals had slower declines in GC, EF, and memory than White individuals when all six cohorts were examined. Two cohorts recruited Hispanic individuals by design. In a sensitivity analysis, Hispanic individuals in these cohorts had faster decline in GC, similar decline in EF, and slower decline in memory than White individuals. Higher time-varying cumulative mean SBP was associated with faster declines in GC, EF, and memory in all analyses. After adjusting for time-varying cumulative mean SBP, differences in cognitive slopes between Hispanic and White individuals did not change. CONCLUSION: We found no evidence that cumulative mean SBP differences explained differences in cognitive decline between Hispanic and White individuals.

Introduction. Detailed or “full” shared decision making (SDM) about cancer screening is difficult in the primary care setting. Time spent discussing cancer screening is time not spent on other important issues. Given time constraints, brief SDM that is incomplete but addresses key elements may be feasible and acceptable. However, little is known about how patients feel about abbreviated SDM. This study assessed patient perspectives on a compromise solution (“everyday SDM”): 1) primary care provided makes a tailored recommendation, 2) briefly presents qualitative information on key tradeoffs, and 3) conveys full support for decisional autonomy and desires for more information. Methods. We recruited a stratified random sample of Veterans from an academic Veterans Affairs medical center who were eligible for lung cancer screening, oversampling women and minority patients, to attend a 6-hour deliberative focus group. Experts informed participants about cancer screening, factors that influence screening benefits, and the role of patient preferences. Then, facilitator-led small groups elicited patient questions and informed opinions about the everyday SDM proposal, its acceptability, and their recommendations for improvement. Results. Thirty-six Veterans with a heavy smoking history participated (50% male, 83% white). There was a strong consensus that everyday SDM was acceptable if patients were the final deciders and could get more information on request. Participants broadly recommended that clinicians only mention downsides directly related to screening and avoid discussion of potential downstream harms (such as biopsies). Discussion. Although further testing in more diverse populations and different conditions is needed, these patients found the everyday SDM approach to be acceptable for routine lung cancer screening discussions, despite its use of an explicit recommendation and presentation of only qualitative information.

[This corrects the article DOI: 10.1371/journal.pmed.1002410.].

BACKGROUND: We previously reported that a median of 5.6 years of intensive as compared with standard glucose lowering in 1791 military veterans with type 2 diabetes resulted in a risk of major cardiovascular events that was significantly lower (by 17%) after a total of 10 years of combined intervention and observational follow-up. We now report the full 15-year follow-up. METHODS: We observationally followed enrolled participants (complete cohort) after the conclusion of the original clinical trial by using central databases to identify cardiovascular events, hospitalizations, and deaths. Participants were asked whether they would be willing to provide additional data by means of surveys and chart reviews (survey cohort). The prespecified primary outcome was a composite of major cardiovascular events, including nonfatal myocardial infarction, nonfatal stroke, new or worsening congestive heart failure, amputation for ischemic gangrene, and death from cardiovascular causes. Death from any cause was a prespecified secondary outcome. RESULTS: There were 1655 participants in the complete cohort and 1391 in the survey cohort. During the trial (which originally enrolled 1791 participants), the separation of the glycated hemoglobin curves between the intensive-therapy group (892 participants) and the standard-therapy group (899 participants) averaged 1.5 percentage points, and this difference declined to 0.2 to 0.3 percentage points by 3 years after the trial ended. Over a period of 15 years of follow-up (active treatment plus post-trial observation), the risks of major cardiovascular events or death were not lower in the intensive-therapy group than in the standard-therapy group (hazard ratio for primary outcome, 0.91; 95% confidence interval [CI], 0.78 to 1.06; P = 0.23; hazard ratio for death, 1.02; 95% CI, 0.88 to 1.18). The risk of major cardiovascular disease outcomes was reduced, however, during an extended interval of separation of the glycated hemoglobin curves (hazard ratio, 0.83; 95% CI, 0.70 to 0.99), but this benefit did not continue after equalization of the glycated hemoglobin levels (hazard ratio, 1.26; 95% CI, 0.90 to 1.75). CONCLUSIONS: Participants with type 2 diabetes who had been randomly assigned to intensive glucose control for 5.6 years had a lower risk of cardiovascular events than those who received standard therapy only during the prolonged period in which the glycated hemoglobin curves were separated. There was no evidence of a legacy effect or a mortality benefit with intensive glucose control. (Funded by the VA Cooperative Studies Program; VADT ClinicalTrials.gov number, .)

Key Points Question Does the risk of cognitive decline among US adults vary by sex? Findings In this cohort study using pooled data from 26 088 participants, women, compared with men, had higher baseline performance in global cognition, executive function, and memory. Women, compared with men, had significantly faster declines in global cognition and executive function, but not memory. Meaning These findings suggest that women may have greater cognitive reserve but faster cognitive decline than men., This pooled cohort study used data from 5 population-based cohort studies to investigate whether cognitive decline among US adults varies by sex., Importance Sex differences in dementia risk are unclear, but some studies have found greater risk for women. Objective To determine associations between sex and cognitive decline in order to better understand sex differences in dementia risk. Design, Setting, and Participants This cohort study used pooled analysis of individual participant data from 5 cohort studies for years 1971 to 2017: Atherosclerosis Risk in Communities Study, Coronary Artery Risk Development in Young Adults Study, Cardiovascular Health Study, Framingham Offspring Study, and Northern Manhattan Study. Linear mixed-effects models were used to estimate changes in each continuous cognitive outcome over time by sex. Data analysis was completed from March 2019 to October 2020. Exposure Sex. Main Outcomes and Measures The primary outcome was change in global cognition. Secondary outcomes were change in memory and executive function. Outcomes were standardized as t scores (mean [SD], 50 [10]); a 1-point difference represents a 0.1-SD difference in cognition. Results Among 34 349 participants, 26 088 who self-reported Black or White race, were free of stroke and dementia, and had covariate data at or before the first cognitive assessment were included for analysis. Median (interquartile range) follow-up was 7.9 (5.3-20.5) years. There were 11 775 (44.7%) men (median [interquartile range] age, 58 [51-66] years at first cognitive assessment; 2229 [18.9%] Black) and 14 313 women (median [interquartile range] age, 58 [51-67] years at first cognitive assessment; 3636 [25.4%] Black). Women had significantly higher baseline performance than men in global cognition (2.20 points higher; 95% CI, 2.04 to 2.35 points; P

ImportanceBlack individuals are more likely than white individuals to develop dementia. Whether higher blood pressure (BP) levels in black individuals explain differences between black and white individuals in dementia risk is uncertain.ObjectiveTo determine whether cumulative BP levels explain racial differences in cognitive decline.Design, Setting, and ParticipantsIndividual participant data from 5 cohorts (January 1971 to December 2017) were pooled from the Atherosclerosis Risk in Communities Study, Coronary Artery Risk Development in Young Adults Study, Cardiovascular Health Study, Framingham Offspring Study, and Northern Manhattan Study. Outcomes were standardized as t scores (mean [SD], 50 [10]); a 1-point difference represented a 0.1-SD difference in cognition. The median (interquartile range) follow-up was 12.4 (5.9-21.0) years. Analysis began September 2018.Main Outcomes and MeasuresThe primary outcome was change in global cognition, and secondary outcomes were change in memory and executive function.ExposuresRace (black vs white).ResultsAmong 34 349 participants, 19 378 individuals who were free of stroke and dementia and had longitudinal BP, cognitive, and covariate data were included in the analysis. The mean (SD) age at first cognitive assessment was 59.8 (10.4) years and ranged from 5 to 95 years. Of 19 378 individuals, 10 724 (55.3%) were female and 15 526 (80.1%) were white. Compared with white individuals, black individuals had significantly faster declines in global cognition (−0.03 points per year faster [95% CI, −0.05 to −0.01]; P = .004) and memory (−0.08 points per year faster [95% CI, −0.11 to −0.06]; P P P P P P = .56) and memory (−0.06 points per year [95% CI, −0.08 to −0.03]; P P Conclusions and RelevanceThese results suggest that black individuals’ higher cumulative BP levels may contribute to racial differences in later-life cognitive decline.

Introduction Little is known about how clinicians make low-dose computed tomography lung cancer screening decisions in practice. Investigators assessed the factors associated with real-world decision making, hypothesizing that lung cancer risk and comorbidity would not be associated with agreeing to or receiving screening. Though these factors are key determinants of the benefit of lung cancer screening, they are often difficult to incorporate into decisions without the aid of decision tools. Methods This was a retrospective cohort study of patients meeting current national eligibility criteria and deemed appropriate candidates for lung cancer screening on the basis of clinical reminders completed over a 2-year period (2013–2015) at 8 Department of Veterans Affairs medical facilities. Multilevel mixed-effects logistic regression models (conducted in 2019–2020) assessed predictors (age, sex, lung cancer risk, Charlson Comorbidity Index, travel distance to facility, and central versus outlying decision-making location) of primary outcomes of agreeing to and receiving lung cancer screening. Results Of 5,551 patients (mean age=67 years, 97% male, mean lung cancer risk=0.7%, mean Charlson Comorbidity Index=1.14, median travel distance=24.2 miles), 3,720 (67%) agreed to lung cancer screening and 2,398 (43%) received screening. Lung cancer risk and comorbidity score were not strong predictors of agreeing to or receiving screening. Empirical Bayes adjusted rates of agreeing to and receiving screening ranged from 22% to 84% across facilities and from 19% to 85% across clinicians. A total of 33.7% of the variance in agreeing to and 34.2% of the variance in receiving screening was associated with the facility or the clinician offering screening. Conclusions Substantial variation was found in Veterans agreeing to and receiving lung cancer screening during the Veterans Affairs Lung Cancer Screening Demonstration Project. This variation was not explained by differences in key determinants of patient benefit, whereas the facility and clinician advising the patient had a large impact on lung cancer screening decisions.

BACKGROUND: Most randomized controlled trials (RCTs) and meta-analyses of RCTs examine effect modification (also called a subgroup effect or interaction), in which the effect of an intervention varies by another variable (e.g., age or disease severity). Assessing the credibility of an apparent effect modification presents challenges; therefore, we developed the Instrument for assessing the Credibility of Effect Modification Analyses (ICEMAN). METHODS: To develop ICEMAN, we established a detailed concept; identified candidate credibility considerations in a systematic survey of the literature; together with experts, performed a consensus study to identify key considerations and develop them into instrument items; and refined the instrument based on feedback from trial investigators, systematic review authors and journal editors, who applied drafts of ICEMAN to published claims of effect modification. RESULTS: The final instrument consists of a set of preliminary considerations, core questions (5 for RCTs, 8 for meta-analyses) with 4 response options, 1 optional item for additional considerations and a rating of credibility on a visual analogue scale ranging from very low to high. An accompanying manual provides rationales, detailed instructions and examples from the literature. Seventeen potential users tested ICEMAN; their suggestions improved the user-friendliness of the instrument. INTERPRETATION: The Instrument for assessing the Credibility of Effect Modification Analyses offers explicit guidance for investigators, systematic reviewers, journal editors and others considering making a claim of effect modification or interpreting a claim made by others.

Self-reported tobacco pack-year history plays a large role in decisions about low-dose CT screening for lung cancer, yet is challenging to measure accurately.To what extent does random measurement error in pack-year information impact screening decisions and screening effectiveness?Retrospective cohort study of 10,449 patients with pack-year history documented at least twice between October 2013 and July 2017 across 8 academic Veterans Affairs sites. Outcome measures included (1) observed reliability of pack-year information based on all repeat measures for the study population and (2) each person's statistically "true" pack-year information based on best linear unbiased predictor from a multilevel linear random effects model. To examine how unreliability leads to misclassification of screening eligibility and inaccuracy in estimating lung cancer risk, we simulated pack-year observations for each person, first comparing simulated pack-year and lung cancer risk values with true values, then comparing outcomes when basing screening decisions on unreliable pack-year information vs true information.Reliability of assessing pack-year information in routine practice varied across sites. Thus, we examined the clinical impact of two different levels of reliability, based on the range of intraclass correlation coefficients observed. Using a ≥ 30-pack-year threshold led to a high rate of eligibility misclassifications (48.1% misclassified with higher reliability pack-year information and 60.7% misclassified with lower reliability information). However, using a lung cancer risk threshold leads to fewer misclassifications (47.3%-49.7% misclassified when using lower reliability pack-year information) and maintains screening effectiveness better when using unreliable pack-year information.Random error in real-world pack-year assessments leads to a substantial rate of misclassifying who should be offered CT screening if a ≥ 30-pack-year criterion is used. However, using a lung cancer risk threshold mitigates the impact of unreliable pack-year information. Decision-makers concerned about the impact of unreliable pack-year information should consider using risk-based approaches to CT screening.

Heterogeneity of treatment effect (HTE) refers to the nonrandom variation in the magnitude or direction of a treatment effect across levels of a covariate, as measured on a selected scale, against a clinical outcome. In randomized controlled trials (RCTs), HTE is typically examined through a subgroup analysis that contrasts effects in groups of patients defined "1 variable at a time" (for example, male vs. female or old vs. young). The authors of this statement present guidance on an alternative approach to HTE analysis, "predictive HTE analysis." The goal of predictive HTE analysis is to provide patient-centered estimates of outcome risks with versus without the intervention, taking into account all relevant patient attributes simultaneously. The PATH (Predictive Approaches to Treatment effect Heterogeneity) Statement was developed using a multidisciplinary technical expert panel, targeted literature reviews, simulations to characterize potential problems with predictive approaches, and a deliberative process engaging the expert panel. The authors distinguish 2 categories of predictive HTE approaches: a "risk-modeling" approach, wherein a multivariable model predicts the risk for an outcome and is applied to disaggregate patients within RCTs to define risk-based variation in benefit, and an "effect-modeling" approach, wherein a model is developed on RCT data by incorporating a term for treatment assignment and interactions between treatment and baseline covariates. Both approaches can be used to predict differential absolute treatment effects, the most relevant scale for clinical decision making. The authors developed 4 sets of guidance: criteria to determine when risk-modeling approaches are likely to identify clinically important HTE, methodological aspects of risk-modeling methods, considerations for translation to clinical practice, and considerations and caveats in the use of effect-modeling approaches. The PATH Statement, together with its explanation and elaboration document, may guide future analyses and reporting of RCTs.

The PATH (Predictive Approaches to Treatment effect Heterogeneity) Statement was developed to promote the conduct of, and provide guidance for, predictive analyses of heterogeneous treatment effects (HTE) in clinical trials. The goal of predictive HTE analysis is to provide patient-centered estimates of outcome risks with versus without the intervention, taking into account all relevant patient attributes simultaneously, to support more personalized clinical decision making than can be made based only on an overall average treatment effect. We distinguished two categories of predictive HTE approaches (a “risk modeling” and an “effect modeling” approach) and developed four sets of guidance statements: 1) criteria to determine when risk modeling approaches are likely to identify clinically meaningful HTE; 2) methodological aspects of risk modeling methods; 3) considerations for translation to clinical practice; and 4) considerations and caveats in the use of effect modeling approaches. We also discuss limitations of these methods and enumerate research priorities for advancing methods designed to generate more personalized evidence. This explanation and elaboration document describes the intent and rationale of each recommendation; and discusses related analytic considerations, caveats, and reservations.

OBJECTIVE To determine the risk factors for severe hypoglycemia and the association between severe hypoglycemia and serious cardiovascular adverse events and cardiovascular and all-cause mortality in the Veterans Affairs Diabetes Trial (VADT). RESEARCH DESIGN AND METHODS This post hoc analysis of data from the VADT included 1,791 military veterans (age 60.5 ± 9.0 years) with suboptimally controlled type 2 diabetes (HbA1c 9.4 ± 2.0%) of 11.5 ± 7.5 years disease duration with or without known cardiovascular disease and additional cardiovascular risk factors. Participants were randomized to intensive (HbA1c RESULTS The rate of severe hypoglycemia in the intensive treatment group was 10.3 per 100 patient-years compared with 3.7 per 100 patient-years in the standard treatment group (P < 0.001). In multivariable analysis, insulin use at baseline (P = 0.02), proteinuria (P = 0.009), and autonomic neuropathy (P = 0.01) were independent risk factors for severe hypoglycemia, and higher BMI was protective (P = 0.017). Severe hypoglycemia within the past 3 months was associated with an increased risk of serious cardiovascular events (P = 0.032), cardiovascular mortality (P = 0.012), and total mortality (P = 0.024). However, there was a relatively greater increased risk for total mortality in the standard group compared with the intensive group (P = 0.019). The association between severe hypoglycemia and cardiovascular events increased significantly as overall cardiovascular risk increased (P = 0.012). CONCLUSIONS Severe hypoglycemic episodes within the previous 3 months were associated with increased risk for major cardiovascular events and cardiovascular and all-cause mortality regardless of glycemic treatment group assignment. Standard therapy further increased the risk for all-cause mortality after severe hypoglycemia.

Hennepin Health, a Medicaid accountable care organization, began serving early expansion enrollees (very low-income childless adults) in 2012. It uses an integrated care model to address social and behavioral needs. We compared health care utilization in Hennepin Health with other Medicaid managed care in the same area from 2012 to 2014, controlling for demographics, chronic conditions, and enrollment patterns. Homelessness and substance use were higher in Hennepin Health. Overall adjusted results showed Hennepin Health had 52% more emergency department visits and 11% more primary care visits than comparators. Over time, modeling a 6-month exposure to Hennepin Health, emergency department and primary care visits decreased and dental visits increased; hospitalizations decreased nonsignificantly but increased among comparators. Subgroup analysis of high utilizers showed lower hospitalizations in Hennepin Health. Integrated, accountable care under Medicaid expansion showed some desirable trends and subgroup benefits, but overall did not reduce acute health care utilization versus other managed care.

OBJECTIVE We sought to validate Risk Equations for Complications of Type 2 Diabetes (RECODe) among diverse populations. RESEARCH DESIGN AND METHODS We compared risk predictions from RECODe equations and from two alternative risk models (UK Prospective Diabetes Study Outcomes Model 2 [UKPDS OM2] and American College of Cardiology/American Heart Association Pooled Cohort Equations) to observed outcomes in two studies: the Multi-Ethnic Study of Atherosclerosis (MESA, n = 1,555 adults with type 2 diabetes, median follow-up 9.1 years) and the Jackson Heart Study (JHS, n = 1,746 adults with type 2 diabetes, median follow-up 8.0 years). Outcomes included nephropathy by multiple measures (microalbuminuria, macroalbuminuria, renal failure, end-stage renal disease, and reduction in glomerular filtration rate), moderate to severe diabetic retinopathy by Airlie House classification, fatal or nonfatal myocardial infarction, fatal or nonfatal stroke, congestive heart failure, and all-cause mortality. RESULTS RECODe equations for microvascular and cardiovascular outcomes had C-statistics for discrimination ranging from 0.71 to 0.85 in MESA and 0.64 to 0.91 in JHS for alternative outcomes. Calibration slopes in MESA ranged from 0.62 for a composite nephropathy outcome, 0.83–1.04 for individual nephropathy outcomes, 1.07 for retinopathy, 1.00–1.05 for cardiovascular outcomes, and 1.03 for all-cause mortality. Slopes in JHS ranged from 0.47 for retinopathy, 0.97–1.16 for nephropathy, 0.72–1.05 for cardiovascular outcomes, and 1.01 for all-cause mortality. The alternative models had C-statistics 0.50–0.72 and calibration slopes 0.07–0.60. CONCLUSIONS RECODe equations improved risk estimation for diverse patients with type 2 diabetes, as compared with two commonly used alternatives.

Background Inflammatory bowel disease (IBD) is a chronic disease characterized by unpredictable episodes of flares and periods of remission. Tools that accurately predict disease course would substantially aid therapeutic decision-making. This study aims to construct a model that accurately predicts the combined end point of outpatient corticosteroid use and hospitalizations as a surrogate for IBD flare. Methods Predictors evaluated included age, sex, race, use of corticosteroid-sparing immunosuppressive medications (immunomodulators and/or anti-TNF), longitudinal laboratory data, and number of previous IBD-related hospitalizations and outpatient corticosteroid prescriptions. We constructed models using logistic regression and machine learning methods (random forest [RF]) to predict the combined end point of hospitalization and/or corticosteroid use for IBD within 6 months. Results We identified 20,368 Veterans Health Administration patients with the first (index) IBD diagnosis between 2002 and 2009. Area under the receiver operating characteristic curve (AuROC) for the baseline logistic regression model was 0.68 (95% confidence interval [CI], 0.67–0.68). AuROC for the RF longitudinal model was 0.85 (95% CI, 0.84–0.85). AuROC for the RF longitudinal model using previous hospitalization or steroid use was 0.87 (95% CI, 0.87–0.88). The 5 leading independent risk factors for future hospitalization or steroid use were age, mean serum albumin, immunosuppressive medication use, and mean and highest platelet counts. Previous hospitalization and corticosteroid use were highly predictive when included in specified models. Conclusions A novel machine learning model substantially improved our ability to predict IBD-related hospitalization and outpatient steroid use. This model could be used at point of care to distinguish patients at high and low risk for disease flare, allowing individualized therapeutic management.

We conducted an analysis of data collected during the Veterans Affairs Diabetes Trial (VADT) and the follow-up study (VADT-F) to determine whether intensive (INT) compared with standard (STD) glycaemic control during the VADT resulted in better long-term kidney outcomes. VADT randomly assigned 1791 veterans from 20 Veterans Affairs (VA) medical centres who had type 2 diabetes mellitus and a mean HbA1c of 9.4 ± 2% (79.2 mmol/mol) at baseline to receive either INT or STD glucose control for a median of 5.6 years (randomisation December 2000 to May 2003; intervention ending in May 2008). After the trial, participants received routine care through their own physicians within the VA. This is an interim analysis of the VADT-F (June 2008 to December 2013). We collected data using VA and National databases and report renal outcomes based on serum creatinine, eGFR and urine albumin to creatinine ratio (ACR) in 1033 people who provided informed consent to participate in the VADT-F. By the end of the VADT-F, significantly more people who received INT treatment during the VADT maintained an eGFR >60 ml min−1 1.73 m−2 (OR 1.34 [95% CI 1.05, 1.71], p = 0.02). This benefit was most evident in those who were classified as at moderate risk (INT vs STD, RR 1.3, p = 0.03) or high risk (RR 2.3, p = 0.04) of chronic kidney disease on the Kidney Disease Improving Global Outcomes (KDIGO-CKD) at the beginning of VADT. At the end of VADT-F, significantly more people from the INT group improved to a low KDIGO risk category (RR 6.1, p = 0.002). During the VADT-F there were no significant differences between INT and STD for average HbA1c, blood pressure or lipid levels. After just over 11 years of follow-up, there was a 34% greater odds of maintaining an eGFR of >60 ml min−1 1.73 m−2 and of improving the KDIGO category in individuals with type 2 diabetes who had received INT for a median of 5.6 years. VADT clinical trials.gov number: NCT 00032487.

Background Accurately estimating cardiovascular risk is fundamental to good decision-making in cardiovascular disease (CVD) prevention, but risk scores developed in one population often perform poorly in dissimilar populations. We sought to examine whether a large integrated health system can use their electronic health data to better predict individual patients' risk of developing CVD. Methods We created a cohort using all patients ages 45-80 who used Department of Veterans Affairs (VA) ambulatory care services in 2006 with no history of CVD, heart failure, or loop diuretics. Our outcome variable was new-onset CVD in 2007-2011. We then developed a series of recalibrated scores, including a fully refit "VA Risk Score-CVD (VARS-CVD)." We tested the different scores using standard measures of prediction quality. Results For the 1,512,092 patients in the study, the Atherosclerotic cardiovascular disease risk score had similar discrimination as the VARS-CVD (c-statistic of 0.66 in men and 0.73 in women), but the Atherosclerotic cardiovascular disease model had poor calibration, predicting 63% more events than observed. Calibration was excellent in the fully recalibrated VARS-CVD tool, but simpler techniques tested proved less reliable. Conclusions We found that local electronic health record data can be used to estimate CVD better than an established risk score based on research populations. Recalibration improved estimates dramatically, and the type of recalibration was important. Such tools can also easily be integrated into health system's electronic health record and can be more readily updated.

Creating accurate risk prediction models from Big Data resources such as Electronic Health Records (EHRs) is a critical step toward achieving precision medicine. A major challenge in developing these tools is accounting for imperfect aspects of EHR data, particularly the potential for misclassified outcomes. Misclassification, the swapping of case and control outcome labels, is well known to bias effect size estimates for regression prediction models. In this paper, we study the effect of misclassification on accuracy assessment for risk prediction models and find that it leads to bias in the area under the curve (AUC) metric from standard ROC analysis. The extent of the bias is determined by the false positive and false negative misclassification rates as well as disease prevalence. Notably, we show that simply correcting for misclassification while building the prediction model is not sufficient to remove the bias in AUC. We therefore introduce an intuitive misclassification-adjusted ROC procedure that accounts for uncertainty in observed outcomes and produces bias-corrected estimates of the true AUC. The method requires that misclassification rates are either known or can be estimated, quantities typically required for the modeling step. The computational simplicity of our method is a key advantage, making it ideal for efficiently comparing multiple prediction models on very large datasets. Finally, we apply the correction method to a hospitalization prediction model from a cohort of over 1 million patients from the Veterans Health Administrations EHR. Implementations of the ROC correction are provided for Stata and R. Published 2017. This article is a U.S. Government work and is in the public domain in the USA

This quality improvement study assesses the potential consequences of systematic underreporting of alcohol use on the observed results in a recent, well-publicized study.

Expansion of episode of care measurement models to include presurgical care is an added opportunity to improve quality, value, and efficiency in healthcare delivery.

BackgroundGenetic studies suggest that the relative risk reduction (RRR) of statins may increase over time, potentially resulting in much greater long-term benefit if statins are started before cardiovascular (CV) risk is high.MethodsWe used a nationally representative sample of American adults to estimate effects of initiating a statin when 10-year CV risk reaches 5%, 10% or 15%. We examined scenarios in which a statin’s initial RRR (30%) gradually doubles over 10 to 30 years of treatment.ResultsInitiating a statin when 10-year CV risk is 5% resulted in a mean of 20.1 years on a statin before age 75 (8 years more than starting when CV risk reaches 10%). If a statin’s RRR doubles over 20 years, starting when CV risk is 5% would save about 5.1 to 6.1 additional QALYs per 1000 additional treatment years than starting when CV risk is 10%. Most of this additional benefit was accrued by those who reach a 5% risk at a younger age. Due to the prolonged treatment period, however, early treatment could also result in net harm if the treatment slowly increased a major complication of aging, such as muscular or neurological aging.ConclusionsIn a thought experiment exploring the impact of delayed effects, we found that if the relative effectiveness of statin therapy gradually doubles over a 10 to 30 year period, starting a statin when 10-year CV risk is 5% could have much more long-term benefit than starting a statin when CV risk is 10%. Most of the additional benefit occurred in those at elevated age-adjusted CV risk. Unfortunately, given the long duration of treatment, substantial delayed statin harms, if present, could outweigh these potential benefits and result in substantial net harm.

Objective Glucocorticoids are common in RA management despite an unfavorable, exposure-dependent risk profile impacted by patient and provider-level factors. Existing work describing glucocorticoid use in RA is not generalizable and does not adequately examine provider factors. We aim to describe how providers prescribe glucocorticoids to commercially insured, newly diagnosed RA patients in the United States. Methods This was a retrospective cohort study which used the national Optum© administrative database. We identified 9221 adults ages 18–65 with RA diagnosed 2010–2014. We assessed glucocorticoid dispensing 3 months pre-diagnosis through 12months post-diagnosis (“study period”), cumulatively stratified by calendar quarter and prescriber specialty (rheumatologist, primary care, other). We examined prescribing variation among individual rheumatologists by dividing quarterly distribution of per-patient dose and days’ supply into quartiles. Results 6717 (72.8%) patients filled ≥1 glucocorticoid prescription during the study period. 2890 (31.3%) patients received ≥3 months’ supply, with median (IQR) daily dose 10 (6.6) mg/day and days’ supply 189 (143) days. 52.6% of patients received glucocorticoids 1–3 months post-diagnosis; 29.2% received glucocorticoids 10–12 months post-diagnosis. Among glucocorticoid users post-diagnosis, quarterly median daily dose and days’ supply were consistently ≥10 mg/day and ≥30 days, respectively. Rheumatologists prescribed most glucocorticoids, with median per-quarter daily dose and days’ supply 10 mg/day and 43–60 days. Individual rheumatologists’ prescribing varied widely across all quarters. Conclusion Among commercially insured incident RA patients, receipt of ≥10 mg/day prednisone equivalent for months is common, typically prescribed by rheumatologists, and persists a year post-diagnosis in 29.2% of patients. Glucocorticoid prescribing varies widely across rheumatologists. Further work is warranted to identify provider factors explaining variation in glucocorticoid prescribing, and assess how these affect health outcomes.

Background: High blood pressure (BP), particularly in mid-life, increases the risk for late-life cognitive impairment and dementia (CID). The extent to which Blacks’ and Hispanics’ worse BP control starting in young adulthood contributes to their greater risk of CID compared to Whites is uncertain. Objective: To describe a pooled, multi-ethnic cohort created to determine the associations between BP levels over the life course and CID risk in Blacks, Hispanics, and Whites. Methods: We pooled individual-level data from 35,473 adults (22.6% Blacks, 8.1% Hispanics, 67.3% Whites, 2% Other) with baseline age ≥18 and cognitive assessments across 6 longitudinal cohorts in the United States from 1971 to 2016: Atherosclerosis Risk in Communities Study (ARIC), Coronary Artery Risk Development in Young Adults Study (CARDIA), Cardiovascular Health Study (CHS), Framingham Offspring Study (FOS), Multi-Ethnic Study of Atherosclerosis (MESA), and Northern Manhattan Study (NOMAS). We harmonized cognitive tests across studies into 3 factors representing global cognitive performance, executive function, and memory using statistically appropriate confirmatory factor analysis. Mean (SD) follow up was 17.1 (10.9) years. Results: The mean (SD) baseline age was 54.3 (16.4) years and 19554 (55.1%) participants were female (Table). There were 3193 (9.0%) first incident strokes (2651 ischemic, 348 hemorrhagic, and 117 other or undetermined type) and 15147 (42.7%) deaths. The Table presents mean BPs at the first and last visits by cohort and the pooled sample. From the initial to the last assessment, mean global cognition decreased over time (p Conclusion: This will be the first comprehensive, large investigation of the association of BP levels from young adulthood to late-life and CID risk in Blacks, Hispanics, and Whites.

IMPACT: Glucocorticoid steroids are commonly used despite known dose-dependent cardiovascular toxicity, yet little is known about a) how patients with other cardiovascular risk factors use glucocorticoids, and b) how risks of glucocorticoid treatment might vary depending on a patient’s baseline cardiovascular risk. OBJECTIVES/GOALS: Up to one-third of RA patients use long-term glucocorticoids (GCs) despite a known, dose-dependent association with increased risk of major adverse cardiovascular events (MACE). We aim to evaluate patterns of GC use among RA patients with other MACE risk factors (i.e. diabetes, smoking), and examine how GC use may potentiate these risk factors. METHODS/STUDY POPULATION: We used claims data from Veterans Health Administration to identify 6,090 RA patients with ≥1 rheumatology clinic visit during 2013-2017. We used logistic regression to evaluate associations between incident MACE between 2013-2018, recent long-term GC use, and 5 MACE risk factors: hypertension, diabetes, hyperlipidemia, smoking, and prior MACE. We included two-way interaction terms between GC use and each risk factor. We used a claims-based algorithm to define MACE as any of acute MI, ischemic stroke, TIA, sudden death, or coronary revascularization, between index date and 12/31/2018. We defined index date as first rheumatology visit after meeting RA diagnostic criteria, and recent long-term GC use as ≥90 days’ supply dispensed over 2 years prior to index date. RESULTS/ANTICIPATED RESULTS: Among 2,884 eligible patients,1,553 (54%) had MACE risk factors, and 97 (3%) had prior MACE (Table 1). Overall, 16% of patients recently used long-term GC, compared to 17% of patients with MACE risk factors, and 22% of patients with prior MACE. Incident MACE occurred in 308 (11%) patients, 24% of whom had recent long-term GC use. Recent long-term GC use was independently associated with increased incident MACE (Table 2). While no interaction term was statistically significant overall, differences in odds of incident MACE were seen across levels of recent GC use for several risk factors, particularly diabetes (OR 2.10, 95% CI [0.93-4.77]), tobacco use (OR 2.88, 95% CI [1.16-7.14]) and prior MACE (OR 2.41, 95% CI [0.73-7.95]). DISCUSSION/SIGNIFICANCE OF FINDINGS: Long-term GC use is common among RA patients with MACE risk factors. In this cohort, 25% of patients with incident MACE had recently used long-term GC. Long-term GC use may potentiate effects of comorbidities like diabetes and smoking, disproportionately increasing MACE risk in certain patients.

Background— The World Health Organization aims to reduce mortality from chronic diseases including cardiovascular disease (CVD) by 25% by 2025. High blood pressure is a leading CVD risk factor. We sought to compare 3 strategies for treating blood pressure in China and India: a treat-to-target (TTT) strategy emphasizing lowering blood pressure to a target, a benefit-based tailored treatment (BTT) strategy emphasizing lowering CVD risk, or a hybrid strategy currently recommended by the World Health Organization. Methods and Results— We developed a microsimulation model of adults aged 30 to 70 years in China and in India to compare the 2 treatment approaches across a 10-year policy-planning horizon. In the model, a BTT strategy treating adults with a 10-year CVD event risk of ≥10% used similar financial resources but averted ≈5 million more disability-adjusted life-years in both China and India than a TTT approach based on current US guidelines. The hybrid strategy in the current World Health Organization guidelines produced no substantial benefits over TTT. BTT was more cost-effective at $205 to $272/disability-adjusted life-year averted, which was $142 to $182 less per disability-adjusted life-year than TTT or hybrid strategies. The comparative effectiveness of BTT was robust to uncertainties in CVD risk estimation and to variations in the age range analyzed, the BTT treatment threshold, or rates of treatment access, adherence, or concurrent statin therapy. Conclusions— In model-based analyses, a simple BTT strategy was more effective and cost-effective than TTT or hybrid strategies in reducing mortality.

Background Evaluation of the clinical importance of outcomes in research studies is an essential element of clinical decision making. Objective To understand how clinicians and trainees weigh the importance of different types of clinical outcomes in drug trials. Methods A self-administered paper survey contained 4 scenarios asking participants to rate (1, “no proof” to 10, “good proof”) the extent to which 4 study outcomes provided “proof that the new drug might help people.” Outcomes included (1) a surrogate outcome; (2) a surrogate-enriched composite outcome; (3) stroke mortality; and (4) all-cause mortality. The primary study metrics were mean ratings for each of the 4 outcome types, and the proportion ranking outcome importance of all-cause mortality > stroke mortality > surrogate-enriched composite or surrogate alone. Results A convenience sample of 549 clinicians and trainees at 2 medical centers completed the survey (response rate: 87% medical students, 80% internal medicine residents, 69% general medicine faculty, and 41% physician experts). The surrogate-enriched composite outcome and stroke mortality were rated the most important evidence for benefit (6.6 and 6.4, respectively), with all-cause mortality and a surrogate outcome being rated significantly lower (5.2 and 4.6, respectively). In addition, 48% of clinicians rated improvement in all-cause mortality as more valuable than an improvement in a surrogate marker. Only 21% rated all-cause mortality as more valuable than a surrogate-enriched composite outcome. Conclusions These findings raise concerns that clinicians and trainees may not interpret trial evidence in a way that promotes the best care for patients.

Objective To investigate the credibility of claims that general practitioners lack time for shared decision making and preventive care. Design Monte Carlo microsimulation study. Setting Primary care, United States. Participants Sample of general practitioners (n=1000) representative of annual work hours and patient panel size (n=2000 patients) in the US, derived from the National Health and Nutrition Examination Survey. Main outcome measures The primary outcome was the time needed to deliver shared decision making for highly recommended preventive interventions in relation to time available for preventive care—the prevention-time-space-deficit (ie, time-space needed by doctor exceeding the time-space available). Results On average, general practitioners have 29 minutes each workday to discuss preventive care services (just over two minutes for each clinic visit) with patients, but they need about 6.1 hours to complete shared decision making for preventive care. 100% of the study sample experienced a prevention-time-space-deficit (mean deficit 5.6 h/day) even given conservative (ie, absurdly wishful) time estimates for shared decision making. However, this time deficit could be easily overcome by reducing personal time and shifting gains to work tasks. For example, general practitioners could reduce the frequency of bathroom breaks to every other day and skip time with older children who don’t like them much anyway. Conclusions This study confirms a widely held suspicion that general practitioners waste valuable time on “personal care” activities. Primary care overlords, once informed about the extent of this vast reservoir of personal time, can start testing methods to “persuade” general practitioners to reallocate more personal time toward bulging clinical demands.

OBJECTIVE Our objective was to understand the reliability of profiling surgeons on average health care spending. SUMMARY OF BACKGROUND DATA Under its Merit-based Incentive Payment System (MIPS), Medicare will measure surgeon spending and tie performance to payments. Although the intent of this cost-profiling is to reward low-cost surgeons, it is unknown whether surgeons can be accurately distinguished from their peers. METHODS We used Michigan Medicare and commercial payer claims data to construct episodes of surgical care and to calculate average annual spending for individual surgeons. We then estimated the "reliability" (ie, the ability to distinguish surgeons from their peers) of these cost-profiles and the case-volume that surgeons would need in order to achieve high reliability [intraclass correlation coefficient (ICC) >0.8]. Finally, we calculated the reliability of 2 alternative methods of profiling surgeons (ie, using multiple years of data and grouping surgeons by hospitals). RESULTS We found that annual cost-profiles of individual surgeons had poor reliability; the ICC ranged from

Research Objective: Polygenic risk scores can improve prediction of first-time cardiovascular disease (CVD). Prediction of future events is the central factor in statin use in current guidelines Therefore, we hypothesized that if polygenic risk scores were added to current risk scores, statin use could become more efficient. We performed a cost-effectiveness analysis to see if genomic testing could be a cost-effective way to prevent CVD. Study Design: Decision-analytic model of genomic testing for statin decision-making. Data sources: Published literature Time Horizon: Lifetime Perspective: Third-party payer Intervention: Statin decision-making based on current guidelines or based on current guidelines augmented with genetic testing in all Americans ages 40-75 with estimated 10-year CVD risk of 5-20%. Population Studied: Primary prevention population in the United States. Outcome Measures: Quality-adjusted life-years (QALYs), incremental cost-effectiveness ratios (ICERs), and total 10-year spending. Principal Findings: At $200 per genetic test and $144 for a year of medium-strength statin use, full adherence to the current guidelines are estimated to cost $66,155 per QALY saved. Genetic testing would be $263,000 per additional QALY saved. These results are sensitive to improvements in genetic prediction and cost of genetic testing. Conclusions: Genomic testing to improve statin use is cost-effective for a very large number of Americans at current prices.