Christine Lundquist

Field Synopsis of the Role of Sex in Stroke Prediction Models

Background Guidelines for stroke prevention recommend development of sex‐specific stroke risk scores. Incorporating sex in Clinical Prediction Models (CPMs) may support sex‐specific clinical decision making. To better understand their potential to guide sex‐specific care, we conducted a field synopsis of the role of sex in stroke‐related CPMs. Methods and Results We identified stroke‐related CPMs in the Tufts Predictive Analytics and Comparative Effectiveness CPM Database, a systematic summary of cardiovascular CPMs published from January 1990 to May 2012. We report the proportion of models including the effect of sex on stroke incidence or prognosis, summarize the directionality of the predictive effects of sex, and explore factors influencing the inclusion of sex. Of 92 stroke‐related CPMs, 30 (33%) contained a coefficient for sex or presented sex‐stratified models. Only 12/58 (21%) CPMs predicting outcomes in patients included sex, compared to 18/30 (60%) models predicting first stroke (P<0.0001). Sex was most commonly included in models predicting stroke among a general population (69%). Female sex was consistently associated with reduced mortality after ischemic stroke (n=4) and higher risk of stroke from arrhythmias or coronary revascularization (n=5). Models predicting first stroke versus outcomes among patients with stroke (odds ratio=5.75, 95% CI 2.18–15.14, P<0.001) and those developed from larger versus smaller sample sizes (odds ratio=4.58, 95% CI 1.73–12.13, P=0.002) were significantly more likely to include sex. Conclusions Sex is included in a minority of published CPMs, but more frequently in models predicting incidence of first stroke. The importance of sex‐specific care may be especially well established for primary prevention.

Field Synopsis of Sex in Clinical Prediction Models for Cardiovascular Disease

Background—Several widely used risk scores for cardiovascular disease (CVD) incorporate sex effects, yet there has been no systematic summary of the role of sex in clinical prediction models (CPMs). To better understand the potential of these models to support sex-specific care, we conducted a field synopsis of sex effects in CPMs for CVD. Methods and Results—We identified CPMs in the Tufts Predictive Analytics and Comparative Effectiveness CPM Registry, a comprehensive database of CVD CPMs published from January 1990 to May 2012. We report the proportion of models including sex effects on CVD incidence or prognosis, summarize the directionality of the predictive effects of sex, and explore factors influencing the inclusion of sex. Of 592 CVD-related CPMs, 193 (33%) included sex as a predictor or presented sex-stratified models. Sex effects were included in 78% (53/68) of models predicting incidence of CVD in a general population, versus only 35% (59/171), 21% (12/58), and 17% (12/72) of models predicting outcomes in patients with coronary artery disease, stroke, and heart failure, respectively. Among sex-including CPMs, women with heart failure were at lower mortality risk in 8 of 8 models; women undergoing revascularization for coronary artery disease were at higher mortality risk in 10 of 12 models. Factors associated with the inclusion of sex effects included the number of outcome events and using cohorts at-risk for CVD (rather than with established CVD). Conclusions—Although CPMs hold promise for supporting sex-specific decision making in CVD clinical care, sex effects are included in only one third of published CPMs.

Tufts PACE Clinical Predictive Model Registry: update 1990 through 2015

BackgroundClinical predictive models (CPMs) estimate the probability of clinical outcomes and hold the potential to improve decision-making and individualize care. The Tufts Predictive Analytics and Comparative Effectiveness (PACE) CPM Registry is a comprehensive database of cardiovascular disease (CVD) CPMs. The Registry was last updated in 2012, and there continues to be substantial growth in the number of available CPMs.MethodsWe updated a systematic review of CPMs for CVD to include articles published from January 1990 to March 2015. CVD includes coronary artery disease (CAD), congestive heart failure (CHF), arrhythmias, stroke, venous thromboembolism (VTE), and peripheral vascular disease (PVD). The updated Registry characterizes CPMs based on population under study, model performance, covariates, and predicted outcomes.ResultsThe Registry includes 747 articles presenting 1083 models, including both prognostic (n = 1060) and diagnostic (n = 23) CPMs representing 183 distinct index condition/outcome pairs. There was a threefold increase in the number of CPMs published between 2005 and 2014, compared to the prior 10-year interval from 1995 to 2004. The majority of CPMs were derived from either North American (n = 455, 42%) or European (n = 344, 32%) populations. The database contains 265 CPMs predicting outcomes for patients with coronary artery disease, 196 CPMs for population samples at risk for incident CVD, and 158 models for patients with stroke. Approximately two thirds (n = 701, 65%) of CPMs report a c-statistic, with a median reported c-statistic of 0.77 (IQR, 0.05). Of the CPMs reporting validations, only 333 (57%) report some measure of model calibration. Reporting of discrimination but not calibration is improving over time (p for trend < 0.0001 and 0.39 respectively).ConclusionsThere is substantial redundancy of CPMs for a wide spectrum of CVD conditions. While the number of CPMs continues to increase, model performance is often inadequately reported and calibration is infrequently assessed. More work is needed to understand the potential impact of this literature.

Do Patient Characteristics Impact Decisions by Clinicians on Hemoglobin A1c Targets

In setting hemoglobin A1c (HbA1c) targets, physicians must consider individualized risks and benefits of tight glycemic control (1,2) by recognizing that the risk-benefit ratio may become unfavorable in certain patients, including the elderly and/or those with multiple comorbidities (3,4). Customization of treatment goals based on patient characteristics is poorly understood, partly due to insufficient data on physicians’ decisions in setting targets. We used the National Health and Nutrition Examination Survey (NHANES) to analyze patient-reported HbA1c targets set by physicians and to test whether targets are correlated with patient characteristics. Data from the NHANES waves 2005–2006, 2007–2008, 2011–2012, and 2013–2014 (the 2009–2010 wave lacked HbA1c data) comprised 2,641 individuals with self-reported diabetes, of which 1,782 responded to the question, “What does [your doctor] say [your] ‘A1C’ level should be?” On the basis of the distribution of responses, we analyzed the following targets: <6%, <7%, and higher cutoffs (<8%, 9%, and 10%) combined. Using ordered logistic regression, we assessed the influence of age; sex; race; diabetes duration; comorbidities; BMI; variables on physical, mental, and biological health; and health care utilization. We used NHANES sample weights to calculate population rates of target HbA …

Clinicians’ perspectives on incidentally discovered silent brain infarcts – A qualitative study

Background While silent brain infarcts (SBIs) in screened cohorts are associated with risk of symptomatic stroke and dementia, the clinical significance of incidentally discovered SBIs (id-SBIs) is unknown. Detection may offer an opportunity to initiate prevention measures, but uncertainties about id-SBIs may impede clinicians from addressing them and complicate further study of this condition. Methods and results This study used semi-structured interviews of practicing clinicians. Interviews were audio recorded, transcribed, and analyzed using a grounded theory approach. A constant comparative method was used to organize emergent themes and examine new themes. Purposeful sampling was employed to achieve participant diversity. Fifteen clinicians were interviewed. Emergent themes centered on uncertainty about id-SBIs, clinical decision making in response to uncertainty, and evidence needed to resolve uncertainty. All clinicians reported uncertainty about id-SBIs: diagnostic, prognostic, or therapeutic. Differential responses to uncertainties resulted in practice variation within and between specialties. Diagnostic and prognostic uncertainty discouraged disclosure of imaging findings to patients. Vascular neurologists viewed the prognostic significance of id-SBIs as similar to symptomatic stroke. Therapeutic uncertainty was common, but most participants endorsed using stroke secondary prevention strategies. Regarding future research, all internists indicated they would consider changing practices in response to observational studies, whereas half of the neurologists expressed reluctance to modify practices based on non-randomized data. Several expressed concerns about clinical trial feasibility and lack of equipoise. Conclusions id-SBIs are a focus of uncertainty for clinicians, leading to practice variation. Future studies must address diagnostic and prognostic uncertainty to facilitate implementation of prevention strategies.

Correction: Clinicians’ perspectives on incidentally discovered silent brain infarcts – A qualitative study

[This corrects the article DOI: 10.1371/journal.pone.0194971.].

Patient Variability Seldom Assessed in Cost-effectiveness Studies:

Background. Cost-effectiveness analysis (CEA) estimates can vary substantially across patient subgroups when patient characteristics influence preferences, outcome risks, treatment effectiveness, life expectancy, or associated costs. However, no systematic review has reported the frequency of subgroup analysis in CEA, what type of heterogeneity they address, and how often heterogeneity influences whether cost-effectiveness ratios exceed or fall below conventional thresholds. Methods. We reviewed the CEA literature cataloged in the Tufts Medical Center CEA Registry, a repository describing cost-utility analyses published through 2016. After randomly selecting 200 of 642 articles published in 2014, we ascertained whether each study reported subgroup results and collected data on the defining characteristics of these subgroups. We identified whether any of the CEA subgroup results crossed conventional cost-effectiveness benchmarks (e.g.,

Effects of Race Are Rarely Included in Clinical Prediction Models for Cardiovascular Disease

100,000 per QALY) and compared characteristics of studies with and without subgroup-specific findings. Results. Thirty-eight studies (19%) reported patient subgroup results. Articles reporting subgroup analyses were more likely to be US-based, government funded (v. drug industry- or nonprofit foundation-funded) studies, with a focus on primary or secondary (v. tertiary) prevention (P < 0.05 for comparisons). One or more patient characteristics were used to stratify CEA results 68 times within the 38 studies, with most stratifications using one characteristic (n = 47), most commonly age (n = 35). Among the 23 stratifications reported alongside average ratios in US studies, 13 produced subgroup ratios that crossed a conventional CEA ratio benchmark. Conclusions. Most CEAs do not report any subgroup results, and those that do most often stratify only by patient age. Over half of the subgroup analyses reported could lead to different value-based decision making for at least some patients.

Change in Testing, Awareness of Hemoglobin A1c Result, and Glycemic Control in US Adults, 2007-2014

Racial/ethnic status is frequently a strong predictor of clinical outcomes for an array of conditions, including cardiovascular disease (CVD). Several popular clinical prediction models (CPMs) that help guide common medical decisions, such as equations for 10-year atherosclerotic CVD risk, estimated glomerular filtration rate, and pulmonary function, include terms for race. Nevertheless, the use of racial classifications in medicine and biomedical research has been contested based on evidence that there are few biological or genetic differences between races and concern that encoding racial/ethnic differences may reinforce discrimination, racism, and health disparities. To date, there has been no systematic evaluation of the role of race and ethnicity in CPMs. Our objective was to conduct a field synopsis of the role of race/ethnicity in a registry of CVD-related CPMs.Racial/ethnic status is frequently a strong predictor of clinical outcomes for an array of conditions, including cardiovascular disease (CVD).1 Several popular clinical prediction models (CPMs) that help guide common medical decisions, such as equations for 10-year atherosclerotic CVD risk, estimated glomerular filtration rate, and pulmonary function, include terms for race. Nevertheless, the use of racial classifications in medicine and biomedical research has been contested based on evidence that there are few biological or genetic differences between races and concern that encoding racial/ethnic differences may reinforce discrimination, racism, and health disparities. To date, there has been no systematic evaluation of the role of race and ethnicity in CPMs. Our objective was to conduct a field synopsis of the role of race/ethnicity in a registry of CVD-related CPMs.