The clinical effectiveness of Fidaxomicin compared to Vancomycin in the treatment of Clostridioides difficile infection, a single center real-world experience.

BACKGROUND: The use of fidaxomicin is recommended as first line therapy for all patients with Clostridioides difficile infection (CDI). However, real-world studies have shown conflicting evidence of superiority. METHODS: We conducted a retrospective single center study of patients diagnosed with CDI between 2011-2021. A primary composite outcome of clinical failure, 30-day relapse or CDI-related death was used. A multivariable cause specific Cox proportional hazards model was used to evaluate fidaxomicin compared to vancomycin in preventing the composite outcome. A separate model was fit on a subset of patients with C. difficile ribotypes adjusting for ribotype. RESULTS: There were 598 patients included, of whom 84 received fidaxomicin. The primary outcome occurred in 8 (9.5%) in the fidaxomicin group compared to 111 (21.6%) in the vancomycin group. The adjusted multivariable model showed fidaxomicin was associated with 63% reduction in the risk of the composite outcome compared to vancomycin (HR = 0.37, 95% CI 0.17-0.80). In the 337 patients with ribotype data after adjusting for ribotype 027, the results showing superiority of fidaxomicin were maintained (HR = 0.19, 95% CI 0.05-0.77). CONCLUSION: In the treatment of CDI, we showed that real-world use of fidaxomicin is associated with lower risk of a composite endpoint of treatment failure.

Treatment effect modification due to comorbidity: Individual participant data meta-analyses of 120 randomised controlled trials.

BACKGROUND: People with comorbidities are underrepresented in clinical trials. Empirical estimates of treatment effect modification by comorbidity are lacking, leading to uncertainty in treatment recommendations. We aimed to produce estimates of treatment effect modification by comorbidity using individual participant data (IPD). METHODS AND FINDINGS: We obtained IPD for 120 industry-sponsored phase 3/4 trials across 22 index conditions (n = 128,331). Trials had to be registered between 1990 and 2017 and have recruited ≥300 people. Included trials were multicentre and international. For each index condition, we analysed the outcome most frequently reported in the included trials. We performed a two-stage IPD meta-analysis to estimate modification of treatment effect by comorbidity. First, for each trial, we modelled the interaction between comorbidity and treatment arm adjusted for age and sex. Second, for each treatment within each index condition, we meta-analysed the comorbidity-treatment interaction terms from each trial. We estimated the effect of comorbidity measured in 3 ways: (i) the number of comorbidities (in addition to the index condition); (ii) presence or absence of the 6 commonest comorbid diseases for each index condition; and (iii) using continuous markers of underlying conditions (e.g., estimated glomerular filtration rate (eGFR)). Treatment effects were modelled on the usual scale for the type of outcome (absolute scale for numerical outcomes, relative scale for binary outcomes). Mean age in the trials ranged from 37.1 (allergic rhinitis trials) to 73.0 (dementia trials) and percentage of male participants range from 4.4% (osteoporosis trials) to 100% (benign prostatic hypertrophy trials). The percentage of participants with 3 or more comorbidities ranged from 2.3% (allergic rhinitis trials) to 57% (systemic lupus erythematosus trials). We found no evidence of modification of treatment efficacy by comorbidity, for any of the 3 measures of comorbidity. This was the case for 20 conditions for which the outcome variable was continuous (e.g., change in glycosylated haemoglobin in diabetes) and for 3 conditions in which the outcomes were discrete events (e.g., number of headaches in migraine). Although all were null, estimates of treatment effect modification were more precise in some cases (e.g., sodium-glucose co-transporter-2 (SGLT2) inhibitors for type 2 diabetes-interaction term for comorbidity count 0.004, 95% CI -0.01 to 0.02) while for others credible intervals were wide (e.g., corticosteroids for asthma-interaction term -0.22, 95% CI -1.07 to 0.54). The main limitation is that these trials were not designed or powered to assess variation in treatment effect by comorbidity, and relatively few trial participants had >3 comorbidities. CONCLUSIONS: Assessments of treatment effect modification rarely consider comorbidity. Our findings demonstrate that for trials included in this analysis, there was no empirical evidence of treatment effect modification by comorbidity. The standard assumption used in evidence syntheses is that efficacy is constant across subgroups, although this is often criticised. Our findings suggest that for modest levels of comorbidities, this assumption is reasonable. Thus, trial efficacy findings can be combined with data on natural history and competing risks to assess the likely overall benefit of treatments in the context of comorbidity.

Comorbidity and health-related quality of life in people with a chronic medical condition in randomised clinical trials: An individual participant data meta-analysis.

BACKGROUND: Health-related quality of life metrics evaluate treatments in ways that matter to patients, so are often included in randomised clinical trials (hereafter trials). Multimorbidity, where individuals have 2 or more conditions, is negatively associated with quality of life. However, whether multimorbidity predicts change over time or modifies treatment effects for quality of life is unknown. Therefore, clinicians and guideline developers are uncertain about the applicability of trial findings to people with multimorbidity. We examined whether comorbidity count (higher counts indicating greater multimorbidity) (i) is associated with quality of life at baseline; (ii) predicts change in quality of life over time; and/or (iii) modifies treatment effects on quality of life. METHODS AND FINDINGS: Included trials were registered on the United States trials registry for selected index medical conditions and drug classes, phase 2/3, 3 or 4, had ≥300 participants, a nonrestrictive upper age limit, and were available on 1 of 2 trial repositories on 21 November 2016 and 18 May 2018, respectively. Of 124 meeting these criteria, 56 trials (33,421 participants, 16 index conditions, and 23 drug classes) collected a generic quality of life outcome measure (35 EuroQol-5 dimension (EQ-5D), 31 36-item short form survey (SF-36) with 10 collecting both). Blinding and completeness of follow up were examined for each trial. Using trials where individual participant data (IPD) was available from 2 repositories, a comorbidity count was calculated from medical history and/or prescriptions data. Linear regressions were fitted for the association between comorbidity count and (i) quality of life at baseline; (ii) change in quality of life during trial follow up; and (iii) treatment effects on quality of life. These results were then combined in Bayesian linear models. Posterior samples were summarised via the mean, 2.5th and 97.5th percentiles as credible intervals (95% CI) and via the proportion with values less than 0 as the probability (PBayes) of a negative association. All results are in standardised units (obtained by dividing the EQ-5D/SF-36 estimates by published population standard deviations). Per additional comorbidity, adjusting for age and sex, across all index conditions and treatment comparisons, comorbidity count was associated with lower quality of life at baseline and with a decline in quality of life over time (EQ-5D -0.02 [95% CI -0.03 to -0.01], PBayes > 0.999). Associations were similar, but with wider 95% CIs crossing the null for SF-36-PCS and SF-36-MCS (-0.05 [-0.10 to 0.01], PBayes = 0.956 and -0.05 [-0.10 to 0.01], PBayes = 0.966, respectively). Importantly, there was no evidence of any interaction between comorbidity count and treatment efficacy for either EQ-5D or SF-36 (EQ-5D -0.0035 [95% CI -0.0153 to -0.0065], PBayes = 0.746; SF-36-MCS (-0.0111 [95% CI -0.0647 to 0.0416], PBayes = 0.70 and SF-36-PCS -0.0092 [95% CI -0.0758 to 0.0476], PBayes = 0.631. CONCLUSIONS: Treatment effects on quality of life did not differ by multimorbidity (measured via a comorbidity count) at baseline-for the medical conditions studied, types and severity of comorbidities and level of quality of life at baseline, suggesting that evidence from clinical trials is likely to be applicable to settings with (at least modestly) higher levels of comorbidity. TRIAL REGISTRATION: A prespecified protocol was registered on PROSPERO (CRD42018048202).

Development of Parkinson Disease and Its Relationship with Incidentally Discovered White Matter Disease and Covert Brain Infarction in a Real-World Cohort.

OBJECTIVE: This study aimed to examine the relationship between covert cerebrovascular disease, comprised of covert brain infarction and white matter disease, discovered incidentally in routine care, and subsequent Parkinson disease. METHODS: Patients were ≥50 years and received neuroimaging for non-stroke indications in the Kaiser Permanente Southern California system from 2009 to 2019. Natural language processing identified incidentally discovered covert brain infarction and white matter disease and classified white matter disease severity. The Parkinson disease outcome was defined as 2 ICD diagnosis codes. RESULTS: 230,062 patients were included (median follow-up 3.72 years). A total of 1,941 Parkinson disease cases were identified (median time-to-event 2.35 years). Natural language processing identified covert cerebrovascular disease in 70,592 (30.7%) patients, 10,622 (4.6%) with covert brain infarction and 65,814 (28.6%) with white matter disease. After adjustment for known risk factors, white matter disease was associated with Parkinson disease (hazard ratio 1.67 [95%CI, 1.44, 1.93] for patients <70 years and 1.33 [1.18, 1.50] for those ≥70 years). Greater severity of white matter disease was associated with increased incidence of Parkinson disease(/1,000 person-years), from 1.52 (1.43, 1.61) in patients without white matter disease to 4.90 (3.86, 6.13) in those with severe disease. Findings were robust when more specific definitions of Parkinson disease were used. Covert brain infarction was not associated with Parkinson disease (adjusted hazard ratio = 1.05 [0.88, 1.24]). INTERPRETATION: Incidentally discovered white matter disease was associated with subsequent Parkinson disease, an association strengthened with younger age and increased white matter disease severity. Incidentally discovered covert brain infarction did not appear to be associated with subsequent Parkinson disease. ANN NEUROL 2022;92:620-630.

Assessing heterogeneity of treatment effect in multiple sclerosis trials.

Multiple sclerosis (MS) is heterogeneous with respect to outcomes, and evaluating possible heterogeneity of treatment effect (HTE) is of high interest. HTE is non-random variation in the magnitude of a treatment effect on a clinical outcome across levels of a covariate (i.e. a patient attribute or set of attributes). Multiple statistical techniques can evaluate HTE. The simplest but most bias-prone is conventional one variable-at-a-time subgroup analysis. Recently, multivariable predictive approaches have been promoted to provide more patient-centered results, by accounting for multiple relevant attributes simultaneously. We review approaches used to estimate HTE in clinical trials of MS.

Stratifying Future Stroke Risk with Incidentally Discovered White Matter Disease Severity and Covert Brain Infarct Site.

BACKGROUND: Covert cerebrovascular disease (CCD) includes white matter disease (WMD) and covert brain infarction (CBI). Incidentally discovered CCD is associated with increased risk of subsequent symptomatic stroke. However, it is unknown whether the severity of WMD or the location of CBI predicts risk. OBJECTIVES: The aim of this study was to examine the association of incidentally discovered WMD severity and CBI location with risk of subsequent symptomatic stroke. METHOD: This retrospective cohort study includes patients aged ≥50 years old in the Kaiser Permanente Southern California health system who received neuroimaging for a nonstroke indication between 2009 and 2019. Incidental CBI and WMD were identified via natural language processing of the neuroimage report, and WMD severity was classified into grades. RESULTS: A total of 261,960 patients received neuroimaging; 78,555 patients (30.0%) were identified to have incidental WMD and 12,857 patients (4.9%) to have incidental CBI. Increasing WMD severity is associated with an increased incidence rate of future stroke. However, the stroke incidence rate in CT-identified WMD is higher at each level of severity compared to rates in MRI-identified WMD. Patients with mild WMD via CT have a stroke incidence rate of 24.9 per 1,000 person-years, similar to that of patients with severe WMD via MRI. Among incidentally discovered CBI patients with a determined CBI location, 97.9% are subcortical rather than cortical infarcts. CBI confers a similar risk of future stroke, whether cortical or subcortical or whether MRI- or CT-detected. CONCLUSIONS: Increasing severity of incidental WMD is associated with an increased risk of future symptomatic stroke, dependent on the imaging modality. Subcortical and cortical CBI conferred similar risks.

Stratifying future stroke risk with incidentally-discovered white matter disease severity and covert brain infarct site.

BACKGROUND: Covert cerebrovascular disease (CCD) includes white matter disease (WMD) and covert brain infarction (CBI). Incidentally-discovered CCD is associated with increased risk of subsequent symptomatic stroke. However, it is unknown whether the severity of WMD or the location of CBI predicts risk. OBJECTIVES: To examine the association of incidentally-discovered WMD severity and CBI location with risk of subsequent symptomatic stroke. METHOD: This retrospective cohort study includes patients 50 years old in the Kaiser Permanente Southern California health system who received neuroimaging for a non-stroke indication between 2009-2019. Incidental CBI and WMD were identified via natural language processing of the neuroimage report, and WMD severity was classified into grades. RESULTS: 261,960 patients received neuroimaging; 78,555 (30.0%) were identified to have incidental WMD, and 12,857 (4.9%) to have incidental CBI. Increasing WMD severity is associated with increased incidence rate of future stroke. However, the stroke incidence rate in CT-identified WMD is higher at each level of severity compared to rates in MRI-identified WMD. Patients with mild WMD via CT have a stroke incidence rate of 24.9 per 1,000 person-years, similar to that of patients with severe WMD via MRI. Among incidentally-discovered CBI patients with a determined CBI location, 97.9% are subcortical rather than cortical infarcts. CBI confers a similar risk of future stroke, whether cortical or subcortical, or whether MRI- or CT-detected. CONCLUSIONS: Increasing severity of incidental WMD is associated with an increased risk of future symptomatic stroke, dependent on the imaging modality. Subcortical and cortical CBI conferred similar risks.

Estimating individualized treatment effects from randomized controlled trials: a simulation study to compare risk-based approaches.

BACKGROUND: Baseline outcome risk can be an important determinant of absolute treatment benefit and has been used in guidelines for "personalizing" medical decisions. We compared easily applicable risk-based methods for optimal prediction of individualized treatment effects. METHODS: We simulated RCT data using diverse assumptions for the average treatment effect, a baseline prognostic index of risk, the shape of its interaction with treatment (none, linear, quadratic or non-monotonic), and the magnitude of treatment-related harms (none or constant independent of the prognostic index). We predicted absolute benefit using: models with a constant relative treatment effect; stratification in quarters of the prognostic index; models including a linear interaction of treatment with the prognostic index; models including an interaction of treatment with a restricted cubic spline transformation of the prognostic index; an adaptive approach using Akaike's Information Criterion. We evaluated predictive performance using root mean squared error and measures of discrimination and calibration for benefit. RESULTS: The linear-interaction model displayed optimal or close-to-optimal performance across many simulation scenarios with moderate sample size (N = 4,250; ~ 785 events). The restricted cubic splines model was optimal for strong non-linear deviations from a constant treatment effect, particularly when sample size was larger (N = 17,000). The adaptive approach also required larger sample sizes. These findings were illustrated in the GUSTO-I trial. CONCLUSIONS: An interaction between baseline risk and treatment assignment should be considered to improve treatment effect predictions.

Overall average treatment effects from clinical trials, one-variable-at-a-time subgroup analyses and predictive approaches to heterogeneous treatment effects: Toward a more patient-centered evidence-based medicine.

Despite the predominance of the evidence-based medicine paradigm, a fundamental incongruity remains: Evidence is derived from groups of people, yet medical decisions are made by and for individuals. Randomization ensures the comparability of treatment groups within a clinical trial, which allows for unbiased estimation of average treatment effects. If we treated groups of patients instead of individuals, or if patients with the same disease were identical to one another in all factors that determined the harms and the benefits of therapy, then these group-level averages would make a perfectly sound foundation for medical decision-making. But patients differ from one another in many ways that determine the likelihood of an outcome, both with and without a treatment. Nevertheless, popular approaches to evidence-based medicine have encouraged a reliance on the average treatment effects estimated from clinical trials and meta-analysis as guides to decision-making for individuals. Here, we discuss the limitations of this approach as well as limitations of conventional, one-variable-at-a-time subgroup analysis; finally, we discuss the rationale for "predictive" approaches to heterogeneous treatment effects. Predictive approaches to heterogeneous treatment effects combine methods for causal inference (e.g. randomization) with methods for prediction that permit inferences about which patients are likely to benefit and which are not, taking into account multiple relevant variables simultaneously to yield "personalized" estimates of benefit-harm trade-offs. We focus on risk modeling approaches, which rely on the mathematical dependence of the absolute treatment effect with the baseline risk, which varies substantially "across patients" in most trials. While there are a number of examples of risk modeling approaches that have been practice-changing, risk modeling does not provide ideal estimates of individual treatment effects, since risk modeling does not account for how individual variables might modify the effects of therapy. In "effect modeling," prediction models are developed directly on clinical trial data, including terms for treatment and treatment effect interactions. These more flexible approaches may better uncover individualized treatment effects, but are also prone to overfitting when dimensionality is high, power is low, and there is limited prior knowledge about effect modifiers.

Prognostic models for COVID-19 needed updating to warrant transportability over time and space.

BACKGROUND: Supporting decisions for patients who present to the emergency department (ED) with COVID-19 requires accurate prognostication. We aimed to evaluate prognostic models for predicting outcomes in hospitalized patients with COVID-19, in different locations and across time. METHODS: We included patients who presented to the ED with suspected COVID-19 and were admitted to 12 hospitals in the New York City (NYC) area and 4 large Dutch hospitals. We used second-wave patients who presented between September and December 2020 (2137 and 3252 in NYC and the Netherlands, respectively) to evaluate models that were developed on first-wave patients who presented between March and August 2020 (12,163 and 5831). We evaluated two prognostic models for in-hospital death: The Northwell COVID-19 Survival (NOCOS) model was developed on NYC data and the COVID Outcome Prediction in the Emergency Department (COPE) model was developed on Dutch data. These models were validated on subsequent second-wave data at the same site (temporal validation) and at the other site (geographic validation). We assessed model performance by the Area Under the receiver operating characteristic Curve (AUC), by the E-statistic, and by net benefit. RESULTS: Twenty-eight-day mortality was considerably higher in the NYC first-wave data (21.0%), compared to the second-wave (10.1%) and the Dutch data (first wave 10.8%; second wave 10.0%). COPE discriminated well at temporal validation (AUC 0.82), with excellent calibration (E-statistic 0.8%). At geographic validation, discrimination was satisfactory (AUC 0.78), but with moderate over-prediction of mortality risk, particularly in higher-risk patients (E-statistic 2.9%). While discrimination was adequate when NOCOS was tested on second-wave NYC data (AUC 0.77), NOCOS systematically overestimated the mortality risk (E-statistic 5.1%). Discrimination in the Dutch data was good (AUC 0.81), but with over-prediction of risk, particularly in lower-risk patients (E-statistic 4.0%). Recalibration of COPE and NOCOS led to limited net benefit improvement in Dutch data, but to substantial net benefit improvement in NYC data. CONCLUSIONS: NOCOS performed moderately worse than COPE, probably reflecting unique aspects of the early pandemic in NYC. Frequent updating of prognostic models is likely to be required for transportability over time and space during a dynamic pandemic.

Redevelopment and validation of the SYNTAX score II to individualise decision making between percutaneous and surgical revascularisation in patients with complex coronary artery disease: secondary analysis of the multicentre randomised controlled SYNTAXES trial with external cohort validation.

BACKGROUND: Randomised controlled trials are considered the gold standard for testing the efficacy of novel therapeutic interventions, and typically report the average treatment effect as a summary result. As the result of treatment can vary between patients, basing treatment decisions for individual patients on the overall average treatment effect could be suboptimal. We aimed to develop an individualised decision making tool to select an optimal revascularisation strategy in patients with complex coronary artery disease. METHODS: The SYNTAX Extended Survival (SYNTAXES) study is an investigator-driven extension follow-up of a multicentre, randomised controlled trial done in 85 hospitals across 18 North American and European countries between March, 2005, and April, 2007. Patients with de-novo three-vessel and left main coronary artery disease were randomly assigned (1:1) to either the percutaneous coronary intervention (PCI) group or coronary artery bypass grafting (CABG) group. The SYNTAXES study ascertained 10-year all-cause deaths. We used Cox regression to develop a clinical prognostic index for predicting death over a 10-year period, which was combined, in a second stage, with assigned treatment (PCI or CABG) and two prespecified effect-modifiers, which were selected on the basis of previous evidence: disease type (three-vessel disease or left main coronary artery disease) and anatomical SYNTAX score. We used similar techniques to develop a model to predict the 5-year risk of major adverse cardiovascular events (defined as a composite of all-cause death, non-fatal stroke, or non-fatal myocardial infarction) in patients receiving PCI or CABG. We then assessed the ability of these models to predict the risk of death or a major adverse cardiovascular event, and their differences (ie, the estimated benefit of CABG versus PCI by calculating the absolute risk difference between the two strategies) by cross-validation with the SYNTAX trial (n=1800 participants) and external validation in the pooled population (n=3380 participants) of the FREEDOM, BEST, and PRECOMBAT trials. The concordance (C)-index was used to measure discriminative ability, and calibration plots were used to assess the degree of agreement between predictions and observations. FINDINGS: At cross-validation, the newly developed SYNTAX score II, termed SYNTAX score II 2020, showed a helpful discriminative ability in both treatment groups for predicting 10-year all-cause deaths (C-index=0·73 [95% CI 0·69-0·76] for PCI and 0·73 [0·69-0·76] for CABG) and 5-year major adverse cardiovascular events (C-index=0·65 [0·61-0·69] for PCI and C-index=0·71 [0·67-0·75] for CABG). At external validation, the SYNTAX score II 2020 showed helpful discrimination (C-index=0·67 [0·63-0·70] for PCI and C-index=0·62 [0·58-0·66] for CABG) and good calibration for predicting 5-year major adverse cardiovascular events. The estimated treatment benefit of CABG over PCI varied substantially among patients in the trial population, and the benefit predictions were well calibrated. INTERPRETATION: The SYNTAX score II 2020 for predicting 10-year deaths and 5-year major adverse cardiovascular events can help to identify individuals who will benefit from either CABG or PCI, thereby supporting heart teams, patients, and their families to select optimal revascularisation strategies. FUNDING: The German Heart Research Foundation and the Patient-Centered Outcomes Research Institute.

The cumulative incidence of dysphagia and dysphagia-free survival in persons diagnosed with amyotrophic lateral sclerosis.

INTRODUCTION/AIMS: Dysphagia worsens mortality and quality of life for persons diagnosed with amyotrophic lateral sclerosis (ALS), yet our understanding of its incidence and timing remains limited. In this study we sought to estimate dysphagia incidence and dysphagia-free survival over time. METHODS: Using data from the Pooled Resource Open-Access ALS Clinical Trials Database, we compared characteristics of persons with and without dysphagia upon study entry. To account for competing mortality risk, we used Kaplan-Meier curves to estimate the cumulative incidence of dysphagia and the median number of days until the development of dysphagia or death in those without dysphagia at study entry. RESULTS: Patients with dysphagia upon study entry were more likely to have bulbar onset and had faster rates of functional decline and shorter diagnostic delays. The cumulative incidence of new-onset dysphagia was 44% at 1 year and 64% at 2 years after trial enrollment for those with spinal onset, and 85% and 92% for those with bulbar onset. The median duration of dysphagia-free survival after trial enrollment was 11.5 months for those with spinal onset and 3.2 months for those with bulbar onset. DISCUSSION: Our findings underscore the high risk for dysphagia development and support the need for early dysphagia referral and evaluation to minimize the risk of serious dysphagia-related complications.

Agreement between neuroimages and reports for natural language processing-based detection of silent brain infarcts and white matter disease.

BACKGROUND: There are numerous barriers to identifying patients with silent brain infarcts (SBIs) and white matter disease (WMD) in routine clinical care. A natural language processing (NLP) algorithm may identify patients from neuroimaging reports, but it is unclear if these reports contain reliable information on these findings. METHODS: Four radiology residents reviewed 1000 neuroimaging reports (RI) of patients age > 50 years without clinical histories of stroke, TIA, or dementia for the presence, acuity, and location of SBIs, and the presence and severity of WMD. Four neuroradiologists directly reviewed a subsample of 182 images (DR). An NLP algorithm was developed to identify findings in reports. We assessed interrater reliability for DR and RI, and agreement between these two and with NLP. RESULTS: For DR, interrater reliability was moderate for the presence of SBIs (k = 0.58, 95 % CI 0.46-0.69) and WMD (k = 0.49, 95 % CI 0.35-0.63), and moderate to substantial for characteristics of SBI and WMD. Agreement between DR and RI was substantial for the presence of SBIs and WMD, and fair to substantial for characteristics of SBIs and WMD. Agreement between NLP and DR was substantial for the presence of SBIs (k = 0.64, 95 % CI 0.53-0.76) and moderate (k = 0.52, 95 % CI 0.39-0.65) for the presence of WMD. CONCLUSIONS: Neuroimaging reports in routine care capture the presence of SBIs and WMD. An NLP can identify these findings (comparable to direct imaging review) and can likely be used for cohort identification.

The Predictive Approaches to Treatment effect Heterogeneity (PATH) Statement: Explanation and Elaboration.

The PATH (Predictive Approaches to Treatment effect Heterogeneity) Statement was developed to promote the conduct of, and provide guidance for, predictive analyses of heterogeneity of treatment effects (HTE) in clinical trials. The goal of predictive HTE analysis is to provide patient-centered estimates of outcome risk with versus without the intervention, taking into account all relevant patient attributes simultaneously, to support more personalized clinical decision making than can be made on the basis of only an overall average treatment effect. The authors distinguished 2 categories of predictive HTE approaches (a "risk-modeling" and an "effect-modeling" approach) and developed 4 sets of guidance statements: criteria to determine when risk-modeling approaches are likely to identify clinically meaningful HTE, methodological aspects of risk-modeling methods, considerations for translation to clinical practice, and considerations and caveats in the use of effect-modeling approaches. They 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.

The Predictive Approaches to Treatment effect Heterogeneity (PATH) Statement.

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.

Heterogeneity of Treatment Effects in an Analysis of Pooled Individual Patient Data From Randomized Trials of Device Closure of Patent Foramen Ovale After Stroke.

Importance: Patent foramen ovale (PFO)-associated strokes comprise approximately 10% of ischemic strokes in adults aged 18 to 60 years. While device closure decreases stroke recurrence risk overall, the best treatment for any individual is often unclear. Objective: To evaluate heterogeneity of treatment effect of PFO closure on stroke recurrence based on previously developed scoring systems. Design, Setting, and Participants: Investigators for the Systematic, Collaborative, PFO Closure Evaluation (SCOPE) Consortium pooled individual patient data from all 6 randomized clinical trials that compared PFO closure plus medical therapy vs medical therapy alone in patients with PFO-associated stroke, and included a total of 3740 participants. The trials were conducted worldwide from 2000 to 2017. Exposures: PFO closure plus medical therapy vs medical therapy alone. Subgroup analyses used the Risk of Paradoxical Embolism (RoPE) Score (a 10-point scoring system in which higher scores reflect younger age and the absence of vascular risk factors) and the PFO-Associated Stroke Causal Likelihood (PASCAL) Classification System, which combines the RoPE Score with high-risk PFO features (either an atrial septal aneurysm or a large-sized shunt) to classify patients into 3 categories of causal relatedness: unlikely, possible, and probable. Main Outcomes and Measures: Ischemic stroke. Results: Over a median follow-up of 57 months (IQR, 24-64), 121 outcomes occurred in 3740 patients. The annualized incidence of stroke with medical therapy was 1.09% (95% CI, 0.88%-1.36%) and with device closure was 0.47% (95% CI, 0.35%-0.65%) (adjusted hazard ratio [HR], 0.41 [95% CI, 0.28-0.60]). The subgroup analyses showed statistically significant interaction effects. Patients with low vs high RoPE Score had HRs of 0.61 (95% CI, 0.37-1.00) and 0.21 (95% CI, 0.11-0.42), respectively (P for interaction = .02). Patients classified as unlikely, possible, and probable using the PASCAL Classification System had HRs of 1.14 (95% CI, 0.53-2.46), 0.38 (95% CI, 0.22-0.65), and 0.10 (95% CI, 0.03-0.35), respectively (P for interaction = .003). The 2-year absolute risk reduction was -0.7% (95% CI, -4.0% to 2.6%), 2.1% (95% CI, 0.6%-3.6%), and 2.1% (95% CI, 0.9%-3.4%) in the unlikely, possible, and probable PASCAL categories, respectively. Device-associated adverse events were generally higher among patients classified as unlikely; the absolute risk increases in atrial fibrillation beyond day 45 after randomization with a device were 4.41% (95% CI, 1.02% to 7.80%), 1.53% (95% CI, 0.33% to 2.72%), and 0.65% (95% CI, -0.41% to 1.71%) in the unlikely, possible, and probable PASCAL categories, respectively. Conclusions and Relevance: Among patients aged 18 to 60 years with PFO-associated stroke, risk reduction for recurrent stroke with device closure varied across groups classified by their probabilities that the stroke was causally related to the PFO. Application of this classification system has the potential to guide individualized decision-making.

Prehospital Triage Strategies for the Transportation of Suspected Stroke Patients in the United States.

BACKGROUND AND PURPOSE: Ischemic stroke patients with large vessel occlusion (LVO) could benefit from direct transportation to an intervention center for endovascular treatment, but non-LVO patients need rapid IV thrombolysis in the nearest center. Our aim was to evaluate prehospital triage strategies for suspected stroke patients in the United States. METHODS: We used a decision tree model and geographic information system to estimate outcome of suspected stroke patients transported by ambulance within 4.5 hours after symptom onset. We compared the following strategies: (1) Always to nearest center, (2) American Heart Association algorithm (ie, directly to intervention center if a prehospital stroke scale suggests LVO and total driving time from scene to intervention center is <30 minutes, provided that the delay would not exclude from thrombolysis), (3) modified algorithms with a maximum additional driving time to the intervention center of <30 minutes, <60 minutes, or without time limit, and (4) always to intervention center. Primary outcome was the annual number of good outcomes, defined as modified Rankin Scale score of 0-2. The preferred strategy was the one that resulted in the best outcomes with an incremental number needed to transport to intervention center (NNTI) <100 to prevent one death or severe disability (modified Rankin Scale score of >2). RESULTS: Nationwide implementation of the American Heart Association algorithm increased the number of good outcomes by 594 (+1.0%) compared with transportation to the nearest center. The associated number of non-LVO patients transported to the intervention center was 16 714 (NNTI 28). The modified algorithms yielded an increase of 1013 (+1.8%) to 1369 (+2.4%) good outcomes, with a NNTI varying between 28 and 32. The algorithm without time limit was preferred in the majority of states (n=32 [65%]), followed by the algorithm with <60 minutes delay (n=10 [20%]). Tailoring policies at county-level slightly reduced the total number of transportations to the intervention center (NNTI 31). CONCLUSIONS: Prehospital triage strategies can greatly improve outcomes of the ischemic stroke population in the United States, but increase the number of non-LVO stroke patients transported to an intervention center. The current American Heart Association algorithm is suboptimal as a nationwide policy and should be modified to allow more delay when directly transporting LVO-suspected patients to an intervention center.

Risk of Paradoxical Embolism (RoPE)-Estimated Attributable Fraction Correlates With the Benefit of Patent Foramen Ovale Closure: An Analysis of 3 Trials.

BACKGROUND AND PURPOSE: In patients with cryptogenic stroke and patent foramen ovale (PFO), the Risk of Paradoxical Embolism (RoPE) Score has been proposed as a method to estimate a patient-specific "PFO-attributable fraction"-the probability that a documented PFO is causally-related to the stroke, rather than an incidental finding. The objective of this research is to examine the relationship between this RoPE-estimated PFO-attributable fraction and the effect of closure in 3 randomized trials. METHODS: We pooled data from the CLOSURE-I (Evaluation of the STARFlex Septal Closure System in Patients With a Stroke and/or Transient Ischemic Attack due to Presumed Paradoxical Embolism through a Patent Foramen Ovale), RESPECT (Randomized Evaluation of Recurrent Stroke Comparing PFO Closure to Established Current Standard of Care Treatment), and PC (Clinical Trial Comparing Percutaneous Closure of Patent Foramen Ovale [PFO] Using the Amplatzer PFO Occluder With Medical Treatment in Patients With Cryptogenic Embolism) trials. We examine the treatment effect of closure in high RoPE score (≥7) versus low RoPE score (<7) patients. We also estimated the relative risk reduction associated with PFO closure across each level of the RoPE score using Cox proportional hazard analysis. We estimated a patient-specific attributable fraction using a PC trial-compatible (9-point) RoPE equation (omitting the neuroradiology variable), as well as a 2-trial analysis using the original (10-point) RoPE equation. We examined the Pearson correlation between the estimated attributable fraction and the relative risk reduction across RoPE strata. RESULTS: In the low RoPE score group (<7, n=912), the rate of recurrent strokes per 100 person-years was 1.37 in the device arm versus 1.68 in the medical arm (hazard ratio, 0.82 [0.42-1.59] P=0.56) compared with 0.30 versus 1.03 (hazard ratio, 0.31 [0.11-0.85] P=0.02) in the high RoPE score group (≥7, n=1221); treatment-by-RoPE score group interaction, P=0.12. The RoPE score estimated attributable fraction anticipated the relative risk reduction across all levels of the RoPE score, in both the 3-trial (r=0.95, P<0.001) and 2-trial (r=0.92, P<0.001) analyses. CONCLUSIONS: The RoPE score estimated attributable fraction is highly correlated to the relative risk reduction of device versus medical therapy. This observation suggests the RoPE score identifies patients with cryptogenic stroke who are likely to have a PFO that is pathogenic rather than incidental.

Predictive approaches to heterogeneous treatment effects: a scoping review.

BACKGROUND: Recent evidence suggests that there is often substantial variation in the benefits and harms across a trial population. We aimed to identify regression modeling approaches that assess heterogeneity of treatment effect within a randomized clinical trial. METHODS: We performed a literature review using a broad search strategy, complemented by suggestions of a technical expert panel. RESULTS: The approaches are classified into 3 categories: 1) Risk-based methods (11 papers) use only prognostic factors to define patient subgroups, relying on the mathematical dependency of the absolute risk difference on baseline risk; 2) Treatment effect modeling methods (9 papers) use both prognostic factors and treatment effect modifiers to explore characteristics that interact with the effects of therapy on a relative scale. These methods couple data-driven subgroup identification with approaches to prevent overfitting, such as penalization or use of separate data sets for subgroup identification and effect estimation. 3) Optimal treatment regime methods (12 papers) focus primarily on treatment effect modifiers to classify the trial population into those who benefit from treatment and those who do not. Finally, we also identified papers which describe model evaluation methods (4 papers). CONCLUSIONS: Three classes of approaches were identified to assess heterogeneity of treatment effect. Methodological research, including both simulations and empirical evaluations, is required to compare the available methods in different settings and to derive well-informed guidance for their application in RCT analysis.

Assessment of the impact of EHR heterogeneity for clinical research through a case study of silent brain infarction.

BACKGROUND: The rapid adoption of electronic health records (EHRs) holds great promise for advancing medicine through practice-based knowledge discovery. However, the validity of EHR-based clinical research is questionable due to poor research reproducibility caused by the heterogeneity and complexity of healthcare institutions and EHR systems, the cross-disciplinary nature of the research team, and the lack of standard processes and best practices for conducting EHR-based clinical research. METHOD: We developed a data abstraction framework to standardize the process for multi-site EHR-based clinical studies aiming to enhance research reproducibility. The framework was implemented for a multi-site EHR-based research project, the ESPRESSO project, with the goal to identify individuals with silent brain infarctions (SBI) at Tufts Medical Center (TMC) and Mayo Clinic. The heterogeneity of healthcare institutions, EHR systems, documentation, and process variation in case identification was assessed quantitatively and qualitatively. RESULT: We discovered a significant variation in the patient populations, neuroimaging reporting, EHR systems, and abstraction processes across the two sites. The prevalence of SBI for patients over age 50 for TMC and Mayo is 7.4 and 12.5% respectively. There is a variation regarding neuroimaging reporting where TMC are lengthy, standardized and descriptive while Mayo's reports are short and definitive with more textual variations. Furthermore, differences in the EHR system, technology infrastructure, and data collection process were identified. CONCLUSION: The implementation of the framework identified the institutional and process variations and the heterogeneity of EHRs across the sites participating in the case study. The experiment demonstrates the necessity to have a standardized process for data abstraction when conducting EHR-based clinical studies.