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

External Validation of the FREEDOM Score for Individualized Decision Making Between CABG and PCI

BACKGROUND

Although randomized trials have established that coronary artery bypass grafting (CABG) is, on average, the most effective revascularization strategy compared with percutaneous coronary intervention (PCI) in patients with diabetes and multivessel disease (MVD), individual patients differ in many characteristics that can affect the benefits and harms of treatment. The FREEDOM (Future Revascularization Evaluation in Patients with Diabetes Mellitus) score was developed to predict different outcomes with CABG vs PCI on the basis of 8 patient characteristics and the smoking-treatment interaction.

OBJECTIVES

This study aimed to assess the ability of the 5-year major adverse cardiovascular event (MACE) model to predict treatment benefit of CABG vs PCI in the SYNTAX (Synergy Between Percutaneous Coronary Intervention With Taxus and Cardiac Surgery) and BEST (Bypass Surgery and Everolimus-Eluting Stent Implantation in the Treatment of Patients with Multivessel Coronary Artery Disease) trials.

METHODS

This study identified 702 patients with diabetes and MVD to mirror the FREEDOM participants. Discrimination was assessed by C-index, and calibration was assessed by calibration plots in the PCI and CABG arms, respectively. The ability of the FREEDOM score to predict treatment benefit of CABG vs PCI was assessed.

RESULTS

Overall, CABG was associated with a lower rate of 5-year MACE compared with PCI (12.4% vs 20.3%; log-rank P = 0.021) irrespective of a history of smoking (P_interaction = 0.975). Both discrimination and calibration were helpful in the PCI arm (C-index: 0.69; slope: 0.96, intercept: -0.24), but moderate in the CABG arm (C-index: 0.61; slope: 0.61; intercept: -0.53). The FREEDOM score showed some heterogeneity of treatment benefit.

CONCLUSIONS

The FREEDOM score could identify some heterogeneity of treatment benefit of CABG vs PCI for 5-year MACE. Until further prospective validations are performed, these results should be taken into consideration when using the FREEDOM score in patients with diabetes and MVD. (Synergy Between Percutaneous Coronary Intervention With Taxus and Cardiac Surgery [SYNTAX]; NCT00114972) (Bypass Surgery and Everolimus-Eluting Stent Implantation in the Treatment of Patients with Multivessel Coronary Artery Disease [BEST]; NCT00997828) (Future Revascularization Evaluation in Patients with Diabetes Mellitus [FREEDOM]; NCT00086450).

An Electronic Health Record-Compatible Model to Predict Personalized Treatment Effects From the Diabetes Prevention Program: A Cross-Evidence Synthesis Approach Using Clinical Trial and Real-World Data

OBJECTIVE

To develop an electronic health record (EHR)-based risk tool that provides point-of-care estimates of diabetes risk to support targeting interventions to patients most likely to benefit.

PATIENTS AND METHODS

A risk prediction model was developed and validated in a large observational database of patients with an index visit date between January 1, 2012, and December 31, 2016, with treatment effect estimates from risk-based reanalysis of clinical trial data. The risk model development cohort included 1.1 million patients with prediabetes from the OptumLabs Data Warehouse (OLDW); the validation cohort included a distinct sample of 1.1 million patients in OLDW. The randomly assigned clinical trial cohort included 3081 people from the Diabetes Prevention Program (DPP) study.

RESULTS

Eleven variables reliably obtainable from the EHR were used to predict diabetes risk. This model validated well in the OLDW (C statistic = 0.76; observed 3-year diabetes rate was 1.8% (95% confidence interval [CI], 1.7 to 1.9) in the lowest-risk quarter and 19.6% (19.4 to 19.8) in the highest-risk quarter). In the DPP, the hazard ratio (HR) for lifestyle modification was constant across all levels of risk (HR, 0.43; 95% CI, 0.35 to 0.53), whereas the HR for metformin was highly risk dependent (HR, 1.1; 95% CI, 0.61 to 2.0 in the lowest-risk quarter vs HR, 0.45; 95% CI, 0.35 to 0.59 in the highest-risk quarter). Fifty-three percent of the benefits of population-wide dissemination of the DPP lifestyle modification and 73% of the benefits of population-wide metformin therapy can be obtained by targeting the highest-risk quarter of patients.

CONCLUSION

The Tufts-Predictive Analytics and Comparative Effectiveness DPP Risk model is an EHR-compatible tool that might support targeted diabetes prevention to more efficiently realize the benefits of the DPP interventions.

Implementation of a Web-Based Tool for Shared Decision-making in Lung Cancer Screening: Mixed Methods Quality Improvement Evaluation

Background

Lung cancer risk and life expectancy vary substantially across patients eligible for low-dose computed tomography lung cancer screening (LCS), which has important consequences for optimizing LCS decisions for different patients. To account for this heterogeneity during decision-making, web-based decision support tools are needed to enable quick calculations and streamline the process of obtaining individualized information that more accurately informs patient-clinician LCS discussions. We created DecisionPrecision, a clinician-facing web-based decision support tool, to help tailor the LCS discussion to a patient’s individualized lung cancer risk and estimated net benefit.

Objective

The objective of our study is to test two strategies for implementing DecisionPrecision in primary care at eight Veterans Affairs medical centers: a quality improvement (QI) training approach and academic detailing (AD).

Methods

Phase 1 comprised a multisite, cluster randomized trial comparing the effectiveness of standard implementation (adding a link to DecisionPrecision in the electronic health record vs standard implementation plus the Learn, Engage, Act, and Process [LEAP] QI training program). The primary outcome measure was the use of DecisionPrecision at each site before versus after LEAP QI training. The second phase of the study examined the potential effectiveness of AD as an implementation strategy for DecisionPrecision at all 8 medical centers. Outcomes were assessed by comparing absolute tool use before and after AD visits and conducting semistructured interviews with a subset of primary care physicians (PCPs) following the AD visits.

Results

Phase 1 findings showed that sites that participated in the LEAP QI training program used DecisionPrecision significantly more often than the standard implementation sites (tool used 190.3, SD 174.8 times on average over 6 months at LEAP sites vs 3.5 SD 3.7 at standard sites; P<.001). However, this finding was confounded by the lack of screening coordinators at standard implementation sites. In phase 2, there was no difference in the 6-month tool use between before and after AD (95% CI −5.06 to 6.40; P=.82). Follow-up interviews with PCPs indicated that the AD strategy increased provider awareness and appreciation for the benefits of the tool. However, other priorities and limited time prevented PCPs from using them during routine clinical visits.

Conclusions

The phase 1 findings did not provide conclusive evidence of the benefit of a QI training approach for implementing a decision support tool for LCS among PCPs. In addition, phase 2 findings showed that our light-touch, single-visit AD strategy did not increase tool use. To enable tool use by PCPs, prediction-based tools must be fully automated and integrated into electronic health records, thereby helping providers personalize LCS discussions among their many competing demands. PCPs also need more time to engage in shared decision-making discussions with their patients.

Trial Registration

ClinicalTrials.gov NCT02765412; https://clinicaltrials.gov/ct2/show/NCT02765412

Dexamethasone 12 mg versus 6 mg for patients with COVID-19 and severe hypoxaemia: a pre-planned, secondary Bayesian analysis of the COVID STEROID 2 trial

PURPOSE

We compared dexamethasone 12 versus 6 mg daily for up to 10 days in patients with coronavirus disease 2019 (COVID-19) and severe hypoxaemia in the international, randomised, blinded COVID STEROID 2 trial. In the primary, conventional analyses, the predefined statistical significance thresholds were not reached. We conducted a pre-planned Bayesian analysis to facilitate probabilistic interpretation.

METHODS

We analysed outcome data within 90 days in the intention-to-treat population (data available in 967 to 982 patients) using Bayesian models with various sensitivity analyses. Results are presented as median posterior probabilities with 95% credible intervals (CrIs) and probabilities of different effect sizes with 12 mg dexamethasone.

RESULTS

The adjusted mean difference on days alive without life support at day 28 (primary outcome) was 1.3 days (95% CrI -0.3 to 2.9; 94.2% probability of benefit). Adjusted relative risks and probabilities of benefit on serious adverse reactions was 0.85 (0.63 to 1.16; 84.1%) and on mortality 0.87 (0.73 to 1.03; 94.8%) at day 28 and 0.88 (0.75 to 1.02; 95.1%) at day 90. Probabilities of benefit on days alive without life support and days alive out of hospital at day 90 were 85 and 95.7%, respectively. Results were largely consistent across sensitivity analyses, with relatively low probabilities of clinically important harm with 12 mg on all outcomes in all analyses.

CONCLUSION

We found high probabilities of benefit and low probabilities of clinically important harm with dexamethasone 12 mg versus 6 mg daily in patients with COVID-19 and severe hypoxaemia on all outcomes up to 90 days.

Can Systems Biology Advance Clinical Precision Oncology?

Precision oncology is perceived as a way forward to treat individual cancer patients. However, knowing particular cancer mutations is not enough for optimal therapeutic treatment, because cancer genotype-phenotype relationships are nonlinear and dynamic. Systems biology studies the biological processes at the systems' level, using an array of techniques, ranging from statistical methods to network reconstruction and analysis, to mathematical modeling. Its goal is to reconstruct the complex and often counterintuitive dynamic behavior of biological systems and quantitatively predict their responses to environmental perturbations. In this paper, we review the impact of systems biology on precision oncology. We show examples of how the analysis of signal transduction networks allows to dissect resistance to targeted therapies and inform the choice of combinations of targeted drugs based on tumor molecular alterations. Patient-specific biomarkers based on dynamical models of signaling networks can have a greater prognostic value than conventional biomarkers. These examples support systems biology models as valuable tools to advance clinical and translational oncological research.

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.

Randomised clinical trials in critical care: past, present and future

Randomised clinical trials (RCTs) are the gold standard for providing unbiased evidence of intervention effects. Here, we provide an overview of the history of RCTs and discuss the major challenges and limitations of current critical care RCTs, including overly optimistic effect sizes; unnuanced conclusions based on dichotomization of results; limited focus on patient-centred outcomes other than mortality; lack of flexibility and ability to adapt, increasing the risk of inconclusive results and limiting knowledge gains before trial completion; and inefficiency due to lack of re-use of trial infrastructure. We discuss recent developments in critical care RCTs and novel methods that may provide solutions to some of these challenges, including a research programme approach (consecutive, complementary studies of multiple types rather than individual, independent studies), and novel design and analysis methods. These include standardization of trial protocols; alternative outcome choices and use of core outcome sets; increased acceptance of uncertainty, probabilistic interpretations and use of Bayesian statistics; novel approaches to assessing heterogeneity of treatment effects; adaptation and platform trials; and increased integration between clinical trials and clinical practice. We outline the advantages and discuss the potential methodological and practical disadvantages with these approaches. With this review, we aim to inform clinicians and researchers about conventional and novel RCTs, including the rationale for choosing one or the other methodological approach based on a thorough discussion of pros and cons. Importantly, the most central feature remains the randomisation, which provides unparalleled restriction of confounding compared to non-randomised designs by reducing confounding to chance.

Harrell Jr F, Moons KGM, Debray TPA, Reitsma JB. A tutorial on individualized treatment effect prediction from randomized trials with a binary endpoint

Randomized trials typically estimate average relative treatment effects, but decisions on the benefit of a treatment are possibly better informed by more individualized predictions of the absolute treatment effect. In case of a binary outcome, these predictions of absolute individualized treatment effect require knowledge of the individual's risk without treatment and incorporation of a possibly differential treatment effect (ie, varying with patient characteristics). In this article, we lay out the causal structure of individualized treatment effect in terms of potential outcomes and describe the required assumptions that underlie a causal interpretation of its prediction. Subsequently, we describe regression models and model estimation techniques that can be used to move from average to more individualized treatment effect predictions. We focus mainly on logistic regression-based methods that are both well-known and naturally provide the required probabilistic estimates. We incorporate key components from both causal inference and prediction research to arrive at individualized treatment effect predictions. While the separate components are well known, their successful amalgamation is very much an ongoing field of research. We cut the problem down to its essentials in the setting of a randomized trial, discuss the importance of a clear definition of the estimand of interest, provide insight into the required assumptions, and give guidance with respect to modeling and estimation options. Simulated data illustrate the potential of different modeling options across scenarios that vary both average treatment effect and treatment effect heterogeneity. Two applied examples illustrate individualized treatment effect prediction in randomized trial data.

Rethinking Table 1

Clinical and translational medicine studies of disease risk or treatment response typically include a table 1 comparing groups on age, sex, and race and/or ethnicity. Although customarily treated as biological variables, each denote biography, elements of a person's lived experience. Capturing these biographical features is essential to achieving the ambition of personalized medicine.

Development and validation of the PORTRET tool to predict recurrence, overall survival, and other-cause mortality in older patients with breast cancer in the Netherlands: a population-based study

BACKGROUND

Current prediction tools for breast cancer outcomes are not tailored to the older patient, in whom competing risk strongly influences treatment effects. We aimed to develop and validate a prediction tool for 5-year recurrence, overall mortality, and other-cause mortality for older patients (aged ≥65 years) with early invasive breast cancer and to estimate individualised expected benefits of adjuvant systemic treatment.

METHODS

We selected surgically treated patients with early invasive breast cancer (stage I-III) aged 65 years or older from the population-based FOCUS cohort in the Netherlands. We developed prediction models for 5-year recurrence, overall mortality, and other-cause mortality using cause-specific Cox proportional hazard models. External validation was performed in a Dutch Cancer registry cohort. Performance was evaluated with discrimination accuracy and calibration plots.

FINDINGS

We included 2744 female patients in the development cohort and 13631 female patients in the validation cohort. Median age was 74·8 years (range 65-98) in the development cohort and 76·0 years (70-101) in the validation cohort. 5-year follow-up was complete for more than 99% of all patients. We observed 343 and 1462 recurrences, and 831 and 3594 deaths, of which 586 and 2565 were without recurrence, in the development and validation cohort, respectively. The area under the receiver-operating-characteristic curve at 5 years in the external dataset was 0·76 (95% CI 0·75-0·76) for overall mortality, 0·76 (0·76-0·77) for recurrence, and 0·75 (0·74-0·75) for other-cause mortality.

INTERPRETATION

The PORTRET tool can accurately predict 5-year recurrence, overall mortality, and other-cause mortality in older patients with breast cancer. The tool can support shared decision making, especially since it provides individualised estimated benefits of adjuvant treatment.

FUNDING

Dutch Cancer Foundation and ZonMw.

Association Between Baseline Diastolic Blood Pressure and the Efficacy of Intensive vs Standard Blood Pressure-Lowering Therapy

IMPORTANCE

Low diastolic blood pressure (DBP) has been found to be associated with increased adverse cardiovascular events; however, it is unknown whether intensifying blood pressure therapy in patients with an already low DBP to achieve a lower systolic blood pressure (SBP) target is safe or effective.

OBJECTIVE

To evaluate whether there is an association of baseline DBP and intensification of blood pressure-lowering therapy with the outcomes of all-cause death and cardiovascular events.

DESIGN, SETTING, AND PARTICIPANTS

This cohort study analyzed patients who were randomized to intensive or standard BP control in the Action to Control Cardiovascular Risk in Diabetes-Blood Pressure (ACCORD-BP) trial and Systolic Blood Pressure Intervention Trial (SPRINT). Data were collected from September 1999 to June 2009 (ACCORD-BP) and from October 2010 to August 2015 (SPRINT). Data were analyzed from December 2020 to June 2021.

EXPOSURES

Baseline DBP as a continuous variable.

MAIN OUTCOMES AND MEASURES

All-cause death and a composite cardiovascular end point (CVE) that included cardiovascular death, nonfatal myocardial infarction, and nonfatal stroke.

RESULTS

A total of 14 094 patients (mean [SD] age, 66.2 [8.9] years; 8504 [60.4%] men) were included in this analysis. There were significant nonlinear associations between baseline DBP and all-cause death (eg, baseline DBP 50 vs 80 mm Hg: hazard ratio [HR], 1.48; 95% CI, 1.06-2.08; P = .02) and the composite CVE (eg, baseline DBP 50 vs 80 mm Hg: HR, 1.45; 95% CI, 1.27-3.04; P = .003) observed among all participants. Findings for the interaction between baseline DBP and treatment group assignment for all cause death did not reach statistical significance. For intensive vs standard therapy, the HR of death for a baseline DBP of 50 mm Hg was 1.80 (95% CI, 0.95-3.39; P = .07) and that for a baseline DBP of 80 mm Hg was 0.77 (95% CI, 0.59-1.01; P = .05). Overall, there was no interaction found between baseline DBP and treatment group assignment for the composite CVE. Over the range of baseline DBP values, significant reductions in the composite CVE for patients assigned to intensive vs standard therapy were found for baseline DBP values of 80 mm Hg (HR, 0.78; 95% CI, 0.62-0.98; P = .03) and 90 mm Hg (HR, 0.74; 95% CI, 0.55-0.98; P = .04).

CONCLUSIONS AND RELEVANCE

This pooled cohort study found no evidence of a significant interaction between baseline DBP and treatment intensity for all-cause death or for a composite CVE. These results are hypothesis generating and merit further study.

The CoVID-TE risk assessment model for venous thromboembolism in hospitalized patients with cancer and COVID-19

BACKGROUND

Hospitalized patients with COVID-19 have increased risks of venous (VTE) and arterial thromboembolism (ATE). Active cancer diagnosis and treatment are well-known risk factors; however, a risk assessment model (RAM) for VTE in patients with both cancer and COVID-19 is lacking.

OBJECTIVES

To assess the incidence of and risk factors for thrombosis in hospitalized patients with cancer and COVID-19.

METHODS

Among patients with cancer in the COVID-19 and Cancer Consortium registry (CCC19) cohort study, we assessed the incidence of VTE and ATE within 90 days of COVID-19-associated hospitalization. A multivariable logistic regression model specifically for VTE was built using a priori determined clinical risk factors. A simplified RAM was derived and internally validated using bootstrap.

RESULTS

From March 17, 2020 to November 30, 2020, 2804 hospitalized patients were analyzed. The incidence of VTE and ATE was 7.6% and 3.9%, respectively. The incidence of VTE, but not ATE, was higher in patients receiving recent anti-cancer therapy. A simplified RAM for VTE was derived and named CoVID-TE (Cancer subtype high to very-high risk by original Khorana score +1, VTE history +2, ICU admission +2, D-dimer elevation +1, recent systemic anti-cancer Therapy +1, and non-Hispanic Ethnicity +1). The RAM stratified patients into two cohorts (low-risk, 0-2 points, n = 1423 vs. high-risk, 3+ points, n = 1034) where VTE occurred in 4.1% low-risk and 11.3% high-risk patients (c statistic 0.67, 95% confidence interval 0.63-0.71). The RAM performed similarly well in subgroups of patients not on anticoagulant prior to admission and moderately ill patients not requiring direct ICU admission.

CONCLUSIONS

Hospitalized patients with cancer and COVID-19 have elevated thrombotic risks. The CoVID-TE RAM for VTE prediction may help real-time data-driven decisions in this vulnerable population.

Generalizability and Transportability of the National Lung Screening Trial Data: Extending Trial Results to Different Populations

BACKGROUND

Randomized controlled trials (RCT) play a central role in evidence-based healthcare. However, the clinical and policy implications of implementing RCTs in clinical practice are difficult to predict as the studied population is often different from the target population where results are being applied. This study illustrates the concepts of generalizability and transportability, demonstrating their utility in interpreting results from the National Lung Screening Trial (NLST).

METHODS

Using inverse-odds weighting, we demonstrate how generalizability and transportability techniques can be used to extrapolate treatment effect from (i) a subset of NLST to the entire NLST population and from (ii) the entire NLST to different target populations.

RESULTS

Our generalizability analysis revealed that lung cancer mortality reduction by LDCT screening across the entire NLST [16% (95% confidence interval [CI]: 4-24)] could have been estimated using a smaller subset of NLST participants. Using transportability analysis, we showed that populations with a higher prevalence of females and current smokers had a greater reduction in lung cancer mortality with LDCT screening [e.g., 27% (95% CI, 11-37) for the population with 80% females and 80% current smokers] than those with lower prevalence of females and current smokers.

CONCLUSIONS

This article illustrates how generalizability and transportability methods extend estimation of RCTs' utility beyond trial participants, to external populations of interest, including those that more closely mirror real-world populations.

IMPACT

Generalizability and transportability approaches can be used to quantify treatment effects for populations of interest, which may be used to design future trials or adjust lung cancer screening eligibility criteria.

Effect of Bedside Compared With Outside the Room Patient Case Presentation on Patients' Knowledge About Their Medical Care : A Randomized, Controlled, Multicenter Trial

BACKGROUND

Although bedside case presentation contributes to patient-centered care through active patient participation in medical discussions, the complexity of medical information and jargon-induced confusion may cause misunderstandings and patient discomfort.

OBJECTIVE

To compare bedside versus outside the room patient case presentation regarding patients' knowledge about their medical care.

DESIGN

Randomized, controlled, parallel-group trial. (ClinicalTrials.gov: NCT03210987).

SETTING

3 Swiss teaching hospitals.

PATIENTS

Adult medical patients who were hospitalized.

INTERVENTION

Patients were randomly assigned to bedside or outside the room case presentation.

MEASUREMENTS

The primary endpoint was patients' average knowledge of 3 dimensions of their medical care (each rated on a visual analogue scale from 0 to 100): understanding their disease, the therapeutic approach being used, and further plans for care.

RESULTS

Compared with patients in the outside the room group (n = 443), those in the bedside presentation group (n = 476) reported similar knowledge about their medical care (mean, 79.5 points [SD, 21.6] vs. 79.4 points [SD, 19.8]; adjusted difference, 0.09 points [95% CI, -2.58 to 2.76 points]; P = 0.95). Also, an objective rating of patient knowledge by the study team was similar for the 2 groups, but the bedside presentation group had higher ratings of confusion about medical jargon and uncertainty caused by team discussions. Bedside ward rounds were more efficient (mean, 11.89 minutes per patient [SD, 4.92] vs. 14.14 minutes per patient [SD, 5.65]; adjusted difference, -2.31 minutes [CI, -2.98 to -1.63 minutes]; P < 0.001).

LIMITATION

Only Swiss hospitals and medical patients were included.

CONCLUSION

Compared with outside the room case presentation, bedside case presentation was shorter and resulted in similar patient knowledge, but sensitive topics were more often avoided and patient confusion was higher. Physicians presenting at the bedside need to be skilled in the use of medical language to avoid confusion and misunderstandings.

PRIMARY FUNDING SOURCE

Swiss National Foundation (10531C_ 182422).

Noninvasive Ventilation Use Is Associated with Better Survival in Amyotrophic Lateral Sclerosis

Rationale: Noninvasive ventilation (NIV) is standard of care in amyotrophic lateral sclerosis (ALS), yet few data exist regarding its benefits.Objectives: We sought to identify whether the use of NIV was associated with survival in ALS.Methods: This was a single-center retrospective cohort study of 452 patients with ALS seen between 2006 and 2015. We matched one or more NIV subjects (prescribed NIV) to non-NIV subjects (never prescribed NIV) without replacement. The outcome was time from NIV prescription date (NIV subjects) or matched date (non-NIV subjects) until death. We performed a multivariable Cox proportional hazards model with NIV hourly usage as a time-varying covariate and stratified by matched groups.Results: After creating 180 matched groups and adjusting for age, body mass index, ALS Functional Rating Scale Revised dyspnea score, and hourly NIV use, NIV was associated with a 26% reduction in the rate of death compared with non-NIV subjects (hazard ratio [HR], 0.74; 95% confidence interval [CI], 0.57-0.98; P = 0.04). Among those with limb-onset ALS, NIV subjects had a 37% lower rate of death compared with non-NIV subjects (HR, 0.63; 95% CI, 0.45-0.87; P = 0.006). Among NIV subjects, we found that NIV use for an average of ≥4 h/d was associated with improved survival.Conclusions: NIV use was associated with significantly better survival in ALS after matching and adjusting for confounders. Increasing duration of daily NIV use was associated with longer survival. Randomized clinical trials should be performed to identify ideal thresholds for improving survival and optimizing adherence in ALS.

Identifying adults with acute rhinosinusitis in primary care that benefit most from antibiotics: protocol of an individual patient data meta-analysis using multivariable risk prediction modelling

INTRODUCTION

Acute rhinosinusitis (ARS) is a prime reason for doctor visits and among the conditions with highest antibiotic overprescribing rates in adults. To reduce inappropriate prescribing, we aim to predict the absolute benefit of antibiotic treatment for individual adult patients with ARS by applying multivariable risk prediction methods to individual patient data (IPD) of multiple randomised placebo-controlled trials.

METHODS AND ANALYSIS

This is an update and re-analysis of a 2008 IPD meta-analysis on antibiotics for adults with clinically diagnosed ARS. First, the reference list of the 2018 Cochrane review on antibiotics for ARS will be reviewed for relevant studies published since 2008. Next, the systematic searches of CENTRAL, MEDLINE and Embase of the Cochrane review will be updated to 1 September 2020. Methodological quality of eligible studies will be assessed using the Cochrane Risk of Bias 2 tool. The primary outcome is cure at 8-15 days. Regression-based methods will be used to model the risk of being cured based on relevant predictors and treatment, while accounting for clustering. Such model allows for risk predictions as a function of treatment and individual patient characteristics and hence gives insight into individualised absolute benefit. Candidate predictors will be based on literature, clinical reasoning and availability. Calibration and discrimination will be evaluated to assess model performance. Resampling techniques will be used to assess internal validation. In addition, internal-external cross-validation procedures will be used to inform on between-study differences and estimate out-of-sample model performance. Secondarily, we will study possible heterogeneity of treatment effect as a function of outcome risk.

ETHICS AND DISSEMINATION

In this study, no identifiable patient data will be used. As such, the Medical Research Involving Humans Subject Act (WMO) does not apply and official ethical approval is not required. Results will be submitted for publication in international peer-reviewed journals.

PROSPERO REGISTRATION NUMBER

CRD42020220108.

Prespecification of subgroup analyses and examination of treatment-subgroup interactions in cancer individual participant data meta-analyses are suboptimal

OBJECTIVES

This study aimed to explore the prespecification and conduct of subgroup analyses in cancer individual participant data meta-analyses (IPDMAs).

STUDY DESIGN AND SETTING

We searched PubMed, Embase.com, Cochrane Library, and Web of Science to identify IPDMAs of randomized controlled trials evaluating intervention effects for cancer. We evaluated how often cancer IPDMAs prespecify subgroup analyses and statistical approaches for examining treatment-subgroup interactions and handling continuous subgroup variables.

RESULTS

We included 89 IPDMAs, of which 41 (46.1%) reported a statistically significant treatment-subgroup interaction (P < 0.05) in at least one subgroup analysis. 47 (52.8%) IPDMAs prespecified methods for conducting subgroup analyses and the remaining 42 (47.2%) did not prespecify subgroup analyses. Of the 47 IPDMAs prespecified subgroup analyses, 19 performed the planned subgroup analyses, 21 added subgroup analyses, 7 reduced subgroup analyses. Eighty IPDMAs examined treatment-subgroup interactions, but 72 IPDMAs did not provide enough information to determine whether an appropriate approach that avoided aggregation bias was used. 85 IPDMAs that used continuous variables in subgroup analyses categorized continuous variables and only 1 IPDMA examined non-linear relationships.

CONCLUSION

Many cancer IPDMAs did not prespecify subgroup analyses, nor did they fully perform planned subgroup analyses. Lack of details for the test of treatment-subgroup interactions and examination of non-linear interactions was suboptimal.

Subgroup discovery in non-inferiority trials

Approaches and guidelines for performing subgroup analysis to assess heterogeneity of treatment effect in clinical trials have been the topic of numerous papers in the statistical and clinical literature, but have been discussed predominantly in the context of conventional superiority trials. Concerns about treatment heterogeneity are the same if not greater in non-inferiority (NI) trials, especially since overall similarity between two treatment arms in a successful NI trial could be due to the existence of qualitative interactions that are more likely when comparing two active therapies. Even in unsuccessful NI trials, subgroup analyses can yield important insights about the potential reasons for failure to demonstrate non-inferiority of the experimental therapy. Recent NI trials have performed a priori subgroup analyses using standard statistical tests for interaction, but there is increasing interest in more flexible machine learning approaches for post-hoc subgroup discovery. The performance and practical application of such methods in NI trials have not been systematically explored, however. We considered the Virtual Twin method for the NI setting, an algorithm for subgroup identification that combines random forest with classification and regression trees, and conducted extensive simulation studies to examine its performance under different NI trial conditions and to devise decision rules for selecting the final subgroups. We illustrate the utility of the method with data from a NI trial that was conducted to compare two acupuncture treatments for chronic musculoskeletal pain.

Commentary: Identifying individualized predictions of response in ADHD pharmacotherapy - a commentary on Rodrigues et al. (2020)

In this issue, Rodrigues et al. (2020) present a systematic review with meta-analyses that reports the efficacy of five treatments for children with attention-deficit hyperactivity disorder symptoms in the context of autism spectrum disorder - (a) methylphenidate; (b) atomoxetine; (c) guanfacine; (d) aripiprazole; and (e) risperidone. In this commentary, we highlight the contrast between the scarce evidence base of treatment for ADHD in the context of autism and other subpopulations, such as tic disorders and intellectual disability, and the extensive evidence base of treatment for ADHD in general. The commentary weighs about the conundrum clinicians face of whether to rely on the limited evidence base of treatment for ADHD in subpopulation, or to derive conclusions from the larger body of evidence of treatment for ADHD in general. The commentary also discusses potential avenues for future research to address this clinical problem.

Screening for regenerative therapy responders in heart failure

Risk of outcome variability challenges therapeutic innovation. Selection of the most suitable candidates is predicated on reliable response indicators. Especially for emergent regenerative biotherapies, determinants separating success from failure in achieving disease rescue remain largely unknown. Accordingly, (pre)clinical development programs have placed increased emphasis on the multi-dimensional decoding of repair capacity and disease resolution, attributes defining responsiveness. To attain regenerative goals for each individual, phenotype-based patient selection is poised for an upgrade guided by new insights into disease biology, translated into refined surveillance of response regulators and deep learning-amplified clinical decision support.

Microvascular Disease and Risk of Cardiovascular Events and Death From Intensive Treatment in Type 2 Diabetes: The ACCORDION Study

OBJECTIVE

To assess whether the presence of microvascular complications modifies the effect of intensive glucose reduction on long-term outcomes in patients with type 2 diabetes.

PATIENTS AND METHODS

Using ACCORD and ACCORDION study data, we investigated the risk of the primary outcome (nonfatal myocardial infarction, nonfatal stroke, or cardiovascular death) or death in relation to the prerandomization type and extent of microvascular complications. Interaction terms were fitted in survival models to estimate the risk of both outcomes across levels of an overall microvascular disease score (range 0 to 100) and its individual components: diabetic nephropathy, retinopathy, and neuropathy.

RESULTS

During a mean follow-up of 7.7 years, 1685 primary outcomes and 1806 deaths occurred in 9405 participants. The outcome-specific microvascular score was ≤30 in 97.9% of subjects for the primary outcome and in 98.5% for death. For participants with scores of 0 and 30, respectively, the 10-year absolute risk difference between intensive glucose control and standard treatment ranged from -0.8% (95% CI, -2.6, 1.1) to -3.0% -7.1, 1.1) for the primary outcome and from -0.5% (-2.1, 1.1) to 0.7% (-4.2, 5.6) for mortality. Retinopathy was associated with the largest effects, with a 10-year absolute risk difference of -6.5% (-11.1 to -2.0) for the primary outcome and -3.9% (-7.8 to 0.1) for mortality.

CONCLUSION

This hypothesis-generating study identifies diabetic retinopathy as predictor of the beneficial effect of intensive glucose control on the risk of cardiovascular disease and possibly death. Further long-term studies are required to confirm these findings.

Patient Selection for Intensive Blood Pressure Management Based on Benefit and Adverse Events

BACKGROUND

Intensive systolic blood pressure (SBP) treatment prevents cardiovascular disease (CVD) events in patients with high CVD risk on average, though benefits likely vary among patients.

OBJECTIVES

The aim of this study was to predict the magnitude of benefit (reduced CVD and all-cause mortality risk) along with adverse event (AE) risk from intensive versus standard SBP treatment.

METHODS

This was a secondary analysis of SPRINT (Systolic Blood Pressure Intervention Trial). Separate benefit outcomes were the first occurrence of: 1) a CVD composite of acute myocardial infarction or other acute coronary syndrome, stroke, heart failure, or CVD death; and 2) all-cause mortality. Treatment-related AEs of interest included hypotension, syncope, bradycardia, electrolyte abnormalities, injurious falls, and acute kidney injury. Modified elastic net Cox regression was used to predict absolute risk for each outcome and absolute risk differences on the basis of 36 baseline variables available at the point of care with intensive versus standard treatment.

RESULTS

Among 8,828 SPRINT participants (mean age 67.9 years, 35% women), 600 CVD composite events, 363 all-cause deaths, and 481 treatment-related AEs occurred over a median follow-up period of 3.26 years. Individual participant risks were predicted for the CVD composite (C index = 0.71), all-cause mortality (C index = 0.75), and treatment-related AEs (C index = 0.69). Higher baseline CVD risk was associated with greater benefit (i.e., larger absolute CVD risk reduction). Predicted CVD benefit and predicted increased treatment-related AE risk were correlated (Spearman correlation coefficient = -0.72), and 95% of participants who fell into the highest tertile of predicted benefit also had high or moderate predicted increases in treatment-related AE risk. Few were predicted as high benefit with low AE risk (1.8%) or low benefit with high AE risk (1.5%). Similar results were obtained for all-cause mortality.

CONCLUSIONS

SPRINT participants with higher baseline predicted CVD risk gained greater absolute benefit from intensive treatment. Participants with high predicted benefit were also most likely to experience treatment-related AEs, but AEs were generally mild and transient. Patients should be prioritized for intensive SBP treatment on the basis of higher predicted benefit. (Systolic Blood Pressure Intervention Trial [SPRINT]; NCT01206062).

Daily practice in guideline adherence to adjuvant chemotherapy in stage III colon cancer and predictors of outcome

INTRODUCTION

Although guidelines recommend adjuvant chemotherapy for stage III colon cancer patients, many patients do not receive adjuvant chemotherapy. The aim of this study was to identify reasons for guideline non-adherence and assess the effect on patient outcomes in a multicenter cohort of stage III colon cancer patients who received surgery plus adjuvant chemotherapy or surgery alone.

METHODS

Patients who underwent surgery between 2007 and 2017 were included. Reasons for non-adherence were determined. Propensity score analyses with inverse probability weighting were performed to adjust for confounding factors. Cox proportional hazards regression and risk stratified analyses were performed to assess the association of guideline adherence and other potential predictors with recurrence free survival (RFS).

RESULTS

Data of 575 patients were included of whom 61% received adjuvant chemotherapy. In 87 of 222 patients (39%) who did not receive adjuvant chemotherapy, no reason was documented. Only age was predictive for receiving chemotherapy. Patients who received adjuvant chemotherapy had longer RFS (HR 0.42, 95%CI 0.29-0.62, p < 0.001). High T- and N-stage were associated with poorer RFS HR 2.0 (95%CI 1.58-2.71, p < 0.001) and HR 2.19 (95%CI 1.60-2.99, p < 0.001) respectively. Risk groups were identified with distinct prognosis and treatment effect and a nomogram is presented to visualize individualized RFS differences.

CONCLUSION

This study shows considerable variation in guideline adherence to adjuvant chemotherapy and poor documentation on reasons for non-adherence. Optimizing adherence and gaining insight in reasons for non-adherence is advocated as this can lead to significant RFS benefit, especially in patients with high T-and N-stage tumors.

Tutorial: A nontechnical explanation of the counterfactual definition of effect modification and interaction

Effect modification and interaction are important concepts for answering causal questions about interdependent effects of two (or more) exposures on some outcome of interest. Although conceptually alike and often mistakenly regarded as synonymous, effect modification and interaction actually refer to slightly different concepts when considered from a causal perspective. Their subtle yet relevant distinction lies in how the interplay between exposures is defined and the causal roles attributed to the exposures involved in the effect modification and interaction. To gain more insight into similarities and differences between the concepts of effect modification and interaction, the counterfactual theory of causation, albeit complicated, can be very helpful. Therefore, this article presents a nontechnical explanation of the counterfactual definition of effect modification and interaction. Essentially, effect modification and interaction are reflections of the reality and complexity of multicausality. The causal effect of an exposure of interest often depends on the levels of other exposures (effect modification) or causal effects of other exposures (interaction). Consequently, exposure effects should not be regarded in isolation but in combination. Understanding the underlying principles of effect modification and interaction on a conceptual level enables researchers to better anticipate, detect, and interpret these causal phenomena when setting up, analyzing, and reporting findings of (clinical) epidemiological studies. Effect modification and interaction are not biases to be avoided but properties of causal effects that ought to be unveiled. Hence, evidence for effect modification and interaction needs to be shown in order to delineate in whom and which instances causal effects occur.

A scoping review of causal methods enabling predictions under hypothetical interventions

BACKGROUND

The methods with which prediction models are usually developed mean that neither the parameters nor the predictions should be interpreted causally. For many applications, this is perfectly acceptable. However, when prediction models are used to support decision making, there is often a need for predicting outcomes under hypothetical interventions.

AIMS

We aimed to identify published methods for developing and validating prediction models that enable risk estimation of outcomes under hypothetical interventions, utilizing causal inference. We aimed to identify the main methodological approaches, their underlying assumptions, targeted estimands, and potential pitfalls and challenges with using the method. Finally, we aimed to highlight unresolved methodological challenges.

METHODS

We systematically reviewed literature published by December 2019, considering papers in the health domain that used causal considerations to enable prediction models to be used for predictions under hypothetical interventions. We included both methodologies proposed in statistical/machine learning literature and methodologies used in applied studies.

RESULTS

We identified 4919 papers through database searches and a further 115 papers through manual searches. Of these, 87 papers were retained for full-text screening, of which 13 were selected for inclusion. We found papers from both the statistical and the machine learning literature. Most of the identified methods for causal inference from observational data were based on marginal structural models and g-estimation.

CONCLUSIONS

There exist two broad methodological approaches for allowing prediction under hypothetical intervention into clinical prediction models: (1) enriching prediction models derived from observational studies with estimated causal effects from clinical trials and meta-analyses and (2) estimating prediction models and causal effects directly from observational data. These methods require extending to dynamic treatment regimes, and consideration of multiple interventions to operationalise a clinical decision support system. Techniques for validating 'causal prediction models' are still in their infancy.

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.

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.

Counterfactual clinical prediction models could help to infer individualized treatment effects in randomized controlled trials-An illustration with the International Stroke Trial

OBJECTIVE

Causal treatment effects are estimated at the population level in randomized controlled trials, while clinical decision is often to be made at the individual level in practice. We aim to show how clinical prediction models used under a counterfactual framework may help to infer individualized treatment effects.

STUDY DESIGN AND SETTING

As an illustrative example, we reanalyze the International Stroke Trial. This large, multicenter trial enrolled 19,435 adult patients with suspected acute ischemic stroke from 36 countries, and reported a modest average benefit of aspirin (vs. no aspirin) on a composite outcome of death or dependency at 6 months. We derive and validate multivariable logistic regression models that predict the patient counterfactual risks of outcome with and without aspirin, conditionally on 23 predictors.

RESULTS

The counterfactual prediction models display good performance in terms of calibration and discrimination (validation c-statistics: 0.798 and 0.794). Comparing the counterfactual predicted risks on an absolute difference scale, we show that aspirin-despite an average benefit-may increase the risk of death or dependency at 6 months (compared with the control) in a quarter of stroke patients.

CONCLUSIONS

Counterfactual prediction models could help researchers and clinicians (i) infer individualized treatment effects and (ii) better target patients who may benefit from treatments.

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.