"Super-covariates": Using predicted control group outcome as a covariate in randomized clinical trials.

The power of randomized controlled clinical trials to demonstrate the efficacy of a drug compared with a control group depends not just on how efficacious the drug is, but also on the variation in patients' outcomes. Adjusting for prognostic covariates during trial analysis can reduce this variation. For this reason, the primary statistical analysis of a clinical trial is often based on regression models that besides terms for treatment and some further terms (e.g., stratification factors used in the randomization scheme of the trial) also includes a baseline (pre-treatment) assessment of the primary outcome. We suggest to include a "super-covariate"-that is, a patient-specific prediction of the control group outcome-as a further covariate (but not as an offset). We train a prognostic model or ensembles of such models on the individual patient (or aggregate) data of other studies in similar patients, but not the new trial under analysis. This has the potential to use historical data to increase the power of clinical trials and avoids the concern of type I error inflation with Bayesian approaches, but in contrast to them has a greater benefit for larger sample sizes. It is important for prognostic models behind "super-covariates" to generalize well across different patient populations in order to similarly reduce unexplained variability whether the trial(s) to develop the model are identical to the new trial or not. In an example in neovascular age-related macular degeneration we saw efficiency gains from the use of a "super-covariate".

Diagnostic Performance of a Machine Learning Algorithm (Asthma/Chronic Obstructive Pulmonary Disease [COPD] Differentiation Classification) Tool Versus Primary Care Physicians and Pulmonologists in Asthma, COPD, and Asthma/COPD Overlap.

BACKGROUND: The differential diagnosis of asthma and chronic obstructive pulmonary disease (COPD) poses a challenge in clinical practice and its misdiagnosis results in inappropriate treatment, increased exacerbations, and potentially death. OBJECTIVE: To investigate the diagnostic accuracy of the Asthma/COPD Differentiation Classification (AC/DC) tool compared with primary care physicians and pulmonologists in asthma, COPD, and asthma-COPD overlap. METHODS: The AC/DC machine learning-based diagnostic tool was developed using 12 parameters from electronic health records of more than 400,000 patients aged 35 years and older. An expert panel of three pulmonologists and four general practitioners from five countries evaluated 119 patient cases from a prospective observational study and provided a confirmed diagnosis (n = 116) of asthma (n = 53), COPD (n = 43), asthma-COPD overlap (n = 7), or other (n = 13). Cases were then reviewed by 180 primary care physicians and 180 pulmonologists from nine countries and by the AC/DC tool, and diagnostic accuracies were compared with reference to the expert panel diagnoses. RESULTS: Average diagnostic accuracy of the AC/DC tool was superior to that of primary care physicians (median difference, 24%; 95% posterior credible interval: 17% to 29%; P < .0001) and was noninferior and superior (median difference, 12%; 95% posterior credible interval: 6% to 17%; P < .0001 for noninferiority and P = .0006 for superiority) to that of pulmonologists. Average diagnostic accuracies were 73%, 50%, and 61% by AC/DC tool, primary care physicians, and pulmonologists versus expert panel diagnosis, respectively. CONCLUSION: The AC/DC tool demonstrated superior diagnostic accuracy compared with primary care physicians and pulmonologists in the diagnosis of asthma and COPD in patients aged 35 years and greater and has the potential to support physicians in the diagnosis of these conditions in clinical practice.

Eliciting judgements about dependent quantities of interest: The SHeffield ELicitation Framework extension and copula methods illustrated using an asthma case study.

Pharmaceutical companies regularly need to make decisions about drug development programs based on the limited knowledge from early stage clinical trials. In this situation, eliciting the judgements of experts is an attractive approach for synthesising evidence on the unknown quantities of interest. When calculating the probability of success for a drug development program, multiple quantities of interest-such as the effect of a drug on different endpoints-should not be treated as unrelated. We discuss two approaches for establishing a multivariate distribution for several related quantities within the SHeffield ELicitation Framework (SHELF). The first approach elicits experts' judgements about a quantity of interest conditional on knowledge about another one. For the second approach, we first elicit marginal distributions for each quantity of interest. Then, for each pair of quantities, we elicit the concordance probability that both lie on the same side of their respective elicited medians. This allows us to specify a copula to obtain the joint distribution of the quantities of interest. We show how these approaches were used in an elicitation workshop that was performed to assess the probability of success of the registrational program of an asthma drug. The judgements of the experts, which were obtained prior to completion of the pivotal studies, were well aligned with the final trial results.

A New Comprehensive Approach to Assess the Probability of Success of Development Programs Before Pivotal Trials.

The point at which clinical development programs transition from early phase to pivotal trials is a critical milestone. Substantial uncertainty about the outcome of pivotal trials may remain even after seeing positive early phase data, and companies may need to make difficult prioritization decisions for their portfolio. The probability of success (PoS) of a program, a single number expressed as a percentage reflecting the multitude of risks that may influence the final program outcome, is a key decision-making tool. Despite its importance, companies often rely on crude industry benchmarks that may be "adjusted" by experts based on undocumented criteria and which are typically misaligned with the definition of success used to drive commercial forecasts, leading to overly optimistic expected net present value calculations. We developed a new framework to assess the PoS of a program before pivotal trials begin. Our definition of success encompasses the successful outcome of pivotal trials, regulatory approval and meeting the requirements for market access as outlined in the target product profile. The proposed approach is organized in four steps and uses an innovative Bayesian approach to synthesize all relevant evidence. The new PoS framework is systematic and transparent. It will help organizations to make more informed decisions. In this paper, we outline the rationale and elaborate on the structure of the proposed framework, provide examples, and discuss the benefits and challenges associated with its adoption.

Improving the assessment of the probability of success in late stage drug development.

There are several steps to confirming the safety and efficacy of a new medicine. A sequence of trials, each with its own objectives, is usually required. Quantitative risk metrics can be useful for informing decisions about whether a medicine should transition from one stage of development to the next. To obtain an estimate of the probability of regulatory approval, pharmaceutical companies may start with industry-wide success rates and then apply to these subjective adjustments to reflect program-specific information. However, this approach lacks transparency and fails to make full use of data from previous clinical trials. We describe a quantitative Bayesian approach for calculating the probability of success (PoS) at the end of phase II which incorporates internal clinical data from one or more phase IIb studies, industry-wide success rates, and expert opinion or external data if needed. Using an example, we illustrate how PoS can be calculated accounting for differences between the phase II data and future phase III trials, and discuss how the methods can be extended to accommodate accelerated drug development pathways.

Predicting drug approvals: The Novartis data science and artificial intelligence challenge.

We describe a novel collaboration between academia and industry, an in-house data science and artificial intelligence challenge held by Novartis to develop machine-learning models for predicting drug-development outcomes, building upon research at MIT using data from Informa as the starting point. With over 50 cross-functional teams from 25 Novartis offices around the world participating in the challenge, the domain expertise of these Novartis researchers was leveraged to create predictive models with greater sophistication. Ultimately, two winning teams developed models that outperformed the baseline MIT model-areas under the curve of 0.88 and 0.84 versus 0.78, respectively-through state-of-the-art machine-learning algorithms and the use of newly incorporated features and data. In addition to validating the variables shown to be associated with drug approval in the earlier MIT study, the challenge also provided new insights into the drivers of drug-development success and failure.

Developing a short-term prediction model for asthma exacerbations from Swedish primary care patients' data using machine learning - Based on the ARCTIC study.

OBJECTIVE: The ability to predict impending asthma exacerbations may allow better utilization of healthcare resources, prevention of hospitalization and improve patient outcomes. We aimed to develop models using machine learning to predict risk of exacerbations. METHODS: Data from 29,396 asthma patients was collected from electronic medical records and national registers covering clinical and epidemiological factors (e.g. comorbidities, health care contacts), between 2000 and 2013. Machine-learning classifiers were used to create models to predict exacerbations within the next 15 days. Model selection was done using the mean cross validation score of area under precision-recall curve (AUPRC). RESULTS: The most important predictors of exacerbation were comorbidity burden and previous exacerbations. Model validation on test data yielded an AUPRC = 0.007 (95% CI: ± 0.0002), indicating that historic clinical information alone may not be sufficient to predict a near future risk of asthma exacerbation. CONCLUSIONS: Supplementation with additional data on environmental triggers, (e.g. weather, pollen count, air quality) and from wearables, might be necessary to improve performance of the short-term predictive model to develop a more clinically useful tool.

Maximizing Adherence and Gaining New Information For Your Chronic Obstructive Pulmonary Disease (MAGNIFY COPD): Study Protocol for the Pragmatic, Cluster Randomized Trial Evaluating the Impact of Dual Bronchodilator with Add-On Sensor and Electronic Monitoring on Clinical Outcomes.

BACKGROUND: Poor treatment adherence in COPD patients is associated with poor clinical outcomes and increased healthcare burden. Personalized approaches for adherence management, supported with technology-based interventions, may offer benefits to patients and providers but are currently unproven in terms of clinical outcomes as opposed to adherence outcomes. METHODS: Maximizing Adherence and Gaining New Information For Your COPD (MAGNIFY COPD study), a pragmatic cluster randomized trial, aims to evaluate the impact of an adherence technology package (interventional package), comprising an adherence review, ongoing provision of a dual bronchodilator but with an add-on inhaler sensor device and a connected mobile application. This will compare time to treatment failure and other clinical outcomes in patients identified at high risk of exacerbations with historic poor treatment adherence as measured by prescription collection to mono/dual therapy over one year (1312 patients) versus usual care. Treatment failure is defined as the first occurrence of one of the following: (1) moderate/severe COPD exacerbation, (2) prescription of triple therapy (inhaled corticosteroid/long-acting ß2-agonist/long-acting muscarinic antagonist [ICS/LABA/LAMA]), (3) prescription of additional chronic therapy for COPD, or (4) respiratory-related death. Adherence, moderate/severe exacerbations, respiratory-related healthcare resource utilization and costs, and intervention package acceptance rate will also be assessed. Eligible primary care practices (N=176) participating in the Optimum Patient Care Quality Improvement Program will be randomized (1:1) to either adherence support cluster arm (suitable patients already receiving or initiated Ultibro® Breezhaler® [indacaterol/glycopyrronium] will be offered interventional package) or the control cluster arm (suitable patients continue to receive usual clinical care). Patients will be identified and outcomes collected from anonymized electronic medical records within the Optimum Patient Care Research Database. On study completion, electronic medical record data will be re-extracted to analyze outcomes in both study groups. REGISTRATION NUMBER: ISRCTN10567920. CONCLUSION: MAGNIFY will explore patient benefits of technology-based interventions for electronic adherence monitoring.

Predicting Hospitalization Due to COPD Exacerbations in Swedish Primary Care Patients Using Machine Learning - Based on the ARCTIC Study.

PURPOSE: Chronic obstructive pulmonary disease (COPD) exacerbations can negatively impact disease severity, progression, mortality and lead to hospitalizations. We aimed to develop a model that predicts a patient's risk of hospitalization due to severe exacerbations (defined as COPD-related hospitalizations) of COPD, using Swedish patient level data. PATIENTS AND METHODS: Patient level data for 7823 Swedish patients with COPD was collected from electronic medical records (EMRs) and national registries covering healthcare contacts, diagnoses, prescriptions, lab tests, hospitalizations and socioeconomic factors between 2000 and 2013. Models were created using machine-learning methods to predict risk of imminent exacerbation causing patient hospitalization due to COPD within the next 10 days. Exacerbations occurring within this period were considered as one event. Model performance was assessed using the Area under the Precision-Recall Curve (AUPRC). To compare performance with previous similar studies, the Area Under Receiver Operating Curve (AUROC) was also reported. The model with the highest mean cross validation AUPRC was selected as the final model and was in a final step trained on the entire training dataset. RESULTS: The most important factors for predicting severe exacerbations were exacerbations in the previous six months and in whole history, number of COPD-related healthcare contacts and comorbidity burden. Validation on test data yielded an AUROC of 0.86 and AUPRC of 0.08, which was high in comparison to previously published attempts to predict COPD exacerbation. CONCLUSION: Our work suggests that clinically available information on patient history collected via automated retrieval from EMRs and national registries or directly during patient consultation can form the basis for future clinical tools to predict risk of severe COPD exacerbations.

Cardiovascular outcomes at recommended blood pressure targets in middle-aged and elderly patients with type 2 diabetes mellitus and hypertension.

PURPOSE: Available data of event-based clinical outcomes trials show that little evidence supports the guidelines recommendations to lower blood pressure (BP) to <130/80 mmHg in middle-aged and elderly people with type 2 diabetes mellitus and hypertension. We addressed this issue by post-hoc analysing the risk of cardiovascular (CV) events in mostly elderly high-risk hypertensive patients with type 2 diabetes mellitus participating in the Valsartan Antihypertensive Long-term Use Evaluation (VALUE) trial. MATERIAL AND METHODS: Patients (n = 5250) were divided into 4 groups according to the proportion of on-treatment visits before the occurrence of an event (<25% to ≥ 75%) in which BP was reduced to <140/90 or <130/80 mmHg. RESULTS: After adjustment for baseline demographic differences between groups, a reduction in the proportion of visits in which BP achieved <140/90 mmHg accompanied a progressive increase in the risk of CV mortality and morbidity as well as of cause-specific events such as stroke, myocardial infarction and heart failure. A progressive reduction in the proportion of visits in which BP was reduced <130/80 mmHg did not have any effect on CV risks. CONCLUSION: In mostly elderly high-risk hypertensive patients with type 2 diabetes mellitus participating in the VALUE trial, achieving more frequently BP <140/90 mmHg showed a marked protective effect on overall and all cause-specific cardiovascular outcomes. This was not the case for a more frequent achievement of the more intensive BP target, i.e. <130/80 mmHg.

Cardiovascular outcomes at recommended blood pressure targets in middle-aged and elderly patients with type 2 diabetes mellitus compared to all middle-aged and elderly hypertensive study patients with high cardiovascular risk.

PURPOSE: Event-based clinical outcome trials have shown limited evidence to support guidelines recommendations to lower blood pressure (BP) to <130/80 mmHg in middle-aged and elderly hypertensive patients with diabetes mellitus or with general high cardiovascular (CV) risk. We addressed this issue by post-hoc analysing the risk of CV events in patients who participated in the Valsartan Antihypertensive Long-term Use Evaluation (VALUE) trial and compared the hypertensive patients with type 2 diabetes mellitus with all high-risk hypertensive patients. MATERIALS AND METHODS: Patients were divided into 4 groups according to the proportion of on-treatment visits before the occurrence of an event (<25% to ≥75%) in which BP was reduced to <140/90 or <130/80 mmHg. Patients with diabetes mellitus (n = 5250) were compared with the entire VALUE population with high CV risk (n = 15,245). RESULTS: After adjustments for baseline differences between groups, a reduction in the proportion of visits in which BP was reduced to <140/90 mmHg, but not to <130/80 mmHg, was accompanied by a progressive increase in the risk of CV morbidity and mortality as well as stroke, myocardial infarction and heart failure in both diabetes mellitus and in all high-risk patients. Target BP <130/80 mmHg reduced stroke risk in the main population but not in the diabetes mellitus patients. Patients with diabetes mellitus had higher event rates for the primary cardiac endpoint and all-cause mortality driven by a higher rate of heart failure. CONCLUSION: In the high-risk hypertensive patients of the VALUE trial achieving more frequently BP <140/90 mmHg, but not <130/80 mmHg, showed principally the same protective effect on overall and cause-specific cardiovascular outcomes in patients with diabetes mellitus and in the general high-risk hypertensive population.

Evidence synthesis from aggregate recurrent event data for clinical trial design and analysis.

Information from historical trials is important for the design, interim monitoring, analysis, and interpretation of clinical trials. Meta-analytic models can be used to synthesize the evidence from historical data, which are often only available in aggregate form. We consider evidence synthesis methods for trials with recurrent event endpoints, which are common in many therapeutic areas. Such endpoints are typically analyzed by negative binomial regression. However, the individual patient data necessary to fit such a model are usually unavailable for historical trials reported in the medical literature. We describe approaches for back-calculating model parameter estimates and their standard errors from available summary statistics with various techniques, including approximate Bayesian computation. We propose to use a quadratic approximation to the log-likelihood for each historical trial based on 2 independent terms for the log mean rate and the log of the dispersion parameter. A Bayesian hierarchical meta-analysis model then provides the posterior predictive distribution for these parameters. Simulations show this approach with back-calculated parameter estimates results in very similar inference as using parameter estimates from individual patient data as an input. We illustrate how to design and analyze a new randomized placebo-controlled exacerbation trial in severe eosinophilic asthma using data from 11 historical trials.

Fevipiprant, an oral prostaglandin DP2 receptor (CRTh2) antagonist, in allergic asthma uncontrolled on low-dose inhaled corticosteroids.

Dose-related efficacy and safety of fevipiprant (QAW039), an oral DP2 (CRTh2) receptor antagonist, was assessed in patients with allergic asthma uncontrolled by low-dose inhaled corticosteroids (ICS).Adult patients were randomised to 12 weeks' treatment with once-daily (1, 3, 10, 30, 50, 75, 150, 300 or 450 mg q.d) or twice-daily (2, 25, 75 or 150 mg b.i.d) fevipiprant (n=782), montelukast 10 mg q.d (n=139) or placebo (n=137). All patients received inhaled budesonide 200 µg b.i.dFevipiprant produced a statistically significant improvement in the primary end-point of change in pre-dose forced expiratory volume in 1 s at week 12 (p=0.0035) with a maximum model-averaged difference to placebo of 0.112 L. The most favourable pairwise comparisons to placebo were for the fevipiprant 150 mg q.d and 75 mg b.i.d groups, with no clinically meaningful differences between q.d and b.i.d Montelukast also demonstrated a significant improvement in this end-point. No impact on other efficacy end-points was observed. Adverse events were generally mild/moderate in severity, and were evenly distributed across doses and treatments.Fevipiprant appears to be efficacious and well-tolerated in this patient population, with an optimum total daily dose of 150 mg. Further investigations into the clinical role of fevipiprant in suitably designed phase III clinical trials are warranted.

Effect of valsartan on kidney outcomes in people with impaired glucose tolerance.

AIMS: To examine the effect of valsartan on kidney outcomes in patients with impaired glucose tolerance (IGT). METHODS: In a double-blind randomized trial, 9306 patients with IGT were assigned to valsartan (160 mg daily) or placebo. The co-primary endpoints were the development of diabetes and two composite cardiovascular outcomes. Prespecified renal endpoints included: the composite of renal death, end-stage renal disease (ESRD) or doubling of serum creatinine; estimated glomerular filtration rate (eGFR) ≤30 mL/min/1.73 m2 ; hospitalization for renal failure; and progression from normoalbuminuria to microalbuminuria, microalbuminuria to macroalbuminuria, and normoalbuminuria to macroalbuminuria. The median follow-up was 6.2 years. RESULTS: Valsartan reduced the incidence of diabetes but not cardiovascular events. In the valsartan group, 25/4631 patients (0.5%), vs 26/4675 (0.6%) patients in the placebo group, developed ESRD or experienced doubling of serum creatinine (hazard ratio [HR] 0.96, 95% confidence interval [CI] 0.55-1.66; P = .87). Few patients in either group developed an eGFR of ≤30 mL/min/1.73 m2 or had a renal hospitalization. Fewer patients on valsartan (237/4084 [5.8%]) than on placebo (342/4092 [8.4%]) developed microalbuminuria (HR 0.68, 95% CI 0.57-0.80; P < .0001), and fewer valsartan-treated patients developed macroalbuminuria. Overall, urinary albumin-to-creatinine ratio (UACR) was 11% lower with valsartan (95% CI 8-13; P < .0001) and 9% lower (95% CI 6-11; P < .0001) after adjusting for both glucose and blood pressure. CONCLUSIONS: The effect of valsartan on UACR was not wholly explained by change in blood pressure or glucose. Valsartan reduced the incidence of microalbuminuria in IGT without increasing the incidence of hyperkalaemia or renal dysfunction compared with placebo.

Meta-analysis of aggregate data on medical events.

Meta-analyses of clinical trials often treat the number of patients experiencing a medical event as binomially distributed when individual patient data for fitting standard time-to-event models are unavailable. Assuming identical drop-out time distributions across arms, random censorship, and low proportions of patients with an event, a binomial approach results in a valid test of the null hypothesis of no treatment effect with minimal loss in efficiency compared with time-to-event methods. To deal with differences in follow-up-at the cost of assuming specific distributions for event and drop-out times-we propose a hierarchical multivariate meta-analysis model using the aggregate data likelihood based on the number of cases, fatal cases, and discontinuations in each group, as well as the planned trial duration and groups sizes. Such a model also enables exchangeability assumptions about parameters of survival distributions, for which they are more appropriate than for the expected proportion of patients with an event across trials of substantially different length. Borrowing information from other trials within a meta-analysis or from historical data is particularly useful for rare events data. Prior information or exchangeability assumptions also avoid the parameter identifiability problems that arise when using more flexible event and drop-out time distributions than the exponential one. We discuss the derivation of robust historical priors and illustrate the discussed methods using an example. We also compare the proposed approach against other aggregate data meta-analysis methods in a simulation study. Copyright © 2016 John Wiley & Sons, Ltd.

Fevipiprant, a prostaglandin D2 receptor 2 antagonist, in patients with persistent eosinophilic asthma: a single-centre, randomised, double-blind, parallel-group, placebo-controlled trial.

BACKGROUND: Eosinophilic airway inflammation is often present in asthma, and reduction of such inflammation results in improved clinical outcomes. We hypothesised that fevipiprant (QAW039), an antagonist of prostaglandin D2 receptor 2, might reduce eosinophilic airway inflammation in patients with moderate-to-severe eosinophilic asthma. METHODS: We performed a single-centre, randomised, double-blind, parallel-group, placebo-controlled trial at Glenfield Hospital (Leicester, UK). We recruited patients with persistent, moderate-to-severe asthma and an elevated sputum eosinophil count (≥2%). After a 2-week single-blind placebo run-in period, patients were randomly assigned (1:1) by the trial pharmacist, using previously generated treatment allocation cards, to receive fevipiprant (225 mg twice per day orally) or placebo, stratified by the use of oral corticosteroid treatment and bronchoscopy. The 12-week treatment period was followed by a 6-week single-blind placebo washout period. The primary outcome was the change in sputum eosinophil percentage from baseline to 12 weeks after treatment, analysed in the intention-to-treat population. All patients who received at least one dose of study drug were included in the safety analyses. This trial is registered with ClinicalTrials.gov, number NCT01545726, and with EudraCT, number 2011-004966-13. FINDINGS: Between Feb 10, 2012, and Jan 30, 2013, 61 patients were randomly assigned to receive fevipiprant (n=30) or placebo (n=31). Three patients in the fevipiprant group and four patients in the placebo group withdrew because of asthma exacerbations. Two patients in the fevipiprant group were incorrectly given placebo (one at the mid-treatment visit and one throughout the course of the study). They were both included in the fevipiprant group for the primary analysis, but the patient who was incorrectly given placebo throughout was included in the placebo group for the safety analyses. Between baseline and 12 weeks after treatment, sputum eosinophil percentage decreased from a geometric mean of 5·4% (95% CI 3·1-9·6) to 1·1% (0·7-1·9) in the fevipiprant group and from 4·6% (2·5-8·7) to 3·9% (CI 2·3-6·7) in the placebo group. Compared with baseline, mean sputum eosinophil percentage was reduced by 4·5 times in the fevipiprant group and by 1·3 times in the placebo group (difference between groups 3·5 times, 95% CI 1·7-7·0; p=0·0014). Fevipiprant had a favourable safety profile, with no deaths or serious adverse events reported. No patient withdrawals were judged by the investigator to be related to the study drug. INTERPRETATION: Fevipiprant reduces eosinophilic airway inflammation and is well tolerated in patients with persistent moderate-to-severe asthma and raised sputum eosinophil counts despite inhaled corticosteroid treatment. FUNDING: Novartis Pharmaceuticals, AirPROM project, and the UK National Institute for Health Research.

Predictors of cardiac morbidity in diabetic, new-onset diabetic and non-diabetic high-risk hypertensive patients: The Valsartan Antihypertensive Long-term Use Evaluation (VALUE) trial.

Diabetic and new-onset diabetic patients with hypertension have higher cardiac morbidity than patients without diabetes. We aimed to investigate whether baseline predictors of cardiac morbidity, the major constituent of the primary endpoint in the Valsartan Antihypertensive Long-term Use Evaluation (VALUE) trial, were different in patients with diabetes and new-onset diabetes compared to patients without diabetes. In total, 15,245 high-risk hypertensive patients in the VALUE trial were followed for an average of 4.2 years. At baseline, 5250 patients were diabetic by the 1999 World Health Organization criteria, 1298 patients developed new-onset diabetes and 8697 patients stayed non-diabetic during follow-up. Cardiac morbidity was defined as a composite of myocardial infarction and heart failure requiring hospitalization, and baseline predictors were identified by univariate and multivariate stepwise Cox regression analyses. History of coronary heart disease (CHD) and age were the most important predictors of cardiac morbidity in both diabetic and non-diabetic patients. History of CHD, history of stroke and age were the only significant predictors of cardiac morbidity in patients with new-onset diabetes. Predictors of cardiac morbidity, in particular history of CHD and age, were essentially the same in high-risk hypertensive patients with diabetes, new-onset diabetes and without diabetes who participated in the VALUE trial.

No evidence for a J-shaped curve in treated hypertensive patients with increased cardiovascular risk: The VALUE trial.

Previous studies have debated the notion that low blood pressure (BP) during treatment, particularly diastolic (DBP), is associated with increased risk of cardiovascular disease. We evaluated the impact of low BP on cardiovascular outcomes in a high-risk population of 15,244 hypertensive patients, almost half of whom had a history of coronary artery disease (CAD). In the prospective Valsartan Antihypertensive Long-term Use Evaluation (VALUE) trial, patients were randomized to valsartan or amlodipine regimens and followed for 4.2 years (mean) with no difference in the primary cardiovascular endpoint. A Cox proportional hazards model was used to evaluate the relationship between average on-treatment BP and clinical outcomes. The relationship between BP and cardiovascular events was adjusted for age, gender and body mass index, and baseline qualifying risk factors and diseases (smoking, high total cholesterol, diabetes mellitus, proteinuria, CAD, previous stroke and left ventricular hypertrophy). DBP ≥ 90 mmHg, compared with < 90 mmHg, was associated with increased incidence of the primary cardiovascular endpoint (all cardiac events); however, DBP < 70 mmHg, compared with ≥ 70 mmHg, was not associated with increased incidence after covariate adjustment (no J-shaped curve). Similar results were observed for death, myocardial infarction (MI), heart failure and stroke, considered separately. Nadir for MI was at DBP of 76 mmHg and for stroke 60 mmHg. The ratio of MI to stroke increased with lower DBP. In CAD patients the MI to stroke ratio was more pronounced than in patients without CAD but there was no significant J-curve in either group. Systolic BP ≥ 150 but not < 130 mmHg, compared with 130-149 mmHg, similarly was associated with increased risk for primary outcome. In conclusion, patients in BP strata ≥ 150/90 mmHg, but not patients in BP strata < 130/70 mmHg, were at increased risk for adverse outcomes in this hypertensive, high-risk population. Although benefit in preventing MI in relation to preventing stroke levels off for the lowest BPs, these data provide no support for a J-curve in the treatment of high-risk hypertensive patients . The increase in the ratio of MI to stroke with lower DBP indicates target organ heterogeneity in that the optimal on-treatment DBP for cerebroprotection is below that for cardioprotection.

Cardiovascular outcomes at different on-treatment blood pressures in the hypertensive patients of the VALUE trial.

AIMS: Recent hypertension guidelines recommend that also in high cardiovascular (CV) risk, hypertensive patients blood pressure (BP) is lowered to <140/90 mmHg as no evidence is available supporting the lower target of <130/80 mmHg recommended in previous guidelines. Whether this represents the optimal treatment strategy is debated, however. METHODS AND RESULTS: The high CV risk hypertensive patients of the Valsartan Antihypertensive Long-term use Evaluation (VALUE) trial were divided into subgroups according to (i) the percentage of on-treatment visits in which BP was reduced to <140/90 or <130/80 mmHg or (ii) the mean systolic or diastolic BP (SBP/DBP) values achieved during the entire treatment period or up to the occurrence of an event. A progressive increase from <25 to ≥75% of the visits in which BP was <140/90 mmHg was accompanied by a significant, progressive marked decrease in the covariate adjusted risk of CV morbidity and mortality, cause specific CV events (myocardial infarction, heart failure, and stroke), and all-cause mortality. Except for a persistent progressive decrease in stroke, no significant trend to a risk decrease occurred for a similar progressive increment of the proportion of visits with BP <130/80 mmHg. Increasing the proportion of visits with a BP <140/90 mmHg (but not <130/80 mmHg) was accompanied by a decreased risk of events also when differences in baseline risk were adjusted using a propensity score. Finally, compared with patients remaining at a mean on-treatment SBP ≥140 or DBP ≥90 mmHg, the risk of all events was markedly reduced when on-treatment mean SBP was lowered to a mean SBP of 130-139 mmHg or a mean DBP of 80-89 mmHg, whereas at on-treatment mean SBP <130 mmHg or DBP <80 mmHg, an additional risk reduction was found for stroke but for any other type of event, the risk of which remained similar or only slightly greater than that seen at the higher BP target. CONCLUSIONS: In the high CV risk, hypertensives of the VALUE trial reducing BP consistently to <140/90 mmHg had marked beneficial effects both when data were calculated as proportion of visits at BP target or as on-treatment mean BP. Reducing BP to <130/80 mmHg led only to some possible further benefit on stroke, whereas the risk of other outcomes remained substantially similar to or slightly greater than that seen at the higher target. Thus, aggressive BP reductions when CV risk is high may not offer substantial advantages, except perhaps in patients or conditions in which stroke risk is particularly common.