The Emergence of De-facto Standards

Increasingly, companies compete on platform technologies that bring together groups of users in two-sided networks. Examples include smartphones and on-line search engines. In industries governed by platform technologies, it is common to see one emerging as the de-facto standard because they are especially prone to network externalities (i.e. when the benefit that can be derived from a technology increases exponentially with the number of users). Competitions for the de-facto standard are high-stakes games. These ‘winner-take-all’ markets demonstrate very different competitive dynamics than markets in which many competitors can coexist relatively peacefully, as they often have a single tipping point which shifts market adoption to one particular technology. The academic field lacks a robust clarification on how firms can shape the odds of their technology emerging as the de-facto standard. This study develops an integrative framework and corresponding methodology for understanding the process by which a technology becomes the de-facto standard. By applying the framework to several technology competitions, insight is provided in how firm-, technology- and market-related elements influence technology competitions. Results indicate that the emergence of every de-facto standard displays a unique path. This path can be divided into six phases, and can be influenced by 40 unique elements, of which 19 are subject to strategic decision making. Patterns between technology competitions indicate a common set of focal points per phase

Internal-external cross-validation helped to evaluate the generalizability of prediction models in large clustered datasets

OBJECTIVE: To illustrate how to evaluate the need of complex strategies for developing generalizable prediction models in large clustered datasets. STUDY DESIGN AND SETTING: We developed eight Cox regression models to estimate the risk of heart failure using a large population-level dataset. These models differed in the number of predictors, the functional form of the predictor effects (non-linear effects and interaction) and the estimation method (maximum likelihood and penalization). Internal-external cross-validation was used to evaluate the models' generalizability across the included general practices. RESULTS: Among 871,687 individuals from 225 general practices, 43,987 (5.5%) developed heart failure during a median follow-up time of 5.8 years. For discrimination, the simplest prediction model yielded a good concordance statistic, which was not much improved by adopting complex strategies. Between-practice heterogeneity in discrimination was similar in all models. For calibration, the simplest model performed satisfactorily. Although accounting for non-linear effects and interaction slightly improved the calibration slope, it also led to more heterogeneity in the observed/expected ratio. Similar results were found in a second case study involving patients with stroke. CONCLUSION: In large clustered datasets, prediction model studies may adopt internal-external cross-validation to evaluate the generalizability of competing models, and to identify promising modelling strategies

Does Heterogeneity Exist in Treatment Associations With Renin–Angiotensin–System Inhibitors or Beta-blockers According to Phenotype Clusters in Heart Failure with Preserved Ejection Fraction?

BACKGROUND: We explored the association between use of renin–angiotensin system inhibitors and beta-blockers, with mortality/morbidity in 5 previously identified clusters of patients with heart failure with preserved ejection fraction (HFpEF). METHODS AND RESULTS: We analyzed 20,980 patients with HFpEF from the Swedish HF registry, phenotyped into young–low comorbidity burden (12%), atrial fibrillation–hypertensive (32%), older–atrial fibrillation (24%), obese–diabetic (15%), and a cardiorenal cluster (17%). In Cox proportional hazard models with inverse probability weighting, there was no heterogeneity in the association between renin–angiotensin system inhibitor use and cluster membership for any of the outcomes: cardiovascular (CV) mortality, all-cause mortality, HF hospitalisation, CV hospitalisation, or non-CV hospitalisation. In contrast, we found a statistical interaction between beta-blocker use and cluster membership for all-cause mortality (P = .03) and non-CV hospitalisation (P = .001). In the young–low comorbidity burden and atrial fibrillation–hypertensive cluster, beta-blocker use was associated with statistically significant lower all-cause mortality and non-CV hospitalisation and in the obese–diabetic cluster beta-blocker use was only associated with a statistically significant lower non-CV hospitalisation. The interaction between beta-blocker use and cluster membership for all-cause mortality could potentially be driven by patients with improved EF. However, patient numbers were diminished when excluding those with improved EF and the direction of the associations remained similar. CONCLUSIONS: In patients with HFpEF, the association with all-cause mortality and non-CV hospitalisation was heterogeneous across clusters for beta-blockers. It remains to be elucidated how heterogeneity in HFpEF could influence personalized medicine and future clinical trial design

Discovering Distinct Phenotypical Clusters in Heart Failure Across the Ejection Fraction Spectrum: a Systematic Review

Review Purpose: This systematic review aims to summarise clustering studies in heart failure (HF) and guide future clinical trial design and implementation in routine clinical practice. Findings: 34 studies were identified (n = 19 in HF with preserved ejection fraction (HFpEF)). There was significant heterogeneity invariables and techniques used. However, 149/165 described clusters could be assigned to one of nine phenotypes: 1) young, low comorbidity burden; 2) metabolic; 3) cardio-renal; 4) atrial fibrillation (AF); 5) elderly female AF; 6) hypertensive-comorbidity; 7) ischaemic-male; 8) valvular disease; and 9) devices. There was room for improvement on important methodological topics for all clustering studies such as external validation and transparency of the modelling process. Summary: The large overlap between the phenotypes of the clustering studies shows that clustering is a robust approach for discovering clinically distinct phenotypes. However, future studies should invest in a phenotype model that can be implemented in routine clinical practice and future clinical trial design. Graphical Abstract: HF = heart failure, EF = ejection fraction, HFpEF = heart failure with preserved ejection fraction, HFrEF = heart failure with reduced ejection fraction, CKD = chronic kidney disease, AF = atrial fibrillation, IHD = ischaemic heart disease, CAD = coronary artery disease, ICD = implantable cardioverter-defibrillator, CRT = cardiac resynchronization therapy, NT-proBNP = N-terminal pro b-type natriuretic peptide, BMI = Body Mass Index, COPD = Chronic obstructive pulmonary disease

Internal-external cross-validation helped to evaluate the generalizability of prediction models in large clustered datasets.

OBJECTIVE: To illustrate how to evaluate the need of complex strategies for developing generalizable prediction models in large clustered datasets. STUDY DESIGN AND SETTING: We developed eight Cox regression models to estimate the risk of heart failure using a large population-level dataset. These models differed in the number of predictors, the functional form of the predictor effects (non-linear effects and interaction) and the estimation method (maximum likelihood and penalization). Internal-external cross-validation was used to evaluate the models' generalizability across the included general practices. RESULTS: Among 871,687 individuals from 225 general practices, 43,987 (5.5%) developed heart failure during a median follow-up time of 5.8 years. For discrimination, the simplest prediction model yielded a good concordance statistic, which was not much improved by adopting complex strategies. Between-practice heterogeneity in discrimination was similar in all models. For calibration, the simplest model performed satisfactorily. Although accounting for non-linear effects and interaction slightly improved the calibration slope, it also led to more heterogeneity in the observed/expected ratio. Similar results were found in a second case study involving patients with stroke. CONCLUSION: In large clustered datasets, prediction model studies may adopt internal-external cross-validation to evaluate the generalizability of competing models, and to identify promising modelling strategies

Risk factors for incident heart failure in age- and sex-specific strata: a population-based cohort using linked electronic health records

AIMS: Several risk factors for incident heart failure (HF) have been previously identified, however large electronic health records (EHR) datasets may provide the opportunity to examine the consistency of risk factors across different subgroups from the general population. METHODS AND RESULTS: We used linked EHR data from 2000 to 2010 as part of the UK-based CALIBER resource to select a cohort of 871 687 individuals 55 years or older and free of HF at baseline. The primary endpoint was the first record of HF from primary or secondary care. Cox proportional hazards analysis was used to estimate hazard ratios for associations between risk factors and incident HF, separately for men and women and by age category: 55-64, 65-74, and > 75 years. During 5.8 years of median follow-up, a total of 47 987 incident HF cases were recorded. Age, social deprivation, smoking, sedentary lifestyle, diabetes, atrial fibrillation, chronic obstructive pulmonary disease, body mass index, haemoglobin, total white blood cell count and creatinine were associated with HF. Smoking, atrial fibrillation and diabetes showed stronger associations with incident HF in women compared to men. CONCLUSION: We confirmed associations of several risk factors with HF in this large population-based cohort across age and sex subgroups. Mainly modifiable risk factors and comorbidities are strongly associated with incident HF, highlighting the importance of preventive strategies targeting such risk factors for HF

Empagliflozin in Heart Failure With Predicted Preserved Versus Reduced Ejection Fraction: Data From the EMPA-REG OUTCOME Trial

Background: In the EMPA-REG OUTCOME trial, ejection fraction (EF) data were not collected. In the subpopulation with heart failure (HF), we applied a new predictive model for EF to determine the effects of empagliflozin in HF with predicted reduced (HFrEF) vs preserved (HFpEF) EF vs no HF. / Methods and Results: We applied a validated EF predictive model based on patient baseline characteristics and treatments to categorize patients with HF as being likely to have HF with mid-range EF (HFmrEF)/HFrEF (EF <50%) or HFpEF (EF ≥50%). Cox regression was used to assess the effect of empagliflozin vs placebo on cardiovascular death/HF hospitalization (HHF), cardiovascular and all-cause mortality, and HHF in patients with predicted HFpEF, HFmrEF/HFrEF and no HF. Of 7001 EMPA-REG OUTCOME patients with data available for this analysis, 6314 (90%) had no history of HF. Of the 687 with history of HF, 479 (69.7%) were predicted to have HFmrEF/HFrEF and 208 (30.3%) to have HFpEF. Empagliflozin's treatment effect was consistent in predicted HFpEF, HFmrEF/HFrEF and no-HF for each outcome (HR [95% CI] for the primary outcome 0.60 [0.31–1.17], 0.79 [0.51–1.23], and 0.63 [0.50–0.78], respectively; P interaction = 0.62). / Conclusions: In EMPA-REG OUTCOME, one-third of the patients with HF had predicted HFpEF. The benefits of empagliflozin on HF and mortality outcomes were consistent in nonHF, predicted HFpEF and HFmrEF/HFrEF

Clinical profile and contemporary management of patients with heart failure with preserved ejection fraction: results from the CHECK-HF registry

Background: Clinical management of heart failure with preserved ejection fraction (HFpEF) centres on treating comorbidities and is likely to vary between countries. Thus, to provide insight into the current management of HFpEF, studies from multiple countries are required. We evaluated the clinical profiles and current management of patients with HFpEF in the Netherlands. Methods: We included 2153 patients with HFpEF (defined as a left ventricular ejection fraction ≥ 50%) from the CHECK-HF registry, which included patients from 2013 to 2016. Results: Median age was 77 (IQR 15) years, 55% were women and the most frequent comorbidities were hypertension (51%), renal insufficiency (45%) and atrial fibrillation (AF, 38%). Patients between 65 and 80 years and those over 80 years had on average more comorbidities (up to 64% and 74%, respectively, with two or more comorbidities) than patients younger than 65 years (38% with two or more comorbidities, p-value < 0.001). Although no specific drugs are available for HFpEF, treating comorbidities is advised. Beta-blockers were most frequently prescribed (78%), followed by loop diuretics (74%), renin-angiotensin system (RAS) inhibitors (67%) and mineralocorticoid receptor antagonists (MRAs, 39%). Strongest predictors for loop-diuretic use were older age, higher New York Heart Association class and AF. Conclusion: The medical HFpEF profile is determined by the underlying comorbidities, sex and age. Comorbidities are highly prevalent in HFpEF patients, especially in elderly HFpEF patients. Despite the lack of evidence, many HFpEF patients receive regular beta-blockers, RAS inhibitors and MRAs, often for the treatment of comorbidities