The role of heart rate as a risk marker for predicting adverse outcomes

Cardiovascular disease is one of the leading causes of death around the world. Resting heart rate has been shown to be a strong and independent risk marker for adverse cardiovascular events and mortality, and yet its role as a predictor of risk is somewhat overlooked in clinical practice. With the aim of highlighting its prognostic value, the role of resting heart rate as a risk marker for death and other adverse outcomes was further examined in a number of different patient populations. A systematic review of studies that previously assessed the prognostic value of resting heart rate for mortality and other adverse cardiovascular outcomes was presented. New analyses of nine clinical trials were carried out. Both the original and extended Cox model that allows for analysis of time-dependent covariates were used to evaluate and compare the predictive value of baseline and time-updated heart rate measurements for adverse outcomes in the CAPRICORN, EUROPA, PROSPER, PERFORM, BEAUTIFUL and SHIFT populations. Pooled individual patient meta-analyses of the CAPRICORN, EPHESUS, OPTIMAAL and VALIANT trials, and the BEAUTIFUL and SHIFT trials, were also performed. The discrimination and calibration of the models applied were evaluated using Harrell’s C-statistic and likelihood ratio tests, respectively. Finally, following on from the systematic review, meta-analyses of the relation between baseline and time-updated heart rate, and the risk of death from any cause and from cardiovascular causes, were conducted. Both elevated baseline and time-updated resting heart rates were found to be associated with an increase in the risk of mortality and other adverse cardiovascular events in all of the populations analysed. In some cases, elevated time-updated heart rate was associated with risk of events where baseline heart rate was not. Time-updated heart rate also contributed additional information about the risk of certain events despite knowledge of baseline heart rate or previous heart rate measurements. The addition of resting heart rate to the models where resting heart rate was found to be associated with risk of outcome improved both discrimination and calibration, and in general, the models including time-updated heart rate along with baseline or the previous heart rate measurement had the highest and similar C-statistics, and thus the greatest discriminative ability. The meta-analyses demonstrated that a 5bpm higher baseline heart rate was associated with a 7.9% and an 8.0% increase in the risk of all-cause and cardiovascular death, respectively (both p less than 0.001). Additionally, a 5bpm higher time-updated heart rate (adjusted for baseline heart rate in eight of the ten studies included in the analyses) was associated with a 12.8% (p less than 0.001) and a 10.9% (p less than 0.001) increase in the risk of all-cause and cardiovascular death, respectively. These findings may motivate health care professionals to routinely assess resting heart rate in order to identify individuals at a higher risk of adverse events. The fact that the addition of time-updated resting heart rate improved the discrimination and calibration of models for certain outcomes, even if only modestly, strengthens the case that it be added to traditional risk models. The findings, however, are of particular importance, and have greater implications for the clinical management of patients with pre-existing disease. An elevated, or increasing heart rate over time could be used as a tool, potentially alongside other established risk scores, to help doctors identify patient deterioration or those at higher risk, who might benefit from more intensive monitoring or treatment re-evaluation. Further exploration of the role of continuous recording of resting heart rate, say, when patients are at home, would be informative. In addition, investigation into the cost-effectiveness and optimal frequency of resting heart rate measurement is required. One of the most vital areas for future research is the definition of an objective cut-off value for the definition of a high resting heart rate.

Business model evolution and firm performance of entrepreneurial companies

This research explores the business model (BM) evolution process of entrepreneurial companies and investigates the relationship between BM evolution and firm performance. Recently, it has been increasingly recognised that the innovative design (and re-design) of BMs is crucial to the performance of entrepreneurial firms, as BM can be associated with superior value creation and competitive advantage. However, there has been limited theoretical and empirical evidence in relation to the micro-mechanisms behind the BM evolution process and the entrepreneurial outcomes of BM evolution. This research seeks to fill this gap by opening up the ‘black box’ of the BM evolution process, exploring the micro-patterns that facilitate the continuous shaping, changing, and renewing of BMs and examining how BM evolutions create and capture value in a dynamic manner. Drawing together the BM and strategic entrepreneurship literature, this research seeks to understand: (1) how and why companies introduce BM innovations and imitations; (2) how BM innovations and imitations interplay as patterns in the BM evolution process; and (3) how BM evolution patterns affect firm performances. This research adopts a longitudinal multiple case study design that focuses on the emerging phenomenon of BM evolution. Twelve entrepreneurial firms in the Chinese Online Group Buying (OGB) industry were selected for their continuous and intensive developments of BMs and their varying success rates in this highly competitive market. Two rounds of data collection were carried out between 2013 and 2014, which generates 31 interviews with founders/co-founders and in total 5,034 pages of data. Following a three-stage research framework, the data analysis begins by mapping the BM evolution process of the twelve companies and classifying the changes in the BMs into innovations and imitations. The second stage focuses down to the BM level, which addresses the BM evolution as a dynamic process by exploring how BM innovations and imitations unfold and interplay over time. The final stage focuses on the firm level, providing theoretical explanations as to the effects of BM evolution patterns on firm performance. This research provides new insights into the nature of BM evolution by elaborating on the missing link between BM dynamics and firm performance. The findings identify four patterns of BM evolution that have different effects on a firm’s short- and long-term performance. This research contributes to the BM literature by presenting what the BM evolution process actually looks like. Moreover, it takes a step towards the process theory of the interplay between BM innovations and imitations, which addresses the role of companies’ actions, and more importantly, reactions to the competitors. Insights are also given into how entrepreneurial companies achieve and sustain value creation and capture by successfully combining the BM evolution patterns. Finally, the findings on BM evolution contributes to the strategic entrepreneurship literature by increasing the understanding of how companies compete in a more dynamic and complex environment. It reveals that, the achievement of superior firm performance is more than a simple question of whether to innovate or imitate, but rather an integration of innovation and imitation strategies over time. This study concludes with a discussion of the findings and their implications for theory and practice.