Practical Data Analytics for Innovation in Medicine: Building Real Predictive and Prescriptive Models in Personalized Healthcare and Medical Research Using AI, ML, and Related Technologies, Second Edition

Practical Data Analytics for Innovation in Medicine: Building Real Predictive and Prescriptive Models in Personalized Healthcare and Medical Research Using AI, ML, and Related Technologies, Second Edition discusses the needs of healthcare and medicine in the 21st century, explaining how data analytics play an important and revolutionary role. With healthcare effectiveness and economics facing growing challenges, there is a rapidly emerging movement to fortify medical treatment and administration by tapping the predictive power of big data, such as predictive analytics, which can bolster patient care, reduce costs, and deliver greater efficiencies across a wide range of operational functions. Sections bring a historical perspective, highlight the importance of using predictive analytics to help solve health crisis such as the COVID-19 pandemic, provide access to practical step-by-step tutorials and case studies online, and use exercises based on real-world examples of successful predictive and prescriptive tools and systems. The final part of the book focuses on specific technical operations related to quality, cost-effective medical and nursing care delivery and administration brought by practical predictive analytics. © 2023 Elsevier Inc. All rights reserved.

Long-term (180-Day) Outcomes in Critically Ill Patients With COVID-19 in the REMAP-CAP Randomized Clinical Trial.

Importance: The longer-term effects of therapies for the treatment of critically ill patients with COVID-19 are unknown. Objective: To determine the effect of multiple interventions for critically ill adults with COVID-19 on longer-term outcomes. Design, Setting, and Participants: Prespecified secondary analysis of an ongoing adaptive platform trial (REMAP-CAP) testing interventions within multiple therapeutic domains in which 4869 critically ill adult patients with COVID-19 were enrolled between March 9, 2020, and June 22, 2021, from 197 sites in 14 countries. The final 180-day follow-up was completed on March 2, 2022. Interventions: Patients were randomized to receive 1 or more interventions within 6 treatment domains: immune modulators (n = 2274), convalescent plasma (n = 2011), antiplatelet therapy (n = 1557), anticoagulation (n = 1033), antivirals (n = 726), and corticosteroids (n = 401). Main Outcomes and Measures: The main outcome was survival through day 180, analyzed using a bayesian piecewise exponential model. A hazard ratio (HR) less than 1 represented improved survival (superiority), while an HR greater than 1 represented worsened survival (harm); futility was represented by a relative improvement less than 20% in outcome, shown by an HR greater than 0.83. Results: Among 4869 randomized patients (mean age, 59.3 years; 1537 [32.1%] women), 4107 (84.3%) had known vital status and 2590 (63.1%) were alive at day 180. IL-6 receptor antagonists had a greater than 99.9% probability of improving 6-month survival (adjusted HR, 0.74 [95% credible interval {CrI}, 0.61-0.90]) and antiplatelet agents had a 95% probability of improving 6-month survival (adjusted HR, 0.85 [95% CrI, 0.71-1.03]) compared with the control, while the probability of trial-defined statistical futility (HR >0.83) was high for therapeutic anticoagulation (99.9%; HR, 1.13 [95% CrI, 0.93-1.42]), convalescent plasma (99.2%; HR, 0.99 [95% CrI, 0.86-1.14]), and lopinavir-ritonavir (96.6%; HR, 1.06 [95% CrI, 0.82-1.38]) and the probabilities of harm from hydroxychloroquine (96.9%; HR, 1.51 [95% CrI, 0.98-2.29]) and the combination of lopinavir-ritonavir and hydroxychloroquine (96.8%; HR, 1.61 [95% CrI, 0.97-2.67]) were high. The corticosteroid domain was stopped early prior to reaching a predefined statistical trigger; there was a 57.1% to 61.6% probability of improving 6-month survival across varying hydrocortisone dosing strategies. Conclusions and Relevance: Among critically ill patients with COVID-19 randomized to receive 1 or more therapeutic interventions, treatment with an IL-6 receptor antagonist had a greater than 99.9% probability of improved 180-day mortality compared with patients randomized to the control, and treatment with an antiplatelet had a 95.0% probability of improved 180-day mortality compared with patients randomized to the control. Overall, when considered with previously reported short-term results, the findings indicate that initial in-hospital treatment effects were consistent for most therapies through 6 months.

Entrepreneurs, platforms, and international technology transformation

Platform business models and their underlying technologies are having a transformational impact on the global business landscape. This Schumpeterian disruption is the direct result of international entrepreneurship spanning 40 years from the battle for the industry standard for personal computers in the 1980s to the present day. The chapter analyses the role of entrepreneurs in laying the technological foundations for the growth of the platform business model before undertaking a literature review of business model innovation and the resource-based view (RBV) of technological disruption. This highlights how platform-driven ecosystems have inverted traditional ways of doing business which incumbent firms struggle to emulate. Due to the global nature of the Internet as a technology platform, the chapter will also consider the importance of the born global firm and why traditional one-sided businesses struggle to match the agility and scope of the new age entrepreneurial platform start-ups. This is evidenced by the surge in platform adoption in response to the Covid-19 pandemic. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

The effect of smoking on COVID-19 severity: A systematic review and meta-analysis.

Various comorbidities represent risk factors for severe coronavirus disease 2019 (COVID-19). The impact of smoking on COVID-19 severity has been previously reported in several meta-analyses limited by small sample sizes and poor methodology. We aimed to rigorously and definitively quantify the effects of smoking on COVID-19 severity. MEDLINE, Embase, CENTRAL, and Web of Science were searched between 1 December 2019 and 2 June 2020. Studies reporting smoking status of hospitalized patients with different severities of disease and/or at least one clinical endpoint of interest (disease progression, intensive care unit admission, need for mechanical ventilation, and mortality) were included. Data were pooled using a random-effects model. This study was registered on PROSPERO: CRD42020180920. We analyzed 47 eligible studies reporting on 32 849 hospitalized COVID-19 patients, with 8417 (25.6%) reporting a smoking history, comprising 1501 current smokers, 5676 former smokers, and 1240 unspecified smokers. Current smokers had an increased risk of severe COVID-19 (risk ratios [RR]: 1.80; 95% confidence interval [CI]: 1.14-2.85; P = .012), and severe or critical COVID-19 (RR: 1.98; CI: 1.16-3.38; P = .012). Patients with a smoking history had a significantly increased risk of severe COVID-19 (RR: 1.31; CI: 1.12-1.54; P = .001), severe or critical COVID-19 (RR: 1.35; CI: 1.19-1.53; P < .0001), in-hospital mortality (RR: 1.26; CI: 1.20-1.32; P < .0001), disease progression (RR: 2.18; CI: 1.06-4.49; P = .035), and need for mechanical ventilation (RR: 1.20; CI: 1.01-1.42; P = .043). Patients with any smoking history are vulnerable to severe COVID-19 and worse in-hospital outcomes. In the absence of current targeted therapies, preventative, and supportive strategies to reduce morbidity and mortality in current and former smokers are crucial.