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Background Adaptive mechanical ventilation automatically adjusts respiratory rate (RR) and tidal volume ( V T ) to deliver the clinically desired minute ventilation, selecting RR and V T based on Otis’ equation on least work of breathing. However, the resulting V T may be relatively high, especially in patients with more compliant lungs. Therefore, a new mode of adaptive ventilation (adaptive ventilation mode 2, AVM2) was developed which automatically minimizes inspiratory power with the aim of ensuring lung-protective combinations of V T and RR. The aim of this study was to investigate whether AVM2 reduces V T , mechanical power, and driving pressure (Δ P stat ) and provides similar gas exchange when compared to adaptive mechanical ventilation based on Otis’ equation. Methods A prospective randomized cross-over study was performed in 20 critically ill patients on controlled mechanical ventilation, including 10 patients with acute respiratory distress syndrome (ARDS). Each patient underwent 1 h of mechanical ventilation with AVM2 and 1 h of adaptive mechanical ventilation according to Otis’ equation (adaptive ventilation mode, AVM). At the end of each phase, we collected data on V T , mechanical power, Δ P , PaO 2 /FiO 2 ratio, PaCO 2 , pH, and hemodynamics. Results Comparing adaptive mechanical ventilation with AVM2 to the approach based on Otis’ equation (AVM), we found a significant reduction in V T both in the whole study population (7.2 ± 0.9 vs. 8.2 ± 0.6 ml/kg, p  <  0.0001) and in the subgroup of patients with ARDS (6.6 ± 0.8 ml/kg with AVM2 vs. 7.9 ± 0.5 ml/kg with AVM, p  <  0.0001). Similar reductions were observed for Δ P stat (whole study population: 11.5 ± 1.6 cmH 2 O with AVM2 vs. 12.6 ± 2.5 cmH 2 O with AVM, p  <  0.0001; patients with ARDS: 11.8 ± 1.7 cmH 2 O with AVM2 and 13.3 ± 2.7 cmH 2 O with AVM, p  = 0.0044) and total mechanical power (16.8 ± 3.9 J/min with AVM2 vs. 18.6 ± 4.6 J/min with AVM, p  = 0.0024; ARDS: 15.6 ± 3.2 J/min with AVM2 vs. 17.5 ± 4.1 J/min with AVM, p  = 0.0023). There was a small decrease in PaO 2 /FiO 2 (270 ± 98 vs. 291 ± 102 mmHg with AVM, p  = 0.03; ARDS: 194 ± 55 vs. 218 ± 61 with AVM, p  = 0.008) and no differences in PaCO 2 , pH, and hemodynamics. Conclusions Adaptive mechanical ventilation with automated minimization of inspiratory power may lead to more lung-protective ventilator settings when compared with adaptive mechanical ventilation according to Otis’ equation. Trial registration The study was registered at the German Clinical Trials Register ( DRKS00013540 ) on December 1, 2017, before including the first patient.

BackgroundImplementation of new practice guidelines for statin use was very poor.ObjectiveTo test a multi-component quality improvement intervention to encourage use of new guidelines for statin use.DesignCluster-randomized, usual-care controlled trial.ParticipantsThe study population was primary care visits for patients who were recommended statins by the 2013 guidelines, but were not receiving them. We excluded patients who were over 75 years old, or had an ICD9 or ICD10 code for end-stage renal disease, muscle pain, pregnancy, or in vitro fertilization in the 2 years prior to the study visit.InterventionsA novel quality improvement intervention consisting of a personalized decision support tool, an educational program, a performance measure, and an audit and feedback system. Randomization was at the level of the primary care team.Main MeasuresOur primary outcome was prescription of a medium- or high-strength statin. We studied how receiving the intervention changed care during the quality improvement intervention compared to before it and if that change continued after the intervention.Key ResultsAmong 3787 visits to 43 primary care providers, being in the intervention arm tripled the odds of patients being prescribed an appropriate statin (OR 3.0, 95% CI 1.8–4.9), though the effect resolved after the personalized decision support ended (OR 1.7, 95% CI 0.99–2.77).ConclusionsA simple, personalized quality improvement intervention is promising for enabling the adoption of new guidelines.ClinicalTrials.gov IdentifierNCT02820870

The magnitude of benefit of cardiac resynchronization therapy (CRT) varies significantly among its recipients; approximately 30% of CRT patients do not report clinical improvement. Optimization of CRT pacing parameters can further improve cardiac function, both acutely and chronically. Echocardiographic optimization is used in clinical practice, but it is time and resource consuming. In addition, optimal settings at rest may change later with activity or cardiac remodeling. The adaptive CRT (aCRT) algorithm was designed to provide automatic ambulatory adjustment of CRT pacing configuration (left ventricular or biventricular pacing) and device delays based on periodic measurement of electrical conduction intervals. The aCRT algorithm is currently undergoing evaluation in a prospective, randomized, double-blinded, worldwide clinical trial. The trial enrolled 522 patients, who satisfied standard clinical indications for a CRT device. Within 2 weeks after the implant, the patients were randomized to aCRT versus echo-optimized biventricular pacing (Echo) settings in 2:1 ratio and followed up at 1-, 3-, 6-, and 12-month postrandomization. The noninferiority primary trial objectives at 6-month postrandomization are to demonstrate that (a) the percentage of aCRT patients who improved in their clinical composite score is at least as high as the percentage of Echo patients; (b) cardiac performance as assessed by echocardiography is similar when using aCRT settings versus echo-optimized settings; and (c) aCRT does not result in inappropriate device settings. First and last patient enrollments occurred in November 2009 and December 2010, respectively. The safety and efficacy of the aCRT algorithm will be evaluated in this ongoing clinical trial.

Medical imaging can assess the tumor and its environment in their entirety, which makes it suitable for monitoring the temporal and spatial characteristics of the tumor. Progress in computational methods, especially in artificial intelligence for medical image process and analysis, has converted these images into quantitative and minable data associated with clinical events in oncology management. This concept was first described as radiomics in 2012. Since then, computer scientists, radiologists, and oncologists have gravitated towards this new tool and exploited advanced methodologies to mine the information behind medical images. On the basis of a great quantity of radiographic images and novel computational technologies, researchers developed and validated radiomic models that may improve the accuracy of diagnoses and therapy response assessments. Here, we review the recent methodological developments in radiomics, including data acquisition, tumor segmentation, feature extraction, and modelling, as well as the rapidly developing deep learning technology. Moreover, we outline the main applications of radiomics in diagnosis, treatment planning and evaluations in the field of oncology with the aim of developing quantitative and personalized medicine. Finally, we discuss the challenges in the field of radiomics and the scope and clinical applicability of these methods.

Within the past decade, the field of immunotherapy has revolutionized the treatment of many cancers with the development and regulatory approval of various immune-checkpoint inhibitors and chimeric antigen receptor T cell therapies in diverse indications. Another promising approach to cancer immunotherapy involves the use of personalized vaccines designed to trigger de novo T cell responses against neoantigens, which are highly specific to tumours of individual patients, in order to amplify and broaden the endogenous repertoire of tumour-specific T cells. Results from initial clinical studies of personalized neoantigen-based vaccines, enabled by the availability of rapid and cost-effective sequencing and bioinformatics technologies, have demonstrated robust tumour-specific immunogenicity and preliminary evidence of antitumour activity in patients with melanoma and other cancers. Herein, we provide an overview of the complex process that is necessary to generate a personalized neoantigen vaccine, review the types of vaccine-induced T cells that are found within tumours and outline strategies to enhance the T cell responses. In addition, we discuss the current status of clinical studies testing personalized neoantigen vaccines in patients with cancer and considerations for future clinical investigation of this novel, individualized approach to immunotherapy.Personalized neoantigen-based therapeutic vaccines hold promise as cancer immunotherapies. This Review provides an overview of the complex personalized neoantigen vaccine production process, vaccine-induced T cell responses and strategies to enhance these responses. Completed and ongoing clinical studies testing such vaccines are discussed, and considerations for future clinical investigation of this novel, individualized form of immunotherapy are outlined.

The identification of molecular targets and the growing knowledge of their cellular functions have led to the development of small molecule inhibitors as a major therapeutic class for cancer treatment. Both multitargeted and highly selective kinase inhibitors are used for the treatment of advanced treatment-resistant cancers, and many have also achieved regulatory approval for early clinical settings as adjuvant therapies or as first-line options for recurrent or metastatic disease. Lessons learned from the development of these agents can accelerate the development of next-generation inhibitors to optimise the therapeutic index, overcome drug resistance, and establish combination therapies. The future of small molecule inhibitors is promising as there is the potential to investigate novel difficult-to-drug targets, to apply predictive non-clinical models to select promising drug candidates for human evaluation, and to use dynamic clinical trial interventions with liquid biopsies to deliver precision medicine.

Remarkable progress has been made in the development of biomarker-driven targeted therapies for patients with multiple cancer types, including melanoma, breast and lung tumours, although precision oncology for patients with colorectal cancer (CRC) continues to lag behind. Nonetheless, the availability of patient-derived CRC models coupled with in vitro and in vivo pharmacological and functional analyses over the past decade has finally led to advances in the field. Gene-specific alterations are not the only determinants that can successfully direct the use of targeted therapy. Indeed, successful inhibition of BRAF or KRAS in metastatic CRCs driven by activating mutations in these genes requires combinations of drugs that inhibit the mutant protein while at the same time restraining adaptive resistance via CRC-specific EGFR-mediated feedback loops. The emerging paradigm is, therefore, that the intrinsic biology of CRC cells must be considered alongside the molecular profiles of individual tumours in order to successfully personalize treatment. In this Review, we outline how preclinical studies based on patient-derived models have informed the design of practice-changing clinical trials. The integration of these experiences into a common framework will reshape the future design of biology-informed clinical trials in this field.Progress in precision medicine for colorectal cancer continues to lag behind the rapid improvements seen in patients with certain other solid tumour types. Nonetheless, owing largely to the availability of better translational models, novel and effective targeted therapy strategies based on tumour biology are beginning to be developed for subsets of patients. In this Review, the authors summarize these developments and discuss future directions in this rapidly evolving area of research.

In recent years, the development of nanoparticles has expanded into a broad range of clinical applications. Nanoparticles have been developed to overcome the limitations of free therapeutics and navigate biological barriers — systemic, microenvironmental and cellular — that are heterogeneous across patient populations and diseases. Overcoming this patient heterogeneity has also been accomplished through precision therapeutics, in which personalized interventions have enhanced therapeutic efficacy. However, nanoparticle development continues to focus on optimizing delivery platforms with a one-size-fits-all solution. As lipid-based, polymeric and inorganic nanoparticles are engineered in increasingly specified ways, they can begin to be optimized for drug delivery in a more personalized manner, entering the era of precision medicine. In this Review, we discuss advanced nanoparticle designs utilized in both non-personalized and precision applications that could be applied to improve precision therapies. We focus on advances in nanoparticle design that overcome heterogeneous barriers to delivery, arguing that intelligent nanoparticle design can improve efficacy in general delivery applications while enabling tailored designs for precision applications, thereby ultimately improving patient outcome overall.Advances in nanoparticle design could make substantial contributions to personalized and non-personalized medicine. In this Review, Langer, Mitchell, Peppas and colleagues discuss advances in nanoparticle design that overcome heterogeneous barriers to delivery, as well as the challenges in translating these design improvements into personalized medicine approaches.

The digital revolution is disrupting the ways in which health research is conducted, and subsequently, changing healthcare. Direct-to-consumer wellness products and mobile apps, pervasive sensor technologies and access to social network data offer exciting opportunities for researchers to passively observe and/or track patients ‘in the wild’ and 24/7. The volume of granular personal health data gathered using these technologies is unprecedented, and is increasingly leveraged to inform personalized health promotion and disease treatment interventions. The use of artificial intelligence in the health sector is also increasing. Although rich with potential, the digital health ecosystem presents new ethical challenges for those making decisions about the selection, testing, implementation and evaluation of technologies for use in healthcare. As the ‘Wild West’ of digital health research unfolds, it is important to recognize who is involved, and identify how each party can and should take responsibility to advance the ethical practices of this work. While not a comprehensive review, we describe the landscape, identify gaps to be addressed, and offer recommendations as to how stakeholders can and should take responsibility to advance socially responsible digital health research.