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Record W2904867303 · doi:10.1097/mcc.0000000000000576

Editorial: Every breath you take…should be monitored

2018· editorial· en· W2904867303 on OpenAlexaffabout
Tài Pham, Laurent Brochard

Bibliographic record

VenueCurrent Opinion in Critical Care · 2018
Typeeditorial
Languageen
FieldMedicine
TopicRespiratory Support and Mechanisms
Canadian institutionsSt. Michael's HospitalUniversity of Toronto
Fundersnot available
KeywordsMedicineIntensive care medicine

Abstract

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It is widely accepted that randomized clinical trials (RCT) provide a high level of scientific evidence. It is undisputable that RCTs have led to great advances in critically ill patient management in general and in patients with acute respiratory failure and the acute respiratory distress syndrome (ARDS) in particular [1–3]. However, RCTs have many limitations and are not exempt of flaws [4]: their sample-size calculations are often ‘optimized’ and the treatment effect overestimated to make study recruitment feasible (thus are actually underpowered); they usually take years from the initial hypothesis to the publication of their results; generally test only one approach versus another; and strict inclusion criteria to obtain a homogeneous population often prevent extrapolation of their results to the global population admitted to intensive care units. Last, examples of RCTs with different or contradictory results when replicated are not infrequent. On the other hand, observational studies have been criticized for their lower internal validity, the impossibility to adjust on unmeasured confounders, and their inability to provide causal inference. Nevertheless, they allow the inclusion of more ‘real life’ populations, focus on a specific subpopulation usually excluded from RCTs and have the practical advantage to be completed in a relatively short time [5]. When it comes to comparative effectiveness research, statistical tools such as propensity score or instrumental variables can markedly improve observational studies design. Taking ARDS as an example, the LUNG SAFE [6] study, an international observational study of patients with hypoxemic respiratory failure and ARDS, provided crucial information such as: underrecognition of the syndrome (how would you enroll patients with ARDS in a RCT when half of the patients meeting the inclusion criteria are not identified?); complexity of treatments choice (early treatment with noninvasive ventilation could be deleterious in patients with PaO2/FiO2 lower than 150 mmHg [7]); importance of the geo-economic variations on management and outcomes (survival was higher in countries with higher gross national income per person [8]); different managements in specific populations such as immunocompromised [9] or a lack of extensive workup when a cause of ARDS was not identified [10]. The bottom line is the paramount importance of identifying specific patients with specific needs. Close monitoring (of the respiratory system) in the ICU could solve this and help to optimize patient management individually. The inherent essence of intensive care is not only to grant organ-supportive treatments but to be able to provide close monitoring and to adapt treatment to this continuous assessment on the basis of physiological responses. The 11 articles written for the respiratory system issue of Current Opinion in Critical Care well reflect the importance of monitoring critically ill patients and personalizing their management. First of all, Beitler (pp. 3–11) discusses the tools available at the bedside to monitor the patient's respiratory system from the most widely available (physical examination) to the most recent technology (electrical impedance tomography) and their potential use to tailor ventilation to specific patients. As different types of patients likely require different investigations and treatments, Sinha and Calfee (pp. 12–20) provide an updated overview of the current research identifying homogeneous subgroups and phenotypes in ARDS and their different trajectories and responses to treatments; Ferreyro and Munshi (pp. 21–28) highlight the specificities and challenges of respiratory failure in immunocompromised patients, including a very informative focus on complications related to recent drugs such as immune checkpoint inhibitors or chimeric antigen receptor T-cell therapy; and Gibelin et al. (pp. 29–36) share their astute approach to rare respiratory diseases in the ICU including useful diagnosis strategy for intensivists facing these challenging cases. Finally, Cordioli et al. (pp. 37–44) present a modern and innovative approach to an old concept. Their physiological view of interactions between ventilation and circulation during cardiopulmonary resuscitation and interpretation of the capnogram may change the future management of cardiac arrest. Monitoring and treatments have to be adapted to the unique ICU environment in which the critically ill patient is admitted as emphasized by Inglis et al. (pp. 45–53) who remind us that there is not one but many different types of resource limited settings. Low and middle-income countries are too often left out of RCTs and observational studies and care providers in these underprivileged contexts develop inventive solutions to tackle the lack of expensive devices or supplies. Following on the theme of monitoring and personalized treatment, the importance of using the appropriate device (from the least to the most invasive) according to the situation is highlighted by the articles on noninvasive ventilation, airway pressure release ventilation (APRV) in pediatric patients, and extracorporeal membrane oxygenation (ECMO). García-de-Acilu et al. (pp. 54–62) take up the challenge of summarizing the current (and contradicting) knowledge and evidences on noninvasive ventilatory supports for acute hypoxemic respiratory failure. They wisely suggest to closely monitor patients receiving such treatments and avoid delaying intubations when time to move to invasive ventilation has come. Lalgudi Ganesan (pp. 63–70) synthesizes the physiology of APRV, its benefits, and limits and provides an insider analysis of the most recent RCT of use of APRV in children. Schmidt et al. (pp. 71–76) give their expert view of the recent advances in veno-venous ECMO in light of the recent ‘EOLIA’ trial which interpretation is controversial: ECMO benefit on mortality did not reach statistical significance but can be considered as being clinically significant. Monitoring and personalizing respiratory system management should not be limited to the lung but should also take into account respiratory muscles and the consequences of respiratory failure (dyspnea). Mirroring the concept of lung protective ventilation, Schepens et al. (pp. 77–85) nicely introduce the concept of diaphragm protective ventilation and explore the mechanisms of diaphragm myotrauma. Last but not least, Decavèle et al. (pp. 86–94) review the appropriate detection and treatment of patients’ dyspnea during mechanical ventilation giving a practical approach to this frequent and invalidating symptom. All these topics covered by acknowledged experts as well as brilliant and promising researchers/intensivists still in the early phase of their carrier give a fantastic overview of the modern approach of respiratory assessment, monitoring and management in critically ill patients. Precision medicine is still in its early days and it is clearer and clearer that the future of ventilation will be optimized by more accurate assessment, continuous monitoring with appropriate tools and personalized treatment based on the patient's trajectory, initial presentation, responses to treatment, and evolution in the ICU. Now take a deep breath and enjoy your reading. Acknowledgements The authors thank Thomas Piraino (RT, Department of Respiratory Therapy, St. Michael's Hospital, Toronto, Canada) for his careful English edition of the manuscript. Financial support and sponsorship L.B. holds the Keenan Chair in Critical Care and Respiratory Medicine. L.B. reports grants from Medtronic Covidien, grants and nonfinancial support from Air Liquide, nonfinancial support from Philips, nonfinancial support and other from General Electric, grants and nonfinancial support from Fisher Paykel, and nonfinancial support from Drager, consulting with Baxter, outside the submitted work. Conflicts of interest There are no conflicts of interest.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesResearch integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.008
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.154
GPT teacher head0.462
Teacher spread0.308 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreEditorial

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2018
Admission routes2
Has abstractyes

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