Effective sample size estimation for a mechanical ventilation trial through Monte-Carlo simulation: Length of mechanical ventilation and Ventilator Free Days

Virtual patient framework for the testing of mechanical ventilation airway pressure and flow settings protocol

BACKGROUND AND OBJECTIVE

Model-based and personalised decision support systems are emerging to guide mechanical ventilation (MV) treatment for respiratory failure patients. However, model-based treatments require resource-intensive clinical trials prior to implementation. This research presents a framework for generating virtual patients for testing model-based decision support, and direct use in MV treatment.

METHODS

The virtual MV patient framework consists of 3 stages: 1) Virtual patient generation, 2) Patient-level validation, and 3) Virtual clinical trials. The virtual patients are generated from retrospective MV patient data using a clinically validated respiratory mechanics model whose respiratory parameters (respiratory elastance and resistance) capture patient-specific pulmonary conditions and responses to MV care over time. Patient-level validation compares the predicted responses from the virtual patient to their retrospective results for clinically implemented MV settings and changes to care. Patient-level validated virtual patients create a platform to conduct virtual trials, where the safety of closed-loop model-based protocols can be evaluated.

RESULTS

This research creates and presents a virtual patient platform of 100 virtual patients generated from retrospective data. Patient-level validation reported median errors of 3.26% for volume-control and 6.80% for pressure-control ventilation mode. A virtual trial on a model-based protocol demonstrates the potential efficacy of using virtual patients for prospective evaluation and testing of the protocol.

CONCLUSION

The virtual patient framework shows the potential to safely and rapidly design, develop, and optimise new model-based MV decision support systems and protocols using clinically validated models and computer simulation, which could ultimately improve patient care and outcomes in MV.

Factors Associated with the Occurrence of Death Outcome in Children with Neonatal Respiratory Distress Syndrome

Neonatal respiratory distress syndrome (NRDS) is a consequence of immaturity at birth and it is still associated with relatively high mortality rate. The aim of this study was to identify the factors associated with the occurrence of fatal outcome in newborns with neonatal respiratory distress syndrome.The research was designed as a case-control study nested in a retrospective cohort, and it enrolled newborns treated during 2015 at Pediatric Clinic of Clinical Center in Kragujevac. Diagnosis of NRDS and decision about the treatment were left at the discretion of attending pediatricians. The cases were patients with fatal outcome, while controls were randomly selected from the pool of survivors and matched with each case by gender in a ratio of 4:1. The study included 371 newborns, of whom 201 (54.2%) were male and 170 (45.8%) female. Lethal outcome occurred in 36 newborns (9,7%). Significant association was found between death and APGAR score (ORadjusted: 0.516, 95% CI: 0.322-0.827), weight on delivery (ORadjusted: 0.996, 95% CI: 0.993-0.999), duration of hospitalization (ORadjusted: 0.901, 95% CI: 0.835-0.972) and mechanical ventilation (ORadjusted: 165.256, 95% CI: 7.616-3585.714). Higher gestational age, higher birth weight, higher APGAR score and longer duration of hospitalization were singled out as protective factors, while use of mechanical ventilation increased the risk of death. Major limitations of the study were retrospective nature and relatively small number of identified cases. Postponing delivery and delivery in institution with neonatal intensive care unit are crucial for survival of newborns with NRDS.

Model-based PEEP titration versus standard practice in mechanical ventilation: a randomised controlled trial

Background

Positive end-expiratory pressure (PEEP) at minimum respiratory elastance during mechanical ventilation (MV) in patients with acute respiratory distress syndrome (ARDS) may improve patient care and outcome. The Clinical utilisation of respiratory elastance (CURE) trial is a two-arm, randomised controlled trial (RCT) investigating the performance of PEEP selected at an objective, model-based minimal respiratory system elastance in patients with ARDS.

Methods and design

The CURE RCT compares two groups of patients requiring invasive MV with a partial pressure of arterial oxygen/fraction of inspired oxygen (PaO2/FiO2) ratio ≤ 200; one criterion of the Berlin consensus definition of moderate (≤ 200) or severe (≤ 100) ARDS. All patients are ventilated using pressure controlled (bi-level) ventilation with tidal volume = 6–8 ml/kg. Patients randomised to the control group will have PEEP selected per standard practice (SPV). Patients randomised to the intervention will have PEEP selected based on a minimal elastance using a model-based computerised method. The CURE RCT is a single-centre trial in the intensive care unit (ICU) of Christchurch hospital, New Zealand, with a target sample size of 320 patients over a maximum of 3 years. The primary outcome is the area under the curve (AUC) ratio of arterial blood oxygenation to the fraction of inspired oxygen over time. Secondary outcomes include length of time of MV, ventilator-free days (VFD) up to 28 days, ICU and hospital length of stay, AUC of oxygen saturation (SpO₂)/FiO₂ during MV, number of desaturation events (SpO₂ < 88%), changes in respiratory mechanics and chest x-ray index scores, rescue therapies (prone positioning, nitric oxide use, extracorporeal membrane oxygenation) and hospital and 90-day mortality.

Discussion

The CURE RCT is the first trial comparing significant clinical outcomes in patients with ARDS in whom PEEP is selected at minimum elastance using an objective model-based method able to quantify and consider both inter-patient and intra-patient variability. CURE aims to demonstrate the hypothesized benefit of patient-specific PEEP and attest to the significance of real-time monitoring and decision-support for MV in the critical care environment.

Trial registration

Australian New Zealand Clinical Trial Registry, ACTRN12614001069640. Registered on 22 September 2014.

(https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=366838&isReview=true) The CURE RCT clinical protocol and data usage has been granted by the New Zealand South Regional Ethics Committee (Reference number: 14/STH/132).

The Lung Physiome and virtual patient models: From morphometry to clinical translation

The understanding or prediction of specific functions of the lung can be made using compact models that have identifiable parameters and that are custom designed to the problem of interest. However, when structure contributes to function - as is the case with most lung pathologies - structure-based, biophysical models become essential. Here we describe the application of structure-based models within the lung Physiome framework to identifying and explaining patient risk in 12patients diagnosed with acute pulmonary embolism. The model integrates perfusion, ventilation, and gas exchange to predict arterial blood gases and pulmonary artery pressure in individual patient models in response to patient-specific blood clot distribution, with full or partial arterial occlusion. The necessity for a patient-specific approach with biophysical models that account for scale-specific structure and function is demonstrated.

Optimising mechanical ventilation through model-based methods and automation

Mechanical ventilation (MV) is a core life-support therapy for patients suffering from respiratory failure or acute respiratory distress syndrome (ARDS). Respiratory failure is a secondary outcome of a range of injuries and diseases, and results in almost half of all intensive care unit (ICU) patients receiving some form of MV. Funding the increasing demand for ICU is a major issue and MV, in particular, can double the cost per day due to significant patient variability, over-sedation, and the large amount of clinician time required for patient management. Reducing cost in this area requires both a decrease in the average duration of MV by improving care, and a reduction in clinical workload. Both could be achieved by safely automating all or part of MV care via model-based dynamic systems modelling and control methods are ideally suited to address these problems. This paper presents common lung models, and provides a vision for a more automated future and explores predictive capacity of some current models. This vision includes the use of model-based methods to gain real-time insight to patient condition, improve safety through the forward prediction of outcomes to changes in MV, and develop virtual patients for in-silico design and testing of clinical protocols. Finally, the use of dynamic systems models and system identification to guide therapy for improved personalised control of oxygenation and MV therapy in the ICU will be considered. Such methods are a major part of the future of medicine, which includes greater personalisation and predictive capacity to both optimise care and reduce costs. This review thus presents the state of the art in how dynamic systems and control methods can be applied to transform this core area of ICU medicine.

COSCA (Core Outcome Set for Cardiac Arrest) in Adults: An Advisory Statement From the International Liaison Committee on Resuscitation

Cardiac arrest effectiveness trials have traditionally reported outcomes that focus on survival. A lack of consistency in outcome reporting between trials limits the opportunities to pool results for meta-analysis. The COSCA initiative (Core Outcome Set for Cardiac Arrest), a partnership between patients, their partners, clinicians, research scientists, and the International Liaison Committee on Resuscitation, sought to develop a consensus core outcome set for cardiac arrest for effectiveness trials. Core outcome sets are primarily intended for large, randomized clinical effectiveness trials (sometimes referred to as pragmatic trials or phase III/IV trials) rather than for pilot or efficacy studies. A systematic review of the literature combined with qualitative interviews among cardiac arrest survivors was used to generate a list of potential outcome domains. This list was prioritized through a Delphi process, which involved clinicians, patients, and their relatives/partners. An international advisory panel narrowed these down to 3 core domains by debate that led to consensus. The writing group refined recommendations for when these outcomes should be measured and further characterized relevant measurement tools. Consensus emerged that a core outcome set for reporting on effectiveness studies of cardiac arrest (COSCA) in adults should include survival, neurological function, and health-related quality of life. This should be reported as survival status and modified Rankin scale score at hospital discharge, at 30 days, or both. Health-related quality of life should be measured with ≥1 tools from Health Utilities Index version 3, Short-Form 36-Item Health Survey, and EuroQol 5D-5L at 90 days and at periodic intervals up to 1 year after cardiac arrest, if resources allow.