Evaluation of the accuracy of established patient inspiratory effort estimation methods during mechanical support ventilation.

There is a clinical need for monitoring inspiratory effort to prevent lung- and diaphragm injury in patients who receive supportive mechanical ventilation in an Intensive Care Unit. Different pressure-based techniques are available to estimate this inspiratory effort at the bedside, but the accuracy of their effort estimation is uncertain since they are all based on a simplified linear model of the respiratory system, which omits gas compressibility of air, and the viscoelasticity and nonlinearities of the respiratory system. The aim of this in-silico study was to provide an overview of the pressure-based estimation techniques and to evaluate their accuracy using a more sophisticated model of the respiratory system and ventilator. The influence of the following parameters on the accuracy of the pressure-based estimation techniques was evaluated using the in-silico model: 1) the patient's respiratory mechanics 2) PEEP and the inspiratory pressure of the ventilator 3) gas compressibility of air 4) viscoelasticity of the respiratory system 5) the strength of the inspiratory effort. The best-performing technique in terms of accuracy was the whole breath occlusion. The average error and maximum error were the lowest for all patient archetypes. We found that the error was related to the expansion of gas in the breathing set and lungs and respiratory compliance. However, concerns exist that other factors not included in the model, such as a changed muscle-force relation during an occlusion, might influence the true accuracy. The estimation techniques based on the esophageal pressure showed an error related to the viscoelastic element in the model which leads to a higher error than the occlusion. The error of the esophageal pressure-based techniques is therefore highly dependent on the pathology of the patient and the settings of the ventilator and might change over time while a patient recovers or becomes more ill.

A model-based approach to generating annotated pressure support waveforms.

Large numbers of asynchronies during pressure support ventilation cause discomfort and higher work of breathing in the patient, and are associated with an increased mortality. There is a need for real-time decision support to detect asynchronies and assist the clinician towards lung-protective ventilation. Machine learning techniques have been proposed to detect asynchronies, but they require large datasets with sufficient data diversity, sample size, and quality for training purposes. In this work, we propose a method for generating a large, realistic and labeled, synthetic dataset for training and validating machine learning algorithms to detect a wide variety of asynchrony types. We take a model-based approach in which we adapt a non-linear lung-airway model for use in a diverse patient group and add a first-order ventilator model to generate labeled pressure, flow, and volume waveforms of pressure support ventilation. The model was able to reproduce basic measured lung mechanics parameters. Experienced clinicians were not able to differentiate between the simulated waveforms and clinical data (P = 0.44 by Fisher's exact test). The detection performance of the machine learning trained on clinical data gave an overall comparable true positive rate on clinical data and on simulated data (an overall true positive rate of 94.3% and positive predictive value of 93.5% on simulated data and a true positive rate of 98% and positive predictive value of 98% on clinical data). Our findings demonstrate that it is possible to generate labeled pressure and flow waveforms with different types of asynchronies.

A Model-based Approach to Generating Annotated Pressure Support Waveforms.

During pressure support ventilation, every breath is triggered by the patient. Mismatches between the patient and the ventilator are called asynchronies. It has been reported that large numbers of asynchronies may be harmful and may lead to increased mortality. Automatic asynchrony detection and classification, with subsequent feedback to clinicians, will improve lung ventilation and, possibly, patient outcome. Machine learning techniques have been used to detect asynchronies. However, large, diverse and high-quality training and verification data sets are needed. In this work, we propose a model for generating a large, realistic, labeled, synthetic dataset for training and testing machine learning algorithms to detect a wide variety of asynchrony types. Next to a morphological evaluation of the obtained waveforms, validation of the proposed model includes a test with a machine learning algorithm trained on clinical data.

Effectiveness, safety and efficacy of INTELLiVENT-adaptive support ventilation, a closed-loop ventilation mode for use in ICU patients - a systematic review.

Introduction: INTELLiVENT-Adaptive Support Ventilation (INTELLiVENT-ASV), an advanced closed-loop ventilation mode for use in intensive care unit (ICU) patients, is equipped with algorithms that automatically adjust settings on the basis of physiologic signals and patient's activity. Here we describe its effectiveness, safety, and efficacy in various types of ICU patients.Areas covered: A systematic search conducted in MEDLINE, EMBASE, the Cochrane Central register of Controlled Trials (CENTRAL), and in Google Scholar identified 10 randomized clinical trials.Expert opinion: Studies suggest INTELLiVENT-ASV to be an effective automated mode with regard to the titrations of tidal volume, airway pressure, and oxygen. INTELLiVENT-ASV is as safe as conventional modes. However, thus far studies have not shown INTELLiVENT-ASV to be superior to conventional modes with regard to duration of ventilation and other patient-centered outcomes. Future studies are needed to test its efficacy.

Differences in identification of patients' deterioration may hamper the success of clinical escalation protocols.

BACKGROUND: Timely and consistent recognition of a 'clinical crisis', a life threatening condition that demands immediate intervention, is essential to reduce 'failure to rescue' rates in general wards. AIM: To determine how different clinical caregivers define a 'clinical crisis' and how they respond to it. DESIGN: An international survey. METHODS: Clinicians working on general wards, intensive care units or emergency departments in the Netherlands, the United Kingdom and Denmark were asked to review ten scenarios based on common real-life cases. Then they were asked to grade the urgency and severity of the scenario, their degree of concern, their estimate for the risk for death and indicate their preferred action for escalation. The primary outcome was the scenarios with a National Early Warning Score (NEWS) ≥7 considered to be a 'clinical crisis'. Secondary outcomes included how often a rapid response system (RRS) was activated, and if this was influenced by the participant's professional role or experience. The data from all participants in all three countries was pooled for analysis. RESULTS: A total of 150 clinicians participated in the survey. The highest percentage of clinicians that considered one of the three scenarios with a NEWS ≥7 as a 'clinical crisis' was 52%, while a RRS was activated by <50% of participants. Professional roles and job experience only had a minor influence on the recognition of a 'clinical crisis' and how it should be responded to. CONCLUSION: This international survey indicates that clinicians differ on what they consider to be a 'clinical crisis' and on how it should be managed. Even in cases with a markedly abnormal physiology (i.e. NEWS ≥7) many clinicians do not consider immediate activation of a RRS is required.

Intelligent dynamic clinical checklists improved checklist compliance in the intensive care unit.

BACKGROUND: Checklists can reduce medical errors. However, the effectiveness of checklists is hampered by lack of acceptance and compliance. Recently, a new type of checklist with dynamic properties has been created to provide more specific checklist items for each individual patient. Our purpose in this simulation-based study was to investigate a newly developed intelligent dynamic clinical checklist (DCC) for the intensive care unit (ICU) ward round. METHODS: Eligible clinicians were invited to participate as volunteers. Highest achievable scores were established for six typical ICU scenarios to determine which items must be checked. The participants compared the DCC with the local standard of care. The primary outcomes were the caregiver satisfaction score and the percentages of checked items overall and of critical items requiring a direct intervention. RESULTS: In total, 20 participants were included, who performed 116 scenarios. The median percentage of checked items was 100.0% with the DCC and 73.6% for the scenarios completed with local standard of care ( P <0.001). Critical items remained unchecked in 23.1% of the scenarios performed with local standard of care and 0.0% of the scenarios where the DCC was available ( P <0.001). The mean satisfaction score of the DCC was 4.13 out of 5. CONCLUSIONS: This simulation study indicates that an intelligent DCC significantly increases compliance with best practice by reducing the percentage of unchecked items during ICU ward rounds, while the user satisfaction rate remains high. Real-life clinical research is required to evaluate this new type of checklist further.

Thinking beyond the mass: ANCA-associated vasculitis mimicking a pancreatic malignancy.

Isolated pancreatic involvement is a rare initial presentation in patients with ANCA-associated vasculitis. We report a patient with a suspected malignant pancreatic mass, referred to our hospital for pancreaticoduodenectomy. However, the pancreatic mass proved to be the initial manifestation of ANCA-associated vasculitis.