An Open-Loop, Physiologic Model–Based Decision Support System Can Provide Appropriate Ventilator Settings:

Clinical practice, decision-making, and use of clinical decision support systems in invasive mechanical ventilation: a narrative review

Invasive mechanical ventilation is a key supportive therapy for patients on intensive care. There is increasing emphasis on personalised ventilation strategies. Clinical decision support systems (CDSS) have been developed to support this. We conducted a narrative review to assess evidence that could inform device implementation. A search was conducted in MEDLINE (Ovid) and EMBASE. Twenty-nine studies met the inclusion criteria. Role allocation is well described, with interprofessional collaboration dependent on culture, nurse:patient ratio, the use of protocols, and perception of responsibility. There were no descriptions of process measures, quality metrics, or clinical workflow. Nurse-led weaning is well-described, with factors grouped by patient, nurse, and system. Physician-led weaning is heterogenous, guided by subjective and objective information, and 'gestalt'. No studies explored decision-making with CDSS. Several explored facilitators and barriers to implementation, grouped by clinician (facilitators: confidence using CDSS, retaining decision-making ownership; barriers: undermining clinician's role, ambiguity moving off protocol), intervention (facilitators: user-friendly interface, ease of workflow integration, minimal training requirement; barriers: increased documentation time), and organisation (facilitators: system-level mandate; barriers: poor communication, inconsistent training, lack of technical support). One study described factors that support CDSS implementation. There are gaps in our understanding of ventilation practice. A coordinated approach grounded in implementation science is required to support CDSS implementation. Future research should describe factors that guide clinical decision-making throughout mechanical ventilation, with and without CDSS, map clinical workflow, and devise implementation toolkits. Novel research design analogous to a learning organisation, that considers the commercial aspects of device design, is required.

Current situation and needs analysis of medical staff first aid ability in China: a cross-sectional study

OBJECTIVES

We aim to understand the current situation of the first aid ability and training needs of Chinese medical personnel to provide a scientific basis for formulating the contents and methods of emergency medical rescue training and thereby improve the first aid level of Chinese medical personnel.

METHODS

A cross-sectional survey was conducted between June 2022 and February 2023 using a two-stage cluster sampling method with a structured questionnaire sent to medical workers in 12 provinces in China. 14,527 questionnaires were included in this study. Data were collected on demographic characteristics, first aid knowledge and skills, and training needs. Variance analysis was used to compare the difference between the first aid ability and training needs of medical staff in different hospitals, and multiple linear regression analysis was carried out to evaluate first aid ability and training needs.

RESULT

The study included 6041 patients (41.6%) in tertiary hospitals, 5838 patients (40.2%) in secondary hospitals, and 2648 patients (18.2%) in primary hospitals. There were significant differences in the first aid ability and training needs of medical staff in hospitals of different levels (p < 0.001). The score of first aid knowledge and skills in tertiary hospitals was the highest (209.7 ± 45.0), and the score of training needs in primary hospitals was the highest (240.6 ± 44.0). There was a significant correlation between first aid ability and training needs score (p < 0.001). Multiple linear regression analysis shows that geographic region, age, work tenure, gender, job title, department, professional title, monthly income, and hospital level are the influencing factors of training demand.

CONCLUSION

Medical staff in primary hospitals generally have low first aid knowledge and skills and a strong willingness to train. Therefore, it is imperative to strengthen the training of first aid ability and research training strategies. The level of the hospital is closely related to the level of first aid, so it is necessary to recognize the commonalities and differences in medical staff's demand for first aid knowledge and skills and carry out targeted education and training.

A long short-temory relation network for real-time prediction of patient-specific ventilator parameters

Accurate prediction of patient-specific ventilator parameters is crucial for optimizing patient-ventilator interaction. Current approaches encounter difficulties in concurrently observing long-term, time-series dependencies and capturing complex, significant features that influence the ventilator treatment process, thereby hindering the achievement of accurate prediction of ventilator parameters. To address these challenges, we propose a novel approach called the long short-term memory relation network (LSTMRnet). Our approach uses a long, short-term memory bank to store rich information and an important feature selection step to extract relevant features related to respiratory parameters. This information is obtained from the prior knowledge of the follow up model. We also concatenate the embeddings of both information types to maintain the joint learning of spatio-temporal features. Our LSTMRnet effectively preserves both time-series and complex spatial-critical feature information, enabling an accurate prediction of ventilator parameters. We extensively validate our approach using the publicly available medical information mart for intensive care (MIMIC-III) dataset and achieve superior results, which can be potentially utilized for ventilator treatment (i.e., sleep apnea-hypopnea syndrome ventilator treatment and intensive care units ventilator treatment.

Computer clinical decision support that automates personalized clinical care: a challenging but needed healthcare delivery strategy

How to deliver best care in various clinical settings remains a vexing problem. All pertinent healthcare-related questions have not, cannot, and will not be addressable with costly time- and resource-consuming controlled clinical trials. At present, evidence-based guidelines can address only a small fraction of the types of care that clinicians deliver. Furthermore, underserved areas rarely can access state-of-the-art evidence-based guidelines in real-time, and often lack the wherewithal to implement advanced guidelines. Care providers in such settings frequently do not have sufficient training to undertake advanced guideline implementation. Nevertheless, in advanced modern healthcare delivery environments, use of eActions (validated clinical decision support systems) could help overcome the cognitive limitations of overburdened clinicians. Widespread use of eActions will require surmounting current healthcare technical and cultural barriers and installing clinical evidence/data curation systems. The authors expect that increased numbers of evidence-based guidelines will result from future comparative effectiveness clinical research carried out during routine healthcare delivery within learning healthcare systems.

Reconstructing asynchrony for mechanical ventilation using a hysteresis loop virtual patient model

BACKGROUND

Patient-specific lung mechanics during mechanical ventilation (MV) can be identified from measured waveforms of fully ventilated, sedated patients. However, asynchrony due to spontaneous breathing (SB) effort can be common, altering these waveforms and reducing the accuracy of identified, model-based, and patient-specific lung mechanics.

METHODS

Changes in patient-specific lung elastance over a pressure-volume (PV) loop, identified using hysteresis loop analysis (HLA), are used to detect the occurrence of asynchrony and identify its type and pattern. The identified HLA parameters are then combined with a nonlinear mechanics hysteresis loop model (HLM) to extract and reconstruct ventilated waveforms unaffected by asynchronous breaths. Asynchrony magnitude can then be quantified using an energy-dissipation metric, Easyn, comparing PV loop area between model-reconstructed and original, altered asynchronous breathing cycles. Performance is evaluated using both test-lung experimental data with a known ground truth and clinical data from four patients with varying levels of asynchrony.

RESULTS

Root mean square errors for reconstructed PV loops are within 5% for test-lung experimental data, and 10% for over 90% of clinical data. Easyn clearly matches known asynchrony magnitude for experimental data with RMS errors < 4.1%. Clinical data performance shows 57% breaths having Easyn > 50% for Patient 1 and 13% for Patient 2. Patient 3 only presents 20% breaths with Easyn > 10%. Patient 4 has Easyn = 0 for 96% breaths showing accuracy in a case without asynchrony.

CONCLUSIONS

Experimental test-lung validation demonstrates the method's reconstruction accuracy and generality in controlled scenarios. Clinical validation matches direct observations of asynchrony in incidence and quantifies magnitude, including cases without asynchrony, validating its robustness and potential efficacy as a clinical real-time asynchrony monitoring tool.

Stochastic integrated model-based protocol for volume-controlled ventilation setting

BACKGROUND AND OBJECTIVE

Mechanical ventilation (MV) is the primary form of care for respiratory failure patients. MV settings are based on general clinical guidelines, intuition, and experience. This approach is not patient-specific and patients may thus experience suboptimal, potentially harmful MV care. This study presents the Stochastic integrated VENT (SiVENT) protocol which combines model-based approaches of the VENT protocol from previous works, with stochastic modelling to take the variation of patient respiratory elastance over time into consideration.

METHODS

A stochastic model of Ers is integrated into the VENT protocol from previous works to develop the SiVENT protocol, to account for both intra- and inter-patient variability. A cohort of 20 virtual MV patients based on retrospective patient data are used to validate the performance of this method for volume-controlled (VC) ventilation. A performance evaluation was conducted where the SiVENT and VENT protocols were implemented in 1080 instances each to compare the two protocols and evaluate the difference in reduction of possible MV settings achieved by each.

RESULTS

From an initial number of 189,000 possible MV setting combinations, the VENT protocol reduced this number to a median of 10,612, achieving a reduction of 94.4% across the cohort. With the integration of the stochastic model component, the SiVENT protocol reduced this number from 189,000 to a median of 9329, achieving a reduction of 95.1% across the cohort. The SiVENT protocol reduces the number of possible combinations provided to the user by more than 1000 combinations as compared to the VENT protocol.

CONCLUSIONS

Adding a stochastic model component into a model-based approach to selecting MV settings improves the ability of a decision support system to recommend patient-specific MV settings. It specifically considers inter- and intra-patient variability in respiratory elastance and eliminates potentially harmful settings based on clinically recommended pressure thresholds. Clinical input and local protocols can further reduce the number of safe setting combinations. The results for the SiVENT protocol justify further investigation of its prediction accuracy and clinical validation trials.

Transparent decision support for mechanical ventilation using visualization of clinical preferences

Background

Systems aiding in selecting the correct settings for mechanical ventilation should visualize patient information at an appropriate level of complexity, so as to reduce information overload and to make reasoning behind advice transparent. Metaphor graphics have been applied to this effect, but these have largely been used to display diagnostic and physiologic information, rather than the clinical decision at hand. This paper describes how the conflicting goals of mechanical ventilation can be visualized and applied in making decisions. Data from previous studies are analyzed to assess whether visual patterns exist which may be of use to the clinical decision maker.

Materials and methods

The structure and screen visualizations of a commercial clinical decision support system (CDSS) are described, including the visualization of the conflicting goals of mechanical ventilation represented as a hexagon. Retrospective analysis is performed on 95 patients from 2 previous clinical studies applying the CDSS, to identify repeated patterns of hexagon symbols.

Results

Visual patterns were identified describing optimal ventilation, over and under ventilation and pressure support, and over oxygenation, with these patterns identified for both control and support modes of mechanical ventilation. Numerous clinical examples are presented for these patterns illustrating their potential interpretation at the bedside.

Conclusions

Visual patterns can be identified which describe the trade-offs required in mechanical ventilation. These may have potential to reduce information overload and help in simple and rapid identification of sub-optimal settings.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12938-021-00974-5.

Decision support system to evaluate ventilation in the acute respiratory distress syndrome (DeVENT study)-trial protocol

Background

The acute respiratory distress syndrome (ARDS) occurs in response to a variety of insults, and mechanical ventilation is life-saving in this setting, but ventilator-induced lung injury can also contribute to the morbidity and mortality in the condition. The Beacon Caresystem is a model-based bedside decision support system using mathematical models tuned to the individual patient’s physiology to advise on appropriate ventilator settings. Personalised approaches using individual patient description may be particularly advantageous in complex patients, including those who are difficult to mechanically ventilate and wean, in particular ARDS.

Methods

We will conduct a multi-centre international randomised, controlled, allocation concealed, open, pragmatic clinical trial to compare mechanical ventilation in ARDS patients following application of the Beacon Caresystem to that of standard routine care to investigate whether use of the system results in a reduction in driving pressure across all severities and phases of ARDS.

Discussion

Despite 20 years of clinical trial data showing significant improvements in ARDS mortality through mitigation of ventilator-induced lung injury, there remains a gap in its personalised application at the bedside. Importantly, the protective effects of higher positive end-expiratory pressure (PEEP) were noted only when there were associated decreases in driving pressure. Hence, the pressures set on the ventilator should be determined by the diseased lungs’ pressure-volume relationship which is often unknown or difficult to determine. Knowledge of extent of recruitable lung could improve the ventilator driving pressure. Hence, personalised management demands the application of mechanical ventilation according to the physiological state of the diseased lung at that time. Hence, there is significant rationale for the development of point-of-care clinical decision support systems which help personalise ventilatory strategy according to the current physiology. Furthermore, the potential for the application of the Beacon Caresystem to facilitate local and remote management of large numbers of ventilated patients (as seen during this COVID-19 pandemic) could change the outcome of mechanically ventilated patients during the course of this and future pandemics.

Trial registration

ClinicalTrials.gov identifier NCT04115709. Registered on 4 October 2019, version 4.0

Supplementary Information

The online version contains supplementary material available at 10.1186/s13063-021-05967-2.

Ventilator-Associated Lung Injury

Ventilatory support, while life saving, can also cause or aggravate lung injury through several mechanisms which are encompassed within ventilator-associated lung injury (VALI). The important realizationin the acute respiratory distress syndrome that the “baby” lung resided in non-dependent areas led to the conceptualization of “lung rest” to reduce stress and strain to exposed alveolar units. We discuss concepts and mechanisms within VALI that ultimately induce maladaptive lung responses, as well as, current and future management strategies to detect and mitigate VALI at the bedside.

Multilayer perceptron neural network model development for mechanical ventilator parameters prediction by real time system learning

Background and objective

In pandemic situations like COVID 19, real time monitoring of patient condition and continuous delivery of inspired oxygen can be made possible only through artificial intelligence-based system modeling. Even now manual control of mechanical ventilator parameters is continuing despite the ever-increasing number of patients in critical epidemic conditions. Here a suggestive multi-layer perceptron neural network model is developed to predict the level of inspired oxygen delivered by the mechanical ventilator along with mode and positive end expiratory pressure (PEEP) changes for reducing the effort of health care professionals.

Methods

The artificial neural network model is developed by Python programming using real time data. Parameter identification for model inputs and outputs is done by in corporating consistent real time patient data including periodical arterial blood gas analysis, continuous pulse oximetry readings and mechanical ventilator settings using statistical pairwise analysis using R programming.

Results

Mean square error values and R values of the model are calculated and found to be an average of 0.093 and 0.81 respectively for various data sets. Accuracy loss will be in good fit with validation loss for a comparable number of epochs.

Conclusions

Comparison of the model output is undertaken with physician’s prediction using statistical analysis and shows an accuracy error of 4.11 percentages which is permissible for a good predictive system.

Model-based estimation of negative inspiratory driving pressure in patients receiving invasive NAVA mechanical ventilation

BACKGROUND

Optimisation of mechanical ventilation (MV) and weaning requires insight into underlying patient breathing effort. Current identifiable models effectively describe lung mechanics, such as elastance (E) and resistance (R) at the bedside in sedated patients, but are less effective when spontaneous breathing is present. This research derives and regularises a single compartment model to identify patient-specific inspiratory effort.

METHODS

Constrained second-order b-spline basis functions (knot width 0.05 s) are used to describe negative inspiratory drive (Pp, cmH₂O) as a function of time. Breath-breath Pp are identified with single E and R values over inspiration and expiration from n = 20 breaths for N = 22 patients on NAVA ventilation. Pp is compared to measured electrical activity of the diaphragm (Eadi) and published results.

RESULTS

Average per-patient root-mean-squared model fit error was (median [interquartile range, IQR]) 0.9 [0.6-1.3] cmH₂O, and average per-patient median Pp was -3.9 [-4.5- -3.0] cmH₂O, with range -7.9 - -1.9 cmH₂O. Per-patient E and R were 16.4 [13.6-21.8] cmH₂O/L and 9.2 [6.4-13.1] cmH₂O.s/L, respectively. Most patients showed an inspiratory volume threshold beyond which Pp started to return to baseline, and Pp at peak Eadi (end-inspiration) was often strongly correlated with peak Eadi (R²=0.25-0.86). Similarly, average transpulmonary pressure was consistent breath-breath in most patients, despite differences in peak Eadi and thus peak airway pressure.

CONCLUSIONS

The model-based inspiratory effort aligns with electrical muscle activity and published studies showing neuro-muscular decoupling as a function of pressure and/or volume. Consistency in coupling/dynamics were patient-specific. Quantification of patient and ventilator work of breathing contributions may aid optimisation of MV modes and weaning.

Feasibility of implementing Extubation Advisor, a clinical decision support tool to improve extubation decision-making in the ICU: a mixed-methods observational study

OBJECTIVES

Although spontaneous breathing trials (SBTs) are standard of care to extubation readiness, no tool exists that optimises prediction and standardises assessment. In this study, we evaluated the feasibility and clinical impressions of Extubation Advisor (EA), a comprehensive clinical extubation decision support (CDS) tool.

DESIGN

Phase I mixed-methods observational study.

SETTING

Two Canadian intensive care units (ICUs).

PARTICIPANTS

We included patients on mechanical ventilation for ≥24 hours and clinicians (respiratory therapists and intensivists) responsible for extubation decisions.

INTERVENTIONS

Components included a predictive model assessment, feasibility evaluation, questionnaires and interviews with clinicians.

RESULTS

We enrolled 117 patients, totalling 151 SBTs and 80 extubations. The incidence of extubation failure was 11% in low-risk patients and 21% in high-risk patients stratified by the predictive model; 38% failed extubation when both the model and clinical impression were at high risk. The tool was well rated: 94% and 75% rated the data entry and EA report as average or better, respectively. Interviews (n=15) revealed favourable impressions regarding its user interface and functionality, but unexpectedly, also concerns regarding EA's potential impact on respiratory therapists' job security.

CONCLUSIONS

EA implementation was feasible, and users perceived it to have potential to support extubation decision-making. This study helps to understand bedside implementation of CDS tools in a multidisciplinary ICU.

TRIAL REGISTRATION NUMBER

NCT02988167.

Enabling a learning healthcare system with automated computer protocols that produce replicable and personalized clinician actions

Clinical decision-making is based on knowledge, expertise, and authority, with clinicians approving almost every intervention-the starting point for delivery of "All the right care, but only the right care," an unachieved healthcare quality improvement goal. Unaided clinicians suffer from human cognitive limitations and biases when decisions are based only on their training, expertise, and experience. Electronic health records (EHRs) could improve healthcare with robust decision-support tools that reduce unwarranted variation of clinician decisions and actions. Current EHRs, focused on results review, documentation, and accounting, are awkward, time-consuming, and contribute to clinician stress and burnout. Decision-support tools could reduce clinician burden and enable replicable clinician decisions and actions that personalize patient care. Most current clinical decision-support tools or aids lack detail and neither reduce burden nor enable replicable actions. Clinicians must provide subjective interpretation and missing logic, thus introducing personal biases and mindless, unwarranted, variation from evidence-based practice. Replicability occurs when different clinicians, with the same patient information and context, come to the same decision and action. We propose a feasible subset of therapeutic decision-support tools based on credible clinical outcome evidence: computer protocols leading to replicable clinician actions (eActions). eActions enable different clinicians to make consistent decisions and actions when faced with the same patient input data. eActions embrace good everyday decision-making informed by evidence, experience, EHR data, and individual patient status. eActions can reduce unwarranted variation, increase quality of clinical care and research, reduce EHR noise, and could enable a learning healthcare system.

Intensive Care Weaning (iCareWean) protocol on weaning from mechanical ventilation: a single-blinded multicentre randomised control trial comparing an open-loop decision support system and routine care, in the general intensive care unit

INTRODUCTION

Automated systems for ventilator management to date have been either fully heuristic rule-based systems or based on a combination of simple physiological models and rules. These have been shown to reduce the duration of mechanical ventilation in simple to wean patients. At present, there are no published studies that evaluate the effect of systems that use detailed physiological descriptions of the individual patient.The BEACON Caresystem is a model-based decision support system that uses mathematical models of patients' physiology in combination with models of clinical preferences to provide advice on appropriate ventilator settings. An individual physiological description may be particularly advantageous in selecting the appropriate therapy for a complex, heterogeneous, intensive care unit (ICU) patient population.

METHODS AND ANALYSIS

Intenive Care weaning (iCareWean) is a single-blinded, multicentre, prospective randomised control trial evaluating management of mechanical ventilation as directed by the BEACON Caresystem compared with that of current care, in the general intensive care setting. The trial will enrol 274 participants across multiple London National Health Service ICUs. The trial will use a primary outcome of duration of mechanical ventilation until successful extubation.

ETHICS AND DISSEMINATION

Safety oversight will be under the direction of an independent committee of the study sponsor. Study approval was obtained from the regional ethics committee of the Health Research Authority (HRA), (Research Ethic Committee (REC) reference: 17/LO/0887. Integrated Research Application System (IRAS) reference: 226610. Results will be disseminated through international critical care conference/symposium and publication in peer-reviewed journal.

TRIAL REGISTRATION NUMBER

ClinicalTrials.gov under NCT03249623. This research is registered with the National Institute for Health Research under CPMS ID: 34831.

A physiology-based mathematical model for the selection of appropriate ventilator controls for lung and diaphragm protection

Mechanical ventilation is used to sustain respiratory function in patients with acute respiratory failure. To aid clinicians in consistently selecting lung- and diaphragm-protective ventilation settings, a physiology-based decision support system is needed. To form the foundation of such a system, a comprehensive physiological model which captures the dynamics of ventilation has been developed. The Lung and Diaphragm Protective Ventilation (LDPV) model centers around respiratory drive and incorporates respiratory system mechanics, ventilator mechanics, and blood acid–base balance. The model uses patient-specific parameters as inputs and outputs predictions of a patient’s transpulmonary and esophageal driving pressures (outputs most clinically relevant to lung and diaphragm safety), as well as their blood pH, under various ventilator and sedation conditions. Model simulations and global optimization techniques were used to evaluate and characterize the model. The LDPV model is demonstrated to describe a CO₂ respiratory response that is comparable to what is found in literature. Sensitivity analysis of the model indicate that the ventilator and sedation settings incorporated in the model have a significant impact on the target output parameters. Finally, the model is seen to be able to provide robust predictions of esophageal pressure, transpulmonary pressure and blood pH for patient parameters with realistic variability. The LDPV model is a robust physiological model which produces outputs which directly target and reflect the risk of ventilator-induced lung and diaphragm injury. Ventilation and sedation parameters are seen to modulate the model outputs in accordance with what is currently known in literature.

Clinical decision support recommending ventilator settings during noninvasive ventilation

NIV therapy is used to provide positive pressure ventilation for patients. There are protocols describing what ventilator settings to use to initialize NIV; however, the guidelines for titrating ventilator settings are less specific. We developed an advisory system to recommend NIV ventilator setting titration and recorded respiratory therapist agreement rates at the bedside. We developed an algorithm (NIV advisor) to recommend when to change the non-invasive ventilator settings of IPAP, EPAP, and FiO₂ based on patient respiratory parameters. The algorithm utilized a multi-target approach; oxygenation, ventilation, and patient effort. The NIV advisor recommended ventilator settings to move the patient's respiratory parameters in a preferred target range. We implemented a pilot study evaluating the usability of the NIV advisor on 10 patients receiving critical care with non-invasive ventilation (NIV). Respiratory therapists were asked their agreement on recommendations from the NIV advisor at the patient's bedside. Bedside respiratory therapists agreed with 91% of the ventilator setting recommendations from the NIV advisor. The POB and VT values were the respiratory parameters that were most often out of the preferred target range. The IPAP ventilator setting was the setting most often considered in need of changing by the NIV advisor. The respiratory therapists agreed with the majority of the recommendations from the NIV advisor. We consider the IPAP recommendations informative in providing the respiratory therapist assistance in targeting preferred POB and Vt values, as these values were frequently out of the target ranges. This pilot implementation was unable to produce the results required to determine the value of the EPAP recommendations. The FiO₂ recommendations from the NIV advisor were treated as ancillary information behind the IPAP recommendations.

Effects of inspired oxygen concentration during emergence from general anaesthesia on postoperative lung impedance changes evaluated by electrical impedance tomography: a randomised controlled trial

We evaluated the effects of three different inspired oxygen concentrations (40%, 80%, and 100%) at anaesthesia emergence on postoperative lung volumes as measured by global impedance of electrical impedance tomography (EIT). This is a randomised, controlled, and assessor-blinded study in single-centre from May 2017 to August 2017. Seventy-one patients undergoing elective laparoscopic colorectal surgery with healthy lung condition were randomly allocated into the three groups based on the concentration of inspired oxygen applied during anaesthesia emergence: 40%-, 80%- or 100%-oxygen. End-expiratory lung impedance (EELI) with normal tidal ventilation and total lung impedance (TLI) with full respiratory effort were measured preoperatively and before discharge in the post-anaesthesia care unit by EIT, and perioperative changes (the ratio of difference between preoperative and postoperative value to preoperative value) were compared among the three groups. Postoperative lung impedances were significantly reduced compared with preoperative values in all patients (P < 0.001); however, perioperative lung impedance reduction (%) did not differ among the three oxygen groups. The mean reduction ratio in each 40%-, 80%-, and 100%-oxygen group were 37% ± 13%, 41% ± 14%, and 46% ± 14% for EELI (P = 0.125) and 40% ± 20%, 44% ± 17% and 49% ± 20% for TLI (P = 0.276), respectively. Inspired oxygen concentrations applied during anaesthesia emergence did not show a significant difference in postoperative lung volume as measured by EIT in patients undergoing laparoscopic colorectal surgery with healthy lungs.Trial registration cris.nih.go.kr (KCT0002642).

A Pilot Bench Study of Decision Support for Proportional Assist Ventilation

The purpose was to develop a bench setup for testing a decision support system (DSS) for proportional assist ventilation (PAV). The test setup was based on a patient simulator connected to a mechanical ventilator with the DSS measurement sensors connected to the respiratory circuit. A test case was developed with parameters of lung mechanics reflecting a patient with mild acute respiratory distress syndrome. Five experiments were performed starting at different levels of percentage support (%Supp) and continuing until the DSS advised to remain at current settings. Final advice ranged from %Supp of 50-70%, indicating some dependence of baseline level, but with resulting patient effort estimates indicating that this may not be clinically important. Further studies are required of test cases reflecting different patient types and in patients.

An open-loop, physiological model based decision support system can reduce pressure support while acting to preserve respiratory muscle function

PURPOSE

To assess whether a clinical decision support system (CDSS) suggests PS and FIO₂ maintaining appropriate breathing effort, and minimizing FIO₂.

MATERIALS

Prospective, cross-over study in PS ventilated ICU patients. Over support (150% baseline) and under support (50% baseline) were applied by changing PS (15 patients) or PEEP (8 patients). CDSS advice was followed. Tension time index of inspiratory muscles (TTies), respiratory and metabolic variables were measured.

RESULTS

PS over support (median 8.0 to 12.0 cmH₂O) reduced respiratory muscle activity (TTies 0.090 ± 0.028 to 0.049 ± 0.030; p < .01), and tended to increase tidal volume (VT: 8.6 ± 3.0 to 10.1 ± 2.9 ml/kg; p = .08). CDSS advice reduced PS (6.0 cmH₂O, p = .005), increased TTies (0.076 ± 0.038, p < .01), and tended to reduce VT (8.9 ± 2.4 ml/kg, p = .08). PS under support (12.0 to 4.0 cmH₂O) slightly increased respiratory muscle activity, (TTies to 0.120 ± 0.044; p = .007) with no significant CDSS advice. CDSS advice reduced FIO₂ by 12-14% (p = .005), resulting in median SpO₂ = 96% (p < .02). PEEP changes did not result in changes in physiological variables, or CDSS advice.

CONCLUSION

The CDSS advised on low values of PS often not prohibiting extubation, while acting to preserve respiratory muscle function and preventing passive lung inflation. CDSS advice minimized FIO₂ maintaining SpO₂ at safe and beneficial values.