Hyperoxemia and excess oxygen use in early acute respiratory distress syndrome : Insights from the LUNG SAFE study

Publisher Copyright: © 2020 The Author(s). Copyright: Copyright 2020 Elsevier B.V., All rights reserved.Background: Concerns exist regarding the prevalence and impact of unnecessary oxygen use in patients with acute respiratory distress syndrome (ARDS). We examined this issue in patients with ARDS enrolled in the Large observational study to UNderstand the Global impact of Severe Acute respiratory FailurE (LUNG SAFE) study. Methods: In this secondary analysis of the LUNG SAFE study, we wished to determine the prevalence and the outcomes associated with hyperoxemia on day 1, sustained hyperoxemia, and excessive oxygen use in patients with early ARDS. Patients who fulfilled criteria of ARDS on day 1 and day 2 of acute hypoxemic respiratory failure were categorized based on the presence of hyperoxemia (PaO2 > 100 mmHg) on day 1, sustained (i.e., present on day 1 and day 2) hyperoxemia, or excessive oxygen use (FIO2 ≥ 0.60 during hyperoxemia). Results: Of 2005 patients that met the inclusion criteria, 131 (6.5%) were hypoxemic (PaO2 < 55 mmHg), 607 (30%) had hyperoxemia on day 1, and 250 (12%) had sustained hyperoxemia. Excess FIO2 use occurred in 400 (66%) out of 607 patients with hyperoxemia. Excess FIO2 use decreased from day 1 to day 2 of ARDS, with most hyperoxemic patients on day 2 receiving relatively low FIO2. Multivariate analyses found no independent relationship between day 1 hyperoxemia, sustained hyperoxemia, or excess FIO2 use and adverse clinical outcomes. Mortality was 42% in patients with excess FIO2 use, compared to 39% in a propensity-matched sample of normoxemic (PaO2 55-100 mmHg) patients (P = 0.47). Conclusions: Hyperoxemia and excess oxygen use are both prevalent in early ARDS but are most often non-sustained. No relationship was found between hyperoxemia or excessive oxygen use and patient outcome in this cohort. Trial registration: LUNG-SAFE is registered with ClinicalTrials.gov, NCT02010073publishersversionPeer reviewe

Linguistic summaries for compliance analysis of a glucose management clinical protocol

Clinical protocols are introduced in hospitals to standardize the care delivery process. Compliance is a measure used to determine whether the protocol has been followed. However compliance measures do not provide enough insight to be used in daily practice. In this paper we propose to use linguistic summaries to analyze the compliance to clinical protocols. As our case we examine a glucose management protocol at ICU. We will demonstrate that linguistic summaries can provide insight, that is actionable and comprehensible by the health-care providers

Towards a Flexible Assessment of Compliance with Clinical Protocols Using Fuzzy Aggregation Techniques

In healthcare settings, compliance with clinical protocols and medical guidelines is important to ensure high-quality, safe and effective treatment of patients. How to measure compliance and how to represent compliance information in an interpretable and actionable way is still an open challenge. In this paper, we propose new metrics for compliance assessments. For this purpose, we use two fuzzy aggregation techniques, namely the OWA operator and the Sugeno integral. The proposed measures take into consideration three factors: (i) the degree of compliance with a single activity, (ii) the degree of compliance of a patient, and (iii) the importance of the activities. The proposed measures are applied to two clinical protocols used in practice. We demonstrate that the proposed measures for compliance can further aid clinicians in assessing the aspect of protocol compliance when evaluating the effectiveness of implemented clinical protocols

Towards a Flexible Assessment of Compliance with Clinical Protocols Using Fuzzy Aggregation Techniques

In healthcare settings, compliance with clinical protocols and medical guidelines is important to ensure high-quality, safe and effective treatment of patients. How to measure compliance and how to represent compliance information in an interpretable and actionable way is still an open challenge. In this paper, we propose new metrics for compliance assessments. For this purpose, we use two fuzzy aggregation techniques, namely the OWA operator and the Sugeno integral. The proposed measures take into consideration three factors: (i) the degree of compliance with a single activity, (ii) the degree of compliance of a patient, and (iii) the importance of the activities. The proposed measures are applied to two clinical protocols used in practice. We demonstrate that the proposed measures for compliance can further aid clinicians in assessing the aspect of protocol compliance when evaluating the effectiveness of implemented clinical protocols. Keywords: clinical protocols; protocol compliance; protocol adherence; protocol conformance; aggregation; OWA operator; Sugeno integra

Linguistic summaries for compliance analysis of a glucose management clinical protocol

Clinical protocols are introduced in hospitals to standardize the care delivery process. Compliance is a measure used to determine whether the protocol has been followed. However compliance measures do not provide enough insight to be used in daily practice. In this paper we propose to use linguistic summaries to analyze the compliance to clinical protocols. As our case we examine a glucose management protocol at ICU. We will demonstrate that linguistic summaries can provide insight, that is actionable and comprehensible by the health-care providers

ACCURACY OF END-TIDAL CO2 CAPNOMETERS IN POST-CARDIAC SURGERY PATIENTS DURING CONTROLLED MECHANICAL VENTILATION

Background: The determination of end-tidal carbon dioxide (etCO(2)) is very helpful in cardiac resuscitation for confirmation and monitoring of endotracheal tube placement and as an indicator of return of circulation and effectiveness of chest compressions. There is now also widespread use of capnometry on-site at emergency and trauma fields. Objective: We studied the accuracy and correlation of three capnometers (EMMA, Medtronic, and Evita) with partial pressure of arterial CO2 (PaCO2) measurements. Methods: The three capnometers were placed in-line in the ventilator tubing of the patient. Forty sedated and mechanically ventilated post-cardiac surgery patients were studied. Twenty consecutive etCO(2) values were collected simultaneously from all three monitors while drawing an arterial blood sample. Paired sample t-test and Pearson correlation were used to compare the capnometers and their correlation with PaCO2. Results: The correlation of etCO(2) measurements between all three capnometers was good (Emma vs. Evita: 0.874, Emma vs. Medtronic: 0.949, Evita vs. Medtronic: 0.878). The correlation of PaCO2 with the Evita is the lowest (0.671) as compared to the EMMA (0.693) and the Medtronic (0.727). The lowest dispersion of the difference between etCO(2) and PaCO2 was seen in EMMA (3.30), the highest in Evita (3.98). Conclusions: A good correlation between etCO(2) and PaCO2 was shown with the three capnometers in the present study. However, etCO(2) measurements were not valid to estimate PaCO2 in these patients. Therefore, capnometry cannot be used to replace serial blood gas analyses completely, but may be a good cardiopulmonary trend monitor and alerting system in catastrophic events

On fuzzy compliance for clinical protocols

\u3cp\u3eClinical protocols are introduced in hospitals to standardize the care delivery process. Compliance is a measure used to determine whether the protocol has been followed. However, so far an activity in the protocol could be either compliant or non-compliant. In this paper we consider the compliance of a single activity as a fuzzy term. We propose to define the rules which can assess the compliance degree of an activity. We proposed the fuzzy compliance measure of clinical protocol that aggregates those compliance degrees. We demonstrate a case of glucose management protocol at Intensive Care Unit (ICU). Initial results are promising.\u3c/p\u3

Prediction of lung mechanics throughout recruitment maneuvers in pressure-controlled ventilation

Mechanical ventilation (MV) is a core therapy in the intensive care unit (ICU). Some patients rely on MV to support breathing. However, it is a difficult therapy to optimise, where interand intrapatient variability leads to significantly increased risk of lung damage. Excessive volume and/or pressure can cause volutrauma or barotrauma, resulting in increased length of time on ventilation, length of stay, cost and mortality. Virtual patient modelling has changed care in other areas of ICU medicine, enabling more personalized and optimal care, and have emerged for volume-controlled MV. This research extends this MV virtual patient model into the increasingly more commonly used pressure-controlled MV mode. The simulation methods are extended to use pressure, instead of both volume and flow, as the known input, increasing the output variables to be predicted (flow and its integral, volume). The model and methods are validated using data from N = 14 pressure-control ventilated patients during recruitment maneuvers, with n = 558 prediction tests over changes of PEEP ranging from 2 to 16 cmH(2)O. Prediction errors for peak inspiratory volume for an increase of 16 cmH(2)O were 80 [30 - 140] mL (15.9 [8.4 - 31.0]%), with RMS fitting errors of 0.05 [0.03 - 0.12] L. These results show very good prediction accuracy able to guide personalised MV care. (C) 2020 Elsevier B.V. All rights reserved.</p