Noninvasive ventilation (NIV) has been widely used in critically ill patients after extubation. However, NIV failure is associated with poor outcomes. This study aimed to determine early predictors of NIV failure and to construct an accurate machine-learning model to identify patients at risks of NIV failure after extubation in intensive care units (ICUs).Patients who underwent NIV after extubation in the eICU Collaborative Research Database (eICU-CRD) were included. NIV failure was defined as need for invasive ventilatory support (reintubation or tracheotomy) or death after NIV initiation. A total of 93 clinical and laboratory variables were assessed, and the recursive feature elimination algorithm was used to select key features. Hyperparameter optimization was conducted with an automated machine-learning toolkit called Neural Network Intelligence. A machine-learning model called Categorical Boosting (CatBoost) was developed and compared with nine other models. The model was then prospectively validated among patients enrolled in the Cardiac Surgical ICU of Zhongshan Hospital, Fudan University.Of 929 patients included in the eICU-CRD cohort, 248 (26.7%) had NIV failure. The time from extubation to NIV, age, Glasgow Coma Scale (GCS) score, heart rate, respiratory rate, mean blood pressure (MBP), saturation of pulse oxygen (SpO2), temperature, glucose, pH, pressure of oxygen in blood (PaO2), urine output, input volume, ventilation duration, and mean airway pressure were selected. After hyperparameter optimization, our model showed the greatest accuracy in predicting NIV failure (AUROC: 0.872 [95% CI 0.82-0.92]) among all predictive methods in an internal validation. In the prospective validation cohort, our model was also superior (AUROC: 0.846 [95% CI 0.80-0.89]). The sensitivity and specificity in the prediction group is 89% and 75%, while in the validation group they are 90% and 70%. MV duration and respiratory rate were the most important features. Additionally, we developed a web-based tool to help clinicians use our model.This study developed and prospectively validated the CatBoost model, which can be used to identify patients who are at risk of NIV failure. Thus, those patients might benefit from early triage and more intensive monitoring.NCT03704324. Registered 1 September 2018, https://register.gov . 

/pdf/early-prediction-of-noninvasive-ventilation-failure-after-3inqiu3e.pdf

Early prediction of noninvasive ventilation failure after extubation: development and validation of a machine-learning model

Critical Care Medicine www.ccmjournal.org 767 The original recommendations for the management of acute respiratory distress syndrome (ARDS) included the use of high-pressure constant volume ventilation, inspiratory plateau, and end-expiratory pressure (1). Webb and Tierney (2) later demonstrated in an animal model that high inflating pressure-induced an ARDS like change in the lung. When limiting ventilation pressures in the management of ARDS was first investigated in the 1980s, it was not clear which pressure was important (3). Although recognizing that the causes of ventilator-induced lung injury were multifactorial, the consensus at the 1993 conference on mechanical ventilation in Northbrook, Illinois was that end-inspiratory occlusion pressure, that is, plateau pressure (Pplat) (also known as inspiratory pause pressure or peak alveolar pressure) was the most important target pressure to avoid alveolar overdistention (4). This consensus, supported by accumulating evidence, influenced ventilation strategies for the next 20 years by advocating the monitoring of Pplat and targeting less than 30 cm H 2 O. More recently, it has been suggested that neither volume based on ideal body weight nor Pplat are always the most appropriate target variables. Amato et al (5) analysis of the large prospectively randomized ventilation trials in patients with ARDS demonstrated a strong association between “driving pressure” (ΔP) (defined as Pplat minus extrinsic positive endexpiratory pressure [PEEP]) and mortality. As these trials delivered a fixed tidal volume (Vt), ΔP is inversely proportional to compliance. The argument made is that respiratory system compliance (CRS) is related to the volume of aerated remaining functional lung during disease (termed functional lung size). Therefore, an elevated “ΔP” (ΔP = Vt/ CRS) represents a low remaining functional lung size–low compliance in volume modes will result in a high ΔP. The analysis was retrospective, and while supported in other studies in patients with ARDS, mortality has an inconstant association with ΔP in patients without ARDS and not appear to be associated with ΔP in obesity in other studies (6–11).Prospective clinical trials controlling ΔP designed to investigate patient focused outcomes are currently lacking (7, 8). Both ΔP and Pplat evaluation require a measure of peak alveolar pressure. When a mode utilizing an inspiratory pause period is used, ventilators will often display a breath by breath “Pplat.” These estimates of Pplat, made in very early pause time, are greater than those measured at 3 or 5 seconds during formal inspiratory hold procedure (12). Although not difficult to perform, accurate monitoring of Pplat requires a repetitive inspiratory hold, generally performed manually, taking 5 seconds or so per measurement. An alternative, the system pressure averaged over the entire ventilatory period, or mean airway pressure (MAP), requires no such intervention and is routinely shown on most intensive care ventilators. In this issue of Critical Care Medicine, Sahetya et al (13), looking at a mixed population of mechanically ventilated patients, found that increasing MAP was associated with increasing mortality and suggest it as an alternative to ΔP or Pplat. What utility does MAP have in the critically ill? First, as long as sufficient PEEP is applied to inflate recruitable lung units, MAP and oxygenation demonstrate a predictable and quantifiable direct relationship and thus could be used to safeguard oxygenation when titrating ventilation (4, 14). Interestingly as Sahetya et al (13) observe, MAP is usually ignored as a monitored variable. Second, lung injury in ARDS occurs at the alveolar level, and strategies to reduce ventilator-induced injury appropriately focus on the pressures and volumes in the alveoli, rather than airway pressures. If the inspiratory and expiratory resistance are known, mean alveolar pressure (MalvP) can be estimated from the MAP by the following formula: MalvP = (VE/60)(RE–RI) + MAP where RI and RE are inspiratory and expiratory resistances, respectively (14). MAP and MalvP are often used interchangeably, as in general situations, they are closely related. However, expiratory resistance usually exceeds inspiratory resistance so MAP displayed on monitoring devices almost always slightly underestimates but tells us something about MalvP (14, 15). This is in contradistinction to peak airway pressure which is never less than the Pplat, and commonly differ by significant amounts. The findings of Sahetya et al (13) that higher MAP is associated with higher mortality and were equally predictive as Pplat and ΔP in predicting mortality now gives a second reason to pay more attention to this recondite variable. What do the different pressures measure? ΔP is required to overcome elastic forces during lung expansion generated by lung parenchyma, pleura, and the chest wall. The proportion each contributes is not easily determined. If we ignore the complexity of spontaneous breathing patients, with their lowered pleural pressures, and thus ΔPs, and concentrate on sedated *See also p. 688.

What Do Mean Airway Pressures Mean

Abstract Background Oxygenation index [OI = (MAP × FiO2 × 100)/PaO2] assesses the severity of hypoxic respiratory failure. Oxygen saturation index [OSI = (MAP × FiO2 × 100)/SpO2] is a noninvasive method to assesses the severity of hypoxic respiratory failure. Conventionally used PaO2/FiO2 (P/F) ratio to measure the severity of ARDS requires arterial blood gas (ABG) sampling. It tenders limited prognostic information mandating the need for better markers. Oxygenation index (needs arterial sampling) and OSI (a noninvasive method) are substitutes to provide mortality information in ARDS patients. We evaluated the correlation between P/F, OI, and OSI in invasively ventilated COVID-19 ARDS patients (C-ARDS) and looked at its relationship with mortality. Patients and methods A retrospective study of invasively ventilated C-ARDS >18 years of age managed in COVID ICU. Ventilator settings (FiO2, mean airway pressure), pulse oximetry (SpO2), and ABG values (PaO2) were simultaneously noted at the time of sample collection. Patient outcomes (alive and deceased) were documented. Differences in parameters between survivors and nonsurvivors were assessed using independent sample t-test. Receiver operating characteristic (ROC) analysis with Youden's index was used to identify cutoff values to determine survival. Results A total of 1557 measurements for 203 patients were collected over the maximum duration of 21 days after ventilation. About 147 (72.4%) were males and 56 (27.6%) were females. On day one of ventilation, 161 (79.3%) had P/F ratio <200, 28 (13.8%) had P/F ratio between 200 and 300, and 14 (6.9%) had P/F ratio >300. There was a linear relationship between P/F ratio and OSI (r = −0.671), P/F and OI (r = −0.753), and OSI and OI (r = 0.893) (p < 0.001). After natural log transform, the correlation between these factors became stronger [P/F ratio and OSI (r = −0.797), PF and OI (r = −0.949), and OSI and OI (r = 0.902) (p < 0.001)]. About 74 (36.5%) patients survived. Survivors had significantly higher P/F ratio as compared with nonsurvivors (p < 0.05). Oxygen saturation index and OI were significantly lower in survivors as compared with nonsurvivors. Based on day-1 reading, a higher OSI (AUC = 0.719, 95% CI = 0.648–0.790) and OI (AUC = 0.752. 95% CI = 0.684–0.819) significantly can predict mortality. On the other hand, a higher P/F ratio can predict survival (AUC = 0.734, 95% CI = 0.664–0.805). P/F ratio of 160 on day 1 can predict survival. Oxygen saturation index values above 10.4% and OI above 13.5% were the cutoff derived for day 1 values to predict mortality. Conclusion Noninvasive OSI can be used to assess the severity of hypoxic respiratory failure in C-ARDS without arterial access in resource-limited settings. Oxygen saturation index can noninvasively provide prognostic information in invasively ventilated C-ARDS patients. How to cite this article Vadi S, Suthar D, Sanwalka N. Correlation and Prognostic Significance of Oxygenation Indices in Invasively Ventilated Adults (OXIVA-CARDS) with COVID-19-associated ARDS: A Retrospective Study. Indian J Crit Care Med 2023;27(11):801–805.

Correlation and Prognostic Significance of Oxygenation Indices in Invasively Ventilated Adults (OXIVA-CARDS) with COVID-19-associated ARDS: A Retrospective Study

The effect of modifying the end inspiratory pause (EIP) on respiratory mechanics and gas distribution of surgical patients ventilated with an open lung approach (OLA) has not been addressed before.Prospective, randomised, crossover study carried out in a tertiary hospital. Subjects were assigned to receive an initial EIP of 10% or 30% of the total inspiratory time. We compared standard ventilation [time 0: tidal volume (VT) 7 mL × kg-1, respiratory rate (RR) 13, inspiratory:expiratory (I:E) rate 1:2, positive end-expiratory pressure (PEEP) 5 cm H2O and EIP 10% and 30% for groups 1 and 2, respectively) with tailored OLA (similar parameters except for a tailored PEEP after a stepwise recruitment manoeuvre), followed by a crossover assignment sequence between groups (times 2-4).We included 32 adult subjects undergoing major surgery. Tailored OLA strategy was associated with a significant increase in PEEP, plateau pressure (Pplat), PaO2, and compliance of the respiratory system (CRS) with a significant decrease in driving pressure (Pdriv) and PaCO2, and a more homogeneous gas distribution in both groups. A significantly lower PEEP (p < 0.001), Pdriv (5 [5-6] versus 6.5 [6-8] cmH2O; p < 0.001) and mean airway pressure (Pmean; p < 0.001) together with a higher CRS (77 [67-87] versus 58 [52-70] ml*cmH2O-1; p < 0.001) were observed when an EIP of 30% was applied as compared to an EIP of 10%.The use of a tailored OLA strategy combined with a longer EIP is associated with a higher CRS, and a lower Pdriv, Pmean and PEEP. Additional studies are necessary to assess if the improved ventilatory conditions observed with a longer EIP are associated with better patients' outcomes. Trial registration at clinicaltrials.gov with identifier: NCT03568786. 

Influence of the end inspiratory pause on respiratory mechanics and tidal gas distribution of surgical patients ventilated under a tailored open lung approach strategy: A randomised, crossover trial.

Impact of the first 24 h of continuous kidney replacement therapy on hemodynamics, ventilation, and analgo-sedation in critically ill children.

OBJECTIVES To determine the association between mean airway pressure and 90-day mortality in patients with acute respiratory failure requiring mechanical ventilation and to compare the predictive ability of mean airway pressure compared with inspiratory plateau pressure and driving pressure. DESIGN Prospective observational cohort. SETTING Five ICUs in Lima, Peru. SUBJECTS Adults requiring invasive mechanical ventilation via endotracheal tube for acute respiratory failure. INTERVENTIONS None. MEASUREMENTS AND MAIN RESULTS Of potentially eligible participants (n = 1,500), 65 (4%) were missing baseline mean airway pressure, while 352 (23.5%) were missing baseline plateau pressure and driving pressure. Ultimately, 1,429 participants were included in the analysis with an average age of 59 ± 19 years, 45% female, and a mean PaO2/FIO2 ratio of 248 ± 147 mm Hg at baseline. Overall, 90-day mortality was 50.4%. Median baseline mean airway pressure was 13 cm H2O (interquartile range, 10-16 cm H2O) in participants who died compared to a median mean airway pressure of 12 cm H2O (interquartile range, 10-14 cm H2O) in participants who survived greater than 90 days (p < 0.001). Mean airway pressure was independently associated with 90-day mortality (odds ratio, 1.38 for difference comparing the 75th to the 25th percentile for mean airway pressure; 95% CI, 1.10-1.74) after adjusting for age, sex, baseline Acute Physiology and Chronic Health Evaluation III, baseline PaO2/FIO2 (modeled with restricted cubic spline), baseline positive end-expiratory pressure, baseline tidal volume, and hospital site. In predicting 90-day mortality, baseline mean airway pressure demonstrated similar discriminative ability (adjusted area under the curve = 0.69) and calibration characteristics as baseline plateau pressure and driving pressure. CONCLUSIONS In a multicenter prospective cohort, baseline mean airway pressure was independently associated with 90-day mortality in mechanically ventilated participants and predicts mortality similarly to plateau pressure and driving pressure. Because mean airway pressure is readily available on all mechanically ventilated patients and all ventilator modes, it is a potentially more useful predictor of mortality in acute respiratory failure.

Mean Airway Pressure As a Predictor of 90-Day Mortality in Mechanically Ventilated Patients.

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Mean Airway Pressure As a Predictor of 90-Day Mortality in Mechanically Ventilated Patients.