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Effect of training on inspiratory load compensation in weaned and unweaned mechanically ventilated ICU patients.

ABSTRACT: While inspiratory muscle weakness is common in prolonged mechanical ventilation, inspiratory muscle strength training (IMST) can facilitate strengthening and ventilator weaning. However, the inspiratory load compensation (ILC) responses to threshold loads are not well characterized in patients. We retrospectively compared ILC responses according to the clinical outcomes of IMST (ie, maximum inspiratory pressure [PImax], weaning outcome), in difficult-to-wean ICU patients.Sixteen tracheostomized subjects (10 weaned, 6 unweaned) from a previous clinical trial underwent IMST 5 days/week, at the highest tolerated load, in conjunction with daily, progressive spontaneous breathing trials. PImax and ILC with a 10 cm H2O load were compared in the subjects before and after IMST. Changes in ILC performance were further characterized (5, 10, 15 cm H2O loads) in the trained subjects who weaned.Demographics, respiratory mechanics, and initial PImax (52 ± 26 cm H2O vs 42 ± 13 cm H2O) did not significantly differ between the groups. Upon enrollment, PImax significantly correlated with flow ILC responses with the 10 cm H2O load (r = 0.64, P = .008). After IMST, PImax significantly increased in the entire sample (P = .03). Both before and after IMST, subjects who weaned generated greater flow and volume ILC than subjects who failed to wean. Additionally, ILC flow, tidal volume, and duty cycle increased upon ventilator weaning, at loads of 5, 10, and 15 cm H2O.Flow ILC at a threshold load of 10 cm H2O in ventilated, tracheostomized subjects positively correlated with PImax. Although PImax improved in both groups, the flow and volume ILC responses of the weaned subjects were more robust, both before and after IMST. The results suggest that ILC response is different in weaned and unweaned subjects, reflecting dynamic inspiratory muscular efforts that could be influential in weaning.

SUBMITTER: Smith BK

PROVIDER: S-EPMC4157996 | biostudies-literature | 2014 Jan

REPOSITORIES: biostudies-literature

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Effect of training on inspiratory load compensation in weaned and unweaned mechanically ventilated ICU patients.

Smith Barbara Kellerman BK Gabrielli Andrea A Davenport Paul W PW Martin A Daniel AD

Respiratory care 20130613 1

<h4>Background</h4>While inspiratory muscle weakness is common in prolonged mechanical ventilation, inspiratory muscle strength training (IMST) can facilitate strengthening and ventilator weaning. However, the inspiratory load compensation (ILC) responses to threshold loads are not well characterized in patients. We retrospectively compared ILC responses according to the clinical outcomes of IMST (ie, maximum inspiratory pressure [PImax], weaning outcome), in difficult-to-wean ICU patients.<h4>M ...[more]

PMID: 23764858

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Project description:BackgroundDiaphragmatic dysfunction is well documented in patients receiving mechanical ventilation. Inspiratory muscle training (IMT) has been used to facilitate weaning by strengthening the inspiratory muscles, yet the optimal approach remains uncertain. Whilst some data on the metabolic response to whole body exercise in critical care exist, the metabolic response to IMT in critical care is yet to be investigated. This study aimed to quantify the metabolic response to IMT in critical care and its relationship to physiological variables.MethodsWe conducted a prospective observational study on mechanically ventilated patients ventilated for ≥ 72 h and able to participate in IMT in a medical, surgical, and cardiothoracic intensive care unit. 76 measurements were taken on 26 patients performing IMT using an inspiratory threshold loading device at 4 cmH2O, and at 30, 50 and 80% of their negative inspiratory force (NIF). Oxygen consumption (VO2) was measured continuously using indirect calorimetry.ResultsFirst session mean (SD) VO2 was 276 (86) ml/min at baseline, significantly increasing to 321 (93) ml/min, 333 (92) ml/min, 351(101) ml/min and 388 (98) ml/min after IMT at 4 cmH2O and 30, 50 and 80% NIF, respectively (p = 0.003). Post hoc comparisons revealed significant differences in VO2 between baseline and 50% NIF and baseline and 80% NIF (p = 0.048 and p = 0.001, respectively). VO2 increased by 9.3 ml/min for every 1 cmH2O increase in inspiratory load from IMT. Every increase in P/F ratio of 1 decreased the intercept VO2 by 0.41 ml/min (CI - 0.58 to - 0.24 p < 0.001). NIF had a significant effect on the intercept and slope, with every 1 cmH2O increase in NIF increasing intercept VO2 by 3.28 ml/min (CI 1.98-4.59 p < 0.001) and decreasing the dose-response slope by 0.15 ml/min/cmH2O (CI - 0.24 to - 0.05 p = 0.002).ConclusionsIMT causes a significant load-dependent increase in VO2. P/F ratio and NIF impact baseline VO2. The dose-response relationship of the applied respiratory load during IMT is modulated by respiratory strength. These data may offer a novel approach to prescription of IMT.Take home messageThe optimal approach to IMT in ICU is uncertain; we measured VO2 at different applied respiratory loads to assess whether VO2 increased proportionally with load and found VO2 increased by 9.3 ml/min for every 1 cmH2O increase in inspiratory load from IMT. Baseline NIF has a significant effect on the intercept and slope, participants with a higher baseline NIF have a higher resting VO2 but a less pronounced increase in VO2 as the inspiratory load increases; this may offer a novel approach to IMT prescription. Trial registration ClinicalTrials.gov, registration number: NCT05101850. Registered on 28 September 2021, https://clinicaltrials.gov/ct2/show/NCT05101850.

Project description:BackgroundMechanical ventilation (MV) is vital for critically ill ICU patients but carries significant mortality risks. This study aims to develop a predictive model to estimate hospital mortality among MV patients, utilizing comprehensive health data to assist ICU physicians with early-stage alerts.MethodsWe developed a Machine Learning (ML) framework to predict hospital mortality in ICU patients receiving MV. Using the MIMIC-III database, we identified 25,202 eligible patients through ICD-9 codes. We employed backward elimination and the Lasso method, selecting 32 features based on clinical insights and literature. Data preprocessing included eliminating columns with over 90% missing data and using mean imputation for the remaining missing values. To address class imbalance, we used the Synthetic Minority Over-sampling Technique (SMOTE). We evaluated several ML models, including CatBoost, XGBoost, Decision Tree, Random Forest, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Logistic Regression, using a 70/30 train-test split. The CatBoost model was chosen for its superior performance in terms of accuracy, precision, recall, F1-score, AUROC metrics, and calibration plots.ResultsThe study involved a cohort of 25,202 patients on MV. The CatBoost model attained an AUROC of 0.862, an increase from an initial AUROC of 0.821, which was the best reported in the literature. It also demonstrated an accuracy of 0.789, an F1-score of 0.747, and better calibration, outperforming other models. These improvements are due to systematic feature selection and the robust gradient boosting architecture of CatBoost.ConclusionThe preprocessing methodology significantly reduced the number of relevant features, simplifying computational processes, and identified critical features previously overlooked. Integrating these features and tuning the parameters, our model demonstrated strong generalization to unseen data. This highlights the potential of ML as a crucial tool in ICUs, enhancing resource allocation and providing more personalized interventions for MV patients.

Project description:BackgroundIn most countries, patients receiving mechanical ventilation (MV) are treated in intensive care units (ICUs). However, in some countries, including Japan, many patients on MV are not treated in ICUs. There are insufficient epidemiological data on these patients. Here, we sought to describe the epidemiology of patients on MV in Japan by comparing and contrasting patients on MV treated in ICUs and in non-ICU settings. A preliminary comparison of patient outcomes between ICU and non-ICU patients was a secondary objective.MethodsData on adult patients receiving MV for at least 3 days in ICUs or non-ICU settings from April 2010 through March 2012 were obtained from the Quality Indicator/Improvement Project, a voluntary data-administration project covering more than 400 acute-care hospitals in Japan. We excluded patients with cancer-related diagnoses. Patient demographic data and the critical care provided were compared between groups.ResultsOver the study period, 17,775 patients on MV were treated only in non-ICU settings, whereas 20,516 patients were treated at least once in ICUs (46.4% vs. 53.6%). Average age was higher in non-ICU patients than in ICU patients (72.8 vs. 70.2, P?<?0.001). Mean number of ventilation days was greater in non-ICU patients (11.7 vs. 9.5, P?<?0.001). Hospital mortality was higher in non-ICU patients (41.4% vs. 38.8%, P?<?0.001). Standard critical care (e.g., arterial line placement, enteral nutrition, and stress-ulcer prevention) was provided significantly less often in non-ICU patients. Multivariate analysis showed that ICU admission significantly decreased hospital mortality (adjusted odds ratio 0.713, 95% CI 0.676 to 0.753).ConclusionsA large proportion of Japanese patients on MV were treated in non-ICU settings. Analysis of administrative data indicated preliminarily that hospital mortality rates in these patients were higher in non-ICU settings than in ICUs. Prospective analyses comparing non-ICU and ICU patients on MV by severity scoring are needed.

Project description:BackgroundBiomarkers can be used to detect the presence of endothelial and/or alveolar epithelial injuries in case of ARDS. Angiopoietin-2 (Ang-2), soluble intercellular adhesion molecule-1 (ICAM-1), vascular cell adhesion protein-1 (VCAM-1), P-selectin and E-selectin are biomarkers of endothelial injury, whereas the receptor for advanced glycation end-products (RAGE) reflects alveolar epithelial injury. The aims of this study were to evaluate whether the plasma concentration of the above-mentioned biomarkers was different 1) in survivors and non-survivors of COVID-19-related ARDS and 2) in COVID-19-related and classical ARDS.MethodsThis prospective study was performed in two COVID-19-dedicated Intensive Care Units (ICU) and one non-COVID-19 ICU at Ferrara University Hospital. A cohort of 31 mechanically ventilated patients with COVID-19 ARDS and a cohort of 11 patients with classical ARDS were enrolled. Ang-2, ICAM-1, VCAM-1, P-selectin, E-selectin and RAGE were determined with a bead-based multiplex immunoassay at three time points: inclusion in the study (T1), after 7 ± 2 days (T2) and 14 ± 2 days (T3). The primary outcome was to evaluate the plasma trend of the biomarker levels in survivors and non-survivors. The secondary outcome was to evaluate the differences in respiratory mechanics variables and gas exchanges between survivors and non-survivors. Furthermore, we compared the plasma levels of the biomarkers at T1 in patients with COVID-19-related ARDS and classical ARDS.ResultsIn COVID-19-related ARDS, the plasma levels of Ang-2 and ICAM-1 at T1 were statistically higher in non-survivors than survivors, (p = 0.04 and p = 0.03, respectively), whereas those of P-selectin, E-selectin and RAGE did not differ. Ang-2 and ICAM-1 at T1 were predictors of mortality (AUROC 0.650 and 0.717, respectively). At T1, RAGE and P-selectin levels were higher in classical ARDS than in COVID-19-related ARDS. Ang-2, ICAM-1 and E-selectin were lower in classical ARDS than in COVID-19-related ARDS (all p < 0.001).ConclusionsCOVID-19 ARDS is characterized by an early pulmonary endothelial injury, as detected by Ang-2 and ICAM-1. COVID-19 ARDS and classical ARDS exhibited a different expression of biomarkers, suggesting different pathological pathways. Trial registration NCT04343053 , Date of registration: April 13, 2020.

Project description:PurposeWhile fever may be a presenting symptom of COVID-19, fever at hospital admission has not been identified as a predictor of mortality. However, hyperthermia during critical illness among ventilated COVID-19 patients in the ICU has not yet been studied. We sought to determine mortality predictors among ventilated COVID-19 ICU patients and we hypothesized that fever in the ICU is predictive of mortality.Materials and methodsWe conducted a retrospective cohort study of 103 ventilated COVID-19 patients admitted to the ICU between March 14 and May 27, 2020. Final follow-up was June 5, 2020. Patients discharged from the ICU or who died were included. Patients still admitted to the ICU at final follow-up were excluded.Results103 patients were included, 40 survived and 63(61.1%) died. Deceased patients were older {66 years[IQR18] vs 62.5[IQR10], (p = 0.0237)}, more often male {48(68%) vs 22(55%), (p = 0.0247)}, had lower initial oxygen saturation {86.0%[IQR18] vs 91.5%[IQR11.5], (p = 0.0060)}, and had lower pH nadir than survivors {7.10[IQR0.2] vs 7.30[IQR0.2] (p < 0.0001)}. Patients had higher peak temperatures during ICU stay as compared to hospital presentation {103.3°F[IQR1.7] vs 100.0°F[IQR3.5], (p < 0.0001)}. Deceased patients had higher peak ICU temperatures than survivors {103.6°F[IQR2.0] vs 102.9°F[IQR1.4], (p = 0.0008)}. Increasing peak temperatures were linearly associated with mortality. Febrile patients who underwent targeted temperature management to achieve normothermia did not have different outcomes than those not actively cooled. Multivariable analysis revealed 60% and 75% higher risk of mortality with peak temperature greater than 103°F and 104°F respectively; it also confirmed hyperthermia, age, male sex, and acidosis to be predictors of mortality.ConclusionsThis is one of the first studies to identify ICU hyperthermia as predictive of mortality in ventilated COVID-19 patients. Additional predictors included male sex, age, and acidosis. With COVID-19 cases increasing, identification of ICU mortality predictors is crucial to improve risk stratification, resource management, and patient outcomes.

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Effect of training on inspiratory load compensation in weaned and unweaned mechanically ventilated ICU patients.

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Effect of training on inspiratory load compensation in weaned and unweaned mechanically ventilated ICU patients.

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