High Respiratory Drive and Excessive Respiratory Efforts Predict Relapse of Respiratory Failure in Critically Ill Patients with COVID-19.
Ontology highlight
ABSTRACT:
SUBMITTER: Esnault P
PROVIDER: S-EPMC7560807 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:BackgroundPatients with obesity are at increased risk of severe COVID-19, requiring mechanical ventilation due to acute respiratory failure. However, conflicting data are obtained for intensive care unit (ICU) mortality.ObjectiveTo analyze the relationship between obesity and in-hospital mortality of ICU patients with COVID-19.Subjects/methodsPatients admitted to the ICU for COVID-19 acute respiratory distress syndrome (ARDS) were included retrospectively. The following data were collected: comorbidities, body mass index (BMI), the severity of ARDS assessed with PaO2/FiO2 (P/F) ratios, disease severity measured by the Simplified Acute Physiology Score II (SAPS II), management and outcomes.ResultsFor a total of 222 patients, there were 34 patients (15.3%) with normal BMI, 92 patients (41.4%) who were overweight, 80 patients (36%) with moderate obesity (BMI:30-39.9 kg/m2), and 16 patients (7.2%) with severe obesity (BMI ≥ 40 kg/m2). Overall in-hospital mortality was 20.3%. Patients with moderate obesity had a lower mortality rate (13.8%) than patients with normal weight, overweight or severe obesity (17.6%, 21.7%, and 50%, respectively; P = 0.011. Logistic regression showed that patients with a BMI ≤ 29 kg/m2 (odds ratio [OR] 3.64, 95% CI 1.38-9.60) and those with a BMI > 39 kg/m2 (OR 10.04, 95% CI 2.45-41.09) had a higher risk of mortality than those with a BMI from 29 to 39 kg/m2. The number of comorbidities (≥2), SAPS II score, and P/F < 100 mmHg were also independent predictors for in-hospital mortality.ConclusionsCOVID-19 patients admitted to the ICU with moderate obesity had a lower risk of death than the other patients, suggesting a possible obesity paradox.
| S-EPMC8190754 | biostudies-literature
Project description:In hypoxemic patients at risk for developing respiratory failure, the decision to initiate invasive mechanical ventilation (IMV) may be extremely difficult, even more so among patients suffering from COVID-19. Delayed recognition of respiratory failure may translate into poor outcomes, emphasizing the need for stronger predictive models for IMV necessity. We developed a two-step model; the first step was to train a machine learning predictive model on a large dataset of non-COVID-19 critically ill hypoxemic patients from the United States (MIMIC-III). The second step was to apply transfer learning and adapt the model to a smaller COVID-19 cohort. An XGBoost algorithm was trained on data from the MIMIC-III database to predict if a patient would require IMV within the next 6, 12, 18 or 24 h. Patients' datasets were used to construct the model as time series of dynamic measurements and laboratory results obtained during the previous 6 h with additional static variables, applying a sliding time-window once every hour. We validated the adaptation algorithm on a cohort of 1061 COVID-19 patients from a single center in Israel, of whom 160 later deteriorated and required IMV. The new XGBoost model for the prediction of the IMV onset was trained and tested on MIMIC-III data and proved to be predictive, with an AUC of 0.83 on a shortened set of features, excluding the clinician's settings, and an AUC of 0.91 when the clinician settings were included. Applying these models "as is" (no adaptation applied) on the dataset of COVID-19 patients degraded the prediction results to AUCs of 0.78 and 0.80, without and with the clinician's settings, respectively. Applying the adaptation on the COVID-19 dataset increased the prediction power to an AUC of 0.94 and 0.97, respectively. Good AUC results get worse with low overall precision. We show that precision of the prediction increased as prediction probability was higher. Our model was successfully trained on a specific dataset, and after adaptation it showed promise in predicting outcome on a completely different dataset. This two-step model successfully predicted the need for invasive mechanical ventilation 6, 12, 18 or 24 h in advance in both general ICU population and COVID-19 patients. Using the prediction probability as an indicator of the precision carries the potential to aid the decision-making process in patients with hypoxemic respiratory failure despite the low overall precision.
| S-EPMC9216294 | biostudies-literature
Project description:This SuperSeries is composed of the SubSeries listed below.
2022-02-28 | GSE197259 | GEO
Project description:Systemic inflammatory illnesses (such as sepsis) are marked by degradation of the endothelial glycocalyx, a layer of glycosaminoglycans (including heparan sulfate, chondroitin sulfate, and hyaluronic acid) lining the vascular lumen. We hypothesized that different pathophysiologic insults would produce characteristic patterns of released glycocalyx fragments. We collected plasma from healthy donors as well as from subjects with respiratory failure due to altered mental status (intoxication, ischemic brain injury), indirect lung injury (non-pulmonary sepsis, pancreatitis), or direct lung injury (aspiration, pneumonia). Mass spectrometry was employed to determine the quantity and sulfation patterns of circulating glycosaminoglycans. We found that circulating heparan sulfate fragments were significantly (23-fold) elevated in patients with indirect lung injury, while circulating hyaluronic acid concentrations were elevated (32-fold) in patients with direct lung injury. N-Sulfation and tri-sulfation of heparan disaccharides were significantly increased in patients with indirect lung injury. Chondroitin disaccharide sulfation was suppressed in all groups with respiratory failure. Plasma heparan sulfate concentrations directly correlated with intensive care unit length of stay. Serial plasma measurements performed in select patients revealed that circulating highly sulfated heparan fragments persisted for greater than 3 days after the onset of respiratory failure. Our findings demonstrate that circulating glycosaminoglycans are elevated in patterns characteristic of the etiology of respiratory failure and may serve as diagnostic and/or prognostic biomarkers of critical illness.
| S-EPMC3961648 | biostudies-literature
Project description:BackgroundPatients with chronic known or unknown interstitial lung disease (ILD) may present with severe respiratory flares that require intensive management. Outcome data in these patients are scarce.Patients and methodsClinical and radiological features were collected in 83 patients with ILD-associated acute respiratory failure (ARF). Determinants of hospital mortality and response to corticosteroid therapy were identified by logistic regression.ResultsHospital and 1-year mortality rates were 41% and 54% respectively. Pulmonary hypertension, computed tomography (CT) fibrosis and acute kidney injury were independently associated with mortality (odds ratio (OR) 4.55; 95% confidence interval (95%CI) (1.20-17.33); OR, 7.68; (1.78-33.22) and OR 10.60; (2.25-49.97) respectively). Response to steroids was higher in patients with shorter time from hospital admission to corticosteroid therapy. Patients with fibrosis on CT had lower response to steroids (OR, 0.03; (0.005-0.21)). In mechanically ventilated patients, overdistension induced by high PEEP settings was associated with CT fibrosis and hospital mortality.ConclusionMortality is high in ILD-associated ARF. CT and echocardiography are valuable prognostic tools. Prompt corticosteroid therapy may improve survival.
| S-EPMC4130629 | biostudies-literature
Project description:BackgroundCoronavirus disease 2019 (COVID-19) has affected individuals worldwide, and patients with cancer are particularly vulnerable to COVID-19-related severe illness, respiratory failure, and mortality. The relationship between COVID-19 and cancer remains a critical concern, and a comprehensive investigation of the factors affecting survival among patients with cancer who develop COVID-19-related respiratory failure is warranted. We aim to compare the characteristics and outcomes of COVID-19-related acute respiratory failure in patients with and without underlying cancer, while analyzing factors affecting in-hospital survival among cancer patients.MethodsWe conducted a retrospective observational study at Taipei Veterans General Hospital in Taiwan from May to September 2022, a period during which the omicron variant of the severe acute respiratory syndrome coronavirus 2 was circulating. Eligible patients had COVID-19 and acute respiratory failure. Clinical data, demographic information, disease severity markers, treatment details, and outcomes were collected and analyzed.ResultsOf the 215 enrolled critically ill patients with COVID-19, 65 had cancer. The patients with cancer were younger and had lower absolute lymphocyte counts, higher ferritin and lactate dehydrogenase (LDH) concentrations, and increased vasopressor use compared with those without cancer. The patients with cancer also received more COVID-19 specific treatments but had higher in-hospital mortality rate (61.5% vs 36%, P = 0.002) and longer viral shedding (13 vs 10 days, P = 0.007) than those without cancer did. Smoking [odds ratio (OR): 5.804, 95% confidence interval (CI): 1.847-39.746], elevated LDH (OR: 1.004, 95% CI: 1.001-1.012), vasopressor use (OR: 5.437, 95% CI: 1.202-24.593), and new renal replacement therapy (OR: 3.523, 95% CI: 1.203-61.108) were independent predictors of in-hospital mortality among patients with cancer and respiratory failure.ConclusionCritically ill patients with cancer experiencing COVID-19-related acute respiratory failure present unique clinical features and worse clinical outcomes compared with those without cancer. Smoking, elevated LDH, vasopressor use, and new renal replacement therapy were risk factors for in-hospital mortality in these patients.
| S-EPMC10789018 | biostudies-literature
Project description:Objective: Adiponectin, an anti-inflammatory cytokine produced by adipose tissue, has been shown to modulate survival in animal models of critical illness. We examined the association between plasma adiponectin and clinical outcomes in critically ill patients with acute respiratory failure.Design: Secondary analysis of a single-center, randomized controlled trial.Setting: Medical intensive care unit of a university-based, tertiary medical center.Patients: One hundred seventy-five subjects with acute respiratory failure enrolled in randomized, controlled pilot trial of Early versus Delayed Enternal Nutrition (EDEN pilot study).Interventions: None.Measurements and main results: Adiponectin measured within 48 hrs of respiratory failure (Apn1) was inversely correlated with body mass index (r=-0.25, p=.007) and was higher in females (median, 12.6 μg/mL; interquartile range, 7.6-17.1) than males (9.45 μg/mL; 6.2-14.2; p=.02). Adiponectin increased at day 6 (Apn1: 11.4 μg/mL [6.6-15.3] vs. Apn6: 14.1 μg/mL [10.3-18.6], p<.001). This increase was significant only in survivors (Δ adiponectin in survivors: 3.9±6 μg/mL, n=80, p<.001 vs. Δ in nonsurvivors: 1.69±4.6 μg/mL, n=14, p=.19). Higher Apn1 was significantly associated with 28-day mortality (odds ratio 1.59 per 5-μg/mL increase; 95% confidence interval 1.15-2.21; p=.006). No measured demographic, clinical, or cytokine covariates, including interleukin-6, interleukin-8, interleukin-10, interleukin-1β, interleukin-12, tumor necrosis factor-α, and interferon-γ, were confounders or effect modifiers of this association between adiponectin and mortality.Conclusions: Independent of measured covariates, increased plasma adiponectin levels measured within 48 hrs of respiratory failure are associated with mortality. This finding suggests that factors derived from adipose tissue play a role in modulating the response to critical illness.
| S-EPMC3703623 | biostudies-literature
Project description:AbstractAlthough the number of deaths due to coronavirus disease 2019 (COVID-19) is higher in men than women, prior studies have provided limited sex-stratified clinical data.We evaluated sex-related differences in clinical outcomes among critically ill adults with COVID-19.Multicenter cohort study of adults with laboratory-confirmed COVID-19 admitted to intensive care units at 67 U.S. hospitals from March 4 to May 9, 2020. Multilevel logistic regression was used to evaluate 28-day in-hospital mortality, severe acute kidney injury (AKI requiring kidney replacement therapy), and respiratory failure occurring within 14 days of intensive care unit admission.A total of 4407 patients were included (median age, 62 years; 2793 [63.4%] men; 1159 [26.3%] non-Hispanic White; 1220 [27.7%] non-Hispanic Black; 994 [22.6%] Hispanic). Compared with women, men were younger (median age, 61 vs 64 years, less likely to be non-Hispanic Black (684 [24.5%] vs 536 [33.2%]), and more likely to smoke (877 [31.4%] vs 422 [26.2%]). During median follow-up of 14 days, 1072 men (38.4%) and 553 women (34.3%) died. Severe AKI occurred in 590 men (21.8%), and 239 women (15.5%), while respiratory failure occurred in 2255 men (80.7%) and 1234 women (76.5%). After adjusting for age, race/ethnicity and clinical variables, compared with women, men had a higher risk of death (OR, 1.50, 95% CI, 1.26-1.77), severe AKI (OR, 1.92; 95% CI 1.57-2.36), and respiratory failure (OR, 1.42; 95% CI, 1.11-1.80).In this multicenter cohort of critically ill adults with COVID-19, men were more likely to have adverse outcomes compared with women.
| S-EPMC8677989 | biostudies-literature
Project description:Patients supported by mechanical ventilation require frequent invasive blood gas samples to monitor and adjust the level of support. We developed a transparent and novel blood gas estimation model to provide continuous monitoring of blood pH and arterial CO2 in between gaps of blood draws, using only readily available noninvasive data sources in ventilated patients. The model was trained on a derivation dataset (1,883 patients, 12,344 samples) from a tertiary pediatric intensive care center, and tested on a validation dataset (286 patients, 4030 samples) from the same center obtained at a later time. The model uses pairwise non-linear interactions between predictors and provides point-estimates of blood gas pH and arterial CO2 along with a range of prediction uncertainty. The model predicted within Clinical Laboratory Improvement Amendments of 1988 (CLIA) acceptable blood gas machine equivalent in 74% of pH samples and 80% of PCO2 samples. Prediction uncertainty from the model improved estimation accuracy by 15% by identifying and abstaining on a minority of high-uncertainty samples. The proposed model estimates blood gas pH and CO2 accurately in a large percentage of samples. The model's abstention recommendation coupled with ranked display of top predictors for each estimation lends itself to real-time monitoring of gaps between blood draws, and the model may help users determine when a new blood draw is required and delay blood draws when not needed.
| S-EPMC9198060 | biostudies-literature
Project description:Infections caused by SARS-CoV-2 may cause a severe disease, termed COVID-19, with significant mortality. Host responses to this infection, mainly in terms of systemic inflammation, have emerged as key pathogenetic mechanisms, and their modulation is the only therapeutic strategy that has shown a mortality benefit. Herein, we used peripheral blood transcriptomes of critically-ill COVID-19 patients obtained at admission in an Intensive Care Unit, to identify two clusters that, in spite of no major clinical differences, have different gene expression profiles that reveal different underlying pathogenetic mechanisms and ultimately have different ICU outcome. A transcriptomic signature was used to identify these clusters in an external validation cohort, yielding a similar result. These results illustrate the potential of transcriptomic profiles to identify patient endotypes and point to relevant pathogenetic mechanisms in COVID-19.
2023-04-29 | GSE206264 | GEO