Gender- and age-based differences in outcomes of mechanically ventilated ICU patients: a Chinese multicentre retrospective study.
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ABSTRACT: Background
Previous studies have suggested that the gender and/or age of a patient may influence the clinical outcomes of critically ill patients. Our aim was to determine whether there are gender- and age-based differences in clinical outcomes for mechanically ventilated patients in intensive care units (ICUs).Methods
We performed a multicentre retrospective study involving adult patients who were admitted to the ICU and received at least 24 h of mechanical ventilation (MV). The patients were divided into two groups based on gender and, subsequently, further grouped based on gender and age < or ≥ 65 years. The primary outcome measure was hospital mortality.Results
A total of 853 mechanically ventilated patients were evaluated. Of these patients, 63.2% were men and 61.5% were ≥ 65 years of age. The hospital mortality rate for men was significantly higher than that for women in the overall study population (P = 0.042), and this difference was most pronounced among elderly patients (age ≥ 65 years; P = 0.006). The durations of MV, ICU lengths of stay (LOS), and hospital LOS were significantly longer for men than for women among younger patients (P ≤ 0.013) but not among elderly patients. Multivariate logistic regression analysis revealed that male gender was independently associated with hospital mortality among elderly patients but not among younger patients.Conclusions
There were important gender- and age-based differences in the outcomes among mechanically ventilated ICU patients. The combination of male gender and advanced age is strongly associated with hospital mortality.
SUBMITTER: Ma JG
PROVIDER: S-EPMC8744292 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundIn recent years, the number of elderly patients receiving mechanical ventilation (MV) in intensive care units (ICUs) has increased. However, the evidence on the outcomes of elderly mechanically ventilated patients is scant in China. Our objective was to evaluate the characteristics and outcomes in elderly patients (≥65 years) receiving MV in the ICU.MethodsWe performed a multicentre retrospective study involving adult patients who were admitted to the ICU and received at least 24 hours of MV. Patients were divided into three age groups: under 65, 65-79, and ≥80 years. The primary outcome was hospital mortality. We performed univariate and multivariate logistic regression analysis to identify factors associated with hospital mortality.ResultsA total of 853 patients were analysed. Of those, 61.5% were ≥65 years of age, and 26.0% were ≥80 years of age. There were significant differences in the principal reason for MV among the three age groups (P<0.001). Advanced age was significantly associated with total duration of MV, ICU length of stay (LOS), and ICU costs (all P<0.001), but not with hospital LOS and hospital costs (P>0.05). In addition, mortality rates in the ICU, hospital, and at 60 days significantly increased with age (all P<0.001). In the age group of 80 years and older, the mortality rates were 47.7%, 49.5%, and 50.0%, respectively. Multivariate logistic regression analysis had found that age, Acute Physiology and Chronic Health Evaluation (APACHE) II score, partial pressure of oxygen in arterial blood/fraction of inspired oxygen (PaO2/FiO2) ratio, total duration of MV, ICU LOS, and the decision to withhold/withdraw life-sustaining treatments were independent influence factors for mortality rates.ConclusionsMechanically ventilated elderly patients (≥65 years) have a higher ICU and hospital mortality, but the hospital LOS and hospital costs are similar to younger patients. Advanced age should be considered as a significant independent risk factor for hospital mortality of mechanically ventilated ICU patients.
| S-EPMC8107518 | biostudies-literature
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.
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| S-EPMC9799109 | biostudies-literature
Project description:ObjectivesTo measure how the ICU Liberation Bundle (aka ABCDEF Bundle or the Bundle) affected clinical outcomes in mechanically ventilated (MV) adult ICU patients, as well as bundle sustainability and spread across a healthcare system.DesignWe conducted a multicenter, prospective, cohort observational study to measure bundle performance versus patient outcomes and sustainability in 11 adult ICUs at six community hospitals. We then prospectively measured bundle spread and performance across the other 28 hospitals of the healthcare system.SettingA large community-based healthcare system.PatientsIn 11 study ICUs, we enrolled 1,914 MV patients (baseline n = 925, bundle performance/outcomes n = 989), 3,019 non-MV patients (baseline n = 1,323, bundle performance/outcomes n = 1,696), and 2,332 MV patients (bundle sustainability). We enrolled 9,717 MV ICU patients in the other 28 hospitals to assess bundle spread.InterventionsWe used evidence-based strategies to implement the bundle in all 34 hospitals.Measurements and main resultsWe compared outcomes for the 12-month baseline and bundle performance periods. Bundle implementation reduced ICU length of stay (LOS) by 0.5 days (p = 0.02), MV duration by 0.6 days (p = 0.01), and ICU LOS greater than or equal to 7 days by 18.1% (p < 0.01). Performance period bundle compliance was compared with the preceding 3-month baseline compliance period. Compliance with pain management and spontaneous awakening trial (SAT) and spontaneous breathing trial (SBT) remained high, and reintubation rates remained low. Sedation assessments increased (p < 0.01) and benzodiazepine sedation use decreased (p < 0.01). Delirium assessments increased (p = 0.02) and delirium prevalence decreased (p = 0.02). Patient mobilization and ICU family engagement did not significantly improve. Bundle element sustainability varied. SAT/SBT compliance dropped by nearly half, benzodiazepine use remained low, sedation and delirium monitoring and management remained high, and patient mobility and family engagement remained low. Bundle compliance in ICUs across the healthcare system exceeded that of study ICUs.ConclusionsThe ICU Liberation Bundle improves outcomes in MV adult ICU patients. Evidence-based implementation strategies improve bundle performance, spread, and sustainability across large healthcare systems.
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Project description:RationalePrediction of patients at risk for mortality can help triage patients and assist in resource allocation.ObjectivesDevelop and evaluate a machine learning-based algorithm which accurately predicts mortality in COVID-19, pneumonia, and mechanically ventilated patients.MethodsRetrospective study of 53,001 total ICU patients, including 9166 patients with pneumonia and 25,895 mechanically ventilated patients, performed on the MIMIC dataset. An additional retrospective analysis was performed on a community hospital dataset containing 114 patients positive for SARS-COV-2 by PCR test. The outcome of interest was in-hospital patient mortality.ResultsWhen trained and tested on the MIMIC dataset, the XGBoost predictor obtained area under the receiver operating characteristic (AUROC) values of 0.82, 0.81, 0.77, and 0.75 for mortality prediction on mechanically ventilated patients at 12-, 24-, 48-, and 72- hour windows, respectively, and AUROCs of 0.87, 0.78, 0.77, and 0.734 for mortality prediction on pneumonia patients at 12-, 24-, 48-, and 72- hour windows, respectively. The predictor outperformed the qSOFA, MEWS and CURB-65 risk scores at all prediction windows. When tested on the community hospital dataset, the predictor obtained AUROCs of 0.91, 0.90, 0.86, and 0.87 for mortality prediction on COVID-19 patients at 12-, 24-, 48-, and 72- hour windows, respectively, outperforming the qSOFA, MEWS and CURB-65 risk scores at all prediction windows.ConclusionsThis machine learning-based algorithm is a useful predictive tool for anticipating patient mortality at clinically useful timepoints, and is capable of accurate mortality prediction for mechanically ventilated patients as well as those diagnosed with pneumonia and COVID-19.
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Project description:BackgroundVasoactive medications are commonly used in the treatment of critically ill patients, but their impact on the development of ICU-acquired weakness is not well described. The objective of this study is to evaluate the relationship between vasoactive medication use and the outcome of ICU-acquired weakness.MethodsThis is a secondary analysis of mechanically ventilated patients (N = 172) enrolled in a randomized clinical trial of early occupational and physical therapy vs conventional therapy, which evaluated the end point of ICU-acquired weakness on hospital discharge. Patients underwent bedside muscle strength testing by a therapist blinded to study allocation to evaluate for ICU-acquired weakness. The effects of vasoactive medication use on the incidence of ICU-acquired weakness in this population were assessed.ResultsOn logistic regression analysis, the use of vasoactive medications increased the odds of developing ICU-acquired weakness (odds ratio [OR], 3.2; P = .01) independent of all other established risk factors for weakness. Duration of vasoactive medication use (in days) (OR, 1.35; P = .004) and cumulative norepinephrine dose (μg/kg/d) (OR, 1.01; P = .02) (but not vasopressin or phenylephrine) were also independently associated with the outcome of ICU-acquired weakness.ConclusionsIn mechanically ventilated patients enrolled in a randomized clinical trial of early mobilization, the use of vasoactive medications was independently associated with the development of ICU-acquired weakness. Prospective trials to further evaluate this relationship are merited.Trial registryClinicalTrials.gov; No.: NCT01777035; URL: www.clinicaltrials.gov.
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Project description:ObjectiveTo identify quantitative electroencephalography (EEG)-based indicators of delirium or coma in mechanically ventilated patients.MethodsWe prospectively enrolled 28 mechanically ventilated intensive care unit (ICU) patients to undergo 24-hour continuous EEG, 25 of whom completed the study. We assessed patients twice daily using the Richmond Agitation-Sedation Scale (RASS) and Confusion Assessment Method for the ICU (CAM-ICU). We evaluated the spectral profile, regional connectivity and complexity of 5-minute EEG segments after each assessment. We used penalized regression to select EEG metrics associated with delirium or coma, and compared mixed-effects models predicting delirium with and without the selected EEG metrics.ResultsDelta variability, high-beta variability, relative theta power, and relative alpha power contributed independently to EEG-based identification of delirium or coma. A model with these metrics achieved better prediction of delirium or coma than a model with clinical variables alone (Akaike Information Criterion: 36 vs 43, p = 0.006 by likelihood ratio test). The area under the receiver operating characteristic curve for an ad hoc hypothetical delirium score using these metrics was 0.94 (95%CI 0.83-0.99).ConclusionsWe identified four EEG metrics that, in combination, provided excellent discrimination between delirious/comatose and non-delirious mechanically ventilated ICU patients.SignificanceOur findings give insight to neurophysiologic changes underlying delirium and provide a basis for pragmatic, EEG-based delirium monitoring technology.
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Project description:BackgroundRespiratory pendelluft phenomenon, defined as intrapulmonary gas redistribution caused by asynchronous alveolar ventilation, could be potentially harmful by inducing lung injury. The aim of the present study was to investigate its prevalence and prognosis in intensive care unit (ICU) patients with acute respiratory failure (ARF).MethodsThis was a retrospective observational study on 200 mechanically ventilated ARF patients treated in a tertiary ICU. The presence of pendelluft was determined using electrical impedance tomography (EIT) within 48 h after admission. Its amplitude was defined as the impedance difference between the sum of all regional tidal impedance variation and the global tidal impedance variation. A value above 2.5% (the 95th percentile from 30 healthy volunteers) was considered confirmative for its occurrence.ResultsPendelluft was found in 61 patients (39 in 94 patients with spontaneous breathing, 22 in 106 receiving controlled ventilation), with an overall prevalence of 31%. Existence of spontaneous breathing and higher global inhomogeneity index were associated with pendelluft. Patients with pendelluft had a longer ICU length of stay [10 (6, 14) vs. 7 (4, 11) days; median (lower, upper quartile); p = 0.022] and shorter 14-day ventilator-free days [8 (1, 10) vs. 10 (6, 12) days; p = 0.015]. Subgroup survival analysis suggested the association between pendelluft and longer ventilation duration, which was significant only in patients with PaO2/FiO2 ratio below 200 mmHg (log-rank p = 0.042). ICU mortality did not differ between the patients with and without pendelluft.ConclusionsRespiratory pendelluft occurred often in our study group and it was associated with longer ventilation duration. Early recognition of this phenomenon should trigger interventions aimed at alleviating pendelluft.
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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.
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Project description:RATIONALE:Physicians are increasingly being held accountable for patient outcomes, yet their specific contribution to the outcomes remains uncertain. OBJECTIVES:To determine variation in outcomes of mechanically ventilated patients among intensivists, as well as associations between intensivist experience and patient outcomes. METHODS:We performed a retrospective cohort study of mechanically ventilated Medicare fee-for-service patients in acute care hospitals in Pennsylvania using administrative, clinical, and physician data from Centers for Medicare and Medicaid Services and the American Medical Association from 2008 and 2009. We identified intensivists by training background, board certification, and claims for services provided to patients admitted to an intensive care unit. We assigned patients to intensivists for outcome attribution based on submitted claims for critical care and in-patient services. We estimated the physician-specific adjusted odds ratios (ORs) for 30-day mortality using a hierarchical model with a random effect for physician, adjusted for patient and hospital characteristics. We tested for independent association of physician experience with patient outcomes using mixed-effects regression for the primary outcome of 30-day mortality. We defined physician experience in two ways: years since training completion ("duration") and annual number of mechanically ventilated patients ("volume"). RESULTS:We assigned 345 physicians to 11,268 patients. The 30-day mortality was 43% and median hospital length of stay was 11 days (interquartile range = 6-18). The physician adjusted OR varied from 0.72 to 1.64 (median = 0.99; interquartile range = 0.92-1.09). A total of 48% of physicians was outliers, with an adjusted OR significantly different from 1. However, among intensivists, physician experience was not associated with 30-day mortality (duration OR = 1.00 per additional year; 95% confidence interval = 1.00-1.01; volume OR = 1.00 per additional patient; 95% confidence interval = 1.00-1.00). CONCLUSIONS:Intensivists independently contribute to outcomes of Medicare patients who undergo mechanical ventilation, as evidenced by the variation in risk-adjusted mortality across intensivists. However, physician experience does not underlie this relationship between intensivists, suggesting the need to identify modifiable physician factors to improve outcomes.
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