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Mortality in Relation to Frailty in Patients Admitted to a Specialized Geriatric Intensive Care Unit.

ABSTRACT: In older adults admitted to intensive care units (ICUs), frailty influences prognosis. We examined the relationship between the frailty index (FI) based on deficit accumulation and early and late survival.Older patients (?65 years) admitted to a specialized geriatric ICU at the Liuhuaqiao Hospital, Guangzhou, China between July-December 2011 (n = 155; age 82.7±7.1 y; 87.1% men) were followed for 300 days. The FI was calculated as the proportion present of 52 health deficits. FI performance was compared with that of several prognostic scores.The 90-day death rate was 38.7% (n = 60; 27 died within 30 days). The FI score was correlated with the Glasgow Coma Scale, Karnofsky Scale, Palliative Performance Scale, Acute Physiology Score-APACHE II and APACHE IV (r (2) = 0.52 to 0.72, p < 0.001). Patients who died within 30 days had higher mean FI scores (0.41±0.11) than those who survived to 300 days (0.22±0.11; F = 38.91, p < 0.001). Each 1% increase in the FI from the previous level was associated with an 11% increase in the 30-day mortality risk (95% CI: 7%-15%) adjusting for age, sex, and the prognostic scores. The FI discriminated patients who died in 30 days from those who survived with moderately high accuracy (AUC = 0.89±0.03). No one with an FI score >0.46 survived past 90 days.ICU survival was strongly associated with the level of frailty at admission. An FI based on health deficit accumulation may help improve critical care outcome prediction in older adults.

SUBMITTER: Zeng A

PROVIDER: S-EPMC4631107 | biostudies-literature | 2015 Dec

REPOSITORIES: biostudies-literature

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Mortality in Relation to Frailty in Patients Admitted to a Specialized Geriatric Intensive Care Unit.

Zeng An A Song Xiaowei X Dong Jiahui J Mitnitski Arnold A Liu Jian J Guo Zhenhui Z Rockwood Kenneth K

The journals of gerontology. Series A, Biological sciences and medical sciences 20150922 12

<h4>Background</h4>In older adults admitted to intensive care units (ICUs), frailty influences prognosis. We examined the relationship between the frailty index (FI) based on deficit accumulation and early and late survival.<h4>Methods</h4>Older patients (≥65 years) admitted to a specialized geriatric ICU at the Liuhuaqiao Hospital, Guangzhou, China between July-December 2011 (n = 155; age 82.7±7.1 y; 87.1% men) were followed for 300 days. The FI was calculated as the proportion present of 52 he ...[more]

PMID: 26400736

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Project description:BackgroundPrognostic information at the time of hospital discharge can help guide goals-of-care discussions for future care. We sought to assess the association between the Hospital Frailty Risk Score (HFRS), which may highlight patients' risk of adverse outcomes at the time of hospital discharge, and in-hospital death among patients admitted to the intensive care unit (ICU) within 12 months of a previous hospital discharge.MethodsWe conducted a multicentre retrospective cohort study that included patients aged 75 years or older admitted at least twice over a 12-month period to the general medicine service at 7 academic centres and large community-based teaching hospitals in Toronto and Mississauga, Ontario, Canada, from Apr. 1, 2010, to Dec. 31, 2019. The HFRS (categorized as low, moderate or high frailty risk) was calculated at the time of discharge from the first hospital admission. Outcomes included ICU admission and death during the second hospital admission.ResultsThe cohort included 22 178 patients, of whom 1767 (8.0%) were categorized as having high frailty risk, 9464 (42.7%) as having moderate frailty risk, and 10 947 (49.4%) as having low frailty risk. One hundred patients (5.7%) with high frailty risk were admitted to the ICU, compared to 566 (6.0%) of those with moderate risk and 790 (7.2%) of those with low risk. After adjustment for age, sex, hospital, day of admission, time of admission and Laboratory-based Acute Physiology Score, the odds of ICU admission were not significantly different for patients with high (adjusted odds ratio [OR] 0.99, 95% confidence interval [CI] 0.78 to 1.23) or moderate (adjusted OR 0.97, 95% CI 0.86 to 1.09) frailty risk compared to those with low frailty risk. Among patients admitted to the ICU, 75 (75.0%) of those with high frailty risk died, compared to 317 (56.0%) of those with moderate risk and 416 (52.7%) of those with low risk. After multivariable adjustment, the risk of death after ICU admission was higher for patients with high frailty risk than for those with low frailty risk (adjusted OR 2.86, 95% CI 1.77 to 4.77).InterpretationAmong patients readmitted to hospital within 12 months, patients with high frailty risk were similarly likely as those with lower frailty risk to be admitted to the ICU but were more likely to die if admitted to ICU. The HFRS at hospital discharge can inform prognosis, which can help guide discussions for preferences for ICU care during future hospital stays.

Project description:ImportanceA high Pao2, termed hyperoxemia, is postulated to have deleterious health outcomes. To date, the association between hyperoxemia during the ongoing management of critical illness and mortality has been incompletely evaluated in children.ObjectiveTo examine whether severe hyperoxemia events are associated with mortality among patients admitted to a pediatric intensive care unit (PICU).Design, setting, and participantsA retrospective cohort study was conducted over a 10-year period (January 1, 2009, to December 31, 2018); all 23 719 PICU encounters at a quaternary children's hospital with a documented arterial blood gas measurement were evaluated.ExposuresSevere hyperoxemia, defined as Pao2 level greater than or equal to 300 mm Hg (40 kPa).Main outcomes and measuresThe highest Pao2 values during hospitalization were dichotomized according to the definition of severe hyperoxemia and assessed for association with in-hospital mortality using logistic regression models incorporating a calibrated measure of multiple organ dysfunction, extracorporeal life support, and the total number of arterial blood gas measurements obtained during an encounter.ResultsOf 23 719 PICU encounters during the inclusion period, 6250 patients (13 422 [56.6%] boys; mean [SD] age, 7.5 [6.6] years) had at least 1 measured Pao2 value. Severe hyperoxemia was independently associated with in-hospital mortality (adjusted odds ratio [aOR], 1.78; 95% CI, 1.36-2.33; P < .001). Increasing odds of in-hospital mortality were observed with 1 (aOR, 1.47; 95% CI, 1.05-2.08; P = .03), 2 (aOR, 2.01; 95% CI, 1.27-3.18; P = .002), and 3 or more (aOR, 2.53; 95% CI, 1.62-3.94; P < .001) severely hyperoxemic Pao2 values obtained greater than or equal to 3 hours apart from one another compared with encounters without hyperoxemia. A sensitivity analysis examining the hypothetical outcomes of residual confounding indicated that an unmeasured binary confounder with an aOR of 2 would have to be present in 37% of the encounters with severe hyperoxemia and 0% of the remaining cohort to fail to reject the null hypothesis (aOR of severe hyperoxemia, 1.31; 95% CI, 0.99-1.72).Conclusions and relevanceGreater numbers of severe hyperoxemia events appeared to be associated with increased mortality in this large, diverse cohort of critically ill children, supporting a possible exposure-response association between severe hyperoxemia and outcome in this population. Although further prospective evaluation appears to be warranted, this study's findings suggest that guidelines for ongoing management of critically ill children should take into consideration the possible detrimental effects of severe hyperoxemia.

Project description:BackgroundThe Warburg effect, characterized by elevated lactate levels without tissue hypoxia or shock, has been described in patients with aggressive lymphoproliferative malignancies. However, the clinical characteristics and long-term outcomes in this population remain poorly understood.MethodsWe retrospectively analyzed 135 patients with aggressive lymphoproliferative malignancies admitted to the ICU between January 2017 and December 2022. Patients were classified into three groups: Clinical Warburg Effect (CWE), No Warburg with High Lactate level (NW-HL), and No Warburg with Normal Lactate level (NW-NL). Clinical characteristics and outcomes were compared between the groups and factors associated with 1-year mortality and CWE were identified using multivariable analyses.ResultsOf the 135 patients, 46 (34%) had a CWE. This group had a higher proportion of Burkitt and T cell lymphomas, greater tumor burden, and more frequent bone and cerebral involvement than the other groups. At 1 year, 72 patients (53%) died, with significantly higher mortality in the CWE and NW-HL groups (70% each) than in the NW-NL group (38%). Factors independently associated with 1-year mortality were age [HR = 1.02 CI 95% (1.00-1.04)], total SOFA score at admission [HR = 1.19 CI 95% (1.12-1.25)], and CWE [HR = 3.87 CI 95% (2.13-7.02)]. The main factors associated with the CWE were tumor lysis syndrome [OR = 2.84 CI 95% (1.14-7.42)], bone involvement of the underlying malignancy [OR = 3.58 CI 95% (1.02-12.91)], the total SOFA score at admission [OR = 0.81 CI 95% (0.69-0.91)] and hypoglycemia at admission [OR = 14.90 CI 95% (5.42-47.18)].ConclusionCWE is associated with a higher tumor burden and increased 1-year mortality compared to patients without this condition. Our findings underscore the importance of recognizing patients with CWE as a high-risk cohort, as their outcomes closely resemble those of individuals with lymphoma and shock, despite not requiring advanced organ support. Clinicians should recognize the urgency of managing these patients and consider early intervention to improve their prognosis.

Project description:BackgroundsThe in-hospital mortality in lung cancer patients admitted to intensive care unit (ICU) is extremely high. This study intended to adopt machine learning algorithm models to predict in-hospital mortality of critically ill lung cancer for providing relative information in clinical decision-making.MethodsData were extracted from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) for a training cohort and data extracted from the Medical Information Mart for eICU Collaborative Research Database (eICU-CRD) database for a validation cohort. Logistic regression, random forest, decision tree, light gradient boosting machine (LightGBM), eXtreme gradient boosting (XGBoost), and an ensemble (random forest+LightGBM+XGBoost) model were used for prediction of in-hospital mortality and important feature extraction. The AUC (area under receiver operating curve), accuracy, F1 score and recall were used to evaluate the predictive performance of each model. Shapley Additive exPlanations (SHAP) values were calculated to evaluate feature importance of each feature.ResultsOverall, there were 653 (24.8%) in-hospital mortality in the training cohort, and 523 (21.7%) in-hospital mortality in the validation cohort. Among the six machine learning models, the ensemble model achieved the best performance. The top 5 most influential features were the sequential organ failure assessment (SOFA) score, albumin, the oxford acute severity of illness score (OASIS) score, anion gap and bilirubin in random forest and XGBoost model. The SHAP summary plot was used to illustrate the positive or negative effects of the top 15 features attributed to the XGBoost model.ConclusionThe ensemble model performed best and might be applied to forecast in-hospital mortality of critically ill lung cancer patients, and the SOFA score was the most important feature in all models. These results might offer valuable and significant reference for ICU clinicians' decision-making in advance.

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Mortality in Relation to Frailty in Patients Admitted to a Specialized Geriatric Intensive Care Unit.

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Mortality in Relation to Frailty in Patients Admitted to a Specialized Geriatric Intensive Care Unit.

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