scholarly journals Differences in Characteristics, Hospital Care, and Outcomes between Acute Critically Ill Emergency Department Patients Receiving Palliative Care and Usual Care

2021 ◽  
Vol 18 (23) ◽  
pp. 12546
Author(s):  
Julia Chia-Yu Chang ◽  
Che Yang ◽  
Li-Ling Lai ◽  
Hsien-Hao Huang ◽  
Shih-Hung Tsai ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Emergency Department ◽  
Palliative Care ◽  
Intensive Care ◽  
Hospital Mortality ◽  
Usual Care ◽  
Critically Ill ◽  
Resource Utilization ◽  
Critically Ill Patients ◽  
Hospital Care

Background: The early integration of palliative care in the emergency department (ED-PC) provides several benefits, including improved quality of life with optimal comfort measures, and symptom control. Whether palliative care could affect the intensive care unit admissions, hospital care and resource utilization requires further investigation. Aim: To determine the differences in inpatient characteristics, hospital care, survival, and resource utilization between patients receiving palliative care (ED-PC) and usual care (UC). Design: Retrospective observational study. Setting/participants: We enrolled consecutive, acute, critically ill patients admitted to the emergency intensive care unit at Taipei Veterans General Hospital from 1 February 2018 to 31 January 2020. Results: A total of 1273 patients were evaluated for unmet palliative care needs; 685 patients received ED-PC and 588 received UC. The palliative care patients were more severely frail (AOR 2.217 (1.295–3.797), p = 0.004), had functional deterioration with three ADLs (AOR 1.348 (1.040–1.748), p = 0.024), biopsychosocial discomfort (AOR 1.696 (1.315–2.187), p < 0.001), higher Taiwan Triage and Acuity Scale 1 (p = 0.024), higher in-hospital mortality (AOR 1.983 (1.540–2.555), p < 0.001), were four times more likely to sign an DNR (AOR 4.536 (2.522–8.158), p < 0.001), and were twice as likely to sign an DNR at admission (AOR 2.1331.619–2.811), p < 0.001). Palliative care patients received less epinephrine (AOR 0.424 (0.265–0.678), p < 0.001), more frequent withdrawal of an endotracheal tube (AOR 8.780 (1.122–68.720), p = 0.038), and more narcotics (AOR1.675 (1.132–2.477), p = 0.010). Palliative care patients exhibited lower 7-day, 30-day, and 90-day survival rates (p < 0.001). There was no significant difference in the hospital length of stay (LOS) (21.2 ± 26.6 vs. 21.7 ± 20.6, p = 0.709) nor total hospital expenses (293,169 ± 350,043 vs. 294,161 ± 315,275, p = 0.958). Conclusion: Acute critically ill patients receiving palliative care were more frail, more critical, and had higher in-hospital mortality. Palliative care patients received less epinephrine, more endotracheal extubation, and more narcotics. There was no difference in the hospital LOS or hospital costs between the palliative and usual care groups. The synthesis of ED-PC is new but achievable with potential benefits to align care with patient goals.

Abstract 15293: Noninvasive Echocardiographic Cardiac Power Output Predicts Mortality in Cardiac Intensive Care Unit Patients

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Barry Burstein ◽  
Vidhu Anand ◽  
Bradley Ternus ◽  
Meir Tabi ◽  
Nandan S Anavekar ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Hospital Mortality ◽  
Critically Ill ◽  
Power Output ◽  
Critically Ill Patients ◽  
Cardiac Intensive Care Unit ◽  
Cardiac Power ◽  
The Mean ◽  
Cardiac Intensive Care

Introduction: A low cardiac power output (CPO), measured invasively, identifies critically ill patients at increased risk of mortality. CPO can also be measured non-invasively with transthoracic echocardiography (TTE), although prognostic data in critically ill patients is not available. Hypothesis: Reduced CPO measured by TTE is associated with increased hospital mortality in cardiac intensive care unit (CICU) patients. Methods: Using a database of CICU patients admitted between 2007 and 2018, we identified patients with TTE within one day (before or after) of CICU admission who had data necessary for calculation of CPO. Multivariable logistic regression determined the relationship between CPO and adjusted hospital mortality. Results: We included 5,585 patients with a mean age of 68.3±14.8 years, including 36.7% females. Admission diagnoses included acute coronary syndrome (ACS) in 57%, heart failure (HF) in 50%, cardiac arrest (CA) in 12%, and cardiogenic shock (CS) in 13%. The mean left ventricular ejection fraction (LVEF) was 47±16%, and the mean CPO was 1.0±0.4 W. CPO was inversely associated with the risk of hospital mortality (Figure A), including among patients with ACS, HF, and CS (Figure B). On multivariable analysis, lower CPO was associated with higher hospital mortality (OR 0.96 per 0.1 W, 95% CI 0.0.93-0.99, p=0.03). Hospital mortality was highest in patients with low CPO coupled with reduced LVEF, increased vasopressor requirements, or higher admission lactate. Hospital mortality was higher among patients with a CPO <0.6 W (adjusted OR 1.57, 95% CI 1.13-2.19, p = 0.007), particularly in the presence of admission lactate level >4 mmol/L (50.9%). Conclusions: Echocardiographic CPO was inversely associated with hospital mortality in CICU patients, particularly among patients with increased lactate and vasopressor requirements. Routine measurement of CPO provides important information beyond LVEF and should be considered in CICU patients.

Feasibility and Acceptability of a Palliative Approach Screening Tool in the Intensive Care Unit

2020 ◽  
Vol 29 (3) ◽  
pp. 214-220
Author(s):  
Jane Venis ◽  
Peter Dodek
Keyword(s):  
Intensive Care Unit ◽  
Palliative Care ◽  
Intensive Care ◽  
Focus Group ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Screening Tool ◽  
Unmet Needs ◽  
Pilot Testing ◽  
Palliative Approach

Background Identifying critically ill patients who have unmet needs for palliative care is the first step in integrating the palliative approach for patients and their families into intensive care units. Objective To explore how palliative care is addressed in an intensive care unit and to develop and test a screening tool for unmet needs that may be met through the palliative approach. Methods A mixed-methods study was conducted in the intensive care unit of a tertiary care hospital to explore the palliative approach. Focus groups and a survey were used to identify items for the screening tool. After pilot testing of the tool, interviews were conducted to refine the content. Results The first focus group (14 participants) revealed participants’ frustration with unclear communication and a desire for better collaboration among health care team members regarding patients with serious life-limiting illnesses and their families. The survey (response rate: 20%; 30 of 150) showed clinicians’ preference for items that identify specific needs rather than diagnoses. The second focus group (8 participants) yielded strategies to operationalize the tool for all patients in the intensive care unit. After 2 separate pilot testing cycles, bedside nurses noted that use of the screening tool prompted earlier discussions and broader assessments of what is meaningful to patients and their families. Conclusion Development of a screening tool for unmet palliative care needs among intensive care unit patients is feasible and acceptable and may help to systematically integrate the palliative approach into routine care for critically ill patients.

scholarly journals Using machine learning tools to predict outcomes for emergency department intensive care unit patients

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Qiangrong Zhai ◽  
Zi Lin ◽  
Hongxia Ge ◽  
Yang Liang ◽  
Nan Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intensive Care Unit ◽  
Emergency Department ◽  
Intensive Care ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Predictive Accuracy ◽  
Medical Center ◽  
Learning Tools ◽  
Critical Patients

AbstractThe number of critically ill patients has increased globally along with the rise in emergency visits. Mortality prediction for critical patients is vital for emergency care, which affects the distribution of emergency resources. Traditional scoring systems are designed for all emergency patients using a classic mathematical method, but risk factors in critically ill patients have complex interactions, so traditional scoring cannot as readily apply to them. As an accurate model for predicting the mortality of emergency department critically ill patients is lacking, this study’s objective was to develop a scoring system using machine learning optimized for the unique case of critical patients in emergency departments. We conducted a retrospective cohort study in a tertiary medical center in Beijing, China. Patients over 16 years old were included if they were alive when they entered the emergency department intensive care unit system from February 2015 and December 2015. Mortality up to 7 days after admission into the emergency department was considered as the primary outcome, and 1624 cases were included to derive the models. Prospective factors included previous diseases, physiologic parameters, and laboratory results. Several machine learning tools were built for 7-day mortality using these factors, for which their predictive accuracy (sensitivity and specificity) was evaluated by area under the curve (AUC). The AUCs were 0.794, 0.840, 0.849 and 0.822 respectively, for the SVM, GBDT, XGBoost and logistic regression model. In comparison with the SAPS 3 model (AUC = 0.826), the discriminatory capability of the newer machine learning methods, XGBoost in particular, is demonstrated to be more reliable for predicting outcomes for emergency department intensive care unit patients.

Outcomes of Critically Ill Gynecological Cancer Patients Admitted to Intensive Care Unit

2012 ◽  
Vol 30 (1) ◽  
pp. 7-11 ◽  
Author(s):  
Silvio A. Ñamendys-Silva ◽  
María O. González-Herrera ◽  
Julia Texcocano-Becerra ◽  
Angel Herrera-Gómez
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Prognostic Factors ◽  
Mortality Rate ◽  
Hospital Mortality ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Gynecological Cancer ◽  
Apache Ii ◽  
Apache Ii Score

Purpose: To assess the characteristics of critically ill patients with gynecological cancer, and to evaluate their prognosis. Methods: Fifty-two critically ill patients with gynecological cancer admitted to intensive care unit (ICU) were included. Univariate and multivariate logistic regressions were used to identify factors associated with hospital mortality. Results: Thirty-five patients (67.3%) had carcinoma of the cervix uteri and 11 (21.2%) had ovarian cancer. The mortality rate in the ICU was 17.3% (9 of 52) and hospital mortality rate were 23%(12 of 52). In the multivariate analysis, independent prognostic factors for hospital mortality were vasopressor use (odds ratio [OR] = 8.60, 95% confidence interval [CI] 2.05-36; P = .03) and the Acute Physiology and Chronic Health Evaluation (APACHE) II score (OR = 1.43, 95% CI 1.01-2.09; P = .048). Conclusions: The independent prognostic factors for hospital mortality were the need for vasopressors and the APACHE II score.

scholarly journals The Comparison between Sepsis 1.0 and Sepsis 3.0 in Database of Severe Infection in Critical Illness: A Retrospective Study

2019 ◽  
Author(s):  
Wei Zhang ◽  
Yan Zheng ◽  
Juan Gu ◽  
Yan Kang
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Hospital Mortality ◽  
Sequential Organ Failure Assessment ◽  
Critically Ill ◽  
Predictive Validity ◽  
Critically Ill Patients ◽  
Severe Infection ◽  
Suspected Infection ◽  
Failure Assessment

Abstract Objective To compared the Sepsis 1.0 criterial with the Sepsis 3.0 criteria predict the efficacy of all-caused mortality of in-hospital in critically ill patients with severe infection. Design This is a retrospective and cohort study based on the database of severe infection. Setting A 48-bed general intensive care unit in affiliated hospital of University. Patients Critically ill patients with suspected infection based on the electronic health records from 1 January to 31 December, 2015. Interventions None. Measurements The variables of exposures included: quick sequential organ failure assessment (qSOFA), systemic inflammatory response syndrome (SIRS) score and sequential organ failure assessment (SOFA). Main outcomes and measures: for predictive validity, we found that the discrimination for hospital mortality was more common with sepsis than with uncomplicated infections. Results are reported as the area under the receiver operating characteristic curve (AUROC).Main Results In the primary cohort, 873 patients had suspected infection cohort (n=634), of whom 188 (29.7%) died; and with the non-infection cohort (n=239), 26 patients died (10.9%). Among intensive care unit (ICU) cases in the infection cohort, the predictive validity for hospital mortality was higher for Sepsis 3.0 (SOFA) criteria (AUROC=0.702; 95%CI, 0.665 −0.737; p≤0.01 for both) than for Sepsis 1.0 (SIRS) criteria (AUROC=0.533; 95% confidence interval [95%CI], 0.493−0.572). Conclusions In our study, we found the Sepsis 3.0 criteria is able to accurately predict the prognosis in critically ill patients with severe infection, and its predictive efficacy is superior to Sepsis 1.0 criteria.

scholarly journals Association between Malnutrition and Hospital Mortality and Duration of Intensive Care Unit Admission in the Critically Ill: A Prospective Cohort Study

2017 ◽  
Author(s):  
Charles Chin Han Lew ◽  
Gabriel Jun Yung Wong ◽  
Ka Po Cheung ◽  
Ai Ping Chua ◽  
Mary Foong Fong Chong ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Cohort Study ◽  
Intensive Care ◽  
Hospital Mortality ◽  
Critically Ill ◽  
Prospective Cohort Study ◽  
Critically Ill Patients ◽  
Prospective Cohort ◽  
Unit Length ◽  
Increased Risk

There is limited evidence for the association between malnutrition and hospital mortality as well as Intensive Care Unit length-of-stay (ICU-LOS) in critically ill patients. We aimed to examine the aforementioned associations by conducting a prospective cohort study in an ICU of a Singapore tertiary hospital. Between August 2015 and October 2016, all adult patients with &ge;24 h of ICU-LOS were included. The 7-point Subjective Global Assessment (7-point SGA) was used to determine patients&rsquo; nutritional status within 48 hours of ICU admission. Multivariate analyses were conducted in two ways: 1) presence versus absence of malnutrition, and 2) dose-dependent association for each 1-point decrease in the 7-point SGA. There were 439 patients of which 28.0% were malnourished, and 29.6% died before hospital discharge. Malnutrition was associated with an increased risk of hospital mortality [adjusted-RR 1.39 (95%CI: 1.10&ndash;1.76)], and this risk increased with a greater degree of malnutrition [adjusted-RR 1.09 (95%CI: 1.01&ndash;1.18) for each 1-point decrease in the 7-point SGA]. No significant association was found between malnutrition and ICU-LOS. Conclusion: There was a clear association between malnutrition and higher hospital mortality in critically ill patients. The association between malnutrition and ICU-LOS could not be replicated and hence requires further evaluation.

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