scholarly journals A lay perspective on prioritization for intensive care in pandemic times: Vaccination status matters

2021 ◽  
Author(s):  
Philipp Sprengholz ◽  
Lars Korn ◽  
Lisa Felgendreff ◽  
Sarah Eitze ◽  
Cornelia Betsch
Keyword(s):  
Intensive Care ◽  
Health Condition ◽  
Vaccination Status ◽  
Public Understanding ◽  
Ingroup Bias ◽  
Vaccination Rates ◽  
Icu Admission ◽  
Treatment Benefits ◽  
Fourth Wave ◽  
Triage Decisions

During a pandemic, demand for intensive care often exceeds availability. Experts agree that allocation should maximize benefits and must not be based on whether patients could have taken preventive measures. However, intensive care units (ICUs) are often overburdened by individuals with severe COVID-19 who have chosen not to be vaccinated to prevent the disease. This article reports an experiment that investigated the German public’s prioritization preferences during the fourth wave of the coronavirus pandemic (N = 1,014). In a series of scenarios, participants were asked to decide on ICU admission for patients who differed in terms of health condition, expected treatment benefits, and vaccination status. The results reveal an ingroup bias, as vaccinated individuals preferred to allocate more resources to the vaccinated than to the unvaccinated. Participants also favored admitting a heart attack patient rather than a COVID-19 patient with the same likelihood of benefiting from ICU admission, indicating a preference for maintaining regular ICU services rather than treating those with severe COVID-19. Finally, participants were more likely to admit a patient to intensive care when this meant withholding rather than withdrawing care from another patient. The results indicate that lay prioritizations violate established allocation principles, presaging potential conflicts between those in need of intensive care and those who provide and allocate it. It is therefore recommended that allocation principles should be explained to enhance public understanding. Additionally, vaccination rates should be increased to relieve ICUs and reduce the need for such triage decisions.

scholarly journals Outcome of cancer patients considered for intensive care unit admission in two university hospitals in the Netherlands: the danger of delayed ICU admissions and off-hour triage decisions

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Esther N. van der Zee ◽  
Dominique D. Benoit ◽  
Marinus Hazenbroek ◽  
Jan Bakker ◽  
Erwin J. O. Kompanje ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
The Netherlands ◽  
Cancer Patients ◽  
Admission Policy ◽  
P Value ◽  
University Hospitals ◽  
Icu Admission ◽  
Risk Of Death ◽  
Triage Decisions

Abstract Background Very few studies assessed the association between Intensive Care Unit (ICU) triage decisions and mortality. The aim of this study was to assess whether an association could be found between 30-day mortality, and ICU admission consultation conditions and triage decisions. Methods We conducted a retrospective cohort study in two large referral university hospitals in the Netherlands. We identified all adult cancer patients for whom ICU admission was requested from 2016 to 2019. Via a multivariable logistic regression analysis, we assessed the association between 30-day mortality, and ICU admission consultation conditions and triage decisions. Results Of the 780 cancer patients for whom ICU admission was requested, 332 patients (42.6%) were considered ‘too well to benefit’ from ICU admission, 382 (49%) patients were immediately admitted to the ICU and 66 patients (8.4%) were considered ‘too sick to benefit’ according to the consulting intensivist(s). The 30-day mortality in these subgroups was 30.1%, 36.9% and 81.8%, respectively. In the patient group considered ‘too well to benefit’, 258 patients were never admitted to the ICU and 74 patients (9.5% of the overall study population, 22.3% of the patients ‘too well to benefit’) were admitted to the ICU after a second ICU admission request (delayed ICU admission). Thirty-day mortality in these groups was 25.6% and 45.9%. After adjustment for confounders, ICU consultations during off-hours (OR 1.61, 95% CI 1.09–2.38, p-value 0.02) and delayed ICU admission (OR 1.83, 95% CI 1.00–3.33, p-value 0.048 compared to “ICU admission”) were independently associated with 30-day mortality. Conclusion The ICU denial rate in our study was high (51%). Sixty percent of the ICU triage decisions in cancer patients were made during off-hours, and 22.3% of the patients initially considered “too well to benefit” from ICU admission were subsequently admitted to the ICU. Both decisions during off-hours and a delayed ICU admission were associated with an increased risk of death at 30 days. Our study suggests that in cancer patients, ICU triage decisions should be discussed during on-hours, and ICU admission policy should be broadened, with a lower admission threshold for critically ill cancer patients.

scholarly journals In-Hospital Outcomes in Patients Admitted to the Intensive Care Unit after a Return Visit to the Emergency Department

Healthcare ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 431
Author(s):  
Chun-Fu Lin ◽  
Yi-Syun Huang ◽  
Ming-Ta Tsai ◽  
Kuan-Han Wu ◽  
Chien-Fu Lin ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Emergency Department ◽  
Regression Analysis ◽  
Intensive Care ◽  
Medical Cost ◽  
Total Medical Cost ◽  
Hospital Outcomes ◽  
Return Visit ◽  
Icu Admission ◽  
Ed Visits

Background: Intensive care unit (ICU) admission following a short-term emergency department (ED) revisit has been considered a particularly undesirable outcome among return-visit patients, although their in-hospital prognosis has not been discussed. We aimed to compare clinical outcomes between adult patients admitted to the ICU after unscheduled ED revisits and those admitted during index ED visits. Method: This retrospective study was conducted at two tertiary medical centers in Taiwan from 1 January 2016 to 31 December 2017. All adult non-trauma patients admitted to the ICU directly via the ED during the study period were included and divided into two comparison groups: patients admitted to the ICU during index ED visits and those admitted to the ICU during return ED visits. The outcomes of interest included in-hospital mortality, mechanical ventilation (MV) support, profound shock, hospital length of stay (HLOS), and total medical cost. Results: Altogether, 12,075 patients with a mean (standard deviation) age of 64.6 (15.7) years were included. Among these, 5.3% were admitted to the ICU following a return ED visit within 14 days and 3.1% were admitted following a return ED visit within 7 days. After adjusting for confounding factors for multivariate regression analysis, ICU admission following an ED revisit within 14 days was not associated with an increased mortality rate (adjusted odds ratio (aOR): 1.08, 95% confidence interval (CI): 0.89 to 1.32), MV support (aOR: 1.06, 95% CI: 0.89 to 1.26), profound shock (aOR: 0.99, 95% CI: 0.84 to 1.18), prolonged HLOS (difference: 0.04 days, 95% CI: −1.02 to 1.09), and increased total medical cost (difference: USD 361, 95% CI: −303 to 1025). Similar results were observed after the regression analysis in patients that had a 7-day return visit. Conclusion: ICU admission following a return ED visit was not associated with major in-hospital outcomes including mortality, MV support, shock, increased HLOS, or medical cost. Although ICU admissions following ED revisits are considered serious adverse events, they may not indicate poor prognosis in ED practice.

scholarly journals Early Prediction of Seven-Day Mortality in Intensive Care Unit Using a Machine Learning Model: Results from the SPIN-UTI Project

2021 ◽  
Vol 10 (5) ◽  
pp. 992
Author(s):  
Martina Barchitta ◽  
Andrea Maugeri ◽  
Giuliana Favara ◽  
Paolo Marco Riela ◽  
Giovanni Gallo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intensive Care ◽  
Intensive Care Units ◽  
Learning Algorithm ◽  
Area Under The Curve ◽  
Support Vector ◽  
Icu Admission ◽  
Risk Of Death ◽  
Saps Ii ◽  
Svm Algorithm

Patients in intensive care units (ICUs) were at higher risk of worsen prognosis and mortality. Here, we aimed to evaluate the ability of the Simplified Acute Physiology Score (SAPS II) to predict the risk of 7-day mortality, and to test a machine learning algorithm which combines the SAPS II with additional patients’ characteristics at ICU admission. We used data from the “Italian Nosocomial Infections Surveillance in Intensive Care Units” network. Support Vector Machines (SVM) algorithm was used to classify 3782 patients according to sex, patient’s origin, type of ICU admission, non-surgical treatment for acute coronary disease, surgical intervention, SAPS II, presence of invasive devices, trauma, impaired immunity, antibiotic therapy and onset of HAI. The accuracy of SAPS II for predicting patients who died from those who did not was 69.3%, with an Area Under the Curve (AUC) of 0.678. Using the SVM algorithm, instead, we achieved an accuracy of 83.5% and AUC of 0.896. Notably, SAPS II was the variable that weighted more on the model and its removal resulted in an AUC of 0.653 and an accuracy of 68.4%. Overall, these findings suggest the present SVM model as a useful tool to early predict patients at higher risk of death at ICU admission.

Correlates of Improved Influenza Vaccination of Healthcare Personnel: A Survey of Hospitals in Louisiana

2013 ◽  
Vol 34 (7) ◽  
pp. 723-729 ◽  
Author(s):  
Kayla L. Fricke ◽  
Mariella M. Gastañaduy ◽  
Renee Klos ◽  
Rodolfo E. Bégué
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Influenza Vaccination ◽  
Vaccination Campaign ◽  
Vaccination Rate ◽  
Healthcare Personnel ◽  
Patient Contact ◽  
Hospital Type ◽  
Vaccination Rates ◽  
Item Questionnaire

Objective.To describe practices for influenza vaccination of healthcare personnel (HCP) with emphasis on correlates of increased vaccination rates.Design.Survey.Participants.Volunteer sample of hospitals in Louisiana.Methods.All hospitals in Louisiana were invited to participate. A 17-item questionnaire inquired about the hospital type, patients served, characteristics of the vaccination campaign, and the resulting vaccination rate.Results.Of 254 hospitals, 153 (60%) participated and were included in the 124 responses that were received. Most programs (64%) required that HCP either receive the vaccine or sign a declination form, and the rest were exclusively voluntary (36%); no program made vaccination a condition of employment. The median vaccination rate was 67%, and the vaccination rate was higher among hospitals that were accredited by the Joint Commission; provided acute care; served children, pregnant women, oncology patients, or intensive care unit patients; required a signed declination form; or imposed consequences for unvaccinated HCP (the most common of which was to require that a mask be worn on patient contact). Hospitals that provided free vaccine, made vaccine widely available, advertised the program extensively, required a declination form, and imposed consequences had the highest vaccination rates (median, 86%; range, 81%–91%).Conclusions.The rate of influenza vaccination of HCP remains low among the hospitals surveyed. Recommended practices may not be enough to reach 90% vaccination rates unless a signed declination requirement and consequences are implemented. Wearing a mask is a strong consequence. Demanding influenza vaccination as a condition of employment was not reported as a practice by the participating hospitals.

scholarly journals Healthcare trajectories before and after critical illness: population-based insight on diverse patients clusters

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Youenn Jouan ◽  
Leslie Grammatico-Guillon ◽  
Noémie Teixera ◽  
Claire Hassen-Khodja ◽  
Christophe Gaborit ◽  
...  
Keyword(s):  
Septic Shock ◽  
Mechanical Ventilation ◽  
Intensive Care ◽  
Critical Illness ◽  
Length Of Stay ◽  
Population Based ◽  
Hospital Length Of Stay ◽  
Healthcare Use ◽  
Icu Admission ◽  
Icu Discharge

Abstract Background The post intensive care syndrome (PICS) gathers various disabilities, associated with a substantial healthcare use. However, patients’ comorbidities and active medical conditions prior to intensive care unit (ICU) admission may partly drive healthcare use after ICU discharge. To better understand retative contribution of critical illness and PICS—compared to pre-existing comorbidities—as potential determinant of post-critical illness healthcare use, we conducted a population-based evaluation of patients’ healthcare use trajectories. Results Using discharge databases in a 2.5-million-people region in France, we retrieved, over 3 years, all adult patients admitted in ICU for septic shock or acute respiratory distress syndrome (ARDS), intubated at least 5 days and discharged alive from hospital: 882 patients were included. Median duration of mechanical ventilation was 11 days (interquartile ranges [IQR] 8;20), mean SAPS2 was 49, and median hospital length of stay was 42 days (IQR 29;64). Healthcare use (days spent in healthcare facilities) was analyzed 2 years before and 2 years after ICU admission. Prior to ICU admission, we observed, at the scale of the whole study population, a progressive increase in healthcare use. Healthcare trajectories were then explored at individual level, and patients were assembled according to their individual pre-ICU healthcare use trajectory by clusterization with the K-Means method. Interestingly, this revealed diverse trajectories, identifying patients with elevated and increasing healthcare use (n = 126), and two main groups with low (n = 476) or no (n = 251) pre-ICU healthcare use. In ICU, however, SAPS2, duration of mechanical ventilation and length of stay were not different across the groups. Analysis of post-ICU healthcare trajectories for each group revealed that patients with low or no pre-ICU healthcare (which represented 83% of the population) switched to a persistent and elevated healthcare use during the 2 years post-ICU. Conclusion For 83% of ARDS/septic shock survivors, critical illness appears to have a pivotal role in healthcare trajectories, with a switch from a low and stable healthcare use prior to ICU to a sustained higher healthcare recourse 2 years after ICU discharge. This underpins the hypothesis of long-term critical illness and PICS-related quantifiable consequences in healthcare use, measurable at a population level.

scholarly journals Development of a machine learning model for predicting pediatric mortality in the early stages of intensive care unit admission

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Bongjin Lee ◽  
Kyunghoon Kim ◽  
Hyejin Hwang ◽  
You Sun Kim ◽  
Eun Hee Chung ◽  
...  
Keyword(s):  
Machine Learning ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Validation Cohort ◽  
External Validation ◽  
Learning Model ◽  
Derivation Cohort ◽  
Icu Admission ◽  
Early Stages ◽  
Machine Learning Model

AbstractThe aim of this study was to develop a predictive model of pediatric mortality in the early stages of intensive care unit (ICU) admission using machine learning. Patients less than 18 years old who were admitted to ICUs at four tertiary referral hospitals were enrolled. Three hospitals were designated as the derivation cohort for machine learning model development and internal validation, and the other hospital was designated as the validation cohort for external validation. We developed a random forest (RF) model that predicts pediatric mortality within 72 h of ICU admission, evaluated its performance, and compared it with the Pediatric Index of Mortality 3 (PIM 3). The area under the receiver operating characteristic curve (AUROC) of RF model was 0.942 (95% confidence interval [CI] = 0.912–0.972) in the derivation cohort and 0.906 (95% CI = 0.900–0.912) in the validation cohort. In contrast, the AUROC of PIM 3 was 0.892 (95% CI = 0.878–0.906) in the derivation cohort and 0.845 (95% CI = 0.817–0.873) in the validation cohort. The RF model in our study showed improved predictive performance in terms of both internal and external validation and was superior even when compared to PIM 3.

scholarly journals Respiratory events in ward are associated with later intensive care unit (ICU) admission and hospital mortality in onco-hematology patients not admitted to ICU after a first request

PLoS ONE ◽  
2017 ◽  
Vol 12 (7) ◽  
pp. e0181808 ◽  
Author(s):  
Laure Doukhan ◽  
Magali Bisbal ◽  
Laurent Chow-Chine ◽  
Antoine Sannini ◽  
Jean Paul Brun ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Hospital Mortality ◽  
Icu Admission ◽  
Respiratory Events

Outpatient Follow-up Care After Hospital Discharge of Children With Complex Chronic Conditions at a Rural Tertiary Care Hospital

2021 ◽  
pp. 000992282110472
Author(s):  
Andrew Brown ◽  
Mary Quaile ◽  
Hannah Morris ◽  
Dmitry Tumin ◽  
Clayten L. Parker ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Hospital Discharge ◽  
Care Coordination ◽  
Chronic Conditions ◽  
Tertiary Care ◽  
Care Hospital ◽  
Icu Admission ◽  
Complex Chronic Conditions

Objective To determine factors associated with completion of recommended outpatient follow-up visits in children with complex chronic conditions (CCCs) following hospital discharge. Methods We retrospectively identified children aged 1 to 17 years diagnosed with a CCC who were discharged from our rural tertiary care children’s hospital between 2017 and 2018 with a diagnosis meeting published CCC criteria. Patients discharged from the neonatal intensive care unit and patients enrolled in a care coordination program for technology-dependent children were excluded. Results Of 113 eligible patients, 77 (68%) had outpatient follow-up consistent with discharge instructions. Intensive care unit (ICU) admission ( P = .020) and prolonged length of stay ( P = .004) were associated with decreased likelihood of completing recommended follow-up. Conclusions Among children with CCCs who were not already enrolled in a care coordination program, ICU admission was associated with increased risk of not completing recommended outpatient follow-up. This population could be targeted for expanded care coordination efforts.

scholarly journals Intensive Care Unit Capacity Strain and Outcomes of Critical Illness in a Resource-Limited Setting: A 2-Hospital Study in South Africa

2018 ◽  
Vol 35 (10) ◽  
pp. 1104-1111 ◽  
Author(s):  
George L. Anesi ◽  
Nicole B. Gabler ◽  
Nikki L. Allorto ◽  
Carel Cairns ◽  
Gary E. Weissman ◽  
...  
Keyword(s):  
South Africa ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Critical Illness ◽  
Primary Outcome ◽  
Resource Limited Setting ◽  
Icu Mortality ◽  
Icu Admission ◽  
Resource Limited ◽  
Limited Setting

Objective: To measure the association of intensive care unit (ICU) capacity strain with processes of care and outcomes of critical illness in a resource-limited setting. Methods: We performed a retrospective cohort study of 5332 patients referred to the ICUs at 2 public hospitals in South Africa using the country’s first published multicenter electronic critical care database. We assessed the association between multiple ICU capacity strain metrics (ICU occupancy, turnover, census acuity, and referral burden) at different exposure time points (ICU referral, admission, and/or discharge) with clinical and process of care outcomes. The association of ICU capacity strain at the time of ICU admission with ICU length of stay (LOS), the primary outcome, was analyzed with a multivariable Cox proportional hazard model. Secondary outcomes of ICU triage decision (with strain at ICU referral), ICU mortality (with strain at ICU admission), and ICU LOS (with strain at ICU discharge), were analyzed with linear and logistic multivariable regression. Results: No measure of ICU capacity strain at the time of ICU admission was associated with ICU LOS, the primary outcome. The ICU occupancy at the time of ICU admission was associated with increased odds of ICU mortality (odds ratio = 1.07, 95% confidence interval: 1.02-1.11; P = .004), a secondary outcome, such that a 10% increase in ICU occupancy would be associated with a 7% increase in the odds of ICU mortality. Conclusions: In a resource-limited setting in South Africa, ICU capacity strain at the time of ICU admission was not associated with ICU LOS. In secondary analyses, higher ICU occupancy at the time of ICU admission, but not other measures of capacity strain, was associated with increased odds of ICU mortality.

Sign in / Sign up

Export Citation Format

Share Document