Predictors of survival in patients with gastrointestinal malignancies admitted to the intensive care unit.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. e18050-e18050
Author(s):  
Heidi Chwan Ko ◽  
Melissa Yan ◽  
Rohan Gupta ◽  
Juhee Song ◽  
Kayla Kebbel ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Hospital Mortality ◽  
Cancer Patients ◽  
Sofa Score ◽  
Icu Mortality ◽  
Gastrointestinal Malignancies ◽  
Icu Admission ◽  
Risk Of Death ◽  
Gi Cancer

e18050 Background: Cancer patients have a high use of healthcare utilization at the end of life which can frequently involve admissions to the intensive care unit (ICU). We sought to evaluate the predictors for outcome in gastrointestinal (GI) cancer patients admitted to the ICU for non-surgical conditions. Methods: The objective of this study was to determine the factors associated with ICU mortality, hospital mortality and overall survival (OS). A total of 200 patients with GI cancer admitted to the ICU at The University of Texas MD Anderson Cancer Center between November 2012 and February 2015 were retrospectively analyzed. Cancer characteristics, treatment characteristics, and Sequential Organ Failure Assessment (SOFA) scores defining severity based on 6 organ systems with scores ranging from 0 to 24 were analyzed for their effects on survival endpoints using multivariate logistic regression models and a multivariate Cox proportional hazards regression model. Results: The characteristics of the 200 patients were: 64.5% male, mean age of 60 years, median admission SOFA score of 6.0, and tumor types of primary intestinal (37.5%), hepatobiliary/pancreatic (36%), and gastroesophageal (GE) (24%). The ICU mortality was 26%, hospital mortality was 41%, and 6-month OS estimate was 25%. In multivariate analysis, ICU admission SOFA score > 10 (odds ratio (OR) 17.1, p < 0.0001), poorly differentiated tumor grade (OR 3.2, p = 0.02), and shorter duration of metastatic disease (OR 2.3, p = 0.07) were associated with increased odds of ICU mortality. These same variables were associated with increased odds of hospital mortality. In multivariate OS analysis, SOFA score 6-10 (hazard ratio (HR) 2.1, p = 0.0006) and SOFA score > 10 (HR 4.4, p < 0.0001), patients with GE primary (HR 2.2, p = 0.002) and patients with a poor outpatient performance status that precluded active chemotherapy (HR 2.2, p = 0.01) were associated with increased risk of death. Conclusions: The SOFA score was the most predictive factor for ICU mortality, hospital mortality, and OS for GI cancer patients admitted to the ICU. It should be utilized in all GI cancer patients upon ICU admission to improve both acute and longer-term prognostication.

Multicenter outcome study of cancer patients admitted to the intensive care unit: a probability of mortality model.

1998 ◽  
Vol 16 (2) ◽  
pp. 761-770 ◽  
Author(s):  
J S Groeger ◽  
S Lemeshow ◽  
K Price ◽  
D M Nierman ◽  
P White ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Logistic Regression ◽  
Intensive Care ◽  
Hospital Mortality ◽  
Cancer Patients ◽  
The United States ◽  
Categorical Variables ◽  
Allogeneic Bone ◽  
Icu Admission ◽  
Hospital Survival

PURPOSE To develop prospectively and validate a model for probability of hospital survival at admission to the intensive care unit (ICU) of patients with malignancy. PATIENTS AND METHODS This was an inception cohort study in the setting of four ICUs of academic medical centers in the United States. Defined continuous and categorical variables were collected on consecutive patients with cancer admitted to the ICU. A preliminary model was developed from 1,483 patients and then validated on an additional 230 patients. Multiple logistic regression modeling was used to develop the models and subsequently evaluated by goodness-of-fit and receiver operating characteristic (ROC) analysis. The main outcome measure was hospital survival after ICU admission. RESULTS The observed hospital mortality rate was 42%. Continuous variables used in the ICU admission model are PaO2/FiO2 ratio, platelet count, respiratory rate, systolic blood pressure, and days of hospitalization pre-ICU. Categorical entries include presence of intracranial mass effect, allogeneic bone marrow transplantation, recurrent or progressive cancer, albumin less than 2.5 g/dL, bilirubin > or = 2 mg/dL, Glasgow Coma Score less than 6, prothrombin time greater than 15 seconds, blood urea nitrogen (BUN) greater than 50 mg/dL, intubation, performance status before hospitalization, and cardiopulmonary resuscitation (CPR). The P values for the fit of the preliminary and validation models are .939 and .314, respectively, and the areas under the ROC curves are .812 and .802. CONCLUSION We report a disease-specific multivariable logistic regression model to estimate the probability of hospital mortality in a cohort of critically ill cancer patients admitted to the ICU. The model consists of 16 unambiguous and readily available variables. This model should move the discussion regarding appropriate use of ICU resources forward. Additional validation in a community hospital setting is warranted.

scholarly journals Disparities in Intensive Care Unit Admission and Mortality Among Patients With Schizophrenia and COVID-19: A National Cohort Study

2020 ◽  
Author(s):  
Guillaume Fond ◽  
Vanessa Pauly ◽  
Marc Leone ◽  
Pierre-Michel Llorca ◽  
Veronica Orleans ◽  
...  
Keyword(s):  
Health Care ◽  
Intensive Care Unit ◽  
Mental Illness ◽  
Cohort Study ◽  
Intensive Care ◽  
Hospital Mortality ◽  
Severe Mental Illness ◽  
Vulnerable Population ◽  
Icu Admission ◽  
Risk Of Death

Abstract Patients with schizophrenia (SCZ) represent a vulnerable population who have been understudied in COVID-19 research. We aimed to establish whether health outcomes and care differed between patients with SCZ and patients without a diagnosis of severe mental illness. We conducted a population-based cohort study of all patients with identified COVID-19 and respiratory symptoms who were hospitalized in France between February and June 2020. Cases were patients who had a diagnosis of SCZ. Controls were patients who did not have a diagnosis of severe mental illness. The outcomes were in-hospital mortality and intensive care unit (ICU) admission. A total of 50 750 patients were included, of whom 823 were SCZ patients (1.6%). The SCZ patients had an increased in-hospital mortality (25.6% vs 21.7%; adjusted OR 1.30 [95% CI, 1.08–1.56], P = .0093) and a decreased ICU admission rate (23.7% vs 28.4%; adjusted OR, 0.75 [95% CI, 0.62–0.91], P = .0062) compared with controls. Significant interactions between SCZ and age for mortality and ICU admission were observed (P = .0006 and P &lt; .0001). SCZ patients between 65 and 80 years had a significantly higher risk of death than controls of the same age (+7.89%). SCZ patients younger than 55 years had more ICU admissions (+13.93%) and SCZ patients between 65 and 80 years and older than 80 years had less ICU admissions than controls of the same age (−15.44% and −5.93%, respectively). Our findings report the existence of disparities in health and health care between SCZ patients and patients without a diagnosis of severe mental illness. These disparities differed according to the age and clinical profile of SCZ patients, suggesting the importance of personalized COVID-19 clinical management and health care strategies before, during, and after hospitalization for reducing health disparities in this vulnerable population.

scholarly journals Severity and Outcome Detection for the Cancer Patients with COVID-19 Using Machine Learning Models

2021 ◽  
Author(s):  
Akbar Davoodi ◽  
Shaghayegh Haghjooy Javanmard ◽  
Golnaz Vaseghi ◽  
Amirreza Manteghinejad
Keyword(s):  
Machine Learning ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Cancer Patients ◽  
Cancer Center ◽  
Support Vector ◽  
Neutrophil Lymphocyte Ratio ◽  
Icu Admission ◽  
Reactive Protein ◽  
Risk Of Death

Abstract Background:The COVID-19 pandemic challenges the healthcare system to provide enough resources to battle the pandemic without jeopardizing routine treatments. As a result, this is important that we can predict the outcomes of patients at the time of admission. This study aims to apply different machine learning (ML) models for predicting Intensive Care Unit (ICU) admission and mortality of Cancer Patients infected with COVID-19.Methods:This study's data were collected from a referral cancer center in Iran. The study included all patients with cancer and a confirmed diagnosis of COVID-19.Different ML prediction algorithms like Logistic Regression (LR), Naïve Bayes (NB), k-Nearest Neighbours (kNN), Random Forest (RF), and Support Vector Machine (SVM) were used. Also, we applied the SelectKBest method to find the most important features for predicting ICU admission and mortality.Results:Three hundred thirty-nine patients enrolled in the study. One hundred fifteen were admitted to the Intensive Care Unit (ICU), and 118 patients died during the hospital admission. The Area Under Curve (AUC) for predicting mortality is 0.61 for LR, 0.74 for NB, 0.61 for kNN, 0.6 for SVM, and 0.79 for RF. The AUC for predicting ICU admission is 0.61 for LR, 0.74 for NB, 0.56 for kNN, 0.55 for SVM, and 0.7 for RF.C-reactive protein (CRP), Aspartate transaminase (AST), and Neutrophil-Lymphocyte Ratio (NLR) also are the most common features in predicting ICU admission and mortality.Conclusion:Our findings show the promise of different AI methods for predicting the risk of death or ICU in cancer patients infected with COVID-19, highlighting the importance of first laboratory results and patients' symptoms.

scholarly journals High Mobility Group Box 1 and Interleukin 6 at Intensive Care Unit Admission as Biomarkers in Critically Ill COVID-19 Patients

2021 ◽  
Author(s):  
Chaisith Sivakorn ◽  
Jutamas Dechsanga ◽  
Lawan Jamjumrus ◽  
Kobporn Boonnak ◽  
Marcus J. Schultz ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Critically Ill ◽  
Sofa Score ◽  
Interleukin 6 ◽  
Plasma Levels ◽  
High Mobility ◽  
High Mobility Group ◽  
Icu Mortality ◽  
Icu Admission

Exuberant inflammation manifesting as a “cytokine storm” has been suggested as a central feature in the pathogenesis of severe coronavirus disease 2019 (COVID-19). This study investigated two prognostic biomarkers, the high mobility group box 1 (HMGB1) and interleukin-6 (IL-6), in patients with severe COVID-19 at the time of admission in the intensive care unit (ICU). Of 60 ICU patients with COVID-19 enrolled and analyzed in this prospective cohort study, 48 patients (80%) were alive at ICU discharge. HMGB1 and IL-6 plasma levels at ICU admission were elevated compared with a healthy control, both in ICU nonsurvivors and ICU survivors. HMGB1 and IL-6 plasma levels were higher in patients with a higher Sequential Organ Failure Assessment (SOFA) score (> 10), and the presence of septic shock or acute kidney injury. HMGB1 and IL-6 plasma levels were also higher in patients with a poor oxygenation status (PaO2/FiO2 < 150 mm Hg) and a longer duration of ventilation (> 7 days). Plasma HMGB1 and IL-6 levels at ICU admission also correlated with other prognostic markers, including the maximum neutrophil/lymphocyte ratio, D-dimer levels, and C-reactive protein levels. Plasma HMGB1 and IL-6 levels at ICU admission predicted ICU mortality with comparable accuracy to the SOFA score and the COVID-GRAM risk score. Higher HMGB1 and IL-6 were not independently associated with ICU mortality after adjustment for age, gender, and comorbidities in multivariate analysis models. In conclusion, plasma HMGB1 and IL6 at ICU admission may serve as prognostic biomarkers in critically ill COVID-19 patients.

scholarly journals Outcomes and Prognostic Factors in Patients with Hematologic Malignancies in the Intensive Care unit: A Single-Center Cohort Study of 233 Cases in Taiwan

2021 ◽  
Author(s):  
Chieh-Lung Chen ◽  
Sing-Ting Wang ◽  
Wen-Chien Cheng ◽  
Chih-Yu Chen ◽  
Wei-Cheng Chen ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Multivariate Analysis ◽  
Intensive Care ◽  
Renal Replacement Therapy ◽  
Sofa Score ◽  
Critically Ill Patients ◽  
Hematologic Malignancies ◽  
Mortality Rates ◽  
Icu Mortality ◽  
Icu Admission

Abstract BackgroundPatients with a hematologic malignancies (HM) have one of the highest mortality rates among cancer patients admitted to the medical intensive care unit (ICU). The aim of this study was to identify outcomes and risk factors that predict the prognosis of critically ill patients with HM in the ICU.MethodsA retrospective observational study was conducted in a tertiary referral hospital in Taiwan over 40 months (January 1, 2017–April 30, 2020). All adult patients with HM who were admitted to medical ICU were enrolled. Clinical data upon hospital and ICU admission were collected. The predictors of ICU mortality were evaluated using a multivariate analysis.ResultsA total of 233 patients with HM met the inclusion criteria. The median age (SD) was 59.3 (15.1) years, and 76% of the HMs were classified as high-grade disease. The median (IQR) Sequential Organ Failure Assessment (SOFA) score at ICU admission was 11 (9–15); Simplified Acute Physiology Score II, 64 (51–80); and Acute Physiology and Chronic Health Evaluation II score, 28 (23–34). The most common reasons for ICU admission were acute respiratory failure (63.1%) and septic shock (19.7%). The ICU and hospital mortality rates were 54.1% and 67.8%, respectively. A multivariate analysis revealed that the initiation of renal replacement therapy in the ICU (odds ratio [OR], 3.88; 95% CI, 1.66–9.08) and SOFA score (OR, 1.16; 95% CI, 1.03–1.31) were independently associated with ICU mortality.ConclusionsThe ICU and hospital outcomes of critically ill patients with HM are improving. Performance status, cancer status, invasive mechanical ventilation, severe neutropenia, and transplantation status were not identified as predictive factors of ICU outcome. Initiation of renal replacement therapy in the ICU and the SOFA score upon ICU admission were independently associated with ICU mortality. We suggest early and timely ICU admission of patients at risk of multiorgan failure.

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 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

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.

scholarly journals P012: Mortality rate of cancer patients by type presenting to the intensive care unit with sepsis.

CJEM ◽  
2019 ◽  
Vol 21 (S1) ◽  
pp. S67
Author(s):  
S. Beckett ◽  
E. Karreman ◽  
R. Hughes
Keyword(s):  
Logistic Regression ◽  
Intensive Care ◽  
Mortality Rate ◽  
Cancer Patients ◽  
Solid Tumors ◽  
Predictive Factors ◽  
Poor Prognosis ◽  
Solid Tumours ◽  
Icu Mortality ◽  
Icu Admission

Introduction: Sepsis in cancer patients is associated with higher mortality rates than non-cancer patients. As a whole, hematological or solid tumor cancers have not demonstrated a prognostic link to sepsis survival rates in intensive care units (ICU), however poor-prognosis solid tumours (less than 25% 5-year survival) have not been investigated. This study examined ICU mortality rate and its predictive factors of patients with sepsis and poor-prognosis solid tumors in comparison to patients with higher prognosis solid tumours. Methods: A 6-year retrospective chart review of 79 patients with sepsis and solid tumour cancers and/or metastatic cancers admitted to the ICU was conducted. Information regarding mortality rate within 14 days, length of ICU stay, incidence of intubation, and other primary reasons for ICU admission was collected. Data was analysed using logistic regression. Results: Logistic regression results showed intubation as the only significant factor contributing to patient mortality (p &lt; .001), with the odds of mortality being 12.3 times higher for intubated than non-intubated patients. Five-year cancer survival rate was the second best predictor (p = .082), while age, sex, and metastasis were also not significant predictive factors for survival. Intubated patients with poor prognosis cancers had the lowest survival chance as further indicated by the 16 patients who met this criterion, of which 14 died within two weeks of ICU admission. Conclusion: The fact that poor prognosis cancers in sepsis were not significantly predictive of ICU mortality supports current literature regarding solid tumors in general, while intubation being a significant predictor for mortality in patients with sepsis and cancer regardless of type builds on previous research. A limitation of this study is the relative low number of included cases with poor-prognosis cancer types. Further evaluation is needed to understand the implications of our results for end-of-life care and ICU admission for patients with these characteristics.

scholarly journals Selection criteria for intensive care unit referral of lung cancer patients: a pilot study

2014 ◽  
Vol 45 (2) ◽  
pp. 491-500 ◽  
Author(s):  
Anne-Claire Toffart ◽  
Carola Alegria Pizarro ◽  
Carole Schwebel ◽  
Linda Sakhri ◽  
Clemence Minet ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Cancer Patients ◽  
Selection Criteria ◽  
Performance Status ◽  
Lung Cancer Patients ◽  
Icu Admission ◽  
Organ Failures ◽  
Intensity Of Care

The decision-making process for the intensity of care delivered to patients with lung cancer and organ failure is poorly understood, and does not always involve intensivists. Our objective was to describe the potential suitability for intensive care unit (ICU) referral of lung cancer in-patients with organ failures.We prospectively included consecutive lung cancer patients with failure of at least one organ admitted to the teaching hospital in Grenoble, France, between December 2010 and October 2012.Of 140 patients, 121 (86%) were evaluated by an oncologist and 49 (35%) were referred for ICU admission, with subsequent admission for 36 (73%) out of those 49. Factors independently associated with ICU referral were performance status ⩽2 (OR 10.07, 95% CI 3.85–26.32), nonprogressive malignancy (OR 7.00, 95% CI 2.24–21.80), and no explicit refusal of ICU admission by the patient and/or family (OR 7.95, 95% CI 2.39–26.37). Factors independently associated with ICU admission were the initial ward being other than the lung cancer unit (OR 6.02, 95% CI 1.11–32.80) and an available medical ICU bed (OR 8.19, 95% CI 1.48–45.35).Only one-third of lung cancer patients with organ failures were referred for ICU admission. The decision not to consider ICU admission was often taken by a non-intensivist, with advice from an oncologist rather than an intensivist.

Sign in / Sign up

Export Citation Format

Share Document