Outcome of Cancer Patients Considered for Intensive Care Unit Admission: A Hospital-Wide Prospective Study

2005 ◽  
Vol 23 (19) ◽  
pp. 4406-4413 ◽  
Author(s):  
Guillaume Thiéry ◽  
Élie Azoulay ◽  
Michael Darmon ◽  
Magali Ciroldi ◽  
Sandra De Miranda ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Cancer Patients ◽  
Probability Model ◽  
Survival Rates ◽  
Area Under The Curve ◽  
Admission Policy ◽  
Good Survival ◽  
Icu Admission ◽  
Admission Status

Purpose To evaluate the outcome of cancer patients considered for admission to the intensive care unit (ICU). Patients and Methods Prospective, one-year hospital-wide study of all cancer and hematology patients, including bone marrow transplantation patients, for whom admission to the ICU was requested. Results Of the 206 patients considered for ICU admission, 105 patients (51%) were admitted. Of the 101 patients who were not admitted, 54 patients (26.2%) were considered too sick to benefit, and 47 patients (22.8%) were considered to be too well to benefit from the ICU. Of these 47 patients, 13 patients were admitted later. Survival rates after 30 and 180 days were significantly associated with admission status (P < .0001). Remission of the malignancy (odds ratio [OR], 3.37; 95% CI, 1.25 to 9.07) was independently associated with ICU admission, whereas poor chronic health status (OR, 0.38; 95% CI, 0.16 to 0.74) and solid tumor (OR, 0.43; 95% CI, 0.24 to 0.78) were associated with ICU refusal. In admitted patients, 30-day and 6-month survival rates were 54.3% and 32.4%, respectively. Of the patients considered too sick to benefit from ICU admission, 26% were alive on day 30 and 16.7% on day 180. Among patients considered too well to benefit, the 30-day survival rate was a worrisome 78.7%. Calibration of the Mortality Probability Model (the only score available at triage) was of limited value for predicting 30-day survival (area under the curve, 0.62). Conclusion Both the excess mortality in too-well patients later admitted to the ICU and the relatively good survival in too-sick patients suggest the need for a broader admission policy.

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 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.

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 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.

Mid-regional pro-adrenomedullin predicts poor outcome in non-selected patients admitted to an intensive care unit

2019 ◽  
Vol 57 (4) ◽  
pp. 549-555 ◽  
Author(s):  
Chiara Bellia ◽  
Luisa Agnello ◽  
Bruna Lo Sasso ◽  
Giulia Bivona ◽  
Maurizio Santi Raineri ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Critically Ill ◽  
Critically Ill Patients ◽  
Area Under The Curve ◽  
Scoring Systems ◽  
Simplified Acute Physiology Score ◽  
Icu Admission ◽  
Open Issue ◽  
The One

Abstract Background Mortality risk and outcome in critically ill patients can be predicted by scoring systems, such as APACHE and SAPS. The identification of prognostic biomarkers, simple to measure upon admission to an intensive care unit (ICU) is an open issue. The aim of this observational study was to assess the prognostic value of plasma mid-regional pro-adrenomedullin (MR-proADM) at ICU admission in non-selected patients in comparison to Acute Physiology and Chronic Health Evaluation II (APACHEII) and Simplified Acute Physiology Score II (SAPSII) scores. Methods APACHEII and SAPSII scores were calculated after 24 h from ICU admission. Plasma MR-proADM levels were measured by TRACE-Kryptor on admission (T0) and after 24 h (T24). The primary endpoint was intra-hospital mortality; secondary endpoint was length of stay (LOS). Results One hundred and twenty-six consecutive non-selected patients admitted to an ICU were enrolled. Plasma MR-proADM levels were correlated with LOS (r=0.28; p=0.0014 at T0; r=0.26; p=0.005 at T24). Multivariate analysis showed that T0 MR-proADM was a significant predictor of mortality (odds ratio [OR]: 1.27; 95% confidence interval [95%CI]: 1.03–1.55; p=0.022). Receiver operating characteristic curves analysis revealed that MR-proADM on ICU admission identified non-survivors with high accuracy, not inferior to the one of APACHEII and SAPSII scores (area under the curve [AUC]: 0.71; 95%CI: 0.62–0.78; p=0.0002 for MR-proADM; AUC: 0.71; 95%CI: 0.62–0.79; p<0.0001 for APACHEII; AUC: 0.8; 95%CI: 0.71–0.87; p<0.0001 for SAPSII). Conclusions Our findings point out a role of MR-proADM as a prognostic tool in non-selected patients in ICUs being a reliable predictor of mortality and LOS and support its use on admission to an ICU to help the management of critically ill patients.

scholarly journals Immune-related adverse events: a retrospective look into the future of oncology in the intensive care unit

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Adrien Joseph ◽  
Audrey Simonaggio ◽  
Annabelle Stoclin ◽  
Antoine Vieillard-Baron ◽  
Guillaume Geri ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Adverse Events ◽  
Cancer Patients ◽  
Immune Checkpoint ◽  
Immune Checkpoint Inhibitors ◽  
Checkpoint Inhibitors ◽  
Primary Tumors ◽  
Icu Admission ◽  
Paris Area

Abstract Background Immune checkpoint inhibitors have reshaped the standard of care in oncology. However, they have been associated with potentially life-threatening immune-related adverse events. With the growing indications of immune checkpoint inhibitors and their position as a pillar of cancer treatment, intensive care physicians will be increasingly confronted with their side effects. The outcome of patients with severe immune-related adverse events in the intensive care unit remains unknown. This retrospective multicentric study aims to describe the characteristics of patients admitted to the intensive care units of 4 academic hospitals in Paris area while receiving immune checkpoint inhibitor treatment between January 2013 and October 2019. Results Over the study period, 112 cancer patients who received immune checkpoint inhibitors were admitted to the intensive care unit within 60 days after the last dose. ICU admission was related to immune-related adverse events (n = 29, 26%), other intercurrent events (n = 39, 35%), or complications related to tumor progression (n = 44, 39%). Immune-related adverse events were pneumonitis (n = 8), colitis (n = 4), myocarditis (n = 3), metabolic disorders related to diabetes (n = 3), hypophysitis (n = 2), nephritis (n = 2), meningitis or encephalitis (n = 2), hepatitis (n = 2), anaphylaxis (n = 2) and pericarditis (n = 1). Primary tumors were mostly melanomas (n = 14, 48%), non-small-cell lung cancers (n = 7, 24%), and urothelial carcinomas (n = 5, 17%). Diagnosis of melanoma and a neutrophil/lymphocyte ratio < 10 were associated with immune-related diagnosis versus other reasons for ICU admission. During their ICU stay, immune-related adverse events patients needed vasopressors (n = 7), mechanical ventilation (n = 6), and extra-corporeal membrane oxygenation (n = 2). One-year survival was significantly higher for patients admitted for irAE compared to patients admitted for other reasons (p = 0.004). Conclusions Admission to the intensive care unit related to immune-related adverse event was associated with better outcome in cancer patients treated with immune checkpoint inhibitors. Our results support the admission for an intensive care unit trial for patients with suspected immune-related adverse events.

scholarly journals Factors associated with survival of patients with solid Cancer alive after intensive care unit discharge between 2005 and 2013

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Hubert Gheerbrant ◽  
Jean-François Timsit ◽  
Nicolas Terzi ◽  
Stéphane Ruckly ◽  
Mathieu Laramas ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Cancer Patients ◽  
Solid Cancer ◽  
Factors Associated ◽  
Icu Admission ◽  
Icu Discharge ◽  
Treatment Limitation ◽  
Anticancer Treatment ◽  
Ecog Ps

Abstract Background At intensive care unit (ICU) admission, the issue about prognosis of critically ill cancer patients is of clinical interest, especially after ICU discharge. Our objective was to assess the factors associated with 3- and 6-month survival of ICU cancer survivors. Methods Based on the French OutcomeRea™ database, we included solid cancer patients discharged alive, between December 2005 and November 2013, from the medical ICU of the university hospital in Grenoble, France. Patient characteristics and outcome at 3 and 6 months following ICU discharge were extracted from available database. Results Of the 361 cancer patients with unscheduled admissions, 253 (70%) were discharged alive from ICU. The main primary cancer sites were digestive (31%) and thoracic (26%). The 3- and 6-month mortality rates were 33 and 41%, respectively. Factors independently associated with 6-month mortality included ECOG performance status (ECOG-PS) of 3–4 (OR,3.74; 95%CI: 1.67–8.37), metastatic disease (OR,2.56; 95%CI: 1.34–4.90), admission for cancer progression (OR,2.31; 95%CI: 1.14–4.68), SAPS II of 45 to 58 (OR,4.19; 95%CI: 1.76–9.97), and treatment limitation decision at ICU admission (OR,4.00; 95%CI: 1.64–9.77). Interestingly, previous cancer chemotherapy prior to ICU admission was independently associated with lower 3-month mortality (OR, 0.38; 95%CI: 0.19–0.75). Among patients with an ECOG-PS 0–1 at admission, 70% (n = 66) and 61% (n = 57) displayed an ECOG-PS 0–2 at 3- and 6-months, respectively. At 3 months, 74 (55%) patients received anticancer treatment, 13 (8%) were given exclusive palliative care. Conclusions Factors associated with 6-month mortality are almost the same as those known to be associated with ICU mortality. We highlight that most patients recovered an ECOG-PS of 0–2 at 3 and 6 months, in particular those with a good ECOG-PS at ICU admission and could benefit from an anticancer treatment following ICU discharge.

scholarly journals A Risk Score to Predict Admission to Intensive Care Unit in Patients With COVID-19: The ABC-GOALS Score

2020 ◽  
Author(s):  
Juan M. Mejia-Vilet ◽  
Bertha M. Cordova-Sanchez ◽  
Dheni Fernandez-Camargo ◽  
R. Angelica Mendez-Perez ◽  
Luis Eduardo Morales-Buenrostro ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Emergency Department ◽  
Intensive Care ◽  
Clinical Laboratory ◽  
Area Under The Curve ◽  
Laboratory Model ◽  
Clinical Model ◽  
Icu Admission ◽  
Department Evaluation ◽  
Development And Validation

Background. COVID-19 pandemic poses a burden on hospital resources and intensive care unit (ICU) occupation. This study aimed to provide a scoring system that, assessed upon first-contact evaluation at the emergency department, predicts the need for ICU admission. Methods. We prospectively assessed patients admitted to a COVID-19 reference center in Mexico City between March 16th and May 21st, and split them into development and validation cohorts. Patients were segregated into a group that required admission to ICU, and a group that never required ICU admission and was discharged from hospitalization. By logistic regression, we constructed predictive models for ICU admission, including clinical, laboratory, and imaging findings from the emergency department evaluation. The ABC-GOALS score was created by assigning values to the weighted odd ratios. The score was compared to other COVID-19 and pneumonia scores through the area under the curve (AUC). Results. We included 569 patients divided into development (n=329) and validation (n=240) cohorts. One-hundred-fifteen patients from each cohort required admission to ICU. The clinical model (ABC-GOALSc) included sex, obesity, the Charlson comorbidity index, dyspnea, arterial pressure, and respiratory rate at triage evaluation. The clinical plus laboratory model (ABC-GOALScl) added serum albumin, glucose, lactate dehydrogenase, and S/F ratio to the clinical model. The model that included imaging (ABC-GOALSclx) added the CT scan finding of >50% lung involvement. The model AUC were 0.79 (95%CI 0.74-0.83) and 0.77 (95%CI 0.71-0.83), 0.86 (95%CI 0.82-0.90) and 0.87 (95%CI 0.83-0.92), 0.88 (95%CI 0.84-0.92) and 0.86 (95%CI 0.81-0.90) for the clinical, laboratory and imaging models in the development and validation cohorts, respectively. The ABC-GOALScl and ABC-GOALSclx scores outperformed other COVID-19 and pneumonia-specific scores. Conclusion. The ABC-GOALS score is a tool to evaluate patients with COVID-19 at admission to the emergency department, which allows to timely predict their risk of admission to an ICU.

scholarly journals A Risk Score to Predict Admission to the Intensive Care Unit in Patients with COVID-19: the ABC-GOALS score

2020 ◽  
Author(s):  
Juan M Mejía-Vilet ◽  
Bertha M. Córdova-Sánchez ◽  
Dheni A. Fernández-Camargo ◽  
R. Angélica Méndez-Pérez ◽  
Luis E Morales-Buenrostro ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Logistic Regression ◽  
Intensive Care ◽  
Risk Score ◽  
Predictive Models ◽  
Clinical Laboratory ◽  
Area Under The Curve ◽  
Imaging Findings ◽  
Icu Admission ◽  
Development And Validation

Objective. To develop a score to predict the need for ICU admission in COVID-19.Methods. We assessed patients admitted to a COVID-19 center in Mexico. Patients were segregated into a group that required ICU admission, and a group that never required ICU admission. By logistic regression, we derived predictive models including clinical, laboratory, and imaging findings. The ABC-GOALS was constructed and compared to other scores.Results. We included 329 and 240 patients in the development and validation cohorts, respectively. One-hundred-fifteen patients from each cohort required ICU admission. The clinical (ABC-GOALSc), clinical+laboratory (ABC-GOALScl), clinical+laboratory+image (ABC-GOALSclx) models area under the curve were 0.79 (95%CI=0.74-0.83) and 0.77 (95%CI=0.71-0.83), 0.86 (95%CI=0.82-0.90) and 0.87 (95%CI=0.83-0.92), 0.88 (95%CI=0.84-0.92) and 0.86 (95%CI=0.81-0.90), in the development and validation cohorts, respectively. The ABC-GOALScl and ABC-GOALSclx outperformed other COVID-19 and pneumonia predictive scores.Conclusion. ABC-GOALS is a tool to timely predict the need for admission to ICU in COVID-19.

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.

Sign in / Sign up

Export Citation Format

Share Document