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 < .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.

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.

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.

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 Early Plasma Osmolality Levels and Clinical Outcomes in Children Admitted to the Pediatric Intensive Care Unit: A Single-Center Cohort Study

2021 ◽  
Vol 9 ◽  
Author(s):  
Huabin Wang ◽  
Zhongyuan He ◽  
Jiahong Li ◽  
Chao Lin ◽  
Huan Li ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Intensive Care ◽  
Hospital Mortality ◽  
Regression Model ◽  
Clinical Outcomes ◽  
Poor Prognosis ◽  
Logistic Regression Model ◽  
Plasma Osmolality ◽  
Pediatric Intensive Care ◽  
Linear Spline

Objective: Identifying high-risk children with a poor prognosis in pediatric intensive care units (PICUs) is critical. The aim of this study was to assess the predictive value of early plasma osmolality levels in determining the clinical outcomes of children in PICUs.Methods: We retrospectively assessed critically ill children in a pediatric intensive care database. The locally weighted-regression scatter-plot smoothing (LOWESS) method was used to explore the approximate relationship between plasma osmolality and in-hospital mortality. Linear spline functions and stepwise expansion models were applied in conjunction with a multivariate logistic regression to further analyze this relationship. A subgroup analysis by age and complications was performed.Results: In total, 5,620 pediatric patients were included in this study. An approximately “U”-shaped relationship between plasma osmolality and mortality was detected using LOWESS. In the logistic regression model using a linear spline function, plasma osmolality ≥ 290 mmol/L was significantly associated with in-hospital mortality [odds ratio (OR) 1.020, 95% confidence interval (CI) 1.010–1.031], while plasma osmolality &lt;290 mmol/L was not significantly associated with in-hospital mortality (OR 0.990, 95% CI 0.966–1.014). In the logistic regression model with plasma osmolality as a tri-categorical variable, only high osmolality was significantly associated with in-hospital mortality (OR 1.90, 95% CI 1.38–2.64), whereas low osmolality was not associated with in-hospital mortality (OR 1.28, 95% CI 0.84–1.94). The interactions between plasma osmolality and age or complications were not significant.Conclusion: High osmolality, rather than low osmolality, can predict a poor prognosis in children in PICUs.

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.

scholarly journals Lactate Clearance as a Predictive Marker of Mortality in Adult Intensive Care Unit

2020 ◽  
Vol 5 (2) ◽  
pp. 32-38
Author(s):  
Shirish Raj Joshi ◽  
Renu Gurung ◽  
Subhash Prasad Acharya ◽  
Bashu Dev Parajuli ◽  
Navindra Raj Bista
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Blood Lactate ◽  
Lactate Concentration ◽  
Blood Lactate Concentration ◽  
Lactate Clearance ◽  
Icu Mortality ◽  
Adult Intensive Care Unit ◽  
Icu Admission ◽  
Significant Difference

Introduction: Lactate clearance has been widely investigated. Serial lactate concentrations can be used to examine disease severity and predict mortality in the intensive care unit. We investigated the diagnostic accuracy of lactate concentration and lactate clearance in predicting mortality in critically ill patients during the first 24 hours in Intensive Care Unit (ICU).Methods: It was a Prospective, observational study conducted in ICU. Sixty eight consecutive patients having blood lactate level >2 mmol/L were included irrespective of disease and postoperative status. We measured blood lactate concentration at ICU admission(H0), at six hours(H6), 12 hours(H12), and 24 hours(H24). Lactate clearance was measured for H0-H6, H0-H12 and H0-H24 time period.Results: ICU mortality was 33.8%. Lactate clearance was 15.80 ± 17.21% in survivors and 1.73±11% in non survivors for the H0-H6 (p = 0.001) and remained higher in survivors than in non survivors over the study period of 24 hours; 17.97±15 vs. -2.04±19.84% for H0-H12 and 27.40 ± 11.41% vs. -14.83 ± 26.84% for the H0-H24 period (p < 0.001 for each studied period). There was significant difference in lactate concentration (static) between survivors and non survivors during the course of initial 24 hours. The best predictor of ICU mortality was lactate clearance for the H0-H24 period (AUC =0.89; 95% CI 0.78-1.01). Logistic regression found that H0-H24 lactate clearance was independently correlated to a survival status (p = 0.005, OR = 0.922 and 95% CI 0.871-0.976).Conclusion: Blood lactate concentration and lactate clearance are both predictive for mortality during initial 24 hours of ICU admission.

scholarly journals Association of hyperchloremia with all-cause mortality in patients admitted to the surgical intensive care unit: a retrospective cohort study

2022 ◽  
Vol 22 (1) ◽  
Author(s):  
Keke Song ◽  
Tingting Yang ◽  
Wei Gao
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Heart Disease ◽  
Mortality Rate ◽  
Proportional Hazards Model ◽  
Cox Proportional Hazards Model ◽  
Surgical Intensive Care Unit ◽  
Surgical Intensive Care ◽  
Icu Admission ◽  
The Relationship

Abstract Background Serum chloride (Cl−) is one of the most essential extracellular anions. Based on emerging evidence obtained from patients with kidney or heart disease, hypochloremia has been recognized as an independent predictor of mortality. Nevertheless, excessive Cl− can also cause death in severely ill patients. This study aimed to investigate the relationship between hyperchloremia and high mortality rate in patients admitted to the surgical intensive care unit (SICU). Methods We enrolled 2131 patients from the Multiparameter Intelligent Monitoring in Intensive Care III database version 1.4 (MIMIC-III v1.4) from 2001 to 2012. Selected SICU patients were more than 18 years old and survived more than 72 h. A serum Cl− level ≥ 108 mEq/L was defined as hyperchloremia. Clinical and laboratory variables were compared between hyperchloremia (n = 664) at 72 h post-ICU admission and no hyperchloremia (n = 1467). The Locally Weighted Scatterplot Smoothing (Lowess) approach was utilized to investigate the correlation between serum Cl- and the thirty-day mortality rate. The Cox proportional-hazards model was employed to investigate whether serum chlorine at 72 h post-ICU admission was independently related to in-hospital, thirty-day and ninety-day mortality from all causes. Kaplan-Meier curve of thirty-day and ninety-day mortality and serum Cl− at 72 h post-ICU admission was further constructed. Furthermore, we performed subgroup analyses to investigate the relationship between serum Cl− at 72 h post-ICU admission and the thirty-day mortality from all causes. Results A J-shaped correlation was observed, indicating that hyperchloremia was linked to an elevated risk of thirty-day mortality from all causes. In the multivariate analyses, it was established that hyperchloremia remained a valuable predictor of in-hospital, thirty-day and ninety-day mortality from all causes; with adjusted hazard ratios (95% CIs) for hyperchloremia of 1.35 (1.02 ~ 1.77), 1.67 (1.28 ~ 2.19), and 1.39 (1.12 ~ 1.73), respectively. In subgroup analysis, we observed hyperchloremia had a significant interaction with AKI (P for interaction: 0.017), but there were no interactions with coronary heart disease, hypertension, and diabetes mellitus (P for interaction: 0.418, 0.157, 0.103, respectively). Conclusion Hyperchloremia at 72 h post-ICU admission and increasing serum Cl− were associated with elevated mortality risk from all causes in severely ill SICU patients.

scholarly journals Predictors of intensive care unit admission in patients with Legionella pneumonia: role of the time to appropriate antibiotic therapy

Infection ◽  
2020 ◽  
Author(s):  
Marco Falcone ◽  
Alessandro Russo ◽  
Giusy Tiseo ◽  
Mario Cesaretti ◽  
Fabio Guarracino ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Mortality Rate ◽  
Antibiotic Therapy ◽  
Tertiary Care ◽  
Initial Therapy ◽  
Community Acquired Pneumonia ◽  
Care Hospital ◽  
Icu Admission ◽  
Definitive Therapy

Abstract Purpose Legionella spp. pneumonia (LP) is a cause of community-acquired pneumonia (CAP) that requires early intervention. The median mortality rate varies from 4 to 11%, but it is higher in patients admitted to intensive care unit (ICU). The objective of this study is to identify predictors of ICU admission in patients with LP. Methods A single-center, retrospective, observational study conducted in an academic tertiary-care hospital in Pisa, Italy. Adult patients with LP consecutively admitted to study center from October 2012 to October 2019. Results During the study period, 116 cases of LP were observed. The rate of ICU admission was 20.7% and the overall 30-day mortality rate was 12.1%. Mortality was 4.3% in patients hospitalized in medical wards versus 41.7% in patients transferred to ICU (p < 0.001). The majority of patients (74.1%) received levofloxacin as definitive therapy, followed by macrolides (16.4%), and combination of levofloxacin plus a macrolide (9.5%). In the multivariate analysis, diabetes (OR 8.28, CI 95% 2.11–35.52, p = 0.002), bilateral pneumonia (OR 10.1, CI 95% 2.74–37.27, p = 0.001), and cardiovascular events (OR 10.91, CI 95% 2.83–42.01, p = 0.001), were independently associated with ICU admission, while the receipt of macrolides/levofloxacin therapy within 24 h from admission was protective (OR 0.20, CI 95% 0.05–0.73, p = 0.01). Patients who received a late anti-Legionella antibiotic (> 24 h from admission) underwent urinary antigen test later compared to those who received early active antibiotic therapy (2 [2–4] vs. 1 [1–2] days, p < 0.001). Conclusions Admission to ICU carries significantly increased mortality in patients with diagnosis of LP. Initial therapy with an antibiotic active against Legionella (levofloxacin or macrolides) reduces the probability to be transferred to ICU and should be provided in all cases until Legionella etiology is excluded.

Priority levels in cancer patients admitted to medical intensive care at a safety net hospital.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e18599-e18599
Author(s):  
Raisa Epistola ◽  
Michael Olufemi Shodiya ◽  
Jordan Epistola ◽  
Dong Chang ◽  
James Jen-Chi Yeh
Keyword(s):  
Intensive Care ◽  
Cancer Patients ◽  
Critically Ill ◽  
Statistical Tests ◽  
Quality Care ◽  
Safety Net ◽  
Close Monitoring ◽  
Icu Admission ◽  
Safety Net Hospital ◽  
Priority Levels

e18599 Background: Admissions of cancer patients to intensive care units (ICU) are increasing with improved mortality. While ICU care can be lifesaving, its higher cost does not always result in reduced mortality. Moreover, timely goals of care (GOC) discussions correlate with less ICU use in those with certain cancers. We investigate if hospital mortality and disposition outcomes for cancer patients correlate to triage by ICU providers. Methods: This subgroup analysis of a prospective cohort of 808 patients admitted to the ICU from 1 July 2015- 15 June 2016 at an academic safety net hospital included 106 patients diagnosed with cancer. Medical records were reviewed by ICU physicians, who assigned priority ranks using Society of Critical Care Medicine guidelines: 1: critically ill, needing treatment/monitoring not provided outside of ICU, 2: not critically ill, but requiring close monitoring/potentially immediate intervention, 3: critically ill patients with reduced likelihood of recovery, 4: not appropriate for ICU, equivalent outcomes achieved with non-ICU care. We did a chart review for factors like prior therapy and documentation of GOC discussions. Statistical tests were conducted to examine if priority levels correlate with disposition, mortality, and length of stay (LOS). Results: χ2-tests revealed priority rank correlated with disposition after hospitalization (p<.05) with group 3 having the highest proportion of deaths and lowest proportion of discharges home. It revealed that mortality rate differed by group (p<.05) with logistic regression showing that priority 3 predicted increased mortality (p<.05). ANCOVA indicated ICU LOS differed by priority group (p<.05), with priority 3 averaging the longest LOS. While priority 3 had the most in-hospital GOC discussions, relatively few were documented pre-hospitalization. Conclusions: Overall, our patients were accurately triaged, with worse mortality and discharge outcomes among priority 3 and a dearth of pre-hospitalization GOC documentation for all groups. Our data show the importance of triaging patients and having early, frequent GOC discussions to minimize ICU admission given increasing demand and costs. GOC discussions are associated with less aggressive medical care near death and better patient quality of life. Thus, holding these talks with our sickest patients prior to potential ICU admission is an area to improve cost-effective high quality care.[Table: see text]

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