Susannah Kish Wallace

Influence of an advance directive on the initiation of life support technology in critically ill cancer patients

Objective To determine whether the presence of an advance directive at admission to an intensive care unit (ICU) influenced the decision to initiate life support therapy in critically ill cancer patients. Design Matched-pairs case-control design. Setting The University of Texas M. D. Anderson Cancer Center ICU. Patients Of 872 patients treated in the ICU from 1994 to 1996, 236 (27%) were identified as having advance directives. One hundred thirty five patients who had advance directives were successfully matched to 135 patients who did not on the basis of type of malignancy, reason for admission to ICU, severity of illness, and age. These pairs comprised the study group. Interventions Life-supporting interventions were compared between the matched groups using the McNemar and Wilcoxon matched-pairs signed ranks tests. Measurements and Main Results No significant difference was found in the frequency with which the following interventions were applied in patients with and without advance directives (respectively): mechanical ventilation, 44% vs. 42%; inotropic support, 31% vs. 31%; pulmonary artery catheterization, 11% vs. 12%; cardiopulmonary resuscitation, 7% vs. 12%; and renal dialysis, 3% vs. 7%. There were also no differences in ICU (75% vs. 73%, respectively) or hospital (56% vs. 59%, respectively) survival. More patients with advance directives than those without had do-not-resuscitate orders within the first 72 hrs (19% vs. 11%, p = .046) and patients with advance directives had shorter ICU durations and lower ICU charges than patients without advance directives. Conclusions After controlling for type of malignancy, reason for admission to the ICU, severity of illness, and age, the decision to initiate life-supporting interventions did not differ significantly among patients with and without advance directives. The presence of an advance directive, however, may have helped guide decisions earlier regarding duration of therapy and resuscitation status.

Automating and simplifying the SOFA score in critically ill patients with cancer

The aim was to demonstrate the performance of a modified version of the Sequential Organ Failure Assessment (SOFA) score to predict mortality in medical and surgical patients with cancer. We performed an electronic retrospective review of databases. We included adult patients with cancer admitted into a 53-bed ICU over 28 months. We electronically calculated a modified SOFA (mSOFA) score at admission. A majority of the patients were admitted into the surgical ICU. Of 328 nonsurvivors, 85.1 per cent were medical patients and only 14.9 per cent surgical patients. The mean admission mSOFA scores for medical and surgical patients were 4.7 ± 3.2 and 1.7 ± 1.9, respectively. The overall area under the curve (AUC) of the mSOFA score was 0.84. The AUCs for medical and surgical patients were 0.72 and 0.78, respectively. Our results demonstrate that electronic assessment of mSOFA score has potential in resource allocation decisions as well as in critical care outreach programs.

Cross-validation of a Sequential Organ Failure Assessment score–based model to predict mortality in patients with cancer admitted to the intensive care unit☆☆☆

PURPOSE This study aims to validate the performance of the Sequential Organ Failure Assessment (SOFA) score to predict death of critically ill patients with cancer. MATERIAL AND METHODS We conducted a retrospective observational study including adults admitted to the intensive care unit (ICU) between January 1, 2006, and December 31, 2008. We randomly selected training and validation samples in medical and surgical admissions to predict ICU and in-hospital mortality. By using logistic regression, we calculated the probabilities of death in the training samples and applied them to the validation samples to test the goodness-of-fit of the models, construct receiver operator characteristics curves, and calculate the areas under the curve (AUCs). RESULTS In predicting mortality at discharge from the unit, the AUC from the validation group of medical admissions was 0.7851 (95% confidence interval [CI], 0.7437-0.8264), and the AUC from the surgical admissions was 0.7847 (95% CI, 0.6319-0.937). The AUCs of the SOFA score to predict mortality in the hospital after ICU admission were 0.7789 (95% CI, 0.74-0.8177) and 0.7572 (95% CI, 0.6719-0.8424) for the medical and surgical validations groups, respectively. CONCLUSIONS The SOFA score had good discrimination to predict ICU and hospital mortality. However, the observed underestimation of ICU deaths and unsatisfactory goodness-of-fit test of the model in surgical patients to indicate calibration of the score to predict ICU mortality is advised in this group.

Place of Death among Hospitalized Patients with Cancer at the End of Life

BACKGROUND The majority of hospital deaths in the United States occur after ICU admission. The characteristics associated with the place of death within the hospital are not known for patients with cancer. OBJECTIVE The study objective was to identify patient characteristics associated with place of death among hospitalized patients with cancer who were at the end of life. METHODS A retrospective cohort study design was implemented. Subjects were consecutive patients hospitalized between 2003 and 2007 at a large comprehensive cancer center in the United States. Multinomial logistic regression analysis was used to identify patient characteristics associated with place of death (ICU, hospital following ICU, hospital without ICU) among hospital decedents. RESULTS Among 105,157 hospital discharges, 3860 (3.7%) died in the hospital: 42% in the ICU, 14% in the hospital following an ICU stay, and 44% in the hospital without ICU services. Individuals with the following characteristics had an increased risk of dying in the ICU: nonlocal residence, newly diagnosed hematologic or nonmetastatic solid tumor malignancies, elective admission, surgical or pediatric services. A palliative care consultation on admission was associated with dying in the hospital without ICU services. CONCLUSIONS Understanding existing patterns of care at the end of life will help guide decisions about resource allocation and palliative care programs. Patients who seek care at dedicated cancer centers may elect more aggressive care; thus the generalizability of this study is limited. Although dying in a hospital may be unavoidable for patients who have uncontrolled symptoms that cannot be managed at home, palliative care consultations with patients and their families in advance regarding end-of-life preferences may prevent unwanted admission to the ICU.

Two Decades of ICU Utilization and Hospital Outcomes in a Comprehensive Cancer Center.

Objective:To investigate ICU utilization and hospital outcomes of oncological patients admitted to a comprehensive cancer center. Design:Observational cohort study. Setting:The University of Texas MD Anderson Cancer Center. Patients:Consecutive adults with cancer discharged over a 20-year period. Interventions:None. Measurements and Main Results:The Cochran-Armitage test for trend was used to evaluate ICU utilization and hospital mortality rates by primary service over time. A negative binomial log linear regression model was fitted to the data to investigate length of stay over time. Among 387,306 adult hospitalized patients, the ICU utilization rate was 12.9%. The overall hospital mortality rate was 3.6%: 16.2% among patients with an ICU stay and 1.8% among non-ICU patients. Among those admitted to the ICU, the mean (SD) admission Sequential Organ Failure Assessment score was 6.1 (3.8) for all ICU patients: 7.3 (4.4) for medical ICU patients and 4.9 (2.8) for surgical ICU patients. Hematologic disorders were associated with the highest hospital mortality rate in ICU patients (42.8%); metastatic disease had the highest mortality rate in non-ICU patients (4.2%); sepsis, pneumonia, and other infections had the highest mortality rate for all inpatients (8.5%). Conclusions:This study provides a longitudinal view of ICU utilization rates, hospital and ICU length of stay, and severity-adjusted mortality rates. Although the data arise from a single institution, it encompasses a large number of hospital admissions over two decades and can serve as a point of comparison for future oncological studies at similar institutions. More studies of this nature are needed to determine whether consolidation of cancer care into specialized large-volume facilities may improve outcomes, while simultaneously sustaining appropriate resource utilization and reducing unnecessary healthcare costs.

Factors associated with anemia in patients with cancer admitted to an intensive care unit

PURPOSE The study aimed to evaluate the relative impact of clinical and demographic factors associated with the prevalence and incidence of anemia (hemoglobin [Hb] <12 g/dL) in critically ill patients with cancer. MATERIALS AND METHODS We performed an electronic chart review for demographic and clinical data of adult patients with cancer with or without anemia admitted to the intensive care unit (ICU). Prevalence of anemia was determined at admission, and incidence determined if anemia developed during ICU stay. Anemia was classified as mild, moderate, or severe. The additive impact of clinical and demographic factors was evaluated by using a hierarchical linear regression model. RESULTS A total of 4705 patients were included in the study. The prevalence and incidence of anemia were 68.0% and 46.6%, respectively. In prevalent cases, we found that the clinical covariates modified sequential organ failure assessment score, admission to the medical ICU, prior chemotherapy, diagnosis of hematologic cancer, and length of hospital stay before ICU admission explained 18.7% of the variance in the model, whereas the demographic covariates (age, sex, and race) explained only an additional 0.6%. The pattern was similar for incidence cases. CONCLUSIONS Clinical factors are more influential than demographic factors in the observed rates of prevalence and incidence of anemia in the ICU; thus, protocols are needed to identify subgroups of patients with cancer who could benefit from novel management strategies.