Mortality of Adult Critically Ill Subjects With Cancer

Outcome of Cancer Patients with an Unplanned Intensive Care Unit Admission: Predictors of Mortality and Long-term Survival

Background

Understanding the characteristics and outcomes of cancer patients with unplanned ICU admission is imperative for therapeutic decisions and prognostication purposes.

Objective

To describe the clinical characteristics of patients with hematological and non-hematological malignancies (NHM) who require unplanned ICU admission and to determine the predictors of mortality and long-term survival.

Methods

This retrospective study included all patients with cancer who had an unplanned ICU admission between 2011 and 2016 at a tertiary hospital in Saudi Arabia. The following variables were collected: age, gender, ICU length of stay (LOS), APACHE II score, type of malignancy, febrile neutropenia, source and time of admission, and need for mechanical ventilation (MV), renal replacement therapy (RRT), and treatment with vasopressors (VP). Predictors of mortality and survival rates at 28 days and 3, 6, and 12 months were calculated.

Results

The study included 410 cancer patients with 466 unplanned ICU admissions. Of these, 52% had NHM. The average LOS in the ICU was 9.6 days and the mean APACHE score was 21.9. MV was needed in 73% of the patients, RRT in 15%, and VP in 24%, while febrile neutropenia was present in 24%. There were statistically significant differences between survivors and non-survivors in the APACHE II score (17.7 ± 8.0 vs. 25.6 ± 9.2), MV use (52% vs. 92%), need for RRT (6% vs. 23%), VP use (42% vs. 85%), and presence of febrile neutropenia (18% vs. 30%). The predictors of mortality were need for MV (OR = 4.97), VP (OR = 3.43), RRT (OR = 3.31), and APACHE II score (OR = 1.10). Survival rates at 28 days, 3, 6, and 12 months were 52%, 28%, 22%, and 15%, respectively.

Conclusion

The survival rate of cancer patients with an unplanned admission to the ICU remains low. Predictors of mortality include need for MV, RRT, and VP and presence of febrile neutropenia. About 85% of cancer patients died within 1 year after ICU admission.

How Does Health-Related Quality of Life Change Over Time in Cancer Survivors Following an Admission to the Intensive Care Unit?: An Integrative Review

BACKGROUND

Cancer survivors account for 15% to 20% of all intensive care unit (ICU) admissions. In general ICU populations, patients are known to experience reduced health-related quality of life (HRQoL). However, little is known about HRQoL impacts among cancer survivors following a critical illness in ICU.

OBJECTIVE

The aim of this study was to critically synthesize the evidence to further understand the impact of a critical illness and ICU admission in cancer survivors.

METHODS

An integrative review was conducted and reported according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analysis) guidelines. Three electronic databases were searched (MEDLINE, CINAHL, and EMBASE) using keywords and Boolean logic. Quality appraisal, data extraction, and a narrative synthesis were completed for all included studies by 2 reviewers.

RESULTS

Eleven publications met inclusion criteria. Health-related quality-of-life domains most frequently reported in cancer survivors after discharge from ICU included the following: physical function limitations, physical symptoms, and anxiety/depression.

CONCLUSIONS

Health-related quality of life decreased immediately after the admission to ICU with a gradual increase in the 3 to 12 months following. Cancer survivors are vulnerable to physical limitations, pain, and social isolation after an admission to ICU.

IMPLICATIONS FOR PRACTICE

Cancer survivors who have been affected by a critical illness are at risk of reduced HRQoL after an admission to ICU. This integrative review will help clinicians and researchers to develop patient-centered models of care during the recovery of critical illness, which are currently lacking in service delivery.

The effect of the underlying malignancy on short- and medium-term survival of critically ill patients admitted to the intensive care unit: a retrospective analysis based on propensity score matching

Background

Advances in oncology led to a substantial increase in the number of patients requiring admission to the ICU. It is significant to confirm which cancer critical patients can benefit from the ICU care like noncancer patients.

Methods

An observational retrospective cohort study using intensive care unit (ICU) admissions of Medical Information Mart for Intensive Care III from the Beth Israel Deaconess Medical Center in Boston, MA, USA between 2001 and 2012 was conducted. Propensity score matching was used to reduce the imbalance between two matched cohorts. ICU patients with cancer were compared with those without cancer in terms of patients’ characteristics and survival.

Results

There were 38,508 adult patients admitted to ICUs during the period. The median age was 65 years (IQR, 52–77) and 8308 (21.6%) had an underlying malignancy diagnosis. The noncancer group had a significant survive advantage at the point of 28-day, 90-day, 365-day and 1095-day after ICU admission compared with cancer group (P < 0.001 for all) after PSM. Subgroup analysis showed that the diagnosis of malignancy didn’t decrease 28-day and 90-day survive when patients’ age ≥ 65-year, patients in surgical intensive care unit or cardiac surgery recovery unit or traumatic surgical intensive care unit, elective admissions, patients with renal replacement therapy or vasopressor support (P > 0.05 for all).

Conclusions

Malignancy is a common diagnosis among ICU patients. Patients without cancer have a survive advantage compared with patients with cancer in the short- and medium-term. However, in selected groups, cancer critical patients can benefit from the ICU care service like noncancer patients in the short-term.

One-year mortality among non-surgical patients with hematological malignancies admitted to the intensive care unit: a Danish nationwide population-based cohort study

PURPOSE

Contemporary data on mortality of hematological patients admitted to the intensive care unit (ICU) are missing. In a Danish nationwide set-up, we assessed 30-day and 1-year mortality in this population including impact of age and comorbidity, with non-hematological patients as reference.

METHODS

This population-based cohort study included all non-surgical patients > 15 years of age admitted to an ICU in Denmark between 2010 and 2015. Data on hematological malignancies were obtained from the Danish Hematological Database, and information on the Charlson Comorbidity Index was obtained from the Danish National Patient Registry. Thirty-day and 1-year mortality was estimated using the Kaplan-Meier method. We used Cox proportional hazards regression to estimate hazard ratios.

RESULTS

We included 2122 ICU patients with a hematological malignancy and 88,951 non-hematological ICU patients. The 30-day mortality was 44% (95% confidence interval: 42-47%) among hematological patients and 27% (27-27%) among non-hematological patients. Similarly, 1-year mortality was 66% (64-68%) and 37% (37-37%), respectively. The corresponding hazard ratio with adjustment for age, sex, and comorbidity was 1.62 (1.54-1.71). Excess mortality was observed in all subgroups of age or of comorbidity. For example, the 1-year mortality for patients with Charlson Comorbidity Index Score > 3: 70% (66-74%) among hematological patients and 62% (61-63%) among non-hematological patients.

CONCLUSION

ICU patients with hematological malignancy had higher mortality than other ICU patients. However, one third of critically ill patients with a hematological malignancy is alive 1 year after ICU admission.

Weaning outcome of solid cancer patients requiring mechanical ventilation in the intensive care unit

BACKGROUND

Whether the weaning outcome of solid cancer patients receiving mechanical ventilation (MV) in the intensive care unit (ICU) is comparable to that in non-cancer patients is unknown. The aim of this study was to compare the weaning outcomes between non-cancer patients and patients with different types of cancer.

METHODS

We studied patients requiring MV during ICU stay for medical reasons between 2012 and 2014. Cancer patients were grouped into those with lung cancer (LC), head and neck cancer (HNC), hepatocellular carcinoma (HCC), and other cancers (OC). The primary endpoint was successful weaning at day 90 after the initiation of MV, and the main secondary endpoints were 28-day and 90-day mortality after ICU admission.

RESULTS

Five hundred and eighteen patients with solid cancers and 1362 non-cancer patients were recruited. The rate of successful weaning at day 90 was 57.9% in cancer patients, which was lower than 68.9% in non-cancer patients (p < 0.001). Compared to non-cancer patients, LC was associated with a lower probability of weaning at day 90 (hazard ratio 0.565, 95% CI 0.446 to 0.715), while HNC, HCC, and OC had similar probabilities. The 28-day and 90-day mortality rates were higher in cancer patients than in non-cancer patients (45.2% vs. 29.4%, and 65.6% vs. 37.7%, respectively, both p < 0.001).

CONCLUSION

Among mechanically ventilated patients in the ICU, those with LC were associated with a lower probability of weaning at day 90 compared to non-cancer patients.

Analysis of causality from observational studies and its application in clinical research in Intensive Care Medicine

Random allocation of treatment or intervention is the key feature of clinical trials and divides patients into treatment groups that are approximately balanced for baseline, and therefore comparable covariates except for the variable treatment of the study. However, in observational studies, where treatment allocation is not random, patients in the treatment and control groups often differ in covariates that are related to intervention variables. These imbalances in covariates can lead to biased estimates of the treatment effect. However, randomized clinical trials are sometimes not feasible for ethical, logistical, economic or other reasons. To resolve these situations, interest in the field of clinical research has grown in designing studies that are most similar to randomized experiments using observational (i.e. non-random) data. Observational studies using propensity score analysis methods have been increasing in the scientific papers of Intensive Care. Propensity score analyses attempt to control for confounding in non-experimental studies by adjusting for the likelihood that a given patient is exposed. However, studies with propensity indexes may be confusing, and intensivists are not familiar with this methodology and may not fully understand the importance of this technique. The objectives of this review are: to describe the fundamentals of propensity index methods; to present the techniques to adequately evaluate propensity index models; to discuss the advantages and disadvantages of these techniques.