Characteristics and outcomes of autologous hematopoietic stem cell transplant recipients admitted to intensive care units: A multicenter study.

PURPOSE: Studies of critically ill hematopoietic stem cell transplantation (HSCT) recipients have mainly been single-center and focused on allogenic HSCT recipients. We aimed to describe a cohort of autologous HSCT with an unplanned intensive care unit (ICU) admission. METHODS: This study is a retrospective cohort study of autologous HSCT performed as a treatment for a hematological malignancy, during their first unplanned ICU admission in 50 hospitals in Brazil. We assessed the hospital mortality and the association between mechanical ventilation, vasopressors, and renal replacement therapy and hospital mortality in autologous HSCT recipients, adjusted for potential confounders. RESULTS: We included 301 patients. Multiple myeloma was the most common malignancy driving to HSCT. ICU and hospital mortality were 22.9% and 37.5%, respectively. After adjustment for potential confounders, mechanical ventilation (OR = 9.10; CI 95%, 4.82-17.15) was associated with hospital mortality, but vasopressors (OR = 1.43; CI 95%, 0.77-2.64) and renal replacement therapy (OR = 1.30; CI 95%, 0.63-2.66) were not. CONCLUSIONS: In this large cohort of critically ill autologous HSCT recipients, mechanical ventilation was the only organ support-therapy associated with increased mortality in autologous HSCT recipients.

Trends in clinical profiles, organ support use and outcomes of patients with cancer requiring unplanned ICU admission: a multicenter cohort study.

PURPOSE: To describe trends in outcomes of cancer patients with unplanned admissions to intensive-care units (ICU) according to cancer type, organ support use, and performance status (PS) over an 8-year period. METHODS: We retrospectively analyzed prospectively collected data from all cancer patients admitted to 92 medical-surgical ICUs from July/2011 to June/2019. We assessed trends in mortality through a Bayesian hierarchical model adjusted for relevant clinical confounders and whether there was a reduction in ICU length-of-stay (LOS) over time using a competing risk model. RESULTS: 32,096 patients (8.7% of all ICU admissions; solid tumors, 90%; hematological malignancies, 10%) were studied. Bed/days use by cancer patients increased up to more than 30% during the period. Overall adjusted mortality decreased by 9.2% [95% credible interval (CI), 13.1-5.6%]. The largest reductions in mortality occurred in patients without need for organ support (9.6%) and in those with need for mechanical ventilation (MV) only (11%). Smallest reductions occurred in patients requiring MV, vasopressors, and dialysis (3.9%) simultaneously. Survival gains over time decreased as PS worsened. Lung cancer patients had the lowest decrease in mortality. Each year was associated with a lower sub-hazard for ICU death [SHR 0.93 (0.91-0.94)] and a higher chance of being discharged alive from the ICU earlier [SHR 1.01 (1-1.01)]. CONCLUSION: Outcomes in critically ill cancer patients improved in the past 8 years, with reductions in both mortality and ICU LOS, suggesting improvements in overall care. However, outcomes remained poor in patients with lung cancer, requiring multiple organ support and compromised PS.

ICU staffing feature phenotypes and their relationship with patients' outcomes: an unsupervised machine learning analysis.

PURPOSE: To study whether ICU staffing features are associated with improved hospital mortality, ICU length of stay (LOS) and duration of mechanical ventilation (MV) using cluster analysis directed by machine learning. METHODS: The following variables were included in the analysis: average bed to nurse, physiotherapist and physician ratios, presence of 24/7 board-certified intensivists and dedicated pharmacists in the ICU, and nurse and physiotherapist autonomy scores. Clusters were defined using the partition around medoids method. We assessed the association between clusters and hospital mortality using logistic regression and with ICU LOS and MV duration using competing risk regression. RESULTS: Analysis included data from 129,680 patients admitted to 93 ICUs (2014-2015). Three clusters were identified. The features distinguishing between the clusters were: the presence of board-certified intensivists in the ICU 24/7 (present in Cluster 3), dedicated pharmacists (present in Clusters 2 and 3) and the extent of nurse autonomy (which increased from Clusters 1 to 3). The patients in Cluster 3 exhibited the best outcomes, with lower adjusted hospital mortality [odds ratio 0.92 (95% confidence interval (CI), 0.87-0.98)], shorter ICU LOS [subhazard ratio (SHR) for patients surviving to ICU discharge 1.24 (95% CI 1.22-1.26)] and shorter durations of MV [SHR for undergoing extubation 1.61(95% CI 1.54-1.69)]. Cluster 1 had the worst outcomes. CONCLUSION: Patients treated in ICUs combining 24/7 expert intensivist coverage, a dedicated pharmacist and nurses with greater autonomy had the best outcomes. All of these features represent achievable targets that should be considered by policy makers with an interest in promoting equal and optimal ICU care.

Association of frailty with short-term outcomes, organ support and resource use in critically ill patients.

PURPOSE: Frail patients are known to experience poor outcomes. Nevertheless, we know less about how frailty manifests itself in patients' physiology during critical illness and how it affects resource use in intensive care units (ICU). We aimed to assess the association of frailty with short-term outcomes and organ support used by critically ill patients. METHODS: Retrospective analysis of prospective collected data from 93 ICUs in Brazil from 2014 to 2015. We assessed frailty using the modified frailty index (MFI). The primary outcome was in-hospital mortality. Secondary outcomes were discharge home without need for nursing care, ICU and hospital length of stay (LOS), and utilization of ICU organ support and transfusion. We used mixed logistic regression and competing risk models accounting for relevant confounders in outcome analyses. RESULTS: The analysis consisted of 129,680 eligible patients. There were 40,779 (31.4%) non-frail (MFI = 0), 64,407 (49.7%) pre-frail (MFI = 1-2) and 24,494 (18.9%) frail (MFI ≥ 3) patients. After adjusted analysis, frailty was associated with higher in-hospital mortality (OR 2.42, 95% CI 1.89-3.08), particularly in patients admitted with lower SOFA scores. Frail patients were less likely to be discharged home (OR 0.36, 95% CI 0.54-0.79) and had higher hospital and ICU LOS than non-frail patients. Use of all forms of organ support (mechanical ventilation, non-invasive ventilation, vasopressors, dialysis and transfusions) were more common in frail patients and increased as MFI increased. CONCLUSIONS: Frailty, as assessed by MFI, was associated with several patient-centered endpoints including not only survival, but also ICU LOS and organ support.

Role of organisational factors on the 'weekend effect' in critically ill patients in Brazil: a retrospective cohort analysis.

INTRODUCTION: Higher mortality for patients admitted to intensive care units (ICUs) during the weekends has been occasionally reported with conflicting results that could be related to organisational factors. We investigated the effects of ICU organisational and staffing patterns on the potential association between weekend admission and outcomes in critically ill patients. METHODS: We included 59 614 patients admitted to 78 ICUs participating during 2013. We defined 'weekend admission' as any ICU admission from Friday 19:00 until Monday 07:00. We assessed the association between weekend admission with hospital mortality using a mixed logistic regression model controlling for both patient-level (illness severity, age, comorbidities, performance status and admission type) and ICU-level (decrease in nurse/bed ratio on weekend, full-time intensivist coverage, use of checklists on weekends and number of institutional protocols) confounders. We performed secondary analyses in the subgroup of scheduled surgical admissions. RESULTS: A total of 41 894 patients (70.3%) were admitted on weekdays and 17 720 patients (29.7%) on weekends. In univariable analysis, weekend admitted patients had higher ICU (10.9% vs 9.0%, P<0.001) and hospital (16.5% vs 13.5%, P<0.001) mortality. After adjusting for confounders, weekend admission was not associated with higher hospital mortality (OR 1.05, 95% CI 0.99 to 1.12, P=0.095). However, a 'weekend effect' was still observed in scheduled surgical admissions, as well as in ICUs not using checklists during the weekends. For unscheduled admissions, no 'weekend effect' was observed regardless of ICU's characteristics. For scheduled surgical admissions, a 'weekend effect' was present only in ICUs with a low number of implemented protocols and those with a reduction in the nurse/bed ratio and not applying checklists during weekends. CONCLUSIONS: ICU organisational factors, such as decreased nurse-to-patient ratio, absence of checklists and fewer standardised protocols, may explain, in part, increases in mortality in patients admitted to the ICU mortality on weekends.

The effects of performance status one week before hospital admission on the outcomes of critically ill patients.

PURPOSE: To assess the impact of performance status (PS) impairment 1 week before hospital admission on the outcomes in patients admitted to intensive care units (ICU). METHODS: Retrospective cohort study in 59,693 patients (medical admissions, 67 %) admitted to 78 ICUs during 2013. We classified PS impairment according to the Eastern Cooperative Oncology Group (ECOG) scale in absent/minor (PS = 0-1), moderate (PS = 2) or severe (PS = 3-4). We used univariate and multivariate logistic regression analyses to investigate the association between PS impairment and hospital mortality. RESULTS: PS impairment was moderate in 17.3 % and severe in 6.9 % of patients. The hospital mortality was 14.4 %. Overall, the worse the PS, the higher the ICU and hospital mortality and length of stay. In addition, patients with worse PS were less frequently discharged home. PS impairment was associated with worse outcomes in all SAPS 3, Charlson Comorbidity Index and age quartiles as well as according to the admission type. Adjusting for other relevant clinical characteristics, PS impairment was associated with higher hospital mortality (odds-ratio (OR) = 1.96 (95 % CI 1.63-2.35), for moderate and OR = 4.22 (3.32-5.35), for severe impairment). The effects of PS on the outcome were particularly relevant in the medium range of severity-of-illness. These results were consistent in the subgroup analyses. However, adding PS impairment to the SAPS 3 score improved only slightly its discriminative capability. CONCLUSION: PS impairment was associated with worse outcomes independently of other markers of chronic health status, particularly for patients in the medium range of severity of illness.

Early determinants of death due to multiple organ failure after noncardiac surgery in high-risk patients.

BACKGROUND: Prediction of perioperative cardiac complications is important in the medical management of patients undergoing noncardiac surgery. However, these patients frequently die as a consequence of primary or secondary multiple organ failure (MOF), often as a result of sepsis. We investigated the early perioperative risk factors for in-hospital death due to MOF in surgical patients. METHODS: This was a prospective, multicenter, observational cohort study performed in 21 Brazilian intensive care units (ICUs). Adult patients undergoing noncardiac surgery who were admitted to the ICU within 24 hours after operation were evaluated. MOF was characterized by the presence of at least 2 organ failures. To determine the relative risk (RR) of in-hospital death due to MOF, we performed a logistic regression multivariate analysis. RESULTS: A total of 587 patients were included (mean age, 62.4 ± 17 years). ICU and hospital mortality rates were 15% and 20.6%, respectively. The main cause of death was MOF (53%). Peritonitis (RR 4.17, 95% confidence interval [CI] 1.38-12.6), diabetes (RR 3.63, 95% CI 1.17-11.2), unplanned surgery (RR 3.62, 95% CI 1.18-11.0), age (RR 1.04, 95% CI 1 0.01-1.08), and elevated serum lactate concentrations (RR 1.52, 95% CI 1.14-2.02), a high central venous pressure (RR 1.12, 95% CI 1.04-1.22), a fast heart rate (RR 3.63, 95% CI 1.17-11.2) and pH (RR 0.04, 95% CI 0.0005-0.38) on the day of admission were independent predictors of death due to MOF. CONCLUSIONS: MOF is the main cause of death after surgery in high-risk patients. Awareness of the risk factors for death due to MOF may be important in risk stratification and can suggest routes for therapy.