Critically ill metastatic cancer patients returning home after unplanned ICU stay: an observational, multicentre retrospective study

 

by Frédéric Gonzalez, Rémi Starka, Laurent Ducros, Magali Bisbal, Laurent Chow-Chine, Luca Servan, Jean-Manuel de Guibert, Bruno Pastene, Marion Faucher, Antoine Sannini, Marc Leone and Djamel Mokart 

 

Annals of Intensive Care volume 13, Article number: 73 (2023) 

 

Background

Data about critically ill metastatic cancer patients functional outcome after unplanned admission to the ICU are scarce. The aim of this study was to assess factors associated with 90-day return home and 1-year survival in this population.

Study design and methods

A multicenter retrospective study included all consecutive metastatic cancer patients admitted to the ICU for unplanned reason between 2017 and 2020.

Results

Among 253 included metastatic cancer patients, mainly with lung cancer, 94 patients (37.2%) could return home on day 90. One-year survival rate was 28.5%. Performance status 0 or 1 (OR, 2.18; 95% CI 1.21–3.93; P = 0.010), no malnutrition (OR, 2.90; 95% CI 1.61–5.24; P < 0.001), female gender (OR, 2.39; 95% CI 1.33–4.29; P = 0.004), recent chemotherapy (OR, 2.62; 95% CI 1.40–4.90; P = 0.003), SOFA score ≤ 5 on admission (OR, 2.62; 95% CI 1.41–4.90; P = 0.002) were significantly predictive for 90-day return home. Malnutrition (HR, 1.66; 95% CI 1.18–2.22; P = 0.003), acute respiratory failure (ARF) as reason for admission (HR, 1.40; 95% CI 1.10–1.95; P = 0.043), SAPS II on admission (HR, 1.03; 95% CI 1.02–1.05; P < 0.001) and decisions to forgo life-sustaining therapies (DFLST) (HR, 2.80; 95% CI 2.04–3.84; P < 0.001) were independently associated with 1-year mortality.

Conclusions

More than one out of three metastatic cancer patients could return home within 3 months after an unplanned admission to the ICU. Previous performance and nutritional status, ongoing specific treatment and low severity of the acute illness were found to be predictive for return home. Such encouraging findings should help change the dismal perception of critically ill metastatic cancer patients.

 

 

 

Clustering of critically ill patients using an individualized learning approach enables dose optimization of mobilization in the ICU

 

by Kristina E. Fuest, Bernhard Ulm, Nils Daum, Maximilian Lindholz, Marco Lorenz, Kilian Blobner, Nadine Langer, Carol Hodgson, Margaret Herridge, Manfred Blobner and Stefan J. Schaller 

 

Critical Care volume 27, Article number: 1 Published: 03 January 2023

 

Background

While early mobilization is commonly implemented in intensive care unit treatment guidelines to improve functional outcome, the characterization of the optimal individual dosage (frequency, level or duration) remains unclear. The aim of this study was to demonstrate that artificial intelligence-based clustering of a large ICU cohort can provide individualized mobilization recommendations that have a positive impact on the likelihood of being discharged home.

Methods

This study is an analysis of a prospective observational database of two interdisciplinary intensive care units in Munich, Germany. Dosage of mobilization is determined by sessions per day, mean duration, early mobilization as well as average and maximum level achieved. A k-means cluster analysis was conducted including collected parameters at ICU admission to generate clinically definable clusters.

Results

Between April 2017 and May 2019, 948 patients were included. Four different clusters were identified, comprising “Young Trauma,” “Severely ill & Frail,” “Old non-frail” and “Middle-aged” patients. Early mobilization (< 72 h) was the most important factor to be discharged home in “Young Trauma” patients (ORadj 10.0 [2.8 to 44.0], p < 0.001). In the cluster of “Middle-aged” patients, the likelihood to be discharged home increased with each mobilization level, to a maximum 24-fold increased likelihood for ambulating (ORadj 24.0 [7.4 to 86.1], p < 0.001). The likelihood increased significantly when standing or ambulating was achieved in the older, non-frail cluster (ORadj 4.7 [1.2 to 23.2], p = 0.035 and ORadj 8.1 [1.8 to 45.8], p = 0.010).

Conclusions

An artificial intelligence-based learning approach was able to divide a heterogeneous critical care cohort into four clusters, which differed significantly in their clinical characteristics and in their mobilization parameters. Depending on the cluster, different mobilization strategies supported the likelihood of being discharged home enabling an individualized and resource-optimized mobilization approach.

 

Diarrhea during critical illness: a multicenter cohort study

Intensive Care Medicine volume 48, pages 570–579 (2022) Published: 11 April 2022

Purpose

To study the incidence, predictors, and outcomes of diarrhea during the stay in the intensive care unit (ICU).

Methods

Prospective cohort of consecutive adults in the ICU for > 24 h during a 10-week period across 12 intensive care units (ICUs) internationally. The explored outcomes were: (1) incidence of diarrhea, (2) Clostridioides difficile-associated diarrhea (CDAD); (3) ICU and hospital length of stay (LOS) and mortality in patients with diarrhea. We fit generalized linear models to evaluate the predictors, management, morbidity and mortality associated with diarrhea.

Results

Among 1109 patients aged 61.4 (17.5) [mean (standard deviation)] years, 981(88.5%) were medical and 645 (58.2%) were mechanically ventilated. The incidence was 73.8% (818 patients, 73.8%, 95% confidence interval [CI] 71.1–76.6) using the definition of the World Health Organisation (WHO). Incidence varied across definitions (Bristol 53.5%, 95% CI 50.4–56.7; Bliss 37.7%, 95% CI 34.9–40.4). Of 99 patients with diarrhea undergoing CDAD testing, 23 tested positive (2.2% incidence, 95% CI 1.5–3.4). Independent predictors included enteral nutrition (RR 1.23, 95% CI 1.16–1.31, p < 0.001), antibiotic days (RR 1.02, 95% CI 1.02–1.03, p < 0.001), and suppositories (RR 1.14 95% CI 1.06–1.22, p < 0.001). Opiates decreased diarrhea risk (RR 0.76, 95% CI 0.68–0.86, p < 0.001). Diarrhea prompted management modifications (altered enteral nutrition or medications: RR 10.25, 95% CI 5.14–20.45, p < 0.001) or other consequences (fecal management device or CDAD testing: RR 6.16, 95% CI 3.4–11.17, p < 0.001). Diarrhea was associated with a longer time to discharge for ICU or hospital stay, but was not associated with hospital mortality.

Conclusion

Diarrhea is common, has several predictors, and prompts changes in patient care, is associated with longer time to discharge but not mortality.

Evaluation of ICU Risk Models Adapted for Use as Continuous Markers of Severity of Illness Throughout the ICU Stay*

Badawi, Omar PharmD, MPH, FCCM1,2,3; Liu, Xinggang MD, PhD1; Hassan, Erkan PharmD, FCCM1; Amelung, Pamela J. MD, FCCP1; Swami, Sunil PhD, MPH, MBBS1

Critical Care Medicine: March 2018 - Volume 46 - Issue 3 - p 361–367

doi: 10.1097/CCM.0000000000002904

Objectives: Evaluate the accuracy of different ICU risk models repurposed as continuous markers of severity of illness.

Design: Nonintervention cohort study.

Setting: eICU Research Institute ICUs using tele-ICU software calculating continuous ICU Discharge Readiness Scores between January 2013 and March 2016.

Patients: Five hundred sixty-one thousand four hundred seventy-eight adult ICU patients with an ICU length of stay between 4 hours and 30 days.

Interventions: Not available.

Measurements and Main Results: Hourly Acute Physiology and Chronic Health Evaluation IV, Sequential Organ Failure Assessment, and Discharge Readiness Scores were calculated beginning hour 4 of the ICU stay. Primary outcome was the area under the receiver operating characteristic curve for the mean score with ICU mortality. Secondary outcomes included area under the receiver operating characteristic curves for ICU mortality with admission, median, maximum and last scores, and for death within 24 hours. The trajectories of each score were visualized by plotting the hourly averages against time in the ICU, stratified by mortality and length of stay. The area under the receiver operating characteristic curves for mean Acute Physiology and Chronic Health Evaluation, Sequential Organ Failure Assessment, and Discharge Readiness Scores were 0.90 (0.89–0.90), 0.86 (0.86–0.86), and 0.94 (0.94–0.94), respectively. The area under the receiver operating characteristic curves for hourly Acute Physiology and Chronic Health Evaluation, Sequential Organ Failure Assessment, and Discharge Readiness Scores predicting 24-hour mortality were 0.81 (0.81–0.81), 0.76 (0.76–0.76), and 0.86 (0.86–0.86). Discharge Readiness Scores had a higher area under the receiver operating characteristic curve than both Acute Physiology and Chronic Health Evaluation and Sequential Organ Failure Assessment for each metric. Acute Physiology and Chronic Health Evaluation and Sequential Organ Failure Assessment scores increased throughout the first 24 hours in both survivors and nonsurvivors; Discharge Readiness Scores continuously decreased in survivors and temporarily decreased before increasing by hour 36 in nonsurvivors with longer length of stays.

Conclusions: Acute Physiology and Chronic Health Evaluation, Sequential Organ Failure Assessment, and Discharge Readiness Scores all have relatively high discrimination for ICU mortality when used continuously; Discharge Readiness Scores tended to have slightly higher area under the receiver operating characteristic curves for each endpoint. These findings validate the use of these models on a population level for continuous risk adjustment in the ICU, although Acute Physiology and Chronic Health Evaluation and Sequential Organ Failure Assessment appear slower to respond to improvements in patient status than Discharge Readiness Scores, and Discharge Readiness Scores may reflect physiologic improvement from interventions, potentially underestimating risk.

Surviving intensive care

Surviving intensive care: A systematic review of healthcare resource use after hospital discharge. Critical care medicine, Aug 2013, Vol. 41(8), p.1832-43.

Lone, N.I., et al.

http://journals.lww.com/ccmjournal/Abstract/2013/08000/Surviving_Intensive_Care___A_Systematic_Review_of.2.aspx

Intensive care survivors continue to experience significant morbidity following acute hospital discharge, but healthcare costs associated with this ongoing morbidity are poorly described. As the demand for intensive care increases, understanding the magnitude of postacute hospital healthcare costs is of increasing relevance to clinicians and healthcare planners. We undertook a systematic review of the literature reporting major healthcare resource use by intensive care survivors following discharge from the hospital and identified factors associated with increased resource use.

Surviving intensive care

Surviving intensive care: A systematic review of healthcare resource use after hospital discharge. Critical care medicine, August 2013, Vol. 41(8), p.1832-43.

Lone, N.I., et al.

http://journals.lww.com/ccmjournal/Abstract/2013/08000/Surviving_Intensive_Care___A_Systematic_Review_of.2.aspx

Intensive care survivors continue to experience significant morbidity following acute hospital discharge, but healthcare costs associated with this ongoing morbidity are poorly described. As the demand for intensive care increases, understanding the magnitude of post-acute hospital healthcare costs is of increasing relevance to clinicians and healthcare planners. We undertook a systematic review of the literature reporting major healthcare resource use by intensive care survivors following discharge from the hospital and identified factors associated with increased resource use.

Can we safely discharge patients from the intensive care unit after hours ?

Brasel , K .
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