Preadmission quality of life can predict mortality in intensive care unit--a prospective cohort study

Health-Related Quality of Life Scores and Values as Predictors of Mortality: A Scoping Review

Health-related quality of life (HRQoL) can be assessed through measures that can be generic or disease specific, encompass several independent scales, or employ holistic assessment (i.e., the derivation of composite scores). HRQoL measures may identify patients with differential risk profiles. However, the usefulness of generic and holistic HRQoL measures in identifying patients at higher risk of death is unclear. The aim of the present study was to undertake a scoping review of generic, holistic assessments of HRQoL as predictors of mortality in general non-patient populations and clinical sub-populations with specified conditions or risk factors in persons 18 years or older. Five databases were searched from 18 June to 29 June 2020 to identify peer-reviewed published articles. The searches were updated in August 2022. Reference lists of included and cited articles were also searched. Of 2552 articles screened, 110 met criteria for inclusion. Over one-third of studies were from North America. Most studies pertained to sub-populations with specified conditions and/or risk factors, almost a quarter for people with cardiovascular diseases. There were no studies pertaining to people with mental health conditions. Nearly three-quarters of the studies used a RAND Corporation QoL instrument, predominantly the SF-36, and nearly a quarter, a utility instrument, predominantly the EQ-5D. HRQoL was associated with mortality in 67 of 72 univariate analyses (92%) and 100 of 109 multivariate analyses (92%). HRQoL was found to be associated with mortality in the general population and clinical sub-populations with physical health conditions. Whether this relationship holds in people with mental health conditions is not known. HRQoL assessment may be useful for screening and/or monitoring purposes to understand how people perceive their health and well-being and as an indicator of mortality risk, encouraging better-quality and timely patient care to support and maximize what may be a patient's only modifiable outcome.

State of the Art of Machine Learning-Enabled Clinical Decision Support in Intensive Care Units: Literature Review

Background

Modern clinical care in intensive care units is full of rich data, and machine learning has great potential to support clinical decision-making. The development of intelligent machine learning–based clinical decision support systems is facing great opportunities and challenges. Clinical decision support systems may directly help clinicians accurately diagnose, predict outcomes, identify risk events, or decide treatments at the point of care.

Objective

We aimed to review the research and application of machine learning–enabled clinical decision support studies in intensive care units to help clinicians, researchers, developers, and policy makers better understand the advantages and limitations of machine learning–supported diagnosis, outcome prediction, risk event identification, and intensive care unit point-of-care recommendations.

Methods

We searched papers published in the PubMed database between January 1980 and October 2020. We defined selection criteria to identify papers that focused on machine learning–enabled clinical decision support studies in intensive care units and reviewed the following aspects: research topics, study cohorts, machine learning models, analysis variables, and evaluation metrics.

Results

A total of 643 papers were collected, and using our selection criteria, 97 studies were found. Studies were categorized into 4 topics—monitoring, detection, and diagnosis (13/97, 13.4%), early identification of clinical events (32/97, 33.0%), outcome prediction and prognosis assessment (46/97, 47.6%), and treatment decision (6/97, 6.2%). Of the 97 papers, 82 (84.5%) studies used data from adult patients, 9 (9.3%) studies used data from pediatric patients, and 6 (6.2%) studies used data from neonates. We found that 65 (67.0%) studies used data from a single center, and 32 (33.0%) studies used a multicenter data set; 88 (90.7%) studies used supervised learning, 3 (3.1%) studies used unsupervised learning, and 6 (6.2%) studies used reinforcement learning. Clinical variable categories, starting with the most frequently used, were demographic (n=74), laboratory values (n=59), vital signs (n=55), scores (n=48), ventilation parameters (n=43), comorbidities (n=27), medications (n=18), outcome (n=14), fluid balance (n=13), nonmedicine therapy (n=10), symptoms (n=7), and medical history (n=4). The most frequently adopted evaluation metrics for clinical data modeling studies included area under the receiver operating characteristic curve (n=61), sensitivity (n=51), specificity (n=41), accuracy (n=29), and positive predictive value (n=23).

Conclusions

Early identification of clinical and outcome prediction and prognosis assessment contributed to approximately 80% of studies included in this review. Using new algorithms to solve intensive care unit clinical problems by developing reinforcement learning, active learning, and time-series analysis methods for clinical decision support will be greater development prospects in the future.

Worse pre-admission quality of life is a strong predictor of mortality in critically ill patients

Objectives

In this study, we aimed to investigate whether quality of life (QoL) before intensive care unit (ICU) admission could predict ICU mortality in critically ill patients.

Patients and methods

Between January 2019 and April 2019, a total of 105 ICU patients (54 males, 51 females; mean age: 58 years; range, 18 to 91 years) from two ICUs of a tertiary care hospital were included in this cross-sectional, prospective study. Pre-admission QoL was measured by the Short Form (SF)-12- Physical Component Scores (PCS) and Mental Component Scores (MCS) and EuroQoL five-dimension, five-level scale (EQ-5D-5L) within 24 h of ICU admission and mortality rates were estimated.

Results

The overall mortality rate was 28.5%. Pre-admission QoL was worse in the non-survivors independent from age, sex, socioeconomic and education status, and comorbidities. During the hospitalization, the rate of sepsis and ventilator/hospital-acquired pneumonia were similar among the two groups (p>0.05). Logistic regression analysis adjusted for sex, age, education status, and Acute Physiology and Chronic Health Evaluation II (APACHE II) scores showed that pre-admission functional status as assessed by the SF-12 MCS (odds ratio [OR]: 14,2; 95% confidence interval [CI]: 2.5-79.0), SF-12 PCS (OR: 10.6; 95% CI: 1.8-62.7), and EQ-5D-5L (OR: 8.0; 95% CI: 1.5-44.5) were found to be independently associated with mortality.

Conclusion

Worse pre-admission QoL is a strong predictor of mortality in critically ill patients. The SF-12 and EQ-5D-5L scores are both valuable tools for this assessment. Not only the physical status, but also the mental status before ICU admission should be evaluated in terms of QoL to better utilize ICU resources.

Self-reported quality of life as a predictor of mortality in renal cell carcinoma

BACKGROUND

This study evaluated the utility of self-reported quality of life (QOL) metrics in predicting mortality among all-comers with renal cell carcinoma (RCC) and externally tested the findings in a registry of patients with small renal masses.

METHODS

The Surveillance, Epidemiology, and End Results-Medicare Health Outcomes Survey (SEER-MHOS) captured QOL metrics composed of mental component summary (MCS) and physical component summary (PCS) scores. Regression models assessed associations of MCS and PCS with all-cause, RCC-specific, and non-RCC-specific mortality. Harrell's concordance statistic (the C-index) and the Akaike information criterion (AIC) determined predictive accuracy and parsimony, respectively. Findings were tested in the prospective Delayed Intervention and Surveillance for Small Renal Masses (DISSRM) registry.

RESULTS

In SEER-MHOS, 1494 patients had a median age of 73.4 years and a median follow-up time of 5.6 years. Each additional MCS and PCS point reduced the hazard of all-cause mortality by 1.3% (95% CI, 0.981-0.993; P < .001) and 2.3% (95% CI, 0.971-0.984; P < .001), respectively. Models with QOL metrics demonstrated higher predictive accuracy (C-index, 72.3% vs 70.1%) and parsimony (AIC, 9376.5 vs 9454.5) than models without QOL metrics. QOL metrics exerted a greater effect on non-RCC-specific mortality than RCC-specific mortality. External testing in the DISSRM registry confirmed these findings with similar results for all-cause mortality.

CONCLUSIONS

Models with self-reported QOL metrics predicted all-cause mortality in patients with RCC with higher accuracy and parsimony than those without QOL metrics. Physical health was a stronger predictor of mortality than mental health. The findings support the incorporation of QOL metrics into prognostic models and patient counseling for RCC.

Physical, Mental, and Cognitive Health Status of ICU Survivors Before ICU Admission: A Cohort Study

OBJECTIVES

Although patient's health status before ICU admission is the most important predictor for long-term outcomes, it is often not taken into account, potentially overestimating the attributable effects of critical illness. Studies that did assess the pre-ICU health status often included specific patient groups or assessed one specific health domain. Our aim was to explore patient's physical, mental, and cognitive functioning, as well as their quality of life before ICU admission.

DESIGN

Baseline data were used from the longitudinal prospective MONITOR-IC cohort study.

SETTING

ICUs of four Dutch hospitals.

PATIENTS

Adult ICU survivors (n = 2,467) admitted between July 2016 and December 2018.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Patients, or their proxy, rated their level of frailty (Clinical Frailty Scale), fatigue (Checklist Individual Strength-8), anxiety and depression (Hospital Anxiety and Depression Scale), cognitive functioning (Cognitive Failure Questionnaire-14), and quality of life (Short Form-36) before ICU admission. Unplanned patients rated their pre-ICU health status retrospectively after ICU admission. Before ICU admission, 13% of all patients was frail, 65% suffered from fatigue, 28% and 26% from symptoms of anxiety and depression, respectively, and 6% from cognitive problems. Unplanned patients were significantly more frail and depressed. Patients with a poor pre-ICU health status were more often likely to be female, older, lower educated, divorced or widowed, living in a healthcare facility, and suffering from a chronic condition.

CONCLUSIONS

In an era with increasing attention for health problems after ICU admission, the results of this study indicate that a part of the ICU survivors already experience serious impairments in their physical, mental, and cognitive functioning before ICU admission. Substantial differences were seen between patient subgroups. These findings underline the importance of accounting for pre-ICU health status when studying long-term outcomes.

Neighborhood Socioeconomic Status and Trajectories of Physical Health-Related Quality of Life Among Stroke Survivors

Background and Purpose- Stroke is the leading cause of serious, long-term disability in the United States, and the number of stroke survivors is projected to rise. Physical functioning status may be compromised in survivors living in low socioeconomic status environments in comparison to higher socioeconomic status environments. Higher socioeconomic status environments may include benefits in the built environment such as sidewalks, accessible transit, or low traffic volume. Investigation is needed to understand the effects of the socioenvironmental context on trajectories of stroke survivors' physical health-related quality of life (PH-QOL) over time. Methods- Participants from the REGARDS (REasons for Geographic and Racial Differences in Stroke) study enrolled in the ancillary Caring for Adults Recovering from the Effects of Stroke project completed the SF-12 around 6 to 12, 18, 27, and 36 months poststroke. Measures of area-level income, wealth, education, and employment at the census tract level were combined to represent participants' neighborhood socioeconomic status. Linear mixed models were used to predict trajectories of PH-QOL over time, controlling for individual characteristics. Results- The average trajectory of PH-QOL was flat over time. However, women and younger stroke survivors had better trajectories over time than men and older stroke survivors. Higher neighborhood socioeconomic status was significantly associated with better PH-QOL across all time points (β=1.73; 95% CI, 0.17-3.30), after controlling for demographic variables and severity of stroke. Conclusions- Our findings demonstrate that neighborhood socioeconomic status, sex, and age are associated with the poststroke recovery process. The results of this study suggest the importance of evaluating the environment surrounding stroke survivors when they return to their home communities. Future research should identify specific features of the environment within different socioeconomic status neighborhoods to better understand how they contribute to PH-QOL among stroke survivors.

Performance of Pediatric Mortality Prediction Scores for PICU Mortality and 90-Day Mortality

OBJECTIVES

The use of mortality prediction scores in clinical trials in the PICU is essential for comparing patient groups. Because of the decline in PICU mortality over the last decades, leading to a shift toward later deaths, recent trials use 90-day mortality as primary outcome for estimating mortality and survival more accurately. This study assessed and compared the performance of two frequently used PICU mortality prediction scores for prediction of PICU and 90-day mortality.

DESIGN

This secondary analysis of the randomized controlled Early versus Late Parenteral Nutrition in the Pediatric Intensive Care Unit trial compared the discrimination (area under the receiver operating characteristic curve) and calibration of the Pediatric Index of Mortality 3 and the Pediatric Risk of Mortality III scores for prediction of PICU and 90-day mortality.

SETTING

Three participating PICUs within academic hospitals in Belgium, the Netherlands, and Canada.

PATIENTS

One-thousand four-hundred twenty-eight critically ill patients 0-17 years old.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Although Pediatric Index of Mortality 3 only includes information available at the time of PICU admission, thus before any intervention in the PICU, it showed good discrimination (area under the receiver operating characteristic curve, 0.894; 95% CI, 0.892-0.896) and good calibration (no deviation from the diagonal, p = 0.58) for PICU mortality. Pediatric Risk of Mortality III, which involves the worst values for the evaluated variables during the first 24 hours of PICU stay, was statistically more discriminant (area under the receiver operating characteristic curve, 0.920; 95% CI, 0.918-0.921; p = 0.04) but poor in calibration (significant deviation from the diagonal; p = 0.04). Pediatric Index of Mortality 3 and Pediatric Risk of Mortality III discriminated equally well between 90-day mortality and survival (area under the receiver operating characteristic curve, 0.867; 95% CI, 0.866-0.869 and area under the receiver operating characteristic curve, 0.882; 95% CI, 0.880-0.884, respectively, p = 0.77), but Pediatric Risk of Mortality III was not well calibrated (p = 0.04), unlike Pediatric Index of Mortality 3 (p = 0.34).

CONCLUSIONS

Pediatric Index of Mortality 3 performed better in calibration for predicting PICU and 90-day mortality than Pediatric Risk of Mortality III and is not influenced by intervention or PICU quality of care. Therefore, Pediatric Index of Mortality 3 seems a better choice for use in clinical trials with 90-day mortality as primary outcome.

Pre-admission quality of life as predictor of outcome after acute care: the role of emotional well-being

PURPOSE

We sought to investigate whether pre-admission quality of life could act as a predictor of mortality among acutely ill patients, and which dimension of QOL has the greater impact on outcomes.

METHODS

Prospective cohort study including patients admitted to an acute medical unit of Rabat Ibn Sina University Hospital, Morocco, between June and September 2014. Characteristics of patients were recorded at admission. The primary exposure was pre-admission quality of life recorded using Euroqol five dimensions three level (EQ-5 D-3 L) and the primary outcome was 90-day mortality. We fit a Cox proportional hazards model to assess their association. We constructed six prediction models; each model included either EQ5D index or one of the five dimensions. We allowed all continuous variables to have a non-linear relationship with the primary outcome using restricted cubic spline with three knots.

RESULTS

We included 251 patients. The mean EQ5D index was 0.46 ± 0.5. The design of each prediction model was based on the significant findings of the univariate analysis including; bedside EQ5D index or one of the five dimensions of the EQ5D; age, history of chronic disease, Charlson Comorbidity Index and hemoglobinemia. Multi-variate Cox proportional adjusted hazard ratio (HR) derived from the six models, identified that EQ5D index was independently associated with 90-day mortality (HR: 0.48; 95% CI: 0.25; 0.91, P = 0.02), and that anxiety and depression dimension has the greater impact on outcome (HR: 2.97; 95% CI: 1.38; 6.41, P = 0.005).

CONCLUSIONS

This study revealed that pre-admission health-related quality of life (HRQoL), and particularly pre-admission psychological HRQoL was associated with outcome of acutely ill patients 90 days after discharge.