Predicting Performance Status 1 Year After Critical Illness in Patients 80 Years or Older: Development of a Multivariable Clinical Prediction Model. Crit Care Med. 2016;44:1718-1726. [PMID: 27075141 DOI: 10.1097/ccm.0000000000001762]

Meeting complex multidimensional needs in older patients and their families during and beyond critical illness

PURPOSE OF REVIEW

To highlight the emerging crisis of critically ill elderly patients and review the unique burden of multidimensional morbidity faced by these patients and caregivers and potential interventions.

RECENT FINDINGS

Physical, psychological, and cognitive sequelae after critical illness are frequent, durable, and robust across the international ICU outcome literature. Elderly patients are more vulnerable to the multisystem sequelae of critical illness and its treatment and the resultant multidimensional morbidity may be profound, chronic, and significantly affect functional independence, transition to the community, and quality of life for patients and families. Recent data reinforce the importance of baseline functional status, health trajectory, and chronic illness as key determinants of long-term functional disability after ICU. These risks are even more pronounced in older patients.

SUMMARY

The current article is an overview of the outcomes of older survivors of critical illness, putative interventions to mitigate the long-term morbidity of patients, and the consequences for families and caregivers. A multimodal longitudinal approach designed to follow patients for one or more years may foster a better understanding of multidimensional morbidity faced by vulnerable older patients and families and provides a detailed understanding of recovery trajectories in this unique population to optimize outcome, goals of care directives, and ongoing informed consent to ICU treatment.

Frailty assessment in critically ill older adults: a narrative review

Frailty, a condition that was first defined 20 years ago, is now assessed via multiple different tools. The Frailty Phenotype was initially used to identify a population of "pre-frail" and "frail" older adults, so as to prevent falls, loss of mobility, and hospitalizations. A different definition of frailty, via the Clinical Frailty Scale, is now actively used in critical care situations to evaluate over 65 year-old patients, whether it be for Intensive Care Unit (ICU) admissions, limitation of life-sustaining treatments or prognostication. Confusion remains when mentioning "frailty" in older adults, as to which tools are used, and what the impact or the bias of using these tools might be. In addition, it is essential to clarify which tools are appropriate in medical emergencies. In this review, we clarify various concepts and differences between frailty, functional autonomy and comorbidities; then focus on the current use of frailty scales in critically ill older adults. Finally, we discuss the benefits and risks of using standardized scales to describe patients, and suggest ways to maintain a complex, three-dimensional, patient evaluation, despite time constraints. Frailty in the ICU is common, involving around 40% of patients over 75. The most commonly used scale is the Clinical Frailty Scale (CFS), a rapid substitute for Comprehensive Geriatric Assessment (CGA). Significant associations exist between the CFS-scale and both short and long-term mortality, as well as long-term outcomes, such as loss of functional ability and being discharged home. The CFS became a mainstream tool newly used for triage during the Covid-19 pandemic, in response to the pressure on healthcare systems. It was found to be significantly associated with in-hospital mortality. The improper use of scales may lead to hastened decision-making, especially when there are strains on healthcare resources or time-constraints. Being aware of theses biases is essential to facilitate older adults' access to equitable decision-making regarding critical care. The aim is to help counteract assessments which may be abridged by time and organisational constraints.

Critical care beyond organ support: the importance of geriatric rehabilitation

Very old critically ill patients pose a growing challenge for intensive care. Critical illness and the burden of treatment in the intensive care unit (ICU) can lead to a long-lasting decline of functional and cognitive abilities, especially in very old patients. Multi-complexity and increased vulnerability to stress in these patients may lead to new and worsening disabilities, requiring careful assessment, prevention and rehabilitation. The potential for rehabilitation, which is crucial for optimal functional outcomes, requires a systematic, multi-disciplinary approach and careful long-term planning during and following ICU care. We describe this process and provide recommendations and checklists for comprehensive and timely assessments in the context of transitioning patients from ICU to post-ICU and acute hospital care, and review the barriers to the provision of good functional outcomes.

A novel score for predicting 1-year mortality of intensive care patients

BACKGROUND

We aimed to develop a simple scoring table for predicting probability of death within 1-year after admission to an intensive care unit. We analysed data on emergency admissions from the nationwide Finnish intensive care quality registry.

METHODS

We included first admissions of adult patients with data available on 1-year vital status (dead or alive) and all five variables included in a premorbid functional status score, which is the number of activities the person can manage independently of the following five: get out of bed, move indoors, dress, climb stairs and walk 400 m. We analysed data on patient characteristics and admission-associated factors from 2012 to 2014 to find predictors of 1-year mortality and to develop a score for predicting probability of death. We tested the performance of this score in data from 2015. We assessed the 1-year functional status score of survivors with data available.

RESULTS

Out of 25,261 patients, 20,628 (81.7%) patients were able to perform all five functional activities independently prior to the intensive care unit admission. At 1-year post admission, 19,625 (77.7%) patients were alive. 1-year functional status score was known for 11,011 patients and 8970 (81.5%) patients achieved functional status score 5, managing all five activities independently. The score based on age, sex, preceding functional status, type of intensive care unit admission, severity of acute illness and the most significant diagnoses predicted 1-year mortality with an area under the receiver operating characteristic curve 0.78 (95% CI, 0.76-0.79). The calibration of our prediction model was good, with calibration intercept -0.01 (-0.07 to 0.05) and calibration slope 0.96 (0.90 to 1.02).

CONCLUSION

Our score based on data available at intensive care unit admission predicted 1-year mortality with fairly good discrimination. Most survivors achieved good functional recovery.

A scoping review of older patients' health-related quality of life, recovery and well-being after intensive care

AIMS

In the present study, we aimed to determine how Health-Related Quality of Life (HRQoL), recovery (function and capacity in daily life) and well-being are followed up and characterised in persons ≥65 years of age who were being cared for in an intensive care unit (ICU).

DESIGN

A scoping review.

METHODS

CINAHL, MEDLINE (Ovid) and PsycINFO databases were searched in October 2021. 20 studies met the inclusion criteria. The scoping review followed the principles outlined by Arksey and O'Malley, and the Preferred Reporting Items for Systematic Review and Meta-analyses (PRISMA) checklist and Joanna Briggs Institute (JBI) framework were used.

RESULTS

Results are presented under five subheadings: Study characteristics, Type of studies, Methods for follow-up, health-related quality of life, and Recovery. Time seems to be an important factor regarding HRQoL among older patients being cared for in an ICU, with most elderly survivors perceiving their HRQoL as acceptable after 1 year. Nevertheless, several studies showed patients' willingness to be readmitted to the ICU if necessary, indicating that life is worth fighting for.

PATIENT OR PUBLIC CONTRIBUTION

Due to the design of the study, this study involves no patient or public contribution.

Incidence and Risk Factors of Worsened Activities of Daily Living Status Three Months after Intensive Care Unit Discharge among Critically Ill Patients: A Prospective Cohort Study

Background: We aimed to determine risk factors associated with worsened activity of daily living (ADL) status three months after intensive care unit (ICU) discharge. Methods: In this prospective, observational study, we enrolled critically ill adult patients that were emergently admitted to an ICU. We assessed ADL status by Barthel index score prior to ICU admission and three months after ICU discharge. The primary outcome was worsened ADL status, defined as a ≥10 decrease in Barthel index score. Results: We enrolled 102 patients (median age was 72 years old, 55% were male, and 87% received mechanical ventilation during ICU stay), and 42 patients (41%) had worsened ADL status three months after discharge from ICU. Multivariate analysis revealed that older age (>70 years old; adjusted odds ratio (aOR) 3.68; 95% confidence interval (95%CI) 1.33−10.19), high burden of chronic illness (aOR 4.11; 95%CI 1.43−11.81), and longer duration of mechanical ventilation (≥4 days; aOR 2.83; 95%CI 1.04−7.69) were independent risk factors for worsened ADL status at three months. Conclusions: Almost half of the critically ill adult patients in this cohort had worsened ADL status after ICU discharge. Older age, high burden of chronic illness, and longer duration of mechanical ventilation were risk factors for worsened ADL status.

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.

Premorbid functional status as an outcome predictor in intensive care patients aged over 85 years

BACKGROUND

Poor premorbid functional status (PFS) is associated with mortality after intensive care unit (ICU) admission in patients aged 80 years or older. In the subgroup of very old ICU patients, the ability to recover from critical illness varies irrespective of age. To assess the predictive ability of PFS also among the patients aged 85 or older we set out the current study.

METHODS

In this nationwide observational registry study based on the Finnish Intensive Care Consortium database, we analysed data of patients aged 85 years or over treated in ICUs between May 2012 and December 2015. We defined PFS as good for patients who had been independent in activities of daily living (ADL) and able to climb stairs and as poor for those who were dependent on help or unable to climb stairs. To assess patients' functional outcome one year after ICU admission, we created a functional status score (FSS) based on how many out of five physical activities (getting out of bed, moving indoors, dressing, climbing stairs, and walking 400 m) the patient could manage. We also assessed the patients' ability to return to their previous type of accommodation.

RESULTS

Overall, 2037 (3.3% of all adult ICU patients) patients were 85 years old or older. The average age of the study population was 87 years. Data on PFS were available for 1446 (71.0%) patients (good for 48.8% and poor for 51.2%). The one-year mortalities of patients with good and those with poor PFS were 29.2% and 50.1%, respectively, p < 0.001. Poor PFS increased the probability of death within 12 months, adjusted odds ratio (OR), 2.15; 95% confidence interval (CI) 1.68-2.76, p < 0.001. For 69.5% of survivors, the FSS one year after ICU admission was unchanged or higher than their premorbid FSS and 84.2% of patients living at home before ICU admission still lived at home.

CONCLUSIONS

Poor PFS doubled the odds of death within one year. For most survivors, functional status was comparable to the premorbid status.

Development of a practically usable prediction model for quality of life of ICU survivors: A sub-analysis of the MONITOR-IC prospective cohort study

PURPOSE

As the goal of ICU treatment is survival in good health, we aimed to develop a prediction model for ICU survivors' change in quality of life (QoL) one year after ICU admission.

MATERIALS & METHODS

This is a sub-study of the prospective cohort MONITOR-IC study. Adults admitted ≥12 h to the ICU of a university hospital between July 2016-January 2019 were included. Moribund patients were excluded. Change in QoL one year after ICU admission was quantified using the EuroQol five-dimensional (EQ-5D-5L) questionnaire, and Short-Form 36 (SF-36). Multivariable linear regression analysis and best subsets regression analysis (SRA) were used. Models were internally validated by bootstrapping.

RESULTS

The PREdicting PAtients' long-term outcome for Recovery (PREPARE) model was developed (n = 1308 ICU survivors). The EQ-5D-models had better predictive performance than the SF-36-models. Explained variance (adjusted R²) of the best model (33 predictors) was 58.0%. SRA reduced the number of predictors to 5 (adjusted R² = 55.3%, SE = 0.3), including QoL, diagnosis of a Cardiovascular Incident and frailty before admission, sex, and ICU-admission following planned surgery.

CONCLUSIONS

Though more long-term data are needed to ascertain model accuracy, in future, the PREPARE model may be used to better inform and prepare patients and their families for ICU recovery.

Clinical characteristics and outcomes of mechanically ventilated elderly patients in intensive care units: a Chinese multicentre retrospective study

Background

In recent years, the number of elderly patients receiving mechanical ventilation (MV) in intensive care units (ICUs) has increased. However, the evidence on the outcomes of elderly mechanically ventilated patients is scant in China. Our objective was to evaluate the characteristics and outcomes in elderly patients (≥65 years) receiving MV in the ICU.

Methods

We performed a multicentre retrospective study involving adult patients who were admitted to the ICU and received at least 24 hours of MV. Patients were divided into three age groups: under 65, 65-79, and ≥80 years. The primary outcome was hospital mortality. We performed univariate and multivariate logistic regression analysis to identify factors associated with hospital mortality.

Results

A total of 853 patients were analysed. Of those, 61.5% were ≥65 years of age, and 26.0% were ≥80 years of age. There were significant differences in the principal reason for MV among the three age groups (P<0.001). Advanced age was significantly associated with total duration of MV, ICU length of stay (LOS), and ICU costs (all P<0.001), but not with hospital LOS and hospital costs (P>0.05). In addition, mortality rates in the ICU, hospital, and at 60 days significantly increased with age (all P<0.001). In the age group of 80 years and older, the mortality rates were 47.7%, 49.5%, and 50.0%, respectively. Multivariate logistic regression analysis had found that age, Acute Physiology and Chronic Health Evaluation (APACHE) II score, partial pressure of oxygen in arterial blood/fraction of inspired oxygen (PaO₂/FiO₂) ratio, total duration of MV, ICU LOS, and the decision to withhold/withdraw life-sustaining treatments were independent influence factors for mortality rates.

Conclusions

Mechanically ventilated elderly patients (≥65 years) have a higher ICU and hospital mortality, but the hospital LOS and hospital costs are similar to younger patients. Advanced age should be considered as a significant independent risk factor for hospital mortality of mechanically ventilated ICU patients.

Long-term outcomes after critical illness: recent insights

Intensive care survivors often experience post-intensive care sequelae, which are frequently gathered together under the term “post-intensive care syndrome” (PICS). The consequences of PICS on quality of life, health-related costs and hospital readmissions are real public health problems. In the present Viewpoint, we summarize current knowledge and gaps in our understanding of PICS and approaches to management.

Frailty status among older critically ill patients with severe acute kidney injury

BACKGROUND

Frailty status among critically ill patients with acute kidney injury (AKI) is not well described despite its importance for prognostication and informed decision-making on life-sustaining therapies. In this study, we aim to describe the epidemiology of frailty in a cohort of older critically ill patients with severe AKI, the outcomes of patients with pre-existing frailty before AKI and the factors associated with a worsening frailty status among survivors.

METHODS

This was a secondary analysis of a prospective multicentre observational study that enrolled older (age > 65 years) critically ill patients with AKI. The clinical frailty scale (CFS) score was captured at baseline, at 6 months and at 12 months among survivors. Frailty was defined as a CFS score of ≥ 5. Demographic, clinical and physiological variables associated with frailty as baseline were described. Multivariable Cox proportional hazard models were constructed to describe the association between frailty and 90-day mortality. Demographic and clinical factors associated with worsening frailty status at 6 months and 12 months were described using multivariable logistic regression analysis and multistate models.

RESULTS

Among the 462 patients in our cohort, median (IQR) baseline CFS score was 4 (3-5), with 141 (31%) patients considered frail. Pre-existing frailty was associated with greater hazard of 90-day mortality (59% (n = 83) for frail vs. 31% (n = 100) for non-frail; adjusted hazards ratio [HR] 1.49; 95% CI 1.11-2.01, p = 0.008). At 6 months, 68 patients (28% of survivors) were frail. Of these, 57% (n = 39) were not classified as frail at baseline. Between 6 and 12 months of follow-up, 9 (4% of survivors) patients transitioned from a frail to a not frail status while 10 (4% of survivors) patients became frail and 11 (5% of survivors) patients died. In multivariable analysis, age was independently associated with worsening CFS score from baseline to 6 months (adjusted odds ratio [OR] 1.08; 95% CI 1.03-1.13, p = 0.003).

CONCLUSIONS

Pre-existing frailty is an independent risk factor for mortality among older critically ill patients with severe AKI. A substantial proportion of survivors experience declining function and worsened frailty status within one year.

Long term outcomes for elderly patients after emergency intensive care admission: A cohort study

Background

Elderly patients (≥ 80 years of age) surviving an episode of critical illness suffer long-term morbidity and risk of mortality. Identifying high risk groups could assist in informing discussions with patients and families.

Aim

To determine factors associated with long-term survival following ICU admission.

Design

A cohort study of patients aged ≥ 80 years of age admitted to the ICU as an emergency.

Methods

Patients admitted from January 2010 to December 2018 were included in the study. Primary outcome was five year survival. Mortality was assessed using a multivariable flexible parametric survival analysis adjusted for demographics, and clinically relevant covariates.

Results

There were 828 patients. Mean age was 84 years (SD 3.2) and 419 (51%) were male. Patients were categorised into medical (423 (51%)) and surgical (405 (49%)) admissions. Adjusted hazard ratios (aHR) for mortality were highest for serum lactate (>8 mmol/l aHR 2.56 (C.I. 1.79–3.67)), lowest systolic blood pressure (< 70 mmHg aHR 2.04 (C.I. 1.36–3.05)) and pH (< 7.05 aHR 4.70 (C.I 2.67–8.21)). There were no survivors beyond one year with severe abnormalities of pH and lactate (< 7.05 and > 8 mmol/l respectively). Relative survival for medical patients was below that expected for the general population for the duration of the study.

Conclusion

Overall five-year survival was 27%. For medical and surgical patients it was 19% and 35% respectively. Survival at 30 days and one year was 61% and 46%. The presence of features of circulatory shock predicted poor short and long term survival.

The wave of very old people in the intensive care unit-A challenge in decision-making

In this paper the authors express the opinion that there is much to be learned about the 80+ year old age group as it relates to critical care and end-of-life matters. We need to learn how to better predict outcome, we need to learn our limitations and deal with uncertainties, we need to better communicate with our elderly patients and their caregivers and we need to engage with our colleagues in Geriatrics. There is a wave of very old people arriving in the intensive care unit and we have much to do to prepare for it and for the ethical, fair and appropriate care of these critically ill, but elderly, patients.

The performance of acute versus antecedent patient characteristics for 1-year mortality prediction during intensive care unit admission: a national cohort study

Background

Multiple factors contribute to mortality after ICU, but it is unclear how the predictive value of these factors changes during ICU admission. We aimed to compare the changing performance over time of the acute illness component, antecedent patient characteristics, and ICU length of stay (LOS) in predicting 1-year mortality.

Methods

In this retrospective observational cohort study, the discriminative value of four generalized mixed-effects models was compared for 1-year and hospital mortality. Among patients with increasing ICU LOS, the models included (a) acute illness factors and antecedent patient characteristics combined, (b) acute component only, (c) antecedent patient characteristics only, and (d) ICU LOS. For each analysis, discrimination was measured by area under the receiver operating characteristics curve (AUC), calculated using the bootstrap method. Statistical significance between the models was assessed using the DeLong method (p value < 0.05).

Results

In 400,248 ICU patients observed, hospital mortality was 11.8% and 1-year mortality 21.8%. At ICU admission, the combined model predicted 1-year mortality with an AUC of 0.84 (95% CI 0.84–0.84). When analyzed separately, the acute component progressively lost predictive power. From an ICU admission of at least 3 days, antecedent characteristics significantly exceeded the predictive value of the acute component for 1-year mortality, AUC 0.68 (95% CI 0.68–0.69) versus 0.67 (95% CI 0.67–0.68) (p value < 0.001). For hospital mortality, antecedent characteristics outperformed the acute component from a LOS of at least 7 days, comprising 7.8% of patients and accounting for 52.4% of all bed days. ICU LOS predicted 1-year mortality with an AUC of 0.52 (95% CI 0.51–0.53) and hospital mortality with an AUC of 0.54 (95% CI 0.53–0.55) for patients with a LOS of at least 7 days.

Conclusions

Comparing the predictive value of factors influencing 1-year mortality for patients with increasing ICU LOS, antecedent patient characteristics are more predictive than the acute component for patients with an ICU LOS of at least 3 days. For hospital mortality, antecedent patient characteristics outperform the acute component for patients with an ICU LOS of at least 7 days. After the first week of ICU admission, LOS itself is not predictive of hospital nor 1-year mortality.

External Validation of Two Models to Predict Delirium in Critically Ill Adults Using Either the Confusion Assessment Method-ICU or the Intensive Care Delirium Screening Checklist for Delirium Assessment

OBJECTIVES

To externally validate two delirium prediction models (early prediction model for ICU delirium and recalibrated prediction model for ICU delirium) using either the Confusion Assessment Method-ICU or the Intensive Care Delirium Screening Checklist for delirium assessment.

DESIGN

Prospective, multinational cohort study.

SETTING

Eleven ICUs from seven countries in three continents.

PATIENTS

Consecutive, delirium-free adults admitted to the ICU for greater than or equal to 6 hours in whom delirium could be reliably assessed.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

The predictors included in each model were collected at the time of ICU admission (early prediction model for ICU delirium) or within 24 hours of ICU admission (recalibrated prediction model for ICU delirium). Delirium was assessed using the Confusion Assessment Method-ICU or the Intensive Care Delirium Screening Checklist. Discrimination was determined using the area under the receiver operating characteristic curve. The predictive performance was determined for the Confusion Assessment Method-ICU and Intensive Care Delirium Screening Checklist cohort, and compared with both prediction models' original reported performance. A total of 1,286 Confusion Assessment Method-ICU-assessed patients and 892 Intensive Care Delirium Screening Checklist-assessed patients were included. Compared with the area under the receiver operating characteristic curve of 0.75 (95% CI, 0.71-0.79) in the original study, the area under the receiver operating characteristic curve of the early prediction model for ICU delirium was 0.67 (95% CI, 0.64-0.71) for delirium as assessed using the Confusion Assessment Method-ICU and 0.70 (95% CI, 0.66-0.74) using the Intensive Care Delirium Screening Checklist. Compared with the original area under the receiver operating characteristic curve of 0.77 (95% CI, 0.74-0.79), the area under the receiver operating characteristic curve of the recalibrated prediction model for ICU delirium was 0.75 (95% CI, 0.72-0.78) for assessing delirium using the Confusion Assessment Method-ICU and 0.71 (95% CI, 0.67-0.75) using the Intensive Care Delirium Screening Checklist.

CONCLUSIONS

Both the early prediction model for ICU delirium and recalibrated prediction model for ICU delirium are externally validated using either the Confusion Assessment Method-ICU or the Intensive Care Delirium Screening Checklist for delirium assessment. Per delirium prediction model, both assessment tools showed a similar moderate-to-good statistical performance. These results support the use of either the early prediction model for ICU delirium or recalibrated prediction model for ICU delirium in ICUs around the world regardless of whether delirium is evaluated with the Confusion Assessment Method-ICU or Intensive Care Delirium Screening Checklist.

Demographic Shifts, Case Mix, Activity, and Outcome for Elderly Patients Admitted to Adult General ICUs in England, Wales, and Northern Ireland

OBJECTIVES

Major increases in the proportion of elderly people in the population are predicted worldwide. These population increases, along with improving therapeutic options and more aggressive treatment of elderly patients, will have major impact on the future need for healthcare resources, including critical care. Our objectives were to explore the trends in admissions, resource use, and risk-adjusted hospital mortality for older patients, admitted over a 20-year period between 1997 and 2016 to adult general ICUs in England, Wales, and Northern Ireland.

DESIGN

RETROSPECTIVE ANALYSIS OF NATIONAL CLINICAL AUDIT DATABASE.

SETTING

The Intensive Care National Audit & Research Centre Case Mix Programme Database, the national clinical audit for adult general ICUs in England, Wales, and Northern Ireland.

PATIENTS

All adult patients 16 years old or older admitted to adult general ICUs contributing data to the Case Mix Programme Database between January 1, 1997, and December 31, 2016.

MEASUREMENTS AND MAIN RESULTS

The annual number, trends, and outcomes for patients across four age bands (16-64, 65-74, 75-84, and 85+ yr) admitted to ICUs contributing to the Case Mix Programme Database from 1997 to 2016 were examined. Case mix, activity, and outcome were described in detail for the most recent cohort of patients admitted in 2015-2016. Between 1997 to 2016, the annual number of admissions to ICU of patients in the older age bands increased disproportionately, with increases that could not be explained solely by general U.K. demographic shifts. The risk-adjusted acute hospital mortality decreased significantly within each age band over the 20-year period of the study. Although acute severity at ICU admission was comparable with that of the younger age group, apart from cardiovascular and renal dysfunction, older patients received less organ support. Older patients stayed longer in hospital post-ICU discharge, and hospital mortality increased with age, but the majority of patients surviving to hospital discharge returned home.

CONCLUSIONS

Over the past two decades, elderly patients have been more commonly admitted to ICU than can be explained solely by the demographic shift. Importantly, as with the wider population, outcomes in elderly patients admitted to ICU are improving over time, with most patients returning home.

Risk factors of frailty and death or only frailty after intensive care in non-frail elderly patients: a prospective non-interventional study

Background

Frailty status is recognized as an important parameter in critically ill elderly patients, but nothing is known about outcomes in non-frail patients regarding the development of frailty or frailty and death after intensive care. The aim of this study was to determine risk factors for frailty and death or only frailty 6 months after intensive care unit (ICU) admission in non-frail patients ≥ 65 years.

Methods

A prospective non-interventional study performed in an academic ICU from February 2015 to February 2016 included non-frail ≥ 65-year-old patients hospitalized for > 24 h in the ICU. Frailty was assessed by calculating the frailty index (FI) at admission and 6 months later. Patients who remained non-frail (FI < 0.2) were compared to patients who presented frailty (FI ≥ 0.2) and those who presented frailty and death at 6 months.

Results

Among 974 admissions, 136 patients were eligible for the study and 88 patients were analysed at 6 months (non-frail n = 34, frail n = 29, death n = 25). Multivariable analysis showed that mechanical ventilation duration was an independent risk factor for frailty/death at 6 months (per day of mechanical ventilation, odds ratio [OR] = 1.11; 95% confidence interval [CI] 1.04–1.19, p = 0.002). When excluding patients who died, mechanical ventilation duration remained the sole risk factor for frailty at 6 months (OR = 1.19; 95% CI 1.07–1.33, p = 0.001).

Conclusion

Mechanical ventilation duration was the sole predictive factor of frailty and death or only frailty 6 months after ICU hospitalization in initially non-frail patients.

Baseline and clinical characteristics of older adults admitted to the intensive care unit through the emergency room: Analysis based on age groups

Background and Objectives:

There is currently no consensus on the criteria for admitting older adults to the intensive care unit.

Methods:

This single-center retrospective study evaluated the baseline and clinical characteristics of older adults admitted to the intensive care unit between January 2017 and June 2017; patients were analyzed according to their age group. Factors associated with in-hospital mortality were specifically determined using logistic regression analysis.

Results:

Among 582 patients included in the present study, 34.2%, 46.6%, and 19.2% were aged 65–74, 75–84, and over 84 years, respectively. In terms of clinical outcomes, although there were no significant differences in the length of intensive care unit and hospital stay and intensive care unit mortality, significant differences were observed in terms of in-hospital mortality, hospital discharge disposition, and neurologic outcomes at discharge ( p = 0.039, p = 0.005, and p = 0.032, respectively). Predictive factors for in-hospital mortality were age (⩾85 years), initial mental status (stupor to coma), a Korean Triage and Acuity Scale level of 1, underlying diagnosis of cancer, abdominal pain or discomfort, apnea, and a chief compliant of dyspnea.

Conclusion:

Compared to those aged 65–84 years, in-hospital mortality was 1.96-fold higher in those aged over 84 years. However, the overall mortality in our cohort was not considerably different from that of the younger population. Intensive care unit admission should be considered in selected older adults after evaluating the risk factors for mortality.

Cumulative Prognostic Score Predicting Mortality in Patients Older Than 80 Years Admitted to the ICU

OBJECTIVES

To develop a scoring system model that predicts mortality within 30 days of admission of patients older than 80 years admitted to intensive care units (ICUs).

DESIGN

Prospective cohort study.

SETTING

A total of 306 ICUs from 24 European countries.

PARTICIPANTS

Older adults admitted to European ICUs (N = 3730; median age = 84 years [interquartile range = 81‐87 y]; 51.8% male).

MEASUREMENTS

Overall, 24 variables available during ICU admission were included as potential predictive variables. Multivariable logistic regression was used to identify independent predictors of 30‐day mortality. Model sensitivity, specificity, and accuracy were evaluated with receiver operating characteristic curves.

RESULTS

The 30‐day‐mortality was 1562 (41.9%). In multivariable analysis, these variables were selected as independent predictors of mortality: age, sex, ICU admission diagnosis, Clinical Frailty Scale, Sequential Organ Failure Score, invasive mechanical ventilation, and renal replacement therapy. The discrimination, accuracy, and calibration of the model were good: the area under the curve for a score of 10 or higher was .80, and the Brier score was .18. At a cut point of 10 or higher (75% of all patients), the model predicts 30‐day mortality in 91.1% of all patients who die.

CONCLUSION

A predictive model of cumulative events predicts 30‐day mortality in patients older than 80 years admitted to ICUs. Future studies should include other potential predictor variables including functional status, presence of advance care plans, and assessment of each patient's decision‐making capacity.

Premorbid functional status as a predictor of 1-year mortality and functional status in intensive care patients aged 80 years or older

PURPOSE

We assessed the association between the premorbid functional status (PFS) and 1-year mortality and functional status of very old intensive care patients.

METHODS

Using a nationwide quality registry, we retrieved data on patients treated in Finnish intensive care units (ICUs) during the period May 2012‒April 2013. Of 16,389 patients, 1827 (11.1%) were very old (aged 80 years or older). We defined a person with good functional status as someone independent in activities of daily living (ADL) and able to climb stairs without assistance; a person with poor functional status was defined as needing assistance for ADL or being unable to climb stairs. We adjusted for severity of illness and calculated the impact of PFS.

RESULTS

Overall, hospital mortality was 21.3% and 1-year mortality was 38.2%. For emergency patients (73.5% of all), hospital mortality was 28% and 1-year mortality was 48%. The functional status at 1 year was comparable to the PFS in 78% of the survivors. PFS was poor for 43.3% of the patients. A poor PFS predicted an increased risk of in-hospital death, adjusted odds ratio (OR) 1.50 (95% confidence interval, 1.07-2.10), and of 1-year mortality, OR 2.18 (1.67-2.85). PFS data significantly improved the prediction of 1-year mortality.

CONCLUSIONS

Of very old ICU patients, 62% were alive 1 year after ICU admission and 78% of the survivors had a functional status comparable to the premorbid situation. A poor PFS doubled the odds of death within a year. Knowledge of PFS improved the prediction of 1-year mortality.

Enhancing Recovery From Sepsis: A Review

Importance

Survival from sepsis has improved in recent years, resulting in an increasing number of patients who have survived sepsis treatment. Current sepsis guidelines do not provide guidance on posthospital care or recovery.

Observations

Each year, more than 19 million individuals develop sepsis, defined as a life-threatening acute organ dysfunction secondary to infection. Approximately 14 million survive to hospital discharge and their prognosis varies. Half of patients recover, one-third die during the following year, and one-sixth have severe persistent impairments. Impairments include development of an average of 1 to 2 new functional limitations (eg, inability to bathe or dress independently), a 3-fold increase in prevalence of moderate to severe cognitive impairment (from 6.1% before hospitalization to 16.7% after hospitalization), and a high prevalence of mental health problems, including anxiety (32% of patients who survive), depression (29%), or posttraumatic stress disorder (44%). About 40% of patients are rehospitalized within 90 days of discharge, often for conditions that are potentially treatable in the outpatient setting, such as infection (11.9%) and exacerbation of heart failure (5.5%). Compared with patients hospitalized for other diagnoses, those who survive sepsis (11.9%) are at increased risk of recurrent infection than matched patients (8.0%) matched patients (P < .001), acute renal failure (3.3% vs 1.2%, P < .001), and new cardiovascular events (adjusted hazard ratio [HR] range, 1.1-1.4). Reasons for deterioration of health after sepsis are multifactorial and include accelerated progression of preexisting chronic conditions, residual organ damage, and impaired immune function. Characteristics associated with complications after hospital discharge for sepsis treatment are not fully understood but include both poorer presepsis health status, characteristics of the acute septic episode (eg, severity of infection, host response to infection), and quality of hospital treatment (eg, timeliness of initial sepsis care, avoidance of treatment-related harms). Although there is a paucity of clinical trial evidence to support specific postdischarge rehabilitation treatment, experts recommend referral to physical therapy to improve exercise capacity, strength, and independent completion of activities of daily living. This recommendation is supported by an observational study involving 30 000 sepsis survivors that found that referral to rehabilitation within 90 days was associated with lower risk of 10-year mortality compared with propensity-matched controls (adjusted HR, 0.94; 95% CI, 0.92-0.97, P < .001).

Conclusions and Relevance

In the months after hospital discharge for sepsis, management should focus on (1) identifying new physical, mental, and cognitive problems and referring for appropriate treatment, (2) reviewing and adjusting long-term medications, and (3) evaluating for treatable conditions that commonly result in hospitalization, such as infection, heart failure, renal failure, and aspiration. For patients with poor or declining health prior to sepsis who experience further deterioration after sepsis, it may be appropriate to focus on palliation of symptoms.

Improving Long-Term Outcomes After Sepsis

Although acute survival from sepsis has improved in recent years, a large fraction of sepsis survivors experience poor long-term outcomes. In particular, sepsis survivors have high rates of weakness, cognitive impairment, hospital readmission, and late death. To improve long-term outcomes, in-hospital care should focus on early, effective treatment of sepsis; minimization of delirium, distress, and immobility; and preparing patients for hospital discharge. In the posthospital setting, medical care should focus on addressing new disability and preventing medical deterioration, providing a sustained period out of the hospital to allow for recovery.

Development of a prediction model for long-term quality of life in critically ill patients

PURPOSE

We developed a prediction model for quality of life (QOL) 1 year after intensive care unit (ICU) discharge based upon data available at the first ICU day to improve decision-making.

METHODS

The database of a 1-year prospective study concerning long-term outcome and QOL (assessed by EuroQol-5D) in critically ill adult patients consecutively admitted to the ICU of a university hospital was used. Cases with missing data were excluded. Utility indices at baseline (UIb) and at 1 year (UI1y) were surrogates for QOL. For 1-year non-survivors UI1y was set at zero. The grouped lasso technique selected the most important variables in the prediction model. R² and adjusted R² were calculated.

RESULTS

1831 of 1953 cases (93.8%) were complete. UI1y depended significantly on: UIb (P<0.001); solid tumor (P<0.001); age (P<0.001); activity of daily living (P<0.001); imaging (P<0.001); APACHE II-score (P=0.001); ≥80 years (P=0.001); mechanical ventilation (P=0.006); hematological patient (P=0.007); SOFA-score (P=0.008); tracheotomy (P=0.018); admission diagnosis surgical P<0.001 (versus medical); and comorbidity (P=0.049). Only baseline health status and surgical patients were positively associated with UI1y. R² was 0.3875 and adjusted R² 0.3807.

CONCLUSION

Although only 40% of variability in long-term QOL could be explained, this prediction model can be helpful in decision-making.

Are we ever too old?: Characteristics and outcome of octogenarians admitted to a medical intensive care unit

The aging population increases the demand of intensive care unit (ICU) treatments. However, the availability of ICU beds is limited. Thus, ICU admission of octogenarians is considered controversial. The population above 80 years is a very heterogeneous group though, and age alone might not be the best predictor. Aim of this study was to analyze resource consumption and outcome of octogenarians admitted to a medical ICU to identify reliable survival predictors in a senescent society.This retrospective observational study analyzes 930 octogenarians and 5732 younger patients admitted to a medical ICU. Admission diagnosis, APACHE II and SAPS II scores, use of ICU resources, and mortality were recorded. Long-term mortality was analyzed using Kaplan-Meier survival curves and multivariate cox regression analysis.Patients ≥80 years old had higher SAPS II (43 vs 38, P < .001) and APACHE II (23 vs 21, P = .001) scores. Consumption of ICU resources by octogenarians was lower in terms of length of stay, mechanical ventilation, and renal replacement therapy. Among octogenarians, ICU survivors got less mechanical ventilation or renal replacement therapy than nonsurvivors. Intra-ICU mortality in the very old was higher (19% vs 12%, P < .001) and long-term survival was lower (HR 1.76, P < .001). Multivariate cox regression analysis of octogenarians revealed that admission diagnosis of myocardial infarction (HR 1.713, P = .023), age (1.08, P = .002), and SAPS II score (HR 1.02, 95%, P = .01) were independent risk factors, whereas admission diagnoses monitoring post coronary intervention (HR .253, P = .002) and cardiac arrhythmia (HR .534, P = .032) had a substantially reduced mortality risk.Octogenarians show a higher intra-ICU and long-term mortality than younger patients. Still, they show a considerable life expectancy after ICU admission even though they get less invasive care than younger patients. Furthermore, some admission diagnoses like myocardial infarction, cardiac arrhythmia and monitoring post cardiac intervention are much stronger predictors for long-term survival than age or SAPS II score in the very old.

Cost analysis of the very elderly admitted to intensive care units

Background

Very elderly patients are often admitted to intensive care units (ICUs) despite poor outcomes and frequent preference to avoid unnecessary prolongation of life. We sought to determine the cost of ICU admission for the very elderly and the factors influencing this cost.

Methods

This prospective, observational cohort study included patients ≥80 years old admitted to 22 Canadian ICUs from 2009 to 2013. A subset of consenting individuals comprised a longitudinal cohort followed over 12 months. Costs were calculated from ICU length of stay and unit costs for ICU admission from a Canadian academic hospital. A generalized linear model was employed to identify cost-predictive variables.

Results

In total, 1671 patients were included; 610 were enrolled in the longitudinal cohort. The average age was 85 years; median ICU length of stay was 4 days. Mortality was 35% (585/1671) in hospital and 41% (253/610) at 12 months. The average cost of ICU admission per patient was $31,679 ± 65,867. Estimated ICU costs were $48,744 per survivor to discharge and $61,783 per survivor at 1 year. For both decedents and survivors, preference for comfort measures over life support was an independent predictor for lower cost (P < 0.01).

Conclusions

Considering the poor clinical outcomes, and that many ICU admissions may be undesired by very elderly patients, ICU costs in this population are substantial. Our finding that a preference for comfort care predicted a lower cost independent of mortality reinforces the importance of early goals of care discussions to avoid both undesired and potentially non-beneficial interventions, consequently reducing costs.

Trial registration

ClinicalTrials.gov, NCT01293708. Registered on 10 February 2011.

Electronic supplementary material

The online version of this article (doi:10.1186/s13054-017-1689-y) contains supplementary material, which is available to authorized users.

A path to precision in the ICU

Precision medicine is increasingly touted as a groundbreaking new paradigm in biomedicine. In the ICU, the complexity and ambiguity of critical illness syndromes have been identified as fundamental justifications for the adoption of a precision approach to research and practice. Inherently protean diseases states such as sepsis and acute respiratory distress syndrome have manifestations that are physiologically and anatomically diffuse, and that fluctuate over short periods of time. This leads to considerable heterogeneity among patients, and conditions in which a “one size fits all” approach to therapy can lead to widely divergent results. Current ICU therapy can thus be seen as imprecise, with the potential to realize substantial gains from the adoption of precision medicine approaches. A number of challenges still face the development and adoption of precision critical care, a transition that may occur incrementally rather than wholesale. This article describes a few concrete approaches to addressing these challenges.

First, novel clinical trial designs, including registry randomized controlled trials and platform trials, suggest ways in which conventional trials can be adapted to better accommodate the physiologic heterogeneity of critical illness. Second, beyond the “omics” technologies already synonymous with precision medicine, the data-rich environment of the ICU can generate complex physiologic signatures that could fuel precision-minded research and practice. Third, the role of computing infrastructure and modern informatics methods will be central to the pursuit of precision medicine in the ICU, necessitating close collaboration with data scientists. As work toward precision critical care continues, small proof-of-concept studies may prove useful in highlighting the potential of this approach.

Will We Ever Agree on Protein Requirements in the Intensive Care Unit?

The precise value of the normal adult protein requirement has long been debated. For many reasons-one of them being the difficulty of carrying out long-term nutrition experiments in free-living people-uncertainty is likely to persist indefinitely. By contrast, the controlled environment of the intensive care unit and relatively short trajectory of many critical illnesses make it feasible to use hard clinical outcome trials to determine protein requirements for critically ill patients in well-defined clinical situations. This article suggests how the physiological principles that underlie our understanding of normal protein requirements can be incorporated into the design of such clinical trials. The main focus is on 3 principles: (1) the rate of body nitrogen loss roughly predicts an individual's minimum protein requirement and is thus essential to measure to identify individual patients and clinical situations in which the minimum protein requirement is importantly increased, (2) existing muscle mass sets an upper limit on the rate at which amino acids can be mobilized from muscle for transfer to central proteins and sites of injury and is thus important to monitor to identify patients who are at greatest risk of protein deficiency-related adverse outcomes, and (3) negative energy balance increases the dietary protein requirement, so calorie-deprived patients-whether obese or not-should be enrolled in hard clinical outcome trials that compare the current practice of "permissive underfeeding" (underprovision of all nutrients, including protein) with hypocaloric nutrition supplemented by a suitably generous amount of protein.

Nutrient stimulation of mesenteric blood flow - implications for older critically ill patients

Nutrient ingestion induces a substantial increase in mesenteric blood flow. In older persons (aged ≥ 65 years), particularly those with chronic medical conditions, the cardiovascular compensatory response may be inadequate to maintain systemic blood pressure during mesenteric blood pooling, leading to postprandial hypotension. In older ambulatory persons, postprandial hypotension is an important pathophysiological condition associated with an increased propensity for syncope, falls, coronary vascular events, stroke and death. In older critically ill patients, the administration of enteral nutrition acutely increases mesenteric blood flow, but whether this pathophysiological response is protective, or precipitates mesenteric ischaemia, is unknown. There are an increasing number of older patients surviving admission to intensive care units, who are likely to be at increased risk of postprandial hypotension, both during, and after, their stay in hospital. In this review, we describe the prevalence, impact and mechanisms of postprandial hypotension in older people and provide an overview of the impact of postprandial hypotension on feeding prescriptions in older critically ill patients. Finally, we provide evidence that postprandial hypotension is likely to be an unrecognised problem in older survivors of critical illness and discuss potential options for management.