On predictions in critical care: The individual prognostication fallacy in elderly patients

A Comprehensive Review of the Role of Biomarkers in the Early Detection of Endocrine Disorders in Critical Illnesses

Endocrine disorders pose significant challenges in the management of critically ill patients, contributing to morbidity and mortality in intensive care settings. Timely detection of these disorders is essential to optimizing patient outcomes. Biomarkers, as measurable indicators of biological processes or disease states, play a crucial role in the early identification and monitoring of endocrine dysfunction. This comprehensive review examines the role of biomarkers in the early detection of endocrine disorders in critical illnesses. We provide an overview of common endocrine disorders encountered in the intensive care unit (ICU) and discuss the impact of endocrine dysregulation on patient outcomes. Additionally, we classify biomarkers and explore their significance in diagnosing and monitoring endocrine disorders, including thyroid dysfunction, adrenal insufficiency, and hypopituitarism. Furthermore, we discuss the clinical applications of biomarkers, including their utility in guiding therapeutic interventions, monitoring disease progression, and predicting outcomes in critical illnesses. Emerging trends and future directions in biomarker research are also highlighted, emphasizing the need for continued investigation into novel biomarkers and technological advancements. Finally, we underscore the potential of biomarkers to revolutionize the early detection and management of endocrine disorders in critical illnesses, ultimately improving patient care and outcomes in the ICU.

Unsupervised machine learning analysis to identify patterns of ICU medication use for fluid overload prediction

INTRODUCTION

Intravenous (IV) medications are a fundamental cause of fluid overload (FO) in the intensive care unit (ICU); however, the association between IV medication use (including volume), administration timing, and FO occurrence remains unclear.

METHODS

This retrospective cohort study included consecutive adults admitted to an ICU ≥72 hours with available fluid balance data. FO was defined as a positive fluid balance ≥7% of admission body weight within 72 hours of ICU admission. After reviewing medication administration record (MAR) data in three-hour periods, IV medication exposure was categorized into clusters using principal component analysis (PCA) and Restricted Boltzmann Machine (RBM). Medication regimens of patients with and without FO were compared within clusters to assess for temporal clusters associated with FO using the Wilcoxon rank sum test. Exploratory analyses of the medication cluster most associated with FO for medications frequently appearing and used in the first 24 hours was conducted.

RESULTS

FO occurred in 127/927 (13.7%) of the patients enrolled. Patients received a median (IQR) of 31 (13-65) discrete IV medication administrations over the 72-hour period. Across all 47,803 IV medication administrations, ten unique IV medication clusters were identified with 121-130 medications in each cluster. Among the ten clusters, cluster 7 had the greatest association with FO; the mean number of cluster 7 medications received was significantly greater in patients in the FO cohort compared to patients who did not experience FO (25.6 vs.10.9. p<0.0001). 51 of the 127 medications in cluster 7 (40.2%) appeared in > 5 separate 3-hour periods during the 72-hour study window. The most common cluster 7 medications included continuous infusions, antibiotics, and sedatives/analgesics. Addition of cluster 7 medications to a prediction model with APACHE II score and receipt of diuretics improved the ability for the model to predict fluid overload (AUROC 5.65, p =0.0004).

CONCLUSIONS

Using ML approaches, a unique IV medication cluster was strongly associated with FO. Incorporation of this cluster improved the ability to predict development of fluid overload in ICU patients compared with traditional prediction models. This method may be further developed into real-time clinical applications to improve early detection of adverse 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.

Machine learning vs. traditional regression analysis for fluid overload prediction in the ICU

Fluid overload, while common in the ICU and associated with serious sequelae, is hard to predict and may be influenced by ICU medication use. Machine learning (ML) approaches may offer advantages over traditional regression techniques to predict it. We compared the ability of traditional regression techniques and different ML-based modeling approaches to identify clinically meaningful fluid overload predictors. This was a retrospective, observational cohort study of adult patients admitted to an ICU ≥ 72 h between 10/1/2015 and 10/31/2020 with available fluid balance data. Models to predict fluid overload (a positive fluid balance ≥ 10% of the admission body weight) in the 48-72 h after ICU admission were created. Potential patient and medication fluid overload predictor variables (n = 28) were collected at either baseline or 24 h after ICU admission. The optimal traditional logistic regression model was created using backward selection. Supervised, classification-based ML models were trained and optimized, including a meta-modeling approach. Area under the receiver operating characteristic (AUROC), positive predictive value (PPV), and negative predictive value (NPV) were compared between the traditional and ML fluid prediction models. A total of 49 of the 391 (12.5%) patients developed fluid overload. Among the ML models, the XGBoost model had the highest performance (AUROC 0.78, PPV 0.27, NPV 0.94) for fluid overload prediction. The XGBoost model performed similarly to the final traditional logistic regression model (AUROC 0.70; PPV 0.20, NPV 0.94). Feature importance analysis revealed severity of illness scores and medication-related data were the most important predictors of fluid overload. In the context of our study, ML and traditional models appear to perform similarly to predict fluid overload in the ICU. Baseline severity of illness and ICU medication regimen complexity are important predictors of fluid overload.

Limiting life-sustaining treatment for very old ICU patients: cultural challenges and diverse practices

BACKGROUND

Decisions about life-sustaining therapy (LST) in the intensive care unit (ICU) depend on predictions of survival as well as the expected functional capacity and self-perceived quality of life after discharge, especially in very old patients. However, prognostication for individual patients in this cohort is hampered by substantial uncertainty which can lead to a large variability of opinions and, eventually, decisions about LST. Moreover, decision-making processes are often embedded in a framework of ethical and legal recommendations which may vary between countries resulting in divergent management strategies.

METHODS

Based on a vignette scenario of a multi-morbid 87-year-old patient, this article illustrates the spectrum of opinions about LST among intensivsts with a special interest in very old patients, from ten countries/regions, representing diverse cultures and healthcare systems.

RESULTS

This survey of expert opinions and national recommendations demonstrates shared principles in the management of very old ICU patients. Some guidelines also acknowledge cultural differences between population groups. Although consensus with families should be sought, shared decision-making is not formally required or practised in all countries.

CONCLUSIONS

This article shows similarities and differences in the decision-making for LST in very old ICU patients and recommends strategies to deal with prognostic uncertainty. Conflicts should be anticipated in situations where stakeholders have different cultural beliefs. There is a need for more collaborative research and training in this field.

Futility and Shared Decision-Making

Medical futility is an ancient and yet consistent challenge in clinical medicine. The means of balancing conflicting priorities and stakeholders' preferences has changed as much as the science that powers the understanding and treatment of disease. The introduction of patient self-determination and choice in medical decision-making shifted the locus of power in the physician-patient relationship but did not obviate the physician's responsibilities to provide benefit and prevent harm. As we have refined the process in time, new paradigms, specialists, and tools have been developed to help navigate the ever-changing landscape.

The role of clinical phenotypes in decisions to limit life-sustaining treatment for very old patients in the ICU

BACKGROUND

Limiting life-sustaining treatment (LST) in the intensive care unit (ICU) by withholding or withdrawing interventional therapies is considered appropriate if there is no expectation of beneficial outcome. Prognostication for very old patients is challenging due to the substantial biological and functional heterogeneity in that group. We have previously identified seven phenotypes in that cohort with distinct patterns of acute and geriatric characteristics. This study investigates the relationship between these phenotypes and decisions to limit LST in the ICU.

METHODS

This study is a post hoc analysis of the prospective observational VIP2 study in patients aged 80 years or older admitted to ICUs in 22 countries. The VIP2 study documented demographic, acute and geriatric characteristics as well as organ support and decisions to limit LST in the ICU. Phenotypes were identified by clustering analysis of admission characteristics. Patients who were assigned to one of seven phenotypes (n = 1268) were analysed with regard to limitations of LST.

RESULTS

The incidence of decisions to withhold or withdraw LST was 26.5% and 8.1%, respectively. The two phenotypes describing patients with prominent geriatric features and a phenotype representing the oldest old patients with low severity of the critical condition had the largest odds for withholding decisions. The discriminatory performance of logistic regression models in predicting limitations of LST after admission to the ICU was the best after combining phenotype, ventilatory support and country as independent variables.

CONCLUSIONS

Clinical phenotypes on ICU admission predict limitations of LST in the context of cultural norms (country). These findings can guide further research into biases and preferences involved in the decision-making about LST. Trial registration Clinical Trials NCT03370692 registered on 12 December 2017.

Contribution of information about acute and geriatric characteristics to decisions about life-sustaining treatment for old patients in intensive care

BACKGROUND

Life-sustaining treatment (LST) in the intensive care unit (ICU) is withheld or withdrawn when there is no reasonable expectation of beneficial outcome. This is especially relevant in old patients where further functional decline might be detrimental for the self-perceived quality of life. However, there still is substantial uncertainty involved in decisions about LST. We used the framework of information theory to assess that uncertainty by measuring information processed during decision-making.

METHODS

Datasets from two multicentre studies (VIP1, VIP2) with a total of 7488 ICU patients aged 80 years or older were analysed concerning the contribution of information about the acute illness, age, gender, frailty and other geriatric characteristics to decisions about LST. The role of these characteristics in the decision-making process was quantified by the entropy of likelihood distributions and the Kullback-Leibler divergence with regard to withholding or withdrawing decisions.

RESULTS

Decisions to withhold or withdraw LST were made in 2186 and 1110 patients, respectively. Both in VIP1 and VIP2, information about the acute illness had the lowest entropy and largest Kullback-Leibler divergence with respect to decisions about withdrawing LST. Age, gender and geriatric characteristics contributed to that decision only to a smaller degree.

CONCLUSIONS

Information about the severity of the acute illness and, thereby, short-term prognosis dominated decisions about LST in old ICU patients. The smaller contribution of geriatric features suggests persistent uncertainty about the importance of functional outcome. There still remains a gap to fully explain decision-making about LST and further research involving contextual information is required.

TRIAL REGISTRATION

VIP1 study: NCT03134807 (1 May 2017), VIP2 study: NCT03370692 (12 December 2017).

Clustering analysis of geriatric and acute characteristics in a cohort of very old patients on admission to ICU

PURPOSE

The biological and functional heterogeneity in very old patients constitutes a major challenge to prognostication and patient management in intensive care units (ICUs). In addition to the characteristics of acute diseases, geriatric conditions such as frailty, multimorbidity, cognitive impairment and functional disabilities were shown to influence outcome in that population. The goal of this study was to identify new and robust phenotypes based on the combination of these features to facilitate early outcome prediction.

METHODS

Patients aged 80 years old or older with and without limitations of life-sustaining treatment and with complete data were recruited from the VIP2 study for phenotyping and from the COVIP study for external validation. The sequential organ failure assessment (SOFA) score and its sub-scores taken on admission to ICU as well as demographic and geriatric patient characteristics were subjected to clustering analysis. Phenotypes were identified after repeated bootstrapping and clustering runs.

RESULTS

In patients from the VIP2 study without limitations of life-sustaining treatment (n = 1977), ICU mortality was 12% and 30-day mortality 19%. Seven phenotypes with distinct profiles of acute and geriatric characteristics were identified in that cohort. Phenotype-specific mortality within 30 days ranged from 3 to 57%. Among the patients assigned to a phenotype with pronounced geriatric features and high SOFA scores, 50% died in ICU and 57% within 30 days. Mortality differences between phenotypes were confirmed in the COVIP study cohort (n = 280).

CONCLUSIONS

Phenotyping of very old patients on admission to ICU revealed new phenotypes with different mortality and potential need for anticipatory intervention.

A retrospective cohort study comparing differences in 30-day mortality among critically ill patients aged ≥ 70 years treated in European tax-based healthcare systems (THS) versus social health insurance systems

In Europe, tax-based healthcare systems (THS) and social health insurance systems (SHI) coexist. We examined differences in 30-day mortality among critically ill patients aged ≥ 70 years treated in intensive care units in a THS or SHI. Retrospective cohort study. 2406 (THS n = 886; SHI n = 1520) critically ill ≥ 70 years patients in 129 ICUs. Generalized estimation equations with robust standard errors were chosen to create population average adjusted odds ratios (aOR). Data were adjusted for patient-specific variables, organ support and health economic data. The primary outcome was 30-day-mortality. Numerical differences between SHI and THS in SOFA scores (6 ± 3 vs. 5 ± 3; p = 0.002) were observed, but clinical frailty scores were similar (> 4; 17% vs. 14%; p = 0.09). Higher rates of renal replacement therapy (18% vs. 11%; p < 0.001) were found in SHI (aOR 0.61 95%CI 0.40-0.92; p = 0.02). No differences regarding intubation rates (68% vs. 70%; p = 0.33), vasopressor use (67% vs. 67%; p = 0.90) and 30-day-mortality rates (47% vs. 50%; p = 0.16) were found. Mortality remained similar between both systems after multivariable adjustment and sensitivity analyses. The retrospective character of this study. Baseline risk and mortality rates were similar between SHI and THS. The type of health care system does not appear to have played a role in the intensive care treatment of critically ill patients ≥ 70 years with COVID-19 in Europe.

Variations in end-of-life care practices in older critically ill patients with COVID-19 in Europe

BACKGROUND

Previous studies reported regional differences in end-of-life care (EoLC) for critically ill patients in Europe.

OBJECTIVES

The purpose of this post-hoc analysis of the prospective multicentre COVIP study was to investigate variations in EoLC practices among older patients in intensive care units during the coronavirus disease 2019 pandemic.

METHODS

A total of 3105 critically ill patients aged 70 years and older were enrolled in this study (Central Europe: n = 1573; Northern Europe: n = 821; Southern Europe: n = 711). Generalised estimation equations were used to calculate adjusted odds ratios (aORs) to population averages. Data were adjusted for patient-specific variables (demographic, disease-specific) and health economic data (gross domestic product, health expenditure per capita). The primary outcome was any treatment limitation, and 90-day mortality was a secondary outcome.

RESULTS

The frequency of the primary endpoint (treatment limitation) was highest in Northern Europe (48%), intermediate in Central Europe (39%) and lowest in Southern Europe (24%). The likelihood for treatment limitations was lower in Southern than in Central Europe (aOR 0.39; 95% confidence interval [CI] 0.21-0.73; p = 0.004), even after multivariable adjustment, whereas no statistically significant differences were observed between Northern and Central Europe (aOR 0.57; 95%CI 0.27-1.22; p = 0.15). After multivariable adjustment, no statistically relevant mortality differences were found between Northern and Central Europe (aOR 1.29; 95%CI 0.80-2.09; p = 0.30) or between Southern and Central Europe (aOR 1.07; 95%CI 0.66-1.73; p = 0.78).

CONCLUSION

This study shows a north-to-south gradient in rates of treatment limitation in Europe, highlighting the heterogeneity of EoLC practices across countries. However, mortality rates were not affected by these results.

Communicating About Time-Limited Trials

Time-limited trials (TLTs) are used in the management of critical care patients undergoing potentially nonbeneficial interventions to improve prognostication and build trust and consensus between family and intensivists. When these trials are not well defined and executed, discordant views of the patient's prognosis, conflict, and continuation of nonbeneficial care can arise. The mnemonic TIME (truth about uncertainty in prognosis, interval of time, measurement of improvement, and end or extend) can help facilitate clear communication surrounding TLTs. This framework allows physicians and families to deal more effectively with the inherent uncertainty and required flexibility needed in caring for complex critical care patients. This can lead to patient-centered decision-making that improves patient-physician relationships and goal-concordant care and also potentially reduces nonbeneficial treatments at the end of life.

The management of multi-morbidity in elderly patients: Ready yet for precision medicine in intensive care?

There is ongoing demographic ageing and increasing longevity of the population, with previously devastating and often-fatal diseases now transformed into chronic conditions. This is turning multi-morbidity into a major challenge in the world of critical care. After many years of research and innovation, mainly in geriatric care, the concept of multi-morbidity now requires fine-tuning to support decision-making for patients along their whole trajectory in healthcare, including in the intensive care unit (ICU). This article will discuss current challenges and present approaches to adapt critical care services to the needs of these patients.

ICU-Mortality in Old and Very Old Patients Suffering From Sepsis and Septic Shock

Purpose: Old (>64 years) and very old (>79 years) intensive care patients with sepsis have a high mortality. In the very old, the value of critical care has been questioned. We aimed to compare the mortality, rates of organ support, and the length of stay in old vs. very old patients with sepsis and septic shock in intensive care.

Methods: This analysis included 9,385 patients, from the multi-center eICU Collaborative Research Database, with sepsis; 6184 were old (aged 65–79 years), and 3,201 were very old patients (aged 80 years and older). A multi-level logistic regression analysis was used to fit three sequential regression models for the binary primary outcome of ICU mortality. A sensitivity analysis in septic shock patients (n = 1054) was also conducted.

Results: In the very old patients, the median length of stay was shorter (50 ± 67 vs. 56 ± 72 h; p < 0.001), and the rate of a prolonged ICU stay was lower (>168 h; 9 vs. 12%; p < 0.001) than the old patients. The mortality from sepsis was higher in very old patients (13 vs. 11%; p = 0.005), and after multi-variable adjustment being very old was associated with higher odds for ICU mortality (aOR 1.32, 95% CI 1.09–1.59; p = 0.004). In patients with septic shock, mortality was also higher in the very old patients (38 vs. 36%; aOR 1.50, 95% CI 1.10–2.06; p = 0.01).

Conclusion: Very old ICU-patients suffer from a slightly higher ICU mortality compared with old ICU-patients. However, despite the statistically significant differences in mortality, the clinical relevance of such minor differences seems to be negligible.

A new multi-national network studying Very old Intensive care Patients (VIPs)

In Europe there are increasing numbers of old (more than 65 years old) and very old (more than 80 years old) patients (very old intensive care patients - VIPs) (Figure 1). In addition to combinations of chronic conditions (multi-morbidity), there are geriatric disabilities and functional limitations, with a profound impact on management in the ICU and afterwards [1].