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

The interplay between frailty status and persistent critical illness on the outcomes of patients with critical COVID-19: A population-based retrospective cohort study

OBJECTIVES

Persistent critical illness (PerCI) occurs when the patient's prolonged intensive care unit (ICU) stay results in complications that become the primary drivers of their condition, rather than the initial reason for their admission. Patients with frailty have a higher risk of developing and dying from PerCI. We aimed to investigate the interplay of frailty and PerCI in critically ill patients with COVID-19.

METHOD

We conducted a retrospective multicentre cohort study including 103 Australian and New Zealand ICUs over the period of January 2020 to December 2021. We included all adult patients with COVID-19 and documented the Clinical Frailty Scale (frail ≥ 5). PerCI is defined as an ICU length of stay of ≥10 days. We aimed to investigate the hospital mortality with and without PerCI across varying degrees of frailty and examined the potential interaction effect between frailty status and PerCI.

RESULTS

The prevalence of PerCI was similar between patients with and without frailty (25.4% vs. 27.9%; p = 0.44). Hospital mortality was higher in patients with PerCI than in those without (28.8% vs. 9.3%; p < 0.001). Mortality in patients with PerCI also increased with increasing frailty (p < 0.001). Frailty independently predicted hospital mortality. When adjusted for Australia and New Zealand risk of death mortality prediction model and sex, the impact of frailty was no different in patients with and without PerCI (odds ratio = 1.30 [95% confidence interval: 1.14-1.49] vs. (odds ratio = 1.46 [95% confidence interval: 1.29-1.64]). Furthermore, increasing frailty did not influence mortality in patients with PerCI more (or less) than in those without PerCI (pinteraction = 0.82).

CONCLUSIONS

The presence of frailty independently predicted hospital mortality in patients with PerCI with COVID-19, but the impact of frailty on mortality was no different in those who developed PerCI from those without PerCI.

Early prediction of mortality upon intensive care unit admission

BACKGROUND

We aimed to develop and validate models for predicting intensive care unit (ICU) mortality of critically ill adult patients as early as upon ICU admission.

METHODS

Combined data of 79,657 admissions from two teaching hospitals' ICU databases were used to train and validate the machine learning models to predict ICU mortality upon ICU admission and at 24 h after ICU admission by using logistic regression, gradient boosted trees (GBT), and deep learning algorithms.

RESULTS

In the testing dataset for the admission models, the ICU mortality rate was 7%, and 38.4% of patients were discharged alive or dead within 1 day of ICU admission. The area under the receiver operating characteristic curve (0.856, 95% CI 0.845-0.867) and area under the precision-recall curve (0.331, 95% CI 0.323-0.339) were the highest for the admission GBT model. The ICU mortality rate was 17.4% in the 24-hour testing dataset, and the performance was the highest for the 24-hour GBT model.

CONCLUSION

The ADM models can provide crucial information on ICU mortality as early as upon ICU admission. 24 H models can be used to improve the prediction of ICU mortality for patients discharged more than 1 day after ICU admission.

Lower gastrointestinal tract dysbiosis in persistent critical illness: a systematic review

Introduction. The human lower gastrointestinal tract microbiome is complex, dynamic and prone to disruption occurring during critical illness.Hypothesis or gap statement. The characteristics of lower gastrointestinal tract microbiome disruption and its association with clinical outcomes in patients with prolonged intensive care stay remain uncertain.Aim. To systematically review studies describing lower gastrointestinal tract molecular sequencing in patients with prolonged intensive care stay and explore associations with clinical outcomes.Methodology. This systematic review was prospectively registered and follows the Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines. OVID MEDLINE, EMBASE and The Cochrane Central Register of Controlled Trials databases were searched for eligible studies describing adults and/or children who underwent molecular sequencing of stool or rectal samples taken on or after 10 days of intensive care.Results. There were 13 studies with 177 patients included. The overall certainty of evidence was low, and no studies reported mortality. Reduced alpha diversity was observed in nine out of nine studies but was not associated with clinical outcomes in four out of four studies. Longitudinal alpha diversity decreased in five out of six studies, and inter-individual beta diversity increased in five out of five studies. After approximately one week of intensive care unit admission, rapid fluctuations in dominant taxa stabilized with trajectories of either recovery or deterioration in five studies. Pathogenic enrichment and commensal depletion were reported in all 13 studies and associated with clinical outcomes in two studies.Conclusion. Lower gastrointestinal tract microbiome disruption is highly prevalent and has consistent characteristics in patients with prolonged intensive care stay. Amongst reported metrics, only relative taxon abundance was associated with clinical outcomes.

The impact of frailty and rapid response team activation on patients admitted to the intensive care unit: A case-control matched, observational, single-centre cohort study

BACKGROUND

Frailty is a multi-dimensional syndrome associated with mortality and adverse outcomes in patients admitted to the intensive care unit (ICU). Further investigation is warranted to explore the interplay among factors such as frailty, clinical deterioration triggering a medical emergency team (MET) review, and outcomes following admission to the ICU.

METHODS

Single-centre, retrospective observational case-control study of adult patients (>18 years) admitted to a medical-surgical ICU with (cases) or without (controls) a preceding MET review between 4 h and 14 days prior. Matching was performed for age, ICU admission diagnosis, Acute Physiology and Chronic Health Evaluation III (APACHE III) score and the 8-point Clinical Frailty Scale (CFS). Cox proportional hazard regression modelling was performed to determine associations with 30-day mortality after admission to ICU.

RESULTS

A total of 2314 matched admissions were analysed. Compared to non-frail patients (CFS 1-4), mortality was higher in all frail patients (CFS 5-8), at 31% vs. 13%, and in frail patients admitted after MET review at 33%. After adjusting for age, APACHE, antecedent MET review and CFS in the Cox regression, mortality hazard ratio increased by 26% per CFS point and by 3% per APACHE III point, while a MET review was not an independent predictor. Limitations of medical treatment occurred in 30% of frail patients, either with or without a MET antecedent, and this was five times higher compared to non-frail patients.

CONCLUSION

Frail patients admitted to ICU have a high short-term mortality. An antecedent MET event was associated with increased mortality but did not independently predict short-term survival when adjusting for confounding factors. The intrinsic significance of frailty should be primarily considered during MET review of frail patients. This study suggests that routine frailty assessment of hospitalised patients would be helpful to set goals of care when admission to ICU could be considered.

Effects of education, income and employment on ICU and post-ICU survival - A nationwide Swedish cohort study of individual-level data with 1-year follow up

PURPOSE

The aim of this study was to examine relationships between education, income, and employment (socioeconomic status, SES) and intensive care unit (ICU) survival and survival 1 year after discharge from ICU (Post-ICU survival).

METHODS

Individual data from ICU patients were linked to register data of education level, disposable income, employment status, civil status, foreign background, comorbidities, and vital status. Associations between SES, ICU survival and 1-year post-ICU survival was analysed using Cox's regression.

RESULTS

We included 58,279 adults (59% men, median length of stay in ICU 4.0 days, median SAPS3 score 61). Survival rates at discharge from ICU and one year after discharge were 88% and 63%, respectively. Risk of ICU death (Hazard ratios, HR) was significantly higher in unemployed and retired compared to patients who worked prior to admission (1.20; 95% CI: 1.10-1.30 and 1.15; (1.07-1.24), respectively. There was no consistent association between education, income and ICU death. Risk of post-ICU death decreased with greater income and was roughly 16% lower in the highest compared to lowest income quintile (HR 0.84; 0.79-0.88). Higher education levels appeared to be associated with reduced risk of death during the first year after ICU discharge.

CONCLUSIONS

Significant relationships between low SES in the critically ill and increased risk of death indicate that it is important to identify and support patients with low SES to improve survival after intensive care. Studies of survival after critical illness need to account for participants SES.

Persistent Critical Illness and Long-Term Outcomes in Patients With COVID-19: A Multicenter Retrospective Cohort Study

OBJECTIVES

A nontrivial number of patients in ICUs experience persistent critical illness (PerCI), a phenomenon in which features of the ICU course more consistently predict mortality than the initial indication for admission. We aimed to describe PerCI among patients with critical illness caused by COVID-19, and these patients' short- and long-term outcomes.

DESIGN

Multicenter retrospective cohort study.

SETTING

Australian and New Zealand Intensive Care Society Adult Patient Database of 114 Australian ICUs between January 1, 2020, and March 31, 2022.

PATIENTS

Patients 16 years old or older with COVID-19, and a documented ICU length of stay.

EXPOSURE

The presence of PerCI, defined as an ICU length of stay greater than or equal to 10 days.

MEASUREMENTS

We compared the survival time up to 2 years from ICU admission using time-varying robust-variance estimated Cox proportional hazards models. We further investigated the impact of PerCI in subgroups of patients, stratifying based on whether they survived their initial hospitalization.

MAIN RESULTS

We included 4961 patients in the final analysis, and 882 patients (17.8%) had PerCI. ICU mortality was 23.4% in patients with PerCI and 6.5% in those without PerCI. Patients with PerCI had lower 2-year (70.9% [95% CI, 67.9-73.9%] vs. 86.1% [95% CI, 85.0-87.1%]; p < 0.001) survival rates compared with patients without PerCI. Patients with PerCI had higher mortality (adjusted hazards ratio: 1.734; 95% CI, 1.388-2.168); this was consistent across several sensitivity analyses. When analyzed as a nonlinear predictor, the hazards of mortality were inconsistent up until 10 days, before plateauing.

CONCLUSIONS

In this multicenter retrospective observational study patients with PerCI tended to have poorer short-term and long-term outcomes. However, the hazards of mortality plateaued beyond the first 10 days of ICU stay. Further studies should investigate predictors of developing PerCI, to better prognosticate long-term outcomes.

Development and evaluation of regression tree models for predicting in-hospital mortality of a national registry of COVID-19 patients over six pandemic surges

BACKGROUND

Objective prognostic information is essential for good clinical decision making. In case of unknown diseases, scarcity of evidence and limited tacit knowledge prevent obtaining this information. Prediction models can be useful, but need to be not only evaluated on how well they predict, but also how stable these models are under fast changing circumstances with respect to development of the disease and the corresponding clinical response. This study aims to provide interpretable and actionable insights, particularly for clinicians. We developed and evaluated two regression tree predictive models for in-hospital mortality of COVID-19 patient at admission and 24 hours (24 h) after admission, using a national registry. We performed a retrospective analysis of observational routinely collected data.

METHODS

Two regression tree models were developed for admission and 24 h after admission. The complexity of the trees was managed via cross validation to prevent overfitting. The predictive ability of the model was assessed via bootstrapping using the Area under the Receiver-Operating-Characteristic curve, Brier score and calibration curves. The tree models were assessed on the stability of their probabilities and predictive ability, on the selected variables, and compared to a full-fledged logistic regression model that uses variable selection and variable transformations using splines. Participants included COVID-19 patients from all ICUs participating in the Dutch National Intensive Care Evaluation (NICE) registry, who were admitted at the ICU between February 27, 2020, and November 23, 2021. From the NICE registry, we included concerned demographic data, minimum and maximum values of physiological data in the first 24 h of ICU admission and diagnoses (reason for admission as well as comorbidities) for model development. The main outcome measure was in-hospital mortality. We additionally analysed the Length-of-Stay (LoS) per patient subgroup per survival status.

RESULTS

A total of 13,369 confirmed COVID-19 patients from 70 ICUs were included (with mortality rate of 28%). The optimism-corrected AUROC of the admission tree (with seven paths) was 0.72 (95% CI: 0.71-0.74) and of the 24 h tree (with 11 paths) was 0.74 (0.74-0.77). Both regression trees yielded good calibration and variable selection for both trees was stable. Patient subgroups comprising the tree paths had comparable survival probabilities as the full-fledged logistic regression model, survival probabilities were stable over six COVID-19 surges, and subgroups were shown to have added predictive value over the individual patient variables.

CONCLUSIONS

We developed and evaluated regression trees, which operate at par with a carefully crafted logistic regression model. The trees consist of homogenous subgroups of patients that are described by simple interpretable constraints on patient characteristics thereby facilitating shared decision-making.

Prior physical illness predicts death better than acute physiological derangement on intensive care unit admission in COVID-19: A Swedish registry study

COVID-19 is associated with prolonged intensive care unit (ICU) stay and considerable mortality. The onset of persistent critical illness, defined as when prior illness predicts death better than acute physiological derangement, has not been studied in COVID-19. This national cohort study based on the Swedish Intensive Care Registry (SIR) included all patients admitted to a Swedish ICU due to COVID-19 from 6 March 2020 to 9 November 2021. Simplified Acute Physiology Score-3 (SAPS3) Box 1 was used as a measure of prior illness and Box 3 as a measure of acute derangement to evaluate the onset and importance of persistent critical illness in COVID-19. To compare predictive capacity, the area under receiver operating characteristic (AUC) of SAPS3 and its constituent Box 1 and 3 was calculated for 30-day mortality. In 7 969 patients, of which 1 878 (23.6%) died within 30 days of ICU admission, the complete SAPS3 score had acceptable discrimination: AUC 0.75 (95% CI 0.74 to 0.76) but showed under prediction in low-risk patients and over prediction in high-risk patients. SAPS3 Box 1 showed markedly better discrimination than Box 3 (AUC 0.74 vs 0.65, P<0,0001). Using custom logistic models, the difference in predictive performance of prior and acute illness was validated, AUC 0.76 vs AUC 0.69, p<0.0001. Prior physical illness predicts death in COVID-19 better than acute physiological derangement during ICU stay, and the whole SAPS3 score is not significantly better than just prior illness. The results suggests that COVID-19 may exhibit similarities to persistent critical illness immediately from ICU admission, potentially because of long median ICU length-of-stay. Alternatively, the variables in the acute physiological derangement model may not adequately capture the severity of illness in COVID-19.

Timing and burden of persistent critical illnessin UK intensive care units: An observational cohort study

Background

Persistent critical illness is a recognisable clinical syndrome defined conceptually as when the patient's reason for being in the intensive care unit (ICU) is more related to their ongoing critical illness than their original reason for admission. Our objectives were: (1) to assess the day in ICU on which chronic factors (e.g., age, gender and comorbidities) were more predictive of survival than acute factors (e.g. admission diagnosis, physiological derangements) measured on the day of admission; (2) to assess the consistency of this finding across major patient subgroups and over time and (3) to compare case mix characteristics and outcomes for patients determined to develop persistent critical illness (based on ICU length of stay) with other patients.

Methods

Observational cohort study using a high-quality clinical database from the national clinical audit of adult critical care. 217 adult ICUs in England, Wales and Northern Ireland. 835,946 adult patients admitted to participating ICUs between 1 April 2009 and 31 March 2016. The main outcome measure was mortality at discharge from acute hospital.

Results

We fitted two statistical models ('chronic' and 'acute') and updated these based upon patients with an ICU length of stay of at least 1, 2, etc., up to 28 days. The discrimination of the chronic model first exceeded that of the acute model on day 11. Patients with longer stays (>10 days) comprised 9% of admissions but used 45% of ICU bed-days. After a mean ICU length of stay of 22 days and a subsequent 28 days in hospital, 30% died.

Conclusions

Persistent critical illness is commonly encountered in clinical practice and is associated with increased healthcare utilisation and adverse outcomes. Improvements in our understanding of the longer term outcomes and in the development of tools to aid prognostication are urgently required - for humane as well as health economic reasons.

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).

A Retrospective Observational Study Exploring 30- and 90-Day Outcomes for Patients With COVID-19 After Percutaneous Tracheostomy and Gastrostomy Placement

To determine the 30- and 90-day outcomes of COVID-19 patients receiving tracheostomy and percutaneous endoscopic gastrostomy (PEG).

Data Science Trends Relevant to Nursing Practice: A Rapid Review of the 2020 Literature

BACKGROUND

The term "data science" encompasses several methods, many of which are considered cutting edge and are being used to influence care processes across the world. Nursing is an applied science and a key discipline in health care systems in both clinical and administrative areas, making the profession increasingly influenced by the latest advances in data science. The greater informatics community should be aware of current trends regarding the intersection of nursing and data science, as developments in nursing practice have cross-professional implications.

OBJECTIVES

This study aimed to summarize the latest (calendar year 2020) research and applications of nursing-relevant patient outcomes and clinical processes in the data science literature.

METHODS

We conducted a rapid review of the literature to identify relevant research published during the year 2020. We explored the following 16 topics: (1) artificial intelligence/machine learning credibility and acceptance, (2) burnout, (3) complex care (outpatient), (4) emergency department visits, (5) falls, (6) health care-acquired infections, (7) health care utilization and costs, (8) hospitalization, (9) in-hospital mortality, (10) length of stay, (11) pain, (12) patient safety, (13) pressure injuries, (14) readmissions, (15) staffing, and (16) unit culture.

RESULTS

Of 16,589 articles, 244 were included in the review. All topics were represented by literature published in 2020, ranging from 1 article to 59 articles. Numerous contemporary data science methods were represented in the literature including the use of machine learning, neural networks, and natural language processing.

CONCLUSION

This review provides an overview of the data science trends that were relevant to nursing practice in 2020. Examinations of such literature are important to monitor the status of data science's influence in nursing practice.

Predictors of death and new disability after critical illness: a multicentre prospective cohort study

PURPOSE

This study aimed to determine the prevalence and predictors of death or new disability following critical illness.

METHODS

Prospective, multicentre cohort study conducted in six metropolitan intensive care units (ICU). Participants were adults admitted to the ICU who received more than 24 h of mechanical ventilation. The primary outcome was death or new disability at 6 months, with new disability defined by a 10% increase in the WHODAS 2.0.

RESULTS

Of 628 patients with the primary outcome available (median age of 62 [49-71] years, 379 [61.0%] had a medical admission and 370 (58.9%) died or developed new disability by 6 months. Independent predictors of death or new disability included age [OR 1.02 (1.01-1.03), P = 0.001], higher severity of illness (APACHE III) [OR 1.02 (1.01-1.03), P < 0.001] and admission diagnosis. Compared to patients with a surgical admission diagnosis, patients with a cardiac arrest [OR (95% CI) 4.06 (1.89-8.68), P < 0.001], sepsis [OR (95% CI) 2.43 (1.32-4.47), P = 0.004], or trauma [OR (95% CI) 6.24 (3.07-12.71), P < 0.001] diagnosis had higher odds of death or new disability, while patients with a lung transplant [OR (95% CI) 0.21 (0.07-0.58), P = 0.003] diagnosis had lower odds. A model including these three variables had good calibration (Brier score 0.20) and acceptable discriminative power with an area under the receiver operating characteristic curve of 0.76 (95% CI 0.72-0.80).

CONCLUSION

Less than half of all patients mechanically ventilated for more than 24 h were alive and free of new disability at 6 months after admission to ICU. A model including age, illness severity and admission diagnosis has acceptable discriminative ability to predict death or new disability at 6 months.

Microbial findings and the role of difficult-to-treat pathogens in patients with periprosthetic infection admitted to the intensive care unit

Little is known about patients with Periprosthetic Joint Infection (PJI) admitted to the Intensive Care Unit (ICU). The purpose of this study was threefold: i) To report the microbiological findings of ICUpatients with PJI. ii) To compare the clinical data between Difficult-To-Treat (DTT) and non-DTT PJI. iii) To identify risk factors for mortality. This is a retrospective study from a tertiary healthcare center in Germany from 2012-2016. A total of 124 patients with 169 pathogens were included. The most common bacteria were Staphyloccous aureus (26.6%), Staphyloccus epidermidis (12.4%), Enterococci ssp. and Escherichia coli (respectively 9.4%). DTT PJI was diagnosed in 28 patients (22.6%). The main pathogens of DTT PJI were Staphylococus epidermidis (14.5%), Escherichia coli (12.7%), Staphylococcus aureus and Candida spp. (respectively 9.1%). Polymicrobial PJI, number of pathogens, ICU stay and mortality were significantly differrent between DTT PJI and non-DTT PJI (p≤0.05). Multivariate logistic regression identified prolonged ICU stay and DTT PJI as risk factors for mortality. In conclusion, we suggest, that the term of DTT pathogens is useful for the intensivist to assess the clinical outcome in ICU-patients with PJI.

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

Predicting the future course of critical conditions involves personal experience, heuristics and statistical models. Although these methods may perform well for some cases and population averages, they suffer from substantial shortcomings when applied to individual patients. The reasons include methodological problems of statistical modeling as well as limitations of cross-sectional data sampling. Accurate predictions for individual patients become crucial when they have to guide irreversible decision-making. This notably applies to triage situations in response to a lack of healthcare resources. We will discuss these issues and argue that analysing longitudinal data obtained from time-limited trials in intensive care can provide a more robust approach to individual prognostication.