SAPS 3--From evaluation of the patient to evaluation of the intensive care unit. Part 1: Objectives, methods and cohort description

A window of opportunity for ICU end-of-life care-A retrospective multicenter cohort study

BACKGROUND

The "window of opportunity" for intensive care staff to deliver end-of-life (EOL) care lies in the timeframe from "documenting the diagnosis of dying" to death. Diagnosing the dying can be a challenging task in the ICU. We aimed to describe the trajectories for dying patients in Danish intensive care units (ICUs) and to examine whether physicians document that patients are dying in time to perform EOL care and, if so, when a window of opportunity for EOL care exists.

METHODS

From the Danish Intensive Care Database, we identified patients ≥18 years old admitted to Danish ICUs between January and December 2020 with an ICU stay of >96 h (four days) and who died during the ICU stay or within 7 days after ICU discharge. A chart review was performed on 250 consecutive patients admitted from January 1, 2020, to ICUs in the Central Denmark Region.

RESULTS

In most charts (223 [89%]), it was documented that the patient was dying. Of those patients who received mechanical ventilation, 171 (68%) died after abrupt discontinuation of mechanical ventilation, and 63 (25%) died after gradual withdrawal. Patients whose mechanical ventilation was discontinued abruptly died after a median of 1 h (interquartile range [IQR]: 0-15) and 5 h (IQR: 2-15) after a diagnosis of dying was recorded. In contrast, patients with a gradual withdrawal died after a median of 108 h (IQR: 71-189) and 22 h (IQR: 5-67) after a diagnosis of dying was recorded.

CONCLUSIONS

EOL care hinges on the ability to diagnose the dying. This study shows that there is a window of opportunity for EOL care, particularly for patients who are weaned from mechanical ventilation. This highlights the importance of intensifying efforts to address EOL care requirements for ICU patients and those discharged from ICUs who are not eligible for readmission.

Comparing causal random forest and linear regression to estimate the independent association of organisational factors with ICU efficiency

PURPOSE

Parametric regression models have been the main statistical method for identifying average treatment effects. Causal machine learning models showed promising results in estimating heterogeneous treatment effects in causal inference. Here we aimed to compare the application of causal random forest (CRF) and linear regression modelling (LRM) to estimate the effects of organisational factors on ICU efficiency.

METHODS

A retrospective analysis of 277,459 patients admitted to 128 Brazilian and Uruguayan ICUs over three years. ICU efficiency was assessed using the average standardised efficiency ratio (ASER), measured as the average of the standardised mortality ratio (SMR) and the standardised resource use (SRU) according to the SAPS-3 score. Using a causal inference framework, we estimated and compared the conditional average treatment effect (CATE) of seven common structural and organisational factors on ICU efficiency using LRM with interaction terms and CRF.

RESULTS

The hospital mortality was 14 %; median ICU and hospital lengths of stay were 2 and 7 days, respectively. Overall median SMR was 0.97 [IQR: 0.76,1.21], median SRU was 1.06 [IQR: 0.79,1.30] and median ASER was 0.99 [IQR: 0.82,1.21]. Both CRF and LRM showed that the average number of nurses per ten beds was independently associated with ICU efficiency (CATE [95 %CI]: -0.13 [-0.24, -0.01] and -0.09 [-0.17,-0.01], respectively). Finally, CRF identified some specific ICUs with a significant CATE in exposures that did not present a significant average effect.

CONCLUSION

In general, both methods were comparable to identify organisational factors significantly associated with CATE on ICU efficiency. CRF however identified specific ICUs with significant effects, even when the average effect was nonsignificant. This can assist healthcare managers in further in-dept evaluation of process interventions to improve ICU efficiency.

Development and validation of a predictive model for in-hospital mortality from perioperative bacteremia in gastrointestinal surgery

PURPOSE

Prognostic scores require fluctuating values, such as respiratory rate, which are unsuitable for retrospective auditing. Therefore, this study aimed to develop and validate a predictive model for in-hospital mortality associated with gastrointestinal surgery for retrospective auditing.

METHODS

Data from patients with bacteremia related to gastrointestinal surgery performed at Shizuoka General Hospital between July 2006 and December 2021 were extracted from a prospectively maintained database. Patients suspected of having a positive blood culture with contaminating bacteria or missing laboratory data were excluded. The remaining patients were randomly assigned in a 2:1 ratio to the deviation and validation cohorts. A logistic regression model estimated the odds ratios (ORs) and created a predictive model for in-hospital mortality. The model was evaluated using receiver operating characteristic (ROC) curves and calibration plots.

RESULTS

Of 20,637 gastrointestinal surgeries, 398 resulted in bacteremia. The median age of patients with bacteremia was 72 years, and 66.1% were male. The most common pathogens were Staphylococcus (13.9%), followed by Bacteroides (12.4%) and Escherichia (11.4%). Multivariable logistic regression showed that creatinine abnormality (P < 0.001, OR = 3.39), decreased prognostic nutritional index (P < 0.001, OR = 0.90/unit), and age ≥ 75 years (P = 0.026, OR = 2.89) were independent prognostic factors for in-hospital mortality. The area under the ROC curve of the predictive model was 0.711 in the validation cohort. The calibration plot revealed that the model slightly overestimated mortality in the validation cohort.

CONCLUSIONS

Using age, creatinine level, albumin level, and lymphocyte count, the model accurately predicted in-hospital mortality after bacteremia infection related to gastrointestinal surgery, demonstrating its suitability for retrospective audits.

ICURE: Intensive care unit (ICU) risk evaluation for 30-day mortality. Developing and evaluating a multivariable machine learning prediction model for patients admitted to the general ICU in Sweden

BACKGROUND

A prediction model that estimates mortality at admission to the intensive care unit (ICU) is of potential benefit to both patients and society. Logistic regression models like Simplified Acute Physiology Score 3 (SAPS 3) and APACHE are the traditional ICU mortality prediction models. With the emergence of machine learning (machine learning) and artificial intelligence, new possibilities arise to create prediction models that have the potential to sharpen predictive accuracy and reduce the likelihood of misclassification in the prediction of 30-day mortality.

METHODS

We used the Swedish Intensive Care Registry (SIR) to identify and include all patients ≥18 years of age admitted to general ICUs in Sweden from 2008 to 2022 with SAPS 3 score registered. Only data collected within 1 h of ICU admission was used. We had 153 candidate predictors including baseline characteristics, previous medical conditions, blood works, physiological parameters, cause of admission, and initial treatment. We stratified the data randomly on the outcome variable 30-day mortality and created a training set (80% of data) and a test set (20% of data). We evaluated several hundred prediction models using multiple ML frameworks including random forest, gradient boosting, neural networks, and logistic regression models. Model performance was evaluated by comparing the receiver operator characteristic area under the curve (AUC-ROC). The best performing model was fine-tuned by optimizing hyperparameters. The model's calibration was evaluated by a calibration belt. Ultimately, we simplified the best performing model with the top 1-20 predictors.

RESULTS

We included 296,344 first-time ICU admissions. We found age, Glasgow Coma Scale, creatinine, systolic blood pressure, and pH being the most important predictors. The AUC-ROC was 0.884 in test data using all predictors, specificity 95.2%, sensitivity 47.0%, negative predictive value of 87.9% and positive predictive value of 70.7%. The final model showed excellent calibration. The ICU risk evaluation for 30-day mortality (ICURE) prediction model performed equally well to the SAPS 3 score with only eight variables and improved further with the addition of more variables.

CONCLUSION

The ICURE prediction model predicts 30-day mortality rate at first-time ICU admission superiorly compared to the established SAPS 3 score.

INhaled Sedation versus Propofol in REspiratory failure in the Intensive Care Unit (INSPiRE-ICU1): protocol for a randomised, controlled trial

INTRODUCTION

Sedation in mechanically ventilated adults in the intensive care unit (ICU) is commonly achieved with intravenous infusions of propofol, dexmedetomidine or benzodiazepines. Significant limitations associated with each can impact their usage. Inhaled isoflurane has potential benefit for ICU sedation due to its safety record, sedation profile, lack of metabolism and accumulation, and fast wake-up time. Administration in the ICU has historically been restricted by the lack of a safe and effective delivery system for the ICU. The Sedaconda Anaesthetic Conserving Device-S (Sedaconda ACD-S) has enabled the delivery of inhaled volatile anaesthetics for sedation with standard ICU ventilators, but it has not yet been rigorously evaluated in the USA. We aim to evaluate the efficacy and safety of inhaled isoflurane delivered via the Sedaconda ACD-S compared with intravenous propofol for sedation of mechanically ventilated ICU adults in USA hospitals.

METHODS AND ANALYSIS

INhaled Sedation versus Propofol in REspiratory failure in the ICU (INSPiRE-ICU1) is a phase 3, multicentre, randomised, controlled, open-label, assessor-blinded trial that aims to enrol 235 critically ill adults in 14 hospitals across the USA. Eligible patients are randomised in a 1.5:1 ratio for a treatment duration of up to 48 (±6) hours or extubation, whichever occurs first, with primary follow-up period of 30 days and additional follow-up to 6 months. Primary outcome is percentage of time at target sedation range. Key secondary outcomes include use of opioids during treatment, spontaneous breathing efforts during treatment, wake-up time at end of treatment and cognitive recovery after treatment.

ETHICS AND DISSEMINATION

Trial protocol has been approved by US Food and Drug Administration (FDA) and central (Advarra SSU00208265) or local institutional review boards ((IRB), Cleveland Clinic IRB FWA 00005367, Tufts HS IRB 20221969, Houston Methodist IRB PRO00035247, Mayo Clinic IRB Mod22-001084-08, University of Chicago IRB21-1917-AM011 and Intermountain IRB 033175). Results will be presented at scientific conferences, submitted for publication, and provided to the FDA.

TRIAL REGISTRATION NUMBER

NCT05312385.

The utility of an algorithm based on procalcitonin monitoring in patients with sepsis

OBJECTIVE

The aim of the study was to develop and validate an algorithm based on procalcitonin (PCT) monitoring to predict the prognosis of patients with sepsis.

DESIGN

The design was a retrospective and observational prospective study.

SETTING

The study was set in intensive care units (ICUs) in 2 different hospitals in Spain.

PATIENTS

Patients in the study included 101 patients with sepsis aged ≥18 years.

INTERVENTIONS

In the retrospective study, PCT results from patients admitted to the ICU in 2011-2012 were collected. In the prospective study, PCT was determined at specific time points as indicated by the algorithm from March 2018 to April 2019. The primary outcome measure, 28-day mortality, was the main variable of interest.

RESULTS

The study developed an algorithm based on early PCT monitoring for predicting the prognosis of patients with sepsis. The algorithm was initially developed retrospectively in 1 cohort and subsequently validated prospectively in another cohort.

CONCLUSIONS

The developed algorithm provides information on the prognosis of patients with sepsis, distinguishing between those with a good prognosis and those with a poor prognosis (defined as mortality).

Desmopressin use in major cardiac surgery is associated with renal impairment: a retrospective single-center analysis

BACKGROUND

Desmopressin acetate (1-deamino-8-d-arginine vasopressin-DDAVP) is a analogue of the antidiuretic hormone vasopressin. DDAVP is suggested to reduce bleeding after cardiac surgery using cardiopulmonary bypass. The aim of this study was to determine if DDAVP has any negative impact on renal function leading to acute kidney injury (AKI) and therefore increases the need for renal replacement therapy (RRT).

METHODS

We performed a retrospective single institutional cohort analysis of 2,179 patients undergoing elective and urgent cardiac surgery with CPB from 2017 to 2021. Logistic regression analysis was used to investigate any association between DDAVP, the incidence of AKI KDIGO class 3 and the need for RRT, respectively. The model was adjusted for relevant covariates, including preexisting renal impairment, pharmacological hemodynamic support with vasopressors, complexity of the surgery and postoperative lactate. Secondary outcomes included, in hospital mortality and the need for allogenic blood transfusion.

RESULTS

A total of 992 (45.5%) patients received DDAVP intraoperatively during surgery or shortly thereafter. The use of DDAVP was associated with a significant increase in in AKI KDIGO class 3 (OR 2.27; 95% CI 1.46-3.55; p < 0,001) and the need for RRT (OR 2.19; 95%CI 1.48-3.24; p < 0,001). Both findings persisted after covariate adjusting. No increased in-hospital mortality was associated with DDAVP.

CONCLUSION

In cardiac surgery, the use of DDAVP was associated with an increased rate of server AKI and the requirement for RRT. Given the severity of the potential harm associated with DDAVP, an evidence-based reevaluation is needed to enable an accurate risk and benefit assessment.

Developing Augmented Pro-SOFA and Pro-SAPS Models by Integrating Biomarkers PCT, NLR, and CRP with SOFA and SAPS-III Scores

Background

Sepsis, a life-threatening condition characterized by a dysregulated immune response to infection, remains a significant clinical challenge globally. This study aims to enhance the predictive accuracy of existing sepsis severity scores by developing augmented versions of the SOFA and SAPS-III models, termed Pro-SOFA and Pro-SAPS, through the integration of biomarkers procalcitonin (PCT), neutrophil-to-lymphocyte ratio (NLR), and C-reactive protein (CRP).

Methods

This prospective observational study was conducted in the medical ICU of a tertiary care hospital in southern India from August 2022 to December 2023. A total of 301 adult patients suspected or confirmed to have sepsis were assessed for eligibility, with 171 patients completing the study. Demographic and clinical data were collected; SOFA and SAPS-III scores were calculated and augmented with PCT, NLR, and CRP to develop Pro-SOFA and Pro-SAPS models. The performance of these models was evaluated using Brier scores, AUC, and net reclassification index (NRI).

Results

The augmented Pro-SOFA and Pro-SAPS models demonstrated superior predictive accuracy compared to their original counterparts. The Brier scores for Pro-SOFA and Pro-SAPS were 0.181 and 0.165, respectively, indicating better calibration than the original scores. The Pro-SAPS showed significant improvement over the original SAPS-III score (NRI = 0.50, SE = 0.14, p < 0.01). Similarly, Pro-SOFA outperformed the original SOFA (NRI = 0.49, SE = 0.13, p < 0.01).

Conclusion and clinical significance

Integrating PCT, CRP, and NLR with SOFA and SAPS-III scores to develop Pro-SOFA and Pro-SAPS significantly improves the predictive accuracy for sepsis mortality and can thus potentially improve sepsis outcomes.

How to cite this article

Nandakumar A, Sudeep S, Sreemohan AC, Vijayakumar S, Sudhakaran GJ, Gutjahr G, et al. Developing Augmented Pro-SOFA and Pro-SAPS Models by Integrating Biomarkers PCT, NLR, and CRP with SOFA and SAPS-III Scores. Indian J Crit Care Med 2024;28(10):935-941.

Comparison of different intensive care scoring systems and Glasgow Aneurysm score for aortic aneurysm in predicting 28-day mortality: a retrospective cohort study from MIMIC-IV database

OBJECTIVE

This study aims to assess the performance of various scoring systems in predicting the 28-day mortality of patients with aortic aneurysms (AA) admitted to the intensive care unit (ICU).

METHODS

We utilized data from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) to perform a comparative analysis of various predictive systems, including the Glasgow Aneurysm Score (GAS), Simplified Acute Physiology Score (SAPS) III, SAPS II, Logical Organ Dysfunction System (LODS), Sequential Organ Failure Assessment (SOFA), Systemic Inflammatory Response Syndrome (SIRS), and The Oxford Acute Illness Severity Score (OASIS). The discrimination abilities of these systems were compared using the area under the receiver operating characteristic curve (AUROC). Additionally, a 4-knotted restricted cubic spline regression was employed to evaluate the association between the different scoring systems and the risk of 28-day mortality. Finally, we conducted a subgroup analysis focusing on patients with abdominal aortic aneurysms (AAA).

RESULTS

This study enrolled 586 patients with AA (68.39% male). Among them, 26 patients (4.4%) died within 28 days. Comparative analysis revealed higher SAPS II, SAPS III, SOFA, LODS, OASIS, and SIRS scores in the deceased group, while no statistically significant difference was observed in GAS scores between the survivor and deceased groups (P = 0.148). The SAPS III system exhibited superior predictive value for the 28-day mortality rate (AUROC 0.805) compared to the LODS system (AUROC 0.771), SOFA (AUROC 0.757), SAPS II (AUROC 0.759), OASIS (AUROC 0.742), SIRS (AUROC 0.638), and GAS (AUROC 0.586) systems. The results of the univariate and multivariate logistic analyses showed that SAPS III was statistically significant for both 28-day and 1-year mortality. Subgroup analyses yielded results consistent with the overall findings. No nonlinear relationship was identified between these scoring systems and 28-day all-cause mortality (P for nonlinear > 0.05).

CONCLUSION

The SAPS III system demonstrated superior discriminatory ability for both 28-day and 1-year mortality compared to the GAS, SAPS II SIRS, SOFA, and OASIS systems among patients with AA.

Predicting ICU Readmission from Electronic Health Records via BERTopic with Long Short Term Memory Network Approach

Background: The increasing rate of intensive care unit (ICU) readmissions poses significant challenges in healthcare, impacting both costs and patient outcomes. Predicting patient readmission after discharge is crucial for improving medical quality and reducing expenses. Traditional analyses of electronic health record (EHR) data have primarily focused on numerical data, often neglecting valuable text data. Methods: This study employs a hybrid model combining BERTopic and Long Short-Term Memory (LSTM) networks to predict ICU readmissions. Leveraging the MIMIC-III database, we utilize both quantitative and text data to enhance predictive capabilities. Our approach integrates the strengths of unsupervised topic modeling with supervised deep learning, extracting potential topics from patient records and transforming discharge summaries into topic vectors for more interpretable and personalized predictions. Results: Utilizing a comprehensive dataset of 36,232 ICU patient records, our model achieved an AUROC score of 0.80, thereby surpassing the performance of traditional machine learning models. The implementation of BERTopic facilitated effective utilization of unstructured data, generating themes that effectively guide the selection of relevant predictive factors for patient readmission prognosis. This significantly enhanced the model's interpretative accuracy and predictive capability. Additionally, the integration of importance ranking methods into our machine learning framework allowed for an in-depth analysis of the significance of various variables. This approach provided crucial insights into how different input variables interact and impact predictions of patient readmission across various clinical contexts. Conclusions: The practical application of BERTopic technology in our hybrid model contributes to more efficient patient management and serves as a valuable tool for developing tailored treatment strategies and resource optimization. This study highlights the significance of integrating unstructured text data with traditional quantitative data to develop more accurate and interpretable predictive models in healthcare, emphasizing the importance of individualized care and cost-effective healthcare paradigms.

Intensive care unit caseload and workload and their association with outcomes in critically unwell patients: a large registry-based cohort analysis

BACKGROUND

Too high or too low patient volumes and work amounts may overwhelm health care professionals and obstruct processes or lead to inadequate personnel routine and process flow. We sought to evaluate, whether an association between current caseload, current workload, and outcomes exists in intensive care units (ICU).

METHODS

Retrospective cohort analysis of data from an Austrian ICU registry. Data on patients aged ≥ 18 years admitted to 144 Austrian ICUs between 2013 and 2022 were included. A Cox proportional hazards model with ICU mortality as the outcome of interest adjusted with patients' respective SAPS 3, current ICU caseload (measured by ICU occupancy rates), and current ICU workload (measured by median TISS-28 per ICU) as time-dependent covariables was constructed. Subgroup analyses were performed for types of ICUs, hospital care level, and pre-COVID or intra-COVID period.

RESULTS

415 584 patient admissions to 144 ICUs were analysed. Compared to ICU caseloads of 76 to 100%, there was no significant relationship between overuse of ICU capacity and risk of death [HR (95% CI) 1.06 (0.99-1.15), p = 0.110 for > 100%], but for lower utilisation [1.09 (1.02-1.16), p = 0.008 for ≤ 50% and 1.10 (1.05-1.15), p < 0.0001 for 51-75%]. Exceptions were significant associations for caseloads > 100% between 2020 and 2022 [1.18 (1.06-1.30), p = 0.001], i.e., the intra-COVID period. Compared to the reference category of median TISS-28 21-30, lower [0.88 (0.78-0.99), p = 0.049 for ≤ 20], but not higher workloads were significantly associated with risk of death. High workload may be associated with higher mortality in local hospitals [1.09 (1.01-1.19), p = 0.035 for 31-40, 1.28 (1.02-1.60), p = 0.033 for > 40].

CONCLUSIONS

In a system with comparably high intensive care resources and mandatory staffing levels, patients' survival chances are generally not affected by high intensive care unit caseload and workload. However, extraordinary circumstances, such as the COVID-19 pandemic, may lead to higher risk of death, if planned capacities are exceeded. High workload in ICUs in smaller hospitals with lower staffing levels may be associated with increased risk of death.

Dapagliflozin for Critically Ill Patients With Acute Organ Dysfunction: The DEFENDER Randomized Clinical Trial

Importance

Sodium-glucose cotransporter 2 (SGLT-2) inhibitors improve outcomes in patients with type 2 diabetes, heart failure, and chronic kidney disease, but their effect on outcomes of critically ill patients with organ failure is unknown.

Objective

To determine whether the addition of dapagliflozin, an SGLT-2 inhibitor, to standard intensive care unit (ICU) care improves outcomes in a critically ill population with acute organ dysfunction.

Design, Setting, and Participants

Multicenter, randomized, open-label, clinical trial conducted at 22 ICUs in Brazil. Participants with unplanned ICU admission and presenting with at least 1 organ dysfunction (respiratory, cardiovascular, or kidney) were enrolled between November 22, 2022, and August 30, 2023, with follow-up through September 27, 2023.

Intervention

Participants were randomized to 10 mg of dapagliflozin (intervention, n = 248) plus standard care or to standard care alone (control, n = 259) for up to 14 days or until ICU discharge, whichever occurred first.

Main Outcomes and Measures

The primary outcome was a hierarchical composite of hospital mortality, initiation of kidney replacement therapy, and ICU length of stay through 28 days, analyzed using the win ratio method. Secondary outcomes included the individual components of the hierarchical outcome, duration of organ support-free days, ICU, and hospital stay, assessed using bayesian regression models.

Results

Among 507 randomized participants (mean age, 63.9 [SD, 15] years; 46.9%, women), 39.6% had an ICU admission due to suspected infection. The median time from ICU admission to randomization was 1 day (IQR, 0-1). The win ratio for dapagliflozin for the primary outcome was 1.01 (95% CI, 0.90 to 1.13; P = .89). Among all secondary outcomes, the highest probability of benefit found was 0.90 for dapagliflozin regarding use of kidney replacement therapy among 27 patients (10.9%) in the dapagliflozin group vs 39 (15.1%) in the control group.

Conclusion and Relevance

The addition of dapagliflozin to standard care for critically ill patients and acute organ dysfunction did not improve clinical outcomes; however, confidence intervals were wide and could not exclude relevant benefits or harms for dapagliflozin.

Trial Registration

ClinicalTrials.gov Identifier: NCT05558098.

APRICOT-Mamba: Acuity Prediction in Intensive Care Unit (ICU): Development and Validation of a Stability, Transitions, and Life-Sustaining Therapies Prediction Model

On average, more than 5 million patients are admitted to intensive care units (ICUs) in the US, with mortality rates ranging from 10 to 29%. The acuity state of patients in the ICU can quickly change from stable to unstable, sometimes leading to life-threatening conditions. Early detection of deteriorating conditions can assist in more timely interventions and improved survival rates. While Artificial Intelligence (AI)-based models show potential for assessing acuity in a more granular and automated manner, they typically use mortality as a proxy of acuity in the ICU. Furthermore, these methods do not determine the acuity state of a patient (i.e., stable or unstable), the transition between acuity states, or the need for life-sustaining therapies. In this study, we propose APRICOT-M (Acuity Prediction in Intensive Care Unit-Mamba), a 1M-parameter state space-based neural network to predict acuity state, transitions, and the need for life-sustaining therapies in real-time among ICU patients. The model integrates ICU data in the preceding four hours (including vital signs, laboratory results, assessment scores, and medications) and patient characteristics (age, sex, race, and comorbidities) to predict the acuity outcomes in the next four hours. Our state space-based model can process sparse and irregularly sampled data without manual imputation, thus reducing the noise in input data and increasing inference speed. The model was trained on data from 107,473 patients (142,062 ICU admissions) from 55 hospitals between 2014-2017 and validated externally on data from 74,901 patients (101,356 ICU admissions) from 143 hospitals. Additionally, it was validated temporally on data from 12,927 patients (15,940 ICU admissions) from one hospital in 2018-2019 and prospectively on data from 215 patients (369 ICU admissions) from one hospital in 2021-2023. Three datasets were used for training and evaluation: the University of Florida Health (UFH) dataset, the electronic ICU Collaborative Research Database (eICU), and the Medical Information Mart for Intensive Care (MIMIC)-IV dataset. APRICOT-M significantly outperforms the baseline acuity assessment, Sequential Organ Failure Assessment (SOFA), for mortality prediction in both external (AUROC 0.95 CI: 0.94-0.95 compared to 0.78 CI: 0.78-0.79) and prospective (AUROC 0.99 CI: 0.97-1.00 compared to 0.80 CI: 0.65-0.92) cohorts, as well as for instability prediction (external AUROC 0.75 CI: 0.74-0.75 compared to 0.51 CI: 0.51-0.51, and prospective AUROC 0.69 CI: 0.64-0.74 compared to 0.53 CI: 0.50-0.57). This tool has the potential to help clinicians make timely interventions by predicting the transition between acuity states and decision-making on life-sustaining within the next four hours in the ICU.

Patient-level cost analysis of intensive care unit acquired infections: A prospective cohort study

INTRODUCTION

Hospital-associated infections (HAIs) are associated with increased mortality and prolonged hospital length-of-stay (LOS). Although some studies have shown that HAIs are associated with increased costs, these studies only used cost estimates, were carried out in a small number of centres, or only in high-income countries.

METHODS

We carried out a prospective cohort study in ten Brazilian intensive care units (ICUs) selected from a collaborative platform study (IMPACTO MR). We included all patients aged 18 years or older admitted from October 2019 to December 2021 and who had an ICU LOS of at least two days. The costs were adjusted for official inflation until December 2022 and converted into international dollars using the 2021 purchasing power parity (PPP) conversion rate. We used a propensity score matching method to compare patients with HAIs and patients without HAIs, and patients with and without ventilator-associated pneumonia (VAP), central-line bloodstream infection (CLABSI), catheter-associated urinary tract infection (CA-UTI) and multidrug-resistant (MDR) HAIs.

RESULTS

We included 7,953 patients in the study, of whom 574 (7.2%) had an HAI during their ICU stay. After propensity-score matching, patients with HAIs had ICU costs that were more than three times higher than those of patients without HAIs [$ 19,642 (IQR; 12,884-35,134) vs. 6,086 (IQR; 3,268-12,550); p <0.001). Patients with VAP, CLABSI, and CA-UTI, but not with MDR-HAIs also had higher total ICU costs.

CONCLUSIONS

HAIs acquired in the ICU are associated with higher ICU costs. These findings were consistent across specific types of infection.

Prognosticating the outcome of intensive care in older patients-a narrative review

Prognosis determines major decisions regarding treatment for critically ill patients. Statistical models have been developed to predict the probability of survival and other outcomes of intensive care. Although they were trained on the characteristics of large patient cohorts, they often do not represent very old patients (age ≥ 80 years) appropriately. Moreover, the heterogeneity within this particular group impairs the utility of statistical predictions for informing decision-making in very old individuals. In addition to these methodological problems, the diversity of cultural attitudes, available resources as well as variations of legal and professional norms limit the generalisability of prediction models, especially in patients with complex multi-morbidity and pre-existing functional impairments. Thus, current approaches to prognosticating outcomes in very old patients are imperfect and can generate substantial uncertainty about optimal trajectories of critical care in the individual. This article presents the state of the art and new approaches to predicting outcomes of intensive care for these patients. Special emphasis has been given to the integration of predictions into the decision-making for individual patients. This requires quantification of prognostic uncertainty and a careful alignment of decisions with the preferences of patients, who might prioritise functional outcomes over survival. Since the performance of outcome predictions for the individual patient may improve over time, time-limited trials in intensive care may be an appropriate way to increase the confidence in decisions about life-sustaining treatment.

Enhancing SAPS-3 Predictive Accuracy with Initial, Peak, and Last Lactate Measurements in Septic Shock

Background/Objectives: Septic shock is a severe condition with high mortality necessitating precise prognostic tools for improved patient outcomes. This study aimed to evaluate the collective predictive value of the Simplified Acute Physiology Score 3 (SAPS-3) and lactate measurements (initial, peak, last, and clearance rates within the first 24 h) in patients with septic shock. Specifically, it sought to determine how these markers enhance predictive accuracy for 28-day mortality beyond SAPS-3 alone. Methods: This retrospective cohort study analyzed data from 66 septic shock patients at two ICUs of Vienna General Hospital (2017-2019). SAPS-3 and lactate levels (initial, peak, last measurement within 24 h, and 24 h clearance) were obtained from electronic health records. Logistic regression models were constructed to identify predictors of 28-day mortality, and receiver operating characteristic (ROC) curves assessed predictive accuracy. Results: Among 66 patients, 36 (55%) died within 28 days. SAPS-3 scores significantly differed between survivors and non-survivors (76 vs. 85 points; p = 0.016). First, last, and peak lactate were significantly higher in non-survivors compared to survivors (all p < 0.001). The combination of SAPS-3 and first lactate produced the highest predictive accuracy (AUC = 80.6%). However, 24 h lactate clearance was not predictive of mortality. Conclusions: Integrating SAPS-3 with lactate measurements, particularly first lactate, improves predictive accuracy for 28-day mortality in septic shock patients. First lactate serves as an early, robust prognostic marker, providing crucial information for clinical decision-making and care prioritization. Further large-scale studies are needed to refine these predictive tools and validate their efficacy in guiding treatment strategies.

Impact of the COVID-19 Pandemic on the Outcomes of Patients Undergoing Oncological Surgeries: CORONAL Study

BACKGROUND

The impact of coronavirus disease 2019 (COVID-19) on postoperative recovery from oncology surgeries should be understood for the clinical decision-making. Therefore, this study was designed to evaluate the postoperative cumulative 28-day mortality and the morbidity of surgical oncology patients during the COVID-19 pandemic.

METHODS

This retrospective cohort study included patients consecutively admitted to intensive care units (ICU) of three centres for postoperative care of oncologic surgeries between March to June 2019 (first phase) and March to June 2020 (second phase). The primary outcome was cumulative 28-day postoperative mortality. Secondary outcomes were postoperative organic dysfunction and the incidence of clinical complications. Because of the possibility of imbalance between groups, adjusted analyses were performed: Cox proportional hazards model (primary outcome) and multiple logistic regression model (secondary outcomes).

RESULTS

After screening 328 patients, 291 were included. The proportional hazard of cumulative 28-day mortality was higher in the second phase than that in the first phase in the Cox model, with the adjusted hazard ratio of 4.35 (95% confidence interval [CI] 2.15-8.82). The adjusted incidences of respiratory complications (odds ratio [OR] 5.35; 95% CI 1.42-20.11) and pulmonary infections (OR 1.53; 95% CI 1.08-2.17) were higher in the second phase. However, the adjusted incidence of other infections was lower in the second phase (OR 0.78; 95% CI 0.67-0.91).

CONCLUSIONS

Surgical oncology patients who underwent postoperative care in the intensive care unit during the COVID-19 pandemic had higher hazard of 28-day mortality. Furthermore, these patients had higher odds of respiratory complications and pulmonary infections. Trials registration The study is registered in the Brazilian Registry of Clinical Trials under the code RBR-8ygjpqm, UTN code U1111-1293-5414.

Recommended dosages of analgesic and sedative drugs in intensive care result in a low incidence of potentially toxic blood concentrations

Background

Standard dosages of analgesic and sedative drugs are given to intensive care patients. The resulting range of blood concentrations and corresponding clinical responses need to be better examined. The purpose of this study was to describe daily dosages, measured blood concentrations, and clinical responses in critically ill patients. The purpose was also to contribute to establishing whole blood concentration reference values of the drugs investigated.

Methods

A descriptive study of prospectively collected data from 302 admissions to a general intensive care unit (ICU) at a university hospital. Ten drugs (clonidine, fentanyl, morphine, dexmedetomidine, ketamine, ketobemidone, midazolam, paracetamol, propofol, and thiopental) were investigated, and daily dosages recorded. Blood samples were collected twice daily, and drug concentrations were measured. Clinical responses were registered using Richmond agitation-sedation scale (RASS) and Numeric rating scale (NRS).

Results

Drug dosages were within recommended dose ranges. Blood concentrations for all 10 drugs showed a wide variation within the cohort, but only 3% were above therapeutic interval where clonidine (57 of 122) and midazolam (38 of 122) dominated. RASS and NRS were not correlated to drug concentrations.

Conclusion

Using recommended dose intervals for analgesic and sedative drugs in the ICU setting combined with regular monitoring of clinical responses such as RASS and NRS leads to 97% of concentrations being below the upper limit in the therapeutic interval. This study contributes to whole blood drug concentration reference values regarding these 10 drugs.

Mortality rates among adult critical care patients with unusual or extreme values of vital signs and other physiological parameters: a retrospective study

BACKGROUND

We evaluated relationships of vital signs and laboratory-tested physiological parameters with in-hospital mortality, focusing on values that are unusual or extreme even in critical care settings.

METHODS

We retrospectively studied Philips Healthcare-MIT eICU data (207 U.S. hospitals, 20142015), including 166,959 adult-patient critical care admissions. Analyzing most-deranged (worst) value measured in the first admission day, we investigated vital signs (body temperature, heart rate, mean arterial pressure, and respiratory rate) as well as albumin, bilirubin, blood pH via arterial blood gas (ABG), blood urea nitrogen, creatinine, FiO2 ABG, glucose, hematocrit, PaO2 ABG, PaCO2 ABG, sodium, 24-hour urine output, and white blood cell count (WBC).

RESULTS

In-hospital mortality was ≥50% at extremes of low blood pH, low and high body temperature, low albumin, low glucose, and low heart rate. Near extremes of blood pH, temperature, glucose, heart rate, PaO2 , and WBC, relatively. Small changes in measured values correlated with several-fold mortality rate increases. However, high mortality rates and abrupt mortality increases were often hidden by the common practice of thresholding or binning physiological parameters. The best predictors of in-hospital mortality were blood pH, temperature, and FiO2 (scaled Brier scores: 0.084, 0.063, and 0.049, respectively).

CONCLUSIONS

In-hospital mortality is high and sharply increasing at extremes of blood pH, body temperature, and other parameters. Common-practice thresholding obscures these associations. In practice, vital signs are sometimes treated more casually than laboratory-tested parameters. Yet, vitals are easier to obtain and we found they are often the best mortality predictors, supporting perspectives that vitals are undervalued.

Predictive validity of the Clinical Frailty Scale-España on the increase in dependency after hospital discharge

INTRODUCTION

The frailty present at hospital admission and the stressors to which patients are subjected during their stay may increase dependency at hospital discharge.

OBJECTIVES

To assess the predictive validity of the Clinical Frailty Scale-España (CFS-Es) on increased dependency at 3 and 12 months (m) after hospital discharge.

METHODOLOGY

Multicentre cohort study in 2020-2022. Including patients with >48 h stay in intensive care units (ICU) and non-COVID-19.

VARIABLES

pre-admission frailty (CFS-Es). Sex, age, days of stay (ICU and hospital), dependency on admission and at 3 m and 12 m after discharge (Barthel index), muscle weakness (Medical Research Council Scale sum score <48), hospital readmissions.

STATISTICS

descriptive and multivariate analysis.

RESULTS

254 cases were included. Thirty-nine per cent were women and the median [Q1-Q3] age was 67 [56-77] years. SAPS 3 on admission (median [Q1-Q3]): 62 [51-71] points. Frail patients on admission (CFS-Es 5-9): 58 (23%). Dependency on admission (n = 254) vs. 3 m after hospital discharge (n = 171) vs. 12 m after hospital discharge (n = 118): 1) Barthel 90-100: 82% vs. 68% vs. 65%. 2) Barthel 60-85: 15% vs. 15% vs. 20%. 3) Barthel 0-55: 3% vs. 17% vs. 15%. In the multivariate analysis, adjusted for the variables recorded, we observed that frail patients on admission (CFS-Es 5-9) are 2.8 times (95%CI: 1.03-7.58; p = 0.043) more likely to increase dependency (Barthel 90-100 to <90 or Barthel 85-60 to <60) at 3 m post-discharge (with respect to admission) and 3.5 times (95%CI: 1.18-10.30; p = 0.024) more likely to increase dependency at 12 m post-discharge. Furthermore, for each additional CFS-Es point there is a 1.6-fold (95%CI: 1.01-2.23; p = 0.016) greater chance of increased dependency in the 12 m following discharge.

CONCLUSIONS

CFS-Es at admission can predict increased dependency at 3 m and 12 m after hospital discharge.

Harnessing Big Data in Critical Care: Exploring a new European Dataset

Freely available datasets have become an invaluable tool to propel data-driven research, especially in the field of critical care medicine. However, the number of datasets available is limited. This leads to the repeated reuse of datasets, inherently increasing the risk of selection bias. Additionally, the need arose to validate insights derived from one dataset with another. In 2023, the Salzburg Intensive Care database (SICdb) was introduced. SICdb offers insights in currently 27,386 intensive care admissions from 21,583 patients. It contains cases of general and surgical intensive care from all disciplines. Amongst others SICdb contains information about: diagnosis, therapies (including data on preceding surgeries), scoring, laboratory values, respiratory and vital signals, and configuration data. Data for SICdb (1.0.6) was collected at one single tertiary care institution of the Department of Anesthesiology and Intensive Care Medicine at the Salzburger Landesklinik (SALK) and Paracelsus Medical University (PMU) between 2013 and 2021. This article aims to elucidate on the characteristics of the dataset, the technical implementation, and provides analysis of its strengths and limitations.

The Influence of Potential Organ Donors on Standardized Mortality Ratios and ICU Benchmarking

OBJECTIVES

The standardized mortality ratio (SMR) is a common metric to benchmark ICUs. However, SMR may be artificially distorted by the admission of potential organ donors (POD), who have nearly 100% mortality, although risk prediction models may not identify them as high-risk patients. We aimed to evaluate the impact of PODs on SMR.

DESIGN

Retrospective registry-based multicenter study.

SETTING

Twenty ICUs in Finland, Estonia, and Switzerland in 2015-2017.

PATIENTS

Sixty thousand forty-seven ICU patients.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

We used a previously validated mortality risk model to calculate the SMRs. We investigated the impact of PODs on the overall SMR, individual ICU SMR and ICU benchmarking. Of the 60,047 patients admitted to the ICUs, 514 (0.9%) were PODs, and 477 (93%) of them died. POD deaths accounted for 7% of the total 6738 in-hospital deaths. POD admission rates varied from 0.5 to 18.3 per 1000 admissions across ICUs. The risk prediction model predicted a 39% in-hospital mortality for PODs, but the observed mortality was 93%. The ratio of the SMR of the cohort without PODs to the SMR of the cohort with PODs was 0.96 (95% CI, 0.93-0.99). Benchmarking results changed in 70% of ICUs after excluding PODs.

CONCLUSIONS

Despite their relatively small overall number, PODs make up a large proportion of ICU patients who die. PODs cause bias in SMRs and in ICU benchmarking. We suggest excluding PODs when benchmarking ICUs with SMR.

A nationwide cohort study on the association between intensive care treatments and mental distress linked psychiatric disorders

Given the psychic strain patients experience in the intensive care unit (ICU), a potential risk of mental disorders has been suggested. However, the effects of intensive care treatment per se are unknown. We investigated whether the level of intensive care treatments is an independent risk factor for developing long-term mental disorders after intensive care. In a national cohort of adult ICU patients we combined data on diagnoses, treatment, and causes of death. We defined extensive ICU treatment as being treated with invasive ventilation for > 24 h, continuous renal replacement therapy, or both. The primary outcome was incident mental disorder 1 year after ICU admission. Extensive ICU treatment was found to be associated with a decreased risk of developing a mental disorder ≥ 1 year after ICU admission (HR 0.90, 95% CI 0.82-0.99, p = 0.04), and increasing severity of acute illness (HR 1.18, 95% CI 1.06-1.32, p < 0.001) were associated with an increased risk of mental disorders. Because death acted as a competing risk for mental illness, mortality might help explain the apparent protective effect of extensive ICU care.Trial registration Clinical Trials Registry (Identification number NCT05137977). Registered 16 November 2021. As a registry trial the patients were already included at the trial registration i.e. it was retrospectively registered.

Comparison between ultrasonography and computed tomography for measuring skeletal muscle mass in critically ill patients with different body mass index

BACKGROUND & AIM

Among critical patients, there is an early onset of changes in both the quantity and quality of muscle mass. It is essential to find tools that promptly identify this muscle mass loss. The aim of this study was to compare the ultrasonography of the quadriceps femoris to the gold standard, thigh computed tomography (CT) for assessing the musculature of critically ill patients with different body mass index who have suffered traumatic brain injury.

METHODS

This is a prospective validation study in an Intensive Care Unit (ICU) specialized in trauma care, located at a tertiary teaching hospital. Our study involved a convenience sample of patients. Sequential ultrasound and CT scans were performed at three distinct time intervals: upon admission, between 24 and 96 h' post-admission, and finally, between 96 and 168 h' post-admission. For all ultrasound measurements, we conducted simultaneous quadriceps CT measurements. The correlation between measurements obtained by ultrasound and computed tomography at three different times and in three BMI ranges was analyzed, in individuals with normal weight, overweight and obese.

RESULTS

Results: We analyzed 252 images in 49 patients in time 1, 40 patients in time 2, and 37 in time 3 to compare the thickness quadriceps muscle using US and CT. Of these, 18 patients had a BMI ≤ 24.9 kg/m2 (normal weight), 18 patients from 25 to 29.9 kg/m2 (overweight), and 8 patients had a BMI ≥ 30 kg/m2 (obese). The mean age was 37 years, the majority (94%) were male and the main comorbidities were: hypertension 12%, diabetes 4% and 14% smoking. The results revealed minor discrepancies between measurements obtained through the two methods, these changes were not influenced by the body mass index, with these variations being practically insignificant in the context of clinical application. Thus, the correlation and concordance between the values obtained found a strong positive correlation with good limits of agreement. The Spearman's correlation coefficients obtained were r = 0.89, 0.91 and 0.88, p < 0.01 at T1, T2 and T3 respectively for normal weight, r = 0.91, 0.80 and 0.81, p < 0.01 at T1, T2 and T3 respectively for overweight and r = 0.89, 0.94 and 0.84, p < 0.01 at T1, T2 and T3 respectively for obesity. In addition to a positive correlation, we observed a high agreement between the methods. The Bland & Altman analysis at time 1 showed, respectively, the bias of 1.46, 2.03 and 0.76. At time 2, the bias was 0.42, 3.11 and 2.12. At time 3, the bias was 2.26, 3.38 and 2.11 mm.

CONCLUSION

Our findings suggest that measure femoral quadriceps muscle thickness ultrasound-based exhibits a comparable performance to thigh CT. This conclusion stems from the excellent correlation and good agreement observed between ultrasound and CT, which is considered the gold standard for muscle assessment in critically ill patients.

TRIAL REGISTRATION

This clinical trial is registered at REBEC https://ensaiosclinicos.gov.br/ identifier: RBR-2bzspnz. The protocol was approved, on July 30, 2019, by the Research Ethics Committee of the Hospital das Clínicas, Faculdade de Medicina de Ribeirão Preto - Trial Registration Number: 3,475,851.

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.

Profiling and Benchmarking Central Nervous System Infections in an Infectious Diseases Intensive Care Unit

BACKGROUND

There is little information comparing the performance of community acquired central nervous system infections (CNSI) treatment by intensive care units (ICUs) specialized in infectious diseases with treatment at other ICUs. Our objective was to reduce these gaps, creating bases for benchmarking and future case-mix classification.

METHODS

This is a retrospective observational cohort of 785 admissions with 82 cases of CNSI admitted to the ICU of an important Brazilian referral center for infectious diseases (INI) between January 2012 and January 2019. Comparisons were made to data retrospectively collected from the 303,500 intensive care admissions from the Brazilian state health care system included in the Epimed Monitor database. Clinical, epidemiologic, and performance indicators: the standardized mortality rate (SMR) and the standardized resource use rate per ICU surviving patient (SRU) were collected.

RESULTS

Case-mix infections profile and SMR/SRU data. SUS Mixed medical/surgical ICUs: SMR = 1.26, SRU = 1.59; SUS Neurological ICUs: SMR = 1.17, SRU = 2.23; INI ICU: SMR = 1.1, SRU = 1.1; INI ICU CNSI patients: SMR = 0.95, SRU = 1.01.

CONCLUSIONS

Severe patients with CNSI can be efficiently and effectively treated in an ICU specialized in infectious diseases when compared to mixed medical/surgical and neurological ICUs from the public health system. At the same time, we provided profiling and a case-mix that can help and encourage benchmarking by other institutions and other countries.

Predictive Performance for Hospital Mortality of SAPS 3, SOFA, ISS, and New ISS in Critically Ill Trauma Patients: A Validation Cohort Study

Background: It is not known whether anatomical scores perform better than general critical care scores for trauma patients admitted to the intensive care unit (ICU). We compare the predictive performance for hospital mortality of general critical care scores (SAPS 3 and SOFA) with anatomical injury-based scores (Injury Severity Score [ISS] and New ISS [NISS]). Methods: Retrospective cohort study of patients admitted to a specialized trauma ICU from a tertiary hospital in São Paulo, Brazil between May, 2012 and January, 2016. We retrieved data from the ICU database for critical care scores and calculated ISS and NISS from chart data and whole body computed tomography results. We compared the predictive performance for hospital mortality of each model through discrimination, calibration, and decision-curve analysis. Results: The sample comprised 1053 victims of trauma admitted to the ICU, with 84.2% male patients and mean age of 40 (±18) years. Main injury mechanism was blunt trauma (90.7%). Traumatic brain injury was present in 67.8% of patients; 43.3% with severe TBI. At the time of ICU admission, 846 patients (80.3%) were on mechanical ventilation and 644 (64.3%) on vasoactive drugs. Hospital mortality was 23.8% (251). Median SAPS 3 was 41; median maximum SOFA within 24 h of admission, 7; ISS, 29; and NISS, 41. AUROCs (95% CI) were: SAPS 3 = 0.786 (0.756-0.817), SOFA = 0.807 (0.778-0.837), ISS = 0.616 (0.577-0.656), and NISS = 0.689 (0.649-0.729). In pairwise comparisons, SAPS 3 and SOFA did not differ, while both outperformed the anatomical scores (p < .001). Maximum SOFA within 24 h of admission presented the best calibration and net benefit in decision-curve analysis. Conclusions: Trauma-specific anatomical scores have fair performance in critically ill trauma patients and are outperformed by SAPS 3 and SOFA. Illness severity is best characterized by organ dysfunction and physiological variables than anatomical injuries.

Delirium During Critical Illness and Subsequent Change of Treatment in Patients With Cancer: A Mediation Analysis

OBJECTIVES

To assess whether delirium during ICU stay is associated with subsequent change in treatment of cancer after discharge.

DESIGN

Retrospective cohort study.

SETTING

A 50-bed ICU in a dedicated cancer center.

PATIENTS

Patients greater than or equal to 18 years old with a previous proposal of cancer treatment (chemotherapy, target therapy, hormone therapy, immunotherapy, radiotherapy, oncologic surgery, and bone marrow transplantation).

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

We considered delirium present if Confusion Assessment Method for the ICU was positive. We assessed the association between delirium and modification of the treatment after discharge. We also performed a mediation analysis to assess both the direct and indirect (i.e., mediated by the development of functional dependence after discharge) of delirium on modification of cancer treatment and whether the modification of cancer treatment was associated with mortality at 1 year. We included 1,134 patients, of whom, 189 (16.7%) had delirium. Delirium was associated with the change in cancer treatment (adjusted odds ratio [OR], 3.80; 95% CI, 2.72-5.35). The association between delirium in ICU and change of treatment was both direct and mediated by the development of functional dependence after discharge. The proportion of the total effect of delirium on change of treatment mediated by the development of functional dependence after discharge was 33.0% (95% CI, 21.7-46.0%). Change in treatment was associated with increased mortality at 1 year (adjusted OR, 2.68; 95% CI, 2.01-3.60).

CONCLUSIONS

Patients who had delirium during ICU stay had a higher rate of modification of cancer treatment after discharge. The effect of delirium on change in cancer treatment was only partially mediated by the development of functional dependence after discharge. Change in cancer treatment was associated with increased 1-year mortality.

Application of ventilator-associated events (VAE) in ventilator-associated pneumonia (VAP) notified in Brazil (IMPACTO MR-PAV): a protocol for a cohort study

INTRODUCTION

Certain criteria for ventilator-associated events (VAE) definition might influence the type of an event, its detection rate and consequently the resource expenditure in intensive care unit. The Impact of Infections by Antimicrobial-Resistant Microorganisms - Ventilator-Associated Pneumonia (IMPACTO MR-PAV) aims to evaluate the incidence and diagnostic accuracy of ventilator-associated pneumonia (VAP) using the current criteria for VAP surveillance in Brazil versus the VAE criteria defined by the US National Healthcare Safety Network-Center for Diseases Control and Prevention (CDC) criteria.

METHODS AND ANALYSIS

The study will be conducted in around 15 centres across Brazil from October 2022 to December 2023. Trained healthcare professionals will collect data and compare the incidence of VAP using both the current criteria for VAP surveillance in Brazil and the VAE criteria defined by the CDC. The accuracy of the two criteria for identifying VAP will also be analysed. It will also characterise other events associated with mechanical ventilation (ventilator-associated condition, infection-related ventilator-associated complication) and adjudicate VAP reported to the Brazilian Health Regulatory Agency (ANVISA) using current epidemiological diagnostic criteria.

ETHICS AND DISSEMINATION

This study was approved by the Institutional Review Board under the number 52354721.0.1001.0070. The study's primary outcome measure will be the incidence of VAP using the two different surveillance criteria, and the secondary outcome measures will be the accuracy of the two criteria for identifying VAP and the adjudication of VAP reported to ANVISA. The results will contribute to the improvement of VAP surveillance in Brazil and may have implications for other countries that use similar criteria.

TRIAL REGISTRATION NUMBER

NCT05589727; Clinicaltrials.gov.

Prognosis after Intensive Care for COPD Exacerbation in Relation to Long-Term Oxygen Therapy: A Nationwide Cohort Study

Decisions to admit or refuse admission to intensive care for acute exacerbations of COPD (AECOPD) can be difficult, due to an uncertainty about prognosis. Few studies have evaluated outcomes after intensive care for AECOPD in patients with chronic respiratory failure requiring long-term oxygen therapy (LTOT). In this nationwide observational cohort study, we investigated survival after first-time admission for AECOPD in all patients aged ≥40 years admitted to Swedish intensive care units between January 2008 and December 2015, comparing patients with and without LTOT. Among the 4,648 patients enrolled in the study, 450 were on LTOT prior to inclusion. Respiratory support data was available for 2,631 patients; 73% of these were treated with noninvasive ventilation (NIV) only, 17% were treated with immediate invasive ventilation, and 10% were intubated after failed attempt with NIV. Compared to patients without LTOT, patients with LTOT had higher 30-day mortality (38% vs. 25%; p < 0.001) and one-year mortality (70% vs. 43%; p < 0.001). Multivariable logistic and Cox regression models adjusted for age, sex and SAPS3 score confirmed higher mortality in LTOT, odds ratio for 30-day mortality was 1.8 ([95% confidence interval] 1.5-2.3) and hazard ratio for one-year mortality was 1.8 (1.6-2.0). In summary, although need for LTOT is a negative prognostic marker for survival after AECOPD requiring intensive care, a majority of patients with LTOT survived the AECOPD and 30% were alive after one year.

Incidence, risk factors and outcome of acute kidney injury in critically ill COVID-19 patients in Tyrol, Austria: a prospective multicenter registry study

INTRODUCTION

Acute kidney injury is a frequent complication in critically ill patients with and without COVID-19. The aim of this study was to evaluate the incidence of, and risk factors for, acute kidney injury and its effect on clinical outcomes of critically ill COVID-19 patients in Tyrol, Austria.

METHODS

This multicenter prospective registry study included adult patients with a SARS-CoV-2 infection confirmed by polymerase chain reaction, who were treated in one of the 12 dedicated intensive care units during the COVID-19 pandemic from February 2020 until May 2022.

RESULTS

In total, 1042 patients were included during the study period. The median age of the overall cohort was 66 years. Of the included patients, 267 (26%) developed acute kidney injury during their intensive care unit stay. In total, 12.3% (n = 126) required renal replacement therapy with a median duration of 9 (IQR 3-18) days. In patients with acute kidney injury the rate of invasive mechanical ventilation was significantly higher with 85% (n = 227) compared to 41% (n = 312) in the no acute kidney injury group (p < 0.001). The most important risk factors for acute kidney injury were invasive mechanical ventilation (OR = 4.19, p < 0.001), vasopressor use (OR = 3.17, p < 0.001) and chronic kidney disease (OR = 2.30, p < 0.001) in a multivariable logistic regression analysis. Hospital and intensive care unit mortality were significantly higher in patients with acute kidney injury compared to patients without acute kidney injury (Hospital mortality: 52.1% vs. 17.2%, p < 0.001, ICU-mortality: 47.2% vs. 14.7%, p < 0.001).

CONCLUSION

As in non-COVID-19 patients, acute kidney injury is clearly associated with increased mortality in critically ill COVID-19 patients. Among known risk factors, invasive mechanical ventilation has been identified as an independent and strong predictor of acute kidney injury.

Long-term mortality of critically ill patients with cancer and delirium who survived to discharge: a retrospective cohort study

PURPOSE

Delirium is common in critically ill patients and has been associated with lower short-term survival; however, its association with long-term survival has been scarcely evaluated and few studies have shown divergent results.

METHODS

We conducted a retrospective cohort study of adult patients with cancer admitted to the intensive care unit (ICU) and discharged from hospital from January 2015 to December 2018. We considered delirium present if the Confusion Assessment Method for Intensive Care Unit (CAM-ICU) result was positive. We assessed the association between delirium during ICU stay and long-term mortality (up to three years after discharge). We also assessed the association between delirium type (hypoactive, hyperactive, and mixed) with long-term mortality.

RESULTS

We included 3,079 patients. Of these, 430 (14%) were considered delirious at some point during their ICU stay. Delirium was associated with one-year mortality after hospital discharge (hazard ratio [HR], 1.58; 95% confidence interval [CI], 1.36 to 1.83) after adjustment for potential confounders, but not with one to three year-mortality (HR, 0.92; 95% CI, 0.61 to 1.39). Hypoactive and mixed delirium were associated with one-year mortality (HR, 1.77; 95% CI, 1.46 to 2.14 and HR, 1.56; 95% CI, 1.21 to 2.00, respectively), but none of the delirium motor types was associated with one to three-year mortality.

CONCLUSIONS

We observed that delirium during ICU stay was associated with increased one-year mortality, but was not with mortality after one year. This association was observed in hypoactive and mixed delirium types but not with hyperactive delirium.

A simple mortality prediction model for sepsis patients in intensive care

Background

Sepsis is common in the intensive care unit (ICU). Two of the ICU's most widely used mortality prediction models are the Simplified Acute Physiology Score 3 (SAPS-3) and the Sequential Organ Failure Assessment (SOFA) score. We aimed to assess the mortality prediction performance of SAPS-3 and SOFA upon ICU admission for sepsis and find a simpler mortality prediction model for these patients to be used in clinical practice and when conducting studies.

Methods

A retrospective study of adult patients fulfilling the Sepsis-3 criteria admitted to four general ICUs was performed. A simple prognostic model was created using backward stepwise multivariate logistic regression. The area under the curve (AUC) of SAPS-3, SOFA and the simple model was assessed.

Results

One thousand nine hundred eighty four admissions were included. A simple six-parameter model consisting of age, immunosuppression, Glasgow Coma Scale, body temperature, C-reactive protein and bilirubin had an AUC of 0.72 (95% confidence interval (CI) 0.69-0.75) for 30-day mortality, which was non-inferior to SAPS-3 (AUC 0.75, 95% CI 0.72-0.77) (p = 0.071). SOFA had an AUC of 0.67 (95% CI 0.64-0.70) and was inferior to SAPS-3 (p < 0.001) and our simple model (p = 0.0019).

Conclusion

SAPS-3 has a lower prognostic value in sepsis than in the general ICU population. SOFA performs less well than SAPS-3. Our simple six-parameter model predicts mortality just as well as SAPS-3 upon ICU admission for sepsis, allowing the design of simple studies and performance monitoring.

Relationship between clinical-epidemiological parameters and outcomes of patients with COVID-19 admitted to the intensive care unit: a report from a Brazilian hospital

Background

People in low-income countries, especially those with low socio-economic conditions, are likelier to test positive for SARS-CoV-2. The unequal conditions of public health systems also increase the infection rate and make early identification and treatment of at-risk patients difficult. Here, we aimed to characterize the epidemiological profile of COVID-19 patients in intensive care and identify laboratory and clinical markers associated with death.

Materials and methods

We conducted an observational, descriptive, and cross-sectional study in a reference hospital for COVID-19 treatment in the Southern Region of Bahia State, in Brazil, to evaluate the epidemiological, clinical, and laboratory characteristics of COVID-19 patients admitted to the intensive care unit (ICU). Additionally, we used the area under the curve (AUC) to classify survivors and non-survivors and a multivariate logistic regression analysis to assess factors associated with death. Data was collected from the hospital databases between April 2020 and July 2021.

Results

The use of bladder catheters (OR 79.30; p < 0.0001) and central venous catheters (OR, 45.12; p < 0.0001) were the main factors associated with death in ICU COVID-19 patients. Additionally, the number of non-survivors increased with age (p < 0.0001) and prolonged ICU stay (p < 0.0001). Besides, SAPS3 presents a higher sensibility (77.9%) and specificity (63.1%) to discriminate between survivors and non-survivor with an AUC of 0.79 (p < 0.0001).

Conclusion

We suggest that multi-laboratory parameters can predict patient prognosis and guide healthcare teams toward more assertive clinical management, better resource allocation, and improved survival of COVID-19 patients admitted to the ICU.

Prediction of episode of hemodynamic instability using an electrocardiogram based analytic: a retrospective cohort study

BACKGROUND

Predicting the onset of hemodynamic instability before it occurs remains a sought-after goal in acute and critical care medicine. Technologies that allow for this may assist clinicians in preventing episodes of hemodynamic instability (EHI). We tested a novel noninvasive technology, the Analytic for Hemodynamic Instability-Predictive Indicator (AHI-PI), which analyzes a single lead of electrocardiogram (ECG) and extracts heart rate variability and morphologic waveform features to predict an EHI prior to its occurrence.

METHODS

Retrospective cohort study at a quaternary care academic health system using data from hospitalized adult patients between August 2019 and April 2020 undergoing continuous ECG monitoring with intermittent noninvasive blood pressure (NIBP) or with continuous intraarterial pressure (IAP) monitoring.

RESULTS

AHI-PI's low and high-risk indications were compared with the presence of EHI in the future as indicated by vital signs (heart rate > 100 beats/min with a systolic blood pressure < 90 mmHg or a mean arterial blood pressure of < 70 mmHg). 4,633 patients were analyzed (3,961 undergoing NIBP monitoring, 672 with continuous IAP monitoring). 692 patients had an EHI (380 undergoing NIBP, 312 undergoing IAP). For IAP patients, the sensitivity and specificity of AHI-PI to predict EHI was 89.7% and 78.3% with a positive and negative predictive value of 33.7% and 98.4% respectively. For NIBP patients, AHI-PI had a sensitivity and specificity of 86.3% and 80.5% with a positive and negative predictive value of 11.7% and 99.5% respectively. Both groups performed with an AUC of 0.87. AHI-PI predicted EHI in both groups with a median lead time of 1.1 h (average lead time of 3.7 h for IAP group, 2.9 h for NIBP group).

CONCLUSIONS

AHI-PI predicted EHIs with high sensitivity and specificity and within clinically significant time windows that may allow for intervention. Performance was similar in patients undergoing NIBP and IAP monitoring.

A generalizable and interpretable model for mortality risk stratification of sepsis patients in intensive care unit

PURPOSE

This study aimed to construct a mortality model for the risk stratification of intensive care unit (ICU) patients with sepsis by applying a machine learning algorithm.

METHODS

Adult patients who were diagnosed with sepsis during admission to ICU were extracted from MIMIC-III, MIMIC-IV, eICU, and Zigong databases. MIMIC-III was used for model development and internal validation. The other three databases were used for external validation. Our proposed model was developed based on the Extreme Gradient Boosting (XGBoost) algorithm. The generalizability, discrimination, and validation of our model were evaluated. The Shapley Additive Explanation values were used to interpret our model and analyze the contribution of individual features.

RESULTS

A total of 16,741, 15,532, 22,617, and 1,198 sepsis patients were extracted from the MIMIC-III, MIMIC-IV, eICU, and Zigong databases, respectively. The proposed model had an area under the receiver operating characteristic curve (AUROC) of 0.84 in the internal validation, which outperformed all the traditional scoring systems. In the external validations, the AUROC was 0.87 in the MIMIC-IV database, better than all the traditional scoring systems; the AUROC was 0.83 in the eICU database, higher than the Simplified Acute Physiology Score III and Sequential Organ Failure Assessment (SOFA),equal to 0.83 of the Acute Physiology and Chronic Health Evaluation IV (APACHE-IV), and the AUROC was 0.68 in the Zigong database, higher than those from the systemic inflammatory response syndrome and SOFA. Furthermore, the proposed model showed the best discriminatory and calibrated capabilities and had the best net benefit in each validation.

CONCLUSIONS

The proposed algorithm based on XGBoost and SHAP-value feature selection had high performance in predicting the mortality of sepsis patients within 24 h of ICU admission.

Who needs clinician attention first? A qualitative study of critical care clinicians' needs that enable the prioritization of care for populations of acutely ill patients

BACKGROUND

To adequately care for groups of acutely ill patients, clinicians maintain situational awareness to identify the most acute needs within the entire intensive care unit (ICU) population through constant reappraisal of patient data from electronic medical record and other information sources. Our objective was to understand the information and process requirements of clinicians caring for multiple ICU patients and how this information is used to support their prioritization of care among populations of acutely ill patients. Additionally, we wanted to gather insights on the organization of an Acute care multi-patient viewer (AMP) dashboard.

METHODS

We conducted and audio-recorded semi-structured interviews of ICU clinicians who had worked with the AMP in three quaternary care hospitals. The transcripts were analyzed with open, axial, and selective coding. Data was managed using NVivo 12 software.

RESULTS

We interviewed 20 clinicians and identified 5 main themes following data analysis: (1) strategies used to enable patient prioritization, (2) strategies used for optimizing task organization, (3) information and factors helpful for situational awareness within the ICU, (4) unrecognized or missed critical events and information, and (5) suggestions for AMP organization and content. Prioritization of critical care was largely determined by severity of illness and trajectory of patient clinical status. Important sources of information were communication with colleagues from the previous shift, bedside nurses, and patients, data from the electronic medical record and AMP, and physical presence and availability in the ICU.

CONCLUSIONS

This qualitative study explored ICU clinicians' information and process requirements to enable the prioritization of care among populations of acutely ill patients. Timely recognition of patients who need priority attention and intervention provides opportunities for improvement of critical care and for preventing catastrophic events in the ICU.

Central nervous system biomarkers GFAp and NfL associate with post-acute cognitive impairment and fatigue following critical COVID-19

A high proportion of patients with coronavirus disease 2019 (COVID-19) experience post-acute COVID-19, including neuropsychiatric symptoms. Objective signs of central nervous system (CNS) damage can be investigated using CNS biomarkers such as glial fibrillary acidic protein (GFAp), neurofilament light chain (NfL) and total tau (t-tau). We have examined whether CNS biomarkers can predict fatigue and cognitive impairment 3-6 months after discharge from the intensive care unit (ICU) in critically ill COVID-19 patients. Fifty-seven COVID-19 patients admitted to the ICU were included with analysis of CNS biomarkers in blood at the ICU and at follow up. Cognitive dysfunction and fatigue were assessed with the Montreal Cognitive Assessment (MoCA) and the Multidimensional Fatigue inventory (MFI-20). Elevated GFAp at follow-up 3-6 months after ICU discharge was associated to the development of mild cognitive dysfunction (p = 0.01), especially in women (p = 0.005). Patients who experienced different dimensions of fatigue at follow-up had significantly lower GFAp in both the ICU and at follow-up, specifically in general fatigue (p = 0.009), physical fatigue (p = 0.004), mental fatigue (p = 0.001), and reduced motivation (p = 0.001). Women showed a more pronounced decrease in GFAp compared to men, except for in mental fatigue where men showed a more pronounced GFAp decrease compared to women. NfL concentration at follow-up was lower in patients who experienced reduced motivation (p = 0.004). Our findings suggest that GFAp and NfL are associated with neuropsychiatric outcome after critical COVID-19.Trial registration The study was registered à priori (clinicaltrials.gov: NCT04316884 registered on 2020-03-13 and NCT04474249 registered on 2020-06-29).

Predictive validity of the EVARUCI scale to evaluate risk for pressure injury in critical care patients

OBJECTIVE

To compare the predictive capacity of the current risk assessment scale for pressure ulcers in intensive care (EVARUCI), translated into Brazilian Portuguese, using the Braden scale.

METHOD

This cross-sectional study collected prospective data from adult patients in three intensive care units. The receiver operating characteristic (ROC) and precision-recall curve (PR curve) were used to analyse the predictive capacity for pressure injury (PI) using both predictive values and odds ratios (ORs).

RESULTS

The incidence of PIs in the study sample of 324 patients was 14.2%. The area under the ROC curve was 0.807 for EVARUCI and 0.798 for the Braden scale. At a cutoff point of 10 on the EVARUCI scale, sensitivity was 69.6%; specificity 78.4%; positive predictive value 34.8%; and OR 8.3. At a cutoff point of 11 on the Braden scale, sensitivity was 76.1%; specificity 75.9%; positive predictive value 34.3%; and OR 10. The area under the PR curve was 0.396 for the EVARUCI scale and 0.348 for the Braden scale, reflecting a smaller area for both. The F1 score value was 0.476 with 37.5% precision and 65.2% recall for the EVARUCI scale, and 0.473 with 34.3% precision and 76.1% recall for the Braden scale.

CONCLUSION

The EVARUCI scale predictive capacity was similar to that of the Braden scale. However, the precision of both scales was low for the accurate prediction of patients at risk of developing PIs.

Comparing severe COVID-19 outcomes of first and second/third waves: a prospective single-centre cohort study of health-related quality of life and pulmonary outcomes 6 months after infection

OBJECTIVE

We aimed to compare long-term outcomes in intensive care unit (ICU) survivors between the first and second/third waves of the COVID-19 pandemic. More specifically, to assess health-related quality of life (HRQL) and respiratory health 6 months post-ICU and to study potential associations between patient characteristic and treatment variables regarding 6-month outcomes.

DESIGN

Prospective cohort study.

SETTING

Single-centre study of adult COVID-19 patients with respiratory distress admitted to two Swedish ICUs during the first wave (1 March 2020-1 September 2020) and second/third waves (2 September 2020- 1 August 2021) with follow-up approximately 6 months after ICU discharge.

PARTICIPANTS

Critically ill COVID-19 patients who survived for at least 90 days.

MAIN OUTCOME MEASURES

HRQL, extent of residual changes on chest CT scan and pulmonary function were compared between the waves. General linear regression and multivariable logistic regression were used to present mean score differences (MSD) and ORs with 95% CIs.

RESULTS

Of the 456 (67%) critically ill COVID-19 patients who survived at least 90 days, 278 (61%) were included in the study. Six months after ICU discharge, HRQL was similar between survivors in the pandemic waves, except that the second/third wave survivors had better role physical (MSD 20.2, 95% CI 7.3 to 33.1, p<0.01) and general health (MSD 7.2, 95% CI 0.7 to 13.6, p=0.03) and less bodily pain (MSD 12.2, 95% CI 3.6 to 20.8, p<0.01), while first wave survivors had better diffusing capacity of the lungs for carbon monoxide (OR 1.9, 95% CI 1.1 to 3.5, p=0.03).

CONCLUSIONS

This study indicates that even though intensive care treatment strategies have changed with time, there are few differences in long-term HRQL and respiratory health seems to remain at 6 months for patients surviving critical COVID-19 in the first and second/third waves of the pandemic.

Critical care: A concept analysis

Objective

The terms critical care and the Intensive Care Unit (ICU) are often used interchangeably to describe a place of care. Defining critical care becomes challenging because of the colloquial use of the term. Using concept analysis allows for the development of definition and meaning. The aim of this concept analysis is to distinguish the use of the term critical care to develop an operational definition which describes what constitutes critical care.

Method

Walker and Avant's eight-step approach to concept analysis guided this study. Five databases (CINAHL, Scopus, PubMed, ProQuest Dissertation Abstracts and Medline in EBSCO) were searched for studies related to critical care. The search included both qualitative and quantitative studies written in English and published between 1990 and 2022.

Results

Of the 439 papers retrieved, 47 met the inclusion criteria. The defining attributes of critical care included 1) a maladaptive response to illness/injury, 2) admission modelling criteria, 3) advanced medical technologies, and 4) specialised health professionals. Antecedents were associated with illness/injury that progressed to a level of criticality with a significant decline in both physical and psychological functioning. Consequences were identified as either death or survival with/without experiencing post-ICU syndrome.

Conclusion

Describing critical care is often challenging because of the highly technical nature of the environment. This conceptual understanding and operational definition will inform future research as to the scope of critical care and allow for the design of robust evaluative instruments to better understand the nature of care in the intensive care environment.

Impact of bedside ultrasound to reduce the incidence of acute renal injury in high-risk surgical patients: a randomized clinical trial

PURPOSE

This study aimed to determine whether performing bedside ultrasound impacts the occurrence of acute kidney injury (AKI) in the immediate postoperative period (POP) of high-risk surgery patients.

METHODS

POP patients were randomly assigned to two groups: (i) ultrasound (US) group, in which hemodynamic management was guided with clinical parameters supplemented with the bedside US findings; (ii) control group, hemodynamic management based solely on clinical parameters. Two exams were performed in the first 24 h of admission.

RESULTS

Fifty-one patients were randomized to the US group and 60 to the control group. There was no significant difference for incidence of AKI in both groups assessed 12 h (31.4% vs 35.0%, P = 0.84), 24 h (27.5% vs 23.3%, P = 0.66), or 7 days (17.6 vs 8.3%, P = 0.16) after surgery. No difference was found in the amounts of volume administered over the first 12 h (1000 [500-2000] vs. 1000 [500-1500], P = 0.72) and 24 h (1000 [0-1500] vs. 1000 [0-1500], P = 0.95) between the groups. Patients without AKI in the control group received higher amounts of volume during the ICU stay.

CONCLUSION

The use of bedside US in the immediate postoperative period of high-risk surgery did not show benefits in reducing AKI incidence.

Development of a dynamic prediction model for unplanned ICU admission and mortality in hospitalized patients

Frequent assessment of the severity of illness for hospitalized patients is essential in clinical settings to prevent outcomes such as in-hospital mortality and unplanned admission to the intensive care unit (ICU). Classical severity scores have been developed typically using relatively few patient features. Recently, deep learning-based models demonstrated better individualized risk assessments compared to classic risk scores, thanks to the use of aggregated and more heterogeneous data sources for dynamic risk prediction. We investigated to what extent deep learning methods can capture patterns of longitudinal change in health status using time-stamped data from electronic health records. We developed a deep learning model based on embedded text from multiple data sources and recurrent neural networks to predict the risk of the composite outcome of unplanned ICU transfer and in-hospital death. The risk was assessed at regular intervals during the admission for different prediction windows. Input data included medical history, biochemical measurements, and clinical notes from a total of 852,620 patients admitted to non-intensive care units in 12 hospitals in Denmark's Capital Region and Region Zealand during 2011-2016 (with a total of 2,241,849 admissions). We subsequently explained the model using the Shapley algorithm, which provides the contribution of each feature to the model outcome. The best model used all data modalities with an assessment rate of 6 hours, a prediction window of 14 days and an area under the receiver operating characteristic curve of 0.898. The discrimination and calibration obtained with this model make it a viable clinical support tool to detect patients at higher risk of clinical deterioration, providing clinicians insights into both actionable and non-actionable patient features.

Nationwide analysis of hospital admissions and outcomes of patients with SARS-CoV-2 infection in Austria in 2020 and 2021

This retrospective study evaluated temporal and regional trends of patient admissions to hospitals, intensive care units (ICU), and intermediate care units (IMCU) as well as outcomes during the COVID-19 pandemic in Austria. We analysed anonymous data from patients admitted to Austrian hospitals with COVID-19 between January 1st, 2020 and December 31st, 2021. We performed descriptive analyses and logistic regression analyses for in-hospital mortality, IMCU or ICU admission, and in-hospital mortality following ICU admission. 68,193 patients were included, 8304 (12.3%) were primarily admitted to ICU, 3592 (5.3%) to IMCU. Hospital mortality was 17.3%; risk factors were male sex (OR 1.67, 95% CI 1.60-1.75, p < 0.001) and high age (OR 7.86, 95% CI 7.07-8.74, p < 0.001 for 90+ vs. 60-64 years). Mortality was higher in the first half of 2020 (OR 1.15, 95% CI 1.04-1.27, p = 0.01) and the second half of 2021 (OR 1.11, 95% CI 1.05-1.17, p < 0.001) compared to the second half of 2020 and differed regionally. ICU or IMCU admission was most likely between 55 and 74 years, and less likely in younger and older age groups. We find mortality in Austrian COVID-19-patients to be almost linearly associated with age, ICU admission to be less likely in older individuals, and outcomes to differ between regions and over time.

Prediction model of in-hospital mortality in intensive care unit patients with cardiac arrest: a retrospective analysis of MIMIC -IV database based on machine learning

BACKGROUND

Both in-hospital cardiac arrest (IHCA) and out-of-hospital cardiac arrest (OHCA) have higher incidence and lower survival rates. Predictors of in-hospital mortality for intensive care unit (ICU) admitted cardiac arrest (CA) patients remain unclear.

METHODS

The Medical Information Mart for Intensive Care IV (MIMIC-IV) database was used to perform a retrospective study. Patients meeting the inclusion criteria were identified from the MIMIC-IV database and randomly divided into training set (n = 1206, 70%) and validation set (n = 516, 30%). Candidate predictors consisted of the demographics, comorbidity, vital signs, laboratory test results, scoring systems, and treatment information on the first day of ICU admission. Independent risk factors for in-hospital mortality were screened using the least absolute shrinkage and selection operator (LASSO) regression model and the extreme gradient boosting (XGBoost) in the training set. Multivariate logistic regression analysis was used to build prediction models in training set, and then validated in validation set. Discrimination, calibration and clinical utility of these models were compared using the area under the curve (AUC) of the receiver operating characteristic (ROC) curves, calibration curves and decision curve analysis (DCA). After pairwise comparison, the best performing model was chosen to build a nomogram.

RESULTS

Among the 1722 patients, in-hospital mortality was 53.95%. In both sets, the LASSO, XGBoost,the logistic regression(LR) model and the National Early Warning Score 2 (NEWS 2) models showed acceptable discrimination. In pairwise comparison, the prediction effectiveness was higher with the LASSO,XGBoost and LR models than the NEWS 2 model (p < 0.001). The LASSO,XGBoost and LR models also showed good calibration. The LASSO model was chosen as our final model for its higher net benefit and wider threshold range. And the LASSO model was presented as the nomogram.

CONCLUSIONS

The LASSO model enabled good prediction of in-hospital mortality in ICU admission CA patients, which may be widely used in clinical decision-making.

Invasive candidiasis: current clinical challenges and unmet needs in adult populations

Invasive candidiasis (IC) is a serious infection caused by several Candida species, and the most common fungal disease in hospitals in high-income countries. Despite overall improvements in health systems and ICU care in the last few decades, as well as the development of different antifungals and microbiological techniques, mortality rates in IC have not substantially improved. The aim of this review is to summarize the main issues underlying the management of adults affected by IC, focusing on specific forms of the infection: IC developed by ICU patients, IC observed in haematological patients, breakthrough candidaemia, sanctuary site candidiasis, intra-abdominal infections and other challenging infections. Several key challenges need to be tackled to improve the clinical management and outcomes of IC patients. These include the lack of global epidemiological data for IC, the limitations of the diagnostic tests and risk scoring tools currently available, the absence of standardized effectiveness outcomes and long-term data for IC, the timing for the initiation of antifungal therapy and the limited recommendations on the optimal step-down therapy from echinocandins to azoles or the total duration of therapy. The availability of new compounds may overcome some of the challenges identified and increase the existing options for management of chronic Candida infections and ambulant patient treatments. However, early identification of patients that require antifungal therapy and treatment of sanctuary site infections remain a challenge and will require further innovations.

Conservative versus conventional oxygen therapy in type I acute respiratory failure patients in respiratory intensive care unit, Zagazig University

The present study aimed to assess the effect of a conservative (permissive hypoxemia) versus conventional (normoxia) protocol for oxygen supplementation on the outcome of type I respiratory failure patients admitted to respiratory intensive care unit (ICU). This randomized controlled clinical trial was carried out at the Respiratory ICU, Chest Department of Zagazig University Hospital, for 18 months, starting in July 2018. On admission, 56 enrolled patients with acute respiratory failure were randomized in a 1:1 ratio into the conventional group [oxygen therapy was supplied to maintain oxygen saturation (SpO2) between 94% and 97%] and the conservative group (oxygen therapy was administered to maintain SpO2 values between 88% and 92%). Different outcomes were assessed, including ICU mortality, the need for mechanical ventilation (MV) (invasive or non-invasive), and ICU length of stay. In the current study, the partial pressure of oxygen was significantly higher among the conventional group at all times after the baseline reading, and bicarbonate was significantly higher among the conventional group at the first two readings. There was no significant difference in serum lactate level in follow-up readings. The mean duration of MV and ICU length of stay was 6.17±2.05 and 9.25±2.22 days in the conventional group versus 6.46±2.0 and 9.53±2.16 days in the conservative group, respectively, without significant differences between both groups. About 21.4% of conventional group patients died, while 35.7% of conservative group patients died without a significant difference between both groups. We concluded that conservative oxygen therapy may be applied safely to patients with type I acute respiratory failure.

Evaluation of Digital Health Strategy to Support Clinician-Led Critically Ill Patient Population Management: A Randomized Crossover Study

To investigate whether a novel acute care multipatient viewer (AMP), created with an understanding of clinician information and process requirements, could reduce time to clinical decision-making among clinicians caring for populations of acutely ill patients compared with a widely used commercial electronic medical record (EMR).

DESIGN

Single center randomized crossover study.

SETTING

Quaternary care academic hospital.

SUBJECTS

Attending and in-training critical care physicians, and advanced practice providers.

INTERVENTIONS

AMP.

MEASUREMENTS AND MAIN RESULTS

We compared ICU clinician performance in structured clinical task completion using two electronic environments-the standard commercial EMR (Epic) versus the novel AMP in addition to Epic. Twenty subjects (10 pairs of clinicians) participated in the study. During the study session, each participant completed the tasks on two ICUs (7-10 beds each) and eight individual patients. The adjusted time for assessment of the entire ICU and the adjusted total time to task completion were significantly lower using AMP versus standard commercial EMR (-6.11; 95% CI, -7.91 to -4.30 min and -5.38; 95% CI, -7.56 to -3.20 min, respectively; p < 0.001). The adjusted time for assessment of individual patients was similar using both the EMR and AMP (0.73; 95% CI, -0.09 to 1.54 min; p = 0.078). AMP was associated with a significantly lower adjusted task load (National Aeronautics and Space Administration-Task Load Index) among clinicians performing the task versus the standard EMR (22.6; 95% CI, -32.7 to -12.4 points; p < 0.001). There was no statistically significant difference in adjusted total errors when comparing the two environments (0.68; 95% CI, 0.36-1.30; p = 0.078).

CONCLUSIONS

When compared with the standard EMR, AMP significantly reduced time to assessment of an entire ICU, total time to clinical task completion, and clinician task load. Additional research is needed to assess the clinicians' performance while using AMP in the live ICU setting.

Comparative effectiveness and safety of bolus vs. continuous infusion of loop diuretics: Results from the MIMIC-III Database

BACKGROUND

It is unclear whether fluid management goals are best achieved by bolus injection or continuous infusion of loop diuretics. In this study, we compared the effectiveness and safety of a continuous infusion with that of a bolus injection when an increased loop diuretic dosage is required in intensive care unit (ICU) patients.

METHODS

We obtained data from the MIMIC-III database for patients who were first-time ICU admissions and required an increased diuretic dosage. Patients were excluded if they had an estimated glomerular filtration rate <15 ml/min/1.73 m², were receiving renal replacement therapy, had a baseline systolic blood pressure <80 mmHg, or required a furosemide dose <120 mg. The patients were divided into a continuous group and a bolus group. Propensity score matching was used to balance patients' background characteristics.

RESULTS

The final dataset included 807 patients (continuous group, n = 409; bolus group, n = 398). After propensity score matching, there were 253 patients in the bolus group and 231 in the continuous group. The 24 h urine output per 40 mg of furosemide was significantly greater in the continuous group than in the bolus group (234.66 ml [95% confidence interval (CI) 152.13-317.18, p < 0.01]). There was no significant between-group difference in the incidence of acute kidney injury (odds ratio 0.96, 95% CI 0.66-1.41, p = 0.85).

CONCLUSIONS

Our results indicate that a continuous infusion of loop diuretics may be more effective than a bolus injection and does not increase the risk of acute kidney injury in patients who need an increased diuretic dosage in the ICU.

Prone positioning in COVID-19 patients with acute respiratory distress syndrome and invasive mechanical ventilation

OBJECTIVE

To identify adverse events related to prone positioning in COVID-19 patients with severe disease and acute respiratory distress syndrome, to analyze the risk factors associated with the development of anterior pressure ulcers, to determine whether the recommendation of prone positioning is associated with improved clinical outcomes.

METHODS

Retrospective study performed in 63 consecutive patients with COVID-19 pneumonia admitted to intensive care unit on invasive mechanical ventilation and treated with prone positioning between March and April 2020. Association between prone-related pressure ulcers and selected variables was explored by the means of logistic regression.

RESULTS

A total of 139 proning cycles were performed. The mean number of cycles were 2 [1-3] and the mean duration per cycle was of 22h [15-24]. The prevalence of adverse events this population was 84.9 %, being the physiologic ones (i.e., hypo/hypertension) the most prevalent. 29 out of 63 patients (46%) developed prone-related pressure ulcers. The risk factors for prone-related pressure ulcers were older age, hypertension, levels of pre-albumin <21mg/dl, the number of proning cycles and severe disease. We observed a significant increase in the PaO₂/FiO₂ at different time points during the prone positioning, and a significant decrease after it.

CONCLUSIONS

There is a high incidence of adverse events due to PD, with the physiological type being the most frequent. The identification of the main risk factors for the development of prone-related pressure ulcers will help to prevent the occurrence of these lesions during the prone positioning. Prone positioning offered an improvement in the oxygenation in these patients.