POStoperative INTELLiVENT-adaptive support VEntilation in cardiac surgery patients (POSITiVE) II-study protocol of a randomized clinical trial.

BACKGROUND: One single-center randomized clinical trial showed that INTELLiVENT-adaptive support ventilation (ASV) is superior to conventional ventilation with respect to the quality of ventilation in post-cardiac surgery patients. Other studies showed that this automated ventilation mode reduces the number of manual interventions at the ventilator in various types of critically ill patients. In this multicenter study in patients post-cardiac surgery, we test the hypothesis that INTELLiVENT-ASV is superior to conventional ventilation with respect to the quality of ventilation. METHODS: "POStoperative INTELLiVENT-adaptive support VEntilation in cardiac surgery patients II (POSITiVE II)" is an international, multicenter, two-group randomized clinical superiority trial. In total, 328 cardiac surgery patients will be randomized. Investigators screen patients aged > 18 years of age, scheduled for elective cardiac surgery, and expected to receive postoperative ventilation in the ICU for longer than 2 h. Patients either receive automated ventilation by means of INTELLiVENT-ASV or ventilation that is not automated by means of a conventional ventilation mode. The primary endpoint is quality of ventilation, defined as the proportion of postoperative ventilation time characterized by exposure to predefined optimal, acceptable, and critical (injurious) ventilatory parameters in the first two postoperative hours. One major secondary endpoint is ICU team staff workload, captured by the ventilator software collecting manual settings on alarms. Patient-centered endpoints include duration of postoperative ventilation and length of stay in ICU. DISCUSSION: POSITiVE II is the first international, multicenter, randomized clinical trial designed to confirm that POStoperative INTELLiVENT-ASV is superior to non-automated conventional ventilation and secondary to determine if this closed-loop ventilation mode reduces ICU team staff workload. The results of POSITiVE II will support intensive care teams in their choices regarding the use of automated ventilation in postoperative care of uncomplicated cardiac surgery patients. TRIAL REGISTRATION: Clinicaltrials.gov NCT06178510 . Registered on December 4, 2023.

Genomic and phenotypic inconsistencies in Pseudomonas aeruginosa resistome among intensive care patients.

Objective: Pseudomonas aeruginosa, a difficult-to-manage nosocomial pathogen, poses a serious threat to clinical outcomes in intensive care (ICU) patients due to its high antimicrobial resistance (AMR). To promote effective management, it is essential to investigate the genomic and phenotypic differences in AMR expression of the isolates. Methods: A prospective observational study was conducted from July 2022 to April 2023 at Liepaja Regional Hospital in Latvia. The study included all adult patients who were admitted to the ICU and had a documented infection with P. aeruginosa, as confirmed by standard laboratory microbiological testing and short-read sequencing. Since ResFinder is the only sequencing-based database offering antibacterial susceptibility testing (AST) data for each antibiotic, we conducted a comparison of the resistance profile with the results of phenotypic testing, evaluating if ResFinder met the US Food and Drug Administration (FDA) requirements for approval as a new AMR diagnostic test. Next, to improve precision, AST data from ResFinder was compared with two other databases - AMRFinderPlus and RGI. Additionally, data was gathered from environmental samples to inform the implementation of appropriate infection control measures in real time. Results: Our cohort consisted of 33 samples from 29 ICU patients and 34 environmental samples. The presence of P. aeruginosa infection was found to be associated with unfavourable clinical outcomes. A third of the patient samples were identified as multi-drug resistant isolates. Apart from resistance against colistin, significant discrepancies were observed when phenotypic data were compared to genotypic data. For example, the aminoglycoside resistance prediction of ResFinder yielded a major errors value of 3.03% for amikacin, which was marginally above the FDA threshold. Among the three positive environmental samples, one sample exhibited multiple AMR genes similar to the patient samples in its cluster. Conclusion: Our findings underscore the importance of utilizing a combination of diagnostic methods for the identification of resistance mechanisms, clusters, and environmental reservoirs in ICUs.

Racial Differences in Accuracy of Predictive Models for High-Flow Nasal Cannula Failure in COVID-19.

OBJECTIVES: To develop and validate machine learning (ML) models to predict high-flow nasal cannula (HFNC) failure in COVID-19, compare their performance to the respiratory rate-oxygenation (ROX) index, and evaluate model accuracy by self-reported race. DESIGN: Retrospective cohort study. SETTING: Four Emory University Hospitals in Atlanta, GA. PATIENTS: Adult patients hospitalized with COVID-19 between March 2020 and April 2022 who received HFNC therapy within 24 hours of ICU admission were included. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Four types of supervised ML models were developed for predicting HFNC failure (defined as intubation or death within 7 d of HFNC initiation), using routine clinical variables from the first 24 hours of ICU admission. Models were trained on the first 60% (n = 594) of admissions and validated on the latter 40% (n = 390) of admissions to simulate prospective implementation. Among 984 patients included, 317 patients (32.2%) developed HFNC failure. eXtreme Gradient Boosting (XGB) model had the highest area under the receiver-operator characteristic curve (AUROC) for predicting HFNC failure (0.707), and was the only model with significantly better performance than the ROX index (AUROC 0.616). XGB model had significantly worse performance in Black patients compared with White patients (AUROC 0.663 vs. 0.808, p = 0.02). Racial differences in the XGB model were reduced and no longer statistically significant when restricted to patients with nonmissing arterial blood gas data, and when XGB model was developed to predict mortality (rather than the composite outcome of failure, which could be influenced by biased clinical decisions for intubation). CONCLUSIONS: Our XGB model had better discrimination for predicting HFNC failure in COVID-19 than the ROX index, but had racial differences in accuracy of predictions. Further studies are needed to understand and mitigate potential sources of biases in clinical ML models and to improve their equitability.

Optimize individualized energy delivery for septic patients using predictive deep learning models.

BACKGROUND AND OBJECTIVES: We aim to establish deep learning models to optimize the individualized energy delivery for septic patients. METHODS AND STUDY DESIGN: We conducted a study of adult septic patients in ICU, collecting 47 indicators for 14 days. We filtered out nutrition-related features and divided the data into datasets according to the three metabolic phases proposed by ESPEN: acute early, acute late, and rehabilitation. We then established optimal energy target models for each phase using deep learning and conducted external validation. RESULTS: A total of 179 patients in training dataset and 98 patients in external validation dataset were included in this study, and total data size was 3115 elements. The age, weight and BMI of the patients were 63.05 (95%CI 60.42-65.68), 61.31(95%CI 59.62-63.00) and 22.70 (95%CI 22.21-23.19), respectively. And 26.0% (72) of the patients were female. The models indicated that the optimal energy targets in the three phases were 900kcal/d, 2300kcal/d, and 2000kcal/d, respectively. Excessive energy intake increased mortality rapidly in the early period of the acute phase. Insufficient energy in the late period of the acute phase significantly raised the mortality as well. For the rehabilitation phase, too much or too little energy delivery were both associated with elevated death risk. CONCLUSIONS: Our study established time-series prediction models for septic patients to optimize energy delivery in the ICU. We recommended permissive underfeeding only in the early acute phase. Later, increased energy intake may improve survival and settle energy debts caused by underfeeding.

Onset of Prolonged High-Potency Benzodiazepine Use Among ICU Survivors: A Nationwide Cohort Study.

OBJECTIVES: Exposure to critical illness and intensive care may lead to long-term psychologic and physical impairments. To what extent ICU survivors become prolonged users of benzodiazepines after exposure to critical care is not fully explored. This study aimed to describe the extent of onset of prolonged high-potency benzodiazepine use among ICU survivors not using these drugs before admission, identify factors associated with this use, and analyze whether such usage is associated with increased mortality. DESIGN: Retrospective cohort study. SETTING: Sweden, including all registered ICU admissions between 2010 and 2017. PATIENTS: ICU patients surviving for at least 3 months, not using high-potency benzodiazepine before admission, were eligible for inclusion. INTERVENTIONS: Admission to intensive care. MEASUREMENTS AND MAIN RESULTS: A total of 237,904 patients were screened and 137,647 were included. Of these 5338 (3.9%) became prolonged users of high-potency benzodiazepines after ICU discharge. A peak in high-potency benzodiazepine prescriptions was observed during the first 3 months, followed by sustained usage throughout the follow-up period of 18 months. Prolonged usage was associated with older age, female sex, and a history of both somatic and psychiatric comorbidities, including substance abuse. Additionally, a longer ICU stay, a high estimated mortality rate, and prior consumption of low-potency benzodiazepines were associated with prolonged use. The risk of death between 6 and 18 months post-ICU admission was significantly higher among high-potency benzodiazepine users, with an adjusted hazard ratio of 1.8 (95% CI, 1.7-2.0; p < 0.001). No differences were noted in causes of death between users and nonusers. CONCLUSIONS: Despite the lack of evidence supporting long-term treatment, prolonged usage of high-potency benzodiazepines 18 months following ICU care was notable and associated with an increased risk of death. Considering the substantial number of ICU admissions, prevention of benzodiazepine misuse may improve long-term outcomes following critical care.

Prognostic Significance of Thrombocytopenia and Mean Platelet Volume in COPD Patients with Acute Exacerbations in ICU Settings.

BACKGROUND Platelets have important modulatory effects on inflammatory and immune-mediated pathways. Thrombocytopenia is a critical condition that is frequently encountered in the intensive care unit (ICU) and increases mortality. This retrospective study of 472 patients admitted to the ICU with acute exacerbation of chronic obstructive pulmonary disease (COPD) aimed to evaluate thrombocytopenia and mean platelet volume (MPV) with prognosis and patient mortality. MATERIAL AND METHODS A total of 472 patients diagnosed with COPD according to GOLD criteria and hospitalized in the tertiary ICU between 1 April 2018 and 11 May 2021 were included in the study. Platelets were calculated by the impetance method and MPV was simultaneously calculated based on the platelet histogram. Patients with platelet count ≤100×109/L and >100×109/L and patients with MPV values <7 fl, 7-11 fl, and >11fl were compared in terms of mortality and prognosis. RESULTS The mortality rate in COPD patients with thrombocytopenia was high, at 61.5%. Thrombocytopenia (P=.002), high MPV (P=.006) Acute Physiology and Chronic Health Evaluation-2 (APACHE-II) score (P=.025), length of stay (LOS) in the ICU (P=.009), mechanical ventilation duration (P<.001), leukocytosis (P<.001), high Sequential Organ Failure Assessment (SOFA) score (P<.001), LOS in the hospital (P=.035), and hypoalbuminemia (P<.001) were significantly associated with mortality. CONCLUSIONS Thrombocytopenia, high MPV, high APACHE-II and SOFA scores, LOS in the ICU and hospital, duration of mechanical ventilation, leukocytosis, and hypoalbuminemia predict mortality in COPD patients. Since infection-sepsis, hypoalbuminemia, and hypoxia can worsen this situation, ensuring early infection control, providing albumin support, and preventing hypoxia contribute significantly to reducing thrombocytopenia and mortality.

Lactate and base excess (BE) as markers of hypoperfusion and mortality in traumatic hemorrhagic shock in patients undergoing Damage Control: a historical cohort.

INTRODUCTION: hemorrhagic shock is a significant cause of trauma-related deaths in Brazil and worldwide. This study aims to compare BE and lactate values at ICU admission and twenty-four hours after in identifying tissue hypoperfusion and mortality. METHODS: examines a historical cohort of trauma patients over eitheen years old submittet to damage control resuscitation approch upon hospital admission and were then admitted to the ICU. We collected and analyzed ISS, mechanism and type of trauma, need for renal replacement therapy, massive transfusion. BE, lactate, pH, bicarbonate at ICU admission and twenty-four hours later, and mortality data. The patients were grouped based on their BE values (≥-6 and <-6mmol/L), which were previously identified in the literature as predictors of severity. They were subsequently redivided using the most accurate values found in this sample. In addition to performing multivariate binary logistic regression. The data were compared using several statistical tests due to diversity and according to the indication for each variable. RESULTS: there were significant changes in perfusion upon admission to the Intensive Care Unit. BE is a statistically significant value for predicting mortality, as determined by using values from previous literature and from this study. CONCLUSION: the results demonstrate the importance of monitoring BE levels in the prediction of ICU mortality. BE proves to be a valuable bedside marker with quick results and wide availability.

A privacy-preserving platform oriented medical healthcare and its application in identifying patients with candidemia.

Federated learning (FL) has emerged as a significant method for developing machine learning models across multiple devices without centralized data collection. Candidemia, a critical but rare disease in ICUs, poses challenges in early detection and treatment. The goal of this study is to develop a privacy-preserving federated learning framework for predicting candidemia in ICU patients. This approach aims to enhance the accuracy of antifungal drug prescriptions and patient outcomes. This study involved the creation of four predictive FL models for candidemia using data from ICU patients across three hospitals in China. The models were designed to prioritize patient privacy while aggregating learnings across different sites. A unique ensemble feature selection strategy was implemented, combining the strengths of XGBoost's feature importance and statistical test p values. This strategy aimed to optimize the selection of relevant features for accurate predictions. The federated learning models demonstrated significant improvements over locally trained models, with a 9% increase in the area under the curve (AUC) and a 24% rise in true positive ratio (TPR). Notably, the FL models excelled in the combined TPR + TNR metric, which is critical for feature selection in candidemia prediction. The ensemble feature selection method proved more efficient than previous approaches, achieving comparable performance. The study successfully developed a set of federated learning models that significantly enhance the prediction of candidemia in ICU patients. By leveraging a novel feature selection method and maintaining patient privacy, the models provide a robust framework for improved clinical decision-making in the treatment of candidemia.

Machine learning-based prediction of clinical outcomes after traumatic brain injury: Hidden information of early physiological time series.

AIMS: To assess the predictive value of early-stage physiological time-series (PTS) data and non-interrogative electronic health record (EHR) signals, collected within 24 h of ICU admission, for traumatic brain injury (TBI) patient outcomes. METHODS: Using data from TBI patients in the multi-center eICU database, we focused on in-hospital mortality, neurological status based on the Glasgow Coma Score (mGCS) motor subscore at discharge, and prolonged ICU stay (PLOS). Three machine learning (ML) models were developed, utilizing EHR features, PTS signals collected 24 h after ICU admission, and their combination. External validation was performed using the MIMIC III dataset, and interpretability was enhanced using the Shapley Additive Explanations (SHAP) algorithm. RESULTS: The analysis included 1085 TBI patients. Compared to individual models and existing scoring systems, the combination of EHR and PTS features demonstrated comparable or even superior performance in predicting in-hospital mortality (AUROC = 0.878), neurological outcomes (AUROC = 0.877), and PLOS (AUROC = 0.835). The model's performance was validated in the MIMIC III dataset, and SHAP algorithms identified six key intervention points for EHR features related to prognostic outcomes. Moreover, the EHR results (All AUROC >0.8) were translated into online tools for clinical use. CONCLUSION: Our study highlights the importance of early-stage PTS signals in predicting TBI patient outcomes. The integration of interpretable algorithms and simplified prediction tools can support treatment decision-making, contributing to the development of accurate prediction models and timely clinical intervention.

Predictive value of the dynamics of absolute lymphocyte counts for 90-day mortality in ICU sepsis patients: a retrospective big data study.

OBJECTIVES: The objective of the study was to assess the clinical predictive value of the dynamics of absolute lymphocyte count (ALC) for 90-day all-cause mortality in sepsis patients in intensive care unit (ICU). DESIGN: Retrospective cohort study using big data. SETTING: This study was conducted using the Medical Information Mart for Intensive Care IV database V.2.0 database. PRIMARY AND SECONDARY OUTCOME MEASURES: The primary outcome was 90-day all-cause mortality. PARTICIPANTS: Patients were included if they were diagnosed with sepsis on the first day of ICU admission. Exclusion criteria were ICU stay under 24 hours; the absence of lymphocyte count on the first day; extremely high lymphocyte count (>10×109/L); history of haematolymphatic tumours, bone marrow or solid organ transplants; survival time under 72 hours and previous ICU admissions. The analysis ultimately included 17 329 sepsis patients. RESULTS: The ALC in the non-survivors group was lower on days 1, 3, 5 and 7 after admission (p<0.001). The ALC on day 7 had the highest area under the curve (AUC) value for predicting 90-day mortality. The cut-off value of ALC on day 7 was 1.0×109/L. In the restricted cubic spline plot, after multivariate adjustments, patients with higher lymphocyte counts had a better prognosis. After correction, in the subgroups with Sequential Organ Failure Assessment score ≥6 or age ≥60 years, ALC on day 7 had the lowest HR value (0.79 and 0.81, respectively). On the training and testing set, adding the ALC on day 7 improved all prediction models' AUC and average precision values. CONCLUSIONS: Dynamic changes of ALC are closely associated with 90-day all-cause mortality in sepsis patients. Furthermore, the ALC on day 7 after admission is a better independent predictor of 90-day mortality in sepsis patients, especially in severely ill or young sepsis patients.

Development and validation of machine learning models to predict MDRO colonization or infection on ICU admission by using electronic health record data.

BACKGROUND: Multidrug-resistant organisms (MDRO) pose a significant threat to public health. Intensive Care Units (ICU), characterized by the extensive use of antimicrobial agents and a high prevalence of bacterial resistance, are hotspots for MDRO proliferation. Timely identification of patients at high risk for MDRO can aid in curbing transmission, enhancing patient outcomes, and maintaining the cleanliness of the ICU environment. This study focused on developing a machine learning (ML) model to identify patients at risk of MDRO during the initial phase of their ICU stay. METHODS: Utilizing patient data from the First Medical Center of the People's Liberation Army General Hospital (PLAGH-ICU) and the Medical Information Mart for Intensive Care (MIMIC-IV), the study analyzed variables within 24 h of ICU admission. Machine learning algorithms were applied to these datasets, emphasizing the early detection of MDRO colonization or infection. Model efficacy was evaluated by the area under the receiver operating characteristics curve (AUROC), alongside internal and external validation sets. RESULTS: The study evaluated 3,536 patients in PLAGH-ICU and 34,923 in MIMIC-IV, revealing MDRO prevalence of 11.96% and 8.81%, respectively. Significant differences in ICU and hospital stays, along with mortality rates, were observed between MDRO positive and negative patients. In the temporal validation, the PLAGH-ICU model achieved an AUROC of 0.786 [0.748, 0.825], while the MIMIC-IV model reached 0.744 [0.723, 0.766]. External validation demonstrated reduced model performance across different datasets. Key predictors included biochemical markers and the duration of pre-ICU hospital stay. CONCLUSIONS: The ML models developed in this study demonstrated their capability in early identification of MDRO risks in ICU patients. Continuous refinement and validation in varied clinical contexts remain essential for future applications.

Factors Associated With Quality-of-Dying-and-Death Classes Among Critically Ill Patients.

Importance: Improving end-of-life care in the intensive care unit (ICU) is a priority, but clinically modifiable factors of quality of dying and death (QODD) are seldom identified. Objectives: To comprehensively identify factors associated with QODD classes of dying ICU patients, emphasizing clinically modifiable factors based on the integrative framework of factors associated with for bereavement outcomes. Design, Setting, and Participants: This observational cohort study was conducted at medical ICUs of 2 Taiwanese medical centers from January 2018 to March 2020 with follow-up through December 2022. Eligible participants included primary family surrogates responsible for decision making for critically ill ICU patients at high risk of death (Acute Physiology and Chronic Health Evaluation II score >20) but who survived more than 3 days after ICU admission. Data analysis was conducted from July to September 2023. Main Outcomes and Measures: QODD was measured by the 23-item ICU-QODD questionnaire. Factors associated with patient membership in 4 previously determined QODD classes (high, moderate, poor to uncertain, and worst) were examined using a 3-step approach for latent class modeling with the high QODD class as the reference category. Results: A total of 309 family surrogates (mean [SD] age, 49.83 [12.55] years; 184 women [59.5%] and 125 men [40.5%]) were included in the study. Of all surrogates, 91 (29.4%) were the patients' spouse and 66 (53.7%) were the patients' adult child. Patient demographics were not associated with QODD class. Two family demographics (age and gender), relationship with the patient (spousal or adult-child), and length of ICU stay were associated with QODD classes. Patients of surrogates perceiving greater social support were less likely to be in the poor to uncertain (adjusted odds ratio [aOR], 0.89; 95% CI, 0.83-0.94) and worst (aOR, 0.92; 95% CI, 0.87-0.96) QODD classes. Family meetings were associated with the poor to uncertain QODD class (aOR, 8.61; 95% CI, 2.49-29.74) and worst QODD class (aOR, 7.28; 95% CI, 1.37-38.71). Death with cardiopulmonary resuscitation was associated with the worst QODD class (aOR, 7.51; 95% CI, 1.12-50.25). Family presence at patient death was uniformly negatively associated with the moderate QODD class (aOR, 0.16; 95% CI, 0.05-0.54), poor to uncertain QODD class (aOR, 0.21; 95% CI, 0.05-0.82), and worst QODD class (aOR, 0.08; 95% CI, 0.02-0.38). Higher family satisfaction with ICU care was negatively associated with the poor to uncertain QODD class (aOR, 0.93; 95% CI, 0.87-0.98) and worst QODD class (aOR, 0.86; 95% CI, 0.81-0.92). Conclusions and Relevance: In this cohort study of critically ill patients and their family surrogates, modifiable end-of-life ICU-care characteristics played a more significant role in associations with patient QODD class than did immutable family demographics, preexisting family health conditions, patient demographics, and patient clinical characteristics, thereby illuminating actionable opportunities to improve end-of-life ICU care.

My Magical Day in the Trauma ICU.

Healing is a difficult concept to describe, quantify, or replicate. It is a complex mixture of personal contributions from the professional providing care, including competence, compassion, and empathy, that conjoins with the needs, sensitivities, and receptivity of the one who is receiving the care. Although it may be difficult to predict all the elements that come together to initiate sustained healing, as well as the long-term impact, it is important to observe the moments that make a difference. For those who study the nature of healing, a patient's reflections can surface the kinds of elements that are present when healing is sustained. [J Contin Educ Nurs. 2024;55(7):326-327.].

The effect of web-based training given to nurses on the prevention of pressure injury in patients hospitalized in the anesthesiology and reanimation intensive care unit: quasi-experimental pre-test post-test research.

BACKGROUND: The knowledge, attitudes, and behaviors of intensive care nurses concerning the prevention of pressure injury (PI) may be positively affected by education. PURPOSE: To evaluate the effect of web-based training given to nurses on their knowledge of, attitudes about, and behaviors in the prevention of PI. METHODS: This study was conducted between May 2019 and December 2019 with a pre-test and post-test design. The study sample consisted of 22 nurses and 80 patients. A link to the educational video prepared for the prevention of PIs was sent to the nurses' mobile phones. RESULTS: The training had a significant positive effect on nurses' level of knowledge of and attitudes toward PI prevention (P < .001 and P = .042, respectively). In group 1, comprising 40 patients who received treatment before nurses' training, 2.5% of patients had stage 1 PI on day 1 and 7.5% had stage 1 PI on day 7, and 2.5% had stage 4 PI on day 7. In group 2, comprising 40 patients who received treatment after nurses' training, 2.5% of patients had stage 1 PI on day 1 and 2.5% had stage 1 PI on day 7. CONCLUSION: Nurses' knowledge of and attitudes and behaviors toward PI prevention were improved following the web-based training, and the stage and rate of PI were lower in patients who received care after nurses received the training.

MORTALITY RISK ASSESSMENT AMONG PATIENTS, HOSPITALIZED FOR COVID-19.

In Georgia, the number of confirmed cases of Coronavirus were 1,85,5289. Among them, 17 132 patients died. Information on risk factors for mortality is insufficient. The purpose of our research is to evaluate clinical features of heavy patients with severe COVID and determine prognostic factors of outcome. Factors associated with critical COVID-19 included older age and certain chronic medical conditions. The clinical material of 250 chronically ill COVID-19 patients admitted to the intensive care unit was retrospectively studied. We divided the patients into two groups. The dead and the survivors. Demographic data, comorbidities, chronic diseases, results of ultrasound, cardiography, computed tomography and laboratory characteristics were studied. In patients with chronic diseases, in the intensive care unit during COVID-19, the relative chance of survival decreases: CRP3 - OR=0.98(95% CI:0.97-0.99Hydrothorax- OR=0.24(95% CI:0.06-0.95); Sepsis/Septic shock - OR=0.07(95% CI:0.01-0.39); WBC - OR=0.86(95% CI:0.74-0.99); Mechanical lung ventilation - OR=0.01(95% CI:0.00-0.05)); increase survival relative chance- pO2 - OR=1.03(95% CI:1.0-1.06). Predictors of mortality in patients with chronic diseases: coagulation characteristics, inflammatory markers, sepsis, and artificial lung ventilation. Risk factors for covid-19 mortality need to be studied to increase pandemic preparedness.

Association between admission pan-immune-inflammation value and short-term mortality in septic patients: a retrospective cohort study.

Pan-Immune-Inflammation Value (PIV) has recently received more attention as a novel indicator of inflammation. We aimed to evaluate the association between PIV and prognosis in septic patients. Data were extracted from the Medical Information Mart for Intensive Care IV database. The primary and secondary outcomes were 28-day and 90-day mortality. The association between PIV and outcomes was assessed by Kaplan-Meier curves, Cox regression analysis, restricted cubic spline curves and subgroup analysis. A total of 11,331 septic patients were included. Kaplan-Meier curves showed that septic patients with higher PIV had lower 28-day survival rate. In multivariable Cox regression analysis, log2-PIV was positively associated with the risk of 28-day mortality [HR (95% CI) 1.06 (1.03, 1.09), P < 0.001]. The relationship between log2-PIV and 28-day mortality was non-linear with a predicted inflection point at 8. To the right of the inflection point, high log2-PIV was associated with an increased 28-day mortality risk [HR (95% CI) 1.13 (1.09, 1.18), P < 0.001]. However, to the left of this point, this association was non-significant [HR (95% CI) 1.01 (0.94, 1.08), P = 0.791]. Similar results were found for 90-day mortality. Our study showed a non-linear relationship between PIV and 28-day and 90-day mortality risk in septic patients.

Performance of the MRI lesion pattern score in predicting neurological outcome after out of hospital cardiac arrest: a retrospective cohort analysis.

BACKGROUND: Despite advances in resuscitation practice, patient survival following cardiac arrest remains poor. The utilization of MRI in neurological outcome prognostication post-cardiac arrest is growing and various classifications has been proposed; however a consensus has yet to be established. MRI, though valuable, is resource-intensive, time-consuming, costly, and not universally available. This study aims to validate a MRI lesion pattern score in a cohort of out of hospital cardiac arrest patients at a tertiary referral hospital in Switzerland. METHODS: This cohort study spanned twelve months from February 2021 to January 2022, encompassing all unconscious patients aged ≥ 18 years who experienced out-of-hospital cardiac arrest of any cause and were admitted to the intensive care unit (ICU) at Inselspital, University Hospital Bern, Switzerland. We included patients who underwent the neuroprognostication process, assessing the performance and validation of a MRI scoring system. RESULTS: Over the twelve-month period, 137 patients were admitted to the ICU, with 52 entering the neuroprognostication process and 47 undergoing MRI analysis. Among the 35 MRIs indicating severe hypoxic brain injury, 33 patients (94%) experienced an unfavourable outcome (UO), while ten (83%) of the twelve patients with no or minimal MRI lesions had a favourable outcome. This yielded a sensitivity of 0.94 and specificity of 0.83 for predicting UO with the proposed MRI scoring system. The positive and negative likelihood ratios were 5.53 and 0.07, respectively, resulting in an accuracy of 91.49%. CONCLUSION: We demonstrated the effectiveness of the MLP scoring scheme in predicting neurological outcome in patients following cardiac arrest. However, to ensure a comprehensive neuroprognostication, MRI results need to be combined with other assessments. While neuroimaging is a promising objective tool for neuroprognostication, given the absence of sedation-related confounders-compared to electroencephalogram (EEG) and clinical examination-the current lack of a validated scoring system necessitates further studies. Incorporating standardized MRI techniques and grading systems is crucial for advancing the reliability of neuroimaging for neuroprognostication. TRIAL REGISTRATION: Registry of all Projects in Switzerland (RAPS) 2020-01761.

Vitamin K: a potential missing link in critical illness-a scoping review.

BACKGROUND: Vitamin K is essential for numerous physiological processes, including coagulation, bone metabolism, tissue calcification, and antioxidant activity. Deficiency, prevalent in critically ill ICU patients, impacts coagulation and increases the risk of bleeding and other complications. This review aims to elucidate the metabolism of vitamin K in the context of critical illness and identify a potential therapeutic approach. METHODS: In December 2023, a scoping review was conducted using the PRISMA Extension for Scoping Reviews. Literature was searched in PubMed, Embase, and Cochrane databases without restrictions. Inclusion criteria were studies on adult ICU patients discussing vitamin K deficiency and/or supplementation. RESULTS: A total of 1712 articles were screened, and 13 met the inclusion criteria. Vitamin K deficiency in ICU patients is linked to malnutrition, impaired absorption, antibiotic use, increased turnover, and genetic factors. Observational studies show higher PIVKA-II levels in ICU patients, indicating reduced vitamin K status. Risk factors include inadequate intake, disrupted absorption, and increased physiological demands. Supplementation studies suggest vitamin K can improve status but not normalize it completely. Vitamin K deficiency may correlate with prolonged ICU stays, mechanical ventilation, and increased mortality. Factors such as genetic polymorphisms and disrupted microbiomes also contribute to deficiency, underscoring the need for individualized nutritional strategies and further research on optimal supplementation dosages and administration routes. CONCLUSIONS: Addressing vitamin K deficiency in ICU patients is crucial for mitigating risks associated with critical illness, yet optimal management strategies require further investigation. IMPACT RESEARCH: To the best of our knowledge, this review is the first to address the prevalence and progression of vitamin K deficiency in critically ill patients. It guides clinicians in diagnosing and managing vitamin K deficiency in intensive care and suggests practical strategies for supplementing vitamin K in critically ill patients. This review provides a comprehensive overview of the existing literature, and serves as a valuable resource for clinicians, researchers, and policymakers in critical care medicine.

Clinical features and risk factors for mortality in patients with Klebsiella pneumoniae bloodstream infections.

INTRODUCTION: Concern about Klebsiella pneumoniae (K. pneumoniae) bloodstream infections (KP-BSIs) is widespread because of their high incidence and lethality. The aim of this study was to investigate the clinical features of, and risk factors for mortality caused by KP-BSIs. METHODOLOGY: This was a single-center retrospective observational study performed between 1 January 2019 and 31 December 2021, at a tertiary hospital. All patients with KP-BSIs were enrolled and their clinical data were retrieved from electronic medical records. RESULTS: A total of 145 patients were included (121 in the survival group and 24 in the non-survival group). There was a higher proportion of lower respiratory tract infections in the non-survival group than in the survival group (33.3% vs. 12.4%) (p < 0.05). There was a higher proportion of multi drug resistant (MDR) strains of K. pneumoniae in the non-survival group than in the survival group (41.7% vs. 16.5%) (p < 0.05). Multivariate analysis revealed that sequential organ failure assessment (SOFA) score > 6.5 (OR, 13.71; 95% CI, 1.05-179.84), admission to the intensive care unit (ICU) (OR, 2.27; 95% CI, 0.26-19.61) and gastrointestinal bleeding (OR, 19.97; 95% CI, 1.11-361.02) were independent risk factors for death in patients with KP-BSIs. CONCLUSIONS: Among all KP-BSIs, a high proportion of K. pneumoniae originated from lower respiratory tract infections, and a high proportion of K. pneumoniae were MDR; however, mortality was not influenced. SOFA score > 6.5, admission to the ICU, and gastrointestinal bleeding were independent risk factors for death in patients with KP-BSI.