Oculomic stratification of COVID-19 patients' intensive therapy unit admission status and mortality by retinal morphological findings.

To investigate if retinal thickness has predictive utility in COVID-19 outcomes by evaluating the statistical association between retinal thickness using OCT and of COVID-19-related mortality. Secondary outcomes included associations between retinal thickness and length of stay (LoS) in hospital. In this retrospective cohort study, OCT scans from 230 COVID-19 patients admitted to the Intensive Care Unit (ITU) were compared with age and gender-matched patients with pneumonia from before March 2020. Total retinal, GCL + IPL, and RNFL thicknesses were recorded, and analysed with systemic measures collected at the time of admission and mortality outcomes, using linear regression models, Pearson's R correlation, and Principal Component Analysis. Retinal thickness was significantly associated with all-time mortality on follow up in the COVID-19 group (p = 0.015), but not 28-day mortality (p = 0.151). Retinal and GCL + IPL layer thicknesses were both significantly associated with LoS in hospital for COVID-19 patients (p = 0.006 for both), but not for patients with pneumonia (p = 0.706 and 0.989 respectively). RNFL thickness was not associated with LoS in either group (COVID-19 p = 0.097, pneumonia p = 0.692). Retinal thickness associated with LoS in hospital and long-term mortality in COVID-19 patients, suggesting that retinal structure could be a surrogate marker for frailty and predictor of disease severity in this group of patients, but not in patients with pneumonia from other causes.

Interpretable mortality prediction model for ICU patients with pneumonia: using shapley additive explanation method.

BACKGROUND: Pneumonia, a leading cause of morbidity and mortality worldwide, often necessitates Intensive Care Unit (ICU) admission. Accurate prediction of pneumonia mortality is crucial for tailored prevention and treatment plans. However, existing mortality prediction models face limited adoption in clinical practice due to their lack of interpretability. OBJECTIVE: This study aimed to develop an interpretable model for predicting pneumonia mortality in ICUs. Leveraging the Shapley Additive Explanation (SHAP) method, we sought to elucidate the Extreme Gradient Boosting (XGBoost) model and identify prognostic factors for pneumonia. METHODS: Conducted as a retrospective cohort study, we utilized electronic health records from the eICU-CRD (2014-2015) for all adult pneumonia patients. The first 24 h of each ICU admission records were considered, with 70% of the dataset allocated for model training and 30% for validation. The XGBoost model was employed, and performance was assessed using the area under the receiver operating characteristic curve (AUC). The SHAP method provided insights into the XGBoost model. RESULTS: Among 10,962 pneumonia patients, in-hospital mortality was 16.33%. The XGBoost model demonstrated superior predictive performance (AUC: 0.778 ± 0.016)) compared to traditional scoring systems and other machine learning method, which achieved an improvement of 10% points. SHAP analysis identified Aspartate Aminotransferase (AST) as the most crucial predictor. CONCLUSIONS: Interpretable predictive models enhance mortality risk assessment for pneumonia patients in the ICU, fostering transparency. AST emerged as the foremost predictor, followed by patient age, albumin, BMI et al. These insights, rooted in strong correlations with mortality, facilitate improved clinical decision-making and resource allocation.

Adiposity and mortality among intensive care patients with COVID-19 and non-COVID-19 respiratory conditions: a cross-context comparison study in the UK.

BACKGROUND: Adiposity shows opposing associations with mortality within COVID-19 versus non-COVID-19 respiratory conditions. We assessed the likely causality of adiposity for mortality among intensive care patients with COVID-19 versus non-COVID-19 by examining the consistency of associations across temporal and geographical contexts where biases vary. METHODS: We used data from 297 intensive care units (ICUs) in England, Wales, and Northern Ireland (Intensive Care National Audit and Research Centre Case Mix Programme). We examined associations of body mass index (BMI) with 30-day mortality, overall and by date and region of ICU admission, among patients admitted with COVID-19 (N = 34,701; February 2020-August 2021) and non-COVID-19 respiratory conditions (N = 25,205; February 2018-August 2019). RESULTS: Compared with non-COVID-19 patients, COVID-19 patients were younger, less often of a white ethnic group, and more often with extreme obesity. COVID-19 patients had fewer comorbidities but higher mortality. Socio-demographic and comorbidity factors and their associations with BMI and mortality varied more by date than region of ICU admission. Among COVID-19 patients, higher BMI was associated with excess mortality (hazard ratio (HR) per standard deviation (SD) = 1.05; 95% CI = 1.03-1.07). This was evident only for extreme obesity and only during February-April 2020 (HR = 1.52, 95% CI = 1.30-1.77 vs. recommended weight); this weakened thereafter. Among non-COVID-19 patients, higher BMI was associated with lower mortality (HR per SD = 0.83; 95% CI = 0.81-0.86), seen across all overweight/obesity groups and across dates and regions, albeit with a magnitude that varied over time. CONCLUSIONS: Obesity is associated with higher mortality among COVID-19 patients, but lower mortality among non-COVID-19 respiratory patients. These associations appear vulnerable to confounding/selection bias in both patient groups, questioning the existence or stability of causal effects.

The red blood cell distribution width to albumin ratio was a potential prognostic biomarker for acute respiratory failure: a retrospective study.

BACKGROUND: The association between red blood cell distribution width (RDW) to albumin ratio (RAR) and prognosis in patients with acute respiratory failure (ARF) admitted to the Intensive Care Unit (ICU) remains unclear. This retrospective cohort study aims to investigate this association. METHODS: Clinical information of ARF patients was collected from the Medical Information Mart for Intensive Care IV (MIMIC-IV) version 2.0 database. The primary outcome was, in-hospital mortality and secondary outcomes included 28-day mortality, 60-day mortality, length of hospital stay, and length of ICU stay. Cox regression models and subgroup analyses were conducted to explore the relationship between RAR and mortality. RESULTS: A total of 4547 patients with acute respiratory failure were enrolled, with 2277 in the low ratio group (RAR < 4.83) and 2270 in the high ratio group (RAR > = 4.83). Kaplan-Meier survival analysis demonstrated a significant difference in survival probability between the two groups. After adjusting for confounding factors, the Cox regression analysis showed that the high RAR ratio had a higher hazard ratio (HR) for in-hospital mortality (HR 1.22, 95% CI 1.07-1.40; P = 0.003), as well as for 28-day mortality and 60-day mortality. Propensity score-matched (PSM) analysis further supported the finding that high RAR was an independent risk factor for ARF. CONCLUSION: This study reveals that RAR is an independent risk factor for poor clinical prognosis in patients with ARF admitted to the ICU. Higher RAR levels were associated with increased in-hospital, 28-day and 60-day mortality rates.

Effectiveness of Zinc Supplementation for Sepsis Treatment: A Single-Center Retrospective Observational Study.

BACKGROUND: Zinc plays an important role in sepsis; however, the effectiveness of zinc supplementation and the appropriate dose remain unclear. This study aimed to verify the effectiveness of zinc supplementation and the appropriate dose in patients with sepsis. METHODS: This single-center retrospective observational study included 247 patients with sepsis from 1 April 2015 to 31 March 2023 who were receiving ventilatory management. The patients were divided into three groups according to the zinc supplementation dose: <15 mg, 15-50 mg, and ≥50 mg. RESULTS: The <15 mg, 15-50 mg, and ≥50 mg groups had 28 (19%), six (21%), and 16 deaths (22%) at discharge, with no statistically significant difference (p = 0.36). No statistically significant differences were observed in the length of intensive care unit (ICU) stay (p = 0.06). A higher supplementation dose corresponded with a statistically significant increase in blood zinc concentration in the first week (38.5 ± 16.6 µg/dL, 58.8 ± 19.7 µg/dL, 74.2 ± 22.5 µg/dL, respectively; p < 0.01) but not in the second or third weeks (p = 0.08, 0.19, respectively). CONCLUSIONS: Zinc supplementation did not reduce the mortality rate or length of ICU stay or contribute to an increased serum zinc concentration. High-dose zinc supplementation may not be effective during acute sepsis.

Frailty and 6-month trajectory of elderly trauma patients over the age of 65 years admitted to intensive care unit for severe trauma: experience of a level 1 trauma center.

BACKGROUND: The aging global population forecasts a significant rise in severe trauma cases among individuals aged 65 and above. Frailty emerges as a paramount predictor of post-traumatic outcomes, surpassing age and trauma severity indices. Despite this, scant attention is given to the trajectory of elderly patients post-intensive care unit (ICU) stay following severe trauma, justifying this study. The objective of this study was to analyze trajectories (frailty, place of residence) following a major trauma requiring an ICU stay. MATERIALS AND METHODS: An observational cohort study was conducted, leveraging data from a level 1 trauma center spanning 2018 to 2023. Inclusion criteria included elderly patients aged 65 and above admitted to the ICU for severe trauma. Data encompassed demographic profiles, trauma severity scores, clinical parameters, and frailty assessments sourced from the Traumabase database. RESULTS: Among 293 patients included 190 were non-frail, frailty was correlated with elevated mortality rates (114 (38.9%) at 6 months), heightened incidence of traumatic brain injuries, and notable declines in post-traumatic autonomy. Only 39.2% of patients had resumed residence at home six months post-injury, with a conspicuous trend towards institutionalization, particularly among frail individuals. CONCLUSION: This study highlights the role of frailty in determining the outcomes of elderly patients following severe trauma. Frailty is associated with higher mortality, increased rates of institutionalization, and a decline in functional status. These results highlight the importance of assessing frailty in the trajectory of severely injured patients over the age of 65 years-old.

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.

Candidaemia and Central Line-Associated Candidaemia in a Network of Indian ICUs: Impact of COVID-19 Pandemic.

BACKGROUND AND OBJECTIVES: Candidaemia is a potentially life-threatening emergency in the intensive care units (ICUs). Surveillance using common protocols in a large network of hospitals would give meaningful estimates of the burden of candidaemia and central line associated candidaemia in low resource settings. We undertook this study to understand the burden and epidemiology of candidaemia in multiple ICUs of India, leveraging the previously established healthcare-associated infections (HAI) surveillance network. Our aim was also to assess the impact that the pandemic of COVID-19 had on the rates and associated mortality of candidaemia. METHODS: This study included adult patients from 67 Indian ICUs in the AIIMS-HAI surveillance network that conducted BSI surveillance in COVID-19 and non-COVID-19 ICUs during and before the COVID-19 pandemic periods. Hospitals identified healthcare-associated candidaemia and central line associated candidaemia and reported clinical and microbiological data to the network as per established and previously published protocols. RESULTS: A total of 401,601 patient days and 126,051 central line days were reported during the study period. A total of 377 events of candidaemia were recorded. The overall rate of candidaemia in our network was 0.93/1000 patient days. The rate of candidaemia in COVID-19 ICUs (2.52/1000 patient days) was significantly higher than in non-COVID-19 ICUs (1.05/patient days) during the pandemic period. The rate of central line associated candidaemia in COVID-19 ICUs (4.53/1000 central line days) was also significantly higher than in non-COVID-19 ICUs (1.73/1000 central line days) during the pandemic period. Mortality in COVID-19 ICUs associated with candidaemia (61%) was higher than that in non-COVID-19 ICUs (41%). A total of 435 Candida spp. were isolated. C. tropicalis (26.7%) was the most common species. C. auris accounted for 17.5% of all isolates and had a high mortality. CONCLUSION: Patients in ICUs with COVID-19 infections have a much higher risk of candidaemia, CLAC and its associated mortality. Network level data helps in understanding the true burden of candidaemia and will help in framing infection control policies for the country.

Exploring Clinical Practices of Critical Alarm Settings in Intensive Care Units: A Retrospective Study of 60,000 Patient Stays from the MIMIC-IV Database.

In Intensive Care Unit (ICU), the settings of the critical alarms should be sensitive and patient-specific to detect signs of deteriorating health without ringing continuously, but alarm thresholds are not always calibrated to operate this way. An assessment of the connection between critical alarm threshold settings and the patient-specific variables in ICU would deepen our understanding of the issue. The aim of this retrospective descriptive and exploratory study was to assess this relationship using a large cohort of ICU patient stays. A retrospective study was conducted on some 70,000 ICU stays taken from the MIMIC-IV database. Critical alarm threshold values and threshold modification frequencies were examined. The link between these alarm threshold settings and 30 patient variables was then explored by computing the Shapley values of a Random Tree Forest model, fitted with patient variables and alarm settings. The study included 57,667 ICU patient stays. Alarm threshold values and alarm threshold modification frequencies exhibited the same trend: they were influenced by the vital sign monitored, but almost never by the patient's overall health status. This exploratory study also placed patients' vital signs as the most important variables, far ahead of medication. In conclusion, alarm settings were rigid and mechanical and were rarely adapted to the evolution of the patient. The management of alarms in ICU appears to be imperfect, and a different approach could result in better patient care and improved quality of life at work for staff.

Limitations of life-sustaining treatments in intensive care units in Croatia: a multicenter retrospective study.

AIM: In order to gain insight into the current prevailing practices regarding the limitation of life-sustaining treatment in intensive care units (ICUs) in Croatia, we assessed the frequency of limitation and provision of certain treatment modalities, as well as the associated patient and ICU-related factors. METHODS: A multicenter retrospective cross-sectional study was conducted in 17 ICUs in Croatia. We reviewed the medical records of patients deceased in 2017 and extracted data on demographic, clinical, and health care variables. A logistic regression analysis was conducted to determine the associations between these variables and treatment modalities. RESULTS: The study enrolled 1095 patients (55% male; mean age 69.9±13.7). Analgesia and sedation were discontinued before the patient's death in 23% and 34% of the cases, respectively. Patients older than 71 years were less often mechanically ventilated (P<0.001), and less frequently received inotropes and vasoactive therapy (P=0.002) than younger patients. Patients hospitalized in the ICU for less than 7 days less frequently had discontinuation of mechanical ventilation and inotropes and vasoactive therapy than patients hospitalized for 8 days and longer (P<0.001). Logistic regression analysis showed that ICU type was a crucial determinant, with multidisciplinary and surgical ICUs being associated with higher odds of intubation, mechanical ventilation, vasoactive and inotropic therapy, analgesia, and sedation. CONCLUSION: Older patients and those diagnosed with stroke and intracranial hemorrhage received fewer therapeutic modalities. All the observed treatment modalities were more frequently discontinued in patients who were hospitalized in the ICU for a prolonged time.

Pheno- and Genotypic Epidemiological Characterization of Vancomycin-Resistant Enterococcus faecium Isolates from Intensive Care Unit Patients in Central Türkiye.

Vancomycin-resistant Enterococcus faecium (VRE) has been detected in Türkiye. Only limited information is available on its dissemination in the central regions of the country. This study describes the first epidemiological characterization of VRE clinical isolates detected in patients in a hospital in the province of Aksaray. In this one-year study conducted between 2021 and 2022, stool samples from intensive care unit patients were screened for VRE using the phenotypic E-test method, and the antibiotic sensitivity test was analyzed by using the VITEK® 2 system. A molecular assay for confirmation of species level was carried out by 16S rRNA gene-based sequencing and testing for antibiotic resistance (vanA or vanB) and virulence factor-encoding genes (esp, asa1, and hyl). Further, genotypic characterization was determined by macro-restriction fragment pattern analysis (MRFPA) of genomic DNA digested with SmaI restriction enzyme. Of the total 350 Enterococcus positive patients from different hospital intensive care units, 22 (6.3%) were positive for VRE using the phenotypic E-test method. All isolates showed resistance to ampicillin, ciprofloxacin, vancomycin, and teicoplanin and positive amplification for the vanA gene. However, none of the isolates was positive for the vanB gene. The most prevalent virulence gene was esp. The results indicate that the isolates are persistent in the hospital environment and subsequently transmitted to hospitalized patients, thus representing challenges to an outbreak and infection control. These study results would also help formulate more effective strategies to reduce the transmission and propagation of VRE contamination in various hospital settings.

Theory-based and evidence-based nursing interventions for the prevention of ICU-acquired weakness in the intensive care unit: A systematic review.

OBJECTIVES: To synthesise and map the evidence of a theory- and evidence-based nursing intervention for the prevention of ICU-acquired weakness and evaluate its effectiveness in terms of the incidence of ICU-acquired weakness, incidence of delirium, and length of hospital stay. METHODS: We searched PubMed, CINAHL, MEDLINE, Academic Search Complete, Embase, Scopus, Web of Science and the Cochrane Library from database inception to November 2023. The eligible studies focused on critically ill patients in the intensive care unit, used a theory- and evidence-based nursing intervention, and reported the incidence of ICU-acquired weakness and/or used the Medical Research Council Scale. The methodological quality of the included studies was critically appraised by two authors using the appropriate Joanna Briggs Institute appraisal tool for randomised controlled trials, quasi-experimental studies, and cohort studies. Additionally, the weighted kappa coefficient was used to assess inter-rater agreement of the quality assessment. Data were reported using a narrative synthesis. This systematic review was registered by the International Prospective Register of Systematic Review (PROSPERO; CRD42023477011). RESULTS: A total of 5162 studies were initially retrieved, and 9 studies were eventually included after screening. This systematic review revealed that preventive nursing interventions for ICU-acquired weakness mainly include (a) physiotherapy, including neuromuscular electrical stimulation and early rehabilitation, and (b) nutritional support. In addition, (c) airway management, (d) sedation and analgesia management, (e) complication prevention (delirium, stress injury and deep vein thrombosis prevention), and (f) psychological care were also provided. The theories are dominated by goal-oriented theories, and the evidence is mainly the ABCDE bundle in the included studies. The results show that theory- or evidence-based nursing interventions are effective in reducing the incidence of ICU-acquired weakness (or improving the Medical Research Council Scale scores), decreasing the incidence of delirium, shortening the length of hospital stay, and improving patients' self-care and quality of life. CONCLUSION: Theory- and evidence-based nursing interventions have good results in preventing ICU-acquired weakness in critically ill patients. Current nursing interventions favour a combination of multiple interventions rather than just a single intervention. Therefore, preventive measures for ICU-acquired weakness should be viewed as complex interventions and should be based on theory or evidence. This systematic review is based on a small number of trials. Thus, more high-quality randomised controlled trials are needed to draw definitive conclusions about the impact of theory- and evidence-based nursing interventions on the prevention of ICU-acquired weakness.

Predisposing and precipitating factors for the development of postoperative delirium in critically ill patients in a university intensive care unit.

OBJECTIVE: to detect the incidence of postoperative delirium in critically ill patients admitted to a surgical intensive care unit and to evaluate the predisposing and precipitating factors associated with postoperative delirium in critically ill patients admitted to a surgical intensive care unit. METHOD: this is a prospective cohort study of 157 critically ill surgical patients. Fisher's exact test and Chi-square test were used for the association between factors and the occurrence of delirium, the Wilcoxon test for numerical variables, and the logistic regression model for the analysis of predisposing and precipitating factors. RESULTS: the incidence of delirium was 28% (n=44). Age was a significant predisposing factor (p=0.001), followed by the length of surgery (p<0.001), blood transfusion (p=0.043), administration of crystalloids (p=0.008), and anti-inflammatory drugs (p=0.037), which were the precipitating factors identified. The best-adjusted models were: age, length of surgery, non-administration of anti-emetics, use of sufentanil, and blood transfusion. CONCLUSION: delirium is a frequent condition in critically ill adults undergoing surgery and the existence of precipitating and predisposing factors is relevant to the outcome, with the anesthetic-surgical procedure as the catalyst event.

Prediction of mortality events of patients with acute heart failure in intensive care unit based on deep neural network.

BACKGROUND: Acute heart failure (AHF) in the intensive care unit (ICU) is characterized by its criticality, rapid progression, complex and changeable condition, and its pathophysiological process involves the interaction of multiple organs and systems. This makes it difficult to predict in-hospital mortality events comprehensively and accurately. Traditional analysis methods based on statistics and machine learning suffer from insufficient model performance, poor accuracy caused by prior dependence, and difficulty in adequately considering the complex relationships between multiple risk factors. Therefore, the application of deep neural network (DNN) techniques to the specific scenario, predicting mortality events of patients with AHF under intensive care, has become a research frontier. METHODS: This research utilized the MIMIC-IV critical care database as the primary data source and employed the synthetic minority over-sampling technique (SMOTE) to balance the dataset. Deep neural network models-backpropagation neural network (BPNN) and recurrent neural network (RNN), which are based on electronic medical record data mining, were employed to investigate the in-hospital death event judgment task of patients with AHF under intensive care. Additionally, multiple single machine learning models and ensemble learning models were constructed for comparative experiments. Moreover, we achieved various optimal performance combinations by modifying the classification threshold of deep neural network models to address the diverse real-world requirements in the ICU. Finally, we conducted an interpretable deep model using SHapley Additive exPlanations (SHAP) to uncover the most influential medical record features for each patient from the aspects of global and local interpretation. RESULTS: In terms of model performance in this scenario, deep neural network models outperform both single machine learning models and ensemble learning models, achieving the highest Accuracy, Precision, Recall, F1 value, and Area under the ROC curve, which can reach 0.949, 0.925, 0.983, 0.953, and 0.987 respectively. SHAP value analysis revealed that the ICU scores (APSIII, OASIS, SOFA) are significantly correlated with the occurrence of in-hospital fatal events. CONCLUSIONS: Our study underscores that DNN-based mortality event classifier offers a novel intelligent approach for forecasting and assessing the prognosis of AHF patients in the ICU. Additionally, the ICU scores stand out as the most predictive features, which implies that in the decision-making process of the models, ICU scores can provide the most crucial information, making the greatest positive or negative contribution to influence the incidence of in-hospital mortality among patients with acute heart failure.

A machine learning model for early candidemia prediction in the intensive care unit: Clinical application.

Candidemia often poses a diagnostic challenge due to the lack of specific clinical features, and delayed antifungal therapy can significantly increase mortality rates, particularly in the intensive care unit (ICU). This study aims to develop a machine learning predictive model for early candidemia diagnosis in ICU patients, leveraging their clinical information and findings. We conducted this study with a cohort of 334 patients admitted to the ICU unit at Ji Ning NO.1 people's hospital in China from Jan. 2015 to Dec. 2022. To ensure the model's reliability, we validated this model with an external group consisting of 77 patients from other sources. The candidemia to bacteremia ratio is 1:1. We collected relevant clinical procedures and eighteen key examinations or tests features to support the recursive feature elimination (RFE) algorithm. These features included total bilirubin, age, platelet count, hemoglobin, CVC, lymphocyte, Duration of stay in ICU and so on. To construct the candidemia diagnosis model, we employed random forest (RF) algorithm alongside other machine learning methods and conducted internal and external validation with training and testing sets allocated in a 7:3 ratio. The RF model demonstrated the highest area under the receiver operating characteristic (AUC) with values of 0.87 and 0.83 for internal and external validation, respectively. To evaluate the importance of features in predicting candidemia, Shapley additive explanation (SHAP) values were calculated and results revealed that total bilirubin and age were the most important factors in the prediction model. This advancement in candidemia prediction holds significant promise for early intervention and improved patient outcomes in the ICU setting, where timely diagnosis is of paramount crucial.

Healthcare Provider Experiences With Unvaccinated COVID-19 Patients: A Qualitative Study.

IMPORTANCE: In the setting of an active pandemic the impact of public vaccine hesitancy on healthcare workers has not yet been explored. There is currently a paucity of literature that examines how patient resistance to disease prevention in general impacts practitioners. OBJECTIVES: The COVID-19 pandemic created unprecedented healthcare challenges with impacts on healthcare workers' wellbeing. Vaccine hesitancy added complexity to providing care for unvaccinated patients. Our study qualitatively explored experiences of healthcare providers caring for unvaccinated patients with severe COVID-19 infection in the intensive care setting. DESIGN: We used interview-based constructivist grounded theory methodology to explore experiences of healthcare providers with critically ill unvaccinated COVID-19 patients. SETTING AND PARTICIPANTS: Healthcare providers who cared for unvaccinated patients with severe COVID-19 respiratory failure following availability of severe acute respiratory syndrome coronavirus 2 vaccines were recruited from seven ICUs located within two large academic centers and one community-based hospital. We interviewed 24 participants, consisting of eight attending physicians, seven registered nurses, six critical care fellows, one respiratory therapist, one physiotherapist, and one social worker between March 2022 and September 2022 (approximately 1.5 yr after the availability of COVID-19 vaccines in Canada). ANALYSIS: Interviews were recorded, transcribed, de-identified, and coded to identify emerging themes. The final data was analyzed to generate the thematic framework. Reflexivity was employed to reflect upon and discuss individual pre-conceptions and opinions that may impact collection and interpretation of the data. RESULTS: Healthcare providers maintained dedication toward professionalism during provision of care, at the cost of suffering emotional turmoil from the pandemic and COVID-19 vaccine hesitancy. Evolving sources of stress associated with vaccine hesitancy included ongoing high volumes of critically ill patients, resource shortages, and visitation restrictions, which contributed to perceived emotional distress, empathy loss, and professional dissatisfaction. As a result, there were profound personal and professional consequences for healthcare professionals, with perceived impacts on patient care. CONCLUSIONS: Our study highlights struggles of healthcare providers in fulfilling professional duties while navigating emotional stressors unique to vaccine hesitancy. System-based interventions should be explored to help providers navigate biases and moral distress, and to foster resilience for the next major healthcare system strain.

To prescribe or not: a two-center retrospective observational study of antibiotics usage and outcomes of COVID-19 in Turkey.

This retrospective cohort study conducted in Turkey between December 2020 and June 2022 aimed to assess antibiotic use, bacterial co-infections, and the associated factors on mortality in hospitalized patients with mild-to-severe COVID-19. Among the 445 patients, 80% received antibiotics, with fluoroquinolones being the most common choice, followed by beta-lactams and combinations. Various clinical and laboratory parameters, including symptoms, comorbidities, CCI, oxygen requirements, and CRP levels were observed to be elevated in the antibiotic group. Non-survivors had more ICU admissions and longer hospital stays compared to survivors. We conducted a multivariate Cox regression analysis to evaluate factors related to mortality. However, we did not find an association between antibiotic use and mortality [HR 2.7 (95% CI 0.4-20)]. The study identified significant factors associated with an antibiotic prescription, such as CCI (OR 1.6), CRP (OR 2.3), and ICU admission (OR 8.8), (p < 0.05). The findings suggest re-evaluating the necessity of antibiotics in COVID-19 cases based on clinical assessments, focusing on the presence of bacterial infections rather than empirical treatment. Further research is necessary to more accurately identify patients with bacterial co-infections who would benefit from antibiotic treatment.

The usage of immunosuppressant agents and secondary infections in patients with COVID-19 in the intensive care unit: a retrospective study.

Although COVID-19 infection is an immunosuppressant disease, many immunosuppressant agents, such as pulse methylprednisolone (PMP), dexamethasone (DXM), and tocilizumab (TCZ), were used during the pandemic. Secondary infections in patients with COVID-19 have been reported recently. This study investigated these agents' effects on secondary infections and outcomes in patients with COVID-19 in intensive care units (ICUs). This study was designed retrospectively, and all data were collected from the tertiary intensive care units of six hospitals between March 2020 and October 2021. All patients were divided into three groups: Group I [GI, PMP (-), DXM (-) and TCZ (-)], Group II [GII, PMP (+), DXM (+)], and Group III [GIII, PMP (+), DXM (+), TCZ (+)]. Demographic data, PaO/FiO2 ratio, laboratory parameters, culture results, and outcomes were recorded. To compare GI-GII and GI-GIII, propensity score matching (PSM) was used by matching 14 parameters. Four hundred twelve patients with COVID-19 in the ICU were included in the study. The number of patients with microorganisms ≥ 2 was 279 (67.7%). After PSM, in GII and GIII, the number of (+) tracheal cultures and (+) bloodstream cultures detected different microorganisms ≥ 2 during the ICU period, neuropathy, tracheotomized patients, duration of IMV, and length of ICU stay were significantly higher than GI. The mortality rate was similar in GI and GII, whereas it was significantly higher in GIII than in GI. The use of immunosuppressant agents in COVID-19 patients may lead to an increase in secondary infections. In addition, increased secondary infections may lead to prolonged ICU stay, prolonged IMV duration, and increased mortality.

Machine learning-based prognostic model for 30-day mortality prediction in Sepsis-3.

BACKGROUND: Sepsis poses a critical threat to hospitalized patients, particularly those in the Intensive Care Unit (ICU). Rapid identification of Sepsis is crucial for improving survival rates. Machine learning techniques offer advantages over traditional methods for predicting outcomes. This study aimed to develop a prognostic model using a Stacking-based Meta-Classifier to predict 30-day mortality risks in Sepsis-3 patients from the MIMIC-III database. METHODS: A cohort of 4,240 Sepsis-3 patients was analyzed, with 783 experiencing 30-day mortality and 3,457 surviving. Fifteen biomarkers were selected using feature ranking methods, including Extreme Gradient Boosting (XGBoost), Random Forest, and Extra Tree, and the Logistic Regression (LR) model was used to assess their individual predictability with a fivefold cross-validation approach for the validation of the prediction. The dataset was balanced using the SMOTE-TOMEK LINK technique, and a stacking-based meta-classifier was used for 30-day mortality prediction. The SHapley Additive explanations analysis was performed to explain the model's prediction. RESULTS: Using the LR classifier, the model achieved an area under the curve or AUC score of 0.99. A nomogram provided clinical insights into the biomarkers' significance. The stacked meta-learner, LR classifier exhibited the best performance with 95.52% accuracy, 95.79% precision, 95.52% recall, 93.65% specificity, and a 95.60% F1-score. CONCLUSIONS: In conjunction with the nomogram, the proposed stacking classifier model effectively predicted 30-day mortality in Sepsis patients. This approach holds promise for early intervention and improved outcomes in treating Sepsis cases.

Impact of the timing of invasive mechanical ventilation in patients with sepsis: a multicenter cohort study.

BACKGROUND: The potential adverse effects associated with invasive mechanical ventilation (MV) can lead to delayed decisions on starting MV. We aimed to explore the association between the timing of MV and the clinical outcomes in patients with sepsis ventilated in intensive care unit (ICU). METHODS: We analyzed data of adult patients with sepsis between September 2019 and December 2021. Data was collected through the Korean Sepsis Alliance from 20 hospitals in Korea. Patients who were admitted to ICU and received MV were included in the study. Patients were divided into 'early MV' and 'delayed MV' groups based on whether they were on MV on the first day of ICU admission or later. Propensity score matching was applied, and patients in the two groups were compared on a 1:1 ratio to overcome bias between the groups. Outcomes including ICU mortality, hospital mortality, length of hospital and ICU stay, and organ failure at ICU discharge were compared. RESULTS: Out of 2440 patients on MV during ICU stay, 2119 'early MV' and 321 'delayed MV' cases were analyzed. The propensity score matching identified 295 patients in each group with similar baseline characteristics. ICU mortality was lower in 'early MV' group than 'delayed MV' group (36.3% vs. 46.4%; odds ratio, 0.66; 95% confidence interval, 0.47-0.93; p = 0.015). 'Early MV' group had lower in-hospital mortality, shorter ICU stay, and required tracheostomy less frequently than 'delayed MV' group. Multivariable logistic regression model identified 'early MV' as associated with lower ICU mortality (odds ratio, 0.38; 95% confidence interval, 0.29-0.50; p < 0.001). CONCLUSION: In patients with sepsis ventilated in ICU, earlier start (first day of ICU admission) of MV may be associated with lower mortality.