Intensive Care Unit Cohort Research Articles

Proteome Signature for Differential Diagnosis of Patients with Bilateral Lung Infiltrates

BackgroundThe differential diagnosis of bilateral lung infiltrates and prognosis prediction can be challenging for clinicians in the intensive care unit (ICU). We analyzed the proteome from bronchoalveolar lavage fluid (BALF) and determine their usefulness for evaluating the infectious causes and mortality associated with bilateral lung infiltrates.MethodsIn the ICU cohort, 136 patients with bilateral infiltrate on chest radiographs were selected, and bronchoscopy with bronchoalveolar lavage (BAL) was performed. Proteomics profiling of the exosomes in the BALF (n=20) was conducted to identify candidate protein biomarkers potentially associated with infection or mortality. The BAL samples (n=116) were used to measure the candidate biomarker levels.ResultsThe candidate biomarkers, CD20, CLIC4, SCFD1, and TAP1, were selected for the differential diagnosis of infection or mortality. The levels of CD20 were significantly elevated in patients with non-infectious causes compared to those with infectious causes (248.6±154.5versus177.6±150.9 ng·mL−1, p=0.014). The levels of CLIC4, SCFD1, and TAP1 did not differ between the two groups. As per the receiver operating characteristic analysis, CD20 was a significant predictor of non-infectious causes (area under curve, 0.668; 95% CI, 0.567–0.769; p=0.002; cut off value, 167.6 ng·mL−1; sensitivity, 74.1%; specificity, 63.2%). There were no significant differences in the concentrations of the biomarkers between survivors and non-survivors.ConclusionsOur results suggested that CD20 levels in BALF might be a useful biomarker for differentiating non-infectious and infectious diseases in patients with bilateral lung infiltrates.

Short-term vital parameter forecasting in the intensive care unit: A benchmark study leveraging data from patients after cardiothoracic surgery.

Patients in an Intensive Care Unit (ICU) are closely and continuously monitored, and many machine learning (ML) solutions have been proposed to predict specific outcomes like death, bleeding, or organ failure. Forecasting of vital parameters is a more general approach to ML-based patient monitoring, but the literature on its feasibility and robust benchmarks of achievable accuracy are scarce. We implemented five univariate statistical models (the naïve model, the Theta method, exponential smoothing, the autoregressive integrated moving average model, and an autoregressive single-layer neural network), two univariate neural networks (N-BEATS and N-HiTS), and two multivariate neural networks designed for sequential data (a recurrent neural network with gated recurrent unit, GRU, and a Transformer network) to produce forecasts for six vital parameters recorded at five-minute intervals during intensive care monitoring. Vital parameters were the diastolic, systolic, and mean arterial blood pressure, central venous pressure, peripheral oxygen saturation (measured by non-invasive pulse oximetry) and heart rate, and forecasts were made for 5 through 120 minutes into the future. Patients used in this study recovered from cardiothoracic surgery in an ICU. The patient cohort used for model development (n = 22,348) and internal testing (n = 2,483) originated from a heart center in Germany, while a patient sub-set from the eICU collaborative research database, an American multicenter ICU cohort, was used for external testing (n = 7,477). The GRU was the predominant method in this study. Uni- and multivariate neural network models proved to be superior to univariate statistical models across vital parameters and forecast horizons, and their advantage steadily became more pronounced for increasing forecast horizons. With this study, we established an extensive set of benchmarks for forecast performance in the ICU. Our findings suggest that supplying physicians with short-term forecasts of vital parameters in the ICU is feasible, and that multivariate neural networks are most suited for the task due to their ability to learn patterns across thousands of patients.

Clinical phenotyping uncovers heterogeneous associations between corticosteroid treatment and survival in critically ill COVID-19 patients.

Disease heterogeneity in coronavirus disease 2019 (COVID-19) may render the current one-size-fits-all treatment approach suboptimal. We aimed to identify and immunologically characterize clinical phenotypes among critically ill COVID-19 patients, and to assess heterogeneity of corticosteroid treatment effect. We applied consensus k-means clustering on 21 clinical parameters obtained within 24h after admission to the intensive care unit (ICU) from 13,279 COVID-19 patients admitted to 82 Dutch ICUs from February 2020 to February 2022. Derived phenotypes were reproduced in 6225 COVID-19 ICU patients from Spain (February 2020 to December 2021). Longitudinal immunological characterization was performed in three COVID-19 ICU cohorts from the Netherlands and Germany, and associations between corticosteroid treatment and survival were assessed across phenotypes. We derived three phenotypes: COVIDICU1 (43% of patients) consisted of younger patients with the lowest Acute Physiology And Chronic Health Evaluation (APACHE) scores, highest body mass index (BMI), lowest PaO2/FiO2 ratio, and a 90-day in-hospital mortality rate of 18%. COVIDICU2 patients (37%) had the lowest BMI, were older and had higher APACHE scores and mortality rate (24%) than COVIDICU1. Patients with COVIDICU3 (20%) were the eldest with the most comorbidities, the highest APACHE scores, acute kidney injury and metabolic dysregulations, and the highest mortality rate (47%). These patients also displayed the most pronounced inflammatory response. Corticosteroid therapy started at day 5 [2-9] after ICU admission and administered for 5 [3-7] days was associated with an increased risk for 90-day mortality in patients with the COVIDICU1 and COVIDICU2 phenotypes (hazard ratio [HR] 1.59 [1.09-2.31], p = 0.015 and HR 1.79 [1.42-2.26], p < 0.001, respectively), but not in patients with the COVIDICU3 phenotype (HR 1.08 [0.76-1.54], p = 0.654). Our multinational study identified three distinct clinical COVID-19 phenotypes, each exhibiting marked differences in demographic, clinical, and immunological features, and in the response to late and short-term corticosteroid treatment.

Postdiscontinuation Antibiotic Exposure in Hospitalized Infants at Risk for Late-onset Sepsis in the Neonatal Intensive Care Unit.

In the neonatal intensive care unit, infants are at risk for late-onset sepsis. When blood cultures are negative, antibiotic stewardship efforts encourage stopping antibiotics, yet the duration of therapeutic exposure after the last dose is unknown. This retrospective cohort study of simulated antibiotic exposures used published population pharmacokinetic models within drug-specific neonatal intensive care unit cohorts of preterm and term infants, postnatal age 7-60 days and exposed to cefepime, piperacillin-tazobactam or tobramycin. Monte Carlo simulations (NONMEM 7.3) were used to predict steady-state exposures after a 72-hour antibiotic course per Neofax dosing. Exposure was assessed relative to drug-specific minimum inhibitory concentration (MIC) targets between 1 and 16 mcg/mL for Pseudomonas and Enterobacteriaceae species. Postdiscontinuation antibiotic exposure (PDAE) was defined as the time from the last dose to when antibiotic concentration decreased below a specific MIC. Piperacillin-tazobactam, cefepime and tobramycin cohorts included infants with median gestation age 29, 32 and 32 weeks and postnatal age 17, 19 and 15 days, respectively. The mean PDAE was 19-68 hours, depending on the specific antibiotic/MIC combination. PDAE was longer for infants <28 days old and preterm (vs. term) infants. Cefepime exhibited the longest mean PDAE of 68 hours for Enterobacteriaceae MIC 1. Piperacillin mean PDAE was 25 hours for Enterobacteriaceae MIC 8. Tobramycin had a short mean PDAE of 19 hours. Piperacillin and cefepime exposures remained therapeutic long after the expected 8- to 12-hour dosing interval. PDAE is an important consideration for antibiotic stewardship among hospitalized infants, particularly premature infants and those within 1 month postbirth.

Serum cytokine dysregulation signatures associated with COVID-19 outcomes in high mortality intensive care unit cohorts across pandemic waves and variants

The aim of this study was to characterize the systemic cytokine signature of critically ill COVID-19 patients in a high mortality setting aiming to identify biomarkers of severity, and to explore their associations with viral loads and clinical characteristics. We studied two COVID-19 critically ill patient cohorts from a referral centre located in Central Europe. The cohorts were recruited during the pre-alpha/alpha (November 2020 to April 2021) and delta (end of 2021) period respectively. We determined both the serum and bronchoalveolar SARS-CoV-2 viral load and identified the variant of concern (VoC) involved. Using a cytokine multiplex assay, we quantified systemic cytokine concentrations and analyzed their relationship with clinical findings, routine laboratory workup and pulmonary function data obtained during the ICU stay. Patients who did not survive had a significantly higher systemic and pulmonary viral load. Patients infected with the pre-alpha VoC showed a significantly lower viral load in comparison to those infected with the alpha- and delta-variants. Levels of systemic CTACK, M-CSF and IL-18 were significantly higher in non-survivors in comparison to survivors. CTACK correlated directly with APACHE II scores. We observed differences in lung compliance and the association between cytokine levels and pulmonary function, dependent on the VoC identified. An intra-cytokine analysis revealed a loss of correlation in the non-survival group in comparison to survivors in both cohorts. Critically ill COVID-19 patients exhibited a distinct systemic cytokine profile based on their survival outcomes. CTACK, M-CSF and IL-18 were identified as mortality-associated analytes independently of the VoC involved. The Intra-cytokine correlation analysis suggested the potential role of a dysregulated systemic network of inflammatory mediators in severe COVID-19 mortality.

#2707 Prediction of plasma sodium changes in hypernatremic patients: the role of tissue sodium content

Abstract Background and Aims Hypo- and hypernatremia are frequently encountered electrolyte disturbances in clinical practice. Rapid correction of these dysnatremias can result in severe neurological complications. Therefore, various formulas have been developed to predict changes in plasma sodium concentration ([Na+]) after treatment. However, these kidney-centered formulas have been shown to be inaccurate. A potential explanation for the discrepancy between predicted and measured plasma [Na+] is the accumulation of sodium in the skin, which is not explicitly taken into account in these formulas. The purpose of this post-hoc analysis is to evaluate the association between factors related to tissue sodium accumulation and the discrepancy between measured and predicted plasma [Na+]. Method We used data from a hypernatremic intensive care unit (ICU) cohort with 66 patients and 1,034 ICU days with complete data on sodium, potassium and water balance. We excluded ICU days with incomplete balances, dialysis and severe diarrhea. The predicted plasma [Na+] was calculated using the Barsoum-Levine (BL) and the Nguyen-Kurtz (NK) formula. Then, we calculated the discrepancy between predicted and measured plasma [Na+] (∆[Na+]). We fitted a linear mixed-effect model to investigate the association between factors related to tissue sodium content and ∆[Na+]. Age, sex, blood pressure, eGFR, CRP, plasma albumin, total body water (TBW), baseline plasma [Na+] and changes in plasma [Na+] were incorporated as fixed effects in the model, while the subjects were introduced as a random effect. TBW was estimated as 60% of body weight for males and 50% of body weight for females, corrected for fluid balances on subsequent ICU days. Results A total of 613 ICU days of 66 patients were included in our analysis. The mean age was 58 ± 15 years and 38% was female. The mean plasma [Na+] at inclusion day was 145 ± 9 mmol/L and mean eGFR CKD-EPI was 52 ± 28 ml/min/1.73 m2. On average, the deviation between predicted and measured plasma [Na+] was 3.8 ± 3.3 mmol/L for the BL formula and 3.9 ± 3.4 mmol/L for the NK formula. For both formulas, TBW (p = 0.01), baseline plasma [Na+] (p &amp;lt; 0.001) and plasma [Na+] change (p &amp;lt; 0.001) were significantly associated with the discrepancy between predicted and measured plasma [Na+] (Table 1). Plasma albumin (p = 0.02) was also significantly associated with this discrepancy when the NK formula was used. Conclusion In a hypernatremic ICU cohort, baseline plasma [Na+], estimated TBW and plasma [Na+] changes are associated with the inaccurateness of plasma [Na+] prediction. As baseline plasma [Na+] and estimated TBW are available when initiating treatment, incorporation of these variables in the currently available formulas may improve the prediction of plasma [Na+].

SOFA score performs worse than age for predicting mortality in patients with COVID-19.

The use of the Sequential Organ Failure Assessment (SOFA) score, originally developed to describe disease morbidity, is commonly used to predict in-hospital mortality. During the COVID-19 pandemic, many protocols for crisis standards of care used the SOFA score to select patients to be deprioritized due to a low likelihood of survival. A prior study found that age outperformed the SOFA score for mortality prediction in patients with COVID-19, but was limited to a small cohort of intensive care unit (ICU) patients and did not address whether their findings were unique to patients with COVID-19. Moreover, it is not known how well these measures perform across races. In this retrospective study, we compare the performance of age and SOFA score in predicting in-hospital mortality across two cohorts: a cohort of 2,648 consecutive adult patients diagnosed with COVID-19 who were admitted to a large academic health system in the northeastern United States over a 4-month period in 2020 and a cohort of 75,601 patients admitted to one of 335 ICUs in the eICU database between 2014 and 2015. We used age and the maximum SOFA score as predictor variables in separate univariate logistic regression models for in-hospital mortality and calculated area under the receiver operator characteristic curves (AU-ROCs) and area under precision-recall curves (AU-PRCs) for each predictor in both cohorts. Among the COVID-19 cohort, age (AU-ROC 0.795, 95% CI 0.762, 0.828) had a significantly better discrimination than SOFA score (AU-ROC 0.679, 95% CI 0.638, 0.721) for mortality prediction. Conversely, age (AU-ROC 0.628 95% CI 0.608, 0.628) underperformed compared to SOFA score (AU-ROC 0.735, 95% CI 0.726, 0.745) in non-COVID-19 ICU patients in the eICU database. There was no difference between Black and White COVID-19 patients in performance of either age or SOFA Score. Our findings bring into question the utility of SOFA score-based resource allocation in COVID-19 crisis standards of care.

The laboratory parameters-derived CoLab score as an indicator of the host response in ICU COVID-19 patients decreases over time: a prospective cohort study

The CoLab score was developed and externally validated to rule out COVID-19 among suspected patients presenting at the emergency department. We hypothesized a within-patient decrease in the CoLab score over time in an intensive care unit (ICU) cohort. Such a decrease would create the opportunity to potentially rule out the need for isolation when the infection is overcome. Using linear mixed-effects models, data from the Maastricht Intensive Care COVID (MaastrICCht) cohort were used to investigate the association between time and the CoLab score. Models were adjusted for sex, APACHE II score, ICU mortality, and daily SOFA score. The CoLab score decreased by 0.30 points per day (95% CI − 0.33 to − 0.27), independent of sex, APACHE II, and Mortality. With increasing SOFA score over time, the CoLab score decreased more strongly (− 0.01 (95% CI − 0.01 to − 0.01) additional decrease per one-point increase in SOFA score.) The CoLab score decreased in ICU patients on mechanical ventilation for COVID-19, with a one-point reduction per three days, independent of sex, APACHE II, and ICU mortality, and somewhat stronger with increasing multi-organ failure over time. This suggests that the CoLab score would decrease below a threshold where COVID-19 can be excluded.

Facial appearance associates with longitudinal multi-organ failure: an ICU cohort study

BackgroundFacial appearance, whether consciously or subconsciously assessed, may affect clinical assessment and treatment strategies in the Intensive Care Unit (ICU). Nevertheless, the association between objective clinical measurement of facial appearance and multi-organ failure is currently unknown. The objective of this study was to examine whether facial appearance at admission is associated with longitudinal evaluation of multi-organ failure.MethodsThis was a sub-study of the Simple Intensive Care Studies-II, a prospective observational cohort study. All adult patients acutely admitted to the ICU between March 26, 2019, and July 10, 2019, were included. Facial appearance was assessed within three hours of ICU admission using predefined pictograms. The SOFA score was serially measured each day for the first seven days after ICU admission. The association between the extent of eye-opening and facial skin colour with longitudinal Sequential Organ Failure Assessment (SOFA) scores was investigated using generalized estimation equations.ResultsSOFA scores were measured in 228 patients. Facial appearance scored by the extent of eye-opening was associated with a higher SOFA score at admission and follow-up (unadjusted 0.7 points per step (95%CI 0.5 to 0.9)). There was no association between facial skin colour and a worse SOFA score over time. However, patients with half-open or closed eyes along with flushed skin had a lower SOFA score than patients with a pale or normal facial skin colour (P-interaction < 0.1).ConclusionsThe scoring of patients’ facial cues, primarily the extent of eye-opening and facial colour, provided valuable insights into the disease state and progression of the disease of critically ill patients. The utilization of advanced monitoring techniques that incorporate facial appearance holds promise for enhancing future intensive care support.

Biomarker Analysis Provides Evidence for Host Response Homogeneity in Patients With COVID-19

BackgroundThe exploration of subphenotypes in hospitalized COVID-19 patients has garnered substantial attention. Most existing studies operate under the assumption of heterogeneity in COVID-19 patient populations and this assumption can lead to erroneous conclusions. Research QuestionDo plasma biomarker profiles reflective of various pathophysiological pathways provide evidence for heterogeneity in hospitalized patients with COVID-19? Study Design and MethodsThis is a secondary analysis of two prospective observational studies of adult patients hospitalized with COVID-19 related respiratory failure in the general ward and intensive care unit (ICU) of two medical centers and with 44 host response biomarkers available. Parsimonious models were used to allocate and validate ARDS inflammatory subphenotypes. Novel biological subphenotypes were identified using latent profile analysis (LPA) and hierarchical clustering. Heterogeneity of treatment effect (HTE) for corticosteroids was assessed using an interaction term in a logistic regression model. ResultsThe cohort consisted of 162 patients admitted to the ICU and 464 to the ward. Using the parsimonious models in ICU patientsonly 3.1-13% of patients were classified as hyperinflammatory subphenotype. Using de novo subphenotyping techniques, neither clustering or LPA revealed significant evidence for heterogeneity in the ward (p = 0.11-0.13), ICU (p = 0.23-0.88) or combined cohort (p = 0.05-0.88). Adding clinical variables did not alter results in the ICU or combined cohort. Using the combined approach in the ward cohort, indices provided borderline significance for two subphenotypes, and there was good agreement between clustering and LPA (87.9%), but no HTE for corticosteroids was observed between these two classes (p = 0.198). InterpretationSystemic inflammatory subphenotypes derived from ARDS patients do not reflect the variation in severity of COVID-19. Empirical evidence, derived from cluster analysis or latent profile analysis, offers limited support for biological heterogeneity in COVID-19.

The Ratio of Red Blood Cell Distribution Width to Albumin as a Predictor for Rehospitalization Risk and Rehospitalization All-Cause Mortality in Middle-Aged and Elderly Survivors with Sepsis: An Ambispective ICU Cohort Study.

To explore the relationship between red blood cell distribution width to albumin (RDW/ALB) ratio (RAR) and the risk of rehospitalization and rehospitalization all-cause mortality in middle-aged and elderly survivors with sepsis based on an ambispective longitudinal cohort from the Intensive Care Unit (ICU). Between 2017 and 2022, 455 adults who survived the first-episode severe sepsis without recurrence for at least 3 months were included in this study. All participants were followed up every 4 weeks for 12 months. According to the tertiles of RAR, participants were divided into three groups: low-level (≤0.36, n = 152), moderate-level (0.37-0.44, n = 152), and high-level (≥0.45, n = 151). The relationship between RAR and the risk of rehospitalization and rehospitalization all-cause mortality was evaluated. Out of 455 participants, 156 experienced rehospitalization (34.3%), of which 44 (28.2%) died. Receiver operating characteristic (ROC) analysis showed that the RAR cut-off values for rehospitalization and rehospitalization all-cause mortality were 0.4251 and 0.4743, respectively. Multivariate Cox regression analysis indicated that the RAR was positively associated with rehospitalization (P = 0.011) and all-cause mortality (P = 0.006). Compared with the low-level, the high-level RAR presented a higher dose-dependent rehospitalization risk (P = 0.02) and rehospitalization all-cause mortality (P = 0.044). The stratified analysis displayed that compared to the low-level, with the RAR increasing by 1.0, the risk for rehospitalization increased 3.602-fold in aged <65 patients (P = 0.002) and 1.721-fold in female patients (P = 0.014). Kaplan-Meier survival analysis implied a significant positive association between the RAR and the cumulative incidence of rehospitalization and rehospitalization all-cause mortality (log-rank, all P < 0.001). RAR has a reliable predictive value for the risk of rehospitalization and rehospitalization all-cause mortality in patients with sepsis. Consequently, monitoring RAR for at least 1 year after surviving sepsis in female patients aged <65 in clinical practice is critical.

Impaired Whole-Blood Fibrinolysis is a Predictor of Mortality in Intensive Care Patients.

Background Altered fibrinolysis is considered to play a crucial role in the development of coagulopathy in sepsis. However, routine laboratory tests for fibrinolysis are currently very limited, and the impact of fibrinolytic capacity on clinical outcome is poorly investigated. Objectives To assess whole-blood fibrinolysis in patients admitted to the intensive care unit (ICU) and compare fibrinolysis in sepsis patients with nonsepsis patients. Further, to investigate associations between fibrinolytic capacity and 30-day mortality and venous thromboembolism (VTE). Methods This study was designed as a prospective cohort study. Adult ICU patients were included at the Aarhus University Hospital, Denmark. All patients had a blood sample obtained the morning after admission. A modified thromboelastometry (ROTEM®) analysis with tissue plasminogen activator (ROTEM®-tPA) was used to assess fibrinolysis. The primary endpoint was difference in ROTEM®-tPA lysis time between sepsis patients and nonsepsis patients. Results ROTEM®-tPA revealed fibrinolytic impairment in sepsis patients ( n = 30) compared with nonsepsis ICU controls ( n = 129), with longer lysis time (median [interquartile range] 3,600 [3,352-3,600] vs. 3,374 seconds [2,175-3,600], p < 0.01), lower maximum lysis (23 [8-90] vs. 94% [14-100], p = 0.02), and lower fibrinolysis speed (0.41 [0.0-1.4] vs. 1.6 mm/min [0.1-2.7], p = 0.01). In the composite ICU population, 61% (97/159) demonstrated prolonged lysis time indicating impaired fibrinolytic capacity. These patients had higher 30-day mortality (adjusted odds ratio [OR]: 2.26 [0.83-6.69]) and VTE risk (OR: 3.84 [0.87-17.8]) than patients with normal lysis time. Conclusion Sepsis patients showed impaired fibrinolysis measured with ROTEM®-tPA compared with nonsepsis patients and ROTEM®-tPA lysis time was associated with 30-day mortality and VTE in the entire ICU cohort.

Development of a clinical practice guideline for physiotherapy management of adults invasively ventilated with community-acquired pneumonia

Background and settingPatients hospitalised with community-acquired pneumonia (CAP) are frequently admitted to an intensive care unit (ICU) for invasive mechanical ventilation and receive treatment by physiotherapists. However, clinical physiotherapy practice is variable for this ICU cohort. ObjectivesTo develop a clinical practice guideline for physiotherapy management of adults invasively ventilated with CAP using the best available evidence. MethodsGuideline development using evidence synthesis according to the GRADE and JBI approaches, incorporating findings from four preceding phases of a mixed-methods research program: systematic review and meta-analysis, national survey of Australian ICU physiotherapy practice, e-Delphi study to determine expert consensus, and multidisciplinary peer-review of the expert consensus statements by senior ICU clinicians to determine validity and applicability of the statements for translation into practice. ResultsThe guideline comprises 26 recommendations, encompassing physiotherapy assessment, patient selection and prioritisation, and treatment. Physiotherapy treatment covers domains of humidification, patient positioning, hyperinflation techniques, manual chest wall techniques, normal saline instillation, active treatment, and mobilisation. Recommendations are rated as strong or conditional based on JBI criteria, and certainty of evidence according to GRADE. Considerations for practice are provided within the guideline to enhance clarity and practicality, particularly for conditional recommendations where evidence is limited or conflicting. ConclusionThis guideline, based on the best available evidence for clinical physiotherapy practice for adults invasively ventilated with CAP, is intended to support clinicians with clinical decision making. Further research is required to evaluate guideline implementation into clinical practice, and incorporate the values and preferences of ICU patients and their families. Contribution of paper•This world first clinical practice guideline was developed based on the best available evidence for physiotherapy management of adults invasively ventilated with non-COVID-19 community-acquired pneumonia.•The evidence-based guideline provides 26 recommendations across nine domains of physiotherapy assessment and treatment, to guide best practice for this common ICU cohort.

Characteristics and prognostic factors of bacterial meningitis in the intensive care unit: a prospective nationwide cohort study

BackgroundPatients with bacterial meningitis can be severely ill necessitating intensive care unit (ICU) treatment. Here, we describe clinical features and prognostic factors of adults with bacterial meningitis admitted to the ICU in a nationwide prospective cohort study.MethodsWe prospectively assessed clinical features and outcome of adults (age > 16 years) with community-acquired bacterial meningitis included in the MeninGene study between March 1, 2006 and July 1, 2022, that were initially admitted to the ICU. We identified independent predictors for initial ICU admission and for unfavourable outcome (Glasgow Outcome Scale score between 1–4) by multivariable logistic regression.ResultsA total of 2709 episodes of bacterial meningitis were included, of which 1369 (51%) were initially admitted to the ICU. We observed a decrease in proportion of patients being admitted to the ICU during the Covid-19 pandemic in 2020 (decreased to 39%, p = 0.004). Median age of the 1369 patients initially admitted to the ICU was 61 years (IQR 49–69), and the rates of unfavourable outcome (47%) and mortality (22%) were high. During the Covid-19 pandemic, we observed a trend towards an increase in unfavourable outcome. Prognostic factors predictive for initial ICU admission were younger age, immunocompromised state, male sex, factors associated with pneumococcal meningitis, and those indicative of systemic compromise. Independent predictors for unfavourable outcome in the initial ICU cohort were advanced age, admittance to an academic hospital, cranial nerve palsies or seizures on admission, low leukocyte count in blood, high C-reactive protein in blood, low CSF: blood glucose ratio, listerial meningitis, need for mechanical ventilation, circulatory shock and persistent fever. 204 of 1340 episodes (15%) that were initially not admitted to the ICU were secondarily transferred to the ICU. The rates of unfavourable outcome (66%) and mortality (30%) in this group were high.ConclusionsThe majority of patients with community-acquired bacterial meningitis are admitted to the ICU, and the unfavourable outcome and mortality rates of these patients remain high. Patients that are initially admitted to non-ICU wards but secondarily transferred to the ICU also had very high rates of unfavourable outcome.

Patients with COVID-19 Pneumonia Admitted to an Intensive Care Unit (ICU) at a Community Hospital in Flint, Michigan, in Early 2020: Characteristics and Mortality

INTRODUCTION Despite the many studies conducted on the factors affecting mortality in patients with COVID-19, there is ongoing debate about the role of race as a risk factor. Several studies have reported a geographic and racial disparity in COVID-19 cases in Michigan. This study aimed to examine the characteristics of the 2020 first cohort of Intensive Care Unit (ICU) COVID-19 patients admitted to a community teaching hospital in Flint, Michigan, and to determine the factors associated with ICU mortality, including race. METHODS This cross-sectional study included adult patients (≥ 18 years) with severe COVID-19 pneumonia admitted to the ICU between March and May 2020. Potential risk factors associated with ICU mortality included demographic characteristics, comorbidities, treatments, and complications. RESULTS The study sample consisted of N = 48 patients, aged 24–85 years, (mean 59.7; SD = 12.8); 56.2% (n=27) were male and 51.1% (n=24) were Black adults. The mortality rate was 51.1%. Age (aOR 1.1, 95% CI [1.01, 1.20]; p =0.03), type 2 diabetes (aOR 5.7, 95% CI [1.2, 29.1]; p =0.03), and essential hypertension (aOR 6.2, 95% CI [1.1, 34.5]; p =0.04) were all found to have statistically significant independent associations with increased risk of ICU mortality in this study sample. On the other hand, race was not found to be associated with ICU mortality. CONCLUSIONS These findings support the literature regarding the association of comorbid conditions, including type 2 diabetes and hypertension, with poorer outcomes in ICU hospitalized patients with severe COVID-19 pneumonia. This study provides insight into mortality of an ICU patient cohort earlier on during the COVID-19 pandemic in Flint, Michigan.

Can Emergency Hematopoiesis Due to Sepsis Lead to Clonal Hematopoiesis? Implications for Understanding the Pathogenesis of Clonal Evolution

O.O &amp; T.K are co-first authors. Clonal (myelo) hematopoiesis of indeterminate potential (CHIP) arises from somatic mutations in myeloid genes, providing a proliferative and survival advantage to the affected “CHIP-initiating” early hematopoietic precursor cells. Understanding the conditions that lead to the emergence of detectable clones (early seeding of myeloid neoplasia-MN) is of great interest due to its clinical implications. Among CH facilitating states, aging has been extensively studied, but a clear distinction between the impact of chronological versus biological aging remains challenging. Additionally, other factors contributing to CHIP development may include chemotherapy, genetic predisposition, bone marrow failure states, or immunodeficiency. However, not all CHIP-promoting factors have been identified because they are subtle, pathophysiologically complex, and thus remain cryptic. We hypothesized that episodes of emergency hematopoiesis (EH), such as those caused by acute infections, could potentially result in acquisition of CHIP with repeated episodes of EH resulting in clonal expansion. If this hypothesis is confirmed, one could envision that both instances of significant hematopoietic expansion (high leukocytosis) and repeated cycles of EH could be at play in conjunction with chronological factors. For instance, the older an individual is, the greater is the number of EH episodes possible. To examine our theory, we prospectively collected blood samples from patients admitted at the intensive care unit (ICU) of Cleveland Clinic and Yale University. Inclusion criteria required a white blood cell (WBC) count ≥ 15K/uL. Patients with known or prior malignancies and those exposed to chemotherapy were excluded. For relevant comparisons, we assessed CH incidence in our cohort against a group of patients with solid tumors screened for CH (n=107) but without acute infections, along with historical CHIP cohorts. CH screening employed a targeted NGS myeloid gene panel with a reported VAF cut-off of 2%. A total of 78 individuals admitted to the ICU with possible infections and leukocytosis were enrolled in this study. The median age was 70 years (IQR: 61-77), with a M:F ratio of 1.7 (Table 1). Time from leukocytosis onset to sampling was 3 days (IQR: 1-5), the median WBC at ICU admission was 18 K/uL (IQR: 16-21), and the median absolute neutrophil count (ANC) was 16 K/uL (IQR: 12-20). Overall, 94 variants were identified including 16 (median VAF 50.1) variants of potentially germline origin, and 4 variants of unknown significance. We observed 74 CHIP-associated mutations in 37 (47%) patients at a median VAF of 14% (4-47%). The landscape of CHIP mutations included alterations in DNMT3A (20%), TET2 (12%), ASXL1 (12%), SMC1A, TP53, and PPM1D genes (6% each). Although higher rates of CHIP were expected in the cancer cohort, no difference was found between the two groups (40% vs. 47% in the ICU, p=0.3). DNMT3A, ASXL1 and PPM1D mutations were more frequently encountered in the cancer cohort (Table 1). The prevalence of CHIP in the ICU cohort was higher in older age groups: 30-39 (29%), 50-69 (49%), and ≥70 (51%), compared to the expected frequency in comparable age ranges (and median range) in a historical healthy population, which was 1%, 4%, and 10%, respectively (&amp;lt;0.00001; Fig.1). Notably, CHIP was more prevalent among smokers (57%; p=0.003). No significant differences were found when comparing CHIP + and CHIP - patients of the ICU cohort with regards to WBC (median 18K/uL both). Similarly, CHIP prevalence did not change according to the degree of leukocytosis in an analysis where patients were ranked by WBC (≥18K/uL at 44% vs. &amp;lt;18K/uL at 46%). When looking at the impact of CHIP on critical care outcomes, a logistic regression model accounting for age, gender, and mutation type revealed that the presence of DNMT3A mutations was associated with a higher risk of diagnosis of overt septic shock (log OR= 2.4, 95% CI 0.19-5.4, p&amp;lt;0.05). In summary, our prospective observational study has shown that sepsis is associated with preferential mutant clone expansion, primarily due to EH rather than the extent of leukocytosis and may be contributing to the acquisition of new clones. Whether such episodes result in permanent acquisition of new CHIP mutations with potential for MN progression will be the subject of longitudinal analysis of these samples, and findings will be presented at the ASH meeting.

Development and validation of a machine learning model to predict the use of renal replacement therapy in 14,374 patients with COVID-19

PurposeTo develop a model to predict the use of renal replacement therapy (RRT) in COVID-19 patients. Materials and methodsRetrospective analysis of multicenter cohort of intensive care unit (ICU) admissions of Brazil involving COVID-19 critically adult patients, requiring ventilatory support, admitted to 126 Brazilian ICUs, from February 2020 to December 2021 (development) and January to May 2022 (validation). No interventions were performed. ResultsEight machine learning models' classifications were evaluated. Models were developed using an 80/20 testing/train split ratio and cross-validation. Thirteen candidate predictors were selected using the Recursive Feature Elimination (RFE) algorithm. Discrimination and calibration were assessed. Temporal validation was performed using data from 2022. Of 14,374 COVID-19 patients with initial respiratory support, 1924 (13%) required RRT. RRT patients were older (65 [53-75] vs. 55 [42-68]), had more comorbidities (Charlson's Comorbidity Index 1.0 [0.00-2.00] vs 0.0 [0.00-1.00]), had higher severity (SAPS-3 median: 61 [51-74] vs 48 [41-58]), and had higher in-hospital mortality (71% vs 22%) compared to non-RRT. Risk factors for RRT, such as Creatinine, Glasgow Coma Scale, Urea, Invasive Mechanical Ventilation, Age, Chronic Kidney Disease, Platelets count, Vasopressors, Noninvasive Ventilation, Hypertension, Diabetes, modified frailty index (mFI) and Gender, were identified. The best discrimination and calibration were found in the Random Forest (AUC [95%CI]: 0.78 [0.75-0.81] and Brier's Score: 0.09 [95%CI: 0.08–0.10]). The final model (Random Forest) showed comparable performance in the temporal validation (AUC [95%CI]: 0.79 [0.75–0.84] and Brier's Score, 0.08 [95%CI: 0.08-0.1]). ConclusionsAn early ML model using easily available clinical and laboratory data accurately predicted the use of RRT in critically ill patients with COVID-19. Our study demonstrates that using ML techniques is feasible to provide early prediction of use of RRT in COVID-19 patients.

2956. Increased Microbiota Alpha Diversity is Associated with Decreased Hospital Mortality in Medical Intensive Care Unit Patients

Abstract Background Characteristics of the gut microbiome may be useful to predict resistance to colonization and expansion of pathogenic organisms such as C. difficile. In this study, we evaluated whether inclusion of microbiome characteristics improved a clinical prediction model for in-hospital mortality. Methods Rectal or fecal swab samples were collected daily from medical intensive care unit (ICU) patients from three hospitals between 2017-2019 and underwent 16S rRNA gene sequencing of the V4 region. Patient admissions with ≥1 rectal swab collected &amp;lt; 48hrs from ICU admission were randomly selected 2:1 for calibration and validation analyses. Independent variables included Shannon Index, Microbiome Health Index, Community Type (CT) and relative abundance (RA) of specific bacterial taxa based on 3% sequence difference or class-level classification. The RA of each bacterial taxon was range-standardized (95%) and scaled to analyze a 10% relative difference within each taxon’s range. Logistic regression models identified microbiome characteristics at admission that were associated with in-hospital mortality. Fixed effects were included in all models to account for microbiome differences between facilities. Results There were 1365 patient admissions (910 calibration, 455 validation) analyzed (Table), of which 134 (96 calibration, 38 validation) died during hospitalization. Increase in Shannon Index by 1 [OR 0.66 (0.51-0.84), p=0.001], 10% relative increase in the RA of Bacilli [OR 1.15 (1.08-1.22), p&amp;lt; 0.001] and 10% relative increase in the RA of Enterococcus [OR 1.10 (1.04-1.14), p=0.001] were associated with in-hospital mortality in calibration models after controlling for Charlson Comorbidity Index [OR 1.23 (1.15-1.32), p&amp;lt; 0.001]. Increase in Shannon Index by 1 [OR 0.88 (0.57-1.38), p=0.581] had the same direction and similar odds in the validation cohort (Figure), supporting the findings of the calibration model. Conclusion In this secondary analysis of a large, multicenter, prospective medical ICU cohort, we found that higher alpha diversity of the gut microbiome was associated with decreased odds of in-hospital mortality. Disclosures Michael Z. David, MD PhD, Covance: Grant/Research Support|GSK: Advisor/Consultant|GSK: Grant/Research Support Erik R. Dubberke, MD, MSPH, Abbott: Advisor/Consultant|AstraZeneca: Advisor/Consultant|Ferring Pharmaceuticals: Advisor/Consultant|Ferring Pharmaceuticals: Grant/Research Support|Merck and Co.: Advisor/Consultant|Pfizer: Advisor/Consultant|Pfizer: Grant/Research Support|Seres Therapeutics: Advisor/Consultant|Summit: Advisor/Consultant|Theriva Biologics: Grant/Research Support Vincent B. Young, MD/PhD, ASM: Senior Editor|Debiopharm: Advisor/Consultant|Vedanta Biosciences: Advisor/Consultant

Proteomics-Based Mapping of Bronchopulmonary Dysplasia-Associated Changes in Noninvasively Accessible Oral Secretions

ObjectiveTo determine if oral secretions (OS) can be used as a non-invasively collected body fluid, in lieu of tracheal aspirates (TA), to track respiratory status and predict bronchopulmonary dysplasia (BPD) development in infants born <32 weeks. Study DesignThis was a retrospective, single-center cohort study that included data and convenience samples from week-of-life (WoL) 3 from two independent preterm infant cohorts. Using previously banked samples, we applied our sample-sparing, high-throughput proteomics technology to compare OS and TA proteomes in infants born <32 weeks admitted to the Neonatology Intensive Care Unit (NICU) (Cohort 1; N=23 infants). In a separate similar cohort, we mapped the BPD-associated changes in the OS proteome (Cohort 2; N=17 infants including 8 with BPD). ResultsIn samples collected during the first month of life, we identified 607 proteins unique to OS, 327 proteins unique to TA, and 687 overlapping proteins belonging to pathways involved in immune effector processes, neutrophil degranulation, leukocyte mediated immunity, and metabolic processes. Furthermore, we identified 37 OS proteins that showed significantly differential abundance between BPD cases and controls: 13 were associated with metabolic and immune dysregulation, 10 of which (eg, SERPINC1, CSTA, BPI) have been linked to BPD or other prematurity-related lung disease based on blood or TA investigations, but not OS. ConclusionOS are a noninvasive, easily accessible alternative to TA and amenable to high-throughput proteomic analysis in preterm newborns. OS samples hold promise to yield actionable biomarkers of BPD development, particularly for prospective categorization of at-risk infants for timely clinical trial enrollment with novel therapies.

Shock Severity Classification and Mortality in Adults With Cardiac, Medical, Surgical, and Neurological Critical Illness

ObjectiveTo evaluate whether the Society for Cardiovascular Angiography and Interventions (SCAI) Shock Classification could perform risk stratification in a mixed cohort of intensive care unit (ICU) patients, similar to its validation in patients with acute cardiac disease. MethodsWe included 21,461 adult Mayo Clinic ICU patient admissions from December 1, 2014, to February 28, 2018, including cardiac ICU (16.7%), medical ICU (37.4%), neurosciences ICU (27.7%), and surgical ICU (18.2%). The SCAI Shock Classification (a 5-stage classification from no shock [A] to refractory shock [E]) was assigned in each 4-hour period during the first 24 hours of ICU admission. ResultsThe median age was 65 years, and 43.2% were female. In-hospital mortality occurred in 1611 (7.5%) patients, with a stepwise increase in in-hospital mortality in each higher maximum SCAI Shock stage overall: A, 4.0%; B, 4.6%; C, 7.0%; D, 13.9%; and E, 40.2%. The SCAI Shock Classification provided incremental mortality risk stratification in each ICU, with the best performance in the cardiac ICU and the worse performance in the neurosciences ICU. The SCAI Shock Classification was associated with higher adjusted in-hospital mortality (adjusted odds ratio, 1.32 per each stage; 95% CI, 1.24 to 1.41; P<.001); this association was not observed in the neurosciences ICU when considered separately. ConclusionThe SCAI Shock Classification provided incremental mortality risk stratification beyond established prognostic markers across the spectrum of medical and surgical critical illness, proving utility outside its original intent.