Predictor Of Intensive Care Unit Mortality Research Articles

Clinical usefulness of presepsin and monocyte distribution width (MDW) kinetic for predicting mortality in critically ill patients in intensive care unit

BackgroundIn this study, we explored the accuracy of two new sepsis biomarkers, monocyte distribution width (MDW) and presepsin (PSP), compared to traditional ones, C-reactive protein (CRP) and Procalcitonin (PCT), to identify sepsis and predict intra-hospital mortality by analyzing their kinetic at different time points during hospitalization stay.MethodsWe enrolled 104 patients admitted to the intensive care unit (ICU) of University Hospital “Paolo Giaccone”, Palermo. Among these, 30 (29%) had a clinical diagnosis of sepsis. MDW, PCT, CRP, and PSP were evaluated at admission (T0), after 24 h (T24), 48 h (T48), 72 h (T72), at day 5 (T5), and at discharge (TD).ResultsPatients with sepsis displayed higher levels of PCT and PSP than patients without sepsis at each timepoint; differently, CRP displayed statistically significant differences only at T0, while MDW only at T0 and T24. Patients with increasing levels of PSP displayed lower median survival time than patients with decreasing levels; differences reached statistical significance only at 48 h (20 vs. 29 days, log rank test, p = 0.046). Interestingly, PSP was an independent predictor of ICU mortality at 48 and 72 h after hospital admission. Also, the kinetic of PSP had prognostic value, with increased values at 48 h after admission being associated with reduced survival.ConclusionOur findings support the role of PSP and its kinetic as a predictor of ICU mortality.

Characteristics and outcomes of patients admitted to intensive care units in Uganda: a descriptive nationwide multicentre prospective study

Intensive care unit (ICU) mortality rates have decreased over time. However, in low-and lower-middle income countries (LMICs), there remains an excess ICU mortality with limited understanding of patient characteristics, treatments, and outcomes from small single centre studies. We aimed therefore, to describe the characteristics, therapies and outcomes of patients admitted to all intensive care units in Uganda. A nationwide prospective observational study including all patients admitted Uganda’s ICUs with available daily charts was conducted from 8th January 2018 to 1st April 2018. Socio-demographics and clinical characteristics including worst vital signs in the first 24 h of admission were recorded with calculation of the National Early Warning Score (NEWS-2) and quick Sequential Organ Function Assessment (qSOFA) score. ICU interventions were recorded during the ICU stay and patients were followed up to 28 days in ICU. The primary outcome was 28 day ICU mortality. Three-hundred fifty-one patients were analysed with mean age 39 (24.1) years, 205 (58.4%) males with 197 (56%) surgical admissions. The commonest indication for ICU admission was postoperative care (42.9%), 214 (61%) had at least one comorbidity, with hypertension 104 (48.6%) most prevalent and 35 (10%) HIV positive. The 28 day ICU mortality was 90/351 (25.6%) with a median ICU stay of 3 (1–7) days. The highest probability of death occurred during the first 10 days with more non-survivors receiving mechanical ventilation (80% vs 34%; p < 0.001), sedation/paralysis (70% vs 50%; p < 0.001), inotropic/vasopressor support (56.7% vs 22.2%; p < 0.001) and renal replacement therapy (14.4% vs 4.2%; p < 0.001). Independent predictors of ICU mortality included mechanical ventilation (HR 3.34, 95% CI 1.48–7.52), sedation/paralysis (HR 2.68, 95% CI 1.39–5.16), inotropes/vasopressor (HR 3.17,95% CI 1.89–5.29) and an HIV positive status (HR 2.28, 95% CI 1.14–4.56). This study provides a comprehensive description of ICU patient characteristics, treatment patterns, and outcomes in Uganda. It not only adds to the global body of knowledge on ICU care in resource-limited settings but also serves as a foundation for future research and policy initiatives aimed at optimizing ICU care in Sub-Saharan Africa.

The relationship between lactate dehydrogenase to albumin ratio and all-cause mortality during ICU stays in patients with sepsis: A retrospective cohort study with propensity score matching

BackgroundSepsis is a prevalent and severe medical condition which is frequently observed in the intensive care unit (ICU). Although numerous biomarkers have been identified to predict the prognosis of sepsis, the lactate dehydrogenase to albumin ratio (LDH/ALB ratio) has not been extensively investigated. The principal objective of this study is to assess the relationship between LDH/ALB ratio and all-cause mortality in patients with sepsis. MethodsThis study included all adult critically ill patients with sepsis from the Medical Information Mart for Intensive Care IV (MIMIC-IV, version 2.0) database. Propensity score matching (PSM) analysis was conducted to mitigate bias, and Kaplan-Meier curves were performed to evaluate the cumulative survival across different groups. The association between the LDH/ALB ratio and mortality was examined through restricted cubic spline (RCS) analysis and Cox regression analysis. The robustness of the findings was confirmed through subgroup analyses. Additionally, the prognostic capability of the LDH/ALB ratio was further evaluated using receiver operating characteristic (ROC) curve analysis. ResultsThere were 6059 adult patients with sepsis enrolled in the final analysis. RCS revealed a non-linear relationship between the LDH/ALB ratio and an increased risk of ICU all-cause mortality (χ2 = 46.900, P < 0.001). Following PSM analysis, 1553 matched pairs were obtained. As comparison to the low LDH/ALB ratio group, the mortality rate in the high LDH/ALB ratio group was significantly higher (P < 0.001). Kaplan-Meier curves, both before and after PSM, revealed that the ICU cumulative survival rate for patients with sepsis was significantly lower in the high LDH/ALB ratio group compared to the low LDH/ALB ratio group (χ2 = 93.360, P < 0.001; χ2 = 14.400, P < 0.001). Even after adjusting for a range of potential confounders, multivariate Cox regression analysis indicated that an elevated LDH/ALB ratio was a significant predictor of all-cause mortality in these patients. ROC curve analysis demonstrated that the LDH/ALB ratio had an area under the ROC curve (AUC) of 0.688 for predicting ICU mortality, with a sensitivity of 69.2% and a specificity of 58.6%. ConclusionsAn elevated LDH/ALB ratio (≥10.57) was associated with all-cause mortality in critically ill patients with sepsis, and it might serve as a prognostic marker. Clinicians should pay closer attention to sepsis patients presenting with an LDH/ALB ratio of 10.57 or higher.

Epidemiology and Outcomes of Critical Illness and Novel Predictors of Mortality in an Ethiopian Medical Intensive Care Unit.

Low- and middle-income countries (LMICs) bear most of the global burden of critical illness. Managing this burden requires improved understanding of epidemiology and outcomes in LMIC intensive care units (ICUs), including LMIC-specific mortality prediction scores. This study was a retrospective observational study at Tikur Anbessa Specialized Hospital in Addis Ababa, Ethiopia, examining all consecutive medical ICU admissions from June 2014 to April 2015. The primary outcome was ICU mortality; secondary outcomes were prolonged ICU stay and prolonged mechanical ventilation. ICU mortality prediction models were created using multivariable logistic regression and compared with the Mortality Probability Model-II (MPM-II). Associations with secondary outcomes were examined with multivariable logistic regression. There were 198 admissions during the study period; mortality was 35%. Age, shock on admission, mechanical ventilation, human immunodeficiency virus, and Glasgow Coma Scale ≤8 were associated with ICU mortality. The receiver operating characteristic curve for this 5-factor model had an AUC of 0.8205 versus 0.7468 for MPM-II, favoring the simplified new model. Mechanical ventilation and lack of shock were associated with prolonged ICU stays. Mortality in an LMIC medical ICU was high. This study examines an LMIC medical ICU population, showing a simplified prediction model may predict mortality as well as complex models.

Machine learning models to evaluate mortality in pediatric patients with pneumonia in the intensive care unit.

This study aimed to predict mortality in children with pneumonia who were admitted to the intensive care unit (ICU) to aid decision-making. Retrospective cohort study conducted at a single tertiary hospital. This study included children who were admitted to the pediatric ICU at the National Taiwan University Hospital between 2010 and 2019 due to pneumonia. Two prediction models were developed using tree-structured machine learning algorithms. The primary outcomes were ICU mortality and 24-h ICU mortality. A total of 33 features, including demographics, underlying diseases, vital signs, and laboratory data, were collected from the electronic health records. The machine learning models were constructed using the development data set, and performance matrices were computed using the holdout test data set. A total of 1231 ICU admissions of children with pneumonia were included in the final cohort. The area under the receiver operating characteristic curves (AUROCs) of the ICU mortality model and 24-h ICU mortality models was 0.80 (95% confidence interval [CI], 0.69-0.91) and 0.92 (95% CI, 0.86-0.92), respectively. Based on feature importance, the model developed in this study tended to predict increased mortality for the subsequent 24 h if a reduction in the blood pressure, peripheral capillary oxygen saturation (SpO2), or higher partial pressure of carbon dioxide (PCO2) were observed. This study demonstrated that the machine learning models for predicting ICU mortality and 24-h ICU mortality in children with pneumonia have the potential to support decision-making, especially in resource-limited settings.

Machine-learning models are superior to severity scoring systems for the prediction of the mortality of critically ill patients in a tertiary medical center.

Intensive care unit (ICU) mortality prediction helps to guide therapeutic decision making for critically ill patients. Several scoring systems based on statistical techniques have been developed for this purpose. In this study, we developed a machine-learning model to predict patient mortality in the very early stage of ICU admission. This study was performed with data from all patients admitted to the intensive care units of a tertiary medical center in Taiwan from 2009 to 2018. The patients' comorbidities, co-medications, vital signs, and laboratory data on the day of ICU admission were obtained from electronic medical records. We constructed random forest and extreme gradient boosting (XGBoost) models to predict ICU mortality, and compared their performance with that of traditional scoring systems. Data from 12,377 patients was allocated to training (n = 9901) and testing (n = 2476) datasets. The median patient age was 70.0 years; 9210 (74.41%) patients were under mechanical ventilation in the ICU. The areas under receiver operating characteristic curves for the random forest and XGBoost models (0.876 and 0.880, respectively) were larger than those for the Acute Physiology and Chronic Health Evaluation II score (0.738), Sequential Organ Failure Assessment score (0.747), and Simplified Acute Physiology Score II (0.743). The fraction of inspired oxygen on ICU admission was the most important predictive feature across all models. The XGBoost model most accurately predicted ICU mortality and was superior to traditional scoring systems. Our results highlight the utility of machine learning for ICU mortality prediction in the Asian population.

A longitudinal study to determine if all critically ill patients should be considered at nutrition risk or is there a highly accurate screening tool to be adopted?

Nutrition risk is prevalent in intensive care unit (ICU) settings and related to poor prognoses. We aimed to evaluate the concurrent and predictive validity of different nutrition risk screening tools in the ICU. Data were collected between 2019 and 2022 in six ICUs (n = 450). Nutrition risk was evaluated by modified Nutrition Risk in Critically ill (mNUTRIC), Nutritional Risk Screening (NRS-2002), Malnutrition Screening Tool (MST), Malnutrition Universal Screening Tool (MUST), and Nutritional Risk in Emergency (NRE-2017). Accuracy and agreement of the tools were assessed; logistic regression was used to verify the association between nutrition risk and prolonged ICU stay; Cox regression was used for mortality in the ICU, both with adjustment for confounders. NRS-2002 ≥5 showed the best accuracy (0.63 [95% CI, 0.58-0.69]) with mNUTRIC, and MST with NRS-2002 ≥5 (0.76 [95% CI, 0.71-0.80]). All tools had a poor/fair agreement with mNUTRIC (k = 0.019-0.268) and moderate agreement with NRS-2002 ≥5 (k = 0.474-0.503). MUST (2.26 [95% CI 1.40-3.63]) and MST (1.69 [95% CI, 1.09-2.60]) predicted death in the ICU, and the NRS-2002 ≥5 (1.56 [95% CI 1.02-2.40]) and mNUTRIC (1.86 [95% CI, 1.26-2.76]) predicted prolonged ICU stay. No nutrition risk screening tool demonstrated a satisfactory concurrent validity; only the MUST and MST predicted ICU mortality and the NRS-2002 ≥5 and mNUTRIC predicted prolonged ICU stay, suggesting that it could be appropriate to adopt the ESPEN recommendation to assess nutrition status in patients with ≥48 h in the ICU.

Evaluation of the FAST HUG implementation effect on the mortality and length of stay in admitted patient in Intensive Care Unit (ICU) of a hospital

Objective: We aimed to determine new version of the FAST HUG implementation effect on the mortality and length of stay in admittedpatient in Intensive Care Unit (ICU) of a hospital. Methods: We conducted a prospective cohort study on 406 patients who admitted in medical and surgical ICU of Imam Hossein Hospitalof Tehran province in Iran from July 1, 2022, to September 30, 2022. We identified predictors of ICU mortality by logistic regression model,predictors of 28-day mortality/re-hospitalization/recovery by generalized ordered logit model, predictors of length of stay (LOS) by linearregression, predictors of mechanical ventilation by generalized ordered logit and predictors of SOFA score by hurdle model. Results: This mnemonic had protective impact (OR = 0.56, P-value = 0.01) on the 28-days mortality. One unit increase in mean of FAST HUGscore had a preventive effect on the outcome of death (OR = 0.21). Surgical admission type (Coefficient = –3.24, P-value = 0.00) and FASTHUG implementation (Coefficient =1.58, P-value= 0.0) were strongest factors on the LOS. Conclusion: Our study results approved the importance of the FAST HUG implementation on the decrease of patient mortality in ICU and28 days after discharge. Additionally, this checklist had a statistically significant role in declining SOFA scores in ICU patients.

The utility of brain natriuretic peptide as a prognosticating marker in critical care patients.

Brain natriuretic peptide (BNP) is an established biomarker of morbidity and mortality in cardiac failure. Data also suggest potential prognostic utility in non-heart failure cohorts. The utility of BNP in predicting intensive care unit (ICU) outcomes has not been well evaluated in a mixed critical care population in the South African (SA) context. To evaluate the ability of BNP to predict ICU mortality in a heterogeneous critical care population in SA. This was a retrospective observational study of 100 patients admitted to a multidisciplinary, closed, intensivist-run ICU in a tertiary academic hospital serving KwaZulu-Natal Province (1 January 2020 - 31 July 2022). Initial BNP was evaluated as a predictor of ICU mortality using univariate and multivariable analyses. There was a statistically significant difference in BNP between survivors and non-survivors in the cohort of patients without heart failure. The median initial BNP in the non-heart failure cohort was 411 (interquartile range (IQR) 116 - 848) ng/L in non-survivors, and 150 (44 - 356) ng/L in survivors (p=0.028). The optimal cut-off for BNP was determined as 366 ng/L. A BNP ≥366 ng/L was an independent predictor of ICU outcome. This study highlights the potential utility of BNP as a predictor of ICU mortality in a heterogeneous ICU population, with the greatest utility in patients without heart failure. Further studies are required to confirm this finding. The study is a retrospective, observational study conducted in multidisciplinary, closed, intensivist-run ICU at a tertiary academic hospital. It showed an elevated BNP is associated with increased ICU mortality, particularly in those without a baseline diagnosis of heart failure. This identifies the need for further prospective studies evaluating BNP as a prognostic marker in non-cardiac critically ill patients, and its utility as an addition in pre-existing ICU outcome prediction scores.

Mortality and Prognostic Factors of Adult Patients with Acute Leukemia Admitted to the ICU: A Systematic Review

Introduction: Acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL) are the most aggressive group of hematologic malignancies (Pulte et al, 2020) and will be responsible for approximately 12,700 deaths in the U.S. during 2023(Seiger et al,2023). Improved overall survival (OS) has been observed in the past decades in both diseases, but high mortality in the Intensive Care Unit (ICU) persists, which may prevent ICU admission of those patients. We aimed to review the literature on mortality data and survival predictors of patients with acute leukemia (AL) admitted to the ICU. Methods: Embase, Pubmed and Cochrane databases were searched for articles published from January of 2005 to December of 2020. Due to OS improvement of patients with AML, we only included studies published as of 2005 (Percival et al, 2015). Editorials, case reports and small case series were excluded. Results: Initial search revealed 1,015 manuscripts from database and references, 650 were screened and 115 were fully assessed, for which 24 met eligibility criteria (Figure 1). A total of 13,350 patients were included in the review. Median age was 54.8 years and 57.3% of patients were male. AML represented the majority of diagnoses (98.2%).Two studies included only newly diagnosed patients, 1 study only included patients with relapsed disease and the rest (N=21) included subjects regardless of the AL status. Only 0.7% of patients had a history of hematopoietic stem cell transplantation . 81.9% of patients required mechanical ventilation, 25.3% needed renal replacement therapy, and 65.9% used vasopressors in studies data was available. Median SOFA score in the first 24h of ICU admission was 10.85 . Pooled ICU mortality and hospital mortality were 45.0% and 43.1%, respectively; 58.2% of patients were deceased at 90-day mark (Table 1). The 12 months OS was 37.3%. Higher SOFA score, need for mechanical ventilation, sepsis, vasopressor use, and age greater than 60 years were the most common predictors of ICU, hospital and 90-day mortality. Relapsed or refractory status of underlying AL was the most significant risk factor for long-term mortality. Conclusions: Our systemic review showed that ICU mortality in patients with AL was 45.0%, which represents a survival improvement compared to prior studies on patients with hematological malignancies, where ICU mortality could be as high as 69% (Ashkenazi et al, 1986). Predictors of ICU mortality were closely related to the severity of acute illness rather than malignancy status. We acknowledge that our patient population was mostly comprised of AML cases and the majority of the original studies included a small number of patients with AL with exception of one large study (Halpern et al, 2017).

A cross-modal clinical prediction system for intensive care unit patient outcome

Electronic Health Records (EHRs) are becoming an encyclopedia of clinical concepts, encompassing diagnoses, medications and clinical descriptions to describe patients’ health conditions. The abundance of EHR data presents unprecedented opportunities for leveraging machine learning in clinical predictive tasks. However, the complex process of acquiring healthcare data in some settings like Intensive Care Unit (ICU) gives rise to various challenges in mining EHR, such as modality diversity, heterogeneity, data silo and data quality problems, etc. In this paper, to overcome the data heterogeneity and data quality issues, we propose a Code-Text cross-modal Contrastive Learning (CTCL) framework specifically designed for predicting ICU patient outcomes. This framework leverages both structured (multi-view medical codes) and unstructured (clinical notes) EHR data to extract patient representations. To capture the complicated intra and inter interactions among medical codes, CTCL employs a multi-view graph convolution network to extract code encodings and a cross-view contrastive loss to regularize the model parameters. To enhance patient representations across different modalities, a cross-modal contrastive learning module is introduced. Experimental evaluations on the eICU-CRD database demonstrate that CTCL outperforms state-of-the-art competitors on the ICU mortality prediction task and in-hospital readmission prediction task. Furthermore, ablation studies and discussions demonstrate the effectiveness of each module of the framework.

Increased Levels of Galectin-3 in Critical COVID-19.

Severe COVID-19 is related to hyperinflammation and multiple organ injury, including respiratory failure, thus requiring intensive care unit (ICU) admission. Galectin-3, a carbohydrate-binding protein exhibiting pleiotropic effects, has been previously recognized to participate in inflammation, the immune response to infections and fibrosis. The aim of this study was to evaluate the relationship between galectin-3 and the clinical severity of COVID-19, as well as assess the prognostic accuracy of galectin-3 for the probability of ICU mortality. The study included 235 COVID-19 patients with active disease, treated in two different Greek hospitals in total. Our results showed that median galectin-3 serum levels on admission were significantly increased in critical COVID-19 patients (7.2 ng/mL), as compared to the median levels of patients with less severe disease (2.9 ng/mL, p = 0.003). Galectin-3 levels of the non-survivors hospitalized in the ICU were significantly higher than those of the survivors (median 9.1 ng/mL versus 5.8 ng/mL, p = 0.001). The prognostic accuracy of galectin-3 for the probability of ICU mortality was studied with a receiver operating characteristic (ROC) curve and a multivariate analysis further demonstrated that galectin-3 concentration at hospital admission could be assumed as an independent risk factor associated with ICU mortality. Our results were validated with galectin-3 measurements in a second patient cohort from a different Greek university hospital. Our results, apart from strongly confirming and advancing previous knowledge with two patient cohorts, explore the possibility of predicting ICU mortality, which could provide useful information to clinicians. Therefore, galectin-3 seems to establish its involvement in the prognosis of hospitalized COVID-19 patients, suggesting that it could serve as a promising biomarker in critical COVID-19.

A new efficient ALignment-driven Neural Network for Mortality Prediction from Irregular Multivariate Time Series data

The irregularity of the time interval between observations in and across the stream is a key factor that leads to a drop in performance when classical machine learning or deep learning models are used for a downstream task requiring multivariate time series. Indeed, irregular multivariate time series not only increase the rate of missing values but also lead to data sparsity, which consequently makes the data almost unleverageable and/or ineffective for models. To tackle this scorching challenge, most of the pioneering approaches apply imputation or interpolation in their core, which might lead to embedding data with noise. To especially address this irregular multivariate time series issue, we introduce, in this paper, a new deep neural network model called ALignment-driven Neural Network. The innovative idea of our model is to transform the irregular multivariate time series into pseudo-aligned (or pseudo-regular) latent values. The latter are shown as a matrix, where the coefficients are the latent values of each feature at user-defined reference time points that are evenly spaced. They are obtained through a duplication process driven by an exponential decay mechanism. The obtained output is then passed to a Recurrent Neural Network model, which is undoubtedly the must-use model for regular time series data. To show that our model added value, we looked at the Intensive Care Unit mortality prediction task. In this unit, the physiological measurements used to make decisions have a problem with time irregularity. Leveraging the publicly available MIMIC-III, we compare the performance of our model to that of flagship models. In addition, we also performed extensive ablation studies to highlight the importance of specific components in our model. Interestingly enough, whenever data is collected 24 and 48 h after a patient’s admission, we outperform our pioneering competitors, i.e., +1.1% and +1.5% for the AUC score, +2.3% and +2.4% for the AUPRC score and +0.6% and +1.7% for the F1-score.

Serum Cholinesterase, C-reactive Protein, Interleukin 6, and Procalcitonin Levels as Predictors of Mortality in Patients in the Intensive Care Unit.

The prognostic utility of inflammatory markers in survival has been suggested in patients with cancer; however, evidence on their prognostic value in severely ill patients is very limited. We aimed to explore the prognostic value of cholinesterase (ChE), C-reactive protein (CRP), interleukin-6 (IL-6), and procalcitonin (PCT) in predicting mortality in patients from the intensive care unit (ICU). Serum levels of ChE, CRP, IL-6 and PCT were measured in ICU patients from December 13th, 2019 to June 28th, 2022. We assessed the predictive power of ChE, CRP, IL-6, and PCT using the receiver operating characteristic (ROC) curves. Furthermore, we evaluated their diagnostic accuracy by comparing the areas under the ROC curve (AUCs) along with their corresponding 95% confidence intervals (CIs). The cut-off values were determined to dichotomise these biomarkers, which were then included in multivariable logistic regression models to examine their relationship with ICU mortality. Among 253 ICU patients included in the study, 66 (26%) died during the ICU stay. The AUCs to predict ICU mortality were 0.643 (95% CI, 0.566-0.719), 0.648 (95% CI, 0.633-0.735), 0.643 (95% CI, 0.563-0.723) and 0.735 (95% CI, 0.664-0.807) for ChE, CRP, IL-6 and PCT, respectively. After adjusting for age, sex and disease severity, lower ChE level (<3.668 × 103 U L-1) and higher levels of CRP (>10.546 mg dL-1), IL-6 (>986.245 pg mL-1) and PCT (>0.505 μg L-1) were associated with higher mortality risk, with odd ratios of 2.70 (95% CI, 1.32-5.54), 4.99 (95% CI, 2.41-10.38), 3.24 (95% CI, 1.54-6.78) and 3.67 (95% CI, 1.45-9.95), respectively. ChE, CRP, IL-6 and PCT were independent ICU mortality risk factors in severely ill patients. Elevated PCT levels exhibited better predictive value than the other three biomarkers that were evaluated.

Discriminative performance of pancreatic stone protein in predicting ICU mortality and infection severity in adult patients with infection: a systematic review and individual patient level meta-analysis

BackgroundSeveral studies suggested pancreatic stone protein (PSP) as a promising biomarker to predict mortality among patients with severe infection. The objective of the study was to evaluate the performance of PSP in predicting intensive care unit (ICU) mortality and infection severity among critically ill adults admitted to the hospital for infection.MethodsA systematic search across Cochrane Central Register of Controlled Trials and MEDLINE databases (1966 to February 2022) for studies on PSP published in English using ‘pancreatic stone protein’, ‘PSP’, ‘regenerative protein’, ‘lithostatin’ combined with ‘infection’ and ‘sepsis’ found 46 records. The search was restricted to the five trials that measured PSP using the enzyme-linked immunosorbent assay technique (ELISA). We used Bayesian hierarchical regression models for pooled estimates and to predict mortality or disease severity using PSP, C-Reactive Protein (CRP) and procalcitonin (PCT) as main predictor. We used statistical discriminative measures, such as the area under the receiver operating characteristic curve (AUC) and classification plots.ResultsAmong the 678 patients included, the pooled ICU mortality was 17.8% (95% prediction interval 4.1% to 54.6%) with a between-study heterogeneity (I-squared 87%). PSP was strongly associated with ICU mortality (OR = 2.7, 95% credible interval (CrI) [1.3–6.0] per one standard deviation increase; age, gender and sepsis severity adjusted OR = 1.5, 95% CrI [0.98–2.8]). The AUC was 0.69 for PSP 95% confidence interval (CI) [0.64–0.74], 0.61 [0.56–0.66] for PCT and 0.52 [0.47–0.57] for CRP. The sensitivity was 0.96, 0.52, 0.30 for risk thresholds 0.1, 0.2 and 0.3; respective false positive rate values were 0.84, 0.25, 0.10.ConclusionsWe found that PSP showed a very good discriminative ability for both investigated study endpoints ICU mortality and infection severity; better in comparison to CRP, similar to PCT. Combinations of biomarkers did not improve their predictive ability.

Efficacy of Neutrophil Lymphocyte Stress Factor in Diagnosis of Sepsis and Prediction of Mortality in Septic Patients Admitted to the ICU

Abstract Background Sepsis is a major public health concern. It is among the most common reasons for admission to intensive care units (ICUs) throughout the world. Severe sepsis and septic shock are associated with 30-60% mortality rate which is very high compared to other common diseases, such as myocardial infarction or breast cancer. Sepsis is now defined as a “life-threatening organ dysfunction due to a dysregulated host response to infection”. Aim of the Work to evaluate the efficacy of neutrophil lymphocyte stress factor (NLSF) in the diagnosis of sepsis and prediction of mortality in septic patients admitted to the ICU. Patients and Methods We conducted this prospective observational study on 60 patients after informed consents were taken from patients or their next of kin. We enrolled adult patients admitted to the ICU with the diagnosis of septic shock. We excluded patients younger than 18 years, pregnant females, patients with terminal malignancy, patients with liver cirrhosis child-pugh C, patients with shock of mixed origin and patients with aplasia, immunosuppressive disease or receiving immunosuppressive therapy. Then we classified patients into 2 main groups according to presence of sepsis into; Septic group (30 patients), and Non-Septic group (30 patients). Results from our study concluded that NLSF can be used in diagnosis of sepsis, although it’s the least accurate diagnostic marker among PCT, CRP and NLSF, it provides the cheapest tool for diagnosis of sepsis. It was also found that NLSF can be used in prediction of mortality in septic patient during the period of our study and that it’s the most accurate prognostic marker of mortality among PCT, CRP and NLSF. Conclusion NLSF in combination with other parameters can be used as a cheap easy tool for the early detection of sepsis in intensive care units. NLSF can be used as a prognostic marker in sepsis for prediction of mortality in ICU. Further studies are required to clarify these results on a larger group of patients and for longer periods of time.

Investigating the Utility of the SOFA Score and Creating a Modified SOFA Score for Predicting Mortality in the Intensive Care Units in a Tertiary Hospital in Jordan.

The utility of the Sequential Organ Failure Assessment (SOFA) score in predicting mortality in the intensive care unit (ICU) has been demonstrated before, but serial testing in various settings is required to validate and improve the score. This study examined the utility of the SOFA score in predicting mortality in Jordanian ICU patients and aimed to find a modified score that required fewer laboratory tests. A prospective observational study was conducted at Jordan University Hospital (JUH). All adult patients admitted to JUH ICUs between June and December 2020 were included in the study. SOFA scores were measured daily during the whole ICU stay. A modified SOFA score (mSOFA) was constructed from the available laboratory, clinical, and demographic data. The performance of the SOFA, mSOFA, qSOFA, and SIRS in predicting ICU mortality was assessed using the area under the receiver operating characteristic curve (AUROC). 194 patients were followed up. SOFA score (mean ± SD) at admission was significantly higher in non-survivors (7.5 ± 3.9) compared to survivors (2.4 ± 2.2) and performed the best in predicting ICU mortality (AUROC = 0.8756, 95% CI: 0.8117-0.9395) compared to qSOFA (AUROC = 0.746, 95% CI: 0.655-0.836) and SIRS (AUROC = 0.533, 95% CI: 0.425-0.641). The constructed mSOFA included points for the hepatic and CNS SOFA scores, in addition to one point each for the presence of chronic kidney disease or the use of breathing support; it performed as well as the SOFA score in this cohort or better than the SOFA score in a subgroup of patients with heart disease. SOFA score was a good predictor of mortality in a Jordanian ICU population and better than qSOFA, while SIRS could not predict mortality. Furthermore, the proposed mSOFA score which employed fewer laboratory tests could be used after validation from larger studies.

The correlation between electrocardiographic parameters and mortality in non-cardiac ICU patients.

This study aimed to determine the correlation between selected electrocardiogram (ECG) parameters (recorded at admission) and mortality in non-cardiac, non-COVID-19 intensive care unit (ICU) patients, and to determine the sensitivity and specificity of a novel admission ECG score (AD-ECG) for predicting mortality. Additionally, the sensitivity and specificity of the AD-ECG and Acute Physiology and Chronic Health Evaluation II (APACHE II) scores for predicting ICU mortality were compared. Clinical and laboratory data, and ECG parameters were compared between ICU survivors and non-survivors. ECG parameters (the QTc and Tpe intervals, and the Tpe/QT and Tpe/QTc ratios) and pulse pressure at ICU admission (baseline) were used to calculate the AD-ECG score. Cut-off values for ECG parameters, pulse pressure, and AD-ECG and APACHE II scores were calculated. The sensitivity and specificity of the APACHE II and AD-ECG scores were determined. The study included 167 patients. Mortality was higher in the patients with comorbidities, mechanical ventilation, and length of ICU stay (p < 0.05). The QTc and Tpe intervals, and the TPe/QT and TPe/QTc ratios differed significantly between the survivors and non-survivors (p < 0.05). The sensitivity and specificity of the AD-ECG score were similar to those of the APACHE II score. When pulse pressure, and the QTc and Tpe intervals were added to APACHE II, the sensitivity of the APACHE II score increased from 78.9% to 85.5%, and its specificity increased from 75% to 86.8%. A novel admission ECG score (AD-ECG) based on ECG parameters (the QTc and Tpe intervals, and the Tpe/QT and Tpe/QTc ratios) and pulse pressure has similar sensitivity and specificity as the APACHE II score for predicting non-cardiac ICU mortality. Adding pulse pressure, and the QTc and Tpe intervals increases the sensitivity and specificity of the APACHE II score; however, as the present study included non-cardiac patients only, additional larger-scale studies are needed to obtain more precise results.

Bacterial and fungal superinfections are detected at higher frequency in critically ill patients affected by SARS CoV-2 infection than negative patients and are associated to a worse outcome.

Patients with viral infections are at higher risk to acquire bacterial and fungal superinfections associated with a worse prognosis. We explored this critical point in the setting of patients with severe COVID-19 disease. The study included 1911 patients admitted to intensive care unit (ICU) during a 2-year study period (March 2020-March 2022). Of them, 713 (37.3%) were infected with SARS-CoV-2 and 1198 were negative (62.7%). Regression analysis was performed to determine risk factors associated with the presence of bacterial and/or fungal superinfections in SARS-CoV-2 patients and to evaluate predictors of ICU mortality. Of the 713 patients with SARS-CoV-2 infection, 473 (66.3%) had respiratory and/or bloodstream bacterial and/or fungal superinfections, while of the 1198 COVID-19-negative patients, only 369 (30%) showed respiratory and/or bloodstream bacterial and/or fungal superinfections (p < 0.0001). Baseline characteristics of COVID-19 patients included a median age of 66 (interquartile range [IQR], 58-73), a predominance of males (72.7%), and the presence of a BMI higher than 24 (median 26; IQR, 24.5-30.4). Seventy-four percent (527, 73.9%) had one or more comorbidities and 135 (18.9%) of them had received previous antibiotic therapy. Furthermore, most of them (473, 66.3%) exhibited severe radiological pictures and needed invasive mechanical ventilation. Multivariate logistic regression analysis showed that 1 unit increment in BMI rises the risk of bacterial and/or fungal superinfections acquisition by 3% and 1-day increment in ICU stays rises the risk of bacterial and/or fungal superinfections acquisition by 11%. Furthermore, 1-day increment in mechanical ventilation rises the risk of bacterial and/or fungal superinfection acquisition by 2.7 times. Furthermore, patients with both bacterial and fungal infections had a significantly higher mortality rate than patients without superinfections (45.8% vs. 26.2%, p < 0.0001). Therefore, bacterial and fungal superinfections are frequent in COVID-19 patients admitted to ICU and their presence is associated with a worse outcome. This is an important consideration for targeted therapies in critically ill SARS-CoV-2 infected patients to improve their clinical course.

Optimized Risk Score to Predict Mortality in Patients With Cardiogenic Shock in the Cardiac Intensive Care Unit.

Background Mortality prediction in critically ill patients with cardiogenic shock can guide triage and selection of potentially high-risk treatment options. Methods and Results We developed and externally validated a checklist risk score to predict in-hospital mortality among adults admitted to the cardiac intensive care unit with Society for Cardiovascular Angiography & Interventions Shock Stage C or greater cardiogenic shock using 2 real-world data sets and Risk-Calibrated Super-sparse Linear Integer Modeling (RiskSLIM). We compared this model to those developed using conventional penalized logistic regression and published cardiogenic shock and intensive care unit mortality prediction models. There were 8815 patients in our training cohort (in-hospital mortality 13.4%) and 2237 patients in our validation cohort (in-hospital mortality 22.8%), and there were 39 candidate predictor variables. The final risk score (termed BOS,MA2) included maximum blood urea nitrogen ≥25 mg/dL, minimum oxygen saturation <88%, minimum systolic blood pressure <80 mm Hg, use of mechanical ventilation, age ≥60 years, and maximum anion gap ≥14 mmol/L, based on values recorded during the first 24 hours of intensive care unit stay. Predicted in-hospital mortality ranged from 0.5% for a score of 0to 70.2% for a score of 6. The area under the receiver operating curve was 0.83 (0.82-0.84) in training and 0.76 (0.73-0.78) in validation, and the expected calibration error was 0.9% in training and 2.6% in validation. Conclusions Developed using a novel machine learning method and the largest cardiogenic shock cohorts among published models, BOS,MA2 is a simple, clinically interpretable risk score that has improved performance compared with existing cardiogenic-shock risk scores and better calibration than general intensive care unit risk scores.