Predicting Intensive Care Unit Admission Research Articles

Development and Validation of a Risk Score for Predicting ICU Admission in Adults with New-Onset Encephalitis.

Timely intensive care unit (ICU) admission for patients with encephalitis is associated with better prognosis. Therefore, our aim was to create a risk score predicting ICU admission in adults with encephalitis, which could aid in optimal management and resource allocation. We initially identified variables that would be most predictive of ICU admission among 372 patients with encephalitis from two hospital systems in Houston, Texas (cohort 1), who met the International Encephalitis Consortium (IEC) criteria from 2005 to 2023. Subsequently, we used a binary logistic regression model to create a risk score for ICU admission, which we then validated externally using a separate cohort of patients from two hospitals in Baltimore, Maryland (cohort 2), who met the IEC criteria from 2006 to 2022. Of 634 patients with encephalitis, 255 (40%) were admitted to the ICU, including 45 of 113 (39.8%) patients with an autoimmune cause, 100 of 272 (36.7%) with an infectious cause, and 110 of 249 (44.1%) with an unknown cause (p = 0.225). After conducting a multivariate analysis in cohort 1, we found that the presence of focal neurological signs, new-onset seizure, a Full Outline of Unresponsiveness score ≤ 14, leukocytosis, and a history of chronic kidney disease at admission were associated with an increased risk of ICU admission. The resultant clinical score for predicting ICU admission had an area under the receiver operating characteristic curve (AUROC) of 0.77 (95% confidence interval [CI] 0.72-0.82, p < 0.001). Patients were classified into three risk categories for ICU admission: low risk (score 0, 12.5%), intermediate risk (scores 1-5, 49.5%), and high risk (scores 6-8, 87.5%). External validation in cohort 2 yielded an AUROC of 0.76 (95% CI 0.69-0.83, p < 0.001). ICU admission is common in patients with encephalitis, regardless of etiology. Our risk score, encompassing neurologic and systemic factors, may aid physicians in decisions regarding intensity of care for adult patients with encephalitis upon hospital admission.

Lactate to albumin ratio as a prognosis predictor in gastrointestinal bleeding in the emergency department.

Gastrointestinal bleeding (GIB) is a common cause of emergency department (ED) visits, and has various prognoses. This study aimed to verify the prognostic ability of the lactate/albumin (L/A) ratio in patients with GIB compared with the AIMS65 score and blood urea nitrogen/albumin (B/A) ratio. This retrospective study included patients with GIB symptoms who visited the ED in 2019. Baseline characteristics and laboratory data were obtained to calculate the L/A and B/A ratios and AIMS65 score. Each score was evaluated as a predictor of intensive care unit (ICU) admission and in-hospital mortality by using the area under the receiver operating characteristic (AUROC) curve. The multivariate logistic regression analysis revealed that the L/A ratio significantly predicted ICU admission and in-hospital mortality. The AUROC curve for predicting ICU admission were 0.788, 0.695, and 0.586 for the L/A, B/A, and AIMS65 scores, respectively, while the AUROC curve for predicting in-hospital mortality were 0.807, 0.799, and 0.683 for the L/A, B/A, and AIMS65 scores, respectively. The L/A ratio, which consists of the serum lactate and albumin levels, showed superior performance relative to the B/A ratio and AIMS65 score in predicting the prognosis of patients with GIB.

An explainable machine learning-based model to predict intensive care unit admission among patients with community-acquired pneumonia and connective tissue disease

BackgroundThere is no individualized prediction model for intensive care unit (ICU) admission on patients with community-acquired pneumonia (CAP) and connective tissue disease (CTD) so far. In this study, we aimed to establish a machine learning-based model for predicting the need for ICU admission among those patients.MethodsThis was a retrospective study on patients admitted into a University Hospital in China between November 2008 and November 2021. Patients were included if they were diagnosed with CAP and CTD during admission and hospitalization. Data related to demographics, CTD types, comorbidities, vital signs and laboratory results during the first 24 h of hospitalization were collected. The baseline variables were screened to identify potential predictors via three methods, including univariate analysis, least absolute shrinkage and selection operator (Lasso) regression and Boruta algorithm. Nine supervised machine learning algorithms were used to build prediction models. We evaluated the performances of differentiation, calibration, and clinical utility of all models to determine the optimal model. The Shapley Additive Explanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) techniques were performed to interpret the optimal model.ResultsThe included patients were randomly divided into the training set (1070 patients) and the testing set (459 patients) at a ratio of 70:30. The intersection results of three feature selection approaches yielded 16 predictors. The eXtreme gradient boosting (XGBoost) model achieved the highest area under the receiver operating characteristic curve (AUC) (0.941) and accuracy (0.913) among various models. The calibration curve and decision curve analysis (DCA) both suggested that the XGBoost model outperformed other models. The SHAP summary plots illustrated the top 6 features with the greatest importance, including higher N-terminal pro-B-type natriuretic peptide (NT-proBNP) and C-reactive protein (CRP), lower level of CD4 + T cell, lymphocyte and serum sodium, and positive serum (1,3)-β-D-glucan test (G test).ConclusionWe successfully developed, evaluated and explained a machine learning-based model for predicting ICU admission in patients with CAP and CTD. The XGBoost model could be clinical referenced after external validation and improvement.

A machine learning framework for interpretable predictions in patient pathways: The case of predicting ICU admission for patients with symptoms of sepsis

Proactive analysis of patient pathways helps healthcare providers anticipate treatment-related risks, identify outcomes, and allocate resources. Machine learning (ML) can leverage a patient’s complete health history to make informed decisions about future events. However, previous work has mostly relied on so-called black-box models, which are unintelligible to humans, making it difficult for clinicians to apply such models. Our work introduces PatWay-Net, an ML framework designed for interpretable predictions of admission to the intensive care unit (ICU) for patients with symptoms of sepsis. We propose a novel type of recurrent neural network and combine it with multi-layer perceptrons to process the patient pathways and produce predictive yet interpretable results. We demonstrate its utility through a comprehensive dashboard that visualizes patient health trajectories, predictive outcomes, and associated risks. Our evaluation includes both predictive performance – where PatWay-Net outperforms standard models such as decision trees, random forests, and gradient-boosted decision trees – and clinical utility, validated through structured interviews with clinicians. By providing improved predictive accuracy along with interpretable and actionable insights, PatWay-Net serves as a valuable tool for healthcare decision support in the critical case of patients with symptoms of sepsis.

Development of Prediction Model for Intensive Care Unit Admission Based on Heart Rate Variability: A Case-Control Matched Analysis.

This study aimed to develop a predictive model for intensive care unit (ICU) admission by using heart rate variability (HRV) data. This retrospective case-control study used two datasets (emergency department [ED] patients admitted to the ICU, and patients in the operating room without ICU admission) from a single academic tertiary hospital. HRV metrics were measured every 5 min using R-peak-to-R-peak (R-R) intervals. We developed a generalized linear mixed model to predict ICU admission and assessed the area under the receiver operating characteristic curve (AUC). Odds ratios (ORs) with 95% confidence intervals (CIs) were calculated from the coefficients. We analyzed 610 (ICU: 122; non-ICU: 488) patients, and the factors influencing the odds of ICU admission included a history of diabetes mellitus (OR [95% CI]: 3.33 [1.71-6.48]); a higher heart rate (OR [95% CI]: 3.40 [2.97-3.90] per 10-unit increase); a higher root mean square of successive R-R interval differences (RMSSD; OR [95% CI]: 1.36 [1.22-1.51] per 10-unit increase); and a lower standard deviation of R-R intervals (SDRR; OR [95% CI], 0.68 [0.60-0.78] per 10-unit increase). The final model achieved an AUC of 0.947 (95% CI: 0.906-0.987). The developed model effectively predicted ICU admission among a mixed population from the ED and operating room.

Obesity Surgery Mortality Risk Score as a Predictor for Intensive Care Unit Admission in Patients Undergoing Laparoscopic Bariatric Surgery.

Background: Laparoscopic bariatric surgery provides many benefits including lower postoperative pain scores, reduced opioid consumption, shorter hospital stays, and improved quality of recovery. However, the anaesthetic management of obese patients requires caution in determining postoperative risk and in planning adequate postoperative pathways. Currently, there are no specific indications for intensive care unit (ICU) admission in this surgical population and most decisions are made on a case-by-case basis. The aim of this study is to investigate whether Obesity Surgery Mortality Risk Score (OS-MRS) is able to predict ICU admission in patients undergoing laparoscopic bariatric surgery (LBS). Methods: We retrospectively reviewed data of patients who underwent LBS during a 2-year period (2017-2019). The collected data included demographics, comorbidities and surgery-related variables. Postoperative ICU admission was decided via bariatric anaesthesiologists' evaluations, based on the high risk of postoperative cardiac or respiratory complications. Anaesthesia protocol was standardized. Logistic regression was used for statistical analysis. Results: ICU admission was required in 2% (n = 15) of the 763 patients. The intermediate risk group of the OS-MRS was detected in 84% of patients, while the American Society of Anaesthesiologists class III was reported in 80% of patients. A greater OS-MRS (p = 0.01), advanced age (p = 0.04), male gender (p = 0.001), longer duration of surgery (p = 0.0001), increased number of patient comorbidities (p = 0.002), and previous abdominal surgeries (p = 0.003) were predictive factors for ICU admission. Conclusions: ICU admission in obese patients undergoing LBS is predicted by OS-MRS together with age, male gender, number of comorbidities, previous abdominal surgeries, and duration of surgery.

Prediction of Admission to Intensive Care Unit and 1-Year Mortality After Acute Pancreatitis With Walled-Off Pancreatic Necrosis: A Retrospective, Single-Center Cohort Study.

Pancreatic walled-off necrosis (WON) carries significant mortality and morbidity risks, often necessitating intensive care unit (ICU) admission. This retrospective study aimed to evaluate whether routine biochemical parameters at the time of the index endoscopic procedure could predict ICU admission and 1-year mortality following endoscopic treatment of WON. We retrospectively identified 201 consecutive patients who underwent endoscopic drainage for WON between January 1, 2010, and December 31, 2020. Associations between routine biochemical blood tests and outcomes were assessed using logistic regression models. Within 1 year of the index endoscopy, 31 patients (15.4%) died, and 40 (19.9%) were admitted to the ICU due to sepsis. Preoperative electrolyte disturbances were more prevalent among ICU-admitted patients and nonsurvivors. Hyperkalemia, hypoalbuminemia, and elevated urea were significant predictors of 1-year mortality, while hypernatremia, elevated serum creatinine, and hypoalbuminemia predicted ICU admission. Predictive models exhibited good discriminative ability, with an AUC of 0.84 (95% CI,0,75-0.93) for 1-year mortality and 0.86 (95%CI, 0.79-0.92) for ICU admission. Preoperative imbalances in routine blood tests effectively predict adverse outcomes in endoscopically treated WON patients.

Assessing the predictive value of neutrophil percentage to albumin ratio for ICU admission in ischemic stroke patients.

Acute ischemic stroke (AIS) remains a substantial global health challenge, contributing to increased morbidity, disability, and mortality. This study aimed at investigating the predictive value of the neutrophil percentage to albumin ratio (NPAR) in determining intensive care unit (ICU) admission among AIS patients. A retrospective observational study was conducted, involving AIS cases admitted to a tertiary hospital in Jordan between 2015 and 2020. Lab data were collected upon admission, and the primary outcome was ICU admission during hospitalization. Descriptive and inferential analyses were performed using SPSS version 29. In this study involving 364 AIS patients, a subset of 77 (21.2%) required admission to the ICU during their hospital stay, most frequently within the first week of admission. Univariable analysis revealed significantly higher NPAR levels in ICU-admitted ischemic stroke patients compared to those who were not admitted (23.3 vs. 15.7, p < 0.001), and multivariable regression models confirmed that higher NPAR (≥19.107) independently predicted ICU admission in ischemic stroke patients (adjusted odds ratio [aOR] = 4.85, 95% CI: 1.83-12.83). Additionally, lower GCS scores and higher neutrophil-to-lymphocyte ratio (NLR) were also associated with increased likelihood of ICU admission. In terms of predictive performance, NPAR showed the highest accuracy with an AUC of 0.885, sensitivity of 0.805, and specificity of 0.854, using a cutoff value of 19.107. NPAR exhibits an AUC of 0.058, significantly outperforming NLR (Z = 2.782, p = 0.005). NPAR emerged as a robust independent predictor of ICU admission in ischemic stroke patients, surpassing the predictive performance of the NLR.

Shock and modified shock indices in predicting poisoning severity and outcomes in acute aluminum phosphide poisoned patients.

Severe refractory hypotension and cardiogenic shock are the main contributors to death in acute aluminum phosphide (ALP) poisoning. Shock index (SI) and modified shock index (MSI) are easily obtained parameters that reflect shock at an early stage. This study aimed to evaluate the role of SI and MSI in the prediction of the severity and outcomes of acute ALP poisoned patients. This cross sectional study was conducted on patients admitted to Tanta University Poison Control Centre with acute ALP poisoning from April 2022 to March 2023. Socio-demographics and toxicological data were taken, findings of clinical examination and laboratory investigations were recoded, SI was calculated by dividing heart rate over systolic blood pressure, and MSI was obtained by dividing heart rate over mean arterial pressure. Poisoning severity was assessed using poisoning severity score (PSS). Patients were divided into groups according to intensive care unit (ICU) admission and mortality. The study enrolled 94 patients. The median values of SI and MSI were significantly higher in ICU-admitted patients and non-survivors rather than their comparable groups. Significant positive correlations were observed between each of SI and MSI and PSS. At cut-off >1.14, SI conveyed fair performance to predict ICU admission and mortality (AUC=0.710 and 0.739, respectively). Similarly, MSI had fair performance to predict ICU admission (AUC=0.731) and mortality (AUC=0.744) at cut-off >1.47 and >1.5, respectively. Both SI and MSI could be considered simple bedside adjuncts to predict ICU admission and mortality in acute ALP poisoning.

Liver dysfunction and clinical outcomes of unvaccinated COVID-19 patients with and without chronic hepatitis B

BackgroundLiver dysfunction is common during coronavirus disease 2019 (COVID-19), while its clinical impact and association with chronic hepatitis B (CHB) remain uncertain. We aimed to investigate liver dysfunction in COVID-19 patients and its impacts on those with/without CHB. MethodsWe conducted a retrospective cohort study of COVID-19 patients at National Taiwan University Hospital, stratified according to hepatitis B surface antigen (HBsAg) serostatus, with demographics, laboratory data, and hospitalization course reviewed, and clinical outcomes compared through multivariable analyses. ResultsWe enrolled 109 COVID-19 patients unvaccinated against SARS-CoV-2 by August 2021. The HBsAg-positive group (n = 34) had significantly higher alanine aminotransferase (ALT) (26 vs. 16 U/L, P = 0.034), platelet (224 vs. 183 k/μL, P = 0.010) and longer hospitalizations (17 vs. 13 days, P = 0.012) compared with HBsAg-negative group (n = 75), while percentages of hepatitis (2-fold ALT elevation), oxygen supplementation, ventilators usage, COVID-specific treatment, intensive care unit (ICU) admission and mortality were comparable. Older age (odds ratio [OR]: 1.04, 95 % confidence interval [CI]: 1.00–1.08, P = 0.032) and higher aspartate aminotransferase (AST) (OR: 1.08, 95 % CI: 1.004–1.16, P = 0.038) were associated with oxygen supplementation according to multivariable analyses. Higher AST predicted ICU admission (OR: 1.11, 95 % CI: 1.03–1.19, P = 0.008). Oxygen usage (OR: 5.64, 95 % CI: 1.67–19.09, P = 0.005) and shock (OR: 5.12, 95 % CI: 1.14–22.91, P = 0.033) were associated with liver dysfunction. ConclusionsCHB patients had higher ALT levels and longer hospitalizations during COVID-19. Higher AST levels predict severe COVID-19 and ICU admission.

Respiratory torque teno virus load at emergency department visit predicts intensive care unit admission of SARS-CoV-2 infected patients.

Accurate prediction of COVID-19 severity remains a challenge. Torque teno virus (TTV), recognized as a surrogate marker of functional immunity in solid organ transplant recipients, holds the potential for assessing infection outcomes. We investigated whether quantifying TTV in nasopharyngeal samples upon emergency department(ED) admission could serve as an early predictor of COVID-19 severity. Retrospective single-center study in the ED of Saint-Louis Hospital in Paris, France. TTV DNA was quantified in nasopharyngeal swab samples collected for SARS-CoV-2 testing. Among 295 SARS-CoV-2 infected patients, 92 returned home, 160 were admitted to medical wards, and 43 to the intensive care unit (ICU). Elevated TTV loads were observed in ICU patients (median: 3.02 log copies/mL, interquartile range [IQR]: 2.215-3.825), exceeding those in discharged (2.215, [0; 2.962]) or hospitalized patients (2.24, [0; 3.29]) (p = 0.006). Multivariate analysis identified diabetes, obesity, hepatitis, fever, dyspnea, oxygen requirement, and TTV load as predictors of ICU admission. A 2.91 log10 copies/mL TTV threshold independently predicted ICU admission. Nasopharyngeal TTV quantification in SARS-CoV-2 infected patients is linked to the likelihood of ICU admission and might reflect respiratory immunosuppression.

Comparative study of the diagnostic power of full outline of unresponsiveness score and Glasgow coma scale in patients with traumatic brain injury in an emergency department

Background: The Glasgow coma scale (GCS) is the most commonly used scale, and the full outline of unresponsiveness (FOUR) score is new validated coma scale as an alternative to GCS to measure the level of consciousness and evaluate the severity of the injury in traumatic brain injury (TBI) patients. Aims and Objectives: The present study compared the performance of FOUR scores and GCS in outcome predictions of TBI cases. Materials and Methods: This prospective and cross-sectional study was conducted over a year by the Emergency Department of a tertiary care hospital, Kovai Medical Center and Hospital, Coimbatore, India. Of 159, 123 patients were recruited from intensive care unit (ICU), and 36 from the ward were included in this study. Data were collected using an observation checklist to determine the similarities and differences in predicting outcomes using the two assessment scales (GCS and FOUR). Results: Most patients were 51–60 years (38%), with a mean age of 41.57. About 82% were male, 18% were female, and 31% had comorbidity of hypertension. Data analysis showed a significant statistical difference in mean FOUR and GCS scores between ICU and ward admission. A multivariate logistic regression study revealed that the probability of ICU admission in trauma patients from the emergency department was associated with a decline in GCS and FOUR scores. The range of predicted ICU admission was similar in both GCS and FOUR score models. Conclusion: Although both scores are good predictors of TBI patients, we concluded that the FOUR score is a recommended predictive model for patients admitted to the medical ICU.

The rate of ward to intensive care transfer and its predictors among hospitalized COPD patients, a retrospective study in a local tertiary center in Saudi Arabia

ObjectiveTo investigate the prevalence of intensive care unit (ICU) admission and its predictors among hospitalized chronic obstructive pulmonary disease (COPD) patients.MethodsAn observational retrospective study was conducted. All patients with a confirmed diagnosis of COPD according to the GOLD guidelines between 28 and 2020 and 1 March 2023 at Al-Noor Specialist Hospital were included in this study. Patients were excluded if a preemptive diagnosis of COPD was made clinically without spirometry evidence of fixed airflow limitation. Descriptive results were presented as frequency (percentage) for categorical variables and mean (SD) for continuous variables and to estimate prevalence of ICU admission. Predictors of ICU admission among hospitalized COPD patients were determined using logistic regression analysis. A SPSS (Statistical Package for the Social Sciences) version 25 was used to perform all statistical analysis.ResultsA total of 705 patients with COPD were included in this study. The mean age was 65.4 (25.3) years. Around 12.4% of the hospitalized patients were admitted to the ICD. Logistic regression analysis identified that older age (OR; 1.92, (1.41–2.62)), smoking (OR; 1.60 (1.17–2.19)), and having specific comorbidities (Hypertension (OR; 1.98 (1.45–2.71)), Diabetes mellitus (OR; 1.42 (1.04–1.93)), GERD (OR; 2.81 (1.99–3.96)), Ischemic heart disease (OR; 3.22 (2.19–4.75)), Obstructive sleep apnea syndrome (OR; 2.14 (1.38–3.33)), stroke (OR; 4.51 (2.20–9.26))) were predictors of ICU admissions among patients with COPD.ConclusionsOur study found that a step-up approach to inpatient COPD management requires admission to the ICU in 12.4%, for which age, smoking status, cardiovascular, and stroke were important predictors. Further clinical research is needed to provide a validated model that can be incorporated into clinical practice to monitor this patient population during their admission and identify at-risk individuals for early transfer to higher acuity settings and intensive care units.

Severity prediction in COVID-19 patients using clinical markers and explainable artificial intelligence: A stacked ensemble machine learning approach

The recent COVID-19 pandemic had wreaked havoc worldwide, causing a massive strain on already-struggling healthcare infrastructure. Vaccines have been rolled out and seem effective in preventing a bad prognosis. However, a small part of the population (elderly and people with comorbidities) continues to succumb to this deadly virus. Due to a lack of available resources, appropriate triaging and treatment planning are vital to improving outcomes for patients with COVID-19. Assessing whether a patient requires the hospital’s Intensive Care Unit (ICU) is very important since these units are not available for every patient. In this research, we automate this assessment with stacked ensemble machine learning models that predict ICU admission based on general patient laboratory data. We have built an explainable decision support model which automatically scores the COVID-19 severity for individual patients. Data from 1925 COVID-19 positive patients, sourced from three top-tier Brazilian hospitals, were used to design the model. Pearson’s correlation and mutual information were utilized for feature selection, and the top 24 features were chosen as input for the model. The final stacked model could provide decision support on whether an admitted COVID-19 patient would require the ICU or not, with an accuracy of 88%. Explainable Artificial Intelligence (EAI) was used to undertake system-level insight discovery and investigate various clinical variables’ impact on decision-making. It was found that the most critical factors were respiratory rate, temperature, blood pressure, lactate dehydrogenase, hemoglobin, and age. Healthcare facilities can use the proposed approach to categorize COVID-19 patients and prevent COVID-19 fatalities.

Validation of the Turkish version of the pediatric early warning score

Aim: The pediatric early warning score (PEWS) identifies pediatric patients at risk for clinical deterioration and can helpless-experienced providers get a sense of which patients may need escalation of care. The purpose of the study was to adapt the PEWS into Turkish and evaluate its validity in pediatric patients admitted to the emergency. &#x0D; Material and Methods: This study was conducted between May and October 2022 on 228 patients aged 17 and under in the pediatric emergency department of a tertiary care hospital. In the pilot phase of the study, scoring of the first 30 patients was performed by three nurses in the emergency department. In the second phase, the validity of the PEWS scale was evaluated. Validity of the scoring system in predicting admission was assessed using area under the receiver operating characteristics (ROC) curve (AUC), sensitivity, and specificity, positive predictive value (PPV) and negative predictive value (NPV).&#x0D; Results: The mean age of the children was 6.37 ± 4.72 years. Phase I demonstrated good inter-rater reliability (kappa = 0.75). In phase II, 22 patients (9.6%) were admitted to the intensive care unit (ICU) during the study period. AUC for predicting was 0.948 (95% CI: 0.915–0.981). According to ROC curve analysis, a cut-off value for PEWS score was found to be 4 (PEWS &gt;4) for admitted to the ICU. Sensitivity and specificity in predicting ICU admission with the cut-off PEWS ≥4 was 86.36% and 90.78%, respectively (PPV, 50%; NPV, 90.48%). The sensitivity and specificity in predicting admission with a cut-off of PEWS ≥1 was 100% and 59.22%, respectively (PPV, 20.75%; NPV, 100%).&#x0D; Conclusion: The Turkish version of PEWS can be helpful in assessing patient status in pediatric emergency department with acceptable validity and can serve as a potentially screening tool for prediction of ICU admission.

Cardiac Parameters Better Predict ICU Admission and Short-Term Mortality in Hospitalized Patients With COVID-19.

Background COVID-19 is a multisystemic disease that affects many organs, and the use of some parameters is recommended both during hospitalization and follow-up. In this study, we investigated the relationship between blood (liver and kidney function tests, lactate, and D-dimer), infection (C-reactive protein (CRP), lymphocyte count, ferritin, and albumin), and cardiac (creatine kinase-myocardial band (CK-MB), troponin, and brain natriuretic peptide (BNP)) parameters with intensive care unit (ICU) admission and mortality. Materials and methods Patients hospitalized in Erzurum City Hospital with the diagnosis of COVID-19 between April 2020 and November 2022 were included in this retrospective study. The patient's files and electronic media records were retrospectively reviewed, and the patient'sanamnesis, physical examination, clinical findings, biochemical parameters, and treatment methods were recorded. The ICU needs of the patients and the treatment processes in intensive care were found in the in-hospital records. The hospital records and six-month mortality data were obtained retrospectively with the necessary permissions. Thus, blood parameters and their relation to each other in terms of prognosis were evaluated in determining the six-month mortality rates of the patients and estimating the need for ICU. Results A total of 5100 patients were included in the study. The mean age of patients with mortality was 74.2 ± 11.2 and that without mortality was 59.9 ± 15.7 (p < 0.001). In the mortality (+) group, 61.5% of patients were male, and in the mortality (-) group, 47.4% of the patients were male (p < 0.001). The mean age of patients with ICU admission was 69.6 ± 13.6 and without ICU admission was 60.3 ± 15.9 years (p < 0.001). In the ICU admission (+) group, 60.5% of patients were male; and in the ICU admission (-) group, 47.2% of patients were male (p < 0.001). Death and ICU admission were observed more frequently in elderly and male patients (p < 0.001 for both mortality and ICU admission). Blood parameters were evaluated both in the mortality and ICU groups, and organ function tests, blood count parameters, inflammatory markers, and cardiac parameters were significantly associated with poor outcomes. Cox regression analysis showed that lactate, albumin, Ln(troponin), and Ln(BNP) were independent predictors of mortality and ICU admission. Receiver operating characteristics (ROC) curve analysis showed that Ln(troponin) and Ln(BNP) levels predicted the development of mortality and ICU admission better than other parameters. Discussion COVID-19 can cause problems in different systems as a result of an inflammatory response, secreted cytokines, hypercoagulability, and direct tissue damage. When treating patients, a more appropriate approach is to evaluate different parameters together rather than focusing on a single parameter and deciding accordingly. However, evaluating alterations in many parameters in a disease that affects many systems is difficult and increases the risk of mistakes. Although each blood parameter separately is important, it was observed that the cardiac parameters troponin I and BNP have better predictive values than others in predicting the course and prognosis of COVID-19. Conclusion Blood parameters are used in COVID-19 diagnosis, treatment, and follow-up. Although it is not primarily a cardiac disease, cardiac markers can provide better results in showing the course and prognosis of COVID-19.

Hematocrit and lactate trends help predict outcomes in trauma independent of CT and other clinical parameters.

Hematocrit and lactate have an established role in trauma as indicators of bleeding and cell death, respectively. The wide availability of CT imaging and clinical data poses the question of how these can be used in combination to predict outcomes. To assess the utility of hematocrit or lactate trends in predicting intensive care unit (ICU) admission and hospital length of stay (LOS) in patients with torso trauma combined with clinical parameters and injury findings on CT. This was a single-center retrospective study of adults with torso trauma in one year. Trends were defined as a unit change per hour. CT findings and clinical parameters were explanatory variables. Outcomes were ICU admission and hospital LOS. Multivariate logistic and negative binomial regression models were used to calculate the odds ratio (OR) and incident rate ratio (IRR). Among 840 patients, 561 (72% males, age 39 ± 18) were included, and 168 patients (30%) were admitted to the ICU. Decreasing hematocrit trend [OR 2.54 (1.41-4.58), p = 0.002] and increasing lactate trend [OR 3.85 (1.35-11.01), p = 0.012] were associated with increased odds of ICU admission. LOS median was 2 (IQR: 1-5) days. Decreasing hematocrit trend [IRR 1.37 (1.13-1.66), p = 0.002] and increasing lactate trend [2.02 (1.43-2.85), p < 0.001] were associated with longer hospital LOS. Hematocrit and lactate trends may be helpful in predicting ICU admission and LOS in torso trauma independent of organ injuries on CT, age, or admission clinical parameters.

FTP7.9 Not all necrotising pancreatitis are the same: does aetiology affect the extent pancreatic necrosis? A single-centre retrospective study

Abstract Aim To assess the extent of pancreatic necrosis (PN) on abdominal computed tomography (CT) in patients with acute gallstone pancreatitis (GP) and alcohol-induced pancreatitis (AIP). Methods Patients ≥ 18-year-old with first-time onset of GP, AIP and PN on CT scan, from January 2010 to September 2018, were included. Extent of PN on CT, organ failure rate, admission to the intensive care unit (ICU), were compared between GP (group 1) and AIP (group 2). PN extent was described as: localised – right or left of the midline; diffuse – across the midline; more/less than 50% of the pancreas. Analysis of continuous and categorical data and multiple logistic regression were conducted. Significance was set at p&amp;lt;0.05. Results One hundred-two consecutive patients, 66 in group 1, 36 in group 2, were included. Overall, diffuse PN occurred in 34.3% of cases, more frequently in group 1 (42.4% vs 19.4%, p=0.03). Single/multiorgan failure developed in 50 patients, 57.6% belonged to group 1 (p=0.03). Predictors of organ failure were diffuse pattern of PN (OR 2.70, 95% CI 1.03 to 7.03, p=0.004) and PN ≥50% (OR 5.50, 95% CI 1.39 to 21.6, p=0.01). Admission to ICU was required in 27 subjects; PN ≥50% predicted ICU admission (OR 8.93, 95% CI 2.68 to 29.73, p=0.0004). The median hospital stay was 12 days (range 1-265), longer in group 1 (12.5 vs 10.5, p=0.02). Conclusions Diffuse PN occurred more frequently in patients with GP. A larger extent of PN predicted the onset of single/multiorgan failure and admission to the ICU.

Red cell distribution width-to-albumin ratio is a simple promising prognostic marker in acute cholangitis requiring biliary drainage

BackgroundIt is crucial to assess the severity of acute cholangitis (AC). There are currently several prognostic markers. However, the accuracies of these markers are not satisfied. The present study aimed to investigate the predictive value of the red cell distribution width (RDW)-to-albumin ratio (RAR) for the prognosis of AC. MethodsWe retrospectively evaluated consecutive patients diagnosed with AC between May 2019 and March 2022. RAR was calculated, and its predictive ability for in-hospital mortality, intensive care unit (ICU) admission, bacteremia, and the length of hospitalization were analyzed. ResultsOut of 438 patients, 34 (7.8%) died. Multivariate analysis showed that malignant etiology [odds ratio (OR) = 4.816, 95% confidence interval (CI): 1.936-11.980], creatinine (OR = 1.649, 95% CI: 1.095-2.484), and RAR (OR = 2.064, 95% CI: 1.494-2.851) were independent risk factors for mortality. When adjusted for relevant covariates, including age, sex, malignant etiology, Tokyo severity grading (TSG), Charlson comorbidity index, and creatinine, RAR significantly predicted mortality (adjusted OR = 1.833, 95% CI: 1.280-2.624). When the cut-off of RAR was set to 3.8, its sensitivity and specificity for mortality were 94.1% and 56.7%, respectively. Patients with an RAR of > 3.8 had a 20.9-fold (OR = 20.9, 95% CI: 4.9-88.6) greater risk of mortality than the remaining patients. The area under the curve value of RAR for mortality was 0.835 (95% CI: 0.770-0.901), which was significantly higher than that of TSG and the other prognostic markers, such as C-reactive protein-to-albumin ratio, and procalcitonin-to-albumin ratio. Lastly, RAR was not inferior to TSG in predicting ICU admission, bacteremia, and the length of hospitalization. ConclusionsRAR successfully predicted the in-hospital mortality, ICU admission, bacteremia, and the length of hospitalization of patients with AC, especially in-hospital mortality. RAR is a promising marker that is more convenient than TSG and other prognostic markers for predicting the prognosis of patients with AC.

Predictive value of ferritin, glucose, urea, and creatinine for COVID-19 severity and mortality in patients from Asir, Saudi Arabia.

To correlate demographics, blood groupings, and laboratory characteristics of hospitalized COVID-19 patients with disease severity and outcomes. This study included 294 COVID-19 patients. Data on patient age, gender, laboratory results, clinical severity, mortality, comorbidities, and blood group were obtained from medical records retrospectively. High levels of ferritin (p<0.01), urea (p<0.0001), and creatinine (p<0.05) were detected in intensive care unit (ICU)-admitted patients. Ferritin (p<0.05), glucose (p<0.0001), urea (p<0.0001), and creatinine (p<0.0001) were significantly higher in non-survivor compared to survivor COVID-19 patients. Predictors for ICU admission among patients were ferritin (odd ratio [OR]=0.999, p=0.0055) and urea (OR=0.991, p=0.0001). Predictors for mortality were: age (OR=0.963, p=0.0001), ferritin (OR=0.999, p=0.0149), glucose (OR=0.993, p=0.0001), urea (OR=0.976, p=0.0001), and creatinine (OR=0.556, p=0.0001). The most reliable laboratory parameters in predicting mortality were: age (area under the curve [AUC]=0.685, p<0.0001), ferritin (AUC=0.610, p<0.05), glucose (AUC=0.681, p<0.0001), urea (AUC=0.856, p<0.0001), and creatinine (AUC=0.823, p<0.0001). High ferritin, glucose, urea, and creatinine levels may predict poor outcomes in COVID-19 patients. These findings could help predict admissions to the ICU and mortality among such patients.