Predicting ICU Admission Research Articles

KLRB1 expression in nasopharyngeal mucosa as a prognostic biomarker in COVID-19 patients

The resurgence of COVID-19 and the rise in severe outcomes emphasize the need for reliable prognostic markers to guide patient care and optimize ICU and hospital resources. This study investigates the potential of nasopharyngeal swabs to identify biomarkers that predict ICU admission or death in hospitalized COVID-19 patients. We analyzed nasopharyngeal exudates from 95 hospitalized patients in 2020 using high-plex RNA quantification on the NanoString® nCounter platform. Comparative analysis identified four genes, with KLRB1 (Killer cell lectin like receptor B1) (Odds Ratio OR 0.5, 95% CI: 0.27–0.96), along with age (OR 3.3, 95% CI: 1.25–8.93) emerging as independent prognostic markers in multivariate analysis. These findings were validated using qRT-PCR in an independent cohort of 168 patients hospitalized in 2022. While univariate analysis identified a significant association between KLRB1 expression and vaccination status (p < 0.05), only low KLRB1 expression (OR 1.135, 95% CI: 1.0-1.280), and age (OR 1.033, 95% CI: 1.006–1.061) were confirmed as independent risk factors for ICU admission or death, regardless of other studied variables such as comorbidities, vaccination status, or smoking habits. Our findings suggest that KLRB1 expression could improve prognostic tools by identifying patients at higher risk upon admission. Incorporating KLRB1 into multiplex diagnostic kits alongside SARS-CoV-2 detection could streamline prognostic assessment, providing a more comprehensive and efficient approach to patient management.

Identification of Risk of Early Decompensation and Predictors of ICU Admission in Patients Triggering Code Sepsis.

Introduction "Code Sepsis" is a protocol, auto-initiated as a system-based trigger in our community-based teaching center. It is designed to be activatedin patients with two or more systemic inflammatory response syndrome (SIRS) criteria and organ dysfunction. The purpose is to identify sepsis early and treat it according to the Surviving Sepsis guidelines. If clinically indicated, patients are rapidly evaluated and treated with antibiotics and fluid boluses.Despite proactive measures to improve patient outcomes, clinical deterioration can still occur, which may necessitate ICU admission, vasopressor initiation, and even increased morbidity and mortality. In this study, we aimed to compare patients who triggered a code sepsis requiring ICU admission at a teaching hospital with those who did not require ICU admission, identifying risk factors for early decompensation. Methods We conducted a retrospective study to gather data on all patients admitted to the hospital between September 1, 2022, and December 31, 2022, who triggeredcode sepsis. Subjects were identified based on whether they triggered code sepsis, and demographic details, admission details, laboratory values, and code sepsis notes were collected using a Redcap questionnaire. The datawas reviewedand evaluated using STATA software (Stata Corp., College Station, TX, USA), and means were calculated and compared between the two outcome cohorts.Multivariate logistic regression analysiswas performedto determineadjusted odds ratios (OR). Results For patients who triggered code sepsis, the mean age (63.94 vs. 63.71, p=0.963) and mean length of stay (11.94 vs. 10.03 days, p=0.422) were comparable between those who required ICU admission and those who did not. However, there were significant differences in other factors.The mean initial lactate (2.22vs. 1.40, p=0.017), initial alanine aminotransferase (ALT) level (74.52vs. 37.11, p<0.05), andaspartate aminotransferase (AST) levels (149.84vs. 61.03, p=0.005) were significantly higher in patients who required ICU levelcare. At the time of code sepsis, patients who needed a 30 cc/kg fluid bolus (OR=12.8, p<0.01),or had hypotension in the first hour after the event (OR=7.94, p<0.01) had a higher chance of requiring ICU admission. Patients meeting quick sequential organ failure assessment (qSOFA) criteria (OR= 4.4 and 5, p<0.01) and requiring escalation of antibiotics (OR=19.33, p<0.01) at the time of code sepsis were also more likely to require ICU. White cell count, glucose level, serum creatinine, and troponins at admission were comparable in both groups. There was no statistically significant difference in the distribution ofco-morbiditiessuch as type 2 diabetes, hypertension, cardiovascular, or kidney disease among the patients who went to the ICU versus thosewho didnot. Conclusion Code sepsis, triggered by the system or physician, identifies the presence of SIRS and organ dysfunction, thus enabling healthcare providers to intervene and manage sepsis or septic shock earlier. Higher lactate levels, the presence of transaminitis during the initial trigger event, and positive qSOFA criteria indicate a worse prognosis. This may require escalation of care to the ICU, closer monitoring, and possible use of vasopressor support. Early identification of such individuals can lead to better management of their condition.

Cerebrospinal fluid multiplex PCR cycle thresholds may predict ICU admission in community-acquired meningoencephalitis.

We investigated the relationship between cerebrospinal fluid (CSF) PCR cycle threshold (Ct) values, clinical characteristics, and outcomes in suspected CAM, while assessing QIAstat-Dx® ME Panel concordance with routine diagnostic methods. Frozen CSF from 30 individuals with suspected CAM were analyzed using the QIAstat-Dx® ME Panel. Ct values were categorized as low (≤ 30) or high (>30). Concordance with CSF FilmArray®/culture results was assessed using the Kappa coefficient. Low Ct values were associated to CSF markers of meningitis and predicted ICU admissions (log-rank P = 0.025), particularly within the first two weeks of hospitalization (85 % for low Ct values versus 50 % for high Ct values, P = 0.041). Ct values were not associated with mortality in the survival analysis (log-rank P = 0.109). Substantial agreement was observed between QIAstat-Dx® and comparators (96.7 %, Kappa 0.839). CSF Ct values can assist in risk stratification for timely ICU admission in individuals with CAM.

Validation of prognostic models predicting mortality or ICU admission in patients with COVID-19 in low- and middle-income countries: a global individual participant data meta-analysis

BackgroundWe evaluated the performance of prognostic models for predicting mortality or ICU admission in hospitalized patients with COVID-19 in the World Health Organization (WHO) Global Clinical Platform, a repository of individual-level clinical data of patients hospitalized with COVID-19, including in low- and middle-income countries (LMICs).MethodsWe identified eligible multivariable prognostic models for predicting overall mortality and ICU admission during hospital stay in patients with confirmed or suspected COVID-19 from a living review of COVID-19 prediction models. These models were evaluated using data contributed to the WHO Global Clinical Platform for COVID-19 from nine LMICs (Burkina Faso, Cameroon, Democratic Republic of Congo, Guinea, India, Niger, Nigeria, Zambia, and Zimbabwe). Model performance was assessed in terms of discrimination and calibration.ResultsOut of 144 eligible models, 140 were excluded due to a high risk of bias, predictors unavailable in LIMCs, or insufficient model description. Among 11,338 participants, the remaining models showed good discrimination for predicting in-hospital mortality (3 models), with areas under the curve (AUCs) ranging between 0.76 (95% CI 0.71–0.81) and 0.84 (95% CI 0.77–0.89). An AUC of 0.74 (95% CI 0.70–0.78) was found for predicting ICU admission risk (one model). All models showed signs of miscalibration and overfitting, with extensive heterogeneity between countries.ConclusionsAmong the available COVID-19 prognostic models, only a few could be validated on data collected from LMICs, mainly due to limited predictor availability. Despite their discriminative ability, selected models for mortality prediction or ICU admission showed varying and suboptimal calibration.

Retrospective validation of the STUMBL score in a Level 1 trauma centre.

A single-centre retrospective validation study was undertaken using information from all patients with an Emergency Department (ED) attendance for isolated blunt chest trauma at a major trauma centre in Australia from 2018. The performance of the STUMBL score was measured including the cut-off score which best predicted 1) the discharge disposition from ED (ward or intensive care unit [ICU]), 2) the development of pulmonary complications, 3) an extended length of stay (LOS) (7 days or more) and 4) any complication (pulmonary, extended LOS, in hospital mortality). The performance measures included sensitivity, specificity, negative and positive predictive values as well discrimination and calibration. There were 300 patients admitted between 1st January 2018 and 31st December 2018 with a median age of 60 years (IQR 44-75) and 65 % were male. The risk prediction cut-off score for our patient cohort ranged from 18.5 for LOS 7 days or more to 11.5 for ward admission from ED. The positive predictive value (PPV) ranged from 56.7 % for ward admission from ED to 21.1 % for pulmonary complications. The negative predictive value (NPV) and sensitivity was highest for ICU admission from ED (96.5 % and 80.6 %) and the specificity ranged from 78 % for all complication prediction to 65.3 % for LOS of 7 or more days. The C statistic ranged from 0.82 for ICU admission to 0.65 for pulmonary morbidity. The performance measures of the STUMBL score are suboptimal in our population. The best performing measure was the ability to predict ICU admission. Further validation work that includes additional factors may improve the positive predictive value and clinical utility of the score in our cohort.

Comparative Analysis of qSOFA, PRIEST, PAINT, and ISARIC4C Scores in Predicting Severe COVID-19 Outcomes Among Patients Aged over 75 Years.

Elderly patients, particularly those over 75 years old, have been disproportionately affected by COVID-19, exhibiting higher rates of severe outcomes, such as ICU admissions and mortality. This study aimed to evaluate and compare the effectiveness of various clinical scoring systems-qSOFA, PRIEST, PAINT, and ISARIC4C-in predicting ICU admission, the need for mechanical ventilation, and mortality among elderly COVID-19 patients. In this retrospective cohort study conducted at two tertiary care hospitals, 131 elderly patients (aged ≥ 75) and 226 younger controls (aged < 65) with confirmed COVID-19 were included. Clinical scores were computed at admission and five days after symptom onset. Kaplan-Meier survival analysis and Receiver Operating Characteristic (ROC) curve analysis were performed to assess the predictive performance of the scores regarding severe outcomes. Kaplan-Meier analysis indicated significantly lower survival probabilities for elderly patients with high scores at admission. Those with an ISARIC4C score above 11.8 had a survival probability of 25% compared to 74% for those below this threshold (p < 0.001). Similarly, elderly patients with a qSOFA score above 2.1 had a survival probability of 36% compared to 72% for those with lower scores (p < 0.001). The PRIEST and PAINT scores also demonstrated predictive validity; patients with a PRIEST score above 6.3 and a PAINT score above 6.5 at admission showed comparable decreases in survival probabilities. ROC analysis at five days post-symptom onset revealed that the ISARIC4C score had the highest area under the curve (AUC) of 0.772, suggesting excellent predictive validity for severe outcomes, including mortality. The optimal cutoffs identified were 11.2 for ISARIC4C, 6.3 for PRIEST, and 6.5 for PAINT, each displaying high sensitivity and specificity. The ISARIC4C, qSOFA, PRIEST, and PAINT scores are robust predictors of severe outcomes in elderly COVID-19 patients over 75 years old, as confirmed by Kaplan-Meier and ROC analyses. These tools can be crucial for early identification of patients at high risk of adverse outcomes, guiding clinical decision making, and optimizing resource allocation. The use of these scoring systems should be encouraged in clinical settings to enhance the management of elderly COVID-19 patients. Further research is necessary to validate these findings across different populations and settings.

HPB SO46 - Risk Stratification for Acute Pancreatitis: A Retrospective Comparative Analysis between Glasgow-Imrie and BISAP Scoring Systems within a District General Hospital

Abstract Background Risk stratification for acute pancreatitis (AP) is an important tool in clinical decision making. The BISAP score has been validated to be a comparable risk prognostication tool to current models[1, 2]. Current trust practice is the Glasgow-Imrie score, which involves more invasive and non-routine investigations, resulting in an increased cost and labour burden. Additionally, a previous local audit demonstrated poor compliance with only 55% of admissions having a documented score, limiting utility and prognostication. By demonstrating non-inferiority, we aim to implement BISAP as the standard of practice with earlier severity assessment, improved patient experience and minimising unnecessary investigations. Method A retrospective analysis of 153 admissions with acute pancreatitis admitted to a single-site district general hospital over a 12-month period was performed, reviewing electronic patient records for documentation, observations and investigations. Both Glasgow-Imrie and BISAP scores were calculated within 48 and 24 hours of admission retrospectively and respectively. Descriptive statistics was used to summarise data and compare trends between both scores and the severity of acute pancreatitis, ICU admissions and mortality. Logistic regression was utilised to model the relationship between both systems and binary outcomes with area under the curve (AUC) and non-inferiority tests derived to determine efficacy. Results The cohort had a mean age of 57 ± 18.53 (52:48 male:female) with an average LOS of 7.25 ± 10.12 days. 6.54% had severe AP, 3.92% required ICU admisison and 3.26% mortality rate. The AUC value for both models was 0.868 in predicting severity (p&amp;lt;0.001) with Glasgow-Imrie performing better in predicting ICU admission (AUC 0.954 &amp;gt; 0.855, p&amp;lt;0.01) and BISAP outperforming Glasgow-Imrie in predicting mortality (AUC 0.903 &amp;gt; 0.867, p&amp;lt;0.01). Bootstrapping resampling demonstrated no significant difference between models in predicting severity (δAUC 0.0007, 95%CI -0.01181, 0.1167), ICU admission (δAUC 0.1010, 95%CI -0.0113, 0.2140) and mortality (δAUC -0.0366, 95%CI -0.0788, 0.0045). Conclusion Both the Glasgow-Imrie and BISAP scoring systems are accurate in predicting the severity of pancreatitis, ICU admission and mortality. The BISAP score is similar in prognostic accuracy to the Glasgow-Imrie score and is advantageous in being simpler to obtain in clinical practice with a higher rate of completeness. We recommend the BISAP score as a non-inferior, pragmatic risk stratification tool in acute pancreatitis.

Circulating inflammatory cytokines predict severity disease in hospitalized COVID-19 patients: A prospective multicenter study of the European DRAGON consortium

BackgroundCOVID-19 has put a huge strain on the healthcare systems worldwide, requiring unprecedented intensive care resources. There is still an unmet clinical need for easily available biomarkers capable of predicting the risk for severe disease. The main goal of this prospective multicenter study was to identify biomarkers that could predict ICU admission and in-hospital mortality. MethodsWe prospectively recruited COVID-19 PCR positive patients in two hospitals, in Belgium and Italy. Blood samples were collected at hospital admission and 20 potential biomarkers were measured with the Luminex technology. Logistic regression models were performed to identify the biomarkers that, alone or together, were associated with patient disease severity. ResultsOur study demonstrates that elevated levels of circulating inflammatory cytokines were associated with disease severity in COVID-19 hospitalized patients. CXCL10, IL-4, IL-6 and MCP-1 values were predictive of ICU admission. Elevated levels of IL-6 and MCP-1 were also associated with in hospital death in COVID-19 hospitalized patients. ConclusionAltogether, elevated and correlated inflammatory cytokines in the blood of COVID-19 patients at hospital admission are predictive of disease severity and suggest a dysregulated inflammation induced by SARS-CoV-2 infection.

National Early Warning Score 2 to Predict ICU Admission of COVID-19 Patients in Bangladeshi Cohort

Objective: To assess the predictive accuracy of the National Early Warning Score 2 (NEWS 2) for early prediction of clinical deterioration (ICU admission) of COVID-19- infected patients in Bangladesh. Methods: Data from 244 patients with COVID-19 infection admitted from July 2021- September 2021, to the Aichi Hospital Ltd, Bangladesh, were retrospectively reviewed. Consecutive adult patients with laboratory-confirmed COVID-19 initially admitted to non- ICU wards were included. Categorical and quantitative variables were expressed as numbers (percentages) and median (interquartile range, IQR). We evaluated the predictive performance of NEWS2 to predict ICU admission by comparing the area under the receiver operating characteristics curve (AUROC) at thresholds 5 and 7 and assessed its association with ICU admission by performing multivariate logistic analyses. STATA conducted all the statistical analyses. Results: Among the included 218 patients, 68 patients were transferred to ICU. The AUROC was 0.96 (Standard error 0.01, 95% confidence interval 0.93-0.98), revealing that NEWS2 at hospital admission was a good predictor of ICU admission. A NEWS2 threshold of 5 had higher sensitivity than a threshold of 7 (72.06% and 29.41%). A point of 7 also had high specificity (99.33% and 63.33%) and a high positive predictive value than a threshold of 5 (95.24% vs. 47.12%). The NEWS2 entries 5 and 7 were related to ICU admission found in multivariate logistic regression analysis. Conclusion: Our results suggest that NEWS2 at hospital admission is a good predictor for ICU admission of COVID-19 patients in Bangladesh. Screening patients at admission with this tool may identify patients at risk of clinical deterioration and help in better management. EWMCJ Vol. 12, No. 1&amp;2, January-July 2024: 60-65

Prognostic performance of the Bova, sPESI, and Qanadli scores in patients with acute pulmonary embolism.

Acute pulmonary embolism (PE) is a disease with a serious prognosis and a high probability of death in the emergency department. To investigate the prediction of PE-related mortality and intensive care admission (ICU) of Qanadli (Qscore), Bova, and simplified Pulmonary Embolism Severity Index (sPESI) scores. This retrospective observational study consisted of all patients diagnosed with acute PE who were imaged under computed tomography pulmonary angiography (CTPA) for a total of 5 years between 1 June 2018 and 1 June 2023. The prediction of radiological and clinical scores for mortality and ICU admission was examined. A total of 95 patients were analyzed. Patients who died and those who were admitted to the ICU had a significantly higher frequency of being found to have a high-risk (≥1) sPESI score (P = 0.04 and P = 0.016, respectively). For mortality, the sPESI score was found to be significant; the sensitivity and specificity were observed as 54% and 66% (area under the curve [AUC]=0.670, 95% confidence interval [CI]=0.527-0.814; P = 0.020). For ICU admission, the sensitivity and specificity of the Qscore, sPESI, and Bova scores were 35%, 77%, and 58%, and 78%, 65%, and 84% respectively (AUC=0.626, 95% CI=0.511-0.740, P = 0.031; AUC=0.769, 95% CI=0.674-0.865, P < 0.001; and AUC=0.767, 95% CI=0.671-0.862, P < 0.001, respectively). It was found that the sPESI score was effective at predicting mortality in patients with acute PE. Qscore, sPESI, and Bova scores have been shown to be useful in predicting ICU admission.

Prognostic value of CURB-65 and pneumonia severity index (PSI) in COVID-19 pneumonia: A retrospective cohort study

Objective: To validate a better scoring system between CURB- 65 and the pneumonia severity index (PSI) to predict the need for intensive care unit admission and 30-day mortality in patients with COVID-19 pneumonia. Methods: A retrospective cohort study was conducted on patients in a tertiary care hospital in Kota, Rajasthan. Relevant data about history, and general and systemic examination with laboratory investigations was collected. Patients were categorized into different risk groups for ICU admission and 30-day mortality according to CURB-65 and PSI. Sensitivity, specificity, positive predictive value, negative predictive value, and area under the curve were calculated. Continuous variables, means and percentages were compared using the t-test for numerical data and the Chi-square test for nominal data. Results: 100 patients who tested positive for SARS-CoV-2 by RT- PCR were included; 67 were male and 33 were female. 58 patients had severe disease (SpO2≤90%) and 30 patients died. CURB- 65 had an area under the curve (AUC) of 0.81 for predicting ICU admission, with a sensitivity of 92% and a specificity of 54% for high risk patients. Meanwhile, PSI had an AUC of 0.88, with a sensitivity of 83% and a specificity of 73% in high risk patients. In addition, CURB-65 had an AUC of 0.79 for predicting 30-day mortality, with a sensitivity of 60% and a specificity of 89% in high risk patients. PSI had an AUC of 0.71 for predicting 30-day mortality, with a sensitivity of 90% and a specificity of 46% in high risk patients. Conclusions: This single-centre retrospective study involving a cohort of COVID-19 patients demonstrates that CURB-65 and PSI are powerful tools for predicting mortality. However, PSI appears to be slightly better at predicting the need for intensive care unit admission in high-risk patients.

Machine learning approaches to predict the need forintensive care unitadmission among Iranian COVID-19 patients based on ICD-10: A cross-sectional study.

Timely identification of the patients requiring intensive care unit admission (ICU) could be life-saving. We aimed to compare different machine learning algorithms to predict the requirements for ICU admission in COVID-19 patients. We screened all patients with COVID-19 at six academic hospitals in Tehran comprising our study population. A total of 44,112 COVID-19 patients (≥18 years old) were included, among which 7722 patients were hospitalized. We used a Random Forest algorithm to select significant variables. Then, prediction models were developed using the Support Vector Machine, Naıve Bayes, logistic regression, lightGBM, decision tree, and K-Nearest Neighbor algorithms. Sensitivity, specificity, accuracy, F1 score, and receiver operating characteristic-Area Under the Curve (AUC) were used to compare the prediction performance of different models. Based on random Forest, the following predictors were selected: age, cardiac disease, cough, hypertension, diabetes, influenza & pneumonia, malignancy, and nervous system disease. Age was found to have the strongest association with ICU admission among COVID-19 patients. All six models achieved an AUC greater than 0.60. Naıve Bayes achieved the best predictive performance (AUC = 0.71). Naïve Bayes and lightGBM demonstrated promising results in predicting ICU admission needs in COVID-19 patients. Machine learning models could help quickly identify high-risk patients upon entry and reduce mortality and morbidity among COVID-19 patients.

Chest Computed Tomography Severity Score to Predict Outcome of Patients with COVID 19

Abstract Background The coronavirus disease 2019 (COVID-19) is an acute infectious pneumonia caused by a severe acute respiratory syndrome-coronavirus-2. Some studies have suggested that C-reactive protein, lymphocyte count, and features from CT scoring as predictors of severity and prognosis of COVID-19. Non-contrast chest computed tomography (CT) scan was found to be a useful diagnostic instrument for screening COVID-19 pneumonia. This score uses lung opacification as a surrogate for extension of the disease in the lungs. Aim To assess the prognostic value of chest CT severity score (CSS) to predict the severity of symptoms and outcomes among patients with typical CT findings. Subject and Methods This Prospective Observational study was conducted at Radiology department, faculty of medicine, Ain Shams University on 94 patients. Results There is a significant difference between the groups regarding symptoms severity. there is no significant difference between the groups regarding CT findings. left lung severity scores and total severity score were significantly higher among poor prognosis patients compared to good prognosis patients. mortality rate was significantly higher among poor prognosis patients compared to good prognosis patients. severity score was significantly higher among severe symptoms patients compared to mild symptoms patients in good prognosis patients. Conclusion The current study showed that CT images and CT-severity scores can predict ICU admission and mortality in patients with COVID-19 pneumonia. Clinicians and radiologists can use the CT-severity score as a measure of disease severity and an indicator of poor prognosis, including the need for mechanical ventilation. Also, our findings showed the superiority of CT findings over other clinical information and co-morbidities in predicting the short-term outcome.

The Role of Trending Shock Index Paediatric Adjusted (SIPA) in Paediatric Trauma as a Predictor of Morbidity and Mortality

Abstract Background and Purpose There are several injury scoring systems, anatomic, physiologic and combined scores, which have been used for prediction of patients' outcome such as mortality. Among physiologic scoring systems evolved shock index (heart rate divided by systolic blood pressure) which has been first described in 1967. In the world of paediatric and due to the physiological differences between different age groups, few adjustments have to be done considering these changes evolving shock index paediatric adjusted (SIPA). This study was conducted to evaluate SIPA as a quick feasible method for triaging and managing pediatric trauma and also to predict ICU admission and need for CT scan as a primary outcome and also secondary outcomes like length of hospital stay and (need for surgical intervention) and mortality rate. Study Design Prospective randomized controlled study. Setting The children’s accident and emergency department in Armed Forces Hospitals and Ain Shams University hospitals. Subjects We included 124 pediatric trauma children, and they were divided into 2 groups: Group A (62 children): assessed by SIPA in an incidence of trauma, and Group B (62 children): assessed by traditional vital signs (blood pressure, pulse and capillary refill). Methods All patients were subjected to full history taking, SIPA assessment on admission and its daily changes (baseline up to 48 hours). Assessment for need for ICU admission, ventilation, CT scan, surgical intervention, and Length of hospital stay, along with mortality rate recording. Results In the studied population, the mean age of all patients was (6.8 ± 3.2) years, with (65.3%) of patient males; and (34.7%) females. Regarding outcome data, comparative study between the 2 groups revealed; significant decrease in ICU admission, need for CT scan, LOS and mortality rate in Group A (SIPA) (p &amp;lt; 0.05). As regards SIPA trend, we found non-significant difference between mortality and no-mortality subgroups in SBP, heart rate, Capillary refill time and SIPA during the serial (baseline up to 48-h) measurements. Also, regarding ICU LOS, we found non-significant difference between mortality and no-mortality subgroups in SBP, heart rate, Capillary refill time and SIPA during the serial (baseline up to 48-h) measurements. Conclusion To conclude, SIPA score proven to be an accurate and specific predictor of morbidity, hospital stay and ICU admission, and mortality rate in pediatric trauma patients and is superior to capillary refill time, tachycardia or hypotension alone for predicting different outcomes.

Using Acidosis as a Surrogate for or Supplement to the Bedside Index of Severity in Acute Pancreatitis Scoring Prediction System Has a Nonsignificant Effect.

Currently, risk stratification calculators for acute pancreatitis (AP) can at best predict acute pancreatitis mortality at 12 hours from the presentation. Given the severe morbidity associated with AP, the identification of additional prognostic indicators, which may afford earlier prediction in length of stay (LOS) and mortality, is desired. Metabolic acidosis can be a prognostic marker for the severity of AP, and venous bicarbonate can reliably and accurately be substituted for arterial base deficit to detect metabolic acidosis. Since serum bicarbonate, anion gap (AG), and corrected AG (CAG) are routinely obtained upon presentation to the emergency department and often daily in the hospital, we conducted a retrospective analysis of 443 patients, evaluating if venous bicarbonate could predict the severity of pancreatitis as well as mortality, admission to the ICU, ICU LOS, and hospital LOS. The inclusion of venous bicarbonate, AG, and CAGin the first 12 hours only slightly improved the predictive capabilities of the Bedside Index for Severity in Acute Pancreatitis (BISAP) score for these secondary outcomes. None of our incorporations of acidemia improved severity predictions more than the BISAP alone. Adding CAG to BISAP scoring had the largest effect on predicting ICU admission and hospital LOS (area under the curve (AUC): 1.12 (confidence interval (CI) 95%: 1.06-1.19), p <.001 and AUC1.02 (CI 95%1.01-1.04), p <.001; respectively). ICU LOS was not impacted by the addition of AG, CAG, or venous bicarbonate. In-hospital death (n=12) was too small to be determined.

A simple electronic medical record-based predictors of illness severity in sepsis (sepsis) score.

Current scores for predicting sepsis outcomes are limited by generalizability, complexity, and electronic medical record (EMR) integration. Here, we validate a simple EMR-based score for sepsis outcomes in a large multi-centre cohort. A simple electronic medical record-based predictor of illness severity in sepsis (SEPSIS) score was developed (4 additive lab-based predictors) using a population-based retrospective cohort study. Internal medicine services across four academic teaching hospitals in Toronto, Canada from April 2010-March 2015 (primary cohort) and 2015-2019 (secondary cohort). We identified patients admitted with sepsis based upon receipt of antibiotics and positive cultures. The primary outcome was in-hospital mortality and secondary outcomes were ICU admission at 72 hours, and hospital length of stay (LOS). We calculated the area under the receiver operating curve (AUROC) for the SEPSIS score, qSOFA, and NEWS2. We then evaluated the SEPSIS score in a secondary cohort (2015-2019) of hospitalized patients receiving antibiotics. Our primary cohort included 1,890 patients with a median age of 72 years (IQR: 56-83). 9% died during hospitalization, 18.6% were admitted to ICU, and mean LOS was 12.7 days (SD: 21.5). In the primary and secondary (2015-2019, 4811 patients) cohorts, the AUROCs of the SEPSIS score for predicting in-hospital mortality were 0.63 and 0.64 respectively, which were similar to NEWS2 (0.62 and 0.67) and qSOFA (0.62 and 0.68). AUROCs for predicting ICU admission at 72 hours, and length of stay > 14 days, were similar between scores, in the primary and secondary cohorts. All scores had comparable calibration for predicting mortality. An EMR-based SEPSIS score shows a similar ability to predict important clinical outcomes compared with other validated scores (qSOFA and NEWS2). Because of the SEPSIS score's simplicity, it may prove a useful tool for clinical and research applications.

A comparative study between curb-65 and psi/port score as predictors for ICU admission and mortality in community acquired pneumonia patients presenting to a tertiary care hospital

Despite recent advances in clinical medicine, mortality due to CAP has remained relatively the same. Therefore, early recognition for ICU admission plays a pivotal role in management of CAP. PSI/PORT (Pneumonia Severity Index/ Pneumonia Patient Outcome Research Team) score and CURB-65 (Confusion, Uremia, Respiratory rate, BP, Age &amp;#62; 65 years) are the two severity assessment scores that have extensively been used by the clinicians to distinguish between high risk and low risk patients. This study aims at comparing the predictive value of these two scores for ICU admission and mortality. The information required for calculating CURB-65 and PSI was extracted with careful history taking, patient assessment and necessary investigations. Patients with &amp;#62;18 years of age, diagnosed clinically and radiologically with CAP were included in the study.CURB-65 and PSI scores were determined in a total of 131 patients on admission. Based on careful clinical assessment and judgment of treating physician 60 patients were admitted in ICU whereas 71 patients were admitted in medical ward. Out of 60 patients admitted in ICU 23 died. The ability to predict ICU admission was almost similar for PSI/PORT score (AUC 0.9605; 95% CI 0.9277-0.9933) and CURB-65 (AUC 0.9694; 95% CI 0.9368-1.9714). The ability to predict mortality was almost similar for PSI/PORT score (AUC 0.9196; 95% CI 0.8670-0.9722) and CURB-65 (AUC 0.9214 95% CI 0.8696-0.9732). CURB-65 score was found almost similar in predicting ICU admission amongst patients coming with CAP to our hospital when compared to PSI/PORT score and when predicting mortality in patients with community-acquired pneumonia, CURB-65 and PSI/PORT score appear to have comparable specificity and sensitivity. Considering the simplicity, CURB-65 score could be preferred over PSI/PORT score. However, larger studies are required to know the exact Indian scenario. Despite of recent advances in the field of medical science the mortality due to CAP has remained relatively the same. Therefore, early diagnosis and admission to ICU with standard treatment play a pivot role in management of patients with CAP. Two scoring system, PSI/PORT score and CURB-65 score, are widely being used to differentiate between low risk and high risk patients. We undertook this study for comparing the predictive value of these two scores for ICU admission and mortality.

Patterns and outcomes of patients with abdominal injury: a multicenter study from Iran

BackgroundInjury is one of the leading causes of death worldwide, and the abdomen is the most common area of trauma after the head and extremities. Abdominal injury is often divided into two categories: blunt and penetrating injuries. This study aims to determine the epidemiological and clinical characteristics of these two types of abdominal injuries in patients registered with the National Trauma Registry of Iran (NTRI).MethodsThis multicenter cross-sectional study was conducted with data from the NTRI from July 24, 2016, to May 21, 2023. All abdominal trauma patients defined by the International Classification of Diseases; 10th Revision (ICD-10) codes were enrolled in this study. The inclusion criteria were one of the following: hospital length of stay (LOS) of more than 24 h, fatal injuries, and trauma patients transferred from the ICU of other hospitals.ResultsAmong 532 patients with abdominal injuries, 420 (78.9%) had a blunt injury, and 435 (81.7%) of the victims were men. The most injured organs in blunt trauma were the spleen, with 200 (47.6%) and the liver, with 171 (40.7%) cases, respectively. Also, the colon and small intestine, with 42 (37.5%) cases, had the highest number of injuries in penetrating injuries. Blood was transfused in 103 (23.5%) of blunt injured victims and 17 (15.2%) of penetrating traumas (p = 0.03). ICU admission was significantly varied between the two groups, with 266 (63.6%) patients in the blunt group and 47 (42%) in penetrating (p < 0.001). Negative laparotomies were 21 (28%) in penetrating trauma and only 11 (7.7%) in blunt group (p < 0.001). In the multiple logistic regression model after adjusting, ISS ≥ 16 increased the chance of ICU admission 3.13 times relative to the ISS 1–8 [OR: 3.13, 95% CI (1.56 to 6.28), P = 0.001]. Another predictor was NOM, which increased ICU chance 1.75 times more than OM [OR: 1.75, 95% CI (1.17 to 2.61), p = 0.006]. Additionally, GCS 3–8 had 5.43 times more ICU admission odds than the GCS 13–15 [OR:5.43, 95%CI (1.81 to 16.25), P = 0.002] respectively.ConclusionThis study found that the liver and spleen are mostly damaged in blunt injuries. Also, in most cases of penetrating injuries, the colon and small intestine had the highest frequency of injuries compared to other organs. Blunt abdominal injuries caused more blood transfusions and ICU admissions. Higher ISS, lower GCS, and NOM were predictors of ICU admission in abdominal injury victims.

GDF-15 Levels and Other Laboratory Findings as Predictors of COVID-19 Severity and Mortality: A Pilot Study.

Growth differentiation factor 15 (GDF-15) is a stress-induced cytokine associated with acute and chronic inflammatory states. This prospective observational study aimed to investigate the prognostic roles of GDF-15 and routine clinical laboratory parameters in COVID-19 patients. Upon the admission of 95 adult hospitalized COVID-19 patients in Croatia, blood analysis was performed, and medical data were collected. The patients were categorized based on survival, ICU admission, and hospitalization duration. Logistic regression and ROC curve methods were employed for the statistical analysis. Logistic regression revealed two independent predictors of negative outcomes: CURB-65 score (OR = 2.55) and LDH (OR = 1.005); one predictor of ICU admission: LDH (OR = 1.004); and one predictor of prolonged hospitalization: the need for a high-flow nasal cannula (HFNC) upon admission (OR = 4.75). The ROC curve showed diagnostic indicators of negative outcomes: age, CURB-65 score, LDH, and GDF-15. The largest area under the curve (AUC = 0.767, specificity = 65.6, sensitivity = 83.9) was represented by GDF-15, with a cutoff value of 3528 pg/mL. For ICU admission, significant diagnostic indicators were LDH, CRP, and IL-6. Significant diagnostic indicators of prolonged hospitalization were CK, GGT, and oxygenation with an HFNC upon admission. This study reaffirms the significance of the commonly used laboratory parameters and clinical scores in evaluating COVID-19. Additionally, it introduces the potential for a new diagnostic approach and research concerning GDF-15 levels in this widespread disease.

Predictors to Intensive Care Unit admission among patient with coronavirus disease in Sukraraj Tropical and Infectious Disease Hospital, Nepal: A case-control study.

The clinical features of COVID-19 are vary widely, ranging from asymptomatic states or mild upper respiratory tract infections to severe pneumonia. Previous studies have shown that 20.0% of COVID-19 patients are hospitalized, out of which 10.0-20.0% are admitted to the Intensive Care Unit. The present study aims to assess predictors associated with COVID-19 leading to Intensive Care Unit admission among reverse transcriptase- polymerase chain reaction (RT-PCR) positive patients in Sukraraj Tropical and infectious disease hospital, Nepal. A case-control study was conducted from June 2022 to July 2022 among patients admitted to Sukraraj Tropical and Infectious Disease Hospital. A hospital-based age (± 2 years) and sex-matched case-control study design were adopted in which ICU admitted (case group, n = 33) and general ward admitted (control group, n = 66) were included. Data were collected using a structured questionnaire comprising of socio-demographic, clinical, and preventive predictors. Data were analyzed using the Statistical Package for Social Science version 11.5. The Chi-square test and conditional logistic regression to determine the predictors associated with ICU admission. High blood pressure, high C-reactive protein and poor application of preventive practices were found to be the predictors of ICU admission. Conditional logistics regression analyses revealed that independent risk factors associated with ICU admission were elevated blood pressure (AOR = 2.22; 95% CI 1.05-4.71, p = 0.015) and abnormal C-Reactive Protein (AOR = 2.92; 95% CI 1.24-6.84, p = 0.012) at the time of hospital admission were more likely to get admitted to ICU. Likewise, patients with poor preventive practice (AOR = 3.34; 95% CI 1.19-9.31, p = 0.02) more likely to get admitted to ICU than patient with good preventive practices.These research findings hold potential significance for facilitating early triage and risk assessment in COVID-19 patients.