Predictors Of Admission Research Articles

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&2, January-July 2024: 60-65

Predictors of heart failure hospitalization after surgery in patients with primary mitral regurgitation: the relevance of left atrial coupling index

Abstract Background and Aims The main indications for surgery in patients with primary mitral regurgitation (PMR) include the presence of symptoms, left ventricular dysfunction (LVEF ≤60%) and dilation (LVESD ≥ 40 mm), but novel markers are needed to detect early myocardial damage. Our aim was to explore the impact of echocardiographic parameters on the incidence of heart failure (HF) hospitalizations after surgery in our cohort of patients with PMR. Methods Retrospective analysis of a prospective cohort of patients with PMR who underwent mitral valve surgery in a tertiary care center from 2014 to 2022. Echocardiographic parameters measured prior to surgery were analyzed. The primary outcome was HF admissions during follow-up. Results 349 patients were included. The mean age was 69 (12) years and 55.9% were male. During a mean follow up of 3.28 (2) years, 13 patients (3.7%) were admitted due to HF. Variables significantly associated with HF admissions were: female gender, atrial fibrillation prior to surgery, LVEF, LV end-diastolic volume index, LV global longitudinal strain, left atrial coupling index [LACI] (end-diastolic left atrial volume/end-diastolic LV volume), TAPSE and right ventricular-arterial coupling. In a multivariable logistic regression analysis, the only independent predictors of HF admissions were female gender (OR 5.68), LVEF (OR 0.93) and LACI (1.87) (p<0.05). Conclusions In our cohort of patients with PMR, apart from LVEF, LACI was the only echocardiographic parameter independently associated with HF hospitalizations after surgery. This easy to obtain echocardiographic parameter, which takes into account the interrelation between LA and LV compliance, could help guide patients’ management in this setting.Table 1

Exploring factors associated with domestic violence among pregnant women in Pakistan and its implications for maternal and perinatal health

ABSTRACT Domestic violence during pregnancy is a critical health issue due to its significant implications for both maternal and fetal well-being. Women in South Asian countries are particularly at a higher risk of experiencing domestic violence. The objective of this study was to identify factors associated with domestic violence among pregnant women attending tertiary care antenatal services in Pakistan. The secondary aim was to evaluate the impact of domestic violence on maternal and perinatal outcomes. The study participants were 681 pregnant women registered for antenatal care and delivery at a tertiary care hospital in Islamabad, Pakistan. Data were collected through in-person surveys and medical chart reviews. Associations between domestic violence and study variables were examined by logistic regression analysis. Among the study participants, 31.4%experienced some form of domestic violence during pregnancy, with psychological abuse (16.4%) being the most prevalent followed by sexual abuse (9.5%) and physical violence (1.8%). Women whose husbands were unemployed were three times more likely to experience domestic violence (aOR; 3.3, 95%CI: 1.6, 7.0). Similarly, husband’s substance abuse was significantly associated with domestic violence during pregnancy (aOR: 120.8, 95%CI: 57.9, 252.2). Domestic violence was a significant predictor of adverse maternal outcomes, including anemia, preterm labor, and recurrent abdominal pain. Similarly, domestic violence was a significant predictor of NICU admission (aOR: 20.6, 95%CI: 12.6, 33.8) and low birthweight (aOR: 24.4, 95%CI: 10.2, 58.4). The study findings emphasize the urgent need for comprehensive interventions targeting domestic violence among women and preventing its cascading effects on pregnancies.

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.

Identification of factors predicting low-risk febrile neutropenia admissions in adults with acute myeloid leukemia

AbstractFebrile neutropenia (FN) is the most common reason for hospital readmission after chemotherapy for acute myeloid leukemia (AML) and is a major driver of health care resource utilization. Although FN risk models exist, they have largely been developed and validated for solid tumors. We therefore examined whether baseline characteristics could predict which patients with AML and FN have a lower risk of progression to severe illness. We identified adults with high-grade myeloid neoplasms (≥10% blasts in the blood/marrow) who received intensive chemotherapy and who were admitted for FN between 2016 and 2023. We collected baseline clinical and disease variables. Outcomes were: infections identified, hospital length of stay (LOS), intensive care unit (ICU) admission, and survival. A lower-risk (LR) outcome was defined as LOS <72 hours without ICU admission or inpatient death. Univariate and multivariable (MV) logistic regression models were used to assess covariate associations with outcomes. We identified 397 FN admissions in 248 patients (median age, 61; [range, 29-77] years). The median hospital LOS was 6 days (range, 1-56) days; 10% required ICU admission, and 3.5% died inpatients. Only 15% of admissions were LR. Infection was identified in 59% of admissions. Physiologic parameters, including heart rate, blood pressure, and fever height, were the best predictors of LR admission and infection. We developed MV models to predict LR admission and infection with area under the curve (AUC) of 0.82 and 0.72, respectively. Established FN and critical illness models were not predictive of outcomes in AML, and we could not identify a LR group; thus, an AML-specific FN risk model requires further development and validation.

Predictors of Care Home Admission and Survival Rate in Patients With Syndromes Associated With Frontotemporal Lobar Degeneration in Europe.

Data on care home admission and survival rates of patients with syndromes associated with frontotemporal lobar degeneration (FTLD) are limited. However, their estimation is essential to plan trials and assess the efficacy of intervention. Population-based registers provide unique samples for this estimate. The aim of this study was to assess care home admission rate, survival rate, and their predictors in incident patients with FTLD-associated syndromes from the European FRONTIERS register-based study. We conducted a prospective longitudinal multinational observational registry study, considering incident patients with FTLD-associated syndromes diagnosed between June 1, 2018, and May 31, 2019, and followed for up to 5 years till May 31, 2023. We enrolled patients fulfilling diagnosis of the behavioral variant frontotemporal dementia (bvFTD), primary progressive aphasia (PPA), progressive supranuclear palsy (PSP) or corticobasal syndrome (CBS), and FTD with motor neuron disease (FTD-MND). Kaplan-Meier analysis and Cox multivariable regression models were used to assess care home admission and survival rates. The survival probability score (SPS) was computed based on independent predictors of survivorship. A total of 266 incident patients with FTLD were included (mean age ± SD = 66.7 ± 9.0; female = 41.4%). The median care home admission rate was 97 months (95% CIs 86-98) from disease onset and 57 months (95% CIs 56-58) from diagnosis. The median survival was 90 months (95% CIs 77-97) from disease onset and 49 months (95% CIs 44-58) from diagnosis. Survival from diagnosis was shorter in FTD-MND (hazard ratio [HR] 4.59, 95% CIs 2.49-8.76, p < 0.001) and PSP/CBS (HR 1.56, 95% CIs 1.01-2.42, p = 0.044) compared with bvFTD; no differences between PPA and bvFTD were found. The SPS proved high accuracy in predicting 1-year survival probability (area under the receiver operating characteristic curve = 0.789, 95% CIs 0.69-0.87), when defined by age, European area of residency, extrapyramidal symptoms, and MND at diagnosis. In FTLD-associated syndromes, survival rates differ according to clinical features and geography. The SPS was able to predict prognosis at individual patient level with an accuracy of ∼80% and may help to improve patient stratification in clinical trials. Future confirmatory studies considering different populations are needed.

D-dimer as a Predictor of ICU Admission and Mortality in COVID-19 Patients: Insights From a Two-Year Retrospective Study From a Tertiary Care Center in South India

D-dimer as a Predictor of ICU Admission and Mortality in COVID-19 Patients: Insights From a Two-Year Retrospective Study From a Tertiary Care Center in South India

Prognostic role of persistent angina after percutaneous revascularization in chronic coronary syndrome with altered angiography and stress CMR

Introduction and objectivesIn patients with established chronic coronary syndrome (CCS), the significance of persistent angina is controversial. We aimed to evaluate the prognostic role of persistent angina in symptomatic CCS patients with abnormal stress cardiovascular magnetic resonance (CMR) and altered angiographic findings undergoing percutaneous revascularization. MethodsWe analyzed 334 CCS patients with Canadian Cardiovascular Society angina class ≥2, perfusion deficits on stress CMR and severe lesions in angiography who underwent medical therapy optimization plus CMR-guided percutaneous revascularization. We investigated the association of persistent angina at 6 months postintervention with subsequent cardiac death, myocardial infarction, and hospital admission. ResultsAll patients had angina class ≥2 (mean: 2.8±0.7), abnormal stress CMR (mean ischemic burden: 5.8±2.7 segments), and severe angiographic lesions. The angina resolution rates were 81% at 6 months, and 81%, 81%, and 77% at 1, 2, and 5 years, respectively. During a median follow-up of 8.9 years, persistent angina was independently associated with higher rates of subsequent cardiac death (13% vs 4%; HR, 3.7; 95%CI, 1.5-9.2; P=.005), myocardial infarction (24% vs 6%; HR, 4.9; 95%CI, 2.4–9.9; P<.001), and hospital admission for heart failure (27% vs 13%; HR, 2.7; 95%CI, 1.5–5.2; P=.001). ConclusionsIn CCS patients with robust diagnostic evidence from symptoms, stress CMR, and angiography, persistent angina after percutaneous revascularization is a strong predictor of subsequent cardiac death, myocardial infarction, and hospital admission for heart failure.

Patients with influenza admitted to a tertiary-care hospital in Riyadh between 2018 and 2022: characteristics, outcomes and factors associated with ICU admission and mortality

BackgroundInfluenza is a common cause of hospital admissions globally with regional variations in epidemiology and clinical profile. We evaluated the characteristics and outcomes of patients with influenza admitted to a tertiary-care center in Riyadh, Saudi Arabia.MethodsThis was a retrospective cohort of adult patients admitted with polymerase chain reaction-confirmed influenza to King Abdulaziz Medical City-Riyadh between January 1, 2018, and May 31, 2022. We compared patients who required intensive care unit (ICU) admission to those who did not and performed multivariable logistic regression to assess the predictors of ICU admission and hospital mortality.ResultsDuring the study period, 675 adult patients were hospitalized with influenza (median age 68.0 years, females 53.8%, hypertension 59.9%, diabetes 55.1%, and chronic respiratory disease 31.1%). Most admissions (83.0%) were in the colder months (October to March) in Riyadh with inter-seasonal cases even in the summertime (June to August). Influenza A was responsible for 79.0% of cases, with H3N2 and H1N1 subtypes commonly circulating in the study period. Respiratory viral coinfection occurred in 12 patients (1.8%) and bacterial coinfection in 42 patients (17.4%). 151 patients (22.4%) required ICU admission, of which 62.3% received vasopressors and 48.0% mechanical ventilation. Risk factors for ICU admission were younger age, hypertension, bilateral lung infiltrates on chest X-ray, and Pneumonia Severity Index. The overall hospital mortality was 7.4% (22.5% for ICU patients, p < 0.0001). Mortality was 45.0% in patients with bacterial coinfection, 30.9% in those requiring vasopressors, and 29.2% in those who received mechanical ventilation. Female sex (odds ratio [OR], 2.096; 95% confidence interval [CI] 1.070, 4.104), ischemic heart disease (OR, 3.053; 95% CI 1.457, 6.394), immunosuppressed state (OR, 7.102; 95% CI 1.803, 27.975), Pneumonia Severity Index (OR, 1.029; 95% CI, 1.017, 1.041), leukocyte count and serum lactate level (OR, 1.394; 95% CI, 1.163, 1.671) were independently associated with hospital mortality.ConclusionsInfluenza followed a seasonal pattern in Saudi Arabia, with H3N2 and H1N1 being the predominant circulating strains during the study period. ICU admission was required for > 20%. Female sex, high Pneumonia Severity Index, ischemic heart disease, and immunosuppressed state were associated with increased mortality.

Predictors of high hospital admission and criminal reoffending in adults experiencing homelessness: a retrospective cohort study

ObjectivesTo examine the costs and predictors of high hospital use and criminal reoffending in adults experiencing homelessness. ApproachRetrospective cohort study of 2,140 adults who attended homeless hostel clinics in New South Wales, Australia using linked clinic, health, criminal offence, and mortality data from 1 July 2008 to 30 June 2021. Multivariable logistic regression examined predictors of high all-cause hospital use (≥4 emergency department presentations in 12 months) and predictors of recidivism. ResultsThere were 27,466 hospitalisations at a total cost of AUD$548.2 million. Twenty-seven percent of the cohort had high hospital use. Factors associated with high use were age ≥45 years, female (AOR: 1.52; 95%CI 1.05-2.22), presence of a mental disorder, substance use disorder (AOR: 1.36; 95%CI: 1.03-1.82), or being homeless for &gt;1 year (AOR: 1.31; 95%CI: 1.06-1.62). There were 1,646 (76.9%) adults who had contact with the criminal justice system. Most (75.3%) reoffended within 24 months after index offence, and reoffenders were more likely to be younger, have a personality disorder (AOR: 1.31; 95% CI: 1.04-1.67), substance use disorder (AOR: 1.60; 95%CI 1.14-2.23), and had a previous charge dismissed on mental health grounds (AOR: 1.79; 95% CI: 1.31-2.46). Reoffenders more likely to have a principal theft-related offence (AOR: 1.85; 95%CI: 1.29-2.66). Total court finalisation costs were AUD$11.3 million. ConclusionsThe high use of hospital and criminal justice services lends support to strategies to develop models of supported housing with a focus on integrated care, mental health support, improved referral pathways, and better coordination with community-based support agencies.

Prediction of medical admissions after psychiatric inpatient hospitalization in bipolar disorder: a retrospective cohort study.

Bipolar Disorder (BD) is a severe mental illness associated with high rates of general medical comorbidity, reduced life expectancy, and premature mortality. Although BD has been associated with high medical hospitalization, the factors that contribute to this risk remain largely unexplored. We used baseline medical and psychiatric records to develop a supervised machine learning model to predict general medical admissions after discharge from psychiatric hospitalization. In this retrospective three-year cohort study of 71 patients diagnosed with BD (mean age=52.19 years, females=56.33%), lasso regression models combining medical and psychiatric records, as well as those using them separately, were fitted and their predictive power was estimated using a leave-one-out cross-validation procedure. The proportion of medical admissions in patients with BD was higher compared with age- and sex-matched hospitalizations in the same region (25.4% vs. 8.48%). The lasso model fairly accurately predicted the outcome (area under the curve [AUC]=69.5%, 95%C.I.=55-84.1; sensitivity=61.1%, specificity=75.5%, balanced accuracy=68.3%). Notably, pre-existing cardiovascular, neurological, or osteomuscular diseases collectively accounted for more than 90% of the influence on the model. The accuracy of the model based on medical records was slightly inferior (AUC=68.7%, 95%C.I. = 54.6-82.9), while that of the model based on psychiatric records only was below chance (AUC=61.8%, 95%C.I.=46.2-77.4). Our findings support the need to monitor medical comorbidities during clinical decision-making to tailor and implement effective preventive measures in people with BD. Further research with larger sample sizes and prospective cohorts is warranted to replicate these findings and validate the predictive model.

541. THE PHOENIX-HH SCORE: A TOOL TO PREDICT ADMISSION TO THE ICU AFTER HIATAL HERNIA REPAIR

Abstract Background Minimally invasive hiatal hernia (HH) repair is usually a safe procedure with low morbidity; however, a subset of patients may require intensive care unit (ICU) admission. To date, no specific and validated tool is available to predict the risk of ICU admission after HH repair. Although multiple tools to predict the risk of admission to the ICU after major surgical procedures have been reported, most of them are complex and require laboratory parameters. We propose a novel and quick intraoperative score that is useful for assessing unexpected ICU admission and tailoring patient management during HH repair. Methods A development cohort was established using a prospectively maintained database containing demographic, clinical, and surgical information of consecutive patients who underwent HH repair between September 2016 and July 2023. Clinical expertise and multiple logistic regressions were employed to select adequate demographic or surgical predictors to create an intuitive risk score (PHOENIX-HH). Further, a specific scoring system for each included variable was designated, and the total score (a scale from 0 to 9 points) was assessed for accuracy using the area under the receiver operating characteristic curve (AUC-ROC). Results The development cohort was formed by 391 patients who underwent primary HH repair (73.7% women; mean age 64.4±12.5 years; mean BMI 28.9±4.9 kg/m2). The mean AUC in the development cohort was 0.845 (95%CI: 0.756-0.934), and when defining a cut-off of ≥6 points, 12 of 15 patients who required ICU admission were identified using the proposed score (sensitivity, 80% [95%CI: 51.9-95.7%]; specificity, 80.1% [95%CI: 75.7-84%]; positive likelihood ratio, 4.0 [95%CI: 2.9-5.6]; negative predictive value, 99.0% [95%CI: 97.3-99.6%]). Conclusion The strongest predictor for unexpected ICU admission and postoperative complications following HH repair was the HH size (defined as the percentage of intrathoracic stomach); hence, the incorporation of this variable and other preoperative and intraoperative factors into a scoring system led to the development of the PHOENIX-HH score. This tool may allow early identification of patients who may require admission to the ICU after the surgery. Future validation using an external cohort is highly desirable.

Physician performance scores used to predict emergency department admission numbers and excessive admissions burden

BackgroundOvercrowding in hospitals is associated with a panoply of adverse events. Inappropriate decisions in the emergency department (ED) contribute to overcrowding. The performance of individual physicians as part of the...

Serum Anion Gap at Admission Predicts All-Cause Mortality in Critically Ill Patients With Cirrhosis: A Retrospective Cohort Study.

The primary objective of this study was to evaluate admission serum anion gap (AG) as a predictor of all-cause mortality in critically ill patients with cirrhosis. A total of 3,084 cirrhotic patients were included and randomly divided into training and validation cohorts (n = 2,159 and 925, respectively). Patients were categorized into high and normal AG groups based on their AG values. Cox regression and Kaplan-Meier survival analysis were used to assess the relationships between AG levels and outcomes. Both cohorts showed strong parameter similarity ( P > 0.05). High AG was associated with significantly lower survival probabilities. Cox models confirmed elevated AG as a risk factor, even after adjusting for covariates (hazard ratio: 1.920, 1.793, and 1.764 for 30-day, 60-day, and hospital mortality, respectively). Subgroup analyses, especially regarding chronic kidney disease, revealed complex interactions. Serum AG displayed predictive power comparable with established scoring systems. Elevated AG at admission is a valuable predictor of poor outcomes and increased mortality risk in critically ill cirrhotic patients. Serum AG can serve as an easily accessible tool for risk assessment and prognosis evaluation in this population.

Hematocrit and Albumin Levels at Admission Predict in-Hospital Mortality in Pediatric COVID-19 Omicron Variant Patients.

The Omicron variant is the present predominant COVID-19 strain worldwide. Accurate mortality prediction can facilitate risk stratification and targeted therapies. The study aimed to evaluate the feasibility of the difference in hematocrit and albumin (HCT-ALB) levels, alone or combined with the pediatric Sequential Organ Failure Assessment (pSOFA) score and lactate level, to predict the in-hospital mortality of COVID-19 Omicron variant-infected pediatric patients. A multicenter retrospective cohort study was performed for children with COVID-19 Omicron variant infection between December 2021 and January 2022. The demographics, clinical characteristics, hospital admission laboratory test results, and treatments were recorded. The in-hospital mortality was documented. The associations between HCT-ALB levels and mortality, and between HCT-ALB+pSOFA+lactate and mortality were analyzed. A total of 119 children were included. The median age was 1.6 (interquartile range: 0.5-6.2) years old. There were 70 boys and 49 girls. The mortality rate was 14.3% (17/119). The univariate and multivariate Cox regression analysis revealed that HCT-ALB was associated to in-hospital mortality (hazard ratio: 1.500, 95% confidence interval: 1.235-1.822, p<0.001). The receiver operating characteristic curve analysis revealed that HCT-ALB can be used to accurately predict in-hospital mortality at a cut-off value of -0.7 (area under the curve: 0.888, sensitivity: 0.882, specificity: 0.225, Youden index: 0.657, p<0.001). These patients were assigned into three groups based on the HCT-ALB level, pSOFA score, and lactate level (low-, medium-, and high-risk groups). The Kaplan-Meier analysis revealed that the mortality increased in the high-risk group, when compared to the medium-risk group (p<0.01). The latter group had a higher mortality, when compared to the low-risk group (p<0.01). The HCT-ALB level can be applied to predict the in-hospital mortality of children infected with the COVID-19 Omicron variant. Its combination with other variables can improve prediction performance.

Prediction of intensive care unit admission using machine learning in patients with odontogenic infection.

This study aimed to develop and validate a model to predict the need for intensive care unit (ICU) admission in patients with dental infections using an automated machine learning (ML) program called H2O-AutoML. Two models were created using only the information available at the initial examination. Model 1 was parameterized with only clinical symptoms and blood tests, excluding contrast-enhanced multi-detector computed tomography (MDCT) images available at the initial visit, whereas model 2 was created with the addition of the MDCT information to the model 1 parameters. Although model 2 was expected to be superior to model 1, we wanted to independently determine this conclusion. A total of 210 patients who visited the Department of Oral and Maxillofacial Surgery at the Dankook University Dental Hospital from March 2013 to August 2023 was included in this study. The patients' demographic characteristics (sex, age, and place of residence), systemic factors (hypertension, diabetes mellitus [DM], kidney disease, liver disease, heart disease, anticoagulation therapy, and osteoporosis), local factors (smoking status, site of infection, postoperative wound infection, dysphagia, odynophagia, and trismus), and factors known from initial blood tests were obtained from their medical charts and retrospectively reviewed. The generalized linear model algorithm provided the best diagnostic accuracy, with an area under the receiver operating characteristic values of 0.8289 in model 1 and 0.8415 in model 2. In both models, the C-reactive protein level was the most important variable, followed by DM. This study provides unprecedented data on the use of ML for successful prediction of ICU admission based on initial examination results. These findings will considerably contribute to the development of the field of dentistry, especially oral and maxillofacial surgery.

GLIM-defined malnutrition in patients with acute abdomen associated with poor prognosis and increased economic burden: A cross-sectional study.

The Global Leadership Initiative on Malnutrition (GLIM) criteria have rapidly developed into a principal methodological framework for nutrition diagnosis. However, the applicability of the GLIM criteria in patients with acute abdomen has not been validated. This is a cross-sectional study conducted on patients diagnosed with acute abdomen and admitted to a tertiary hospital in southwest China. Nutrition risk screening was conducted using the Nutrition Risk Screening 2002, and patients identified with nutrition risk were assessed for malnutrition based on the GLIM criteria. We enrolled a total of 440 patients with acute abdomen. The top three diagnoses of acute abdomen were intestinal obstruction (47.2%), acute appendicitis (23.1%), and digestive system perforation (8.8%). The prevalence of nutrition risk was 46.5%, with a malnutrition rate of 32.5% based on the GLIM. Patients with malnutrition according to the GLIM showed significantly higher rates of intensive care unit (ICU) admission (13.28% vs 7.07%; P = 0.003), increased hospitalization costs (median: 3315USD [interquartile range (IQR): 978-7852] vs 1641 [IQR: 816-3523] USD; P < 0.001), and longer length of hospital stay (LOS) (median: 8 [IQR: 5-13] vs 6 [IQR: 4-8] days; P < 0.001) compared with patients without malnutrition. Multivariate analysis indicated that GLIM-defined malnutrition was an independent predictor of hospitalization costs, and severe malnutrition was an independent predictor of ICU admission. GLIM criteria are applicable for diagnosing malnutrition in patients with acute abdomen. The prevalence of malnutrition was high in patients with acute abdomen. Malnutrition was associated with increased ICU admission and LOS, along with higher economic burden.

Characteristics of paediatric injuries as predictors of 24-hour disposition from the Emergency Department of a teaching hospital in Southwestern Uganda

BackgroundPaediatric injuries are among the leading causes of morbidity and mortality globally, especially in low- and middle-income countries. We aimed to characterize paediatric injuries as predictors of disposition from Mbarara Regional Referral Hospital Emergency Department (ED) Southwestern Uganda. MethodsThis was a prospective cohort study done from 12th December 2022 to 31st March 2023. We described the characteristics of injuries sustained by children and evaluated the predictors of 24-hour disposition from the ED using logistic regression. ResultsOf the 160 children followed up, 64.4% were male with a median age of 7 years, brought in with road traffic accidents (RTAs) (40.6%) and falls (35.6%) as the commonest mechanism of injury. Over half of the patients were triaged as yellow (urgent); polytrauma and head injuries were the top injury patterns. The majority (45.6%) of the children were admitted to the inpatient surgical ward. Only 1.9% and 5.0% ended up in intensive care unit (ICU) and died (to mortuary), respectively. The median time to disposition was 8 h and 14% stayed in the ED beyond 24-hours. Patients who needed more intensive initial treatment, including additional medications or interventions, were significantly more likely to be admitted to the ward (AOR= 5.3, 95%CI: 2.0-13.0, p <0.01). ConclusionPaediatric injuries were caused mainly by RTAs and presenting with polytrauma and head injuries. Most patients were disposed of to the inpatient surgical ward within 24 h with severe KTS and initial management being strongest predictors of admission. These findings can be used to tailor quick risk stratification and decision-making tools and improve ED disposition of paediatric injuries in Low- and Middle- income countries.

Utilizing injury severity score, Glasgow coma scale, and revised trauma score for trauma-related in-hospital mortality and ICU admission prediction; originated from 7-year results of a nationwide multicenter registry

Objective: During the past few decades, many scoring systems have been developed to evaluate the severity of injury and predict the outcome in trauma patients. This study aimed to assess the capacity of three common trauma scoring systems: injury severity score (ISS), Glasgow coma scale (GCS), and revised trauma score (RTS) in predicting in-hospital mortality and ICU admission in patients with traumatic injury. Methods: This is a multicenter study of the hospital-based national trauma registry of Iran (NTRI), an ongoing registry-based trauma database. This study included trauma cases from 12 major trauma centers throughout the country admitted between July 2016 and November 2023. The inclusion criteria were all patients admitted to the emergency department due to trauma, hospitalized for at least 24 hours, deceased within the first 24 hours of admission, and patients transferred from the intensive care unit (ICU)s of other hospitals. Results: A total of 50,458 traumatic patients, with 38,740 (76.9%) being male, were included in this study. After adjustment for confounders, head, face, and neck injuries were associated with the highest odds of death (OR: 7.51, P-value&lt;0.001), whereas abdominal injuries were associated with the highest odds of ICU admission (OR: 4.58, P-value&lt;0.001). Each Unit increase in RTS score was accompanied by a 61% decrease in odds of death (OR: 0.39, P-value&lt;0.001). The area under the ROC curve for predicting in-hospital mortality was 0.81 (0.79 to 0.82) in ISS, 0.78 (0.77 to 0.80) in GCS, and 0.75 (0.73 to 0.76) in RTS. There was a significant difference between RTS and GCS, as well as RTS and ISS for in-hospital mortality prediction (P-values&lt; 0.001). The area under the ROC curve for the prediction of ICU admission was 0.75 (0.74 to 0.75) in ISS, 0.63 (0.62 to 0.63) in GCS, and 0.62 (0.61 to 0.63) in RTS. There was a statistically significant difference between ISS and GCS, as well as ISS and RTS, for ICU admission prediction (P-value&lt;0.001). Conclusion: ISS is the best predictor of in-hospital mortality and ICU admission, compared to GCS and RTS.

Causes and predictors of recurrent unplanned hospital admissions in heart failure patients: a cohort study.

Despite progress in therapy, heart failure (HF) inflicts a heavy burden of hospital admissions. In this study, we identified among 1360 community-dwelling HF patients (mean age 70.7 ± 11.3 years, 72.5% men) subgroups sharing similar profiles of unplanned hospital admissions, based on the admission causes and frequency of each cause. Hospital discharge summaries were reviewed for the main admission cause. Patient subgroups were identified via cluster analysis. We investigated baseline predictors associated with these subgroups, using multinomial logistic models. During 3421 patient-years, there were 5192 hospital admissions, of which 4252 (82%) were unplanned. We identified five patient subgroups (clusters 1-5) with distinctive hospitalization profiles. HF accounted for approximately one-third of admissions in the first patient cluster (23% of the patient sample). In contrast, patients in the second cluster (39% of the patient sample) were hospitalized for various reasons, with no single prominent admission cause identified. The other three clusters, comprising 16% of the patient sample, accounted for 42% of all unplanned hospitalizations. While patients in the third cluster were hospitalized mainly due to ischemic heart disease and arrhythmia, patients in the fourth and fifth clusters shared a high burden of recurrent HF admissions. The five patient clusters differed by baseline predictors, including age, functional capacity, comorbidity burden, hemoglobin, and cause of HF. HF patients differ significantly in the causes and overall burden of unplanned hospitalizations. The patient subgroups identified and predictors for these subgroups may guide personalized interventions to reduce the burden of unplanned hospitalizations among HF patients. Trial registration: ClinicalTrials.gov, NCT00533013. Registered 20 September 2007. https://clinicaltrials.gov/study/NCT00533013 .