Predictors Of 30-day Mortality Research Articles

ABC score is a better predictor for 30-day mortality in upper gastrointestinal bleeding: A prospective single-center study.

Acute upper gastrointestinal bleeding (UGIB) still has a mortality rate of about 10%. Several pre-endoscopy scoring systems have been developed to predict the outcome, but none accurately predict mortality. The present study was aimed at comparing the new ABC score (age, blood tests and comorbidities) with other pre-existing scoring systems to predict mortality. This prospective single-center study was done at a tertiary hospital in India in 2022-2023. Patients > 18years presenting with UGIB within 48hours were included in the study. They were divided into variceal and non-variceal UGIB cohorts and were followed for 30days after receiving standard-of-care treatment. Out of 296 patients, 168 (56.7%) had variceal (V) bleed, while 128 (43.2%) individuals had a non-variceal (NV) type of GI bleed. The mortality rate was 9.8% (n = 29), which was higher among the V bleed group compared to the NV bleed group (8.7% vs. 1.1%). The area under the receiver operating characteristics(AUROC) for ABC score was the highest (0.75) compared to other scoring systems and was also more significant among deaths related to V bleed (0.76) than NV bleed (0.64). Hypoalbuminemia and > 3 blood transfusions are significant factors in predicting mortality. Our study demonstrates that the ABC score is superior to other scores in predicting 30-day mortality in patients with UGIB. ABC score may be a better predictor of mortality among V bleed patients than NV bleeds.

Risk factors and comorbidities associated with cardiac arrests and medical emergencies in interventional radiology patients

PurposeTo investigate the incidence, predictors, and outcomes of medical emergencies in patients undergoing IR procedures at a tertiary care center.Materials and methodsSeven-year retrospective review of all medical emergencies in patients undergoing IR procedures at King Abdulaziz Medical City, Riyadh, Saudi Arabia. Medical emergencies included Cardiopulmonary arrest (CPA), or emergencies that required activation of the critical care response team (CCRT). Variables included demographics, procedure details and outcome data including complications and 30-day mortality. Multivariate logistic regression analysis was conducted to identify independent predictors of CPA and 30-day mortality.ResultsNinety-four patients (50% male) were included with a median age of 60.5 years. Recent or current ICU admission was recorded in 39 patients (43.8%). Comorbidities included diabetes (50%), hypertension (59.6%), coronary artery disease (25.5%), heart failure (21.5%), ESRD (28.7%), active infection 28 (31%), with ASA3 in 64 patients (68%) and ASA4 in 23 (24.5%).The incidence of CPA and CCRT activation was 0.045% and 0.049%, respectively, among 100,000 patients who underwent IR procedures during the study period. Half the events were with venous procedures, followed by non-vascular (33%) and arterial procedures (10.6%). 30-day mortality was 30.5%. Independent predictors of CPA included pulmonary disease (aOR 16.79, 95% CI 2.334–195.3, p = 0.0097), emergency procedures (aOR 11.63, 95% CI 2.517–72.46, p = 0.0035), general anesthesia (aOR 19.41, 95% CI 1.854–491.8, p = 0.0254), and sedation (aOR 13.04, 95% CI 2.081–118.8, p = 0.0108). Predictors of 30-day mortality were CPA (aOR 9.830, 95% CI 2.439–66.66, p = 0.0045) and hypotension as a complication (aOR 16.81, 95% CI 3.766–122.3, p = 0.0009).ConclusionOur findings highlight the complexity of patients undergoing IR procedures and the importance of identifying high-risk patients to prevent adverse events in the IR setting.

Urinary Output as a Predictor of Mortality in Cardiogenic Shock: An Explorative Retrospective Analysis

Background/Objectives: Cardiogenic shock (CS) remains a critical condition with high mortality rates despite advances in treatment. This study aimed to evaluate the prognostic significance of urinary output at various time intervals during CS and its effectiveness as a predictor of 30-day mortality, particularly in comparison to the Simplified Acute Physiology Score 3 (SAPS 3). Methods: We conducted a retrospective analysis of 96 patients diagnosed with CS, assessing urinary output at different intervals (0–6 h, 6–12 h, 12–24 h, and 0–24 h) as potential predictors of 30-day mortality. SAPS 3 was calculated for all patients, and its predictive value was compared to that of urinary output using both univariate and multivariate analyses. Additional analyses included ROC curve assessment and Kaplan–Meier survival analysis. Results: Urinary output at 6–12 h was significantly associated with 30-day mortality in univariate analysis. Area under the receiver operating characteristic curves (AUROCs) for urinary output at 0–6 h, 6–12 h, and 12–24 h was 0.61 (p = 0.07), 0.63 (p = 0.04), and 0.61 (p = 0.08), respectively. These AUROCs did not differ significantly between the three urinary output parameters. Regarding the cumulative urinary output of 0–24 h, the most pronounced impact was observed in patients producing less than 0.5 mL/kg/h. In multivariate analysis, when combined with SAPS 3, the predictive power of urinary output diminished. SAPS 3 alone demonstrated significant predictive value with an AUROC of 0.77 (p < 0.001). Conclusions: While early urinary output is a valuable predictor of 30-day mortality in patients with CS, its prognostic strength is limited when considered alongside comprehensive risk assessments like SAPS 3. These findings suggest that a multifaceted approach, incorporating both early and comprehensive indicators, is essential for accurately predicting outcomes in CS patients.

Diabetes does not increase in-hospital or short-term mortality in patients undergoing surgical repair for type A aortic dissection: insight from the national readmission database

BackgroundPrevious studies have reported a protective effect of type 2 diabetes on the incidence and progression of aortic aneurysms. We investigated whether this protective effect extends to aortic dissections.MethodsData from the US Nationwide Readmission Database (2016–2019) were analyzed. Patients admitted for open surgery repair of acute type A aortic dissection (TAAD) were initially analyzed (index group). Those discharged alive were followed for up to 30 days (readmission group). The co-primary outcomes were in-hospital and 30-day mortality.ResultsBetween 2016 and 2019, 7,324 patients were admitted for open surgical repair of acute TAAD, of whom 965 (13.2%) had diabetes. Patients with diabetes were older and had a higher prevalence of obesity, hypertension, smoking, dyslipidemia, and chronic kidney disease (CKD). 15.2% of patients with diabetes and 14.6% without diabetes died; hence, diabetes did not have a significant impact on in-hospital mortality (adjusted odd ratio [aOR] = 1.02 [0.84–1.24]). Similarly, diabetes was not associated with a higher adjusted risk of atrial fibrillation (aOR = 1.03 [0.89–1.20]), stroke (aOR = 0.83 [0.55–1.26]), cardiogenic shock (aOR = 1.18 [0.98–1.42]), but increased the risk of acute renal failure (aOR = 1.20 [1.04–1.39]). Within 30 days of discharge, 154 (15.9%) patients with diabetes and 952 (15%) from the non-diabetes group were readmitted. Readmitted patients with diabetes were older and had a higher prevalence of cardiovascular comorbidities. We didn’t observe any significant difference in the adjusted risk of 30-day mortality between the diabetes and non-diabetes groups (adjusted hazard ratio [aHR] = 0.81 [0.41–1.60]). However, diabetes was associated with a lower risk of readmission (aHR = 0.81 [0.68–0.97]). Age was the most significant predictor of all outcomes. CKD was the most significant predictor of 30-day mortality, with the risk increasing five-fold in patients with diabetes (HR = 5.58 [2.58–6.62]. Cardiovascular-related conditions were the most common causes of readmission in both groups. However, respiratory-related conditions were more prevalent in the diabetes group compared to the non-diabetes group (19.5% vs. 13%, respectively, p = 0.032).ConclusionsDiabetes does not increase in-hospital or short-term mortality in patients undergoing surgical repair for Type A aortic dissection.Graphical abstract

Impact of Inflammation After Cardiac Surgery on 30-day Mortality and Machine Learning Risk Prediction

Impact of Inflammation After Cardiac Surgery on 30-day Mortality and Machine Learning Risk Prediction

In Reply to the Letter to the Editor Regarding: Predictors of 30-Day Mortality for Surgically Treated Patients with Spontaneous Supratentorial Intracerebral Hemorrhage and Validation of the Surgical Swedish Intracerebral Hemorrhage Score: A Retrospective Single-Center Analysis of 136 Cases

In Reply to the Letter to the Editor Regarding: Predictors of 30-Day Mortality for Surgically Treated Patients with Spontaneous Supratentorial Intracerebral Hemorrhage and Validation of the Surgical Swedish Intracerebral Hemorrhage Score: A Retrospective Single-Center Analysis of 136 Cases

Letter to the Editor Regarding: Predictors of 30-Day Mortality for Surgically Treated Patients with Spontaneous Supratentorial Intracerebral Hemorrhage and Validation of the Surgical Swedish Intracerebral Hemorrhage Score: A Retrospective Single-Center Analysis of 136 Cases

Letter to the Editor Regarding: Predictors of 30-Day Mortality for Surgically Treated Patients with Spontaneous Supratentorial Intracerebral Hemorrhage and Validation of the Surgical Swedish Intracerebral Hemorrhage Score: A Retrospective Single-Center Analysis of 136 Cases

Nutritional status as a predictor of 30-day mortality among intensive care unit patients in sub-Saharan Africa: a prospective cohort study

BackgroundMalnutrition is a major public health issue, causing significant mortality and morbidity, especially in developing nations. However, the magnitude and its impact on clinical outcomes in Intensive Care Unit (ICU) patients need to be investigated better in Sub-Saharan Africa, and this study was intended to address these issues.ObjectiveThe objective of this study was to assess the effect of malnutrition on clinical outcomes among ICU patients.MethodA prospective cohort study was conducted among 436 ICU patients, 218 of whom were malnourished. After obtaining ethical approval, malnourished and well-nourished patients were followed for thirty days to examine the effects of nutritional status on clinical outcomes and its determinants. At admission, nutritional screening and evaluation were performed with Subjective Global Assessment (SGA) and Malnutrition Universal Screening Tool (MUST), and during the next thirty days, it was evaluated every seven days with SGA, MUST, and modified Nutrition Risk in Critically Ill (NUTRIC).ResultThis study demonstrated that the incidence of 30-day mortality was 47.9%( 95% CI: 43.2 to 52.6). The hazards of death in patients with malnutrition increased by 40% as compared to well-nourished patients (aHR = 1.4, 95% CI: 1.33 to 2.56), and patients with diabetes mellitus had 4 times the hazards of death (aHR = 4.2, 95% CI: 2.12 to 8.28).ConclusionMalnutrition is prevalent in adult ICU patients and has been linked to a higher 30-day mortality and a more extended ICU stay. MUST, SGA, and NUTRIC, well-validated, practical, cost-effective, and non-invasive techniques for routinely evaluating nutritional status in critically ill patients, were good predictors of mortality.

Association of albumin to urea nitrogen ratio with 30-day mortality in adult hemophagocytic lymphohistiocytosis: a retrospective cohort study.

Hemophagocytic lymphohistiocytosis (HLH) is a severe disease with a grim prognosis. This study aims to investigate the potential role of albumin to urea nitrogen ratio (AUR) as a predictor of 30-day mortality in adult HLH patients. This retrospective analysis involved patients admitted to the hospital with a first-time diagnosis of HLH between January 2015 and September 2021. The primary outcome was defined as 30-day all-cause mortality. Patients were categorized as survivors and non-survivors, as well as test and validation cohorts. Clinical signs and laboratory biomarkers on admission were picked up. A total of 467 patients were included in the study, with a 30-day mortality rate of 31.0% (n = 145). There were no significant differences observed between the test and validation cohorts. Surviving patients exhibited significantly higher levels of AUR. Multivariate analysis indicated that an AUR < 3.40 was deemed to be an independent risk factor (test cohort: HR: 3.663, P < 0.001; validation cohort: 2.475, P = 0.013; total cohort: 2.976, P < 0.001). The area under the receiver operating characteristic curve (AUC) values were 0.734 in the test cohort, 0.690 in the validation cohort, and 0.711 in the total cohort. AUR emerged as an independent and reliable risk indicator for 30-day mortality in adults with HLH, offering clinicians a tool to identify high-risk patients efficiently.

SCAI cardiogenic shock classification for predicting short-term mortality in acute myocardial infarction

Background: Cardiogenic shock (CS) remains a critical complication of acute myocardial infarction (AMI) with severe outcomes. The Society for Cardiovascular Angiography and Interventions (SCAI) classification is a valuable tool for assessing the severity and progression of CS. Objectives: Our study aimed to evaluate the association between initial SCAI classification and 30-day mortality in AMI patients and assess the prognostic value of SCAI changes within 24 hours post-admission. Methods: All patients with AMI were classified according to the SCAI stages at admission and re-evaluated after 24 hours. 30-day mortality rates were compared across SCAI stages, and Cox regression analysis was used to assess the risk of death based on 24-hour transition SCAI classification. Results: At admission, among 232 AMI patients, 50.8% were classified as SCAI A, 21.6% as SCAI B, 23.3% as SCAI C, and 4.3% as SCAI D/E. The 30-day mortality rates for each classification were 1.7% for SCAI A, 30.0% for SCAI B, 68.5% for SCAI C, and 90% for SCAI D/E. Within 24 hours of admission, 14.5% of patients experienced a worsening SCAI stage, 73.4% remained unchanged SCAI, and 12.1% showed improved SCAI. Patients with a worsening SCAI classification had a significantly higher risk of 30-day mortality, with an adjusted hazard ratio (HR) of 50.4 compared to those with stable SCAI status (p log-rank = 0.00001). Conversely, patients with stable or improved SCAI stages had notably lower mortality rates. Conclusion: The initial SCAI stage at admission and its changes within 24 hours are crucial predictors of 30-day mortality in AMI patients with CS.

The effect of resampling techniques on the performances of machine learning clinical risk prediction models in the setting of severe class imbalance: development and internal validation in a retrospective cohort

PurposeThe availability of population datasets and machine learning techniques heralded a new era of sophisticated prediction models involving a large number of routinely collected variables. However, severe class imbalance in clinical datasets is a major challenge. The aim of this study is to investigate the impact of commonly-used resampling techniques in combination with commonly-used machine learning algorithms in a clinical dataset, to determine whether combination(s) of these approaches improve upon the original multivariable logistic regression with no resampling.MethodsWe previously developed and internally validated a multivariable logistic regression 30-day mortality prediction model in 30,619 patients using preoperative and intraoperative features.Using the same dataset, we systematically evaluated and compared model performances after application of resampling techniques [random under-sampling, near miss under-sampling, random oversampling, and synthetic minority oversampling (SMOTE)] in combination with machine learning algorithms (logistic regression, elastic net, decision trees, random forest, and extreme gradient boosting).ResultsWe found that in the setting of severe class imbalance, the impact of resampling techniques on model performance varied by the machine learning algorithm and the evaluation metric. Existing resampling techniques did not meaningfully improve area under receiving operating curve (AUROC). The area under the precision recall curve (AUPRC) was only increased by random under-sampling and SMOTE for decision trees, and oversampling and SMOTE for extreme gradient boosting. Importantly, some combinations of algorithm and resampling technique decreased AUROC and AUPRC compared to no resampling.ConclusionExisting resampling techniques had a variable impact on models, depending on the algorithms and the evaluation metrics. Future research is needed to improve predictive performances in the setting of severe class imbalance.

A 10-year Retrospective Comparison of Endovascular and Open Aneurysm Repair for Infective Native Abdominal Aortic Aneurysm

BackgroundThis study compared outcomes of endovascular aneurysm repair (EVAR) and open aneurysm repair (OAR) in patients with infective native abdominal aortic aneurysms (INAAA), evaluating perioperative and in-hospital mortality, antibiotic treatment duration, complications, overall survival rates, and reintervention-free times at 10 years. MethodsA retrospective cohort study of 125 INAAA patients (80 EVAR, 45 OAR) diagnosed between January 2004 and December 2019 was conducted. Patients were categorized as fit or unfit for open surgery based on cardiac, respiratory, and renal status, following the guidelines established in the EVAR-1 and EVAR-2 trials. Primary outcomes included 30-day and in-hospital mortality. Secondary outcomes encompassed early and late complications requiring reintervention, antibiotic treatment duration, 10-year overall survival, 10-year reintervention-free survival, and factors influencing 30-day mortality. Statistical analysis used chi-square, t-tests, and Mann-Whitney U tests. Logistic regression assessed mortality. Kaplan-Meier estimation evaluated survival. Analyses used SPSS version 18.0 (p < 0.05 considered significant) ResultsMales predominated in both OAR (37/45, 82.2%) and EVAR (62/80, 77.5%) groups (P = 0.693). Mean age was 64.8 ± 9.8 years for OAR and 69.0 ± 12.6 years for EVAR (P = 0.063). The abdominal aorta was the most common aneurysm location, accounting for 91/125 (72.8%) cases. Salmonella spp. accounted for 19/45 (42.2%) of positive culture cases, and 34/125 (27.2%) patients had ruptured aneurysms. The EVAR group had a higher proportion of unfit patients (41/80, 51.2%) compared to OAR (10/45,22.2%; P=0.003). 30-day mortality rates were 6/80 (7.5%) for EVAR and 2/45 (4.4%) for OAR; OR = 1.75 (95% CI: 0.34 - 9.06), P = 0.508, while in-hospital mortality rates were 7/80 (8.8%) and 5/45 (11.1%); OR = 0.77 (95% CI: 0.23 - 2.58), P = 0.668. No significant differences were found in antibiotic treatment duration (median 11 vs 6 months, P=0.594), 10-year overall survival rates (62.8% vs 64.8%, P=0.90), or reintervention-free time (83.8% vs 82.2%, P=0.922), and aneurysm-related death (84.7% vs 92.9%, P=0.159), Unfit patient status was an independent predictor of 30-day mortality (adjusted OR, 10.654; 95% CI, 1.041-109.030; P=0.046). ConclusionsOur study demonstrates that EVAR and OAR provide comparable outcomes in INAAA management, despite EVAR being performed more frequently in unfit patients. The similar early mortality rates, antibiotic treatment durations, and long-term survival between the two approaches support EVAR as a viable alternative to OAR. Importantly, our finding that unfit patient status independently predicts perioperative mortality emphasizes the critical role of patient selection in treatment decisions. These results collectively suggest that EVAR may be particularly beneficial for high-risk INAAA patients unsuitable for OAR, potentially expanding treatment options for this challenging condition.

CT parameters of psoas muscle predicts 28-day mortality in older patients with sepsis: a retrospective study

OdjectivesTo investigate the predictive value of computed tomography (CT)-based transverse diameter (TD) and longitudinal diameter (LD) of the psoas muscle at the level of third lumbar vertebra for 28-day mortality in older patients with sepsis.MethodsThis retrospective single-center cohort study included 115 septic patients aged over 65 years old who were admitted to ICU in Beijing-Chaoyang Hospital between July 2017 and July 2022. The TD and LD of psoas muscle were obtained by measurement on abdominal CT images. Logistic regression analysis was conducted to identify the prognostic factors for 28-day mortality in older patients with sepsis, and their predictive performances were evaluated using the receiver operating characteristic (ROC) curve.ResultsMultivariable logistic analysis indicated that TD (OR: 0.405, 95% CI: 0.190–0.864) was a protective factor for 28-day mortality in older patients with sepsis, whereas LD was not. The areas under the curve (AUCs) of TD, APACHE II, and SOFA were 0.666 (95% CI: 0.565–0.767), 0.660 (95% CI: 0.561–0.760), and 0.679 (95% CI: 0.581–0.777), respectively. Furthermore, the AUCs for the combination of TD with APACHE II or SOFA were 0.766 (95% CI: 0.679–0.853, P < 0.001) and 0.765 (95% CI: 0.679–0.852, P < 0.001).ConclusionsTD of psoas muscle was a predictor for 28-day mortality in older patients with sepsis. The combination of TD with APACHE II or SOFA scores enhances the early identification of patients at high risk for poor prognosis.Clinical trial numbernot applicable.

Prevalence, Risk Factors, and Mortality of New-Onset Atrial Fibrillation in Mechanically Ventilated Critically Ill Patients.

Background/Objectives: Critically ill patients admitted to the intensive care unit (ICU) frequently develop new-onset atrial fibrillation (NOAF) due to numerous risk factors. While NOAF has been associated with increased mortality, it remains unclear whether it serves merely as a marker of illness severity or directly contributes to adverse outcome. This study aimed to determine the incidence and risk factors for NOAF in a homogenized population of mechanically ventilated patients at ICU admission, excluding well-established predisposing factors. Additionally, we examined the impact of NOAF on mortality in this context. Methods: We prospectively studied consecutive patients over a 3-year period to identify triggers for NOAF. Factors associated with 30-day mortality during the ICU stay were recorded. Demographic data, medical history, laboratory findings, and the severity of illness at admission were compared between patients who developed NOAF and those remaining in sinus rhythm. In NOAF patients, the course of atrial fibrillation (resolution, persistence, or recurrence) was evaluated during the 30-day ICU stay. Results: Of the 1330 patients screened, 685 were eligible for analysis, with 110 (16.1%) developing NOAF. Septic episodes occurred more frequently in the NOAF group compared to the no-NOAF group (92.7% vs. 58.1%, p < 0.001). Notably, 80% of NOAF patients developed a septic episode concurrently with the atrial fibrillation, often stemming from secondary infections, and 85.3% presented with septic shock. When focusing on patients with at least one septic episode during the 30-day ICU stay, 23.4% of them developed NOAF. Additionally, patients with NOAF were older and had a higher prevalence of hypertension; disease severity at admission was not a triggering factor. Mainly sepsis, but also advanced age, and a history of hypertension remained independent factors associated with its occurrence. Sepsis, primarily, along with advanced age and a history of hypertension, was identified as independent factors associated with the occurrence of NOAF. Mortality was higher in the NOAF group compared to the control group (39 patients (35.5%) vs. 138 patients (24%), p = 0.01). NOAF occurrence, sepsis, disease severity at admission, and age were associated with increased ICU mortality; however, NOAF was not found to be an independent predictor of ICU mortality in multivariate analysis. Instead, sepsis, age, and disease severity at admission remained independent predictors of 30-day mortality. Sinus rhythm was restored in 60.9% of NOAF patients within 48 h, with the improvement or stabilization of sepsis being crucial for rhythm restoration. Conclusions: NOAF is a common complication in intubated ICU patients and is independently associated with sepsis, advanced age, and hypertension. While NOAF is linked to increased ICU mortality, it is more likely a marker of disease severity than a direct cause of death. Sepsis improvement appears critical for restoring and maintaining sinus rhythm.

Interpretable prediction of 30-day mortality in patients with acute pancreatitis based on machine learning and SHAP.

Severe acute pancreatitis (SAP) can be fatal if left unrecognized and untreated. The purpose was to develop a machine learning (ML) model for predicting the 30-day all-cause mortality risk in SAP patients and to explain the most important predictors. This research utilized six ML methods, including logistic regression (LR), k-nearest neighbors(KNN), support vector machines (SVM), naive Bayes (NB), random forests(RF), and extreme gradient boosting(XGBoost), to construct six predictive models for SAP. An extensive evaluation was conducted to determine the most effective model and then the Shapley Additive exPlanations (SHAP) method was applied to visualize key variables. Utilizing the optimized model, stratified predictions were made for patients with SAP. Further, the study employed multivariable Cox regression analysis and Kaplan-Meier survival curves, along with subgroup analysis, to explore the relationship between the machine learning-based score and 30-day mortality. Through LASSO regression and recursive feature elimination (RFE), 25 optimal feature variables are selected. The XGBoost model performed best, with an area under the curve (AUC) of 0.881, a sensitivity of 0.5714, a specificity of 0.9651 and an F1 score of 0.64. The first six most important feature variables were the use of vasopressor, high Charlson comorbidity index, low blood oxygen saturation, history of malignant tumor, hyperglycemia and high APSIII score. Based on the optimal threshold of 0.62, patients were divided into high and low-risk groups, and the 30-day survival rate in the high-risk group decreased significantly. COX regression analysis further confirmed the positive correlation between high-risk scores and 30-day mortality. In the subgroup analysis, the model showed good risk stratification ability in patients with different gender, renal replacement therapy and with or without a history of malignant tumor, but it was not effective in predicting peripheral vascular disease. the XGBoost model effectively predicts the severity of SAP, serving as a valuable tool for clinicians to identify SAP early.

Construction and validation of a clinical prediction model for sepsis using peripheral perfusion index to predict in-hospital and 28-day mortality risk.

Sepsis is a clinical syndrome caused by infection, leading to organ dysfunction due to a dysregulated host response. In recent years, its high mortality rate has made it a significant cause of death and disability worldwide. The pathophysiological process of sepsis is related to the body's dysregulated response to infection, with microcirculatory changes serving as early warning signals that guide clinical treatment. The Peripheral Perfusion Index (PI), as an indicator of peripheral microcirculation, can effectively evaluate patient prognosis. This study aims to develop two new prediction models using PI and other common clinical indicators to assess the mortality risk of sepsis patients during hospitalization and within 28 days post-ICU admission. This retrospective study analyzed data from sepsis patients treated in the Intensive Care Unit of Peking Union Medical College Hospital between December 2019 and June 2023, ultimately including 645 patients. LASSO regression and logistic regression analyses were used to select predictive factors from 35 clinical indicators, and two clinical prediction models were constructed to predict in-hospital mortality and 28-day mortality. The models' performance was then evaluated using ROC curve, calibration curve, and decision curve analyses. The two prediction models performed excellently in distinguishing patient mortality risk. The AUC for the in-hospital mortality prediction model was 0.82 in the training set and 0.73 in the validation set; for the 28-day mortality prediction model, the AUC was 0.79 in the training set and 0.73 in the validation set. The calibration curves closely aligned with the ideal line, indicating consistency between predicted and actual outcomes. Decision curve analysis also demonstrated high net benefits for the clinical utility of both models. The study shows that these two prediction models not only perform excellently statistically but also hold high practical value in clinical applications. The models can help physicians accurately assess the mortality risk of sepsis patients, providing a scientific basis for personalized treatment.

A Comparative Analysis of Age, BMI, Age/BMI Ratio, Nottingham Hip Fracture Score, and American Society of Anesthesiologists (ASA) Grade as Predictors of 30-Day Mortality After Neck of Femur Fractures: A Retrospective Cohort Study.

Background and objective Hip fracture is a condition associated with high mortality rates, necessitating the use of risk assessment tools to optimise patient care. This study aimed to introduce and describe a novel score using Age/BMIas an improved predictor of 30-day mortality Methods A retrospective cohort study was conducted at a high-volume neck of the femur centre. Data from 574 patients treated over one year were collected and analysed. Multivariate logistic regression analysis was used to determine variables that significantly increased the risk of 30-day mortality. Results A total of 574 patients were identified: 388 females and 186 males.The overall mortality of the patient cohort at the time of data collection was21.78% (n=125). The 30-day mortality was found to be5.75% (n=33) while the one-year mortality rate was 21.08% (n=121). The key risk factors for mortality in neck of femur fractures, highlighted in the literature review, were compared against the binomial outcome variable of 30-day mortality. Categorical data analysis was first completed to highlight key trends. A regression analysis thendemonstrated the significance of each factor. Age (p=0.75207), BMI (p=0.97674), and Age/BMI (p=0.92205) showed no statistical significance. The Nottingham Hip Fracture Score (NHFS) was marginally significant (p=0.05749). The American Society of Anesthesiologists (ASA) grade was shown to be statistically significant, emergingas the strongest predictor of 30-day mortality(p=0.00953). Conclusions Our findings showthat current guidelines utilising ASA and NHFS are excellent predictors of 30-day mortality in hip fracture patients. The proposed Age/BMI score did not demonstrate efficacy in this cohort. Further research is warranted to explore alternative predictors and enhance risk assessment in this population.

Predicting 30-Day Outcomes in Primary Intracerebral Hemorrhage Using the Intracerebral Hemorrhage Score: A Study in Bangladesh.

Introduction Intracerebral hemorrhage (ICH) is a severe subtype of stroke associated with high rates of mortality and morbidity. Accurate early prognostication is essential for optimizing treatment strategies and improving patient outcomes. The ICH score, which includes clinical and imaging variables, is widely used to predict mortality and functional outcomes in ICH patients. However, limited data on the applicability of this scoring system are available from low-income countries. This study aims to evaluate the ICH score as a predictor of 30-day mortality and functional outcome, as measured by the modified Rankin Scale (mRS), in ICH patients at a tertiary care hospital in Bangladesh. Methods We conducted a prospective cohort study over one year at the National Institute of Neurosciences and Hospital in Dhaka, Bangladesh. One hundred patients aged over 18 years with confirmed primary ICH were enrolled. We collected data on demographics, clinical presentation, risk factors, and imaging findings, including the Glasgow Coma Scale (GCS), ICH score components, and hematoma volume. Patients were followed up for 30 days, and outcomes were assessed using the mRS. Statistical analyses included univariate and multivariate logistic regression and Kaplan-Meier survival estimates. Results A total of 100 participants were enrolled for the study. The mean age of participants was 59.2 ± 14.5 years, and 57 were men, and 43 were women. The overall 30-day mortality rate was 44%. Mortality rates increased significantly with higher ICH scores (p < 0.001), with all patients scoring 4 or 5 on the ICH score dying within 30 days. Lower GCS scores, larger hematoma volumes, the presence of intraventricular hemorrhage (IVH), and increasing age were associated with higher mortality. Multivariate analysis identified increasing age (p = 0.023), lower GCS score (p = 0.003), and higher ICH score (p = 0.025) as independent predictors of 30-day mortality. Higher ICH scores were also significantly associated with poor functional outcomes (mRS ≥ 4) at both discharge and 30 days (p < 0.001). GCS score emerged as an independent predictor of poor functional outcomes at 30 days (p = 0.012). Conclusions The ICH score is an effective tool for predicting 30-day mortality and functional outcomes in patients with primary ICH. Incorporating the ICH score and GCS assessment into routine clinical practice can aid healthcare providers in early risk stratification, optimizing treatment plans, and improving resource allocation.

The Hemoglobin, Albumin, Lymphocyte, and Platelet (HALP) Score Predicts the Prognosis of Adults with Status Epilepticus: A Retrospective Study.

The hemoglobin, albumin, lymphocyte, and platelet (HALP) score has been indicated to be a novel indicator that reflects systemic inflammation and is associated with the functional outcome in various neurological diseases. Since inflammation is closely involved in the pathogenesis of status epilepticus (SE), this study aimed to explore the predictive value of HALP for the prognosis of SE patients. In the retrospective study, we investigated and validated the predictive value of HALP for the prognosis of adult SE patients. We collected patient's clinical characteristics and laboratory test data. Information of 30-day mortality after SE onset was obtained, and modified Rankin scale (mRS) scores were used to evaluate the clinical outcomes. Multivariable logistic regression models were applied to explore the association between HALP and SE prognosis. Receiver operating characteristic (ROC) curve analysis was performed to determine the optimal cutoff of HALP to predict 30-day mortality in SE patients. We first analyzed the discovery cohort of 116 patients with SE. We observed that 19.0% (22/116) of the patients died within 30 days after SE onset, and 26.3% (20/76) had poor outcomes during the 6-month follow-up. Multivariate logistic regression analysis indicated that lower HALP on admission was an independent predictor of 30-day mortality (odds ratio: 0.980; 95% confidence interval, 0.962-0.999; P = 0.044). In ROC analysis, a HALP of 20.32 was identified as the optimal cutoff value to predict 30-day mortality in SE patients. Furthermore, multivariate logistic regression analysis showed that HALP < 20.32 was related to an increased risk of poor SE outcomes (odds ratio: 27.051; 95% confidence interval, 3.589-203.909; P = 0.001), which was validated in the external cohort. Lower HALP is independently associated with an increased risk of 30-day mortality and poor functional outcomes in patients with SE. Our findings suggest that HALP may be a promising biomarker for predicting SE prognosis.

Establishment and validation of a sepsis 28-day mortality prediction model based on the lactate dehydrogenase-to-albumin ratio in patients with sepsis

To develop and validate a predictive model of 28-day mortality in sepsis based on lactate dehydrogenase-to-albumin ratio (LAR). Sepsis patients diagnosed in the department of intensive care medicine of the First Affiliated Hospital of Soochow University from August 1, 2017 to September 1, 2022 were retrospective selected. Clinical data, laboratory indicators, disease severity scores [acute physiology and chronic health evaluation II (APACHE II), sequential organ failure assessment (SOFA)] were collected. Patients were divided into death group and survival group according to whether they died at 28 days, and the difference between the two groups was compared. The dataset was randomly divided into training set and validation set according to 7 : 3. Lasso regression method was used to screen the risk factors affecting the 28-day death of sepsis patients, and incorporating multivariate Logistic regression analysis (stepwise regression) were included, a prediction model was constructed based on the independent risk factors obtained, and a nomogram was drawn. The nomogram prediction model was established. Receiver operator characteristic curve (ROC curve) was drawn to analyze and evaluate the predictive efficacy of the model. Hosmer-Lemeshow test, calibration curve and decision curve analysis (DCA) were used to evaluate the accuracy and clinical practicability of the model, respectively. A total of 394 patients with sepsis were included, with 248 survivors and 146 non-survivors at 28 days. Compared with the survival group, the age, proportion of chronic obstructive pneumonia, respiratory rate, lactic acid, red blood cell distribution width, prothrombin time, activated partial thromboplastin time, alanine aminotransferase, aspartate aminotransferase, blood urea nitrogen, creatinine, blood potassium, blood phosphorus, LAR, SOFA score, and APACHE II score in the death group were significantly increased, while oxygenation index, monocyte count, platelet count, fibrinogen, total cholesterol, triglycerides, high-density lipoprotein, low-density lipoprotein, and blood calcium were significantly reduced. In the training set, LAR, age, oxygenation index, blood urea nitrogen, lactic acid, total cholesterol, fibrinogen, blood potassium and blood phosphorus were screened by Lasso regression. Multivariate Logistic regression analysis finally included LAR [odds ratio (OR) = 1.029, 95% confidence interval (95%CI) was 1.014-1.047, P < 0.001], age (OR = 1.023, 95%CI was 1.005-1.043, P = 0.012), lactic acid (OR = 1.089, 95%CI was 1.003-1.186, P = 0.043), oxygenation index (OR = 0.996, 95%CI was 0.993-0.998, P = 0.002), total cholesterol (OR = 0.662, 95%CI was 0.496-0.865, P = 0.003) and blood potassium (OR = 1.852, 95%CI was 1.169-2.996, P = 0.010). A total of 6 predictor variables were used to establish a prediction model. ROC curve showed that the area under the curve (AUC) of the model in the training set and validation set were 0.773 (95%CI was 0.715-0.831) and 0.793 (95%CI was 0.703-0.884), which was better than APACHE II score (AUC were 0.699 and 0.745) and SOFA score (AUC were 0.644 and 0.650), and the cut-off values were 0.421 and 0.309, the sensitivity were 62.4% and 82.2%, and the specificity were 82.2% and 68.9%, respectively. The results of Hosmer-Lemeshow test and calibration curve showed that the predicted results of the model were in good agreement with the actual clinical observation results, and the DCA showed that the model had good clinical application value. The prediction model based on LAR has a good predictive value for 28-day mortality in patients with sepsis and can guide clinical decision-making.