Accurate Predictor Of Mortality Research Articles

Derivation and validation of a simple prognostic risk score to predict short-term mortality in acute cardiogenic pulmonary edema: SABIHA Score.

In the context of acute cardiogenic pulmonary edema (ACPE), a frequently encountered medical emergency associated with high early mortality rates, there is a need to predict short-term outcomes for risk stratification.Our aim was to derive and validate a model, a simple clinical scoring system using baseline vital signs, clinical and presenting characteristics, and readily available laboratory tests, that allows accurate prediction of short-term mortality in individuals experiencing ACPE. This retrospective cohort study included 1088 patients with ACPE from six health centers. Subjects were randomly allocated into derivation and validation cohorts at a 4:3 ratio, facilitating comprehensive examination and validation of prognostic model. Independent predictors of mortality (p<0.05) from the multivariable model were included in the risk score. The discriminant ability of the score was tested by ROC analysis. In the derivation cohort (n=623), age, blood urea nitrogen, heart rate, intubation, anemia, and systolic blood pressure were identified as independent predictors of mortality in multivariable analysis. These variables were used to develop a risk score ranging from 0 to 6 by scoring 0 and 1. The SABIHA score provided a good calibration with a concordance index of 0.879 (95% CI: 0.821-0.937). While the probability of short-term mortality was 80.0% in the high risk group, this rate was only 3.3% in the low risk group. This score also performed well on the validation set. A simple clinical score consisting of routinely obtained variables can be used in risk stratification to predict short-term outcomes in patients with ACPE.

Multimodal deep learning for predicting in-hospital mortality in heart failure patients using longitudinal chest X-rays and electronic health records.

Amid an aging global population, heart failure has become a leading cause of hospitalization among older people. Its high prevalence and mortality rates underscore the importance of accurate mortality prediction for swift disease progression assessment and better patient outcomes. The evolution of artificial intelligence (AI) presents new avenues for predicting heart failure mortality. Yet current research has predominantly leveraged structured data and unstructured clinical notes from electronic health records (EHR), underutilizing the prognostic value of chest X-rays (CXRs).This study aims to harness deep learning methodologies to explore the feasibility of enhancing the precision of predicting in-hospital all-cause mortality in heart failure patients using CXRs data.We propose a novel multimodal deep learning network based on the spatially and temporally decoupled Transformer (MN-STDT) for in-hospital mortality prediction in heart failure by integrating longitudinal CXRs and structured EHR data. The MN-STDT captures spatial and temporal information from CXRs through a Hybrid Spatial Encoder and a Distance-Aware Temporal Encoder, ultimately fusing features from both modalities for predictive modeling. Initial pre-training of the spatial encoder was conducted on CheXpert, followed by full model training and evaluation on the MIMIC-IV and MIMIC-CXR datasets for mortality prediction tasks. In a comprehensive view, the MN-STDT demonstrated the best performance, with an AUC-ROC of 0.8620, surpassing all baseline models. Comparative analysis revealed that the AUC-ROC of the multimodal model (0.8620) was significantly higher than that of models using only structured data (0.8166) or chest X-ray data alone (0.7479).This study demonstrates the value of CXRs in the prognosis of heart failure, showing that the combination of longitudinal CXRs with structured EHR data can significantly improve the accuracy of mortality prediction in heart failure. Feature importance analysis based on SHAP provides interpretable decision support, paving the way for potential clinical applications.

Mortality prediction of inpatients with NSTEMI in a premier hospital in China based on stacking model.

Acute myocardial infarction (AMI) remains a leading cause of hospitalization and death in China. Accurate mortality prediction of inpatient is crucial for clinical decision-making of non-ST-segment elevation myocardial infarction (NSTEMI) patients. In this study, a total of 3061 patients between January 1, 2017 and December 31, 2022 diagnosed with NSTEMI were enrolled in this study. A new method based on Stacking ensemble model is proposed to predict the in-hospital mortality risk of NSTEMI using clinical data. This method mainly consists of three parts. Firstly, oversampling technique was used to alleviate the class imbalance problem. Secondly, the feature selection method of Recursive Feature Elimination (RFE) was selected for effective feature selection. Finally, a unique double-layer stacking model is designed to improve the performance of the algorithm. Seven classical artificial intelligence methods of Logistic Regression (LR), Decision Tree (DT), Support Vector Machine (SVM), Random Forest (RF), Adaptive Boosting (ADB), Extra Tree (ET), and Gradient Boosting Decision Tree (GBDT) were selected as candidate models for the base model of the first layer of the model, and extreme gradient enhancement (XGBOOST) was selected as the meta-model for the second layer. Patient were divided into the surviving group and the death group, and a total of 57 clinical features showed statistically significant for the two groups and finally included in the subsequent model. The results show that the Area Under Curve (AUC) of the Stacking model proposed in this paper is 0.987, which is higher than that of LR (0.934), DT (0.946), SVM (0.942), RF (0.948), ADB (0.949), ET (0.938) and GBDT (0.920). At the same time, the proposed Stacking model has higher performance than each single model in terms of Accuracy, Precision, Recall and F1 evaluation indicators. The Stacking model proposed in this paper can integrate the advantages of LR, DT, SVM, RF, ADB, ET and GBDT models to achieve better prediction performance. This model can provide valuable insights for physicians to identify high-risk patients more precisely and timely, thereby maximizing the potential for early clinical interventions to reduce the mortality rate.

Predictive Mortality and Gastric Cancer Risk Using Clinical and Socio-Economic Data: A Nationwide Multicenter Cohort Study

Background/Objectives: Gastric cancer is a leading cause of cancer-related mortality, particularly in East Asia, with a notable burden in Republic of Korea. This study aimed to construct and develop machine learning models for the prediction of gastric cancer mortality and the identification of risk factors. Methods: All data were acquired from the Korean Clinical Data Utilization for Research Excellence by multiple medical centers in South Korea. A total of 23,717 gastric cancer patients were divided into two groups by cause of mortality (all-cause of 2664 and disease-specific of 1620) and investigated. We used comprehensive data integrating clinical, pathological, lifestyle, and socio-economic factors. Cox proportional hazards analysis was conducted to estimate hazard ratios for mortality. Five machine learning models (random forest, gradient boosting machine, XGBoost, light GBM, and cat boosting) were developed to predict mortality. The models were interpreted by SHAP, one of the explainable AI techniques. Results: For all-cause mortality, the gradient-boosting machine learning model demonstrated the highest performance with an AUC-ROC of 0.795. For disease-specific mortality, the light GBM model outperformed others, achieving an AUC-ROC of 0.867. Significant predictors included the AJCC7 stage, tumor size, lymph node count, and lifestyle factors such as smoking, drinking, and diabetes. Conclusions: This study underscores the importance of integrating both clinical and lifestyle data to enhance mortality prediction accuracy in gastric cancer patients. The findings highlight the need for personalized treatment approaches in the Korean population and emphasize the role of demographic-specific data in predictive modeling.

A stacked ensemble deep learning model for predicting the intensive care unit patient mortality

Accurate mortality prediction in intensive care units (ICUs) is essential for optimizing patient treatment, nursing care, and resource allocation. Traditional models, such as Acute Physiology and Chronic Health Evaluation and Simplified Acute Physiology Score, have been very important in clinical practice, but they frequently have issues with prediction accuracy and adaptability, especially when dealing with complex and evolving patient data. These issues can be resolved, and the accuracy of mortality prediction increased due to recent developments in machine learning, especially deep learning. The present study introduces a new deep learning ensemble model that achieves a significant improvement over existing methods. Using stacked ensemble learning, our approach combines the advantages of one Random Forests model and two CatBoost models. We achieved a notable performance in mortality prediction by carefully training and optimizing this ensemble using the electronic ICU Collaborative Research Database. Our model boasts an accuracy of 94.19%, precision of 94.097%, recall of 94.29%, and F1-score of 94.191%, demonstrating a substantial improvement over conventional approaches. The prediction of ICU mortality has been significantly improved using ensemble learning, which helps medical and nursing staff to better treat patients individually, allocate resources efficiently, and enhance patient outcomes. This approach gives healthcare experts the ability to make data-driven decisions, leading to more effective and efficient care within the ICU.

Predicting Short-Term Mortality in Patients With Acute Pulmonary Embolism With Deep Learning.

Accurate prediction of short-term mortality in patients with acute pulmonary embolism (PE) is critical for optimizing treatment strategies and improving patient outcomes. The Pulmonary Embolism Severity Index (PESI) is the current reference score used for this purpose, but it has limitations regarding predictive accuracy. Our aim was to develop a new short-term mortality prediction model for PE patients based on deep learning (DL) with multimodal data, including imaging and clinical/demographic data. We developed a novel multimodal deep learning (mmDL) model using contrast-enhanced multidetector computed tomography scans combined with clinical and demographic data to predict short-term mortality in patients with acute PE. We benchmarked various machine learning architectures, including XGBoost, convolutional neural networks (CNNs), and Transformers. Our cohort included 207 acute PE patients, of whom 53 died during their hospital stay. The mmDL model achieved an area under the receiver operating characteristic curve (AUC) of 0.98 (P<0.001), significantly outperforming the PESI score, which had an AUC of 0.86 (P<0.001). Statistical analysis confirmed that the mmDL model was superior to PESI in predicting short-term mortality (P<0.001). Our proposed mmDL model predicts short-term mortality in patients with acute PE with high accuracy and significantly outperforms the current standard PESI score.

Abstract A026: Assessing the impact of obesity on postoperative outcomes in RCC using machine learning

Abstract Introduction Obesity is associated with an increased risk of developing renal cell carcinoma (RCC) but paradoxically correlates with improved outcomes in metastatic cases. Moreover, the data on the relationship between obesity and postoperative outcomes following nephrectomy are inconsistent. Accurate prediction of postoperative outcomes, including complications, 30-day readmissions, and mortality, is essential for improving patient outcomes in surgical procedures. In this study, we utilized ML models to predict such outcomes in RCC patients undergoing nephroureterectomy, radical nephrectomy, partial nephrectomy, and other excision procedures on the kidney using data from the National Surgical Quality Improvement Program (NSQIP; 2016- 2021). Methods A gradient-boosted tree (GBT) ML model was developed and trained to predict the primary outcomes of interest: major complications, minor complications, 30-day readmission, and mortality. Patients with a BMI ≥30 kg/m2 were considered obese, and patients with a BMI &amp;lt;30 kg/m2 were used as controls. The model's performance was rigorously evaluated using various measures, such as AUROC, generalized R-square, and misclassification rate. The model's predictions were validated using separate training (70%) and validation (30%) cohorts to ensure generalizability and applicability in diverse patient populations. Results In the analysis, 36,284 cases were included. Bivariate analysis revealed that obesity was associated with an increased rate of minor complications (5.48% vs. 4.41%, p&amp;lt;0.0001) compared to non-obese individuals with similar major complications (3.33 vs. 3.21, p=0.49), mortality (0.54 vs. 0.64, p=0.22) and 30-day readmission rates (5.74 vs. 5.83, p=0.70). The GBT model demonstrated substantial predictive power across all four outcomes. In predicting mortality, the model achieved an AUROC of 0.80 in the validation set, with a misclassification rate of 0.68%. The prediction of major complications was also robust, with an AUROC of 0.91 in the validation set and a misclassification rate of 2.2%. Similarly, the model's performance in predicting minor complications and 30-day readmissions was strong, with AUROC values of 0.70 and 0.66, respectively, in the validation sets. Conclusions Obesity was linked to a higher rate of minor complications but did not affect other postoperative outcomes in RCC patients undergoing nephrectomy. By integrating a wide range of patient-specific variables, ML models are powerful tools for clinicians to identify high-risk patients and tailor perioperative care accordingly. The interaction between RCC and the surrounding adipose tissue microenvironment may have clinical relevance, warranting further research to elucidate the role of BMI in postoperative complications. Our findings advocate for the expanded use of ML techniques in surgical decision-making, which could enhance patient safety, optimize resource use, and improve overall surgical outcomes. Citation Format: Atulya A. Khosla, Manas Pustake, Sufal Chhabra, Yanjia Zhang, Mukesh Roy, Muni Rubens, Venkataraghavan Ramamoorthy, Anshul Saxena, Ishmael A. Jaiyesimi. Assessing the impact of obesity on postoperative outcomes in RCC using machine learning [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Tumor-body Interactions: The Roles of Micro- and Macroenvironment in Cancer; 2024 Nov 17-20; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(22_Suppl):Abstract nr A026.

The Usefulness of Carotid Artery Doppler Measurement as a Predictor of Early Death in Sepsis Patients Admitted to the Emergency Department.

Background: This study aims to verify whether the blood flow velocity and the diameter size, measured through intra-carotid artery Doppler measurements performed on sepsis patients visiting the emergency department, are useful as tools for predicting the risk of early death. Methods: As a prospective study, this research was performed on sepsis patients who visited a local emergency medical center from August 2021 to February 2023. The sepsis patients' carotid artery was measured using Doppler imaging, and they were divided into patients measured for the size of systolic and diastolic mean blood flow velocity and diameter size: those measured for their qSOFA (quick sequential organ failure assessment) score and those measured using the SIRS (systemic inflammatory response syndrome) criteria. By measuring and comparing their mortality prediction accuracies, this study sought to verify the usefulness of blood flow velocity and the diameter size of the intra-carotid artery as tools to predict early death. Results: This study was conducted on 1026 patients, excluding 45 patients out of the total of 1071 patients. All sepsis patients were measured using systolic and diastolic blood flow velocity and diameter by Doppler imaging of the intra-carotid artery, assessed using qSOFA and evaluated using SIRS criteria. The results of the analysis performed to compare the mortality prediction accuracy were as follows. First, the hazard ratio (95% CI) of the intra-carotid artery was significant (p < 0.05), at 1.020 (1.004-1.036); the hazard ratio (95% CI) of qSOFA was significant (p < 0.05), at 3.871 (2.526-5.931); and the hazard ratio (95% CI) of SIRS showed no significant difference, at 1.002 (0.995-1.009). After 2 h of infusion treatment, the diameter size was 4.72 ± 1.23, showing a significant difference (p < 0.05). After 2 h of fluid treatment, the blood flow velocity was 101 m/s ± 21.12, which showed a significant difference (p < 0.05). Conclusions: Measuring the mean blood flow velocity in the intra-carotid arteries of sepsis patients who visit the emergency department is useful for predicting the risk of death at an early stage. And this study showed that Doppler measurement of the diameter size of the carotid artery significantly increased after performing fluid treatment after early recognition.

Neutrophil-to-lymphocyte ratio as a prognostic indicator of mortality in Polycythemia Vera: insights from a prospective cohort analysis

We analyzed the neutrophil-to-lymphocyte ratio (NLR) in 1508 patients with PV and found that those with an NLR ≥ 5 were generally older, had a longer disease history, and had higher cardiovascular risk factors, more arterial thrombosis, and more aggressive blood counts, indicating a more proliferative disease. NLR was an accurate predictor of mortality, with patients with NLR ≥ 5 having significantly worse overall survival and more than twice the mortality rate compared to those with NLR < 5. Multivariable models confirmed that increasing age, previous venous thrombosis and NLR ≥ 5 were strong predictors of death, further influenced by cardiovascular risk factors. We examined the interaction between NLR and the number of cardiovascular risk factors and found a progressive trend of increased mortality risk for NLR values ≥ 5 in addition to the presence of more than one risk factor. In conclusion, patients with NLR ≥ 5 require careful monitoring and management of cardiovascular risk factors because they increase mortality when associated with progressive levels of NLR.

Prediction of Severe Acute Pancreatitis at a Very Early Stage of the Disease Using Artificial Intelligence Techniques, Without Laboratory Data or Imaging Tests: The PANCREATIA Study.

To evaluate machine learning models' performance in predicting acute pancreatitis severity using early-stage variables while excluding laboratory and imaging tests. Severe acute pancreatitis (SAP) affects approximately 20% of acute pancreatitis (AP) patients and is associated with high mortality rates. Accurate early prediction of SAP and in-hospital mortality is crucial for effective management. Traditional scores such as APACHE-II and BISAP are complex and require laboratory tests, while early predictive models are lacking. Machine learning (ML) has shown promising results in predictive modelling, potentially outperforming traditional methods. We analysed data from a prospective database of AP patients admitted to Vall d'Hebron Hospital from November 2015 to January 2022. Inclusion criteria were adults diagnosed with AP according to the 2012 Atlanta classification. Data included basal characteristics, current medication, and vital signs. We developed machine learning models to predict SAP, in-hospital mortality, and intensive care unit (ICU) admission. The modelling process included two stages: Stage 0, which used basal characteristics and medication, and Stage 1, which included data from Stage 0 and vital signs. Out of 634 cases, 594 were analysed. The Stage 0 model showed AUC values of 0.698 for mortality, 0.721 for ICU admission, and 0.707 for persistent organ failure. The Stage 1 model improved performance with AUC values of 0.849 for mortality, 0.786 for ICU admission, and 0.783 for persistent organ failure. The models demonstrated comparable or superior performance to APACHE-II and BISAP scores. The ML models showed good predictive capacity for SAP, ICU admission, and mortality using early-stage data without laboratory or imaging tests. This approach could revolutionise AP patients' initial triage and management, providing a personalised prediction method based on early clinical data.

Leveraging machine learning to enhance postoperative risk assessment in coronary artery bypass grafting patients with unprotected left main disease: a retrospective cohort study.

Risk stratification for patients undergoing coronary artery bypass surgery (CABG) for left main coronary artery (LMCA) disease is essential for informed decision-making. This study explored the potential of machine learning (ML) methods to identify key risk factors associated with mortality in this patient group. This retrospective cohort study was conducted on 866 patients from the Gulf Left Main Registry who presented between 2015 and 2019. The study outcome was hospital all-cause mortality. Various machine learning models [logistic regression, random forest (RF), k-nearest neighbor, support vector machine, naïve Bayes, multilayer perception, boosting] were used to predict mortality, and their performance was measured using accuracy, precision, recall, F1 score, and area under the receiver operator characteristic curve (AUC). Nonsurvivors had significantly greater EuroSCORE II values (1.84 (10.08-3.67) vs. 4.75 (2.54-9.53) %, P <0.001 for survivors and nonsurvivors, respectively). The EuroSCORE II score significantly predicted hospital mortality (OR: 1.13 (95% CI: 1.09-1.18), P <0.001), with an AUC of 0.736. RF achieved the best ML performance (accuracy=98, precision=100, recall=97, and F1 score=98). Explainable artificial intelligence using SHAP demonstrated the most important features as follows: preoperative lactate level, emergency surgery, chronic kidney disease (CKD), NSTEMI, nonsmoking status, and sex. QLattice identified lactate and CKD as the most important factors for predicting hospital mortality this patient group. This study demonstrates the potential of ML, particularly the Random Forest, to accurately predict hospital mortality in patients undergoing CABG for LMCA disease and its superiority over traditional methods. The key risk factors identified, including preoperative lactate levels, emergency surgery, chronic kidney disease, NSTEMI, nonsmoking status, and sex, provide valuable insights for risk stratification and informed decision-making in this high-risk patient population. Additionally, incorporating newly identified risk factors into future risk-scoring systems can further improve mortality prediction accuracy.

Adjustment of the SMART risk score by bioactive adrenomedullin enables a more accurate prediction of mortality in patients with ASCVD

Abstract Background Bioactive adrenomedullin 1-52 (bio-ADM) is a dynamic blood biomarker for real-time assessment of endothelial function. Bio-ADM was recently shown to be a prognostic marker in patients with acute heart failure and cardiogenic shock. The SMART (Second Manifestations of Arterial Disease) score is a validated tool for risk assessment in patients with established atherosclerotic cardiovascular disease (ASCVD). The aim of this study was to assess whether measurement of bio-ADM adds incremental value to the SMART score in stable patients with ASCVD. Methods Circulating bio-ADM levels were assessed in n=695 stable patients with ASCVD in an all-comer cohort. Endpoints evaluated were all-cause mortality and cardiovascular mortality; follow up was 3 years. Results Bio-ADM was higher in non-survivors (all-cause death: n=54, median 33.1 pg/mL) compared to survivors (n=641, median 17.9 pg/mL; p&amp;lt;0.0001). Univariable Cox regression analyses showed bio-ADM to be associated with adverse outcome [standardized hazard ratio (HR) of bio-ADM values: All-cause death: 2.4, 95% confidence interval (CI): 2.0-2.9; p&amp;lt;0.001, cardiovascular death: 2.5, 95% CI: 1.9-3.3; p&amp;lt;0.001]. This association remained significant in various multivariable Cox regression models. Bio-ADM was found to be a strong marker for mortality (c-index: 0.80, Chi²: 54.1) and proved to be superior to other markers including hs-Troponin T (c-index: 0.61, Chi²: 2.0) and eGFR CKD-EPI 2021 (c-index: 0.687, Chi²: 33.5). Addition of bio-ADM to the SMART score significantly improved model performance in predicting mortality (SMART score: c-index: 0.717, Chi²: 24.73; SMART score + bio-ADM: c-index: 0.832, Chi²: 63.24; Delta c-index: 0.115; Delta Chi²: 38.51; all p&amp;lt;0.001) and showed net reclassification improvement in risk categorization with 11.6% in the group of survivors and 9.8% for non-survivors. Conclusion Bio-ADM provides incremental added value (improved discrimination, calibration and reclassification) on top of the SMART risk score in patients with ASCVD.

Prediction of in-hospital mortality risk for patients with acute ST-elevation myocardial infarction after primary PCI based on predictors selected by GRACE score and two feature selection methods.

Accurate in-hospital mortality prediction following percutaneous coronary intervention (PCI) is crucial for clinical decision-making. Machine Learning (ML) and Data Mining methods have shown promise in improving medical prognosis accuracy. We analyzed a dataset of 4,677 patients from the Regional Vascular Center of Primorsky Regional Clinical Hospital No. 1 in Vladivostok, collected between 2015 and 2021. We utilized Extreme Gradient Boosting, Histogram Gradient Boosting, Light Gradient Boosting, and Stochastic Gradient Boosting for mortality risk prediction after primary PCI in patients with acute ST-elevation myocardial infarction. Model selection was performed using Monte Carlo Cross-validation. Feature selection was enhanced through Recursive Feature Elimination (RFE) and Shapley Additive Explanations (SHAP). We further developed hybrid models using Augmented Grey Wolf Optimizer (AGWO), Bald Eagle Search Optimization (BES), Golden Jackal Optimizer (GJO), and Puma Optimizer (PO), integrating features selected by these methods with the traditional GRACE score. The hybrid models demonstrated superior prediction accuracy. In scenario (1), utilizing GRACE scale features, the Light Gradient Boosting Machine (LGBM) and Extreme Gradient Boosting (XGB) models optimized with BES achieved Recall values of 0.944 and 0.954, respectively. In scenarios (2) and (3), employing SHAP and RFE-selected features, the LGB models attained Recall values of 0.963 and 0.977, while the XGB models achieved 0.978 and 0.99. The study indicates that ML models, particularly the XGB optimized with BES, can outperform the conventional GRACE score in predicting in-hospital mortality. The hybrid models' enhanced accuracy presents a significant step forward in risk assessment for patients post-PCI, offering a potential alternative to existing clinical tools. These findings underscore the potential of ML in optimizing patient care and outcomes in cardiovascular medicine.

Development and validation of a chronic kidney disease progression model using patient-level simulations

Chronic disease progression models are available for several highly prevalent conditions. For chronic kidney disease (CKD), the scope of existing progression models is limited to the risk of kidney failure and major cardiovascular (CV) events. The aim of this project was to develop a comprehensive CKD progression model (CKD-PM) that simulates the risk of CKD progression and a broad range of complications in patients with CKD. A series of literature reviews informed the selection of risk factors and identified existing risk equations/algorithms for kidney replacement therapy (KRT), CV events, other CKD-related complications, and mortality. Risk equations and transition probabilities were primarily sourced from publications produced by large US and international CKD registries. A patient-level, state-transition model was developed with health states defined by the Kidney Disease Improving Global Outcomes categories. Model validation was performed by comparing predicted outcomes with observed outcomes in the source cohorts used in model development (internal validation) and other cohorts (external validation). The CKD-PM demonstrated satisfactory modeling properties. Accurate prediction of all-cause and CV mortality was achieved without calibration, while prediction of CV events through CKD-specific equations required implementation of a calibration factor to balance time-dependent versus baseline risk. Predicted annual changes in estimated glomerular filtration rate (eGFR) and urine albumin-creatinine ratio were acceptable in comparison to external values. A flexible eGFR threshold for KRT equations enabled accurate prediction of these events. This CKD-PM demonstrated reliable modeling properties. Both internal and external validation revealed robust outcomes.

Comprehensive risk factor-based nomogram for predicting one-year mortality in patients with sepsis-associated encephalopathy

Sepsis-associated encephalopathy (SAE) is a frequent and severe complication in septic patients, characterized by diffuse brain dysfunction resulting from systemic inflammation. Accurate prediction of long-term mortality in these patients is critical for improving clinical outcomes and guiding treatment strategies. We conducted a retrospective cohort study using the MIMIC IV database to identify adult patients diagnosed with SAE. Patients were randomly divided into a training set (70%) and a validation set (30%). Least absolute shrinkage and selection operator regression and multivariate logistic regression were employed to identify significant predictors of 1-year mortality, which were then used to develop a prognostic nomogram. The model’s discrimination, calibration, and clinical utility were assessed using the area under the receiver operating characteristic curve (AUC), calibration plots, and decision curve analysis, respectively. A total of 3,882 SAE patients were included in the analysis. The nomogram demonstrated strong predictive performance with AUCs of 0.881 (95% CI: 0.865, 0.896) in the training set and 0.859 (95% CI: 0.830, 0.888) in the validation set. Calibration plots indicated good agreement between predicted and observed 1-year mortality rates. The decision curve analysis showed that the nomogram provided greater net benefit across a range of threshold probabilities compared to traditional scoring systems such as Glasgow Coma Scale and Sequential Organ Failure Assessment. Our study presents a robust and clinically applicable nomogram for predicting 1-year mortality in SAE patients. This tool offers superior predictive performance compared to existing severity scoring systems and has significant potential to enhance clinical decision-making and patient management in critical care settings.

“An Analytical Study To Compare The Effectiveness Of P-Possum And Possum Scores In Predicting Mortality And Morbidity Following Emergency Laparotomy"

Background: Surgical outcomes are increasingly scrutinized as part of efforts to ensure accountability and improve the quality of care. The Physiological and Operative Severity Score for the enumeration of Mortality and Morbidity (POSSUM) and its modification, the Portsmouth POSSUM (P-POSSUM), are widely used tools for predicting surgical outcomes. Objective: This study aimed to assess the accuracy of POSSUM in predicting morbidity and P-POSSUM in predicting mortality for patients undergoing emergency laparotomies in a North Indian tertiary care center. Methods: A hospital-based, analytic observational study was conducted at S.M.S. Medical College and Hospital, Jaipur, involving 160 patients who underwent emergency laparotomy. Preoperative physiological and intraoperative data were collected and used to calculate POSSUM and P-POSSUM scores. Mortality predictions were compared using linear and exponential analyses, and the observed-to-expected (O:E) mortality ratio was calculated. Statistical significance was determined using the Chi-square test. Results: The mean age of the cohort was 49.65 years, with males comprising 64.37% of the population. Peptic perforation (20.6%) was the most common cause of surgery. POSSUM significantly over-predicted mortality (O:E ratio = 0.26, p=0.04), while P-POSSUM provided a more accurate prediction (O:E ratio = 0.45, p=0.81). POSSUM also overestimated morbidity (O:E ratio = 0.8, p=0.51). Conclusion: While POSSUM tends to over-predict both mortality and morbidity, P-POSSUM offers a more accurate prediction of mortality in patients undergoing emergency laparotomy. These scoring systems can serve as valuable tools for surgical audits and improving the quality of care, though careful selection of the appropriate predictive model is essential

Machine Learning of Laboratory Data in Predicting 30-Day Mortality for Adult Hemophagocytic Lymphohistiocytosis.

Hemophagocytic Lymphohistiocytosis (HLH) carries a high mortality rate. Current existing risk-evaluation methodologies fall short and improved predictive methods are needed. This study aimed to forecast 30-day mortality in adult HLH patients using 11 distinct machine learning (ML) algorithms. A retrospective analysis on 431 adult HLH patients from January 2015 to September 2021 was conducted. Feature selection was executed using the least absolute shrinkage and selection operator. We employed 11 ML algorithms to create prediction models. The area under the curve (AUC), sensitivity, specificity, positive predictive value, negative predictive value, F1 score, calibration curve and decision curve analysis were used to evaluate these models. We assessed feature importance using the SHapley Additive exPlanation (SHAP) approach. Seven independent predictors emerged as the most valuable features. An AUC between 0.65 and 1.00 was noted among the eleven ML algorithms. The gradient boosting decision tree (GBDT) algorithms demonstrated the most optimal performance (1.00 in the training cohort and 0.80 in the validation cohort). By employing the SHAP method, we identified the variables that contributed to the model and their correlation with 30-day mortality. The AUC of the GBDT algorithms wasthe highest when using the top 4 (ferritin, UREA, age and thrombin time (TT)) features, reaching 0.99 in the training cohort and 0.83 in the validation cohort. Additionally, we developed a web-based calculator to estimate the risk of 30-day mortality. With GBDT algorithms applied to laboratory data, accurate prediction of 30-day mortality is achievable. Integrating these algorithms into clinical practice could potentially improve 30-day outcomes.

Interpretable mortality prediction model for ICU patients with pneumonia: using shapley additive explanation method

BackgroundPneumonia, a leading cause of morbidity and mortality worldwide, often necessitates Intensive Care Unit (ICU) admission. Accurate prediction of pneumonia mortality is crucial for tailored prevention and treatment plans. However, existing mortality prediction models face limited adoption in clinical practice due to their lack of interpretability.ObjectiveThis study aimed to develop an interpretable model for predicting pneumonia mortality in ICUs. Leveraging the Shapley Additive Explanation (SHAP) method, we sought to elucidate the Extreme Gradient Boosting (XGBoost) model and identify prognostic factors for pneumonia.MethodsConducted as a retrospective cohort study, we utilized electronic health records from the eICU-CRD (2014–2015) for all adult pneumonia patients. The first 24 h of each ICU admission records were considered, with 70% of the dataset allocated for model training and 30% for validation. The XGBoost model was employed, and performance was assessed using the area under the receiver operating characteristic curve (AUC). The SHAP method provided insights into the XGBoost model.ResultsAmong 10,962 pneumonia patients, in-hospital mortality was 16.33%. The XGBoost model demonstrated superior predictive performance (AUC: 0.778 ± 0.016)) compared to traditional scoring systems and other machine learning method, which achieved an improvement of 10% points. SHAP analysis identified Aspartate Aminotransferase (AST) as the most crucial predictor.ConclusionsInterpretable predictive models enhance mortality risk assessment for pneumonia patients in the ICU, fostering transparency. AST emerged as the foremost predictor, followed by patient age, albumin, BMI et al. These insights, rooted in strong correlations with mortality, facilitate improved clinical decision-making and resource allocation.

Assessing the Utility of Prediction Scores PAINT, ISARIC4C, CHIS, and COVID-GRAM at Admission and Seven Days after Symptom Onset for COVID-19 Mortality.

The COVID-19 pandemic underscores the need for accurate prognostic tools to predict patient outcomes. This study evaluates the effectiveness of four prominent COVID-19 prediction scores-PAINT, ISARIC4C, CHIS, and COVID-GRAM-at two critical time points: at admission and seven days post-symptom onset, to assess their utility in predicting mortality among hospitalized patients. Conducted at the Clinical Emergency Hospital Pius Brînzeu in Timișoara, this retrospective analysis included adult patients hospitalized with confirmed SARS-CoV-2 infection. Eligible patients had complete data for the scores at both time points. Statistical analysis involved ROC curves and logistic regression to assess the scores' predictive accuracy for mortality. The study included 215 patients, split into 139 survivors and 76 non-survivors. At admission, the PAINT, ISARIC4C, CHIS, and COVID-GRAM scores significantly differentiated between the survival outcomes (p < 0.0001). The best cutoff values at admission were 6.26 for PAINT, 7.95 for ISARIC4C, 5.58 for CHIS, and 0.63 for COVID-GRAM, corresponding to sensitivities of 85.47%, 80.56%, 88.89%, and 83.33% and specificities of 77.34%, 82.12%, 75.01%, and 78.45%, respectively. By day seven, the cutoff values increased, indicating deteriorating conditions in patients who eventually succumbed to the virus. The hazard ratios at admission for exceeding these cutoffs were significant: PAINT (HR = 3.45), ISARIC4C (HR = 2.89), CHIS (HR = 4.02), and COVID-GRAM (HR = 3.15), highlighting the scores' abilities to predict severe outcomes. One week post symptom onset, these scores' predictive values and corresponding hazard ratios increased, further validating their prognostic significance over time. The evaluated COVID-19 prediction scores robustly predict mortality at admission and become more predictive by the seventh day of symptom onset. These findings support the use of these scores in clinical settings to facilitate early identification and intervention for high-risk patients, potentially improving patient outcomes during the ongoing global health crisis.

The Naples Score: Can it Outperform Existing Scores in Predicting Gastric Cancer Mortality?

Objective: Gastric surgery, including curative and palliative procedures, is crucial in managing gastric cancer. Despite advancements, postoperative mortality remains a significant concern, influenced by factors such as patient nutritional status. Accurate assessment of nutritional status is essential for risk stratification and improving patient outcomes.This study aims to investigate the correlation between four nutritional scores: Naples Prognostic Score , Controlling Nutritional Status score, Prognostic Nutritional Index, and Systemic Inflammation Score and the mortality rate following gastric surgery, to identify the most reliable predictors of postoperative mortality. Materials and methods: This retrospective study evaluated 50 patients diagnosed with gastric adenocarcinoma and operated on at Erol Olçok Training and Research Hospital between April 2021 and September 2023. Data collected included age, gender, operation type, laparoscopy usage, albumin, cholesterol, neutrophil-to-lymphocyte ratio, lymphocyte-to-monocyte ratio, mortality rates, and TNM stages. Nutritional scores were calculated, and their predictive accuracy for mortality was assessed using time-dependent Receiver Operating Characteristic curve analysis. Results: The study included 50 patients. Significant differences in albumin levels, neutrophil-to-lymphocyte rate, lymphocyte-to-monocyte ratio, and nutritional scores were found between deceased and surviving patients. Prognostic Nutritional Index had the highest predictive accuracy with an Area under the curve of 0.792, sensitivity of 76%, and specificity of 86.2%, outperforming others. Conclusion: The Prognostic Nutritional Index is the most effective predictor of postoperative mortality among the evaluated scores.