How to cite this article: Patnaik RK, Karan N. Synergizing Survival: Uniting Acute Gastrointestinal Injury Grade and Disease Severity Scores in Critical Care Prognostication. Indian J Crit Care Med 2024;28(6):529-530.

BACKGROUND: There is a growing demand for advanced methods to improve the understanding and prediction of illnesses. This study focuses on Sepsis, a critical response to infection, aiming to enhance early detection and mortality prediction for Sepsis-3 patients to improve hospital resource allocation.
METHODS: In this study, we developed a Machine Learning (ML) framework to predict the 30-day mortality rate of ICU patients with Sepsis-3 using the MIMIC-III database. Advanced big data extraction tools like Snowflake were used to identify eligible patients. Decision tree models and Entropy Analyses helped refine feature selection, resulting in 30 relevant features curated with clinical experts. We employed the Light Gradient Boosting Machine (LightGBM) model for its efficiency and predictive power.
RESULTS: The study comprised a cohort of 9118 Sepsis-3 patients. Our preprocessing techniques significantly improved both the AUC and accuracy metrics. The LightGBM model achieved an impressive AUC of 0.983 (95% CI: [0.980-0.990]), an accuracy of 0.966, and an F1-score of 0.910. Notably, LightGBM showed a substantial 6% improvement over our best baseline model and a 14% enhancement over the best existing literature. These advancements are attributed to (I) the inclusion of the novel and pivotal feature Hospital Length of Stay (HOSP_LOS), absent in previous studies, and (II) LightGBM\'s gradient boosting architecture, enabling robust predictions with high-dimensional data while maintaining computational efficiency, as demonstrated by its learning curve.
CONCLUSIONS: Our preprocessing methodology reduced the number of relevant features and identified a crucial feature overlooked in previous studies. The proposed model demonstrated high predictive power and generalization capability, highlighting the potential of ML in ICU settings. This model can streamline ICU resource allocation and provide tailored interventions for Sepsis-3 patients.

OBJECTIVE: To evaluate the predictive ability of mortality prediction scales in cancer patients admitted to intensive care units (ICUs).
METHODS: A systematic review of the literature was conducted using a search algorithm in October 2022. The following databases were searched: PubMed, Scopus, Virtual Health Library (BVS), and Medrxiv. The risk of bias was assessed using the QUADAS-2 scale.
METHODS: ICUs admitting cancer patients.
METHODS: Studies that included adult patients with an active cancer diagnosis who were admitted to the ICU.
METHODS: Integrative study without interventions.
METHODS: Mortality prediction, standardized mortality, discrimination, and calibration.
RESULTS: Seven mortality risk prediction models were analyzed in cancer patients in the ICU. Most models (APACHE II, APACHE IV, SOFA, SAPS-II, SAPS-III, and MPM II) underestimated mortality, while the ICMM overestimated it. The APACHE II had the SMR (Standardized Mortality Ratio) value closest to 1, suggesting a better prognostic ability compared to the other models.
CONCLUSIONS: Predicting mortality in ICU cancer patients remains an intricate challenge due to the lack of a definitive superior model and the inherent limitations of available prediction tools. For evidence-based informed clinical decision-making, it is crucial to consider the healthcare team\'s familiarity with each tool and its inherent limitations. Developing novel instruments or conducting large-scale validation studies is essential to enhance prediction accuracy and optimize patient care in this population.

Extensive research has been devoted to predicting ICU mortality, to assist clinical teams managing critical patients. Electronic health records (EHR) contain both static and dynamic medical data, with the latter accumulating during ICU stays. Existing models often rely on a fixed time window (e.g., first 24 h) for prediction, potentially missing vital post-24-hour data. The present study aims to improve mortality prediction for ICU patients following Cardiac Arrest (CA) using a dynamic sliding window approach that accommodates evolving data characteristics. Our cohort included 2331 CA patients, of whom 684 died in the ICU and 1647 survived. Applying the sliding window technique, we created six different time windows and used each separately for model training and validation. We compared our results to a baseline accumulative window. The different time windows created by the sliding window technique differed in their prediction performance and outperformed the baseline 24-hour window significantly. The XGBoost model outperformed all other models, with the 30-42 h time window achieving the best results (AUC = 0.8, accuracy = 0.77). Our work shows that the sliding window technique is effective in improving mortality prediction. We demonstrated how important time-window selection is and showed that enhancing it can save time and thus improve mortality prediction. These findings promise to improve the clinical team\'s efficiency in prioritizing patients and giving greater attention to higher-risk patients. To conclude, mortality prediction in the ICU can be improved if we consider alternative time windows instead of the 24-hour window, which is currently the most widely accepted among scoring systems today.

BACKGROUND: The Parkland Trauma Index of Mortality (PTIM) is an integrated, machine learning 72-h mortality prediction model that automatically extracts and analyzes demographic, laboratory, and physiological data in polytrauma patients. We hypothesized that this validated model would perform equally as well at another level 1 trauma center.
METHODS: A retrospective cohort study was performed including ∼5000 adult level 1 trauma activation patients from January 2022 to September 2023. Demographics, physiologic and laboratory values were collected. First, a test set of models using PTIM clinical variables (CVs) was used as external validation, named PTIM+. Then, multiple novel mortality prediction models were developed considering all CVs designated as the Cincinnati Trauma Index of Mortality (CTIM). The statistical performance of the models was then compared.
RESULTS: PTIM CVs were found to have similar predictive performance within the PTIM + external validation model. The highest correlating CVs used in CTIM overlapped considerably with those of the PTIM, and performance was comparable between models. Specifically, for prediction of mortality within 48 h (CTIM versus PTIM): positive prediction value was 35.6% versus 32.5%, negative prediction value was 99.6% versus 99.3%, sensitivity was 81.0% versus 82.5%, specificity was 97.3% versus 93.6%, and area under the curve was 0.98 versus 0.94.
CONCLUSIONS: This external cohort study suggests that the variables initially identified via PTIM retain their predictive ability and are accessible in a different level 1 trauma center. This work shows that a trauma center may be able to operationalize an effective predictive model without undertaking a repeated time and resource intensive process of full variable selection.

UNASSIGNED: The application of machine learning in health care often necessitates the use of hierarchical codes such as the International Classification of Diseases (ICD) and Anatomical Therapeutic Chemical (ATC) systems. These codes classify diseases and medications, respectively, thereby forming extensive data dimensions. Unsupervised feature selection tackles the \"curse of dimensionality\" and helps to improve the accuracy and performance of supervised learning models by reducing the number of irrelevant or redundant features and avoiding overfitting. Techniques for unsupervised feature selection, such as filter, wrapper, and embedded methods, are implemented to select the most important features with the most intrinsic information. However, they face challenges due to the sheer volume of ICD and ATC codes and the hierarchical structures of these systems.
UNASSIGNED: The objective of this study was to compare several unsupervised feature selection methods for ICD and ATC code databases of patients with coronary artery disease in different aspects of performance and complexity and select the best set of features representing these patients.
UNASSIGNED: We compared several unsupervised feature selection methods for 2 ICD and 1 ATC code databases of 51,506 patients with coronary artery disease in Alberta, Canada. Specifically, we used the Laplacian score, unsupervised feature selection for multicluster data, autoencoder-inspired unsupervised feature selection, principal feature analysis, and concrete autoencoders with and without ICD or ATC tree weight adjustment to select the 100 best features from over 9000 ICD and 2000 ATC codes. We assessed the selected features based on their ability to reconstruct the initial feature space and predict 90-day mortality following discharge. We also compared the complexity of the selected features by mean code level in the ICD or ATC tree and the interpretability of the features in the mortality prediction task using Shapley analysis.
UNASSIGNED: In feature space reconstruction and mortality prediction, the concrete autoencoder-based methods outperformed other techniques. Particularly, a weight-adjusted concrete autoencoder variant demonstrated improved reconstruction accuracy and significant predictive performance enhancement, confirmed by DeLong and McNemar tests (P<.05). Concrete autoencoders preferred more general codes, and they consistently reconstructed all features accurately. Additionally, features selected by weight-adjusted concrete autoencoders yielded higher Shapley values in mortality prediction than most alternatives.
UNASSIGNED: This study scrutinized 5 feature selection methods in ICD and ATC code data sets in an unsupervised context. Our findings underscore the superiority of the concrete autoencoder method in selecting salient features that represent the entire data set, offering a potential asset for subsequent machine learning research. We also present a novel weight adjustment approach for the concrete autoencoders specifically tailored for ICD and ATC code data sets to enhance the generalizability and interpretability of the selected features.

UNASSIGNED: The Charlson Comorbidity Index ≥2, in-Hospital onset, Albumin <2.5 g/dL, altered Mental status, Eastern Cooperative Oncology Group Performance status ≥2, Steroid use (CHAMPS) score is a novel and promising prognostic tool. We present an initial external validation of the CHAMPS score for predicting mortality in acute nonvariceal upper gastrointestinal bleeding (NVUGIB) across multiple clinical outcomes.
UNASSIGNED: A prospective cohort study was conducted on adult patients with NVUGIB admitted to the Department of Gastroenterology between November 2022 and June 2023. The CHAMPS score performance in predicting in-hospital outcomes was evaluated by employing area under the receiver operating characteristic (AUROC) curves, followed by a comparative analysis with five pre-existing scores.
UNASSIGNED: A total of 140 patients were included in the study. The CHAMPS score showed its highest performance in predicting mortality rates (AUROC = 0.89), significantly outperforming the Glasgow-Blatchford Bleeding Score (GBS) as well as the Albumin level <3.0 mg/dL, International normalized ratio >1.5, altered Mental status, Systolic blood pressure ≤90 mmHg, and age >65 years (AIMS65) score (AUROC = 0.72 and 0.71, respectively; all p < 0.05). Subgroup analysis for bleeding-related and non-bleeding-related mortality further confirmed the robust predictive capability of the CHAMPS score (AUROC = 0.88 and 0.87, respectively). The CHAMPS score failed to predict rebleeding and intervention reliably, exhibiting AUROC values of 0.43 and 0.55, respectively. The optimal CHAMPS score cutoff value for predicting mortality was 3 points, achieving 100% sensitivity and 71.2% specificity. In the low-risk category defined by both CHAMPS and GBS scores, mortality and rebleeding rates were 0%. However, within the CHAMPS score-based low-risk group, 58.8% required intervention, contrasting with a 0% intervention rate for the GBS score-based low-risk group (GBS score ≤1).
UNASSIGNED: The CHAMPS score consistently demonstrated a robust predictive performance for mortality (AUROC > 0.8), facilitating the identification of high-risk patients requiring aggressive treatment and low-risk patients in need of localized treatment or safe discharge after successful bleeding control.

UNASSIGNED: Heart failure (HF) is a global health challenge affecting millions, with significant variations in patient characteristics and outcomes based on ejection fraction. This study aimed to differentiate between HF with reduced ejection fraction (HFrEF) and HF with preserved ejection fraction (HFpEF) with respect to patient characteristics, risk factors, comorbidities, and clinical outcomes, incorporating advanced machine learning models for mortality prediction.
UNASSIGNED: The study included 1861 HF patients from 21 centers in Jordan, categorized into HFrEF (EF <40%) and HFpEF (EF ≥ 50%) groups. Data were collected from 2021 to 2023, and machine learning models were employed for mortality prediction.
UNASSIGNED: Among the participants, 29.7% had HFpEF and 70.3% HFrEF. Significant differences were noted in demographics and comorbidities, with a higher prevalence of males, younger age, smoking, and familial history of premature ASCVD in the HFrEF group. HFpEF patients were typically older, with higher rates of diabetes, hypertension, and obesity. Machine learning analysis, mainly using the Random Forest Classifier, demonstrated significant predictive capability for mortality with an accuracy of 0.9002 and an AUC of 0.7556. Other models, including Logistic Regression, SVM, and XGBoost, also showed promising results. Length of hospital stay, need for mechanical ventilation, and number of hospital admissions were the top predictors of mortality in our study.
UNASSIGNED: The study underscores the heterogeneity in patient profiles between HFrEF and HFpEF. Integrating machine learning models offers valuable insights into mortality risk prediction in HF patients, highlighting the potential of advanced analytics in improving patient care and outcomes.

UNASSIGNED: This study investigated potential predictive models associated with natural killer (NK) cell mitochondrial membrane potential (MMP or ΔΨm) in predicting death among critically ill patients with COVID-19.
UNASSIGNED: We included 97 patients with COVID-19 of different severities attending Peking Union Medical College Hospital from December 2022 to January 2023. Patients were divided into three groups according to oxygen and mechanical ventilation use during specimen collection and were followed for survival and death at 3 months. The lymphocyte subpopulation MMP was detected via flow cytometry. We constructed a joint diagnostic model by integrating identified key indicators and generating receiver operating curves (ROCs) and evaluated its predictive performance for mortality risk in critically ill patients.
UNASSIGNED: The NK-cell MMP median fluorescence intensity (MFI) was significantly lower in critically ill patients who died from COVID-19 (p<0.0001) and significantly and positively correlated with D-dimer content in critically ill patients (r=0.56, p=0.0023). The random forest model suggested that fibrinogen levels and NK-cell MMP MFI were the most important indicators. Integrating the above predictive models for the ROC yielded an area under the curve of 0.94.
UNASSIGNED: This study revealed the potential of combining NK-cell MMP with key clinical indicators (D-dimer and fibrinogen levels) to predict death among critically ill patients with COVID-19, which may help in early risk stratification of critically ill patients and improve patient care and clinical outcomes.

UNASSIGNED: Pulmonary hypertension (PH) frequently complicates the course of patients with left heart disease (PH-LHD) and is associated with worse clinical outcomes. Mortality calculators for PH-LHD are lacking, and it is unclear whether any risk prediction tools originally derived from other forms of PH can accurately predict outcomes in patients with PH-LHD.
UNASSIGNED: We retrospectively analyzed data from 161 patients diagnosed with PH-LHD referred to our pulmonary hypertension center from 2016 to 2022. We calculated the Registry to Evaluate Early and Long-Term PAH Disease Management (REVEAL 2.0) risk score and categorized patients as low, intermediate, or high-risk. We assessed survival at 1 and 3 years using Kaplan-Meier and Cox proportional hazards, as well as classification performance using a concordance index.
UNASSIGNED: At the first outpatient visit, 15% of patients were stratified as low-risk, 27% as intermediate, and 57% as high-risk. Cumulative 1-year survival rates were 100%, 94%, and 91% for the low, intermediate, and high-risk strata, respectively. Cumulative 3-year survival rates were 96%, 89%, and 70% for the low, intermediate, and high-risk strata, respectively. We found no difference in outcomes at 1 year between risk groups. High-risk patients had an increased risk of death at 3 years using REVEAL 2.0 (HR 5.32, p < 0.001). However, while REVEAL 2.0 accurately discriminated high-risk patients, the hazard ratio was not statistically different between patients classified as intermediate-risk compared to low-risk.
UNASSIGNED: REVEAL 2.0 accurately predicted 3-year survival in PH-LHD patients with high-risk features. However, the mortality risk between patients classified as intermediate-risk was not different from the low-risk stratum, suggesting inaccurate classification for this group of patients.