Accurate Predictor Of Mortality Research Articles

Predictive value of serum albumin and calcium levels in burn patients with Pseudomonas aeruginosa infection: A comprehensive analysis of clinical outcomes.

In the ongoing challenge to reduce burn-associated mortality rates, this study explores the predictive capacity of clinical factors in burn patients, focusing on vitamin D, calcium, and serum albumin levels during hospitalisation in cases with Pseudomonas aeruginosa infection. Our research involves a comprehensive analysis of 100 burn patients, encompassing crucial clinical parameters such as the burn severity index, serum albumin, vitamin D, and calcium levels at admission. Data were meticulously entered into IBM Statistics SPSS software version 28 and subjected to statistical analysis. The study reveals an average patient age of 39.75 years and a notable 34% mortality rate. Additionally, the average lengths of hospital and intensive care unit (ICU) stays are determined to be 11.33 and 7.79 days, respectively. Significantly, a correlation between calcium and albumin variables and treatment outcomes is established, showcasing their potential to predict variable changes in patient mortality rates. Furthermore, a noteworthy association is observed between serum calcium levels and the duration of ICU hospitalisation. In conclusion, albumin and calcium variables emerge as sensitive and specific indicators for predicting outcomes in burn patients. Importantly, the independence of these factors from the physician's experience and diagnosis reduces human error and thus increases the accuracy of mortality prediction in this patient population.

Predicting Individual Tree Mortality of Larix gmelinii var. Principis-rupprechtii in Temperate Forests Using Machine Learning Methods

Accurate prediction of individual tree mortality is essential for informed decision making in forestry. In this study, we proposed machine learning models to forecast individual tree mortality within the temperate Larix gmelinii var. principis-rupprechtii forests in Northern China. Eight distinct machine learning techniques including random forest, logistic regression, artificial neural network, generalized additive model, support vector machine, gradient boosting machine, k-nearest neighbors, and naive Bayes models were employed, to construct an ensemble learning model based on comprehensive dataset from this specific ecosystem. The random forest model emerged as the most accurate, demonstrating 92.9% accuracy and 92.8% sensitivity, making it the best model among those tested. We identified key variables impacting tree mortality, and the results showed that a basal area larger than the target trees (BAL), a diameter at 130 cm (DBH), a basal area (BA), an elevation, a slope, NH4-N, soil moisture, crown density, and the soil’s available phosphorus are important variables in the Larix Principis-rupprechtii individual mortality model. The variable importance calculation results showed that BAL is the most important variable with an importance value of 1.0 in a random forest individual tree mortality model. By analyzing the complex relationships of individual tree factors, stand factors, environmental, and soil factors, our model aids in decision making for temperate Larix gmelinii var. principis-rupprechtii forest conservation.

Data analytics approach for short- and long-term mortality prediction following acute non-ST-elevation myocardial infarction (NSTEMI) and Unstable Angina (UA) in Asians.

Traditional risk assessment tools often lack accuracy when predicting the short- and long-term mortality following a non-ST-segment elevation myocardial infarction (NSTEMI) or Unstable Angina (UA) in specific population. To employ machine learning (ML) and stacked ensemble learning (EL) methods in predicting short- and long-term mortality in Asian patients diagnosed with NSTEMI/UA and to identify the associated features, subsequently evaluating these findings against established risk scores. We analyzed data from the National Cardiovascular Disease Database for Malaysia (2006-2019), representing a diverse NSTEMI/UA Asian cohort. Algorithm development utilized in-hospital records of 9,518 patients, 30-day data from 7,133 patients, and 1-year data from 7,031 patients. This study utilized 39 features, including demographic, cardiovascular risk, medication, and clinical features. In the development of the stacked EL model, four base learner algorithms were employed: eXtreme Gradient Boosting (XGB), Support Vector Machine (SVM), Naive Bayes (NB), and Random Forest (RF), with the Generalized Linear Model (GLM) serving as the meta learner. Significant features were chosen and ranked using ML feature importance with backward elimination. The predictive performance of the algorithms was assessed using the area under the curve (AUC) as a metric. Validation of the algorithms was conducted against the TIMI for NSTEMI/UA using a separate validation dataset, and the net reclassification index (NRI) was subsequently determined. Using both complete and reduced features, the algorithm performance achieved an AUC ranging from 0.73 to 0.89. The top-performing ML algorithm consistently surpassed the TIMI risk score for in-hospital, 30-day, and 1-year predictions (with AUC values of 0.88, 0.88, and 0.81, respectively, all p < 0.001), while the TIMI scores registered significantly lower at 0.55, 0.54, and 0.61. This suggests the TIMI score tends to underestimate patient mortality risk. The net reclassification index (NRI) of the best ML algorithm for NSTEMI/UA patients across these periods yielded an NRI between 40-60% (p < 0.001) relative to the TIMI NSTEMI/UA risk score. Key features identified for both short- and long-term mortality included age, Killip class, heart rate, and Low-Molecular-Weight Heparin (LMWH) administration. In a broad multi-ethnic population, ML approaches outperformed conventional TIMI scoring in classifying patients with NSTEMI and UA. ML allows for the precise identification of unique characteristics within individual Asian populations, improving the accuracy of mortality predictions. Continuous development, testing, and validation of these ML algorithms holds the promise of enhanced risk stratification, thereby revolutionizing future management strategies and patient outcomes.

Dynamic Prediction of Mortality Using Longitudinally Measured Pediatric Sequential Organ Failure Assessment Scores: A Joint Modeling Approach.

The pediatric Sequential Organ Failure Assessment (pSOFA) score was designed to track illness severity and predict mortality in critically ill children. Most commonly, pSOFA at a point in time is used to assess a static patient condition. However, this approach has a significant drawback because it fails to consider any changes in a patients' condition during their PICU stay and, especially, their response to initial critical care treatment. We aimed to evaluate the performance of longitudinal pSOFA scores for predicting mortality. Single-center, retrospective cohort study. Quaternary 40-bed PICU. All patients admitted to the PICU between 2015 and 2021 with at least 24 hours of ICU stay. None. We calculated daily pSOFA scores up to 30 days, or until death or discharge from the PICU, if earlier. We used the joint longitudinal and time-to-event data model for the dynamic prediction of 30-day in-hospital mortality. The dataset, which included 9146 patients with a 30-day in-hospital mortality of 2.6%, was divided randomly into training (75%) and validation (25%) subsets, and subjected to 40 repeated stratified cross-validations. We used dynamic area under the curve (AUC) to evaluate the discriminative performance of the model. Compared with the admission-day pSOFA score, AUC for predicting mortality between days 5 and 30 was improved on average by 6.4% (95% CI, 6.3-6.6%) using longitudinal pSOFA scores from the first 3 days and 9.2% (95% CI, 9.0-9.5%) using scores from the first 5 days. Compared with admission-day pSOFA score, longitudinal pSOFA scores improved the accuracy of mortality prediction in PICU patients at a single center. The pSOFA score has the potential to be used dynamically for the evaluation of patient conditions.

Multidisciplinary-derived clinical score for accurate prediction of long-term mortality in fibrotic lung disease patients

BackgroundIdiopathic pulmonary fibrosis (IPF) stands out as one of the most aggressive forms of interstitial lung diseases (ILDs), currently without a definitive cure. Multidisciplinary discussion (MDD) is now considered a cornerstone in diagnosing and differentiating ILD subtypes. The Gender-Age-Physiology (GAP) score, developed to assess IPF prognosis based on sex, age, forced vital capacity, and diffusion capacity for carbon monoxide (DLCO), is limited in not considering dyspnea and functional impairment during the walking test. We proposed a MDD-based clinical score for mortality prediction among those patients.MethodsFrom December 2018 to December 2019, we enrolled ILD patients with IPF and non-IPF and followed-up them till December 2020. Based on DLCO, modified Medical Research Council (mMRC) Dyspnea Scale, and six-minute walking test (6MWT) distance, a functional score was developed for mortality prediction.ResultsWe enrolled 104 ILD patients, 12 (11.5%) died by the one-year follow-up. In receiver operating characteristic (ROC) curve analysis, DLCO (% predicted) was the most accurate variable predicting one-year mortality with an area under curve (AUC) of 0.88 (95% confidence interval [CI] = 0.80–0.94), followed by mMRC Dyspnea Score (AUC = 0.82 [95% CI = 0.73–0.89]), 6MWT distance (AUC = 0.80 [95% CI = 0.71–0.88]), and GAP score (AUC = 0.77 [95% CI = 0.67–0.84]). Only the GAP score (hazard ratio [HR] = 1.55, 95% CI = 1.03–2.34, p = 0.0.37) and functional score (HR = 3.45, 95% CI = 1.11–10.73, p = 0.032) were significantly associated with one-year mortality in multivariable analysis.ConclusionThe clinical score composite of DLCO, mMRC Dyspnea Scale, and 6MWT distance could provide an accurate prediction for long-term mortality in ILD patients, laying out a helpful tool for managing and following these patients.

Heart rate complexity helps mortality prediction in the intensive care unit: A pilot study using artificial intelligence

BackgroundIn intensive care units (ICUs), accurate mortality prediction is crucial for effective patient management and resource allocation. The Simplified Acute Physiology Score II (SAPS-2), though commonly used, relies heavily on comprehensive clinical data and blood samples. This study sought to develop an artificial intelligence (AI) model utilizing key hemodynamic parameters to predict ICU mortality within the first 24 h and assess its performance relative to SAPS-2. MethodsWe conducted an analysis of select hemodynamic parameters and the structure of heart rate curves to identify potential predictors of ICU mortality. A machine-learning model was subsequently trained and validated on distinct patient cohorts. The AI algorithm’s performance was then compared to the SAPS-2, focusing on classification accuracy, calibration, and generalizability. Measurements and main resultsThe study included 1298 ICU admissions from March 27th, 2015, to March 27th, 2017. An additional cohort from 2022 to 2023 comprised 590 patients, resulting in a total dataset of 1888 patients. The observed mortality rate stood at 24.0%. Key determinants of mortality were the Glasgow Coma Scale score, heart rate complexity, patient age, duration of diastolic blood pressure below 50 mmHg, heart rate variability, and specific mean and systolic blood pressure thresholds. The AI model, informed by these determinants, exhibited a performance profile in predicting mortality that was comparable, if not superior, to the SAPS-2. ConclusionsThe AI model, which integrates heart rate and blood pressure curve analyses with basic clinical parameters, provides a methodological approach to predict in-hospital mortality in ICU patients. This model offers an alternative to existing tools that depend on extensive clinical data and laboratory inputs. Its potential integration into ICU monitoring systems may facilitate more streamlined mortality prediction processes.

Reduced IgG2 with thrombocytopenia predicts mortality in patients with influenza pneumonia

BackgroundThrombocytopenia is a common disorder during influenza that is related to high mortality. ObjectivesA prospective study was performed to investigate the association of immunoglobulin subclass changes accompanying incident thrombocytopenia with clinical outcomes in patients with severe influenza. Methods96 influenza patients were recruited and divided into two groups, patients with thrombocytopenia (n = 30) and patients without thrombocytopenia (n = 66). Plasma microarrays were used for quantitative analysis of immunoglobulins. The endpoint was 28-day mortality. Continuous platelet count, d-dimer, level of each Ig subclass and other variables were compared between the two groups. Kaplan-Meier curve was taken to analyze the 28-day survival rate of the two groups and Cox regression analysis was performed to identify variables independently associated with 28-day mortality. ResultsPatients with thrombocytopenia had significantly high values of d-dimer at admission and when platelet lowest with high SOFA score. Their IgA2, IgG2, and IgG4 values were also lower than those without thrombocytopenia. Patients without thrombocytopenia had a higher 28-day survival rate than those in the thrombocytopenia group. In the multivariate Cox regression model, age (HR = 1.036, 95%CI = 1.011–1.062), IgG2 (HR = 0.990, 95%CI = 0.982–0.998), platelet minimum within 28 days (HR = 0.991, 95%CI = 0.982–0.999) and d-dimer when platelet lowest (HR = 1.091, 95%CI = 1.047–1.137) were independently related to 28-day mortality. ConclusionDecreased IgG2 may be associated with thrombocytopenia. A coexistence of thrombocytopenia, IgG2 reduction and d-dimer elevation may improve the accuracy of mortality prediction in patients with influenza pneumonia.

Prognostic evaluation of heart failure in women: insight from the MECKI score database

Abstract Background Heart failure (HF) in women often assumes extremely peculiar characteristics. The risk stratification and prognostic evaluation of HF in women is a challenge for clinicians. The actual prognostic scores are, in fact, lacking a specific sex-oriented assessment. In HF women show better survival despite a comparatively low peak oxygen consumption (VO2) at cardiopulmonary exercise testing: this raises doubt about the accuracy of risk assessment in women. Previous studies on MECKI score database have demonstrated that female prognostic advantage is lost when sex-specific differences are correctly considered. Purpose The present analysis has the aim to identify parameters which could be differently associated to prognosis in men and women and to re-calibrate the MECKI score according to these differences. Methods The new weights of the MECKI score variables according to gender, were calculated using the means of the 200 repetitions obtained to cross-validation procedure. The primary outcome of the study was the composite of all-cause mortality, urgent heart transplant and LVAD implant. The difference in predictive ability between the native and gender re-calibrated MECKI was calculated with the ROC curve at 2 years follow-up and calibration plot. Results We retrospectively analyzed 7900 HF patients with reduced ejection fraction included in the MECKI score registry (61±13 years, 6456 M/ 1444 F, LVEF 33±10%, VO2 /kg 14.9±4.9 mL/min Kg). Patients follow up was: 4.05 years [1.72-7.47]. Table 1 presents the weights of the individual MECKI variables for males and females. The main differences have been detected for the intercept value, hemoglobin and Na+. We used these variables to recalibrate the algorithm of MECKI score. We obtained higher AUC for MECKI re-calibrated by gender than for Native MECKI (0.7893 vs. 0.7799, p = 0.0194, Fig. 1 left panel). Moreover, the gender MECKI was better calibrated (Fig. 1, right panels). Conclusions The calibration of MECKI Score according to gender specific differences improves the prognostic power and accuracy of mortality prediction at two years follow up.Fig 1Table 1

Growth differentiation factor-15 is associated with myocardial infarction and death at 30 and 90 days in ED patients with suspected acute coronary syndrome

Abstract Background Growth differentiation factor (GDF)-15 is a novel biomarker of cardiac stress that may be an accurate predictor of mortality and acute myocardial infarction (AMI) in patients with acute chest pain. However, data from US populations are lacking. Purpose Our objective was to determine whether GDF-15 is an independent predictor of index, 30-day, and 90-day day AMI and death in a U.S cohort of ED patients with possible acute coronary syndrome. Methods This study was a secondary analysis of the STOP-CP (High Sensitivity Cardiac Troponin T to Optimize Chest Pain Risk Stratification) trial, which prospectively enrolled adults (≥ 21 years) presenting to eight emergency departments in the US with suspected AMI (1/25/2017-9/6/2018). Blood sampling was performed at baseline and 3 hours later, and hs-TnT, NT-pro-BNP, and GDF-15 assays were performed at a central laboratory. The primary outcome was the composite of AMI and all-cause death at index visit, 30 days, and 90 days and secondary outcomes were AMI and death separately. Multivariable logistic regression models were fitted for the outcomes at each timepoint with the following independent variables: age, sex, number of cardiac risk factors, ECG findings, hs-TnT, and NT-pro-BNP. Odds ratios (ORs) are reported per 100 ng/L increase in GDF-15 levels. Results In 1,428 patients the median age was 58 (IQR: 49, 66) years and 25% (353/1,428) had a history of coronary artery disease. The median GDF-15 level was 1,233 pg/mL (IQR: 754, 2206) at baseline and 1,363 pg/mL (IQR: 851, 2434) at 3 hours. The composite outcome of AMI or death occurred in 12% (169) at index visit, an additional 2% (34) at 30 days, and an additional 3% (37) at 90 days. In multivariable regression models, GDF-15 was independently associated with the composite outcome of AMI or death at 30 days (OR ng/L 1.01, 95% CI 1.00-1.02) and 90 days (OR 1.01, 95% CI 1.01-1.02), but not during the index visit (OR 1.00, 95% CI 1.00-1.00). It was also associated with both AMI (OR 1.01, 95% CI 1.00-1.02) and death (OR 1.01, 95% CI 1.01-1.02) separately at 90 days, but was only associated with death (OR 1.01, 95% CI 1.01-1.02) at 30 days. Conclusion Among adult ED patients with suspected AMI, GDF-15 was independently associated with subsequent 30-day and 90-day AMI or death. These data suggest a potential role for GDF-15 in risk stratifying ED patients with regard to future cardiac events.

Machine learning-based prediction of 30-day major adverse cardiac and cerebrovascular events in non-cardiac surgery patients

Abstract Background The number of non-cardiac surgeries performed worldwide has been steadily increasing, presenting a challenge for clinicians to accurately identify patients at high risk of complications and to allocate the appropriate level of perioperative care. Accurate prediction of postoperative mortality is crucial not only for successful patient care, but also for information-based shared decision-making with patients and efficient allocation of medical resources. Purpose In this study, we aimed to develop a novel predictive model using machine learning methods applied to electronic health record data. Our objective is to identify the risk factors most likely to lead to 30-day major adverse cardiac and cerebrovascular events after non-cardiac surgery Methods We conducted a retrospective analysis of data from a single tertiary care institution that included patients aged 65 years or over who underwent non-cardiac surgery from May 2003 and December 2020. The Observational Medical Outcomes Partnership (OMOP) common data model (CDM) data was used to build predictive models, which allowed for the utilization of demographic data, as well as preoperative characteristics such as diagnosis, lab results, vital signs, medications, and information on operations and procedures from the electronic health records (EHRs) in a standardized way. We employed machine learning models, which were developed and validated using the OHDSI Patient-Level-Prediction framework. Results We included a total of 47,915 patients to train (75%) and test (25%) our predictive models. To compare prediction performances, we applied gradient boosting machine (GBM), logistic regression (LR), random forest (RF), AdaBoost (AB), and decision tree (DT). Our results for a test data (Fig 1.) showed that the GBM model had the best performance in terms of the area under the receiver operating characteristic curve (AUROC) (0.903) and the area under the precision-recall curve (AUPRC) (0.395). Conclusions Our study demonstrates that applying machine learning algorithms to electronic health record data can effectively identify patients at high risk of major adverse cardiac and cerebrovascular events following non-cardiac surgery. This algorithm has the potential to support clinicians in effectively identifying patients at high risk and provide appropriate perioperative care. Further work is needed to validate and refine the proposed model to ensure its external validity and broader applicability in clinical practice.We plan to validate the proposed model externally by testing it on a cohort of approximately 280,000 patients from other tertiary care institution, and present the results at the 2023 ESC Congress.

Improving mortality forecasting using a hybrid of Lee–Carter and stacking ensemble model

BackgroundMortality forecasting is a critical component in various fields, including public health, insurance, and pension planning, where accurate predictions are essential for informed decision-making. This study introduces an innovative hybrid approach that combines the classical Lee–Carter model with advanced machine learning techniques, particularly the stack ensemble model, to enhance the accuracy and efficiency of mortality forecasts.ResultsThrough an extensive analysis of mortality data from Ghana, the hybrid model’s performance is assessed, showcasing its superiority over individual base models. The proposed hybrid Lee–Carter model with a stack ensemble emerges as a powerful tool for mortality forecasting based on the performance metrics utilized. Additionally, the study highlights the impact of incorporating additional base models within the stack ensemble framework to enhance predictive performance.ConclusionThrough this innovative approach, the study provides valuable insights into enhancing mortality prediction accuracy. By bridging classic mortality modeling with advanced machine learning, the hybrid model offers a powerful tool for policymakers, actuaries, and healthcare practitioners to inform decisions and plan for the future. The findings of this research pave the way for further advancements and improvements in mortality forecasting methodologies, thus contributing to the broader understanding and management of mortality risks in various sectors.

Can mean platelet volume be a prognosis predictor in viral infections: An example of Covid-19

PurposeThis study revealed the utility of mean platelet volume (MPV) as a mortality marker in SARS-CoV-2 infection, as well as its connection with other inflammatory indicators such as procalcitonin (PCT) and neutrophil-lymphocyte ratio (NLR). MethodsA total of 1528 patients (853 males and 653 females; mean age: 64.33 ± 16.36 years; range, 18–100 years) were hospitalized with COVID-19 between March 2020 and December 2022. The patients' demographic and clinical information, including ward and critical care data, were gathered from their medical records. On the first and last days, the PCT, NLR, and MPV values of the patients, who were divided into groups based on their hospitalization and outcomes, were analyzed. ResultsWhen the relevant laboratory data from the first and last days were compared, each group was statistically significant (p < 0.05). There was a moderate association between the final MPV values and the PCT and NLR values of the patients admitted to the ward (r = 0.448 and r = 0.397, respectively, where p < 0.01). There was also a substantial and moderate correlation between the final MPV levels and the PCT and NLR values of patients admitted to the intensive care unit (r = 0.613 and r = 0.361, respectively, p < 0.01). When compared to the patients' outcomes, the MPV had greater specificity and AUC values than the PCT and NLR (94.4 %, 0.968, 80.6 %, 0.923, 81 %, 0.845, respectively). ConclusionIn patients hospitalized with COVID-19, the specificity of MPV values at the point of sickness severity and outcome was shown to be greater than PCT and NLR values, and MPV values may be a more accurate predictor of mortality than PCR and NLR.

Accuracy of Bleeding Volume as a Predictor of Mortality in Epidural Hemorrhage Patients Undergoing Surgery at Dr. Mohammad Hoesin General Hospital, Palembang, Indonesia

Introduction: EDH is a collection of blood clots between the dura layer and the cranium. This is usually caused by rupture of the middle meningeal artery but can also be caused by rupture of the dural venous sinus, diploic vein, meningeal vein, or bleeding from the fracture line. CT scan is the investigation of choice to detect intracranial injury after trauma. CT scans can also identify additional features that influence the results, namely, midline shifting, blood clot thickness and hematoma volume, cerebral edema, and cranium fractures. This study aimed to determine the accuracy of bleeding volume as a predictor of mortality in epidural hemorrhage patients undergoing surgery at Dr. Mohammad Hoesin General Hospital, Palembang Indonesia.&#x0D; Methods: This study is a retrospective study of prognostic tests to determine the accuracy of bleeding volume as a predictor of mortality in patients with EDH who underwent surgery at Dr. Mohammad Hoesin General Hospital. This study uses secondary data obtained from medical record data at the medical records installation of Dr. Mohammad Hoesin General Hospital, Palembang, Indonesia. A total of 106 research subjects participated in this study. &#x0D; Results: The sensitivity value of bleeding volume was 77.7%, and specificity was 78.3%, and also obtained a positive predictive value of 7.21% and a negative predictive value of 97.4%, it can be concluded that bleeding volume is not accurate as a predictor of mortality.&#x0D; Conclusion: Bleeding volume is not an accurate predictor of mortality in epidural hemorrhage patients undergoing surgery at Dr. Mohammad Hoesin General Hospital, Palembang Indonesia.

Reconstructing the cytokine view for the multi-view prediction of COVID-19 mortality

BackgroundCoronavirus disease 2019 (COVID-19) is a rapidly developing and sometimes lethal pulmonary disease. Accurately predicting COVID-19 mortality will facilitate optimal patient treatment and medical resource deployment, but the clinical practice still needs to address it. Both complete blood counts and cytokine levels were observed to be modified by COVID-19 infection. This study aimed to use inexpensive and easily accessible complete blood counts to build an accurate COVID-19 mortality prediction model. The cytokine fluctuations reflect the inflammatory storm induced by COVID-19, but their levels are not as commonly accessible as complete blood counts. Therefore, this study explored the possibility of predicting cytokine levels based on complete blood counts.MethodsWe used complete blood counts to predict cytokine levels. The predictive model includes an autoencoder, principal component analysis, and linear regression models. We used classifiers such as support vector machine and feature selection models such as adaptive boost to predict the mortality of COVID-19 patients.ResultsComplete blood counts and original cytokine levels reached the COVID-19 mortality classification area under the curve (AUC) values of 0.9678 and 0.9111, respectively, and the cytokine levels predicted by the feature set alone reached the classification AUC value of 0.9844. The predicted cytokine levels were more significantly associated with COVID-19 mortality than the original values.ConclusionsIntegrating the predicted cytokine levels and complete blood counts improved a COVID-19 mortality prediction model using complete blood counts only. Both the cytokine level prediction models and the COVID-19 mortality prediction models are publicly available at http://www.healthinformaticslab.org/supp/resources.php.

ANCOC Score to Predict Mortality in Different SARS-CoV-2 Variants and Vaccination Status.

More than three years after the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic outbreak, hospitals worldwide are still affected by coronavirus disease 19 (COVID-19). The availability of a clinical score that can predict the risk of death from the disease at the time of diagnosis and that can be used even if population characteristics change and the virus mutates can be a useful tool for emergency physicians to make clinical decisions. During the first COVID-19 waves, we developed the ANCOC (age, blood urea nitrogen, C-reactive protein, oxygen saturation, comorbidities) score, a clinical score based on five main parameters (age, blood urea nitrogen, C-reactive protein, oxygen saturation, comorbidities) that accurately predicts the risk of death in patients infected with SARS-CoV-2. A score of less than -1 was associated with 0% mortality risk, whereas a score of 6 was associated with 100% risk of death, with an overall accuracy of 0.920. The aim of our study is to internally validate the ANCOC score and evaluate whether it can predict 60-day mortality risk independent of vaccination status and viral variant. We retrospectively enrolled 843 patients admitted to the emergency department (ED) of our hospital with a diagnosis of COVID-19. A total of 515 patients were admitted from July 2021 to September 2021, when the Delta variant was prevalent, and 328 in January 2022, when the Omicron 1 variant was predominant. All patients included in the study had a diagnosis of COVID-19 confirmed by polymerase chain reaction (PCR) on an oropharyngeal swab. Demographic data, comorbidities, vaccination data, and various laboratory, radiographic, and blood gas parameters were collected from all patients to determine differences between the two waves. ANCOC scores were then calculated for each patient, ranging from -6 to 6. Patients infected with the Omicron variant were significantly older and had a greater number of comorbidities, of which hypertension and chronic obstructive pulmonary disease (COPD) were the most common. Immunization was less common in Delta patients than in Omicron patients (34% and 56%, respectively). To assess the accuracy of mortality prediction, we constructed a receiver operating characteristic (ROC) curve and found that the area under the ROC curve was greater than 0.8 for both variants. These results suggest that the ANCOC score is able to predict 60-day mortality regardless of viral variant and whether the patient is vaccinated or not. In a population with increasingly high vaccination rates, several parameters may be considered prognostic for the risk of fatal outcomes. This study suggests that the ANCOC score can be very useful for the clinician in an emergency setting to quickly understand the patient's evolution and provide proper attention and the most appropriate treatments.

Evaluating the Potential of Machine Learning and Wearable Devices in End-of-Life Care in Predicting 7-Day Death Events Among Patients With Terminal Cancer: Cohort Study.

An accurate prediction of mortality in end-of-life care is crucial but presents challenges. Existing prognostic tools demonstrate moderate performance in predicting survival across various time frames, primarily in in-hospital settings and single-time evaluations. However, these tools may fail to capture the individualized and diverse trajectories of patients. Limited evidence exists regarding the use of artificial intelligence (AI) and wearable devices, specifically among patients with cancer at the end of life. This study aimed to investigate the potential of using wearable devices and AI to predict death events among patients with cancer at the end of life. Our hypothesis was that continuous monitoring through smartwatches can offer valuable insights into the progression of patients at the end of life and enable the prediction of changes in their condition, which could ultimately enhance personalized care, particularly in outpatient or home care settings. This prospective study was conducted at the National Taiwan University Hospital. Patients diagnosed with cancer and receiving end-of-life care were invited to enroll in wards, outpatient clinics, and home-based care settings. Each participant was given a smartwatch to collect physiological data, including steps taken, heart rate, sleep time, and blood oxygen saturation. Clinical assessments were conducted weekly. The participants were followed until the end of life or up to 52 weeks. With these input features, we evaluated the prediction performance of several machine learning-based classifiers and a deep neural network in 7-day death events. We used area under the receiver operating characteristic curve (AUROC), F1-score, accuracy, and specificity as evaluation metrics. A Shapley additive explanations value analysis was performed to further explore the models with good performance. From September 2021 to August 2022, overall, 1657 data points were collected from 40 patients with a median survival time of 34 days, with the detection of 28 death events. Among the proposed models, extreme gradient boost (XGBoost) yielded the best result, with an AUROC of 96%, F1-score of 78.5%, accuracy of 93%, and specificity of 97% on the testing set. The Shapley additive explanations value analysis identified the average heart rate as the most important feature. Other important features included steps taken, appetite, urination status, and clinical care phase. We demonstrated the successful prediction of patient deaths within the next 7 days using a combination of wearable devices and AI. Our findings highlight the potential of integrating AI and wearable technology into clinical end-of-life care, offering valuable insights and supporting clinical decision-making for personalized patient care. It is important to acknowledge that our study was conducted in a relatively small cohort; thus, further research is needed to validate our approach and assess its impact on clinical care. ClinicalTrials.gov NCT05054907; https://classic.clinicaltrials.gov/ct2/show/NCT05054907.

Prognostic accuracy of Neonatal SOFA score versus SIRS criteria in preterm infants with late-onset sepsis

Neonatal SOFA score was reported as an accurate predictor of mortality while the prognostic accuracy of SIRS criteria is unknown. The aim was to compare neonatal SOFA and SIRS criteria for the prediction of late onset sepsis-related mortality in preterm newborns. Newborns ≤ 32 weeks with late onset sepsis were retrospectively studied. Neonatal SOFA and SIRS criteria were calculated at onset of sepsis (T0), and after 6 ± 1 (T1), 12 ± 3 (T2) and 24 ± 3 h (T3). Outcome was death during antibiotic treatment for late onset sepsis. We studied 112 newborns with gestational age 26.9 ± 2.3 weeks; 11% met the study outcome. Neonatal SOFA was significantly higher in non-survivors vs. survivors at all time intervals; SIRS criteria were significantly higher in non-survivors vs. survivors at T1, T2 and T3. Neonatal SOFA increased over time in non-survivors (p = 0.003). At T0, the area under receiver operating characteristics curve was significantly higher for neonatal SOFA score than SIRS criteria (0.950 vs. 0.569; p = 0.0002), and the best calculated cut-off for T0 neonatal SOFA score was 4. In multivariate analysis T0 and T1 neonatal SOFA were predictors of late onset sepsis-related mortality (p = 0.048 and p < 0.001). Conclusion: Neonatal SOFA score showed greater discriminatory capacity for mortality than SIRS criteria and might be helpful to plan management for patients at higher risk of death.What is Known:• Neonatal SOFA score may be an accurate prognostic tool.• No prognostic score has been fully standardized for septic newborns in NICU.What is New:• Neonatal SOFA score outperformed SIRS criteria for the prediction of prognosis in preterm infants with late onset sepsis.• Neonatal SOFA score assessed at onset of sepsis and 6 hrs later is a predictor of mortality.

Machine Learning Approach for the Prediction of In-Hospital Mortality in Traumatic Brain Injury Using Bio-Clinical Markers at Presentation to the Emergency Department.

Accurate prediction of in-hospital mortality is essential for better management of patients with traumatic brain injury (TBI). Machine learning (ML) algorithms have been shown to be effective in predicting clinical outcomes. This study aimed to identify predictors of in-hospital mortality in TBI patients using ML algorithms. A retrospective study was performed using data from both the trauma registry and electronic medical records among TBI patients admitted to the Hamad Trauma Center in Qatar between June 2016 and May 2021. Thirteen features were selected for four ML models including a Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), and Extreme Gradient Boosting (XgBoost), to predict the in-hospital mortality. A dataset of 922 patients was analyzed, of which 78% survived and 22% died. The AUC scores for SVM, LR, XgBoost, and RF models were 0.86, 0.84, 0.85, and 0.86, respectively. XgBoost and RF had good AUC scores but exhibited significant differences in log loss between the training and testing sets (% difference in logloss of 79.5 and 41.8, respectively), indicating overfitting compared to the other models. The feature importance trend across all models indicates that aPTT, INR, ISS, prothrombin time, and lactic acid are the most important features in prediction. Magnesium also displayed significant importance in the prediction of mortality among serum electrolytes. SVM was found to be the best-performing ML model in predicting the mortality of TBI patients. It had the highest AUC score and did not show overfitting, making it a more reliable model compared to LR, XgBoost, and RF.

The outcomes and prognostic factors of patients with hepatocellular carcinoma and Child-Turcotte-Pugh class B.

The Child-Turcotte-Pugh (CTP) score is widely used for assessing the liver's functional reserve in patients with advanced chronic liver disease (ACLD) and hepatocellular carcinoma (HCC). This study aims to explore the outcomes of patients with HCC and CTP class B and to investigate the prognostic accuracy of prediction models for ACLD in these patients. We retrospectively enrolled 1143 patients with HCC and CTP class B between 2007 and 2022. We divided the patients into three subgroups based on their CTP scores: CTP-B7, CTP-B8, and CTP-B9. We compared the corrected Akaike information criterion among each mortality prediction model, including the CTP score, albumin-bilirubin (ALBI) score, modified ALBI score, the model for end-stage liver disease (MELD), and MELD 3.0. Among the enrolled patients, 576 (50.3%) were in the CTP-B7 group, 363 (31.8%) were in the CTP-B8 group, and 204 (17.9%) were in the CTP-B9 group. After a median follow-up of 4.6 months (interquartile range IQR 1.8-17.2 months), 963 patients died, and the 5-year overall survival (OS) rate was 11.4%. The 5-year OS rates were 11.6%, 13.6%, and 8.3% in the CTP-B7, CTP-B8, and CTP-B9 groups, respectively. Patients in the CTP-B7 group and CTP-B8 group had comparable OS ( p = 0.089), both of which were better than those in the CTP-B9 group ( p < 0.001). Furthermore, the MELD 3.0 score had the lowest corrected akaike information criteria value and provided a more accurate mortality prediction than the MELD score, ALBI grade, modified ALBI grade, and CTP score. Patients in the CTP-B7 and CTP-B8 groups had comparable OS, both of which were better than those in the CTP-B9 group. Moreover, MELD 3.0 provided the most accurate mortality prediction in patients with HCC and CTP class B.

Development and verification of a nomogram for predicting short-term mortality in elderly ischemic stroke populations

Stroke is a major healthcare problem worldwide, particularly in the elderly population. Despite limited research on the development of prediction models for mortality in elderly individuals with ischemic stroke, our study aimed to address this knowledge gap. By leveraging data from the Medical Information Mart for Intensive Care IV database, we collected comprehensive raw data pertaining to elderly patients diagnosed with ischemic stroke. Through meticulous screening of clinical variables associated with 28-day mortality, we successfully established a robust nomogram. To assess the performance and clinical utility of our nomogram, various statistical analyses were conducted, including the concordance index, integrated discrimination improvement (IDI), net reclassification index (NRI), calibration curves and decision curve analysis (DCA). Our study comprised a total of 1259 individuals, who were further divided into training (n = 894) and validation (n = 365) cohorts. By identifying several common clinical features, we developed a nomogram that exhibited a concordance index of 0.809 in the training dataset. Notably, our findings demonstrated positive improvements in predictive performance through the IDI and NRI analyses in both cohorts. Furthermore, calibration curves indicated favorable agreement between the predicted and actual incidence of mortality (P > 0.05). DCA curves highlighted the substantial net clinical benefit of our nomogram compared to existing scoring systems used in routine clinical practice. In conclusion, our study successfully constructed and validated a prognostic nomogram, which enables accurate short-term mortality prediction in elderly individuals with ischemic stroke.