Prediction Of Events Research Articles

Machine learning-based risk assessment of gene editing: degeneration during gene editing possibility of target effect

CRISPI-Cas 9 technology is currently the most popular gene editing tool, which has been used in biomedical research and clinical treatment, but it also has the problem of off-target effects that cause unexpected gene mutations, which limits its safety and efficacy. Now, various machine learning models have been trained to predict off-target events in order to: Gene editing is more precise. In terms of both accuracy and interpretability there are still issues in the existing models. This paper has focused on reviewing Machine learning archery Off Target Prediction models and their performance. The results of this study show that the deep learning model has high accuracy and interpretability in the prediction of off-target events, which provides a reference for the optimization of gene editing strategies. The results reveal that there is a lot of room for development of deep learning technology in improving the safety of gene editing, which provides an in-depth reference for the sustainable development of gene editing technology. The application of this study to this paper also has its limitations, including the inadequacy of the applicability of the model in multiple gene editing contexts. In the future research of gene editing, more attention can be paid to the study of new biological indicators, the establishment of more accurate prediction models, and the improvement of models with more explanatory interpretation, so as to more safely apply gene editing technology in clinical and agricultural treatment and improvement.

A comprehensive framework for evaluating time to event predictions using the restricted mean survival time

AbstractThe restricted mean survival time (RMST) is a widely used quantity in survival analysis due to its straightforward interpretation. For instance, predicting the time to event based on patient attributes is of great interest when analyzing medical data. In this paper, we propose a novel framework for evaluating RMST estimations. A criterion that estimates the mean squared error of an RMST estimator using Inverse Probability Censoring Weighting (IPCW) is presented. A model‐agnostic conformal algorithm adapted to right‐censored data is also introduced to compute prediction intervals and to evaluate local variable importance. Finally, a model‐agnostic statistical test is developed to assess global variable importance. Our framework is valid for any RMST estimator that is asymptotically convergent and works under model misspecification.

Examination Process Modeling for Intelligent Patent Management: A Multi-aspect Neural Sequential Approach

Recently, the booming growth of patent applications has brought an unprecedented challenge in performing efficient intellectual property management. Therefore, intelligent approaches are urgently needed to analyze intrinsic patterns of patents. However, a long-standing obstacle is the lack of effective methods for modeling the dynamic and diverse examination process of patent applications, which can benefit a wide range of downstream tasks for patent management. In fact, the major challenges lie in how to discover and integrate domain-specific properties from large-scale unlabeled examination data. To this end, in this paper, we propose a Self-supervised Examination Process Modeling (SEPM) framework to learn the contextualized embedding for patents through modeling their examination processes. Specifically, we first design a multi-aspect event embedding layer, which leverages the fine-tuned language model, frequent-pattern embedding, and time encoding to capture the semantic, frequent-pattern and temporal information of examination events, respectively. Then, a mutual-information-aware integration layer is applied to fuse the extracted features into multi-aspect embedding considering their mutual interactions. Further, we develop a multi-objective sequential neural network for learning the contextualized patent representation, which is achieved through jointly learning two self-supervised objectives, namely event code and event lag auto-regression. To explore the application potential of SEPM, we fine-tune the well-trained model for three important downstream tasks of patent management, including the prediction of next events, patent classification, and grant prediction. In the end, extensive experiments with real-world data from the US Patent and Trademark Office verify the effectiveness and application prospects of the proposed framework.

Adjustment of the Framingham index by abdominal aortic calcification scores enables a more accurate prediction of long-term cardiac events in general population aged 40 years and above: evidence from NHANES 2013–2014

BackgroundAbdominal aortic calcification (AAC) is considered as a strong predictor of cardiovascular disease (CVD) events. Our study aimed to investigate whether the predicted risk for cardiac death with the Framingham risk score (FRS) could be further improved with the addition of AAC score in general population aged ≥ 40 years.MethodsA total of 2971 participants aged ≥ 40 years in the National Health and Nutrition Examination Surveys (NHANES) 2013–2014 were followed up. The Spearman’s rank correlation was performed to explore the correlation between the AAC score and FRS. the Kaplan-Meier method was used to make cumulative cardiac death curve. Predictors of cardiac death was identified by multivariate Cox regression. Additionally, the predictive value of FRS alone and combined with AAC score was estimated by comparing the area under the receiver-operating characteristic (ROC) curve (AUC) and calculating Integrated Discrimination Improvement (IDI).ResultsDuring a median of 70.50 months (interquartile range 58.43–82.57 months), 49 (1.649%) experienced cardiac death in the follow-up. The FRS was positively associated with AAC score. Both FRS and AAC score were independent predictors of cardiac death (hazard ratio 1.216; 95%CI 1.135 to 1.302; p < 0.00001; 1.159; 95%CI 1.109 to 1.211; p < 0.00001; respectively). Furthermore, Kaplan-Meier analysis demonstrated increased risk of cardiac death with increasing AAC score (log-rank 54.548, p<0.0001). The predictive value of FRS significantly was improved by adding with AAC score [increase in AUC from 0.734 to 0.780, p < 0.0001; IDI = 0.007, p = 0.013 ].ConclusionsAAC score is positively related with Framingham index, and FBS adjusted by AAC score further improved the prediction risk for cardiac death in general population aged ≥ 40 years.

Recent changes in ENSO’s impacts on the summertime circumglobal teleconnection and mid-latitude extremes

The boreal summer circumglobal teleconnection (CGT) provides a primary predictability source for mid-latitude Northern Hemisphere climate anomalies and extreme events. Here, we show that the CGT’s circulation structure has been displaced westward by half a wavelength since the late 1970s, more severely impacting heatwaves and droughts over East Europe, East Asia, and southwestern North America. We present empirical and modelling evidence of the essential role of El Niño-Southern Oscillation (ENSO) in shaping this change. Before the late 1970s, ENSO indirectly promoted CGT by modulating the Indian summer monsoon rainfall (ISMR). Since 1980s, the ENSO–ISMR link has weakened, but the westward-displaced ENSO forcing has been able to directly trigger a Rossby wave response at the exit of the East Asian westerly jet, resulting in a shift of the previous CGT’s North Pacific and downstream centers westward along the westerly jet waveguide. State-of-the-art climate models with prescribed anthropogenic forcing cannot simulate these changes, suggesting that they are driven by natural variability. This work highlights the importance of studying the impacts of changing ENSO to improve seasonal prediction of mid-latitude extreme events.

Prognostic model for esophagogastric variceal rebleeding after endoscopic treatment in liver cirrhosis: A Chinese multicenter study.

Rebleeding after recovery from esophagogastric variceal bleeding (EGVB) is a severe complication that is associated with high rates of both incidence and mortality. Despite its clinical importance, recognized prognostic models that can effectively predict esophagogastric variceal rebleeding in patients with liver cirrhosis are lacking. To construct and externally validate a reliable prognostic model for predicting the occurrence of esophagogastric variceal rebleeding. This study included 477 EGVB patients across 2 cohorts: The derivation cohort (n = 322) and the validation cohort (n = 155). The primary outcome was rebleeding events within 1 year. The least absolute shrinkage and selection operator was applied for predictor selection, and multivariate Cox regression analysis was used to construct the prognostic model. Internal validation was performed with bootstrap resampling. We assessed the discrimination, calibration and accuracy of the model, and performed patient risk stratification. Six predictors, including albumin and aspartate aminotransferase concentrations, white blood cell count, and the presence of ascites, portal vein thrombosis, and bleeding signs, were selected for the rebleeding event prediction following endoscopic treatment (REPET) model. In predicting rebleeding within 1 year, the REPET model exhibited a concordance index of 0.775 and a Brier score of 0.143 in the derivation cohort, alongside 0.862 and 0.127 in the validation cohort. Furthermore, the REPET model revealed a significant difference in rebleeding rates (P < 0.01) between low-risk patients and intermediate- to high-risk patients in both cohorts. We constructed and validated a new prognostic model for variceal rebleeding with excellent predictive performance, which will improve the clinical management of rebleeding in EGVB patients.

Improving 10-year cardiovascular risk prediction in patients with type 2 diabetes with metabolomics

BackgroundExisting cardiovascular risk prediction models still have room for improvement in patients with type 2 diabetes who represent a high-risk population. This study evaluated whether adding metabolomic biomarkers could enhance the 10-year prediction of major adverse cardiovascular events (MACE) in these patients.MethodsData from 10,257 to 1,039 patients with type 2 diabetes from the UK Biobank (UKB) and the German ESTHER cohort, respectively, were used for model derivation, internal and external validation. A total of 249 metabolites were measured with nuclear magnetic resonance (NMR) spectroscopy. Sex-specific LASSO regression with bootstrapping identified significant metabolites. The enhanced model’s predictive performance was evaluated using Harrell’s C-index.ResultsSeven metabolomic biomarkers were selected by LASSO regression for enhanced MACE risk prediction (three for both sexes, three male- and one female-specific metabolite(s)). Especially albumin and the omega-3-fatty-acids-to-total-fatty-acids-percentage among males and lactate among females improved the C-index. In internal validation with 30% of the UKB, adding the selected metabolites to the SCORE2-Diabetes model increased the C-index statistically significantly (P = 0.037) from 0.660 to 0.678 in the total sample. In external validation with ESTHER, the C-index increase was higher (+ 0.043) and remained statistically significant (P = 0.011).ConclusionsIncorporating seven metabolomic biomarkers in the SCORE2-Diabetes model enhanced its ability to predict MACE in patients with type 2 diabetes. Given the latest cost reduction and standardization efforts, NMR metabolomics has the potential for translation into the clinical routine.Graphical abstract

Predicting Seizures Episodes and High-Risk Events in Autism Through Adverse Behavioral Patterns.

Individuals with Autism Spectrum Disorder may display interfering behaviors that limit their inclusion in educational and community settings, negatively impacting their quality of life. These behaviors may also signal potential medical conditions or indicate upcoming high-risk behaviors. This study explores behavior patterns that precede high-risk, challenging behaviors or seizures the following day. We analyzed an existing dataset of behavior and seizure data from 331 children with profound ASD over nine years. We developed a deep learning-based algorithm designed to predict the likelihood of aggression, elopement, and self-injurious behavior (SIB) as three high-risk behavioral events, as well as seizure episodes as a high-risk medical event occurring the next day. The proposed model attained accuracies of 78.4%, 80.68%, 85.43%, and 69.95% for predicting the next-day occurrence of aggression, SIB, elopement, and seizure episodes, respectively. The results were proven significant for more than 95% of the population for all high-risk event predictions using permutation-based statistical tests. Our findings emphasize the potential of leveraging historical behavior data for the early detection of high-risk behavioral and medical events, paving the way for behavioral interventions and improved support in both social and educational environments.

RELATIONSHIP BETWEEN HEPATORENAL INDEX AND ECHOGENICITY OF CAROTID ATHEROSCLEROTIC PLAQUES ACCORDING TO GSM-ANALYSIS IN PATIENTS WITH ASYMPTOMATIC CAROTID ATHEROSCLEROSIS

HighlightsThe severity of liver steatosis is inversely correlated with the echogenicity of carotid atherosclerotic plaques in middle-aged patients with carotid atherosclerosis.Hepatorenal index is an ultrasound marker of hypoechoic carotid atherosclerotic plaques.The inclusion of liver steatosis in cardiovascular risk stratification systems can potentially improve the prediction of adverse cardiovascular events. AnnotationAim. To study the relationship between hepatorenal index (HRI) and echogenicity of carotid atherosclerotic plaques (CAP).Methods. The study included patients with CVD risk factors. The patients' age ranged from 40 to 64 years. The ultrasound study of the brachiocephalic arteries was carried out in standard modes. Echogenicity of CAP in carotid arteries was assessed by GSM-analysis. HRI was defined as the ratio of the median gray scale of the liver to the median gray scale of the kidney. Severity of hepatic steatosis was determined by semiquantitative method using Hamaguchi scale.Results. 139 patients underwent examination according to a unified protocol. Among those patients 104 were selected for the analysis. Assessing the relationship between echogenicity of carotid CAPs, Hamaguchi scale score and HRI, we have found inverse statistically significant correlations. The median GSM of CAP in the study group was 57,0 (39,7; 80,0) conventional units. In order to determine the potential diagnostic value of Hamaguchi scale and HRI for detecting CAPs with lower than median echogenicity for the given group of patients, we performed ROC analysis. According to logistic regression analysis adjusted for sex, age, obesity, and abdominal obesity, an increase in HRI greater than 1,34 was associated with increased odds ratio of carotid CAPs with GSM &lt; 57 conventional units (less than 50th percentile) by a factor of 2,66 (95% CI 1,09–6,45; p = 0,031), whereas the increase associated with CAPs with GSM &lt; 39,7 conventional units (less than 25th percentile) was 2,95-fold (95% CI 1,08–8,08; p = 0,035).Conclusion. The severity of hepatic steatosis as assessed by the Hamaguchi scale and HRI was inversely correlated with the echogenicity of carotid CAPs in middle-aged patients with carotid atherosclerosis. An increase in HRI &gt; 1,34 with 65,8% probability predicted the presence of carotid CAPs with a GSM of less than 57. Odds ratio of carotid CAPs with GSM &lt; 57 conventional units (less than the 50th percentile) and GSM &lt; 39,7 (less than 25th percentile) with HRI values &gt; 1,34 were 2,66 and 2,95, respectively, after adjustment for intervening factors.

Correction of CAMS PM10 Reanalysis Improves AI-Based Dust Event Forecast

Correction of CAMS PM10 Reanalysis Improves AI-Based Dust Event Forecast

MKER: multi-modal knowledge extraction and reasoning for future event prediction

Humans can predict what will happen shortly, which is essential for survival, but machines cannot. To equip machines with the ability, we introduce the innovative multi-modal knowledge extraction and reasoning (MKER) framework. This framework combines external commonsense knowledge, internal visual relation knowledge, and basic information to make inference. This framework is built on an encoder-decoder structure with three essential components: a visual language reasoning module, an adaptive cross-modality feature fusion module, and a future event description generation module. The visual language reasoning module extracts the object relationships among the most informative objects and the dynamic evolution of the relationship, which comes from the sequence scene graphs and commonsense graphs. The long short-term memory model is employed to explore changes in the object relationships at different times to form a dynamic object relationship. Furthermore, the adaptive cross-modality feature fusion module aligns video and language information by using object relationship knowledge as guidance to learn vision-language representation. Finally, the future event description generation module decodes the fused information and generates the language description of the next event. Experimental results demonstrate that MKER outperforms existing methods. Ablation studies further illustrate the effectiveness of the designed module. This work advances the field by providing a way to predict future events, enhance machine understanding, and interact with dynamic environments.

Prediction Of Adverse Events After Left Ventricular Assist Device Implantation Based On Pre-implantation Comorbidities

Prediction Of Adverse Events After Left Ventricular Assist Device Implantation Based On Pre-implantation Comorbidities

From intensive care monitors to cloud environments: a structured data pipeline for advanced clinical decision support.

Clinical decision-making is increasingly shifting towards data-driven approaches and requires large databases to develop state-of-the-art algorithms for diagnosing, detecting and predicting diseases. The intensive care unit (ICU), a data-rich setting, faces challenges with high-frequency, unstructured monitor data. Here, we showcase a successful example of a data pipeline to efficiently move patient data to the cloud environment for structured storage. This supports individual patient analysis, enables largescale retrospective research, and the development of data-driven algorithms. Since June 2021, ICU data of the Amsterdam UMC have been collected and stored in a third-party cloud environment which is hosted on large virtual servers. The feasibility of the pipeline will be demonstrated with the available data through research and clinical use cases. Furthermore, privacy, safety, data quality, and environmental impact are carefully considered in the cloud storage transition. Over two years, data from over 9000 patients have been stored in the cloud. The availability, agility, computational power, high uptime, and streaming data pipelines allow for large retrospective analyses as well as the opportunity to implement real-time prediction of critical events with machine learning algorithms. Critical events can be accessed by applying keyword search in the natural language data, annotated by the treating team. Besides, the cloud environment offers storage of institutional data enabling evaluation of healthcare. The combined data and features of cloud environments offer support for predictive algorithm development and implementation, healthcare evaluation, and improved individual patient care. University of Amsterdam Research Priority Agenda Program AI for Heath Decision-Making.

Harmful algal bloom prediction using empirical dynamic modeling.

Harmful Algal Blooms (HABs) can originate from a variety of reasons, including water pollution coming from agriculture, effluent from treatment plants, sewage system leaks, pH and light levels, and the consequences of climate change. In recent years, HAB events have become a serious environmental problem, paralleling population growth, agricultural development, increasing air temperatures, and declining precipitation. Hence, it is crucial to identify the mechanisms responsible for the formation of HABs, accurately assess their short- and long-term impacts, and quantify their variations based on climate projections for developing accurate action plans and effectively managing resources. From this point of view, this present study utilizes empirical dynamic modeling (EDM) to predict chlorophyll-a concentration of Lake Erie. This method is characterized by its nonlinearity and nonparametric nature. EDM has a key advantage in that it overcomes the limitations of traditional statistical modeling by utilizing data-driven attractor reconstruction. Chlorophyll-a is a critical parameter in the prediction of HAB events. Lake Erie is an inland water body that experiences frequent HAB phenomena due to its location. The EDM demonstrated exceptional performance, and these findings imply that the EDM model can effectively capture the underlying dynamics of chlorophyll-a changes.

Enhancing drug-drug interaction event prediction from knowledge graphs by multimodal deep neural networks

Enhancing drug-drug interaction event prediction from knowledge graphs by multimodal deep neural networks

Predictive nomogram of ultrasound indicators for the termination outcome of caesarean scar pregnancy

To develop and validate a nomogram for predicting the risk of adverse events (intraoperative massive haemorrhage or retained products of conception) associated with the termination of Caesarean scar pregnancy (CSP). Data were retrospectively collected from patients diagnosed with CSP who underwent Dilation and Curettage (D&C) at two hospitals. This data was divided into internal and external cohorts for analysis. The internal cohort was randomly split, with 70% of the data designated for a training set and 30% for an internal validation set. The external cohort served exclusively as the external validation set. LASSO and logistic regression techniques were employed to select variables and construct the nomogram. The performance of the nomogram was evaluated using various methods, including C-index, calibration curve, decision curve analysis (DCA), and clinical impact curve analysis (CICA), to assess its identification, calibration, and clinical effectiveness. The prediction nomogram included several predictors, such as scar thickness, type of CSP, gestational sac diameter, and blood flow. It demonstrated strong discrimination, with a C-index of 0.83 (95% confidence interval: 0.77–0.89). Furthermore, in the internal validation set, a high C-index of 0.78 was achieved, while in the external validation set, it reached 0.83. Additional assessments using calibration curve analysis, DCA, and CICA indicated robust agreement between the nomogram’s predictions and actual observations, highlighting its utility and reliability. The developed nomogram shows excellent discriminative ability and calibration, with the potential for effective local prediction of adverse events in CSP.

Distilling the knowledge from large-language model for health event prediction

Health event prediction is empowered by the rapid and wide application of electronic health records (EHR). In the Intensive Care Unit (ICU), precisely predicting the health related events in advance is essential for providing treatment and intervention to improve the patients outcomes. EHR is a kind of multi-modal data containing clinical text, time series, structured data, etc. Most health event prediction works focus on a single modality, e.g., text or tabular EHR. How to effectively learn from the multi-modal EHR for health event prediction remains a challenge. Inspired by the strong capability in text processing of large language model (LLM), we propose the framework CKLE for health event prediction by distilling the knowledge from LLM and learning from multi-modal EHR. There are two challenges of applying LLM in the health event prediction, the first one is most LLM can only handle text data rather than other modalities, e.g., structured data. The second challenge is the privacy issue of health applications requires the LLM to be locally deployed, which may be limited by the computational resource. CKLE solves the challenges of LLM scalability and portability in the healthcare domain by distilling the cross-modality knowledge from LLM into the health event predictive model. To fully take advantage of the strong power of LLM, the raw clinical text is refined and augmented with prompt learning. The embedding of clinical text are generated by LLM. To effectively distill the knowledge of LLM into the predictive model, we design a cross-modality knowledge distillation (KD) method. A specially designed training objective will be used for the KD process with the consideration of multiple modality and patient similarity. The KD loss function consists of two parts. The first one is cross-modality contrastive loss function, which models the correlation of different modalities from the same patient. The second one is patient similarity learning loss function to model the correlations between similar patients. The cross-modality knowledge distillation can distill the rich information in clinical text and the knowledge of LLM into the predictive model on structured EHR data. To demonstrate the effectiveness of CKLE, we evaluate CKLE on two health event prediction tasks in the field of cardiology, heart failure prediction and hypertension prediction. We select the 7125 patients from MIMIC-III dataset and split them into train/validation/test sets. We can achieve a maximum 4.48% improvement in accuracy compared to state-of-the-art predictive model designed for health event prediction. The results demonstrate CKLE can surpass the baseline prediction models significantly on both normal and limited label settings. We also conduct the case study on cardiology disease analysis in the heart failure and hypertension prediction. Through the feature importance calculation, we analyse the salient features related to the cardiology disease which corresponds to the medical domain knowledge. The superior performance and interpretability of CKLE pave a promising way to leverage the power and knowledge of LLM in the health event prediction in real-world clinical settings.

The Estimated Glomerular Filtration Rate in the Prediction of Major Adverse Cardiovascular Events in Patients with Normal Renal Function after Coronary Artery Bypass Grafting: A Single-Center Retrospective Cohort Study

Background: The estimated glomerular filtration rate (eGFR) has emerged as a risk factor for coronary artery disease (CAD), but there are currently insufficient data on the association of eGFR with the postoperative prognosis in patients undergoing coronary artery bypass grafting (CABG). This study aimed to investigate the potential utility of eGFR as a prognostic indicator for major adverse cardiovascular events (MACE) in patients with normal renal function after CABG. Methods: This study included a total of 222 consecutive patients with normal renal function (eGFR ≥90 mL/min/1.73 m2) who underwent CABG between June 2018 and December 2019. These patients were divided into two groups based on the occurrence of MACE: without MACE group (n = 202) and with MACE group (n = 20). The eGFR was calculated from serum creatinine (SCr). The eGFR (mL/min/1.73 m2) = 175 × SCr (mg/dL)–1.234 × age (year)–0.179 × 0.79 (in the case of females). Kaplan–Meier survival curve analysis was conducted followed by the log-rank test. Cox proportional hazards regression models were used to explore the association between the eGFR and MACE. Receiver operating characteristics (ROC) curve analysis was performed to assess the predictive performance of the eGFR and identify the optimal cutoff point. Results: A progressively lower eGFR was associated with an increasingly higher cumulative incidence of MACE (log-rank test, p = 0.017). The hazard ratio (95% CI) of MACE was 0.068 (0.005–0.941) in tertile 3 of the eGFR and 0.204 (0.051–0.817) per SD increase in the eGFR. The AUC of the eGFR was 0.678 (p = 0.009) and the optimal cut-off value to predict MACE was &gt;118.3 mL/min/1.73 m2. Conclusion: The eGFR may be an independent prognostic determinant in patients with normal renal function undergoing CABG surgery. In patients with normal renal function, MACE was more likely to occur in patients with lower eGFR compared with those with higher eGFR. The eGFR may have an important predictive value in predicting MACE in patients with normal renal function undergoing CABG surgery.

Hemogram-Based Phenotypes of the Immune Response and Coagulopathy in Blunt Thoracic Trauma.

Background: Blunt thoracic trauma possesses unique physiopathological traits due to the complex interaction of immune and coagulation systems in the lung tissue. Hemogram-based ratios such as neutrophil-to-lymphocyte (NLR), platelet-to-lymphocyte (PLR), neutrophil-to-lymphocyte × platelet (NLPR) ratios have been studied as proxies for immune dysregulation and survival in trauma. We hypothesized that blunt thoracic trauma patients exhibit distinct patterns of coagulation and inflammation abnormalities identifiable by the use of readily available hemogram-derived markers. Methods: The present study represents a retrospective observational analysis that included 86 patients with blunt thoracic trauma from a single high-volume level one trauma center. The primary outcome was mortality prediction in blunt thoracic trauma patients using these derived biomarkers. Secondary outcomes included phenotypes of the immune response and coagulopathy and the prediction of non-fatal adverse events. Results: A U-shaped distribution of mortality was found, with high rates of early deaths in patients with an NLPR value of <3.1 and high rates of late deaths in patients with NLPR > 9.5. A subgroup of blunt thoracic trauma patients expressing moderate inflammation and inflammation-induced hypercoagulation objectified as NLPR between 3.1 and 9.5 may have a survival benefit (p < 0.0001). The NLPR cut-off for predicting early deaths and the need for massive transfusion was 3.1 (sensitivity = 80.00% and specificity = 71.05%). Conclusions: These findings suggest that blunt thoracic trauma patients exhibit distinct phenotypes of the immune response and coagulopathy from the early stages. A controlled, balanced interaction of immune, coagulation, and fibrinolytic systems might effectively achieve tissue repair and increase survival in thoracic trauma patients and should be subject to further research.

Prediction of major adverse cardiovascular events following acute myocardial infarction using electrocardiogram DETERMINE score

BackgroundAcute myocardial infarction (AMI) remains a major cause of mortality and morbidity globally, with a high incidence of major adverse cardiovascular events (MACE) post-primary percutaneous coronary intervention (PPCI). The DETERMINE score, derived from electrocardiographic (ECG) markers, has shown promise as a predictor of adverse outcomes, but its clinical utility requires further validation.ObjectiveTo evaluate the predictive value of the DETERMINE score for MACE and provide early clinical warnings for high-risk patients.MethodsThis bidirectional cohort study included AMI patients from the Second Affiliated Hospital of Anhui Medical University between 2019 and 2023. The training cohort comprised 545 patients between January 2019 and January 2023, while the validation cohort consisted of 122 patients between February 2023 and July 2023. The primary endpoint was MACE within one-year post-PPCI. The relationship between the DETERMINE score and MACE was analyzed using Cox regression, trend tests, and restricted cubic splines to assess linear and nonlinear associations. Patients were stratified into risk groups based on tertiles or optimal cutoffs, and Kaplan-Meier survival curves compared MACE incidence across groups. Predictive accuracy was evaluated through time-dependent C-index, ROC curves, decision curve analysis, and calibration, and compared to other prognostic scores, including the Selvester, GRACE, and SYNTAX scores, as well as left ventricular ejection fraction (LVEF). Subgroup analyses by sex, age, and culprit artery involvement were also conducted.ResultsCox multivariate regression indicated that the DETERMINE score was an independent risk factor for MACE (HR = 1.56, 95% CI 1.38–1.75, P < 0.001). Trend test and RCS showed a positive correlation and non-linear relationship between the DETERMINE score and MACE (P-trend < 0.001, P-overall < 0.001, P-nonlinear: 0.003). Kaplan-Meier survival analysis revealed that, in both the training and validation datasets, groups with a higher DETERMINE score showed a higher cumulative risk of MACE. The DETERMINE score outperformed traditional prognostic scores (Selvester, GRACE, SYNTAX) in terms of predictive accuracy, with an AUROC of 0.840 at 12 months in the training cohort. The score also provided a substantial clinical net benefit, particularly over longer follow-up periods. Subgroup analyses confirmed its predictive power across different demographics and clinical presentations.ConclusionThe DETERMINE score has outstanding predictive power for MACE post-PPCI, which can guide the early identification of high-risk patients with poor prognosis of AMI in clinical practice.