Early Prediction Model Research Articles

Development and validation of predictive models of early immune effector cell-associated hematotoxicity (eIPMs).

Immune effector cell-associated hematotoxicity (ICAHT) is associated with morbidity and mortality after chimeric antigen receptor (CAR) T-cell therapy. To date, the factors associated with ICAHT are poorly characterized, and there is no validated predictive model of ICAHT specifically. Therefore, we performed comprehensive univariate analyses to identify factors associated with severe (grade 3-4) early ICAHT (eICAHT) in 691 patients who received commercial or investigational CAR T-cell therapy for hematologic malignancies. In univariate logistic regression, pre-infusion factors associated with severe eICAHT included disease type (acute lymphoblastic leukemia), pre-lymphodepletion (LD) blood counts including absolute neutrophil count (ANC), lactate dehydrogenase (LDH), and inflammatory (C-reactive protein [CRP]; ferritin, interleukin-6 [IL-6]), and coagulopathy biomarkers (D-dimer). Post-infusion laboratory markers associated with severe eICAHT included early and peak levels of inflammatory biomarkers (CRP, ferritin, IL-6), coagulopathy biomarkers (D-dimer), peak cytokine release syndrome (CRS) grade, and peak neurotoxicity grade. We trained (n = 483) and validated (n = 208) two Early ICAHT Prediction Models (eIPM): eIPMPre including pre-infusion factors only (disease type and pre-LD ANC, platelet count, LDH, and ferritin) and eIPMPost containing both pre- (disease type and pre-LD ANC, platelet count, and LDH) and post-infusion (day +3 ferritin) factors. Both models generated calibrated predictions and high discrimination (area under the receiver operating characteristic curve [AUROC] in test set: 0.87 for eIPMPre and 0.88 for eIPMPost), with higher net benefit in decision curve analysis for eIPMPost. Individualized predictions of severe eICAHT can be generated from both eIPMs utilizing our online tool (available at https://eipm.fredhutch.org).

Abnormal microRNA expression profile at early stages of gestation in pregnancies destined to develop placenta previa

BackgroundPlacenta previa is the abnormal implantation of the placenta into the lower segment of the uterus, is associated with adverse maternal and fetal outcomes such as placenta accreta spectrum disorders, antepartum and postpartum hemorrhage, fetal growth restriction, prematurity, stillbirth and neonatal death, thrombophlebitis, and septicemia. The aim of the study was to assess retrospectively how the later onset of placenta previa affects the microRNA expression profile in the whole peripheral blood during the first trimester of gestation.MethodsRegarding the occurrence of the association between aberrant microRNA expression profiles at early stages of gestation and later onset of various pregnancy-related complications, we selected for the study pregnancies developing placenta previa as the only pregnancy-related disorder. In total, 24 singleton pregnancies diagnosed with placenta previa that underwent first-trimester prenatal screening and delivered on-site within the period November 2012–May 2018 were included in the study. Overall, 80 normal pregnancies that delivered appropriate-for-gestational age newborns after completing 37 weeks of gestation were selected as the control group based on the equality of the length of biological sample storage.ResultsDownregulation of multiple microRNAs (miR-20b-5p, miR-24-3p, miR-26a-5p, miR-92a-3p, miR-103a-3p, miR-130b-3p, miR-133a-3p, miR-145-5p, miR-146a-5p, miR-155-5p, miR-181a-5p, miR-195-5p, miR-210-3p, miR-342-3p, and miR-574-3p) was observed in pregnancies destined to develop placenta previa. The combination of seven microRNAs (miR-130b-3p, miR-145-5p, miR-155-5p, miR-181a-5p, miR-210-3p, miR-342-3p, and miR-574-3p) showed the highest accuracy (AUC 0.937, p < 0.001, 100.0% sensitivity, 83.75% specificity) to differentiate, at early stages of gestation, between pregnancies with a normal course of gestation and those with placenta previa diagnosed in the second half of pregnancy. Overall, 75% of pregnancies destined to develop placenta previa were correctly identified at 10.0% FPR.ConclusionConsecutive large-scale analyses must be performed to verify the reliability of the proposed novel early predictive model for placenta previa occurring as the only pregnancy-related disorder.

Genetic risk stratification and risk factors of early menopause in women: a multi-center study utilizing polygenic risk scores

ObjectiveThis study aims to evaluate the utility of polygenic risk scores (PRS) in women with early menopause (EM) and to investigate the clinical characteristics and risk factors associated with EM based on genetic risk.Study designGenotyping data and clinical data from women with EM and women with normal age of menopause retrieved from UK Biobank were used for early menopause risk prediction model establishment. Subsequently, 99 women diagnosed with EM and 1027 control women underwent PGT-M were recruited for model validation from across eight hospitals in China. According to PRS percentiles, these participants were further classified into high risk and intermediate risk groups. Characteristics among women at different risk levels were compared, and risk factors with early menopause were also statistical analyzed.Main outcome measuresThe proportion of women at high risk in EM and control groups; Characteristics with significant difference among women at different risk levels; risk factors associated with EM.ResultsThe proportion of high-risk women in the EM group was significantly higher than that in control women underwent PGT-M (Group PGT-M) (OR = 3.78), and that in women with normal age menopause from UK Biobank (Group UKB) (OR = 5.11). Notably, the women with high risk of EM exhibited distinct characteristics compared to women with the intermediate-risk of EM, and identified several risk factors associated with EM.ConclusionsWe established a PRS model to serves as a valuable instrument for EM risk prediction. The exploratory analysis revealed that women with high risk of EM exhibited a higher height, suggesting EM related genetic loci may also influence growth and development level. Several risk factors were found to be potentially associated with EM, such as excessive familial contentment, COVID-19 vaccination, staying up late, and the husband’s engagement in smoking and alcohol abuse.

Multimodal transformer for early alarm prediction

Alarms are an essential part of distributed control systems designed to help plant operators keep the processes stable and safe. In reality, however, alarms are often noisy and thus can be easily overlooked. Early alarm prediction can give the operator more time to assess the situation and introduce corrective actions to avoid downtime and negative impact on human safety and environment. Existing studies on alarm prediction typically rely on signals directly coupled with these alarms. However, using more sources of information could benefit early prediction by letting the model learn characteristic patterns in the interactions of signals and events. Meanwhile, multimodal deep learning has recently seen impressive developments. Combination (or fusion) of modalities has been shown to be a key success factor, yet choosing the best fusion method for a given task introduces a new degree of complexity, in addition to existing architectural choices and hyperparameter tuning. This is one of the reasons why real-world problems are still typically tackled with unimodal approaches. To bridge this gap, we introduce a multimodal Transformer model for early alarm prediction based on a combination of recent events and signal data. The model learns the optimal representation of data from multiple fusion strategies automatically. The model is validated on real-world industrial data. We show that our model is capable of predicting alarms with the given horizon and that the proposed multimodal fusion method yields state-of-the-art predictive performance while eliminating the need to choose among conventional fusion techniques, thus reducing tuning costs and training time.

Analyzing the Efficacy of K-Means Clustering and Logistic Regression For Diabetes Prediction

Diabetes causes a large number of deaths each year and a large number of people living with the disease do not realize their health condition early enough. In this study, we propose a data mining based model for early diagnosis and prediction of diabetes using the UCI database. Although K-means is simple and can be used for a wide variety of data types, it is quite sensitive to initial positions of cluster centers which determine the final cluster result, which either provides a sufficient and efficiently clustered dataset for the logistic regression model, or gives a lesser amount of data as a result of incorrect clustering of the original dataset, thereby limiting the performance of the logistic regression model. Our findings offer insights into the comparative strengths and weaknesses of each method, shedding light on their potential applications in diabetes diagnosis and risk assessment. A further experiment with a new dataset showed the applicability of our model for the predication of diabetes.

Prediction of risk for early or very early preterm births using high-resolution urinary metabolomic profiling

BackgroundPreterm birth (PTB) is a serious health problem. PTB complications is the main cause of death in infants under five years of age worldwide. The ability to accurately predict risk for PTB during early pregnancy would allow early monitoring and interventions to provide personalized care, and hence improve outcomes for the mother and infant.ObjectiveThis study aims to predict the risks of early preterm (< 35 weeks of gestation) or very early preterm (≤ 26 weeks of gestation) deliveries by using high-resolution maternal urinary metabolomic profiling in early pregnancy.DesignA retrospective cohort study was conducted by two independent preterm and term cohorts using high-density weekly urine sampling. Maternal urine was collected serially at gestational weeks 8 to 24. Global metabolomics approaches were used to profile urine samples with high-resolution mass spectrometry. The significant features associated with preterm outcomes were selected by Gini Importance. Metabolite biomarker identification was performed by liquid chromatography tandem mass spectrometry (LCMS-MS). XGBoost models were developed to predict early or very early preterm delivery risk.Setting and participantsThe urine samples included 329 samples from 30 subjects at Stanford University, CA for model development, and 156 samples from 24 subjects at the University of Alabama, Birmingham, AL for validation.Results12 metabolites associated with PTB were selected and identified for modelling among 7,913 metabolic features in serial-collected urine samples of pregnant women. The model to predict early PTB was developed using a set of 12 metabolites that resulted in the area under the receiver operating characteristic (AUROCs) of 0.995 (95% CI: [0.992, 0.995]) and 0.964 (95% CI: [0.937, 0.964]), and sensitivities of 100% and 97.4% during development and validation testing, respectively. Using the same metabolites, the very early PTB prediction model achieved AUROCs of 0.950 (95% CI: [0.878, 0.950]) and 0.830 (95% CI: [0.687, 0.826]), and sensitivities of 95.0% and 60.0% during development and validation, respectively.ConclusionModels for predicting risk of early or very early preterm deliveries were developed and tested using metabolic profiling during the 1st and 2nd trimesters of pregnancy. With patient validation studies, risk prediction models may be used to identify at-risk pregnancies prompting alterations in clinical care, and to gain biological insights of preterm birth.

Clinical characteristics and early identification of augmented renal clearance in PICU patients with severe sepsis associated with MRSA infection

ObjectivesTo investigate the epidemiological characteristics of Augmented Renal Clearance (ARC) in severe sepsis children with MRSA infection and find risk factors to establish a model predicting ARC onset in PICU.DesignRetrospective study, in which ARC was defined by estimated glomerular filtration rate (eGFR) measured by the modified Schwartz formula above 130 ml/min/1.73 m2. Univariable and multivariable logistic regression analyses were performed to find the predictor for ARC. Multi-strategy modeling was used to form an early prediction model for ARC, which was evaluated by the area under the ROC curve (AUC), accuracy (ACC) and other indicators.SettingOne China PICU.PatientsSevere sepsis children with MRSA infection admitted to PICU from May 2017 to June 2022 at Children's Hospital of Nanjing Medical University.InterventionsNone.Measurements and main results125 of 167 (74.9%) patients with severe sepsis with MRSA infection have occurred ARC during the hospitalization of PICU, of which 44% have an absolute decrease in vancomycin trough level (VTL), patients with ARC have a longer length of stay in both hospital and PICU, lower VTL and require longer anti-infective treatment. 20 different models were established for the early recognition of ARC. Among them, the best performer had an AUC of 0.746 and a high application prospect.ConclusionARC is a phenomenon significantly underestimated in pediatric patients with severe sepsis associated with MRSA infection, which can affect 74.9% of these patients and affects the process of anti-infection treatment and clinical outcomes. To achieve early prediction only by specific risk factors is unreliable, a model based on Multivariate Logistic Regression in this study was chosen to be used clinically.

Machine learning-based infection diagnostic and prognostic models in post-acute care settings: a systematic review.

This study aims to (1) review machine learning (ML)-based models for early infection diagnostic and prognosis prediction in post-acute care (PAC) settings, (2) identify key risk predictors influencing infection-related outcomes, and (3) examine the quality and limitations of these models. PubMed, Web of Science, Scopus, IEEE Xplore, CINAHL, and ACM digital library were searched in February 2024. Eligible studies leveraged PAC data to develop and evaluate ML models for infection-related risks. Data extraction followed the CHARMS checklist. Quality appraisal followed the PROBAST tool. Data synthesis was guided by the socio-ecological conceptual framework. Thirteen studies were included, mainly focusing on respiratory infections and nursing homes. Most used regression models with structured electronic health record data. Since 2020, there has been a shift toward advanced ML algorithms and multimodal data, biosensors, and clinical notes being significant sources of unstructured data. Despite these advances, there is insufficient evidence to support performance improvements over traditional models. Individual-level risk predictors, like impaired cognition, declined function, and tachycardia, were commonly used, while contextual-level predictors were barely utilized, consequently limiting model fairness. Major sources of bias included lack of external validation, inadequate model calibration, and insufficient consideration of data complexity. Despite the growth of advanced modeling approaches in infection-related models in PAC settings, evidence supporting their superiority remains limited. Future research should leverage a socio-ecological lens for predictor selection and model construction, exploring optimal data modalities and ML model usage in PAC, while ensuring rigorous methodologies and fairness considerations.

Machine learning model for early prediction of survival in gallbladder adenocarcinoma: A comparison study

The prognosis for gallbladder adenocarcinoma (GBAC), a highly malignant cancer, is not good. In order to facilitate individualized risk stratification and improve clinical decision-making, this study set out to create and validate a machine learning model that could accurately predict early survival outcomes in GBAC patients. Five models—RSF, Cox regression, GBM, XGBoost, and Deepsurv—were compared using data from the SEER database (2010–2020). The dataset was divided into training (70 %) and validation (30 %) sets, and the C-index, ROC curves, calibration curves, and decision curve analysis (DCA) were used to assess the model's performance. At 1, 2, and 3-year survival intervals, the RSF model performed better than the others in terms of calibration, discrimination, and clinical net benefit. The most important predictor of survival, according to SHAP analysis, is AJCC stage. Patients were divided into high, medium, and low-risk groups according to RSF-derived risk scores, which revealed notable variations in survival results. These results demonstrate the RSF model's potential as an early survival prediction tool for GBAC patients, which could enhance individualized treatment and decision-making.

Prognostic value of serum complement cleavage factor Bb in idiopathic membranous nephropathy and establishment of nomogram model

Complement activation is involved in idiopathic membranous nephropathy (IMN). We aimed to investigate the relationship of serum complement cleavage factor Bb with IMN progression, and to establish a model for early prediction of kidney outcomes. We measured serum factor Bb in a retrospective cohort of 449 IMN patients at the time of kidney biopsy. Cox regression analysis showed that higher levels of serum factor Bb were independently associated with IMN progression event defined as end-stage renal disease or ≥ 40% decline in estimated glomerular filtration rate. Patients in the middle and highest tertiles of serum factor Bb had respectively 2.1-, and 2.6-fold higher risk for disease progression compared with those in the lowest tertile. We developed an optimized prognostic nomogram model incorporating age, log serum anti-PLA2R antibody, log serum Bb, proteinuria and tubular atrophy/interstitial fibrosis. The model demonstrated good predictive ability with a concordance index of 0.77 (bootstrap-corrected of 0.76) for predicting 3-, and 5-year kidney survival. Calibration curves and decision curve analysis confirmed the model’s good calibration and clinical utility. Our findings suggest that serum factor Bb may serve as an essential prognostic indicator of IMN. The novel nomogram model may offer important guidance on the management of this patient population.

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

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

Development and validation of an interpretable machine learning model for predicting the risk of distant metastasis in papillary thyroid cancer: a multicenter study

The survival rate of patients with distant metastasis (DM) of papillary thyroid carcinoma (PTC) is significantly reduced. It is of great significance to find an effective method for early prediction of the risk of DM for formulating individualized diagnosis and treatment plans and improving prognosis. Previous studies have significant limitations, and it is still necessary to develop new models for predicting the risk of DM of PTC. We aimed to develop and validate interpretable machine learning (ML) models for early prediction of DM in patients with PTC using a multicenter cohort. We collected data on patients with PTC who were admitted between June 2013 and May 2023. Data from 1430 patients at Yunnan Cancer Hospital (YCH) served as the training and internal validation set, while data from 434 patients at the First Affiliated Hospital of Kunming Medical University (KMU 1st AH) was used as the external test set. Nine ML methods such as random forest (RF) were used to construct the model. Model prediction performance was compared using evaluation indicators such as the area under the receiver operating characteristic curve (AUC). The SHapley Additive exPlanation (SHAP) method was used to rank the feature importance and explain the final model. Among the nine ML models, the RF model performed the best. The RF model accurately predicted the risk of DM in patients with PTC in both the internal validation of the training set [AUC: 0.913, 95% confidence interval (CI) (0.9075-0.9185)] and the external test set [AUC: 0.8996, 95% CI (0.8483-0.9509)]. The calibration curve showed high agreement between the predicted and observed risks. In the sensitivity analysis focusing on DM sites of PTC, the RF model exhibited outstanding performance in predicting "lung-only metastasis" showing high AUC, specificity, sensitivity, F1 score, and a low Brier score. SHAP analysis identified variables that contributed to the model predictions. An online calculator based on the RF model was developed and made available for clinicians at https://predictingdistantmetastasis.shinyapps.io/shiny1/. 11 variables were included in the final RF model: age of the patient with PTC, whether the tumor size is>2cm, whether the tumor size is≤1cm, lymphocyte (LYM) count, monocyte (MONO) count, monocyte/lymphocyte ratio (MLR), thyroglobulin (TG) level, thyroid peroxidase antibody (TPOAb) level, whether the T stage is T1/2, whether the T stage is T3/4, and whether the N stage is N0. On the basis of large-sample and multicenter data, we developed and validated an explainable ML model for predicting the risk of DM in patients with PTC. The model helps clinicians to identify high-risk patients early and provides a basis for individualized patient treatment plans. This work was supported by the National Natural Science Foundation of China (No. 81960426, 82360345 and 82001986), the Outstanding Youth Science Foundation of Yunnan Basic Research Project (No. 202401AY070001-316), Yunnan Province Applied and Basic Research Foundation (No. 202401AT070008), and Ten Thousand Talent Plans for Young Top-notch Talents of Yunnan Province.

Development and validation of an early acute kidney injury risk prediction model for patients with sepsis in emergency departments

In this study, we aimed to develop and validate a nomogram to predicting the risk of sepsis-associated acute kidney injury (SA-AKI) in patients admitted to emergency departments (EDs). We randomly divided a retrospective dataset of 391 patients with sepsis into a 294-person training cohort and a 97-person validation cohort, and developed three predictive models using multivariate logistic regression analysis and clinical insight. No difference was observed between the three models using the DeLong test and Model 3 was selected as the risk prediction model based on the principle of least inclusion indicators. The use of vasopressor drugs, patient age, platelet count, procalcitonin, and D-dimer levels were included. The training and validation cohorts had a consistency index of 0.832 and 0.866, respectively, indicating high accuracy and stability in predicting SA-AKI risk. The area under the receiver operating characteristic curve was 0.832, showing excellent discrimination. The calibration curves for the training and validation cohorts showed excellent calibration. The decision curve and clinical impact curve analyses showed that the net clinical benefit of using the nomogram was greatest over a probability threshold of 0.05–0.90. In addition, the model showed moderate validity in predicting the 30-day survival and the incidence of major adverse renal events within 30 days. The nomogram developed for SA-AKI risk assessment in patients in EDs showed good discriminability and clinical utility. It can provide a theoretical basis for emergency physicians to prevent SA-AKI.

Predictive modeling of bronchopulmonary dysplasia in premature infants: the impact of new diagnostic standards.

To provide a risk prediction for bronchopulmonary dysplasia (BPD) in premature infants under the new diagnostic criteria and establish a prediction model. In this study, we retrospectively collected case data on preterm infants admitted to the NICU from August 2015 to August 2018. A lasso analysis was performed to identify the risk factors associated with the development of BPD. A nomogram predictive model was constructed in accordance with the new diagnostic criteria for BPD. A total of 276 preterm infants were included in the study.The incidence of BPD under the 2018 diagnostic criteria was 11.2%. Mortality was significantly higher in the BPD group than the non-BPD group under the 2018 diagnostic criteria (P < 0.05). Fourteen possible variables were selected by the Lasso method, with a penalty coefficient λ=0.0154. The factors that eventually entered the logistic regression model included birth weight [BW, OR = 0.9945, 95% CI: 0.9904-0.9979], resuscitation way (OR = 4.8249, 95% CI: 1.3990-19.4752), intrauterine distress (OR = 8.0586, 95% CI: 1.7810-39.5696), score for SNAPPE-II (OR = 1.0880, 95% CI: 1.0210-1.1639), hematocrit (OR = 1.1554, 95% CI: 1.0469-1.2751) and apnea (OR = 7.6916, 95% CI: 1.4180-52.1236). The C-index after adjusting for fitting deviation was 0.894. This study made a preliminary exploration of the risk model for early prediction of BPD and indicated good discrimination and calibration in premature infants.

Early prediction of sepsis in emergency department patients using various methods and scoring systems.

Early recognition of sepsis, a common life-threatening condition in intensive care units (ICUs), is beneficial for improving patient outcomes. However, most sepsis prediction models were trained and assessed in the ICU, which might not apply to emergency department (ED) settings. To establish an early predictive model based on basic but essential information collected upon ED presentation for the follow-up diagnosis of sepsis observed in the ICU. This study developed and validated a reliable model of sepsis prediction among ED patients by comparing 10 different methods based on retrospective electronic health record data from the MIMIC-IV database. In-ICU sepsis was identified as the primary outcome. The potential predictors encompassed baseline demographics, vital signs, pain scale, chief complaints and Emergency Severity Index (ESI). 80% and 20% of the total of 425 737 ED visit records were randomly selected for the train set and the test set for model development and validation, respectively. Among the methods evaluated, XGBoost demonstrated an optimal predictive performance with an area under the curve (AUC) of 0.90 (95% CI: 0.90-0.91). Logistic regression exhibited a comparable predictive ability to XGBoost, with an AUC of 0.89 (95% CI: 0.89-0.90), along with a sensitivity and specificity of 85% (95% CI: 0.83-0.86) and 78% (95% CI: 0.77-0.80), respectively. Neither of the five commonly used severity scoring systems demonstrated satisfactory performance for sepsis prediction. The predictive ability of using ESI as the sole predictor (AUC: 0.79, 95% CI: 0.78-0.80) was also inferior to the model integrating ESI and other basic information. The use of ESI combined with basic clinical information upon ED presentation accurately predicted sepsis among ED patients, strengthening its application in ED. The proposed model may assist nurses in risk stratification management and prioritize interventions for potential sepsis patients, even in low-resource settings.

Development and validation of a risk prediction model for feeding intolerance in neurocritical patients with enteral nutrition.

This study collects and analyzes clinical data on enteral nutrition therapy in neurocritical patients, develops and validates a feeding intolerance (FI) risk prediction model, and provides a theoretical basis for screening patients with high risk of feeding intolerance (FI) and delivering personalized care. A convenience sampling method was employed to select 300 patients who were admitted to a tertiary hospital in China for early enteral nutrition therapy in the neurointensive care unit between April 2022 and December 2022. Independent risk factors for FI were identified using univariate and logistic regression analyses. A prediction model was established, and the goodness of fit and discriminant validity of the model were evaluated. The incidence of FI in neurocritical patients receiving enteral nutrition was 71%. Logistic regression analysis identified age, Glasgow Coma Scale (GCS) scores, Acute Physiology and Chronic Health Evaluation II (APACHE II) scores, mechanical ventilation, feeding via the nasogastric tube route, hyperglycemia, and low serum albumin as independent risk factors for the development of FI (p < 0.05). The predictive formula for FI risk was established as follows: Logit p = -14.737 + 1.184 × mechanical ventilation +2.309 × feeding route +1.650 × age + 1.336 × GCS tertile (6-8 points) + 1.696 × GCS tertile (3-5 points) + 1.753 × APACHE II score + 1.683 × blood glucose value +1.954 × serum albumin concentration. The Hosmer-Lemeshow test showed χ2 = 9.622, p = 0.293, and the area under the ROC curve was 0.941 (95% confidence interval: 0.912-0.970, p < 0.001). The optimal critical value was 0.767, with a sensitivity of 85.9%, a specificity of 90.8%, and a Youden index of 0.715. The early enteral nutrition FI risk prediction model developed in this study demonstrated good predictive ability. This model can serve as a valuable reference for effectively assessing the risk of FI in neurocritical patients, thereby enhancing clinical outcomes.

Evaluation of a Risk Model for Sepsis Early Prediction in Infants with Epidermolysis Bullosa

Abstract Objective Epidermolysis bullosa (EB) is a severe hereditary condition characterized by fragile skin that can lead to complications, including severe infections such as sepsis. Current research on sepsis in children with EB is limited, and there is a need for specific biomarkers that can aid in early detection and management. Methods This study analyzed blood samples from 92 children diagnosed with EB, 42 of whom developed sepsis. We investigated various inflammatory proteins and clinical parameters as potential biomarkers. Multivariate analysis was used to determine predictors of sepsis occurrence. Results Elevated levels of C-reactive protein (CRP), interleukin-6 (IL-6), lactate, and decreased oxygen saturation were significantly associated with sepsis in children with EB. The predictive model displayed an area under the receiver operating characteristic curve of 0.80 in the training set and 0.77 in the validation set, indicating good predictive accuracy. Conclusion Our findings suggest that CRP, IL-6, lactate, and oxygen saturation are reliable predictors of sepsis in children with EB. These biomarkers should be monitored closely to facilitate early diagnosis and improve outcomes in this vulnerable population. The study underscores the need for tailored research and diagnostic strategies for children with EB at risk of sepsis.

Hardware-accelerated neural network model for early prediction of sudden cardiac arrest based on heart rate variability metrics

AbstractSudden Cardiac Arrest (SCA) constitutes a dire medical condition, marked by the abrupt cessation of effective blood circulation due to the heart's failure to contract properly. This leads to acute circulatory collapse, often culminating in loss of consciousness within an hour and potentially resulting in fatality within minutes if left unattended. Heart rate variability (HRV) serves as a critical biometric, derived from electrocardiogram (ECG) signals with ventricular depolarization waves to further calculate the R-R Intervals (RRIs). These intervals provide the basis for extracting various characteristics of cardiac rhythm, encompassing time-domain, frequency-domain, and nonlinear features. This study presents a neural network-based classification algorithm that leverages HRV metrics to categorize patients into SCA and Normal Sinus Rhythm (NSR) cohorts. The proposed neural network (NN) model showcased impressive results, achieving an accuracy of 96.23%, a sensitivity of 94.35%, and a specificity of 98.12% in detecting SCA, as evaluated through leave-one-subject-out analysis. In order to harness the benefits of hardware acceleration, the algorithm is implemented on a Field-Programmable Gate Array (FPGA). Its computational efficiency is subsequently benchmarked against traditional software-based methodologies. The hardware-level implementation is made possible in Verilog hardware description language (HDL) and was verified successfully with expected performance by register-transfer level (RTL) simulation via Vivado 2020.2.

APT: Alarm Prediction Transformer

Distributed control systems (DCS) are essential to operate complex industrial processes. A major part of a DCS is the alarm system, which helps plant operators to keep the processes stable and safe. Alarms are defined as threshold values on individual signals taking into account minimum reaction time of the human operator. In reality, however, alarms are often noisy and overwhelming, and thus can be easily overlooked by the operators. Early alarm prediction can give the operator more time to react and introduce corrective actions to avoid downtime and negative impact on human safety and the environment. In this context, we introduce Alarm Prediction Transformer (APT), a multimodal Transformer-based machine learning model for early alarm prediction based on the combination of recent events and signal data. Specifically, we propose two novel fusion strategies and three methods of label encoding with various levels of granularity. Given a window of several minutes of event logs and signal data, our model predicts whether an alarm is going to be triggered after a few minutes and, if yes, it also predicts its location. Our experiments on two novel real industrial plant data sets and a simulated data set show that the model is capable of predicting alarms with the given horizon and that our proposed fusion technique combining inputs from different modalities, i. e. events and signals, yields more accurate results than any of the modalities alone or conventional fusion techniques.

Air pollution’s numerical, spatial, and temporal heterogeneous impacts on childhood hand, foot and mouth disease: a multi-model county-level study from China

BackgroundWhile stationary links between childhood hand, foot and mouth disease (HFMD) and air pollution are known, a comprehensive study on their heterogeneous relationships (nonstationarity), jointly considering numerical, temporal and spatial dimensions, has not been reported.MethodsMonthly HFMD incidence and air pollution data were collected at the county level from Sichuan-Chongqing, China (2009–2011), alongside meteorological and social environmental covariates. Key influential factors were identified using random forest (RF) under the stationary assumption. Factors’ numerically, temporally, and spatially heterogeneous relationships with HFMD were assessed using generalized additive model (GAM) and geographically and temporally weighted regression (GTWR).ResultsOur findings highlighted the relatively higher stationary contributions of fine particulate matter (PM2.5) and ozone (O3) to HFMD incidence across Sichuan-Chongqing counties. We further uncovered heterogeneous impacts of PM2.5 and O3 from three nonstationary perspectives. Numerically, PM2.5 showed an inverse ‘V’-shaped relationship with HFMD incidence, while O3 exhibited a complex pattern, with increased HFMD incidence at low PM2.5 and moderate O3 concentrations. Temporally, PM2.5’s impact peaked in autumn and was weakest in spring, whereas O3’s effect was strongest in summer. Spatially, hotspot mapping revealed high-risk clusters for PM2.5 impact across all seasons, with notable geographical variations, and for O3 in spring, summer, and autumn, concentrated in specific regions of Sichuan-Chongqing.ConclusionsThis study underscores the nuanced and three-perspective heterogeneous influences of air pollution on HFMD in small areas, emphasizing the need for differentiated, localized, and time-sensitive prevention and control strategies to enhance the precision of dynamic early warnings and predictive models for HFMD and other infectious diseases, particularly in the fields of environmental and spatial epidemiology.