Predictive Index Research Articles

Optimizing Kernel Density Estimation Bandwidth for Road Traffic Accident Hazard Identification: A Case Study of the City of London

Road traffic accidents pose significant challenges to sustainable urban safety and intelligent transportation management. The effective hazard identification of crash hotspots is crucial in implementing targeted safety measures. A severity-weighted system was adopted to quantify crash hazard levels. Using 1059 valid crash records of the City of London, the spatial correlations of crash points were first examined via average nearest neighbor analysis. Then, the optimal KDE bandwidth was determined via ArcGIS’s automatic extraction method, multi-distance spatial cluster analysis, and incremental spatial autocorrelation (ISA) analysis. The predictive accuracy index (PAI) was used to evaluate the accuracy of KDE results at various bandwidths. The results revealed a clustered spatial distribution of crash points. The optimized KDE bandwidth obtained via ISA analysis was 134 m, and the yielded PAI was 4.381, indicating better predictive accuracies and balanced hotspot distributions and reflecting both local concentrations and the overall continuity of crash hazard hotspots. Applying this bandwidth to the validation data allowed the successful identification of most high-risk areas and potential crash hazard hotspots attributed to traffic environmental factors; this method exhibits reliability, accuracy, and robustness over medium to long time scales. This workflow can serve as an analytical template for assisting planners in improving the identification accuracy of hazard hotspots, thereby reducing crash occurrences, actively promoting sustainable traffic safety development, and providing valuable insights for targeted crash prevention and intelligent traffic safety management in urban areas.

Doege-Potter syndrome due to a solitary fibrous tumor of the pleura: a case report

BackgroundDoege-Potter syndrome is a rare paraneoplastic phenomenon associated with solitary fibrous tumors of the pleura (SFTPs). It is characterized by the presence of severe, sustained, and treatment-refractory hypoglycemia. Hypoglycaemia, which may be the sole symptom at disease onset, is mediated by the secretion of high-molecular-weight insulin-like growth factor (IGF-2). Most tumors exhibit benign behavior, with a 100% survival rate at 5 years. However, 10% of these tumors may display aggressive behavior with local or metastatic recurrence. We present a clinical case of a patient with a benign solitary fibrous tumor of the pleura who presented with symptomatic hypoglycemia and required pulmonary and pleural surgical resection to control the paraneoplastic phenomenon.Case presentationA Hispanic 46-year-old man presented with a 15-day history of transient alterations in consciousness worsened by fasting. The relevant medical history included obstructive sleep apnea treated with continuous positive air pressure (CPAP) and previous smoking. In-hospital studies revealed noninsulinemic hypoglycemia and a benign SFTP. Complete surgical resection was performed while the patient received dextrose fluids and corticosteroids perioperatively for hypoglycemia. Subsequently, the hypoglycemia resolved, and the patient was followed-up without disease recurrence.ConclusionDoege-Potter syndrome is challenging to recognize. However, effective treatment can be achieved with a high survival rate. Raising awareness among healthcare professionals about the recognition of this paraneoplasic syndrome patients will improve diagnostic suspicion, biochemical confirmation, the development of diagnostic and therapeutic guidelines, and the creation of predictive indices for aggressive presentations requiring closer monitoring.

Association between adherence to life’s simple 7 metrics and risk of obstructive sleep apnea among adults in the United States

BackgroundWe aimed to explore the impact of adherence to Life’s Simple 7 (LS7) metrics on risk of obstructive sleep apnea (OSA), and the impact of inflammation on the association, in adults in the United States.MethodsData from 13,825 community-dwelling adults aged ≥ 20 years recruited in the National Health and Nutrition Examination Surveys (NHANES) 2005–2008, 2015–2018 was analyzed. The LS7 score was calculated based on the AHA definition of LS7 metrics. The diagnosis of OSA was based on self-reported symptoms of sleep disturbance using a standard questionnaire. The Multivariable Apnea Prediction (MAP) Index score was also calculated to assess the risk of OSA. Log-binominal regression and negative binomial regression were performed to estimate the associations between LS7 and OSA and MAP index, with odds ratios (ORs) and prevalence ratios (PRs) and their 95% confidence intervals (CIs) calculated. Mediation analysis was performed to estimate the mediating effects of inflammatory indicators on the associations.ResultsA total of 4473 participants (32.4%) had OSA, and the mean MAP index was 0.39. In fully adjusted log-binominal regression models, with total score < 6 as the reference, the ORs (95% CIs) for risk of OSA were 0.90 (0.73, 1.10), 0.76 (0.65, 0.89), 0.78 (0.64, 0.95), and 0.45 (0.38, 0.54) for total score = 6, total score = 7, total score = 8, and total score > 8, respectively (P for trend < 0.001). When LS7 score was analyzed as a continuous variable, each 1-point increase in LS7 score was associated with a 15% decrease in OSA risk (P < 0.001). In negative binominal regression models, the adjusted PRs (95% CIs) for the MAP index were 0.93 (0.90, 0.97), 0.87 (0.84, 0.91), 0.80 (0.77, 0.84), and 0.55 (0.53, 0.57) for total score = 6, total score = 7, total score = 8, and total score > 8, respectively (P for trend < 0.001). For each 1-point increase in LS7 score, the risk of OSA decreased by 13% (P < 0.001). Consistent results were observed in subgroup analysis. Mediation analysis indicated that inflammatory factors, including blood cell count, neutrophil count, and C-reactive protein, positively mediated the association of LS7 with OSA, with a mediation proportion of 0.022 (P = 0.04), 0.02 (P = 0.04), and 0.02 (P = 0.02), respectively.ConclusionsIn a nationally representative sample of US adults, adherence to LS7 metrics was independently associated with reduced OSA risk. Inflammation plays a mediating role in the association between LS7 and OSA.

Explainable highway performance degradation prediction model based on LSTM

With the dense and huge highway network in China, the highway maintenance management system has become the most concerned issue for Chinese highway managers in recent years. Therefore, based on the highway network in Guizhou Province of China, this paper established the semi-rigid asphalt pavement performance multi-output Long Short-Term Memory (LSTM) prediction model with regional applicability, including Pavement Surface Condition Index (PCI), Pavement Rutting Depth Index (RDI), Pavement Riding Quality Index (RQI) and Pavement Skidding Resistance Index (SRI) and the hyperparameters of the model were optimized by Bayesian optimization algorithm and the casual analysis of the model was conducted based on SHapley Additive exPlanations (SHAP), providing a reference for China's highway network-level preventive maintenance decision-making. The results prove that the established model can effectively predict the performance of the following year through the data of the previous two years. The established model has a better comprehensive prediction effect in comparison to similar multi-output models. The average coefficient of determination (R2) of the four prediction indexes can reach 0.823. Besides, the prediction effect among the indexes is stable and multiple pavement performance indexes can be effectively and reliably predicted at the same time. In addition, the contribution of input feature variables to output is quantified based on SHAP. According to quantified the contribution, the association relationship of the neural network model is set-valued mapped to the causality relationship in the post hoc. The input feature variables with the main contribution are extracted for causal analysis. The causal analysis shows that the degradation of the four pavement performances all have obvious time memory characteristics and have significant differences, but their pavement performance degradation is the result of nonlinear changes caused by the coupling effects of traffic load, road age, climate, and pavement structure.

Comparing the efficacy of different climate indices for prediction of labor loss, body temperatures, and thermal perception in a wide variety of warm and hot climates.

The purpose of this study was to investigate which climate/heat indices perform best in predicting heat-induced loss of physical work capacity (PWCloss). Integrating data from earlier studies, data from 982 exposures (75 conditions) exercising at a fixed cardiovascular load of 130 beats·min-1, in varying temperatures (15-50°C), humidities (20-80%), solar radiation (0-800 W·m-2), wind (0.2-3.5 m·s-1), and two clothing levels, were used to model the predictive power of ambient temperature, universal thermal climate index (UTCI), wet bulb globe temperature (WBGT), modified physiologically equivalent temperature (mPET), heat index, apparent temperature (AT), and wet bulb temperature (Twb) for the calculation of PWCloss, skin temperature (Tskin) and core-to-skin temperature gradient, and thermal perception (thermal sensation vote, TSV) in the heat. R2, RMSE, and Akaike information criterion were used indicating model performance. Indices not including wind/radiation in their calculation (Ta, heat index, AT, and Twb) struggled to provide consistent predictions across variables. For PWCloss and TSV, UTCI and WBGT had the highest predictive power. For Tskin, and core-to-skin temperature gradient, the physiological models UTCI and mPET worked best in seminude conditions, but clothed, AT, WBGT, and UTCI worked best. For all index predictions, Ta, vapor pressure, and Twb were shown to be the worst heat strain predictors. Although UTCI and WBGT had similar model performance using the full dataset, WBGT did not work appropriately in windy, hot-dry, conditions where WBGT predicted lower strain due to wind, whereas the empirical data, UTCI and mPET indicated that wind in fact increased the overall level of thermal strain. The findings of the current study highlight the advantages of using a physiological model-based index like UTCI when evaluating heat stress in dynamic thermal environments.NEW & NOTEWORTHY There is an urgent need to determine the optimal heat stress metric when forecasting the impact of heat stress on human performance, physiological stress, and perception. We analyzed a wealth of laboratory data, simulating heart rate (HR)-paced work with wide variations in air temperature, humidity, wind speed, solar radiation, and clothing. We conclude that the universal thermal climate index (UTCI) [followed by wet-bulb globe temperature (WBGT)] is the optimal heat index to reliably predict reductions in performance, and elevations in physiological and perceptual stress.

Assessment of perioperative cardiac risk using preoperative quantitative flow ratio in patients with coronary artery disease undergoing noncardiac surgery: a retrospective cohort study.

Quantitative flow ratio (QFR) is a novel diagnostic modality for the functional testing of coronary artery stenosis, but evidence concerning the postoperative prognostic implication of QFR in noncardiac surgery (NCS) of patients with coronary artery disease (CAD) is limited. The purpose of this study was to examine the role of QFR in perioperative risk prediction in patients with coronary heart disease. This retrospective cohort study was conducted in The First Affiliated Hospital of Wenzhou Medical University between 2013 and 2022, and consecutively included patients with CAD who had undergone NCS <1 year after coronary angiography. The primary endpoint was major adverse cardiovascular events (MACEs), which were defined as a composite of cardiovascular death, nonfatal myocardial infarction, nonfatal stroke, cardiopulmonary arrest, malignant ventricular arrhythmia (MVA), congestive heart failure, and revascularization. Univariate and multifactorial Cox regression was used to identify the independent risk factors for perioperative cardiovascular events and to construct new models. The area under the curve (AUC), net reclassification improvement (NRI), and integrated discrimination improvement (IDI) were used to compare the newly constructed model with existing traditional models. Among the 929 participants enrolled (median age 68 years; 72.0% male), the primary endpoint was met in 67 (7.2%) patients within 30 days of follow-up. There was no significant difference in the incidence of the primary endpoint between patients with QFR <0.75 and those with "gray zone" lesions (0.75≤ QFR ≤0.8) (log-rank P=0.325). Patients with QFR <0.75 and those with "gray zone" lesions (0.75≤ QFR ≤0.8) had a higher incidence of primary endpoint events compared to patients with QFR >0.8. [QFR <0.75 vs. QFR >0.8: adjusted hazard ratio (HR) =20.70, P<0.001; 0.75≤ QFR ≤0.8 vs. QFR >0.8: HR =15.99, P<0.001]. The independent predictors of MACEs events within 30 days after NCS were albumin level [HR =0.92, 95% confidence interval (CI): 0.87-0.98; P=0.008], emergency surgery (HR =4.12, 95% CI: 1.66-10.23; P=0.002), and QFR ≤0.8 (HR =15.92, 95% CI: 5.96-42.51; P<0.001). In addition, adjusting the original Revised Cardiac Risk Index (RCRI) with QFR ≤0.8 as a risk factor significantly improved the risk stratification of postoperative adverse events, with the adjusted AUC rising from 0.574 to 0.740 (P<0.001). QFR ≤0.8 could independently predict perioperative cardiovascular adverse events in patients with CAD undergoing NCS and improve the predictive value of original predictive index. Gray-zone lesions (0.75≤ QFR ≤0.8) should be actively treated.

What is the best predictor of body fat percentage for older brazilian women?

This study aimed to investigate the validity of several equations and predictive indices for estimating body fat percentage (%BF) in 152 older women, with an average age of 67.4 years and an average body mass index (BMI) of 28.65 kg/m². To this end, anthropometric measurements including height, body weight, circumferences (waist and hip), and a dual energy X-ray absorptiometry (DXA) scan were performed. All measurements were performed by trained researchers following specific protocols. The results were compared to the dual energy X-ray absorptiometry (DXA) technique, which is considered the reference method. The analyzed equations showed moderate to good correlation coefficients with DXA, with particular emphasis on Visser’s equation 6, which showed the best correlation (r = 0.752, p &lt; 0.001). However, the agreement between the equations and DXA, as assessed by the Lin concordance coefficient, was classified as poor (ρc &lt; 0.90). This indicates that although the equations have a positive correlation with body composition, they tend to deviate from the identity line when compared to the reference method. Additionally, the equations showed high sensitivity for detecting obesity when the cut-off point of 30% body fat was adopted, indicating a good ability to identify the presence of the condition. However, the equations, with the exception of equation 4, showed low specificity, meaning they had limited ability to detect normal individuals, resulting in a low negative predictive value. The results suggest that BF% equations and indices are dependent on the populations in which they were developed. The specificity and sensitivity of these equations may vary, and it is important to carefully select the most appropriate equation for estimating BF% in older Brazilian women. Keywords: Anthropometry; body composition; older adults.

A retrospective study of the effects of a vasopressor bolus on systolic slope (dP/dt) and dynamic arterial elastance (Eadyn)

BackgroundTo enhance the utility of functional hemodynamic monitoring, the variables systolic slope (dP/dt) and dynamic arterial elastance (Eadyn) are calculated by the Hypotension Prediction Index (HPI) Acumen® Software. This study was designed to characterize the effects of phenylephrine and ephedrine on dP/dt and Eadyn.MethodsThis was a retrospective, non-randomized analysis of data collected during two clinical studies. All patients required intra-operative controlled mechanical ventilation and had an indwelling radial artery catheter connected to an Acumen IQ sensor. Raw arterial pressure waveform data was downloaded from the patient monitor and all hemodynamic measurements were calculated off-line. The anesthetic record was reviewed for bolus administrations of either phenylephrine or ephedrine. Cardiovascular variables prior to drug administration were compared to those following vasopressor administrations. The primary outcome was the difference for dP/dt and Eadyn at baseline compared with the average after the bolus administration. All data sets demonstrated non-normal distributions so statistical analysis of paired and unpaired data followed the Wilcoxon matched pairs signed-rank test or Mann-Whitney U test, respectively.Results201 doses of phenylephrine and 100 doses of ephedrine were analyzed. All data sets are reported as median [95% CI]. Mean arterial pressure (MAP) increased from 62 [54,68] to 78 [76,80] mmHg following phenylephrine and from 59 [55,62] to 80 [77,83] mmHg following ephedrine. Stroke volume and cardiac output both increased. Stroke volume variation and pulse pressure variation decreased. Both drugs produced significant increases in dP/dt, from 571 [531, 645] to 767 [733, 811] mmHg/sec for phenylephrine and from 537 [509, 596] to 848 [779, 930] mmHg/sec for ephedrine. No significant changes in Eadyn were observed.ConclusionBolus administration of phenylephrine or ephedrine increases dP/dt but does not change Eadyn. dP/dt demonstrates potential for predicting the inotropic response to phenylephrine or ephedrine, providing guidance for the most efficacious vasopressor when treating hypotension.Trial registrationData was collected from two protocols. The first was deemed to not require written, informed consent by the Institutional Review Board (IRB). The second was IRB-approved (Effect of Diastolic Dysfunction on Dynamic Cardiac Monitors) and registered on ClinicalTrials.gov (NCT04177225).

Leveraging Hypotension Prediction Index to Forecast LPS-Induced Acute Lung Injury and Inflammation in a Porcine Model: Exploring the Role of Hypoxia-Inducible Factor in Circulatory Shock.

Acute respiratory distress syndrome (ARDS) is a critical illness in critically unwell patients, characterized by refractory hypoxemia and shock. This study evaluates an early detection tool and investigates the relationship between hypoxia and circulatory shock in ARDS, to improve diagnostic precision and therapy customization. We used a porcine model, inducing ARDS with mechanical ventilation and intratracheal plus intravenous lipopolysaccharide (LPS) injection. Hemodynamic changes were monitored using an Acumen IQ sensor and a ForeSight Elite sensor connected to the HemoSphere platform. We evaluated tissue damage, inflammatory response, and hypoxia-inducible factor (HIF) alterations using enzyme-linked immunosorbent assay and immunohistochemistry. The results showed severe hypotension and increased heart rates post-LPS exposure, with a notable rise in the hypotension prediction index (HPI) during acute lung injury (p = 0.024). Tissue oxygen saturation dropped considerably in the right brain region. Interestingly, post-injury HIF-2α levels were lower at the end of the experiment. Our findings imply that the HPI can effectively predict ARDS-related hypotension. HIF expression levels may serve as possible markers of rapid ARDS progression. Further research should be conducted on the clinical value of this novel approach in critical care, as well as the relationship between the HIF pathway and ARDS-associated hypotension.

The combination of handgrip strength and CONUT predicts overall survival in patients with gastrointestinal cancer: A multicenter cohort study

BackgroundThe controlled nutritional status score (CONUT) and handgrip strength (HGS) were both predictive indexes for the prognosis of cancers. However, the combination of CONUT and HGS for predicting the prognosis of gastrointestinal cancer had not been developed. This study aimed to explore the combination of CONUT and HGS as the potential predictive prognosis in patients with gastric and colorectal cancer. MethodsA cohort study was conducted with gastric and colorectal cancer patients in multicenter in China. Based on the optimal HGS cutoff value for different sex, the HGS cutoff value was determined. The patients were divided into high and low HGS groups based on their HGS scores. A CONUT score of 4 or less was defined as a low CONUT, whereas scores higher than 4 were defined as high CONUT. The Kaplan-Meier method was used to create survival curves, and the log-rank test was used to compare time-event relationships between groups. A Cox proportional hazard regression model was used to determine independent risk factors for overall survival (OS). ResultsA total 2177 gastric and colorectal patients were enrolled in this study, in which 1391 (63.9%) were men (mean [SD] age, 66.11 [11.60] years). Multivariate analysis revealed that patients with high HGS had a lower risk of death than those with low HGS (hazard ratio [HR],0.87; 95% confidence interval [CI], 0.753-1.006, P=0.06), while high CONUT had a higher risk of death than those with low CONUT (HR, 1.476; 95% CI, 1.227-1.777, P< 0.001). Patients with both low HGS and high CONUT had 1.712 fold increased risk of death (HR, 1.712; 95% CI, 1.364-2.15, P < 0.001). Moreover, cancer type and sex were stratified and found that patients with high CONUT and low HGS had lower survival rate than those with low CONUT and high HGS in both gastric or colorectal cancer, and both male and female. ConclusionA combination of low HGS and high CONUT was associated with poor prognosis in patients with gastrointestinal cancer, which could probably predict the prognosis of gastrointestinal cancer more accurate than HGS or CONUT alone.

Beyond the debut: unpacking six years of Hypotension Prediction Index software in intraoperative hypotension prevention - a systematic review and meta-analysis.

Intraoperative hypotension (IOH) during general anesthesia is associated with higher morbidity and mortality, although randomized trials have not established a causal relation. Historically, our approach to IOH has been reactive. The Hypotension Prediction Index (HPI) is a machine learning software that predicts hypotension minutes in advance. This systematic review and meta-analysis explores whether using HPI alongside a personalized treatment protocol decreases intraoperative hypotension. A systematic search was performed in Pubmed and Scopus to retrieve articles published from January 2018 to February 2024 regarding the impact of the HPI software on reducing IOH in adult patients undergoing non-cardio/thoracic surgery. Excluded were case series, case reports, meta-analyses, systematic reviews, and studies using non-invasive arterial waveform analysis. The risk of bias was assessed by the Cochrane risk-of-bias tool (RoB 2) and the Risk Of Bias In Non-randomised Studies (ROBINS-I). A meta-analysis was undertaken solely for outcomes where sufficient data were available from the included studies. 9 RCTs and 5 cohort studies were retrieved. The overall median differences between the HPI-guided and the control groups were - 0.21 (95% CI:-0.33, -0.09) - p < 0.001 for the Time-Weighted Average (TWA) of Mean Arterial Pressure (MAP) < 65mmHg, -3.71 (95% CI= -6.67, -0.74)-p = 0.014 for the incidence of hypotensive episodes per patient, and - 10.11 (95% CI= -15.82, -4.40)-p = 0.001 for the duration of hypotension. Notably a large amount of heterogeneity was detected among the studies. While the combination of HPI software with personalized treatment protocols may prevent intraoperative hypotension (IOH), the large heterogeneity among the studies and the lack of reliable data on its clinical significance necessitate further investigation.

The increasing water stress projected for China could shift the agriculture and manufacturing industry geographically

The sustainable development of China has been challenged by the misalignment of water demand and supply across regions under varying climate change scenarios. Here we develop a water stress prediction index using a fuzzy decision-making approach, which analyzes spatiotemporal variations of water stress and concomitant effects on the populace within China. Our results indicate that water stress will increase from 2020 to 2099 under both low and high emission scenarios, primarily due to decreased water supplies like surface runoff and snow water content. Seasonal analysis reveals that annual fluctuations in water stress are mainly driven by changes in spring and autumn. Water stress is projected to be considerably lower in southeastern provinces compared to northwestern ones, where, on average, over 20% of the Chinese population could be severely impacted. These changes in water stress could lead to the north-to-south migration of the agriculture sector, manufacturing sector, and human population.

Construction of a predictive index to create new high-value genotypes of cows

Construction of a predictive index to create new high-value genotypes of cows

Comparative analysis of body mass index and obesity-related anthropometric indices for mortality prediction: a study of the Namwon and Dong-gu cohort in Korea.

This study investigated the associations between several obesity-related anthropometric indices and mortality in middle-aged and elderly populations to compare the indices' predictive ability with that of the body mass index (BMI). We analyzed data on 12 indices calculated from 19,805 community-based cohort participants (average age, 63.27 years; median follow-up, 13.49 years). Each index was calculated using directly measured values of height, weight, waist circumference (WC), and hip circumference (HC). We calculated hazard ratios (HRs) and 95% confidence intervals (CIs) for each index using Cox regression and evaluated mortality prediction with the Harrell concordance index (c-index). Adding anthropometric indices to the basic mortality model (c-index, 0.7723; 95% CI, 0.7647 to 0.7799) significantly increased the predictive power of BMI (c-index, 0.7735; 95% CI, 0.7659 to 0.7811), a body shape index (ABSI; c-index, 0.7735; 95% CI, 0.7659 to 0.7810), weight-adjusted waist index (WWI; c-index, 0.7731; 95% CI, 0.7656 to 0.7807), and waist to hip index (WHI; c-index, 0.7733; 95% CI, 0.7657 to 0.7809). The differences between the BMI model and the other 3 models were not statistically significant. In predicting all-cause mortality, the ABSI, WWI, and WHI models based on WC or HC had stronger predictive power than conventional risk factors but were not significantly different from the BMI model.

Development and validation of a deep learning-based model for diabetes diagnosis

Diabetes is a systemic disease caused by hyperglycemia, and the number of people with diabetes worldwide may reach 1.31 billion by 2050. The traditional diagnosis of early diabetes is difficult and inaccurate. Computer-aided automatic method has been widely applied in diabetes diagnosis at early stage. In order to realize automatic diagnosis of diabetes, this study proposed a deep neural network-based model for diabetes diagnosis, and its performance was compared with those of other classical machine leaning models. After data cleaning, Synthetic Minority Over-sampling Technique (SMOTE) sampling and feature selecting of the diabetes dataset, multiple models were applied to the features such as Body Mass Index (BMI), Age and other features for prediction. The results showed that Deep Neural Network (DNN) is the best diagnostic solution for diabetes with excellent performance with an accuracy of 99.5%. More specific features will be considered to ensure the accuracy and credibility of clinical diagnosis of diabetes.

Predicting wheat yield from 2001 to 2020 in Hebei Province at county and pixel levels based on synthesized time series images of Landsat and MODIS

To obtain seasonable and precise crop yield information with fine resolution is very important for ensuring the food security. However, the quantity and quality of available images and the selection of prediction variables often limit the performance of yield prediction. In our study, the synthesized images of Landsat and MODIS were used to provide remote sensing (RS) variables, which can fill the missing values of Landsat images well and cover the study area completely. The deep learning (DL) was used to combine different vegetation index (VI) with climate data to build wheat yield prediction model in Hebei Province (HB). The results showed that kernel NDVI (kNDVI) and near-infrared reflectance (NIRv) slightly outperform normalized difference vegetation index (NDVI) in yield prediction. And the regression algorithm had a more prominent effect on yield prediction, while the yield prediction model using Long Short-Term Memory (LSTM) outperformed the yield prediction model using Light Gradient Boosting Machine (LGBM). The model combining LSTM algorithm and NIRv had the best prediction effect and relatively stable performance in single year. The optimal model was then used to generate 30 m resolution wheat yield maps in the past 20 years, with higher overall accuracy. In addition, we can define the optimum prediction time at April, which can consider simultaneously the performance and lead time. In general, we expect that this prediction model can provide important information to understand and ensure food security.

Unmanned Aerial Vehicle-Measured Multispectral Vegetation Indices for Predicting LAI, SPAD Chlorophyll, and Yield of Maize

Predicting crop yield at preharvest is pivotal for agricultural policy and strategic decision making. Despite global agricultural targets, labour-intensive surveys for yield estimation pose challenges. Using unmanned aerial vehicle (UAV)-based multispectral sensors, this study assessed crop phenology and biotic stress conditions using various spectral vegetation indices. The goal was to enhance the accuracy of predicting key agricultural parameters, such as leaf area index (LAI), soil and plant analyser development (SPAD) chlorophyll, and grain yield of maize. The study’s findings demonstrate that during the kharif season, the wide dynamic range vegetation index (WDRVI) showcased superior correlation coefficients (R), coefficients of determination (R2), and the lowest root mean square errors (RMSEs) of 0.92, 0.86, and 0.14, respectively. However, during the rabi season, the atmospherically resistant vegetation index (ARVI) achieved the highest R and R2 and the lowest RMSEs of 0.83, 0.79, and 0.15, respectively, indicating better accuracy in predicting LAI. Conversely, the normalised difference red-edge index (NDRE) during the kharif season and the modified chlorophyll absorption ratio index (MCARI) during the rabi season were identified as the predictors with the highest accuracy for SPAD chlorophyll prediction. Specifically, R values of 0.91 and 0.94, R2 values of 0.83 and 0.82, and RMSE values of 2.07 and 3.10 were obtained, respectively. The most effective indices for LAI prediction during the kharif season (WDRVI and NDRE) and for SPAD chlorophyll prediction during the rabi season (ARVI and MCARI) were further utilised to construct a yield model using stepwise regression analysis. Integrating the predicted LAI and SPAD chlorophyll values into the model resulted in higher accuracy compared to individual predictions. More exactly, the R2 values were 0.51 and 0.74, while the RMSE values were 9.25 and 6.72, during the kharif and rabi seasons, respectively. These findings underscore the utility of UAV-based multispectral imaging in predicting crop yields, thereby aiding in sustainable crop management practices and benefiting farmers and policymakers alike.

An Albumin, Neutrophil, and Lymphocyte-Related Risk Estimation Tool in Hospitalised Patients.

The neutrophil-to-lymphocyte ratio (NLR) is commonly used as a prognostic indicator for microbiological and inflammatory conditions in clinical settings. However, the quotient to albumin levels, which is another nutritional and clinical predictor, may also have an interesting diagnostic and prognostic value.This study aimed to primarily investigate the predictive performances of the neutrophils to albumin and lymphocytes ratio (NALR) compared to the NLRin predicting poor outcomes during hospital admission, particularly the decomposition of respiratory, renal, liver, and circulatory systems, resulting in longer hospital stays or mortality. An observational study was performed on a cohort of 270 hospitalised patients admitted to Rashid bin Al-Hussein Military Hospital during the period from October 2023 to early November 2023. The study specifically targeted adult patients (age >17 years) who had a minimum of 80% availability of their initial and follow-up data during admission. We dichotomised all eligible test patients into two groups: Group I, which represented better outcomes of interest, and Group II, which represented poorer outcomes of interest. Statistically, we conducted binary logistic, receiver operating, and sensitivity analyses to explore the predictive performances and indices for NALR and NLR. We also conducted chi-square and independent T analyses to uncover the distribution rates of the independent variables across Groups I and II. We considered a p-value of less than 0.05 as the level of significance. Out of a total sample size of 270, 82 patients (30.37%) were allocated to Group I, and 188 patients (69.63%) were allocated to Group II. Males outnumbered females in this study by 184 (68.1%) to 86 (31.9%).Patients in the study had an average age of 58.08±10.02 years. The average hospitalisation took 13.71±6.38 days, significantly longer in Group II compared to Group I (15.43±6.76 days vs. 9.77±2.69 days, p-value<0.05). We found that the area under the receiver operating characteristic (ROC) curves was estimated at [0.808±0.031 (0.748-0.868), p-value=0.000] and [0.667±0.034 (0.601-0.733), p-value=0.000] for NALR and NLR, respectively. The optimal operating thresholds for NALR and NLR were 1.5 and 5.37, with sensitivities and specificities of 86.7% versus 73.4% and 70.73% versus 70.73%, respectively. The proposed NALRshowed superior predictive performance, sensitivity, and correlation compared to the parent NLR. Both tools can be used in clinical practice to prioritise clinical and pharmacotherapeutics for hospitalised patients based on unfavourable outcomes.

The IMPACT Score: Does Sex Matter?

ObjectiveThe Index for Mortality Prediction After Cardiac Transplantation (IMPACT) score is a quantitative risk index that predicts 1-yr mortality risk, derived from United Network for Organ Sharing (UNOS) data in which women are underrepresented. The validity of the IMPACT score in 1-yr mortality risk after OHT in women is unknown. The objective of this study is to assess differences in score performance by sex. We hypothesize that the IMPACT score is a poor predictor of 1-yr mortality risk after OHT in women. DesignExternal validation study including patients 18 years and older first-time single organ OHT recipients from The International Society of Heart and Lung Transplantation (ISHLT) data from 2009-2018. Demographic and clinical characteristics were compared by sex. IMPACT score was calculated and regression models constructed for the entire sample, and stratified by sex. Model discrimination was assessed with the area under the receiver operating characteristic curve (AUROC) and calibration was assessed graphically. SettingN/A InterventionsN/A ParticipantsPatients 18 years and older first time single OHT recipients from ISHLT registry from 2009-2018. Measurements and Main ResultsFor 1-yr mortality, the AUROC (95% CI) for the full sample was 0.59 (0.57-0.60); 0.58 (0.55-0.61) for women; 0.59 (0.58-0.61) for men. 1-yr mortality was 9.4% in the overall cohort, with no difference in mortality by sex (9.0% vs 9.6% women vs men, p=0.22). ConclusionsThe IMPACT score exhibited poor discrimination and calibration in the ISHLT 2009-2019 cohort, overall and by sex. There was no difference in 1-yr mortality between women and men.

Design of Consumer Confidence Prediction Index Model based on DEGWO Algorithm

Based on the CCI index released by China Economic Information Network, combined with the DEGWO difference algorithm and BP neural network regression; Constructed a DEGWO-BP synthesis algorithm under machine learning mode to predict and fit consumer confidence index; The empirical results show that the cumulative error of the consumer confidence index using the DEGWO-BP algorithm is the lowest, only 37.8273; The average absolute error of the model is the lowest, and the model has the minimum level of deviation; The model with the minimum extreme deviation value has the strongest stability.