Area Under ROC Curve Research Articles

Quantitative imaging of dynamic myocardial blood flow using dual‐head rapidly rotating gantry single‐photon emission computed tomography to enhance coronary artery disease diagnosis

AbstractBackgroundThis study aimed to evaluate the performance of myocardial blood flow (MBF) and myocardial flow reserve (MFR) for diagnosing coronary artery disease (CAD) using dynamic single‐photon emission computed tomography/computed tomography (SPECT/CT) with a dual‐head rapidly rotating gantry (RRG) and to compare this method with conventional myocardial perfusion imaging (MPI), which can be obtained in a one‐stop manner.MethodsA total of 93 patients with suspected or confirmed CAD who underwent RRG SPECT/CT were retrospectively enrolled. They underwent invasive coronary angiography (ICA) and conventional MPI within 3 weeks. Based on the ICA results, the performance of MBF quantitative and semi‐quantitative parameters were compared at patient and vessel levels using ≥50% and ≥75% stenosis as the criteria for CAD diagnosis. Receiver operating characteristic (ROC) curve was used to evaluate the diagnostic efficacy of two types of parameters.ResultsFor patient‐level analysis, the area under ROC curves (AUCs) for stress MBF (sMBF), MFR, summed stress score (SSS), and summed different score (SDS) were 0.971, 0.939, 0.612, and 0.546, respectively, for ≥50% stenosis as positive, and were 0.983, 0.932, 0.735, and 0.509, respectively, for ≥75% stenosis as positive criteria. For vessel‐level analysis, the AUCs of sMBF, MFR, SSS, and SDS were 0.981, 0.933, 0.636, and 0.560, respectively, for ≥50% stenosis as positive and were 0.984, 0.933, 0.767, and 0.583, respectively, for ≥75% stenosis as positive criteria.ConclusionsCompared with semi‐quantitative parameters of conventional MPI, MBF quantitative parameters of dual‐head RRG SPECT had higher diagnostic performance for CAD.

The prognostic importance of the pan-immune-inflammation value in patients with septic shock

IntroductionThe purpose of this study was to determine whether the pan-immune-inflammation value (PIV), a novel biomarker combining neutrophil platelet, monocyte, and lymphocyte counts, some of the most widespread indicators of systemic inflammation, can predict mortality and prognosis in patients admitted to the intensive care unit (ICU) with septic shock.MethodThis prospective study was performed with 82 patients aged 18 or over admitted to a tertiary ICU with diagnoses of septic shock. Patients with hematological disease and neutropenia were excluded. PIV was calculated with the formula [neutrophil count (103/μL) × platelet count (103/μL) × monocyte count (103/μL)]/lymphocyte count (103/μL).ResultsMedian age, presence of hypertension, Acute Physiology and Chronic Health Evaluation II (APACHE II) levels, and neutrophil, monocyte, and platelet counts were lower in the low-PIV group than in the high-PIV group (p < 0.05). The highest area under ROC curve (AUC) was determined for Sequential Organ Failure Assessment (SOFA) (0.94 (0.89 – 0.99)), followed by Glasgow Coma Scale (GCS) (0.81 (0.70 – 0.91)), APACHE II (0.80 (0.69 – 0.91)) and lactate (0.77 (0.67 – 0.88)). Median survival was longer in the low-PIV group than in the high-PIV group (28 (15.25 – 40.76) vs 16 (9.46 – 22.55) days, respectively, p < 0.05). The univariate Cox proportional hazards (CPH) model showed that high PIV (HR = 2.13 (1.03—4.38)), low GCS (HR = 3.31 (1.34 – 8.15)), high SOFA (HR = 9.41 (2.86 – 30.95)), high APACHE II (HR = 3.08 (1.47 – 6.45)), high lactate (HR = 6.56 (2.73 – 15.75)), and high procalcitonin (PCT) (HR = 2.73 (1.11 – 6.69)) values were associated with a decreased survival time among ICU patients (p < 0.05). The multivariate CPH model showed the age-adjusted risk estimates for these six laboratory parameters. High lactate (HR = 7.97 (2.19 – 29.08)) and high SOFA scores (HR = 4.85 (1.22 – 19.32)) were significantly associated with shorter survival in ICU patients (p < 0.05).ConclusionThe findings of this research suggest that PIV could predict the longer survival in patients with septic shock. Despite PIV score’s capability to show inflammation, it is not significantly associated with mortality in the multivariate analysis.

Value of high frame rate contrast enhanced ultrasound in gallbladder wall thickening in non-acute setting

BackgroundUltrasound (US) has been widely used in screening and differential diagnosis of gallbladder wall thickening (GWT). However, the sensitivity and specificity for diagnosing wall-thickening type gallbladder cancer are limited, leading to delayed treatment or overtreatment. We aim to explore the value of high frame rate contrast enhanced ultrasound (H-CEUS) in distinguishing wall-thickening type gallbladder cancer (malignant) from GWT mimicking malignancy (benign).MethodsThis retrospective study enrolled consecutive patients with non-acute GWT who underwent US and H-CEUS examination before cholecystectomy. Clinical information, US image and H-CEUS image characteristics between malignant and benign GWT were compared. The independent risk factors for malignant GWT on H-CEUS images were selected by multivariate logistic regression analysis. The diagnostic performance of H-CEUS in determining malignant GWT was compared with that of the gallbladder reporting and data system (GB-RADS) score.ResultsForty-six patients included 30 benign GWTs and 16 malignant GWTs. Only mural layering and interface with liver on US images were significantly different between malignant and benign GWT (P < 0.05). Differences in enhancement direction, vascular morphology, serous layer continuity, wash-out time and mural layering in the venous phase of GWT on H-CEUS images were significant between malignant and benign GWT (P < 0.05). The sensitivity, specificity and accuracy of H-CEUS based on enhancement direction, vascular morphology and wash-out time in the diagnosis of malignant GWT were 93.75%, 90.00%, and 91.30%, respectively. However, the sensitivity, specificity and accuracy of the GB-RADS score were only 68.75%, 73.33% and 71.74%, respectively. The area under ROC curve (AUC) of H-CEUS was significantly higher than that of the GB-RADS score (AUC = 0.965 vs. 0.756).ConclusionsH-CEUS can accurately detect enhancement direction, vascular morphology and wash-out time of GWT, with a higher diagnostic performance than the GB-RADS score in determining wall-thickening type gallbladder cancer. This study provides a novel imaging means with high accuracy for the diagnosis of wall-thickening type gallbladder cancer, thus may be better avoiding delayed treatment or overtreatment.

Advanced CT measures of coronary artery disease with intermediate stenosis in patients with severe aortic valve stenosis

BackgroundCoronary artery disease (CAD) and severe aortic valve stenosis (AS) frequently coexist. While pre-transcatheter aortic valve replacement (TAVR) computed tomography angiography (CTA) allows to rule out obstructive CAD, interpreting hemodynamic significance of intermediate stenoses is challenging. This study investigates the incremental value of CT-derived fractional flow reserve (CT-FFR), quantitative coronary plaque characteristics (e.g., stenosis degree, plaque volume, and composition), and peri-coronary adipose tissue (PCAT) density to detect hemodynamically significant lesions among those with AS and CAD.Materials and methodsWe included patients with severe AS and intermediate coronary lesions (20–80% diameter stenosis) who underwent pre-TAVR CTA and invasive coronary angiogram (ICA) with resting full-cycle ratio (RFR) assessment between 08/16 and 04/22. CTA image analysis included assessment of CT-FFR, quantitative coronary plaque analysis, and PCAT density. Coronary lesions with RFR ≤ 0.89 indicated hemodynamic significance as reference standard.ResultsOverall, 87 patients (age 77.9 ± 7.4 years, 38% female) with 95 intermediate coronary artery lesions were included. CT-FFR showed good discriminatory capacity (area under receiver operator curve (AUC) = 0.89, 95% confidence interval (CI) 0.81–0.96, p < 0.001) to identify hemodynamically significant lesions, superior to anatomical assessment, plaque morphology, and PCAT density. Plaque composition and PCAT density did not differ between lesions with and without hemodynamic significance. Univariable and multivariable analyses revealed CT-FFR as the only predictor for functionally significant lesions (odds ratio 1.28 (95% CI 1.17–1.43), p < 0.001). Overall, CT-FFR ≤ 0.80 showed diagnostic accuracy, sensitivity, and specificity of 88.4% (95%CI 80.2–94.1), 78.5% (95%CI 63.2–89.7), and 96.2% (95%CI 87.0–99.5), respectively.ConclusionCT-FFR was superior to CT anatomical, plaque morphology, and PCAT assessment to detect functionally significant stenoses in patients with severe AS.Clinical relevance statementCT-derived fractional flow reserve in patients with severe aortic valve stenosis may be a useful tool for non-invasive hemodynamic assessment of intermediate coronary lesions, while CT anatomical, plaque morphology, and peri-coronary adipose tissue assessment have no incremental or additional benefit. These findings might help to reduce pre-transcatheter aortic valve replacement invasive coronary angiogram.Key Points• Interpreting the hemodynamic significance of intermediate coronary stenoses is challenging in pre-transcatheter aortic valve replacement CT.• CT-derived fractional flow reserve (CT-FFR) has a good discriminatory capacity in the identification of hemodynamically significant coronary lesions.• CT-derived anatomical, plaque morphology, and peri-coronary adipose tissue assessment did not improve the diagnostic capability of CT-FFR in the hemodynamic assessment of intermediate coronary stenoses.

The GRAPHS-CRAFITY score: a novel efficacy predictive tool for unresectable hepatocellular carcinoma treated with immunotherapy.

To investigate MR features associated with prognosis of unresectable HCC receiving immunotherapy and establish a MR feature-based scoring system to predict efficacy of immunotherapy. This retrospective study included patients with unresectable HCC who received immunotherapy at 2 hospitals between August 2018 and February 2022. The last follow-up was October 2022. Clinical variables and MR features were assessed using univariate and multivariate Cox regression analyses. A new scoring system was constructed based on independent risk factors and the CRAFITY score consisting of AFP (≥ 100ng/ml) and CRP (≥ 1mg/dl). And the predictive performance of CRAFITY core and new score were compared by receiver-operating-characteristics curves (ROCs), area under ROCs (AUCs), and calibration curves. A total of 166 patients (55.6 ± 10.4years) were included in training cohort and 77 patients (55.4 ± 10.7years) were included in validation cohort. There were significant differences in BCLC stage, max size, macrovascular invasion, intratumoral artery, and enhancing capsule between the 2 groups. Based on independent risk factors (gross GRowtH type, intratumoral fAt, enhancing tumor caPsule, Sex and CRAFITY score), a novel efficacy predictive tool named the GRAPHS-CRAFITY score was developed to predict OS. The OS was significantly different among the 3 groups according to GRAPHS-CRAFITY score (p value < 0.001). The GRAPHS-CRAFITY score could predict tumor response and disease control (p value < 0.001, p value < 0.001). The GRAPHS-CRAFITY score is a reliable and easily applicable tool to predict the efficacy of unresectable HCC receiving immunotherapy.

The utility of heart rate and heart rate variability to identify limits of tolerance to moderate-intensity work in the heat: a secondary analysis.

We investigated the utility of heart rate (HR) and heart rate variability (HRV) for identifying individuals who may terminate work early due to excessive heat strain. Forty-eight men and women (median=36 years; Q1=20 years; Q3=54 years) attempted 180 min of moderate-intensity work at a fixed metabolic rate (∼200W/m2; ∼3.5 METs) in a hot environment (wet-bulb globe temperature: 32 °C). Receiver operating characteristics (ROC) curves were used to identify the ability of indices of HR (absolute HR, percentage of maximum HR, percentage of HR reserve) and HRV (root-mean-square of successive differences (RMSSD), high-frequency power, and detrended fluctuation analysis component alpha 1 (DFA α1)) to discriminate between participants who completed the 180 min work bout or terminated prematurely. Participants who terminated work prematurely (n=26) exhibited higher HR and percentage of HR measures, as well as reduced RMSSD and DFA α1 after the first hour of work compared to participants who completed the bout. The discriminative utility of HR and HRV indices was strongest within the first hour of work, with percentage of HR reserve demonstrating excellent discriminative power (ROC area under curve (AUC) of 0.8). Stratifying participants by age and sex improved ROC AUC point estimates for most indices, particularly in female participants. The study provides preliminary evidence supporting the use of noninvasive cardiac monitoring for predicting work tolerance in healthy individuals exposed to occupational heat stress. HR and percentage of HR reserve were suggested to discriminate work termination most effectively. Further investigations are warranted to explore the influence of individual factors and refine the discriminative thresholds for early identification of excessive occupational heat strain.

Development and validation of a clinical prediction model for the risk of distal metastasis in intrahepatic cholangiocarcinoma: a real-world study

BackgroundCholangiocarcinoma (CCA) is a highly malignant and easily metastatic bile duct tumor with poor prognosis. We aimed at studying the associated risk factors affecting distal metastasis of CCA and using nomogram to guide clinicians in predicting distal metastasis of CCA.MethodsBased on inclusion and exclusion criteria, 345 patients with CCA were selected from the Fifth Medical Center of Chinese PLA General Hospital and were divided into distal metastases (N = 21) and non-distal metastases (N = 324). LASSO regression models were used to screen for relevant parameters and to compare basic clinical information between the two groups of patients. Risk factors for distal metastasis were identified based on the results of univariate and multivariate logistic regression analyses. The nomogram was established based on the results of multivariate logistic regression, and we drawn the corresponding correlation heat map. The predictive accuracy of the nomogram was evaluated by receiver operating characteristic (ROC) curves and calibration plots. The utility of the model in clinical applications was illustrated by applying decision curve analysis (DCA), and overall survival(OS) analysis was performed using the method of Kaplan-meier.ResultsThis study identified 4 independent risk factors for distal metastasis of CCA, including CA199, cholesterol, hypertension and margin invasion, and developed the nomogram based on this. The result of validation showed that the model had significant accuracy for diagnosis with the area under ROC (AUC) of 0.882 (95% CI: 0.843-0.914). Calibration plots and DCA showed that the model had high clinical utility.ConclusionsThis study established and validated a model of nomogram for predicting distal metastasis in patients with CCA. Based on this, it could guide clinicians to make better decisions and provide more accurate prognosis and treatment for patients with CCA.

Validation of oxygen saturations measured in the community by emergency medical services as a marker of clinical deterioration in patients with confirmed COVID-19: a retrospective cohort study

ObjectivesTo evaluate oxygen saturation and vital signs measured in the community by emergency medical services (EMS) as clinical markers of COVID-19-positive patient deterioration.DesignA retrospective data analysis.SettingPatients were conveyed by EMS...

Exploring the diagnostic performance of machine learning in prediction of metabolic phenotypes focusing on thyroid function.

In this study, we employed various machine learning models to predict metabolic phenotypes, focusing on thyroid function, using a dataset from the National Health and Nutrition Examination Survey (NHANES) from 2007 to 2012. Our analysis utilized laboratory parameters relevant to thyroid function or metabolic dysregulation in addition to demographic features, aiming to uncover potential associations between thyroid function and metabolic phenotypes by various machine learning methods. Multinomial Logistic Regression performed best to identify the relationship between thyroid function and metabolic phenotypes, achieving an area under receiver operating characteristic curve (AUROC) of 0.818, followed closely by Neural Network (AUROC: 0.814). Following the above, the performance of Random Forest, Boosted Trees, and K Nearest Neighbors was inferior to the first two methods (AUROC 0.811, 0.811, and 0.786, respectively). In Random Forest, homeostatic model assessment for insulin resistance, serum uric acid, serum albumin, gamma glutamyl transferase, and triiodothyronine/thyroxine ratio were positioned in the upper ranks of variable importance. These results highlight the potential of machine learning in understanding complex relationships in health data. However, it's important to note that model performance may vary depending on data characteristics and specific requirements. Furthermore, we emphasize the significance of accounting for sampling weights in complex survey data analysis and the potential benefits of incorporating additional variables to enhance model accuracy and insights. Future research can explore advanced methodologies combining machine learning, sample weights, and expanded variable sets to further advance survey data analysis.

Automating quality control in cardiac magnetic resonance: Artificial intelligence for discriminative assessment of planning and motion artifacts and real-time reacquisition guidance

BackgroundAccurate measurements from cardiovascular magnetic resonance (CMR) images require precise positioning of scan planes and elimination of motion artifacts from arrhythmia or breathing. Unidentified or incorrectly managed artifacts degrade image quality, invalidate clinical measurements, and decrease diagnostic confidence. Currently, radiographers must manually inspect each acquired image to confirm diagnostic quality and decide whether reacquisition or a change in sequences is warranted. We aimed to develop artificial intelligence (AI) to provide continuous quality scores across different quality domains, and from these, determine whether cines are clinically adequate, require replanning, or warrant a change in protocol. MethodsA three-dimensional convolutional neural network was trained to predict cine quality graded on a continuous scale by a level 3 CMR expert, focusing separately on planning and motion artifacts. It incorporated four distinct output heads for the assessment of image quality in terms of (a, b, c) 2-, 3- and 4-chamber misplanning, and (d) long- and short-axis arrhythmia/breathing artifact. Backpropagation was selectively performed across these heads based on the labels present for each cine. Each image in the testing set was reported by four level 3 CMR experts, providing a consensus on clinical adequacy. The AI's assessment of image quality and ability to identify images requiring replanning or sequence changes were evaluated with Spearman’s rho and the area under receiver operating characteristic curve (AUROC), respectively. ResultsA total of 1940 cines across 1387 studies were included. On the test set of 383 cines, AI-judged image quality correlated strongly with expert judgment, with Spearman’s rho of 0.84, 0.84, 0.81, and 0.81 for 2-, 3- and 4-chamber planning quality and the extent of arrhythmia or breathing artifacts, respectively. The AI also showed high efficacy in flagging clinically inadequate cines (AUROC 0.88, 0.93, and 0.93 for identifying misplanning of 2-, 3- and 4-chamber cines, and 0.90 for identifying movement artifacts). ConclusionAI can assess distinct domains of CMR cine quality and provide continuous quality scores that correlate closely with a consensus of experts. These ratings could be used to identify cases where reacquisition is warranted and guide corrective actions to optimize image quality, including replanning, prospective gating, or real-time imaging.

Multitask learning to predict successful weaning in critically ill ventilated patients: A retrospective analysis of the MIMIC-IV database.

Weaning is an essential issue in critical care. This study explores the efficacy of multitask learning models in predicting successful weaning in critically ill ventilated patients using the Medical Information Mart for Intensive Care (MIMIC) IV database. We employed a multitask learning framework with a shared bottom network to facilitate common knowledge extraction across all tasks. We used the Shapley additive explanations (SHAP) plot and partial dependence plot (PDP) for model explainability. Furthermore, we conducted an error analysis to assess the strength and limitation of the model. Area under receiver operating characteristic curve (AUROC), calibration plot and decision curve analysis were used to determine the performance of the model. A total of 7758 critically ill patients were included in the analyses, and 78.5% of them were successfully weaned. Multitask learning combined with spontaneous breath trial achieved a higher performance to predict successful weaning compared with multitask learning combined with shock and mortality (area under receiver operating characteristic curve, AUROC, 0.820 ± 0.002 vs 0.817 ± 0.001, p < 0.001). We assessed the performance of the model using calibration and decision curve analyses and further interpreted the model through SHAP and PDP plots. The error analysis identified a relatively high error rate among those with low disease severities, including low mean airway pressure and high enteral feeding. We demonstrated that multitask machine learning increased predictive accuracy for successful weaning through combining tasks with a high inter-task relationship. The model explainability and error analysis should enhance trust in the model.

Copeptin and the syndrome of inappropriate antidiuresis (SIAD) after pituitary transsphenoidal surgery.

This study evaluates the predictive value of copeptin for syndrome of inappropriate antidiuresis (SIAD) postpituitary transsphenoidal surgery (TSS). Data from 133 consecutive patients undergoing TSS (November 2017-October 2022) at Oxford University Hospitals NHS trust are presented in this retrospective study. Logistic regression (LR) and receiver operating characteristic (ROC) curves were performed to evaluate the diagnostic utility of copeptin. The Mann-Whitney U test was used to compare copeptin levels between the SIAD and no SIAD groups. Fourteen patients (10.8%) developed SIAD. Copeptin was available in 121, 53 and 87 patients for Days 1, 241 and 8 post-TSS, respectively. LR for Day 1 copeptin to predict SIAD gave an odds ratio (OR) of 1.0 (95%CI 42 0.84-1.20, p = .99), area under-ROC curve (AUC) was 0.49; Day 2 copeptin OR was 0.65 (95%CI 0.39-1.19, 43 p = .77), AUC was 0.57 LR for Day 1 sodium to predict SIAD gave an odds ratio (OR) of 1.0 (95%CI 0.85-1.21, p = .99), AUC was 0.50. In conclusion, our data provide no evidence for copeptin as a predictive marker for post-TSS SIAD.

Comparison of Pneumonia-specific Scores, Sepsis Score and Generic Score in Predicting the Severity of Community-acquired Pneumonia: A Cross-sectional Study

Introduction: Pneumonia is defined as inflammation of the pulmonary parenchyma caused by an infectious agent. Community-acquired Pneumonia (CAP) is a heterogeneous disease with a significant disease burden, morbidity, and mortality. Severe Community-acquired Pneumonia (SCAP) has been proven to be associated with increased Intensive Care Unit (ICU) admission, mechanical ventilation, and mortality. Although several severity assessment tools are available, there is a lack of evidence to support one tool over another in patients with pneumonia. Aim: To compare the ability of pneumonia-specific scores {{Confusion, Urea, Respiratory rate, Blood pressure (CURB)- 65 and Expanded CURB-65)}, Sepsis score {quick Sepsisrelated Organ Failure Assessment (qSOFA)}, and Generic score {National Early Warning Score (NEWS)} in predicting SCAP patients at the time of hospital admission. Materials and Methods: This was a hospital-based crosssectional study conducted in the Department of Pulmonary Medicine, Government Hospital for Chest and Communicable Diseases, Andhra Medical College, Visakhapatnam, India, on 100 patients with clinically and radiologically diagnosed CAP over a period of six months from April 2023 to September 2023 after obtaining Institutional ethics clearance and informed consent. All four severity scores (CURB-65, eCURB-65, qSOFA, NEWS) were documented in each patient at the time of admission. Outcomes such as 30-day mortality and ICU admission were measured. Receiver Operating Characteristic (ROC) curve analysis was performed for mortality prediction and ICU admission for all four scoring systems, and statistical analysis was carried out using Statistical Packages for Social Sciences (SPSS) version 24.0. Results: Out of 100 patients, 62 (62%) were males, and the remaining 38 (38%) were females with a mean age of 56±15 years. The number of patients with co-morbidities was 48 (48%). Regarding addictive habits, smoking and alcohol played a significant role at 38% and 33%, respectively. A 30- day mortality was observed in 18 (18%) patients, and 20 (20%) patients received ICU treatment. The frequency of patients with co-morbidities such as Diabetes Mellitus (DM), Hypertension (HTN), Ischaemic Heart Disease (IHD), and Chronic Obstructive Pulmonary Disease (COPD) was 21%, 33%, 5%, and 3%, respectively. For ICU admission as an outcome measure, the Area Under Receiver Operating Characteristics (AUROC) values were as follows: CURB-65: 0.977 (95% CI: 0.949-1.00, p-value &lt;0.001); Expanded CURB-65: 0.966 (95% CI: 0.931-1.00, p-value &lt;0.001); qSOFA: 0.935 (95% CI: 0.881-0.989, p-value &lt;0.001); NEWS score: 0.967 (95% CI: 0.934-1.00, p-value &lt;0.001). Conclusion: In the present study, all four scoring systems were equally effective in detecting the need for ICU admission and predicting 30-day mortality among CAP patients at the time of admission. However, organ-specific tools (CURB-65 (2-3) moderate) have demonstrated valid and effective means of assessing severity compared to sepsis scores and generic tools.

Mapping groundwater potential zone in the subarnarekha basin, India, using a novel hybrid multi-criteria approach in Google earth Engine

Assessing groundwater potential for sustainable resource management is critically important. In addressing this concern, this study aims to advance the field by developing an innovative approach for Groundwater potential zone (GWPZ) mapping using advanced techniques, such as FuzzyAHP, FuzzyDEMATEL, and Logistic regression (LR) models. GWPZ was carried out by integrating various primary factors, such as hydrologic, soil permeability, morphometric, terrain distribution, and anthropogenic influences, incorporating twenty-seven individual criteria using multi-criteria decision models along with a hybrid approach for the Subarnarekha River basin, India, in Google earth engine (GEE). The predictive capability of the model was evaluated using a Multi-Collinearity test (VIF <10.0), followed by applying a random forest model, considering the weighted impact of the five primary factors. The hybrid model for GWPZ classification showed that 21.97 % (4256.3 km2) of the area exhibited very high potential, while 11.37 % (2202.1 km2) indicated very low potential for GW in this area. Validation of the groundwater level data from 72 observation wells, performed by the Area under receiver operating characteristic (AUROC) curve technique, yielded values ranging between 75 % and 78 % for different models, underscoring the robust predictability of GWPZ. The hybrid and LR-FuzzyAHP models demonstrated remarkable effectiveness in GWPZ mapping, indicating that the downstream and southern regions boast substantial groundwater potential attributed to alluvial soil and favorable recharge conditions. Conversely, the central part grapples with a scarcity of groundwater. It holds the potential to assist planners and managers in formulating strategies for managing groundwater levels and alleviating the impacts of future droughts.

Association of Delta Neutrophil Index with the 30-day Mortality in Adult Cardiac Surgical Patients.

Purpose: This study aimed to assess the predictive accuracy of 30-day mortality with delta neutrophil index (DNI) in adult cardiac surgical patients. Methods: This study enrolled patients who underwent cardiac surgery under general anesthesia between March 2016 and May 2022 at a tertiary hospital in the Republic of Korea. DNI was measured preoperatively, on postoperative arrival to the surgical intensive care unit (ICU), and 12, 24, 48, and 72 h postoperatively. Receiver operating characteristic (ROC) analysis was employed to identify the prediction accuracy of DNI. An area under ROC curve (AUROC) ≥0.700 was defined as satisfactory predictive accuracy. An optimal cutoff point for the DNI value to maximize predictive accuracy was revealed in the ROC curve, where [sensitivity + specificity] was maximum. Results: This study included a total of 843 patients in the final analyses. The mean age of the study population was 66.9±12.2 years and 38.4% of them were female patients. The overall 30-day mortality rate was 5.2%. Surgery involving the thoracic aorta, history of prior cardiac surgery, or emergency surgery were associated with a higher mortality rate. The DNI showed satisfactory predictive accuracy at 24 h, 48 h, and 72 h postoperatively, with AUROC of 0.729, 0.711, and 0.755, respectively. The optimal cutoff points of DNI at each time point were 3.2, 3.8, and 2.3, respectively. Conclusions: Postoperative DNI is a good predictor of 30-day mortality after cardiac surgery and has the benefit of no additional financial costs or time.

The validity and reliability of the Thai version of the asthma control test.

The Asthma Control Test (ACT) has been widely used for the assessment of asthma control. However, it has never been validated in adult Thai asthmatic patients. To determine the validity and reliability of the Thai version of the ACT in adult Thai asthmatic patients. Any correlation between ACT and level of asthma controlled was determined using the Spearman's rank correlation coefficient. The ACT was carried out at 2 visits to a physician (4-12 weeks apart) to ascertain the level of reliability. Discriminant validity was determined using an area under receiver operating characteristic curve (AuROC) to identify the optimum cut-off point of the levels of control. Seventy-one asthmatic adult patients, 40 (56.3%) female with a mean age of 54.2 ± 14.7 years were enrolled. The Thai version of ACT showed an acceptable internal consistency reliability with a Cronbach's alpha = 0.75. Test-retest reliability was 0.82. There was a significant correlation between the ACT scores and GINA symptom control tool (r = 0.87, p < 0.001). An ACT ≤ 22 was used to screen "not well-controlled" asthma with a sensitivity of 96.4% and specificity of 93.0% and an ACT score ≤ 19 was used to screen "uncontrolled" asthma. The Thai version of ACT is valid and a reliable tool for use in adult Thai asthmatic patients. However, the cut off points of ACT for levels of control should be changed to 22 and 19 for differentiation between well vs. partly controlled and partly vs. uncontrolled asthma, respectively.

Identifying risk zones and landscape features that affect common leopard depredation.

Human-wildlife conflict (HWC) is a pressing issue worldwide but varies by species over time and place. One of the most prevalent forms of HWC in the mid-hills of Nepal is human-common-leopard conflict (HLC). Leopard attacks, especially in forested areas, can severely impact villagers and their livestock. Information on HLC in the Gorkha district was scarce, thus making it an ideal location to identify high-risk zones and landscape variables associated with such events. Registered cases were collected and reviewed from the Division Forest Office (DFO) during 2019-2021. Claims from DFO records were confirmed with herders and villagers via eight focus group discussions. To enhance modeling success, researchers identified a total of 163 leopard attack locations on livestock, ensuring a minimum distance of at least 100 meters between locations. Using maximum entropy (MaxEnt) and considering 13 environmental variables, we mapped common leopard attack risk zones. True Skill Statistics (TSS) and area under receiver-operator curve (AUC) were used to evaluate and validate the Output. Furthermore, 10 replications, 1,000 maximum iterations, and 1000 background points were employed during modeling. The average AUC value for the model, which was 0.726 ± 0.021, revealed good accuracy. The model performed well, as indicated by a TSS value of 0.61 ± 0.03. Of the total research area (27.92 km2), about 74% was designated as a low-risk area, 19% as a medium-risk area, and 7% as a high-risk area. Of the 13 environmental variables, distance to water (25.2%) was the most significant predictor of risk, followed by distance to road (16.2%) and elevation (10.7%). According to response curves, the risk of common leopard is highest in the areas between 1.5 to 2 km distances from the water sources, followed by the closest distance from a road and an elevation of 700 to 800 m. Results suggest that managers and local governments should employ intervention strategies immediately to safeguard rural livelihoods in high-risk areas. Improvements include better design of livestock corrals, insurance, and total compensation of livestock losses. Settlements near roads and water sources should improve the design and construction of pens and cages to prevent livestock loss. More studies on the characteristics of victims are suggested to enhance understanding of common leopard attacks, in addition to landscape variables. Such information can be helpful in formulating the best management practices.

The Optimal Cut-off Value of Upper Arm Circumference and Calf Circumference for Assessing Sarcopenia Among Chinese Community-Dwelling Older Adults.

To explore the cut-off values and health evaluations of upper arm circumference (AC) and calf circumference (CC) on sarcopenia in Chinese community-dwelling older people. In this cross-sectional study, AC, CC, handgrip strength, muscle mass and gait speed were measured in 1537 Chinese community-dwelling older people in Sub-study 1. Correlation analysis, receiver operator characteristic curve (ROC curve) analysis, and consistency analysis were used for determination of AC and CC cut-off values for sarcopenia diagnosis (sarcopenia-AC and CC). Thereafter, 269 participants accepted additional assessments on physical function, body composition and muscle strength in Sub-study 2. T-test or Mann-Whitney U-test was used to explore the differential effects of sarcopenia-AC and CC on health indicators between sarcopenic and non-sarcopenic participants. In Sub-study 1, the Area Under ROC (AUC) of AC and CC for sarcopenia screening were greater than 0.700 (P<0.05). The cut-off values, sensitivity and specificity of AC and CC on sarcopenia in males were 25.9 cm (86.0%, 83.6%) and 33.7 cm (90.7%, 81.4%) whereas in females were 26.5 cm (70.8%, 69.7%) and 33.0 cm (86.5%, 69.4%), respectively. In Sub-study 2, the participants with sarcopenia-AC or sarcopenia-CC showed lower muscle strength and lower fat and muscle mass than the ones without (P<0.05). Additionally, males instead of females with sarcopenia-AC or sarcopenia-CC showed worse performance in time-up and go test and 6-Minute Walk Test (P<0.05). However, the 30-second chair stand test was not different between participants with and without sarcopenia-AC or sarcopenia-CC in both sexes. We found accurate and Chinese population targeted cut-off values of AC and CC on sarcopenia diagnosis (25.9 cm and 33.7 cm in males; 26.5 cm and 33.0 cm in females) and a good evaluation effect of AC and CC on fat and muscle mass, muscle strength and physical functions in males, not females.

Artificial Intelligence in Laboratory Technologies for Early Detection and Prognostication of Sepsis: A Systematic Review

Background: Sepsis: a complex clinical syndrome--represents life-threatening organ dysfunction instigated by an infection's dysregulated host response. Early detection and accurate prognostication of sepsis are crucial; they pave the way for timely intervention, ultimately enhancing patient outcomes. The rise in interest towards Artificial Intelligence (AI) applications within laboratory technologies is directly related to its potential for improving early detection and prognosis forecasting in sepsis cases; this interest comes as AI continues its advancement. Methods: We conducted a systematic review of studies utilizing AI algorithms in laboratory settings for early sepsis detection and prognostication: our methods entailed searching relevant databases for research published until October 2023. Our inclusion criteria spanned original articles; these applied machine learning (ML) and deep learning (DL) techniques to laboratory data--with the aim being sepsis prediction. We assessed the quality of the studies, extracted and synthesized data on AI model performance metrics - including: area under receiver operating characteristic curve (AUROC), sensitivity, specificity, and accuracy. Results: The review encompassed eight studies meeting the inclusion criteria; AI models showcased exceptional predictive capabilities--evidenced by a range of AUROC values from 0.799 to 0.9213, signifying noticeably acceptable performance. However, there was wide variation in sensitivity and specificity among these analyses: an indicator of heterogeneity in model performance. Superior prognostic accuracy and potential for real-time monitoring of patients' early sepsis signs emerged in several models: notably, within the first 12 hours of patient admission - their highest predictive period. The models frequently outperformed traditional scoring systems. Conclusion: Laboratory technology's AI applications significantly promise sepsis' early detection and prognostication. Reviewed studies suggest AI models may surpass traditional methods, offering potential integration into clinical workflows for rapid sepsis identification aid. Nevertheless, we also acknowledged both the variability in model performance and necessity of additional validation across diverse clinical settings. Future research: it must concentrate on two key aspects--the refinement of AI algorithms to enhance sensitivity and precision; furthermore, it should delve into evaluating the clinical impact of tools for sepsis prediction that are assisted by AI.

Multimodal Intraoperative Neurophysiological Monitoring in Intramedullary Spinal Cord Tumors: A 10-Year Single Center Experience.

The study aimed at evaluating the efficacy and the ability of D-wave monitoring combined with somatosensory evoked potentials (SSEPs) and motor evoked potentials (MEPs) to predict functional outcomes in intramedullary spinal cord tumor (IMSCT) surgery. Between December 2011 and December 2020, all patients harboring IMSCT who underwent surgery at our institution were prospectively collected in a surgical spinal registry and retrospectively analyzed. Patient charts and surgical and histological reports were analyzed. The multimodal IONM included SSEPs, MEPs, and-whenever possible-D-waves. All patients were evaluated using the modified McCormick and Frankel grade at admission and 3, 6, and 12 months of follow-up. Sixty-four patients were enrolled in the study. SSEP and MEP monitoring was performed in all patients. The D-wave was not recordable in seven patients (11%). Significant IONM changes (at least one evoked potential modality) were registered in 26 (41%) of the 64 patients. In five cases (8%) where the SSEPs and MEPs lost and the D-wave permanently dropped by about 50%, patients experienced a permanent deterioration of their neurological status. Multimodal IONM (SSEP, MEP, and D-wave neuromonitoring) significantly predicted postoperative deficits (p = 0.0001), with a sensitivity of 100.00% and a specificity of 95.65%. However, D-waves demonstrated significantly higher sensitivity (100%) than MEPs (62.5%) and SSEPs (71.42%) alone. These tests' specificities were 85.10%, 13.89%, and 17.39%, respectively. Comparing the area under ROC curves (AUCs) of these evoked potentials in 53 patients (where all three modalities of IONM were registered) using the pairwise t-test, D-wave monitoring appeared to have higher accuracy and ability to predict postoperative deficits with strong statistical significance compared with MEP and SSEP alone (0.992 vs. 0.798 vs. 0.542; p = 0.018 and p < 0.001). The use of multimodal IONM showed a statistically significant greater ability to predict postoperative deficits compared with SSEP, MEP, and D-wave monitoring alone. D-wave recording significantly increased the accuracy and clinical value of neurophysiological monitoring in IMSCT tumor resection.