Area Under ROC Curve Research Articles

Analysis of the effects of early screening combined with blood lactate on the severity of patients with sepsis

This work aimed to investigate the adoption value of blood lactic acid (BLA) combined with the National Early Warning Score (NEWS) in the early screening of sepsis patients and assessing their severity. The data and materials utilized in this work were obtained from the electronic medical record system of 537 anonymized sepsis patients who received emergency rescue in the emergency rescue area of Liuzhou People's Hospital, Guangxi, from July 1, 2020, to December 26, 2020. Based on the 28-day outcomes of sepsis patients, the medical records were rolled into Group S (407 survival cases) and Group D (130 dead cases). Basic information such as the mode of hospital admission, initial management, use of emergency ventilator within 24 h of admission, NEWS score, arterial oxygen pressure/alveolar oxygen pressure ratio (PaO2/PAO2), alveolar-arterial oxygen difference (A-aDO2), serum creatinine (SCr), blood urea nitrogen (BUN), oxygenation index (OI), Glasgow Coma Scale (GCS), D-dimer, use of vasoactive drugs within 24 h of admission, C-reactive protein (CRP), procalcitonin (PCT), interleukin-6 (IL-6), N-terminal pro-B-type natriuretic peptide (NT-proBNP), quick Sequential Organ Failure Assessment (qSOFA) score, SOFA score, BLA level, NEWS with lactate (NEWS-L) score, SOFA score including lactate level (SOFA-L) score, Intensive Care Unit (ICU) length of stay, total hospital stay, ICU stay/total hospital stay, and septic shock condition were compared between groups. Logistic regression analysis was performed to assess the impact of various predictive factors on prognosis and to plot the receiver operating characteristic (ROC) curve. The results suggested marked differences between Group S and Group D in terms of mean age (t = −5.620; OR = −9.96, 95 % CI: −13.44∼-6.47; P < 0.001). Group S showed drastic differences in terms of mode of hospital admission (χ2 = 9.618, P < 0.01), method of initial management (χ2 = 51.766, P < 0.001), use of emergency ventilator within 24 h of admission (χ2 = 98.564, P < 0.001), incidence of septic shock (χ2 = 77.545, P < 0.001), use of vasoactive drugs within 24 h of admission (χ2 = 102.453, P < 0.001), heart rate (t = −4.063, P < 0.001), respiratory rate (t = −4.758, P < 0.001), oxygenation status (χ2 = 20.547, P < 0.001), NEWS score (t = −6.120, P < 0.001), PaO2/PAO2 ratio (t = 2.625, P < 0.01), A-aDO2 value (Z = −3.581, P < 0.001), OI value (Z = −3.106, P < 0.01), PLT value (Z = −2.305, P < 0.05), SCr value (Z = −3.510, P < 0.001), BUN value (Z = −3.170, P < 0.01), D-dimer (Z = −4.621, P < 0.001), CRP level (Z = −4.057, P < 0.001), PCT value (Z = −2.783, P < 0.01), IL-6 level (Z = −2.904, P < 0.001), length of hospital stay (Z = −4.138, P < 0.001), total hospital stay (Z = −8.488, P < 0.001), CCU/total hospital stay (Z = −9.118, P < 0.001), NEWS score (t = −6.120, P < 0.001), SOFA score (t = −6.961, P < 0.001), SOFA-L score (Z = −4.609, P < 0.001), NEWS-L score (Z = −5.845, P < 0.001), BLA level (Z = −6.557, P < 0.001), and GCS score (Z = 6.909, P < 0.001) when compared to Group D. The use of ventilators, septic shock, PCT, NEWS score, GCS score, SOFA score, SOFA-L score, NEWS-L score, and BLA level were identified as independent risk factors for predicting the prognosis of sepsis patients (P < 0.001). The areas under ROC curve (AUC) of blood lactic acid, PCT, NEWS, NEWS-L, GCS, SOFA, and SOFA-L were 0.695, 0.665, 0.692, 0.698, 0.477, 0.700, and 0.653, respectively. These findings indicate that the combination of BLA with NEWS (NEWS-L) score and SOFA score has certain advantages in assessing the prognosis of sepsis.

Deep learning driven diagnosis of malignant soft tissue tumors based on dual-modal ultrasound images and clinical indexes

BackgroundSoft tissue tumors (STTs) are benign or malignant superficial neoplasms arising from soft tissues throughout the body with versatile pathological types. Although Ultrasonography (US) is one of the most common imaging tools to diagnose malignant STTs, it still has several drawbacks in STT diagnosis that need improving.ObjectivesThe study aims to establish this deep learning (DL) driven Artificial intelligence (AI) system for predicting malignant STTs based on US images and clinical indexes of the patients.MethodsWe retrospectively enrolled 271 malignant and 462 benign masses to build the AI system using 5-fold validation. A prospective dataset of 44 malignant masses and 101 benign masses was used to validate the accuracy of system. A multi-data fusion convolutional neural network, named ultrasound clinical soft tissue tumor net (UC-STTNet), was developed to combine gray scale and color Doppler US images and clinic features for malignant STTs diagnosis. Six radiologists (R1-R6) with three experience levels were invited for reader study.ResultsThe AI system achieved an area under receiver operating curve (AUC) value of 0.89 in the retrospective dataset. The diagnostic performance of the AI system was higher than that of one of the senior radiologists (AUC of AI vs R2: 0.89 vs. 0.84, p=0.022) and all of the intermediate and junior radiologists (AUC of AI vs R3, R4, R5, R6: 0.89 vs 0.75, 0.81, 0.80, 0.63; p &amp;lt;0.01). The AI system also achieved an AUC of 0.85 in the prospective dataset. With the assistance of the system, the diagnostic performances and inter-observer agreement of the radiologists was improved (AUC of R3, R5, R6: 0.75 to 0.83, 0.80 to 0.85, 0.63 to 0.69; p&amp;lt;0.01).ConclusionThe AI system could be a useful tool in diagnosing malignant STTs, and could also help radiologists improve diagnostic performance.

#357 Sex differences in the aldosterone-renin system must be taken into account during screening for primary aldosteronism

Abstract Background and Aims Primary aldosteronism (PA) is one of the most frequent causes of secondary hypertension. Patients with PA are at higher risk for cardiovascular and renal complications than those with essential hypertension. Screening for PA is rarely considered in the assessment of a hypertensive patient, in part because of difficulties in the diagnostic process. Nevertheless, identification of PA is crucial to guide patients towards optimal management, either by mineralocorticoid receptor blockade or by unilateral adrenalectomy, with the latter offering potential cure for patients with aldosterone-producing adenomas (APAs). Numerous studies show inadequate diagnostic accuracy of the aldosterone:renin ratio (ARR) for screening for PA, particularly in females, who have higher ARR variability and lower renin compared to males. Women have a higher risk of false-positive screening results leading to unnecessary confirmatory tests. Use of oral contraception (OC) or hormone replacement therapy (HRT) may also confound the ARR. The aim of this study was to develop improved screening tools for PA, including adjustments for sex-specific differences. Method PROSALDO is a multicenter prospective cohort study designed to test new diagnostic procedures for patients with suspected PA. Medication that interfere with the renin-angiotensin-aldosterone system are withdrawn before laboratory measurements. For screening, the routine ARR is calculated along with additional probability scores based on a peripheral venous steroid hormone profile (PVSP). The latter allows for identification of potentially false-negative ARR results. After a positive screening result of the ARR and/or a high probability score based on the PVSP, PA is confirmed or excluded with a saline infusion test (SIT). PA confirmation is followed by adrenal venous sampling (AVS) to identify patients with unilateral PA. We compared aldosterone, renin and ARR levels between females and males with and without PA. Renin- and sex dependent formulas for aldosterone screening cut-offs were derived from the study population. Logistic regression and the corresponding receiver operating characteristic (ROC) curves were used to determine and compare diagnostic accuracies. Results PA was confirmed in 159 (24.5%) of 649 (328 (50.5%) females and 321 (49.5%) males). Lateralization was shown in 58 out of 117 patients who underwent AVS. In hypertensive patients without PA, ARR was higher in females vs. males (26.4 vs. 11.7 pmol/mU, P &amp;lt; 0.0001), mainly due to a lower renin (9.2 vs. 16.2 mU/L, P &amp;lt; 0.0001). Furthermore, ARRs in patients with vs. without PA showed more overlap in females compared to males. Areas under ROC curves (AUROCs) for the ARR were 0.857 (CI 95%: 0.797-0.902) vs. 0.912 (CI 95%: 0.876-0.938) for females and males respectively. Using liquid chromatography mass spectrometry measurements (LC-MS/MS) of aldosterone, an ARR cut-off of 21.75 pmol/L yielded a sensitivity of 93% with only 45% specificity in females, while for males, a specificity of 66% was obtained for a sensitivity of 97.7%. This meant for females having a higher risk of false-positive screening results (OR 2.36 (CI 95%: 1.65-3.41)), even after excluding patients on OC or HRT (OR 2.17 (CI 95%: 1.5-3.17)). We derived a renin- and sex-dependent formula for the calculation of aldosterone cut-offs, which improved diagnostic accuracy for both males and females (ΔAUROC = 0.05 and 0.045, P &amp;lt; 0.0001 and 0.02 respectively): for a diagnostic sensitivity of 94-95%, specificity was improved to 61% in females and 82% in males. Conclusion Lower renin in females without PA leads to higher ARRs and thus a higher risk of false-positive screening results. Higher overlap of the ARR between females with and without PA compared to their male counterparts weakens utility of the ARR for discrimination of patients with and without PA. Sex- and renin-specific aldosterone cut-offs for screening for PA offer improved diagnostic accuracy. Diagnostic accuracy remains however superior in males, which highlights the need for further optimization of diagnostic procedures specifically in women.

Risk factors and prediction model of delirium in elderly patients after hip arthroplasty.

To analyze the risk factors of delirium in elderly patients after hip arthroplasty and to construct a prediction model. Clinical data of 248 elderly patients who underwent hip arthroplasty in the Department of Traumatology and Orthopedics at Wuhan Fourth Hospital were retrospectively collected from November 2021 to February 2023. Logistic regression analysis was used to identify the risk factors of delirium after hip arthroplasty, and a nomogram prediction model was constructed using the RMS package of R4.1.2 software. The accuracy and stability of the model was evaluated based on the Hosmer-Lemeshow goodness-of-fit test and the receiver operating characteristic (ROC) curve. Age, nighttime sleep, anesthesia method, intraoperative blood loss, hypoxemia, and C-reactive protein (CRP) level were all risk factors of delirium after the hip arthroplasty (P<0.05). These factors were used to construct a nomogram prediction model that was internally validated using the Bootstrap method. The prediction model had the area under ROC curve (AUC) of 0.980 (95% CI: 0.964-0.996), indicating that it has certain predictive value for postoperative delirium. When the optimal cut off value was selected, the sensitivity and specificity were 92.7% and 92.3%, respectively, indicating that the prediction model is effective. Age, short nighttime sleep, general anesthesia, high intraoperative blood loss, hypoxemia, and high CRP levels are independent risk factors for delirium after hip arthroplasty. The nomogram prediction model constructed based on these risk factors can effectively predict delirium in elderly patients after hip arthroplasty.

Detecting anomalies in blockchain transactions using machine learning classifiers and explainability analysis

As the use of blockchain for digital payments continues to rise, it becomes susceptible to various malicious attacks. Successfully detecting anomalies within blockchain transactions is essential for bolstering trust in digital payments. However, the task of anomaly detection in blockchain transaction data is challenging due to the infrequent occurrence of illicit transactions. Although several studies have been conducted in the field, a limitation persists: the lack of explanations for the model's predictions. This study seeks to overcome this limitation by integrating explainable artificial intelligence (XAI) techniques and anomaly rules into tree-based ensemble classifiers for detecting anomalous Bitcoin transactions. The shapley additive explanation (SHAP) method is employed to measure the contribution of each feature, and it is compatible with ensemble models. Moreover, we present rules for interpreting whether a Bitcoin transaction is anomalous or not. Additionally, we introduce an under-sampling algorithm named XGBCLUS, designed to balance anomalous and non-anomalous transaction data. This algorithm is compared against other commonly used under-sampling and over-sampling techniques. Finally, the outcomes of various tree-based single classifiers are compared with those of stacking and voting ensemble classifiers. Our experimental results demonstrate that: (i) XGBCLUS enhances true positive rate (TPR) and receiver operating characteristic-area under curve (ROC-AUC) scores compared to state-of-the-art under-sampling and over-sampling techniques, and (ii) our proposed ensemble classifiers outperform traditional single tree-based machine learning classifiers in terms of accuracy, TPR, and false positive rate (FPR) scores.

Landslide susceptibility assessment through multi-model stacking and meta-learning in Poyang County, China

This study aims to evaluate the effectiveness of various individual machine learning and their ensemble techniques such as Stacking, Voting and Meta-learning in landslide susceptibility assessment taking Poyang, Jiangxi, China as an example. Multi-source geo-environmental data including field surveys, Sentinel-2A/B satellite images, Digital Elevation Models (DEM), and geological and hydrological data were utilized to construct and validate landslide susceptibility models. Results show that the Stacking Classifier outperformed other models, achieving the highest F1 Score of 0.846 and AUC (Area Under ROC Curve) of 0.923, demonstrating its strong predictivity, followed by the Voting Classifier with the F1 Score of 0.829 and AUC of 0.922. Among the individual models, the Multi-Layer Perceptron (MLP) performed best with the F1 Score of 0.828 and AUC of 0.904. Furthermore, the explainable Artificial Intelligence (XAI) technique was applied to better understand the mechanism of classifiers in predicting landslide susceptibility and it suggests a significant correlation between land use, distance to fault, and landslide occurrences. In conclusion, Stacking and Voting hybrid learning models show clear advantages over the individual ones for landslide risk zoning. The results of study may provide technical support for disaster mitigation efforts and future urban planning in areas prone to landslides in Poyang.

PREDICTION OF FLUID RESPONSIVENESS WITH CRYSTALLOID MINI-FLUID CHALLENGE IN CRITICALLY ILL PATIENTS

Background: The mini-fluid challenge test is a good predictor and has minimal limitations in determining fluid responsiveness in the critically ill. However, it primarily relies on colloid infusion. The availability of colloids may vary among hospitals in resource-limited countries, and they can be more expensive than crystalloid fluids. Objective: The study aimed to use crystalloids instead of colloids to predict fluid responsiveness. Methods: This study was conducted from August 2022 to February 2023 among patients with shock in the medical ICU to assess fluid responsiveness. Arterial and central venous catheters were used for the intravenous infusion and monitoring. Data were collected during two consecutive crystalloid mini-fluid challenges of 50 mL each in 1 minute, 100 mL in 2 minutes, and a standard fluid challenge of 300 mL over 15 minutes. The objective was to predict a stroke volume index (SVI) increase of &gt; 10%. Diagnostic accuracy was evaluated using the Receiver Operating Characteristic (ROC) curve, and hemodynamic variables in the positive fluid challenge group were compared for different volume expansions. Results: Twenty-nine patients (62% males; median age, 75 years) were included, with 42 fluid challenge test events. Septic shock was the primary condition in 83% of the cases. The 50 mL crystalloid mini-fluid challenge showed an accuracy of 80% with a sensitivity of 69% and specificity of 100% at the cutoff level of SVI &gt; 5%, Area Under ROC (AUROC)=0.79. In comparison, the 100 mL crystalloid mini-fluid challenge demonstrated an accuracy of 85% (sensitivity of 79% and specificity of 100%) at the cutoff level of SVI &gt; 10% (AUROC= 0.89). In the positive fluid challenge group, administration of crystalloids led to a significant increase in mean arterial pressure and SVI, while other parameters remained comparable. Conclusions: A mini-fluid challenge test with 100 mL crystalloids can predict fluid responsiveness in critically ill patients. The best cutoff level was a change in SVI &gt; 10% from baseline.

Impact of an artificial intelligence based model to predict non-transplantable recurrence among patients with hepatocellular carcinoma

ObjectiveWe sought to develop Artificial Intelligence (AI) based models to predict non-transplantable recurrence (NTR) of hepatocellular carcinoma (HCC) following hepatic resection (HR). MethodsHCC patients who underwent HR between 2000-2020 were identified from a multi-institutional database. NTR was defined as recurrence beyond Milan Criteria. Different machine learning (ML) and deep learning (DL) techniques were used to develop and validate two prediction models for NTR, one using only preoperative factors and a second using both preoperative and postoperative factors. ResultsOverall, 1763 HCC patients were included. Among 877 patients with recurrence, 364 (41.5%) patients developed NTR. An ensemble AI model demonstrated the highest area under ROC curves (AUC) of 0.751 (95% CI: 0.719–0.782) and 0.717 (95% CI:0.653–0.782) in the training and testing cohorts, respectively which improved to 0.858 (95% CI: 0.835–0.884) and 0.764 (95% CI: 0.704–0.826), respectively after incorporation of postoperative pathologic factors. Radiologic tumor burden score and pathological microvascular invasion were the most important preoperative and postoperative factors, respectively to predict NTR. Patients predicted to develop NTR had overall 1- and 5-year survival of 75.6% and 28.2%, versus 93.4% and 55.9%, respectively, among patients predicted to not develop NTR (p < 0.0001). ConclusionThe AI preoperative model may help inform decision of HR versus LT for HCC, while the combined AI model can frame individualized postoperative care (https://altaf-pawlik-hcc-ntr-calculator.streamlit.app/).

Identifying potential (re)hemorrhage among sporadic cerebral cavernous malformations using machine learning

The (re)hemorrhage in patients with sporadic cerebral cavernous malformations (CCM) was the primary aim for CCM management. However, accurately identifying the potential (re)hemorrhage among sporadic CCM patients in advance remains a challenge. This study aims to develop machine learning models to detect potential (re)hemorrhage in sporadic CCM patients. This study was based on a dataset of 731 sporadic CCM patients in open data platform Dryad. Sporadic CCM patients were followed up 5 years from January 2003 to December 2018. Support vector machine (SVM), stacked generalization, and extreme gradient boosting (XGBoost) were used to construct models. The performance of models was evaluated by area under receiver operating characteristic curves (AUROC), area under the precision-recall curve (PR-AUC) and other metrics. A total of 517 patients with sporadic CCM were included (330 female [63.8%], mean [SD] age at diagnosis, 42.1 [15.5] years). 76 (re)hemorrhage (14.7%) occurred during follow-up. Among 3 machine learning models, XGBoost model yielded the highest mean (SD) AUROC (0.87 [0.06]) in cross-validation. The top 4 features of XGBoost model were ranked with SHAP (SHapley Additive exPlanations). All-Elements XGBoost model achieved an AUROCs of 0.84 and PR-AUC of 0.49 in testing set, with a sensitivity of 0.86 and a specificity of 0.76. Importantly, 4-Elements XGBoost model developed using top 4 features got a AUROCs of 0.83 and PR-AUC of 0.40, a sensitivity of 0.79, and a specificity of 0.72 in testing set. Two machine learning-based models achieved accurate performance in identifying potential (re)hemorrhages within 5 years in sporadic CCM patients. These models may provide insights for clinical decision-making.

Urban explosion and hotspots of forest loss in western Himalaya: Mapping land use/cover change trends since 1975

The Western Himalayan Region (WHR) is under constant threat from Land Use and Land Cover Change (LULCC) brought on by various socioeconomic activities such as urbanization, agricultural expansion, and population growth. Detailed research into the scope of these changes is necessary to comprehend the challenges they represent. This study aimed at (1) examining and quantifying spatiotemporal patterns of LULCC from 1975 to 2015, (2) mapping the hotspots of forest loss, and (3) modelling future LULCC for the year 2055 using medium spatial resolution multispectral satellite imagery (MSS, TM, LISS-I, LISS-III, AWiFS), field data, visual interpretation and logistic regression model in the WHR. Logistic regression integrated various drivers of LULCC, including climatic, topographic, and socio-economic factors and past LULCC trends to forecast potential LULC in the WHR by 2055. The results highlight an outrageous 184% increase in regional urbanization. Expansion of urban and cropland (12%) led to deforestation which reduced the area of natural habitats, specifically forests (−11%), water bodies (−8%) and scrubland (−6%). A significant rise in barren land by 30% was noted, while there was a decrease of 20% in glacier/snow melt. The future projection for 2055 anticipates continuing current trends, emphasizing an intensified urban expansion of 63% and an approximate 9% reduction in overall forest coverage. The accuracy of the predicted 2015 LULC map was assessed using ROC/AUC (Receiver Operating Characteristic/Area under Curve) which was found to be 0.79.

A comprehensive evaluation of serum circCSPP1 as a novel diagnostic and prognostic biomarker for gastric cancer

PurposeGastric cancer (GC) has high incidence and mortality due to its low early screening efficiency. Circular RNAs (CircRNAs) are a new class of non-coding RNAs which is closely related to GC. Nevertheless, the clinical application value of circRNAs in GC are largely unknown. Therefore, we studied the role of a novel circRNA named circCSPP1 in patients with GC. MethodsCircRNA sequencing was performed to screen out the target molecule. Real-time fluorescent quantitative PCR (RT-qPCR) was utilized to detect the expression level of circCSPP1 in GC tissues, cells, and serum. Gel and Sanger sequencing were utilized to verify the ring structure of circCSPP1. RNase R enzyme digestion experiment and actinomycin D experiment were verifed the advantage of circCSPP1 as a diagnostic biomarker in patients with GC when that compared with linear RNA. The correlation between the expression level of serum circCSPP1 and clinicopathological data of GC patients was further analyzed. Receiver operating characteristic curve (ROC) and the area under ROC curve (AUC) were utilied to evaluate the diagnostic performance. ResultsCircCSPP1 has a circular structure which with resistance to RNA exonuclease digestion and long half-life compared with linear RNA. In our study, circCSPP1 was first found up-regulated in patients with GC. Serum circCSPP1 level was decreased significantly after surgical resection whereas increased after recurrence. High expression of circCSPP1 was associated with poor survival rates. The expression level of circCSPP1 was significantly correlated to tumor size, T stage, lymph node metastasis, and TNM stage. The AUC of serum circCSPP1 was 0.834, with high sensitivity and specificity in discriminating patients with GC from healthy donors. More importantly, the combined diagnosis of circCSPP1, CEA, and CA19–9 achieved the superior AUC of 0.882, with the highest specificity. ConclusionSerum circCSPP1 may prove to be a potential non-invasive auxiliary diagnostic biomarker for patients with GC.

Circulating long noncoding RNA, Zfpm2-As1, and XIST based on medical data analysis are potential plasma biomarkers for gastric cancer diagnosis.

Long noncoding RNAs (lncRNAs) participate in diseases, especially tumorigenesis, including gastric cancer (GC). Although lncRNAs in GC tissues have been extensively studied in previous research, the possible significance of circulating lncRNAs in diagnosing GC is still unknown. The present work investigated lncRNAs ZFPM2-AS1 and XIST with high expression in GC tissues proved as potential plasma biomarkers from 20 early GC cases, 100 GC cases, and 90 normal subjects. The possible correlation between ZFPM2-AS1 and XIST expression levels was analyzed with general characteristics and clinicopathological features. The performance in diagnosis was assessed according to receiver operating characteristic (ROC) analysis. According to the results, XIST and ZFPM2-AS1 expression remarkably increased within GC plasma relative to normal subjects (P< 0.01); besides, lncRNA XIST expression after surgery had a tendency of downregulation compared with preoperative levels (P< 0.05). Moreover, the area under ROC curve (AUC) values were 0.62 for ZFPM2-AS1 and 0.68 for XIST, while the pooled AUC value of CA-724 and two lncRNAs was 0.751. Circulating lncRNAs ZFPM2-AS1 and XIST can serve as the candidate plasma biomarkers used to diagnose GC.

Age-adjusted Charlson comorbidity index is associated with the risk of osteoporosis in older fall-prone men: a retrospective cohort study

BackgroundThere is growing evidence linking the age-adjusted Charlson comorbidity index (aCCI), an assessment tool for multimorbidity, to fragility fracture and fracture-related postoperative complications. However, the role of multimorbidity in osteoporosis has not yet been thoroughly evaluated. We aimed to investigate the association between aCCI and the risk of osteoporosis in older adults at moderate to high risk of falling.MethodsA total of 947 men were included from January 2015 to August 2022 in a hospital in Beijing, China. The aCCI was calculated by counting age and each comorbidity according to their weighted scores, and the participants were stratified into two groups by aCCI: low (aCCI < 5), and high (aCCI ≥5). The Kaplan Meier method was used to assess the cumulative incidence of osteoporosis by different levels of aCCI. The Cox proportional hazards regression model was used to estimate the association of aCCI with the risk of osteoporosis. Receiver operating characteristic (ROC) curve was adapted to assess the performance for aCCI in osteoporosis screening.ResultsAt baseline, the mean age of all patients was 75.7 years, the mean BMI was 24.8 kg/m2, and 531 (56.1%) patients had high aCCI while 416 (43.9%) were having low aCCI. During a median follow-up of 6.6 years, 296 participants developed osteoporosis. Kaplan–Meier survival curves showed that participants with high aCCI had significantly higher cumulative incidence of osteoporosis compared with those had low aCCI (log-rank test: P < 0.001). When aCCI was examined as a continuous variable, the multivariable-adjusted model showed that the osteoporosis risk increased by 12.1% (HR = 1.121, 95% CI 1.041–1.206, P = 0.002) as aCCI increased by one unit. When aCCI was changed to a categorical variable, the multivariable-adjusted hazard ratios associated with different levels of aCCI [low (reference group) and high] were 1.00 and 1.557 (95% CI 1.223–1.983) for osteoporosis (P < 0.001), respectively. The aCCI (cutoff ≥5) revealed an area under ROC curve (AUC) of 0.566 (95%CI 0.527–0.605, P = 0.001) in identifying osteoporosis in older fall-prone men, with sensitivity of 64.9% and specificity of 47.9%.ConclusionsThe current study indicated an association of higher aCCI with an increased risk of osteoporosis among older fall-prone men, supporting the possibility of aCCI as a marker of long-term skeletal-related adverse clinical outcomes.

The prognostic value of HEART score in patients with known heart failure evaluated at the emergency department for acute chest pain

Abstract Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): National Heart Lung and Blood Institute (United States). Background Chest pain is a common and concerning symptom in emergency care, often necessities further risk stratification for adverse cardiovascular events on short-term follow up. Patients with known heart failure (HF) constitutes a unique sub-population that would greatly benefit from short-term risk stratification to guide appropriate clinical interventions and optimize resource allocation. HEART (History, ECG, Age, Risk factors, Troponin) is a well-validated risk score that has been shown to outperform other clinical risk scores in patients with chest pain. It remains unknown if HEART score can be safely used in patients with known HF presenting to the emergency department with chest pain. Purpose We sought to evaluate the prognostic value of HEART score in patients with known HF compared to their counterparts. Method This was a prospective observational cohort study of consecutive adult patients evaluated at the emergency department for non-traumatic chest pain. HEART score was computed from charts by independent physician reviewers. The primary study outcome was major adverse cardiac events (MACE) defined as acute coronary syndrome, invasive revascularization, reinfarction, death, cardiac arrest, ventricular tachyarrhythmia, or acute exacerbation within 30 days of indexed admission. Outcomes were adjudicated by two independent reviewers (Kappa = 0.908). Classification performance between groups was compared using area under receiver operator characteristic curve (AUROC), sensitivity, and negative predictive value (NPV). Results Our cohort included 4,132 patients (age 59±16, 47% female, 33% Black). Overall, 921 patients (22.3%) experienced 30-day MACE. The classification performance of HEART score in those without HF (n = 3,329, 81%) was AUROC of 0.831 (95% CI 0.813-0.848), compared to only 0.633 (95% CI 0.592-0.673) in those with known HF (n = 803, 19%) (z score = 8.771, p &amp;lt; 0.001, Figure). The sensitivity and NPV of HEART score among those without HF were 87.4% and 95.3%, compared to 81.2% and 73.3%, respectively, among those with known HF. Conclusion Our findings suggest that the prognostic value of HEART score for predicting 30-day MACE is inferior and suboptimal in patients with known HF evaluated for chest pain at the emergency department compared to their counterparts. These findings emphasize the need for more tailored risk assessment and management strategies in this specific patient population. Further research is warranted to better understand the limitations and potential enhancements of the HEART score in patients with HF.Classification Performance of HEART score

Diabetes Prediction Using Machine Learning

Diabetes, characterized by high blood sugar levels over time, poses challenges for accurate prediction due to limited labelled data and data quality issues like outliers and missing values. To address these challenges, our research introduces a robust prediction framework. This framework integrates techniques such as outlier rejection, filling missing values, standardizing data, selecting relevant features, and employing K-fold cross-validation. We utilize a range of machine learning algorithms including k-nearest Neighbour, Decision Trees, Random Forest, AdaBoost, Naive Bayes, XGBoost, and Multilayer Perceptron (MLP). Additionally, we propose a weighted ensembling technique to enhance prediction accuracy. The weights for ensembling are determined based on the performance of each classifier using the Area Under ROC Curve (AUC) metric. We optimize model performance through hyperparameter tuning using grid search. Our experiments are conducted using the Pima Indian Diabetes Dataset under uniform conditions. The ensembling classifier we propose achieves superior performance with a sensitivity of 0.789, specificity of 0.934, false omission rate of 0.092, diagnostic odds ratio of 66.234, and AUC of 0.950. This outperforms existing methods by 2.00% in terms of AUC. Our framework surpasses other approaches discussed in the literature and shows promise for improving diabetes prediction. We have released our source code for diabetes prediction to the public.

Prognostic factors and novel prediction models for overall survival of patients with submandibular gland cancer: A population-based retrospective cohort study

BackgroundAccurately predicting the survival rate of submandibular gland cancer (SGC) is of significant importance for guiding treatment decision-making and improving patient outcomes. This study was aimed to identify the independent prognostic factors of overall survival (OS) in SGC patients, and develop novel prediction models to aid clinicians in predicting the survival probability. Materials and methodsPatients diagnosed with primary SGC after the year 2010 were extracted from SEER database and then randomly allocated into training and test samples in a 7:3 ratio. Uni- and multi-variable COX analyses were employed using the training sample to ascertain independent prognostic factors for OS. Subsequently, graphic and online dynamic nomograms were established basing on the independent prognostic factors. We utilized C-index, calibration curve, receiver operating characteristic (ROC) curve, and area under ROC curve (AUC) value to evaluate the discrimination capacity and the consistency between predicted and actual survival. ResultsA total of 527 SGC patients were included (369 assigned to training group and 158 assigned to test group). The multivariable COX analysis showed that age, sex, marital status, tumor histology, summary stage, metastases to bone, and tumor size were independently associated with OS. Novel graphical and online dynamic (URL: https://yangxg1209.shinyapps.io/overall_survival_submandibular_gland_tumor/) nomograms were established. The C-indices (training: 0.77, 95%CI 0.71–0.84; test: 0.77, 95%CI 0.68–0.85) indicate favorable discrimination ability of the model, and the calibration curves demonstrated favorable consistency between the predicted and actual survival rates. ConclusionsOur study identified the independent prognostic factors influencing OS in patients with SGC, and successfully established and validated novel nomograms, which provide accurate prediction of survival rates and allows for personalized risk assessment.

Human circular RNA hsa_circ_0000231 clinical diagnostic effectiveness as a new tumor marker in gastric cancer.

Owing to the subtlety of initial symptoms associated with gastric cancer (GC), the majority of patients are diagnosed at later stages. Given the absence of reliable diagnostic markers, it is imperative to identify novel markers that exhibit high sensitivity and specificity. Circular RNA, a non-coding RNA, plays an important role in tumorigenesis and development and is well expressed in body fluids. In this study, we aimed to identify hsa_circ_0000231 as a new biomarker for the diagnosis of GC and to assess its clinical diagnostic value in serum. The stability and correctness of hsa_circ_0000231 was determined by agarose gel electrophoresis, Rnase R assay and Sanger sequencing. Real-time quantitative polymerase chain reaction (qRT-PCR) was designed to discover the expression level of hsa_circ_0000231 and whether it has dynamic serum monitoring capability. The correlation between hsa_circ_0000231 and clinicopathological parameters was analyzed by collecting clinical and pathological data from GC patients. In addition, diagnostic efficacy was assessed by constructing receiver operating characteristic curves (ROC). Hsa_circ_0000231 exhibits a stable and consistently expressed structure. In GC serum, cells, and tissues, it demonstrates reduced expression levels. Elevated expression levels observed postoperatively suggest its potential for dynamic monitoring. Additionally its expression level correlates with TNM staging and neuro/vascular differentiation. The area under ROC curve (AUC) for hsa_circ_0000231 is 0.781, indicating its superior diagnostic value compared to CEA, CA19-9, and CA72-4. The combination of these four indicators enhances diagnostic accuracy, with an AUC of 0.833. The stable expression of hsa_circ_0000231 in the serum of gastric cancer patients holds promise as a novel biomarker for both the diagnosis and dynamic monitoring of GC.

Impact of Pretransplant C-Reactive Protein, Neutrophiles, Platelets, and Albumin Levels on Recipient Survival After Simultaneous Pancreas and Kidney Transplantation

BackgroundLong-lasting diabetes mellitus type 1 and end-stage renal disease induce severe metabolic and immunological deterioration. Pretransplant C-reactive protein (CRP) and albumin (ALB) levels have impact on kidney transplantation. We evaluated impact of preoperative CRP, ALB, neutrophiles (NEU) and platelets (PLT) count, on one- and five-year recipients survival after simultaneous pancreas and kidney transplantation (SPK). MethodsAmong 103 SPK recipients, parameters: CRP (mean: 4.5±4.97mg/L); NEU (mean: 5.12 ±2.13∗103/mm3); PLT (mean: 244±84∗103/mm3); ALB (mean 4.5±0.75 g/dL) were obtained before transplantation. Cox regression, uni-, multivariate analysis for one- and five-year survivals were performed with 95% Confidence Intervals (CI) and area under ROC curve (AUC) was assessed. ResultsIn Cox regression, ALB <3,65g/dL had significant impact for one-year and five-year survivors with hazard ratios: 8 (CI:1.5-38.28, p<0.05) and 3.13 (CI:1.45-6.73, p<0.05), respectively. In univariate analysis, we found significantly decreased one-year survival if: PLT<180∗103/mm3, ALB <3.65g/dL, NEU>5.8∗103/mm3 and CRP>2.25mg/L with odds ratios (OR): 6.75 (CI: 2.12-21.15); 4.05(CI:1.3-12.09); 2.97 (CI: 1.02-8.64) and 5.51 (CI:1.67-18.19), respectively. Independent factors for five-year survival were: CRP, ALB and PLT with OR: 4.72(CI:1.67–13.29); 3.31(CI:1.18–9.25) and 4.2(CI:1.39–12.68), respectively. In multivariate analysis, we built two models for one-year survival. Model 1 (ALB+PLT) with OR: 3.12 (CI:0.97-10.07) and 5.55 (CI:1.67-18.4); and model 2 (CRP+PLT) with OR: 5.51 (CI:1.5-17.3) and 4.3 (CI:1.2-15.06), respectively. AUC for models 1 and 2 were: 0.74 and 0.759, respectively. ConclusionsNEU, PLT, ALB and CRP levels assessed before transplantation are independent factors for one- and five-year SPK recipients survival.

A Neoteric Feature Extraction Technique to Predict the Survival of Gastric Cancer Patients.

At the time of cancer diagnosis, it is crucial to accurately classify malignant gastric tumors and the possibility that patients will survive. This study aims to investigate the feasibility of identifying and applying a new feature extraction technique to predict the survival of gastric cancer patients. A retrospective dataset including the computed tomography (CT) images of 135 patients was assembled. Among them, 68 patients survived longer than three years. Several sets of radiomics features were extracted and were incorporated into a machine learning model, and their classification performance was characterized. To improve the classification performance, we further extracted another 27 texture and roughness parameters with 2484 superficial and spatial features to propose a new feature pool. This new feature set was added into the machine learning model and its performance was analyzed. To determine the best model for our experiment, Random Forest (RF) classifier, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Naïve Bayes (NB) (four of the most popular machine learning models) were utilized. The models were trained and tested using the five-fold cross-validation method. Using the area under ROC curve (AUC) as an evaluation index, the model that was generated using the new feature pool yields AUC = 0.98 ± 0.01, which was significantly higher than the models created using the traditional radiomics feature set (p < 0.04). RF classifier performed better than the other machine learning models. This study demonstrated that although radiomics features produced good classification performance, creating new feature sets significantly improved the model performance.

Prediction of neoplastic gallbladder polyps in patients with different age level based on preoperative ultrasound: a multi-center retrospective real-world study

BackgroundThe prevalence of neoplastic polyps in gallbladder polyps (GPs) increases sharply with age and is associated with gallbladder carcinoma (GBC). This study aims to predict neoplastic polyps and provide appropriate treatment strategies based on preoperative ultrasound features in patients with different age level.MethodsAccording to the age classification of WHO, 1523 patients with GPs who underwent cholecystectomy from January 2015 to December 2019 at 11 tertiary hospitals in China were divided into young adults group (n=622), middle-aged group (n=665) and elderly group (n=236). Linear scoring models were established based on independent risk variables screened by the Logistic regression model in different age groups. The area under ROC (AUC) to evaluate the predictive ability of linear scoring models, long- and short- diameter of GPs.ResultsIndependent risk factors for neoplastic polyps included the number of polyps, polyp size (long diameter), and fundus in the young adults and elderly groups, while the number of polyps, polyp size (long diameter), and polyp size (short diameter) in the middle-aged groups. In different age groups, the AUCs of its linear scoring model were higher than the AUCs of the long- and short- diameter of GPs for differentiating neoplastic and non-neoplastic polyps (all P<0.05), and Hosmer-Lemeshow goodness of fit test showed that the prediction accuracy of the linear scoring models was higher than the long- and short- diameter of GPs (all P>0.05).ConclusionThe linear scoring models of the young adults, middle-aged and elderly groups can effectively distinguish neoplastic polyps from non-neoplastic polyps based on preoperative ultrasound features.