Validación de test de flexión-relajación para definir un déficit lumbar con electrodos tetrapolares / Validation of the flexion-relaxation test to define a lumbar deficit with tetrapolar electrodes

Introducción y objetivo: Obtener un nuevo punto de corte (PC) para un test de flexión-relajación (FR) lumbar efectuado con electrodos (e.) tetrapolares, desde valores ya definidos con dispositivos bipolares.Materiales y métodosLa muestra del estudio consta de 47 pacientes en situación de incapacidad temporal por dolor lumbar (DL). Fueron evaluados mediante un test de dinamometría isométrica, una prueba cinemática y una valoración del fenómeno FR.Se plantean dos experimentos con curvas ROC. El primero, con 47 pacientes que efectuaron de modo consecutivo el test FR con ambos tipos de electrodos, utilizándose como variable de clasificación el punto de corte conocido para los e. bipolares (2,49uV). En el segundo, con los datos de la EMGs registrados con e. tetrapolares en 17 pacientes, se efectúa un test de DeLong que compara las 2 curvas ROC que construimos, por un lado, al clasificar la muestra desde pruebas de dinamometría y cinemática, y por el otro, al clasificarlos con los valores de la EMGs bipolar.ResultadosUn total de 34 pacientes completaron adecuadamente las valoraciones del primer experimento y 17 pacientes el segundo. El primer estudio arroja un punto de corte de 1,2uV, con un AUC del 87,7%; sensibilidad 84,2% y especificidad 80%. El segundo muestra un PC para los e. bipolares de 1,21uV (AUC 87,5%) y para los e. tetrapolares de 1,43 (AUC 82,5%) con un test de DeLong sin diferencias significativas entre ambas curvas (p>0,4065).ConclusionesLa metodología de validación con curvas ROC ha permitido obtener un nuevo PC para la prueba FR de modo práctico, simplemente simultaneando ambos test sobre el mismo grupo de pacientes hasta obtener una muestra significativa. (AU)

Introduction and objective: To obtain a new cut-off point (CP) for a lumbar flexion-relaxation (RF) test established with tetrapolar (e.) electrodes, from values already defined with bipolar devices.Materials and methodsThe study sample consists of 47 patients in a situation of temporary disability due to low back pain (DL). They were evaluated by means of an isometric dynamometry test, a kinematic test and an assessment of the FR phenomenon.Two experiments with ROC curves are proposed. The first, with 47 patients who consecutively performed the RF test with both types of electrodes, using the cut-off point (CP) known for the e. bipolar (2.49μV). In the second, with the EMG data recorded with e. tetrapolar in 17 patients, a DeLong test was performed that compares the 2 ROC curves that were constructed on the one hand, by classifying the sample from dynamometry and kinematic tests, and on the other, by classifying them with the bipolar EMG values.ResultsA total of 34 patients adequately completed the evaluations of the first experiment and 17 patients the second. The first study shows a cut-off point of 1.2μV, with an AUC of 87.7%; Sensitivity 84.2% and Specificity 80%. The second shows a PC for e. bipolars of 1.21μV (AUC 87.5%) and for e. tetrapolar values of 1.43 (AUC 82.5%) with a DeLong test without significant differences between both curves (p>0.4065).ConclusionsThe validation methodology with ROC curves has made it possible to obtain a new PC for the RF test in a practical way, simply by simultaneously performing both tests on the same group of patients until a significant sample is obtained. (AU)

[The predictive value of dynamic changes of neutrophil/lymphocyte ratio (NLR) combined with the model of end-stage liver disease (MELD) score in patients with acute-on-chronic hepatitis B liver failure].

Objective: To determine the predictive value of dynamic changes of neutrophil/lymphocyte ratio (NLR) combined with the model of end-stage liver disease (MELD) score in patients with acute-on-chronic hepatitis B liver failure. Methods: Patients with acute-on-chronic hepatitis B liver failure who were hospitalized in the Department of Hepatology of Qilu Hospital of Shandong University from January 2010 to July 2023 were retrospectively enrolled. According to the clinical outcomes of patients within 30 days of admission, they were divided into the survival group and the death group. The dynamic changes in NLR and initial values on day 3, 5, 8, and 12 in two groups were analyzed for the diagnostic value of 30-day prognosis in patients with acute-on-chronic hepatitis B liver failure. Logistic regression analysis and machine learning XGBoost algorithm were used to evaluate the risk factors influencing the prognosis of patients at 30 days. Receiver operating characteristic(ROC) curve was used to evaluate the diagnostic value of NLR and initial value change combined with MELD score on day 12 of admission in patients with chronic acute hepatitis B liver failure. Results: A total of 243 patients were enrolled in the study, including 145 patients in the survival group [115 males, 30 females, aged 25-74 (47±11)] and 98 patients in the death group [80 males, 18 females, aged 22-80 (49±13) ]. The median initial NLR of survival group and death group were 3.5 (2.1, 5.3) and 4.9 (2.9, 8.3), respectively, and the difference was statistically significant (P=0.003). The variation of NLR from the initial value on day 3, 5, 8, and 12 in the survival group [1.6 (0, 4.3), 1.9 (-0.2, 4.1), 2.0 (-0.1, 4.3) and 2.9 (0.3, 7.0), respectively] were lower than that in the death group [3.2 (0.9, 7.5), 5.1 (1.8, 7.6), 5.8 (2.0, 10.6) and 9.6 (3.5, 16.4), respectively] (all P<0.001). Logistic regression multivariate analysis showed that the changes in NLR on the 12th day and initial value (OR=1.07,95%CI:1.01-1.14, P=0.014), the changes in NLR on the 3rd day and initial value (OR=2.71, 95%CI: 1.32-5.55, P=0.007), the initial value of NLR (OR=1.18,95%CI:1.01-1.37,P=0.035) and fibrinogen (OR=0.21,95%CI:0.05-0.96,P=0.044) were related factors for death within 30 days. Machine learning XGBoost algorithm showed that the weight of the change between the NLR on the 12th day and the initial value was the highest. The area under the ROC curve of the combined MELD score was 0.812 (95%CI: 0.728-0.895), the specificity was 67.78%, and the sensitivity was 82.35%. Conclusion: Dynamic change of NLR combined with MELD score has high predictive value for the short-term prognosis of patients with acute-on-chronic hepatitis B liver failure.

Identifying Predictors of Mortality in Sepsis Patients with Malignancy: A Retrospective Cohort Study.

BACKGROUND: Sepsis is a major problem that contributes to a high mortality rate. Its mortality is especially high in patients with malignancy. One study reported that sepsis patients with malignancy have a 2.32 times higher risk of mortality compared to patients without malignancy. For this reason, factors that influence mortality in sepsis patients with malignancy become especially important to provide effective and efficient therapy. This study aims to identify factors that influence mortality in sepsis patients with malignancy. METHODS: This study is a retrospective cohort study using medical records of sepsis patients with malignancy who were treated at Cipto Mangunkusumo Hospital from 2020 to 2022. A bivariate analysis was carried out and followed by a logistic regression analysis on variables with p-value<0.25 on the bivariate analysis. RESULTS: Among the 350 eligible sepsis subjects with malignancy, there was an 82% mortality rate (287 subjects). Bivariate and multivariate analyses revealed significant associations between mortality and both SOFA score (adjusted Odds Ratio of 5.833, 95%CI 3.214-10.587) and ECOG performance status (adjusted Odds Ratio of 3.490, 95%CI 1.690-7.208). CONCLUSION: SOFA score and ECOG performance status are significantly associated with sepsis patient mortality in malignancy cases.

Identifying diagnostic indicators for type 2 diabetes mellitus from physical examination using interpretable machine learning approach.

Background: Identification of patients at risk for type 2 diabetes mellitus (T2DM) can not only prevent complications and reduce suffering but also ease the health care burden. While routine physical examination can provide useful information for diagnosis, manual exploration of routine physical examination records is not feasible due to the high prevalence of T2DM. Objectives: We aim to build interpretable machine learning models for T2DM diagnosis and uncover important diagnostic indicators from physical examination, including age- and sex-related indicators. Methods: In this study, we present three weighted diversity density (WDD)-based algorithms for T2DM screening that use physical examination indicators, the algorithms are highly transparent and interpretable, two of which are missing value tolerant algorithms. Patients: Regarding the dataset, we collected 43 physical examination indicator data from 11,071 cases of T2DM patients and 126,622 healthy controls at the Affiliated Hospital of Southwest Medical University. After data processing, we used a data matrix containing 16004 EHRs and 43 clinical indicators for modelling. Results: The indicators were ranked according to their model weights, and the top 25% of indicators were found to be directly or indirectly related to T2DM. We further investigated the clinical characteristics of different age and sex groups, and found that the algorithms can detect relevant indicators specific to these groups. The algorithms performed well in T2DM screening, with the highest area under the receiver operating characteristic curve (AUC) reaching 0.9185. Conclusion: This work utilized the interpretable WDD-based algorithms to construct T2DM diagnostic models based on physical examination indicators. By modeling data grouped by age and sex, we identified several predictive markers related to age and sex, uncovering characteristic differences among various groups of T2DM patients.

Validity and reliability of the Turkish version of the Informant Assessment of Geriatric Delirium Scale.

Background and purpose:

Delirium is a common complication developing in el­der­ly patients. Therefore, it is important to diagnose delirium earlier. Family caregivers play an active role in early diagnosis of de­lirium and build a bridge between health pro­fessionals and patients. The purpose of this research was to achieve the validity and reliability of the Turkish version of the Informant Assessment of Geriatric Delirium Scale (I-AGeD).

This is a methodological study. The sample comprised 125 caregivers ac­cepting to participate in the study and offering care to older patients with hip fracture aged ≥60 years. Data were gathered preoperatively and on postoperative days 0, 1 and 2. After achieving the linguistic and content validity of the scale, the known-groups comparison was used to achieve its construct validity. The ROC curve analysis was made to determine the sensitivity and specificity of the scale. Item-total correlations, item analysis based on the difference between the upper 27% and lower 27%, Kuder–Richardson 20 (KR-20) coefficient and parallel forms reliability with the NEECHAM Confusion Scale were adapted to assess discriminant indices of the items in the I-AGeD.

The item-total correlation coeffi­cients of the scale ranged from 0.54 to 0.89 and KR-20 coefficient ranged from 0.09 to 0.91 depending on the measurement times. According to the ROC curve analysis, the sensitivity and specificity of the scale were ≥ 91% and ≥ 96% respectively. The parallel forms reliability analysis showed a highly significant, strong negative relation at each measurement between the I-AGeD and the NEECHAM Confusion Scale. 

The I-AGeD is valid and reliable to diagnose delirium in older Turkish patients in perioperative processes.

[Prognostic nutritional index application value for acute-on-chronic liver failure co-infection].

Objective: To explore the predictive value of the prognostic nutritional index (PNI) in concurrently infected patients with acute-on-chronic liver failure (ACLF). Methods: 220 cases with ACLF diagnosed and treated at the First Affiliated Hospital of Xi'an Jiaotong University from January 2011 to December 2016 were selected. Patients were divided into an infection and non-infection group according to whether they had co-infections during the course of the disease. Clinical data differences were compared between the two groups of patients. Binary logistic regression analysis was used to screen out influencing factors related to co-infection. The receiver operating characteristic curve was used to evaluate the predictive value of PNI for ACLF co-infection. The measurement data between groups were compared using the independent sample t-test and the Mann-Whitney U rank sum test. The enumeration data were analyzed using the Fisher exact probability test or the Pearson χ(2) test. The Pearson method was performed for correlation analysis. The independent risk factors for liver failure associated with co-infection were analyzed by multivariate logistic analysis. Results: There were statistically significant differences in ascites, hepatorenal syndrome, PNI score, and albumin between the infection and the non-infection group (P < 0.05). Among the 220 ACLF cases, 158 (71.82%) were infected with the hepatitis B virus (HBV). The incidence rate of infection during hospitalization was 69.09% (152/220). The common sites of infection were intraabdominal (57.07%) and pulmonary infection (29.29%). Pearson correlation analysis showed that PNI and MELD-Na were negatively correlated (r = -0.150, P < 0.05). Multivariate logistic analysis results showed that low PNI score (OR=0.916, 95%CI: 0.865~0.970), ascites (OR=4.243, 95%CI: 2.237~8.047), and hepatorenal syndrome (OR=4.082, 95%CI : 1.106~15.067) were risk factors for ACLF co-infection (P < 0.05). The ROC results showed that the PNI curve area (0.648) was higher than the MELD-Na score curve area (0.610, P < 0.05). The effectiveness of predicting infection risk when PNI was combined with ascites and hepatorenal syndrome complications was raised. Patients with co-infections had a good predictive effect when PNI ≤ 40.625. The sensitivity and specificity were 84.2% and 41.2%, respectively. Conclusion: Low PNI score and ACLF co-infection have a close correlation. Therefore, PNI has a certain appraisal value for ACLF co-infection.

Methods of determining optimal cut-point of diagnostic biomarkers with application of clinical data in ROC analysis: an update review.

INTRODUCTION: An important application of ROC analysis is the determination of the optimal cut-point for biomarkers in diagnostic studies. This comprehensive review provides a framework of cut-point election for biomarkers in diagnostic medicine. METHODS: Several methods were proposed for the selection of optional cut-points. The validity and precision of the proposed methods were discussed and the clinical application of the methods was illustrated with a practical example of clinical diagnostic data of C-reactive protein (CRP), erythrocyte sedimentation rate (ESR) and malondialdehyde (MDA) for prediction of inflammatory bowel disease (IBD) patients using the NCSS software. RESULTS: Our results in the clinical data suggested that for CRP and MDA, the calculated cut-points of the Youden index, Euclidean index, Product and Union index methods were consistent in predicting IBD patients, while for ESR, only the Euclidean and Product methods yielded similar estimates. However, the diagnostic odds ratio (DOR) method provided more extreme values for the optimal cut-point for all biomarkers analyzed. CONCLUSION: Overall, the four methods including the Youden index, Euclidean index, Product, and IU can produce quite similar optimal cut-points for binormal pairs with the same variance. The cut-point determined with the Youden index may not agree with the other three methods in the case of skewed distributions while DOR does not produce valid informative cut-points. Therefore, more extensive Monte Carlo simulation studies are needed to investigate the conditions of test result distributions that may lead to inconsistent findings in clinical diagnostics.

[Correlation Analysis between Neutrophil-to-lymphocyte Ratio 
and the Risk of Malnutrition in Stage IV Primary Lung Cancer].

BACKGROUND: Malnutrition is commonly associated with poor prognosis in patients with malignant tumors. The neutrophil-to-lymphocyte ratio (NLR) is an indicator of inflammation in the body and predicts the risk of malnutrition in a variety of diseases; however, its association with malnutrition in lung cancer patients is unclear. The aim of this study is to clarify the association between NLR and nutritional status in stage IV primary lung cancer and to further determine the optimal NLR cut-off that best predicts the risk of malnutrition. METHODS: A retrospective analysis of 209 patients admitted to the Department of Medical Oncology, Tianjin Medical University General Hospital with a primary diagnosis of stage IV lung cancer from May 2019 to February 2021 was performed, and the nutritional risk screening 2002 (NRS 2002) was used to examine their nutritional status. Patient demographic information, pathology, Karnofsky performance status (KPS) score, body mass index (BMI), comorbidities and clinical biochemical indicators were also included. The correlation between NLR and NRS 2002 was investigated. Receiver operating characteristic (ROC) curve was used to determine the best NLR cut-off predi cting malnutrition risk. Multivariable Logistic regression was used to assess the association between NLR and malnutrition risk. RESULTS: The rate of patients with stage IV primary lung cancer at nutritional risk was 36.36% (76/209). A significant positive correlation was observed between NLR values and NRS 2002 risk score (r=0.765, P<0.001). The ROC curve analysis indicated that an NLR of 3.94 was the optimal cut-off for predicting malnutrition risk (area under the curve=0.747, 95%CI: 0.678-0.815, P<0.001), which showed a sensitivity of 55%, a specificity of 86%, a positive predictive value of 68%, and a negative predictive value of 77%. Patients in the NLR>3.94 group had a significantly higher risk of malnutrition compared to those in the NLR≤3.94 group (69.49% vs 23.33%, P<0.001). Furthermore, NLR was identified as a risk factor for malnutrition in stage IV primary lung cancer patients. CONCLUSIONS: NLR is associated with the risk of malnutrition in stage IV primary lung cancer, and NLR can be used as one of the indicators for screening nutritional risk in patients with stage IV primary lung cancer.

Effect of subclavian vein diameter combined with perioperative fluid therapy on preventing post-induction hypotension in patients with ASA status I or II.

BACKGROUND: Perioperative hypotension is frequently observed following the initiation of general anesthesia administration, often associated with adverse outcomes. This study assessed the effect of subclavian vein (SCV) diameter combined with perioperative fluid therapy on preventing post-induction hypotension (PIH) in patients with lower ASA status. METHODS: This two-part study included patients aged 18 to 65 years, classified as ASA physical status I or II, and scheduled for elective surgery. The first part (Part I) included 146 adult patients, where maximum SCV diameter (dSCVmax), minimum SCV diameter (dSCVmin), SCV collapsibility index (SCVCI) and SCV variability (SCVvariability) assessed using ultrasound. PIH was determined by reduction in mean arterial pressure (MAP) exceeding 30% from baseline measurement or any instance of MAP < falling below 65 mmHg for ≥ a duration of at least 1 min during the period from induction to 10 min after intubation. Receiver Operating Characteristic (ROC) curve analysis was employed to determine the predictive values of subclavian vein diameter and other relevant parameters. The second part comprised 124 adult patients, where patients with SCV diameter above the optimal cutoff value, as determined in Part I study, received 6 ml/kg of colloid solution within 20 min before induction. The study evaluated the impact of subclavian vein diameter combined with perioperative fluid therapy by comparing the observed incidence of PIH after induction of anesthesia. RESULTS: The areas under the curves (with 95% confidence intervals) for SCVCI and SCVvariability were both 0.819 (0.744-0.893). The optimal cutoff values were determined to be 45.4% and 14.7% (with sensitivity of 76.1% and specificity of 86.7%), respectively. Logistic regression analysis, after adjusting for confounding factors, demonstrated that both SCVCI and SCVvariability were significant predictors of PIH. A threshold of 45.4% for SCVCI was chosen as the grouping criterion. The incidence of PIH in patients receiving fluid therapy was significantly lower in the SCVCI ≥ 45.4% group compared to the SCVCI < 45.4% group. CONCLUSIONS: Both SCVCI and SCVvariability are noninvasive parameters capable of predicting PIH, and their combination with perioperative fluid therapy can reduce the incidence of PIH.

Prognostic value of the neutrophil-to-lymphocyte ratio and C-reactive-protein-to-prealbumin ratio in hospitalized older patients with coronavirus disease 2019.

The neutrophil-to-lymphocyte ratio (NLR) and C-reactive protein-to-prealbumin ratio (CPAR) are novel markers of inflammation. The CPAR is an indicator of inflammation and malnutrition. We evaluated NLR and CPAR in combination as indicators of disease severity and prognosis in hospitalized older patients with coronavirus disease 2019 (COVID-19). A total of 222 hospitalized patients with COVID-19 (aged > 60 years) were divided into non-severe and severe groups. The severe group was subdivided into the surviving and deceased subgroups. We retrospectively assessed the predictive power of NLR and CPAR in combination (NLR + CPAR) to determine the prognosis of hospitalized older patients with COVID-19. The NLR and CPAR were significantly higher in the severe group than in the non-severe group (P < .001). Furthermore, the NLR and CPAR were higher in the deceased subgroup than in the surviving subgroup (P < .001). Pearson correlation analysis showed a highly significant positive correlation between NLR and CPAR (P < .001, r = 0.530). NLR + CPAR showed an area under the curve of 0.827 and sensitivity of 83.9% in the severe group; the area under the curve was larger (0.925) and sensitivity was higher (87.1%) in the deceased subgroup. The receiver operating characteristic curve of NLR + CPAR was significantly different from the receiver operating characteristic curves of either biomarker alone (P < .001). Kaplan-Meier analysis showed that patients in the severe group with elevated NLR + CPAR had a significantly lower 90-day survival rate than patients who lacked this finding (odds ratio 7.87, P < .001). NLR + CPAR may enable early diagnosis and assessment of disease severity in hospitalized older patients with COVID-19. This may also enable the identification of high-risk older patients with COVID-19 at the time of admission.

Multiple-matrix metabolomics analysis for the distinct detection of colorectal cancer and adenoma.

OBJECTIVES: Although colorectal cancer (CRC) is the leading cause of cancer-related morbidity and mortality, current diagnostic tests for early-stage CRC and colorectal adenoma (CRA) are suboptimal. Therefore, there is an urgent need to explore less invasive screening procedures for CRC and CRA diagnosis. METHODS: Untargeted gas chromatography-mass spectrometry (GC-MS) metabolic profiling approach was applied to identify candidate metabolites. We performed metabolomics profiling on plasma samples from 412 subjects including 200 CRC patients, 160 CRA patients and 52 normal controls (NC). Among these patients, 45 CRC patients, 152 CRA patients and 50 normal controls had their fecal samples tested simultaneously. RESULTS: Differential metabolites were screened in the adenoma-carcinoma sequence. Three diagnostic models were further developed to identify cancer group, cancer stage, and cancer microsatellite status using those significant metabolites. The three-metabolite-only classifiers used to distinguish the cancer group always keeps the area under the receiver operating characteristic curve (AUC) greater than 0.7. The AUC performance of the classifiers applied to discriminate CRC stage is generally greater than 0.8, and the classifiers used to distinguish microsatellite status of CRC is greater than 0.9. CONCLUSION: This finding highlights potential early-driver metabolites in CRA and early-stage CRC. We also find potential metabolic markers for discriminating the microsatellite state of CRC. Our study and diagnostic model have potential applications for non-invasive CRC and CRA detection.

Specific mortality in patients with diffuse large B-cell lymphoma: a retrospective analysis based on the surveillance, epidemiology, and end results database.

BACKGROUND: The full potential of competing risk modeling approaches in the context of diffuse large B-cell lymphoma (DLBCL) patients has yet to be fully harnessed. This study aims to address this gap by developing a sophisticated competing risk model specifically designed to predict specific mortality in DLBCL patients. METHODS: We extracted DLBCL patients' data from the SEER (Surveillance, Epidemiology, and End Results) database. To identify relevant variables, we conducted a two-step screening process using univariate and multivariate Fine and Gray regression analyses. Subsequently, a nomogram was constructed based on the results. The model's consistency index (C-index) was calculated to assess its performance. Additionally, calibration curves and receiver operator characteristic (ROC) curves were generated to validate the model's effectiveness. RESULTS: This study enrolled a total of 24,402 patients. The feature selection analysis identified 13 variables that were statistically significant and therefore included in the model. The model validation results demonstrated that the area under the receiver operating characteristic (ROC) curve (AUC) for predicting 6-month, 1-year, and 3-year DLBCL-specific mortality was 0.748, 0.718, and 0.698, respectively, in the training cohort. In the validation cohort, the AUC values were 0.747, 0.721, and 0.697. The calibration curves indicated good consistency between the training and validation cohorts. CONCLUSION: The most significant predictor of DLBCL-specific mortality is the age of the patient, followed by the Ann Arbor stage and the administration of chemotherapy. This predictive model has the potential to facilitate the identification of high-risk DLBCL patients by clinicians, ultimately leading to improved prognosis.

Predictive Value of Combined HbA1c and Neutrophil-to-Lymphocyte Ratio for Diabetic Peripheral Neuropathy in Type 2 Diabetes.

BACKGROUND Diabetic peripheral neuropathy (DPN) is a prevalent complication affecting over 60% of type 2 diabetes patients. Early diagnosis is challenging, leading to irreversible impacts on quality of life. This study explores the predictive value of combining HbA1c and Neutrophil-to-Lymphocyte Ratio (NLR) for early DPN detection. MATERIAL AND METHODS An observational study was conducted at the First People's Hospital of Linping District, Hangzhou spanning from May 2019 to July 2020. Data on sex, age, biochemical measurements were collected from electronic medical records and analyzed. Employing multivariate logistic regression analysis, we sought to comprehend the factors influencing the development of DPN. To assess the predictive value of individual and combined testing for DPN, a receiver operating characteristic (ROC) curve was plotted. The data analysis was executed using R software (Version: 4.1.0). RESULTS The univariate and multivariate logistic regression analysis identified the level of glycated hemoglobin (HbA1C) (OR=1.94, 95% CI: 1.27-3.14) and neutrophil-to-lymphocyte ratio (NLR) (OR=4.60, 95% CI: 1.15-22.62, P=0.04) as significant risk factors for the development of DPN. Receiver operating characteristic (ROC) curve analysis demonstrated that HbA1c, NLR, and their combined detection exhibited high sensitivity in predicting the development of DPN (71.60%, 90.00%, and 97.2%, respectively), with moderate specificity (63.8%, 45.00%, and 50.00%, respectively). The area under the curve (AUC) for these predictors was 0.703, 0.661, and 0.733, respectively. CONCLUSIONS HbA1c and NLR emerge as noteworthy risk indicators associated with the manifestation of DPN in patients with type 2 diabetes. The combined detection of HbA1c and NLR exhibits a heightened predictive value for the development of DPN.

Value of long non-coding RNA HAS2-AS1 as a diagnostic and prognostic marker of glioma.

BACKGROUND: Glioma presents high incidence and poor prognosis, and therefore more effective treatments are needed. Studies have confirmed that long non-coding RNAs (lncRNAs) basically regulate various human diseases including glioma. It has been theorized that HAS2-AS1 serves as an lncRNA to exert an oncogenic role in varying cancers. This study aimed to assess the value of lncRNA HAS2-AS1 as a diagnostic and prognostic marker for glioma. METHODS: The miRNA expression data and clinical data of glioma were downloaded from the TCGA database for differential analysis and survival analysis. In addition, pathological specimens and specimens of adjacent normal tissue from 80 patients with glioma were used to observe the expression of HAS2-AS1. The receiver operating characteristic (ROC) curve was used to analyze the diagnostic ability and prognostic value of HAS2-AS1 in glioma. Meanwhile, a Kaplan-Meier survival curve was plotted to evaluate the survival of glioma patients with different HAS2-AS1 expression levels. RESULTS: HAS2-AS1 was significantly upregulated in glioma tissues compared with normal tissue. The survival curves showed that overexpression of HAS2-AS1 was associated with poor overall survival (OS) and progression-free survival (PFS). Several clinicopathological factors of glioma patients, including tumor size and WHO grade, were significantly correlated with HAS2-AS1 expression in tissues. The ROC curve showed an area under the curve (AUC) value of 0.863, indicating that HAS2-AS1 had good diagnostic value. The ROC curve for the predicted OS showed an AUC of 0.906, while the ROC curve for predicted PFS showed an AUC of 0.88. Both suggested that overexpression of HAS2-AS1 was associated with poor prognosis. CONCLUSIONS: Normal tissues could be clearly distinguished from glioma tissues based on HAS2-AS1 expression. Moreover, overexpression of HAS2-AS1 indicated poor prognosis in glioma patients. Therefore, HAS2-AS1 could be used as a diagnostic and prognostic marker for glioma.

Predictive value of CD86 for the occurrence of sepsis (Sepsis-3) in patients with infection.

This prospective observational study explored the predictive value of CD86 in the early diagnosis of sepsis in the emergency department. The primary endpoint was the factors associated with a diagnosis of sepsis. The secondary endpoint was the factors associated with mortality among patients with sepsis. It enrolled inpatients with infection or high clinical suspicion of infection in the emergency department of a tertiary Hospital between September 2019 and June 2021. The patients were divided into the sepsis and non-sepsis groups according to the Sepsis-3 standard. The non-sepsis group included 56 patients, and the sepsis group included 65 patients (19 of whom ultimately died). The multivariable analysis showed that CD86% (odds ratio [OR] = 1.22, 95% confidence interval [CI]: 1.04-1.44, P = 0.015), platelet count (OR = 0.99, 95%CI: 0.986-0.997, P = 0.001), interleukin-10 (OR = 1.01, 95%CI: 1.004-1.025, P = 0.009), and procalcitonin (OR = 1.17, 95%CI: 1.01-1.37, P = 0.043) were independent risk factors for sepsis, while human leukocyte antigen (HLA%) (OR = 0.96, 05%CI: 0.935-0.995, P = 0.022), respiratory rate (OR = 1.16, 95%CI: 1.03-1.30, P = 0.014), and platelet count (OR = 1.01, 95%CI: 1.002-1.016, P = 0.016) were independent risk factors for death in patients with sepsis. The model for sepsis (CD86%, platelets, interleukin-10, and procalcitonin) and the model for death (HLA%, respiratory rate, and platelets) had an area under the curve (AUC) of 0.870 and 0.843, respectively. CD86% in the first 24 h after admission for acute infection was independently associated with the occurrence of sepsis in the emergency department.

Cardiovascular disease detection using a novel stack-based ensemble classifier with aggregation layer, DOWA operator, and feature transformation.

Due to their widespread prevalence and impact on quality of life, cardiovascular diseases (CVD) pose a considerable global health burden. Early detection and intervention can reduce the incidence, severity, and progression of CVD and prevent premature death. The application of machine learning (ML) techniques to early CVD detection is therefore a valuable approach. In this paper, A stack-based ensemble classifier with an aggregation layer and the dependent ordered weighted averaging (DOWA) operator is proposed for detecting cardiovascular diseases. We propose transforming features using the Johnson transformation technique and normalizing feature distributions. Three diverse first-level classifiers are selected based on their accuracy, and predictions are combined using the aggregation layer and DOWA. A linear support vector machine (SVM) meta-classifier makes the final classification. Adding the aggregation layer to the stacking classifier improves classification accuracy significantly, according to the study. The accuracy is enhanced by 5%, resulting in an impressive overall accuracy of 94.05%. Moreover, the proposed system significantly increases the area under the receiver operating characteristic (ROC) curve compared to recent studies, reaching 97.14%. It further reinforces the classifier's reliability and effectiveness in classifying cardiovascular disease by distinguishing between positive and negative instances. With improved accuracy and a high area under the curve (AUC), the proposed classifier exhibits robustness and superior performance in the detection of cardiovascular diseases.

Detection of lung cancer through SERS analysis of serum.

Early detection and postoperative assessment are crucial for improving overall survival among lung cancer patients. Here, we report a non-invasive technique that integrates Raman spectroscopy with machine learning for the detection of lung cancer. The study encompassed 88 postoperative lung cancer patients, 73 non-surgical lung cancer patients, and 68 healthy subjects. The primary aim was to explore variations in serum metabolism across these cohorts. Comparative analysis of average Raman spectra was conducted, while principal component analysis was employed for data visualization. Subsequently, the augmented dataset was used to train convolutional neural networks (CNN) and Resnet models, leading to the development of a diagnostic framework. The CNN model exhibited superior performance, as verified by the receiver operating characteristic curve. Notably, postoperative patients demonstrated an increased likelihood of recurrence, emphasizing the crucial need for continuous postoperative monitoring. In summary, the integration of Raman spectroscopy with CNN-based classification shows potential for early detection and postoperative assessment of lung cancer.

Structural or/and functional MRI-based machine learning techniques for attention-deficit/hyperactivity disorder diagnosis: A systematic review and meta-analysis.

BACKGROUND: The aim of this study was to investigate the diagnostic value of ML techniques based on sMRI or/and fMRI for ADHD. METHODS: We conducted a comprehensive search (from database creation date to March 2024) for relevant English articles on sMRI or/and fMRI-based ML techniques for diagnosing ADHD. The pooled sensitivity, specificity, positive likelihood ratio (LR+), negative likelihood ratio (LR-), summary receiver operating characteristic (SROC) curve and area under the curve (AUC) were calculated to assess the diagnostic value of sMRI or/and fMRI-based ML techniques. The I2 test was used to assess heterogeneity and the source of heterogeneity was investigated by performing a meta-regression analysis. Publication bias was assessed using the Deeks funnel plot asymmetry test. RESULTS: Forty-three studies were included in the systematic review, 27 of which were included in our meta-analysis. The pooled sensitivity and specificity of sMRI or/and fMRI-based ML techniques for the diagnosis of ADHD were 0.74 (95 % CI 0.65-0.81) and 0.75 (95 % CI 0.67-0.81), respectively. SROC curve showed that AUC was 0.81 (95 % CI 0.77-0.84). Based on these findings, the sMRI or/and fMRI-based ML techniques have relatively good diagnostic value for ADHD. LIMITATIONS: Our meta-analysis specifically focused on ML techniques based on sMRI or/and fMRI studies. Since EEG-based ML techniques are also used for diagnosing ADHD, further systematic analyses are necessary to explore ML methods based on multimodal medical data. CONCLUSION: sMRI or/and fMRI-based ML technique is a promising objective diagnostic method for ADHD.

SAGL: A self-attention-based graph learning framework for predicting survival of colorectal cancer patients.

BACKGROUND AND OBJECTIVE: Colorectal cancer (CRC) is one of the most commonly diagnosed cancers worldwide. The accurate survival prediction for CRC patients plays a significant role in the formulation of treatment strategies. Recently, machine learning and deep learning approaches have been increasingly applied in cancer survival prediction. However, most existing methods inadequately represent and leverage the dependencies among features and fail to sufficiently mine and utilize the comorbidity patterns of CRC. To address these issues, we propose a self-attention-based graph learning (SAGL) framework to improve the postoperative cancer-specific survival prediction for CRC patients. METHODS: We present a novel method for constructing dependency graph (DG) to reflect two types of dependencies including comorbidity-comorbidity dependencies and the dependencies between features related to patient characteristics and cancer treatments. This graph is subsequently refined by a disease comorbidity network, which offers a holistic view of comorbidity patterns of CRC. A DG-guided self-attention mechanism is proposed to unearth novel dependencies beyond what DG offers, thus augmenting CRC survival prediction. Finally, each patient will be represented, and these representations will be used for survival prediction. RESULTS: The experimental results show that SAGL outperforms state-of-the-art methods on a real-world dataset, with the receiver operating characteristic curve for 3- and 5-year survival prediction achieving 0.849±0.002 and 0.895±0.005, respectively. In addition, the comparison results with different graph neural network-based variants demonstrate the advantages of our DG-guided self-attention graph learning framework. CONCLUSIONS: Our study reveals that the potential of the DG-guided self-attention in optimizing feature graph learning which can improve the performance of CRC survival prediction.

Dynamic contrast-enhanced MR imaging in identifying active anal fistula after surgery.

BACKGROUND: It is challenging to identify residual or recurrent fistulas from the surgical region, while MR imaging is feasible. The aim was to use dynamic contrast-enhanced MR imaging (DCE-MRI) technology to distinguish between active anal fistula and postoperative healing (granulation) tissue. METHODS: Thirty-six patients following idiopathic anal fistula underwent DCE-MRI. Subjects were divided into Group I (active fistula) and Group IV (postoperative healing tissue), with the latter divided into Group II (≤ 75 days) and Group III (> 75 days) according to the 75-day interval from surgery to postoperative MRI reexamination. MRI classification and quantitative analysis were performed. Correlation between postoperative time intervals and parameters was analyzed. The difference of parameters between the four groups was analyzed, and diagnostic efficiency was tested by receiver operating characteristic curve. RESULTS: Wash-in rate (WI) and peak enhancement intensity (PEI) were significantly higher in Group I than in Group II (p = 0.003, p = 0.040), while wash-out rate (WO), time to peak (TTP), and normalized signal intensity (NSI) were opposite (p = 0.031, p = 0.007, p = 0.010). Area under curves for discriminating active fistula from healing tissue within 75 days were 0.810 in WI, 0.708 in PEI, 0.719 in WO, 0.783 in TTP, 0.779 in NSI. All MRI parameters were significantly different between Group I and Group IV, but not between Group II and Group III, and not related to time intervals. CONCLUSION: In early postoperative period, DCE-MRI can be used to identify active anal fistula in the surgical area. TRIAL REGISTRATION: Chinese Clinical Trial Registry: ChiCTR2000033072.