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)

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.

Quantitative evaluation of meniscus injury using synthetic magnetic resonance imaging.

BACKGROUND: Magnetic resonance imaging (MRI) can diagnose meniscal lesions anatomically, while quantitative MRI can reflect the changes of meniscal histology and biochemical structure. Our study aims to explore the association between the measurement values obtained from synthetic magnetic resonance imaging (SyMRI) and Stoller grades. Additionally, we aim to assess the diagnostic accuracy of SyMRI in determining the extent of meniscus injury. This potential accuracy could contribute to minimizing unnecessary invasive examinations and providing guidance for clinical treatment. METHODS: Total of 60 (n=60) patients requiring knee arthroscopic surgery and 20 (n=20) healthy subjects were collected from July 2022 to November 2022. All subjects underwent conventional MRI and SyMRI. Manual measurements of the T1, T2 and proton density (PD) values were conducted for both normal menisci and the most severely affected position of injured menisci. These measurements corresponded to the Stoller grade of meniscus injuries observed in the conventional MRI. All patients and healthy subjects were divided into normal group, degeneration group and torn group according to the Stoller grade on conventional MRI. One-way analysis of variance (ANOVA) was employed to compare the T1, T2 and PD values of the meniscus among 3 groups. The accuracy of SyMRI in diagnosing meniscus injury was assessed by comparing the findings with arthroscopic observations. The diagnostic efficiency of meniscus degeneration and tear between conventional MRI and SyMRI were analyzed using McNemar test. Furthermore, a receiver operating characteristic curve (ROC curve) was constructed and the area under the curve (AUC) was utilized for evaluation. RESULTS: According to the measurements of SyMRI, there was no statistical difference of T1 value or PD value measured by SyMRI among the normal group, degeneration group and torn group, while the difference of T2 value was statistically significant among 3 groups (P=0.001). The arthroscopic findings showed that 11 patients were meniscal degeneration and 49 patients were meniscal tears. The arthroscopic findings were used as the gold standard, and the difference of T1 and PD values among the 3 groups was not statistically significant, while the difference of T2 values (32.81±2.51 of normal group, 44.85±3.98 of degeneration group and 54.42±3.82 of torn group) was statistically significant (P=0.001). When the threshold of T2 value was 51.67 (ms), the maximum Yoden index was 0.787 and the AUC value was 0.934. CONCLUSIONS: The measurement values derived from SyMRI could reflect the Stoller grade, illustrating that SyMRI has good consistency with conventional MRI. Moreover, the notable consistency observed between SyMRI and arthroscopy suggests a potential role for SyMRI in guiding clinical diagnoses.

Total muscle-to-fat ratio influences urinary incontinence in United States adult women: a population-based study.

Purpose: This study aims to investigate the relationship between the total muscle-to-fat ratio (tMFR) and female urinary incontinence (UI), determine whether tMFR can serve as a useful index for predicting UI, and identify factors that may influence this relationship. Methods: We retrospectively analyzed data from 4391 adult women participating in the National Health and Nutrition Examination Survey (NHANES) conducted between 2011 and 2018. The correlation between tMFR and UI was examined using a dose-response curve generated through a restricted cubic spline (RCS) function, LASSO and multivariate logistic regression. Furthermore, predictive models were constructed incorporating factors such as age, race, hypertension, diabetes, cotinine levels, and tMFR. The performance of these predictive models was evaluated using training and test datasets, employing calibration curves, receiver operating characteristic curves, and clinical decision curves. Mediation effects were also analyzed to explore potential relationships between tMFR and female UI. Results: In a sample of 4391 adult women, 1073 (24.4%) self-reported experiencing UI, while 3318 (75.6%) reported not having UI. Based on the analyses involving LASSO regression and multivariate logistic regression, it was found that tMFR exhibited a negative association with UI (OR = 0.599, 95% CI: 0.497-0.719, P < 0.001). The results from the restricted cubic spline chart indicated a decreasing risk of UI in women as tMFR increased. Furthermore, the model constructed based on logistic regression analysis demonstrated a certain level of accuracy (in the training dataset: area under the curve (AUC) = 0.663; in the test dataset: AUC = 0.662) and clinical applicability. The mediation analysis revealed that the influence of tMFR on the occurrence of UI in women might potentially occur through the blood index lymphocyte count (P = 0.040). Conclusion: A high tMFR serves as a protective factor against UI in women. Furthermore, lymphocyte might be involved in the relationship between tMFR and female UI.

Association of the neutrophil-to-lymphocyte ratio with the occurrence of venous thromboembolism and arterial thrombosis.

OBJECTIVE: This study aimed to assess the association of the neutrophil-to-lymphocyte ratio (NLR) with the occurrence of venous thromboembolism (VTE) and arterial thrombosis (AT). METHODS: This was a retrospective cross-sectional study including 585 medical records obtained from all consecutive patients who were suspected of having thrombosis. RESULTS: The AT group had a higher neutrophil count and NLR and a lower lymphocyte count than the non-thrombosis group. Receiver operating characteristic curve analysis showed the ability of the NLR to predict the presence of AT. The cut-off value for the NLR was 4.44. No distinction was found in the NLR between the VTE and non-thrombosis groups. Regression analysis showed that a high NLR was an independent factor related to the presence of AT. Patients with an NLR ≥ 4.44 had a higher risk of AT than those with an NLR < 4.44 (odds ratio = 2.015, 95% confidence interval: 1.180-3.443). CONCLUSION: A high NLR may be considered a predictive factor for the occurrence of AT, but an association with the presence of VTE was not found.

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.

Elevated venous lactate level as an early predictive marker of organ failure in acute pancreatitis: a retrospective study.

OBJECTIVE: The aim of this study was to elucidate the relationship between venous lactate levels and the severity of acute pancreatitis (AP). PATIENTS AND METHODS: Retrospective data analysis was conducted on patients diagnosed with acute pancreatitis. The comparative assessment encompassed baseline characteristics, laboratory data, illness severity, local consequences, and organ failure instances. This comparison was performed between patients exhibiting normal serum lactic acid levels (HL) and those displaying elevated HL levels. The association between serum HL levels and other pertinent clinical markers was investigated using linear regression. Logistic regression analysis was employed to evaluate the utility of elevated serum lactate levels in identifying high-risk groups. RESULTS: Significantly elevated serum HL levels were observed in patients with moderately severe acute pancreatitis (MSAP) and severe acute pancreatitis (SAP) in contrast to those with mild acute pancreatitis (MAP) (p<0.01). Multivariate logistic analysis demonstrated that higher lactate levels independently predicted organ failure (95% CI 0.738-0.902, p<0.05). Receiver operating characteristic (ROC) curve analysis indicated that the lactate (LAC) cut-off value of 2.45 mmol/L yielded sensitivity and specificity values of 76.5% and 79.1%, respectively, for predicting AP-associated organ failure. The corresponding area under the curve (AUC) was 0.820. CONCLUSIONS: In AP patients, elevated serum HL levels signify disease severity and hold predictive potential for assessing the risk of organ failure.

Analysis of risk factors affecting the prognosis of patients with sepsis and construction of nomogram prediction model.

OBJECTIVE: This study analyzed the clinical data of 200 sepsis patients, exploring the risk factors that affect patient prognosis and providing the basis for clinically targeted intervention to improve patient prognosis. PATIENTS AND METHODS: 200 septic patients were admitted to Yulin Second Hospital, and they were divided into a survival group of 151 patients and a death group of 49 patients, according to their clinical outcomes on admission. The relevant clinical parameters within 24 h of admission were collected, and the independent risk factors affecting the prognosis of septic patients were analyzed by multivariate Logistic regression. R language 4.21 software was used to construct a nomogram prediction model. The receiver operating characteristic curve was used to evaluate the discrimination of the nomogram model, and decline curve analysis was drawn to evaluate the effectiveness of the model. RESULTS: In the nomogram prediction model, age, the Acute Physiology and Chronic Health Scoring System Domain (APACHE II) score, the Sequential Organ Failure Assessment (SOFA) score, C-reactive protein (CRP), total bilirubin, albumin (Alb), urea nitrogen, creatinine, and lactate (Lac) were independent risk factors for death in septic patients. The area under the receiver operating characteristic (ROC) curve for predicting the prognosis of septic patients was 0.597-1.000, and the calibration curve tends to be the ideal curve. The model had good discrimination and calibration and had high accuracy in evaluating septic patients. The modeling curves in the decline curve analysis (DCA) were all above the two extreme curves, which had good clinical value. CONCLUSIONS: Nine clinical variables have been found to be independent risk factors for death in septic patients. The prediction model established based on this has good accuracy, discrimination, and consistency in predicting the prognosis of sepsis patients.

Evaluation of Serum CA 19-9 as a Diagnostic Marker for Pancreatic Malignancy.

In biliary-pancreatic malignancy, the serum CA 19-9 is considered as a tumor marker. Its high level may indicate the presence of a malignant disorder, but it can also be raised in benign conditions and also in malignancies from other organs. The value may be normal even in malignant condition. This comparative study was conducted in the Department of Surgery of Bangabandhu Sheikh Mujib Medical University (BSMMU), Bangladesh from 1st June 2016 to 31st May 2017 to determine the sensitivity and specificity of CA 19-9 as a tumor marker in pancreatic malignancy in our perspective and to establish a cut-off value of CA 19-9 which might prove as a definitive indication of pancreatic malignancy. We found that when the cut off value of CA 19-9 is 38 U/ml (according to ROC curve), the sensitivity, specificity, PPV and NPV were 77.8%, 77.8%, 77.8%, 77.8% respectively. And if the serum CA 19-9 threshold was raised to 100 and 120 to diagnose pancreatic cancer, sensitivity became 72.2% and 66.7% and NPV 76.2% and 73.9% respectively. However, the specificity increased to 88.9% and 94.4% and the PPV increased to 86.7% and 92.3% respectively.

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.

[Construction and validation of an in-hospital mortality risk prediction model for patients receiving VA-ECMO: a retrospective multi-center case-control study].

OBJECTIVE: To investigate the risk factors of in-hospital mortality and establish a risk prediction model for patients receiving venoarterial extracorporeal membrane oxygenation (VA-ECMO). METHODS: We retrospectively collected the data of 302 patients receiving VA-ECMO in ICU of 3 hospitals in Guangdong Province between January, 2015 and January, 2022 using a convenience sampling method. The patients were divided into a derivation cohort (201 cases) and a validation cohort (101 cases). Univariate and multivariate logistic regression analyses were used to analyze the risk factors for in-hospital death of these patients, based on which a risk prediction model was established in the form of a nomogram. The receiver operator characteristic (ROC) curve, calibration curve and clinical decision curve were used to evaluate the discrimination ability, calibration and clinical validity of this model. RESULTS: The in-hospital mortality risk prediction model was established based the risk factors including hypertension (OR=3.694, 95% CI: 1.582-8.621), continuous renal replacement therapy (OR=9.661, 95%CI: 4.103-22.745), elevated Na2 + level (OR=1.048, 95% CI: 1.003-1.095) and increased hemoglobin level (OR=0.987, 95% CI: 0.977-0.998). In the derivation cohort, the area under the ROC curve (AUC) of this model was 0.829 (95% CI: 0.770-0.889), greater than those of the 4 single factors (all AUC < 0.800), APACHE II Score (AUC=0.777, 95% CI: 0.714-0.840) and the SOFA Score (AUC=0.721, 95% CI: 0.647-0.796). The results of internal validation showed that the AUC of the model was 0.774 (95% CI: 0.679-0.869), and the goodness of fit test showed a good fitting of this model (χ2=4.629, P>0.05). CONCLUSION: The risk prediction model for in-hospital mortality of patients on VA-ECMO has good differentiation, calibration and clinical effectiveness and outperforms the commonly used disease severity scoring system, and thus can be used for assessing disease severity and prognostic risk level in critically ill 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.

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.

Application of roc-analysis to assess the quality of predicting the risk of chronic rhinosinusitis recurrence.

OBJECTIVE: Aim: To propose a new, original approach to assessing the quality of a multivariate regression model for predicting the risk of recurrence in patients with chronic rhinosinusitis based on ROC analysis with the construction of appropriate curves, estimating the area under them, as well as calculating the sensitivity, accuracy, specificity, and predictive value of a positive and negative classification results, the likelihood ratio of positive and negative patient detection results. PATIENTS AND METHODS: Materials and Methods: 204 patients aged with a diagnosis of chronic rhinosinusitis were examined. RESULTS: Results: To build a multivariate regression model 14 probable factors of chronic rhinosinusitis occurrence were selected to determine the diagnostic value of the proposed model we calculate the sensitivity (Se), specificity (Sp), positive predictive value (PPV), negative predictive value (NPV), the likelihood ratio of a positive test (LR+), the likelihood ratio of a negative test (LR-) and prediction accuracy % of the proposed mathematical model. In order to determine the prognostic value of the risk ratio of CRS recurrence model, ROC- analysis was performed, ROC curves were obtained. CONCLUSION: Conclusions: The multivariate regression model makes it possible to predict potential complications and the possibility of disease recurrence. The construction of ROC-curves allows us to assert the excellent classification quality of chronic rhinosinusitis recurrence.

Predicting pressure injury risk in hospitalised patients using machine learning with electronic health records: a US multilevel cohort study.

OBJECTIVE: To predict the risk of hospital-acquired pressure injury using machine learning compared with standard care. DESIGN: We obtained electronic health records (EHRs) to structure a multilevel cohort of hospitalised patients at risk for pressure injury and then calibrate a machine learning model to predict future pressure injury risk. Optimisation methods combined with multilevel logistic regression were used to develop a predictive algorithm of patient-specific shifts in risk over time. Machine learning methods were tested, including random forests, to identify predictive features for the algorithm. We reported the results of the regression approach as well as the area under the receiver operating characteristics (ROC) curve for predictive models. SETTING: Hospitalised inpatients. PARTICIPANTS: EHRs of 35 001 hospitalisations over 5 years across 2 academic hospitals. MAIN OUTCOME MEASURE: Longitudinal shifts in pressure injury risk. RESULTS: The predictive algorithm with features generated by machine learning achieved significantly improved prediction of pressure injury risk (p<0.001) with an area under the ROC curve of 0.72; whereas standard care only achieved an area under the ROC curve of 0.52. At a specificity of 0.50, the predictive algorithm achieved a sensitivity of 0.75. CONCLUSIONS: These data could help hospitals conserve resources within a critical period of patient vulnerability of hospital-acquired pressure injury which is not reimbursed by US Medicare; thus, conserving between 30 000 and 90 000 labour-hours per year in an average 500-bed hospital. Hospitals can use this predictive algorithm to initiate a quality improvement programme for pressure injury prevention and further customise the algorithm to patient-specific variation by facility.

Influential factors on disease activity in Crohn's disease and their Roc curve evaluation: a cross-sectional study.

OBJECTIVE: This study aims to investigate the factors influencing disease activity in patients with Crohn's disease (CD) and provide insights and references for the management and prevention of CD. METHODS: We recruited CD patients who met the inclusion and exclusion criteria and were treated at the First Affiliated Hospital of Soochow University from November 2022 to June 2023. Generalized linear mixed models were used to analyze the factors affecting disease activity in CD patients. Receiver operating characteristic (ROC) curve analysis was employed to assess the predictive value of these factors for disease activity. RESULTS: A total of 268 CD participants aged 18 to 65 were included in the study, with over 68% of them in remission or experiencing mild disease activity, indicating relatively good disease control. The results of the generalized linear mixed models showed that older age, absence of diabetes, high levels of physical activity, and a low dietary inflammatory index (DII) were protective factors for lower disease activity in CD patients (p < 0.05). ROC curve analysis demonstrated that physical activity level, age, and DII all had ROC areas greater than 0.6 in predicting disease activity in CD patients (p < 0.05). CONCLUSION: The factors influencing the disease activity of CD patients are numerous and should be given attention. CD patients who are younger, have low levels of physical activity, high DII, and have diabetes are at a higher risk of increased disease activity. By reducing or avoiding the mentioned risk factors and leveraging protective factors, it is possible to mitigate the disease activity of CD to some extent.

Value of modified qSOFA, glucose and lactate in predicting prognosis in children with sepsis in the PICU.

The purpose was to investigate how well age-adjusted modified quick Sequential Organ Failure Assessment (qSOFA) scores paired with blood glucose and lactate levels predict the outcomes of septicemic children in the pediatric intensive care unit (PICU). One hundred children who were diagnosed with sepsis and septic shock in the PICU of Henan Children's Hospital were eligible, and other 20 patients in the same hospital at different times were selected as a validation set. Respiratory rate (RR), heart rate (HR), capillary refill time (CRT), and Alert, Voice, Pain, Unresponsive (AVPU) scale were included in the age-adjusted modified qSOFA scoring criteria for scoring. The primary outcome was 28-day all-cause mortality. The predictive values were evaluated by the ROC curve. In the sepsis group, 50 patients were male, and 50 patients were female. The 28-day all-cause mortality rate was 52%. Fifty-one patients with age-adjusted modified qSOFA scores >1. The serum lactate level was 2.4 mmol/L, and the blood glucose level was 9.3 mmol/L. The AUCs for the age-adjusted modified qSOFA score, serum lactate and blood glucose levels for the prediction of 28-day all-cause mortality in children with sepsis were 0.719, 0.719 and 0.737, respectively. The cut-off values were one point, 3.8 mmol/L and 10 mmol/L, respectively. The AUC of the age-adjusted modified qSOFA score for the validation set of was 0.925. When the three indices were combined, the AUC was 0.817, the Hosmer-Lemeshow goodness-of-fit test showed χ2 = 2.428 and p = .965. When children with sepsis are admitted to the ICU, we recommend performing rapid scoring and rapid bedside lactate and glucose testing to determine the early prognosis.

Bayesian and deep-learning models applied to the early detection of ovarian cancer using multiple longitudinal biomarkers.

BACKGROUND: Ovarian cancer is the most lethal of all gynecological cancers. Cancer Antigen 125 (CA125) is the best-performing ovarian cancer biomarker which however is still not effective as a screening test in the general population. Recent literature reports additional biomarkers with the potential to improve on CA125 for early detection when using longitudinal multimarker models. METHODS: Our data comprised 180 controls and 44 cases with serum samples sourced from the multimodal arm of UK Collaborative Trial of Ovarian Cancer Screening (UKCTOCS). Our models were based on Bayesian change-point detection and recurrent neural networks. RESULTS: We obtained a significantly higher performance for CA125-HE4 model using both methodologies (AUC 0.971, sensitivity 96.7% and AUC 0.987, sensitivity 96.7%) with respect to CA125 (AUC 0.949, sensitivity 90.8% and AUC 0.953, sensitivity 92.1%) for Bayesian change-point model (BCP) and recurrent neural networks (RNN) approaches, respectively. One year before diagnosis, the CA125-HE4 model also ranked as the best, whereas at 2 years before diagnosis no multimarker model outperformed CA125. CONCLUSIONS: Our study identified and tested different combination of biomarkers using longitudinal multivariable models that outperformed CA125 alone. We showed the potential of multivariable models and candidate biomarkers to increase the detection rate of ovarian cancer.

ChatGPT-Enhanced ROC Analysis (CERA): A shiny web tool for finding optimal cutoff points in biomarker analysis.

Diagnostic tests play a crucial role in establishing the presence of a specific disease in an individual. Receiver Operating Characteristic (ROC) curve analyses are essential tools that provide performance metrics for diagnostic tests. Accurate determination of the cutoff point in ROC curve analyses is the most critical aspect of the process. A variety of methods have been developed to find the optimal cutoffs. Although the R programming language provides a variety of package programs for conducting ROC curve analysis and determining the appropriate cutoffs, it typically needs coding skills and a substantial investment of time. Specifically, the necessity for data preprocessing and analysis can present a significant challenge, especially for individuals without coding experience. We have developed the CERA (ChatGPT-Enhanced ROC Analysis) tool, a user-friendly ROC curve analysis web tool using the shiny interface for faster and more effective analyses to solve this problem. CERA is not only user-friendly, but it also interacts with ChatGPT, which interprets the outputs. This allows for an interpreted report generated by R-Markdown to be presented to the user, enhancing the accessibility and understanding of the analysis results.