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)

Prevalence of dyslipidaemia within Polish nurses. Cross-sectional study - single and multiple linear regression models and ROC analysis.

BACKGROUND: Maintaining good health is vital not only for own well-being, but also to ensure high-quality patient care. The aim of this study was to evaluate the prevalence of dyslipidaemia and to determine the factors responsible for the development of this disorder among Polish nurses. Lipid profile disorders are the most prevalent and challenging risk factors for the development of cardiovascular disease. Nurses have significant potential and play a crucial role in providing care and treatment services. METHODS: This cross-sectional study involved nurses and included measurements of body weight composition (Tanita MC-980), body mass index, waist circumference, blood pressure (Welch Allyn 4200B), lipid profile, and fasting blood glucose (CardioChek PA). RESULTS: The results revealed that more than half of the nurses (60.09%) were overweight or obese, with 57.28% exhibiting elevated blood pressure, 32.25% having fasting glucose levels, and 69.14% experiencing dyslipidaemia. Multiple model evaluation using ROC curves demonstrated that multiple models accurately predicted hypercholesterolemia (AUC = 0.715), elevated LDL (AUC = 0.727), and elevated TC (AUC = 0.723) among Polish nurses. CONCLUSION: Comprehensive education programmes should be implemented that include the latest advances in cardiovascular disease prevention. Regular check-ups, as well as the promotion and availability of healthy food in hospital canteens, are essential.

[Exploration and validation of optimal cut-off values for tPSA and fPSA/tPSA screening of prostate cancer at different ages].

Objective: To determine the total and age-specific cut-off values of total prostate specific antigen (tPSA) and the ratio of free PSA divided total PSA (fPSA/tPSA) for screening prostate cancer in China. Methods: Based on the Chinese Colorectal, Breast, Lung, Liver, and Stomach cancer Screening Trial (C-BLAST) and the Tianjin Common Cancer Case Cohort (TJ4C), males who were not diagnosed with any cancers at baseline since 2017 and received both tPSA and fPSA testes were selected. Based on Cox regression, the overall and age-specific (＜60, 60-＜70, and ≥70 years) accuracy and optimal cut-off values of tPSA and fPSA/tPSA ratio for screening prostate cancer were evaluated with time-dependent receiver operating characteristic curve (tdROC) and area under curve (AUC). Bootstrap resampling was used to internally validate the stability of the optimal cut-off value, and the PLCO study was used to externally validate the accuracy under different cut-off values. Results: A total of 5 180 participants were included in the study, and after a median follow-up of 1.48 years, a total of 332 prostate cancer patients were included. In the total population, the tdAUC of tPSA and fPSA/tPSA screening for prostate cancer were 0.852 and 0.748, respectively, with the optimal cut-off values of 5.08 ng/ml and 0.173, respectively. After age stratification, the age specific cut-off values of tPSA in the <60, 60-<70, and ≥70 age groups were 3.13, 4.82, and 11.54 ng/ml, respectively, while the age-specific cut-off values of fPSA/tPSA were 0.153, 0.135, and 0.130, respectively. Under the age-specific cut-off values, the sensitivities of tPSA screening for prostate cancer in males <60, 60-70, and ≥70 years old were 92.3%, 82.0%, and 77.6%, respectively, while the specificities were 84.7%, 81.3%, and 75.4%, respectively. The age-specific sensitivities of fPSA/tPSA for screening prostate cancer were 74.4%, 53.3%, and 55.9%, respectively, while the specificities were 83.8%, 83.7%, and 83.7%, respectively. Both bootstrap's internal validation and PLCO external validation provided similar results. The combination of tPSA and fPSA/tPSA could further improve the accuracy of screening. Conclusion: To improve the screening effects, it is recommended that age-specific cut-off values of tPSA and fPSA/tPSA should be used to screen for prostate cancer in the general risk population.

[Characteristics and diagnostic value of serum bile acids profile in pregnant women with intrahepatic cholestasis of pregnancy and asymptomatic hypercholanemia of pregnancy].

Objective: To analyze serum bile acid profiles in pregnant women with normal pregnancy, intrahepatic cholestasis of pregnancy (ICP) and asymptomatic hypercholanemia of pregnancy (AHP), and to evaluate the application value of serum bile acid profiles in the diagnosis of ICP and AHP. Methods: The clinical data of 122 pregnant women who underwent prenatal examination in Xuzhou Maternal and Child Health Care Hospital from June 2022 to May 2023 were collected, including 54 cases of normal pregnancy group, 28 cases of ICP group and 40 cases of AHP group. Ultraperformance liquid chromatography-tandem mass spectrometry was used to measure the levels of 15 serum bile acids in each group, including cholic acid (CA), chenodeoxycholic acid (CDCA), deoxycholic acid (DCA), lithocholic acid (LCA), ursodeoxycholic acid (UDCA), glycolcholic acid (GCA), glycochenodeoxycholic acid (GCDCA), glycodeoxycholic acid (GDCA), glycolithocholic acid (GLCA), glycoursodeoxycholic acid (GUDCA), taurocholic acid (TCA), taurochenodeoxycholic acid (TCDCA), taurodeoxycholic acid (TDCA), taurolithocholic acid (TLCA) and tauroursodeoxycholic acid (TUDCA). Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were used to screen differential bile acids. The receiver operating characteristic (ROC) curve was used to analyze the diagnostic efficacy of differential bile acids and combined indicators between groups. Results: (1) Compared with normal pregnancy group, the serum levels of LCA, GCA, GCDCA, GDCA, GLCA, UDCA, TCA, TCDCA, TDCA, TLCA, GUDCA and TUDCA in ICP group were significantly different (all P<0.05), while the levels of LCA, DCA, GCA, GCDCA, GDCA, GLCA, TCA, TCDCA, TDCA, TLCA, GUDCA and TUDCA in AHP group were significantly different (all P<0.05). Compared with ICP group, the serum levels of CDCA, DCA, UDCA, TDCA, GUDCA and TUDCA in AHP group were significantly different (all P<0.05). (2) In the OPLS-DA model, the differential bile acids between ICP group and AHP group were TUDCA, TCA, UDCA, GUDCA and GCA, and their variable importance in projection (VIP) were 1.489, 1.345, 1.344, 1.184 and 1.111, respectively. TCA, GCDCA, GCA, TDCA, GDCA and TCDCA were the differentially expressed bile acids between AHP group and normal pregnancy group, and their VIP values were 1.236, 1.229, 1.197, 1.145, 1.139 and 1.138, respectively. (3) ROC analysis showed that the area under the curve (AUC) of TUDCA, TCA, UDCA, GUDCA and GCA in the differential diagnosis of ICP and AHP was 0.860, and the sensitivity and specificity were 67.9% and 95.0%, respectively. The AUC of TCA, GCDCA, GCA, TDCA, GDCA and TCDCA in the diagnosis of AHP was 0.964, and the sensitivity and specificity were 95.0% and 93.1%, respectively. Conclusions: There are differences in serum bile acid profiles among normal pregnant women, ICP and AHP. The serum bile acid profiles of pregnant women have potential application value in the differential diagnosis of ICP and AHP and the diagnosis of AHP.

Pretreatment mortality risk prediction model in patients with polymyositis/dermatomyositis-associated interstitial lung disease.

OBJECTIVES: Risk prediction for patients with polymyositis/dermatomyositis-associated interstitial lung disease (PM/DM-ILD) is challenging due to heterogeneity in the disease course. We aimed to develop a mortality risk prediction model for PM/DM-ILD. METHODS: This prognostic study analysed patients with PM/DM-ILD admitted to Nanjing Drum Hospital from 2016 to 2021. The primary outcome was mortality within 1 year. We used a least absolute shrinkage and selection operator (LASSO) logistic regression model to identify predictive laboratory indicators. These indicators were used to create a laboratory risk score, and we developed a mortality risk prediction model by incorporating clinical factors. The evaluation of model performance encompassed discrimination, calibration, clinical utility and practical application for risk prediction and prognosis. RESULTS: Overall, 418 patients with PM/DM-ILD were enrolled and randomly divided into development (n=282) and validation (n=136) cohorts. LASSO logistic regression identified four optimal features in the development cohort, forming a laboratory risk score: C reactive protein, lactate dehydrogenase, CD3+CD4+ T cell counts and PO2/FiO2. The final prediction model integrated age, arthralgia, anti-melanoma differentiation-associated gene 5 antibody status, high-resolution CT pattern and the laboratory risk score. The prediction model exhibited robust discrimination (area under the receiver operating characteristic: 0.869, 95% CI 0.811 to 0.910), excellent calibration and valuable clinical utility. Patients were categorised into three risk groups with distinct mortality rates. The internal validation, sensitivity analyses and comparative assessments against previous models further confirmed the robustness of the prediction model. CONCLUSIONS: We developed and validated an evidence-based mortality risk prediction model with simple, readily accessible clinical variables in patients with PM/DM-ILD, which may inform clinical decision-making.

A dynamic nomogram for predict individual risk of malignant brain edema after endovascular thrombectomy in acute ischemic stroke.

The aim of this study was to develop a dynamic nomogram combining clinical and imaging data to predict malignant brain edema (MBE) after endovascular thrombectomy (EVT) in patients with large vessel occlusion stroke (LVOS). We analyzed the data of LVOS patients receiving EVT at our center from October 2018 to February 2023, and divided a 7:3 ratio into the training cohort and internal validation cohort, and we also prospectively collected patients from another stroke center for external validation. MBE was defined as a midline shift or pineal gland shift > 5 mm, as determined by computed tomography (CT) scans obtained within 7 days after EVT. A nomogram was constructed using logistic regression analysis, and its receiver operating characteristic curve (ROC) and calibration were assessed in three cohorts. A total of 432 patients were enrolled in this study, with 247 in the training cohort, 100 in the internal validation cohort, and 85 in the external validation cohort. MBE occurred in 24% (59) in the training cohort, 16% (16) in the internal validation cohort and 14% (12) in the external validation cohort. After adjusting for various confounding factors, we constructed a nomogram including the clot burden score (CBS), baseline neutrophil count, core infarct volume on CTP before EVT, collateral index, and the number of retrieval attempts. The AUCs of the training cohorts were 0.891 (95% CI 0.840-0.942), the Hosmer-Lemeshow test showed good calibration of the nomogram (P = 0.879). And our nomogram performed well in both internal and external validation data. Our nomogram demonstrates promising potential in identifying patients at elevated risk of MBE following EVT for LVOS.

Development and validation of a smartwatch algorithm for differentiating physical activity intensity in health monitoring.

To develop and validate a machine learning based algorithm to estimate physical activity (PA) intensity using the smartwatch with the capacity to record PA and determine outdoor state. Two groups of participants, including 24 adults (13 males) and 18 children (9 boys), completed a sequential activity trial. During each trial, participants wore a smartwatch, and energy expenditure was measured using indirect calorimetry as gold standard. The support vector machine algorithm and the least squares regression model were applied for the metabolic equivalent (MET) estimation using raw data derived from the smartwatch. Exercise intensity was categorized based on MET values into sedentary activity (SED), light activity (LPA), moderate activity (MPA), and vigorous activity (VPA). The classification accuracy was evaluated using area under the ROC curve (AUC). The METs estimation accuracy were assessed via the mean absolute error (MAE), the correlation coefficient, Bland-Altman plots, and intraclass correlation (ICC). A total of 24 adults aged 21-34 years and 18 children aged 9-13 years participated in the study, yielding 1790 and 1246 data points for adults and children respectively for model building and validation. For adults, the AUC for classifying SED, MVPA, and VPA were 0.96, 0.88, and 0.86, respectively. The MAE between true METs and estimated METs was 0.75 METs. The correlation coefficient and ICC were 0.87 (p < 0.001) and 0.89, respectively. For children, comparable levels of accuracy were demonstrated, with the AUC for SED, MVPA, and VPA being 0.98, 0.89, and 0.85, respectively. The MAE between true METs and estimated METs was 0.80 METs. The correlation coefficient and ICC were 0.79 (p < 0.001) and 0.84, respectively. The developed model successfully estimated PA intensity with high accuracy in both adults and children. The application of this model enables independent investigation of PA intensity, facilitating research in health monitoring and potentially in areas such as myopia prevention and control.

Sex differences in the diagnostic value of optic nerve sheath diameter for assessing intracranial pressure.

The optic nerve sheath diameter (ONSD) can predict elevated intracranial pressure (ICP) but it is not known whether diagnostic characteristics differ between men and women. This observational study was performed at the Karolinska University Hospital in Sweden to assess sex differences in diagnostic accuracy for ONSD. We included 139 patients (65 women), unconscious and/or sedated, with invasive ICP monitoring. Commonly used ONSD derived measurements and associated ICP measurements were collected. Linear regression analyses were performed with ICP as dependent variable and ONSD as independent variable. Area under the receiver operator characteristics curve (AUROC) analyses were performed with a threshold for elevated ICP ≥ 20 mmHg. Analyses were stratified by sex. Optimal cut-offs and diagnostic characteristics were estimated. The ONSD was associated with ICP in women. The AUROCs in women ranged from 0.70 to 0.83. In men, the ONSD was not associated with ICP and none of the AUROCs were significantly larger than 0.5. This study suggests that ONSD is a useful predictor of ICP in women but may not be so in men. If this finding is verified in further studies, this would call for a re-evaluation of the usage and interpretation of ONSD to estimate ICP.

Development and validation of machine learning models and nomograms for predicting the surgical difficulty of laparoscopic resection in rectal cancer.

BACKGROUND: The objective of this study is to develop and validate a machine learning (ML) prediction model for the assessment of laparoscopic total mesorectal excision (LaTME) surgery difficulty, as well as to identify independent risk factors that influence surgical difficulty. Establishing a nomogram aims to assist clinical practitioners in formulating more effective surgical plans before the procedure. METHODS: This study included 186 patients with rectal cancer who underwent LaTME from January 2018 to December 2020. They were divided into a training cohort (n = 131) versus a validation cohort (n = 55). The difficulty of LaTME was defined based on Escal's et al. scoring criteria with modifications. We utilized Lasso regression to screen the preoperative clinical characteristic variables and intraoperative information most relevant to surgical difficulty for the development and validation of four ML models: logistic regression (LR), support vector machine (SVM), random forest (RF), and decision tree (DT). The performance of the model was assessed based on the area under the receiver operating characteristic curve(AUC), sensitivity, specificity, and accuracy. Logistic regression-based column-line plots were created to visualize the predictive model. Consistency statistics (C-statistic) and calibration curves were used to discriminate and calibrate the nomogram, respectively. RESULTS: In the validation cohort, all four ML models demonstrate good performance: SVM AUC = 0.987, RF AUC = 0.953, LR AUC = 0.950, and DT AUC = 0.904. To enhance visual evaluation, a logistic regression-based nomogram has been established. Predictive factors included in the nomogram are body mass index (BMI), distance between the tumor to the dentate line ≤ 10 cm, radiodensity of visceral adipose tissue (VAT), area of subcutaneous adipose tissue (SAT), tumor diameter >3 cm, and comorbid hypertension. CONCLUSION: In this study, four ML models based on intraoperative and preoperative risk factors and a nomogram based on logistic regression may be of help to surgeons in evaluating the surgical difficulty before operation and adopting appropriate responses and surgical protocols.

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.

Prognostic model on pregnancy outcomes for women with recurrent spontaneous abortions treated with cyclosporin A: A single-institution experience.

BACKGROUND: This study aimed to identify prognostic factors for pregnancy outcomes and construct a prognostic model for pregnancy outcomes in women with Recurrent Spontaneous Abortions (RSA) treated with cyclosporin A. METHODS: A total of 154 RSA patients treated with cyclosporin A between October 2016 and October 2018 were retrospectively recruited. Multivariate logistic regression was applied to identify the prognostic factors for pregnancy success in RSA women treated with cyclosporin A. The Receiver Operating Characteristic (ROC) curve was applied to construct prognostic value, and the prognostic performance was assessed using area under the ROC. RESULTS: After adjusting potential confounding factors, the authors noted increased age (OR = 0.771; 95 % CI 0.693‒0.858; p < 0.001) and positive antinuclear antibodies (OR = 0.204; 95 % CI 0.079‒0.526; p = 0.001) were associated with a reduced incidence of pregnancy success, while positive anti-ß2 glycoprotein-I-antibody (OR = 21.941; 95 % CI 1.176‒409.281; p = 0.039) was associated with an increased incidence of pregnancy success after treated with cyclosporin A. The AUC of combining these variables for predicting pregnancy failure was 0.809 (95 % CI 0.735‒0.880). CONCLUSIONS: This study systematically identified the prognostic factors for pregnancy success in women treated with cyclosporin A, and the constructed prognostic model based on these factors with relatively higher prognostic value. Further large-scale prospective studies should be performed to validate the prognostic value of the constructed model.

Development and application of a deep learning-based comprehensive early diagnostic model for chronic obstructive pulmonary disease.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a frequently diagnosed yet treatable condition, provided it is identified early and managed effectively. This study aims to develop an advanced COPD diagnostic model by integrating deep learning and radiomics features. METHODS: We utilized a dataset comprising CT images from 2,983 participants, of which 2,317 participants also provided epidemiological data through questionnaires. Deep learning features were extracted using a Variational Autoencoder, and radiomics features were obtained using the PyRadiomics package. Multi-Layer Perceptrons were used to construct models based on deep learning and radiomics features independently, as well as a fusion model integrating both. Subsequently, epidemiological questionnaire data were incorporated to establish a more comprehensive model. The diagnostic performance of standalone models, the fusion model and the comprehensive model was evaluated and compared using metrics including accuracy, precision, recall, F1-score, Brier score, receiver operating characteristic curves, and area under the curve (AUC). RESULTS: The fusion model exhibited outstanding performance with an AUC of 0.952, surpassing the standalone models based solely on deep learning features (AUC = 0.844) or radiomics features (AUC = 0.944). Notably, the comprehensive model, incorporating deep learning features, radiomics features, and questionnaire variables demonstrated the highest diagnostic performance among all models, yielding an AUC of 0.971. CONCLUSION: We developed and implemented a data fusion strategy to construct a state-of-the-art COPD diagnostic model integrating deep learning features, radiomics features, and questionnaire variables. Our data fusion strategy proved effective, and the model can be easily deployed in clinical settings. TRIAL REGISTRATION: Not applicable. This study is NOT a clinical trial, it does not report the results of a health care intervention on human participants.

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.

A novel TCGA-validated programmed cell-death-related signature of ovarian cancer.

BACKGROUND: Ovarian cancer (OC) is a gynecological malignancy tumor with high recurrence and mortality rates. Programmed cell death (PCD) is an essential regulator in cancer metabolism, whose functions are still unknown in OC. Therefore, it is vital to determine the prognostic value and therapy response of PCD-related genes in OC. METHODS: By mining The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx) and Genecards databases, we constructed a prognostic PCD-related genes model and performed Kaplan-Meier (K-M) analysis and Receiver Operating Characteristic (ROC) curve for its predictive ability. A nomogram was created via Cox regression. We validated our model in train and test sets. Quantitative real-time PCR (qRT-PCR) was applied to identify the expression of our model genes. Finally, we analyzed functional analysis, immune infiltration, genomic mutation, tumor mutational burden (TMB) and drug sensitivity of patients in low- and high-risk group based on median scores. RESULTS: A ten-PCD-related gene signature including protein phosphatase 1 regulatory subunit 15 A (PPP1R15A), 8-oxoguanine-DNA glycosylase (OGG1), HECT and RLD domain containing E3 ubiquitin protein ligase family member 1 (HERC1), Caspase-2.(CASP2), Caspase activity and apoptosis inhibitor 1(CAAP1), RB transcriptional corepressor 1(RB1), Z-DNA binding protein 1 (ZBP1), CD3-epsilon (CD3E), Clathrin heavy chain like 1(CLTCL1), and CCAAT/enhancer-binding protein beta (CEBPB) was constructed. Risk score performed well with good area under curve (AUC) (AUC3 - year =0.728, AUC5 - year = 0.730). The nomogram based on risk score has good performance in predicting the prognosis of OC patients (AUC1 - year =0.781, AUC3 - year =0.759, AUC5 - year = 0.670). Kyoto encyclopedia of genes and genomes (KEGG) analysis showed that the erythroblastic leukemia viral oncogene homolog (ERBB) signaling pathway and focal adhesion were enriched in the high-risk group. Meanwhile, patients with high-risk scores had worse OS. In addition, patients with low-risk scores had higher immune-infiltrating cells and enhanced expression of checkpoints, programmed cell death 1 ligand 1 (PD-L1), indoleamine 2,3-dioxygenase 1 (IDO-1) and lymphocyte activation gene-3 (LAG3), and were more sensitive to A.443,654, GDC.0449, paclitaxel, gefitinib and cisplatin. Finally, qRT-PCR confirmed RB1, CAAP1, ZBP1, CEBPB and CLTCL1 over-expressed, while PPP1R15A, OGG1, CASP2, CD3E and HERC1 under-expressed in OC cell lines. CONCLUSION: Our model could precisely predict the prognosis, immune status and drug sensitivity of OC patients.

Relationship between lymphocytes and idiopathic macular hole.

BACKGROUND: An idiopathic macular hole (IMH) is a full-thickness anatomic defect extending from the internal limiting membrane to the photoreceptor layer of the macula without any known cause. Recently, clinical laboratory markers of systemic inflammatory status derived from complete blood counts have been evaluated in ocular diseases. This study aimed to explore whether they could predict the development and progression of IMHs. METHODS: A retrospective review of 36 patients with IMH and 36 sex-and-age-matched patients with cataracts was conducted. We collected complete blood counts of all participating individuals and calculated systemic immunoinflammatory indicators. The maximum base diameter of the IMH (BD), minimum diameter of the IMH (MIN), height of the IMH (H), area of the intraretinal cyst (IRC), and curve lengths of the detached photoreceptor arms were measured on optical coherence tomography (OCT) images. We used these values to calculate the macular hole index (MHI), tractional hole index (THI), diameter hole index (DHI), hole form factor (HFF), and macular hole closure index (MHCI). We performed a receiver operating characteristic (ROC) curve analysis of 30 patients with IMH who were followed up 1 month after surgery. RESULTS: Lymphocyte counts were significantly higher in the IMH group. No other significant differences were observed between the IMH and control groups. Lymphocyte counts in the IMH group were significantly negatively correlated with MIN and BD and were significantly positively correlated with MHI, THI, and MHCI. However, lymphocyte counts were not significantly correlated with H, IRC, DHI, and HFF. In the ROC analysis, BD, MIN, MHI, THI, and MHCI were significant predictors of anatomical outcomes. According to the cut-off points of the ROC analysis, lymphocyte counts were compared between the above-cut-off and below-cut-off groups. Lymphocyte counts were significantly higher in the MIN ≤ 499.61 µm, MHI ≥ 0.47, THI ≥ 1.2, and MHCI ≥ 0.81 groups. There were no significant differences between the above-cut-off and below-cut-off BD groups. CONCLUSIONS: Although inflammation may not be an initiating factor, it may be involved in IMH formation. Lymphocytes may play a relatively important role in tissue repair during the developmental and postoperative recovery phases of IMH.

A new nomogram model for the individualized prediction of mild cognitive impairment in elderly patients with type 2 diabetes mellitus.

Background: A high risk of developing mild cognitive impairment (MCI) is faced by elderly patients with type 2 diabetes mellitus (T2DM). In this study, independent risk factors for MCI in elderly patients with T2DM were investigated, and an individualized nomogram model was developed. Methods: In this study, clinical data of elderly patients with T2DM admitted to the endocrine ward of the hospital from November 2021 to March 2023 were collected to evaluate cognitive function using the Montreal Cognitive Assessment scale. To screen the independent risk factors for MCI in elderly patients with T2DM, a logistic multifactorial regression model was employed. In addition, a nomogram to detect MCI was developed based on the findings of logistic multifactorial regression analysis. Furthermore, the accuracy of the prediction model was evaluated using calibration and receiver operating characteristic curves. Finally, decision curve analysis was used to evaluate the clinical utility of the nomogram. Results: In this study, 306 patients were included. Among them, 186 patients were identified as having MCI. The results of multivariate logistic regression analysis demonstrated that educational level, duration of diabetes, depression, glycated hemoglobin, walking speed, and sedentary duration were independently correlated with MCI, and correlation analyses showed which influencing factors were significantly correlated with cognitive function (p <0.05). The nomogram based on these factors had an area under the curve of 0.893 (95%CI:0.856-0.930)(p <0.05), and the sensitivity and specificity were 0.785 and 0.850, respectively. An adequate fit of the nomogram in the predictive value was demonstrated by the calibration plot. Conclusions: The nomogram developed in this study exhibits high accuracy in predicting the occurrence of cognitive dysfunction in elderly patients with T2DM, thereby offering a clinical basis for detecting MCI in patients with T2DM.

The relationship between complement C1q and coronary plaque vulnerability based on optical coherence tomography analysis.

To determine the association between complement C1q and vulnerable plaque morphology among coronary artery disease (CAD) patients. We conducted a retrospective observational study of 221 CAD patients admitted to The Second Affiliated Hospital of Xi'an Jiaotong University. Intravascular optical coherence tomography was utilized to describe the culprit plaques' morphology. Using logistic regression analysis to explore the correlation between C1q and vulnerable plaques, and receiver operator characteristic (ROC) analysis assess the predictive accuracy. As reported, the complement C1q level was lower in ACS patients than CCS patients (18.25 ± 3.88 vs. 19.18 ± 4.25, P = 0.045). The low complement-C1q-level group was more prone to develop vulnerable plaques. In lipid-rich plaques, the complement C1q level was positively correlated with the thickness of fibrous cap (r = 0.480, P = 0.041). Univariate and multivariate logistic regression analyses suggested that complement C1q could be an independent contributor to plaques' vulnerability. For plaque rupture, erosion, thrombus, and cholesterol crystals, the areas under the ROC curve of complement C1q level were 0.873, 0.816, 0.785, and 0.837, respectively (P < 0.05 for all). In CAD patients, the complement C1q could be a valuable indicator of plaque vulnerability.

Quantitative characterization of breast lesions and normal fibroglandular tissue using compartmentalized diffusion-weighted model: comparison of intravoxel incoherent motion and restriction spectrum imaging.

BACKGROUND: To compare the compartmentalized diffusion-weighted models, intravoxel incoherent motion (IVIM) and restriction spectrum imaging (RSI), in characterizing breast lesions and normal fibroglandular tissue. METHODS: This prospective study enrolled 152 patients with 157 histopathologically verified breast lesions (41 benign and 116 malignant). All patients underwent a full-protocol preoperative breast MRI, including a multi-b-value DWI sequence. The diffusion parameters derived from the mono-exponential model (ADC), IVIM model (Dt, Dp, f), and RSI model (C1, C2, C3, C1C2, F1, F2, F3, F1F2) were quantitatively measured and then compared among malignant lesions, benign lesions and normal fibroglandular tissues using Kruskal-Wallis test. The Mann-Whitney U-test was used for the pairwise comparisons. Diagnostic models were built by logistic regression analysis. The ROC analysis was performed using five-fold cross-validation and the mean AUC values were calculated and compared to evaluate the discriminative ability of each parameter or model. RESULTS: Almost all quantitative diffusion parameters showed significant differences in distinguishing malignant breast lesions from both benign lesions (other than C2) and normal fibroglandular tissue (all parameters) (all P < 0.0167). In terms of the comparisons of benign lesions and normal fibroglandular tissues, the parameters derived from IVIM (Dp, f) and RSI (C1, C2, C1C2, F1, F2, F3) showed significant differences (all P < 0.005). When using individual parameters, RSI-derived parameters-F1, C1C2, and C2 values yielded the highest AUCs for the comparisons of malignant vs. benign, malignant vs. normal tissue and benign vs. normal tissue (AUCs = 0.871, 0.982, and 0.863, respectively). Furthermore, the combined diagnostic model (IVIM + RSI) exhibited the highest diagnostic efficacy for the pairwise discriminations (AUCs = 0.893, 0.991, and 0.928, respectively). CONCLUSIONS: Quantitative parameters derived from the three-compartment RSI model have great promise as imaging indicators for the differential diagnosis of breast lesions compared with the bi-exponential IVIM model. Additionally, the combined model of IVIM and RSI achieves superior diagnostic performance in characterizing breast lesions.

Feasibility Study of Computed Tomographic Radiomics Model for the Prediction of Early and Intermediate Stage Hepatocellular Carcinoma Using BCLC Staging.

BACKGROUND: Hepatocellular carcinoma (HCC) is a serious health concern because of its high morbidity and mortality. The prognosis of HCC largely depends on the disease stage at diagnosis. Computed tomography (CT) image textural analysis is an image analysis technique that has emerged in recent years. OBJECTIVE: To probe the feasibility of a CT radiomic model for predicting early (stages 0, A) and intermediate (stage B) HCC using Barcelona Clinic Liver Cancer (BCLC) staging. METHODS: A total of 190 patients with stages 0, A, or B HCC according to CT-enhanced arterial and portal vein phase images were retrospectively assessed. The lesions were delineated manually to construct a region of interest (ROI) consisting of the entire tumor mass. Consequently, the textural profiles of the ROIs were extracted by specific software. Least absolute shrinkage and selection operator dimensionality reduction was used to screen the textural profiles and obtain the area under the receiver operating characteristic curve values. RESULTS: Within the test cohort, the area under the curve (AUC) values associated with arterial-phase images and BCLC stages 0, A, and B disease were 0.99, 0.98, and 0.99, respectively. The overall accuracy rate was 92.7%. The AUC values associated with portal vein phase images and BCLC stages 0, A, and B disease were 0.98, 0.95, and 0.99, respectively, with an overall accuracy of 90.9%. CONCLUSION: The CT radiomic model can be used to predict the BCLC stage of early-stage and intermediate-stage HCC.

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.