Leveraging diverse cellular stress patterns for predicting clinical outcomes and therapeutic responses in patients with multiple myeloma.

Tumour microenvironment harbours diverse stress factors that affect the progression of multiple myeloma (MM), and the survival of MM cells heavily relies on crucial stress pathways. However, the impact of cellular stress on clinical prognosis of MM patients remains largely unknown. This study aimed to provide a cell stress-related model for survival and treatment prediction in MM. We incorporated five cell stress patterns including heat, oxidative, hypoxic, genotoxic, and endoplasmic reticulum stresses, to develop a comprehensive cellular stress index (CSI). Then we systematically analysed the effects of CSI on survival outcomes, clinical characteristics, immune microenvironment, and treatment sensitivity in MM. Molecular subtypes were identified using consensus clustering analysis based on CSI gene profiles. Moreover, a prognostic nomogram incorporating CSI was constructed and validated to aid in personalised risk stratification. After screening from five stress models, a CSI signature containing nine genes was established by Cox regression analyses and validated in three independent datasets. High CSI was significantly correlated with cell division pathways and poor clinical prognosis. Two distinct MM subtypes were identified through unsupervised clustering, showing significant differences in prognostic outcomes. The nomogram that combined CSI with clinical features exhibited good predictive performances in both training and validation cohorts. Meanwhile, CSI was closely associated with immune cell infiltration level and immune checkpoint gene expression. Therapeutically, patients with high CSI were more sensitive to bortezomib and antimitotic agents, while their response to immunotherapy was less favourable. Furthermore, in vitro experiments using cell lines and clinical samples verified the expression and function of key genes from CSI. The CSI signature could be a clinically applicable indicator of disease evaluation, demonstrating potential in predicting prognosis and guiding therapy for patients with MM.

Establishment of a nomogram model for predicting therapy complications in patients with polycythemia and deep venous thrombosis.

BACKGROUND: Patients with deep venous thrombosis (DVT) residing at high altitudes can only rely on anticoagulation therapy, missing the optimal window for surgery or thrombolysis. Concurrently, under these conditions, patient outcomes can be easily complicated by high-altitude polycythemia (HAPC), which increases the difficulty of treatment and the risk of recurrent thrombosis. To prevent reaching this point, effective screening and targeted interventions are crucial. Thus, this study analyzes and provides a reference for the clinical prediction of thrombosis recurrence in patients with lower-extremity DVT combined with HAPC. AIM: To apply the nomogram model in the evaluation of complications in patients with HAPC and DVT who underwent anticoagulation therapy. METHODS: A total of 123 patients with HAPC complicated by lower-extremity DVT were followed up for 6-12 months and divided into recurrence and non-recurrence groups according to whether they experienced recurrence of lower-extremity DVT. Clinical data and laboratory indices were compared between the groups to determine the influencing factors of thrombosis recurrence in patients with lower-extremity DVT and HAPC. This study aimed to establish and verify the value of a nomogram model for predicting the risk of thrombus recurrence. RESULTS: Logistic regression analysis showed that age, immobilization during follow-up, medication compliance, compliance with wearing elastic stockings, and peripheral blood D-dimer and fibrin degradation product levels were indepen-dent risk factors for thrombosis recurrence in patients with HAPC complicated by DVT. A Hosmer-Lemeshow goodness-of-fit test demonstrated that the nomogram model established based on the results of multivariate logistic regression analysis was effective in predicting the risk of thrombosis recurrence in patients with lower-extremity DVT complicated by HAPC (χ 2 = 0.873; P > 0.05). The consistency index of the model was 0.802 (95%CI: 0.799-0.997), indicating its good accuracy and discrimination. CONCLUSION: The column chart model for the personalized prediction of thrombotic recurrence risk has good application value in predicting thrombotic recurrence in patients with lower-limb DVT combined with HAPC after discharge.

[Biological prediction model of insulin resistance in obese adolescents based on LASSO-Logistic regression].

OBJECTIVE: To identify risk factors affecting the development of insulin resistance in obese adolescents, and to build a nomograph model for predicting the risk of insulin resistance and achieve early screening of insulin resistance. METHODS: A total of 404 obese adolescents aged 10 to 17 years were randomly recruited through a weight loss camp for the detection and diagnosis of lipids and insulin resistance between 2019 and 2021, and key lipid indicators affecting the development of insulin resistance were screened by Lasso regression, nomogram model was constructed, and internal validation of the models was performed by Bootstrap method, and the area under the working characteristic curve(ROC-AUC) and clinical decision curve were used to assess the calibration degree and stability of the column line graph. RESULTS: The AUC was 0.825(95% CI 0.782-0.868), the internal validation result C-Index was 0.804, the mean absolute error of the column line graph model to predict the risk of insulin resistance was 0.015 and the Brier score was 0.163. The Hosmer-Lemeshow goodness-of-fit test showed that model is ideal and acceptable(χ~2=5.59, P=0.70). CONCLUSION: The nomogram model of triglyceride, low-density lipoprotein cholesterol and total cholesterol/high-density lipoprotein cholesterol based on Lasso-logistic regression can effectively predict the risk of insulin resistance in obese children and adolescents.

Construction of a nomogram model based on biomarkers for liver metastasis in non-small cell lung cancer.

BACKGROUND: Patients with non-small cell lung cancer (NSCLC) with liver metastasis have a poor prognosis, and there are no reliable biomarkers for predicting disease progression. Currently, no recognized and reliable prediction model exists to anticipate liver metastasis in NSCLC, nor have the risk factors influencing its onset time been thoroughly explored. METHODS: This study conducted a retrospective analysis of 434 NSCLC patients from two hospitals to assess the association between the risk and timing of liver metastasis, as well as several variables. RESULTS: The patients were divided into two groups: those without liver metastasis and those with liver metastasis. We constructed a nomogram model for predicting liver metastasis in NSCLC, incorporating elements such as T stage, N stage, M stage, lack of past radical lung cancer surgery, and programmed death ligand 1 (PD-L1) levels. Furthermore, NSCLC patients with wild-type EGFR, no prior therapy with tyrosine kinase inhibitors (TKIs), and no prior radical lung cancer surgery showed an elevated risk of early liver metastasis. CONCLUSION: In conclusion, the nomogram model developed in this study has the potential to become a simple, intuitive, and customizable clinical tool for assessing the risk of liver metastasis in NSCLC patients following validation. Furthermore, it provides a framework for investigating the timing of metachronous liver metastasis.

Exploring prognostic DNA methylation genes in bladder cancer: a comprehensive analysis.

The current study aimed to investigate the status of genes with prognostic DNA methylation sites in bladder cancer (BLCA). We obtained bulk transcriptome sequencing data, methylation data, and single-cell sequencing data of BLCA from public databases. Initially, Cox survival analysis was conducted for each methylation site, and genes with more than 10 methylation sites demonstrating prognostic significance were identified to form the BLCA prognostic methylation gene set. Subsequently, the intersection of marker genes associated with epithelial cells in single-cell sequencing analysis was obtained to acquire epithelial cell prognostic methylation genes. Utilizing ten machine learning algorithms for multiple combinations, we selected key genes (METRNL, SYT8, COL18A1, TAP1, MEST, AHNAK, RPP21, AKAP13, RNH1) based on the C-index from multiple validation sets. Single-factor and multi-factor Cox analyses were conducted incorporating clinical characteristics and model genes to identify independent prognostic factors (AHNAK, RNH1, TAP1, Age, and Stage) for constructing a Nomogram model, which was validated for its good diagnostic efficacy, prognostic prediction ability, and clinical decision-making benefits. Expression patterns of model genes varied among different clinical features. Seven immune cell infiltration prediction algorithms were used to assess the correlation between immune cell scores and Nomogram scores. Finally, drug sensitivity analysis of Nomogram model genes was conducted based on the CMap database, followed by molecular docking experiments. Our research offers a reference and theoretical basis for prognostic evaluation, drug selection, and understanding the impact of DNA methylation changes on the prognosis of BLCA.

Functional genetic variants and susceptibility and prediction of gestational diabetes mellitus.

The aetiological mechanism of gestational diabetes mellitus (GDM) has still not been fully understood. The aim of this study was to explore the associations between functional genetic variants screened from a genome-wide association study (GWAS) and GDM risk among 554 GDM patients and 641 healthy controls in China. Functional analysis of single nucleotide polymorphisms (SNPs) positively associated with GDM was further performed. Univariate regression and multivariate logistic regression analyses were used to screen clinical risk factors, and a predictive nomogram model was established. After adjusting for age and prepregnancy BMI, rs9283638 was significantly associated with GDM susceptibility (P < 0.05). Moreover, an obvious interaction between rs9283638 and clinical variables was detected (Pinteraction < 0.05). Functional analysis confirmed that rs9283638 can regulate not only target gene transcription factor binding, but it also regulates the mRNA levels of SAMD7 (P < 0.05). The nomogram model constructed with the factors of age, FPG, 1hPG, 2hPG, HbA1c, TG and rs9283638 revealed an area under the ROC curve of 0.920 (95% CI 0.902-0.939). Decision curve analysis (DCA) suggested that the model had greater net clinical benefit. Conclusively, genetic variants can alter women's susceptibility to GDM by affecting the transcription of target genes. The predictive nomogram model constructed based on genetic and clinical variables can effectively distinguish individuals with different GDM risk factors.

Establishment of a nomogram model for predicting distant metastasis in pancreatic ductal adenocarcinoma: a comparative analysis of different lymph node staging systems based on the SEER database.

The purpose of this study was to compare the predictive value of different lymph node staging systems and to develop an optimal prognostic nomogram for predicting distant metastasis in pancreatic ductal adenocarcinoma (PDAC). Our study involved 6364 patients selected from the Surveillance, Epidemiology, and End Results (SEER) database and 126 patients from China. Independent risk factors for distant metastasis were screened by univariate and multivariate logistic regression analyses, and a model-based comparison of different lymph node staging systems was conducted. Furthermore, we developed a nomogram for predicting distant metastasis using the optimal performance lymph node staging system. The lymph node ratio (LNR), log odds of positive lymph nodes (LODDS), age, primary site, grade, tumor size, American Joint Committee on Cancer (AJCC) 7th Edition T stage, and radiotherapy recipient status were significant predictors of distant metastasis in PDAC patients. The model with the LODDS was a better fit than the model with the LNR. We developed a nomogram model based on LODDS and six clinical parameters. The area under the curve (AUC) and concordance index (C-index) of 0.753 indicated that this model satisfied the discrimination criteria. Kaplan-Meier curves indicate a significant difference in OS among patients with different metastasis risks. LODDS seems to have a superior ability to predict distant metastasis in PDAC patients compared with the AJCC 8th Edition N stage, PLN and LNR staging systems. Moreover, we developed a nomogram model for predicting distant metastasis. Clinicians can use the model to detect patients at high risk of distant metastasis and to make further clinical decisions.

Risk Factors for Early Recurrence After Radical Resection of Hepatocellular Carcinoma Based on Preoperative Contrast-Enhanced Ultrasound and Clinical Features.

OBJECTIVES: To investigate risk factors for the early recurrence (ER) of hepatocellular carcinoma (HCC) after radical resection based on preoperative contrast-enhanced ultrasound (CEUS) and clinical features to provide guidance for clinical treatment. METHODS: The retrospective analysis selected 130 HCC patients who underwent radical tumor resection from October 2019 to November 2021. All patients underwent preoperative routine ultrasound examination and CEUS, and the pathology was confirmed as HCC after surgery. The patients were divided into two groups based on whether there is an ER, namely the ER group and the non-ER group. The general clinical, routine and CEUS data of patients were collected, and the factors were selected by using the least absolute shrinkage and selection operator (LASSO) regression. Multivariate logistic regression was used to screen the independent influencing factors of ER. Then a nomogram model was established to predict the risk of ER, and the application value of nomogram through internal validation was evaluated. RESULTS: Multivariate logistic regression identified several independent factors influencing ER after radical HCC resection. Significant factors included early wash-out phase (95%CI = 0.003-0.206, P = 0.001), liver cirrhosis (95%CI = 2.835-221.224, P = 0.004), incomplete envelope (95%CI = 5.247-1056.130,P = 0.001), multiple lesions (95%CI = 1.110-135.424,P = 0.041), Albumin <40 g/L (95%CI = 2.496-127.223,P = 0.004), and Golgi Protein 73 (GP73) ≥ 85 ng/mL (95%CI = 1.594-30.002, P = 0.010), with all P-values <0.05. The nomogram prediction model constructed based on the results of multivariate logistic regression, demonstrated a ROC curve AUC of 0.879, a sensitivity of 93.5%, a specificity of 66.7%, and a C-index of 0.602, indicating superior diagnostic efficiency compared to independent influencing factors. The ER nomogram prediction model confirmed good discrimination and calibration in internal validation. CONCLUSION: The CEUS-Clinical combined model effectively monitors the risk of ER in high-risk populations following radical resection of HCC, timely interventions to improve patient prognosis.

Body fat ratio as a novel predictor of complications and survival after rectal cancer surgery.

Background: The present study aimed to evaluate the association between body fat ratio (BFR), visceral fat area (VFA), body mass index (BMI) and visceral fat density (VFD) and assess their reliability in assessing risk of postoperative complications and survival status in patients with rectal cancer (RC). Materials and methods: The present study retrospectively included 460 patients who underwent surgical treatment for RC at the First Affiliated Hospital of Wannan Medical College (Yijishan Hospital of Wannan Medical College, Wuhu, China) between September 2018 and July 2021. BFR, VFA, BMI, and VFD were measured and basic information, clinical data, complications and survival were recorded. Results: Statistical analysis was performed to determine optimal BFR cut-off and evaluate group differences. BFR demonstrated a significant positive correlation with VFA (R = 0.739) and BMI (R = 0.783) and significant negative correlation with VFD (R = -0.773). The areas under the receiver operating characteristic curve of BFR, VFA, BMI, and VFD in predicting postoperative complications in RC were all >0.7 and the optimal cut-off value of BFR was 24.3. Patients in the BFR-low group had fewer postoperative complications, lower intraoperative indices, shorter hospitalization times and lower costs than those in the BFR-high group. BFR predicted complications with high diagnostic significance and was validated by multiple models. Furthermore, patients in the BFR-high group had a longer overall survival compared with patients in the BFR-low group. Conclusion: BFR was associated with BMI, VFA, and VFD. A BFR threshold of 24.3 was correlated with decreased complications and enhanced long-term survival.

The Predictive Role of Cardiac Troponin Elevation Ratio Combined With Heart Function Index Model in the Prognosis of Non-ST-Segment Elevation Myocardial Infarction Patients.

Background: Non-ST-segment elevation myocardial infarction (NSTEMI) is a common form of coronary artery disease, and its prognosis is influenced by multiple factors. This study aimed to analyze the predictive role of the combined application of cardiac troponin and cardiac function indices in NSTEMI patients' prognosis. Methods: NSTEMI patients were screened and included in the study. Cardiac troponin elevation ratio (cardiac troponin I (cTnI)/upper limit of normal (ULN)) was measured upon admission, and cardiac function was assessed. General clinical data, laboratory parameters, Grace score, New York Heart Association (NYHA) functional class, complications, and mortality data were collected. The correlation between mortality in NSTEMI patients and clinical parameters was analyzed, and a nomogram prediction model for NSTEMI patient mortality was established. Results: A total of 252 NSTEMI patients were included. Female gender, elevated high-sensitivity C-reactive protein (H-CRP), left ventricular ejection fraction (LVEF) < 50%, NYHA class III and IV, and cTnI/ULN elevation by 36.25-fold were significantly independently associated with mortality outcomes. Multifactorial logistic analysis indicated that these indices remained associated with mortality. A nomogram model predicting NSTEMI patient mortality was constructed using these indices, with an area under the curve (AUC) of 0.911, sensitivity of 97.5%, and specificity of 72.8%. This predictive model outperformed the Grace score (AUC = 0.840). Conclusions: In NSTEMI patients, a 36.25-fold increase in cTnI/ULN, coupled with NYHA class III and IV, independently predicted prognosis. We developed a nomogram model integrating cTnI/ULN and cardiac function indices, aiding clinicians in assessing risk and implementing early interventions for improved outcomes.

Nomogram for predicting the risk of anxiety and depression in patients with non-mild burns.

BACKGROUND: Post-burn anxiety and depression affect considerably the quality of life and recovery of patients; however, limited research has demonstrated risk factors associated with the development of these conditions. AIM: To predict the risk of developing post-burn anxiety and depression in patients with non-mild burns using a nomogram model. METHODS: We enrolled 675 patients with burns who were admitted to The Second Affiliated Hospital, Hengyang Medical School, University of South China between January 2019 and January 2023 and met the inclusion criteria. These patients were randomly divided into development (n = 450) and validation (n = 225) sets in a 2:1 ratio. Univariate and multivariate logistic regression analyses were conducted to identify the risk factors associated with post-burn anxiety and depression diagnoses, and a nomogram model was constructed. RESULTS: Female sex, age < 33 years, unmarried status, burn area ≥ 30%, and burns on the head, face, and neck were independent risk factors for developing post-burn anxiety and depression in patients with non-mild burns. The nomogram model demonstrated predictive accuracies of 0.937 and 0.984 for anxiety and 0.884 and 0.923 for depression in the development and validation sets, respectively, and good predictive performance. Calibration and decision curve analyses confirmed the clinical utility of the nomogram. CONCLUSION: The nomogram model predicted the risk of post-burn anxiety and depression in patients with non-mild burns, facilitating the early identification of high-risk patients for intervention and treatment.

A nomogram prediction model for postoperative seroma/hematoma in elderly subjects after TAPP.

BACKGROUND: Formation of seroma/hematoma is one of the most common postoperative complications following laparoscopic inguinal hernia repair. This study aimed to identify risk factors associated with seroma/hematoma and construct a prediction model. METHODS: Elderly subjects undergoing laparoscopic Transabdominal preperitoneal Patch Plasty (TAPP) were included in this study. The observation endpoint was set as the occurrence of seroma/hematoma within 3 months after TAPP surgery. Independent risk factors were identified through preliminary univariate screening and binary logistic regression analysis. These risk factors were then used to construct a nomogram predictive model using R software. RESULTS: A total of 330 patients were included in the analysis, of which 51 developed seroma/hematoma, resulting in an incidence rate of 15.5%. Obesity (OR: 3.54, 95%CI: 1.45-8.66, P = 0.006), antithrombotic drug use (OR: 2.73, 95%CI: 1.06-7.03, P = 0.037), C-reactive protein (CRP) ≥ 8 (OR: 2.72, 95%CI: 1.04-7.10, P = 0.041, albumin/fibrinogen ratio (AFR) < 7.85 (OR: 2.99, 95%CI: 1.28-7.00, P = 0.012), and lymphocyte/monocyte ratio (LMR) < 4.05 (OR: 12.62, 95%CI: 5.69-28.01, P < 0.001) were five independent risk factors for seroma/hematoma. The nomogram model has well predictive value for seroma/hematoma, with an AUC of 0.879. CONCLUSIONS: The nomogram model based on obesity, antithrombotic drug, CRP, AFR, and LMR has a proved good predictive value and it has potential in clinical practice.

A nomogram model based on SII, AFR, and NLR to predict infectious complications of laparoscopic hysterectomy for cervical cancer.

BACKGROUND: This study aimed to investigate the potential risk factors associated with postoperative infectious complications following laparoscopic hysterectomy for cervical cancer and to develop a prediction model based on these factors. METHODS: This study enrolled patients who underwent selective laparoscopic hysterectomy for cervical cancer between 2019 and 2024. A multivariate regression analysis was performed to identify independent risk factors associated with postoperative infectious complications. A nomogram prediction model was subsequently constructed and evaluated using R software. RESULTS: Out of 301 patients were enrolled and 38 patients (12.6%) experienced infectious complications within one month postoperatively. Six variables were independent risk factors for postoperative infectious complications: age ≥ 60 (OR: 3.06, 95% confidence interval (CI): 1.06-8.79, P = 0.038), body mass index (BMI) ≥ 24.0 (OR: 3.70, 95%CI: 1.4-9.26, P = 0.005), diabetes (OR: 2.91, 95% CI: 1.10-7.73, P = 0.032), systemic immune-inflammation index (SII) ≥ 830 (OR: 6.95, 95% CI: 2.53-19.07, P < 0.001), albumin-to-fibrinogen ratio (AFR) < 9.25 (OR: 4.94, 95% CI: 2.02-12.07, P < 0.001), and neutrophil-to-lymphocyte ratio (NLR) ≥ 3.45 (OR: 7.53, 95% CI: 3.04-18.62, P < 0.001). Receiver operator characteristic (ROC) curve analysis indicated an area under the curve (AUC) of this nomogram model of 0.928, a sensitivity of 81.0%, and a specificity of 92.1%. CONCLUSIONS: The nomogram model, incorporating age, BMI, diabetes, SII, AFR, and NLR, demonstrated strong predictive capabilities for postoperative infectious complications following laparoscopic hysterectomy for cervical cancer.

Myeloperoxidase and its derivative hypochlorous acid combined clinical indicators predict new-onset atrial fibrillation in sepsis: a case-control study.

BACKGROUD: New-onset atrial fibrillation (NOAF) is a common complication of sepsis and linked to higher death rates in affected patients. The lack of effective predictive tools hampers early risk assessment for the development of NOAF. This study aims to develop practical and effective predictive tools for identifying the risk of NOAF. METHODS: This case-control study retrospectively analyzed patients with sepsis admitted to the emergency department of Xinhua Hospital, Shanghai Jiao Tong University School of Medicine from September 2017 to January 2023. Based on electrocardiographic reports and electrocardiogram monitoring records, patients were categorized into NOAF and non-NOAF groups. Laboratory tests, including myeloperoxidase (MPO) and hypochlorous acid (HOCl), were collected, along with demographic data and comorbidities. Least absolute shrinkage and selection operator regression and multivariate logistic regression analyses were employed to identify predictors. The area under the curve (AUC) was used to evaluate the predictive model's performance in identifying NOAF. RESULTS: A total of 389 patients with sepsis were included in the study, of which 63 developed NOAF. MPO and HOCl levels were significantly higher in the NOAF group compared to the non-NOAF group. Multivariate logistic regression analysis identified MPO, HOCl, tumor necrosis factor-α (TNF-α), white blood cells (WBC), and the Acute Physiology and Chronic Health Evaluation II (APACHE II) score as independent risk factors for NOAF in sepsis. Additionally, a nomogram model developed using these independent risk factors achieved an AUC of 0.897. CONCLUSION: The combination of MPO and its derivative HOCl with clinical indicators improves the prediction of NOAF in sepsis. The nomogram model can serve as a practical predictive tool for the early identification of NOAF in patients with sepsis.

A preliminary probabilistic nomogram model for predicting renal arteriolar damage in IgA nephropathy from clinical parameters.

Background: IgA nephropathy (IgAN) is a significant contributor to chronic kidney disease (CKD). Renal arteriolar damage is associated with IgAN prognosis. However, simple tools for predicting arteriolar damage of IgAN remain limited. We aim to develop and validate a nomogram model for predicting renal arteriolar damage in IgAN patients. Methods: We retrospectively analyzed 547 cases of biopsy-proven IgAN patients. Least absolute shrinkage and selection operator (LASSO) regression and logistic regression were applied to screen for factors associated with renal arteriolar damage in patients with IgAN. A nomogram was developed to evaluate the renal arteriolar damage in patients with IgAN. The performance of the proposed nomogram was evaluated based on a calibration plot, ROC curve (AUC) and Harrell's concordance index (C-index). Results: In this study, patients in the arteriolar damage group had higher levels of age, mean arterial pressure (MAP), serum creatinine, serum urea nitrogen, serum uric acid, triglycerides, proteinuria, tubular atrophy/interstitial fibrosis (T1-2) and decreased eGFR than those without arteriolar damage. Predictors contained in the prediction nomogram included age, MAP, eGFR and serum uric acid. Then, a nomogram model for predicting renal arteriolar damage was established combining the above indicators. Our model achieved well-fitted calibration curves and the C-indices of this model were 0.722 (95%CI 0.670-0.774) and 0.784 (95%CI 0.716-0.852) in the development and validation groups, respectively. Conclusion: With excellent predictive abilities, the nomogram may be a simple and reliable tool to predict the risk of renal arteriolar damage in patients with IgAN.

Establishment of Risk Nomogram Model of Postpartum Hemorrhage After Second Cesarean Section.

Objective: To establish and evaluate a nomogram model for predicting the risk of postpartum hemorrhage in second cesarean section. Methods: A total of 440 parturients who underwent the second cesarean section surgery and were registered in our hospital from August 2019 to July 2021 were selected as the study subjects. They were randomly divided into 220 modeling group and 220 validation group based on simple randomization. The two groups were divided into postpartum hemorrhage group and postpartum non bleeding group according to whether postpartum hemorrhage occurred. Results: In the modeling group, the incidence of postpartum hemorrhage in the second cesarean section was 15.00%; the Logistic regression model showed that placenta previa, operation time, prenatal anemia, placenta accreta, uterine inertia were the independent risk factors of postpartum hemorrhage in the second cesarean section (P < 0.05). ROC results showed that AUC of predicting the risk of postpartum hemorrhage in the second cesarean section was 0.824. The slope of calibration curve is close to 1, Hosmer-Lemeshow goodness of fit test showed x2= 7.585, P = 0.250. The external verification results show that the AUC is 0.840, and the predicted probability of the calibration curve is close to the actual probability. Conclusion: Based on the five risk factors of postpartum hemorrhage in the second cesarean section, including placenta previa, operation time, prenatal anemia, placenta accreta and uterine inertia, the nomogram model for predicting the risk of postpartum hemorrhage in the second cesarean section has good accuracy and differentiation.

Clinicopathological significance of cancer stem cell marker CD44/SOX2 in esophageal squamous cell carcinoma (ESCC) patients and construction of a nomogram to predict overall survival.

Background: Esophageal squamous cell carcinoma (ESCC), a prevalent malignancy within the upper gastrointestinal system, is characterized by its unfavorable prognosis and the absence of specific indicators for outcome prediction and high-risk case identification. In our research, we examined the expression levels of cancer stem cells (CSCs), markers CD44/SOX2 in ESCC, scrutinized their association with clinicopathological parameters, and developed a predictive nomogram model. This model, which incorporates CD44/SOX2, aims to forecast the overall survival (OS) of patients afflicted with ESCC. Methods: Immunohistochemistry was utilized to detect the expression levels of CD44 and SOX2 in both cancerous and paracancerous tissues of 68 patients with ESCC. The correlation between CD44/SOX2 expression and clinicopathological parameters was subsequently analyzed. Factors impacting the prognosis of ESCC patients were assessed through univariate and multivariate Cox regression analyses. Leveraging the results of these multivariate regression analyses, a nomogram prognostic model was established to provide individualized predictions of ESCC patient survival outcomes. The predictive accuracy of the nomogram prognostic model was evaluated using the consistency index (C-index) and calibration curves. Results: The expression levels of CD44 were markedly elevated in the tumor tissues of ESCC patients. Similarly, SOX2 was significantly overexpressed in the tumor tissues of ESCC patients. The positive expression of SOX2 in ESCC demonstrated a strong correlation with both the pathological T-stage and the presence of carcinoembryonic antigen. CD44 and SOX2 co-positive expression was significantly associated with the pathological T-stage and tumor node metastasis (TNM) stage. Furthermore, ESCC patients exhibiting CD44-positive expression in their tumor tissue generally had a more adverse prognosis. The co-expression of CD44 and SOX2 resulted in a grimmer prognosis compared to patients with other combinations. Multivariate Cox regression analysis identified the co-expression of CD44 and SOX2, the pathological T-stage, and lymph node metastasis as independent prognostic indicators for ESCC patients. The three identified variables were subsequently incorporated into a nomogram for predicting OS. The C-index of the measurement model and the area under the curve of the subjects' work characteristics showed good individual prediction. This prognostic model stratified patients into low- and high-risk categories. Analysis revealed that the 5-year OS rate was significantly higher in the low-risk group compared to the high-risk group. Conclusions: Elevated CD44 levels, indicative of CSC presence, are intimately linked with the oncogenesis of ESCC and are strongly predictive of unfavorable patient outcomes. Concurrently, the SOX2 gene exhibits a heightened expression in ESCC, markedly accelerating tumor progression and fostering more extensive disease infiltration. The co-expression of CD44 and SOX2 correlates significantly with ESCC patient prognosis, serving as a reliable, independent prognostic marker. Our constructed nomogram, incorporating CD44/SOX2 expression, enhances the prediction of OS and facilitates risk stratification in ESCC patients.

Risk factor analysis and nomogram prediction model construction of postoperative complications of thoracoscopic non-small cell lung cancer.

Background: A series of complications will inevitably occur after thoracoscopic pulmonary resection. How to avoid or reduce postoperative complications is an important research area in the perioperative treatment of thoracic surgery. This study analyzed the risk factors for thoracoscopic postoperative complications of non-small cell lung cancer (NSCLC) and established a nomogram prediction model in order to provide help for clinical decision-making. Methods: Patients with NSCLC who underwent thoracoscopic surgery from January 2017 to December 2021 were selected as study subjects. The relationship between patient characteristics, surgical factors, and postoperative complications was collected and analyzed. Based on the results of the statistical regression analysis, a nomogram model was constructed, and the predictive performance of the nomogram model was evaluated. Results: A total of 872 patients who met the study criteria were included in the study. A total of 171 patients had complications after thoracoscopic surgery, accounting for 19.6% of the study population. Logistic regression analysis showed that thoracic adhesion, history of respiratory disease, and lymphocyte-monocyte ratio (LMR) were independent risk factors for complications after thoracoscopic surgery (P<0.05). Variables with P<0.1 in logistic regression analysis were included in the nomogram model. The verification results showed that the area under curve (AUC) of the model was 0.734 [95% confidence interval (CI): 0.693-0.775], and the calibration curve showed that the model had good differentiation. The decision curve analysis (DCA) curve showed that this model has good clinical application value. In subgroup analysis of complications, gender, history of respiratory disease, body mass index (BMI), type of surgical procedure, thoracic adhesion, and Time of operation were identified as significant risk factors for prolonged air leak (PAL) after surgery. Tumor location and forced expiratory volume in the first second (FEV1) were identified as important risk factors for postoperative pulmonary infection. N stage and thoracic adhesion were identified as significant risk factors for postoperative pleural effusion. The AUC for PAL was 0.823 (95% CI: 0.768-0.879). The AUC of postoperative pulmonary infection was 0.714 (95% CI: 0.627-0.801). The AUC of postoperative pleural effusion was 0.757 (95% CI: 0.650-0.864). The calibration curve and DCA curve indicated that the model had good predictive performance and clinical application value. Conclusions: This study analyzed the risk factors affecting the postoperative complications of NSCLC through thoracoscopic surgery, and the nomogram model built based on the influencing factors has certain significance for the identification and reduction of postoperative complications.

Development of a Nomogram That Predicts the Risk of Atrial Fibrillation in Patients with Coronary Heart Disease.

Objective: To explore the risk factors of atrial fibrillation (AF) in patients with coronary heart disease (CHD), and to construct a risk prediction model. Methods: The participants in this case-control study were from the cardiovascular Department of Changzhou Affiliated Hospital of Nanjing University of Chinese Medicine from June 2016 to June 2023, and they were divided into AF group and non-AF group according to whether AF occurred during hospitalization. The clinical data of the two groups were compared by retrospective analysis. Multivariate Logistic regression analysis was used to investigate the risk factors of AF occurrence in CHD patients. The nomogram model was constructed with R 4.2.6 language "rms" package, and the model's differentiation, calibration and effectiveness were evaluated by drawing ROC curve, calibration curve and decision curve. Results: A total of 1258 patients with CHD were included, and they were divided into AF group (n=92) and non-AF group (n=1166) according to whether AF was complicated. Logistic regression analysis showed that age, coronary multiple branch lesion, history of heart failure, history of drinking, pulmonary hypertension, left atrial diameter, left ventricular end-diastolic diameter and diabetes mellitus were independent risk factors for the occurrence of AF in CHD patients (P < 0.05). The ROC curve showed that the AUC of this model was 0.956 (95% CI (0.916, 0.995)) and the consistency index was 0.966. The calibration curve of the model is close to the ideal curve. The analysis of decision curve shows that the prediction value of the model is better when the probability threshold of the model is 0.042~0.963. Conclusion: The nomogram model established in this study for predicting the risk of AF in patients with CHD has better predictive performance and has certain reference value for clinical identification of high-risk groups prone to AF in patients with CHD.

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OBJECTIVES: Peritoneal free cancer cells can negatively impact disease progression and patient outcomes in gastric cancer. This study aimed to investigate the feasibility of using golden-angle radial sampling dynamic contrast-enhanced magnetic resonance imaging (GRASP DCE-MRI) to predict the presence of peritoneal free cancer cells in gastric cancer patients. METHODS: All enrolled patients were consecutively divided into analysis and validation groups. Preoperative magnetic resonance imaging (MRI) scans and perfusion were performed in patients with gastric cancer undergoing surgery, and peritoneal lavage specimens were collected for examination. Based on the peritoneal lavage cytology (PLC) results, patients were divided into negative and positive lavage fluid groups. The data collected included clinical and MR information. A nomogram prediction model was constructed to predict the positive rate of peritoneal lavage fluid, and the validity of the model was verified based on data from the verification group. RESULTS: There was no statistical difference between the proportion of PLC-positive cases predicted by GRASP DCE-MR and the actual PLC test. MR tumor stage, tumor thickness, and perfusion parameter Tofts-Ketty model volume transfer constant (Ktrans) were independent predictors of positive peritoneal lavage fluid. The nomogram model featured a concordance index (C-index) of 0.785 and 0.742 for the modeling and validation groups, respectively. CONCLUSIONS: GRASP DCE-MR could effectively predict peritoneal free cancer cells in gastric cancer patients. The nomogram model constructed using these predictors may help clinicians to better predict the risk of peritoneal free cancer cells being present in gastric cancer patients.