Risk analysis of visceral pleural invasion in malignant solitary pulmonary nodules that appear touching the pleural surface.

BACKGROUND: The preoperative determination of visceral pleural invasion (VPI) in patients with malignant solitary pulmonary nodules (SPNs) is essential for determining the surgical range and selecting adjuvant chemotherapy. OBJECTIVES: This study aimed to systematically investigate risk factors of VPI in patients with SPN and construct a preoperative predictive model for such patients. DESIGN: This is a retrospective study. The clinical, radiological, and pathological characteristics of study subjects were reviewed, and the groups with and without VPI were compared. METHODS: Multivariate logistic analysis was utilized to identify independent risk factors for VPI. Moreover, a predictive nomogram was constructed to assess the likelihood of VPI occurrence. RESULTS: Of the 364 enrolled cases, SPNs adjacent to the pleura with VPI were found in 110 (30.2%) patients. By incorporating four preoperative variables, including tumor diameter (>2 cm), maximum computed tomography value (>200 Hu), air bronchogram sign, and age, a preoperative predictive nomogram was constructed. The nomogram demonstrated good discriminative ability, with a C-index of 0.736 (95% CI (0.662-0.790)). Furthermore, our data indicated that the air bronchogram sign (odd ratio (OR) 1.81, 95% CI (0.99-3.89), p = 0.048), a maximum diameter >2 cm (OR 24.48, 95% CI (8.43-71.07), p < 0.001), pathological type (OR 5.01, 95% CI (2.61-9.64), p < 0.001), and Ki-67 >30% (OR 2.95, 95% CI (1.40-6.21), p = 0.004) were overall independent risk factors for VPI. CONCLUSION: This study investigated the risk factors for VPI in malignant SPNs touching the pleural surface. Additionally, a nomogram was developed to predict the likelihood of VPI in such patients, facilitating informed decision-making regarding surgical approaches and treatment protocols.

Development and validation of a nomogram-based model to predict combined esophageal injury post-esophagoscopy for esophageal foreign bodies.

OBJECTIVE: The aim of this study is to create and validate a nomogram model for predicting complications of esophageal injury post-esophagoscopy in patients with esophageal foreign bodies (EFB). METHODS: We examined 303 patients who underwent esophagoscopy from January 2019 to December 2022 at a leading hospital in Anhui, known for its expertise in otorhinolaryngology-head and neck surgery. The patients were split into a modeling group and a validation group in a 7:3 ratio. Logistic regression analysis was employed to determine the risk factors for esophageal injury after undergoing esophagoscopy in patients with EFB. Based on these factors, a nomogram risk prediction model was developed and assessed using a goodness of fit test. RESULTS: Logistic regression analysis revealed that the type of foreign body, failure of gastroscopic retrieval, duration of lodgment, lodgment site, and presence of combined cardiovascular and cerebrovascular diseases were significant (p < 0.05) independent risk factors for esophageal injury following esophagoscopy for EFB. The area under the ROC curve for the training set was 0.850, and the Hosmer-Lemeshow goodness of fit test resulted in a p value of 0.908. For the validation set, the area under the ROC curve was 0.848, and the Hosmer-Lemeshow test gave a p value of 0.665. The calibration curve showed a close alignment between the predicted and observed values. CONCLUSION: The type of foreign body, duration of lodgment, lodgment site, previous failure of gastroscopic retrieval, and history of combined cardiovascular and cerebrovascular diseases are significant risk factors for esophageal injury following EFB esophagoscopy. This model accurately quantifies the risk of esophageal injury after EFB esophagoscopy.

Enhancing Patient Outcomes: A Novel Nomogram Prediction Model Based on Systemic Immune-Inflammation Index for Esophageal Stricture After Endoscopic Submucosal Dissection.

BACKGROUND: Endoscopic submucosal dissection (ESD) is a widely utilized treatment for early esophageal cancer. However, the rising incidence of postoperative esophageal stricture poses a significant challenge, adversely affecting patients' quality of life and treatment outcomes. Developing precise predictive models is urgently required to enhance treatment outcomes. MATERIALS AND METHODS: This study retrospectively analyzed clinical data from 124 patients with early esophageal cancer who underwent ESD at Ningbo Medical Center Lihuili Hospital. Patients were followed up to assess esophageal stricture incidence. Binary logistic regression analysis was used to identify factors associated with post-ESD esophageal stricture. A novel nomogram prediction model based on Systemic Immune-inflammation Index (SII) was constructed and evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). RESULTS: ROC curve analysis showed that the optimal value of SII for predicting esophageal stricture was 312.67. Both univariate and multivariate analyses identified lesion infiltration depth (< M2 vs. ≥ M2, p = 0.002), lesion longitudinal length (< 4 cm vs. ≥ 4 cm, p = 0.008), circumferential resection range (< 0.5, 0.5-0.75, ≥ 0.75, p = 0.014), and SII (< 312.67 vs. ≥ 312.67, p = 0.040) as independent risk factors for post-ESD esophageal stricture. A novel nomogram prediction model incorporating these four risk factors was developed. Validation using ROC curve analysis demonstrated satisfactory model performance, while calibration curves indicated good agreement between model-predicted risk and observed outcomes. CONCLUSION: We successfully constructed a novel nomogram prediction model based on SII, which can accurately and intuitively predict the occurrence of esophageal stricture after ESD, providing guidance for clinicians and improving treatment outcomes.

Analysis of prognostic risk factors and development of a predictive model for fetuses with right aortic arch with mirror-image branching: a prognostic evaluation method based on ultrasound parameter features.

Background: Prenatal ultrasound plays a crucial role in the diagnosis and classification of right aortic arch (AO) with mirror-image branching (RAA-MB). The recent research in this area has primarily focused on qualitative diagnosis, neglecting the quantitative analysis of ultrasound factors that impact RAA-MB outcomes. This study used echocardiography to measure prenatal ultrasound parameters for vascular ring and trachea in fetuses with RAA-MB, employing a nomogram model to evaluate factors influencing their prognosis, thereby providing a comprehensive characterization of potential outcomes. Methods: A retrospective case-control study was conducted from March 2019 to March 2023. A systematic gathering of prenatal echocardiograms and clinical data was completed for a cohort comprising 92 cases of fetal RAA-MB at the Ultrasound Medicine Center of Gansu Provincial Maternity and Child Care Hospital. Participant recruitment was executed through random selection from among those receiving outpatient medical care. Within the cohort, 42 cases were categorized as fetuses with isolated RAA-MB, while the remaining 50 cases were characterized as fetuses with RAA-MB and associated anomalies. Measurements were taken of the angle between the right AO and the ductus arteriosus (DA) (AO-DA), the distance between the AO and DA, the diameter of AO and DA, and the distance growth rate (DGR) of the AO-DA distance. Additionally, measurements were taken of the tracheal anterior-posterior diameter, tracheal left-right diameter, and tracheal circumference in the three-vessel tracheal view. In the AO view, measurements were taken of the tracheal cross-sectional area (TA) and the vessel ring cross-sectional area (VRA). The relationship between these parameters and the prognosis of fetuses with RAA-MB was assessed using logistic regression analysis. A receiver operating characteristic (ROC) curve was constructed to evaluate the diagnostic performance of the predictive model based on these factors. Results: The multivariate logistic regression analysis revealed that the independent predictive factors for the prognosis of fetuses with RAA-MB were the AO-DA distance [odds ratio (OR) =0.012], TA (OR =0.401), and VRA (OR =1.103) (all P values <0.001). The area under the ROC curve was 0.891 [95% confidence interval (CI): 0.789-0.914; P<0.001], indicating a high accuracy of the model's predictions. Conclusions: The AO-DA distance, TA, and VRA are factors that influence the prognosis of fetuses with RAA-MB. The column chart model constructed based on these parameters can effectively provide a reference for predicting the risk of adverse outcomes in fetuses with RAA-MB.

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.

The significance of risk stratification through nomogram-based assessment in determining postmastectomy radiotherapy for patients diagnosed with pT1 - 2N1M0 breast cancer.

OBJECTIVE: To explore the high-risk factors affecting the prognosis of pT1 - 2N1M0 patients after mastectomy, establish a nomogram prediction model, and screen the radiotherapy benefit population. METHOD: The clinical data of 936 patients with pT1 - 2N1M0 who underwent mastectomy in the fourth hospital of Hebei Medical University from 2010 to 2016 were retrospectively analyzed. There were 583 patients received postmastectomy radiotherapy(PMRT), and 325 patients without PMRT. Group imbalances were mitigated using the propensity score matching (PSM) method, and the log-rank test was employed to compare overall survival (OS) and disease-free survival (DFS) between the cohorts. The efficacy of PMRT across various risk groups was evaluated using a nomogram model. RESULT: The median follow-up period was 98 months, Patients who received PMRT demonstrated significantly improved 5-year and 8-year OS and DFS compared to those who did not (P < 0.001). Multivariate analysis revealed that age, primary tumor site, positive lymph node, stage, and Ki-67 level independently influenced OS, while age, primary tumor site, and stage independently affected DFS. PMRT drastically enhanced OS in the high-risk group (P = 0.001), but did not confer benefits in the low-risk and intermediate risk groups (P = 0.057, P = 0.099). PMRT led to a significant improvement in disease-free survival (DFS) among patients in the intermediate and high-risk groups (P = 0.036, P = 0.001), whereas the low-risk group did not experience a significant benefit (P = 0.475). CONCLUSION: Age ≤ 40 years, tumor located in the inner quadrant or central area, T2 stage, 2-3 lymph nodes metastasis, and Ki67 > 30% were the high-risk factors affecting the prognosis of this cohort of patients. In OS nomogram, patients with a risk score of 149 or higher who received PMRT exhibited improved OS. Similarly, in DFS nomogram, patients with a risk score of 123 or higher who received PMRT demonstrated enhanced DFS.

Analysis of risk factors for PCI no-reflow in coronary heart disease and construction of related prediction models.

OBJECTIVE: To analyze the risk factors of percutaneous coronary intervention (PCI) no-reflow in patients with coronary heart disease (CHD) and construct a predictive nomogram model. METHODS: This retrospective study included 260 patients with CHD who underwent PCI in the Third Affiliated Hospital of Chongqing Medical University from January 2022 to December 2023. The subjects were divided into a PCI no-reflow group (n = 86) and normal reflow group (n = 174) based on thrombolysis in myocardial infarction (TIMI) blood flow grading. General data, PCI related data and laboratory indexes of patients were collected. Logistic regression was used to analyze the risk factors of no-reflow after PCI in CHD patients. Based on the significant variables from regression analysis, a nomogram prediction model was constructed by using R language. The accuracy of the model was evaluated by receiver operating characteristic (ROC) curve and calibration curve, and the decision curve was drawn to clarify the clinical utility of the model. Model performance metrics included area under the curve (AUC), accuracy, sensitivity and specificity. RESULTS: Multivariate logistic regression analysis showed that hypertension, cystatin C (Cys-C), hypersensitive c-reactive protein (hs-CRP) and platelet-to-lymphocyte ratio (PLR) were risk factors for no-reflow after PCI in CHD patients (OR > 1, P < 0.001), while ADAM metallopeptidase with thrombospondin type 1 motif 13 (ADAMTS-13) and lymphocyte (LYM) were protective factors (OR < 1, P < 0.001). The nomogram prediction model based on the above risk factors showed good predictive value. The AUC of the nomogram prediction model in the training set was 0.967 (95% CI: 0.946-0.989), with a specificity of 0.923 and a sensitivity of 0.908. In the validation set, the AUC was 0.894 (95% CI: 0.817-0.971), with a specificity of 0.807 and a sensitivity of 0.857. The calibration curve indicated good agreement between the predicted and actual probabilities, and the decision curve showed clinical benefit across a range of threshold probabilities in both the training and validation sets (0.0-0.99). CONCLUSION: The risk factors affecting the occurrence of no-reflow after PCI in patients with CHD include hypertension, serum Cys-C, hs-CRP, PLR, ADAMTS-13 and LYM levels. The nomogram risk prediction model based on the above factors is valuable for identifying patients with high risk of no-reflow after PCI.

Development and validation of a TRIM27-based nomogram for predicting metachronous liver metastasis and prognosis in postoperative colorectal cancer patients.

BACKGROUND: Colorectal cancer ranks among the most prevalent malignancies globally. Accurate prediction of metachronous liver metastasis is crucial for optimizing postoperative management. Tripartite motif-containing protein 27 (TRIM27), an E3 ubiquitin ligase, is implicated in diverse cellular functions and tumorigenesis. METHODS: This study aimed to develop and validate a TRIM27-based nomogram for prognostication in colorectal cancer patients. Transcriptome sequencing of five paired tumor and normal tissue samples identified TRIM27 as a potential prognostic biomarker. Immunohistochemistry was employed to assess TRIM27 expression in colorectal cancer cohorts from two institutions. RESULTS: TRIM27 expression correlated significantly with both the prognosis of colorectal cancer patients and the occurrence of metachronous liver metastasis. A nomogram incorporating TRIM27 and clinical factors was constructed and demonstrated robust predictive accuracy in an independent validation cohort. CONCLUSION: The TRIM27-based nomogram is a valuable prognostic tool for predicting prognosis and metachronous liver metastasis in colorectal cancer patients, aiding in personalized treatment 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.

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.

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.

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.

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.

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.

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.

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.

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.

Prediction of peritoneal free cancer cells in gastric cancer patients by golden-angle radial sampling dynamic contrast-enhanced magnetic resonance imaging. / ç£å ±æŒ¯é‡‘è§’å¾„å‘é‡‡æ ·åŠ¨æ€å¢žå¼ºæˆåƒé¢„æµ‹èƒƒç™Œæ‚£è€ è ¹è ”æ¸¸ç¦»ç™Œç»†èƒž.

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.