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.

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.

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.

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.

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.

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.

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.

Predictive value of inflammation and nutritional index in immunotherapy for stage IV non-small cell lung cancer and model construction.

Identifying individuals poised to gain from immune checkpoint inhibitor (ICI) therapies is a pivotal element in the realm of tailored healthcare. The expression level of Programmed Death Ligand 1 (PD-L1) has been linked to the response to ICI therapy, but its assessment typically requires substantial tumor tissue, which can be challenging to obtain. In contrast, blood samples are more feasible for clinical application. A number of promising peripheral biomarkers have been proposed to overcome this hurdle. This research aims to evaluate the prognostic utility of the albumin-to-lactate dehydrogenase ratio (LAR), the Pan-immune-inflammation Value (PIV), and the Prognostic Nutritional Index (PNI) in predicting the response to ICI therapy in individuals with advanced non-small cell lung cancer (NSCLC). Furthermore, the study seeks to construct a predictive nomogram that includes these markers to facilitate the selection of patients with a higher likelihood of benefiting from ICI therapy. A research initiative scrutinized the treatment records of 157 advanced NSCLC patients who received ICI therapy across two Jiangxi medical centers. The cohort from Jiangxi Provincial People's Hospital (comprising 108 patients) was utilized for the training dataset, while the contingent from Jiangxi Cancer Hospital (49 patients) served for validation purposes. Stratification was based on established LAR, PIV, and PNI benchmarks to explore associations with DCR and ORR metrics. Factorial influences on ICI treatment success were discerned through univariate and multivariate Cox regression analysis. Subsequently, a Nomogram was devised to forecast outcomes, its precision gauged by ROC and calibration curves, DCA analysis, and cross-institutional validation. In the training group, the optimal threshold values for LAR, PIV, and PNI were identified as 5.205, 297.49, and 44.6, respectively. Based on these thresholds, LAR, PIV, and PNI were categorized into high (≥ Cut-off) and low (< Cut-off) groups. Patients with low LAR (L-LAR), low PIV (L-PIV), and high PNI (H-PNI) exhibited a higher disease control rate (DCR) (P < 0.05) and longer median progression-free survival (PFS) (P < 0.05). Cox multivariate analysis indicated that PS, malignant pleural effusion, liver metastasis, high PIV (H-PIV), and low PNI (L-PNI) were risk factors adversely affecting the efficacy of immunotherapy (P < 0.05). The Nomogram model predicted a concordance index (C-index) of 0.78 (95% CI: 0.73-0.84). The areas under the ROC curve (AUC) for the training group at 6, 9, and 12 months were 0.900, 0.869, and 0.866, respectively, while the AUCs for the external validation group at the same time points were 0.800, 0.886, and 0.801, respectively. Throughout immunotherapy, PIV and PNI could act as prospective indicators for forecasting treatment success in NSCLC patients, while the devised Nomogram model exhibits strong predictive performance for patient prognoses.

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.

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.

Predicting Kawasaki disease shock syndrome in children.

Background: Kawasaki disease shock syndrome (KDSS) is a critical manifestation of Kawasaki disease (KD). In recent years, a logistic regression prediction model has been widely used to predict the occurrence probability of various diseases. This study aimed to investigate the clinical characteristics of children with KD and develop and validate an individualized logistic regression model for predicting KDSS among children with KD. Methods: The clinical data of children diagnosed with KDSS and hospitalized between January 2021 and December 2023 were retrospectively analyzed. The best predictors were selected by logistic regression and lasso regression analyses. A logistic regression model was built of the training set (n = 162) to predict the occurrence of KDSS. The model prediction was further performed by logistic regression. A receiver operating characteristic curve was used to evaluate the performance of the logistic regression model. We built a nomogram model by visualizing the calibration curve using a 1000 bootstrap resampling program. The model was validated using an independent validation set (n = 68). Results: In the univariate analysis, among the 24 variables that differed significantly between the KDSS and KD groups, further logistic and Lasso regression analyses found that five variables were independently related to KDSS: rash, brain natriuretic peptide, serum Na, serum P, and aspartate aminotransferase. A logistic regression model was established of the training set (area under the receiver operating characteristic curve, 0.979; sensitivity=96.2%; specificity=97.2%). The calibration curve showed good consistency between the predicted values of the logistic regression model and the actual observed values in the training and validation sets. Conclusion: Here we established a feasible and highly accurate logistic regression model to predict the occurrence of KDSS, which will enable its early identification.

Impact of Combined Chemotherapy and Targeted Therapy on Pancreatic Neuroendocrine Carcinoma with Liver Metastasis: A Single-Center Modified Nomogram Analysis.

Objective: To establish a modified nomogram model for pancreatic neuroendocrine carcinoma (pNEC) patients with liver metastasis via single-center clinical data, and to provide guidelines for improving the diagnosis and treatment of patients. Methods: A retrospective analysis of clinical data from pNEC patients with liver metastasis at Peking Union Medical College Hospital (January 2000 to November 2023) was conducted. Univariate and multivariate Cox regression analyses were employed to identify prognostic factors for overall survival (OS). Kaplan-Meier curves were generated, and a modified nomogram predictive model was developed to illustrate the prognosis of pNEC patients with liver metastasis. Calibration plots and C-index were used to validate the model's feasibility, accuracy, and reliability. Results: Forty-five participants with the rare cancer type pNEC and liver metastasis were included in the study. Kaplan-Meier curves revealed that primary tumor resection (PTR), chemotherapy or targeted therapy, and tumor size equal to or less than 5cm significantly improved OS compared to those without PTR, chemotherapy or targeted therapy, and tumor size larger than 5cm. Multivariate Cox regression analysis identified PTR, a combination of chemotherapy and targeted therapy, and tumor size as independent prognostic factors for OS. The predictive nomogram model exhibited acceptable performance with a C-index of 0.744 (0.639-0.805) through bootstrapping. Conclusion: Combining chemotherapy with targeted therapy enhances the survival of pNEC patients with liver metastasis. The modified nomogram model and predictive score table offer valuable references and insights for both clinicians and patients.

Development and Validation of a Predictive Model for Postoperative Intracranial Infections in Neurosurgery with Risk Factor Analysis.

BACKGROUND: Currently, the diagnosis of postneurosurgical intracranial infection is mainly dependent on cerebrospinal fluid (CSF) bacterial culture, which has the disadvantages of being time-consuming, having a low detection rate, and being easily affected by other factors. These disadvantages bring some difficulties to early diagnosis. Therefore, it is very important to construct a nomogram model to predict the risk of infection and provide a basis for early diagnosis and treatment. METHODS: This retrospective study analyzed postneurosurgical patient data from the Fourth Affiliated Hospital of Harbin Medical University between January 2019 and September 2023. The patients were randomly assigned in an 8:2 ratio into the training cohort and the internal validation cohort. In the training cohort, initial screening of relevant indices was conducted via univariate analysis. Subsequently, the least absolute shrinkage and selection operator logistic regression identified significant potential risk factors for inclusion in the nomogram model. The model's discriminative ability was assessed using the area under the receiver operating characteristic curve, and its calibration was evaluated through calibration plots. The clinical utility of the model was determined using decision curve analysis and further validated by the internal validation cohort. RESULTS: Multivariate logistic regression analysis of the training cohort identified 7 independent risk factors for postoperative intracranial infection: duration of postoperative external drainage (odds ratio [OR] 1.19, P = 0.005), continued fever (OR 2.11, P = 0.036), CSF turbidity (OR 2.73, P = 0.014), CSF pressure (OR 1.01, P = 0.018), CSF total protein level (OR 1.26, P = 0.026), CSF glucose concentration (OR 0.74, P = 0.029), and postoperative serum albumin level (OR 0.84, P < 0.001). Using these variables to construct the final model. The area under the receiver operating characteristic curve value of the model was 0.868 in the training cohort and 0.900 in the internal validation cohort. Calibration and the decision curve analysis indicated high accuracy and clinical benefit of the nomogram, findings that were corroborated in the validation cohort. CONCLUSIONS: This study successfully developed a novel nomogram for predicting postoperative intracranial infection, demonstrating excellent predictive performance. It offers a pragmatic tool for the early diagnosis of intracranial infection.

Construction of a preoperative nomogram model for predicting perineural invasion in advanced gastric cancer.

Objective: This study aimed to develop and validate a clinical and imaging-based nomogram for preoperatively predicting perineural invasion (PNI) in advanced gastric cancer. Methods: A retrospective cohort of 351 patients with advanced gastric cancer who underwent surgical resection was included. Multivariable logistic regression analysis was conducted to identify independent risk factors for PNI and to construct the nomogram. The performance of the nomogram was assessed using calibration curves, the concordance index (C-index), the area under the curve (AUC), and decision curve analysis (DCA). The disparity in disease-free survival (DFS) between the nomogram-predicted PNI-positive group and the nomogram-predicted PNI-negative group was evaluated using the Log-Rank test and Kaplan-Meier analysis. Results: Extramural vascular invasion (EMVI), Borrmann classification, tumor thickness, and the systemic inflammation response index (SIRI) emerged as independent risk factors for PNI. The nomogram model demonstrated a commendable AUC value of 0.838. Calibration curves exhibited excellent concordance, with a C-index of 0.814. DCA indicated that the model provided good clinical net benefit. The DFS of the nomogram-predicted PNI-positive group was significantly lower than that of the nomogram-predicted PNI-negative group (p < 0.001). Conclusion: This study successfully developed a preoperative nomogram model that not only effectively predicted PNI in gastric cancer but also facilitated postoperative risk stratification.

The predictive value of serum tumor markers for EGFR mutation in non-small cell lung cancer patients with non-stage IA.

Objective: The predictive value of serum tumor markers (STMs) in assessing epidermal growth factor receptor (EGFR) mutations among patients with non-small cell lung cancer (NSCLC), particularly those with non-stage IA, remains poorly understood. The objective of this study is to construct a predictive model comprising STMs and additional clinical characteristics, aiming to achieve precise prediction of EGFR mutations through noninvasive means. Materials and methods: We retrospectively collected 6711 NSCLC patients who underwent EGFR gene testing. Ultimately, 3221 stage IA patients and 1442 non-stage IA patients were analyzed to evaluate the potential predictive value of several clinical characteristics and STMs for EGFR mutations. Results: EGFR mutations were detected in 3866 patients (57.9 %) of all NSCLC patients. None of the STMs emerged as significant predictor for predicting EGFR mutations in stage IA patients. Patients with non-stage IA were divided into the study group (n = 1043) and validation group (n = 399). In the study group, univariate analysis revealed significant associations between EGFR mutations and the STMs (carcinoembryonic antigen (CEA), squamous cell carcinoma antigen (SCC), and cytokeratin-19 fragment (CYFRA21-1)). The nomogram incorporating CEA, CYFRA 21-1, pathology, gender, and smoking history for predicting EGFR mutations with non-stage IA was constructed using the results of multivariate analysis. The area under the curve (AUC = 0.780) and decision curve analysis demonstrated favorable predictive performance and clinical utility of nomogram. Additionally, the Random Forest model also demonstrated the highest average C-index of 0.793 among the eight machine learning algorithms, showcasing superior predictive efficiency. Conclusion: CYFRA21-1 and CEA have been identified as crucial factors for predicting EGFR mutations in non-stage IA NSCLC patients. The nomogram and 8 machine learning models that combined STMs with other clinical factors could effectively predict the probability of EGFR mutations.

A novel multivariate logistic model for predicting risk factors of failed treatment with carbapenem-resistant Acinetobacter baumannii ventilator-associated pneumonia.

Background: This study aimed to explore the risk factors for failed treatment of carbapenem-resistant Acinetobacter baumannii ventilator-associated pneumonia (CRAB-VAP) with tigecycline and to establish a predictive model to predict the incidence of failed treatment and the prognosis of CRAB-VAP. Methods: A total of 189 CRAB-VAP patients were included in the safety analysis set from two Grade 3 A national-level hospitals between 1 January 2022 and 31 December 2022. The risk factors for failed treatment with CRAB-VAP were identified using univariate analysis, multivariate logistic analysis, and an independent nomogram to show the results. Results: Of the 189 patients, 106 (56.1%) patients were in the successful treatment group, and 83 (43.9%) patients were in the failed treatment group. The multivariate logistic model analysis showed that age (OR = 1.04, 95% CI: 1.02, 1.07, p = 0.001), yes. of hypoproteinemia (OR = 2.43, 95% CI: 1.20, 4.90, p = 0.013), the daily dose of 200 mg (OR = 2.31, 95% CI: 1.07, 5.00, p = 0.034), yes. of medication within 14 days prior to surgical intervention (OR = 2.98, 95% CI: 1.19, 7.44, p = 0.019), and no. of microbial clearance (OR = 0.31, 95% CI: 0.14, 0.70, p = 0.005) were risk factors for the failure of tigecycline treatment. Receiver operating characteristic (ROC) analysis showed that the AUC area of the prediction model was 0.745 (0.675-0.815), and the decision curve analysis (DCA) showed that the model was effective in clinical practice. Conclusion: Age, hypoproteinemia, daily dose, medication within 14 days prior to surgical intervention, and microbial clearance are all significant risk factors for failed treatment with CRAB-VAP, with the nomogram model indicating that high age was the most important factor. Because the failure rate of CRAB-VAP treatment with tigecycline was high, this prediction model can help doctors correct or avoid risk factors during clinical treatment.