Predictive Nomogram Research Articles

Risk factors analysis and nomogram conduction for major adverse events after lumbar fusion surgery in older patients: a prospective cohort study

This study aimed to identify risk factors for major adverse events (AEs) after lumbar fusion surgery in patients aged 75 and over and evaluate the role of several common geriatric comprehensive assessment items in predicting postoperative major AEs. This is a prospective cohort study of patients aged ≥75 years who underwent open lumbar fusion for degenerative spine disease from August 2019 to August 2022. The primary outcome measure was the incidence of major postoperative AEs within 90 days after surgery. Clavien-Dindo III-IV complications and unplanned readmission were defined as major AEs. Patients' characteristics, laboratory tests, assessment results, and surgery-related variables were compared between the major AEs and the non-major AEs groups. Multivariable logistic regression analysis was used to identify independent risk factors for major AEs. The logistic regression model was evaluated in another prospective cohort of patients from October 2022 to October 2023. A total of 301 patients (mean [SD] age, 79.7 [3.5] years; 60.5% male) were included in the study. Five features, including female (OR 1.99, p = 0.040), higher body mass index (BMI) (OR 1.090, p = 0.024), frailty (OR 2.043, p = 0.032), hypoalbuminemia (OR 2.489, p = 0.040), and higher Charlson comorbidity index (CCI) (OR 1.397, p = 0.024), were independently associated with major AEs and were selected to develop a predictive nomogram of major AEs. The area under the curve values for the development set and validation set were 0.75 and 0.71, respectively. Preoperative frailty, hypoalbuminemia, female sex, greater BMI, and higher CCI grade were risk factors for 90-day major AEs after lumbar fusion surgery in older patients. The predictive nomogram that we developed using this data can enhance preoperative risk counseling and optimization for older patients.

Association of aging related genes and immune microenvironment with major depressive disorder

ObjectiveTo study the relationship between aging related genes (ARGs) and Major Depressive Disorder (MDD). MethodsThe datasets GSE98793, GSE52790 and GSE39653 for MDD were obtained from the GEO database, and ARGs were obtained from the Human Aging Genome Resources database. Differential expression genes (DEGs) screening and GO, KEGG enrichment analysis were performed to uncover the underlying mechanisms. To identify key ARGs associated with MDD (key ARG-DEGs), we employed machine learning methods such as LASSO, SVM, and Random Forest, as well as the plug-ins CytoHubba-MCC and MCODE methods. SsGSEA was used to analyze the immune infiltration of MDD and healthy controls. Furthermore, we created risk prediction nomograms model and ROC curves to assess not only the ability of key ARG-DEGs to diagnose MDD, but also predicted miRNAs and transcription factors (TFs) that might interact. Finally, a two-sample Mendelian randomization (MR) study was performed to confirm the association of identified key ARG-DEGs with depression. ResultsDEGs of ARGs between MDD and healthy controls led to the identification of eight ARG-DEGs. GO and KEGG analysis revealed that the pathways associated with these eight ARG-DEGs were primarily concentrated in Foxo pathway, JAK-STAT pathway, Pl3K-AKT pathway, and metabolic diseases. A comprehensive analysis further narrowed down the 8 ARG-DEGs to 4 key ARG-DEGs: MMP9, IL7R, S100B, and EGF. Immune infiltration analysis indicated significant differences in CD8(+) T cells, macrophages, neutrophils, Th2 cells, and TIL cells between MDD and control groups, correlating with these four key ARG-DEGs. Based on these four key ARG-DEGs, a risk prediction model for MDD was developed. The miRNA-TF-mRNA interaction network of the key ARG-DEGs highlights the complexity of the regulatory process, providing valuable insights for future related research. The MR study suggested a potential causal relationship between MMP9 and the risk of depression. ConclusionThe process of aging, immune dysregulation, and MDD are closely interconnected. MMP9, IL7R, S100B, and EGF may be used as novel diagnostic biomarkers and potential therapeutic targets for MDD, especially MMP9.

The value of nomogram model combined with contrast-enhanced ultrasound in the differential diagnosis of cervical tuberculosis lymphadenitis and metastatic lymph node.

This study aimed to construct an effective Nomogram for the differential diagnosis of cervical tuberculosis lymphadenitis (CTBL) and cervical metastatic lymph node (CMLN) based on ultrasound (US). Retrospectively analyzed 227 patients with CTBL and CMLN who attended Hangzhou Red Cross Hospital from January 2020 to October 2022, and were divided into a training set (n = 163) and a validation set (n = 64) according to the clinical data, US, and CEUS qualitative and quantitative analysis data were recorded to establish the prediction model and perform validation. The area under curve (AUC) of the receiver operating characteristic curve (ROC) was used to assess the discrimination of the model; the calibration curve and brier coefficient were used to assess the calibration of the model; and a Nomogram prediction model was constructed to visualize the results nomogram prediction model was constructed to visualize the results. Gender (OR = 0.200, 95% CI:0.090-0.470, P < 0.001), age (OR = 0.170, 95% CI:0.070-0.410, P < 0.001), liquefaction necrosis (OR = 2.560, 95% CI:1.080-6.040, P = 0.033), perfusion defect (OR = 2.570, 95% CI:1.010-6.580, P = 0.048), and Standard deviation (StdDev) (OR = 3.040, 95% CI:1.220-7.570, P = 0.017) were the independent predictors of the constructed model. The AUCs of the constructed predictive model in the training set and validation set were 0.844 and 0.927, respectively; from the calibration curves, it was observed that the predicted values of the model and the actual observed values fell near the 45° diagonal, and the brier scores were 0.145 and 0.109 in the training set and validation set, respectively. StdDev combined with Gender, age, and the presence of liquefaction necrosis and perfusion defects are important features to identify CTBL and CMLN, and the constructed visual nomogram is intuitive and convenient to improve the efficiency of clinical work.

Integration of ubiquitination-related genes in predictive signatures for prognosis and immunotherapy response in sarcoma.

Ubiquitination is one of the most prevalent and complex post-translational modifications of proteins in eukaryotes, playing a critical role in regulating various physiological and pathological processes. Targeting ubiquitination pathways, either through inhibition or activation, holds promise as a novel therapeutic approach for cancer treatment. However, the expression patterns, prognostic significance, and underlying mechanisms of ubiquitination-related genes (URGs) in sarcoma (SARC) remain unclear. We analyzed URG expression patterns and prognostic implications in TCGA-SARC using public databases, identifying DEGs related to ubiquitination among SARC molecular subtypes. Functional enrichment analysis elucidated their biological significance. Prognostic signatures were developed using LASSO-Cox regression, and a predictive nomogram was constructed. External validation was performed using GEO datasets and clinical tissue samples. The association between URG risk scores and various clinical parameters, immune response, drug sensitivity, and RNA modification regulators was investigated. Integration of data from multiple sources and RT-qPCR confirmed upregulated expression of prognostic URGs in SARC. Single-cell RNA sequencing data analyzed URG distribution across immune cell types. Prediction analysis identified potential target genes of microRNAs and long non-coding RNAs. We identified five valuable genes (CALR, CASP3, BCL10, PSMD7, PSMD10) and constructed a prognostic model, simultaneously identifying two URG-related subtypes in SARC. The UEGs between subtypes in SARC are mainly enriched in pathways such as Cell cycle, focal adhesion, and ECM-receptor interaction. Analysis of URG risk scores reveals that patients with a low-risk score have better prognoses compared to those with high-risk scores. There is a significant correlation between DRG riskscore and clinical features, immune therapy response, drug sensitivity, and genes related to pan-RNA epigenetic modifications. High-risk SARC patients were identified as potential beneficiaries of immune checkpoint inhibitor therapy. We established regulatory axes in SARC, including CALR/hsa-miR-29c-3p/LINC00943, CASP3/hsa-miR-143-3p/LINC00944, and MIR503HG. RT-qPCR data further confirmed the upregulation of prognostic URGs in SARC. Finally, we validated the prognostic model's excellent predictive performance in predicting outcomes for SARC patients. We discovered a significant correlation between aberrant expression of URGs and prognosis in SARC patients, identifying a prognostic model related to ubiquitination. This model provides a basis for individualized treatment and immunotherapy decisions for SARC patients.

A non-invasive predictive model based on multimodality ultrasonography images to differentiate malignant from benign focal liver lesions

We have developed a non-invasive predictive nomogram model that combines image features from Sonazoid contrast-enhanced ultrasound (SCEUS) and Sound touch elastography (STE) with clinical features for accurate differentiation of malignant from benign focal liver lesions (FLLs). This study ultimately encompassed 262 patients with FLLs from the First Hospital of Shanxi Medical University, covering the period from March 2020 to April 2023, and divided them into training set (n = 183) and test set (n = 79). Logistic regression analysis was used to identify independent indicators and develop a predictive model based on image features from SCEUS, STE, and clinical features. The area under the receiver operating characteristic (AUC) curve was determined to estimate the diagnostic performance of the nomogram with CEUS LI-RADS, and STE values. The C-index, calibration curve, and decision curve analysis (DCA) were further used for validation. Multivariate and LASSO logistic regression analyses identified that age, ALT, arterial phase hyperenhancement (APHE), enhancement level in the Kupffer phase, and Emean by STE were valuable predictors to distinguish malignant from benign lesions. The nomogram achieved AUCs of 0.988 and 0.978 in the training and test sets, respectively, outperforming the CEUS LI-RADS (0.754 and 0.824) and STE (0.909 and 0.923) alone. The C-index and calibration curve demonstrated that the nomogram offers high diagnostic accuracy with predicted values consistent with actual values. DCA indicated that the nomogram could increase the net benefit for patients. The predictive nomogram innovatively combining SCEUS, STE, and clinical features can effectively improve the diagnostic performance for focal liver lesions, which may help with individualized diagnosis and treatment in clinical practice.

Nomograms predicting benefit after immunotherapy in oral bifidobacteria supplementation ICC patients: a retrospective study

PurposeThe objective of this study was to develop nomograms for predicting outcomes following immunotherapy in patients diagnosed with intrahepatic cholangiocarcinoma (ICC).Patients and methodsA retrospective analysis was conducted on data from 75 ICC patients who received immunotherapy at Jinling Hospital and Drum Hospital. The discriminative power, accuracy, and clinical applicability of the nomograms were assessed using the concordance index (C-index), calibration curve, and decision curve analysis (DCA). The predictive performance of the nomograms for overall survival (OS) and progression-free survival (PFS) was evaluated using the area under the receiver operating characteristic (ROC) curve. Kaplan-Meier curves were also generated for validation purposes.ResultsMultivariable analysis identified independent prognostic factors for OS, including CA19-9 levels, portal vein tumor thrombus (PVTT) grade, bifidobacteria administration, and surgery. The C-index of the nomogram for OS prediction was 0.722 (95% confidence interval [CI]: 0.661–0.783). Independent prognostic factors for PFS included CA19-9 levels, albumin, and bilirubin, with a C-index of 0.678 (95% CI: 0.612–0.743) for the nomogram predicting PFS. Calibration curves demonstrated strong concordance between predicted and observed outcomes, while DCA and Kaplan-Meier curves further supported the clinical utility of the nomogram.ConclusionThe nomogram developed in this study demonstrated favorable performance in predicting the prognosis of ICC patients undergoing immunotherapy. Additionally, our findings, for the first time, identified probiotics as a potential prognostic marker for immunotherapy. This prognostic model has the potential to enhance patient selection for immunotherapy and improve clinical decision-making.

Overall survival and disease-free survival prediction in Chinese women with breast cancer aged 70 years or older by using nomograms.

By analyzing the existing data of this study, a prediction tool for the overall breast cancer survival and disease-free survival (DFS) of elderly women was established. Clinicopathologic data were collected from elderly women with BC who were admitted to the Tianjin Medical University Cancer Institute and Hospital from August 2014 to December 2017. Independent prognostic factors for BC in elderly patients were confirmed using the Cox proportional hazards model. Nomograms were developed with these factors for predicting the 3- and 5-year overall survival (OS) as well as DFS. The nomograms' discrimination ability and calibration were assessed through the area under the curve (AUC), concordance index (C-index), decision curve analysis (DCA), and calibration plots. We enroled 889 elderly patients with BC, and the results showed that the 3-year OS rate was 93.4% (95%CI = 91.8%-95.1%), the 3-year DFS rate was 87.8% (95%CI = 85.7%-90.0%), the 5-year OS rate was 85.6% (95%CI = 83.3%-87.9%), and the 5-year DFS rate was 80.1%(95%CI = 77.5%-82.8%). The corrected C-indices of the OS and DFS nomograms were 0.799 and 0.667, respectively (95%CI = 0.767-0.830 and 0.632-0.702, respectively). Relatively high AUC values were shown by the nomograms for estimating OS and DFS. The DCA revealed that the constructed nomograms had net benefits for clinical application. The calibration curves demonstrated an excellent correspondence between the data predicted by the nomograms and the actual survival data. Survival curves indicated that risk stratification could differentiate OS and DFS. This study developed novel and practical nomograms for individual prediction of DFS and OS in elderly BC patients. These nomograms can predict 3- and 5-year OS as well as DFS in the elderly BC patient population, thereby enabling personalized risk assessment and risk-based therapy.

Nomogram based on virtual hyperemic pullback pressure gradients for predicting the suboptimal post-PCI QFR outcome after stent implantation.

Growing evidence shows an association between higher post-PCI quantitative flow ratios (QFR) and improved clinical prognosis, however, no models are available to predict suboptimal QFRs (< 0.91) after angiographically successful PCI. This study aims to establish a prediction nomogram for this domain. This study included 450 vessels derived from 421 consecutive patients enrolled in the PIONEER IV trial, which were randomly assigned in a 1:1 ratio to a training (N = 225) and internal validation (N = 225) set, with external validation performed in 97 vessels from 95 consecutive patients enrolled in the ASET Japan trial. LASSO regression was used for optimal feature selection, and multivariate logistic regression was subsequently utilized to construct the nomogram. The performance of the nomograms was assessed and validated by area under the receiver operating characteristics curve (AUC), calibration curves, decision curve analysis, and clinical impact curves. The nomogram was constructed incorporating a novel metric, quantitative flow ratio derived pullback pressure gradient (QFR-PPG), alongside four conventional parameters: left anterior descending artery disease, pre-procedural QFR, reference vessel diameter, and percent diameter stenosis. AUCs of the nomogram were 0.866 (95%CI:0.818-0.914), 0.784 (95% CI:0.722-0.847), and 0.781 (95% CI:0.682-0.879) in the training, internal validation and external validation sets, respectively. Bias-corrected curves revealed a strong consistency between actual observations and prediction. The risk of a suboptimal post-PCI QFR in patients after angiographically successful PCI can be effectively predicted using a nomogram incorporating five variables available pre-PCI, with its performance and clinical predictive value confirming its utility in helping clinicians with decision-making and planning revascularization. Registered on clinicaltrial.gov (NCT04923191 and NCT05117866).

Development and validation of a nomogram for the estimation of the prognosis of patients presenting with a febrile seizure

BackgroundFebrile seizures constitute a prevalent emergency in early childhood. Previous studies indicated that certain febrile seizures in children may progress to epilepsy, exerting a substantial impact on children's health and developmental trajectory. The objective of this study was to formulate a predictive nomogram to assess the likelihood of transitioning from febrile seizures to epilepsy in pediatric patients, thereby facilitating informed decisions regarding medical interventions for febrile seizures.MethodsA total of 306 patients were enrolled and categorized into training (70%) and test (30%) cohorts. Clinical characteristics were subjected to comparison utilizing chi-squared and t tests. Multivariate logistic regression was employed to identify significant factors for predicting the risk of transitioning from febrile seizures to epilepsy, leading to the development of a nomogram. The nomogram’s performance was assessed through receiver operating characteristic curves, calibration, and decision curve analysis.ResultsPredictive factors associated with the transition to epilepsy encompassed lower Na, elevated RDW, IL-6, and increased background slow rhythm and epileptiform discharges in EEG. The nomogram, incorporating five factors, exhibited commendable predictive value (AUC train = 0.812, AUC test = 0.791) for assessing the risk of transitioning from febrile seizures to epilepsy. Calibration analyses confirmed reliability, and decision curve analysis underscored its clinical utility.ConclusionsLower Na, elevated RDW, IL-6, background slow rhythm, and epileptiform discharges are associated with the risk of transitioning from febrile seizures to epilepsy. The nomogram stands as a valuable tool for predicting this risk, aiding in the strategic implementation of medical interventions to enhance outcomes for patients with febrile seizures.

Deciphering a hydrogen sulfide-related signature to supervise prognosis and therapeutic response in colon adenocarcinoma.

Hydrogen sulfide (H2S) is a critical molecule that participates in various molecular, physiological, and pathophysiological processes in biological systems. Emerging evidence has revealed that H2S is implicated in the progression of colon cancer and immune escape. Against this backdrop, the present study aimed to construct a prognostic risk feature for colon adenocarcinoma (COAD) by leveraging hydrogen sulfide-related genes (HSRG). Transcriptomic data and corresponding clinical-pathological information of colon cancer were obtained from The Cancer Genome Atlas and gene expression omnibus databases. Univariate Cox regression analysis was employed to assess the prognostic relevance of HSRG. Consensus clustering was utilized to perform molecular subtyping of COAD, followed by comparison of immune cell infiltration, drug sensitivity, and immune therapy response between subtypes. Differential expression gene and gene set enrichment analyses were conducted between subtypes. Univariate, lasso, and multivariate Cox regression analyses were applied to construct a prognostic model derived from HSRG. A nomogram model for predicting COAD prognosis was constructed and evaluated. In this study, we identified 12 HSRGs that were associated with COAD prognosis. Consensus clustering analysis revealed 3 COAD molecular subtypes that exhibited significant differences in terms of prognosis, tumor immune cell infiltration, drug sensitivity, and immune therapy response. Gene set enrichment analysis demonstrated that immunoregulatory processes were significantly suppressed in the poor-prognosis subtype while Wnt-related pathways and processes were significantly upregulated. Based on the differentially expressed genes between subtypes, we constructed a risk model comprising 11 genes that effectively distinguished high-risk patients from low-risk patients with significant associations with patient survival outcomes, drug treatment, pathological staging, and T staging. The HSRG-derived risk feature was an independent prognostic factor for COAD in drug treatment and pathological staging and could be integrated into a nomogram for prognosis prediction. Calibration curve, receiver operating characteristic curve, and decision curve analysis demonstrated excellent performance of the nomogram in evaluating COAD prognosis. Our study systematically assessed the prognostic significance of HSRG in COAD, identified HSRG-based molecular subtypes and risk features, and highlighted their potential utility in predicting prognosis and treatment response.

Development and validation of a clinical-radiomics nomogram for the early prediction of Klebsiella pneumoniae liver abscess

Background and aim Pyogenic liver abscess (PLA) is a devastating and potentially life-threatening disease globally, with Klebsiella pneumoniae liver abscess (KPLA) being the most prevalent in Asia. This study aims to develop an effective and comprehensive nomogram combining clinical and radiomics features for early prediction of KPLA. Methods 255 patients with PLA from 2013 to 2023 were enrolled and randomly divided into the training and validation cohorts at a 7:3 ratio. The differences between the two cohorts of patients were assessed via univariate analysis. The radiomics features were extracted from imaging data from enhanced CT of liver abscesses. The optimal radiomics features were filtered using the independent sample t-test and least absolute shrinkage and selection operator, and a radiomics score (Rad-score) was calculated by weighting their respective coefficients. Clinically independent predictors were identified from the clinical data and combined with the Rad-score to develop a nomogram by multivariate logistic regression. The predictive performance was evaluated using the area under the receiver operating characteristic curve (AUC), calibration curve, and clinical decision curve. Results The nomogram incorporated four clinical features of diabetes mellitus, cryptogenic liver abscess, C-reactive protein level, and splenomegaly, and the Rad-score that was constructed based on seven optimal radiomics features. It had an AUC of 0.929 (95% CI, 0.894-0.964) and 0.923 (95% CI, 0.864-0.981) in the training and validation cohorts, respectively. The calibration and decision curves showed that the nomogram had good agreement and clinical applicability. Conclusions The clinical-radiomics nomogram performed well in predicting KPLA, hopefully serving as a reference for early diagnosis of KPLA.

Prediction of post-ESD esophageal stricture by a nomogram and risk factor analysis of ineffective oral steroids prophylaxis

Background and aimsSeveral risk models for esophageal stricture after endoscopic submucosal dissection have been developed. However, some of them did not include the use of steroids in the risk analysis. Glucocorticoid sensitivity mediated by glucocorticoid receptor expression has not been discussed in this condition.MethodsClinical and endoscopic characteristics were included in the logistic regression model to establish a nomogram for stenosis prediction. The score for each risk factor was estimated. Risk factors of ineffective oral steroid prophylaxis were analyzed and glucocorticoid receptor expressions were detected by immunohistochemistry.ResultsThree hundred fourteen patients of endoscopic submucosal dissection for esophageal superficial neoplasms were included to develop the nomogram. The circumferential range(≤ 3/4, 3/4–1 or the whole circumference), longitudinal diameter reached 4 cm (yes or not) and lesion location (the cervical and upper thoracic part, the middle thoracic part or the lower thoracic part) consisted of the nomogram. Patients have a high risk of esophageal stricture if they have a total point greater than 36. In the simplified risk score model, the corresponding cutoff score was 1. 92 patients with oral steroid prophylaxis were separately analyzed and the circumferential mucosal defect involving 7/8 or more was an independent risk factor of ineffective prevention (OR 12.2, 95%CI 5.27–28.11). The expression of glucocorticoid receptor β was higher in the stricture group (p = 0.042 for AOD; p = 0.016 for the scoring system).ConclusionsWe established a nomogram for esophageal stricture prediction. Depending on the characteristics of lesions, it is possible to estimate the risk of stricture under routine post-ESD treatments (no steroids or oral steroids). Alternative treatments should be considered if the risk is extremely high, especially for patients with mucosal defects involving 7/8 or more of circumference in which oral steroid treatment tends to be ineffective. The higher glucocorticoid receptor β may indicate potential glucocorticoid resistance.

Development of a novel nomogram for patients with SCLC and comparison with other models

BackgroundThough several nomograms have been established to predict the survival probability of patients with small-cell lung cancer (SCLC), none involved enough variables. This study aimed to construct a novel prognostic nomogram and compare its performance with other models.MethodsSeven hundred twenty-two patients were pathologically diagnosed with SCLC in Shanxi Province Cancer Hospital, Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences, Cancer Hospital Affiliated to Shanxi Medical University from January 2016 to December 2018. We input Forty-one factors by reviewing the medical records. The nomogram was constructed based on the variables identified by univariate and multivariate analyses in the training set and validated in the validation set. Then we compared the performance of the models in terms of discrimination, calibration, and clinical net benefit.ResultsThere were eight variables involved in the nomogram: gender, monocyte (MON), neuron-specific enolase (NSE), cytokeratin 19 fragments (Cyfra211), M stage, radiotherapy (RT), chemotherapy cycles (CT cycles), and prophylactic cranial irradiation (PCI). The calibration curve showed a good correlation between the nomogram prediction and actual observation for overall survival (OS). The area under the curve (AUC) of the nomogram was higher, and the Integrated Brier score (IBS) was lower than other models, indicating a more accurate prediction. Decision curve analysis (DCA) showed a significant improvement in the clinical net benefit compared to the other models.ConclusionsWe constructed a novel nomogram to predict OS for patients with SCLC using more comprehensive and objective variables. It performed better than existing models and would assist clinicians in individually estimating risk and making a therapeutic regimen.

Risk factors and prediction model for low-birth-weight infants born to women with gestational diabetes mellitus.

To explore the risk factors for low-birth-weight infants born to pregnant women with GDM to develop a prediction model and to construct a prediction nomogram for the risk of low birth weight infants born to pregnant women with GDM. The clinical data of singleton infants diagnosed with gestational diabetes mellitus in Southeast China in 2019 were retrospectively reviewed. Gestational conditions and neonatal weight were assessed. A total of 386 diabetic mothers and infants were enrolled, including 193 in the low birth weight group and 193 in the normal birth weight group. The statistically significant factors were age over 36 years (OR = 1.916, 95% CI 1.048-3.505), junior high school education (OR = 4.454, 95% CI 1.882-10.543), history of fetal distress (OR = 0.120, 95% CI 0.016-0.925), gestational hypertension (OR = 3.681, 95% CI 1.357-9.986), preeclampsia (OR = 24.652, 95% CI 5.956-102.036), threatened preterm birth (OR = 18.393, 95% CI 8.457-39.999), triglycerides (OR = 0.642, 95% CI 0.485-0.850), and inadequate gestational weight gain (OR = 1.997, 95% CI 1.162-3.432). The area under the receiver operating characteristic curve (AUC) was 0.834 (95% CI: 0.794-0.874, p < 0.001), and the sensitivity and specificity were 82.38 and 87.56%, respectively. The goodness-of-fit test likelihood ratio 2 was 2.089 (p = 0.978). The comprehensive nomogram model showed that the discrimination and mean absolute error were 0.834 and 0.015, respectively. The calibration curves showed acceptable agreement between the predictions of the column line plots and the observations. The DCA curves showed good positive net yields in the prediction model. This study established a prediction model and risk score for low birth weight in pregnant women with GDM. It helps pregnancy clinics to identify the risk of low birth weight in newborns promptly, in addition to glycemic control and weight management for pregnant women with GDM, and should improve the appropriate treatment plan for pregnant women with higher risk, to provide personalized and precise treatment for pregnant women with GDM and improve infant outcomes.

The value evaluation of Nomogram prediction model based on CTA imaging features for selecting treatment methods for isolated superior mesenteric artery dissection

ObjectiveTo evaluate value of Nomogram prediction model based on CTA imaging features for selecting treatment methods for isolated superior mesenteric artery dissection (ISMAD).MethodsSymptomatic ISMAD patients were randomly divided into a training set and a validation set in a 7:3 ratio. In the training set, relevant risk factors for conservative treatment failure in ISMAD patients were analyzed, and a Nomogram prediction model for treatment outcome of ISMAD was constructed with risk factors. The predictive value of the model was evaluated.ResultsLow true lumen residual ratio (TLRR), long dissection length, and large arterial angle (superior mesenteric artery [SMA]/abdominal aorta [AA]) were identified as independent high-risk factors for conservative treatment failure (P < 0.05). The receiver operating characteristic curve (ROC) results showed that the area under curve (AUC) of Nomogram prediction model was 0.826 (95% CI: 0.740–0.912), indicating good discrimination. The Hosmer-Lemeshow goodness-of-fit test showed good consistency between the predicted curve and the ideal curve of the Nomogram prediction model. The decision curve analysis (DCA) analysis results showed that when probability threshold for the occurrence of conservative treatment failure predicted was 0.05–0.98, patients could obtain more net benefits. Similar results were obtained for the predictive value in the validation set.ConclusionLow TLRR, long dissection length, and large arterial angle (SMA/AA) are independent high-risk factors for conservative treatment failure in ISMAD. The Nomogram model constructed with independent high-risk factors has good clinical effectiveness in predicting the failure.

Construction and validation of a predictive model for the return to work of patients with permanent colorectal stoma: a cross-sectional study

BackgroundThe issue of patients returning to work is increasingly garnering attention from countries worldwide. This study aims to investigate the risk factors associated with patients returning to work after undergoing permanent enterostomies. Additionally, it seeks to establish and validate a nomogram prediction model, thereby providing a more effective reference for patients aiming to return to work.MethodsThis study was a cross-sectional investigation conducted between September 2022 and September 2023. We conveniently selected 293 postoperative patients with permanent colorectal stomas due to colorectal cancer from three tertiary hospitals in Liaoning Province. Participants were categorized into Returned and Non-Returned groups based on their return to work status. Data were collected using a general information questionnaire, a Stoma Acceptance Questionnaire, and the Ostomy Adjustment Inventory. Binary logistic regression analysis was performed using SPSS 25.0 software to identify independent influencing factors. A predictive model was constructed using R Studio 4.3.0 software. Internal validation was conducted through 1,000 rounds of Bootstrap resampling, and model performance was assessed using Receiver Operating Characteristic (ROC) curves, the Hosmer-Lemeshow (H-L) test, and calibration curves.ResultsAfter surgery, the return-to-work rate for patients with permanent colorectal stomas was 29.69%. Age, education level, postoperative time, stoma complication, adjuvant therapy, stoma acceptance score, and ostomy adjustment inventory score were identified as independent factors influencing the return-to-work status of these patients (P < 0.05). These factors were incorporated into a logistic regression model generated by R software, resulting in a ROC curve with an area under the curve (AUC) of 0.916 (95% CI: 0.884–0.947). The Youden index was 0.731, and the cutoff value was 0.228. Sensitivity and specificity were 0.920 and 0.811, respectively. The H-L test demonstrated good model fit (χ2 = 12.858, P = 0.117, P > 0.05). Calibration curves indicated a close alignment between predicted and actual probabilities.ConclusionsThe postoperative return-to-work rate is low in patients with permanent enterostomies. The prediction model developed in this study demonstrates strong performance and offers predictive value, providing a scientific foundation for assessing patients’ return to work. Caregivers should prioritize the early identification of various patient types for proactive intervention to enhance the rate of postoperative return to work.

A predictive model for treatment effectiveness in severe primary immune thrombocytopenia during pregnancy: A retrospective study in a tertiary critical maternity referral center.

Objectives The study aimed to identify factors influencing the severity of primary immune thrombocytopenia (ITP) during pregnancy, develop a predictive model for treatment response, and report maternal and neonatal outcomes associated with severe ITP. Design A retrospective analysis was conducted on 155 pregnancies with severe ITP between January 2018 and April 2023 at a tertiary critical maternity referral center in Shanghai, China. Participants/Materials The study included 155 pregnancies diagnosed with severe ITP, divided into groups based on the lowest platelet count (&lt;30×109/L vs. 30-50×109/L) and first-line treatment response (Non-response vs. Response). Setting The study was conducted at Ren Ji Hospital, Shanghai Jiao Tong University School of Medicine, a tertiary critical maternity rescue referral center. Methods Clinical characteristics and outcomes were compared between groups. A multivariable logistic regression model was used to identify factors associated with the severity of ITP. A prediction model for treatment response was established using LASSO-logistic regression and internally validated. Results ITP severity was found to be correlated with low maximum amplitude (MA) of thrombelastography (TEG) (OR 5.43, 95% CI 1.48 to 16.00, p=0.002), bleeding events (OR 4.91, 95% CI 1.62 to 14.86, p=0.005), and low reticulocytes (OR 2.40×10-7, 95% CI 1.06×10-13 to 0.55, p=0.04). Of the 118 patients who received first-line therapy, 52 (44%) responded. The dataset was randomly split into a training (N=99) and test (N=23) set with a ratio of 8:2. A predictive nomogram was created and internally validated showing good discrimination. The model yielded an area under receiver operating characteristic curve (ROC) of 0.78 (0.69 to 0.87) and 0.85 (0.67 to 1.00) in the training and validation cohort, respectively. Earlier delivery and high rate of NICU admission occurred with severe ITP and treatment failure. Limitations The study was limited by a relatively small sample size and the retrospective observational design, which imposed limitations on the assessment of treatment efficacy. Conclusions We identified clinical predictors of ITP severity and treatment resistance during pregnancy. A nomogram predicting first-line response was validated. These findings can facilitate clinical decision-making and counseling regarding this challenging pregnancy complication.

Computed tomography-based radiomics nomogram for prediction of lympho-vascular and perineural invasion in esophageal squamous cell cancer patients: a retrospective cohort study

PurposeLympho-vascular invasion (LVI) and perineural invasion (PNI) have been established as prognostic factors in various types of cancers. The preoperative prediction of LVI and PNI has the potential to guide personalized medicine strategies for patients with esophageal squamous cell cancer (ESCC). This study investigates whether radiomics features derived from preoperative contrast-enhanced CT could predict LVI and PNI in ESCC patients.Methods and materialsA retrospective cohort of 544 ESCC patients who underwent esophagectomy were included in this study. Preoperative contrast-enhanced CT images, pathological results of PNI and LVI, and clinical characteristics were collected. For each patient, the gross tumor volume (GTV-T) and lymph nodes volume (GTV-N) were delineated and four categories of radiomics features (first-order, shape, textural and wavelet) were extracted from GTV-T and GTV-N. The Mann–Whitney U test was used to select significant features associated with LVI and PNI in turn. Subsequently, radiomics signatures for LVI and PNI were constructed using LASSO regression with ten-fold cross-validation. Significant clinical characteristics were combined with radiomics signature to develop two nomogram models for predicting LVI and PNI, respectively. The area under the curve (AUC) and calibration curve were used to evaluate the predictive performance of the models.ResultsThe radiomics signature for LVI prediction consisted of 28 features, while the PNI radiomics signature comprised 14 features. The AUCs of the LVI radiomics signature were 0.77 and 0.74 in the training and validation groups, respectively, while the AUCs of the PNI radiomics signature were 0.69 and 0.68 in the training and validation groups. The nomograms incorporating radiomics signatures and significant clinical characteristics such as age, gender, thrombin time and D-Dimer showed improved predictive performance for both LVI (AUC: 0.82 and 0.80 in the training and validation group) and PNI (AUC: 0.75 and 0.72 in the training and validation groups) compared to the radiomics signature alone.ConclusionThe radiomics features extracted from preoperative contrast-enhanced CT of gross tumor and lymph nodes have demonstrated their potential in predicting LVI and PNI in ESCC patients. Furthermore, the incorporation of clinical characteristics has shown additional value, resulting in improved predictive performance.

Development of biomarker signatures associated with anoikis to predict prognosis in patients with esophageal cancer: An observational study.

Anoikis, a form of programmed cell death linked to cancer, has garnered significant research attention. Esophageal cancer (ESCA) ranks among the most prevalent malignant tumors and represents a major global health concern. To ascertain whether anoikis-related genes (ARGs) can accurately predict ESCA prognosis, we evaluated the predictive value and molecular mechanisms of ARGs in ESCA and constructed an optimal model for prognostic prediction. Using the Cancer Genome Atlas (TCGA)-ESCA database, we identified ARGs with differences in ESCA. ARG signatures were generated using Cox regression. A predictive nomogram model was developed to forecast ARG signatures and patient outcomes in ESCA. Gene set enrichment analysis (GSEA) was employed to uncover potential biological pathways associated with ARG signatures. Estimation of stromal and immune cells in malignant tumor tissues using expression data (ESTIMATE) and cell-type identification by estimating relative subsets of RNA transcripts analyses were used to assess differences in the immune microenvironment of the ARG signature model. Based on ARGs, the patients with ESCA were divided into high and low groups, and the sensitivity of patients to drugs in the database of genomics of drug sensitivity in cancer was analyzed. Finally, the correlation between drug sensitivity and risk score was then evaluated based on the ARG signatures. Prognostic relevance was significantly linked to the ARG profiles of 5 genes: MYB binding protein 1a (MYBBP1A), plasminogen activator, urokinase (PLAU), budding uninhibited by benzimidazoles 3, HOX transcript antisense RNA, and euchromatic histone-lysine methyltransferase 2 (EHMT2). Using the risk score as an independent prognostic factor combined with clinicopathological features, the nomogram accurately predicted the overall survival (OS) of individual patients with ESCA. Gene ontology (GO) enrichment analysis indicated that the primary molecular roles included histone methyltransferase function, binding to C2H2 zinc finger domains, and histone-lysine N-methyltransferase activity. GSEA revealed that the high-risk cohort was connected to cytokine-cytokine receptor interaction, graft-versus-host disease, and hematopoietic cell lineage, whereas the low-risk cohort was related to arachidonic acid metabolism, drug metabolism via cytochrome P450 and fatty acid metabolism. Drug sensitivity tests showed that 16 drugs were positively correlated, and 3 drugs were negatively correlated with ARG characteristic scores. Our study developed 5 ARG signatures as biomarkers for patients with ESCA, providing an important reference for the individualized treatment of this disease.

A radiomics model for predicting perineural invasion in stage II-III colon cancer based on computer tomography

BackgroundColon cancer, a frequently encountered malignancy, exhibits a comparatively poor survival prognosis. Perineural invasion (PNI), highly correlated with tumor progression and metastasis, is a substantial effective predictor of stage II-III colon cancer. Nonetheless, the lack of effective and facile predictive methodologies for detecting PNI prior operation in colon cancer remains a persistent challenge.MethodPre-operative computer tomography (CT) images and clinical data of patients diagnosed with stage II-III colon cancer between January 2015 and December 2023 were obtained from two sub-districts of Sun Yat-sen Memorial Hospital (SYSUMH). The LASSO/RF/PCA filters were used to screen radiomics features and LR/SVM models were utilized to construct radiomics model. A comprehensive model, shown as nomogram finally, combining with radiomics score and significant clinical features were developed and validated by area under the curve (AUC) and decision curve analysis (DCA).ResultThe total cohort, comprising 426 individuals, was randomly divided into a development cohort and a validation cohort as a 7:3 ratio. Radiomics scores were extracted from LASSO-SVM models with AUC of 0.898/0.726 in the development and validation cohorts, respectively. Significant clinical features (CA199, CA125, T-stage, and N-stage) were used to establish combining model with radiomics scores. The combined model exhibited superior reliability compared to single radiomics model in AUC value (0.792 vs. 0.726, p = 0.003) in validation cohorts. The radiomics-clinical model demonstrated an AUC of 0.918/0.792, a sensitivity of 0.907/0.813 and a specificity of 0.804/0.716 in the development and validation cohorts, respectively.ConclusionThe study developed and validated a predictive nomogram model combining radiomics scores and clinical features, and showed good performance in predicting PNI pre-operation in stage II-III colon cancer patients.