Predictive Nomogram Research Articles

Identification of Lipophagy-Related Gene Signature for Diagnosis and Risk Prediction of Alzheimer’s Disease

Background: Recent research indicates that lipid metabolism and autophagy play crucial roles in the development of Alzheimer’s disease (AD). Investigating the relationship between AD diagnosis and gene expression related to lipid metabolism, autophagy, and lipophagy may improve early diagnosis and the identification of therapeutic targets. Methods: Transcription datasets from AD patients were obtained from the Gene Expression Omnibus (GEO). Genes associated with lipid metabolism, autophagy, and lipophagy were sourced from the Gene Set Enrichment Analysis (GSEA) database and the Human Autophagy Database (HADb). Lipophagy-related hub genes were identified using a combination of Limma analysis, weighted gene co-expression network analysis (WGCNA), and machine learning techniques. Based on these hub genes, we developed an AD risk prediction nomogram and validated its diagnostic accuracy using three external validation datasets. Additionally, the expression levels of the hub genes were assessed through quantitative reverse transcription polymerase chain reaction (qRT-PCR). Results: Our analysis identified three hub genes—ACBD5, GABARAPL1, and HSPA8—as being associated with AD progression. The nomogram constructed from these hub genes achieved an area under the curve (AUC) value of 0.894 for AD risk prediction, with all validation sets yielding AUC values greater than 0.8, indicating excellent diagnostic efficacy. qRT-PCR results further corroborated the associations between these hub genes and AD development. Conclusions: This study identified and validated three lipophagy-related hub genes and developed a reliable diagnostic model, offering insights into the pathology of AD and facilitating the diagnosis of AD patients.

Association between 5-item modified frailty index and clinical outcomes in elderly rectal cancer patients after radical surgery.

Frailty is a significant factor contributing to an increased risk of adverse clinical outcomes in older cancer patients. This study aims to investigate the impact of the 5-item modified frailty index (mFI) on both short-term and long-term prognosis in elderly rectal cancer patients who underwent radical resection. And, by integrating the 5-item mFI with clinicopathological characteristics, a postoperative severe complications nomogram and a cancer-specific survival (CSS) prediction nomogram were further constructed. Demographic, clinical, and therapeutic data were collected from 1,034 patients aged ≥ 70 years with rectal cancer who underwent proctectomy between 2017 and 2022. Patients were categorized into three groups based on their mFI scores: 318 patients with mFI = 0, 404 patients with mFI = 1, and 312 patients with mFI ≥ 2. Comparisons among these groups revealed that higher 5-item mFI scores were associated with an increased incidence of both overall and severe postoperative morbidity, prolonged recovery times, and elevated total medical costs. Multivariate logistic regression analysis indicated that an mFI score of ≥ 2 [odds ratio = 2.856, 95% confidence interval (CI): 1.542-5.290, P < 0.001] was an independent risk factor for severe postoperative complications. Similarly, in competing risk analysis, the 5-item mFI was identified as an independent prognostic factor for CSS (subdistribution hazard ratio = 2.00, 95% CI: 1.47-2.72, P < 0.001). The postoperative severe complications nomogram and CSS prediction nomogram AUC values were 0.726 and 0.844, respectively, both demonstrating promising predictive capabilities. In conclusion, the 5-item mFI serves as a concise and effective tool for preoperative frailty stratification and for predicting clinical outcomes in elderly rectal cancer patients.

Ultrasonic radiomics-based nomogram for preoperative prediction of residual tumor in advanced epithelial ovarian cancer: a multicenter retrospective study

ObjectivesTo identify radiomic features extracted from ultrasound images and to develop and externally validate a comprehensive model that combines clinical data with ultrasound radiomics features to predict the residual tumor status in patients with advanced epithelial ovarian cancer (OC).MethodsThe study included 112 patients with advanced epithelial OC who underwent preoperative transvaginal ultrasound. Of these, 78 patients were assigned to the development cohort and 34 to the external validation cohort. Tumor contours were manually delineated as regions of interest (ROI) on the ultrasound images, and radiomic features were extracted. The final set of variables was identified using LASSO (least absolute shrinkage and selection operator) regression. Clinical features were also collected and incorporated into the model. A combination model integrating ultrasound radiomics and clinical variables was developed and externally validated. The performance of the predictive models was assessed.ResultsA total of 1,561 radiomic features and 18 clinical features were extracted. The final model included 10 significant ultrasound radiomic variables and 4 clinical features. The comprehensive model outperformed models based on either clinical or radiomic features alone, achieving an accuracy of 0.84, a sensitivity of 0.80, a specificity of 0.75, a precision of 0.88, a positive predictive value of 0.81, a negative predictive value of 0.86, an F1-score of 0.78, and an AUC of 0.82 in the external validation set.ConclusionsThe comprehensive model, which integrated clinical and ultrasound radiomic features, exhibited strong performance and generalizability in preoperatively identifying patients likely to achieve complete resection of all visible disease.

A prediction nomogram for residual after negative pressure aspiration for endogenic cesarean scar ectopic pregnancy: a retrospective study

BackgroundWe aimed to establish a predictive nomogram to evaluate the incidence of residual tissue in patients with endogenic cesarean scar ectopic pregnancy after negative pressure aspiration.MethodsThis retrospective study included patients treated in the gynecology department of our institution from May 2017 to August 2023 who underwent negative pressure suction treatment, ultrasound examinations before and after treatment, and received telephone follow-up for at least 6 months. A total of 899 patients met the inclusion criteria and were divided into a training cohort (629 patients, 70%) and a validation cohort (270 patients, 30%). Independent predictive factors were established using multivariate logistic regression. The resulting nomogram was validated using 1,000 bootstrap resampling, and calibration curves were plotted. Receiver operating characteristic (ROC) analysis was performed to calculate the area under the curve, sensitivity, specificity, and other metrics to assess its discriminative performance. Clinical decision curves were constructed to evaluate clinical applicability and quantify the net benefit within a range of threshold probabilities. The model was externally validated in the validation cohort.ResultsPredictive factors included in the nomogram included age (hazard ratio [HR]: 1.220, 95% confidence interval [CI]: 1.135—1.316), BMI (HR: 0.890, 95% CI: 0.796—0.986), intraoperative major hemorrhage (HR: 4.457, 95% CI: 1.610—12.292), maximum diameter of the gestational sac (HR: 1.572, 95% CI: 1.295, 1.914), and thickness of the remaining muscle layer of the lower uterine segment (HR: 1.572, 95% CI: 0.014, 0.430). The ROC curve of the resulting nomogram showed similar area under the curve values for the training (0.809, 95% CI: 0.751—0.867) and validation cohorts (0.814, 95% CI: 0.739, 0.888). The Hosmer–Lemeshow test indicated good model fit (P = 0.861), and the calibration curve was close to the ideal diagonal line. Decision curve analysis demonstrated good net benefit, and external validation confirmed its reliability.ConclusionsThe model may aid in individual clinical decision-making, allowing clinicians to perform immediate postoperative assessments for patients with endogenous ectopic pregnancy in cesarean section scars treated with negative pressure suction, identify high-risk subpopulations, and select appropriate supplementary treatment in advance, making it particularly suitable for low-income areas and resource-limited primary hospitals.

Prediction of postoperative disease-free survival in colorectal cancer patients using CT radiomics nomogram: a multicenter study.

Radiomics analysis is widely used to assess tumor prognosis. To explore the value of computed tomography (CT) radiomics nomogram in predicting disease-free survival (DFS) of patients with colorectal cancer (CRC) after operation. A total of 522 CRC patients from three centers were retrospectively included. Radiomics features were extracted from CT images, and the least absolute shrinkage and selection operator Cox regression algorithm was employed to select radiomics features. Clinical risk factors associated with DFS were selected through univariate and multivariate Cox regression analysis to build the clinical model. A predictive nomogram was developed by amalgamating pertinent clinical risk factors and radiomics features. The predictive performance of the nomogram was evaluated using the C-index, calibration curve, and decision curve. DFS probabilities were estimated using the Kaplan-Meier method. Integrating the retained eight radiomics features and three clinical risk factors (pathological N stage, microsatellite instability, perineural invasion), a nomogram was constructed. The C-index for the nomogram were 0.819 (95% CI=0.794-0.844), 0.782 (95% CI=0.740-0.824), 0.786 (95% CI=0.753-0.819), and 0.803 (95% CI=0.765-0.841) in the training set, internal validation set, external validation set 1, and external validation set 2, respectively. The calibration curves demonstrated a favorable congruence between the predicted and observed values as depicted by the nomogram. The decision curve analysis underscored that the nomogram yielded a heightened clinical net benefit. The constructed radiomics nomogram, amalgamating the radiomics features and clinical risk factors, exhibited commendable performance in the individualized prediction of postoperative DFS in CRC patients.

Multi-sequence MRI-based Nomogram for Prediction of Human Epidermal Growth Factor Receptor 2 Expression in Breast Cancer

Multi-sequence MRI-based Nomogram for Prediction of Human Epidermal Growth Factor Receptor 2 Expression in Breast Cancer

Serum Irisin as a Predictor for Peripheral arterial disease: Insights from a Clinical Study

ObjectiveThe purpose of this study was to investigate the correlation between serum irisin concentration and peripheral arterial disease (PAD), and to establish clinical prediction nomograms for PAD occurrence by comparing and analyzing clinical data from patients with PAD patients and healthy controls. MethodsA total of 112 patients with PAD and 90 healthy individuals were recruited for the study. Clinical data from both groups were collected and serum irisin concentration was measured using enzyme-linked immunosorbent assay (ELISA). Correlation analysis was conducted. Risk factors for PAD were identified through univariate and multivariate logistic regression. The clinical prediction nomograms were established and validated. ResultsA total of 202 patients were enrolled in this study, with an average age of 63.98 ± 10.40 years. Of these, 123 were male (60.9%) and 79 were female (39.1%). Hypertension was present in 104 patients (51.5%), diabetes in 59 patients (29.2%), dyslipidemia in 94 patients (46.5%), and 105 patients (52.0%) were smokers. Among them, 90 patients were assigned to the PAD group, which included 78 males (69.6%) and 34 females (30.4%), with an average age of 67.54 ± 10.31 years. In this group, 62 patients (55.4%) had hypertension, 53 (47.3%) had diabetes, 62 (55.4%) had dyslipidemia, and 78 (69.6%) were smokers. The Rutherford classification of these patients showed that 64 (57.1%) were at stage I, 25 (22.3%) at stage II, 16 (14.3%) at stage III, and 7 (6.3%) at stage IV. Serum irisin concentration in patients with PAD showed a significant positive correlation with serum high-density lipoprotein (HDL) (r=0.255) and a significant negative correlation with Rutherford classification (r=-0.374) and smoking status (r=-0.263). Univariate and multivariate logistic regression analyses identified irisin, age, diabetes, dyslipidemia, smoking, creatinine(CR), and neutrophil/lymphocyte ratio (NLR) as independent risk factors for the development of PAD ( P < 0.05). Based on these findings, a clinical prediction nomogram was established. Internal validation of the nomogram demonstrated strong discriminatory ability, with an area under the curve (AUC) of 0.942, indicating the model's excellent performance. Calibration curves and decision curve analyses further confirmed the model’s robust calibration and clinical applicability. ConclusionThis study concluded that serum irisin concentrations were significantly lower in the PAD group compared to the healthy control group, and that serum irisin concentrations in the PAD group were significantly correlated with serum HDL, Rutherford classification, and smoking status. Additionally, Irisin level, age, diabetes, dyslipidemia, smoking, CR, and NLR were identified as independent risk factors for PAD development. The clinical prediction nomogram based on these factors may aid in accurately predicting the risk of PAD development.

Comprehensive characteristics of pulmonary antineutrophil cytoplasmic antibody-associated vasculitis and the development of a predictive nomogram for mortality.

Antineutrophil cytoplasmic antibody (ANCA) associated vasculitis (AAV) is a rare and potential devastating disease with high mortality, frequently with pulmonary involvement. Our study aimed to explore the pulmonary features of AAV and identify predictors of long-term survival. We retrospectively analyzed 538 AAV patients diagnosed between January 2013 and July 2019, with follow-up data extending to August 2020. The Least absolute shrinkage and selection operator (LASSO) regression analysis was employed to identify variables predictive of mortality. Subsequently, a nomogram was developed, with its predictive accuracy and discrimination assessed by the concordance index and calibration curves, respectively. A total of 460 (85.5%) AAV patients presented with pulmonary involvement. The mortality was 36.8%. Patients with pulmonary involvement more frequently exhibited respiratory symptoms, predominantly interstitial lung disease on radiographs, and were at higher risk for respiratory failure, diffuse alveolar hemorrhage, mechanic ventilation and death (All P<0.05). The LASSO regression pinpointed 16 predictors of mortality, and the predictive model demonstrated an area under the curve of 0.810. The nomogram, based on these variables, achieved a concordance index of 0.825, with calibration curve indicating excellent predictive agreement. The study establishes a predictive model for AAV mortality with high accuracy, offering insights crucial for patient care. Pulmonary involvement, prevalent and linked to higher mortality, underscores the need for precise predictive tools in AAV management.

Development and Validation of a Predictive Model for Fall Risk in Pre-Frail Older Adults.

To develop a predictive model for fall risk in pre-frail older adults, providing a basis for early identification and prevention of falls among this population. This was a multicenter prospective cohort study. A total of 473 pre-frail older adults were included, 335 as the training set and 142 as the test set. Univariate and stepwise binary logistic regression analyses were conducted to identify the relationship between pre-frail and fall risk and establish the frailty risk prediction nomogram. The nomogram was constructed based on the results of logistic regression. The model assessment relied on the receiver operating characteristic (ROC) curve, Hosmer-Lemeshow test, calibration curves, and decision curve analysis. Fall incidence rate among pre-frail older adults within 6 months was 13.63%. The final fall risk prediction model identified that sex, history of falls in the past year, visual impairment, increased nocturia, and fear of falling are the most critical risk factors for falls in pre-frail older adults. The model exhibited good accuracy in the testing set, with an area under the ROC curve of 0.825 (95% confidence interval [0.736, 0.914]). Pre-frail older adults have a higher incidence of falls. The logistic regression model constructed in this study shows promising predictive capabilities and can be used as a screening tool to identify pre-frail older adults at high risk of falls in clinical practice. We anticipate that this model will assist clinical nurses in enhancing the efficiency of fall prevention efforts and reducing the incidence of falls among pre-frail older adults. [Research in Gerontological Nursing, 18(1), 29-39.].

Effect of visceral fat area on prognosis of patients undergoing radical gastrectomy and construction of nomogram

BackgroundWe aim to investigate the impact of visceral fat area (VFA) on the prognosis of patients following radical gastric resection and develop a nomogram prediction model to forecast the prognosis of gastric cancer patients.MethodsWe retrospectively analyzed 156 patients who underwent laparoscopic radical gastrectomy for distal gastric cancer in the 900th hospital of the Joint Logistics Support Force from April 2018 to April 2020. We collected the CT image data and clinicopathological data one week prior to the operation and then used software to calculate the VFA, dividing it into two groups: a low VFA group (n = 71) and a high VFA group (n = 85). We compared the clinicopathological characteristics and early postoperative complications of the two groups. The Pearson χ2 test was used to analyze the correlation between body mass index (BMI) and VFA. We used the Kaplan-Meier method to draw the survival curve, analyzed the independent risk factors affecting the prognosis of gastric cancer patients using univariate and multivariate Cox regression models, and established a nomogram model for patient prognosis prediction.ResultsThe results of CT showed that VFA value was (95.89 ± 41.40) cm², and body mass index (BMI) was positively correlated with VFA value (r = 0.291, P < 0.001). The ROC curve shows that VFA can predict the prognosis of patients with gastric cancer significantly better than BMI (AUC = 0.826 vs. AUC = 0.707, P = 0.016). The incidence of incision fat liquefaction, pancreatic fistula, and abdominal infection in the high VFA group was higher than that in the low VFA group (P < 0.05). We followed up with all patients for 0.5–48.5 months, with a median follow-up time of 30 months. We used the Kaplan-Meier method to draw the survival curve. The results showed that the overall survival rate of patients in the high VFA group was significantly higher than that in the low VFA group (χ2 = 38.208, P < 0.001), and the high BMI group was significantly higher than that in the low BMI group (χ2 = 29.767, P < 0.001). Age, the degree of differentiation, complications after surgery, VFA, ASA grading, and TNM staging were all found to have independent effects on the prognosis of gastric cancer patients (Multivariate Cox regression analysis). Multivariate Cox regression analysis led to the construction of a nomogram prediction model for the total survival of gastric cancer patients. Its internal verification C-index was 0.881 (95% CI: 0.852–0.910), and the calibration chart showed good consistency.ConclusionsAge, differentiation degree, postoperative complications, VFA, ASA grading, and TNM staging are independent influencing factors for the prognosis of patients with gastric cancer. The constructed nomogram has excellent prediction accuracy and is helpful to evaluate the prognosis of patients.

Development of a modified nutritional index model based on nutritional status and sarcopenia to predict long-term survival and chemotherapy benefits in elderly patients with advanced gastric cancer

BackgroundElderly patients with advanced gastric cancer have poor prognoses. This study aims to develop a prediction model for long-term survival after radical surgery and to identify patients who may benefit from chemotherapy. MethodsData from 555 elderly patients with advanced gastric cancer admitted to two medical centers from 2009 to 2018 were retrospectively analyzed. Sarcopenia was combined with the Controlling Nutritional Status (CONUT) score to create a modified nutritional index (mCONUT). Cox regression analyses were used to develop a novel nomogram prediction model (mCNS) that combined mCONUT, pN, and tumor size, and its performance was further verified both internally and externally. ResultsMultivariate Cox analysis revealed that tumor size, pN, and mCONUT were independent prognostic risk factors for overall survival (OS). The mCNS model showed good fit and high predictive value (AUC: training set 0.711; validation set 0.707), outperforming the pTNM model (p<0.05). To further investigate the association between the model and adjuvant chemotherapy, we categorized the model into two risk groups : a high-risk group and a low-risk group. Further analysis revealed that, in the low-risk group, the OS and recurrence-free survival(RFS) for patients receiving adjuvant chemotherapy was significantly better than that of those who did not receive chemotherapy (p=0.047,p=0.019). In the high-risk group, this result was not observed (p=0.120, p=0.053). ConclusionThe mCNS model has high predictive value in predicting long-term survival of elderly patients with advanced gastric cancer. Patients with mCNS-L were able to benefit from chemotherapy after laparoscopic radical gastrectomy.

Simultaneous curative resection may improve the long-term survival of patients diagnosed with colorectal liver metastases: A propensity score-matching study.

The benefit of simultaneous curative resection in patients with colorectal liver metastases has been unclear. Adjuvant chemotherapy is still considered an effective and priority treatment for advanced-stage colorectal patients. We retrospectively collected patients with colorectal liver metastases from January 2012 to October 2023 at the Guangxi Medical University Cancer Hospital. The baseline information was compared between a simultaneous curative resection group and a palliative treatment group. Propensity score matching with a 1:1 ratio was applied to develop comparable cohorts of curative resection and palliative treatment resection. Kaplan-Meier survival and Cox regression analyses were performed to determine the impact of curative resection on survival of colorectal liver metastasis patients. Prognostic nomogram and a web-version calculator were developed based on the multivariate Cox regression method. Then, the concordance index (C-index), receiver operating characteristic, calibration plots, and decision curves analysis were applied to evaluate the prognostic performance of the nomogram. A total of 716 patients with colorectal liver metastases were enrolled in the study, of whom 131 patients received curative resection. There was no significant difference in terms of baseline information between the curative resection group and the palliative treatment group after propensity score matching. Multivariable Cox regression analysis showed that curative resection was an independent prognostic factor affecting overall survival (P=.001, hazard ratio=1.95, 95% confidence interval1.30-2.91). Compared with patients who did not receive curative resection, patients who received simultaneous curative resection had a significant improvement in overall survival before and after propensity score matching (P < .0001 and P= .0047, respectively). Overall survival nomogram showed excellent predictive performance with the C-indexes of 0.686 (95% confidence interval 0.556-0.792). The areas under the receiver operating characteristic curves were 0.75 (95% confidence interval 64.43-96.05), 0.75 (67.22-82.58), and 0.76 (66.10-85.98) for predicting 1-, 3-, and 5-year survival, respectively. The calibration plots and decision curves analysis also indicated the good predictability of the predictive nomogram. Finally, subgroup analysis further demonstrated a favorable impact of curative resection on overall survival in colorectal liver metastasis patients after propensity score matching. Simultaneous curative resection may improve the overall survival of patients with colorectal liver metastases and is an independent and effective indicator for predicting overall survival. The nomogram may provide a personalized treatment strategy.

Development and validation of a risk prediction model for gestational diabetes mellitus in women of advanced maternal age during the first trimester.

With the global rise in advanced maternal age (AMA) pregnancies, the risk of gestational diabetes mellitus (GDM) increases. However, few GDM prediction models are tailored for AMA women. This study aims to develop a practical risk prediction model for GDM in AMA women. Data were obtained from a prospective observational cohort of AMA pregnant women from the Obstetrics and Gynecology Hospital in Shanghai, China. Singleton pregnancies with complete OGTT results at 24-28 weeks were selected and divided into training (70%) and validation (30%) sets. First-trimester predictors, including demographic, metabolic parameters, and clinical history, were evaluated for statistical significance. A multivariate logistic regression model was developed, with performance evaluated using receiver operating characteristic (ROC) curves and calibration plots. Predictors were primarily incorporated as categorical variables in a nomogram to enhance model convenience. A model using continuous predictors was also tested for comparison. A total of 1904 AMA women were included, with GDM incidence rates of 18.3% (243/1333) in the training set and 19.3% (110/571) in the validation set. Significant predictors for GDM diagnosis at 24-28 weeks included maternal age, GDM history, first-trimester fasting plasma glucose, mean arterial pressure, and triglyceride levels. The categorical model achieved an area under the ROC curve of 0.717 (95% CI: 0.682-0.753) in the training set and 0.702 (95% CI: 0.645-0.758) in the validation set. The Hosmer-Lemeshow test indicated good calibration (p = .97 in the training set; p = .66 in the validation set). The model with category and continuous predictors exhibited similar performance. This study developed and validated a practical early risk prediction nomogram for GDM in AMA women, using commonly available clinical data. The model shows good predictive performance and is resource-efficient, making it suitable for real-world clinical implementation.

Nomograms for Predicting Risk and Prognosis of Liver Metastases in Ovarian Cancer Patients.

Liver metastases (LiM) commonly manifest in ovarian cancer (OC) patients. We intended to establish nomograms for predicting the risk and prognostic factors in OCLiM patients. Data from the SEER database (Nov 2022, Sub 1992-2020) were analyzed, excluding patients with missing data on liver metastases, survival months, race, AJCC T stage, marital status, rural/urban status, and metastases to bone, brain, or lung. Logistic and Cox regression analyses identified risk and prognostic factors for liver metastases. Predictive nomograms were developed from the multivariable regression results. The nomograms were evaluated using Harrell's C-index, ROC curve, calibration curve, DCA, NRI, and IDI. Moreover, the efficacy of the treatment in the new risk stratification subgroups was demonstrated by Kaplan-Meier (KM) curves. Among 17,056 OC patients, 5.67% (n = 967) had liver metastases. Nomograms were constructed based on identified risk and prognostic factors, with dynamic web-based nomograms developed for clinical use. The nomogram demonstrated C-index values of 81.9% (training) and 82.9% (validation) for predicting liver metastases. For OS and CSS, the C-index values were 73.3% and 73.7% (training), and 73.3% and 72.8% (validation), respectively. The ROC curves for OS at 1-, 3-, 5-year showed AUC values of 84.1%, 79.8%, 75.9% (training) and 82.9%, 78.5%, 82.2% (validation), respectively. For CSS, AUC values at 1-, 3-, and 5-year were 84.5%, 80.2%, 76.1% (training) and 82.6%, 78.0, 82.0% (validation), respectively. The calibration and DCA curves confirmed favorable performance. NRI and IDI analyses showed superiority over the Grade and AJCC stage systems. Surgery improved prognosis in the low-risk group, while chemotherapy was more effective in both low- and medium-risk groups. we developed nomograms and risk stratification systems to assist clinicians in the individualized prediction, risk stratification, and prognostic assessment of OCLiM patients.

How to estimate the probability of a live birth after one or more complete IVF cycles? the development of a novel model in a single-center.

To develop a predictive tool in the form of a Nomogram based on the Cox regression model, which incorporates the impact of the length of treatment cycles on the outcome of live birth, to evaluate the probability of infertile couples having a live birth after one or more complete cycles of In Vitro Fertilization (IVF), and to provide patients with a risk assessment that is easy to understand and visualize. A retrospective study for establishing a prediction model was conducted in the reproductive center of Shenzhen Zhongshan Obstetrics & Gynecology Hospital (formerly Shenzhen Zhongshan Urology Hospital). A total of 4413 patients who completed ovarian stimulation treatment and reached the trigger were involved. 70% of the patients were randomly placed into the training set (n = 3089) and the remaining 30% of the patients were placed into the validation set (n = 1324) randomly. Live birth rate (LBR) and cumulative LBR (CLBR) were calculated for one retrieval cycle and the subsequent five frozen embryo transfer (FET) cycles. Proportional Hazards (PH) Assumption test was used for selecting the parameter in the predictive model. A Cox regression model was built based on the basis of training set, and ROC curves were used to test the specificity and sensitivity of the prediction model. Subsequently, the validation set was applied to verify the validity of the model. Finally, for a more intuitive assessment of the CLBR more intuitively for clinicians and patients, a Nomogram model was established based on predictive model. By calculating the scores of the model, the clinicians could more effectively predict the probability for an individual patient to obtain at least one live birth. In the fresh embryo transfer cycle, the LBR was 38.7%. In the first to fifth FET cycle, the optimal estimate and conservative estimate CLBRs were 59.95%, 65.41%, 66.35%, 66.58%, 66.61% and 56.81%, 60.84%, 61.50%, 61.66%, 61.68%, respectively. Based on PH test results, the potential predictive factors for live birth were insemination method, infertility factors, serum progesterone level (R = 0.043, p = 0.059), and luteinizing hormone level (R = 0.015, p = 0.499) on the day initiated with gonadotropin, basal follicle-stimulating hormone (R = -0.042, p = 0.069) and BMI (R = -0.035, p = 0.123). We used ROC curve to test the predictive power of the model. The AUC was 0.782 (p < 0.01, 95% CI: 0.764-0.801). Then the model was verified using the validation data. The AUC was 0.801 (p < 0.01, 95% CI: 0.774-0.828). A Nomogram model was built based on potential predictive factors that might influence the event of a live birth. The Cox regression and Nomogram prediction models effectively predicted the probability of infertile couples having a live birth. Therefore, this model could assist clinicians with making clinical decisions and providing guidance for patients. N/A.

Development and external validation of a dynamic nomogram for predicting the risk of functional outcome after 90 days in patients with acute intracerebral hemorrhage

Background and purposeIntracerebral hemorrhage remains a significant cause of death and disability worldwide, highlighting the urgent need for accurate prognostic assessments to optimize patient management. This study aimed to develop a practical nomogram for risk prediction of poor prognosis after 90 days in patients with intracerebral hemorrhage.MethodsA retrospective study was conducted on 638 patients with intracerebral hemorrhage in the Second Hospital of Fujian Medical University, China, who were divided into a training set (n = 446) and a test set (n = 192) by random splitting. Then the data on demographics, clinical symptoms, imaging characteristics, and laboratory findings were collected. In this study, adverse outcomes were defined as a Modified Rankin Scale (mRS) score of 3–6 at 90 days post-ICH onset, as assessed during follow-up. Later, least absolute shrinkage and selection operator (LASSO) regression and multifactorial logistic regression were used to screen the variables and construct a nomogram. Next, the evaluation was performed using the Receiver Operating Characteristic (ROC) curve, calibration curve, and decision curve analysis. Finally, the external validation was completed using the data of 496 patients with intracerebral hemorrhage from the Jinjiang Hospital of Traditional Chinese Medicine.ResultsIn the training and test sets of intracerebral hemorrhage, the incidence of poor prognosis was 60.53 and 61.46%, respectively. Through variable screening, this study identified age, Glasgow Coma Scale (GCS), blood glucose, uric acid, hemoglobin, and hematoma location as independent predictors of poor prognosis in intracerebral hemorrhage. The developed dynamic nomogram was easy to use and demonstrated strong predictive performance (training set AUC: 0.87; test set AUC: 0.839; external validation set AUC: 0.774), excellent calibration, and clinical applicability.ConclusionThe dynamic nomogram we developed using five independent risk factors serves as a practical tool for real-time risk assessment and can help facilitate early intervention and personalized patient management, thereby improving clinical outcomes in high-risk patients.

Association between dietary inflammatory index and osteoporosis in the US population: evidence from NHANES 2003–2010

ObjectiveThis study aimed to explore the association between the Dietary Inflammatory Index (DII) and the prevalence of osteoporosis in the U.S. population, using data from the National Health and Nutrition Examination Survey (NHANES) 2003–2010.MethodsData from 7,290 participants in the NHANES 2003–2010 survey were analyzed. The relationship between the DII and osteoporosis was evaluated using weighted multivariate logistic regression, and potential non-linear associations were explored through restricted cubic spline (RCS) regression. Subgroup analyses were conducted with stratified models, and the findings were depicted in a forest plot. To pinpoint key dietary factors associated with osteoporosis, we applied least absolute shrinkage and selection operator (LASSO) regression. These factors were integrated into a nomogram for risk prediction, with the model’s discriminative ability assessed via the receiver operating characteristic (ROC) curve.ResultsOsteoporosis patients had higher DII scores than those without the condition (1.61 vs. 1.18, p &amp;lt; 0.001). After adjusting for covariates, participants in the highest DII quartile had an 88% greater risk of osteoporosis (OR: 1.88, 95% CI: 1.41–2.52, P for trend &amp;lt;0.001). Restricted cubic spline analysis confirmed a linear relationship between DII and osteoporosis risk. Subgroup analyses showed similar patterns across different groups, as illustrated by the forest plot. LASSO regression identified key dietary factors, which were used to build a nomogram with an AUC of 83.6%, indicating strong predictive accuracy.ConclusionA higher DII is strongly linked to increased osteoporosis risk, underscoring the importance of reducing dietary inflammation to help prevent osteoporosis.

Factors associated with pulmonary complications after hepatectomy and establishment of nomogram: A real-world retrospective study

Background and Aims: Hepatectomy is currently the most effective way to treat liver diseases, and its safety has observably improved. However, the incidence of postoperative complications (POCs) remains high. Therefore, exploring the related influencing factors helps identify high-risk groups early and improve patient prognosis. Methods: Clinical data were retrospectively collected from a real-world setting. Patients were divided into two groups based on the incidence of postoperative pulmonary complications (PPCs). Univariate analysis, LASSO regression, and logistic regression were applied to analyse the correlation between PPCs and perioperative indicators. A nomogram prediction model was constructed, whose discrimination, accuracy, and clinical effectiveness were evaluated. Results: The incidence of PPCs was 36.33% among the 1244 patients in this study. The total length of hospital stay and perioperative mortality in the PPCs group were markedly higher (P &lt; 0.001) than in the non-PPCs group. Logistic regression showed that surgical method [odds ratio (OR) =2.469 (95% CI: 1.665, 3.748); P &lt; 0.001], duration of surgery [OR = 1.003 (95% CI: 1.002, 1.005); P &lt; 0.001], postoperative patient destination [OR = 1.453 (95% CI: 1.115, 1.893); P = 0.006], and postoperative international normalised ratio (INR) [OR = 2.245 (95% CI: 1.287, 4.120); P = 0.007] were independent risk factors of PPCs; the number of clamping [OR = 0.988 (95% CI: 0.980, 0.995); P = 0.001] was an independent protective factor of PPCs. The area under the receiver operating characteristic (ROC) curve was 0.675 (95% CI: 0.638, 0.703), the consistency index of the calibration curve was 0.675 (95% CI: 0.641, 0.703), and the Hosmer-Lemeshow goodness-of-fit test yielded P = 0.327. Conclusions: In this study, the incidence of PPCs after hepatectomy was the highest. Our nomogram model can predict the probability of PPCs after hepatectomy.

Construction and validation of a nomogram prediction model for the occurrence of complications in patients following robotic radical surgery for gastric cancer.

In the last two decades, robotic-assisted gastrectomy has become a widely adopted surgical option for gastric cancer (GC) treatment. Despite its popularity, postoperative complications can significantly deteriorate patient quality of life and prognosis. Therefore, identifying risk factors for these complications is crucial for early detection and intervention. This research is designed to construct and validate a predictive model for assessing the risk of postoperative complications in patients undergoing robotic-assisted radical gastrectomy. A retrospective analysis was conducted on 500 GC patients from Gansu Provincial People's Hospital between December 2016 and October 2023. These patients formed the training cohort. An additional 136 patients from the 940th Hospital of Joint Logistic Support Force, the Chinese People's Liberation Army as the external validation cohort. Patients were categorized into groups with and without complications. Data collected included demographic details, laboratory results, CT quantitative body composition analysis, and clinical information. Variable selection was conducted through Lasso regression, succeeded by multivariable logistic regression to pinpoint independent risk factors. These elements facilitated the construction of a nomogram for prediction. The model's performance underwent internal validation via bootstrap techniques and external validation through a validation cohort. The efficacy of the model was quantified by the area under the receiver operating characteristic (ROC) curve (AUC), evaluated for calibration using calibration curves and the Hosmer-Lemeshow test, and assessed for clinical utility through decision curve analysis (DCA). Of the 500 patients in the training cohort, 65 experienced complications, a rate of 13%. The validation cohort had a similar complication rate of 13.24% (18 out of 136 patients). Independent risk factors identified included tumor diameter (OR = 1.99, 95% CI = 1.07-3.73), TNM stage III (OR = 2.12, 95% CI = 1.03-4.36), ASA class I (OR = 0.26, 95% CI = 0.13-0.53), ASA class III (OR = 4.75, 95% CI = 2.12-10.62), and visceral fat area (VFA) (OR = 2.52, 95% CI = 1.10-5.79). The nomogram demonstrated good discrimination (AUC = 0.81, 95% CI: 0.76-0.87) in internal validation and (AUC = 0.79, 95% CI: 0.67-0.90) in external validation. Both validations confirmed the model's accurate calibration and significant clinical utility, with net benefits observed at probability thresholds ranging from 2 to 79% and 2-71%. The developed nomogram, based on five independent risk factors-tumor diameter, TNM stage III, ASA class I, ASA class III, and VFA-effectively predicts the risk of complications in patients undergoing robotic-assisted radical gastrectomy, offering a valuable tool for clinical decision-making.

Development and validation of a nomogram combining clinical and 18F-FDG PET/CT parameters for prediction of high-grade patterns in invasive lung adenocarcinoma

Objective: To establish and validate a nomogram based on clinical characteristics and metabolic parameters derived from 18F-fluorodeoxyglucose positron emission tomography and computed tomography (18F-FDG PET/CT) for prediction of high-grade patterns (HGP) in invasive lung adenocarcinoma. Methods: The clinical and PET/CT image data of 311 patients who were confirmed invasive lung adenocarcinoma and underwent pre-treatment 18F-FDG PET/CT scan in Beijing Hospital between October 2017 and March 2022 were retrospectively collected. The enrolled patients were divided into HGP group (196 patients) and non-HGP group (115 patients) according to the presence and absence of HGP. The data were divided into training set and validation set at 7∶3 ratio using R statistical software and simple random allocation. A nomogram prediction model was constructed in training set. The area under the curve (AUC) of receiver operating characteristic (ROC) was depicted in the training and validation set respectively for assessing the prediction efficacy. The goodness of fit, consistency between predicted and observed probability and clinical usefulness of the model were evaluated by Hosmer-Lemeshow test, calibration curve and decision curve analysis (DCA). Results: The age of 311 patients were (65.6±10.9) years and included 148 males (47.6%). In training set of 217 patients, 141 (65.0%) contained HGP while in validation set of 94 patients, 55 (58.5%) contained HGP. Gender in training set, serum carcino-embryonic antigen (CEA) in validation set, smoking history, clinical stage, cytokeratin fragments (CYFRA21-1), maximum standardized uptake value (SUVmax), mean standardized uptake value (SUVmean), metabolic tumor volume (MTV), total lesion glycolysis (TLG) and maximum diameter (Dmax) in both sets showed significant differences between HGP and non-HGP groups (all P<0.05). The variables integrated in the model were gender, clinical stage, CYFRA21-1, SUVmean and TLG. The AUC (95%CI) of the ROC curve in training and validation set were 0.888 (0.844-0.932) and 0.925 (0.872-0.977), the sensitivity and specificity were 85.1%, 79.0% and 83.6%, 89.7%, respectively. The model showed good goodness of fit (training set: χ2=8.247, P=0.410, validation set: χ2=1.636, P=0.990). Calibration curve and DCA also indicated good consistency and clinical net benefit of the nomogram model. Conclusion: The nomogram model based on clinical features and metabolic parameters derived from 18F-FDG PET/CT could effectively predict the presence of HGP in invasive lung adenocarcinoma and be beneficial to treatment planning.