Nomogram Model Research Articles

Association between visceral lipid accumulation indicators and gallstones: a cross-sectional study based on NHANES 2017–2020

BackgroundObesity is a major contributing factor to the formation of gallstones. As early identification typically results in improved outcomes, we explored the relationship between visceral lipid accumulation indicators and the occurrence of gallstones.MethodsThis cross-sectional study involved 3,224 adults. The researchers employed multivariable logistic regression, smoothed curve fitting (SCF), threshold effects analysis, and subgroup analysis to examine the relationship between metabolic scores for visceral fat (METS-VF), waist circumference (WC), lipid accumulation products (LAP), and visceral adiposity index (VAI) and gallstones. A Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis was used to identify key factors which were then used in the construction of a nomogram model. The diagnostic efficacy of this model in detecting gallstones was then determined using receiver operating characteristic curves.ResultsVisceral lipid accumulation indicators were strongly linked to the likelihood of having gallstones. Specific saturation effects for METS-VF, WC, LAP, and VAI and gallstones were determined using SCF. The inflection points for these effects were found to be 8.565, 108.400, 18.056, and 1.071, respectively. Subgroup analyses showed that associations remained consistent in most subgroups. The nomogram model, which was developed using critical features identified by LASSO regression, demonstrated excellent discriminatory ability, as indicated by an area under the curve value of 0.725.ConclusionsStudies have shown that increases in METS-VF, WC, LAP, and VAI are linked to increased prevalences of gallstones. The nomogram model, designed with critical parameters identified using LASSO regression, exhibits a strong association with the presence of gallstones.

Integrative analysis of bulk and single-cell transcriptomic data reveals novel insights into lipid metabolism and prognostic factors in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is associated with high mortality rate. This study investigated the status of lipid metabolism-related genes in HCC. Bulk transcriptomic and single-cell sequencing data for HCC were retrieved from public databases. The single-cell sequencing data was subjected to dimensionality reduction, which facilitated the annotation of distinct cell subpopulations and marker gene expression analysis within each subpopulation. Genes associated with lipid metabolism in liver cells were identified, and a machine-learning model was developed using the bulk transcriptomic data randomly partitioned into training and validation sets. The efficacy of the model was validated using these two sets. A multifactorial Cox analysis on the model genes combined with clinical features, led to the identification of age, HMGCS2, HNRNPU, and RAN as independent prognostic factors, which were included in the nomogram model construction and validation. A weighted gene co-expression analysis of all genes of the bulk transcriptome samples revealed the correlation between gene modules and risk score. Genes with cor > 0.4 in the highest-expressing module were selected for Gene Ontology and Kyoto Encyclopedia of Genes and Genomes functional enrichment analysis. Immune-related analysis was conducted based on seven algorithms for immune cell infiltration prediction. For the genes in the nomogram model, the expression in clinical pathological factors was also analyzed. The drug sensitivity analysis offered a reference for the selection of targeting drugs. This investigation provides novel insights and a theoretical basis for the prognosis, treatment, and pharmaceutical advancements for patients diagnosed with HCC.

Development and validation of a novel nomogram to predict worsening of gastroesophageal reflux symptoms after laparoscopic sleeve gastrectomy using Lasso-logistic regression

Background Gastroesophageal reflux disease (GERD) is among the most common complications of bariatric surgery. This study aimed to analyse the risk factors affecting the worsening of GERD symptoms after laparoscopic sleeve gastrectomy (LSG), and to establish and validate a related nomogram model. Methods The study recruited 236 participants and randomly divided them into training and validation sets in a ratio of 7:3. LASSO regression technique was used to select the optimal predictive features, and multivariate logistic regression was used to construct the column line graphs. The performance of the nomogram was evaluated and validated by analyzing the area under the receiver operating characteristic (ROC) curve, calibration curve, and decision curve. Results In this study, Lasso-logistic regression was applied to select 5 predictors from the relevant variables, which were body mass index (BMI), diabetes, hiatal hernia, GERD, and triglyceride levels. These 5 predictor variables constructed a model with moderate predictive power, with an area under the ROC curve of 0.779 for the training set and 0.796 for the validation set. Decision curve analysis showed that in external validation, if the risk thresholds were between 4 and 98% and 14–95%, then the nomogram can be applied to the clinic. Conclusions We have developed and validated a nomogram that effectively predicts the risk of worsening gastroesophageal reflux symptoms following LSG.

Clinical characteristics and nomogram model for predicting the risk of recurrence of complicated urinary tract infection in pediatric patients

Complicated urinary tract infection (cUTI) has higher incidences of antibiotic resistance, recurrence, chronicity, and progression. There exist differences in the immune functions, anatomical structures and physiological functions of urinary tract system between children and adults. In addition, the immature stage of hygiene habit development and the inability to express discomfort accurately all contribute to cUTI recurrence in pediatric patients. cUTI recurrence in children can lead to serious consequences such as growth and development delay, renal scars, and progression to end-stage kidney diseases. Despite the high incidence of cUTI recurrence, no predictive model currently exists to guide targeted intervention in pediatric patients. This study aimed to establish a nomogram to predict the risk of cUTI recurrence in children, thereby facilitating targeted prevention and treatment strategies in pediatric patients. The nomogram was developed based on a retrospective cohort that included 421 pediatric patients with cUTI at West China Second University Hospital from January 2020 to August 2023. The patients were randomly divided into a training set and a validation set in a 3:1 ratio. Logistic regression analysis was used to identify risk factors and construct the nomogram for predicting the risk of cUTI recurrence, followed by validation and performance analysis. Of the 421 children with cUTI, the recurrence rate of cUTI was 68.4% (288 cases) during an average follow-up duration of 22.9 months. The nomogram comprised female gender, history of urinary tract surgery, Escherichia coli in urine culture, renal dysfunction, and vesicoureteral reflux as predictors of cUTI recurrence in pediatric patients. The model showed favorable performance with C-index values of 0.735 and 0.750 in the training dataset and the validation dataset, respectively. The decision curve analysis revealed that the nomogram might be clinically useful. The reliable nomogram would be beneficial for clinicians to identify children with high risks of cUTI recurrence for targeted intervention.

Development and validation of a nomogram for predicting bacterial infections in patients with acute exacerbation of chronic obstructive pulmonary disease.

Bacterial infection is a significant contributory factor in the pathogenesis of acute exacerbations of chronic obstructive pulmonary disease (AECOPD) and it has a pivotal role in exacerbating symptoms and precipitating mortality among patients with chronic obstructive pulmonary disease (COPD). The early identification of bacterial infection in individuals with COPD remains a challenge. Therefore, the present study aimed to create and validate a risk assessment tool using easily accessible serum biomarkers to predict bacterial infection in individuals with AECOPD. A retrospective cohort study was carried out at Pingxiang People's Hospital (Pingxiang, China) from January 2023 to December 2023, involving individuals diagnosed with AECOPD. A total of 544 patients with AECOPD were randomly allocated to the two following groups: The training set, which included 70% (n=384) of the patients, and the validation set, which included 30% (n=160) of the patients. Subsequently, a nomogram model was constructed using multivariate logistic regression analysis in the training set. Its discriminatory ability and calibration were internally validated, while decision curve analyses were employed to assess the clinical utility of the nomogram. The incidence of bacterial infection in hospitalized patients with AECOPD was 50% in the training set and 48.1% in the validation set. The nomogram model incorporated independent factors associated with bacterial infection, including C-reactive protein, neutrophil elastase, procalcitonin and eosinophils, identified by univariate and multivariate logistic regression analyses. The area under the curve of the nomogram model was 0.835 [95% confidence interval (CI): 0.795-0.875] in the training set and 0.785 (95% CI: 0.715-0.856) in the validation set. The model demonstrated excellent discrimination and calibration in the validation set [c-statistic: 0.79 (95% CI: 0.68-0.90)]. Furthermore, the discrimination and overfitting bias of the model were assessed through internal validation, revealing a C-index of 0.836 for the initial group and 0.788 for the subsequent validation set. The straightforward risk prediction model for early identification of bacterial infections is valuable for hospitalized patients with AECOPD.

The preoperative prediction of lymph node metastasis of resectable pancreatic ductal adenocarcinoma using dual-layer spectral computed tomography.

To investigate the value of dual-layer spectral computed tomography (DLCT) parameters derived from primary tumors in predicting lymph node metastasis (LNM) of resectable pancreatic ductal adenocarcinoma (PDAC). In this retrospective study, patients with resectable PDAC who underwent DLCT within 2-week intervals before surgery were enrolled and randomly divided into training and validation sets at a 7:3 ratio. The patients' clinical data, CT morphological features, and DLCT parameters were analyzed. Univariate and multivariate logistic analyses were used to identify the predictors and construct a predictive model, and receiver operator characteristic (ROC) curves were programmed to evaluate the predictive efficacy. We enrolled 107 patients (44 patients with LNM and 63 patients without LNM). Among all variables, iodine concentration in the venous phase, extracellular volume, and tumor size were identified as independent predictors of LNM. The nomogram model, incorporating the two DLCT parameters and the morphological feature, achieved an area under the curve (AUC) of 0.877 (95% confidence interval [CI]: 0.803-0.952) and 0.842 (95% CI: 0.707-0.977) for predicting LNM in the training and validation sets, respectively. Furthermore, the AUC of the nomogram model was greater than that of morphological features of lymph nodes in the training (AUC = 0.877 vs. 0.570) and validation (AUC = 0.842 vs. 0.583) sets. DLCT has the potential to predict LNM in patients with resectable PDAC and show a better predictive value than morphological features of lymph nodes. Question Morphological features of lymph nodes are of limited value in detecting metastatic lymph nodes in pancreatic ductal adenocarcinoma (PDAC). Findings Dual-layer spectral computed tomography (DLCT) parameters and morphological features derived from PDAC lesions show good preoperatively predictive efficacy for lymph node metastasis. Clinical relevance The proposed DLCT-based nomogram model may serve as an effective and convenient tool for preoperatively predicting lymph node metastasis of resectable PDAC.

Construction of Survival Nomogram for Ventilator-Associated Pneumonia Patients: Based on MIMIC Database.

Objective: To construct and validate a predictive nomogram model for the survival of patients with ventilator-associated pneumonia (VAP) to enhance prediction of 28-day survival rate in critically ill patients with VAP. Methods: A total of 1,438 intensive care unit (ICU) patients with VAP were screened through Medical Information Mart for Intensive Care (MIMIC)-IV. On the basis of multi-variable Cox regression analysis data, nomogram performance in predicting survival status of patients with VAP at ICU admission for 7, 14, and 28 days was evaluated using the C-index and area under the curve (AUC). Calibration and decision curve analysis curves were generated to assess clinical value and effectiveness of model, and risk stratification was performed for patients with VAP. Result: Through stepwise regression screening of uni-variable and multi-variable Cox regression models, independent prognostic factors for predicting nomogram were determined, including age, race, body temperature, Sequential Organ Failure Assessment score, anion gap, bicarbonate concentration, partial pressure of carbon dioxide, mean corpuscular hemoglobin, and liver disease. The model had C-index values of 0.748 and 0.628 in the train and test sets, respectively. The receiver operating characteristic curve showed that nomogram had better performance in predicting 28-day survival status in the train set (AUC = 0.74), whereas it decreased in the test set (AUC = 0.66). Calibration and decision curve analysis curve results suggested that nomogram had favorable predictive performance and clinical efficacy. Kaplan-Meier curves showed significant differences in survival between low, medium, and high-risk groups in the total set and training set (log-rank p < 0.05), further validating the effectiveness of the model. Conclusion: The VAP patient admission ICU 7, 14, and 28-day survival prediction nomogram was constructed, contributing to risk stratification and decision-making for such patients. The model is expected to play a positive role in supporting personalized treatment and management of VAP.

Nomogram Predictive Model for Vaginal Birth after One Prior Cesarean Section: A Retrospective Study

Background: Repeat cesareans pose significant maternal risks, whereas vaginal birth after cesarean (VBAC) offers better outcomes, with a complication rate of 2.4% compared with 3.6% for elective repeat cesareans and 14.1% for failed trial of labor after cesarean (TOLAC). TOLAC rates are low in China, and success rates vary between 60% and 80%. This study aims to develop a nomogram-based predictive model for VBAC in China and improve existing, less rigorous scoring models. Methods: This retrospective cohort study was conducted at Hangzhou Women's Hospital from February 2015 to March 2020, and included 159 parturient attempts at labor after one prior low transverse cesarean section. The participants were divided into two groups based on their mode of delivery for comparison: the VBAC and the TOLAC failure group. Univariate and multivariate logistic regression analyses were conducted to identify independent predictors for VBAC and develop a nomogram predictive model to estimate the success rate of TOLAC. The bootstrap method was used for internal validation of the models. Three different VBAC prediction models were evaluated by plotting receiver operating characteristic (ROC) curves, calibration curves, and decision curves. Results: A total of 127 women had VBAC and 32 women failed TOLAC, with a success rate of 79.9%. Three independent factors affecting the VBAC were identified: gestational age at delivery, Bishop score and newborn birth weight. A predictive nomogram model for the VBAC was constructed that incorporates these three factors. The model showed a good fit (χ2 = 11.94, p = 0.154) with an overall prediction accuracy of 81.1%. The area under the ROC curve was 0.83 (95% CI (confidence interval), 0.76–0.90) (p &lt; 0.001) and the optimal cut-off value was 83.4%. The bootstrap internal validation showed that our predictive model maintained high overall accuracy and specificity, but exhibited low sensitivity and a low Kappa coefficient. Compared to the Grobman model and Jiaming Rao et al. model, our developed prediction model possesses the strongest discriminatory ability and the highest net benefit, followed by the model by Jiaming Rao et al. All three models demonstrate a high degree of fit. Conclusions: Shorter gestational age at delivery, lower newborn birth weight and higher cervical Bishop score are favorable factors for VBAC. The predictive nomogram model for the VBAC after a single cesarean section, constructed with these three factors, has good predictive efficacy. The model is simple to calculate and has practical value in the clinical selection of suitable candidates for TOLAC after a single cesarean section.

A nomogram for predicting postpartum post-traumatic stress disorder: a prospective cohort study

BackgroundPostpartumpost-traumatic stress disorder (PTSD), as a psychological stress disorder, has long-term and widespread harm. Still, compared with other postpartum psychiatric disorders, postpartum PTSD has received relatively littleattention in China. This study aims to investigate the risk factors of postpartum PTSD and to develop a convenient and rapid nomogram screening tool to help clinical staff identify high-risk pregnant womenin time and take preventative and management measures.MethodsRecruited pregnant women hospitalized for delivery in Qingdao Municipal Hospital and Jinzhou Maternal and Child Health Hospital from November 2022 to October 2023 as convenient samples for the questionnaire survey. Telephone follow-up was conducted 42 days after delivery. After univariate analysis, multicollinearity analysis, and logistic regression analysis, the risk factors of postnatal PTSD were obtained, a prediction model was established, and a nomogram was drawn by R software. G*power3.1.9.7 calculated the effectiveness of the test. The model was validated internally using the Bootstrap approach, and external validation was carried out using a verification group. The accuracy of the model's predictions and its clinical application value were evaluated by the area under the curve, calibration plot, and decision curve analysis.ResultsA total of 602 women were recruited in this study, and the incidence of postpartum PTSD was 11.1% (67/602). Multifactorial logistic regression analysis showed that poor self-assessment of sleep status in late pregnancy (OR = 5.336), cesarean section (OR = 2.825), instrumental delivery (OR = 5.994), having fear of labor (OR = 4.857), and a high score of Five Factors Inventory Neuroticism subscale (OR = 1.244) were independent risk factors for developing postpartum PTSD. A high Quality of Relationship Index score (OR = 0.891) was a protective factor for postpartum PTSD. In the training and validation sets, the nomogram model's area under the ROC curve was 0.928 and 0.907, respectively. The calibration curves showed that the nomogram model was well-fitted, and the Decision Curve Analysis indicated that the nomogram model had good value for clinical application.ConclusionsWith its strong predictive capacity, the prediction model built using postpartum PTSD risk factors can help clinical caregivers identify high-risk pregnant women early on and implement timely preventive intervention strategies.

Prognostic evaluation of segmental ureterectomy combined with chemotherapy in high-grade non-metastatic ureteral cancer: a study based on the SEER database

This study evaluates the survival outcomes of segmental ureterectomy (SU) combined with chemotherapy in patients with high-grade non-metastatic ureteral cancer (UC) using data from the SEER database. A total of 1757 patients with Grade III-IV non-metastatic UC were analyzed. Overall survival (OS) was assessed through Kaplan–Meier analysis, and independent prognostic factors were identified via Cox regression. A Nomogram model was developed and evaluated using the concordance index, area under the time-dependent ROC curve, calibration curves, and decision curve analysis. The 1-, 3-, and 5-year OS rates were 82.8%, 55.6%, and 42.8%, respectively. Age, treatment protocol, T stage, and N stage were significant prognostic factors. Both SU + chemotherapy and radical nephroureterectomy (RNU) + chemotherapy demonstrated comparable survival outcomes, outperforming surgery alone, particularly in patients aged 70 and older. The Nomogram demonstrated high predictive accuracy and clinical utility. These findings suggest that SU + chemotherapy offers survival benefits similar to RNU + chemotherapy, making it a viable option, especially for elderly patients or those with impaired renal function.

Nomogram predicts cervical lymph node metastasis of pathological subtypes of papillary thyroid carcinoma.

Pathological subtypes of papillary thyroid carcinoma (PTC) are important factors in thyroid cancer. Some rare subtypes exhibit extensive lymph node metastasis. These pathological subtypes should receive more attention in clinical practice. Patients with different pathological subtypes of PTC were selected from the SEER database. Logistic regression, random forest, and bootstrap aggregating (bagging) methods were employed to screen for risk factors associated with cervical lymph node metastasis in the training cohort. A nomogram was established based on the model with the largest area under the curve (AUC) and evaluated using calibration curves. Decision curve analysis (DCA) was used to evaluate the clinical benefit to patients. The nomogram was validated in depth by 200 iterations of tenfold cross-validation. A total of 7,882 patients were included in the analysis, with 5,516 patients in the training group and 2,366 patients in the testing group. The logistic regression model achieved the highest AUC of 0.7396. Sex, age, race, extension (extrathyroidal extension), pathological type, and primary tumour size were identified as independent risk factors for cervical lymph node metastasis (p < 0.05). The calibration curve indicated that the model was well calibrated. DCA indicated that the nomogram model had good clinical practicability. In clinical practice, it is important to consider the pathological subtypes of PTC. The established nomogram can serve as a predictive tool for assessing cervical lymph node metastasis.

Development and validation of an inflammation-nutrition indices-based nomogram for predicting early recurrence in patients with stage IB lung adenocarcinoma

To explore the inflammation-nutrition indices and related clinical factors affecting early recurrence in patients with stage IB LUAD. A retrospective analysis was conducted on clinical and pathological data of patients diagnosed with stage IB LUAD who underwent radical surgery in our hospital from January 2016 to January 2021. Using R software, patients were randomly divided into training (n = 140) and validation (n = 59) cohorts in a 7:3 ratio. Univariate and multivariate Cox regression analyses were performed to identify risk factors for RFS and construct a predictive model. The performance of the model was evaluated using the area under the receiver operating characteristic curve (AUC), concordance index (C-index), and calibration curve. Clinical utility of the model was assessed using decision curve analysis (DCA). Multivariate Cox regression analysis revealed that vascular invasion, visceral pleural invasion, predominant pattern, preoperative NLR > 2.33, preoperative PLR > 127.62, and preoperative PNI ≤ 48.3 were independent risk factors for RFS. The C-index of the nomogram model constructed based on these independent risk factors was 0.825 (95% CI: 0.762–0.881) in the training cohort and 0.772 (95% CI: 0.667–0.876) in the validation cohort. The ROC curves showed AUCs of 0.902, 0.881, and 0.877 for 1-year, 2-year, and 3-year RFS in the training cohort and AUCs of 0.782, 0.825, and 0.732 in the validation cohort respectively. Calibration curve and decision curve analysis indicated good clinical value of the model. The nomogram model based on inflammation-nutrition indices has predictive value for early recurrence in patients with stage IB LUAD.

Predicting the prognosis of patients with renal cell carcinoma based on the systemic immune inflammation index and prognostic nutritional index

The aim of the study was to analyze and discuss the value of preoperative systemic immune inflammation index (SII) and prognostic nutritional index (PNI) in predicting the prognosis of patients with renal cell carcinoma (RCC) after operation, and to establish a nomogram prediction model for patients with RCC after operation based on SII and PNI. From January 2014 to December 2018, 210 patients with RCC who underwent surgical treatment at the Xuzhou Central Hospital were selected as the research object. The receiver operating characteristic curve (ROC) was used to determine the optimal cut-off value for preoperative SII, PNI, LMR, PLR, NLR and the patients were divided into groups according to the optimal cutoff values. The survival rate of patients was evaluated. The risk factors that affect the prognosis of patients with RCC were determined by LASSO and Cox regression analysis, and a prognostic nomogram was constructed based on this result. The bootstrap method was used for internal verification of the nomogram model. The prediction efficiency and discrimination of the nomogram model were evaluated by the calibration curve and index of concordance (C-index), respectively. The average overall survival (OS) of all patients was 75.385 months, and the 1-, 2-and 3-year survival rates were 95.5%, 86.6% and 77.2%, respectively. The survival curve showed that the 5-year OS rate of low SII group was significantly higher than that of high SII group (89.0% vs. 64.5%; P < 0.05), and low PNI group was significantly lower than those in high PNI group (43.4% vs. 87.9%; p < 0.05). There were significant differences between preoperative SII and CRP, NLR, PLR, LMR, postoperative recurrence, pathological type and AJCC stage (P < 0.05). There were significant differences between preoperative PNI and BMI, platelet, NLR, PLR, LMR, postoperative recurrence, surgical mode and Fuhrman grade (P < 0.05). The ROC curve analysis showed that the AUC of PNI (AUC = 0.736) was higher than that of other inflammatory indicators, followed by the AUC of SII (0.718), and the difference in AUC area between groups was statistically significant (P < 0.05). The results from multivariate Cox regression analysis showed that SII, PNI, tumor size, tumor necrosis, surgical mode, pathological type, CRP, AJCC stage and Fuhrman grade were independent risk factors for postoperative death of patients with RCC. According to the results of Cox regression analysis, a prediction model for the prognosis of RCC patients was established, and the C-index (0.918) showed that the model had good calibration and discrimination. The subject’s operating characteristic curve indicates that the nomogram has good prediction efficiency (the AUC = 0.953). Preoperative SII and PNI, tumor size, tumor necrosis, surgical mode, pathological type, CRP, AJCC stage and Fuhrman grade are closely related to the postoperative prognosis of patients with renal cell carcinoma. The nomogram model based on SII, PNI, tumor size, tumor necrosis, surgical mode, pathological type, CRP, AJCC stage and Fuhrman grade has good accuracy, discrimination and clinical prediction efficiency.

Development and validation of a nomogram to predict the risk of adjacent segment disease after transforaminal lumbar interbody fusion in patients with lumbar degenerative diseases

BackgroundAdjacent segment disease (ASD) is a common and serious complication that can develop in the mid- to long-term after lumbar fusion surgery. However, the underlying mechanisms of ASD are not yet fully understood. This study aimed to develop and validate a risk prediction model for ASD in patients who underwent transforaminal lumbar interbody fusion (TLIF) for lumbar degenerative diseases.MethodsPatients with lumbar degenerative disease who underwent TLIF between January 2015 and December 2016 were included in the retrospective study. The participants were divided into two groups: ASD and non-ASD. Univariate and multivariate logistic regression analyses were performed to identify factors influencing ASD after TLIF surgery. The receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) were used to evaluate the model’s discrimination, calibration and clinical value, respectively.ResultsA total of 11.5% (59/513) of patients developed ASD. Higher BMI, lower BMD, higher disc grade, and reduced disc height were identified as independent risk factors for ASD after TLIF. The model demonstrated good discrimination in both the training and validation sets, with calibration and Hosmer-Lemeshow tests confirming accuracy, and DCA demonstrating clinical applicability.ConclusionsThe nomogram model demonstrated promise in predicting ASD in patients who underwent TLIF, aiding clinicians in selecting the most suitable surgical approach and optimizing surgical decisions.

Risk Factors and Risk Prediction Model of Poor Prognosis in Patients with Chronic Dacryocystitis Treated by Endoscopic Nasal Dacryocystostomy.

Chronic dacryocystitis often leads to a poor prognosis due to factors like chronic inflammation resulting in lacrimal duct obstruction and recurrent infections. Therefore, this study aims to investigate the risk factors of poor prognosis in patients with chronic dacryocystitis treated by endoscopic nasal dacryocystostomy and to establish a risk prediction model. We retrospectively analyzed the clinical data of chronic dacryocystitis patients (n = 101) treated between January 2022 and February 2024. They were divided into a training set (n = 71) and a validation set (n = 30). Patients were followed up for three months post-operation, and recurrence rates were assessed. Logistic regression analysis was used to identify risk factors for poor prognosis, and a nomogram model was developed utilizing these risk factors. Model validation involved the bootstrap method, calibration curves, receiver operator characteristic (ROC) curves, and the Hosmer-Lemeshow test. Out of the 101 patients, 27 (26.73%) experienced recurrence. The older age, longer operation time, and greater intraoperative bleeding were all associated with poor prognosis (all p < 0.05). Multivariate regression indicated that age (odds ratio (OR) = 2.18, 95% CI: 1.30-3.68), operation time (OR = 1.89, 95% confidence interval (CI): 1.13-3.17), and intraoperative bleeding (OR = 1.69, 95% CI: 1.06-2.69) were significant risk factors. The nomogram model incorporating these factors showed an area under the curve (AUC) of 0.666 for the training set and 0.585 for the validation set. Furthermore, for the training set, sensitivity and specificity were 0.654 and 0.621, and for the validation set, they were 0.598 and 0.548, respectively, with calibration curves indicating good agreement. Age, operation time, and intraoperative bleeding are significant factors affecting the prognosis of patients with chronic dacryocystitis.

Construction and verification of the prediction model for risk of sleep disturbance in elderly patients with hypertension: a cross-sectional survey based on NHANES database from 2005 to 2018

ObjectiveTo construct and verify a risk prediction model for sleep disturbance in elderly patients with hypertension, aiming to offer guidance for sleep management in this demographic.MethodsA cohort of 6,708 elderly hypertensive patients from the NHANES database, spanning 2005 to 2018, met the inclusion criteria and were selected for this study. Participants were randomly assigned to a development group (n = 4,696) and a verification group (n = 2,012) in a 7:3 ratio. The occurrence of sleep disturbance was assessed across the subjects. Independent risk factors for sleep disturbance were analyzed using weighted multivariate logistic regression within the development group. A predictive model for sleep disturbance risk in elderly hypertensive patients was developed and verified using Stata 17.0. The model's predictive accuracy and stability were evaluated using the verification group's data.ResultsOf the 6,708 subjects, 2,014 (30.02%) were identified with sleep disturbance, and the weighted prevalence of sleep disturbance among elderly hypertensive patients was 33.283%. Weighted multivariate logistic regression analysis in the development group revealed that six factors were independently associated with sleep disturbance: higher total depression scores, higher education level, asthma, overweight, arthritis, and work restriction (OR > 1 and P < 0.05). The area under the receiver operating characteristic (ROC) curve (AUC) for the nomogram prediction model was 0.709 in the development group and 0.707 in the verification group, indicating good discrimination ability. Brier scores for the nomogram model were 0.185 in the development group and 0.189 in the verification group, both below 0.25, suggesting good calibration. Decision Curve Analysis (DCA) determined that the nomogram's clinical net benefit was maximized when the threshold probability for sleep disturbance in elderly hypertensive patients was 0.13–0.67 in the development group and 0.14–0.61 in the verification group, highlighting the model's clinical utility.LimitationsThis study is not without its limitations, including issues with data collection, the absence of external validation, and the non-extrapolation of results.ConclusionThe prevalence of sleep disturbance among elderly hypertensive patients stands at 33.283%. The nomogram model, based on identified risk factors for sleep disturbance in this population, has demonstrated good predictive efficiency and clinical relevance. It serves as a valuable tool to assist healthcare providers in identifying elderly hypertensive patients at high risk for sleep disturbance.

Exploring diagnostic biomarkers of type 2 cardio-renal syndrome based on secreted proteins and bioinformatics analysis

Chronic heart failure (CHF) can induce chronic kidney disease (CKD), called type 2 cardio-renal syndrome (CRS2). The mechanism is not completely clear, and there is a lack of early warning biomarkers of CKD in the context of CHF. Two CKD, one CHF-PBMC and four CHF-cardiac tissue expression profile datasets were obtained from GEO database. Differential expression analysis and WGCNA were used to detect CKD key genes and CHF-related secreted proteins. Protein–protein interaction (PPI), functional enrichment, and cMAP analysis reveal potential mechanisms and drugs of CHF-related CKD. Five machine learning algorithms were used to screen candidate biomarkers, construct a diagnostic nomogram for CKD and validate it in two external cohorts. Clinical serum samples were collected in our hospital to evaluate the correlation and diagnostic value of biomarkers and CKD. 225 CKD key genes and 316 CHF-related secreted proteins were identified. Four key subgroups, including 204 genes, were identified as CRS2-related pathogenic genes by PPI analysis. Enrichment analysis revealed that the identified subgroups exhibited significant enrichment in cytokine action, immune responses, and inflammatory processes. The cMAP analysis highlighted metiradone as a drug with greater potential for therapeutic intervention for CRS2. Utilizing five machine learning algorithms, three hub genes (CD48, COL3A1, LOXL1) were pinpointed as potential biomarkers for CKD, and a nomogram model was constructed. Receiver operating characteristic analysis demonstrated that the nomogram’s area under the curve (AUC) exceeded 0.80 in both the CKD combined dataset and two external cohorts. In addition, the three biomarkers were significantly correlated with the glomerular filtration rate, and the AUC of the model predicting disease progression was 0.944. Furthermore, analysis of immune cell infiltration indicated a correlation between the three biomarkers and the infiltration fraction of macrophages, neutrophils, and other immune cells in CKD. Our clinical cohort validated the expression patterns of three biomarkers in serum, and the diagnostic model achieved an AUC of 0.876. CHF has the potential to facilitate the progression of CKD via the release of cardiac and PBMC secreted proteins. Furthermore, CD48, COL3A1, and LOXL1 have been identified as potential biomarkers for the detection of CKD.

Prognostic value of red blood cell distribution width in sepsis induced cardiomyopathy patients.

The potential association between red blood cell distribution width (RDW) at admission and prognosis in patients with sepsis-induced cardiomyopathy(SIC) remains uncertain. The purpose of this study was to explore the prognostic value of RDW on mortality in patients with SIC. Data for this retrospective study were obtained from the MIMIC IV2.2 database. We used propensity score matching (PSM) and Cox proportional hazards regression analysis to evaluate the main risk factors associated with mortality in SIC patients. This analysis was utilized to develop a predictive nomogram. To assess the predictive accuracy and clinical usefulness of the model, we employed the concordance index (C-index) and decision curve analysis. To define the high- and low-RDW groups among patients with SIC, we determined the optimal cut-off value by maximizing the Youden index. According to the screening criteria, we identified a cohort of 1051 patients diagnosed with SIC. When comparing the high-RDW group to the low-RDW group, it was found that the high-RDW group exhibited longer Los_ICU(4.5 days vs.3.8 days, respectively, P = 0.009) and higher mortality rates at 28 days (33.8% vs. 7.8%, respectively, P < 0.001). A nomogram model was created using matched patients which included various factors such as Age, RDW, LDH, CKMB, creatinine and the administration of β-blocker. The C-index predicting 28-day survival probability was 0.846. Decision curves analysis demonstrated that the inclusion of RDW in the model provided a greater net benefit compared to excluding RDW. The prognosis of patients with SIC can be predicted by the RDW value. The nomogram model provides a useful tool in identifying and managing SIC patients.

Risk stratification model for foreseeing overall survival in Chinese patients with initially metastatic small-cell lung cancer.

The study was outlined to develop and approve a nomogram and chance stratification demonstrate for foreseeing overall survival of Chinese patients with at first metastatic small-cell lung cancer (SCLC). We collected information from the Surveillance, Epidemiology, and End Results (SEER) database approximately Chinese SCLC patients with at first distant metastases between 2010 and 2015. Patients with incomplete data about the follow-up time or clinicopathological information were excluded. The included patients were randomized into the training and validation set. Univariate and multivariate Cox proportional hazard regression models were performed. By integrating the significant variables screened, a prescient nomogram and risk stratification model were developed. In addition, we collected 198 small-cell lung cancer patients with metastasis at diagnosis from the case database of the Affiliated Hospital of Shandong University of Traditional Chinese Medicine as an external validation cohort. In all, 421 patients were screened from the SEER database. Multivariate examination showed that age (P = .049), sex (P = .001), grade (P = .008), chemotherapy (P = .001), liver metastasis (P = .001), and pleural invasion (P = .012) were independent prognostic factors. The C-indicator of the nomogram to anticipate overall survival was higher than that of the eighth edition of the American Joint Committee on Cancer Tumor Node Metastasis classification system (0.75 vs 0.543; P < .001). A risk stratification model was encouraged to be created to precisely classify patients into 2 prognostic bunches. The survival rates anticipated by the nomogram appeared to have critical qualifications from the Kaplan-Meier curves in the entire SEER cohort. Calibration curves and survival predictions also showed strong accuracy and consistency in the external validation cohort. The nomogram provided a clear prognostic superiority over the traditional Tumor Node Metastasis system. It could help clinicians make individual risk predictions for initially metastatic Chinese SCLC cancer patients and give necessary treatment recommendations.

The value of nomogram based on MRI functional imaging in differentiating cerebral alveolar echinococcosis from brain metastases

ObjectiveThis study aims to evaluate the effectiveness of a nomogram model constructed using Diffusion Kurtosis Imaging (DKI) and 3D Arterial Spin Labeling (3D-ASL) functional imaging techniques in distinguishing between cerebral alveolar echinococcosis (CAE) and brain metastases (BM).MethodsProspectively collected were 24 cases (86 lesions) of patients diagnosed with CAE and 16 cases (69 lesions) of patients diagnosed with BM at the affiliated hospital of Qinghai University from 2018 to 2023, confirmed either pathologically or through comprehensive diagnosis. Both patient groups underwent DKI and 3D-ASL scanning. DKI parameters (Kmean, Dmean, FA, ADC) and cerebral blood flow (CBF) were analyzed for the parenchymal area, edema area, and symmetrical normal brain tissue area in both groups. There were 155 lesions in total in the two groups of patients. We used SPSS to randomly select 70% as the training set (108 lesions) and the remaining 30% as the test set (47 lesions) and performed a difference analysis between the two groups. The independent factors distinguishing CAE from BM were identified using univariate and multivariate logistic regression analyses. Based on these factors, a diagnostic model was constructed and expressed as a nomogram.ResultUnivariate and multivariate logistic regression analyses identified nDmean1 and nCBF1 in the lesion parenchyma area, as well as nKmean2 and nDmean2 in the edema area, as independent factors for distinguishing CAE from BM. The model's performance, measured by the area under the ROC curve (AUC), had values of 0.942 and 0.989 for the training and test sets, respectively. Calibration curves demonstrated that the predicted probabilities were highly consistent with the actual values, and DCA confirmed the model's high clinical utility.ConclusionThe nomogram model, which incorporates DKI and 3D-ASL functional imaging, effectively distinguishes CAE from BM. It offers an intuitive, accurate, and non-invasive method for differentiation, thus providing valuable guidance for subsequent clinical decisions.