Nomogram Prediction Model Research Articles

A nomogram combining CT-based radiomic features with clinical features for the differentiation of benign and malignant cystic pulmonary nodules

BackgroundCurrently, the differentiation between benign and malignant cystic pulmonary nodules poses a significant challenge for clinicians. The objective of this retrospective study was to construct a predictive model for determining the likelihood of malignancy in patients with cystic pulmonary nodules.MethodsThe current study involved 129 patients diagnosed with cystic pulmonary nodules between January 2017 and June 2023 at the Neijiang First People’s Hospital. The study gathered the clinical data, preoperative imaging features of chest CT, and postoperative histopathological results for both cohorts. Univariate and multivariate logistic regression analyses were employed to identify independent risk factors, from which a prediction model and nomogram were developed. In addition, The model's performance was assessed through receiver operating characteristic (ROC) curve analysis, calibration curve analysis, and decision curve analysis (DCA).ResultsA cohort of 129 patients presenting with cystic pulmonary nodules, consisting of 92 malignant and 37 benign lesions, was examined. Logistic data analysis identified a cystic airspace with a mural nodule, spiculation, mural morphology, and the number of cystic cavities as significant independent predictors for discriminating between benign and malignant cystic lung nodules. The nomogram prediction model demonstrated a high level of predictive accuracy, as evidenced by an area under the ROC curve (AUC) of 0.874 (95% CI: 0.804–0.944). Furthermore, the calibration curve of the model displayed satisfactory calibration. DCA proved that the prediction model was useful for clinical application.ConclusionIn summary, the risk prediction model for benign and malignant cystic pulmonary nodules has the potential to assist clinicians in the diagnosis of such nodules and enhance clinical decision-making processes.

Development of a prediction nomogram for 1-month mortality in neonates with congenital diaphragmatic hernia

ObjectivesAlthough many prognostic factors in neonates with congenital diaphragmatic hernia (CDH) have been described, no consensus thus far has been reached on which and how many factors are involved. The aim of this study is to analyze the association of multiple prenatal and postnatal factors with 1-month mortality of neonates with CDH and to construct a nomogram prediction model based on significant factors.MethodsA retrospective analysis of neonates with CDH at our center from 2013 to 2022 was conducted. The primary outcome was 1-month mortality. All study variables were obtained either prenatally or on the first day of life. Risk for 1-month mortality of CDH was quantified by odds ratio (OR) with 95% confidence interval (CI) in multivariable logistic regression models.ResultsAfter graded multivariable adjustment, six factors were found to be independently and consistently associated with the significant risk of 1-month mortality in neonates with CDH, including gestational age of prenatal diagnosis (OR, 95% CI, P value: 0.845, 0.772 to 0.925, < 0.001), observed-to-expected lung-to-head ratio (0.907, 0.873 to 0.943, < 0.001), liver herniation (3.226, 1.361 to 7.648, 0.008), severity of pulmonary hypertension (6.170, 2.678 to 14.217, < 0.001), diameter of defect (1.560, 1.084 to 2.245, 0.017), and oxygen index (6.298, 3.383 to 11.724, < 0.001). Based on six significant factors identified, a nomogram model was constructed to predict the risk for 1-month mortality in neonates with CDH, and this model had decent prediction accuracy as reflected by the C-index of 94.42%.ConclusionsOur findings provide evidence for the association of six preoperational and intraoperative factors with the risk of 1-month mortality in neonates with CDH, and this association was reinforced in a nomogram model.

Quantitative parameter analysis of pretreatment dual-energy computed tomography in nasopharyngeal carcinoma cervical lymph node characteristics and prediction of radiotherapy sensitivity

BackgroundTreatment efficacy may differ among patients with nasopharyngeal carcinoma (NPC) at similar tumor–node–metastasis stages. Moreover, end-of-treatment tumor regression is a reliable indicator of treatment sensitivity. This study aimed to investigate whether quantitative dual-energy computed tomography (DECT) parameters could predict sensitivity to neck–lymph node radiotherapy in patients with NPC.MethodsOverall, 388 lymph nodes were collected from 98 patients with NPC who underwent pretreatment DECT. The patients were divided into complete response (CR) and partial response (PR) groups. Clinical characteristics and quantitative DECT parameters were compared between the groups, and the optimal predictive ability of each parameter was determined using receiver operating characteristic (ROC) analysis. A nomogram prediction model was constructed and validated using univariate and binary logistic regression.ResultsDECT parameters were higher in the CR group than in the PR group. The iodine concentration (IC), normalized IC, Mix-0.6, spectral Hounsfield unit curve slope, effective atomic number, and virtual monoenergetic images were significantly different between the groups. The area under the ROC curve of the DECT parameters was 0.73–0.77. Based on the binary logistic regression, a column chart was constructed using 10 predictive factors, including age, sex, N stage, maximum lymph node diameter, arterial phase NIC, venous phase NIC, λHU and spectral Hounsfield units at 70 keV. The area under the ROC curve value of the constructed model was 0.813, with a sensitivity and specificity of 85.6% and 81.3%, respectively.ConclusionQuantitative DECT parameters could effectively predict the sensitivity of NPC to radiotherapy. Therefore, DECT parameters and NPC clinical features can be combined to construct a nomogram with high predictive power and used as a clinical analytical tool.

Integrative nomogram model based on anoikis-related genes enhances prognostic evaluation in colorectal cancer

BackgroundRevealing the role of anoikis resistance plays in CRC is significant for CRC diagnosis and treatment. This study integrated the CRC anoikis-related key genes (CRC-AKGs) and established a novel model for improving the efficiency and accuracy of the prognostic evaluation of CRC. MethodsCRC-ARGs were screened out by performing differential expression and univariate Cox analysis. CRC-AKGs were obtained through the LASSO machine learning algorithm and the LASSO Risk-Score was constructed to build a nomogram clinical prediction model combined with the clinical predictors. In parallel, this work developed a web-based dynamic nomogram to facilitate the generalization and practical application of our model. ResultsWe identified 10 CRC-AKGs and a risk-related prognostic Risk-Score was calculated. Multivariate COX regression analysis indicated that the Risk-Score, TNM stage, and age were independent risk factors that significantly associated with the CRC prognosis(p < 0.05). A prognostic model was built to predict the outcome with satisfied accuracy (3-year AUC = 0.815) for CRC individuals. The web interactive nomogram (https://yuexiaozhang.shinyapps.io/anoikisCRC/) showed strong generalizability of our model. In parallel, a substantial correlation between tumor microenvironment and Risk-Score was discovered in the present work. ConclusionThis study reveals the potential role of anoikis in CRC and sets new insights into clinical decision-making in colorectal cancer based on both clinical and sequencing data. Also, the interactive tool provides researchers with a user-friendly interface to input relevant clinical variables and obtain personalized risk predictions or prognostic assessments based on our established model.

Multivariate analysis and construction and validation of a nomogram model from data of 1610 patients with non-tumor-related anastomotic stenosis after rectal cancer surgery

Objective: To assess the risk factors affecting development of non-tumor- related anastomotic stenosis after rectal cancer and to construct a nomogram prediction model. Methods: This was a retrospective study of data of patients who had undergone excision with one-stage intestinal anastomosis for rectal cancer between January 2003 and September 2018 in Nanfang Hospital of Southern Medical University. The exclusion criteria were as follows: (1) pathological examination of the operative specimen revealed residual tumor on the incision margin of the anastomosis; (2) pathological examination of postoperative colonoscopy specimens revealed tumor recurrence at the anastomotic stenosis, or postoperative imaging evaluation and tumor marker monitoring indicated tumor recurrence; (3) follow-up time <3 months; and (4) simultaneous multiple primary cancers. Univariate analysis using the χ2 or Fisher's exact test was performed to assess the study patients' baseline characteristics and variables such as tumor-related factors and surgical approach (P<0.05). Multivariate analysis using binary logistic regression was then performed to identify independent risk factors for development of non-tumor-related anastomotic stenosis after rectal cancer. Finally, a nomogram model for predicting non-tumor-related anastomotic stenosis after rectal cancer surgery was constructed using R software. The reliability and accuracy of this prediction model was evaluated using internal validation and calculation of the area under the curve of the model's receiver characteristic curve (ROC). Results: The study cohort comprised 1,610 patients, including 1,008 men and 602 women of median age 59 (50, 67) years and median body mass index 22.4 (20.2, 24.5) kg/m². Non-tumor-related anastomotic stenosis developed in 121 (7.5%) of these patients. The incidence of non-tumor-related anastomotic stenosis in patients who had undergone neoadjuvant chemotherapy, neoadjuvant radiotherapy, and surgery alone was 11.2% (10/89), 26.4% (47/178), and 4.8% (64/1,343), respectively. Neoadjuvant treatment (neoadjuvant chemotherapy: OR=2.455, 95%CI: 1.148-5.253, P=0.021; neoadjuvant chemoradiotherapy, OR=3.882, 95%CI: 2.425-6.216, P<0.001), anastomotic leakage (OR=7.960, 95%CI: 4.550-13.926, P<0.001), open laparotomy (OR=3.412, 95%CI: 1.772-6.571, P<0.001), and tumor location (distance of tumor from the anal verge 5-10 cm: OR=2.381, 95%CI:1.227-4.691, P<0.001; distance of tumor from the anal verge <5 cm: OR=5.985,95% CI: 3.039-11.787, P<0.001) were identified as independent risk factors for non-tumor-related anastomotic stenosis. Thereafter, a nomogram prediction model incorporating the four identified risk factors for development of anastomotic stenosis after rectal cancer was developed. The area under the curve of the model ROC was 0.815 (0.773-0.857, P<0.001), and the C-index of the predictive model was 0.815, indicating that the model's calibration curve fitted well with the ideal curve. Conclusion: Non-tumor-related anastomotic stenosis after rectal cancer surgery is significantly associated with neoadjuvant treatment, anastomotic leakage, surgical procedure, and tumor location. A nomogram based on these four factors demonstrated good discrimination and calibration, and would therefore be useful for screening individuals at risk of anastomotic stenosis after rectal cancer surgery.

Which factors are associated with adverse prognosis in multiple myeloma patients after surgery? - preliminary establishment and validation of the nomogram

BackgroundTo investigate the prognosis of patients with Multiple Myeloma (MM) after surgery, analyze the risk factors leading to adverse postoperative outcomes, and establish a nomogram.MethodsClinical data from 154 patients with MM who underwent surgery at our institution between 2007 and 2019 were retrospectively analyzed. Assessing and comparing patients’ pain levels, quality of life, and functional status before and after surgery (P < 0.05) were considered statistically significant. The Kaplan-Meier survival curve was used to estimate the median survival time. Adverse postoperative outcomes were defined as worsened symptoms, lesion recurrence, complication grade ≥ 2, or a postoperative survival period < 1 year. Logistic regression analysis was used to determine the prognostic factors. Based on the logistic regression results, a nomogram predictive model was developed and calibrated.ResultsPostoperative pain was significantly alleviated in patients with MM, and there were significant improvements in the quality of life and functional status (P < 0.05). The median postoperative survival was 41 months. Forty-nine patients (31.8%) experienced adverse postoperative outcomes. Multivariate logistic regression analysis identified patient age, duration of MM, International Staging System, preoperative Karnofsky Performance Status, and Hb < 90 g/L as independent factors influencing patient prognosis. Based on these results, a nomogram was constructed, with a C-index of 0.812. The calibration curve demonstrated similarity between the predicted and actual survival curves. Decision curve analysis favored the predictive value of the model at high-risk thresholds from 10% to-69%.ConclusionThis study developed a nomogram risk prediction model to assist in providing quantifiable assessment indicators for preoperative evaluation of surgical risk.

A nomogram for predicting the risk of cancer-related cognitive impairment in breast cancer patients based on a scientific symptom model

Cancer-related cognitive impairment is a significant clinical challenge observed in patients with breast cancer, manifesting during or after treatment. This impairment leads to deteriorations in memory, processing speed, attention, and executive functioning, which profoundly impact patients' occupational performance, daily living activities, and overall quality of life. Grounded in the Symptom Science Model 2.0, this study investigates the contributing factors to Cancer-related cognitive impairment in breast cancer patients and develops a predictive nomogram for this demographic. Employing both univariate and multivariate logistic regression analyses, this investigation delineates the predictive factors influencing outcomes in breast cancer patients. A nomogram was constructed leveraging these identified predictive factors, accompanied by internal validation through bootstrap resampling methodology (1000 bootstrap samples). The efficacy of the predictive model was assessed by employing the Hosmer–Lemeshow goodness-of-fit test and calibration curves. The prevalence of cognitive impairment in breast cancer patients was identified to be 45.83%.Multivariate logistic regression analysis identified the independent predictors of Cancer-related cognitive impairment in breast cancer patients as place of residence, educational level, chemotherapy, benefit finding, post-traumatic growth, anxiety, fear of cancer progression, and fasting blood glucose levels. these factors were integrated into the nomogram. The Hosmer–Lemeshow goodness-of-fit test demonstrated that the prediction model was appropriately calibrated (χ2 = 11.520, P = 0.174). Furthermore, the model exhibited an area under the curve of 0.955 (95% CI 0.939 to 0.971) and a sensitivity of 0.906, evidencing its robust discriminative capacity and accuracy. Utilizing the Symptom Science Model 2.0 as a framework, this study comprehensively examines the multifaceted factors influencing Cancer-related cognitive impairment in breast cancer patients, spanning five critical domains: complex symptoms, phenotypic characterization, biobehavioral factors, social determinants of health, and patient-centered experiences. A predictive nomogram model was established, demonstrating satisfactory predictive accuracy and capability. This model is capable of identifying breast cancer patients with cognitive impairments with high precision. The findings furnish empirical evidence in support of the early detection, diagnosis, and intervention strategies for high-risk breast cancer patients afflicted with Cancer-related cognitive impairment.

A novel nomogram model to predict in-hospital mortality in patients with acute type A aortic dissection after surgery

BackgroundAcute type A aortic dissection is a dangerous disease that threatens public health. In recent years, with the progress of medical technology, the mortality rate of patients after surgery has been gradually reduced, leading that previous prediction models may not be suitable for nowadays. Therefore, the present study aims to find new independent risk factors for predicting in-hospital mortality and construct a nomogram prediction model. Methods: The clinical data of 341 consecutive patients in our center from 2019 to 2023 were collected, and they were divided into two groups according to the death during hospitalization. The independent risk factors were analyzed by univariate and multivariate logistic regression, and the nomogram was constructed and verified based on these factors. Results: age, preoperative lower limb ischemia, preoperative activated partial thromboplastin time (APTT), preoperative platelet count, Cardiopulmonary bypass (CPB) time and postoperative acute kidney injury (AKI) independently predicted in-hospital mortality of patients with acute type A aortic dissection after surgery. The area under the receiver operating characteristic curve (AUC) for the nomogram was 0.844. The calibration curve and decision curve analysis verified that the model had good quality. Conclusion: The new nomogram model has a good ability to predict the in-hospital mortality of patients with acute type A aortic dissection after surgery.

Constructing a personalized nomogram model for predicting anastomotic leakage after laparoscopic radical resection of rectal cancer

The purpose of this study was to explore the risk factors for anastomotic leakage (AL) after laparoscopic radical resection of rectal cancer and to construct a nomogram prediction model for AL after laparoscopic radical resection of rectal cancer. We selected 366 patients with rectal cancer who underwent laparoscopic radical resection for rectal cancer in our hospital between January 2021 and December 2023 as the research subjects. Logistic regression analysis was used to screen the risk factors for AL after laparoscopic radical resection for rectal cancer, and a nomogram model for AL after radical resection for rectal cancer was constructed and validated. Our results showed among 366 patients with rectal cancer, 42 patients developed AL after surgery, and the incidence rate of AL was 11.48%. Logistic regression analysis results showed that gender, preoperative intestinal obstruction, distance between the tumor and the anal edge ≤7 cm, and diabetes were risk factors for AL after laparoscopic radical resection of rectal cancer (P < 0.05). The calibration curve of the nomogram model for AL after laparoscopic radical resection of rectal cancer showed that the model's predicted value and actual value fit well, and the area under the curve of the model was 0.859, (95%CI: 0.807–0.912). Overall, gender, preoperative intestinal obstruction, distance between the tumor and the anal verge ≤7 cm, and diabetes are risk factors for AL after laparoscopic radical resection of rectal cancer. The nomogram model of AL after laparoscopic radical resection of rectal cancer has high accuracy and has a certain guiding significance for formulating prevention and treatment strategies in advance.

Development and validation of a nomogram predictive model for cognitive impairment in cerebral small vessel disease: a comprehensive retrospective analysis.

Cerebral small vessel disease (CSVD) is a common neurodegenerative condition in the elderly, closely associated with cognitive impairment. Early identification of individuals with CSVD who are at a higher risk of developing cognitive impairment is crucial for timely intervention and improving patient outcomes. The aim of this study is to construct a predictive model utilizing LASSO regression and binary logistic regression, with the objective of precisely forecasting the risk of cognitive impairment in patients with CSVD. The study utilized LASSO regression for feature selection and logistic regression for model construction in a cohort of CSVD patients. The model's validity was assessed through calibration curves and decision curve analysis (DCA). A nomogram was developed to predict cognitive impairment, incorporating hypertension, CSVD burden, apolipoprotein A1 (ApoA1) levels, and age. The model exhibited high accuracy with AUC values of 0.866 and 0.852 for the training and validation sets, respectively. Calibration curves confirmed the model's reliability, and DCA highlighted its clinical utility. The model's sensitivity and specificity were 75.3 and 79.7% for the training set, and 76.9 and 74.0% for the validation set. This study successfully demonstrates the application of machine learning in developing a reliable predictive model for cognitive impairment in CSVD. The model's high accuracy and robust predictive capability provide a crucial tool for the early detection and intervention of cognitive impairment in patients with CSVD, potentially improving outcomes for this specific condition.

Developing and validating a predictive model of delivering large-for-gestational-age infants among women with gestational diabetes mellitus

BACKGROUND The birth of large-for-gestational-age (LGA) infants is associated with many short-term adverse pregnancy outcomes. It has been observed that the proportion of LGA infants born to pregnant women with gestational diabetes mellitus (GDM) is significantly higher than that born to healthy pregnant women. However, traditional methods for the diagnosis of LGA have limitations. Therefore, this study aims to establish a predictive model that can effectively identify women with GDM who are at risk of delivering LGA infants. AIM To develop and validate a nomogram prediction model of delivering LGA infants among pregnant women with GDM, and provide strategies for the effective prevention and timely intervention of LGA. METHODS The multivariable prediction model was developed by carrying out the following steps. First, the variables that were associated with LGA risk in pregnant women with GDM were screened by univariate analyses, for which the P value was &lt; 0.10. Subsequently, Least Absolute Shrinkage and Selection Operator regression was fit using ten cross-validations, and the optimal combination factors were selected by choosing lambda 1se as the criterion. The final predictors were determined by multiple backward stepwise logistic regression analysis, in which only the independent variables were associated with LGA risk, with a P value &lt; 0.05. Finally, a risk prediction model was established and subsequently evaluated by using area under the receiver operating characteristic curve, calibration curve and decision curve analyses. RESULTS After using a multistep screening method, we establish a predictive model. Several risk factors for delivering an LGA infant were identified (P &lt; 0.01), including weight gain during pregnancy, parity, triglyceride-glucose index, free tetraiodothyronine level, abdominal circumference, alanine transaminase-aspartate aminotransferase ratio and weight at 24 gestational weeks. The nomogram’s prediction ability was supported by the area under the curve (0.703, 0.709, and 0.699 for the training cohort, validation cohort, and test cohort, respectively). The calibration curves of the three cohorts displayed good agreement. The decision curve showed that the use of the 10%-60% threshold for identifying pregnant women with GDM who are at risk of delivering an LGA infant would result in a positive net benefit. CONCLUSION Our nomogram incorporated easily accessible risk factors, facilitating individualized prediction of pregnant women with GDM who are likely to deliver an LGA infant.

Risk factors for avascular necrosis of the femoral head after developmental hip dislocation reduction surgery and construction of Nomogram prediction model

BackgroundTo analyze the risk factors for the development of avascular necrosis (AVN) of the femoral head after reduction surgery in children with developmental hip dysplasia (DDH), and to establish a prediction nomogram.MethodsThe clinical data of 134 children with DDH (169 hips) treated with closure reduction or open reduction from December 2016 to December 2019 were retrospectively analyzed. Independent risk factors for AVN after DDH reduction being combined with cast external immobilization were determined by univariate analysis and multivariate logistic regression and used to generate nomograms predicting the occurrence of AVN.ResultsA total of 169 hip joints in 134 children met the inclusion criteria, with a mean age at surgery of 10.7 ± 4.56 months (range: 4–22 months) and a mean follow-up duration of 38.32 ± 27.00 months (range: 12–94 months). AVN developed in 42 hip joints (24.9%); univariate analysis showed that the International Hip Dysplasia Institute (IHDI) grade, preoperative development of the femoral head ossification nucleus, cartilage acetabular index, femoral head to acetabular Y-shaped cartilage distance, residual acetabular dysplasia, acetabular abduction angle exceeding 60°, and the final follow-up acetabular index (AI) were associated with the development of AVN (P < 0.05). Multivariate logistic regression analysis showed that the preoperative IHDI grade, development of the femoral head ossification nucleus, acetabular abduction angle exceeding 60°, and the final follow-up AI were independent risk factors for AVN development (P < 0.05). Internal validation of the Nomogram prediction model showed a consistency index of 0.833.ConclusionPreoperative IHDI grade, preoperative development of the femoral head ossification nucleus, final AI, and acetabular abduction angle exceeding 60° are risk factors for AVN development. This study successfully constructed a Nomogram prediction model for AVN after casting surgery for DDH that can predict the occurrence of AVN after casting surgery for DDH.

Predictive nomogram model for severe coronary artery calcification in end-stage kidney disease patients

Introduction The Agatston coronary artery calcification score (CACS) is an assessment index for coronary artery calcification (CAC). This study aims to explore the characteristics of CAC in end-stage kidney disease (ESKD) patients and establish a predictive model to assess the risk of severe CAC in patients. Methods CACS of ESKD patients was assessed using an electrocardiogram-gated coronary computed tomography (CT) scan with the Agatston scoring method. A predictive nomogram model was established based on stepwise regression. An independent validation cohort comprised of patients with ESKD from multicentres. Results 369 ESKD patients were enrolled in the training set, and 127 patients were included in the validation set. In the training set, the patients were divided into three subgroups: no calcification (CACS = 0, n = 98), mild calcification (0 < CACS ≤ 400, n = 141) and severe calcification (CACS > 400, n = 130). Among the four coronary branches, the left anterior descending branch (LAD) accounted for the highest proportion of calcification. Stepwise regression analysis showed that age, dialysis vintage, β-receptor blocker, calcium-phosphorus product (Ca × P), and alkaline phosphatase (ALP) level were independent risk factors for severe CAC. A nomogram that predicts the risk of severe CAC in ESKD patients has been internally and externally validated, demonstrating high sensitivity and specificity. Conclusion CAC is both prevalent and severe in ESKD patients. In the four branches of the coronary arteries, LAD calcification is the most common. Our validated nomogram model, based on clinical risk factors, can help predict the risk of severe coronary calcification in ESKD patients who cannot undergo coronary CT analysis.

Construction and validation of a predictive model for lower extremity deep vein thrombosis after total knee arthroplasty.

The aim was to investigate the independent risk factors for lower extremity deep vein thrombosis (DVT) after total knee arthroplasty, and to establish a nomogram prediction model accordingly. Data were collected from total knee replacement patients from January 2022 to December 2023 in our hospital. Unifactorial and multifactorial logistic regression analyses were used to determine the independent risk factors for lower extremity DVT after total knee arthroplasty and to establish the corresponding nomogram. The receiver operating characteristic curves were plotted and the area under the curve was calculated, and the calibration curves and decision curves were plotted to evaluate the model performance. A total of 652 patients with total knee arthroplasty were included in the study, and 142 patients after total knee arthroplasty developed deep veins in the lower extremities, with an incidence rate of 21.78%. After univariate and multivariate logistic regression analyses, a total of 5 variables were identified as independent risk factors for lower extremity DVT after total knee arthroplasty: age > 60 years (OR: 1.70; 95% CI: 1.23-3.91), obesity (OR: 1.51; 95% CI: 1.10-1.96), diabetes mellitus (OR: 1.80; 95% CI: 1.23-2.46), D-dimer > 0.5 mg/L (OR: 1.47; 95% CI: 1.07-1.78), and prolonged postoperative bed rest (OR: 1.64; 95% CI: 1.15-3.44). the nomogram constructed in this study for lower extremity DVT after total knee arthroplasty has good predictive accuracy, which helps physicians to intervene in advance in patients at high risk of lower extremity DVT after total knee arthroplasty.

Screening of co-expressed genes in hypopharyngeal carcinoma with esophageal carcinoma based on RNA sequencing and Clinical Research

To explore the hub comorbidity genes and potential pathogenic mechanisms of hypopharyngeal carcinoma with esophageal carcinoma, and evaluate their diagnostic value for hypopharyngeal carcinoma with co-morbid esophageal carcinoma. We performed gene sequencing on tumor tissues from 6 patients with hypopharyngeal squamous cell carcinoma with esophageal squamous cell carcinoma (hereafter referred to as “group A”) and 6 patients with pure hypopharyngeal squamous cell carcinoma (hereafter referred to as “group B”). We analyzed the mechanism of hub genes in the development and progression of hypopharyngeal squamous cell carcinoma with esophageal squamous cell carcinoma through bioinformatics, and constructed an ROC curve and Nomogram prediction model to analyze the value of hub genes in clinical diagnosis and treatment. 44,876 genes were sequenced in 6 patients with group A and 6 patients with group B. Among them, 76 genes showed significant statistical differences between the group A and the group B.47 genes were expressed lower in the group A than in the group B, and 29 genes were expressed higher. The top five hub genes were GABRG2, CACNA1A, CNTNAP2, NOS1, and SCN4B. GABRG2, CNTNAP2, and SCN4B in the hub genes have high diagnostic value in determining whether hypopharyngeal carcinoma patients have combined esophageal carcinoma (AUC: 0.944, 0.944, 0.972). These genes could possibly be used as potential molecular markers for assessing the risk of co-morbidity of hypopharyngeal carcinoma combined with esophageal carcinoma.

Nomogram-based risk assessment for emergency cervical cerclage failure in patients with cervical insufficiency

BackgroundCervical insufficiency can lead to preterm birth and neonatal mortality. Emergency cervical cerclage is a surgical intervention aimed at preventing preterm birth in patients with cervical insufficiency. However, some patients may experience cerclage failure. This study aimed to identify the risk factors associated with cerclage failure and develop a predictive nomogram model for patients with cervical insufficiency undergoing emergency cervical cerclage. MethodsData of 200 patients who underwent emergency cervical cerclage for cervical insufficiency were retrospectively analyzed. Patients were categorized into successful and failed groups based on their ability to take the infant home. Univariate and multivariate logistic regression analyses were performed to identify risk factors for cerclage failure. A nomogram model was developed based on multivariate logistic regression results, and its performance was assessed using receiver operating characteristic curves, calibration plots, and decision curve analysis (DCA). ResultsUnivariate logistic regression analysis identified 11 potential risk factors for cerclage failure, including the presence of polycystic ovary syndrome (PCOS), vaginitis, cervical dilation, preoperative C-reactive protein, routine vaginal lavage after cervical cerclage, delivery, gestational age, extended days, chorioamnionitis, intrauterine infection, cervical laceration, and premature rupture of membranes. Multivariate logistic regression analysis revealed that PCOS, cervical dilation after cervical cerclage were independent risk factors for cerclage failure while routine vaginal lavage was a protective factor against failure. The nomogram predictive model demonstrated an area under the curve value of 0.975, indicating excellent discriminatory ability. The calibration plot showed good consistency between the nomogram predictions and actual observations. DCA demonstrated the strong clinical applicability of the nomogram. ConclusionsThis study successfully identified risk factors associated with emergency cervical cerclage failure in patients with cervical insufficiency and developed a predictive nomogram model. This model can assist clinicians in making informed decisions and accurately predicting the risk of cerclage failure in these patients.

Development and application of a risk nomogram for the prediction of risk of carbapenem-resistant Acinetobacter baumannii infections in neuro-intensive care unit: a mixed method study

ObjectiveThis study aimed to develop and apply a nomogram with good accuracy to predict the risk of CRAB infections in neuro-critically ill patients. In addition, the difficulties and expectations of application such a tool in clinical practice was investigated.MethodsA mixed methods sequential explanatory study design was utilized. We first conducted a retrospective study to identify the risk factors for the development of CRAB infections in neuro-critically ill patients; and further develop and validate a nomogram predictive model. Then, based on the developed predictive tool, medical staff in the neuro-ICU were received an in-depth interview to investigate their opinions and barriers in using the prediction tool during clinical practice. The model development and validation is carried out by R. The transcripts of the interviews were analyzed by Maxqda.ResultsIn our cohort, the occurrence of CRAB infections was 8.63% (47/544). Multivariate regression analysis showed that the length of neuro-ICU stay, male, diabetes, low red blood cell (RBC) count, high levels of procalcitonin (PCT), and number of antibiotics ≥ 2 were independent risk factors for CRAB infections in neuro-ICU patients. Our nomogram model demonstrated a good calibration and discrimination in both training and validation sets, with AUC values of 0.816 and 0.875. Additionally, the model demonstrated good clinical utility. The significant barriers identified in the interview include “skepticism about the accuracy of the model”, “delay in early prediction by the indicator of length of neuro-ICU stay”, and “lack of a proper protocol for clinical application”.ConclusionsWe established and validated a nomogram incorporating six easily accessed indicators during clinical practice (the length of neuro-ICU stay, male, diabetes, RBC, PCT level, and the number of antibiotics used) to predict the risk of CRAB infections in neuro-ICU patients. Medical staff are generally interested in using the tool to predict the risk of CRAB, however delivering clinical prediction tools in routine clinical practice remains challenging.

PAX1 methylation as a robust predictor: developing and validating a nomogram for assessing endocervical curettage (ECC) necessity in human papillomavirus16/18-positive women undergoing colposcopy

ObjectiveThe major challenge in routine endocervical curettage (ECC) among Human Papillomavirus (HPV) 16/18-positive patients is that only a small fraction benefit. Nevertheless, current reported models often overestimate the validity and necessity of ECC, making it difficult to improve benefits for patients. This research hypothesized that assessing paired boxed gene 1 methylation levels (PAX1m) and clinical characteristics could enhance the predictive accuracy of detecting additional high-grade squamous intraepithelial lesions or worse (HSIL +) through ECC that were not identified by colposcopy-directed biopsy (CDB).MethodsData from 134 women with HPV16/18 positivity undergoing CDB and ECC between April 2018 and April 2022 were collected and analyzed. Quantitative methylation-specific polymerase chain reaction (qMSP) was utilized to measure PAX1m, expressed as ΔCp. Univariate and multivariate regression analyses were conducted to screen variables and select predictive factors. A nomogram was constructed using multivariate logistic regression to predict additional HSIL + detected by ECC. The discrimination, calibration, and clinical utility of the nomogram were evaluated using receiver operating characteristic curves (ROC) and the calibration plot.ResultsAge (odds ratio [OR], 5.654; 95% confidence interval [CI], 1.131–37.700), cytology (OR, 24.978; 95% CI, 3.085–540.236), and PAX1 methylation levels by grade (PAX1m grade) (OR, 7.801; 95% CI, 1.548–44.828) were independent predictive factors for additional detection of HSIL + by ECC. In HPV16/18-positive women, the likelihood of additional detection of HSIL + through ECC increased with the severity of cytological abnormalities, peaking at 43.8% for high-grade cytological lesions. Moreover, when cytological findings indicated low-grade lesions, PAX1 methylation levels were positively correlated with the additional detection of HSIL + by ECC (P value < 0.001). A nomogram prediction model was developed (area under curve (AUC) = 0.946; 95% CI, 0.901–0.991), demonstrating high sensitivity (90.9%) and specificity (90.5%) at the optimal cutoff point of 107. Calibration analysis confirmed the model’s strong agreement between predicted and observed probabilities.ConclusionThe clinical nomogram presented promising predictive performance for the additional detection of HSIL + through ECC among women with HPV16/18 infection. PAX1 methylation level could serve as a valuable tool in guiding individualized clinical decisions regarding ECC for patients with HPV 16/18 infection, particularly in cases of low-grade cytological findings.

Prediction of peritoneal free cancer cells in gastric cancer patients by golden-angle radial sampling dynamic contrast-enhanced magnetic resonance imaging.

Peritoneal free cancer cells can negatively impact disease progression and patient outcomes in gastric cancer. This study aimed to investigate the feasibility of using golden-angle radial sampling dynamic contrast-enhanced magnetic resonance imaging (GRASP DCE-MRI) to predict the presence of peritoneal free cancer cells in gastric cancer patients. All enrolled patients were consecutively divided into analysis and validation groups. Preoperative magnetic resonance imaging (MRI) scans and perfusion were performed in patients with gastric cancer undergoing surgery, and peritoneal lavage specimens were collected for examination. Based on the peritoneal lavage cytology (PLC) results, patients were divided into negative and positive lavage fluid groups. The data collected included clinical and MR information. A nomogram prediction model was constructed to predict the positive rate of peritoneal lavage fluid, and the validity of the model was verified based on data from the verification group. There was no statistical difference between the proportion of PLC-positive cases predicted by GRASP DCE-MR and the actual PLC test. MR tumor stage, tumor thickness, and perfusion parameter Tofts-Ketty model volume transfer constant (Ktrans) were independent predictors of positive peritoneal lavage fluid. The nomogram model featured a concordance index (C-index) of 0.785 and 0.742 for the modeling and validation groups, respectively. GRASP DCE-MR could effectively predict peritoneal free cancer cells in gastric cancer patients. The nomogram model constructed using these predictors may help clinicians to better predict the risk of peritoneal free cancer cells being present in gastric cancer patients.

Nomogram for Predicting Survival in Locally Advanced Cervical Cancer with Concurrent Chemoradiotherapy plus or Not Adjuvant Chemotherapy: A Retrospective Analysis Based on 2018 FIGO Staging.

Background: The comprehensive treatment mode of combining concurrent chemoradiotherapy (CCRT) with adjuvant chemotherapy (AC) is a commonly used mainstream model in the clinical practice of locally advanced cervical cancer (LACC). However, the necessity for AC after CCRT lacks sufficient evidence-based medical support. This study constructs a predictive model for the survival time dependence of CCRT ± AC for LACC based on the 2018 International Federation of Gynecology and Obstetrics (FIGO) staging with internal validation, the prognosis was assessed with intensity-modulated radiotherapy (IMRT) and concurrent cisplatin, and provides guidance for future stratified treatment. Materials and Methods: The retrospective analysis included 482 patients with LACC who CCRT from January 2016 to January 2023. Patients who used the 2009 FIGO staging were all standardized for the 2018 FIGO staging. The 482 patients with LACC were divided into a training set (n = 290) and a validation set (n = 192) at a ratio of 6:4. COX multivariate regression model and LASSO regression were used to screen for independent prognostic factors affecting progression-free survival (PFS) and overall survival (OS), and a nomogram clinical prediction model was constructed based on these factors. Evaluate the effectiveness of the model through the receiver operating characteristic curve, calibration curve, decision curve, risk heat map, and survival curves for risk stratification. Results: The PFS and OS independent prognostic risk factors affecting the 2018 FIGO staging of LACC during CCRT were validated to be similar to the 2009 FIGO staging prediction model reported in previous literature. In the training cohort, area under the curve (AUC) values at 1, 3, and 5 years were 0.941, 0.882, and 0.885 for PFS, and 0.946, 0.946, and 0.969 for OS, respectively. When applied to a test cohort, the model also showed accurate prediction result (AUC at 1, 3, and 5 years were 0.869, 0.891, and 0.899 for PFS, and 0.891, 0.941 and 0.878 for OS, respectively). Subgroup analysis suggests that patients with LACC, adenocarcinoma, stage IVA, pelvic lymph node metastasis, pretreatment hemoglobin ≤100 g/l and residual tumor diameter >2 cm, who received CCRT in the 2018 FIGO stage, may benefit more from adjuvant chemtherapy. Conclusions: Based on the 2018 FIGO staging, a nomogram prediction model for PFS and OS in patients with LACC undergoing CCRT was developed. The model, established by combining weighted clinical and pathological factors, can provide more personalized treatment predictions in clinical practice. For patients with high-risk factors such as residual tumor diameter > 2 cm after CCRT for LACC, AC may bring benefits.