Nomogram for predicting overall survival after curative gastrectomy using inflammatory, nutritional and pathological factors / Nomograma para predecir la supervivencia global después de una gastrectomía curativa utilizando factores inflamatorios, nutricionales y patológicos

Purpose: To establish a nomogram for predicting the overall survival (OS) in patients with gastric cancer (GC) based on inflammatory, nutritional and pathological factors. Methods: GC patients underwent curative gastrectomy from January 2012 to June 2017 in our hospital were included, and were classified into training set and validation set with a ratio of 7:3. Then variables associated with OS were analyzed using univariate and multivariate Cox regression analysis. Nomograms predicting OS were built using variables from multivariable Cox models. Finally, Kaplan–Meier curve and Log-rank test were also conducted to analyze the 1-yr, 3-yr and 5-yr OS to validate the efficiency of risk stratification of the nomogram. Results: A total of 366 GC patients were included. After univariate and multivariate Cox regression analysis, age (HR = 1.52, 95% CI = 1.01–2.30, P = 0.044), CA50 (HR = 1.90, 95% CI = 1.12–3.21, P = 0.017), PNI (HR = 1.65, 95% CI = 1.13–2.39, P = 0.009), SII (HR = 1.46, 95% CI = 1.03–2.08, P = 0.036), T stage (HR = 2.26, 95% CI = 1.01–5.05, P = 0.048; HR = 7.24, 95% CI = 3.64–14.40, P < 0.001) were independent influencing factors on the survival time of GC patients. Five factors including CEA, prognostic nutritional index (PNI), systemic immune-inflammation index (SII), ln (tumor size), T stage, and N stage were identified and entered the nomogram, which showed good discrimination and calibration in both sets. On internal validation, 1-yr, 3-yr and 5-yr nomogram demonstrated a good discrimination with an area under the ROC curve (AUC) of 0.77, 0.84 and 0.86, respectively. The AUC for 1-yr, 3-yr and 5-yr nomogram in validation set was 0.77, 0.79 and 0.81, respectively. The OS in low risk group of training cohort and validation cohort was significantly higher than that of intermediate risk group and high risk group, respectively...(AU)

Nomogram for predicting early hypophosphatemia in term infants.

BACKGROUND: Physiological processes rely on phosphate, which is an essential component of adenosine triphosphate (ATP). Hypophosphatasia can affect nearly every organ system in the body. It is crucial to monitor newborns with risk factors for hypophosphatemia and provide them with the proper supplements. We aimed to evaluate the risk factors and develop a nomogram for early hypophosphatemia in term infants. METHODS: We conducted a retrospective study involving 416 term infants measured serum phosphorus within three days of birth. The study included 82 term infants with hypophosphatemia (HP group) and 334 term infants without hypophosphatemia (NHP group). We collected data on the characteristics of mothers, newborn babies, and childbirth. Furthermore, univariate and multivariate logistic regression analyses were performed to identify independent risk factors for hypophosphatemia in term infants, and a nomogram was developed and validated based on the final independent risk factors. RESULTS: According to our analysis, the multivariate logistic regression analysis showed that male, maternal diabetes, cesarean delivery, lower serum magnesium, and lower birth weight were independent risk factors for early hypophosphatemia in term infants. In addition, the C-index of the developed nomogram was 0.732 (95% CI = 0.668-0.796). Moreover, the calibration curve indicated good consistency between the hypophosphatemia diagnosis and the predicted probability, and a decision curve analysis (DCA) confirmed the clinical utility of the nomogram. CONCLUSIONS: The analysis revealed that we successfully developed and validated a nomogram for predicting early hypophosphatemia in term infants.

Nomogram predicting the efficacy of transurethral surgery in benign prostatic hyperplasia patients.

PURPOSE: This study aimed to develop and validate a nomogram for predicting the efficacy of transurethral surgery in benign prostatic hyperplasia (BPH) patients. METHODS: Patients with BPH who underwent transurethral surgery in the West China Hospital and West China Shang Jin Hospital were enrolled. Patients were retrospectively involved as the training group and were prospectively recruited as the validation group for the nomogram. Logistic regression analysis was utilized to generate nomogram for predicting the efficacy of transurethral surgery. The discrimination of the nomogram was assessed using the area under the receiver operating characteristic curve (AUC) and calibration plots were applied to evaluate the calibration of the nomogram. RESULTS: A total of 426 patients with BPH who underwent transurethral surgery were included in the study, and they were further divided into a training group (n = 245) and a validation group (n = 181). Age (OR 1.07, 95% CI 1.02-1.15, P < 0.01), the compliance of the bladder (OR 2.37, 95% CI 1.20-4.67, P < 0.01), the function of the detrusor (OR 5.92, 95% CI 2.10-16.6, P < 0.01), and the bladder outlet obstruction (OR 2.21, 95% CI 1.07-4.54, P < 0.01) were incorporated in the nomogram. The AUC of the nomogram was 0.825 in the training group, and 0.785 in the validation group, respectively. CONCLUSION: The nomogram we developed included age, the compliance of the bladder, the function of the detrusor, and the severity of bladder outlet obstruction. The discrimination and calibration of the nomogram were confirmed by internal and external validation.

Construction and validation of a nomogram for predicting lateral lymph node metastasis in pediatric and adolescent with differentiated thyroid carcinoma.

BACKGROUND: Limited research has been conducted to specifically investigate the identification of risk factors and the development of prediction models for lateral lymph node metastasis (LNM) in pediatric and adolescent differentiated thyroid carcinoma (DTC) populations, despite its significant association with unfavorable prognosis. METHODS: This study entails a retrospective analysis of the clinical characteristics exhibited by pediatric and adolescent patients who have been diagnosed with DTC. The data utilized for this analysis was sourced from the Surveillance, Epidemiology, and End Results (SEER) database, spanning the time frame from 2000 to 2020. Furthermore, the study incorporates patients who were treated at the Departments of Breast and Thyroid Surgery in the Second Clinical Medical College, Affiliated Fifth People's Hospital of Chengdu University of Traditional Chinese Medicine, as well as The General Hospital of Western Theater Command, during the period from 2010 to 2020. RESULTS: A cohort of 2631 patients from the SEER database, along with an additional 339 patients from our departments who met the specified inclusion criteria, were included in this study. Subsequently, four clinical variables, namely age, tumor size, multifocality, and extrathyroidal invasion, were identified as being significantly associated with lateral LNM in pediatric and adolescent DTC patients. These variables were then utilized to construct a nomogram, which demonstrated effective discrimination with a concordance index (C-index) of 0.731. Furthermore, the performance of this model was validated through both internal and external assessments, yielding C-index values of 0.721 and 0.712, respectively. Afterward, a decision curve analysis was conducted to assess the viability of this nomogram in predicting lymph node metastasis. CONCLUSION: The current investigation has effectively constructed a nomogram model utilizing visualized multipopulationsal data. Our findings demonstrate a significant association between various clinical characteristics and lateral LNM in pediatric and adolescent DTC patients. These outcomes hold substantial significance for healthcare practitioners, as they can employ this model to inform individualized clinical judgments for the pediatric and adolescent cohorts.

Development and validation of a nomogram for predicting new vertebral compression fractures after percutaneous kyphoplasty in postmenopausal patients.

BACKGROUND: Postmenopausal women face a heightened risk of developing new vertebral compression fractures (NVCFs) following percutaneous kyphoplasty (PKP) for osteoporotic vertebral compression fractures (OVCFs). This study aimed to develop and validate a visual nomogram model capable of accurately predicting NVCF occurrence post-PKP to optimize treatment strategies and minimize occurrence. METHODS: This retrospective study included postmenopausal women diagnosed with OVCF who underwent PKP at the Affiliated Hospital of Shandong University of Traditional Chinese Medicine between January 2016 and January 2021. Patient data, including basic information, surgical details, imaging records, and laboratory findings, were collected. The patients were categorized into two groups based on NVCF occurrence within 2 years post-PKP: the NVCF group and the non-NVCF group. Following the utilization of least absolute shrinkage and selection operator (LASSO) regression for feature selection, a nomogram was constructed. Model differentiation, calibration, and clinical applicability were evaluated using receiver operating characteristic (ROC), calibration, and decision (DCA) curve analyses. RESULTS: In total, 357 patients were included in the study. LASSO regression analysis indicated that cement leakage, poor cement diffusion, and endplate fracture were independent predictors of NVCF. The nomogram demonstrated excellent predictive accuracy and clinical applicability. CONCLUSIONS: This study used LASSO regression to identify three independent predictors of NVCF and developed a predictive model that could effectively predict NVCF occurrence in postmenopausal women. This simple prediction model can support medical decision-making and is feasible for clinical practice.

Development of a nomogram based on body composition analysis of quantitative computed tomography combined with clinical prognostic factors to predict disease-free survival after surgery and adjuvant chemotherapy in patients with gastric cancer.

Background: Patients with gastric cancer (GC) have a high recurrence rate after surgery. To predict disease-free survival (DFS), we investigated the value of body composition changes (BCCs) measured by quantitative computed tomography (QCT) in assessing the prognosis of patients with GC undergoing resection combined with adjuvant chemotherapy and to construct a nomogram model in combination with clinical prognostic factors (CPFs). Methods: A retrospective study of 60 patients with GC between February 2015 and June 2019 was conducted. Pre- and posttreatment CT images of patients was used to measure bone mineral density (BMD), subcutaneous fat area (SFA), visceral fat area (VFA), total fat area (TFA), paravertebral muscle area (PMA), and the rate of BCC was calculated. CPFs such as maximum tumor diameter (MTD), human epidermal growth factor receptor-2 (HER2), and Ki-67 were derived from postoperative pathological findings. Independent prognostic factors affecting DFS in GC were screened via univariate and multivariate Cox regression analysis. The Kaplan-Meier method and log-rank test were used to plot survival curves and compare the curves between groups, respectively. Receiver operating characteristic (ROC) curves, calibration curves, and decision curves to evaluate the efficacy of the nomogram. Results: The results of multivariate Cox regression analysis showed that ΔBMD [hazard ratio (HR): 4.577; 95% confidence interval (CI): 1.483-14.132; P=0.008], ΔPMA (HR: 5.784; 95% CI: 1.251-26.740; P=0.025), HER2 (HR: 4.819; 95% CI: 2.201-10.549; P<0.001), and maximal tumor diameter (HR: 3.973; 95% CI: 1.893-8.337; P<0.001) were independent factors influencing DFS. ΔBMD, ΔSFA, ΔVFA, ΔTFA, and ΔPMA were -3.86%, -23.44%, -19.57%, -22.45%, and -5.94%, respectively. The prognostic model of BCCs combined with CPFs had the highest predictive performance. Decision curve analysis (DCA) indicated good clinical benefit for the prognostic nomogram. The concordance index of the prognostic nomogram was 0.814, and the area under the curve (AUC) of predicting 2- and 3-year DFS were 0.879 and 0.928, respectively. The calibration curve showed that the nomogram-predicted DFS aligned well with the actual DFS. Conclusions: The prognostic nomogram combining BCCs and CPFs was able to reliably predict the DFS of patients with GC.

Exploring the potential mechanisms of Tongmai Jiangtang capsules in treating diabetic nephropathy through multi-dimensional data.

Background: Diabetic nephropathy (DN) is a prevalent and debilitating disease that represents the leading cause of chronic kidney disease which imposes public health challenges Tongmai Jiangtang capsule (TMJT) is commonly used for the treatment of DN, albeit its underlying mechanisms of action are still elusive. Methods: This study retrieved databases to identify the components and collect the targets of TMJT and DN. Target networks were constructed to screen the core components and targets. Samples from the GEO database were utilized to perform analyses of targets and immune cells and obtain significantly differentially expressed core genes (SDECGs). We also selected a machine learning model to screen the feature genes and construct a nomogram. Furthermore, molecular docking, another GEO dataset, and Mendelian randomization (MR) were utilized for preliminary validation. We subsequently clustered the samples based on SDECG expression and consensus clustering and performed analyses between the clusters. Finally, we scored the SDECG score and analyzed the differences between clusters. Results: This study identified 13 SDECGs between DN and normal groups which positively regulated immune cells. We also identified five feature genes (CD40LG, EP300, IL1B, GAPDH, and EGF) which were used to construct a nomogram. MR analysis indicated a causal link between elevated IL1B levels and an increased risk of DN. Clustering analysis divided DN samples into four groups, among which, C1 and CI were mainly highly expressed and most immune cells were up-regulated. C2 and CII were the opposite. Finally, we found significant differences in SDECG scores between C1 and C2, CI and CII, respectively. Conclusion: TMJT may alleviate DN via core components (e.g. Denudatin B, hancinol, hirudinoidine A) targeting SDECGs (e.g. SRC, EGF, GAPDH), with the involvement of feature genes and modulation of immune and inflammation-related pathways. These findings have potential implications for clinical practice and future investigations.

Development and External Validation of a Nomogram for Predicting the Effect of RTX on the Treatment of Membranous Nephropathy.

Introduction: Rituximab (RTX) has been shown to be effective in inducing immunological remission in patients with membranous nephropathy (MN). Some patients required more than one course of RTX to achieve immunological remission. Identifying patients who need more courses of RTX to achieve immunological remission is beneficial for better physician-patient communication, the assessment of treatment course, and the evaluation of medical costs. This study aims to establish a practical model to predict the probability of immunological remission after receiving one cycle of RTX. Methods: This study enrolled 106 patients from the First Affiliated Hospital of Zhengzhou University in the modeling group and 30 patients from Henan Provincial Hospital of Traditional Chinese Medicine in the external validation group. Patients in the modeling group were divided into responders or nonresponders according to whether they achieved immunological remission or not after following up for 6 months. A nomogram was established based on the results of logistic regression analysis. The predictive performance of the nomogram was evaluated by the area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis (DCAs). Results: In the modeling group, 75 (70.8%) patients achieved immunological remission within 6 months after receiving one cycle of RTX. Significant differences were observed between nonresponders and responders. Risk factors used in nomogram included PLA2R antibody, hemoglobin, and gender. The AUC value of nomogram was 0.797 (95% CI 0.701-0.894, P<0.001). The calibration curves demonstrated acceptable agreement between the predicted outcomes by the nomogram and the actual values. DCA curves showed good positive net benefits in the predictive model. The external validation also demonstrated the reliability of the prediction nomogram. Conclusion: A predictive nomogram including PLA2R antibody, hemoglobin, and gender may provide a basis to predict the doses of RTX needed in MN patients.

Lifestyle-based nomogram for identifying the Chaoshan inhabitants of China at high risk of Helicobacter pylori infection.

BACKGROUND: Helicobacter pylori (HP) infection is associated with various diseases. Early detection can prevent the onset of illness. We constructed a nomogram to predict groups at high risk of HP infection. METHODS: Patients who underwent regular medical check-ups at hospital in Chaoshan, China from March to September 2022 were randomly allocated to the training and validation cohorts. Risk factors including basic characteristics and lifestyle habits associated with HP infection were analyzed by logistic regression analyses. The independent varieties were calculated and plotted into a nomogram. The nomogram was internally validated by receiver operating characteristic curve, calibration, and decision curve analyses (DCAs). RESULTS: Of the 945 patients, 680 were included in the training cohort and 265 in the validation cohort. 356 patients in training cohort with positive 13 C-UBT results served as the infected group, and 324 without infection were the control group. The multivariate regression analyses showed that the risk factors for HP infection included alcohol consumption (OR = 1.29, 95%CI = 0.78-2.13, P = 0.03), family history of gastric disease (OR = 4.35, 95%CI = 1.47-12.84, P = 0.01), living with an HP-positive individual (OR = 18.09, 95%CI = 10.29-31.82, P < 0.0001), drinking hot tea (OR = 1.58, 95%CI = 1.05-2.48, P = 0.04), and infection status of co-drinkers unknown (OR = 2.29, 95%CI = 1.04-5.06, P = 0.04). However, drinking tea > 3 times per day (OR = 0.56, 95%CI = 0.33-0.95, P = 0.03), using serving chopsticks (OR = 0.30, 95%CI = 0.12-0.49, P < 0.0001) were protective factors for HP infection. The nomogram had an area under the curve (AUC) of 0.85 in the training cohort. The DCA was above the reference line within a large threshold range, indicating that the model was better. The calibration analyses showed the actual occurrence rate was basically consistent with the predicted occurrence rate. The model was validated in the validation cohort, and had a good AUC (0.80), DCA and calibration curve results. CONCLUSIONS: This nomogram, which incorporates basic characteristics and lifestyle habits, is an efficient model for predicting those at high risk of HP infection in the Chaoshan region.

Development and validation of risk prediction model for premenstrual syndrome in nurses: results from the nurses-based the TARGET cohort study.

Objective: Premenstrual syndrome (PMS) stands as a significant concern within the realm gynecological disorders, profoundly impacting women of childbearing age in China. However, the elusive nature of its risk factors necessitates investigation. This study, therefore, is dedicated to unraveling the intricacies of PMS by focusing on nurses, a cohort with unique occupational stressors, to develop and validate a predictive model for assessing the risk of PMS. Methods: This investigation employed a multi-center cross-sectional analysis drawing upon data from the TARGET Nurses' health cohort. Utilizing online survey versions of the Premenstrual Syndrome Scale (PMSS), a comprehensive dataset encompassing physiological, social, psychological, occupational, and behavioral variables was collected from 18,645 participants. A stepwise multivariate logistic regression analysis was conducted to identify independent risk factors for PMS. Furthermore, a refined variable selection process was executed, combining the Least Absolute Shrinkage and Selection Operator (LASSO) method with 10-fold cross-validation. The visualization of the risk prediction model was achieved through a nomogram, and its performance was evaluated using the C index, receiver operating characteristic (ROC) curves, and the calibration curves. Results: Among the diverse variables explored, this study identified several noteworthy predictors of PMS in nurses, including tea or coffee consumption, sleep quality, menstrual cycle regularity, intermenstrual bleeding episodes, dysmenorrhea severity, experiences of workplace bullying, trait coping style, anxiety, depression and perceived stress levels. The prediction model exhibited robust discriminatory power, with an area under the curve of 0.765 for the training set and 0.769 for the test set. Furthermore, the calibration curve underscored the model's high degree of alignment with observed outcomes. Conclusion: The developed model showcases exceptional accuracy in identifying nurses at risk of PMS. This early alert system holds potential to significantly enhance nurses' well-being and underscore the importance of professional support.

Enhancing prognostic accuracy: a SEER-based analysis for overall and cancer-specific survival prediction in cervical adenocarcinoma patients.

BACKGROUND: Cervical adenocarcinoma (CA) is the second most prevalent histological subtype of cervical cancer, following cervical squamous cell carcinoma (CSCC). As stated in the guidelines provided by the National Comprehensive Cancer Network, they are staged and treated similarly. However, compared with CSCC patients, CA patients are more prone to lymph node metastasis and recurrence with a poorer prognosis. The objective of this research was to discover prognostic indicators and develop nomograms that can be utilized to anticipate the overall survival (OS) and cancer-specific survival (CSS) of patients diagnosed with CA. METHODS: Using the Surveillance, Epidemiology, and End Result (SEER) database, individuals with CA who received their diagnosis between 2004 and 2015 were identified. A total cohort (n = 4485) was randomly classified into two separate groups in a 3:2 ratio, to form a training cohort (n = 2679) and a testing cohort (n = 1806). Overall survival (OS) was the primary outcome measure and cancer-specific survival (CSS) was the secondary outcome measure. Univariate and multivariate Cox analyses were employed to select significant independent factors and Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression analysis was utilized to develop predictive nomogram models. The predictive accuracy and discriminatory ability of the nomogram were assessed by employing metrics such as the calibration curve, receiver operating characteristic (ROC) curve, and the concordance index (C-index). RESULTS: Age, Tumor Node Metastasis stages (T, N, and M), SEER stage, grade, and tumor size were assessed as common independent predictors of both OS and CSS. The C-index value of the nomograms for predicting OS was 0.832 (95% CI 0.817-0.847) in the training cohort and 0.823 (95% CI 0.805-0.841) in the testing cohort. CONCLUSION: We developed and verified nomogram models for predicting 1-, 3- and 5-year OS and CSS among patients with cervical adenocarcinoma. These models exhibited excellent performance in prognostic prediction, providing support and assisting clinicians in assessing survival prognosis and devising personalized treatments for CA patients.

The use of transcutaneous bilirubin nomograms for the prevention of bilirubin neurotoxicity in the neonates.

Purpose: Although neonatal jaundice is a ubiquitous and predominantly benign phenomenon, the risk of neurotoxicity exists in a number of infants with unconjugated hyperbilirubinemia. Plotting bilirubin values on nomograms enables clinicians to employ an anticipatory and individualized approach with the goal of avoiding excessive hyperbilirubinemia and preventing acute bilirubin encephalopathy and its progression to kernicterus. We aimed to construct nomograms for White term infants based on transcutaneous bilirubin (TcB) measurements using a JM-105 device. Methods: TcB measurements were taken in infants at ages ranging from 0 to 96 postnatal hours. We then constructed hour-specific TcB nomograms from forehead and sternum measurements in infants who did not require subsequent phototherapy. Results: We included 2,981 TcB measurements taken on the forehead and 2,977 measurements taken on the sternum in 301 White term newborn infants. We assessed the predictive abilities of the nomograms at six postnatal time intervals using receiver operating characteristic curves. The areas under the curves indicated reasonable prediction of hyperbilirubinemia requiring phototherapy, except for the forehead measurement taken within the first 12 h of life. Sensitivity tended to rise as postnatal age increased. Conclusion: The nomograms illustrate dermal bilirubin dynamics in White term neonates during the first 4 days of life. They may be useful tools to predict individualized risk of hyperbilirubinemia requiring treatment, and to plan optimal follow-up of infants at risk of bilirubin neurotoxicity.

A new risk predictive scoring system of vasovagal reactions in patients with preoperative autologous blood donation.

BACKGROUND AND OBJECTIVES: Vasovagal response (VVR) is the most common adverse reaction during blood donation and it is the main element for the safety of the patients with preoperative autologous blood donation (PABD). Accurate identification high-risk group is of great significance for PABD. Our study aimed to establish a scoring system based on the nomogram to screen the high-risk population and provide evidence for preventing the occurrence of VVRs. MATERIALS AND METHODS: A number of 4829 patients underwent PABD between July 2017 and June 2020 in the first medical center of Chinese PLA Hospital were recruited, 3387 of whom were included in the training group (70 %; 108 VVRs patients vs 3279 Non-VVRs patients), 1442 were included in the validation group (30 %; 46 VVRs patients vs 1396 Non-VVRs patients). The data were analyzed by univariate and multivariate logistic regression. The nomogram of the scoring system was created by using the RMS tool in R software. RESULTS: Seven variables including BMI, hematocrit, pre-phlebotomy heart rate and systolic blood pressure, history of blood donation, age group and primary disease were selected to build the nomogram, which was shown as prediction model. And the score was 0-1 for BMI, 0-2 for hematocrit, systolic blood pressure, heart rate and no blood donation history, 0-10 for age, 0-3 for primary disease. When the total cutoff score was 11, the predictive system for identifying VVRs displayed higher diagnostic accuracy. The area under the curve, specificity, and sensitivity of the training group were 0.942, 82.41 % and 97.17 %, respectively, whereas those of the validation group were 0.836, 78.26 % and 78.15 %, respectively. CONCLUSION: A risk predictive scoring system was successfully developed to identify high-risk VVRs group form PABD patients that performed well.

Nomogram predicting overall survival after surgical resection for retroperitoneal leiomyosarcoma patients.

Background: Surgery is the best way to cure the retroperitoneal leiomyosarcoma (RLMS), and there is currently no prediction model on RLMS after surgical resection. The objective of this study was to develop a nomogram to predict the overall survival (OS) of patients with RLMS after surgical resection. Methods: Patients who underwent surgical resection from September 2010 to December 2020 were included. The nomogram was constructed based on the COX regression model, and the discrimination was assessed using the concordance index. The predicted OS and actual OS were evaluated with the assistance of calibration plots. Results: 118 patients were included. The median OS for all patients was 47.8 (95% confidence interval (CI), 35.9-59.7) months. Most tumor were completely resected (n=106, 89.8%). The proportions of French National Federation of Comprehensive Cancer Centres (FNCLCC) classification were equal as grade 1, grade 2, and grade 3 (31.4%, 30.5%, and 38.1%, respectively). The tumor diameter of 73.7% (n=85) patients was greater than 5 cm, the lesions of 23.7% (n=28) were multifocal, and 55.1% (n=65) patients had more than one organ resected. The OS nomogram was constructed based on the number of resected organs, tumor diameter, FNCLCC grade, and multifocal lesions. The concordance index of the nomogram was 0.779 (95% CI, 0.659-0.898), the predicted OS and actual OS were in good fitness in calibration curves. Conclusion: The nomogram prediction model established in this study is helpful for postoperative consultation and the selection of patients for clinical trial enrollment.

Analysis and prediction of the efficacy and influencing factors of camrelizumab combined with TACE and sorafenib in the treatment of advanced hepatocellular carcinoma.

BACKGROUND: TACE combined with targeted therapy is a method for the treatment of hepatocellular carcinoma. After adding camrelizumab, some patients had gained benefits, but some patients have produced serious adverse reactions. Therefore, more studies are needed to prove the efficacy and adverse reactions, and prediction models are needed to help with decision-making. METHODS: With ethics committee approval, a bi-center retrospective study was finished. A total of 235 patients were enrolled and divided into the treatment group of camrelizumab combined with TACE and sorafenib and the treatment group of TACE and sorafenib. The survival rate, short-term efficacy and adverse reactions were compared, and the efficacy prediction model was established. RESULTS: The 2-year survival time and objective response rate of the treatment group of camrelizumab combined with TACE plus sorafenib were higher than those of TACE plus sorafenib. Camrelizumab increased the proportion of reactive capillary proliferation, but had no effect on other adverse reactions. The established nomogram can accurately predict the response to the treatment. CONCLUSIONS: Camrelizumab combined with TACE and sorafenib can improve the survival rate of patients with hepatocellular carcinoma, and it is an effective treatment. The nomogram model can predict the efficacy, which is beneficial for patients.

Prediction nomogram based on 18F-FDG PET/CT and clinical parameters for patients with diffuse large B-cell lymphoma.

The objective of this study was to develop a nomogram including parameters assessed by 18F-FDG PET/CT and clinical parameters for patients with diffuse large B-cell lymphoma (DLBCL) to predict progression-free survival (PFS). A total of 181 patients with pathologically diagnosed DLBCL at Sichuan Cancer Hospital and Institute from March 2015 to December 2020 were enrolled in this retrospective study. The area under the receiver operating characteristic (ROC) curve (AUC) was used to calculate the optimal cutoff values of the semiquantitative parameters (SUVmax, TLG, MTV, and Dmax) for PFS. A nomogram was constructed according to multivariate Cox proportional hazards regression. The predictive and discriminatory capacities of the nomogram were then measured using the concordance index (C-index), calibration plots, and Kaplan-Meier curves. The predictive and discriminatory capacities of the nomogram and the International Prognostic Index of the National Comprehensive Cancer Network (NCCN-IPI) were compared via the C-index and AUC. Multivariate analysis demonstrated that male gender and pretreatment Ann Arbor stage III-IV, non-GCB, elevated lactate dehydrogenase (LDH), number of extranodal organ involvement (Neo)>1, MTV≥152.8 cm3, and Dmax ≥53.9 cm were associated with unfavorable PFS (all p<0.05). The nomogram, including gender, Ann Arbor stage, pathology type, Neo, LDH levels, MTV, and Dmax, showed good prediction accuracy, with a C-index of 0.760 (95% CI: 0.727-0.793), which was higher than that of NCCN-IPI (0.710; 95% CI: 0.669-751). The calibration plots for 2-year demonstrated good consistency between the predicted and observed probabilities for survival time. We established a nomogram including MTV, Dmax, and several clinical parameters to predict the PFS of patients with DLBCL, and the nomogram showed better predictability and higher accuracy than NCCN-IPI.

A nomogram diagnostic cardiovascular disease in patients with erythrodermic psoriasis in Chinese.

Objective: Patients with erythrodermic psoriasis (EP) are associated with an increased risk of cardiovascular disease (CVD), because of the more severe inflammation in the skin areas. This study aimed to develop a diagnostic model for the risk of CVD in EP patients based on the available features and multidimensional clinical data. Methods: A total of 298 EP patients from Beijing Hospital of Traditional Chinese Medicine were retrospectively included in this study from May 5th, 2008, to March 3rd, 2022. Of them, 213 patients were selected as the development set by random sampling, and clinical parameters were analyzed by univariate and backward stepwise regression. Whereas the remaining 85 patients were randomly selected as the validation set. The model performance was later assessed in terms of discrimination, calibration, and clinical usefulness. Results: In the development set, the CVD rate was 9%, which was independently correlated with age, glycated albumin (GA>17%), smoking, albumin (ALB<40 g/L), and lipoprotein(a) (Lp(a)>300 mg/L). The area under the ROC curve (AUC) value was 0.83 (95% confidence interval CI, 0.73,0.93). For the validation set of EP patients, the AUC value was 0.85 (95%CI, 0.76,0.94). According to decision curve analysis, our model exhibited favorable clinical applicability. Conclusion: EP patients with age, GA>17%, smoking, ALB<40 g/L, and Lp(a)>300 mg/L are associated with a higher risk of CVD. The nomogram model performs well in predicting the probability of CVD in EP patients, which may help improve perioperative strategies and good treatment outcomes.

External Tertiary-Care-Hospital Validation of the Epidemiological SEER-Based Nomogram Predicting Downgrading in High-Risk Prostate Cancer Patients Treated with Radical Prostatectomy.

We aimed to externally validate the SEER-based nomogram used to predict downgrading in biopsied high-risk prostate cancer patients treated with radical prostatectomy (RP) in a contemporary European tertiary-care-hospital cohort. We relied on an institutional tertiary-care database to identify biopsied high-risk prostate cancer patients in the National Comprehensive Cancer Network (NCCN) who underwent RP between January 2014 and December 2022. The model's downgrading performance was evaluated using accuracy and calibration. The net benefit of the nomogram was tested with decision-curve analyses. Overall, 241 biopsied high-risk prostate cancer patients were identified. In total, 51% were downgraded at RP. Moreover, of the 99 patients with a biopsy Gleason pattern of 5, 43% were significantly downgraded to RP Gleason pattern ≤ 4 + 4. The nomogram predicted the downgrading with 72% accuracy. A high level of agreement between the predicted and observed downgrading rates was observed. In the prediction of significant downgrading from a biopsy Gleason pattern of 5 to a RP Gleason pattern ≤ 4 + 4, the accuracy was 71%. Deviations from the ideal predictions were noted for predicted probabilities between 30% and 50%, where the nomogram overestimated the observed rate of significant downgrading. This external validation of the SEER-based nomogram confirmed its ability to predict the downgrading of biopsy high-risk prostate cancer patients and its accurate use for patient counseling in high-volume RP centers.

Construction and validation of a nomogram for hepatocellular carcinoma patients treated by traditional Chinese medicine based on inflammation, nutrition, and blood lipid indicators.

PURPOSE: To establish a nomogram for hepatocellular carcinoma (HCC) patients treated by traditional Chinese medicine (TCM). METHODS: Clinical cases of HCC patients treated by TCM at Hunan Integrated Traditional Chinese and Western Medicine Hospital, and it was randomly divided into the training cohort (n = 222) and the validation cohort (n = 95). In the training cohort, independent risk factors were determined by Cox regression analysis and a nomogram was constructed. The efficiency and clinical applicability of nomograms were evaluated using time-dependent curves, calibration, and the decision curve (DCA), and the patients were divided into high-risk, middle-risk and low-risk groups using X-tile software. RESULTS: Multivariate Cox regression analysis screened 6 independent risk factors to construct a nomogram of HCC patients, including TNM stage, treatment methods, high-density lipoprotein (HDL), neutrophil-to-lymphocyte ratio (NLR), albumin-to-globulin ratio (AGR), and prognostic nutritional index (PNI). The consistency index (C-index) of the nomogram of the training was 0.811 (0.794-0.829) and the validation cohort was 0.825 (0.800-0.849). The time dependency showed the AUC values of the nomogram for 3 and 5 years in training cohort were 0.894 (95% CI 0.840-0.948) and 0.952 (95% CI 0.914-0.990), and the validation cohort were 0.928 (95% CI 0.865-0.990) and 0.96 3(95% CI 0.916-1.010). The calibration plot showed the nomogram fits well onto perfect curves, and the DCA curve showed the net benefit of the nomogram at a certain probability threshold is significantly higher than the net benefit of the TNM stage at the same threshold probability. Finally, all the patients were divided into high-risk, middle-risk and low-risk groups based on the total score of nomogram, and it showed effectively how to identify to high-risk patients. CONCLUSION: The nomogram established by the independent risk factors of TNM stage, treatment methods, HDL, AGR, NLR and PNI can predict the prognosis of HCC patients treated by TCM, providing an effective tool to clinical workers to evaluate the prognosis and survival time of HCC patients.

Myofascial Frequency Syndrome: A novel syndrome of bothersome lower urinary tract symptoms associated with myofascial pelvic floor dysfunction.

Background: Patients presenting with lower urinary tract symptoms (LUTS) are historically classified to several symptom clusters, primarily overactive bladder (OAB) and interstitial cystitis/bladder pain syndrome (IC/BPS). Accurate diagnosis, however, is challenging due to overlapping symptomatic features, and many patients do not readily fit into these categories. To enhance diagnostic accuracy, we previously described an algorithm differentiating OAB from IC/BPS. Herein, we sought to validate the utility of this algorithm for identifying and classifying a real-world population of individuals presenting with OAB and IC/BPS and characterize patient subgroups outside the traditional LUTS diagnostic paradigm. Methods: An Exploratory cohort of 551 consecutive female subjects with LUTS evaluated in 2017 were administered 5 validated genitourinary symptom questionnaires. Application of the LUTS diagnostic algorithm classified subjects into controls, IC/BPS, and OAB, with identification of a novel group of highly bothered subjects lacking pain or incontinence. Symptomatic features of this group were characterized by statistically significant differences from the OAB, IC/BPS and control groups on questionnaires, comprehensive review of discriminate pelvic exam, and thematic analysis of patient histories. In a Reassessment cohort of 215 subjects with known etiologies of their symptoms (OAB, IC/BPS, asymptomatic microscopic hematuria, or myofascial dysfunction confirmed with electromyography), significant associations with myofascial dysfunction were identified in a multivariable regression model. Pre-referral and specialist diagnoses for subjects with myofascial dysfunction were catalogued. Findings: Application of a diagnostic algorithm to an unselected group of 551subjects presenting for urologic care identified OAB and IC/BPS in 137 and 96 subjects, respectively. An additional 110 patients (20%) with bothersome urinary symptoms lacked either bladder pain or urgency characteristic of IC/BPS and OAB, respectively. In addition to urinary frequency, this population exhibited a distinctive symptom constellation suggestive of myofascial dysfunction characterized as "persistency": bothersome urinary frequency resulting from bladder discomfort/pelvic pressure conveying a sensation of bladder fullness and a desire to urinate. On examination, 97% of persistency patients demonstrated pelvic floor hypertonicity with either global tenderness or myofascial trigger points, and 92% displayed evidence of impaired muscular relaxation, hallmarks of myofascial dysfunction. We therefore classified this symptom complex "myofascial frequency syndrome". To confirm this symptom pattern was attributable to the pelvic floor, we confirmed the presence of "persistency" in 68 patients established to have pelvic floor myofascial dysfunction through comprehensive evaluation corroborated by symptom improvement with pelvic floor myofascial release. These symptoms distinguish subjects with myofascial dysfunction from subjects with OAB, IC/BPS, and asymptomatic controls, confirming that myofascial frequency syndrome is a distinct LUTS symptom complex. Interpretation: This study describes a novel, distinct phenotype of LUTS we classified as myofascial frequency syndrome in approximately one-third of individuals with urinary frequency. Common symptomatic features encompass elements in other urinary syndromes, such as bladder discomfort, urinary frequency and urge, pelvic pressure, and a sensation of incomplete emptying, causing significant diagnostic confusion for providers. Inadequate recognition of myofascial frequency syndrome may partially explain suboptimal overall treatment outcomes for women with LUTS. Recognition of the distinct symptom features of MFS (persistency) should prompt referral to pelvic floor physical therapy. To improve our understanding and management of this as-yet understudied condition, future studies will need to develop consensus diagnostic criteria and objective tools to assess pelvic floor muscle fitness, ultimately leading to corresponding diagnostic codes. Funding: This work was supported by the AUGS/Duke UrogynCREST Program (R25HD094667 (NICHD)) and by NIDDK K08 DK118176 and Department of Defense PRMRP PR200027, and NIA R03 AG067993.