Novel prognostic nomograms for postoperative patients with oral cavity squamous cell carcinoma in the central region of China.

BACKGROUND: Oral cavity squamous cell carcinoma (OCSCC) is the most common pathological type in oral tumors. This study intends to construct a novel prognostic nomogram model based on China populations for these resectable OCSCC patients, and then validate these nomograms. METHODS: A total of 607 postoperative patients with OCSCC diagnosed between June 2012 and June 2018 were obtained from two tertiary medical institutions in Xinxiang and Zhengzhou. Then, 70% of all the cases were randomly assigned to the training group and the rest to the validation group. The endpoint time was defined as overall survival (OS) and disease-free survival (DFS). The nomograms for predicting the 3-, and 5-year OS and DFS in postoperative OCSCC patients were established based on the independent prognostic factors, which were identified by the univariate analysis and multivariate analysis. A series of indexes were utilized to assess the performance and net benefit of these two newly constructed nomograms. Finally, the discrimination capability of OS and DFS was compared between the new risk stratification and the American Joint Committee on Cancer (AJCC) stage by Kaplan-Meier curves. RESULTS: 607 postoperative patients with OCSCC were selected and randomly assigned to the training cohort (n = 425) and validation cohort (n = 182). The nomograms for predicting OS and DFS in postoperative OCSCC patients had been established based on the independent prognostic factors. Moreover, dynamic nomograms were also established for more convenient clinical application. The C-index for predicting OS and DFS were 0.691, 0.674 in the training group, and 0.722, 0.680 in the validation group, respectively. Besides, the calibration curve displayed good consistency between the predicted survival probability and actual observations. Finally, the excellent performance of these two nomograms was verified by the NRI, IDI, and DCA curves in comparison to the AJCC stage system. CONCLUSION: The newly established and validated nomograms for predicting OS and DFS in postoperative patients with OCSCC perform well, which can be helpful for clinicians and contribute to clinical decision-making.

Development and validation of an online dynamic nomogram system for pulmonary consolidation in children with Mycoplasma pneumoniae pneumonia.

INTRODUCTION: The occurrence of pulmonary consolidation in children with Mycoplasma pneumoniae pneumonia (MPP) can lead to exacerbation of the disease. Therefore, early identification of children with MPP in combination with pulmonary consolidation is critical. The purpose of this study was to develop a straightforward, easy-to-use online dynamic nomogram for the identification of children with MPP who are at high risk of developing pulmonary consolidation. METHODS: 491 MPP patients were chosen and divided randomly into a training cohort and an internal validation cohort at a 4:1 ratio. Multi-factor logistic regression was used to identify the risk variables for mixed pulmonary consolidation in children with Mycoplasma pneumoniae (MP). The selected variables were utilized to build the nomograms and validated using the C-index, decision curve analysis, calibration curves, and receiver operating characteristic (ROC) curves. RESULTS: Seven variables were included in the Nomogram model: age, fever duration, lymphocyte count, C-reactive protein (CRP), ferritin, T8 lymphocyte percentage, and T4 lymphocyte percentage. We created a dynamic nomogram that is accessible online ( https://ertong.shinyapps.io/DynNomapp/ ). The C-index was 0.90. The nomogram calibration curves in the training and validation cohorts were highly comparable to the standard curves. The area under the curve (AUC) of the prediction model was, respectively, 0.902 and 0.883 in the training cohort and validation cohort. The decision curve analysis (DCA) curve shows that the model has a significant clinical benefit. CONCLUSIONS: We developed a dynamic online nomogram for predicting combined pulmonary consolidation in children with MP based on 7 variables for the first time. The predictive value and clinical benefit of the nomogram model were acceptable.

A dynamic model to predict early occurrence of acute kidney injury in ICU hospitalized cirrhotic patients: a MIMIC database analysis.

BACKGROUND: This study aimed to develop a tool for predicting the early occurrence of acute kidney injury (AKI) in ICU hospitalized cirrhotic patients. METHODS: Eligible patients with cirrhosis were identified from the Medical Information Mart for Intensive Care database. Demographic data, laboratory examinations, and interventions were obtained. After splitting the population into training and validation cohorts, the least absolute shrinkage and selection operator regression model was used to select factors and construct the dynamic online nomogram. Calibration and discrimination were used to assess nomogram performance, and clinical utility was evaluated by decision curve analysis (DCA). RESULTS: A total of 1254 patients were included in the analysis, and 745 developed AKI. The mean arterial pressure, white blood cell count, total bilirubin level, Glasgow Coma Score, creatinine, heart rate, platelet count and albumin level were identified as predictors of AKI. The developed model had a good ability to differentiate AKI from non-AKI, with AUCs of 0.797 and 0.750 in the training and validation cohorts, respectively. Moreover, the nomogram model showed good calibration. DCA showed that the nomogram had a superior overall net benefit within wide and practical ranges of threshold probabilities. CONCLUSIONS: The dynamic online nomogram can be an easy-to-use tool for predicting the early occurrence of AKI in critically ill patients with cirrhosis.

Development and external validation of a nomogram for predicting lymph node metastasis in 1-3 cm lung adenocarcinoma.

Aim: This study aimed to investigate the risk factors for lymph node metastasis in 1-3 cm adenocarcinoma and develop a new nomogram to predict the probability of lymph node metastasis.Materials & methods: This study collected clinical data from 1656 patients for risk factor analysis and an additional 500 patients for external validation. The logistic regression analyses were employed for risk factor analysis. The least absolute shrinkage and selection operator regression was used to select variables, and important variables were used to construct the nomogram and an online calculator.Results: The nomogram for predicting lymph node metastasis comprises six variables: tumor size (mediastinal window), consolidation tumor ratio, tumor location, lymphadenopathy, preoperative serum carcinoembryonic antigen level and pathological grade. According to the predicted results, the risk of lymph node metastasis was divided into low-risk group and high-risk group. We confirmed the exceptional clinical efficacy of the model through multiple evaluation methods.Conclusion: The importance of intraoperative frozen section is increasing. We discussed the risk factors for lymph node metastasis and developed a nomogram to predict the probability of lymph node metastasis in 1-3 cm adenocarcinomas, which can guide lymph node resection strategies during surgery.

[Box: see text].

Acute Pancreatitis in Pregnancy: A Propensity Score Matching Analysis and Dynamic Nomogram for Risk Assessment.

BACKGROUND: Acute pancreatitis is easily confused with abdominal pain symptoms, and it could lead to serious complications for pregnant women and fetus, the mortality was as high as 3.3% and 11.6-18.7%, respectively. However, there is still lack of sensitive laboratory markers for early diagnosis of APIP and authoritative guidelines to guide treatment. OBJECTIVE: The purpose of this study was to explore the risk factors of acute pancreatitis in pregnancy, establish, and evaluate the dynamic prediction model of risk factors in acute pancreatitis in pregnancy patients. STUDY DESIGN: Clinical data of APIP patients and non-pregnant acute pancreases patients who underwent regular antenatal check-ups during the same period were collected. The dataset after propensity matching was randomly divided into training set and verification set at a ratio of 7:3. The model was constructed using Logistic regression, least absolute shrinkage and selection operator regression, R language and other methods. The training set model was used to construct the diagnostic nomogram model and the validation set was used to validate the model. Finally, the accuracy and clinical practicability of the model were evaluated. RESULTS: A total of 111 APIP were included. In all APIP patients, hyperlipidemic pancreatitis was the most important reason. The levels of serum amylase, creatinine, albumin, triglyceride, high-density lipoprotein cholesterol, and apolipoprotein A1 were significantly different between the two groups. The propensity matching method was used to match pregnant pancreatitis patients and pregnant non-pancreatic patients 1:1 according to age and gestational age, and the matching tolerance was 0.02. The multivariate logistic regression analysis of training set showed that diabetes, triglyceride, Body Mass Index, white blood cell, and C-reactive protein were identified and entered the dynamic nomogram. The area under the ROC curve of the training set was 0.942 and in validation set was 0.842. The calibration curve showed good predictive in training set, and the calibration performance in the validation set was acceptable. The calibration curve showed the consistency between the nomogram model and the actual probability. CONCLUSION: The dynamic nomogram model we constructed to predict the risk factors of acute pancreatitis in pregnancy has high accuracy, discrimination, and clinical practicability.

A dynamic online nomogram predicting post-traumatic arrhythmias: A retrospective cohort study.

BACKGROUND: A nomogram is a visualized clinical prediction models, which offer a scientific basis for clinical decision-making. There is a lack of reports on its use in predicting the risk of arrhythmias in trauma patients. This study aims to develop and validate a straightforward nomogram for predicting the risk of arrhythmias in trauma patients. METHODS: We retrospectively collected clinical data from 1119 acute trauma patients who were admitted to the Advanced Trauma Center of the Affiliated Hospital of Zunyi Medical University between January 2016 and May 2022. Data recorded included intra-hospital arrhythmia, ICU stay, and total hospitalization duration. Patients were classified into arrhythmia and non-arrhythmia groups. Data was summarized according to the occurrence and prognosis of post-traumatic arrhythmias, and randomly allocated into a training and validation sets at a ratio of 7:3. The nomogram was developed according to independent risk factors identified in the training set. Finally, the predictive performance of the nomogram model was validated. RESULTS: Arrhythmias were observed in 326 (29.1%) of the 1119 patients. Compared to the non-arrhythmia group, patients with arrhythmias had longer ICU and hospital stays and higher in-hospital mortality rates. Significant factors associated with post-traumatic arrhythmias included cardiovascular disease, catecholamine use, glasgow coma scale (GCS) score, abdominal abbreviated injury scale (AIS) score, injury severity score (ISS), blood glucose (GLU) levels, and international normalized ratio (INR). The area under the receiver operating characteristic curve (AUC) values for both the training and validation sets exceeded 0.7, indicating strong discriminatory power. The calibration curve showed good alignment between the predicted and actual probabilities of arrhythmias. Decision curve analysis (DCA) indicated a high net benefit for the model in predicting arrhythmias. The Hosmer-Lemeshow goodness-of-fit test confirmed the model's good fit. CONCLUSION: The nomogram developed in this study is a valuable tool for accurately predicting the risk of post-traumatic arrhythmias, offering a novel approach for physicians to tailor risk assessments to individual patients.

A dynamic nomogram predicting symptomatic pneumonia in patients with lung cancer receiving thoracic radiation.

PURPOSE: The most common and potentially fatal side effect of thoracic radiation therapy is radiation pneumonitis (RP). Due to the lack of effective treatments, predicting radiation pneumonitis is crucial. This study aimed to develop a dynamic nomogram to accurately predict symptomatic pneumonitis (RP ≥ 2) following thoracic radiotherapy for lung cancer patients. METHODS: Data from patients with pathologically diagnosed lung cancer at the Zhongshan People's Hospital Department of Radiotherapy for Thoracic Cancer between January 2017 and June 2022 were retrospectively analyzed. Risk factors for radiation pneumonitis were identified through multivariate logistic regression analysis and utilized to construct a dynamic nomogram. The predictive performance of the nomogram was validated using a bootstrapped concordance index and calibration plots. RESULTS: Age, smoking index, chemotherapy, and whole lung V5/MLD were identified as significant factors contributing to the accurate prediction of symptomatic pneumonitis. A dynamic nomogram for symptomatic pneumonitis was developed using these risk factors. The area under the curve was 0.89(95% confidence interval 0.83-0.95). The nomogram demonstrated a concordance index of 0.89(95% confidence interval 0.82-0.95) and was well calibrated. Furthermore, the threshold values for high- risk and low- risk were determined to be 154 using the receiver operating curve. CONCLUSIONS: The developed dynamic nomogram offers an accurate and convenient tool for clinical application in predicting the risk of symptomatic pneumonitis in patients with lung cancer undergoing thoracic radiation.

A dynamic online nomogram for predicting renal outcomes of idiopathic membranous nephropathy.

BACKGROUND: Because spontaneous remission is common in IMN, and there are adverse effects of immunosuppressive therapy, it is important to assess the risk of progressive loss of renal function before deciding whether and when to initiate immunosuppressive therapy. Therefore, this study aimed to establish a risk prediction model to predict patient prognosis and treatment response to help clinicians evaluate patient prognosis and decide on the best treatment regimen. METHODS: From September 2019 to December 2020, a total of 232 newly diagnosed IMN patients from three hospitals in Liaoning Province were enrolled. Logistic regression analysis selected the risk factors affecting the prognosis, and a dynamic online nomogram prognostic model was constructed based on extreme gradient boost, random forest, logistic regression machine learning algorithms. Receiver operating characteristic and calibration curves and decision curve analysis were utilized to assess the performance and clinical utility of the developed model. RESULTS: A total of 130 patients were in the training cohort and 102 patients in the validation cohort. Logistic regression analysis identified four risk factors: course ≥ 6 months, UTP, D-dimer and sPLA2R-Ab. The random forest algorithm showed the best performance with the highest AUROC (0.869). The nomogram had excellent discrimination ability, calibration ability and clinical practicability in both the training cohort and the validation cohort. CONCLUSIONS: The dynamic online nomogram model can effectively assess the prognosis and treatment response of IMN patients. This will help clinicians assess the patient's prognosis more accurately, communicate with the patient in advance, and jointly select the most appropriate treatment plan.

New online dynamic nomograms to predict recurrence-free and overall survival after resection of endometrial cancer: a single-institution retrospective cohort study.

PURPOSE: The significant global burden of endometrial cancer (EC) and the challenges associated with predicting EC recurrence indicate the need for a dynamic prediction model. This study aimed to propose nomograms based on clinicopathological variables to predict recurrence-free survival (RFS) and overall survival (OS) after surgical resection for EC. METHODS: This single-institution retrospective cohort study included patients who underwent surgical resection for EC. Web-based nomograms were developed to predict RFS and OS following resection for EC, and their discriminative and calibration abilities were assessed. RESULTS: This study included 289 patients (median age, 56 years). At a median follow-up of 51.1 (range, 4.1-128.3) months, 13.5% (39/289) of patients showed relapse or died, and 10.7% (31/289) had non-endometrioid tumors (median size: 2.8 cm). Positive peritoneal cytology result (hazard ratio [HR], 35.06; 95% confidence interval [CI], 1.12-1095.64; P = 0.0428), age-adjusted Charlson comorbidity index (AACCI) (HR, 52.08; 95% CI, 12.35-219.61; P < 0.001), and FIGO (Federation of Gynecology and Obstetrics) stage IV (HR, 138.33; 95% CI, 17.38-1101.05; P < 0.001) were predictors of RFS. Similarly, depth of myometrial invasion ≥ 1/2 (HR, 1; 95% CI, 0.46-2.19; P = 0.995), AACCI (HR, 93.63; 95% CI, 14.87-589.44; P < 0.001), and FIGO stage IV (HR, 608.26; 95% CI, 73.41-5039.66; P < 0.001) were predictors of OS. The nomograms showed good predictive capability, positive discriminative ability, and calibration (RFS: 0.895 and OS: 0.891). CONCLUSION: The nomograms performed well in internal validation when patients were stratified into prognostic groups, offering a personalized approach for risk stratification and treatment decision-making.

Development and validation of an online dynamic nomogram based on the atherogenic index of plasma to screen nonalcoholic fatty liver disease.

BACKGROUND: Nonalcoholic fatty liver disease (NAFLD), a common liver disease worldwide, can be reversed early in life with lifestyle and medical interventions. This study aimed to develop a noninvasive tool to screen NAFLD accurately. METHODS: Risk factors for NAFLD were identified using multivariate logistic regression analysis, and an online NAFLD screening nomogram was developed. The nomogram was compared with reported models (fatty liver index (FLI), atherogenic index of plasma (AIP), and hepatic steatosis index (HSI)). Nomogram performance was evaluated through internal and external validation (National Health and Nutrition Examination Survey (NHANES) database). RESULTS: The nomogram was developed based on six variables. The diagnostic performance of the present nomogram for NAFLD (area under the receiver operator characteristic curve (AUROC): 0.863, 0.864, and 0.833, respectively) was superior to that of the HSI (AUROC: 0.835, 0.833, and 0.810, respectively) and AIP (AUROC: 0.782, 0.773, and 0.728, respectively) in the training, validation, and NHANES sets. Decision curve analysis and clinical impact curve analysis presented good clinical utility. CONCLUSION: This study establishes a new online dynamic nomogram with excellent diagnostic and clinical performance. It has the potential to be a noninvasive and convenient method for screening individuals at high risk for NAFLD.

A dynamic nomogram for predicting the probability of irreversible neurological dysfunction after cervical spinal cord injury: research based on clinical features and MRI data.

BACKGROUND: Irreversible neurological dysfunction (IND) is an adverse event after cervical spinal cord injury (CSCI). However, there is still a shortage of objective criteria for the early prediction of neurological function. We aimed to screen independent predictors of IND and use these findings to construct a nomogram that could predict the development of neurological function in CSCI patients. METHODS: Patients with CSCI attending the Affiliated Hospital of Southwest Medical University between January 2014 and March 2021 were included in this study. We divided the patients into two groups: reversible neurological dysfunction (RND) and IND. The independent predictors of IND in CSCI patients were screened using the regularization technique to construct a nomogram, which was finally converted into an online calculator. Concordance index (C-index), calibration curves analysis and decision curve analysis (DCA) evaluated the model's discrimination, calibration, and clinical applicability. We tested the nomogram in an external validation cohort and performed internal validation using the bootstrap method. RESULTS: We enrolled 193 individuals with CSCI in this study, including IND (n = 75) and RND (n = 118). Six features, including age, American spinal injury association Impairment Scale (AIS) grade, signal of spinal cord (SC), maximum canal compromise (MCC), intramedullary lesion length (IMLL), and specialized institution-based rehabilitation (SIBR), were included in the model. The C-index of 0.882 from the training set and its externally validated value of 0.827 demonstrated the model's prediction accuracy. Meanwhile, the model has satisfactory actual consistency and clinical applicability, verified in the calibration curve and DCA. CONCLUSION: We constructed a prediction model based on six clinical and MRI features that can be used to assess the probability of developing IND in patients with CSCI.

Development and validation of an online dynamic nomogram system for predicting cancer cachexia among inpatients: a real-world cohort study in China.

BACKGROUND: Early recognition of cachexia is essential for ensuring the prompt intervention and treatment of cancer patients. However, the diagnosis of cancer cachexia (CC) usually is delayed. This study aimed to establish an accurate and high-efficiency diagnostic system for CC. METHODS: A total of 4834 cancer inpatients were enrolled in the INSCOC project from July 2013 to June 2020. All cancer patients in the study were randomly assigned to a development cohort (n=3384, 70%) and a validation cohort (n=1450, 30%). The least absolute shrinkage and selection operator (LASSO) method and multivariable logistic regression were used to identify the independent predictors for developing the dynamic nomogram. Discrimination and calibration were adopted to evaluate the ability of nomogram. A decision curve analysis (DCA) was used to evaluate clinical use. RESULTS: We combined 5 independent predictive factors (age, NRS2002, PG-SGA, QOL by the QLQ-C30, and cancer categories) to establish the online dynamic nomogram system. The C-index, sensitivity, and specificity of the nomo-system to predict CC was 0.925 (95%CI, 0.916-0.934, P < 0.001), 0.826, and 0.862 in the development set, while the values were 0.923 (95%CI, 0.909-0.937, P < 0.001), 0.854, and 0.829 in the validation set. In addition, the calibration curves of the diagnostic nomogram also presented good agreement with the actual situation. DCA showed that the model is clinically useful and can increase the clinical benefit in cancer patients. CONCLUSIONS: This study developed an online dynamic nomogram system with outstanding accuracy to help clinicians and dieticians estimate the probability of cachexia. This simple-to-use online nomogram can increase the clinical benefit in cancer patients and is expected to be widely adopted.

Establishment of dynamic nomogram and risk score models for T2DM: a retrospective cohort study in Beijing.

BACKGROUND: Health interventions can delay or prevent the occurrence and development of diabetes. Dynamic nomogram and risk score (RS) models were developed to predict the probability of developing type 2 diabetes mellitus (T2DM) and identify high-risk groups. METHODS: Participants (n = 44,852) from the Beijing Physical Examination Center were followed up for 11 years (2006-2017); the mean follow-up time was 4.06 ± 2.09 years. Multivariable Cox regression was conducted in the training cohort to identify risk factors associated with T2DM and develop dynamic nomogram and RS models using weighted estimators corresponding to each covariate derived from the fitted Cox regression coefficients and variance estimates, and then undergone internal validation and sensitivity analysis. The concordance index (C-index) was used to assess the accuracy and reliability of the model. RESULTS: Of the 44,852 individuals at baseline, 2,912 were diagnosed with T2DM during the follow-up period, and the incidence density rate per 1,000 person-years was 16.00. Multivariate analysis indicated that male sex (P < 0.001), older age (P < 0.001), high body mass index (BMI, P < 0.05), high fasting plasma glucose (FPG, P < 0.001), hypertension (P = 0.015), dyslipidaemia (P < 0.001), and low serum creatinine (sCr, P < 0.05) at presentation were risk factors for T2DM. The dynamic nomogram achieved a high C-index of 0.909 in the training set and 0.905 in the validation set. A tenfold cross-validation estimated the area under the curve of the nomogram at 0.909 (95% confidence interval 0.897-0.920). Moreover, the dynamic nomogram and RS model exhibited acceptable discrimination and clinical usefulness in subgroup and sensitivity analyses. CONCLUSIONS: The T2DM dynamic nomogram and RS models offer clinicians and others who conduct physical examinations, respectively, simple-to-use tools to assess the risk of developing T2DM in the urban Chinese current or retired employees.

Prognostic value of preoperative lymphocyte-to-monocyte ratio in oral cancer patients and establishment of a dynamic nomogram.

OBJECTIVE: To assess the association of preoperative lymphocyte-to-monocyte ratio (LMR) and overall survival (OS) in patients with oral cancer and develop a dynamic nomogram for individualized survival prediction. METHOD: The prognostic value of LMR was evaluated in a large-scale cohort with 651 postoperative patients with oral cancer between January 2010 and December 2017. Propensity score-matched (PSM) analysis and inverse probability of treatment weighting (IPTW) analysis were performed to further verify the prognostic value of LMR. A dynamic nomogram was then developed based on the LMR and clinicopathological features, and its predictive performance and clinical utility were evaluated. RESULTS: A high LMR was significantly associated with better OS of patients with oral cancer (HR = 0.65; 95% CI = 0.44-0.98). The similar association was also observed in the PSM and IPTW analyses. Moreover, compared with TNM staging system, the dynamic nomogram based on the LMR exhibited more excellent predictive performance (0.72 versus 0.64, p < .001), with calibration curves (1,000 bootstrap resamples) suggesting good match between the actual and predicted probabilities. Decision curve analyses (DCAs) showed a more significant positive net benefit in the practical ranges of threshold probabilities using the dynamic nomogram. CONCLUSION: Preoperative LMR may serve as an easily accessible and non-invasive prognostic biomarker for predicting the prognosis of patients with oral cancer. A dynamic nomogram based on the LMR may show more convenience in survival prediction for patients with oral cancer. Further future studies are warranted to confirm our findings.

A web-based dynamic Nomogram for predicting instrumental activities of daily living disability in older adults: a nationally representative survey in China.

BACKGROUND: Instrumental Activities of Daily Living (IADL) disability is a common health burden in aging populations. The identification of high-risk individuals is essential for timely targeted interventions. Although predictors for IADL disability have been well described, studies constructing prediction tools for IADL disability among older adults were not adequately explored. Our study aims to develop and validate a web-based dynamic nomogram for individualized IADL disability prediction in older adults. METHODS: Data were obtained from the China Health and Retirement Longitudinal Study (CHARLS). We included 4791 respondents aged 60 years and over, without IADL disability at baseline in the 2011 to 2013 cohort (training cohort) and 371 respondents in the 2013 to 2015 cohort (validation cohort). Here, we defined IADL disability as needing any help in any items of the Lawton and Brody's scale. A web-based dynamic nomogram was built based on a logistic regression model in the training cohort. We validated the nomogram internally with 1000 bootstrap resamples and externally in the validation cohort. The discrimination and calibration ability of the nomogram was assessed using the concordance index (C-index) and calibration plots, respectively. RESULTS: The nomogram incorporated ten predictors, including age, education level, social activity frequency, drinking frequency, smoking frequency, comorbidity condition, self-report health condition, gait speed, cognitive function, and depressive symptoms. The C-index values in the training and validation cohort were 0.715 (bootstrap-corrected C-index = 0.702) and 0.737, respectively. The internal and external calibration plots for predictions of IADL disability were in excellent agreement. An online web server was built ( https://lilizhang.shinyapps.io/DynNomapp/ ) to facilitate the use of the nomogram. CONCLUSIONS: We developed a dynamic nomogram to evaluate the risk of IADL disability precisely and expediently. The application of this nomogram would be helpful for health care physicians in decision-making.

A dynamic nomogram for predicting the risk of asthma: Development and validation in a database study.

BACKGROUND: Asthma remains a serious health problem with increasing prevalence and incidence. This study was to develop and validate a dynamic nomogram for predicting asthma risk. METHODS: Totally 597 subjects whose age ≥18 years old with asthma, an accurate age at first cigarette, and clear smoking status were selected from the National Health and Nutrition Examination Survey (NHANES) database (2013-2018). The dataset was randomly split into the training set and the testing set at a ratio of 4:6. Simple and multiple logistic regressions were used for identifying independent predictors. Then the nomogram was developed and internally validated using data from the testing set. The receiver operator characteristic (ROC) curve was used for assessing the performance of the nomogram. RESULTS: According to the simple and multiple logistic regressions, smoking ≥40 years, female gender, the age for the first smoking, having close relative with asthma were independently associated with the risk of an asthma attack. The nomogram was thereby developed with the link of https://yanglifen.shinyapps.io/Dynamic_Nomogram_for_Asthma/. The ROC analyses showed an AUC of 0.726 (0.724-0.728) with a sensitivity of 0.887 (0.847-0.928) in the training set, and an AUC of 0.702 (0.700-0.703) with a sensitivity of 0.860 (0.804-0.916) in the testing set, fitting well in calibration curves. Decision curve analysis further confirmed the clinical usefulness of the nomogram. CONCLUSION: Our dynamic nomogram could help clinicians to assess the individual probability of asthma attack, which was helpful for improving the treatment and prognosis of asthma.

Predictive value of preoperative and postoperative peripheral lymphocyte difference in hepatitis B virus-related hepatocellular cancer patients: Based on the analysis of dynamic nomogram.

BACKGROUND: Inflammation plays an important role in the progression and prognosis of hepatocellular carcinoma (HCC). Our aim is to explore the prognostic value of preoperative and postoperative peripheral lymphocyte differences and to develop a dynamic prognosis nomogram in hepatitis B virus-related HCC patients. METHODS: Important indicators related to overall survival (OS) are screened out by Cox proportional hazard models. The receiver operating characteristic curves, decision curve analysis curves, net reclassification improvement, and integrated discrimination improvement were used to evaluate the performance of the model. RESULTS: Lymphocyte (L) difference was an independent risk factor. It was further verified that the performance of the nomogram was significantly improved after the L difference was incorporated into the nomogram. The nomogram generated had the area under curves of 0.779, 0.775, and 0.793 at 3, 5, and 7 years after surgery, respectively. Our nomogram models showed significantly better performance in predicting the HCC prognosis compared to other models. And online webserver and scoring system table was built based on the proposed nomogram for convenient clinical use. CONCLUSIONS: It is newly found that L difference is an effective predictor of OS, and the nomogram based on this indicator can accurately predict the prognosis of HCC patients.

Dynamic nomogram for predicting infected pancreatic necrosis in female patients of childbearing age with hypertriglyceridemia-induced acute pancreatitis.

BACKGROUND: Hypertriglyceridemia is a common cause of acute pancreatitis. Pregnant women are at risk of developing hypertriglyceridemia-induced acute pancreatitis (HTG-AP); however, whether pregnancy increases the risk of infected pancreatic necrosis (IPN) is unknown. AIM: We aimed to assess the association between pregnancy and IPN. METHODS: This 10-year retrospective cohort study was conducted at Jinling Hospital. Adult female patients of childbearing age with HTG-AP between January 2013 and September 2022 were screened. Logistic regression analyses were performed to assess the risk factors for IPN. Patients admitted within 7 days were assigned to the training and validation sets to develop a dynamic nomogram for IPN prediction. RESULTS: 489 patients were included, and 144 developed IPN. Logistic regression analyses revealed pregnancy (OR: 2.578 95% CI: 1.474-4.510) as an independent risk factor for IPN. Gestation weeks, ARDS, albumin level, and serum creatinine level were selected as the predictors of the dynamic nomogram for IPN prediction, with good discrimination in the training set (AUC 0.867 95% CI: 0.794-0.940) and validation set (AUC 0.957 95% CI: 0.885-1.000). CONCLUSION: Pregnancy increases the risk of IPN in adult patients of childbearing age with HTG-AP, and the dynamic nomogram may help risk stratification for IPN.

Age-related nomogram revealed optimal therapeutic option for older patients with primary liver cancer: less is more.

BACKGROUND: Age bias in therapeutic decisions for older patients with cancer exists. There is a clear need to individualize such decisions. METHODS: Based on the Surveillance, Epidemiology and End Results (SEER) database, 5081 primary liver cancer (PLC) patients between 2010 and 2014 were identified and divided into <64, 64-74 and >74 years group. Each group was randomly divided into training and internal validation cohorts, and patients who were diagnosed between 2015 and 2016 were included as an external validation. The nomogram model predicting overall survival (OS) was generated and evaluated based on the Cox regression for the influencing factors in prognosis. The K-M analysis was used to compare the difference among different treatments. RESULTS: KM analysis showed a significant difference for OS in three age groups (P < 0.001). At the same time, we also found different prognostic factors and their importance in different age groups. Therefore, we created three nomograms based on the results of Cox regression results for each age group. The c-index was 0.802, 0.766, 0.781 respectively. The calibration curve and ROC curve show that our model has a good predictive efficacy and the reliability was also confirmed in the internal and external validation set. An available online page was established to simplify and visualize our model (http://124.222.247.135/). The results of treatment analysis revealed that the optimal therapeutic option for PLCs was surgery alone. CONCLUSIONS: The optimal therapeutic option for older PLCs was surgery alone. The generated dynamic nomogram in this study may be a useful tool for personalized clinical decisions.

An Online Dynamic Radiomics-Clinical Nomogram to Predict Recurrence in Patients with Spontaneous Intracerebral Hemorrhage.

OBJECTIVE: Radiomics can reflect the heterogeneity within the focus. We aim to explore whether radiomics can predict recurrent intracerebral hemorrhage (RICH) and develop an online dynamic nomogram to predict it. METHODS: This retrospective study collected the clinical and radiomics features of patients with spontaneous intracerebral hemorrhage seen in our hospital from October 2013 to October 2016. We used the minimum redundancy maximum relevancy and the least absolute shrinkage and selection operator methods to screen radiomics features and calculate the Rad-score. We use the univariate and multivariate analyses to screen clinical predictors. Optimal clinical features and Rad-score were used to construct different logistics regression models called the clinical model, radiomics model, and combined-logistic regression model. DeLong testing was performed to compare performance among different models. The model with the best predictive performance was used to construct an online dynamic nomogram. RESULTS: Overall, 304 patients with intracerebral hemorrhage were enrolled in this study. Fourteen radiomics features were selected to calculate the Rad-score. The patients with RICH had a significantly higher Rad-score than those without (0.5 vs. -0.8; P< 0.001). The predictive performance of the combined-logistic regression model with Rad-score was better than that of the clinical model for both the training (area under the receiver operating curve, 0.81 vs. 0.71; P = 0.02) and testing (area under the receiver operating curve, 0.65 vs. 0.58; P = 0.04) cohorts statistically. CONCLUSIONS: Radiomics features were determined related to RICH. Adding Rad-score into conventional clinical models significantly improves the prediction efficiency. We developed an online dynamic nomogram to accurately and conveniently evaluate RICH.