Web-based Nomogram Research Articles

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

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

Development and Validation of a Predictive Model of Prostate Screening Compliance: A Nationwide Population-Based Study.

Prostate cancer (PCa) is the second most common cancer in men worldwide, with significant incidence and mortality, particularly in Mexico, where diagnosis at advanced stages is common. Early detection through screening methods such as digital rectal examination and prostate-specific antigen testing is essential to improve outcomes. Despite current efforts, compliance with prostate screening (PS) remains low due to several barriers. This study aims to develop and validate a predictive model for PCa screening compliance in Mexican men. Retrospective observational design with data from the Mexican Health and Aging Study (MHAS). Participants were men aged 50-69 from three cohorts: development/internal validation, temporal validation, and external validation. Key predictors were identified using relaxed Least Absolute Shrinkage and Selection Operator (LASSO) regression, and model performance was assessed using the area under the curve (AUC) from receiver operating characteristic (ROC) analyses, along with calibration and decision curve analysis (DCA). A web nomogram was also developed. The final model included seven key predictors. AUC values indicated good predictive performance: 0.783 for the training subgroup, 0.722 for the test subgroup, 0.748 for the time cohort, and 0.756 for the external cohort, with sensitivities of 73.5%. The DCA demonstrated the superior clinical utility of the model compared to the reference strategies. The predictive model developed for performance to PCa screening is robust across different cohorts and highlights critical factors influencing performance. The accompanying web-based nomogram enhances clinical applicability and supports interventions aimed at improving PCa screening rates among Mexican men.

A web-based dynamic nomogram for estimating talaromycosis risk in hospitalized HIV-positive patients.

Our study aimed to develop and validate a nomogram to assess talaromycosis risk in hospitalized HIV-positive patients. Prediction models were built using data from a multicentre retrospective cohort study in China. On the basis of the inclusion and exclusion criteria, we collected data from 1564 hospitalized HIV-positive patients in four hospitals from 2010 to 2019. Inpatients were randomly assigned to the training or validation group at a 7:3 ratio. To identify the potential risk factors for talaromycosis in HIV-infected patients, univariate and multivariate logistic regression analyses were conducted. Through multivariate logistic regression, we determined ten variables that were independent risk factors for talaromycosis in HIV-infected individuals. A nomogram was developed following the findings of the multivariate logistic regression analysis. For user convenience, a web-based nomogram calculator was also created. The nomogram demonstrated excellent discrimination in both the training and validation groups [area under the ROC curve (AUC)=0.883 vs. 0.889] and good calibration. The results of the clinical impact curve (CIC) analysis and decision curve analysis (DCA) confirmed the clinical utility of the model. Clinicians will benefit from this simple, practical, and quantitative strategy to predict talaromycosis risk in HIV-infected patients and can implement appropriate interventions accordingly.

Construction, validation, and visualization of a web-based nomogram to predict survival in male breast cancer patients with second primary prostate cancer.

The advancement of early detection and treatment has brought about a significant concern for male breast cancer (MBC) survivors-the emergence of a second primary malignancy (SPM) poses a grave threat to their lives. Among them, second primary prostate cancer (spPCa) holds particular significance. This study aimed to investigate the impact of spPCa on the prognosis of MBC patients. We performed a retrospective analysis using information from the Surveillance, Epidemiology, and End Results (SEER) database to investigate individuals diagnosed with MBC who also experienced an SPM between 2000 and 2020. Propensity score matching (PSM) was employed to balance the baseline characteristics of individuals with spPCa and those with second primary non-prostate cancer (non-PCa). The impact of spPCa on participant survival was assessed using the Kaplan-Meier method. Furthermore, two nomograms were developed, based on univariate and multifactor Cox regression analyses, to predict overall survival (OS) and cancer-specific survival (CSS). The capacity of the nomograms was evaluated using the concordance index (C-index), calibration curve, receiver operating characteristic (ROC) analysis, and decision curve analysis (DCA). Additionally, a risk stratification system was devised, taking into account the cumulative score of each patient in the nomogram. This study enrolled a total of 885 MBC patients who experienced an SPM, of which 265 (29.9%) were diagnosed with spPCa. Through PSM, 257 pairs of eligible participants were selected. Survival analysis revealed that patients with prostate cancer (PCa) as an SPM have longer OS and CSS compared to those with other types of cancer as an SPM. The participants were randomly divided into a training set and a validation set in a ratio of 7:3. The Cox proportional hazards model was utilized to assess the risk factors associated with survival outcomes. Two nomograms were developed to forecast the 3-, 5-, 8-, and 10-year OS and CSS of male patients who had breast cancer and SPM. The two nomograms exhibited excellent performance in terms of the C-index, ROC curves, calibration plots, and DCA curves, demonstrating their exceptional clinical discriminative ability and predictive utility. In the risk stratification system predicated on the total score of the nomogram, patients deemed high-risk exhibited diminished OS and CSS. Additionally, we created user-friendly web applications to enhance the accessibility of the nomogram in clinical practices, which can be accessed at https://mbcpre.shinyapps.io/DynNomapp_OS/ for OS and https://mbcpre.shinyapps.io/DynNomapp_CSS/ for CSS. MBC patients with spPCa exhibit a more favorable prognosis than those with other SPMs. The two nomograms we constructed could accurately forecast the OS and CSS for MBC patients with spPCa. Patients whose nomograms are stratified as high-risk should gain additional attention. Our nomograms may aid clinicians in personalizing treatment strategies and supporting clinical decisions.

Development, Validation, and Visualization of a Web-Based Nomogram to Predict the Risk of Psychological Distress in Patients Undergoing Chemotherapy After Breast Cancer Surgery.

Psychological distress of postoperative chemotherapy patients with breast cancer is significant and has a serious impact on their quality of survival. Risk prediction models can efficiently assess patients' psychological distress and risk factors. To investigate the factors influencing psychological distress in postoperative chemotherapy patients with breast cancer and construct a nomogram model to predict the occurrence of psychological distress in patients. Two hundred forty-seven women in treatment for breast cancer who were postoperative and receiving chemotherapy were recruited. Participants completed a distress management screening measure, a numerical rating scale, the Pittsburgh Sleep Quality Index, and demographic and clinical items. Experiencing significant psychological distress was reported by 65.6% of participants. The psychological distress risk prediction model included 5 variables: financial problems, appearance/shape, distant metastases, the Numerical Rating Scale Pain score, and fatigue. A web calculator was designed based on the model (https://77nomogram.shinyapps.io/dynnomapp/). This study found that financial and appearance/shape problems, distant metastases, pain scores, and fatigue were predictors of greater psychological distress in women undergoing chemotherapy after breast cancer surgery. The model constructed in this study has good predictive efficacy. This web-based nomogram model can help healthcare professionals quickly assess the likelihood of psychological distress in patients and screen for risk factors for psychological distress.

The continuous improvement of digital assistance in the radiation oncologist's work: from web-based nomograms to the adoption of large-language models (LLMs). A systematic review by the young group of the Italian association of radiotherapy and clinical oncology (AIRO).

Recently, the availability of online medical resources for radiation oncologists and trainees has significantly expanded, alongside the development of numerous artificial intelligence (AI)-based tools. This review evaluates the impact of web-based clinical decision-making tools in the clinical practice of radiation oncology. We searched databases, including PubMed, EMBASE, and Scopus, using keywords related to web-based clinical decision-making tools and radiation oncology, adhering to PRISMA guidelines. Out of 2161 identified manuscripts, 70 were ultimately included in our study. These papers all supported the evidence that web-based tools can be transversally integrated into multiple radiation oncology fields, with online applications available for dose and clinical calculations, staging and other multipurpose intents. Specifically, the possible benefit of web-based nomograms for educational purposes was investigated in 35 of the evaluated manuscripts. As regards to the applications of digital and AI-based tools to treatment planning, diagnosis, treatment strategy selection and follow-up adoption, a total of 35 articles were selected. More specifically, 19 articles investigated the role of these tools in heterogeneous cancer types, while nine and seven articles were related to breast and head & neck cancers, respectively. Our analysis suggests that employing web-based and AI tools offers promising potential to enhance the personalization of cancer treatment.

Development, validation, and visualization of a web-based nomogram for predicting chronic kidney disease incidence at health examination centers

Purpose To develop and validate a web-based nomogram for predicting new incident chronic kidney disease (CKD) within 4 years in a cohort undergoing routine physical examination from two health examination centers. Methods One center was utilized for training and internal validation of a nomogram model involving 6515 patients, while a separate center was employed for external validation with 3152 patients. Sixteen candidate predictors, including patient demographics, medical histories, physical examination, and laboratory test data, were included in this study to ascertain factors linked to new incident CKD. A nomogram was created to predict CKD risks using a logistic model. The nomogram’s performance was assessed using the area under the receiver operating characteristic curve (AUC), calibration plot, and decision curve analysis. Results Out of the 9667 healthy individuals included in the study with mean age of 46 years, sex ratio (male/female) of 1.69 (6075/3592), 118 (2.59%), 51 (2.61%), and 60 (1.90%) individuals developed CKD in the training (n = 4563), internal validation (n = 1952), and external validation (n = 3152) datasets, respectively. Age, history of diabetes mellitus, systolic blood pressure, serum creatinine, albumin, and triglyceride levels were used to build the nomogram, which yielded excellent discrimination ability (training cohort, AUC = 0.8806, 95% confidence interval [CI] 0.8472–0.9141; internal validation cohort, AUC = 0.8506, 95% CI 0.7856–0.9156; external validation cohort, AUC = 0.9183, 95% CI 0.8698–0.9669). We further developed a web-based calculator for convenient application (https://luochuxuan.shinyapps.io/dynnomapp/). Conclusion Our web-based nomogram accurately predicted CKD risks in Chinese health individuals and can be easily used in clinical settings.

Development and validation of a web-based nomogram for acute kidney injury in acute non-variceal upper gastrointestinal bleeding patients.

Acute kidney injury (AKI) is a common and serious complication in patients with acute non-variceal upper gastrointestinal bleeding (NVUGIB). Early prediction and intervention are crucial for improving patient outcomes. Data for patients presenting with acute NVUGIB in this retrospective study were sourced from the MIMC-IV database. Patients were randomly allocated into training and validation cohorts for further analysis. Independent predictors for AKI were identified using least absolute shrinkage and selection operator regression and multivariable logistic regression analyses in the training cohort. Based on the logistic regression results, a nomogram was developed to predict early AKI onset in acute NVUGIB patients, and implemented as a web-based calculator for clinical application. The nomogram's performance was evaluated through discrimination, using the C-index, calibration curves, and decision curve analysis (DCA) to assess its clinical value. The study involved 1082 acute NVUGIB patients, with 406 developing AKI. A multivariable logistic regression identified five key AKI predictors: CKD, use of human albumin, chronic liver disease, glucose, and blood urea nitrogen. The nomogram was constructed based on independent predictors. The nomogram exhibited robust accuracy, evidenced by a C-index of 0.73 in the training cohort and 0.72 in the validation cohort. Calibration curves demonstrated satisfactory concordance between predicted and observed AKI occurrences. DCA revealed that the nomogram offered considerable clinical benefit within a threshold probability range of 7% to 54%. Our nomogram is a valuable tool for predicting AKI risk in patients with acute NVUGIB, offering potential for early intervention and improved clinical outcomes.

Assessing microvascular invasion in HBV-related hepatocellular carcinoma: an online interactive nomogram integrating inflammatory markers, radiomics, and convolutional neural networks.

The early recurrence of hepatocellular carcinoma (HCC) correlates with decreased overall survival. Microvascular invasion (MVI) stands out as a prominent hazard influencing post-resection survival status and metastasis in patients with HBV-related HCC. The study focused on developing a web-based nomogram for preoperative prediction of MVI in HBV-HCC. 173 HBV-HCC patients from 2017 to 2022 with complete preoperative clinical data and Gadopentetate dimeglumine-enhanced magnetic resonance images were randomly divided into two groups for the purpose of model training and validation, using a ratio of 7:3. MRI signatures were extracted by pyradiomics and the deep neural network, 3D ResNet. Clinical factors, blood-cell-inflammation markers, and MRI signatures selected by LASSO were incorporated into the predictive nomogram. The evaluation of the predictive accuracy involved assessing the area under the receiver operating characteristic (ROC) curve (AUC), the concordance index (C-index), along with analyses of calibration and decision curves. Inflammation marker, neutrophil-to-lymphocyte ratio (NLR), was positively correlated with independent MRI radiomics risk factors for MVI. The performance of prediction model combined serum AFP, AST, NLR, 15 radiomics features and 7 deep features was better than clinical and radiomics models. The combined model achieved C-index values of 0.926 and 0.917, with AUCs of 0.911 and 0.907, respectively. NLR showed a positive correlation with MRI radiomics and deep learning features. The nomogram, incorporating NLR and MRI features, accurately predicted individualized MVI risk preoperatively.

A Machine Learning Model Based on CT Imaging Metrics and Clinical Features to Predict the Risk of Hospital-Acquired Pneumonia After Traumatic Brain Injury.

To develop a validated machine learning (ML) algorithm for predicting the risk of hospital-acquired pneumonia (HAP) in patients with traumatic brain injury (TBI). We employed the Least Absolute Shrinkage and Selection Operator (LASSO) to identify critical features related to pneumonia. Five ML models-Logistic Regression (LR), Extreme Gradient Boosting (XGB), Random Forest (RF), Naive Bayes Classifier (NB), and Support Vector Machine (SVC)-were developed and assessed using the training and validation datasets. The optimal model was selected based on its performance metrics and used to create a dynamic web-based nomogram. In a cohort of 858 TBI patients, the HAP incidence was 41.02%. LR was determined to be the optimal model with superior performance metrics including AUC, accuracy, and F1-score. Key predictive factors included Age, Glasgow Coma Score, Rotterdam Score, D-dimer, and the Systemic Immune Response to Inflammation Index (SIRI). The nomogram developed based on these predictors demonstrated high predictive accuracy, with AUCs of 0.818 and 0.819 for the training and validation datasets, respectively. Decision curve analysis (DCA) and calibration curves validated the model's clinical utility and accuracy. We successfully developed and validated a high-performance ML algorithm to assess the risk of HAP in TBI patients. The dynamic nomogram provides a practical tool for real-time risk assessment, potentially improving clinical outcomes by aiding in early intervention and personalized patient management.

Derivation and validation of the first web-based nomogram to predict the spontaneous pregnancy after reproductive surgery using machine learning models.

Infertility remains a significant global burden over the years. Reproductive surgery is an effective strategy for infertile women. Early prediction of spontaneous pregnancy after reproductive surgery is of high interest for the patients seeking the infertility treatment. However, there are no high-quality models and clinical applicable tools to predict the probability of natural conception after reproductive surgery. The eligible data involving 1013 patients who operated for infertility between June 2016 and June 2021 in Yantai Yuhuangding Hospital in China, were randomly divided into training and internal testing cohorts. 195 subjects from the Linyi People's Hospital in China were considered for external validation. Both univariate combining with multivariate logistic regression and the least absolute shrinkage and selection operator (LASSO) algorithm were performed to identify independent predictors. Multiple common machine learning algorithms, namely logistic regression, decision tree, random forest, support vector machine, k-nearest neighbor, and extreme gradient boosting, were employed to construct the predictive models. The optimal model was verified by evaluating the model performance in both the internal and external validation datasets. Six clinical indicators, including female age, infertility type, duration of infertility, intraoperative diagnosis, ovulation monitoring, and anti-Müllerian hormone (AMH) level, were screened out. Based on the logistic regression model's superior clinical predictive value, as indicated by the area under the receiver operating characteristic curve (AUC) in both the internal (0.870) and external (0.880) validation sets, we ultimately selected it as the optimal model. Consequently, we utilized it to generate a web-based nomogram for predicting the probability of spontaneous pregnancy after reproductive surgery. Furthermore, the calibration curve, Hosmer-Lemeshow (H-L) test, the decision curve analysis (DCA) and clinical impact curve analysis (CIC) demonstrated that the model has superior calibration degree, clinical net benefit and generalization ability, which were confirmed by both internal and external validations. Overall, our developed first nomogram with online operation provides an early and accurate prediction for the probability of natural conception after reproductive surgery, which helps clinicians and infertile couples make sensible decision of choosing the mode of subsequent conception, natural or IVF, to further improve the clinical practices of infertility treatment.

Development of a novel nomogram for predicting delayed methotrexate excretion following high-dose methotrexate in adult patients with hematologic malignancies.

High-dose methotrexate (HDMTX) is integral in treating hematologic malignancies but carries risks of severe toxicities due to prolonged MTX exposure. However, knowledge of delayed MTX excretion is primarily derived from pediatric and adolescent cohorts, with the reported predictors being presented as rough dichotomous values. This study aimed to identify risk factors for delayed MTX excretion exclusively in adult patients with hematologic malignancies and develop a more applicable predictive nomogram based on continuous clinical and laboratory variables. 517 HDMTX cycles in 194 patients were retrospectively analyzed. Delayed MTX excretion was defined as either MTX concentration ≥ 1.0 µmol/L at 48h or ≥ 0.1 µmol/L at 72h after HDMTX initiation. Multivariate logistic regression analysis was used to construct the nomogram internally validated with the bootstrap method. Delayed MTX excretion was observed in 24.0% of cycles. Six significant predictors were identified: relapsed/refractory disease (Odds ratio [OR] 2.03), fewer HDMTX cycles (OR 0.771), treatment intent (OR 2.13), lower albumin (OR 0.563) and creatinine clearance levels (OR 0.993), and increased γ-glutamyl transpeptidase levels (OR 1.004, all P < 0.05). These were incorporated into a web-based nomogram as continuous variables with good prediction accuracy (area under the curve, 0.73) and without significant overfitting. Delayed MTX excretion increased risks of developing acute kidney injury, even solely at the 72h timepoint (OR 2.57, P = 0.025), without providing any benefit of clinical outcomes. This study comprehensively characterized MTX elimination failure following HDMTX in adult patients and could pave the way for individualized risk prediction.

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

PurposeThe 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.MethodsThis 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.ResultsThis 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).ConclusionThe 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, validation and visualization of a web-based nomogram for predicting risk of new-onset diabetes after percutaneous coronary intervention

Simple and practical tools for screening high-risk new-onset diabetes after percutaneous coronary intervention (PCI) (NODAP) are urgently needed to improve post-PCI prognosis. We aimed to evaluate the risk factors for NODAP and develop an online prediction tool using conventional variables based on a multicenter database. China evidence-based Chinese medicine database consisted of 249, 987 patients from 4 hospitals in mainland China. Patients ≥ 18 years with implanted coronary stents for acute coronary syndromes and did not have diabetes before PCI were enrolled in this study. According to the occurrence of new-onset diabetes mellitus after PCI, the patients were divided into NODAP and Non-NODAP. After least absolute shrinkage and selection operator regression and logistic regression, the model features were selected and then the nomogram was developed and plotted. Model performance was evaluated by the receiver operating characteristic curve, calibration curve, Hosmer–Lemeshow test and decision curve analysis. The nomogram was also externally validated at a different hospital. Subsequently, we developed an online visualization tool and a corresponding risk stratification system to predict the risk of developing NODAP after PCI based on the model. A total of 2698 patients after PCI (1255 NODAP and 1443 non-NODAP) were included in the final analysis based on the multicenter database. Five predictors were identified after screening: fasting plasma glucose, low-density lipoprotein cholesterol, hypertension, family history of diabetes and use of diuretics. And then we developed a web-based nomogram (https://mr.cscps.com.cn/wscoringtool/index.html) incorporating the above conventional factors for predicting patients at high risk for NODAP. The nomogram showed good discrimination, calibration and clinical utility and could accurately stratify patients into different NODAP risks. We developed a simple and practical web-based nomogram based on multicenter database to screen for NODAP risk, which can assist clinicians in accurately identifying patients at high risk of NODAP and developing post-PCI management strategies to improved patient prognosis.

Differentiation between viral and autoimmune limbic encephalitis: a prospective cohort study with development and validation of a diagnostic model.

Distinguishing between viral encephalitis (VE) and autoimmune limbic encephalitis (ALE) presents a clinical challenge due to the overlap in symptoms. We aimed to develop and validate a diagnostic prediction model to differentiate VE and ALE. A prospective observational multicentre cohort study, which continuously enrolled patients diagnosed with either ALE or VE from October 2011 to April 2023. The demographic data, clinical features, and laboratory test results were collected and subjected to logistic regression analyses. The model was displayed as a web-based nomogram and then modified into a scored prediction tool. Model performance was assessed in both derivation and external validation cohorts. A total of 2423 individuals were recruited, and 1001 (496 VE, 505 ALE) patients were included. Based on the derivation cohort (389 VE, 388 ALE), the model was developed with eight variables including age at onset, acuity, fever, headache, nausea/vomiting, psychiatric or memory complaints, status epilepticus, and CSF white blood cell count. The model showed good discrimination and calibration in both derivation (AUC 0.890; 0.868-0.913) and external validation (107 VE, 117 ALE, AUC 0.872; 0.827-0.917) cohorts. The scored prediction tool had a total point that ranged from - 4 to 10 also showing good discrimination and calibration in both derivation (AUC 0.885, 0.863-0.908) and external validation (AUC 0.868, 0.823-0.913) cohorts. The prediction model provides a reliable and user-friendly tool for differentiating between the VE and ALE, which would benefit early diagnosis and appropriate treatment and alleviate economic burdens on both patients and society.

A dynamic nomogram to predict invasive fungal super-infection during healthcare-associated bacterial infection in intensive care unit patients: an ambispective cohort study in China.

Invasive fungal super-infection (IFSI) is an added diagnostic and therapeutic dilemma. We aimed to develop and assess a nomogram of IFSI in patients with healthcare-associated bacterial infection (HABI). An ambispective cohort study was conducted in ICU patients with HABI from a tertiary hospital of China. Predictors of IFSI were selected by both the least absolute shrinkage and selection operator (LASSO) method and the two-way stepwise method. The predictive performance of two models built by logistic regression was internal-validated and compared. Then external validity was assessed and a web-based nomogram was deployed. Between Jan 1, 2019 and June 30, 2023, 12,305 patients with HABI were screened in 14 ICUs, of whom 372 (3.0%) developed IFSI. Among the fungal strains causing IFSI, the most common was C.albicans (34.7%) with a decreasing proportion, followed by C.tropicalis (30.9%), A.fumigatus (13.9%) and C.glabrata (10.1%) with increasing proportions year by year. Compared with LASSO-model that included five predictors (combination of priority antimicrobials, immunosuppressant, MDRO, aCCI and S.aureus), the discriminability of stepwise-model was improved by 6.8% after adding two more predictors of COVID-19 and microbiological test before antibiotics use (P<0.01).And the stepwise-model showed similar discriminability in the derivation (the area under curve, AUC=0.87) and external validation cohorts (AUC=0.84, P=0.46). No significant gaps existed between the proportion of actual diagnosed IFSI and the frequency of IFSI predicted by both two models in derivation cohort and by stepwise-model in external validation cohort (P=0.16, 0.30 and 0.35, respectively). The incidence of IFSI in ICU patients with HABI appeared to be a temporal rising, and our externally validated nomogram will facilitate the development of targeted and timely prevention and control measures based on specific risks of IFSI.

The development and external validation of a web-based nomogram for predicting overall survival with Ewing sarcoma in children.

Ewing sarcoma remains the second most prevalent primary aggressive bone tumor in teens and young adults. The aim of our study was to develop and validate a web-based nomogram to predict the overall survival for Ewing sarcoma in children. A total of 698 patients, with 640 cases from the Surveillance, Epidemiology, and End Results (the training set) and 58 cases (the external validation set), were included in this study. Cox analyses were carried out to determine the independent prognostic indicators, which were further included to establish a web-based nomogram. The predictive abilities were tested through the concordance index, calibration curve, decision curve analysis, and area under the receiver operating characteristic curve. As suggested by univariate and multivariate Cox analyses, age, primary site, tumor size, metastasis stage (M stage), and chemotherapy were included as the independent predictive variables. The area under the receiver operating characteristic curve values, calibration curves, concordance index, and decision curve analysis from training and validation groups suggested the model has great clinical applications. We developed a convenient and precise web-based nomogram to evaluate overall survival for Ewing sarcoma in children. The application of this nomogram would assist physicians and patients in making decisions.

A dynamic visualization clinical tool constructed and validated based on the SEER database for screening the optimal surgical candidates for bone metastasis in primary kidney cancer

The implementation of primary tumor resection (PTR) in the treatment of kidney cancer patients (KC) with bone metastases (BM) has been controversial. This study aims to construct the first tool that can accurately predict the likelihood of PTR benefit in KC patients with BM (KCBM) and select the optimal surgical candidates. This study acquired data on all patients diagnosed with KCBM during 2010–2015 from the Surveillance, Epidemiology, and End Results (SEER) database. Propensity score matching (PSM) was utilized to achieve balanced matching of PTR and non-PTR groups to eliminate selection bias and confounding factors. The median overall survival (OS) of the non-PTR group was used as the threshold to categorize the PTR group into PTR-beneficial and PTR-Nonbeneficial subgroups. Kaplan–Meier (K–M) survival analysis was used for comparison of survival differences and median OS between groups. Risk factors associated with PTR-beneficial were identified using univariate and multivariate logistic regression analyses. Receiver operating characteristic (ROC), area under the curve (AUC), calibration curves, and decision curve analysis (DCA) were used to validate the predictive performance and clinical utility of the nomogram. Ultimately, 1963 KCBM patients meeting screening criteria were recruited. Of these, 962 patients received PTR and the remaining 1061 patients did not receive PTR. After 1:1 PSM, there were 308 patients in both PTR and non-PTR groups. The K–M survival analysis results showed noteworthy survival disparities between PTR and non-PTR groups, both before and after PSM (p < 0.001). In the logistic regression results of the PTR group, histological type, T/N stage and lung metastasis were shown to be independent risk factors associated with PTR-beneficial. The web-based nomogram allows clinicians to enter risk variables directly and quickly obtain PTR beneficial probabilities. The validation results showed the excellent predictive performance and clinical utility of the nomograms for accurate screening of optimal surgical candidates for KCBM. This study constructed an easy-to-use nomogram based on conventional clinicopathologic variables to accurately select the optimal surgical candidates for KCBM patients.

Construction and validation of a novel web-based nomogram for primary ocular adnexal lymphoma: a real-world analysis based on the Surveillance, Epidemiology, and End Results database.

The occurrence rate of primary ocular adnexal lymphoma (POAL) is relatively low, and estimation of prognosis of these patients poses significant challenges. This study aims to investigate the independent prognostic factors of POAL patients and establish a predictive model to provide clinical data for the formulation of standardized treatment plans. We conducted a retrospective analysis by extracting data of POAL patients diagnosed between 2000 and 2017 from the Surveillance, Epidemiology, and End Results (SEER) database. The enrolled patients were randomly divided into a training group and a testing group in a 7:3 ratio. To identify independent prognostic factors, we used both univariate and multivariate Cox regression analyses. Conditional survival (CS) pattern of these patients was analyzed. We formulated a nomogram model to forecast survival rates at intervals of 2, 5, 10, and 15 years. The reliability of the model's predictions was assessed through the concordance index (C-index) and the area under the receiver operating characteristic (ROC) curve (AUC). Moreover, we designed an online survival calculator using the nomogram model. The study ultimately analyzed 3,324 patients with POAL, of which 2,327 and 997 were respectively assigned to a training group and a testing group. Important prognostic factors including age, sex, tumor site, tumor histology, coexistence of other malignancy, surgery, radiotherapy (RT), and marital status were identified. Based on these predictors, a novel nomogram model was successfully developed with excellent predictive performance, which can also be accessed on the website: https://helloshinyweb.shinyapps.io/eye_dynamic_nomogram/. The calibration curves demonstrated good consistency between the predicted and actual survival rates. Additionally, the C-index and AUC demonstrated good discriminative ability. This study has successfully developed and validated a prognostic nomogram model that accurately predicts the survival rate of patients with POAL. The model proves invaluable in enabling clinical doctors to assess patients' risk factors and formulate personalized treatment strategies, thereby enhancing survival assessment and clinical management for POAL patients.

Development and validation of web-based nomograms for predicting survival status in patients with intrahepatic cholangiocarcinoma depending on the surgical status: a SEER database analysis

This study aimed to develop and validate prognostic nomograms that can estimate the probability of 1-, 3- and 5-year overall survival (OS) as well as cancer-specific survival (CSS) for Intrahepatic cholangiocarcinoma (ICCA) patients. Clinical data of 1446 patients diagnosed with ICCA between 2010 and 2017 from the Surveillance, Epidemiology, and End Results (SEER) database were analyzed. In both the OS and the CSS group, the training cohort and validation cohort were divided into a 7:3 ratio. Age, sex, AJCC T stage, AJCC N stage, AJCC M stage, surgical status, and tumor grade were selected as independent prognostic risk factors to build the nomograms. To compare the efficacy of predicting 1-, 3-, and 5-year OS and CSS rates of the nomogram with the 8th edition of the American Joint Committee on Cancer (AJCC) staging system, we evaluated the Harrell’s index of concordance (C-index), area under the receiver operating characteristic curve (AUC) and decision curve analysis (DCA) in both cohorts. The results showed the nomogram for 1-, 3-, and 5-year OS and CSS prediction performed better than the AJCC staging system. In the subgroup analysis for patients could not receive surgery as the primary treatment. We developed two nomograms for predicting the 1-, and 2-year OS and CSS rates following the same analysis procedure. Results indicate that the performance of both nomograms, which contained sex, AJCC T stage, AJCC M stage, chemotherapy, and tumor grade and prognostic factors, was also superior to the AJCC staging system. Meanwhile, four dynamic network-based nomograms were published. The survival analysis showed the survival rate of patients classified as high-risk based on the nomogram score was significantly lower compared to those categorized as low-risk (P < 0.0001). Finally, accurate and convenient nomograms were established to assist clinicians in making more personalized prognosis predictions for ICCA patients.