Decision Curve Analysis Research Articles

The Demographic and Clinical Characteristics, Prognostic Factors, and Survival Outcomes of Head and Neck Carcinosarcoma: A SEER Database Analysis.

Head and neck carcinosarcoma (HNCS) is a rare and highly aggressive malignancy with limited research, resulting in an incomplete understanding of disease progression and a lack of reliable prognostic tools. This study aimed to retrospectively analyze the clinical characteristics and outcomes of HNCS patients using data from the Surveillance, Epidemiology, and End Results (SEER) database and to develop a nomogram to predict overall survival (OS) and cancer-specific survival (CSS). Patients diagnosed with HNCS from 1975 to 2020 were identified in the SEER database. Univariate and multivariate Cox regression analyses were conducted to identify independent prognostic indicators, with the optimal model selected using the minimal Akaike Information Criterion (AIC). The identified prognostic factors were incorporated into nomograms to predict OS and CSS. Model performance was assessed using the concordance index (C-index), area under the curve (AUC), calibration curves, and decision curve analysis (DCA). Survival curves were generated using Kaplan-Meier analysis and compared via the log-rank test. A total of 152 HNCS patients were included, with 108 assigned to the training cohort and 44 to the validation cohort in a 7:3 ratio. Prognostic factors including age, primary tumor site, marital status, radiotherapy, chemotherapy, tumor size, pathological grade, and tumor stage were incorporated into the nomogram models. The models demonstrated strong predictive performance, with C-index values for OS and CSS of 0.757 and 0.779 in the training group, and 0.777 and 0.776 in the validation group, respectively. AUC values for predicting 3-, 5-, and 10-year OS were 0.662, 0.713, and 0.761, and for CSS the values were 0.726, 0.703, and 0.693. Kaplan-Meier analysis indicated significantly improved survival for patients with lower risk scores. The 3-, 5-, and 10-year OS rates for the entire cohort were 54.1%, 45.6%, and 35.1%, respectively, and the CSS rates were 62.9%, 57.5%, and 52.2%, respectively. This study provides validated nomograms for predicting OS and CSS in HNCS patients, offering a reliable tool to support clinical decision-making for this challenging malignancy. These nomograms enhance the ability to predict patient prognosis and personalize treatment strategies.

Development and validation of a nomogram for predicting histologic subtypes of subpleural non-small cell lung cancer using ultrasound parameters and clinical data.

To develop and validate an individualized nomogram for differentiating the histologic subtypes (adenocarcinoma and squamous cell carcinoma) of subpleural non-small cell lung cancer (NSCLC) based on ultrasound parameters and clinical data. This study was conducted retrospectively between March 2018 and December 2019. Patients were randomly assigned to a development cohort (DC, n=179) and a validation cohort (VC, n=77). A total of 7 clinical parameters and 16 ultrasound parameters were collected. Least absolute shrinkage and selection operator regression analysis was employed to identify the most significant predictors utilizing a 10-fold cross-validation. The multivariate logistic regression model was applied to investigate the relevant factors. An individualized nomogram was then developed. Receiver operating characteristic (ROC) curve, calibration plot and decision curve analysis (DCA) were applied for model validation in both DC and VC. Following the final regression analysis, gender, serum carcinoembryonic antigen, lesion size and perfusion defect in contrast-enhanced ultrasound were entered into the nomogram. The model showed moderate predictive ability, with an area under the ROC curve of 0.867 for DC and 0.838 for VC. The calibration curves of the model showed good agreement between actual and predicted probabilities. The ROC and DCA curves demonstrated that the nomogram exhibited a good predictive performance. We developed a nomogram that can predict the histologic subtypes of subpleural NSCLC. Both internal and external validation revealed optimal discrimination and calibration, indicating that the nomogram may have clinical utility. This model has the potential to assist clinicians in making treatment recommendations.

M6A-related genes and their role in Parkinson's disease: Insights from machine learning and consensus clustering.

Parkinson disease (PD) is a chronic neurological disorder primarily characterized by a deficiency of dopamine in the brain. In recent years, numerous studies have highlighted the substantial influence of RNA N6-methyladenosine (m6A) regulators on various biological processes. Nevertheless, the specific contribution of m6A-related genes to the development and progression of PD remains uncertain. In this study, we performed a differential analysis of the GSE8397 dataset in the Gene Expression Omnibus database and selected important m6A-related genes. Candidate m6A-related genes were then screened using a random forest model to predict the risk of PD. A nomogram model was built based on the candidate m6A-related genes. By employing a consensus clustering method, PD was divided into different m6A clusters based on the selected significant m6A-related genes. Finally, we performed immune cell infiltration analysis to explore the immune infiltration between different clusters. We performed a differential analysis of the GSE8397 dataset in the Gene Expression Omnibus database and selected 11 important m6A-related genes. Four candidate m6A-related genes (YTH Domain Containing 2, heterogeneous nuclear ribonucleoprotein C, leucine-rich pentatricopeptide repeat motif containing protein and insulin-like growth factor binding protein-3) were then screened using a random forest model to predict the risk of PD. A nomogram model was built based on the 4 candidate m6A-related genes. The decision curve analysis indicated that patients can benefit from the nomogram model. By employing a consensus clustering method, PD was divided into 2 m6A clusters (cluster A and cluster B) based on the selected significant m6A-related genes. The immune cell infiltration analysis revealed that cluster A and cluster B exhibit distinct immune phenotypes. In conclusion, m6A-related genes play a significant role in the development of PD and our study on m6A clustering may potentially guide personalized treatment strategies for PD in the future.

Prognostic nomograms for locally advanced cervical cancer based on the SEER database: Integrating Cox regression and competing risk analysis.

Locally advanced cervical carcinoma (LACC) remains a significant global health challenge owing to its high recurrence rates and poor outcomes, despite current treatments. This study aimed to develop a comprehensive risk stratification model for LACC by integrating Cox regression and competing risk analyses. This was done to improve clinical decision making. We analyzed data from 3428 patients with LACC registered in the Surveillance, Epidemiology, and End Results program and diagnosed them between 2010 and 2015. Cox regression and competing risk analyses were used to identify the prognostic factors. We constructed and validated nomograms for overall survival (OS) and disease-specific survival (DSS). Multivariate Cox regression identified key prognostic factors for OS, including advanced International Federation of Gynecology and Obstetrics stage, age, marital status, ethnicity, and tumor size. Notably, International Federation of Gynecology and Obstetrics stages IIIA, IIIB, and IVA had hazard ratios of 2.227, 2.451, and 4.852, respectively, significantly increasing the mortality risk compared to stage IB2. Ethnic disparities were evident, with African Americans facing a 39.8% higher risk than Caucasians did. Competing risk analyses confirmed the significance of these factors in DSS, particularly tumor size. Our nomogram demonstrated high predictive accuracy, with area under the curve values ranging from 0.706 to 0.784 for DSS and 0.717 to 0.781 for OS. Calibration plots and decision curve analyses further validated the clinical utility of this nomogram. We present effective nomograms for LACC risk stratification that incorporate multiple prognostic factors. These models provide a refined approach for individualized patient management and have the potential to significantly enhance therapeutic strategies for LACC.

Prognostic value of serum complement cleavage factor Bb in idiopathic membranous nephropathy and establishment of nomogram model

Complement activation is involved in idiopathic membranous nephropathy (IMN). We aimed to investigate the relationship of serum complement cleavage factor Bb with IMN progression, and to establish a model for early prediction of kidney outcomes. We measured serum factor Bb in a retrospective cohort of 449 IMN patients at the time of kidney biopsy. Cox regression analysis showed that higher levels of serum factor Bb were independently associated with IMN progression event defined as end-stage renal disease or ≥ 40% decline in estimated glomerular filtration rate. Patients in the middle and highest tertiles of serum factor Bb had respectively 2.1-, and 2.6-fold higher risk for disease progression compared with those in the lowest tertile. We developed an optimized prognostic nomogram model incorporating age, log serum anti-PLA2R antibody, log serum Bb, proteinuria and tubular atrophy/interstitial fibrosis. The model demonstrated good predictive ability with a concordance index of 0.77 (bootstrap-corrected of 0.76) for predicting 3-, and 5-year kidney survival. Calibration curves and decision curve analysis confirmed the model’s good calibration and clinical utility. Our findings suggest that serum factor Bb may serve as an essential prognostic indicator of IMN. The novel nomogram model may offer important guidance on the management of this patient population.

An Artificial Intelligence Approach for Test-Free Identification of Sarcopenia.

The diagnosis of sarcopenia relies extensively on human and equipment resources and requires individuals to personally visit medical institutions. The objective of this study was to develop a test-free, self-assessable approach to identify sarcopenia by utilizing artificial intelligence techniques and representative real-world data. This multicentre study enrolled 11 661 middle-aged and older adults from a national survey initialized in 2011. Follow-up data from the baseline cohort collected in 2013 (n = 9403) and 2015 (n = 10 356) were used for validation. Sarcopenia was retrospectively diagnosed using the Asian Working Group for Sarcopenia 2019 framework. Baseline age, sex, height, weight and 20 functional capacity (FC)-related binary indices (activities of daily living = 6, instrumental activities of daily living = 5 and other FC indices = 9) were considered as predictors. Multiple machine learning (ML) models were trained and cross-validated using 70% of the baseline data to predict sarcopenia. The remaining 30% of the baseline data, along with two follow-up datasets (n = 9403 and n = 10 356, respectively), were used to assess model performance. The study included 5634 men and 6027 women (median age = 57.0 years). Sarcopenia was identified in 1288 (11.0%) individuals. Among the 20 FC indices, the running/jogging 1 km item showed the highest predictive value for sarcopenia (AUC [95%CI] = 0.633 [0.620-0.647]). From the various ML models assessed, a 24-variable gradient boosting classifier (GBC) model was selected. This GBC model demonstrated favourable performance in predicting sarcopenia in the holdout data (AUC [95%CI] = 0.831 [0.808-0.853], accuracy = 0.889, recall = 0.441, precision = 0.475, F1 score = 0.458, Kappa = 0.396 and Matthews correlation coefficient = 0.396). Further model validation on the temporal scale using two longitudinal datasets also demonstrated good performance (AUC [95%CI]: 0.833 [0.818-0.848] and 0.852 [0.840-0.865], respectively). The model's built-in feature importance ranking and the SHapley Additive exPlanations method revealed that lifting 5 kg and running/jogging 1 km were relatively important variables among the 20 FC items contributing to the model's predictive capacity, respectively. The calibration curve of the model indicated good agreement between predictions and actual observations (Hosmer and Lemeshow p = 0.501, 0.451 and 0.374 for the three test sets, respectively), and decision curve analysis supported its clinical usefulness. The model was implemented as an online web application and exported as a deployable binary file, allowing for flexible, individualized risk assessment. We developed an artificial intelligence model that can assist in the identification of sarcopenia, particularly in settings lacking the necessary resources for a comprehensive diagnosis. These findings offer potential for improving decision-making and facilitating the development of novel management strategies of sarcopenia.

Development and validation of a novel comorbidity score specific for prostate cancer patients treated with robotic platform and its implication on DaVinci single-port system.

To develop and validate a novel Comorbidity score for Robotic Surgery (CRS) in predicting severe complications after robot-assisted radical prostatectomy (RARP). Furthermore, we investigated the impact of the surgical platform (Multi-Port - MP vs Single-Port - SP) according to this score. We included 2085 ("development cohort") and 595 ("validation cohort") patients undergoing RARP at two tertiary referral centers between 2014 and March 2024 in a retrospective study. Statistical analyses included validation of the Charlson Comorbidity Index (CCI) to predict 30-day severe complications (Clavien-Dindo ≥ 3a), development and external validation of CRS using calibration plots and decision curve analysis. Lastly, locally weighted scatterplot smoothing (LOWESS) analysis was used to graphically explore the impact of the robotic platform according to novel CRS. CCI exhibited limited predictive ability for severe complications (60% in the validation cohort). In multivariable logistic regression analyses testing the correlation between each condition included in CCI and severe complications, diabetes and myocardial infarction resulted as independent predictors (OR 1.75 [95%CI 1.05-2.82]; OR 1.92 [95%CI 1.26-2.88]) and were subsequently fitted into a multivariable logistic model including age, previous abdominal surgery and obesity (BMI > 30). The resulting predictive model demonstrated superior discrimination and clinical net benefit in predicting severe complications compared to CCI (AUC 64 vs 60%). At LOWESS analysis, SP platform was associated with lower risk of severe complications as CRS increased compared to MP system. The validated CRS showed better accuracy compared to CCI in predicting severe complications after RARP. Additionally, the use of SP robotic platform may reduce the risk of severe complications in highly comorbid patients according to CRS.

Accurate prediction of the lymph node status in ampullary duodenal carcinoma: potential guidance for clinical management

PurposeThis study aimed to identify the risk factors associated with ampullary duodenal carcinoma (a-DC) and develop a clinical model to dynamically and accurately predict the risk of lymph node metastasis (LNM) in a-DC patients.MethodsData from 4077 patients (2004–2020) were extracted from the Surveillance, Epidemiology, and End Results database to form a training cohort, while 173 cases (2010–2020) from Zhejiang Provincial People’s Hospital in China were used as an external validation cohort. A reliable LASSO-logistic method was employed to identify independent risk factors for a-DC LNM, and a nomogram was developed based on these factors to assess the risk of a-DC LNM. The nomogram was evaluated using the Akaike information criterion, misclassification error, area under the curve, and likelihood ratio test. Finally, the nomogram’s accuracy and generalizability were externally validated..ResultsAfter screening using LASSO and logistic regression four variables were identified as independent risk factors for a-DC LNM: sex (P < 0.001), tumor size (P < 0.001), grade (P < 0.001), and tumor extension (P < 0.001). The area under the curve of the nomogram was 74.8% in the training group and 88.9% in the external validation group. The calibration curves demonstrated that the LNM predictions made by the nomogram were in satisfactory agreement with the actual observed LNM. Additionally, the decision curve analysis curves indicated effective clinical utility of the nomogram.ConclusionsA nomogram based on the LASSO-logistic analysis was constructed to predict a-DC LNM, demonstrating good performance and clinical application value.

Analysis of risk factors and establishment of early warning model for recent postoperative complications of colorectal cancer.

This study aimed to analyze factors associated with recent complications after colorectal cancer surgery, constructing a nomogram to aid gastrointestinal surgeons in preoperative decision-making for patients at risk of such complications. In this retrospective study, clinical data were collected from patients undergoing radical colorectal cancer surgery at the Department of Gastrointestinal Surgery of the Affiliated Cancer Hospital of Guizhou Medical University and the Second People's Hospital of Chengdu from November 1, 2021, to January 26, 2024. Univariable and multivariable logistic regression analyses were conducted to assess risk factors for recent postoperative complications and develop a prediction model. External validation was performed using data from 48 postoperative colorectal cancer patients in the Second People's Hospital of Chengdu City from January 1, 2023, to May 30, 2023. Evaluation included receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis using R4.2.2 statistical software. A total of 324 patients who underwent radical colorectal cancer surgery were enrolled. The training cohort (n=176) identified four independent risk factors for recent complications: PNI ≥45 (OR=4.17, P<0.001), Albumin <40 g/L (OR=3.9, P<0.001), ASA score III-IV (OR=6.29, P<0.001), and Tumor diameter ≥5 cm (OR=4.24, P<0.001). A nomogram was constructed incorporating these factors. The AUC of the nomogram model in the training cohort was 0.835, with subsequent internal and external validation cohort AUCs of 0.815 and 0.819, respectively, indicating strong discriminatory ability. The calibration curve demonstrated good consistency, and decision curve analysis indicated high clinical utility. PNI ≥45, Albumin <40 g/L, ASA score III-IV, and Tumor diameter ≥5 cm emerged as independent risk factors for recent complications following colorectal cancer surgery. We developed a nomogram model for these complications, potentially aiding gastrointestinal surgeons in preoperative patient evaluation and treatment planning for colorectal cancer surgery.

A new nomogram for predicting extraurothelial recurrence in patients with upper urinary tract urothelial carcinoma following radical nephroureterectomy.

We sought to develop and validate a nomogram for predicting extra-urinary recurrence (EUR) following radical nephroureterectomy (RNU) in patients with upper urinary tract urothelial carcinoma (UTUC). Data from 556 UTUC patients post-RNU at the First Hospital of Jilin University were retrospectively analyzed. These patients were categorized into a training group (n=389) and a validation group (n=167). Variables significantly associated with prognosis were identified using univariate Cox regression and most minor absolute shrinkage and selection operator (LASSO) analysis. These independent predictors were incorporated into the nomogram to estimate extra-urinary recurrence-free survival (EURFS). Validation of the nomogram involved ROC curves, calibration plots, and decision curve analysis (DCA). Patients were stratified into two risk categories based on their nomogram scores to compare EURFS using the Kaplan-Meier method. Eight predictors were identified: T-stage, N-stage, tumor grade, local and nerve invasion, preoperative hemoglobin level, neutrophil-to-lymphocyte ratio (NLR), and creatinine, all proving to be independent predictors of EUR. A nomogram was created based on these eight factors, and using the ROC, calibration curves, and DCA, good prediction results were shown in both the training and validation groups. The training and validation groups also showed reliable predictive performance. In particular, there was a significant difference in survival between the high-risk and low-risk groups (P<0.0001). We have also built a network calculator that calculates patient survival time. The URL is https://haowu24.shinyapps.io/dynnomapp. A nomogram for predicting distant metastases in UTUC patients was successfully developed, and its accuracy, reliability, and clinical value were demonstrated. This new tool helps to improve the clinical management of UTUC cases.

Development and validation of a risk prediction model for postpartum urinary incontinence in primiparas with singleton pregnancies: a multicenter clinical investigation.

Postpartum urinary incontinence (UI) is a serious condition that significantly affects the quality of life. Several studies have demonstrated that it is associated with pelvic floor dysfunction. This study aimed to develop and validate a UI risk prediction model to identify primiparas with singleton pregnancies at high risk. A multistage stratified random sampling process was used. UI was measured using the International Standard Consultation on Incontinence Questionnaire Form (a modified Bristol questionnaire, ICIQ-FLUTS). Records of 1,340 primiparas with singleton pregnancies were reviewed, and data were collected from January 2014 to December 2014 in multiple centers. A univariate logistic regression analysis was performed, followed by a multivariable logistic regression analysis of the data. Using bootstrap resampling, we constructed a nomogram to assess postpartum UI risk. A total of 1,340 patients were enrolled, including 345 with postpartum UI and 995 with non-postpartum UI. The occurrence of postpartum UI was significantly related to the mode of delivery, family history of UI, coffee or tea consumption, antenatal UI, and frequent cough. The nomogram exhibited good discriminatory ability with a C-index of 0.718 (95% confidence interval: 0.684-0.752) and a bootstrap-corrected C-index of 0.716. Additionally, the calibration curve demonstrated that the predicted outcomes aligned well with the actual observations. Ultimately, the decision curve analysis indicated that the nomogram exhibited favorable clinical applicability. The decision curve analysis suggests that the nomogram could provide clinical value. The clinician will then feel more confident about making clinical recommendations regarding postpartum UI screening for primiparous women with singleton pregnancies.

Development and validation of the competing risk nomogram and risk classification system for predicting cancer-specific mortality in patients with cervical adenosquamous carcinoma treated via radical hysterectomy.

In this study, we established and validated a competing risk nomogram for predicting the cumulative incidence of cervical adenosquamous carcinoma (ASC)-specific death in patients undergoing radical hysterectomy. Patients diagnosed with ASC between 2010 and 2019 were retrieved from the Surveillance, Epidemiology, and End Results (SEER) database. The cumulative incidence function (CIF) for various variables influencing ASC-specific mortality was computed. A Fine-Gray competing risk model was used to identify independent predictors, formulating a competing risk nomogram. A multivariate Cox proportional hazards model was also applied for comparative analysis. The performance of the nomogram was assessed using metrics such as the concordance index (C-index), receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). A corresponding risk classification system was constructed based on nomogram-derived scores. Factors such as advanced age, racial background (Black race), higher tumor grade, increased tumor size, advanced TNM stage, and receipt of radiotherapy without chemotherapy were found to be positively associated with elevated ASC-specific mortality. Additionally, age, T stage, M stage, and chemotherapy were identified as independent predictors correlated with ASC-specific mortality. The established nomogram exhibited accurate discriminatory capabilities and superior net benefits compared to the traditional TNM staging system. Additionally, the high-risk group consistently demonstrated higher probabilities of ASC-specific death in both the training and validation sets. The developed nomogram proficiently quantified the incidence of ASC-specific death in patients subjected to radical hysterectomy for ASC. This tool could help clinicians in formulating personalized treatment strategies and devising follow-up protocols.

Construction of a nomogram for personalized prediction of lower limb lymphedema risk after cervical cancer surgery

ObjectiveThis study aims to construct and evaluate an individualized nomogram for predicting the risk of lower limb lymphedema after cervical cancer surgery. Healthcare professionals can utilize line chart models to predict the probability of postoperative lower limb lymphedema in different patients, allowing for the early identification of high-risk patients and facilitating early prevention and treatment.MethodsA retrospective study was conducted among 411 cervical cancer patients treated at our hospital from May 2021 to December 2023. The patients were randomly divided into a modeling group (313 cases) and a validation group (98 cases) according to an approximate 3:1 ratio. The modeling group was further divided into a lower limb lymphedema group (61 cases) and a non-lower limb lymphedema group (252 cases) based on the presence of postoperative lower limb lymphedema. Multiple factors Logistic regression was used to identify risk factors, and a nomogram was constructed using R software version 4.0.2, with internal and external validation performed.ResultsRisk factors for lower limb lymphedema following cervical cancer surgery include age 60 years or older, a Body Mass Index (BMI) of 24 kg/m² or higher, hypertension, the removal of 30 or more lymph nodes, adjuvant radiotherapy and chemotherapy, and prolonged standing for six hours or more (P < 0.05). Internal and external validation results demonstrated that the calibration curve closely aligned with the ideal curve. The Area Under Curve(AUC) of the Receiver Operating Characteristic(ROC) curve was 0.890 (95% CI: 0.844 ∼ 0.936) and 0.876 (95% CI: 0.821 ∼ 0.930), indicating high model calibration and discrimination. Decision Curve Analysis(DCA) curve revealed that the Logistic model had good net returns and high clinical practicality when the probability range of the high-risk threshold was 0.11 ∼ 0.98.ConclusionThe nomogram, developed using factors such as age, BMI, hypertension, number of lymph nodes dissected, adjuvant radiotherapy and chemotherapy, and duration of standing, has strong predictive value and offers significant clinical benefits, making it a valuable tool for clinical decision-making.

CT-based radiomics model for predicting perineural invasion status in gastric cancer.

Perineural invasion (PNI) is an independent risk factor for poor prognosis in gastric cancer (GC) patients. This study aimed to develop and validate predictive models based on CT imaging and clinical features to predict PNI status in GC patients. This retrospective study included 291 GC patients (229 in the training cohort and 62 in the validation cohort) who underwent gastrectomy between January 2020 and August 2022. The clinical data and preoperative abdominal contrast-enhanced computed tomography (CECT) images were collected. Radiomics features were extracted from the venous phase of CECT images. The intraclass correlation coefficient (ICC), Pearson correlation coefficient, and t-test were applied for radiomics feature selection. The random forest algorithm was used to construct a radiomics signature and calculate the radiomics feature score (Rad-score). A hybrid model was built by aggregating the Rad-score and clinical predictors. The area under the receiver operating characteristic curve (ROC) and decision curve analysis (DCA) were used to evaluate the prediction performance of the radiomics, clinical, and hybrid models. A total of 994 radiomics features were extracted from the venous phase images of each patient. Finally, 5 radiomics features were selected and used to construct a radiomics signature. The hybrid model demonstrated strong predictive ability for PNI, with AUCs of 0.833 (95% CI: 0.779-0.887) and 0.806 (95% CI: 0.628-0.983) in the training and validation cohorts, respectively. The DCA showed that the hybrid model had good clinical utility. We established three models, and the hybrid model that combined the Rad-score and clinical predictors had a high potential for predicting PNI in GC patients.

Predictive modeling of COVID-19 mortality risk in chronic kidney disease patients using multiple machine learning algorithms

The coronavirus disease 2019 (COVID-19) has a significant impact on the global population, particularly on individuals with chronic kidney disease (CKD). COVID-19 patients with CKD will face a considerably higher risk of mortality than the general population. This study developed a predictive model for assessing mortality in COVID-19-affected CKD patients, providing personalized risk prediction to optimize clinical management and reduce mortality rates. We developed machine learning algorithms to analyze 219 patients’ clinical laboratory test data retrospectively. The performance of each model was assessed using a calibration curve, decision curve analysis, and receiver operating characteristic (ROC) curve. It was found that the LightGBM model showed the most satisfied performance, with an area under the ROC curve of 0.833, sensitivity of 0.952, and specificity of 0.714. Prealbumin, neutrophil percent, respiratory index in arterial blood, half-saturated pressure of oxygen, carbon dioxide in serum, glucose, neutrophil count, and uric acid were the top 8 significant variables in the prediction model. Validation by 46 patients demonstrated acceptable accuracy. This model can serve as a powerful tool for screening CKD patients at high risk of COVID-19-related mortality and providing decision support for clinical staff, enabling efficient allocation of resources, and facilitating timely and targeted management for those who need the relevant interference urgently.

Preoperative prediction of occult lymph node metastasis in patients with non-small cell lung cancer: a simple and widely applicable model.

Lymph node metastasis (LNM) is one of the most common pathways of metastasis in non-small cell lung cancer (NSCLC). Preoperative assessment of occult lymph node metastasis (OLNM) in NSCLC patients is beneficial for selecting appropriate treatment plans and improving patient prognosis. A total of 370 NSCLC patients were included in the study. Univariate and multivariate logistic regression analysis were used to screen potential risk factors for OLNM in preoperative NSCLC patients. And establish a nomogram for OLNM in NSCLC patients before surgery. Receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) were used to evaluate the established nomogram. Both univariate and multivariate logistic regression analyses suggested that multiple tumors, ERBB2 missense mutation, CA125 levels, CA153 levels, tumor site, tumor length, and serum ferritin are potential risk factors for OLNM in NSCLC patients. The constructed nomogram was evaluated, and the consistency index (C-index) and area under the ROC curve of the model were both 0.846. The calibration curve showed that the predicted values of the model had a high degree of fit with the actual observed values, and DCA suggested that the above indicators had good utility. The personalized scoring prediction model constructed based on multiple tumors, ERBB2 miss mutation, CA125 levels, CA153 levels, tumor site, tumor length, and serum ferritin can screen NSCLC patients who may have OLNM.

Development and validation of a clinical nomogram prediction model for surgical site infection following lumbar disc herniation surgery

Surgical site infection (SSI) following lumbar disc herniation (LDH) surgery leads to prolonged hospital stays, increased costs and reoperations. Therefore, we aim to develop and validate a nomogram to predict the risk of SSI following LDH surgery, thereby helping spine surgeons design personalized prevention strategies and promote early recovery. Data from 647 patients with SSI who underwent LDH surgery at the First Affiliated Hospital of Air Force Medical University (AFMU) from 2020 to 2023 were collected. Ultimately, 241 patients with SSI were selected based on inclusion and exclusion criteria. Patients were randomly divided into training and validation sets with a ratio of 7:3. LASSO regression, univariate, and multivariate logistic regression were utilized to identify target variables and establish the prediction model, which was subsequently validated. Six factors—Age, Body Mass Index (BMI), Postoperative Suction Drainage (PSD), Gelatin Sponge (GS), None-Preoperative Antibiotic (NPTA), and Thrombin Time (TT)—were selected to construct the nomogram model. In the training set, the area under the curve (AUC) for the nomogram was 0.818 (95% CI 0.779–0.857). In the validation set, the AUC was 0.782 (95% CI 0.717–0.846). Calibration curves for both sets showed satisfactory agreement between predicted and actual SSI probabilities. Decision curve analysis indicated that the nomogram is clinically useful with a threshold range of 1–90%. The Clinical Impact Curve (CIC) demonstrated an acceptable cost-benefit ratio. The developed nomogram model effectively predicts the risk of SSI following LDH surgery, enabling spine surgeons to formulate more professional and rational clinical prevention strategies.

Construction and validation of a clinical prediction model for sepsis using peripheral perfusion index to predict in-hospital and 28-day mortality risk.

Sepsis is a clinical syndrome caused by infection, leading to organ dysfunction due to a dysregulated host response. In recent years, its high mortality rate has made it a significant cause of death and disability worldwide. The pathophysiological process of sepsis is related to the body's dysregulated response to infection, with microcirculatory changes serving as early warning signals that guide clinical treatment. The Peripheral Perfusion Index (PI), as an indicator of peripheral microcirculation, can effectively evaluate patient prognosis. This study aims to develop two new prediction models using PI and other common clinical indicators to assess the mortality risk of sepsis patients during hospitalization and within 28 days post-ICU admission. This retrospective study analyzed data from sepsis patients treated in the Intensive Care Unit of Peking Union Medical College Hospital between December 2019 and June 2023, ultimately including 645 patients. LASSO regression and logistic regression analyses were used to select predictive factors from 35 clinical indicators, and two clinical prediction models were constructed to predict in-hospital mortality and 28-day mortality. The models' performance was then evaluated using ROC curve, calibration curve, and decision curve analyses. The two prediction models performed excellently in distinguishing patient mortality risk. The AUC for the in-hospital mortality prediction model was 0.82 in the training set and 0.73 in the validation set; for the 28-day mortality prediction model, the AUC was 0.79 in the training set and 0.73 in the validation set. The calibration curves closely aligned with the ideal line, indicating consistency between predicted and actual outcomes. Decision curve analysis also demonstrated high net benefits for the clinical utility of both models. The study shows that these two prediction models not only perform excellently statistically but also hold high practical value in clinical applications. The models can help physicians accurately assess the mortality risk of sepsis patients, providing a scientific basis for personalized treatment.

Development and validation of a prognostic model for cachexia in postoperative gastric cancer patients with low nutritional risk: a dual-center retrospective cohort study.

Gastric cancer can lead to excessive catabolism in patients. After undergoing gastric surgery, patients may experience additional unintended weight loss, resulting in severe malnutrition and potentially cachexia. We selected and incorporated patients from two centers. Cohort 1 (n = 1393) served as the development cohort, while cohort 2 (n = 501) was designated as an external validation cohort. Within cohort 1, 70% of the patients were utilized for model training, with the remaining 30% reserved for internal validation. The training set initially underwent univariate logistic regression, followed by multivariate logistic regression. The factors ultimately incorporated were used to construct the model and create nomograms. The discriminative ability was assessed using ROC curves in all three datasets, calibration curves were used to evaluate consistency, and decision curves analysis was performed to assess the clinical application value. The model incorporated 12 factors, specifically: age (OR = 1.07), preoperative BMI (OR = 0.89), surgery type (Total Gastrectomy (TG), OR = 1.83), chemotherapy (yes, OR = 1.52), stage (III, OR = 1.40), anastomotic obstruction (yes, OR = 6.85), Postsurgical Gastroparesis Syndrome (PGS) (yes, OR = 2.27), albumin (OR = 0.95), hemoglobin (OR = 0.98), triglycerides (OR = 0.36), CRP (OR = 1.07), and Neutrophil to Lymphocyte Ratio (NLR) (OR = 1.05). The model demonstrated robust performance in ROC with AUC values of 0.805 in the training set, 0.767 in the validation set, and 0.795 in Cohort 2. Calibration curves in all three datasets exhibited a high degree of concordance between actual and predicted probabilities. Decision curve analysis (DCA) indicated that the model holds substantial clinical utility across all three datasets. We have developed a predictive model for cachexia in patients undergoing gastric cancer surgery. This model enables healthcare professionals to accurately estimate the risk of cachexia in postoperative patients with nutritional deficits, allowing for timely nutritional interventions to enhance patient quality of life and prognosis.

Study on the predictive model of delirium risk after surgery for elderly hip fractures based on meta-analysis.

To develop and validate a risk prediction model for postoperative delirium in elderly patients with hip fractures, aiming to identify high-risk patients and implement preventive measures. A systematic search of five authoritative medical databases was conducted, retrieving a total of 1368 relevant articles. After screening, 44 high-quality studies were included in the meta-analysis, analyzing 13 potential risk factors, such as age, gender, diabetes, and history of stroke. A risk prediction model was constructed and validated in a cohort of 189 elderly hip fracture patients. The model's predictive performance was evaluated using ROC curves, with calibration assessed through the Hosmer-Lemeshow test, and clinical utility examined via Decision Curve Analysis (DCA) and Clinical Impact Curves (CIC). The meta-analysis identified the following as independent risk factors for postoperative delirium: age (≥ 70years), male gender, diabetes, history of stroke, preoperative comorbidities (≥ 2), previous delirium, preoperative cognitive impairment, low preoperative albumin levels (≤ 40g/L), prolonged preoperative waiting time (≥ 48h), anemia (≤ 100g/L), ASA classification (≥ 3), use of general anesthesia, and prolonged surgery duration (≥ 2h). The prediction model demonstrated strong efficiency in the validation cohort, with an AUC of 0.956, sensitivity of 87.3%, specificity of 94.8%, and a Brier score of 0.144, indicating high predictive accuracy and calibration. DCA and CIC analyses showed the model to have strong clinical decision-making value and impact across most thresholds. The risk prediction model developed in this study shows high predictive accuracy and clinical utility, making it valuable for identifying high-risk patients and implementing preventive measures in clinical practice. However, the study has limitations, such as potential retrospective bias, and further validation in larger, multicenter prospective studies is needed to confirm the model's broader applicability and stability.