Development and validation of a risk prediction model for chemical cystitis in patients with non-muscle-invasive bladder cancer undergoing intravesical instillation

Colorectal cancer remains an important public health problem in the context of the COVID-19 (Corona virus disease 2019) pandemic. The decline in detection rates and delayed diagnosis of the disease necessitate the exploration of novel approaches to identify individuals with a heightened risk of developing colorectal cancer. The study aids clinicians in the rational allocation and utilization of healthcare resources, thereby benefiting patients, physicians, and the healthcare system. The present study retrospectively analyzed the clinical data of colorectal cancer cases diagnosed at the Affiliated Hospital of Guilin Medical University from September 2022 to September 2023, along with a control group. The study employed univariate and multivariate logistic regression as well as LASSO (Least absolute shrinkage and selection operator) regression to screen for predictors of colorectal cancer risk. The optimal predictors were selected based on the area under the curve (AUC) of the receiver operating characteristic (ROC) curve. These predictors were then utilized in constructing a Nomogram Model for predicting colorectal cancer risk. The accuracy of the risk prediction Nomogram Model was assessed through calibration curves, ROC curves, and decision curve analysis (DCA) curves. Clinical data of 719 patients (302 in the case group and 417 in the control group) were included in this study. Based on univariate logistic regression analysis, there is a correlation between Body Mass Index (BMI), red blood cell count (RBC), anemia, Mean Corpuscular Volume (MCV), mean corpuscular hemoglobin (MCH), mean corpuscular hemoglobin concentration (MCHC), platelet count (PLT), Red Cell Distribution Width-Standard Deviation (RDW-SD), and the incidence of colorectal cancer. Based on the findings of multivariate logistic regression analysis, the variables of BMI and RBC exhibit a decrease, while anemia and PLT demonstrate an increase, all of which are identified as risk factors for the occurrence of colorectal cancer. LASSO regression selected BMI, RBC, anemia, and PLT as prediction factors. LASSO regression and multivariate logistic regression analysis yielded the same results. A nomogram was constructed based on the 4 prediction factors identified by LASSO regression analysis to predict the risk of colorectal cancer. The AUC of the nomogram was 0.751 (95% CI, OR: 0.708-0.793). The calibration curves in the validation and training sets showed good performance, indicating that the constructed nomogram model has good predictive ability. Additionally, the DCA demonstrated that the nomogram model has diagnostic accuracy. The Nomogram Model offers precise prognostications regarding the likelihood of Colorectal Cancer in patients, thereby helping healthcare professionals in their decision-making processes and promoting the rational categorization of patients as well as the allocation of medical resources.

BackgroundMany diabetic patients develop and progress to diabetic foot ulcers, which seriously affect health and quality of life and cause great economic and psychological stress, especially in elderly diabetic patients who often have various underlying diseases, and the consequences of their progression to diabetic foot ulcers are more serious and seriously affect elderly patients in surgery. Therefore, it is particularly important to analyze the influencing factors related to the progression of elderly diabetic patients to diabetic foot, and the column line graph prediction model is drawn based on regression analysis to derive the influencing factors of the progression of elderly diabetic patients to diabetic foot, and the total score derived from the combination of various influencing factors can visually calculate the probability of the progression of elderly diabetic patients to diabetic foot.ObjectiveThe influencing factors of progression deterioration to diabetic foot in elderly diabetic patients based on LASSO regression analysis and logistics regression analysis, and the column line graph prediction model was established by statistically significant risk factors.MethodsThe clinical data of elderly diabetic patients aged 60 years or older in the orthopedic ward and endocrine ward of the Third Hospital of Shanxi Medical University from 2015-01-01 to 2021-12-31 were retrospectively analyzed and divided into a modeling population (211) and an internal validation population (88) according to the random assignment principle. Firstly, LASSO regression analysis was performed based on the modeling population to screen out the independent influencing factors for progression to diabetic foot in elderly diabetic patients; Logistics univariate and multifactor regressions were performed by the screened influencing factors, and then column line graph prediction models for progression to diabetic foot in elderly diabetic patients were made by these influencing factors, using ROC (subject working characteristic curve) and AUC (their area under the curve), C-index validation, and calibration curve to initially evaluate the model discrimination and calibration. Model validation was performed by the internal validation set, and the ROC curve, C-index and calibration curve were used to further evaluate the column line graph model performance. Finally, using DCA (decision curve analysis), we observed whether the model could be used better in clinical settings.Results and conclusions(1) LASSO (Least absolute shrinkage and selection operator) regression analysis yielded a more significant significance on risk factors for progression to diabetic foot in elderly diabetic patients, such as age, presence of peripheral neuropathy, history of smoking, duration of disease, serum lactate dehydrogenase, and high-density cholesterol; (2) Based on the influencing factors and existing theories, a column line graph prediction model for progression to diabetic foot in elderly diabetic patients was constructed. The working characteristic curves of subjects in the training group and their area under the curve (area under the curve = 0.840) were also analyzed simultaneously with the working characteristic curves of subjects in the external validation population and their area under the curve (area under the curve = 0.934), which finally showed that the model was effective in predicting column line graphs; (iii) the C-index in the modeled cohort was 0.840 (95%CI: 0.779-0.901) and the C-index in the validation cohort was 0.934 (95%CI: 0.887-0.981), indicating that the model had good predictive accuracy; the calibration curve fit was good; (iv) the results of the decision curve analysis showed that the model would have good results in clinical use; (v) it indicated that the established predictive model for predicting progression to diabetic foot in elderly diabetic patients had good test efficacy and helped clinically screen the possibility of progression to diabetic foot in elderly diabetic patients and give personalized interventions to different patients in time.

IntroductionThis study aimed to develop a dynamic nomogram model to predict the risk of Clostridioides difficile infection (CDI) in children with ulcerative colitis (UC).MethodsThis was a retrospective study that clinical data from pediatric diagnosis and treatment with UC at Zhengzhou University Children's Hospital between January 2018 and December 2024 were retrospectively reviewed. Patients were classified into CDI (n = 35) and non-CDI (n = 86) groups based on the presence or absence of CDI. Predictor variables were selected using least absolute shrinkage and selection operator (LASSO) regression and subsequently entered into a multivariate logistic regression model. Nomograms were then constructed based on the final logistic regression analysis. The model's performance and clinical utility were assessed using receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis (DCA). Internal validation was performed using 1,000 bootstrap resamples.ResultsA total of 121 children were included in the study. Based on LASSO and multivariate logistic regression analysis of 24 candidate variables, five independent risk factors for CDI in children with UC were identified: Pediatric Ulcerative Colitis Activity Index (PUCAI), erythrocyte sedimentation rate (ESR), vitamin D (Vit D), fecal calprotectin (FC), and antibiotic use exceeding seven days (all p < 0.05). The nomograms constructed with the above variables demonstrated excellent discriminative ability (C-index = 0.964, 95% CI: 0.932–0.997). The Hosmer-Lemeshow test (χ2 = 12.529, p = 0.129) and bootstrap validation revealed good concordance between the predicted probabilities and actual outcomes. Decision curve analysis (DCA) indicated significant net clinical benefit, and the model maintained robust consistency across relevant clinical subgroups.ConclusionsPUCAI, ESR, Vit D, FC, and use of antibiotic use exceeding seven days were the five independent risk factors for CDI in children with UC. The resulting nomogram may support clinicians in early diagnosis and timely adjustment of therapeutic strategies.

Coronavirus disease 2019 (COVID-19) is an infectious disease spreading rapidly worldwide. As it quickly spreads and can cause severe disease, early detection and treatment may reduce mortality. Therefore, the study aims to construct a risk model and a nomogram for predicting the mortality of COVID-19. The original data of this study were from the article "Neurologic Syndromes Predict Higher In-Hospital Mortality in COVID-19." The database contained 4,711 multiethnic patients. In this secondary analysis, a statistical difference test was conducted for clinical demographics, clinical characteristics, and laboratory indexes. The least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression analysis were applied to determine the independent predictors for the mortality of COVID-19. A nomogram was conducted and validated according to the independent predictors. The area under the curve (AUC), the calibration curve, and the decision curve analysis (DCA) were carried out to evaluate the nomogram. The mortality of COVID-19 is 24.4%. LASSO and multivariate logistic regression analysis suggested that risk factors for age, PCT, glucose, D-dimer, CRP, troponin, BUN, LOS, MAP, AST, temperature, O2Sats, platelets, Asian, and stroke were independent predictors of CTO. Using these independent predictors, a nomogram was constructed with good discrimination (0.860 in the C index) and internal validation (0.8479 in the C index), respectively. The calibration curves and the DCA showed a high degree of reliability and precision for this clinical prediction model. An early warning model based on accessible variates from routine clinical tests to predict the mortality of COVID-19 were conducted. This nomogram can be conveniently used to facilitate identifying patients who might develop severe disease at an early stage of COVID-19. Further studies are warranted to validate the prognostic ability of the nomogram.

Despite several previous studies that have explored the predictors of high morbidity in coronary artery disease (CAD) and developed nomograms for CAD patients prior to coronary angiography (CAG), there is a lack of models available to predict chronic total occlusion (CTO). The aim of this study is to develop a risk model and a nomogram for predicting the probability of CTO prior to CAG. The study included 1,105 patients with CAG-diagnosed CTO in the derivation cohort and 368 patients in the validation cohort. Clinical demographics, echocardiography results, and laboratory indexes were analyzed using statistical difference tests. Independent risk factors affecting the CTO indication were selected using least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression analysis. A nomogram was built and validated based on these independent indicators. The performance of the nomogram was evaluated using area under the curve (AUC), calibration curve, and decision curve analysis (DCA). LASSO and multivariate logistic regression analysis revealed that 6 variables, including sex (male), lymphocyte percentage (LYM%), ejection fraction (EF), myoglobin (Mb), non-high-density lipoprotein cholesterol (non-HDL), and N-terminal pro-B-type natriuretic peptide (NT-proBNP), were independent predictors of CTO. The nomogram constructed based on these variables showed good discrimination (C index of 0.744) and external validation (C index of 0.729). The calibration curves and DCA demonstrated high reliability and precision for this clinical prediction model. The nomogram based on sex (male), LYM%, EF, Mb, non-HDL, and NT-proBNP could be used to predict CTO in CAD patients, enhancing the ability to predict their prognosis in clinical practice. Further research is needed to validate the efficacy of the nomogram in other populations.

BackgroundLymphovascular invasion (LVI) is mostly used as a preoperative predictor to establish lymph node metastasis (LNM) prediction models for superficial esophageal squamous cell carcinoma (SESCC). However, LVI still needs to be confirmed by postoperative pathology. In this study, we combined LNM and LVI as a unified outcome and named it LNM/LVI, and aimed to develop an LNM/LVI prediction model in SESCC using preoperative factors.MethodsA total of 512 patients who underwent radical resection of SESCC were retrospectively collected. Logistic regression and least absolute shrinkage and selection operator (LASSO) regression were adopted to identify the predictive factors of LNM/LVI. Integrated discrimination improvement (IDI) and net reclassification improvement (NRI) were calculated to select the potential predictive factors from the results of LASSO and logistic regression. A nomogram for predicting LNM/LVI was established by incorporating these factors. The efficacy, accuracy, and clinical utility of the nomogram were, respectively, assessed with the area under the curve (AUC), calibration curve, and decision curve analysis (DCA). Finally, the random forest (RF) algorithm was used to further evaluate the impact of these factors included in the nomogram on LNM/LVI.ResultsTumor size, tumor location, tumor invasion depth, tumor differentiation, and macroscopic type were confirmed as independent risk factors for LNM/LVI according to the results of logistic regression, LASSO regression, IDI, and NRI analyses. A nomogram including these five variables showed a good performance in LNM/LVI prediction (AUC = 0.776). The calibration curve revealed that the predictive results of this nomogram were nearly consistent with actual observations. Significant clinical utility of our nomogram was demonstrated by DCA. The RF model with the same five variables also had similar predictive efficacy with the nomogram (AUC = 0.775).ConclusionThe nomogram was adopted as a final tool for predicting LNM/LVI because its risk score system made it more user-friendly and clinically useful than the random forest model, which can help clinicians make optimal treatment decisions for patients with SESCC.

IntroductionThis study aimed to elucidate the relationship between neutrophil‐to‐lymphocyte ratio (neutrophil–lymphocyte ratio, NLR) and 28‐day mortality in critically ill elderly sepsis patients.MethodsRetrieval of information on patients &gt; 65 years of age who meet the diagnostic criteria for sepsis in sepsis 3.0 using the Medical Information Marketplace for Intensive Care‐IV (MIMIC‐IV) database. Optimal NLR truncation was identified using the X‐tile software and corresponding Kaplan–Meier (K‐M) survival curves were plotted. The ability of the NLR in combination with quick Sequential Organ Failure Assessment (qSOFA) and both alone to predict 28‐day mortality in critically ill elderly septic patients was also assessed using the receiver operating characteristic curve. Least absolute shrinkage and selection operator (LASSO) and logistic regressions were applied to factors with p &lt; 0.05 to establish a 28‐day mortality model. Area under the curve (AUC), calibration curve, and decision curve analysis were utilized to assess nomogram predictive accuracy.ResultsAmong 2032 elderly sepsis patients, 44.6% expired. The K–M survival analysis suggested that NLR ≥ 13.6 significantly increased the risk of death. The AUC of the NLR combined with the qSOFA score to predict 28‐day mortality in critically ill elderly septic patients was 0.764, which was significantly higher than that of the NLR (0.728) and qSOFA (0.644) alone and stronger than the predictive power of the SOFA (0.738) but weaker than that of the Simplified Acute Physiology Score (p = 0.785). A nomogram of 28‐day mortality in critically ill elderly septic patients was constructed using regression analysis of the collected variables, culminating in the inclusion of five factors: age, NLR, lactate, mechanical ventilation, and qSOFA. This nomogram showed good discrimination and calibration in predicting mortality in critically ill elderly septic patients.ConclusionThis study showed that low levels of NLR (&lt; 13.6) were significantly associated with better survival. The ability of the NLR combined with the qSOFA score to predict 28‐day mortality in elderly patients with severe sepsis was significantly higher than when the two were used alone and was also stronger than the predictive ability of the SOFA but weaker than that of the SAPSII. The nomogram constructed using the LASSO regression and logistic regression analysis effectively integrates multiple vital factors, showing good predictive ability and clinical practicability, and providing a powerful tool for evaluating the prognosis of critically ill elderly patients with sepsis.

BackgroundVertebroplasty is the main minimally invasive operation for osteoporotic vertebral compression fracture (OVCF), which has the advantages of rapid pain relief and shorter recovery time. However, new adjacent vertebral compression fracture (AVCF) occurs frequently after vertebroplasty. The purpose of this study was to investigate the risk factors of AVCF and establish a clinical prediction model.MethodsWe retrospectively collected the clinical data of patients who underwent vertebroplasty in our hospital from June 2018 to December 2019. The patients were divided into a non-refracture group (289 cases) and a refracture group (43 cases) according to the occurrence of AVCF. The independent predictive factors for postoperative new AVCF were determined by univariate analysis, least absolute shrinkage and selection operator (LASSO) logistic regression, and multivariable logistic regression analysis. A nomogram clinical prediction model was established based on relevant risk factors, and the receiver operating characteristic curve (ROC), calibration curve, and decision curve analysis (DCA) were used to evaluate the prediction effect and clinical value of the model. After internal validation, patients who underwent vertebroplasty in our hospital from January 2020 to December 2020, including a non-refracture group (156 cases) and a refracture group (21 cases), were included as the validation cohort to evaluate the prediction model again.ResultsThree independent risk factors of low bone mass density (BMD), leakage of bone cement and “O” shaped distribution of bone cement were screened out by LASSO regression and logistic regression analysis. The area under the curve (AUC) of the model in the training cohort and the validation cohort was 0.848 (95%CI: 0.786–0.909) and 0.867 (95%CI: 0.796–0.939), respectively, showing good predictive ability. The calibration curves showed the correlation between prediction and actual status. The DCA showed that the prediction model was clinically useful within the whole threshold range.ConclusionLow BMD, leakage of bone cement and “O” shaped distribution of bone cement are independent risk factors for AVCF after vertebroplasty. The nomogram prediction model has good predictive ability and clinical benefit.

Autoimmune hepatitis (AIH) is a relatively rare, immune-mediated chronic inflammatory liver disease characterized predominantly by hepatocellular injury. Liver fibrosis assessment is crucial in AIH management. We aimed to develop and validate a novel non-invasive nomogram to predict advanced liver fibrosis in AIH patients. Patients with AIH who had undergone liver biopsy and met the inclusion and exclusion criteria were included in this retrospective study; subsequently, they were randomly assigned to the training set and the validation set in a ratio of 7:3. Least absolute shrinkage and selection operator (LASSO) regression with 10-fold cross-validation identified predictors from candidate variables. Multivariable logistic regression established independent predictors used to construct the nomogram. Performance was evaluated using area under the receiver operating characteristic curve (AUC), calibration curves, Hosmer-Lemeshow test, and decision curve analysis (DCA). This study ultimately included 141 patients with AIH (n = 98 for the training set and n = 43 for the validation set). LASSO and multivariate logistic regression analysis showed that liver stiffness measurement (LSM), platelet (PLT), and prothrombin time (PT) were independent risk factors for advanced liver fibrosis in patients with AIH, and a nomogram for diagnosing advanced liver fibrosis was established based on these factors. Calibration was excellent (Hosmer-Lemeshow P > 0.05), and DCA showed significant clinical net benefit. The nomogram demonstrates excellent discriminatory ability: the AUC of the training set is 0.851 (95% CI: 0.777–0.925), and the AUC of the validation set is 0.922 (95% CI: 0.847–0.997). It outperforms single indicators (LSM, PLT, PT), as well as well-known serological models including aspartate aminotransferase to platelet ratio (APRI) and fibrosis-4 (FIB-4). This novel nomogram model has excellent diagnostic performance and can more intuitively and personalizedly assess the probability of advanced liver fibrosis in patients with AIH, thereby potentially reducing the reliance on liver biopsy.

Although recurrence rates after radiofrequency catheter ablation (RFCA) in patients with atrial fibrillation (AF) remain high, there are a limited number of novel, high-quality mathematical predictive models that can be used to assess early recurrence after RFCA in patients with AF. To identify the preoperative serum biomarkers and clinical characteristics associated with post-RFCA early recurrence of AF and develop a novel risk model based on least absolute shrinkage and selection operator (LASSO) regression to select important variables for predicting the risk of early recurrence of AF after RFCA. This study collected a dataset of 136 atrial fibrillation patients who underwent RFCA for the first time at Peking University Shenzhen Hospital from May 2016 to July 2022. The dataset included clinical characteristics, laboratory results, medication treatments, and other relevant parameters. LASSO regression was performed on 100 cycles of data. Variables present in at least one of the 100 cycles were selected to determine factors associated with the early recurrence of AF. Then, multivariable logistic regression analysis was applied to build a prediction model introducing the predictors selected from the LASSO regression analysis. A nomogram model for early post-RFCA recurrence in AF patients was developed based on visual analysis of the selected variables. Internal validation was conducted using the bootstrap method with 100 resamples. The model's discriminatory ability was determined by calculating the area under the curve (AUC), and calibration analysis and decision curve analysis (DCA) were performed on the model. In a 3-month follow-up of AF patients (n = 136) who underwent RFCA, there were 47 recurrences of and 89 non-recurrences of AF after RFCA. P, PLR, RDW, LDL, and CRI-II were associated with early recurrence of AF after RFCA in patients with AF (p < 0.05). We developed a predictive model using LASSO regression, incorporating four robust factors (PLR, RDW, LDL, CRI-II). The AUC of this prediction model was 0.7248 (95% CI 0.6342-0.8155), and the AUC of the internal validation using the bootstrap method was 0.8403 (95% CI 0.7684-0.9122). The model demonstrated a strong predictive capability, along with favorable calibration and clinical applicability. The Hosmer-Lemeshow test indicated that there was good consistency between the predicted and observed values. Additionally, DCA highlighted the model's advantages in terms of its clinical application. We have developed and validated a risk prediction model for the early recurrence of AF after RFCA, demonstrating strong clinical applicability and diagnostic performance. This model plays a crucial role in guiding physicians in preoperative assessment and clinical decision-making. This novel approach also provides physicians with personalized management recommendations.

BACKGROUND: The factors influencing relapse after radiofrequency operation of the V2 branch of the trigeminal neuralgia are yet to be identified. OBJECTIVES: The risk factors affecting recurrence after radiofrequency operation of the V2 branch of the trigeminal neuralgia were analyzed, and a curative effect prediction model was constructed. STUDY DESIGN: A retrospective study. SETTING: This study was conducted at the Affiliated Hospital of Jiaxing University, People’s Republic of China. METHODS: The records of patients with maxillary nerve pain in the V2 branch of the trigeminal nerve who underwent computed tomography-guided foramen rotundum radiofrequency treatment at the Pain Department of the Affiliated Hospital of Jiaxing College from April 2014 through December 2020 were collected and randomly divided into training (n = 137) and test (n = 59) groups at a 7:3 ratio. The outcome variable was whether or not recurrence was observed 2 years postsurgery. Independent predictors were screened by LASSO (least absolute shrinkage and selection operator) regression analysis. Based on these findings, a nomogram prediction model was explored further and developed using multifactor logistic regression analysis. Also, the feasibility of the nomogram prediction model for recurrence after radiofrequency was assessed using a validation group. Finally, the discriminatory power, accuracy, and clinical utility of the prediction model were evaluated using the receiver operating characteristic (ROC), area under the curve (AUC), calibration curve, and decision curve analysis (DCA), respectively. RESULTS: LASSO regression, combined with multifactorial logistic regression analysis, identified factors such as age, duration, branches, and numbness that influence V2 trigeminal nerve pain recurrence in patients 2 years post-radiofrequency surgery (P &lt; 0.05). The above variables were used to construct the nomogram prediction models. The AUC of the nomogram prediction model predicted that the recurrence post V2 radiofrequency was 0.726 in the training group and 0.611 in the test group. The DCA showed that the columnar plot prediction model predicted the risk of recurrence post-radiofrequency of the V2 branch of the trigeminal nerve had a threshold probability of 0 – 0.9. LIMITATIONS: This was a single-center study. CONCLUSION: A highly accurate nomogram prediction model (predictor variables include age, duration, branches, and numbness) was developed to improve the early identification and screening of patients at high risk of recurrence after V2 trigeminal nerve radiofrequency surgery. KEY WORDS: Trigeminal neuralgia, maxillary neuralgia, radiofrequency, risk factors, prediction model

This study aimed to develop and validate a biomarker-based prediction model for assessing the individual risk of coronary artery lesions (CAL) in Kawasaki disease (KD). A retrospective analysis was performed on 345 pediatric KD patients admitted between June 2018 and June 2022. Patients were randomly divided into training (n = 241) and validation (n = 104) sets. Univariate analysis identified candidate predictors, and Least Absolute Shrinkage and Selection Operator (LASSO) regression was used for feature selection. Multivariable logistic regression and machine learning models—random forest (RF), support vector machine, and k-nearest neighbors—were developed. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis. A nomogram was constructed, and SHapley Additive exPlanations (SHAP) values were applied to interpret feature contributions. Seven biomarkers were significantly associated with CAL in univariate analysis (P < 0.05). LASSO and multivariable logistic regression analysis identified age, N-terminal pro-B-type natriuretic peptide, interleukin-6, calprotectin, endothelial microparticles, Matrix Metalloproteinase-9, and Galectin-3 as independent predictors. The RF model demonstrated superior performance, with AUCs of 0.888 (training) and 0.860 (validation). SHAP analysis confirmed these three variables as the top contributors to CAL prediction. The nomogram exhibited strong calibration and clinical utility. The machine learning-based prediction model incorporating novel biomarkers enables individualized risk assessment for CAL development in KD patients. This model exhibits excellent predictive performance and clinical applicability, facilitating early identification of high-risk patients and the implementation of targeted interventions, thereby optimizing healthcare resource allocation and improving long-term cardiovascular outcomes.

Predicting the consciousness recovery for comatose patients with acute brain injury is an important issue. Although some efforts have been made in the study of prognostic assessment methods, it is still unclear which factors can be used to establish model to directly predict the probability of consciousness recovery. We aimed to establish a model using clinical and neuroelectrophysiological indicators to predict consciousness recovery of comatose patients after acute brain injury. The clinical data of patients with acute brain injury admitted to the neurosurgical intensive care unit of Xiangya Hospital of Central South University from May 2019 to May 2022, who underwent electroencephalogram (EEG) and auditory mismatch negativity (MMN) examinations within 28 days after coma onset, were collected. The prognosis was assessed by Glasgow Outcome Scale (GOS) at 3 months after coma onset. The least absolute shrinkage and selection operator (LASSO) regression analysis was applied to select the most relevant predictors. We combined Glasgow coma scale (GCS), EEG, and absolute amplitude of MMN at Fz to develop a predictive model using binary logistic regression and then presented by a nomogram. The predictive efficiency of the model was evaluated with AUC and verified by calibration curve. The decision curve analysis (DCA) was used to evaluate the clinical utility of the prediction model. A total of 116 patients were enrolled for analysis, of which 60 had favorable prognosis (GOS ≥ 3). Five predictors, including GCS (OR = 13.400, P < 0.001), absolute amplitude of MMN at Fz site (FzMMNA, OR = 1.855, P = 0.038), EEG background activity (OR = 4.309, P = 0.023), EEG reactivity (OR = 4.154, P = 0.030), and sleep spindles (OR = 4.316, P = 0.031), were selected in the model by LASSO and binary logistic regression analysis. This model showed favorable predictive power, with an AUC of 0.939 (95% CI: 0.899-0.979), and calibration. The threshold probability of net benefit was between 5% and 92% in the DCA. This predictive model for consciousness recovery in patients with acute brain injury is based on a nomogram incorporating GCS, EEG background activity, EEG reactivity, sleep spindles, and FzMMNA, which can be conveniently obtained during hospitalization. It provides a basis for care givers to make subsequent medical decisions.

BackgroundSarcopenia is a common complication in patients with stroke, adversely affecting recovery and increasing mortality risk. However, no standardised tool exists for its screening in this population. This study aims to identify factors influencing sarcopenia in patients with stroke, develop a risk prediction model and evaluate its predictive performance.MethodsData from 794 patients with stroke were analysed to assess demographic and clinical characteristics. Variable selection was performed using least absolute shrinkage and selection operator (LASSO) regression, followed by multivariate regression analysis. Logistic regression (LR), random forest (RF) and XGBoost algorithms were used to construct prediction models, with the optimal model subjected to external validation. Internal validation was conducted via bootstrap resampling, and external validation involved an additional cohort of 159 patients with stroke. Model performance was assessed using the area under the curve (AUC), calibration curves and decision curve analysis (DCA).ResultsSeven variables were identified through LASSO and multivariate regression analysis. The LR model achieved the highest AUC (0.805), outperforming the RF (0.796) and XGBoost (0.780) models. Additionally, the LR model exhibited superior accuracy, precision, recall, specificity and F1-score. External validation confirmed the LR model’s robustness, with an AUC of 0.816. Calibration and DCA curves demonstrated their accuracy and clinical applicability.ConclusionsA predictive model, presented as a nomogram and an online risk calculator, was developed to assess sarcopenia risk in patients with stroke. Early screening using this model may facilitate timely interventions and improve patient outcomes.

This study aimed to explore the factors associated with orthostatic hypotension and to develop a nomogram to predict the risk of orthostatic hypotension. Orthostatic hypotension is defined as a fall in systolic blood pressure (SBP) of at least 20 mmHg or diastolic blood pressure (DBP) of at least 10 mmHg within 3 min of standing. In this cross-sectional analysis, 1708 patients were collected from January 2016 to June 2018. These patients were divided into the orthostatic hypotension group and the non-orthostatic hypotension group. The variables were selected by least absolute shrinkage and selection operator (LASSO) regression. The characteristic variables selected in the LASSO regression were analyzed using multivariable logistic regression to construct the predictive model. The predictive model was displayed using a nomogram. The model performances were evaluated by the area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis. The prevalence of orthostatic hypotension was 14.3% in this study. The LASSO and multivariable logistic regression analyses suggested that age, diabetes duration, seat SBP, supine SBP, and albumin were associated with orthostatic hypotension. The AUC of the nomogram was 0.796 [95% confidence interval (CI): 0.759-0.834] in the training set and 0.832 (95% CI: 0.780-0.885) in the validation set. Calibration curves were drawn and showed acceptable predictive performance, and the decision curve analysis showed that the proposed nomogram had strong clinical applicability. Age, diabetes duration, seat SBP, supine SBP, and albumin were associated with orthostatic hypotension. The nomogram model established by the factors provided an effective way to forecast the risk of orthostatic hypotension.