Nomogram Model Research Articles

Radiomics predict the WHO/ISUP nuclear grade and survival in clear cell renal cell carcinoma

ObjectivesThis study aimed to assess the predictive value of radiomics derived from intratumoral and peritumoral regions and to develop a radiomics nomogram to predict preoperative nuclear grade and overall survival (OS) in patients with clear cell renal cell carcinoma (ccRCC).MethodsThe study included 395 patients with ccRCC from our institution. The patients in Center A (anonymous) institution were randomly divided into a training cohort (n = 284) and an internal validation cohort (n = 71). An external validation cohort comprising 40 patients from Center B also was included. Computed tomography (CT) radiomics features were extracted from the internal area of the tumor (IAT) and IAT combined peritumoral areas of the tumor at 3 mm (PAT 3 mm) and 5 mm (PAT 5 mm). Independent predictors from both clinical and radiomics scores (Radscore) were used to construct a radiomics nomogram. Kaplan–Meier analysis with a log-rank test was performed to evaluate the correlation between factors and OS.ResultsThe PAT 5-mm radiomics model (RM) exhibited exceptional predictive capability for grading, achieving an area under the curves of 0.80, 0.80, and 0.90 in the training, internal validation, and external validation cohorts. The nomogram and RM gained from the PAT 5-mm region were more clinically useful than the clinical model. The association between OS and predicted nuclear grade derived from the PAT 5-mm Radscore and the nomogram-predicted score was statistically significant (p < 0.05).ConclusionThe CT-based radiomics and nomograms showed valuable predictive capabilities for the World Health Organization/International Society of Urological Pathology grade and OS in patients with ccRCC.Critical relevance statementThe intratumoral and peritumoral radiomics are feasible and promising to predict nuclear grade and overall survival in patients with clear cell renal cell carcinoma, which can contribute to the development of personalized preoperative treatment strategies.Key PointsThe multi-regional radiomics features are associated with clear cell renal cell carcinoma (ccRCC) grading and prognosis.The combination of intratumoral and peritumoral 5 mm regional features demonstrated superior predictive performance for grading.The nomogram and radiomics models have a broad range of clinical applications.Graphical

Analysis of Risk Factors of Postoperative Arrhythmia in Patients with Coronary Heart Disease and Establishment of Nomogram Risk Model

Background: To explore the risk factors of postoperative arrhythmia in patients with coronary heart disease (CHD) and to establish a Nomogram model for predicting the risk of postoperative arrhythmia in CHD patients. Methods: Retrospectively, the medical data of CHD patients (from January 2021 to January 2024, n = 390) were collected. According to whether arrhythmia occurred after percutaneous coronary intervention surgery, patients were divided into the arrhythmia group (n = 130) and non-arrhythmia group (n = 260). The risk factors of postoperative arrhythmia were obtained by multi-factor logistic regression analysis. A Nomogram model for predicting the risk of postoperative arrhythmia in CHD patients was established using R language and underwent verification. Results: The results of multi-factor logistic regression analysis showed that diastolic pressure, heart function grade at III–IV, creatinine, C-reactive protein (CRP), N-terminal pro-brain natriuretic peptide (NT-ProBNP) peak value, total bilirubin (TBIL) and red cell distribution width (RDW) were the risk factors inducing postoperative arrhythmia in CHD patients. Based on these risk factors, a Nomogram model was successfully established. The verification results revealed that the predicted values were basically consistent with the actual values, indicating that the Nomogram model had good prediction ability. The area under the curve (AUC) value was 0.974, suggesting the high prediction efficiency of Nomogram model. Conclusion: Diastolic pressure, heart function grade at III–IV, creatinine, CRP, NT-ProBNP peak value, TBIL and RDW are the risk factors of postoperative arrhythmia in CHD patients. Nomogram model based on these risk factors has good prediction efficiency and underlying clinical value.

Preoperative prediction of histopathological grading in patients with chondrosarcoma using MRI-based radiomics with semantic features

BackgroundDistinguishing high-grade from low-grade chondrosarcoma is extremely vital not only for guiding the development of personalized surgical treatment but also for predicting the prognosis of patients. We aimed to establish and validate a magnetic resonance imaging (MRI)-based nomogram for predicting preoperative grading in patients with chondrosarcoma.MethodsApproximately 114 patients (60 and 54 cases with high-grade and low-grade chondrosarcoma, respectively) were recruited for this retrospective study. All patients were treated via surgery and histopathologically proven, and they were randomly divided into training (n = 80) and validation (n = 34) sets at a ratio of 7:3. Next, radiomics features were extracted from two sequences using the least absolute shrinkage and selection operator (LASSO) algorithms. The rad-scores were calculated and then subjected to logistic regression to develop a radiomics model. A nomogram combining independent predictive semantic features with radiomic by using multivariate logistic regression was established. The performance of each model was assessed by the receiver operating characteristic (ROC) curve analysis and the area under the curve, while clinical efficacy was evaluated via decision curve analysis (DCA).ResultsUltimately, six optimal radiomics signatures were extracted from T1-weighted imaging (T1WI) and T2-weighted imaging with fat suppression (T2WI-FS) sequences to develop the radiomics model. Tumour cartilage abundance, which emerged as an independent predictor, was significantly related to chondrosarcoma grading (p < 0.05). The AUC values of the radiomics model were 0.85 (95% CI, 0.76 to 0.95) in the training sets, and the corresponding AUC values in the validation sets were 0.82 (95% CI, 0.65 to 0.98), which were far superior to the clinical model AUC values of 0.68 (95% CI, 0.58 to 0.79) in the training sets and 0.72 (95% CI, 0.57 to 0.87) in the validation sets. The nomogram demonstrated good performance in the preoperative distinction of chondrosarcoma. The DCA analysis revealed that the nomogram model had a markedly higher clinical usefulness in predicting chondrosarcoma grading preoperatively than either the rad-score or clinical model alone.ConclusionThe nomogram based on MRI radiomics combined with optimal independent factors had better performance for the preoperative differentiation between low-grade and high-grade chondrosarcoma and has potential as a noninvasive preoperative tool for personalizing clinical plans.

Metabolism score and machine learning models for the prediction of esophageal squamous cell carcinoma progression.

The incomplete prediction of prognosis in esophageal squamous cell carcinoma (ESCC) patients is attributed to various therapeutic interventions and complex prognostic factors. Consequently, there is a pressing demand for enhanced predictive biomarkers that can facilitate clinical management and treatment decisions. This study recruited 491 ESCC patients who underwent surgical treatment at Huashan Hospital, Fudan University. We incorporated 14 blood metabolic indicators and identified independent prognostic indicators for overall survival through univariate and multivariate analyses. Subsequently, a metabolism score formula was established based on the biochemical markers. We constructed a nomogram and machine learning models utilizing the metabolism score and clinically significant prognostic features, followed by an evaluation of their predictive accuracy and performance. We identified alkaline phosphatase, free fatty acids, homocysteine, lactate dehydrogenase, and triglycerides as independent prognostic indicators for ESCC. Subsequently, based on these five indicators, we established a metabolism score that serves as an independent prognostic factor in ESCC patients. By utilizing this metabolism score in conjunction with clinical features, a nomogram can precisely predict the prognosis of ESCC patients, achieving an area under the curve (AUC) of 0.89. The random forest (RF) model showed superior predictive ability (AUC = 0.90, accuracy = 86%, Matthews correlation coefficient = 0.55). Finally, we used an RF model with optimal performance to establish an online predictive tool. The metabolism score developed in this study serves as an independent prognostic indicator for ESCC patients.

Integrating Tumor and Nodal Radiomics to Predict the Response to Neoadjuvant Chemotherapy and Recurrence Risk for Locally Advanced Gastric Cancer.

To develop and evaluate machine learning models using tumor and nodal radiomics features for predicting the response to neoadjuvant chemotherapy (NAC) and recurrence risk in locally advanced gastric cancer (LAGC). Early and accurate response prediction is vital to stratify LAGC patients and select proper candidates for NAC. A total of 218 patients with LAGC undergoing NAC followed by gastrectomy were enrolled in our study and were randomly divided into a training cohort (n = 153) and a validation cohort (n = 65). We extracted 1316 radiomics features from the volume of interest of the primary lesion and maximal lymph node on venous phase CT images. We built 3 radiomics signatures for distinguishing good responders and poor responders based on tumor radiomics (TR), nodal radiomics (NR), and a combination of the two (TNR), respectively. A nomogram was then developed by integrating the radiomics signature and clinical factors. Kaplan- Meier survival curves were used to evaluate the prognostic value of the nomogram. The TNR signature achieved improved predictive value, with AUCs of 0.755 and 0.744 in the training and validation cohorts. Our proposed nomogram model (TNRN) showed a good performance for GR prediction in the prediction efficacy, calibration ability, and clinical benefit, with AUCs of 0.779 and 0.732 in the training and validation cohorts, superior to the clinical model. Moreover, the TNRN could accurately classify the patients into high-risk and low-risk groups in both training and validation cohorts with regard to postoperative recurrence and metastasis. The TNRN performed well in identifying good responders and provided valuable information for predicting progression-free survival time (PFS) in patients with LAGC who underwent NAC.

Construction and validation of a nomogram for blood transfusion after open reduction and internal fixation (ORIF) of proximal humeral fractures in the elderly: a cross-sectional study

PurposeFew studies have focused on the risk factors leading to postoperative blood transfusion after open reduction and internal fixation (ORIF) of proximal humeral fractures (PHFs) in the elderly. Therefore, we designed this study to explore potential risk factors of blood transfusion after ORIF for PHFs. We have also established a nomogram model to integrate and quantify our research results and give feedback.MethodsIn this study, we retrospectively analyzed the clinical data of elderly PHF patients undergoing ORIF from January 2020 to December 2021. We have established a multivariate regression model and nomograph. The prediction performance and consistency of the model were evaluated by the consistency coefficient and calibration curve, respectively.Results162 patients met our inclusion criteria and were included in the final study. The following factors are related to the increased risk of transfusion after ORIF: time to surgery, fibrinogen levels, intraoperative blood loss, and surgical duration.ConclusionsOur patient-specific transfusion risk calculator uses a robust multivariable model to predict transfusion risk.The resulting nomogram can be used as a screening tool to identify patients with high transfusion risk and provide necessary interventions for these patients (such as preoperative red blood cell mobilization, intraoperative autologous blood transfusion, etc.).

Development and validation of a novel nomogram model predicting the unfavorable outcome based on NAR and collaterals status for patients with AIS

IntroductionStroke is a leading cause of disability and mortality globally. This study aimed to develop a prognostic nomogram based on neutrophil-to-albumin ratio (NAR) and collateral status in acute ischemic stroke (AIS) patients with anterior large vessel occlusion (LVO). Material & method590 AIS patients with LVO assessed for regional leptomeningeal collateral (rLMC) were retrospectively enrolled, and randomly divided into a training set (n = 414) and a testing set (n = 176). Unfavorable functional outcome was defined as a modified Rankin scale (mRS) score of 3 to 6 at 3 months. We assessed the accuracy and clinical utility of the nomogram using calibration plots, area under the curve (AUC), decision curve analysis (DCA), net reclassification index (NRI), and integrated discrimination improvement (IDI). ResultsBoth NAR and rLMC were independently associated with unfavorable outcome at 3 months (OR=8.96, p=0.0341; OR=0.89, p=0.0002, respectively). The developed nomogram (akaike information criterion (AIC)=398.77), which included NAR, rLMC and other factors, showed good performance (the AUC for the development and validation cohorts was 0.848 and 0.840 respectively) and improved the predictive value compared to a model without NAR and rLMC, according to an overall NRI of 3.27% (p=0.2401), overall IDI of 3.27% (p=0.2414), and a higher AUC (0.848 vs 0.831). ConclusionsNAR can serve as an independent predictor in AIS patients with anterior LVO, and the nomogram incorporating NAR and rLMC is reliable in predicting unfavorable outcome. Further studies with larger sample sizes are needed to validate and extend these findings.

Establishment and validation of a prediction model for apnea on bronchiolitis.

The objective of this study is to examine the risk factors associated with apnea in hospitalized patients diagnosed with bronchiolitis and to develop a nomogram prediction model for the early identification of patients who are at risk of developing apnea. The clinical data of patients diagnosed with acute bronchiolitis and hospitalized at the Children's Hospital of Nanjing Medical University between February 2018 and May 2021 were retrospectively analyzed. LASSO regression and logistic regression analysis were used to determine the risk factors for apnea in these patients. A nomogram was constructed based on variables selected through multivariable logistic regression analysis. Receiver operating characteristic (ROC) curve and calibration curve were used to assess the accuracy and discriminative ability of the nomogram model, and decision curve analysis (DCA) was performed to evaluate the model's performance and clinical effectiveness. A retrospective analysis was conducted on 613 children hospitalized with bronchiolitis, among whom 53 (8.6%) experienced apnea. The results of Lasso regression and Logistic regression analyses showed that underlying diseases, feeding difficulties, tachypnea, WBC count, and lung consolidation were independent risk factors for apnea. A nomogram prediction model was constructed based on the five predictors mentioned above. After internal validation, the nomogram model demonstrated an AUC of 0.969 (95% CI 0.951-0.987), indicating strong predictive performance for apnea in bronchiolitis. Calibration curve analysis confirmed that the nomogram prediction model had good calibration, and the clinical decision curve analysis (DCA) indicated that the nomogram was clinically useful in estimating the net benefit to patients. In this study, a nomogram model was developed to predict the risk of apnea in hospitalized children with bronchiolitis. The model showed good predictive performance and clinical applicability, allowing for timely identification and intensified monitoring and treatment of high-risk patients to improve overall clinical prognosis.

A nomogram prediction model for short-term aortic-related adverse events in patients with acute Stanford type B aortic intramural hematoma: development and validation.

This study is to examine the factors associated with short-term aortic-related adverse events in patients with acute type B aortic intramural hematoma (IMH). Additionally, we develop a risk prediction nomogram model and evaluate its accuracy. This study included 197 patients diagnosed with acute type B IMH. The patients were divided into stable group (n = 125) and exacerbation group (n = 72) based on the occurrence of aortic-related adverse events. Logistic regression and the Least Absolute Shrinkage and Selection Operator (LASSO) method for variables based on baseline assessments with significant differences in clinical and image characteristics were employed to identify independent predictors. A nomogram risk model was constructed based on these independent predictors. The nomogram model was evaluated using various methods such as the receiver operating characteristic curve, calibration curve, decision analysis curve, and clinical impact curve. Internal validation was performed using the Bootstrap method. A nomogram risk prediction model was established based on four variables: absence of diabetes, anemia, maximum descending aortic diameter (MDAD), and ulcer-like projection (ULP). The model demonstrated a discriminative ability with an area under the curve (AUC) of 0.813. The calibration curve indicated a good agreement between the predicted probabilities and the actual probabilities. The Hosmer-Lemeshow goodness of fit test showed no significant difference (χ 2 = 7.040, P = 0.532). The decision curve analysis (DCA) was employed to further confirm the clinical effectiveness of the nomogram. This study introduces a nomogram prediction model that integrates four important risk factors: ULP, MDAD, anemia, and absence of diabetes. The model allows for personalized prediction of patients with type B IMH.

Construction of a nomogram model for predicting the outcome of debulking surgery for ovarian cancer on the basis of clinical indicators.

This study aimed to investigate the risk factors affecting satisfaction with debulking surgery for ovarian cancer and establish a preoperative clinical predictive model. Clinical data from 131 patients who underwent ovarian cancer debulking surgery at Jiangnan University Affiliated Hospital between 2016 and 2022 were collected. Patients were randomly separated into an experimental group and a control group in a 7:3 ratio. On the basis of intraoperative outcomes, patients were grouped as either surgery-satisfactory or surgery-unsatisfactory. Clinical indicators were compared through single-factor analysis between groups. Significantly different factors (p < 0.1) were further analyzed through multivariate logistic regression. A predictive nomogram model was developed and validated by receiver operating characteristic (ROC), calibration, and clinical decision curves. Single-factor analysis revealed the significance of factors such as albumin levels, alkaline phosphatase (ALP), ECOG scores, CA125, HE4, and lymph node metastasis. Multivariate regression analysis identified albumin levels, ALP, ECOG scores, HE4, and lymph node metastasis as independent risk factors for satisfactory surgical outcomes in patients with ovarian cancer undergoing debulking surgery as (p < 0.05). A clinical predictive model was successfully constructed. ROC curves showed AUC values of 0.818 and 0.796 for the experimental and validation groups, respectively. Internal validation through the bootstrap method confirmed the model's fit in both groups. Meanwhile, the clinical decision curve demonstrated the model's high utility. Independent risk factors associated with satisfactory tumor reduction in patients with ovarian cancer undergoing debulking surgery included decreased albumin levels, ALP > 137 U/L, ECOG = 1 score, HE4 > 140 pmol/L, and lymph node metastasis. Constructing a clinical predictive model through logistic regression analysis enables individualized testing and maximizes clinical benefits.

Nomogram development for predicting ovarian tumor malignancy using inflammatory biomarker and CA-125

Global challenges in ovarian cancer underscore the need for cost-effective screening. This study aims to assess the role of pretreatment Neutrophil-to-Lymphocyte Ratio (NLR), Lymphocyte-to-Monocyte-Ratio (LMR), Platelet-to-Lymphocyte Ratio (PLR), and CA-125 in distinguishing benign and malignant ovarian tumors, while also constructing nomogram models for distinguish benign and malignant ovarian tumor using inflammatory biomarkers and CA-125. This is a retrospective study of 206 ovarian tumor patients. We conducted bivariate analysis to compare mean values of CA-125, LMR, NLR, and PLR with histopathology results. Multiple regression logistic analysis was then employed to establish predictive models for malignancy. NLR, PLR, and CA-125 exhibited statistically higher levels in malignant ovarian tumors compared to benign ones (5.56 ± 4.8 vs. 2.9 ± 2.58, 278.12 ± 165.2 vs. 180.64 ± 89.95, 537.2 ± 1621.47 vs. 110.08 ± 393.05, respectively), while lower LMR was associated with malignant tumors compared to benign (3.2 ± 1.6 vs. 4.24 ± 1.78, p = 0.0001). Multiple logistic regression analysis revealed that both PLR and CA125 emerged as independent risk factors for malignancy in ovarian tumors (P(z) 0.03 and 0.01, respectively). Utilizing the outcomes of multiple regression logistic analysis, a nomogram was constructed to enhance malignancy prediction in ovarian tumors. In conclusion, our study emphasizes the significance of NLR, PLR, CA-125, and LMR in diagnosing ovarian tumors. PLR and CA-125 emerged as independent risk factors for distinguishing between benign and malignant tumors. The nomogram model offers a practical way to enhance diagnostic precision.

Development of a Predictive Nomogram for Sepsis in Patients with Urolithiasis-Related Obstructive Pyelonephritis.

Background and Objectives: In patients with urolithiasis-related obstructive pyelonephritis (UROP), sepsis represents a critical and concerning complication that can substantially increase the mortality rate. This study aimed to identify the risk factors for sepsis in UROP patients and to develop a predictive nomogram model. Materials and Methods: We analyzed data from 148 patients who met the UROP criteria and were admitted to Chang Gung Memorial Hospital between 1 January 2016 and 31 December 2021. The primary outcome evaluated was the incidence of sepsis, as defined by the most recent Sepsis-3 guidelines. To identify potential risk factors for sepsis, we employed the Least Absolute Shrinkage and Selection Operator (LASSO) regression technique. Subsequently, we utilized multivariable logistic regression to construct the predictive model. Results: There was a total of 102 non-sepsis cases and 46 sepsis cases. Risk factors for sepsis in multivariable analysis were a history of diabetes mellitus (DM) (OR = 4.24, p = 0.007), shock index (SI) (×10-1) (OR = 1.55, p < 0.001), C-reactive protein (CRP) (mg/dL) (OR = 1.08, p = 0.005), and neutrophil to lymphocyte ratio (NLR) (×10) (OR = 1.58, p = 0.007). The nomogram exhibited an area under the receiver operating characteristic curve of 0.890 (95% CI 0.830-0.949). Conclusions: Our study demonstrated that patients with UROP who have DM, higher SI, higher NLR, and elevated CRP levels are significantly more likely to develop sepsis. These insights may aid in risk stratification, and it is imperative that clinicians promptly initiate treatment for those identified as high risk.

A Prediction Nomogram for Fractured Vertebra Recollapse After Posterior Reduction and Pedicle Screw Fixation in Thoracolumbar Fractures

This study aimed to establish a predictive nomogram model for recollapse of fractured vertebra after posterior pedicle screw fixation in thoracolumbar fractures (TLFs). Patients undergoing posterior pedicle screw fixation for TLFs at our hospital between January 2016 and December 2021 were retrospectively reviewed. Patients were divided into 2 groups according to the presence or absence of recollapse of the fractured vertebra at the final follow-up. The predictors for fractured vertebra recollapse were identified by univariate and multivariable logistic regression analysis, and a nomogram model was developed. The prediction performance and internal validation were established. A total of 224 patients were included in this study. Of these, 46 (20.5%) patients developed recollapse of fractured vertebra. Age, thoracic and lumbar injury severity score, screw distribution in the fractured vertebra, and anterior vertebral height compression ratio were associated with vertebral recollapse. These predictors were used to construct a predictive nomogram. The area under the receiver operating characteristic curve of the nomogram model was 0.891. The concordance index was 0.891, and it was 0.877 with bootstrapping validation. The calibration curves and decision curve analysis also suggested that the nomogram model had excellent predictive performances for fractured vertebra recollapse. A clinical nomogram incorporating 4 variables was constructed to predict fractured vertebra recollapse after posterior pedicle screw fixation for TLFs. The nomogram demonstrated good calibration and discriminative abilities, which may help clinicians to make better treatment decisions.

Predictive Factors for Delayed Recovery From Anesthesia in Patients Receiving Free Vascularized Flap Reconstruction for Head and Neck Defects: A Retrospective Cohort Study.

Free flap reconstruction for head and neck defects is currently a common procedure. This study aimed to create and validate a predictive model for identifying patients at risk of delayed recovery from anesthesia after free flap reconstruction for head and neck defect. Electronic medical records from all patients were retrospectively collected. The primary outcome variable was delayed recovery from anesthesia. The least absolute shrinkage and selection operator regression model was employed to identify the most relevant features, followed by the construction of a nomogram model using multivariable logistic regression analysis. The discriminatory power, calibration, and clinical utility of the nomogram model were assessed using receiver operating characteristic curve analysis, calibration curve analysis, and decision curve analysis, respectively. This novel nomogram model incorporated 4 predictors for delayed recovery from anesthesia: preoperative albumin, intraoperative fresh frozen plasma infusion, preoperative platelet-to-lymphocyte ratio, and duration of intraoperative hypotension. The area under the receiver operating characteristic curve (area under the curve) for the nomogram model was determined to be 0.821 (95% CI: 0.803-0.836). After internal validation, the corrected area under the curve was found to be 0.768 (95% CI: 0.639-0.812). In addition, the model exhibited well-fitted calibration curves and demonstrated favorable clinical usability as indicated by the calibration curve and decision curve analysis curve. The authors created and validated a novel predictive model utilizing a limited number of 4 predictors, yet exhibiting commendable predictive performance. This innovative tool holds the potential to mitigate delayed recovery from anesthesia and enhance the efficient allocation of medical resources.

Development of a nomogram for predicting malnutrition in elderly hospitalized cancer patients: a cross-sectional study in China.

The Patient-Generated Subjective Global Assessment (PG-SGA) serves as a specialized nutritional assessment instrument designed for cancer patients. Despite its specificity, the complexity and time requirements of this tool, along with the necessity for administration by trained professionals, limit its practicality in clinical settings. Our objective is to identify a straightforward, efficient, and dependable nutritional assessment tool to promote broader adoption in clinical practice. This study encompassed a total of 450 patients diagnosed with cancer. Of these, 315 individuals constituted the training set, and the remaining 135 were allocated to the external validation set. The model variables were identified through the Least Absolute Shrinkage and Selection Operator (LASSO) regression method. Binary logistic regression outcomes facilitated the development of a nomogram, offering a visual depiction of the predicted probabilities. The predictive accuracy of the nomogram model was evaluated by calculating the area under the Receiver Operating Characteristic (ROC) curve. The LASSO method detected four variables that were included in the final prediction model: age, serum albumin levels (ALB), body mass index (BMI), and activities of daily living (ADL). The area under the curve (AUC) for this prediction model was 0.905. Both the internal and external calibration curves for malnutrition showed that the predictive nomogram model was highly accurate. The study has developed a prediction model that demonstrates remarkable accuracy in forecasting malnutrition. Furthermore, it presents a streamlined nutritional assessment tool aimed at swiftly identifying cancer patients at nutritional risk, thereby facilitating oncologists in delivering targeted nutritional support to these individuals.

Analysis of MTHFR C677T genotype and related factors in H-type hypertension in Tibet, China

Background and aimsH-type hypertension is essential hypertension combined with high homocysteine, and both synergistically increase the risk of cardiovascular and cerebrovascular events. The aim of this study was to investigate the risk factors of H-type hypertension in Tibetan plateau population and correlation with MTHFR C677T gene.Methods and resultsA multi-stage cluster random sampling method was used to select the research subjects in Tibet Autonomous Region from June 2020 to November 2021. Among Tibetans, the incidence of H-type hypertension accounted for 84.31% of hypertensive patients. The logistic regression analysis demonstrated that age, uric acid (UA), triglyceride (TG) and low-density lipoprotein cholesterol (LDL-C) were risk factors for the prevalence of H-type hypertension, the OR (95% CI) was 1.083(1.073–1.094), 1.002(1.001–1.004), 1.240(1.050–1.464) and 2.274(1.432–3.611), respectively. MTHFR C677T TT genotype patients with H-type hypertension OR (95% CI) was 1.629(1.004–2.643). Based on this, a nomogram model was established, and the reliability of the model was proved by area under ROC curve, Brier score and average absolute error. The model’s results indicate that for every five years of age, the score increases by 6 points; for a 2mmol/L increase in TG, the score increases by 5.5 points; for a 1mmol/L increase in LDL-C, the score increases by 10 points; and individuals with the TT genotype receive 8 points. The higher the score, the greater the risk of disease.ConclusionThe MTHFR C677T TT genotype is a risk locus for Tibetan patients with H-type hypertension, with age, TG, and LDL-C were identified as risk factors for the disease.

Construction and validation of a nomogram for predicting survival in elderly patients with severe acute pancreatitis: a retrospective study from a tertiary center

PurposeThere is a lack of adequate models specifically designed for elderly patients with severe acute pancreatitis (SAP) to predict the risk of death. This study aimed to develop a nomogram for predicting the overall survival of SAP in elderly patients.MethodsElderly patients diagnosed with SAP between January 1, 2017 and December 31, 2022 were included in the study. Risk factors were identified through least absolute shrinkage and selection operator regression analysis. Subsequently, a novel nomogram model was developed using multivariable logistic regression analysis. The predictive performance of the nomogram was evaluated using metrics such as the receiver operating characteristic curve, calibration curve, and decision curve analysis (DCA).ResultsA total of 326 patients were included in the analysis, with 260 in the survival group and 66 in the deceased group. Multivariate logistic regression indicated that age, respiratory rate, arterial pH, total bilirubin, and calcium were independent prognostic factors for the survival of SAP patients. The nomogram demonstrated a performance comparable to sequential organ failure assessment (P = 0.065). Additionally, the calibration curve showed satisfactory predictive accuracy, and the DCA highlighted the clinical application value of the nomogram.ConclusionWe have identified key demographic and laboratory parameters that are associated with the survival of elderly patients with SAP. These parameters have been utilized to create a precise and user-friendly nomogram, which could be an effective and valuable clinical tool for clinicians.

An externally validated prognostic model for critically ill patients with traumatic brain injury.

Patients with traumatic brain injury (TBI) who are admitted to the intensive care unit often exhibit critical conditions; thus, early prediction of in-hospital mortality is crucial. In this study, we aimed to develop a reliable and easily promotable model for predicting the in-hospital mortality of critically ill patients with TBI using easily accessible indicators and validate the model using external data. Patient data from the Medical Information Mart for Intensive Care-IV 2.2 database were used as training and internal validation sets to establish and internally validate the prognostic model. Data from the Affiliated Dongyang Hospital of Wenzhou Medical University were used for external validation. The Boruta algorithm was used for the initial feature selection, followed by univariate and multivariate logistic regression analyses to identify the final independent predictors. The predictive performance was evaluated using a receiver operating characteristic curve, calibration curve, clinical practicality decision curve analysis, and clinical impact curve. This study included 3225 patients (training set: 2042; internal validation set: 874; and external validation set: 309). Ten variables were selected for inclusion in the nomogram model: age, mechanical ventilation usage, vasoactive agent usage, intracerebral hemorrhage, temperature, respiration rate, white blood cell count, platelet count, red blood cell distribution width, and glucose. The nomogram demonstrated good predictive performance in both the internal and external validation sets. We developed an externally validated nomogram that exhibited good discrimination, calibration, and clinical utility for predicting in-hospital mortality in critically ill patients with TBI.

Development and validation of a nomogram to predict overall survival in patients with glioma: a population-based study.

The objective is to investigate the prognostic factors associated with gliomas and to develop and assess a predictive nomogram model connected to survival that may serve as an additional resource for the clinical management of glioma patients. From 2010 to 2015, participants included in the study were chosen from the Surveillance Epidemiology and End Results (SEER) database. Gliomas were definitively diagnosed in each of them. They were divided into the training group and the validation cohort at random (7/3 ratio) using a random number table. To identify the independent predictive markers for overall survival (OS), Cox regression analysis was utilized. Subsequently, the training cohort's survival-related nomogram predictive model for OS was created by incorporating the fundamental patient attributes. Following that, the training cohort's model underwent internal validation. The nomogram model's authenticity and reliability were assessed through the computation of receiver operating characteristic (ROC) curves and concordance index (C-index). To evaluate the degree of agreement between the observed and predicted values in the training and validation cohorts, calibration plots were created. Age, primary site, histological type, surgery, chemotherapy, marital status, and grade were the independent predictive factors for OS in the training cohort, according to Cox regression analysis. Moreover, the nomogram model for predicting 1-year, 3-year, and 5-year OS was built using these variables. The C-indexes of OS for glioma patients in the training cohort and internal validation cohort were found to be 0.779 (95% CI=0.769-0.789) and 0.776 (95% CI=0.760-0.792), respectively, according to the results. The ROC curves also demonstrated good discrimination. Additionally, calibration plots demonstrated a fair amount of agreement. In summary, the nomogram prediction model of OS demonstrated a moderate level of reliability in its predictive performance, offering valuable reference data to enable doctors to quickly and easily determine the survival likelihood of patients with gliomas.

Multiparametric mri-based radiomics nomogram for predicting lymph-vascular space invasion in cervical cancer

PurposeTo develop and validate a multiparametric magnetic resonance imaging (mpMRI)-based radiomics model for predicting lymph-vascular space invasion (LVSI) of cervical cancer (CC).MethodsThe data of 177 CC patients were retrospectively collected and randomly divided into the training cohort (n=123) and testing cohort (n = 54). All patients received preoperative MRI. Feature selection and radiomics model construction were performed using max-relevance and min-redundancy (mRMR) and the least absolute shrinkage and selection operator (LASSO) on the training cohort. The models were established based on the extracted features. The optimal model was selected and combined with clinical independent risk factors to establish the radiomics fusion model and the nomogram. The diagnostic performance of the model was assessed by the area under the curve.ResultsFeature selection extracted the thirteen most important features for model construction. These radiomics features and one clinical characteristic were selected showed favorable discrimination between LVSI and non-LVSI groups. The AUCs of the radiomics nomogram and the mpMRI radiomics model were 0.838 and 0.835 in the training cohort, and 0.837 and 0.817 in the testing cohort.ConclusionThe nomogram model based on mpMRI radiomics has high diagnostic performance for preoperative prediction of LVSI in patients with CC.