Practical Nomogram Research Articles (Page 1)

Background Diabetic ketoacidosis (DKA) is a severe hyperglycemic emergency characterized by metabolic acidosis and electrolyte disturbances. The optimal strategy for potassium replenishment in DKA remains incomplete. This study comprehensively characterized potassium disturbances in DKA and evaluated the effectiveness of potassium replenishment strategies, with a focus on the risk of hypokalemia during treatment. Methods In this multicentre retrospective cohort study, we enrolled the consecutive DKA patients admitted to seven tertiary centres across eastern, central and western China (1 January 2021–31 December 2023). Demographics, biochemical parameters and daily potassium chloride (KCl) replenishment were extracted and evaluated. We used multivariable logistic regression to identify predictors of hypokalaemia during treatment, internally validated the model, and constructed a practical nomogram. Results A total of 571 eligible subjects were included in the analysis. On admission, blood glucose, arterial pH, HCO 3 - , and electrolyte profiles were seriously deteriorated. Among the patients, 95 patients (16.6%) were hypokalemic, 352 (61.6%) normokalemic and 124 (21.7%) hyperkalemic. Hyperkalemia was more frequent in severe DKA and associated with renal impairment and the severity of DKA ( p < 0.05). During treatment, 388 (67.9%) patients developed hypokalemia, the proportion rose to 73.6% among severe DKA cases. The occurrence of hypokalemia during treatment was independently associated with potassium concentration, HbA1c, and arterial pH at admission ( p < 0.05). The statistical model predicted the risk of hypokalemia during treatment. A daily 6.0 g KCl supplement offered superior predictive efficacy for hypokalemia compared to lower doses throughout the treatment course. Conclusions Potassium imbalances were highly prevalent in DKA. Although hyperkalemia was more common on admission, hypokalemia frequently developed during treatment. Daily 6.0 g KCl replenishment was superior to lower doses in predicting hypokalaemia. This study provided the full spectrum of potassium disorders in DKA and delivered an evidence-based, patient-specific replenishment framework.

Development and multi-cohort validation of a prognostic risk score model for oral squamous cell carcinoma based on a three-gene signature.

ObjectivesUltrasound soft markers (USMs) are associated with increased risk of fetal chromosomal abnormalities but lack standardized risk assessment methods, often leading to unnecessary amniocentesis procedures. We aimed to develop a practical nomogram tool to quantify this risk and help clinicians make more objective decisions about invasive testing, particularly in resource-limited settings.ResultsWe retrospectively analyzed 565 pregnancies with USMs who underwent amniocentesis between 2016 and 2024. Our nomogram integrated six readily available clinical factors: maternal age, thickened nuchal translucency, adverse pregnancy history, structural malformations, fetal growth restriction, and short long bones. The tool demonstrated moderate discriminatory ability with an AUC of 0.738 (95% CI 0.652–0.823) in the training set and 0.647 (95% CI 0.511–0.784) in the validation set. Calibration curves confirmed good agreement between predicted and observed outcomes. Rather than discovering new associations between USMs and chromosomal abnormalities, this tool simply converts established clinical knowledge into a user-friendly format that allows clinicians to objectively assess the need for amniocentesis in clinical practice.Supplementary InformationThe online version contains supplementary material available at 10.1186/s13104-025-07512-9.

Factors influencing the prognosis of patients with terminal-stage cancer remain poorly understood. In this study, we examined these factors and developed a visual model to predict patient survival. Data were collected from patients with terminal-stage cancer treated at the Air Force Hospital of the People’s Liberation Army Eastern Theater Command between 2011 and 2020 were collected. Patients were categorized into the training and validation cohorts. Clinical and laboratory characteristics were collected for analysis and prognostic factors were identified to construct a predictive model, develop a nomogram in the training set (n = 193) and verify it in the validation set (n = 85). Our findings revealed that survival predictions for terminal-stage cancer were not associated with common factors such as tumor type, stage, patient age at diagnosis, or Eastern Cooperative Oncology Group performance status score. Instead, factors such as willingness to receive treatment, dyspnea, serum urea, serum albumin, and neutrophil count proved to be critical. These factors were used to create a highly accurate and reliable nomogram. A comprehensive analysis of prognostic factors in patients with terminal-stage cancer resulted in the development of a practical nomogram model for clinical application.

BackgroundSepsis-associated encephalopathy (SAE) is a fatal complication of sepsis, with a high mortality rate worldwide. This study aimed to reduce mortality and improve the quality of life of patients with SAEs by developing a practical nomogram to predict the risk factors associated with ICU mortality.MethodThe MIMIC database was used as the training set to build the model, and the eICU-CRD served as the validation set for external verification. LASSO regression analysis was conducted to identify predictive variables and develop the nomogram model. Receiver Operating Characteristic (ROC) curves were generated to assess the model’s discriminative ability. Model calibration was assessed using calibration curves and the Hosmer-Lemeshow goodness-of-fit tests. Clinical decision curves were plotted to assess the model’s net benefit and evaluate its clinical applicability.ResultsA total of 5,242 patients from the MIMIC database and 3,103 from the eICU-CRD were included in the study. LASSO regression, identified eight predictive variables for inclusion in the final model. The nomogram was evaluated against standard ICU scoring systems, including SAPS II, SOFA and GOS scores, with AUROC values of 0.832, 0.769, 0.607, and 0.575, respectively, in the training set. Conversely, the validation set demonstrated AUROC values of 0.825, 0.715, 0.714, and 0.587. P-values from the Hosmer-Lemeshow goodness-of-fit test for both the training and validation sets were 0.129 and 0.583, respectively, indicating a good fit quality. DCA revealed that the nomogram consistently provides greater net benefits compared to SAPS II, SOFA, and GCS scores.ConclusionDeveloping mortality prediction models for SAE patients in the ICU can facilitate early intervention strategies and potentially reduce mortality rates in this high-risk population.Clinical trial numberNot applicable.Supplementary InformationThe online version contains supplementary material available at 10.1186/s12911-025-03222-1.

BackgroundPostoperative delirium (POD) is a serious complication in geriatric patients admitted to the ICU following abdominal surgery. Malnutrition is a significant modifiable risk factor for POD, yet the comparative predictive value of established nutritional indices—Geriatric Nutritional Risk Index (GNRI), Prognostic Nutritional Index (PNI), and Controlling Nutritional Status (CONUT)—remains unclear in this high-risk population. This study aimed to directly compare these indices to identify the optimal preoperative predictor for POD.MethodsThis single-center retrospective study analyzed 333 patients (≥65 years) admitted post-abdominal surgery to the ICU (from October 2021 to December 2024). POD was diagnosed using CAM-ICU. A clinical prediction nomogram was developed based on significant predictors from the multivariate model. The discriminative ability of preoperative GNRI, PNI, and CONUT scores was compared using receiver operating characteristic (ROC) curves, DeLong’s test for the area under the ROC curve (AUC) differences, along with net reclassification improvement (NRI) and integrated discrimination improvement (IDI) to assess model performance enhancements. Optimal cut-off values were determined by maximizing the Youden index, and corresponding sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and kappa statistics were reported. The study was approved by the Institutional Ethics Committee of Jinling Hospital (Approval No. 2024NZKY-038-02).ResultsFactors identified from multivariable analysis (diabetes mellitus, hypoalbuminemia, reduced total cholesterol) were incorporated into a clinical prediction nomogram, which demonstrated good discrimination (AUC = 0.769, 95%CI: 0.707–0.832, p<0.001) and calibration (Hosmer-Lemeshow test p = 0.444; Brier score = 0.137). Decision curve analysis confirmed its clinical utility. Among the nutritional indices, the CONUT score demonstrated superior predictive performance (AUC = 0.751, 95% CI: 0.686–0.816, p<0.001), significantly outperforming PNI (AUC = 0.673, p<0.001) and GNRI (AUC = 0.666, p<0.001). At an optimal cutoff of 7.5, CONUT achieved 60.9% sensitivity and 81.1% specificity. However, adding CONUT to the clinical nomogram did not significantly improve the predictive performance compared to the clinical model alone (p > 0.05).ConclusionWe developed a practical nomogram and identified the CONUT score as a valuable preoperative predictor for POD—both demonstrating comparable predictive utility. The CONUT score outperformed PNI and GNRI by integrating key biomarkers (albumin, cholesterol, lymphocytes) into a single metric. Although its components overlap with the clinical model, CONUT offers high specificity and simplicity, making it an efficient tool for rapid preoperative risk stratification.

BackgroundTriple-negative breast cancer (TNBC), a highly heterogeneous breast cancer subtype, poses significant challenges to human health. Intra-tumor heterogeneity (ITH) limits the reliability of conventional prognostic models.MethodsUsing multi-region RNA-seq, we quantified TNBC transcriptomic heterogeneity through an integrative heterogeneity score (IHS). After evaluating inter-patient heterogeneity (IPH) and ITH, prognostic and low-heterogeneity genes were identified and used to build a prognostic risk model with a random survival forest (RSF) algorithm. This model was combined with TNM staging into a nomogram for clinical applicability. We further revealed the distinct immune microenvironment features, somatic mutations, and chemotherapy responses between risk subgroups. Gene expression was validated via RT-qPCR.ResultsSpatial characterization uncovered substantial ITH, evidenced by sharp shifts in PAM50 subtypes and immune infiltration. Two low-heterogeneity biomarkers, CYP4B1 and GBP1, were identified to develop a robust prognostic signature with consistent predictive performance across 3- to 9-year survival endpoints (AUC > 0.6). The high-risk subgroup exhibited reduced immune infiltration, reduced immune checkpoint molecule expression, and poor immunotherapy response rates. Integration of the risk signature with TNM staging created a clinically practical nomogram with superior predictive accuracy (C-index >0.67). Therapeutic vulnerability profiling identified six targeted agents showing increased efficacy in high-risk patients. Dysregulation of signature genes was demonstrated in two TNBC cell lines.ConclusionsThis study established a transcriptomic heterogeneity-resilient prognostic model for TNBC, enabling precise survival stratification and immune microenvironment assessment. The integrative nomogram and risk-guided therapeutic predictions address clinical challenges in TNBC management, advancing personalized treatment strategies.

ObjectiveThis study aimed to identify the risk factors for bronchiolitis obliterans (BO) development in children with severe adenovirus pneumonia (SAP) and to construct and validate a nomogram prediction model.MethodsThis retrospective study included 152 pediatric patients with SAP between January 2019 and December 2023. We categorized these patients as having developed BO (n=36) and non-BO (n=116) based on long-term follow-up outcomes. Key clinical features were optimized using the least absolute shrinkage and selection operator (LASSO) regression and a nomogram was developed using logistic regression. Model performance was assessed and validated through receiver operating characteristic (ROC) curve analysis, calibration curves, and decision curve analysis (DCA).ResultsThe LASSO regression analysis initially identified nine potential clinical predictors. Subsequent univariable and multivariable logistic regression revealed four independent risk factors significantly associated with BO development, namely, younger age, Odds ratio (OR) =0.94, 95% CI, 0.90–0.99, p=0.010; longer duration of fever, OR=2.27, 95% CI, 1.52–3.39, p<0.001; requirement for tracheoscopy, OR=5.25, 95% CI, 1.06–26.09, p=0.040; and extended oxygen therapy, OR=1.64, 95% CI, 1.10–2.43, p=0.010. The final prediction model incorporated three key predictors (months of age, fever duration, and oxygen therapy duration) into a clinically practical nomogram. The model demonstrated excellent discrimination, with an area under the curve (AUC) of 0.95, 95% CI, 0.91–0.98, a sensitivity of 0.83, and a specificity of 0.93. The Hosmer-Lemeshow test, χ2=5.24, p=0.732 indicated good calibration, and the DCA demonstrated positive clinical benefits.ConclusionWe developed and validated a clinically practical nomogram, incorporating three key predictors mainly, months of age, fever duration, and oxygen therapy duration in predicting BO in children with SAP.The model demonstrates strong discriminatory power, reliable calibration, and clinical utility. This tool enables early risk stratification, facilitating timely intervention for high-risk pediatric SAP patients.

BackgroundSevere pneumonia often leads to acute respiratory failure requiring mechanical ventilation (MV), significantly increasing patient morbidity and mortality. Early prediction of MV requirement could optimize patient management and resource allocation. This study aimed to identify key risk factors and develop a practical nomogram model to predict the need for mechanical ventilation among patients with severe pneumonia.MethodsIn this retrospective study, patients with severe pneumonia admitted between January 2021 and December 2024 were analyzed at a single tertiary institution. Patients were stratified based on the use of MV within 24 h of admission. Multivariable logistic regression identified independent predictors of MV, which were used to construct a nomogram. Model performance was evaluated via receiver operating characteristic (ROC) curves, bootstrap validation, calibration, and decision curve analysis (DCA).ResultsA total of 216 patients were included, with 165 in the MV group and 51 in the non-MV group. Patients requiring MV were significantly older and demonstrated lower oxygenation index (OI), partial pressure of oxygen [p(O₂)], central venous oxygen saturation (ScvO₂), and procalcitonin (PCT) levels, along with higher partial pressure of carbon dioxide [p(CO₂)], alveolar-arterial oxygen gradient [p(A-a)O₂], and APACHE II scores (all p < 0.01). Age, OI, p(O₂), p(CO₂), and p(A-a)O₂ were independent predictors included in the nomogram. The model showed excellent discrimination (area under the ROC curve, AUC = 0.819), calibration (concordance index, C-index = 0.805), and substantial clinical utility.ConclusionThis retrospective study suggests that age, OI, p(O₂), p(CO₂), and p(A-a)O₂ could help predict MV in severe pneumonia. The proposed nomogram might offer good predictive accuracy, calibration, and clinical utility, potentially aiding early risk stratification. Prospective multicenter validation is needed to confirm its generalizability and clinical utility.

ObjectiveThe retrospective study established a prognostic nomogram based on red blood cell distribution width-coefficient of variation (RDW-CV) for elderly septic patients.MethodsWe analyzed 1997 critically ill patients admitted between December 2016 and June 2019, and 986 elderly septic patients were included in the study and stratified into survival and non-survival groups. Using machine learning-based feature importance analysis and multivariate logistic regression, we evaluated predictors of mortality in the elderly septic patients, with particular focus on RDW-CV. We constructed a nomogram incorporating RDW-CV to predict clinical outcomes in elderly septic patients and evaluated its performance.ResultsThe mortality of 986 elderly sepsis patients was 27.48%. Importance analysis showed that RDW-CV demonstrated superior predictive value for mortality. The RDW-CV (17.22 ±3.98%) in the non-survival group was significantly higher than that (15.30 ±2.81%) in the survival group, p < 0.0001. The RDW-CV was used to predict the mortality of patients and the AUC was 0.65 (95% CI: 0.61, 0.69). Multivariate logistic regression showed that mechanical ventilation, drug-resistant bacterial infection, hemofiltration, and RDW-CV independently influenced mortality, a predictive nomogram was developed based on a final model that included RDW-CV and other clinical indicators, the area under the curve (AUC) was found to be 0.755 (95% CI: 0.714, 0.797), decision curve analyses (DCA) revealed superior net benefit of the nomogram across threshold probabilities of 0.30–1.00 in both derivation and validation cohorts. The calibration curve demonstrates strong agreement between the model’s predicted probabilities and the validation cohort’s predicted probabilities.ConclusionHigher RDW-CV was found to have a significant association with mortality prediction, the nomogram based on RDW-CV with other clinical indicators could more accurately predict the clinical outcome of elderly septic patients, validation analysis confirmed the accuracy of the nomogram, the predictive model offered clinical applicability.

ObjectiveTo develop and validate a machine learning-based nomogram for predicting heart failure (HF) in elderly patients with relapsed/refractory multiple myeloma (RRMM) receiving carfilzomib-based therapy, facilitating early identification and individualized clinical management.MethodsThis retrospective study analyzed clinical data from 192 elderly RRMM patients treated with carfilzomib-based therapy at Shaanxi Provincial Cancer Hospital (from January 1, 2023, to December 31, 2024). Machine learning algorithms, including the Least Absolute Shrinkage and Selection Operator (LASSO) regression, Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost), were used for variable selection. Robust predictors identified through cross-model consistency evaluation and bootstrap resampling were incorporated into a nomogram. Model performance was assessed using concordance index (C-index), calibration curves, and decision curve analysis (DCA).ResultsHF occurred in 25.5% (49/192) of patients. Machine learning models consistently identified coronary artery disease (CAD), hypertension, renal insufficiency, and albumin (Alb) levels as significant HF risk factors. The nomogram showed good predictive performance (C-index: 0.780, 95% CI: 0.704–0.841), internal calibration (Hosmer–Lemeshow χ² = 1.334, P = 0.970), and external validation (Hosmer-Lemeshow χ² = 1.054, P = 0.788). DCA confirmed clinical utility across a wide range of threshold probabilities (1% to 83%), with a peak net benefit of 0.248.ConclusionThis study provides a practical nomogram for cardiovascular risk assessment in elderly RRMM patients receiving carfilzomib-based therapy, which may assist clinicians in early risk stratification and support tailored monitoring and management throughout treatment.

Elucidating the correlation between polychlorinated dibenzo-p-dioxins and prostate cancer progression: Insights from gene expression and molecular docking.

BackgroundCurrent research on breast-conserving surgery (BCS) focuses on recurrence and survival but overlooks the issue of residual tumors post-excisional biopsy. These remnants, crucial for surgical planning, often necessitate additional excisions, impacting BCS success. Our 5-year study of excisional biopsy patients identifies risk factors for residual tumors, offering insights to improve surgical decisions.MethodsThis study examined 233 breast cancer patients split into training and validation groups (2:1 ratio). Logistic regression models identified predictors of post-biopsy residual tumors status, leading to the creation and validation of a preoperative nomogram for residual risk.ResultsIn this study of 233 patients, 23.9% with BCS had residual tumors after biopsy, significantly less than those in the non-BCS group (P<0.001). Tumor size, biopsy method, and histopathological subtype were crucial in predicting residual tumors and were used to develop a nomogram, which showed strong predictive accuracy for preoperative residual tumor status. This tool enhances preoperative risk stratification and aids in the formulation of personalized surgical strategies by providing visual quantification of the probabilities associated with oncological clearance parameters.ConclusionsWe developed a clinically practical nomogram for predicting residual tumor status following excisional biopsy, facilitating preoperative risk stratification and personalized surgical strategy. Further prospective studies are necessary to evaluate its generalizability and accuracy.

This study aimed to develop and validate a practical nomogram for differentiating between benign and malignant pancreatic masses. A total of 494 patients with pancreatic mass lesions, confirmed by histopathology, were enrolled from Wuhan Union Medical College Hospital between January 2020 and May 2022. The participants were randomly divided into development and validation groups in a 7:3 ratio. Using multivariate logistic regression, the nomogram was constructed based on five independent predictors: blood type, CA19-9, IgG4, anorexia, and weight loss. The model's performance was assessed using receiver operating characteristic (ROC) curve analysis and calibration curves. In the development and validation sets, the areas under the ROC curve were 0.932 and 0.957, respectively. The nomogram demonstrated a high net benefit in the clinical decision curve analysis. Based on the model, pancreatic malignancy risk was classified as low (< 4%), moderate (4%-71%), and high (> 71%). This nomogram provides an easy-to-use, efficient tool for the early differentiation of pancreatic malignancies and could be implemented in primary, secondary, and emergency care settings to facilitate the timely referral of patients to higher-level hospitals.

BackgroundAlthough identifying factors contributing to aggressive behavior in individuals with schizophrenia is crucial for developing targeted prevention strategies and intervention, most studies were cross-sectional or short-term, and did not take into account the factor of urbanicity. This study aimed to develop a predictive model of aggressive behavior in individuals with schizophrenia in rural China.MethodA total of 205 individuals with schizophrenia who were identified in 1994 and followed up in 2015 were included in the study. Aggressive behavior was assessed using the Modified Overt Aggression Scale (MOAS). The final predictive model was developed by backward stepwise regression. The model’s predictive performance was evaluated using the C statistic and calibration curve.ResultThe rate of aggressive behavior in individuals with schizophrenia in rural China was 36.1% during 1994-2015. The final model of aggressive behavior incorporated the following factors: male, lower educational level, unmarried, with delusion, worse social functioning, and with previous treatment. The model demonstrated acceptable discriminative ability, with an AUC of 0.73, sensitivity of 0.82, and specificity of 0.53. The calibration curve indicated a good fit of the model.ConclusionThe predictive model developed in this study showed good discriminative ability. A clinically practical nomogram was built to assess the risk of aggressive behavior in individuals with schizophrenia in rural China, which may facilitate early detection and intervention of these individuals, particularly in rural areas with limited resources. This approach may be relevant to similar settings internationally.

Lymphoma in children and adolescents represents a distinct clinical entity, often associated with aggressive biological behavior and poor cancer-specific outcomes. Accurate prediction of cancer-specific death (CSD) in this population is essential for guiding personalized treatment and follow-up strategies. This study aimed to develop and validate a nomogram for predicting the risk of CSD in children and adolescents with lymphoma using a competing risk model based on a large population-based cohort. Lymphoma cases diagnosed in patients aged 1 to 17 years were extracted from the surveillance, epidemiology, and end results database. Eligible patients were randomly assigned to a training cohort (70%) and a validation cohort (30%). Independent prognostic factors for CSD were identified using univariate and multivariate competing risk regression analyses. A nomogram was constructed based on significant variables, and its performance was evaluated by the concordance index (C-index), receiver operating characteristic curves, and calibration plots. A total of 6954 pediatric and adolescent lymphoma cases were included. Six variables, including age, race, diagnosis time, lymphoma subtype, tumor grade, and tumor stage, were identified as independent predictors of CSD. The nomogram showed strong discriminative power, with 5-, 10-, and 15-year area under curves of 0.814, 0.794, and 0.787 in the training cohort, 0.818, 0.792, and 0.764 in the validation cohort. Calibration curves demonstrated good agreement between predicted and observed outcomes. Survival analysis showed that patients with high-risk score had a poor clinical outcome. We developed a robust and clinically practical nomogram for predicting CSD in children and adolescents with lymphoma. This tool may assist clinicians in identifying high-risk patients and formulating individualized management strategies.

ABSTRACTAimsTo develop and validate a nomogram model based on clinical and laboratory parameters to predict the risk of metabolic dysfunction‐associated fatty liver disease (MAFLD) in the early stage of type 2 diabetes.Materials and MethodsWe performed this study among 883 inpatients with new‐onset type 2 diabetes, and the data were divided randomly into training and validation groups. The logistic regression method was used to identify the independent risk factors of MAFLD, and a nomogram was established according to the logistic regression analysis and these selected parameters. The discrimination, calibration, and clinical utility of the nomogram were measured by receiver operating characteristic curve analysis, calibration curves, and decision‐curve analysis, respectively.ResultsEight variables were identified and included in the nomogram (body mass index, alanine aminotransferase, triglyceride, low‐density lipoprotein cholesterol, high‐density lipoprotein cholesterol, fasting plasma glucose, urea nitrogen and serum uric acid). The value of the area under the receiver operating characteristic (ROC) curve was 0.898 for the training group and 0.92 for the validation group. The calibration plots indicated that this model had good accuracy, and the decision‐curve analysis revealed high‐clinical practicability of the nomogram.ConclusionsThis study established a convenient and practical nomogram model, which can be used as an easy‐to‐use tool to evaluate the risk of MAFLD among patients with newly diagnosed T2DM.

ObjectivesThis study aimed to establish a novel and practical nomogram for use upon hospital admission to identify coinfections among elderly patients with coronavirus disease 2019 (COVID-19) to provide timely intervention, limit antimicrobial agent overuse, and finally reduce unfavourable outcomes.MethodsThis prospective cohort study included COVID-19 patients consecutively admitted at multicenter medical facilities in a two-stage process. The nomogram was built on the multivariable logistic regression analysis. The performance of the nomogram was assessed for discrimination and calibration using receiver operating characteristic curves, calibration plots, and decision curve analysis (DCA) in rigorous internal and external validation settings. Two different cutoff values were determined to stratify coinfection risk in elderly patients with COVID-19.ResultsThe coinfection rates in elderly patients determined to be and 26.61%. The nomogram was developed with the parameters of diabetes comorbidity, previous invasive procedure, and procalcitonin (PCT) level, which together showed areas under the curve of 0.86, 0.82, and 0.83 in the training, internal validation, and external validation cohorts, respectively. The nomogram outperformed both PCT or C-reactive protein level alone in detecting coinfections in elderly patients with COVID-19; in addition, we found the nomogram was specific for the elderly compared to non-elderly group. To facilitate clinical decision-making among elderly patients with COVID-19, we defined two cutoff values of prediction probability: a low cutoff of 6.65% to rule out coinfections and a high cutoff of 27.79% to confidently confirm coinfections.ConclusionsThis novel nomogram will assist in the early identification of coinfections in elderly patients with COVID-19.

A Predictive Nomogram Model Incorporating Contrast Extravasation for Early Prediction of Postthrombectomy Malignant Cerebral Edema.

The CLIMA study [Relationship between Optical Coherence Tomography (OCT) Coronary Plaque Morphology and Clinical Outcome; NCT02883088] introduced the concept of high-risk plaque (HRP) and demonstrated that HRP was associated with a high risk of major coronary events. HRP is defined by four simultaneous characteristics: minimum lumen area (MLA) <3.5 mm2, fibrous cap thickness (FCT) <75 μm, lipid arc circumferential extension >180°, and macrophage infiltration. Early prediction of HRP formation is critical for preventing and treating acute coronary syndrome (ACS), but no studies have been conducted on this topic. To identify the risk factors associated with OCT HRP in ACS and develop a risk prediction model for HRPs in ACS. A prospective observational study was conducted on patients with ACS between September 2019 and August 2022. A total of 169 patients were divided into two groups: OCT HRP (n = 55) and OCT non-HRP (n = 114) groups. Clinical data, laboratory results, and OCT characteristics of the patients were collected. Least absolute shrinkage and selection operator (LASSO) regression was used to screen variables, while multivariate logistic regression was used to create a risk prediction model. A nomogram was created, and the receiver operating characteristic curve was used to assess the model's discrimination, as well as the bootstrap method to internally validate it. The most commonly observed HRP characteristic was lipid plague >180° (147 patients), followed by MLA < 3.5 mm2 (141 patients), macrophages (127 patients), and FCT < 75 μm (64 patients). The LASSO regression model was used to screen variables and develop an HRP risk factor model. The nomogram includes five predictors: age, BMI ≥ 25 kg/m2, triglycerides, low-density lipoprotein cholesterol, and Log N-terminal brain natriuretic peptide precursor. The model is highly differentiated (area under the curve 0.780, 95% confidence interval 0.705-855) and calibrated. The calibration curve and decision curve analysis demonstrated the model's clinical usefulness. A simple and practical nomogram for predicting HRPs accurately in patients with ACS was developed and validated, and is expected to help clinicians diagnose and prevent plaque stability.