Circulating biomarker- and magnetic resonance-based nomogram predicting long-term outcomes in dilated cardiomyopathy / 中华医学杂志(英文版)

BACKGROUND@#Dilated cardiomyopathy (DCM) has a high mortality rate and is the most common indication for heart transplantation. Our study sought to develop a multiparametric nomogram to assess individualized all-cause mortality or heart transplantation (ACM/HTx) risk in DCM patients.@*METHODS@#The present study is a retrospective cohort study. The demographic, clinical, blood test, and cardiac magnetic resonance imaging (CMRI) data of DCM patients in the tertiary center (Fuwai Hospital) were collected. The primary endpoint was ACM/HTx. The least absolute shrinkage and selection operator (LASSO) Cox regression model was applied for variable selection. Multivariable Cox regression was used to develop a nomogram. The concordance index (C-index), area under the receiver operating characteristic curve (AUC), calibration curve, and decision curve analysis (DCA) were used to evaluate the performance of the nomogram.@*RESULTS@#A total of 218 patients were included in the present study. They were randomly divided into a training cohort and a validation cohort. The nomogram was established based on eight variables, including mid-wall late gadolinium enhancement, systolic blood pressure, diastolic blood pressure, left ventricular ejection fraction, left ventricular end-diastolic diameter, left ventricular end-diastolic volume index, free triiodothyronine, and N-terminal pro-B type natriuretic peptide. The AUCs regarding 1-year, 3-year, and 5-year ACM/HTx events were 0.859, 0.831, and 0.840 in the training cohort and 0.770, 0.789, and 0.819 in the validation cohort, respectively. The calibration curve and DCA showed good accuracy and clinical utility of the nomogram.@*CONCLUSIONS@#We established and validated a circulating biomarker- and CMRI-based nomogram that could provide a personalized prediction of ACM/HTx for DCM patients, which might help risk stratification and decision-making in clinical practice.

Assessment of risk factors for bronchopulmonary dysplasia with pulmonary hypertension and construction of a prediction nomogram model / 中华儿科杂志

Objective: To explore the risk factors of pulmonary hypertension (PH) in premature infants with bronchopulmonary dysplasia (BPD), and to establish a prediction model for early PH. Methods: This was a retrospective cohort study. Data of 777 BPD preterm infants with the gestational age of <32 weeks were collected from 7 collaborative units of the Su Xinyun Neonatal Perinatal Collaboration Network platform in Jiangsu Province from January 2019 to December 2022. The subjects were randomly divided into a training cohort and a validation cohort at a ratio of 8∶2 by computer, and non-parametric test or χ2 test was used to examine the differences between the two retrospective cohorts. Univariate Logistic regression and multivariate logistic regression analyses were used in the training cohort to screen the risk factors affecting the PH associated with BPD. A nomogram model was constructed based on the severity of BPD and its risk factors,which was internally validated by the Bootstrap method. Finally, the differential, calibration and clinical applicability of the prediction model were evaluated using the training and verification queues. Results: A total of 130 among the 777 preterm infants with BPD had PH, with an incidence of 16.7%, and the gestational age was 28.7 (27.7, 30.0) weeks, including 454 males (58.4%) and 323 females (41.6%). There were 622 preterm infants in the training cohort, including 105 preterm infants in the PH group. A total of 155 patients were enrolled in the verification cohort, including 25 patients in the PH group. Multivariate Logistic regression analysis revealed that low 5 min Apgar score (OR=0.87, 95%CI 0.76-0.99), cesarean section (OR=1.97, 95%CI 1.13-3.43), small for gestational age (OR=9.30, 95%CI 4.30-20.13), hemodynamically significant patent ductus arteriosus (hsPDA) (OR=4.49, 95%CI 2.58-7.80), late-onset sepsis (LOS) (OR=3.52, 95%CI 1.94-6.38), and ventilator-associated pneumonia (VAP) (OR=8.67, 95%CI 3.98-18.91) were all independent risk factors for PH (all P<0.05). The independent risk factors and the severity of BPD were combined to construct a nomogram map model. The area under the receiver operating characteristic (ROC) curve of the nomogram model in the training cohort and the validation cohort were 0.83 (95%CI 0.79-0.88) and 0.87 (95%CI 0.79-0.95), respectively, and the calibration curve was close to the ideal diagonal. Conclusions: Risk of PH with BPD increases in preterm infants with low 5 minute Apgar score, cesarean section, small for gestational age, hamodynamically significant patent ductus arteriosus, late-onset sepsis, and ventilator-associated pneumonia. This nomogram model serves as a useful tool for predicting the risk of PH with BPD in premature infants, which may facilitate individualized early intervention.

A Prediction Model for Human Immunodeficiency Virus Infection and Mother-to-Child Transmission Based on the Expression Levels of Selenoprotein Genes / 中国医学科学院学报

Objective To study the expression of selenoprotein genes in human immunodeficiency virus(HIV)infection and its mother-to-child transmission,so as to provide a theoretical basis for the prevention,diagnosis,and treatment of acquired immunodeficiency syndrome.Methods The dataset GSE4124 was downloaded from the Gene Expression Omnibus(GEO).Two groups of HIV-positive mothers(n=25)and HIV-negative mothers(n=20)were designed.HIV-positive mothers included a subset of transmitter(TR)mothers(n=11)and non-transmitter(NTR)mothers(n=14).Then,t-test was carried out to compare the expression levels of selenoprotein genes between the four groups(HIV-positive vs. HIV-negative,NTR vs. HIV-negative,TR vs. HIV-negative,TR vs. NTR).Univariate and multivariate Logistic regression were adopted to analyze the effects of differentially expressed genes on HIV infection and mother-to-child transmission.R software was used to establish a nomogram prediction model and evaluate the model performance.Results Compared with the HIV-negative group,HIV-positive,NTR,and TR groups had 8,5 and 8 down-regulated selenoprotein genes,respectively.Compared with the NTR group,the TR group had 4 down-regulated selenoprotein genes.Univariate Logistic regression analysis showed that abnormally high expression of GPX1,GPX3,GPX4,TXNRD1,TXNRD3,and SEPHS2 affected HIV infection and had no effect on mother-to-child transmission.The multivariate Logistic regression analysis showed that the abnormally high expression of TXNRD3(OR=0.032,95%CI=0.002-0.607,P=0.022)was positively correlated with HIV infection.As for the nomogram prediction model,the area under the receiver-operating characteristic curve for 1-year survival of HIV-infected patients was 0.840(95%CI=0.690-1.000),and that for 3-year survival of HIV-infected patients was 0.870(95%CI=0.730-1.000).Conclusions Multiple selenoprotein genes with down-regulated expression levels were involved in the regulation of HIV infection and mother-to-child transmission.The abnormal high expression of TXNRD3 was positively correlated with HIV infection.The findings provide new ideas for the prevention,diagnosis,and treatment of acquired immunodeficiency syndrome.

Construction and evaluation of a nomogram prediction model for periprosthetic fractures after total hip arthroplasty / 中国骨伤

OBJECTIVE@#To construct and evaluate nomogram prediction model for periprosthetic fractures in patients undergoing total hip arthroplasty (THA).@*METHODS@#A total of 538 patients who underwent THA from April 2013 to February 2019 were selected as the research subjects, including 318 males and 220 females, aged 40 to 60 years old with an average age of (50.79±6.37) years old. All patients with THA were divided into non-fracture group (506 patients) and fracture group (32 pathents) according to the 3-year follow-up results. Univariate and multivariate Logistic regression analyses were performed to analyze the influencing factors of postoperative periprosthetic fractures in patients with THA. A nomogram prediction model for periprosthetic fractures in patients undergoing THA was constructed, and the validity and discrimination of the prediction model were evaluated.@*RESULTS@#The proportion of patients with osteoporosis, trauma history, and hip revision in the fracture group were higher than those in the non-fracture group(P<0.05), and the proportion of bone cement prosthesis was lower than that in the non-fracture group(P<0.05). The osteoporosis status[OR=4.177, 95%CI(1.815, 9.617), P<0.05], trauma history[OR=7.481, 95%CI(3.104, 18.031), P<0.05], and hip revision[OR=11.371, 95%CI(3.220, 40.153, P<0.05] were independent risk factors for postoperative periprosthetic fractures in patients undergoing THA, cemented prosthesis [OR=0.067, 95%CI(0.019, 0.236), P<0.05] was an independent protective factor for postoperative periprosthetic fractures in patients undergoing THA(P<0.05). Hosmer-Lemeshow goodness of fit test showed that χ2=7.864, P=0.325;the area under the curve (AUC) for periprosthetic fractures in patients undergoing THA was 0.892 with a sensitivity of 87.5% and a specificity of 77.7% by receiver operating characteristic(ROC) curve.@*CONCLUSION@#The nomogram prediction model for periprosthetic fractures after THA constructed in this study has good discrimination, which is beneficial to clinical prediction of periprosthetic fractures in patients undergoing THA, and facilitates individualized fracture prevention.

Keloid nomogram prediction model based on weighted gene co-expression network analysis and machine learning / 生物医学工程学杂志

Keloids are benign skin tumors resulting from the excessive proliferation of connective tissue in wound skin. Precise prediction of keloid risk in trauma patients and timely early diagnosis are of paramount importance for in-depth keloid management and control of its progression. This study analyzed four keloid datasets in the high-throughput gene expression omnibus (GEO) database, identified diagnostic markers for keloids, and established a nomogram prediction model. Initially, 37 core protein-encoding genes were selected through weighted gene co-expression network analysis (WGCNA), differential expression analysis, and the centrality algorithm of the protein-protein interaction network. Subsequently, two machine learning algorithms including the least absolute shrinkage and selection operator (LASSO) and the support vector machine-recursive feature elimination (SVM-RFE) were used to further screen out four diagnostic markers with the highest predictive power for keloids, which included hepatocyte growth factor (HGF), syndecan-4 (SDC4), ectonucleotide pyrophosphatase/phosphodiesterase 2 (ENPP2), and Rho family guanosine triphophatase 3 (RND3). Potential biological pathways involved were explored through gene set enrichment analysis (GSEA) of single-gene. Finally, univariate and multivariate logistic regression analyses of diagnostic markers were performed, and a nomogram prediction model was constructed. Internal and external validations revealed that the calibration curve of this model closely approximates the ideal curve, the decision curve is superior to other strategies, and the area under the receiver operating characteristic curve is higher than the control model (with optimal cutoff value of 0.588). This indicates that the model possesses high calibration, clinical benefit rate, and predictive power, and is promising to provide effective early means for clinical diagnosis.

Construction and evaluation of a nomogram for predicting the prognosis of patients with colorectal cancer with peritoneal carcinomatosis treated with cytoreductive surgery plus hyperthermic intraperitoneal chemotherapy / 中华胃肠外科杂志

Objectives: To construct a nomogram incorporating important prognostic factors for predicting the overall survival of patients with colorectal cancer with peritoneal metastases treated with cytoreductive surgery (CRS) plus hyperthermic intraperitoneal chemotherapy (HIPEC), the aim being to accurately predict such patients' survival rates. Methods: This was a retrospective observational study. Relevant clinical and follow-up data of patients with colorectal cancer with peritoneal metastases treated by CRS + HIPEC in the Department of Peritoneal Cancer Surgery, Beijing Shijitan Hospital, Capital Medical University from 2007 January to 2020 December were collected and subjected to Cox proportional regression analysis. All included patients had been diagnosed with peritoneal metastases from colorectal cancer and had no detectable distant metastases to other sites. Patients who had undergone emergency surgery because of obstruction or bleeding, or had other malignant diseases, or could not tolerate treatment because of severe comorbidities of the heart, lungs, liver or kidneys, or had been lost to follow-up, were excluded. Factors studied included: (1) basic clinicopathological characteristics; (2) details of CRS+HIPEC procedures; (3) overall survival rates; and (4) independent factors that influenced overall survival; the aim being to identify independent prognostic factors and use them to construct and validate a nomogram. The evaluation criteria used in this study were as follows. (1) Karnofsky Performance Scale (KPS) scores were used to quantitatively assess the quality of life of the study patients. The lower the score, the worse the patient's condition. (2) A peritoneal cancer index (PCI) was calculated by dividing the abdominal cavity into 13 regions, the highest score for each region being three points. The lower the score, the greater is the value of treatment. (3) Completeness of cytoreduction score (CC), where CC-0 and CC-1 denote complete eradication of tumor cells and CC-2 and CC-3 incomplete reduction of tumor cells. (4) To validate and evaluate the nomogram model, the internal validation cohort was bootstrapped 1000 times from the original data. The accuracy of prediction of the nomogram was evaluated with the consistency coefficient (C-index), and a C-index of 0.70-0.90 suggest that prediction by the model was accurate. Calibration curves were constructed to assess the conformity of predictions: the closer the predicted risk to the standard curve, the better the conformity. Results: The study cohort comprised 240 patients with peritoneal metastases from colorectal cancer who had undergone CRS+HIPEC. There were 104 women and 136 men of median age 52 years (10-79 years) and with a median preoperative KPS score of 90 points. There were 116 patients (48.3%) with PCI≤20 and 124 (51.7%) with PCI>20. Preoperative tumor markers were abnormal in 175 patients (72.9%) and normal in 38 (15.8%). HIPEC lasted 30 minutes in seven patients (2.9%), 60 minutes in 190 (79.2%), 90 minutes in 37 (15.4%), and 120 minutes in six (2.5%). There were 142 patients (59.2%) with CC scores 0-1 and 98 (40.8%) with CC scores 2-3. The incidence of Grade III to V adverse events was 21.7% (52/240). The median follow-up time is 15.3 (0.4-128.7) months. The median overall survival was 18.7 months, and the 1-, 3- and 5-year overall survival rates were 65.8%, 37.2% and 25.7%, respectively. Multivariate analysis showed that KPS score, preoperative tumor markers, CC score, and duration of HIPEC were independent prognostic factors. In the nomogram constructed with the above four variables, the predicted and actual values in the calibration curves for 1, 2 and 3-year survival rates were in good agreement, the C-index being 0.70 (95% CI: 0.65-0.75). Conclusions: Our nomogram, which was constructed with KPS score, preoperative tumor markers, CC score, and duration of HIPEC, accurately predicts the survival probability of patients with peritoneal metastases from colorectal cancer treated with cytoreductive surgery plus hyperthermic intraperitoneal chemotherapy.

Establishment and validation of a nomogram-based predictive model for idiopathic aldosteronism / 中华内科杂志

Objective: To establish and validate a nomogram-based predictive model for idiopathic hyperaldosteronism (IHA). Methods: This cross-sectional study was conducted with the collected clinical and biochemical data of patients with primary aldosteronism (PA) including 249 patients with unilateral primary aldosteronism (UPA) and 107 patients with IHA, who were treated at the Department of Endocrinology of the First Affiliated Hospital of Chongqing Medical University from November 2013 to November 2022. Plasma aldosterone concentration (PAC) and plasma renin concentration (PRC) were measured by chemiluminescence. Stepwise regression analysis was applied to select the key predictors of IHA, and a nomogram-based scoring model was developed. The model was validated in another external independent cohort of patients with PA including 62 patients with UPA and 43 patients with IHA, who were diagnosed at the Department of Endocrinology, First Affiliated Hospital of Zhengzhou University. An independent-sample t test, Mann-Whitney U test, and χ2 test were used for statistical analysis. Results: In the training cohort, in comparison with the UPA group, the IHA group showed a higher serum potassium level [M(Q1, Q3), 3.4 (3.1, 3.8) mmol/L vs. 2.7 (2.1, 3.1) mmol/L] and higher PRC [4.0 (2.1, 8.2) mU/L vs. 1.5 (0.6, 3.4) mU/L] and a lower PAC post-saline infusion test (SIT) [305 (222, 416) pmol/L vs. 720 (443, 1 136) pmol/L] and a lower rate of unilateral adrenal nodules [33.6% (36/107) vs. 81.1% (202/249)]; the intergroup differences in these measurements were statistically significant (all P<0.001). Serum potassium level, PRC, PAC post-SIT, and the rate of unilateral adrenal nodules showed similar performance in the IHA group in the validation cohort. After stepwise regression analysis for all significant variables in the training cohort, a scoring model based on a nomogram was constructed, and the predictive parameters included the rate of unilateral adrenal nodules, serum potassium concentration, PAC post-SIT, and PRC in the standing position. When the total score was ≥14, the model showed a sensitivity of 0.65 and specificity of 0.90 in the training cohort and a sensitivity of 0.56 and specificity of 1.00 in the validation cohort. Conclusion: The nomogram was used to successfully develop a model for prediction of IHA that could facilitate selection of patients with IHA who required medication directly.

Establishment and validation of a nomogram for predicting prognosis of gastric neuroendocrine neoplasms based on data from 490 cases in a single center / 南方医科大学学报

OBJECTIVE@#To develop and validate a nomogram for predicting outcomes of patients with gastric neuroendocrine neoplasms (G-NENs).@*METHODS@#We retrospectively collected the clinical data from 490 patients with the diagnosis of G-NEN at our medical center from 2000 to 2021. Log-rank test was used to analyze the overall survival (OS) of the patients. The independent risk factors affecting the prognosis of G-NEN were identified by Cox regression analysis to construct the prognostic nomogram, whose performance was evaluated using the C-index, receiver-operating characteristic (ROC) curve, area under the ROC curve (AUC), calibration curve, DCA, and AUDC.@*RESULTS@#Among the 490 G-NEN patients (mean age of 58.6±10.92 years, including 346 male and 144 female patients), 130 (26.5%) had NET G1, 54 (11.0%) had NET G2, 206 (42.0%) had NEC, and 100 (20.5%) had MiNEN. None of the patients had NET G3. The numbers of patients in stage Ⅰ-Ⅳ were 222 (45.3%), 75 (15.3%), 130 (26.5%), and 63 (12.9%), respectively. Univariate and multivariate analyses identified age, pathological grade, tumor location, depth of invasion, lymph node metastasis, distant metastasis, and F-NLR as independent risk factors affecting the survival of the patients (P < 0.05). The C-index of the prognostic nomogram was 0.829 (95% CI: 0.800-0.858), and its AUC for predicting 1-, 3- and 5-year OS were 0.883, 0.895 and 0.944, respectively. The calibration curve confirmed a good consistency between the model prediction results and the actual observations. For predicting 1-year, 3-year and 5-year OS, the TNM staging system and the nomogram had AUC of 0.033 vs 0.0218, 0.191 vs 0.148, and 0.248 vs 0.197, respectively, suggesting higher net benefit and better clinical utility of the nomogram.@*CONCLUSION@#The prognostic nomogram established in this study has good predictive performance and clinical value to facilitate prognostic evaluation of individual patients with G-NEN.

Nomogram-Based Prediction of Risk of Cervical Lymph Node Metastasis in Differentiated Thyroid Carcinoma / 中国医学科学院学报

Objective To establish a nomogram for predicting the risk of cervical lymph node metastasis in differentiated thyroid carcinoma (DTC). Methods The patients with complete clinical data of DTC and cervical lymph node ultrasound and diagnosed based on pathological evidence from January 2019 to December 2021 were assigned into a training group (n=444) and a validation group (n=125).Lasso regression was performed to screen the data with differences between groups,and multivariate Logistic regression to establish a prediction model with the factors screened out by Lasso regression.C-index and calibration chart were employed to evaluate the prediction performance of the established model. Results The predictive factors for establishing the model were lymph node short diameter≥0.5 cm,long-to-short-axis ratio<2,disappearance of lymph node hilum,cystic transformation,hyperechogenicity,calcification,and abnormal blood flow (all P<0.001).The established model demonstrated a good discriminative ability,with the C index of 0.938 (95%CI=0.926-0.961) in the training group. Conclusion The nomogram established based on the ultrasound image features of cervical lymph nodes in DTC can accurately predict the risk of cervical lymph node metastasis in DTC.

Predicting survival and prognosis of postoperative breast cancer brain metastasis: a population-based retrospective analysis / 中华医学杂志(英文版)

BACKGROUND@#Breast cancer is one of the most common cancer in women and a proportion of patients experiences brain metastases with poor prognosis. The study aimed to construct a novel predictive clinical model to evaluate the overall survival (OS) of patients with postoperative brain metastasis of breast cancer (BCBM) and validate its effectiveness.@*METHODS@#From 2010 to 2020, a total of 310 female patients with BCBM were diagnosed in The Affiliated Cancer Hospital of Xinjiang Medical University, and they were randomly assigned to the training cohort and the validation cohort. Data of another 173 BCBM patients were collected from the Surveillance, Epidemiology, and End Results Program (SEER) database as an external validation cohort. In the training cohort, the least absolute shrinkage and selection operator (LASSO) Cox regression model was used to determine the fundamental clinical predictive indicators and the nomogram was constructed to predict OS. The model capability was assessed using receiver operating characteristic, C-index, and calibration curves. Kaplan-Meier survival analysis was performed to evaluate clinical effectiveness of the risk stratification system in the model. The accuracy and prediction capability of the model were verified using the validation and SEER cohorts.@*RESULTS@#LASSO Cox regression analysis revealed that lymph node metastasis, molecular subtype, tumor size, chemotherapy, radiotherapy, and lung metastasis were statistically significantly correlated with BCBM. The C-indexes of the survival nomogram in the training, validation, and SEER cohorts were 0.714, 0.710, and 0.670, respectively, which showed good prediction capability. The calibration curves demonstrated that the nomogram had great forecast precision, and a dynamic diagram was drawn to increase the maneuverability of the results. The Risk Stratification System showed that the OS of low-risk patients was considerably better than that of high-risk patients ( P < 0.001).@*CONCLUSION@#The nomogram prediction model constructed in this study has a good predictive value, which can effectively evaluate the survival rate of patients with postoperative BCBM.

A diagnostic prediction model for hypertension in Han and Yugur population from the China National Health Survey (CNHS) / 中华医学杂志(英文版)

BACKGROUND@#The prevalence of hypertension is high among Chinese adults, thus, identifying non-hypertensive individuals at high risk for intervention will help to improve the efficiency of primary prevention strategies.@*METHODS@#The cross-sectional data on 9699 participants aged 20 to 80 years were collected from the China National Health Survey in Gansu and Hebei provinces in 2016 to 2017, and they were nonrandomly split into the training set and validation set based on location. Multivariable logistic regression analysis was performed to develop the diagnostic prediction model, which was presented as a nomogram and a website with risk classification. Predictive performances of the model were evaluated using discrimination and calibration, and were further compared with a previously published model. Decision curve analysis was used to calculate the standardized net benefit for assessing the clinical usefulness of the model.@*RESULTS@#The Lasso regression analysis identified the significant predictors of hypertension in the training set, and a diagnostic model was developed using logistic regression. A nomogram with risk classification was constructed to visualize the model, and a website ( https://chris-yu.shinyapps.io/hypertension_risk_prediction/ ) was developed to calculate the exact probabilities of hypertension. The model showed good discrimination and calibration, with the C-index of 0.789 (95% confidence interval [CI]: 0.768, 0.810) through internal validation and 0.829 (95% CI: 0.816, 0.842) through external validation. Decision curve analysis demonstrated that the model was clinically useful. The model had a higher area under receiver operating characteristic curves in training and validation sets compared with a previously published diagnostic model based on Northern China population.@*CONCLUSION@#This study developed and validated a diagnostic model for hypertension prediction in Gansu Province. A nomogram and a website were developed to make the model conveniently used to facilitate the individualized prediction of hypertension in the general population of Han and Yugur.

Establishment of a prognostic nomogram model for predicting acute renal injury in patients with moderate and severe burns / 中华危重病急救医学

OBJECTIVE@#To establish a prediction model of acute kidney injury (AKI) in moderate and severe burn patients, so as to provide basic research evidence for early identification of burn-related AKI.@*METHODS@#Patients who were admitted to the department of plastic burn surgery of the Affiliated Hospital of Southwest Medical University from November 2018 to January 2021 were selected, and their clinical characteristics, laboratory examinations and other indicators were recorded. Multivariate Logistic regression analysis was used to screen out the risk factors of AKI related to moderate and severe burns, and R software was used to establish the nomogram of moderate and severe burn patients complicated with AKI. The Bootstrap method model was used for internal verification by repeating sample for 1 000 times. Consistency index and calibration curve were used to evaluate the accuracy of the model, and the receiver operator characteristic curve (ROC curve) and the area under the curve (AUC) were used to evaluate the prediction efficiency, decision curve analysis (DCA) was used to evaluate the clinical utility of the model.@*RESULTS@#A total of 186 patients with moderate and severe burn were included, among which 54 patients suffered from AKI, and the incidence rate was 29.03%. Multivariate Logistic regression analysis showed that the total burn surface area [TBSA; odds ratio (OR) = 1.072, 95% confidence interval (95%CI) was 1.031-1.115, P = 0.001], estimated glomerular filtration rate (eGFR; OR = 0.960, 95%CI was 0.931-0.990, P = 0.010), neutrophil (NEU; OR = 1.190, 95%CI was 1.021-1.386, P = 0.026), neutrophil/lymphocyte ratio (NLR; OR = 0.867, 95%CI was 0.770-0.977, P = 0.019), D-dimer (OR = 4.603, 95%CI was 1.792-11.822, P = 0.002) were the risk factors for patients with moderate and severe burn complicated with AKI. Taking the above indexes as predictive factors, a nomogram prediction model was established, the ROC curve was plotted with AUC of 0.998 (95%CI was 0.988-1.000). Optimum threshold of ROC curve was -0.862, the sensitivity was 98.0% and the specificity was 98.2%, and the consistency index was 0.998 (95%CI was 0.988-1.000). The calibration curve showed that the prognostic nomogram model was accurate, DCA showed that most patients can benefit from this model.@*CONCLUSIONS@#The burned patients with higher TBSA, NEU, NLR, D-dimer and lower eGFR tend to suffer from AKI. The nomogram based on the above five risk factors has high accuracy and clinical value, which can be used as a predictive tool to evaluate the risk of AKI in moderate and severe burn patients.

Development and validation of a mechanical power-oriented nomogram model for predicting the risk of weaning failure in mechanically ventilated patients: an analysis using the data from MIMIC-IV / 中华危重病急救医学

OBJECTIVE@#To develop and validate a mechanical power (MP)-oriented nomogram prediction model of weaning failure in mechanically ventilated patients.@*METHODS@#Patients who underwent invasive mechanical ventilation (IMV) for more than 24 hours and were weaned using a T-tube ventilation strategy were collected from the Medical Information Mart for Intensive Care-IV v1.0 (MIMIC-IV v1.0) database. Demographic information and comorbidities, respiratory mechanics parameters 4 hours before the first spontaneous breathing trial (SBT), laboratory parameters preceding the SBT, vital signs and blood gas analysis during SBT, length of intensive care unit (ICU) stay and IMV duration were collected and all eligible patients were enrolled into the model group. Lasso method was used to screen the risk factors affecting weaning outcomes, which were included in the multivariate Logistic regression analysis. R software was used to construct the nomogram prediction model and build the dynamic web page nomogram. The discrimination and accuracy of the nomogram were assessed by receiver operator characteristic curve (ROC curve) and calibration curves, and the clinical validity was assessed by decision curve analysis (DCA). The data of patients undergoing mechanical ventilation hospitalized in ICU of the First People's Hospital of Lianyungang City and the Second People's Hospital of Lianyungang City from November 2021 to October 2022 were prospectively collected to externally validate the model.@*RESULTS@#A total of 3 695 mechanically ventilated patients were included in the model group, and the weaning failure rate was 38.5% (1 421/3 695). Lasso regression analysis finally screened out six variables, including positive end-expiratory pressure (PEEP), MP, dynamic lung compliance (Cdyn), inspired oxygen concentration (FiO2), length of ICU stay and IMV duration, with coefficients of 0.144, 0.047, -0.032, 0.027, 0.090 and 0.098, respectively. Logistic regression analysis showed that the six variables were all independent risk factors for predicting weaning failure risk [odds ratio (OR) and 95% confidence interval (95%CI) were 1.155 (1.111-1.200), 1.048 (1.031-1.066), 0.968 (0.963-0.974), 1.028 (1.017-1.038), 1.095 (1.076-1.113), and 1.103 (1.070-1.137), all P < 0.01]. The MP-oriented nomogram prediction model of weaning failure in mechanically ventilated patients showed accurate discrimination both in the model group and external validation group, with area under the ROC curve (AUC) and 95%CI of 0.832 (0.819-0.845) and 0.879 (0.833-0.925), respectively. Furthermore, its predictive accuracy was significantly higher than that of individual indicators such as MP, Cdyn, and PEEP. Calibration curves showed good correlation between predicted and observed outcomes. DCA indicated that the nomogram model had high net benefits, and was clinically beneficial.@*CONCLUSIONS@#The MP-oriented nomogram prediction model of weaning failure accurately predicts the risk of weaning failure in mechanical ventilation patients and provides valuable information for clinicians making decisions on weaning.

Preliminary exploration of clinical prediction model of severe swallowing disorder after acute ischemic stroke based on nomogram model / 中华危重病急救医学

OBJECTIVE@#To establish a predictive model for severe swallowing disorder after acute ischemic stroke based on nomogram model, and evaluate its effectiveness.@*METHODS@#A prospective study was conducted. The patients with acute ischemic stroke admitted to Mianyang Central Hospital from October 2018 to October 2021 were enrolled. Patients were divided into severe swallowing disorder group and non-severe swallowing disorder group according to whether severe swallowing disorder occurred within 72 hours after admission. The differences in general information, personal history, past medical history, and clinical characteristics of patients between the two groups were compared. The risk factors of severe swallowing disorder were analyzed by multivariate Logistic regression analysis, and the relevant nomogram model was established. The bootstrap method was used to perform self-sampling internal validation on the model, and consistency index, calibration curve, receiver operator characteristic curve (ROC curve), and decision curve were used to evaluate the predictive performance of the model.@*RESULTS@#A total of 264 patients with acute ischemic stroke were enrolled, and the incidence of severe swallowing disorder within 72 hours after admission was 19.3% (51/264). Compared with the non-severe swallowing disorder group, the severe swallowing disorder group had a higher proportion of patients aged of ≥ 60 years old, with severe neurological deficits [National Institutes of Health stroke scale (NIHSS) score ≥ 7], severe functional impairments [Barthel index, an activity of daily living functional status assessment index, < 40], brainstem infarction and lesions ≥ 40 mm (78.43% vs. 56.81%, 52.94% vs. 28.64%, 39.22% vs. 12.21%, 31.37% vs. 13.62%, 54.90% vs. 24.41%), and the differences were statistically significant (all P < 0.01). Multivariate Logistic regression analysis showed that age ≥ 60 years old [odds ratio (OR) = 3.542, 95% confidence interval (95%CI) was 1.527-8.215], NIHSS score ≥ 7 (OR = 2.741, 95%CI was 1.337-5.619), Barthel index < 40 (OR = 4.517, 95%CI was 2.013-10.136), brain stem infarction (OR = 2.498, 95%CI was 1.078-5.790) and lesion ≥ 40 mm (OR = 2.283, 95%CI was 1.485-3.508) were independent risk factors for severe swallowing disorder after acute ischemic stroke (all P < 0.05). The results of model validation showed that the consistency index was 0.805, and the trend of the calibration curve was basically consistent with the ideal curve, indicating that the model had good prediction accuracy. ROC curve analysis showed that the area under the ROC curve (AUC) predicted by nomogram model for severe swallowing disorder after acute ischemic stroke was 0.817 (95%CI was 0.788-0.852), indicating that the model had good discrimination. The decision curve showed that within the range of 5% to 90%, the nomogram model had a higher net benefit value for predicting the risk of severe swallowing disorder after acute ischemic stroke, indicating that the model had good clinical predictive performance.@*CONCLUSIONS@#The independent risk factors of severe swallowing disorder after acute ischemic stroke include age ≥ 60 years old, NIHSS score ≥ 7, Barthel index < 40, brainstem infarction and lesion size ≥ 40 mm. The nomogram model established based on these factors can effectively predict the occurrence of severe swallowing disorder after acute ischemic stroke.

Establishment and validation of a preoperative nomogram model for predicting the risk of hepatocellular carcinoma with microvascular invasion / 中华外科杂志

Objective: To establish and validate a nomogram model for predicting the risk of microvascular invasion(MVI) in hepatocellular carcinoma. Methods: The clinical data of 210 patients with hepatocellular carcinoma who underwent hepatectomy at Department of Hepatobiliary and Pancreatic Surgery,the Affiliated Hospital of Qingdao University from January 2013 to October 2021 were retrospectively analyzed. There were 169 males and 41 females, aged(M(IQR)) 57(12)years(range:30 to 80 years). The patients were divided into model group(the first 170 cases) and validation group(the last 40 cases) according to visit time. Based on the clinical data of the model group,rank-sum test and multivariate Logistic regression analysis were used to screen out the independent related factors of MVI. R software was used to establish a nomogram model to predict the preoperative MVI risk of hepatocellular carcinoma,and the validation group data were used for external validation. Results: Based on the modeling group data,the receiver operating characteristic curve was used to determine that cut-off value of DeRitis ratio,γ-glutamyltransferase(GGT) concentration,the inverse number of activated peripheral blood T cell ratio (-aPBTLR) and the maximum tumor diameter for predicting MVI, which was 0.95((area under curve, AUC)=0.634, 95%CI: 0.549 to 0.719), 38.2 U/L(AUC=0.604, 95%CI: 0.518 to 0.689),-6.05%(AUC=0.660, 95%CI: 0.578 to 0.742),4 cm(AUC=0.618, 95%CI: 0.533 to 0.703), respectively. Univariate and multivariate Logistic regression analysis showed that DeRitis≥0.95,GGT concentration ≥38.2 U/L,-aPBTLR>-6.05% and the maximum tumor diameter ≥4 cm were independent related factors for MVI in hepatocellular carcinoma patients(all P<0.05). The nomogram prediction model based on the above four factors established by R software has good prediction efficiency. The C-index was 0.758 and 0.751 in the model group and the validation group,respectively. Decision curve analysis and clinical impact curve showed that the nomogram model had good clinical benefits. Conclusions: DeRitis ratio,serum GGT concentration,-aPBTLR and the maximum tumor diameter are valuable factors for preoperative prediction of hepatocellular carcinoma with MVI. A relatively reliable nomogram prediction model could be established on them.

Establishment and Clinical Significance of Prognostic Nomogram Model for Diffuse Large B-Cell Lymphoma Based on Immunohistochemistry Markers and International Prognostic Index Scores / 中国实验血液学杂志

OBJECTIVE@#To retrospectively analyze clinical characteristics and survival time of patients with diffuse large B-cell lymphoma (DLBCL), detect prognosis-related markers, and establish a nomogram prognostic model of clinical factors combined with biomarkers.@*METHODS@#One hundred and thirty-seven patients with DLBCL were included in this study from January 2014 to March 2019 in the First Affiliated Hospital of Nanchang University. The expression of GCET1, LMO2, BCL-6, BCL-2 and MYC protein were detected by immunohistochemistry (IHC), then the influences of these proteins on the survival and prognosis of the patients were analyzed. Univariate and multivariate Cox regression analysis were used to gradually screen the prognostic factors in nomogram model. Finally, nomogram model was established according to the result of multivariate analysis.@*RESULTS@#The positive expression of GCET1 protein was more common in patients with Ann Arbor staging I/II (P =0.011). Compared with negative patients, patients with positive expression of LMO2 protein did not often show B symptoms (P =0.042), and could achieve better short-term curative effect (P =0.005). The overall survival (OS) time of patients with positive expression of LMO2 protein was significantly longer than those with negative expression of LMO2 protein (P =0.018), though the expression of LMO2 protein did not correlate with progression-free survival (PFS) (P >0.05). However, the expression of GCET1 protein had no significant correlation with OS and PFS. Multivariate Cox regression analysis showed that nomogram model consisted of 5 prognostic factors, including international prognostic index (IPI), LMO2 protein, BCL-2 protein, MYC protein and rituximab. The C-index applied to the nomogram model for predicting 4-year OS rate was 0.847. Moreover, the calibrated curve of 4-year OS showed that nomogram prediction had good agreement with actual prognosis.@*CONCLUSION@#The nomogram model incorporating clinical characteristics and IHC biomarkers has good discrimination and calibration, which provides a useful tool for the risk stratification of DLBCL.

Clinical Characteristics and Nomogram Model of Nosocomial Infection in Patients with Newly Diagnosed Multiple Myeloma / 中国实验血液学杂志

OBJECTIVE@#To explore the clinical characteristics of nosocomial infection in newly diagnosed multiple myeloma(NDMM) patients, and establish a predictive nomogram model.@*METHODS@#The clinical data of 164 patients with MM who were treated in Shanxi Bethune Hospital from January 2017 to December 2021 were retrospectively analyzed. The clinical characteristics of infection were analyzed. Infections were grouped as microbiologically defined infections and clinically defined infections. Univariate and multivariate regression models were used to analyze the risk factors of infection. A nomogram was established.@*RESULTS@#164 patients with NDMM were included in this study, and 122 patients (74.4%) were infected. The incidence of clinically defined infection was the highest (89 cases, 73.0%), followed by microbial infection (33 cases, 27.0%). Among 122 cases of infection, 89 cases (73.0%) had CTCAE grade 3 or above. The most common site of infection was lower respiratory in 52 cases (39.4%), upper respiratory tract in 45 cases (34.1%), and urinary system in 13 cases (9.8%). Bacteria(73.1%) were the main pathogens of infection. Univariate analysis showed that ECOG ≥2, ISS stage Ⅲ, C-reactive protein ≥10 mg/L, serum Creatinine ≥177 μmol/L had higher correlation with nosocomial infection in patients with NDMM. Multivariate regression analysis showed that C-reactive protein ≥10 mg/L (P＜0.001), ECOG ≥2 (P=0.011) and ISS stage Ⅲ （P=0.024） were independent risk factors for infection in patients with NDMM. The nomogram model established based on this has good accuracy and discrimination. The C-index of the nomogram was 0.779（95%CI: 0.682-0.875）. Median follow-up time was 17.5 months, the median OS of the two groups was not reached (P=0.285).@*CONCLUSION@#Patients with NDMM are prone to bacterial infection during hospitalization. C-reactive protein ≥10 mg/L, ECOG ≥2 and ISS stage Ⅲ are the risk factors of nosocomial infection in NDMM patients. The nomogram prediction model established based on this has great prediction value.

Risk factors for neonatal asphyxia and establishment of a nomogram model for predicting neonatal asphyxia in Hubei Enshi Tujia and Miao Autonomous Prefecture: a multicenter study / 中国当代儿科杂志

OBJECTIVES@#To investigate the risk factors for neonatal asphyxia in Hubei Enshi Tujia and Miao Autonomous Prefecture and establish a nomogram model for predicting the risk of neonatal asphyxia.@*METHODS@#A retrospective study was conducted with 613 cases of neonatal asphyxia treated in 20 cooperative hospitals in Enshi Tujia and Miao Autonomous Prefecture from January to December 2019 as the asphyxia group, and 988 randomly selected non-asphyxia neonates born and admitted to the neonatology department of these hospitals during the same period as the control group. Univariate and multivariate analyses were used to identify risk factors for neonatal asphyxia. R software (4.2.2) was used to establish a nomogram model. Receiver operator characteristic curve, calibration curve, and decision curve analysis were used to assess the discrimination, calibration, and clinical usefulness of the model for predicting the risk of neonatal asphyxia, respectively.@*RESULTS@#Multivariate logistic regression analysis showed that minority (Tujia), male sex, premature birth, congenital malformations, abnormal fetal position, intrauterine distress, maternal occupation as a farmer, education level below high school, fewer than 9 prenatal check-ups, threatened abortion, abnormal umbilical cord, abnormal amniotic fluid, placenta previa, abruptio placentae, emergency caesarean section, and assisted delivery were independent risk factors for neonatal asphyxia (P<0.05). The area under the curve of the model for predicting the risk of neonatal asphyxia based on these risk factors was 0.748 (95%CI: 0.723-0.772). The calibration curve indicated high accuracy of the model for predicting the risk of neonatal asphyxia. The decision curve analysis showed that the model could provide a higher net benefit for neonates at risk of asphyxia.@*CONCLUSIONS@#The risk factors for neonatal asphyxia in Hubei Enshi Tujia and Miao Autonomous Prefecture are multifactorial, and the nomogram model based on these factors has good value in predicting the risk of neonatal asphyxia, which can help clinicians identify neonates at high risk of asphyxia early, and reduce the incidence of neonatal asphyxia.

New model of PIRADS and adjusted prostatespecific antigen density of peripheral zone improves the detection rate of initial prostate biopsy: a diagnostic study / 亚洲男科学杂志(英文版)

This study explored a new model of Prostate Imaging Reporting and Data System (PIRADS) and adjusted prostate-specific antigen density of peripheral zone (aPSADPZ) for predicting the occurrence of prostate cancer (PCa) and clinically significant prostate cancer (csPCa). The demographic and clinical characteristics of 853 patients were recorded. Prostate-specific antigen (PSA), PSA density (PSAD), PSAD of peripheral zone (PSADPZ), aPSADPZ, and peripheral zone volume ratio (PZ-ratio) were calculated and subjected to receiver operating characteristic (ROC) curve analysis. The calibration and discrimination abilities of new nomograms were verified with the calibration curve and area under the ROC curve (AUC). The clinical benefits of these models were evaluated by decision curve analysis and clinical impact curves. The AUCs of PSA, PSAD, PSADPZ, aPSADPZ, and PZ-ratio were 0.669, 0.762, 0.659, 0.812, and 0.748 for PCa diagnosis, while 0.713, 0.788, 0.694, 0.828, and 0.735 for csPCa diagnosis, respectively. All nomograms displayed higher net benefit and better overall calibration than the scenarios for predicting the occurrence of PCa or csPCa. The new model significantly improved the diagnostic accuracy of PCa (0.945 vs 0.830, P < 0.01) and csPCa (0.937 vs 0.845, P < 0.01) compared with the base model. In addition, the number of patients with PCa and csPCa predicted by the new model was in good agreement with the actual number of patients with PCa and csPCa in high-risk threshold. This study demonstrates that aPSADPZ has a higher predictive accuracy for PCa diagnosis than the conventional indicators. Combining aPSADPZ with PIRADS can improve PCa diagnosis and avoid unnecessary biopsies.

Study on risk factors of abnormal pulmonary function among dust-exposed workers and prediction model / 中华劳动卫生职业病杂志

Objective: To explore the influencing factors of abnormal pulmonary function in dust-exposed workers and establish the risk prediction model of abnormal pulmonary function. Methods: In April 2021, a total of 4255 dust exposed workers from 47 enterprises in 2020 were included in the study. logistic regression was used to analyze the influencing factors of abnormal pulmonary function in dust-exposed workers, and the corresponding nomogram prediction model was established. The model was evaluated by ROC curve, Calibrationpolt and decision analysis curve. Results: logistic regression analysis showed that age (OR=1.03, 95%CI=1.02~1.05, P<0.001) , physical examination type (OR=4.52, 95%CI=1.69~12.10, P=0.003) , dust type (Comparison with coal dust, Cement dust, OR=3.45, 95%CI=1.45~8.18, P=0.005, Silica dust (OR=2.25, 95%CI=1.01~5.03, P=0.049) , blood pressure (OR=1.63, 95%CI=1.22~2.18, P=0.001) , creatinine (OR=0.08, 95%CI=0.05~0.12, P<0.001) , daily exposure time (OR=1.06, 95%CI=1.10~1.12, P=0.034) and total dust concentration (OR=1.29, 95%CI=1.08~1.54, P=0.005) were the influencing factors of abnormal pulmonary function. The area under the ROC curve of risk prediction nomogram model was 0.764. The results of decision analysis curve showed that the nomogram model had reference value in the prevention and intervention of abnormal pulmonary function when the threshold probability exceeded 0.05. Conclusion: The accuracy ofthe nomogram model constructed by logistic regression werewell in predicting the risk of abnormal lung function of dust-exposed workers.