Objective To investigate the risk factors for early neurological deterioration in mild acute ischemic stroke，to construct a clinical predictive model，and to perform internal validation of this model. Methods A retrospective analysis was performed for 739 patients with mild acute ischemic stroke who were admitted to Department of Neurology，Kuntong Hospital of Zunhua，from October 2020 to December 2023，and they were randomly divided into a training set with 534 patients （72.3%） and a validation set with 205 patients （27.7%） at a ratio of 7∶3. Univariate and multivariate logistic regression analyses were performed for the training set to determine the risk factors for early neurological deterioration in mild acute ischemic stroke. A clinical predictive model was constructed，and internal validation was performed in terms of discriminatory ability，calibration，and clinical decision making. A nomogram was plotted. Results The multivariate logistic regression analysis showed that female sex （OR=1.87，95% CI 1.14~3.09，P=0.014），time window ≤6 hours （OR=3.10，95%CI 1.56~6.19，P=0.001），a baseline NIHSS score of 2 points （OR=3.72，95%CI 1.30~10.61，P=0.014），a baseline NIHSS score of 3 points （OR=4.24，95%CI 1.45~12.35，P=0.008），a TOAST classification of large artery atherosclerosis （OR=3.88，95%CI 2.20~6.83，P<0.001），and the responsible arteries of the basilar artery，the middle cerebral artery，and the internal carotid artery （OR=8.39，95%CI 2.28~30.85，P=0.001； OR=6.22，95%CI 1.78~21.71，P=0.004； OR=5.38，95%CI 1.15~25.13，P=0.032） were independent risk factors for early neurological deterioration in mild acute ischemic stroke. The clinical predictive model constructed showed a moderate discriminatory ability （AUC>0.7），good calibration （P>0.05） in the Hosmer-Lemeshow goodness-of-fit test），and good clinical benefits in both the training set and the validation set. Conclusion This clinical predictive model can effectively predict the onset of early neurological deterioration in mild acute ischemic stroke and guide clinicians to make decisions，and therefore，it holds promise for clinical application.
Nomograms

Objective To investigate the influencing factors for white matter hyperintensity （WMH） in middle-aged and elderly patients with hypertension， and to establish and verity a nomogram prediction model. Methods A total of 198 middle-aged and elderly patients with hypertension and WMH who were hospitalized in our hospital from January 2022 to April 2023 were enrolled. Related clinical data were analyzed， and related data were recorded. A binary logistic regression analysis was used to investigate the independent risk factors for WMH and establish a nomogram， and the receiver operating characteristic （ROC） curve and the calibration curve were used to evaluate the diagnostic efficacy of the nomogram. Results Age， course of hypertension， cystatin C， homocysteine，red blood cell distribution width， and cognitive impairment were the independent influencing factors for WMH in the middle-aged and elderly patients with hypertension. The nomogram established showed good diagnostic efficacy （AUC=0.815， 95% CI 0.756~0.874，P<0.001） and calibration ability （C index=0.794）. Conclusion The nomogram established in this study has a good predictive ability for moderate-to-severe WMH in middle-aged and elderly patients with hypertension and can provide certain help for clinical workers.
Nomograms

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.
Humans ; Female ; HIV Infections ; Acquired Immunodeficiency Syndrome ; Infectious Disease Transmission, Vertical ; Nomograms ; Selenoproteins/genetics*

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.
Humans ; Keloid/genetics* ; Nomograms ; Algorithms ; Calibration ; Machine Learning

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.
Male ; Female ; Humans ; Adult ; Middle Aged ; Arthroplasty, Replacement, Hip/adverse effects* ; Periprosthetic Fractures/surgery* ; Nomograms ; Reoperation/adverse effects* ; Risk Factors ; Osteoporosis/surgery* ; Retrospective Studies ; Hip Prosthesis

BACKGROUND: In recent years, immunotherapy represented by programmed cell death 1 (PD-1)/programmed cell death ligand 1 (PD-L1) immunosuppressants has greatly changed the status of non-small cell lung cancer (NSCLC) treatment. PD-L1 has become an important biomarker for screening NSCLC immunotherapy beneficiaries, but how to easily and accurately detect whether PD-L1 is expressed in NSCLC patients is a difficult problem for clinicians. The aim of this study was to construct a Nomogram prediction model of PD-L1 expression in NSCLC patients based on 18F-fluorodeoxy glucose (18F-FDG) positron emission tomography/conputed tomography (PET/CT) metabolic parameters and to evaluate its predictive value. METHODS: Retrospective collection of 18F-FDG PET/CT metabolic parameters, clinicopathological information and PD-L1 test results of 155 NSCLC patients from Inner Mongolia People's Hospital between September 2016 and July 2021. The patients were divided into the training group (n=117) and the internal validation group (n=38), and another 51 cases of NSCLC patients in our hospital between August 2021 and July 2022 were collected as the external validation group according to the same criteria. Then all of them were categorized according to the results of PD-L1 assay into PD-L1+ group and PD-L1- group. The metabolic parameters and clinicopathological information of patients in the training group were analyzed by univariate and binary Logistic regression, and a Nomogram prediction model was constructed based on the screened independent influencing factors. The effect of the model was evaluated by receiver operating characteristic (ROC) curve, calibration curve and decision curve analysis (DCA) in both the training group and the internal and external validation groups. RESULTS: Binary Logistic regression analysis showed that metabolic tumor volume (MTV), gender and tumor diameter were independent influences on PD-L1 expression. Then a Nomogram prediction model was constructed based on the above independent influences. The ROC curve for the model in the training group shows an area under the curve (AUC) of 0.769 (95%CI: 0.683-0.856) with an optimal cutoff value of 0.538. The AUC was 0.775 (95%CI: 0.614-0.936) in the internal validation group and 0.752 (95%CI: 0.612-0.893) in the external validation group. The calibration curves were tested by the Hosmer-Lemeshow test and showed that the training group (χ2=0.040, P=0.979), the internal validation group (χ2=2.605, P=0.271), and the external validation group (χ2=0.396, P=0.820) were well calibrated. The DCA curves show that the model provides clinical benefit to patients over a wide range of thresholds (training group: 0.00-0.72, internal validation group: 0.00-0.87, external validation group: 0.00-0.66). CONCLUSIONS The Nomogram prediction model constructed on the basis of 18F-FDG PET/CT metabolic parameters has greater application value in predicting PD-L1 expression in NSCLC patients.
Humans ; Carcinoma, Non-Small-Cell Lung/drug therapy* ; Positron Emission Tomography Computed Tomography ; Lung Neoplasms/drug therapy* ; Fluorodeoxyglucose F18/therapeutic use* ; Nomograms ; Retrospective Studies ; B7-H1 Antigen/metabolism* ; Glucose/therapeutic use* ; Positron-Emission Tomography/methods*

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.
Infant ; Male ; Infant, Newborn ; Humans ; Pregnancy ; Female ; Bronchopulmonary Dysplasia/epidemiology* ; Infant, Premature ; Hypertension, Pulmonary/epidemiology* ; Retrospective Studies ; Nomograms ; Ductus Arteriosus, Patent/epidemiology* ; Pneumonia, Ventilator-Associated/complications* ; Cesarean Section/adverse effects* ; Gestational Age ; Risk Factors ; Sepsis

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.
Female ; Humans ; Male ; Carcinoma, Hepatocellular/pathology* ; Liver Neoplasms/pathology* ; Neoplasm Invasiveness ; Nomograms ; Retrospective Studies ; Risk Factors ; Adult ; Middle Aged ; Aged ; Aged, 80 and over

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.
Humans ; Dust/analysis* ; Lung ; Nomograms ; Risk Factors ; ROC Curve

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.
Male ; Humans ; Prostate/pathology* ; Prostate-Specific Antigen/analysis* ; Prostatic Neoplasms/diagnostic imaging* ; Biopsy ; Nomograms ; Retrospective Studies