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 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

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.
Humans ; Male ; Female ; Middle Aged ; Aged ; Nomograms ; Retrospective Studies ; Prognosis ; Neoplasm Staging ; Stomach Neoplasms/pathology*

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 investigate the correlation between the human epidermal growth factor receptor-2-related genes (HRGs) and survival prognosis of bladder cancer and to construct a predictive model for survival prognosis of bladder cancer patients based on HRGs. METHODS: HRGs in bladder cancer were found by downloading bladder tumor tissue mRNA sequencing data and clinical data from the cancer genome atlas (TCGA), downloading HER-2 related genes from the molecular signatures database (MsigDB), and crossing the two databases. Further identifying HRGs associated with bladder cancer survival (P < 0.05) by using single and multi-factor Cox regression analysis and constructing HRGs risk score model (HRSM), the bladder cancer patients were categorized into high-risk and low-risk groups accor-ding to the median risk score. Survival analysis of the patients in high- and low-risk groups was conducted using R language and correlation of HRGs with clinical characteristics. A multi-factor Cox regression analysis was used to verify the independent factors affecting the prognosis of the patients with bladder cancer. The area under the curve (AUC) of the receiver operating characteristic curve (ROC) of HRSM was calculated, and a nomogram was constructed for survival prediction of the bladder cancer patients. Analysis of HRSM and patient immune cell infiltration correlation was made using the TIMER database. RESULTS: A total of 13 HRGs associated with patient survival were identified in this study. Five genes (BTC, CDC37, EGF, PTPRR and EREG) were selected for HRSM by multi-factor Cox regression analysis. The 5-year survival rate of the bladder cancer patients in the high-risk group was significantly lower than that of the patients in the low-risk group. High expression of PTPRR was found to be significantly and negatively correlated with tumor grade and stage by clinical correlation analysis, while EREG was found to be the opposite; Increased expression of EGF was associated with high grade, however, the high expression ofCDC37showed the opposite result. And no significant correlation was found between BTC expression and clinical features. Correlation analysis of HRSM with immune cells revealed a positive correlation between risk score and infiltration of dendritic cells, CD8⁺T cells, CD4⁺T cells, neutrophils and macrophages. CONCLUSION HRGs have an important role in the prognosis of bladder cancer patients and may serve as new predictive biomarkers and potential targets for treatment.
Humans ; Epidermal Growth Factor ; Prognosis ; Urinary Bladder Neoplasms/genetics* ; Nomograms ; Urinary Bladder

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.
Infant, Newborn ; Humans ; Male ; Pregnancy ; Female ; Nomograms ; Retrospective Studies ; Cesarean Section ; Risk Factors ; Asphyxia Neonatorum/etiology*

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.
Humans ; Nomograms ; Multiple Myeloma/metabolism* ; Prognosis ; Retrospective Studies ; Cross Infection ; C-Reactive Protein