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Resumo Fundamento: O no-reflow (NR) é caracterizado por uma redução aguda no fluxo coronário que não é acompanhada por espasmo coronário, trombose ou dissecção. O índice prognóstico inflamatório (IPI) é um novo marcador que foi relatado como tendo um papel prognóstico em pacientes com câncer e é calculado pela razão neutrófilos/linfócitos (NLR) multiplicada pela razão proteína C reativa/albumina. Objetivo: Nosso objetivo foi investigar a relação entre IPI e NR em pacientes com infarto do miocárdio com supradesnivelamento do segmento ST (IAMCSST) submetidos a intervenção coronária percutânea primária (ICPp). Métodos: Um total de 1.541 pacientes foram incluídos neste estudo (178 com NR e 1.363 com refluxo). A regressão penalizada LASSO (Least Absolute Shrinkage and Select Operator) foi usada para seleção de variáveis. Foi criado um nomograma baseado no IPI para detecção do risco de desenvolvimento de NR. A validação interna com reamostragem Bootstrap foi utilizada para reprodutibilidade do modelo. Um valor de p bilateral <0,05 foi aceito como nível de significância para análises estatísticas. Resultados: O IPI foi maior em pacientes com NR do que em pacientes com refluxo. O IPI esteve associado de forma não linear com a NR. O IPI apresentou maior capacidade discriminativa do que o índice de imunoinflamação sistêmica, NLR e relação PCR/albumina. A adição do IPI ao modelo de regressão logística multivariável de base melhorou a discriminação e o efeito do benefício clínico líquido do modelo para detecção de pacientes com NR, e o IPI foi a variável mais proeminente no modelo completo. Foi criado um nomograma baseado no IPI para prever o risco de NR. A validação interna do nomograma Bootstrap mostrou uma boa capacidade de calibração e discriminação. Conclusão: Este é o primeiro estudo que mostra a associação de IPI com NR em pacientes com IAMCSST submetidos a ICPp.
Abstract Background: No-reflow (NR) is characterized by an acute reduction in coronary flow that is not accompanied by coronary spasm, thrombosis, or dissection. Inflammatory prognostic index (IPI) is a novel marker that was reported to have a prognostic role in cancer patients and is calculated by neutrophil/lymphocyte ratio (NLR) multiplied by C-reactive protein/albumin ratio. Objective: We aimed to investigate the relationship between IPI and NR in ST-segment elevation myocardial infarction (STEMI) patients undergoing primary percutaneous coronary intervention (pPCI). Methods: A total of 1541 patients were enrolled in this study (178 with NR and 1363 with reflow). Lasso panelized shrinkage was used for variable selection. A nomogram was created based on IPI for detecting the risk of NR development. Internal validation with Bootstrap resampling was used for model reproducibility. A two-sided p-value <0.05 was accepted as a significance level for statistical analyses. Results: IPI was higher in patients with NR than in patients with reflow. IPI was non-linearly associated with NR. IPI had a higher discriminative ability than the systemic immune-inflammation index, NLR, and CRP/albumin ratio. Adding IPI to the baseline multivariable logistic regression model improved the discrimination and net-clinical benefit effect of the model for detecting NR patients, and IPI was the most prominent variable in the full model. A nomogram was created based on IPI to predict the risk of NR. Bootstrap internal validation of nomogram showed a good calibration and discrimination ability. Conclusion: This is the first study that shows the association of IPI with NR in STEMI patients who undergo pPCI.
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ObjectiveTo investigate the influencing factors for the clinical outcome of patients with drug-induced liver injury （DILI）， and to establish a nomogram prediction model for validation. MethodsA retrospective analysis was performed for the general information and laboratory data of 188 patients with DILI who were admitted to Heilongjiang Provincial Hospital Affiliated to Harbin Institute of Technology from January 2017 to December 2022， and according to their clinical outcome， they were divided into good outcome group with 146 patients and poor outcome group with 42 patients. The independent-samples t test was used for comparison of normally distributed continuous data between two groups， and the Mann-Whitney U test was used for comparison of non-normally distributed continuous data between two groups； the chi-square test was used for comparison of categorical data between two groups. Univariate and multivariate Logistic regression analyses were used to investigate the independent influencing factors for the clinical outcome of DILI patients. R Studio 4.1.2 software was used to establish a nomogram model， and calibration curve， receiver operating characteristic （ROC） curve， and decision curve analysis （DCA） were used to perform internal validation. ResultsThe univariate Logistic regression analysis showed that liver biopsy for the diagnosis of DILI， platelet count， cholinesterase， albumin， prothrombin time activity， IgM， and IgG were associated with adverse outcomes in patients with DILI. The multivariate Logistic regression analysis showed that liver biopsy for the diagnosis of DILI （odds ratio ［OR］=0.072， 95% confidence interval ［CI］： 0.022 — 0.213， P<0.001）， clinical classification （OR=0.463， 95%CI： 0.213 — 0.926， P=0.039）， alanine aminotransferase （OR=0.999， 95%CI： 0.998 — 1.000， P=0.025）， prothrombin time activity （OR=0.973， 95%CI： 0.952 — 0.993， P=0.011）， and IgM （OR=1.456， 95%CI： 1.082 — 2.021， P=0.015） were independent influencing factors for clinical outcome in patients with DILI. The nomogram prediction model was established， and after validation， the calibration curve was close to the reference curve. The area under the ROC curve was 0.829， and the DCA curve showed that the model had good net clinical benefit. ConclusionThe nomogram prediction model established in this study has good clinical calibration， discriminative ability， and application value in evaluating the clinical outcome of patients with DILI.
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ObjectiveTo investigate the value of aspartate aminotransferase-to-platelet ratio index （APRI） and platelet-albumin-bilirubin （PALBI） score in predicting the risk of esophagogastric variceal bleeding in patients with liver cirrhosis. MethodsA total of 119 patients with liver cirrhosis who were admitted to The First Affiliated Hospital of Soochow University from May 2021 and June 2022 were enrolled， and clinical data， routine blood test results， serum biochemistry， and coagulation test results were collected from all patients. According to the presence or absence of esophagogastric variceal bleeding， the patients were divided into non-bleeding group with 59 patients and bleeding group with 60 patients， and a comparative analysis was performed for the two groups. The independent samples t-test was used for comparison of normally distributed continuous data between two groups， and the Mann-Whitney U test was used for comparison of non-normally distributed continuous data between two groups； the chi-squared test or the Fisher’s exact test was used for comparison of categorical data between groups. The multivariate Logistic regression analysis was used to identify the independent risk factors for esophagogastric variceal bleeding in patients with liver cirrhosis and establish a nomogram predictive model. ResultsThe male patients accounted for 75.00% in the bleeding group and 40.68% in the non-bleeding group， and there was a significant difference in sex composition between the two groups （χ2=14.384， P<0.001）. Chronic hepatitis B was the main etiology in both the bleeding group and the non-bleeding group （53.33% vs 38.98%）， and there was no significant difference in composition ratio between the two groups （χ2=2.464， P=0.116）. Compared with the non-bleeding group， the bleeding group had a significantly higher activity of AT-IIIA （t=3.329， P=0.001） and significantly lower levels of PLT， TBil， Ca， TC， and TT （all P<0.05）. There were significant differences in APRI and PALBI between the two groups （χ2=6.175 and 19.532， both P<0.05）. The binary logistic regression analysis showed that APRI （odds ratio ［OR］=0.309， 95% confidence interval ［CI］： 0.109 — 0.881， P=0.028）， PALBI （OR=7.667， 95%CI： 2.005 — 29.327， P=0.003）， Ca （OR=0.001， 95%CI： 0.000 — 0.141， P=0.007）， TC （OR=0.469， 95%CI： 0.226 — 0.973， P=0.042）， and TT （OR=0.599， 95%CI： 0.433 — 0.830， P=0.002） were independent influencing factors for esophagogastric variceal bleeding in liver cirrhosis. A nomogram model was established based on the above factors and had an index of concordance of 0.899 and a well-fitted calibration curve. ConclusionAPRI and PALBI have a good value in predicting esophagogastric variceal bleeding in patients with liver cirrhosis， and the nomogram model established based on this study can predict the incidence rate of esophagogastric variceal bleeding in patients with liver cirrhosis.
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Objective@#To explore the relationship between lifestyle and myopia and construct Nomogram model to predict myopia risk among primary school students in Tianjin, so as to provide a scientific basis for precision myopia prevention and control.@*Methods@#From April to July of 2022, a census method was used to conduct vision testing and lifestyle related questionnaires among 373 180 primary school students in 15 districts of Tianjin. The relationship between lifestyle and myopia was analyzed by the multivariate Logistic regression, and a nomogram prediction model was constructed to predict myopia risk.@*Results@#The detection rate of myopia among primary school students in Tianjin was 37.6%. The results of the multivariate Logistic regression showed that daily outdoor activity time of 1-2 h ( OR =0.94) and >2 h ( OR =0.84), time of using daily electronic devices of >2 h ( OR =1.03), daily paper materials reading and writing time of 1-2 h ( OR =1.02) and >2 h ( OR =1.09), weekly fresh vegetable intake of 2-6 times ( OR =0.93) and ≥7 times ( OR =0.88) were statistically correlated with myopia ( P <0.01). The Nomogram prediction model showed that the factors associated with myopia were grade, family history of myopia, gender, daily outdoor activity time, weekly frequency of fresh vegetable intake, daily paper materials reading and writing time, and time of using daily electronic devices time.@*Conclusions@#The lifestyle of primary school students in Tianjin is associated with myopia. The constructed nomogram model could provide a scientific basis for identifying key intervention populations for myopia prevention and taking targeted prevention and control measures.
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ABSTRACT Purpose: To investigate the risk factors associated with adverse outcomes in patients with residual stones after percutaneous nephrolithotomy (PCNL) and to establish a nomogram to predict the probability of adverse outcomes based on these risk factors. Methods: We conducted a retrospective review of 233 patients who underwent PCNL for upper urinary tract calculi and had postoperative residual stones. The patients were divided into two groups according to whether adverse outcomes occurred, and the risk factors for adverse outcomes were explored by univariate and multivariate analyses. Finally, we created a nomogram for predicting the risk of adverse outcomes in patients with residual stones after PCNL. Results: In this study, adverse outcomes occurred in 125 (53.6%) patients. Multivariate logistic regression analysis indicated that the independent risk factors for adverse outcomes were the diameter of the postoperative residual stones (P < 0.001), a positive urine culture (P = 0.022), and previous stone surgery (P = 0.004). The above independent risk factors were used as variables to construct the nomogram. The nomogram model was internally validated. The calculated concordance index was 0.772. The Hosmer- Lemeshow goodness-of-fit test was performed (P > 0.05). The area under the ROC curve of this model was 0.772. Conclusions: Larger diameter of residual stones, positive urine culture, and previous stone surgery were significant predictors associated with adverse outcomes in patients with residual stones after PCNL. Our nomogram could help to assess the risk of adverse outcomes quickly and effectively in patients with residual stones after PCNL
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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.
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Humains , Nomogrammes , Myélome multiple/métabolisme , Pronostic , Études rétrospectives , Infection croisée , Protéine C-réactiveRésumé
Objective To investigate the value of HALP score in evaluating the prognosis of patients with hepatocellular carcinoma (HCC) after hepatectomy and whether the nomogram based on HALP score could effectively predict the postoperative survival of patients. Methods A retrospective study was performed for the clinical data of 253 HCC patients who underwent surgical treatment in Department of Hepatobiliary Surgery, The Affiliated Hospital of Southwest Medical University, from July 2013 to March 2020. The receiver operating characteristic (ROC) curve was plotted to calculate the optimal cut-off values of HALP score and other related indicators; the chi-square test was used to investigate the association between HALP score and clinicopathological features; the Kaplan-Meier method was used to plot survival curves, and the Log-rank test method was used for comparison. The univariate and multivariate Cox regression analyses were used to investigate the association of HALP score and other clinical parameters with the prognosis of patients. R3.6 was used to establish a nomogram; C-index and calibration curve were used to evaluate the predictive ability of the nomogram, and net reclassification index (NRI) and integrated discrimination improvement (IDI) were used to compare predictive ability between the nomogram model and the conventional model. Results The Kaplan-Meier analysis showed that the high HALP group had significantly better overall survival (OS) and recurrence-free survival (RFS) than the low HALP group ( P < 0.001). The univariate Cox regression analysis showed that white blood cell count, gamma-glutamyl transpeptidase (GGT), alkaline phosphatase (ALP), alpha-fetoprotein (AFP), surgical approach, microvascular invasion, TNM stage, degree of tumor differentiation, HALP, aspartate aminotransferase (AST)/alanine aminotransferase (ALT) ratio, neutrophil-to-lymphocyte ratio (NLR), and monocyte-to-lymphocyte ratio (MLR) were significantly associated with OS (all P < 0.05). The variables with statistical significance in the univariate Cox regression analysis were included in the multivariate Cox regression analysis, and the results showed that ALP, AST/ALT ratio, ALP, AFP, degree of tumor differentiation, and TNM stage were independent influencing factors for OS after surgery in HCC patients (all P < 0.05). The univariate Cox regression analysis showed that GGT, ALP, AFP, microvascular invasion, TNM stage, degree of tumor differentiation, HALP, AST/ALT ratio, NLR, and MLR were significantly associated with RFS (all P < 0.05), and the multivariate Cox regression analysis showed that HALP, AST/ALT ratio, NLR, ALP, AFP, and TNM stage were independent influencing factors for RFS after surgery in HCC patients (all P < 0.05). The nomograms for OS and RFS of HCC patients were established based on the multivariate analysis. The nomogram for OS had a C-index of 0.732 (95% confidence interval [ CI ]: 0.691-0.774) and an area under the ROC curve of 0.795, 0.791, and 0.775, respectively, in predicting 1-, 3-, and 5-year survival rates, and the nomogram for RFS had a C-index of 0.677 (95% CI : 0.637-0.717) and an area under the ROC curve of 0.742, 0.733, and 0.716, respectively, in predicting 1-, 3-, and 5-year survival rates. The calibration curves of 1-, 3-, and 5-year OS were well fitted to those of 1-, 3-, and 5-year RFS. Conclusion A low level of HALP before surgery is a predictive factor for poor long-term prognosis in HCC patients undergoing surgical treatment, and the nomogram model based on HALP score is superior to the BCLC staging model and can better predict the prognosis of HCC.
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Objective@#To explore the influencing factors of exposure to campus bullying among junior and senior school students, and to establish a column line diagram model for risk prediction, while providing a theoretical basis for campus bullying prevention and control in secondary schools.@*Methods@#A total of 22 034 junior and senior school students were selected via direct sampling technique from September to November 2021 in 13 cities in Jiangsu Province, China, and questionnaires were administered using the Student Health Behavior Questionnaire. The Chi squared test and multifactor Logistic regression analysis were used to derive the influencing factors of exposure to campus bullying, and a column line graph prediction model was drawn.@*Results@#A total of 540 students reported that they had experienced campus bullying, with a prevalence rate of 2.45%. Being in a non conventional family ( OR =1.30,95% CI =1.02-1.65), overweight/obesity ( OR =1.35,95% CI =1.09-1.67), scolding by parents in the past 30 days ( OR =2.27,95% CI =1.82-2.84), cigarette smoking in the past 30 days ( OR =1.54,95% CI =1.11-2.15), Internet addiction ( OR =2.03，95% CI =1.34-3.08), and depressive symptoms( OR =5.24，95% CI =4.16-6.61), all of which were positively correlated with exposure to campus bullying among junior and senior school students ( P <0.05). Furthermore, the following factors were negatively associated with junior and senior school students protection from campus bullying in female students ( OR = 0.58 , 95% CI =0.46-0.72)，senior school students ( OR =0.68，95% CI =0.54-0.83), eating breakfast sometimes ( OR =0.37，95% CI = 0.22 -0.62), and eating breakfast everyday ( OR =0.28，95% CI =0.17-0.49) ( P <0.05). The column line graph established based on the above influencing factors had an area under the curve of 0.792 (95% CI =0.769-0.815), and the calibration curve showed that the predicted value was basically the same as the measured value.@*Conclusions@#Non conventional families, overweight/obesity, male students, junior school students, scolding by parents, cigarette smoking, Internet addiction, and depressive symptoms are correlated with school bullying among middle school students. The predictive model constructed in the study can provide an effective basis to predict the risk of school bullying and facilitate the implementation of proactive interventions for junior and senior school students.
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ObjectiveTo establish an early predictive model using serological markers based on LASSO regression for predicting the possibility of HBsAg clearance in HBeAg-negative chronic hepatitis B （CHB） patients treated with pegylated interferon α-2b （PEG-IFNα-2b）， and to investigate the diagnostic value of the model. MethodsA total of 136 HBeAg-negative CHB patients who received PEG-IFNα-2b treatment in the Affiliated Hospital of Xuzhou Medical University from April 2020 to October 2021 were enrolled， among whom 47 received PEG-IFNα-2b for the first time （previously untreated） and 89 received PEG-IFNα-2b after 48 weeks of treatment with nucleos（t）ide analogues （treatment-experienced）. The patients were randomly assigned to a training set with 95 patients and a validation set with 41 patients at a ratio of 7∶3， and related data were collected for both groups， including virological markers， routine blood test results， and liver function at baseline and week 12 of treatment. According to HBsAg status at week 48 of treatment， the patients were divided into seroconversion group with 38 patients and non-seroconversion group with 98 patients. The independent-samples t test was used for comparison of normally distributed continuous data between two groups， and the Wilcoxon rank-sum test was used for comparison of non-normally distributed continuous data between two groups； the chi-square test was used for comparison of categorical variables between two groups. The LASSO regression analysis and univariate and multivariate logistic regression analyses were used to establish a nomogram model； the receiver operating characteristic （ROC） curve was used to assess its predictive ability， and the area under the ROC curve （AUC） was used for comparison of predictive value. ResultsIn the training set， 95 HBeAg-negative CHB patients were treated with PEG-IFNα-2b for 48 weeks， among whom there were 27 patients in the seroconversion group and 68 in the non-seroconversion group. The univariate Logistic regression analysis， with P<0.2 as the criterion for screening， showed that 9 indicators were included in the LASSO regression analysis， i.e.， sex， baseline HBV DNA level， the reduction in HBV DNA in 0 — 12 weeks， baseline HBsAg level， the reduction in HBsAg in 0 — 12 weeks， baseline aspartate aminotransferase （AST） level， the reduction in AST in 0 — 12 weeks， baseline alanine aminotransferase （ALT） level， and the reduction in ALT in 0 — 12 weeks. The LASSO regression analysis showed that sex， baseline HBsAg level， the reduction in HBsAg in 0 — 12 weeks， and the reduction in ALT in 0 — 12 weeks were non-zero variables and were included in the multivariate Logistic regression analysis. The multivariate Logistic regression analysis obtained 4 independent predictive factors， i.e.， sex （odds ratio ［OR］=5.38， 95% confidence interval ［CI］： 1.11 — 34.21， P=0.049）， baseline HBsAg level （OR=0.12， 95%CI： 0.04 — 0.26， P<0.001）， the reduction in HBsAg in 0 — 12 weeks （OR=5.54， 95%CI： 1.97 — 19.18， P=0.003）， and the reduction in ALT in 0 — 12 weeks （OR=0.99， 95%CI： 0.97 — 1.00， P=0.039）. A nomogram model was established based on the results of the multivariate Logistic regression analysis， and the ROC curve was used to assess the predictive value of this nomogram model. This nomogram model had an AUC of 0.934 （95%CI： 0.886 — 0.981） in the training set and an AUC of 0.921 （95%CI： 0.838 — 1.000） in the validation set. In addition， the results of calibration curve and decision curve analyses showed that the model had good consistency and accuracy. ConclusionBased on general information and serological markers， the LASSO regression analysis is used to establish a nomogram model using sex， baseline HBsAg level， the reduction in HBsAg in 0 — 12 weeks， and the reduction in ALT in 0 — 12 weeks， and this model can be used to predict the probability of achieving HBsAg clearance in HBeAg-negative CHB patients treated with PEG-IFNα-2b， which provides important reference and theoretical support for the clinical treatment of patients.
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Objective@#To explore the related factors of myopia among children and adolescents in Yunnan Province, and to predict and evaluate the influencing factors, so as to provide a scientific theoretical basis for the prevention and control of myopia.@*Methods@#From March 9 to 14, 2023, 848 students from 6 primary and secondary schools in Dali and Lijiang of Yunnan Province were selected by multi stage stratified random cluster sampling method for visual acuity detection and questionnaire survey on myopia related factors. Multivariate Logistic regression analysis was used to establish a Nomogram prediction model for the selected influencing factors.@*Results@#The overall myopia rate of the respondents was 68.3%, the myopia rate of boys (63.4%) was lower than that of girls (72.9%), and the myopia rate of primary school students (46.7%) was lower than that of junior high school students (81.1%), and the difference was statistically significant（ χ 2=8.71, 108.07, P <0.05). Daily eye exercises, activities outside the teaching building during recess, having daily sleep time of 7-9 and >9 h, having both parents without myopia were negatively correlated with the occurrence of myopia in children and adolescents in Yunnan Province ( OR=0.64, 0.63, 0.56, 0.28, 0.48, P < 0.05 ). The reading and writing time after school ≥3 h per day and parents unrestricted time to play video games were positively correlated with myopia ( OR=1.94, 1.78, P <0.05). Based on the influencing factors, a Nomogram prediction model was established to quantitatively evaluate the risk of myopia. The results showed that greater risk for myopia was associated with sleep duration, parental history of myopia, and the time spent reading and writing after school every day.@*Conclusion@#Both genetic factors and environmental factors are related to myopia in children and adolescents. The prediction model of nomogram is beneficial for screening high risk factors of myopia and taking corresponding prevention and treatment measures.
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Objective:To investigate the factors influencing the prognosis of hepatitis B-related hepatocellular carcinoma treated with programmed death receptor 1 (PD-1) inhibitors, and to construct a prognostic nomogram model for these patients and evaluate its clinical significances.Methods:The clinical data of 121 patients with hepatitis B-related hepatocellular carcinoma treated with PD-1 inhibitors at the First Affiliated Hospital of Xinxiang Medical College from July 2018 to July 2021 were retrospectively analyzed. Follow-up was performed from the beginning of PD-1 inhibitor use, and the Kaplan-Meier method was used to analyze the overall survival of patients. The variables screened by the univariate Cox proportional hazards model analysis and variables clinically believed to be related to the prognosis were included in the multivariate Cox proportional hazards model for overall survival, and the stepwise regression method was used to screen the independent factors influencing overall survival. Based on the independent influencing factors of overall survival, R 3.5.1 software was used to construct a prognostic nomogram model for overall survival of hepatitis B-related hepatocellular carcinoma treated with PD-1 inhibitors. Calibration curve was used to the consistency of model prediction and practice. The Harrell consistency index and receiver operating characteristic (ROC) curve (with imaging diagnosis as the gold standard) were used to analyze the efficacy of model in predicting the 1-year and 2-year overall survival rates.Results:The median follow-up time of 121 patients was 12.40 months, and the median overall survival time was 14.30 months, with overall survival rates of 82.60% and 62.30% at 6 and 12 months. Multivariate Cox regression analysis showed that albumin (ALB) ( HR = 0.946, 95% CI 0.901-0.992), international normalized ratio (INR) ( HR = 32.034, 95% CI 5.046-203.362), aspartate aminotransferase (AST) ( HR = 1.010, 95% CI 1.007-1.012) were independent influencing factors for overall survival of patients. According to the three factors, a prognostic nomogram model for hepatitis B-related hepatocellular carcinoma treated with PD-1 inhibitors was constructed. The slope of the calibration curve of the model predicting 1-year and 2-year overall survival rates was close to 1. The Harrell consistency index of the nomogram model was 0.809 (95% CI 0.760-0.858). ROC curve analysis showed that the area under the curve (AUC) of the nomogram model predicting 1-year and 2-year overall survival rates of patients was 0.794 (95% CI 0.744-0.887, P < 0.001) and 0.791 (95% CI 0.708-0.860, P = 0.002). Conclusions:ALB, INR and AST are the influencing factors of prognosis of hepatitis B-related hepatocellular carcinoma patients treated with PD-1 inhibitors, and the nomogram model constructed based on prognostic influencing factors has a good effect on predicting the 1-year and 2-year overall survival rates of patients, which can be used to screen the population suitable for immunotherapy and is conducive to the clinical formulation of individualized and precise treatment plans.
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Objective:To investigate the risk factors and establish a prediction model of primary non-response (PNR) to anti-tumor necrosis factor-α(TNF-α) monoclonal antibody in Crohn′s disease (CD) patients.Methods:From December 1, 2018 to July 31, 2022, 103 patients with CD treated with the anti-TNF-α monoclonal antibody in Renmin Hospital of Wuhan University were enrolled (modeling group), and at the same time, 109 patients with CD treated with anti-TNF-α monoclonal antibody in Zhongnan Hospital of Wuhan University were selected (validation group). The baseline clinical data of all the patients before the first treatment of anti-TNF-α monoclonal antibody were collected, which included C-reactive protein (CRP), the simplified Crohn′s disease activity index (CDAI), and modified multiplier simple endoscopic score for Crohn′s disease (MM-SES-CD), etc. Multivariate logistic regression was used to screen the independent risk factors of PNR in patients with CD treated with the anti-TNF-α monoclonal antibody, and to establish the nomograms prediction model. The area under the curve (AUC) of the receiver operating characteristic curve (ROC), the net reclassification index (NRI), integrated discrimination improvement index (IDI), and decision curve analysis (DCA) were used to evaluate the predictive efficacy and clinical application value of the prediction model. DeLong test was used for statistical analysis.Results:The results of multivariate logistic regression analysis showed that high level of CRP ( OR=1.030, 95% confidence interval (95% CI) 1.002 to 1.059), simplified CDAI ( OR=1.399, 95% CI 1.023 to 1.913), and MM-SES-CD ( OR=1.100, 95% CI 1.025 to 1.181) in baseline were independent risk factors of PNR in patients with CD treated with the anti-TNF-α monoclonal antibody ( P=0.033, 0.036 and 0.008). The results of ROC analysis showed that the AUCs of CRP, simplified CDAI, MM-SES-CD, and the prediction model in the modeling group and the validation group were 0.697(95% CI 0.573 to 0.821), 0.772(95% CI 0.666 to 0.879), 0.819(95% CI 0.725 to 0.912), 0.869 (95% CI 0.786 to 0.951) and 0.856 (95% CI 0.756 to 0.955), respectively. The AUC of the prediction model in the modeling group was greater than those of CRP and simplified CDAI, and the differences were statistically significant ( Z=3.00 and 2.75, P=0.003 and 0.006), while compared with MM-SES-CD and the validation group, the differences were not statistically significant (both P>0.05). However, compared with MM-SES-CD, the NRI and IDI of the prediction model in the modeling group were 0.205(95% CI 0.002 to 0.409, P=0.048) and 0.098(95% CI 0.022 to 0.174, P=0.011), respectively, suggesting that the predictive ability of the prediction model was better than that of MM-SES-CD. The results of DCA indicated that the prediction model had significant clinical benefits in both the modeling group and the validation group. Conclusions:A prediction model was successfully constructed based on the independent risk factors for PNR in patients with CD treated with the anti-TNF-α monoclonal antibody. After verification, the prediction model has good prediction performance and significant clinical benefits.
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Objective:To establish a nomogram to evaluate the adequacy of bowel preparation before colonoscopy and to guide clinical decision-making.Methods:A total of 1 023 valid questionnaires from subjects who underwent diagnosis and treatment of colonoscopy at the digestive endoscopy center, Xiangya Hospital, Central South University from September 2020 to March 2021 were finally returned. The contents of the questionnaire mainly included the clinical characteristics, defecation habits, the number of defecation and the time of the last defecation after taking the medicine and the self-assessment results of bowel preparation before colonoscopy. Subjects' bowel preparation was graded with the Boston bowel preparation scale (BBPS) by a designated endoscopist in a single blinded method. Multivariate analyse was used to explore the influencing factors for bowel preparation adequacy, and a nomogram was drawn accordingly.Results:Based on BBPS scores, bowel preparation of 674 subjects were adequate and 349 were inadequate. Multivariate analyse identified the number of defecation per week ( OR=1.649,95% CI:1.233-2.204, P=0.001), the number of defecation after medication ( OR=3.963, 95% CI: 1.851-8.485, P<0.001), the time of the last defecation after medication ( OR=5.151, 95% CI: 1.152-23.037, P=0.032), and self-assessment of bowel preparation before examination ( OR=8.284, 95% CI: 2.042-33.601, P=0.003) were influencing factors for the adequacy of bowel preparation for colonoscopy. The area under the receiver operating characteristic curve of assessment of colonoscopic bowel preparation adequacy with nomogram visualization according to influencing factors was 0.913, optimal cutoff value was 0.824, the sensitivity was 0.746, and the specificity was 0.971 under the internal validation cohort. Conclusion:The nomogram based on the number of defecation per week, the number of defecation after medication, the time of the last defecation after medication, and self-assessment of bowel preparation before examination could evaluate the adequacy of bowel preparation before colonoscopy, which is worthy of application.
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Objective:To investigate the risk factors of bronchopulmonary dysplasia (BPD) in very low birth weight (VLBW) infants with gestational age ≤32 weeks within 28 days after birth and to establish and validate the nomogram model for BPD prediction.Methods:We retrospectively chose VLBW infants with gestational age ≤32 weeks who survived to postmenstrual age (PMA) 36 weeks and were admitted to the neonatal intensive care unit of Peking University Third Hospital from January 2016 to April 2020 as the training cohort. BPD was diagnosed in accordance with the 2018 criteria. The clinical data of these infants were collected, and the risk factors of BPD were analyzed by Chi-square test, Mann-Whitney U test, and multivariate logistic regression, and a nomogram model was established. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve was used to assess the predictive performance. Decision curve analysis (DCA) was constructed for differentiation evaluation, and the calibration chart and Hosmer-Lemeshow goodness of fit test were used for the calibration evaluation. Bootstrap was used for internal validation. VLBW infants with gestational age ≤32 weeks survived to PMA 36 weeks and admitted to Hebei Chengde Maternal and Child Health Hospital from October 2017 to February 2022 were included as the validation cohort. ROC curve and calibration plot were conducted in the validation cohort for external validation. Results:Of the 467 premature infants included in the training cohort, 104 were in the BPD group; of the 101 patients in the external validation cohort, 16 were in the BPD group. Multivariate logistic regression analysis showed that low birth weight ( OR=0.03, 95% CI: 0.01-0.13), nosocomial pneumonia ( OR=2.40, 95% CI: 1.41-4.09), late-onset sepsis ( OR=2.18, 95% CI: 1.18-4.02), and prolonged duration of endotracheal intubation ( OR=1.61, 95% CI: 1.26-2.04) were risk factors for BPD in these groups of infants (all P<0.05). According to the multivariate logistic regression analysis results, a nomogram model for predicting BPD risk was established. The AUC of the training cohort was 0.827 (95% CI: 0.783-0.872), and the ideal cut-off value for predicted probability was 0.206, with a sensitivity of 0.788 (95% CI: 0.697-0.862) and specificity of 0.744 (95% CI: 0.696-0.788). The AUC of the validation cohort was 0.951 (95% CI:0.904-0.999). Taking the prediction probability of 0.206 as the high-risk threshold, the sensitivity and specificity corresponding to this value were 0.812 (95% CI: 0.537-0.950) and 0.882 (95% CI: 0.790-0.939). The Hosmer-Lemeshow goodness-of-fit test in the training and validation cohort showed a good fit ( P>0.05). DCA results showed a high net benefit of clinical intervention in very preterm infants when the threshold probability was 5%~80% for the training cohort. Conclusion:Low birth weight, nosocomial pneumonia, late-onset sepsis, and prolonged tracheal intubation duration are risk factors for BPD. The established nomogram model has a certain value in predicting the risk of BPD in VLBW less than 32 weeks.
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Objective:To investigate the risk factors of diabetic nephropathy (DN) in primary type 2 diabetes mellitus (T2DM) patients and to quantitatively analyze the risk of DN by nomogram modeling.Methods:A total of 1 588 primary T2DM patients from 17 townships and streets in Zhejiang Province were enrolled from June 2018 to August 2018 in this cross-sectional study, with an average age of (56.8±10.1) years (50.06% male) and a mean disease duration of 9 years. The clinical data, biochemical test results, and fundus photographs of all T2DM patients were collected, and logistic regression analysis was used to screen the risk factors of DN. Then, a nomogram model was used to quantitatively analyze the risk of DN.Results:DN occurred in 27.71% (440/1 588 cases) primary type 2 diabetes patients. Hemoglobin A 1c (HbA 1c) ( OR=1.159, 95% CI 1.039-1.292), systolic blood pressure ( OR=1.041, 95% CI 1.031-1.051), serum creatinine (Scr) ( OR=1.011, 95% CI 1.004-1.017), serum globulin (GLOB) ( OR=1.072, 95% CI 1.039-1.105), diabetic retinopathy (DR) ( OR=1.463, 95% CI 1.073-1.996), education level of more than junior high school ( OR=2.018, 95% CI 1.466-2.777), and moderate-intensity exercise ( OR=0.751, 95% CI 0.586-0.961) were influencing factors of DN. Nomogram model analysis showed that the total score of each factor of DN ranged from 64-138 points, and the corresponding risk rate ranged from 0.1-0.9. The nomogram model also predicted a C-index value of 0.753 (95% CI 0.726-0.781) and an area under the receiver operating characteristic curve of DN of 0.753. Internal verification of the C-index reached 0.738. The model displayed medium predictive power and could be applied in clinical practice. Conclusions:HbA 1c, systolic blood pressure, Scr, GLOB, DR, and more than a junior high school education are independent risk factors of DN. Nomogram modeling can more intuitively evaluate the risk of DN in primary T2DM patients.
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Objective:Development and validation of a nomogram for predicting the 4-year incidence of type-2 diabetes mellitus (T2DM) in a Chinese population was attempted.Methods:This prospective cohort study was conducted in Shijingshan District Pingguoyuan Community (Beijing, China) from December 2011 to April 2012 among adults aged≥40 years not suffering from T2DM. Finally, 8 058 adults free of T2DM were included with a median duration of follow-up of 4 years. Participants were divided into a modeling group and verification group using simple random sampling at a ratio of 7∶3. Univariate and multivariate Cox proportional risk models were applied to identify the independent risk predictors in the modeling group. A nomogram was constructed to predict the 4-year incidence of T2DM based on the results of multivariate analysis. The Concordance Index and calibration plots were used to evaluate the differentiation and calibration of the nomogram in both groups.Results:A total of 5 641 individuals were in the modeling group and 2 417 people were in the validation group, of which 265 and 106 had T2DM, respectively, at 4-year follow-up. In the modeling group, age ( HR=1.349, 95% CI 1.011-1.800), body mass index ( HR=1.347, 95% CI 1.038-1.746), hyperlipidemia ( HR=1.504, 95% CI 1.133-1.996), fasting blood glucose ( HR=4.189, 95% CI 3.010-5.830), 2-h blood glucose level according to the oral glucose tolerance test ( HR=3.005, 95% CI 2.129-4.241), level of glycosylated hemoglobin ( HR=3.162, 95% CI 2.283-4.380), and level of γ-glutamyl transferase ( HR=1.920, 95% CI 1.385-2.661) were independent risk factors for T2DM. Validation of the nomogram revealed the Concordance Index of the modeling group and validation group to be 0.906 (95% CI 0.888-0.925) and 0.844 (95% CI 0.796-0.892), respectively. Calibration plots showed good calibration in both groups. Conclusion:These data suggest that our nomogram could be a simple and reliable tool for predicting the 4-year risk of developing T2DM in a high-risk Chinese population.
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Objective:To investigate the risk factors of gout and establish a columnar graph model to predict the risk of gout development.Methods:A total of 1 032 Han Chinese men attending the Affiliated Hospital of Traditional Chinese Medicine of Xinjiang Medical University, People′s Hospital of Xinjiang Uygur Autonomous Region, and the First Affiliated Hospital of Xinjiang Medical University from 2018 to 2020 were selected as study subjects and divided into training set(722 cases)and validation set(310 cases)by simple random sampling method in the ratio of 7∶3. General information and biochemical indices of the subjects were collected. The collected information was used to assess the risk of gout prevalence. LASSO regression analysis of R Studio software was used to screen the best predictors, and was introduced to construct a column line graph model for predicting gout risk using receiver operating characteristic(ROC)curves, and the Hosmer-Lemeshow test was used to assess the discrimination and calibration of the column line graph model. Finally, decision curve analysis(DCA)was performed using the rmda program package to assess the clinical utility of the model in validation data.Results:Age, uric acid, body mass index, total cholesterol, and waist-to-hip ratio were risk factors for gout( P<0.05). The column line graph prediction model based on the above five independent risk factors had good discrimination(AUC value: 0.923 for training set validation and 0.922 for validation set validation)and accuracy(Hosmer-Lemeshow test: P>0.05 for validation set validation); decision curve analysis showed that the prediction model curve had clinical practical value. Conclusion:The nomogram model established by combining age, uric acid, body mass index, total cholesterol, and waist-to-hip ratio indicators can predict the risk of gout more accurately.
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Objective:To construct and validate a predictive model of fecal/urinary incontinence among older adults in China.Methods:Data was obtained from the Seventh Chinese Longitudinal Healthy Longevity Survey in 2018.In the questionnaire, "Are you able to control your bowel and urine" , was regarded as the main effect indicator.Receiver operating curves(ROC)were used to find the best cut-off values of calf circumference for predicting fecal/urinary incontinence, and univariate Logistic model method was used to explore the potential factors associated with fecal/urinary incontinence among community-living older adults in China.A random sampling method was used to extract 70% of the survey data as the training set, and the remaining 30% of the survey data as the test set.A multivariate Logistic regression analysis was conducted in the training set to build a prediction model that encompassed all predictors, and a nomogram was plotted.Results:Logistic regression analysis showed that age, small calf circumference(male <28.5 cm, female <26.5 cm), inability to walk 1 km continuously, inability to lift 5 kg items, inability to do three consecutive squats, limited daily activities, and a history of urinary system disorders, nervous system disorders, and cerebrovascular disorders were all risk factors for fecal/urinary incontinence for older adults in China.Female, better socioeconomic status, and normal body mass index were protective factors for fecal/urinary incontinence.The Logistic regression model for predicting fecal/urinary incontinence among Chinese older adults was constructed using the above twelve factors.The consistency index(C-index)value of the model was 0.907, indicating that the model had good predictive ability.The area under the ROC curve(AUC)of the overall sample, training set and test set were 0.906(95% CI: 0.896-0.917), 0.907(95 % CI: 0.894-0.921)and 0.910(95% CI: 0.892-0.928), respectively, indicating that the model had high prediction ability and good discrimination. Conclusions:Age, sex, calf circumference, ability to walk 1 km continuously, ability to lift 5 kg items, ability to do three consecutive squats, daily activities, history of urinary system disorders, nervous system disorders and cerebrovascular disorders, socioeconomic status, and body mass index were independent predictors for fecal/urinary incontinence among older adults in China.The nomogram based on the above indicators has a good predictive effect on fecal/urinary incontinence for older adults.
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Objective:To study the risk factors for early recurrence of patients undergoing radical pancreaticoduodenectomy (PD) for pancreatic ductal adenocarcinoma (PDAC) and construct a normogram model.Methods:Patients undergoing open radical PD for PDAC at Faculty of Hepato-Pancreato-Biliary Surgery, Chinese PLA General Hospital from January 2014 to December 2021 were retrospectively screened. A total of 213 patients were enrolled, including 145 males and 68 females, aged (58.4±9.8) years. Patients were divided into the early recurrence group ( n=59, recurrence within 6 months after surgery) and a control group ( n=154, no recurrence within 6 months after surgery). Using minimum absolute value convergence and selection operator regression (LASSO) and multi-factor logistic regression analysis, we screened out the best predictor of early recurrence after PD for PDAC, and then established a nomogram model. The effectiveness of the model was validated by receiver operating characteristic (ROC) curve, calibration curves, and decision analysis curves. Results:Multivariate logistic regression analysis showed that patients with obstructive jaundice, vascular invasion, massive intraoperative bleeding, high-risk tumors (poorly differentiated or undifferentiated), high carbohydrate antigen 19-9 to total bilirubin ratio, and high fibrinogen and neutrophil to lymphocyte ratio scores had a higher risk of early postoperative recurrence. Based on the indexes above, a nomogram prediction model was constructed. The area under the ROC curve was 0.797 (95% CI: 0.726-0.854). Validation of the calibration curve exhibited good concordance between the predicted probability and ideal probability, decision curve analysis showed that the net benefits of the groupings established according to the model were all greater than 0 within the high risk threshold of 0.08 to 1.00. Conclusion:The nomogram for predicting early recurrence after PD for PDAC has a good efficiency, which could be helpful to screen out the high-risk patients for adjuvant or neoadjuvant therapy.
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Objective:To analyze the influencing factors of abnormal 15-minute retention rate of indocyanine green (ICG R15) (≥10%) in patients with hepatocellular carcinoma, and to construct a nomogram model, and to evaluate the prediction efficiency of the nomogram model.Methods:The clinical data of 190 patients with hepatocellular carcinoma in Zhengzhou University People's Hospital from December 2017 to June 2022 were retrospectively analyzed, including 148 males and 42 females, aged (57.8±9.9) years. According to ICG R15, the patients were divided into ICG R15 normal group ( n=134, ICG R15<10%) and ICG R15 abnormal group ( n=56, ICG R15≥10%). Univariate and multivariate logistic regression were used to analyze the influencing factors of abnormal ICG R15, and the nomogram model was established. The predictive ability of the model was evaluated by receiver operating characteristic (ROC) curve and C-index, and the model was verified by calibration curve and decision analysis curve. Results:Abnormal ICG R15 group the proportion of liver cirrhosis, albumin ≤35 g/L, hemoglobin ≤110 g/L, platelet count ≤100×10 9/L, prothrombin time >13 s, alanine aminotransferase >40 U/L, aspartate aminotransferase >40 U/L, total bilirubin >34.2 μmol/L, and the largest tumor diameter >5.0 cm, spleen volume >383.1 cm 3, spleen volume to of non-tumor liver volume (SNLR) >0.276 and liver tumor volume >117.2 cm 3 were higher than that of ICG R15 normal group, and the differences were statistically significant (all P<0.05). Logistic regression analysis showed that liver cirrhosis ( OR=3.89, 95% CI: 1.28-11.80, P=0.016), spleen volume >383.1 cm 3( OR=5.17, 95% CI: 1.38-19.38, P=0.015), SNLR >0.276 ( OR=5.54, 95% CI: 1.44-21.26, P=0.013) and total bilirubin >34.2 μmol/L( OR=10.20, 95% CI: 1.88-55.39, P=0.007) increased the risk of abnormal ICG R15. A nomogram model was constructed based on the above risk factors. The C-index of the model was 0.915 (95% CI: 0.872-0.957), and the area under the ROC curve predicted by the nomogram model was 0.915 (95% CI: 0.871-0.958). The calibration curve showed that the correlation index of the abnormal ICG R15 predicted by the nomogram was similar to actual situation. Decision analysis curve showed high returns. Conclusion:Liver cirrhosis, spleen volume >383.1 cm 3, SNLR>0.276 and total bilirubin >34.2 μmol/L were indepentlent risk factors for abnormal ICG R15 in patients with hepatocellur carcinoma. The clinical prediction model of ICG R15 abnormality constructed by nomogram has good prediction efficiency, which can provide a reference for evaluating preoperative liver reserve function of patients with hepatocellular carcinoma.