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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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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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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 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.
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Objective:To construct a nomogram prediction model for survival after radical surgical resection of intrahepatic cholangiocarcinoma (ICC) based on the albumin-bilirubin index (ALBI), and to evaluate its predictive efficacy.Methods:From January 2016 to January 2020, 170 patients with ICC who underwent radical surgical resection at the People's Hospital of Zhengzhou University were retrospectively analyzed. There were 90 males and 80 females, aged (58.5±10.6) years old. Based on a ratio of 7∶3 by the random number table, the patients were divided into the training set ( n=117) and the internal validation set ( n=53). The training set was used for nomogram model construction, and the validation set was used for model validation and evaluation. Follow up was conducted through outpatient reexamination and telephone contact. The Kaplan-Meier method was used for survival analysis, and a nomogram was drawn based on variables with a P<0.05 in multivariate Cox regression analysis. The predictive strength of the predictive model was evaluated by analyzing the consistency index (C-index), calibration curve, and clinical decision curve of the training and validation sets. Results:Multivariate Cox regression analysis showed that carbohydrate antigen 19-9 (CA19-9) ≥37 U/ml ( HR=1.99, 95% CI: 1.10-3.60, P=0.024), ALBI≥-2.80 ( HR=2.43, 95% CI: 1.40-4.22, P=0.002), vascular tumor thrombus ( HR=2.34, 95% CI: 1.40-3.92, P=0.001), and the 8th edition AJCC N1 staging ( HR=2.18, 95% CI: 1.21-3.95, P=0.010) were independent risk factors affecting postoperative survival of ICC patients after curative resection. The predictive model constructed based on the above variables was then evaluated, and the C-index of the model was 0.76. Calibration curve showed the predicted survival curve of ICC patients at 3 years after surgery based on the model was well-fitted to the 45° diagonal line which represented actual survival. Clinical decision curve analysis showed that the model had a significant positive net benefit in both the training and validation sets. Conclusion:The nomograph model for survival rate after radical resection of ICC was constructed based on four variables: ALBI, CA19-9, vascular tumor thrombus, and AJCC N staging (8th edition) in this study. This model provided a reference for more accurate prognosis evaluation and treatment selection plan for ICC patients.
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Objective:To analyze the risk factors of short-term recurrence of hepatocellular carcinoma (HCC) treated by transcatheter arterial chemoembolization combined with radiofrequency ablation (TACE-RFA), and to predict the short-term recurrence rate by establishing a nomogram model.Methods:The clinical data of patients with hepatocellular carcinoma treated with TACE-RFA at the PLA General Hospital from January 2010 to December 2019 were retrospectively analyzed. Of 125 patients who were included, there were 103 males and 22 females, aged (56.6±8.9) years old. Based on whether tumors had recurred within 12 months after treatment, the patients were divided into two groups: the recurrent group ( n=86) and the non-recurrent group ( n=39). The baseline conditions, tumor characteristics and preoperative laboratory examination resultss were collected and the patients were followed-up by outpatient reexaminations. Multivariate logistic regression analysis was used to study the risk factors of short-term recurrence. C-index, correction model and ROC curve were used to evaluate the model. Results:Multivariate logistics regression analysis showed that the neutrophil to lymphocyte ratio (NLR) >1.25 ( OR=2.87, P=0.048), albumin-γ-glutamyltransferase ratio (AGR)≤0.3 ( OR=3.40, P=0.043), incomplete tumor encapsulation ( OR=3.81, P=0.007) and maximum tumor diameter ( OR=1.98, P=0.003) were independent risk factors for short-term recurrence after TACE-RFA. Applying the above factors to construct the nomograph, the C-index was 0.767, the area under the curve was 0.77 (95% CI: 0.67-0.85), and the calibration curve had a good consistency. Conclusion:NLR>1.25, AGR≤0.3, incomplete tumor encapsulation and tumor maximum diameter were risk factors of short-term recurrence after TACE-RFA in patients with HCC. The nomogram model based on the above factors was of good value in predicting short-term recurrence after TACE-RFA.
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Objective:To analyze the prognostic value of nomogram model for cervical cancer based on the imaging features of diffusion kurtosis imaging (DKI) histogram.Methods:The DKI and clinical data of 272 patients with cervical cancer who were admitted to Affiliated Hospital of Guangdong Medical University from March 2015 to February 2022 were collected and retrospectively analyzed. All patients were randomly divided into the training group ( n=190) and validation group ( n=82) at a ratio of 7 vs. 3. The parameters of DKI histogram were obtained by GE AW 4.2 MRI software. The best prognostic imaging features were screened by LASSO regression. The DKI radiomics score was calculated by linear combination. The independent risk factors of prognosis were identified by univariate and multivariate regression analyses, and a nomogram model was constructed. The model discrimination was assessed by the area under the receiver operating characteristic (ROC) curve (AUC). The internal consistency of the model was evaluated by the calibration map. Results:Adenocarcinoma ( HR=2.496, 95% CI=1.312-4.749, P=0.005), DKI score ( HR=24.087, 95% CI=6.062-95.711, P<0.001), depth of invasion ≥ 1/2 muscular layer ( HR=2.277, 95% CI=1.156-4.487, P=0.017) and neutrophil to lymphocyte ratio (NLR) ( HR=1.800, 95% CI=1.313-2.468, P<0.001) were the independent risk factors for prognosis of cervical cancer. The AUC of the nomogram model in the training and validation groups were 0.860 and 0.757, respectively. The calibration curve was well fitted with the 45° diagonal. The prediction results of long-term prognosis of this model were in good agreement with the actual situation. Conclusions:Adenocarcinoma, NLR, DKI score and depth of invasion ≥ 1/2 muscular layer are the independent risk factors for the prognosis of patients with cervical cancer. The constructed nomogram model could reliably predict the 3-year survival rate of patients with cervical cancer.
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Objective:To evaluate the value of radiomics based on contrast-enhanced spectral mammography (CESM) of internal and peripheral regions combined with clinical factors in predicting benign and malignant breast lesions of breast imaging reporting and data system category 4 (BI-RADS 4).Methods:A retrospective analysis was performed on the clinical and imaging data of patients with breast lesions who were treated in Yantai Yuhuangding Hospital (Center 1) Affiliated to Qingdao University from July 2017 to July 2020 and in Fudan University Cancer Hospital (Center 2) from June 2019 to July 2020. Center 1 included 835 patients, all female, aged 17-80 (49±12) years, divided into training set (667 cases) and test set (168 cases) according to the "train-test-split" function in Python software at a ratio of 8∶2; and 49 patients were included from Center 2 as external validation set, all female, aged 34-70 (51±8) years. The radiomics features were extracted from the intralesional region (ITR), the perilesional regions of 5, 10 mm (PTR 5 mm, PTR10 mm) and the intra-and perilesional regions of 5, 10 mm (IPTR 5 mm, IPTR 10 mm) and were selected by variance filtering, SelectKBest algorithm, and least absolute shrinkage and selection operator. Then five radiomics signatures were constructed including ITR signature, PTR 5 mm signature, PTR 10 mm signature, IPTR 5 mm signature, IPTR 10 mm signature. In the training set, univariable and multivariable logistic regressions were used to construct nomograms by selecting radiomics signatures and clinical factors with significant difference between benign and malignant BI-RADS type 4 breast lesions. The efficacy of nomogram in predicting benign and malignant BI-RADS 4 breast lesions was evaluated by the receiver operating characteristic curve and area under the curve (AUC). Decision curve and calibration curve were used to evaluate the net benefit and calibration capability of the nomogram.Results:The nomogram included ITR signature, PTR 5 mm signature, PTR 10 mm signature, IPTR 5 mm signature, age, and BI-RADS category 4 subclassification for differentiating malignant and benign BI-RADS category 4 breast lesions and obtained AUCs of 0.94, 0.92, and 0.95 in the training set, test set, and external validation set, respectively. The calibration curve showed good agreement between the predicted probabilities and actual results and the decision curve indicated a good net benefit of the nomogram for predicting malignant BI-RADS 4 lesions in the training set, test set, and external validation set.Conclusion:The nomogram constructed from the radiomics features of the internal and surrounding regions of CESM breast lesions combined with clinical factors is attributed to differentiate benign from malignant BI-RADS category 4 breast lesions.
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Objective:To explore the independent risk factor for in-hospital mortality of patients with multiple trauma, and to construct a prediction model of risk of death and validate its efficacy.Methods:A retrospective cohort study was performed to analyze the clinical data of 1 028 patients with multiple trauma admitted to Affiliated Hospital of Jiangsu University from January 2011 to December 2021. There were 765 males and 263 females, aged 18-91 years[(53.8±12.4)years]. The injury severity score (ISS) was 16-57 points [(26.3±7.6)points]. There were 153 deaths and 875 survivals. A total of 777 patients were enrolled as the training set from January 2011 to December 2018 for building the prediction model, while another 251 patients were enrolled as validation set from January 2019 to December 2021. According to the outcomes, the training set was divided into the non-survival group (115 patients) and survival group (662 patients). The two groups were compared in terms of the gender, age, underlying disease, injury mechanism, head and neck injury, maxillofacial injury, chest injury, abdominal injury, extremity and pelvis injury, body surface injury, damage control surgery, pre-hospital time, number of injury sites, Glasgow coma score (GCS), ISS, shock index, and laboratory test results within 6 hours on admission, including blood lactate acid, white blood cell counts, neutrophil to lymphocyte ratio (NLR), platelet counts, hemoglobin, activated partial thromboplastin time (APTT), fibrinogen, D-dimer and blood glucose. Univariate analysis and multivariate Logistic regression analysis were performed to determine the independent risk factors for in-hospital mortality in patients with multiple trauma. The R software was used to establish a nomogram prediction model based on the above risk factors. Area under the receiver operating characteristic (ROC) curve (AUC), calibration curve and clinical decision curve analysis (DCA) were plotted in the training set and the validation set, and Hosmer-Lemeshow goodness-of-fit test was performed.Results:Univariate analysis showed that abdominal injury, extremity and pelvis injury, damage control surgery, GCS, ISS, shock index, blood lactic acid, white blood cell counts, NLR, platelet counts, hemoglobin, APTT, fibrinogen, D-dimer and blood glucose were correlated with in-hospital mortality in patients with multiple trauma ( P<0.05 or 0.01). Logistic regression analysis showed that GCS≤8 points ( OR=1.99, 95% CI 1.12,3.53), ISS>25 points ( OR=7.39, 95% CI 3.50, 15.61), shock index>1.0 ( OR=3.43, 95% CI 1.94,6.08), blood lactic acid>2 mmol/L ( OR=9.84, 95% CI 4.97, 19.51), fibrinogen≤1.5 g/L ( OR=2.57, 95% CI 1.39,4.74) and blood glucose>10 mmol/L ( OR=3.49, 95% CI 2.03, 5.99) were significantly correlated with their in-hospital mortality ( P<0.05 or 0.01). The ROC of the nomogram prediction model indicated that AUC of the training set was 0.91 (95% CI 0.87, 0.93) and AUC of the validation set was 0.90 (95% CI 0.84, 0.95). The calibration curve showed that the predicted probability was consistent with the actual situation in both the training set and validation set. DCA showed that the nomogram prediction model presented excellent performance in predicting in-hospital mortality. In Hosmer-Lemeshow goodness-of-fit test, χ2 value of the training set was 9.69 ( P>0.05), with validation set of 9.16 ( P>0.05). Conclusions:GCS≤8 points, ISS>25 points, shock index>1.0, blood lactic acid>2 mmol/L, fibrinogen≤1.5 g/L and blood glucose>10 mmol/L are independent risk factors for in-hospital mortality in patients with multiple trauma. The nomogram prediction model based on these 6 predictive variables shows a good predictive performance, which can help clinicians comprehensively assess the patient′s condition and identify the high-risk population.
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Objective:To investigate the risk factors associated with mortality in patients with severe traumatic liver injury (TLI) and to establish and validate an early prediction model for mortality.Methods:A retrospective cohort study was conducted to analyze the clinical data of 273 patients with severe TLI admitted to the ICU from the medical information mart for the intensive care-IV (MIMIC-IV) database. The cohort consisted of 176 males and 97 females, with age ranging from 18 to 83 years [35.6 years(25.7,57.5)years]. The patients were divided into two groups based on in-hospital mortality: the survival group (253 patients, 92.7%) and the death group (20 patients, 7.3%). The two groups were compared with regards to gender, age, cause and type of injury, treatment method, massive blood transfusion, comorbidities as well as vital signs and laboratory tests measured within 24 hours of ICU admission. Univariate analysis was used to screen for risk factors associated with mortality in severe TLI patients. Independent risk factors for mortality were determined using multivariate Logistic regression analysis. Lasso regression was used to screen for predictors of mortality, and a nomogram prognostic model was then established through a multivariate Logistic regression analysis. The area under the receiver operating characteristic (ROC) curve (AUC) was used to evaluate the discrimination of the model, while the Hosmer-Lemeshow goodness-of-fit test and calibration curve were used to evaluate the calibration of the model. The model′s clinical applicability was evaluated through decision curve analysis (DCA). Internal validation was performed by the 200 Bootstrap samples, and external validation was performed by using 163 patients with severe TLI from the emergency ICU collaborative research database (eICU-CRD). Finally, the predictive efficacy of the nomogram model was compared to other trauma or severity scores.Results:Univariate analysis showed that the age, cause of injury, massive blood transfusion, chronic liver disease and laboratory tests measured within 24 hours of ICU admission, including temperature, systolic blood pressure, diastolic blood pressure, mean arterial pressure, shock index, platelets, red blood cell distribution width (RDW), mean red blood cell hemoglobin concentration (MCHC), blood glucose, blood urea nitrogen, creatinine, anion gap, bicarbonate, prothrombin time (PT), activated partial thromboplastin time (APTT) and international normalized ratio (INR) were associated with the mortality of severe TLI patients ( P<0.05 or 0.01). Multivariate Logistic regression analysis revealed that age ( OR=1.08, 95% CI 1.03, 1.12, P<0.01), body temperature <36 ℃ ( OR=8.00, 95% CI 2.17, 29.53, P<0.01), shock index ( OR=9.59, 95% CI 1.76, 52.18, P<0.01) and anion gap ( OR=1.32, 95% CI 1.15, 1.53, P<0.01) were significantly associated with mortality in severe TLI patients. Lasso regression analysis selected 7 predictors, including age, body temperature<36 ℃, shock index, anion gap, chronic liver disease, creatinine and APTT. Based on these 7 predictors, a nomogram prediction model was developed. The AUC of the nomogram for predicting mortality was 0.96 (95% CI 0.94, 0.99), and the Hosmer-Lemeshow goodness-of-fit test indicated a good fit ( P>0.05). The calibration curve demonstrated excellent consistency between the predicted and actual probabilities, and DCA demonstrated that the model had good clinical net benefit at all risk threshold probability ranges. Internal validation confirmed the stability of the model ( AUC=0.96, 95% CI 0.92, 0.98), and external validation demonstrated good generalization ability ( AUC=0.95, 95% CI 0.91, 0.98). Moreover, the nomogram exhibited superior predictive efficacy compared with injury severity score (ISS), revised trauma score (RTS), trauma injury severity score (TRISS), sequential organ failure score (SOFA), acute physiological score III (APS III), Logistic organ dysfunction score (LODS), Oxford acute severity of illness score (OASIS) and simplified acute physiological score II (SAPS II). Conclusions:Age, body temperature <36 ℃, shock index and anion gap are independent risk factors for mortality in severe TLI patients. A nomogram prognosis model based on 7 predictors, namely age, body temperature <36 ℃, shock index, anion gap, chronic liver disease, creatinine and APTT exhibits good predictive efficacy and robustness, and is contributive to accurately assess the risk of mortality in severe TLI patients at an early stage.
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Objective:To analyze risk factors for prognosis of adult patients with traumatic brain injury (TBI), construct the prognostic model of TBI and evaluate its predictive value.Methods:A case-control study was used to analyze the clinical data of 522 patients with TBI admitted to Xijing Hospital of Air Force Medical University from March 2011 to September 2019, including 438 males and 84 females; aged 18-75 years [(44.9±15.0)years]. According to the Glasgow outcome score (GOS) at discharge, the patients were divided into good prognosis group (GOS 4-5 points, n=165) and poor prognosis group (GOS 1-3 points, n=357). The two groups were compared with regards to qualitative data such as sex, underlying diseases, causes of injury, multiple injuries, open injuries, intracranial foreign bodies, cerebral herniation, consciousness status on admission and at discharge, surgery, lung infection on admission, tracheostomy, ventilator-assisted ventilation, hospital-acquired pneumonia/pathogenic bacteria and intracranial infection, and quantitative data such as Glasgow coma score (GCS) on admission and at discharge, age, measurements on admission [systolic blood pressure, diastolic blood pressure, mean arterial pressure, temperature, heart rate, creatinine, urea nitrogen, blood sodium, blood potassium, blood glucose, prothrombin time (PT), activated partial thromboplastin time (APTT), platelets, international normalized ratio (INR), pupil size of both eyes] and length of hospital stay. Univariate analysis and Lasso regression analysis were used to screen the risk factors affecting the prognosis of TBI patients, and the selected influencing factors were included in multivariate Logistic regression analysis to identify independent risk factors and construct regression equations. R was used to draw a visual nomogram based on regression equation for predicting the prognosis of TBI patients. The prognostic predictive value of the nomogram was evaluated by using the receiver operating characteristic (ROC) curve, and the area under the curve (AUC), Youden index, sensitivity, specificity and consistency index (C index) were calculated. Results:Univariate analysis showed that there were significant differences between the two groups in underlying diseases, open injuries, cerebral herniation, consciousness status on admission and at discharge, lung infection on admission, tracheostomy, ventilator-assisted ventilation, hospital-acquired pneumonia/pathogenic bacteria, GCS on admission and at discharge, age, and measurements on admission (systolic blood pressure, mean arterial pressure, body temperature, heart rate, creatinine, urea nitrogen, blood potassium, blood glucose, PT, INR, pupil size of right eye) (all P<0.05 or 0.01). There were no significant differences between the two groups in gender, causes of injury, multiple injuries, intracranial foreign bodies, surgery, intracranial infection, measurements on admission (diastolic blood pressure, blood sodium, APTT, platelets, pupil size of left eye) and length of hospital stay (all P>0.05). After screening by Lasso regression model, the results of multivariate Logistic regression analysis showed that GCS on admission ( OR=0.67, 95% CI 0.62, 0.73, P<0.01), age ( OR=1.03, 95% CI 1.01, 1.04, P<0.01), blood glucose on admission ( OR=1.17, 95% CI 1.06, 1.30, P<0.01) and INR on admission ( OR=17.08, 95% CI 2.12, 137.89, P<0.01) could be used as the main risk factors to construct the prediction model, and the regression equation was constructed: Logit [ P/(1- P)]=-0.398× "GCS on admission"+0.024× "age"+0.158×"blood glucose on admission"+2.838×"INR on admission"-1.693. The AUC for the prognosis prediction in adult patients with TBI using R based on a visual nomogram model was 0.87 (95% CI 0.83, 0.89, P<0.01). The Youden index for the predicted probability was 0.60 (sensitivity of 85.2% and specificity of 75.2%), with the C index of 0.87. Conclusion:Age, GCS on admission, blood glucose on admission and INR on admission are the main risk factors affecting the prognosis of TBI in adults, and the nomogram drawn by these parameters can better predict their clinical outcome.