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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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Objective To establish a nomogram prediction model of hyperuricemia(HUA)onset risk in overweight and obese children and adolescents in order to provide reference for the prevention and treatment of HUA in this population.Methods The clinical data of 1 410 overweight and obese children and adolescents aged 6-17 years old visiting in this hospital from September 2021 to August 2022 were retrospectively analyzed.A total of 987 overweight and obese children and adolescents were randomly extracted according to a ratio of 7:3 to establish the model,and the remaining 423 cases were validated internally.Referring to the definition of high uric acid in"Zhu-futang Practical Pediatrics",the subjects were divided into high uric acid group and non-high uric acid group.The logis-tic regression analysis was used to analyze the influencing factors of HUA in overweight and obese children and adoles-cents.The nomogram model was constructed by using the R language.The area under the receiver operating character-istic(ROC)curve(AUC),decision analysis curve(DIC),clinical impact curve(CIC)and C-index were used to evalu-ate the predictive ability of the model,and the Bootstrap repeated sampling method(taking samples for 1000 times)was used for internal validation of the model.Results The results of multivariate analysis showed that the age(OR=2.324,95％CI:1.155-4.672,P=0.018),gender(OR=0.456,95％CI:0.256-0.810,P=0.007),triglycerides(OR=3.775,95％CI:2.321-6.138,P＜0.001),blood calcium(OR=26.986,95％CI:3.186-228.589,P=0.003)and blood creatinine(OR=1.047,95％CI:1.026-1.070,P＜0.001)were the influen-cing factors of HUA in overweight and obese children and adolescents.AUC of the ROC curve of the model was 0.840,the sensitivity was 0.786,the specificity was 0.762,the Youden index was 0.548,and the C-index was 0.840.The risk probability of DC A was 0.1-0.8,the net benefit rate of both models was＞0,AUC of ROC curve in the internal verification was 0.871.Conclusion The constructed nomogram in this study has a good predictive efficiency for the onset risk of HUA in overweight and obese children and adolescents,and may provide reference for the early diagnosis and treatment of this population.
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Objective:To investigate the efficacy of Shuotong ureteroscope combined with flexible ureteroscope in the treatment of 2-3 cm lower calyceal calculi, and analyze the influencing factors.Methods:A total of 102 patients with lower calyceal calculi were treated in the Second People′s Hospital of Yulin from February 2019 to December 2022, and they were divided into the observation group and the control group, with 51 cases in each group. The patients of the observation group were treated with Shuotong ureteroscope combined with flexible ureteroscope, while the patients of the control group were treated with flexible ureteroscope. According to whether the stones were completely removed after operation, all patients were divided into non-stone removal group ( n=13) and stone removal group ( n=89). The operation time, hospitalization time, lithotripsy time, intraoperative blood loss, complication rate and stone clearance rate were compared between the observation group and the control group. Generalized Estimation Equation was used to analyze and evaluate the effects of treatment time, treatment scheme and their interaction on visual analogue scale (VAS), white blood cell (WBC), blood urea nitrogen (BUN), blood creatinine (Cr), hemoglobin (HGB) and procalcitonin (PCT). Univariate and multivariate Logistic regression were used to analyze the risk factors of stone removal rate. Nomogram model was constructed based on risk factors and evaluate the model. Results:Compared with the control group, operation time [(118.72±9.61) min vs (136.65±11.27) min], hospitalization stay [(6.43±1.12) d vs (10.29±2.23) d] and the lithotripsy time [ (51.23±10.38) min vs (56.62±11.43) min] of the observation group were shorter, and the amount of intraoperative blood loss [(128.52±10.20) mL vs (157.53±15.31) mL] were significantly less than those of the control group ( P< 0.05). The results of Generalized Estimation Equation analysis showed that treatment time, treatment regimen and their interaction had significant effects on WBC, HGB, BUN, Cr, PCT and VAS ( P< 0.05). Compared with the control group, the incidence of complications (5.88% vs 19.61%) of the observation group was lower and the stone clearance rate ( 94.12% vs 80.39%) was significantly higher than those in the control group ( P< 0.05). The mode of operation, infundibulopelvic angle(IPA), caliceal pelvic height (CPH) and the maximum diameter of stones were all influencing factors of stone removal rate in patients with 2-3 cm lower calyceal calculi. The nomogram model constructed in this study has good differentiation, calibration and clinical practicability, and can better identify high-risk patients with incomplete removal of 2-3 cm lower calyceal calculi. Conclusions:Shuotong ureteroscope combined with flexible ureteroscope is a safe, effective method for the treatment of 2-3 cm lower calyceal calculi. It has the advantages of simple operation, less intraoperative bleeding, less postoperative complications and high stone clearance rate. IPA, CPH, the maximum diameter of calculi and the mode of operation were all independent factors affecting the stone clearance rate of 2-3 cm lower calyceal calculi. The nomogram model constructed in this study can well identify the high-risk patients with incomplete clearance of 2-3 cm lower calyceal calculi.
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Objective:To analyze the influencing factors of postoperative frailty in elderly patients with gastrointestinal tumors, establish a nomogram model for predicting postoperative frailty and evaluate its efficacy, so as to provide reference basis for formulating perioperative frailty management plans for elderly gastrointestinal tumor patients in the later stage.Methods:Convenience sampling method was used to select 376 elderly patients with gastrointestinal tumors who underwent surgical treatment in the First Affiliated Hospital of Anhui Medical University from December 2021 to August 2022 as the study objects. On the 5th day after surgery according to Tilburg Frailty Indicator, the patients were diagnosed whether they developed postoperative frailty and were divided into postoperative frailty group and postoperative non-frailty group. General data, laboratory indicators and clinical data of patients were collected. Univariate analysis and multivariate Logistic regression analysis were used to screen the independent influencing factors of postoperative frailty in elderly patients with gastrointestinal tumors. R software was used to establish a nomogram prediction model and conduct internal validation to evaluate the differentiation, calibration and clinical applicability of the model.Results:A total of 265 males and 111 females included aged (70.04 ± 5.89) years old, with 222 (59.0%) patients experienced postoperative frailty in this study. Multivariate analysis showed that low Barthel Index score ( OR=0.941, 95% CI 0.903-0.980), low hemoglobin ( OR=0.976, 95% CI 0.963-0.989), high Charison Comorbid Index score ( OR=1.457, 95% CI 1.128-1.882), preoperative frailty ( OR=4.369, 95% CI 1.486-12.841), and pathological stage Ⅲ-Ⅳ ( OR=2.053, 95% CI 1.253-3.364) were independent influencing factors for postoperative frailty of elderly gastrointestinal tumors (all P<0.05). The AUC before and after internal validation of the nomogram model was 0.811 (95% CI 0.768-0.854) and 0.803 (95% CI 0.762-0.856) respectively. The results of Hosmer-Lemeshow test showed good goodness of fit ( χ2=4.09, P>0.05). Decision curve analysis showed that the model had certain clinical applicability. Conclusions:Based on the risk factors of postoperative frailty in elderly patients with gastrointestinal tumors, the nomogram prediction model was established, which has good differentiation, consistency and clinical applicability, and can provide reference for clinical staff to make perioperative frailty management plan.
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Objective:To investigate the risk factors for periprosthetic joint infection (PJI) after primary total knee arthroplasty (TKA) and construct a nomogram model for prediction of such risks.Methods:In this retrospective study, we enrolled 69 patients with PJI after primary TKA (the infection group, n=69) who had been admitted to Department of Orthopedics, Nanjing Jinling Hospital, The First School of Clinical Medicine, Southern Medical University from January 2010 to December 2019. The non-infection group included the patients of the same kind but without postoperative infection during the same period who were matched according to time of admission, age, and gender in a ratio of 1∶3 ( n=207). The data on body mass index, anesthesia method, operation time, preoperative C-reactive protein, preoperative albumin, and comorbid medical conditions were collected from both groups to screen the risk factors for postoperative development of PJI using univariate and multivariate conditional logistic regression analyses. After a nomogram of the risk factors was plotted using R software, the consistency index (C-index) was calculated. The receiver operating characteristic curve, calibration curve, and clinical decision curve were drawn. Results:Multivariate conditional logistic regression analysis showed that preoperative albumin <35 g/L ( OR=7.166, 95% CI: 3.427 to 14.983, P<0.001), operation time >90 min ( OR=3.163, 95% CI: 1.476 to 6.779, P=0.003), diabetes mellitus ( OR=3.966, 95% CI: 1.833 to 8.578, P<0.001), rheumatic diseases ( OR=3.531, 95% CI: 1.362 to 9.156, P=0.009), and chronic lung diseases ( OR=4.734, 95% CI: 1.790 to 12.521, P=0.002) were risk factors for development of PJI after primary TKA. The nomogram constructed with R software visualized the model. The C-index of the nomogram was 0.809 (95% CI: 0.751 to 0.867), indicating a good predictive capability of the model. The calibration curves of the model showed that the nomogram was in good agreement with the actual observations. The decision curves showed that the threshold probabilities of the model ranged from 0.08 to 0.75, providing a good net clinical benefit. Conclusions:Preoperative low albumin, prolonged operation time, diabetes, rheumatic diseases, and chronic lung diseases may be the risk factors for PJI after primary TKA. The nomogram prediction model based on these factors can provide a reference for clinicians to prevent PJI.
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Objective:To develop and validate a postoperative infection nomogram of hepatitis B-associated hepatocellular carcinoma (HCC) after hepatectomy.Methods:Clinical data of 229 patients with HCC undergoing hepatectomy at the Department of Hepatobiliary Surgery of Tianjin Third Central Hospital from January 2014 to December 2022 were retrospectively analyzed, including 174 males and 55 females, aged (58.2±11.4) years. LASSO regression analysis screened the factors associated with hepatitis B-associated HCC infection after hepatectomy, which were further incorporated into multivariate logistic regression analysis. A nomographic prediction model was established based on the results of multivariate logistic regression analysis. Concordance index (C-index), calibration curve and receiver operating characteristic (ROC) curve were used to evaluate the model, and decision curve analysis (DCA) was used to analyze the clinical applicability of the model. Internal validation of the model was performed using bootstrap method.Results:A total of nine variables were screened as factors associated with the postoperative infections using LASSO regression, including gender, smoking history, body mass index (BMI), serum level of alpha fetoprotein, resection fashion (anatomical or non-anatomical), intraoperative blood loss, surgical method (laparoscopy or open), serum level of creatinine, and postoperative biliary fistula. Multivariate logistic regression analysis showed that BMI, resection fashion, intraoperative blood loss >500 ml, and postoperative biliary fistula were risk factors for postoperative infection (all P<0.05). Based on the above risk factors, a postoperative infection nomogram of hepatitis B-associated HCC after hepatectomy was established. The C-index was 0.839 (95% CI: 0.768-0.910), and the area under ROC curve was 0.853 (95% CI: 0.795-0.912), indicating that the model had a good predictive ability. The calibration curve was basically consistent with the ideal curve. The DCA showed that the model had a good clinical applicability. Internal validation C-index was 0.829 (95% CI: 0.766-0.892). Conclusion:The nomogram based on BMI, surgical resection fashion, intraoperative blood loss >500 ml, and postoperative biliary fistula has a high predictive accuracy and can be used to predict postoperative infections after hepatectomy for HCC.
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Objective To construct a nomogram prediction model for major adverse cardiovascular events(MACE)within 1 year after percutaneous coronary intervention(PCI)in elderly patients with acute coronary syndrome(ACS).Methods A retrospective analysis was conducted on 551 patients with diagnosed ACS and undergoing PCI in Department of Cardiovascular Medicine of Air Force Medical Center from 1 January 2020 to 1 April 2022.According to the occurrence of MACE during 1 year of follow-up,they were classified into MACE group(n=176)and non-MACE group(n=375).Risk factors for the occurrence of MACE in elderly ACS patients within 1 year after PCI were analysed using univariate and multivariate logistic regression,a nomogram prediction model was constructed,and the predictive power of the model was assessed using the area under the ROC curve(AUC).Results The MACE group had significantly higher Gensini score,systemic immune-inflammation index,and GRACE score,but obviously lower prognostic nutritional index than the non-MACE group(P＜0.01).Multivariate logistic regression analysis showed that recent smoking(OR=2.222,95％CI:1.361-3.628,P=0.010),hyperlipidaemia(OR=1.881,95％CI:1.145-3.089,P=0.013),prognostic nutritional index(OR=4.645,95％CI:2.788-7.739,P=0.001),LVEF(OR=5.177,95％CI:3.160-8.483,P=0.001),systemic immune-inflammation index(OR=5.396,95％CI:3.179-9.159,P=0.001),and preoperative di-agnosis of non-STEMI(OR=2.829,95％CI:1.356-5.901,P=0.006)or STEMI(OR=3.451,95％CI:1.596-7.463,P=0.002)were independent influencing factors for occurrence of MACE after PCI in elderly ACS patients.ROC curve analysis showed that the AUC value of the nomo-gram model for predicting MACE within 1 year after PCI in elderly ACS patients was 0.888.Con-clusion Our developed nomogram model is simple and practical,and can effectively predict the occurrence of MACE within 1 year after PCI in elderly ACS patients.And external validation should be carried out to ensure its generality.
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Objective To construct a nomogram model for predicting the risk of in-hospital death in CHF patients by using noninvasive hemodynamic monitoring combined with age,DBP,CRP and renal insufficiency(serum creatinine≥ 442 μmol/L).Methods A total of 223 elderly patients with acute onset of CHF admitted in First,Second Medical Centre of Chinese PLA General Hos-pital from September 2022 to March 2023 were recruited in this study.According to their clinical outcomes,they were divided into survival group(196 cases)and death group(27 cases).Based on the in-hospital death and other related indicators,a nomogram model was constructed to predict the risk factors of in-hospital death in CHF.Results Noninvasive hemodynamic mornitoring indi-cated that the death group had significantly higher LVEF and LCWI values but lower LVEDV value than the survival group(P＜0.05,P＜0.01).Multivariate logistic regression analysis showed that age(OR=1.131,95％CI:1.052-1.213,P=0.001),DBP(OR=0.932,95％CI:0.882-0.982,P=0.011),CRP(OR=1.171,95％CI:1.021-1.352,P=0.024),LVEDV(OR=0.984,95％CI:0.962-0.992,P=0.011)and renal insufficiency(OR=5.863,95％CI:1.351-1.731,P=0.004)were independent risk factors for the short-term prognosis of the elderly CHF patients.The AUC value of the nomogram model was 0.902(95％CI:0.819-0.948,P＜0.05),and calibration curve analysis showed the C-index was 0.902,indicating accurate predictive perform-ance.Conclusion Age,DBP,LVEDV,CRP and renal insufficiency are independent risk factors for the short-term prognosis of the elderly CHF patients.
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Objective:To construct the risk prediction nomogram model of acute kidney injury (AKI) with R language and traditional statistical methods based on the large sample clinical database, and verify the accuracy of the model.Methods:It was a a retrospective case control study. The patients who met the diagnostic criteria of AKI in Tongji Hospital of Tongji University from January 1 to December 31, 2021 were screened in the clinical database, and the patients with monitored serum creatinine within 48 hours but without AKI were included as the control group. The demographic data, disease history, surgical history, medication history and laboratory test data were collected to screen the risk factors of AKI in clinic.Firstly, based on multivariate logistic regression analysis and forward stepwise logistic regression analysis, the selected risk factors were included to construct the nomogram model. At the same time, cross validation, bootstrap validation and randomly split sample validation were used for internal verification, and clinical data of patients in the sane hospital after one year (January to December, 2022) were collected for external verification. The receiver-operating characteristic curve was used to determine the discrimination of the model, and calibration curve and decision curve analysis were carried out to evaluate the accuracy and clinical net benefit, respectively.Results:A total of 5 671 patients were enrolled in the study, with 1 884 AKI patients (33.2%) and 3 787 non-AKI patients (66.7%). Compared with non-AKI group, age, and proportions of surgical history, renal replacement therapy, hypertension, diabetes, cerebrovascular accident,chronic kidney disease, drug use histories and mortality in AKI group were all higher (all P<0.05). Multivariate logistic regression analysis showed that the independent influencing factors of AKI were surgical history, hypertension, cerebrovascular accident, diabetes, chronic kidney disease, diuretics, nitroglycerin, antidiuretic hormones, body temperature, serum creatinine, C-reactive protein, red blood cells, white blood cells, D-dimer, myoglobin, hemoglobin, blood urea nitrogen, brain natriuretic peptide, aspartate aminotransferase, alanine aminotransferase, triacylglycerol, lactate dehydrogenase, total bilirubin, activated partial thromboplastin time, blood uric acid and potassium ion (all P<0.05). Finally, the predictive factors in the nomogram were determined by forward stepwise logistic regression analysis, including chronic kidney disease, hypertension, myoglobin, serum creatinine and blood urea nitrogen, and the area under the curve of the prediction nomogram model was 0.926 [95% CI 0.918-0.933, P<0.001]. The calibration curve showed that the calibration effect of nomogram was good ( P>0.05). The decision curve showed that when the risk threshold of nomogram model was more than 0.04, the model construction was useful in clinic. In addition, the area under the curve of receiver-operating characteristic curve predicted by nomograph model in external validation set was 0.876 (95% CI 0.865-0.886), which indicated that nomograph model had a high discrimination degree. Conclusion:A nomogram model for predicting the occurrence of AKI is established successfully, which is helpful for clinicians to find high-risk AKI patients early, intervene in time and improve the prognosis.
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Objective:To explore the risk factors for malnutrition after a tracheotomy and to construct a predictive model useful for its prevention through early intervention.Methods:Clinical data describing 440 tracheotomy patients were subjected to a retrospective analysis. The variables examined were age, sex, etiology, Glasgow Coma Score (GCS), activities of daily living (ADL) score, age-corrected Charlson comorbidity index (aCCI), food intake, swallowing function, incidence of infections, as well as any history of diabetes mellitus, hypertension, smoking or alcohol consumption. Patients identified as being at risk of malnutrition (NRS-2002≥3) were screened using the Nutritional Risk Screening tool (NRS-2002) and the European Society of Clinical Nutrition and Metabolism′s ESPEN2015 criteria. The subjects were thus categorized into a malnutrition group of 343 and a control group of 97. Unifactorial and multifactorial logistic regression analyses were performed, and stepwise regression was applied to include the factors found significant in the unifactorial analysis into the multifactorial logistic regression analysis, and to construct a column-line graph prediction model. The clinical utility of the model was assessed by applying the receiver operator characteristics (ROC) curves, calibration plots and decision curve analysis (DCA).Results:Of the 440 persons studied, 343 (78%) were malnourished. The multivariate logistic regression analysis showed that pulmonary infection, dysphagia, low GCS score and high aCCI score were significant risk factors for malnutrition after a tracheotomy. A prediction nomograph was constructed. After fitting and correcting, the area under the curve (AUC) of the prediction model′s ROC curve was 0.911, the specificity was 80.4%, and the sensitivity was 91.3%. That was significantly higher than the AUCs for pulmonary infection (0.809), dysphagia (0.697), aCCI (0.721) and GCS (0.802). Bootstrap self-sampling was used to verify the model internally. After 1000 samples the average absolute error between the predicted risk and the actual risk was 0.013, indicating good prediction ability. The DCA results demonstrated that the model has substantial clinical applicability across a range of nutritional interventions, particularly for threshold probability values ranging from 0 to 0.96.Conclusion:Pulmonary infection, dysphagia, low GCS score, and high aCCI score are risk factors for malnutrition among tracheotomy patients. The nomogram model constructed in this study has good predictive value for the occurrence of malnutrition among such patients.
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Objective:To construct and analyze the visual nomogram predictive model for the prognosis of elderly advanced lung adenocarcinoma patients after surgery based on the Surveillance, Epidemiology, and End Results (SEER) database.Methods:SEER*Stat8.4.0.1 software was used to screen out the data from 17 register in SEER database between 2000 and 2019, and finally 4 453 lung adenocarcinoma patients aged ≥ 65 years who underwent surgical treatment and were diagnosed as stage Ⅲ and Ⅳ according to the 7th edition of the American Joint Committee on Cancer (AJCC) staging criteria were enrolled. The data were randomly divided into the training set (3 117 cases) and the validation set (1 336 cases) in a 7:3 ratio; the epidemilogical data and clinicopathological characteristics of the two groups were compared. LASSO regression was used for data dimensionality reduction to select the best predictors from the prognostic factors of patients. Cox proportional risk model was used to perform univariate and multivariate analyses of the screened variables, and based on R software rms package and the prognostic independent risk factors, the nomogram was constructed to predict the 1-, 3-, and 5-year cancer-specific survival (CSS) rates of the patients. The validation set was validated by using Bootstrap method with 1 000 equal repeated samples with playback, and the accuracy of the nomogram model was verified by using the C-index, receiving operating characteristic (ROC) curves and calibration curves.Results:There were no statistically significant differences in age, gender, race, tumor location, Grade grading, surgery methods, the number of lymph node dissection, radiotherapy, tumor diameter, tumor metastasis, marriage, living condition, TNM staging, radiochemotherapy of training set and validation set (all P > 0.05). In training set, 18 variables were included into LASSO regression analysis and were performed with dimensionality reduction; ultimately, 11 optimal predictive variables were selected, including age ≥ 85 years ( HR = 2.34, 95% CI: 1.803-3.037, P < 0.01), male ( HR = 1.326, 95% CI: 1.228-1.432, P < 0.01), Grade grading Ⅲ-Ⅳ ( HR = 1.333, 95% CI: 0.844-2.105, P < 0.01), undissected lymph nodes ( HR = 2.261, 95% CI: 2.023-2.527, P < 0.01), tumor diameter ≥3.7 cm ( HR = 1.445, 95% CI: 1.333-1.566, P < 0.01), bone metastasis ( HR = 1.535, 95% CI: 1.294-1.819, P < 0.01), brain metastasis ( HR = 1.308, 95% CI: 1.117-1.532, P < 0.01), lung metastasis ( HR = 1.229, 95% CI: 1.056-1.431, P = 0.01), living in rural areas ( HR = 1.215, 95% CI: 1.084-1.363, P < 0.01), TNM staging Ⅳ ( HR = 1.155, 95% CI: 1.044-1.278, P = 0.01), postoperative radiotherapy ( HR = 1.148, 95% CI: 1.054-1.250, P < 0.01); lung adenocarcinoma patients with the above 11 factors had worse prognosis. Based on the variables, the nomogram predictive model was constructed to predict 1-, 3-, and 5-year CSS rates of elderly advanced lung adenocarcinoma patients. Bootstrap method was used for repeated sampling for 1 000 times to verify the modeling effect of nomogram. In the model group, C-index was 0.654 (95% CI: 0.641-0.668), 0.666 (95% CI: 0.646-0.685), respectively in the training set and the validation set. The nomogram was drawn to predict ROC curves of 1-, 3-, and 5-year CSS rates for elderly advanced lung adenocarcinoma patients after operation in the training set and validation set; the area under the curve (AUC) of 1-year, 3-year, and 5-year CSS rates was 0.730 (95% CI: 0.708-0.754) and 0.689 (95% CI: 0.672-0.710), 0.687 (95% CI: 0.668-0.711) and 0.731 (95% CI: 0.697-0.765), 0.712 (95% CI:0.684-0.740) and 0.714 (95% CI: 0.683-0.745), respectively in the training and validation sets. The calibration curve showed a high consistency between the predicted probability of the model and the actual probability. Conclusions:The nomogram model constructed by optimal predictive variables for predicting the prognosis of elderly advanced lung adenocarcinoma patients after surgery may be a convenient tool for survival prediction of these patients.
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ObjectiveTo investigate the influencing factors for overt hepatic encephalopathy （OHE） in patients with hepatitis B cirrhosis after transjugular intrahepatic portosystemic shunt （TIPS）， and to construct an individualized risk prediction model. MethodsA total of 302 patients with hepatitis B cirrhosis who underwent TIPS in Department of Gastroenterology， The General Hospital of Western Theater Command， from January 2017 to December 2021 were enrolled， and according to the presence or absence of OHE after surgery， they were divided into non-OHE group with 237 patients and OHE group with 65 patients. The two groups were compared in terms of general data， laboratory markers， Child-Turcotte-Pugh （CTP） score， MELD combined with serum sodium concentration （MELD-Na） score， and albumin-bilirubin （ALBI） score before surgery. The independent-samples t test or the Mann-Whitney U test was used for comparison of continuous data between two groups， and the chi-square test was used for comparison of categorical data between two groups. The univariate and multivariate logistic regression analyses were used to identify the influencing factors for OHE after TIPS in patients with hepatitis B cirrhosis， and independent influencing factors were used to construct a nomogram model. The receiver operating characteristic （ROC） curve analysis and the calibration curve analysis were used to evaluate the discriminatory ability and calibration of the model， and the decision curve analysis and the clinical impact curve （CIC） were used to evaluate the clinical effectiveness of the model . ResultsAge （odds ratio ［OR］=1.035， 95% confidence interval ［CI］： 1.004 — 1.066， P<0.05）， white blood cell count （WBC）/platelet count （PLT） ratio （OR=33.725， 95%CI： 1.220 — 932.377， P<0.05）， international normalized ratio （INR） （OR=5.149， 95%CI： 1.052 — 25.207， P<0.05）， and pre-albumin （PAB） （OR=0.992， 95%CI： 0.983 — 1.000， P<0.05） were independent predictive factors for OHE after TIPS in patients with hepatitis B cirrhosis. The nomogram model constructed based on age， WBC/PLT ratio， INR， and PAB had an area under the ROC curve of 0.716 （95%CI： 0.649 — 0.781）， with a sensitivity of 78.5% and a specificity of 56.1%. ConclusionThe nomogram model constructed based on age， WBC/PLT ratio， INR， and PAB can help to predict the risk of OHE after TIPS in patients with hepatitis B cirrhosis.
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ObjectiveTo investigate the performance of a nomogram model established based on clinical indices and magnetic resonance imaging （MRI） signs in determining the traditional Chinese medicine （TCM） syndrome types of primary liver cancer. MethodsA retrospective analysis was performed for the clinical data of 138 patients with primary liver cancer who were hospitalized in The Affiliated Hospital of Shaanxi University of Chinese Medicine from September 2018 to July 2023， and the patients were divided into excess syndrome group with 84 patients and deficiency syndrome group with 54 patients. All patients underwent Gd-EOB-DTPA contrast-enhanced MRI scan before treatment. The independent-samples t test was used for comparison of continuous data between two groups， and the chi-square or the Fisher’s exact test was used for comparison of categorical data between groups. A Logistic regression analysis was used to investigate the independent predictive factors for the TCM syndrome type of primary liver cancer， and a nomogram model was established. The patients were randomly divided into training group with 110 patients and validation group with 28 patients at a ratio of 8∶2， and the calibration curve， the receiver operating characteristic （ROC） curve， and the decision curve were used to evaluate the clinical performance of this model. ResultsThere were significant differences between the excess syndrome group and the deficiency syndrome group in neutrophils， lymphocyte count （LYM）， platelet count， albumin （Alb）， neutrophil-lymphocyte ratio （NLR）， prothrombin time （PT）， alpha-fetoprotein （AFP）， direct bilirubin （DBil）， indirect bilirubin， total bilirubin， presence or absence of portal vein invasion， number of tumors， hepatobiliary tumor signal， and apparent diffusion coefficient （ADC） （all P<0.05）. The Logistic regression analysis showed that AFP （odds ratio ［OR］=0.003， 95% confidence interval ［CI］： 0.000 — 0.052， P<0.001）， PT （OR=0.032， 95%CI： 0.004 — 0.286， P=0.002）， LYM （OR=0.032， 95%CI： 0.004 — 0.286， P=0.002）， Alb （OR=0.009， 95%CI： 0.001 — 0.163， P=0.001）， NLR （OR=0.040， 95%CI： 0.003 — 0.457， P=0.010）， DBil （OR=0.014， 95%CI： 0.001 — 0.198， P=0.002）， portal vein cancer thrombus （OR=0.005， 95%CI： 0.000 — 0.115， P=0.001）， number of tumors （OR=12.740， 95%CI： 1.212 — 133.937， P=0.034）， and ADC （OR=19.269， 95%CI： 3.163 — 117.387， P=0.001） were independent predictive factors for TCM syndrome types of primary liver cancer. In the training group， the model had an area under the ROC curve （AUC） of 0.962， a sensitivity of 84.1%， a specificity of 92.4%， and an accuracy of 89.1%， and in the validation group， the model had an AUC of 0.848， a sensitivity of 63.6%， a specificity of 100.0%， and an accuracy of 85.7%. The calibration curve showed that the nomogram model had good consistency between predicted syndrome types and actual syndrome types in the training group and the validation group， and the decision curve showed that the nomogram model had good net benefits within a relatively wide range of threshold probability. ConclusionThe nomogram model based on clinical indices and MRI signs has good clinical efficacy and value in judging the TCM syndrome type of primary liver cancer.
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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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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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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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Objective:To analyze the risk factors of postoperative infection in patients with colon cancer, and construct a nomogram model.Methods:The clinical data of 220 patients with colon cancer in Anhui Cancer Hospital from May 2019 to June 2022 were retrospectively analyzed. Among them, 55 patients developed postoperative infection (infection group), and 165 patients did not develop postoperative infection (non-infection group). The receiver operating characteristic (ROC) curve was used to analyze the efficacy of each index in predicting postoperative infection in patients with colon cancer. Multivariate Logistic regression analysis was used to analyze the independent risk factors of postoperative infection in patients with colon cancer. R language 3.5.2 software was used to construct a nomogram model for predicting postoperative infection in patients with colon cancer, and it was verified and evaluated.Results:There were no significant differences in gender composition, body mass index, tumor stage, intraoperative blood transfusion, hypertension, smoking history, alcohol consumption history, tumor diameter and hemoglobin between the two groups ( P>0.05); the age, diabetes mellitus ratio, operation time and exhaust time in the infection group were significantly higher than those in the non-infection group: (49.60 ± 4.40) years old vs. (47.20 ± 4.12) years old, 63.64% (35/55) vs. 30.30% (50/165), (197.80 ± 12.55) min vs. (192.23 ± 12.05) min and (3.42 ± 1.18) d vs. (2.60 ± 0.80) d, the albumin was significantly lower than that in the non-infected group: (28.29 ± 3.02) g/L vs. (32.80 ± 3.21) g/L, and there were statistical differences ( P<0.01). ROC curve analysis result showed that the area under the curve of age, operation time, exhaust time and albumin for predicting postoperative infection in patients with colon cancer were 0.672, 0.610, 0.706 and 0.846, and the optimal cut-off values were 49 years old, 184 min, 3 d and 30 g/L, respectively. Multivariate Logistic regression analysis result showed that age (>49 years old), diabetes mellitus, operation time (>184 min), exhaust time (>3 d) and albumin (≤30 g/L) were independent risk factors of postoperative infection in patients with colon cancer ( OR = 2.131, 1.758, 1.449, 1.841 and 2.325; 95% CI 1.269 to 2.696, 1.354 to 3.059, 1.201 to 1.965, 1.018 to 2.365 and 1.582 to 3.051; P<0.01). A nomogram model was constructed with age, diabetes mellitus, operation time, exhaust time, and albumin as predictors for predicting postoperative infection in patients with colon cancer. The correction curve of the nomogram model for predicting postoperative infection in patients with colon cancer was close to the ideal curve (C-index = 0.764, 95% CI 0.657 to 0.834); decision curve analysis result showed that the nomogram model provided clinical net benefit when the risk threshold was > 0.07; and the clinical net benefit of the model was higher than that of age, diabetes mellitus, operation time, exhaust time and albumin. Conclusions:The age (>49 years old), diabetes mellitus, operation time (>184 min), exhaust time (>3 d) and albumin (≤30 g/L) are the independent risk factors of postoperative infection in patients with colon cancer, and the nomogram model based on the above variables could predict postoperative infection.
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Objective Most of the clinical manifestations of aortic dissection and myocardial infarction are chest pain,which can easily lead to misdiagnosis and disastrous consequences.Therefore,this study intends to establish a differential diagnosis model and verify it in order to achieve early accurate prediction.Methods The relevant information of 200 patients with myocardial infarction and 120 pa-tients with aortic dissection diagnosed in the Second Affiliated Hospital of Zhengzhou University was collected,including age,gender,blood routine examination,electrolytes,markers of myocardial necrosis and blood coagulation function at admission.The patients were di-vided into myocardial infarction group and aortic dissection group.The independent risk factors were found out through t-test,AN OVA and binary Logistic regression analysis,and the nomogram was further drawn using R language to develop and validate the differential diag-nosis scoring table.Results The procalcitonin,prothrombin time(PT)、international normalized ratio(INR)、fibrin degradation product(FDP),D-dimer,white blood cell(WBC),percentage of neutrophil,percentage of lymphocyte,absolute value of neutrophil,absolute value of lymphocyte,C-reactive protein,cardiac troponin T(cTNT)、creatine kinase isozyme(CK-MB),systolic blood pressure of pa-tients in the two groups were statistically significant(P＜0.05),There was no significant difference in other indexes(P＞0.05).Binary Logistic regression analysis further showed that procalcitonin,D-dimer,C-reactive protein and systolic blood pressure were independent risk factors for diagnosing aortic dissection,while percentage of lymphocyte and absolute value of lymphocyte were independent risk factors for diagnosing myocardial infarction.According to the validation results of the score table developed by the nomogram,the the area under the receiver operating characteristic curve was 0.978,and the best cut-off value was 40.70 points.The sensitivity and specificity were 92.5％and 96.0％.Conclusion This study confirms that procalcitonin,D-dimer,C-reactive protein and systolic blood pressure are independent risk factors for diagnosing aortic dissection,while percentage of lymphocyte and absolute value of lymphocyte are independent risk factors for diagnosing myocardial infarction.The differential diagnosis scoring table proposed in this study can effectively differentiate patients with aortic dissection and myocardial infarction at an early stage,so as to guide further clinical diagnosis and treatment.
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Objective:To explore the diagnostic value of serum tumor marker detection for lung adenocarcinoma and its predictive value for postoperative recurrence.Methods:A total of 100 lung adenocarcinoma patients (modeling group) and 100 benign lung disease patients (control group) admitted to the Cangzhou Hospital of Integrated Traditional and Western Medicine from December 2018 to December 2020 were selected as the research subjects. In addition, 50 lung adenocarcinoma patients admitted from December 2016 to December 2017 were selected as the validation group. The serum carbohydrate antigen 125 (CA125) and carcinoembryonic antigen (CEA) levels of the modeling group and the control group were compared Levels of cytokeratin 21 fragment (CYFRA21-1), carbohydrate antigen 19-9 (CA19-9), and squamous cell carcinoma antigen (SCC-Ag). The receiver operating characteristic (ROC) curve was used to evaluate the diagnostic value of CA125, CEA, CYFRA21-1, CA19-9, and SCC-Ag alone and in combination for lung adenocarcinoma; Single and multiple factor analyses were conducted to identify the risk factors for postoperative recurrence in lung adenocarcinoma patients; A column chart model for predicting postoperative recurrence in lung adenocarcinoma patients was constructed using R software, and a risk stratification system was constructed.Results:The average water levels of CA125, SCC-Ag, CEA, CYFRA21-1, and CA19-9 in the modeling group were significantly higher than those in the control group (all P<0.05). The ROC curve showed that the combined detection of CA125, CEA, CYFRA21-1, CA19-9, and SCC-Ag had high diagnostic value for lung adenocarcinoma, with an area under the curve (AUC) of 0.903 (95% CI: 0.865-0.954); Elevated levels of CEA, CA125, CA19-9, and CYFRA21-1 were independent risk factors for postoperative recurrence in lung adenocarcinoma patients (all P<0.05). Patients were divided into extremely low-risk group (total score<56 points), low-risk group (56 points≤total score<132 points), medium risk group (132 points≤total score<186 points), and high-risk group (total score≥186 points) through risk stratification. The survival curve results showed that there was a statistically significant difference in postoperative recurrence rate among patients with different risk stratification systems ( P<0.05). Conclusions:The combined detection of CA125, CEA, CYFRA21-1, CA19-9, and SCC-Ag has high diagnostic value for lung adenocarcinoma. Among them, CA125, CEA, CYFRA21-1, and CA19-9 have certain predictive value for postoperative recurrence in patients.
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Objective@#To investigate the epidemiological characteristics of varicella outbreaks in primary and middle schools, and to establish a risk predictive model, so as to provide scientific guidance for the prevention of varicella outbreaks in schools.@*Methods@#Based on a nested case-control study, primary and middle schools in 4 districts of Shanghai (Yangpu District and Jingan District) and Hangzhou (Xiaoshan District and Linping District) from January to December 2023 were selected to observe the status of varicella outbreaks. Associated factors of varicella outbreaks were investigated and used for establishing the predictive model, which was evaluated by the Hosmer-Lemeshow(H-L) goodness of fit test, receiver operating characteristic (ROC) curve, Calibration curve, decision curve analysis (DCA).@*Results@#A total of 98 varicella outbreaks were included, with 195 schools without varicella outbreaks during the same period as controls. Eight factors, including the availability of warm water in restroom, availability of hand soap in restroom, average class size, duration of student attendance at school per day, presence of a fulltime school doctor, hesitancy of the school principal towards varicella vaccination, and rates of first and second doses of varicella vaccination, were identified as potential factors for school varicella outbreaks, with statistically significant differences (χ2/Z=10.01, 20.49, 17.43, 9.74, 32.17, 6.60, 2.20, 3.39, P<0.05). The 8 variables above were employed to construct a risk predictive model, and Hosmer-Lemeshow goodness of fit test yielded a χ2 value of 5.863 (P>0.05); the area under the ROC curve (AUC) was 0.846 (95%CI=0.799-0.893); Calibration curve analysis indicated good consistency between predicted and actual values of the model. DCA demonstrated favorable predictive performance of the model over a wide range. @*Conclusions@#The predictive model for school varicella outbreaks demonstrates satisfactory accuracy and efficacy. It suggested to make good use of this prediction model and take relevant measures to reduce the risk of varicella transmission in schools.