Development and validation of a diagnostic prediction model for severe periventricular-intraventricular hemorrhage in newborns: insights from a retrospective analysis utilizing the MIMIC-III database

Abstract Objective: Periventricular-intraventricular hemorrhage is the most common type of intracranial bleeding in newborns, especially in the first 3 days after birth. Severe periventricular-intraventricular hemorrhage is considered a progression from mild periventricular-intraventricular hemorrhage and is often closely associated with severe neurological sequelae. However, no specific indicators are available to predict the progression from mild to severe periventricular-intraventricular in early admission. This study aims to establish an early diagnostic prediction model for severe PIVH. Method: This study was a retrospective cohort study with data collected from the MIMIC-III (v1.4) database. Laboratory and clinical data collected within the first 24 h of NICU admission have been used as variables for both univariate and multivariate logistic regression analyses to construct a nomogram-based early prediction model for severe periventricular-intraventricular hemorrhage and subsequently validated. Results: A predictive model was established and represented by a nomogram, it comprised three variables: output, lowest platelet count and use of vasoactive drugs within 24 h of NICU admission. The model's predictive performance showed by the calculated area under the curve was 0.792, indicating good discriminatory power. The calibration plot demonstrated good calibration between observed and predicted outcomes, and the Hosmer-Lemeshow test showed high consistency (p = 0.990). Internal validation showed the calculated area under a curve of 0.788. Conclusions: This severe PIVH predictive model, established by three easily obtainable indicators within the NICU, demonstrated good predictive ability. It offered a more user-friendly and convenient option for neonatologists.

A prediction model for coronary artery abnormalities in children with Kawasaki disease older than 5 years

Abstract Objective: Reliably prediction models for coronary artery abnormalities (CAA) in children aged > 5 years with Kawasaki disease (KD) are still lacking. This study aimed to develop a nomogram model for predicting CAA at 4 to 8 weeks of illness in children with KD older than 5 years. Methods: A total of 644 eligible children were randomly assigned to a training cohort (n = 450) and a validation cohort (n = 194). The least absolute shrinkage and selection operator (LASSO) analysis was used for optimal predictors selection, and multivariate logistic regression was used to develop a nomogram model based on the selected predictors. Area under the receiver operating characteristic curve (AUC), calibration curves, Hosmer-Lemeshow test, Brier score, and decision curve analysis (DCA) were used to assess model performance. Results: Neutrophil to lymphocyte ratio, intravenous immunoglobulin resistance, and maximum baseline z-score ≥ 2.5 were identified by LASSO as significant predictors. The model incorporating these variables showed good discrimination and calibration capacities in both training and validation cohorts. The AUC of the training cohort and validation cohort were 0.854 and 0.850, respectively. The DCA confirmed the clinical usefulness of the nomogram model. Conclusions: A novel nomogram model was established to accurately assess the risk of CAA at 4-8 weeks of onset among KD children older than 5 years, which may aid clinical decisionmaking.

Development and validation of a clinical prediction model for prognostic factors in patients with primary pontine hemorrhage

Abstract We aimed to develop a prognostic model for primary pontine hemorrhage (PPH) patients and validate the predictive value of the model for a good prognosis at 90 days. A total of 254 PPH patients were included for screening of the independent predictors of prognosis, and data were analyzed by univariate and multivariable logistic regression tests. The cases were then divided into training cohort (n=219) and validation cohort (n=35) based on the two centers. A nomogram was developed using independent predictors from the training cohort to predict the 90-day good outcome and was validated from the validation cohort. Glasgow Coma Scale score, normalized pixels (used to describe bleeding volume), and mechanical ventilation were significant predictors of a good outcome of PPH at 90 days in the training cohort (all P<0.05). The U test showed no statistical difference (P=0.892) between the training cohort and the validation cohort, suggesting the model fitted well. The new model showed good discrimination (area under the curve=0.833). The decision curve analysis of the nomogram of the training cohort indicated a great net benefit. The PPH nomogram comprising the Glasgow Coma Scale score, normalized pixels, and mechanical ventilation may facilitate predicting a 90-day good outcome.

Construction and Verification of A Nomogram Model for Predicting Invasive Risk of Ground Glass Nodules / 肿瘤防治研究

Objective To investigate the importance of a nomogram model based on biomarkers and CT signs in the prediction of the invasive risk of ground glass nodules. Methods A total of 322 patients with ground glass nodule, including 240 and 82 patients in the model and verification groups, respectively, were retrospectively analyzed. Independent risk factors for the invasive risk of ground glass nodules were screened out after using single and multiple Logistic analysis. R software was used to construct the nomogram model, and clinical decision curve analysis (DCA), receiver operating curve (ROC), and calibration curve were used for internal and external verification of the model. Results In this study, the independent risk factors for the invasive risk of ground glass nodules included systemic immune-inflammation index (SII), CYFRA21-1, edge, vascular cluster sign, and nodular consolidation tumor ratio (CTR). The area under the ROC curve of the constructed nomogram model had a value of 0.946, and that of the external validation group reached 0.932, which suggests the good capability of the model in predicting the invasive risk of ground glass nodules. The model was internally verified through drawing of calibration curves of Bootstrap 1000 automatic sampling. The results showed that the consistency index between the model and actual curves reached 0.955, with a small absolute error and good fit. The DCA curve revealed a good clinical practicability. In addition, nodule margin, vascular cluster sign, and CTR were correlated with the grade of pathological subtype of invasive adenocarcinoma. Conclusion A nomogram model based on biomarkers and CT signs has good value and clinical practicability in the prediction of the invasive risk of ground glass nodules.

Clinical radiomics nomogram and deep learning based on CT in discriminating atypical pulmonary hamartoma from lung adenocarcinoma / 安徽医科大学学报

Objective To discuss the value of clinical radiomic nomogram(CRN)and deep convolutional neural network(DCNN)in distinguishing atypical pulmonary hamartoma(APH)from atypical lung adenocarcinoma(ALA).Methods A total of 307 patients were retrospectively recruited from two institutions.Patients in institu-tion 1 were randomly divided into the training(n=184:APH=97,ALA=87)and internal validation sets(n=79:APH=41,ALA=38)in a ratio of 7∶3,and patients in institution 2 were assigned as the external validation set(n=44:APH=23,ALA=21).A CRN model and a DCNN model were established,respectively,and the performances of two models were compared by delong test and receiver operating characteristic(ROC)curves.A human-machine competition was conducted to evaluate the value of AI in the Lung-RADS classification.Results The areas under the curve(AUCs)of DCNN model were higher than those of CRN model in the training,internal and external validation sets(0.983 vs 0.968,0.973 vs 0.953,and 0.942 vs 0.932,respectively),however,the differences were not statistically significant(p=0.23,0.31 and 0.34,respectively).With a radiologist-AI com-petition experiment,AI tended to downgrade more Lung-RADS categories in APH and affirm more Lung-RADS cat-egories in ALA than radiologists.Conclusion Both DCNN and CRN have higher value in distinguishing APH from ALA,with the former performing better.AI is superior to radiologists in evaluating the Lung-RADS classification of pulmonary nodules.

Establishment and verification of a prognostic nomogram for survival of tongue squa-mous cell carcinoma patients who underwent cervical dissection / 北京大学学报(医学版)

Objective:To evaluate the prognostic significance of inflammatory biomarkers,prognostic nutritional index and clinicopathological characteristics in tongue squamous cell carcinoma(TSCC)patients who underwent cervical dissection.Methods:The retrospective cohort study consisted of 297 patients undergoing tumor resection for TSCC between January 2017 and July 2018.The study population was divided into the training set and validation set by 7:3 randomly.The peripheral blood indices of interest were preoperative neutrophil-to-lymphocyte ratio(NLR),lymphocyte-to-monocyte ratio(LMR),platelet-to-lymphocyte ratio(PLR),systemic immune-inflammation index(SII),systemic inflammation score(SIS)and prognostic nutritional index(PNI).Kaplan-Meier survival analysis and multivariable Cox regression analysis were used to evaluate independent prognostic factors for overall survival(OS)and disease-specific survival(DSS).The nomogram's accuracy was internally validated using concordance index,receiver operating characteristic(ROC)curve,area under the curve(AUC),calibration plot and decision curve analysis.Results:According to the univariate Cox regression analysis,clinical TNM stage,clinical T category,clinical N category,differentiation grade,depth of invasion(DOI),tumor size and pre-treatment PNI were the prognostic factors of TSCC.Multivariate Cox regression analysis revealed that pre-treatment PNI,clinical N category,DOI and tumor size were independent prognostic factors for OS or DSS(P＜0.05).Positive neck nodal status(N≥1),PNI≤50.65 and DOI＞2.4 cm were associated with the poorer 5-year OS,while a positive neck nodal status(N≥1),PNI≤50.65 and tumor size＞3.4 cm were associated with poorer 5-year DSS.The concordance index of the nomograms based on independent prognostic factors was 0.708(95％CI,0.625-0.791)for OS and 0.717(95％CI,0.600-0.834)for DSS.The C-indexes for external validation of OS and DSS were 0.659(95％CI,0.550-0.767)and 0.780(95％CI,0.669-0.890),respectively.The 1-,3-and 5-year time-dependent ROC analyses(AUC=0.66,0.71 and 0.72,and AUC=0.68,0.77 and 0.79,respec-tively)of the nomogram for the OS and DSS pronounced robust discriminative ability of the model.The calibration curves showed good agreement between the predicted and actual observations of OS and DSS,while the decision curve confirmed its pronounced application value.Conclusion:Pre-treatment PNI,clinical N category,DOI and tumor size can potentially be used to predict OS and DSS of patients with TSCC.The prognostic nomogram based on these variables exhibited good accurary in predicting OS and DSS in patients with TSCC who underwent cervical dissection.They are effective tools for predicting sur-vival and helps to choose appropriate treatment strategies to improve the prognosis.

Establishment and validation of nomogram of cancer specific survival of patients with hepatocellular carcinoma with negative alpha fetoprotein based on SEER Database / 吉林大学学报(医学版)

Objective:To discuss the factors related to the prognosis in the alpha fetoprotein(AFP)negative hepatocellular carcinoma(HCC)patients,and to construct the nomogram for predicting the survival time of the patients.Methods:The retrospective analysis on data of 2 064 cases of AFP negative HCC patients extracted from the Surveillance,Epidemiology,and End Results(SEER)Database was conducted,and all the patients were divided into training cohort and internal validation cohort at a ratio of 7∶3,and 101 AFP negative HCC patients from the Integrated Traditional Chinese and Western Medicine Hospital in Hunan Province were regarded as the external validation cohort.The univariate Cox regression analysis results were incorporated into the multivariate analysis,and the independent risk factors for the AFP negative HCC patients were obtained by multivariate Cox analysis to build a cancer specific survival(CSS)prognosis nomogram for the AFP negative HCC patients.The predictive efficacy and clinical utility of the nomogram were evaluated by time-dependent receiver operating characteristic curve(ROC),calibration plots,and decision curve analysis(DCA).The total score obtained from the nomogram was used for the risk stratification to compare the degree of risk discrimination between the nomogram and the American Joint Committee on Cancer(AJCC)staging system.Results:Ten independent risk factors were selected by multivariate Cox regression analysis to construct 3-year,4-year,and 5-year CSS prognostic nomograms for the AFP negative HCC patients,including the patient's age,pathological grade,surgical status,radiotherapy status,chemotherapy status,lung metastasis status,tumor size,tumor T stage,tumor M stage,and marital status.The area under curve(AUC)for the 3-year,4-year,and 5-year time-dependent ROC in the training cohort were 0.807(95%CI:0.786-0.828),0.804(95%CI:0.782-0.826),and 0.813(95%CI:0.790-0.835),respectively.In the internal validation cohort,they were 0.776(95%CI:0.743-0.810),0.772(95%CI:0.737-0.808),and 0.789(95%CI:0.752-0.826),and in the external validation cohort,they were 0.773(95%CI:0.677-0.868),0.746(95%CI:0.620-0.872),and 0.736(95%CI:0.577-0.895).The calibration plots verified that the nomogram fitted well with the perfect line.The DCA curve revealed that the net benefit of the nomogram was significatly higer than that of the AJCC staging system at certain probability thresholds compared with AJCC staging,the nomogram had a better ability to identify high-risk individuals.Conclusion:The serum AFP expression is one of the prognostic markers for the HCC patients.For those patients with AFP negative expression in serum,different considerations should be taken.The nomogram model based on multiple risk factors is a promising clinical tool for assessing the CSS in the AFP negative HCC patients.

Establishment of a nomogram prediction model for risk of COPD complicating pulmonary interstitial fibrosis / 重庆医学

Objective To analyze the influencing factors of chronic obstructive pulmonary disease(COPD)complicating pulmonary interstitial fibrosis,and to establish a nomogram prediction model.Methods The clinical data of 195 patients with COPD admitted and treated in this hospital from January 2019 to Feb-ruary 2021 were retrospectively analyzed.The factors possibly affecting the patients with COPD complicating pulmonary interstitial fibrosis were collected,and the patients were divided into 2 groups according to whether having pulmonary interstitial fibrosis.The independent risk factors were analyzed and screened by the multi-variate logistic regression.Then the nomogram prediction model was constructed.Results Among the includ-ed 195 cases of COPD in this study,there were 50 cases(25.64％)of complicating pulmonary interstitial fi-brosis.The univariate and multivariate logistic regression analysis results showed that the smoking history,duration of COPD,frequency of acute exacerbations onset,serum transforming growth factor β1(TGF-β1),basic fibroblast growth factor(bFGF)and angiotensin Ⅱ(AngⅡ)were the independent influencing factors of COPD complicating pulmonary interstitial fibrosis(P＜0.05).The nomogram model was constructed accord-ing to the results of multivariate analysis results,and the area under the receiver operating characteristic(ROC)curve was 0.956(95％CI:0.930-0.983),the average absolute error of internal verification by the Bootstrap method was 0.025,and the prediction model performance basically fitted the ideal model.Conclusion The nomogram model constructed by this study for predicting the pulmonary interstitial fibrosis in COPD patients has high accuracy and distinction degree.

Development and validation of a prediction model for severe community-acquired pneumonia in adults based on peripheral blood inflammatory indicators / 国际检验医学杂志

Objective To explore the development and validation of a prediction model for severe communi-ty-acquired pneumonia in adults based on peripheral blood inflammatory indicators.Methods Venous blood samples of 204 community-acquired pneumonia in adults patients admitted to 7 hospitals in Chongqing area from April 2021 to August 2022 were collected to detect C-reactive protein(CRP),peripheral white blood cell count(WBC),neutrophil to lymphocyte ratio(NLR),cytokines,lymphocyte subgroups and neutrophil CD64 index.All of patients were divided into a training group and a validation group according to the time of admis-sion.Univariate and multivariate Logistic regression were used to analyze the data of the training group,the characteristic factors of severe progression for pneumonia were selected to construct the nomogram model,and the data of the validation group was used to verify the model.The receiver operating characteristic(ROC)curve,calibration curve and decision curve analysis(DCA)were used to evaluate the prediction ability of the model for severe community-acquired pneumonia in adults.Results Logistic regression analysis showed that age,CRP,WBC,interleukin(IL)-4/interferon gamma ratio and IL-6/IL-10 ratio were independent risk factors for severe community-acquired pneumonia in adults.The area under the ROC curve of the nomogram model in the training group and the validation group was 0.893 and 0.880,respectively.The calibration curve and DCA results shown that the model had a good prediction effect for severe community-acquired pneumonia in adults.Conclusion The inflammatory indicators included in this model are simple and easy to obtain clinically.This model with good differentiation and accuracy,it can be used as a practical tool to predict severe community-ac-quired pneumonia in adults,and has certain clinical application value.

Role of serum cholinesterase and inflammatory markers in the prognosis of stage ⅠA -ⅢA breast cancer / 国际肿瘤学杂志

Objective:To analyze the preoperative and postoperative serum cholinesterase (CHE) levels in patients with stage ⅠA-ⅢA breast cancer who underwent surgical treatment, and to explore the roles of them and peripheral blood inflammatory markers in the prognostic prediction of stage ⅠA-ⅢA breast cancer.Methods:The relevant blood indicators of 152 patients with stage ⅠA-ⅢA breast cancer who underwent surgery and postoperative adjuvant therapy from January 2012 to December 2017 at Affiliated Huai'an Hospital of Xuzhou Medical University were retrospectively studied. The optimal cut-off values of serum CHE levels and peripheral blood inflammatory markers [systemic immune-inflammation index (SII) and systemic inflammatory response index (SIRI) ] were calculated using X-tile 3.6.1 software. Patients were categorized into low and high value groups based on the optimal cutoff values. Kaplan-Meier curves and Cox regression analysis were used to assess the correlation between CHE and peripheral blood inflammation indexes and disease-free survival (DFS). Spearman correlation coefficient and Wilcoxon test were used to assess the correlation and changes of CHE and inflammation indexes before and after treatment. In addition to this, a nomogram prediction model was conscturcted based on independent prognostic factors by R software, which was validated by Bootstrap method.Results:The CHE levels of patients before and after treatment was 8 645.0 (7 251.3, 10 229.3) and 9 309.0 (7 801.0, 10 835.3) U/L, respectively, with a statistically significant difference ( Z=2.73, P=0.006) .The optimal cut-off values for postoperative CHE (Post-CHE), postoperative SII (Post-SII), and postoperative SIRI (Post-SIRI) associated with patients' DFS, being 7 773 U/L, 741, and 0.9, respectively. Univariate analysis showed that tumor size (≤2 cm vs.>2 cm and ≤5 cm: HR=2.55, 95% CI: 1.30-4.99, P=0.006; ≤2 cm vs. >5 cm: HR=8.95, 95% CI: 4.15-19.32, P<0.001), number of positive lymph nodes ( HR=3.84, 95% CI: 2.24-6.58, P<0.001), clinical stage (stage Ⅰ vs. stage Ⅱ: HR=1.52, 95% CI: 0.68-3.39, P=0.309, stage Ⅰ vs. stage Ⅲ: HR=8.12, 95% CI: 3.76-17.55, P<0.001), Ki-67 expression ( HR=2.19, 95% CI: 1.24-3.84, P=0.007), whether radiotherapy ( HR=2.05, 95% CI: 1.19-3.53, P=0.010), Post-CHE ( HR=6.81, 95% CI: 3.94-11.76, P<0.001), Pre-neutrophil to lymphocyte ratio (NLR) ( HR=1.11, 95% CI: 1.02-1.21, P=0.014), Post-NLR ( HR=5.23, 95% CI: 2.78-9.85, P<0.001), Pre-platelet to lymphocyte ratio (PLR) ( HR=2.08, 95% CI: 1.01-4.26, P=0.046), Post-PLR ( HR=7.11, 95% CI: 3.78-13.37, P<0.001), Pre-lymphocyte to monocyte ratio (LMR) ( HR=0.37, 95% CI: 0.20-0.66, P<0.001), Post-LMR ( HR=0.23, 95% CI: 0.13-0.41, P<0.001), Pre-SII ( HR=1.81, 95% CI: 1.05-3.12, P=0.033), Post-SII ( HR=6.12, 95% CI: 3.48-10.76, P<0.001), Pre-SIRI ( HR=2.12, 95% CI: 1.24-3.63, P=0.006), and Post-SIRI ( HR=4.93, 95% CI: 2.87-8.48, P<0.001) were associated with DFS in patients with stage ⅠA-ⅢA breast cancer. Multivariate analysis showed that tumor size (≤2 cm vs. >2 cm and ≤5 cm: HR=2.86, 95% CI: 1.41-5.78, P=0.003; ≤2 cm vs. >5 cm: HR=3.72, 95% CI: 1.50-9.26, P=0.005), number of positive lymph nodes ( HR=4.66, 95% CI: 2.28-9.54, P<0.001), Ki-67 expression ( HR=2.13, 95% CI: 1.15-3.94, P=0.016), Post-CHE ( HR=0.18, 95% CI: 0.10-0.33, P<0.001), Post-SII ( HR=2.71, 95% CI: 1.39-5.29, P=0.004), and Post-SIRI ( HR=3.77, 95% CI: 1.93-7.36, P<0.001) were independent influencing factors for DFS in patients with stage ⅠA-ⅢA breast cancer. Kaplan-Meier survival curve analysis showed that the median DFS of patients in the Ki-67<30% group was not reached, and the median DFS of patients in the Ki-67≥30% group was 89.0 months, and the 3- and 5-year DFS rates were 84.9% vs. 75.9% and 80.8% vs. 64.3%, respectively, with a statistically significant difference ( χ2=7.65, P=0.006) ; the median DFS of patients in the tumor size≤2 cm group was not reached, the median DFS of the 2 cm<tumor size≤5 cm group was 93.5 months, and the median DFS of the tumor size>5 cm group was 26.3 months, and the 3- and 5-year DFS rates were 95.5% vs. 74.6% vs. 42.1%, 86.3% vs. 68.6% vs. 25.3%, with a statistically significant difference ( χ2=40.46, P<0.001) ; the median DFS of patients in the group with the number of positive lymph nodes<4 was not reached, and the median DFS of the group with the number of positive lymph nodes≥4 was 30.7 months, and the 3- and 5-year DFS rates were 87.9% vs. 46.4% and 81.4% vs. 28.6%, respectively, with a statistically significant difference ( χ2= 47.34, P<0.001) ; the median DFS of patients in the Post-CHE<7 773 U/L group was 47.3 months, and the median DFS of patients in the Post-CHE≥7 773 U/L group was not reached, and the 3- and 5-year DFS rates were 52.8 % vs. 88.6% and 27.8% vs. 81.2%, respectively, with a statistically significant difference ( χ2=62.17, P<0.001) ; the median DFS was not achieved in patients in the Post-SII<741 group, and the median DFS was 30.5 months in the Post-SII≥741 group, with 3- and 5-year DFS rates of 88.1% vs. 38.5% and 80.1% vs. 30.8%, respectively, with a statistically significant difference ( χ2=50.78, P<0.001) ; the median DFS of patients in Post-SIRI<0.9 group was not reached, the median DFS of Post-SIRI≥0.9 group was 33.3 months, and the 3- and 5-year DFS rates were 93.5% vs. 46.7% and 84.9% vs. 39.9%, respectively, with a statistically significant difference ( χ2=40.67, P<0.001). Spearman correlation analysis revealed that Post-CHE was not correlated with Post-SII ( r=-0.111, P=0.175), and Post-CHE was negatively correlated with Post-SIRI ( r=-0.228, P=0.005). Post-treatment CHE was elevated compared to preoperative and the median DFS was not reached in patients with elevated CHE group and 61.8 months in patients with reduced CHE group after treatment, with a statistically significant difference ( χ2=25.67, P<0.001). The nomogram based on independent prognostic factors had good predictive performance, with a C-index of 0.893. Conclusion:The serum CHE level exhibited a significant increase following treatment. Postoperative serum CHE combined with SII and SIRI can effectively predict DFS in patients with stage ⅠA-ⅢA breast cancer, and the prognosis of patients with elevated CHE after treatment is better. The nomogram constructed based on independent prognostic factors has good predictive performance for DFS in breast cancer patients.

Exploration of prognostic factors and nomogram construction for advanced non-small cell lung cancer treated with immunotherapy based on hematologic indexes / 国际肿瘤学杂志

Objective:To explore influencing factors affecting the prognosis of patients with advanced non-small cell lung cancer (NSCLC) receiving immunotherapy based on hematologic indexes, thus to construct and evaluate a nomogram prediction model.Methods:The clinical data of 80 patients with advanced NSCLC treated with programmed death-1 inhibitor monotherapy or combination regimen from January 2018 to June 2020 at the Affiliated Hospital of North China University of Science and Technology and Tangshan People's Hospital were retrospectively analyzed. Hematologic indexes at the baseline, the optimal remission and the progressive disease (PD) were collected separately, and independent influencing factors for patient prognosis were analyzed using Cox proportional hazards regression model. A nomogram prediction model was constructed based on the results of the multifactorial analysis, and the predictive performance of the model was evaluated by receiver operating characteristic (ROC) curve, concordance index (C-index) and calibration curves.Results:As of the follow-up cut-off date, of the 80 patients, 63 had PD, with a median overall survival (OS) of 16.9 months. Univariate analysis showed that, age ( HR=2.09, 95% CI: 1.17-3.74, P=0.013) , number of treatment lines ( HR=2.23, 95% CI: 1.21-4.12, P=0.010) , lymphocyte to monocyte ratio (LMR) at the baseline ( HR=0.75, 95% CI: 0.57-0.97, P=0.028) , D-dimer ( HR=1.00, 95% CI: 1.00-1.00, P=0.002) and lactate dehydrogenase (LDH) ( HR=1.01, 95% CI: 1.00-1.01, P=0.006) at the optimal remission, haemoglobin ( HR=0.97, 95% CI: 0.96-0.99, P<0.001) , D-dimer ( HR=1.00, 95% CI: 1.00-1.00, P=0.002) , C-reactive protein ( HR=1.01, 95% CI: 1.00-1.01, P=0.011) , albumin (ALB) ( HR=0.91, 95% CI: 0.87-0.96, P=0.001) , neutrophil to lymphocyte ratio (NLR) ( HR=1.16, 95% CI: 1.05-1.27, P=0.002) and LMR ( HR=0.62, 95% CI: 0.42-0.90, P=0.012) at the PD were all influencing factors for the prognosis of advanced NSCLC patients receiving immunotherapy. Least absolute shrinkage and selection operator regression were used to screen the variables for P<0.10 in the univariate analysis, and nine possible influencing factors were obtained, which were age, fibrinogen and LDH at the optimal remission, haemoglobin, D-dimer, C-reactive protein, LDH, ALB and LMR at the PD. Multivariate analysis of the above variables showed that, age ( HR=0.91, 95% CI: 0.86-0.97, P=0.004) , LDH ( HR=1.01, 95% CI: 1.00-1.01, P=0.013) and ALB ( HR=0.82, 95% CI: 0.67-0.99, P=0.041) at the PD were independent influencing factors for the prognosis of patients with advanced NSCLC who received immunotherapy. The area under curve of the nomogram predicting model based on the above indexes, 1- and 2-year OS rates of patients were 0.77 (95% CI: 0.65-0.89) and 0.75 (95% CI: 0.66-0.88) , respectively, and C-index was 0.71 (95% CI: 0.64-0.78) , the calibration curves showed good consistency between predicted and actual probability of occurrence. Patients in the low-risk group ( n=40) had a median OS of 29.9 months (95% CI: 22.5 months-NA) , which was significantly better than that of the high-risk group ( n=40) [13.4 months (95% CI: 11.4-23.5 months) , χ2=11.30, P<0.001]. Conclusion:Age, LDH and ALB at the PD are independent influencing factors affecting the prognosis of patients with advanced NSCLC receiving immunotherapy, and the nomogram model constructed based on the above indexes has good differentiation and calibration for predicting 1- and 2-year OS rates in advanced NSCLC patients receiving immunotherapy.

Investigation on the risk of recurrent immune thrombocytopenia in children and establishment of a predictive model / 基础医学与临床

Objective To investigate the recurrence of immune thrombocytopenia(ITP)in children and to establish a predictive model.Methods A total of 288 children with ITP admitted to Children's Hospital of Wujiang District and Children's Hospital Affiliated to Suzhou University from January 2018 to April 2022 were collected.The factors potentially related to the recurrence of ITP in children were screened.The children in the model group were divided into 2 groups according to the presence or absence of recurrence and the indicators of the 2 groups were compared.After screening the potential influencing factors by LASSO regression and the independent influencing factors of relapse in children with ITP patients by Logstic regression analysis,we constructed a column-line graph model by using R language and validated it.Results A total of 37(18.47%)of 201 patients in the model group experienced relapse.The age,blood type,duration of disease before treatment,antecedent infections,bleeding,initial treatment regimen,antinuclear antibody titer,initial count and mean platelet volume,initial platelet distri-bution width,initial peripheral blood lymphocyte count and time length to effective platelet count after treatment were found in the recurrence group versus the non-recurrence group The difference was statistically significant(P＜0.05).The results of multifactorial logistic regression analysis performed on the basis of LASSO regression showed that blood type,duration of illness before treatment,antecedent infection,initial treatment regimen,ini-tial peripheral blood lymphocyte count,and time to effective platelet count after treatment were independent influ-ences on the conversion of cough variant asthma to classic asthma in children.Based on the results of the multifac-torial analysis,a column chart model for predicting ITP recurrence in children was developed in R.The results of the receiver operating characteristic(ROC)analysis showed that the area under curve(AUC)of the column chart model for predicting ITP recurrence in children in the modeling group was 0.867[95%CI(0.796,0.938)]with a sensitivity of 84.2%and a specificity of 73.1%,and that in the validation group,the AUC was 0.838[95%CI(0.765),0.911]with a sensitivity of 82.3%and a specificity of 78.4%,0.911)]sensitivity was 82.3%and specificity was 78.4%.The Bootstrap method was used to repeat the sampling 1000 times,and the validation group was used for validation.The results of the calibration curve showed that the prediction curves of the model group and the validation group were basically fitted with the standard curve,suggesting that the model prediction accuracy was high.The results of the decision curve analysis of the model group showed that the net benefit rate of patients was greater than zero when the probability threshold of the column line graph model of pre-dicting ITP recurrence in children was 0.15-0.75.Conclusions ITP recurrence in children is mainly affected by the patient's age,blood type,and pre-treatment course of the disease,and the column-line diagram model based on these factors has a high accuracy and differentiation for ITP recurrence in parenting children.

The predictive value of a nomogram model based on aspartate aminotransferase-platelet ratio index for hepatocellular carcinoma recurrence after radiofrequency ablation / 介入放射学杂志

Objective To investigate the relationship between aspartate aminotransferase-platelet ratio index(APRI)and hepatocellular carcinoma(HCC)recurrence after radiofrequency ablation(RFA),and to construct a nomogram model for predicting the prognosis.Methods The clinical data of a total of 204 patients,whose initial diagnosis was HCC and received RFA at the Wujin Hospital Affiliated to Jiangsu University of China between January 2017 and December 2020,were retrospectively analyzed.The optimal cut-off value of APRI was determined using receiver operating characteristic(ROC)curve.Kaplan-Meier curves were plotted to estimate the recurrence-free survival(RFS)of high-APRI group patients and low-APRI group patients.The independent predictors of HCC recurrence after RFA were identified by using univariate and multivariate Cox regression analysis,and significant variables were selected to construct a nomogram model.The predictive ability of the nomogram model for HCC recurrence was evaluated by the consistency index(C-index)and calibration curves.Results The incidence of HCC recurrence after RFA was 57.4%(117/204),the optimal cut-off value of APRI for predicting HCC recurrence was 0.501,and the area under curve(AUC)value was 0.678(95%CI=0.603-0.752).High-APRI group(≥0.501)had 121 patients and low-APRI group(＜0.501)had 83 patients.High APRI index was significantly correlated with low RFS(χ2=12.929,P＜0.01).The univariate and multivariate Cox regression analysis revealed that the number of tumors(HR=1.541,95%CI=1.039-2.286,P=0.031),maximum tumor diameter(HR=1.461,95%CI=1.011-2.112,P=0.044),serum AFP level(HR=2.286,95%CI=1.576-3.318,P＜0.01)and APRI index(HR=1.873,95%CI=1.257-2.790,P=0.002)were the independent risk factors for HCC recurrence.Based on the above four variables,a nomogram model for predicting HCC recurrence after RFA was constructed,the C-index was 0.769(95%CI=0.676-0.862),and the AUC values for 1-,2-,and 3-year RFS prediction were 0.707,0.719,and 0.707,respectively.The calibration curves showed that a good consistency existed between the predicted probability and actual probability.Conclusion The nomogram model based on APRI and tumor biological characteristics has an excellent predictive ability for HCC recurrence after RFA.(J Intervent Radiol,2024,32:38-43)

Application of MRI-based radiomics model in predicting the tumor response in patients with advanced hepatocellular carcinoma after transcatheter arterial chemoembolization / 介入放射学杂志

Objective To develop a nomogram model based on the clinical features and the radiomics texture analysis of multimodal magnetic resonance imaging(MRI),so as to predict the tumor response in patients with advanced hepatocellular carcinoma(HCC)3 months after receiving transcatheter arterial chemoembolization(TACE).Methods A total of 105 patients with advanced HCC,whose diagnosis was pathologically-confirmed at the Suzhou Municipal Ninth People's Hospital between January 2017 and December 2021,were enrolled in this study.The patients were randomly divided into training group(n=63)and verification group(n=42).Before chemotherapy,T1WI,T2WI,dynamic contrast-enhanced(DCE)scan,and diffusion-weighted imaging(DWI)were performed by using a 3.0T MRI scanner.A.K.software was used to extract the texture.Three months after chemotherapy,according to the modified Response Evaluation Criteria in Solid Tumors(mRECIST)the patients were divided into response group(n=63)and non-response group(n=42).Results Compared with the response group,in the non-response group the percentage of Child-Pugh grade B and BCLC stage C was obviously higher and the serum alpha fetoprotein(AFP)level was remarkably elevated(P＜0.05).A.K.software extracted 396 MRI texture features,and LASSO regression analysis screened out 6 optimal predictors.The radiation score(Rad-score)was calculated by ROC.The AUC of Rad-score for predicting tumor non-response after TACE by ROC in the training group and verification group were 0.842 and 0.803 respectively.Multivariate logistic regression model analysis showed that AFP≥50 ng/mL(OR=1.568,95%CI=1.234-1.902,P=0.003),Child-Pugh grade B(OR=1.754,95%CI=1.326-2.021,P=0.001),BCLC stage C(OR=1.847,95%CI=1.412-2.232,P=0.001)and Rad-score(OR=2.023,95%CI=1.569-2.457,P＜0.001)were the independent risk factors for tumor non-response.Clinico-radiomics combination had the highest AUC value for predicting tumor non-response.The correction curve showed that the nomogram model had a good agreement.Conclusion The quantitative score of radiomics texture analysis of multimodal MRI has a certain value in predicting tumor non-response in advanced HCC patients 3 months after TACE,and the nomogram model,which is constructed if combined with clinical factors,carries good practical potential.(J Intervent Radiol,2024,32:63-68)

Construction and validation of a model for predicting the risk of in-hospital cardiac arrest in emergency rooms / 中华急诊医学杂志

Objective:The predictive model of cardiac arrest in the emergency room was constructed and validated based on Logistic regression.Methods:This study was a retrospective cohort study. Patients admitted to the emergency room of the First Affiliated Hospital of Xinjiang Medical University from January 2020 to July 2021 were included. The general information, vital signs, clinical symptoms, and laboratory examination results of the patients were collected, and the outcome was cardiac arrest within 24 hours. The patients were randomly divided into modeling and validation group at a ratio of 7:3. LASSO regression and multivariable logistic regression were used to select predictive factors and construct a prediction model for cardiac arrest in the emergency room. The value of the prediction model was evaluated using the area under the receiver operator characteristic curve (AUC), calibration curve, and decision curve analysis (DCA).Results:A total of 784 emergency room patients were included in the study, 384 patients occurred cardiac arrest. The 10 variables were ultimately selected to construct a risk prediction model for cardiac arrest: Logit( P)= -4.503+2.159×modified early warning score (MEWS score)+2.095×chest pain+1.670×abdominal pain+ 2.021×hematemesis+2.015×cold extremities+5.521×endotracheal intubation+0.388×venous blood lactate-0.100×albumin+0.768×K ++0.001×D-dimer. The AUC of the model group was 0.984 (95% CI: 0.976-0.993) and that of the validation group was 0.972 (95% CI: 0.951-0.993). This prediction model demonstrates good calibration, discrimination, and clinical applicability. Conclusions:Based on the MEWS score, chest pain, abdominal pain, hematemesis, cold extremities, tracheal intubation, venous blood lactate, albumin, K +, and D-dimer, a predictive model for cardiac arrest in the in-hospital emergency room was constructed to predict the probability of cardiac arrest in emergency room patients and adjust the treatment strategy in time.

Establishment of a nomogram model to predict sepsis in patients with multiple trauma / 中华急诊医学杂志

Objective:To explore the risk factors of sepsis in patients with multiple trauma and construct a nomogram prediction model.Methods:The data of patients with multiple injuries admitted to the emergency intensive care unit (EICU) of the General Hospital of Ningxia Medical University from January 2021 to April 2022 were respectively collected. Inclusion criteria: (1) meet the diagnostic criteria for multiple injuries; (2) the time from injury to admission ≤ 24 hours; (3) age>18 years old; (4) all examination or rescue measures were approved by the patient or the patient's family; (5) the patient's clinical data were complete. The patients were divided into sepsis group and non-sepsis group according to the definition of Sepsis 3.0 at the 28-day of EICU hospitalization. The receiver operating characteristic curve was drawn. Logistic regression analysis was applied to determine the independent predictors for sepsis, and the nomogram was constructed.Results:A total of 291 patients were included, including 102 in the sepsis group and 189 in the non-sepsis group. Multivariate logistic analysis revealed that age, acute physiology and chronic health status score (APACHE) Ⅱ, Glasgow Coma Scale (GCS), injury severity score (ISS), sequential organ failure assessment (SOFA) within 24 hours after admission, blood transfusion frequency, the application of norepinephrine, mechanical ventilation, pathogenic culture results, and history of diabetes were independent factors influencing the occurrence of sepsis. A nomogram model was constructed by combining these variables (AUC=0.913, 95% CI: 0.847-0.942), and the model had a good fitting calibration curve. Conclusions:The nomogram constructed by age, APACHE-Ⅱ, GCS score, SOFA score, ISS score, number of blood transfusions, mechanical ventilation, norepinephrine drug use, pathogenic culture and diabetes has a good predictive value for sepsis in patients with multiple trauma in the later stage, which is worth promoting.

Predictive value of inflammatory markers on the risk of stroke-associated pneumonia in acute ischemic stroke patients / 临床神经病学杂志

Objective To explore the predictive value of inflammatory markers for stroke-associated pneumonia(SAP)in patients with acute ischemic stroke(AIS)based on the nomogram model.Methods According to whether pneumonia occurred,259 AIS patients were divided into SAP group(81 cases)and non-SAP group(178 cases).The clinical data of the two groups were compared.The systemic inflammatory response index(SIRI),systemic immunoinflammatory index(SII)and neutrophil to lymphocyte ratio(NLR)were calculated according to the formula.The variables with statistically significant differences were included in the multivariate binary Logistic regression model to screen out the independent risk factors for SAP in AIS patients.The independent risk factors were used to construct a predictive model,and the predictive ability of the two models,which only included traditional factors and included inflammatory indicators at the same time,was further compared from the aspects of discrimination,calibration,clinical practicability and so on.Reclassification analysis was used to evaluate the extent to which the nomogram model improved the predictive value of SAP risk in AIS patients.Results Compared with those in the non-SAP group,the rates of smoking,diabetes,dysphagia,leukocytes,neutrophils,lymphocytes,triglyceride level,NIHSS score on admission,SIRI,SII and NLR were significantly increased in the SAP group,and the rate of hypertension was decreased(all P＜0.05).Diabetes mellitus(OR =2.505,95%CI:1.070-5.850,P =0.034),dysphagia(OR =3.492,95%CI:1.501-8.119,P =0.004),NIHSS score on admission(OR = 1.310,95%CI:1.188-1.446,P＜0.001),SIRI(OR =2.417,95%CI:1.327-4.401,P =0.008),NLR(OR =1.434,95%CI:1.101-1.860,P =0.007)were independent risk factors for SAP in AIS patients.The area under the curve was 0.788(95%CI:0.725-0.852,P＜0.001)for the prediction model without inflammatory factors and 0.884(95%CI:0.838-0.930,P＜0.001)for the prediction model with independent risk factors.The calibration curve showed a good consistency between the predicted risk and the observed results.The decision curve showed that the model had a significant net benefit for predicting SAP.In addition,by calculating the net reclassification index(NRI)and the comprehensive discriminant improvement index(IDI),it was found that the nomogram model had a significant improvement in predicting the risk of SAP in AIS patients.Internal verification also proves the reliability of the nomogram model.Conclusions SIRI and NLR are independent predictors of SAP in AIS patients on admission.Adding SIRI and NLR to the traditional model can significantly improve the ability to identify the risk of SAP occurrence in AIS patients.

Establishment of a Predictive Model for Chronic Cough after Pulmonary Resection / 中国肺癌杂志

BACKGROUND@#Chronic cough after pulmonary resection is one of the most common complications, which seriously affects the quality of life of patients after surgery. Therefore, the aim of this study is to explore the risk factors of chronic cough after pulmonary resection and construct a prediction model.@*METHODS@#The clinical data and postoperative cough of 499 patients who underwent pneumonectomy or pulmonary resection in The First Affiliated Hospital of University of Science and Technology of China from January 2021 to June 2023 were retrospectively analyzed. The patients were randomly divided into training set (n=348) and validation set (n=151) according to the principle of 7:3 randomization. According to whether the patients in the training set had chronic cough after surgery, they were divided into cough group and non-cough group. The Mandarin Chinese version of Leicester cough questionnare (LCQ-MC) was used to assess the severity of cough and its impact on patients' quality of life before and after surgery. The visual analog scale (VAS) and the self-designed numerical rating scale (NRS) were used to evaluate the postoperative chronic cough. Univariate and multivariate Logistic regression analysis were used to analyze the independent risk factors and construct a model. Receiver operator characteristic (ROC) curve was used to evaluate the discrimination of the model, and calibration curve was used to evaluate the consistency of the model. The clinical application value of the model was evaluated by decision curve analysis (DCA).@*RESULTS@#Multivariate Logistic analysis screened out that preoperative forced expiratory volume in the first second/forced vital capacity (FEV1/FVC), surgical procedure, upper mediastinal lymph node dissection, subcarinal lymph node dissection, and postoperative closed thoracic drainage time were independent risk factors for postoperative chronic cough. Based on the results of multivariate analysis, a Nomogram prediction model was constructed. The area under the ROC curve was 0.954 (95%CI: 0.930-0.978), and the cut-off value corresponding to the maximum Youden index was 0.171, with a sensitivity of 94.7% and a specificity of 86.6%. With a Bootstrap sample of 1000 times, the predicted risk of chronic cough after pulmonary resection by the calibration curve was highly consistent with the actual risk. DCA showed that when the preprobability of the prediction model probability was between 0.1 and 0.9, patients showed a positive net benefit.@*CONCLUSIONS@#Chronic cough after pulmonary resection seriously affects the quality of life of patients. The visual presentation form of the Nomogram is helpful to accurately predict chronic cough after pulmonary resection and provide support for clinical decision-making.

Construction of a column-line graph for predicting the probability of survival in early-stage elderly HER2-positive breast cancer based on the SEER database / 中国临床药理学与治疗学

AIM: To construct column-line plots to predict survival in elderly patients with early-stage HER2-positive breast cancer using the Surveillance, Epidemiology and End Results (SEER) database. METHODS: 5 220 (based on the era of single-targeted therapy) and 1 176 (based on the era of dual-targeted therapy) patients screened in the SEER database were randomized into a training group and an internal validation group. COX proportional risk regression was used to screen survival-related predictors and build a column-line graphical model, and the accuracy and utility of the model were tested using the consistency index (C-index), calibration curves, and time-dependent ROC curves. Patients receiving chemotherapy and non-chemotherapy were statistically paired using two-group propensity score matching, and subgroup analyses were performed on the screened variables. RESULTS: The single-targeted therapy era line graph was constructed from seven variables: age, marital status, T-stage, N-stage, surgery, chemotherapy, and radiotherapy. The dual-targeted therapy era line graph was constructed from five variables: age, AJCC staging, surgery, chemotherapy, and radiotherapy. The results of the subgroup analysis showed that older HER2-positive breast cancer patients who received chemotherapy had better OS. CONCLUSION: Based on the SEER database, an accurate column-line graph predicting survival in elderly patients with early-stage HER2-positive breast cancer was established and validated. This study suggests that chemotherapy increases survival benefit in elderly patients.

Development and validation of a nomogram diagnostic model for the diagnosis of Prosthetic Joint Infections based on serum and joint fluid inflammatory markers / 中华骨科杂志

Objective:To construct a column-line diagram diagnostic model based on serum and joint fluid inflammatory markers for the diagnosis of periprosthetic joint infections (PJI) after joint arthroplasty and to validate its predictive ability.Methods:The clinical data of 181 patients diagnosed with PJI or aseptic loosening in the Department of Orthopedics of the First Affiliated Hospital of Chongqing Medical University from January 2015 to June 2020 were retrospectively collected as a modeling group. The best indicators for diagnosing PJI were screened by lasso regression, single-factor and multifactor analysis. By comprehensively considering the weights and intrinsic connections of the indicators, a column-line diagram diagnostic model was constructed and used to develop a clinical decision support system (CDSS). Prospectively, the clinical data of patients diagnosed with PJI or aseptic loosening in the Department of Orthopedics of the First Hospital of Chongqing Medical University from July 2020 to December 2022 were collected as a validation group, and the diagnostic performance of the column-line diagram model was externally validated by methods such as receiver operating characteristic curve (ROC).Results:There were 85 cases of PJI in the 181 cases modeling group and 23 cases of PJI in the 49 cases validation group. Among the 27 potential factors analyzed by lasso regression analysis, body mass index (BMI), blood tests including platelet (PLT), absolute lymphocyte value, interferon γ (IFN-γ), ESR, IL-6, C-reactive protein, D-dimer, and joint fluid tests including C-reactive protein, IL-1, IL-4, IL-6, percentage of multinucleated neutrophils (PMN%), and CD64 may be potential indicators for the diagnosis of PJI. Univariate found significant differences between hematologic tests including sedimentation, C-reactive protein, IL-6, D-dimer and joint fluid tests including C-reactive protein, joint fluid CD64 index, C-reactive protein, IL-1, IL-4, IL-6, PMN%( P<0.05). Further multifactorial regression analysis screened serum IL-6, D-dimer, joint fluid CD64 index, C-reactive protein, IL-1, IL-4, IL-6, and percentage of multinucleated neutrophils, and based on that, the column-line graph model and CDSS system were constructed. The area under the ROC in the validation group was 0.978, and the AUC in the internal validation was 0.995; the C-index of the calibration curve was 99.50%, and the C-index of the internal validation was 99.53%, suggesting that the column-line diagram model has a good predictive ability. Conclusions:The column-line diagram for diagnosing PJI based on multiple diagnostic indicators showed good diagnostic performance. The CDSS system constructed by column-line diagrams could assist clinicians in diagnosing PJI and making reasonable strategies in time.