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.

Significance of radiologic extranodal extension in locoregionally advanced nasopharyngeal carcinoma with lymph node metastasis: a comprehensive nomogram

Abstract Objective We aimed to assess the significance of rENE and creat a predictive tool (nomogram) for estimating Overall Survival (OS) in locoregionally advanced Nasopharyngeal Carcinoma (NPC) patients with Lymph Node Metastasis (LNM) based on their clinical characteristics and Radiologic Extranodal Extension (rENE). Methods Five hundred and sixty-nine NPC patients with LNM were randomly divided into training and validation groups. Significant factors were identified using univariate and multivariate analyses in the training cohort. Then, the nomogram based on the screening results was established to predict the Overall Survival (OS). Calibration curves and the Concordance index (C-index) gauged predictive accuracy and discrimination. Receiver Operating Characteristic (ROC) analysis assessed risk stratification, and clinical utility was measured using Decision Curve Analysis (DCA). The nomogram's performance was validated for discrimination and calibration in an independent validation cohort. Results A total of 360 (63.2%) patients were present with radiologic extranodal extension at initial diagnosis. Patients with rENE had significantly lower OS than other patients. Multivariate analysis identified the five factors, including rENE, for the nomogram model. The C-index was 0.75 (0.71-0.78) in the training cohort and 0.76 (0.69-0.83) in the validation cohort. Notably, the nomogram outperformed the 8th TNM staging system, as evident from the higher AUC values (0.77 vs. 0.60 for 2 year and 0.75 vs. 0.65 for 3 year) and well-calibrated calibration curves. Decision curve analysis indicated improved Net Benefit (NB) with the nomogram for predicting OS. The log-rank test confirmed significant survival distinctions between risk groups in both training and validation cohorts. Conclusions We demonstrated the prognostic value of rENE in nasopharyngeal carcinoma and developed a nomogram based on rENE and other factors to provide individual prediction of OS for locoregionally advanced nasopharyngeal carcinoma with lymph node metastasis. Level of evidence: III.

Establishment and validation of nomogram prediction model of cefoperazone/sulbactam-induced thrombocytopenia / 中国药房

OBJECTIVE To explore the predictive factors of cefoperazone/sulbactam-induced thrombocytopenia in adult inpatients， and to establish and validate the nomogram prediction model. METHODS Data of adult inpatients treated with cefoperazone/sulbactam in Xi’an Central Hospital from Jun. 30th， 2021 to Jun. 30th， 2023 were retrospectively collected. The training set and internal validation set were randomly constructed in a 7∶3 ratio. Singler factor and multifactor Logistic regression analysis were used to screen the independent predictors of cefoperazone/sulbactam-induced thrombocytopenia. The nomogram was drawn by using “RMS” of R 4.0.3 software， and the predictive performance of the model was evaluated by the receiver operating characteristic curve and C-index curve. Hosmer-Lemeshow goodness-of-fit test was used to evaluate the calibration degree of the model. Using the same standard， the clinical data of hospitalized patients receiving cefoperazone/sulbactam in Xi’an First Hospital in the same period were collected for external validation of the nomogram prediction model. RESULTS A total of 1 045 patients in Xi’an Central Hospital were included in this study， among which 67 patients suffered from cefoperazone/sulbactam-induced thrombocytopenia， with an incidence of 6.41%. After the false positive patients were excluded， 473 patients were included finally， including 331 in the training set and 142 in theinternal validation set. Multifactor Logistic regression analysis showed that age [OR＝1.043， 95%CI （1.017， 1.070）]， estimated glomerular filtration rate （eGFR） [OR＝0.988，95%CI（0.977， 0.998）]， baseline platelet （PLT） [OR＝0.989， 95%CI（0.982， 0.996）]， nutritional risk [OR＝3.863， 95%CI（1.884， 7.921）] and cumulative defined daily doses （DDDs） [OR＝1.082， 95%CI（1.020， 1.147）] were independent predictors for cefoperazone/sulbactam-induced thrombocytopenia （P＜0.05）. The C-index values of the training set and the internal validation set were 0.824 [95%CI （0.759， 0.890）] and 0.828 [95%CI （0.749， 0.933）]， respectively. The results of the Hosmer-Lemeshow test showed that χ 2 values were 0.441 （P＝0.802） and 1.804 （P＝0.406）. In the external validation set， the C-index value was 0.808 [95%CI （0.672， 0.945）]， the χ 2 value of the Hosmer-Lemeshow test was 0.899 （P＝0.638）. CONCLUSIONS The independent predictors of cefoperazone/sulbactam-induced thrombocytopenia include age， baseline PLT， eGFR， nutritional risk and cumulative DDDs. The model has good predictive efficacy and extrapolation ability， which can help clinic identify the potential risk of cefoperazone/sulbactam-induced thrombocytopenia quickly and accurately.

Nomogram including serum ferritin to predict the occurrence of diabetic retinopathy / 国际眼科杂志(Guoji Yanke Zazhi)

AIM:To establish a nomogram model to predict the effect of serum ferritin on diabetic retinopathy and evaluate the model.METHODS:A total of 21 variables, including ferritin, were screened by univariate and multivariate regression analysis to determine the risk factors of diabetic retinopathy. A nomogram prediction model was established for evaluation and calibration.RESULTS:Ferritin, duration of diabetes, hemoglobin, urine microalbumin, regularity of medication and body mass index were included in the nomogram model. The consistency index of the prediction model with serum ferritin was 0.813(95%CI: 0.748-0.879). The calibration curves of internal and external verification showed good performance, and the probability of the threshold suggested by the decision curve was in the range 10% to 90%. The model had a high net profit value.CONCLUSIONS:Serum ferritin is an important risk factor for diabetic retinopathy. A new nomogram model, which includes body mass index, duration of diabetes, ferritin, hemoglobin, urine microalbumin and regularity of medication, has a high predictive accuracy and could provide early prediction for clinicians.

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.

A predictive model of lymph node metastasis after thoracoscopic surgery for lung adenocarcinoma with a diameter≤3 cm / 中国胸心血管外科临床杂志

@#Objective    To predict the probability of lymph node metastasis after thoracoscopic surgery in patients with lung adenocarcinoma based on nomogram. Methods    We analyzed the clinical data of the patients with lung adenocarcinoma treated in the department of thoracic surgery of our hospital from June 2018 to May 2021. The patients were randomly divided into a training group and a validation group. The variables that may affect the lymph node metastasis of lung adenocarcinoma were screened out by univariate logistic regression, and then the clinical prediction model was constructed by multivariate logistic regression. The nomogram was used to show the model visually, the receiver operating characteristic (ROC) curve, calibration curve and clinical decision curve to evaluate the calibration degree and practicability of the model. Results    Finally 249 patients were collected, including 117 males aged 53.15±13.95 years and 132 females aged 47.36±13.10 years. There were 180 patients in the training group, and 69 patients in the validation group. There was a significant correlation between the 6 clinicopathological characteristics and lymph node metastasis of lung adenocarcinoma in the univariate logistic regression. The area under the ROC curve in the training group was 0.863, suggesting the ability to distinguish lymph node metastasis, which was confirmed in the validation group (area under the ROC curve was 0.847). The nomogram and clinical decision curve also performed well in the follow-up analysis, which proved its potential clinical value. Conclusion    This study provides a nomogram combined with clinicopathological characteristics, which can be used to predict the risk of lymph node metastasis in patients with lung adenocarcinoma with a diameter≤3 cm.

Construction of a individualized model for predicting the risk of recurrence in patients with retinal vein occlusion induced macular edema / 国际眼科杂志(Guoji Yanke Zazhi)

AIM: To analyze the recurrence factors of patients with retinal vein occlusion(RVO)induced macular edema(ME)and construct a nomogram model.METHODS: Retrospective study. A total of 306 patients with RVO induced ME admitted to our hospital from January 2019 to June 2022 were included as study objects, and they were divided into modeling group with 214 cases(214 eyes)and 92 cases(92 eyes)in the verification group by 7:3. All patients were followed up for 1 a after receiving anti-vascular endothelial growth factor(VEGF)treatment, and patients in the modeling group were separated into a recurrence group(n=66)and a non recurrence group(n=148)based on whether they had recurrence. Clinical data were collected and multivariate Logistic regression was applied to analyze and determine the factors affecting recurrence in patients with RVO induced ME; R3.6.3 software was applied to construct a nomogram model for predicting the recurrence risk of patients with RVO induced ME; ROC curve and calibration curve were applied to evaluate the discrimination and consistency of nomogram model in predicting the recurrence risk of patients with RVO induced ME.RESULTS: There were statistically significant differences in central retinal thickness(CRT), course of disease, hyperreflective foci(HF), disorder of retinal inner layer structure, and injection frequency between the non recurrence group and the recurrence group before treatment(all P&#x0026;#x003C;0.05). The multivariate Logistic regression analysis showed that pre-treatment CRT(OR=1.011), course of disease(OR=1.104), HF(OR=5.074), retinal inner layer structural disorder(OR=4.640), and injection frequency(OR=4.036)were influencing factors for recurrence in patients with RVO induced ME(all P&#x0026;#x003C;0.01). The area under the ROC curve of the modeling group was 0.924(95%CI: 0.882-0.966), the slope of the calibration curve was close to 1, and the results of the Hosmer-Lemeshow goodness of fit test showed that χ2=11.817, P=0.160; the area under the ROC curve of the verification group was 0.939(95%CI: 0.892-0.985), the slope of the calibration curve was close to 1, and the results of the Hosmer-Lemeshow goodness of fit test showed χ2=6.082, P=0.638.CONCLUSION: Pre-treatment CRT, course of disease, HF, disorder of retinal inner layer structure, and injection frequency are independent risk factors for recurrence in patients with RVO induced ME. The nomogram model constructed based on this has a high discrimination and consistency in predicting the recurrence risk of patients with RVO induced ME.

Construction and validation of a prediction model for public acceptance of kidney xenotransplantation in China / 器官移植

Objective To explore the public attitude towards kidney xenotransplantation in China by constructing and validating the prediction model based on xenotransplantation questionnaire. Methods A convenient sampling survey was conducted among the public in China with the platform of Wenjuanxing to analyze public acceptance of kidney xenotransplantation and influencing factors. Using random distribution method, all included questionnaires (n=2 280) were divided into the training and validation sets according to a ratio of 7:3. A prediction model was constructed and validated. Results A total of 2 280 questionnaires were included. The public acceptance rate of xenotransplantation was 71.3%. Multivariate analysis showed that gender, marital status, resident area, medical insurance coverage, religious belief, vegetarianism, awareness of kidney xenotransplantation and whether on the waiting list for kidney transplantation were the independent influencing factors for public acceptance of kidney xenotransplantation (all P<0.05). The area under the curve (AUC) of receiver operating characteristic (ROC) of the prediction model in the training set was 0.773, and 0.785 in the validation set. The calibration curves in the training and validation sets indicated that the prediction models yielded good prediction value. Decision curve analysis (DCA) suggested that the prediction efficiency of the model was high. Conclusions In China, public acceptance of kidney xenotransplantation is relatively high, whereas it remains to be significantly enhanced. The prediction model based on questionnaire survey has favorable prediction efficiency, which provides reference for subsequent research.

Classification of bladder cancer based on immune cell infiltration and construction of a risk prediction model for prognosis / 浙江大学学报·医学版

OBJECTIVES@#To classify bladder cancer based on immune cell infiltration score and to construct a risk assessment model for prognosis of patients.@*METHODS@#The transcriptome data and data of breast cancer patients were obtained from the TCGA database. The single sample gene set enrichment analysis was used to calculate the infiltration scores of 16 immune cells. The classification of breast cancer patients was realized by unsupervised clustering, and the sensitivity of patients with different types to immunotherapy and chemotherapy was analyzed. The key modules significantly related to the infiltration of key immune cells were identified by weighted correlation network analysis (WGCNA), and the key genes in the modules were extracted. A risk scoring model and a nomogram for risk assessment of prognosis for bladder cancer patients were constructed and verified.@*RESULTS@#The immune cell infiltration scores of normal tissues and tumor tissues were calculated, and B cells, mast cells, neutrophils, T helper cells and tumor infiltrating lymphocytes were determined to be the key immune cells of bladder cancer. Breast cancer patients were clustered into two groups (Cluster 1 and Custer 2) based on immune cell infiltration scores. Compared with patients with Cluster 1, patients with Cluster 2 were more likely to benefit from immunotherapy (P<0.05), and patients with Cluster 2 were more sensitive to Enbeaten, Docetaxel, Cyclopamine, and Akadixin (P<0.05). WGCNA screened out 35 genes related to key immune cells, and 4 genes (GPR171, HOXB3, HOXB5 and HOXB6) related to the prognosis of bladder cancer were further screened by LASSO Cox regression. The areas under the ROC curve (AUC) of the bladder cancer prognosis risk scoring model based on these 4 genes to predict the 1-, 3- and 5-year survival of patients were 0.735, 0.765 and 0.799, respectively. The nomogram constructed by combining risk score and clinical parameters has high accuracy in predicting the 1-, 3-, and 5-year overall survival of bladder cancer patients.@*CONCLUSIONS@#According to the immune cell infiltration score, bladder cancer patients can be classified. And the bladder cancer prognosis risk scoring model and nomogram based on key immune cell-related genes have high accuracy in predicting the prognosis of bladder cancer patients.

Risk factors for ketoacidosis in children/adolescents with type 1 diabetes mellitus and establishment of a predictive model / 中国当代儿科杂志

OBJECTIVES@#To investigate the risk factors for diabetic ketoacidosis (DKA) in children/adolescents with type 1 diabetes mellitus (T1DM) and to establish a model for predicting the risk of DKA.@*METHODS@#A retrospective analysis was performed on 217 children/adolescents with T1DM who were admitted to General Hospital of Ningxia Medical University from January 2018 to December 2021. Among the 217 children/adolescents,169 cases with DKA were included as the DKA group and 48 cases without DKA were included as the non-DKA group. The risk factors for DKA in the children/adolescents with T1DM were analyzed, and a nomogram model was established for predicting the risk of DKA in children/adolescents with T1DM.@*RESULTS@#For the 217 children/adolescents with T1DM, the incidence rate of DKA was 77.9% (169/217). The multivariate logistic regression analysis showed that high levels of random blood glucose, hemoglobin A1c (HbA1c), blood ketone body, and triglyceride on admission were closely associated with the development of DKA in the children/adolescents with T1DM (OR=1.156, 3.2031015, 20.131, and 9.519 respectively; P<0.05). The nomogram prediction model had a C-statistic of 0.95, with a mean absolute error of 0.004 between the risk of DKA predicted by the nomogram model and the actual risk of DKA, indicating that the model had a good overall prediction ability.@*CONCLUSIONS@#High levels of random blood glucose, HbA1c, blood ketone body, and triglyceride on admission are closely associated with the development of DKA in children/adolescents with T1DM, and targeted intervention measures should be developed to reduce the risk of DKA.

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.

Comprehensive analysis of clinicopathologic and sonographic features in thyroid cancer with skip lymph node metastasis: establish and assessment of a prediction nomogram

Abstract Lateral Lymph Node Metastasis (LLNM) is common in Papillary Thyroid Carcinoma (PTC) and is associated with a poor prognosis. LLNM without central lymph node metastasis as skip metastasis is not common. We aimed to investigate clinicopathologic and sonographic risk factors for skip metastasis in PTC patients, and to establish a nomogram for predicting the possibility of skip metastasis in order to determine the therapeutic strategy. We retrospectively reviewed the data of 1037 PTC patients who underwent surgery from 2016 to 2020 at a single institution. Univariate and multivariate analyses were used to identify the clinicopathologic and preoperative sonographic risk factors of skip metastasis. A nomogram including the risk factors for predicting skip metastasis was further developed and validated. The incidence of skip metastasis was 10.7%. The univariate and multivariate analyses suggested that gender (p = 0.001), tumor location (p = 0.000), extrathyroidal extension (p = 0.000), and calcification (p = 0.000) were independent risk factors. For papillary thyroid microcarcinoma, tumor location (p = 0.000) and calcification (p = 0.001) were independent risk factors. A nomogram according to the clinicopathologic and sonographic predictors was developed. The receiver operating characteristic curve indicated that AUC was 0.824 and had an excellent consistency. The calibration plot analysis showed a good performance and clinical utility of the model. Decision curve analysis revealed it was clinically useful. A nomogram for predicting the probability of skip metastasis was developed, which exhibited a favorable predictive value and consistency. For the female PTC patient, tumor located at the upper pole is more likely to have skip metastasis. Surgeons and sonographers should pay close attention to the patients who have the risk factors. Evidence level: This article's evidence level is 3. Level 3 evidence is derived from nonrandomized, controlled clinical trials. In this study, patients who receive an intervention are compared to a control group. Authors may detect a statistically significant and clinically relevant outcome.

Impact of crossplay between ocular aberrations and depth of focus in topo-guided laser-assisted in situ keratomileusis outcomes

Purpose: To develop a nomogram in cases with mismatch between subjective and Topolyzer cylinder, and based on the magnitude of the mismatch, customize a treatment plan to attain good visual outcomes post?laser?assisted in situ keratomileusis (LASIK) surgery. Methods: The patients were evaluated preoperatively using corneal tomography with Pentacam. Five optimal corneal topography scans were obtained from the Topolyzer Vario were used for planning the LASIK treatment. For the nomogram purpose, the patients were divided into three categories based on the difference between the subjective cylinder and Topolyzer (corneal) cylinder. The first group (group 1) consisted of eyes of patients, where the difference was less than or equal to 0.4 D. The second group (group 2) consisted of eyes, where the difference was more than 0.4 D and the subjective cylinder was lesser than the Topolyzer cylinder. The third group (group 3) included eyes where the difference was more than 0.4 D but the subjective cylinder was greater than the Topolyzer cylinder. LASIK was performed with the WaveLight FS 200 femtosecond laser and WaveLight EX500 excimer laser. Assessment of astigmatism correction for the three groups was done using Aplins vector analysis. For comparison of proportions, Chi?square test was used. A P value less than 0.05 was considered statistically significant. Results: The UDVA was statistically significantly different when compared between groups 1 and 2 (P = 0.02). However, the corrected distance visual acuity (CDVA) was similar among all the three groups (P = 0.1). Group 3 showed an increase of residual cylinder by ?0.25 D, which was significant at intermediate and near reading distances (P < 0.05). Group 3 showed significantly higher target?induced astigmatism (TIA) compared to groups 1 and 2 (P = 0.01). The mean surgically induced astigmatism (SIA) was the least in group 2, which was statistically significant (P < 0.01). Conclusion: The outcomes for distance vision using our nomogram postoperatively were excellent, but further refinement for improving the near vision outcomes is required

Establishment of risk prediction model for nosocomial infection in preterm very low birth weight infants / 中国热带医学

@#Abstract: Objective　To establish a risk prediction model for nosocomial infection in preterm very low birth weight infants, and conduct internal validation. Methods　A total of 206 cases of very low birth weight premature infants hospitalized in the Department of Neonatology of Union Hospital Affiliated to Tongji Medical College from January 2018 to June 2020 were included in this study, factors that may affect the nosocomial infection of children were collected, and the infants were divided into two groups according to whether there is nosocomial infection. The influencing factors were compared between the two groups, and multivariate Logistic regression analysis was performed after screening variables with LASSO regression. According to the results of multi factor analysis, the nomogram model was constructed and verified internally. Results　A total of 29 of 206 children had nosocomial infection (14.08%), and 33 pathogenic bacteria were detected, including 23 Gram-negative bacteria, 9 Gram-positive bacteria and 1 fungus. The results of multivariate logistic regression analysis based on LASSO regression showed that the risk factors for nosocomial infection of VLBW premature infants were 28-31+6 weeks of gestation, amniotic fluid pollution, mechanical ventilation, indwelling gastric tube, unreasonable use of antibiotics, and hospitalization time ≥ 7 days. The protective factors were Apgar score ≥ 7 points at 1 min and breast feeding accounting for 50% or more (P<0.05). The Area Under Curve (AUC) of ROC curve of nomogram model was 0.946 [95%CI(0.923, 1.000)]. The calibration curve showed that the probability of hospital infection predicted by the model was basically consistent with the actual probability. The decision curve showed that when the probability threshold of nomogram model to predict the risk of nosocomial infection of very low birth weight premature infants was 0-0.85, the net rate of return was greater than 0. Conclusion　Preterm infants with extremely low birth weight are at high risk of nosocomial infection, mainly affected by factors such as gestational weeks, hospitalization time, amniotic fluid pollution, etc. The nomogram model constructed by the above factors has high accuracy and discrimination for predicting nosocomial infection in such children.

Construction and Validation of Risk Prediction Model for Colorectal Polyps / 胃肠病学

Background: Colorectal polyp is a common lower gastrointestinal disease. Study of its risk factors is of great significance for prevention and treatment of colorectal polyps in clinical practice. Aims: To construct and verify a prediction model for risk of colorectal polyps. Methods: According to the inclusion and exclusion criteria, 254 subjects who were hospitalized for health examination in the Special Internal Medicine Ward of Shanghai Huadong Hospital from January 2019 to June 2021 were enrolled in the study. They were allocated into colorectal polyps group and non⁃polyp group based on the results of colonoscopy. The relevant risk factors of colorectal polyp were collected, including gender, age, cigarette smoking, alcohol drinking, hypertension, diabetes, hyperlipidemia, hyperuricemia, polyps/stones of gallbladder, fatty liver, etc. After screened by LASSO regression model, the selected factors were analyzed by multivariate Logistic regression to build the prediction model and nomogram. Furthermore, the prediction model was evaluated by ROC curve, C index, calibration curve and decision curve, and validated by internal samples. Results: Of the 254 subjects enrolled in the study, 116 cases were in colorectal polyps group and 138 in non⁃polyp group. The risk prediction model identified that gender (OR=2.11, 95% CI: 1.06⁃4.27), age (OR=2.76, 95% CI: 1.17⁃6.73), hypertension (OR=3.23, 95% CI: 1.52⁃7.12), diabetes (OR=4.37, 95% CI: 1.52⁃14.64), hyperlipidemia (OR=3.20, 95% CI: 1.74⁃5.95) and fatty liver (OR= 2.21, 95% CI: 1.13⁃4.35) were independent risk factors for colorectal polyps. The model showed good area under the ROC curve (0.807) and C index (0.807). The decision curve demonstrated that if the threshold probability of colorectal polyps was more than 12%, the model would be of clinical significance. Internal samples were randomly selected for validation, and the C index was 0.793. Conclusions: The prediction model and nomogram constructed by combination of risk factors including gender, age, hypertension, diabetes, hyperlipidemia and fatty liver have a substantial reference value for risk prediction of colorectal polyps.

Prediction of Pulmonary Disease Progression in Patients with COVID-19 Based on CT Radiomics / 中山大学学报(医学科学版)

ObjectivesBased on the changes of lung lesions in patients with COVID-19 at different stages， a nomogram model describing CT image features was established by radiomics method to explore its efficacy in predicting the progression of the disease. MethodsThis retrospective study enrolled 136 patients with COVID-19 pneumonia who received at least two CTs including three cohorts （training cohort and validation cohort 1 and 2）. Patients in the training cohort were divided into three groups according to time between onset of fever symptoms and the first CT. The clinical manifestations and CT features of each group were analyzed and compared. A nomogram to predict disease progression was constructed according to the CT features of the patients， and its performance was evaluated. ResultsThe training cohort consisted of 41 patients.A nomogram was generated to predict disease progression based on three CT features： irregular strip shadow， air bronchial sign， and the proportion of lesions with irregular shape ≥50%. AUC（95%CI）=0.906（0.817，0.995）.The C index of the training cohort was 0.906， and the C index of the internal verification was 0.892. AUC（95%CI）of the validation cohort 1 （34 cases） =0.889（0.793，0.984）；AUC（95%CI）of the validation cohort 2 （61 cases）=0.876（0.706，1.000）.The calibration curves show that the predicted values of the nomogram are in good agreement with the observed values. ConclusionThe nomogram model based on CT radiomics can predict the outcome of lung lesions in patients with high sensitivity and specificity.According to the changes of CT image characteristics of patients with COVID-19， lung lesions will be improved when the proportion of irregular cable shadow， air bronchogram and irregular lesions is greater than 50%.

A Prediction Model for Human Immunodeficiency Virus Infection and Mother-to-Child Transmission Based on the Expression Levels of Selenoprotein Genes / 中国医学科学院学报

Objective To study the expression of selenoprotein genes in human immunodeficiency virus(HIV)infection and its mother-to-child transmission,so as to provide a theoretical basis for the prevention,diagnosis,and treatment of acquired immunodeficiency syndrome.Methods The dataset GSE4124 was downloaded from the Gene Expression Omnibus(GEO).Two groups of HIV-positive mothers(n=25)and HIV-negative mothers(n=20)were designed.HIV-positive mothers included a subset of transmitter(TR)mothers(n=11)and non-transmitter(NTR)mothers(n=14).Then,t-test was carried out to compare the expression levels of selenoprotein genes between the four groups(HIV-positive vs. HIV-negative,NTR vs. HIV-negative,TR vs. HIV-negative,TR vs. NTR).Univariate and multivariate Logistic regression were adopted to analyze the effects of differentially expressed genes on HIV infection and mother-to-child transmission.R software was used to establish a nomogram prediction model and evaluate the model performance.Results Compared with the HIV-negative group,HIV-positive,NTR,and TR groups had 8,5 and 8 down-regulated selenoprotein genes,respectively.Compared with the NTR group,the TR group had 4 down-regulated selenoprotein genes.Univariate Logistic regression analysis showed that abnormally high expression of GPX1,GPX3,GPX4,TXNRD1,TXNRD3,and SEPHS2 affected HIV infection and had no effect on mother-to-child transmission.The multivariate Logistic regression analysis showed that the abnormally high expression of TXNRD3(OR=0.032,95%CI=0.002-0.607,P=0.022)was positively correlated with HIV infection.As for the nomogram prediction model,the area under the receiver-operating characteristic curve for 1-year survival of HIV-infected patients was 0.840(95%CI=0.690-1.000),and that for 3-year survival of HIV-infected patients was 0.870(95%CI=0.730-1.000).Conclusions Multiple selenoprotein genes with down-regulated expression levels were involved in the regulation of HIV infection and mother-to-child transmission.The abnormal high expression of TXNRD3 was positively correlated with HIV infection.The findings provide new ideas for the prevention,diagnosis,and treatment of acquired immunodeficiency syndrome.

Construction of a competing risk model for disease-free survival of patients with non-muscle invasive bladder cancer / 现代泌尿外科杂志

【Objective】 To construct an easy-to-use individual survival prognostic tool based on competing risk analyses to predict the risk of 1-, 2- and 3- year recurrence for patients with non-muscle invasive bladder cancer (NMIBC). 【Methods】 The follow-up data of 419 NMIBC patients were obtained. The patients were randomly divided into training cohort (n=293) and validation cohort (n=126). The variables included age at diagnosis, sex, history of smoking, tumor number, tumor size, histolo-gic grade, pathological stage, and bladder perfusion drug. The cumulative incidence function (CIF) of recurrence was estimated using all variables in the training cohort and potential prognostic variables were determined with Gray’s test. The Fine-Gray subdistribution proportional hazard approach was used as a multivariate competitive risk analysis to identify independent pro-gnostic variables. A competing risk nomogram was developed to predict the recurrence. The performance of the competing risk model was evaluated with the area under the receiver operating characteristic curve (AUC), calibration curve, and Brier score. 【Results】 Five independent prognostic factors including age, number of tumors, tumor size, histologic grade and pathological stage were used to construct the competing risk model. In the validation cohort, the AUC of 1-, 2- and 3- year recurrence were 0.895 (95%CI: 0.831-0.959), 0.861(95%CI: 0.774-0.948) and 0.827(95%CI: 0.721-0.934), respectively, indicating that the model had a high predictive performance. 【Conclusion】 We successfully constructed a competing risk model to predict the risk of 1-, 2- and 3-year recurrence for NMIBC patients. It may help clinicians to improve the postoperative management of patients.