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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.
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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.
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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.
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@#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.
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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<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<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.
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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.
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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.
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Humanos , Tos Crónica , Tos/etiología , Neoplasias Pulmonares , Neumonectomía/efectos adversos , Calidad de Vida , Estudios RetrospectivosRESUMEN
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.
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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.
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Niño , Adolescente , Humanos , Diabetes Mellitus Tipo 1/complicaciones , Glucemia , Hemoglobina Glucada , Estudios Retrospectivos , Cetosis , Factores de Riesgo , Cuerpos Cetónicos , TriglicéridosRESUMEN
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.
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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
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ObjectiveTo investigate the risk factors for pulmonary fungal infection in lung cancer patients， construct and validate a risk prediction model using available clinical data to predict the risk of pulmonary fungal infections in patients with lung cancer. MethodsWe conducted a retrospective study and collected information of 390 lung cancer patients treated at Zhongshan People's Hospital from January 2021 to March 2023. Demographic and clinical characteristics of the patients with and without pulmonary fungal infections were used to construct column line graphs to predict the occurrence of pulmonary fungal infections. All enrolled patients were randomly assigned to training set and internal validation set in the ratio of 7：3. For the modelling group， LASSO regression was applied to screen variables and select predictors， and multivariate logistic regression with a training set was used to construct the Noe column line graph model. The judgment ability of the model was determined by calculating the area under the curve （AUC）， and in addition， calibration analysis and decision curve analysis （DCA） were performed on the model. ResultsLASSO regression identified 14 potential predictive factors， and further logistic regression analysis showed that hepatic injury， surgery， anemia， hypoalbuminemia， illness course， invasive operation， hospital stay at least 2 weeks and glucocorticoid used for at least 2 weeks were independent predictors for the occurrence of pulmonary fungal infection in lung cancer patients. A predictive model was established based on these variables， with an AUC95%CI of 0.980 （0.973， 0.896） for the training set and an AUC95%CI of 0.956 （0.795， 1.000） for internal validation， indicating high discriminative ability. The calibration curves for both the training set and validation set were distributed along the 45°line， and the decision curve analysis （DCA） showed net benefit for threshold probabilities greater than 0.03. ConclusionsThe construction and validation of a predictive model for the risk of lung fungal infections in lung cancer patients will help clinical practitioners to identify high-risk groups and give timely intervention or adjust treatment decisions.
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ObjectiveTo develop and validate a predictive risk model for vision-threatening diabetic retinopathy in patients with type 2 diabetes using readily accessible clinical data， which may provide a convenient and effective prediction tool for early identification and referral of at-risk populations. MethodsA nomogram model was developed using a dataset obtained from patients with T2DM who participated in the Guangzhou Diabetic Eye Study from November 2017 to December 2020. Logistic regression was used to construct the model， and model performance was evaluated using receiver operating characteristic curve， Hosmer-Lemeshow test， calibration curve and decision curve analysis. The model underwent internal validation through the mean AUC of k-fold cross-validation method， and further external validation was conducted in the Dongguan Eye Study. ResultsA total of 2 161 individuals were included in the model development dataset， of whom 135 （6.25%） people were diagnosed with VTDR. Age （P＜0.001，OR=0.927，95%CI：0.898~0.957） and body mass index （P＜0.001，OR =0.845，95%CI：0.821~0.932） were found to be negatively correlated with VTDR， whereas diabetes duration （P＜0.001，OR=1.064，95%CI：1.035~1.094）， insulin use （P =0.045，OR =1.534，95%CI：1.010~2.332）， systolic blood pressure （P＜0.001，OR =1.019，95%CI：1.008~1.029）， glycated hemoglobin （P＜0.001，OR =1.484，95%CI：1.341~1.643）， and serum creatinine （P＜0.001，OR =1.017，95%CI：1.010~1.023） were positively correlated with VTDR. All these variables were included in the model as predictors. The model showed strong discrimination in the development dataset with an area under the receiver operating characteristic curve （AUC） of 0.797 and in the external validation dataset （AUC 0.762）. The Hosmer-Lemeshow test（P＞0.05）and the calibration curve displayed good agreement. Decision curve analysis showed that the nomogram produced net benefit in the two datasets. ConclusionsIndependent factors influencing VTDR include age， duration of diabetes mellitus， insulin use， body mass index， systolic blood pressure， glycosylated hemoglobin， and serum creatinine. The nomogram constructed using these variables demonstrates a high degree of predictive validity. The model can serve as a valuable tool for early detection and referral of VTDR in primary care clinics. Therefore， its application and promotion are highly recommended.
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Objective To analyze the factors affecting the prognosis of soft tissue sarcomas originating from the mediastinum and lung using relevant data from the SEER database. Methods The data of 376 patients were collected from the SEER database, and were randomly divided into the train set (n=263) and validation set (n=113). The relationship between each variable and patient survival and prognosis was analyzed using the Kaplan-Meier method and Cox proportional risk regression to establish a nomogram, to predict the overall survival of patients. The calibration curves, consistency index, and ROC curves were used to evaluate the performance of the nomogram. Results Histological type, surgery, chemotherapy, tumor size, and tumor stage were the factors affecting the prognosis of primary mediastinal and pulmonary soft tissue sarcomas. The established nomogram could predict the 6-month, 1-year, and 2-year overall survival of patients, and the calibration curves showed good prediction accuracy with measured values. C index of the train set and validation set were 0.754 and 0.745, respectively. The areas under the curve of ROC were 0.849 and 0.924. Conclusion The nomogram established in this study can predict 6-month, 1-year, and 2-year overall survival in patients with primary mediastinal and pulmonary soft tissue sarcoma.
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OBJECTIVE@#Constructing a predictive model for urinary incontinence after laparoscopic radical prostatectomy (LRP) based on prostatic gland related MRI parameters.@*METHODS@#In this study, 202 cases were included. All the patients were diagnosed with prostate cancer by prostate biopsy and underwent LRP surgery in Peking University Third Hospital. The preoperative MRI examination of all the patients was completed within 1 week before the prostate biopsy. Prostatic gland related parameters included prostate length, width, height, prostatic volume, intravesical prostatic protrusion length (IPPL), prostate apex shape, etc. From the first month after the operation, the recovery of urinary continence was followed up every month, and the recovery of urinary continence was based on the need not to use the urine pad all day long. Logistic multivariate regression analysis was used to analyze the influence of early postoperative recovery of urinary continence. Risk factors were used to draw the receiver operator characteristic (ROC) curves of each model to predict the recovery of postoperative urinary continence, and the difference of the area under the curve (AUC) was compared by DeLong test, and the clinical net benefit of the model was evaluated by decision curve analysis (DCA).@*RESULTS@#The average age of 202 patients was 69.0 (64.0, 75.5) years, the average prostate specific antigen (PSA) before puncture was 12.12 (7.36, 20.06) μg/L, and the Gleason score < 7 points and ≥ 7 points were 73 cases (36.2%) and 129 cases (63.9%) respectively, with 100 cases (49.5%) at T1/T2 clinical stage, and 102 cases (50.5%) at T3 stage. The prostatic volume measured by preoperative MRI was 35.4 (26.2, 51.1) mL, the ratio of the height to the width was 0.91 (0.77, 1.07), the membranous urethral length (MUL) was 15 (11, 16) mm, and the IPPL was 2 (0, 6) mm. The prostatic apex A-D subtypes were 67 cases (33.2%), 80 cases (39.6%), 24 cases (11.9%) and 31 cases (15.3%), respectively. The training set and validation set were 141 cases and 61 cases, respectively. The operations of all the patients were successfully completed, and the urinary continence rate was 59.4% (120/202) in the 3 months follow-up. The results of multivariate analysis of the training set showed that the MUL (P < 0.001), IPPL (P=0.017) and clinical stage (P=0.022) were independent risk factors for urinary incontinence in the early postoperative period (3 months). The nomogram and clinical decision curve were made according to the results of multivariate analysis. The AUC value of the training set was 0.885 (0.826, 0.944), and the AUC value of the validation set was 0.854 (0.757, 0.950). In the verification set, the Hosmer-Lemeshow goodness-of-fit test was performed on the model, and the Chi-square value was 5.426 (P=0.711).@*CONCLUSION@#Preoperative MUL, IPPL, and clinical stage are indepen-dent risk factors for incontinence after LRP. The nomogram developed based on the relevant parameters of MRI glands can effectively predict the recovery of early urinary continence after LRP. The results of this study require further large-scale clinical research to confirm.
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Masculino , Humanos , Próstata/cirugía , Prostatectomía/efectos adversos , Neoplasias de la Próstata/patología , Incontinencia Urinaria/etiología , Laparoscopía/métodos , Imagen por Resonancia Magnética/efectos adversos , Recuperación de la Función , Estudios RetrospectivosRESUMEN
Objective To investigate the independent risk factors affecting prognosis of patients with retinoblastoma (RB) and construct a nomogram to predict prognosis of patients with RB. Methods Data of 759 RB patients were collected from the SEER database. Patients were randomly assigned to the training group and validation group in a 7:3 ratio. Univariate and multivariate Cox proportional hazard regression analyses were used to determine the independent prognostic factors, based on which a nomogram was constructed. C index, calibration curve, and ROC curve were used to evaluate the predictive efficiency and calibration degree of the nomogram. Results Multivariate analysis identified independent risk factors associated with overall survival, namely, T stage and SEER stage. The C-index of SEER training set was 0.765 (95%CI: 0.744-0.786), the calibration curve was drawn, and the observed and predicted values overlapped well, indicating good consistency. The ROC curve showed that the nomogram could accurately predict three-year (AUC=0.743), five-year (AUC=0.734) and 10-year (AUC=0.720) survival rates of RB patients. Conclusion T stage and SEER stage are independent risk factors related to prognosis of RB patients, and the nomogram can accurately predict the three-year, five-year, and 10-year overall survival rates of patients.
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Objective:To explore the independent predictive factors of cirrhosis in patients with hepatitis B e antigen (HBeAg)-negative chronic hepatitis B virus (HBV) infection, and establish a nomogram model based on clinical laboratory data and analyze the predictive value of this model.Methods:The laboratory data of 596 patients with HBeAg-negative chronic HBV infection and 677 patients with hepatitis B cirrhosis, who were hospitalized in the First Hospital of China Medical University from 2011 to 2021, were retrospectively analyzed. Patients were randomly divided into training group ( n=892) and validation group ( n=381) at the ratio of 7∶3. The independent predictive factors of cirrhosis were analyzed by univariate logistic regression, multiple collinearity test and multivariate logistic regression. The nomogram model was established and the prediction value of this model was evaluated. Results:According to multivariate logistic regression analysis, hepatitis B core antibody ( OR=1.492, 95% CI 1.316-1.706), glutamine transpeptidase ( OR=1.015, 95% CI 1.010-1.022), platelet ( OR=0.986, 95% CI 0.982-0.988) and albumin ( OR=0.853, 95% CI 0.824-0.882) were independent predictors of cirrhosis ( P<0.05), and the nomogram was established based on the four indicators. Receiver operating characteristic curves showed that area under the curve of the nomogram was 0.933 (95% CI 0.916-0.950), and that of the validation group was 0.931 (95% CI 0.905-0.956). The calibration curves indicated the nomogram model was highly consistent with the actual outcome. Decision curves and clinical impact curves confirmed that the model had high net benefit and good clinical application performance. Conclusions:Hepatitis B core antibody, glutamine transpeptidase, platelet and albumin are independent predictors of cirrhosis among patients with HBeAg-negative chronic HBV infection. The newly developed nomogram model based on these factors could be used to predict cirrhosis risk in these patients.
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Objective:To investigate the risk factors of high intraocular pressure (IOP) after femtosecond laser in situ keratomileusis (FS-LASIK) in patients with high myopia, and construct and verify nomogram model.Methods:A retrospective clinical study. From January 2019 to January 2021, 327 patients (654 eyes) with high myopia treated with FS-LASIK in the Department of Ophthalmology of the 910th Hospital of the People's Liberation Army Coalition Security Force were included in the study. The patients were categorized into high IOP group and non-high IOP group according to whether high IOP occurred after surgery, which were 60 cases and 120 eyes (18.35%, 60/327) and 267 cases and 534 eyes (81.65%, 267/327), respectively. The clinical data of patients in the two groups were analyzed and observed, and the indicators with differences were subjected to one-way and multifactorial logistic regression analyses, and the results of the regression analyses were visualized to obtain the column line graphs using R3.5.3 software, and the accuracy of the column line graphs was verified by the consistency index (C-index), the calibration curves, and the subject's work characteristic curves (ROC curves).Results:Comparison of the number of cases of affected corneal thickness ( χ2=7.424), corneal curvature ( χ2=9.849), glucocorticoid treatment ( χ2=7.222), intraoperative IOP fluctuation ( χ2=11.475), corneal hysteresis ( χ2=6.368), and the incidence of intraoperative complications ( χ2=6.673) in the hypertensive IOP group and the nonvisualized IOP group were statistically significant ( P<0.05). Binary logistic regression analysis showed that corneal thickness >450 μm, corneal curvature≤38 D, glucocorticoid treatment, intraoperative IOP fluctuation, corneal hysteresis ≤8.0 mm Hg (1 mm Hg=0.133 kPa), and intraoperative complications were the risk factors for the occurrence of high IOP after FS-LASIK surgery in patients with high myopia ( P<0.05). The C-index of the column-line graph prediction model based on this was 0.722 (95% confidence interval 0.684-0.760), the calibration curve and the ideal curve were basically the same, and the area under the ROC curve was 0.709. Conclusions:Corneal thickness> 450 μm, keratometric curvature ≤38 D, glucocorticoid treatment, intraoperative fluctuation of intraocular pressure, and corneal hysteresis ≤8.0 mm Hg are the risk factors for the development of hyperopic IOP in highly risk factors for the development of high IOP after FS-LASIK surgery in myopic patients. The column-line diagram model constructed on the basis of the risk factors hava good accuracy.
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Objective:To analyze the risk factors associated with retinal detachment in patients with myopia, and to establish and validate the predictive column-line diagram model.Methods:A cross-sectional clinical study. From January 2020 to November 2021, 90 patients with myopia combined with retinal detachment who were diagnosed by ophthalmologic examination in the People's Hospital of Ningxia Hui Autonomous Region were included in the study (observation group). Ninety myopic patients with age- and gender-matched myopia who underwent ophthalmologic examination for myopia during the same period were selected as the control group. The clinical data of the two groups were analyzed, and the indicators with differences were subjected to univariate and multivariate logistic regression analyses. The results of the regression analyses were visualized by using R software to obtain the column charts, and the accuracy of the column charts was verified by the ROC curves of the subjects' work characteristics; the clinical efficacy of the column chart model was verified by the internal data.Results:Compared with the control group, patients in the observation group were older, had higher myopic refraction, had more visual fatigue, ocular trauma, and cataracts, had lower choroidal and retinal thickness, and had more history of ophthalmic surgery, and the differences were statistically significant ( P<0.05). The area under the ROC curve (AUC) for age, myopic refraction, retinal thickness, and choroidal thickness were 0.612, 0.613, 0.720, and 0.704, respectively; the optimal cutoff values were 43 years old, -3.5 D, 225 μm, and 144 μm. the ROC values were 0.612, 0.613, 0.720, and 0.704 for age (>43 years old), myopic refraction (>-3.5 D), visual fatigue (yes), ocular trauma (yes), cataracts (yes), retinal thickness (≤225 μm), and choroidal thickness (≤144 μm) were the risk factors affecting the development of retinal detachment in myopic patients ( P<0.05). The consistency index of the column chart model for predicting the risk of retinal detachment in patients with myopia was 0.731 (95% confidence interval 0.665-0.824); the risk threshold for predicting the development of retinal detachment in patients was >0.07. Conclusions:Age >43 years, myopic refraction >-3.5 D, presence of visual fatigue, ocular trauma, cataract, retinal thickness ≤225 μm, choroidal thickness ≤144 μm are the risk factors affecting the development of retinal detachment in myopic patients. The column-line diagram model constructed on the basis of the risk factors has good accuracy.