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<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.

Risk prediction of low birth weight infants in Shanghai / 上海预防医学

ObjectiveTo investigate the risk factors of fertility behaviors with preterm birth and low birth weight， and to develop a nomogram model to predict the occurrence of low birth weight. MethodsBirth registration information in Shanghai from 2010 to 2020 was collected， and ANOVA and Chi-square tests were used to compare the differences in reproductive behavior factors and newborn health status across time. The odds ratio （OR） value and 95%CI were calculated by a multi-classification logistic regression model to determine the association between reproductive behavior factors and preterm birth or low birth weight infants. A nomogram model was established based on logistic model and the area under the ROC curve was used to assess the effect of the model. ResultsThis analysis included 2 089 384 live newborns. The incidence of full-term low birth weight， preterm normal weight and preterm low birth weight in Shanghai was 0.94%， 2.48% and 2.01%， respectively. From 2010 to 2020， 40.00% women had a history of abortion， the proportion of women who gave birth at age ≥40 years old increased from 1.05% to 2.24%， the proportion of fathers aged ≥40 years increased from 4.79% to 7.48%， and the proportion of women with postgraduate or above increased from 4.81% to 11.74%. The incidence of preterm low birth weight in Shanghai showed an increasing trend over time. Logistic regression analysis showed that the risk of preterm low birth weight was lower in female than in male infants （OR=0.97， 95%CI： 0.95‒0.98）， and the risk of full-term low birth weight was higher than in male infants （OR=1.85， 95%CI： 1.80‒1.90）. The risk of preterm birth and low birth weight was lower for couples of childbearing age with higher education. The risk of preterm low birth weight in newborns tended to increase with maternal age at childbirth >30 years， paternal age ≥40 years， and the number of abortions >2 times. Mother <25 or >35 years， father aged 30‒34 years， and the number of abortions >3 times were the risk factors of full-term low birth weight infants. ConclusionCouples of childbearing age who choose to have children at too high or too low age may increase the risk of preterm birth or low birth weight， so it is necessary to strengthen population awareness and promote age-appropriate childbirth. Multiple abortions are also associated with preterm birth and low birth weight， and it is advisable to popularize the scientific knowledge of contraception and birth control to reduce unnecessary abortions. The nomogram in the study can visualize the risk of full-term and low birth weight infant at different levels of factors， which can assist couples preparing for pregnancy in making decisions about the timing of childbirth and understanding the level of risk.

Analysis of risk factors for neonatal preterm birth and construction of nomogram prediction model / 中国热带医学

@#Abstract: Objective To analyze the risk factors for neonatal preterm birth in 12 hospitals in Yunnan Province from 2016 to 2017, and to establish a nomogram prediction model for neonatal preterm birth, providing scientific evidence for the prevention of preterm birth. Methods A total of 20 445 pregnant women who gave birth in 12 hospitals in Yunnan Province from 2016 to 2017 were collected and grouped into a preterm group (n=1 186) and a full-term group (n=19 259) according to whether they had a premature delivery. The general information questionnaire of pregnant women designed by the research team was applied to understand the basic conditions and pregnancy information of the two groups, and the risk factors of preterm birth were determined by logistic regression analysis, R software was applied to draw a nomogram prediction model of neonatal preterm birth, and its predictive performance was tested. Results There were significant differences in the proportions of twins and above (9.11% vs 7.10%), pregnancy-induced hypertension (21.67% vs 18.57%), gestational diabetes mellitus (18.21% vs 15.90%), anemia (24.28% vs 20.70%), premature rupture of membranes (11.64% vs 9.76%), and abnormal placenta (7.08% vs 5.51%) between the preterm group and the full-term group (χ2=6.731, 7.055, 4.441, 8.691, 4.437, 5.232, all P<0.05); the logistic regression analysis showed that the risk factors for neonatal preterm birth were twins and above (OR=2.378), pregnancy-induced hypertension (OR=2.039), gestational diabetes mellitus (OR=1.824), anemia (OR=1.825), and premature rupture of membranes (OR=2.313) (all P<0.05); the discrimination (area under the curve was 0.794, 95%CI=0.738-0.850) and precision (goodness of fit HL test, χ2=8.864, P=0.312) of the nomogram model constructed to predict the occurrence of neonatal preterm birth were both good. Conclusions The nomogram model for preterm birth constructed based on 5 factors including number of fetuses, pregnancy-induced hypertension, gestational diabetes mellitus, anemia and premature rupture of membranes can predict the occurrence of neonatal preterm birth well, thus providing reference for the prevention of neonatal preterm birth.

Clinical Prediction of Lung Cancer Complicated with Pulmonary Infection After Thoracoscopic Surgery Based on Nomogram Model / 肿瘤防治研究

Objective To analyze the risk factors of lung cancer patients complicated with pulmonary infection after thoracoscopic surgery and establish a predictive nomogram model. Methods A total of 315 patients with primary lung cancer who had undergone thoracoscopic surgery from January 2018 to October 2021 in our hospital were divided into two groups according to the incidence of pulmonary infection. Two groups of clinical data were collected for single-factor and regression analyses, and independent predictors were obtained. On this basis, a risk model was constructed and its predictive effectiveness was evaluated. Results The independent risk factors of lung cancer patients complicated with pulmonary infection after thoracoscopic radical operation were as follows: age≥62.5 years, smoking index≥100, PEF≤72.1 ml/s, TNM stage Ⅲ/Ⅳ, and operation time≥188.5 min (P < 0.05). Based on the above factors, the risk model of the column chart was established. Model-verification results showed that the C-index of the model was 0.909, and the correction curve showed that the column chart model had good differentiation and consistency. Conclusion Lung cancer patients' age, smoking index, TNM stage, PEF, and operation time are closely related to pulmonary infection after thoracoscopic radical operation. The nomogram model is useful for identifying high-risk patients and reducing postoperative complications.

Based on CT radiomics model for predicting the response to first-line chemotherapy of diffuse large B-cell lymphoma / 中华肿瘤杂志

Objective: To investigate the potential value of CT Radiomics model in predicting the response to first-line chemotherapy in diffuse large B-cell lymphoma (DLBCL). Methods: Pre-treatment CT images and clinical data of DLBCL patients treated at Shanxi Cancer Hospital from January 2013 to May 2018 were retrospectively analyzed and divided into refractory patients (73 cases) and non-refractory patients (57 cases) according to the Lugano 2014 efficacy evaluation criteria. The least absolute shrinkage and selection operator (LASSO) regression algorithm, univariate and multivariate logistic regression analyses were used to screen out clinical factors and CT radiomics features associated with efficacy response, followed by radiomics model and nomogram model. Receiver operating characteristic (ROC) curve, calibration curve and clinical decision curve were used to evaluate the models in terms of the diagnostic efficacy, calibration and clinical value in predicting chemotherapy response. Results: Based on pre-chemotherapy CT images, 850 CT texture features were extracted from each patient, and 6 features highly correlated with the first-line chemotherapy effect of DLBCL were selected, including 1 first order feature, 1 gray level co-occurence matrix, 3 grey level dependence matrix, 1 neighboring grey tone difference matrix. Then, the corresponding radiomics model was established, whose ROC curves showed AUC values of 0.82 (95% CI: 0.76-0.89) and 0.73 (95% CI: 0.60-0.86) in the training and validation groups, respectively. The nomogram model, built by combining validated clinical factors (Ann Arbor stage, serum LDH level) and CT radiomics features, showed an AUC of 0.95 (95% CI: 0.90-0.99) and 0.91 (95% CI: 0.82-1.00) in the training group and the validation group, respectively, with significantly better diagnostic efficacy than that of the radiomics model. In addition, the calibration curve and clinical decision curve showed that the nomogram model had good consistency and high clinical value in the assessment of DLBCL efficacy. Conclusion: The nomogram model based on clinical factors and radiomics features shows potential clinical value in predicting the response to first-line chemotherapy of DLBCL patients.

Establishment and validation of nomogram model for intraocular hypertension after femtosecond laser in situ keratomileusis for high myopia / 中华眼底病杂志

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.

Analysis of risk factors for retinal detachment in myopic patients and construction of Nomogram prediction model / 中华眼底病杂志

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.

Establishment and validation of nomogram model for visual prognosis of macular edema secondary to retinal branch vein occlusion treated with ranibizumab / 中华眼底病杂志

Objective:To explore the influencing factors of visual prognosis of macular edema secondary to branch retinal vein occlusion (BRVO-ME) after treatment with ranibizumab, and construct and verify the nomogram model.Methods:A retrospective study. A total of 130 patients with BRVO-ME diagnosed by ophthalmology examination in the Department of Ophthalmology, Liuzhou Red Cross Hospital from January 2019 to December 2021 were selected in this study. All patients received intravitreal injection of ranibizumab. According to the random number table method, the patients were divided into the training set and the test set with a ratio of 3:1, which were 98 patients (98 eyes) and 32 patients (32 eyes), respectively. According to the difference of logarithm of the minimum angle of resolution (logMAR) best corrected visual acuity (BCVA) at 6 months after treatment and logMAR BCVA before treatment, 98 patients (98 eyes) in the training set were divided into good prognosis group (difference ≤-0.3) and poor prognosis group (difference >-0.3), which were 58 patients (58 eyes) and 40 patients (40 eyes), respectively. The clinical data of patients in the two groups were analyzed, univariate and multivariate logistic regression analysis were carried out for the different indicators, and the visualization regression analysis results were obtained by using R software. The consistency index (C-index), convolutional neural network (CNN), calibration curve and receiver operating characteristic (ROC) curve were used to verify the accuracy of the nomogram model.Results:Univariate analysis showed that age, disease course, outer membrane (ELM) integrity, elliptical zone (EZ) integrity, BCVA, center macular thickness (CMT), outer hyperreflective retinal foci (HRF), inner retina HRF, and the blood flow density of retinal deep capillary plexus (DCP) were risk factors affecting the visual prognosis after treatment with ranibizumab in BRVO-ME patients ( P<0.05). Multivariate logistic regression analysis showed that course of disease, ELM integrity, BCVA and outer HRF were independent risk factors for visual prognosis after ranibizumab treatment for BRVO-ME patients ( P<0.05). The ROC area under the curve of the training set and the test set were 0.846[95% confidence interval ( CI) 0.789-0.887) and 0.852 (95% CI 0.794 -0.873)], respectively; C-index were 0.836 (95% CI 0.793-0.865) and 0.845 (95% CI 0.780-0.872), respectively. CNN showed that the error rate gradually stabilized after 300 cycles, with good model accuracy and strong prediction ability. Conclusions:Course of disease, ELM integrity, BCVA and outer HRF were independent risk factors of visual prognosis after ranibizumab treatment in BRVO-ME patients. The nomogram model based on risk factors has good differentiation and accuracy.

Predictive value of proximal and distal ureteral diameter ratio for impacted stones in the middle and upper ureter / 中华泌尿外科杂志

Objective:To evaluate the predictive value of proximal ureteral diameter (D1)to distal ureteral diameter (D2)ratio (DDR) for impacted stones in the middle and upper ureter.Methods:The clinical data of 173 patients with middle and upper ureteral calculi admitted to the Third Hospital of Shanxi Medical University from January 2014 to November 2021 were retrospectively analyzed. There were 75 males and 98 females, with the median age of 56.0 (51.0, 62.0) years old and median body mass index of 26.1 (24.8, 27.2) kg/m 2. The imaging data of the patients were analyzed. The impacted stones were defined as the inability of the contrast agent to pass through the site of obstruction when intravenous urography or CT urography was performed, resulting in the inability of the ureter to visualize normally in parts below the site of obstruction. D1 was defined as the proximal ureteral diameter at the lower pole of the kidney on horizontal CT images. D2 was defined as the ureteral diameter 3 cm from the calculi. The stone diameter, stone CT value, D1, D2, and DDR were compared between impacted stone group and non-impacted stone group. Univariate logistic regression analysis was used to analyze the different indicators. Random number table was used to divide the training set and validation set according to the ratio of 7∶3. Through least absolute shrinkage and selection operator(LASSO) regression analysis, the independent influencing factors were obtained and the nomogram model was established (Model 1). Receiver operating characteristic (ROC) curve and area under the curve (AUC) were used to verify the predictive efficacy of the model, and the other three effective models (Model 2-4) were constructed by stepwise multivariate logistic regression. The deLong test was used to compare whether there was a significant difference in the AUC between Model 1 and the other three models, and the net benefit of patients was analyzed by clinical decision curve analysis(DCA). Results:In this study, 64 cases (37.0%) were impacted ureteral calculi and 109 cases (63.0%) were non-impacted ureteral calculi, and there were significant differences in diameter[7.8(6.2, 8.8)mm vs. 6.3(5.2, 8.1)mm] , CT value[878.5(763.8, 940.5)HU vs.764.0 (613.0, 854.0) HU], D1[11.1(8.9, 14.9) mm vs. 9.1(7.1, 10.8) mm], D2[4.1(3.1, 4.9) mm vs. 5.0(4.1, 5.9) mm] and DDR[3.1(2.3, 3.9) vs. 1.8(1.4, 2.4)] between the two groups( P < 0.05). The results of univariate logistic regression analysis showed that stone diameter ( OR = 1.333, P < 0.001), CT value ( OR = 1.002, P=0.002), D1 ( OR = 1.146, P<0.001), D2 ( OR = 0.652, P < 0.001) and DDR ( OR = 2.995, P<0.001) were the influencing factors of impacted stones. The training set and validation set included 122 cases and 51 cases, respectively, without significant differences in their image characteristics and outcomes ( P > 0.05). The results of LASSO regression analysis showed that λ corresponding to the simplest result in the optimal range was 0.0908, and three variables were included at this time, and the influencing factors of impacted stones were stone diameter (coefficient 0.0700, OR = 1.073), CT value (coefficient 0.0003, OR = 1.001) and DDR (coefficient 0.5960, OR = 1.815). Moreover, Model 1 was established. According to the model fitting results, ROC curves were plotted, and the AUC of Model 1 was 0.862, and the AUCs of Model 2-4 were 0.859, 0.762, and 0.793, respectively. After deLong test, there was no significant difference between Model 1 and Model 2 ( Z = 0.248, P = 0.804). The AUC of Model 1 was superior to that of Model 3 ( Z = 2.888, P = 0.004) and Model 4 ( Z = 2.321, P = 0.020). The DCA suggested that Model 1 could improve the net benefit rate by up to approximately 21% of patients. Conclusions:DDR is the influencing factor of impacted ureteral calculi, and the model constructed by DDR, stone CT value and stone diameter can effectively predict the probability of impacted ureteral calculi in the middle and upper ureter.

Long-term survival patients with advanced non-small cell lung cancer receiving thoracic radiotherapy: clinical characteristics and the construction of a nomogram prognostic model / 中华放射医学与防护杂志

Objective:To analyze the clinical characteristics of long-term survival patients with advanced non-small cell lung cancer (NSCLC) treated with chemotherapy combined with primary tumor radiotherapy, and to establish a Nomogram prognostic model, aiming to provide a certain reference for making a decision about the treatment of advanced NSCLC.Methods:A retrospective analysis was made on the data of 260 NSCLC patients who participated in two prospective clinical studies from January 2003 to May 2012 and the data of 138 NSCLC patients admitted to the Affiliated Cancer Hospital of Guizhou Medical University from January 2014 to August 2020. The former 260 cases were used as a training set and the latter 138 cases were used as the validation set. The overall survival (OS) of ≥ 18 months was defined as long-term survival (LTS). The clinical characteristics of LTS patients were compared with those with OS less than 18 months. The clinical characteristics and treatment-related parameters between the two types of patients were compared using the χ2 test. A multivariate analysis was made using logistic regression, and a nomogram model was built using RStudio. Results:The median OS of the training set was 13.4 months (95% CI: 11.9-14.9), with 1-, 2-, and 3-year OS rates of 55.4%, 19.1%, and 11.9%, respectively. In the training set, 87 cases had LTS and were classified as the LTS group, while 173 cases had OS less than 18 months and were classified as the non-LTS group. The univariate analysis showed that the prognostic factors affecting LST included the KPS score, T status, the number of metastatic organs, the number of metastatic lesions, brain metastasis, bone metastasis, the number of chemotherapy cycles, the biologically effective dose (BED) to the primary tumor, hemoglobin level, platelet count, plasma D-dimer, fibrinogen level, lactate dehydrogenase, and lung immune prognostic index (LIPI; χ2=4.72-12.63, P < 0.05). The multivariable analysis showed that the independent prognostic factors of LTS included a number of chemotherapy cycles ≥ 4, BED ≥ 70 Gy, platelets ≤ 220×10 9/L, D-dimer ≤ 0.5 mg/L, and a good LIPI score ( P= 0.002, 0.036, 0.005, 0.008, and 0.002). A nomogram model was established using the meaningful parameters obtained in the multivariable analysis, determining that the training and validation sets had a consistency index (C-index) of 0.750 and 0.727, respectively. As shown by the analytical result of the corrected curves, for the advanced NSCLC patients treated with thoracic radiotherapy, their LTS probability predicted using the nomogram prognostic model was highly consistent with their actual LTS probability. Both the analytical result of the receiver operating characteristic (ROC) curves and the decision curve analysis (DCA) result showed that the composite prediction model was more beneficial than a single prediction model. Conclusions:For patients with advanced NSCLC treated with thoracic radiotherapy, the independent prognostic factors of LTS included the number of chemotherapy cycles, BED, platelet count, pre-chemotherapy D-dimer, and LIPI score. The Nomogram prognostic model built based on these prognostic factors is a convenient, intuitive, and personalized prediction model used to screen patients who can benefit from thoracic radiotherapy.

Factors affecting cardiac autonomic neuropathy in diabetic patients with complications and construction of Nomogram prediction model / 中国医师进修杂志

Objective:To investigate the risk factors for concomitant cardiac autonomic neuropathy in diabetic patients and to develop a Nomogram prediction model.Methods:One hundred and fifty-eight diabetic patients admitted to in Southern Hospital Zengcheng Branch from March 2019 to March 2021 were selected. Patients with normal heart rate variability were the diabetic group, and patients with abnormal heart rate variability were the group with diabetes mellitus complicated by cardiac autonomic neuropathy. Logistic regression analysis was used to analyze the risk factors of cardiac autonomic neuropathy. Nomogram models were developed and model performance was evaluated. Decision curve analysis (DCA) was used to assess the net clinical benefit of the Nomogram model.Results:Comparison of general data showed that fasting blood glucose, tumour necrosis factor-α (TNF-α), glomerular filtration rate (eGER), uric acid, C-reactive protein (CRP), interleukin-6 (IL-6), free fatty acids (FFA), standard deviation of sinus heart beat RR interval (SDNN), and duration of diabetes compared to the diabetic group had statistically significant ( P<0.05); the results of the subject work characteristics (ROC) curve analysis showed that the best cut-off values for fasting glucose, TNF-α, eGFR, uric acid, CRP, IL-6, FFA, SDNN and duration of diabetes were >7.53 mmol/L, >98.45 ng/L, ≤94.79 ml/(min·1.73 m 2), > 87.3 μmol/L, >6.22 μmol/L, >37.84 ng/L, >839.19 μmol/L, ≤ 95.88 ms, >9 years; multi-factorial Logistic regression analysis showed that fasting glucose (>7.53 mmol/L), TNF-α (>98.45 ng/L), CRP (>6.22 μmol/L), IL-6 (>37.84 ng/L), FFA (>839.19 μmol/L), SDNN (≤95.88 ms), and duration of diabetes (>9 years) were risk factors for the development of cardiac autonomic neuropathy in diabetic patients; internal validation showed that the Nomogram model predicted a C-index of 0.706 (95% CI 0.668 - 0.751) for the risk of cardiac autonomic neuropathy. The DCA results showed that the Nomogram model predicted a risk threshold of >0.25 for the development of cardiac autonomic neuropathy and that the Nomogram model provided a net clinical benefit. Conclusions:There are many risk factors for cardiac autonomic neuropathy, and the nomogram model based on risk factors in this study has good predictive power and may provide a reference for clinical screening of high-risk patients and further improvement of treatment planning.

Influencing factors of refractory anastomotic stenosis after laparoscopic intersphincteric resec-tion for rectal cancer and construction of nomogram prediction model / 中华消化外科杂志

Objective:To investigate the influencing factors of refractory anastomotic stenosis after laparoscopic intersphincteric resection (Ls-ISR) for rectal cancer and construction of nomogram prediction model.Methods:The retrospective case-control study was conducted. The clinicopatho-logical data of 495 patients who underwent Ls-ISR for rectal cancer in two medical centers, including 448 patients in Peking University First Hospital and 47 patients in Cancer Hospital Chinese Academy of Medical Sciences, from June 2012 to December 2021 were collected. There were 311 males and 184 females, aged 61 (range, 20-84)years. Observation indicators: (1) incidence of anastomotic stenosis; (2) influencing factors of refractory anastomotic stenosis after Ls-ISR; (3) construction and evaluation of nomogram prediction model for refractory anastomotic stenosis after Ls-ISR. Follow-up was conducted using outpatient examination and telephone interview to detect the incidence of postoperative anastomotic leakage and anastomotic stenosis up to August 2022. Measurement data with normal distribution were represented as Mean± SD, and comparison between groups was conducted using the t test. Measurement data with skewed distribution were represented as M(range). Count data were described as absolute numbers, and comparison between groups was conducted using the chi-square test. Univariate and multivariate analyses were conducted using the Logistic regression model. Factors with P<0.10 in univariate analysis were included in multivariate analysis. The R software (3.6.3 version) was used to construct nomogram prediction model. The receiver operating characteristic (ROC) curve was drawn and the area under curve (AUC) was used to evaluate the efficacy of nomogram prediction model. Results:(1) Incidence of anastomotic stenosis. All 495 patients underwent Ls-ISR successfully, without conversion to laparotomy, and all patients were followed up for 47(range, 8-116)months. During the follow-up period, there were 458 patients without anas-tomotic stenosis, and 37 patients with anastomotic stenosis. Of the 37 patients, there were 15 cases with grade A anastomotic stenosis, 3 cases with grade B anastomotic stenosis and 19 cases with grade C anastomotic stenosis, including 22 cases being identified as the refractory anastomotic stenosis. Fifteen patients with grade A anastomotic stenosis were relieved after anal dilation treat-ment. Three patients with grade B anastomotic stenosis were improved after balloon dilation and endoscopic treatment. Nineteen patients with grade C anastomotic stenosis underwent permanent stoma. During the follow-up period, there were 42 cases with anastomotic leakage including 17 cases combined with refractory anastomotic stenosis, and 453 cases without anastomotic leakage including 5 cases with refractory anastomotic stenosis. There was a significant difference in the refractory anastomotic stenosis between patients with and without anastomotic leakage ( χ2=131.181, P<0.05). (2) Influencing factors of refractory anastomotic stenosis after Ls-ISR. Results of multivariate analysis showed that neoadjuvant therapy, distance from tumor to anal margin ≤4 cm, clinic N+ stage were independent risk factors of refractory anastomotic stenosis after Ls-ISR ( hazard ratio=7.297, 3.898, 2.672, 95% confidence interval as 2.870-18.550, 1.050-14.465, 1.064-6.712, P<0.05). (3) Construction and evaluation of nomogram prediction model for refractory anastomotic stenosis after Ls-ISR. Based on the results of multivariate analysis, neoadjuvant therapy, distance from tumor to anal margin and clinic N staging were included to constructed the nomogram prediction model for refractory anastomotic stenosis after Ls-ISR. Results of ROC curve showed the AUC of nomogram prediction model for refractory anastomotic stenosis after Ls-ISR was 0.739 (95% confidence interval as 0.646-0.833). Conclusions:Neoadjuvant therapy, distance from tumor to anal margin ≤4 cm, clinic N+ stage are independent risk factors of refractory anastomotic stenosis after Ls-ISR. Nomogram prediction model based on these factors can predict the incidence of refractory anastomotic stenosis after Ls-ISR.

Risk factors of endometrial lesions after breast cancer surgery and construction of a graph model / 中华内分泌外科杂志

Objective:To explore the risk factors affecting endometrial lesions after breast cancer surgery, and build a nomogram prediction model.Methods:From Oct. 2019 to Nov. 2021, 103 patients with abnormal bleeding after breast cancer surgery were selected, the clinical data of the patients were collected, and they were divided into the non-lesion group and the lesion group according to whether the endometrial lesion occurred. A Logistic risk regression model was established to analyze the risk factors affecting endometrial lesions in postoperative patients with breast cancer, a nomogram prediction model was constructed and verified, and receiver operating characteristic curve (ROC) analysis was performed to analyze the nomogram model for predicting sensitivityof endometrial lesions.Results:Childbirth history ( OR=37.100, 95% CI: 3.777-527.7, P=0.004), endometrial thickness ( OR=2.489, 95% CI: 1.699-4.007, P<0.001), menopause ( OR=0.099, 95% CI: 0.015-0.499, P=0.009), abnormal bleeding time ( OR=6.922, 95% CI: 2.221-24.800, P=0.002), and types of treatment drugs ( OR=3.738, 95% CI: 1.187-13.200, P=0.030) had statistical significance in predicting endometrial lesions in postoperative patients with breast cancer. Using the above five variables to construct a nomogram model, the consistency of the nomogram in predicting endometrial lesions in postoperative patients with breast cancer was 0.739, and the discrimination was good. The calibration curve showed that the average absolute error between the predicted probability and the actual probability was 0.041,and ROC curve showed that the AUC value of the nomogram model for predicting endometrial lesions was 0.800. Conclusion:Establishing a nomogram model for predicting the risk of endometrial lesions in postoperative patients with breast cancer has good accuracy and high clinical value.

Construction and study of nomograph model for prognosis of multiple trauma patients / 中华急诊医学杂志

Objective:To explore the prognostic risk factors of patients with multiple injuries and establish a nomogram prediction model.Methods:The clinical data of 291 patients with multiple injuries admitted to the Emergency Intensive Care Unit (EICU) of General Hospital of Ningxia Medical University were collected, including sex, age, open injury, norepinephrine use, mechanical ventilation, time to hospital after injury, distance to hospital, relative lymphocyte value, platelet count, lactic acid, injury severity score (ISS), acute physiology and chronic health evaluationⅡ (APACHE Ⅱ), Glasgow coma scale (GCS), number of blood transfusions, number of operations, and previous history of diabetes, hypertension and smoking within 24 h after admission. According to whether the condition worsened during the hospitalization of EICU, the patients were divided into the deterioration group and improvement group. SPSS26.0 software was used for statistical analysis of the data, univariate and multivariate analysis were used to screen the factors affecting the prognosis of patients with multiple injuries, receiver operating characteristic (ROC) curve and forest chart were drawn, and the influencing factors in binary Logistic regression model were used to make the nomogram.Results:Mechanical ventilation, norepinephrine use, age, relative lymphocyte value, lactic acid, APACHE-II score, GCS score, and number of operations were significant for predicting the prognosis of patients with multiple injuries ( P<0.05). The independent influencing factors obtained by binary Logistic regression model were age, lactic acid, APACHE-Ⅱ score and number of operations. ROC curve analysis showed that the area under the curve was the largest in multi-factor combined prediction, followed by APACHE-Ⅱ score. The diagnostic cut-off value of each index was as follows: age >58 years old, relative lymphocyte value≤ 8.62%, lactic acid >1.72, APACHE-Ⅱ score >16, GCS score≤ 6, and number of operations≤ 0. The R software was used to establish a nomogram of the influencing factors in the binary Logistic regression model, which had good predictive value. Conclusions:The nomogram constructed by age, relative lymphocyte value, lactic acid, APACHE-Ⅱ score, GCS score, number of operations, mechanical ventilation, and norepinephrine use has a good predictive value for the prognosis of patients with multiple injuries, and is worthy of promotion..

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.

Establishment of a nomogram model for predicting the risk of postpartum stress urinary incontinence based on pelvic floor ultrasound and clinical data / 现代泌尿外科杂志

【Objective】 To analyze the risk factors of postpartum stress urinary incontinence (SUI) and to establish a nomogram model. 【Methods】 A total of 278 puerpera who gave birth at our hospital during Dec.2018 and Aug.2020 were selected as the modeling group, and 132 puerpera who gave birth during Sep.2020 and Sep.2021 were involved in the verification group. Factors affecting postpartum SUI were identified with univariate and multivariate logistic regression, and a nomogram prediction model was constructed with R software. The predictive effectiveness and discrimination of the model were assessed, and the decision curve analysis (DCA) was drawn to evaluate the clinical application value of the model. 【Results】 A total of 84 cases (30.22%) in the modeling group developed SUI 2 months after delivery. Fetal weight, delivery method, maternal age, mobility (Δhy) and rotation Angle (Δβ) were factors affecting postpartum SUI (P<0.05). Multivariate logistic regression analysis showed that increased fetal weight, normal delivery, increased Δhy, and increased Δβ were independent risk factors of postpartum SUI (P<0.05). The constructed nomogram fitted well. The H-L fit curve of the modeling group and verification group were (χ2=7.514, P=0.312) and (χ2=6.157, P=0.267), respectively. The area under the receiver operating characteristic curve of the modeling group and verification group were 0.815 and 0.760, respectively, indicating high specificity and consistency. DCA indicated that when the high-risk threshold probability of the model was between 0.06-0.80, the nomogram model had a high clinical value. 【Conclusion】 Increased fetal weight, normal delivery, increased Δhy and elevated Δβ are independent risk factors that affect postpartum SUI. The nomogram model constructed has good predictive effectiveness and discrimination, and high clinical application value.

Construction of a prediction model for prognosis of severe pneumonia patients combined with sepsis / 中华临床感染病杂志

Objective:To construct a prediction model for prognosis of severe pneumonia patients combined with sepsis.Methods:Clinical data of 318 severe pneumonia patients combined with sepsis admitted at Taizhou People’s Hospital affiliated to Nanjing Medical University from March 2019 to March 2022 were retrospectively analyzed. Patients were randomized into a modeling set ( n=233) and a validation set ( n=85) with a 3∶1 ratio. In the modeling set there were 180 survival cases and 53 fatal cases according to the clinical outcomes within 30 days of admission. Multivariate Cox regression analysis was used to evaluate the independent prognostic factors for patients in the modeling set. A nomogram prediction model was constructed by R based on these prognostic factors and further verified using the data of the validation set with receiver operating curve (ROC), decision curve analysis (DCA), and calibrated with calibration curve analyses. Results:Multivariate Cox regression analysis suggested that septic shock ( HR=2.32, 95% CI 1.37-3.89, P=0.013) and neutrophil/lymphocyte ratio (NLR) ( HR=2.52, 95% CI 1.23-5.61, P=0.017) were independent risk factors for mortality in severe pneumonia patients combined with sepsis within 30 days of admission, while albumin/fibrinogen ratio (AFR) ( HR=0.64, 95% CI 0.41-0.83, P=0.011) and prognostic nutritional index (PNI) ( HR=0.68, 95% CI 0.57-0.83, P=0.009) were independent protective factors. The area under ROC curve (AUC) of the nomogram model based on these four indicators in the modeling and validation sets were 0.875 and 0.880, respectively. The DCA curve analysis indicated that the clinical benefit of this model was better than "All" or "None" curves in both the modeling and verification sets.The calibrate curve analysis indicated that the actual and corrected curves fitted well and were close to the ideal curve. Conclusion:The constructed nomogram model based on septic shock, AFR, NLR and PNI has a well prognostic value in severe pneumonia patients combined with sepsis.

Kupperman index-based analysis of factors influencing perimenopausal symptoms and establishment of risk nomogram in female petrochemical workers / 环境与职业医学

Background Women face more reproductive health problems in their whole life cycle. Occupational exposure to harmful factors in the petrochemical industry may have a synergistic effect on women’s existing health problems. Objective To analyze the influencing factors of perimenopausal syndrome (PMS) in female workers in petrochemical industry, and establish a nomogram model of the risk of PMS in female workers, so as to provide a easy and quick health monitoring and evaluation method for female workers. Methods A total of 2653 perimenopausal female workers aged 45-55 years old were selected from a petrochemical enterprise. A questionnaire survey was conducted to collect information on demographic characteristics, occupational characteristics, psychological status, and reproductive health information. The prevalence of PMS of female workers was evaluated by the Kupperman Index Scale, the physical fatigue and mental fatigue were evaluated by the Fatigue Scale. A linear graph prediction model was established by multiple logistic regression. A nomogram was presented and C-index was used to verify the differentiation of the model. Then Bootstrap method was used for internal validation. Results Among the 2653 female worker, a total of 1306 cases (49.2%) presented PMS with a Kupperman score ≥7. The main symptoms were fatigue (79.95%), irritability (71.32%), and insomnia (66.79%). Significant differences in PMS prevalence were found among female workers of different age, body mass index, and working posture groups (P < 0.05). The participants with alcohol drinking, maternal premature or late menopause, hypertension, lack of physical exercise, heavy lifting, sick leave in the last 6 months, combined occupational exposures to dust, chemicals, noise [> 80 dB(A)], or electromagnetic field, and not wearing protective masks, gloves or protective earplugs reported higher prevalence rates of PMS (P < 0.05). The prevalence rate of PMS in female workers with sleep duration ≤ 6 h was higher than that with > 6 h (P < 0.05), and higher in female workers with physical and mental fatigue than in those without (P < 0.05). The results of logistic regression analysis showed that those with maternal premature or late menopause (OR=1.572, 95%CI: 1.320−1.872), hypertension (OR=1.579, 95%CI: 1.127−2.213), alcohol drinking (OR=1.286, 95%CI: 1.080−1.532), no physical exercise (OR=1.598, 95%CI: 1.330−1.920), sleep duration ≤ 6 h (OR=1.853, 95%CI: 1.518−2.263), sick leave in recent 6 months (OR=1.614, 95%CI: 1.226−2.123), physical fatigue (OR=2.384, 95%CI: 1.887−3.012), mental fatigue (OR=5.649, 95%CI: 4.382−7.283), combined exposure to occupational harmful factors (OR=1.329, 95%CI: 1.108−1.593), long-time sitting (OR=2.014, 95%CI: 1.271−3.190), and heavy lifting (OR=1.505, 95%CI: 1.178−1.923) showed a higher risk of reporting PMS (P<0.05). The C-index from the ROC curve of the nomogram model was 0.748 (95%CI: 0.729−0.766). The results of Bootstrap validation showed that the standard curve and the predicted curve almost overlapped, and the absolute error was 0.008, indicating that the model fitness was good. Conclusion PMS in female petrochemical workers may occur due to long-term exposures to multiple factors. The established nomogram model has good predictive ability and could be applied to monitor and evaluate female reproductive health in petroleum industry.

A risk prediction model of conversion to open surgery during laparoscopic splenectomy and esophagogastric devascularization / 中华普通外科杂志

Objective:To establish a risk prediction model of conversion to open surgery during laparoscopic splenectomy and esophagogastric devascularization (LSED) and evaluate the impact of this conversion on patients' short-term prognosis.Methods:A total of 358 cirrhotic portal hypertension patients admitted to the Department of General Surgery , Second Affiliated Hospital, Air Force Military Medical University from Feb 2011 to Nov 2020 were retrospectively analyzed. All patients underwent attempted LSED. Univariate and least absolute shrinkage and selection operator (LASSO) Logistic regression were used to analyze the independent risk factors for conversion to laparotomy, and the R language was used to build a nomogram prediction model for conversion to laparotomy. The intraoperative and postoperative conditions of the two groups were compared.Results:A total of 358 patients were included in this study, of which 31(8.7%). patients were converted to open surgery. In univariate analysis, high MELD score, BMI ≥24 kg/m 2, history of upper abdominal surgery, red sign of the varicose, low platelet count and prolonged PT are risk factors for conversion . LASSO regression finally identified 5 factors: MELD, BMI, PLT, history of surgery, and red sign. In the nomogram prediction model the area under ROC curve was 0.831. The conversion led to longer operation time; increased blood loss; prolonged postoperative abdominal drainage , longer hospital stay, and increased perioperative complications ( t=-2.167, P=0.031; Z=-4.350, P<0.01; Z=-3.102, P=0.002; Z=-3.454, P=0.001; χ2=8.773, P=0.003). Conclusions:LASSO regression selected five indicators with greatest impact on intraoperative conversion: MELD, BMI, PLT, red sign, and previous history of abdominal surgery. The nomogram prediction model established has good prediction ability. Patients converted to open surgery had worse short-term outcomes.

Establishment of nomogram model for the risk factors of cerebral hemorrhage in young people / 中国综合临床

Objective:To explore the common risk factors of intracerebral hemorrhage(ICH) in young people and to establish a predictive model of nomogram.Methods:The relevant data of young patients with ICH (≤45 years ) hospitalized in the Department of Neurosurgery of Dezhou people's Hospital from January 2014 to August 2021 were retrospectively studied, and the young group who underwent physical examination in the Physical Examination Center of Dezhou people's Hospital at the same time were randomly selected as the control group. Analyze the risk factors that may affect cerebral hemorrhage in young people, screen the risk factors with statistical differences through single factor analysis, screen the independent risk factors according to multi factor Logistic regression analysis, construct the risk nomogram model of cerebral hemorrhage in young people, and test the efficiency, goodness of fit and benefit of the constructed model through internal validation.Results:Compared with the control group, there were statistically significant differences in family history (χ 2=115.66, P<0.001), hypertension grade( Z=17.67, P<0.001), smoking history (χ 2=33.91, P<0.001), drinking grade ( Z=4.84, P<0.001), body mass index (BMI) ( t=11.76, P<0.001), low density lipoprotein ( t=4.78, P<0.001), high density lipoprotein cholesterol ( t=5.83, P<0.001),blood glucose ( Z=5.68, P<0.001) and homocysteine ( Z=2.22, P<0.001) in the case group. Binary Logistic regression analysis showed that hypertension grade ( OR=3.457, 95%CI: 2.809-4.254, P<0.001), family history ( OR=2.871, 95%CI:1.868-4.413, P<0.001), BMI ( OR=1.093, 95%CI:1.040-1.148, P<0.001), high density lipoprotein cholesterol ( OR=0.230, 95%CI:0.111-0.480, P<0.001), blood glucose ( OR=3.457, 95%CI:2.809-4.254, P<0.001), homocysteine (O R=3.457, 95%CI:2.809-4.254, P<0.001) was an independent risk factor for intracerebral hemorrhage in young adults. The nomogram prediction model showed that BMI was 96 points, hypertension grade was 100 points, family history was 30 points, high density lipoprotein cholesterol was 76 points, homocysteine was 48 points, blood glucose was 52 points,homocysteine was 48 points and blood glucose was 52 points, respectively. The consistency coefficient of the prediction model was 0.874. The nomogram dependent ROC curve AUC was 0.891, and the corresponding sensitivity and specificity were 74.5% (263/353) and 89.7% (437/487), respectively, a nomogram model was established with good diagnostic efficiency. Conclusion:The nomogram model established in this study can predict the probability of intracerebral hemorrhage in high-risk population, and take intervention measures as early as possible to prevent the occurrence of intracerebral hemorrhage in young people.