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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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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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Objective:To construct a risk prediction model for infection with Klebsiella pneumonia (KP) for patients with severe acute pancreatitis (SAP).Methods:Retrospective analysis was done on the clinical data of 109 SAP patients who were admitted to Shanghai General Hospital, between March 2016 and December 2021. Patients were classified into infection group ( n=25) and non-infection group ( n=84) based on the presence or absence of KP infection, and the clinical characteristics of the two groups were compared. The least absolute shrinkage and selection operator (LASSO) algorithm was used to reduce the dimension of the variables with statistical significance in univariate analysis. A nomogram prediction model was created by incorporating the optimized features from the LASSO regression model into the multivariate logistic regression analysis. Receiver operating characteristic curve (ROC) was drawn and the area under curve (AUC) was calculated; and consistency index (C-index) were used to assess the prediction model's diagnostic ability. Results:A total of 25 strains of KP were isolated from 109 patients with SAP, of which 21(84.0%) had multi-drug resistance. 20 risk factors (SOFA score, APACHEⅡ score, Ranson score, MCTSI score, mechanical ventilation time, fasting time, duration of indwelling of the peritoneal drainage tube, duration of deep vein indwelling, number of invasive procedures, without or with surgical intervention, without or with endoscopic retrograde cholangiopancreatography (ERCP), types of high-level antibiotics used, digestion disorders, abnormalities in blood coagulation, metabolic acidosis, pancreatic necrosis, intra-abdominal hemorrhage, intra-abdominal hypertension, length of ICU stay and total length of hospital stay) were found to be associated with KP infection in SAP patients by univariate analysis. The four variables (APACHEⅡ score, duration of indwelling of the peritoneal drainage tube, types of high-level antibiotics used, and total length of hospital stay) were extracted after reduced by LASSO regression. These four variables were found to be risk factors for KP infection in SAP patients by multiple logistic regression analysis (all P value <0.05). Nomogram prediction model for KP infection in SAP was established based on the four variables above. The verification results of the model showed that the C-index of the model was 0.939, and the AUC was 0.939 (95% CI 0.888-0.991), indicating that the nomogram model had relatively accurate prediction ability. Conclusions:This prediction model establishes integrated the basic clinical data of patients, which could facilitate the risk prediction for KP infection in patients with SAP and thus help to formulate better therapeutic plans for patients.
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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.
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Corneal refractive surgery is widely used to correct myopia and astigmatism because of its safety, effectiveness and stability.Precision medicine is the future direction of development, and the demands for accuracy in corneal refractive surgery are also increasing, which has a direct impact on patient satisfaction.Nomogram, as a key design in refractive surgery, needs to be combined with several important predictors to quantify individual risk.Different surgical methods need different nomograms.In this paper, the effects of corneal surface surgery, lamellar surgery, modeling algorithm and possible factors such as patient's sex, age, expected correction, corneal curvature, preoperative spherical equivalent, etc.on the predicted values were discussed.At the same time, the application of nomograms in corneal refractive surgery at home and abroad in recent years and the research progress of nomogram influencing factors were explored, in order to provide more and more accurate reference for clinical practice, to improve the accuracy of corneal refractive surgery and help patients achieve satisfactory postoperative visual quality.
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Objective:To investigate the influencing factors of recurrence after radical resection of middle and low rectal cancer, and to establish a prediction model based on magnetic resonance imaging (MRI) measurement of perirectal fat content and investigate its application value.Methods:The retrospective cohort study was constructed. The clinicopathological data of 254 patients with middle and low rectal cancer who were admitted to Tianjin Union Medical Center from December 2016 to December 2021 were collected. There were 188 males and 66 females, aged (61±9)years. All patients underwent radical resection of rectal cancer and routine pelvic MRI examina-tion. Observation indicators: (1) follow-up and quantitative measurement of perirectal fat content; (2) factors influencing tumor recurrence after radical resection of middle and low rectal cancer; (3) construction and evaluation of the nomogram prediction model of tumor recurrence after radical resection of middle and low rectal cancer. Measurement data with normal distribution were represented as Mean± SD, and measurement data with skewed distribution were represented as M(rang) and M( Q1, Q2). Count data were described as absolute numbers. Univariate and multivariate analyses were conducted using the COX regression model. The rms software package (4.1.3 version) was used to construct the nomogram and calibration curve. The survival software package (4.1.3 version) was used to calculate the C-index. The ggDCA software package (4.1.3 version) was used for decision curve analysis. Results:(1) Follow-up and quantitative measurement of perirectal fat content. All 254 patients were followed up for 41.0(range, 1.0?59.0)months after surgery. During the follow-up period, there were 81 patients undergoing tumor recurrence with the time to tumor recurrence as 15.0(range, 1.0?43.0)months, and there were 173 patients without tumor recurrence. The preoperative rectal mesangial fascia envelope volume, preoperative rectal mesangial fat area, preoperative rectal posterior mesangial thickness were 159.1(68.6,266.5)cm3, 17.0(5.1,34.4)cm2, 1.2(0.4,3.2)cm in the 81 patients with tumor recurrence, and 178.5(100.1,310.1)cm3, 19.8(5.3,40.2)cm2 and 1.6(0.3,3.7)cm in the 173 patients without tumor recurrence. (2) Factors influencing tumor recurrence after radical resection of middle and low rectal cancer. Results of multivariate analysis showed that poorly differentiated tumor, tumor pathological N staging as N1?N2 stage, rectal posterior mesangial thickness ≤1.43 cm, magnetic resonance extra mural vascular invasion, tumor invasion surrounding structures were independent risk factors of tumor recurrence after radical resection of middle and low rectal cancer ( hazard ratio=1.64, 2.20, 3.19, 1.69, 4.20, 95% confidence interval as 1.03?2.61, 1.29?3.74, 1.78?5.71, 1.02?2.81, 2.05?8.63, P<0.05). (3) Construction and evaluation of the nomogram prediction model of tumor recurrence after radical resection of middle and low rectal cancer. Based on the results of multivariate analysis, the tumor differentiation, tumor pathological N staging, rectal posterior mesangial thickness, magnetic resonance extra mural vascular invasion, tumor invasion surrounding structures were included to construct the nomogram predic-tion model of tumor recurrence after radical resection of middle and low rectal cancer. The total score of these index in the nomogram prediction model corresponded to the probability of post-operative tumor recurrence. The C-index of the nomogram was 0.80, indicating that the prediction model with good prediction accuracy. Results of calibration curve showed that the nomogram prediction model with good prediction ability. Results of decision curve showed that the prediction probability threshold range was wide when the nomogram prediction model had obvious net benefit rate, and the model had good clinical practicability. Conclusions:Poorly differentiated tumor, tumor pathological N staging as N1?N2 stage, rectal posterior mesangial thickness ≤1.43 cm, magnetic resonance extra mural vascular invasion, tumor invasion surrounding structures are independent risk factors of tumor recurrence after radical resection of middle and low rectal cancer. Nomogram prediction model based on MRI measurement of perirectal fat content can effectively predict the probability of postoperative tumor recurrence.
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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.
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Objective:To investigate the value of number of negative lymph nodes (NLNs) in predicting the prognosis of patients with esophageal cancer after neoadjuvant therapy and the construction of nomogram prodiction model.Methods:The retrospective cohort study was conducted. The clinicopathological data of 1 924 patients with esophageal cancer after neoadjuvant therapy uploaded to the Surveillance, Epidemiology, and End Results Database of the National Cancer Institute from 2004 to 2015 were collected. There were 1 624 males and 300 females, aged 63 (range, 23?85)years. All 1 924 patients were randomly divided into the training dataset of 1 348 cases and the validation dataset of 576 cases with a ratio of 7:3 based on random number method in the R software (3.6.2 version). The training dataset was used to constructed the nomogram predic-tion model, and the validation dataset was used to validate the performance of the nomogrram prediction model. The optimal cutoff values of number of NLNs and number of examined lymph nodes (ELNs) were 8, 14 and 10, 14, respectively, determined by the X-tile software (3.6.1 version), and then data of NLNs and ELNs were converted into classification variables. Observation indicators: (1) clinicopathological characteristics of patients in the training dataset and the validation dataset; (2) survival of patients in the training dataset and the validation dataset; (3) prognostic factors analysis of patients in the training dataset; (4) survival of patients in subgroup of the training dataset; (5) prognostic factors analysis in subgroup of the training dataset; (6) construction of nomogram prediction model and calibration curve. 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), and comparison between groups was conducted using the Mann-Whitney U test. Count data were described as absolute numbers, and comparison between groups was conducted using the chi-square test. The Kaplan-Meier method was used to draw survival curve and Log-Rank test was used for survival analysis. The COX proportional hazard model was used for univariate and multivariate analyses. Based on the results of multivariate analysis, the nomogram prediction model was constructed. The prediction efficacy of nomogram prediction model was evaluated using the area under curve (AUC) of the receiver operating characteristic curve and the Harrell′s c index. Errors of the nomogram prediction model in predicting survival of patients for the training dataset and the validation dataset were evaluated using the calibration curve. Results:(1) Clinicopathological characteristics of patients in the training dataset and the validation dataset. There was no significant difference in clinicopatholo-gical characteristics between the 1 348 patients of the training dataset and the 576 patients of the validation dataset ( P>0.05). (2) Survival of patients in the training dataset and the validation dataset. All 1 924 patients were followed up for 50(range, 3?140)months, with 3-year and 5-year cumulative survival rate as 59.4% and 49.5%, respectively. The 3-year cumulative survival rate of patients with number of NLNs as <8, 8?14 and >14 in the training dataset was 46.7%, 62.0% and 66.0%, respectively, and the 5-year cumulative survival rate was 38.1%, 52.1% and 59.7%, respectively. There was a significant difference in the survival of these patients in the training dataset ( χ2=33.70, P<0.05). The 3-year cumulative survival rate of patients with number of NLNs as <8, 8?14 and >14 in the validation dataset was 51.1%, 54.9% and 71.2%, respectively, and the 5-year cumulative survival rate was 39.3%, 42.5% and 55.7%, respectively. There was a significant difference in the survival of these patients in the validation dataset ( χ2=14.49, P<0.05). The 3-year cumulative survival rate of patients with number of ELNs as <10, 10?14 and >14 in the training dataset was 53.9%, 60.0% and 62.7%, respectively, and the 5-year cumulative survival rate was 44.7%, 49.1% and 56.9%, respectively. There was a significant difference in the survival of these patients in the training dataset ( χ2=9.88, P<0.05). The 3-year cumulative survival rate of patients with number of ELNs as <10, 10?14 and >14 in the validation dataset was 56.2%, 47.9% and 69.3%, respectively, and the 5-year cumula-tive survival rate was 44.9%, 38.4% and 51.9%, respectively. There was a significant difference in the survival of these patients in the validation dataset ( χ2=9.30, P<0.05). (3) Prognostic factors analysis of patients in the training dataset. Results of multivariate analysis showed that gender, neoadjuvant pathological (yp) T staging, ypN staging (stage N1, stage N2, stage N3) and number of NLNs (8?14, >14) were independent influencing factors for the prognosis of patients with esophageal cancer after neoadjuvant therapy ( hazard ratio=0.65, 1.44, 1.96, 2.41, 4.12, 0.69, 0.56, 95% confidence interval as 0.49?0.87, 1.17?1.78, 1.59?2.42, 1.84?3.14, 2.89?5.88, 0.56?0.86, 0.45?0.70, P<0.05). (4) Survival of patients in subgroup of the training dataset. Of the patients with NLNs in the training dataset, the 3-year cumulative survival rate of patients with number of NLNs as <8, 8?14 and >14 was 61.1%, 71.6% and 76.8%, respectively, and the 5-year cumulative survival rate was 50.7%, 59.9% and 70.1%, respectively. There was a significant difference in the survival of these patients in the training dataset ( χ2=12.66, P<0.05). Of the patients with positive lymph nodes in the training dataset, the 3-year cumulative survival rate of patients with number of NLNs as <8, 8?14 and >14 was 26.1%, 42.9% and 44.7%, respectively, and the 5-year cumulative survival rate was 20.0%, 36.5% and 39.3%, respectively. There was a significant difference in the survival of these patients in the training dataset ( χ2=20.39, P<0.05). (5) Prognostic factors analysis in subgroup of the training dataset. Results of multivariate analysis in patients with NLNs in the training dataset showed that gender, ypT staging and number of NLNs (>14) were independent influencing factors for the prognosis of patients with esophageal cancer after neoadju-vant therapy ( hazard ratio=0.67, 1.44, 0.56, 95% confidence interval as 0.47?0.96, 1.09?1.90, 0.41?0.77, P<0.05). Results of multi-variate analysis in patients with positive lymph nodes in the training dataset showed that race as others, histological grade as G2, ypN staging as stage N3 and number of NLNs (8?14, >14) were independent influencing factors for the prognosis of patients with esophageal cancer after neoadjuvant therapy ( hazard ratio=2.73, 0.70, 2.08, 0.63, 0.59, 95% confidence interval as 1.43?5.21, 0.54?0.91, 1.44?3.02, 0.46?0.87, 0.44?0.78, P<0.05). (6) Construction of nomogram prediction model and calibration curve. Based on the multivariate analysis of prognosis in patients of the training dataset ,the nomogram prediction model for the prognosis of patients with esophageal cancer after neoadju-vant treatment was constructed based on the indicators of gender, ypT staging, ypN staging and number of NLNs. The AUC of nomogram prediction model in predicting the 3-, 5-year cumulative survival rate of patients in the training dataset and the validation dataset was 0.70, 0. 70 and 0.71, 0.71, respectively. The Harrell′s c index of nomogram prediction model of patients in the training dataset and the validation dataset was 0.66 and 0.63, respectively. Results of calibration curve showed that the predicted value of the nomogram prediction model of patients in the training dataset and the validation dataset was in good agreement with the actual observed value. Conclusion:The number of NLNs is an independent influencing factor for the prognosis of esophageal cancer patients after neoadjuvant therapy, and the nomogram prediction model based on number of NLNs can predict the prognosis of esophageal cancer patients after neoadjuvant therapy.
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Objective:To investigate the influencing factors of blood glucose fluctuation after surgery for type A aortic dissection in non-diabetic patients.Methods:A total of 109 patients with non-diabetic type A aortic dissection who underwent surgical treatment in Ningbo Medical Center Li Huili Hospital from Dec. 2016 to Mar. 2022 were selected as the research subjects. Logistic regression analysis was used to explore the influencing factors of blood glucose fluctuation in non-diabetic patients with type A aortic dissection surgery, and a nomogram model of blood glucose fluctuation in non-diabetic patients with type A aortic dissection surgery was constructed.Results:Logistic regression analysis showed that BMI≥24 kg/m 2, poor sleep quality, depression, cardiopulmonary bypass time>5 h and intraoperative bleeding were the risk factors for postoperative blood glucose fluctuation in non-diabetic type A aortic dissection patients ( P<0.05). The C-index of the nomogram model was 0.746 (95% CI: 0.711-0.781) ; the calibration curve was in good agreement with the ideal curve; the AUC of the nomogram model was 0.804. Conclusion:BMI≥24 kg/m 2, poor sleep quality, depression, cardiopulmonary bypass time>5 h and intraoperative bleeding are risk factors for postoperative blood glucose fluctuation in non-diabetic type A aortic dissection patients.
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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.
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Objective:To investigate the clinical characteristics of patients with acute aortic dissection (AAD) through a retrospective and observational study, and to construct an early warning model of AAD that could be used in the emergency room.Methods:The data of 11 583 patients in the Emergency Chest Pain Center from January to December 2019 were retrospectively collected from the Chest Pain Database of Zhongshan Hospital Affiliated to Fudan University. Inclusion criteria: patients with chest pain who attended the Emergency Chest Pain Center between January and December 2019. Exclusion criteria were 1) younger than 18 years, 2) no chest/back pain, 3) patients with incomplete clinical information, and 4) patients with a previous definite diagnosis of aortic dissection who had or had not undergone surgery. The clinical data of 9668 patients with acute chest/back pain were finally collected, excluding 53 patients with previous definite diagnosis of AAD and/or without surgical aortic dissection. A total of 9 615 patients were enrolled as the modeling cohort for early diagnosis of AAD. The patients were divided into the AAD group and non-AAD group according to whether AAD was diagnosed. Risk factors were screened by univariate and multivariate logistic regression, the best fitting model was selected for inclusion in the study, and the early warning model was constructed and visualized based on the nomogram function in R software. The model performance was evaluated by accuracy, specificity, sensitivity, positive likelihood ratio and negative likelihood ratio. The model was validated by a validation cohort of 4808 patients who met the inclusion/exclusion criteria from January 2020 to June 2020 in the Emergency Chest Pain Center of the hospital. The effect of early diagnosis and early warning model was evaluated by calibration curve.Results:After multivariate analysis, the risk factors for AAD were male sex ( OR=0.241, P<0.001), cutting/tear-like pain ( OR=38.309, P<0.001), hypertension ( OR=1.943, P=0.007), high-risk medical history ( OR=12.773, P<0.001), high-risk signs ( OR=7.383, P=0.007), and the first D-dimer value ( OR=1.165, P<0.001), Protective factors include diabetes( OR=0.329, P=0.027) and coronary heart disease ( OR=0.121, P<0.001). The area under the ROC curve (AUC) of the early diagnosis and warning model constructed by combining the risk factors was 0.939(95 CI:0.909-0.969). Preliminary validation results showed that the AUC of the early diagnosis and warning model was 0.910(95 CI:0.870-0.949). Conclusions:Sex, cutting/tear-like pain, hypertension, high-risk medical history, high-risk signs, and first D-dimer value are independent risk factors for early diagnosis of AAD. The model constructed by these risk factors has a good effect on the early diagnosis and warning of AAD, which is helpful for the early clinical identification of AAD patients.
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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..
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Objective:To explore the independent risk factors of in-hospital cardiac arrest (IHCA) in critically ill patients and construct a nomogram model to predict the risk of IHCA based on the identified risk factors.Methods:Patients who were admitted to the intensive care units (ICUs) from 2008 to 2019 were retrospectively enrolled from the Medical Information Mart for Intensive Care -Ⅳ database. The patients were excluded if they (1) were younger than 18 years old, (2) had repeated ICU admission records, or (3) had an ICU stay shorter than 24 h. The patients were randomly divided into the training and internal validation cohorts (7 : 3). Univariate and multivariate logistic regression models were used to identify independent risk factors of IHCA, and a nomogram was constructed based on these independent risk factors. Calibration curve, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA) were used to evaluate the nomogram model. Finally, the nomogram was externally validated using the emergency ICU collaborative research database.Results:A total of 41,951 critically ill patients were enrolled (training cohort, n=29 366; internal validation cohort, n=12 585). Multivariate analysis showed that myocardial infarction, pulmonary heart disease, cardiogenic shock, respiratory failure, acute kidney injury, respiratory rate, glucose, hematocrit, sodium, anion gap, vasoactive drug use, and invasive mechanical ventilation were independent risk factors of IHCA. Based on the above risk factors, a nomogram for predicting IHCA was constructed. The area under the ROC curve (AUC) of the nomogram was 0.817 (95% CI: 0.785–0.847). The calibration curve showed that the predicted and actual probabilities of the nomogram were consistent. Moreover, DCA showed that the nomogram had clinical benefits for predicting IHCA. In the internal validation cohort, the nomogram had a similar predictive value of IHCA (AUC=0.807, 95% CI: 0.760–0.862). In an external validation cohort of 87,626 critically ill patients, the nomogram had stable ability for predicting IHCA (AUC=0.804, 95% CI: 0.786–0.822). In addition, the nomogram also had predictive value for in-hospital mortality (AUC=0.818, 95% CI: 0.802-0.834). Conclusions:The nomogram is constructed based on identified independent risk factors, which has good predictive value for IHCA. Moreover, the performance of the nomogram in the external validation cohort is robust. The study findings may help clinicians to assess the risk of IHCA in critically ill patients.
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ObjectiveTo establish and validate a clinical prediction model for 1-year major adverse cardiovascular events（MACEs）risk after percutaneous coronary intervention （PCI） in coronary heart disease （CHD） patients with blood stasis syndrome. MethodThe consecutive CHD patients diagnosed with blood stasis syndrome in the Department of Integrative Cardiology at China-Japan Friendship Hospital from September 1， 2019 to March 31， 2021 were selected for a retrospective study， and basic clinical features and relevant indicators were collected. Eligible patients were classified into a derivation set and a validation set at a ratio of 7∶3， and each set was further divided into a MACEs group and a non-MACEs group. The factors affecting the outcomes were screened out by least absolute shrinkage and selection operator （Lasso） and used to establish a logistic regression model and identify independent prediction variables. The goodness-of-fit of the model was evaluated by the Hosmer-Lemeshow test， and the area under curve （AUC） of the receiver operating characteristic （ROC） curve， calibration curve， decision curve analysis （DCA）， and clinical impact curve （CIC） were employed to evaluate the discrimination， calibration， and clinical impact of the model. ResultA total of 731 consecutive patients were assessed and 404 eligible patients were enrolled， including 283 patients in the derivation set and 121 patients in the validation set. Lasso identified ten variables influencing outcomes， which included age， sex， fasting plasma glucose （FPG）， triglyceride （TG）， low-density lipoprotein cholesterol （LDL-C）， homocysteine （Hcy）， brachial-ankle pulse wave velocity （baPWV）， flow-mediated dilatation （FMD）， left ventricular ejection fraction （LVEF）， and Gensini score. The multivariate Logistic regression preliminarily identified age， FPG， TG， Hcy， LDL-C， LVEF， and Gensini score as the independent variables that influenced the outcomes. Of these variables， male， high FMD and high LVEF were protective factors， and the rest were risk factors. The prediction model for 1-year MACEs risk after PCI in CHD patients with blood stasis syndrome showed χ2=12.371 （P=0.14） in Hosmer-Lemeshow test and the AUC of 0.90. With the threshold probability > 10%， the model showed better prediction performance for 1-year MACEs risk after PCI in CHD patients with blood stasis syndrome than for that in all the patients. With the threshold probability > 60%， the estimated value was much closer to the real number of patients. ConclusionThe established clinical prediction model facilitates the early prediction of 1-year MACEs risk after PCI in CHD patients with blood stasis syndrome， which can provide ideas for the precise treatment of CHD patients after PCI and has guiding significance for improving the prognosis of the patients. Meanwhile， multi-center studies with larger sample sizes are expected to further validate， improve， and update the model.
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Objective To investigate the predictive value of preoperative fibrinogen/albumin ratio (FAR) and systemic immune inflammation index (SII) on the postoperative prognosis of patients with pancreatic ductal adenocarcinoma. Methods An ROC curve was used in determining the best cutoff values of FAR and SII and then grouped. The Cox proportional hazards model was used in analyzing the prognostic factors of radical pancreatic cancer surgery, and then a Nomogram prognostic model was established. C-index, AUC, and calibration curve were used in evaluating the discrimination and calibration ability of the Nomogram. DCA curves were used in assessing the clinical validity of the Nomograms. Results The optimal cutoff values for preoperative FAR and SII were 0.095 and 532.945, respectively. FAR≥ 0.095, SII≥ 532.945, CA199≥ 450.9 U/ml, maximum tumor diameter≥ 4 cm, and the absence of postoperative chemotherapy were independent risk factors for the poor prognosis of pancreatic cancer (P<0.05). The discrimination ability, calibration ability, and clinical effectiveness of Nomogram prognostic model were better than those of the TNM staging system. Conclusion The constructed Nomogram prognostic model has higher accuracy and level of discrimination and more clinical benefits than the TNM staging prognostic model.
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Objective To construct a Nomogram model that can accurately predict early death of metastatic colon cancer (mCC). Methods A total of 6 669 patients from the SEER database were identified using inclusion and exclusion criteria. Multivariate logistic regression was used to identify risk factors for early mortality and to construct a Nomogram. The predictive performance of the Nomogram was evaluated by C-index, calibration curve, and decision curve analysis (DCA). Results Primary tumor location, differentiation grade, T stage, M stage, bone metastases, brain metastases, CEA, tumor size, age and marital status were independent factors for early death in patients with mCC. A Nomogram was constructed based on these variables. The C-index and the calibration curve of the Nomogram showed the good predictive ability of the nomogram. DCA showed that the Nomogram had a superior clinical net benefit in predicting early death compared with TNM stage. Conclusion The developed Nomogram has good predictive ability and can help guide clinicians to identify patients with high-risk mCC for individualized diagnosis and treatment.
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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.