Objective To explore the potential indicators of cervical lymph node metastasis in papillary thyroid microcarcinoma (PTMC) patients and to develop a nomogram model. Methods The clinicopathologic features of PTMC patients in the SEER database from 2004 to 2015 and PTMC patients who were admitted to the Center for Thyroid and Breast Surgery of Xuanwu Hospital from 2019 to 2020 were retrospectively analyzed. The records of SEER database were divided into training set and internal verification set according to 7∶3. The patients data of Xuanwu Hospital were used as the external verification set. Logistic regression and Lasso regression were used to analyze the potential indicators for cervical lymph node metastasis. A nomogram was developed and whose predictive value was verified in the internal and external validation sets. According to the preoperative ultrasound imaging characteristics, the risk scores for PTMC patients were further calculated. The consistency between the scores based on pathologic and ultrasound imaging characteristics was verified. Results The logistic regression analysis results illustrated that male, age<55 years old, tumor size, multifocality, and extrathyroidal extension were associated with cervical lymph node metastasis in PTMC patients (P<0.001). The C index of the nomogram was 0.722, and the calibration curve exhibited to be a fairly good consistency with the perfect prediction in any set. The ROC curve of risk score based on ultrasound characteristics for predicting lymph node metastasis in PTMC patients was 0.701 [95%CI was (0.637 4, 0.765 6)], which was consistent with the risk score based on pathological characteristics (Kappa value was 0.607, P<0.001). Conclusions The nomogram model for predicting the lymph node metastasis of PTMC patients shows a good predictive value, and the risk score based on the preoperative ultrasound imaging characteristics has good consistency with the risk score based on pathological characteristics.
ObjectiveTo analyze the factors affecting the prognosis of patients with primary tracheal malignancy, and establish a nomogram model for prediction its prognosis.MethodsA total of 557 patients diagnosed with primary tracheal malignancy from 1975 to 2016 in the Surveillance, Epidemiology, and End Results Data were collected. The factors affecting the overall survival rate of primary tracheal malignancy were screened and modeled by univariate and multivariate Cox regression analysis. The nomogram prediction model was performed by R 3.6.2 software. Using the C-index, calibration curves and receiver operating characteristic (ROC) curve to evaluate the consistency and predictive ability of the nomogram prediction model.ResultsThe median survival time of 557 patients with primary tracheal malignancy was 21 months, and overall survival rates of the 1-year, 3-year and 5-year were 59.1%±2.1%, 42.5%±2.1%, and 35.4%±2.2%. Univariate and multivariate Cox regression analysis showed that age, histology, surgery, radiotherapy, tumor size, tumor extension and the range of lymph node involvement were independent risk factors affecting the prognosis of patients with primary tracheal malignancy (P<0.05). Based on the above 7 risk factors to establish the nomogram prediction model, the C-index was 0.775 (95%CI 0.751-0.799). The calibration curve showed that the prediction model established in this study had a good agreement with the actual survival rate of the 1 year, 3 year and 5 years. The area under curve of 1-year, 3-year and 5-year predicting overall survival rates was 0.837, 0.827 and 0.836, which showed that the model had a high predictive power.ConclusionThe nomogram prediction model established in this study has a good predictive ability, high discrimination and accuracy, and high clinical value. It is useful for the screening of high-risk groups and the formulation of personalized diagnosis and treatment plans, and can be used as an evaluation tool for prognostic monitoring of patients with primary tracheal malignancy.
ObjectiveTo retrospectively analyze the causes and risk factors of unplanned extubation (UE) in cancer patients during peripherally inserted central catheter (PICC) retention, so as to provide references for effectively predicting the occurrence of UE. Methods27 998 cancer patients who underwent PICC insertion, maintenance and removal in the vascular access nursing center of our hospital from January 2016 to June 2023 were retrospectively analyzed. General information, catheterization information, and maintenance information were collected. The Chi-squared test was used for univariate analysis, multivariate analysis was used by binary unconditional logistic regression. They were randomly divided into modeling group and internal validation group according to the ratio of 7∶3. The related nomogram prediction model and internal validation were established. ResultsThe incidence of UE during PICC retention in tumor patients was 2.80% (784/27 998 cases). Univariate analysis showed that age, gender, diagnosis, catheter retention time, catheter slipping, catheter related infection, catheter related thrombosis, secondary catheter misplacement, dermatitis, and catheter blockage had an impact on UE (P<0.05). Age, diagnosis, catheter retention time, catheter slipping, catheter related infection, catheter related thrombosis, secondary catheter misplacement, and catheter blockage are independent risk factors for UE (P<0.05). Based on the above 8 independent risk factors, a nomogram model was established to predict the risk of UE during PICC retention in tumor patients. The ROC area under the predicted nomogram was 0.90 (95%CI 0.89 to 0.92) in the modeling group, and the calibration curve showed good predictive consistency. Internal validation showed that the area under the ROC curve of the prediction model was 0.91 (95%CI 0.89 to 0.94), and the trend of the prediction curve was close to the standard curve. ConclusionPatients aged ≥60 years, non chest tumor patients, catheter retention time (≤6 months), catheter slipping, catheter related infections, catheter related thrombosis, secondary catheter misplacement, and catheter blockage increase the risk of UE. The nomogram model established in this study has good predictive ability and discrimination, which is beneficial for clinical screening of patients with different degrees of risk, in order to timely implement targeted prevention and effective treatment measures, and ultimately reduce the occurrence of UE.
Objective To explore independent risk factors for 30-day mortality in critical patients with pulmonary infection and sepsis, and build a prediction model. Methods Patients diagnosed with pulmonary infection and sepsis in the MIMIC-Ⅲ database were analyzed. The CareVue database was the training cohort (n=934), and the Metavision database was the external validation cohort (n=687). A COX proportional hazards regression model was established to screen independent risk factors and draw a nomogram. We conducted internal cross-validation and external validation of the model. Using the receiver operator characteristic (ROC) curve, Calibration chart, and decision curve analysis, we detected the discrimination, calibration, and benefit of the model respectively, comparing with the SOFA scoring model. Results Age, SOFA score, white blood cell count≤4×109/L, neutrophilic granulocyte percentage (NEU%)>85%, platelet count (PLT)≤100×109/L, PLT>300×109/L, red cell distribution width >15%, blood urea nitrogen, and lactate dehydrogenase were independent risk factors. The areas under the ROC curve of the model were 0.747 (training cohort) and 0.708 (external validation cohort), respectively, which was superior to the SOFA scoring model in terms of discrimination, calibration, and benefit. Conclusion The model established in this study can accurately and effectively predict the risk of the disease mortality, and provide a visual assessment method for early identification of high-risk patients.
Objective To evaluate the relationship of systemic immune inflammatory index (SII) with the clinical features and prognosis of osteosarcoma patients. Methods The clinical data of patients with osteosarcoma surgically treated in Fuzhou Second Hospital between January 2012 and December 2017 were retrospectively collected. The preoperative SII value was calculated, which was defined as platelet × neutrophil/lymphocyte count. The best critical value of SII was determined by receiver operating characteristic (ROC) curve analysis, and the relationship between SII and clinical features of patients was analyzed by χ2 test. Kaplan-Meier method and Cox proportional hazard model were used to study the effect of SII on overall survival (OS). The nomogram prediction model was established according to the independent risk factors of patients’ prognosis. Results A total of 108 patients with osteosarcoma were included in this study. Preoperative high SII was significantly correlated with tumor diameter, Enneking stage, local recurrence and metastasis (P<0.05). The median follow-up time was 62 months. The 1-, 3-, 5-year survival rates of the low SII group were significantly higher than those of the high SII group (100.0%, 96.4%, 85.1% vs. 95.4%, 73.7%, 30.7%), and the survival of the two groups were statistically different (P<0.05). Univariate Cox regression analyses showed that tumor diameter, Enneking stage, local recurrence, metastasis and SII were associated with OS (P<0.05). Multiple Cox regression analysis showed that Enneking stage (P=0.031), local recurrence (P=0.035) and SII (P=0.001) were independent risk factors of OS. The nomogram constructed according to the independent risk factors screened by the Cox regression model had good discrimination and consistency (C-index=0.774), and the calibration curve showed that the nomogram had a high consistency with the actual results. In addition, the ROC curve indicated that the nomogram had a good prediction efficiency (area under the curve=0.880). Conclusions The preoperative SII level is expected to become an important prognostic parameter for patients with osteosarcoma. The higher the SII level is, the worse the prognosis of patients will be. The nomogram prediction model built on preoperative SII level, Enneking stage and local recurrence has a good prediction efficiency, and can be used to guide the diagnosis and treatment of clinical osteosarcoma.
ObjectiveTo study the differential expression of minichromosome maintenance protein (MCM) gene family in hepatocellular carcinoma (HCC) and to explore its survival predictive value.MethodsTranscriptome data, clinical data, and survival information of patients with HCC were extracted from The Cancer Genome Atlas (TCGA), and the differential expression of MCM gene was analyzed. The prognostic value of differentially expressed of MCM gene was studied by Cox proportional hazards regression model, the prognostic model and risk score system were constructed. On the basis of risk score, a number of indicators were included to construct a nomogram to predict the3- and 5-year survival probability of HCC patients, and to verify and evaluate their predictive ability and accuracy.ResultsThe expressions of MCM2, MCM3, MCM4, MCM5, MCM6, MCM7, MCM8, and MCM10 in HCC tissues were higher than those of normal liver tissues (P<0.05), and univariate analysis showed that they were all related to prognosis (P<0.05). Multivariate analysis showed that MCM6 and MCM10 were independent factors affecting survival of HCC patients (P<0.05). Through multivariate analysis, a prognostic model consisting of MCM6, MCM8, and MCM10 was constructed, and a risk scoring system was established. It had been verified that this risk score was an independent risk factor affecting the prognosis of patients with HCC, and the prognosis of patients with high scores were worse than those of patients with low scores (P<0.001). We used TNM stage, T stage, and risk score to construct a nomogram with a consistency index (C index) of 0.723 and draw a time-dependent receiver operating characteristic curve, the results showed that area under the curve of 3- and 5-year were 0.731 and 0.704, respectively.ConclusionsMCM6,MCM8, and MCM10 in the MCM gene family have important prognostic value in HCC. The nomogram constructed in this study can better predict the survival probability of HCC patients.
Objective To develop and validate a nomogram prediction model of early knee function improvement after total knee arthroplasty (TKA). Methods One hundred and sixty-eight patients who underwent TKA at Sichuan Province Orthopedic Hospital between January 2018 and February 2021 were prospectively selected to collect factors that might influence the improvement of knee function in the early postoperative period after TKA, and the improvement of knee function was assessed using the Knee Score Scale of the Hospital for Special Surgery (HSS) at 6 months postoperatively. The patients were divided into two groups according to the postoperative knee function improvement. The preoperative, intraoperative and postoperative factors were compared between the two groups; multiple logistic regression was performed after the potential factors screened by LASSO regression; then, a nomogram predictive model was established by R 4.1.3 language and was validated internally. Results All patients were followed up at 6 months postoperatively, and the mean HSS score of the patients increased from 55.19±8.92 preoperatively to 89.27±6.18 at 6 months postoperatively (t=?40.706, P<0.001). LASSO regression screened eight influencing factors as potential factors, with which the results of multiple logistic regression analysis showed that preoperative body mass index, etiology, preoperative joint mobility, preoperative HSS scores, postoperative lower limb force line, and postoperative analgesia were independent influencing factors for the improvement of knee function in the early postoperative period after TKA (P<0.05). A nomogram model was established based on the multiple logistic regression results, and the calibration curve showed that the prediction curve basically fitted the standard curve; the receiver operating characteristic curve showed that the area under the curve of the nomogram model for the prediction of suboptimal knee function in the early postoperative period after TKA was 0.894 [95% confidence interval (0.825, 0.963)]. Conclusions There is a significant improvement in knee function in patients after TKA, and the improvement of knee function in the early postoperative period after TKA is influenced by preoperative body mass index, etiology, and preoperative joint mobility, etc. The nomogram model established accordingly can be used to predict the improvement of knee function in the early postoperative period after TKA with a high degree of differentiation and accuracy.
ObjectiveTo explore the relation between preoperative serum gamma-glutamyl transpeptidase to platelet ratio (GPR) and overall survival (OS) of patients with hepatitis B virus-associated hepatocellular carcinoma (Abbreviated as “patients with HCC”), and to establish a nomogram for predicting OS. MethodsAccording to the inclusion and exclusion criteria, the clinicopathologic data of patients with HCC who underwent radical resection in the Department of Hepatobiliary Surgery of Xianyang Central Hospital, from January 15, 2012 to December 15, 2018, were retrospectively analyzed. The optimal critical value of GPR was determined by receiver operating characteristic curve, then the patients were divided into a low GPR group (GPR was optimal critical value or less ) and high GPR group (GPR was more optimal critical value). The Kaplan-Meier method was used to draw the survival curve and analyze the OS of patients. The univariate and multivariate Cox proportional hazards regression model were used to analyze the factors influencing prognosis in the patients with HCC. According to the risk factors of OS for patients with HCC, a nomogram was established. The consistency index and calibration curve in predicting the 3-year and 5-year accumulative OS rates of patients with HCC were evaluated. ResultsA total of 213 patients were gathered. The optimal critical value of GPR was 0.906. There were 114 patients in the low GPR group and 99 patients in the high GPR group. The Kaplan-Meier survival curve analysis showed that the 1-, 3- and 5-year accumulative OS rates were 99.1%, 81.8%, 60.6% in the low GPR group, respectively, which were 74.2%, 49.1%, 35.7% in the low GPR group, respectively. The OS curve of the low GPR group was better than that of the high GPR group (χ2=25.893, P<0.001). The multivariate analysis results showed that the microvascular invasion, incomplete capsule, intraoperative bleeding ＞1 000 mL, postoperative complications, GPR >0.906, low tumor differentiation, and late TNM stage did not contribute to accumulative OS in the patients with HCC (P<0.05). The consistency index (95%CI) of the nomogram in predicting accumulative OS rates at 3- and 5-year for patients with HCC were 0.761 (0.739, 0.783) and 0.735 (0.702, 0.838), respectively. The calibration curves of 3- and 5-year accumulative OS rates of the nomogram were in good agreement with the actual results. ConclusionsPreoperative GPR is associated with OS, and patients with higher GPR have worse prognosis. The nomogram based on GPR has a good accuracy and differentiation.
ObjectiveTo analyze the current status and risk factors of postoperative complications in patients with retroperitoneal tumor (RPT) and to establish a nomogram for predicting the occurrence of postoperative complications. MethodsThe clinicopathologic data of patients with RPT who met the inclusion criteria in the West China Hospital of Sichuan University from June 2019 to May 2022 were retrospectively collected. The risk factors of postoperative complications were analyzed by using univariate and multivariate analyses, and the nomogram was constructed based on the risk factors and validated. ResultsA total of 205 patients were collected in this study, 70 (34.1%) of whom had postoperative complications. The multivariate analysis results of logistic regression showed that the preoperative serum albumin ＜35 g/L [OR=2.355, 95%CI (1.256, 4.416), P=0.008], tumor sarcoma [OR=2.498, 95%CI (1.219, 5.120), P=0.012], and visceral resection [OR=2.008, 95%CI (1.042, 3.868), P=0.037] increased the probability of postoperative complications for the patients with RPT. The area under the receiver operating characteristic curve of the nomogram based on the risk factors in predicting the occurrence of postoperative complications was 0.704 [95%CI (0.626, 0.781), P<0.001]. The consistency index of the nomogram by internal verification was 0.704 [95%CI (0.628, 0.779)]. The calibration curve of the nomogram showed that the predicted value was basically consistent with the actual value, the Hosmer-Lemeshow goodness-of-fit test model had a good goodness-of-fit (χ2=3.407, P=0.906). ConclusionsFrom the results of this study, the tumor sarcoma, lower preoperative serum albumin, and visceral resection are associated with postoperative complications for patients with RPT. The nomogram based on risk factors has a good predictive value for postoperative complications.
ObjectiveTo analyze the correlation between folate receptor-positive circulating tumor cells (FR+CTC) and the benign or malignant lesions of the lung, and to establish a malignant prediction model for pulmonary neoplasm based on clinical data, imaging and FR+CTC tests.MethodsA retrospective analysis was done on 1 277 patients admitted to the Affiliated Hospital of Qingdao University from September 2018 to December 2019, including 518 males and 759 females, with a median age of 57 (29-85) years. They underwent CTC examination of peripheral blood and had pathological results of pulmonary nodules and lung tumors. The patients were randomly divided into a trial group and a validation group. Univariate and multivariate analyses were performed on the data of the two groups. Then the nomogram prediction model was established and verified internally and externally. Receiver operating characteristic (ROC) curve was used to test the differentiation of the model and calibration curve was used to test the consistency of the model.ResultsTotally 925 patients suffered non-small cell lung cancer and 113 patients had benign diseases in the trial group; 219 patients suffered non-small cell lung cancer and 20 patients had benign diseases in the verification group. The FR+CTC in the peripheral blood of non-small cell lung cancer patients was higher than that found in the lungs of the patients who were in favorite conditions (P<0.001). Multivariate analysis showed that age≥60 years, female, FR+CTC value>8.7 FU/3 mL, positive pleural indenlation sign, nodule diameter, positive burr sign, consolidation/tumor ratio<1 were independent risk factors for benign and malignant lung tumors with a lesion diameter of ≤4 cm. Thereby, the nomogram prediction model was established. The area under the ROC curve (AUC) of the trial group was 0.918, the sensitivity was 86.36%, and the specificity was 83.19%. The AUC value of the verification group was 0.903, the sensitivity of the model was 79.45%, and the specificity was 90.00%, indicating nomogram model discrimination was efficient. The calibration curve also showed that the nomogram model calibration worked well.ConclusionFR+CTC in the peripheral blood of non-small cell lung cancer patients is higher than that found in the lungs of the patients who carry benign pulmonary diseases. The diagnostic model of clinical stage Ⅰ non-small cell lung cancer established in this study owns good accuracy and can provide a basis for clinical diagnosis.