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.
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.
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 explore the relationship between circadian rhythm genes and the occurrence, development, prognosis, and tumor microenvironment (TME) of lung adenocarcinoma (LUAD). MethodsThe Cancer Genome Atlas data were used to evaluate the expression, copy number variation, and somatic mutation frequency of circadian gene sets in LUAD. GO, KEGG, and GSEA enrichment analyses were used to explore the potential mechanisms by which circadian rhythm genes affected LUAD progression. Cox regression, least absolute shrinkage and selection operator regression, support vector machine recursive feature elimination, and random forest screened circadian genes and established prognostic models, and on this basis constructed nomogram to predict patients' 1-, 3-, and 5-year survival rates. Kaplan-Meier survival curves, receiver operating characteristic (ROC) curves, and time-dependent ROC curves were drawn to evaluate the predictive ability of the model, and the external dataset of GEO further verified the prognostic value of the prediction model. In addition, we evaluated the association of the prognostic model with immune cells and immune checkpoint genes. Single cell RNA sequencing (scRNA-seq) analysis was used to explore the molecular characteristics between prognostically relevant circadian genes and different immune cell populations in TME. ResultsDifferentially expressed circadian rhythm genes were mainly enriched in biological processes related to cGMP-PKG signaling pathway, lipid and atherosclerosis, and JAK-STAT signaling pathway. Seven circadian rhythm genes: LGR4, CDK1, KLF10, ARNTL2, RORA, NPAS2, PTGDS were screened out, and a RiskScore model was established. According to the median RiskScore, samples were divided into a high-risk group and a low-risk group. Compared with patients in the low-risk group, patients in the high-risk group showed a poorer prognosis (P<0.001). Immunological characterization analysis showed that there were differences in the infiltration of multiple immune cells between the low-risk group and high-risk group. Most immune checkpoint genes had higher expression levels in the high-risk group than those in the low-risk group, and RiskScore was positively correlated with the expression of CD276, TNFSF4, PDCD1LG2, CD274, and TNFRSF9, and negatively correlated with the expression of CD40LG and TNFSF15. The scRNA-seq analysis showed that RORA and KLF10 were mainly expressed in natural killer cells. ConclusionThe prognostic model based on seven feature circadian rhythm genes has certain predictive value for predicting survival of LUAD patients. Dysregulated expression of circadian genes may regulate the occurrence, progression as well as prognosis of LUAD through affecting TME, which provides a possible direction for finding potential strategies for treating LUAD from the perspective of mechanism by which circadian disorder affects immune cells.
ObjectiveTo investigate the prognostic value of preoperative serum albumin-to-globulin ratio (AGR) and neutrophil-lymphocyte ratio (NLR) in the overall survival (OS) of patients with esophageal squamous cell carcinoma (ESCC), and to establish an individualized nomogram model and evaluate its efficacy, in order to provide a possible evaluation basis for the clinical treatment and postoperative follow-up of ESCC patients. MethodsAGR, NLR, clinicopathological and follow-up data of ESCC patients diagnosed via pathology in the Department of Thoracic Surgery, The First Affiliated Hospital of Xinjiang Medical University from 2010 to 2017 were collected. The correlation between NLR/AGR and clinicopathological data were analyzed. Kaplan-Meier analysis and log-rank test were used for survival analysis. The optimal cut-off values of AGR and NLR were determined by X-tile software, and the patients were accordingly divided into a high-level group and a low-level group. At the same time, univariate and multivariate Cox regression analyses were used to identify independent risk factors affecting OS in the ESCC patients, and a nomogram prediction model was constructed and internally verified. The diagnostic efficacy of the model was evaluated by receiver operating characteristic (ROC) curve and calibration curve, and the clinical application value was evaluated by decision curve analysis. ResultsA total of 150 patients were included in this study, including 105 males and 45 females with a mean age of 62.3±9.3 years, and the follow-up time was 1-5 years. The 5-year OS rate of patients in the high-level AGR group was significantly higher than that in the low-level group (χ2=6.339, P=0.012), and the median OS of the two groups was 25 months and 12.5 months, respectively. The 5-year OS rate of patients in the high-level NLR group was significantly lower than that in the low-level NLR group (χ2=5.603, P=0.018), and the median OS of the two groups was 18 months and 39 months, respectively. Multivariate Cox analysis showed that AGR, NLR, T stage, lymph node metastasis, N stage, and differentiation were independent risk factors for the OS of ESCC patients. The C-index of the nomogram model was 0.689 [95%CI (0.640, 0.740)] after internal validation. The area under the ROC curve of predicting 1-, 3-, and 5-year OS rate was 0.773, 0.724 and 0.725, respectively. At the same time, the calibration curve and the decision curve suggest that the model had certain efficacy in predicting survival and prognosis. ConclusionPreoperative AGR and NLR are independent risk factors for ESCC patients. High level of AGR and low level of NLR may be associated with longer OS in the patients; the nomogram model based on AGR, NLR and clinicopathological features may be used as a method to predict the survival and prognosis of ESCC patients, which is expected to provide a reference for the development of personalized treatment for patients.
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 identify and analyze risk factors for acute renal failure (ARF) following lung transplantation and to develop a predictive model. Methods Data for this study were obtained from the United Network for Organ Sharing (UNOS) database, encompassing patients who underwent unilateral or bilateral lung transplantation between 2015 and 2022. We analyzed both preoperative and postoperative clinical characteristics of the patients. A combined approach utilizing random forest and least absolute shrinkage and selection operator (LASSO) regression was employed to identify key factors associated with the incidence of ARF post-transplantation, based on which a nomogram model was developed. The predictive performance of the constructed model was evaluated in both training and validation sets, using receiver operating characteristic (ROC) curves and area under the curve (AUC) metrics to verify and compare model effectiveness. ResultsA total of 15 110 lung transplantation patients were included in the study, consisting of6 041 males and 9 069 females, with a median age of 62.00 years (interquartile range: 54.00 to 67.00). The analysis revealed statistically significant differences between postoperative renal dialysis and non-dialysis patients regarding preoperative lung diagnosis, estimated glomerular filtration rate (eGFR), mechanical ventilation, preoperative ICU treatment, extracorporeal membrane oxygenation (ECMO) support, infections occurring within two weeks prior to transplantation, Karnofsky Performance Status (KPS) score, waitlist duration, double-lung transplantation, and ischemia time (P<0.05). Five key variables associated with ARF after lung transplantation were identified through random forest and LASSO regression: recipients’ eGFR, preoperative ICU treatment, ECMO support, bilateral lung transplantation, and ischemia time. A nomogram model was subsequently established. Model evaluation demonstrated that the constructed predictive model achieved high accuracy in both training and validation sets, with favorable AUC values, confirming its validity and reliability. ConclusionThis study identifies common risk factors for ARF following lung transplantation and introduces an effective predictive model with potential clinical applications.
ObjectiveTo analyze the prognostic factors of patients with bacterial bloodstream infection sepsis and to identify independent risk factors related to death, so as to potentially develop one predictive model for clinical practice. Method A non-intervention retrospective study was carried out. The relative data of adult sepsis patients with positive bacterial blood culture (including central venous catheter tip culture) within 48 hours after admission were collected from the electronic medical database of the First Affiliated Hospital of Dalian Medical University from January 1, 2018 to December 31, 2019, including demographic characters, vital signs, laboratory data, etc. The patients were divided into a survival group and a death group according to in-hospital outcome. The risk factors were analyzed and the prediction model was established by means of multi-factor logistics regression. The discriminatory ability of the model was shown by area under the receiver operating characteristic curve (AUC). The visualization of the predictive model was drawn by nomogram and the model was also verified by internal validation methods with R language. Results A total of 1189 patients were retrieved, and 563 qualified patients were included in the study, including 398 in the survival group and 165 in the death group. Except gender and pathogen type, other indicators yielded statistical differences in single factor comparison between the survival group and the death group. Independent risk factors included in the logistic regression prediction model were: age [P=0.000, 95% confidence interval (CI) 0.949 - 0.982], heart rate (P=0.000, 95%CI 0.966 - 0.987), platelet count (P=0.009, 95%CI 1.001 - 1.006), fibrinogen (P=0.036, 95%CI 1.010 - 1.325), serum potassium ion (P=0.005, 95%CI 0.426 - 0.861), serum chloride ion (P=0.054, 95%CI 0.939 - 1.001), aspartate aminotransferase (P=0.03, 95%CI 0.996 - 1.000), serum globulin (P=0.025, 95%CI 1.006 - 1.086), and mean arterial pressure (P=0.250, 95%CI 0.995 - 1.021). The AUC of the prediction model was 0.779 (95%CI 0.737 - 0.821). The prediction efficiency of the total score of the model's nomogram was good in the 210 - 320 interval, and mean absolute error was 0.011, mean squared error was 0.00018. Conclusions The basic vital signs within 48 h admitting into hospital, as well those homeostasis disordering index indicated by coagulation, liver and renal dysfunction are highly correlated with the prognosis of septic patients with bacterial bloodstream infection. Early warning should be set in order to achieve early detection and rescue patients’ lives.
ObjectiveTo analyze the risk factors influencing major postoperative complications (MPC) after minimally invasive radical gastrectomy for gastric cancer following neoadjuvant chemotherapy (NACT), and to construct a nomogram for accurately predicting MPC risk factors, and provide a reference for clinical decision-making. MethodsThe gastric cancer patients who underwent minimally invasive radical gastrectomy in the Department of General Surgery of the First Medical Center of the Chinese PLA General Hospital from February 2012 to December 2022 and met the inclusion criteria of this study were retrospectively collected. The univariate and multivariate logistic regression model were used to evaluate the risk factors influencing MPC and a nomogram model was constructed. The MPC were defined as Clavien-Dindo classification grade Ⅱ and beyond. The area under the receiver operating characteristic curve (AUC) and the calibration curve were used to evaluate the discrimination and accuracy of the nomogram model. ResultsA total of 362 patients were included in this study, among whom 65 cases (18.0%) experienced MPC. The multivariate logistic regression analysis showed that the age ≥58 years old, body mass index (BMI) ≥25 kg/m2, tumor long diameter ≥30 mm, operative time ≥300 min, and preoperative neutrophil-to-lymphocyte ratio (NLR) ≥3.7 were the risk factors influencing MPC. The nomogram model constructed using the above variables showed that the AUC (95%CI) was 0.731 (0.662, 0.801) in predicting the risk of MPC. The calibration curves showed that the prediction curve of the nomogram in predicting the MPC was agree well with the actual MPC (Hosmer-Lemeshow test: χ2=9.293, P=0.056). ConclusionFrom the results of this study, nomogram model constructed by combining age, BMI, tumor long diameter, operative time, and preoperative NLR can distinguish between patients with and without MPC after minimally invasive radical gastrectomy for gastric cancer following NACT, and has a better accuracy.
ObjectiveTo explore the value of geriatric nutritional risk index (GNRI) and sarcopenia on predicting postoperative complications in elderly patients with gastric cancer. MethodsAccording to the inclusion and exclusion criteria, the elderly (aged ≥60 years) patients with gastric cancer underwent radical gastrectomy in the Department of Gastrointestinal Surgery of Xuzhou Central Hospital from January 1, 2017 to December 31, 2021 were retrospectively gathered. The occurrence of postoperative complications (grade 2 or beyond by the Clavien-Dindo classification) was analyzed. The risk factors affecting postoperative complications were analyzed by univariate and multivariate logistic regression analyses to construct the prediction model, then was visualized by drawing a nomogram. The differentiation of the nomogram between the patients with postoperative complications and without postoperative complications was evaluated by the receiver operating characteristic (ROC) curve. The accuracy of the nomogram was evaluated by the calibration curve. Further, the clinical net benefit rate was analyzed by the decision curve analysis (DCA) to evaluate the clinical practicability. ResultsA total of 236 patients were gathered, 97 (41.1%) of whom had postoperative complications during hospitalization. The results of multivariate logistic regression analysis showed that the age, gender, GNRI, sarcopenia, surgical mode, and American Society of Aneshesiologists classification were the factors influencing the postoperative complications (P<0.05). The differentiation of nomogram based on the influencing factors was well, the area under the ROC curve was 0.732. The calibration curve showed that the model prediction curve was close to the ideal curve. The clinical net benefit rate by the DCA was higher when the probability of postoperative complications was 0.18 to 0.72. ConclusionsThe efficiency of nomogram based on GNRI and sarcopenia is well for predicting the occurrence of postoperative complications in elderly patients with gastric cancer. However, the nomogram needs to be further validated by prospective studies and external data.