Relationship between self-reported sleep duration during week-/work-days and metabolic syndrome from NHANES 2013 to 2016.

PURPOSE: This research aimed at determining the relationship between self-reported sleep duration during week-/work-days and metabolic syndrome (MetS) from NHANES 2013 to 2016. METHODS: This study analyzed data from 11,181 people aged 16 or older who took part in the NHANES (National Health and Nutrition Examination Surveys) from 2013 to 2016. A standard questionnaire was used to define self-reported sleep duration, and MetS was defined on the basis of the NCEP (National Cholesterol Education Program)/ATP III revised diagnostic criteria. Logistic regression and restricted cubic splines (RCS) models were used to assess the relationship between self-reported sleep duration and MetS. RESULTS: The overall prevalence of MetS in the study cohort was 26.1%, with 24.8% for males and 27.3% for females. After adjusting for potential confounding factors, MetS was significantly associated with self-reported short sleep duration (odds ratio = 1.16, 95% confidence interval = 1.03-1.31, P = 0.013) but not with long sleep duration (P = 0.117). RCS regression revealed that self-reported sleep duration was nonlinearly related to MetS (P for nonlinearity = 0.0026). The risk of MetS decreased with increased sleep duration for durations of less than 7 h/day, while there was no association for longer sleep durations. CONCLUSION: These results suggest that self-reported short sleep duration is a risk factor for MetS, while long sleep duration is not.

Developing and verifying a multivariate model to predict the survival probability after coronary artery bypass grafting in patients with coronary atherosclerosis based on the MIMIC-III database.

BACKGROUND: Coronary atherosclerosis is one of the main cardiovascular diseases affecting the global population. Coronary artery bypass grafting (CABG) is commonly used to improve the survival probability of patients with coronary atherosclerosis. However, the prognosis of patients after CABG remains unclear. OBJECTIVES: We aimed to construct a novel nomogram comprising readily available indicators to predict the 1-, 2-, and 3-year survival rates after CABG in patients with coronary atherosclerosis. METHODS: We utilized the Medical Information Mart for Intensive Care III (MIMIC-III) database for the study. The calibration plot, concordance index (C-index), net reclassification index (NRI), integrated discrimination improvement (IDI), and area under the receiver operating characteristic curve (AUROC) were used to evaluate the performance of the model, and to compare the nomogram with the Sequential Organ Failure Assessment (SOFA) score and Simplified Acute Physiology Score II (SAPS II) in order to illustrate the clinical effectiveness of the model. RESULTS: The multivariate Cox regression model showed that age, marital status, body mass index, creatinine, platelet count, red cell distribution width, heart rate, intensive-care unit stay time, and Elixhauser Comorbidity Index were risk factors. The C-indexes of the nomogram exceeded 0.75, and its NRI and IDI were both higher than 0. The AUROCs were larger for the nomogram than for the SAPS II and SOFA score. CONCLUSION: Our new nomogram is a personalized tool that helps clinicians choose treatment options and predict the long-term prognosis of patients.

Body mass index linked to short-term and long-term all-cause mortality in patients with acute myocardial infarction.

PURPOSES OF STUDY: This study aimed to elucidate the relationship between obesity and short-term and long-term mortality in patients with acute myocardial infarction (AMI) by analysing the body mass index (BMI). STUDY DESIGN: A retrospective cohort study was performed on adult intensive care unit (ICU) patients with AMI in the Medical Information Mart for Intensive Care III database. The WHO BMI classification was used in the study. The Kaplan-Meier curve was used to show the likelihood of survival in patients with AMI. The relationships of the BMI classification with short-term and long-term mortality were assessed using Cox proportional hazard regression models. RESULTS: This study included 1295 ICU patients with AMI, who were divided into four groups according to the WHO BMI classification. Our results suggest that obese patients with AMI tended to be younger (p<0.001), be men (p=0.001) and have higher blood glucose and creatine kinase (p<0.001) compared with normal weight patients. In the adjusted model, compared with normal weight AMI patients, those who were overweight and obese had lower ICU risks of death HR=0.64 (95% CI 0.46 to 0.89) and 0.55 (0.38 to 0.78), respectively, inhospital risks of death (0.77 (0.56 to 1.09) and 0.61 (0.43 to 0.87)) and long-term risks of death (0.78 0.64 to 0.94) and 0.72 (0.59 to 0.89). On the other hand, underweight patients had higher risks of short-term(ICU or inhospital mortality) and long-term mortality compared with normal weight patients (HR=1.39 (95% CI 0.58 to 3.30), 1.46 (0.62 to 3.42) and 1.99 (1.15 to 3.44), respectively). CONCLUSIONS: Overweight and obesity were protective factors for the short-term and long-term risks of death in patients with AMI.

Obesity Paradox of All-Cause Mortality in 4,133 Patients Treated with Coronary Revascularization.

OBJECTIVES: The purpose of this study was to determine whether there is a dose-response relationship between body mass index (BMI) and all-cause mortality in patients after coronary revascularization. METHODS: The MIMIC-III database (version 1.4) was used as the sample population. For variables with less than 10% of values missing, we used the mice package of R software for multiple imputations. Cox regression was used to determine the risk factors of all-cause mortality in patients. RCSs were used to observe the relationship between BMI and all-cause mortality. Additional subgroup and sensitivity analyses were also performed to explore whether the conclusion can be applied to specific groups. RESULTS: Both univariate and multivariate Cox models indicated that the mortality risk was lower for overweight patients than for normal-weight patients (P < 0.05). In RCS models, BMI had a U-shaped relationship with all-cause mortality of patients after coronary artery bypass grafting (CABG) (P for nonlinearity = 0.0028). There was a weak U-shaped relationship between BMI and all-cause mortality after percutaneous coronary intervention (PCI), but the nonlinear relationship between these two parameters was not significant (P for nonlinearity = 0.1756). CONCLUSIONS: The obesity paradox does exist in patients treated with CABG and PCI. RCS analysis indicated that there was a U-shaped relationship between BMI and all-cause mortality in patients after CABG. After sex stratification, the relationship between BMI and all-cause mortality in male patients who received PCI was L-shaped, while the nonlinear relationship among females was not significant.

A Novel Nomogram for Predicting Survival in Patients with Severe Acute Pancreatitis: An Analysis Based on the Large MIMIC-III Clinical Database.

BACKGROUND: Severe acute pancreatitis (SAP) can cause various complications. Septic shock is a relatively common and serious complication that causes uncontrolled systemic inflammatory response syndrome, which is one of the main causes of death. This study aimed to develop a nomogram for predicting the overall survival of SAP patients during the initial 24 hours following admission. MATERIALS AND METHODS: All the data utilized in this study were obtained from the MIMIC-III (Medical Information Mart for Intensive Care III) database. The data were analyzed using multivariate Cox regression, and the performance of the proposed nomogram was evaluated based on Harrell's concordance index (C-index) and the area under the receiver operating characteristic curve (AUC). The clinical value of the prediction model was tested using decision-curve analysis (DCA). The primary outcomes were 28-day, 60-day, and 90-day mortality rates. RESULTS: The 850 patients included in the analysis comprised 595 in the training cohort and 255 in the validation cohort. The training cohort consisted of 353 (59.3%) males and 242 (40.7%) females with SAP. Multivariate Cox regression showed that weight, sex, insurance status, explicit sepsis, SAPSII score, Elixhauser score, bilirubin, anion gap, creatinine, hematocrit, hemoglobin, RDW, SPO2, and respiratory rate were independent prognostic factors for the survival of SAP patients admitted to an intensive care unit. The predicted values were compared using C-indexes, calibration plots, integrated discrimination improvement, net reclassification improvement, and DCA. CONCLUSIONS: We have identified some important demographic and laboratory parameters related to the prognosis of patients with SAP and have used them to establish a more accurate and convenient nomogram for evaluating their 28-day, 60-day, and 90-day mortality rates.

A New Scoring System for Predicting In-hospital Death in Patients Having Liver Cirrhosis With Esophageal Varices.

Introduction: Liver cirrhosis is caused by the development of various acute and chronic liver diseases. Esophageal varices is a common and serious complication of liver cirrhosis during decompensation. Despite the development of various treatments, the prognosis for liver cirrhosis with esophageal varices (LCEV) remains poor. We aimed to establish and validate a nomogram for predicting in-hospital death in LCEV patients. Methods: Data on LCEV patients were extracted from the Medical Information Mart for Intensive Care III and IV (MIMIC-III and MIMIC-IV) database. The patients from MIMIC-III were randomly divided into training and validation cohorts. Training cohort was used for establishing the model, validation and MIMIC-IV cohorts were used for validation. The independent prognostic factors for LCEV patients were determined using the least absolute shrinkage and selection operator (LASSO) method and forward stepwise logistic regression. We then constructed a nomogram to predict the in-hospital death of LCEV patients. Multiple indicators were used to validate the nomogram, including the area under the receiver operating characteristic curve (AUC), calibration curve, Hosmer-Lemeshow test, integrated discrimination improvement (IDI), net reclassification index (NRI), and decision curve analysis (DCA). Results: Nine independent prognostic factors were identified by using LASSO and stepwise regressions: age, Elixhauser score, anion gap, sodium, albumin, bilirubin, international normalized ratio, vasopressor use, and bleeding. The nomogram was then constructed and validated. The AUC value of the nomogram was 0.867 (95% CI = 0.832-0.904) in the training cohort, 0.846 (95% CI = 0.790-0.896) in the validation cohort and 0.840 (95% CI = 0.807-0.872) in the MIMIC-IV cohort. High AUC values indicated the good discriminative ability of the nomogram, while the calibration curves and the Hosmer-Lemeshow test results demonstrated that the nomogram was well-calibrated. Improvements in NRI and IDI values suggested that our nomogram was superior to MELD-Na, CAGIB, and OASIS scoring system. DCA curves indicated that the nomogram had good value in clinical applications. Conclusion: We have established the first prognostic nomogram for predicting the in-hospital death of LCEV patients. The nomogram is easy to use, performs well, and can be used to guide clinical practice, but further external prospective validation is still required.

Nomogram for predicting cancer-specific survival in undifferentiated pleomorphic sarcoma: A Surveillance, Epidemiology, and End Results -based study.

INTRODUCTION: The purpose of this study was to construct and validate a nomogram for predicting cancer-specific survival (CSS) in undifferentiated pleomorphic sarcoma (UPS) patients at 3, 5, and 8 years after the diagnosis. METHODS: Data for UPS patients were extracted from the SEER (Surveillance, Epidemiology, and End Results) database. The patients were randomly divided into a training cohort (70%) and a validation cohort (30%). The backward stepwise Cox regression model was used to select independent prognostic factors. All of the factors were integrated into the nomogram to predict the CSS rates in UPS patients at 3, 5, and 8 years after the diagnosis. The nomogram' s performance was then validated using multiple indicators, including the area under the time-dependent receiver operating characteristic curve (AUC), consistency index (C-index), calibration curve, decision-curve analysis (DCA), integrated discrimination improvement (IDI), and net reclassification improvement (NRI). RESULTS: This study included 2,009 UPS patients. Ten prognostic factors were identified after analysis of the Cox regression model in the training cohort, which were year of diagnosis, age, race, primary site, histological grade, T, N, M stage, surgery status, and insurance status. The nomogram was then constructed and validated internally and externally. The relatively high C-indexes and AUC values indicated that the nomogram has good discrimination ability. The calibration curves revealed that the nomogram was well calibrated. NRI and IDI values were both improved, indicating that our nomogram was superior to the AJCC (American Joint Committee on Cancer) system. DCA curves demonstrated that the nomogram was clinically useful. CONCLUSIONS: The first nomogram for predicting the prognosis of UPS patients has been constructed and validated. Its usability and performance showed that the nomogram can be applied to clinical practice. However, further external validation is still needed.

Incidence trends and survival prediction of urothelial cancer of the bladder: a population-based study.

BACKGROUND: The aim of this study is to determine the incidence trends of urothelial cancer of the bladder (UCB) and to develop a nomogram for predicting the cancer-specific survival (CSS) of postsurgery UCB at a population-based level based on the SEER database. METHODS: The age-adjusted incidence of UCB diagnosed from 1975 to 2016 was extracted, and its annual percentage change was calculated and joinpoint regression analysis was performed. A nomogram was constructed for predicting the CSS in individual cases based on independent predictors. The predictive performance of the nomogram was evaluated using the consistency index (C-index), net reclassification index (NRI), integrated discrimination improvement (IDI), a calibration plot and the receiver operating characteristics (ROC) curve. RESULTS: The incidence of UCB showed a trend of first increasing and then decreasing from 1975 to 2016. However, the overall incidence increased over that time period. The age at diagnosis, ethnic group, insurance status, marital status, differentiated grade, AJCC stage, regional lymph nodes removed status, chemotherapy status, and tumor size were independent prognostic factors for postsurgery UCB. The nomogram constructed based on these independent factors performed well, with a C-index of 0.823 and a close fit to the calibration curve. Its prediction ability for CSS of postsurgery UCB is better than that of the existing AJCC system, with NRI and IDI values greater than 0 and ROC curves exhibiting good performance for 3, 5, and 8 years of follow-up. CONCLUSIONS: The nomogram constructed in this study might be suitable for clinical use in improving the clinical predictive accuracy of the long-term survival for postsurgery UCB.

Competing-risks nomogram for predicting cancer-specific death in upper tract urothelial carcinoma: a population-based analysis.

OBJECTIVE: This study aimed to use a competing-risks model to establish a nomogram to accurately analyse the prognostic factors for upper tract urothelial carcinoma (UTUC) cancer-specific death (CSD). DESIGN: Retrospective observational cohort study. SETTING: The programme has yielded a database of all patients with cancer in 18 defined geographical regions of the USA. PARTICIPANTS: We selected patients with UTUC from the latest edition of the Surveillance, Epidemiology, and End Results database from 1975 to 2016. After excluding patients with unknown histological grade, tumour size and lymph node status, 2576 patients were finally selected. PRIMARY AND SECONDARY OUTCOME MEASURES: We used the Fine-Gray proportional subdistribution hazards model for multivariate analysis and compared the results with cause-specific hazards model. We finally constructed a nomogram for 3-year, 5-year and 8-year CSD rates and tested these rates in a validation cohort. RESULTS: The proportional subdistribution hazards model showed that sex, tumour size, distant metastasis, surgery status, number of lymph nodes positive (LNP) and lymph nodes ratio (LNR) were independent prognostic factors for CSD. All significant factors associated with CSD were included in the nomogram. The 3-year, 5-year and 8-year concordance indexes were 0.719, 0.702 and 0.692 in the training cohort and 0.701, 0.675 and 0.668 in the validation cohort, respectively. CONCLUSIONS: The competing-risks model showed that sex, tumour size, distant metastasis, surgery status, LNP and LNR were associated with CSD. The nomogram predicts the probability of CSD in patients with UTUC at 3, 5 and 8 years, which may help clinicians in predicting survival probabilities in individual patients.

Nomograms for Differentiated Thyroid Carcinoma Patients Based on the Eighth AJCC Staging and Competing Risks Model.

Background: Differentiated thyroid carcinoma (DTC) patients have a long survival period and good prognosis, so they are easily affected by competing risk events. The purpose of this study was to use the competing risks model to identify prognostic factors for cause-specific death (CSD) and death due to other causes (DOC) in patients with DTC. Methods: Our screening process identified 34 585 DTC patients in the Surveillance, Epidemiology, and End Results database and randomly divided them into a training cohort and a validation cohort. We used the Fine and Gray subdistribution hazards model to establish the CSD and DOC nomograms. The distinguishing ability and consistency of the nomograms were evaluated using the consistency indexes and calibration plots. Results: Our analysis of a competing risks model revealed that pathological grade, tumor size, histological type, American Joint Committee on Cancer (AJCC)-8 stage, surgery status, adjuvant radiotherapy status, adjuvant chemotherapy status, and log odds of positive lymph nodes are prognostic factors for CSD, and age at diagnosis, year of diagnosis, sex, pathological grade, tumor size, AJCC-8 stage, surgery status, adjuvant radiotherapy status, and lymph node ratio are prognostic factors for DOC. The 1-year, 3-year, and 5-year concordance indexes in the validation cohorts were 0.942, 0.931, and 0.913 for the CSD nomogram and 0.813, 0.746, and 0.776 for the DOC nomogram. The calibration plots showed good consistency in both nomograms. Conclusions: Our nomograms can be used as a tool to help clinicians individually predict the probability of CSD and DOC in DTC patients at 1 year, 3 years, and 5 years, which has certain guiding value in clinical applications.

Prognostic Value of Blood Urea Nitrogen/Creatinine Ratio for Septic Shock: An Analysis of the MIMIC-III Clinical Database.

BACKGROUND: Research has previously been done into the risk factors for mortality in septic shock patients. However, there has been no epidemiological study investigating the effect of the blood urea nitrogen/creatinine ratio (BCR) on the prognosis of critically ill septic shock patients. This study is aimed at determining the relationship between BCR and all-cause mortality in adult septic shock patients. METHODS: Data were extracted from the MIMIC-III database. The clinical endpoints were 28-, 90-, and 365-day all-cause mortality rates in critically ill septic shock patients. Cox proportional hazards models and subgroup analyses were used to analyze the relationship between BCR quartiles and all-cause mortality in septic shock patients. Receiver operator characteristic (ROC) curves and areas under the ROC curves (AUCs) were calculated to evaluate how accurately BCR predicts the mortality of septic shock patients. RESULTS: Among the 2484 septic shock patients extracted from the database, 619, 563, 677, and 625 fell into the first (<14.4 mg/dL), second (≥14.4 mg/dL and <20.0 mg/dL), third (≥20.0 mg/dL and <27.3 mg/dL), and fourth (≥27.3 mg/dL) quartiles of BCR, respectively. Male and white patients accounted for 53.8% (1336 patients) and 74.8% (1857 patients) of the population, respectively. The mean age of the population was 67.7 ± 15.8 years. An inverse M-shaped relationship between BCR and mortality in septic shock patients was identified, with a value of ≥27.3 mg/dL providing the highest risk (HR = 1.596, 95% CI: 1.396-1.824, P < 0.001). In the Cox regression model adjusted for different confounding variables, BCR values in the fourth quartiles were significantly associated with increased mortality, using the first quartiles as a reference. The areas under the ROC curves (AUCs) for BCR plus the Sequential Organ Failure Assessment (SOFA) score and BCR plus Acute Physiology Score III (APSIII) were 0.694 (95% CI: 0.673-0.716) and 0.724 (95% CI: 0.703-0.744), respectively. CONCLUSION: An inverse M-shaped curve was determined between BCR and the mortality of septic shock patients. BCR was identified as a readily available and independent prognostic biomarker for septic shock patients, and higher BCRs were associated with increased mortality in these patients.

Competing-risks nomograms for predicting cause-specific mortality in parotid-gland carcinoma: A population-based analysis.

INTRODUCTION: Parotid-gland carcinoma (PGC) is a relatively rare tumor that comprises a group of heterogeneous histologic subtypes. We used the Surveillance, Epidemiology, and End Results (SEER) program database to apply a competing-risks analysis to PGC patients, and then established and validated predictive nomograms for PGC. METHODS: Specific screening criteria were applied to identify PGC patients and extract their clinical and other characteristics from the SEER database. We used the cumulative incidence function to estimate the cumulative incidence rates of PGC-specific death (GCD) and other cause-specific death (OCD), and tested for differences between groups using Gray's test. We then identified independent prognostic factors by applying the Fine-Gray proportional subdistribution hazard approach, and constructed predictive nomograms based on the results. Calibration curves and the concordance index (C-index) were employed to validate the nomograms. RESULTS: We finally identified 4,075 eligible PGC patients who had been added to the SEER database from 2004 to 2015. Their 1-, 3-, and 5-year cumulative incidence rates of GCD were 10.1%, 21.6%, and 25.7%, respectively, while those of OCD were 2.9%, 6.6%, and 9.0%. Age, race, World Health Organization histologic risk classification, differentiation grade, American Joint Committee on Cancer (AJCC) T stage, AJCC N stage, AJCC M stage, and RS (radiotherapy and surgery status) were independent predictors of GCD, while those of OCD were age, sex, marital status, AJCC T stage, AJCC M stage, and RS. These factors were integrated for constructing predictive nomograms. The results for calibration curves and the C-index suggested that the nomograms were well calibrated and had good discrimination ability. CONCLUSION: We have used the SEER database to establish-to the best of our knowledge-the first competing-risks nomograms for predicting the 1-, 3-, and 5-year cause-specific mortality in PGC. The nomograms showed relatively good performance and can be used in clinical practice to assist clinicians in individualized treatment decision-making.

Nomograms for Estimating Cause-Specific Death Rates of Patients With Inflammatory Breast Cancer: A Competing-Risks Analysis.

PURPOSE: Inflammatory breast cancer (IBC) is a rare, aggressive and special subtype of primary breast cancer. We aimed to establish competing-risks nomograms to predict the IBC-specific death (BCSD) and other-cause-specific death (OCSD) of IBC patients. METHODS: We extracted data on primary IBC patients from the SEER (Surveillance, Epidemiology, and End Results) database by applying specific inclusion and exclusion criteria. Cumulative incidence function (CIF) was used to calculate the cumulative incidence rates and Gray's test was used to evaluate the difference between groups. Fine-Gray proportional subdistribution hazard method was applied to identify the independent predictors. We then established nomograms to predict the 1-, 3-, and 5-year cumulative incidence rates of BCSD and OCSD based on the results. The calibration curves and concordance index (C-index) were adopted to validate the nomograms. RESULTS: We enrolled 1699 eligible IBC patients eventually. In general, the 1-, 3-, and 5-years cumulative incidence rates of BCSD were 15.3%, 41.0%, and 50.7%, respectively, while those of OCSD were 3.0%, 5.1%, and 7.4%. The following 9 variables were independent predictive factors for BCSD: race, lymph node ratio (LNR), AJCC M stage, histological grade, ER (estrogen receptor) status, PR (progesterone receptor) status, HER-2 (human epidermal growth factor-like receptor 2) status, surgery status, and radiotherapy status. Meanwhile, age, ER, PR and chemotherapy status could predict OCSD independently. These factors were integrated for the construction of the competing-risks nomograms. The results of calibration curves and C-indexes indicated the nomograms had good performance. CONCLUSIONS: Based on the SEER database, we established the first competing-risks nomograms to predict BCSD and OCSD of IBC patients. The good performance indicated that they could be incorporated in clinical practice to provide references for clinicians to make individualized treatment strategies.

Establishment of a prognostic model based on the Sequential Organ Failure Assessment score for patients with first-time acute myocardial infarction.

OBJECTIVE: This study aimed to identify the prognostic factors of patients with first-time acute myocardial infarction (AMI) and to establish a nomogram for prognostic modeling. METHODS: We studied 985 patients with first-time AMI using data from the Multi-parameter Intelligent Monitoring for Intensive Care database and extracted their demographic data. Cox proportional hazards regression was used to examine outcome-related variables. We also tested a new predictive model that includes the Sequential Organ Failure Assessment (SOFA) score and compared it with the SOFA-only model. RESULTS: An older age, higher SOFA score, and higher Acute Physiology III score were risk factors for the prognosis of AMI. The risk of further cardiovascular events was 1.54-fold higher in women than in men. Patients in the cardiac surgery intensive care unit had a better prognosis than those in the coronary heart disease intensive care unit. Pressurized drug use was a protective factor and the risk of further cardiovascular events was 1.36-fold higher in nonusers. CONCLUSION: The prognosis of AMI is affected by age, the SOFA score, the Acute Physiology III score, sex, admission location, type of care unit, and vasopressin use. Our new predictive model for AMI has better performance than the SOFA model alone.

Developing and validating a novel nomogram used a competing-risks model for predicting the prognosis of primary fallopian tube carcinoma: a retrospective study based on the SEER database.

BACKGROUND: The current prognostic methods for primary fallopian tube carcinoma (PFTC) are inadequate. This study is the first to use a competing-risks model to perform an accurate analysis of the prognostic factors for PFTC cause-specific death (CSD). We used the model to established a nomogram for the 3-, 5-, and 8-year CSD rates based on the identified prognostic factors. METHODS: This study selected 1,924 patients from the SEER (Surveillance, Epidemiology, and End Results) database. The cumulative incidence function (CIF) was used in univariate analyses, and Gray's test was used to determine the intergroup difference in the CIF. We then used the subdistribution proportional hazards model in a multivariate analysis. We finally used the prognostic factors identified in the analysis of the competing-risks model to construct a 3-, 5-, and 8-year CSD nomogram for PFTC patients. The concordance index (C-index) and calibration plots were used to evaluate the discrimination ability and consistency of the model. RESULTS: The subdistribution proportional hazards model showed that age, histological type, FIGO stage, and the log of the ratio between the numbers of positive and negative lymph nodes (LODDS) were independent prognostic factors for CSD. The 3-, 5-, and 8-year C-indexes were 0.744, 0.744, and 0.733 in the training cohort, and 0.737, 0.748, and 0.721 in the validation cohort. In the calibration plots, the forecast lines were very close to the reference lines. CONCLUSIONS: This study is the first to analyze the prognostic factors for PFTC based on a competing-risks model. This model indicates that age, histological type, FIGO stage, and LODDS are significant prognostic factors affecting CSD in PFTC patients. We have also constructed the first 3-, 5-, and 8-year CSD nomogram for PFTC patients. This nomogram exhibits good discrimination ability and accuracy and can help clinicians to provide individualized prognostic analysis for PFTC patients.

Prognostic Exploration of All-Cause Death in Gingival Squamous Cell Carcinoma: A Retrospective Analysis of 2076 Patients.

BACKGROUND: We aimed to establish a prognostic model for gingival squamous cell carcinoma (GSCC) that was superior to traditional AJCC staging and to perform a comprehensive comparison of the newly established nomogram with the AJCC staging system. METHODS: We extracted 2,076 patients with gingival squamous cell carcinoma who had been entered into the SEER (Surveillance, Epidemiology, and End Results) database between 2004 and 2015, and randomly divided 70% of them into the training cohort and the other 30% into the validation cohort. Cox regression analysis was performed in combination with clinical experience and age, race, sex, marital status, tumor location, histological subtype, tumor grade, AJCC stage, chemotherapy status, radiotherapy status, and surgery status as possible prognostic factors. We evaluated and compared the two cohorts using the consistency index (C-index), area under the receiver operating characteristic curves, calibration curves, discriminant improvement index, and decision-curve analysis. RESULTS: The Cox retrospective analysis showed that age, AJCC stage, tumor grade, histological subtype, radiotherapy status, and surgery status were significant factors to include in the new model of gingival squamous cell carcinoma. The other indicators were also better for the new model than for the AJCC staging system. CONCLUSION: We have developed and validated a nomogram for performing reliable gingival squamous cell carcinoma prognoses. The prognostic value of the nomogram is higher than that of the AJCC staging system. We expect that the inclusion of more-comprehensive and authoritative data (i.e., not just limited to residents of the United States) would also allow the construction of reliable nomograms for other populations.

Competing-Risk Nomograms for Predicting the Prognosis of Patients With Infiltrating Lobular Carcinoma of the Breast.

BACKGROUND: Infiltrating lobular carcinoma (ILC) is the second most common histologic subtype of breast cancer. We assessed the rates of cause-specific death in ILC patients with the aim of establishing competing-risk nomograms for predicting their prognosis. PATIENTS AND METHODS: Data on ILC patients were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. The cumulative incidence function was used to calculate the cumulative incidence rates of cause-specific death, and Gray's test was applied to test the differences in cumulative incidence rates among groups. We then identified independent prognostic factors by applying the Fine-Gray proportional subdistribution hazard analysis method and established nomograms based on the results. Calibration curves and the concordance index were employed to validate the nomograms. RESULTS: The study enrolled 11,361 patients. The 3-, 5-, and 10-year overall cumulative incidence rates for those who died of ILC were 3.1%, 6.2%, and 12.2%, respectively, whereas the rates for those who died from other causes were 3.2%, 5.8%, and 14.1%. Age, marriage, grade, size, regional node positivity, American Joint Committee on Cancer M stage, progesterone receptor, and surgery were independent prognostic factors for dying of ILC, whereas the independent prognostic factors for dying of other causes were age, race, marriage, size, radiation, and chemotherapy. The nomograms were well calibrated and had good discrimination ability. CONCLUSION: We applied competing-risk analysis to ILC patients based on the SEER database and established nomograms that perform well in predicting the cause-specific death rates at 3, 5, and 10 years after the diagnosis.

UV-activated permanganate process for micro-organic pollutant degradation: efficiency, mechanism and influencing factors.

Ultraviolet-activated permanganate (UV/PM) process is a novel advanced oxidation process (AOP), but its application potential remains to be evaluated. This work investigates the degradation of refractory organic pollutant by UV/PM in terms of efficiency, mechanism, and influencing factors. The target compound benzoic acid (BA), which is a micro-organic pollutant and is resistant to PM and UV treatment, can be efficiently degraded by UV/PM. The electron paramagnetic resonance spectra directly supported the formation of hydroxyl radical (HO•) and superoxide radical (O2•-) from UV photolysis of PM. Competitive kinetics experiments verified that O2•- acted as precursor of HO• and the good degradation performance of BA was due to the involvement of HO• and manganese(V). The rate constants of BA degradation showed a positive linear relationship with PM dosage in the range of 0.5-20 mg·L-1, and the degradation process was significantly influenced by solution pH and natural organic matters but insensitive to chloride and bicarbonate at environmentally relevant concentrations. Compared to the typical UV-based AOP UV/hydrogen peroxide, UV/PM is a little inferior, indicating that optimization and enhancement is needed for this process before its possible practical application.

A pan-cancer study of selenoprotein genes as promising targets for cancer therapy.

BACKGROUND: The most important health benefit of selenium (Se) is in the prevention and control of cancer. Glutathione peroxidases (GPXs) and thioredoxin reductases (TXNRDs) are selenoenzymes that are thought to play a role in oxidative stress. The differential expression of genes of the TXNRD and GPX families is closely related to carcinogenesis and the occurrence of cancer. This study comprehensively analyzed the expression profiles of seven genes in the TXNRD and GPX families, in terms of their correlations with patient survival and immune-cell subtypes, tumor microenvironment, and drug sensitivity. RESULTS: The expression profiles of genes in the TXNRD and GPX families differ between different types of cancer, and also between and within individual cancer cases. The expression levels of the seven analyzed genes are related to the overall survival of patients. The TXNRD1 and TXNRD3 genes are mainly related to poor prognoses, while other genes are related to good or poor prognoses depending on the type of cancer. All of the genes were found to be correlated to varying degrees with immune-cell subtypes, level of mechanistic cell infiltration, and tumor cell stemness. The TXNRD1, GPX1, and GPX2 genes may exert dual effects in tumor mutagenesis and development, while the TXNRD1, GPX1, GPX2, and GPX3 genes were found to be related to drug sensitivity or the formation of drug resistance. CONCLUSIONS: The results will greatly help in identifying the association between genes and tumorigenesis, especially in the immune response, tumor microenvironment, and drug resistance, and very important when attempting to identify new therapeutic targets.

Global, regional, and national burdens of bladder cancer in 2017: estimates from the 2017 global burden of disease study.

BACKGROUND: The aim of this study is to describe the prevalence and mortality of bladder cancer (BCa) using data obtained in the Global Burden of Disease study performed in 2017 (GBD 2017). METHODS: Data on BCa for 2017, including prevalence, mortality, and disability-adjusted life years (DALYs), were obtained from GBD 2017 at the global, regional, and national levels. We also analyzed the association of BCa burden with the country development level. RESULTS: There were 2.63 million BCa cases estimated from the GBD 2017 data, with 200,000 persons dying of BCa, resulting in 3.60 million DALYs in 2017. The age-standardized prevalence (ASP) of BCa was 32.91/100,000 persons, and age-standardized death rate (ASDR) was 2.57/100,000 persons. The ASP and ASDR of BCa were higher in males than in females, and higher in people older than 60 years. The ASP and ASDR of BCa were higher in Western Europe and Central Europe than in South Asia, Andean Latin America, and Central Latin America, and higher in countries with a higher sociodemographic index (SDI). Correlation analysis identified that the ASP and ASDR of BCa were positively correlated with the country SDI (P < 0.0001 and ρ = 0.68 for ASP, and P = 0.0048 and ρ = 0.20 for ASDR). In addition, 33.72% deaths and 36.80% DALYs caused by BCa could be attributed to smoking globally. CONCLUSION: The prevalence and mortality of BCa were very high in 2017, especially in high-SDI countries. Smoking-cessation strategies should be strengthened to control the burden associated with BCa.