Thiamine supplementation may be associated with improved prognosis in patients with sepsis.

Sepsis is a clinical syndrome characterised by a severe disorder of pathophysiology caused by infection of pathogenic micro-organisms. The addition of antioxidant micronutrient therapies such as thiamine to sepsis treatment remains controversial. This study explored the effect of thiamine on the prognosis of patients with sepsis. This study was a retrospective study involving patients with sepsis from the Medical Information Mart for Intensive Care IV. Patients were divided into two groups, the thiamine received group (TR) and the thiamine unreceived group (TUR), according to whether they were supplemented with thiamin via intravenous while in the intensive care unit (ICU). The primary outcome was ICU mortality. The association between thiamine and outcome was analysed using the Cox proportional hazards regression model, propensity score matching (PSM), generalised boosted model-based inverse probability of treatment weighting (IPTW) and doubly robust estimation. A total of 11 553 sepsis patients were enrolled in this study. After controlling for potential confounders using Cox regression models, the TR group had a statistically significantly lower ICU mortality risk than the TUR group. The hazard ratio of ICU mortality for the TR group was 0·80 (95 % CI 0·70, 0·93). We obtained the same results after using PSM, IPTW and doubly robust estimation. Supplementation with thiamine has a beneficial effect on the prognosis of patients with sepsis. More randomised controlled trials are needed to confirm the effectiveness of thiamine supplementation in the treatment of sepsis.

How socioeconomic and clinical factors impact prostate-cancer-specific and other-cause mortality in prostate cancer stratified by clinical stage: Competing-risk analysis.

BACKGROUND: The aim of this study was to analyze the causes of death and risk factors of prostate-cancer-specific mortality (PCSM) and other-cause mortality (OCM) at different clinical stages using data from the Surveillance, Epidemiology, and End Results database. METHODS: The characteristics and cause-specific death classifications of males with prostate cancer (PCa) were extracted. Multivariate competing-risk regression analysis was used to identify significant predictors and quantify the cumulative incidence of PCSM and OCM at different clinical stages. RESULTS: Of the 244,433 PCa patients who were included, 19,274 died from 7356 PCSM, and 11,918 from OCM. The proportion of PCSM gradually increased from 2010 to 2016. The risk factors for PCSM in the localized PCa stage included older age, not being married, living in a county with higher poverty rates, and higher PSA levels and Gleason scores. Meanwhile, Medicaid and lower education levels were the additional risk factors of OCM. The risk factors for PCSM in the regional PCa stage included older age, not being married, Medicaid, living in a county with higher poverty rates, and higher PSA levels and Gleason scores. Meanwhile, the income level did not affect OCM risk. The risk factors for PCSM in the distant metastatic PCa stage included a separated/divorced/widowed marital status, Medicaid, and higher PSA levels and Gleason scores. Meanwhile, older age, an unmarried or separated/divorced/widowed marital status, and higher PSA levels were risk factors for OCM. In addition, receiving both surgery and radiation was worse than just receiving surgery for PCa specific survival in localized and regional PCa patients. CONCLUSION: Some pretreatment and treatment factors may influence OCM that are not identical to those for PCSM at the corresponding stage. Decision-makers and managers should fully consider OCM to maximize treatment benefits for PCa.

Nomogram to predict cause-specific mortality of patients with rectal adenocarcinoma undergoing surgery: a competing risk analysis.

BACKGROUND: Rectal adenocarcinoma is one of major public health problems, severely threatening people's health and life. Cox proportional hazard models have been applied in previous studies widely to analyze survival data. However, such models ignore competing risks and treat them as censored, resulting in excessive statistical errors. Therefore, a competing-risk model was applied with the aim of decreasing risk of bias and thereby obtaining more-accurate results and establishing a competing-risk nomogram for better guiding clinical practice. METHODS: A total of 22,879 rectal adenocarcinoma cases who underwent primary-site surgical resection were collected from the SEER (Surveillance, Epidemiology, and End Results) database. Death due to rectal adenocarcinoma (DRA) and death due to other causes (DOC) were two competing endpoint events in the competing-risk regression analysis. The cumulative incidence function for DRA and DOC at each time point was calculated. Gray's test was applied in the univariate analysis and Gray's proportional subdistribution hazard model was adopted in the multivariable analysis to recognize significant differences among groups and obtain significant factors that could affect patients' prognosis. Next, A competing-risk nomogram was established predicting the cause-specific outcome of rectal adenocarcinoma cases. Finally, we plotted calibration curve and calculated concordance indexes (c-index) to evaluate the model performance. RESULTS: 22,879 patients were included finally. The results showed that age, race, marital status, chemotherapy, AJCC stage, tumor size, and number of metastasis lymph nodes were significant prognostic factors for postoperative rectal adenocarcinoma patients. We further successfully constructed a competing-risk nomogram to predict the 1-year, 3-year, and 5-year cause-specific mortality of rectal adenocarcinoma patients. The calibration curve and C-index indicated that the competing-risk nomogram model had satisfactory prognostic ability. CONCLUSION: Competing-risk analysis could help us obtain more-accurate results for rectal adenocarcinoma patients who had undergone surgery, which could definitely help clinicians obtain accurate prediction of the prognosis of patients and make better clinical decisions.

The relationship between hematocrit and serum albumin levels difference and mortality in elderly sepsis patients in intensive care units-a retrospective study based on two large database.

BACKGROUND: Sepsis still threatens the lives of more than 300 million patients annually and elderly patients with sepsis usually have a more complicated condition and a worse prognosis. Existing studies have shown that both Hematocrit (HCT) and albumin (ALB) can be used as potential predictors of sepsis, and their difference HCT-ALB has a significant capacity to diagnose infectious diseases. Currently, there is no relevant research on the relationship between HCT-ALB and the prognosis of elderly sepsis patients. Therefore, this study aims to explore the association between HCT-ALB and mortality in elderly patients with sepsis. METHODS: This study was a multi-center retrospective study based on the Medical Information Mart for Intensive Care (MIMIC-IV) database and the eICU Collaborative Research Database (eICU-CRD) in elderly patients with sepsis. The optimal HCT-ALB cut-off point for ICU mortality was calculated by the Youden Index based on the eICU-CRD dataset, and multivariate logistic regressions were conducted to explore the association between HCT-ALB and ICU/hospital mortality in the two databases. Subgroup analyses were performed for different parameters and comorbidity status. RESULTS: The number of 16,127 and 3043 elderly sepsis patients were selected from two large intensive care databases (eICU-CRD and MIMIC-IV, respectively) in this study. Depending on the optimal cut-off point, patients in both eICU-CRD and MIMIC-IV were independently divided into low HCT-ALB (< 6.7) and high HCT-ALB (≥ 6.7) groups. The odds ratio (95%confidence interval) [OR (95CI%)] of the high HCT-ALB group were 1.50 (1.36,1.65) and 1.71 (1.58,1.87) for ICU and hospital mortality in the eICU-CRD database after multivariable adjustment. Similar trends in the ICU and hospital mortality [OR (95%CI) 1.41 (1.15,1.72) and 1.27 (1.07,1.51)] were observed in MIMIC-IV database. Subgroup analysis showed an interaction effect with SOFA score in the eICU-CRD database however not in MIMIC-IV dataset. CONCLUSIONS: High HCT-ALB (≥ 6.7) is associated with 1.41 and 1.27 times ICU and hospital mortality risk in elderly patients with sepsis. HCT-ALB is simple and easy to obtain and is a promising clinical predictor of early risk stratification for elderly sepsis patients in ICU.

Influence of ambulatory blood pressure-related indicators within 24 h on in-hospital death in sepsis patients.

Background: Sepsis is a serious public health problem worldwide. Blood pressure is one of the indicators that is closely monitored in intensive-care units, and it reflects complex interactions between the internal cardiovascular control mechanism and the external environment. We aimed to determine the impact of indicators related to the ambulatory blood pressure on the prognosis of sepsis patients. Methods: This retrospective study was based on the Medical Information Mart for Intensive Care IV database. Relevant information about sepsis patients was extracted according to specific inclusion and exclusion criteria. Examined parameters included the average blood pressure, blood pressure variability (BPV), and circadian rhythm, and the study outcome was in-hospital death. We investigated the effects of these indicators on the risk of in-hospital death among sepsis patients using Cox proportional-hazards models, restricted cubic splines analysis, and subgroup analysis. Results: This study enrolled 10,316 sepsis patients, among whom 2,117 died during hospitalization. All parameters except the nighttime variation coefficient of the diastolic blood pressure (DBP) were associated with in-hospital death of sepsis patients. All parameters except for fluctuations in DBP exhibited nonlinear correlations with the outcome. The subgroup analysis revealed that some of the examined parameters were associated with in-hospital death only in certain subgroups. Conclusion: Indicators related to the ambulatory blood pressure within 24 h are related to the prognosis of sepsis patients. When treating sepsis, in addition to blood pressure, attention should also be paid to BPV and the circadian rhythm in order to improve the prognosis and the survival rate.

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.

Sleep duration, genetic susceptibility, and Alzheimer's disease: a longitudinal UK Biobank-based study.

BACKGROUND: Alzheimer's disease (AD) is the most frequently occurring type of dementia. Concurrently, inadequate sleep has been recognized as a public health epidemic. Notably, genetic and environmental factors are now considered contributors to AD progression. OBJECTIVE: To assess the association between sleep duration, genetic susceptibility, and AD. METHODS AND RESULTS: Based on 483,507 participants from the UK Biobank (UKB) with an average follow-up of 11.3 years, there was a non-linear relationship between AD incidence and sleep duration (P for non-linear < 0.001) by restricted cubic splines (RCS). Sleep duration was categorized into short sleep duration (< 6 h/night), normal sleep duration (6-9 h/night), and long sleep duration (> 9 h/night). No statistically significant interaction was identified between sleep duration and the AD-GRS (Alzheimer's disease genetic risk score, P for interaction = 0.45) using Cox proportional risk model. Compared with the participants who had a low AD-GRS and normal sleep duration, there was associated with a higher risk of AD in participants with a low AD-GRS and long sleep duration (HR = 3.4806; 95% CI 2.0011-6.054, p < 0.001), participants with an intermediate AD-GRS and long sleep duration (HR = 2.0485; 95% CI 1.3491-3.1105, p < 0.001), participants with a high AD-GRS and normal sleep duration (HR = 1.9272; 95% CI 1.5361-2.4176, p < 0.001), and participants with a high AD-GRS and long sleep duration (HR = 5.4548; 95% CI 3.1367-9.4863, p < 0.001).In addition, there was no causal association between AD and sleep duration using Two Sample Mendelian randomization (MR). CONCLUSION: In the UKB population, though there was no causal association between AD and sleep duration analyzed using Two Sample MR, long sleep duration (> 9 h/night) was significantly associated with a higher risk of AD, regardless of high, intermediate or low AD-GRS. Prolonged sleep duration may be one of the clinical predictors of a higher risk of AD.

Antithrombotic Therapy Improves ICU Mortality of Septic Patients with Peripheral Vascular Disease.

Objective: The effectiveness of antithrombotic drugs for treating sepsis is controversial. Here, we explore the association between antithrombotic therapy and intensive care unit (ICU) mortality for septic patients with peripheral vascular disease. Methods: This retrospective cohort study uses data from the Medical Information Mart for Intensive Care (MIMIC)-III database. Kaplan-Meier survival analyses were used to examine mortality among different groups. Cox regression and marginal structural Cox models (MSCMs) were used to adjust for confounding factors. Main Results. The final cohort from the MIMIC-III database included 776 patients, of which 701 survived and 75 perished. The anticoagulant (AC) group and the antiplatelet-anticoagulation (AC-AP) group survived better than the group without antithrombotic treatment (non-AT). The AC and AC-AP groups showed a 0.363-fold and 0.373-fold risk of ICU mortality, respectively, compared with the non-AT group when controlling for age, gender, CRRT, alcohol, heart failure, hypertension, diabetes, obesity, renal failure, liver disease, INR, PT, PPT, and SpO2. Antiplatelet therapy did not reduce ICU mortality. The same trends were apparent from the MSCM. In addition, the AC-AP group exhibited a lower risk of bleeding complications. Conclusion: Although the antithrombotic group (AC and AC-AP groups) demonstrated a higher sequential organ failure assessment (SOFA) score than the group without antithrombotic treatment (non-AT group), the risk of ICU mortality was lower without increasing the risk of bleeding complications. Our study further suggested that anticoagulation therapy may benefit the prognosis of septic patients with peripheral vascular disease.

Risk factor analysis and nomogram for predicting in-hospital mortality in ICU patients with sepsis and lung infection.

BACKGROUND: Lung infection is a common cause of sepsis, and patients with sepsis and lung infection are more ill and have a higher mortality rate than sepsis patients without lung infection. We constructed a nomogram prediction model to accurately evaluate the prognosis of and provide treatment advice for patients with sepsis and lung infection. METHODS: Data were retrospectively extracted from the Medical Information Mart for Intensive Care (MIMIC-III) open-source clinical database. The definition of Sepsis 3.0 [10] was used, which includes patients with life-threatening organ dysfunction caused by an uncontrolled host response to infection, and SOFA score ≥ 2. The nomogram prediction model was constructed from the training set using logistic regression analysis, and was then internally validated and underwent sensitivity analysis. RESULTS: The risk factors of age, lactate, temperature, oxygenation index, BUN, lactate, Glasgow Coma Score (GCS), liver disease, cancer, organ transplantation, Troponin T(TnT), neutrophil-to-lymphocyte ratio (NLR), and CRRT, MV, and vasopressor use were included in the nomogram. We compared our nomogram with the Sequential Organ Failure Assessment (SOFA) score and Simplified Acute Physiology Score II (SAPSII), the nomogram had better discrimination ability, with areas under the receiver operating characteristic curve (AUROC) of 0.743 (95% C.I.: 0.713-0.773) and 0.746 (95% C.I.: 0.699-0.790) in the training and validation sets, respectively. The calibration plot indicated that the nomogram was adequate for predicting the in-hospital mortality risk in both sets. The decision-curve analysis (DCA) of the nomogram revealed that it provided net benefits for clinical use over using the SOFA score and SAPSII in both sets. CONCLUSION: Our new nomogram is a convenient tool for accurate predictions of in-hospital mortality among ICU patients with sepsis and lung infection. Treatment strategies that improve the factors considered relevant in the model could increase in-hospital survival for these ICU 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.

The association between continuous renal replacement therapy as treatment for sepsis-associated acute kidney injury and trend of lactate trajectory as risk factor of 28-day mortality in intensive care units.

BACKGROUND: Sepsis has high incidence and fatality rates in intensive care units, often leading to renal failure. The effectiveness of continuous renal replacement therapy (CRRT) in sepsis-associated acute kidney injury (S-AKI) patients is currently uncertain. AIM: Joint model was used to determine the association between CRRT and the lactate trajectory trend and how it correlated to 28-day mortality for S-AKI patient in ICU. METHODS: A retrospective study was applied to patients with sepsis and AKI, which were extracted from the MIMIC-III public database, with the endpoint being 28-day mortality. Every lactate level measurement within 28 days was observed and calculated using logarithms. Joint model combined the longitudinal analysis of the natural logarithm of the lactate level [log(lactate)] in longitudinal submodel and Cox regression by trajectory function, demonstrating the effects of CRRT on 28-day survival and log(lactate) changes, and its final relationship with the event status. RESULTS: Among the 717 S-AKI patients, 157 received CRRT. CRRT was not associated with 28-day mortality. After adjustments, the relationship between CRRT use and log(lactate) elevation was statistically significant. The parameter estimation of CRRT and log(lactate) indicated that using CRRT will increase log(lactate) by 0.041 in S-AKI patients. The joint model also instigated a fixed association between changes in the lactate level and the event result, revealing an exp value of (0.755) = 2.12, indicating that an increase of one unit in log(lactate) will increase the risk of 28-day mortality 2.12-times. CONCLUSION: There was no significant association between CRRT use and 28-day survival in S-AKI patients, and JM showed that CRRT use might be associated with elevation of longitudinal lactate levels. Therefore, additional attention should be paid to other treatments to control lactate levels when providing renal support for patients with S-AKI.

Prediction of prognosis in elderly patients with sepsis based on machine learning (random survival forest).

BACKGROUND: Elderly patients with sepsis have many comorbidities, and the clinical reaction is not obvious. Thus, clinical treatment is difficult. We planned to use the laboratory test results and comorbidities of elderly patients with sepsis from a large-scale public database Medical Information Mart for Intensive Care (MIMIC) IV to build a random survival forest (RSF) model and to evaluate the model's predictive value for these patients. METHODS: Clinical information of elderly patients with sepsis in MIMIC IV database was collected retrospectively. Machine learning (RSF) was used to select the top 30 variables in the training cohort to build the final RSF model. The model was compared with the traditional scoring systems SOFA, SAPSII, and APSIII. The performance of the model was evaluated by C index and calibration curve. RESULTS: A total of 6,503 patients were enrolled in the study. The top 30 important variables screened by RSF were used to construct the final RSF model. The new model provided a better C-index (0.731 in the validation cohort). The calibration curve described the agreement between the predicted probability of RSF model and the observed 30-day survival. CONCLUSIONS: We constructed a prognostic model to predict a 30-day mortality risk in elderly patients with sepsis based on machine learning (RSF algorithm), and it proved superior to the traditional scoring systems. The risk factors affecting the patients were also ranked. In addition to the common risk factors of vasopressors, ventilator use, and urine output. Newly added factors such as RDW, type of ICU unit, malignant cancer, and metastatic solid tumor also significantly influence prognosis.

Influence of the trajectory of the urine output for 24 h on the occurrence of AKI in patients with sepsis in intensive care unit.

BACKGROUND: Sepsis-associated acute kidney injury (S-AKI) is a common and life-threatening complication in hospitalized and critically ill patients. This condition is an independent cause of death. This study was performed to investigate the correlation between the trajectory of urine output within 24 h and S-AKI. METHODS: Patients with sepsis were studied retrospectively based on the Medical Information Mart for Intensive Care IV. Latent growth mixture modeling was used to classify the trajectory of urine output changes within 24 h of sepsis diagnosis. The outcome of this study is AKI that occurs 24 h after sepsis. Cox proportional hazard model, Fine-Gray subdistribution proportional hazard model, and doubly robust estimation method were used to explore the risk of AKI in patients with different trajectory classes. RESULTS: A total of 9869 sepsis patients were included in this study, and their 24-h urine output trajectories were divided into five classes. The Cox proportional hazard model showed that compared with class 1, the HR (95% CI) values for classes 3, 4, and 5 were 1.460 (1.137-1.875), 1.532 (1.197-1.961), and 2.232 (1.795-2.774), respectively. Competing risk model and doubly robust estimation methods reached similar results. CONCLUSIONS: The trajectory of urine output within 24 h of sepsis patients has a certain impact on the occurrence of AKI. Therefore, in the early treatment of sepsis, close attention should be paid to changes in the patient's urine output to prevent the occurrence of S-AKI.

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.

Examining More Lymph Nodes May Improve the Prognosis of Patients With Right Colon Cancer: Determining the Optimal Minimum Lymph Node Count.

OBJECTIVES: The objective is to determine the optimal minimum lymph node examination number for right colon cancer (RCC) patients. METHODS: We comprehensively analysed the Surveillance, Epidemiology and End Results database data from 2004 to 2016 to determine the 13-year trend in the number of lymph nodes examined among 108,703 left colon cancer and 165,937 RCC patients. 133,137 RCC patients eligible for inclusion were used to determine the optimal minimum for lymph node examination. We used restricted cubic splines to analyse the dose-response relationship between the number of lymph nodes examined and prognosis. X-tiles and decision trees were used to determine the optimal cutoff for the number of lymph nodes based on the survival outcomes of patients with RCC. The Kaplan-Meier method and COX model were used to estimate the overall survival and independent prognostic factors, and a prediction model was constructed. The C-index, calibration curve, net reclassification improvement and integrated discrimination improvement were used to determine the predictive performance of the model, and decision curve analysis was used to evaluate the benefits. RESULTS: Lymph node examinations were common among colon cancer patients over the 13-year study period. It is generally agreed that at least 12 lymph nodes must be examined to ensure proper dissection and accurate staging of RCC; however, the optimal number of lymph nodes to be examined is controversial. The dose-response relationship indicated that 12 was not the optimal minimum number of lymph nodes for RCC patients. X-tile and survival decision-tree analysis indicated that 20 nodes was the optimal number. Survival analysis indicated that <20 nodes examined was a risk factor for poor prognosis, and the classification performance was superior for 20 nodes compared to 12 nodes. CONCLUSION: Lymph node examination in RCC patients should be altered. Our research suggests that a 20-node measure may be more suitable for RCC patients.

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.

Influence of fluid balance on the prognosis of patients with sepsis.

BACKGROUND: Early and timely fluid treatment or resuscitation are the basic measures for the active treatment of sepsis. Our aim is to further explore the relationship between fluid balance and prognosis in patients with sepsis on a daily basis for 5 days. METHODS: Sepsis patients in eICU Collaborative Research Database were divided into the negative balance group (NB/-) and the positive balance group (PB/+) according to daily fluid balance. The primary outcome was in-hospital mortality. Survival differences between the groups were analyzed by using Cox regression. Then dose-response relationship between fluid balance and in-hospital mortality was studied using restricted cubic splines (RCSs). Furthermore, patients with fluid balance data for the previous three consecutive days were selected and divided into eight groups ("+/+/+", "+/+/-", "+/ -/-", "+/ -/+", "-/ -/-", "-/ -/+", "-/+/+", and "-/+/-"). Kaplan-Meier curves and Cox regression were used to show the survival difference between groups. RESULTS: Our study, which included 19,557 patients in a multicenter database, showed that positive fluid balances on days 1, 2, and 3 after sepsis diagnosis were associated with poor prognosis with the HRs of 1.29 (1.20,1.40), 1.13 (1.01,1.27), and 1.25 (1.08,1.44), respectively, while the fluid balance on days 4 and 5 had no effect on the primary outcome. Then RCSs showed an overall trend that the risk of in-hospital mortality on days 1, 2, and 3 increased with increasing fluid balance. For three consecutive days of fluid balance, we studied 9205 patients and Kaplan-Meier curves revealed survival differences among patients in the eight groups. The cox model demonstrated that compared with the "+/+/+" group, the "+/ -/-", "-/ -/-", "-/ -/+", "-/+/+", and "-/+/-" groups had a lower risk of in-hospital mortality, with HRs of 0.65 (0.45,0.93), 0.72 (0.60,0.86), 0.63 (0.43,0.93), 0.69 (0.48,0.98), and 0.63 (0.42,0.96), respectively. CONCLUSIONS: In patients with sepsis, positive fluid balance on days 1, 2, and 3 was associated with adverse outcomes. For patients with fluid balance for three consecutive days, the "+/-/-", "-/ -/-", "-/-/+", "-/+/+", and "-/+/-" groups were less likely to die in hospital than the "+/+/+" group.

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.

Relationship Between Body Mass Index and Kidney Stones Based on Dose-Response Analyses Using Restricted Cubic Splines Applied to NHANES 2011-2016 Data.

OBJECTIVE: The objective of the study was to estimate the dose-response relationship between body mass index (BMI) and the self-reported prevalence of kidney stones based on a restricted cubic spline (RCS) method. METHODS: This cross-sectional study analyzed 13,223 adults aged ≥20 years who had participated in the National Health and Nutrition Examination Survey performed during 2011-2016. Kidney stones were identified using a standard questionnaire, and physical examinations were used to determine BMI. Logistic regression was used to assess the relationship between BMI and the prevalence of kidney stones, with the dose-response relationship explored using RCSs. RESULTS: The overall prevalence of kidney stones was 9.7%: 10.6% in males and 8.9% in females. After adjusting for potential confounders, compared with those with a BMI in quartile 1, the odds ratios of kidney stones among those with BMIs in quartiles 2, 3, and 4 were 1.45, 1.60, and 2.00, respectively (95% confidence interval = 1.21-1.75, 1.33-1.92, and 1.67-2.39; all P < .001). Multivariate RCS regression revealed that BMI was related to kidney stones in a nonlinear manner (P for nonlinearity <0.001). There was a significant positive relationship, with the curves being steeper when BMI was <28 kg/m2. CONCLUSION: This analysis of National Health and Nutrition Examination Survey data has demonstrated that BMI is significantly associated with the prevalence of kidney stones.

A prognostic nomogram for the cancer-specific survival of patients with upper-tract urothelial carcinoma based on the Surveillance, Epidemiology, and End Results Database.

BACKGROUND: The aim of this study was to establish a comprehensive nomogram for the cancer-specific survival (CSS) of patients with upper-tract urothelial carcinoma (UTUC) and compare it with the traditional American Joint Committee on Cancer (AJCC) staging system in order to determine its reliability. METHODS: This study analyzed 9505 patients with UTUC in the Surveillance, Epidemiology, and End Results (SEER) database. R software was used to randomly divided the patients in a 7-to-3 ratio to form a training cohort (n = 6653) and a validation cohort (n = 2852). Multivariable Cox regression was used to identify predictive variables. The new survival model was compared with the AJCC prognosis model using the concordance index (C-index), the area under the time-dependent receiver operating characteristics curve (AUC), the net reclassification improvement (NRI), the integrated discrimination improvement (IDI), calibration plotting, and decision-curve analysis (DCA). RESULTS: We have established a nomogram for determining the 3-, 5-, and 8-year CSS probabilities of UTUC patients. The nomogram indicates that the AJCC stage has the greatest influence on CSS in UTUC, followed by the age at diagnosis, surgery status, tumor size, radiotherapy status, histological grade, marital status, chemotherapy status, race, and finally sex. The C-index was higher for the nomogram than the AJCC staging system in both the training cohort (0.785 versus 0.747) and the validation cohort (0.779 versus 0.739). Calibration plotting demonstrated that the model has good calibration ability. The AUC, NRI, IDI, and DCA of the nomogram showed that it performs better than the AJCC staging system alone. CONCLUSIONS: This study is the first to establish a comprehensive UTUC nomogram based on the SEER database and evaluate it using a series of indicators. Our novel nomogram can help clinical staff to predict the 3-, 5-, and 8-year CSS probabilities of UTUC patients more accurately than using the AJCC staging system.