Development and validation of prognostic nomogram for cirrhotic patients with acute kidney injury upon ICU admission.

This study aims to develop and validate a prognostic nomogram that accurately predicts the short-term survival rate of cirrhotic patients with acute kidney damage (AKI) upon ICU admission. For this purpose, we examined the admission data of 3060 cirrhosis patients with AKI from 2008 to 2019 in the MIMIC-IV database. All included patients were randomly assigned to derivation and validation cohorts in a 7:3 ratio. The derivation cohort used the least absolute shrinkage and selection operator (LASSO) regression model to identify independent predictors of AKI. A prognostic nomogram was constructed via multivariate logistic regression analysis in the derivation cohort and subsequently verified in the validation cohort. Nomogram's discrimination, calibration, and clinical utility were evaluated using the C-index, calibration plot, and decision curve analysis (DCA). A total of 2138 patients were enrolled in the derivation cohort, with a median follow-up period of 15 days, a median survival time of 41 days, and a death rate of 568 patients (26.6%). The cumulative survival rates at 15 and 30 days were 75.8% and 57.5%, respectively. The results of the multivariate analysis indicated that advanced AKI stage, use of vasoactive drugs, advanced age, lower levels of ALB, lower mean sBp, longer INR, and longer PT were all independent risk factors that significantly influenced the all-cause mortality of cirrhosis patients with AKI (all p < 0.01). The C-indices for the derivation and the validation cohorts were 0.821 (95% CI 0.800-0.842) and 0.831 (95% CI 0.810-0.852), respectively. The model's calibration plot demonstrated high consistency between predicted and actual probabilities. Furthermore, the DCA showed that the nomogram was clinically valuable. Therefore, the developed and internally validated prognostic nomogram exhibited favorable discrimination, calibration, and clinical utility in forecasting the 15-day and 30-day survival rates of cirrhosis patients with AKI upon admission to the ICU.

Development and validation of a dynamic online nomogram for predicting acute kidney injury in cirrhotic patients upon ICU admission.

Background: Acute kidney injury (AKI) is one of the most common and deadly complications among cirrhotic patients at intensive care unit (ICU) admission. We aimed to develop and validate a simple and clinically useful dynamic nomogram for predicting AKI in cirrhotic patients upon ICU admission. Methods: We analyzed the admission data of 4,375 patients with liver cirrhosis in ICU from 2008 to 2019 in the intensive care unit IV (MIMIC-IV) database. The eligible cirrhotic patients were non-randomly divided into derivation (n = 2,188) and validation (n = 2,187) cohorts at a ratio of 1:1, according to the order of admission. The least absolute shrinkage and selection operator regression model was used to identify independent predictors of AKI in the derivation cohort. A dynamic online nomogram was built using multivariate logistic regression analysis in the derivation cohort and then validated in the validation cohort. The C-index, calibration curve, and decision curve analysis were used to assess the nomogram's discrimination, calibration, and clinical usefulness, respectively. Results: The incidence of AKI in 4,375 patients was 71.3%. Ascites, chronic kidney disease, shock, sepsis, diuretic drugs, hepatic encephalopathy, bacterial infections, vasoactive drugs, admission age, total bilirubin, and blood urea nitrogen were identified using the multivariate logistic regression analysis as significant predictors of AKI upon ICU admission. In the derivation cohort, the model showed good discrimination (C-index, 0.786; 95% CI, 0.765-0.806) and good calibration. The model in the validation cohort yielded good discrimination (C-index, 0.774; 95% CI, 0.753-0.795) and good calibration. Decision curve analysis demonstrated that the dynamic online nomogram was clinically useful. Conclusion: Our study presents a dynamic online nomogram that incorporates clinical predictors and can be conveniently used to facilitate the individualized prediction of AKI in cirrhotic patients upon ICU admission.

Inhibition of TNF-α and JNK Signaling Pathway Can Reduce Paclitaxel-Induced Apoptosis of Mouse Cardiomyocytes.

Paclitaxel (PTX) is a widely used chemotherapeutic drug for treating tumors. However, studies have shown that it can cause cardiac problems such as arrhythmia, myocarditis, chronic cardiomyopathy, and heart failure. Therefore, it is essential to study the mechanism behind the cardiotoxicity of PTX in tumor treatment. In this study, we initially injected PTX into mice to establish a myocardial cell apoptosis model to observe the degree of damage to mouse myocardium caused by PTX. Upon determining the levels of mouse myocardial creatine phosphokinase (CK), myokinase isoenzyme (CK-MB), aspartate transaminase (AST), and lactate dehydrogenase (LDH), we found that all of these levels showed apparent increases in mice treated with PTX. Further analyses of the TNF-α level and the expression of Jun N-terminal kinase (JNK) and Bcl-2 family-related proteins in myocardial tissue were performed. It was found that PTX increased the protein levels of TNF-α, Bax, p-JNK, and JNK in myocardial tissue but decreased the protein level of Bcl-2. After 1 month of PTX treatment in mice, we inhibited the expression of TNF-α and JNK proteins, which reduced the effect of paclitaxel on the apoptosis of mouse cardiomyocytes. The protein levels of Bax, p-JNK, and TNF-α in cardiomyocytes were reduced, while there was a relative increase in the Bcl-2 protein level. The findings suggested that inhibition of the NK signaling pathway and TNF-α can lessen the effect of PTX on mouse cardiomyocytes.

A model for predicting the overall survival of gastroenteropancreatic neuroendocrine neoplasms after surgery.

BACKGROUND: The increase in the incidence of gastroenteropancreatic neuroendocrine tumors (GEP-NENs) and refined morphological imaging techniques have led to a rise in the number of patients undergoing surgery. However, there is still a paucity of objective, clinically reliable and personalized tools to evaluate patient prognosis. MATERIALS AND METHODS: We identified patients from the Surveillance, Epidemiology, and End Results (SEER) database who underwent surgery for GEP-NEN from 1975 to 2018. The predictors associated with OS were investigated by Multivariate Cox proportional hazards (PHs) regression analysis in the primary cohort; a prognostic nomogram was then built based on the multivariate analysis results. The performance of the nomogram was assessed by Harrell's concordance index (C-index) and calibration curve and compared with the eighth edition of the American Joint Committee on Cancer (AJCC) staging system. RESULTS: A total of 45,889 patients were enrolled in our study; 32,321 were included in the primary cohort, and 13,568 were included in the validation cohort. A nomogram incorporating Age, Differentiation, M staging, and AJCC staging was subsequently built based on the multivariate analysis. The C-index (0.833 for the primary cohort and 0.845 for the validation cohort) and calibration curves indicated good discriminative ability and calibration of the nomogram. Further analysis demonstrated that the nomogram had superior discriminatory ability than the AJCC staging system (C-index= 0.706). CONCLUSION: The proposed nomogram showed excellent prediction with good calibration and discrimination, which can be used to make well-informed and individualized clinical decisions regarding the clinical management of GEP-NENs.

Development and validation of a novel nomogram for predicting survival rate in pancreatic neuroendocrine neoplasms.

BACKGROUND: Over the past decades, the incidence and prevalence of pancreatic neuroendocrine neoplasms (pNENs) have steadily increased. However, accurate prediction of the prognosis and treatment of this condition are currently challenging. This study aims to develop and validate a personalized nomogram to predict the survival of patients with pNENs. MATERIALS AND METHODS: A total of 9739 patients with pNENs were downloaded from the Surveillance, Epidemiology, and End Results (SEER) database. Subsequently, the patients were randomly assigned to a derivation cohort (n = 6874) and a validation cohort (n = 2865). The survival of patients was assessed using the Cox proportional hazards (PHs) regression analysis. Then, the nomogram that predicted 3-and 5-year survival rates were developed in the derivation cohort. Further, the predictive performance of the nomogram was evaluated through discrimination and calibration. RESULTS: The Cox regression analysis revealed that age, differentiation, the extent of tumor, M staging, and surgery were independent prognostic predictors for pNENs. The nomogram showed superior discrimination capability than AJCC staging in both derived and validation cohorts (C-index: 0.874 versus 0.721 and 0.833 versus 0.721). The calibration curves showed that the practical and predicted survival rates effectively coincided, specifically for the 3-year survival rate. CONCLUSION: Our nomogram is a valuable tool for the prediction of the survival rate for patients with pNENs; this may promote individualized prognostic evaluation and treatment.

A novel risk-predicted nomogram for sepsis associated-acute kidney injury among critically ill patients.

BACKGROUND: Acute kidney injury (AKI) is a prevalent and severe complication of sepsis contributing to high morbidity and mortality among critically ill patients. In this retrospective study, we develop a novel risk-predicted nomogram of sepsis associated-AKI (SA-AKI). METHODS: A total of 2,871 patients from the Medical Information Mart for Intensive Care III (MIMIC-III) critical care database were randomly assigned to primary (2,012 patients) and validation (859 patients) cohorts. A risk-predicted nomogram for SA-AKI was developed through multivariate logistic regression analysis in the primary cohort while the nomogram was evaluated in the validation cohort. Nomogram discrimination and calibration were assessed using C-index and calibration curves in the primary and external validation cohorts. The clinical utility of the final nomogram was evaluated using decision curve analysis. RESULTS: Risk predictors included in the prediction nomogram included length of stay in intensive care unit (LOS in ICU), baseline serum creatinine (SCr), glucose, anemia, and vasoactive drugs. Nomogram revealed moderate discrimination and calibration in estimating the risk of SA-AKI, with an unadjusted C-index of 0.752, 95 %Cl (0.730-0.774), and a bootstrap-corrected C index of 0.749. Application of the nomogram in the validation cohort provided moderate discrimination (C-index, 0.757 [95 % CI, 0.724-0.790]) and good calibration. Besides, the decision curve analysis (DCA) confirmed the clinical usefulness of the nomogram. CONCLUSIONS: This study developed and validated an AKI risk prediction nomogram applied to critically ill patients with sepsis, which may help identify reasonable risk judgments and treatment strategies to a certain extent. Nevertheless, further verification using external data is essential to enhance its applicability in clinical practice.

Crizotinib resistance conferred by BRAF V600E mutation in non-small cell lung cancer harboring an oncogenic ROS1 fusion.

Non-small cell lung cancer (NSCLC) patients with oncogenic ROS1 rearrangements would inevitably develop drug resistance and disease progression after receiving targeted oncogene therapy. Here, we present a metastatic lung adenocarcinoma patient harboring a CD74-ROS1 fusion who initially responded to crizotinib and then developed resistance after acquiring a rarely reported BRAF V600E mutation.

A clinical prediction nomogram to assess risk of colorectal cancer among patients with type 2 diabetes.

Colorectal cancer remains a major health burden worldwide and is closely related to type 2 diabetes. This study aimed to develop and validate a colorectal cancer risk prediction model to identify high-risk individuals with type 2 diabetes. Records of 930 patients with type 2 diabetes were reviewed and data were collected from 1 November 2013 to 31 December 2019. Clinical and demographic parameters were analyzed using univariable and multivariable logistic regression analysis. The nomogram to assess the risk of colorectal cancer was constructed and validated by bootstrap resampling. Predictors in the prediction nomogram included age, sex, other blood-glucose-lowering drugs and thiazolidinediones. The nomogram demonstrated moderate discrimination in estimating the risk of colorectal cancer, with Hosmer-Lemeshow test P = 0.837, an unadjusted C-index of 0.713 (95% CI 0.670-0.757) and a bootstrap-corrected C index of 0.708. In addition, the decision curve analysis demonstrated that the nomogram would be clinically useful. We have developed a nomogram that can predict the risk of colorectal cancer in patients with type 2 diabetes. The nomogram showed favorable calibration and discrimination values, which may help clinicians in making recommendations about colorectal cancer screening for patients with type 2 diabetes.

Nomogram for Predicting Risk of Digestive Carcinoma Among Patients with Type 2 Diabetes.

PURPOSE: Digestive carcinomas remain a major health burden worldwide and are closely related to type 2 diabetes. The aim of this study was to develop and validate a digestive carcinoma risk prediction model to identify high-risk individuals among those with type 2 diabetes. PATIENTS AND METHODS: The prediction model was developed in a primary cohort that consisted of 655 patients with type 2 diabetes. Data were collected from November 2013 to December 2018. Clinical parameters and demographic characteristics were analyzed by logistic regression to develop a model to predict the risk of digestive carcinomas; then, a nomogram was constructed. The performance of the nomogram was assessed with respect to calibration, discrimination, and clinical usefulness. The results were internally validated by a bootstrapping procedure. The independent validation cohort consisted of 275 patients from January 2019 to December 2019. RESULTS: Predictors in the prediction nomogram included sex, age, insulin use, and body mass index. The model showed good discrimination (C-index 0.747 [95% CI, 0.718-0.791]) and calibration (Hosmer-Lemeshow test P=0.541). The nomogram showed similar discrimination in the validation cohort (C-index 0.706 [95% CI, 0.682-0.755]) and good calibration (Hosmer-Lemeshow test P=0.418). Decision curve analysis demonstrated that the nomogram would be clinically useful. CONCLUSION: We developed a low-cost and low-risk model based on clinical and demographic parameters to help identify patients with type 2 diabetes who might benefit from digestive cancer screening.

Clinical observation and analysis of thyroid hormone levels in patients with idiopathic membranous nephropathy.

We sought to investigate the effect of total triiodothyronine (TT3) reduction in the follow-up of patients with idiopathic membranous nephropathy (IMN). A total of 121 patients were enrolled and classified into a low TT3 group or a normal group. Clinical indicators were compared between the groups, and changes in estimated glomerular filtration rate (eGFR), albumin (ALB), thyroid-stimulating hormone, serum creatinine, total protein, total cholesterol (TC), triglyceride (TG), and low-density lipoprotein cholesterol (LDL-C) during follow-up were analysed. In the analysis by TT3 level, ALB was significantly lower in the low TT3 group (P < .05), while TC, TG, LDL-C, fibrinogen, and renal pathological staging were significantly higher in the low TT3 group (P < .05). Analysis of variance for repeated measurement during follow-up showed that there were no significant differences in eGFR and ALB between the groups. TC, TG, and LDL-C levels were significantly higher in the low TT3 group (P < .05). Approximately 37% of patients with IMN showed a decrease in TT3, which was accompanied by significantly decreased ALB level, higher pathological stage, and increased serum lipid level compared with patients having a normal TT3 level. The management of TT3, and appropriate intervention, may therefore help to prevent the kidney damage progress in patients with IMN.

A Differential Diagnosis Model For Diabetic Nephropathy And Non-Diabetic Renal Disease In Patients With Type 2 Diabetes Complicated With Chronic Kidney Disease.

PURPOSE: Differentiating between diabetic nephropathy (DN) and non-diabetic renal disease (NDRD) is difficult and inefficient. The aim of the present study was to create a model for the differential diagnosis of DN and NDRD in patients with type 2 diabetes mellitus (T2DM). PATIENTS AND METHODS: We consecutively screened 213 patients with T2DM complicated with chronic kidney disease, who underwent renal biopsy at The First Affiliated Hospital of Guangxi Medical University (Nanning, China) between 2011 and 2017. According to the pathological results derived from the renal biopsy, the patients were divided into three groups (74, 130, and nine in the DN, NDRD, and NDRD superimposed with DN group, respectively). Clinical and laboratory data were compared and a diagnostic model was developed based on the following logistic regression model: logit(P)=+++ … +. RESULTS: We observed a high incidence of NDRD (61.0% of all patients), including various pathological types; the most common type was idiopathic membranous nephropathy. By comparing clinical variables, we identified a number of differences between DN and NDRD. Logistic regression analyses showed that the following variables were statistically significant: the absence of diabetic retinopathy (DR), proteinuria within the non-nephrotic range, the absence of anemia and an estimated glomerular filtration rate (eGFR) ≥90 mL/min/1.73 m2. We subsequently constructed a diagnostic model for predicting NDRD, as follows: PNDRD=1/[1+exp(-17.382-3.339×DR-1.274×Proteinuria-2.217×Anemia-1.853×eGFR-0.993×DM+20.892Bp)]. PNDRD refers to the probability of a diagnosis of NDRD (a PNDRD≥0.5 predicts NDRD while a PNDRD <0.5 predicts DN); while DM refers to the duration of diabetes. This model had a sensitivity of 95.4%, a specificity of 83.8%, and the area under the receiver operating characteristic curve was 0.925. CONCLUSION: Our diagnostic model may facilitate the clinical differentiation of DN and NDRD, and assist physicians in developing more effective and rational criteria for kidney biopsy in patients with T2DM complicated with chronic kidney disease.