Publications by authors named "Lu-Huai Feng"
Article Synopsis
- This study developed a machine-learning algorithm to predict survival rates for patients with gastric neuroendocrine neoplasms (gNENs) using data from 775 patients.
- The researchers utilized various machine learning models, identifying the optimal random survival forest (RSF) model, which showed high predictive accuracy for 1-, 3-, and 5-year survival rates calculated from demographic and clinical data.
- The final model allowed for risk stratification, classifying patients into high- and low-risk groups based on predicted outcomes, confirmed by Kaplan-Meier analysis for survival comparison.
Objective: The objective of this study is to develop and validate an effective prognostic nomogram for predicting the short-term survival rate of patients with acute heart failure (AHF) complicated by acute kidney injury (AKI) who are admitted to the intensive care unit (ICU).
Patients And Methods: We conducted an analysis of data from patients of AHF with AKI spanning the period from 2008 to 2019, utilizing the MIMIC-IV database. Patients were randomly divided into training and validation sets.
Background: Acute kidney injury (AKI) is a common and serious complication in patients with acute non-variceal upper gastrointestinal bleeding (NVUGIB). Early prediction and intervention are crucial for improving patient outcomes.
Methods: Data for patients presenting with acute NVUGIB in this retrospective study were sourced from the MIMC-IV database.
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.
View Article and Find Full Text PDFBackground: 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.
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.
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.
View Article and Find Full Text PDFBackground: 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.
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.
View Article and Find Full Text PDFNomogram for Predicting Risk of Digestive Carcinoma Among Patients with Type 2 Diabetes.
Diabetes Metab Syndr Obes
May 2020
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.