Related Experiment Video
Updated: Sep 19, 2025
Cutoff Value of Phase Angle by Bioelectrical Impedance Analysis at Admission as a Prognostic Factor in Patients with Acute Heart Failure
Published on: June 10, 2025
Predicting 14-day readmission in middle-aged and elderly patients with pneumonia using emergency department data: a
Nguyen Thanh Nhu1, Jiunn-Horng Kang2,3,4,5,6, Tian-Shin Yeh4,7,8,9
1Faculty of Medicine, Can Tho University of Medicine and Pharmacy, Can Tho, Viet Nam.
Objectives:
Unplanned pneumonia readmissions increase patient morbidity, mortality and healthcare costs. Among pneumonia patients, the middle-aged and elderly (≥45 years old) have a significantly higher risk of readmission compared with the young. Given that the 14-day readmission rate is considered a healthcare quality indicator, this study is the first to develop survival machine learning (ML) models using emergency department (ED) data to predict 14-day readmission risk following pneumonia-related admissions.
Design:
A retrospective multicentre cohort study.
Setting:
This study used the Taipei Medical University Clinical Research Database, including data from patients at three affiliated hospitals.
Participants:
11 989 hospital admissions for pneumonia among patients aged ≥45 years admitted from 2014 to 2021.
Primary And Secondary Outcome Measures:
The dataset was randomly split into training (80%), validation (10%) and independent test (10%) sets. Input features included demographics, comorbidities, clinical events, vital signs, laboratory results and medical interventions. Four survival ML models-CoxNet, Survival Tree, Gradient Boosting Survival Analysis and Random Survival Forest-were developed and compared on the validation set. The best performance model was tested on the independent test set.
Results:
The RSF model outperformed the other models. Validation on an independent test set confirmed the model's robustness (C-index=0.710; AUC=0.693). The most important predictive features included creatinine levels, age, haematocrit levels, Charlson Comorbidity Index scores, and haemoglobin levels, with their predictive value changing over time.
Conclusions:
The RSF model effectively predicts 14-day readmission risk among pneumonia patients. The ED data-based model allows clinicians to estimate readmission risk before ward admission or discharge from the ED, enabling timely interventions. Accurately predicting short-term readmission risk might also further support physicians in designing the optimal healthcare programme and controlling individual medical status to prevent readmissions.
More Related Videos
07:13Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
Published on: April 18, 2025
12:18A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Related Concept Videos
01:30Pneumonia I: Introduction
Risk Factors
Various factors influence the likelihood of developing pneumonia. Age plays a crucial role, with infants, children under two, and individuals over 65 at increased risk due to their...
01:30Pneumonia III: Complications and Assessment
01:30Pneumonia V: Nursing management and Prevention
The nurse must practice strict medical asepsis and adhere to infection control guidelines to minimize healthcare-associated infections.
Enhance airway patency
Position the patient correctly to facilitate drainage of the affected lung segments. Manual or mechanical percussion and vibration can also be employed....
01:28Pneumonia IV: Management
Bacterial Pneumonia Treatment
For bacterial pneumonia, antibiotics serve as the cornerstone of therapy. Initial treatment often begins with empirical antibiotics, tailored to the anticipated causative organism and adjusted based on culture results. Key antibiotic choices include:
01:18Steps in Outbreak Investigation
01:25Statistical Methods for Analyzing Epidemiological Data