Background: The Rapid Emergency Medicine Score (REMS) and Worthing Physiological Scoring system (WPS) have been developed for predicting in-hospital mortality in nonsurgical emergency department (ED) patients. The prognostic performance of the scoring systems in independent populations has not been clear. The aim of the study is to evaluate the prognostic accuracy of REMS and WPS systems in the estimation of 30-day mortality risk among medical patients in ED.
Methods: The study was designed as a prospective investigation, with the setting being the ED of the National Hospital of Can Tho, Vietnam. We enrolled medical patients aged 16+ years who met the study entry criteria. Clinical data were obtained as required for each scoring system. The primary outcome was mortality within 30 days since hospitalization. The association between each scoring system and mortality was assessed by the hazard ratio (HR) of the Cox's proportional hazard model.
Results: The study involved 1746 patients, average age 65.9 years (SD 17). During the period of follow-up, 172 patients (9.9 %) died. The risk of 30-day mortality was increased by 30 % for each additional REMS unit (HR: 1.28; 95 % confidence interval (CI): 1.23-1.34) and by 60 % for each additional WPS unit (HR: 1.6; 95 % CI: 1.5-1.7). The AUC of the REMS was 0.71 (95 % CI: 0.67-0.76) which was significantly lower than that of the WPS (0.80; 95 % CI: 0.76-0.83).
Conclusions: Both REMS and WPS have good prognostic value in the prediction of death in ED patients. The WPS appeared to have a better prognostic performance than the REMS system.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4457731 | PMC |
| http://dx.doi.org/10.1186/s12245-015-0066-3 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Autonomous International Classification of Diseases Coding Using Pretrained Language Models and Advanced Prompt Learning Techniques: Evaluation of an Automated Analysis System Using Medical Text.
JMIR Med Inform
January 2025
Medical Big Data Research Center, Chinese PLA General Hospital, Beijing, China.
Background: Machine learning models can reduce the burden on doctors by converting medical records into International Classification of Diseases (ICD) codes in real time, thereby enhancing the efficiency of diagnosis and treatment. However, it faces challenges such as small datasets, diverse writing styles, unstructured records, and the need for semimanual preprocessing. Existing approaches, such as naive Bayes, Word2Vec, and convolutional neural networks, have limitations in handling missing values and understanding the context of medical texts, leading to a high error rate.
View Article and Find Full Text PDFDeep Learning Reconstruction for Enhanced Resolution and Image Quality in Breath-Hold MRCP: A Preliminary Study.
J Comput Assist Tomogr
November 2024
From the Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, Kumamoto, Japan.
Objective: This preliminary study aims to assess the image quality of enhanced-resolution deep learning reconstruction (ER-DLR) in magnetic resonance cholangiopancreatography (MRCP) and compare it with non-ER-DLR MRCP images.
Methods: Our retrospective study incorporated 34 patients diagnosed with biliary and pancreatic disorders. We obtained MRCP images using a single breath-hold MRCP on a 3T MRI system.
Prostate Cancer Risk Prediction Model Using Clinical and Magnetic Resonance Imaging-Related Findings: Impact of Combining Lesions' Locations and Apparent Diffusion Coefficient Values.
J Comput Assist Tomogr
November 2024
From the Department of Radiology, Mayo Clinic, Rochester, MN.
Objectives: The aims of the study are to develop a prostate cancer risk prediction model that combines clinical and magnetic resonance imaging (MRI)-related findings and to assess the impact of adding Prostate Imaging-Reporting and Data System (PI-RADS) ≥3 lesions-level findings on its diagnostic performance.
Methods: This 3-center retrospective study included prostate MRI examinations performed with clinical suspicion of clinically significant prostate cancer (csPCa) between 2018 and 2022. Pathological diagnosis within 1 year after the MRI was used to diagnose csPCa.
Veterans May Be Seeing Lower-Quality Clinicians In The VHA Community Care Network.
Health Aff (Millwood)
January 2025
Thomas C. Tsai Harvard University and Brigham and Women's Hospital.
With the rapid expansion of veterans' access to community care under the Veterans Affairs Maintaining Internal Systems and Strengthening Integrated Outside Networks (VA MISSION) Act of 2018, ensuring that veterans receive high-quality community care has become a national priority. Using Veterans Health Administration (VHA) data and Medicare performance scores, we assessed how clinicians' performance on quality measures differed between those who treated veterans within the VHA Community Care Network and those who did not. We found that in 2022, 66.
View Article and Find Full Text PDFCombining Patient Survey Data With Diagnosis Codes Improved Medicare Advantage Risk-Adjustment Accuracy.
Health Aff (Millwood)
January 2025
David J. Meyers, Brown University.
Under the current Medicare Advantage (MA) risk-adjustment system, plans are incentivized to report diagnosis codes on enrollees' medical claims reflecting additional and more severe health conditions to increase enrollees' risk scores and corresponding plan payments. To improve the integrity of risk adjustment, researchers have proposed four alternative methods to construct risk scores: calculate Hierarchical Condition Categories (HCC) scores excluding diagnosis codes from health risk assessments and chart reviews, calculate HCC scores excluding diagnosis codes most subject to score inflation, use pharmaceutical claims alone, and use self-reported survey responses alone or in combination with diagnosis codes. Using 2016-19 medical and pharmaceutical claims linked to Consumer Assessment of Healthcare Providers and Systems survey responses from 151,432 MA enrollees, we compared the predictive accuracy of each alternative strategy with the standard HCC approach.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!