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Construction and Verification of A Prediction Model for Stranded Risk Nomograms in Emergency Department Patients

This study developed and validated a nomogram model using data from 25,391 emergency department patients to effectively predict the risk of prolonged stranded duration, thereby offering a practical tool for managing ED overcrowding.

Original authors: Manman SHANG, Qin WEI

Published 2026-07-01

📖 5 min read🧠 Deep dive

CC BY 4.0

Original authors: Manman SHANG, Qin WEI

Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

Imagine the Emergency Department (ED) as a busy, high-stakes train station. People arrive with all sorts of "tickets" (medical conditions), hoping to catch a train to the right destination (a hospital bed, a specialist, or going home). Sometimes, the station gets so crowded that passengers get "stranded" on the platform for hours, waiting for a train that hasn't arrived yet.

This paper is like a detective report from a specific train station (Fujian Medical University Union Hospital) that tried to figure out why some passengers get stuck for a long time and how to predict who will be stuck before they even arrive.

Here is the story of their investigation, broken down simply:

1. The Mission: Predicting the "Stranded"

The researchers looked at over 25,000 patients who visited the ER between 2021 and 2023. They wanted to solve a puzzle: What makes a patient stay in the ER for more than 16 hours? (They called this "stranded time").

To solve this, they split the data into two groups:

The "Normal" Group: Patients who got through in 16 hours or less.
The "Stranded" Group: Patients who waited longer than 16 hours.

They excluded data from early 2023 because a massive wave of pneumonia cases (due to the pandemic) would have messed up their math, like trying to measure traffic flow during a massive parade.

2. The Clues: What Makes People Wait?

The researchers acted like detectives, looking for clues that separated the "quick exits" from the "long waits." They found several key factors that acted like heavy backpacks, slowing patients down:

How You Arrive: If you walked in or were carried by family, you were more likely to wait longer. Interestingly, if you arrived by ambulance (120), you actually got through faster. It's like having a VIP pass; the ambulance crew gets you priority treatment immediately.
Your Age: Older passengers tended to wait longer. As we get older, our bodies are more complex, and it takes more time to figure out the right train to catch.
The "Triage" Ticket: When you arrive, a nurse gives you a "ticket" based on how sick you are (Grade I is critical, Grade IV is minor). Surprisingly, patients with less urgent tickets (Grade III and IV) waited longer than the critical ones. Why? Because the critical patients get immediate attention, while the "not quite urgent" ones get stuck in a backlog.
Your Medical History: Certain diseases were like heavy luggage. Patients with infections, tumors, or blood disorders waited the longest. These conditions often require many tests and careful observation.
The "Test" Load: If a patient needed a lot of tests (more than 12 different blood work items) or many scans (more than 2 X-rays/CTs), they were much more likely to be stranded. It's like having to go through ten different security checkpoints before you can board.
Time of Day: The station gets most crowded during the day (8 AM to midnight). If you arrive then, you are more likely to wait. The night shift (midnight to 8 AM) was surprisingly faster.
The "Regulars": People who visit the ER four or more times a year were slightly more likely to be stranded, perhaps because their conditions are complex or chronic.

3. The Solution: The "Stranded Risk" Scorecard (Nomogram)

Once they figured out the clues, the researchers built a prediction tool called a Nomogram.

Think of this Nomogram as a customized weather forecast for your ER visit.

Instead of predicting rain, it predicts "stranding risk."
You take a patient's details (Age? Yes. Arrived by ambulance? No. Needs 15 blood tests? Yes.) and plug them into the scorecard.
The scorecard adds up points and gives a percentage chance: "This patient has an 80% chance of being stranded for more than 16 hours."

4. Did the Scorecard Work?

The researchers tested their scorecard in two ways:

Internal Test: They checked it against the data they used to build it. It worked well, correctly identifying about 75% of the risks.
External Test: They tried it on a new group of patients from a different time period. It still worked well, proving it wasn't just a lucky guess.

They also used a "Decision Curve" (a fancy way of saying "Is this useful in real life?"). The result was yes: if a doctor uses this tool, they can make better decisions about managing the crowd.

5. The Big Takeaway

The paper concludes that this Nomogram is a reliable tool. It helps hospital managers and doctors see the "traffic jam" before it happens. By knowing who is likely to get stuck, they can potentially rearrange resources to clear the platform faster.

In short: The researchers turned a chaotic, crowded ER into a predictable system by creating a simple scorecard that tells them, "Hey, this patient is likely to wait a long time because they are older, arrived by walking, and need a lot of tests." This helps the station run smoother for everyone.

Technical Summary: Construction and Verification of a Prediction Model for Stranded Risk Nomograms in Emergency Department Patients

Problem Statement
Emergency Departments (EDs) globally face increasing pressure due to rising patient volumes and resource constraints, leading to prolonged patient "stranded" times (extended stays within the ED before transfer, discharge, or death). This congestion compromises patient safety, increases medical costs, and strains human resources. While risk warning models exist, there is a need for specific, validated tools to predict the likelihood of patient stranding in the ED to facilitate early intervention and better resource allocation. This study addresses the gap by investigating risk factors for ED stranded duration and developing a predictive nomogram model.

Methodology
The study employed a retrospective analysis of 25,391 patients treated at the Emergency Department of Fujian Medical University Union Hospital between January 2021 and August 2023.

Data Partitioning: The dataset was split into a modeling group (19,057 patients from Jan 2021–Oct 2022) for factor identification and model construction, and a validation group (6,334 patients from Mar–Aug 2023) for performance evaluation.
Exclusion Criteria: Data from Nov 2022–Feb 2023 were excluded to avoid bias from the COVID-19 pneumonia surge. Patients with missing critical data (e.g., duration of stay, diagnosis, arrival mode) were also excluded.
Variable Definition: The dependent variable was "stranded time," categorized into a normal group (≤16 hours) and a stranded group (>16 hours) based on the median. Thirteen independent variables were analyzed across three categories:
1. Patient Factors: Age, gender, arrival method, and frequency of ED visits.
2. Disease Factors: Pre-examination triage level, Modified Early Warning Score (MEWS), ICD-10 diagnosis classification, and number of test/examination items.
3. Institutional Factors: Diversion destination, specialty attribution, treatment time period, and adherence to standard waiting times.
Statistical Analysis: Univariate analysis (Chi-square and rank-sum tests) identified significant factors. Binary logistic regression (backward elimination) was used to determine independent predictors. A nomogram was constructed using R software (rms package).
Model Evaluation: Performance was assessed using the Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC), calibration curves, goodness-of-fit tests, and Decision Curve Analysis (DCA) for clinical utility.

Key Contributions

Identification of Predictors: The study identified specific independent factors influencing ED stranding, including arrival method, triage level, specialty, time period, wait-time compliance, ICD-10 diagnosis, and the volume of tests performed.
Nomogram Development: A visual prediction tool (nomogram) was constructed to calculate the probability of a patient being stranded (>16 hours) based on the cumulative score of the identified risk factors.
Validation: The model underwent rigorous internal and external validation, demonstrating consistent performance across different patient cohorts.

Results

Descriptive Statistics: The median stranded time for the cohort was 15.69 hours (IQR: 5.64–26.73). Approximately 50.54% of patients stayed ≤16 hours, while 17.92% stayed >32 hours.
Regression Findings: Multivariate analysis confirmed that arrival method (e.g., walking vs. ambulance), triage level (Grades III and IV showed higher risk than Grade I), specific disease categories (e.g., respiratory diseases, tumors, infectious diseases), and institutional factors (e.g., non-compliance with standard waiting times) were significant independent predictors.
Model Performance:
- Discrimination: The model achieved an AUC of 0.748 in the internal validation (modeling group) and 0.732 in the external validation group.
- Calibration: Goodness-of-fit tests indicated a strong alignment between predicted and actual incidence rates (Internal $\chi^2=0.086, p=0.931$ ; External $\chi^2=0.193, p=0.847$ ).
- Clinical Utility: Decision Curve Analysis (DCA) demonstrated that the nomogram provided a higher net benefit than alternative strategies when the threshold probability for predicting prolonged stay was between 0.35 and 0.65 (internal) and 0.35 and 0.60 (external).

Significance and Claims
The authors claim that the developed nomogram is a validated tool that effectively predicts the risk of patient stranding in the ED. The study posits that this model offers practical decision support for managing ED overcrowding. By enabling medical staff to accurately assess stranding risks and providing early warnings, the tool aims to facilitate timely interventions. Furthermore, the model is presented as a resource for ED managers to better understand operational dynamics, optimize staffing, and improve human resource allocation and quality management. The study concludes that the nomogram serves as a valuable instrument for clinical emergency response efforts.