Risk Model for Preoperative Anxiety in Surgical Patients, 2021-2024
by Cheng Wu·Updated 1mo ago
15.3 KB1files
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Description
Cheng Wu's study presents a multivariable risk stratification model for preoperative anxiety, developed from 425 adult elective surgery patients at a tertiary teaching hospital in Southwest China between January 2021 and October 2024. The model, with an area under the ROC curve of 0.848, identifies factors like sex, BMI, ASA status, sleep quality, and smartphone use. The dataset includes demographic, clinical, perioperative, psychological, behavioral, and social variables extracted from electronic medical records.
Use Cases
Developing risk prediction models for preoperative anxiety based on identified factors like sex, BMI, and sleep quality.
Analyzing the association between behavioral factors such as smartphone use duration and clinical anxiety outcomes.
Validating clinical prediction models using the reported discrimination and calibration metrics.
Studying psychological and social determinants of surgical outcomes in an elective surgery population.
Strengths
Model performance is quantitatively reported with an area under the ROC curve of 0.848 and sensitivity/specificity metrics.
Data covers multiple variable domains including demographic, clinical, psychological, behavioral, and social factors.
Study includes 425 patient records with a clear outcome prevalence (39.5% meeting anxiety criteria).
Limitations
Row count and column-level documentation are unknown, which may limit suitability assessment.
Data is from a single center in Southwest China, which may limit generalizability.
The primary file format is DOCX, which may require extraction or conversion for analysis.
Provenance
Source
Cheng Wu via figshare.
Collection Method
Retrospective observational study extracting data from electronic medical records and routine preoperative assessments.
Time Range
January 2021 to October 2024.
Freshness
Last updated 2026-04-21 05:35:18; freshness should be verified.
Geography
A tertiary teaching hospital in Southwest China.
Dataset is very small (15.3 KB). The primary data is likely contained within a DOCX document describing the study and model, which may require parsing to access structured data.