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Machine learning-based diagnostic model construction and validation for primary thoracogenic chest pain

Machine learning-based diagnostic model construction and validation for primary thoracogenic chest pain

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2500105074
Enrollment
Unknown
Registered
2025-06-27
Start date
2025-09-01
Completion date
Unknown
Last updated
2025-06-30

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Primary thoracogenic chest pain

Interventions

Gold Standard:After excluding diseases such as cardiac chest pain, pulmonary chest pain, digestive tract chest pain, and herpes zoster neuralgia, the diagnosis of this disease can be made based on par
Index test:AI Diagnosis Model for Primary Thoracic Pain Originating from the Thoracic

Sponsors

Shenzhen Third People's Hospital
Lead Sponsor

Eligibility

Sex/Gender
All
Age
18 Years to No maximum

Inclusion criteria

Inclusion criteria: 1. The patient complains of chest pain or chest and back discomfort; 2. Be over 18 years old; 3. Those with chest pain whose cause is not clear.

Exclusion criteria

Exclusion criteria: 1. Combined with structural heart disease (coronary heart disease, heart failure, myocardial infarction, etc.); 2. Patients with new-onset reflux esophagitis or peptic ulcer; 3. Patients with pulmonary infection or pleurisy; 4. Those with a recent history of herpes zoster on the neck and chest wall; 5. New onset of neck and chest fractures, tumors, tuberculosis, infections and other diseases; 6. Suffering from rheumatic immune diseases, etc.; 7. Patients with severe abnormal liver and kidney function; 8. Patients with abnormal coagulation function; 9. Patients with skin infection at the puncture site; 10. Those who refuse to receive diagnostic treatment; 11. Those who are unable to cooperate due to various reasons; 12. Lost to follow-up.

Design outcomes

Primary

MeasureTime frame
Sensitive;Specific;Positive predictive value;Negative predictive value;AUC-ROC;Kappa coefficient;Visual analogue scale score;Short Form (36) Health Survey Score;

Countries

China

Contacts

Public ContactZha Xiaoliang

Shenzhen Third People's Hospital

784104863@qq.com+86 137 3927 7467

Outcome results

None listed

Source: ChiCTR (via WHO ICTRP) · Data processed: Feb 4, 2026