Aims Upper gastrointestinal endoscopy (UGIE) is the mainstay for diagnosing major organic
diseases of the upper GI tract including esophageal and gastric cancer, as well as
peptic ulcer and Barrett’s esophagus (BE). However, its efficacy and availability
are hampered by inappropriate and over-prescriptions.
Aim was to derive a predictive model for major organic diseases of the upper GI tract
to risk-stratify patients before prescribing UGIE.
Methods Retrospective analysis of prospectively collected data through endoscopy reporting
system (Tesi EndoxWeb) at our center, including adult patients undergoing UGIE for
clinical indication. A predictive model for major organic diseases of the upper GI
tract was derived based on indication to UGIE and patients’ demographics through logistic
regression, computing odds ratio (OR) with 95% confidence interval (95%CI) and its
discriminant power assessed through area under ROC curve (AUROC), whereas calibration
was assessed through calibration plot. Two risk categories were defined, i.e. low-risk
(< 9%), and high-risk (≥ 9%). The number needed to scope (NNS) was computed.
Results Overall, 3254 patients who underwent UGIE were included. Mean patient age was 64 + 16
years, and 56% were female. Major organic disease was found in 330 (10%) patients,
including 23 esophageal or gastric cancer, 255 peptic ulcers, and 65 BE cases. The
best performing predictive model included age≥70 years (OR 1.65, 95%CI 1.15-2.38),
male sex (OR 1.94, 95%CI 1.53-2.45) and the presence of at least one alarm feature
including dysphagia, vomiting, anemia, or weight loss (OR 2.74, 95%CI 2.14-3.49).
The model discriminative performance was fair (AUROC 0.68, 95%CI 0.65-0.71), whereas
calibration was good according to visual inspection of calibration plot. The NNS was
19 in the low-risk group and 7 in the high-risk group.
Conclusions We derived a simple prediction tool to risk-stratify patients before prescribing
UGIE, with two-fold implication, i.e., to help clinicians in appropriate UGIE prescription,
and to help healthcare providers defining the priority of UGIE and optimizing resource
allocation.