Table 4 Summary of studies that evaluated factors and scoring system in predicting endoscopic intervention and outcome in UGIB
Author (year) | Factors/model | Country | No. of factors | Derivative N (validation N) | Data collection | Intervention (%) | AUC Model | AUC GBS | Limitation |
|---|---|---|---|---|---|---|---|---|---|
Lee CH [31] | Hematemesis, INR | Korea | 2 | 270 (N/A) | 2013–2017 | 59.0 | N/A | N/A | OR 2.0–2.5 |
Kim SS [32] | Hb, bloody NG | Korea | 2 | 613 (N/A) | 2009–2013 | 53.7 | N/A | N/A | OR 2.68, P < 0.001 |
Redondo CE [34] | MAP(ASH) | Spain | 6 | 547 (3012) | 2013–2017 | 40.8 | 0.61 (0.69)* | 0.75 | Fair performance |
Veisman I [30] | Machine learning model | Israel | 25 | 883 (N/A) | 2012–2018 | 16.4 | 0.68 | 0.64 |  |
Ito N [35] | Nagoya University Score | Japan | 4 | 509 (160) | 2016–2018 | 34.0 | 0.78 | 0.62 | Exclude variceal bleeding |
Sasaki Y [36] | H3B2 score | Japan | 5 | 675 (N/A) | 2015–2019 | 34.5 | 0.73 | 0.72 | Exclude variceal bleeding |
Marks I [37] | London Hemostat Score | UK | 6 | 466 (404) | 2015–2020 | 25.0 | 0.82 (0.8)* | 0.72 | Need calculation of positive and negative score |
Acehan F [33] | Syncope, MAP, BUN | Türkiye | 3 | 406 (N/A) | 2019–2022 | 30.3 | 0.65 | 0.60 | Exclude variceal bleeding |