An External Validation Study of the Oakland and Glasgow-Blatchford Scores for Predicting Adverse Outcomes of Acute Lower Gastrointestinal Bleeding in an Asian Population

Aims This study is aimed at (1) validating the performance of Oakland and Glasgow-Blatchford (GBS) scores and (2) comparing these scores with the SALGIB score in predicting adverse outcomes of acute lower gastrointestinal bleeding (ALGIB) in a Vietnamese population. Methods A multicenter cohort study was conducted on ALGIB patients admitted to seven hospitals across Vietnam. The adverse outcomes of ALGIB consisted of blood transfusion; endoscopic, radiologic, or surgical interventions; severe bleeding; and in-hospital death. The Oakland and GBS scores were calculated, and their performance was compared with that of SALGIB, a locally developed prediction score for adverse outcomes of ALGIB in Vietnamese, based on the data at admission. The accuracy of these scores was measured using the area under the receiver operating characteristic curve (AUC) and compared by the chi-squared test. Results There were 414 patients with a median age of 60 (48–71). The rates of blood transfusion, hemostatic intervention, severe bleeding, and in-hospital death were 26.8%, 15.2%, 16.4, and 1.4%, respectively. The SALGIB score had comparable performance with the Oakland score (AUC: 0.81 and 0.81, respectively; p = 0.631) and outperformed the GBS score (AUC: 0.81 and 0.76, respectively; p = 0.002) for predicting the presence of any adverse outcomes of ALGIB. All of the three scores had acceptable and comparable performance for in-hospital death but poor performance for hemostatic intervention. The Oakland score had the best performance for predicting severe bleeding. Conclusions The Oakland and SALGIB scores had excellent and comparable performance and outperformed the GBS score for predicting adverse outcomes of ALGIB in Vietnamese.


Introduction
Background and objectives 3a Explain the medical context (including whether diagnostic or prognostic) and rationale for developing or validating the multivariable prediction model, including references to existing models. 3b Specify the objectives, including whether the study describes the development or validation of the model or both. Methods

Source of data 4a
Describe the study design or source of data (e.g., randomized trial, cohort, or registry data), separately for the development and validation data sets, if applicable. 4b Specify the key study dates, including start of accrual; end of accrual; and, if applicable, end of follow-up.

Participants 5a
Specify key elements of the study setting (e.g., primary care, secondary care, general population) including number and location of centres. 5b Describe eligibility criteria for participants. 5c Give details of treatments received, if relevant.

Outcome 6a
Clearly define the outcome that is predicted by the prediction model, including how and when assessed. 6b Report any actions to blind assessment of the outcome to be predicted.

Predictors 7a
Clearly define all predictors used in developing or validating the multivariable prediction model, including how and when they were measured. 7b Report any actions to blind assessment of predictors for the outcome and other predictors. Sample size 8 Explain how the study size was arrived at.
Missing data 9 Describe how missing data were handled (e.g., complete-case analysis, single imputation, multiple imputation) with details of any imputation method.

Statistical analysis methods 10a
Describe how predictors were handled in the analyses.
10b Specify type of model, all model-building procedures (including any predictor selection), and method for internal validation.
10d Specify all measures used to assess model performance and, if relevant, to compare multiple models. Risk groups 11 Provide details on how risk groups were created, if done. Results

Participants 13a
Describe the flow of participants through the study, including the number of participants with and without the outcome and, if applicable, a summary of the follow-up time. A diagram may be helpful.
13b Describe the characteristics of the participants (basic demographics, clinical features, available predictors), including the number of participants with missing data for predictors and outcome.

Model development 14a
Specify the number of participants and outcome events in each analysis. 14b If done, report the unadjusted association between each candidate predictor and outcome.

Model specification 15a
Present the full prediction model to allow predictions for individuals (i.e., all regression coefficients, and model intercept or baseline survival at a given time point). 15b Explain how to the use the prediction model. Model performance 16 Report performance measures (with CIs) for the prediction model.

Limitations 18
Discuss any limitations of the study (such as nonrepresentative sample, few events per predictor, missing data).

Interpretation 19b
Give an overall interpretation of the results, considering objectives, limitations, and results from similar studies, and other relevant evidence.
Implications 20 Discuss the potential clinical use of the model and implications for future research.

Other information
Supplementary information 21 Provide information about the availability of supplementary resources, such as study protocol, Web calculator, and data sets. Funding 22 Give the source of funding and the role of the funders for the present study.
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