2.6 GI complication risk score
The derivation cohort was randomly divided into internal training (70%)
and internal hold-out validation (30%) datasets [14]. Candidate
risk predictors with p>0.10 in the univariate logistic
regression using the internal training dataset were included in the
multivariate analysis. We performed three repeated five-fold
cross-validations for predictor variable selection [14]. This was
based on the principle of randomly dividing the dataset into five equal
subsamples and using four subsamples for training and the remaining
subsample for the test [13]. As each of the five subsamples was used
once during the cross-validation process, the analysis was performed
five times per cross-validation. Fifteen analyses were performed when
this was repeated three times. This process has also been used for
internal cross-validation [15].
After selecting the final predictors through model optimization, the
regression coefficient for the risk factors in the final model was used
to calculate the integer assigned to the risk prediction score [13].
The integer points closest to each regression coefficient ×10 was chosen
for each risk factor [6]. Individual risk was based on the sum of
the weighted scores for each assigned risk factor score [13]. For
use in clinical decision-making, we derived a cut-off value for the risk
prediction score to distinguish between high-risk and non-high-risk
groups based on the Youden index [16].
We used an internal hold-out validation and an external validation
dataset to validate the final model. The discrimination of the
prediction score was evaluated by calculating the Area under the
receiver operating characteristic (ROC) curves (AUROC) [13].
Calibration of the model for comparing the predicted and observed risks
was evaluated using the Hosmer–Lemeshow goodness-of-fit test and
calibration plots [13].
After applying the prediction model to the external validation dataset,
we classified patients with a risk score greater than or equal to the
cut-off value as high-risk cases. We then evaluated predictors of
high-risk cases.