Geriatric Risk Prediction Models for Major Gastroenterological Surgery using the National Clinical Database in Japan: A Multicenter Prospective Cohort Study

Dynamic Nomogram for Predicting the Risk of Perioperative Neurocognitive Disorders in Adults

BACKGROUND

Simple and rapid tools for screening high-risk patients for perioperative neurocognitive disorders (PNDs) are urgently needed to improve patient outcomes. We developed an online tool with machine-learning algorithms using routine variables based on multicenter data.

METHODS

The entire dataset was composed of 49,768 surgical patients from 3 representative academic hospitals in China. Surgical patients older than 45 years, those undergoing general anesthesia, and those without a history of PND were enrolled. When the patient's discharge diagnosis was PND, the patient was in the PND group. Patients in the non-PND group were randomly extracted from the big data platform according to the surgical type, age, and source of data in the PND group with a ratio of 3:1. After data preprocessing and feature selection, general linear model (GLM), artificial neural network (ANN), and naive Bayes (NB) were used for model development and evaluation. Model performance was evaluated by the area under the receiver operating characteristic curve (ROCAUC), the area under the precision-recall curve (PRAUC), the Brier score, the index of prediction accuracy (IPA), sensitivity, specificity, etc. The model was also externally validated on the multiparameter intelligent monitoring in intensive care (MIMIC) Ⅳ database. Afterward, we developed an online visualization tool to preoperatively predict patients' risk of developing PND based on the models with the best performance.

RESULTS

A total of 1051 patients (242 PND and 809 non-PND) and 2884 patients (6.2% patients with PND) were analyzed on multicenter data (model development, test [internal validation], external validation-1) and MIMIC Ⅳ dataset (external validation-2). The model performance based on GLM was much better than that based on ANN and NB. The best-performing GLM model on validation-1 dataset achieved ROCAUC (0.874; 95% confidence interval [CI], 0.833-0.915), PRAUC (0.685; 95% CI, 0.584-0.786), sensitivity (72.6%; 95% CI, 61.4%-81.5%), specificity (84.4%; 95% CI, 79.3%-88.4%), Brier score (0.131), and IPA (44.7%), and of which the ROCAUC (0.761, 95% CI, 0.712-0.809), the PRAUC (0.475, 95% CI, 0.370-0.581), Brier score (0.053), and IPA (76.8%) on validation-2 dataset. Afterward, we developed an online tool (https://pnd-predictive-model-dynnom.shinyapps.io/ DynNomapp/) with 10 routine variables for preoperatively screening high-risk patients.

CONCLUSIONS

We developed a simple and rapid online tool to preoperatively screen patients' risk of PND using GLM based on multicenter data, which may help medical staff's decision-making regarding perioperative management strategies to improve patient outcomes.

Surgical outcomes in gastroenterological surgery in Japan: Report of the National Clinical Database 2011-2019

BACKGROUND

We aimed to present the 2019 annual report of the gastroenterological section of the National Clinical Database (NCD).

METHODS

We reviewed 609,589 cases recorded in 2019 and 5,029,764 cases registered from 2011 to 2019 for the 115 selected gastroenterological surgical procedures.

RESULTS

The main features of gastroenterological surgery in Japan were similar to those described in the 2018 annual report, namely, that 1) operative numbers gradually increased in all procedures, except gastrectomy and hepatectomy, which decreased in these years; 2) in all eight major gastroenterological surgeries, the age distribution tended toward older patients; 3) the morbidity of esophagectomy, hepatectomy, and pancreaticoduodenectomy increased, but mortality was minimized in all procedures; 4) all eight major gastroenterological procedures have increasingly been performed under laparoscopy; and 5) board-certified surgeons were increasingly involved. These trends in recent years were more prominent in 2019.

CONCLUSIONS

Thanks to the continuous cooperation and dedication of the surgeons, medical staff, and surgical clinical reviewers who registered the clinical data into the NCD, it is possible to understand the comprehensive landscape of surgery in Japan and to disclose new evidence in this field. The Japanese Society of Gastroenterological Surgery will continue to promote the value of this database and encourage the use of feedback and clinical studies using the NCD, now and in the future. Generating further approaches to surgical quality improvement are important directions for future research.