Predicting surgical outcomes of acute diffuse peritonitis: Updated risk models based on real‐world clinical data

Abstract

Aim

The existing predictive risk models for the surgical outcome of acute diffused peritonitis (ADP) need renovation by adding relevant variables such as ADP's definition or causative etiology to pursue outstanding data collection reflecting the real world. We aimed to revise the risk models predicting mortality and morbidities of ADP using the latest Japanese Nationwide Clinical Database (NCD) variable set.

Methods

Clinical dataset of ADP patients who underwent surgery, and registered in the NCD between 2016 and 2019, were used to develop a risk model for surgical outcomes. The primary outcome was perioperative mortality.

Results

After data cleanup, 45 379 surgical cases for ADP were derived for analysis. The perioperative and 30‐day mortality were 10.6% and 7.2%, respectively. The prediction models have been created for the mortality and 10 morbidities associated with the mortality. The top five relevant predictors for perioperative mortality were age >80, advanced cancer with multiple metastases, platelet count of <50 000/mL, serum albumin of <2.0 g/dL, and unknown ADP site. The C‐indices of perioperative and 30‐day mortality were 0.859 and 0.857, respectively. The predicted value calculated with the risk models for mortality was highly fitted with the actual probability from the lower to the higher risk groups.

Conclusions

Risk models for postoperative mortality and morbidities with good predictive performance and reliability were revised and validated using the recent real‐world clinical dataset. These models help to predict ADP surgical outcomes accurately and are available for clinical settings.

Abstract

Risk models for predicting postoperative surgical outcomes of acute diffused peritonitis (ADP) were created and validated using the recent real‐world clinical dataset of the Japanese Nationwide Clinical Database (NCD). Evaluation of predictive power and calibration of these risk models showed they were sufficiently reliable to be used in clinical settings.