Abdominal perfusion pressure is critical for survival analysis in patients with intra-abdominal hypertension: mortality prediction using incomplete data.

BACKGROUND

Abdominal perfusion pressure (APP) is a salient feature in the design of a prognostic model for patients with intra-abdominal hypertension (IAH). However, incomplete data significantly limits the size of the beneficiary patient population in clinical practice. Using advanced artificial intelligence methods, the authors developed a robust mortality prediction model with APP from incomplete data.

METHODS

The authors retrospectively evaluated the patients with IAH from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. Incomplete data were filled in using generative adversarial imputation nets (GAIN). Lastly, demographic, clinical, and laboratory findings were combined to build a 7-day mortality prediction model.

RESULTS

The authors included 1354 patients in this study, of which 63 features were extracted. Data imputation with GAIN achieved the best performance. Patients with an APP <60 mmHg had significantly higher all-cause mortality within 7-90 days. The difference remained significant in long-term survival even after propensity score matching (PSM) eliminated other mortality risks between groups. Lastly, the built machine learning model for 7-day modality prediction achieved the best results with an AUC of 0.80 in patients with confirmed IAH outperforming the other four traditional clinical scoring systems.

CONCLUSIONS

APP reduction is an important survival predictor affecting the survival prognosis of patients with IAH. The authors constructed a robust model to predict the 7-day mortality probability of patients with IAH, which is superior to the commonly used clinical scoring systems.