Construction and Validation of a Prediction Model for All - Cause Mortality Within 30 Days After Surgery in Critically Ill Patients Undergoing Emergency Gastrointestinal Surgery

Plain-language summary

Gastrointestinal surgeries are crucial for treating digestive tract diseases. However, they often lead to high postoperative infection rates due to long durations, bacterial translocation, weakened immunity, and reduced intestinal function post-surgery. This not only impacts surgical outcomes and patient recovery but also extends hospital stays and increases financial burdens. In severe instances, it can even result in sepsis and death. The mortality risk for emergency gastrointestinal surgeries ranges from 15% to 25%. Existing scoring systems like APACHE - Ⅱ and SOFA, designed mainly for ICU patients, are insufficient for predicting the death risk of emergency gastrointestinal surgery patients. Some machine learning models for common gastric and colorectal cancer patients lack independent prospective validation. To overcome these limitations, this study at the First Hospital of Jilin University aims to construct and validate a model predicting all - cause mortality within 30 days post - emergency gastrointestinal surgery. The study proceeds in two phases. The retrospective development phase examines patients who underwent emergency gastrointestinal surgery from July 2019 to July 2024. Data is collected via the electronic medical record system, and eligible patients form the development cohort for model building. The prospective validation phase, planned to last 5 months for patient enrollment and 30 days for follow - up, is part of a half - year study. Inclusion criteria involve patients over 18, undergoing emergency gastrointestinal surgery (ICD - 10), and meeting specific critical conditions post - surgery. Exclusion criteria include superficial surgeries, significant data loss, intraoperative death, multiple injuries, and participation in other studies. Sample size calculation, based on methods by Harrell et al. and Peduzzi et al., requires at least 80 patients with events. With a 15% event incidence, the training set needs about 534 cases, the validation set 229, for a total of 763 cases (7:3 ratio). An additional 100 cases are for external validation. Investigated factors include demographics, medical history, preoperative, intraoperative, and postoperative indicators, plus pathology. The primary endpoint is 30 - day all - cause mortality, and the secondary is 30 - day postoperative complications, assessed by Clavien - Dindo classification. Data management involves CRC and double - entry. Analysis uses SPSS 25.0 and R 4.0.2. Bias is controlled through surgeon screening and surgical quality evaluation. The study has ethical approval, and patients provide informed consent. This research aims to offer clinicians a reliable model for early high - risk patient identification and precise interventions, ultimately enhancing patient outcomes.

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