Modelo de Predicción de Riesgo de Lesión Renal Aguda Postoperatoria en una Población Quirúrgica Amplia

Postoperative acute kidney injury (AKI) significantly impacts patient recovery, increasing morbidity, mortality, and healthcare costs. This study aims to develop and validate a supervised learning model to predict the risk of AKI in patients undergoing surgical procedures, utilizing data from the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP).

ACS-NSQIP data were collected from 12 hospitals in the Tennessee Surgical Quality Collaborative (TSQC) from 2020 to 2023. The dataset was divided into training (2020-2022) and temporal validation (2023) sets, with an external validation set from the 2023 ACS-NSQIP Participant Use Data File (PUF). Patients with a history of dialysis and ASA classification of 5 were excluded. AKI was defined based on postoperative renal insufficiency or dialysis within 30 days of surgery.

The study included 59,706 surgical cases in the training set and 980,323 cases in the external validation set, with AKI incidences of 1.8% and 2.4%, respectively. An additive logistic model was selected as the most parsimonious, achieving an AUC of 0.87-0.88 in both validation sets (Figure1). Key predictors included inpatient status, ascites, renal failure, preoperative creatinine, sepsis, American Society of Anesthesiologists (ASA) classification, and age (Figure 2).

The model demonstrated excellent discrimination and calibration, particularly in the temporal validation set. The model has potential to help identify high-risk patients preoperatively, guiding perioperative interventions to reduce AKI incidence and the associated healthcare costs. Further studies are needed to validate, refine, and study real-world applications of the model in diverse clinical settings.