Postoperative mid-to-long-term adverse event prediction model for patients receiving non-cardiac surgery: An extension of the Simple Postoperative AKI RisK (SPARK) model

  • Kwon, Soie; 
  • Park, Sehoon; 
  • Yang, Sunah; 
  • Shin, Chaiho; 
  • Lee, Jeonghwan; 
  • 외 10명
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초록

Background Postoperative acute kidney injury (PO-AKI) is a critical complication of adverse kidney outcomes, both short and long-term. We aimed to expand our pre-existing PO-AKI prediction model to predict mid-to long-term adverse kidney outcomes. Methods We included patients who underwent major non-cardiac surgeries from the original SPARK cohort, two external validation cohorts, and a temporal validation cohort. Mid-to-long-term adverse kidney outcomes were defined as end-stage kidney disease progression or death within 1 or 3 years after surgery. We verified and tuned the original Simple Postoperative AKI RisK (SPARK) model to predict mid-to-long-term adverse kidney events. Results We included 33 636 patients in development, 71 232 patients in external validation, and 33 944 patients in temporal validation cohorts, respectively. One- and 3-year adverse kidney events occurred in 5.5% and 13.2% in the development cohort, respectively. The original SPARK score demonstrated an acceptable discriminative power for 1-year and 3-year adverse outcome risks with C indices mostly >0.7. However, the power was relatively poor when restricted to high-risk patients or those who actually developed PO-AKI. When we re-calculated the regression coefficients from a Cox model, the discriminative performances were better, especially for those with high-risk characteristics (e.g. 1-year outcome, C-index 0.72 in developmental and 0.73‒0.77 in validation datasets). Furthermore, when the model integrated the PO-AKI stage and history of malignancy with the SPARK variables, the performance was significantly enhanced (1-year, C-index 0.86 in development and 0.86‒0.88 in validation results). With the above findings, we constructed an online postoperative risk prediction system (https://snuhnephrology.github.io/postop/). Conclusions The addition of two clinical factors and recalibration of SPARK variables significantly improved mid-to-long-term postoperative risk prediction for mortality or dialysis after non-cardiac surgery. Our calculator helps clinicians easily predict a mid-to-long-term risk and PO-AKI occurrence by entering a few variables.

키워드

acute kidney injury; forecasting; long term adverse effect; postoperative; ACUTE KIDNEY INJURY; ACUTE-RENAL-FAILURE; CARDIAC-SURGERY; END-POINTS; VALIDATION; MORTALITY; SURVIVAL
제목
Postoperative mid-to-long-term adverse event prediction model for patients receiving non-cardiac surgery: An extension of the Simple Postoperative AKI RisK (SPARK) model
저자
Kwon, Soie; Park, Sehoon; Yang, Sunah; Shin, Chaiho; Lee, Jeonghwan; Ryu, Jiwon; Kim, Sejoong; Cho, Jeong Min; Yoon, Hyung-Jin; Kim, Dong Ki; Joo, Kwon Wook; Kim, Yon Su; Park, Minsu; Kim, Kwangsoo; Lee, Hajeong
DOI
10.1093/ckj/sfaf045
발행일
2025-05
유형
Article
저널명
CKJ: Clinical Kidney Journal
권
18
호
5

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