Scholarlink

Background <p>Cardiac surgery-associated acute kidney injury (AKI) remains a common postoperative complication and is associated with adverse short- and long-term outcomes. Conventional risk scores may have limited performance in complex and dynamically evolving postoperative courses. This study aimed to identify data-driven postoperative clinical states from time-series electronic medical record (EMR) data and characterize how state occupancy evolves before AKI onset.</p> Methods <p>We conducted a retrospective cohort study of adults undergoing major open-heart surgery at a tertiary cardiovascular center. AKI was defined using the modified Kidney Disease Improving Global Outcomes criteria. Each postoperative day was represented as a patient-day vector comprising laboratory and static clinical features. An internal benchmark guided preprocessing decisions, including feature normalization and selection. Principal component analysis was used for dimensionality reduction, and multiple clustering algorithms were evaluated using validity, resampling-based stability, and reproducibility metrics.</p> Results <p>Among 3,526 patients, 567 developed AKI. Five clinically interpretable postoperative states were identified. C5 represented an evolving or near-established AKI state, characterized by older age, hyperglycemia, elevated urea and creatinine, hyperkalemia, and hypocalcemia. C4 represented a near-term alarm state, marked by relatively higher hemoglobin, hematocrit, RBC count, calcium, and sodium. C3 reflected a microcytic-hypochromic erythrocyte phenotype with lower MCV, MCH, and MCHC. C1 was characterized by relatively prolonged PT and elevated INR, whereas C2 reflected an overall stable postoperative profile. In patients who developed AKI, state occupancy shifted toward C4 in the immediate pre-onset period and increased further in C5 around the day of AKI diagnosis. By contrast, patients without AKI showed increasing occupancy of the stable C2 state and the relatively protective C1 state as the endpoint approached.</p> Conclusions <p>Phenotyping of postoperative EMR data identified five interpretable states with distinct laboratory profiles and temporal relationships to cardiac surgery-associated AKI. The AKI trajectory was characterized by transition from an alarm state to an evolving AKI state, whereas the Non-AKI trajectory converged toward stable or protective postoperative states. These findings suggest that dynamic phenotyping may provide a framework for postoperative AKI risk stratification and trajectory monitoring.</p> Clinical trial number <p>Not applicable.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Discovery of postoperative clinical states linked to cardiac surgery-associated acute kidney injury

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月3日

查看全文
  • Motahare Shabestari,
  • Vinod Kumar Chauhan,
  • Mohammadtaghi Sarebanhassanabadi,
  • Seyed Jalil Mirhosseini

摘要

Background

Cardiac surgery-associated acute kidney injury (AKI) remains a common postoperative complication and is associated with adverse short- and long-term outcomes. Conventional risk scores may have limited performance in complex and dynamically evolving postoperative courses. This study aimed to identify data-driven postoperative clinical states from time-series electronic medical record (EMR) data and characterize how state occupancy evolves before AKI onset.

Methods

We conducted a retrospective cohort study of adults undergoing major open-heart surgery at a tertiary cardiovascular center. AKI was defined using the modified Kidney Disease Improving Global Outcomes criteria. Each postoperative day was represented as a patient-day vector comprising laboratory and static clinical features. An internal benchmark guided preprocessing decisions, including feature normalization and selection. Principal component analysis was used for dimensionality reduction, and multiple clustering algorithms were evaluated using validity, resampling-based stability, and reproducibility metrics.

Results

Among 3,526 patients, 567 developed AKI. Five clinically interpretable postoperative states were identified. C5 represented an evolving or near-established AKI state, characterized by older age, hyperglycemia, elevated urea and creatinine, hyperkalemia, and hypocalcemia. C4 represented a near-term alarm state, marked by relatively higher hemoglobin, hematocrit, RBC count, calcium, and sodium. C3 reflected a microcytic-hypochromic erythrocyte phenotype with lower MCV, MCH, and MCHC. C1 was characterized by relatively prolonged PT and elevated INR, whereas C2 reflected an overall stable postoperative profile. In patients who developed AKI, state occupancy shifted toward C4 in the immediate pre-onset period and increased further in C5 around the day of AKI diagnosis. By contrast, patients without AKI showed increasing occupancy of the stable C2 state and the relatively protective C1 state as the endpoint approached.

Conclusions

Phenotyping of postoperative EMR data identified five interpretable states with distinct laboratory profiles and temporal relationships to cardiac surgery-associated AKI. The AKI trajectory was characterized by transition from an alarm state to an evolving AKI state, whereas the Non-AKI trajectory converged toward stable or protective postoperative states. These findings suggest that dynamic phenotyping may provide a framework for postoperative AKI risk stratification and trajectory monitoring.

Clinical trial number

Not applicable.