Impact of Developing Dialysis-Requiring Acute Kidney Injury on Long-Term Mortality in Cancer Patients with Septic Shock.
June-Sung KimYe-Jee KimYoun-Jung KimWon Young KimPublished in: Cancers (2023)
(1) Background: Considering recent advances in both cancer and sepsis management, we chose to evaluate the associated factors for occurrence of septic acute kidney injury in cancer patients using a nationwide population-based cohort data. (2) Methods: Using data from the National Health Insurance Service of Korea, adult cancer patients who presented to emergency departments with septic shock from 2009 to 2017 were analyzed. A Cox-proportional hazard model was conducted to evaluate the clinical effect of sepsis-related acute kidney injury requiring dialysis. (3) Results: Among 42,477 adult cancer patients with septic shock, dialysis-requiring acute kidney injury occurred in 5449 (12.8%). Recovery from dialysis within 30 days was 77.9% and, overall, 30-day and 2-year mortality rates were 52.1% and 85.1%, respectively. Oncologic patients with dialysis-requiring acute kidney injury frequently occurred in males and patients with hematologic cancer. A multivariate Cox-proportional hazard model showed that dialysis-requiring acute kidney injury had the highest adjusted hazard ratio of 1.353 (95% confidence interval 1.313-1.395) for 2-year mortality. (4) Conclusions: Dialysis-requiring septic acute kidney injury did not occur commonly. However, it had a significant association with increased long-term mortality, which suggests emphasis should be placed on the prevention of acute kidney injury, particularly in male hematologic cancer patients.
Keyphrases
- acute kidney injury
- septic shock
- cardiac surgery
- chronic kidney disease
- end stage renal disease
- papillary thyroid
- health insurance
- squamous cell
- peritoneal dialysis
- cardiovascular events
- childhood cancer
- risk factors
- healthcare
- electronic health record
- mental health
- coronary artery disease
- type diabetes
- squamous cell carcinoma
- cardiovascular disease
- risk assessment
- machine learning
- young adults
- big data
- deep learning