Background Etiologies for acute kidney injury (AKI) vary by geographic region and socioeconomic status. While considerable information is now available on AKI in the Americas, Europe and China, large comprehensive epidemiologic studies of AKI from Southeast Asia (SEA) are still lacking. The aim of this study was to investigate the rates and characteristics of AKI among intensive care unit (ICU) patients in Thailand. Methods We conducted the largest prospective observational study of AKI in SEA. The data were serially collected on the first 28 days of ICU admission by registration in electronic web-based format. AKI status was defined by full Kidney Disease: Improving Global Outcome criteria. We used AKI occurrence as the clinical outcome and explored the impact of modifiable and non-modifiable risk factors on the development and progression of AKI. Results We enrolled 5476 patients from 17 ICU centres across Thailand from February 2013 to July 2015. After excluding patients with end-stage renal disease and those with incomplete data, AKI occurred in 2471 of 4668 patients (52.9%). Overall, the maximum AKI stage was Stage 1 in 7.5%, Stage 2 in 16.5% and Stage 3 in 28.9%. In the multivariable adjusted model, we found that older age, female sex, admission to a regional hospital, medical ICU, high body mass index, primary diagnosis of cardiovascular-related disease and infectious disease, higher Acute Physiology and Chronic Health Evaluation II, non-renal Sequential Organ Failure Assessment scores, underlying anemia and use of vasopressors were all independent risk factors for AKI development. Conclusions In Thai ICUs, AKI is very common. Identification of risk factors of AKI development will help in the development of a prognostic scoring model for this population and should help in decision making for timely intervention, ultimately leading to better clinical outcomes.

IMPORTANCE Systematic differences between patients included in randomized clinical trials (RCTs) and the general patient population may influence the generalizability of RCT findings. Comprehensive national registries of patients with end-stage kidney disease who are undergoing dialysis provide a unique opportunity to compare trial and real-world patient cohorts. OBJECTIVE To determine if participants in large, multicenter dialysis trials were similar to the general population undergoing dialysis in terms of age, comorbidities, and mortality rate.

Background: There has been a global increase in the incidence of acute kidney injury (AKI), including among critically-ill surgical patients. AKI prediction score provides an opportunity for early detection of patients who are at risk of AKI; however, most of the AKI prediction scores were derived from cardiothoracic surgery. Therefore, we aimed to develop an AKI prediction score for major non-cardiothoracic surgery patients who were admitted to the intensive care unit (ICU). Methods: The data of critically-ill patients from non-cardiothoracic operations in the Thai Surgical Intensive Care Unit (THAI-SICU) study were used to develop an AKI prediction score. Independent prognostic factors from regression analysis were included as predictors in the model. The outcome of interest was AKI within 7 days after the ICU admission. The AKI diagnosis was made according to the Kidney Disease Improving Global Outcomes (KDIGO)-2012 serum creatinine criteria. Diagnostic function of the model was determined by area under the Receiver Operating Curve (AuROC). Risk scores were categorized into four risk probability levels: low (0-2.5), moderate (3.0-8.5), high (9.0-11.5), and very high (12.0-16.5) risk. Risk of AKI was presented as likelihood ratios of positive (LH+). Results: A total of 3474 critically-ill surgical patients were included in the model; 333 (9.6%) developed AKI. Using multivariable logistic regression analysis, older age, high Sequential Organ Failure Assessment (SOFA) non-renal score, emergency surgery, large volume of perioperative blood loss, less urine output, and sepsis were identified as independent predictors for AKI. Then AKI prediction score was created from these predictors. The summation of the score was 16.5 and had a discriminative ability for predicting AKI at AuROC = 0.839 (95% CI 0.825-0.852). LH+ for AKI were: low risk = 0.117 (0.063-0.200); moderate risk = 0.927 (0.745-1.148); high risk = 5.190 (3.881-6.910); and very high risk = 9.892 (6.230-15.695), respectively. Conclusions: The function of AKI prediction score to predict AKI among critically ill patients who underwent noncardiothoracic surgery was good. It can aid in early recognition of critically-ill surgical patients who are at risk from ICU admission. The scores could guide decision making for aggressive strategies to prevent AKI during the perioperative period or at ICU admission.