Acute Kidney Injury Risk Prediction Score for Critically-ill Surgical Patients

Konlawij Trongtrakul; Jayanton Patumanond; Suneerat Kongsayreepong; Sunthiti Morakul; Tanyong Pipanmekaporn; Osaree Akaraborworn; Sujaree Poopipatpab

Disclosures

BMC Anesthesiol. 2020;20(140) 

In This Article

Discussion

According to a nation-wide, multicenter surgical ICUs dataset – the THAI-SICU Study, in a week after a major non-cardiothoracic operation, patients who stayed in surgical ICUs, nearly 10% of them suffered AKI, with an almost 10-times greater risk of ICU mortality than non-AKI patients. Moreover, AKI in critically-ill surgical patients could be simply predicted by the means of just six simple pre-ICU demographics, combining both patient baseline characteristics and perioperative data. The predictors that determine AKI are patient age, SOFA non-renal score, sepsis at ICU admission, emergency surgery, peri-operative blood loss, and peri-operative urine output. The last three predictors collected from the perioperative period made the score unique for critically-ill surgical patients, and have rarely been reported before.[21] In a total score of 16.5, increasing the score increases the probability of AKI.

Our AKI prediction score has a good discriminative ability (c-statistic of 0.839; 95%CI, 0.825–0.852 and 0.821; 95%CI, 0.797–0.845, after internal validating by bootstrapping). Previous studies about AKI prediction score, which studied in different populations and timing of prediction, have usually reported good diagnostic function. Most of them range above 0.80. For instance, the study form patients who had undergone liver resection, in their development cohort, AKI prediction score's C-statistic was at 0.81 (95%CI, 0.76–0.86).[22] Another study from Kheterpal et al., who built a scoring system for predicting AKI following major general surgery (not specify only critically-ill surgical patients), reported a good diagnostic model with of 0.80 (95%CI, 0.79–0.81).[8] Another study by Malhotra et al. stated just a moderate to good function of their AKI prediction model, at 0.79 (95%CI, 0.70–0.89).[9] However, their study populations were mixed both medical and surgical critically ill patients.

The AKI prediction scores in patients who underwent major operation have been reported from other settings. For instance, the AKI prediction score reported by Bell and colleagues.[23] They addressed an importance of AKI prediction score in orthopedic surgical patients and its impact on short and long-term survival outcomes. The AKI predictive ability of their score was (AuROC) 0.74 (95%CI, 0.73–0.75) in the derivative cohort and 0.73 (no 95%CI reported) when internally validated. However, all of the predictors were only derived from preoperative data, without any aggregated data regarding peri-operation and severity of illness after an operation. The other two AKI prediction scores were reported from general major non cardiothoracic surgery, not specified only critically ill patient, by Park et al[24] and Lei et al.[25] The study by Park and colleagues[24] reported quite good AKI prediction ability, an AuROC of 0.80 (95%CI, 0.79–0.81) in the derivation cohort, but decreased slightly to 0.72 (95%CI, 0.71–0.73) when externally validated. However, this study used only preoperative data for developing the AKI prediction score. Another study by Lei and colleagues,[25] they demonstrated an AuROC of 0.712 (95%CI, 0.694–0.731), when the score was derived from the pre-operative data. When added peri-operative and post-operative data to pre-operative data, a significant increase in model performance was found (p < 0.001). The AuROC increased to be 0.804 (95%CI, 0.788–0.899) and 0.871 (95%CI, 0.802–0.832), respectively. The results from this study confirmed our concern regarding the importance of peri-operative and post-operative data should be co-operated into the AKI prediction score.

The diagnostic indices, comprising sensitivity, specificity, PPV, and NPV, in our prediction score were 72.3, 80.6, 28.8, and 96.4%, respectively. A high percentage of NPV made our score beneficial for including most of the patients who are at risk of AKI. Thus, fewer cases will be missed by our prediction score. Moreover, the diagnostic properties of our study were quite similar to studies from Malhotra et al. (74, 72, 23, and 96%, respectively)[9] and Rueggeberg et al. (78, 92, 62, and 96%, respectively).[26] However, it might be very difficult to interpret differences between prediction models in the details, because of diverse definitions of some variables and study populations.

The predictors that determine AKI in our study were comparable to previous studies. For instance, patient age,[8,24,27] sepsis at ICU admission,[9] SOFA non-renal score,[1,28] emergency surgery,[8,24] and perioperative blood lost.[29]

Increased patient age increased the risk of AKI. However, with some differences in the cut-off value, our used 65, most frequently and acceptably used, whereas, in mixed critically-ill patients, age of ≥56 years was used.[8] Somehow, in another study created the scores corresponding the increase in ranges of age.[24]

The report from mixed critically-ill patients by Malhotra et al. showed that severe infection and sepsis were associated with AKI.[9] However, in major non-cardiac surgery studies, sepsis was lacking as one of AKI predictors.[8,22–24,27]

The spectrum of illness severity, as measured by SOFA non-renal score, was included as one of our predictors. To the best of our knowledge, no preceding studies contained severity of illness in their AKI prediction scores. The use of SOFA non- renal score, after categorization into 3 levels (0–1, 2–5, and ≥ 6), represented the risk of AKI, sequentially. We excluded SOFA renal score domain to eliminate the effect of individual baseline renal function on ICU admission from total SOFA score, as per some recommendations from previous reports.[1,28]

Regarding surgical information, emergency surgery was undoubtfully found as one of the AKI prediction score, similar to the previous studies.[8,24] Other perioperative risk factors including peri-operative blood loss and peri-operative urine output, recently, there has been no definitive consensus on how much blood loss is correlated to the risk of AKI. However, a study by Kim et al,[30] showed that every 1 l of perioperative blood loss in liver transplant recipients increased the risk of continuous renal replacement therapy significantly. As for urine output during operation, there were some differences in our data compared to other study. Slankamenac K et al. found oliguria raised the possibility of AKI,[22] they defined oliguria as urinary output < 400 ml/24 h. Though, in our study, we arranged perioperative differently.

To the best of our knowledge, our study represents one of the largest series of AKI in critically-ill surgical patients who underwent major non-cardiothoracic surgery. The availability of intensive monitoring for every case in the ICU might be difficult in some centers, particularly in resource-limited countries, such as Thailand. This AKI prediction score can be utilized in daily clinical practice for early AKI detection. Selected cases that are at high risk of AKI will benefit from more frequent serum creatinine blood sampling, hourly urine output measurement, aggressive fluid resuscitation, optimization of fluid balance management, and avoidance of unnecessary nephrotoxic agents to mitigate the occurrence of AKI and to augment renal function recovery.

Limitations

There were some limitations in our study. First, AKI was diagnosed based on serum creatinine only. This could underestimate the overall incidence of AKI. Urine output is another criterion for an AKI diagnosis, but it was not used because of the lack of information on this variable available from the THAI-SICU Study. Second, the AKI prediction score was only able to determine AKI in a period of 1 week following ICU admission. It was thought to be based upon the fact that how long does perioperative AKI has no clear definition,[27] and how long this usually acute disease lasted for after the operation is unknown. Moreover, during ICU admissions of more than a week, AKI may occur due to other factors; for instance, nosocomial infection, surgical site infection, or an exposure to nephrotoxic agents. Third, perioperative urine output was categorized into 3 orders. The urine output should be adjusted by body weight and perioperative period. We attempted to use urine output/kg/hour as the predictor, but the results after testing by statistical analysis showed that an ordination of raw urine output was more suitable and had more discriminative ability for AKI prediction than urine output/kg/hour. Finally, the intraoperative hypotension was not accounted for in out AKI predictors due to a lack of this information in our dataset. As previous studies had been shown that intraoperative hypotension has significantly impacted on the occurrence of AKI post-operatively.[31,32]

Further Study

We hope to apply the AKI prediction score into our clinical practice. It would be of great value in validating the scoring system in critically-ill surgical patients from other centers. Moreover, some prediction scores for predicting other kidney issues in the ICU might be topics of interest, such as a score for predicting patients who will benefit from commencing early replacement therapy, or a score for forecasting patients who will experience renal function recovery.

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