Associated Factors And Short-term Mortality Of Early Versus Late Acute Kidney Injury Following On-pump Cardiac Surgery

Abstract

1. objectives

Acute kidney injury (AKI) is common following cardiac surgery. The aim was to investigate the characteristics of AKI that occurred within 48 h and during 48 h to 7 days after cardiac surgery.

2. methods

Patient data were extracted from Medical Information Mart for Intensive Care III database. AKI was defined according to the Kidney Disease Improving Global Outcomes guideline and divided into early (within 48 h) and late (during 48 h to 7 days) AKI. Multivariable logistic regression models were established to investigate risk factors for AKI. Cox proportional hazards model was used to analyze 90-day survival.

3. results

AKI occurred in 51.2% (2741/5356) patients within the first 7 days following cardiac surgery, with the peak occurrence at 36– 48 h. The incidence of early and late AKI was 41.9% and 9.2%, respectively. Patients with late AKI were older and had more comorbidities compared to early AKI patients. Risk factors associated with early AKI included age, body mass index, congestive heart failure, and diabetes. While late AKI was related to atrial fibrillation, estimated glomerular filtration rate, sepsis, norepinephrine, mechanical ventilation, and packed red blood cell transfusion. In Cox proportional model, both late and early AKIs were independently associated with 90-day mortality, and patients with early AKI had better survival than those with late AKI.

4. conclusions

AKI that occurred earlier was distinguishable from AKI that occurred later after cardiac surgery. The time frame should be taken into consideration.

5. keywords

Acute kidney injury • Cardiac surgery • Early AKI • Late AKI • Risk factors

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Introduction

Acute kidney injury (AKI) is common following cardiac surgery and has been tied to prolonged hospitalization, higher economic cost, increased readmissions, and poor prognosis [1–3]. The incidence of AKI following cardiac surgery is reported to range from 0.3% to 81.2% according to different definitions as well as different populations [4, 5]. Mild AKI, even a small increment of serum creatine (sCr), or transient oliguria has been reported to be associated with poor prognosis [6, 7]. Furthermore, previous studies have also shown that AKI, even with complete recovery, is still a risk factor for long-term mortality [8, 9].

During cardiac surgery, cardiopulmonary bypass is a well-known deleterious factor to the kidney, for the process itself could lead to potential hypo-perfusion, hypoxia, and/or ischaemic–reperfusion, oxidative stress, and exacerbate inflammation [10]. While some postoperative complications or therapeutic measurements, like low cardiac output, sepsis, and use of vasoactive agents or antibiotics, may also hurt kidneys by specific mechanisms [1]. Although several models have been established to focus on risk factors or prediction of AKI following cardiac surgery, they seldom of them consider the time frame and try to distinguish AKI occurring earlier from that occurring later. Recently, AKI following non-cardiac major surgery is 2 different phenotypes by occurring time [11].

In this retrospective database analysis, we sought to compare risk factors between patients with AKI occurring within 48 h postcardiac surgery (called early AKI) and patients with AKI occurring during 48 h to 7 days post-cardiac surgery (called late AKI). We also evaluated their effects on 90-day mortality.

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Patients and methods

1. Ethics statement

Medical Information Mart for Intensive Care (MIMIC) III v1.4 database was approved by the Institutional Review Boards of Beth Israel Deaconess Medical Center (number 2001-P-001699/14) and the Massachusetts Institute of Technology (number 0403000206). Requirements for individual patient consent were waived. Shengnan Li obtained access to this public database (certification number 36216625) and signed a data use agreement including not identifying subjects. Institutional Review Boards approval for this records-based research was exempted according to Common Rule (45 CFR 46).

2. Data source

We conducted a retrospective analysis based on MIMIC III v1.4 database, which is a restricted-access clinical database hosted on Physionet. It includes 61 532 intensive care unit (ICU) stays admitted into 5 ICUs at Beth Israel Deaconess Medical Center from June 2001 to October 2012 [12–14]. The database includes demographics, vital sign measurements, laboratory test results, procedures, medications, caregiver notes, imaging reports, and mortality. Health-related data are de-identified in compliance with Health Insurance Portability and Accountability Act. Protected health information was also deleted from the structured data source.

3. Patient population

Adult patients undergoing on-pump cardiac surgery were included in this study. Exclusive criteria were (i) a patient who was lack of sCr, urine output, and renal replacement therapy (RRT) information and (ii) a patient who had the end-stage renal disease or estimated glomerular filtration rate (eGFR) <15 ml/min, or a patient was on RRT before surgery. For those who performed >1 surgery during the time interval, we only selected the first surgery. International Classification of Diseases Version 9 (ICD-9) code was used to identify surgical procedures and extracted data on demographics, health characteristics, lab test data, postoperative complications, and treatments. Surgery was divided into 5 categories: coronary artery bypass grafting, valve surgery, aorta surgery, combined surgery, and others. Here, we defined combined surgery as >1 procedure done during the surgery.

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4. Outcomes

The primary outcome was AKI. AKI was defined according to Kidney Disease Improving Global Outcomes (KDIGO) rule by sCr criteria or urine output criteria [15]. Baseline sCr was defined in the same way as a previous study [11]. ‘Early AKI’ was defined as AKI first diagnosed within 48 h following surgery. ‘Late AKI’ was defined as AKI first diagnosed during 48 h to 7 days after surgery. Secondary outcomes included 90-day survival, length of ICU stay, and length of hospital stay. Patient survival following discharge was contained in the database, which was acquired from the social security death registry.

5. Statistical analysis

For a detailed description, see Supplementary Material. Continuous variables were presented with a mean (standard deviation) or median (interquartile range), while categorical variables were presented with frequency (%). Normality was accessed by Skewness/Kurtosis test. Missing data were imputed using the multiple imputations by chained equation [16]. Baseline characteristics were compared among groups using the Kruskal–Wallis test or analysis of variance (ANOVA) for continuous variables and the chi-squared test or Fisher’s exact test for categorical variables. The survival model was initiated at the time of surgery and followed until death or the last following up. Survival curves were constructed by Kaplan–Meier method and the pairwise log-rank test was used to test the difference in survival. Bonferroni correction was used to offset multiple comparisons. Multivariable Cox’s proportional hazard model was constructed to compare hazards ratio across the groups (no AKI, early AKI, late AKI) adjusting by potential factors that may affect survival. Two multivariable logistic regression models were established to investigate independent risk factors for early AKI and late AKI, respectively (both compared with patients who did not develop AKI).

For the machine learning algorithm, a copy of data was split into a 70% training set for model development and a 30% testing set for validation in a stratified fashion. Two separate fine-tuned random forest models were developed using 2 sets of features from above to classify early AKI patients and late AKI patients (both versus patients without AKI). The feature selection pipeline in these 2 models was both based on univariate feature selection and random-forest-based recursive feature selection. Trained models were validated on the testing set. Discrimination was evaluated by area under the receiver operating characteristic curve (AUC), and calibration was assessed by Hosmer–Lemeshow test. Statistical analysis was performed with Python 3.7.5 under an anaconda environment (http://www.anaconda.com/). P-value <0.05 was considered statistically significant.

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Discussion

In this retrospective study, most of the AKI manifested within 48 h postoperatively. We classified AKI following cardiac surgery into early AKI (within 48 h post-surgery) and late AKI (during 48 h to 7 days post-surgery) deliberately. Our major results confirm similar findings with a previous study that early AKI and late AKI following surgery may be 2 different phenotypes [11]. In the present study, most of the risk factors appear to be specific to patients with early AKI or late AKI. Furthermore, the effects of early and late AKI on 90-day mortality were also distinguishable. These findings may add extra evidence that AKI occurring after cardiac surgery may also need to be treated separately according to its onset, especially when designing clinical trials.

The definition of AKI has changed over time, and it mainly relies on sCr, urine output, or RRT. AKI has been defined as a 0.1 mg/ dl increment in sCr to RRT in the previous literature [5]. Thus, the incidence of AKI varies [5]. Here, we define AKI according to the KIDGO rule by both sCr or urine output criteria. And the incidence of AKI was 51.2% in our population, which was consistent with recent reports that defined AKI according to the same criteria in recent data [4, 17, 18].

AKI following cardiac surgery has been discussed previously. Predictive models have been established to predict severe AKI [19– 25]. Some of them have great performance [19–24]. However, very few studies focused on the onset of AKI following cardiac surgery. Acute Dialysis Quality Initiative is used to advocate subdividing cardiac surgery-associated AKI into early AKI (within 7 days) and late AKI (between 7 and 30 days after cardiac surgery) based on the median length of hospital stay [26]. They suggested that it might be disc surgery associated with AKI [26]. Another literature defined early AKI as that occurred within 24 h following cardiac surgery by RIFLE criteria when they explored attributable cardiopulmonary bypass-related variables on AKI [27]. Among critically ill patients following non-cardiac major surgery, 48 h was set as a cut-off to define early and late AKI [11]. However, when we looked into the occurring time distribution of AKI after cardiac surgery, the peak was 36– 48 h, and 81.9% of AKI occurred within 48 h. Thus, we identified 48 as a reasonable watershed to separate early AKI and late AKI. This time window is also consistent with the plausible delay of serum creatinine until it reaches diagnostic criteria and is codified in the KDIGO definition of AKI. As predicted, the random forest model for late AKI showed better discrimination performance than the early AKI model, for the former was more informative.

Risk factors for AKI after cardiac surgery have been demonstrated before. Variables like age, body mass index, congestive heart failure, and diabetes have been frequently mentioned as risk factors for AKI post-cardiac surgery previously [19, 20, 23, 28]. However, they affected only early AKI in our study. Medications like beta-blocker and furosemide were also only related to early AKI rather than late AKI. Decreased eGFR is a component in the simplified renal index score system [21], but it only had an impact on late AKI. Compared to coronary artery bypass grafting surgery, combined surgery has been reported as a significant risk factor for AKI after cardiac surgery [20, 21]. However, it was a risk factor only for late AKI in our study. Moreover, surgery type did not have a relative effect on early AKI. Considering decreased eGFR and a series of postoperative complications, the occurrence of late AKI is more likely due to the ‘long-term’ accumulation of repeated insults, which is also confirmed by focused analysis on the incidence of AKI among patients with multiple cardiac surgeries. Thus, it seems that it is necessary to consider the time frame when evaluating the risk for AKI following cardiac surgery. Different predictive or evaluating systems should be established for early and late AKI, respectively.

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Here, we also found some modifiable factors. Anaemia has been recognized as modifiable in previous studies [28–30]. In the present study, the difference between maximum and minimum preoperative hemoglobin was protective for early AKI, while packed red blood cell transfusion was a risk factor for late AKI. Usually, if the hemoglobin value was normal, it would be relatively stable and would not be interfered with too much before surgery. A significant increment in hemoglobin before surgery may be explained by alleviating anemia. It seemed that we added indirect but significant evidence on the benefit of anemia improvement before surgery. Demirjian et al. demonstrated that serum potassium was a risk factor for postoperative dialysis [23]. In our study, fluctuation of potassium 1 month before hospital admission was a risk factor for early AKI. It is usual for cardiac patients to take both diuretics and potassium supplements. Congestive heart failure might damage potassium absorption from the gastrointestinal tract. Diuretic itself could result in loss of potassium. Significant fluctuation of potassium may be a reflection of poorly controlled cardiac disease. Carefully monitoring and maintaining potassium levels normal should be helpful.

Limitations

Our study also has some limitations. First, it was a retrospective study and, thus, had its inherent nature. We established the association between independent risk factors and AKI, but not causality. However, if carefully validated, it could help clinicians to identify patients with high risks and take preventive steps to improve patient care. Second, our data came from a single center. However, we studied a relatively large population. We had complete data to define the AKI stage with precise timing information. Third, we did not include intraoperative variables, which were also very important for outcomes. It was unfortunate that this information was not available in our dataset. However, we had reasonably comprehensive preoperative and postoperative variables, which could be reasonably expected to impact the AKI rate.

Conclusion

In summary, we classified AKI following on-pump cardiac surgery into 2 categories, early AKI, and late AKI according to the occurring time. Furthermore, we identified both common and distinct risk factors for early and late AKIs. They had different risk factor spectrums and prognoses. Therefore, it may need to treat the situations distinctively when designing trials or conducting quality improvement.

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Shengnan Lia,b, Ming Liu c , Xiang Liu d , Dong Yang d , Nianguo Dong c and Fei Li c

a Department of Anesthesiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China

b Institute of Anesthesiology and Critical Care Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China

c Department of Cardiovascular Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China

d Guangzhou AID Cloud Technology Co., LTD, Guangzhou, China