Research article Open Access
Usefulness of STS-Renal and ACEF scores as predictive models for acute kidney injury after cardiac surgery in UMAE Cardiology Hospital-CMN IMSS Mexico
Nalleli Adriana Pérez-Rubio1, Alberto Ramírez Castañeda1, Martin Rosas-Peralta2, Gabriela Borrayo-Sánchez2, Jaime Salgado Vázquez1, Carolina Álvarez Moreno1, Lucelli Yañes Gutierrez3, Horacio Márquez González3, Luis Zúñiga- Alanís1, and Carlos Riera-Kinkel1*
1 Division of Cardiothoracic surgery, Cardiology Hospital XXI century, Instituto Mexicano Del Seguro Social, Mexico, CDMX, Mexico
2 “A todo corazón-Código Infarto” program, National Medical Center XXI Century, IMSS, CDMX, México
3 Congenital Heart Disease department, Cardiology Hospital XXI century, Instituto Mexicano Del Seguro Social, Mexico, CDMX, Mexico
*Corresponding author: Carlos Riera Kinkel, Head of Cardiac Surgery Division, Hospital de Cardio logia, Centro Medico National Siglo XXI, IMSS, Cuauhtémoc 330, Col Doctors, Deleg Cuauhtémoc, CP 06720, México, D F, México; Phone: (+52) 56276927; E-mail: @
Received: November 21, 2018; Accepted: December 03, 2018; Published: December 05, 2018
Citation: Carlos Riera K, Nalleli Adriana PR, Castañeda AR, et al. (2018) Usefulness of STS-Renal and ACEF scores as predictive models for acute kidney injury after cardiac surgery in UMAE Cardiology Hospital-CMN IMSS Mexico. Cardiovascular Thoracic Surgery 3(4):1-6. DOI: 10.15226/2573-864X/3/3/00149
Abstract
Objective: To Evaluate the STS-renal and ACEF scores which are short-Term Risk Calculators for developing renal failure in patients after cardiac surgery at a unique 3rd level Medical Center in Mexico City.

Methods: We conducted a retrospective study, consecutive patients between August 1st 2015 and July 31st 2016 were included. We applied the STS-renal and ACEF scores as predictive methods for acute kidney injury (AKI) after cardiac surgery with cardiopulmonary bypass.

Results: They were 525 patients who met the inclusion criteria. Arose kidney injury in 135 (25.5%) of the patients. We found that age, time of aortic clamping, pre-operative creatinine, hypertension and complexity of the procedure level are the principal risk factors for kidney injury, and as a consequence, we observed a correlation among the severity of kidney injury and increases of the hospital stay and mortality. 26(5%) patients died, six of them directly attributable to kidney injury.

Conclusions: We assessed two risk scores to predict early kidney injury after cardiac surgery. We found as the most applicable to our population the STS-kidney short term risk calculator. The use of this kind of risk calculators should be applied routinely in México.

Key words: Cardiac Surgery; Kidney Injury; ACEF; STS
Introduction
Postoperative kidney injury after Cardiac surgery is a complication which increases mortality and hospital stay. American Society of thoracic surgery defines acute kidney injury after surgery as the increase of serum creatinine 2 mg/dL or twice the preoperative value, or new requirement of haemodialysis. [1] Several short term risk calculators to predict kidney injury after cardiac surgery have been developed; however, they are focused on the prediction of dialysis requirement and/or severe kidney injury. Nevertheless, the prediction of mild and moderate kidney injury is also important. Birnie et al., [2] conducted an analysis of about 30,000 patients undergoing cardiac surgery in three hospitals in the United Kingdom, and they developing a model for short risk prediction of all stages of kidney injury, and it was useful for slight and severe types of kidney injury.

Most commonly used risk models include the Score of the American Society of Thoracic Surgeons (STS), published in 2008, and the Age, Creatinine and Ejection Fraction (ACEF) score, published at 2009. For those patients undergoing bypass surgery the STS score has been reported more accurately to predict the risk of post-surgical dialysis requirement, however the validity of the method for predicting slight kidney injury or requirement for dialysis is not weak. [3] It is assumed a greater accuracy of the ACEF score for the prediction of kidney injury with minimum requirements for replacement therapy.

The predictive models in cardiac surgery have been developed from certain population groups, in a defined period of time, and taking into account certain variables selected previously. Therefore, there is doubt of their applicability to different population groups or another point in time. Kidney injury is a complication of cardiac surgery that becomes present in ~30% of patients and as a consequence it increases either mortality or hospital stay, and as a result increased costs for the institution.

Detection of kidney injury in its early stages, yet more identifies patients at high risk, is important to provide correctly early treatment and therefore, decrease mortality and hospital stay. For this reason, it is essential to establish a predictive method applicable to our population.

Considering the above, it was decided to conduct this study to validate predictive methods already established, such as STSkidney and the ACEF score for the presentation of acute kidney injury in our population of patients undergoing cardiac surgery.
Primary end point
To Validate the score STS-kidney and ACEF in post-operated patients of valvular heart surgery and coronary artery bypass under cardiopulmonary bypass in Mexico City at a 3rd level Hospital of Cardiology-CMN XXI century, IMSS
Secondary Aims
1) To determine which predictive short term risk calculators is more sensitive for post-surgical kidney injury

2) to assess other risk factors already known for kidney injury such as cardiopulmonary bypass time, time of aortic clamping, smoking, sex, weight, hypertension, diabetes mellitus, peripheral vascular disease, dyslipidemia, level of hematocrit in our population

3) To determine mortality associated with kidney lesion in the population studied

4) To determine length of hospital stay both hospitalization and therapy post-surgical due to kidney injury.
Patients and Methods
We include consecutive patients undergoing cardiac bypass surgery and valvular surgery under cardiopulmonary bypass from a 3rd level Cardiology Hospital at National Medical Center XXI-Century, IMSS, of Mexico City. All patients had age > 18 years old, both genders and with a full medical record including all variables to evaluate the predictive short term risk calculators (STS and ACEF).
Statistical analysis
Continuous variables are described as mean and standard deviation or medium and inter quartile range according to their distribution. The qualitative variables are expressed in number, frequency or percentage. The comparison between groups for continuous variables was with student’s t or U-Mann Whitney test, as appropriate. Comparison of qualitative variables was performed with the chi square test. Regression and correlation as well as matching models were generated for the predictive models in study. We used the SPSS statistical package version 22.0 software. A value of p less than 0.05 was considered statistically significant.
Results
548 consecutive patients were selected for this study, we excluded those cases with incomplete record (n=23) and eight with kidney injury prior to the surgery, for a total loss of 31 (5.6%) cases, resulting in 525 patients undergoing surgery cardiovascular that they fulfilled the inclusion criteria. 308 (58.6%) of them were male. The mean age observed was 62 (± 11.4 years). The case history is presented in the table 1.

Of the surgeries performed 303 (57.7%) were valve surgery, 162 (30.8%) Coronary Artery Bypass Grafting (CABG) surgery and 60 (11.5%) valve surgery plus bypass surgery. The most frequently performed surgery was the aortic valve surgery at 150 (28.6%) patients, followed by the operation of two or more valves with 91(17.3%), mitral valve with 56 (10.7%) and tricuspid with six (1.1%). Three or more vessels myocardial revascularization was performed at 154 (29.3%) and two vessels in eight (1.5%). The average time of cardiopulmonary bypass was 98.6 (±38. 4) minutes and aortic clamping time was 69.3(±28.3) minutes
Table 1: General characteristics by gender of the total study population

Variable

Gender

Total

%

Men

Woman

Body Mass Index

Under weight = <18.5

2

0

2

0.4

Normal = 18.5–24.9

110

62

172

32.8

Overweight = 25–29.9

146

102

248

47.2

Obesity = 30 or greater

50

53

103

19.6

Type of  Surgery

Elective

133

168

301

57.4

Urgent

134

30

164

31.2

Emergency

41

19

60

11.4

Diabetes mellitus

Yes

114

59

173

33

No

194

158

352

67

Hypertension

Yes

184

132

316

60

No

124

85

209

40

Smoking

Yes

141

77

218

41.5

No

167

140

307

58.5

Dyslipidemia

Yes

127

60

187

35.6

No

181

157

338

64.4

Prior Myocardial Infarction

Yes

42

7

49

9.4

No

266

210

476

90.6

Hemodynamic Profile

Uncompensated

7

9

16

3

Compensated

301

208

509

97

COPD

Yes

6

14

20

3.8

No

302

203

505

96.2

Cardiac Arrhythmia

Yes

28

56

84

16

No

280

161

441

84

COPD : chronic obstructive  pulmonary disease

Distribution of Study Population by the Presence of Kidney Injury

From the analyzed patients (n=525), 390 (74.5%) had no kidney injury and 135 (25.5%) with some degree of kidney injury according to the classification of AKIN, 100 (19%) of them with stage 1, 20 (3.8%) with stage 2, and 15 (2.8%) with stage 3. We developed bivariate crosses between acute kidney injury and other modifying variables such as: history of diabetes mellitus, index of body mass, chronic obstructive pulmonary disease, hypertension, cardiac arrhythmias, dyslipidemia, blood trans-operative transfusion, smoking, preoperative infarction, hemodynamic status, type of surgery procedure performed, cardiopulmonary bypass time and aortic clamping time.

You can observe that age, the presence of high blood pressure and the type of procedure have statistical significance in the presentation of kidney injury in terms of the background (Table 2). Was also performed Chi2 for the analysis of qualitative variables finding a stochastic significance in preoperative creatinine (p = < 0.001) and aortic clamp time (p = < 0.001) for the presence of kidney injury.

The analysis of hospital stay in post-surgical therapy area shown increasing days of stay as more increased the degree of kidney injury (p = < 0.001), having as average 5.7 days for posoperated total evaluated patients.

In terms of mortality 26 (5%) deaths were recorded as is shown in table 3. From the total number of reported deaths six were due to kidney injury in a direct way, eight to septic shock and 12 due to cardiogenic shock.
Table 2:AKIN Kidney injury level by variables of study

Variables

Without Kidney Injury (n=390)

ARI

AKIN 1 (n=100)

AKIN 2 (n=20)

AKIN 3 (n=15)

TOTAL (525)

p Value

Age, mean Gender

61.26 ± 10.5

65.55± 4.1

67±13.1

68±12.7

=0.001

Men

225

61

14

10

310

=0.27

Female

165

39

6

5

215

BMI

<25 Kg/m2

2

0

0

0

2

=0.61

25-29 kg/m2

131

26

6

9

172

Overweight

179

51

13

5

248

Obesity

78

23

1

1

103

Diabetes mellitus

Yes

125

38

4

6

173

=0.375

No

265

62

16

9

352

Hypertension

Yes

226

71

9

10

316

=0.051

No

164

29

11

5

209

Smoking

Yes

157

41

11

9

218

= 0.282

No

233

59

9

6

307

Dyslipidemia

Yes

142

36

5

4

187

=0.651

No

248

64

15

11

338

Prior Myocardial Infarction

Yes

34

10

3

2

49

=0.742

No

356

90

17

13

476

Hemodynamic Profile

Unstable

8

2

0

6

16

= <0.001

Stable

381

98

20

9

508

COPD

Yes

14

6

0

0

20

=0.436

No

376

94

20

15

505

Arrhythmia

Yes

60

20

2

2

84

=0.596

No

330

80

18

13

441

Type of Surgery

Elective

240

49

7

5

301

=0.316

Urgent

133

20

6

5

164

Emergency

17

31

7

5

60

Procedure

Valvular

227

60

15

1

303

= <0.001

Coronary artery bypass grafting (CABG)

130

22

5

5

162

Valvular + CABG

40

18

0

4

60

ARI: acute renal injury; * t test, U-Mann Whitney, ANOVA or Chi Square as appropriate

Table 3:Kidney injury and mortality

Kidney Injury level

Total Number

%

Without

ARI 1

ARI 2

ARI 3

Die

12

5

4

5

26

5

Alive

378

95

16

10

499

95

Total

390

100

20

15

525

100

Table 4:Staging of acute kidney injury

RIFLE

AKIN

Creatinine

Urinary Output

R-Risk

1

Up 1-2 times from base value

<0.5 mL/Kg/h / 6 h

I-Injury

2

Elevation 2-3 times form basal value

<0.5 mL/Kg/h /12 h

F-Failure

3

Elevation > 3 times, creatinine > 4 mg/dL, with drastic up > 0.5 mg/dL or, therapy replacement required.

<0.3 mL/Kg/h/24h o anury by 12 hrs

L-Lost

kidney persistent failure < 4 weeks

 

E-End kidney

Failure kidney failure  >   3 months

kidney terminal

Reliability Test
To assess the internal consistency of the proposed scores, applied a Cronbach alpha test, obtaining a statistician of 0.125 for two items, which is not reliable, which means that, together, STSkidney and ACEF not present one enough internal consistency to be able to be used in conjunction.

A logistic regression model diagnosis test is applied to each score to evaluate its predictive capacity with kidney injury. Observed that the STS shown a better diagnosis for kidney injury and every one of its degrees in comparison with the ACEF shown no statistically significant differences.
Discussion
Secondary kidney injury after cardiac surgery ranges between 5-30%, therefore it is considered that it is one of the most frequent complications after cardiac surgery, which increases both the duration of the hospital stay and mortality rate. In our hospital arose in 25% of cases largely in ARI stage 1. The AKI, has been associated with important risk factors such as male sex in ratio of 2:1 on the female, diabetes mellitus and hypertension, other risk factors dependent on the procedure surgical and anesthetic are very important such as the duration of cardiopulmonary bypass and aortic clamping time, blood transfusion requirements and high doses of vasopressors. In our study the most important risk factors were age, hypertension, aortic clamping time, creatinine levels pre-surgical and the complexity of the procedure and they acquire statistical significance.

Diagnosis of kidney injury is based on two basic although late criteria such as the elevation of plasmatic creatinine and decrease of the urinary output. In this study the approach most commonly used for diagnosis and classification of kidney injury was the plasmatic creatinine.

It is estimated that approximately 30% of patients undergoing cardiac surgery develop some degree of kidney injury [4] Global mortality cardiac surgery is around 0.9%, and increases up to 20% if the acute kidney injury is developed, and up to 60% when this AKI required a replacement therapy [5] In addition, it is known that increases up to 20% in the plasmatic concentration of creatinine in the postoperative period can result in a significant effect on the clinical evolution [6]

Many of the risk factors associated with kidney injury are not modifiable, such as age, gender, high blood pressure, diabetes mellitus, dyslipidemia and peripheral vascular disease. In the case of age for example, it is estimated that the glomerular filtrate decreases 1 mL/year old, which means that the number of nephrons of an elderly patient has decreased by 30% itself. Kidney failure appears more frequently in males with a 2:1 ratio, probably due to some vascular factor linked to sex, with a major condition of the arterial lights in men which predispose to poor response mechanisms. In the case of the chronic degenerative diseases such as diabetes mellitus and hypertension, kidney injury is related to the micro vessel disease, especially if the patient has more than 10 years with these alterations [7]

Other risk factors are dependent on the surgical and anesthetic procedure, including cardiopulmonary bypass time, aortic clamping time, ejection fraction, blood transfusion and require high doses of vasopressors [8, 9]

These factors modify kidney function, induced by cycles of ischemia and reperfusion, with increased oxidative damage and increasing systemic and kidney inflammation, all these mechanisms are involved in the development of kidney injury [10]

Various strategies have been created to decrease its occurrence such as management of intravenous fluids, extracorporeal circulation and techniques for hemodynamic stability. Pharmacological and non-pharmacological treatments have been developed without having the desired effectiveness. The diagnosis includes the taking of serum creatinine and urine output measurement. However, urine output is not as specific as the increase in serum creatinine, because it takes several days to up, which might slow the onset of treatment to kidney injury.

In addition, mechanisms associated with kidney injury include peri-operatoria ischemia, reperfusion injury, and haemolysis by cardiopulmonary bypass and its nephropathy by pigments, oxidative stress and inflammation [11]

Kidney perfusion is a complex mechanism. Around 20% of the cardiac output is directed to kidneys. Most of the blood is filtered by the glomerular crust; this derivation maintains the concentration of electrolytes and water in the kidney Medulla that is required for resorption in the tubule and the collector system. The O2 pressure at the spinal cord level is 10-20 mm Hg, which may be a protective mechanism to oxidative injury but it makes more susceptible to ischemia.

During cardiac surgery, various mechanisms can cause alteration in kidney perfusion. The cardiopulmonary bypass provides a non-pulsatile blood flow leading to an imbalance between cortical and medullary perfusion. Paradoxically, the increase in perfusion may precipitate ischemia, due to the increase in the consumption of oxygen by the increase of transport of solutes [12]

Aortic clamping increases the risk of athero-embolism toward the kidneys, and they exacerbate ischemia and induce inflammation [13] other factors such as the cascade of the Reninangiotensin- aldosterone system and activation of the sympathetic nervous system may alter kidney oxygenation during surgery [14, 15]

The cardiopulmonary bypass circuit contains a pump, an Oxygenator, suction catheters and filters that damage red blood cells and increases plasma free hemoglobin [16] the free hemoglobin decreases the haptoglobin and injures the kidneys, increasing the production of radical free products, precipitating proteins in the collector system and inducing arteriolar vasoconstriction by elimination of nitric oxide. In addition free iron increases the toxic reaction of oxygen, particularly at kidney level [17, 18]

Various systems have been proposed to classify and staging acute kidney injury. The most recent classification of the AKIN has adapted to the previous criteria from the RIFLE, and it is based on the changes in serum creatinine and urinary output (table 4) [19]

Recently some proteins have been identified that are related with kidney damage. These markers of kidney function and kidney injury (NGL, KIM 1, IL-18, NAG and GST) (Cystatin C) offer many advantages over the taking of serum creatinine, since its increase is earlier and are more specific and sensitive for the detection of kidney injury, however it is still continue in validation due to low reproduction and access to the laboratories that process them [20]

It was further noted that as more severe is the kidney injury as more the risk of both direct and indirect mortality and length of hospital stay is developed, which raises the costs for the care. As for the internal validation of the risk scores, there is controversy about what is best for the timely prediction of kidney injury. This is secondary to the instruments used are applied to a given population, on the time required. In this study we found that the predictive capacity was better determinate by the short term risk calculator STS-kidney. A proper correlation was found in each of the degrees of severity, while the lowest was for AKIN 2.
Conclusions
According with our results we can conclude:
1. Advanced age and the presence of hypertension are common entities that cannot be prevented and are directly related with the risk of AKI.

2. Variables inherent in the procedure such as complexity of surgery and aortic clamp time, increases the risk of kidney injury.

3. Preoperative creatinine is another variable with stochastic significance associated with the presentation of acute kidney injury.

4. Kidney injury increases hospital stay in the post-surgical therapy.

5. As greater severity of kidney injury is correlated with increases of mortality risk.

6. The STS-kidney showed to be better for acute kidney injury prediction, than the ACEF score, after cardiac surgery.
ReferencesTop
  1. Ho A M, Chan SK. Kidney dysfunction and CABG. Curr Opin Pharmacol. 2012;12(2):181-188.
  2. Birnie K, Verheyden V, Pagano D, Bhabra M, Tilling K, Sterne JA, Murphy GJ, et al. Predictive models for kidney disease: improving global outcomes (KDIGO) in acute kidney injury in UK cardiac surgery. Crit Care. 2014; 18(6):606.  doi: 10.1186/s13054-014-0606-x
  3. Ranucci M, Castelvecchio S, Menicanti L, Frigiola A, Pelissero G. Risk of assessing mortality risk in elective cardiac operations: age, creatinine, ejection fraction, and the law of parsimony. Circulation. 2009;119(24):3053-3061. doi: 10.1161/CIRCULATIONAHA.108.842393
  4. Karkouti K, Wijeysundera DN, Yau TM, Callum JL, Chdeng DC, Crowther M, Dupuis JY, et al. Acute Kidney injury after cardiac surgery: focus on modifiable risk factors. Circulation. 2009;119(4):495-502. doi: 10.1161/CIRCULATIONAHA.108.786913
  5. Weisberg AD, Weisber E, Wilson JM, Collard CD. Preoperative evaluation and preparation of the patient for cardiac surgery. Anesthesiol Clin. 2009;27(4):633-648.
  6. Mehta RL, Kellum JA, Shah SV, Molitoris BA, Ronco C, Warnock DG, Levin A, et al. Acute Kidney Injury Network: report of an initiative to improve outcomes in acute kidney injury. Crit Care. 2007;11(2):R31.
  7. Khan IH, Catto GRD, Edward N, MacLeod AM. Acute kidney failure: factors influencing nephrology prekidney and outcome. QJM. 1997;90:781-785.
  8. Wijeysundera DN, Karkouti K, Beattle WS. Improving the identification of patients at risk of postoperative kidney failure after cardiac surgery. Anesthesiology 2006;104(1):65-72.
  9. Lopez-Delgado JC, Esteve F, Torrado H, Rodríguez-Castro D, Carrio ML, Farrero E y col, Javierre C, et al. Influence of acute kidney injury on short and long term outcomes in patients undergoing cardiac surgery: risk factors and prognostic value of a modified RIFLE classification. Crit Care. 2013;17(6):R293. doi: 10.1186/cc13159
  10. Guerrero AF, Camacho MJ, Sandoval NF, Umana JP, Obandoa CE, Carreño M. Factors asociados a insuficiencia kidney postoperatoria en cirugía de revascularización miocárdica. Rev Colom Cardiol. 2016;23(3):230-236.
  11. Gomez H, Ince C, De Backer D, Pickkers P, Payen D, Hotchkiss J, Kellum JA, et al. A unified theory of sepsis-induced acute kidney injury: inflammation, microcirculatory dysfunction, bioenergetics and the tubular cell adaptation to injury. Shock. 2014;41(1): 3-11. doi: 10.1097/SHK.0000000000000052
  12. O´Neal JB, Shaw AD, Billings FT. Acute kidney injury following cardiac surgery: current understanding and future directions. Critical Care. 2016;20:187-195.
  13. Granata A, Insalaco O. Atheroembolism kidney disease: diagnosis and etiologic factors. Clin Ther. 2012;163(4):313-322.
  14. Fuji T, Kurata H, Takaoaka M, Muraoka T, Fujisawa Y, Shokoji T, Nishiyama A, et al. The role of kidney sympathetic nervous system in the pathogenesis of ischemic acute kidney failure. Eur J Pharmacol. 2003;481(2-3):241-248.
  15. Schrier CW. Pathophysiology of ischemic acute kidney injury: In: Diseases of the kidney and urinary tract. 8th ed. Philadelphia: Lippincott Williams and Wilkins. 2007;930-961.
  16. Billings FT, Yu Ch, Byrne JG, Petracek MR, Pretorius M. Heme oxigenase-1 and acute kidney injury following cardiac surgery. Cardiokidney Med. 2014;4(1):12-21. doi: 10.1159/000357871
  17. Haase M, Bellomo R, Haase-Fielitz  A. Novel biomarkers, oxidative stress, and the role of labile iron toxicity in cardiopulmonary bypass-associated acute kidney injury. J Am Coll Cardiol. 2010;55(19):2024-2033. doi: 10.1016/j.jacc.2009.12.046
  18. Khwaja A. KDIGO clinical practice guidelines for acute kidney injury. Nephron Clin Pract. 2012;120(4):179-184. doi: 10.1159/000339789
  19. Claure R. Lesión kidney aguda: ya no más insuficiencia kidney aguda. Residente. 2008;3(3):79-85.
  20. Haase M, Devarajan P, Haase-Fielitz A, Bellomo R, Cruz DN, Wagener G, Krawczeski CD, et al. The Outcome of neutrophil gelatinase-associated lipocalin-positive subclinical acute kidney injury: a multicenter pooled analysis of prospective studies. J Am Coll Cardiol. 2011;57(17):1752-1761. doi: 10.1016/j.jacc.2010.11.051
 
Listing : ICMJE   

Creative Commons License Open Access by Symbiosis is licensed under a Creative Commons Attribution 3.0 Unported License