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Using acute kidney injury severity and scoring systems to predict outcome in patients with burn injury
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Journal of the Formosan Medical Association (2016) xx, 1e7
Available online at www.sciencedirect.com
ScienceDirect journal homepage: www.jfma-online.com
ORIGINAL ARTICLE
Using acute kidney injury severity and scoring systems to predict outcome in patients with burn injury George Kuo a,d, Shih-Yi Yang b,d, Shiow-Shuh Chuang b, Pei-Chun Fan a,c, Chih-Hsiang Chang a,c, Yen-Chang Hsiao b, Yung-Chang Chen a,c,* a
Department of Nephrology, Kidney Research Center, Taoyuan City, Taiwan Department of Plastic Surgery, Linkou Burn Center, Chang Gung Memorial Hospital, Taipei, Taiwan c College of Medicine, Chang Gung University, Taoyuan, Taiwan b
Received 7 September 2016; received in revised form 16 October 2016; accepted 28 October 2016
KEYWORDS acute kidney injury; Acute Physiology and Chronic Health Evaluation; burn; prognosis; Sequential Organ Failure Assessment
Background/Purpose: Acute kidney injury (AKI) is a frequent complication of severe burn injury and is associated with mortality. The definition of AKI was modified by the Kidney Disease Improving Global Outcomes Group in 2012. So far, no study has compared the outcome accuracy of the new AKI staging guidelines with that of the complex score system. Hence, we compared the accuracy of these approaches in predicting mortality. Methods: This was a post hoc analysis of prospectively collected data from an intensive care burn unit in a tertiary care university hospital. Patients admitted to this unit from July 2004 to December 2006 were enrolled. Demographic, clinical, and laboratory data and prognostic risk scores were used as predictors of mortality. Results: A total of 145 adult patients with a mean age of 41.9 years were studied. Thirty-five patients (24.1%) died during the hospital course. Among the prognostic risk models, the Acute Physiology and Chronic Health Evaluation III system exhibited the strongest discriminative power and the AKI staging system also predicted mortality well (areas under the receiver operating characteristic curve: 0.889 vs. 0.835). Multivariate logistic regression analysis identified total burn surface area, ventilator use, AKI, and toxic epidermal necrolysis as independent risk factors for mortality.
Conflicts of interest: The authors have no conflicts of interest relevant to this article. * Corresponding author. Division of Critical Care Nephrology, Department of Nephrology, Chang Gung Memorial Hospital, College of Medicine, Chang Gung University, 199 Tung Hwa North Road, Taipei, 105 Taiwan. E-mail address: [email protected] (Y.-C. Chen). d These two authors contributed equally to this manuscript. http://dx.doi.org/10.1016/j.jfma.2016.10.012 0929-6646/Copyright ª 2016, Formosan Medical Association. Published by Elsevier Taiwan LLC. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Please cite this article in press as: Kuo G, et al., Using acute kidney injury severity and scoring systems to predict outcome in patients with burn injury, Journal of the Formosan Medical Association (2016), http://dx.doi.org/10.1016/j.jfma.2016.10.012
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G. Kuo et al. Conclusion: Our results revealed that AKI stage has considerable discriminative power for predicting mortality. Compared with other prognostic models, AKI stage is easier to use to assess outcome in patients with severe burn injury. Copyright ª 2016, Formosan Medical Association. Published by Elsevier Taiwan LLC. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/bync-nd/4.0/).
Introduction Acute kidney injury (AKI) is a common, harmful, and potentially treatable complication responsible for increased medical expenditure and poor outcomes in hospital settings.1e3 Its incidence varies from 28% to 75%, depending on the etiologies, and has increased in the past decade.4e8 In addition, AKI developed after admission to a burn unit is associated with 20-fold increased mortality and long-term complications.9 Even a minor acute reduction in kidney function is associated with an adverse prognosis. In 2012, the Kidney Disease Improving Global Outcomes (KDIGO) Group modified the definition of AKI, merging it with that of the Acute Kidney Injury Network (AKIN) and risk of renal failure, injury to the kidney, failure of kidney function, loss of kidney function, and end-stage renal failure (RIFLE) criteria. The criteria defined AKI as occurring in patients who exhibited a 0.3 mg/dL increase in serum creatinine within 48 hours, a 1.5-time increase in serum creatinine from baseline within 7 days, or urine volume less than 0.5 mL/kg/h for 6 hours (http://kdigo.org/home/ guidelines/acute-kidney-injury/). Acute Physiology and Chronic Health Evaluation (APACHE) II and APACHE III, the Sequential Organ Failure Assessment (SOFA) score, and the Organ System Failure (OSF) score have been used to predict mortality in patients with burns.10e13 However, these risk models are complex and difficult to use clinically. Although research has argued that RIFLE and AKIN also predict outcome, so far no study has examined the new AKI staging developed by the KDIGO Group. Thus, this study compared the efficacy of using these scores in accurately predicting mortality in a burn unit.
Methods Study design, patient information, and data collection This is a post hoc analysis of prospectively collected data from the intensive care burn unit at a tertiary care referral center in Taiwan. The Institutional Review Board of the study hospital approved the study and waived the need for informed consent because there was no breach of privacy. The study protocol was approved by the Institutional Review Board of the Chang Gung Memorial Hospital (CGMH; Taipei, Taiwan; IRB approval number: 201600665B0). Patients admitted to the burn unit between July 2004 and December 2006 were enrolled in the database. Patients who were receiving dialysis, aged under 18 years, or reported prior organ transplantation were excluded.
The diagnosis and severity of AKI were confirmed based on data obtained within 3 days after patient admission using the KDIGO Clinical Practice Guidelines for Acute Kidney Injury.14,15 The hourly urine amount was recorded by the nursing staff as intensive care unit (ICU) routine. In determining the short-term outcome, the primary end point of this research was mortality. Three-month mortality was considered the secondary outcome. After hospital discharge, 3-month follow-up examinations were performed by reviewing the follow-up records. None of the patients was lost to follow-up in the study period. Patient demographics; reason for admission; clinical and laboratory information; APACHE II, APACHE III, and SOFA scores; ICU length of stay; and hospital mortality data were recorded from the health information system in CGMH.16,17 A simple model for classifying AKI severity was developed as follows: non-AKI (0 points), Stage 1 (1 point), Stage 2 (2 points), and Stage 3 (3 points).6,18
Statistical analysis Descriptive statistical results were expressed as mean standard error. All variables were tested for normal distribution using the KolmogoroveSmirnov test. The Student t test was used to compare the means of continuous variables and normally distributed data. Categorical data were tested using the c2 test or Fisher’s exact test. Calibration was assessed using the HosmereLemeshow goodness-of-fit test (C statistic) to compare the number of observed and predicted deaths in the different risk groups for the entire range of death probabilities. The discrimination of each receiver operating characteristic curve was assessed using the area under the curve, which was compared using a nonparametric approach. Area under the receiver operating characteristic (AUROC) curve analysis was also conducted to estimate cutoff values, sensitivity, specificity, overall correctness, and positive and negative predictive values. Finally, cutoff points were calculated by determining the optimal Youden index (sensitivity þ specificity e 1). Cumulative survival curves over time were generated using the KaplaneMeier approach and compared using the log rank test. All statistical tests were two-tailed; a p value < 0.05 was considered statistically significant. Data were analyzed using SPSS 13.0 for Windows (SPSS Inc., Chicago, IL, USA).
Results Overall, 145 consecutive patients with a mean age of 41.9 years were investigated. AKI was diagnosed in 52 patients (35.8%). Thirty-five patients died during hospitalization
Please cite this article in press as: Kuo G, et al., Using acute kidney injury severity and scoring systems to predict outcome in patients with burn injury, Journal of the Formosan Medical Association (2016), http://dx.doi.org/10.1016/j.jfma.2016.10.012
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AKI severity in burn patients Table 1
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Demographic data and clinical characteristics according to survivors and nonsurvivors.
Age (y) Male Burn surface area (%) Burn degree Inhalation injury high grade Ventilator Shock Underlying disease DM CKD Laboratory data GCS (points) Leukocyte (103/mL) Hemoglobin (g/dL) Platelet (103/mL) Cr (mg/dL) Albumin (mg/dL) AST (IU/L) Ca corrected (mg/dL) P (mg/dL) PaO2/FiO2 (mmHg) Urine amount (mL) Outcome AKI Non-AKI Stage 1 Stage 2 Stage 3 RRT ICU stay (d) ICU score AKI SOFA APACHE II APACHE III OSF
All patients (n Z 145)
Survivors (n Z 110)
Nonsurvivors (n Z 35)
p
41.9 1.6 110 (75.9) 47.9 1.9 2.5 0.1 6 (4.1) 93 (64.1) 15 (10.3)
38.9 1.7 84 (77.3) 39.3 1.5 2.5 0.1 3 (2.7) 60 (54.5) 11 (10.0)
51.2 3.8 25 (71.4) 74.9 3.6 2.6 0.3 3 (8.8) 33 (94.3) 4 (11.4)
0.001 NS (0.482) <0.001 NS (0.154) NS (0.131) <0.001 NS (0.809)
20 (13.8) 4 (1.8)
14 (12.7) 2 (1.8)
6 (17.1) 2 (5.7)
NS (0.509) NS (0.220)
13.9 0.2 14.9 0.8 13.9 0.3 201.7 6.8 1.0 0.1 2.7 0.1 64.6 9.0 8.7 0.1 3.6 0.1 379.1 17.2 3467 172
14.3 0.2 14.2 0.7 14.5 0.3 203.6 7.4 0.9 0.1 2.8 0.1 63.8 11.3 8.6 0.1 3.4 0.1 388.6 10.1 3810 193
12.5 0.5 17.3 2.1 12.1 0.6 195.6 16.6 1.5 0.2 2.2 0.2 67.0 12.1 9.1 0.2 4.1 0.3 350.8 33.1 2389 315
<0.001 NS (0.166) <0.001 NS (0.611) 0.004 0.005 NS (0.884) NS (0.067) 0.025 NS (0.118) <0.001
86 35 12 12 9 (6.2) 35.6 2.0
79 24 5 2 2 (1.8) 43.8 23.2
7 11 7 10 7 (20.0) 9.6 1.6
<0.001 <0.001
0.7 0.1 3.5 0.2 9.7 0.5 39.6 2.4 0.7 0.1
0.4 0.1 2.8 0.2 7.6 0.5 29.6 1.9 0.5 0.1
1.6 0.2 5.8 0.6 16.1 1.2 71.8 4.9 1.2 0.2
<0.001 <0.001 <0.001 <0.001 <0.001
<0.001
Data are presented as n, n (%), or mean standard error, unless otherwise indicated. AKI Z acute kidney injury; APACHE Z Acute Physiology and Chronic Health Evaluation; AST Z aspartate transaminase; Ca Z calcium; CKD Z chronic kidney disease; Cr Z creatinine; DM Z diabetes mellitus; FiO2 Z fraction of inspiration O2; GCS Z Glasgow Coma Scale; ICU Z intensive care unit; NS Z not significant; OSF Z Organ System Failure; P Z phosphorus; PaO2 Z partial pressure of oxygen; RRT Z renal replacement therapy; SOFA Z Sequential Organ Failure Assessment.
(mortality rate: non-AKI, 7/86 Z 8.1%; Stage 1, 11/ 35 Z 31.4%; Stage 2, 7/12 Z 58.3%; Stage 3, 10/ 12 Z 83.3%; p < 0.001). Patient data, including age, sex, laboratory parameters, and risk model scores, are listed in Tables 1 and 2 according to mortality and AKI, respectively. Patients who died during the hospital course were older and had larger total burn surface area (TBSA), more ventilator use, poor conscious levels, worse renal function, and higher phosphate levels at admission. These patients also exhibited lower hemoglobin (Hb), albumin, and urine output levels. In the admission period, they had longer ICU stays and renal replacement therapy. Patients with AKI were older and had larger TBSA, more ventilator use, lower Glasgow Coma Scale scores, and lower Hb. Patients with StevenseJohnson syndrome (SJS) and toxic epidermal
necrolysis (TEN) exhibited a significantly higher mortality rate compared to patients with other etiologies. The etiologies on admission are listed in Table 3. The predictive ability of APACHE II, APACHE III, SOFA, and AKI staging was compared, and the calibration and discrimination of the models are listed in Table 4. Among these risk models, APACHE III exhibited the highest prediction ability (AUROC, 0.889 0.029), followed by APACHE II (AUROC, 0.841 0.037) and AKI staging (AUROC, 0.819 0.045). Both the APACHE III and APACHE II were significantly superior to SOFA and OSF in evaluating the mortality (APACHE II vs. SOFA p Z 0.004; APACHE II vs. OSF p < 0.001; APACHE III vs. SOFA p Z 0.033; APACHE III vs. OSF p Z 0.002, respectively). Severity of AKI was also superior to OSF (p Z 0.022).
Please cite this article in press as: Kuo G, et al., Using acute kidney injury severity and scoring systems to predict outcome in patients with burn injury, Journal of the Formosan Medical Association (2016), http://dx.doi.org/10.1016/j.jfma.2016.10.012
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G. Kuo et al. Table 2
Demographic data and clinical characteristics according to AKI and non-AKI.
Age (y) Male Burn surface area (%) Inhalation injury Ventilator Shock Underlying disease DM CKD Laboratory data GCS (points) Leukocyte (103/mL) Hemoglobin (g/dL) Platelet (103/mL) Cr (mg/dL) Albumin (mg/dL) AST (IU/L) Ca (mg/dL) P (mg/dL) PaO2/FiO2 (mmHg) Urine amount (mL) Outcome Mortality ICU stay (d) ICU score SOFA APACHE II APACHE III OSF
AKI (n Z 52)
Non-AKI (n Z 93)
p
47.3 2.6 42 (73.7) 60.5 3.3 3 (5.3) 43 (75.4) 5 (8.8)
38.4 2.1 68 (77.3) 39.9 1.9 3 (3.4) 50 (56.4) 10 (11.4)
0.010 NS (0.622) <0.001 NS (0.584) 0.022 NS (0.617)
7 (12.3) 2 (2.3)
13 (14.8) 2 (3.5)
NS (0.671) NS (0.657)
13.1 0.4 15.3 1.2 13.0 0.5 187.7 11.7 1.3 0.1 2.6 0.1 76.4 16.6 8.9 0.2 3.8 0.2 375.0 21.1 3359 294
14.4 0.2 14.6 0.9 14.4 0.2 210.8 8.2 0.9 0.1 2.8 0.1 56.3 10.2 8.6 0.1 3.5 0.1 381.8 11.8 3536 212
0.006 NS (0.681) 0.010 NS (0.099) 0.011 NS (0.263) NS (0.280) NS (0.173) NS (0.170) NS (0.763) NS (0.617)
29 (50.9) 43.6 2.6
6 (6.8) 23.3 2.6
<0.001 <0.001
4.4 0.5 11.9 0.9 51.3 4.4 1.0 0.1
2.9 0.2 8.1 0.6 31.8 2.4 0.5 0.1
0.011 0.001 <0.001 0.010
Data are presented as n (%) or mean standard deviation, unless otherwise indicated. AKI Z acute kidney injury; APACHE Z Acute Physiology and Chronic Health Evaluation; AST Z aspartate transaminase; Ca Z calcium; CKD Z chronic kidney disease; Cr Z creatinine; DM Z diabetes mellitus; FiO2 Z fraction of inspiration O2; GCS Z Glasgow Coma Scale; ICU Z intensive care unit; NS Z not significant; OSF Z Organ System Failure; P Z phosphorus; PaO2 Z partial pressure of oxygen; SOFA Z Sequential Organ Failure Assessment.
Table 3
Etiologies on admission.
Flame burn injury Scald burn injury Electric burn injury Chemical burn injury SJS/TEN
All patients (n Z 145)
Survivors (n Z 110)
Nonsurvivors (n Z 35)
p
77 (53.1) 40 (27.6) 5 (3.4) 7 (4.8) 16 (11.0)
59 (53.6) 33 (30.0) 5 (4.5) 7 (6.4) 6 (5.5)
18 (51.4) 7 (20.0) 0 (0.0) 0 (0.0) 10 (28.6)
NS (0.820) NS (0.249) NS (0.199) NS (0.126) <0.001
Data are presented as n (%). NS Z not significant; SJS Z StevenseJohnson syndrome; TEN Z toxic epidermal necrolysis.
To determine the selected cutoff points for predicting in-hospital mortality, the sensitivity, specificity, and overall accuracy of the prediction were determined (Table 5). Among the models, APACHE III exhibited the highest Youden index score and overall correctness at the 48-point cutoff. The AKI system exhibited the highest sensitivity for prognostic prediction. Multivariate logistic regression analysis with backward elimination was conducted to identify demographic and clinical characteristics associated with mortality in patients
with burns (Table 6). Age, TBSA, ventilator usage, Glasgow Coma Scale scores, Hb, creatinine, albumin, phosphate, and SJS/TEN that were statistically significant were included in the multivariate analysis by applying a multiple logistic regression based on forward stepwise regression to obtain independent predictors. Only TBSA, ventilator usage, AKI, and SJS/TEN were associated with mortality. Figure 1 shows the cumulative survival rates according to the AKI stage. The cumulative survival rates revealed that mortality increased with the AKI stage.
Please cite this article in press as: Kuo G, et al., Using acute kidney injury severity and scoring systems to predict outcome in patients with burn injury, Journal of the Formosan Medical Association (2016), http://dx.doi.org/10.1016/j.jfma.2016.10.012
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AKI severity in burn patients Table 4
5
Calibration and discrimination for the scoring methods in predicting mortality. Calibration
AKI APACHE II APACHE III SOFA OSF
Discrimination
Goodness-of-fit (c )
df
p
AUROC standard error
95% CI
p
0.360 7.121 3.701 7.237 0.118
2 8 8 6 1
0.835 0.524 0.883 0.299 0.732
0.819 0.045 0.841 0.037 0.889 0.029 0.750 0.053 0.690 0.053
0.730e0.908 0.833e0.946 0.769e0.912 0.585e0.794 0.730e0.908
0.042 <0.001 <0.001 <0.001 <0.001
2
AKI Z acute kidney injury; APACHE Z Acute Physiology and Chronic Health Evaluation; AUROC Z area under the receiver operating characteristic curve; CI Z confidence interval; OSF Z Organ System Failure; SOFA Z Sequential Organ Failure Assessment.
Table 5
Prediction of different scores for mortality.
Predictive factors
Cutoff point
Youden index
Sensitivity (%)
Specificity (%)
Overall correctness (%)
AKI APACHE II APACHE III SOFA OSF
1 13 48 4 1
0.539 0.489 0.690 0.425 0.290
82 62 76 76 78
72 86 84 66 50
77 74 80 71 64
AKI Z acute kidney injury; APACHE Z Acute Physiology and Chronic Health Evaluation; SOFA Z Sequential Organ Failure Assessment; OSF Z Organ System Failure.
Table 6
Multivariate logistic regression analysis for mortality.
Parameter
Beta coefficient
Multivariate logistic regression Surface area 0.079 Ventilator use 3.939 Acute kidney injury 2.688 SJS/TEN 3.341 Constant e10.908
Standard error
Odds ratio (95% CI)
p
0.021 0.845 1.704 1.704 2.640
1.082 (1.038e1.129) 51.368 (1.440e832.372) 14.701 (2.803e77.096) 28.237 (1.000e797.213) d
<0.001 0.031 0.001 0.050 d
CI Z confidence interval; SJS Z StevenseJohnson syndrome (SJS); TEN Z toxic epidermal necrolysis.
Discussion AKI is not uncommon in ICUs and has been associated with increased mortality, ICU length of stay, and medical costs for critically ill patients.19,20 Patients admitted to burn units typically exhibit complex syndromes with numerous pathways that affect renal function and involve hemodynamic changes, tissue breakdown products, inflammatory cytokines, septicemia, and drug toxicity.21 AKI is also a key factor in mortality. Recent investigations using APACHE III examined acute physiology scores, age, and chronic health problem scores, which are currently widely used to predict clinical outcomes in ICU settings and exhibit the accuracy of APACHE III in estimating mortality in patients with burns.11,22 Moore et al11 reported an AUROC of 0.833 while using APACHE III, which is similar to our results. Further investigation supported the efficacy of SOFA scores in assessing the extent of organ dysfunction in various groups, including critically ill patients with burn injuries, but did not provide related AUROC information.12 Coca et al9 reported the association between different RIFLE stages of AKI with morbidity and mortality in burn patients, but did
not compare the complex scoring systems with AKI stage. Chung et al23 compared the outcome between early intensive renal replacement therapy and conventional therapy in which AKI was defined by AKIN criteria. Patients with either Stage 2 AKI in combination with shock or Stage 3 AKI received continuous venovenous hemofiltration. Patients with early treatment had less 28-day mortality compared with those who had conventional treatment (38% vs. 71% p Z 0.011). To our knowledge, this study is the first to compare the usefulness of these complex scoring systems with the 2012 AKI definition for outcome prediction. Although the discriminative power of APACHE III is higher, the KDIGO AKI staging approach is a considerably simpler method that requires fewer parameters to assess the prognosis in patients with burns. For the further interventional trial, our result also provided an overview of the definition. Compared with survivors, nonsurvivors were older, had anemia, and exhibited poor conscious levels, all of which have been exhibited in other study groups. Moreau et al24 emphasized the importance of age in burn-related mortality and developed an ageerisk score calculated from TBSA
Please cite this article in press as: Kuo G, et al., Using acute kidney injury severity and scoring systems to predict outcome in patients with burn injury, Journal of the Formosan Medical Association (2016), http://dx.doi.org/10.1016/j.jfma.2016.10.012
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G. Kuo et al. single measurement of ICU scores; repeating the SOFA or AKI stage measurement might improve the score accuracy.
Conclusion Our results provide additional evidence that AKI stage is easier to use to predict outcome than are complex prognostic scores. We also demonstrated that TBSA, ventilator use, AKI, and SJS/TEN are critical in determining prognosis in patients with burns. When considering the cost effectiveness and ease of implementation, the AKI scale is recommended for evaluating short-term prognosis in critically ill patients with burns.
Acknowledgments The authors thank Shu-Yun Wang for assistance in the analysis, sampling, and data collection. This study was supported by grants from the Ministry of Science and Technology (NSC 103-2314-B-182A-040, 103-2314-B-182A018-MY3). Figure 1 Cumulative survival rates of patients according to different stages of acute kidney injury (AKI).
References
and an age cubic functional form. Furthermore, anemia was also reported in other study groups.25 Patients with large burns experience the anemia of critical illness. Acute hemolysis, red blood cell sequestration, reduced red blood cell survival, operative blood loss, nutritional deficiencies, phlebotomies, and reduced erythropoiesis were the main etiologies. A poor conscious level after severe burn injury might be related to cerebral edema and cytokine storm. However, the pathophysiology of these possible relationships has not been widely discussed. Using multivariate logistic regression analysis, our investigation revealed that TBSA, AKI, ventilator use, and SJS/TEN are independent factors for death. The percentage of TBSA is widely accepted to be a risk factor for death, and our analyses confirmed that it is. For a 1% increase in TBSA, we expect to see an approximately 8.2% increase in the odds of death. In addition, AKI exerts a strong impact on mortality in patients with burns.9,26 We found that patients with AKI exhibited a 14.7-fold increase in likelihood of death in the odds ratio. Although use of mechanical ventilation may have been misinterpreted as the patient having received airway burns, Galeiras et al27 demonstrated a strong correlation between early mechanical ventilation use and mortality in patients with burns. Prior studies have identified SJS/TEN as a strong factor of mortality in burn units.28,29 Novel biomarkers such as cystatin-C and NGAL initiated a new edge of early diagnosis of AKI in ICUs.30e32 Further study combining the riskevaluating strategy and biomarkers might improve outcomes by facilitating early detection. Despite the encouraging results of this study, several potential limitations should be considered. First, this was a post hoc analysis of prospectively collected data from a single tertiary care medical center, which limits the generalization of the findings. We also could not compare cost efficacy when using the AKI staging system. Second, our research used a
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AKI severity in burn patients 13. Chang CH, Fan PC, Chang MY, Tian YC, Hung CC, Fang JT, et al. Acute kidney injury enhances outcome prediction ability of Sequential Organ Failure Assessment score in critically ill patients. PLoS One 2014;9:e109649. 14. Palevsky PM, Liu KD, Brophy PD, Chawla LS, Parikh CR, Thakar CV, et al. KDOQI US commentary on the 2012 KDIGO clinical practice guideline for acute kidney injury. Am J Kidney Dis 2013;61:649e72. 15. Okusa MD, Davenport A. Reading between the (guide)linesdthe KDIGO practice guideline on acute kidney injury in the individual patient. Kidney Int 2014;85:39e48. 16. Knaus WA, Wagner DP, Draper EA, Zimmerman JE, Bergner M, Bastos PG, et al. The APACHE III prognostic system. Risk prediction of hospital mortality for critically ill hospitalized adults. Chest 1991;100:1619e36. 17. Vincent JL, Moreno R, Takala J, Willatts S, De Mendonc ¸a A, Bruining H, et al. The SOFA (Sepsis-related Organ Failure Assessment) score to describe organ dysfunction/failure. On behalf of the Working Group on Sepsis-Related Problems of the European Society of Intensive Care Medicine. Intensive Care Med 1996;22:707e10. 18. Chang CH, Lin CY, Tian YC, Jenq CC, Chang MY, Chen YC, et al. Acute kidney injury classification: comparison of AKIN and RIFLE criteria. Shock 2010;33:247e52. 19. Chen YC, Tsai FC, Fang JT, Yang CW. Acute kidney injury in adults receiving extracorporeal membrane oxygenation. J Formos Med Assoc 2014;113:778e85. 20. Zhu JC, Chen SL, Jin GZ, Shao MX, Kan J, Lu CY, et al. Acute renal injury after thoracic endovascular aortic repair of Stanford type B aortic dissection: incidence, risk factors, and prognosis. J Formos Med Assoc 2014;113:612e9. 21. Wang TH, Lin TY. Responding to mass burn casualties caused by corn powder at the Formosa Water Park in 2015. J Formos Med Assoc 2015;114:1151e3. 22. Tanaka Y, Shimizu M, Hirabayashi H. Acute Physiology, Age, and Chronic Health Evaluation (APACHE) III score is an alternative efficient predictor of mortality in burn patients. Burns 2007; 33:316e20.
7 23. Chung KK, Lundy JB, Matson JR, Renz EM, White CE, King BT, et al. Continuous venovenous hemofiltration in severely burned patients with acute kidney injury: a cohort study. Crit Care 2009;13:R62. 24. Moreau AR, Westfall PH, Cancio LC, Mason Jr AD. Development and validation of an age-risk score for mortality predication after thermal injury. J Trauma 2005;58:967e72. 25. Posluszny Jr JA, Gamelli RL. Anemia of thermal injury: combined acute blood loss anemia and anemia of critical illness. J Burn Care Res 2010;31:229e42. 26. Steinvall I, Bak Z, Sjoberg F. Acute kidney injury is common, parallels organ dysfunction or failure, and carries appreciable mortality in patients with major burns: a prospective exploratory cohort study. Crit Care 2008;12:R124. 27. Galeiras R, Lorente JA, Pe ´rtega S, Vallejo A, Tomicic V, de la Cal MA, et al. A model for predicting mortality among critically ill burn victims. Burns 2009;35:201e9. 28. Sekula P, Liss Y, Davidovici B, Dunant A, Roujeau JC, Kardaun S, et al. Evaluation of SCORTEN on a cohort of patients with StevenseJohnson syndrome and toxic epidermal necrolysis included in the RegiSCAR study. J Burn Care Res 2011;32: 237e45. 29. Khoo AK, Foo CL. Toxic epidermal necrolysis in a burns centre: a 6-year review. Burns 1996;22:275e8. 30. Yang HT, Yim H, Cho YS, Kym D, Hur J, Kim JH, et al. Assessment of biochemical markers in the early post-burn period for predicting acute kidney injury and mortality in patients with major burn injury: comparison of serum creatinine, serum cystatin-C, plasma and urine neutrophil gelatinase-associated lipocalin. Crit Care 2014;18:R151. 31. Lin CY, Chang CH, Fan PC, Tian YC, Chang MY, Jenq CC, et al. Serum interleukin-18 at commencement of renal replacement therapy predicts short-term prognosis in critically ill patients with acute kidney injury. PLoS One 2013;8:e66028. 32. Chen TH, Chang CH, Lin CY, Jenq CC, Chang MY, Tian YC, et al. Acute kidney injury biomarkers for patients in a coronary care unit: a prospective cohort study. PLoS One 2012;7:e32328.
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Journal of the Formosan Medical Association (2016) xx, 1e7
Available online at www.sciencedirect.com
ScienceDirect journal homepage: www.jfma-online.com
ORIGINAL ARTICLE
Using acute kidney injury severity and scoring systems to predict outcome in patients with burn injury George Kuo a,d, Shih-Yi Yang b,d, Shiow-Shuh Chuang b, Pei-Chun Fan a,c, Chih-Hsiang Chang a,c, Yen-Chang Hsiao b, Yung-Chang Chen a,c,* a
Department of Nephrology, Kidney Research Center, Taoyuan City, Taiwan Department of Plastic Surgery, Linkou Burn Center, Chang Gung Memorial Hospital, Taipei, Taiwan c College of Medicine, Chang Gung University, Taoyuan, Taiwan b
Received 7 September 2016; received in revised form 16 October 2016; accepted 28 October 2016
KEYWORDS acute kidney injury; Acute Physiology and Chronic Health Evaluation; burn; prognosis; Sequential Organ Failure Assessment
Background/Purpose: Acute kidney injury (AKI) is a frequent complication of severe burn injury and is associated with mortality. The definition of AKI was modified by the Kidney Disease Improving Global Outcomes Group in 2012. So far, no study has compared the outcome accuracy of the new AKI staging guidelines with that of the complex score system. Hence, we compared the accuracy of these approaches in predicting mortality. Methods: This was a post hoc analysis of prospectively collected data from an intensive care burn unit in a tertiary care university hospital. Patients admitted to this unit from July 2004 to December 2006 were enrolled. Demographic, clinical, and laboratory data and prognostic risk scores were used as predictors of mortality. Results: A total of 145 adult patients with a mean age of 41.9 years were studied. Thirty-five patients (24.1%) died during the hospital course. Among the prognostic risk models, the Acute Physiology and Chronic Health Evaluation III system exhibited the strongest discriminative power and the AKI staging system also predicted mortality well (areas under the receiver operating characteristic curve: 0.889 vs. 0.835). Multivariate logistic regression analysis identified total burn surface area, ventilator use, AKI, and toxic epidermal necrolysis as independent risk factors for mortality.
Conflicts of interest: The authors have no conflicts of interest relevant to this article. * Corresponding author. Division of Critical Care Nephrology, Department of Nephrology, Chang Gung Memorial Hospital, College of Medicine, Chang Gung University, 199 Tung Hwa North Road, Taipei, 105 Taiwan. E-mail address: [email protected] (Y.-C. Chen). d These two authors contributed equally to this manuscript. http://dx.doi.org/10.1016/j.jfma.2016.10.012 0929-6646/Copyright ª 2016, Formosan Medical Association. Published by Elsevier Taiwan LLC. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Please cite this article in press as: Kuo G, et al., Using acute kidney injury severity and scoring systems to predict outcome in patients with burn injury, Journal of the Formosan Medical Association (2016), http://dx.doi.org/10.1016/j.jfma.2016.10.012
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G. Kuo et al. Conclusion: Our results revealed that AKI stage has considerable discriminative power for predicting mortality. Compared with other prognostic models, AKI stage is easier to use to assess outcome in patients with severe burn injury. Copyright ª 2016, Formosan Medical Association. Published by Elsevier Taiwan LLC. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/bync-nd/4.0/).
Introduction Acute kidney injury (AKI) is a common, harmful, and potentially treatable complication responsible for increased medical expenditure and poor outcomes in hospital settings.1e3 Its incidence varies from 28% to 75%, depending on the etiologies, and has increased in the past decade.4e8 In addition, AKI developed after admission to a burn unit is associated with 20-fold increased mortality and long-term complications.9 Even a minor acute reduction in kidney function is associated with an adverse prognosis. In 2012, the Kidney Disease Improving Global Outcomes (KDIGO) Group modified the definition of AKI, merging it with that of the Acute Kidney Injury Network (AKIN) and risk of renal failure, injury to the kidney, failure of kidney function, loss of kidney function, and end-stage renal failure (RIFLE) criteria. The criteria defined AKI as occurring in patients who exhibited a 0.3 mg/dL increase in serum creatinine within 48 hours, a 1.5-time increase in serum creatinine from baseline within 7 days, or urine volume less than 0.5 mL/kg/h for 6 hours (http://kdigo.org/home/ guidelines/acute-kidney-injury/). Acute Physiology and Chronic Health Evaluation (APACHE) II and APACHE III, the Sequential Organ Failure Assessment (SOFA) score, and the Organ System Failure (OSF) score have been used to predict mortality in patients with burns.10e13 However, these risk models are complex and difficult to use clinically. Although research has argued that RIFLE and AKIN also predict outcome, so far no study has examined the new AKI staging developed by the KDIGO Group. Thus, this study compared the efficacy of using these scores in accurately predicting mortality in a burn unit.
Methods Study design, patient information, and data collection This is a post hoc analysis of prospectively collected data from the intensive care burn unit at a tertiary care referral center in Taiwan. The Institutional Review Board of the study hospital approved the study and waived the need for informed consent because there was no breach of privacy. The study protocol was approved by the Institutional Review Board of the Chang Gung Memorial Hospital (CGMH; Taipei, Taiwan; IRB approval number: 201600665B0). Patients admitted to the burn unit between July 2004 and December 2006 were enrolled in the database. Patients who were receiving dialysis, aged under 18 years, or reported prior organ transplantation were excluded.
The diagnosis and severity of AKI were confirmed based on data obtained within 3 days after patient admission using the KDIGO Clinical Practice Guidelines for Acute Kidney Injury.14,15 The hourly urine amount was recorded by the nursing staff as intensive care unit (ICU) routine. In determining the short-term outcome, the primary end point of this research was mortality. Three-month mortality was considered the secondary outcome. After hospital discharge, 3-month follow-up examinations were performed by reviewing the follow-up records. None of the patients was lost to follow-up in the study period. Patient demographics; reason for admission; clinical and laboratory information; APACHE II, APACHE III, and SOFA scores; ICU length of stay; and hospital mortality data were recorded from the health information system in CGMH.16,17 A simple model for classifying AKI severity was developed as follows: non-AKI (0 points), Stage 1 (1 point), Stage 2 (2 points), and Stage 3 (3 points).6,18
Statistical analysis Descriptive statistical results were expressed as mean standard error. All variables were tested for normal distribution using the KolmogoroveSmirnov test. The Student t test was used to compare the means of continuous variables and normally distributed data. Categorical data were tested using the c2 test or Fisher’s exact test. Calibration was assessed using the HosmereLemeshow goodness-of-fit test (C statistic) to compare the number of observed and predicted deaths in the different risk groups for the entire range of death probabilities. The discrimination of each receiver operating characteristic curve was assessed using the area under the curve, which was compared using a nonparametric approach. Area under the receiver operating characteristic (AUROC) curve analysis was also conducted to estimate cutoff values, sensitivity, specificity, overall correctness, and positive and negative predictive values. Finally, cutoff points were calculated by determining the optimal Youden index (sensitivity þ specificity e 1). Cumulative survival curves over time were generated using the KaplaneMeier approach and compared using the log rank test. All statistical tests were two-tailed; a p value < 0.05 was considered statistically significant. Data were analyzed using SPSS 13.0 for Windows (SPSS Inc., Chicago, IL, USA).
Results Overall, 145 consecutive patients with a mean age of 41.9 years were investigated. AKI was diagnosed in 52 patients (35.8%). Thirty-five patients died during hospitalization
Please cite this article in press as: Kuo G, et al., Using acute kidney injury severity and scoring systems to predict outcome in patients with burn injury, Journal of the Formosan Medical Association (2016), http://dx.doi.org/10.1016/j.jfma.2016.10.012
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AKI severity in burn patients Table 1
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Demographic data and clinical characteristics according to survivors and nonsurvivors.
Age (y) Male Burn surface area (%) Burn degree Inhalation injury high grade Ventilator Shock Underlying disease DM CKD Laboratory data GCS (points) Leukocyte (103/mL) Hemoglobin (g/dL) Platelet (103/mL) Cr (mg/dL) Albumin (mg/dL) AST (IU/L) Ca corrected (mg/dL) P (mg/dL) PaO2/FiO2 (mmHg) Urine amount (mL) Outcome AKI Non-AKI Stage 1 Stage 2 Stage 3 RRT ICU stay (d) ICU score AKI SOFA APACHE II APACHE III OSF
All patients (n Z 145)
Survivors (n Z 110)
Nonsurvivors (n Z 35)
p
41.9 1.6 110 (75.9) 47.9 1.9 2.5 0.1 6 (4.1) 93 (64.1) 15 (10.3)
38.9 1.7 84 (77.3) 39.3 1.5 2.5 0.1 3 (2.7) 60 (54.5) 11 (10.0)
51.2 3.8 25 (71.4) 74.9 3.6 2.6 0.3 3 (8.8) 33 (94.3) 4 (11.4)
0.001 NS (0.482) <0.001 NS (0.154) NS (0.131) <0.001 NS (0.809)
20 (13.8) 4 (1.8)
14 (12.7) 2 (1.8)
6 (17.1) 2 (5.7)
NS (0.509) NS (0.220)
13.9 0.2 14.9 0.8 13.9 0.3 201.7 6.8 1.0 0.1 2.7 0.1 64.6 9.0 8.7 0.1 3.6 0.1 379.1 17.2 3467 172
14.3 0.2 14.2 0.7 14.5 0.3 203.6 7.4 0.9 0.1 2.8 0.1 63.8 11.3 8.6 0.1 3.4 0.1 388.6 10.1 3810 193
12.5 0.5 17.3 2.1 12.1 0.6 195.6 16.6 1.5 0.2 2.2 0.2 67.0 12.1 9.1 0.2 4.1 0.3 350.8 33.1 2389 315
<0.001 NS (0.166) <0.001 NS (0.611) 0.004 0.005 NS (0.884) NS (0.067) 0.025 NS (0.118) <0.001
86 35 12 12 9 (6.2) 35.6 2.0
79 24 5 2 2 (1.8) 43.8 23.2
7 11 7 10 7 (20.0) 9.6 1.6
<0.001 <0.001
0.7 0.1 3.5 0.2 9.7 0.5 39.6 2.4 0.7 0.1
0.4 0.1 2.8 0.2 7.6 0.5 29.6 1.9 0.5 0.1
1.6 0.2 5.8 0.6 16.1 1.2 71.8 4.9 1.2 0.2
<0.001 <0.001 <0.001 <0.001 <0.001
<0.001
Data are presented as n, n (%), or mean standard error, unless otherwise indicated. AKI Z acute kidney injury; APACHE Z Acute Physiology and Chronic Health Evaluation; AST Z aspartate transaminase; Ca Z calcium; CKD Z chronic kidney disease; Cr Z creatinine; DM Z diabetes mellitus; FiO2 Z fraction of inspiration O2; GCS Z Glasgow Coma Scale; ICU Z intensive care unit; NS Z not significant; OSF Z Organ System Failure; P Z phosphorus; PaO2 Z partial pressure of oxygen; RRT Z renal replacement therapy; SOFA Z Sequential Organ Failure Assessment.
(mortality rate: non-AKI, 7/86 Z 8.1%; Stage 1, 11/ 35 Z 31.4%; Stage 2, 7/12 Z 58.3%; Stage 3, 10/ 12 Z 83.3%; p < 0.001). Patient data, including age, sex, laboratory parameters, and risk model scores, are listed in Tables 1 and 2 according to mortality and AKI, respectively. Patients who died during the hospital course were older and had larger total burn surface area (TBSA), more ventilator use, poor conscious levels, worse renal function, and higher phosphate levels at admission. These patients also exhibited lower hemoglobin (Hb), albumin, and urine output levels. In the admission period, they had longer ICU stays and renal replacement therapy. Patients with AKI were older and had larger TBSA, more ventilator use, lower Glasgow Coma Scale scores, and lower Hb. Patients with StevenseJohnson syndrome (SJS) and toxic epidermal
necrolysis (TEN) exhibited a significantly higher mortality rate compared to patients with other etiologies. The etiologies on admission are listed in Table 3. The predictive ability of APACHE II, APACHE III, SOFA, and AKI staging was compared, and the calibration and discrimination of the models are listed in Table 4. Among these risk models, APACHE III exhibited the highest prediction ability (AUROC, 0.889 0.029), followed by APACHE II (AUROC, 0.841 0.037) and AKI staging (AUROC, 0.819 0.045). Both the APACHE III and APACHE II were significantly superior to SOFA and OSF in evaluating the mortality (APACHE II vs. SOFA p Z 0.004; APACHE II vs. OSF p < 0.001; APACHE III vs. SOFA p Z 0.033; APACHE III vs. OSF p Z 0.002, respectively). Severity of AKI was also superior to OSF (p Z 0.022).
Please cite this article in press as: Kuo G, et al., Using acute kidney injury severity and scoring systems to predict outcome in patients with burn injury, Journal of the Formosan Medical Association (2016), http://dx.doi.org/10.1016/j.jfma.2016.10.012
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G. Kuo et al. Table 2
Demographic data and clinical characteristics according to AKI and non-AKI.
Age (y) Male Burn surface area (%) Inhalation injury Ventilator Shock Underlying disease DM CKD Laboratory data GCS (points) Leukocyte (103/mL) Hemoglobin (g/dL) Platelet (103/mL) Cr (mg/dL) Albumin (mg/dL) AST (IU/L) Ca (mg/dL) P (mg/dL) PaO2/FiO2 (mmHg) Urine amount (mL) Outcome Mortality ICU stay (d) ICU score SOFA APACHE II APACHE III OSF
AKI (n Z 52)
Non-AKI (n Z 93)
p
47.3 2.6 42 (73.7) 60.5 3.3 3 (5.3) 43 (75.4) 5 (8.8)
38.4 2.1 68 (77.3) 39.9 1.9 3 (3.4) 50 (56.4) 10 (11.4)
0.010 NS (0.622) <0.001 NS (0.584) 0.022 NS (0.617)
7 (12.3) 2 (2.3)
13 (14.8) 2 (3.5)
NS (0.671) NS (0.657)
13.1 0.4 15.3 1.2 13.0 0.5 187.7 11.7 1.3 0.1 2.6 0.1 76.4 16.6 8.9 0.2 3.8 0.2 375.0 21.1 3359 294
14.4 0.2 14.6 0.9 14.4 0.2 210.8 8.2 0.9 0.1 2.8 0.1 56.3 10.2 8.6 0.1 3.5 0.1 381.8 11.8 3536 212
0.006 NS (0.681) 0.010 NS (0.099) 0.011 NS (0.263) NS (0.280) NS (0.173) NS (0.170) NS (0.763) NS (0.617)
29 (50.9) 43.6 2.6
6 (6.8) 23.3 2.6
<0.001 <0.001
4.4 0.5 11.9 0.9 51.3 4.4 1.0 0.1
2.9 0.2 8.1 0.6 31.8 2.4 0.5 0.1
0.011 0.001 <0.001 0.010
Data are presented as n (%) or mean standard deviation, unless otherwise indicated. AKI Z acute kidney injury; APACHE Z Acute Physiology and Chronic Health Evaluation; AST Z aspartate transaminase; Ca Z calcium; CKD Z chronic kidney disease; Cr Z creatinine; DM Z diabetes mellitus; FiO2 Z fraction of inspiration O2; GCS Z Glasgow Coma Scale; ICU Z intensive care unit; NS Z not significant; OSF Z Organ System Failure; P Z phosphorus; PaO2 Z partial pressure of oxygen; SOFA Z Sequential Organ Failure Assessment.
Table 3
Etiologies on admission.
Flame burn injury Scald burn injury Electric burn injury Chemical burn injury SJS/TEN
All patients (n Z 145)
Survivors (n Z 110)
Nonsurvivors (n Z 35)
p
77 (53.1) 40 (27.6) 5 (3.4) 7 (4.8) 16 (11.0)
59 (53.6) 33 (30.0) 5 (4.5) 7 (6.4) 6 (5.5)
18 (51.4) 7 (20.0) 0 (0.0) 0 (0.0) 10 (28.6)
NS (0.820) NS (0.249) NS (0.199) NS (0.126) <0.001
Data are presented as n (%). NS Z not significant; SJS Z StevenseJohnson syndrome; TEN Z toxic epidermal necrolysis.
To determine the selected cutoff points for predicting in-hospital mortality, the sensitivity, specificity, and overall accuracy of the prediction were determined (Table 5). Among the models, APACHE III exhibited the highest Youden index score and overall correctness at the 48-point cutoff. The AKI system exhibited the highest sensitivity for prognostic prediction. Multivariate logistic regression analysis with backward elimination was conducted to identify demographic and clinical characteristics associated with mortality in patients
with burns (Table 6). Age, TBSA, ventilator usage, Glasgow Coma Scale scores, Hb, creatinine, albumin, phosphate, and SJS/TEN that were statistically significant were included in the multivariate analysis by applying a multiple logistic regression based on forward stepwise regression to obtain independent predictors. Only TBSA, ventilator usage, AKI, and SJS/TEN were associated with mortality. Figure 1 shows the cumulative survival rates according to the AKI stage. The cumulative survival rates revealed that mortality increased with the AKI stage.
Please cite this article in press as: Kuo G, et al., Using acute kidney injury severity and scoring systems to predict outcome in patients with burn injury, Journal of the Formosan Medical Association (2016), http://dx.doi.org/10.1016/j.jfma.2016.10.012
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AKI severity in burn patients Table 4
5
Calibration and discrimination for the scoring methods in predicting mortality. Calibration
AKI APACHE II APACHE III SOFA OSF
Discrimination
Goodness-of-fit (c )
df
p
AUROC standard error
95% CI
p
0.360 7.121 3.701 7.237 0.118
2 8 8 6 1
0.835 0.524 0.883 0.299 0.732
0.819 0.045 0.841 0.037 0.889 0.029 0.750 0.053 0.690 0.053
0.730e0.908 0.833e0.946 0.769e0.912 0.585e0.794 0.730e0.908
0.042 <0.001 <0.001 <0.001 <0.001
2
AKI Z acute kidney injury; APACHE Z Acute Physiology and Chronic Health Evaluation; AUROC Z area under the receiver operating characteristic curve; CI Z confidence interval; OSF Z Organ System Failure; SOFA Z Sequential Organ Failure Assessment.
Table 5
Prediction of different scores for mortality.
Predictive factors
Cutoff point
Youden index
Sensitivity (%)
Specificity (%)
Overall correctness (%)
AKI APACHE II APACHE III SOFA OSF
1 13 48 4 1
0.539 0.489 0.690 0.425 0.290
82 62 76 76 78
72 86 84 66 50
77 74 80 71 64
AKI Z acute kidney injury; APACHE Z Acute Physiology and Chronic Health Evaluation; SOFA Z Sequential Organ Failure Assessment; OSF Z Organ System Failure.
Table 6
Multivariate logistic regression analysis for mortality.
Parameter
Beta coefficient
Multivariate logistic regression Surface area 0.079 Ventilator use 3.939 Acute kidney injury 2.688 SJS/TEN 3.341 Constant e10.908
Standard error
Odds ratio (95% CI)
p
0.021 0.845 1.704 1.704 2.640
1.082 (1.038e1.129) 51.368 (1.440e832.372) 14.701 (2.803e77.096) 28.237 (1.000e797.213) d
<0.001 0.031 0.001 0.050 d
CI Z confidence interval; SJS Z StevenseJohnson syndrome (SJS); TEN Z toxic epidermal necrolysis.
Discussion AKI is not uncommon in ICUs and has been associated with increased mortality, ICU length of stay, and medical costs for critically ill patients.19,20 Patients admitted to burn units typically exhibit complex syndromes with numerous pathways that affect renal function and involve hemodynamic changes, tissue breakdown products, inflammatory cytokines, septicemia, and drug toxicity.21 AKI is also a key factor in mortality. Recent investigations using APACHE III examined acute physiology scores, age, and chronic health problem scores, which are currently widely used to predict clinical outcomes in ICU settings and exhibit the accuracy of APACHE III in estimating mortality in patients with burns.11,22 Moore et al11 reported an AUROC of 0.833 while using APACHE III, which is similar to our results. Further investigation supported the efficacy of SOFA scores in assessing the extent of organ dysfunction in various groups, including critically ill patients with burn injuries, but did not provide related AUROC information.12 Coca et al9 reported the association between different RIFLE stages of AKI with morbidity and mortality in burn patients, but did
not compare the complex scoring systems with AKI stage. Chung et al23 compared the outcome between early intensive renal replacement therapy and conventional therapy in which AKI was defined by AKIN criteria. Patients with either Stage 2 AKI in combination with shock or Stage 3 AKI received continuous venovenous hemofiltration. Patients with early treatment had less 28-day mortality compared with those who had conventional treatment (38% vs. 71% p Z 0.011). To our knowledge, this study is the first to compare the usefulness of these complex scoring systems with the 2012 AKI definition for outcome prediction. Although the discriminative power of APACHE III is higher, the KDIGO AKI staging approach is a considerably simpler method that requires fewer parameters to assess the prognosis in patients with burns. For the further interventional trial, our result also provided an overview of the definition. Compared with survivors, nonsurvivors were older, had anemia, and exhibited poor conscious levels, all of which have been exhibited in other study groups. Moreau et al24 emphasized the importance of age in burn-related mortality and developed an ageerisk score calculated from TBSA
Please cite this article in press as: Kuo G, et al., Using acute kidney injury severity and scoring systems to predict outcome in patients with burn injury, Journal of the Formosan Medical Association (2016), http://dx.doi.org/10.1016/j.jfma.2016.10.012
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G. Kuo et al. single measurement of ICU scores; repeating the SOFA or AKI stage measurement might improve the score accuracy.
Conclusion Our results provide additional evidence that AKI stage is easier to use to predict outcome than are complex prognostic scores. We also demonstrated that TBSA, ventilator use, AKI, and SJS/TEN are critical in determining prognosis in patients with burns. When considering the cost effectiveness and ease of implementation, the AKI scale is recommended for evaluating short-term prognosis in critically ill patients with burns.
Acknowledgments The authors thank Shu-Yun Wang for assistance in the analysis, sampling, and data collection. This study was supported by grants from the Ministry of Science and Technology (NSC 103-2314-B-182A-040, 103-2314-B-182A018-MY3). Figure 1 Cumulative survival rates of patients according to different stages of acute kidney injury (AKI).
References
and an age cubic functional form. Furthermore, anemia was also reported in other study groups.25 Patients with large burns experience the anemia of critical illness. Acute hemolysis, red blood cell sequestration, reduced red blood cell survival, operative blood loss, nutritional deficiencies, phlebotomies, and reduced erythropoiesis were the main etiologies. A poor conscious level after severe burn injury might be related to cerebral edema and cytokine storm. However, the pathophysiology of these possible relationships has not been widely discussed. Using multivariate logistic regression analysis, our investigation revealed that TBSA, AKI, ventilator use, and SJS/TEN are independent factors for death. The percentage of TBSA is widely accepted to be a risk factor for death, and our analyses confirmed that it is. For a 1% increase in TBSA, we expect to see an approximately 8.2% increase in the odds of death. In addition, AKI exerts a strong impact on mortality in patients with burns.9,26 We found that patients with AKI exhibited a 14.7-fold increase in likelihood of death in the odds ratio. Although use of mechanical ventilation may have been misinterpreted as the patient having received airway burns, Galeiras et al27 demonstrated a strong correlation between early mechanical ventilation use and mortality in patients with burns. Prior studies have identified SJS/TEN as a strong factor of mortality in burn units.28,29 Novel biomarkers such as cystatin-C and NGAL initiated a new edge of early diagnosis of AKI in ICUs.30e32 Further study combining the riskevaluating strategy and biomarkers might improve outcomes by facilitating early detection. Despite the encouraging results of this study, several potential limitations should be considered. First, this was a post hoc analysis of prospectively collected data from a single tertiary care medical center, which limits the generalization of the findings. We also could not compare cost efficacy when using the AKI staging system. Second, our research used a
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Please cite this article in press as: Kuo G, et al., Using acute kidney injury severity and scoring systems to predict outcome in patients with burn injury, Journal of the Formosan Medical Association (2016), http://dx.doi.org/10.1016/j.jfma.2016.10.012