Combination of biomarker with clinical risk factors for prediction of severe acute kidney injury in critically ill patients

doi:10.21203/rs.3.rs-39774/v2

Background: Acute kidney injury (AKI) occur commonly in the intensive care unit (ICU). Insulin-like growth factor-binding protein 7 (IGFBP7) and tissue inhibitor of metalloproteinase-2 (TIMP-2), known as [TIMP-2] x [IGFBP7] (NephroCheck), have been identified as novel biomarkers for the prediction of AKI risk. However, disease biomarkers require appropriate clinical context to be used effectively. We conducted a retrospective cohort study to find risk factors and assess the performance of the combination of NephroCheck with risk factors would provide feasible information for AKI prediction.

Methods: All patients who admitted in the ICU (June 2016 to July 2017) participated in the study. The primary outcome was severe AKI within the first 7 days of the ICU. The predictors were separated into 3 categories: chronic risk factors, acute risk factors and biochemical indicators.

Results: The study included 577 patients. 96 patients developed to severe AKI (16.6%) within 7 days. In addition to NephroCheck (+) (OR=2.139, 95% CI (1.260-3.630), P =0.005), age >65 years (OR=1.961, 95% CI (1.153-3.336), P=0.013), CKD (OR=2.573, 95% CI (1.319-5.018), P=0.006) and PCT (+)(OR=3.223, 95% CI (1.643-6.321), P=0.001) were also the independent predictor of severe AKI within 7 days. Compared to NephroCheck (+) only (AUC=0.66, 95% CI:0.60-0.72), the combination of NephroCheck (+) and risk factors (age>65years, CKD and PCT positive) (AUC=0.75, 95% CI:0.70-0.81) led to a significant increase in the area under ROC curve for severe AKI prediction within 7 days.

Conclusions: Although NephroCheck is an effective screening tool for recognizing patients at high risk, we found that combination with biomarker and risk factors (age>65years, CKD, procalcitonin positive) for risk assessment of AKI has the greatest significance for patients with uncertain disease trajectories.

Urology & Nephrology

Acute kidney injury

Clinical prediction

Intensive care

Risk factors

Insulin-like growth factor-binding protein 7

Tissue inhibitor of metalloproteinase-2.

Acute kidney injury (AKI) is common clinical condition occurring in intensive care unit (ICU) patients and is confirmed as a strong independent risk factor for high mortality. 50% of ICU patients will develop AKI and more than 20% of critically ill patients will reach severe AKI, stage 2 and 3(Kidney Disease: Improving Global Outcomes, KDIGO) [1]. However, AKI is usually unpredictable. In a large proportion of patients, the development of AKI has no obvious warnings or symptoms and remains clinically silent until a sudden drop on renal function[2].

AKI could be identified by reduced urine output (urine volume <0.5 mL/kg/h for 6 hours) and increased serum creatinine(SCr) level (≥26.5 μmol/L within 48 hours), however, they have been shown to be a lagging marker[3]. Owing to the limitations of SCr and urine output, much efforts have been made to find biomarkers that can be used as "kidney troponin", which ideally predicts the severity and prognosis of AKI before markers of nephrological function change [4]. Therefore, many studies were conducted to discover and validate new AKI biomarkers. Until September 2014, following the publications of two multicenter ICU cohort studies, the combination of insulin-like growth factor-binding protein 7 (IGFBP7) and tissue inhibitor of metalloproteinase-2 (TIMP-2), known as [TIMP-2] x [IGFBP7] (NephroCheck), has been approved for marketing by the US Food and Drug Administration (FDA)[5-7]. This is the first biomarker for AKI risk assessment to help intensive care physicians and nephrologists make early predictions for AKI in intensive care settings and optimize the timing of resuscitation and promote supportive care for patients at risk for AKI.

Recent research has focused on the use of biomarkers for AKI to recognize high risk patients, however, most of these studies have not integrated with clinical risk factors of AKI. The random and non-directional use of any biomarker will reduce its effectiveness[5, 6, 8, 9]. Basu et al[10] have shown that combining clinical data with biomarkers can improve the accuracy of predicting risk of severe AKI in pediatric ICU patients. Disease biomarkers require appropriate clinical context to be used effectively[11]. We hypothesized that the combination of the biomarker NephroCheck with risk factors would provide feasible information for the assessment of AKI and promote early intervention to improve clinical outcomes.

Study population

All patients (age ≥ 18 years old) who admitted in the ICU of San Bortolo Hospital (Vicenza, Italy) from June 2016 to July 2017 participated in the study. End stage renal disease (ESRD) patients, anuria patients and patients diagnosed with severe AKI (stage 2 and 3) were excluded. This study was approved by the ethics committee of St. Bortolo Hospital, Vicenza, Italy (Comitato Etico provinciale a ULSS 8 Vicenza) (Exp. number: 03/17). The clinical research was conducted according to the principles expressed in the Declaration of Helsinki.

Study endpoint

The primary outcome was severe AKI (stage 2 and 3) within the first 7 days of the ICU. Secondary outcomes included continuous renal replacement therapy (CRRT) initiation, ICU mortality, and length of stay (LOS). LOS means the duration of ICU stay.

Data collection

Urine samples for measuring [TIMP-2] x [IGFBP7] concentrations were obtained and analyzed immediately following enrollment. The concentration of the two proteins ([TIMP-2] and [IGFBP7]) was analyzed with the Vitros Platform (Ortho Clinical Diagnostics) using NephroCheck Kits (Astute Medical). An important point to note is that the test was acquired by the central laboratory at market price. In order to develop a new diagnostic tool, the hospital allocated a special budget. Other data were collected from hospital records including demographics, anthropometry, admission diagnosis, comorbidities, simplified acute physiology score II (SAPS II) [12]on admission, mean arterial pressure (MAP) on admission, SCr levels on admission, lactate levels on admission and procalcitonin (PCT) levels on admission. A single SCr was recorded per day. In addition, data on CRRT, death and ICU discharge were recorded.

Definitions

Severe AKI was defined as 2.0 or more multiplied by baseline SCr according to KDIGO consensus guidelines. NephroCheck >0.3 (ng/ml)²/1000 was considered positive and a value of ≤0.3 (ng/ml)²/1000 was considered negative. PCT >0.5 μg/l was considered positive and a value of ≤0.5μg/l was considered negative.

Risk factor profiling

By reviewing the literature[13-17], the predictors we identified were separated into 3 categories: chronic risk factors, acute risk factors and biochemical indicators. Chronic risk factors included advanced age (age>65 years), obesity (BMI>30 kg/m²), hypertension, diabetes mellitus (DM), chronic kidney disease (CKD), lung disease and cardiovascular disease (CVD). Acute risk factors were sepsis, high-risk surgery, MAP<70mmHg, patients requiring vasopressors and mechanical ventilation. Biochemical indicators were SCr level on admission, lactate levels on admission Nephrocheck levels on admission and procalcitonin (PCT) levels on admission.

Statistical Analysis

The percentage was calculated for category variable. Continuous variables are described as median (interquartile range). The categorical variables between the two groups were compared using Fisher's exact test or chi-square test. The Mann-Whitney test was used for comparisons of 2 groups and the Kruskal-Wallis test was used for comparisons of 3 groups. In the pretreatment step, variables were pre-screened using univariate logistic regression analysis. Once the univariate predictor of the AKI is determined, then multivariate logistic regression is used to select the variables, which eliminated the collinearity and interaction of the selected predictors. Comparison of the areas under the receiver operating characteristic (ROC) curve was tested using the nonparametric method. P<0.05 was considered statistically significant. Analysis was performed using SPSS Version 24.0 (IBM Corp, Somers, NY, USA).

Study population

In consecutive 866 adult patients (age ≥ 18 years) who underwent screening, we excluded 289 unsuitable patients. Therefore, the study included 577 patients and 96 of them developed to severe AKI (16.6%) within 7 days in ICU. The flowchart of this study and the number of patients is presented in Fig.1. Admission diagnoses of the patients included surgery (14.7%), sepsis (10.6%), cardiovascular disease (11.8%), trauma (22.7%), respiratory diseases (8.8%), neurological diseases (21%) and other causes (10.4%).

Patient characteristics

Patient characteristics are listed in Table 1. Severe AKI group enrolled 96 (16.6%) patients and no severe AKI group enrolled 481 (83.4%) patients. Compared with patients of no severe AKI group, patients of severe AKI group were significantly older, had a higher body mass index and had more CKD, sepsis and hypotension (MAP<70mmHg). ICU patients in the severe AKI group also had a higher SCr level, lactate level, NephroCheck value and PCT level on admission.

Table 1 Baseline characteristics and outcomes of the study population by presence or absence of severe AKI within 7 days
Variable	Severe AKI	No Severe AKI	P Value
N	96(16.6)	481(83.4)
Age (years)	73(58-81)	67(51-77)	0.004
Male	64(66.7)	301(62.6)	0.448
BMI (kg/m²)	26.18(23.03-29.41）	24.8(22.86-27.68)	0.037
SAPSII	44(34-52)	38(27-50)	0.153
Chronic risk factors
Age>65years	66(68.8)	256(53.2)	0.005
BMI>30kg/m²	21(21.9)	61(12.7)	0.039
Hypertension	48(50)	212(44.1)	0.49
DM	21(21.9)	66(13.7)	0.138
CKD	20(20.8)	39(8.1)	<0.001
Lung diseases	8(8.3)	43(8.9)	0.848
CVD	10(10.4)	58(12.1)	0.649
Acute risk factors
Sepsis	20(20.8)	41(8.5)	<0.001
Surgery	18(18.8)	67(13.9)	0.224
Vasopressor	46(47.9)	179(37.2)	0.05
Mechanical ventilation	71(74)	329(68.4)	0.281
MAP<70mmHg	40(41.7)	109(22.7)	<0.001
Biochemical indicators
Serum creatinine, admission(mg/dl)	1.08 (0.75-1.45)	0.83（0.65-1.08）	<0.001
Serum lactate, admission (mmol/l)	2.1(1.4-3.8)	1.6(1.2-2.6)	0.001
NephroCheck value, admission((ng/ml)²/1000)	0.66(0.23-2.49)	0.29(0.08-0.86)	<0.001
PCT, admission(ug/l)	1.19(0.28-6.81)	0.26(0.10-1.45)	<0.001
Nephrocheck (+)	69(71.9)	233(48.4)	<0.001
PCT (+)	54(56.3)	115(23.9)	<0.001
Outcomes
CRRT	11(11.5)	6(1.2)	<0.001
Death	32(33.3)	63(13.1)	<0.001
LOS(d)	5(2-8)	3(2-7)	0.034

Data are expressed as n (%) or median (interquartile range).

BMI, body mass index; SAPS II, simplified acute physiology score II; DM, diabetes mellitus; CKD, chronic kidney disease; CVD, cardiovascular disease; MAP, mean arterial pressure; PCT, procalcitonin; CRRT, continuous renal replacement therapy; LOS, length of stay.

Severe AKI within 7 days is associated with poor outcomes in ICU patients

Table 1 shows 11.5% of patients in severe AKI group and 1.2% of patients in no severe AKI group required CRRT (P <0.001). Severe AKI was also associated with ICU mortality. The mortality incidence was 33.3% in severe AKI group and 13.1% in no severe AKI group respectively (P <0.001). Severe AKI also increased LOS in ICU. LOS of severe AKI group and no severe AKI group were 5 (2-8) and 3 (2-7) respectively (P = 0.034).

Univariate variables associated with severe AKI within 7 days

Table 2 provides a list of significant univariate variables associated with severe AKI within 7 days. The presence of hypertension, CVD, lung disease, high-risk surgery, mechanical ventilation and SAPSII cannot predict the development of AKI in our study. Among the chronic risk factors, age >65 years, BMI>30 kg/m², DM and CKD could predict severe AKI, the relative risk was 1.934 (95% CI(1.212-3.085,) P = 0.006), 1.887 (95% CI(1.085-3.282), P = 0.025), 1.748 (95% CI (1.009-3.027), P=0.046), 2.982 (95% CI (1.651-5.388), P < 0.001). Among the acute risk factors, sepsis and MAP<70mmHg could predict severe AKI, with a relative risk of 2.824 (95% CI (1.570-5.081), P=0.001) and 2.431 (95% CI (1.537-3.845), P<0.001). Among biochemical indicators, elevated SCr level was associated with a relative risk of 1.697 of developing severe AKI (95% CI (1.263-2.28), P<0.001). For increase in serum lactate concentration, there was a 11.5% increased relative risk of developing severe AKI (OR=1.115, 95% CI (1.036-1.199), P=0.003). In addition, NephroCheck (+) predicts the development of severe AKI with a relative risk of 2.72 (95% CI (1.684-4.394), P<0.001). PCT (+) predicts the development of severe AKI with a relative risk of 4.883 (95% CI (2.625-9.084), P<0.001).

Table 2 Logistic regression analysis for predictor of severe AKI within 7 days

Variable	Univariate	Multivariate
Chronic risk factors
Age>65years	1.934 (1.212-3.085)	1.961(1.153-3.336)
BMI>30kg/m²	1.887 (1.085-3.282)	NS
DM	1.748 (1.009-3.027)	NS
CKD	2.982 (1.651-5.388)	2.573(1.319-5.018)
Acute risk factors
Sepsis	2.824 (1.570-5.081)	NS
MAP<70mmHg	2.431 (1.537-3.845)	NS
Biochemical indicators
Serum creatinine, admission(mg/dl)	1.697 (1.263-2.281)	NS
Serum lactate, admission (mmol/l)	1.115 (1.036-1.199)	NS
Nephrocheck (+)	2.720 (1.684-4.394)	2.139(1.260-3.630)
PCT (+)	4.883 (2.625-9.084)	3.223(1.643-6.321)

Data are expressed as odds ratio (95% CI). NS: Nonsignificant predictors.

BMI, body mass index; DM, diabetes mellitus; CKD, chronic kidney disease; MAP, mean arterial pressure; PCT, procalcitonin.

Independent predictors of severe AKI within 7 days

Multivariate logistic regression was performed with univariate variables related to severe AKI within seven days. Following the variable selection, 4 independent predictors, including age >65 years (OR=1.961, 95% CI (1.153-3.336), P=0.013), CKD (OR=2.573, 95% CI (1.319-5.018), P=0.006), NephroCheck (+) on admission (OR=2.139, 95% CI (1.260-3.630), P =0.005) and PCT (+) on admission (OR=3.223, 95% CI (1.643-6.321), P=0.001), predicted the development of severe AKI (Table 2).

NephroCheck level on admission was associated with incidence of severe AKI within 7 days and its poor outcomes

For the 577 patients, 275 (47.7%) were NephroCheck≤0.3 (ng/ml)²/1000, 220 (38.1%) were NephroCheck (0.3-2) (ng/ml)²/1000 and 82 (14.2%) were NephroCheck≥2(ng/ml)²/1000. Severe AKI incidence within seven days, CRRT initiation and ICU mortality were highest in NephroCheck≥2(ng/ml)²/1000 group. The incidence of severe AKI within seven days increased from 9.8% in NephroCheck ≤0.3 (ng/ml)²/1000 patients to 19.1% in NephroCheck (0.3-2) (ng/ml)²/1000 patients and 32.9% in NephroCheck≥2(ng/ml)²/1000 patients (compared in 3 groups, P < 0.001). The treatment of CRRT increased from 1.1% in NephroCheck ≤0.3 (ng/ml)²/1000 patients to 2.7% in NephroCheck (0.3-2) (ng/ml)²/1000 patients and 9.6% in NephroCheck≥2(ng/ml)²/1000 patients (compared in 3 groups, P < 0.001). ICU mortality increased from 13.5% in NC (-) patients to 16.8% in NephroCheck (0.3-2) (ng/ml)²/1000 patients and 25.6% in NephroCheck≥2(ng/ml)²/1000 patients (compared with 3 groups, P = 0.033) (Fig. 2).

Incorporation of risk factors augments the predictive performance of the NephroCheck

Compared to NephroCheck (+) only (AUC=0.66, 95% CI:0.60-0.72), the combination of NephroCheck (+) and risk factors (age>65years, CKD and PCT positive) (AUC=0.75, 95% CI:0.70-0.81) led to a significant increase in the area under ROC curve for severe AKI prediction within 7 days (Fig. 3).

AKI is a major complication of major diseases which are associated with poor outcomes, high mortality and increased resource use[17-19]. The recognition of patients at high risk for developing AKI is attracting more and more attention recently. Early identification of AKI allows for rapid therapeutic intervention to achieve significant clinical benefit. AKI animal models using ischemic, toxic, and septic models have shown that multiple therapeutic agents appear to reduce kidney injury if administered before or shortly after injury[20-23].

Cell cycle arrest may be the first process of neurological cells activation upon stress. The proteins [TIMP-2] and [IGFBP7] associated with cell cycle arrest are promising markers for the detection of AKI. It has been verified that [TIMP-2] x [IGFBP7] are superior to other biomarkers on detecting AKI in previous cohorts study[5, 6], which provide early warnings and allow physicians to modify risk, promote intervention and avoid further complications.

Our study demonstrated that NephroCheck is an effective screening tool for recognizing patients at high risk for AKI and indicates that early NephroCheck (+) is a good predictor of severe AKI. In this study, we found that patients with NephroCheck >0.3 (ng/ml)²/1000 will significantly increase predictive discrimination against subsequent severe AKI. In addition to severe AKI, we also observed that patients with NephroCheck (+) were more likely to have other poor outcomes, such as CRRT initiation and death. Furthermore, as the level of NephroCheck increases, the risk of severe AKI and its poor outcomes also increases. NephroCheck≥2(ng/ml)²/1000 patients had highest incidence of severe AKI within seven days, CRRT initiation and ICU mortality.

Although NephroCheck is a good tool for recognizing the increased risk of AKI and its poor outcomes, indiscriminate biomarker measurement in every patient, regardless of age and comorbidities, will render any test virtually useless[24]. Therefore, integration of clinical risk factors with AKI biomarkers (kidney troponin) can increase sensitivity and discriminating power to kidney attacks[25]. In our study, we have determined predictors of severe AKI, including age >65 years, CKD, NephroCheck (+) and PCT (+). Some risk factors identified for AKI in our study are consistent with previous literature reports: age and CKD[13, 16, 17, 26, 27]. The epidemiological association between CKD and AKI makes CKD a risk factor for AKI[13, 17]. Among the various hypotheses it is possible that patients with CKD have lost renal self-regulation and hemodynamic stability, which explains the small changes in SCr level and the predisposition to subsequent damage [11].

We also observed that PCT is a predictor of severe AKI in our study. PCT is closely related to the severity of systemic inflammation and bacterial infection[28]. High PCT levels have been considered a good diagnostic indicator of poor prognosis in sepsis patients[29]. Increased PCT levels in patients with pancreatitis and contrast-induced AKI are associated with the development of AKI[30, 31]. Nie et al[32] reported that PCT can be used as a predictor of AKI for infective patients.

In this study, we found that combination with biomarker NephroCheck and risk factors for risk assessment of AKI has the greatest significance for patients with uncertain disease trajectories. Future research needs to clinically or electronically identify patients at high risk of AKI development or progression to CKD or ESRD[1, 33]. We plan to develop an electronic alert system including the assessment of NephroCheck and risk factors. This risk prediction tool can automatically detect high-risk patients for AKI and help early management and individualized treatment of AKI [13, 34-38]. For example, patients identified as high-risk AKI don’t need to wait for the development of AKI, but begin to optimize volume, adjust drug dose and avoid potential nephrotoxicity based on their AKI risk profile. The aim of the tool is to reduce the severity of AKI and decrease the number of patients requiring dialysis.

Our research also has limitations. First, our study has a small number of severe AKI events. It should be noted that we have excluded 102 patients who had already developed moderate or severe AKI on admission. All this may reduce the morbidity of AKI. Second, we were unable to determine all risk variables associated with AKI. For example, insufficient blood volume, proteinuria, bilirubinuria and exposure to nephrotoxic drugs and contrast media. Furthermore, when a family member is unable to provide a medical history, it is not always possible to determine the chronic risk factors for a comatose ICU patient. Third, the urine criterion was not applied to diagnose AKI in the patient population, as urine output data were not uniformly available. This may have decreased the overall incidence of AKI diagnosis. Finally, by studying NephroCheck as a biomarker in all patients admitted to the ICU, we had a larger sample size. However, this was at the expense of not exclusively focusing on those at risk of AKI. Additionally, biomarkers require appropriate clinical context to be used effectively and should be used in high-risk patients for AKI aiming to avoid excessive clinical use.

In this study, we have determined predictors of severe AKI, including age >65 years, CKD, NephroCheck (+) and PCT (+). Furthermore, we found that combination with biomarker NephroCheck and risk factors (age >65 years, CKD and PCT) for risk assessment of AKI has the greatest significance for patients with uncertain disease trajectories.

AKI： Acute kidney injury

ICU： Intensive care unit

KDIGO：Kidney Disease: Improving Global Outcomes

IGFBP7：Insulin-like growth factor-binding protein 7

TIMP-2：Tissue inhibitor of metalloproteinase-2

SCr： Serum creatinine

FDA： Food and Drug Administration

CRRT：Continuous renal replacement therapy

LOS： Length of stay

SAPS II：Simplified acute physiology score II

MAP： Mean arterial pressure

PCT：Procalcitonin

DM：Diabetes mellitus

CKD： Chronic kidney disease

ESRD：End stage renal disease

CVD：Cardiovascular disease

ROC：Receiver operating characteristic

Ethics approval and consent to participate

This study was approved by the ethics committee of St. Bortolo Hospital, Vicenza, Italy (Comitato Etico provinciale aULSS 8 Vicenza) (Exp. number: 03/17). The informed consent obtained from study participants was written.

Consent for publication

Not applicable.

Availability of data and materials

The datasets used and analyzed during the current study are available from the corresponding author on reasonable request.

Competing interests

All the authors declared no competing interests.

Funding

This study was supported by National Natural Science Foundation of China (NSFC) (Grant number: 81600591).

Authors' contributions

LJ and CR designed the study; AZ, YX, VC, MC, DPM and CC collected data; LJ, XHS and YX analyzed the data; LJ and XHS drafted the manuscript; LJ and SC revised the manuscript; all authors have read and approved the manuscript.

Acknowledgements

Not applicable.

Ronco C, Rizo-Topete L, Serrano-Soto M, Kashani K: Pro: Prevention of acute kidney injury: time for teamwork and new biomarkers. Nephrol Dial Transplant 2017, 32(3):408-413.
Uettwiller-Geiger DL, Vijayendran R, Kellum JA, Fitzgerald RL: Analytical characteristics of a biomarker-based risk assessment test for acute kidney injury (AKI). Clin Chim Acta 2016, 455:93-98.
Srisawat N, Kellum JA: Acute kidney injury: definition, epidemiology, and outcome. Curr Opin Crit Care 2011, 17(6):548-555.
Ostermann M, Philips BJ, Forni LG: Clinical review: Biomarkers of acute kidney injury: where are we now? Critical care (London, England) 2012, 16(5):233.
Kashani K, Al-Khafaji A, Ardiles T, Artigas A, Bagshaw SM, Bell M, Bihorac A, Birkhahn R, Cely CM, Chawla LS et al: Discovery and validation of cell cycle arrest biomarkers in human acute kidney injury. Critical care (London, England) 2013, 17(1):R25.
Bihorac A, Chawla LS, Shaw AD, Al-Khafaji A, Davison DL, Demuth GE, Fitzgerald R, Gong MN, Graham DD, Gunnerson K et al: Validation of cell-cycle arrest biomarkers for acute kidney injury using clinical adjudication. Am J Respir Crit Care Med 2014, 189(8):932-939.
US Food and Drug Administration. Letter to Astute Medical. http://www. accessdata.fda.gov/cdrh_docs/pdf13/den130031.pdf.
Coca SG, Yalavarthy R, Concato J, Parikh CR: Biomarkers for the diagnosis and risk stratification of acute kidney injury: a systematic review. Kidney Int 2008, 73(9):1008-1016.
Vijayan A, Faubel S, Askenazi DJ, Cerda J, Fissell WH, Heung M, Humphreys BD, Koyner JL, Liu KD, Mour G et al: Clinical Use of the Urine Biomarker [TIMP-2] x [IGFBP7] for Acute Kidney Injury Risk Assessment. Am J Kidney Dis 2016, 68(1):19-28.
Basu RK, Wang Y, Wong HR, Chawla LS, Wheeler DS, Goldstein SL: Incorporation of biomarkers with the renal angina index for prediction of severe AKI in critically ill children. Clin J Am Soc Nephrol 2014, 9(4):654-662.
Cruz DN, Ferrer-Nadal A, Piccinni P, Goldstein SL, Chawla LS, Alessandri E, Belluomo Anello C, Bohannon W, Bove T, Brienza N et al: Utilization of small changes in serum creatinine with clinical risk factors to assess the risk of AKI in critically lll adults. Clin J Am Soc Nephrol 2014, 9(4):663-672.
Le Gall JR, Lemeshow S, Saulnier F: A new Simplified Acute Physiology Score (SAPS II) based on a European/North American multicenter study. Jama 1993, 270(24):2957-2963.
Malhotra R, Kashani KB, Macedo E, Kim J, Bouchard J, Wynn S, Li G, Ohno-Machado L, Mehta R: A risk prediction score for acute kidney injury in the intensive care unit. Nephrol Dial Transplant 2017, 32(5):814-822.
Liano F, Pascual J: Epidemiology of acute renal failure: a prospective, multicenter, community-based study. Madrid Acute Renal Failure Study Group. Kidney Int 1996, 50(3):811-818.
Chertow GM, Lazarus JM, Christiansen CL, Cook EF, Hammermeister KE, Grover F, Daley J: Preoperative renal risk stratification. Circulation 1997, 95(4):878-884.
Chawla LS, Abell L, Mazhari R, Egan M, Kadambi N, Burke HB, Junker C, Seneff MG, Kimmel PL: Identifying critically ill patients at high risk for developing acute renal failure: a pilot study. Kidney Int 2005, 68(5):2274-2280.
Sileanu FE, Murugan R, Lucko N, Clermont G, Kane-Gill SL, Handler SM, Kellum JA: AKI in low-risk versus high-risk patients in intensive care. Clin J Am Soc Nephrol 2015, 10(2):187-196.
Hoste EA, Bagshaw SM, Bellomo R, Cely CM, Colman R, Cruz DN, Edipidis K, Forni LG, Gomersall CD, Govil D et al: Epidemiology of acute kidney injury in critically ill patients: the multinational AKI-EPI study. Intensive Care Med 2015, 41(8):1411-1423.
Bouchard J, Acharya A, Cerda J, Maccariello ER, Madarasu RC, Tolwani AJ, Liang X, Fu P, Liu ZH, Mehta RL: A Prospective International Multicenter Study of AKI in the Intensive Care Unit. Clin J Am Soc Nephrol 2015, 10(8):1324-1331.
Wang W, Falk SA, Jittikanont S, Gengaro PE, Edelstein CL, Schrier RW: Protective effect of renal denervation on normotensive endotoxemia-induced acute renal failure in mice. Am J Physiol Renal Physiol 2002, 283(3):F583-587.
Yamasaki N, Nagano T, Mori-Kudo I, Tsuchida A, Kawamura T, Seki H, Taiji M, Noguchi H: Hepatocyte growth factor protects functional and histological disorders of HgCl(2)-induced acute renal failure mice. Nephron 2002, 90(2):195-205.
Okusa MD, Ye H, Huang L, Sigismund L, Macdonald T, Lynch KR: Selective blockade of lysophosphatidic acid LPA3 receptors reduces murine renal ischemia-reperfusion injury. Am J Physiol Renal Physiol 2003, 285(3):F565-574.
Miyaji T, Hu X, Yuen PS, Muramatsu Y, Iyer S, Hewitt SM, Star RA: Ethyl pyruvate decreases sepsis-induced acute renal failure and multiple organ damage in aged mice. Kidney Int 2003, 64(5):1620-1631.
Stein R, Gupta B, Agarwal S, Golub J, Bhutani D, Rosman A, Eng C: Prognostic implications of normal (<0.10 ng/ml) and borderline (0.10 to 1.49 ng/ml) troponin elevation levels in critically ill patients without acute coronary syndrome. Am J Cardiol 2008, 102(5):509-512.
Kellum JA, Bellomo R, Ronco C: Kidney attack. Jama 2012, 307(21):2265-2266.
Hoste EA, Lameire NH, Vanholder RC, Benoit DD, Decruyenaere JM, Colardyn FA: Acute renal failure in patients with sepsis in a surgical ICU: predictive factors, incidence, comorbidity, and outcome. J Am Soc Nephrol 2003, 14(4):1022-1030.
Mehta RL, Pascual MT, Gruta CG, Zhuang S, Chertow GM: Refining predictive models in critically ill patients with acute renal failure. J Am Soc Nephrol 2002, 13(5):1350-1357.
Al-Nawas B, Shah PM: Procalcitonin, a new diagnostic and prognostic marker for severe infections. Clin Microbiol Infect 1998, 4(5):237-241.
Guo SY, Zhou Y, Hu QF, Yao J, Wang H: Procalcitonin is a marker of gram-negative bacteremia in patients with sepsis. Am J Med Sci 2015, 349(6):499-504.
Huang HL, Nie X, Cai B, Tang JT, He Y, Miao Q, Song HL, Luo TX, Gao BX, Wang LL et al: Procalcitonin levels predict acute kidney injury and prognosis in acute pancreatitis: a prospective study. PLoS One 2013, 8(12):e82250.
Kurtul A, Murat SN, Yarlioglues M, Duran M, Ocek AH, Celik IE, Kilic A, Koseoglu C, Oksuz F, Baris VO: Procalcitonin as an Early Predictor of Contrast-Induced Acute Kidney Injury in Patients With Acute Coronary Syndromes Who Underwent Percutaneous Coronary Intervention. Angiology 2015, 66(10):957-963.
Nie X, Wu B, He Y, Huang X, Dai Z, Miao Q, Song H, Luo T, Gao B, Wang L et al: Serum procalcitonin predicts development of acute kidney injury in patients with suspected infection. Clin Chem Lab Med 2013, 51(8):1655-1661.
De Rosa S, Samoni S, Ronco C: Creatinine-based definitions: from baseline creatinine to serum creatinine adjustment in intensive care. Critical care (London, England) 2016, 20:69.
Porter CJ, Juurlink I, Bisset LH, Bavakunji R, Mehta RL, Devonald MA: A real-time electronic alert to improve detection of acute kidney injury in a large teaching hospital. Nephrol Dial Transplant 2014, 29(10):1888-1893.
Wallace K, Mallard AS, Stratton JD, Johnston PA, Dickinson S, Parry RG: Use of an electronic alert to identify patients with acute kidney injury. Clin Med (Lond) 2014, 14(1):22-26.
Parikh CR, Moledina DG, Coca SG, Thiessen-Philbrook HR, Garg AX: Application of new acute kidney injury biomarkers in human randomized controlled trials. Kidney Int 2016, 89(6):1372-1379.
Wilson FP, Shashaty M, Testani J, Aqeel I, Borovskiy Y, Ellenberg SS, Feldman HI, Fernandez H, Gitelman Y, Lin J et al: Automated, electronic alerts for acute kidney injury: a single-blind, parallel-group, randomised controlled trial. Lancet 2015, 385(9981):1966-1974.
Selby NM, Crowley L, Fluck RJ, McIntyre CW, Monaghan J, Lawson N, Kolhe NV: Use of electronic results reporting to diagnose and monitor AKI in hospitalized patients. Clin J Am Soc Nephrol 2012, 7(4):533-540.

Combination of biomarker with clinical risk factors for prediction of severe acute kidney injury in critically ill patients

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Journal Publication

Version 2

Abstract

Figures

Background

Methods

Results

Discussion

Conclusions

List of Abbreviations

Declarations

References

Status:

Journal Publication

Version 2