Acute kidney injury (AKI) is a clinical syndrome with multiple etiologies (1) afflicting 18–39 people per thousand population (2). It occurs in approximately 11–13% of hospital admissions (3) and over 50% of patients in ICUs (4). AKI is an important risk factor for chronic kidney disease and accelerated progression to end-stage kidney disease, leading to poor quality of life, disability, and increased costs (5).
Early recognition and management of patients at risk for or with AKI but prior to clinical manifestations are likely to translate into better outcomes (1,5). Even when identifying risk factors for AKI (e.g., advanced age and underlying disease, sepsis, radiocontrast, nephrotoxic drugs), there is no reliable way for a clinician to use this information to establish a clear and actionable risk profile (6). Furthermore, AKI is usually silent, with no early signs or symptoms, and serum creatinine may only increase once significant injury has occurred (7). All this leads to delays in recognizing AKI and applying treatments to preserve kidney function (6).
Sepsis is the most common cause of AKI in critically ill patients, playing a role in 40–50% of AKI cases (8). Sepsis is defined as a life-threatening organ dysfunction caused by a dysregulated host response to infection (9). The Global Burden of Disease Study estimated that in 2017 sepsis caused almost 20% of all global preventable deaths (10). The development of sepsis-associated AKI (SA-AKI) is associated with reduced survival and longer hospital/ICU stay (11).
Biomarkers that provide an early indicator of kidney stress could be useful in clinical practice to detect silent episodes of AKI or for early identification of patients at risk (6,12). Two such biomarkers, tissue inhibitor of metalloproteinases-2 (TIMP-2) and insulin-like growth factor-binding protein 7 (IGFBP7) (13,14), are measured by a commercial test (NephroCheck Test; Astute Medical, San Diego, CA) and are combined into a risk score ([TIMP-2]•[IGFBP7]) that can predict the development of moderate or severe AKI (Kidney Disease: Improving Global Outcomes [KDIGO] stages 2–3) within 12 hours (13,15,16).
Currently, no study has assessed the clinical utility and economic effects of [TIMP-2]•[IGFBP7] in patients at risk of SA-AKI. We designed “Limiting AKI Progression In Sepsis” (LAPIS), a phase 4, multicenter, adaptive, randomized controlled trial (RCT) of biomarker-guided delivery of kidney-sparing care measures in patients with sepsis admitted to the ICU. Here, we sought to describe the LAPIS trial design and to estimate the rates of the primary endpoint as a function of the biomarker patterns by simulating the study protocol using similar cohorts from the Sapphire study (13) and the Protocolized Care for Early Septic Shock (ProCESS) trial (17).
MATERIAL AND METHODS
The study will be performed in accordance with the Declaration of Helsinki, the International Council for Harmonization Good Clinical Practice, and all applicable laws and regulations of the countries in which the trial is conducted. An independent Data and Safety Monitoring Board will review the progress of the study. More details are provided in the Supplemental Digital Content (https://links.lww.com/CCM/G472). Trial registration is available at ClinicalTrials.gov number NCT04434209.
The primary objective of the LAPIS study is to evaluate the effects of biomarker-guided implementation of kidney-sparing care measures (intervention arm) in comparison with standard of care (SOC) assessment and treatment (control arm) on clinical outcomes in patients with sepsis. The secondary objective is to evaluate the effect of the intervention on economic outcomes.
The primary endpoint is a composite of progression of two or more stages of AKI (from KDIGO stage 0 to 2/3 or from stage 1 to 3), death, or dialysis within 72 hours after enrollment. For the purposes of the endpoint, dialysis is defined as any form of renal replacement therapy (RRT).
Secondary endpoints are as follows: 1) death, dialysis, or AKI stage 2/3 within 48 and 72 hours of enrollment; 2) stage 2 or 3 AKI within 72 hours of enrollment; and 3) ICU length of stay. A detailed description of all the endpoints is in the Supplemental Digital Content (https://links.lww.com/CCM/G472).
Study Design and Study Population.
LAPIS is an adaptive, multicenter, open label, RCT of patients with sepsis. We plan to enroll approximately 540 patients at 18 sites in Europe and the United States. The study will compare SOC patient management with management guided by [TIMP-2]•[IGFBP7] using protocol-defined care measures. After any eligible patient is diagnosed with sepsis, the patient may be approached for study participation (Supplemental Fig. S1, https://links.lww.com/CCM/G472).
We will consider eligible adults (age 21 or older) with a diagnosis of sepsis or septic shock according to Sepsis-3 definitions (9) without AKI stage 2/3 at the time of screening. Patients must be admitted to the ICU or have a planned admission to the ICU with an expected stay in the hospital of more than 48 hours. The full list of inclusion and exclusion criteria is reported in Supplemental Table S1 (https://links.lww.com/CCM/G472), and a more detailed discussion is provided in the Supplemental Digital Content-Study Population (https://links.lww.com/CCM/G472). Consented patients will be randomly assigned 1:1 to either intervention or control (Supplemental Fig. S2, https://links.lww.com/CCM/G472).
Patients randomly assigned to the control arm will be treated according to the caring team plan and any site approaches for treating sepsis patients.
As it is SOC to promote the deescalation of care in low-risk patients for SA-AKI, when subjects have three negative [TIMP-2]•[IGFBP7] values, and if medically appropriate according to their judgment, the treating clinician may consider deescalation of care.
Intervention arm—kidney-sparing sepsis bundles
Patients with any (TIMP-2)•(IGFBP7) test result greater than 0.3 will be recommended for kidney-sparing sepsis bundles (KSSBs) with three possible levels of care depending on the quantitative value of the test results and test result trends over time (Fig. 1). Once started, the assigned KSSB level will not deescalate to a lower level for at least 72 hours after enrollment. KSSB interventions are based on the international KDIGO guidelines for the prevention of AKI (1) routinely used in ICUs around the world. The treating clinician has the option to decline the use of any KSSB intervention, if they feel it is not in the best interest of the patient. The three levels of the KSSB interventions are listed in Table 1 and described in the Supplemental Digital Content (https://links.lww.com/CCM/G472).
TABLE 1. -
Summary and List of the Interventions of Limiting acute kidney injury Progression In Sepsis Trial
|Trial summary (PICO)
||Critically ill patients with sepsis or septic shock at risk for AKI.
||Serial tests of urinary [TIMP-2]•[IGFBP7] to guide and escalating series of interventions (see Level 1–3 KSSB below).
||Clinicians blinded to [TIMP-2]•[IGFBP7] results; application of standard of care for sepsis.
||Progression of 2 or more Kidney Disease: Improving Global Outcomes stages of AKI, death, or dialysis within 72 hr after enrollment.
|Level 1 KSSB
||Discontinuation of potentially nephrotoxic agents (full list in Supplemental Table S2, [http://links.lww.com/CCM/G472]).
||Discontinuation of nonsteroidal anti-inflammatory drugs, angiotensin-converting enzyme inhibitors, angiotensin receptor blockers, aminoglycosides, radiocontrast agents.
||Vancomycin or aminoglycosides dosing based upon therapeutic drug level monitoring.
||Review all medications for potential nephrotoxicity as soon as possible in consultation with available hospital resources (e.g., clinical pharmacists).
||Use of only balanced crystalloid for fluid boluses.
||Accurate daily measurement of total fluids intake and output.
||Limit use of diuretics and fluids only after determining fluid status and need.
||Provision of alternative options to radiocontrast procedures (consideration of alternative imaging methods, use of the lowest possible dose of contrast medium, and avoidance of all unnecessary IV iodinated contrast dye).
|Level 2 KSSB
||institution of functional hemodynamic monitoring (e.g., with FloTrac, Pulse Contour Cardiac Output, ultrasound) to optimize the volume status and hemodynamic variables and to assess fluid responsiveness
|Level 3 KSSB
||Review the study subject’s kidney status with available hospital resources chiefly to identify any unrecognized cause of AKI (e.g., consultation with nephrologist)
||Review the study subject’s infectious disease management with available hospital resources (e.g., infectious disease specialist).
||Consideration of seeking other sources of infection (interventions could include imaging procedures, skin examination, etc.)
AKI = acute kidney injury, IGFBP7 = insulin-like growth factor-binding protein 7, KSSB = kidney-sparing sepsis bundle, TIMP-2 = tissue inhibitor of metalloproteinases-2, PICO = Population-Intervention-Comparison-Outcome.
This study will use an adaptive design using prespecified changes to the protocol based on the observed relative risk reduction (RRR). If the RRR is different from the assumed 30%, the sample size will be modified as detailed in the Supplemental Digital Content (https://links.lww.com/CCM/G472). Furthermore, if the overall RRR is less than 17%, the effect of excluding septic shock patients will be evaluated.
We selected two cohorts of patients from the Sapphire (13) and ProCESS (17) studies to simulate and inform the event rate estimates for the LAPIS primary endpoint in association with urinary [TIMP-2]•[IGFBP7] values over time. The Sapphire study (13) included 723 adults admitted to the ICU within 24 hours of enrollment. Among these patients, we considered only those with sepsis. For [TIMP-2]•[IGFBP7] measurements, the first urine sample was collected at enrollment, the second sample 12 ± 6 hours later, and the third sample at 24 ± 6 hours after enrollment. Time of enrollment varied among patients, but for most, it was when the patient was already admitted in the ICU and/or completed the first resuscitative treatments. For this reason and for the purposes of our trial simulation, these time points represent 6, 18, and 30 hours from sepsis diagnosis and start of the treatment.
The ProCESS trial (17) enrolled 1,341 patients 18 years or older, recruited in the emergency department within 2 hours after the detection of septic shock. For [TIMP-2]•[IGFBP7] measurements, the first urine sample was collected at 6 hours after enrollment and a second sample at 24 hours after enrollment.
From both cohorts, we excluded patients with AKI stage 2 or 3 at enrollment. Further details on inclusion and exclusion criteria and on the sensitivity analysis for the ProCESS trial are provided in the Supplemental Digital Content-LAPIS simulation (https://links.lww.com/CCM/G472). In both studies, urine samples were centrifuged, and supernatants were frozen and stored at less than –70°C; the supernatant was then thawed immediately prior to testing for [TIMP-2]•[IGFBP7].
We assessed the primary endpoint for each cohort as a whole and for all branches created using the protocol treatment algorithm (Fig. 1). We described the general characteristics of each cohort overall and according to the presence or absence of the primary endpoint. We used Pearson’s chi-square test to compare categorical variables, and we used Mann-Whitney U test to compare continuous variables. Logistic regression for the primary endpoint was performed in both cohorts. Among the statistically significant variables (p < 0.05) at univariate analysis (Supplemental Tables S3 and S4, https://links.lww.com/CCM/G472), we chose least absolute shrinkage and selection operator (LASSO) regularization to select the best covariates for the logistic regression models. Analyses were conducted using SPSS Statistics Version 26 (IBM Corp., Armonk, NY) and R 4.0.2 (R Foundation for Statistical Computer, Vienna, Austria) with alpha set at two-tailed p value of less than 0.05.
Characteristics of the Two Cohorts
The Sapphire evaluation cohort consisted of 203 patients (of 723). In this cohort, 22% of patients (45/203) experienced the primary endpoint. Supplemental Table S3 (https://links.lww.com/CCM/G472) shows the general characteristics of the Sapphire cohort and compares patients with and without the primary endpoint. Age, sex, and race did not differ in the two groups nor did comorbidities. Acute Physiology and Chronic Health Evaluation (APACHE) III score, but not SOFA score, was higher in the endpoint positive group (median 93 vs 73; p = 0.002). All three [TIMP-2]•[IGFBP7] median values were higher in the endpoint positive group.
The ProCESS evaluation cohort consisted of 607 patients (of 1,341). In this cohort, 24% of patients (144/607) experienced the primary endpoint. Supplemental Table S4 (https://links.lww.com/CCM/G472) shows the general characteristics of the ProCESS cohort and compares patients with and without the primary endpoint. Patients who developed the primary endpoint were older and had a higher prevalence of chronic heart failure, history of renal disease, cerebral vascular disease, and dementia. Furthermore, APACHE II score, SOFA score, and both the [TIMP-2]•[IGFBP7] median values were higher in the endpoint positive group.
Finally, LASSO regularization yielded a reduced model with the [TIMP-2]•[IGFBP7] measurement as covariates. In Sapphire, only the third [TIMP-2]•[IGFBP7] test had a statistically significant odds ratio of 2.29 (95% CI, 1.37–4.08; p = 0.003) after adjustment for the other two [TIMP-2]•[IGFBP7] tests in the model (Supplemental Table S5, https://links.lww.com/CCM/G472), whereas for the ProCESS cohort, both tests were significant (Supplemental Table S6, https://links.lww.com/CCM/G472).
Figure 2 shows in detail the results of simulating LAPIS protocol algorithm using the Sapphire cohort. The percentage of patients who met the primary endpoint ranged from 14% when the “first” [TIMP-2]•[IGFBP7] value was less than or equal to 0.3 (ng/mL)2/1,000, to 20% for values between 0.3 and 1.0, and finally to 35% when the value was greater than or equal to 1.0. Figure 3 shows in detail the results of simulating LAPIS protocol algorithm using the ProCESS cohort. The percentage of patients who met the primary endpoint ranged from 16% when the “first” [TIMP-2]•[IGFBP7] value was less than or equal to 0.3 (ng/mL)2/1,000, to 25% for values between 0.3 and 1.0, and finally to 40% when the value was greater than or equal to 1.0.
Table 2 summarized the results of both simulations. We pooled together all patients who should have reached the same level of treatment at the end of the algorithm according to their [TIMP-2]•[IGFBP7] values. In Sapphire, only 6% of patients with three consecutive [TIMP-2]•[IGFBP7] less than or equal to 0.3 (ng/mL)2/1,000 developed the primary endpoint, whereas in ProCESS, 14% of patients with two consecutive values less than or equal to 0.3 developed the primary endpoint. In addition, the results indicate that the LAPIS algorithm proposes increased complexity of care (reflected by a higher level of KSSB) for patients with a higher risk of developing the primary endpoint. At the end of the simulation, the endpoint positivity rate increased from 10% for level 1 KSSB to 41% for level 3 KSSB in Sapphire cohort, and from 21% for level 1 KSSB to 46% for level 3 KSSB in ProCESS. Supplemental Table S7 (https://links.lww.com/CCM/G472) and Supplemental Figure S3 (https://links.lww.com/CCM/G472) show similar results for the simulation in the sensitivity analysis cohort for ProCESS trial, and they are discussed in the Supplemental Digital Content (https://links.lww.com/CCM/G472).
Our simulation helps inform the upcoming LAPIS trial and adds insight into AKI development in septic patients. The aim of the LAPIS trial is to assess the effects of a biomarker-guided kidney-sparing sepsis protocol on patient outcomes. Previous trials in patients undergoing major surgery measured [TIMP-2]•[IGFBP7] to enrich for patients at higher risk of AKI, and patients testing positive were randomized to interventions (18, 19), whereas LAPIS will be the first trial that randomizes patients to receive the biomarker test itself. Furthermore, LAPIS will be the first to use serial [TIMP-2]•[IGFBP7] measurements, and our algorithm was specifically designed to consider biomarker trends over time. Recent observational studies have supported this approach (20,21).
The 0.3 cut off is the only Food and Drug Administration–approved cut off for its high sensitivity in identifying patients at higher risk for moderate-severe AKI. This cut off will be sensitive enough to provide at least level 1 interventions to most of our patients (around 64–83% according to our simulations). In addition, the higher 1.0 cut off has a higher specificity (22) that will allow us to select patients deserving a higher level of treatment.
We chose to obtain our first biomarker sample at approximately 6 hours from sepsis diagnosis because, in sepsis, patients with high [TIMP-2]•[IGFBP7] values upon presentation that decline with resuscitation have much better outcomes compared with patients with high [TIMP-2]•[IGFBP7] values after the initial 6–12 hours of therapy suggesting that a high value after 6 hours may be more predictive of outcome (21,23). In addition, a 6-hour sepsis bundle is already a well-established practice at many hospitals, and it would be quite difficult to enroll patients in time to modify this practice.
In our simulation, patients with sepsis in Sapphire were quite similar to patients in ProCESS, a study that exclusively enrolled septic shock, with regard to both the general patient characteristics (Supplemental Tables S3 and S4, https://links.lww.com/CCM/G472) and the overall proportion of patients who experience the primary endpoint. In ProCESS, the definition of septic shock was based on the presence of systemic inflammatory response syndrome criteria plus shock, so we are cautious in generalizing these data since LAPIS will use Sepsis-3 criteria; it is also for this reason that we designed the specific adaption regarding septic shock. Also, both regression models for prediction of the primary endpoint included only [TIMP-2]•[IGFBP7] and no other clinical variables; [TIMP-2]•[IGFBP7] values were all associated with the endpoint at univariate analysis, but when combined in a model, the association was strongest for the last [TIMP-2]•[IGFBP7] likely because this value was the closest to the time the patient developed the endpoint.
Table 2 emphasizes the LAPIS protocol’s ability to appropriately increase the complexity and intensity of care, corresponding to a higher level of KSSB, for patients who have a higher risk of experiencing the endpoint. The endpoint rates for level 3 KSSB were similar in the two simulations (41% vs 46%), whereas the higher difference in the other levels could be because ProCESS simulation was based only on two [TIMP-2]•[IGFBP7] measurements.
TABLE 2. -
Summary of Protocol Simulation in Sapphire and Protocolized Care for Early Septic Shock Studies
|Treatment Levels After [Tissue Inhibitor of Metalloproteinases-2]•[Insulin-Like Growth Factor-Binding Protein 7] Testsa
||Progression of 2 Acute Kidney Injury Stagesb
| L1 KSSB
| L2 KSSB
| L3 KSSB
|Protocolized Care for Early Septic Shock triale
| L1 KSSB
| L2 KSSB
| L3 KSSB
KSSB = kidney-sparing sepsis bundle, L = level of the kidney-sparing sepsis bundle, SOC = standard of care.
aPatients in neither study received these interventions but these interventions would have been recommended based on the biomarker results.
bWithin 72 hr from enrollment.
cFor Sapphire study within 72 hr from enrollment. For Protocolized Care for Early Septic Shock trial within 48 hr from enrollment.
dPresence of at least one between progression of two or more stages of acute kidney injury, death, or dialysis.
e Based only on two [tissue inhibitor of metalloproteinases-2]•[insulin-like growth factor-binding protein 7] tests.
The KSSB interventions were derived from the KDIGO AKI clinical practice guideline (1) and represent an implementation plan for therapies (e.g., balanced fluids). Other KDIGO care bundles (e.g., optimization of fluid status, maintenance of perfusion pressure, discontinuation of nephrotoxic agents) have been implemented for different types of patients—cardiac (18) and major abdominal surgery (19)—and have been shown to reduce AKI rates compared with standard of care.
Experts from Europe and North America have recommended many of the interventions proposed in LAPIS trial guided by the results of [TIMP-2]•[IGFBP7] (24). KDIGO bundles applied after interpreting the results of [TIMP-2]•[IGFBP7] seem to be effective in reducing AKI both in real-life (25) and in clinical trials settings (18,19,26). In particular, nephrotoxic exposure (e.g., vancomycin, piperacillin/tazobactam, nonsteroidal anti-inflammatory drugs, radiocontrast agents) is known to be frequent in ICU patients and may lead to drug-associated AKI in around 15–25% of the patients (27). Cumulative and longer nephrotoxic exposures further increase the risk for AKI; additionally, [TIMP-2]•[IGFBP7] seems to be able to identify patients at risk of developing AKI after this kind of exposure (27,28).
We assigned functional hemodynamic monitoring as part of the level 2 KSSB because we think it plays a crucial role in the management of patients with sepsis at higher risk of AKI and because this intervention was included in other previous studies (18,26). Furthermore, a recent trial underlined how using fluid responsiveness assessment to guide resuscitation therapy is able to reduce the rates of RRT and mechanical ventilation without compromising safety (29).
As a level 3 KSSB, we suggest nephrology and infectious disease consultation. Early nephrologist involvement in managing patients with AKI is likely to be beneficial (30), as delayed consultation is associated with higher mortality in patients with AKI (31). Similarly, infectious disease consultation may be helpful in patients with sepsis and septic shock who are not responding to standard therapy (32–34). These subspecialists are normally brought in when cases are complex or refractory, but, as also suggested by our simulation, a situation of high-risk of death, dialysis, or progression of AKI should warrant their early involvement. We are aware that a potential limitation of our study, with a complex intervention with different levels of KSSB, is that we will not know the relative contribution of each component to the final effect. However, we will be able to determine the effect of a consecutive biomarker-guided protocol in patients with sepsis, which is our primary goal.
We described our study rationale, protocol, intervention, and analysis approach before starting the LAPIS trial. Our simulation of the LAPIS protocol algorithm using patient-level data from two prior studies allowed us to better understand the role of urinary [TIMP-2]•[IGFBP7] in selecting patients with markedly different rates of progression to moderate/severe AKI, death, or dialysis in 72 hours.
1. Kellum JA, Lameire N, Aspelin P, et al.: Kidney disease: Improving global outcomes (KDIGO) acute kidney injury work group. KDIGO clinical practice guideline for acute kidney injury. Kidney Int Suppl. 2012; 2:1–138
2. Ronco C, Bellomo R, Kellum JA: Acute kidney injury. Lancet. 2019; 394:1949–1964
3. Al-Jaghbeer M, Dealmeida D, Bilderback A, et al.: Clinical decision support for in-hospital AKI. J Am Soc Nephrol. 2018; 29:654–660
4. Hoste EA, Bagshaw SM, Bellomo R, et al.: Epidemiology of acute kidney injury in critically ill patients: The multinational AKI-EPI study. Intensive Care Med. 2015; 41:1411–1423
5. Mehta RL, Cerdá J, Burdmann EA, et al.: International society of nephrology’s 0by25 initiative for acute kidney injury (zero preventable deaths by 2025): A human rights case for nephrology. Lancet. 2015; 385:2616–2643
6. Ronco C, Ricci Z: The concept of risk and the value of novel markers of acute kidney injury. Crit Care. 2013; 17:117
7. Kellum JA, Devarajan P: What can we expect from biomarkers for acute kidney injury? Biomark Med. 2014; 8:1239–1245
8. Gómez H, Kellum JA: Sepsis-induced acute kidney injury. Curr Opin Crit Care. 2016; 22:546–553
9. Singer M, Deutschman CS, Seymour CW, et al.: The third international consensus definitions for sepsis and septic shock (Sepsis-3). JAMA. 2016; 315:801–810
10. Rudd KE, Johnson SC, Agesa KM, et al.: Global, regional, and national sepsis incidence and mortality, 1990-2017: Analysis for the global burden of disease study. Lancet. 2020; 395:200–211
11. Suh SH, Kim CS, Choi JS, et al.: Acute kidney injury in patients with sepsis and septic shock: risk factors and clinical outcomes. Yonsei Med J. 2013; 54:965–972
12. Srisawat N, Kellum JA: The role of biomarkers in acute kidney injury. Crit Care Clin. 2020; 36:125–140
13. Kashani K, Al-Khafaji A, Ardiles T, et al.: Discovery and validation of cell cycle arrest biomarkers in human acute kidney injury. Crit Care. 2013; 17:R25
14. Kellum JA, Chawla LS: Cell-cycle arrest and acute kidney injury: The light and the dark sides. Nephrol Dial Transplant. 2016; 31:16–22
15. Bihorac A, Chawla LS, Shaw AD, et al.: Validation of cell-cycle arrest biomarkers for acute kidney injury using clinical adjudication. Am J Respir Crit Care Med. 2014; 189:932–939
16. Hoste EA, McCullough PA, Kashani K, et al.; Sapphire Investigators: Derivation and validation of cutoffs for clinical use of cell cycle arrest biomarkers. Nephrol Dial Transplant. 2014; 29:2054–2061
17. Yealy DM, Kellum JA, Huang DT, et al.: A randomized trial of protocol-based care for early septic shock. N Engl J Med. 2014; 370:1683–1693
18. Meersch M, Schmidt C, Hoffmeier A, et al.: Prevention of cardiac surgery-associated AKI by implementing the KDIGO guidelines in high risk patients identified by biomarkers: The PrevAKI randomized controlled trial. Intensive Care Med. 2017; 43:1551–1561
19. Göcze I, Jauch D, Götz M, et al.: Biomarker-guided intervention to prevent acute kidney injury after major surgery: The prospective randomized BigpAK study. Ann Surg. 2018; 267:1013–1020
20. McCullough PA, Ostermann M, Forni LG, et al.; the Sapphire Investigators: Serial urinary tissue inhibitor of metalloproteinase-2 and insulin-like growth factor-binding protein 7 and the prognosis for acute kidney injury over the course of critical illness. Cardiorenal Med. 2019; 9:358–369
21. Fiorentino M, Xu Z, Smith A, et al.: Serial measurement of cell-cycle arrest biomarkers [TIMP-2]•[IGFBP7] and risk for progression to death, dialysis or severe acute kidney injury in patients with septic shock. Am J Respir Crit Care Med. 2020; 202:1262–1270
22. Honore PM, Nguyen HB, Gong M, et al.; Sapphire and Topaz Investigators: Urinary tissue inhibitor of metalloproteinase-2 and insulin-like growth factor-binding protein 7 for risk stratification of acute kidney injury in patients with sepsis. Crit Care Med. 2016; 44:1851–1860
23. Nusshag C, Rupp C, Schmitt F, et al.: Cell cycle biomarkers and soluble urokinase-type plasminogen activator receptor for the prediction of sepsis-induced acute kidney injury requiring renal replacement therapy: A prospective, exploratory study. Crit Care Med. 2019; 47:e999–e1007
24. Guzzi LM, Bergler T, Binnall B, et al.: Clinical use of [TIMP-2]•[IGFBP7] biomarker testing to assess risk of acute kidney injury in critical care: Guidance from an expert panel. Crit Care. 2019; 23:225
25. Kane-Gill SL, Peerapornratana S, Wong A, et al.: Use of tissue inhibitor of metalloproteinase 2 and insulin-like growth factor binding protein 7 [TIMP2]•[IGFBP7] as an AKI risk screening tool to manage patients in the real-world setting. J Crit Care. 2020; 57:97–101
26. Engelman DT, Crisafi C, Germain M, et al.: Using urinary biomarkers to reduce acute kidney injury following cardiac surgery. J Thorac Cardiovasc Surg. 2020; 160:1235–1246.e2
27. Ostermann M, McCullough PA, Forni LG, et al.; all SAPPHIRE Investigators: Kinetics of urinary cell cycle arrest markers for acute kidney injury following exposure to potential renal insults. Crit Care Med. 2018; 46:375–383
28. Kane-Gill SL, Ostermann M, Shi J, et al.: Evaluating renal stress using pharmacokinetic urinary biomarker data in critically ill patients receiving vancomycin and/or piperacillin-tazobactam: A secondary analysis of the Multicenter Sapphire Study. Drug Saf. 2019; 42:1149–1155
29. Douglas IS, Alapat PM, Corl KA, et al.: Fluid response evaluation in sepsis hypotension and shock: A randomized clinical trial. Chest. 2020; 158:1431–1445
30. Balasubramanian G, Al-Aly Z, Moiz A, et al.: Early nephrologist involvement in hospital-acquired acute kidney injury: A pilot study. Am J Kidney Dis. 2011; 57:228–234
31. Soares DM, Pessanha JF, Sharma A, et al.: delayed nephrology consultation and high mortality on acute kidney injury: A meta-analysis. Blood Purif. 2017; 43:57–67
32. Viale P, Tedeschi S, Scudeller L, et al.: Infectious diseases team for the early management of severe sepsis and septic shock in the emergency department. Clin Infect Dis. 2017; 65:1253–1259
33. Madaline T, Wadskier Montagne F, Eisenberg R, et al.: Early infectious disease consultation is associated with lower mortality in patients with severe sepsis or septic shock who complete the 3-hour sepsis treatment bundle. Open Forum Infect Dis. 2019; 6:ofz408
34. Lee RA, Vo DT, Zurko JC, et al.: Infectious diseases consultation is associated with decreased mortality in enterococcal bloodstream infections. Open Forum Infect Dis. 2020; 7:ofaa064