We recently reported that tissue inhibitor of metalloproteinases-2 (TIMP-2) and insulin-like growth factor binding protein 7 (IGFBP7) augment acute kidney injury (AKI) staging [1] using a framework developed by international consensus [2]. We found that, in patients with sepsis who developed AKI according to functional staging [3], urinary [TIMP-2]•[IGFBP7] >2.0 (ng/mL)2/1000 identified patients with lower 30-day survival within functional stages while this was not true for patients without AKI. In this report we sought to test whether an alternative biomarker of AKI, urinary kidney injury molecule-1 (KIM-1), can differentiate survival within the same functional stage.
We measured KIM-1 in a random subset of 480 patients drawn from all patients enrolled in the ProCESS trial, a multicenter, randomized clinical trial of three different resuscitation strategies in patients with septic shock, whose methods and main results have been published elsewhere [4, 5]. We excluded patients with end-stage kidney disease, reference serum creatinine (sCr) ≥4 mg/dL and missing admission sCr. We collected patient demographics and prior health history, and we classified AKI according to Kidney Disease: Improving Global Outcomes (KDIGO) criteria using both sCr and urine output [3]. Urine samples for KIM-1, obtained 6 h after the start of resuscitation, were centrifuged right after collection, and the supernatant was frozen and stored at <–70°C. The supernatant was thawed immediately prior to testing for KIM-1 with the enzyme-linked immunosorbent assay kits obtained from EKF Diagnostics (Cardiff, UK) and performed according to the manufacturer's specifications. This retrospective study was in accordance with the ethical standards of the institutional and national research committee and with the 1964 Declaration of Helsinki.
We evaluated the presence of AKI by KDIGO between 0 and 24 h from enrollment and its highest stage, and urinary KIM-1 at 6 h from enrollment. To be consistent with our previous analysis [1] and to find a comparable ‘high-specificity’ cutoff for KIM-1, we: (i) determined the specificity for the 2.0 cutoff of [TIMP-2]•[IGFBP7] to identify patients with AKI; (ii) created a receiver operating characteristic curve for KIM-1 for AKI; and (iii) selected the cutoff for KIM-1 with the most similar specificity to that of [TIMP-2]•[IGFBP7]. We used this cutoff to categorize each KDIGO stage as biomarker negative or positive [2]. Our endpoints were survival and all-cause mortality at 30 days following enrollment. We compared survival between and within AKI stage (biomarker positive vs. biomarker negative) using Kaplan–Meier plots and Log-rank test. We compared mortality within AKI stage using Pearson's Chi-square test together with relative risk (RR) [95% confidence interval (CI)]. We performed an exploratory sensitivity analysis comparing mortality using other exploratory cutoffs (3.0, 2.0 and 1.0 ng/mL) and this is reported in the Supplemental data. We conducted all the analyses using SPSS Statistics Version 26 (IBM Corp., Armonk, NY, USA) and set the per-comparison significance at a two-tailed P < .05.
After excluding patients without a valid KIM-1 result, our analysis cohort included 467 patients. Table 1 summarizes the general characteristics of our cohort. A total of 284/467 (60.8%) patients had AKI between 0 and 24 h from enrollment. The 2.0 cutoff for [TIMP-2]•[IGFBP7] corresponded to a sensitivity (Sn) of 26.7% and specificity (Sp) of 92.5% for AKI. For KIM-1, the closest Sp was 92.3% and it corresponded to a cutoff value of 4.0 ng/mL (with Sn 20.4%). A total of 72/467 (15.4%) patients had [KIM-1] greater than 4.0 ng/mL. We then applied this cutoff to each functional KDIGO AKI stage. Patients with KIM-1 >4.0 were 14/183 (7.7%) among patients with no AKI (stage 1S), 8/70 (11.4%) among patients with AKI stage 1 (stage 1B), 36/139 (25.9%) for stage 2 (stage 2B) and 14/75 (18.7%) for stage 3 (stage 3B). The survival was different across all eight stages obtained with KIM-1 (P = .001) and the corresponding survival curves are shown in Fig. 1. The pairwise comparisons for survival within the same functional stage were statistically different for no AKI vs stage 1S (P = .03) and stage 3A vs 3B (P = .02). All-cause mortality at 30 days was increased for stage 1S vs no AKI with corresponding mortality rates of 28.6% (95% CI 10.5–54.5) vs 10.7% (95% CI 6.7–16.6; P = .048 for Chi-Square test) and RR 2.68 (95% CI 1.05–6.84), and for stage 3B vs 3A, with corresponding mortality rates of 42.9% (95% CI 20.3–68.1) vs 14.8% (95% CI 7.6–25.2; P = .02) and RR 2.91 (95% CI 1.24–6.83). Mortality was not statistically different for stage 1B vs 1A and for stage 2B vs 2A (Supplementary data, Table S1). In Supplementary data, Table S1 reported the results of the exploratory sensitivity analysis regarding mortality rates for other cutoffs.
Table 1:
General characteristics of the analysis cohort.
| Analysis cohort for KIM-1 (N = 467) | |
|---|---|
| Age, years | 61 [49–72] |
| Sex, male/female | 275 (58.9%)/192 (41.1%) |
| Racea | |
| Caucasian/white | 316 (67.7%) |
| African American/Black | 114 (24.4%) |
| Other | 37 (7.9%) |
| Cardiovascular diseaseb | 312 (66.8%) |
| Arterial hypertension | 281 (60.2%) |
| Congestive heart failure | 53 (11.3%) |
| Previous myocardial infarction | 45 (9.6%) |
| Cerebral vascular disease | 51 (10.9%) |
| Peripheral vascular disease | 31 (6.6%) |
| Diabetes mellitus | 169 (36.2%) |
| Chronic respiratory disease | 100 (21.4%) |
| Renal disease history | 38 (8.1%) |
| Active cancer | 90 (19.3%) |
| Dementia | 32 (6.9%) |
| Liver cirrhosis | 30 (6.4%) |
| Peptic ulcer disease | 17 (3.6%) |
| HIV infection | 13 (2.8%) |
| Charlson comorbidity index | 2 [1–4] |
| SOFA score at enrollment | 7 [4–9] |
| APACHE II score at enrollment | 19 [15–25] |
| AKI (0–24 h from enrollment) | 284 (60.8%) |
| No AKI | 183 (39.2%) |
| KDIGO Stage 1 | 70 (15.0%) |
| KDIGO Stage 2 | 139 (29.8%) |
| KDIGO Stage 3 | 75 (16.1%) |
| Mortality at 30 days | 84 (18.0%) |
| [TIMP-2]•[IGFBP7], (ng/mL)2/1000 | 0.33 [0.12–1.18] |
| [TIMP-2]•[IGFBP7] >2.0 (ng/mL)2/1000 | 79 (16.9%) |
| KIM-1, ng/mL | 0.90 [0.50–2.51] |
| KIM-1 >4.0 ng/mL | 72 (15.4%) |
Categorical variables are presented as numbers (%), continuous variables as medians [interquartile range].
Race was determined by patient self‐report or by patient's legally authorized representative. ‘Other’ race corresponds to Asian, American Indian or native Alaskan, Native Hawaiian or other Pacific islander, unknown, or other.
Presence of any among arterial hypertension, congestive heart failure, previous myocardial infarction, cerebral vascular disease and peripheral vascular disease.
APACHE, Acute Physiology And Chronic Health Evaluation; SOFA, Sequential Organ Failure Assessment.
Figure 1:
Survival by new AKI stages. The figure shows the Kaplan–Meier curves for survival up to 30 days stratified for the new AKI stages based on urinary KIM-1 ≤4 or >4.0 ng/mL. Dashed lines indicate biomarker-negative patients and solid lines indicate those who were biomarker positive. Blue lines: no AKI/stage 1S; green: stage 1A/B; orange: stage 2A/B; red: 3A/B (for details about the definition of stages refer to reference [2]). Below the figure are the numbers of patients at risk of death at the beginning of Day 0, 10, 20 and 30 from enrollment. The Log-rank test showed P = .001. Pairwise comparison between survival curves within the same functional stage were as follows: no AKI vs stage 1S (P = .03), stage 1A vs 1B (P = .82), stage 2A vs 2B (P = .27) and stage 3A vs 3B (P = .02).
Markers of structural damage to the kidney may or may not be associated with function impairment. KIM-1, produced by proximal tubular cells in response to various insults [6], can predict AKI [7, 8] and adverse outcomes such as death or need for renal replacement therapy [9, 10]. KIM-1 >4.0 identified patients with subclinical AKI (stage 1S), whereas [TIMP-2]•[IGFBP7] >2.0 did not [1]. This is also consistent with prior work indicating that kidney damage is detected by this marker below the sCr threshold [10–12]. As in our previous report, important limitations exist for our analysis and approach (e.g. retrospective analysis, selection bias related to missing value for biomarkers, missing data regarding long-term renal function, etc.) [1]. This new exploratory analysis faced other potential limitations related to smaller sample size. Nevertheless, the results are qualitatively like those using [TIMP-2]•[IGFBP7]. To be consistent with our work for [TIMP-2]•[IGFBP7], we measured KIM-1 at 6 h after initial sepsis resuscitation, but while this may be the ideal timing for [TIMP-2]•[IGFBP7] [13], this might not be the same for KIM-1 due to its intrinsically different characteristics (i.e. damage marker upregulated in the injured proximal tubular epithelium after different types of injury/toxic exposures [14]). In the sensitivity analysis, KIM-1 was not able to identify subclinical AKI at lower cutoffs, so it is possible that our results for the 4.0 cutoff is related to low sample size.
In conclusion, in patients with septic shock, high-specificity cutoff of 4.0 for KIM-1 was able to identify patients with lower 30-day survival in patients with AKI stage 3.
Supplementary Material
ACKNOWLEDGEMENTS
The authors thank Ms Karen Nieri (Executive Assistant at the Center for Critical Care Nephrology, Department of Critical Care Medicine, University of Pittsburgh School of Medicine) for her editorial assistance in preparing this article.
Notes
A complete list of ProCESS and ProGReSS-AKI Investigators is available in the Supplementary Data.
Contributor Information
Luca Molinari, Center for Critical Care Nephrology, Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA; Department of Translational Medicine, Università del Piemonte Orientale, Novara, Italy.
Douglas P Landsittel, Department of Biomedical Informatics, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.
John A Kellum, Center for Critical Care Nephrology, Department of Critical Care Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.
for the ProCESS and ProGReSS-AKI Investigators*:
Derek C Angus, Lakhmir S Chawla, David T Huang, Christopher Keener, John A Kellum, Nicole Lucko, Paul M Palevsky, Francis Pike, Kai Singbartl, Ali Smith, Donald M Yealy, Sachin Yende, Derek C Angus, Amber E Barnato, Tammy L Eaton, Elizabeth Gimbel, David T Huang, Christopher Keener, John A Kellum, Kyle Landis, Francis Pike, Diana K Stapleton, Lisa A Weissfeld, Michael Willochell, Kourtney A Wofford, and Donald M Yealy
FUNDING
The ProCESS trial and its follow-up studies were funded by National Institutes of Health/National Institute of Diabetes and Digestive and Kidney Diseases (R01DK083961) and National Institutes of Health/National Institute of General Medical Sciences (P50GM076659). The funding organization had no role in the design and conduct of the study; in the collection, management, analysis and interpretation of the data; in the preparation, review or approval of the manuscript; and in the decision to submit the manuscript for publication.
AUTHORS’ CONTRIBUTIONS
Conceptualization: J.A.K., L.M. Methodology: L.M., D.P.L. Formal analysis and investigation: L.M., D.P.L. Draft of the manuscript: L.M., J.AK. Critical revision of the manuscript for important intellectual content: J.A.K., L.M., D.P.L. Funding acquisition: J.A.K.
DATA AVAILABILITY STATEMENT
The primary data used in this analysis is available to outside parties under a data use agreement with the University of Pittsburgh and in accordance with the procedures of the ProCESS Publications Committee.
CONFLICT OF INTEREST STATEMENT
J.A.K. discloses research support and consulting fees from Astute Medical and Astute Medical has licensed unrelated technology from the University of Pittsburgh. Astute Medical is now part of bioMérieux. None of the other authors declare any conflict of interests.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The primary data used in this analysis is available to outside parties under a data use agreement with the University of Pittsburgh and in accordance with the procedures of the ProCESS Publications Committee.