What does this research mean for the field?

After lung transplantation, urinary [TIMP-2]*[IGFBP-7] has limited utility for acute kidney injury risk stratification, whereas plasma biomarker-derived risk phenotypes are significantly associated with AKI severity and postoperative outcomes. Novelty: ClaimNovelty.INCREMENTAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

To evaluate the performance of subclinical acute kidney injury and creatinine-independent plasma biomarker phenotypes for early post-lung transplantation risk stratification.

September 3, 2026JHLT OpenOpen Access

Early Biomarkers of Acute Kidney Injury After Lung Transplantation

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Authors

VSVittorio Scaravilli AUAlessandro Uslenghi GTGloria Turconi

Discussion

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Overview

Prospective cohort study shows plasma biomarker phenotypes predict acute kidney injury after lung transplantation, indicating plasma profiles outperform urinary markers for risk stratification.

Key Points

To evaluate the performance of subclinical acute kidney injury and creatinine-independent plasma biomarker phenotypes for early post-lung transplantation risk stratification.
Conducted a prospective single-center cohort study including 84 bilateral lung transplant recipients.
Measured urinary TIMP-2*IGFBP-7 alongside plasma H-FABP, midkine, sTNFR1, and sTNFR2 at 6 and 36 hours post-reperfusion using point-of-care platforms.
Diagnosed AKI via KDIGO criteria, defined subclinical AKI as urinary TIMP-2*IGFBP-7 > 0.3 without AKI criteria, and identified risk phenotypes using latent profile analysis.
AKI occurred in 30 of 84 recipients (36%), whereas urinary TIMP-2*IGFBP-7 showed modest discrimination with a peak AUROC of 0.63 (95% CI 0.58–0.70) at 36 hours.
Subclinical AKI was identified in 25 recipients but exhibited postoperative outcomes comparable to those in recipients without AKI.
Latent profile analysis stratified patients into low-risk (30%), intermediate-risk (51%), and high-risk (19%) phenotypes, with AKI rates of 16%, 42%, and 72% (p=0.006) and 72-hour primary graft dysfunction grade 3 rates of 4%, 13%, and 35% (p=0.045).

Cite This Study

Scaravilli et al. (2026) studied this question.

synapsesocial.com/papers/6a99365f636c6408cfa7f8ac https://doi.org/10.1016/j.jhlto.2026.100679

View Full Paper

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September 3, 2026JHLT OpenOpen Access

Early Biomarkers of Acute Kidney Injury After Lung Transplantation

View Full Paper

Ask AI

Bookmark

Authors

VSVittorio Scaravilli AUAlessandro Uslenghi GTGloria Turconi

Discussion

Member takes

Overview

Prospective cohort study shows plasma biomarker phenotypes predict acute kidney injury after lung transplantation, indicating plasma profiles outperform urinary markers for risk stratification.

Key Points

To evaluate the performance of subclinical acute kidney injury and creatinine-independent plasma biomarker phenotypes for early post-lung transplantation risk stratification.
Conducted a prospective single-center cohort study including 84 bilateral lung transplant recipients.
Measured urinary TIMP-2*IGFBP-7 alongside plasma H-FABP, midkine, sTNFR1, and sTNFR2 at 6 and 36 hours post-reperfusion using point-of-care platforms.
Diagnosed AKI via KDIGO criteria, defined subclinical AKI as urinary TIMP-2*IGFBP-7 > 0.3 without AKI criteria, and identified risk phenotypes using latent profile analysis.
AKI occurred in 30 of 84 recipients (36%), whereas urinary TIMP-2*IGFBP-7 showed modest discrimination with a peak AUROC of 0.63 (95% CI 0.58–0.70) at 36 hours.
Subclinical AKI was identified in 25 recipients but exhibited postoperative outcomes comparable to those in recipients without AKI.
Latent profile analysis stratified patients into low-risk (30%), intermediate-risk (51%), and high-risk (19%) phenotypes, with AKI rates of 16%, 42%, and 72% (p=0.006) and 72-hour primary graft dysfunction grade 3 rates of 4%, 13%, and 35% (p=0.045).

Cite This Study

Scaravilli et al. (2026) studied this question.

synapsesocial.com/papers/6a99365f636c6408cfa7f8ac https://doi.org/10.1016/j.jhlto.2026.100679

View Full Paper

Ask AI

Bookmark