Background
Combining tubular damage and functional biomarkers may improve prediction precision of acute kidney injury (AKI). Serum cystatin C (sCysC) represents functional damage of kidney, while urinary N-acetyl-β-D-glucosaminidase (uNAG) is considered as a tubular damage biomarker. So far, there is no nomogram containing this combination to predict AKI in septic cohort. We aimed to compare the performance of AKI prediction models with or without incorporating these two biomarkers and develop an effective nomogram for septic patients in intensive care unit (ICU).
Methods
This was a prospective study conducted in the mixed medical-surgical ICU of a tertiary care hospital. Adults with sepsis were enrolled. The patients were divided into development and validation cohorts in chronological order of ICU admission. A logistic regression model for AKI prediction was first constructed in the development cohort. The contribution of the biomarkers (sCysC, uNAG) to this model for AKI prediction was assessed with the area under the receiver operator characteristic curve (AUC), continuous net reclassification index (cNRI), and incremental discrimination improvement (IDI). Then nomogram was established based on the model with the best performance. This nomogram was validated in the validation cohort in terms of discrimination and calibration. The decision curve analysis (DCA) was performed to evaluate the nomogram’s clinical utility.
Results
Of 358 enrolled patients, 232 were in the development cohort (69 AKI), while 126 in the validation cohort (52 AKI). The first clinical model included the APACHE II score, serum creatinine, and vasopressor used at ICU admission. Adding sCysC and uNAG to this model improved the AUC to 0.831. Furthermore, incorporating them significantly improved risk reclassification over the predictive model alone, with cNRI (0.575) and IDI (0.085). A nomogram was then established based on the new model including sCysC and uNAG. Application of this nomogram in the validation cohort yielded fair discrimination with an AUC of 0.784 and good calibration. The DCA revealed good clinical utility of this nomogram.
Conclusions
A nomogram that incorporates functional marker (sCysC) and tubular damage marker (uNAG), together with routine clinical factors may be a useful prognostic tool for individualized prediction of AKI in septic patients.
Emerging epidemiological researches have been performed to assess the association of ESR1 PvuII (rs2234693 T>C) polymorphism with the risk of cancer, yet with conflicting conclusions. Therefore, this updated meta-analysis was performed to make a more accurate evaluation of such relationship. We adopted EMBASE, PubMed, CNKI, and WANFANG database to search relevant literature before January 2018. Odds ratios (ORs) and 95% confidence intervals (CIs) were employed to estimate the relationship strengths. In final, 80 studies (69 publications) involving 26428 cases and 43381 controls were enrolled. Our results failed to provide significant association between overall cancer risk and PvuII polymorphism under homozygous (TT vs. CC) and heterozygous (TT vs. CT) models. Statistically significant relationship was only observed for PvuII polymorphism in allele model T vs. C (OR=0.95, 95% CI=0.91-0.99). Stratification analysis by cancer type suggested that T genotype significantly decreased prostate cancer risk (TT vs. CC: OR=0.79, 95% CI=0.66-0.94; T vs. C: OR=0.89, 95% CI=0.82-0.98), Leiomyoma risk (T vs. C: OR=0.82, 95% CI=0.68-0.98), and HCC risk (TT vs. CC: OR=0.45, 95% CI=0.28-0.71; T vs. C: OR=0.67, 95% CI=0.47-0.95). Furthermore, significantly decreased risk was also found for Africans, population-based and hospital-based studies in the stratified analyses. These results suggest that ESR1 PvuII (rs2234693 T>C) polymorphism may only have little impact on cancer susceptibility. In the future, large-scale epidemical studies are warranted to verify these results.
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