A nomogram incorporating functional and tubular damage biomarkers to predict the risk of acute kidney injury for septic patients.

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-ß-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.

Combining Serum Cystatin C and Urinary N-Acetyl-Beta-D-Glucosaminidase Improves the Precision for Acute Kidney Injury Diagnosis after Resection of Intracranial Space-Occupying Lesions.

BACKGROUND: Postoperative acute kidney injury (AKI) is frequent and associated with adverse outcomes. Unfortunately, the early diagnosis of AKI remains a challenge. Combining functional and tubular damage biomarkers may provide better precision for AKI detection. However, the diagnostic accuracy of this combination for AKI after neurosurgery is unclear. Serum cystatin C (sCysC) and urinary albumin/creatinine ratio (uACR) are considered functional biomarkers, while urinary N-acetyl-ß-D-glucosaminidase (uNAG) represents tubular damage. We aimed to assess the performances of these clinical available biomarkers and their combinations for AKI prediction after resection of intracranial space-occupying lesions. METHODS: A prospective study was conducted, enrolling adults undergoing resection of intracranial space-occupying lesions and admitted to the neurosurgical intensive care unit. The discriminative abilities of postoperative sCysC, uNAG, uACR, and their combinations in predicting AKI were compared using the area under the receiver operating characteristic curve (AUC-ROC), continuous net reclassification index (cNRI), and incremental discrimination improvement (IDI). RESULTS: Of 605 enrolled patients, AKI occurred in 67 patients. The cutoff values of sCysC, uNAG, and uACR to predict postoperative AKI were 0.72 mg/L, 19.98 U/g creatinine, and 44.21 mg/g creatinine, respectively. For predicting AKI, the composite of sCysC and uNAG (AUC-ROC = 0.785) outperformed either individual biomarkers or the other two panels (uNAG plus uACR or sCysC plus uACR). Adding this panel to the predictive model improved the AUC-ROC to 0.808. Moreover, this combination significantly improved risk reclassification over the clinical model alone, with cNRI (0.633) and IDI (0.076). Superior performance of this panel was further confirmed with bootstrap internal validation. CONCLUSIONS: Combination of functional and tubular damage biomarkers improves the predictive accuracy for AKI after resection of intracranial space-occupying lesions.

The effect of glucocorticoids on serum cystatin C in identifying acute kidney injury: a propensity-matched cohort study.

BACKGROUND: Glucocorticoids may impact the accuracy of serum cystatin C (sCysC) in reflecting renal function. We aimed to assess the effect of glucocorticoids on the performance of sCysC in detecting acute kidney injury (AKI) in critically ill patients. METHODS: A prospective observational cohort study was performed in a general intensive care unit (ICU). Using propensity score matching, we successfully matched 240 glucocorticoid users with 960 non-users among 2716 patients. Serum creatinine (SCr) and sCysC were measured for all patients at ICU admission. Patients were divided into four groups based on cumulative doses of glucocorticoids within 5 days before ICU admission (Group I: non-users; Group II: 0 mg < prednisone ≤50 mg; Group III: 50 mg < prednisone ≤150 mg; Group IV: prednisone > 150 mg). We compared the performance of sCysC for diagnosing and predicting AKI in different groups using the area under the receiver operator characteristic curve (AUC). RESULTS: A total of 240 patients received glucocorticoid medication within 5 days before ICU admission. Before and after matching, the differences of sCysC levels between glucocorticoid users and non-users were both significant (P <  0.001). The multiple linear regression analysis revealed that glucocorticoids were independently associated with sCysC (P <  0.001). After matching, the group I had significantly lower sCysC levels than the group III and group IV (P <  0.05), but there were no significant differences in sCysC levels within different glucocorticoids recipient groups (P > 0.05). Simultaneously, we did not find significant differences in the AUC between any two groups in the matched cohort (P > 0.05). CONCLUSIONS: Glucocorticoids did not impact the performance of sCysC in identifying AKI in critically ill patients.

C-reactive protein concentration as a risk predictor of mortality in intensive care unit: a multicenter, prospective, observational study.

BACKGROUND: It is not clear whether there are valuable inflammatory markers for prognosis judgment in the intensive care unit (ICU). We therefore conducted a multicenter, prospective, observational study to evaluate the prognostic role of inflammatory markers. METHODS: The clinical and laboratory data of patients at admission, including C-reactive protein (CRP), were collected in four general ICUs from September 1, 2018, to August 1, 2019. Multivariate logistic regression was used to identify factors independently associated with nonsurvival. The area under the receiver operating characteristic curve (AUC-ROC), net reclassification improvement (NRI), and integrated discrimination improvement (IDI) were used to evaluate the effect size of different factors in predicting mortality during ICU stay. 3 -knots were used to assess whether alternative cut points for these biomarkers were more appropriate. RESULTS: A total of 813 patients were recruited, among whom 121 patients (14.88%) died during the ICU stay. The AUC-ROC values of PCT and CRP for discriminating ICU mortality were 0.696 (95% confidence interval [CI], 0.650-0.743) and 0.684 (95% CI, 0.633-0.735), respectively. In the multivariable analysis, only APACHE II score (odds ratio, 1.166; 95% CI, 1.129-1.203; P = 0.000) and CRP concentration > 62.8 mg/L (odds ratio, 2.145; 95% CI, 1.343-3.427; P = 0.001), were significantly associated with an increased risk of ICU mortality. Moreover, the combination of APACHE II score and CRP > 62.8 mg/L significantly improved risk reclassification over the APACHE II score alone, with NRI (0.556) and IDI (0.013). Restricted cubic spline analysis confirmed that CRP concentration > 62.8 mg/L was the optimal cut-off value for differentiating between surviving and nonsurviving patients. CONCLUSION: CRP markedly improved risk reclassification for prognosis prediction.

Defining a postoperative mean arterial pressure threshold in association with acute kidney injury after cardiac surgery: a prospective observational study.

Acute kidney injury (AKI) is a common but fatal complication after cardiac surgery. In the absence of effective treatments, the identification and modification of risk factors has been a major component of disease management. However, the optimal blood pressure target for preventing cardiac surgery-associated acute kidney injury (CSA-AKI) remains unclear. We sought to determine the effect of postoperative mean arterial pressure (MAP) in CSA-AKI. It is hypothesized that longer periods of hypotension after cardiac surgery are associated with an increased risk of AKI. This prospective cohort study was conducted on adult patients who underwent cardiac surgery requiring cardiopulmonary bypass at a tertiary center between October 2018 and May 2020. The primary outcome is the occurrence of CSA-AKI. MAP and its duration in the ranges of less than 65, 65 to 74, and 75 to 84 mmHg within 24 h after surgery were recorded. The association between postoperative MAP and CSA-AKI was examined by using logistic regression. Among the 353 patients enrolled, 217 (61.5%) had a confirmed diagnosis of CSA-AKI. Each 1 h epoch of postoperative MAP less than 65 mmHg was associated with an adjusted odds ratio of 1.208 (95% CI, 1.007 to 1.449; P = 0.042), and each 1 h epoch of postoperative MAP between 65 and 74 mmHg was associated with an adjusted odds ratio of 1.144 (95% CI, 1.026 to 1.275; P = 0.016) for CSA-AKI. A potentially modifiable risk factor, postoperative MAP less than 75 mmHg for 1 h or more is associated with an increased risk of CSA-AKI.

Variations of urinary N-acetyl-ß-D-glucosaminidase levels and its performance in detecting acute kidney injury under different thyroid hormones levels: a prospectively recruited, observational study.

OBJECTIVE: Changes in thyroid function will be accompanied by changes in urinary N-acetyl-ß-D-glucosaminidase (uNAG) levels. Therefore, whether thyroid hormones interfere the ability of uNAG in detecting acute kidney injury (AKI) has raised concern in patients with critical illness. DESIGN: A prospectively recruited, observational study was performed. SETTING: Adults admitted to the intensive care unit of a grade A tertiary hospital in China. PARTICIPANTS: A total of 1919 critically ill patients were enrolled in the study. MAIN OUTCOME MEASURES: To investigate the variations of the ability of uNAG to detect AKI in patients with critical illness under different thyroid hormones levels (differences in area under the curve (AUC) for uNAG diagnosis and prediction of AKI with different thyroid hormones levels). RESULTS: The bivariate correlation analysis revealed that FT3 and TT3 levels were independently associated with uNAG levels (p<0.001). FT3 and uNAG also showed correlation in multivariable linear regression analysis (p<0.001). After stratification according to the levels of FT3 or TT3, significant variation was observed in the uNAG levels with different quartiles (p<0.05). However, in patients with varying FT3 and TT3 levels, no significant difference was found in the AUCs of uNAG to detect AKI (p>0.05). CONCLUSIONS: Even if uNAG levels varied with FT3 and TT3 levels, these hormones did not interfere with uNAG's ability to detect AKI in patients with critical illness.

Dysbiosis of intestinal microbiota in critically ill patients and risk of in-hospital mortality.

BACKGROUND: Despite the essential functions of the intestinal microbiota in human physiology, little research was reported on gut microbiota alterations in intensive care patients. This investigation examined the dysbacteriosis of intestinal flora in critically ill patients and evaluated the prognostic performance of this dysbiosis to predict in-hospital mortality. METHODS: A prospective cohort of patients were consecutively recruited in the Intensive Care Units (ICUs) in Guangdong Provincial People's Hospital from March 2017 through October 2017. Acute Physiology and Chronic Health Evaluation (APACHE) II score and Sequential Organ Failure Assessment (SOFA) score were assessed, and fecal samples were taken for examination within 24 hours of ICU admission. The taxonomic composition of the intestinal microbiome was determined using 16S rDNA gene sequencing. Patients were divided into survival and death groups based on hospital outcomes. The two groups were statistically compared using the Wilcoxon test and Metastats analysis. The genera of bacteria showing significantly different abundance between groups were assessed as predictors of in-hospital death. The prognostic value of bacterial abundance alone and in combination with APACHE II or SOFA score was evaluated using the area under the receiver operating characteristic curve (AUROC). RESULTS: Among the 61 patients examined, 12 patients (19.7%) died during their hospital stay. Bifidobacterium abundance was higher in the survival group than the death group (P = 0.031). The AUROC of Bifidobacterium abundance in identifying in-hospital death at a cut-off probability of 0.0041 was 0.718 (95% confidence interval [CI], 0.588-0.826). The panel of Bifidobacterium abundance plus SOFA (AUROC, 0.882; 95% CI, 0.774-0.950) outperformed SOFA (AUROC, 0.649; 95% CI, 0.516-0.767; P = 0.012) and Bifidobacterium abundance alone (P = 0.007). The panel of Bifidobacterium abundance plus APACHE II (AUROC, 0.876; 95% CI, 0.766-0.946) outperformed APACHE II (AUROC, 0.724; 95% CI, 0.595-0.831; P = 0.035) and Bifidobacterium abundance alone (P = 0.012). CONCLUSIONS: Dysbiosis of intestinal microbiota with variable degrees of reduction in Bifidobacterium abundance exhibited promising performance in the predicting of in-hospital mortality and provides incremental prognostic value to existing scoring systems in the adult intensive care unit (ICU) setting.