Proenkephalin as a biomarker correlates with acute kidney injury: a systematic review with meta-analysis and trial sequential analysis.

BACKGROUND: Proenkephalin A 119-159 (PENK) is freely filtered in the glomerulus with plasma levels correlating with glomerular filtration rate. Therefore, PENK has been proposed as an early indicator of acute kidney injury (AKI) although its performance is dependent on the clinical setting. This meta-analysis aimed to investigate the correlation between PENK levels and the development of AKI. METHODS: We conducted a comprehensive search on the PubMed, Embase, Cochrane databases, the website ClinicalTrials.gov and Cnki.net until June 26, 2023. Summary receiver operating characteristic (SROC) curves were used to amalgamate the overall test performance. Diagnostic odds ratio (DOR) was employed to compare the diagnostic accuracy of PENK with other biomarkers. Quality of the evidence was assessed using the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) criteria. RESULTS: We incorporated 11 observational studies with 3969 patients with an incidence of AKI of 23.4% (929 out of 3969 patients) with the best optimal cutoff value of PENK for early detection of AKI being 57.3 pmol/L. The overall sensitivity and specificity of PENK in identifying AKI were 0.69 (95% CI 0.62-0.75) and 0.76 (95% CI 0.68-0.82), respectively. The combined positive likelihood ratio (LR) stood at 2.83 (95% CI 2.06-3.88), and the negative LR was 0.41 (95% CI 0.33-0.52). The SROC curve showcased pooled diagnostic accuracy of 0.77 (95% CI 0.73-0.81). Interestingly, patients with a history of hypertension or heart failure demonstrated a lower specificity of PENK in correlating the development of AKI. CONCLUSION: Our results indicate that PENK possesses significant potential as a biomarker for the early detection of the development of AKI, using a cutoff point of 57.3 pmol/L for PENK.

Improvement of composite kidney outcomes by AKI care bundles: a systematic review and meta-analysis.

INTRODUCTION: Various approaches have been suggested to identify acute kidney injury (AKI) early and to initiate kidney-protective measures in patients at risk or with AKI. The objective of this study was to evaluate whether care bundles improve kidney outcomes in these patients. METHODS: We conducted a systematic review of the literature to evaluate the clinical effectiveness of AKI care bundles with or without urinary biomarkers in the recognition and management of AKI. The main outcomes were major adverse kidney events (MAKEs) consisting of moderate-severe AKI, receipt of renal replacement therapy (RRT), and mortality. RESULTS: Out of 7434 abstracts screened, 946 published studies were identified. Thirteen studies [five randomized controlled trials (RCTs) and eight non-RCTs] including 16,540 patients were eligible for inclusion in the meta-analysis. Meta-analysis showed a lower incidence of MAKE in the AKI care bundle group [odds ratio (OR) 0.73, 95% confidence interval (CI) 0.66-0.81] with differences in all 3 individual outcomes [moderate-severe AKI (OR 0.65, 95% CI 0.51-0.82), RRT (OR 0.63, 95% CI = 0.46-0.88) and mortality]. Subgroup analysis of the RCTs, all adopted biomarker-based approach, decreased the risk of MAKE (OR 0.55, 95% CI 0.41-0.74). Network meta-analysis could reveal that the incorporation of biomarkers in care bundles carried a significantly lower risk of MAKE when compared to care bundles without biomarkers (OR = 0.693, 95% CI = 0.50-0.96), while the usual care subgroup had a significantly higher risk (OR = 1.29, 95% CI = 1.09-1.52). CONCLUSION: Our meta-analysis demonstrated that care bundles decreased the risk of MAKE, moderate-severe AKI and need for RRT in AKI patients. Moreover, the inclusion of biomarkers in care bundles had a greater impact than care bundles without biomarkers.

Cardiometabolic factors explaining the association between physical activity and quality of life: U.S. National Health and Nutrition Examination Survey.

Purpose: To test the Clustered Cardiometabolic Risk (CCMR) factor explaining the relationship between physical activity and physical quality of life (QOL). Methods: Using the U.S. National Health and Nutrition Examination Survey 2003-2006, 2,445 adults completed the CDC Healthy Days Questionnaire for measuring QOL, wore the accelerometer for assessing physical activity pattern (PAP), and completed triglyceride, glucose, serum insulin, waist circumference, blood pressure, and HDL-cholesterol tests from which the CCMR factor was created. Physical QOL was classified as poor (≥14 days with poor physical health within past 30 days) vs. good (<14 days). We classified PAP by moderate-to-vigorous physical activity (MVPA), light-intensity physical activity (LIPA), and sedentary behavior (SB). We defined MVPA, LIPA, and SB as ≥2020 counts/minute, 100-2019 counts/minute, and ≤99 counts/minute, respectively. We further classified PAP status as unhealthy (MVPA <150 min/week & SB>LIPA) or healthy (MVPA <150 min/week & SBLIPA or SB

Unsupervised clustering identifies sub-phenotypes and reveals novel outcome predictors in patients with dialysis-requiring sepsis-associated acute kidney injury.

INTRODUCTION: Heterogeneity exists in sepsis-associated acute kidney injury (SA-AKI). This study aimed to perform unsupervised consensus clustering in critically ill patients with dialysis-requiring SA-AKI. PATIENTS AND METHODS: This prospective observational cohort study included all septic patients, defined by the Sepsis-3 criteria, with dialysis-requiring SA-AKI in surgical intensive care units in Taiwan between 2009 and 2018. We employed unsupervised consensus clustering based on 23 clinical variables upon initializing renal replacement therapy. Multivariate-adjusted Cox regression models and Fine-Gray sub-distribution hazard models were built to test associations between cluster memberships with mortality and being free of dialysis at 90 days after hospital discharge, respectively. RESULTS: Consensus clustering among 999 enrolled patients identified three sub-phenotypes characterized with distinct clinical manifestations upon renal replacement therapy initiation (n = 352, 396 and 251 in cluster 1, 2 and 3, respectively). They were followed for a median of 48 (interquartile range 9.5-128.5) days. Phenotypic cluster 1, featured by younger age, lower Charlson Comorbidity Index, higher baseline estimated glomerular filtration rate but with higher severity of acute illness was associated with an increased risk of death (adjusted hazard ratio of 3.05 [95% CI, 2.35-3.97]) and less probability to become free of dialysis (adjusted sub-distribution hazard ratio of 0.55 [95% CI, 0.38-0.8]) than cluster 3. By examining distinct features of the sub-phenotypes, we discovered that pre-dialysis hyperlactatemia ≥3.3 mmol/L was an independent outcome predictor. A clinical model developed to determine high-risk sub-phenotype 1 in this cohort (C-static 0.99) can identify a sub-phenotype with high in-hospital mortality risk (adjusted hazard ratio of 1.48 [95% CI, 1.25-1.74]) in another independent multi-centre SA-AKI cohort. CONCLUSIONS: Our data-driven approach suggests sub-phenotypes with clinical relevance in dialysis-requiring SA-AKI and serves an outcome predictor. This strategy represents further development toward precision medicine in the definition of high-risk sub-phenotype in patients with SA-AKI.Key messagesUnsupervised consensus clustering can identify sub-phenotypes of patients with SA-AKI and provide a risk prediction.Examining the features of patient heterogeneity contributes to the discovery of serum lactate levels ≥ 3.3 mmol/L upon initializing RRT as an independent outcome predictor.This data-driven approach can be useful for prognostication and lead to a better understanding of therapeutic strategies in heterogeneous clinical syndromes.

Evaluating the reliability, validity and minimally important difference of the Taiwanese version of the diabetes quality of life (DQOL) measurement.

BACKGROUND: Few diabetes HRQOL instruments are available in Chinese language. We tested psychometric properties of a Diabetes Quality of Life (DQOL) in Chinese language for diabetes patients in Taiwan and estimated its minimally important differences (MIDs). METHODS: Data were collected from 337 patients treated in diabetes clinics of a Taiwan teaching hospital. Pearson's correlations among domain scores of the DQOL (satisfaction, impact, and worry), the D-39S (a diabetes-specific instrument, including domains of diabetes control, energy and mobility, social burden and anxiety and worry, and sexual functioning) and the RAND-12 (a generic instrument, including physical health composite (PHC) and mental health composite (MHC)) were estimated to determine convergent/discriminant validity. Known-groups validity was examined using 2-hour postprandial plasma glucose (2 h PPG), hemoglobin A1c (HbA1c)) and presence of complications (retinopathy, neuropathy, and diabetic foot complications rather than the known groups of cardiovascular and cerebrovascular complications). We used a combined anchor- and distribution-based approach to establish MIDs. RESULTS: The DQOL scores were more strongly correlated with the physical domains of the D-39S (diabetes control and energy and mobility) and RAND-12 PHC than psychological domains of the D-39S (social burden, anxiety and worry, and sexual functioning) and RAND-12 MHC. The DQOL showed satisfactory discriminative ability for the known groups of 2 h PPG and HbA1c (effect size (ES) > or = 0.2) and retinopathy, neuropathy, and diabetic foot complications (ES > or = 0.3), but less satisfactory for the known groups of cardiovascular and cerebrovascular complications. MIDs for the DQOL domains were 3-5 points for satisfaction, 4-5 points for impact, 6-8 points for worry, and 3-4 points for overall HRQOL. CONCLUSION: We validated a DQOL in Chinese language for diabetes patients in Taiwan and provided MIDs to facilitate the measure of diabetes HRQOL.