Clinical Outcome of Twice-Weekly Hemodialysis Patients with Long-Term Dialysis Vintage

Background/Aims: Twice-weekly hemodialysis(HD) is prevalent in the developing countries, scarce data are available for this treatment in patients with long-term dialysis vintage. Methods: 106 patients with more than 5 years HD vintage undergoing twice-weekly HD or thrice-weekly HD in a hemodialysis center in Shanghai between December 1, 2013 and December 31, 2013 were enrolled into the cohort study with 3 years follow-up. Kaplan–Meier analysis and Cox proportional hazards models were used to compare patient survival between the two groups. Subgroup analysis of 62 patients more than 10 years HD vintage was also performed according to their different dialysis frequency. Results: Compared with patients on thrice-weekly HD, twice-weekly HD patients had significantly longer HD session time and higher single-pool Kt/V (spKt/V) (session time, 4.59±0.45 vs 4.14±0.31 hours/per session, P< 0.001; spKt/V, 2.12±0.31 vs 1.83±0.30, P< 0.001). Kaplan–Meier survival analysis indicated that the two groups had similar survival (P=0.983). Multivariate Cox regression analysis showed that age and time-dependent serum albumin were predictors of patient mortality. Subgroup analysis of 62 patients more than 10 years HD vintage also indicated that the two groups had similar survival. During the follow-up, 4 patients dropped out from the twice-weekly HD group and transferred to thrice-weekly HD. Conclusion: The similar survival between twice-weekly HD and thrice-weekly HD in patients with long-term dialysis vintage is likely relating to patient selection, individualized treatment for dialysis patients based on clinical features and socioeconomic factors remains a tough task for the clinicians

Development and validation of the prediction model for mortality in patients with diabetic kidney disease in intensive care unit: a study based on medical information Mart for intensive care

AbstractWe aimed to explore factors associated with mortality of diabetic kidney disease (DKD), and to establish a prediction model for predicting the mortality of DKD. This was a cohort study. In total, 1,357 DKD patients were identified from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database, with 505 DKD patients being identified from the MIMIC-III as the testing set. The outcome of the study was 1-year mortality. COX proportional hazard models were applied to screen the predictive factors. The prediction model was conducted based on the predictive factors. A receiver operating characteristic (ROC) curve with the area under the curve (AUC) was calculated to evaluate the performance of the prediction model. The median follow-up time was 365.00 (54.50,365.00) days, and 586 patients (43.18%) died within 1 year. The predictive factors for 1-year mortality in DKD included age, weight, sepsis, heart rate, temperature, Charlson Comorbidity Index (CCI), Simplified Acute Physiology Score (SAPS) II, and Sequential Organ Failure Assessment (SOFA), lymphocytes, red cell distribution width (RDW), serum albumin, and metformin. The AUC of the prediction model for predicting 1-year mortality in the training set was 0.771 [95% confidence interval (CI): 0.746-0.795] and the AUC of the prediction model in the testing set was 0.795 (95% CI: 0.756-0.834). This study establishes a prediction model for predicting mortality of DKD, providing a basis for clinical intervention and decision-making in time

Clinical Usefulness of Novel Biomarkers for the Detection of Acute Kidney Injury following Elective Cardiac Surgery

&lt;i&gt;Background/Aims:&lt;/i&gt; Acute kidney injury (AKI) is common following cardiac surgery and predicts a poor outcome. However, the early detection of AKI has proved elusive and most cases are diagnosed only following a significant rise in serum creatinine (SCr). We compared a panel of early biomarkers of AKI for the detection of AKI in patients undergoing heart surgery. This study included serum cystatin C (CyC) and urinary levels of neutrophil gelatinase-associated lipocalin (NGAL), interleukin-18 (IL-18), retinol-binding protein (RBP) and N-acetyl-β-&lt;i&gt;D&lt;/i&gt;-glucosaminidase (NAG). &lt;i&gt;Methods:&lt;/i&gt; We retrospectively identified 15 patients undergoing open cardiac surgery who developed AKI within 72 h postoperatively. For these, we identified 15 matched controls also having undergone surgery but without AKI. Serial serum and urine samples had prospectively been postoperatively obtained from all patients at 0, 2, 4, 6, 10, 24, 48 and 72 h after admission to the intensive care unit. AKI was defined as a &gt;50% increase in SCr. CyC was measured by nephelometry, while NGAL, IL-18, and RBP were measured by ELISA and NAG was measured by spectrophotometry. The urinary biomarkers were normalized to urinary creatinine (UCr) concentration. Each marker was assessed at each time point for its predictive value using receiver operating characteristic curves to predict AKI. &lt;i&gt;Results:&lt;/i&gt; Following the exclusion of 1 case due to a urinary tract infection, the final cohort consisted of 29 patients aged 62.9 ± 13.7 years with baseline SCr of 73.2 ± 11.9 µmol/l. While there were no differences in the demographics between cases and controls, the aortic clamp time was predictably higher in AKI cases than in controls (60.6 ± 13.9 vs. 43.0 ± 9.2 min, p &lt; 0.05). Each biomarker differed significantly between cases and controls for at least one time point. The optimal area under the curve (AUC) was for CyC at 10 h (sensitivity 0.71, specificity 0.92, cutoff 1.31 mg/l), NGAL at 0 h (sensitivity 0.84, specificity 0.80, cutoff 49.15 µg/g UCr), IL-18 at 2 h (sensitivity 0.85, specificity 0.73, cutoff 285.65 ng/g UCr), RBP at 0 h (sensitivity 0.75, specificity 0.67, cutoff 2,934.65 µg/g UCr) and NAG at 4 h (sensitivity 0.86, specificity 0.67, cutoff 37.05 U/mg UCr). Using a combination of all 5 biomarkers analyzed at the optimal time point as above, we were able to obtain an AUC of 0.98 (0.93–1.02, p &lt; 0.001) in this limited sample. &lt;i&gt;Conclusion:&lt;/i&gt; The use of serum and urinary biomarkers for the prediction of AKI in patients undergoing cardiac surgery is highly dependent on the sampling time. Of the evaluated markers urinary NGAL had the best predictive profile. The previously unstudied marker of urinary RBP showed similar predictive power as more established markers. By combining all 5 studied biomarkers we were able to predict significantly more cases, suggesting that the use of more than one marker may be beneficial clinically.</jats:p

Prevalence of Acute Kidney Injury following Cardiac Surgery and Related Risk Factors in Chinese Patients

&lt;i&gt;Background/Aims:&lt;/i&gt; Acute kidney injury (AKI) following surgery is a major complication, but the prevalence and risk factors in the Asian population are unclear. Recently, a consensus definition of AKI (AKIN) was proposed. We studied a cohort of cardiac surgery patients and identified AKI by AKIN and associated risk factors. &lt;i&gt;Methods:&lt;/i&gt; We retrospectively evaluated 1,056 consecutive patients undergoing cardiac surgery in Renji Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China from January 1, 2004 to June 30, 2007. We recorded AKIN stage, clinical characteristics, perioperative variables and complications, as well as clinical outcomes. Univariate and multivariate regression as well as survival analysis was performed. &lt;i&gt;Results:&lt;/i&gt; AKI occurred in 328 (31.1%) patients, stage 1 in 21.1%, stage 2 in 6.3% and stage 3 in 3.7%. Patients with AKI were older (65.8 vs. 53.5 years, p &lt; 0.001), more often male (66.8 vs. 54.1%, p &lt; 0.001), and had higher Charlson Comorbidity Index (CCI) (CCI &gt;2: 22.6 vs. 7.8%, p &lt; 0.001). In logistic regression, advanced age (OR 1.48 per decade, 95% CI 1.32–1.67), CCI &gt;2 (OR 2.82, 95% CI 1.80–4.41), hypertension (OR 2.13, 95% CI 1.47–3.09), left ventricular ejection fraction (LVEF) &lt;45% (OR 1.97, 95% CI 1.14–3.40), postoperative central venous pressure (CVP) &lt;6 cm H&lt;sub&gt;2&lt;/sub&gt;O (OR 13.28, 95% CI 8.72–20.14) and postoperative use of ACEI/ARB (OR 1.90, 95% CI 1.27–2.85) were risk factors of AKI. Mortality rose progressively with increased AKIN stage (non-AKI 0.7%, stage 1 4.9%, stage 2 12.1% and stage 3 48.7%). In ROC analysis, AKIN classification was identified to be associated with in-hospital mortality with an AUC of 0.865 (95% CI 0.801–0.929, sensitivity 0.884, specificity 0.714, p &lt; 0.001). Finally, in a Cox proportional hazards model, AKIN stage (HR 2.40, p &lt; 0.001), re-exploration (HR 6.30, p = 0.002) and multiple organ dysfunction syndrome (MODS) (HR 4.42, p = 0.001) were associated risk factors for in-hospital mortality. &lt;i&gt;Conclusion:&lt;/i&gt; We evaluated AKIN as a marker of AKI and mortality risk in a large, unselected Chinese cohort of incident patients undergoing cardiac surgery. AKI following cardiac surgery was diagnosed by AKIN criteria in around one third of the patients, and AKI may be associated with outcome. The value of preventative strategies to reduce AKI and their effect on in-hospital mortality should be studied.</jats:p

The Level of the Biomarkers at the Time of Nephrology Consultation Might Predict the Prognosis of Acute Kidney Injury in Hospitalized Patients

&lt;b&gt;&lt;i&gt;Introduction:&lt;/i&gt;&lt;/b&gt; The value of biomarkers at the time of nephrology consultation in predicting the prognosis of acute kidney injury (AKI) in hospitalized patients has not been well described. This study aimed to evaluate the possibility of biomarkers at the time of nephrology consultation in predicting the prognosis of AKI. &lt;b&gt;&lt;i&gt;Methods:&lt;/i&gt;&lt;/b&gt; We prospectively enrolled 103 hospitalized patients who developed AKI. Urinary Neutrophil Gelatinase Associated Lipocalin (NGAL), IL-6, IL-18, N-Acetyl-F-D-Glucosaminidase (NAG), and serum Cystatin C (CysC) were measured at the time of nephrology consultation. Baseline values of serum creatinine (bScr), serum creatinine on consultation (cScr) and the peak level of serum creatinine (pScr) were recorded. All the patients were followed-up till 28 days since consultation. Serum and urinary levels of the biomarkers were compared according to the patient or kidney prognosis. Each marker was assessed for its predictive value using receiver operator characteristic (ROC) curves to predict AKI prognosis. &lt;b&gt;&lt;i&gt;Results:&lt;/i&gt;&lt;/b&gt; Patients were aged 54.28 w 19.05. Male patients constituted 65% of the study group and baseline Scr was 93.54 w 35.98 Vmol/l. The mortality rate of the patients was 25.2% and kidney loss rate was 18.8% at 28 days after consultation. The level of urinary NGAL was significantly higher in death patients than that in survival patients [147.00 (31.59, 221.87) Vg/ml vs. 22.43 (6.48, 89.77) Vg/ml, p = 0.001], while the level of bScr, cScr, pScr, urinary IL-6 and NAG were similar in both these groups. The AUC of urinary NGAL for predicting patients' death was 0.723 (p = 0.001). Serum CysC and urinary IL-18 concentration was higher in the death patients with a marginal p value (p = 0.065 and 0.059 respectively). All the urinary markers, including NAG, NGAL, IL-6 and IL-18 were significantly higher in patients with kidney loss than in those with kidney survival, while no difference was found in Scr and serum CysC levels. The AUCs of these urinary biomarkers for predicting kidney survival were 0.663, 0.655, 0.705 and 0.663 respectively (p &lt; 0.05). The concentrations of cScr, pScr, serum CysC, urinary IL-6 and NGAL were significantly higher in RRT patients (p &lt; 0.05). The AUCs for predicting RRT were 0.628, 0.781, 0.768, 0.672 and 0.775 respectively. &lt;b&gt;&lt;i&gt;Conclusions:&lt;/i&gt;&lt;/b&gt; The level of biomarkers at the time of nephrology consultation might predict the prognosis of AKI in hospitalized patients. Further studies should be done to understand the role of the serum and urinary markers in the prognosis of AKI. i 2014 S. Karger AG, Basel</jats:p

Development and validation of the prediction model for mortality in patients with diabetic kidney disease in intensive care unit: a study based on medical information Mart for intensive care

We aimed to explore factors associated with mortality of diabetic kidney disease (DKD), and to establish a prediction model for predicting the mortality of DKD. This was a cohort study. In total, 1,357 DKD patients were identified from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database, with 505 DKD patients being identified from the MIMIC-III as the testing set. The outcome of the study was 1-year mortality. COX proportional hazard models were applied to screen the predictive factors. The prediction model was conducted based on the predictive factors. A receiver operating characteristic (ROC) curve with the area under the curve (AUC) was calculated to evaluate the performance of the prediction model. The median follow-up time was 365.00 (54.50,365.00) days, and 586 patients (43.18%) died within 1 year. The predictive factors for 1-year mortality in DKD included age, weight, sepsis, heart rate, temperature, Charlson Comorbidity Index (CCI), Simplified Acute Physiology Score (SAPS) II, and Sequential Organ Failure Assessment (SOFA), lymphocytes, red cell distribution width (RDW), serum albumin, and metformin. The AUC of the prediction model for predicting 1-year mortality in the training set was 0.771 [95% confidence interval (CI): 0.746-0.795] and the AUC of the prediction model in the testing set was 0.795 (95% CI: 0.756-0.834). This study establishes a prediction model for predicting mortality of DKD, providing a basis for clinical intervention and decision-making in time.</p