Acute Kidney Injury in Malaysian Intensive Care Setting: Incidences, Risk Factors, and Outcome.

INTRODUCTION: Acute kidney injury (AKI) is common in the intensive care unit (ICU) with a high risk of morbidity and mortality. The high incidence of AKI in our population may be attributed to sepsis. We investigated the incidence, risk factors, and outcome of AKI in four tertiary Malaysian ICUs. We also evaluated its association with sepsis. MATERIALS AND METHODS: This retrospective cohort study extracted de-identified data from the Malaysian Registry of Intensive Care in four Malaysian tertiary ICUs between January 2010 and December 2014. The study was registered under the NMRR and approved by the ethics committee. AKI was defined as twice the baseline creatinine or urine output <0.5 ml/kg/h for 12 h. RESULTS: Of 26,663 patients, 24.2% had AKI within 24 h of admission. Patients with AKI were older and had higher severity of illness compared to those without AKI. AKI patients had a longer duration of mechanical ventilation, length of ICU, and hospital stay. Age, Simplified Acute Physiological II Score, and the presence of sepsis and preexisting hypertension, chronic cardiovascular disease independently associated with AKI. About 32.3% had sepsis. Patients with both AKI and sepsis had the highest risk of mortality (relative risk 3.43 [3.34-3.53]). CONCLUSIONS: AKI is common in our ICU, with higher morbidity and mortality. Independent risk factors of AKI include age, the severity of illness, sepsis and preexisting hypertension, and chronic cardiovascular disease. AKI independently contributes to mortality. The presence of AKI and sepsis increased the risk of mortality by three times.

Levels and Diagnostic Value of Model-based Insulin Sensitivity in Sepsis: A Preliminary Study.

BACKGROUND AND AIMS: Currently, there is a lack of real-time metric with high sensitivity and specificity to diagnose sepsis. Insulin sensitivity (SI) may be determined in real-time using mathematical glucose-insulin models; however, its effectiveness as a diagnostic test of sepsis is unknown. Our aims were to determine the levels and diagnostic value of model-based SI for identification of sepsis in critically ill patients. MATERIALS AND METHODS: In this retrospective, cohort study, we analyzed SI levels in septic (n = 18) and nonseptic (n = 20) patients at 1 (baseline), 4, 8, 12, 16, 20, and 24 h of their Intensive Care Unit admission. Patients with diabetes mellitus Type I or Type II were excluded from the study. The SI levels were derived by fitting the blood glucose levels, insulin infusion and glucose input rates into the Intensive Control of Insulin-Nutrition-Glucose model. RESULTS: The median SI levels were significantly lower in the sepsis than in the nonsepsis at all follow-up time points. The areas under the receiver operating characteristic curve of the model-based SI at baseline for discriminating sepsis from nonsepsis was 0.814 (95% confidence interval, 0.675-0.953). The optimal cutoff point of the SI test was 1.573 × 10-4 L/mu/min. At this cutoff point, the sensitivity was 77.8%, specificity was 75%, positive predictive value was 73.7%, and negative predictive value was 78.9%. CONCLUSIONS: Model-based SI ruled in and ruled out sepsis with fairly high sensitivity and specificity in our critically ill nondiabetic patients. These findings can be used as a foundation for further, prospective investigation in this area.

Dynamic Changes of Plasma Neutrophil Gelatinase-Associated Lipocalin Predicted Mortality in Critically Ill Patients with Systemic Inflammatory Response Syndrome.

BACKGROUND AND AIMS: About 50% of patients admitted to the Intensive Care Unit have systemic inflammatory response syndrome (SIRS), and about 10%-20% of them died. Early risk stratification is important to reduce mortality. Plasma neutrophil gelatinase-associated lipocalin (NGAL) is increased by inflammation and infection. Its ability to predict mortality in SIRS patients is of interest. We evaluated the ability of serial measurement of NGAL for the prediction of mortality in critically ill patients with SIRS. MATERIALS AND METHODS: This is a secondary analysis of a single-center, prospective, observational study. Patients who fulfill the SIRS criteria were recruited in the study. Delta NGAL at 24 and 48 h (ΔNGAL-24 and ΔNGAL-48) was defined as 24 and 48 h NGAL minus day 1 NGAL; NGAL clearance (NGALc) was defined as percentage of ΔNGAL over day 1 NGAL. The primary outcome of the study is in-hospital mortality. RESULTS: A total of 151 patients were analyzed, of which 53 (35%) died. Nonsurvivors were older (51 vs. 45, P = 0.03) and had higher Sequential Organ Failure Assessment (9 ± 7 vs. 7 ± 4, P = 0.02) and Simplified Acute Physiology Score II (47 ± 15 vs. 40 ± 15, P = 0.01) scores as compared to survivors. NGAL concentrations over 3 days were higher in nonsurvivors compared to survivors (repeated measures analysis of variance, P = 0.02). Day 1 NGAL, ΔNGAL-24, and NGALc-24 were not independently predictive of mortality. However, day 3 NGAL, ΔNGAL-48, and NGALc-48 were predictive after adjusted for age and severity of illness (odds ratio 9.1 [1.97-41.7]). CONCLUSIONS: NGAL dynamics over 48 h independently predicted mortality in critically ill patients with SIRS. This could assist clinicians in risk stratification of this group of high-risk patients.

The clinical utility window for acute kidney injury biomarkers in the critically ill.

INTRODUCTION: Acute Kidney Injury (AKI) biomarker utility depends on sample timing after the onset of renal injury. We compared biomarker performance on arrival in the emergency department (ED) with subsequent performance in the intensive care unit (ICU). METHODS: Urinary and plasma Neutrophil Gelatinase-Associated Lipocalin (NGAL), and urinary Cystatin C (CysC), alkaline phosphatase, γ-Glutamyl Transpeptidase (GGT), α- and π-Glutathione S-Transferase (GST), and albumin were measured on ED presentation, and at 0, 4, 8, and 16 hours, and days 2, 4 and 7 in the ICU in patients after cardiac arrest, sustained or profound hypotension or ruptured abdominal aortic aneurysm. AKI was defined as plasma creatinine increase ≥ 26.5 µmol/l within 48 hours or ≥ 50% within 7 days. RESULTS: In total, 45 of 77 patients developed AKI. Most AKI patients had elevated urinary NGAL, and plasma NGAL and CysC in the period 6 to 24 hours post presentation. Biomarker performance in the ICU was similar or better than when measured earlier in the ED. Plasma NGAL diagnosed AKI at all sampling times, urinary NGAL, plasma and urinary CysC up to 48 hours, GGT 4 to 12 hours, and π-GST 8 to 12 hours post insult. Thirty-one patients died or required dialysis. Peak 24-hour urinary NGAL and albumin independently predicted 30-day mortality and dialysis; odds ratios 2.87 (1.32 to 6.26), and 2.72 (1.14 to 6.48), respectively. Urinary NGAL improved risk prediction by 11% (IDI event of 0.06 (0.002 to 0.19) and IDI non-event of 0.04 (0.002 to 0.12)). CONCLUSION: Early measurement in the ED has utility, but not better AKI diagnostic performance than later ICU measurement. Plasma NGAL diagnosed AKI at all time points. Urinary NGAL best predicted mortality or dialysis compared to other biomarkers. TRIAL REGISTRATION: Australian and New Zealand Clinical Trials Registry ACTRN12610001012066. Registered 12 February 2010.

Combining creatinine and volume kinetics identifies missed cases of acute kidney injury following cardiac arrest.

INTRODUCTION: Fluid resuscitation in the critically ill often results in a positive fluid balance, potentially diluting the serum creatinine concentration and delaying diagnosis of acute kidney injury (AKI). METHODS: Dilution during AKI was quantified by combining creatinine and volume kinetics to account for fluid type, and rates of fluid infusion and urine output. The model was refined using simulated patients receiving crystalloids or colloids under four glomerular filtration rate (GFR) change scenarios and then applied to a cohort of critically ill patients following cardiac arrest. RESULTS: The creatinine concentration decreased during six hours of fluid infusion at 1 litre-per-hour in simulated patients, irrespective of fluid type or extent of change in GFR (from 0% to 67% reduction). This delayed diagnosis of AKI by 2 to 9 hours. Crystalloids reduced creatinine concentration by 11 to 19% whereas colloids reduced concentration by 36 to 43%. The greatest reduction was at the end of the infusion period. Fluid dilution alone could not explain the rapid reduction of plasma creatinine concentration observed in 39 of 49 patients after cardiac arrest. Additional loss of creatinine production could account for those changes. AKI was suggested in six patients demonstrating little change in creatinine, since a 52 ± 13% reduction in GFR was required after accounting for fluid dilution and reduced creatinine production. Increased injury biomarkers within a few hours of cardiac arrest, including urinary cystatin C and plasma and urinary Neutrophil-Gelatinase-Associated-Lipocalin (biomarker-positive, creatinine-negative patients) also indicated AKI in these patients. CONCLUSIONS: Creatinine and volume kinetics combined to quantify GFR loss, even in the absence of an increase in creatinine. The model improved disease severity estimation, and demonstrated that diagnostic delays due to dilution are minimally affected by fluid type. Creatinine sampling should be delayed at least one hour following a large fluid bolus to avoid dilution. Unchanged plasma creatinine post cardiac arrest signifies renal injury and loss of function. TRIAL REGISTRATION: Australian and New Zealand Clinical Trials Registry ACTRN12610001012066.

Test characteristics of urinary biomarkers depend on quantitation method in acute kidney injury.

The concentration of urine influences the concentration of urinary biomarkers of AKI. Whether normalization to urinary creatinine concentration, as commonly performed to quantitate albuminuria, is the best method to account for variations in urinary biomarker concentration among patients in the intensive care unit is unknown. Here, we compared the diagnostic and prognostic performance of three methods of biomarker quantitation: absolute concentration, biomarker normalized to urinary creatinine concentration, and biomarker excretion rate. We measured urinary concentrations of alkaline phosphatase, γ-glutamyl transpeptidase, cystatin C, neutrophil gelatinase-associated lipocalin, kidney injury molecule-1, and IL-18 in 528 patients on admission and after 12 and 24 hours. Absolute concentration best diagnosed AKI on admission, but normalized concentrations best predicted death, dialysis, or subsequent development of AKI. Excretion rate on admission did not diagnose or predict outcomes better than either absolute or normalized concentration. Estimated 24-hour biomarker excretion associated with AKI severity, and for neutrophil gelatinase-associated lipocalin and cystatin C, with poorer survival. In summary, normalization to urinary creatinine concentration improves the prediction of incipient AKI and outcome but provides no advantage in diagnosing established AKI. The ideal method for quantitating biomarkers of urinary AKI depends on the outcome of interest.

Sepsis mortality score for the prediction of mortality in septic patients.

PURPOSE: To derive a prediction equation for 30-day mortality in sepsis using a multi-marker approach and compare its performance to the Sequential Organ Failure Assessment (SOFA) score. METHODS: This study included 159 septic patients admitted to an intensive care unit. Leukocytes count, procalcitonin (PCT), interleukin-6 (IL-6), and paraoxonase (PON) and arylesterase (ARE) activities of PON-1 were assayed from blood obtained on ICU presentation. Logistic regression was used to derive sepsis mortality score (SMS), a prediction equation describing the relationship between biomarkers and 30-day mortality. RESULTS: The 30-day mortality rate was 28.9%. The SMS was [еlogit(p)/(1+еlogit(p))]×100; logit(p)=0.74+(0.004×PCT)+(0.001×IL-6)-(0.025×ARE)-(0.059×leukocytes count). The SMC had higher area under the receiver operating characteristic curve (95% Cl) than SOFA score [0.814 (0.736-0.892) vs. 0.767 (0.677-0.857)], but is not statistically significant. When the SMS was added to the SOFA score, prediction of 30-day mortality improved compared to SOFA score used alone [0.845 (0.777-0.899), p=0.022]. CONCLUSIONS: A sepsis mortality score using baseline leukocytes count, PCT, IL-6 and ARE was derived, which predicted 30-day mortality with very good performance and added significant prognostic information to SOFA score.

Assessing mechanical ventilation asynchrony through iterative airway pressure reconstruction.

BACKGROUND AND OBJECTIVE: Respiratory mechanics estimation can be used to guide mechanical ventilation (MV) but is severely compromised when asynchronous breathing occurs. In addition, asynchrony during MV is often not monitored and little is known about the impact or magnitude of asynchronous breathing towards recovery. Thus, it is important to monitor and quantify asynchronous breathing over every breath in an automated fashion, enabling the ability to overcome the limitations of model-based respiratory mechanics estimation during asynchronous breathing ventilation. METHODS: An iterative airway pressure reconstruction (IPR) method is used to reconstruct asynchronous airway pressure waveforms to better match passive breathing airway waveforms using a single compartment model. The reconstructed pressure enables estimation of respiratory mechanics of airway pressure waveform essentially free from asynchrony. Reconstruction enables real-time breath-to-breath monitoring and quantification of the magnitude of the asynchrony (MAsyn). RESULTS AND DISCUSSION: Over 100,000 breathing cycles from MV patients with known asynchronous breathing were analyzed. The IPR was able to reconstruct different types of asynchronous breathing. The resulting respiratory mechanics estimated using pressure reconstruction were more consistent with smaller interquartile range (IQR) compared to respiratory mechanics estimated using asynchronous pressure. Comparing reconstructed pressure with asynchronous pressure waveforms quantifies the magnitude of asynchronous breathing, which has a median value MAsyn for the entire dataset of 3.8%. CONCLUSION: The iterative pressure reconstruction method is capable of identifying asynchronous breaths and improving respiratory mechanics estimation consistency compared to conventional model-based methods. It provides an opportunity to automate real-time quantification of asynchronous breathing frequency and magnitude that was previously limited to invasively method only.