Association of Prior to Intensive Care Unit Statin Use With Outcomes on Patients With Acute Kidney Injury.
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SUBMITTER: Tu B
PROVIDER: S-EPMC8739269 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundThe incidence of acute kidney injury (AKI) is high in intensive care units (ICUs), and a better understanding of AKI is needed. Early chronic kidney disease is associated with urinary concentration inability and AKI recovery with increased urinary solutes in humans. Whether the inability of the kidneys to concentrate urine and excrete solutes at appropriate levels could occur prior to the diagnosis of AKI is still uncertain, and the associated mechanisms have not been studied.MethodsIn this single-center prospective observational study, high AKI risk in ICU patients was followed up for 7 days or until ICU discharge. They were grouped as "AKI" or "No AKI" according to their AKI status throughout admission. We collected daily urine samples to measure solute concentrations and osmolality. Data were analyzed 1 day before AKI, or from the first to the fifth day of admission in the "No AKI" group. We used logistic regression models to evaluate the influence of the variables on future AKI diagnosis. The expression of kidney transporters in urine was evaluated by Western blotting.ResultsWe identified 29 patients as "No AKI" and 23 patients as "AKI," the latter being mostly low severity AKI. Urinary sodium excretion was lower in "AKI" patients prior to AKI diagnosis, particularly in septic patients. The expression of Na+/H+ exchanger (NHE3), a urinary sodium transporter, was higher in "AKI" patients.ConclusionsUrinary sodium excretion is low before an AKI episode in ICU patients, and high expressions of proximal tubule sodium transporters might contribute to this.
| S-EPMC10479577 | biostudies-literature
Project description:IntroductionAcute kidney injury (AKI) is commonly observed in the intensive care unit (ICU), where it can be caused by a variety of factors. The objective of this study was to evaluate the prognostic value of urinary angiotensinogen, a candidate prognostic AKI biomarker identified in post-cardiac surgery patients, in this heterogeneous population.MethodsUrinary angiotensinogen was measured by ELISA and corrected for urine creatinine in 45 patients who developed AKI in the ICU. Patients were grouped by AKI etiology, and the angiotensinogen-to-creatinine ratio (uAnCR) was compared among the groups using the Kruskal-Wallis test. The ability of uAnCR to predict the following endpoints was tested using the area under the ROC curve (AUC): the need for renal replacement therapy (RRT) or death, increased length of stay (defined as hospital discharge>7 days or death?7 days from sample collection), and worsening AKI (defined as an increase in serum creatinine>0.3 mg/dL after sample collection or RRT).ResultsuAnCR was significantly elevated in patients who met the composite outcome RRT or death (89.4 vs 25.4 ng/mg; P=0.01), and it was a strong predictor of this outcome (AUC=0.73). Patients with uAnCR values above the median for the cohort (55.21 ng/mg) had increased length of stay compared to patients with uAnCR?55.21 ng/mg (22 days vs 7 days after sample collection; P=0.01). uAnCR was predictive of the outcome increased length of stay (AUC=0.77). uAnCR was also a strong predictor of worsening of AKI (AUC=0.77). The uAnCR of patients with pre-renal AKI was lower compared to patients with AKI of other causes (median uAnCR 11.3 vs 80.2 ng/mg; P=0.02).ConclusionsElevated urinary angiotensinogen is associated with adverse events in AKI patients in the ICU. It could be used to identify high risk patients who would benefit from timely intervention that could improve their outcomes.
| S-EPMC3672721 | biostudies-literature
Project description:ObjectiveTo evaluate the impact of pre-intensive care unit admission (pre-ICU) statin use on all-cause in-hospital mortality and ICU length of stay (LOS).DesignRetrospective cohort study.SettingAdult ICUs at tertiary hospitals.PatientsAdult critically ill patients diagnosed with sepsis admitted to the ICUs.InterventionThe exposure was pre-ICU statin prescription (statin users); unexposed represented absence of pre-ICU prescription (non-users).Measurement and main resultsWe used the 2001-2012 Medical Information Mart for Intensive Care-III (MIMIC-III) database to determine average treatment effect (ATE) of pre-ICU statin use on 30-day ICU mortality, ICU LOS, and 30-day in-hospital mortality using the Augmented Inverse Propensity Weighted technique (AIPW), after adjusting for confounding factors (age, race, health insurance, corticosteroids use, vital signs, laboratory tests, and Sequential Organ Failure Assessment score (SOFA). We measured 30-day ICU mortality as deaths within 30 days of admission to the ICU, and ICU LOS was measured in fractional days. A 30-day in-hospital mortality was measured as death within 30 days of hospital admission. A total of 8200 patients with sepsis were identified; 19.8% (1623) were statin users, and 80.2% (6577) were non-users. Most were Caucasian, aged 80 years and above, and male. After adjusting for confounding factors, pre-ICU statin use decreased 30-day ICU mortality (ATE, -0.026; 95% confidence interval [CI], -0.048 to -0.009); ICU LOS (ATE, -0.369; 95% Cl, -0.849 to -0.096); and 30-day in-hospital mortality (ATE, -0.039; 95% CI, -0.084 to -0.026) on average compared with non-statin use, respectively. In a stratified analysis, the result for ICU LOS (ATE, -0.526; 95% CI, -0.879 to -0.241) and 30-day in-hospital mortality (ATE, -0.023; 95% CI, -0.048 to -0.002) was consistent among patients admitted to the medical ICU.ConclusionsAmong patients with sepsis admitted to the medical ICU, pre-ICU statin use is causally associated with a decrease in 30-day ICU mortality, ICU LOS, and 30-day in-hospital mortality compared to non-use. This study adds to the totality of evidence on the pleiotropic effect of statin use in patients with sepsis.
| S-EPMC7920641 | biostudies-literature
Project description:Background and objectivesLittle is known about the long-term burden of AKI in the pediatric intensive care unit. We aim to evaluate if pediatric AKI is associated with higher health service use post-hospital discharge.Design, setting, participants, & measurementsThis is a retrospective cohort study of children (?18 years old) admitted to two tertiary centers in Montreal, Canada. Only the first admission per patient was included. AKI was defined in two ways: serum creatinine alone or serum creatinine and/or urine output. The outcomes were 30-day, 1-year, and 5-year hospitalizations, emergency room visits, and physician visits per person-time using provincial administrative data. Univariable and multivariable Poisson regression were used to evaluate AKI associations with outcomes.ResultsA total of 2041 children were included (56% male, mean admission age 6.5±5.8 years); 299 of 1575 (19%) developed AKI defined using serum creatinine alone, and when urine output was included in the AKI definition 355 of 1622 (22%) children developed AKI. AKI defined using serum creatinine alone and AKI defined using serum creatinine and urine output were both associated with higher 1- and 5-year hospitalization risk (AKI by serum creatinine alone adjusted relative risk, 1.42; 95% confidence interval, 1.12 to 1.82; and 1.80; 1.54 to 2.11, respectively [similar when urine output was included]) and higher 5-year physician visits (adjusted relative risk, 1.26; 95% confidence interval, 1.14 to 1.39). AKI was not associated with emergency room use after adjustments.ConclusionsAKI is independently associated with higher hospitalizations and physician visits postdischarge.
| S-EPMC5969475 | biostudies-literature
Project description:IntroductionAcute kidney injury (AKI) is common among hospitalized patients and has a significant impact on morbidity and mortality. Although early prediction of AKI has the potential to reduce adverse patient outcomes, it remains a difficult condition to predict and diagnose. The purpose of this study was to evaluate the ability of a machine learning algorithm to predict for AKI as defined by Kidney Disease: Improving Global Outcomes (KDIGO) stage 2 or 3 up to 48 hours in advance of onset using convolutional neural networks (CNNs) and patient electronic health record (EHR) data.MethodsA CNN prediction system was developed to use EHR data gathered during patients' stays to predict AKI up to 48 hours before onset. A total of 12,347 patient encounters were retrospectively analyzed from the Medical Information Mart for Intensive Care III (MIMIC-III) database. An XGBoost AKI prediction model and the sequential organ failure assessment (SOFA) scoring system were used as comparators. The outcome was AKI onset. The model was trained on routinely collected patient EHR data. Measurements included area under the receiver operating characteristic (AUROC) curve, positive predictive value (PPV), and a battery of additional performance metrics for advance prediction of AKI onset.ResultsOn a hold-out test set, the algorithm attained an AUROC of 0.86 and PPV of 0.24, relative to a cohort AKI prevalence of 7.62%, for long-horizon AKI prediction at a 48-hour window before onset.ConclusionA CNN machine learning-based AKI prediction model outperforms XGBoost and the SOFA scoring system, revealing superior performance in predicting AKI 48 hours before onset, without reliance on serum creatinine (SCr) measurements.
| S-EPMC8116756 | biostudies-literature
Project description:BackgroundYoung adults with congenital heart disease (CHD) are increasing in number with an increased risk for acute kidney injury. Little is known concerning the impact of non-recovery of kidney function for these patients. Therefore, we sought to explore the rates of acute kidney disease, persistent renal dysfunction, and their associations with adverse outcomes in young adults with CHD.MethodsThis is a single-centre retrospective study including all patients at the ages of 18-40 with CHD who were admitted to an intensive care unit between 2010 and 2014. Patients with a creatinine ≥ 1.5 times the baseline at the time of hospital discharge were deemed to have persistent renal dysfunction, while acute kidney disease was defined as a creatinine ≥ 1.5 times the baseline 7-28 days after a diagnosis of acute kidney injury. Outcomes of death at 5 years and length of hospital stay were examined using multivariable logistic regression and negative binomial regression, respectively.ResultsOf the (89/195) 45.6% of patients with acute kidney injury, 33.7% had persistent renal dysfunction and 23.6% met the criteria for acute kidney disease. Persistent renal dysfunction [odds ratio (OR), 3.27; 95% confidence interval (CI): 1.15-9.29] and acute kidney disease (OR: 11.79; 95% CI: 3.75-39.09) were independently associated with mortality at 5 years. Persistent renal dysfunction was associated with a longer duration of hospital stay (Incidence Rate Ratio: 1.96; 95% CI: 1.53-2.51).ConclusionsIn young adults with CHD, acute kidney injury was common and persistent renal dysfunction, as well as acute kidney disease, were associated with increased mortality and length of hospitalisation.
| S-EPMC7897224 | biostudies-literature
Project description:BackgroundAcute kidney injury (AKI) represents a significant global health challenge, leading to increased patient distress and financial health care burdens. The development of AKI in intensive care unit (ICU) settings is linked to prolonged ICU stays, a heightened risk of long-term renal dysfunction, and elevated short- and long-term mortality rates. The current diagnostic approach for AKI is based on late indicators, such as elevated serum creatinine and decreased urine output, which can only detect AKI after renal injury has transpired. There are no treatments to reverse or restore renal function once AKI has developed, other than supportive care. Early prediction of AKI enables proactive management and may improve patient outcomes.ObjectiveThe primary aim was to develop a machine learning algorithm, NAVOY Acute Kidney Injury, capable of predicting the onset of AKI in ICU patients using data routinely collected in ICU electronic health records. The ultimate goal was to create a clinical decision support tool that empowers ICU clinicians to proactively manage AKI and, consequently, enhance patient outcomes.MethodsWe developed the NAVOY Acute Kidney Injury algorithm using a hybrid ensemble model, which combines the strengths of both a Random Forest (Leo Breiman and Adele Cutler) and an XGBoost model (Tianqi Chen). To ensure the accuracy of predictions, the algorithm used 22 clinical variables for hourly predictions of AKI as defined by the Kidney Disease: Improving Global Outcomes guidelines. Data for algorithm development were sourced from the Massachusetts Institute of Technology Lab for Computational Physiology Medical Information Mart for Intensive Care IV clinical database, focusing on ICU patients aged 18 years or older.ResultsThe developed algorithm, NAVOY Acute Kidney Injury, uses 4 hours of input and can, with high accuracy, predict patients with a high risk of developing AKI 12 hours before onset. The prediction performance compares well with previously published prediction algorithms designed to predict AKI onset in accordance with Kidney Disease: Improving Global Outcomes diagnosis criteria, with an impressive area under the receiver operating characteristics curve (AUROC) of 0.91 and an area under the precision-recall curve (AUPRC) of 0.75. The algorithm's predictive performance was externally validated on an independent hold-out test data set, confirming its ability to predict AKI with exceptional accuracy.ConclusionsNAVOY Acute Kidney Injury is an important development in the field of critical care medicine. It offers the ability to predict the onset of AKI with high accuracy using only 4 hours of data routinely collected in ICU electronic health records. This early detection capability has the potential to strengthen patient monitoring and management, ultimately leading to improved patient outcomes. Furthermore, NAVOY Acute Kidney Injury has been granted Conformite Europeenne (CE)-marking, marking a significant milestone as the first CE-marked AKI prediction algorithm for commercial use in European ICUs.
| S-EPMC10755657 | biostudies-literature
Project description:Acute kidney injury is a frequent postoperative complication that confers increased mortality, morbidity, and costs. The purpose of this study was to evaluate whether preoperative statin use is associated with a decreased risk of postoperative acute kidney injury.We assembled a retrospective cohort of 98,939 patients who underwent a major open abdominal, cardiac, thoracic, or vascular procedure between 2000 and 2010. Statin users were pair-matched to nonusers on the basis of surgery type, baseline kidney function, days from admission until surgery, and propensity score based on demographics, comorbid conditions, and concomitant medications. Acute kidney injury was defined based on changes in serum creatinine measurements applying Acute Kidney Injury Network and Risk-Injury-Failure staging systems, and on the need for renal replacement therapy. Associations between statin use and acute kidney injury were estimated by conditional logistic regression.Across various acute kidney injury definitions, statin use was consistently associated with a decreased risk: adjusted odds ratios (95% confidence intervals) varied from 0.74 (0.58-0.95) to 0.80 (0.71-0.90). Associations were similar among diabetics and nondiabetics, and across strata of baseline kidney function. The protective association of statins was most pronounced among patients undergoing vascular surgery and least among patients undergoing cardiac surgery.Preoperative statin use is associated with a decreased risk of postoperative acute kidney injury. Future randomized clinical trials are needed to determine causality.
| S-EPMC3597342 | biostudies-literature
Project description:IntroductionThe incidence of acute kidney injury (AKI) in hospitalized patients is rising, and survivors are at high risk for cardiovascular events and mortality. Effective strategies that improve long-term outcomes of AKI are unknown.MethodsA retrospective cohort study was performed between 2008 and 2011. All subjects were followed until 31 March 2013, with a minimum follow-up of 2 years. Participants were adults 18 years of age or older, who developed AKI during a hospitalization and had chronic kidney disease (CKD) following discharge (n = 19,707 mean age 69.9 years, mean postdischarge estimated glomerular filtration rate (eGFR) 43.0 ml/min/1.73 m2). Exposure to statins was examined prior to the index hospitalization as well as within 2 years following hospital discharge. The primary outcome was mortality; secondary outcomes included all-cause re-hospitalization and cardiovascular events.ResultsWithin 2 years of discharge, only 38.3% of the participants were prescribed a statin. After adjustment for comorbidities, statin use prior to admission, demographics, baseline kidney function, and a number of other factors, statin use was associated with lower mortality (hazard ratio, 0.74; 95% confidence interval, 0.69, 0.79) in AKI survivors with CKD. Patients who received a statin also had a lower risk of all cause rehospitalization (adjusted hazarad ratio, 0.90; 95% confidence interval, 0.85, 0.94). Statin use was not associated with a reduction in cardiovascular events.DiscussionAmong AKI survivors with CKD, statin use was associated with a lower risk of mortality and rehospitalization rates. This finding suggests that there is an opportunity to improve postdischarge care in AKI survivors.
| S-EPMC5678614 | biostudies-literature