Acute kidney injury in the elderly hospitalized patients

Kayataş, Kadir; Şahin, Gülizar Manga; Tepe, Mehmet; Kaya, Zeynep Ece; Apaydın, Süheyla; Demirtunç, Refik

doi:10.3109/0886022x.2014.934693

Cited by 22 publications

(20 citation statements)

References 19 publications

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“…In our cohort, the rate of all-cause mortality was 26.8, 34.1, 37.1, and 39.3% at 1, 2, 3 and 4 years, respectively. A few studies investigating predictors of mortality following hip fracture have identified age, dementia, malnutrition, and comorbidities [4,7,10,13,20]. Our study suggests that AKI during hospitalization is an important predictor of long-term mortality.…”

Section: Discussionmentioning

confidence: 52%

“…Early diagnosis of AKI would be possible, and elderly patients with severe AKI did not undergo surgery. The reported incidence of AKI in elderly patients undergoing hip fracture surgery ranges from 15.3–24.4% according to the definition of AKI and varies with the considered population [2,3,6,8,10,30]. Elderly patients are particularly susceptible to developing AKI.…”

Section: Discussionmentioning

confidence: 99%

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Acute kidney injury can predict in-hospital and long-term mortality in elderly patients undergoing hip fracture surgery

et al. 2017

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BackgroundHip fracture is a common health problem in the elderly that is associated with increased mortality. Acute kidney injury (AKI) is a frequent complication in elderly patients undergoing surgery and is associated with the clinical outcome. We evaluated the incidence and risk factors of AKI in elderly patients undergoing hip fracture surgery and the impact of AKI on short- and long-term clinical outcomes.MethodsWe performed a retrospective cohort study of 450 elderly patients who underwent hip fracture surgery between January 2010 and December 2012. We defined AKI according to the Acute Kidney Injury Network (AKIN) criteria and investigated the effect of AKI on the duration of hospital stay and in-hospital and long-term mortality.ResultsOf the 450 patients, 95 (21.1%) developed AKI during hospitalization and 178 (39.6%) died, with a mean follow-up of 3.6 ± 1.0 years. The baseline serum creatinine level, use of angiotensin-converting enzyme inhibitors or angiotensin-II receptor antagonists, red blood cell transfusion volume, and history of coronary artery disease were independent risk factors for AKI. Patients with AKI during hospitalization had significantly longer hospital stays and higher in-hospital and long-term mortality than those without AKI. Multivariate analysis revealed that age, history of coronary artery disease, serum albumin level, and AKI were independent predictors of long-term mortality.ConclusionsAKI is a frequent complication in elderly patients undergoing hip fracture surgery and is independently associated with increased in-hospital and long-term mortality.

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Section: Discussionmentioning

confidence: 52%

Section: Discussionmentioning

confidence: 99%

Acute kidney injury can predict in-hospital and long-term mortality in elderly patients undergoing hip fracture surgery

et al. 2017

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“…Mortality in the elderly with AKI varies in the literature from 23% [17] to 54% [13] according to the mean age of the patients, severity of illness and criteria used to define AKI. In our series, almost 70% of the nonagenarians with AKI died.…”

Section: Discussionmentioning

confidence: 99%

Incidence, predictors and prognosis of acute kidney injury in nonagenarians: an in-hospital cohort study

et al. 2020

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Background: Given the aging of the population, nephrologists are ever more frequently assisting nonagenarians with acute kidney injury (AKI). The management of these patients presents unique characteristics, including bioethical dilemmas, such as the utilization of renal replacement therapy (RRT) at this extreme age. Methods: We conducted a retrospective cohort study at a tertiary hospital. Over a 10-year period, 832 nonagenarians were hospitalized for two or more days. A random sample of 461 patients was obtained; 25 subjects were excluded due to lack of essential data. AKI was defined and staged according to the Kidney Disease Improving Global Outcomes (KDIGO) criteria.Results: We analyzed data from 436 patients, mean age 93.5 ± 3.3 years, 74.3% female; 76.4% required intensive care unit (ICU). The incidence of AKI was 45%. Length of hospital stay, ICU admission, vasopressors, and mechanical ventilation (MV) were independent predictors of AKI. Overall in-hospital mortality was 43.1%. Mortality was higher in the AKI compared to the no AKI group (66.8% vs. 23.8%, p < 0.001). Only 13 patients underwent RRT; all were critically ill, requiring vasopressors and 76.9% in MV. Mortality for this RRT group was 100% but not significantly higher than that observed in 26 non-RRT controls (96.1%, p = 1.0) obtained by proportional random sampling, matched by variables related to illness severity. In multivariable analysis, age, Charlson's score, vasopressors, MV, and AKIbut not RRTwere independent predictors of mortality.Conclusions: AKI is common in hospitalized nonagenarians and carries a grave prognosis, especially in those who are critically iil. The use of RRT was not able to change the fatal prognosis of this subgroup of patients. Our data may help guide informed decisions about the utility of RRT in this scenario.

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“…Tang et al demonstrated that elevated serum CRP concentrations were associated with increased serum creatinine and urea concentrations ( P < 0.01) in patients with AKI; CRP concentrations subsequently fell after recovery from AKI . In older patients with AKI, CRP was an independent risk factor for mortality . CRP has also been studied for its ability to predict risk for AKI.…”

Section: Discussionmentioning

confidence: 99%

A clinical, proteomics, and artificial intelligence‐driven model to predict acute kidney injury in patients undergoing coronary angiography

Ibrahim

McCarthy

Shrestha

et al. 2019

Clinical Cardiology

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Background Standard measures of kidney function are only modestly useful for accurate prediction of risk for acute kidney injury (AKI). Hypothesis Clinical and biomarker data can predict AKI more accurately. Methods Using Luminex xMAP technology, we measured 109 biomarkers in blood from 889 patients prior to undergoing coronary angiography. Procedural AKI was defined as an absolute increase in serum creatinine of ≥0.3 mg/dL, a percentage increase in serum creatinine of ≥50%, or a reduction in urine output (documented oliguria of <0.5 mL/kg per hour for >6 hours) within 7 days after contrast exposure. Clinical and biomarker predictors of AKI were identified using machine learning and a final prognostic model was developed with least absolute shrinkage and selection operator (LASSO). Results Forty‐three (4.8%) patients developed procedural AKI. Six predictors were present in the final model: four (history of diabetes, blood urea nitrogen to creatinine ratio, C‐reactive protein, and osteopontin) had a positive association with AKI risk, while two (CD5 antigen‐like and Factor VII) had a negative association with AKI risk. The final model had a cross‐validated area under the receiver operating characteristic curve (AUC) of 0.79 for predicting procedural AKI, and an in‐sample AUC of 0.82 ( P < 0.001). The optimal score cutoff had 77% sensitivity, 75% specificity, and a negative predictive value of 98% for procedural AKI. An elevated score was predictive of procedural AKI in all subjects (odds ratio = 9.87; P < 0.001). Conclusions We describe a clinical and proteomics‐supported biomarker model with high accuracy for predicting procedural AKI in patients undergoing coronary angiography.

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Acute kidney injury in the elderly hospitalized patients

Cited by 22 publications

References 19 publications

Acute kidney injury can predict in-hospital and long-term mortality in elderly patients undergoing hip fracture surgery

Acute kidney injury can predict in-hospital and long-term mortality in elderly patients undergoing hip fracture surgery

Incidence, predictors and prognosis of acute kidney injury in nonagenarians: an in-hospital cohort study

A clinical, proteomics, and artificial intelligence‐driven model to predict acute kidney injury in patients undergoing coronary angiography

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