scholarly journals Impact of a computerized decision support tool deployed in two intensive care units on acute kidney injury progression and guideline compliance: a prospective observational study

Critical Care ◽  
2020 ◽  
Vol 24 (1) ◽  
Author(s):  
Christopher Bourdeaux ◽  
Erina Ghosh ◽  
Louis Atallah ◽  
Krishnamoorthy Palanisamy ◽  
Payaal Patel ◽  
...  
Keyword(s):  
Acute Kidney Injury ◽  
Intensive Care ◽  
Decision Support ◽  
Observational Study ◽  
Patient Outcomes ◽  
Kidney Injury ◽  
Decision Support Tool ◽  
Care Bundle ◽  
Support Tool ◽  
Stage 1

Abstract Background Acute kidney injury (AKI) affects a large proportion of the critically ill and is associated with worse patient outcomes. Early identification of AKI can lead to earlier initiation of supportive therapy and better management. In this study, we evaluate the impact of computerized AKI decision support tool integrated with the critical care clinical information system (CCIS) on patient outcomes. Specifically, we hypothesize that integration of AKI guidelines into CCIS will decrease the proportion of patients with Stage 1 AKI deteriorating into higher stages of AKI. Methods The study was conducted in two intensive care units (ICUs) at University Hospitals Bristol, UK, in a before (control) and after (intervention) format. The intervention consisted of the AKIN guidelines and AKI care bundle which included guidance for medication usage, AKI advisory and dashboard with AKI score. Clinical data and patient outcomes were collected from all patients admitted to the units. AKI stage was calculated using the Acute Kidney Injury Network (AKIN) guidelines. Maximum AKI stage per admission, change in AKI stage and other metrics were calculated for the cohort. Adherence to eGFR-based enoxaparin dosing guidelines was evaluated as a proxy for clinician awareness of AKI. Results Each phase of the study lasted a year, and a total of 5044 admissions were included for analysis with equal numbers of patients for the control and intervention stages. The proportion of patients worsening from Stage 1 AKI decreased from 42% (control) to 33.5% (intervention), p = 0.002. The proportion of incorrect enoxaparin doses decreased from 1.72% (control) to 0.6% (intervention), p < 0.001. The prevalence of any AKI decreased from 43.1% (control) to 37.5% (intervention), p < 0.05. Conclusions This observational study demonstrated a significant reduction in AKI progression from Stage 1 and a reduction in overall development of AKI. In addition, a reduction in incorrect enoxaparin dosing was also observed, indicating increased clinical awareness. This study demonstrates that AKI guidelines coupled with a newly designed AKI care bundle integrated into CCIS can impact patient outcomes positively.

scholarly journals Evaluation of Aetiology and Outcome of Acute Kidney Injury in a Tertiary Care Hospital of India- A Prospective Observational Study

2020 ◽  
Author(s):  
VS Gaurav Narayan ◽  
SG Ramya ◽  
Sonal Rajesh Kumar ◽  
SK Nellaiappa Ganesan
Keyword(s):  
Acute Kidney Injury ◽  
Intensive Care ◽  
Observational Study ◽  
Prospective Observational Study ◽  
Tertiary Care ◽  
Kidney Injury ◽  
Rapid Decline ◽  
Care Hospital ◽  
Medical Intensive Care

Introduction: The Acute Kidney Injury (AKI) is a rapid decline in renal filtration function. The aetiological spectrum, prevalence of AKI and outcome is highly variable. This variation exists due to the difference in the criteria used, study population and demographic features. Huge differences are noted when AKI is compared in developing and developed countries. Hence, it is important to analyse the spectrum of AKI to facilitate earlier diagnosis and treatment which shall help in improving the outcome. Aim: To study the prevalence, aetiology and outcome of AKI in the medical intensive care. Materials and Methods: This was a prospective observational study conducted in a medical intensive care for 18 months where 1490 patients were screened and 403 patients were included as AKI by KDIGO criteria. History, examination, appropriate investigations and treatment details including dialysis were noted. The serum creatinine levels were obtained every day, to know the time of onset of AKI, at the time of death or discharge, and after one month for patients who turned up for follow-up. Patients were categorised based on outcome as survivors and nonsurvivors. Survivors were divided into as fully recovered and partially recovered and those who left the Intensive Care Unit (ICU) against medical advice were termed as lost to follow-up. Results: A total of 403 patients (27.04% of 1490) of medical intensive care admissions were found to have AKI. Sepsis was the most common cause of AKI. At the end of the month, 78.4% of AKI patients fully recovered, 1.2% partially recovered and the mortality was 14.9%. Mortality was higher in AKI associated with chronic medical conditions like cardiac failure, chronic liver disease and stroke. Conclusion: If treated early, AKI is mostly reversible. Regional differences in AKI should be studied extensively and local guidelines should be formulated by experts for prevention and early treatment, to improve the disease outcome.

scholarly journals Epidemiology and short-term outcomes of acute kidney injury among patients in the intensive care unit in Laos: a nationwide multicenter, prospective, and observational study

BMC Medicine ◽  
2020 ◽  
Vol 18 (1) ◽  
Author(s):  
Noot Sengthavisouk ◽  
Nuttha Lumlertgul ◽  
Chanmaly Keomany ◽  
Phonepadith Banouvong ◽  
Phetvilay Senavong ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Acute Kidney Injury ◽  
Intensive Care ◽  
Observational Study ◽  
Kidney Injury ◽  
Short Term

scholarly journals Nursing Activities Score and Acute Kidney Injury

2017 ◽  
Vol 70 (3) ◽  
pp. 475-480 ◽  
Author(s):  
Filipe Utuari de Andrade Coelho ◽  
Mirian Watanabe ◽  
Cassiane Dezoti da Fonseca ◽  
Katia Grillo Padilha ◽  
Maria de Fátima Fernandes Vattimo
Keyword(s):  
Intensive Care Unit ◽  
Acute Kidney Injury ◽  
Intensive Care ◽  
Kidney Disease ◽  
Quantitative Study ◽  
Kidney Injury ◽  
Nursing Workload ◽  
Intensive Care Patients ◽  
Stage 1

ABSTRACT Objective: to evaluate the nursing workload in intensive care patients with acute kidney injury (AKI). Method: A quantitative study, conducted in an intensive care unit, from April to August of 2015. The Nursing Activities Score (NAS) and Kidney Disease Improving Global Outcomes (KDIGO) were used to measure nursing workload and to classify the stage of AKI, respectively. Results: A total of 190 patients were included. Patients who developed AKI (44.2%) had higher NAS when compared to those without AKI (43.7% vs 40.7%), p <0.001. Patients with stage 1, 2 and 3 AKI showed higher NAS than those without AKI. A relationship was identified between stage 2 and 3 with those without AKI (p = 0.002 and p <0.001). Conclusion: The NAS was associated with the presence of AKI, the score increased with the progression of the stages, and it was associated with AKI, stage 2 and 3.

Using a Decision Wheel to Reduce Use of Restraints in a Medical-Surgical Intensive Care Unit

2016 ◽  
Vol 25 (6) ◽  
pp. 479-486 ◽  
Author(s):  
Stacy Hevener ◽  
Barbara Rickabaugh ◽  
Toby Marsh
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Decision Support ◽  
Intensive Care Units ◽  
Decision Support Tool ◽  
Educational Activity ◽  
Surgical Intensive Care Unit ◽  
Surgical Intensive Care ◽  
Support Tool ◽  
Unplanned Extubations

Background Little information is available on the use of tools in intensive care units to help nurses determine when to restrain a patient. Patients in medical-surgical intensive care units are often restrained for their safety to prevent them from removing therapeutic devices. Research indicates that restraints do not necessarily prevent injuries or removal of devices by patients. Objectives To decrease use of restraints in a medical-surgical intensive care unit and to determine if a decision support tool is useful in helping bedside nurses determine whether or not to restrain a patient. Methods A quasi-experimental study design was used for this pilot study. Data were collected for each patient each shift indicating if therapeutic devices were removed and if restraints were used. An online educational activity supplemented by 1-on-1 discussion about proper use of restraints, alternatives, and use of a restraint decision tool was provided. Frequency of restraint use was determined. Descriptive statistics and thematic analysis were used to examine nurses’ perceptions of the decision support tool. Results Use of restraints was reduced 32%. No unplanned extubations or disruption of life-threatening therapeutic devices by unrestrained patients occurred. Conclusions With implementation of the decision support tool, nurses decreased their use of restraints yet maintained patients’ safety. A decision support tool may help nurses who are undecided or who need reassurance on their decision to restrain or not restrain a patient.

scholarly journals Epidemiological profile of acute kidney injury in critically ill patients admitted to intensive care units: A Prospective Brazilian Cohort

2021 ◽  
Author(s):  
Antônio José Inda-Filho ◽  
Heitor Siqueira Ribeiro ◽  
Edilene Almeida Vieira ◽  
Aparecido Pimentel Ferreira
Keyword(s):  
Acute Kidney Injury ◽  
Intensive Care ◽  
Intensive Care Units ◽  
Meta Analysis ◽  
Kidney Injury ◽  
Federal District ◽  
Incidence And Mortality ◽  
Epidemiological Profile ◽  
Stage 1 ◽  
Disease Stages

Abstract Introduction Acute kidney injury (AKI) is a frequent syndrome affecting patients admitted to intensive care units (ICU), and it is associated with poor clinical outcomes. The aim of the present study was to understand the epidemiological profile of patients with AKI admitted to ICUs. Methods Prospective cohort study, carried out in three ICUs in the Federal District, Brazil. Between October/2017 and December/2018, 8,131 patients were included in the cohort. AKI was defined according to the KDIGO criteria. The main outcomes assessed were AKI development and mortality within 28 days of hospitalization. Results Of the 8,131 patients followed up, 1,728 developed AKI (21.3%). Of the 1,728 patients with AKI, 1,060 (61.3%) developed stage 1, while stages 2 and 3 represented 154 (8.9%) and 514 (29.7%), respectively. Of these, 459 (26.6%) underwent renal replacement therapy. The mortality was 25.7% for those with AKI, and 4.9% for those without AKI. Discussion Patients with AKI had higher mortality rates when compared to those without AKI. Likewise, among patients with AKI, higher disease stages were associated with higher death occurrences. AKI incidence (21.3%) and mortality (25.7%) in our study is in line with the largest meta-analysis ever conducted, in which incidence and mortality of 21.6 and 23.9% were observed, respectively. These findings confirm the importance of establishing the KDIGO guideline for the definition and management of AKI in Brazilian ICUs.

scholarly journals Machine Learning for the Prediction of Progression in Patients with Acute Kidney Injury in Critical Care

2021 ◽  
Author(s):  
Lifan Zhang ◽  
Canzheng Wei ◽  
Yunxia Feng ◽  
Aijia Ma ◽  
Yan Kang
Keyword(s):  
Machine Learning ◽  
Acute Kidney Injury ◽  
Logistic Regression ◽  
Critical Care ◽  
Intensive Care ◽  
Logistic Regression Model ◽  
Kidney Injury ◽  
Gradient Boosting ◽  
Extreme Gradient Boosting ◽  
Stage 1

Abstract Background: Acute kidney injury (AKI) is a serve and harmful syndrome in the intensive care unit. The purpose of this study is to develop a prediction model that predict whether patients with AKI stage 1/2 will progress to AKI stage 3. Methods: Patients with AKI stage 1/2, when they were first diagnosed with AKI in the Medical Information Mart for Intensive Care (MIMIC-III), were included. We excluded patients who had underwent RRT or progressed to AKI stage 3 within 72 hours of the first AKI diagnosis. We also excluded patients with chronic kidney disease (CKD). We used the Logistic regression and machine learning extreme gradient boosting (XGBoost) to build two models which can predict patients who will progress to AKI stage 3. Established models were evaluated by cross-validation, receiver operating characteristic curve (ROC), and precision-recall curves (PRC). Results: We included 25711 patients, of whom 2130 (8.3%) progressed to AKI stage 3. Creatinine, multiple organ failure syndromes (MODS), blood urea nitrogen (BUN), sepsis, and respiratory failure were the most important in AKI progression prediction. The XGBoost model has a better performance than the Logistic regression model on predicting AKI stage 3 progression (AU-ROC, 0.926; 95%CI, 0.917 to 0.931 vs. 0.784; 95%CI, 0.771 to 0.796, respectively). Conclusions: The XGboost model can better identify patients with AKI progression than Logistic regression model. Machine learning techniques may improve predictive modeling in medical research. Keywords: Acute kidney injury; Critical care; Logistic Models; Extreme gradient boosting

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