Acute kidney injury in non-critical care setting: elaboration and validation of an in-hospital death prognosis score.
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SUBMITTER: Bamoulid J
PROVIDER: S-EPMC6868787 | biostudies-literature | 2019 Nov
REPOSITORIES: biostudies-literature
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Acute kidney injury in non-critical care setting: elaboration and validation of an in-hospital death prognosis score.
Bamoulid Jamal J Philippot Hélène H Kazory Amir A Yannaraki Maria M Crepin Thomas T Vivet Bérengère B Devillard Nadège N Roubiou Caroline C Bresson-Vautrin Catherine C Chalopin Jean-Marc JM Courivaud Cécile C Ducloux Didier D
BMC nephrology 20191121 1
<h4>Background</h4>Acute kidney injury (AKI) is still characterized by a high mortality rate. While most patients with AKI are admitted in conventional medical units, current available data are still obtained from studies designed for patients admitted in intensive care units (ICU). Our study aimed to elaborate and validate an in-hospital death prognosis score for AKI admitted in conventional medical care units.<h4>Methods</h4>We included two prospective cohorts of consecutive patients with AKI ...[more]
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Project description:Background. Out-of-hospital cardiac arrest (OHCA) remains a challenge for emergency physicians, given the poor prognosis. In 2020, MIRACLE2, a new and easier to apply score, was established to predict the neurological outcome of OHCA. Objective. The aim of this study is to compare the discrimination of MIRACLE2 score with cardiac arrest hospital prognosis (CAHP) score for OHCA neurologic outcomes. Methods. This retrospective cohort study was conducted between January 2015 and December 2019. Adult patients (>17 years) with cardiac arrest who were brought to the hospital by an emergency medical service crew were included. Deaths due to trauma, burn, drowning, resuscitation not initiated due to pre-ordered “do not resuscitate” orders, and patients who did not achieve return of spontaneous circulation were excluded. Receiver operating characteristic curve analysis with Youden Index was performed to calculate optimal cut-off values for both scores. Results. Overall, 200 adult OHCA cases were analyzed. The threshold of the MIRACLE2 score for favorable neurologic outcomes was 5.5, with an area under the curve (AUC) value of 0.70 (0.61−0.80, p < 0.001); the threshold of the CAHP score was 223.4, with an AUC of 0.77 (0.68−0.86, p < 0.001). On setting the MIRACLE2 score cut-off value, we documented 64.7% sensitivity (95% confidence interval [CI], 56.9−71.9%), 66.7.0% specificity (95% CI, 48.2−82.0%), 90.8% positive predictive value (PPV; 95% CI, 85.6−94.2%), and 27.2% negative predictive value (NPV; 95% CI, 21.4−33.9%). On establishing a CAHP cut-off value, we observed 68.2% sensitivity (95% CI, 60.2−75.5%), 80.6% specificity (95% CI, 62.5−92.6%), 94.6% PPV (95% CI, 88.6%−98.0%), and 33.8% NPV (95% CI, 23.2−45.7%) for unfavorable neurologic outcomes. Conclusions. The CAHP score demonstrated better discrimination than the MIRACLE2 score, affording superior sensitivity, specificity, PPV, and NPV; however, the CAHP score remains relatively difficult to apply. Further studies are warranted to establish scores with better discrimination and ease of application.
| S-EPMC8950818 | biostudies-literature
Project description:ImportanceAcute kidney injury (AKI) is associated with increased morbidity and mortality in hospitalized patients. Current methods to identify patients at high risk of AKI are limited, and few prediction models have been externally validated.ObjectiveTo internally and externally validate a machine learning risk score to detect AKI in hospitalized patients.Design, setting, and participantsThis diagnostic study included 495 971 adult hospital admissions at the University of Chicago (UC) from 2008 to 2016 (n = 48 463), at Loyola University Medical Center (LUMC) from 2007 to 2017 (n = 200 613), and at NorthShore University Health System (NUS) from 2006 to 2016 (n = 246 895) with serum creatinine (SCr) measurements. Patients with an SCr concentration at admission greater than 3.0 mg/dL, with a prior diagnostic code for chronic kidney disease stage 4 or higher, or who received kidney replacement therapy within 48 hours of admission were excluded. A simplified version of a previously published gradient boosted machine AKI prediction algorithm was used; it was validated internally among patients at UC and externally among patients at NUS and LUMC.Main outcomes and measuresPrediction of Kidney Disease Improving Global Outcomes SCr-defined stage 2 AKI within a 48-hour interval was the primary outcome. Discrimination was assessed by the area under the receiver operating characteristic curve (AUC).ResultsThe study included 495 971 adult admissions (mean [SD] age, 63 [18] years; 87 689 [17.7%] African American; and 266 866 [53.8%] women) across 3 health systems. The development of stage 2 or higher AKI occurred in 15 664 of 48 463 patients (3.4%) in the UC cohort, 5711 of 200 613 (2.8%) in the LUMC cohort, and 3499 of 246 895 (1.4%) in the NUS cohort. In the UC cohort, 332 patients (0.7%) required kidney replacement therapy compared with 672 patients (0.3%) in the LUMC cohort and 440 patients (0.2%) in the NUS cohort. The AUCs for predicting at least stage 2 AKI in the next 48 hours were 0.86 (95% CI, 0.86-0.86) in the UC cohort, 0.85 (95% CI, 0.84-0.85) in the LUMC cohort, and 0.86 (95% CI, 0.86-0.86) in the NUS cohort. The AUCs for receipt of kidney replacement therapy within 48 hours were 0.96 (95% CI, 0.96-0.96) in the UC cohort, 0.95 (95% CI, 0.94-0.95) in the LUMC cohort, and 0.95 (95% CI, 0.94-0.95) in the NUS cohort. In time-to-event analysis, a probability cutoff of at least 0.057 predicted the onset of stage 2 AKI a median (IQR) of 27 (6.5-93) hours before the eventual doubling in SCr concentrations in the UC cohort, 34.5 (19-85) hours in the NUS cohort, and 39 (19-108) hours in the LUMC cohort.Conclusions and relevanceIn this study, the machine learning algorithm demonstrated excellent discrimination in both internal and external validation, supporting its generalizability and potential as a clinical decision support tool to improve AKI detection and outcomes.
| S-EPMC7420241 | biostudies-literature
Project description:BackgroundPredictive models and clinical risk scores for hospital-acquired acute kidney injury (AKI) are mainly focused on critical and surgical patients. We have used the electronic clinical records from a tertiary care general hospital to develop a risk score for new-onset AKI in general inpatients that can be estimated automatically from clinical records.MethodsA total of 47 466 patients met inclusion criteria within a 2-year period. Of these, 2385 (5.0%) developed hospital-acquired AKI. Step-wise regression modelling and Bayesian model averaging were used to develop the Madrid Acute Kidney Injury Prediction Score (MAKIPS), which contains 23 variables, all obtainable automatically from electronic clinical records at admission. Bootstrap resampling was employed for internal validation. To optimize calibration, a penalized logistic regression model was estimated by the least absolute shrinkage and selection operator (lasso) method of coefficient shrinkage after estimation.ResultsThe area under the curve of the receiver operating characteristic curve of the MAKIPS score to predict hospital-acquired AKI at admission was 0.811. Among individual variables, the highest odds ratios, all >2.5, for hospital-acquired AKI were conferred by abdominal, cardiovascular or urological surgery followed by congestive heart failure. An online tool (http://www.bioestadistica.net/MAKIPS.aspx) will facilitate validation in other hospital environments.ConclusionsMAKIPS is a new risk score to predict the risk of hospital-acquired AKI, based on variables present at admission in the electronic clinical records. This may help to identify patients who require specific monitoring because of a high risk of AKI.
| S-EPMC7857831 | biostudies-literature
Project description: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 AKI - but not RRT - were 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.
| S-EPMC6993395 | biostudies-literature
Project description:Individualize treatment after cardiac arrest could potentiate future clinical trials selecting patients most likely to benefit from interventions. We assessed the Cardiac Arrest Hospital Prognosis (CAHP) score for predicting reason for death to improve patient selection. Consecutive patients in two cardiac arrest databases were studied between 2007 and 2017. Reasons for death were categorised as refractory post-resuscitation shock (RPRS), hypoxic-ischaemic brain injury (HIBI) and other. We computed the CAHP score, which relies on age, location at OHCA, initial cardiac rhythm, no-flow and low-flow times, arterial pH, and epinephrine dose. We performed survival analyses using the Kaplan-Meier failure function and competing-risks regression. Of 1543 included patients, 987 (64%) died in the ICU, 447 (45%) from HIBI, 291 (30%) from RPRS, and 247 (25%) from other reasons. The proportion of deaths from RPRS increased with CAHP score deciles; the sub-hazard ratio for the tenth decile was 30.8 (9.8-96.5; p < 0.0001). The sub-hazard ratio of the CAHP score for predicting death from HIBI was below 5. Higher CAHP score values were associated with a higher proportion of deaths due to RPRS. This score may help to constitute uniform patient populations likely to benefit from interventions assessed in future randomised controlled trials.
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Project description:AKI is associated with increased risk of death, prolonged length of stay and development of de-novo chronic kidney disease. The aim of our study is the development and validation of prediction models to identify the risk of AKI in ICU patients up to 7 days. We retrospectively recruited 692 consecutive patients admitted to the ICU at San Bortolo Hospital (Vicenza, Italy) from 1 June 2016 to 31 March 2017: 455 patients were treated as the derivation group and 237 as the validation group. Candidate variables were selected based on a literature review and expert opinion. Admission eGFR< 90 ml/min /1.73 mq (OR 2.78; 95% CI 1.78-4.35; p<0.001); SOFAcv ≥ 2 (OR 2.23; 95% CI 1.48-3.37; p<0.001); lactate ≥ 2 mmol/L (OR 1.81; 95% CI 1.19-2.74; p = 0.005) and (TIMP-2)•(IGFBP7) ≥ 0.3 (OR 1.65; 95% CI 1.08-2.52; p = 0.019) were significantly associated with AKI. For the q-AKI score, we stratified patients into different AKI Risk score levels: 0-2; 3-4; 5-6; 7-8 and 9-10. In both cohorts, we observed that the proportion of AKI patients was higher in the higher score levels.
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Project description:BackgroundAcute respiratory failure occurs frequently in hospitalized patients and often starts before ICU admission. A risk stratification tool to predict mortality and risk for mechanical ventilation (MV) may allow for earlier evaluation and intervention. We developed and validated an automated electronic health record (EHR)-based model-Accurate Prediction of Prolonged Ventilation (APPROVE)-to identify patients at risk of death or respiratory failure requiring >= 48 h of MV.MethodsThis was an observational study of adults admitted to four hospitals in 2013 or a fifth hospital in 2017. Clinical data were extracted from the EHRs. The 2013 patients were randomly split 50:50 into a derivation/validation cohort. The qualifying event was death or intubation leading to MV >= 48 h. Random forest method was used in model derivation. APPROVE was calculated retrospectively whenever data were available in 2013, and prospectively every 4 h after hospital admission in 2017. The Modified Early Warning Score (MEWS) and National Early Warning Score (NEWS) were calculated at the same times as APPROVE. Clinicians were not alerted except for APPROVE in 2017cohort.ResultsThere were 68,775 admissions in 2013 and 2258 in 2017. APPROVE had an area under the receiver operator curve of 0.87 (95% CI 0.85-0.88) in 2013 and 0.90 (95% CI 0.84-0.95) in 2017, which is significantly better than the MEWS and NEWS in 2013 but similar to the MEWS and NEWS in 2017. At a threshold of > 0.25, APPROVE had similar sensitivity and positive predictive value (PPV) (sensitivity 63% and PPV 21% in 2013 vs 64% and 16%, respectively, in 2017). Compared to APPROVE in 2013, at a threshold to achieve comparable PPV (19% at MEWS > 4 and 22% at NEWS > 6), the MEWS and NEWS had lower sensitivity (16% for MEWS and NEWS). Similarly in 2017, at a comparable sensitivity threshold (64% for APPROVE > 0.25 and 67% for MEWS and NEWS > 4), more patients who triggered an alert developed the event with APPROVE (PPV 16%) while achieving a lower false positive rate (FPR 5%) compared to the MEWS (PPV 7%, FPR 14%) and NEWS (PPV 4%, FPR 25%).ConclusionsAn automated EHR model to identify patients at high risk of MV or death was validated retrospectively and prospectively, and was determined to be feasible for real-time risk identification.Trial registrationClinicalTrials.gov, NCT02488174 . Registered on 18 March 2015.
| S-EPMC6206729 | biostudies-literature
Project description:Background and objectivesCoronavirus disease 2019 is spreading rapidly across the world. This study aimed to assess the characteristics of kidney injury and its association with disease progression and death of patients with coronavirus disease 2019.Design, setting, participants, & measurementsThis is a retrospective study. Two representative cohorts were included. Cohort 1 involved severe and critical patients with coronavirus disease 2019 from Wuhan, China. Cohort 2 was all patients with coronavirus disease 2019 in Shenzhen city (Guangdong province, China). Any kidney injury was defined as the presence of any of the following: hematuria, proteinuria, in-hospital AKI, or prehospital AKI. AKI was defined according to the Kidney Disease Improving Global Outcomes (KDIGO) creatinine criteria. The primary outcome was death at the end of follow-up. The secondary outcome was progression to critical illness during the study period.ResultsA total of 555 patients were enrolled; 42% of the cases (229 of 549) were detected with any kidney injury, 33% of the cases (174 of 520) were detected with proteinuria, 22% of the cases (112 of 520) were detected with hematuria, and 6% of the cases (29 of 520) were detected with AKI. Of the 29 patients with AKI, 21 cases were recognized as in-hospital AKI, and eight were recognized as prehospital AKI. Altogether, 27 (5%) patients died at the end of follow-up. The death rate was 11% (20 of 174) in patients with proteinuria, 16% (18 of 112) in patients with hematuria, and 41% (12 of 29) in the AKI settings. Multivariable Cox regression analysis showed that proteinuria (hazard ratio, 4.42; 95% confidence interval, 1.22 to 15.94), hematuria (hazard ratio, 4.71; 95% confidence interval, 1.61 to 13.81), and in-hospital AKI (hazard ratio, 6.84; 95% confidence interval, 2.42 to 19.31) were associated with death. Among the 520 patients with noncritical illness at admission, proteinuria (hazard ratio, 2.61; 95% confidence interval, 1.22 to 5.56) and hematuria (hazard ratio, 2.50; 95% confidence interval, 1.23 to 5.08) were found to be associated with progression to critical illness during the study period.ConclusionsKidney injury is common in coronavirus disease 2019, and it is associated with poor clinical outcomes.PodcastThis article contains a podcast at https://www.asn-online.org/media/podcast/CJASN/2020_09_18_CJN04780420.mp3.
| S-EPMC7646240 | biostudies-literature
Project description:BackgroundThe Kidney Disease: Improving Global Outcomes (KDIGO) Acute Kidney Injury (AKI) guidelines assign the same stage of AKI to patients whether they fulfil urine output criteria, serum creatinine criteria or both criteria for that stage. This study explores the validity of the KDIGO guidelines as a tool to stratify the risk of adverse outcomes in cardiac surgery patients.MethodsProspective data from consecutive adult patients admitted to the cardiac intensive care unit (CICU) following cardiac surgery between January 2013 and May 2015 were analysed. Patients were assigned to groups based on the criteria they met for each stage of AKI according to the KDIGO guidelines. Short and mid-term outcomes were compared between these groups.ResultsA total of 2267 patients were included with 772 meeting criteria for AKI-1 and 222 meeting criteria for AKI-2. After multivariable adjustment, patients meeting both urine output and creatinine criteria for AKI-1 were more likely to experience prolonged CICU stay (OR 4.9, 95%CI 3.3-7.4, p < 0.01) and more likely to require renal replacement therapy (OR 10.5, 95%CI 5.5-21.9, p < 0.01) than those meeting only the AKI-1 urine output criterion. Patients meeting both urine output and creatinine criteria for AKI-1 were at an increased risk of mid-term mortality compared to those diagnosed with AKI-1 by urine output alone (HR 2.8, 95%CI 1.6-4.8, p < 0.01). Patients meeting both urine output and creatinine criteria for AKI-2 were more likely to experience prolonged CICU stay (OR 16.0, 95%CI 3.2-292.0, p < 0.01) or require RRT (OR 11.0, 95%CI 4.2-30.9, p < 0.01) than those meeting only the urine output criterion. Patients meeting both urine output and creatinine criteria for AKI-2 were at a significantly increased risk of mid-term mortality compared to those diagnosed with AKI-2 by urine output alone (HR 3.6, 95%CI 1.4-9.3, p < 0.01).ConclusionsPatients diagnosed with the same stage of AKI by different KDIGO criteria following cardiac surgery have significantly different short and mid-term outcomes. The KDIGO criteria need to be revisited before they can be used to stratify reliably the severity of AKI in cardiac surgery patients. The utility of the criteria also needs to be explored in other settings.
| S-EPMC6020229 | biostudies-literature
Project description:BackgroundImproving recognition of patients at increased risk of acute kidney injury (AKI) in the community may facilitate earlier detection and implementation of proactive prevention measures that mitigate the impact of AKI. The aim of this study was to develop and externally validate a practical risk score to predict the risk of AKI in either hospital or community settings using routinely collected data.MethodsRoutinely collected linked datasets from Tayside, Scotland, were used to develop the risk score and datasets from Kent in the UK and Alberta in Canada were used to externally validate it. AKI was defined using the Kidney Disease: Improving Global Outcomes serum creatinine-based criteria. Multivariable logistic regression analysis was performed with occurrence of AKI within 1 year as the dependent variable. Model performance was determined by assessing discrimination (C-statistic) and calibration.ResultsThe risk score was developed in 273 450 patients from the Tayside region of Scotland and externally validated into two populations: 218 091 individuals from Kent, UK and 1 173 607 individuals from Alberta, Canada. Four variables were independent predictors for AKI by logistic regression: older age, lower baseline estimated glomerular filtration rate, diabetes and heart failure. A risk score including these four variables had good predictive performance, with a C-statistic of 0.80 [95% confidence interval (CI) 0.80-0.81] in the development cohort and 0.71 (95% CI 0.70-0.72) in the Kent, UK external validation cohort and 0.76 (95% CI 0.75-0.76) in the Canadian validation cohort.ConclusionWe have devised and externally validated a simple risk score from routinely collected data that can aid both primary and secondary care physicians in identifying patients at high risk of AKI.
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