Feature Ranking in Predictive Models for Hospital-Acquired Acute Kidney Injury.
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SUBMITTER: Wu L
PROVIDER: S-EPMC6251919 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BACKGROUND:Hospital-acquired acute kidney injury (AKI) is associated with increased mortality and resource consumption. Little is known about the association of AKI with short-term hospital readmissions. STUDY DESIGN:Retrospective cohort study. SETTING & PARTICIPANTS:We investigated whether adult survivors of hospital-acquired AKI were at increased odds for early hospital readmission. PREDICTOR:The peak-to-nadir serum creatinine difference during the index hospitalization was used to define AKI according to the KDIGO (Kidney Disease: Improving Global Outcomes) classification and staging system. MEASUREMENTS:Multivariable logistic regression analyses examined the association of AKI with 30-, 60-, and 90-day hospital readmission, adjusting for age, sex, race, Charlson-Deyo comorbidity index score, acute hospital-related factors, common causes of hospitalization, and baseline estimated glomerular filtration rate. RESULTS:3,345 (15%) of 22,001 included patients experienced AKI during the index hospitalization. Compared to the non-AKI group, the AKI group had a significantly higher 30-day hospital readmission rate (11% vs 15%; P<0.001), which persisted at 60 and 90 days. The AKI group also was more likely to be readmitted to the hospital within 30 days for cardiovascular-related conditions, mainly heart failure (P<0.001) and acute myocardial infarction (P=0.01). AKI associated independently with higher odds of 30-day hospital readmission (OR, 1.21; 95% CI, 1.08-1.36), which persisted at 60 (OR, 1.15; 95% CI, 1.03-1.27) and 90 days (adjusted OR, 1.13; 95% CI, 1.02-1.25). Results were attenuated in a propensity score-matched cohort of 5,912 patients. LIMITATIONS:Single-center study of mild forms of AKI; ascertainment bias and outcome misclassification due to the use of administrative codes. CONCLUSIONS:Our results suggest that survivors of hospital-acquired AKI experience higher odds of early hospital readmission. Transitions of care services may be warranted for such patients to prevent readmissions and reduce health care costs.
| S-EPMC6556889 | biostudies-literature
Project description:IntroductionIn patients with cirrhosis, differences between acute kidney injury (AKI) at the time of hospital admission (community-acquired) and AKI occurring during hospitalization (hospital-acquired) have not been explored. We aimed to compare patients with hospital-acquired AKI (H-AKI) and community-acquired AKI (C-AKI) in a large, prospective study.MethodsHospitalized patients with cirrhosis were enrolled (N = 519) and were followed for 90 days after discharge for mortality. The primary outcome was mortality within 90 days; secondary outcomes were the development of de novo chronic kidney disease (CKD)/progression of CKD after 90 days. Cox proportional hazards and logistic regressions were used to determine the independent association of either AKI for primary and secondary outcomes, respectively.ResultsH-AKI occurred in 10%, and C-AKI occurred in 25%. In multivariable Cox models adjusting for significant confounders, only patients with C-AKI had a higher risk for mortality adjusting for model for end-stage liver disease-Na: (hazard ratio 1.64, 95% confidence interval [CI] 1.04-2.57, P = 0.033) and adjusting for acute on chronic liver failure: (hazard ratio 2.44, 95% CI 1.63-3.65, P < 0.001). In univariable analysis, community-acquired-AKI, but not hospital-acquired-AKI, was associated with de novo CKD/progression of CKD (odds ratio 2.13, 95% CI 1.09-4.14, P = 0.027), but in multivariable analysis, C-AKI was not independently associated with de novo CKD/progression of CKD. However, when AKI was dichotomized by stage, C-AKI stage 3 was independently associated with de novo CKD/progression of CKD (odds ratio 4.79, 95% CI 1.11-20.57, P = 0.035).DiscussionCompared with H-AKI, C-AKI is associated with increased mortality and de novo CKD/progression of CKD in patients with cirrhosis. Patients with C-AKI may benefit from frequent monitoring after discharge to improve outcomes.
| S-EPMC7483791 | biostudies-literature
Project description:Previous studies have described the incidence, risk factors, and outcomes for patients with acute exacerbations of COPD (AECOPD) developing acute kidney injury (AKI). However, little is known about the differences between community-acquired AKI (CA-AKI) and hospital-acquired AKI (HA-AKI) in patients with AECOPD. Thus, in this study, we compared prevalence, risk factors, and outcomes for these patients with CA-AKI and HA-AKI.This study was conducted from January 2014 to January 2017, and data from adult inpatients with AECOPD were analyzed retrospectively. A total of 1,768 patients were included, 280 patients were identified with CA-AKI and 97 patients were with HA-AKI.Prevalence of CA-AKI was 15.8% and that of HA-AKI was 5.5%, giving an overall AKI prevalence of 21.3%. Patients with CA-AKI had a higher prevalence of chronic kidney disease (CKD) and lower prevalence of chronic cor pulmonale than patients with HA-AKI. Risk factors for developing HA-AKI and CA-AKI were similar, such as being elderly, requirement for mechanical ventilation, and a history of coronary artery disease and CKD. Patients with HA-AKI were more likely to have stage 3 AKI and worse short-term outcomes. In comparison with patients with CA-AKI, those with HA-AKI were more likely to require non-invasive mechanical ventilation (31.3% versus 16.8%; P = 0.003) and had a longer duration of mechanical ventilation (11 days versus 8 days; P = 0.020), longer hospitalization (14 days versus 12 days; P = 0.038), and higher inpatient mortality (32.0% versus 13.2%; P < 0.001). Patients with HA-AKI had worse (multivariate-adjusted) inpatient survival than those with CA-AKI (hazard ratio, 1.7 [95% confidence interval, 1.03-2.81; P = 0.038] for the HA-AKI group).AKI was common in patients with AECOPD requiring hospitalization. CA-AKI was more common than HA-AKI but otherwise demonstrated similar demographics and risk factors. Nevertheless, patients with HA-AKI had worse short-term outcomes.
| S-EPMC6054768 | biostudies-other
Project description:Background and objectivesDespite modern treatment, the case fatality rate of hospital-acquired acute kidney injury (HA-AKI) is still high. We retrospectively described the prevalence and the outcome of HA-AKI without nephrology referral (nrHA-AKI) and late referred HA-AKI patients to nephrologists (lrHA-AKI) compared with early referral patients (erHA-AKI) with respect to renal function recovery, renal replacement therapy (RRT) requirement, and in-hospital mortality of HA-AKI.Design, setting, participants, & measurementsNoncritically ill patients admitted to the tertiary care academic center of Lausanne, Switzerland, between 2004 and 2008 in the medical and surgical services were included. Acute kidney injury was defined using the Acute Kidney Injury Network (AKIN) classification.ResultsDuring 5 years, 4296 patients (4.12% of admissions) experienced 4727 episodes of HA-AKI during their hospital stay. The mean ± SD age of the patients was 61 ± 15 years with a 55% male predominance. There were 958 patients with nrHA-AKI (22.3%) and 2504 patients with lrHA-AKI (58.3%). RRT was required in 31% of the patients with lrHA-AKI compared with 24% of the patients with erHA-AKI. In the multiple risk factor analysis, compared with erHA-AKI, nrHA-AKI and lrHA-AKI were significantly associated with worse renal outcome and higher in-hospital mortality.ConclusionsThese data suggest that HA-AKI is frequent and the patients with nrHA-AKI or lrHA-AKI are at increased risk for in-hospital morbidity and mortality.
| S-EPMC3359001 | biostudies-literature
Project description:IntroductionDrug-induced acute kidney injury (D-AKI) is one of the important types of AKI. The incidence of D-AKI in China has rarely been studied.ObjectiveThis study aims to explore the disease burden, related drugs, and risk factors of D-AKI.MethodsA nationwide cross-sectional survey was conducted in adult patients from 23 academic hospitals in 17 provinces in China. Suspected AKI was screened based on serum creatinine changes in accordance with the 2012 Kidney Disease: Improving Global Outcomes Clinical Practice Guideline for AKI, patients who met the diagnosis of hospital-acquired AKI in January and July of 2014 were defined. Suspected AKI was firstly evaluated for the possibility of D-AKI by pharmacists using the Naranjo Scale and finally defined as D-AKI by nephrologists through reviewing AKI clinical features.ResultsAltogether 280,255 hospitalized patients were screened and 1,960 cases were diagnosed as hospital-acquired AKI, among which 735 cases were defined as having D-AKI (37.50%, 735/1,960) with an in-hospital mortality rate of 13.88% and 54.34% of the survivors did not achieve full renal recovery. 1,642 drugs were related to AKI in these patients. Anti-infectives, diuretics, and proton pump inhibitors were the top 3 types of drugs relevant to D-AKI, accounting for 66.63% cumulatively. Besides age, AKI staging, severe disease, hypoalbuminemia, plasma substitute, and carbapenem related D-AKI were independent risk factors for in-hospital mortality of D-AKI patients.ConclusionIn China, D-AKI has caused a substantial medical burden. Efforts should be made to pursue nephrotoxic drug stewardship to minimize attributable risk and improve the prevention, diagnosis, and treatment of D-AKI.
| S-EPMC8010232 | 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:The disease burden and outcomes of community-acquired (CA-) and hospital-acquired acute kidney injury (HA-AKI) are not well understood. The aim of the study was to investigate the incidence, outcomes, and risk factors of AKI in a large Taiwanese adult cohort.This retrospective cohort study examined 734,340 hospital admissions from a group of hospitals within an organization in Taiwan between January 1, 2010 and December 31, 2014. Patients with AKI at discharge were classified as either CA- or HA-AKI based on the RIFLE (risk, injury, failure, loss of function, end stage of kidney disease) classification criteria. Outcomes were in-hospital mortality, dialysis, recovery of renal function, and length of stay. Risks of developing AKI were determined using multivariate logistic regression based on demographic and baseline clinical characteristics and nephrotoxin use before admission.AKI occurred in 1.68% to 2% hospital discharges among adults without and with preexisting chronic kidney disease (CKD), respectively. The incidence of CA-AKI was 17.25 and HA-AKI was 8.14 per 1000 admissions. The annual rate of CA-AKI increased from 12.43 to 19.96 per 1000 people, but the change in HA-AKI was insignificant. Comparing to CA-AKI, those with HA-AKI had higher levels of in-hospital mortality (26.07% vs 51.58%), mean length of stay (21.25 ± 22.35 vs 35.84 ± 34.62 days), and dialysis during hospitalization (1.45% vs 2.06%). Preexisting systemic diseases, including CKD were associated with increased risks of CA-AKI, and nephrotoxic polypharmacy increased risk of both CA- and HA-AKI.Patients with HA-AKI had more severe outcomes than patients with CA-AKI, and demonstrated different spectrum of risk factors. Although patients with CA-AKI with better outcomes, the incidence increased over time. It is also clear that optimal preventive and management strategies of HA- and CA-AKI are urgently needed to limit the risks in susceptible individuals.
| S-EPMC4902543 | biostudies-literature
Project description:Acute Kidney Injury (AKI) has evoked much interest over the past decade and is reported to be associated with high inpatient mortality. Preventable death and increased readmission rates related to AKI have been the focus of considerable interest.We studied hospital acquired AKI in all emergency hospital admissions, except transfers from ICU to ICU or patients known to renal services, to ascertain mortality and readmission rates, and trackable modifiable factors for death, using cox regression and Kaplan Meier survival curves. Data was extracted from the electronic patient records and a series of case notes reviewed. Admissions were included between April 2006 and March 2010 (and patients followed up until September 2011).Overall incidence of AKI was 2.2%, (AKI stage 1, 61%, stage 2,27% and stage 3, 12%). In patients who sustain in-hospital AKI, 34% die in hospital, 42% are dead at 90 days and 48% at 1 year post discharge, compared to 12% 1 year mortality in patients without AKI. In multivariable analyses, AKI is an independent risk factor for in-hospital mortality (Hazard Ratio 1.6: 95% confidence intervals 1.43-1.75: P < 0.001), death within 90 days of discharge (Hazard Ratio 1.5: 95% confidence intervals 1.3-1.9: P < 0.001) and subsequent mortality beyond 90 days (Hazard Ratio 2.9: 95% confidence intervals 2.7-3.1: P < 0.001) after adjustment for co-morbidities and peak C-reactive protein. Thirty percent of the patients who died in the first 90 days post discharge and had AKI, also had malignancy. Readmission rates at 30 and 90 days were not increased by AKI after adjustment for co-morbidities and peak C-reactive protein.A significant proportion of deaths in the first 90 days post-discharge may not be avoidable, due to malignancy and other end-stage disease. Readmission rates were not higher in patients who had had AKI.
| S-EPMC5651577 | biostudies-literature
Project description:BackgroundAcute kidney injury (AKI) is a common complication after surgery that is associated with increased morbidity and mortality. The majority of existing perioperative AKI risk prediction models are limited in their generalizability and do not fully utilize intraoperative physiological time-series data. Thus, there is a need for intelligent, accurate, and robust systems to leverage new information as it becomes available to predict the risk of developing postoperative AKI.MethodsA retrospective single-center cohort of 2,911 adults who underwent surgery at the University of Florida Health between 2000 and 2010 was utilized for this study. Machine learning and statistical analysis techniques were used to develop perioperative models to predict the risk of developing AKI during the first three days after surgery, first seven days after surgery, and overall (after surgery during the index hospitalization). The improvement in risk prediction was examined by incorporating intraoperative physiological time-series variables. Our proposed model enriched a preoperative model that produced a probabilistic AKI risk score by integrating intraoperative statistical features through a machine learning stacking approach inside a random forest classifier. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, and Net Reclassification Improvement (NRI).ResultsThe predictive performance of the proposed model is better than the preoperative data only model. The proposed model had an AUC of 0.86 (accuracy of 0.78) for the seven-day AKI outcome, while the preoperative model had an AUC of 0.84 (accuracy of 0.76). Furthermore, by integrating intraoperative features, the algorithm was able to reclassify 40% of the false negative patients from the preoperative model. The NRI for each outcome was AKI at three days (8%), seven days (7%), and overall (4%).ConclusionsPostoperative AKI prediction was improved with high sensitivity and specificity through a machine learning approach that dynamically incorporated intraoperative data.
| S-EPMC6448850 | biostudies-literature
Project description:BackgroundModels developed to predict hospital-acquired acute kidney injury (HA-AKI) in non-critically ill patients have a low sensitivity, do not include dynamic changes of risk factors and do not allow the establishment of a time relationship between exposure to risk factors and AKI. We developed and externally validated a predictive model of HA-AKI integrating electronic health databases and recording the exposure to risk factors prior to the detection of AKI.MethodsThe study set was 36 852 non-critically ill hospitalized patients admitted from January to December 2017. Using stepwise logistic analyses, including demography, chronic comorbidities and exposure to risk factors prior to AKI detection, we developed a multivariate model to predict HA-AKI. This model was then externally validated in 21 545 non-critical patients admitted to the validation centre in the period from June 2017 to December 2018.ResultsThe incidence of AKI in the study set was 3.9%. Among chronic comorbidities, the highest odds ratios (ORs) were conferred by chronic kidney disease, urologic disease and liver disease. Among acute complications, the highest ORs were associated with acute respiratory failure, anaemia, systemic inflammatory response syndrome, circulatory shock and major surgery. The model showed an area under the curve (AUC) of 0.907 [95% confidence interval (CI) 0.902-0.908), a sensitivity of 82.7 (95% CI 80.7-84.6) and a specificity of 84.2 (95% CI 83.9-84.6) to predict HA-AKI, with an adequate goodness-of-fit for all risk categories (χ2 = 6.02, P = 0.64). In the validation set, the prevalence of AKI was 3.2%. The model showed an AUC of 0.905 (95% CI 0.904-0.910), a sensitivity of 81.2 (95% CI 79.2-83.1) and a specificity of 82.5 (95% CI 82.2-83) to predict HA-AKI and had an adequate goodness-of-fit for all risk categories (χ2 = 4.2, P = 0.83). An online tool (predaki.amalfianalytics.com) is available to calculate the risk of AKI in other hospital environments.ConclusionsBy using electronic health data records, our study provides a model that can be used in clinical practice to obtain an accurate dynamic and updated assessment of the individual risk of HA-AKI during the hospital admission period in non-critically ill patients.
| S-EPMC8690094 | biostudies-literature