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Prediction Models and Their External Validation Studies for Mortality of Patients with Acute Kidney Injury: A Systematic Review.

ABSTRACT: To systematically review AKI outcome prediction models and their external validation studies, to describe the discrepancy of reported accuracy between the results of internal and external validations, and to identify variables frequently included in the prediction models.We searched the MEDLINE and Web of Science electronic databases (until January 2016). Studies were eligible if they derived a model to predict mortality of AKI patients or externally validated at least one of the prediction models, and presented area under the receiver-operator characteristic curves (AUROC) to assess model discrimination. Studies were excluded if they described only results of logistic regression without reporting a scoring system, or if a prediction model was generated from a specific cohort.A total of 2204 potentially relevant articles were found and screened, of which 12 articles reporting original prediction models for hospital mortality in AKI patients and nine articles assessing external validation were selected. Among the 21 studies for AKI prediction models and their external validation, 12 were single-center (57%), and only three included more than 1,000 patients (14%). The definition of AKI was not uniform and none used recently published consensus criteria for AKI. Although good performance was reported in their internal validation, most of the prediction models had poor discrimination with an AUROC below 0.7 in the external validation studies. There were 10 common non-renal variables that were reported in more than three prediction models: mechanical ventilation, age, gender, hypotension, liver failure, oliguria, sepsis/septic shock, low albumin, consciousness and low platelet count.Information in this systematic review should be useful for future prediction model derivation by providing potential candidate predictors, and for future external validation by listing up the published prediction models.

SUBMITTER: Ohnuma T

PROVIDER: S-EPMC5215838 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Prediction Models and Their External Validation Studies for Mortality of Patients with Acute Kidney Injury: A Systematic Review.

Ohnuma Tetsu T Uchino Shigehiko S

PloS one 20170105 1

<h4>Objectives</h4>To systematically review AKI outcome prediction models and their external validation studies, to describe the discrepancy of reported accuracy between the results of internal and external validations, and to identify variables frequently included in the prediction models.<h4>Methods</h4>We searched the MEDLINE and Web of Science electronic databases (until January 2016). Studies were eligible if they derived a model to predict mortality of AKI patients or externally validated ...[more]

PMID: 28056039

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Project description:ImportanceSome patients will develop chronic kidney disease after a hospitalization with acute kidney injury; however, no risk-prediction tools have been developed to identify high-risk patients requiring follow-up.ObjectiveTo derive and validate predictive models for progression of acute kidney injury to advanced chronic kidney disease.Design, setting, and participantsData from 2 population-based cohorts of patients with a prehospitalization estimated glomerular filtration rate (eGFR) of more than 45 mL/min/1.73 m2 and who had survived hospitalization with acute kidney injury (defined by a serum creatinine increase during hospitalization > 0.3 mg/dL or > 50% of their prehospitalization baseline), were used to derive and validate multivariable prediction models. The risk models were derived from 9973 patients hospitalized in Alberta, Canada (April 2004-March 2014, with follow-up to March 2015). The risk models were externally validated with data from a cohort of 2761 patients hospitalized in Ontario, Canada (June 2004-March 2012, with follow-up to March 2013).ExposuresDemographic, laboratory, and comorbidity variables measured prior to discharge.Main outcomes and measuresAdvanced chronic kidney disease was defined by a sustained reduction in eGFR less than 30 mL/min/1.73 m2 for at least 3 months during the year after discharge. All participants were followed up for up to 1 year.ResultsThe participants (mean [SD] age, 66 [15] years in the derivation and internal validation cohorts and 69 [11] years in the external validation cohort; 40%-43% women per cohort) had a mean (SD) baseline serum creatinine level of 1.0 (0.2) mg/dL and more than 20% had stage 2 or 3 acute kidney injury. Advanced chronic kidney disease developed in 408 (2.7%) of 9973 patients in the derivation cohort and 62 (2.2%) of 2761 patients in the external validation cohort. In the derivation cohort, 6 variables were independently associated with the outcome: older age, female sex, higher baseline serum creatinine value, albuminuria, greater severity of acute kidney injury, and higher serum creatinine value at discharge. In the external validation cohort, a multivariable model including these 6 variables had a C statistic of 0.81 (95% CI, 0.75-0.86) and improved discrimination and reclassification compared with reduced models that included age, sex, and discharge serum creatinine value alone (integrated discrimination improvement, 2.6%; 95% CI, 1.1%-4.0%; categorical net reclassification index, 13.5%; 95% CI, 1.9%-25.1%) or included age, sex, and acute kidney injury stage alone (integrated discrimination improvement, 8.0%; 95% CI, 5.1%-11.0%; categorical net reclassification index, 79.9%; 95% CI, 60.9%-98.9%).Conclusions and relevanceA multivariable model using routine laboratory data was able to predict advanced chronic kidney disease following hospitalization with acute kidney injury. The utility of this model in clinical care requires further research.

Project description:ImportanceThe Simple Postoperative AKI Risk (SPARK) index is a prediction model for postoperative acute kidney injury (PO-AKI) in patients undergoing noncardiac surgery. External validation has not been performed.ObjectiveTo externally validate the SPARK index.Design, setting, and participantsThis single-center retrospective cohort study included adults who underwent noncardiac surgery under general anesthesia from 2007 to 2011. Those with obstetric or urological surgery, estimated glomerular filtration rate (eGFR) of less than 15 mL/min/1.73 m2, preoperative dialysis, or an expected surgical duration of less than 1 hour were excluded. The study was conducted at Nara Medical University Hospital. Data analysis was conducted from January to July 2021.ExposuresRisk factors for AKI included in SPARK index.Main outcomes and measuresPO-AKI, defined as an increase in serum creatinine of at least 0.3 mg/dL within 48 hours or 150% compared with preoperative baseline value or urine output of less than 0.5 mL/kg/h for at least 6 hours within 1 week after surgery, and critical AKI, defined as either AKI stage 2 or greater and/or any AKI connected to postoperative death or requiring kidney replacement therapy before discharge. The discrimination and calibration of the SPARK index were examined with area under the receiver operating characteristic curves (AUC) and calibration plots, respectively.ResultsAmong 5135 participants (2410 [46.9%] men), 303 (5.9%) developed PO-AKI, and 137 (2.7%) developed critical AKI. Compared with the SPARK cohort, participants in our cohort were older (median [IQR] age, 56 [44-66] years vs 63 [50-73] years), had lower baseline eGFR (median [IQR], 82.1 [71.4-95.1] mL/min/1.73 m2 vs 78.2 [65.6-92.2] mL/min/1.73 m2), and had a higher prevalence of comorbidities (eg, diabetes: 3956 of 51 041 [7.8%] vs 802 [15.6%]). The incidence of PO-AKI and critical AKI increased as the scores on the SPARK index increased. For example, 10 of 593 participants (1.7%) in SPARK class A, indicating lowest risk, experienced PO-AKI, while 53 of 332 (16.0%) in SPARK class D, indicating highest risk, experienced PO-AKI. However, AUCs for PO-AKI and critical AKI were 0.67 (95% CI, 0.63-0.70) and 0.62 (95% CI, 0.57-0.67), respectively, and the calibration was poor (PO-AKI: y = 0.24x + 3.28; R2 = 0.86; critical AKI: y = 0.20x + 2.08; R2 = 0.51). Older age, diabetes, expected surgical duration, emergency surgery, renin-angiotensin-aldosterone system blockade use, and hyponatremia were not associated with PO-AKI in our cohort, resulting in overestimation of the predicted probability of AKI in our cohort.Conclusions and relevanceIn this study, the incidence of PO-AKI increased as the scores on the SPARK index increased. However, the predicted probability might not be accurate in cohorts with older patients with more comorbidities.

Project description:Rationale & objectiveThere is limited evidence to guide follow-up after acute kidney injury (AKI). Knowledge gaps include which patients to prioritize, at what time point, and for mitigation of which outcomes. In this study, we sought to compare the net benefit of risk model-based clinical decisions following AKI.Study designExternal validation of 2 risk models of AKI outcomes: the Grampian -Aberdeen (United Kingdom) AKI readmissions model and the Alberta (Canada) kidney disease risk model of chronic kidney disease (CKD) glomerular (G) filtration rate categories 4 and 5 (CKD G4 and G5). Process mining to delineate existing care pathways.Setting & participantsValidation was based on data from adult hospital survivors of AKI from Grampian, 2011-2013.PredictorsKDIGO-based measures of AKI severity and comorbidities specified in the original models.OutcomesDeath or readmission within 90 days for all hospital survivors. Progression to new CKD G4-G5 for patients surviving at least 90 days after AKI.Analytical approachDecision curve analysis to assess the "net benefit" of use of risk models to guide clinical care compared to alternative approaches (eg, prioritizing all AKI, severe AKI, or only those without kidney recovery).Results26,575 of 105,461 hospital survivors in Grampian (mean age, 60.9 ± 19.8 [SD] years) were included for validation of the death or readmission model, and 9,382 patients (mean age, 60.9 ± 19.8 years) for the CKD G4-G5 model. Both models discriminated well (area under the curve [AUC], 0.77 and 0.86, respectively). Decision curve analysis showed greater net benefit for follow up of all AKI than only severe AKI in most cases. Both original and refitted models provided net benefit superior to any other decision strategy. In process mining of all hospital discharges, 41% of readmissions and deaths occurred among people recovering after AKI. 1,464 of 3,776 people (39%) readmitted after AKI had received no intervening monitoring.LimitationsBoth original models overstated risks, indicating a need for regular updating.ConclusionsFollow up after AKI has potential net benefit for preempting readmissions, death, and subsequent CKD progression. Decisions could be improved by using risk models and by focusing on AKI across a full spectrum of severity. The current lack of monitoring among many with poor outcomes indicates possible opportunities for implementation of decision support.

Project description:BackgroundPersistent acute kidney injury (AKI) portends worse clinical outcomes and remains a therapeutic challenge for clinicians. A recent study found that urinary C-C motif chemokine ligand 14 (CCL14) can predict the development of persistent AKI. We aimed to externally validate urinary CCL14 for the prediction of persistent AKI in critically ill patients.MethodsThis was a secondary analysis of the prospective multi-center SAPPHIRE study. We evaluated critically ill patients with cardiac and/or respiratory dysfunction who developed Kidney Disease: Improving Global Outcomes (KDIGO) stage 2-3 AKI within one week of enrollment. The main exposure was the urinary concentration of CCL14 measured at the onset of AKI stage 2-3. The primary endpoint was the development of persistent severe AKI, defined as ≥ 72 h of KDIGO stage 3 AKI or death or renal-replacement therapy (RRT) prior to 72 h. The secondary endpoint was a composite of RRT and/or death by 90 days. We used receiver operating characteristic (ROC) curve analysis to assess discriminative ability of urinary CCL14 for the development of persistent severe AKI and multivariate analysis to compare tertiles of urinary CCL14 and outcomes.ResultsWe included 195 patients who developed KDIGO stage 2-3 AKI. Of these, 28 (14%) developed persistent severe AKI, of whom 15 had AKI ≥ 72 h, 12 received RRT and 1 died prior to ≥ 72 h of KDIGO stage 3 AKI. Persistent severe AKI was associated with chronic kidney disease, diabetes mellitus, higher non-renal APACHE III score, greater fluid balance, vasopressor use, and greater change in baseline serum creatinine. The AUC for urinary CCL14 to predict persistent severe AKI was 0.81 (95% CI, 0.72-0.89). The risk of persistent severe AKI increased with higher values of urinary CCL14. RRT and/or death at 90 days increased within tertiles of urinary CCL14 concentration.ConclusionsThis secondary analysis externally validates urinary CCL14 to predict persistent severe AKI in critically ill patients.

Project description:BackgroundSepsis-associated acute kidney injury (SA-AKI) is common in critically ill patients, which is associated with significantly increased mortality. Existing mortality prediction tools showed insufficient predictive power or failed to reflect patients' dynamic clinical evolution. Therefore, the study aimed to develop and validate machine learning-based models for real-time mortality prediction in critically ill patients with SA-AKI.MethodsThe multi-center retrospective study included patients from two distinct databases. A total of 12,132 SA-AKI patients from the Medical Information Mart for Intensive Care IV (MIMIC-IV) were randomly allocated to the training, validation, and internal test sets. An additional 3,741 patients from the eICU Collaborative Research Database (eICU-CRD) served as an external test set. For every 12 h during the ICU stays, the state-of-the-art eXtreme Gradient Boosting (XGBoost) algorithm was used to predict the risk of in-hospital death in the following 48, 72, and 120 h and in the first 28 days after ICU admission. Area under the receiver operating characteristic curves (AUCs) were calculated to evaluate the models' performance.ResultsThe XGBoost models, based on routine clinical variables updated every 12 h, showed better performance in mortality prediction than the SOFA score and SAPS-II. The AUCs of the XGBoost models for mortality over different time periods ranged from 0.848 to 0.804 in the internal test set and from 0.818 to 0.748 in the external test set. The shapley additive explanation method provided interpretability for the XGBoost models, which improved the understanding of the association between the predictor variables and future mortality.ConclusionsThe interpretable machine learning XGBoost models showed promising performance in real-time mortality prediction in critically ill patients with SA-AKI, which are useful tools for early identification of high-risk patients and timely clinical interventions.

Project description:ObjectiveCritically appraise prediction models for hospital-acquired acute kidney injury (HA-AKI) in general populations.DesignSystematic review.Data sourcesMedline, Embase and Web of Science until November 2016.EligibilityStudies describing development of a multivariable model for predicting HA-AKI in non-specialised adult hospital populations. Published guidance followed for data extraction reporting and appraisal.Results14 046 references were screened. Of 53 HA-AKI prediction models, 11 met inclusion criteria (general medicine and/or surgery populations, 474 478 patient episodes) and five externally validated. The most common predictors were age (n=9 models), diabetes (5), admission serum creatinine (SCr) (5), chronic kidney disease (CKD) (4), drugs (diuretics (4) and/or ACE inhibitors/angiotensin-receptor blockers (3)), bicarbonate and heart failure (4 models each). Heterogeneity was identified for outcome definition. Deficiencies in reporting included handling of predictors, missing data and sample size. Admission SCr was frequently taken to represent baseline renal function. Most models were considered at high risk of bias. Area under the receiver operating characteristic curves to predict HA-AKI ranged 0.71-0.80 in derivation (reported in 8/11 studies), 0.66-0.80 for internal validation studies (n=7) and 0.65-0.71 in five external validations. For calibration, the Hosmer-Lemeshow test or a calibration plot was provided in 4/11 derivations, 3/11 internal and 3/5 external validations. A minority of the models allow easy bedside calculation and potential electronic automation. No impact analysis studies were found.ConclusionsAKI prediction models may help address shortcomings in risk assessment; however, in general hospital populations, few have external validation. Similar predictors reflect an elderly demographic with chronic comorbidities. Reporting deficiencies mirrors prediction research more broadly, with handling of SCr (baseline function and use as a predictor) a concern. Future research should focus on validation, exploration of electronic linkage and impact analysis. The latter could combine a prediction model with AKI alerting to address prevention and early recognition of evolving AKI.

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Prediction Models and Their External Validation Studies for Mortality of Patients with Acute Kidney Injury: A Systematic Review.

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Prediction Models and Their External Validation Studies for Mortality of Patients with Acute Kidney Injury: A Systematic Review.

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