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Derivation and External Validation of Prediction Models for Advanced Chronic Kidney Disease Following Acute Kidney Injury.

ABSTRACT: Some 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.To derive and validate predictive models for progression of acute kidney injury to advanced chronic kidney disease.Data 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).Demographic, laboratory, and comorbidity variables measured prior to discharge.Advanced 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.The 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%).A 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.

SUBMITTER: James MT

PROVIDER: S-EPMC5820711 | biostudies-other | 2017 Nov

REPOSITORIES: biostudies-other

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Derivation and External Validation of Prediction Models for Advanced Chronic Kidney Disease Following Acute Kidney Injury.

James Matthew T MT Pannu Neesh N Hemmelgarn Brenda R BR Austin Peter C PC Tan Zhi Z McArthur Eric E Manns Braden J BJ Tonelli Marcello M Wald Ron R Quinn Robert R RR Ravani Pietro P Garg Amit X AX

JAMA 20171101 18

<h4>Importance</h4>Some 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.<h4>Objective</h4>To derive and validate predictive models for progression of acute kidney injury to advanced chronic kidney disease.<h4>Design, setting, and participants</h4>Data from 2 population-based cohorts of patients with a prehospitalization estimated glomerular filtr ...[more]

PMID: 29136443

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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:ImportanceSevere acute kidney injury (AKI) is a serious postoperative complication. A tool for predicting the risk of AKI requiring kidney replacement therapy (KRT) after major noncardiac surgery might assist with patient counseling and targeted use of measures to reduce this risk.ObjectiveTo derive and validate a predictive model for AKI requiring KRT after major noncardiac surgery.Design, setting, and participantsIn this prognostic study, 5 risk prediction models were derived and internally validated in a population-based cohort of adults without preexisting kidney failure who underwent noncardiac surgery in Alberta, Canada, between January 1, 2004, and December 31, 2013. The best performing model and corresponding risk index were externally validated in a population-based cohort of adults without preexisting kidney failure who underwent noncardiac surgery in Ontario, Canada, between January 1, 2007, and December 31, 2017. Data analysis was conducted from September 1, 2019, to May 31, 2021.ExposuresDemographic characteristics, surgery type, laboratory measures, and comorbidities before surgery.Main outcomes and measuresAcute kidney injury requiring KRT within 14 days after surgery. Discrimination was assessed using the C statistic; calibration was assessed using calibration intercept and slope. Logistic recalibration was used to optimize model calibration in the external validation cohort.ResultsThe derivation cohort included 92 114 patients (52.2% female; mean [SD] age, 62.3 [18.0] years), and the external validation cohort included 709 086 patients (50.8% female; mean [SD] age, 61.0 [16.0] years). A total of 529 patients (0.6%) developed postoperative AKI requiring KRT in the derivation cohort, and 2956 (0.4%) developed postoperative AKI requiring KRT in the external validation cohort. The following factors were consistently associated with the risk of AKI requiring KRT: younger age (40-69 years: odds ratio [OR], 2.07 [95% CI, 1.69-2.53]; <40 years: OR, 3.73 [95% CI, 2.61-5.33]), male sex (OR, 1.55; 95% CI, 1.28-1.87), surgery type (colorectal: OR, 4.86 [95% CI, 3.28-7.18]; liver or pancreatic: OR, 6.46 [95% CI, 3.85-10.83]; other abdominal: OR, 2.19 [95% CI, 1.66-2.89]; abdominal aortic aneurysm repair: OR, 19.34 [95% CI, 14.31-26.14]; other vascular: OR, 7.30 [95% CI, 5.48-9.73]; thoracic: OR, 3.41 [95% CI, 2.07-5.59]), lower estimated glomerular filtration rate (OR, 0.97; 95% CI, 0.97-0.97 per 1 mL/min/1.73 m2 increase), lower hemoglobin concentration (OR, 0.99; 95% CI, 0.98-0.99 per 0.1 g/dL increase), albuminuria (mild: OR, 1.88 [95% CI, 1.52-2.33]; heavy: OR, 3.74 [95% CI, 2.98-4.69]), history of myocardial infarction (OR, 1.63; 95% CI, 1.32-2.03), and liver disease (mild: OR, 2.32 [95% CI, 1.66-3.24]; moderate or severe: OR, 4.96 [95% CI, 3.58-6.85]). In external validation, a final model including these variables showed excellent discrimination (C statistic, 0.95; 95% CI, 0.95-0.96), with sensitivity of 21.2%, specificity of 99.9%, positive predictive value of 38.1%, and negative predictive value of 99.7% at a predicted risk threshold of 10% or greater.Conclusions and relevanceThe findings suggest that this risk model can predict AKI requiring KRT after noncardiac surgery using routine preoperative data. The model may be feasible for implementation in clinical perioperative risk stratification for severe AKI.

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:IntroductionExternal validation studies are essential to study the generalizability of prediction models. Recently a permutation test, focusing on discrimination as quantified by the c-statistic, was proposed to judge whether a prediction model is transportable to a new setting. We aimed to evaluate this test and compare it to previously proposed procedures to judge any changes in c-statistic from development to external validation setting.MethodsWe compared the use of the permutation test to the use of benchmark values of the c-statistic following from a previously proposed framework to judge transportability of a prediction model. In a simulation study we developed a prediction model with logistic regression on a development set and validated them in the validation set. We concentrated on two scenarios: 1) the case-mix was more heterogeneous and predictor effects were weaker in the validation set compared to the development set, and 2) the case-mix was less heterogeneous in the validation set and predictor effects were identical in the validation and development set. Furthermore we illustrated the methods in a case study using 15 datasets of patients suffering from traumatic brain injury.ResultsThe permutation test indicated that the validation and development set were homogenous in scenario 1 (in almost all simulated samples) and heterogeneous in scenario 2 (in 17%-39% of simulated samples). Previously proposed benchmark values of the c-statistic and the standard deviation of the linear predictors correctly pointed at the more heterogeneous case-mix in scenario 1 and the less heterogeneous case-mix in scenario 2.ConclusionThe recently proposed permutation test may provide misleading results when externally validating prediction models in the presence of case-mix differences between the development and validation population. To correctly interpret the c-statistic found at external validation it is crucial to disentangle case-mix differences from incorrect regression coefficients.

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:BackgroundAcute kidney injury (AKI) is common in the kidney transplant population.ObjectiveTo derive a multivariable survival model that predicts time to graft loss following AKI.DesignRetrospective cohort study using health care administrative and laboratory databases.SettingSouthwestern Ontario (1999-2013) and Ottawa, Ontario, Canada (1996-2013).PatientsWe included first-time kidney only transplant recipients who had a hospitalization with AKI 6 months or greater following transplant.MeasurementsAKI was defined using the Acute Kidney Injury Network criteria (stage 1 or greater). The first episode of AKI was included in the analysis. Graft loss was defined by return to dialysis or repeat kidney transplant.MethodsWe performed a competing risk survival regression analysis using the Fine and Gray method and modified the model into a simple point system. Graft loss with death as a competing event was the primary outcome of interest.ResultsA total of 315 kidney transplant recipients who had a hospitalization with AKI 6 months or greater following transplant were included. The median (interquartile range) follow-up time was 6.7 (3.3-10.3) years. Graft loss occurred in 27.6% of the cohort. The final model included 6 variables associated with an increased risk of graft loss: younger age, increased severity of AKI, failure to recover from AKI, lower baseline estimated glomerular filtration rate, increased time from kidney transplant to AKI admission, and receipt of a kidney from a deceased donor. The risk score had a concordance probability of 0.75 (95% confidence interval [CI], 0.69-0.82). The predicted 5-year risk of graft loss fell within the 95% CI of the observed risk more than 95% of the time.LimitationsThe CIs of the estimates were wide, and model overfitting is possible due to the limited sample size; the risk score requires validation to determine its clinical utility.ConclusionsOur prognostic risk score uses commonly available information to predict the risk of graft loss in kidney transplant patients hospitalized with AKI. If validated, this predictive model will allow clinicians to identify high-risk patients who may benefit from closer follow-up or targeted enrollment in future intervention trials designed to improve outcomes.

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Derivation and External Validation of Prediction Models for Advanced Chronic Kidney Disease Following Acute Kidney Injury.

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Derivation and External Validation of Prediction Models for Advanced Chronic Kidney Disease Following Acute Kidney Injury.

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