Dataset Information

Acute Kidney Injury Risk Prediction in Patients Undergoing Coronary Angiography in a National Veterans Health Administration Cohort With External Validation.

ABSTRACT:

Background

Acute kidney injury (AKI) occurs frequently after cardiac catheterization and percutaneous coronary intervention. Although a clinical risk model exists for percutaneous coronary intervention, no models exist for both procedures, nor do existing models account for risk factors prior to the index admission. We aimed to develop such a model for use in prospective automated surveillance programs in the Veterans Health Administration.

Methods and results

We collected data on all patients undergoing cardiac catheterization or percutaneous coronary intervention in the Veterans Health Administration from January 01, 2009 to September 30, 2013, excluding patients with chronic dialysis, end-stage renal disease, renal transplant, and missing pre- and postprocedural creatinine measurement. We used 4 AKI definitions in model development and included risk factors from up to 1 year prior to the procedure and at presentation. We developed our prediction models for postprocedural AKI using the least absolute shrinkage and selection operator (LASSO) and internally validated using bootstrapping. We developed models using 115 633 angiogram procedures and externally validated using 27 905 procedures from a New England cohort. Models had cross-validated C-statistics of 0.74 (95% CI: 0.74-0.75) for AKI, 0.83 (95% CI: 0.82-0.84) for AKIN2, 0.74 (95% CI: 0.74-0.75) for contrast-induced nephropathy, and 0.89 (95% CI: 0.87-0.90) for dialysis.

Conclusions

We developed a robust, externally validated clinical prediction model for AKI following cardiac catheterization or percutaneous coronary intervention to automatically identify high-risk patients before and immediately after a procedure in the Veterans Health Administration. Work is ongoing to incorporate these models into routine clinical practice.

SUBMITTER: Brown JR

PROVIDER: S-EPMC4845295 | biostudies-literature | 2015 Dec

REPOSITORIES: biostudies-literature

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Publications

Acute Kidney Injury Risk Prediction in Patients Undergoing Coronary Angiography in a National Veterans Health Administration Cohort With External Validation.

Brown Jeremiah R JR MacKenzie Todd A TA Maddox Thomas M TM Fly James J Tsai Thomas T TT Plomondon Mary E ME Nielson Christopher D CD Siew Edward D ED Resnic Frederic S FS Baker Clifton R CR Rumsfeld John S JS Matheny Michael E ME

Journal of the American Heart Association 20151211 12

<h4>Background</h4>Acute kidney injury (AKI) occurs frequently after cardiac catheterization and percutaneous coronary intervention. Although a clinical risk model exists for percutaneous coronary intervention, no models exist for both procedures, nor do existing models account for risk factors prior to the index admission. We aimed to develop such a model for use in prospective automated surveillance programs in the Veterans Health Administration.<h4>Methods and results</h4>We collected data on ...[more]

PMID: 26656858

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Project description:Acute kidney injury (AKI) occurred in 12.8% of patients undergoing surgery and is associated with increased mortality. Chronic kidney disease (CKD) is a well-known risk for death and cardiovascular disease (CVD). Effects of AKI and CKD on patients undergoing coronary angiography (CAG) remain incompletely defined. The aim of our study was to investigate the relationship between acute and CKD and mortality in patients undergoing CAG. The cohort study included 49,194 patients in the multicenter cohort from January 2007 to December 2018. Cox regression analyses and Fine-Gray proportional subdistribution risk regression analysis are used to examine the association between kidney disease and all-cause and cardiovascular mortality. In the present study, 13,989 (28.4%) patients had kidney disease. During follow-up, 6144 patients died, of which 4508 (73.4%) were due to CVD. AKI without CKD (HR: 1.54, 95% CI: 1.36-1.74), CKD without AKI (HR: 2.02, 95% CI: 1.88-2.17), AKI with CKD (HR: 3.26, 95% CI: 2.90-3.66), and end-stage kidney disease (ESKD; HR: 5.63, 95% CI: 4.40-7.20) were significantly associated with all-cause mortality. Adjusted HR (95% CIs) for cardiovascular mortality was significantly elevated among patients with AKI without CKD (1.78 [1.54-2.06]), CKD without AKI (2.28 [2.09-2.49]), AKI with CKD (3.99 [3.47-4.59]), and ESKD (6.46 [4.93-8.46]). In conclusion, this study shows that acute or CKD is present in up to one-third of patients undergoing CAG and is associated with a substantially increased mortality. These findings highlight the importance of perioperative management of kidney function, especially in patients with CKD.Impact StatementWhat is already known on this subject? Acute kidney injury (AKI) occurred in 12.8% of patients undergoing surgery and is linked to a 22.2% increase in mortality. Chronic kidney disease (CKD) is a well-known risk for death and cardiovascular events. Effects of AKI and CKD on patients undergoing coronary angiography (CAG) remain incompletely defined.What do the results of this study add? This study shows that kidney disease is present in up to one-third of patients undergoing CAG and is associated with a substantially increased mortality. AKI and CKD are independent predicators for mortality in patients undergoing CAG.What are the implications of these findings for clinical practice and/or further research? These findings highlight the importance of perioperative management of kidney function, especially in patients with CKD.

Project description:BackgroundEuroLung Risk scores were established to predict postoperative morbidity and mortality in patients undergoing anatomic lung resections. We aimed to perform an external validation of the EuroLung scores, which were calculated from data of the European Society of Thoracic Surgeons database, in our video-assisted thoracoscopic surgery cohort.MethodsAll available EuroLung scores were calculated for 718 patients scheduled for anatomic video-assisted thoracoscopic surgery resections between 2009 and 2019. Morbidity and mortality according to the definitions of the EuroLung scores were analyzed in a prospectively maintained database.ResultsOverall observed complication rate was 10.45%. Scores showed weak individual correlation (η = 0.155-0.174). The EuroLung1 app score showed the biggest area under the receiver operative characteristic (ROC) curve with 0.660. Binary logistic regression analysis showed that predicted postoperative forced expiratory volume in 1 s was associated with increased complications in both EuroLung1 and parsimonious EuroLung1 scores. Thirty-day mortality was 0.7% (predicted 1.10-1.40%) and was associated with predicted postoperative forced expiratory volume in 1 s for both EuroLung2 and parsimonious EuroLung2 scores. The EuroLung2 (2016) showed the biggest area under the ROC curve with 0.673. Only a very weak eta correlation between predicted and observed mortality was found for both aggregate EuroLung2, EuroLung2 (2016), EuroLung2 (2019), and parsimonious EuroLung2 (2016) (η = 0.025/0.015/0.011/0.009).ConclusionEuroLung scores help to estimate postoperative morbidity. However, even with the highest aggregate EuroLung scores possible only 50% suffer from postoperative morbidity. Although calibration of the scores was acceptable, discrimination between predicted and observed events was poor. Therefore, individual correlation between predicted and observed events is weak. Therefore, EuroLung scores may be best used to compare institutional quality of care to the European Society of Thoracic Surgeons database but should not be used to preclude patients from surgical treatment.

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.

Dataset Information

Acute Kidney Injury Risk Prediction in Patients Undergoing Coronary Angiography in a National Veterans Health Administration Cohort With External Validation.

Background

Methods and results

Conclusions

Publications

Acute Kidney Injury Risk Prediction in Patients Undergoing Coronary Angiography in a National Veterans Health Administration Cohort With External Validation.

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