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Utility of Biomarkers to Improve Prediction of Readmission or Mortality After Cardiac Surgery.

ABSTRACT: BACKGROUND:Hospital readmission within 30 days is associated with higher risks of complications, death, and increased costs. Accurate statistical models to stratify the risk of 30-day readmission or death after cardiac surgery could help clinical teams focus care on those patients at highest risk. We hypothesized biomarkers could improve prediction for readmission or mortality. METHODS:Levels of ST2, galectin-3, N-terminal pro-brain natriuretic peptide, cystatin C, interleukin-6, and interleukin-10 were measured in samples from 1,046 patients discharged after isolated coronary artery bypass graft surgery from eight medical centers, with external validation in 1,194 patients from five medical centers. Thirty-day readmission or mortality were ascertained using Medicare, state all-payer claims, and the National Death Index. We tested and externally validated the clinical models and the biomarker panels using area under the receiver-operating characteristics (AUROC) statistics. RESULTS:There were 112 patients (10.7%) who were readmitted or died within 30 days after coronary artery bypass graft surgery. The Society of Thoracic Surgeons augmented clinical model resulted in an AUROC of 0.66 (95% confidence interval: 0.61 to 0.71). The biomarker panel with The Society of Thoracic Surgeons augmented clinical model resulted in an AUROC of 0.74 (bootstrapped 95% confidence interval: 0.69 to 0.79, p < 0.0001). External validation of the model showed limited improvement with the addition of a biomarker panel, with an AUROC of 0.51 (95% confidence interval: 0.45 to 0.56). CONCLUSIONS:Although biomarkers significantly improved prediction of 30-day readmission or mortality in our derivation cohort, the external validation of the biomarker panel was poor. Biomarkers perform poorly, much like other efforts to improve prediction of readmission, suggesting there are many other factors yet to be explored to improve prediction of readmission.

SUBMITTER: Brown JR

PROVIDER: S-EPMC6668355 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature

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Utility of Biomarkers to Improve Prediction of Readmission or Mortality After Cardiac Surgery.

Brown Jeremiah R JR Jacobs Jeffrey P JP Alam Shama S SS Thiessen-Philbrook Heather H Everett Allen A Likosky Donald S DS Lobdell Kevin K Wyler von Ballmoos Moritz C MC Parker Devin M DM Garg Amit X AX Mackenzie Todd T Jacobs Marshall L ML Parikh Chirag R CR

The Annals of thoracic surgery 20180804 5

<h4>Background</h4>Hospital readmission within 30 days is associated with higher risks of complications, death, and increased costs. Accurate statistical models to stratify the risk of 30-day readmission or death after cardiac surgery could help clinical teams focus care on those patients at highest risk. We hypothesized biomarkers could improve prediction for readmission or mortality.<h4>Methods</h4>Levels of ST2, galectin-3, N-terminal pro-brain natriuretic peptide, cystatin C, interleukin-6, ...[more]

PMID: 30086283

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Project description:Background: The recent integration of genomic data with electronic health records has enabled large scale genomic studies on a variety of perioperative complications, yet genome-wide association studies on acute kidney injury have been limited in size or confounded by composite outcomes. Genome-wide association studies can be leveraged to create a polygenic risk score which can then be integrated with traditional clinical risk factors to better predict postoperative complications, like acute kidney injury. Methods: Using integrated genetic data from two academic biorepositories, we conduct a genome-wide association study on cardiac surgery-associated acute kidney injury. Next, we develop a polygenic risk score and test the predictive utility within regressions controlling for age, gender, principal components, preoperative serum creatinine, and a range of patient, clinical, and procedural risk factors. Finally, we estimate additive variant heritability using genetic mixed models. Results: Among 1,014 qualifying procedures at Vanderbilt University Medical Center and 478 at Michigan Medicine, 348 (34.3%) and 121 (25.3%) developed AKI, respectively. No variants exceeded genome-wide significance (p < 5 × 10-8) threshold, however, six previously unreported variants exceeded the suggestive threshold (p < 1 × 10-6). Notable variants detected include: 1) rs74637005, located in the exonic region of NFU1 and 2) rs17438465, located between EVX1 and HIBADH. We failed to replicate variants from prior unbiased studies of post-surgical acute kidney injury. Polygenic risk was not significantly associated with post-surgical acute kidney injury in any of the models, however, case duration (aOR = 1.002, 95% CI 1.000-1.003, p = 0.013), diabetes mellitus (aOR = 2.025, 95% CI 1.320-3.103, p = 0.001), and valvular disease (aOR = 0.558, 95% CI 0.372-0.835, p = 0.005) were significant in the full model. Conclusion: Polygenic risk score was not significantly associated with cardiac surgery-associated acute kidney injury and acute kidney injury may have a low heritability in this population. These results suggest that susceptibility is only minimally influenced by baseline genetic predisposition and that clinical risk factors, some of which are modifiable, may play a more influential role in predicting this complication. The overall impact of genetics in overall risk for cardiac surgery-associated acute kidney injury may be small compared to clinical risk factors.

Project description:Background Commonly used cardiovascular risk calculators do not provide risk estimation of stroke, a major postoperative complication with high morbidity and mortality. We developed and validated an accurate cardiovascular risk prediction tool for stroke, major cardiac complications (myocardial infarction or cardiac arrest), and mortality after non-cardiac surgery. Methods and Results This retrospective cohort study included 1 165 750 surgical patients over a 4-year period (2007-2010) from the American College of Surgeons National Surgical Quality Improvement Program Database. A predictive model was developed with the following preoperative conditions: age, history of coronary artery disease, history of stroke, emergency surgery, preoperative serum sodium (≤130 mEq/L, >146 mEq/L), creatinine >1.8 mg/dL, hematocrit ≤27%, American Society of Anesthesiologists physical status class, and type of surgery. The model was trained using American College of Surgeons National Surgical Quality Improvement Program data from 2007 to 2009 (n=809 880) and tested using data from 2010 (n=355 870). Risk models were developed using multivariate logistic regression. The outcomes were postoperative 30-day stroke, major cardiovascular events (myocardial infarction, cardiac arrest, or stroke), and 30-day mortality. Major cardiac complications occurred in 0.66% (n=5332) of patients (myocardial infarction, 0.28%; cardiac arrest, 0.41%), postoperative stroke in 0.25% (n=2005); 30-day mortality was 1.66% (n=13 484). The risk prediction model had high predictive accuracy with area under the receiver operating characteristic curve for stroke (training cohort=0.869, validation cohort=0.876), major cardiovascular events (training cohort=0.871, validation cohort=0.868), and 30-day mortality (training cohort=0.922, validation cohort=0.925). Surgery types, history of stroke, and coronary artery disease are significant risk factors for stroke and major cardiac complications. Conclusions Postoperative stroke, major cardiac complications, and 30-day mortality can be predicted with high accuracy using this web-based predictive model.

Dataset Information

Utility of Biomarkers to Improve Prediction of Readmission or Mortality After Cardiac Surgery.

Publications

Utility of Biomarkers to Improve Prediction of Readmission or Mortality After Cardiac Surgery.

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