Development of an automated phenotyping algorithm for hepatorenal syndrome.

OBJECTIVE: Hepatorenal Syndrome (HRS) is a devastating form of acute kidney injury (AKI) in advanced liver disease patients with high morbidity and mortality, but phenotyping algorithms have not yet been developed using large electronic health record (EHR) databases. We evaluated and compared multiple phenotyping methods to achieve an accurate algorithm for HRS identification. MATERIALS AND METHODS: A national retrospective cohort of patients with cirrhosis and AKI admitted to 124 Veterans Affairs hospitals was assembled from electronic health record data collected from 2005 to 2013. AKI was defined by the Kidney Disease: Improving Global Outcomes criteria. Five hundred and four hospitalizations were selected for manual chart review and served as the gold standard. Electronic Health Record based predictors were identified using structured and free text clinical data, subjected through NLP from the clinical Text Analysis Knowledge Extraction System. We explored several dimension reduction techniques for the NLP data, including newer high-throughput phenotyping and word embedding methods, and ascertained their effectiveness in identifying the phenotype without structured predictor variables. With the combined structured and NLP variables, we analyzed five phenotyping algorithms: penalized logistic regression, naïve Bayes, support vector machines, random forest, and gradient boosting. Calibration and discrimination metrics were calculated using 100 bootstrap iterations. In the final model, we report odds ratios and 95% confidence intervals. RESULTS: The area under the receiver operating characteristic curve (AUC) for the different models ranged from 0.73 to 0.93; with penalized logistic regression having the best discriminatory performance. Calibration for logistic regression was modest, but gradient boosting and support vector machines were superior. NLP identified 6985 variables; a priori variable selection performed similarly to dimensionality reduction using high-throughput phenotyping and semantic similarity informed clustering (AUC of 0.81 - 0.82). CONCLUSION: This study demonstrated improved phenotyping of a challenging AKI etiology, HRS, over ICD-9 coding. We also compared performance among multiple approaches to EHR-derived phenotyping, and found similar results between methods. Lastly, we showed that automated NLP dimension reduction is viable for acute illness.

Higher defibrillation threshold in methamphetamine cardiomyopathy patients with implantable cardioverter-defibrillator.

INTRODUCTION: Identification of patients with an increased risk of high defibrillation thresholds (DFTs) is important in planning implantable cardioverter-defibrillator (ICD) procedures. Clinical observations have suggested that patients with methamphetamine cardiomyopathy (MACMP) have significantly elevated defibrillation thresholds. We hypothesized that MACMP patients would have higher DFT thresholds than controls and would require procedural changes during ICD implantation to accommodate higher thresholds. METHODS: We identified consecutive patients with MACMP undergoing ICD implantation at the academic center from 2003 to 2007. We then compared DFTs against age-and sex-matched controls. RESULTS: The MACMP (n = 10) group showed significantly increased DFT thresholds (23.7 ± 6.7 J) compared with age and sex-matched controls (14.5 ± 4.6 J, p < 0.005). Additionally, patients with MACMP had evidence of more severe congestive heart failure, with increased B-type natrieutic protein (BNP) levels (1173 ± 784 vs 260 ± 349, p = 0.02) and decreased left ventricular ejection fraction (LVEF) (17.8 ± 9.4 vs 35.9 ± 15.2, p = 0.02). MACMP patients required high output devices than controls (50% versus 0%, p = 0.03). Differences between groups remained significant despite adjusting for LVEF. CONCLUSIONS: Planning for ICD implantation should take into consideration a history of methamphetamine abuse, mandating DFT testing and empiric consideration of high output devices for such patients.