Robert M. Cronin

Development and evaluation of an ensemble resource linking medications to their indications

Objective To create a computable MEDication Indication resource (MEDI) to support primary and secondary use of electronic medical records (EMRs). Materials and methods We processed four public medication resources, RxNorm, Side Effect Resource (SIDER) 2, MedlinePlus, and Wikipedia, to create MEDI. We applied natural language processing and ontology relationships to extract indications for prescribable, single-ingredient medication concepts and all ingredient concepts as defined by RxNorm. Indications were coded as Unified Medical Language System (UMLS) concepts and International Classification of Diseases, 9th edition (ICD9) codes. A total of 689 extracted indications were randomly selected for manual review for accuracy using dual-physician review. We identified a subset of medication–indication pairs that optimizes recall while maintaining high precision. Results MEDI contains 3112 medications and 63 343 medication–indication pairs. Wikipedia was the largest resource, with 2608 medications and 34 911 pairs. For each resource, estimated precision and recall, respectively, were 94% and 20% for RxNorm, 75% and 33% for MedlinePlus, 67% and 31% for SIDER 2, and 56% and 51% for Wikipedia. The MEDI high-precision subset (MEDI-HPS) includes indications found within either RxNorm or at least two of the three other resources. MEDI-HPS contains 13 304 unique indication pairs regarding 2136 medications. The mean±SD number of indications for each medication in MEDI-HPS is 6.22±6.09. The estimated precision of MEDI-HPS is 92%. Conclusions MEDI is a publicly available, computable resource that links medications with their indications as represented by concepts and billing codes. MEDI may benefit clinical EMR applications and reuse of EMR data for research.

Combining billing codes, clinical notes, and medications from electronic health records provides superior phenotyping performance

OBJECTIVE To evaluate the phenotyping performance of three major electronic health record (EHR) components: International Classification of Disease (ICD) diagnosis codes, primary notes, and specific medications. MATERIALS AND METHODS We conducted the evaluation using de-identified Vanderbilt EHR data. We preselected ten diseases: atrial fibrillation, Alzheimers disease, breast cancer, gout, human immunodeficiency virus infection, multiple sclerosis, Parkinsons disease, rheumatoid arthritis, and types 1 and 2 diabetes mellitus. For each disease, patients were classified into seven categories based on the presence of evidence in diagnosis codes, primary notes, and specific medications. Twenty-five patients per disease category (a total number of 175 patients for each disease, 1750 patients for all ten diseases) were randomly selected for manual chart review. Review results were used to estimate the positive predictive value (PPV), sensitivity, andF-score for each EHR component alone and in combination. RESULTS The PPVs of single components were inconsistent and inadequate for accurately phenotyping (0.06-0.71). Using two or more ICD codes improved the average PPV to 0.84. We observed a more stable and higher accuracy when using at least two components (mean ± standard deviation: 0.91 ± 0.08). Primary notes offered the best sensitivity (0.77). The sensitivity of ICD codes was 0.67. Again, two or more components provided a reasonably high and stable sensitivity (0.59 ± 0.16). Overall, the best performance (Fscore: 0.70 ± 0.12) was achieved by using two or more components. Although the overall performance of using ICD codes (0.67 ± 0.14) was only slightly lower than using two or more components, its PPV (0.71 ± 0.13) is substantially worse (0.91 ± 0.08). CONCLUSION Multiple EHR components provide a more consistent and higher performance than a single one for the selected phenotypes. We suggest considering multiple EHR components for future phenotyping design in order to obtain an ideal result.

Phenome Wide Association Studies demonstrating pleiotropy of genetic variants within FTO with and without adjustment for body mass index

Phenome-wide association studies (PheWAS) have demonstrated utility in validating genetic associations derived from traditional genetic studies as well as identifying novel genetic associations. Here we used an electronic health record (EHR)-based PheWAS to explore pleiotropy of genetic variants in the fat mass and obesity associated gene (FTO), some of which have been previously associated with obesity and type 2 diabetes (T2D). We used a population of 10,487 individuals of European ancestry with genome-wide genotyping from the Electronic Medical Records and Genomics (eMERGE) Network and another population of 13,711 individuals of European ancestry from the BioVU DNA biobank at Vanderbilt genotyped using Illumina HumanExome BeadChip. A meta-analysis of the two study populations replicated the well-described associations between FTO variants and obesity (odds ratio [OR] = 1.25, 95% Confidence Interval = 1.11–1.24, p = 2.10 × 10−9) and FTO variants and T2D (OR = 1.14, 95% CI = 1.08–1.21, p = 2.34 × 10−6). The meta-analysis also demonstrated that FTO variant rs8050136 was significantly associated with sleep apnea (OR = 1.14, 95% CI = 1.07–1.22, p = 3.33 × 10−5); however, the association was attenuated after adjustment for body mass index (BMI). Novel phenotype associations with obesity-associated FTO variants included fibrocystic breast disease (rs9941349, OR = 0.81, 95% CI = 0.74–0.91, p = 5.41 × 10−5) and trends toward associations with non-alcoholic liver disease and gram-positive bacterial infections. FTO variants not associated with obesity demonstrated other potential disease associations including non-inflammatory disorders of the cervix and chronic periodontitis. These results suggest that genetic variants in FTO may have pleiotropic associations, some of which are not mediated by obesity.

Growth of Secure Messaging Through a Patient Portal as a Form of Outpatient Interaction across Clinical Specialties

OBJECTIVE Patient portals are online applications that allow patients to interact with healthcare organizations. Portal adoption is increasing, and secure messaging between patients and healthcare providers is an emerging form of outpatient interaction. Research about portals and messaging has focused on medical specialties. We characterized adoption of secure messaging and the contribution of messaging to outpatient interactions across diverse clinical specialties after broad portal deployment. METHODS This retrospective cohort study at Vanderbilt University Medical Center examined use of patient-initiated secure messages and clinic visits in the three years following full deployment of a patient portal across adult and pediatric specialties. We measured the proportion of outpatient interactions (i.e., messages plus clinic visits) conducted through secure messaging by specialty over time. Generalized estimating equations measured the likelihood of message-based versus clinic outpatient interaction across clinical specialties. RESULTS Over the study period, 2,422,114 clinic visits occurred, and 82,159 unique portal users initiated 948,428 messages to 1,924 recipients. Medicine participated in the most message exchanges (742,454 messages; 78.3% of all messages sent), followed by surgery (84,001; 8.9%) and obstetrics/gynecology (53,424; 5.6%). The proportion of outpatient interaction through messaging increased from 12.9% in 2008 to 33.0% in 2009 and 39.8% in 2010 (p<0.001). Medicine had the highest proportion of outpatient interaction conducted through messaging in 2008 (23.3% of outpatient interactions in medicine). By 2010, this proportion was highest for obstetrics/gynecology (83.4%), dermatology (71.6%), and medicine (56.7%). Growth in likelihood of message-based interaction was greater for anesthesiology, dermatology, obstetrics/gynecology, pediatrics, and psychiatry than for medicine (p<0.001). CONCLUSIONS This study demonstrates rapid adoption of secure messaging across diverse clinical specialties, with messaging interactions exceeding face-to-face clinic visits for some specialties. As patient portal and secure messaging adoption increase beyond medicine and primary care, research is needed to understand the implications for provider workload and patient care.

A genome-wide association study of heparin-induced thrombocytopenia using an electronic medical record

Heparin-induced thrombocytopenia (HIT) is an unpredictable, potentially catastrophic adverse effect of heparin treatment resulting from an immune response to platelet factor 4 (PF4)/heparin complexes. No genome-wide evaluations have been performed to identify potential genetic influences on HIT. Here, we performed a genome-wide association study (GWAS) and candidate gene study using HIT cases and controls identified using electronic medical records (EMRs) coupled to a DNA biobank and attempted to replicate GWAS associations in an independent cohort. We subsequently investigated influences of GWAS-associated single nucleotide polymorphisms (SNPs) on PF4/heparin antibodies in non-heparin treated individuals. In a recessive model, we observed significant SNP associations (odds ratio [OR] 18.52; 95% confidence interval [CI] 6.33-54.23; p=3.18×10(-9)) with HIT near the T-Cell Death-Associated Gene 8 (TDAG8). These SNPs are in linkage disequilibrium with a missense TDAG8 SNP. TDAG8 SNPs trended toward an association with HIT in replication analysis (OR 5.71; 0.47-69.22; p=0.17), and the missense SNP was associated with PF4/heparin antibody levels and positive PF4/heparin antibodies in non-heparin treated patients (OR 3.09; 1.14-8.13; p=0.02). In the candidate gene study, SNPs at HLA-DRA were nominally associated with HIT (OR 0.25; 0.15-0.44; p=2.06×10(-6)). Further study of TDAG8 and HLA-DRA SNPs is warranted to assess their influence on the risk of developing HIT.

Assisted annotation of medical free text using RapTAT.

OBJECTIVE To determine whether assisted annotation using interactive training can reduce the time required to annotate a clinical document corpus without introducing bias. MATERIALS AND METHODS A tool, RapTAT, was designed to assist annotation by iteratively pre-annotating probable phrases of interest within a document, presenting the annotations to a reviewer for correction, and then using the corrected annotations for further machine learning-based training before pre-annotating subsequent documents. Annotators reviewed 404 clinical notes either manually or using RapTAT assistance for concepts related to quality of care during heart failure treatment. Notes were divided into 20 batches of 19-21 documents for iterative annotation and training. RESULTS The number of correct RapTAT pre-annotations increased significantly and annotation time per batch decreased by ~50% over the course of annotation. Annotation rate increased from batch to batch for assisted but not manual reviewers. Pre-annotation F-measure increased from 0.5 to 0.6 to >0.80 (relative to both assisted reviewer and reference annotations) over the first three batches and more slowly thereafter. Overall inter-annotator agreement was significantly higher between RapTAT-assisted reviewers (0.89) than between manual reviewers (0.85). DISCUSSION The tool reduced workload by decreasing the number of annotations needing to be added and helping reviewers to annotate at an increased rate. Agreement between the pre-annotations and reference standard, and agreement between the pre-annotations and assisted annotations, were similar throughout the annotation process, which suggests that pre-annotation did not introduce bias. CONCLUSIONS Pre-annotations generated by a tool capable of interactive training can reduce the time required to create an annotated document corpus by up to 50%.

Evaluating electronic health record data sources and algorithmic approaches to identify hypertensive individuals

Objective: Phenotyping algorithms applied to electronic health record (EHR) data enable investigators to identify large cohorts for clinical and genomic research. Algorithm development is often iterative, depends on fallible investigator intuition, and is time- and labor-intensive. We developed and evaluated 4 types of phenotyping algorithms and categories of EHR information to identify hypertensive individuals and controls and provide a portable module for implementation at other sites. Materials and Methods: We reviewed the EHRs of 631 individuals followed at Vanderbilt for hypertension status. We developed features and phenotyping algorithms of increasing complexity. Input categories included International Classification of Diseases, Ninth Revision (ICD9) codes, medications, vital signs, narrative-text search results, and Unified Medical Language System (UMLS) concepts extracted using natural language processing (NLP). We developed a module and tested portability by replicating 10 of the best-performing algorithms at the Marshfield Clinic. Results: Random forests using billing codes, medications, vitals, and concepts had the best performance with a median area under the receiver operator characteristic curve (AUC) of 0.976. Normalized sums of all 4 categories also performed well (0.959 AUC). The best non-NLP algorithm combined normalized ICD9 codes, medications, and blood pressure readings with a median AUC of 0.948. Blood pressure cutoffs or ICD9 code counts alone had AUCs of 0.854 and 0.908, respectively. Marshfield Clinic results were similar. Conclusion: This work shows that billing codes or blood pressure readings alone yield good hypertension classification performance. However, even simple combinations of input categories improve performance. The most complex algorithms classified hypertension with excellent recall and precision.

Complex Febrile Seizures Followed by Complete Recovery in an Infant with High-Titer 2009 Pandemic Influenza A (H1N1) Virus Infection

ABSTRACT We describe a 2009 H1N1 virus infection with a high viral load in a previously healthy infant who presented with complex febrile seizures and improved on oseltamivir without neurologic sequelae. Febrile seizures may be a complication in young children experiencing infection with high viral loads of 2009 H1N1 influenza virus.

National veterans health administration inpatient risk stratification models for hospital-acquired acute kidney injury

OBJECTIVE Hospital-acquired acute kidney injury (HA-AKI) is a potentially preventable cause of morbidity and mortality. Identifying high-risk patients prior to the onset of kidney injury is a key step towards AKI prevention. MATERIALS AND METHODS A national retrospective cohort of 1,620,898 patient hospitalizations from 116 Veterans Affairs hospitals was assembled from electronic health record (EHR) data collected from 2003 to 2012. HA-AKI was defined at stage 1+, stage 2+, and dialysis. EHR-based predictors were identified through logistic regression, least absolute shrinkage and selection operator (lasso) regression, and random forests, and pair-wise comparisons between each were made. Calibration and discrimination metrics were calculated using 50 bootstrap iterations. In the final models, we report odds ratios, 95% confidence intervals, and importance rankings for predictor variables to evaluate their significance. RESULTS The area under the receiver operating characteristic curve (AUC) for the different model outcomes ranged from 0.746 to 0.758 in stage 1+, 0.714 to 0.720 in stage 2+, and 0.823 to 0.825 in dialysis. Logistic regression had the best AUC in stage 1+ and dialysis. Random forests had the best AUC in stage 2+ but the least favorable calibration plots. Multiple risk factors were significant in our models, including some nonsteroidal anti-inflammatory drugs, blood pressure medications, antibiotics, and intravenous fluids given during the first 48 h of admission. CONCLUSIONS This study demonstrated that, although all the models tested had good discrimination, performance characteristics varied between methods, and the random forests models did not calibrate as well as the lasso or logistic regression models. In addition, novel modifiable risk factors were explored and found to be significant.

A technology-based patient and family engagement consult service for the pediatric hospital setting

Objective The Vanderbilt Childrens Hospital launched an innovative Technology-Based Patient and Family Engagement Consult Service in 2014. This paper describes our initial experience with this service, characterizes health-related needs of families of hospitalized children, and details the technologies recommended to promote engagement and meet needs. Materials and Methods We retrospectively reviewed consult service documentation for patient characteristics, health-related needs, and consultation team recommendations. Needs were categorized using a consumer health needs taxonomy. Recommendations were classified by technology type. Results Twenty-two consultations were conducted with families of patients ranging in age from newborn to 15 years, most with new diagnoses or chronic illnesses. The consultation team identified 99 health-related needs (4.5 per consultation) and made 166 recommendations (7.5 per consultation, 1.7 per need). Need categories included 38 informational needs, 26 medical needs, 23 logistical needs, and 12 social needs. The most common recommendations were websites (50, 30%) and mobile applications (30, 18%). The most frequent recommendations by need category were websites for informational needs (39, 50%), mobile applications for medical needs (15, 40%), patient portals for logistical needs (12, 44%), and disease-specific support groups for social needs (19, 56%). Discussion Families of hospitalized pediatric patients have a variety of health-related needs, many of which could be addressed by technology recommendations from an engagement consult service. Conclusion This service is the first of its kind, offering a potentially generalizable and scalable approach to assessing health-related needs, meeting them with technologies, and promoting patient and family engagement in the inpatient setting.