OARD: Open annotations for rare diseases and their phenotypes based on real-world data.

Diagnosis for rare genetic diseases often relies on phenotype-driven methods, which hinge on the accuracy and completeness of the rare disease phenotypes in the underlying annotation knowledgebase. Existing knowledgebases are often manually curated with additional annotations found in published case reports. Despite their potential, real-world data such as electronic health records (EHRs) have not been fully exploited to derive rare disease annotations. Here, we present open annotation for rare diseases (OARD), a real-world-data-derived resource with annotation for rare-disease-related phenotypes. This resource is derived from the EHRs of two academic health institutions containing more than 10 million individuals spanning wide age ranges and different disease subgroups. By leveraging ontology mapping and advanced natural-language-processing (NLP) methods, OARD automatically and efficiently extracts concepts for both rare diseases and their phenotypic traits from billing codes and lab tests as well as over 100 million clinical narratives. The rare disease prevalence derived by OARD is highly correlated with those annotated in the original rare disease knowledgebase. By performing association analysis, we identified more than 1 million novel disease-phenotype association pairs that were previously missed by human annotation, and >60% were confirmed true associations via manual review of a list of sampled pairs. Compared to the manual curated annotation, OARD is 100% data driven and its pipeline can be shared across different institutions. By supporting privacy-preserving sharing of aggregated summary statistics, such as term frequencies and disease-phenotype associations, it fills an important gap to facilitate data-driven research in the rare disease community.

Seek COVER: using a disease proxy to rapidly develop and validate a personalized risk calculator for COVID-19 outcomes in an international network.

BACKGROUND: We investigated whether we could use influenza data to develop prediction models for COVID-19 to increase the speed at which prediction models can reliably be developed and validated early in a pandemic. We developed COVID-19 Estimated Risk (COVER) scores that quantify a patient's risk of hospital admission with pneumonia (COVER-H), hospitalization with pneumonia requiring intensive services or death (COVER-I), or fatality (COVER-F) in the 30-days following COVID-19 diagnosis using historical data from patients with influenza or flu-like symptoms and tested this in COVID-19 patients. METHODS: We analyzed a federated network of electronic medical records and administrative claims data from 14 data sources and 6 countries containing data collected on or before 4/27/2020. We used a 2-step process to develop 3 scores using historical data from patients with influenza or flu-like symptoms any time prior to 2020. The first step was to create a data-driven model using LASSO regularized logistic regression, the covariates of which were used to develop aggregate covariates for the second step where the COVER scores were developed using a smaller set of features. These 3 COVER scores were then externally validated on patients with 1) influenza or flu-like symptoms and 2) confirmed or suspected COVID-19 diagnosis across 5 databases from South Korea, Spain, and the United States. Outcomes included i) hospitalization with pneumonia, ii) hospitalization with pneumonia requiring intensive services or death, and iii) death in the 30 days after index date. RESULTS: Overall, 44,507 COVID-19 patients were included for model validation. We identified 7 predictors (history of cancer, chronic obstructive pulmonary disease, diabetes, heart disease, hypertension, hyperlipidemia, kidney disease) which combined with age and sex discriminated which patients would experience any of our three outcomes. The models achieved good performance in influenza and COVID-19 cohorts. For COVID-19 the AUC ranges were, COVER-H: 0.69-0.81, COVER-I: 0.73-0.91, and COVER-F: 0.72-0.90. Calibration varied across the validations with some of the COVID-19 validations being less well calibrated than the influenza validations. CONCLUSIONS: This research demonstrated the utility of using a proxy disease to develop a prediction model. The 3 COVER models with 9-predictors that were developed using influenza data perform well for COVID-19 patients for predicting hospitalization, intensive services, and fatality. The scores showed good discriminatory performance which transferred well to the COVID-19 population. There was some miscalibration in the COVID-19 validations, which is potentially due to the difference in symptom severity between the two diseases. A possible solution for this is to recalibrate the models in each location before use.

Implementation of the COVID-19 Vulnerability Index Across an International Network of Health Care Data Sets: Collaborative External Validation Study.

BACKGROUND: SARS-CoV-2 is straining health care systems globally. The burden on hospitals during the pandemic could be reduced by implementing prediction models that can discriminate patients who require hospitalization from those who do not. The COVID-19 vulnerability (C-19) index, a model that predicts which patients will be admitted to hospital for treatment of pneumonia or pneumonia proxies, has been developed and proposed as a valuable tool for decision-making during the pandemic. However, the model is at high risk of bias according to the "prediction model risk of bias assessment" criteria, and it has not been externally validated. OBJECTIVE: The aim of this study was to externally validate the C-19 index across a range of health care settings to determine how well it broadly predicts hospitalization due to pneumonia in COVID-19 cases. METHODS: We followed the Observational Health Data Sciences and Informatics (OHDSI) framework for external validation to assess the reliability of the C-19 index. We evaluated the model on two different target populations, 41,381 patients who presented with SARS-CoV-2 at an outpatient or emergency department visit and 9,429,285 patients who presented with influenza or related symptoms during an outpatient or emergency department visit, to predict their risk of hospitalization with pneumonia during the following 0-30 days. In total, we validated the model across a network of 14 databases spanning the United States, Europe, Australia, and Asia. RESULTS: The internal validation performance of the C-19 index had a C statistic of 0.73, and the calibration was not reported by the authors. When we externally validated it by transporting it to SARS-CoV-2 data, the model obtained C statistics of 0.36, 0.53 (0.473-0.584) and 0.56 (0.488-0.636) on Spanish, US, and South Korean data sets, respectively. The calibration was poor, with the model underestimating risk. When validated on 12 data sets containing influenza patients across the OHDSI network, the C statistics ranged between 0.40 and 0.68. CONCLUSIONS: Our results show that the discriminative performance of the C-19 index model is low for influenza cohorts and even worse among patients with COVID-19 in the United States, Spain, and South Korea. These results suggest that C-19 should not be used to aid decision-making during the COVID-19 pandemic. Our findings highlight the importance of performing external validation across a range of settings, especially when a prediction model is being extrapolated to a different population. In the field of prediction, extensive validation is required to create appropriate trust in a model.

Relative Risk of Cervical Neoplasms Among Copper and Levonorgestrel-Releasing Intrauterine System Users.

OBJECTIVE: To evaluate the relative risk of cervical neoplasms among copper intrauterine device (Cu IUD) and levonorgestrel-releasing intrauterine system (LNG-IUS) users. METHODS: We performed a retrospective cohort analysis of 10,674 patients who received IUDs at Columbia University Medical Center. Our data were transformed to a common data model and are part of the Observational Health Data Sciences and Informatics network. The cohort patients and outcomes were identified by a combination of procedure codes, condition codes, and medication exposures in billing and claims data. We adjusted for confounding with propensity score stratification and propensity score 1:1 matching. RESULTS: Before propensity score adjustment, the Cu IUD cohort included 8,274 patients and the LNG-IUS cohort included 2,400 patients. The median age for both cohorts was 29 years at IUD placement. More than 95% of the LNG-IUS cohort used a device with 52 mg LNG. Before propensity score adjustment, we identified 114 cervical neoplasm outcomes. Seventy-seven (0.9%) cervical neoplasms were in the Cu IUD cohort and 37 (1.5%) were in the LNG-IUS cohort. The propensity score matching analysis identified 7,114 Cu IUD and 2,174 LNG-IUS users, with covariate balance achieved over 16,827 covariates. The diagnosis of high-grade cervical neoplasia was 0.7% in the Cu IUD cohort and 1.8% in the LNG-IUS cohort (2.4 [95% CI 1.5-4.0] cases/1,000 person-years and 5.2 [95% CI 3.7-7.1] cases/1,000 person-years, respectively). The relative risk of high-grade cervical neoplasms among Cu IUD users was 0.38 (95% CI 0.16-0.78, P<.02) compared with LNG-IUS users. By inspection, the Kaplan-Meier curves for each cohort diverged over time. CONCLUSION: Copper IUD users have a lower risk of high-grade cervical neoplasms compared with LNG-IUS users. The relative risk of cervical neoplasms of LNG-IUS users compared with the general population is unknown.

Characterizing database granularity using SNOMED-CT hierarchy.

Multi-center observational studies require recognition and reconciliation of differences in patient representations arising from underlying populations, disparate coding practices and specifics of data capture. This leads to different granularity or detail of concepts representing the clinical facts. For researchers studying certain populations of interest, it is important to ensure that concepts at the right level are used for the definition of these populations. We studied the granularity of concepts within 22 data sources in the OHDSI network and calculated a composite granularity score for each dataset. Three alternative SNOMED-based approaches for such score showed consistency in classifying data sources into three levels of granularity (low, moderate and high), which correlated with the provenance of data and country of origin. However, they performed unsatisfactorily in ordering data sources within these groups and showed inconsistency for small data sources. Further studies on examining approaches to data source granularity are needed.

Relation of QRS shortening to cardiac output during temporary resynchronization therapy after cardiac surgery.

Cardiac resynchronization therapy (CRT) can improve cardiac function in heart failure without increasing myocardial oxygen consumption. However, CRT optimization based on hemodynamics or echocardiography is difficult. QRS duration (QRSd) is a possible alternative optimization parameter. Accordingly, we assessed QRSd optimization of CRT during cardiac surgery. We hypothesized that QRSd shortening during changes in interventricular pacing delay (VVD) would increase cardiac output (CO). Seven patients undergoing coronary artery bypass, aortic or mitral valve surgery with left ventricular (LV) ejection fraction < or =40%, and QRSd > or =100 msec were studied. CRT was implemented at epicardial pacing sites in the left and right ventricle and right atrium during VVD variation after cardiopulmonary bypass. QRSd was correlated with CO from an electromagnetic aortic flow probe. Both positive and negative correlations were observed. Correlation coefficients ranged from 0.70 to -0.74 during VVD testing. Clear minima in QRSd were observed in four patients and were within 40 msec of maximum CO in two. We conclude that QRSd is not useful for routine optimization of VVD after cardiac surgery but may be useful in selected patients. Decreasing QRSd is associated with decreasing CO in some patients, suggesting that CRT can affect determinants of QRSd and ventricular function independently.