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Identification of people with acquired hemophilia in a large electronic health record database.

ABSTRACT: Electronic health records (EHRs) can provide insights into diagnoses, treatment patterns, and clinical outcomes. Acquired hemophilia (AH) is an ultrarare bleeding disorder characterized by factor VIII inhibiting autoantibodies.To identify patients with AH using an EHR database.Records were accessed from a large EHR database (Humedica) between January 1, 2007 and July 31, 2013. Broad selection criteria were applied using the International Classification of Diseases, Ninth Revision, clinical modification (ICD-9-CM) code for intrinsic circulating anticoagulants (286.5 and all subcodes) and confirmation of records 6 months before and 12 months after the first diagnosis. Additional selection criteria included mention of "bleeding" within physician notes identified via natural language processing output and a normal prothrombin time and prolonged activated partial thromboplastin time.Of 6,348 patients with a diagnosis code of 286.5 or any subcodes, 16 males and 15 females met the selection criteria. The most common bleeding locations reported was gastrointestinal (23%), vaginal (16%), and endocrine (13%). A wide range of comorbidities was reported. Natural language processing identified chart note mention of "hemophilia" in 3 patients (10%), "bruise" in 15 patients (48%), and "pain" in all 31 patients. No patients received a prescription for approved/recommended AH treatments. Four patient cases were reviewed to validate whether the identified cohort had AH; each patient had bleeding symptoms and a normal prothrombin time and prolonged activated partial thromboplastin time, although none received hemostatic treatments.In ultrarare disorders, ICD-9-CM coding alone may be insufficient to identify patient cohorts; multimodal analysis combined with in-depth reviews of physician notes may be more effective.

SUBMITTER: Wang M

PROVIDER: S-EPMC5529096 | biostudies-other | 2017

REPOSITORIES: biostudies-other

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Project description:BackgroundElectronic health record (EHR) databases provide an opportunity to facilitate characterization and trends in patients with COVID-19.MethodsPatients with COVID-19 were identified based on an ICD-10 diagnosis code for COVID-19 (U07.1) and/or a positive SARS-CoV-2 viral lab result from January 2020 to November 2020. Patients were characterized in terms of demographics, healthcare utilization, clinical comorbidities, therapies, laboratory results, and procedures/care received, including critical care, intubation/ventilation, and occurrence of death were described, overall and by month.ResultsThere were 393,773 patients with COVID-19 and 56,996 with a COVID-19 associated hospitalization. A greater percentage of patients hospitalized with COVID-19 relative to all COVID-19 cases were older, male, African American, and lived in the Northeast and South. The most common comorbidities before admission/infection date were hypertension (40.8%), diabetes (29.5%), and obesity (23.8%), and the most common diagnoses during hospitalization were pneumonia (59.6%), acute respiratory failure (44.8%), and dyspnea (28.0%). A total of 85.7% of patients hospitalized with COVID-19 had CRP values > 10 mg/L, 75.5% had fibrinogen values > 400 mg/dL, and 76.8% had D-dimer values > 250 ng/mL. Median values for platelets, CRP, lactate dehydrogenase, D-dimer, and fibrinogen tended to decrease from January-March to November. The use of chloroquine/hydroxychloroquine during hospitalization peaked by March (71.2%) and was used rarely by May (5.1%) and less than 1% afterwards, while the use of remdesivir had increased by May (10.0%) followed by dexamethasone by June (27.7%). All-cause mortality was 3.2% overall and 15.0% among those hospitalized; 21.0% received critical care and 16.0% received intubation/ventilation/ECMO.ConclusionsThis study characterizes US patients with COVID-19 and their management during hospitalization over the first eleven months of this disease pandemic.

Project description:BackgroundSarcopenia, cachexia and frailty have overlapping features and clinical consequences, but often go unrecognized. The objective was to detect patients described by clinicians as having sarcopenia, cachexia or frailty within electronic health records (EHR) and compare clinical variables between cases and matched controls.MethodsWe conducted a case-control study using retrospective data from the Indiana Network for Patient Care multi-health system database from 2016 to 2017. The computable phenotype combined ICD codes for sarcopenia, cachexia and frailty, with clinical note text terms for sarcopenia, cachexia and frailty detected using natural language processing. Cases with these codes or text terms were matched to controls without these codes or text terms matched on birth year, sex and race. Two physicians reviewed EHR for all cases and a subset of controls. Comorbidity codes, laboratory values, and other coded clinical variables were compared between groups using Wilcoxon matched-pair sign-rank test for continuous variables and conditional logistic regression for binary variables.ResultsCohorts of 9594 cases and 9594 matched controls were generated. Cases were 59% female, 69% white, and a median (1st, 3rd quartiles) age 74.9 (62.2, 84.8) years. Most cases were detected by text terms without ICD codes n = 8285 (86.4%). All cases detected by ICD codes (total n = 1309) also had supportive text terms. Overall 1496 (15.6%) had concurrent terms or codes for two or more of the three conditions (sarcopenia, cachexia or frailty). Of text term occurrence, 97% were used positively for sarcopenia, 90% for cachexia, and 95% for frailty. The remaining occurrences were negative uses of the terms or applied to someone other than the patient. Cases had lower body mass index, albumin and prealbumin, and significantly higher odds ratios for diabetes, hypertension, cardiovascular and peripheral vascular diseases, chronic kidney disease, liver disease, malignancy, osteoporosis and fractures (all p < 0.05). Cases were more likely to be prescribed appetite stimulants and caloric supplements.ConclusionsPatients detected with a computable phenotype for sarcopenia, cachexia and frailty differed from controls in several important clinical variables. Potential uses include detection among clinical cohorts for targeting recruitment for research and interventions.

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Identification of people with acquired hemophilia in a large electronic health record database.

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