Dataset Information

Electronic Algorithm Is Superior to Hospital Discharge Codes for Diagnoses of Hypertensive Disorders of Pregnancy in Historical Cohorts.

ABSTRACT: OBJECTIVES:To develop and validate criteria for the retrospective diagnoses of hypertensive disorders of pregnancy that would be amenable to the development of an electronic algorithm, and to compare the accuracy of diagnoses based on both the algorithm and diagnostic codes with the gold standard, of physician-made diagnoses based on a detailed review of medical records using accepted clinical criteria. PATIENTS AND METHODS:An algorithm for hypertensive disorders of pregnancy was developed by first defining a set of criteria for retrospective diagnoses, which included relevant clinical variables and diagnosis of hypertension that required blood pressure elevations in greater than 50% of readings ("the 50% rule"). The algorithm was validated using the Rochester Epidemiology Project (Rochester, Minnesota). A stratified random sample of pregnancies and deliveries between January 1, 1976, and December 31, 1982, with the algorithm-based diagnoses was generated for review and physician-made diagnoses (normotensive, gestational hypertension, and preeclampsia), which served as the gold standard; the targeted cohort size for analysis was 25 per diagnosis category according to the gold standard. Agreements between (1) algorithm-based diagnoses and (2) diagnostic codes and the gold standard were analyzed. RESULTS:Sensitivities of the algorithm for 25 normotensive pregnancies, 25 with gestational hypertension, and 25 with preeclampsia were 100%, 88%, and 100%, respectively, and specificities were 94%, 100%, and 100%, respectively. Diagnostic code sensitivities were 96% for normotensive pregnancies, 32% for gestational hypertension, and 96% for preeclampsia, and specificities were 78%, 96%, and 88%, respectively. CONCLUSION:The electronic diagnostic algorithm was highly sensitive and specific in identifying and classifying hypertensive disorders of pregnancy and was superior to diagnostic codes.

SUBMITTER: Milic NM

PROVIDER: S-EPMC6353636 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature

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Electronic Algorithm Is Superior to Hospital Discharge Codes for Diagnoses of Hypertensive Disorders of Pregnancy in Historical Cohorts.

Milic Natasa M NM Codsi Elisabeth E Butler Tobah Yvonne S YS White Wendy M WM Kattah Andrea G AG Weissgerber Tracey L TL Saiki Mie M Parashuram Santosh S Vaughan Lisa E LE Weaver Amy L AL Savic Marko M Mielke Michelle M MM Garovic Vesna D VD

Mayo Clinic proceedings 20181201 12

<h4>Objectives</h4>To develop and validate criteria for the retrospective diagnoses of hypertensive disorders of pregnancy that would be amenable to the development of an electronic algorithm, and to compare the accuracy of diagnoses based on both the algorithm and diagnostic codes with the gold standard, of physician-made diagnoses based on a detailed review of medical records using accepted clinical criteria.<h4>Patients and methods</h4>An algorithm for hypertensive disorders of pregnancy was ...[more]

PMID: 30522591

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Project description:ObjectiveTo 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 methodsWe conducted the evaluation using de-identified Vanderbilt EHR data. We preselected ten diseases: atrial fibrillation, Alzheimer's disease, breast cancer, gout, human immunodeficiency virus infection, multiple sclerosis, Parkinson's 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.ResultsThe 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).ConclusionMultiple 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.

Project description:BackgroundDischarge diagnoses are used to track national trends and patterns of maternal morbidity. There are few data regarding the validity of the International Classification of Diseases (ICD) codes used for this purpose. The goal of our study was to try to better understand the validity of administrative data being used to monitor and assess trends in morbidity.MethodsHospital stay billing records were queried to identify all delivery admissions at the Massachusetts General Hospital for the time period 2001 to 2011 and the University of Michigan Health System for the time period 2005 to 2011. From this, we identified patients with ICD-9-Clinical Modification (CM) diagnosis and procedure codes indicative of severe maternal morbidity. Each patient was classified with 1 of 18 different medical/obstetric categories (conditions or procedures) based on the ICD-9-CM code that was recorded. Within each category, 20 patients from each institution were selected at random, and the corresponding medical charts were reviewed to determine whether the ICD-9-CM code was assigned correctly. The percentage of correct codes for each of 18 preselected clinical categories was calculated yielding a positive predictive value (PPV) and 99% confidence interval (CI).ResultsThe overall number of correctly assigned ICD-9-CM codes, or PPV, was 218 of 255 (86%; CI, 79%-90%) and 154 of 188 (82%; CI, 74%-88%) at Massachusetts General Hospital and University of Michigan Health System, respectively (combined PPV, 372/443 [84%; CI, 79-88%]). Codes within 4 categories (Hysterectomy, Pulmonary edema, Disorders of fluid, electrolyte and acid-base balance, and Sepsis) had a 99% lower confidence limit ≥75%. Codes within 8 additional categories demonstrated a 99% lower confidence limit between 74% and 50% (Acute respiratory distress, Ventilation, Other complications of obstetric surgery, Disorders of coagulation, Cardiomonitoring, Acute renal failure, Thromboembolism, and Shock). Codes within 6 clinical categories demonstrated a 99% lower confidence limit <50% (Puerperal cerebrovascular disorders, Conversion of cardiac rhythm, Acute heart failure [includes arrest and fibrillation], Eclampsia, Neurotrauma, and Severe anesthesia complications).ConclusionsICD-9-CM codes capturing severe maternal morbidity during delivery hospitalization demonstrate a range of PPVs. The PPV was high when objective supportive evidence, such as laboratory values or procedure documentation supported the ICD-9-CM code. The PPV was low when greater judgment, interpretation, and synthesis of the clinical data (signs and symptoms) was required to support a code, such as with the category Severe anesthesia complications. As a result, these codes should be used for administrative research with more caution compared with codes primarily defined by objective data.

Project description:BackgroundElectronic health records are invaluable for medical research, but much information is stored as free text rather than in a coded form. For example, in the UK General Practice Research Database (GPRD), causes of death and test results are sometimes recorded only in free text. Free text can be difficult to use for research if it requires time-consuming manual review. Our aim was to develop an automated method for extracting coded information from free text in electronic patient records.MethodsWe reviewed the electronic patient records in GPRD of a random sample of 3310 patients who died in 2001, to identify the cause of death. We developed a computer program called the Freetext Matching Algorithm (FMA) to map diagnoses in text to the Read Clinical Terminology. The program uses lookup tables of synonyms and phrase patterns to identify diagnoses, dates and selected test results. We tested it on two random samples of free text from GPRD (1000 texts associated with death in 2001, and 1000 general texts from cases and controls in a coronary artery disease study), comparing the output to the U.S. National Library of Medicine's MetaMap program and the gold standard of manual review.ResultsAmong 3310 patients registered in the GPRD who died in 2001, the cause of death was recorded in coded form in 38.1% of patients, and in the free text alone in 19.4%. On the 1000 texts associated with death, FMA coded 683 of the 735 positive diagnoses, with precision (positive predictive value) 98.4% (95% confidence interval (CI) 97.2, 99.2) and recall (sensitivity) 92.9% (95% CI 90.8, 94.7). On the general sample, FMA detected 346 of the 447 positive diagnoses, with precision 91.5% (95% CI 88.3, 94.1) and recall 77.4% (95% CI 73.2, 81.2), which was similar to MetaMap.ConclusionsWe have developed an algorithm to extract coded information from free text in GP records with good precision. It may facilitate research using free text in electronic patient records, particularly for extracting the cause of death.

Project description:Several lung diseases are increasingly recognized as comorbidities with HIV; however, few data exist related to the spectrum of respiratory symptoms, diagnostic testing, and diagnoses in the current HIV era. The objective of the study is to determine the impact of HIV on prevalence and incidence of respiratory disease in the current era of effective antiretroviral treatment.A pulmonary-specific questionnaire was administered yearly for three years to participants in the Multicenter AIDS Cohort Study (MACS) and Women's Interagency HIV Study (WIHS). Adjusted prevalence ratios for respiratory symptoms, testing, or diagnoses and adjusted incidence rate ratios for diagnoses in HIV-infected compared to HIV-uninfected participants were determined. Risk factors for outcomes in HIV-infected individuals were modeled.Baseline pulmonary questionnaires were completed by 907 HIV-infected and 989 HIV-uninfected participants in the MACS cohort and by 1405 HIV-infected and 571 HIV-uninfected participants in the WIHS cohort. In MACS, dyspnea, cough, wheezing, sleep apnea, and incident chronic obstructive pulmonary disease (COPD) were more common in HIV-infected participants. In WIHS, wheezing and sleep apnea were more common in HIV-infected participants. Smoking (MACS and WIHS) and greater body mass index (WIHS) were associated with more respiratory symptoms and diagnoses. While sputum studies, bronchoscopies, and chest computed tomography scans were more likely to be performed in HIV-infected participants, pulmonary function tests were no more common in HIV-infected individuals. Respiratory symptoms in HIV-infected individuals were associated with history of pneumonia, cardiovascular disease, or use of HAART. A diagnosis of asthma or COPD was associated with previous pneumonia.In these two cohorts, HIV is an independent risk factor for several respiratory symptoms and pulmonary diseases including COPD and sleep apnea. Despite a higher prevalence of chronic respiratory symptoms, testing for non-infectious respiratory diseases may be underutilized in the HIV-infected population.

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Electronic Algorithm Is Superior to Hospital Discharge Codes for Diagnoses of Hypertensive Disorders of Pregnancy in Historical Cohorts.

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Electronic Algorithm Is Superior to Hospital Discharge Codes for Diagnoses of Hypertensive Disorders of Pregnancy in Historical Cohorts.

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