Dataset Information

Practice-based evidence: profiling the safety of cilostazol by text-mining of clinical notes.

ABSTRACT:

Background

Peripheral arterial disease (PAD) is a growing problem with few available therapies. Cilostazol is the only FDA-approved medication with a class I indication for intermittent claudication, but carries a black box warning due to concerns for increased cardiovascular mortality. To assess the validity of this black box warning, we employed a novel text-analytics pipeline to quantify the adverse events associated with Cilostazol use in a clinical setting, including patients with congestive heart failure (CHF).

Methods and results

We analyzed the electronic medical records of 1.8 million subjects from the Stanford clinical data warehouse spanning 18 years using a novel text-mining/statistical analytics pipeline. We identified 232 PAD patients taking Cilostazol and created a control group of 1,160 PAD patients not taking this drug using 1:5 propensity-score matching. Over a mean follow up of 4.2 years, we observed no association between Cilostazol use and any major adverse cardiovascular event including stroke (OR = 1.13, CI [0.82, 1.55]), myocardial infarction (OR = 1.00, CI [0.71, 1.39]), or death (OR = 0.86, CI [0.63, 1.18]). Cilostazol was not associated with an increase in any arrhythmic complication. We also identified a subset of CHF patients who were prescribed Cilostazol despite its black box warning, and found that it did not increase mortality in this high-risk group of patients.

Conclusions

This proof of principle study shows the potential of text-analytics to mine clinical data warehouses to uncover 'natural experiments' such as the use of Cilostazol in CHF patients. We envision this method will have broad applications for examining difficult to test clinical hypotheses and to aid in post-marketing drug safety surveillance. Moreover, our observations argue for a prospective study to examine the validity of a drug safety warning that may be unnecessarily limiting the use of an efficacious therapy.

SUBMITTER: Leeper NJ

PROVIDER: S-EPMC3662653 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Publications

Practice-based evidence: profiling the safety of cilostazol by text-mining of clinical notes.

Leeper Nicholas J NJ Bauer-Mehren Anna A Iyer Srinivasan V SV Lependu Paea P Olson Cliff C Shah Nigam H NH

PloS one 20130523 5

<h4>Background</h4>Peripheral arterial disease (PAD) is a growing problem with few available therapies. Cilostazol is the only FDA-approved medication with a class I indication for intermittent claudication, but carries a black box warning due to concerns for increased cardiovascular mortality. To assess the validity of this black box warning, we employed a novel text-analytics pipeline to quantify the adverse events associated with Cilostazol use in a clinical setting, including patients with c ...[more]

PMID: 23717437

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Project description:BackgroundPrior literature suggests that psychosocial factors adversely impact health and health care utilization outcomes. However, psychosocial factors are typically not captured by the structured data in electronic medical records (EMRs) but are rather recorded as free text in different types of clinical notes.ObjectiveWe here propose a text-mining approach to analyze EMRs to identify older adults with key psychosocial factors that predict adverse health care utilization outcomes, measured by 30-day readmission. The psychological factors were appended to the LACE (Length of stay, Acuity of the admission, Comorbidity of the patient, and Emergency department use) Index for Readmission to improve the prediction of readmission risk.MethodsWe performed a retrospective analysis using EMR notes of 43,216 hospitalization encounters in a hospital from January 1, 2017 to February 28, 2019. The mean age of the cohort was 67.51 years (SD 15.87), the mean length of stay was 5.57 days (SD 10.41), and the mean intensive care unit stay was 5% (SD 22%). We employed text-mining techniques to extract psychosocial topics that are representative of these patients and tested the utility of these topics in predicting 30-day hospital readmission beyond the predictive value of the LACE Index for Readmission.ResultsThe added text-mined factors improved the area under the receiver operating characteristic curve of the readmission prediction by 8.46% for geriatric patients, 6.99% for the general hospital population, and 6.64% for frequent admitters. Medical social workers and case managers captured more of the psychosocial text topics than physicians.ConclusionsThe results of this study demonstrate the feasibility of extracting psychosocial factors from EMR clinical notes and the value of these notes in improving readmission risk prediction. Psychosocial profiles of patients can be curated and quantified from text mining clinical notes and these profiles can be successfully applied to artificial intelligence models to improve readmission risk prediction.

Project description:BackgroundA cancer diagnosis is a source of psychological and emotional stress, which are often maintained for sustained periods of time that may lead to depressive disorders. Depression is one of the most common psychological conditions in patients with cancer. According to the Global Cancer Observatory, breast and colorectal cancers are the most prevalent cancers in both sexes and across all age groups in Spain.ObjectiveThis study aimed to compare the prevalence of depression in patients before and after the diagnosis of breast or colorectal cancer, as well as to assess the usefulness of the analysis of free-text clinical notes in 2 languages (Spanish or Catalan) for detecting depression in combination with encoded diagnoses.MethodsWe carried out an analysis of the electronic health records from a general hospital by considering the different sources of clinical information related to depression in patients with breast and colorectal cancer. This analysis included ICD-9-CM (International Classification of Diseases, Ninth Revision, Clinical Modification) diagnosis codes and unstructured information extracted by mining free-text clinical notes via natural language processing tools based on Systematized Nomenclature of Medicine Clinical Terms that mentions symptoms and drugs used for the treatment of depression.ResultsWe observed that the percentage of patients diagnosed with depressive disorders significantly increased after cancer diagnosis in the 2 types of cancer considered-breast and colorectal cancers. We managed to identify a higher number of patients with depression by mining free-text clinical notes than the group selected exclusively on ICD-9-CM codes, increasing the number of patients diagnosed with depression by 34.8% (441/1269). In addition, the number of patients with depression who received chemotherapy was higher than those who did not receive this treatment, with significant differences (P<.001).ConclusionsThis study provides new clinical evidence of the depression-cancer comorbidity and supports the use of natural language processing for extracting and analyzing free-text clinical notes from electronic health records, contributing to the identification of additional clinical data that complements those provided by coded data to improve the management of these patients.

Project description:IntroductionHospital falls are a continuing clinical concern, with over one million falls occurring each year in the United States. Annually, hospital-acquired falls result in an estimated $34 billion in direct medical costs. Falls are considered largely preventable and, as a result, the Centers for Medicare and Medicaid Services have announced that fall-related injuries are no longer a reimbursable hospital cost. While policies and practices have been implemented to reduce falls, little sustained reduction has been achieved. Little empirical evidence supports the validity of published fall risk factors. While chart abstraction has been used to operationalize risk factors, few studies have examined registered nurses' (RNs') narrative notes as a source of actionable data. Therefore, the purpose of our study was to explore whether there is meaningful fall risk and prevention information in RNs' electronic narrative notes.MethodsThis study utilized a natural language processing design. Data for this study were extracted from the publicly available Medical Information Mart for Intensive Care (MIMIC-III) database. The date comprises deidentified EHR data associated with patients who stayed in critical care units between 2001 and 2012. Text mining procedures were performed on RN's narrative notes following the traditional steps of knowledge discovery.ResultsThe corpus of data extracted from MIMIC-III database was comprised of 1,046,053 RNs' notes from 36,583 unique patients. We identified 3,972 notes (0.4 percent) representing 1,789 (5 percent) patients with explicit documentation related to fall risk/prevention. Around 10 percent of the notes (103,685) from 23,025 patients mentioned intrinsic (patient-related) factors that have been theoretically associated with risk of falling. An additional 1,322 notes (0.1 percent) from 692 patients (2 percent) mentioned extrinsic risk factors, related to organizational design and environment. Moreover, 7672 notes (0.7 percent) from 2,571 patients (7 percent) included information on interventions that could theoretically impact patient falls.ConclusionsThis exploratory study using a NLP approach revealed that meaningful information related to fall risk and prevention may be found in RNs' narrative notes. In particular, RNs' notes can contain information about clinical as well as environmental and organizational factors that could affect fall risk but are not explicitly recorded by the provider as a fall risk factors. In our study, potential fall risk factors were documented for more than half of the sample. Further research is needed to determine the predictive value of these factors.Implications for policy or practiceThis study highlights a potentially rich but understudied source of actionable fall risk data. Furthermore, the application of novel methods to identify quality and safety measures in RNs' notes can facilitate inclusion of RNs' voices in patient outcomes and health services research.

Dataset Information

Practice-based evidence: profiling the safety of cilostazol by text-mining of clinical notes.

Background

Methods and results

Conclusions

Publications

Practice-based evidence: profiling the safety of cilostazol by text-mining of clinical notes.

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