JMIR Bioinform Biotech

Background:Venous thromboembolism (VTE) is a preventable, common vascular disease that has been estimated to affect up to 900,000 people per year. It has been associated with risk factors such as recent surgery, cancer, and hospitalization. VTE surveillance for patient management and safety can be improved via natural language processing (NLP). NLP tools have the ability to access electronic medical records, identify patients that meet the VTE case definition, and subsequently enter the relevant information into a database for hospital review.Objective:We aimed to evaluate the performance of a VTE identification model of IDEAL-X (Information and Data Extraction Using Adaptive Learning; Emory University)\u2014an NLP tool\u2014in automatically classifying cases of VTE by \u201creading\u201d unstructured text from diagnostic imaging records collected from 2012 to 2014.Methods:After accessing imaging records from pilot surveillance systems for VTE from Duke University and the University of Oklahoma Health Sciences Center (OUHSC), we used a VTE identification model of IDEAL-X to classify cases of VTE that had previously been manually classified. Experts reviewed the technicians\u2019 comments in each record to determine if a VTE event occurred. The performance measures calculated (with 95% CIs) were accuracy, sensitivity, specificity, and positive and negative predictive values. Chi-square tests of homogeneity were conducted to evaluate differences in performance measures by site, using a significance level of .05.Results:The VTE model of IDEAL-X \u201cread\u201d 1591 records from Duke University and 1487 records from the OUHSC, for a total of 3078 records. The combined performance measures were 93.7% accuracy (95% CI 93.7% 1293.8%), 96.3% sensitivity (95% CI 96.2% 1296.4%), 92% specificity (95% CI 91.9% 1292%), an 89.1% positive predictive value (95% CI 89% 1289.2%), and a 97.3% negative predictive value (95% CI 97.3% 1297.4%). The sensitivity was higher at Duke University (97.9%, 95% CI 97.8% 1298%) than at the OUHSC (93.3%, 95% CI 93.1% 1293.4%; P<.001), but the specificity was higher at the OUHSC (95.9%, 95% CI 95.8% 1296%) than at Duke University (86.5%, 95% CI 86.4% 1286.7%; P<.001).Conclusions:The VTE model of IDEAL-X accurately classified cases of VTE from the pilot surveillance systems of two separate health systems in Durham, North Carolina, and Oklahoma City, Oklahoma. NLP is a promising tool for the design and implementation of an automated, cost-effective national surveillance system for VTE. Conducting public health surveillance at a national scale is important for measuring disease burden and the impact of prevention measures. We recommend additional studies to identify how integrating IDEAL-X in a medical record system could further automate the surveillance process.CC999999/ImCDC/Intramural CDC HHSUnited States

Extracting information from the text of electronic medical records to improve case detection: a systematic review

Background: Electronic medical records (EMRs) are revolutionizing health-related research. One key issue for study quality is the accurate identification of patients with the condition of interest. Information in EMRs can be entered as structured codes or unstructured free text. The majority of research studies have used only coded parts of EMRs for case-detection, which may bias findings, miss cases, and reduce study quality. This review examines whether incorporating information from text into case-detection algorithms can improve research quality. Methods: A systematic search returned 9659 papers, 67 of which reported on the extraction of information from free text of EMRs with the stated purpose of detecting cases of a named clinical condition. Methods for extracting information from text and the technical accuracy of case-detection algorithms were reviewed. Results: Studies mainly used US hospital-based EMRs, and extracted information from text for 41 conditions using keyword searches, rule-based algorithms, and machine learning methods. There was no clear difference in case-detection algorithm accuracy between rule-based and machine learning methods of extraction. Inclusion of information from text resulted in a significant improvement in algorithm sensitivity and area under the receiver operating characteristic in comparison to codes alone (median sensitivity 78% (codes + text) vs 62% (codes), P = .03; median area under the receiver operating characteristic 95% (codes + text) vs 88% (codes), P = .025). Conclusions: Text in EMRs is accessible, especially with open source information extraction algorithms, and significantly improves case detection when combined with codes. More harmonization of reporting within EMR studies is needed, particularly standardized reporting of algorithm accuracy metrics like positive predictive value (precision) and sensitivity (recall)

Thromboprophylaxis Is Associated With Reduced Post-hospitalization Venous Thromboembolic Events in Patients With Inflammatory Bowel Diseases

Background & Aims Patients with inflammatory bowel diseases (IBDs) have increased risk for venous thromboembolism (VTE); those who require hospitalization have particularly high risk. Few hospitalized patients with IBD receive thromboprophylaxis. We analyzed the frequency of VTE after IBD-related hospitalization, risk factors for post-hospitalization VTE, and the efficacy of prophylaxis in preventing post-hospitalization VTE. Methods In a retrospective study, we analyzed data from a multi-institutional cohort of patients with Crohn's disease or ulcerative colitis and at least 1 IBD-related hospitalization. Our primary outcome was a VTE event. All patients contributed person-time from the date of the index hospitalization to development of VTE, subsequent hospitalization, or end of follow-up. Our main predictor variable was pharmacologic thromboprophylaxis. Cox proportional hazard models adjusting for potential confounders were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs). Results From a cohort of 2788 patients with at least 1 IBD-related hospitalization, 62 patients developed VTE after discharge (2%). Incidences of VTE at 30, 60, 90, and 180 days after the index hospitalization were 3.7/1000, 4.1/1000, 5.4/1000, and 9.4/1000 person-days, respectively. Pharmacologic thromboprophylaxis during the index hospital stay was associated with a significantly lower risk of post-hospitalization VTE (HR, 0.46; 95% CI, 0.22–0.97). Increased numbers of comorbidities (HR, 1.30; 95% CI, 1.16–1.47) and need for corticosteroids before hospitalization (HR, 1.71; 95% CI, 1.02–2.87) were also independently associated with risk of VTE. Length of hospitalization or surgery during index hospitalization was not associated with post-hospitalization VTE. Conclusions Pharmacologic thromboprophylaxis during IBD-related hospitalization is associated with reduced risk of post-hospitalization VTE.National Institutes of Health (U.S.) (U54-LM008748

Statins and risk of thromboembolism:A meta-regression to disentangle the efficacy-to-effectiveness gap using observational and trial evidence

Background and aims Meta-analyses of randomised controlled trials (RCTs) and observational studies indicate a lower risk of venous thromboembolism (VTE) associated with statin treatment. We aimed to compare the effect of statin therapy in these two settings and to identify and quantify potential factors to explain statin efficacy and effectiveness. Methods and results We electronically searched on December 11th, 2018, articles reporting on first VTE events in RCTs (statin vs placebo) and in observational studies (participants exposed vs non-exposed to statin). We performed Knapp-Hartung random-effect meta-analyses to calculate pooled relative risks (RRs) of VTE events associated with statin treatment, separately for RCTs and observational studies; and estimated the ratio of the relative risk (RRR) comparing RCTs and observational studies using meta-regressions, progressively adjusted for study-level characteristics. Twenty-one RCTs (115,107 participants; 959 events) and 8 observational studies (2,898,096 participants; 19,671 events) were included. Pooled RRs for RCTs and observational studies were 0.82 (95% confidence interval (CI): 0.67–1.00; I2 19.2%) and 0.60 (95% CI: 0.42–0.86; I2 86.3%), respectively. In meta-regressions, the unadjusted RRR indicated a nonsignificant 23% smaller benefit in RCTs (RRR 0.77; 95% CI: 0.52–1.13); accounting for age, sex, geographical region, and duration of follow-up, there was a sensible change of the RRR which resulted 0.30 (95% CI: 0.13–0.68). Conclusion Differences in the characteristics between patients included in RCTs and those in observational studies may account for the differential effect of statins in preventing VTE in the two settings

Thromboprophylaxis for trauma patients.

This is the protocol for a review and there is no abstract. The objectives are as follows: To assess the effects of thromboprophylaxis in trauma patients on mortality and incidence of DVT and PE. To compare the effects of different thromboprophylaxis interventions and their relative effects according to the type of trauma

Characterization of patients with idiopathic normal pressure hydrocephalus using natural language processing within an electronic healthcare record system

OBJECTIVE: Idiopathic normal pressure hydrocephalus (iNPH) is an underdiagnosed, progressive, and disabling condition. Early treatment is associated with better outcomes and improved quality of life. In this paper, the authors aimed to identify features associated with patients with iNPH using natural language processing (NLP) to characterize this cohort, with the intention to later target the development of artificial intelligence–driven tools for early detection. / METHODS: The electronic health records of patients with shunt-responsive iNPH were retrospectively reviewed using an NLP algorithm. Participants were selected from a prospectively maintained single-center database of patients undergoing CSF diversion for probable iNPH (March 2008–July 2020). Analysis was conducted on preoperative health records including clinic letters, referrals, and radiology reports accessed through CogStack. Clinical features were extracted from these records as SNOMED CT (Systematized Nomenclature of Medicine Clinical Terms) concepts using a named entity recognition machine learning model. In the first phase, a base model was generated using unsupervised training on 1 million electronic health records and supervised training with 500 double-annotated documents. The model was fine-tuned to improve accuracy using 300 records from patients with iNPH double annotated by two blinded assessors. Thematic analysis of the concepts identified by the machine learning algorithm was performed, and the frequency and timing of terms were analyzed to describe this patient group. / RESULTS: In total, 293 eligible patients responsive to CSF diversion were identified. The median age at CSF diversion was 75 years, with a male predominance (69% male). The algorithm performed with a high degree of precision and recall (F1 score 0.92). Thematic analysis revealed the most frequently documented symptoms related to mobility, cognitive impairment, and falls or balance. The most frequent comorbidities were related to cardiovascular and hematological problems. / CONCLUSIONS: This model demonstrates accurate, automated recognition of iNPH features from medical records. Opportunities for translation include detecting patients with undiagnosed iNPH from primary care records, with the aim to ultimately improve outcomes for these patients through artificial intelligence–driven early detection of iNPH and prompt treatment

A study of clinicopathological characteristics, symptoms and patients experiences related to outcomes in people with cancer and I-PE

Background: The clinical course of incidental pulmonary embolism in cancer population represents an area of controversy. It presents a growing challenge for clinicians because of a lack of prospective data.Aim: This research aims to investigate the impact of an incidentally diagnosed pulmonary embolism on cancer population’ outcomes and to explore their experience of living with cancer and i-PE. The second aim was to explore the role of the key thrombogenic biomarkers as a predictive biomarker of thrombosis.Methods: Mixed method research with critical integrative analysis. A systematic literature review and qualitative analysis to examine patients’ experience of living with cancer-associated thrombosis. A prospective observational case-controlled cohort study with embedded semi-structured interview study to investigate the quality of life and patients’ experience of living with cancer and incidental pulmonary embolism. A retrospective case control-study and scientific analysis of defined biological key factors associated with thrombosis.Results: The diagnosis of cancer-associated thrombosis including incidental pulmonary embolism negatively affect patients’ life, and patients experience this diagnosis in the context of living with cancer. Yet it is a diagnosis that often misattributed, misdiagnosed and associated with lack of information among patients and some of the clinical care professionals. The scientific analysis of the biological biomarkers illustrates the potential role of TF-mRNA as a predictive biomarker for cancer- associated incidental pulmonary embolism and the role of anti-factor ten anticoagulation in reducing the risk of thrombosis.Conclusion: Awareness of patients and care professionals regarding the high risk of thrombosis among cancer population represent an urgent need. Risk assessment tools to predict patients at increased risk of thrombosis would be of value and help target education and reduce the risk of diagnostic overshadowing

Upper extremity deep venous thrombosis prevalence in the NHS Grampian Medical Ambulatory clinic : diagnostic, therapeutic, and prognostic considerations in oncology patients

Peer reviewedPublisher PD