scholarly journals Predicting risk of undiagnosed COPD: development and validation of the TargetCOPD score

2017 ◽  
Vol 49 (6) ◽  
pp. 1602191 ◽  
Author(s):  
Shamil Haroon ◽  
Peymane Adab ◽  
Richard D. Riley ◽  
David Fitzmaurice ◽  
Rachel E. Jordan
Keyword(s):  
Primary Care ◽  
Electronic Health Records ◽  
Risk Score ◽  
Smoking Status ◽  
Cohort Analysis ◽  
Case Finding ◽  
Chronic Obstructive ◽  
Health Records ◽  
C Statistic ◽  
Electronic Health

Chronic obstructive pulmonary disease (COPD) is greatly underdiagnosed worldwide and more efficient methods of case-finding are required. We developed and externally validated a risk score to identify undiagnosed COPD using primary care records.We conducted a retrospective cohort analysis of a pragmatic cluster randomised controlled case-finding trial in the West Midlands, UK. Participants aged 40–79 years with no prior diagnosis of COPD received a postal or opportunistic screening questionnaire. Those reporting chronic respiratory symptoms were assessed with spirometry. COPD was defined as presence of relevant symptoms with a post-bronchodilator forced expiratory volume in 1 s/forced vital capacity ratio below the lower limit of normal. A risk score was developed using logistic regression with variables available from electronic health records for 2398 participants who returned a postal questionnaire. This was externally validated among 1097 participants who returned an opportunistic questionnaire to derive the c-statistic, and the sensitivity and specificity of cut-points.A risk score containing age, smoking status, dyspnoea, prescriptions of salbutamol and prescriptions of antibiotics discriminated between patients with and without undiagnosed COPD (c-statistic 0.74, 95% CI 0.68–0.80). A cut-point of ≥7.5% predicted risk had a sensitivity of 68.8% (95% CI 57.3–78.9%) and a specificity of 68.8% (95% CI 65.8.1–71.6%).A novel risk score using routine data from primary care electronic health records can identify patients at high risk for undiagnosed symptomatic COPD. This score could be integrated with clinical information systems to help primary care clinicians target patients for case-finding.

Preliminary results from the COPE study using primary-care electronic health records and environmental modelling to examine COPD exacerbations

2018 ◽  
Vol 68 (suppl 1) ◽  
pp. bjgp18X696749 ◽  
Author(s):  
Maimoona Hashmi ◽  
Mark Wright ◽  
Kirin Sultana ◽  
Benjamin Barratt ◽  
Lia Chatzidiakou ◽  
...  
Keyword(s):  
Primary Care ◽  
Electronic Health Records ◽  
Health Management ◽  
Practice Research ◽  
Chronic Obstructive ◽  
Clinical Practice Research Datalink ◽  
Health Records ◽  
Preliminary Results ◽  
Copd Exacerbations ◽  
Electronic Health

BackgroundChronic Obstructive Airway Disease (COPD) is marked by often severely debilitating exacerbations. Efficient patient-centric research approaches are needed to better inform health management primary-care.AimThe ‘COPE study’ aims to develop a method of predicting COPD exacerbations utilising personal air quality sensors, environmental exposure modelling and electronic health records through the recruitment of patients from consenting GPs contributing to the Clinical Practice Research Datalink (CPRD).MethodThe study made use of Electronic Healthcare Records (EHR) from CPRD, an anonymised GP records database to screen and locate patients within GP practices in Central London. Personal air monitors were used to capture data on individual activities and environmental exposures. Output from the monitors were then linked with the EHR data to obtain information on COPD management, severity, comorbidities and exacerbations. Symptom changes not equating to full exacerbations were captured on diary cards. Linear regression was used to investigate the relationship between subject peak flow, symptoms, exacerbation events and exposure data.ResultsPreliminary results on the first 80 patients who have completed the study indicate variable susceptibility to environmental stressors in COPD patients. Some individuals appear highly susceptible to environmental stress and others appear to have unrelated triggers.ConclusionRecruiting patients through EHR for a study is feasible and allows easy collection of data for long term follow up. Portable environmental sensors could now be used to develop personalised models to predict risk of COPD exacerbations in susceptible individuals. Identification of direct links between participant health and activities would allow improved health management thus cost savings.

scholarly journals Identifying COPD in routinely collected electronic health records: a systematic scoping review

2021 ◽  
pp. 00167-2021
Author(s):  
Shanya Sivakumaran ◽  
Mohammad A. Alsallakh ◽  
Ronan A. Lyons ◽  
Jennifer K. Quint ◽  
Gwyneth A. Davies
Keyword(s):  
Electronic Health Records ◽  
Smoking Status ◽  
Case Definition ◽  
Chronic Obstructive ◽  
Health Records ◽  
Obstructive Pulmonary Disease ◽  
Electronic Healthcare Databases ◽  
Patient Reported ◽  
Icd 10 ◽  
Electronic Health

Although routinely collected electronic health records (EHR) are widely used to examine outcomes related to chronic obstructive pulmonary disease (COPD), consensus regarding the identification of cases from electronic healthcare databases is lacking. We systematically examine and summarise approaches from the recent literature.MEDLINE via EBSCOhost was searched for COPD-related studies using EHR published from January 1, 2018 to November 30, 2019. Data were extracted relating to the case definition of COPD and determination of COPD severity and phenotypes.From 185 eligible studies, we found widespread variation in the definitions used to identify people with COPD in terms of code sets used (with 20 different code sets in use based on the ICD-10 classification alone) and requirement of additional criteria (relating to age (n=139), medication (n=31), multiplicity of events (n=21), spirometry (n=19) and smoking status (n=9)). Only 7 studies used a case definition which had been validated against a reference standard in the same dataset. Various proxies of disease severity were used since spirometry results and patient-reported outcomes were not often available.To enable the research community to draw reliable insights from electronic health records and aid comparability between studies, clear reporting and greater consistency of the definitions used to identify COPD and related outcome measures is key.

scholarly journals Estimating the population health burden of musculoskeletal conditions using primary care electronic health records

Rheumatology ◽  
2021 ◽  
Author(s):  
Dahai Yu ◽  
George Peat ◽  
Kelvin P Jordan ◽  
James Bailey ◽  
Daniel Prieto-Alhambra ◽  
...  
Keyword(s):  
Primary Care ◽  
Chronic Pain ◽  
Low Back Pain ◽  
Back Pain ◽  
Electronic Health Records ◽  
High Impact ◽  
Low Back ◽  
Musculoskeletal Health ◽  
Health Records ◽  
Electronic Health

Abstract Objectives Better indicators from affordable, sustainable data sources are needed to monitor population burden of musculoskeletal conditions. We propose five indicators of musculoskeletal health and assessed if routinely available primary care electronic health records (EHR) can estimate population levels in musculoskeletal consulters. Methods We collected validated patient-reported measures of pain experience, function and health status through a local survey of adults (≥35 years) presenting to English general practices over 12 months for low back pain, shoulder pain, osteoarthritis and other regional musculoskeletal disorders. Using EHR data we derived and validated models for estimating population levels of five self-reported indicators: prevalence of high impact chronic pain, overall musculoskeletal health (based on Musculoskeletal Health Questionnaire), quality of life (based on EuroQoL health utility measure), and prevalence of moderate-to-severe low back pain and moderate-to-severe shoulder pain. We applied models to a national EHR database (Clinical Practice Research Datalink) to obtain national estimates of each indicator for three successive years. Results The optimal models included recorded demographics, deprivation, consultation frequency, analgesic and antidepressant prescriptions, and multimorbidity. Applying models to national EHR, we estimated that 31.9% of adults (≥35 years) presenting with non-inflammatory musculoskeletal disorders in England in 2016/17 experienced high impact chronic pain. Estimated population health levels were worse in women, older aged and those in the most deprived neighbourhoods, and changed little over 3 years. Conclusion National and subnational estimates for a range of subjective indicators of non-inflammatory musculoskeletal health conditions can be obtained using information from routine electronic health records.

The relationship between usage intention and adoption of electronic health records at primary care clinics

2013 ◽  
Vol 112 (3) ◽  
pp. 731-737 ◽  
Author(s):  
Usman Iqbal ◽  
Cheng-Hsun Ho ◽  
Yu-Chuan(Jack) Li ◽  
Phung-Anh Nguyen ◽  
Wen-Shan Jian ◽  
...  
Keyword(s):  
Primary Care ◽  
Electronic Health Records ◽  
Health Records ◽  
Primary Care Clinics ◽  
Electronic Health ◽  
The Relationship

Development of algorithm for classification smoking status from unstructured bilingual electronic health records based on natural language processing (Preprint)

2021 ◽  
Author(s):  
Ye Seul Bae ◽  
Kyung Hwan Kim ◽  
Han Kyul Kim ◽  
Sae Won Choi ◽  
Taehoon Ko ◽  
...  
Keyword(s):  
Natural Language Processing ◽  
Electronic Health Records ◽  
Natural Language ◽  
Language Processing ◽  
Smoking Status ◽  
Svm Classifier ◽  
Keyword Extraction ◽  
Health Records ◽  
Clinical Notes ◽  
Electronic Health

BACKGROUND Smoking is a major risk factor and important variable for clinical research, but there are few studies regarding automatic obtainment of smoking classification from unstructured bilingual electronic health records (EHR). OBJECTIVE We aim to develop an algorithm to classify smoking status based on unstructured EHRs using natural language processing (NLP). METHODS With acronym replacement and Python package Soynlp, we normalize 4,711 bilingual clinical notes. Each EHR notes was classified into 4 categories: current smokers, past smokers, never smokers, and unknown. Subsequently, SPPMI (Shifted Positive Point Mutual Information) is used to vectorize words in the notes. By calculating cosine similarity between these word vectors, keywords denoting the same smoking status are identified. RESULTS Compared to other keyword extraction methods (word co-occurrence-, PMI-, and NPMI-based methods), our proposed approach improves keyword extraction precision by as much as 20.0%. These extracted keywords are used in classifying 4 smoking statuses from our bilingual clinical notes. Given an identical SVM classifier, the extracted keywords improve the F1 score by as much as 1.8% compared to those of the unigram and bigram Bag of Words. CONCLUSIONS Our study shows the potential of SPPMI in classifying smoking status from bilingual, unstructured EHRs. Our current findings show how smoking information can be easily acquired and used for clinical practice and research.

scholarly journals Epidemiology of dementia: prevalence and incidence estimates using validated electronic health records from primary care

2019 ◽  
Vol Volume 11 ◽  
pp. 217-228 ◽  
Author(s):  
Anna Ponjoan ◽  
Josep Garre-Olmo ◽  
Jordi Blanch ◽  
Ester Fages ◽  
Lia Alves-Cabratosa ◽  
...  
Keyword(s):  
Primary Care ◽  
Electronic Health Records ◽  
Health Records ◽  
Dementia Prevalence ◽  
Epidemiology Of Dementia ◽  
Electronic Health
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