Development and validation of a case definition to estimate the prevalence and incidence of cirrhosis in pan-Canadian primary care databases.

Aims: To develop and validate case definitions to identify patients with cirrhosis and alcohol-related cirrhosis using primary care electronic medical records (EMRs) and to estimate cirrhosis prevalence and incidence in pan-Canadian primary care databases, between 2011 and 2019. Methods: A total of 689,301 adult patients were included with ≥1 visit to a primary care provider within the Canadian Primary Care Sentinel Study Network between January 1, 2017, and December 31, 2018. A subsample of 17,440 patients was used to validate the case definitions. Sensitivity, specificity, predictive values were calculated with their 95% CIs and then determined the population-level prevalence and incidence trends with the most accurate case definition. Results: The most accurate case definition included: ≥1 health condition, billing, or encounter diagnosis for International Classification of Diseases, Ninth Revision codes 571.2, 571.5, 789.59, or 571. Sensitivity (84.6; 95% CI 83.1%-86.%), specificity (99.3; 95% CI 99.1%-99.4%), positive predictive values (94.8; 95% CI 93.9%-95.7%), and negative predictive values (97.5; 95% CI 97.3%-97.7%). Application of this definition to the overall population resulted in a crude prevalence estimate of (0.46%; 95% CI 0.45%-0.48%). Annual incidence of patients with a clinical diagnosis of cirrhosis nearly doubled between 2011 (0.05%; 95% CI 0.04%-0.06%) and 2019 to (0.09%; 95% CI 0.08%-0.09%). Conclusions: The EMR-based case definition accurately captured patients diagnosed with cirrhosis in primary care. Future work to characterize patients with cirrhosis and their primary care experiences can support improvements in identification and management in primary care settings.

Characterizing primary care patients with posttraumatic stress disorder using electronic medical records: a retrospective cross-sectional study.

BACKGROUND: Posttraumatic stress disorder (PTSD) has significant morbidity and economic costs. This study describes the prevalence and characteristics of patients with PTSD using primary care electronic medical record (EMR) data. METHODS: This retrospective cross-sectional study used EMR data from the Canadian Primary Care Sentinel Surveillance Network (CPCSSN). This study included 1,574 primary care providers located in 7 Canadian provinces. There were 689,301 patients that visited a CPCSSN provider between 1 January 2017 and 31 December 2019. We describe associations between PTSD and patient characteristics using descriptive statistics, chi-square, and multiple logistic regression models. RESULTS: Among the 689,301 patients included, 8,817 (1.3%, 95% CI 1.2-1.3) had a diagnosis of PTSD. On multiple logistic regression analysis, patients with depression (OR 4.4, 95% CI 4.2-4.7, P < 0.001), alcohol abuse/dependence (OR 1.7, 95% CI 1.6-1.9, P < 0.001), and/or drug abuse/dependence (OR 2.6, 95% CI 2.5-2.8, P < 0.001) had significantly higher odds of PTSD compared with patients without those conditions. Patients residing in community areas considered the most material deprived (OR 2.1, 95% CI 1.5-2.1, P < 0.001) or the most socially deprived (OR 2.8, 95% CI 2.7-5.3, P < 0.001) had higher odds of being diagnosed with PTSD compared with patients in the least deprived areas. CONCLUSIONS: The prevalence of PTSD in Canadian primary care is 1.3% (95% CI 1.25-1.31). Using EMR records we confirmed the co-occurrence of PTSD with other mental health conditions within primary care settings suggesting benefit for improved screening and evidence-based resources to manage PTSD.

Posttraumatic stress disorder (PTSD) is a mental health disorder with symptoms presenting after having experienced or witnessed a traumatic event. PTSD symptoms continue for more than 1 month after the event and negatively impact the health and social wellbeing of an individual. Primary care, including family doctors, nurse practitioners, and community paediatricians, are often the first point of healthcare for an individual. This study found that PTSD is diagnosed and managed in primary care. Patients with PTSD had comorbidities, substance use, and visited their primary care provider more frequently. Additionally, patients with PTSD often live in a community area that is experiencing high material and social deprivation. The presence of PTSD in primary care suggests the need for new and additional evidence-based resources to assist in managing this complex condition.

Pan-Canadian Electronic Medical Record Diagnostic and Unstructured Text Data for Capturing PTSD: Retrospective Observational Study.

BACKGROUND: The availability of electronic medical record (EMR) free-text data for research varies. However, access to short diagnostic text fields is more widely available. OBJECTIVE: This study assesses agreement between free-text and short diagnostic text data from primary care EMR for identification of posttraumatic stress disorder (PTSD). METHODS: This retrospective cross-sectional study used EMR data from a pan-Canadian repository representing 1574 primary care providers at 265 clinics using 11 EMR vendors. Medical record review using free text and short diagnostic text fields of the EMR produced reference standards for PTSD. Agreement was assessed with sensitivity, specificity, positive predictive value, negative predictive value, and accuracy. RESULTS: Our reference set contained 327 patients with free text and short diagnostic text. Among these patients, agreement between free text and short diagnostic text had an accuracy of 93.6% (CI 90.4%-96.0%). In a single Canadian province, case definitions 1 and 4 had a sensitivity of 82.6% (CI 74.4%-89.0%) and specificity of 99.5% (CI 97.4%-100%). However, when the reference set was expanded to a pan-Canada reference (n=12,104 patients), case definition 4 had the strongest agreement (sensitivity: 91.1%, CI 90.1%-91.9%; specificity: 99.1%, CI 98.9%-99.3%). CONCLUSIONS: Inclusion of free-text encounter notes during medical record review did not lead to improved capture of PTSD cases, nor did it lead to significant changes in case definition agreement. Within this pan-Canadian database, jurisdictional differences in diagnostic codes and EMR structure suggested the need to supplement diagnostic codes with natural language processing to capture PTSD. When unavailable, short diagnostic text can supplement free-text data for reference set creation and case validation. Application of the PTSD case definition can inform PTSD prevalence and characteristics.

Characterizing posttraumatic stress disorder in primary care using electronic medical records: a retrospective cohort study.

Context: Posttraumatic stress disorder (PTSD) is a chronic mental health disorder associated with significant morbidity and economic cost. Primary care providers are frequently involved in the ongoing management of patients experiencing PTSD, as well as related comorbid conditions. Despite recognized need to enhance PTSD management in primary care settings, knowledge regarding its prevalence in these settings is limited. Objective: To apply a validated case definition of PTSD to electronic medical records (EMRs) of family physicians and nurse practitioners participating in the Canadian Primary Care Sentinel Surveillance Network (CPCSSN). Study Design: Retrospective cross-sectional study. Dataset: This study accessed de-identified EMR from 1,574 primary care providers participating in the CPCSSN. Population Studied: The study population included all patients with at least one visit to a primary care provider participating in the CPCSSN between January 1, 2017 and December 31, 2019 (N = 689,301). Outcome Measures: We identified patients with PTSD and described associations between PTSD and patient characteristics (including sex, age, geography, depression, anxiety, medical comorbidities, substance use and social and material deprivation) using multivariable logistic regression models. Results: Among the 689,301 patients meeting inclusion criteria, 8,213 (1.2%) had a diagnosis of PTSD. Patients with PTSD were significantly more likely to reside in an urban location (84.9% vs. 80.4%; p-value <.0001) and have one or more comorbid conditions (90.8% vs. 70.2%; p-value <.0001). On multivariable logistic regression analysis, patients with depression (OR 4.8; 95%CI 4.6-5.1) and anxiety (OR 2.2; 95%CI 2.1-2.3) had increased odds of having PTSD compared to patients without depression or anxiety. Patients with alcohol (OR 1.8; 95%CI 1.6-1.9) and drug (OR 3.1; 95%CI 2.9-3.3) use disorders had significantly higher odds of PTSD compared to patients without these disorders. Patients in the most deprived neighborhoods based on census data had 4.2 times higher odds of have PTSD (95%CI 3.2-5.43) compared to patients in the least deprived areas. Conclusions: This is the first study to describe PTSD prevalence in a large Canadian sample of primary care patients using an EMR-based case definition. Characterizing patients with PTSD in primary care may improve disease surveillance and inform the interdisciplinary care required to manage PTSD symptoms.

AI in predicting COPD in the Canadian population.

Chronic obstructive pulmonary disease (COPD) is a progressive lung disease that produces non-reversible airflow limitations. Approximately 10% of Canadians aged 35 years or older are living with COPD. Primary care is often the first contact an individual will have with the healthcare system providing acute care, chronic disease management, and services aimed at health maintenance. This study used Electronic Medical Record (EMR) data from primary care clinics in seven provinces across Canada to develop predictive models to identify COPD in the Canadian population. The comprehensive nature of this primary care EMR data containing structured numeric, categorical, hybrid, and unstructured text data, enables the predictive models to capture symptoms of COPD and discriminate it from diseases with similar symptoms. We applied two supervised machine learning models, a Multilayer Neural Networks (MLNN) model and an Extreme Gradient Boosting (XGB) to identify COPD patients. The XGB model achieved an accuracy of 86% in the test dataset compared to 83% achieved by the MLNN. Utilizing feature importance, we identified a set of key symptoms from the EMR for diagnosing COPD, which included medications, health conditions, risk factors, and patient age. Application of this XGB model to primary care structured EMR data can identify patients with COPD from others having similar chronic conditions for disease surveillance, and improve evidence-based care delivery.

Diagnosing post-traumatic stress disorder using electronic medical record data.

This study proposes a predictive model that uses structured data and unstructured narrative notes from Electronic Medical Records to accurately identify patients diagnosed with Post-Traumatic Stress Disorder (PTSD). We utilize data from primary care clinicians participating in the Manitoba Primary Care Research Network (MaPCReN) representing 154,118 patients. A reference sample of 195 patients that had their PTSD diagnosis confirmed using a manual chart review of structured data and narrative notes, and PTSD negative patients is used as the gold standard data for model training, validation and testing. We assess structured and unstructured data from eight tables in the MaPCReN namely, patient demographics, disease case, examinations, medication, billing records, health condition, risk factors, and encounter notes. Feature engineering is applied to convert data into proper representation for predictive modeling. We explore serial and parallel mixed data models that are trained on both structured and unstructured data to identify PTSD. Model performances were calculated based on a highly skewed hold-out test dataset. The serial model that uses both structured and text data as input, yielded the highest values in sensitivity (0.77), F-measure (0.76), and AUC (0.88) and the parallel model that uses both structured and text data as the input obtained the highest positive predicted value (PPV) (0.75). Diseases such as PTSD are difficult to diagnose. Information recorded in the chart note over multiple visits of the patients with the primary care physicians has higher predictive power than structured data and combining these two data types can increase the predictive capabilities of machine learning models in diagnosing PTSD. While the deep-learning model outperformed the traditional ensemble model in processing text data, the ensemble classifier obtained better results in ingesting a combination of features obtained from both data types in the serial mixed model. The study demonstrated that unstructured encounter notes enhance a model's ability to identify patients diagnosed with PTSD. These findings can enhance quality improvement, research, and disease surveillance related to PTSD in primary care populations.