Adverse Childhood Experiences (ACEs) in English Electronic Health Records of Linked Mothers and Children: Validation Study Using a Multistage Risk-Prediction Model

2021 ◽  
Author(s):  
Shabeer Syed ◽  
Arturo González-Izquierdo ◽  
Janice Allister ◽  
Gene Feder ◽  
Leah Li ◽  
...  
Keyword(s):  
Electronic Health Records ◽  
Prediction Model ◽  
Risk Prediction ◽  
Validation Study ◽  
Adverse Childhood Experiences ◽  
Risk Prediction Model ◽  
Health Records ◽  
Childhood Experiences ◽  
Adverse Childhood ◽  
Mothers And Children

scholarly journals Mining 100 million notes to find homelessness and adverse childhood experiences: 2 case studies of rare and severe social determinants of health in electronic health records

2017 ◽  
Vol 25 (1) ◽  
pp. 61-71 ◽  
Author(s):  
Cosmin A Bejan ◽  
John Angiolillo ◽  
Douglas Conway ◽  
Robertson Nash ◽  
Jana K Shirey-Rice ◽  
...  
Keyword(s):  
Electronic Health Records ◽  
Social Determinants Of Health ◽  
Social Determinants ◽  
Adverse Childhood Experiences ◽  
Determinants Of Health ◽  
Homeless Population ◽  
Health Records ◽  
Childhood Experiences ◽  
Adverse Childhood ◽  
Electronic Health

Abstract Objective Understanding how to identify the social determinants of health from electronic health records (EHRs) could provide important insights to understand health or disease outcomes. We developed a methodology to capture 2 rare and severe social determinants of health, homelessness and adverse childhood experiences (ACEs), from a large EHR repository. Materials and Methods We first constructed lexicons to capture homelessness and ACE phenotypic profiles. We employed word2vec and lexical associations to mine homelessness-related words. Next, using relevance feedback, we refined the 2 profiles with iterative searches over 100 million notes from the Vanderbilt EHR. Seven assessors manually reviewed the top-ranked results of 2544 patient visits relevant for homelessness and 1000 patients relevant for ACE. Results word2vec yielded better performance (area under the precision-recall curve [AUPRC] of 0.94) than lexical associations (AUPRC = 0.83) for extracting homelessness-related words. A comparative study of searches for the 2 phenotypes revealed a higher performance achieved for homelessness (AUPRC = 0.95) than ACE (AUPRC = 0.79). A temporal analysis of the homeless population showed that the majority experienced chronic homelessness. Most ACE patients suffered sexual (70%) and/or physical (50.6%) abuse, with the top-ranked abuser keywords being “father” (21.8%) and “mother” (15.4%). Top prevalent associated conditions for homeless patients were lack of housing (62.8%) and tobacco use disorder (61.5%), while for ACE patients it was mental disorders (36.6%–47.6%). Conclusion We provide an efficient solution for mining homelessness and ACE information from EHRs, which can facilitate large clinical and genetic studies of these social determinants of health.

scholarly journals Prediction of Incident Hypertension Within the Next Year: Prospective Study Using Statewide Electronic Health Records and Machine Learning (Preprint)

2017 ◽  
Author(s):  
Chengyin Ye ◽  
Tianyun Fu ◽  
Shiying Hao ◽  
Yan Zhang ◽  
Oliver Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Essential Hypertension ◽  
Electronic Health Records ◽  
Prediction Model ◽  
Risk Prediction ◽  
Risk Prediction Model ◽  
Risk Category ◽  
Health Records ◽  
Incident Hypertension ◽  
Category Score

BACKGROUND As a high-prevalence health condition, hypertension is clinically costly, difficult to manage, and often leads to severe and life-threatening diseases such as cardiovascular disease (CVD) and stroke. OBJECTIVE The aim of this study was to develop and validate prospectively a risk prediction model of incident essential hypertension within the following year. METHODS Data from individual patient electronic health records (EHRs) were extracted from the Maine Health Information Exchange network. Retrospective (N=823,627, calendar year 2013) and prospective (N=680,810, calendar year 2014) cohorts were formed. A machine learning algorithm, XGBoost, was adopted in the process of feature selection and model building. It generated an ensemble of classification trees and assigned a final predictive risk score to each individual. RESULTS The 1-year incident hypertension risk model attained areas under the curve (AUCs) of 0.917 and 0.870 in the retrospective and prospective cohorts, respectively. Risk scores were calculated and stratified into five risk categories, with 4526 out of 381,544 patients (1.19%) in the lowest risk category (score 0-0.05) and 21,050 out of 41,329 patients (50.93%) in the highest risk category (score 0.4-1) receiving a diagnosis of incident hypertension in the following 1 year. Type 2 diabetes, lipid disorders, CVDs, mental illness, clinical utilization indicators, and socioeconomic determinants were recognized as driving or associated features of incident essential hypertension. The very high risk population mainly comprised elderly (age>50 years) individuals with multiple chronic conditions, especially those receiving medications for mental disorders. Disparities were also found in social determinants, including some community-level factors associated with higher risk and others that were protective against hypertension. CONCLUSIONS With statewide EHR datasets, our study prospectively validated an accurate 1-year risk prediction model for incident essential hypertension. Our real-time predictive analytic model has been deployed in the state of Maine, providing implications in interventions for hypertension and related diseases and hopefully enhancing hypertension care.

scholarly journals The M6 risk prediction model and two-step strategy to characterize pregnancies of unknown location: a multicentre external validation study

Authorea ◽  
2020 ◽  
Author(s):  
Evangelia Christodoulou ◽  
Shabnam Bobdiwala ◽  
Christopher Kyriacou ◽  
Jessica Farren ◽  
Nicola Mitchell Jones ◽  
...  
Keyword(s):  
Prediction Model ◽  
Risk Prediction ◽  
Validation Study ◽  
External Validation ◽  
Risk Prediction Model ◽  
External Validation Study

scholarly journals Adverse Childhood Experiences and Risk of Binge Drinking and Drunkenness in Middle-Aged Finnish Men

2011 ◽  
Vol 2011 ◽  
pp. 1-12 ◽  
Author(s):  
Laura Kauhanen ◽  
Janne Leino ◽  
Hanna-Maaria Lakka ◽  
John W. Lynch ◽  
Jussi Kauhanen
Keyword(s):  
Binge Drinking ◽  
Socioeconomic Position ◽  
Adverse Childhood Experiences ◽  
School Health ◽  
Disease Risk ◽  
Health Records ◽  
Childhood Experiences ◽  
Adverse Childhood ◽  
Increased Risk ◽  
Behavioural Factors

Objective. The purpose of this study was to investigate associations between adverse childhood experiences and binge drinking and drunkenness in adulthood using both historical and recalled data from childhood.Methods. Data on childhood adverse experiences were collected from school health records and questionnaires completed in adulthood. Adulthood data were obtained from the baseline examinations of the male participants (n=2682) in the Kuopio Ischaemic Heart Disease Risk Factor Study (KIHD) in 1984–1989 from eastern Finland. School health records from the 1930s to 1950s were available for a subsample of KIHD men (n=952).Results. According to the school health records, men who had adverse childhood experiences had a 1.51-fold (95% CI 1.05 to 2.18) age- and examination-year adjusted odds of binge drinking in adulthood. After adjustment for socioeconomic position in adulthood or behavioural factors in adulthood, the association remained unchanged. Adjustment for socioeconomic position in childhood attenuated these effects. Also the recalled data showed associations with adverse childhood experiences and binge drinking with different beverages.Conclusions. Our findings suggest that childhood adversities are associated with increased risk of binge drinking in adulthood.

Assessing the added value of breast tumor markers in breast cancer genetic risk prediction model BRCAPRO.

2011 ◽  
Vol 29 (27_suppl) ◽  
pp. 164-164
Author(s):  
B. Arun ◽  
S. Biswas ◽  
N. Tankhiwale ◽  
A. L. Blackford ◽  
A. M. Gutierrez-Barrera ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Prediction Model ◽  
Tumor Markers ◽  
Risk Prediction ◽  
Validation Study ◽  
Genetic Risk ◽  
Breast Tumor ◽  
Model Building ◽  
Risk Prediction Model ◽  
Genetic Risk Prediction

164 Background: BRCAPRO is a widely used genetic risk prediction model for estimating the carrier probabilities of mutations in BRCA1/2 genes. BRCAPRO has been enhanced to utilize information on molecular markers ER, PR, CK5/6, and CK14. However, no independent validation study on the utility of these markers in risk prediction exists to support using these in actual clinical settings. Further, an important predictive and prognostic marker for breast cancer, Her-2/neu (Her2) is not utilized in BRCAPRO. Therefore, the aim of our study was to: 1) incorporate Her2 in BRCAPRO; 2) conduct a validation study of the markers. Methods: Patients with breast cancer at the UT M. D. Anderson Cancer Center’s breast clinic who were referred for genetic evaluation were included. Separate sets of cohort were used for model building with Her2 and validation to avoid bias. This study was approved by the IRB. For the model building, we estimated the joint probabilities of ER and Her2 status for carriers and non-carriers of BRCA1/2 mutations. For the validation, BRCAPRO was run at two settings: 1) no marker data used and 2) ER/PR used. We calculated the Area Under the receiving operator characteristic Curve (AUC) using the probabilities of carrying any of BRCA1 or BRCA2 and conditional probabilities of carrying BRCA1 (CondBRCA1) and BRCA2 (CondBRCA2) given a proband is carrier. Results: The model-building set for Her2 was based on 409 probands and validation set on 796 probands wherein 23% of the probands were carriers. In the model-building step, we found that joint consideration of Her2 and ER/PR is useful in discriminating between carriers and non-carriers in some subgroups, e.g., a proband with ER-, Her2+ is much more likely to be a non-carrier than a carrier. In the validation step, the AUC for CondBRCA1 and CondBRCA2 improved substantially when ER/PR was used. We are in the process of coding Her2 in BRCAPRO and then validating its utility. Conclusions: Breast tumor markers are useful for prediction of BRCA1/2 mutation status in the BRCAPRO model. ER/PR helps discriminate between BRCA1 and BRCA2 mutation carriers. In our ongoing validation study Her2 is expected to improve discrimination between carriers and non-carriers in certain sub-groups.

scholarly journals Exploring the health and service utilisation of general practice patients with a history of adverse childhood experiences (ACEs): an observational study using electronic health records

BMJ Open ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. e036239
Author(s):  
Katie Hardcastle ◽  
Mark A Bellis ◽  
Catherine A Sharp ◽  
Karen Hughes
Keyword(s):  
Mental Health ◽  
Health Service ◽  
Adverse Childhood Experiences ◽  
Mental Health Problems ◽  
Health Problems ◽  
Service Utilisation ◽  
Health Records ◽  
Childhood Experiences ◽  
Adverse Childhood ◽  
History Of

ObjectivesTo examine the relationships between adverse childhood experiences (ACEs), chronic health and health service utilisation among a sample of general practice patients.DesignCross-sectional observational study using anonymised data from electronic health records for 763 patients.SettingFour general practices in northwest England and North Wales.Outcome measuresPatient demographic data (age, gender); body mass index; self-reported smoking status; self-reported ACEs; diagnosis of chronic health conditions; current mental health problems; total number of service contacts and repeat medication use in the previous 6 months.ResultsA history of ACEs (experiencing abuse or neglect as a child, and/or growing up in a household characterised by violence, substance use, mental health problems or criminal behaviour) was strongly independently associated with current mental health problems, smoking and chronic obstructive pulmonary disease, showing a dose–response relationship with level of ACE exposure. Medication use and contact were significantly greater among patients with high ACE exposure (≥4 ACEs), compared with those with no ACEs. However, contrary to findings from population studies, health service utilisation was not significantly different for patients with increased ACE exposure (1–3 ACEs) and their ACE-free counterparts.ConclusionsFindings highlight the contribution ACEs make to unequal distributions of risk to health and well-being and patterns of health service use in the UK.

scholarly journals Prediction of the 1-Year Risk of Incident Lung Cancer: Prospective Study Using Electronic Health Records from the State of Maine (Preprint)

2018 ◽  
Author(s):  
Xiaofang Wang ◽  
Yan Zhang ◽  
Shiying Hao ◽  
Le Zheng ◽  
Jiayu Liao ◽  
...  
Keyword(s):  
Lung Cancer ◽  
At Risk ◽  
High Risk ◽  
Electronic Health Records ◽  
Prediction Model ◽  
Risk Prediction ◽  
Prospective Cohort ◽  
Retrospective Cohort ◽  
Health Records ◽  
History Of

BACKGROUND Lung cancer is the leading cause of cancer death worldwide. Early detection of individuals at risk of lung cancer is critical to reduce the mortality rate. OBJECTIVE The aim of this study was to develop and validate a prospective risk prediction model to identify patients at risk of new incident lung cancer within the next 1 year in the general population. METHODS Data from individual patient electronic health records (EHRs) were extracted from the Maine Health Information Exchange network. The study population consisted of patients with at least one EHR between April 1, 2016, and March 31, 2018, who had no history of lung cancer. A retrospective cohort (N=873,598) and a prospective cohort (N=836,659) were formed for model construction and validation. An Extreme Gradient Boosting (XGBoost) algorithm was adopted to build the model. It assigned a score to each individual to quantify the probability of a new incident lung cancer diagnosis from October 1, 2016, to September 31, 2017. The model was trained with the clinical profile in the retrospective cohort from the preceding 6 months and validated with the prospective cohort to predict the risk of incident lung cancer from April 1, 2017, to March 31, 2018. RESULTS The model had an area under the curve (AUC) of 0.881 (95% CI 0.873-0.889) in the prospective cohort. Two thresholds of 0.0045 and 0.01 were applied to the predictive scores to stratify the population into low-, medium-, and high-risk categories. The incidence of lung cancer in the high-risk category (579/53,922, 1.07%) was 7.7 times higher than that in the overall cohort (1167/836,659, 0.14%). Age, a history of pulmonary diseases and other chronic diseases, medications for mental disorders, and social disparities were found to be associated with new incident lung cancer. CONCLUSIONS We retrospectively developed and prospectively validated an accurate risk prediction model of new incident lung cancer occurring in the next 1 year. Through statistical learning from the statewide EHR data in the preceding 6 months, our model was able to identify statewide high-risk patients, which will benefit the population health through establishment of preventive interventions or more intensive surveillance.

Sign in / Sign up

Export Citation Format

Share Document