Yanting Guo

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: For many elderly patients, a “disproportionate” amount of health care resources and expenditures is spent during the last year of life, despite the discomfort and reduced quality of life associated with many aggressive medical approaches. However, few prognostic tools have focused on predicting all-cause one-year mortality among elderly patients at a statewide level, an issue that has implications for improving quality of life while distributing scarce resource fairly. Objective: Using data from a statewide elderly population (≥65 years old), we sought to validate prospectively an algorithm to identify patients at risk for dying in the future one year, for the purpose of minimizing decision uncertainty, improving quality of life, and reducing futile treatment. Methods: Analysis was performed using electronic medical records (EMRs) from the Health Information Exchange (HIE) in the state of Maine, which covered records of nearly 95% of the statewide population. The model was developed from 125,896 patients aged at least 65 years who were discharged from any care facility in the HIE network from September 5, 2013 to September 4, 2015. Validation was conducted using 153,199 patients with same inclusion and exclusion criteria from September 5, 2014 to September 4, 2016. Patients were stratified into risk groups. The association between all-cause one-year mortality and risk factors was screened by chi-squared test and manually reviewed by two clinicians. We calculated risk scores for individual patients using a gradient tree based boost algorithm, which measured the probability of mortality within the next 1 year based on the preceding one-year clinical profile. Results: The development sample included 125,896 patients (72,572 women [57.6%]; mean [SD] age, 74.2 [7.7] years). The final validation cohort included 153,199 patients (88,177 women [57.5%]; mean [SD] age, 74.3 [7.8] years). The C-statistic for discrimination was 0.96 (95% CI, 0.93-0.98) in the development group and 0.91 (95% CI, 0.90-0.94) in the validation cohort. The mortality was 0.99% in the low-risk group, 16.75% in the intermediate-risk group, and 72.12 % in the high-risk group. A total of 99 independent risk factors (n=99) for mortality were identified (reported as odds ratios; 95%CIs). Different tumor sites were recognized as driving risk factors, such as ovary (14.42; 2.24-53.04), colon (14.07; 10.08-19.08), stomach (13.64; 3.26-86.57), and bronchus/lung (12.38; 2.91-36.04). Disparities were also found in patients’ social determinants like respiratory hazard index (1.24; 0.92-1.40), and unemployment rate (1.18; 0.98-1.24). Among high-risk patients who expired in our dataset, cerebrovascular accident, amputation, and type 1 diabetes were the top 3 diseases in terms of average cost in the last year of life. Conclusions: With statewide EMRs datasets, our study prospectively validated an accurate one-year risk prediction model and stratification for the elderly population (≥65 years old) at risk of mortality. It should be a valuable adjunct for helping patients to make better quality-of-life choices, and alerting care givers to target high-risk elderly for appropriate care and discussions, thus cutting back on futile treatment.

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). 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.

Clinical informatics, decision support for health professionals, electronic health records, and ehealth infrastructures.