Eric Widen

Background: Cardiac dysrhythmia is currently an extremely common disease. Severe arrhythmias often cause a series of complications, including congestive heart failure, fainting or syncope, stroke, and sudden death. Objective: The aim of this study was to predict incident arrhythmia prospectively within a 1-year period to provide early warning of impending arrhythmia. Methods: Retrospective (1,033,856 individuals enrolled between October 1, 2016, and October 1, 2017) and prospective (1,040,767 individuals enrolled between October 1, 2017, and October 1, 2018) cohorts were constructed from integrated electronic health records in Maine, United States. An ensemble learning workflow was built through multiple machine learning algorithms. Differentiating features, including acute and chronic diseases, procedures, health status, laboratory tests, prescriptions, clinical utilization indicators, and socioeconomic determinants, were compiled for incident arrhythmia assessment. The predictive model was retrospectively trained and calibrated using an isotonic regression method and was prospectively validated. Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC). Results: The cardiac dysrhythmia case-finding algorithm (retrospective: AUROC 0.854; prospective: AUROC 0.827) stratified the population into 5 risk groups: 53.35% (555,233/1,040,767), 44.83% (466,594/1,040,767), 1.76% (18,290/1,040,767), 0.06% (623/1,040,767), and 0.003% (27/1,040,767) were in the very low-risk, low-risk, medium-risk, high-risk, and very high-risk groups, respectively; 51.85% (14/27) patients in the very high-risk subgroup were confirmed to have incident cardiac dysrhythmia within the subsequent 1 year. Conclusions: Our case-finding algorithm is promising for prospectively predicting 1-year incident cardiac dysrhythmias in a general population, and we believe that our case-finding algorithm can serve as an early warning system to allow statewide population-level screening and surveillance to improve cardiac dysrhythmia care.

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 Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or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: Some hospitalized patients rapidly deteriorate due to either disease progression or imperfect triage and level of care assignment after their admission. An EWS to identify patients at high risk of subsequent intra-hospital death can be an effective tool to benefit patient safety and quality of care, and also reduce avoidable harm and costs. Objective: To prospectively validate a real-time early warning system (EWS) with the capacity to predict patients at high risk of in-hospital mortality during their inpatient episodes. Methods: Data were collected from the system-wide electronic medical record (EMR) of two acute Berkshire Health System hospitals, comprised of 54,246 inpatient admissions from January 01, 2015 to September 30, 2017, of which 3.97% resulted in intra-hospital deaths. After constructing the model using statistical methods and modern machine learning algorithms, we prospectively validated the algorithms as a real-time inpatient early warning system (EWS) for mortality. Results: Our EWS algorithm scored patients’ daily and long-term risk of in-hospital mortality probability after admission and stratified them into distinct risk groups. In the prospective validation, our EWS prospectively attained a c-statistic of 0.884, where 99 encounters were captured in the highest risk group, 68.69% of which died during the episodes. Furthermore, our real-time EWS successfully forecasted the top 13.33% of the expired patients at least 40.8 hours before death. Important clinical utilization features, together with coded diagnoses, vital signs and laboratory test results were recognized as impactful predictors in the final EWS. Conclusions: In this study, we prospectively demonstrated our new EWS’s capability of real-time monitoring and alerting clinicians to patients at high risk of in-hospital death, providing opportunities for timely interventions. This real-time EWS is able to assist clinical decision making and enable more actionable and effective individualized care for better patients’ health outcomes in target medical facilities.

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: 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 receiving a new diagnosis of lung cancer (incident lung cancer) within the next one year in a 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 having at least one EHR between April 1, 2016 and March 31, 2018, who had no history of lung cancer. A retrospective (N=873,598) cohort and a prospective (N=836,659) cohort 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 that measured the probability of receiving a new diagnosis of lung cancer from October 1, 2016 to September 31, 2017, based on the clinical profile in the retrospective cohort from the preceding 6 months. The model was validated using the prospective cohort to predict risk of incident lung cancer from April 1, 2017 to March 31, 2018, using the preceding 6 months’ clinical profile. 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 the incidence in the overall cohort (1,167/836,659, 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 incident lung cancer. Conclusions: We developed and prospectively validated an accurate risk prediction model of incident lung cancer in the next one year by statistically learning from the statewide EHR data in the preceding 6 months. Our model was able to identify patients at high risk of incident lung cancer, which will benefit population health through preventive interventions or more intensive surveillance.

BACKGROUND The rapid deterioration observed in the condition of some hospitalized patients can be attributed to either disease progression or imperfect triage and level of care assignment after their admission. An early warning system (EWS) to identify patients at high risk of subsequent intrahospital death can be an effective tool for ensuring patient safety and quality of care and reducing avoidable harm and costs. OBJECTIVE The aim of this study was to prospectively validate a real-time EWS designed to predict patients at high risk of inpatient mortality during their hospital episodes. METHODS Data were collected from the system-wide electronic medical record (EMR) of two acute Berkshire Health System hospitals, comprising 54,246 inpatient admissions from January 1, 2015, to September 30, 2017, of which 2.30% (1248/54,246) resulted in intrahospital deaths. Multiple machine learning methods (linear and nonlinear) were explored and compared. The tree-based random forest method was selected to develop the predictive application for the intrahospital mortality assessment. After constructing the model, we prospectively validated the algorithms as a real-time inpatient EWS for mortality. RESULTS The EWS algorithm scored patients’ daily and long-term risk of inpatient mortality probability after admission and stratified them into distinct risk groups. In the prospective validation, the EWS prospectively attained a c-statistic of 0.884, where 99 encounters were captured in the highest risk group, 69% (68/99) of whom died during the episodes. It accurately predicted the possibility of death for the top 13.3% (34/255) of the patients at least 40.8 hours before death. Important clinical utilization features, together with coded diagnoses, vital signs, and laboratory test results were recognized as impactful predictors in the final EWS. CONCLUSIONS In this study, we prospectively demonstrated the capability of the newly-designed EWS to monitor and alert clinicians about patients at high risk of in-hospital death in real time, thereby providing opportunities for timely interventions. This real-time EWS is able to assist clinical decision making and enable more actionable and effective individualized care for patients’ better health outcomes in target medical facilities.

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.

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.

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