A Comprehensive Index for Predicting Risk of Anemia from Patients' Diagnoses

Improving the Accuracy of Predictive Models for Outcomes of Antidepressants by Using an Ontological Adjustment Approach

For patients with rare comorbidities, there are insufficient observations to accurately estimate the effectiveness of treatment. At the same time, all diagnosis, including rare diagnosis, are part of the International Classification of Disease (ICD). Grouping ICD into broader concepts (i.e., ontology adjustment) can not only increase accuracy of estimating antidepressant effectiveness for patients with rare conditions but also prevent overfitting in big data analysis. In this study, 3,678,082 depressed patients treated with antidepressants were obtained from OptumLabs® Data Warehouse (OLDW). For rare diagnoses, adjustments were made by using the likelihood ratio of the immediate broader concept in the ICD hierarchies. The accuracy of models in training (90%) and test (10%) sets was examined using the area under the receiver operating curves (AROC). The gap in training and test AROC shows how much random noise was modeled. If the gap is large, then the parameters of the model, including the reported effectiveness of the antidepressant for patients with rare conditions, are suspect. There was, on average, a 9.0% reduction in the AROC gap after using the ontological adjustment. Therefore, ontology adjustment can reduce model overfitting, leading to better parameter estimates from the training set.

Relative accuracy of social and medical determinants of suicide in electronic health records

OBJECTIVE

This paper compares the accuracy of predicting suicide from Social Determinants of Health (SDoH) or history of illness.

POPULATION STUDIED

5 313 965 Veterans who at least had two primary care visits between 2008 and 2016.

STUDY DESIGN

The dependent variable was suicide or intentional self-injury. The independent variables were 10 495 International Classification of Disease (ICD) Version 9 codes, age, and gender. The ICD codes included 40 V-codes used for measuring SDoH, such as family disruption, family history of substance abuse, lack of education, legal impediments, social isolation, unemployment, and homelessness. The sample was randomly divided into training (90 percent) and validation (10 percent) sets. Area under the receiver operating characteristic (AROC) was used to measure accuracy of predictions in the validation set.

PRINCIPAL FINDINGS

Separate analyses were done for inpatient and outpatient codes; the results were similar. In the hospitalized group, the mean age was 67.2 years, and 92.1 percent were male. The mean number of medical diagnostic codes during the study period was 37; and 12.9 percent had at least one SDoH V-code. At least one episode of suicide or intentional self-injury occurred in 1.89 percent of cases. SDoH V-codes, on average, elevated the risk of suicide or intentional self-injury by 24-fold (ranging from 4- to 86-fold). An index of 40 SDoH codes predicted suicide or intentional self-injury with an AROC of 0.64. An index of 10 445 medical diagnoses, without SDoH V-codes, had AROC of 0.77. The combined SDoH and medical diagnoses codes also had AROC of 0.77.

CONCLUSION

In predicting suicide or intentional self-harm, SDoH V-codes add negligible information beyond what is already available in medical diagnosis codes.

IMPLICATIONS FOR PRACTICE

Policies that affect SDoH (eg, housing policies, resilience training) may not have an impact on suicide rates, if they do not change the underlying medical causes of SDoH.

Predictive, Personalized, Preventive and Participatory (4P) Medicine Applied to Telemedicine and eHealth in the Literature

The main objective of this work is to provide a review of existing research work into predictive, personalized, preventive and participatory medicine in telemedicine and ehealth. The academic databases used for searches are IEEE Xplore, PubMed, Science Direct, Web of Science and ResearchGate, taking into account publication dates from 2010 up to the present day. These databases cover the greatest amount of information on scientific texts in multidisciplinary fields, from engineering to medicine. Various search criteria were established, such as ("Predictive" OR "Personalized" OR "Preventive" OR "Participatory") AND "Medicine" AND ("eHealth" OR "Telemedicine") selecting the articles of most interest. A total of 184 publications about predictive, personalized, preventive and participatory (4P) medicine in telemedicine and ehealth were found, of which 48 were identified as relevant. Many of the publications found show how the P4 medicine is being developed in the world and the benefits it provides for patients with different illnesses. After the revision that was undertaken, it can be said that P4 medicine is a vital factor for the improvement of medical services. It is hoped that one of the main contributions of this study is to provide an insight into how P4 medicine in telemedicine and ehealth is being applied, as well as proposing outlines for the future that contribute to the improvement of prevention and prediction of illnesses.

Electronic Health Record-Based Screening for Substance Abuse

Existing methods of screening for substance abuse (standardized questionnaires or clinician's simply asking) have proven difficult to initiate and maintain in primary care settings. This article reports on how predictive modeling can be used to screen for substance abuse using extant data in electronic health records (EHRs). We relied on data available through Veterans Affairs Informatics and Computing Infrastructure (VINCI) for the years 2006 through 2016. We focused on 4,681,809 veterans who had at least two primary care visits; 829,827 of whom had a hospitalization. Data included 699 million outpatient and 17 million inpatient records. The dependent variable was substance abuse as identified from 89 diagnostic codes using the Agency for Healthcare Quality and Research classification of diseases. In addition, we included the diagnostic codes used for identification of prescription abuse. The independent variables were 10,292 inpatient and 13,512 outpatient diagnoses, plus 71 dummy variables measuring age at different years between 20 and 90 years. A modified naive Bayes model was used to aggregate the risk across predictors. The accuracy of the predictions was examined using area under the receiver operating characteristic (AROC) curve in 20% of data, randomly set aside for the evaluation. Many physical/mental illnesses were associated with substance abuse. These associations supported findings reported in the literature regarding the impact of substance abuse on various diseases and vice versa. In randomly set-aside validation data, the model accurately predicted substance abuse for inpatient (AROC = 0.884), outpatient (AROC = 0.825), and combined inpatient and outpatient (AROC = 0.840) data. If one excludes information available after substance abuse is known, the cross-validated AROC remained high, 0.822 for inpatient and 0.817 for outpatient data. Data within EHRs can be used to detect existing or predict potential future substance abuse.

The Multimorbidity Index: A Tool for Assessing the Prognosis of Patients from Their History of Illness

BACKGROUND

The Multimorbidity (MM) Index predicts the prognosis of patients from their diagnostic history. In contrast to existing approaches with broad diagnostic categories, it treats each diagnosis as a separate independent variable using individual International Classification of Disease, Revision 9 (ICD-9) codes.

OBJECTIVE

This paper describes the MM Index, reviews the published data on its accuracy, and provides procedures for implementing the Index within electronic health record (EHR) systems. Methods: The MM Index was tested on various patient populations by using data from the United States Department of Veterans Affairs data warehouse and claims data within the Healthcare Cost and Utilization Project of the Agency for Health Care Research and Quality.

RESULTS

In cross-validated studies, the MM Index outperformed prognostic indices based on physiological markers, such as CD4 cell counts in HIV/AIDS, HbAlc levels in diabetes, ejection fractions in heart failure, or the 13 physiological markers commonly used for patients in intensive care units. When predicting the prognosis of nursing home patients by using the cross-validated area under a receiver operating characteristic (ROC) curve, the MM Index was 15 percent outperformed the Quan variant of the Charlson Index, 27 percent more accurate than the Deyo variant of the Charlson Index, and 22 percent more accurate than the von Walraven variant of the Elixhauser Index. For patients in intensive care units, the MM Index was 13 percent outperformed the cross-validated area under ROC associated with Elixhauser's categories. The MM Index also demonstrated greater accuracy than a number of commercially available measures of illness severity; including a fivefold greater accuracy than the All Patient Refined Diagnosis-Related Groups and a threefold greater accuracy than All Payer Severity-Adjusted Diagnosis-Related Groups.

CONCLUSION

The MM Index is statistically more accurate than many existing measures of prognosis. The magnitude of improvement is large and may lead to a clinically meaningful difference in patient care. Given the large improvements in accuracy, the use of the MM Index for policy and comparative effectiveness analysis is recommended.