A 20-Year Evaluation of LOINC in the United States' Largest Integrated Health System

Automated Versus Semi-automated Lab Value Extraction for the VA Cardiac Surgical Quality Improvement Program

INTRODUCTION

The Veterans Affairs Surgical Quality Improvement Program (VASQIP) trains surgical quality nurses (SQNs) at each Veterans Affairs (VA) hospital to extract or verify 187 variables from the medical record for all cardiac surgical cases. For ten preoperative laboratory values, VASQIP has a semiautomated (SA) system in which local lab values are automatically extracted, verified by SQNs, and lab values recorded at other VA facilities are manually extracted. The objective of this study was to develop and validate a method to automate the extraction of these ten preoperative laboratory values and compare results with the current SA method.

MATERIALS AND METHODS

We developed methods to extract ten preoperative laboratory values and measurement dates from the VA Corporate Data Warehouse using Logical Observation Identifiers Names and Codes. Automated (A) versus SA information extraction was compared in terms of agreement, conformance to data definitions, proximity to surgery, and missingness.

RESULTS

For surgeries with both A and SA lab values, the intraclass correlation coefficients for the ten variables ranged from 0.90 to 0.98. For several variables, the A method resulted in much lower rates of missing data (e.g., 2.4% versus 22.5% missing data for high-density lipoprotein) and eliminated out-of-date-range entries.

CONCLUSIONS

Although SQN-extracted data are widely considered the gold standard within National Surgical Quality Improvement Programs, there may be advantages to fully automating extraction of lab values, including high congruence with SA SQN-extracted or verified values and lower rates of missingness and out-of-date-range data.

Inflammatory Biomarkers Differ among Hospitalized Veterans Infected with Alpha, Delta, and Omicron SARS-CoV-2 Variants

Mortality due to COVID-19 has been correlated with laboratory markers of inflammation, such as C-reactive protein (CRP). The lower mortality during Omicron variant infections could be explained by variant-specific immune responses or host factors, such as vaccination status. We hypothesized that infections due to Omicron variant cause less inflammation compared to Alpha and Delta, correlating with lower mortality. This was a retrospective cohort study of veterans hospitalized for COVID-19 at the Veterans Health Administration. We compared inflammatory markers among patients hospitalized during Omicron infection with those of Alpha and Delta. We reported the adjusted odds ratio (aOR) of the first laboratory results during hospitalization and in-hospital mortality, stratified by vaccination status. Of 2,075,564 Veterans tested for COVID-19, 29,075 Veterans met the criteria: Alpha (45.1%), Delta (23.9%), Omicron (31.0%). Odds of abnormal CRP in Delta (aOR = 1.85, 95% CI:1.64-2.09) and Alpha (aOR = 1.94, 95% CI:1.75-2.15) were significantly higher compared to Omicron. The same trend was observed for Ferritin, Alanine aminotransferase, Aspartate aminotransferase, Lactate dehydrogenase, and Albumin. The mortality in Delta (aOR = 1.92, 95% CI:1.73-2.12) and Alpha (aOR = 1.68, 95% CI:1.47-1.91) were higher than Omicron. The results remained significant after stratifying the outcomes based on vaccination status. Veterans infected with Omicron showed milder inflammatory responses and lower mortality than other variants.

Elevated Risk of Chronic Respiratory Conditions within 60 Days of COVID-19 Hospitalization in Veterans

SARS-CoV-2 infection prominently affects the respiratory system, and patients hospitalized with COVID-19 are at an increased risk of developing respiratory conditions. We examined the risk of new respiratory conditions of COVID-19 among hospitalized patients in the national Veterans Health Administration between 15 February 2020 and 16 June 2021. The study cohort included all COVID-19-tested, hospitalized individuals who survived the index admission and did not have any previously diagnosed chronic respiratory conditions (asthma, bronchitis, chronic lung disease, chronic obstructive pulmonary disease (COPD), emphysema, or venous thromboembolism) before SARS-CoV-2 testing. Of 373,048 patients hospitalized after SARS-CoV-2 testing, 18,686 positive and 37,372 negative patients met the inclusion/exclusion criteria and were matched by age, sex, and race using propensity score matching. The results showed that the SARS-CoV-2 positive group had a greater risk of developing asthma (adjusted odds ratio (aOR) = 1.37), bronchitis (aOR = 2.81), chronic lung disease (aOR = 2.14), COPD (aOR = 1.56), emphysema (aOR = 1.52), and venous thromboembolism (aOR = 1.92) within 60 days after the index COVID date of testing. These findings could inform that the clinical care team considers a risk of new respiratory conditions and address these conditions in the post-hospitalization management of the patient, which could potentially lead to reduce the risk of complications and optimize recovery.

A Machine Learning Model to Successfully Predict Future Diagnosis of Chronic Myelogenous Leukemia With Retrospective Electronic Health Records Data

BACKGROUND

Chronic myelogenous leukemia (CML) is a clonal stem cell disorder accounting for 15% of adult leukemias. We aimed to determine if machine learning models could predict CML using blood cell counts prior to diagnosis.

METHODS

We identified patients with a diagnostic test for CML (BCR-ABL1) and at least 6 consecutive prior years of differential blood cell counts between 1999 and 2020 in the largest integrated health care system in the United States. Blood cell counts from different time periods prior to CML diagnostic testing were used to train, validate, and test machine learning models.

RESULTS

The sample included 1,623 patients with BCR-ABL1 positivity rate 6.2%. The predictive ability of machine learning models improved when trained with blood cell counts closer to time of diagnosis: 2 to 5 years area under the curve (AUC), 0.59 to 0.67, 0.5 to 1 years AUC, 0.75 to 0.80, at diagnosis AUC, 0.87 to 0.92.

CONCLUSIONS

Blood cell counts collected up to 5 years prior to diagnostic workup of CML successfully predicted the BCR-ABL1 test result. These findings suggest a machine learning model trained with blood cell counts could lead to diagnosis of CML earlier in the disease course compared to usual medical care.

Smoking Status and Factors associated with COVID-19 In-hospital Mortality among U.S. Veterans

Introduction

The role of smoking in risk of death among patients with COVID-19 remains unclear. We examined the association between in-hospital mortality from COVID-19 and smoking status and other factors in the United States Veterans Health Administration (VHA).

Methods

This is an observational, retrospective cohort study using the VHA COVID-19 shared data resources for February 1 to September 11, 2020. Veterans admitted to the hospital who tested positive for SARS-CoV-2 and hospitalized by VHA were grouped into Never (as reference, NS), Former (FS), and Current smokers (CS). The main outcome was in-hospital mortality. Control factors were the most important variables (among all available) determined through a cascade of machine learning. We reported adjusted odds ratios (aOR) and 95% confidence intervals (95%CI) from logistic regression models, imputing missing smoking status in our primary analysis.

Results

Out of 8 667 996 VHA enrollees, 505 143 were tested for SARS-CoV-2 (NS = 191 143; FS = 240 336; CS = 117 706; Unknown = 45 533). The aOR of in-hospital mortality was 1.16 (95%CI 1.01, 1.32) for FS vs. NS and 0.97 (95%CI 0.78, 1.22; p > .05) for CS vs. NS with imputed smoking status. Among other factors, famotidine and nonsteroidal anti-inflammatory drugs (NSAID) use before hospitalization were associated with lower risk while diabetes with complications, kidney disease, obesity, and advanced age were associated with higher risk of in-hospital mortality.

Conclusions

In patients admitted to the hospital with SARS-CoV-2 infection, our data demonstrate that FS are at higher risk of in-hospital mortality than NS. However, this pattern was not seen among CS highlighting the need for more granular analysis with high-quality smoking status data to further clarify our understanding of smoking risk and COVID-19-related mortality. Presence of comorbidities and advanced age were also associated with increased risk of in-hospital mortality.

Implications

Veterans who were former smokers were at higher risk of in-hospital mortality compared to never smokers. Current smokers and never smokers were at similar risk of in-hospital mortality. The use of famotidine and nonsteroidal anti-inflammatory drugs (NSAIDs) before hospitalization were associated with lower risk while uncontrolled diabetes mellitus, advanced age, kidney disease, and obesity were associated with higher risk of in-hospital mortality.

Gaps in standards for integrating artificial intelligence technologies into ophthalmic practice

PURPOSE OF REVIEW

The purpose of this review is to provide an overview of healthcare standards and their relevance to multiple ophthalmic workflows, with a specific emphasis on describing gaps in standards development needed for improved integration of artificial intelligence technologies into ophthalmic practice.

RECENT FINDINGS

Healthcare standards are an essential component of data exchange and critical for clinical practice, research, and public health surveillance activities. Standards enable interoperability between clinical information systems, healthcare information exchange between institutions, and clinical decision support in a complex health information technology ecosystem. There are several gaps in standards in ophthalmology, including relatively low adoption of imaging standards, lack of use cases for integrating apps providing artificial intelligence -based decision support, lack of common data models to harmonize big data repositories, and no standards regarding interfaces and algorithmic outputs.

SUMMARY

These gaps in standards represent opportunities for future work to develop improved data flow between various elements of the digital health ecosystem. This will enable more widespread adoption and integration of artificial intelligence-based tools into clinical practice. Engagement and support from the ophthalmology community for standards development will be important for advancing this work.

Differences in COVID-19-Related Testing and Healthcare Utilization by Race and Ethnicity in the Veterans Health Administration

Importance

Recent reports indicate differences in COVID-19-related care and outcomes between Black and White Americans.

Objective

We examine the COVID-19-related healthcare utilization and mortality by race and ethnicity of patients tested for SARS-CoV-2 in the Veterans Health Administration (VHA).

Design

A retrospective cohort study.

Setting

We used the VHA COVID-19 shared data resources between February 1 and June 30, 2020.

Participants

Veterans tested for SARS-CoV-2 virus by VHA.

Exposure(s)

Three racial-ethnicity groups of Black, Hispanic, and White (as reference) veterans.

Main Outcome(s) and Measure(s)

Main outcomes are testing rate, positivity rate, hospitalization rate, ICU admission rate, and in-hospital mortality. Controlling for sex, age, and Elixhauser comorbidity index, we report adjusted odds ratios (aOR) and 95% confidence intervals (95% CI) from logistic regression models.

Results

Of the 8,667,996 active veteran enrollees, 252,702 were tested by VHA from February to June, 2020, with 20,500 positive results and 4,790 hospitalizations. The testing rate was 4.4% among Black and 4.7% among Hispanic veterans compared to White veterans, 2.8%. The testing positivity rate was similarly elevated among Black (12.2%) and Hispanic (11.6%) veterans compared to White veterans (6.0%). The aORs of hospitalization in Black veterans (1.88; 95% CI 1.74, 2.03) and Hispanic veterans (1.41; 95% CI 1.25, 1.60) were higher compared to White veterans. No significant differences by race and ethnicity were observed in OR or aOR of ICU admission and in-hospital death among hospitalized patients.

Conclusions and Relevance

On a national level, the VHA was more likely to test and hospitalize Black and Hispanic veterans compared to White veterans, but there were no significant differences in ICU admission or in-hospital mortality among those hospitalized. This pattern of differences may relate to social determinants of health, factors affecting access to non-VHA care, or preferences for VHA care affecting initial care seeking, but not in-hospital outcomes.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40615-021-00982-0.

What Role Can Process Mining Play in Recurrent Clinical Guidelines Issues? A Position Paper

In the age of Evidence-Based Medicine, Clinical Guidelines (CGs) are recognized to be an indispensable tool to support physicians in their daily clinical practice. Medical Informatics is expected to play a relevant role in facilitating diffusion and adoption of CGs. However, the past pioneering approaches, often fragmented in many disciplines, did not lead to solutions that are actually exploited in hospitals. Process Mining for Healthcare (PM4HC) is an emerging discipline gaining the interest of healthcare experts, and seems able to deal with many important issues in representing CGs. In this position paper, we briefly describe the story and the state-of-the-art of CGs, and the efforts and results of the past approaches of medical informatics. Then, we describe PM4HC, and we answer questions like how can PM4HC cope with this challenge? Which role does PM4HC play and which rules should be employed for the PM4HC scientific community?

Information Is Selection-A Review of Basics Shows Substantial Potential for Improvement of Digital Information Representation

Any piece of information is a selection from a set of possibilities. In this paper, this set is called a "domain". Digital information consists of number sequences, which are selections from a domain. At present, these number sequences are defined contextually in a very variable way, which impairs their comparability. Therefore, global uniformly defined "domain vectors" (DVs), with a structure containing a "Uniform Locator" ("UL"), referred to as "UL plus number sequence", are proposed. The "UL" is an efficient global pointer to the uniform online definition of the subsequent number sequence. DVs are globally defined, identified, comparable, and searchable by criteria which users can define online. In medicine, for example, patients, doctors, and medical specialists can define DVs online and can, therefore, form global criteria which are important for certain diagnoses. This allows for the immediate generation of precise diagnostic specific statistics of "similar medical cases", in order to discern the best therapy. The introduction of a compact DV data structure may substantially improve the digital representation of medical information.