Extending an open-source tool to measure data quality: case report on Observational Health Data Science and Informatics (OHDSI)

Association between malnutrition and post-acute COVID-19 sequelae: A retrospective cohort study

BACKGROUND

Long coronavirus disease consists of health problems people experience after being infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). These can be severe and include respiratory, neurological, and gastrointestinal symptoms, with resulting detrimental impacts on quality of life. Although malnutrition has been shown to increase risk of severe disease and death during acute infection, less is known about its influence on post-acute COVID-19 outcomes. We addressed this critical gap in knowledge by evaluating malnutrition's impact on post-COVID-19 sequelae.

METHODS

This study leveraged the National COVID Cohort Collaborative to identify a cohort of patients who were at least 28 days post-acute COVID-19 infection. Multivariable Cox proportional hazard models evaluated the impact of malnutrition on the following postacute sequelae of SARS-CoV-2: (1) death, (2) long COVID diagnosis, (3) COVID-19 reinfection, and (4) other phenotypic abnormalities. A subgroup analysis evaluated these outcomes in a cohort of hospitalized patients with COVID-19 with hospital-acquired (HAC) malnutrition.

RESULTS

The final cohort included 4,372,722 individuals, 78,782 (1.8%) with a history of malnutrition. Individuals with malnutrition had a higher risk of death (adjusted hazard ratio [aHR]: 2.10; 95% CI: 2.04-2.17) and SARS-CoV-2 reinfection (aHR: 1.52; 95% CI: 1.43-1.61) in the postacute period than those without malnutrition. In the subgroup, those with HAC malnutrition had a higher risk of death and long COVID diagnosis.

CONCLUSION

Nutrition screening for individuals with acute SARS-CoV-2 infection may be a crucial step in mitigating life-altering, negative postacute outcomes through early identification and intervention of patients with malnutrition.

New Tools for Data Harmonization and Their Potential Applications in Organ Transplantation

In organ transplantation, accurate analysis of clinical outcomes requires large, high-quality data sets. Not only are outcomes influenced by a multitude of factors such as donor, recipient, and transplant characteristics and posttransplant events but they may also change over time. Although large data sets already exist and are continually expanding in transplant registries and health institutions, these data are rarely combined for analysis because of a lack of harmonization. Promoted by the digitalization of the healthcare sector, effective data harmonization tools became available, with potential applications also for organ transplantation. We discuss herein the present problems in the harmonization of organ transplant data and offer solutions to enhance its accuracy through the use of emerging new tools. To overcome the problem of inadequate representation of transplantation-specific terms, ontologies and common data models particular to this field could be created and supported by a consortium of related stakeholders to ensure their broad acceptance. Adopting clear data-sharing policies can diminish administrative barriers that impede collaboration between organizations. Secure multiparty computation frameworks and the artificial intelligence (AI) approach federated learning can facilitate decentralized and harmonized analysis of data sets, without sharing sensitive data and compromising patient privacy. A common image data model built upon a standardized format would be beneficial to AI-based analysis of pathology images. Implementation of these promising new tools and measures, ideally with the involvement and support of transplant societies, is expected to produce improved integration and harmonization of transplant data and greater accuracy in clinical decision-making, enabling improved patient outcomes.

Second-Line Pharmaceutical Treatments for Patients with Type 2 Diabetes

Importance

Assessing the relative effectiveness and safety of additional treatments when metformin monotherapy is insufficient remains a limiting factor in improving treatment choices in type 2 diabetes.

Objective

To determine whether data from electronic health records across the University of California Health system could be used to assess the comparative effectiveness and safety associated with 4 treatments in diabetes when added to metformin monotherapy.

Design, Setting, and Participants

This multicenter, new user, multidimensional propensity score-matched retrospective cohort study with leave-one-medical-center-out (LOMCO) sensitivity analysis used principles of emulating target trial. Participants included patients with diabetes receiving metformin who were then additionally prescribed either a sulfonylurea, dipeptidyl peptidase-4 inhibitor (DPP4I), sodium-glucose cotransporter-2 inhibitor (SGLT2I), or glucagon-like peptide-1 receptor agonist (GLP1RA) for the first time and followed-up over a 5-year monitoring period. Data were analyzed between January 2022 and April 2023.

Exposure

Treatment with sulfonylurea, DPP4I, SGLT2I, or GLP1RA added to metformin monotherapy.

Main Outcomes and Measures

The main effectiveness outcome was the ability of patients to maintain glycemic control, represented as time to metabolic failure (hemoglobin A1c [HbA1c] ≥7.0%). A secondary effectiveness outcome was assessed by monitoring time to new incidence of any of 28 adverse outcomes, including diabetes-related complications while treated with the assigned drug. Sensitivity analysis included LOMCO.

Results

This cohort study included 31 852 patients (16 635 [52.2%] male; mean [SD] age, 61.4 [12.6] years) who were new users of diabetes treatments added on to metformin monotherapy. Compared with sulfonylurea in random-effect meta-analysis, treatment with SGLT2I (summary hazard ratio [sHR], 0.75 [95% CI, 0.69-0.83]; I2 = 37.5%), DPP4I (sHR, 0.79 [95% CI, 0.75-0.84]; I2 = 0%), GLP1RA (sHR, 0.62 [95% CI, 0.57-0.68]; I2 = 23.6%) were effective in glycemic control; findings from LOMCO sensitivity analysis were similar. Treatment with SGLT2I showed no significant difference in effectiveness compared with GLP1RA (sHR, 1.26 [95% CI, 1.12-1.42]; I2 = 47.3%; no LOMCO) or DPP4I (sHR, 0.97 [95% CI, 0.90-1.04]; I2 = 0%). Patients treated with DPP4I and SGLT2I had fewer cardiovascular events compared with those treated with sulfonylurea (DPP4I: sHR, 0.84 [95% CI, 0.74-0.96]; I2 = 0%; SGLT2I: sHR, 0.78 [95% CI, 0.62-0.98]; I2 = 0%). Patients treated with a GLP1RA or SGLT2I were less likely to develop chronic kidney disease (GLP1RA: sHR, 0.75 [95% CI 0.6-0.94]; I2 = 0%; SGLT2I: sHR, 0.77 [95% CI, 0.61-0.97]; I2 = 0%), kidney failure (GLP1RA: sHR, 0.69 [95% CI, 0.56-0.86]; I2 = 9.1%; SGLT2I: sHR, 0.72 [95% CI, 0.59-0.88]; I2 = 0%), or hypertension (GLP1RA: sHR, 0.82 [95% CI, 0.68-0.97]; I2 = 0%; SGLT2I: sHR, 0.73 [95% CI, 0.58-0.92]; I2 = 38.5%) compared with those treated with a sulfonylurea. Patients treated with an SGLT2I, vs a DPP4I, GLP1RA, or sulfonylurea, were less likely to develop indicators of chronic hepatic dysfunction (sHR vs DPP4I, 0.68 [95% CI, 0.49-0.95]; I2 = 0%; sHR vs GLP1RA, 0.66 [95% CI, 0.48-0.91]; I2 = 0%; sHR vs sulfonylurea, 0.60 [95% CI, 0.44-0.81]; I2 = 0%), and those treated with a DPP4I were less likely to develop new incidence of hypoglycemia (sHR, 0.48 [95% CI, 0.36-0.65]; I2 = 22.7%) compared with those treated with a sulfonylurea.

Conclusions and Relevance

These findings highlight familiar medication patterns, including those mirroring randomized clinical trials, as well as providing new insights underscoring the value of robust clinical data analytics in swiftly generating evidence to help guide treatment choices in diabetes.

Integrating statistical and visual analytic methods for bot identification of health-related survey data

OBJECTIVE

In recent years, we have increasingly observed issues concerning quality of online information due to misinformation and disinformation. Aside from social media, there is growing awareness that questionnaire data collected using online recruitment methods may include suspect data provided by bots. Issues with data quality can be particularly problematic in health and/or biomedical contexts; thus, developing robust methods for suspect data identification and removal is of paramount importance in informatics. In this study, we describe an interactive visual analytics approach to suspect data identification and removal and demonstrate the application of this approach on questionnaire data pertaining to COVID-19 derived from different recruitment venues, including listservs and social media.

METHODS

We developed a pipeline for data cleaning, pre-processing, analysis, and automated ranking of data to address data quality issues. We then employed the ranking in conjunction with manual review to identify suspect data and remove them from subsequent analyses. Last, we compared differences in the data before and after removal.

RESULTS

We performed data cleaning, pre-processing, and exploratory analysis on a survey dataset (N = 4,163) collected using multiple recruitment mechanins using the Qualtrics survey platform. Based on these results, we identified suspect features and used these to generate a suspect feature indicator for each survey response. We excluded survey responses that did not fit the inclusion criteria for the study (n = 29) and then performed manual review of the remaining responses, triangulating with the suspect feature indicator. Based on this review, we excluded 2,921 responses. Additional responses were excluded based on a spam classification by Qualtrics (n=13), and the percentage of survey completion (n=328), resulting in a final sample size of 872. We performed additional analyses to demonstrate the extent to which the suspect feature indicator was congruent with eventual inclusion, as well as compared the characteristics of the included and excluded data.

CONCLUSION

Our main contributions are: 1) a proposed framework for data quality assessment, including suspect data identification and removal; 2) the analysis of potential consequences in terms of representation bias in the dataset; and 3) recommendations for implementation of this approach in practice.

Privacy-Preserving Federated Model Predicting Bipolar Transition in Patients With Depression: Prediction Model Development Study

BACKGROUND

Mood disorder has emerged as a serious concern for public health; in particular, bipolar disorder has a less favorable prognosis than depression. Although prompt recognition of depression conversion to bipolar disorder is needed, early prediction is challenging due to overlapping symptoms. Recently, there have been attempts to develop a prediction model by using federated learning. Federated learning in medical fields is a method for training multi-institutional machine learning models without patient-level data sharing.

OBJECTIVE

This study aims to develop and validate a federated, differentially private multi-institutional bipolar transition prediction model.

METHODS

This retrospective study enrolled patients diagnosed with the first depressive episode at 5 tertiary hospitals in South Korea. We developed models for predicting bipolar transition by using data from 17,631 patients in 4 institutions. Further, we used data from 4541 patients for external validation from 1 institution. We created standardized pipelines to extract large-scale clinical features from the 4 institutions without any code modification. Moreover, we performed feature selection in a federated environment for computational efficiency and applied differential privacy to gradient updates. Finally, we compared the federated and the 4 local models developed with each hospital's data on internal and external validation data sets.

RESULTS

In the internal data set, 279 out of 17,631 patients showed bipolar disorder transition. In the external data set, 39 out of 4541 patients showed bipolar disorder transition. The average performance of the federated model in the internal test (area under the curve [AUC] 0.726) and external validation (AUC 0.719) data sets was higher than that of the other locally developed models (AUC 0.642-0.707 and AUC 0.642-0.699, respectively). In the federated model, classifications were driven by several predictors such as the Charlson index (low scores were associated with bipolar transition, which may be due to younger age), severe depression, anxiolytics, young age, and visiting months (the bipolar transition was associated with seasonality, especially during the spring and summer months).

CONCLUSIONS

We developed and validated a differentially private federated model by using distributed multi-institutional psychiatric data with standardized pipelines in a real-world environment. The federated model performed better than models using local data only.

Leveraging multi-source to resolve inconsistency across pharmacogenomic datasets in drug sensitivity prediction

Pharmacogenomics datasets have been generated for various purposes, such as investigating different biomarkers. However, when studying the same cell line with the same drugs, differences in drug responses exist between studies. These variations arise from factors such as inter-tumoral heterogeneity, experimental standardization, and the complexity of cell subtypes. Consequently, drug response prediction suffers from limited generalizability. To address these challenges, we propose a computational model based on Federated Learning (FL) for drug response prediction. By leveraging three pharmacogenomics datasets (CCLE, GDSC2, and gCSI), we evaluate the performance of our model across diverse cell line-based databases. Our results demonstrate superior predictive performance compared to baseline methods and traditional FL approaches through various experimental tests. This study underscores the potential of employing FL to leverage multiple data sources, enabling the development of generalized models that account for inconsistencies among pharmacogenomics datasets. By addressing the limitations of low generalizability, our approach contributes to advancing drug response prediction in precision oncology.

Life course of retrospective harmonization initiatives: key elements to consider

Optimizing research on the developmental origins of health and disease (DOHaD) involves implementing initiatives maximizing the use of the available cohort study data; achieving sufficient statistical power to support subgroup analysis; and using participant data presenting adequate follow-up and exposure heterogeneity. It also involves being able to undertake comparison, cross-validation, or replication across data sets. To answer these requirements, cohort study data need to be findable, accessible, interoperable, and reusable (FAIR), and more particularly, it often needs to be harmonized. Harmonization is required to achieve or improve comparability of the putatively equivalent measures collected by different studies on different individuals. Although the characteristics of the research initiatives generating and using harmonized data vary extensively, all are confronted by similar issues. Having to collate, understand, process, host, and co-analyze data from individual cohort studies is particularly challenging. The scientific success and timely management of projects can be facilitated by an ensemble of factors. The current document provides an overview of the 'life course' of research projects requiring harmonization of existing data and highlights key elements to be considered from the inception to the end of the project.

Higher hospitalization and mortality rates among SARS-CoV-2-infected persons in rural America

PURPOSE

Rural communities are among the most underserved and resource-scarce populations in the United States. However, there are limited data on COVID-19 outcomes in rural America. This study aims to compare hospitalization rates and inpatient mortality among SARS-CoV-2-infected persons stratified by residential rurality.

METHODS

This retrospective cohort study from the National COVID Cohort Collaborative (N3C) assesses 1,033,229 patients from 44 US hospital systems diagnosed with SARS-CoV-2 infection between January 2020 and June 2021. Primary outcomes were hospitalization and all-cause inpatient mortality. Secondary outcomes were utilization of supplemental oxygen, invasive mechanical ventilation, vasopressor support, extracorporeal membrane oxygenation, and incidence of major adverse cardiovascular events or hospital readmission. The analytic approach estimates 90-day survival in hospitalized patients and associations between rurality, hospitalization, and inpatient adverse events while controlling for major risk factors using Kaplan-Meier survival estimates and mixed-effects logistic regression.

FINDINGS

Of 1,033,229 diagnosed COVID-19 patients included, 186,882 required hospitalization. After adjusting for demographic differences and comorbidities, urban-adjacent and nonurban-adjacent rural dwellers with COVID-19 were more likely to be hospitalized (adjusted odds ratio [aOR] 1.18, 95% confidence interval [CI], 1.16-1.21 and aOR 1.29, CI 1.24-1.1.34) and to die or be transferred to hospice (aOR 1.36, CI 1.29-1.43 and 1.37, CI 1.26-1.50), respectively. All secondary outcomes were more likely among rural patients.

CONCLUSIONS

Hospitalization, inpatient mortality, and other adverse outcomes are higher among rural persons with COVID-19, even after adjusting for demographic differences and comorbidities. Further research is needed to understand the factors that drive health disparities in rural populations.

A topic-based hierarchical publish/subscribe messaging middleware for COVID-19 detection in X-ray image and its metadata

Putting real-time medical data processing applications into practice comes with some challenges such as scalability and performance. Processing medical images from different collaborators is an example of such applications, in which chest X-ray data are processed to extract knowledge. It is not easy to process data and get the required information in real time using central processing techniques when data get very large in size. In this paper, real-time data are filtered and forwarded to the right processing node by using the proposed topic-based hierarchical publish/subscribe messaging middleware in the distributed scalable network of collaborating computation nodes instead of classical approaches of centralized computation. This enables processing streaming medical data in near real time and makes a warning system possible. End users have the capability of filtering/searching. The returned search results can be images (COVID-19 or non-COVID-19) and their meta-data are gender and age. Here, COVID-19 is detected using a novel capsule network-based model from chest X-ray images. This middleware allows for a smaller search space as well as shorter times for obtaining search results.

Modeling the Potential Impact of Missing Race and Ethnicity Data in Infectious Disease Surveillance Systems on Disparity Measures: Scenario Analysis of Different Imputation Strategies

BACKGROUND

Monitoring progress toward population health equity goals requires developing robust disparity indicators. However, surveillance data gaps that result in undercounting racial and ethnic minority groups might influence the observed disparity measures.

OBJECTIVE

This study aimed to assess the impact of missing race and ethnicity data in surveillance systems on disparity measures.

METHODS

We explored variations in missing race and ethnicity information in reported annual chlamydia and gonorrhea diagnoses in the United States from 2007 to 2018 by state, year, reported sex, and infection. For diagnoses with incomplete demographic information in 2018, we estimated disparity measures (relative rate ratio and rate difference) with 5 imputation scenarios compared with the base case (no adjustments). The 5 scenarios used the racial and ethnic distribution of chlamydia or gonorrhea diagnoses in the same state, chlamydia or gonorrhea diagnoses in neighboring states, chlamydia or gonorrhea diagnoses within the geographic region, HIV diagnoses, and syphilis diagnoses.

RESULTS

In 2018, a total of 31.93% (560,551/1,755,510) of chlamydia and 22.11% (128,790/582,475) of gonorrhea diagnoses had missing race and ethnicity information. Missingness differed by infection type but not by reported sex. Missing race and ethnicity information varied widely across states and times (range across state-years: from 0.0% to 96.2%). The rate ratio remained similar in the imputation scenarios, although the rate difference differed nationally and in some states.

CONCLUSIONS

We found that missing race and ethnicity information affects measured disparities, which is important to consider when interpreting disparity metrics. Addressing missing information in surveillance systems requires system-level solutions, such as collecting more complete laboratory data, improving the linkage of data systems, and designing more efficient data collection procedures. As a short-term solution, local public health agencies can adapt these imputation scenarios to their aggregate data to adjust surveillance data for use in population indicators of health equity.

Impact of malnutrition on clinical outcomes in patients diagnosed with COVID-19

BACKGROUND

Coronavirus disease 2019 (COVID-19) is now the third leading cause of death in the United States. Malnutrition in hospitalized patients increases risk of complications. However, the effect of malnutrition on outcomes in patients infected is unclear. This study aims to identify the impact of malnutrition on mortality and adverse hospital events in patients hospitalized with COVID-19.

METHODS

This study used data from the National COVID Cohort Collaborative (N3C), a COVID-19 repository containing harmonized, longitudinal electronic health record data from US health systems. Malnutrition was categorized into three groups based on condition diagnosis: (1) none documented, (2) history of malnutrition, and (3) hospital-acquired malnutrition. Multivariable logistic regression was performed to determine whether malnutrition was associated with mortality and adverse events, including mechanical ventilation, acute respiratory distress syndrome, extracorporeal membrane oxygenation, and hospital-acquired pressure injury, in hospitalized patients with COVID-19.

RESULTS

Of 343,188 patients hospitalized with COVID-19, 11,206 had a history of malnutrition and 15,711 had hospital-acquired malnutrition. After adjustment for potential confounders, odds of mortality were significantly higher in patients with a history of malnutrition (odds ratio [OR], 1.71; 95% confidence interval [CI], 1.63-1.79; P < 0.001) and hospital-acquired malnutrition (OR, 2.5; 95% CI, 2.4-2.6; P < 0.001). Adjusted odds of adverse hospital events were also significantly elevated in both malnutrition groups.

CONCLUSIONS

Results indicate the risk of mortality and adverse inpatient events in adults with COVID-19 is significantly higher in patients with malnutrition. Prevention, diagnosis, and treatment of malnutrition could be a key component in improving outcomes in these patients.

Key Contributions in Clinical Research Informatics

Objectives: To summarize key contributions to current research in the field of Clinical Research Informatics (CRI) and to select best papers published in 2020.

Method: A bibliographic search using a combination of Medical Subject Headings (MeSH) descriptors and free-text terms on CRI was performed using PubMed, followed by a double-blind review in order to select a list of candidate best papers to be then peer-reviewed by external reviewers. After peer-review ranking, a consensus meeting between two section editors and the editorial team was organized to finally conclude on the selected four best papers.

Results: Among the 877 papers published in 2020 and returned by the search, there were four best papers selected. The first best paper describes a method for mining temporal sequences from clinical documents to infer disease trajectories and enhancing high-throughput phenotyping. The authors of the second best paper demonstrate that the generation of synthetic Electronic Health Record (EHR) data through Generative Adversarial Networks (GANs) could be substantially improved by more appropriate training and evaluation criteria. The third best paper offers an efficient advance on methods to detect adverse drug events by computer-assisting expert reviewers with annotated candidate mentions in clinical documents. The large-scale data quality assessment study reported by the fourth best paper has clinical research informatics implications, in terms of the trustworthiness of inferences made from analysing electronic health records.

Conclusions: The most significant research efforts in the CRI field are currently focusing on data science with active research in the development and evaluation of Artificial Intelligence/Machine Learning (AI/ML) algorithms based on ever more intensive use of real-world data and especially EHR real or synthetic data. A major lesson that the coronavirus disease 2019 (COVID-19) pandemic has already taught the scientific CRI community is that timely international high-quality data-sharing and collaborative data analysis is absolutely vital to inform policy decisions.

Barriers to using clinical decision support in ambulatory care: Do clinics in health systems fare better?

OBJECTIVE

We quantify the use of clinical decision support (CDS) and the specific barriers reported by ambulatory clinics and examine whether CDS utilization and barriers differed based on clinics' affiliation with health systems, providing a benchmark for future empirical research and policies related to this topic.

MATERIALS AND METHODS

Despite much discussion at the theoretic level, the existing literature provides little empirical understanding of barriers to using CDS in ambulatory care. We analyze data from 821 clinics in 117 medical groups, based on in Minnesota Community Measurement's annual Health Information Technology Survey (2014-2016). We examine clinics' use of 7 CDS tools, along with 7 barriers in 3 areas (resource, user acceptance, and technology). Employing linear probability models, we examine factors associated with CDS barriers.

RESULTS

Clinics in health systems used more CDS tools than did clinics not in systems (24 percentage points higher in automated reminders), but they also reported more barriers related to resources and user acceptance (26 percentage points higher in barriers to implementation and 33 points higher in disruptive alarms). Barriers related to workflow redesign increased in clinics affiliated with health systems (33 points higher). Rural clinics were more likely to report barriers to training.

CONCLUSIONS

CDS barriers related to resources and user acceptance remained substantial. Health systems, while being effective in promoting CDS tools, may need to provide further assistance to their affiliated ambulatory clinics to overcome barriers, especially the requirement to redesign workflow. Rural clinics may need more resources for training.