Extending Achilles Heel Data Quality Tool with New Rules Informed by Multi-Site Data Quality Comparison

Large healthcare datasets of Electronic Health Record data became indispensable in clinical research. Data quality in such datasets recently became a focus of many distributed research networks. Despite the fact that data quality is specific to a given research question, many existing data quality platform prove that general data quality assessment on dataset level (given a spectrum of research questions) is possible and highly requested by researchers. We present comparison of 12 datasets and extension of Achilles Heel data quality software tool with new rules and data characterization measures

Risk of post-acute sequelae of SARS-CoV-2 infection associated with pre-coronavirus disease obstructive sleep apnea diagnoses: an electronic health record-based analysis from the RECOVER initiative

Obstructive sleep apnea (OSA) has been associated with more severe acute coronavirus disease-2019 (COVID-19) outcomes. We assessed OSA as a potential risk factor for Post-Acute Sequelae of SARS-CoV-2 (PASC).We assessed the impact of preexisting OSA on the risk for probable PASC in adults and children using electronic health record data from multiple research networks. Three research networks within the REsearching COVID to Enhance Recovery initiative (PCORnet Adult, PCORnet Pediatric, and the National COVID Cohort Collaborative [N3C]) employed a harmonized analytic approach to examine the risk of probable PASC in COVID-19-positive patients with and without a diagnosis of OSA prior to pandemic onset. Unadjusted odds ratios (ORs) were calculated as well as ORs adjusted for age group, sex, race/ethnicity, hospitalization status, obesity, and preexisting comorbidities.Across networks, the unadjusted OR for probable PASC associated with a preexisting OSA diagnosis in adults and children ranged from 1.41 to 3.93. Adjusted analyses found an attenuated association that remained significant among adults only. Multiple sensitivity analyses with expanded inclusion criteria and covariates yielded results consistent with the primary analysis.Adults with preexisting OSA were found to have significantly elevated odds of probable PASC. This finding was consistent across data sources, approaches for identifying COVID-19-positive patients, and definitions of PASC. Patients with OSA may be at elevated risk for PASC after SARS-CoV-2 infection and should be monitored for post-acute sequelae

Empiric Potassium Supplementation and Increased Survival in Users of Loop Diuretics

<div><p>Background</p><p>The effectiveness of the clinical strategy of empiric potassium supplementation in reducing the frequency of adverse clinical outcomes in patients receiving loop diuretics is unknown. We sought to examine the association between empiric potassium supplementation and 1) all-cause death and 2) outpatient-originating sudden cardiac death (SD) and ventricular arrhythmia (VA) among new starters of loop diuretics, stratified on initial loop diuretic dose.</p><p>Methods</p><p>We conducted a one-to-one propensity score-matched cohort study using 1999–2007 US Medicaid claims from five states. Empiric potassium supplementation was defined as a potassium prescription on the day of or the day after the initial loop diuretic prescription. Death, the primary outcome, was ascertained from the Social Security Administration Death Master File; SD/VA, the secondary outcome, from incident, first-listed emergency department or principal inpatient SD/VA discharge diagnoses (positive predictive value = 85%).</p><p>Results</p><p>We identified 654,060 persons who met eligibility criteria and initiated therapy with a loop diuretic, 27% of whom received empiric potassium supplementation (N = 179,436) and 73% of whom did not (N = 474,624). The matched hazard ratio for empiric potassium supplementation was 0.93 (95% confidence interval, 0.89–0.98, p = 0.003) for all-cause death. Stratifying on initial furosemide dose, hazard ratios for empiric potassium supplementation with furosemide <40 and ≥40 milligrams/day were 0.93 (0.86–1.00, p = 0.050) and 0.84 (0.79–0.89, p<0.0001). The matched hazard ratio for empiric potassium supplementation was 1.02 (0.83–1.24, p = 0.879) for SD/VA.</p><p>Conclusions</p><p>Empiric potassium supplementation upon initiation of a loop diuretic appears to be associated with improved survival, with a greater apparent benefit seen with higher diuretic dose. If confirmed, these findings support the use of empiric potassium supplementation upon initiation of a loop diuretic.</p></div

Baseline characteristics of beneficiaries in the primary outcome (all-cause death) cohort, before and after propensity score matching.

<p>* not included in the propensity score.</p><p>** health-related behavior or state ascertained via diagnostic codes alone.</p><p>PS: propensity score; K⁺: empiric potassium supplementation; SDiff: standardized difference; IQR = interquartile range; COPD: chronic obstructive pulmonary disease; HIV: human immunodeficiency virus; AIDS: acquired immunodeficiency syndrome; Mg²⁺: magnesium; ACEIs: angiotensin-converting enzyme inhibitors; ATIIRBs: angiotensin-II receptor blocker.</p

Risk of death for empiric potassium supplementation vs. no empiric potassium supplementation among furosemide initiators: propensity score-matched analyses examining patient subgroups.

<p>HR = hazard ratio; CI = confidence interval. p-values for the difference in effect estimates within stratum, in users of furosemide <40 mg/day and in users of furosemide ≥40 mg/day. 1, p = 0.86 and p = 0.24, respectively. 2, p = 0.75 and p = 0.81, respectively. 3, p = 0.66 and p = 0.49, respectively. 4, p = 0.74 and p = 0.37, respectively. 5, p = 0.08 and p<0.01, respectively.</p

Can Multisystem Inflammatory Syndrome in Children Be Managed in the Outpatient Setting? An EHR-Based Cohort Study From the RECOVER Program

Using electronic health record data combined with primary chart review, we identified seven children across nine participant pediatric medical centers with a diagnosis of Multisystem Inflammatory Syndrome in Children (MIS-C) managed exclusively as outpatients. These findings should raise awareness of mild presentations of MIS-C and the option of outpatient management

Behavioral Health Diagnoses in Youth with Differences of Sex Development or Congenital Adrenal Hyperplasia Compared with Controls: A PEDSnet Study

Objective: To evaluate the odds of a behavioral health diagnosis among youth with differences of sex development (DSD) or congenital adrenal hyperplasia (CAH) compared with matched controls in the PEDSnet database. Study design: All youth with a diagnosis of DSD (n = 1216) or CAH (n = 1647) and at least 1 outpatient encounter were extracted from the PEDSnet database and propensity-score matched on 8 variables (1:4) with controls (n = 4864 and 6588, respectively) using multivariable logistic regression. The likelihood of having behavioral health diagnoses was examined using generalized estimating equations. Results: Youth with DSD had higher odds of a behavioral health diagnosis (OR, 1.7; 95% CI, 1.4-2.1; P \u3c.0001) and neurodevelopmental diagnosis (OR, 1.7; 95% CI, 1.4, 2.0; P \u3c.0001) compared with matched controls. Youth with CAH did not have an increased odds of a behavioral health diagnosis (OR, 1.0; 95% CI, 0.9, 1.1; P =.9) compared with matched controls but did have higher odds of developmental delay (OR, 1.8; 95% CI, 1.4, 2.4; P \u3c.0001). Conclusions: Youth with DSD diagnosis have higher odds of a behavioral health or neurodevelopmental diagnosis compared with matched controls. Youth with CAH have higher odds of developmental delay, highlighting the need for screening in both groups

A machine learning-based phenotype for long COVID in children: An EHR-based study from the RECOVER program.

As clinical understanding of pediatric Post-Acute Sequelae of SARS CoV-2 (PASC) develops, and hence the clinical definition evolves, it is desirable to have a method to reliably identify patients who are likely to have post-acute sequelae of SARS CoV-2 (PASC) in health systems data. In this study, we developed and validated a machine learning algorithm to classify which patients have PASC (distinguishing between Multisystem Inflammatory Syndrome in Children (MIS-C) and non-MIS-C variants) from a cohort of patients with positive SARS- CoV-2 test results in pediatric health systems within the PEDSnet EHR network. Patient features included in the model were selected from conditions, procedures, performance of diagnostic testing, and medications using a tree-based scan statistic approach. We used an XGboost model, with hyperparameters selected through cross-validated grid search, and model performance was assessed using 5-fold cross-validation. Model predictions and feature importance were evaluated using Shapley Additive exPlanation (SHAP) values. The model provides a tool for identifying patients with PASC and an approach to characterizing PASC using diagnosis, medication, laboratory, and procedure features in health systems data. Using appropriate threshold settings, the model can be used to identify PASC patients in health systems data at higher precision for inclusion in studies or at higher recall in screening for clinical trials, especially in settings where PASC diagnosis codes are used less frequently or less reliably. Analysis of how specific features contribute to the classification process may assist in gaining a better understanding of features that are associated with PASC diagnoses

A machine learning-based phenotype for long COVID in children: an EHR-based study from the RECOVER program

Background: As clinical understanding of pediatric Post-Acute Sequelae of SARS CoV-2 (PASC) develops, and hence the clinical definition evolves, it is desirable to have a method to reliably identify patients who are likely to have post-acute sequelae of SARS CoV-2 (PASC) in health systems data. Methods and findings: In this study, we developed and validated a machine learning algorithm to classify which patients have PASC (distinguishing between Multisystem Inflammatory Syndrome in Children (MIS-C) and non-MIS-C variants) from a cohort of patients with positive SARS-CoV-2 test results in pediatric health systems within the PEDSnet EHR network. Patient features included in the model were selected from conditions, procedures, performance of diagnostic testing, and medications using a tree-based scan statistic approach. We used an XGboost model, with hyperparameters selected through cross-validated grid search, and model performance was assessed using 5-fold cross-validation. Model predictions and feature importance were evaluated using Shapley Additive exPlanation (SHAP) values. Conclusions: The model provides a tool for identifying patients with PASC and an approach to characterizing PASC using diagnosis, medication, laboratory, and procedure features in health systems data. Using appropriate threshold settings, the model can be used to identify PASC patients in health systems data at higher precision for inclusion in studies or at higher recall in screening for clinical trials, especially in settings where PASC diagnosis codes are used less frequently or less reliably. Analysis of how specific features contribute to the classification process may assist in gaining a better understanding of features that are associated with PASC diagnoses.Funding source: This research was funded by the National Institutes of Health (NIH) Agreement OT2HL161847-01 as part of the Researching COVID to Enhance Recovery (RECOVER) program of research

Spectrum of severity of multisystem inflammatory syndrome in children: an EHR-based cohort study from the RECOVER program

Abstract Multi-system inflammatory syndrome in children (MIS-C) is a severe post-acute sequela of SARS-CoV-2 infection in children, and there is a critical need to unfold its highly heterogeneous disease patterns. Our objective was to characterize the illness spectrum of MIS-C for improved recognition and management. We conducted a retrospective cohort study using data from March 1, 2020–September 30, 2022, in 8 pediatric medical centers from PEDSnet. We included 1139 children hospitalized with MIS-C and used their demographics, symptoms, conditions, laboratory values, and medications for analyses. We applied heterogeneity-adaptive latent class analyses and identified three latent classes. We further characterized the sociodemographic and clinical characteristics of the latent classes and evaluated their temporal patterns. Class 1 (47.9%) represented children with the most severe presentation, with more admission to the ICU, higher inflammatory markers, hypotension/shock/dehydration, cardiac involvement, acute kidney injury and respiratory involvement. Class 2 (23.3%) represented a moderate presentation, with 4–6 organ systems involved, and some overlapping features with acute COVID-19. Class 3 (28.8%) represented a mild presentation. Our results indicated that MIS-C has a spectrum of clinical severity ranging from mild to severe and the proportion of severe or critical MIS-C decreased over time