Browsing by Author "Razzaghi, Hanieh"

Now showing 1 - 7 of 7
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    A machine learning-based phenotype for long COVID in children: an EHR-based study from the RECOVER program
    (Cold Spring Harbor Laboratory, 2022-12-26) Lorman, Vitaly; Razzaghi, Hanieh; Song, Xing; Morse, Keith; Utidjian, Levon; Allen, Andrea J.; Rao, Suchitra; Rogerson, Colin; Bennett, Tellen D.; Morizono, Hiroki; Eckrich, Daniel; Jhaveri, Ravi; Huang, Yungui; Ranade, Daksha; Pajor, Nathan; Lee, Grace M.; Forrest, Christopher B.; Bailey, L. Charles; Pediatrics, School of Medicine
    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.
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    Can Multisystem Inflammatory Syndrome in Children Be Managed in the Outpatient Setting? An EHR-Based Cohort Study From the RECOVER Program
    (Oxford University Press, 2023) Jhaveri, Ravi; Webb, Ryan; Razzaghi, Hanieh; Schuchard, Julia; Mejias, Asuncion; Bennett, Tellen D.; Jone, Pei-Ni; Thacker, Deepika; Schulert, Grant S.; Rogerson, Colin; Cogen, Jonathan D.; Bailey, L. Charles; Forrest, Christopher B.; Lee, Grace M.; Rao, Suchitra; RECOVER consortium; Pediatrics, School of Medicine
    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.
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    EHR-based Case Identification of Pediatric Long COVID: A Report from the RECOVER EHR Cohort
    (medRxiv, 2024-05-23) Botdorf, Morgan; Dickinson, Kimberley; Lorman, Vitaly; Razzaghi, Hanieh; Marchesani, Nicole; Rao, Suchitra; Rogerson, Colin; Higginbotham, Miranda; Mejias, Asuncion; Salyakina, Daria; Thacker, Deepika; Dandachi, Dima; Christakis, Dimitri A.; Taylor, Emily; Schwenk, Hayden; Morizono, Hiroki; Cogen, Jonathan; Pajor, Nate M.; Jhaveri, Ravi; Forrest, Christopher B.; Bailey, L. Charles; RECOVER Consortium; Pediatrics, School of Medicine
    Objective: Long COVID, marked by persistent, recurring, or new symptoms post-COVID-19 infection, impacts children's well-being yet lacks a unified clinical definition. This study evaluates the performance of an empirically derived Long COVID case identification algorithm, or computable phenotype, with manual chart review in a pediatric sample. This approach aims to facilitate large-scale research efforts to understand this condition better. Methods: The algorithm, composed of diagnostic codes empirically associated with Long COVID, was applied to a cohort of pediatric patients with SARS-CoV-2 infection in the RECOVER PCORnet EHR database. The algorithm classified 31,781 patients with conclusive, probable, or possible Long COVID and 307,686 patients without evidence of Long COVID. A chart review was performed on a subset of patients (n=651) to determine the overlap between the two methods. Instances of discordance were reviewed to understand the reasons for differences. Results: The sample comprised 651 pediatric patients (339 females, M age = 10.10 years) across 16 hospital systems. Results showed moderate overlap between phenotype and chart review Long COVID identification (accuracy = 0.62, PPV = 0.49, NPV = 0.75); however, there were also numerous cases of disagreement. No notable differences were found when the analyses were stratified by age at infection or era of infection. Further examination of the discordant cases revealed that the most common cause of disagreement was the clinician reviewers' tendency to attribute Long COVID-like symptoms to prior medical conditions. The performance of the phenotype improved when prior medical conditions were considered (accuracy = 0.71, PPV = 0.65, NPV = 0.74). Conclusions: Although there was moderate overlap between the two methods, the discrepancies between the two sources are likely attributed to the lack of consensus on a Long COVID clinical definition. It is essential to consider the strengths and limitations of each method when developing Long COVID classification algorithms.
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    Extending Achilles Heel Data Quality Tool with New Rules Informed by Multi-Site Data Quality Comparison
    (IOS, 2019) Huser, Vojtech; Li, Xiaochun; Zhang, Zuoyi; Jung, Sungjae; Woong Park, Rae; Banda, Juan; Razzaghi, Hanieh; Londhe, Ajit; Natarajan, Karthik; Biostatistics, School of Public Health
    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.
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    Real-world Effectiveness of BNT162b2 Against Infection and Severe Diseases in Children and Adolescent
    (medRxiv, 2023-11-13) Wu, Qiong; Tong, Jiayi; Zhang, Bingyu; Zhang, Dazheng; Chen, Jiajie; Lei, Yuqing; Lu, Yiwen; Wang, Yudong; Li, Lu; Shen, Yishan; Xu, Jie; Bailey, L. Charles; Bian, Jiang; Christakis, Dimitri A.; Fitzgerald, Megan L.; Hirabayashi, Kathryn; Jhaveri, Ravi; Khaitan, Alka; Lyu, Tianchen; Rao, Suchitra; Razzaghi, Hanieh; Schwenk, Hayden T.; Wang, Fei; Witvliet, Margot I.; Tchetgen Tchetgen, Eric J.; Morris, Jeffrey S.; Forrest, Christopher B.; Chen, Yong; Pediatrics, School of Medicine
    Background: The efficacy of the BNT162b2 vaccine in pediatrics was assessed by randomized trials before the Omicron variant's emergence. The long-term durability of vaccine protection in this population during the Omicron period remains limited. Objective: To assess the effectiveness of BNT162b2 in preventing infection and severe diseases with various strains of the SARS-CoV-2 virus in previously uninfected children and adolescents. Design: Comparative effectiveness research accounting for underreported vaccination in three study cohorts: adolescents (12 to 20 years) during the Delta phase, children (5 to 11 years) and adolescents (12 to 20 years) during the Omicron phase. Setting: A national collaboration of pediatric health systems (PEDSnet). Participants: 77,392 adolescents (45,007 vaccinated) in the Delta phase, 111,539 children (50,398 vaccinated) and 56,080 adolescents (21,180 vaccinated) in the Omicron period. Exposures: First dose of the BNT162b2 vaccine vs. no receipt of COVID-19 vaccine. Measurements: Outcomes of interest include documented infection, COVID-19 illness severity, admission to an intensive care unit (ICU), and cardiac complications. The effectiveness was reported as (1-relative risk)*100% with confounders balanced via propensity score stratification. Results: During the Delta period, the estimated effectiveness of BNT162b2 vaccine was 98.4% (95% CI, 98.1 to 98.7) against documented infection among adolescents, with no significant waning after receipt of the first dose. An analysis of cardiac complications did not find an increased risk after vaccination. During the Omicron period, the effectiveness against documented infection among children was estimated to be 74.3% (95% CI, 72.2 to 76.2). Higher levels of effectiveness were observed against moderate or severe COVID-19 (75.5%, 95% CI, 69.0 to 81.0) and ICU admission with COVID-19 (84.9%, 95% CI, 64.8 to 93.5). Among adolescents, the effectiveness against documented Omicron infection was 85.5% (95% CI, 83.8 to 87.1), with 84.8% (95% CI, 77.3 to 89.9) against moderate or severe COVID-19, and 91.5% (95% CI, 69.5 to 97.6)) against ICU admission with COVID-19. The effectiveness of the BNT162b2 vaccine against the Omicron variant declined after 4 months following the first dose and then stabilized. The analysis revealed a lower risk of cardiac complications in the vaccinated group during the Omicron variant period. Limitations: Observational study design and potentially undocumented infection. Conclusions: Our study suggests that BNT162b2 was effective for various COVID-19-related outcomes in children and adolescents during the Delta and Omicron periods, and there is some evidence of waning effectiveness over time.
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    The Preserving Kidney Function in Children With CKD (PRESERVE) Study: Rationale, Design, and Methods
    (Elsevier, 2023-09-14) Denburg, Michelle R.; Razzaghi, Hanieh; Goodwin Davies, Amy J.; Dharnidharka, Vikas; Dixon, Bradley P.; Flynn, Joseph T.; Glenn, Dorey; Gluck, Caroline A.; Harshman, Lyndsay; Jovanovska, Aneta; Pefkaros Katsoufis, Chryso; Kratchman, Amy L.; Levondosky, Mark; Levondosky, Rebecca; McDonald, Jill; Mitsnefes, Mark; Modi, Zubin J.; Musante, Jordan; Neu, Alicia M.; Pan, Cynthia G.; Patel, Hiren P.; Patterson, Larry T.; Schuchard, Julia; Verghese, Priya S.; Wilson, Amy C.; Wong, Cynthia; Forrest, Christopher B.; Pediatrics, School of Medicine
    Rationale & objective: PRESERVE seeks to provide new knowledge to inform shared decision-making regarding blood pressure (BP) management for pediatric chronic kidney disease (CKD). PRESERVE will compare the effectiveness of alternative strategies for monitoring and treating hypertension on preserving kidney function; expand the National Patient-Centered Clinical Research Network (PCORnet) common data model by adding pediatric- and kidney-specific variables and linking electronic health record data to other kidney disease databases; and assess the lived experiences of patients related to BP management. Study design: Multicenter retrospective cohort study (clinical outcomes) and cross-sectional study (patient-reported outcomes [PROs]). Setting & participants: PRESERVE will include approximately 20,000 children between January 2009-December 2022 with mild-moderate CKD from 15 health care institutions that participate in 6 PCORnet Clinical Research Networks (PEDSnet, STAR, GPC, PaTH, CAPRiCORN, and OneFlorida+). The inclusion criteria were ≥1 nephrologist visit and ≥2 estimated glomerular filtration rate (eGFR) values in the range of 30 to <90 mL/min/1.73 m2 separated by ≥90 days without an intervening value ≥90 mL/min/1.73 m2 and no prior dialysis or kidney transplant. Exposures: BP measurements (clinic-based and 24-hour ambulatory BP); urine protein; and antihypertensive treatment by therapeutic class. Outcomes: The primary outcome is a composite event of a 50% reduction in eGFR, eGFR of <15 mL/min/1.73 m2, long-term dialysis or kidney transplant. Secondary outcomes include change in eGFR, adverse events, and PROs. Analytical approach: Longitudinal models for dichotomous (proportional hazards or accelerated failure time) and continuous (generalized linear mixed models) clinical outcomes; multivariable linear regression for PROs. We will evaluate heterogeneity of treatment effect by CKD etiology and degree of proteinuria and will examine variation in hypertension management and outcomes based on socio-demographics. Limitations: Causal inference limited by observational analyses. Conclusions: PRESERVE will leverage the PCORnet infrastructure to conduct large-scale observational studies that address BP management knowledge gaps for pediatric CKD, focusing on outcomes that are meaningful to patients. Plain-language summary: Hypertension is a major modifiable contributor to loss of kidney function in chronic kidney disease (CKD). The purpose of PRESERVE is to provide evidence to inform shared decision-making regarding blood pressure management for children with CKD. PRESERVE is a consortium of 16 health care institutions in PCORnet, the National Patient-Centered Clinical Research Network, and includes electronic health record data for >19,000 children with CKD. PRESERVE will (1) expand the PCORnet infrastructure for research in pediatric CKD by adding kidney-specific variables and linking electronic health record data to other kidney disease databases; (2) compare the effectiveness of alternative strategies for monitoring and treating hypertension on preserving kidney function; and (3) assess the lived experiences of patients and caregivers related to blood pressure management.
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    Using Electronic Health Record Data to Rapidly Identify Children with Glomerular Disease for Clinical Research
    (American Society of Nephrology, 2019-12) Denburg, Michelle R.; Razzaghi, Hanieh; Bailey, L. Charles; Soranno, Danielle E.; Pollack, Ari H.; Dharnidharka, Vikas R.; Mitsnefes, Mark M.; Smoyer, William E.; Somers, Michael J. G.; Zaritsky, Joshua J.; Flynn, Joseph T.; Claes, Donna J.; Dixon, Bradley P.; Benton, Maryjane; Mariani, Laura H.; Forrest, Christopher B.; Furth, Susan L.; Pediatrics, School of Medicine
    Background: The rarity of pediatric glomerular disease makes it difficult to identify sufficient numbers of participants for clinical trials. This leaves limited data to guide improvements in care for these patients. Methods: The authors developed and tested an electronic health record (EHR) algorithm to identify children with glomerular disease. We used EHR data from 231 patients with glomerular disorders at a single center to develop a computerized algorithm comprising diagnosis, kidney biopsy, and transplant procedure codes. The algorithm was tested using PEDSnet, a national network of eight children's hospitals with data on >6.5 million children. Patients with three or more nephrologist encounters (n=55,560) not meeting the computable phenotype definition of glomerular disease were defined as nonglomerular cases. A reviewer blinded to case status used a standardized form to review random samples of cases (n=800) and nonglomerular cases (n=798). Results: The final algorithm consisted of two or more diagnosis codes from a qualifying list or one diagnosis code and a pretransplant biopsy. Performance characteristics among the population with three or more nephrology encounters were sensitivity, 96% (95% CI, 94% to 97%); specificity, 93% (95% CI, 91% to 94%); positive predictive value (PPV), 89% (95% CI, 86% to 91%); negative predictive value, 97% (95% CI, 96% to 98%); and area under the receiver operating characteristics curve, 94% (95% CI, 93% to 95%). Requiring that the sum of nephrotic syndrome diagnosis codes exceed that of glomerulonephritis codes identified children with nephrotic syndrome or biopsy-based minimal change nephropathy, FSGS, or membranous nephropathy, with 94% sensitivity and 92% PPV. The algorithm identified 6657 children with glomerular disease across PEDSnet, ≥50% of whom were seen within 18 months. Conclusions: The authors developed an EHR-based algorithm and demonstrated that it had excellent classification accuracy across PEDSnet. This tool may enable faster identification of cohorts of pediatric patients with glomerular disease for observational or prospective studies.