Definitions of Drug-Resistant Epilepsy for Administrative Claims Data Research

Neurology ◽  
2021 ◽  
pp. 10.1212/WNL.0000000000012514
Author(s):  
Chloe E Hill ◽  
Chun Chieh Lin ◽  
Samuel W Terman ◽  
Subhendu Rath ◽  
Jack M Parent ◽  
...  
Keyword(s):  
Claims Data ◽  
Administrative Claims ◽  
Drug Resistant ◽  
Operating Characteristics ◽  
Administrative Claims Data ◽  
Current Drug ◽  
Receiver Operating Characteristics Curve ◽  
Epilepsy Diagnosis ◽  
Drug Resistant Epilepsy ◽  
International League

Objective:To assess accuracy of definitions of drug-resistant epilepsy applied to administrative claims data.Methods:We randomly sampled 450 patients from a tertiary health system with >1 epilepsy/convulsion encounter and >2 distinct antiseizure medications (ASMs) from 2014-2020 and >2 years of electronic medical records (EMR) data. We established a drug-resistant epilepsy diagnosis at a specific visit by reviewing EMR data and employing a rubric based in the 2010 International League Against Epilepsy definition. We performed logistic regressions to assess clinically-relevant predictors of drug-resistant epilepsy and to inform claims-based definitions.Results:Of 450 patients reviewed, 150 were excluded for insufficient EMR data. Of the 300 patients included, 98 (33%) met criteria for current drug-resistant epilepsy. The strongest predictors of current drug-resistant epilepsy were drug-resistant epilepsy diagnosis code (OR 16.9, 95% CI 8.8-32.2), >2 ASMs in the prior two years (OR 13.0, 95% CI 5.1-33.3), >3 non-gabapentinoid ASMs (OR 10.3, 95% CI 5.4-19.6), neurosurgery visit (OR 45.2, 95% CI 5.9-344.3), and epilepsy surgery (OR 30.7, 95% CI 7.1-133.3). We created claims-based drug-resistant epilepsy definitions to: 1) maximize overall predictiveness (drug-resistant epilepsy diagnosis; sensitivity 0.86, specificity 0.74, area under the receiver operating characteristics curve [AUROC] 0.80), 2) maximize sensitivity (drug-resistant epilepsy diagnosis or >3 ASMs; sensitivity 0.98, specificity 0.47, AUROC 0.72), and 3) maximize specificity (drug-resistant epilepsy diagnosis and >3 non-gabapentinoid ASMs; sensitivity 0.42, specificity 0.98, AUROC 0.70).Conclusions:Our findings provide validation for several claims-based definitions of drug-resistant epilepsy that can be applied to a variety of research questions.

scholarly journals Predicting drug-resistant epilepsy — A machine learning approach based on administrative claims data

2018 ◽  
Vol 89 ◽  
pp. 118-125 ◽  
Author(s):  
Sungtae An ◽  
Kunal Malhotra ◽  
Cynthia Dilley ◽  
Edward Han-Burgess ◽  
Jeffrey N. Valdez ◽  
...  
Keyword(s):  
Machine Learning ◽  
Claims Data ◽  
Administrative Claims ◽  
Learning Approach ◽  
Drug Resistant ◽  
Administrative Claims Data ◽  
Machine Learning Approach ◽  
Drug Resistant Epilepsy

scholarly journals Characterization of bariatric surgery and outcomes using administrative claims data in the research network of a nationwide commercial health plan

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Qinli Ma ◽  
Michael Mack ◽  
Sonali Shambhu ◽  
Kathleen McTigue ◽  
Kevin Haynes
Keyword(s):  
Bariatric Surgery ◽  
Health Systems ◽  
Comparative Effectiveness ◽  
Claims Data ◽  
Research Network ◽  
Hospital Death ◽  
Administrative Claims ◽  
Administrative Claims Data ◽  
Abdominal Operations

Abstract Background The supplementation of electronic health records data with administrative claims data may be used to capture outcome events more comprehensively in longitudinal observational studies. This study investigated the utility of administrative claims data to identify outcomes across health systems using a comparative effectiveness study of different types of bariatric surgery as a model. Methods This observational cohort study identified patients who had bariatric surgery between 2007 and 2015 within the HealthCore Anthem Research Network (HCARN) database in the National Patient-Centered Clinical Research Network (PCORnet) common data model. Patients whose procedures were performed in a member facility affiliated with PCORnet Clinical Research Networks (CRNs) were selected. The outcomes included a 30-day composite adverse event (including venous thromboembolism, percutaneous/operative intervention, failure to discharge and death), and all-cause hospitalization, abdominal operation or intervention, and in-hospital death up to 5 years after the procedure. Outcomes were classified as occurring within or outside PCORnet CRN health systems using facility identifiers. Results We identified 4899 patients who had bariatric surgery in one of the PCORnet CRN health systems. For 30-day composite adverse event, the inclusion of HCARN multi-site claims data marginally increased the incidence rate based only on HCARN single-site claims data for PCORnet CRNs from 3.9 to 4.2%. During the 5-year follow-up period, 56.8% of all-cause hospitalizations, 31.2% abdominal operations or interventions, and 32.3% of in-hospital deaths occurred outside PCORnet CRNs. Incidence rates (events per 100 patient-years) were significantly lower when based on claims from a single PCORnet CRN only compared to using claims from all health systems in the HCARN: all-cause hospitalization, 11.0 (95% Confidence Internal [CI]: 10.4, 11.6) to 25.3 (95% CI: 24.4, 26.3); abdominal operations or interventions, 4.2 (95% CI: 3.9, 4.6) to 6.1 (95% CI: 5.7, 6.6); in-hospital death, 0.2 (95% CI: 0.11, 0.27) to 0.3 (95% CI: 0.19, 0.38). Conclusions Short-term inclusion of multi-site claims data only marginally increased the incidence rate computed from single-site claims data alone. Longer-term follow up captured a notable number of events outside of PCORnet CRNs. The findings suggest that supplementing claims data improves the outcome ascertainment in longitudinal observational comparative effectiveness studies.

scholarly journals Attribution of Adverse Events Following Coronary Stent Placement Identified Using Administrative Claims Data

2020 ◽  
Vol 9 (4) ◽  
Author(s):  
Sanket S. Dhruva ◽  
Craig S. Parzynski ◽  
Ginger M. Gamble ◽  
Jeptha P. Curtis ◽  
Nihar R. Desai ◽  
...  
Keyword(s):  
Adverse Events ◽  
Stent Placement ◽  
Claims Data ◽  
Coronary Stent ◽  
Administrative Claims ◽  
Administrative Claims Data

Validation of diabetes case definitions using administrative claims data

2011 ◽  
Vol 28 (4) ◽  
pp. 424-427 ◽  
Author(s):  
S. Amed ◽  
S. E. Vanderloo ◽  
D. Metzger ◽  
J.-P. Collet ◽  
K. Reimer ◽  
...  
Keyword(s):  
Claims Data ◽  
Administrative Claims ◽  
Case Definitions ◽  
Administrative Claims Data ◽  
Diabetes Case

Abstract 173: A Comparison of Administrative Claims Discharge Diagnosis with Electronic Medical Record Problem List in the Diagnosis of Acute Myocardial Infarction

2014 ◽  
Vol 7 (suppl_1) ◽  
Author(s):  
Michael D McCulloch ◽  
Tim Sobol ◽  
Joy Yuhas ◽  
Bill Ahern ◽  
Eric D Hixson ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Medical Record ◽  
Chart Review ◽  
Claims Data ◽  
Administrative Claims ◽  
Problem List ◽  
Principal Diagnosis ◽  
Administrative Claims Data ◽  
Patient Admissions

Background: Administrative claims data are commonly used for measurement of mortality and readmissions in Acute Myocardial Infarction (AMI). With advent of the Electronic Medical Record (EMR), the electronic problem list offers new ways to capture diagnosis data. However, no data comparing the accuracy of administrative claims data and the EMR problem list exists. Methods: Two years of admissions at a single, quaternary medical center were analyzed to compare the presence of AMI diagnosis in administrative claims and EMR problem list data using a 2x2 matrix. To gain insights into this novel method, 25 patient admissions were randomly selected from each group to undergo physician chart review to adjudicate a clinical diagnosis of myocardial infarction based on the universal definition. Results: A total of 105,929 admissions from January 1, 2010 to December 31, 2011 were included. Where EMR problem list and administrative claims data were in agreement for or against AMI diagnosis they were highly accurate. Where administrative claims data, but not EMR problem list, reported AMI the most common explanation was true AMI with missing EMR problem list diagnoses (60%). Less common reasons for discordance in this category include: (1) administrative coding error (20%), (2) computer algorithm error (8%), (3) patient death before EMR problem list created (4%), (4) EMR problem list not used (4%) and (5) AMI diagnosis was removed from EMR problem list (4%). Where EMR problem list, but not administrative claims data, reported AMI the most common explanation was no AMI with historical diagnosis of AMI from a previous admission (60%). Less common reasons for discordance in this category include: (1) AMI present but not the principal diagnosis (32%), (2) administrative coding error (4%) and (3) erroneous EMR problem list entry (4%). Conclusion: Compared to administrative and chart review diagnoses, we found that using the EMR problem list to identify patient admissions with a principal diagnosis of AMI will overlook a subset of patients primarily due to inadequate clinical documentation. Additionally, the EMR problem list does not discriminate the admission principal diagnosis from the secondary diagnoses.

Selection of children with drug resistant epilepsy who are candidates for epilepsy surgery: pre and post-operative evaluation

QJM ◽  
2021 ◽  
Vol 114 (Supplement_1) ◽  
Author(s):  
Osama Salah Mohamed El Sharkawy ◽  
Zeinab Anwar El kabbany ◽  
Neveen Tawakol Younis ◽  
Khaled Aboulfotouh Ahmad ◽  
Ahmed Darwish Mahmoud ◽  
...  
Keyword(s):  
Epilepsy Surgery ◽  
Preoperative Evaluation ◽  
Drug Resistant ◽  
Surgery Outcome ◽  
Class 1 ◽  
Operative Evaluation ◽  
Drug Resistant Epilepsy ◽  
Stage 1 ◽  
International League ◽  
Selection Of

Abstract Objective To select patients with drug resistant epilepsy following up in Pediatrics Neurology Outpatient Clinic of Children's Hospital, Ain Shams University who are candidates for epilepsy surgery and to detect outcome of epilepsy surgery in such children as regards seizures control. Methods This prospective study was conducted over a period of 36 months and comprises of 3 stages. Stage 1 includes selection of candidates for epilepsy surgery and preoperative evaluation. Evaluation included clinical assessment, video EEG, MRI epilepsy protocol. Stage 2 include surgery phase where decision of surgery was made by a multidisciplinary team. Stage 3 includes post-operative evaluation as regards Seizures frequency, Seizures Severity using Chalfont score, Engel Epilepsy Surgery Outcome Scale and the International League Against Epilepsy (ILAE) outcome classification. Data was tabulated and analyzed with SSPS package for windows. Results 17 patients underwent epilepsy surgery. Results revealed significant decrease in seizures frequency and severity at 6 and 12 months after surgery. As regards Engel Epilepsy Surgery Outcome Scale 11 (64.7%) patients were class I at 12 months. As regards the ILAE outcome classification 10 (58.8%) patients are class 1 at 12 months. Conclusions epilepsy surgery can be a hope for patients with drug resistant epilepsy who are well selected and evaluated preoperatively. New studies on larger number and for longer duration are recommended.

scholarly journals A risk-model for hospital mortality among patients with severe sepsis or septic shock based on German national administrative claims data

PLoS ONE ◽  
2018 ◽  
Vol 13 (3) ◽  
pp. e0194371 ◽  
Author(s):  
Daniel Schwarzkopf ◽  
Carolin Fleischmann-Struzek ◽  
Hendrik Rüddel ◽  
Konrad Reinhart ◽  
Daniel O. Thomas-Rüddel
Keyword(s):  
Septic Shock ◽  
Severe Sepsis ◽  
Hospital Mortality ◽  
Claims Data ◽  
Risk Model ◽  
Administrative Claims ◽  
Administrative Claims Data
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