scholarly journals Risk of Myopericarditis following COVID-19 mRNA vaccination in a Large Integrated Health System: A Comparison of Completeness and Timeliness of Two Methods

2021 ◽  
Author(s):  
Katie Sharff ◽  
David M Dancoes ◽  
Jodi L Longueil ◽  
Eric S Johnson ◽  
Paul F Lewis
Keyword(s):  
Hospital Discharge ◽  
Search Algorithm ◽  
Acute Myocarditis ◽  
Case Definition ◽  
Emergency Department Visits ◽  
Old Patients ◽  
True Incidence ◽  
Diagnosis Codes ◽  
Initial Care ◽  
Icd 10

Purpose: How completely do hospital discharge diagnoses identify cases of myopericarditis after an mRNA vaccine? Methods: We assembled a cohort 12 to 39 years old patients, insured by Kaiser Permanente Northwest, who received at least one dose of an mRNA vaccine (Pfizer BioNTech or Moderna) between December 2020 and October 2021. We followed them for up to 30 days after their second dose of an mRNA vaccine to identify encounters for myocarditis, pericarditis or myopericarditis. We compared two identification methods: A method that searched all encounter diagnoses using a brief text description (e.g., ICD-10-CM code I40.9 is defined as acute myocarditis, unspecified). We searched the text description of all inpatient or outpatient encounter diagnoses (in any position) for myocarditis or pericarditis. The other method was developed by the Centers for Disease Control and Preventions Vaccine Safety Datalink (VSD), which searched for emergency department visits or hospitalizations with a select set of discharge ICD-10-CM diagnosis codes. For both methods, two physicians independently reviewed the identified patient records and classified them as confirmed, probable or not cases using the CDCs case definition. Results: The encounter methodology identified 14 distinct patients who met the confirmed or probable CDC case definition for acute myocarditis or pericarditis with an onset within 21 days of receipt of COVID-19 vaccination. Three of these 14 patients had an ICD-10 code of I51.4 Myocarditis, Unspecified which was overlooked by the VSD algorithm. The VSD methodology identified 11 patients who met the CDC case definition for acute myocarditis or pericarditis. Seven (64%) of the eleven patients had initial care for myopericarditis outside of a KPNW facility and their diagnosis could not be ascertained by the VSD methodology until claims were submitted (median delay of 33 days; range of 12-195 days). Among those who received a second dose of vaccine (n=146,785), we estimated a risk as 95.4 cases of myopericarditis per million second doses administered (95% CI, 52.1 to 160.0). Conclusion: We identified additional valid cases of myopericarditis following an mRNA vaccination that would be missed by the VSDs search algorithm, which depends on select hospital discharge diagnosis codes. The true incidence of myopericarditis is markedly higher than the incidence reported to US advisory committees. The VSD should validate its search algorithm to improve its sensitivity for myopericarditis.

scholarly journals Illnesses Associated With Increased Length of Stay for Individuals Experiencing Homelessness: A Retrospective Cohort Study of Emergency Department Visits and Hospitalizations.

2020 ◽  
Author(s):  
Sumantra Monty Ghosh ◽  
Khokan Sikdar ◽  
Adetola Koleade ◽  
Peter Farris ◽  
Jordan Ross ◽  
...  
Keyword(s):  
Cohort Study ◽  
Length Of Stay ◽  
Retrospective Cohort Study ◽  
Retrospective Cohort ◽  
Emergency Department Visits ◽  
Targeted Interventions ◽  
Diagnosis Codes ◽  
Icd 10 ◽  
Inpatient Settings ◽  
Artery Disease

Abstract Background: Individuals experiencing homelessness (IEH) tend to have increased length of stay (LOS) in acute care settings, which negatively impacts health care costs and resource utilization. It is unclear however, what specific factors account for this increased LOS. This study attempts to define which diagnoses most impact LOS for IEH and if there are differences based on their demographics. Methods: A retrospective cohort study was conducted looking at ICD-10 diagnosis codes and LOS for patients identified as IEH seen in Emergency Departments (ED) and also for those admitted to. Data were stratified based on diagnosis, gender and age. Statistical analysis was conducted to determine which ICD-10 diagnoses were significantly associated with increased ED and inpatient LOS for IEH compared to housed individuals.Results: Homelessness admissions were associated with increased LOS regardless of gender or age group. The absolute mean difference of LOS between IEH and housed individuals was 1.62 hours [95% CI 1.49 – 1.75] in the ED and 3.02 days [95% CI 2.42-3.62] for inpatients. Males age 18-24 years spent on average 7.12 more days in hospital, and females aged 25-34 spent 7.32 more days in hospital compared to their housed counterparts. Thirty-one diagnoses were associated with increased LOS in EDs for IEH compared to their housed counterparts; maternity concerns and coronary artery disease were associated with significantly increased inpatient LOS. Conclusion: Homelessness significantly increases the LOS of individuals within both ED and inpatient settings. We have identified numerous diagnoses that are associated with increased LOS in IE; these inform the prioritization and development of targeted interventions to improve the health of IEH.

scholarly journals Validation of a Syndromic Case Definition for Detecting Emergency Department Visits Potentially Related to Marijuana

2017 ◽  
Vol 132 (4) ◽  
pp. 471-479 ◽  
Author(s):  
Kathryn DeYoung ◽  
Yushiuan Chen ◽  
Robert Beum ◽  
Michele Askenazi ◽  
Cali Zimmerman ◽  
...  
Keyword(s):  
Public Health ◽  
Emergency Department ◽  
Health Impact ◽  
Case Definition ◽  
Emergency Department Visits ◽  
Free Text ◽  
Search Term ◽  
Diagnosis Codes ◽  
Ed Visits ◽  
The Impact

Objectives: Reliable methods are needed to monitor the public health impact of changing laws and perceptions about marijuana. Structured and free-text emergency department (ED) visit data offer an opportunity to monitor the impact of these changes in near-real time. Our objectives were to (1) generate and validate a syndromic case definition for ED visits potentially related to marijuana and (2) describe a method for doing so that was less resource intensive than traditional methods. Methods: We developed a syndromic case definition for ED visits potentially related to marijuana, applied it to BioSense 2.0 data from 15 hospitals in the Denver, Colorado, metropolitan area for the period September through October 2015, and manually reviewed each case to determine true positives and false positives. We used the number of visits identified by and the positive predictive value (PPV) for each search term and field to refine the definition for the second round of validation on data from February through March 2016. Results: Of 126 646 ED visits during the first period, terms in 524 ED visit records matched ≥1 search term in the initial case definition (PPV, 92.7%). Of 140 932 ED visits during the second period, terms in 698 ED visit records matched ≥1 search term in the revised case definition (PPV, 95.7%). After another revision, the final case definition contained 6 keywords for marijuana or derivatives and 5 diagnosis codes for cannabis use, abuse, dependence, poisoning, and lung disease. Conclusions: Our syndromic case definition and validation method for ED visits potentially related to marijuana could be used by other public health jurisdictions to monitor local trends and for other emerging concerns.

ICD-10 hospital discharge diagnosis codes were sensitive for identifying pulmonary embolism but not deep vein thrombosis

2010 ◽  
Vol 63 (7) ◽  
pp. 790-797 ◽  
Author(s):  
Pierre Casez ◽  
José Labarère ◽  
Marie-Antoinette Sevestre ◽  
Myriam Haddouche ◽  
Xavier Courtois ◽  
...  
Keyword(s):  
Pulmonary Embolism ◽  
Deep Vein Thrombosis ◽  
Hospital Discharge ◽  
Discharge Diagnosis ◽  
Vein Thrombosis ◽  
Hospital Discharge Diagnosis ◽  
Diagnosis Codes ◽  
Icd 10 ◽  
Deep Vein

scholarly journals Defining indicators for drug overdose emergency department visits and hospitalisations in ICD-10-CM coded discharge data

2021 ◽  
Vol 27 (Suppl 1) ◽  
pp. i56-i61 ◽  
Author(s):  
Alana Vivolo-Kantor ◽  
Emilia Pasalic ◽  
Stephen Liu ◽  
Pedro D Martinez ◽  
Robert Matthew Gladden
Keyword(s):  
Emergency Department ◽  
Drug Overdose ◽  
International Classification Of Diseases ◽  
Case Definition ◽  
Emergency Department Visits ◽  
Health Departments ◽  
Discharge Data ◽  
Classification Of Diseases ◽  
Icd 10 ◽  
Key Aspects

IntroductionThe drug overdose epidemic has worsened over the past decade; however, efforts have been made to better understand and track nonfatal overdoses using various data sources including emergency department and hospital admission data from billing and discharge files.Methods and findingsThe Centers for Disease Control and Prevention (CDC) has developed surveillance case definition guidance using standardised discharge diagnosis codes for public health practitioners and epidemiologists using lessons learnt from CDC’s funded recipients and the Council for State and Territorial Epidemiologists (CSTE) International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) Drug Poisoning Indicators Workgroup and General Injury ICD-10-CM Workgroup. CDC’s guidance was informed by health departments and CSTE’s workgroups and included several key aspects for assessing drug overdose in emergency department and hospitalisation discharge data. These include: (1) searching all diagnosis fields to identify drug overdose cases; (2) estimating drug overdose incidence using visits for initial encounter but excluding subsequent encounters and sequelae; (3) excluding underdosing and adverse effects from drug overdose incidence indicators; and (4) using codes T36–T50 for overdose surveillance. CDC’s guidance also suggests analysing intent separately for ICD-10-CM coding.ConclusionsCDC’s guidance provides health departments a key tool to better monitor drug overdoses in their community. The implementation and validation of this standardised guidance across all CDC-funded health departments will be key to ensuring consistent and accurate reporting across all entities.

scholarly journals A Revised ICD–10–CM Surveillance Case Definition for Injury-related Emergency Department Visits

2021 ◽  
Author(s):  
Holly Hedegaard ◽  
Matthew Garnett ◽  
Renee Johnson ◽  
Karen Thomas
Keyword(s):  
Emergency Department ◽  
Case Definition ◽  
Emergency Department Visits ◽  
Icd 10

This report summarizes updates and presents the 2021 revised ICD–10–CM surveillance case definition.

scholarly journals Estimated Prevalence of Unreported IGD Cases in Routine Outpatient Children and Adolescent Psychotherapy

2021 ◽  
Vol 18 (13) ◽  
pp. 6787
Author(s):  
Sonja Kewitz ◽  
Eva Vonderlin ◽  
Lutz Wartberg ◽  
Katajun Lindenberg
Keyword(s):  
Children And Adolescents ◽  
Internet Gaming Disorder ◽  
Self Report ◽  
Clinical Population ◽  
Gaming Disorder ◽  
Parental Report ◽  
Outpatient Psychotherapy ◽  
Old Patients ◽  
Internet Gaming ◽  
Icd 10

Internet Gaming Disorder (IGD) has been included in the DSM-5 as a diagnosis for further study, and Gaming Disorder as a new diagnosis in the ICD-11. Nonetheless, little is known about the clinical prevalence of IGD in children and adolescents. Additionally, it is unclear if patients with IGD are already identified in routine psychotherapy, using the ICD-10 diagnosis F 63.8 (recommended classification of IGD in ICD-10). This study investigated N = 358 children and adolescents (self and parental rating) of an outpatient psychotherapy centre in Germany using the Video Game Dependency Scale. According to self-report 4.0% of the 11- to 17-year-old patients met criteria for a tentative IGD diagnosis and 14.0% according to the parental report. Of the 5- to 10-year-old patients, 4.1% were diagnosed with tentative IGD according to parental report. Patients meeting IGD criteria were most frequently diagnosed with hyperkinetic disorders, followed by anxiety disorders, F 63.8, conduct disorders, mood disorders and obsessive-compulsive disorders (descending order) as primary clinical diagnoses. Consequently, this study indicates that a significant amount of the clinical population presents IGD. Meaning, appropriate diagnostics should be included in routine psychological diagnostics in order to avoid “hidden” cases of IGD in the future.

scholarly journals Multisite medical record review of emergency department visits for traumatic brain injury

2021 ◽  
Vol 27 (S1) ◽  
pp. i42-i48
Author(s):  
Barbara A Gabella ◽  
Jeanne E Hathaway ◽  
Beth Hume ◽  
Jewell Johnson ◽  
Julia F Costich ◽  
...  
Keyword(s):  
Traumatic Brain Injury ◽  
Emergency Department ◽  
Brain Injury ◽  
Skull Fracture ◽  
Signs And Symptoms ◽  
International Classification Of Diseases ◽  
Emergency Department Visits ◽  
Intracranial Injury ◽  
Imaging Results ◽  
Icd 10

BackgroundIn 2016, the CDC in the USA proposed codes from the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) for identifying traumatic brain injury (TBI). This study estimated positive predictive value (PPV) of TBI for some of these codes.MethodsFour study sites used emergency department or trauma records from 2015 to 2018 to identify two random samples within each site selected by ICD-10-CM TBI codes for (1) intracranial injury (S06) or (2) skull fracture only (S02.0, S02.1-, S02.8-, S02.91) with no other TBI codes. Using common protocols, reviewers abstracted TBI signs and symptoms and head imaging results that were then used to assign certainty of TBI (none, low, medium, high) to each sampled record. PPVs were estimated as a percentage of records with medium-certainty or high-certainty for TBI and reported with 95% confidence interval (CI).ResultsPPVs for intracranial injury codes ranged from 82% to 92% across the four samples. PPVs for skull fracture codes were 57% and 61% in the two university/trauma hospitals in each of two states with clinical reviewers, and 82% and 85% in the two states with professional coders reviewing statewide or nearly statewide samples. Margins of error for the 95% CI for all PPVs were under 5%.DiscussionICD-10-CM codes for traumatic intracranial injury demonstrated high PPVs for capturing true TBI in different healthcare settings. The algorithm for TBI certainty may need refinement, because it yielded moderate-to-high PPVs for records with skull fracture codes that lacked intracranial injury codes.

scholarly journals Interrupted time series design to evaluate ICD-9-CM to ICD-10-CM coding changes on trends in Colorado emergency department visits related to traumatic brain injury

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Lauren Alexis De Crescenzo ◽  
Barbara Alison Gabella ◽  
Jewell Johnson
Keyword(s):  
Public Health ◽  
Traumatic Brain Injury ◽  
Emergency Department ◽  
Brain Injury ◽  
Interrupted Time Series ◽  
Emergency Department Visits ◽  
Control And Prevention ◽  
Icd 10 ◽  
Ed Visits ◽  
The Impact

Abstract Background The transition in 2015 to the Tenth Revision of the International Classification of Disease, Clinical Modification (ICD-10-CM) in the US led the Centers for Disease Control and Prevention (CDC) to propose a surveillance definition of traumatic brain injury (TBI) utilizing ICD-10-CM codes. The CDC’s proposed surveillance definition excludes “unspecified injury of the head,” previously included in the ICD-9-CM TBI surveillance definition. The study purpose was to evaluate the impact of the TBI surveillance definition change on monthly rates of TBI-related emergency department (ED) visits in Colorado from 2012 to 2017. Results The monthly rate of TBI-related ED visits was 55.6 visits per 100,000 persons in January 2012. This rate in the transition month to ICD-10-CM (October 2015) decreased by 41 visits per 100,000 persons (p-value < 0.0001), compared to September 2015, and remained low through December 2017, due to the exclusion of “unspecified injury of head” (ICD-10-CM code S09.90) in the proposed TBI definition. The average increase in the rate was 0.33 visits per month (p < 0.01) prior to October 2015, and 0.04 visits after. When S09.90 was included in the model, the monthly TBI rate in Colorado remained smooth from ICD-9-CM to ICD-10-CM and the transition was no longer significant (p = 0.97). Conclusion The reduction in the monthly TBI-related ED visit rate resulted from the CDC TBI surveillance definition excluding unspecified head injury, not necessarily the coding transition itself. Public health practitioners should be aware that the definition change could lead to a drastic reduction in the magnitude and trend of TBI-related ED visits, which could affect decisions regarding the allocation of TBI resources. This study highlights a challenge in creating a standardized set of TBI ICD-10-CM codes for public health surveillance that provides comparable yet clinically relevant estimates that span the ICD transition.

The Accuracy of Hospital Discharge Diagnosis Codes for Major Birth Defects

2016 ◽  
Vol 22 (3) ◽  
pp. E9-E19 ◽  
Author(s):  
Jason L. Salemi ◽  
Jean Paul Tanner ◽  
Diana Sampat ◽  
Suzanne B. Anjohrin ◽  
Jane A. Correia ◽  
...  
Keyword(s):  
Hospital Discharge ◽  
Birth Defects ◽  
Discharge Diagnosis ◽  
Hospital Discharge Diagnosis ◽  
Diagnosis Codes

Dispersion in total cost of care for Medicare fee-for-service (FFS) patients with metastatic pancreatic cancer receiving FDA-approved/NCCN Category 1 regimens at 340B verus non-340B institutions.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e18843-e18843
Author(s):  
Helen Latimer ◽  
Samantha Tomicki ◽  
Gabriela Dieguez ◽  
Paul Cockrum ◽  
George P. Kim
Keyword(s):  
Pancreatic Cancer ◽  
Cost Of Care ◽  
Metastatic Pancreatic Cancer ◽  
Fee For Service ◽  
Total Cost ◽  
Diagnosis Codes ◽  
Heavily Pretreated ◽  
Icd 10 ◽  
Liposomal Irinotecan ◽  
Line Of Therapy

e18843 Background: The Department of Health and Human Services (HHS) designed the 340B drug pricing program to allow institutions that service specialty populations to acquire drugs at lower prices. Objective: To analyze the dispersion in total cost of care (TCOC) for Medicare FFS patients (pts) with metastatic pancreatic cancer (m-PANC) treated at 340B or non-340B institutions, by NCCN Category 1 regimen. Methods: We identified pts with m-PANC using ICD-10 diagnosis codes in the 2016-18 Medicare Parts A/B/D 100% Research Identifiable Files. Study pts had 2+ claims with a pancreatic cancer diagnosis and Medicare FFS coverage for 6 months pre- and 3 months post-metastasis diagnosis. Study pts were treated with NCCN Category 1 regimens: 1L gemcitabine monotherapy (gem-mono), 1L gemcitabine/nab-paclitaxel (gem-nab), 1L FOLFIRINOX (FFX), and 2L liposomal irinotecan-based regimen (nal-IRI). Pts were attributed to 340B or non-340B institutions based on plurality of chemotherapy claims. TCOC reflects insurer-paid services per line of therapy (LOT) for 3 categories: chemotherapy/supportive drugs (chemo/Rx), inpatient care (IP), and other outpatient care (OP). We grouped pts by quartile (qrt) and evaluated drivers of TCOC and mean rates of admissions (admits/pt). Results: We identified 2,697 (340B) and 3,839 (non-340B) pts taking NCCN Category 1 regimens. Gem-mono represented 1% and 4% of all pts in 340B and non-340B institutions, respectively. Gem-nab accounted for 72% of pts in both cohorts. For gem-nab, FFX, and nal-IRI pts, median TCOC was similar in both cohorts, although mean TCOC by qrt was lower at 340B institutions than non-340B institutions, except for gem-nab in the 1st qrt. The components of TCOC were similar between 340B and non-340B institutions in all qrts. In both cohorts, % IP costs increased between the 1st and 4th qrt (340B:15% to 23%, non-340B:14% to 25%). From the 1st to the 4th qrt, admits/pt increased in both cohorts. In the 340B cohort, nal-IRI pts had the lowest admits/pt while gem-nab pts had the highest in all qrts. In the non-340B cohort, nal-IRI pts had the lowest admits/pt except for in the 1st qrt. Conclusions: Median TCOC was lower at 340B institutions than non-340B institutions for all regimens, and the range of TCOC dispersion was also smaller at 340B institutions. Across qrts, chemotherapy accounted for approximately half the TCOC; however, IP costs were proportionally higher in the 4th qrt. Comparing regimens, despite 2L nal-IRI pts being more heavily pretreated, median costs in each cohort were similar to 1L gem-nab and 1L FFX, while admits/pt were generally lower than 1L gem-nab and 1L FFX across qrts and cohorts.

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