scholarly journals Diagnostic Accuracy of Health Care Administrative Diagnosis Codes to Identify Nontuberculous Mycobacteria Disease: A Systematic Review

2021 ◽  
Vol 8 (5) ◽  
Author(s):  
Carlos Mejia-Chew ◽  
Lauren Yaeger ◽  
Kevin Montes ◽  
Thomas C Bailey ◽  
Margaret A Olsen
Keyword(s):  
Health Care ◽  
Diagnostic Accuracy ◽  
Administrative Data ◽  
International Classification Of Diseases ◽  
Administrative Database ◽  
Cochrane Central Register ◽  
Predictive Values ◽  
Diagnosis Codes ◽  
Database Research ◽  
Classification Of Diseases

Abstract Background Health care administrative database research frequently uses standard medical codes to identify diagnoses or procedures. The aim of this review was to establish the diagnostic accuracy of codes used in administrative data research to identify nontuberculous mycobacterial (NTM) disease, including lung disease (NTMLD). Methods We searched Ovid Medline, Embase, Scopus, Cochrane Database of Systematic Reviews, Cochrane Central Register of Controlled Trials, and ClinicalTrials.gov from inception to April 2019. We included studies assessing the diagnostic accuracy of International Classification of Diseases, 9th edition, Clinical Modification (ICD-9-CM) diagnosis codes to identify NTM disease and NTMLD. Studies were independently assessed by 2 researchers, and the Quality Assessment of Diagnostic Accuracy Studies 2 tool was used to assess bias and quality. Results We identified 5549 unique citations. Of the 96 full-text articles reviewed, 7 eligible studies of moderate quality (3730 participants) were included in our review. The diagnostic accuracy of ICD-9-CM diagnosis codes to identify NTM disease varied widely across studies, with positive predictive values ranging from 38.2% to 100% and sensitivity ranging from 21% to 93%. For NTMLD, 4 studies reported diagnostic accuracy, with positive predictive values ranging from 57% to 64.6% and sensitivity ranging from 21% to 26.9%. Conclusions Diagnostic accuracy measures of codes used in health care administrative data to identify patients with NTM varied across studies. Overall the positive predictive value of ICD-9-CM diagnosis codes alone is good, but the sensitivity is low; this method is likely to underestimate case numbers, reflecting the current limitations of coding systems to capture NTM diagnoses.

scholarly journals Processing of Electronic Medical Records for Health Services Research in an Academic Medical Center: Methods and Validation (Preprint)

2018 ◽  
Author(s):  
Nabilah Rahman ◽  
Debby D Wang ◽  
Sheryl Hui-Xian Ng ◽  
Sravan Ramachandran ◽  
Srinath Sridharan ◽  
...  
Keyword(s):  
Health Care ◽  
Health Services Research ◽  
Health Services ◽  
International Classification Of Diseases ◽  
Care Utilization ◽  
Housing Type ◽  
Services Research ◽  
Diagnosis Codes ◽  
Classification Of Diseases

BACKGROUND Electronic medical records (EMRs) contain a wealth of information that can support data-driven decision making in health care policy design and service planning. Although research using EMRs has become increasingly prevalent, challenges such as coding inconsistency, data validity, and lack of suitable measures in important domains still hinder the progress. OBJECTIVE The objective of this study was to design a structured way to process records in administrative EMR systems for health services research and assess validity in selected areas. METHODS On the basis of a local hospital EMR system in Singapore, we developed a structured framework for EMR data processing, including standardization and phenotyping of diagnosis codes, construction of cohort with multilevel views, and generation of variables and proxy measures to supplement primary data. Disease complexity was estimated by Charlson Comorbidity Index (CCI) and Polypharmacy Score (PPS), whereas socioeconomic status (SES) was estimated by housing type. Validity of modified diagnosis codes and derived measures were investigated. RESULTS Visit-level (N=7,778,761) and patient-level records (n=549,109) were generated. The International Classification of Diseases, Tenth Revision, Australian Modification (ICD-10-AM) codes were standardized to the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) with a mapping rate of 87.1%. In all, 97.4% of the ICD-9-CM codes were phenotyped successfully using Clinical Classification Software by Agency for Healthcare Research and Quality. Diagnosis codes that underwent modification (truncation or zero addition) in standardization and phenotyping procedures had the modification validated by physicians, with validity rates of more than 90%. Disease complexity measures (CCI and PPS) and SES were found to be valid and robust after a correlation analysis and a multivariate regression analysis. CCI and PPS were correlated with each other and positively correlated with health care utilization measures. Larger housing type was associated with lower government subsidies received, suggesting association with higher SES. Profile of constructed cohorts showed differences in disease prevalence, disease complexity, and health care utilization in those aged above 65 years and those aged 65 years or younger. CONCLUSIONS The framework proposed in this study would be useful for other researchers working with EMR data for health services research. Further analyses would be needed to better understand differences observed in the cohorts.

scholarly journals Association Between Diagnosis Code Expansion and Changes in 30‐Day Risk‐Adjusted Outcomes for Cardiovascular Diseases

2021 ◽  
Author(s):  
Lauren Gilstrap ◽  
Rishi K. Wadhera ◽  
Andrea M. Austin ◽  
Stephen Kearing ◽  
Karen E. Joynt Maddox ◽  
...  
Keyword(s):  
Logistic Regression ◽  
International Classification Of Diseases ◽  
Fee For Service ◽  
Readmission Rates ◽  
Difference In Differences ◽  
Diagnosis Codes ◽  
Classification Of Diseases ◽  
Changes In Risk ◽  
The Impact

BACKGROUND In January 2011, Centers for Medicare and Medicaid Services expanded the number of inpatient diagnosis codes from 9 to 25, which may influence comorbidity counts and risk‐adjusted outcome rates for studies spanning January 2011. This study examines the association between (1) limiting versus not limiting diagnosis codes after 2011, (2) using inpatient‐only versus inpatient and outpatient data, and (3) using logistic regression versus the Centers for Medicare and Medicaid Services risk‐standardized methodology and changes in risk‐adjusted outcomes. METHODS AND RESULTS Using 100% Medicare inpatient and outpatient files between January 2009 and December 2013, we created 2 cohorts of fee‐for‐service beneficiaries aged ≥65 years. The acute myocardial infarction cohort and the heart failure cohort had 578 728 and 1 595 069 hospitalizations, respectively. We calculate comorbidities using (1) inpatient‐only limited diagnoses, (2) inpatient‐only unlimited diagnoses, (3) inpatient and outpatient limited diagnoses, and (4) inpatient and outpatient unlimited diagnoses. Across both cohorts, International Classification of Diseases, Ninth Revision ( ICD‐9 ) diagnoses and hierarchical condition categories increased after 2011. When outpatient data were included, there were no significant differences in risk‐adjusted readmission rates using logistic regression or the Centers for Medicare and Medicaid Services risk standardization. A difference‐in‐differences analysis of risk‐adjusted readmission trends before versus after 2011 found that no significant differences between limited and unlimited models for either cohort. CONCLUSIONS For studies that span 2011, researchers should consider limiting the number of inpatient diagnosis codes to 9 and/or including outpatient data to minimize the impact of the code expansion on comorbidity counts. However, the 2011 code expansion does not appear to significantly affect risk‐adjusted readmission rate estimates using either logistic or risk‐standardization models or when using or excluding outpatient data.

scholarly journals P135: Administrative codes for heat illness: a validation study in Ontario, Canada

CJEM ◽  
2020 ◽  
Vol 22 (S1) ◽  
pp. S113-S113
Author(s):  
H. Baassiri ◽  
T. Varghese ◽  
M. Columbus ◽  
K. Clemens ◽  
J. Yan
Keyword(s):  
Gold Standard ◽  
Heat Stroke ◽  
Heat Exposure ◽  
Sample Size Calculation ◽  
Physical Exertion ◽  
International Classification Of Diseases ◽  
Administrative Database ◽  
Heat Illness ◽  
Predictive Values ◽  
Icd 10

Introduction: Extreme heat events due to climate change are becoming increasingly frequent and severe, and may have an impact on human health. Administrative database studies using International Classification of Diseases 10th revision codes (ICD-10) are powerful tools to measure the burden of acute heat illness (AHI) in Canada. We aimed to assess the validity of the coding algorithm for emergency department (ED) encounters for AHI in our region. Methods: Two independent reviewers retrospectively abstracted data from 507 medical records of patients presenting at two EDs in Ontario between May-September 2015-2018. The Gold Standard definition of an AHI is chart-documented heat exposure with a heat related complaint, such as syncope while working outdoors on a hot day. To determine ICD coding algorithm positive predictive value (PPV), records that were previously coded as ICD-10 heat illnesses were compared to the Gold Standard for AHI. To determine sensitivity (Sn), specificity (Sp) and negative predictive values (NPV), the Gold Standard was compared to randomly selected records. A total of 326,702 ED visits were included in study period with 208 having an ICD-10 code related to heat illness. Sample size calculation demonstrated a need to manually review 62 previously coded heat illnesses and 931 random cases, of which 50 and 474 have been reviewed, respectively. In both abstractions, 20% of cases underwent a blinded duplicate review. Results: In our review of 474 random records, 2 cases were identified as AHI but without an appropriate ICD-10 code, 445 were not AHIs, and no cases had been identified as having an AHI ICD-10 inappropriately applied. In our review of 50 previously coded heat illnesses, 34 were found to be appropriately coded and 16 inappropriately coded, as AHI ICD-10. Average patient age and gender of heat illness vs non-heat illness ED presentations were 32 and 48 years of age and 49% and 64% male, respectively. The leading complaint in AHI was heat stroke/exhaustion (39%), followed by headaches (15%), dizziness (9%), shortness of breath (9%) and syncope/presyncope (6%). 76% of all heat illness presentations presented following a period of physical exertion. Conclusion: Final calculation of Sn, Sp, PPV, NPV for the algorithm will occur upon completion of the review. Preliminary results suggest that ICD-10 coding for AHI may be applied correctly in the ED. This study will help to determine if administrative data can accurately be used to measure the burden of heat illness in Canada.

scholarly journals Use of Diagnosis Codes and/or Wound Culture Results for Surveillance of Surgical Site Infection after Mastectomy and Breast Reconstruction

2010 ◽  
Vol 31 (05) ◽  
pp. 544-547 ◽  
Author(s):  
Margaret A. Olsen ◽  
Victoria J. Fraser
Keyword(s):  
Surgical Site Infection ◽  
Claims Data ◽  
Breast Surgery ◽  
International Classification Of Diseases ◽  
Site Infection ◽  
Wound Culture ◽  
Diagnosis Codes ◽  
Classification Of Diseases ◽  
Major Breast

We compared surveillance of surgical site infection (SSI) after major breast surgery by using a combination of International Classification of Diseases, Ninth Revision, Clinical Modification diagnosis codes and microbiology-based surveillance. The sensitivity of the coding algorithm for identification of SSI was 87.5%, and the sensitivity of wound culture for identification of SSI was 78.1%. Our results suggest that SSI surveillance can be reliably performed using claims data.

The prevalence of localised scleroderma in childhood assessed in the administrative claims data from the United States

2018 ◽  
Vol 4 (1) ◽  
pp. 77-78
Author(s):  
Timothy Beukelman ◽  
Fenglong Xie ◽  
Ivan Foeldvari
Keyword(s):  
United States ◽  
Systemic Sclerosis ◽  
Claims Data ◽  
International Classification Of Diseases ◽  
The United States ◽  
Administrative Claims ◽  
Administrative Claims Data ◽  
Diagnosis Codes ◽  
Classification Of Diseases

Juvenile localised scleroderma is believed an orphan autoimmune disease, which occurs 10 times more often than systemic sclerosis in childhood and is believed to have a prevalence of 1 per 100,000 children. To gain data regarding the prevalence of juvenile localised scleroderma, we assessed the administrative claims data in the United States using the International Classification of Diseases, Ninth Revision diagnosis codes. We found an estimated prevalence in each year ranging from 3.2 to 3.6 per 10,000 children. This estimate is significantly higher as found in previous studies.

scholarly journals Validating Use of Electronic Health Data to Identify Patients with Urinary Tract Infections in Outpatient Settings

Antibiotics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 536
Author(s):  
George Germanos ◽  
Patrick Light ◽  
Roger Zoorob ◽  
Jason Salemi ◽  
Fareed Khan ◽  
...  
Keyword(s):  
Urinary Tract ◽  
Urinary Tract Infections ◽  
Chart Review ◽  
International Classification Of Diseases ◽  
Primary Care Settings ◽  
Diagnosis Codes ◽  
Classification Of Diseases ◽  
Icd 10 ◽  
Tract Infections ◽  
Outpatient Settings

Objective: To validate the use of electronic algorithms based on International Classification of Diseases (ICD)-10 codes to identify outpatient visits for urinary tract infections (UTI), one of the most common reasons for antibiotic prescriptions. Methods: ICD-10 symptom codes (e.g., dysuria) alone or in addition to UTI diagnosis codes plus prescription of a UTI-relevant antibiotic were used to identify outpatient UTI visits. Chart review (gold standard) was performed by two reviewers to confirm diagnosis of UTI. The positive predictive value (PPV) that the visit was for UTI (based on chart review) was calculated for three different ICD-10 code algorithms using (1) symptoms only, (2) diagnosis only, or (3) both. Results: Of the 1087 visits analyzed, symptom codes only had the lowest PPV for UTI (PPV = 55.4%; 95%CI: 49.3–61.5%). Diagnosis codes alone resulted in a PPV of 85% (PPV = 84.9%; 95%CI: 81.1–88.2%). The highest PPV was obtained by using both symptom and diagnosis codes together to identify visits with UTI (PPV = 96.3%; 95%CI: 94.5–97.9%). Conclusions: ICD-10 diagnosis codes with or without symptom codes reliably identify UTI visits; symptom codes alone are not reliable. ICD-10 based algorithms are a valid method to study UTIs in primary care settings.

Sign in / Sign up

Export Citation Format

Share Document