Validation of an ICD code for accurately identifying emergency department patients who suffer an out-of-hospital cardiac arrest.

AIM: International classification of disease (ICD-9) code 427.5 (cardiac arrest) is utilized to identify cohorts of patients who suffer out-of-hospital cardiac arrest (OHCA), though the use of ICD codes for this purpose has never been formally validated. We sought to validate the utility of ICD-9 code 427.5 by identifying patients admitted from the emergency department (ED) after OHCA. METHODS: Adult visits to a single ED between January 2007 and July 2012 were retrospectively examined and a keyword search of the electronic medical record (EMR) was used to identify patients. Cardiac arrest was confirmed; and ICD-9 information and location of return of spontaneous circulation (ROSC) were collected. Separately, the EMR was searched for patients who received ICD-9 code 427.5. The kappa coefficient (κ) was calculated, as was the sensitivity and specificity of the code for identifying OHCA. RESULTS: The keyword search identified 1717 patients, of which 385 suffered OHCA and 333 were assigned the code 427.5. The agreement between ICD-9 code and cardiac arrest was excellent (κ = 0.895). The ICD-9 code 427.5 was both specific (99.4%) and sensitive (86.5%). Of the 52 cardiac arrests that were not identified by ICD-9 code, 33% had ROSC before arrival to the ED. When searching independently on ICD-9 code, 347 patients with ICD-9 code 427.5 were found, of which 320 were true arrests. This yielded a positive predictive value of 92% for ICD-9 code 427.5 in predicting OHCA. CONCLUSIONS: ICD-9 code 427.5 is sensitive and specific for identifying ED patients who suffer OHCA with a positive predictive value of 92%

Use of International Classification of Diseases, Ninth Revision Codes for Obesity: Trends in the United States from an Electronic Health Record-Derived Database.

Obesity is a potentially modifiable risk factor for many diseases, and a better understanding of its impact on health care utilization, costs, and medical outcomes is needed. The ability to accurately evaluate obesity outcomes depends on a correct identification of the population with obesity. The primary objective of this study was to determine the prevalence and accuracy of International Classification of Diseases, Ninth Revision (ICD-9) coding for overweight and obesity within a US primary care electronic health record (EHR) database compared against actual body mass index (BMI) values from recorded clinical patient data; characteristics of patients with obesity who did or did not receive ICD-9 codes for overweight/obesity also were evaluated. The study sample included 5,512,285 patients in the database with any BMI value recorded between January 1, 2014, and June 30, 2014. Based on BMI, 74.6% of patients were categorized as being overweight or obese, but only 15.1% of patients had relevant ICD-9 codes. ICD-9 coding prevalence increased with increasing BMI category. Among patients with obesity (BMI ≥30 kg/m2), those coded for obesity were younger, more often female, and had a greater comorbidity burden than those not coded; hypertension, dyslipidemia, type 2 diabetes mellitus, and gastroesophageal reflux disease were the most common comorbidities. KEY FINDINGS: US outpatients with overweight or obesity are not being reliably coded, making ICD-9 codes undependable sources for determining obesity prevalence and outcomes. BMI data available within EHR databases offer a more accurate and objective means of classifying overweight/obese status

Capturing cases of distal symmetric polyneuropathy in a community

Introduction: Little is known about what constitutes appropriate diagnostic testing in patients with distal symmetric polyneuropathy (DSP). Methods: Utilizing an ICD‐9 screening method and medical record abstraction, we determined the number of new cases of DSP within community neurology practices in Nueces County, Texas. We then compared 2 case capture methods (ICD‐9 vs. all‐case review screening). Results: The ICD‐9 case capture method identified 52 cases over a 3‐month period. Comparing case capture methods, the ICD‐9 method identified 16 of 17 cases identified by the all‐case review method (94%). The ICD‐9 method required screening of 84% fewer charts compared with the all‐case review. Conclusions: Many new cases of DSP occur each month within Nueces County. The ICD‐9 screening technique combined with medical abstraction is an efficient method to identify new DSP cases in this community. These findings are critical for future epidemiological investigations into patients with DSP. Muscle Nerve, 2012Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/94499/1/23449_ftp.pd

ICD-9 Codes and Surveillance for Clostridium difficile–associated Disease

We conducted a retrospective cohort study to compare Clostridium difficile–associated disease rates determined by C. difficile–toxin assays and International Classification of Diseases, 9th Revision (ICD-9) codes. The correlation between toxin assay results and ICD-9 codes was good (κ = 0.72, p<0.01). The sensitivity of the ICD-9 codes was 78% and the specificity was 99.7%

Reclassification of ICD-9 Codes into Meaningful Categories for Oncology Survivorship Research

Background. The International Classification of Disease, ninth revision (ICD-9) is designed to code disease into categories which are placed into administrative databases. These databases have been used for epidemiological studies. However, the categories used in the ICD9-codes are not always the most effective for evaluating specific diseases or their outcomes, such as the outcomes of cancer treatment. Therefore a re-classification of the ICD-9 codes into new categories specific to cancer outcomes is needed. Methods. An expert panel comprised of two physicians created broad categories that would be most useful to researchers investigating outcomes and morbidities associated with the treatment of cancer. A Senior Data Coordinator with expertise in ICD-9 coding, then joined this panel and each code was re-classified into the new categories. Results. Consensus was achieved for the categories to go from the 17 categories in ICD-9 to 39 categories. The ICD-9 Codes were placed into new categories, and subcategories were also created for more specific outcomes. The results of this re-classification is available in tabular form. Conclusions. ICD-9 codes were re-classified by group consensus into categories that are designed for oncology survivorship research. The novel re-classification system can be used by those involved in cancer survivorship research

Validation of ICD-9-CM diagnosis codes for surgical site infection and noninfectious wound complications after mastectomy

BACKGROUNDFew studies have validated ICD-9-CM diagnosis codes for surgical site infection (SSI), and none have validated coding for noninfectious wound complications after mastectomy.OBJECTIVESTo determine the accuracy of International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) diagnosis codes in health insurer claims data to identify SSI and noninfectious wound complications, including hematoma, seroma, fat and tissue necrosis, and dehiscence, after mastectomy.METHODSWe reviewed medical records for 275 randomly selected women who were coded in the claims data for mastectomy with or without immediate breast reconstruction and had an ICD-9-CM diagnosis code for a wound complication within 180 days after surgery. We calculated the positive predictive value (PPV) to evaluate the accuracy of diagnosis codes in identifying specific wound complications and the PPV to determine the accuracy of coding for the breast surgical procedure.RESULTSThe PPV for SSI was 57.5%, or 68.9% if cellulitis-alone was considered an SSI, while the PPV for cellulitis was 82.2%. The PPVs of individual noninfectious wound complications ranged from 47.8% for fat necrosis to 94.9% for seroma and 96.6% for hematoma. The PPVs for mastectomy, implant, and autologous flap reconstruction were uniformly high (97.5%–99.2%).CONCLUSIONSOur results suggest that claims data can be used to compare rates of infectious and noninfectious wound complications after mastectomy across facilities, even though PPVs vary by specific type of postoperative complication. The accuracy of coding was highest for cellulitis, hematoma, and seroma, and a composite group of noninfectious complications (fat necrosis, tissue necrosis, or dehiscence).Infect Control Hosp Epidemiol 2017;38:334–339</jats:sec

Updating and Validating the Rheumatic Disease Comorbidity Index to ICD-10-CM

Background/Objective: Comorbidities can contribute to increased risk for mortality and disability in individuals with rheumatoid arthritis (RA)1,2. The Rheumatic Disease Comorbidity Index (RDCI) assesses 11 comorbidities and produces a weighted score (0-9) that accurately predicts several health outcomes3. The RDCI was developed with self-report data and later validated with ICD-9-CM codes collected from administrative data3,4. On October 1, 2015, the U.S. transitioned to ICD-10-CM, resulting in a nearly five-fold increase in the number of codes available to classify conditions5. Our objective was to update the RDCI by translating it into ICD-10-CM. Methods: We defined an ICD-9-CM cohort and an ICD-10-CM cohort using patient data from the Veterans Affairs Rheumatoid Arthritis Registry (VARA). ICD-10-CM codes were generated by converting ICD-9-CM codes using tools that provide suggested crosswalks, and the codes were reviewed by a physician to assess clinical relevance. Comorbidities were collected from national VA administrative data over a two-year period in both cohorts (ICD-9-CM: October 1, 2013 to September 30, 2015; ICD-10-CM: January 1, 2016 to December 31, 2017). Comorbidity frequencies were compared using Cohen’s Kappa, and RDCI scores were compared using Intraclass Correlation Coefficients (ICC). Results: Both the ICD-9-CM cohort (n=1,082) and ICD-10-CM cohort (n=1,446) were predominantly male (ICD-9-CM: 89%; ICD-10-CM: 87%), Caucasian (ICD-9-CM: 76%; ICD-10-CM: 73%), and middle to old-aged (ICD-9-CM: 67.3 ± 10.2 years; ICD-10-CM: 68.2 ± 10.0 years). Prevalence of comorbidities were similar between coding systems, with absolute differences less than 4% (range: 0.28 to 3.91). Myocardial infarction, hypertension, diabetes mellitus, depression, stroke, other cardiovascular, lung disease, and cancer had moderate agreement or higher (range κ: 0.47 to 0.84), while fracture and ulcer/stomach problem had slight and fair agreement, respectively (κ = 0.13; κ = 0.27)6,7. The RDCI scores were 2.95 ± 1.73 (mean ± SD) for the ICD-9-CM cohort and 2.93 ± 1.75 for the ICD-10-CM cohort. RDCI scores had moderate agreement (ICC: 0.71; 95% CI: 0.68-0.74)8 among individuals who were observed during both the ICD-9-CM and ICD-10-CM eras. Conclusion: We have mapped the RDCI from ICD-9-CM to ICD-10-CM codes, generating comparable RDCI scores in a large RA registry. Individual comorbidity agreement varied, with more chronic conditions such as diabetes and hypertension having higher agreement and more acute conditions such as fractures and ulcer/stomach problems having lower agreement. The updated RDCI can be used in clinical outcomes research with ICD-10-CM era patient data.https://digitalcommons.unmc.edu/surp2021/1043/thumbnail.jp

The validity of using ICD-9 codes and pharmacy records to identify patients with chronic obstructive pulmonary disease

Background: Administrative data is often used to identify patients with chronic obstructive pulmonary disease (COPD), yet the validity of this approach is unclear. We sought to develop a predictive model utilizing administrative data to accurately identify patients with COPD. Methods: Sequential logistic regression models were constructed using 9573 patients with postbronchodilator spirometry at two Veterans Affairs medical centers (2003-2007). COPD was defined as: 1) FEV1/FVC <0.70, and 2) FEV1/FVC < lower limits of normal. Model inputs included age, outpatient or inpatient COPD-related ICD-9 codes, and the number of metered does inhalers (MDI) prescribed over the one year prior to and one year post spirometry. Model performance was assessed using standard criteria. Results: 4564 of 9573 patients (47.7%) had an FEV1/FVC < 0.70. The presence of ≥1 outpatient COPD visit had a sensitivity of 76% and specificity of 67%; the AUC was 0.75 (95% CI 0.74-0.76). Adding the use of albuterol MDI increased the AUC of this model to 0.76 (95% CI 0.75-0.77) while the addition of ipratropium bromide MDI increased the AUC to 0.77 (95% CI 0.76-0.78). The best performing model included: ≥6 albuterol MDI, ≥3 ipratropium MDI, ≥1 outpatient ICD-9 code, ≥1 inpatient ICD-9 code, and age, achieving an AUC of 0.79 (95% CI 0.78-0.80). Conclusion: Commonly used definitions of COPD in observational studies misclassify the majority of patients as having COPD. Using multiple diagnostic codes in combination with pharmacy data improves the ability to accurately identify patients with COPD.Department of Veterans Affairs, Health Services Research and Development (DHA), American Lung Association (CI- 51755-N) awarded to DHA, the American Thoracic Society Fellow Career Development AwardPeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/84155/1/Cooke - ICD9 validity in COPD.pd

Limitations Of Administrative Databases In Orthopaedic Surgery Research: A Study In Obesity And Anemia

The use of national inpatient databases for orthopaedic surgery research has been increasing. However, large databases that rely on administrative data, such as International Classification of Diseases Ninth Revision (ICD-9) codes, may misrepresent patient information, thus affecting the results of studies using this data. The present study uses easily quantified and objective variables of obesity and anemia as example comorbidities to assess the accuracy of ICD-9 codes in the setting of their continued use in orthopaedic surgery database studies. For each study arm, a large inpatient population was obtained from the Yale-New Haven hospital. Each patient\u27s medical record was reviewed, and the presence of ICD-9 discharge codes for obesity and anemia was directly compared to documented body mass index (BMI) and preoperative hematocrit, respectively. ICD-9 discharge codes for both non-morbid obesity and anemia had a sensitivity of just 0.19. The sensitivity of the ICD-9 code for morbid obesity was 0.48. Using obesity and anemia as examples, this study highlights the potential errors inherent to ICD-9 codes. This calls into serious question the utility of administrative databases for research purposes. Moreover, it is likely that these inaccuracies apply to additional variables as well. As database research continues to increase within orthopaedic surgery, it is important to realize that study outcomes can be skewed by data accuracy, and thus should not be blindly accepted simply by virtue of large sample sizes