Clostridioides difficile infections in Alberta: The validity of administrative data using ICD-10 diagnostic codes for CDI surveillance versus clinical infection surveillance

2020 ◽  
Vol 48 (12) ◽  
pp. 1431-1436
Author(s):  
Ted Pfister ◽  
Elissa Rennert-May ◽  
Jennifer Ellison ◽  
Kathryn Bush ◽  
Jenine Leal
Keyword(s):  
Administrative Data ◽  
Clinical Infection ◽  
Diagnostic Codes ◽  
Clostridioides Difficile ◽  
Infection Surveillance ◽  
Icd 10

scholarly journals Accuracy of Administrative Data for the Coding of Acute Stroke and TIAs

2016 ◽  
Vol 43 (6) ◽  
pp. 765-773 ◽  
Author(s):  
Ruth Hall ◽  
Luke Mondor ◽  
Joan Porter ◽  
Jiming Fang ◽  
Moira K. Kapral
Keyword(s):  
Ischemic Stroke ◽  
Acute Stroke ◽  
Administrative Data ◽  
Data Validation ◽  
Reference Standard ◽  
Stroke Registry ◽  
Diagnostic Codes ◽  
Icd 10 ◽  
Stroke Type ◽  
Ischemic Attack

AbstractObjective: Administrative data validation is essential for identifying biases and misclassification in research. The objective of this study was to determine the accuracy of diagnostic codes for acute stroke and transient ischemic attack (TIA) using the Ontario Stroke Registry (OSR) as the reference standard. Methods: We identified stroke and TIA events in inpatient and emergency department (ED) administrative data from eight regional stroke centres in Ontario, Canada, from April of 2006 through March of 2008 using ICD–10–CA codes for subarachnoid haemorrhage (I60, excluding I60.8), intracerebral haemorrhage (I61), ischemic (H34.1 and I63, excluding I63.6), unable to determine stroke (I64), and TIA (H34.0 and G45, excluding G45.4). We linked administrative data to the Ontario Stroke Registry and calculated sensitivity and positive predictive value (PPV). Results:: We identified 5,270 inpatient and 4,411 ED events from the administrative data. Inpatient administrative data had an overall sensitivity of 82.2% (95% confidence interval [CI95%]=81.0, 83.3) and a PPV of 68.8% (CI95%=67.5, 70.0) for the diagnosis of stroke, with notable differences observed by stroke type. Sensitivity for ischemic stroke increased from 66.5 to 79.6% with inclusion of I64. The sensitivity and PPV of ED administrative data for diagnosis of stroke were 56.8% (CI95%=54.8, 58.7) and 59.1% (CI95%=57.1, 61.1), respectively. For all stroke types, accuracy was greater in the inpatient data than in the ED data. Conclusion: The accuracy of stroke identification based on administrative data from stroke centres may be improved by including I64 in ischemic stroke type, and by considering only inpatient data.

scholarly journals Development of an Electronic Algorithm to Target Outpatient Antimicrobial Stewardship Efforts for Acute Bronchitis

2020 ◽  
Vol 41 (S1) ◽  
pp. s32-s32
Author(s):  
Ebbing Lautenbach ◽  
Keith Hamilton ◽  
Robert Grundmeier ◽  
Melinda Neuhauser ◽  
Lauri Hicks ◽  
...  
Keyword(s):  
Antimicrobial Stewardship ◽  
Predictive Value ◽  
Gold Standard ◽  
Chart Review ◽  
Acute Bronchitis ◽  
Antibiotic Use ◽  
Antibiotic Prescribing ◽  
Diagnostic Codes ◽  
Icd 10 ◽  
The Impact

Background: Antibiotic resistance has increased at alarming rates, driven predominantly by antibiotic overuse. Although most antibiotic use occurs in outpatients, antimicrobial stewardship programs have primarily focused on inpatient settings. A major challenge for outpatient stewardship is the lack of accurate and accessible electronic data to target interventions. We sought to develop and validate an electronic algorithm to identify inappropriate antibiotic use for outpatients with acute bronchitis. Methods: This study was conducted within the University of Pennsylvania Health System (UPHS). We used ICD-10 diagnostic codes to identify encounters for acute bronchitis at any outpatient UPHS practice between March 15, 2017, and March 14, 2018. Exclusion criteria included underlying immunocompromising condition, other comorbidity influencing the need for antibiotics (eg, emphysema), or ICD-10 code at the same visit for a concurrent infection (eg, sinusitis). We randomly selected 300 (150 from academic practices and 150 from nonacademic practices) eligible subjects for detailed chart abstraction that assessed patient demographics and practice and prescriber characteristics. Appropriateness of antibiotic use based on chart review served as the gold standard for assessment of the electronic algorithm. Because antibiotic use is not indicated for this study population, appropriateness was assessed based upon whether an antibiotic was prescribed or not. Results: Of 300 subjects, median age was 61 years (interquartile range, 50–68), 62% were women, 74% were seen in internal medicine (vs family medicine) practices, and 75% were seen by a physician (vs an advanced practice provider). On chart review, 167 (56%) subjects received an antibiotic. Of these subjects, 1 had documented concern for pertussis and 4 had excluding conditions for which there were no ICD-10 codes. One received an antibiotic prescription for a planned dental procedure. Thus, based on chart review, 161 (54%) subjects received antibiotics inappropriately. Using the electronic algorithm based on diagnostic codes, underlying and concurrent conditions, and prescribing data, the number of subjects with inappropriate prescribing was 170 (56%) because 3 subjects had antibiotic prescribing not noted based on chart review. The test characteristics of the electronic algorithm (compared to gold standard chart review) for identification of inappropriate antibiotic prescribing were the following: sensitivity, 100% (161 of 161); specificity, 94% (130 of 139); positive predictive value, 95% (161 of 170); and negative predictive value, 100% (130 of 130). Conclusions: For outpatients with acute bronchitis, an electronic algorithm for identification of inappropriate antibiotic prescribing is highly accurate. This algorithm could be used to efficiently assess prescribing among practices and individual clinicians. The impact of interventions based on this algorithm should be tested in future studies.Funding: NoneDisclosures: None

Comparison of routine blood alcohol tests and ICD-10-AM coding of alcohol involvement for major trauma patients

2021 ◽  
pp. 183335832110371
Author(s):  
Georgina Lau ◽  
Belinda J Gabbe ◽  
Biswadev Mitra ◽  
Paul M Dietze ◽  
Sandra Braaf ◽  
...  
Keyword(s):  
Administrative Data ◽  
Major Trauma ◽  
Blood Alcohol Concentration ◽  
Trauma Centre ◽  
Trauma Patients ◽  
Blood Alcohol ◽  
Alcohol Involvement ◽  
Preventable Risk Factor ◽  
Major Trauma Centre ◽  
Icd 10

Background: Alcohol use is a key preventable risk factor for serious injury. To effectively prevent alcohol-related injuries, we rely on the accurate surveillance of alcohol involvement in injury events. This often involves the use of administrative data, such as International Statistical Classification of Diseases and Related Health Problems, Tenth Revision, Australian Modification (ICD-10-AM) coding. Objective: To evaluate the completeness and accuracy of using administrative coding for the surveillance of alcohol involvement in major trauma injury events by comparing patient blood alcohol concentration (BAC) with ICD-10-AM coding. Method: This retrospective cohort study examined 2918 injury patients aged ≥18 years who presented to a major trauma centre in Victoria, Australia, over a 2-year period, of which 78% ( n = 2286) had BAC data available. Results: While 15% of patients had a non-zero BAC, only 4% had an ICD-10-AM code suggesting acute alcohol involvement. The agreement between blood alcohol test results and ICD-10-AM coding of acute alcohol involvement was fair ( κ = 0.33, 95% confidence interval: 0.27–0.38). Of the 341 patients with a non-zero BAC, 82 (24.0%) had ICD-10-AM codes related to acute alcohol involvement. Supplementary factors Y90 Evidence of alcohol involvement determined by blood alcohol level codes, which specifically describe patient BAC, were assigned to just 29% of eligible patients with a non-zero BAC. Conclusion: ICD-10-AM coding underestimated the proportion of alcohol-related injuries compared to patient BAC. Implications: Given the current role of administrative data in the surveillance of alcohol-related injuries, these findings may have significant implications for the implementation of cost-effective strategies for preventing alcohol-related injuries.

Abstract P250: A Strategy for Reliable Identification of Ischemic Stroke, Thrombolytics, and Thrombectomy in Large Administrative Databases

Stroke ◽  
2021 ◽  
Vol 52 (Suppl_1) ◽  
Author(s):  
Kori S Zachrison ◽  
Sijia Li ◽  
Mathew J Reeves ◽  
Opeolu M Adeoye ◽  
Carlos A Camargo ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Administrative Data ◽  
Administrative Databases ◽  
Stroke Patients ◽  
Inpatient Hospitalization ◽  
Accurate Identification ◽  
Stroke Research ◽  
Diagnosis Codes ◽  
Icd 10 ◽  
Icd Codes

Background: Administrative data are frequently used in stroke research. Ensuring accurate identification of ischemic stroke patients, and those receiving thrombolysis and endovascular thrombectomy (EVT) is critical to ensure representativeness and generalizability. We examined differences in patient samples based on different modes of identification, and propose a strategy for future patient and procedure identification in large administrative databases. Methods: We used nonpublic administrative data from the state of California to identify all ischemic stroke patients discharged from an emergency department or inpatient hospitalization from 2010-2017 based on ICD-9 (2010-2015), ICD-10 (2015-2017), and MS-DRG discharge codes. We identified patients with interhospital transfers, patients receiving thrombolytics, and patients treated with EVT based on ICD, CPT and MS-DRG codes. We determined what proportion of these transfers and procedures would have been identified with ICD versus MS-DRG discharge codes. Results: Of 365,099 ischemic stroke encounters, most (87.7%) had both a stroke-related ICD-9 or ICD-10 code and stroke-related MS-DRG code; 12.3% had only an ICD-9 or ICD-10 code, and 0.02% had only a MS-DRG code. Nearly all transfers (99.9%) were identified using ICD codes. We identified32,433 thrombolytic-treated patients (8.9% of total) using ICD, CPT, and MS-DRG codes; the combination of ICD and CPT codes identified nearly all (98%). We identified 7,691 patients treated with EVT (2.1% of total) using ICD and MS-DRG codes; both MS-DRG and ICD-9/-10 codes were necessary because ICD codes alone missed 13.2% of EVTs. CPT codes only pertain to outpatient/ED patients and are not useful for EVT identification. Conclusions: ICD-9/-10 diagnosis codes capture nearly all ischemic stroke encounters and transfers, while the combination of ICD-9/-10 and CPT codes are adequate for identifying thrombolytic treatment in administrative datasets. However, MS-DRG codes are necessary in addition to ICD codes for identifying EVT, likely due to favorable reimbursement for EVT-related MS-DRG codes incentivizing accurate coding.

scholarly journals Validation of a Case Definition for Pediatric Brain Injury Using Administrative Data

2017 ◽  
Vol 44 (2) ◽  
pp. 161-169 ◽  
Author(s):  
Jane McChesney-Corbeil ◽  
Karen Barlow ◽  
Hude Quan ◽  
Guanmin Chen ◽  
Samuel Wiebe ◽  
...  
Keyword(s):  
Brain Injury ◽  
Administrative Data ◽  
Predictive Value ◽  
External Validation ◽  
Secondary Data ◽  
Population Based ◽  
Case Definition ◽  
Case Definitions ◽  
Icd 10 ◽  
Pediatric Tbi

AbstractBackground: Health administrative data are a common population-based data source for traumatic brain injury (TBI) surveillance and research; however, before using these data for surveillance, it is important to develop a validated case definition. The objective of this study was to identify the optimal International Classification of Disease , edition 10 (ICD-10), case definition to ascertain children with TBI in emergency room (ER) or hospital administrative data. We tested multiple case definitions. Methods: Children who visited the ER were identified from the Regional Emergency Department Information System at Alberta Children’s Hospital. Secondary data were collected for children with trauma, musculoskeletal, or central nervous system complaints who visited the ER between October 5, 2005, and June 6, 2007. TBI status was determined based on chart review. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated for each case definition. Results: Of 6639 patients, 1343 had a TBI. The best case definition was, “1 hospital or 1 ER encounter coded with an ICD-10 code for TBI in 1 year” (sensitivity 69.8% [95% confidence interval (CI), 67.3-72.2], specificity 96.7% [95% CI, 96.2-97.2], PPV 84.2% [95% CI 82.0-86.3], NPV 92.7% [95% CI, 92.0-93.3]). The nonspecific code S09.9 identified >80% of TBI cases in our study. Conclusions: The optimal ICD-10–based case definition for pediatric TBI in this study is valid and should be considered for future pediatric TBI surveillance studies. However, external validation is recommended before use in other jurisdictions, particularly because it is plausible that a larger proportion of patients in our cohort had milder injuries.

scholarly journals Validating International Classification of Disease 10th revision algorithms for identifying influenza and respiratory syncytial virus hospitalizations

2021 ◽  
Vol 2 (2) ◽  
Author(s):  
Mackenzie A Hamilton ◽  
Andrew Calzavara ◽  
Scott D Emerson ◽  
Jeffrey C Kwong
Keyword(s):  
Respiratory Syncytial Virus ◽  
Administrative Data ◽  
Predictive Value ◽  
Hospitalized Patients ◽  
International Classification ◽  
Health Administrative Data ◽  
International Classification Of Disease ◽  
Icd 10 ◽  
Syncytial Virus

Objective: Routinely collected health administrative data can be used to efficiently assess disease burden in large populations, but it is important to evaluate the validity of these data. The objective of this study was to develop and validate International Classification of Disease 10PthP revision (ICD -10) algorithms that identify laboratory-confirmed influenza or laboratory-confirmed respiratory syncytial virus (RSV) hospitalizations using population-based health administrative data from Ontario, Canada. Study Design and Setting: Influenza and RSV laboratory data from the 2014-15 through to 2017-18 respiratory virus seasons were obtained from the Ontario Laboratories Information System (OLIS) and were linked to hospital discharge abstract data to generate influenza and RSV reference cohorts. These reference cohorts were used to assess the sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of the ICD-10 algorithms. To minimize misclassification in future studies, we prioritized specificity and PPV in selecting top-performing algorithms. Results: 83,638 and 61,117 hospitalized patients were included in the influenza and RSV reference cohorts, respectively. The best influenza algorithm had a sensitivity of 73% (95% CI 72% to 74%), specificity of 99% (95% CI 99% to 99%), PPV of 94% (95% CI 94% to 95%), and NPV of 94% (95% CI 94% to 95%). The best RSV algorithm had a sensitivity of 69% (95% CI 68% to 70%), specificity of 99% (95% CI 99% to 99%), PPV of 91% (95% CI 90% to 91%) and NPV of 97% (95% CI 97% to 97%). Conclusion: We identified two highly specific algorithms that best ascertain patients hospitalized with influenza or RSV. These algorithms may be applied to hospitalized patients if data on laboratory tests are not available, and will thereby improve the power of future epidemiologic studies of influenza, RSV, and potentially other severe acute respiratory infections.

Abstract TP187: Low Sensitivity and High Specificity of Administrative Data for Transient Ischemic Attacks in the Emergency Department

Stroke ◽  
2017 ◽  
Vol 48 (suppl_1) ◽  
Author(s):  
Amy Y Yu ◽  
Hude Quan ◽  
Andrew McRae ◽  
Gabrielle O Wagner ◽  
Shelagh B Coutts ◽  
...  
Keyword(s):  
Emergency Department ◽  
Administrative Data ◽  
Temporal Trends ◽  
High Specificity ◽  
Reference Standards ◽  
Administrative Database ◽  
Minor Stroke ◽  
Stroke Mimics ◽  
Icd 10 ◽  
Low Sensitivity

Introduction: Accurate surveillance of TIA is important for monitoring disease burden and evaluating temporal trends. Passive surveillance is a time and cost-effective method to identify TIA using administrative data. Although TIA is primarily managed in the emergency department (ED) without admission to hospital, prior administrative data validation studies have mainly evaluated inpatient databases. We determined the validity of the ICD-10 codes to identify TIA in an ED administrative database. Methods: The study population was obtained from two ongoing studies on the diagnosis of TIA and minor stroke versus stroke mimic. Stroke mimics were actively recruited. Patients enrolled between December 1st 2013 and October 30th 2015 with an ED visit were included in the current study. ED discharge diagnoses were obtained from the National Ambulatory Care Reporting System database. We determined the sensitivity, specificity, and positive predictive value (PPV) of the ICD-10 TIA codes by using two reference standards: 1) the ED chart abstraction and 2) the 90-day final diagnosis, both adjudicated by stroke neurologists. Different case definition algorithms were tested. Results: We included 417 patients. ED adjudication showed 163 (39.1%) TIA, 155 (37.2%) ischemic stroke, and 99 (23.7%) stroke mimics. The most restrictive algorithm, defined as a TIA code in the main position had the lowest sensitivity (36.8%), but highest specificity (92.5%) and PPV (76.0%). The most inclusive algorithm, defined as a TIA code in any positions with and without query prefix had the highest sensitivity (63.8%), but lowest specificity (81.5%) and PPV (68.9%). Comparing the final 90-day diagnosis with coding showed similar results. Conclusions: TIA can be identified with high specificity, but low sensitivity from ED discharge diagnoses. By including patients with stroke mimics, we determined both the false positive and negative rates, allowing for the calculation of sensitivity and specificity. We used two reference standards to verify the accuracy of administrative data. Future studies are necessary to understand the reasons for the low sensitivity of administrative data for TIA and whether the miscoded patients are systematically different from the accurately coded ones.

scholarly journals Strategy for reliable identification of ischaemic stroke, thrombolytics and thrombectomy in large administrative databases

2020 ◽  
pp. svn-2020-000533
Author(s):  
Kori S Zachrison ◽  
Sijia Li ◽  
Mathew J Reeves ◽  
Opeolu Adeoye ◽  
Carlos A Camargo ◽  
...  
Keyword(s):  
Ischaemic Stroke ◽  
Administrative Data ◽  
Administrative Databases ◽  
Accurate Identification ◽  
Stroke Research ◽  
Diagnosis Codes ◽  
Inpatient Hospitalisation ◽  
Icd 10 ◽  
Related Group ◽  
Icd Codes

BackgroundAdministrative data are frequently used in stroke research. Ensuring accurate identification of patients who had an ischaemic stroke, and those receiving thrombolysis and endovascular thrombectomy (EVT) is critical to ensure representativeness and generalisability. We examined differences in patient samples based on mode of identification, and propose a strategy for future patient and procedure identification in large administrative databases.MethodsWe used non-public administrative data from the state of California to identify all patients who had an ischaemic stroke discharged from an emergency department (ED) or inpatient hospitalisation from 2010 to 2017 based on International Classification of Disease (ICD-9) (2010–2015), ICD-10 (2015–2017) and Medicare Severity-Diagnosis-related Group (MS-DRG) discharge codes. We identified patients with interhospital transfers, patients receiving thrombolytics and patients treated with EVT based on ICD, Current Procedural Terminology (CPT) and MS-DRG codes. We determined what proportion of these transfers and procedures would have been identified with ICD versus MS-DRG discharge codes.ResultsOf 365 099 ischaemic stroke encounters, most (87.70%) had both a stroke-related ICD-9 or ICD-10 code and stroke-related MS-DRG code; 12.28% had only an ICD-9 or ICD-10 code and 0.02% had only an MS-DRG code. Nearly all transfers (99.99%) were identified using ICD codes. We identified 32 433 thrombolytic-treated patients (8.9% of total) using ICD, CPT and MS-DRG codes; the combination of ICD and CPT codes identified nearly all (98%). We identified 7691 patients treated with EVT (2.1% of total) using ICD and MS-DRG codes; both MS-DRG and ICD-9/ICD-10 codes were necessary because ICD codes alone missed 13.2% of EVTs. CPT codes only pertain to outpatient/ED patients and are not useful for EVT identification.ConclusionsICD-9/ICD-10 diagnosis codes capture nearly all ischaemic stroke encounters and transfers, while the combination of ICD-9/ICD-10 and CPT codes are adequate for identifying thrombolytic treatment in administrative datasets. However, MS-DRG codes are necessary in addition to ICD codes for identifying EVT, likely due to favourable reimbursement for EVT-related MS-DRG codes incentivising accurate coding.

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