Search

Background: Electronic medical records (EMRs) contain large amounts of detailed clinical information. Using medical record review to identify conditions within large quantities of EMRs can be time-consuming and inefficient. EMR-based phenotyping using machine learning and natural language processing algorithms is a continually developing area of study that holds potential for numerous mental health disorders., Objective: This review evaluates the current state of EMR-based case identification for depression and provides guidance on using current algorithms and constructing new ones., Methods: A scoping review of EMR-based algorithms for phenotyping depression was completed. This research encompassed studies published from January 2000 to May 2023. The search involved 3 databases: Embase, MEDLINE, and APA PsycInfo. This was carried out using selected keywords that fell into 3 categories: terms connected with EMRs, terms connected to case identification, and terms pertaining to depression. This study adhered to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines., Results: A total of 20 papers were assessed and summarized in the review. Most of these studies were undertaken in the United States, accounting for 75% (15/20). The United Kingdom and Spain followed this, accounting for 15% (3/20) and 10% (2/20) of the studies, respectively. Both data-driven and clinical rule-based methodologies were identified. The development of EMR-based phenotypes and algorithms indicates the data accessibility permitted by each health system, which led to varying performance levels among different algorithms., Conclusions: Better use of structured and unstructured EMR components through techniques such as machine learning and natural language processing has the potential to improve depression phenotyping. However, more validation must be carried out to have confidence in depression case identification algorithms in general., (© Allison Grothman, William J Ma, Kendra G Tickner, Elliot A Martin, Danielle A Southern, Hude Quan. Originally published in JMIR Medical Informatics (https://medinform.jmir.org).)

Objective: This systematic review aimed to identify ICD-10 based validated algorithms for chronic conditions using health administrative data., Methods: A comprehensive systematic literature search using Ovid MEDLINE, Embase, PsycINFO, Web of Science and CINAHL was performed to identify studies, published between 1983 and May 2023, on validated algorithms for chronic conditions using administrative health data. Two reviewers independently screened titles and abstracts and reviewed full text of selected studies to complete data extraction. A third reviewer resolved conflicts arising at the screening or study selection stages. The primary outcome was validated studies of ICD-10 based algorithms with both sensitivity and PPV of ≥70 %. Studies with either sensitivity or PPV <70 % were included as secondary outcomes., Results: Overall, the search identified 1686 studies of which 54 met the inclusion criteria. Combining a previously published literature search, a total of 61 studies were included for data extraction. The study identified 40 chronic conditions with high validity and 22 conditions with moderate validity. The validated algorithms were based on administrative data from different countries including Canada, USA, Australia, Japan, France, South Korea, and Taiwan. The algorithms identified included several types of cancers, cardiovascular conditions, kidney diseases, gastrointestinal disorders, and peripheral vascular diseases, amongst others., Conclusion: With ICD-10 prominently used across the world, this up-to-date systematic review can prove to be a helpful resource for research and surveillance initiatives using administrative health data for identifying chronic conditions., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Masson SAS.. All rights reserved.)

Risk prediction models are frequently used to identify individuals at risk of developing hypertension. This study evaluates different machine learning algorithms and compares their predictive performance with the conventional Cox proportional hazards (PH) model to predict hypertension incidence using survival data. This study analyzed 18,322 participants on 24 candidate features from the large Alberta's Tomorrow Project (ATP) to develop different prediction models. To select the top features, we applied five feature selection methods, including two filter-based: a univariate Cox p-value and C-index; two embedded-based: random survival forest and least absolute shrinkage and selection operator (Lasso); and one constraint-based: the statistically equivalent signature (SES). Five machine learning algorithms were developed to predict hypertension incidence: penalized regression Ridge, Lasso, Elastic Net (EN), random survival forest (RSF), and gradient boosting (GB), along with the conventional Cox PH model. The predictive performance of the models was assessed using C-index. The performance of machine learning algorithms was observed, similar to the conventional Cox PH model. Average C-indexes were 0.78, 0.78, 0.78, 0.76, 0.76, and 0.77 for Ridge, Lasso, EN, RSF, GB and Cox PH, respectively. Important features associated with each model were also presented. Our study findings demonstrate little predictive performance difference between machine learning algorithms and the conventional Cox PH regression model in predicting hypertension incidence. In a moderate dataset with a reasonable number of features, conventional regression-based models perform similar to machine learning algorithms with good predictive accuracy., (© 2023. The Author(s).)

Background: Primary care electronic medical record (EMR) data are emerging as a useful source for secondary uses, such as disease surveillance, health outcomes research, and practice improvement. These data capture clinical details about patients' health status, as well as behavioural risk factors, such as smoking. While the importance of documenting smoking status in a healthcare setting is recognized, the quality of smoking data captured in EMRs is variable. This study was designed to test methods aimed at improving the quality of patient smoking information in a primary care EMR database., Methods: EMR data from community primary care settings extracted by two regional practice-based research networks in Alberta, Canada were used. Patients with at least one encounter in the previous 2 years (2016-2018) and having hypertension according to a validated definition were included (n = 48,377). Multiple imputation was tested under two different assumptions for missing data (smoking status is missing at random and missing not-at-random). A third method tested a novel pattern matching algorithm developed to augment smoking information in the primary care EMR database. External validity was examined by comparing the proportions of smoking categories generated in each method with a general population survey., Results: Among those with hypertension, 40.8% (n = 19,743) had either no smoking information recorded or it was not interpretable and considered missing. Those with missing smoking data differed statistically by demographics, clinical features, and type of EMR system used in the clinic. Both multiple imputation methods produced fully complete smoking status information, with the proportion of current smokers estimated at 25.3% (data missing at random) and 12.5% (data missing not-at-random). The pattern-matching algorithm classified 18.2% of patients as current smokers, similar to the population-based survey (18.9%), but still resulted in missing smoking information for 23.6% of patients. The algorithm was estimated to be 93.8% accurate overall, but varied by smoking status category., Conclusion: Multiple imputation and algorithmic pattern-matching can be used to improve EMR data post-extraction but the recommended method depends on the purpose of secondary use (e.g. practice improvement or epidemiological analyses).

Background: Recurrence is not explicitly documented in cancer registry data that are widely used for research. Patterns of events after initial treatment such as oncology visits, re-operation, and receipt of subsequent chemotherapy or radiation may indicate recurrence. This study aimed to develop and validate algorithms for identifying breast cancer recurrence using routinely collected administrative data., Methods: The study cohort included all young (≤ 40 years) breast cancer patients (2007-2010), and all patients receiving neoadjuvant chemotherapy (2012-2014) in Alberta, Canada. Health events (including mastectomy, chemotherapy, radiation, biopsy and specialist visits) were obtained from provincial administrative data. The algorithms were developed using classification and regression tree (CART) models and validated against primary chart review., Results: Among 598 patients, 121 (20.2%) had recurrence after a median follow-up of 4 years. The high sensitivity algorithm achieved 94.2% (95% CI: 90.1-98.4%) sensitivity, 93.7% (91.5-95.9%) specificity, 79.2% (72.5-85.8%) positive predictive value (PPV), and 98.5% (97.3-99.6%) negative predictive value (NPV). The high PPV algorithm had 75.2% (67.5-82.9%) sensitivity, 98.3% (97.2-99.5%) specificity, 91.9% (86.6-97.3%) PPV, and 94% (91.9-96.1%) NPV. Combining high PPV and high sensitivity algorithms with additional (7.5%) chart review to resolve discordant cases resulted in 94.2% (90.1-98.4%) sensitivity, 98.3% (97.2-99.5%) specificity, 93.4% (89.1-97.8%) PPV, and 98.5% (97.4-99.6%) NPV., Conclusion: The proposed algorithms based on routinely collected administrative data achieved favorably high validity for identifying breast cancer recurrences in a universal healthcare system in Canada.

Background: Validation of administrative data case definitions is key for accurate passive surveillance of disease. Transient ischemic attack (TIA) is a condition primarily managed in the emergency department. However, prior validation studies have focused on data after inpatient hospitalization. We aimed to determine the validity of the Canadian 10th International Classification of Diseases (ICD-10-CA) codes for TIA in the national ambulatory administrative database., Methods: We performed a diagnostic accuracy study of four ICD-10-CA case definition algorithms for TIA in the emergency department setting. The study population was obtained from two ongoing studies on the diagnosis of TIA and minor stroke versus stroke mimic using serum biomarkers and neuroimaging. Two reference standards were used 1) the emergency department clinical diagnosis determined by chart abstractors and 2) the 90-day final diagnosis, both obtained by stroke neurologists, to calculate the sensitivity, specificity, positive and negative predictive values (PPV and NPV) of the ICD-10-CA algorithms for TIA., Results: Among 417 patients, emergency department adjudication showed 163 (39.1%) TIA, 155 (37.2%) ischemic strokes, 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 position with and without query prefix had the highest sensitivity (63.8%), but lowest specificity (81.5%) and PPV (68.9%). Sensitivity, specificity, PPV, and NPV were overall lower when using the 90-day diagnosis as reference standard., Conclusions: Emergency department administrative data reflect diagnosis of suspected TIA with high specificity, but underestimate the burden of disease. Future studies are necessary to understand the reasons for the low to moderate sensitivity.

Background: Epidemiologic studies of alcoholic hepatitis (AH) have been hindered by the lack of a validated International Classification of Disease (ICD) coding algorithm for use with administrative data. Our objective was to validate coding algorithms for AH using a hospitalization database., Methods: The Hospital Discharge Abstract Database (DAD) was used to identify consecutive adults (≥18 years) hospitalized in the Calgary region with a diagnosis code for AH (ICD-10, K70.1) between 01/2008 and 08/2012. Medical records were reviewed to confirm the diagnosis of AH, defined as a history of heavy alcohol consumption, elevated AST and/or ALT (<300 U/L), serum bilirubin >34 μmol/L, and elevated INR. Subgroup analyses were performed according to the diagnosis field in which the code was recorded (primary vs. secondary) and AH severity. Algorithms that incorporated ICD-10 codes for cirrhosis and its complications were also examined., Results: Of 228 potential AH cases, 122 patients had confirmed AH, corresponding to a positive predictive value (PPV) of 54% (95% CI 47-60%). PPV improved when AH was the primary versus a secondary diagnosis (67% vs. 21%; P < 0.001). Algorithms that included diagnosis codes for ascites (PPV 75%; 95% CI 63-86%), cirrhosis (PPV 60%; 47-73%), and gastrointestinal hemorrhage (PPV 62%; 51-73%) had improved performance, however, the prevalence of these diagnoses in confirmed AH cases was low (29-39%)., Conclusions: In conclusion the low PPV of the diagnosis code for AH suggests that caution is necessary if this hospitalization database is used in large-scale epidemiologic studies of this condition.

Background: Sternal wound infection (SWI) in patients undergoing coronary artery bypass grafting (CABG) can carry a significant risk of morbidity and mortality. The objective of this work is to describe the methods used to identify cases of SWI in an administrative database and to demonstrate the effectiveness of using an International Classification of Diseases, Tenth Revision (ICD-10) coding algorithm for this purpose., Methods: ICD-10 codes were used to identify cases of SWI within one year of CABG between April 2002 and November 2009. We randomly chose 200 charts for detailed chart review (100 from each of the groups coded as having SWI and not having SWI) to determine the utility of the ICD-10 coding algorithm., Results: There were 2,820 patients undergoing CABG. Of these, 264 (9.4 percent) were coded as having SWI. Thirty-eight cases of SWI were identified by chart review. The ICD-10 coding algorithm of T81.3 or T81.4 was able to identify incident SWI with a positive predictive value of 35 percent and a negative predictive value of 97 percent. The agreement between the ICD-10 coding algorithm and presence of SWI remained fair, with an overall kappa coefficient of 0.32 (95 percent confidence interval, 0.22-0.43). The effectiveness of identifying deep SWI cases is also presented., Conclusions: This article describes an effective algorithm for identifying a cohort of patients with SWI following open sternotomy in large databases using ICD-10 coding. In addition, alternative search strategies are presented to suit researchers' needs.

Background: Administrative data are commonly used for surveillance of chronic medical conditions. The purpose of this study was to determine the validity of an algorithm derived from administrative data for identifying chronic kidney disease (CKD) compared to the reference standard of estimated glomerular filtration rate (eGFR)., Methods: We identified adults from the province of Alberta with at least two outpatient serum creatinine measurements within a 1-year time period. Validity indices were estimated for CKD using up to 3 years of administrative data (physician billing claims and hospital discharge abstracts) for various case-definition combinations. For each algorithm, the sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were calculated against two reference standard definitions of CKD (two eGFR measurements <60 mL/min/1.73m(2) or mean eGFR < 30 mL/min/1.73m(2))., Results: A total of 321 293 eligible subjects were identified. Irrespective of the algorithm, sensitivities for defining CKD (eGFR < 60 mL/min/1.73m(2)) using administrative codes were low. A case-definition algorithm employing two physician claims or one hospitalization within a 2-year period had sensitivity of 19.4%, specificity of 97.2%, PPV of 60.1% and NPV of 84.8% for detecting CKD. Estimates of sensitivity were higher when <30 mL/min/1.73m(2) was used as the reference standard, although PPVs were lower and consistently less than 50%., Conclusion: These results, using eGFR as a reference standard, suggest that administrative data have insufficient sensitivity and PPV for CKD surveillance, although they may be useful when highly specific algorithms are required for research purposes.

Aim: To assess validity of diabetes International Classification of Disease (ICD) 9 and 10 coding algorithms from administrative data using physicians' charts as the 'gold standard' across time periods and geographic regions., Methods: From 48 urban and 16 rural general practitioners' clinics in Alberta and British Columbia, Canada, we randomly selected 50patient charts/clinic for those who visited the clinic in either 2001 or 2004. Reviewed chart data were linked with inpatient discharge abstract and physician claims administrative data. We identified patients with diabetes in the administrative databases using ICD-9 code 250.xx and ICD-10 codes E10.x-E14.x., Results: The prevalence of diabetes was 8.1% among clinic charts. The coding algorithm of "2 physician claims within 2 years or 1 hospitalization with the relevant diabetes ICD codes" had higher validity than other 7 algorithms assessed (sensitivity 92.3%, specificity 96.9%, positive predictive value 77.2%, and negative predictive value 99.3%). After adjustment for age, sex, and comorbid conditions, sensitivity and positive predictive values were not significantly different between time periods and regions., Conclusion: Diabetes could be accurately identified in administrative data using the following case definition "2 physician claims within 2 years or 1 hospital discharge abstract record with diagnosis codes 250.xx or E10.x-E14.x"., (Copyright 2010 Elsevier Ireland Ltd. All rights reserved.)

Background: Large-scale epidemiological studies of primary biliary cirrhosis (PBC) have been hindered by difficulties in case ascertainment., Objective: To develop coding algorithms for identifying PBC patients using administrative data--a widely available data source., Methods: Population-based administrative databases were used to identify patients with a diagnosis code for PBC from 1994 to 2002. Coding algorithms for confirmed PBC (two or more of antimitochondrial antibody positivity, cholestatic liver biochemistry and/or compatible liver histology) were derived using chart abstraction data as the reference. Patients with a recorded PBC diagnosis but insufficient confirmatory data were classified as 'suspected PBC'., Results: Of 189 potential PBC cases, 119 (60%) had confirmed PBC and 28 (14%) had suspected PBC. The optimal algorithm including two or more uses of a PBC code had a sensitivity of 94% (95% CI 71% to 100%) and positive predictive values of 73% (95% CI 61% to 75%) for confirmed PBC, and 89% (95% CI 82% to 94%) for confirmed or suspected PBC. Sensitivity analyses revealed greater accuracy among women, and with the use of multiple data sources and one or more years of data. Inclusion of diagnosis codes for conditions frequently misclassified as PBC did not improve algorithm performance., Conclusions: Administrative databases can reliably identify patients with PBC and may facilitate epidemiological investigations of this condition.

Background: With the introduction of ICD-10 throughout Canada, it is important to ensure that Acute Myocardial Infarction (AMI) comorbidities employed in risk adjustment methods remain valid and robust. Therefore, we developed ICD-10 coding algorithms for nine AMI comorbidities, examined the validity of the ICD-10 and ICD-9 coding algorithms in detection of these comorbidities, and assessed their performance in predicting mortality. The nine comorbidities that we examined were shock, diabetes with complications, congestive heart failure, cancer, cerebrovascular disease, pulmonary edema, acute renal failure, chronic renal failure, and cardiac dysrhythmias., Methods: Coders generated a comprehensive list of ICD-10 codes corresponding to each AMI comorbidity. Physicians independently reviewed and determined the clinical relevance of each item on the list. To ensure that the newly developed ICD-10 coding algorithms were valid in recording comorbidities, medical charts were reviewed. After assessing ICD-10 algorithms' validity, both ICD-10 and ICD-9 algorithms were applied to a Canadian provincial hospital discharge database to predict in-hospital, 30-day, and 1-year mortality., Results: Compared to chart review data as a 'criterion standard', ICD-9 and ICD-10 data had similar sensitivities (ranging from 7.1-100%), and specificities (above 93.6%) for each of the nine AMI comorbidities studied. The frequencies for the comorbidities were similar between ICD-9 and ICD-10 coding algorithms for 49,861 AMI patients in a Canadian province during 1994-2004. The C-statistics for predicting 30-day and 1 year mortality were the same for ICD-9 (0.82) and for ICD-10 data (0.81)., Conclusion: The ICD-10 coding algorithms developed in this study to define AMI comorbidities performed similarly as past ICD-9 coding algorithms in detecting conditions and risk-adjustment in our sample. However, the ICD-10 coding algorithms should be further validated in external databases.

Objectives: Implementation of the International Statistical Classification of Disease and Related Health Problems, 10th Revision (ICD-10) coding system presents challenges for using administrative data. Recognizing this, we conducted a multistep process to develop ICD-10 coding algorithms to define Charlson and Elixhauser comorbidities in administrative data and assess the performance of the resulting algorithms., Methods: ICD-10 coding algorithms were developed by "translation" of the ICD-9-CM codes constituting Deyo's (for Charlson comorbidities) and Elixhauser's coding algorithms and by physicians' assessment of the face-validity of selected ICD-10 codes. The process of carefully developing ICD-10 algorithms also produced modified and enhanced ICD-9-CM coding algorithms for the Charlson and Elixhauser comorbidities. We then used data on in-patients aged 18 years and older in ICD-9-CM and ICD-10 administrative hospital discharge data from a Canadian health region to assess the comorbidity frequencies and mortality prediction achieved by the original ICD-9-CM algorithms, the enhanced ICD-9-CM algorithms, and the new ICD-10 coding algorithms., Results: Among 56,585 patients in the ICD-9-CM data and 58,805 patients in the ICD-10 data, frequencies of the 17 Charlson comorbidities and the 30 Elixhauser comorbidities remained generally similar across algorithms. The new ICD-10 and enhanced ICD-9-CM coding algorithms either matched or outperformed the original Deyo and Elixhauser ICD-9-CM coding algorithms in predicting in-hospital mortality. The C-statistic was 0.842 for Deyo's ICD-9-CM coding algorithm, 0.860 for the ICD-10 coding algorithm, and 0.859 for the enhanced ICD-9-CM coding algorithm, 0.868 for the original Elixhauser ICD-9-CM coding algorithm, 0.870 for the ICD-10 coding algorithm and 0.878 for the enhanced ICD-9-CM coding algorithm., Conclusions: These newly developed ICD-10 and ICD-9-CM comorbidity coding algorithms produce similar estimates of comorbidity prevalence in administrative data, and may outperform existing ICD-9-CM coding algorithms.

Objective: Coding of obesity using the International Classification of Diseases (ICD) in healthcare administrative databases is under‐reported and thus unreliable for measuring prevalence or incidence. This study aimed to develop and test a rule‐based algorithm for automating the detection and severity of obesity using height and weight collected in several sections of the Electronic Medical Records (EMRs). Methods: In this cross‐sectional study, 1904 inpatient charts randomly selected in three hospitals in Calgary, Canada between January and June 2015 were reviewed and linked with AllScripts Sunrise Clinical Manager EMRs. A rule‐based algorithm was created which looks for patients' height and weight values recorded in EMRs. Clinical notes were split into sentences and searched for height and weight, and BMI was computed. Results: The study cohort consisted of 1904 patients with 50.8% females and 43.3% > 64 years of age. The final model to identify obesity within EMRs resulted in a sensitivity of 92.9%, specificity of 98.4%, positive predictive value of 96.7%, negative predictive value of 96.6%, and F1 score of 94.8%. Conclusions: This study developed a highly valid rule‐based EMR algorithm that detects height and weight. This could allow large‐scale analyses using obesity that were previously not possible. [ABSTRACT FROM AUTHOR]

BackgroundHealth administrative data are frequently used for health services and population health research. Comparative research using these data has been facilitated by the use of a standard system for coding diagnoses, the International Classification of Diseases (ICD). Research using the data must deal with data quality and validity limitations which arise because the data are not created for research purposes. This paper presents a list of high-priority methodological areas for researchers using health administrative data.MethodsA group of researchers and users of health administrative data from Canada, the United States, Switzerland, Australia, China and the United Kingdom came together in June 2005 in Banff, Canada to discuss and identify high-priority methodological research areas. The generation of ideas for research focussed not only on matters relating to the use of administrative data in health services and population health research, but also on the challenges created in transitioning from ICD-9 to ICD-10. After the brain-storming session, voting took place to rank-order the suggested projects. Participants were asked to rate the importance of each project from 1 (low priority) to 10 (high priority). Average ranks were computed to prioritise the projects.ResultsThirteen potential areas of research were identified, some of which represented preparatory work rather than research per se. The three most highly ranked priorities were the documentation of data fields in each country's hospital administrative data (average score 8.4), the translation of patient safety indicators from ICD-9 to ICD-10 (average score 8.0), and the development and validation of algorithms to verify the logic and internal consistency of coding in hospital abstract data (average score 7.0).ConclusionThe group discussions resulted in a list of expert views on critical international priorities for future methodological research relating to health administrative data. The consortium's members welcome contacts from investigators involved in research using health administrative data, especially in cross-jurisdictional collaborative studies or in studies that illustrate the application of ICD-10.

Background: Measurement of care quality and safety mainly relies on abstracted administrative data. However, it is well studied that administrative data-based adverse event (AE) detection methods are suboptimal due to lack of clinical information. Electronic medical records (EMR) have been widely implemented and contain detailed and comprehensive information regarding all aspects of patient care, offering a valuable complement to administrative data. Harnessing the rich clinical data in EMRs offers a unique opportunity to improve detection, identify possible risk factors of AE and enhance surveillance. However, the methodological tools for detection of AEs within EMR need to be developed and validated. The objectives of this study are to develop EMR-based AE algorithms from hospital EMR data and assess AE algorithm's validity in Canadian EMR data. Methods: Patient EMR structured and text data from acute care hospitals in Calgary, Alberta, Canada will be linked with discharge abstract data (DAD) between 2010 and 2020 (n~1.5 million). AE algorithms development. First, a comprehensive list of AEs will be generated through a systematic literature review and expert recommendations. Second, these AEs will be mapped to EMR free texts using Natural Language Processing (NLP) technologies. Finally, an expert panel will assess the clinical relevance of the developed NLP algorithms. AE algorithms validation: We will test the newly developed AE algorithms on 10,000 randomly selected EMRs between 2010 to 2020 from Calgary, Alberta. Trained reviewers will review the selected 10,000 EMR charts to identify AEs that had occurred during hospitalization. Performance indicators (e.g., sensitivity, specificity, positive predictive value, negative predictive value, F1 score, etc.) of the developed AE algorithms will be assessed using chart review data as the reference standard. Discussion: The results of this project can be widely implemented in EMR based healthcare system to accurately and timely detect in-hospital AEs. [ABSTRACT FROM AUTHOR]

Background: Epidemiologic studies of alcoholic hepatitis (AH) have been hindered by the lack of a validated International Classification of Disease (ICD) coding algorithm for use with administrative data. Our objective was to validate coding algorithms for AH using a hospitalization database.Methods: The Hospital Discharge Abstract Database (DAD) was used to identify consecutive adults (≥18 years) hospitalized in the Calgary region with a diagnosis code for AH (ICD-10, K70.1) between 01/2008 and 08/2012. Medical records were reviewed to confirm the diagnosis of AH, defined as a history of heavy alcohol consumption, elevated AST and/or ALT (<300 U/L), serum bilirubin >34 μmol/L, and elevated INR. Subgroup analyses were performed according to the diagnosis field in which the code was recorded (primary vs. secondary) and AH severity. Algorithms that incorporated ICD-10 codes for cirrhosis and its complications were also examined.Results: Of 228 potential AH cases, 122 patients had confirmed AH, corresponding to a positive predictive value (PPV) of 54% (95% CI 47-60%). PPV improved when AH was the primary versus a secondary diagnosis (67% vs. 21%; P < 0.001). Algorithms that included diagnosis codes for ascites (PPV 75%; 95% CI 63-86%), cirrhosis (PPV 60%; 47-73%), and gastrointestinal hemorrhage (PPV 62%; 51-73%) had improved performance, however, the prevalence of these diagnoses in confirmed AH cases was low (29-39%).Conclusions: In conclusion the low PPV of the diagnosis code for AH suggests that caution is necessary if this hospitalization database is used in large-scale epidemiologic studies of this condition. [ABSTRACT FROM AUTHOR]

Background: Multimorbidity is common and associated with poor clinical outcomes and high health care costs. Administrative data are a promising tool for studying the epidemiology of multimorbidity. Our goal was to derive and apply a new scheme for using administrative data to identify the presence of chronic conditions and multimorbidity. Methods: We identified validated algorithms that use ICD-9 CM/ICD-10 data to ascertain the presence or absence of 40 morbidities. Algorithms with both positive predictive value and sensitivity ⩾70% were graded as "high validity"; those with positive predictive value ⩾70% and sensitivity <70% were graded as "moderate validity". To show proof of concept, we applied identified algorithms with high to moderate validity to inpatient and outpatient claims and utilization data from 574,409 people residing in Edmonton, Canada during the 2008/2009 fiscal year. Results: Of the 40 morbidities, we identified 30 that could be identified with high to moderate validity. Approximately one quarter of participants had identified multimorbidity (2 or more conditions), one quarter had a single identified morbidity and the remaining participants were not identified as having any of the 30 morbidities. Conclusions: We identified a panel of 30 chronic conditions that can be identified from administrative data using validated algorithms, facilitating the study and surveillance of multimorbidity. We encourage other groups to use this scheme, to facilitate comparisons between settings and jurisdictions. [ABSTRACT FROM AUTHOR]

Administrative databases could be useful in studying the epidemiology of primary sclerosing cholangitis (PSC); however, there is no information regarding the validity of the diagnostic code in administrative databases. The aims of this study were to determine the validity of administrative data for a diagnosis of PSC and generate algorithms for the identification of PSC patients. The sensitivity (Se) and positive predictive value (PPV) of a PSC diagnosis based on administrative data from 2000 to 2003 were determined through chart review data. Algorithms were developed by considering variables associated with PSC and coding details. A logistic regression model was constructed using covariates associated with PSC. Based on this model, each subject was assigned a probability of having PSC. A cutoff value was selected that maximized the Se and specificity (Sp) of correctly predicting PSC cases. In the administrative data, the initial Se and PPV were 83.7 and 7.2% respectively. The optimal algorithm included one PSC code and one inflammatory bowel disease code and had Se 56% and PPV 59%. Overall, the algorithms yielded inadequate PPV and Se estimates to identify a cohort of true PSC cases. The predictive model was constructed using six covariates. For this model, the area under the receiver operating characteristic curve was 93.5%. A cutoff of 0.0729 was used, which maximized the Se 81.9% and Sp 90.7%; however, the PPV was 41.0%. An algorithm for the identification of true PSC cases from administrative data was not possible. We recommend that PSC receives a distinct ICD code from ascending cholangitis. [ABSTRACT FROM AUTHOR]

Background: The performance of the Charlson and Elixhauser comorbidity measures in predicting patient outcomes have been well validated with ICD-9 data but not with ICD-10 data, especially in disease specific patient cohorts. The objective of this study was to assess the performance of these two comorbidity measures in the prediction of in-hospital and 1 year mortality among patients with congestive heart failure (CHF), diabetes, chronic renal failure (CRF), stroke and patients undergoing coronary artery bypass grafting (CABG). Methods: A Canadian provincial hospital discharge administrative database was used to define 17 Charlson comorbidities and 30 Elixhauser comorbidities. C-statistic values were calculated to evaluate the performance of two measures. One year mortality information was obtained from the provincial Vital Statistics Department. Results: The absolute difference between ICD-9 and ICD-10 data in C-statistics ranged from 0 to 0.04 across five cohorts for the Charlson and Elixhauser comorbidity measures predicting inhospital or 1 year mortality. In the models predicting in-hospital mortality using ICD-10 data, the C-statistics ranged from 0.62 (for stroke) -- 0.82 (for diabetes) for Charlson measure and 0.62 (for stroke) to 0.83 (for CABG) for Elixhauser measure. Conclusion: The change in coding algorithms did not influence the performance of either the Charlson or Elixhauser comorbidity measures in the prediction of outcome. Both comorbidity measures were still valid prognostic indicators in the ICD-10 data and had a similar performance in predicting short and long term mortality in the ICD-9 and ICD-10 data. [ABSTRACT FROM AUTHOR]

Summary: The objective of this study was to develop and validate coding algorithms for epilepsy using ICD-coded inpatient claims, physician claims, and emergency room (ER) visits. 720/2049 charts from 2003 and 1533/3252 charts from 2006 were randomly selected for review from 13 neurologists’ practices as the “gold standard” for diagnosis. Epilepsy status in each chart was determined by 2 trained physicians. The optimal algorithm to identify epilepsy cases was developed by linking the reviewed charts with three administrative databases (ICD 9 and 10 data from 2000 to 2008) including hospital discharges, ER visits and physician claims in a Canadian health region. Accepting chart review data as the gold standard, we calculated sensitivity, specificity, positive, and negative predictive value for each ICD-9 and ICD-10 administrative data algorithm (case definitions). Of 18 algorithms assessed, the most accurate algorithm to identify epilepsy cases was “2 physician claims or 1 hospitalization in 2 years coded” (ICD-9 345 or G40/G41) and the most sensitive algorithm was “1 physician clam or 1 hospitalization or 1 ER visit in 2 years.” Accurate and sensitive case definitions are available for research requiring the identification of epilepsy cases in administrative health data. [Copyright &y& Elsevier]