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Medical researchers and clinicians have shown much interest in developing machine learning (ML) algorithms to detect/predict surgical site infections (SSIs). However, little is known about the overall performance of ML algorithms in predicting SSIs and how to improve the algorithm's robustness. We conducted a systematic review and meta-analysis to summarize the performance of ML algorithms in SSIs case detection and prediction and to describe the impact of using unstructured and textual data in the development of ML algorithms.MEDLINE, EMBASE, CINAHL, CENTRAL and Web of Science were searched from inception to March 25, 2021. Study characteristics and algorithm development information were extracted. Performance statistics (e.g., sensitivity, area under the receiver operating characteristic curve [AUC]) were pooled using a random effect model. Stratified analysis was applied to different study characteristic levels. Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Diagnostic Test Accuracy Studies (PRISMA-DTA) was followed.Of 945 articles identified, 108 algorithms from 32 articles were included in this review. The overall pooled estimate of the SSI incidence rate was 3.67%, 95% CI: 3.58-3.76. Mixed-use of structured and textual data-based algorithms (pooled estimates of sensitivity 0.83, 95% CI: 0.78-0.87, specificity 0.92, 95% CI: 0.86-0.95, AUC 0.92, 95% CI: 0.89-0.94) outperformed algorithms solely based on structured data (sensitivity 0.56, 95% CI:0.43-0.69, specificity 0.95, 95% CI:0.91-0.97, AUC = 0.90, 95% CI: 0.87-0.92).ML algorithms developed with structured and textual data provided optimal performance. External validation of ML algorithms is needed to translate current knowledge into clinical practice.

BACKGROUND Surveillance of hospital-acquired pressure injuries (HAPI) is often suboptimal when relying on administrative health data, as International Classification of Diseases (ICD) codes are known to have long delays and are undercoded. We leveraged natural language processing (NLP) applications on free-text notes, particularly the inpatient nursing notes, from electronic medical records (EMRs), to more accurately and timely identify HAPIs. OBJECTIVE This study aimed to show that EMR-based phenotyping algorithms are more fitted to detect HAPIs than ICD-10-CA algorithms alone, while the clinical logs are recorded with higher accuracy via NLP using nursing notes. METHODS Patients with HAPIs were identified from head-to-toe skin assessments in a local tertiary acute care hospital during a clinical trial that took place from 2015 to 2018 in Calgary, Alberta, Canada. Clinical notes documented during the trial were extracted from the EMR database after the linkage with the discharge abstract database. Different combinations of several types of clinical notes were processed by sequential forward selection during the model development. Text classification algorithms for HAPI detection were developed using random forest (RF), extreme gradient boosting (XGBoost), and deep learning models. The classification threshold was tuned to enable the model to achieve similar specificity to an ICD-based phenotyping study. Each model’s performance was assessed, and comparisons were made between the metrics, including sensitivity, positive predictive value, negative predictive value, and F1-score. RESULTS Data from 280 eligible patients were used in this study, among whom 97 patients had HAPIs during the trial. RF was the optimal performing model with a sensitivity of 0.464 (95% CI 0.365-0.563), specificity of 0.984 (95% CI 0.965-1.000), and F1-score of 0.612 (95% CI of 0.473-0.751). The machine learning (ML) model reached higher sensitivity without sacrificing much specificity compared to the previously reported performance of ICD-based algorithms. CONCLUSIONS The EMR-based NLP phenotyping algorithms demonstrated improved performance in HAPI case detection over ICD-10-CA codes alone. Daily generated nursing notes in EMRs are a valuable data resource for ML models to accurately detect adverse events. The study contributes to enhancing automated health care quality and safety surveillance. CLINICALTRIAL

Background The International Classification of Diseases (ICD) is widely used by clinical coders worldwide for clinical coding morbidity data into administrative health databases. Accordingly, hospital data quality largely depends on the coders’ skills acquired during ICD training, which varies greatly across countries. Objective To characterise the current landscape of international ICD clinical coding training. Method An online questionnaire was created to survey the 194 World Health Organization (WHO) member countries. Questions focused on the training provided to clinical coding professionals. The survey was distributed to potential participants who met specific criteria, and to organisations specialised in the topic, such as WHO Collaborating Centres, to be forwarded to their representatives. Responses were analysed using descriptive statistics. Results Data from 47 respondents from 26 countries revealed disparities in all inquired topics. However, most participants reported clinical coders as the primary person assigning ICD codes. Although training was available in all countries, some did not mandate training qualifications, and those that did differed in type and duration of training, with college or university degree being most common. Clinical coding certificates most frequently entailed passing a certification exam. Most countries offered continuing training opportunities, and provided a range of support resources for clinical coders. Conclusion Variability in clinical coder training could affect data collection worldwide, thus potentially hindering international comparability of health data. Implications These findings could encourage countries to improve their resources and training programs available for clinical coders and will ultimately be valuable to the WHO for the standardisation of ICD training.

Background: The initiatives of precision medicine and learning health systems require databases with rich and accurately captured data on patient characteristics. We introduce the Clinical Registry, AdminisTrative Data and Electronic Medical Records (CREATE) database, which includes linked data from 4 population databases: Alberta Provincial Project for Outcome Assessment in Coronary Heart Disease (APPROACH; a national clinical registry), Sunrise Clinical Manager (SCM) electronic medical record (city-wide), the Discharge Abstract Database (DAD), and the National Ambulatory Care Reporting System (NACRS). The intent of this work is to introduce a cardiovascular-specific database for pursuing precision health activities using big data analytics. Methods: We used deterministic data linkage to link SCM electronic medical record data to APPROACH clinical registry data using patient identifier variables. The APPROACH-SCM data set was subsequently linked to DAD and NACRS to obtain inpatient and outpatient cohort data. We further validated the quality of the linkage, where applicable, in these databases by comparing against the Alberta Health Insurance Care Plan registry database. Results: We achieved 99.96% linkage across these 4 databases. Currently, there are 30,984 patients with 35,753 catheterizations in the CREATE database. The inpatient cohort contained 65.75% (20,373/30,984) of the patient sample, whereas the outpatient cohort contained 29.78% (9226/30,984). The infrastructure and the process to update and expand the database has been established. Conclusions: CREATE is intended to serve as a database for supporting big data analytics activities surrounding cardiac precision health. The CREATE database will be managed by the Centre for Health Informatics at the University of Calgary, and housed in a secure high-performance computing environment. Résumé: Contexte: Les initiatives en matière de médecine de précision et les systèmes de santé apprenants ont besoin de bases de données riches et exactes sur les caractéristiques des patients. Nous présentons ici la base de données CREATE (Clinical Registry, Administrative Data and Electronic Medical Records), qui regroupe les données couplées de quatre bases de données populationnelles : le registre clinique national APPROACH (Alberta Provincial Project for Outcome Assessment in Coronary Heart Disease), le système de gestion des dossiers médicaux électroniques SCM (Sunrise Clinical Manager, utilisé à l’échelle municipale), la Base de données sur les congés des patients (BDCP), et le Système national d’information sur les soins ambulatoires (SNISA). Notre objectif est d’offrir une base de données portant précisément sur les maladies cardiovasculaires, afin de soutenir les activités en santé de précision nécessitant l’analyse de mégadonnées. Méthodologie: Nous avons utilisé une méthode de couplage déterministe pour apparier les données du système SCM à celles du registre APPROACH à l’aide de variables d’identification des patients. L’ensemble de données SCM-APPROACH a ensuite été couplé aux données de la BDCP et du SNISA, afin d’obtenir les données des cohortes des patients hospitalisés et des patients ambulatoires. Lorsque c’était possible, nous avons en outre validé la qualité du couplage en comparant les données à celles de la base de données du Régime d’assurance maladie de l’Alberta. Résultats: Nous avons obtenu un taux de couplage de 99,96 % pour les quatre bases de données. À l’heure actuelle, la base de données CREATE compte 30 984 patients ayant subi 35 753 cathétérismes. La cohorte des patients hospitalisés représente 65,75 % (20 373/30 984) de l’échantillon, tandis que la cohorte des patients ambulatoires représente 29,78 % (9226/30 984). L’infrastructure et le processus de mise à jour et d’expansion de la base de données ont été définis. Conclusions: La base de données CREATE est destinée à soutenir les activités d’analyse de mégadonnées nécessaires à la santé cardiaque de précision. Elle sera gérée par le Centre for Health Informatics de l’Université de Calgary et hébergée dans un environnement informatique à haut rendement sécurisé.

A systematic scoping review of health system Performance and Quality Indicators (PQI) related literature for evaluating publicly funded home care programming was conducted. Through the scoping review we identify PQIs, map these based on quadrants of the Institute for Healthcare Improvement’s (IHI) Quadruple Aim, classify these PQIs under general “areas of focus” categories, and present recommendations for use. We selected all articles that identified, referenced, proposed, and/or described at least 1 PQI to measure home care service programming. Minimum inclusion criteria were a description of 1 or more PQIs designed to evaluate and/or measure home care programming, outcomes of home care clients and/or caregivers, or overall system impacts of home care service provision. The review identified 3,475 citations. A review of the abstracts led to the retrieval of 510 full-text articles for assessment of which 105 articles were selected for detailed review and extraction of identified PQIs. Of the 105 identified articles a total of 829 unique PQIs were gathered. Based on the IHI Quadruple Aim, 661 unique measures were identified as Clinical Outcome, 35 as Healthcare Provider Satisfaction, 99 Patient Experience, and 34 in the Financial/Sustainability quadrants. Many patient outcome specific PQIs exist, followed by patient experience measures. Very few PQIs exist in the financial or healthcare provider experience quadrants. The lack of indicators across all the IHI Quadruple Aim quadrants is an opportunity for further development supporting home care quality oversight.

Background Surveillance and outcome studies for heart failure (HF) require accurate identification of patients with HF. Algorithms based on International Classification of Diseases (ICD) codes to identify HF from administrative data are inadequate owing to their relatively low sensitivity. Detailed clinical information from electronic medical records (EMRs) is potentially useful for improving ICD algorithms. This study aimed to enhance the ICD algorithm for HF definition by incorporating comprehensive information from EMRs. Methods The study included 2106 inpatients in Calgary, Alberta, Canada. Medical chart review was used as the reference gold standard for evaluating developed algorithms. The commonly used ICD codes for defining HF were used (namely, the ICD algorithm). The performance of different algorithms using the free text discharge summaries from a population-based EMR were compared with the ICD algorithm. These algorithms included a keyword search algorithm looking for HF-specific terms, a machine learning–based HF concept (HFC) algorithm, an EMR structured data based algorithm, and combined algorithms (the ICD and HFC combined algorithm). Results Of 2106 patients, 296 (14.1%) were patients with HF as determined by chart review. The ICD algorithm had 92.4% positive predictive value (PPV) but low sensitivity (57.4%). The EMR keyword search algorithm achieved a higher sensitivity (65.5%) than the ICD algorithm, but with a lower PPV (77.6%). The HFC algorithm achieved a better sensitivity (80.0%) and maintained a reasonable PPV (88.9%) compared with the ICD algorithm and the keyword algorithm. An even higher sensitivity (83.3%) was reached by combining the HFC and ICD algorithms, with a lower PPV (83.3%). The structured EMR data algorithm reached a sensitivity of 78% and a PPV of 54.2%. The combined EMR structured data and ICD algorithm had a higher sensitivity (82.4%), but the PPV remained low at 54.8%. All algorithms had a specificity ranging from 87.5% to 99.2%. Conclusions Applying natural language processing and machine learning on the discharge summaries of inpatient EMR data can improve the capture of cases of HF compared with the widely used ICD algorithm. The utility of the HFC algorithm is straightforward, making it easily applied for HF case identification.

Background Surveillance of hospital-acquired pressure injuries (HAPI) is often suboptimal when relying on administrative health data, as International Classification of Diseases (ICD) codes are known to have long delays and are undercoded. We leveraged natural language processing (NLP) applications on free-text notes, particularly the inpatient nursing notes, from electronic medical records (EMRs), to more accurately and timely identify HAPIs. Objective This study aimed to show that EMR-based phenotyping algorithms are more fitted to detect HAPIs than ICD-10-CA algorithms alone, while the clinical logs are recorded with higher accuracy via NLP using nursing notes. Methods Patients with HAPIs were identified from head-to-toe skin assessments in a local tertiary acute care hospital during a clinical trial that took place from 2015 to 2018 in Calgary, Alberta, Canada. Clinical notes documented during the trial were extracted from the EMR database after the linkage with the discharge abstract database. Different combinations of several types of clinical notes were processed by sequential forward selection during the model development. Text classification algorithms for HAPI detection were developed using random forest (RF), extreme gradient boosting (XGBoost), and deep learning models. The classification threshold was tuned to enable the model to achieve similar specificity to an ICD-based phenotyping study. Each model’s performance was assessed, and comparisons were made between the metrics, including sensitivity, positive predictive value, negative predictive value, and F1-score. Results Data from 280 eligible patients were used in this study, among whom 97 patients had HAPIs during the trial. RF was the optimal performing model with a sensitivity of 0.464 (95% CI 0.365-0.563), specificity of 0.984 (95% CI 0.965-1.000), and F1-score of 0.612 (95% CI of 0.473-0.751). The machine learning (ML) model reached higher sensitivity without sacrificing much specificity compared to the previously reported performance of ICD-based algorithms. Conclusions The EMR-based NLP phenotyping algorithms demonstrated improved performance in HAPI case detection over ICD-10-CA codes alone. Daily generated nursing notes in EMRs are a valuable data resource for ML models to accurately detect adverse events. The study contributes to enhancing automated health care quality and safety surveillance.

Insomnia and sleep apnea are associated with a variety of comorbid conditions and carry a symptom burden to patients. As the prevalence of insomnia and sleep apnea continue to rise, it is imperative that appropriate tools are implemented to accurately capture their prevalence in acute care settings. A retrospective chart review was conducted on 3,074 inpatient charts in Calgary, Alberta. The estimated prevalence of insomnia was 10.36 percent, and sleep apnea was 6.56 percent in inpatient visits between January 1, 2015, and June 30, 2015. The sensitivity of insomnia and sleep apnea were low, and the specificity was high when comparing the chart review to the ICD-10. As both insomnia and sleep apnea were associated with various comorbid conditions, it would be imperative that alternate methods are identified to capture and code them. This would enable clinicians to better identify and treat them, and ultimately improve patient care.

Background: The new International Classification of Diseases, Eleventh Revision for Mortality and Morbidity Statistics (ICD-11) was developed and released by the World Health Organization (WHO) in June 2018. Because ICD-11 incorporates new codes and features, training materials for coding with ICD-11 are urgently needed prior to its implementation. Objective: This study outlines the development of ICD-11 training materials, training processes and experiences of clinical coders while learning to code using ICD-11. Method: Six certified clinical coders were recruited to code inpatient charts using ICD-11. Training materials were developed with input from experts from the Canadian Institute for Health Information and the WHO, and the clinical coders were trained to use the new classification. Monthly team meetings were conducted to enable discussions on coding issues and to select the correct ICD-11 codes. The training experience was evaluated using qualitative interviews, a questionnaire and a coding quiz. Results: total of 3011 charts were coded using ICD-11. In general, clinical coders provided positive feedback regarding the training program. The average score for the coding quiz (multiple choice, True/False) was 84%, suggesting that the training program was effective. Feedback from the coders enabled the ICD-11 code content, electronic tooling and terminologies to be updated. Conclusion: This study provides a detailed account of the processes involved with training clinical coders to use ICD-11. Important findings from the interviews were reported at the annual WHO conferences, and these findings helped improve the ICD-11 browser and reference guide.

Background: It is essential that clinical documentation and clinical coding be of high quality for the production of healthcare data. Objective: This study assessed qualitatively the strengths and barriers regarding clinical coding quality from the perspective of health information managers. Method: Ten health information managers and clinical coding quality coordinators who oversee clinical coders (CCs) were identified and recruited from nine provinces across Canada. Semi-structured interviews were conducted, which included questions on data quality, costs of clinical coding, education for health information management, suggestions for quality improvement and barriers to quality improvement. Interviews were recorded, transcribed and analysed using directed content analysis and informed by institutional ethnography. Results: Common barriers to clinical coding quality included incomplete and unorganised chart documentation, and lack of communication with physicians for clarification. Further, clinical coding quality suffered as a result of limited resources (e.g. staffing and budget) being available to health information management departments. Managers unanimously reported that clinical coding quality improvements can be made by (i) offering interactive training programmes to CCs and (ii) streamlining sources of information from charts. Conclusion: Although clinical coding quality is generally regarded as high across Canada, clinical coding managers perceived quality to be limited by incomplete and inconsistent chart documentation, and increasing expectations for data collection without equal resources allocated to clinical coding professionals. Implications: This study presents novel evidence for clinical coding quality improvement across Canada.

ObjectiveNew codes developed in the International Classification of Diseases, 11th Revision for Mortality and Morbidity Statistics (ICD-11) needed testing. Field-testing involves real-world application of the new codes to examine data quality. This paper describes the field trial methods to create a dually coded database to field test ICD-11 against ICD-10-CA (a Canadian modification of the International Classification of Diseases, Tenth Revision), and a reference standard data set of diagnoses. ResultsA random sample of discharge records previously coded using ICD-10-CA was selected. Nurses re-examined these entire charts for specific conditions and patient safety events. Clinical coders re-coded the same charts using ICD-11 codes. Inpatients discharged from hospitals in Calgary, Alberta, were identified and a dually coded database was created (n=2897). Inter-rater reliability and coding time improved with ICD-11 coding experience. Clinical coder comments enabled content to be improved in the ICD-11 browser, Coding Tool, and ICD-11 Reference Guide. This paper describes the field trial, database creation methods, and contributions for ICD-11 improvement. Crucial future research will use this database to test ICD-11 before implementation in Canada.

Background Electronic medical records (EMRs) contain large amounts of rich clinical information. Developing EMR-based case definitions, also known as EMR phenotyping, is an active area of research that has implications for epidemiology, clinical care, and health services research. Objective This review aims to describe and assess the present landscape of EMR-based case phenotyping for the Charlson conditions. Methods A scoping review of EMR-based algorithms for defining the Charlson comorbidity index conditions was completed. This study covered articles published between January 2000 and April 2020, both inclusive. Embase (Excerpta Medica database) and MEDLINE (Medical Literature Analysis and Retrieval System Online) were searched using keywords developed in the following 3 domains: terms related to EMR, terms related to case finding, and disease-specific terms. The manuscript follows the Preferred Reporting Items for Systematic reviews and Meta-analyses extension for Scoping Reviews (PRISMA) guidelines. Results A total of 274 articles representing 299 algorithms were assessed and summarized. Most studies were undertaken in the United States (181/299, 60.5%), followed by the United Kingdom (42/299, 14.0%) and Canada (15/299, 5.0%). These algorithms were mostly developed either in primary care (103/299, 34.4%) or inpatient (168/299, 56.2%) settings. Diabetes, congestive heart failure, myocardial infarction, and rheumatology had the highest number of developed algorithms. Data-driven and clinical rule–based approaches have been identified. EMR-based phenotype and algorithm development reflect the data access allowed by respective health systems, and algorithms vary in their performance. Conclusions Recognizing similarities and differences in health systems, data collection strategies, extraction, data release protocols, and existing clinical pathways is critical to algorithm development strategies. Several strategies to assist with phenotype-based case definitions have been proposed.

Background Because the collection of mental health information through interviews is expensive and time consuming, interest in using population-based administrative health data to conduct research on depression has increased. However, there is concern that misclassification of disease diagnosis in the underlying data might bias the results. Our objective was to determine the validity of International Classification of Disease (ICD)-9 and ICD-10 administrative health data case definitions for depression using review of family physician (FP) charts as the reference standard. Methods Trained chart reviewers reviewed 3362 randomly selected charts from years 2001 and 2004 at 64 FP clinics in Alberta (AB) and British Columbia (BC), Canada. Depression was defined as presence of either: 1) documentation of major depressive episode, or 2) documentation of specific antidepressant medication prescription plus recorded depressed mood. The charts were linked to administrative data (hospital discharge abstracts and physician claims data) using personal health numbers. Validity indices were estimated for six administrative data definitions of depression using three years of administrative data. Results Depression prevalence by chart review was 15.9–19.2% depending on year, region, and province. An ICD administrative data definition of ‘2 depression claims with depression ICD codes within a one-year window OR 1 discharge abstract data (DAD) depression diagnosis’ had the highest overall validity, with estimates being 61.4% for sensitivity, 94.3% for specificity, 69.7% for positive predictive value, and 92.0% for negative predictive value. Stratification of the validity parameters for this case definition showed that sensitivity was fairly consistent across groups, however the positive predictive value was significantly higher in 2004 data compared to 2001 data (78.8 and 59.6%, respectively), and in AB data compared to BC data (79.8 and 61.7%, respectively). Conclusions Sensitivity of the case definition is often moderate, and specificity is often high, possibly due to undercoding of depression. Limitations to this study include the use of FP charts data as the reference standard, given the potential for missed or incorrect depression diagnoses. These results suggest that that administrative data can be used as a source of information for both research and surveillance purposes, while remaining aware of these limitations.

Commentary on: Shan Z, Li Y, Baden MY, et al . Association between healthy eating patterns and risk of cardiovascular disease. JAMA Intern Med 2020; 180:1090–100. doi: 10.1001/jamainternmed.2020.2176 Historically, long-term risk management of CVD is composed of healthy dietary recommendations.1 2 A plethora of information about foods and nutrients occupy media and health research spaces,3 yet consumers often possess a suboptimal understanding of healthy dietary patterns.4 Information about isolated nutrients and foods is insufficient to manage CVD risks; instead, elevated public awareness of various recommended dietary patterns is required. …

Introduction Administrative health data from emergency departments play important roles in understanding health needs of the public and reasons for health care resource use. International Classification of Disease (ICD) diagnostic codes have been widely used to code reasons of clinical encounters for administrative purposes in emergency departments. Objective The purpose of the study is to examine the coding agreement and reliability of ICD diagnosis codes in emergency department records through auditing the routinely collected data. Methods We randomly sampled 1 percent of records (n=1636) between October and December 2013 from 11 emergency departments in Alberta, Canada. Auditors were employed to review the same chart and independently assign main diagnosis codes. We assessed coding agreement and reliability through comparison of codes assigned by auditors and hospital coders using proportion of agreement and Cohen’s kappa. Error analysis was conducted to review diagnosis codes with disagreement and categorized them into six groups. Results Overall, the agreement was 86.5% and 82.2% at 3 and 4 digits levels respectively, and reliability was 0.86 and 0.82 respectively. Variations of agreement and reliability were identified across different emergency departments. The major two categories of coding discrepancy were the use of different codes for same condition (23.6%) and the use of codes at different levels of specificity (20.9%). Conclusions Diagnosis codes in emergency departments show high agreement and reliability, although there are variations of coding quality across different hospitals. Stricter coding guidelines regarding the use of unspecified codes are needed to enhance coding consistency.

Objectives Prevalence, and associated morbidity and mortality of chronic sleep disorders have been limited to small cohort studies, however, administrative data may be used to provide representation of larger population estimates of disease. With no guidelines to inform the identification of cases of sleep disorders in administrative data, the objective of this study was to develop and validate a set of ICD-codes used to define sleep disorders including narcolepsy, insomnia, and obstructive sleep apnea (OSA) in administrative data. Methods A cohort of adult patients, with medical records reviewed by two independent board-certified sleep physicians from a sleep clinic in Calgary, Alberta between January 1, 2009 and December 31, 2011, was used as the reference standard. We developed a general ICD-coded case definition for sleep disorders which included conditions of narcolepsy, insomnia, and OSA using: 1) physician claims data, 2) inpatient visit data, 3) emergency department (ED) and ambulatory care data. We linked the reference standard data and administrative data to examine the validity of different case definitions, calculating estimates of sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Results From a total of 1186 patients from the sleep clinic, 1045 (88.1%) were classified as sleep disorder positive, with 606 (51.1%) diagnosed with OSA, 407 (34.4%) with insomnia, and 59 (5.0%) with narcolepsy. The most frequently used ICD-9 codes were general codes of 307.4 (Nonorganic sleep disorder, unspecified), 780.5 (unspecified sleep disturbance) and ICD-10 codes of G47.8 (other sleep disorders), G47.9 (sleep disorder, unspecified). The best definition for identifying a sleep disorder was an ICD code (from physician claims) 2 years prior and 1 year post sleep clinic visit: sensitivity 79.2%, specificity 28.4%, PPV 89.1%, and NPV 15.6%. ICD codes from ED/ambulatory care data provided similar diagnostic performance when at least 2 codes appeared in a time period of 2 years prior and 1 year post sleep clinic visit: sensitivity 71.9%, specificity 54.6%, PPV 92.1%, and NPV 20.8%. The inpatient data yielded poor results in all tested ICD code combinations. Conclusion Sleep disorders in administrative data can be identified mainly through physician claims data and with some being determined through outpatient/ambulatory care data ICD codes, however these are poorly coded within inpatient data sources. This may be a function of how sleep disorders are diagnosed and/or reported by physicians in inpatient and outpatient settings within medical records. Future work to optimize administrative data case definitions through data linkage are needed.

ObjectivePressure ulcer development is a quality of care indicator, as pressure ulcers are potentially preventable. Yet pressure ulcer is a leading cause of morbidity, discomfort and additional healthcare costs for inpatients. Methods are lacking for accurate surveillance of pressure ulcer in hospitals to track occurrences and evaluate care improvement strategies. The main study aim was to validate hospital discharge abstract database (DAD) in recording pressure ulcers against nursing consult reports, and to calculate prevalence of pressure ulcers in Alberta, Canada in DAD. We hypothesised that a more inclusive case definition for pressure ulcers would enhance validity of cases identified in administrative data for research and quality improvement purposes.SettingA cohort of patients with pressure ulcers were identified from enterostomal (ET) nursing consult documents at a large university hospital in 2011.ParticipantsThere were 1217 patients with pressure ulcers in ET nursing documentation that were linked to a corresponding record in DAD to validate DAD for correct and accurate identification of pressure ulcer occurrence, using two case definitions for pressure ulcer.ResultsUsing pressure ulcer definition 1 (7 codes), prevalence was 1.4%, and using definition 2 (29 codes), prevalence was 4.2% after adjusting for misclassifications. The results were lower than expected. Definition 1 sensitivity was 27.7% and specificity was 98.8%, while definition 2 sensitivity was 32.8% and specificity was 95.9%. Pressure ulcer in both DAD and ET consultation increased with age, number of comorbidities and length of stay.ConclusionDAD underestimate pressure ulcer prevalence. Since various codes are used to record pressure ulcers in DAD, the case definition with more codes captures more pressure ulcer cases, and may be useful for monitoring facility trends. However, low sensitivity suggests that this data source may not be accurate for determining overall prevalence, and should be cautiously compared with other prevalence studies.

Background Determination of factors increasing the likelihood of early readmission after hospitalization for heart failure (HF) is fundamental for identifying potential targets for intervention. Thus, we studied the characteristics of patients readmitted within 7 and 30 days after hospitalization for HF in Alberta, Canada. Methods Using hospital discharge abstract data, we followed patients with incident HF discharged from April 2004-March 2012 and determined their readmission status within 7 and 30 days after an index hospitalization. Logistic regression was used to determine variables associated with readmission. Results Of 18,590 patients with HF (49.8% women; mean age 76.4 years), 5.6% were readmitted within 7 days and 18% were readmitted within 30 days. Readmission rates within 7 and 30 days increased significantly with age. Seven-day all-cause readmissions were associated with history of kidney disease (adjusted odds ratio [aOR], 1.28; 95% confidence interval [CI], 1.08-1.53), and 30-day all-cause readmissions were associated with cancer, pulmonary, liver, and kidney disease. Discharge with home care services at the time of discharge was a risk factor for readmission within 7 days (aOR, 1.26; 95% CI, 1.07-1.49) and 30 days (aOR, 1.23; 95% CI, 1.11-1.35). Discharge from a hospital with HF services was associated with lower readmission at both 7 days (aOR, 0.65; 95% CI, 0.57-0.74) and 30 days (aOR, 0.71; 95% CI, 0.65-0.77). Conclusions Several factors were associated with increased risk of readmission, whereas patients discharged from hospitals with HF services had a lower risk of readmission within 7 and 30 days of discharge. The interaction of provision of home care and higher early readmission deserves further study.

BACKGROUND Hospital administrative health data create separate records for each hospital stay of patients. Treating a hospital transfer as a readmission could lead to biased results in health service research. METHODS This is a cross-sectional study. We used the hospital discharge abstract database in 2013 from Alberta, Canada. Transfer cases were defined by transfer institution code and were used as the reference standard. Four time gaps between 2 hospitalizations (6, 9, 12, and 24 h) and 2 day gaps between hospitalizations [same day (up to 24 h), ≤1 d (up to 48 h)] were used to identify transfer cases. We compared the sensitivity and positive predictive value (PPV) of 6 definitions across different categories of sex, age, and location of residence. Readmission rates within 30 days were compared after episodes of care were defined at the different time gaps. RESULTS Among the 6 definitions, sensitivity ranged from 93.3% to 98.7% and PPV ranged from 86.4% to 96%. The time gap of 9 hours had the optimal balance of sensitivity and PPV. The time gaps of same day (up to 24 h) and 9 hours had comparable 30-day readmission rates as the transfer indicator after defining episode of care. CONCLUSIONS We recommend the use of a time gap of 9 hours between 2 hospitalizations to define hospital transfer in inpatient databases. When admission or discharge time is not available in the database, a time gap of same day (up to 24 h) can be used to define hospital transfer.

This paper presents the main causes of heart failure (HF) and an update on the genetics studies on each cause. The review includes a delineation of the etiology and fundamental pathophysiology of HF and provides rational for treatment for the patient and family. Various cardiomyopathies are discussed, includingprimary cardiomyopathies, mixed cardiomyopathies, cardiomyopathies that involve altered cardiac muscle along with generalized multi-organ disorders, and various cardiovascular conditions, such as coronary artery disease (ischemic cardiomyopathy) and hypertension (hypertensive cardiomyopathy).1 A brief review of pharmacogenetics and HF is presented. The application of the genetic components of cardiomyopathy and pharmacogenetics is included to enhance cardiovascular nursing care.

Despite a trend toward a reduction in bedrest time after left heart catheterization (LHC) in many Canadian centres, an evidence-based standard of practice has not been established. Canadian bedrest times range from two to four hours post-LHC. Two recent prospective non-randomized studies (n = 1,000) indicate safety of ambulation at 60 and 90 minutes post-LHC.The purpose of this study was to determine safety of ambulating patients at 90 minutes post-LHC sheath removal compared to the current practice of ambulation at three to four hours post-sheath removal.The study was a prospective non-concurrent design with a retrospective control. METHOD/SAMPLE: Retrospective data from the APPROACH database and chart reviews were analyzed for a period of six months for the control group on the traditional three- to four-hour ambulation protocol (n = 402). Prospective data were gathered for six months for the experimental group (n = 193).There was no difference in complication rates for the two groups.The results suggest that early ambulation for selected patients at 90 minutes is safe and has the potential to increase both patient comfort and quality of care.

The Canadian Council of Cardiovascular Nurses (CCCN) applauds the work done by the Canadian Cardiovascular Society in setting benchmarks for wait times. The Canadian Cardiovascular Society is to be commended for developing the benchmark documents, as well as for establishing strategies for systematic dissemination to increase awareness, advocacy and implementation of the benchmarks across Canada. Quality nursing care, as defined within the CCCN framework, includes working with health teams to ensure that patients have timely access to specialized personnel, tests and procedures as required to prevent disease, promote health, address acute and episodic interventions, and to provide rehabilitative and palliative services, depending on patient need. To extend the access to care discussion, the CCCN suggests that further engagement of all stakeholders, especially clients/patients, is needed to find solutions to wait times and define benchmarks. In addition, preventing heart disease and promoting 'health care' should be recognized and acted on as central to reducing wait times for cardiovascular care. Finally, access to cardiovascular services will be more efficient when the first point of care is broadened to include nurses and other health care professionals. Nurses occupy creative, cost-effective roles directly aimed at reducing wait times and improving care while patients wait. The expanded role of interprofessional education and health care teams, as well as the inclusion of patients and families in program improvement, are solutions that the CCCN suggests may contribute to improved access to cardiovascular care and a sustainable health care system in Canada.

Congestive heart failure is a major and growing health care concern worldwide, and mortality in patients with severe heart failure is high. Few options are available to patients with New York Heart Association class IV heart failure refractory to oral medical therapy. Over the last 15-20 years milrinone, a phosphodiesterase-III inhibitor, has been used occasionally to treat patients with acute heart failure and as a bridge to heart transplantation and, more recently, has been used intermittently or continuously on an outpatient basis. We report a patient with severe, chronic congestive heart failure, whom we treated successfully with continuous milrinone infusions as an outpatient. We were able to wean him of the milrinone after successful up-titration of carvedilol. Nine months after discontinuation of milrinone the patient remains stable in New York Heart Association class I on high dose carvedilol. Research is required to validate the possibility that patients with severe heart failure may be successfully weaned from milrinone using carvedilol and achieve significant improvement of their functional status and quality of life. This may prove to be an effective strategy for the treatment of selected patients with severe, chronic congestive heart failure. (c)2001 by CHF, Inc.

Objective: To assess if the Agency for Healthcare Research and Quality patient safety indictors (PSIs) could be used for case findings in the International Classification of Disease 10th revision (ICD-10) hospital discharge abstract data. Design: We identified and randomly selected 490 patients with a foreign body left during a procedure (PSI 5—foreign body), selected infections (IV site) due to medical care (PSI 7—infection), postoperative pulmonary embolism (PE) or deep vein thrombosis (DVT; PSI 12—PE/DVT), postoperative sepsis (PSI 13—sepsis)and accidental puncture or laceration (PSI 15—laceration) among patients discharged from three adult acute care hospitals in Calgary, Canada in 2007 and 2008. Their charts were reviewed for determining the presence of PSIs and used as the reference standard, positive predictive value (PPV) statistics were calculated to determine the proportion of positives in the administrative data representing ‘true positives’. Results: The PPV for PSI 5—foreign body was 62.5% (95% CI 35.4% to 84.8%), PSI 7—infection was 79.1% (67.4% to 88.1%), PSI 12—PE/DVT was 89.5% (66.9% to 98.7%), PSI 13—sepsis was 12.5% (1.6% to 38.4%) and PSI 15—laceration was 86.4% (75.0% to 94.0%) after excluding those who presented to the hospital with the condition. Conclusions: Several PSIs had high PPV in the ICD administrative data and are thus powerful tools for true positive case finding. The tools could be used to identify potential cases from the large volume of admissions for verification through chart reviews. In contrast, their sensitivity has not been well characterised and users of PSIs should be cautious if using them for ‘quality of care reporting’ presenting the rate of PSIs because under-coded data would generate falsely low PSI rates.