A World Health Organization field trial assessing a proposed ICD-11 framework for classifying patient safety events.

OBJECTIVE: To assess the utility of the proposed World Health Organization (WHO)'s International Classification of Disease (ICD) framework for classifying patient safety events. SETTING: Independent classification of 45 clinical vignettes using a web-based platform. STUDY PARTICIPANTS: The WHO's multi-disciplinary Quality and Safety Topic Advisory Group. MAIN OUTCOME MEASURE(S): The framework consists of three concepts: harm, cause and mode. We defined a concept as 'classifiable' if more than half of the raters could assign an ICD-11 code for the case. We evaluated reasons why cases were nonclassifiable using a qualitative approach. RESULTS: Harm was classifiable in 31 of 45 cases (69%). Of these, only 20 could be classified according to cause and mode. Classifiable cases were those in which a clear cause and effect relationship existed (e.g. medication administration error). Nonclassifiable cases were those without clear causal attribution (e.g. pressure ulcer). Of the 14 cases in which harm was not evident (31%), only 5 could be classified according to cause and mode and represented potential adverse events. Overall, nine cases (20%) were nonclassifiable using the three-part patient safety framework and contained significant ambiguity in the relationship between healthcare outcome and putative cause. CONCLUSIONS: The proposed framework enabled classification of the majority of patient safety events. Cases in which potentially harmful events did not cause harm were not classifiable; additional code categories within the ICD-11 are one proposal to address this concern. Cases with ambiguity in cause and effect relationship between healthcare processes and outcomes remain difficult to classify.

Comparing physical assessment with administrative data for detecting pressure ulcers in a large Canadian academic health sciences centre.

OBJECTIVE: This study aimed to compare classification of pressure ulcers from administrative data with a gold standard assessment, specifically; pressure ulcers confirmed by an independent physical assessment performed by trained nurse surveyors. SETTING: A retrospective analysis of pooled cross-sectional samples of inpatients assessed across 3 consecutive prevalence surveys in a large academic health sciences centre between 2012 and 2013. PARTICIPANTS: There were 2001 patients for whom physical and chart assessments were completed, and for whom a discharge abstract was also available at the time of analysis. The cohort's mean age was 65 years and 55% were women. RESULTS: Based on the physical assessment findings, 14.6% of patients (n=292) had at least 1 pressure ulcer, with a total of 345 pressure ulcers documented among these patients: (stage I=162; stage II=120; stage III=22; stage IV=22 and unstageable=19). Based on coded information, 78 (3.9%) of patients had a pressure ulcer. Of patients with a pressure ulcer determined by the physical assessment, only 21% also had a pressure ulcer captured in the administrative data. Furthermore, only 6% of the patients with a hospital-acquired pressure ulcer, stage II or greater determined by the physical assessment were coded in the Discharge Abstract Database (DAD). CONCLUSIONS: The results of this study demonstrate that coding in the DAD may under-report and fail to accurately reflect the true burden of pressure ulcers in hospitalised patients. This may occur because the presence of pressure ulcers is currently documented in the health record by nurses and not by physicians, yet the administrative data recorded in the DAD only includes physician documented pressure ulcers. We recommend enhancements to the coding methods to monitor and report on pressure ulcers.

Concordance between self-report and a survey-based algorithm for classification of type 1 and type 2 diabetes using the 2011 population-based Survey on Living with Chronic Diseases in Canada (SLCDC)-Diabetes component.

OBJECTIVE: There are 2 major forms of diabetes mellitus: types 1 and 2. A major limitation of most current population-based diabetes surveillance systems is the classification of diabetes types. Our objective was to examine the concordance of self-reported diabetes type with a previously developed classification algorithm, using a nationally representative survey sample. METHODS: Self-reported data were available from 2544 adults with self-reported diabetes, aged ≥20 years and older, who responded to the diabetes component of the 2011 Survey of Living with Chronic Diseases in Canada. We examined the concordance of self-reported diabetes type with an algorithm based on self-reported, but objective, respondent characteristics, such as age of diagnosis and treatment patterns. Concordance was measured using kappa coefficients. Sensitivity, specificity and positive and negative predictive values were calculated using the algorithm as the reference "standard." RESULTS: Approximately 11% of the estimated population did not self-report diabetes type; almost all of these respondents would be classified as having type 2 diabetes by the algorithm. Of those self-reporting diabetes type, we found moderate overall agreement between the algorithm and self-reported type (kappa, 0.52; 95% confidence interval [CI], 0.52 to 0.53). Perfect agreement was noted in the youngest age group (kappa, 1.0; 95% CI, 1.0-1.0) but agreement was poor for the oldest age group (kappa, 0.20; 95% CI, 0.19 to 0.20). CONCLUSIONS: An algorithm based on self-reported, objective characteristics related to diabetes diagnosis and treatment patterns may have the potential to overcome limitations of simple self-report diabetes type for the classification of diabetes type in older adults.

Comparative safety and effectiveness of metformin in patients with diabetes mellitus and heart failure: systematic review of observational studies involving 34,000 patients.

BACKGROUND: There is an ongoing controversy regarding the safety and effectiveness of metformin in the setting of heart failure (HF). Therefore, we undertook a systematic review of the trial and nontrial evidence for metformin in patients with diabetes mellitus and HF. METHODS AND RESULTS: We conducted a comprehensive search for controlled studies, evaluating the association between metformin and morbidity and mortality in people with diabetes mellitus and HF. Two reviewers independently identified citations, extracted data, and evaluated quality. Risk estimates were abstracted and pooled where appropriate. As measures of overall safety, we examined all-cause mortality and all-cause hospitalizations. Nine cohort studies were included; no randomized controlled trials were identified. Most (5 of 9) studies were published in 2010 and were of good quality. Metformin was associated with reduced mortality compared with controls (mostly sulfonylurea therapy): 23% versus 37% (pooled adjusted risk estimates: 0.80; 0.74-0.87; I(2)=15%; P<0.001). No increased risk was observed for metformin in those with reduced left ventricular ejection fraction (mortality pooled adjusted risk estimate: 0.91; 0.72-1.14; I(2)=0%; P=0.34), nor in those with HF and chronic kidney disease (pooled adjusted risk estimate: 0.81; 0.64-1.02; P=0.08). Metformin was associated with a small reduction in all-cause hospitalizations (pooled adjusted risk estimate: 0.93; 0.89-0.98; I(2)=0%; P=0.01). Metformin was not associated with increased risk of lactic acidosis. CONCLUSIONS: The totality of evidence indicates that metformin is at least as safe as other glucose-lowering treatments in patients with diabetes mellitus and HF and even in those with reduced left ventricular ejection fraction or concomitant chronic kidney disease. Until trial data become available, metformin should be considered the treatment of choice for patients with diabetes mellitus and HF.

Validation of classification algorithms for childhood diabetes identified from administrative data.

OBJECTIVE: Type 1 diabetes is the most common form of diabetes among children; however, the proportion of cases of childhood type 2 diabetes is increasing. In Canada, the National Diabetes Surveillance System (NDSS) uses administrative health data to describe trends in the epidemiology of diabetes, but does not specify diabetes type. The objective of this study was to validate algorithms to classify diabetes type in children <20 yr identified using the NDSS methodology. PATIENTS AND METHODS: We applied the NDSS case definition to children living in British Columbia between 1 April 1996 and 31 March 2007. Through an iterative process, four potential classification algorithms were developed based on demographic characteristics and drug-utilization patterns. Each algorithm was then validated against a gold standard clinical database. RESULTS: Algorithms based primarily on an age rule (i.e., age <10 at diagnosis categorized type 1 diabetes) were most sensitive in the identification of type 1 diabetes; algorithms with restrictions on drug utilization (i.e., no prescriptions for insulin ± glucose monitoring strips categorized type 2 diabetes) were most sensitive for identifying type 2 diabetes. One algorithm was identified as having the optimal balance of sensitivity (Sn) and specificity (Sp) for the identification of both type 1 (Sn: 98.6%; Sp: 78.2%; PPV: 97.8%) and type 2 diabetes (Sn: 83.2%; Sp: 97.5%; PPV: 73.7%). CONCLUSIONS: Demographic characteristics in combination with drug-utilization patterns can be used to differentiate diabetes type among cases of pediatric diabetes identified within administrative health databases. Validation of similar algorithms in other regions is warranted.