A Bayesian framework for modeling COVID-19 case numbers through longitudinal monitoring of SARS-CoV-2 RNA in wastewater.

Wastewater-based surveillance has become an important tool for research groups and public health agencies investigating and monitoring the COVID-19 pandemic and other public health emergencies including other pathogens and drug abuse. While there is an emerging body of evidence exploring the possibility of predicting COVID-19 infections from wastewater signals, there remain significant challenges for statistical modeling. Longitudinal observations of viral copies in municipal wastewater can be influenced by noisy datasets and missing values with irregular and sparse samplings. We propose an integrative Bayesian framework to predict daily positive cases from weekly wastewater observations with missing values via functional data analysis techniques. In a unified procedure, the proposed analysis models severe acute respiratory syndrome coronavirus-2 RNA wastewater signals as a realization of a smooth process with error and combines the smooth process with COVID-19 cases to evaluate the prediction of positive cases. We demonstrate that the proposed framework can achieve these objectives with high predictive accuracies through simulated and observed real data.

Exploring data reduction strategies in the analysis of continuous pressure imaging technology.

BACKGROUND: Science is becoming increasingly data intensive as digital innovations bring new capacity for continuous data generation and storage. This progress also brings challenges, as many scientific initiatives are challenged by the shear volumes of data produced. Here we present a case study of a data intensive randomized clinical trial assessing the utility of continuous pressure imaging (CPI) for reducing pressure injuries. OBJECTIVE: To explore an approach to reducing the amount of CPI data required for analyses to a manageable size without loss of critical information using a nested subset of pressure data. METHODS: Data from four enrolled study participants excluded from the analytical phase of the study were used to develop an approach to data reduction. A two-step data strategy was used. First, raw data were sampled at different frequencies (5, 30, 60, 120, and 240 s) to identify optimal measurement frequency. Second, similarity between adjacent frames was evaluated using correlation coefficients to identify position changes of enrolled study participants. Data strategy performance was evaluated through visual inspection using heat maps and time series plots. RESULTS: A sampling frequency of every 60 s provided reasonable representation of changes in interface pressure over time. This approach translated to using only 1.7% of the collected data in analyses. In the second step it was found that 160 frames within 24 h represented the pressure states of study participants. In total, only 480 frames from the 72 h of collected data would be needed for analyses without loss of information. Only ~ 0.2% of the raw data collected would be required for assessment of the primary trial outcome. CONCLUSIONS: Data reduction is an important component of big data analytics. Our two-step strategy markedly reduced the amount of data required for analyses without loss of information. This data reduction strategy, if validated, could be used in other CPI and other settings where large amounts of both temporal and spatial data must be analysed.

Interpreting and coding causal relationships for quality and safety using ICD-11.

Many circumstances necessitate judgments regarding causation in health information systems, but these can be tricky in medicine and epidemiology. In this article, we reflect on what the ICD-11 Reference Guide provides on coding for causation and judging when relationships between clinical concepts are causal. Based on the use of different types of codes and the development of a new mechanism for coding potential causal relationships, the ICD-11 provides an in-depth transformation of coding expectations as compared to ICD-10. An essential part of the causal relationship interpretation relies on the presence of "connecting terms," key elements in assessing the level of certainty regarding a potential relationship and how to proceed in coding a causal relationship using the new ICD-11 coding convention of postcoordination (i.e., clustering of codes). In addition, determining causation involves using documentation from healthcare providers, which is the foundation for coding health information. The coding guidelines and examples (taken from the quality and patient safety domain) presented in this article underline how new ICD-11 features and coding rules will enhance future health information systems and healthcare.

Decision algorithm for when to use the ICD-11 3-part model for healthcare harms.

Accurate data collection of healthcare-related adverse events provides a foundation for quality and health system improvement. The International Classification of Diseases for Mortality and Morbidity Statistics, 11th revision (ICD-11 MMS) includes new codes to identify harm or injury and the events or actions leading to the adverse events. However, it is difficult to choose the correct codes without in-depth understanding of which event may be classified as an injury or harm. A 3-part model will be available in the ICD-11 MMS to cluster the codes for the harm or injury that occurred, the causal factors, and the mode (mechanism) involved. While field testing coding of adverse events, our team developed a decision tree (algorithm), which guides when to use the 3-part model. The decision tree now resides in the ICD-11 Reference Guide. This paper is part of a special ICD-11 paper series and outlines the steps used in the decision-tree (algorithm) and provides examples to help understand the process.While it may take coders some time to gain experience to use the 3-part model and decision-tree, the ICD-11 Reference Guide and this paper can be helpful resources to help clarify the process.

Coding mechanisms for diagnosis timing in the International Classification of Diseases, Version 11.

BACKGROUND: Diagnoses that arise after admission are of interest because they can represent complications of health care, acute conditions arising de novo, or acute decompensation of a chronic comorbidity occurring during the hospital stay. Three countries in the world have adopted diagnosis timing codes for a number of years. Their experience demonstrates the feasibility and utility of associating an International Classification of Diseases, Version 9 or International Classification of Diseases, Version 10 diagnostic code with information on diagnosis timing, either as part of a diagnostic field or as a separate field. However, diagnosis timing is not an integrated feature of these two classifications as it will be for International Classification of Diseases, Version 11. METHODS: We examine the different types of diagnosis timing that can be used to describe complex patients and present examples of how the new International Classification of Diseases, Version 11 codes may be used. RESULTS: Extension codes are one of the important new features of International Classification of Diseases, Version 11 and allow more specificity in diagnosis timing. CONCLUSION: Imbedded and standardized diagnosis timing information is possible within the International Classification of Diseases, Version 11 classification system.

The three-part model for coding causes and mechanisms of healthcare-related adverse events.

ICD-11 provides a promising new way to capture healthcare-related harm or injury. In this paper, we elaborate on the framework for describing healthcare-related events where there is a presumed causal link between an event and underlying healthcare-related factors. The three-part model for describing healthcare-related harm or injury in ICD-11 consists of (1) a healthcare-related activity that is the cause of injury or other harm (selected from Chapter 23 of ICD-11); (2) a mode or mechanism of injury or harm, related to the underlying cause (also from Chapter 23 of ICD-11); and (3) the harmful consequences of the event to the patient, selected from any of Chapters 1 through 22 of ICD-11 (most importantly, the injury or harm experienced by the patient). Concepts from these three elements are linked/clustered through postcoordination to reflect the three-part model in a single coded expression. ICD-11 contains many novel features, and the three-part model described here for healthcare-related adverse events is a notable example.

Strategic Clinical Network Teams Improve Effectiveness, Team and Leadership Processes and Inputs: Theory-Based Longitudinal Survey.

Strategic Clinical Networks (SCNs) in Alberta include multidisciplinary teams that work toward health system innovation and improvement; however, what contributes to team effectiveness is unclear. This theory-informed longitudinal survey (n = 826) evaluated team effectiveness within SCNs and predictors of effectiveness. Satisfaction, inter-team relationships and seven predictors including team inputs and team and leadership processes improved over two years. Attitudinal outputs were predicted by the same factors over time, whereas performance outputs were predicted by different factors. This innovative study emphasizes that SCN teams and their effectiveness evolve over time and that team-based research can refine network evaluations.

Mitigating imperfect data validity in administrative data PSIs: a method for estimating true adverse event rates.

QUESTION: Are there ways to mitigate the challenges associated with imperfect data validity in Patient Safety Indicator (PSI) report cards? FINDINGS: Applying a methodological framework on simulated PSI report card data, we compare the adjusted PSI rates of three hospitals with variable quality of data and coding. This framework combines (i) a measure of PSI rates using existing algorithms; (ii) a medical record review on a small random sample of charts to produce a measure of hospital-specific data validity and (iii) a simple Bayesian calculation to derive estimated true PSI rates. For example, the estimated true PSI rate, for a theoretical hospital with a moderately good quality of coding, could be three times as high as the measured rate (for example, 1.4% rather than 0.5%). For a theoretical hospital with relatively poor quality of coding, the difference could be 50-fold (for example, 5.0% rather than 0.1%). MEANING: Combining a medical chart review on a limited number of medical charts at the hospital level creates an approach to producing health system report cards with estimates of true hospital-level adverse event rates.

Psychometric evaluation of a Canadian version of the Seattle Angina Questionnaire (SAQ-CAN).

BACKGROUND: The Seattle Angina Questionnaire (SAQ) is a widely-used patient-reported outcomes measure in patients with heart disease. This study assesses the validity and reliability of the SAQ in a Canadian cohort of individuals with stable angina. METHODS AND RESULTS: Data are from the Alberta Provincial Project for Outcome Assessment in Coronary Heart Disease (APPROACH) registry, a population-based registry of patients who received cardiac catheterization in Alberta, Canada. The cohort consists of 4052 patients undergoing cardiac catheterization for stable angina and completed the SAQ within 2 weeks. Exploratory factor analysis and confirmatory factor analysis (CFA) were used to assess the factorial structure of the SAQ. Internal and test-retest reliabilities of a new measure (i.e., SAQ-CAN) was measured using Cronbach α and intraclass correlation coefficient, respectively. CFA model fit was assessed using the root mean square error of approximation (RMSEA) and comparative fit index (CFI). Construct validity of the SAQ-CAN was assessed in relation to Hospital Anxiety and Depression Scales (HADS), Euro Quality of life 5 dimension (EQ5D), and original SAQ. Of the 4052 patients included in this analysis, 3281 (80.97%) were younger than 75 years old, while 3239 (79.94%) were male. Both exploratory and confirmatory factor analyses revealed a four-factorial structure consisting of 16 items that provided a better fit to the data (RMSEA = 0.049 [90% CI = (0.047, 0.052)]; CFI = 0.975). The 16-item SAQ demonstrated good to excellent internal reliability (Cronbach's α range from 0.77 to 0.90), moderate to strong correlation with the Original SAQ and EQ5D but negligible correlations with HADS. CONCLUSION: The SAQ-CAN has acceptable psychometric properties that are comparable to the original SAQ. We recommend its use for assessing coronary health outcomes in Canadian patients with Coronary Artery Disease.

Comparative Prognostic Accuracy of Risk Prediction Models for Cardiogenic Shock.

OBJECTIVES: Despite advances in medical therapy, reperfusion, and mechanical support, cardiogenic shock remains associated with excess morbidity and mortality. Accurate risk stratification may improve patient management. We compared the accuracy of established risk scores for cardiogenic shock. METHODS: Patients admitted to tertiary care center cardiac care units in the province of Alberta in 2015 were assessed for cardiogenic shock. The Acute Physiology and Chronic Health Evaluation-II (APACHE-II), CardShock, intra-aortic balloon pump (IABP) Shock II, and sepsis-related organ failure assessment (SOFA) risk scores were compared. Receiver operating characteristic curves were used to assess discrimination of in-hospital mortality and compared using DeLong's method. Calibration was assessed using the Hosmer-Lemeshow goodness-of-fit test. RESULTS: The study included 3021 patients, among whom 510 (16.9%) had cardiogenic shock. Patients with cardiogenic shock had longer median hospital stays (median 11.0 vs 4.1 days, P < .001) and were more likely to die (29.0% vs 2.5%, P < .001). All risk scores were adequately calibrated for predicting hospital morality except for the APACHE-II score (Hosmer-Lemeshow P < .001). Discrimination of in-hospital mortality with the APACHE-II (area under the curve [AUC]: 0.72, 95% confidence interval [CI]: 0.66-0.76) and IABP-Shock II (AUC: 0.73, 95% CI: 0.68-0.77) scores were similar, while the CardShock (AUC: 0.76, 95% CI: 0.72-0.81) and SOFA (AUC: 0.76, 95%CI: 0.72-0.81) scores had better discrimination for predicting in-hospital mortality. CONCLUSIONS: In a real-world population of patients with cardiogenic shock, existing risk scores had modest prognostic accuracy, with no clear superior score. Further investigation is required to improve the discriminative abilities of existing models or establish novel methods.

A disease-specific comorbidity index for predicting mortality in patients admitted to hospital with a cardiac condition.

BACKGROUND: Comorbidity indexes derived from administrative databases are essential tools of research in global health. We sought to develop and validate a novel cardiac-specific comorbidity index, and to compare its accuracy with the generic Charlson-Deyo and Elixhauser comorbidity indexes. METHODS: We derived the cardiac-specific comorbidity index from consecutive patients who were admitted to hospital at a tertiary-care cardiology hospital in Quebec. We used logistic regression analysis and incorporated age, sex and 22 clinically relevant comorbidities to build the index. We compared the cardiac-specific comorbidity index with refitted Charlson-Deyo and Elixhauser comorbidity indexes using the C-statistic and net reclassification improvement to predict in-hospital death, and the Akaike information criterion to predict length of stay. We validated our findings externally in an independent cohort obtained from a provincial registry of coronary disease in Alberta. RESULTS: The novel cardiac-specific comorbidity index outperformed the refitted generic Charlson-Deyo and Elixhauser comorbidity indexes for predicting in-hospital mortality in the derivation population (n = 10 137): C-statistic 0.95 (95% confidence interval [CI] 0.94-0.9) v. 0.81 (95% CI 0.77-0.84) and 0.86 (95% CI 0.82-0.89), respectively. In the validation population (n = 17 877), the cardiac-specific comorbidity index was similarly better: C-statistic 0.92 (95% CI 0.89-0.94) v. 0.76 (95% CI 0.71-0.81) and 0.82 (95% CI 0.78-0.86), respectively, and also numerically outperformed the Charlson-Deyo and Elixhauser comorbidity indexes for predicting 1-year mortality (C-statistic 0.78 [95% CI 0.76-0.80] v. 0.75 [95% CI 0.73-0.77] and 0.77 [95% CI 0.75-0.79], respectively). Similarly, the cardiac-specific comorbidity index showed better fit for the prediction of length of stay. The net reclassification improvement using the cardiac-specific comorbidity index for the prediction of death was 0.290 compared with the Charlson-Deyo comorbidity index and 0.192 compared with the Elixhauser comorbidity index. INTERPRETATION: The cardiac-specific comorbidity index predicted in-hospital and 1-year death and length of stay in cardiovascular populations better than existing generic models. This novel index may be useful for research of cardiology outcomes performed with large administrative databases.

Trajectories of perceived social support in acute coronary syndrome.

PURPOSE: Perceived social support is known to be an important predictor of health outcomes in patients with acute coronary syndrome (ACS). This study investigates patterns of longitudinal trajectories of patient-reported perceived social support in individuals with ACS. METHODS: Data are from 3013 patients from the Alberta Provincial Project for Outcome Assessment in Coronary Heart Disease registry who had their first cardiac catheterization between 2004 and 2011. Perceived social support was assessed using the 19-item Medical Outcomes Study Social Support Survey (MOS) 2 weeks, 1 year, and 3 years post catheterization. Group-based trajectory analysis based on longitudinal multiple imputation model was used to identify distinct subgroups of trajectories of perceived social support over a 3-year follow-up period. RESULTS: Three distinct social support trajectory subgroups were identified, namely: "High" social support group (60%), "Intermediate" social support group (30%), and "Low" social support subgroup (10%). Being female (OR = 1.67; 95% CI = [1.18-2.36]), depression (OR = 8.10; 95% CI = [4.27-15.36]) and smoking (OR = 1.70; 95% CI = [1.23-2.35]) were predictors of the differences among these trajectory subgroups. CONCLUSION: Although the majority of ACS patients showed increased or fairly stable trajectories of social support, about 10% of the cohort reported declining social support. These findings can inform targeted psycho-social interventions to improve their perceived social support and health outcomes.

The Association between Chronic Obstructive Pulmonary Disease and Coronary Artery Disease in Patients Undergoing Coronary Angiography.

Chronic obstructive pulmonary disease (COPD) and coronary artery disease (CAD) are leading causes of morbidity and mortality. There are conflicting results regarding the association between COPD and CAD. We sought to measure the association between COPD and angiographically diagnosed CAD in a population-based cohort. We performed a retrospective analysis using data from the Alberta Provincial Project for Outcomes Assessment in Coronary Heart Disease (APPROACH), a prospectively collected registry capturing all patients undergoing coronary angiography in Alberta, Canada, since 1995. We included adult patients who had undergone coronary angiogram between April 1, 2007 and March 31, 2014. CAD was present if at least one coronary artery had a significant stenosis ≥50%. COPD was present if the patient had a documented COPD history and was prescribed bronchodilators or inhaled steroids. We evaluated the association between COPD and CAD using univariable and multivariable logistic regression. There were 26,137 patients included with a mean age of 63.3 ± 12.2 years, and 19,542 (74.8%) were male. The crude odds ratio (OR) of having CAD was 0.83 (95% CI 0.74-0.92) for patients with COPD compared to those without COPD. The adjusted OR was 0.75 (95% CI 0.67-0.84) after controlling for age, sex, smoking history, body mass index, hypertension, diabetes, hyperlipidemia, peripheral artery disease and cardiac family history. In patients undergoing coronary angiography, COPD was negatively associated with CAD with and without the adjustment for classic risk factors. COPD patients should be properly examined for heart disease to reduce premature mortality.

Left ventricular ejection fraction reassessment post-myocardial infarction: Current clinical practice and determinants of adverse remodeling.

BACKGROUND: Left ventricular (LV) dysfunction may be sustained or aggravated during the convalescent months following an acute myocardial infarction (MI) and is difficult to predict. We sought to determine current practice patterns of LV ejection fraction (LVEF) reassessment during the months following MI and evaluate the predictors and clinical significance of LVEF change in a prospective post-MI patient cohort. METHODS: Patients with an acute MI between June 2010 and August 2014 were identified using the Alberta Provincial Project for Outcome Assessment in Coronary Heart Disease registry. Patients with initial LV dysfunction (LVEF <40% with first MI or <45% with multiple MI events) underwent a protocol-driven repeat LVEF assessment in follow-up if routine LVEF reassessment was not performed. RESULTS: Of 5,964 MI patients, follow-up LVEF assessments were attained for 442 of the 695 patients who had significant LV dysfunction. A sizable proportion (25%) had either no increase or a decline in LVEF. Adverse remodeling was associated with an anterior MI location, a greater peak serum troponin T, and a higher baseline LVEF at time of MI. Adverse LV remodeling conferred a 3-fold risk of death (hazard ratio 3.0, 95% CI 1.6-5.7, P=.001) adjusted for baseline LVEF, anterior MI location, and medication use. CONCLUSIONS: Current practice of LVEF reassessment during the convalescent months post-MI is suboptimal despite a sizeable proportion of patients that undergo adverse LV remodeling. Targeting processes affecting low rates of LVEF reassessment may reduce missed care opportunities and ensure that patients consistently receive appropriate evidence-based and guideline-recommended care.

Impact of clinical presentation and presence of coronary sclerosis on long-term outcome of patients with non-obstructive coronary artery disease.

BACKGROUND: Non-obstructive coronary artery disease (NOCAD) is a common finding on coronary angiography. Our goal was to evaluate the long-term prognosis of NOCAD patients with stable angina (SA). METHODS: The study cohort consisted of 7478 NOCAD patients with normal EF (≥ 50%), and SA who underwent coronary angiography between 1995 and 2012. We compared NOCAD patients (stenosis< 50%) with 10,906 patients with stable obstructive CAD (≥ 50%). The primary endpoint was all-cause mortality. Secondary endpoints included repeat angiography, progressive CAD, and PCI. A second comparison group consisted of 7344 patients with NOCAD presenting with an ACS. Rates of all-cause mortality of NOCAD ACS patients were compared to NOCAD SA patients. RESULTS: Median follow-up time was 6.5 years. NOCAD patients had a lower risk of all-cause mortality compared to CAD patients (HR CAD vs. NOCAD 1.33 (1.19-1.49); p < 0.001). This was driven by patients with normal coronary arteries (HR CAD vs. normal 1.63 (1.36-1.94), p < 0.001), whereas patients with minimal disease (> 0% and < 50%) were at similar risk as CAD patients (HR CAD vs. minimal 1.08 (0.99-1.29), p = 0.06). In NOCAD patients, the strongest predictors of all-cause mortality were age and minimal disease. SA patients with NOCAD had low rates of repeat angiography (7.3%), future CAD (2.3%) and PCI (1.7%). NOCAD ACS patients had a 41% increase in all-cause mortality risk compared to NOCAD SA patients (HR 1.41 (1.25-1.6), p < 0.001). CONCLUSIONS: This study underlines the importance of minimal CAD, as it is not a benign disease entity and portends a similar risk as stable obstructive CAD.

Effectiveness of a standardized electronic admission order set for acute exacerbation of chronic obstructive pulmonary disease.

BACKGROUND: Variation in hospital management of patients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD) may prolong length of stay, increasing the risk of hospital-acquired complications and worsening quality of life. We sought to determine whether an evidence-based computerized AECOPD admission order set could improve quality and reduce length of stay. METHODS: The order set was designed by a provincial COPD working group and implemented voluntarily among three physician groups in a Canadian tertiary-care teaching hospital. The primary outcome was length of stay for patients admitted during order set implementation period, compared to the previous 12 months. Secondary outcomes included length of stay of patients admitted with and without order set after implementation, all-cause readmissions, and emergency department visits. RESULTS: There were 556 admissions prior to and 857 admissions after order set implementation, for which the order set was used in 47%. There was no difference in overall length of stay after implementation (median 6.37 days (95% confidence interval 5.94, 6.81) pre-implementation vs. 6.02 days (95% confidence interval 5.59, 6.46) post-implementation, p = 0.26). In the post-implementation period, order set use was associated with a 1.15-day reduction in length of stay (95% confidence interval - 0.5, - 1.81, p = 0.001) compared to patients admitted without the order set. There was no difference in readmissions. CONCLUSIONS: Use of a computerized guidelines-based admission order set for COPD exacerbations reduced hospital length of stay without increasing readmissions. Interventions to increase order set use could lead to greater improvements in length of stay and quality of care.

Fragmented QRS complexes after acute myocardial infarction are independently associated with unfavorable left ventricular remodeling.

BACKGROUND: Recovery of left ventricular ejection fraction (LVEF) after acute myocardial infarction (MI) is not universal and is difficult to predict. Fragmented QRS (fQRS) complexes are thought to be markers of myocardial scar. We hypothesized that fQRS complexes on 12­lead surface ECGs during the initial post-MI period would be associated with adverse LV remodeling over the following year. METHODS: Change in LVEF between the early (0-2 month) and later (2-12 month) post-MI periods was assessed in two independent cohorts of post-MI patients with initial LV dysfunction. A decline or no recovery in LVEF (ΔLVEF ≤0%) was used as a primary outcome. fQRS complexes were measured on 12­lead ECGs within a week of acute MI. A subset of patients underwent cardiac magnetic resonance imaging (CMR) for scar quantification. RESULTS: Of 705 patients in the combined cohort, 27% experienced the primary outcome (average ΔLVEF of -4%). fQRS complexes were associated with a two-fold higher risk of no LVEF recovery, independent of prior MI or CABG, baseline LVEF, MI location and QRS duration or axis. Of 113 patients undergoing CMR, fQRS was associated with increased peri-infarct zone late gadolinium enhancement (13 ±â€¯5% vs 11 ±â€¯4%, p = 0.02), but not core infarct. CONCLUSIONS: Despite contemporary post-MI therapy, >1 in 4 patients will show a decline in LVEF during follow-up. Fragmented QRS complexes on 12­lead surface ECG early post-MI may be a valuable marker of unfavorable LV remodeling and correlate to increased peri-infarct scar on CMR imaging.

Constructing Episodes of Inpatient Care: How to Define Hospital Transfer in Hospital Administrative Health Data?

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.

Deriving ICD-10 Codes for Patient Safety Indicators for Large-scale Surveillance Using Administrative Hospital Data.

BACKGROUND: Existing administrative data patient safety indicators (PSIs) have been limited by uncertainty around the timing of onset of included diagnoses. OBJECTIVE: We undertook de novo PSI development through a data-driven approach that drew upon "diagnosis timing" information available in some countries' administrative hospital data. RESEARCH DESIGN: Administrative database analysis and modified Delphi rating process. SUBJECTS: All hospitalized adults in Canada in 2009. MEASURES: We queried all hospitalizations for ICD-10-CA diagnosis codes arising during hospital stay. We then undertook a modified Delphi panel process to rate the extent to which each of the identified diagnoses has a potential link to suboptimal quality of care. We grouped the identified quality/safety-related diagnoses into relevant clinical categories. Lastly, we queried Alberta hospital discharge data to assess the frequency of the newly defined PSI events. RESULTS: Among 2,416,413 national hospitalizations, we found 2590 unique ICD-10-CA codes flagged as having arisen after admission. Seven panelists evaluated these in a 2-round review process, and identified a listing of 640 ICD-10-CA diagnosis codes judged to be linked to suboptimal quality of care and thus appropriate for inclusion in PSIs. These were then grouped by patient safety experts into 18 clinically relevant PSI categories. We then analyzed data on 2,381,652 Alberta hospital discharges from 2005 through 2012, and found that 134,299 (5.2%) hospitalizations had at least 1 PSI diagnosis. CONCLUSION: The resulting work creates a foundation for a new set of PSIs for routine large-scale surveillance of hospital and health system performance.

Acute coronary syndrome patients admitted to a cardiology vs non-cardiology service: variations in treatment & outcome.

BACKGROUND: Specialized cardiology services have contributed to reduced mortality in acute coronary syndromes (ACS).  We sought to evaluate the outcomes of ACS patients admitted to non-cardiology services in Southern Alberta. METHODS: Retrospective chart review performed on all troponin-positive patients in the Calgary Health Region identified those diagnosed with ACS by their attending team. Patients admitted to non-cardiology and cardiology services were compared, using linked data from the Alberta Provincial Project for Outcomes Assessment in Coronary Heart Disease (APPROACH) registry and the Strategic Clinical Network for Cardiovascular Health and Stroke. RESULTS: From January 1, 2007 to December 31, 2008, 2105 ACS patients were identified, with 1636 (77.7%) admitted to cardiology and 469 (22.3%) to non-cardiology services. Patients admitted to non-cardiology services were older, had more comorbidities, and rarely received cardiology consultation (5.1%). Cardiac catheterization was underutilized (5.1% vs 86.4% in cardiology patients (p < 0.0001)), as was evidence-based pharmacotherapy (p < 0.0001). Following adjustment for baseline comorbidities, 30-day through 4-year mortality was significantly higher on non-cardiology vs. cardiology services (49.1% vs. 11.0% respectively at 4-years, p < 0.0001). CONCLUSION: In a large ACS population in the Calgary Health Region, 25% were admitted to non-cardiology services. These patients had worse outcomes, despite adjustment for baseline risk factor differences. Although many patients were appropriately admitted to non-cardiology services, the low use of investigations and secondary prevention medications may contribute to poorer patient outcome. Further research is required to identify process of care strategies to improve outcomes and lessen the burden of illness for patients and the health care system.