Health Services Use and Outcomes for Hospital Admissions With a Major Cardiovascular Event Recorded in Health Care Administrative Data in Patients Receiving Maintenance Hemodialysis: A Retrospective Cohort Study
Bibliographic record
Abstract
Background:Administrative data are used in studies of hemodialysis care to report cardiovascular-related hospitalizations. Showing recorded events are associated with significant health care resource use and poor outcomes would confirm that administrative data algorithms identify clinically meaningful events.Objective:The objective of this study was to describe the 30-day health service use and outcomes when a hospital admission with myocardial infarction, congestive heart failure, or ischemic stroke is recorded in administrative databases.Design:This is a retrospective review of linked administrative data.Patients and Setting:Patients receiving maintenance in-center hemodialysis in Ontario, Canada, between April 1, 2013, and March 31, 2017, were included.Measurements:Records from linked health care databases at ICES in Ontario, Canada were considered. We identified hospital admission with the most responsible diagnosis recorded as myocardial infarction, congestive heart failure, or ischemic stroke. We then assessed the frequency of common tests, procedures, consultations, post-discharge outpatient drug prescriptions, and outcomes within 30 days following the hospital admission.Methods:We used descriptive statistics to summarize results using counts and percentages for categorical variables and means with standard deviations or medians with quartile ranges for continuous variables.Results:There were 14 368 patients who received maintenance hemodialysis between April 1, 2013, and March 31, 2017. The number of events per 1000 person-years was 33.5 for hospital admissions with myocardial infarction, 34.2 for congestive heart failure, and 12.9 for ischemic stroke. The median (25th, 75th percentile) duration of hospital stay was 5 (3-10) days for myocardial infarction, 4 (2-8) days for congestive heart failure, and 9 (4-18) days for ischemic stroke. The chance of death within 30 days was 21% for myocardial infarction, 11% for congestive heart failure, and 19% for ischemic stroke.Limitations:Events, procedures, and tests recorded in administrative data can be misclassified compared with medical charts.Conclusions:In patients receiving maintenance hemodialysis, hospital admissions of major cardiovascular events routinely recorded in health administrative databases are associated with significant use of health service resources and poor health outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".