Accuracy of Administrative Hospital Data to Identify Use of Life Support Modalities. A Canadian Study
Bibliographic record
Abstract
Abstract Rationale Accurately identifying use of life support in hospital administrative data enhances the data’s value for quality improvement and research in critical illness. Objectives To assess the accuracy of administrative hospital data for identifying invasive mechanical ventilation (IMV), acute renal replacement therapy (RRT), and intravenous vasoactive drugs in unselected adult intensive care unit (ICU) patients. Methods We employed the administrative dataset of the Discharge Abstract Database from the Province of Manitoba during 2007–2012, using nationally standardized diagnosis and procedure codes to identify the three types of life support. The criterion standard was the Winnipeg ICU Database, which contains daily clinical information about all admissions to all 11 adult ICUs within the Winnipeg Regional Health Authority. For all individuals aged 40 years or older at ICU admission, we calculated sensitivity, specificity, positive predictive value (PPV), and negative predictive value of the administrative data for identifying life support. We also assessed the ability of the administrative data to identify overlapping use of the forms of life support. Results Over the study period, there were 20,764 eligible ICU admissions; 52.6% (10,914) involved IMV, 46.8% (9,724) involved vasoactive agents, and 4.4% (907) involved acute RRT. Identification of IMV from administrative data procedure codes was good, with all four parameters exceeding 90%. The procedure code for use of selected vasoactive drugs had a sensitivity of zero; addition of diagnosis codes for shock raised the sensitivity to only 23% (95% confidence interval [CI], 22–24%). Both the sensitivity and specificity for acute RRT procedure codes exceeded 92%, but owing to low prevalence of RRT, the PPV was only 55% (95% CI, 53–58%). Addition of diagnosis codes for acute renal failure did not appreciably improve performance. Overlapping use of the three types of life support was substantial. Among those receiving any one of the types of life support, 68–76% received at least one of the two other types assessed. Considering use of any one or more of the three forms of life support, the administrative data had a PPV of 97% (95% CI, 96–97%) and a negative predictive value of 69% (95% CI, 68–70%). Conclusions Administrative data accurately identify IMV but not use of vasoactive drugs or acute RRT.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.015 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".