Thirty-day readmission rate as a surrogate marker for quality of care in neurosurgical patients: a single-center Canadian experience
Bibliographic record
Abstract
OBJECTIVE: Readmission to the hospital within 30 days of discharge is used as a surrogate marker for quality and value of care in the United States (US) healthcare system. Concern exists regarding the value of 30-day readmission as a quality of care metric in neurosurgical patients. Few studies have assessed 30-day readmission rates in neurosurgical patients outside the US. The authors performed a retrospective review of all adult neurosurgical patients admitted to a single Canadian neurosurgical academic center and who were discharged to home to assess for the all-cause 30-day readmission rate, unplanned 30-day readmission rate, and avoidable 30-day readmission rate. METHODS: A retrospective review was performed assessing 30-day readmission rates after discharge to home in all neurosurgical patients admitted to a single academic neurosurgical center from January 1, 2011, to December 31, 2011. The primary outcomes included rates of all-cause, unplanned, and avoidable readmissions within 30 days of discharge. Secondary outcomes included factors associated with unplanned and avoidable 30-day readmissions. RESULTS: A total of 184 of 950 patients (19.4%) were readmitted to the hospital within 30 days of discharge. One-hundred three patients (10.8%) were readmitted for an unplanned reason and 81 (8.5%) were readmitted for a planned or rescheduled operation. Only 19 readmissions (10%) were for a potentially avoidable reason. Univariate analysis identified factors associated with readmission for a complication or persistent/worsening symptom, including age (p = 0.009), length of stay (p = 0.007), general neurosurgery diagnosis (p < 0.001), cranial pathology (p < 0.001), intensive care unit (ICU) admission (p < 0.001), number of initial admission operations (p = 0.01), and shunt procedures (p < 0.001). Multivariate analysis identified predictive factors of readmission, including diagnosis (p = 0.002, OR 2.4, 95% CI 1.4-5.3), cranial pathology (p = 0.002, OR 2.7, 95% CI 1.4-5.3), ICU admission (p = 0.004, OR 2.4, 95% CI 1.3-4.2), and number of first admission operations (p = 0.01, OR 0.51, 95% CI 0.3-0.87). Univariate analysis performed to identify factors associated with potentially avoidable readmissions included length of stay (p = 0.03), diagnosis (p < 0.001), cranial pathology (p = 0.02), and shunt procedures (p < 0.001). Multivariate analysis identified only shunt procedures as a predictive factor for avoidable readmission (p = 0.02, OR 5.6, 95% CI 1.4-22.8). CONCLUSIONS: Almost one-fifth of neurosurgical patients were readmitted within 30 days of discharge. However, only about half of these patients were admitted for an unplanned reason, and only 10% of all readmissions were potentially avoidable. This study demonstrates unique challenges encountered in a publicly funded healthcare setting and supports the growing literature suggesting 30-day readmission rates may serve as an inappropriate quality of care metric in neurosurgical patients. Potentially avoidable readmissions can be predicted, and further research assessing predictors of avoidable readmissions is warranted.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".