Bedspacing and clinical outcomes in general internal medicine: A retrospective, multicenter cohort study

Abstract

Background Admitting hospitalized patients to off‐service wards (“bedspacing”) is common and may affect quality of care and patient outcomes. Objective To compare in‐hospital mortality, 30‐day readmission to general internal medicine (GIM), and hospital length‐of‐stay among GIM patients admitted to GIM wards or bedspaced to off‐service wards. Design, Participants, and Measures Retrospective cohort study including all emergency department admissions to GIM between 2015 and 2017 at six hospitals in Ontario, Canada. We compared patients admitted to GIM wards with those who were bedspaced, using multivariable regression models and propensity score matching to control for patient and situational factors. Key Results Among 40,440 GIM admissions, 10,745 (26.6%) were bedspaced to non‐GIM wards and 29,695 (73.4%) were assigned to GIM wards. After multivariable adjustment, bedspacing was associated with no significant difference in mortality (adjusted hazard ratio 0.95, 95% confidence interval [CI]: 0.86–1.05, p = .304), slightly shorter median hospital length‐of‐stay (−0.10 days, 95% CI:−0.20 to −0.001, p = .047) and lower 30‐day readmission to GIM (adjusted OR 0.89, 95% CI: 0.83–0.95, p = .001). Results were consistent when examining each hospital individually and outcomes did not significantly differ between medical or surgical off‐service wards. Sensitivity analyses focused on the highest risk patients did not exclude the possibility of harm associated with bedspacing, although adverse outcomes were not significantly greater. Conclusions Overall, bedspacing was associated with no significant difference in mortality, slightly shorter hospital length‐of‐stay, and fewer 30‐day readmissions to GIM, although potential harms in high‐risk patients remain uncertain. Given that hospital capacity issues are likely to persist, future research should aim to understand how bedspacing can be achieved safely at all hospitals, perhaps by strengthening the selection of low‐risk patients.

Temporal Clustering of Critical Illness Events on Medical Wards

“…Because hospital capacity strain has been associated with adverse patient outcomes, we calculated the ratio of the inpatient GIM census on every day of the study period to the median GIM census for the hospital in that year. This census ratio has been used in previous studies to account for time-varying changes in patient volume compared with typical patient volumes and is a proxy for capacity strain on the GIM wards. Among patients who experienced an outcome, we further described the events with the day of outcome, time of outcome, and the LAPS at 24 and 48 hours before the outcome.…”

“…Among the 8785 patients who experienced the primary outcome, 7381 (84.0%) were exposed to no critical illness events in the 6-hour interval before the outcome, 1086 (12.4%) were exposed to 1 event, and 318 (3.6%) were exposed to more than 1 event (Table 3). Compared with unexposed patients, those with exposure to prior events were younger (77 vs 72 years [exposed to 1 event] or vs 71 years [exposed to >1 event]), had similar sex distribution (55.0% vs 56.4% male [exposed to 1 event] or vs 58.5% male [exposed to >1 event]; 45.0% vs 43.6% female [exposed to 1 event] or vs 41.5% female [exposed to >1 event]), had similar Charlson Comorbidity Index scores, had lower LAPS values calculated at hospital admission (median score, 24 [IQR, 12-38] vs 21 [IQR, for those exposed to 1 event or vs 19 [IQR, [11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30] for those exposed to >1 event), and were more likely to have laboratory test results measured at 24 ). There was no apparent imbalance between groups in day or time of hospital admission, duration of emergency department boarding time, or GIM census ratio on the day of the event.…”

“…Among patients transferred to the ICU, LAPS values at admission appeared somewhat lower for those exposed to more than 1 prior event (median score, 19 [IQR,[11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30]) compared with the other groups (median score for those exposed to 1 event, 20 [IQR, ; median score for those exposed to 0 events, 23 [IQR, 12-37]) (eTable 1 in Supplement 1). An ICU transfer occurred more often at night after exposures (1600 of 3179 patients [50.3%] unexposed, 410 of 656 [62.5%] exposed to 1 event, and 141 of 227 [62.1%] exposed to >1 event).…”

Imaging delays among medical inpatients in Toronto, Ontario: A cohort study

“…We included variables that could impact the time-to-test, at the level of test, hospitalization, patient, and physician. These included system-level variables, measured at test-level: time of ordering relative to admission, whether ordered in the emergency department (ED) or in an Intensive Care Unit (ICU) setting, or ordered during off-hours (evenings or weekends), GIM inpatient census on the day the test was ordered, and designation as bedspaced [19] (patient located on an inpatient unit different from admitting service, excluding ICU). Hospitalizationlevel variables included age, most responsible diagnosis, Charlson comorbidity index, laboratory-based acute physiology score (LAPS, a score based on presenting laboratory test results that is a validated predictor of mortality when combined with age, comorbidity and sex), neighbourhood income quintile, and fiscal year of hospitalization [20][21][22][23].…”