Handing over an anaesthetic mid-case was associated with slightly longer stays and more readmissions

Design

Retrospective multicentre cohort

Population

145,383 adults having non-cardiac inpatient surgery under general anaesthesia

Primary outcome

Hospital length of stay; 30-day readmission

Effect

Stay estimate 1.02 (1.02–1.03); readmission 14.0% vs 12.3%, adjusted RR 1.06 (1.02–1.11)

This retrospective cohort included 145,383 adults having non-cardiac inpatient surgery under general anaesthesia at two US academic networks between 2007 and 2021. In 11.7%, care passed from one attending anaesthesiologist to another during the operation.

Handover was associated with longer hospital stay (median 4 vs 3 days; adjusted estimate 1.02, 95% CI 1.02–1.03) and more unplanned 30-day readmissions (14.0% vs 12.3%; adjusted RR 1.06, 1.02–1.11). The association was stronger in major surgery, among handovers between residents or nurse anaesthetists, and when handover happened late in the day (1.08) or at night (1.12).

The effect per patient is small, and handovers often happen in longer, more complex cases that would do worse anyway. But it is consistent with earlier studies, and late and night handovers are where a structured process would most likely help.

• Use a structured handover tool for every intraoperative change of anaesthetist
• Include airway, lines, drugs given and due, blood loss, fluids and the surgical plan
• Avoid non-essential handovers during critical phases of surgery
• Plan rotas to reduce late-evening handovers in major cases where possible

Why it matters

Handovers are routine in shift-based anaesthesia, and each one carries a measurable, if small, cost to the patient.

Don't overread it

Observational data — longer, harder cases are more likely to be handed over, so the handover itself may not be the cause.

The statistics, in plain English

An adjusted estimate of 1.02 for length of stay means about 2% longer on average; a risk ratio of 1.06 means 6% more readmissions relative to baseline. These are small effects in a very large sample, and residual confounding by case length and complexity could explain part of them.

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