using richer data

Findings weakly suggest a potential mortality benefit associated with HEMS but are limited by statistical uncertainty and less-than-ideal outcome granularity.

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Younger age and higher SAPS scores were also associated with an increased likelihood of HEMS use.

The posterior probability that HEMS increases 30-day mortality in the complier group remains relatively large at 31.5%

A central principle of neurocritical care is the prevention and management of “secondary insults” — physiological disturbances that exacerbate primary brain injury (44). Hypotension and hypoxia, for example, are such insults that are also commonly reported complications during interfacility transfers (3–6). Future prospective studies should incorporate detailed records of interventions and physiological parameters during transfer to better understand how care delivery impacts patient trajectories and to identify opportunities for improvement. (Delete?)

Of note, we observed an unadjusted association between nighttime ICU admission and lower 30-day mortality in this cohort, which may warrant further investigation.

The primary outcome, 30-day mortality, is relatively coarse. For patients with severe brain injury, functional outcomes are more informative. As a proxy, we included Days Alive and Out of Hospital (DAOH) within 90 days as a secondary outcome.

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by at least 2.5 percentage points

complier group

meaningfully

Table 4: Multivariate models predicting 30-day mortality (A, B) and DAOH-90 (C)

Table 3: Multivariate model predicting HEMS transfer

Methods

Figure 3: Routes travelled by patients in the full cohort by all modes of transport. The opacity of the line denotes the relative number of transfers. Receiving tertiary centers are maroon. Centers where >10% of patients are transferred by HEMS are circled. Note that one of the receiving tertiary centers, Örebro University Hospital, also is also acting as a sending center.

Figure 2: Flowchart of patient inclusion.

A 2018 meta-analysis found no association between nighttime ICU admissions and mortality (39). In our cohort, nighttime admission (22:00–07:00) was associated with lower rates of 30-day mortality: OR 0.7 (95% CI: 0.5 to 0.9). Also, a more recent study reported a lower mortality risk for off-hours ICU admissions (40). Therefore, in sensitivity analyses, we included nighttime admission as a covariate in the regression model and used weather as an instrumental variable conditional on admission time.

Instrumental variable relevance (assumption 1) was assessed by fitting a generalized linear model with HEMS minima as the sole predictor of transport modality. The estimated odds ratio (OR) was 18.9 (95% CI: 10.6 to 37.2). However, in subgroups from ICUs that rarely or never use HEMS, the instrumental variable was weak. Including these patients may introduce bias (38), so they were excluded. The cut-off for low HEMS use was set at <10% of transfers, selected pragmatically in the absence of a validated threshold. While untestable, the exclusion restriction (assumption 2) is plausible given that weather at ICU discharge is unlikely to affect mortality directly. Further, the assumption can be challenged by instrumental inequalities or by detecting an association between the instrumental variable and outcome in subgroups where the instrumental variable cannot influence exposure (36, 38). In our analysis, the instrumental inequality was not violated. Additionally, in the subgroup of patients from ICUs with low HEMS utilization, the instrumental variable showed no clear association with outcome: the univariate GLM yielded an odds ratio of 0.8 (95% CI: 0.5 to 1.3).

Figure 1: Distribution of time differences between helicopter departure from the sending hospital and the recorded ICU discharge time. Positive values indicate that the helicopter departed after ICU discharge; negative values indicate departure before the documented discharge time. The ±120-minute window was used to define a matched helicopter transfer as the tails of the distribution reached a uniform plateau.

In Sweden, two similar patients may be transferred by different means, and under markedly different standards of care. Broadly, there are two provider configurations for interhospital transfers. Specialized retrieval teams — typically composed of an anesthesiologist-intensivist and a nurse trained in anesthesia and/or intensive care — operate at a regional level and have access to both air and ground transport. In contrast, local solutions are based at the referring hospital. These may consist of a single provider, typically a nurse with anesthetic training or a physician in training. Local providers or teams rely solely on ground ambulances. All patients transferred between hospitals via HEMS in Sweden are managed by specialized retrieval teams.

Helicopter flight movements were parsed from Automatic Dependent Surveillance–Broadcast (ADS-B) data provided by Flightradar24 AB, covering flights within Swedish airspace. Details on the flight-matching algorithm and processing steps are provided in Supplement A.