Investigators performed a retrospective cohort analysis of patients with suspected sepsis presenting to a large, publicly funded academic emergency department. The Doppler recordings were de-identified; the dataset contains no linked patient health information. Two approaches were used to provoke a preload change: administration of 300 mL of crystalloid infused over three minutes, or a Trendelenburg maneuver. Carotid Doppler spectrograms were acquired with a wireless, wearable critical-care Doppler ultrasound system. The team analyzed these spectrograms for changes in corrected flow time of the common carotid artery, reported as ccFTΔ.
The cohort reflects emergency department practice where rapid, bedside hemodynamic assessment is often required. The device and protocol permitted continuous capture of carotid cardiac cycles during the preload maneuver, enabling temporal analysis of the Doppler-derived flow-time signal across many beats.
The study specified several primary physiological objectives. First, investigators planned to estimate the proportion of patients who met previously described ccFTΔ thresholds for preload responsiveness, specifically the 4 ms and 7 ms cutoffs that have been described in prior work. Second, the team described time-to-peak (TTP) of the ccFTΔ response during the preload challenge, an endpoint intended to characterize the temporal dynamics of the carotid flow-time change. Third, they evaluated whether baseline corrected flow time could discriminate which patients would demonstrate a ccFTΔ during preload challenge by applying two correction formulas: the Wodey equation and Bazett’s correction. The abstract identifies these endpoints explicitly; it does not report every downstream operational definition or specific decision rules beyond those correction equations.
Investigators reported a multi-pronged statistical approach. Pairwise and related outcome comparisons were adjusted for multiple testing using the Holm procedure. The ability of baseline corrected flow time to discriminate responders from non-responders was quantified using the area under the receiver operating characteristic curve (AUC). Time-to-peak estimates were derived through patient-level bootstrap resampling, a resampling technique suited to estimate uncertainty around temporal metrics in clustered beat-level data.
Beyond standard threshold-based analyses, the study team applied an unsupervised, threshold-agnostic approach: k-means clustering of patient-level temporal ccFTΔ responses, performed separately for each correction equation. That exploratory analysis was intended to reveal common temporal response patterns without imposing predefined ms thresholds.
The study attempted a preload challenge in 4,367 patients. Investigators reported that an adequate ccFTΔ was successfully captured in 4,128 patients, representing a 95% capture rate for the attempted maneuvers. The analytic record includes 982,937 Doppler cardiac cycles, a volume that permits beat-by-beat characterization of physiologic response and supports temporal-resampling approaches.
Across the analyzed cohort the median ccFTΔ was 7.3 milliseconds. That summary statistic provides a central tendency benchmark against which the pre-specified 4 ms and 7 ms thresholds were to be compared.
The abstract text provided in the source truncates mid-sentence immediately after reporting a percentage that begins “51.9% of pati…”. The remaining portion of the results section, including the proportion of preload-responsive patients according to each threshold, receiver-operating characteristics (AUC values), estimated time-to-peak values, cluster characteristics from the k-means analyses, inferential statistics, and the authors’ conclusions are not present in the supplied source text. The study’s full outcome data, effect sizes, confidence intervals, and any statement about clinical implications or limitations beyond what is shown here therefore cannot be summarized from this source excerpt.
Unanswered questions left by the available abstract include: the final proportions of patients classified as preload responsive by the 4 ms and 7 ms ccFTΔ thresholds; numerical AUC values and their confidence intervals for baseline corrected flow time using Wodey versus Bazett corrections; reported TTP estimates with uncertainty bounds; the characteristics and clinical interpretation of clusters derived from the k-means analysis; and any safety or feasibility observations tied to either preload maneuver. Those items were not reported in the excerpt and would require access to the complete article.
Limitations stated here are limited to the information the authors provided in the abstract fragment. The study design is retrospective and based on an emergency-department convenience cohort; investigators reported use of established statistical controls (Holm adjustment, bootstrap, AUC), but the source did not provide further methodological details such as handling of missing beats, inter-operator variability in Doppler placement, or prespecified subgroup analyses. For readers seeking complete outcome metrics, the DOI 10.1186/s40635-026-00987-1 and the full text in Intensive Care Medicine Experimental should be consulted, as the abstract text in the supplied source does not include the remainder of the results or the authors’ final interpretation.