Novel pulse pressure pattern monitoring in critical care of elderly sepsis patients
Year: 2025
Type: article
Study design: Observational Study
Abstract: OBJECTIVE: Our research aimed to explore the application of pulse pressure (PP) at the bedside of elderly intensive care unit (ICU) patients with sepsis through a large-scale retrospective cohort study. METHODS: We obtained data from four heterogeneous datasets, which included information on elderly sepsis patients (≥ 65 years). The data were divided into the inference and validation datasets. Thereby enhancing the generalizability of the study. The primary outcome was mortality at 28 days, and piecewise exponential additive mixed model (PAMM) were employed to estimate the strength of the associations over time. RESULTS: We included 12,525 elderly patients with sepsis in the initial inference dataset. Based on the PAMM's inference results, we identified a specific low PP phenotype from the time-dependent endpoint dataset. The phenotype indicates an imbalance between the patient's cardiac pumping ability and circulatory resistance, contributing to an increased 28-day mortality (hazard ratio, 2.36; 95% CI, 2.12-2.63). The consistency of these results was validated using data from various sources. CONCLUSION: Low PP phenotype (PP < 45 mmHg 72 h after intensive care unit admission and lasting for > 3h) may provide an early dynamic warning of the therapeutic effects of resuscitation interventions in long-hospitalized elderly patients with sepsis. IMPLICATIONS FOR CLINICAL PRACTICE: The results demonstrate acceptable consistency across heterogeneous datasets and hold promise for further development and integration into bedside monitoring systems for elderly sepsis patients.
Authors: Wanjun Liu, Shijun Zhou, Manru Li, Pengyue Zhang, Mengshu Pan, Lijun Wei, Zhenhua Zhang, Rui Gong
Language: English
Subfield: Epidemiology
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $4,140
Funder: Anhui Medical University
Awards: G362408977, G508086789
OpenAlex ID: W4409785147
