Evidence map›Paper›PMID 42422678›Full record

ArticleFrontiers in public health2026

Electronic health record-derived care-exposure burden and central line-associated bloodstream infection among critically ill adults: a multi-source retrospective cohort study.

Na Li, Jiali Huang, Jinglan Liu, Yang He

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Article in Frontiers in public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Na LiDepartment of Rehabilitation Medicine, Yichang Central People's Hospital, Yichang, Hubei, China.
Jiali HuangYichang Central People's Hospital, Yichang, Hubei, China.
Jinglan LiuYichang Central People's Hospital, Yichang, Hubei, China.
Yang HeDepartment of Critical Care Medicine, Yichang Central People's Hospital, The First College of Clinical Medical Science, China Three Gorges University, Yichang, Hubei, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Central line-associated bloodstream infection (CLABSI) remains a clinically important healthcare-associated infection in intensive care units. Routinely collected electronic health record (EHR) data may capture patient-level care-process complexity not reflected by conventional catheter-related variables alone. Objective: To examine whether an EHR-derived care-exposure burden score was associated with adjudicated CLABSI or CLABSI-like bloodstream infection among critically ill adults with central venous catheter exposure. Methods: This multi-source retrospective cohort study used harmonised data from MIMIC-IV, the eICU Collaborative Research Database, and a Chinese real-world critical care cohort. Adult patients with documented central venous catheter exposure and an ascertainable post-catheterisation observation window were included. The primary care-exposure burden score summarised cumulative care-process complexity using charting frequency, unique caregivers, concurrent vascular lines, catheter duration, pre-insertion mechanical ventilation duration, antibiotic-type count, and 24-h fluid input and output after catheterisation. Components were log-transformed, robustly standardised within each data source, averaged into a composite score, and categorised into database-specific quartiles. Multivariable logistic regression estimated adjusted odds ratios (aORs) and 95% confidence intervals (CIs). A 48-h landmark-compatible sensitivity analysis recalculated the score after excluding total catheter duration. Results: The final cohort included 12,693 patients: 10,868 from MIMIC-IV, 1,489 from eICU, and 336 from the Chinese cohort. Overall, 910 patients developed adjudicated CLABSI or CLABSI-like bloodstream infection (7.2%). Event rates were 4.7, 19.1, and 33.6%, respectively. Patients with events had higher care-exposure burden scores than those without events [median 0.27 (IQR, 0.00-0.46) vs. -0.02 (IQR, -0.21 to 0.20); Conclusion: Higher EHR-derived care-exposure burden was associated with higher odds of adjudicated CLABSI or CLABSI-like bloodstream infection. The score should be interpreted as a pragmatic patient-level marker of cumulative care complexity and infection-prevention prioritisation, rather than as a causal measure of care quality or nursing performance. Prospective validation is required before clinical implementation.

Indexed as

Catheterization, Central VenousCatheter-Related InfectionsCritical IllnessElectronic Health RecordsAdultAgedChinaCross InfectionFemaleHumansIntensive Care UnitsMaleMiddle AgedRetrospective Studiescare processcentral line-associated bloodstream infectioncritical care nursingelectronic health recordshealthcare-associated infectionretrospective cohort study

Identifiers

PMID42422678
PMCPMC13341585

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.