Evidence map›Paper›PMID 42682581›Full record

ArticleFrontiers in public health2026

Safety incidents during intrahospital transport of hemodialysis patients in China: a multicenter cross-sectional study.

Jing Li, Yao Liu, Liyun Cao, Meng Zhao, Huipeng Xing, Yinbilige Se, Yuqing Chen

Abstract readMulticenter Study
In one paragraph

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

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

7 authors.

Jing Li *Renal Division, Department of Medicine, Peking University First Hospital, Beijing, China.
Yao Liu *Renal Division, Department of Medicine, Peking University First Hospital, Beijing, China.
Liyun CaoRenal Division, Department of Medicine, Peking University First Hospital, Beijing, China.
Meng ZhaoRenal Division, Department of Medicine, Peking University First Hospital, Beijing, China.
Huipeng XingRenal Division, Department of Medicine, Peking University First Hospital, Beijing, China.
Yinbilige SeRenal Division, Department of Medicine, Peking University First Hospital, Beijing, China.
Yuqing ChenRenal Division, Department of Medicine, Peking University First Hospital, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: To characterize safety incidents during intrahospital transport of hemodialysis patients in Chinese tertiary hospitals to guide evidence-based safety standards. Methods: A multicenter cross-sectional study (STROBE-guided) included 4,253 registered nurses consisting of 418 head nurses and 3,835 clinical nurses. While the WHO's International Classification for Patient Safety served as the basis for the classification of safety incidents, the structured questionnaires were created based on clinical observation and a review of the literature. Binary logistic regression identified organizational and operational risk factors. Results: Over 12 months, 268 safety incidents were reported from 135,459 intrahospital transports. Hemodialysis facilities accounted for 81.72% reports. Survey report from 3,232 clinical nurses showed that the predominant incident categories were medical device/equipment malfunctions (61.14%), patient accidents (45.24%), blood/blood product-related events (43.94%), oxygen/gas/vapor issues (42.39%), and clinical administration errors (41.83%). Key independent risk factor was a monthly hemodialysis caseload exceeding 3,000 procedures (OR = 2.580; 95% CI: 1.198-5.556; Conclusion: High-volume dialysis units are at significantly elevated risk for transport safety incidents. Units implementing quality control measures reported more safety incidents, which likely reflects the role of a proactive reporting culture in enhancing the detection of hidden incidents. These findings support targeted resource allocation and policy standardization for safer intrahospital transport in hemodialysis settings.

Indexed as

Medical ErrorsPatient SafetyPatient TransferRenal DialysisTransportation of PatientsChinaCross-Sectional StudiesFemaleHumansRisk FactorsSurveys and QuestionnairesTertiary Care Centershemodialysisintrahospital transportmedical qualitypatient safety incidentrenal replacement therapytransition of care

Identifiers

PMID42682581
PMCPMC13529773

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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.