Conectar
To investigate the prevalence of rapid response team delays, survival distribution of admission to rapid response team delay and its prognostic factors.
A retrospective single-centre study.
Data on rapid response team activations from 1 January 2018 to 31 December 2022 were retrieved from electronic medical records at a tertiary hospital in Hangzhou, China. All patients who met the eligibility criteria were included. Multivariable Cox regression analysis was conducted to analyse the data.
Out of 636 patients included, 18.4% (117) experienced a delay, with a median (interquartile range) of 8.5 (12) days from admission to rapid response team activation. Six significant prognostic factors were found to be associated with the higher hazard ratio of rapid response team delay, including call time (05:01 PM and 7:59 AM), emergency admission, a higher Modified Early Warning Score, an admission diagnosis of infection, a comorbidity of respiratory failure/Acute Respiratory Distress Syndrome and the absence of lung infection.
The prevalence of rapid response team delays was lower, and the days from admission to rapid response team delay was longer than in previous studies. Healthcare providers are suggested to prioritise the care of high-risk patient groups and provide proactive monitoring to ensure timely identification and management.
Implementing artificial intelligence in continuous monitoring systems for high-risk patients is recommended. The findings help nurses anticipate potential delays in rapid response team activation, enabling better preparedness.
The study highlights the prevalence of rapid response team delays, timing from admission to rapid response team activation and six prognostic factors influencing delays. It could shape patient care and inform future research. Hospital administrators should review staffing, especially during night shifts, to minimise delays. Further qualitative research is needed to explore why nurses may delay rapid response team activation.
The STROBE checklist was adhered to when reporting this study.
‘No patient or public contribution’.
Individuals with systemic lupus erythematosus (SLE) often suffer from sleep disturbance, which exhibits heterogeneity. Whether it could be grouped into different clusters remains unknown, posing challenges to the development of personalised interventions to address sleep disturbance.
To examine clusters of sleep disturbance and associated factors in people with SLE.
Cross-sectional design.
From November 2023 to January 2024, people diagnosed with SLE were recruited by a convenience sampling approach. Data were collected via an online platform Wenjuanxing. Sleep disturbance was evaluated by the Pittsburgh Sleep Quality Index (PSQI). Other information, such as disease activity, pain, fatigue, depression and anxiety was also collected using validated instruments. Latent profile analysis was performed to reveal the distinct clusters of sleep disturbance. Multiple logistic regression analysis was performed to investigate factors associated with the clusters.
A total of 538 participants were included, with a response rate of 85.1% (538/632). Only those with sleep disturbance (PSQI > 5) were included in the final analyses. Participant mean age was 32.9 (SD = 8.4) years and 402 (92.6%) were females. All had sleep disturbance (PSQI > 5) and their mean PSQI was 8.8 (SD = 2.9). Three distinct clusters were identified: mild sleep disturbance with poor sleep quality, adequate sleep duration and good daytime functioning (50.7%), mild sleep disturbance with poor sleep quality, adequate sleep duration and poor daytime functioning (30.9%) and moderate sleep disturbance with poor sleep quality, inadequate sleep duration and impaired daytime functioning (18.4%). There are both overlaps and unique aspects in terms of factors associated with each cluster of sleep disturbance, including age, body mass index, cardiovascular system damage, musculoskeletal system damage, depression and anxiety.
Sleep disturbance in patients with SLE showed three distinct clusters, with each cluster having slightly different predisposing factors.
In clinical practice, nurses are recommended to prioritise assessment and interventions for those at-risk subgroups. They could also use the above information to develop and provide personalised interventions to address the unique needs of each cluster of sleep disturbance.
Checklist for reporting of survey studies.
No patient or public contribution.
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