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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’.
To explore and analyse the adaptation process of patients and their families at the point of lung cancer diagnosis.
Totally 23 operable lung cancer patients were included in this study. Colaizzi's method of phenomenology was employed for data analysis.
This study found two different aspects of family adaptation at the diagnosis of lung cancer. For family resilience, three themes emerged: (1) Positive family belief systems (giving meaning to a cancer diagnosis and maintaining a positive/optimistic attitude), (2) Flexible family organizational patterns (maintaining the stability of family structure and function, adjusting the relationship between patients and family members and receiving external support and help) and (3) Good communication and problem-solving strategies (open communication on an equal basis, positive and open expression of emotions and collaborative problem-solving). For family vulnerability, three themes were as follows: (1) Negative family belief systems (negative attitudes and concealment and self-isolation due to stigma), (2) Rigid family organizational patterns (adaptation lost, conflicts between family support and patients' willingness and pressure upon social support) and (3) Unhealthy communication and problem-solving (poor communication, emotional asymmetry of family members and tendency to solve problems alone).
The study highlights the existence of the family resilience and family vulnerability at the point of lung cancer diagnosis and provides patient's perspective for understanding family resilience in specific cultural contexts.
The data were collected through face-to-face interviews.
Trail registration number: ChiCTR2300074801.
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