Nurse-led intensive care follow-up interventions in low- and middle-income countries: A scoping review.

INTRODUCTION

Post-intensive care syndrome affects more than half of intensive care unit (ICU) survivors, resulting in long-term physical, psychological, and cognitive impairments. Structured post-ICU recovery programs, including nurse-led follow-up, have improved patient outcomes in high-income countries. However, little is known about such models in low- and middle-income countries (LMICs), where post-ICU care is often lacking.

OBJECTIVE

The aim of this scoping review was to identify and map the existing nurse-led follow-up care interventions for ICU survivors in LMICs to inform future development and implementation.

METHODS

This scoping review followed the Arksey and O'Malley framework and the Joanna Briggs Institute methodology. Relevant articles were searched through five electronic databases. Data were extracted and synthesised using the Patterns, Advances, Gaps, Evidence for practice and Research recommendations (PAGER) framework.

RESULTS

Seven studies met the inclusion criteria. Most interventions were delivered during the ICU-to-ward transition and were led by ICU liaison nurses. One study extended follow-up beyond hospital discharge using a smartphone-supported nurse-led model for postoperative cardiac survivors. Three main identified patterns were the structure and delivery models of nurse-led follow-up care, nurses' roles and competencies, and outcomes and implications. Across studies, nurse-led follow-up care played primarily as a multitarget transition program supporting patients, families, and ward nurses. Outcomes were mostly short term and transition focused.

CONCLUSIONS

Nurse-led in-hospital follow-up care in LMICs appears feasible and potentially beneficial but remains underdeveloped and largely focused on ICU-to-ward transition. Future studies should focus on developing a structured intervention, defining roles and competencies with thorough outcome measures, validating competency frameworks, and expanding the program for long-term care. Incorporating digital or predictive tools could improve care delivery and support clinical decision-making systems, but evidence for their use in LMIC settings remains limited.

REGISTRATION: This study was registered to Open Science Framework Registries https://osf.io/ps6j3.