Development of a quality indicator set to measure and improve quality of ICU care in low- and middle-income countries

PURPOSE: To develop a set of actionable quality indicators for critical care suitable for use in low- or middle-income countries (LMICs). METHODS: A list of 84 candidate indicators compiled from a previous literature review and stakeholder recommendations were categorised into three domains (foundation, process, and quality impact). An expert panel (EP) representing stakeholders from critical care and allied specialties in multiple low-, middle-, and high-income countries was convened. In rounds one and two of the Delphi exercise, the EP appraised (Likert scale 1–5) each indicator for validity, feasibility; in round three sensitivity to change, and reliability were additionally appraised. Potential barriers and facilitators to implementation of the quality indicators were also reported in this round. Median score and interquartile range (IQR) were used to determine consensus; indicators with consensus disagreement (median < 4, IQR ≤ 1) were removed, and indicators with consensus agreement (median ≥ 4, IQR ≤ 1) or no consensus were retained. In round four, indicators were prioritised based on their ability to impact cost of care to the provider and recipient, staff well-being, patient safety, and patient-centred outcomes. RESULTS: Seventy-one experts from 30 countries (n = 45, 63%, representing critical care) selected 57 indicators to assess quality of care in intensive care unit (ICU) in LMICs: 16 foundation, 27 process, and 14 quality impact indicators after round three. Round 4 resulted in 14 prioritised indicators. Fifty-seven respondents reported barriers and facilitators, of which electronic registry-embedded data collection was the biggest perceived facilitator to implementation (n = 54/57, 95%) Concerns over burden of data collection (n = 53/57, 93%) and variations in definition (n = 45/57, 79%) were perceived as the greatest barrier to implementation. CONCLUSION: This consensus exercise provides a common set of indicators to support benchmarking and quality improvement programs for critical care populations in LMICs

Time to Recovery and Its Predictors in Patients with Traumatic Brain Injury Who Underwent Urgent Neurosurgical Intervention at ALERT Trauma Center, Ethiopia

Ermiyas Belay Woldesenbet,1,2 Fitsum Kifle Belachew,2,3 Kebede Embaye Gezae,4 Gebrekiros Gebremichael Meles,4 Fedila Yassin Ali,1 Yared Boru Firissa,5 Victor Meza Kyaruzi6,7 1Department of Public Health, College of Health Science, Wolkite University, Wolkite, Ethiopia; 2Debre Berhan University Asrat Woldyes Health Sciences Campus, Network for Perioperative and Critical Care, Addis Ababa, Ethiopia; 3Division of Global Surgery, University of Cape Town, Cape Town, South Africa; 4Department of Biostatistics, School of Public Health, College of Health Sciences, Mekelle University, Mekelle, Tigray, Ethiopia; 5Emergency Medicine and Critical Care Department, ALERT Hospital, Addis Ababa, Ethiopia; 6Research Department, Winners Foundation, Yaounde, Cameroon; 7Department of Surgery, School of Medicine, Muhimbili University of Health Sciences, Dar Es Salaam, TanzaniaCorrespondence: Ermiyas Belay Woldesenbet, Email [email protected]: This study aimed to assess and compare the in-hospital recovery times between two groups: those exposed to early intervention and those with late intervention in a cohort of Traumatic Brain Injury (TBI) patients requiring urgent neurosurgical intervention in ALERT Trauma Center in Addis Ababa, Ethiopia.Methods: The study was conducted over seven consecutive months, from March 14, 2020, to October 13, 2020. Patients were consecutively recruited from the emergency department until the final sample size was fulfilled. The recovery time between the early and late surgery groups was compared using the Log rank test. The Cox proportional hazard model was used to analyze the event data, with the assumption of proportional hazards being checked. The measure of effect was reported using the adjusted hazard ratio, and a stepwise approach was used to build the final model.Results: A total of 117 TBI patients undergoing urgent neurosurgical intervention were observed and the median survival time for the early surgery group was 4.1 days, and for the late surgery group, it was 6.4 days, with no statistically significant difference (CHR: 0.73; 95% CI; 0.47– 1.11). On the other hand, severe TBI grade emerged as a significant independent predictor, indicating an 86% lower rate of recovery compared to mild TBI cases. Additionally, higher diastolic blood pressure within the range of 50 to 100 was associated with a 24% increased rate of recovery.Conclusion: This study identified factors influencing recovery outcomes and predictors of prolonged recovery, specifically severe TBI grade and lower diastolic blood pressure. The results emphasize the importance of timely intervention and provide specific considerations for optimizing patient outcomes in TBI cases and guiding further research in the area.Keywords: Glasgow outcome score, severe, TBI, neurosurger

Determining the Minimum Dataset for Surgical Patients in Africa: A Delphi Study.

BACKGROUND: It is often difficult for clinicians in African low- and middle-income countries middle-income countries to access useful aggregated data to identify areas for quality improvement. The aim of this Delphi study was to develop a standardised perioperative dataset for use in a registry. METHODS: A Delphi method was followed to achieve consensus on the data points to include in a minimum perioperative dataset. The study consisted of two electronic surveys, followed by an online discussion and a final electronic survey (four Rounds). RESULTS: Forty-one members of the African Perioperative Research Group participated in the process. Forty data points were deemed important and feasible to include in a minimum dataset for electronic capturing during the perioperative workflow by clinicians. A smaller dataset consisting of eight variables to define risk-adjusted perioperative mortality rate was also described. CONCLUSIONS: The minimum perioperative dataset can be used in a collaborative effort to establish a resource accessible to African clinicians in improving quality of care