RESUMEN
Objective: to describe clinical, management and outcome features of critically ill patients admitted to intensive care units (ICUs) and high dependency units (HDUs) in Kenya. Design: prospective registry-based observational study. Setting: three HDUs and eight ICUs in Kenya. Patients: consecutive adult patients admitted between January 2021 and June 2022. Interventions: none. Measurements and main results: data was entered in a cloud based platform using a common data model. Study endpoints included case mix variables, management features and patient centred outcomes. Patients with Coronavirus disease 2019 (COVID-19) were reported separately. Of the 3892/4546 patients without COVID-19, 2445 patients (62.8%) were from HDUs and 1447 (37.2%) from ICUs. Patients had a median age of 53 years (interquartile range [IQR] 38-68), with HDU patients being older but with a lower severity (APACHE II 6 [3-9] in HDUs vs 12 [7-17] in ICUs; p<0.001). One out of four patients were postoperative with 604 (63.4%) receiving emergency surgery. Readmission rate was 4.8%. Hypertension and diabetes were prevalent comorbidities, with a 4.0% HIV/AIDS rate. Invasive mechanical ventilation (IMV) was applied in 3.4% in HDUs vs. 47.6% in ICUs (P<0.001), with a duration of 7 days (IQR 3-21). There was a similar use of renal replacement therapy (4.0% vs. 4.7%; P<0.001). Vasopressor use was infrequent while half of patients received antibiotics. Average length of stay was 2 days (IQR 1-5). Crude HDU mortality rate was 6.5% in HDUs versus 30.5% in the ICUs (P<0.001). Of the 654 COVID-19 admissions, most were admitted in ICUs (72.3%) with a 33.2% mortality. Conclusions: We provide the first multicenter observational cohort study from an African ICU national registry. Distinct management features and outcomes characterise HDU from ICU patients. Study registration: Clinicaltrials.gov (reference number NCT05456217, date of registration 07 Nov 2022).
RESUMEN
OBJECTIVE: To describe the organisation, staffing patterns and resources available in critical care units in Kenya. The secondary objective was to explore variations between units in the public and private sectors. MATERIALS AND METHODS: An online cross-sectional survey was used to collect data on organisational characteristics (model of care, type of unit, quality- related activities, use of electronic medical records and participation in the national ICU registry), staffing and available resources for monitoring, ventilation and general critical care. RESULTS: The survey included 60 of 75 identified units (80% response rate), with 43% (n = 23) located in government facilities. A total of 598 critical care beds were reported with a median of 6 beds (interquartile range [IQR] 5-11) per unit, with 26% beds (n = 157) being non functional. The proportion of ICU beds to total hospital beds was 3.8% (IQR 1.9-10.4). Most of the units (80%, n = 48) were mixed/general units with an open model of care (60%, n = 36). Consultants-in-charge were mainly anesthesiologists (69%, n = 37). The nurse-to-bed ratio was predominantly 1:2 with half of the nurses formally trained in critical care. Most units (83%, n = 47) had a dedicated ventilator for each bed, however 63% (n = 39) lacked high flow nasal therapy. While basic multiparametric monitoring was ubiquitous, invasive blood pressure measurement capacity was low (3% of beds, IQR 0-81%), and capnography moderate (31% of beds, IQR 0-77%). Blood gas analysers were widely available (93%, n = 56), with 80% reported as functional. Differences between the public and private sector were narrow. CONCLUSION: This study shows an established critical care network in Kenya, in terms of staffing density, availability of basic monitoring and ventilation resources. The public and private sector are equally represented albeit with modest differences. Potential areas for improvement include training, use of invasive blood pressure and functionality of blood gas analysers.
