Pulse oximetry adoption and oxygen orders at paediatric admission over 7 years in Kenya: a multihospital retrospective cohort study

Abstract

ObjectivesTo characterise adoption and explore specific clinical and patient factors that might influence pulse oximetry and oxygen use in low-income and middle-income countries (LMICs) over time; to highlight useful considerations for entities working on programmes to improve access to pulse oximetry and oxygen.DesignA multihospital retrospective cohort study.SettingsAll admissions (n=132 737) to paediatric wards of 18 purposely selected public hospitals in Kenya that joined a Clinical Information Network (CIN) between March 2014 and December 2020.OutcomesPulse oximetry use and oxygen prescription on admission; we performed growth-curve modelling to investigate the association of patient factors with study outcomes over time while adjusting for hospital factors.ResultsOverall, pulse oximetry was used in 48.8% (64 722/132 737) of all admission cases. Use rose on average with each month of participation in the CIN (OR: 1.11, 95% CI 1.05 to 1.18) but patterns of adoption were highly variable across hospitals suggesting important factors at hospital level influence use of pulse oximetry. Of those with pulse oximetry measurement, 7% (4510/64 722) had hypoxaemia (SpO2 <90%). Across the same period, 8.6% (11 428/132 737) had oxygen prescribed but in 87%, pulse oximetry was either not done or the hypoxaemia threshold (SpO2 <90%) was not met. Lower chest-wall indrawing and other respiratory symptoms were associated with pulse oximetry use at admission and were also associated with oxygen prescription in the absence of pulse oximetry or hypoxaemia.ConclusionThe adoption of pulse oximetry recommended in international guidelines for assessing children with severe illness has been slow and erratic, reflecting system and organisational weaknesses. Most oxygen orders at admission seem driven by clinical and situational factors other than the presence of hypoxaemia. Programmes aiming to implement pulse oximetry and oxygen systems will likely need a long-term vision to promote adoption, guideline development and adherence and continuously examine impact.

Improving the quality of in-patient neonatal routine data as a pre-requisite for monitoring and improving quality of care at scale: A multi-site retrospective cohort study in Kenyan hospitals

“…Time elapsed was captured as months since the hospitals joined the CIN-N and was treated as a continuous fixed effect. From previous studies within the CIN, the effect of time on adherence to recommended clinical practice was found to vary across hospitals [28]. We used a likelihood ratio test (LRT) to determine the most suitable random effects model (hospital random intercepts vs hospital random intercepts with random slopes for time).…”

Oxygen as an Essential Medicine

“…For example, a retrospective study of pulse oximeter use in pediatric patients at 18 Kenyan hospitals found that 8.6% of patients had oxygen prescribed, but 87% of patients either did not have pulse oximetry performed or did not have oxygen saturations lower than 90%. 55 In situations where oxygen supply is limited, liberal and nontitrated dosing of supplemental oxygen may lead to or exacerbate supply shortages. In a study of patients in the adult emergency department of a Rwandan teaching hospital, Sutherland and colleagues found that 12% of patients were hypoxemic, but on over 80% of days, patients were either over or undertreated with oxygen to achieve a target oxygen saturation of 90% to 95%.…”

Improving in-patient neonatal data quality as a pre-requisite for monitoring and improving quality of care at scale: A multisite retrospective cohort study in Kenya

“…Time elapsed was captured as months since the hospitals joined the CIN-N and was treated as a continuous fixed effect. From previous studies within the CIN, the effect of time on adherence to recommended clinical practice was found to vary across hospitals [28]. Likelihood ratio tests (LRT) were used to determine the most suitable random effects model (hospital random intercepts vs hospital random intercepts with random slopes for time).…”