The algorithmic approach to guidelines has been introduced and promoted on a large scale since the 1970s. This study aims at comparing the performance of three algorithms for the management of chronic cough in patients with HIV infection, and at reassessing the current position of algorithmic guidelines in clinical decision making through an analysis of accuracy, harm and complexity.
Data were collected at the University Hospital of Kigali (CHUK) in a total of 201 HIV-positive hospitalised patients with chronic cough. We simulated management of each patient following the three algorithms. The first was locally tailored by clinicians from CHUK, the second and third were drawn from publications by Médecins sans Frontières (MSF) and the World Health Organisation (WHO). Semantic analysis techniques known as Clinical Algorithm Nosology were used to compare them in terms of complexity and similarity. For each of them, we assessed the sensitivity, delay to diagnosis and hypothetical harm of false positives and false negatives.
The principal diagnoses were tuberculosis (21%) and pneumocystosis (19%). Sensitivity, representing the proportion of correct diagnoses made by each algorithm, was 95.7%, 88% and 70% for CHUK, MSF and WHO, respectively. Mean time to appropriate management was 1.86 days for CHUK and 3.46 for the MSF algorithm. The CHUK algorithm was the most complex, followed by MSF and WHO. Total harm was by far the highest for the WHO algorithm, followed by MSF and CHUK.
This study confirms our hypothesis that sensitivity and patient safety (i.e. less expected harm) are proportional to the complexity of algorithms, though increased complexity may make them difficult to use in practice.
Keywords: HIV, chronic cough, algorithms, clinical decision making, harm, complexity