Identifying Candida and other yeast-like fungi: utility of an identification algorithm in resource limited setting.

AIM The increasing recovery rates of unusual yeasts with innate drug resistance make accurate identification crucial for successful therapy and infection control measures. The current study was undertaken to study the utility of CHROMagar Candida (CC) and evaluate an identification algorithm, using germ tube test (GT), CC and a commercial identification kit, API ID 32C. SETTINGS AND DESIGN The prospective study was carried out at a private laboratory in Mumbai, India. MATERIALS AND METHODS Identification of 533 yeast and yeast like isolates was carried out using an identification algorithm, comprising of the GT, CC and API tests. RESULTS CC was useful to detect mixed cultures. We were able to identify 393/533, i.e. 73.7 % of isolates using GT and CC Tests only. This was because C. albicans and C. tropicalis, which can be reliably identified using CC, constituted 75.2 % of the isolates. We were unable to identify 140 isolates, i.e. 26.3 %, using GT and CC tests only and performed additional testing using API ID 32C. CC was not found to be reliable in identifying C. krusei. CONCLUSION The diverse identification profile obtained in our study substantiates the need for all diagnostic microbiology laboratories to be better prepared for identifying unusual yeasts. Though GT or CC testing cannot alone suffice for identification of all clinically encountered Candida and yeast-like fungi, use of GT, CC and automated identification systems in a step-wise algorithm can enable the same in a more cost effective manner.