Detecting Preanalytical Errors Using Quality Indicators in a Hematology Laboratory.

BACKGROUND AND OBJECTIVES: Monitoring laboratory performance continuously is crucial for recognizing errors and fostering further improvements in laboratory medicine. This study aimed to review the quality indicators (QIs) and describe the laboratory errors in the preanalytical phase of hematology testing in a clinical laboratory. METHODS: All samples received in the Hematology Laboratory of the Maternity and Pediatric Hospital in Hail for 3 years were retrospectively reviewed and evaluated for preanalytical issues using a set of QIs. The rate of each QI was compared to the quality specifications cited in the literature. RESULTS: A total of 95002 blood samples were collected for analysis in the hematology laboratory from January 2017 through December 2019. Overall, 8852 (9.3%) were considered to show preanalytical errors. The most common were "clotted specimen" (3.6%) and "samples not received" (3.5%). Based on the quality specifications, the preanalytical QIs were classified generally as low and medium level of performance. In contrast, the sigma-based performance level indicates acceptable performance on all the key processes. Further analysis of the study showed a decreasing rate of preanalytical errors from 11.6% to 6.5%. CONCLUSIONS: Preanalytical errors remain a challenge to hematology laboratories. The errors in this case were predominantly related to specimen collection procedures that compromised the specimen quality. Quality indicators are a valuable instrument in the preanalytical phase that allows an opportunity to improve and explore clinical laboratory process performance and progress. Continual monitoring and management of QI data are critical to ensure ongoing satisfactory performance and to enhance the quality in the preanalytical phase.

Antimicrobial Surveillance for Bacterial Uropathogens in Ha'il, Saudi Arabia: A Five-Year Multicenter Retrospective Study.

OBJECTIVE: Urinary tract infections (UTIs) are among the most common infections that require antibiotic intervention. Antibiotic surveillance programs are crucial to assess resistance patterns of microorganisms associated with UTIs and to tailor antibiotic therapy accordingly. Therefore, the aim of the current study is to investigate the prevalence of uropathogens and their antimicrobial susceptibility patterns in Ha'il region. METHODS: We conducted a retrospective study in two main hospitals in Ha'il over a 5-year period (January 2015 to December 2019). Laboratory reports and clinical data of patients with a positive urine culture (≥105 CFU/mL) were included in the study. RESULTS: A total of 428 patients were included in this study. The majority of positive cultures belonged to female patients (94.4%), adults (76.9%) and outpatients (74.3%). Generally, Escherichia coli was the most common pathogen (45%), followed by Klebsiella pneumoniae (17%) and Staphylococcus aureus (12%). Extended-spectrum ß-lactamase (ESBL) strains of E. coli (15.7%) and K. pneumoniae (19.7%) showed resistance to most tested antibiotics, while almost all Gram-negative uropathogens including ESBL strains presented low resistance to cefoxitin, imipenem and meropenem. Most strains of Staphylococcus aureus and Enterococcus faecalis were resistant to trimethoprim/sulfamethoxazole including methicillin-resistant Staphylococcus aureus (MRSA) and vancomycin-resistant Enterococcus faecalis (VRE), whereas the activity of linezolid was monitored against almost all different strains of Gram-positive uropathogens. CONCLUSION: This surveillance study which was conducted over a 5-year period in the Ha'il region revealed that the most common UTI-associated pathogen was E. coli. Based on the current sensitivity profiles obtained from this surveillance, carbapenems and linezolid can be considered as a first therapeutic choice treating UTIs in Ha'il caused by Gram-negative and positive uropathogens, respectively. Frequent targeted surveillance programs for antibiotic-resistant pathogens and their susceptibility profiles are crucial to enable tailored empirical treatment for patients.