Back

An audit of antibiotic prescriptions at the Base Hospital of Wathupitiwala in Sri Lanka.

Samaranayake, W. A. M. P.; Jayawardhana, G.; Sampath Perera, U.; Roshan, A.; Vidanagamage, U.; Wijayawardana, M.; N, H.; Rannolu, P.

2022-05-20 infectious diseases
10.1101/2022.05.18.22275287 medRxiv
Show abstract

Antimicrobial resistance is a serious global public threat. A retrospective audit was conducted in December 2021 in seven units to identify the prevalence and antibiotic prescription patterns at Base Hospital Wathupitiwala, Sri Lanka. Data on antibiotic use was extracted from randomly selected 30 bead head tickets (BHTs) from each unit using a standard questionnaire form. A total of 210 patient records were examined, 107 (50.95%) were females, and the mean age was 41.43 years. Ninety-five patients (45.23%) were on antibiotics, and approximately one-third of them (33.68%) were found to be wrong choices. The prevalence of antimicrobial use varied across the ward types, with the highest being in surgical units (86.66%), followed by pediatric units (55.66%). Community-acquired infections were responsible for the majority (97.89%) of prescriptions. Skin and soft tissue infections are the most common reason (23.35%) for starting antibiotics, followed by respiratory tract infections (16.84%). The majority of prescriptions 90 (70.53%) were made empirically, and 16.4% were related to surgical or medical prophylaxis. Amoxicillin-clavulanate and third-generation cephalosporin were the most commonly used antibiotics, while the duplicate anaerobic cover and two beta-lactam therapies were most widely prescribed as combination therapy. The reason for antibiotics was not documented in 11 (11.58%) patients, while 4 (4.21%) patients had received sub-therapeutic doses. Appropriate microbiological cultures were not sent in the vast majority (71.87%) of prescriptions. Implementation of an antimicrobial stewardship program with the help of all stakeholders is crucial to curbing antimicrobial resistance and inappropriate antimicrobial therapy at Base Hospital Wathupitiwala.

Matching journals

The top 7 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.