ArticleHealth science reports2023
Prescription patterns in an intensive care unit of COVID-19 patients in Bangladesh: A cross-sectional study.
Article in Health science reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Assessment of Level of Awareness for Management and Treatment of Diabetes Mellitus Among Patients in Bangladesh: A Cross-Sectional Study.Health science reports · 2026Article
- Antithrombotic Use Patterns in COVID-19 Patients from Spain: A Real-World Data Study.Journal of clinical medicine · 2024Article
- Prescription patterns in an intensive care unit of COVID-19 patients in Bangladesh: A cross-sectional study.Health science reports · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background and Aims: To reduce death rates for critical patients hospitalized in intensive care units (ICUs), coronavirus (COVID-19) lacks proven and efficient treatment methods. This cross-sectional study aims to evaluate how physicians treat severe and suspected COVID-19 patients in the ICU department in the absence of an established approach, as well as assess the rational use of the medication in the ICU department. Methods: Between June 16, 2021, and December 10, 2022, a total of 428 prescriptions were randomly gathered, including both suspected (yellow zone) and confirmed (red zone) COVID-19 patients. For data management, Microsoft Excel 2021 was utilized, while STATA 17 provided statistical analysis. To find associations between patients' admission status and demographic details, exploratory and bivariate analyses were conducted. Results: Of the 428 patients admitted to the ICU, 228 (53.27%) were in the yellow zone and 200 (46.73%) were in the verified COVID-19 red zone. The majority of patients were male (54.44%), and the age range from 41 to 60 was the most common (41.82%). No significant deviation was detected to the yellow and red groups' prescription patterns. A total of 4001 medicines (mean 9.35/patient) were prescribed. Antiulcerants, antibiotics, respiratory, analgesics, anticoagulants, vitamins and minerals, steroids, cardiovascular, antidiabetic drugs, antivirals, antihistamines, muscle relaxants, and antifungal treatments were widely prescribed drugs. Enoxaparin (67.06%) appeared as the most prescribed medicine, followed by montelukast (60.51%), paracetamol (58.41%), and dexamethasone (51.64%). Conclusion: The prescription patterns for the yellow and red groups were comparable and mostly included symptomatic treatment. Respiratory drugs constituted the most frequent therapeutic class. Polypharmacy should be taken under considerations. In ICU settings, the outcomes emphasize the need of correct diagnosis, cautious antibiotic usage, suitable therapy, and attentive monitoring.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.