Renin-angiotensin-system blockers and IgG antibodies in end-stage renal disease hemodialysis-receiving patients diagnosed with COVID-19 infection.

OBJECTIVES: To evaluate the prevalence of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infections among patients receiving in-center hemodialysis (ICHD), the relationship between the IgG antibody levels against the virus and SARS-CoV-2-associated symptoms, hemodialysis adequacy, and the antihypertensives used in order to control blood pressure. METHODS: A prospective observational study was carried out at a tertiary care center, King Fahad Kidney Center, Riyadh, Kingdom of Saudi Arabia, between November 2020 and January 2021. A total of 214 ICHD patients with end-stage renal disease (ESRD) were included, and the levels of their anti-SARS-CoV-2 IgG antibodies were assessed after obtaining their informed consent. RESULTS: Our tests indicated that 15% of the patients in the study's population had detectable SARS-CoV-2 IgG antibodies, with more than half of them (53%) being asymptomatic. We also found that ESRD patients on angiotensin converting enzyme inhibitors or angiotensin receptor blockers (ACEIs/ARBs) had higher levels of SARS-CoV-2 IgG antibodies than patients not receiving this group of medications. CONCLUSION: More studies are required to assess whether patients with a SARS-CoV-2 infection that do not have an indication for being prescribed ACEIs/ARBs would benefit from receiving these medications.

Prescribing errors among adult patients in a large tertiary care system in Saudi Arabia.

BACKGROUND: Multiple studies have investigated medication errors in hospitals in Saudi Arabia; however, prevalence data on prescribing errors and associated factors remains uncertain. OBJECTIVE: Assess the prevalence, type, severity, and factors associated with prescribing errors. DESIGN: Retrospective database review. SETTING: Large tertiary care setting in Riyadh. PATIENTS AND METHODS: We described and analyzed data related to prescribing errors in adults (>14 years of age) from the Medication Error Electronic Report Forms database for the two-year period from January 2017 to December 2018. MAIN OUTCOME MEASURE: The prevalence of prescribing errors and associated factors among adult patients. SAMPLE SIZE: 315 166 prescriptions screened. RESULTS: Of the total number of inpatient and outpatient prescriptions screened, 4934 prescribing errors were identified for a prevalence of 1.56%. The most prevalent types of prescribing errors were improper dose (n=1516; 30.7%) and frequency (n=987; 20.0%). Two-thirds of prescribing errors did not cause any harm to patients. Most prescribing errors were made by medical residents (n=2577; 52%) followed by specialists (n=1629; 33%). Prescribing errors were associated with a lack of documenting clinical information (adjusted odds ratio: 14.1; 95% CI 7.7-16.8, P<.001) and prescribing anti-infective medications (adjusted odds ratio 2.9; 95% CI 1.3-5.7, P<.01). CONCLUSION: Inadequate documentation in electronic health records and prescribing of anti-infective medications were the most common factors for predicting prescribing errors. Future studies should focus on testing innovative measures to control these factors and their impact on minimizing prescribing errors. LIMITATIONS: Polypharmacy was not considered; the data are from a single healthcare system. CONFLICT OF INTEREST: None.

Use of Early-Onset Sepsis Risk Calculator for Neonates ≥ 34 Weeks in a Large Tertiary Neonatal Centre, Saudi Arabia.

Early-onset sepsis (EOS) refers to sepsis with onset before 72 hours of life. Kaiser Permanente Calculator (KPC) or EOS risk calculator is an advanced multivariate risk model for predicting EOS in infants. Objective To examine the EOS risk calculator effect for predicting neonatal EOS, the necessity for laboratory tests, antibiotic usage, and length of hospital stay among the term and late-preterm newborns. Method In this cross-sectional study, we evaluated 44 cases of neonates ≥34 weeks of gestation started on empiric antibiotics within 72 hours after birth due to suspected EOS at the neonatal intensive care unit (NICU). The study site is a 1,500-bed teaching hospital, with around 4,500 annual deliveries, 70 beds in the level II and level III tertiary care NICU. We calculated the risk of the incidence of EOS as one per 1000 live births. Then we retrospectively calculated the probability of neonatal early-onset infection at birth based on the EOS risk calculator and assigned each neonate to one of the recommended categories of the calculator. The primary outcome was to evaluate the infection risk calculator's effect for predicting neonatal EOS and antibiotic usage among the term and late-preterm newborns ≥34 weeks of gestation. Results In our data, EOS calculator showed unnecessary antibiotic usage for 12 (27.3%) neonates [relative risk reduction (RRR) 27.2%; 95% confidence interval (CI) 20.3% - 35.7%)]. EOS risk calculator implementation may decrease in the number of NICU admission (RRR 20.4%; 95% CI 14.3% - 28%), laboratory tests (RRR 20.4%; 95% CI 14.3% - 28%), and length of stay (RRR 25%; 95% CI 38% - 95%). Conclusion EOS calculator could be considered a strategic and objective implementation for managing EOS that can limit unnecessary laboratory tests, reduce antibiotic usage, and length of stay related to EOS. Our findings ensure a multicenter, randomized study evaluating the safety and general use of the calculator for EOS sepsis in Saudi Arabia's clinical practice.

Prevalence of Preterm Birth Rate During COVID-19 Lockdown in a Tertiary Care Hospital, Riyadh.

Background On March 3, 2020, the first case of coronavirus disease (COVID-19) was reported by the Ministry of Health, Kingdom of Saudi Arabia. Within days, the government confirmed more cases and adopted lockdown measures with travel restrictions from March to June 2020. A distinctive coronavirus was isolated from 190,823 patients by June 30. The pandemic resulted in a significant risk to public health. The study aimed to evaluate the impact of COVID-19 lockdown on the rate of premature births. Method In this cross-sectional study, we observed premature births at the Neonatal Intensive Care Unit (NICU). The study site is a 1,500-bed teaching hospital, with around 4,500 annual deliveries, 70 beds in level II and level III, and tertiary care NICU. We compared the birth rates among preterm infants between March 1 to June 30, 2017-2019, to the similar calendar months of 2020. Information on nationality, gestational age, and maternal conditions were collected from the medical records. We used the Poisson regression model to assess the preterm birth rate's temporal trends before lockdown versus during lockdown. Results Among 7,226 total live neonates, we recorded 1,320 preterm infants during the study period of 2017-2020. The preterm birth rate per 1,000 live births during lockdown showed a 23% drop in the overall preterm birth rate with Prevented Fraction of 36% in extremely preterm (<28 weeks gestational age) births and 26% in moderate/late premature (32 weeks to 36 weeks + 6 days gestational age) births. The estimated preterm birth rate among the Saudi expats (15.11/1,000 live births) showed an increased tendency compared to Saudi nationals (odds ratio [OR]=1.07; 95% CI: 0.75-1.52) and was statistically not significant during the strict lockdown. Conclusion There was a significant reduction in the birth rate of extremely preterm and moderate/late preterm infants during lockdown when compared to the preceding three years. A national dataset is required to evaluate the extent of lockdown's impact on the preterm birth rate.