Antiviral drugs were rapidly implemented into clinical practice for the treatment of high-risk patients with COVID-19, prompting the development of statewide guidelines. This South-Australian study reviewed guideline adherence, assessed prescribing patterns and highlighted the inappropriate management of relative drug-drug interactions and dosing for renal function. Additionally, it evaluated the impact of inappropriate antiviral drug use and suggested methods to improve quality use of medicines.

The aim of this umbrella review was to identify tools and guidelines to aid the deprescribing process of potentially inappropriate medications (PIMs), evaluate development and validation methods, and describe evidence levels for medication inclusion.
Searches were conducted on MEDLINE (Ovid), Embase.com, Cochrane CDSR, CINAHL (EBSCO), Web of Science Core Collection and guideline databases from the date of inception to 7 July 2022. Following the initial search, an additional search was conducted to identify an updated versions of tools on 17 July 2023. We analysed the contents of tools and guidelines.
From 23 systematic reviews and guidelines, we identified 95 tools (72 explicit, 12 mixed and 11 implicit) and nine guidelines. Most tools (83.2%) were developed to use for older persons, including 14 for those with limited life expectancy. Seven tools were for children <18 years (7.37%). Most explicit/mixed tools (78.57%) and all guidelines were validated. We found 484 PIMs and 202 medications with different appropriateness independent of disease for older persons with normal and limited life expectancy, respectively. Only two tools and eight guidelines reported the evidence level, and a quarter of medications had high-quality evidence.
Tools are available for a diversity of populations. There were discrepancies, with the same medication being classified as inappropriate in some tools and appropriate in others, possibly due to low-quality evidence. In particular, tools for patients with limited life expectancy were developed based on very limited evidence, and research to generate this evidence is urgently needed. Our medication lists, along with the level of evidence, could facilitate efforts to strengthen the evidence.

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UNASSIGNED: Patients with heart failure and reduced ejection fraction (HFrEF) are consistently underprescribed guideline-directed medications. Although many barriers to prescribing are known, identification of these barriers has relied on traditional a priori hypotheses or qualitative methods. Machine learning can overcome many limitations of traditional methods to capture complex relationships in data and lead to a more comprehensive understanding of the underpinnings driving underprescribing. Here, we used machine learning methods and routinely available electronic health record data to identify predictors of prescribing.
UNASSIGNED: We evaluated the predictive performance of machine learning algorithms to predict prescription of four types of medications for adults with HFrEF: angiotensin converting enzyme inhibitor/angiotensin receptor blocker (ACE/ARB), angiotensin receptor-neprilysin inhibitor (ARNI), evidence-based beta blocker (BB), or mineralocorticoid receptor antagonist (MRA). The models with the best predictive performance were used to identify the top 20 characteristics associated with prescribing each medication type. Shapley values were used to provide insight into the importance and direction of the predictor relationships with medication prescribing.
UNASSIGNED: For 3,832 patients meeting the inclusion criteria, 70% were prescribed an ACE/ARB, 8% an ARNI, 75% a BB, and 40% an MRA. The best-predicting model for each medication type was a random forest (area under the curve: 0.788-0.821; Brier score: 0.063-0.185). Across all medications, top predictors of prescribing included prescription of other evidence-based medications and younger age. Unique to prescribing an ARNI, the top predictors included lack of diagnoses of chronic kidney disease, chronic obstructive pulmonary disease, or hypotension, as well as being in a relationship, nontobacco use, and alcohol use.
UNASSIGNED: We identified multiple predictors of prescribing for HFrEF medications that are being used to strategically design interventions to address barriers to prescribing and to inform further investigations. The machine learning approach used in this study to identify predictors of suboptimal prescribing can also be used by other health systems to identify and address locally relevant gaps and solutions to prescribing.

BACKGROUND: Antibiotic resistance is a global health crisis. Ensuring responsible, appropriate use (stewardship) is an important for keeping antibiotics working as long as possible. Around 10% of antibiotics across health care are prescribed by oral health care professionals, with high rates of unnecessary use. To maximise the value from research to optimise antibiotic use in dentistry, this study developed international consensus on a core outcome set for dental antibiotic stewardship.
METHODS: Candidate outcomes were sourced from a literature review. International participants were recruited via professional bodies, patient organisations, and social media, with at least 30 dentists, academics, and patient contributors in total. Outcomes scored \"critical for inclusion\" by >70% of the participants (dentists, academics, and patients) after 2 Delphi rounds were included in the core outcome set following a final consensus meeting. The study protocol was registered with the COMET Initiative and published in BMC Trials.
RESULTS: A total of 33 participants from 15 countries, including 8 low- and middle-income countries, completed both rounds of the Delphi study. Antibiotic use outcomes (eg, appropriateness of prescribing), adverse or poor outcomes (eg, complications from disease progression), and a patient-reported outcome were included in the final, agreed core set. Outcomes relating to quality, time, and cost were not included.
CONCLUSIONS: This core outcome set for dental antibiotic stewardship represents the minimum which future studies of antibiotic stewardship in dentistry should report. By supporting researchers to design and report their studies in a way meaningful to multiple stakeholders and enabling international comparisons, the oral health profession\'s contribution to global efforts to tackle antibiotic resistance can be further improved.

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Pressurised metered-dose inhalers (MDIs) have a much higher carbon footprint than dry powder inhalers (DPIs). We aimed to describe variations of inhaler options in local adult asthma prescribing guidance.
We reviewed local clinical commissioning group (CCG) adult asthma prescribing guidance for primary care in England in 2019 and recorded DPI and MDI inclusion. The relationship to prescribing data from OpenPrescribing.net was examined.
In total, 58 unique guidance documents were analysed covering 144 out of 191 CCGs in England. Only 3% of CCG guidelines expressed an overall preference for DPIs, while 12% explicitly preferred MDIs. The inclusion of DPIs first-line was 77% for short-acting β-agonists, 78% for low-dose inhaled corticosteroid (ICS) inhalers and 90-96% for combination long-acting β-agonist/ICS inhalers. MDIs were included first-line in 98-100% of these classes. In 26% of CCGs, there was no first-line DPI option for at least 1 asthma management step. Ten percent of CCGs had no DPI included first-line for any of the 5 classes examined. Many CCGs recommended higher carbon footprint options; Ventolin MDI (25.6%), inhalers containing HFA227ea (57.9%) and ICS regimes recommending 2 puffs of a lower dose over 1 puff of higher dose (94.2%). MDIs were prescribed more in CCGs that recommended them.
Before the COVID pandemic, there was substantial variation between CCGs in adult asthma prescribing guidance regarding higher and lower carbon footprint options. There may still be scope to amend local guidance to improve clinical and environmental outcomes. This study provides a method and baseline for further investigation of this.