Read the FDA’s recommendations related to Accolade pacemaker devices by Boston Scientific and a potential need for early device replacement.

Hamilton Medical is recalling certain coaxial breathing sets due to a defect that may cause cracks in the inner tube, risking patient rebreathing exhaled gases.

Abbott Medical is removing power cords associated with the HeartMate Mobile Power Unit due to a potential risk of the cord not locking into place.

A blocked manometer port on the Ambu SPUR II resuscitator may delay ventilation or cause barotrauma

Costa Mesa, California August 28, 2025 - Green Lumber Holding, LLC (“Green Lumber”) is alerting consumers that counterfeit products misrepresented as genuine Green Lumber have been identified and may pose health risks.

DermaRite Industries, LLC is expanding the voluntary recall initiated on July 16, due to potential microbial contamination identified as Burkholderia cepacia complex. Out of an abundance of caution the voluntary recall has expanded to include additional products and lots, which can be found in the t

Hamilton is repairing certain HAMILTON-C6 ventilators to replace a defective circuit board that can interrupt ventilation, risking serious injury or death.

Extended Tip Applicator recall due to potential non-sterile product and high endotoxin levels. May cause inflammation or infection if used.

ICU Medical is correcting the Plum Duo infusion system due to pump software issues that may cause the device to become unresponsive.

08/27/2025– East Brunswick, NJ, Unichem Pharmaceuticals (USA), Inc. is voluntarily recalling one (1) lot of Cyclobenzaprine Hydrochloride Tablets USP 10 mg, to the consumer level. The Cyclobenzaprine 10mg (90ct) label was inadvertently placed on a bottle containing Meloxicam

The FDA is reminding the public about the safe use of hyperbaric oxygen therapy devices and is providing recommendations to help reduce potential risks.

On August 22, 2025,the US FDA’s Center for Biologics Evaluation and Research has suspended the biologics license for Valneva Austria GmbH’s Ixchiq (Chikungunya Vaccine, Live). This vaccine was initially approved by FDA under the accelerated approval pathway in November of 2023 for the prevention of

Boston Scientific Removes Carotid WALLSTENT Monorail Endoprosthesis due to risk of resistance during device withdrawal

For Immediate Release - BETHLEHEM, PA – August 19, 2025 – B. Braun Medical Inc. (B. Braun) is voluntarily recalling two lots of Lactated Ringers Injection USP 1000 mL,and 0.9% Sodium Chloride Injection USP 1000 mL to the hospital level due to the presence of particulate matter inside the container.

Prusaczyk B, Burke RE. It’s time for the field of geriatrics to invest in implementation science. BMJ Qual Saf 2023;32:700-703.

In this article, the affiliation ‘Central and North West London NHS foundation Trust’ has been added to Simon Conroy and the funding statement has been updated to acknowledge the funding from the NHS Elect and Central and North West London NHS foundation Trust.

doi: 10.1136/bmjqs-2023-016263corr1

Tackling overuse in healthcare is now more necessary than ever. Movements such as Choosing Wisely and Preventing Overdiagnosis have highlighted that some healthcare services offer no added value and may even cause harm to patients. Estimates of overdiagnosis and overtreatment vary widely between services, providers and regions.1 2 Overuse is a persistent challenge in high-income countries and is increasingly recognised in low-income settings.3 Action is needed to prevent patient harm, reduce resource waste and preserve the limited time of healthcare professionals. In addition, since healthcare services have a significant environmental impact, minimising overuse can contribute to achieving climate goals.

De-implementation science

To accelerate the reduction of overuse, robust de-implementation science is essential.4 This field studies the drivers, strategies and processes involved in reducing or eliminating ineffective, unnecessary or harmful healthcare practices, and in replacing them with evidence-based, high-value alternatives. Rigorous...

Sepsis remains a leading cause of morbidity and mortality worldwide.1 The use of artificial intelligence (AI), and particularly machine-learning (ML) approaches, to predict which patients are at risk for sepsis in the hospital may improve patient-centred outcomes through early recognition and timely antibiotics. Yet, despite major interest in the use of ML applications in sepsis care, there are only a handful of successful examples of model implementation that save lives through early detection.2 3 The high cost and extensive system architecture required to test and implement novel ML applications have limited many institutions’ abilities to bring these models to the bedside. Unfortunately, this has resulted in a preponderance of studies on model development rather than implementation and reliance on proprietary models disseminated to health systems without validation or testing. One well-known sepsis predictive model developed by an electronic health record vendor (Epic Systems,...

Background

Low-value care (LVC) describes practices that persist in healthcare, despite being ineffective, inefficient or causing harm. Several determinants for the provision of LVC have been identified, but understanding how these factors influence professionals’ decisions, individually and jointly, is a necessary next step to guide deimplementation.

Methods

A factorial survey experiment was employed using vignettes that presented hypothetical medical scenarios among 593 Swedish primary care physicians. Each vignette varied systematically by factors such as patient age, patient request for the LVC, physician’s perception of this practice, practice cost to the primary care centre and time taken to deliver it. For each scenario, we measured the reported likelihood of providing the LVC. We collected information on the physician’s worry about missing a serious illness.

Results

Patient requests and physicians’ positive perceptions of the practice were the factors that increased the reported likelihood of providing LVC the most (by 14 and 13 percentage points (pp), respectively). When the LVC was low in cost or not time-consuming, patient requests further boosted the likelihood of provision by 29 and 18 pp. In contrast, credible evidence against the LVC reduced the role of patient requests by 11 pp. Physicians’ fear of missing a serious illness was linked with higher reported probability of providing LVC, and the credibility of the evidence against the LVC reduced the role of this concern.

Conclusions

The findings highlight that patient requests enhance the role of many determinants, while the credibility of evidence diminishes the impact of others. Overall, these findings point to the relevance of increased clinician knowledge about LVC, tools for patient communication and the use of decision support tools to reduce the uncertainty in decision-making.

Objective

To identify bias in using a single machine learning (ML) sepsis prediction model across multiple hospitals and care locations; evaluate the impact of six different bias mitigation strategies and propose a generic modelling approach for developing best-performing models.

Methods

We developed a baseline ML model to predict sepsis using retrospective data on patients in emergency departments (EDs) and wards across nine hospitals. We set model sensitivity at 70% and determined the number of alerts required to be evaluated (number needed to evaluate (NNE), 95% CI) for each case of true sepsis and the number of hours between the first alert and timestamped outcomes meeting sepsis-3 reference criteria (HTS3). Six bias mitigation models were compared with the baseline model for impact on NNE and HTS3.

Results

Across 969 292 admissions, mean NNE for the baseline model was significantly lower for EDs (6.1 patients, 95% CI 6 to 6.2) than for wards (7.5 patients, 95% CI 7.4 to 7.5). Across all sites, median HTS3 was 20 hours (20–21) for wards vs 5 (5–5) for EDs. Bias mitigation models significantly impacted NNE but not HTS3. Compared with the baseline model, the best-performing models for NNE with reduced interhospital variance were those trained separately on data from ED patients or from ward patients across all sites. These models generated the lowest NNE results for all care locations in seven of nine hospitals.

Conclusions

Implementing a single sepsis prediction model across all sites and care locations within multihospital systems may be unacceptable given large variances in NNE across multiple sites. Bias mitigation methods can identify models demonstrating improved performance across most sites in reducing alert burden but with no impact on the length of the prediction window.

Background

There is limited evidence from antimicrobial stewardship programmes in less-resourced settings. This study aimed to improve the quality of antibacterial prescriptions by mitigating overuse and promoting the use of narrow-spectrum agents in intensive care units (ICUs) in a middle-income country.

Methods

We established a quality improvement collaborative (QIC) model involving nine Argentine ICUs over 11 months with a 16-week baseline period (BP) and a 32-week implementation period (IP). Our intervention package included audits and feedback on antibacterial use, facility-specific treatment guidelines, antibacterial timeouts, pharmacy-based interventions and education. The intervention was delivered in two learning sessions with three action periods along with coaching support and basic quality improvement training.

Results

We included 912 patients, 357 in BP and 555 in IP. The latter had higher APACHE II (17 (95% CI: 12 to 21) vs 15 (95% CI: 11 to 20), p=0.036), SOFA scores (6 (95% CI: 4 to 9) vs 5 (95% CI: 3 to 8), p=0.006), renal failure (41.6% vs 33.1%, p=0.009), sepsis (36.1% vs 31.6%, p<0.001) and septic shock (40.0% vs 33.8%, p<0.001). The days of antibacterial therapy (DOT) were similar between the groups (change in the slope from BP to IP 28.1 (95% CI: –17.4 to 73.5), p=0.2405). There were no differences in the antibacterial defined daily dose (DDD) between the groups (change in the slope from BP to IP 43.9, (95% CI: –12.3 to 100.0), p=0.1413).

The rate of antibacterial de-escalation based on microbiological culture was higher during the IP (62.0% vs 45.3%, p<0.001).

The infection prevention control (IPC) assessment framework was increased in eight ICUs.

Conclusion

Implementing an antimicrobial stewardship program in ICUs in a middle-income country via a QIC demonstrated success in improving antibacterial de-escalation based on microbiological culture results, but not on DOT or DDD. In addition, eight out of nine ICUs improved their IPC Assessment Framework Score.