Despite reports of an elevated risk of breast cancer associated with antipsychotic use in women, existing evidence remains inconclusive. We aimed to examine existing observational data in the literature and determine this hypothesised association.

We searched Embase, PubMed and Web of Science™ databases on 27 January 2022 for articles reporting relevant cohort or case-control studies published since inception, supplemented with hand searches of the reference lists of the included articles. Quality of studies was assessed using the Newcastle-Ottawa Scale. We generated the pooled odds ratio (OR) and pooled hazard ratio (HR) using a random-effects model to quantify the association. This study was registered with PROSPERO (CRD42022307913).

Nine observational studies, including five cohort and four case-control studies, were eventually included for review (N = 2 031 380) and seven for meta-analysis (N = 1 557 013). All included studies were rated as high-quality (seven to nine stars). Six studies reported a significant association of antipsychotic use with breast cancer, and a stronger association was reported when a greater extent of antipsychotic use, e.g. longer duration, was operationalised as the exposure. Pooled estimates of HRs extracted from cohort studies and ORs from case-control studies were 1.39 [95% confidence interval (CI) 1.11–1.73] and 1.37 (95% CI 0.90–2.09), suggesting a moderate association of antipsychotic use with breast cancer.

Antipsychotic use is moderately associated with breast cancer, possibly mediated by prolactin-elevating properties of certain medications. This risk should be weighed against the potential treatment effects for a balanced prescription decision.

During the COVID-19 pandemic, telesimulation became particularly important to continue the education of medical students during disrupted clerkships while maintaining social distancing.

To describe our experiences of adapting to telesimulation and evaluate this from student and faculty perspectives.

The intervention was evaluated using anonymous surveys consisting of statements rated on a five-point Likert scale from strongly disagree to strongly agree and open-ended questions asking students and facilitators what went well, what they would change and why, and for any other comments.

Adaptations addressed the logistics of online delivery and the structure and content of scenarios. Logistical considerations included central organization of sessions to relieve pressures on clinicians. Pre-session case discussions were introduced to maximise time with simulated patients and give students space to socialise. Content was modified to ensure functionality online and reflect the context of the pandemic. A total of 278 students and 24 facilitators participated in the telesimulation sessions. 98.1% of students (N=109) rated the sessions as very good or good. Students benefited from practicing skills, especially clinical situations which they would rarely encounter as students, and receiving feedback. Facilitators (N=6) felt that students learnt both skills for online consultations and skills that can be transferred to face-to-face situations, but were ambivalent on whether students would benefit more from face-to-face sessions.

Telesimulation is a safe and effective option that offers additional opportunities for students to develop telemedicine skills. Going forward, telesimulation should complement face-to-face delivery to develop future clinicians who are proficient in both remote and face-to-face working.

No significant relationships.

To describe the evolution of respiratory antibiotic prescribing during the coronavirus disease 2019 (COVID-19) pandemic across 3 large hospitals that maintained antimicrobial stewardship services throughout the pandemic.

Retrospective interrupted time-series analysis.

A multicenter study was conducted including medical and intensive care units (ICUs) from 3 hospitals within a Canadian epicenter for COVID-19.

Interrupted time-series analysis was used to analyze rates of respiratory antibiotic utilization measured in days of therapy per 1,000 patient days (DOT/1,000 PD) in medical units and ICUs. Each of the first 3 waves of the pandemic were compared to the baseline.

Within the medical units, use of respiratory antibiotics increased during the first wave of the pandemic (rate ratio [RR], 1.76; 95% CI, 1.38–2.25) but returned to the baseline in waves 2 and 3 despite more COVID-19 admissions. In ICU, the use of respiratory antibiotics increased in wave 1 (RR, 1.30; 95% CI, 1.16–1.46) and wave 2 of the pandemic (RR, 1.21; 95% CI, 1.11–1.33) and returned to the baseline in the third wave, which had the most COVID-19 admissions.

After an initial surge in respiratory antibiotic prescribing, we observed the normalization of prescribing trends at 3 large hospitals throughout the COVID-19 pandemic. This trend may have been due to the timely generation of new research and guidelines developed with frontline clinicians, allowing for the active application of new research to clinical practice.

Only a limited number of patients with major depressive disorder (MDD) respond to a first course of antidepressant medication (ADM). We investigated the feasibility of creating a baseline model to determine which of these would be among patients beginning ADM treatment in the US Veterans Health Administration (VHA).

A 2018–2020 national sample of n = 660 VHA patients receiving ADM treatment for MDD completed an extensive baseline self-report assessment near the beginning of treatment and a 3-month self-report follow-up assessment. Using baseline self-report data along with administrative and geospatial data, an ensemble machine learning method was used to develop a model for 3-month treatment response defined by the Quick Inventory of Depression Symptomatology Self-Report and a modified Sheehan Disability Scale. The model was developed in a 70% training sample and tested in the remaining 30% test sample.

In total, 35.7% of patients responded to treatment. The prediction model had an area under the ROC curve (s.e.) of 0.66 (0.04) in the test sample. A strong gradient in probability (s.e.) of treatment response was found across three subsamples of the test sample using training sample thresholds for high [45.6% (5.5)], intermediate [34.5% (7.6)], and low [11.1% (4.9)] probabilities of response. Baseline symptom severity, comorbidity, treatment characteristics (expectations, history, and aspects of current treatment), and protective/resilience factors were the most important predictors.

Although these results are promising, parallel models to predict response to alternative treatments based on data collected before initiating treatment would be needed for such models to help guide treatment selection.

Airway management is a controversial topic in modern Emergency Medical Services (EMS) systems. Among many concerns regarding endotracheal intubation (ETI), unrecognized esophageal intubation and observations of unfavorable neurologic outcomes in some studies raise the question of whether alternative airway techniques should be first-line in EMS airway management protocols. Supraglottic airway devices (SADs) are simpler to use, provide reliable oxygenation and ventilation, and may thus be an alternative first-line airway device for paramedics. In 2019, Alachua County Fire Rescue (ACFR; Alachua, Florida USA) introduced a novel protocol for advanced airway management emphasizing first-line use of a second-generation SAD (i-gel) for patients requiring medication-facilitated airway management (referred to as “rapid sequence airway” [RSA] protocol).

This was a one-year quality assurance review of care provided under the RSA protocol looking at compliance and first-pass success rate of first-line SAD use.

Records were obtained from the agency’s electronic medical record (EMR), searching for the use of the RSA protocol, advanced airway devices, or either ketamine or rocuronium. If available, hospital follow-up data regarding patient condition and emergency department (ED) airway exchange were obtained.

During the first year, 33 advanced airway attempts were made under the protocol by 23 paramedics. Overall, compliance with the airway device sequence as specified in the protocol was 72.7%. When ETI was non-compliantly used as first-line airway device, the first-pass success rate was 44.4% compared to 87.5% with adherence to first-line SAD use. All prehospital SADs were exchanged in the ED in a delayed fashion and almost exclusively per physician preference alone. In no case was the SAD exchanged for suspected dislodgement evidenced by lack of capnography.

First-line use of a SAD was associated with a high first-pass attempt success rate in a real-life cohort of prehospital advanced airway encounters. No SAD required emergent exchange upon hospital arrival.

Fewer than half of patients with major depressive disorder (MDD) respond to psychotherapy. Pre-emptively informing patients of their likelihood of responding could be useful as part of a patient-centered treatment decision-support plan.

This prospective observational study examined a national sample of 807 patients beginning psychotherapy for MDD at the Veterans Health Administration. Patients completed a self-report survey at baseline and 3-months follow-up (data collected 2018–2020). We developed a machine learning (ML) model to predict psychotherapy response at 3 months using baseline survey, administrative, and geospatial variables in a 70% training sample. Model performance was then evaluated in the 30% test sample.

32.0% of patients responded to treatment after 3 months. The best ML model had an AUC (SE) of 0.652 (0.038) in the test sample. Among the one-third of patients ranked by the model as most likely to respond, 50.0% in the test sample responded to psychotherapy. In comparison, among the remaining two-thirds of patients, <25% responded to psychotherapy. The model selected 43 predictors, of which nearly all were self-report variables.

Patients with MDD could pre-emptively be informed of their likelihood of responding to psychotherapy using a prediction tool based on self-report data. This tool could meaningfully help patients and providers in shared decision-making, although parallel information about the likelihood of responding to alternative treatments would be needed to inform decision-making across multiple treatments.

To determine whether: the N95 respirator affects nasal valve patency; placement on the bony vault improves patency; and external nasal anatomy affects the outcome.

A prospective study with 50 participants was conducted. Nasal patency was measured by the minimal cross-sectional area via acoustic rhinometry, and using the Nasal Obstruction Symptom Evaluation survey, before and after wearing the N95 respirator and after adjustment.

The minimal cross-sectional area was narrowed by 27 per cent when wearing the N95 respirator (p < 0.001), and improved by 9.2 per cent after adjustment (p = 0.003). The total Nasal Obstruction Symptom Evaluation score increased from 10.2 to 25.4 after donning the N95 respirator (p < 0.001), and decreased from 25.4 to 15.6 after adjustment (p < 0.001). There was no correlation with external nasal anatomy parameters.

Wearing the N95 respirator causes narrowing of the nasal valve, and adjustment onto the bony vault improves symptoms. The findings were not affected by external nasal anatomy.