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Robot-assisted total joint arthroplasty (robotic-TJA) has become more widespread over the last 20 years due to higher patient satisfaction and reduced complications. However, robotic TJA may have longer operative times and increased operating room traffic, which are known risk factors for contamination events. Contamination of surgical instruments may be contact- or airborne-related with documented scalpel blade contamination rates up to 9%. The robot arm is a novel instrument that comes in and out of the surgical field, so our objective was to assess whether the robot arm is a source of contamination when used in robotic TJA compared to other surgical instruments. This was a prospective, single-institution, single-surgeon pilot study involving 103 robotic TJAs. The robot arm was swabbed prior to incision and after closure. Pre- and postoperative control swabs were also collected from the suction tip and scalpel blade. Swabs were incubated for 24 hours on tryptic soy agar followed by inspection for growth of any contaminating bacteria. A contamination event was detected in 10 cases (10%). The scalpel blade was the most common site of contamination (8%) followed by the robot arm (2%) and suction tip (0%). Robotic TJA is contaminated with bacteria at a rate around 10%. Although the robot arm is an additional source of potential contamination, the robot arm accrues bacterial contamination infrequently compared to the scalpel blade. Contamination of the robot arm during robotic TJA is minimal when compared to contamination of the scalpel blade. • Contamination during robotic total joint arthroplasty occurs at a rate up to 10%. • There is minimal contamination of the robot arm during robotic TJA. • The scalpel was the most common site of perioperative contamination. [ABSTRACT FROM AUTHOR]

Sodium-glucose cotransporter-2 (SGLT2) inhibitors are effective in adults with diabetes mellitus (DM) and heart failure (HF) based on randomized clinical trials. We compared SGLT2 inhibitor uptake and outcomes in two cohorts: a population-based cohort of all adults with DM and HF in Alberta, Canada and a specialized heart function clinic (HFC) cohort. The population-based cohort was derived from linked provincial healthcare datasets. The specialized clinic cohort was created by chart review of consecutive patients prospectively enrolled in the HFC between February 2018 and August 2022. We examined the association between SGLT2 inhibitor use (modeled as a time-varying covariate) and all-cause mortality or deaths/cardiovascular hospitalizations. Of the 4,885 individuals from the population-based cohort, 64.2% met the eligibility criteria of the trials proving the effectiveness of SGLT2 inhibitors. Utilization of SGLT2 inhibitors increased from 1.2% in 2017 to 26.4% by January 2022. In comparison, of the 530 patients followed in the HFC, SGLT2 inhibitor use increased from 9.8% in 2019 to 49.1 % by March 2022. SGLT2 inhibitor use in the population-based cohort was associated with fewer all-cause mortality (aHR 0.51, 95%CI 0.41-0.63) and deaths/cardiovascular hospitalizations (aHR 0.65, 95%CI 0.54-0.77). However, SGLT2 inhibitor usage rates were far lower in HF patients without DM (3.5% by March 2022 in the HFC cohort). Despite robust randomized trial evidence of clinical benefit, the uptake of SGLT2 inhibitors in patients with HF and DM remains low, even in the specialized HFC. Clinical care strategies are needed to enhance the use of SGLT2 inhibitors and improve implementation. [Display omitted] [ABSTRACT FROM AUTHOR]

To date, there are no randomized, double-blinded clinical trials comparing catheter ablation to DC cardioversion (DCCV) with medical therapy in patients with persistent atrial fibrillation (PersAF). Conducting a large-scale trial to address this question presents considerable challenges, including recruitment, blinding, and implementation. We conducted a pilot study to evaluate the feasibility of conducting a definitive placebo-controlled trial. This prospective trial was carried out at Barts Heart Centre, United Kingdom, employing a randomized, double-blinded, placebo-controlled design. Twenty patients with PersAF (duration <2 years) were recruited, representing 10% of the proposed larger trial as determined by a power calculation. The patients were randomized in a 1:1 ratio to receive either PVI ± DCCV (PVI group) or DCCV + Placebo (DCCV group). The primary endpoint of this feasibility study was to evaluate patient blinding. Patients remained unaware of their treatment allocation until end of study. During the study, 35% of patients experienced recurrence of PersAF prior to completion of 12 months follow-up. Blinding was successfully maintained amongst both patients and medical staff. The DCCV group had a trend to higher recurrence and repeat procedure rate compared to the PVI group (recurrence of PersAF 60% vs 30%; p =.07 and repeat procedure 70% vs 40%; p =.4). The quality of life experienced by individuals in the PVI group showed improvement, as evidenced by enhanced scores on the AF specific questionnaire (AF PROMS) (3 [±4] vs 21 [±8]) and SF-12 mental-component raw score (51.4 [±7] vs 43.24 [±15]) in patients who maintained sinus rhythm at 12 months. This feasibility study establishes the potential for conducting a blinded, placebo-controlled trial to evaluate the efficacy of PVI versus DCCV in patients with PersAF. [Display omitted] [ABSTRACT FROM AUTHOR]

• Semen transfers more readily when wet than dry. • Semen transfers more readily from a non-porous primary surface than from a porous primary surface. • Examination findings can assist in determining whether a stain was wet or dry when a transfer occurred. • Low levels of sperm cells can be detected from transfers even in the absence of acid phosphatase reactions. There is limited data available on the subject of indirect transfer of non-visible body fluids, particularly semen, and often forensic science practitioner experience alone must be used to guide expectations. It can be difficult to assess the likelihood of proposed transfer scenarios without knowledge of how different variables can affect a transfer. The following work carried out by the Association of Forensic Service Providers UK and Ireland Body Fluid Forum explores how the features of transferred semen change with differences in the primary and secondary surface (porous and non– porous), different contact types (passive, pressure and pressure+) and with wet and dry primary stains. It was concluded that the primary surface type and whether the stain was wet or dry when contact occurred had the most significant effect on the transfer of semen, with wet transfers and transfers from the tested non-porous surface producing significantly more, and larger, visible stains under white light, stains with stronger fluorescence as viewed using Crime-lite® ML2, stains with stronger and faster acid phosphatase reactions and greater numbers of spermatozoa viewed using high power microscopy, compared to dry transfers and transfers from the tested porous surface. Pressure with movement transfers resulted in significantly more visible stains under white light and greater numbers of spermatozoa viewed using high power microscopy compared to passive transfers, however this only occurred when transfers were from a porous primary surface. The secondary surface type was not found to have a significant effect on the numbers of spermatozoa viewed using high power microscopy. [ABSTRACT FROM AUTHOR]

• The DNA concentration in liquid semen and saliva samples decreased after seven days. • Enzyme activity in dilute liquid semen & saliva samples & neat vaginal material decreased over time. • Drying times were positively correlated with sample volumes. • Surface type affected semen & saliva drying times but not vaginal material. • Full ejaculates took around 2 & 5 h to dry on porous and non-porous surfaces respectively. The stability of enzyme activity and the amount of detectable DNA within liquid samples of semen, saliva and vaginal material were tested across a number of days. The concentration of DNA within neat semen and saliva samples fell significantly after one week of refrigeration. No apparent change in acid phosphatase or amylase enzyme activity was observed in neat semen and saliva samples over 96 or 72 h respectively. Changes in the enzyme activity of most of the dilute semen and saliva samples, as well as the neat vaginal material sample, were noted after 24 h. The drying times and sizes of stains produced from various volumes of neat semen, saliva and vaginal material were tested on porous and non-porous surfaces at room temperature. Larger volumes of body fluid took longer to dry and generally resulted in larger stains. Body fluids on a non-porous surface took longer to dry than on the porous surface tested. [ABSTRACT FROM AUTHOR]

The rate for periprosthetic joint infection (PJI) exceeds 1% for primary arthroplasties. Over 30% of patients who have a primary arthroplasty require an additional arthroplasty, and the impact of PJI on this population is understudied. Our objective was to assess the prevalence of recurrent, synchronous, and metachronous PJI in patients who had multiple arthroplasties and to identify risk factors for a subsequent PJI. We identified 337 patients who had multiple arthroplasties and at least 1 PJI that presented between 2003 and 2021. The mean follow-up after revision arthroplasty was 3 years (range, 0 to 17.2). Patients who had multiple infected prostheses were categorized as synchronous (ie, presenting at the same time as the initial infection) or metachronous (ie, presenting at a different time as the initial infection). The PJI diagnosis was made using the MusculoSkeletal Infection Society (MSIS) criteria. There were 39 (12%) patients who experienced recurrent PJI in the same joint, while 31 (9%) patients developed PJI in another joint. Positive blood cultures were more likely in the second joint PJI (48%) compared to recurrent PJI (23%) or a single PJI (15%, P <.001). Synchronous PJI represented 42% of the second joint PJI cases (n = 13), while metachronous PJI represented 58% (n = 18). Tobacco users had 75% higher odds of metachronous PJI (odds ratio 1.75, 95% confidence interval: 1.1-2.9, P =.041). Over 20% of the patients with multiple arthroplasties and a single PJI will develop a subsequent PJI in another arthroplasty with 12% recurring in the initial arthroplasty and nearly 10% ocurring in another arthroplasty. Particular caution should be taken in patients who use tobacco, have bacteremia, or have Staphylococcus aureus isolation at time of their initial PJI. Optimizing the management of this high-risk patient population is necessary to reduce the additional burden of subsequent PJI. Prognostic Level IV. [ABSTRACT FROM AUTHOR]

• Using a customized set-up employing an Electrical Low Pressure Impactor, the size-specific filtration efficiency, pressure drop, and filter quality of children's masks (surgical, KN95, 100% cotton, and partially-cotton-based masks) were tested at face velocities representative of adults and school-aged children. • As with adults' masks, children's nonwoven masks (such as surgical and KN95) showed a higher filtration efficiency and filter quality than woven masks. • In accordance with the single fiber theory, the lower face velocity for school-aged children yielded a higher filtration efficiency and lower pressure drop, but FE at particle sizes larger than 0.04 µm was low for woven masks. Differences in physiology and breathing patterns between children and adults lead to disparate responses to aerosols of varying sizes. No standardized method exists for measuring the filtration efficiency (FE) of children's masks to reflect such differences. Using an adult N95 mask as a control and two different face velocities (v f) (9.3 cm/s representing adults and 4.0 cm/s representing school-aged children), we tested the pressure drop (ΔP) through children's nonwoven masks (surgical and KN95) and children's woven masks (100% cotton and partially-cotton-based masks), as well as their size-specific FE between aerodynamic particle diameters of 0.02 and 2.01 μm. All three types of mask showed a 1 to 9% absolute increase in minimum FE at the lower v f and a significant decrease in ΔP. For children's surgical masks the increase in FE was significant for most of the examined particle sizes, but for children's woven masks the increase was limited to particles smaller than 0.04 μm. Lower v f for children is likely to lead to a higher FE, lower ΔP, and consequently higher filter qualities in children's masks. For woven masks, the FE for particles larger than 0.04 μm was low (typically <50%) for both v f 's studied. [ABSTRACT FROM AUTHOR]

• Despite long-standing guideline recommendations, triple therapy remains under-used in clinical practice, with only one-fifth of patients with heart failure (HF) and reduced ejection fraction (HFrEF) receiving all 3 classes within 1 year after the index HF diagnosis. • Younger patients with lower LVEF, higher natriuretic peptide levels and better renal function were more likely to be initiated on triple therapy, and even after adjustment for potential confounders, triple therapy was associated with better clinical outcomes. • Identifying barriers to the timely initiation of triple therapy could help clinicians and policymakers in devising implementation strategies to improve the initiation of quadruple therapy. Quadruple therapy is recommended for the management of patients with heart failure (HF) and reduced ejection fraction (HFrEF). In order to provide background and identify barriers to quadruple therapy, in this study, the aim was to explore the time to initiation of triple therapy in a population-based cohort of patients with de novo HF. Adult patients with de novo hospital or emergency department (ED) diagnosis of HF between April 1, 2008, and March 31, 2018, in Alberta, Canada, were included and were linked to echocardiography data to identify patients with HFrEF (EF ≤ 40%). Any treatment with angiotensin-converting enzyme inhibitors/ angiotensin receptor blockers/ angiotensin receptor neprilysin inhibitors, beta-blockers, and mineralocorticoid receptor antagonists was captured if prescribed for ≥ 28 days and filled at least once during the 12 months after the index episode. Among 14,092 patients with de novo HF and available echocardiography data, 54.9% had HFrEF. By 1 year after diagnosis, of those in the HFrEF cohort, 9.5% had received no therapy, 27.5% monotherapy, 41.6% dual therapy, and 21.4% triple therapy. The median (interquartile range) of time to mono-, dual- and triple therapy in patients with HFrEF were 1 (0, 26), 8 (0, 44), and 14 (0, 52) days, respectively. Patients who received triple therapy were younger, more likely to be male and to have higher frequencies of coronary artery disease, higher glomerular filtration rates and lower left ventricular ejection fraction levels compared to their counterparts. Patients with triple therapy had lower rates of clinical outcomes compared to those on no, mono or dual therapy (adjusted hazard ratio 0.15, 95% confidence interval 0.13, 0.17 for the composite outcome of death, hospitalization due to HF, or ED visit due to HF). Despite guideline recommendations, triple therapy is underused and is slowly deployed in patients with HFrEF, even after hospitalization and ED presentation. Visual Take Home Graphic. ACEi, angiotensin converting enzyme inhibitor; BB, beta-blocker; ED, emergency department; eGFR, estimated glomerular filtration rate; HF, heart failure; HFrEF, heart failure with reduced ejection fraction; LVEF, left ventricular ejection fraction; MRA, mineralocorticoid receptor antagonists; Tx, therapy. [Display omitted] [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic brought unprecedented challenges for youth with neurodevelopmental disabilities (NDD) and their families. Although health measures were implemented to contain the COVID-19 virus, they disrupted public service, profoundly impacting youth and their families' access to services. This study sought to better understand the perspectives and experiences of parents and caregivers of youth with NDD across Canada in accessing services and their mental health needs during the pandemic. The study used a qualitative research design in which we interviewed 40 parents and caregivers across Canada. The results enabled us to understand the impact of service disruptions in significant areas of life, including health, education, employment, and risk mitigation. Policymakers must consider a disability-inclusive lens during public health emergency planning and response to reduce the disproportionate impacts faced by youth with NDD and their families. [ABSTRACT FROM AUTHOR]

Novel strategies are needed to curb the opioid overdose epidemic. Smart home sensors have been successfully deployed as digital biomarkers to monitor health conditions, yet they have not been used to assess symptoms important to opioid use and overdose risks. This study piloted smart home sensors and investigated their ability to accurately detect clinically pertinent symptoms indicative of opioid withdrawal or respiratory depression in adults prescribed methadone. Participants (n = 4; 3 completed) were adults with opioid use disorder exhibiting moderate levels of pain intensity, withdrawal symptoms, and sleep disturbance. Participants were invited to two 8-hour nighttime sleep opportunities to be recorded in a sleep research laboratory, using observed polysomnography and ambient smart home sensors attached to lab bedroom walls. Measures of feasibility included completeness of data captured. Accuracy was determined by comparing polysomnographic data of sleep/wake and respiratory status assessments with time and event sensor data. Smart home sensors captured overnight data on 48 out of 64 hours (75% completeness). Sensors detected sleep/wake patterns in alignment with observed sleep episodes captured by polysomnography 89.4% of the time. Apnea events (n = 118) were only detected with smart home sensors in two episodes where oxygen desaturations were less severe (>80%). Smart home technology could serve as a less invasive substitute for biologic monitoring for adults with pain, sleep disturbances, and opioid withdrawal symptoms. Supplemental sensors should be added to detect apnea events. Such innovations could provide a step forward in assessing overnight symptoms important to populations taking opioids. [ABSTRACT FROM AUTHOR]

Background: Mobile Cardiac Outpatient Telemetry (MCOT) can be used to screen high risk patients for atrial fibrillation (AF). These devices rely primarily on algorithmic detection of AF events, which are then stored and transmitted to a clinician for review. It is critical the positive predictive value (PPV) of MCOT detected AF is high, and this often leads to reduced sensitivity, as device manufacturers try to limit false positives.Objective: The purpose of this study was to design a two stage classifier using artificial intelligence (AI) to improve the PPV of MCOT detected atrial fibrillation episodes whilst maintaining high levels of detection sensitivity.Methods: A low complexity, RR-interval based, AF classifier was paired with a deep convolutional neural network (DCNN) to create a two-stage classifier. The DCNN was limited in size to allow it to be embedded on MCOT devices. The DCNN was trained on 491,727 ECGs from a proprietary database and contained 128,612 parameters requiring only 158 KB of storage. The performance of the two-stage classifier was then assessed using publicly available datasets.Results: The sensitivity of AF detected by the low complexity classifier was high across all datasets (>93%) however the PPV was poor (<76%). Subsequent analysis by the DCNN increased episode PPV across all datasets substantially (>11%), with only a minor loss in sensitivity (<5%). This increase in PPV was due to a decrease in the number of false positive detections. Further analysis showed that DCNN processing was only required on around half of analysis windows, offering a significant computational saving against using the DCNN as a one-stage classifier.Conclusion: DCNNs can be combined with existing MCOT classifiers to increase the PPV of detected AF episodes. This reduces the review burden for physicians and can be achieved with only a modest decrease in sensitivity. [ABSTRACT FROM AUTHOR]

Asymptomatic patients with a remote history of transient ischemic attack (TIA) or stroke are not well studied as a separate population from asymptomatic patients with no prior history of TIA or stroke. We compared in-hospital outcomes after transcarotid artery revascularization (TCAR) and transfemoral carotid artery stenting (TFCAS) among symptomatic patients, patients with a remote history of neurologic symptoms, and asymptomatic patients. Data from patients in the Vascular Quality Initiative database who underwent TCAR (January 2017 to April 2020) or TFCAS (May 2005 to April 2020) were analyzed. Symptomatic status was defined as TIA and/or stroke occurring within 180 days before the procedure. Asymptomatic status was divided into patients with no history of TIA/stroke (asymptomatic) and patients with a history of TIA/stroke occurring more than 180 days before the procedure (remote history of neurologic symptoms). The Student t -test and Pearson χ2 test were used to compare baseline patient characteristics and outcomes. Multivariate logistic regression was used to adjust for significant between-group differences in baseline characteristics. There were 7158 patients who underwent TCAR (symptomatic: 2574, asymptomatic: 3689, and asymptomatic with a remote history of neurologic symptoms: 895) and 18,023 patients who underwent TFCAS (symptomatic: 6195, asymptomatic: 10,333, and asymptomatic with a remote history of neurologic symptoms: 1495). Regardless of symptom status, the mean patient age was 73 years for TCAR and 69 years for TFCAS. A total of 64% of patients in the study were male and 36% of patients were female. The mean long-term follow-up data ranged between 208 and 331 days within the three patient groups. Carotid stenosis patients with a remote history of neurologic symptoms had higher rates of TIA, stroke, TIA/stroke, stroke/death, and stroke/death/myocardial infarction than asymptomatic patients, and these rates were similar to those of symptomatic patients. Comparing TCAR and TFCAS among patients with a remote history of neurologic symptoms, there were statistically significant reductions in the odds of stroke/death (odds ratio: 0.46, 95% confidence interval: 0.27-0.84, P =.011) and stroke/death/myocardial infarction (odds ratio: 0.51, 95% confidence interval: 0.30-0.87, P =.013) after TCAR. This was likely driven by the increased rate of death after TFCAS in patients with a remote history of neurologic symptoms (0.9%) compared with asymptomatic patients (0.6%). Asymptomatic patients with a remote history of TIA/stroke do not have the same outcomes as asymptomatic patients without a history of TIA/stroke and are at higher risk of adverse in-hospital events. Patients with a remote history of TIA/stroke have increased risk of in-hospital death after TFCAS and may benefit from TCAR. [ABSTRACT FROM AUTHOR]

Deep Convolutional Neural Networks (DCNNs) have been shown to provide improved performance over traditional heuristic algorithms for the detection of arrhythmias from ambulatory ECG recordings. However, these DCNNs have primarily been trained and tested on device-specific databases with standardized electrode positions and uniform sampling frequencies. This work explores the possibility of training a DCNN for Atrial Fibrillation (AF) detection on a database of single‑lead ECG rhythm strips extracted from resting 12‑lead ECGs. We then test the performance of the DCNN on recordings from ambulatory ECG devices with different recording leads and sampling frequencies. We developed an extensive proprietary resting 12‑lead ECG dataset of 549,211 patients. This dataset was randomly split into a training set of 494,289 patients and a testing set of the remaining 54,922 patients. We trained a 34-layer convolutional DCNN to detect AF and other arrhythmias on this dataset. The DCNN was then validated on two Physionet databases commonly used to benchmark automated ECG algorithms (1) MIT-BIH Arrhythmia Database and (2) MIT-BIH Atrial Fibrillation Database. Validation was performed following the EC57 guidelines, with performance assessed by gross episode and duration sensitivity and positive predictive value (PPV). Finally, validation was also performed on a selection of rhythm strips from an ambulatory ECG patch that a committee of board-certified cardiologists annotated. On MIT-BIH, The DCNN achieved a sensitivity of 100% and 84% PPV in detecting episodes of AF. and 100% sensitivity and 94% PPV in quantifying AF episode duration. On AFDB, The DCNN achieved a sensitivity of 94% and PPV of 98% in detecting episodes of AF, and 98% sensitivity and 100% PPV in quantifying AF episode duration. On the patch database, the DCNN demonstrated performance that was closely comparable to that of a cardiologist. The results indicate that DCNN models can learn features that generalize between resting 12‑lead and ambulatory ECG recordings, allowing DCNNs to be device agnostic for detecting arrhythmias from single‑lead ECG recordings and enabling a range of clinical applications. [ABSTRACT FROM AUTHOR]

Background: Patients with heart failure (HF) and a reduced ejection fraction (HFrEF) who experience worsening HF (WHF) events are at increased risk of adverse outcomes and experience significant morbidity and mortality. We herein describe the epidemiology of these patients and identify those potentially eligible for vericiguat therapy in this population-based study.Methods and Results: This retrospective cohort study included hospitalized or emergency department patients with a primary diagnosis of HF and a left ventricular ejection fraction (LVEF) of less than 45% diagnosed between April 1, 2009, and March 31, 2019 in Alberta, Canada, with follow-up to March 31, 2020. Inclusion criteria from the VerICiguaT Global Study in Subjects with Heart Failure with Reduced Ejection (VICTORIA) trial were applied to explore eligibility for vericiguat. Among 25,629 patients with HF and LVEF data, 9948 (38.8%) had HFrEF, of which 5259 (52.8%) experienced WHF at some point during a median 5.8 years of follow-up, and 38.3% of those met the vericiguat trial eligibility criteria. Compared with patients with HFrEF without WHF, those with WHF were older, with more comorbidities, worse renal function, and similar LVEF status, but greater use of HF medications at baseline. At the time of WHF, 27% of those with HFrEF and WHF were on triple therapy, 50.6% were on dual therapy, and 15.4% were on monotherapy. All-cause mortality and the composite outcome of all-cause mortality or cardiovascular hospitalization at 1-year of follow-up were higher in the HFrEF with WHF cohort compared with HFrEF without WHF (adjusted hazard ratios of 1.92 and 1.51, respectively, both P < .0001).Conclusions: Approximately one-half of patients with HFrEF experienced WHF over the long-term follow-up. Most were not on triple therapy, highlighting the underuse of the existing standard-of-care treatments and opportunities for application of newer therapies; more than one-third of patients with HFrEF may be eligible for vericiguat.Lay Summary: Among patients with heart failure (HF), those who experience worsening HF (WHF) are at increased risk of adverse outcomes. A few new therapies, including vericiguat, have emerged recently for patients with HF and reduced ejection fraction. However, the epidemiology, treatment patterns, and outcomes of patients with WHF in large representative populations is unclear. In the current study, approximately one-half of the patients with HF and reduced ejection fraction experienced WHF and 38.3% were potentially eligible for vericiguat therapy. The guideline-recommended therapies were under-utilized among patients with WHF, which highlights the need for initiatives to address this care gap. [ABSTRACT FROM AUTHOR]

In this commentary paper, we discuss the use of the electrocardiogram to help clinicians make diagnostic and patient referral decisions in acute care settings. The paper discusses the factors that are likely to contribute to the variability and noise in the clinical decision making process for catheterization lab activation. These factors include the variable competence in reading ECGs, the intra/inter rater reliability, the lack of standard ECG training, the various ECG machine and filter settings, cognitive biases (such as automation bias which is the tendency to agree with the computer-aided diagnosis or AI diagnosis), the order of the information being received, tiredness or decision fatigue as well as ECG artefacts such as the signal noise or lead misplacement. We also discuss potential research questions and tools that could be used to mitigate this 'noise' and improve the quality of ECG based decision making. [ABSTRACT FROM AUTHOR]

Background: We sought to develop machine learning (ML) models trained on administrative data which predict risk of readmission in patients with heart failure and to evaluate and compare the ML model with the currently used LaCE score using clinically informative metrics.Methods and Results: This prognostic study was conducted in Alberta, Canada, on 9845 patients with confirmed heart failure admitted to hospital between 2012 and 2019. The outcome was unplanned all-cause hospital readmission within 30 days of discharge. We used 80% of the data for the ML model development and 20% for independent validation. We reported, using the validation set, c-statistics (area under the receiver operating characteristic curves)and performance metrics (likelihood ratio, positive predictive values) for the XGBoost model and a modified LaCE score within their respective predictive thresholds. Boosted tree-based classifiers had higher area under the receiver operating characteristic curves (0.65 for XGBoost) compared with others (0.58 for neural networks) and 0.57 for the modified LaCE. Within the predicted threshold range of the XGBoost classifier, the positive likelihood ratio was 1.00 at the low end of predicted risk and 6.12 at the high end, resulting in a positive predictive value (post-test probability) range of 21%-62%; the pretest probability of readmission was 20.9% using prevalence. The corresponding positive likelihood ratios and positive predictive values across LaCE score thresholds were 1.00-1.20 and 21%-24%, respectively.Conclusions: Despite predicting readmissions better than the LaCE, even the best ML model trained on administrative health data (XGBoost) did not provide substantially informative prediction performance as it only generated a moderate shift from pre to post-test probability. Health systems wishing to deploy such a tool should consider training ML models with additional data. Adding other techniques like natural language processing, along with ML, to use other clinical information (like chart notes) might improve prediction performance. [ABSTRACT FROM AUTHOR]

Free-living saprotrophic fungi and symbiotic mycorrhizal fungi affect organic matter dynamics differently because of contrasting ecological adaptations. We investigated how mass-loss, C:N-ratio and stable isotope dynamics of leaf litter and humus substrates depended on presence of living tree roots and associated fungal communities in a forest-to-tundra ecotone over three years. Litter mass-loss was stimulated by tree roots, contrary to a Gadgil effect. Increases in the litter nitrogen pool and δ15N suggested import of nitrogen from deeper soil by the dominating saprotrophic fungi. Over time, humus first lost, then gained, mass, and corresponding shifts in δ15N and δ13C suggested fluctuating pools of fine roots and fungal mycelium. Ectomycorrhizal tree roots consistently reduced longer-term humus mass-gain, counteracting positive effects of ericoid roots and associated fungi. Across all substrates, mass dynamics correlated with the balance between ectomycorrhizal and litter-saprotrophic fungi, both linked to mass-loss, and ericaceous shrubs and associated fungi, linked to mass-gain. [ABSTRACT FROM AUTHOR]

Background: Despite the improved awareness of cardiac amyloidosis among clinicians, its incidence and prevalence is not well-described in a community setting. We sought to investigate the incidence and prevalence of cardiac amyloidosis in the community.Methods and Results: In the adult population of Alberta, we examined 3 cohorts: (1) probable cases of cardiac amyloidosis: the presence of physician-assigned diagnosis of amyloidosis (International Classification of Diseases [ICD]-10 code E85; ICD-9 277.3) and 1 or more health care encounter for heart failure (HF) (ICD-10 I50; ICD-9 428); (2) possible cardiac amyloidosis: the presence of clinical phenotypes suggestive of amyloidosis; and (3) a comparator HF cohort without amyloidosis. Between 2004 and 2018, 982 of the 145,329 patients with HF were identified as probable cardiac amyloidosis. During the same period, the incidence rates of probable cardiac amyloidosis increased from 1.38 to 3.69 per 100,000 person-years and the prevalence rates increased from 3.42 to 14.85 per 100,000 person-years (Ptrend < .0001). Patients with probable cardiac amyloidosis were more likely to be male, have a higher comorbidity burden, greater health care use, and poorer outcomes as compared with patients with HF without amyloidosis. A much larger group of patients was identified as possible cardiac amyloidosis (n = 46,255), with similar increase in prevalence from 2004 to 2018 (from 416 to 850 per 100,000 person-years).Conclusions: The incidence and prevalence of cardiac amyloidosis has increased over the last decade. Given the advent of new therapies for cardiac amyloidosis and considering their high cost, it is imperative to devise strategies to screen, identify, and track patients with cardiac amyloidosis from administrative databases. [ABSTRACT FROM AUTHOR]

Automated interpretation of the 12-lead ECG has remained an underpinning interest in decades of research that has seen a diversity of computing applications in cardiology. The application of computers in cardiology began in the 1960s with early research focusing on the conversion of analogue ECG signals (voltages) to digital samples. Alongside this, software techniques that automated the extraction of wave measurements and provided basic diagnostic statements, began to emerge. In the years since then there have been many significant milestones which include the widespread commercialisation of 12-lead ECG interpretation software, associated clinical utility and the development of the related regulatory frameworks to promote standardised development. In the past few years, the research community has seen a significant rejuvenation in the development of ECG interpretation programs. This is evident in the research literature where a large number of studies have emerged tackling a variety of automated ECG interpretation problems. This is largely due to two factors. Specifically, the technical advances, both software and hardware, that have facilitated the broad adoption of modern artificial intelligence (AI) techniques, and, the increasing availability of large datasets that support modern AI approaches. In this article we provide a very high-level overview of the operation of and approach to the development of early 12-lead ECG interpretation programs and we contrast this to the approaches that are now seen in emerging AI approaches. Our overview is mainly focused on highlighting differences in how input data are handled prior to generation of the diagnostic statement. [ABSTRACT FROM AUTHOR]

This paper provides a brief description of how computer programs are used to automatically interpret electrocardiograms (ECGs), and also provides a discussion regarding new opportunities. The algorithms that are typically used today in hospitals are knowledge engineered where a computer programmer manually writes computer code and logical statements which are then used to deduce a possible diagnosis. The computer programmer's code represents the criteria and knowledge that is used by clinicians when reading ECGs. This is in contrast to supervised machine learning (ML) approaches which use large, labelled ECG datasets to induct their own 'rules' to automatically classify ECGs. Although there are many ML techniques, deep neural networks are being increasingly explored as ECG classification algorithms when trained on large ECG datasets. Whilst this paper presents some of the pros and cons of each of these approaches, perhaps there are opportunities to develop hybridised algorithms that combine both knowledge and data driven techniques. In this paper, it is pointed out that open ECG data can dramatically influence what international ECG ML researchers focus on and that, ideally, open datasets could align with real world clinical challenges. In addition, some of the pitfalls and opportunities for ML with ECGs are outlined. A potential opportunity for the ECG community is to provide guidelines to researchers to help guide ECG ML practices. For example, whilst general ML guidelines exist, there is perhaps a need to recommend approaches for 'stress testing' and evaluating ML algorithms for ECG analysis, e.g. testing the algorithm with noisy ECGs and ECGs acquired using common lead and electrode misplacements. This paper provides a primer on ECG ML and discusses some of the key challenges and opportunities. [ABSTRACT FROM AUTHOR]

Background: Postoperative arrhythmias are associated with increased morbidity and mortality in total joint arthroplasty (TJA) patients. HMG-CoA (3-hydroxy-3-methyl-glutaryl-CoA) reductase inhibitors (statins) decrease atrial fibrillation rates after cardiac surgery, but it is unknown if this cardioprotective effect is maintained after joint reconstruction surgery. We aim to determine if perioperative statin use decreases the incidence of 90-day postoperative arrhythmias in patients undergoing primary TJA.Methods: We performed a single-center retrospective cohort study in which 231 primary TJA patients (109 hips, 122 knees) received simvastatin 80 mg daily during their hospitalization as part of a single surgeon's standard postoperative protocol. This cohort was matched to 966 primary TJA patients (387 hips and 579 knees) that did not receive simvastatin. New-onset arrhythmias (bradycardia, atrial fibrillation/tachycardia/flutter, paroxysmal supraventricular tachycardia, and ventricular tachycardia) and complications (readmissions, thromboembolism, infection, and dislocation) within 90 days of the procedure were documented. Categorical variables were analyzed using Fisher's exact tests. Our study was powered to detect a 3% difference in arrhythmia rates.Results: Within 90 days postoperatively, arrhythmias occurred in 1 patient (0.4%) who received a perioperative statin, 39 patients (4.0%) who did not receive statins (P = .003), and 24 patients (4.2%) who were on outpatient statins (P = .005). This is 10-fold reduction in the relative risk of developing a postoperative arrhythmia within 90 days of arthroplasty and an absolute risk reduction of 3.6%.Conclusion: Treating as few as 28 patients with perioperative simvastatin prevents one new cardiac arrhythmia within 90 days in statin-naïve patients undergoing TJA. [ABSTRACT FROM AUTHOR]

[Display omitted] • The public is sometimes exposed to damaging small particle aerosols including SARS-Cov-2. • During shortages, facepieces available to the public do not protect against small particle aerosols. • We report designing homemade masks from home air purifier "true HEPA" filters. • These homemade masks function similar to an N95. Shortages of efficient filtering facepiece respirators leave the public vulnerable to transmission of infectious diseases in small particle aerosols. This study demonstrates that a high-filtration-efficiency facepiece capable of filtering out >95% of 0.05μm particles while being worn can be simply produced with available materials. [ABSTRACT FROM AUTHOR]

Background: Renal dysfunction is a strong predictor of outcomes in patients with acute heart failure (AHF). However, less is known about how sex may influence the prognostic import of renal function in AHF.Methods and Results: In a post hoc analysis of the ASCEND-HF trial including 5377 patients with AHF (33% female), patients were categorized into 3 groups based on the changes in renal function during their hospital stay. Worsening, stable, and improving renal functions were defined as a ≥20% decrease, a <20% change, and a ≥20% increase in the estimated glomerular filtration rate, respectively. The primary outcome was the composite of 30-day all-cause mortality or HF rehospitalization. The median baseline and discharge estimated glomerular filtration rate were 58.4 and 56.9 mL/min/1.73 m2, respectively. Worsening, stable, and improving renal function was observed in 31.9%, 63.2, and 4.9% of patients, respectively. Worsening renal function was associated with adverse outcomes at 30 days (adjusted hazard ratio [aHR] 1.47, 95% confidence interval [CI] 1.22-1.76). This association existed in both males and females (aHR 1.42 and aHR 1.56, respectively, both P < .01). There was an interaction between renal function changes and sex (P = .025), because improving renal function was associated with better outcomes in men (aHR 0.29, 95% CI 0.13-0.66) as compared with women (aHR 1.18, 95% CI 0.59-2.35). There was no interaction between the ejection fraction and renal function in association with subsequent outcomes.Conclusions: Irrespective of sex, worsening renal function was associated with poorer outcomes at 30 days in patients with AHF. More studies are warranted to further delineate the possible sex differences in this setting. [ABSTRACT FROM AUTHOR]

Reduced-dose apixaban is recommended in patients fulfilling 2 of 3 criteria: age ≥80 years, body weight ≤60 kg, and serum creatinine ≥1.5 mg/dL. However, patient weight is often not available in electronic health data. We examined the validity of alternative definitions based on age and renal function alone using an observational dataset of patients with atrial fibrillation and chronic kidney disease which included weight measurements. [ABSTRACT FROM AUTHOR]

• First systematic gas geochemical study to track CO 2 migration at the Shizhuang CO 2 injection site. • Significant southward migration that is not picked up by geophysical methods. • Less than 1 % of injected CO 2 has migrated a few 100s m from the injection site within one year since injection ceased. • Shallow coal seams are a potential reservoir for CO 2 sequestration. Shallow coal seams are a target reservoir for CO 2 sequestration. So far efforts to trace CO 2 sequestration by coal beds have largely focussed on short-term monitoring or theoretical modelling. To evaluate the feasibility of safe long-term storage of CO 2 in shallow coal beds, we report the first systematic gas geochemical study at a shallow injection site in Shizhuang, Qinshui Basin, one year after CO 2 injection ceased. The injected CO 2 is isotopically distinctive and appears to be enriched in heavy noble gases (Kr and Xe). The high CO 2 content and light δ13C CO2 of gases from production wells confirm the migration of injected gas to the northeast and east of the main injection well previously tracked by geophysical studies. The migration of injected gas to the southeast has not been identified before, and exceeds that to the northeast, implying that gas geochemistry provides a more robust method of tracking migration. Less than 1 % of the total injected CO 2 has been extracted from production wells in the year since injection ceased. We estimate that the target coal seam in the study area can store about 0.08 billion tonnes of CO 2. Scaling up we estimate that the Qinshui Basin has the potential to store about 19 billion tonnes of CO 2. This is significantly more than previous estimates. This study demonstrates the importance of geochemical tracers in quantifying and monitoring CO 2 sequestration and also implies that unmined coal seams have high potential for the long-term storage of CO 2. [ABSTRACT FROM AUTHOR]

An increasingly key consideration in Canadian immigration policy is rural settlement patterns. As Canada's largest census metropolitan areas (CMAs) continue to see increases in overall population, smaller and mid-sized communities face greater challenges in attracting and retaining newcomers as a means of economic and population improvements. As such, it is becoming increasingly important to look at the characteristics of individuals who opt for rural settlement in order to improve the outcomes of future attraction initiatives. While a substantial body of literature looks at the movement of immigrants and native-born Canadians across the country, far fewer look at the characteristics that positively predict rural settlement. This study helps to close the knowledge gap by investigating these characteristics and how they change over a 25-year period, using two identical logistic regression models and a pooled cross-sectional model based on the 1991 and 2016 Canadian Census. Overall, age, lower levels of education and income, as well as non-immigrant and non-visible minority statuses lead to higher propensities for rural living. However, the general trend is a move away from rural settlement in favour of larger cities. • Canada's immigration policy focuses on high human capital newcomers. • Most newcomers immediately move to cities when coming to Canada. • All non-white immigrant groups are less likely to live in rural Canada. • Canada's immigration policy inadvertently leads to urban growth at the expense of rural Canada. [ABSTRACT FROM AUTHOR]

Rationale and Objectives: The relative competitiveness of radiology and the number of first-choice applicants to diagnostic radiology have steadily declined over the past decade. The purpose of this study was to identify factors contributing to the declining interest in diagnostic radiology as a career and to explore factors affecting specialty choice.Materials and Methods: A retrospective survey was distributed to resident physicians at a single academic center between July and August 2017. Participants identified factors affecting career choice and evaluated level of agreement with statements regarding radiology using 5-point Likert scales. Higher scores indicated stronger agreement.Results: One hundred and fifty-two resident physicians from Canada participated (21.5% response rate): 20 radiology and 132 nonradiology. Of the total, 27% were registered in postgraduate year (PGY) 1, 23% in PGY 2, 15% in PGY 3, 19% in PGY 4, and 16% in PGY 5, or above. Sixty-one percent of the respondents self-reported as female, 34% as male, and 5% as other/unknown. Of those in radiology, 40% self-reported as female, 55% as male, and 5% as other/unknown, compared to 64% female, 31% male, and 5% other/unknown in other specialties. Regardless of specialty, positive clinical/mentoring experiences strongly affected career choice. Radiology residents were attracted to diverse pathology (M = 4.5) and positive staff/resident interactions (M = 4.4). Nonradiology residents were deterred by lack of patient contact (M = 3.9) and dark work environment (M = 3.6). Resident physicians who had applied to radiology were more likely to report positive mentorship during medical school, disagree that technology will replace radiologists, and desire a higher income specialty (Wald = 56.6, p < 0.001). More recent graduates showed a higher level of concern regarding the potential negative impact of technology and outsourcing on the profession (F (3, 189) = 2.6, p = 0.05). Several trainees (21%) considered radiology, but lacked mentorship (52%) and identified job market concerns (29%).Conclusion: More recent graduates are relatively more concerned about technology replacing radiologists, and radiology applicants have less concern about artificial intelligence replacing radiologists. As positive interactions with radiologists and mentorship are key influencers, our results advocate for early training exposure and reinforcement regarding the positive outlook of the profession. [ABSTRACT FROM AUTHOR]

Introduction: Electrode misplacement and interchange errors are known problems when recording the 12‑lead electrocardiogram (ECG). Automatic detection of these errors could play an important role for improving clinical decision making and outcomes in cardiac care. The objectives of this systematic review and meta-analysis is to 1) study the impact of electrode misplacement on ECG signals and ECG interpretation, 2) to determine the most challenging electrode misplacements to detect using machine learning (ML), 3) to analyse the ML performance of algorithms that detect electrode misplacement or interchange according to sensitivity and specificity and 4) to identify the most commonly used ML technique for detecting electrode misplacement/interchange. This review analysed the current literature regarding electrode misplacement/interchange recognition accuracy using machine learning techniques.Method: A search of three online databases including IEEE, PubMed and ScienceDirect identified 228 articles, while 3 articles were included from additional sources from co-authors. According to the eligibility criteria, 14 articles were selected. The selected articles were considered for qualitative analysis and meta-analysis.Results: The articles showed the effect of lead interchange on ECG morphology and as a consequence on patient diagnoses. Statistical analysis of the included articles found that machine learning performance is high in detecting electrode misplacement/interchange except left arm/left leg interchange.Conclusion: This review emphasises the importance of detecting electrode misplacement detection in ECG diagnosis and the effects on decision making. Machine learning shows promise in detecting lead misplacement/interchange and highlights an opportunity for developing and operationalising deep learning algorithms such as convolutional neural network (CNN) to detect electrode misplacement/interchange. [ABSTRACT FROM AUTHOR]

Oncological resections have become more radical in pursuit of disease free margins. Consequently, vascular structures may be injured inadvertently or purposely resected, with or without subsequent reconstruction. Thus, vascular surgeons have an increasing role in oncological surgery. The present authors sought to review their experience and examine the effect of timing of referral to a Vascular Surgeon (VS) on patient and surgical outcomes following tumour resection. A retrospective review was conducted of a prospectively maintained database at a public hospital network in Adelaide, Australia. All cases of collaboration between a VS and other surgeons for resection of cancer or non-malignant tumour were included. Medical records and operative, pathological, and transfusion data were reviewed, with particular attention to referring team, timing of VS referral (pre- or intra-operative), and the operative role of the VS. Seventy-two cases were identified from January 2004 to June 2018. The most common collaborators were General Surgery and Urology. Of the cases, 86% were elective and 71% were referred to the VS pre-operatively. Pre-operative referral was associated with a predominant VS role of dissection and exposure. Pre-operative referral was associated with lower odds of vessel repair and reconstruction compared with intra-operative referral (adjusted OR = 0.20; 95% CI 0.04–0.93; p =.040) and a lower incidence of positive surgical margins (35% vs. 80%, p =.028). The rate of blood product units required was lower among pre-operative referrals relative to intra-operative referrals, but the effect of timing was not significant after adjustment for potential confounders (IRR = 0.80, 95% CI 0.26–2.44; p =.70). Pre-operative planned involvement of vascular surgery in oncological operations can improve surgical outcomes, with additional expected benefits for surgical training and cross specialty collaboration. [ABSTRACT FROM AUTHOR]