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Aims The current guidelines recommend aortic valve intervention in patients with severe aortic regurgitation (AR) with the onset of symptoms, left ventricular enlargement, or systolic dysfunction. Recent studies have suggested that we might be missing the window of early intervention in a significant number of patients by following the guidelines. Methods and results The overarching goal was to determine if machine learning (ML)-based algorithms could be trained to identify patients at risk for death from AR independent of aortic valve replacement (AVR). Models were trained with five-fold cross-validation on a dataset of 1035 patients, and performance was reported on an independent dataset of 207 patients. Optimal predictive performance was observed with a conditional random survival forest model. A subset of 19/41 variables was selected for inclusion in the final model. Variable selection was performed with 10-fold cross-validation using random survival forest model. The top variables included were age, body surface area, body mass index, diastolic blood pressure, New York Heart Association class, AVR, comorbidities, ejection fraction, end-diastolic volume, and end-systolic dimension, and the relative variable importance averaged across five splits of cross-validation in each repeat were evaluated. The concordance index for predicting survival of the best-performing model was 0.84 at 1 year, 0.86 at 2 years, and 0.87 overall, respectively. Conclusion Using common echocardiographic parameters and patient characteristics, we successfully trained multiple ML models to predict survival in patients with severe AR. This technique could be applied to identify high-risk patients who would benefit from early intervention, thereby improving patient outcomes.

Aims Current non-invasive screening methods for cardiac allograft rejection have shown limited discrimination and are yet to be broadly integrated into heart transplant care. Given electrocardiogram (ECG) changes have been reported with severe cardiac allograft rejection, this study aimed to develop a deep-learning model, a form of artificial intelligence, to detect allograft rejection using the 12-lead ECG (AI-ECG). Methods and results Heart transplant recipients were identified across three Mayo Clinic sites between 1998 and 2021. Twelve-lead digital ECG data and endomyocardial biopsy results were extracted from medical records. Allograft rejection was defined as moderate or severe acute cellular rejection (ACR) based on International Society for Heart and Lung Transplantation guidelines. The extracted data (7590 unique ECG-biopsy pairs, belonging to 1427 patients) was partitioned into training (80%), validation (10%), and test sets (10%) such that each patient was included in only one partition. Model performance metrics were based on the test set (n = 140 patients; 758 ECG-biopsy pairs). The AI-ECG detected ACR with an area under the receiver operating curve (AUC) of 0.84 [95% confidence interval (CI): 0.78–0.90] and 95% (19/20; 95% CI: 75–100%) sensitivity. A prospective proof-of-concept screening study (n = 56; 97 ECG-biopsy pairs) showed the AI-ECG detected ACR with AUC = 0.78 (95% CI: 0.61–0.96) and 100% (2/2; 95% CI: 16–100%) sensitivity. Conclusion An AI-ECG model is effective for detection of moderate-to-severe ACR in heart transplant recipients. Our findings could improve transplant care by providing a rapid, non-invasive, and potentially remote screening option for cardiac allograft function.

Photodamage is caused by chronic sun exposure and ultraviolet radiation and presents as wrinkles, sagging, and pigmented spots. An increase in the ultraviolet index can increase a person's perceived age by worsening skin photodamage. However, since the ultraviolet index varies considerably between geographical regions, perceived age might vary substantially among them. This review aims to describe the differences in chronological and perceived age in regions of the world with different ultraviolet indexes. A literature search of three databases was conducted for studies analyzing perceived age and its relationship to sun exposure. Ultraviolet indexes from the included studies were retrieved from the National Weather Service and the Tropospheric Emission Monitoring Internet Service. Out of 104 studies, seven fulfilled the inclusion criteria. Overall, 3,352 patients were evaluated for perceived age. All studies found that patients with the highest daily sun exposures had the highest perceived ages for their chronological age (p

Although artificial intelligence (AI) algorithms have been shown to be capable of identifying cardiac dysfunction, defined as ejection fraction (EF) ≤ 40%, from 12-lead electrocardiograms (ECGs), identification of cardiac dysfunction using the single-lead ECG of a smartwatch has yet to be tested. In the present study, a prospective study in which patients of Mayo Clinic were invited by email to download a Mayo Clinic iPhone application that sends watch ECGs to a secure data platform, we examined patient engagement with the study app and the diagnostic utility of the ECGs. We digitally enrolled 2,454 unique patients (mean age 53 ± 15 years, 56% female) from 46 US states and 11 countries, who sent 125,610 ECGs to the data platform between August 2021 and February 2022; 421 participants had at least one watch-classified sinus rhythm ECG within 30 d of an echocardiogram, of whom 16 (3.8%) had an EF ≤ 40%. The AI algorithm detected patients with low EF with an area under the curve of 0.885 (95% confidence interval 0.823–0.946) and 0.881 (0.815–0.947), using the mean prediction within a 30-d window or the closest ECG relative to the echocardiogram that determined the EF, respectively. These findings indicate that consumer watch ECGs, acquired in nonclinical environments, can be used to identify patients with cardiac dysfunction, a potentially life-threatening and often asymptomatic condition.

Importance: Diagnosis of heart failure with preserved ejection fraction (HFpEF) among dyspneic patients without overt congestion is challenging. Multiple diagnostic approaches have been proposed but are not well validated against the independent gold standard for HFpEF diagnosis of an elevated pulmonary capillary wedge pressure (PCWP) during exercise. Objective: To evaluate H 2FPEF and HFA-PEFF scores and a PCWP/cardiac output (CO) slope of more than 2 mm Hg/L/min to diagnose HFpEF. Design, Setting, and Participants: This retrospective case-control study included patients with unexplained dyspnea from 6 centers in the US, the Netherlands, Denmark, and Australia from March 2016 to October 2020. Diagnosis of HFpEF (cases) was definitively ascertained by the presence of elevated PCWP during exertion; control individuals were those with normal rest and exercise hemodynamics. Main Outcomes and Measures: Logistic regression was used to evaluate the accuracy of HFA-PEFF and H 2FPEF scores to discriminate patients with HFpEF from controls. Results: Among 736 patients, 563 (76%) were diagnosed with HFpEF (mean [SD] age, 69 [11] years; 334 [59%] female) and 173 (24%) represented controls (mean [SD] age, 60 [15] years; 109 [63%] female). H 2FPEF and HFA-PEFF scores discriminated patients with HFpEF from controls, but the H 2FPEF score had greater area under the curve (0.845; 95% CI, 0.810-0.875) compared with the HFA-PEFF score (0.710; 95% CI, 0.659-0.756) (difference, -0.134; 95% CI, -0.177 to -0.094; P 2FPEF score. Use of the PCWP/CO slope to redefine HFpEF rather than exercise PCWP reclassified 20% (117 of 583) of patients, but patients reclassified from HFpEF to control by this metric had clinical, echocardiographic, and hemodynamic features typical of HFpEF, including elevated resting PCWP in 66% (46 of 70) of reclassified patients. Conclusions and Relevance: In this case-control study, despite requiring fewer data, the H 2FPEF score had superior diagnostic performance compared with the HFA-PEFF score and PCWP/CO slope in the evaluation of unexplained dyspnea and HFpEF in the outpatient setting..

Aims Some artificial intelligence models applied in medical practice require ongoing retraining, introduce unintended racial bias, or have variable performance among different subgroups of patients. We assessed the real-world performance of the artificial intelligence-enhanced electrocardiogram to detect left ventricular systolic dysfunction with respect to multiple patient and electrocardiogram variables to determine the algorithm’s long-term efficacy and potential bias in the absence of retraining. Methods and results Electrocardiograms acquired in 2019 at Mayo Clinic in Minnesota, Arizona, and Florida with an echocardiogram performed within 14 days were analyzed (n = 44 986 unique patients). The area under the curve (AUC) was calculated to evaluate performance of the algorithm among age groups, racial and ethnic groups, patient encounter location, electrocardiogram features, and over time. The artificial intelligence-enhanced electrocardiogram to detect left ventricular systolic dysfunction had an AUC of 0.903 for the total cohort. Time series analysis of the model validated its temporal stability. Areas under the curve were similar for all racial and ethnic groups (0.90–0.92) with minimal performance difference between sexes. Patients with a ‘normal sinus rhythm’ electrocardiogram (n = 37 047) exhibited an AUC of 0.91. All other electrocardiogram features had areas under the curve between 0.79 and 0.91, with the lowest performance occurring in the left bundle branch block group (0.79). Conclusion The artificial intelligence-enhanced electrocardiogram to detect left ventricular systolic dysfunction is stable over time in the absence of retraining and robust with respect to multiple variables including time, patient race, and electrocardiogram features.

Aims Artificial intelligence (AI) enabled electrocardiography (ECG) can detect latent atrial fibrillation (AF) in patients with sinus rhythm (SR). However, the change of AI-ECG probability before and after the first AF episode is not well characterized. We sought to characterize the temporal trend of AI-ECG AF probability around the first episode of AF. Methods and results We retrospectively studied adults who had at least one ECG in SR prior to an ECG that documented AF. An AI network calculated the AF probability from ECGs during SR (positive defined >8.7%, based on optimal sensitivity and specificity). The AI-ECG probability was reported prior to and after the first episode of AF and stratified by age and CHA2DS2-VASc score. Mixed effect models were used to assess the rate of change between time points. A total of 59 212 patients with 544 330 ECGs prior to AF and 413 486 ECGs after AF were included. The mean time between the first positive AI-ECG and first AF was 5.4 ± 5.7 years. The mean AI-ECG probability was 19.8% 2–5 years prior to AF, 23.6% 1–2 years prior to AF, 34.0% 0–3 months prior to AF, 40.9% 0–3 months after AF, 35.2% 1–2 years after AF, and 42.2% 2–5 years after AF (P 50 years CHA2DS2-VASc score ≥4. Conclusion The AI-ECG probability progressively increases with time prior to the first AF episode, transiently decreases 1–2 years following AF and continues to increase thereafter.

Prospective nonrandomized studies have found less postoperative fatigue and improved quality of life in patients undergoing awake spine surgery under spinal anesthesia compared with general anesthesia. Randomized trials are needed to validate these findings.To prospectively investigate patients' willingness to enroll in randomized trials of lumbar spine surgery under spinal versus general anesthesia and identify any potential barriers.We recruited patients undergoing lumbar spine surgery for degenerative disease. We described a randomized trial of spine surgery under spinal versus general anesthesia and assessed patients' willingness to participate in such trial. We elicited preferences for treatment along with demographics. The association between these factors and willingness to participate in the trial was examined.Fifty patients completed interviews; 58% were female, mean age of 60.9 ± 12.5 years. A total of 52% patients stated that they were definitely willing to participate in the hypothetical randomized trial, and 8% probably willing. Only 16% of patients were aware of spinal anesthesia as an option for low back surgery, and 60% indicated no strong preference for the anesthesia techniques. Patients without strong preferences stated a greater willingness to participate than those with strong preferences (80% vs. 10% definitely willing, P0.0001). Age, sex, education, work status, and race were not significantly associated with willingness to participate.Sixty percent of patients stated that they were either definitely or probably willing to participate in the randomized trial. Subjects lacking strong preferences for the anesthesia technique stated a greater willingness to enroll than those with strong preference.

Objective To describe the clinical data from the first 108 patients seen in the Mayo Clinic post–COVID-19 care clinic (PCOCC). Methods After Institutional Review Board approval, we reviewed the charts of the first 108 patients seen between January 19, 2021, and April 29, 2021, in the PCOCC and abstracted from the electronic medical record into a standardized database to facilitate analysis. Patients were grouped into phenotypes by expert review. Results Most of the patients seen in our clinic were female (75%; 81/108), and the median age at presentation was 46 years (interquartile range, 37 to 55 years). All had post-acute sequelae of SARS-CoV-2 infection, with 6 clinical phenotypes being identified: fatigue predominant (n=69), dyspnea predominant (n=23), myalgia predominant (n=6), orthostasis predominant (n=6), chest pain predominant (n=3), and headache predominant (n=1). The fatigue-predominant phenotype was more common in women, and the dyspnea-predominant phenotype was more common in men. Interleukin 6 (IL-6) was elevated in 61% of patients (69% of women; P=.0046), which was more common than elevation in C-reactive protein and erythrocyte sedimentation rate, identified in 17% and 20% of cases, respectively. Conclusion In our PCOCC, we observed several distinct clinical phenotypes. Fatigue predominance was the most common presentation and was associated with elevated IL-6 levels and female sex. Dyspnea predominance was more common in men and was not associated with elevated IL-6 levels. IL-6 levels were more likely than erythrocyte sedimentation rate and C-reactive protein to be elevated in patients with post-acute sequelae of SARS-CoV-2 infection.

Cardiovascular disease remains the leading cause of death in women. Given accumulating evidence on sex- and gender-based differences in cardiovascular disease development and outcomes, the need for more effective approaches to screening for risk factors and phenotypes in women is ever urgent. Public health surveillance and health care delivery systems now continuously generate massive amounts of data that could be leveraged to enable both screening of cardiovascular risk and implementation of tailored preventive interventions across a woman’s life span. However, health care providers, clinical guidelines committees, and health policy experts are not yet sufficiently equipped to optimize the collection of data on women, use or interpret these data, or develop approaches to targeting interventions. Therefore, we provide a broad overview of the key opportunities for cardiovascular screening in women while highlighting the potential applications of artificial intelligence along with digital technologies and tools.

IMPORTANCEMany hospitalized patients with COVID-19 have been treated with convalescent plasma. However, it is uncertain whether this therapy lowers mortality and if so, if the mortality benefit is larger among specific subgroups, such as recipients of plasma with high antibody content and patients treated early in the disease course.OBJECTIVETo examine the association of COVID-19 convalescent plasma transfusion with mortality and the differences between subgroups in hospitalized patients with COVID-19.DATA SOURCESOn October 26, 2022, a systematic search was performed for clinical studies of COVID-19 convalescent plasma in the literature.STUDY SELECTIONRandomized clinical trials and matched cohort studies investigating COVID-19 convalescent plasma transfusion compared with standard of care treatment or placebo among hospitalized patients with confirmed COVID-19 were included. The electronic search yielded 3,841 unique records, of which 744 were considered for full-text screening. The selection process was performed independently by a panel of five reviewers.DATA EXTRACTION AND SYNTHESISThe study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Data were extracted by 5 independent reviewers in duplicate and pooled using inverse-variance random-effects model.MAIN OUTCOMES AND MEASURESPrespecified end point was all-cause mortality during hospitalization.RESULTSThirty-nine randomized clinical trials enrolling 21,529 participants and 70 matched cohort studies enrolling 50,160 participants were included in the systematic review. Separate meta-analyses demonstrated that transfusion of COVID-19 convalescent plasma was associated with a significant decrease in mortality compared with the control cohort for both randomized clinical trials (odds ratio (OR), 0.87 [95% CI, 0.76-1.00]) and matched cohort studies (OR, 0.77 [95% CI, 0.64-0.94]). Meta-analysis of subgroups revealed two important findings. First, treatment with convalescent plasma containing high antibody levels was associated with a decrease in mortality compared to convalescent plasma containing low antibody levels (OR, 0.85 [95% CI, 0.73 to 0.99]). Second, earlier treatment with COVID-19 convalescent plasma was associated with a significant decrease in mortality compared with the later treatment cohort (OR, 0.63 [95% CI, 0.48 to 0.82]).CONCLUSIONS AND RELEVANCECOVID-19 convalescent plasma use was associated with a 13% reduced risk in mortality, implying a mortality benefit for hospitalized patients with COVID-19, particularly those treated with convalescent plasma containing high antibody levels treated earlier in the disease course.Key PointsQuestionWhat is the evidence regarding the potential mortality benefit associated with transfusion of convalescent plasma in hospitalized patients with COVID-19?FindingsIn this meta-analysis of 39 randomized clinical trials enrolling 21,529 participants and 70 matched cohort studies enrolling 50,160 participants, transfusion of convalescent plasma was associated with a 13% mortality benefit. Subgroup analyses revealed that patients treated with plasma containing higher levels of antibodies and patients treated earlier in the course of the disease had a greater mortality benefit associated with COVID-19 convalescent plasma transfusion.MeaningThese findings suggest that transfusion of COVID-19 convalescent plasma is associated with a mortality benefit for hospitalized patients, particularly those treated earlier in the disease course.

Resumen Objetivo: Los informes iniciales de la COVID-19 describieron diversas presentaciones clinicas, pero las anomalias de las vias respiratorias bajas son las mas frecuentes y los hallazgos de la tomografia computarizada (TC) de torax difieren entre pacientes adultos y pediatricos. Nuestro objetivo es resumir los hallazgos tempranos de la TC para informar a los profesionales sanitarios sobre la frecuencia de manifestaciones especificas de la COVID-19 en los pacientes adultos o pediatricos, y determinar si la sensibilidad de la TC justifica su uso en estas poblaciones. Metodos: Se busco en PubMed la presencia de las palabras “TC”, “tecnicas de imagen”, “COVID-19” en el titulo o en el resumen y se seleccionaron para la sintesis de datos 17 estudios a gran escala de PubMed y/o Scopus y casos clinicos publicados entre el 1 de enero de 2020 y el 15 de abril de 2020. Resultados: Las TC iniciales identificaron que las opacidades en vidrio esmerilado (OVE) y las anomalias bilaterales eran mas frecuentes en los adultos (74%, n = 698 y 89%, n = 378, respectivamente) que en los ninos (60%, n = 25 y 37%, n = 46). A partir de los 14 dias, las TC evidenciaron diversos grados de mejoria en los adultos, pero no se observo resolucion hasta al menos 26 dias despues del inicio de los sintomas seudogripales. En los pacientes pediatricos, un tercio (n = 9) mostro pequenas OVE nodulares adicionales limitadas a un solo lobulo 3-5 dias despues de una TC inicial. Conclusion: Los primeros hallazgos de la TC en adultos sugieren el uso limitado de la TC como herramienta complementaria en el diagnostico de la COVID-19 en pacientes adultos sintomaticos, con especial atencion a la identificacion de las anomalias de los lobulos inferiores derecho e izquierdo, las OVE y el engrosamiento del tabique interlobulillar. Los primeros hallazgos de la TC pediatrica desaconsejan el uso de la TC si se dispone de RT-PCR, debido a su sensibilidad significativamente menor en esta poblacion y a la exposicion a radiacion.

The number of applications for facial recognition technology is increasing due to the improvement in image quality, artificial intelligence, and computer processing power that has occurred during the last decades. Algorithms can be used to convert facial anthropometric landmarks into a computer representation, which can be used to help identify nonverbal information about an individual's health status. This article discusses the potential ways a facial recognition tool can perform a health assessment. Because facial attributes may be considered biometric data, clinicians should be informed about the clinical, ethical, and legal issues associated with its use.

To estimate rates and identify factors associated with asymptomatic COVID-19 in the population of Olmsted County during the prevaccination era.We screened first responders (n=191) and Olmsted County employees (n=564) for antibodies to SARS-CoV-2 from November 1, 2020 to February 28, 2021 to estimate seroprevalence and asymptomatic infection. Second, we retrieved all polymerase chain reaction (PCR)-confirmed COVID-19 diagnoses in Olmsted County from March 2020 through January 2021, abstracted symptom information, estimated rates of asymptomatic infection and examined related factors.Twenty (10.5%; 95% CI, 6.9%-15.6%) first responders and 38 (6.7%; 95% CI, 5.0%-9.1%) county employees had positive antibodies; an additional 5 (2.6%) and 10 (1.8%) had prior positive PCR tests per self-report or medical record, but no antibodies detected. Of persons with symptom information, 4 of 20 (20%; 95% CI, 3.0%-37.0%) first responders and 10 of 39 (26%; 95% CI, 12.6%-40.0%) county employees were asymptomatic. Of 6020 positive PCR tests in Olmsted County with symptom information between March 1, 2020, and January 31, 2021, 6% (n=385; 95% CI, 5.8%-7.1%) were asymptomatic. Factors associated with asymptomatic disease included age (0-18 years [odds ratio {OR}, 2.3; 95% CI, 1.7-3.1] and65 years [OR, 1.40; 95% CI, 1.0-2.0] compared with ages 19-44 years), body mass index (overweight [OR, 0.58; 95% CI, 0.44-0.77] or obese [OR, 0.48; 95% CI, 0.57-0.62] compared with normal or underweight) and tests after November 20, 2020 ([OR, 1.35; 95% CI, 1.13-1.71] compared with prior dates).Asymptomatic rates in Olmsted County before COVID-19 vaccine rollout ranged from 6% to 25%, and younger age, normal weight, and later tests dates were associated with asymptomatic infection.

General anesthesia (GA) and spinal anesthesia (SA) have been adopted for lumbar spine surgery (LSS), but GA is used far more widely. We conducted a survey of spine surgeons to explore their attitudes and preferences regarding awake spine surgery under SA.A survey was emailed to 150 spine surgeons. Exposure and attitudes towards spine surgery under SA were elicited. A five-point Likert scale of agreement examined perceptions of SA, while attitudes towards SA were recorded by categorizing free text into themes.Seventy-five surgeons completed the survey, 50 % response rate. Only 27 % said they perform LSS under SA. Most surgeons, 83 %, would recommend GA to a healthy patient undergoing lumbar laminectomy. Only 41 % believes SA to be as safe as GA, and only 30 % believes SA is associated with better postoperative pain control. The most common reasons why SA is not favored was lack of proven benefits over GA (65 %). When asked if a randomized trial finds SA to lead to less postoperative fatigue, 50 % said they would be more likely to offer SA, a significant increase from the baseline response of 27 % (p = 0.002).Our survey indicates that the low adoption of SA for LSS is due to lack of surgeons' belief in the benefits of SA over GA, and that a randomized patient-centered trial has the potential of changing surgeons' perspective and increasing adoption of SA for LSS.

Background Substantial interreader variability exists for common tasks in CT imaging, such as detection of hepatic metastases. This variability can undermine patient care by leading to misdiagnosis. Purpose To determine the impact of interreader variability associated with

Objective Observational studies utilising diffusion tractography have suggested a common mechanism for tremor alleviation in deep brain stimulation for essential tremor: the decussating portion of the dentato-rubro-thalamic tract. We hypothesised that directional stimulation of the dentato-rubro-thalamic tract would result in greater tremor improvement compared to sham programming, as well as comparable improvement as more tedious standard-of-care programming. Methods A prospective, blinded crossover trial was performed to assess the feasibility, safety and outcomes of programming based solely on dentato-rubro-thalamic tract anatomy. Using magnetic resonance imaging diffusion-tractography, the dentato-rubro-thalamic tract was identified and a connectivity-based treatment setting was derived by modelling a volume of tissue activated using directional current steering oriented towards the dentato-rubro-thalamic tract centre. A sham setting was created at approximately 180° opposite the connectivity-based treatment. Standard-of-care programming at 3 months was compared to connectivity-based treatment and sham settings that were blinded to the programmer. The primary outcome measure was percentage improvement in the Fahn–Tolosa–Marín tremor rating score compared to the preoperative baseline. Results Among the six patients, tremor rating scores differed significantly among the three experimental conditions ( P=0.030). The mean tremor rating score improvement was greater with the connectivity-based treatment settings (64.6% ± 14.3%) than with sham (44.8% ± 18.6%; P=0.031) and standard-of-care programming (50.7% ± 19.2%; P=0.062). The distance between the centre of the dentato-rubro-thalamic tract and the volume of tissue activated inversely correlated with the percentage improvement in the tremor rating score (R2=0.24; P=0.04). No significant adverse events were encountered. Conclusions Using a blinded, crossover trial design, we have shown the technical feasibility, safety and potential efficacy of connectivity-based stimulation settings in deep brain stimulation for treatment of essential tremor.

The aim of the European Heart Journal (EHJ) is to attract innovative, methodologically sound, and clinically relevant research manuscripts able to change clinical practice and/or substantially advance knowledge on cardiovascular diseases. As the reference journal in cardiovascular medicine, the EHJ is committed to publishing only the best cardiovascular science adhering to the highest ethical principles. EHJ uses highly rigorous peer-review, critical statistical review and the highest quality editorial process, to ensure the novelty, accuracy, quality, and relevance of all accepted manuscripts with the aim of inspiring the clinical practice of EHJ readers and reducing the global burden of cardiovascular diseases. This review article summarizes the quality standards pursued by the EHJ to fulfill its mission.

Neurosurgeons represent 0.5% of all physicians and currently face a high burden of disease. Physician-scientists are essential to advance the mission of National Academies of Science (NAS) and National Institutes of Health (NIH) through discovery and bench to bedside translation. We investigated trends in NIH neurosurgeon-scientist funding over time as an indicator of physician-scientist workforce training. We used NIH Research Portfolio Online Reporting Tools (RePORTER) to extract grants to neurosurgery departments and neurosurgeons from 1993 to 2017. Manual extraction of each individual grant awardee was conducted. After adjusting for U.S. inflation (base year: 1993), NIH funding to neurosurgery departments increased yearly (P

Predictive models have played a critical role in local, national, and international response to the COVID-19 pandemic. In the United States, health care systems and governmental agencies have relied on several models, such as the Institute for Health Metrics and Evaluation, Youyang Gu (YYG), Massachusetts Institute of Technology, and Centers for Disease Control and Prevention ensemble, to predict short- and long-term trends in disease activity. The Mayo Clinic Bayesian SIR model, recently made publicly available, has informed Mayo Clinic practice leadership at all sites across the United States and has been shared with Minnesota governmental leadership to help inform critical decisions during the past year. One key to the accuracy of the Mayo Clinic model is its ability to adapt to the constantly changing dynamics of the pandemic and uncertainties of human behavior, such as changes in the rate of contact among the population over time and by geographic location and now new virus variants. The Mayo Clinic model can also be used to forecast COVID-19 trends in different hypothetical worlds in which no vaccine is available, vaccinations are no longer being accepted from this point forward, and 75% of the population is already vaccinated. Surveys indicate that half of American adults are hesitant to receive a COVID-19 vaccine, and lack of understanding of the benefits of vaccination is an important barrier to use. The focus of this paper is to illustrate the stark contrast between these 3 scenarios and to demonstrate, mathematically, the benefit of high vaccine uptake on the future course of the pandemic.

To evaluate the cost-effectiveness of an artificial intelligence electrocardiogram (AI-ECG) algorithm under various clinical and cost scenarios when used for universal screening at age 65.We used decision analytic modeling to perform a cost-effectiveness analysis of the use of AI-ECG to screen for asymptomatic left ventricular dysfunction (ALVD) once at age 65 compared with no screening. This screening consisted of an initial screening decision tree and subsequent construction of a Markov model. One-way sensitivity analysis on various disease and cost parameters to evaluate cost-effectiveness at both $50,000 per quality-adjusted life year (QALY) and $100,000 per QALY willingness-to-pay threshold.We found that for universal screening at age 65, the novel AI-ECG algorithm would cost $43,351 per QALY gained, test performance, disease characteristics, and testing cost parameters significantly affect cost-effectiveness, and screening at ages 55 and 75 would cost $48,649 and $52,072 per QALY gained, respectively. Overall, under most of the clinical scenarios modeled, coupled with its robust test performance in both testing and validation cohorts, screening with the novel AI-ECG algorithm appears to be cost-effective at a willingness-to-pay threshold of $50,000.Universal screening for ALVD with the novel AI-ECG appears to be cost-effective under most clinical scenarios with a cost of$50,000 per QALY. Cost-effectiveness is particularly sensitive to both the probability of disease progression and the cost of screening and downstream testing. To improve cost-effectiveness modeling, further study of the natural progression and treatment of ALVD and external validation of AI-ECG should be undertaken.

Wearable electronic devices are a convenient solution to pain intensity assessment as they can provide continuous monitoring for more precise medication adjustments. However, there is little evidence regarding the use of wearable electronic devices for chronic pain intensity assessment. Our primary objective was to examine the physiologic parameters used by wearable electronic devices for chronic pain intensity assessment. We initially inquired PubMed, CINAHL, and Embase for studies evaluating the use of wearable electronic devices for chronic pain intensity assessment. We updated our inquiry by searching on PubMed, Embase, Scopus, and Google Scholar. English peer-reviewed studies were included, with no exclusions based on time frame or publication status. Of 348 articles that were identified on the first inquiry, 8 fulfilled the eligibility criteria. Of 179 articles that were identified on the last inquiry, only 1 fulfilled the eligibility criteria. We found articles evaluating wristbands, smartwatches, and belts. Parameters evaluated were psychomotor and sleep patterns, space and time mobility, heart rate variability, and skeletal muscle electrical activity. Most of the studies found significant positive associations between physiological parameters measured by wearable electronic devices and self-reporting pain scales. Wearable electronic devices reliably reflect physiologic or biometric parameters, providing a physiological correlation for pain. Early stage investigation suggests that the degree of pain intensity can be discerned, which ideally will reduce the bias inherent to existing numeric/verbal scales. Further research on the use of these devices is vital.

Contains fulltext : 235066.pdf (Publisher’s version ) (Open Access) To determine the effect of COVID-19 convalescent plasma on mortality, we aggregated patient outcome data from 10 randomized clinical trials, 20 matched control studies, 2 dose-response studies, and 96 case reports or case series. Studies published between January 1, 2020, and January 16, 2021, were identified through a systematic search of online PubMed and MEDLINE databases. Random effects analyses of randomized clinical trials and matched control data demonstrated that patients with COVID-19 transfused with convalescent plasma exhibited a lower mortality rate compared with patients receiving standard treatments. Additional analyses showed that early transfusion (within 3 days of hospital admission) of higher titer plasma is associated with lower patient mortality. These data provide evidence favoring the efficacy of human convalescent plasma as a therapeutic agent in hospitalized patients with COVID-19.

BACKGROUND. Background parenchymal uptake (BPU) on molecular breast imaging (MBI) was identified in a case-control study as a breast cancer risk factor beyond mammographic density. To our knowledge, this finding has not yet been confirmed in a cohort study. OBJECTIVE. The objectives of this study were to examine the association of BPU with breast cancer and to estimate the absolute risk and discriminatory accuracy of BPU in a cohort study. METHODS. A retrospective cohort was established that included women without a history of breast cancer who underwent MBI from 2004 to 2015. Radiologists who were blinded to future breast cancer diagnoses assessed BPU on baseline MBI examinations as low (photopenic or minimal) or elevated (mild, moderate, or marked). Associations of BPU with breast cancer were estimated using multivariable Cox proportional hazards models of the time to diagnosis. The 5-year absolute risk was calculated for study subgroups. The discriminatory accuracy of BPU was also assessed. RESULTS. Among 2992 women (mean age, 56.3 years; SD, 10.6 years) who underwent MBI, breast cancer events occurred in 144 women (median follow-up, 7.3 years). Median time to diagnosis after MBI was 4.2 years (range, 0.5-11.6 years). Elevated BPU was associated with a greater breast cancer risk (hazard ratio [HR], 2.39; 95% CI, 1.68-3.41; p ≤ .001). This association remained in postmenopausal women (HR, 3.50; 95% CI, 2.31-5.31; p < .001) but was not significant in premenopausal women (HR, 1.29; 95% CI, 0.72-2.32; p = .39). The 5-year absolute risk of breast cancer was 4.3% (95% CI, 2.9-5.7%) for women with elevated BPU versus 2.5% (95% CI, 1.8-3.1%) for those with low BPU. Postmenopausal women with dense breasts and elevated BPU had a 5-year absolute risk of 8.1% (95% CI, 4.3-11.8%) versus 2.8% (1.8-3.8%) for those with low BPU. Among postmenopausal women, discriminatory accuracy for invasive cancer was improved with the addition of BPU versus use of the Gail risk score alone (C statistic, 65.1 vs 59.1; p = .04) or use of the Breast Cancer Surveillance Consortium risk score alone (C statistic, 66.4 vs 60.4; p = .04). CONCLUSION. BPU on MBI is an independent risk factor for breast cancer, with the strongest association observed among postmenopausal women with dense breasts. In postmenopausal women, BPU provides incremental discrimination in predicting breast cancer when combined with either the Gail model or the Breast Cancer Surveillance Consortium model. CLINICAL IMPACT. Observation of elevated BPU on MBI may identify a subset of women with dense breasts who would benefit most from supplemental screening or preventive options.

In this short narrative, we highlight some of our experiences leading the US Convalescent Plasma Program at the beginning of the pandemic in the spring and summer of 2020. This includes a brief summary of how the program emerged and high-level lessons we learned. We also share our impressions about why convalescent plasma was used at scale in the United States, early in the pandemic and share ideas that might inform the use of convalescent plasma in future outbreaks of novel infectious diseases.