bluuebunny/crossref_metadata_2025 huggingface dataset:
Hemolytic parasites affect survival in migrating red-tailed hawks
Authors: Briggs Christopher W , Dudus Kris A , Ely Teresa E , Kwasnoski Laura A , Downs Cynthia J
Published: December 2022

Migrating birds face a myriad of hazards, including higher exposure to parasites and numerous competing energy demands. It follows that migration may act as a selective filter and limit population growth. Understanding how individual-level physiological condition and disease status scale up to population dynamics through differential survival of individuals is necessary to identify threats and management interventions for migratory populations, many of which face increasing conservation challenges. However, linking individual physiological condition, parasite infection status and survival can be difficult. We examined the relationship among two measures of physiological condition [scaled-mass index and heterophil/leukocyte (H/L) ratio], hematozoa (i.e. hemoparasites) presence and abundance, and constitutive immunity in 353 autumn migrating red-tailed hawks (Buteo jamaicensis calurus) from 2004 to 2018. Hematazoa (i.e. Haemoproteus and Leucocytozoon) were in the blood smears from 139 red-tailed hawks (39.4%). H/L ratio decreased with scaled-mass index. Adults had a significantly higher H/L ratio than juveniles. Our two measures of immune defences, hemolytic-complement activity and bacteria-killing ability, were highly positively correlated. Our most notable finding was a negative relationship between Haemoproteus parasitemia and survival (i.e. documented individual mortality), indicating that haemosporidian parasites influence survival during a challenging life stage. The effect of haemosporidian parasites on individuals is often debated, and we provide evidence that parasitemia can affect individual survival. In contrast, we did not find evidence of trade-offs between survival and immune defences.
Clinical validation of an artificial intelligence algorithm offering cross-platform detection of atrial fibrillation using smart device electrocardiograms
Authors: Mannhart D , Lefebvre B , Gardella C , Henry C , Serban T , Knecht S , Kuehne M , Sticherling C , Badertscher P
Published: December 2022

Introduction

Multiple smart devices capable of “screening” for atrial fibrillation (AF) based on single-lead electrocardiogram (SL ECG) are presently available. Manufacturers' algorithm capabilities and accuracy for the automated detection of AF vary. Reliable artificial intelligence (AI) algorithms would be valuable to assist physicians with managing the large amount of data. We aimed to assess the clinical value of applying a smart device agnostic AI-based algorithm for the detection of AF from five different smart devices (four smartwatches, one handheld device) and compared the results to the cardiologist-interpreted 12-lead ECG in a real world cohort of patients.

Methods

This is a prospective, observational study enrolling patients presenting to a cardiology service at a tertiary referral center. Patients were prescribed a 12-lead ECG, followed by five consecutive smart device recordings from five different manufacturers. SL ECGs were exported as PDF files from the devices and analyzed by a deep neural network (DNN) based platform which allows automated AI assisted cardiac rhythm interpretation.

Results

We prospectively enrolled 157 patients (32% female, median age 66 years). AF was present in 48 patients (31%) at time of recording, as documented by the 12-lead ECG. Accuracy for the detection of AF by the DNN-based algorithm was 96.6% for the Apple Watch 6, 95.2% for the AliveCor Kardia Mobile, 96.0% for the Fitbit Sense, 95.7% for the Samsung Galaxy Watch 3 and 93.8% for the Withings Scanwatch, respectively (Figure 1, left). While diagnostic accuracy of the DNN-based algorithm was similar compared to each manufacturer's individual algorithm, the proportion of SL ECGs with a conclusive diagnosis was significantly higher for all smart devices when using the DNN-based algorithm, p<0.001 (Figure 1, right). As complementary analysis, we assessed sensitivity and specificity detection capabilities in both algorithms (Figure 2).

Conclusion

In this clinical validation, a DNN-based algorithm reported significantly more conclusive diagnoses for each smart device compared to the manufacturers' algorithms, whilst showing similarly high accuracy in the detection of AF compared to the cardiologist-interpreted standard 12-lead ECG. Given further validation, SL ECG assisted rhythm interpretation through a cross-platform AI-algorithm presents a promising clinical value for AF detection and offers a possible solution for managing the data surge for smart device-acquired ECGs.

Funding Acknowledgement

Type of funding sources: None.
Deep learning-based electrocardiogram analysis detecting paroxysmal atrial fibrillation during sinus rhythm in patients with cryptogenic stroke: validation study using implantable cardiac monitoring
Authors: Jeon K H , Kwon J M , Lee M S , Cho Y J , Oh I Y , Lee J H
Published: December 2022

Background

Atrial fibrillation (AF) is the most cause of cardioembolic source causing cryptogenic stroke. In these, anticoagulation therapy could reduce recurrence of stroke. However, paroxysmal AF would not be detected even by 24 hours Holter monitoring. Deep learning-based electrocardiogram (ECG) analysis models were recently developed to detect AF during sinus rhythm.

Purpose

We aimed to develop a deep learning algorithm (DLA) to detect AF during sinus rhythm and validate the model in patients with cryptogenic stroke who underwent implantable cardiac monitoring (ICM) to diagnose paroxysmal AF.

Methods

This cohort study involved three hospitals (A, B, and C). We developed a DLA to detect AF using sinus rhythm 10 s 12-lead ECG. We included adult patients aged ≥18 years from hospital A and B. We used development data from AF adult patients who had at least one atrial fibrillation rhythm in the study period (Jan 2016 to Dec 2021) and non-AF patients who had no reference to AF in the ECG and electronic medical record. DLA was based on convolutional neural network (CNN) using 10 s 12-lead. For external validation, the ECGs from 217 patients (hospital C) with cryptogenic stroke who underwent ICM were analyzed by using the DLA for validating the accuracy in the real-world clinical situations.

Results

We included 10,605 AF adult patients and 50,522 non-AF patients as development data. During the internal validation, the area under the curve (AUC) of the final DLA based on CNN was 0.793 (95% Confidence interval 0.778–0.807). In external validation data from cryptogenic stroke patients, the mean ICM duration was 15.1 months, and AF >5 mins was detected in 32 patients (14.5%). The diagnostic accuracy of DLA was 0.793 to detect AF during sinus rhythm, and AUC was 0.824. The sensitivity, specificity, positive predictive value, and negative predictive value of the model were 0.844, 0.784, 0.403, and 0.967, respectively, which outperformed other conventional predictive methods based on clinical factors, such as CHARGE-AF, C2hest, and HATCH.

Conclusions

In this study, DLA accurately detected paroxysmal AF using 12-leads normal sinus rhythm ECG in patients with cryptogenic stroke and outperformed the conventional models. The DLA could be used as a screening tool to identify the cause of stroke in the future.

Funding Acknowledgement

Type of funding sources: None.
Cardiovascular disease risk prediction via machine learning using mental health data
Authors: Dorraki M , Liao Z , Abbott D , Psaltis P J , Baker E , Bidargaddi N , Van Den Hengel A , Narula J , Verjans J W
Published: December 2022

Background

Robust and accurate risk prediction models are much needed in cardiovascular disease. It is well-known that mental health is associated with the risk of developing cardiovascular disease. It is unknown whether mental health markers can enhance existing risk prediction models for cardiovascular disease.

Purpose

The main purpose of this study was to assess capability of mental health factors along with traditional risk factors to be used in cardiovascular predictive machine learning models, and to develop a combined machine learning approach using both traditional risk and psychological factors in 375,145 participants of the UK Biobank.

Methods

A comprehensive Pearson correlation analysis is carried out on UK Biobank data. Subsequently, an ensemble model containing decision tree, random forest, XGBoost, support vector machine (SVM), and deep neural network (DNN) classification approaches was built to predict cardiovascular diseases (CVD) in UK Biobank participants. The model was first trained using traditional cardiovascular risk factors, and subsequently trained using a combination of cardiovascular risk and psychological factors.

Results

The correlation analysis revealed that there is a correlation between CVD and mental health factors suggesting the potential of mental health application for machine learning models. Our ensemble machine learning model was able to predict CVD with an accuracy of 73.49% using CVD risk factors alone. However, by combining psychological factors with CVD risk factors in the training data, an improved accuracy of 95.70% was achieved. The accuracy and robustness of ensemble machine learning model outperformed any of five constituent learning algorithms alone.

Conclusions

Our results suggest that mental health assessment data along with traditional risk factors provides a powerful, safe and affordable machine learning model enrichment that can be used for state-of-the-art prediction of CVD.

Funding Acknowledgement

Type of funding sources: None.
PMHnet-alpha: development and validation of a neural network based discrete-time survival model for mortality prediction in ischemic heart disease
Authors: Holm P , Haue A D , Westergaard D , Banasik K , Koeber L , Brunak S , Bundgaard H
Published: December 2022

Background

Current risk prediction models in ischemic heart disease (IHD) use a small set of well-known risk factors, have limited predictive capabilities, and are largely the same as they were twenty years ago.

We developed and externally validated PMHnet-alpha, a neural-network based survival model for risk-stratification in ischemic heart disease that leverages the multitude of clinical features available in modern electronical health records.

Methods

We included 39,746 IHD patients from the regional Heart Registry that had been subjected to a coronary angiography between 2006 and 2017 with confirmed coronary artery disease.

Clinical data was extracted from the Danish National Patient Registry, and electronic health records.

595 different features, consisting of diagnosis codes, procedure codes, biochemical test results, and clinical measurements were used as model inputs.

Prior to model development, patients were randomly divided into a training set (n=34,746) and a tesing set (n=5,000).

The testing set was not used for model development.

Model performance was evaluated at six months, one years, three-, and five years of follow-up using time-dependent ROC curve analysis and Harrels' C-index.

Lastly, we also assessed the calibration of the model.

We benchmarked the performance of PMHnet-alpha against the GRACE Risk Score 2.0, which is widely considered to the best-performing model in current clinical use.

We explored the importance of individual features using SHAP values on the trained models.

Findings

PMHnet-alpha had very high model discrimination on the testing data with time-dependent AUCs of 0.88 (95% CI 0.86–0.90) at six months, 0.88 (95% CI 0.86–0.90) at one year, 0.84 (95% CI 0.82–0.86) at three years, and 0.82 (95% CI 0.80–0.84) at five years.

The discrimination of the benchmark model GRACE2.0 on the same data was considerably lower, 0.77 (95% CI 0.73–0.80) at six months, 0.77 (95% CI 0.74–0.80) at one year, and 0.73 (95% CI 0.70–0.75) at three years.

PMHnet-alpha is undergoing external validation in other nordic countries.

We identified that on-average, age, coronary pathology and smoking status were the most impactful features.

Interpretation

Here we present a significant improvement of the state of the art in cardiac risk prediction.

PMHnet-alpha supports better and optimized use of available healthcare data, signified by the vast improvement compared to GRACE2.0.

This also signifies an important paradigm shift in which data-driven strategies are necessary to transform the increasing amount of data generated in the modern healthcare system into evidence-based clinical decision making.

Funding Acknowledgement

Type of funding sources: Foundation. Main funding source(s): The Novo Nordisk Foundation, NordForsk
The use of handheld ultrasound device in cardiac examination of patients with history of COVID-19
Authors: Filipiak D , Kasprzak J D , Lipiec P
Published: December 2022

Background

Clinical usefulness of Handheld Ultrasound Device [HUD] was previously confirmed in numerous clinical scenarios. During the previous two years Covid-19 patients become a focal point of healthcare worldwide. The assessment of long term consequences of this infection is bound to overload already burdened healthcare system.

Purpose

To assess clinical usefulness of HUD as an adjunct to physical cardiac examination of patients with history of COVID-19.

Methods

Study population consisted of randomly selected patients with no symptoms of cardiovascular pathology, who had been hospitalized due to COVID-19 one year prior to examination. Physical examination and clinical assessment was augmented with short examination with the use of HUD, which included: visual evaluation of the global and regional LV function, measurement of RV size, screening for the significant valve defects and the presence of pericardial effusion. Subsequently full echocardiographic examination with the use of high-end workstation was performed, which results were treated as reference.

Results

54 patients (35 men, mean age 63±13 years) were enrolled into the study. In clinical examination no significant cardiovascular abnormalities were discovered. In 30 [56%] of patients cardiac abnormalities in HUD examination were detected. In 18 patients [33%] LV function assessment was not performed, due to insufficient quality of registered view. In the remaining group significant impairment of LV ejection fraction (<50%) was detected in HUD examination in 3 [6%] patients (2 confirmed in full examination, positive predictive value [PPV] 57%, negative predictive value [NPV] 97%, AUC 0,82±0,17, P 0,057). WMA were diagnosed in 6 [11%] patients (4 confirmed in full examination, PPV 84% NPV 78%, AUC 0,69±0,17, P 0,02). RV enlargement was identified in 21 [39%] patients (PPV 57%, NPV 97%, AUC 0,85±0,05, P<0,0001), mild pericardial effusion in 3 [6%] patient (1 confirmed in full echocardiographic examination; 2 false positive, no false negative), at least moderate mitral/tricuspid/aortic valve insufficiency in 7 [13%] patients (3 confirmed, 4 false positive cases, no false negative). A total mean time of the heart and lungs HUD examination was 2,1±0,6 minute.

Conclusion

Cardiac abnormalities exposed in brief assessment with the use HUD are a relatively common finding in asymptomatic patients previously hospitalized due to COVID infection in a 1-year follow-up, despite normal physical examination. Normal HUD examination excludes the presence of significant cardiac abnormalities with high probability. However one should keep in mind a relatively high percentage of false positive results, which may lead to an exceeding number of patients referred for a full echocardiographic examination.

Funding Acknowledgement

Type of funding sources: None.
Causation of potential drug to drug interactions alerts, alert overrides, and adverse drug events in critical cardiac patients
Authors: Mohamed Ariff A , Nalliah S N , Abdul Hadi H A H , Win N T W , Thoulath M I T , Aktifanus A T J , Mutaya S M M , Yusof A K M
Published: December 2022

Background

Drug-drug interactions (DDIs) leading to adverse drug events (ADEs) are of special interest because they represent preventable medication errors. Preventable ADE can result in errors involving the manifestation of adverse patient outcomes. Given the high complexity of critically ill cardiac patients, it is important to learn how Clinical Drug Decision Support System (CDDSS) affects outcomes in this population and the number of alerts that are likely to be safely suppressed.

Purpose

Identify adverse DDIs that are clinically detected and review the appropriateness of the doctor's actions to the potential DDIs (PDDIs) alert. Study Design: This is a prospective observational study conducted at a critical cardiac care unit (CCU) in a selected tertiary cardiac center for a duration of six months.

Methods

Physicians treating critically ill cardiac patients were presented with PDDIs data which were acquired from two commercially available CDDSS. The relationship between the decision to prescribe and factors hypothesized to affect physicians' decisions was examined.

Results

Evaluation of 709 patient medication profiles were conducted, resulting in 521 assessed patient profiles having had one or more PDDIs with 87% of them being influenced by polypharmacy. Ninety-one patients (17.5%) were associated with one or more adverse DDIs. Of the total 3284 potential DDIs alerts, 95.5% of the alerts were overridden. Preventable ADE as an outcome of inappropriate override has resulted in 83.1%. (236/284) of adverse DDIs. Whereas appropriate overrides as an outcome of clinically irrelevant ADE were 16.9% (48/284).

Conclusion

Poor preventive actions taken by the doctors caused drug-related harm to the patients despite having CDDSS in place. This suggests that CDDSS is an important application to minimize the harm associated with adverse DDIs by alerting physicians of potentially unsafe situations.

Funding Acknowledgement

Type of funding sources: Public Institution(s). Main funding source(s): International Medical UniversityNational Heart Institute
Perspectives on the use of digital health technologies in cardiology among specialists from an ESC member country: results from a survey
Authors: Kocyigit D , Keser N , Simsek E , Bozyel S , Dalgic Y , Seker M , Korkmaz Y , Guler A , Cinar T , Erturk M
Published: December 2022

Background

Digital health technologies (DHTs) have the potential of facilitating both physicians' and patients' lives with regards to the diagnosis, treatment and follow-up of cardiovascular disease. A huge acceleration in relevant research has been noted particularly after the COVID-19 pandemic.

Purpose

The goal of this study was to determine the reasons for using DHTs in cardiology, as well as the perceived barriers to its use.

Methods

An electronic survey with 43 question multiple-choice questionnaire was conducted between January 10-March 3, 2022. National cardiac society member cardiology fellows in training and specialists were contacted via e-mail (n=2789).

Results

308/2789 (11.04%) subjects responded to the survey (72.40% males, 62.01% aged 30–44 years). 42.53% and 44.81% were affiliated with university hospitals and state hospitals, respectively. 88/297 (29.63%) stated having at least good understanding of DHTs in cardiology. 44.16% utilized smart devices to track their own health status. 117/290 (40.34%) and 193/299 (64.55%) used social media platforms to share medical information with their patients and other physicians, respectively. WhatsApp and Instagram were the most popular platforms for sharing with patients (92/117, 78.63% and 48/117, 41.03%), while WhatsApp and Twitter were the most popular platforms for sharing with other physicians (151/193, 78.24% and 91/193, 47.15%). Considerations and recommendation/utilization patterns of DHTs by physicians are summarized in Table 1. Perceived barriers to the use of DHTs in cardiology is shown on Figure 1 (A: physician-related, B: patient-related, C: technical).

Conclusion

Findings suggest that nearly half of the physicians use DHTs to collect their own health data and use social media to disseminate health information. The majority of physicians believe that DHT is beneficial to both themselves and their patients, and that DHT use in cardiology has increased as a result of the COVID-19 pandemic. To overcome the challenges to the use of DHTs in cardiology, a multilayered collaborative effort involving patient and professional organizations, as well as technical stakeholders and lawmakers, is required.

Funding Acknowledgement

Type of funding sources: None.
Assessment of heart failure parameters by telemonitoring patients with an implantable cardioverter defibrillator
Authors: Fily M , Feliachi S , Leven F , Inamo J C , Hamonic J B , Mansourati J
Published: December 2022

Introduction

Different heart failure (HF) parameters have been evaluated in patients with an implantable cardioverter defibrillator (ICD). Recently, a multi-parameters HF index available in some ICDs has been proposed for patients monitoring in order to prevent hospitalizations for HF. We sought to analyze this index in our patients, based on the occurrence of hospitalizations for HF and/or all-cause mortality. As a second endpoint, the level of NT-pro-BNP, the treatment of HF and the Minnesota score (quality of life) were as well evaluated.

Method

This was on an open, single-center, case-control study. Inclusions started on March 2017 to end up in November 2020 with the main criteria of HF with left ventricular ejection fraction (LVEF) <35%, DDD or CRT-D implanted in primary prevention. A control group was composed of patients receiving telemonitoring and a control group composed of patients receiving standard monitoring. A 1/1 pairing was carried out, based on 3 criteria: age, sex and the date of ICD implantation. Data collection was carried out in August 2021.

Results

74 patients were included in the study; none was lost to follow-up. The occurrence of hospitalization for HF and/or death from any cause was found at the end of the study in 7 of 37 patients in the remote monitoring group versus 22 events of 37 in patients receiving standard monitoring (p=0.011). The number of deaths from any cause was 4 in the study group vs 8 in the control group (NS, p=0.21). The average length of hospital stay is 6.27 days (± 23.6) for patients receiving remote monitoring compared to 9.14 days (± 22.1) p=0.64. NT-pro-BNP was found with a median level of 553 pg/mL [146; 1541] in the remotely monitored group against 498 pg/mL [211; 1400] in controls (p=0.77). The assessment of quality of life by the Minnesota score shows a similar result (p=0.7). Analysis of the treatment of HF shows a significantly (p<0.01) greater presence of anti-aldosterone in patients receiving remote monitoring.

Conclusion

Our study demonstrates the benefit of the telemonitoring protocol on the occurrence of hospitalizations for heart failure and/or all-cause mortality in heart failure patients with an ICD. We observe a significantly higher percentage of anti-aldosterone treatment in monitored patients, suggesting a more optimized medical treatment.

Funding Acknowledgement

Type of funding sources: None.
Wearable device for continuously blood pressure monitoring enable atrial fibrillation risk
Authors: Guo Y , Zhang H
Published: December 2022

Background

The epidemiological data have linked atrial fibrillation (AF) risk with hypertension.

The surging wearable devices allow noninvasive beat-by-beat blood pressure monitoring.

Objective

The present study aimed to investigate dynamic changes on blood pressure with wearable blood pressure monitors and its-related risk for AF occurrence.

Methods

In the mobile Atrial Fibrillation Application (mAFA II) programme, the subjects used the photoplethysmography (PPG)-based smart devices to detect the AF episodes. They simultaneously freely decided whether to take the wearables to monitor their blood pressure.

Results

There were 25388 subjects (mean age ± standard deviation, SD, 48±12 years old, 90.4% male) who received simultaneously blood pressure and cardiac rhythm monitoring with the wearables between Jan 1, 2022 and March 19, 2022. The monitored time (median, interquartile) were 6 (12–18) days and the number of measurements of blood pressure were 16 (3–56).

The 8830 subjects (34.8%, 8830/25388) monitored with average 24-hour BP >130/80 mm Hg and 945 subjects (10%, 945/8830) with pulse differential over 60 mmHg.

The extreme-dippers blood pressure and reverse-dippers blood pressure were in 8563 subjects (33.7%, 8563/25388) and 7018 subjects (27.6%, 7018/25388), respectively.

There were 498 subjects (58±12 years, 89% male) who identified with AF. After adjusting age, gender, diabetes, obstructive sleep apnea, heart failure, coronary artery disease, and hyperthyroidism, the average 24-hour BP >130/80 mm Hg (Odd ratio, OR, 95% confidential interval, CI, 1.38, 1.12–1.69, p=0.002), the reverse-dippers blood pressure (OR, 95% CI, 1.29, 1.07–1.56, p=0.008) and pulse differential over 60 mmHg (OR, 95% CI, 1.76,1.17–2.64, p=0.006) independently predicted the risk of AF.

Conclusion

Wearable devices with beat-by-beat monitoring demonstrated that not only high average 24-hour BP, but also reverse-dippers blood pressure and pulse differential over 60 mmHg increased AF susceptibility, which informing the clinical application of personalized “upstream risk control” of AF.

Funding Acknowledgement

Type of funding sources: Public grant(s) – National budget only. Main funding source(s): National Natural Science Foundation of China
Efficacy of controlvit as a complementary strategy in reducing hospitalizations in patients with heart failure. Randomized clinical trial
Authors: Achury D M , Garcia A G , Marino A M , Bohorquez W B , Gonzalez R G
Published: December 2022

Introduction

In patients with heart failure, the main cause of hospital admissions is due to episodes of decompensation, which are associated with an increase in the costs and resources needed for care; they have a negative impact on the quality of life and prognosis of the disease. Therefore, it is necessary to design, evaluate and implement alternatives to achieve efficient follow-up and monitoring, improving adherence to treatment and helping identify decompensation episodes that facilitate their early treatment, so new hospital admissions can be avoided. For this reason, the use of information and communication technologies (apps) is emerging as an excellent strategy for the possibility of improving coverage, real-time monitoring and timeliness of care. ControlVit® is an electronic application that allows permanent, real-time monitoring of biomedical measurements for early detection of complications.

Objective

To determine the efficacy of ControlVit® as a complementary strategy in reducing hospital admissions in patients in a heart failure program.

Methodology

A randomized, placebo-controlled, open-label clinical trial was conducted, (n=140), included patients with heart failure under controlled clinical follow-up in a structured heart failure program, into NYHA FC II–IV, capable of handling smartphones and with permanent internet connectivity. All patients were provided with educational elements, a scale and a blood pressure monitor to enter clinical data and a symptom questionnaire necessary for follow-up. The patients were randomized to 2 groups: intervention: use of ControlVit® vs. placebo, with a 6-month follow-up. The primary outcome was the difference in readmissions for heart failure between the 2 groups. Patients on the waiting list for heart transplantation or use of ventricular assist devices were excluded. All participants signed informed consent and the protocol was approved by the institutional ethics committee.

Results

A total of 140 patients were included in the study (intervention = 71, placebo = 69), with an average age of 66 years, 71% were men, the main etiology of heart failure was ischemic (60%), the main comorbidities were arterial hypertension (44%), dyslipidemia (42%), hypothyroidism (38%), chronic kidney disease (38%), and diabetes mellitus (27%). During follow-up, there were 6 deaths (3 in each group) and 17 readmissions (3 in the intervention group and 14 in the control group). After adjustment for comorbidities, HR 0.72 CI95 (0.52; 0.92), p=0.0325 was found.

Conclusion

In patients with heart failure, ControlVit® is a useful and complementary tool for follow-up, which reduces hospital admissions due to episodes of decompensation.

Funding Acknowledgement

Type of funding sources: Other. Main funding source(s): German Saldarriega award.Javeriana University
Cardiovascular risk profile after a complicated pregnancy across ethnic groups: the HELIUS study
Authors: Burger Renée J , Gordijn Sanne J , Bolijn Renee , Reilingh Annemarie , Moll Van Charante Eric P , Van Den Born Bert-Jan H , De Groot Christianne J M , Ravelli Anita C J , Galenkamp Henrike , Van Valkengoed Irene G M , Ganzevoort Wessel
Published: December 2022

Aims

Little is known about how pregnancy complications and cardiovascular disease (CVD) risk are associated, specifically among ethnic minorities. In this study, we examined this association in women from six ethnic groups, and the potential value of pregnancy complications as eligibility criterion for CVD risk screening.

Methods and results

We conducted a cross-sectional study combining obstetric history from the Dutch perinatal registry with data on cardiovascular risk up to 15 years after pregnancy from the multi-ethnic HELIUS study. We included 2466 parous women of Dutch, South-Asian Surinamese, African Surinamese, Ghanaian, Turkish, and Moroccan origin. Associations were studied across ethnicities and predictive value of pregnancy complications for CVD risk factors above traditional eligibility criteria for CVD risk screening was assessed using Poisson regression. History of hypertensive disorders of pregnancy and preterm birth were associated with higher prevalence of chronic hypertension and chronic kidney disease across most groups [prevalence ratio (PR): 1.6–1.9]. Gestational diabetes mellitus was associated with increased type 2 diabetes mellitus risk, particularly in ethnic minority groups (PR: 4.5–7.7). Associations did not significantly differ across ethnic groups. The prediction models did not improve substantially after adding pregnancy complications to traditional eligibility criteria for CVD risk screening.

Conclusion

History of hypertensive disorders of pregnancy, preterm birth, and gestational diabetes mellitus is associated with CVD risk factors in parous women, without evidence of a differential association across ethnic groups. However, addition of pregnancy complications to traditional eligibility criteria for CVD risk screening does not substantially improve the prediction of prevalent CVD risk factors.
Global financing for health policy and systems research: a review of funding opportunities
Authors: Kentikelenis Alexander , Ghaffar Abdul , McKee Martin , Dal Zennaro Livia , Stuckler David
Published: December 2022

Health policy and systems research (HPSR) is a neglected area in global health financing. Despite repeated calls for greater investment, it seems that there has been little growth. We analysed trends in reported funding and activity between 2015 and 2021 using a novel real-time source of global health data, the Devex.com database, the world’s largest source of funding opportunities related to international development. We performed a systematic search of the Devex.com database for HPSR-related terms with a focus on low- and middle-income countries. We included ‘programs’, ‘tenders & grants’ and ‘contract awards’, covering all call statuses (open, closed or forecast). Such funding opportunities were included if they were related specifically to HPSR funding or had an HPSR component; pure biomedical funding was excluded. Our findings reveal a relative neglect of HPSR, as only ∼2% of all global health funding calls included a discernible HPSR component. Despite increases in funding calls until 2019, this situation reversed in 2020, likely reflecting the redirection of resources to rapid assessments of the impacts of the coronavirus disease 2019 (COVID-19) pandemic. Most identified projects represented small-scale opportunities—commonly for consultancies or technical assistance. To the extent that new data were generated, these projects were either tied to a specific large intervention or were narrow in scope to meet a specific challenge—with many examples informing policy responses to the Covid-19 pandemic. Nearly half of advertised funding opportunities were multi-country projects, usually addressing global policy priorities like health systems strengthening or development of coordinated public health policies at a regional level. The Covid-19 pandemic has shown why investing in HPSR is more important than ever to enable the delivery of effective health interventions and avoid costly implementation failures. The evidence presented here highlights the need to scale up efforts to convince global health funders to institutionalize the inclusion of HPSR components in all funding calls.
Feeding by larval fish: how taxonomy, body length, mouth size, and behaviour contribute to differences among individuals and species from a coastal ecosystem
Authors: Pepin Pierre
Published: December 2022

Data on individual stomach contents were used to describe length-dependent differences in feeding success of larvae of 11 species of fish found in coastal Newfoundland, Canada. Copepods dominated the diet with a gradual shift from nauplii to copepodites in all species. Differences in feeding success in both prey number and gut fullness among individual larvae was linked to increasing individual diet diversity in all taxa, although there was a weak decline in mean prey size. Maxilla and body length, within and among taxa, have a dominant positive influence on the potential feeding success of larval fish. In addition to differences in average stomach weight, the variability in number of prey per stomach among individuals indicates that each species perceives their prey environment in different ways. Taxonomic proximity had limited effect on differences in feeding success among taxa. The results suggest that behavioural differences among individuals and taxa, that likely reflect swimming capacity and/or prey perception/capture ability, are likely to be important elements contributing to feeding success. Body and mouth size may represent key characteristics that should be considered in evaluating differences in feeding success among species as well as among individuals within and among cohorts.
Residues, Grothendieck Polynomials, and K-Theoretic Thom Polynomials
Authors: Rimányi Richárd , Szenes András
Published: December 2022

Grothendieck polynomials were introduced by Lascoux and Schützenberger and play an important role in K-theoretic Schubert calculus. In this paper, we give a new definition of double stable Grothendieck polynomials based on an iterated residue operation. We illustrate the power of our definition by calculating the Grothendieck expansion of K-theoretic Thom polynomials of ${\mathcal {A}}_{2}$ singularities. We present this expansion in two versions: one displays its stabilization property, while the other displays its expected finiteness property.
Relationship between piperacillin concentrations, clinical factors and piperacillin/tazobactam-associated acute kidney injury
Authors: Tang Girdwood Sonya , Hasson Denise , Caldwell J Timothy , Slagle Cara , Dong Shun , Fei Lin , Tang Peter , Vinks Alexander A , Kaplan Jennifer , Goldstein Stuart L
Published: December 2022

Background

Piperacillin/tazobactam, a commonly used antibiotic, is associated with acute kidney injury (AKI). The relationship between piperacillin concentrations and AKI remains unknown.

Objective

Estimate piperacillin exposures in critically ill children and young adults administered piperacillin/tazobactam to identify concentrations and clinical factors associated with piperacillin-associated AKI.

Patients and methods

We assessed piperacillin pharmacokinetics in 107 patients admitted to the paediatric ICU who received at least one dose of piperacillin/tazobactam. Piperacillin AUC, highest peak (Cmax) and highest trough (Cmin) in the first 24 hours of therapy were estimated. Piperacillin-associated AKI was defined as Kidney Disease: Improving Global Outcomes (KDIGO) Stage 2/3 AKI present >24 hours after initial piperacillin/tazobactam dose. Likelihood of piperacillin-associated AKI was rated using the Naranjo Adverse Drug Reaction Probability Scale. Multivariable logistic regression was performed to identify patient and clinical predictors of piperacillin-associated AKI.

Results

Out of 107 patients, 16 (15%) were rated as possibly or probably having piperacillin-associated AKI. Estimated AUC and highest Cmin in the first 24 hours were higher in patients with piperacillin-associated AKI (2042 versus 1445 mg*h/L, P = 0.03; 50.1 versus 10.7 mg/L, P < 0.001). Logistic regression showed predictors of piperacillin-associated AKI included higher Cmin (OR: 5.4, 95% CI: 1.7–23) and age (OR: 1.13, 95% CI: 1.05–1.25).

Conclusions

We show a relationship between estimated piperacillin AUC and highest Cmin in the first 24 hours of piperacillin/tazobactam therapy and piperacillin-associated AKI, suggesting total piperacillin exposure early in the course is associated with AKI development. These data could serve as the foundation for implementation of model-informed precision dosing to reduce AKI incidence in patients given piperacillin/tazobactam.
Characterizing the relative frequency of clinician engagement with structured social determinants of health data
Authors: Iott Bradley E , Adler-Milstein Julia , Gottlieb Laura M , Pantell Matthew S
Published: December 2022

Objective

Electronic health records (EHRs) are increasingly used to capture social determinants of health (SDH) data, though there are few published studies of clinicians’ engagement with captured data and whether engagement influences health and healthcare utilization. We compared the relative frequency of clinician engagement with discrete SDH data to the frequency of engagement with other common types of medical history information using data from inpatient hospitalizations.

Materials and Methods

We created measures of data engagement capturing instances of data documentation (data added/updated) or review (review of data that were previously documented) during a hospitalization. We applied these measures to four domains of EHR data, (medical, family, behavioral, and SDH) and explored associations between data engagement and hospital readmission risk.

Results

SDH data engagement was associated with lower readmission risk. Yet, there were lower levels of SDH data engagement (8.37% of hospitalizations) than medical (12.48%), behavioral (17.77%), and family (14.42%) history data engagement. In hospitalizations where data were available from prior hospitalizations/outpatient encounters, a larger proportion of hospitalizations had SDH data engagement than other domains (72.60%).

Discussion

The goal of SDH data collection is to drive interventions to reduce social risk. Data on when and how clinical teams engage with SDH data should be used to inform informatics initiatives to address health and healthcare disparities.

Conclusion

Overall levels of SDH data engagement were lower than those of common medical, behavioral, and family history data, suggesting opportunities to enhance clinician SDH data engagement to support social services referrals and quality measurement efforts.
Growth curve modeling of virtual events and online engagement in a palliative care peer support online health community for adolescents and young adults
Authors: Walker Andrew L , Swygert Anna , Marchi Emily , Lebeau Kelsea , Haardörfer Regine , Livingston Melvin D
Published: December 2022

Objective

Online health communities (OHCs) have been identified as important outlets for social support and community connection for adolescents and young adults (AYAs) living with chronic illnesses. Despite evident benefits, there remains a gap in research on methods to maximize sustained patient engagement within OHCs. This study assessed per-patient daily commenting rates over time, as well as associations with program staff and volunteer-facilitated events and engagement.

Materials and Methods

We utilized data from 662 daily patient, volunteer, and staff comment totals within a Discord server hosted through the Streetlight at UF Health Streetlight Gaming and Online Team, between January 2019 and January 2022. Multilevel models were used to assess per-patient daily commenting rates and examine associations with OHC-level predictors of staff and volunteer-facilitated daily and seasonal events, as well as the number of daily active users.

Results

Per-patient comment rates showed an overall negative slope with time in most models. Unadjusted and adjusted growth curve models showed that daily events (β = .21), seasonal events (β = .18), and total daily active users (β = .09) were all significantly associated with increases in per-patient daily comment rates.

Discussion

Results suggest that social event facilitation strategies can be applied to increase AYA patient engagement in OHCs. Seasonal events and staff and volunteer engagement may be the effective means of maintaining engagement among long-term patients.

Conclusion

Our findings highlight the importance of staff and volunteer presence in OHCs in driving long-term patient engagement and in considering patient needs and perspectives in developing OHC features.
Characterization of the enzymatic properties of human RNPEPL1/aminopeptidase Z
Authors: Ohnishi Atsushi , Tsujimoto Masafumi
Published: December 2022

It is now evident that the M1 family of aminopeptidases play important roles in many pathophysiological processes. Among them, the enzymatic properties of arginyl aminopeptidase-like 1 (RNPEPL1) are characterized only by its truncated form. No peptide substrate has been identified. To characterize the enzymatic properties of RNPEPL1 in more detail, the full-length protein was expressed in Escherichia coli and purified to homogeneity. The full-length RNPEPL1 showed rather restricted substrate specificity and basic amino acid preference towards synthetic substrates, which was different from the previously reported specificity characterized by the truncated form. Searching for peptide substrates, we found that several peptides, such as Met-enkephalin and kallidin, were cleaved. RNPEPL1 cleaved bradykinin to de-[Arg]-bradykinin despite the presence of proline at the P2’-position. The enzyme cleaved Met-enkephalin but not dynorphin A1–17. Similar to aminopeptidase B, the full-length RNPEPL1 showed basic amino acid preference towards both synthetic and peptide substrates. In addition to the unusual cleavage of bradykinin, this enzyme shows chain length-dependent cleavage of peptide substrates sharing N-terminal amino acid sequence. This is the first study to report the enzymatic properties of the full-length human RNPEPL1 as an aminopeptidase enzyme.
History Does Not Repeat Itself; It Rhymes: Range Expansion and Outbreak of Plecia longiforceps (Diptera: Bibionidae) in East Asia
Authors: Kim Sangil , Jung Seunghun , Choi Jonghwan , Tsai Cheng-Lung , Farrell Brian D , Shin Seunggwan
Published: December 2022

Plecia longiforceps Duda (Diptera: Bibionidae) is reported for the first time from Korea. P. longiforceps has been previously known from the East Asian subtropics, south of the 33rd parallel, including southeastern China, Taiwan, and the Ryukyu Islands of Japan. An integrative taxonomic approach based on morphological examination of male genitalia and molecular analysis of mitochondrial cytochrome c oxidase subunit I gene sequences confirms the species identification. The recent outbreak of P. longiforceps in the Seoul Metropolitan Area, Korea, documented herein represents the northernmost record of this species and suggests its possible range expansion into the temperate zone. Similar to the range expansion and outbreak history of Plecia nearctica Hardy (Diptera: Bibionidae) in North America around the Gulf of Mexico in the 1960–1970s, P. longiforceps may become a new invasive pest in temperate East Asia. Here, we evaluate range expansion and invasion potential of P. longiforceps through Ensemble species distribution modeling and show that a great portion of Northeast Asia and Japan will likely become habitable for P. longiforceps in the next 50 years.
Attitudes About Honey Bees and Pollinator-Friendly Practices: A Survey of Iowan Beekeepers, Farmers, and Landowners
Authors: Cass Randall P , Hodgson Erin W , O’Neal Matthew E , Toth Amy L , Dolezal Adam G
Published: December 2022

The north central state of Iowa is heavily dominated by agriculture, with limited nesting and foraging habitat for wild and managed pollinators. This region has been identified as critical for pollinator conservation for bees and non-bee species (e.g., monarch butterfly [Danaus plexippus]). Pollinator sustainability (i.e., wild bee conservation and sustainable beekeeping) relies upon management decisions by beekeepers, farmers, and landowners, but it is unclear if these stakeholders are aware of and use best practices. We present results of surveys administered to Iowan beekeepers, farmers, and landowners after extension programming on best management practices for sustaining honey bees (Apis mellifera) and other pollinators. Results show beekeepers, farmers, and landowners believe implementing pollinator-friendly practices is important. However, knowledge of tools to implement these practices, such as registering apiaries to avoid pesticide drift or cost-share programs for establishing habitat on their land, was very low. These results suggest opportunities exist for connecting beekeepers seeking higher quality apiaries with landowners that have established habitat with added floral resources. Further, we reviewed practices that reconstruct tallgrass prairie with support of federal cost-share funds, specifically CP42 and CP43 of the USDA’s conservation reserve program. Overall, we document strong support and enthusiasm for pollinator-friendly practices in Iowa but note gaps in knowledge of practices that are most effective and what tools are available to implement them. These results suggest a way for pollinator extension programs to address these knowledge gaps in a receptive group of beekeepers, farmers, and landowners.
Risk factors for long-term decline in post-operative pulmonary function after lung resection
Authors: Tsubokawa Norifumi , Mimura Takeshi , Tadokoro Kazuki , Yamashita Yoshinori
Published: December 2022

Objectives

The study aimed to examine the risk factors for long-term decline in pulmonary function after anatomical resection for lung cancer and the effects of the decrease on survival.

Methods

We retrospectively examined 489 patients who underwent anatomical resection for lung cancer between 2010 and 2020. Pulmonary function tests were performed preoperatively and at 1, 3, 6 and 12 months after surgery. The lower interquartile medians of the reduction rates of forced expiratory volume in 1 s and vital capacity at 12 months after surgery were taken as the cut-off values of risk factors for the decrease in post-operative pulmonary function.

Results

Forced expiratory volume in 1 s and vital capacity decreased the most in the first month after surgery and then gradually recovered. Vital capacity continued to increase even after 6 months post-surgery, whereas forced expiratory volume in 1 s stabilized. Multivariable logistic analysis showed that the number of resected segments (odds ratio, 2.09; 95% confidence interval, 1.12–3.89; P = 0.019) was a risk factor for the decrease in forced expiratory volume in 1 s at 12 months, and the numbers of resected segments (odds ratio, 1.36; 95% confidence interval, 1.13–1.63; P < 0.001) and post-operative complications (odds ratio, 2.32; 95% confidence interval, 1.01–5.35; P = 0.047) were independent risk factors for decrease in vital capacity. Multivariate cox regression analysis showed that the decrease in vital capacity at 12 months was significantly associated with overall survival (hazard ratio, 2.02; 95% confidence interval, 1.24–3.67; P = 0.004).

Conclusions

Long-term decrease in vital capacity, which was influenced by the number of resected segments and post-operative complications, adversely affected survival.