PeptiVAX: A new adaptable peptides-delivery platform for development of CTL-based, SARS-CoV-2 vaccines.

The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) posed a threat to public health and the global economy, necessitating the development of various vaccination strategies. Mutations in the SPIKE protein gene, a crucial component of mRNA and adenovirus-based vaccines, raised concerns about vaccine efficacy, prompting the need for rapid vaccine updates. To address this, we leveraged PeptiCRAd, an oncolytic vaccine based on tumor antigen decorated oncolytic adenoviruses, creating a vaccine platform called PeptiVAX. First, we identified multiple CD8 T-cell epitopes from highly conserved regions across coronaviruses, expanding the range of T-cell responses to non-SPIKE proteins. We designed short segments containing the predicted epitopes presented by common HLA-Is in the global population. Testing the immunogenicity, we characterized T-cell responses to candidate peptides in peripheral blood mononuclear cells (PBMCs) from pre-pandemic healthy donors and ICU patients. As a proof of concept in mice, we selected a peptide with epitopes predicted to bind to murine MHC-I haplotypes. Our technology successfully elicited peptide-specific T-cell responses, unaffected by the use of unarmed adenoviral vectors or adeno-based vaccines encoding SPIKE. In conclusion, PeptiVAX represents a fast and adaptable SARS-CoV-2 vaccine delivery system that broadens T-cell responses beyond the SPIKE protein, offering potential benefits for vaccine effectiveness.

Risk factors associated with 31-day unplanned hospital readmission in newborns: a systematic review.

The purpose of this study is to synthesize evidence on risk factors associated with newborn 31-day unplanned hospital readmissions (UHRs). A systematic review was conducted searching CINAHL, EMBASE (Ovid), and MEDLINE from January 1st 2000 to 30th June 2021. Studies examining unplanned readmissions of newborns within 31 days of discharge following the initial hospitalization at the time of their birth were included. Characteristics of the included studies examined variables and statistically significant risk factors were extracted from the inclusion studies. Extracted risk factors could not be pooled statistically due to the heterogeneity of the included studies. Data were synthesized using content analysis and presented in narrative and tabular form. Twenty-eight studies met the eligibility criteria, and 17 significant risk factors were extracted from the included studies. The most frequently cited risk factors associated with newborn readmissions were gestational age, postnatal length of stay, neonatal comorbidity, and feeding methods. The most frequently cited maternal-related risk factors which contributed to newborn readmissions were parity, race/ethnicity, and complications in pregnancy and/or perinatal period. CONCLUSION: This systematic review identified a complex and diverse range of risk factors associated with 31-day UHR in newborn. Six of the 17 extracted risk factors were consistently cited by studies. Four factors were maternal (primiparous, mother being Asian, vaginal delivery, maternal complications), and two factors were neonatal (male infant and neonatal comorbidities). Implementation of evidence-based clinical practice guidelines for inpatient care and individualized hospital-to-home transition plans, including transition checklists and discharge readiness assessments, are recommended to reduce newborn UHRs. WHAT IS KNOWN: • Attempts have been made to identify risk factors associated with newborn UHRs; however, the results are inconsistent. WHAT IS NEW: • Six consistently cited risk factors related to newborn 31-day UHRs. Four maternal factors (primiparous, mother being Asian, vaginal delivery, maternal complications) and 2 neonatal factors (male infant and neonatal comorbidities). • The importance of discharge readiness assessment, including newborn clinical fitness for discharge and parental readiness for discharge. Future research is warranted to establish standardised maternal and newborn-related variables which healthcare providers can utilize to identify newborns at greater risk of UHRs and enable comparison of research findings.

A smartphone-based algorithm comprising cough analysis and patient-reported symptoms identifies acute exacerbations of asthma: a prospective, double blind, diagnostic accuracy study.

Objective: Early and accurate recognition of asthma exacerbations reduces the duration and risk of hospitalization. Current diagnostic methods depend upon patient recognition of symptoms, expert clinical examination, or measures of lung function. Here, we aimed to develop and test the accuracy of a smartphone-based diagnostic algorithm that analyses five cough events and five patient-reported features (age, fever, acute or productive cough and wheeze) to detect asthma exacerbations.Methods: We conducted a double-blind, prospective, diagnostic accuracy study comparing the algorithm with expert clinical opinion and formal lung function testing. Results: One hundred nineteen participants >12 years with a physician-diagnosed history of asthma were recruited from a hospital in Perth, Western Australia: 46 with clinically confirmed asthma exacerbations, 73 with controlled asthma. The groups were similar in median age (54yr versus 60yr, p=0.72) and sex (female 76% versus 70%, p=0.5). The algorithm's positive percent agreement (PPA) with the expert clinical diagnosis of asthma exacerbations was 89% [95% CI: 76%, 96%]. The negative percent agreement (NPA) was 84% [95% CI: 73%, 91%]. The algorithm's performance for asthma exacerbations diagnosis exceeded its performance as a detector of patient-reported wheeze (sensitivity, 63.7%). Patient-reported wheeze in isolation was an insensitive marker of asthma exacerbations (PPA=53.8%, NPA=49%). Conclusions: Our diagnostic algorithm accurately detected the presence of an asthma exacerbation as a point-of-care test without requiring clinical examination or lung function testing. This method could improve the accuracy of telehealth consultations and might be helpful in Asthma Action Plans and patient-initiated therapy.

Accuracy, interpretability and usability study of a wireless self-guided fetal heartbeat monitor compared to cardiotocography.

Fetal Cardiography is usually performed using in-hospital Cardiotocographic (CTG) devices to assess fetal wellbeing. New technologies may permit home-based, self-administered examinations. We compared the accuracy, clinical interpretability, and user experience of a patient-administered, wireless, fetal heartbeat monitor (HBM) designed for home use, to CTG. Initially, participants had paired HBM and CTG examinations performed in the clinic. Women then used the HBM unsupervised and rated the experience. Sixty-three women had paired clinic-based HBM and CTG recordings, providing 6982 fetal heart rate measures for point-to-point comparison from 126 min of continuous recording. The accuracy of the HBM was excellent, with limits of agreement (95%) for mean fetal heart rate (FHR) between 0.72 and -1.78 beats per minute. The FHR was detected on all occasions and confirmed to be different from the maternal heart rate. Both methods were equally interpretable by Obstetricians, and had similar signal loss ratios. Thirty-four (100%) women successfully detected the FHR and obtained clinically useful cardiographic data using the device at home unsupervised. They achieved the required length of recording required for non-stress test analysis. The monitor ranked in the 96-100th percentile for usability and learnability. The HBM is as accurate as gold-standard CTG, and provides equivalent clinical information enabling use in non-stress test analyses conducted outside of hospitals. It is usable by expectant mothers with minimal training.

Chronic exposure to carbon black ultrafine particles reprograms macrophage metabolism and accelerates lung cancer.

Chronic exposure to airborne carbon black ultrafine (nCB) particles generated from incomplete combustion of organic matter drives IL-17A-dependent emphysema. However, whether and how they alter the immune responses to lung cancer remains unknown. Here, we show that exposure to nCB particles increased PD-L1+ PD-L2+ CD206+ antigen-presenting cells (APCs), exhausted T cells, and Treg cells. Lung macrophages that harbored nCB particles showed selective mitochondrial structure damage and decreased oxidative respiration. Lung macrophages sustained the HIF1α axis that increased glycolysis and lactate production, culminating in an immunosuppressive microenvironment in multiple mouse models of non-small cell lung cancers. Adoptive transfer of lung APCs from nCB-exposed wild type to susceptible mice increased tumor incidence and caused early metastasis. Our findings show that nCB exposure metabolically rewires lung macrophages to promote immunosuppression and accelerates the development of lung cancer.

Consistency of pediatric pain ratings between dyads: an updated meta-analysis and metaregression.

Accurate assessment of pediatric pain remains a challenge, especially for children who are preverbal or unable to communicate because of their health condition or a language barrier. A 2008 meta-analysis of 12 studies found a moderate correlation between 3 dyads (child-caregiver, child-nurse, and caregiver-nurse). We updated this meta-analysis, adding papers published up to August 8, 2021, and that included intraclass correlation/weighted kappa statistics (ICC/WK) in addition to standard correlation. Forty studies (4,628 children) were included. Meta-analysis showed moderate pain rating consistency between child and caregiver (ICC/WK = 0.51 [0.39-0.63], correlation = 0.59 [0.52-0.65], combined = 0.55 [0.48-0.62]), and weaker consistency between child and health care provider (HCP) (ICC/WK = 0.38 [0.19-0.58], correlation = 0.49 [0.34-0.55], combined = 0.45; 95% confidence interval 0.34-0.55), and between caregiver and HCP (ICC/WK = 0.27 [-0.06 to 0.61], correlation = 0.49 [0.32 to 0.59], combined = 0.41; 95% confidence interval 0.22-0.59). There was significant heterogeneity across studies for all analyses. Metaregression revealed that recent years of publication, the pain assessment tool used by caregivers (eg, Numerical Rating Scale, Wong-Baker Faces Pain Rating Scale, and Visual Analogue Scale), and surgically related pain were each associated with greater consistency in pain ratings between child and caregiver. Pain caused by surgery was also associated with improved rating consistency between the child and HCP. This updated meta-analysis warrants pediatric pain assessment researchers to apply a comprehensive pain assessment scale Patient-Reported Outcomes Measurement Information System to acknowledge psychological and psychosocial influence on pain ratings.

Diagnostic Errors Are Common in Acute Pediatric Respiratory Disease: A Prospective, Single-Blinded Multicenter Diagnostic Accuracy Study in Australian Emergency Departments.

Background: Diagnostic errors are a global health priority and a common cause of preventable harm. There is limited data available for the prevalence of misdiagnosis in pediatric acute-care settings. Respiratory illnesses, which are particularly challenging to diagnose, are the most frequent reason for presentation to pediatric emergency departments. Objective: To evaluate the diagnostic accuracy of emergency department clinicians in diagnosing acute childhood respiratory diseases, as compared with expert panel consensus (reference standard). Methods: Prospective, multicenter, single-blinded, diagnostic accuracy study in two well-resourced pediatric emergency departments in a large Australian city. Between September 2016 and August 2018, a convenience sample of children aged 29 days to 12 years who presented with respiratory symptoms was enrolled. The emergency department discharge diagnoses were reported by clinicians based upon standard clinical diagnostic definitions. These diagnoses were compared against consensus diagnoses given by an expert panel of pediatric specialists using standardized disease definitions after they reviewed all medical records. Results: For 620 participants, the sensitivity and specificity (%, [95% CI]) of the emergency department compared with the expert panel diagnoses were generally poor: isolated upper respiratory tract disease (64.9 [54.6, 74.4], 91.0 [88.2, 93.3]), croup (76.8 [66.2, 85.4], 97.9 [96.2, 98.9]), lower respiratory tract disease (86.6 [83.1, 89.6], 92.9 [87.6, 96.4]), bronchiolitis (66.9 [58.6, 74.5], 94.3 [80.8, 99.3]), asthma/reactive airway disease (91.0 [85.8, 94.8], 93.0 [90.1, 95.3]), clinical pneumonia (63·9 [50.6, 75·8], 95·0 [92·8, 96·7]), focal (consolidative) pneumonia (54·8 [38·7, 70·2], 86.2 [79.3, 91.5]). Only 59% of chest x-rays with consolidation were correctly identified. Between 6.9 and 14.5% of children were inappropriately prescribed based on their eventual diagnosis. Conclusion: In well-resourced emergency departments, we have identified a previously unrecognized high diagnostic error rate for acute childhood respiratory disorders, particularly in pneumonia and bronchiolitis. These errors lead to the potential of avoidable harm and the administration of inappropriate treatment.

Identifying acute exacerbations of chronic obstructive pulmonary disease using patient-reported symptoms and cough feature analysis.

Acute exacerbations of chronic obstructive pulmonary disease (AECOPD) are commonly encountered in the primary care setting, though the accurate and timely diagnosis is problematic. Using technology like that employed in speech recognition technology, we developed a smartphone-based algorithm for rapid and accurate diagnosis of AECOPD. The algorithm incorporates patient-reported features (age, fever, and new cough), audio data from five coughs and can be deployed by novice users. We compared the accuracy of the algorithm to expert clinical assessment. In patients with known COPD, the algorithm correctly identified the presence of AECOPD in 82.6% (95% CI: 72.9-89.9%) of subjects (n = 86). The absence of AECOPD was correctly identified in 91.0% (95% CI: 82.4-96.3%) of individuals (n = 78). The diagnostic agreement was maintained in milder cases of AECOPD (PPA: 79.2%, 95% CI: 68.0-87.8%), who typically comprise the cohort presenting to primary care. The algorithm may aid early identification of AECOPD and be incorporated in patient self-management plans.

Using machine learning to predict paediatric 30-day unplanned hospital readmissions: a case-control retrospective analysis of medical records, including written discharge documentation.

Objectives To assess whether adding clinical information and written discharge documentation variables improves prediction of paediatric 30-day same-hospital unplanned readmission compared with predictions based on administrative information alone. Methods A retrospective matched case-control study audited the medical records of patients discharged from a tertiary paediatric hospital in Western Australia (WA) between January 2010 and December 2014. A random selection of 470 patients with unplanned readmissions (out of 3330) were matched to 470 patients without readmissions based on age, sex, and principal diagnosis at the index admission. Prediction utility of three groups of variables (administrative, administrative and clinical, and administrative, clinical and written discharge documentation) were assessed using standard logistic regression and machine learning. Results Inclusion of written discharge documentation variables significantly improved prediction of readmission compared with models that used only administrative and/or clinical variables in standard logistic regression analysis (χ2 17=29.4, P=0.03). Highest prediction accuracy was obtained using a gradient boosted tree model (C-statistic=0.654), followed closely by random forest and elastic net modelling approaches. Variables highlighted as important for prediction included patients' social history (legal custody or patient was under the care of the Department for Child Protection), languages spoken other than English, completeness of nursing admission and discharge planning documentation, and timing of issuing discharge summary. Conclusions The variables of significant social history, low English language proficiency, incomplete discharge documentation, and delay in issuing the discharge summary add value to prediction models. What is known about the topic? Despite written discharge documentation playing a critical role in the continuity of care for paediatric patients, limited research has examined its association with, and ability to predict, unplanned hospital readmissions. Machine learning approaches have been applied to various health conditions and demonstrated improved predictive accuracy. However, few published studies have used machine learning to predict paediatric readmissions. What does this paper add? This paper presents the findings of the first known study in Australia to assess and report that written discharge documentation and clinical information improves unplanned rehospitalisation prediction accuracy in a paediatric cohort compared with administrative data alone. It is also the first known published study to use machine learning for the prediction of paediatric same-hospital unplanned readmission in Australia. The results show improved predictive performance of the machine learning approach compared with standard logistic regression. What are the implications for practitioners? The identified social and written discharge documentation predictors could be translated into clinical practice through improved discharge planning and processes, to prevent paediatric 30-day all-cause same-hospital unplanned readmission. The predictors identified in this study include significant social history, low English language proficiency, incomplete discharge documentation, and delay in issuing the discharge summary.

Accuracy, Clinical Utility, and Usability of a Wireless Self-Guided Fetal Heart Rate Monitor.

OBJECTIVE: To evaluate the accuracy, clinical utility, and usability of a wireless fetal and maternal heartbeat monitor to monitor fetal heart rate (FHR). METHODS: We conducted a prospective, single-center study of a convenience sample of women aged 18 years or older with a singleton pregnancy of at least 12 weeks of gestation. Fetal heart rate recordings were performed using both the heartbeat monitor and cardiotocography to evaluate accuracy. Clinicians used the heartbeat monitor in the clinic. Women used the device, unassisted, during a clinic visit or at home. Obstetricians assessed the clinical utility of FHR traces. Women rated the heartbeat monitor using the System Usability Scale. RESULTS: A total of 81 participants provided 126 recordings. The accuracy of the heartbeat monitor was excellent compared with cardiotocography, with limits of agreement (95%) for mean FHR between -1.6 (CI -2.0 to 1.3) and +1.0 (CI 0.7-1.4) beats per minute (bpm), mean difference -0.3 bpm, intraclass coefficient 0.99. The FHR was detected on all occasions. Clinicians took a median (interquartile range) of 0.5 (0.2-1.2) minutes to detect the FHR, obtaining a continuous trace of longer than 1 minute in 95% (39/41) of occasions. Home users took a median of 0.5 (0.2-2.0) minutes to detect the FHR, obtaining a continuous trace of longer than 1 minute in 92% (24/26) of occasions, with a median total trace time of 4.6 (4.4-4.8) minutes. The traces were deemed clinically useful in 100% (55/55) of clinician and 97% (31/32) of home recordings. The heartbeat monitor ranked in the 96-100th percentile for usability and learnability. CONCLUSION: The heartbeat monitor was accurate and easy for clinicians and participants to use. Data recorded at home were equivalent to those obtained using current assessment protocols for low-risk pregnancies, potentially allowing the device to be used in telehealth consultations. CLINICAL TRIAL REGISTRATION: Australian New Zealand Clinical Trial Registry, ACTRN12620000739910. FUNDING SOURCES: The HeraBEAT devices used in this study were loaned by HeraMED Pty Ltd (HeraMED, Netanya, ISRAEL). The study was supported by PHI Research Group (not-for-profit), which was responsible for Statistician fees and Research Assistants' salaries. Joondalup Health Campus provided infrastructure support, and IT services in-kind to the PHI research group.

Diagnosing community-acquired pneumonia via a smartphone-based algorithm: a prospective cohort study in primary and acute-care consultations.

BACKGROUND: Community-acquired pneumonia (CAP) is an essential consideration in patients presenting to primary care with respiratory symptoms; however, accurate diagnosis is difficult when clinical and radiological examinations are not possible, such as during telehealth consultations. AIM: To develop and test a smartphone-based algorithm for diagnosing CAP without need for clinical examination or radiological inputs. DESIGN AND SETTING: A prospective cohort study using data from participants aged >12 years presenting with acute respiratory symptoms to a hospital in Western Australia. METHOD: Five cough audio-segments were recorded and four patient-reported symptoms (fever, acute cough, productive cough, and age) were analysed by the smartphone-based algorithm to generate an immediate diagnostic output for CAP. Independent cohorts were recruited to train and test the accuracy of the algorithm. Diagnostic agreement was calculated against the confirmed discharge diagnosis of CAP by specialist physicians. Specialist radiologists reported medical imaging. RESULTS: The smartphone-based algorithm had high percentage agreement (PA) with the clinical diagnosis of CAP in the total cohort (n = 322, positive PA [PPA] = 86.2%, negative PA [NPA] = 86.5%, area under the receiver operating characteristic curve [AUC] = 0.95); in participants 22-<65 years (n = 192, PPA = 85.7%, NPA = 87.0%, AUC = 0.94), and in participants aged ≥65 years (n = 86, PPA = 85.7%, NPA = 87.5%, AUC = 0.94). Agreement was preserved across CAP severity: 85.1% (n = 80/94) of participants with CRB-65 scores 1 or 2, and 87.7% (n = 57/65) with a score of 0, were correctly diagnosed by the algorithm. CONCLUSION: The algorithm provides rapid and accurate diagnosis of CAP. It offers improved accuracy over current protocols when clinical evaluation is difficult. It provides increased capabilities for primary and acute care, including telehealth services, required during the COVID-19 pandemic.

The diagnosis of respiratory disease in children using a phone-based cough and symptom analysis algorithm: The smartphone recordings of cough sounds 2 (SMARTCOUGH-C 2) trial design.

The diagnosis of acute respiratory diseases in children can be challenging, and no single objective diagnostic test exists for common pediatric respiratory diseases. Previous research has demonstrated that ResAppDx, a cough sound and symptom-based analysis algorithm, can identify common respiratory diseases at the point of care. We present the study protocol for SMARTCOUGH-C 2, a prospective diagnostic accuracy trial of a cough and symptom-based algorithm in a cohort of children presenting with acute respiratory diseases. The objective of the study is to assess the performance characteristics of the ResAppDx algorithm in the diagnosis of common pediatric acute respiratory diseases.

Stratifying asthma severity in children using cough sound analytic technology.

Introduction: Asthma is a common childhood respiratory disorder characterized by wheeze, cough and respiratory distress responsive to bronchodilator therapy. Asthma severity can be determined by subjective, manual scoring systems such as the Pulmonary Score (PS). These systems require significant medical training and expertise to rate clinical findings such as wheeze characteristics, and work of breathing. In this study, we report the development of an objective method of assessing acute asthma severity based on the automated analysis of cough sounds.Methods: We collected a cough sound dataset from 224 children; 103 without acute asthma and 121 with acute asthma. Using this database coupled with clinical diagnoses and PS determined by a clinical panel, we developed a machine classifier algorithm to characterize the severity of airway constriction. The performance of our algorithm was then evaluated against the PS from a separate set of patients, independent of the training set.Results: The cough-only model discriminated no/mild disease (PS 0-1) from severe disease (PS 5,6) but required a modified respiratory rate calculation to separate very severe disease (PS > 6). Asymptomatic children (PS 0) were separated from moderate asthma (PS 2-4) by the cough-only model without the need for clinical inputs.Conclusions: The PS provides information in managing childhood asthma but is not readily usable by non-medical personnel. Our method offers an objective measurement of asthma severity which does not rely on clinician-dependent inputs. It holds potential for use in clinical settings including improving the performance of existing asthma-rating scales and in community-management programs.AbbreviationsAMaccessory muscleBIbreathing indexCIconfidence intervalFEV1forced expiratory volume in one secondLRlogistic regressionPEFRpeak expiratory flow ratePSpulmonary scoreRRrespiratory rateSDstandard deviationSEstandard errorWAWestern Australia.

COVID-19 drive-through testing survey: Measuring the burden on healthcare workers.

Objective: To survey individuals who tested positive for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) at 1 of 4 Trinity Health of New England drive-through testing centers to assess their demographic information, hospitalization rate, preexisting conditions, possible routes of exposures, duration of symptoms, and subsequent household infections of healthcare workers (HCWs) when compared to non-HCWs. Methods: Data were collected via a telephone survey using a standardized script. Between March 1, 2020 and June 17, 2020, 28,903 people were tested at 4 Connecticut drive-through testing centers. Individuals who tested positive between March 16 and April 21, 2020 were randomly contacted. Of those individuals, 100 people agreed to complete the survey. Bivariate analysis and logistic regression were performed. Results: HCWs comprised 46% of the 100 survey respondents during the study period. Similarly, HCWs comprised 42.1% of all individuals who tested positive and listed an employer between March 1 and June 17, 2020. HCWs reported a longer duration of symptoms (17.39 vs 13.44 days) and were more likely to report work as their route of exposure (80.4% vs 27.8%) than non-HCWs. Conclusions: HCWs may face a disproportionate risk of contracting COVID-19 and self-report a longer duration of symptoms than the general public. The data suggest a need for an increased recovery time away from work than is currently recommended by the Centers for Disease Control and Prevention, as well as an increase in infection precautions for HCWs.

Cigarette Smoke Exposure in Mice using a Whole-Body Inhalation System.

Close to 14% of adults in the United States were reported to smoke cigarettes in 2018. The effects of cigarette smoke (CS) on lungs and cardiovascular diseases have been widely studied, however, the impact of CS in other tissues and organs such as blood and bone marrow remain incompletely defined. Finding the appropriate system to study the effects of CS in rodents can be prohibitively expensive and require the purchase of commercially available systems. Thus, we set out to build an affordable, reliable, and versatile system to study the pathologic effects of CS in mice. This whole-body inhalation exposure system (WBIS) set-up mimics the breathing and puffing of cigarettes by alternating exposure to CS and clean air. Here we show that this do-it-yourself (DIY) system induces airway inflammation and lung emphysema in mice after 4-months of cigarette smoke exposure. The effects of whole-body inhalation (WBI) of CS on hematopoietic stem and progenitor cells (HSPCs) in the bone marrow using this apparatus are also shown.

Diagnosing Chronic Obstructive Airway Disease on a Smartphone Using Patient-Reported Symptoms and Cough Analysis: Diagnostic Accuracy Study.

BACKGROUND: Rapid and accurate diagnosis of chronic obstructive pulmonary disease (COPD) is problematic in acute care settings, particularly in the presence of infective comorbidities. OBJECTIVE: The aim of this study was to develop a rapid smartphone-based algorithm for the detection of COPD in the presence or absence of acute respiratory infection and evaluate diagnostic accuracy on an independent validation set. METHODS: Participants aged 40 to 75 years with or without symptoms of respiratory disease who had no chronic respiratory condition apart from COPD, chronic bronchitis, or emphysema were recruited into the study. The algorithm analyzed 5 cough sounds and 4 patient-reported clinical symptoms, providing a diagnosis in less than 1 minute. Clinical diagnoses were determined by a specialist physician using all available case notes, including spirometry where available. RESULTS: The algorithm demonstrated high positive percent agreement (PPA) and negative percent agreement (NPA) with clinical diagnosis for COPD in the total cohort (N=252; PPA=93.8%, NPA=77.0%, area under the curve [AUC]=0.95), in participants with pneumonia or infective exacerbations of COPD (n=117; PPA=86.7%, NPA=80.5%, AUC=0.93), and in participants without an infective comorbidity (n=135; PPA=100.0%, NPA=74.0%, AUC=0.97). In those who had their COPD confirmed by spirometry (n=229), PPA was 100.0% and NPA was 77.0%, with an AUC of 0.97. CONCLUSIONS: The algorithm demonstrated high agreement with clinical diagnosis and rapidly detected COPD in participants presenting with or without other infective lung illnesses. The algorithm can be installed on a smartphone to provide bedside diagnosis of COPD in acute care settings, inform treatment regimens, and identify those at increased risk of mortality due to seasonal or other respiratory ailments. TRIAL REGISTRATION: Australian New Zealand Clinical Trials Registry ACTRN12618001521213; http://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=375939.

Identifying Patients at Greatest Risk of Mortality due to COVID-19: A New England Perspective.

INTRODUCTION: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread rapidly since December 2019, resulting in a pandemic that has, as of May 24, 2020, yielded over 5.3 million confirmed cases and over 340,000 deaths. As businesses move to safely reopen and frontline healthcare workers (HCW) continue to face this crisis, it is essential that health officials know who in the population is at the greatest risk of mortality if hospitalized and, therefore, has the greatest need to protect themselves from being infected. We examined the factors that increase the risk of mortality among hospitalized COVID-19 patients. METHODS: This was a retrospective cohort study including confirmed COVID-19 patients admitted to the four Trinity Health of New England hospitals (THONE) in Connecticut and Massachusetts who either died or were discharged between March 1-April 22, 2020. Demographics, comorbidities, and outcomes of care were extracted from the electronic health record. A model of in-hospital mortality was made using a generalized linear model with binomial distribution and log link. RESULTS: The analysis included 346 patients: 229 discharged and 117 deceased. The likelihood of in-hospital mortality was increased for patients who were aged 60 or older (relative risk [RR] = 2.873; 95% confidence interval [CI], 1.733-4.764; p = <0.001), had diabetes (RR = 1.432; 95% CI,1.068-1.921; p = 0.016), or had chronic obstructive pulmonary disease (COPD) (RR = 1.410; 95% CI, 1.058-1.878; p = 0.019). Hyperlipidemia had a protective effect, reducing the likelihood of mortality (RR = 0.745; 95% CI, 0.568-0.975; p = 0.032). Sensitivity and specificity of the model were 51.4% and 88.4%, respectively. CONCLUSIONS: Being age 60 or older or having a history of diabetes or COPD are the most useful risk factors associated with mortality in hospitalized COVID-19 patients. As states ease stay-at-home orders, risk factors of severe disease can be used to identify those more likely to have worse outcomes if infected and hospitalized and, therefore, who in particular should continue to follow public health guidelines for avoiding infection: stay home if possible; practice physical distancing; and wear a facemask.

Towards sustainable maize production in the U.S. upper Midwest with interseeded cover crops.

The incorporation of cover crops into the maize (Zea mays L.)-soybean [Glycine max (L.) Merr.] rotation in the U.S. upper Midwest may improve sustainability. Long, cold winters in the region make identifying successful cover crop species and management practices a challenge. Two experiments were conducted in Minnesota, USA from fall 2016 through spring 2019 to examine the effect of cover crops interseeded at four- to six-leaf collar (early-interseeded) and dent to physiological maturity (late-interseeded) on biomass and grain yield of maize. Annual ryegrass (Lolium multiflorum L.) and cereal rye (Secale cereale L.) were evaluated as monocultures and in mixtures with crimson clover (Trifolium incarnatum L.) and forage radish (Raphanus sativus L.). Differences in canopy cover and biomass of late-interseeded cover crops were observed at the southernmost location in 2018. Additional accumulated growing-degree days in fall 2018 did not translate into increased cover crop canopy coverage of late-interseeded cover crops. Differences in cover crop canopy cover and biomass of early-interseeded cover crops were observed by fall frost at all locations in 2017 and at the northernmost location in 2018. Cover crop canopy cover and biomass at termination before planting maize, soil moisture at maize planting as well as maize aboveground biomass and yield were not affected by spring cereal rye regrowth of cover crops late-interseeded the previous year. Similarly, early-interseeded cover crops did not affect maize aboveground biomass or yield. We attribute these results to limited cover crop growth. This highlights the potential of a variety of cover crop strategies interseeded into maize in the U.S. upper Midwest; however, efforts to fine-tuning cover crop management and weather conditions are needed to benefit from such practice.

Risk factors associated with 30-day all-cause unplanned hospital readmissions at a tertiary children's hospital in Western Australia.

AIM: To identify risk factors associated with 30-day all-cause unplanned hospital readmission at a tertiary children's hospital in Western Australia. METHODS: An administrative paediatric inpatient dataset was analysed retrospectively. Patients of all ages discharged between 1 January 2010 and 31 December 2014 were included. Demographic and clinical information at the index admission was examined using multivariate logistic regression analysis. RESULTS: A total of 3330 patients (4.55%) experienced at least one unplanned readmission after discharge. Readmission was more likely to occur in patients who were either older than 16 years (odds ratio (OR) = 1.46; 95% confidence interval (CI) 1.07-1.98), utilising private insurance as an inpatient (OR = 1.16; 95% CI 1.00-1.34), with greater socio-economic advantage (OR = 1.20; 95% CI 1.02-1.41), admitted on Friday (OR = 1.21; 95% CI 1.05-1.39), discharged on Friday/Saturday/Sunday (OR = 1.26, 95% CI 1.10-1.44; OR = 1.34, 95% CI 1.15-1.57; OR = 1.24, 95% CI 1.05-1.47, respectively), with four or more diagnoses at the index admission (OR = 2.41; 95% CI 2.08-2.80) or hospitalised for 15 days or longer (OR = 2.39; 95% CI 1.88-2.98). Area under receiver operating characteristic curve of the predictive model is 0.645. CONCLUSIONS: A moderate discriminative ability predictive model for 30-day all-cause same hospital readmission was developed. A structured discharge plan is suggested to be commenced from admission to ensure continuity of care for patients identified as being at higher risk of readmission. A recommendation is made that a designated staff member be assigned to co-ordinate the plan, including assessment of patients' and primary carers' readiness for discharge. Further research is required to establish comprehensive paediatric readmission rates by accessing linkage data to capture different hospital readmissions.