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1.
PLoS One ; 16(12): e0261921, 2021.
Article in English | MEDLINE | ID: covidwho-1635925

ABSTRACT

Universal screening for suicidal ideation in primary care and mental health settings has become a key prevention tool in many healthcare systems, including the Veterans Healthcare Administration (VHA). In response to the coronavirus pandemic, healthcare providers faced a number of challenges, including how to quickly adapt screening practices. The objective of this analyses was to learn staff perspectives on how the pandemic impacted suicide risk screening in primary care and mental health settings. Forty semi-structured interviews were conducted with primary care and mental health staff between April-September 2020 across 12 VHA facilities. A multi-disciplinary team employed a qualitative thematic analysis using a hybrid inductive/deductive approach. Staff reported multiple concerns for patients during the crisis, especially regarding vulnerable populations at risk for social isolation. Lack of clear protocols at some sites on how to serve patients screening positive for suicidal ideation created confusion for staff and led some sites to temporarily stop screening. Sites had varying degrees of adaptability to virtual based care, with the biggest challenge being completion of warm hand-offs to mental health specialists. Unanticipated opportunities that emerged during this time included increased ability of patients and staff to conduct virtual care, which is expected to continue benefit post-pandemic.


Subject(s)
COVID-19/epidemiology , COVID-19/psychology , Health Personnel , Mass Screening/methods , Pandemics , SARS-CoV-2 , Suicidal Ideation , Veterans Health , Veterans/psychology , COVID-19/prevention & control , COVID-19/virology , Humans , Mental Health , Physical Distancing , Primary Health Care , Risk Assessment/methods , Telemedicine/methods
3.
Viruses ; 13(7)2021 07 13.
Article in English | MEDLINE | ID: covidwho-1597522

ABSTRACT

Environmental surveillance was recommended for risk mitigation in a novel oral polio vaccine-2 (nOPV2) clinical trial (M5-ABMG) to monitor excretion, potential circulation, and loss of attenuation of the two nOPV2 candidates. The nOPV2 candidates were developed to address the risk of poliovirus (PV) type 2 circulating vaccine-derived poliovirus (cVDPV) as part of the global eradication strategy. Between November 2018 and January 2020, an environmental surveillance study for the clinical trial was conducted in parallel to the M5-ABMG clinical trial at five locations in Panama. The collection sites were located upstream from local treatment plant inlets, to capture the excreta from trial participants and their community. Laboratory analyses of 49 environmental samples were conducted using the two-phase separation method. Novel OPV2 strains were not detected in sewage samples collected during the study period. However, six samples were positive for Sabin-like type 3 PV, two samples were positive for Sabin-like type 1 PV, and non-polio enteroviruses NPEVs were detected in 27 samples. One of the nOPV2 candidates has been granted Emergency Use Listing by the World Health Organization and initial use started in March 2021. This environmental surveillance study provided valuable risk mitigation information to support the Emergency Use Listing application.


Subject(s)
Environmental Monitoring/methods , Poliomyelitis/prevention & control , Poliovirus/immunology , Humans , Panama/epidemiology , Poliomyelitis/virology , Poliovirus/pathogenicity , Poliovirus Vaccine, Oral/analysis , Risk Assessment/methods , Sewage/virology , Vaccines
4.
CMAJ Open ; 9(4): E1223-E1231, 2021.
Article in English | MEDLINE | ID: covidwho-1593829

ABSTRACT

BACKGROUND: The COVID-19 pandemic has led to an increased demand for health care resources and, in some cases, shortage of medical equipment and staff. Our objective was to develop and validate a multivariable model to predict risk of hospitalization for patients infected with SARS-CoV-2. METHODS: We used routinely collected health records in a patient cohort to develop and validate our prediction model. This cohort included adult patients (age ≥ 18 yr) from Ontario, Canada, who tested positive for SARS-CoV-2 ribonucleic acid by polymerase chain reaction between Feb. 2 and Oct. 5, 2020, and were followed up through Nov. 5, 2020. Patients living in long-term care facilities were excluded, as they were all assumed to be at high risk of hospitalization for COVID-19. Risk of hospitalization within 30 days of diagnosis of SARS-CoV-2 infection was estimated via gradient-boosting decision trees, and variable importance examined via Shapley values. We built a gradient-boosting model using the Extreme Gradient Boosting (XGBoost) algorithm and compared its performance against 4 empirical rules commonly used for risk stratifications based on age and number of comorbidities. RESULTS: The cohort included 36 323 patients with 2583 hospitalizations (7.1%). Hospitalized patients had a higher median age (64 yr v. 43 yr), were more likely to be male (56.3% v. 47.3%) and had a higher median number of comorbidities (3, interquartile range [IQR] 2-6 v. 1, IQR 0-3) than nonhospitalized patients. Patients were split into development (n = 29 058, 80.0%) and held-out validation (n = 7265, 20.0%) cohorts. The gradient-boosting model achieved high discrimination (development cohort: area under the receiver operating characteristic curve across the 5 folds of 0.852; validation cohort: 0.8475) and strong calibration (slope = 1.01, intercept = -0.01). The patients who scored at the top 10% captured 47.4% of hospitalizations, and those who scored at the top 30% captured 80.6%. INTERPRETATION: We developed and validated an accurate risk stratification model using routinely collected health administrative data. We envision that modelling such risk stratification based on routinely collected health data could support management of COVID-19 on a population health level.


Subject(s)
COVID-19/epidemiology , Decision Trees , Hospitalization/statistics & numerical data , Risk Assessment , Adult , Aged , COVID-19/therapy , Female , Humans , Male , Middle Aged , Models, Statistical , Ontario/epidemiology , Risk Assessment/methods , Risk Factors
5.
Crit Care ; 25(1): 328, 2021 09 08.
Article in English | MEDLINE | ID: covidwho-1582035

ABSTRACT

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic caused by the SARS-Cov2 virus has become the greatest health and controversial issue for worldwide nations. It is associated with different clinical manifestations and a high mortality rate. Predicting mortality and identifying outcome predictors are crucial for COVID patients who are critically ill. Multivariate and machine learning methods may be used for developing prediction models and reduce the complexity of clinical phenotypes. METHODS: Multivariate predictive analysis was applied to 108 out of 250 clinical features, comorbidities, and blood markers captured at the admission time from a hospitalized cohort of patients (N = 250) with COVID-19. Inspired modification of partial least square (SIMPLS)-based model was developed to predict hospital mortality. Prediction accuracy was randomly assigned to training and validation sets. Predictive partition analysis was performed to obtain cutting value for either continuous or categorical variables. Latent class analysis (LCA) was carried to cluster the patients with COVID-19 to identify low- and high-risk patients. Principal component analysis and LCA were used to find a subgroup of survivors that tends to die. RESULTS: SIMPLS-based model was able to predict hospital mortality in patients with COVID-19 with moderate predictive power (Q2 = 0.24) and high accuracy (AUC > 0.85) through separating non-survivors from survivors developed using training and validation sets. This model was obtained by the 18 clinical and comorbidities predictors and 3 blood biochemical markers. Coronary artery disease, diabetes, Altered Mental Status, age > 65, and dementia were the topmost differentiating mortality predictors. CRP, prothrombin, and lactate were the most differentiating biochemical markers in the mortality prediction model. Clustering analysis identified high- and low-risk patients among COVID-19 survivors. CONCLUSIONS: An accurate COVID-19 mortality prediction model among hospitalized patients based on the clinical features and comorbidities may play a beneficial role in the clinical setting to better management of patients with COVID-19. The current study revealed the application of machine-learning-based approaches to predict hospital mortality in patients with COVID-19 and identification of most important predictors from clinical, comorbidities and blood biochemical variables as well as recognizing high- and low-risk COVID-19 survivors.


Subject(s)
COVID-19/mortality , Hospital Mortality/trends , Machine Learning/standards , Severity of Illness Index , COVID-19/epidemiology , Cohort Studies , Female , Humans , Male , Prognosis , Respiration, Artificial/statistics & numerical data , Risk Assessment/methods , Risk Factors
6.
PLoS One ; 16(12): e0260820, 2021.
Article in English | MEDLINE | ID: covidwho-1581771

ABSTRACT

INTRODUCTION: The COVID-19 pandemic has caused widespread disruptions including to health services. In the early response to the pandemic many countries restricted population movements and some health services were suspended or limited. In late 2020 and early 2021 some countries re-imposed restrictions. Health authorities need to balance the potential harms of additional SARS-CoV-2 transmission due to contacts associated with health services against the benefits of those services, including fewer new HIV infections and deaths. This paper examines these trade-offs for select HIV services. METHODS: We used four HIV simulation models (Goals, HIV Synthesis, Optima HIV and EMOD) to estimate the benefits of continuing HIV services in terms of fewer new HIV infections and deaths. We used three COVID-19 transmission models (Covasim, Cooper/Smith and a simple contact model) to estimate the additional deaths due to SARS-CoV-2 transmission among health workers and clients. We examined four HIV services: voluntary medical male circumcision, HIV diagnostic testing, viral load testing and programs to prevent mother-to-child transmission. We compared COVID-19 deaths in 2020 and 2021 with HIV deaths occurring now and over the next 50 years discounted to present value. The models were applied to countries with a range of HIV and COVID-19 epidemics. RESULTS: Maintaining these HIV services could lead to additional COVID-19 deaths of 0.002 to 0.15 per 10,000 clients. HIV-related deaths averted are estimated to be much larger, 19-146 discounted deaths per 10,000 clients. DISCUSSION: While there is some additional short-term risk of SARS-CoV-2 transmission associated with providing HIV services, the risk of additional COVID-19 deaths is at least 100 times less than the HIV deaths averted by those services. Ministries of Health need to take into account many factors in deciding when and how to offer essential health services during the COVID-19 pandemic. This work shows that the benefits of continuing key HIV services are far larger than the risks of additional SARS-CoV-2 transmission.


Subject(s)
COVID-19/transmission , Health Services Accessibility/trends , Health Services/trends , COVID-19/complications , COVID-19/epidemiology , HIV Infections/complications , HIV Infections/epidemiology , HIV Infections/therapy , HIV-1/pathogenicity , Health Services Administration , Humans , Models, Theoretical , Pandemics/prevention & control , Risk Assessment/methods , SARS-CoV-2/pathogenicity
7.
Ann Med ; 53(1): 402-409, 2021 12.
Article in English | MEDLINE | ID: covidwho-1574118

ABSTRACT

INTRODUCTION: Coronavirus disease 2019 (COVID-19) has a high burden on the healthcare system. Prediction models may assist in triaging patients. We aimed to assess the value of several prediction models in COVID-19 patients in the emergency department (ED). METHODS: In this retrospective study, ED patients with COVID-19 were included. Prediction models were selected based on their feasibility. Primary outcome was 30-day mortality, secondary outcomes were 14-day mortality and a composite outcome of 30-day mortality and admission to medium care unit (MCU) or intensive care unit (ICU). The discriminatory performance of the prediction models was assessed using an area under the receiver operating characteristic curve (AUC). RESULTS: We included 403 patients. Thirty-day mortality was 23.6%, 14-day mortality was 19.1%, 66 patients (16.4%) were admitted to ICU, 48 patients (11.9%) to MCU, and 152 patients (37.7%) met the composite endpoint. Eleven prediction models were included. The RISE UP score and 4 C mortality scores showed very good discriminatory performance for 30-day mortality (AUC 0.83 and 0.84, 95% CI 0.79-0.88 for both), significantly higher than that of the other models. CONCLUSION: The RISE UP score and 4 C mortality score can be used to recognise patients at high risk for poor outcome and may assist in guiding decision-making and allocating resources.


Subject(s)
COVID-19/mortality , Emergency Service, Hospital/statistics & numerical data , Aged , COVID-19/diagnosis , Feasibility Studies , Female , Hospital Mortality , Humans , Length of Stay/statistics & numerical data , Logistic Models , Male , Middle Aged , Netherlands/epidemiology , Prognosis , ROC Curve , Retrospective Studies , Risk Assessment/methods , SARS-CoV-2/isolation & purification
8.
Epidemiol Infect ; 148: e168, 2020 08 04.
Article in English | MEDLINE | ID: covidwho-1537262

ABSTRACT

This study aimed to identify clinical features for prognosing mortality risk using machine-learning methods in patients with coronavirus disease 2019 (COVID-19). A retrospective study of the inpatients with COVID-19 admitted from 15 January to 15 March 2020 in Wuhan is reported. The data of symptoms, comorbidity, demographic, vital sign, CT scans results and laboratory test results on admission were collected. Machine-learning methods (Random Forest and XGboost) were used to rank clinical features for mortality risk. Multivariate logistic regression models were applied to identify clinical features with statistical significance. The predictors of mortality were lactate dehydrogenase (LDH), C-reactive protein (CRP) and age based on 500 bootstrapped samples. A multivariate logistic regression model was formed to predict mortality 292 in-sample patients with area under the receiver operating characteristics (AUROC) of 0.9521, which was better than CURB-65 (AUROC of 0.8501) and the machine-learning-based model (AUROC of 0.4530). An out-sample data set of 13 patients was further tested to show our model (AUROC of 0.6061) was also better than CURB-65 (AUROC of 0.4608) and the machine-learning-based model (AUROC of 0.2292). LDH, CRP and age can be used to identify severe patients with COVID-19 on hospital admission.


Subject(s)
Coronavirus Infections/mortality , Coronavirus Infections/therapy , Logistic Models , Machine Learning , Pneumonia, Viral/mortality , Pneumonia, Viral/therapy , Adolescent , Adult , Aged , COVID-19 , China/epidemiology , Female , Hospitalization , Humans , Male , Middle Aged , Pandemics , Prognosis , ROC Curve , Reproducibility of Results , Retrospective Studies , Risk Assessment/methods , Young Adult
9.
J Vasc Surg Venous Lymphat Disord ; 9(3): 605-614.e2, 2021 05.
Article in English | MEDLINE | ID: covidwho-1510080

ABSTRACT

OBJECTIVE: Early reports suggest that patients with novel coronavirus disease-2019 (COVID-19) infection carry a significant risk of altered coagulation with an increased risk for venous thromboembolic events. This report investigates the relationship of significant COVID-19 infection and deep venous thrombosis (DVT) as reflected in the patient clinical and laboratory characteristics. METHODS: We reviewed the demographics, clinical presentation, laboratory and radiologic evaluations, results of venous duplex imaging and mortality of COVID-19-positive patients (18-89 years) admitted to the Indiana University Academic Health Center. Using oxygen saturation, radiologic findings, and need for advanced respiratory therapies, patients were classified into mild, moderate, or severe categories of COVID-19 infection. A descriptive analysis was performed using univariate and bivariate Fisher's exact and Wilcoxon rank-sum tests to examine the distribution of patient characteristics and compare the DVT outcomes. A multivariable logistic regression model was used to estimate the adjusted odds ratio of experiencing DVT and a receiver operating curve analysis to identify the optimal cutoff for d-dimer to predict DVT in this COVID-19 cohort. Time to the diagnosis of DVT from admission was analyzed using log-rank test and Kaplan-Meier plots. RESULTS: Our study included 71 unique COVID-19-positive patients (mean age, 61 years) categorized as having 3% mild, 14% moderate, and 83% severe infection and evaluated with 107 venous duplex studies. DVT was identified in 47.8% of patients (37% of examinations) at an average of 5.9 days after admission. Patients with DVT were predominantly male (67%; P = .032) with proximal venous involvement (29% upper and 39% in the lower extremities with 55% of the latter demonstrating bilateral involvement). Patients with DVT had a significantly higher mean d-dimer of 5447 ± 7032 ng/mL (P = .0101), and alkaline phosphatase of 110 IU/L (P = .0095) than those without DVT. On multivariable analysis, elevated d-dimer (P = .038) and alkaline phosphatase (P = .021) were associated with risk for DVT, whereas age, sex, elevated C-reactive protein, and ferritin levels were not. A receiver operating curve analysis suggests an optimal d-dimer value of 2450 ng/mL cutoff with 70% sensitivity, 59.5% specificity, and 61% positive predictive value, and 68.8% negative predictive value. CONCLUSIONS: This study suggests that males with severe COVID-19 infection requiring hospitalization are at highest risk for developing DVT. Elevated d-dimers and alkaline phosphatase along with our multivariable model can alert the clinician to the increased risk of DVT requiring early evaluation and aggressive treatment.


Subject(s)
Alkaline Phosphatase/blood , COVID-19 , Extremities , Fibrin Fibrinogen Degradation Products/analysis , Risk Assessment/methods , Ultrasonography, Doppler, Duplex , Venous Thrombosis , Anticoagulants/administration & dosage , Biomarkers/blood , Blood Coagulation , COVID-19/blood , COVID-19/complications , COVID-19/mortality , COVID-19/therapy , Early Diagnosis , Extremities/blood supply , Extremities/diagnostic imaging , Female , Humans , Indiana/epidemiology , Male , Middle Aged , Predictive Value of Tests , Retrospective Studies , SARS-CoV-2/isolation & purification , Time-to-Treatment/statistics & numerical data , Ultrasonography, Doppler, Duplex/methods , Ultrasonography, Doppler, Duplex/statistics & numerical data , Venous Thrombosis/diagnosis , Venous Thrombosis/drug therapy , Venous Thrombosis/etiology , Venous Thrombosis/prevention & control
10.
Arch Dis Child Fetal Neonatal Ed ; 106(6): 627-634, 2021 Nov.
Article in English | MEDLINE | ID: covidwho-1503592

ABSTRACT

OBJECTIVE: To identify risk factors associated with delivery room respiratory support in at-risk infants who are initially vigorous and received delayed cord clamping (DCC). DESIGN: Prospective cohort study. SETTING: Two perinatal centres in Melbourne, Australia. PATIENTS: At-risk infants born at ≥35+0 weeks gestation with a paediatric doctor in attendance who were initially vigorous and received DCC for >60 s. MAIN OUTCOME MEASURES: Delivery room respiratory support defined as facemask positive pressure ventilation, continuous positive airway pressure and/or supplemental oxygen within 10 min of birth. RESULTS: Two hundred and ninety-eight infants born at a median (IQR) gestational age of 39+3 (38+2-40+2) weeks were included. Cord clamping occurred at a median (IQR) of 128 (123-145) s. Forty-four (15%) infants received respiratory support at a median of 214 (IQR 156-326) s after birth. Neonatal unit admission for respiratory distress occurred in 32% of infants receiving delivery room respiratory support vs 1% of infants who did not receive delivery room respiratory support (p<0.001). Risk factors independently associated with delivery room respiratory support were average heart rate (HR) at 90-120 s after birth (determined using three-lead ECG), mode of birth and time to establish regular cries. Decision tree analysis identified that infants at highest risk had an average HR of <165 beats per minute at 90-120 s after birth following caesarean section (risk of 39%). Infants with an average HR of ≥165 beats per minute at 90-120 s after birth were at low risk (5%). CONCLUSIONS: We present a clinical decision pathway for at-risk infants who may benefit from close observation following DCC. Our findings provide a novel perspective of HR beyond the traditional threshold of 100 beats per minute.


Subject(s)
Critical Pathways/standards , Delivery, Obstetric , Electrocardiography/methods , Oxygen Inhalation Therapy , Umbilical Cord , Australia/epidemiology , Cesarean Section/adverse effects , Cesarean Section/methods , Clinical Decision-Making , Constriction , Continuous Positive Airway Pressure/methods , Delivery, Obstetric/adverse effects , Delivery, Obstetric/methods , Delivery, Obstetric/statistics & numerical data , Female , Gestational Age , Heart Rate , Humans , Infant, Newborn , Male , Monitoring, Physiologic/methods , Oxygen Inhalation Therapy/adverse effects , Oxygen Inhalation Therapy/instrumentation , Oxygen Inhalation Therapy/methods , Risk Assessment/methods , Risk Factors , Time-to-Treatment/standards , Time-to-Treatment/statistics & numerical data
11.
Ann Med ; 53(1): 1863-1874, 2021 Dec.
Article in English | MEDLINE | ID: covidwho-1483235

ABSTRACT

OBJECTIVE: To compare the performance of the Risk-stratification of Emergency Department suspected Sepsis (REDS) score to the SIRS criteria, NEWS2, CURB65, SOFA, MEDS and PIRO scores, to risk-stratify Emergency Department (ED) suspected sepsis patients for mortality. METHOD: A retrospective observational cohort study of prospectively collected data. Adult patients admitted from the ED after receiving intravenous antibiotics for suspected sepsis in the year 2020, were studied. Patients with COVID-19 were excluded. The scores stated above were calculated for each patient. Receiver operator characteristics (ROC) curves were constructed for each score for the primary outcome measure, all-cause in-hospital mortality. The area under the ROC (AUROC) curves and cut-off points were identified by the statistical software. Scores above the cut-off point were deemed high-risk. The test characteristics of the high-risk groups were calculated. Comparisons were based on the AUROC curve and sensitivity for mortality of the high-risk groups. Previously published cut-off points were also studied. Calibration was also studied. RESULTS: Of the 2594 patients studied, 332 (12.8%) died. The AUROC curve for the REDS score 0.73 (95% confidence interval [CI] 0.72-0.75) was significantly greater than the AUROC curve for the SIRS criteria 0.51 (95% CI 0.49-0.53), p < .0001 and the NEWS2 score 0.69 (95% CI 0.67-0.70), p = .005, and similar to all other scores studied. Sensitivity for mortality at the respective cut-off points identified (REDS ≥3, NEWS2 ≥ 8, CURB65 ≥ 3, SOFA ≥3, MEDS ≥10 and PIRO ≥10) was greatest for the REDS score at 80.1% (95% CI 75.4-84.3) and significantly greater than the other scores. The sensitivity for mortality for an increase of two points from baseline in the SOFA score was 63% (95% CI 57.5-68.2). CONCLUSIONS: In this single centre study, the REDS score had either a greater AUROC curve or sensitivity for mortality compared to the comparator scores, at the respective cut-off points identified.KEY MESSAGESThe REDS score is a simple and objective scoring system to risk-stratify for mortality in emergency department (MED) patients with suspected sepsis.The REDS score is better or equivalent to existing scoring systems in its discrimination for mortality.


Subject(s)
Anti-Bacterial Agents/administration & dosage , Emergency Service, Hospital/statistics & numerical data , Intensive Care Units/statistics & numerical data , Sepsis/mortality , Severity of Illness Index , Administration, Intravenous , Aged , Aged, 80 and over , Female , Hospital Mortality , Humans , Male , Middle Aged , Prognosis , Prospective Studies , ROC Curve , Retrospective Studies , Risk Assessment/methods , Sepsis/diagnosis , Sepsis/drug therapy
12.
Expert Rev Clin Immunol ; 17(11): 1211-1220, 2021 11.
Article in English | MEDLINE | ID: covidwho-1483218

ABSTRACT

INTRODUCTION: In daily practice management of psoriasis, evaluation of risk factors for infections is having a growing influence. Indeed, in psoriatic patients, risk of infections may be due to psoriasis itself, immunomodulatory therapy, and comorbidities that may increase this risk and patient hospitalization. AREAS COVERED: Given the greater understanding of psoriasis pathogenesis and the increasing number of treatment options, it is particularly important to customize therapy according to each, single patient; psoriasis features and comorbidities are also essential to tailor treatment goals. EXPERT OPINION: In this perspective, the current knowledge on the infectious risk in psoriatic patient, related to comorbidities, such as diabetes mellitus, cardiovascular disease, and chronic obstructive pulmonary, to 'special populations,' to chronic infections, such as latent tuberculosis, chronic hepatitis B and C, and HIV, and to the most recent Covid-19 pandemic scenario, is reviewed and discussed in order to suggest the most appropriate approach and achieve the best available therapeutic option.


Subject(s)
COVID-19/prevention & control , Psoriasis/therapy , Risk Assessment/methods , SARS-CoV-2/isolation & purification , COVID-19/epidemiology , COVID-19/virology , Cardiovascular Diseases/epidemiology , Comorbidity , Humans , Pandemics , Psoriasis/epidemiology , Risk Assessment/statistics & numerical data , Risk Factors , SARS-CoV-2/physiology , Virus Diseases/epidemiology
13.
Infect Dis Poverty ; 10(1): 128, 2021 Oct 24.
Article in English | MEDLINE | ID: covidwho-1482013

ABSTRACT

BACKGROUND: Coronaviruses can be isolated from bats, civets, pangolins, birds and other wild animals. As an animal-origin pathogen, coronavirus can cross species barrier and cause pandemic in humans. In this study, a deep learning model for early prediction of pandemic risk was proposed based on the sequences of viral genomes. METHODS: A total of 3257 genomes were downloaded from the Coronavirus Genome Resource Library. We present a deep learning model of cross-species coronavirus infection that combines a bidirectional gated recurrent unit network with a one-dimensional convolution. The genome sequence of animal-origin coronavirus was directly input to extract features and predict pandemic risk. The best performances were explored with the use of pre-trained DNA vector and attention mechanism. The area under the receiver operating characteristic curve (AUROC) and the area under precision-recall curve (AUPR) were used to evaluate the predictive models. RESULTS: The six specific models achieved good performances for the corresponding virus groups (1 for AUROC and 1 for AUPR). The general model with pre-training vector and attention mechanism provided excellent predictions for all virus groups (1 for AUROC and 1 for AUPR) while those without pre-training vector or attention mechanism had obviously reduction of performance (about 5-25%). Re-training experiments showed that the general model has good capabilities of transfer learning (average for six groups: 0.968 for AUROC and 0.942 for AUPR) and should give reasonable prediction for potential pathogen of next pandemic. The artificial negative data with the replacement of the coding region of the spike protein were also predicted correctly (100% accuracy). With the application of the Python programming language, an easy-to-use tool was created to implements our predictor. CONCLUSIONS: Robust deep learning model with pre-training vector and attention mechanism mastered the features from the whole genomes of animal-origin coronaviruses and could predict the risk of cross-species infection for early warning of next pandemic.


Subject(s)
Coronavirus Infections , Coronavirus , Pandemics , Animals , Coronavirus/isolation & purification , Coronavirus Infections/epidemiology , Coronavirus Infections/veterinary , Deep Learning , Humans , Models, Statistical , Risk Assessment/methods
14.
Public Health Res Pract ; 31(3)2021 Sep 08.
Article in English | MEDLINE | ID: covidwho-1471205

ABSTRACT

Emerging evidence, based on the synthesis of reports from past infectious disease-related public health emergencies, supports an association between previous pandemics and a heightened risk of suicide or suicide-related behaviours and outcomes. Anxiety associated with pandemic media reporting appears to be one critical contributing factor. Social isolation, loneliness, and the disconnect that can result from public health strategies during global pandemics also appear to increase suicide risk in vulnerable individuals. Innovative suicide risk assessment and prevention strategies are needed to recognise and adapt to the negative impacts of pandemics on population mental health.


Subject(s)
COVID-19/epidemiology , Pandemics , Suicide/prevention & control , Suicide/statistics & numerical data , Anxiety/epidemiology , Anxiety/psychology , COVID-19/psychology , Humans , Loneliness/psychology , Mental Health , Public Health , Risk Assessment/methods , SARS-CoV-2 , Social Isolation/psychology , Suicide/psychology
16.
Cancer Prev Res (Phila) ; 14(11): 1021-1032, 2021 11.
Article in English | MEDLINE | ID: covidwho-1463067

ABSTRACT

Up to 10% of patients with pancreatic ductal adenocarcinoma (PDAC) carry underlying germline pathogenic variants in cancer susceptibility genes. The GENetic Education Risk Assessment and TEsting (GENERATE) study aimed to evaluate novel methods of genetic education and testing in relatives of patients with PDAC. Eligible individuals had a family history of PDAC and a relative with a germline pathogenic variant in APC, ATM, BRCA1, BRCA2, CDKN2A, EPCAM, MLH1, MSH2, MSH6, PALB2, PMS2, STK11, or TP53 genes. Participants were recruited at six academic cancer centers and through social media campaigns and patient advocacy efforts. Enrollment occurred via the study website (https://GENERATEstudy.org) and all participation, including collecting a saliva sample for genetic testing, could be done from home. Participants were randomized to one of two remote methods that delivered genetic education about the risks of inherited PDAC and strategies for surveillance. The primary outcome of the study was uptake of genetic testing. From 5/8/2019 to 5/6/2020, 49 participants were randomized to each of the intervention arms. Overall, 90 of 98 (92%) of randomized participants completed genetic testing. The most frequently detected pathogenic variants included those in BRCA2 (N = 15, 17%), ATM (N = 11, 12%), and CDKN2A (N = 4, 4%). Participation in the study remained steady throughout the onset of the Coronavirus disease (COVID-19) pandemic. Preliminary data from the GENERATE study indicate success of remote alternatives to traditional cascade testing, with genetic testing rates over 90% and a high rate of identification of germline pathogenic variant carriers who would be ideal candidates for PDAC interception approaches. PREVENTION RELEVANCE: Preliminary data from the GENERATE study indicate success of remote alternatives for pancreatic cancer genetic testing and education, with genetic testing uptake rates over 90% and a high rate of identification of germline pathogenic variant carriers who would be ideal candidates for pancreatic cancer interception.


Subject(s)
BRCA1 Protein/genetics , BRCA2 Protein/genetics , Genetic Predisposition to Disease , Genetic Testing/methods , Germ-Line Mutation , Pancreatic Neoplasms/genetics , Risk Assessment/methods , Adolescent , Adult , Aged , Aged, 80 and over , Carcinoma, Pancreatic Ductal/genetics , Carcinoma, Pancreatic Ductal/pathology , Carcinoma, Pancreatic Ductal/therapy , Female , Humans , Male , Middle Aged , Models, Genetic , Pancreatic Neoplasms/pathology , Pancreatic Neoplasms/therapy , Patient Participation , Risk Factors , Surveys and Questionnaires , Telemedicine , Young Adult
17.
Eur J Radiol ; 127: 109019, 2020 Jun.
Article in English | MEDLINE | ID: covidwho-1454121

ABSTRACT

PURPOSE: Assessment of a woman's risk of breast cancer is essential when moving towards personalized screening. Breast density is a well-known risk factor and has the potential to improve accuracy of risk prediction models. In this study we reviewed the impact on model performance of adding breast density to clinical breast cancer risk prediction models. METHODS: We conducted a systematic review using a pre-specified search strategy for PubMed, EMBASE, Web of Science, and Cochrane Library from January 2007 until November 2019. Studies were screened using the Covidence software. Eligible studies developed or modified existing breast cancer risk prediction models applicable to the general population of women by adding breast density to the model. Improvement in discriminatory accuracy was measured as an increase in the Area Under the Curve or concordance statistics. RESULTS: Eleven eligible studies were identified by the search and one by reference check. Four studies modified the Gail model, four modified the Tyrer-Cuzick model, and five studies developed new models. Several methods were used to measure breast density, including visual, semi- and fully automated methods. Eleven studies reported discriminatory accuracy and one study reported calibration. Seven studies found a statistically significantly increased discriminatory accuracy when including density in the model. The increase in AUC ranged 0.03 to 0.14. Four studies did not report on statistical significance, but reported an increased AUC ranging from 0.01 to 0.06. CONCLUSION: Including mammographic breast density has the potential to improve breast cancer risk prediction models. However, all models demonstrated limited discrimination accuracy.


Subject(s)
Breast Density , Breast Neoplasms/diagnostic imaging , Mammography/methods , Aged , Breast/diagnostic imaging , Female , Humans , Middle Aged , Risk Assessment/methods
18.
Sci Rep ; 11(1): 18959, 2021 09 23.
Article in English | MEDLINE | ID: covidwho-1437695

ABSTRACT

The COVID-19 pandemic has put massive strains on hospitals, and tools to guide hospital planners in resource allocation during the ebbs and flows of the pandemic are urgently needed. We investigate whether machine learning (ML) can be used for predictions of intensive care requirements a fixed number of days into the future. Retrospective design where health Records from 42,526 SARS-CoV-2 positive patients in Denmark was extracted. Random Forest (RF) models were trained to predict risk of ICU admission and use of mechanical ventilation after n days (n = 1, 2, …, 15). An extended analysis was provided for n = 5 and n = 10. Models predicted n-day risk of ICU admission with an area under the receiver operator characteristic curve (ROC-AUC) between 0.981 and 0.995, and n-day risk of use of ventilation with an ROC-AUC between 0.982 and 0.997. The corresponding n-day forecasting models predicted the needed ICU capacity with a coefficient of determination (R2) between 0.334 and 0.989 and use of ventilation with an R2 between 0.446 and 0.973. The forecasting models performed worst, when forecasting many days into the future (for large n). For n = 5, ICU capacity was predicted with ROC-AUC 0.990 and R2 0.928, and use of ventilator was predicted with ROC-AUC 0.994 and R2 0.854. Random Forest-based modelling can be used for accurate n-day forecasting predictions of ICU resource requirements, when n is not too large.


Subject(s)
COVID-19/epidemiology , Forecasting/methods , Intensive Care Units/trends , Area Under Curve , Computational Biology/methods , Critical Care/statistics & numerical data , Critical Care/trends , Denmark/epidemiology , Hospitalization/trends , Hospitals/trends , Humans , Machine Learning , Pandemics , ROC Curve , Respiration, Artificial/statistics & numerical data , Respiration, Artificial/trends , Retrospective Studies , Risk Assessment/methods , Risk Factors , SARS-CoV-2/pathogenicity , Ventilators, Mechanical/trends
20.
J Cardiovasc Med (Hagerstown) ; 22(11): 828-831, 2021 11 01.
Article in English | MEDLINE | ID: covidwho-1406806

ABSTRACT

AIMS: Controversial data have been published regarding the prognostic role of cardiac troponins in patients who need hospitalization because of coronavirus disease 2019 (COVID-19). The aim of the study was to assess the role of high-sensitivity troponin plasma levels and of respiratory function at admission on all-cause deaths in unselected patients hospitalized because of COVID-19. METHODS: We pooled individual patient data from observational studies that assessed all-cause mortality of unselected patients hospitalized for COVID-19. The individual data of 722 patients were included. The ratio of partial pressure arterial oxygen to fraction of inspired oxygen (PaO2/FiO2) and high-sensitivity troponins was reported at admission in all patients. This meta-analysis was registered on PROSPERO (CRD42020213209). RESULTS: After a median follow-up of 14 days, 180 deaths were observed. At multivariable regression analysis, age [hazard ratio (HR) 1.083, 95% confidence interval (CI) 1.061-1.105, P < 0.0001], male sex (HR 2.049, 95% CI 1.319-3.184, P = 0.0014), moderate-severe renal dysfunction (estimated glomerular filtration rate  < 30 mL/min/m2) (HR 2.108, 95% CI 1.237-3.594, P = 0.0061) and lower PaO2/FiO2 (HR 0.901, 95% CI 0.829-0.978, P = 0.0133) were the independent predictors of death. A linear increase in the HR was associated with decreasing values of PaO2/FiO2 below the normality threshold. On the contrary, the HR curve for troponin plasma levels was near-flat with large CI for values above the normality thresholds. CONCLUSION: In unselected patients hospitalized for COVID-19, mortality is mainly driven by male gender, older age and respiratory failure. Elevated plasma levels of high-sensitivity troponins are not an independent predictor of worse survival when respiratory function is accounted for.


Subject(s)
COVID-19 , Oxygen/analysis , Respiratory Function Tests/methods , Troponin/blood , Age Factors , Biomarkers/analysis , Biomarkers/blood , Blood Gas Analysis/methods , Breath Tests/methods , COVID-19/blood , COVID-19/diagnosis , COVID-19/mortality , Humans , Prognosis , Risk Assessment/methods , SARS-CoV-2 , Sex Factors
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