scholarly journals Developing a prediction model to estimate the true burden of respiratory syncytial virus (RSV) in hospitalised children in Western Australia

2022 ◽  
Vol 12 (1) ◽  
Author(s):  
Amanuel Tesfay Gebremedhin ◽  
Alexandra B. Hogan ◽  
Christopher C. Blyth ◽  
Kathryn Glass ◽  
Hannah C. Moore
Keyword(s):  
Respiratory Syncytial Virus ◽  
Western Australia ◽  
Predictive Accuracy ◽  
Predictive Performance ◽  
Population Based ◽  
Incidence Rates ◽  
Predictive Values ◽  
Childhood Morbidity ◽  
Syncytial Virus ◽  
Sensitivity Specificity

AbstractRespiratory syncytial virus (RSV) is a leading cause of childhood morbidity, however there is no systematic testing in children hospitalised with respiratory symptoms. Therefore, current RSV incidence likely underestimates the true burden. We used probabilistically linked perinatal, hospital, and laboratory records of 321,825 children born in Western Australia (WA), 2000–2012. We generated a predictive model for RSV positivity in hospitalised children aged < 5 years. We applied the model to all hospitalisations in our population-based cohort to determine the true RSV incidence, and under-ascertainment fraction. The model’s predictive performance was determined using cross-validated area under the receiver operating characteristic (AUROC) curve. From 321,825 hospitalisations, 37,784 were tested for RSV (22.8% positive). Predictors of RSV positivity included younger admission age, male sex, non-Aboriginal ethnicity, a diagnosis of bronchiolitis and longer hospital stay. Our model showed good predictive accuracy (AUROC: 0.87). The respective sensitivity, specificity, positive predictive value and negative predictive values were 58.4%, 92.2%, 68.6% and 88.3%. The predicted incidence rates of hospitalised RSV for children aged < 3 months was 43.7/1000 child-years (95% CI 42.1–45.4) compared with 31.7/1000 child-years (95% CI 30.3–33.1) from laboratory-confirmed RSV admissions. Findings from our study suggest that the true burden of RSV may be 30–57% higher than current estimates.

scholarly journals Association of Age at First Severe Respiratory Syncytial Virus Disease With Subsequent Risk of Severe Asthma: A Population-Based Cohort Study

2018 ◽  
Vol 220 (4) ◽  
pp. 550-556 ◽  
Author(s):  
Nusrat Homaira ◽  
Nancy Briggs ◽  
Ju-Lee Oei ◽  
Lisa Hilder ◽  
Barbara Bajuk ◽  
...  
Keyword(s):  
Cohort Study ◽  
Respiratory Syncytial Virus ◽  
Virus Disease ◽  
Population Based ◽  
Incidence Rates ◽  
Severe Respiratory Syncytial Virus ◽  
Respiratory Syncytial Virus Disease ◽  
Syncytial Virus ◽  
Rate Ratios ◽  
Subsequent Risk

Abstract Objective In a population-based cohort study, we determined the association between the age at first severe respiratory syncytial virus (RSV) disease and subsequent asthma. Methods Incidence rates and rate ratios of the first asthma-associated hospitalization after 2 years of age in children hospitalized for RSV disease at <3 months, 3 to <6 months, 6 to <12 months, and 12–24 months of age were calculated. Results The incidence of asthma-associated hospitalization per 1000 child-years among children hospitalized for RSV disease at <3 months of age was 0.5 (95% confidence interval [CI], .2–.7); at 3 to <6 months of age, 0.9 (95% CI,.5–1.3); at 6 to <12 months of age, 2.0 (95% CI, 1.4–2.7); and at 12–24 months of age, 1.7 (95% CI, 1.0–2.5). The rate ratio of hospitalization for asthma was 2–7-fold greater among children hospitalized for RSV disease at ages ≥6 months than that among those hospitalized for RSV disease at ages 0 to <6 months. Conclusions Although the burden of RSV disease is highest in children aged <6 months, the burden of subsequent asthma is higher in children who develop RSV disease at ages ≥6 months.

scholarly journals Development of Machine Learning Models to Predict Probabilities and Types of Stroke at Prehospital Stage: the Japan Urgent Stroke Triage Score Using Machine Learning (JUST-ML)

2021 ◽  
Author(s):  
Kazutaka Uchida ◽  
Junichi Kouno ◽  
Shinichi Yoshimura ◽  
Norito Kinjo ◽  
Fumihiro Sakakibara ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Random Forests ◽  
Prediction Models ◽  
Characteristic Curve ◽  
Predictive Performance ◽  
Vessel Occlusion ◽  
Predictive Values ◽  
Training Cohort ◽  
Sensitivity Specificity

AbstractIn conjunction with recent advancements in machine learning (ML), such technologies have been applied in various fields owing to their high predictive performance. We tried to develop prehospital stroke scale with ML. We conducted multi-center retrospective and prospective cohort study. The training cohort had eight centers in Japan from June 2015 to March 2018, and the test cohort had 13 centers from April 2019 to March 2020. We use the three different ML algorithms (logistic regression, random forests, XGBoost) to develop models. Main outcomes were large vessel occlusion (LVO), intracranial hemorrhage (ICH), subarachnoid hemorrhage (SAH), and cerebral infarction (CI) other than LVO. The predictive abilities were validated in the test cohort with accuracy, positive predictive value, sensitivity, specificity, area under the receiver operating characteristic curve (AUC), and F score. The training cohort included 3178 patients with 337 LVO, 487 ICH, 131 SAH, and 676 CI cases, and the test cohort included 3127 patients with 183 LVO, 372 ICH, 90 SAH, and 577 CI cases. The overall accuracies were 0.65, and the positive predictive values, sensitivities, specificities, AUCs, and F scores were stable in the test cohort. The classification abilities were also fair for all ML models. The AUCs for LVO of logistic regression, random forests, and XGBoost were 0.89, 0.89, and 0.88, respectively, in the test cohort, and these values were higher than the previously reported prediction models for LVO. The ML models developed to predict the probability and types of stroke at the prehospital stage had superior predictive abilities.

scholarly journals Development and performance evaluation of the Medicines Optimisation Assessment Tool (MOAT): a prognostic model to target hospital pharmacists’ input to prevent medication-related problems

2019 ◽  
Vol 28 (8) ◽  
pp. 645-656 ◽  
Author(s):  
Cathy Geeson ◽  
Li Wei ◽  
Bryony Dean Franklin
Keyword(s):  
Risk Factors ◽  
Assessment Tool ◽  
Predictive Accuracy ◽  
External Validation ◽  
Predictive Performance ◽  
Study Data ◽  
Predictive Values ◽  
Hospital Pharmacists ◽  
Potential Risk Factors ◽  
Medicines Optimisation

BackgroundMedicines optimisation is a key role for hospital pharmacists, but with ever-increasing demands on services, there is a need to increase efficiency while maintaining patient safety.ObjectiveTo develop a prediction tool, the Medicines Optimisation Assessment Tool (MOAT), to target patients most in need of pharmacists’ input in hospital.MethodsPatients from adult medical wards at two UK hospitals were prospectively included into this cohort study. Data on medication-related problems (MRPs) were collected by pharmacists at the study sites as part of their routine daily clinical assessments. Data on potential risk factors, such as number of comorbidities and use of ‘high-risk’ medicines, were collected retrospectively. Multivariable logistic regression modelling was used to determine the relationship between risk factors and the study outcome: preventable MRPs that were at least moderate in severity. The model was internally validated and a simplified electronic scoring system developed.ResultsAmong 1503 eligible admissions, 610 (40.6%) experienced the study outcome. Eighteen risk factors were preselected for MOAT development, with 11 variables retained in the final model. The MOAT demonstrated fair predictive performance (concordance index 0.66) and good calibration. Two clinically relevant decision thresholds (ie, the minimum predicted risk probabilities to justify pharmacists’ input) were selected, with sensitivities of 90% and 66% (specificity 30% and 61%); these equate to positive predictive values of 47% and 54%, respectively. Decision curve analysis suggests that the MOAT has potential value in clinical practice in guiding decision-making.ConclusionThe MOAT has potential to predict those patients most at risk of moderate or severe preventable MRPs, experienced by 41% of admissions. External validation is now required to establish predictive accuracy in a new group of patients.

scholarly journals Population-based trends and underlying risk factors for infant respiratory syncytial virus and bronchiolitis hospitalizations

PLoS ONE ◽  
2018 ◽  
Vol 13 (10) ◽  
pp. e0205399 ◽  
Author(s):  
Mihoko V. Bennett ◽  
Kimmie McLaurin ◽  
Christopher Ambrose ◽  
Henry C. Lee
Keyword(s):  
Risk Factors ◽  
Respiratory Syncytial Virus ◽  
Population Based ◽  
Syncytial Virus ◽  
Underlying Risk

scholarly journals Invasive pneumococcal and meningococcal disease: association with influenza virus and respiratory syncytial virus activity?

2008 ◽  
Vol 136 (11) ◽  
pp. 1448-1454 ◽  
Author(s):  
A. G. S. C. JANSEN ◽  
E. A. M. SANDERS ◽  
A. VAN DER ENDE ◽  
A. M. VAN LOON ◽  
A. W. HOES ◽  
...  
Keyword(s):  
Influenza Virus ◽  
Respiratory Syncytial Virus ◽  
Meningococcal Disease ◽  
Invasive Disease ◽  
Population Based ◽  
Older Children ◽  
Virus Activity ◽  
Syncytial Virus ◽  
Pneumococcal Bacteraemia ◽  
Rate Ratios

SUMMARYFew studies have examined the relationship between viral activity and bacterial invasive disease, considering both influenza virus and respiratory syncytial virus (RSV). This study aimed to assess the potential relationship between invasive pneumococcal disease (IPD), meningococcal disease (MD), and influenza virus and RSV activity in The Netherlands. Correlations were determined between population-based data on IPD and MD during 1997–2003 and influenza virus and RSV surveillance data. Incidence rate ratios of disease during periods of high influenza virus and RSV activity over the peri-seasonal and summer baseline periods were calculated. The analyses comprised 7266 and 3072 cases of IPD and MD. When data from all seasons were included, the occurrence of pneumococcal bacteraemia and MD correlated significantly with influenza virus and RSV activity both in children and adults. Periods of increased influenza virus and RSV activity showed higher rates of pneumococcal bacteraemia in older children and adults than the peri-season period. Rates of MD in children were also higher during periods of increased influenza virus activity; the same appeared true for MD in older children during periods of increased RSV activity. Although no causal relationship may be inferred from these data, they support a role for influenza virus and RSV in the pathogenesis of IPD and MD.

scholarly journals Accuracy of diagnostic tests for respiratory syncytial virus infection within a paediatric hospital population in Kilifi County, Kenya

2020 ◽  
Vol 5 ◽  
pp. 155
Author(s):  
Marshal M. Mweu ◽  
Nickson Murunga ◽  
Juliet W. Otieno ◽  
D. James Nokes
Keyword(s):  
Respiratory Syncytial Virus ◽  
Diagnostic Tests ◽  
Latent Class ◽  
Respiratory Illness ◽  
Acute Respiratory Illness ◽  
Predictive Values ◽  
Paediatric Hospital ◽  
Rsv Infection ◽  
Hospital Population ◽  
Syncytial Virus

Background: Respiratory syncytial virus (RSV)-induced lower respiratory tract disease is a prominent cause of hospitalisation among children aged <5 years in developing countries. Accurate and rapid diagnostic tests are central to informing effective patient management and surveillance efforts geared towards quantifying RSV disease burden. This study sought to estimate the sensitivity (Se), specificity (Sp) (along with the associated factors) and predictive values of a direct immunofluorescence test (IFAT), and two real-time reverse transcription polymerase chain reaction (rRT-PCR) assays for RSV infection within a paediatric hospital population: a multiplex rRT-PCR (MPX) and Fast-Track Diagnostics® (FTD) Respiratory Pathogens 33 (Resp-33) rRT-PCR. Methods: The study enlisted 1458 paediatrics aged ≤59 months admitted with acute respiratory illness at the Kilifi County Hospital between August 2011 and December 2013. A Bayesian latent class modelling framework was employed to infer the tests’ estimates based on the patients’ diagnostic data from the three tests. Results: The tests posted statistically similar Se estimates: IFAT (93.7%, [90.7; 95.0]), FTD (97.8%, [94.6; 99.4]) and MPX (97.5%, [94.2; 99.3]). As for Sp, FTD registered a lower estimate (97.4%, [96.2; 98.2]) than MPX (99.7%, [99.0; 100.0]) but similar to IFAT (99.0%, [98.2; 99.6]). The negative and positive predictive values were strong (>91%) and closely mimicked the pattern given by the Se and Sp values respectively. None of the examined covariates (age, sex and pneumonia status) significantly influenced the accuracy of the tests. Conclusions: The evaluation found little to choose between the three diagnostic tests. Nonetheless, with its relative affordability, the conventional IFAT continues to hold promise for use in patient care and surveillance activities for RSV infection within settings where children are hospitalised with severe acute respiratory illness.

scholarly journals 998. Forward and Reverse Translational Approaches to Predict Efficacy of the Neutralizing Respiratory Syncytial Virus (RSV) Antibody MK-1654

2021 ◽  
Vol 8 (Supplement_1) ◽  
pp. S590-S590
Author(s):  
Brian M Maas ◽  
Jos Lommerse ◽  
Nele Plock ◽  
Radha Railkar ◽  
S Y Amy Cheung ◽  
...  
Keyword(s):  
Viral Load ◽  
Respiratory Syncytial Virus ◽  
Tract Infection ◽  
Lower Respiratory Tract ◽  
Incidence Rates ◽  
Independent Contractor ◽  
Phase 3 ◽  
Rsv Infection ◽  
Syncytial Virus ◽  
The Relationship

Abstract Background MK-1654 is a respiratory syncytial virus (RSV) F glycoprotein neutralizing monoclonal antibody (mAb) with an extended half-life in late development to prevent RSV infection in infants. Neutralizing mAbs, like MK-1654, have great potential for prophylaxis against viral infection. However, well-validated approaches for clinical dose and efficacy predictions are lacking. Methods Summary-level literature data from RSV prevention studies were used in a model-based meta-analysis (MBMA) to describe the relationship between RSV incidence rates and serum neutralizing antibody (SNA) titer. The model was validated using viral challenge experiments in cotton rats and phase 3 RSV-A efficacy results in infants for an anti-RSV F mAb, REGN-2222. A phase 2b human RSV challenge study (HCS) in adults was also conducted with MK-1654. Participants (N=70) received 100, 200, 300, or 900 mg of MK-1564 or placebo and were challenged intranasally with RSV 29 days later. RSV viral load and symptomatic infection were monitored. Data from the HCS were compared to model predictions. The MBMA was used to predict efficacy of MK-1654 in a virtual population of pre- and full- term infants. Results The relationship between SNA titer and RSV incidence rate defined using the viral load data from the cotton rat approximated the relationship identified for infants from the clinical MBMA. The MBMA was quantitatively consistent with the phase 3 efficacy results against RSV A for REGN-2222. In the HCS, RSV nasal viral load measured by RT-qPCR and quantitative culture as well as symptomatic infections were decreased in MK-1654 recipients compared to placebo. Incidence rates of RSV infection in the HCS were also consistent with MBMA predictions. The model-based clinical trial simulations for MK-1654 indicated a high probability of substantial efficacy against RSV-associated medically attended lower respiratory tract infection ( &gt;75% for 5 months) for doses ≥75 mg. Conclusion Our MBMA successfully quantified the relationship between RSV SNA and clinically relevant endpoints, including lower respiratory tract infection in infants. MBMA-based efficacy predictions support continued development of the MK-1654 antibody for the prevention of RSV in infants. Disclosures Brian M. Maas, PharmD, Merck & Co., Inc. (Employee, Shareholder) Jos Lommerse, PhD, Certara (Employee, Shareholder)Merck & Co., Inc. (Independent Contractor) Nele Plock, PhD, Certara (Employee, Shareholder)Merck & Co., Inc. (Independent Contractor) Radha Railkar, PhD, Merck & Co., Inc. (Employee, Shareholder) S. Y. Amy Cheung, PhD, Certara (Employee, Shareholder) Luzelena Caro, PhD, Merck & Co., Inc. (Employee, Shareholder) Jingxian Chen, PhD, Merck & Co., Inc. (Employee, Shareholder) Wen Liu, MPH, Merck & Co., Inc. (Employee, Shareholder) Ying Zhang, PhD, Merck & Co., Inc. (Employee, Shareholder) Qinlei Huang, MS, Merck & Co., Inc. (Employee, Shareholder) Wei Gao, PhD, Merck & Co., Inc. (Employee, Shareholder) Li Qin, PhD, Certara (Employee, Shareholder)Merck & Co., Inc. (Independent Contractor) Jie Meng, MSc, Certara (Employee, Shareholder)Merck & Co., Inc. (Independent Contractor) Han Witjes, PhD, Certara (Employee, Shareholder)Merck & Co., Inc. (Independent Contractor) Emilie Schindler, PhD, Certara (Employee, Shareholder)Merck & Co., Inc. (Independent Contractor) Benjamin Guiastrennec, PharmD, PhD, Certara (Employee, Shareholder)Merck & Co., Inc. (Independent Contractor) Francesco Bellanti, PhD, Certara (Employee, Shareholder)Merck & Co., Inc. (Independent Contractor) Daniel Spellman, PhD, Merck & Co., Inc. (Employee, Shareholder) Brad Roadcap, MS, Merck & Co., Inc. (Employee, Shareholder) Amy Espeseth, PhD, Merck & Co., Inc. (Employee, Shareholder) S. Aubrey Stoch, MD, Merck & Co., Inc. (Employee, Shareholder) Eseng Lai, MD, PhD, Merck & Co., Inc. (Employee, Shareholder) Kalpit A. Vora, PhD, Merck & Co., Inc. (Employee, Shareholder) Antonios O. Aliprantis, MD, PhD, Merck & Co., Inc. (Employee, Shareholder) Jeffrey R. Sachs, PhD, Merck & Co., Inc. (Employee, Shareholder)

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