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Nevin Manimala Statistics

Full compliance with Respiratory syncytial virus prophylaxis was associated with fewer respiratory-related hospital admissions in preterm children: a cohort study.

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Full compliance with Respiratory syncytial virus prophylaxis was associated with fewer respiratory-related hospital admissions in preterm children: a cohort study.

Acta Paediatr. 2020 Nov 28;:

Authors: Torchin H, Charkaluk ML, Rousseau J, Marchand-Martin L, Treluyer L, Nuytten A, Truffert P, Jarreau PH, Ancel PY

Abstract
AIM: Although well documented in randomised trials, the efficacy of prophylaxis against respiratory syncytial virus (RSV) in real-word conditions is less studied. The objective was to assess the impact of partial versus full RSV prophylaxis for acute respiratory infections (ARIs) and ARI-related hospital admissions in preterm children.
METHODS: This study included children born preterm in 2011 in France who were eligible for RSV prophylaxis and received at least one palivizumab dose from October 2011 to March 2012. Full prophylaxis was defined as receiving at least one palivizumab dose for each month of RSV exposure in the community. Children with full and partial prophylaxis were matched, and odds of ARIs and ARI-related hospital admission were compared by logistic regression.
RESULTS: Full prophylaxis concerned 861/1083 (80%) children. As compared with full prophylaxis, partial prophylaxis was not associated with ARI occurrence (odds ratio OR 1.3, 95% confidence interval CI 0.9-1.9) but was significantly associated with ARI-related hospital admission during the RSV epidemic (OR 1.9, 95% CI 1.2-2.9).
CONCLUSION: During the 2011-2012 RSV epidemic, hospital admission rates were higher for preterm children with partial than full RSV prophylaxis. Improving compliance could help alleviate the burden of RSV on healthcare systems.

PMID: 33249609 [PubMed – as supplied by publisher]

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Nevin Manimala Statistics

Derivation with Internal Validation of a Multivariable Predictive Model to Predict COVID-19 Test Results in Emergency Department Patients.

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Derivation with Internal Validation of a Multivariable Predictive Model to Predict COVID-19 Test Results in Emergency Department Patients.

Acad Emerg Med. 2020 Nov 28;:

Authors: McDonald SA, Medford RJ, Basit MA, Diercks DB, Courtney DM

Abstract
OBJECTIVES: The COVID-19 pandemic has placed acute care providers in demanding situations in predicting disease given the clinical variability, desire to cohort patients, and high variance in testing availability. An approach to stratify patients by likelihood of disease based on rapidly available emergency department (ED) clinical data would offer significant operational and clinical value. The purpose of this study was to develop and internally validate a predictive model to aid in the discrimination of patients undergoing investigation for COVID-19.
METHODS: All patients greater than 18 years presenting to a single academic ED who were tested for COVID-19 during this index ED evaluation were included. Outcome was defined as the result of COVID-19 PCR testing during the index visit or any positive result within the following 7 days. Variables included chest radiograph interpretation, disease specific screening questions, and laboratory data. Three models were developed with a split-sample approach to predict outcome of the PCR test utilizing logistic regression, random forest, and gradient boosted decision-tree methods. Model discrimination was evaluated comparing AUC and point statistics at a predefined threshold.
RESULTS: 1026 patients were included in the study collected between March and April 2020. Overall, there was disease prevalence of 9.6% in the population under study during this time frame. The logistic regression model was found to have an AUC of 0.89 (95% CI 0.84 – 0.94) when including four features: exposure history, temperature, WBC, and chest radiograph result. Random forest method resulted in AUC of 0.86 (95% CI 0.79 – 0.92) and gradient boosting had an AUC of 0.85 (95% CI 0.79-0.91). With a consistently held negative predictive value, the logistic regression model had a positive predictive value of 0.29 (0.2-0.39) compared to 0.2 (0.14-0.28) for random forest and 0.22 (0.15 – 0.3) for the gradient boosted method.
CONCLUSION: The derived predictive models offer good discriminating capacity for COVID-19 disease and provide interpretable and usable methods for those providers caring for these patients at the important crossroads of the community and the health system. We found utilization of the logistic regression model utilizing exposure history, temperature, WBC, and Chest XR result had the greatest discriminatory capacity with the most interpretable model. Integrating a predictive model-based approach to COVID-19 testing decisions and patient care pathways and locations could add efficiency and accuracy to decrease uncertainty.

PMID: 33249683 [PubMed – as supplied by publisher]

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Nevin Manimala Statistics

MCMC with Gaussian Processes for fast parameter estimation and uncertainty quantification in a 1D fluid-dynamics model of the pulmonary circulation.

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MCMC with Gaussian Processes for fast parameter estimation and uncertainty quantification in a 1D fluid-dynamics model of the pulmonary circulation.

Int J Numer Method Biomed Eng. 2020 Nov 28;:e3421

Authors: Paun LM, Husmeier D

Abstract
The past few decades have witnessed an explosive synergy between physics and the life sciences. In particular, physical modelling in medicine and physiology is a topical research area. The present work focuses on parameter inference and uncertainty quantification in a 1D fluid-dynamics model for quantitative physiology: the pulmonary blood circulation. The practical challenge is the estimation of the patient-specific biophysical model parameters, which cannot be measured directly. In principle this can be achieved based on a comparison between measured and predicted data. However, predicting data requires solving a system of partial differential equations (PDEs), which usually have no closed-form solution, and repeated numerical integrations as part of an adaptive estimation procedure are computationally expensive. In the present article, we demonstrate how fast parameter estimation combined with sound uncertainty quantification can be achieved by a combination of statistical emulation and Markov chain Monte Carlo (MCMC) sampling. We compare a range of state-of-the-art MCMC algorithms and emulation strategies, and assess their performance in terms of their accuracy and computational efficiency. The long-term goal is to develop a method for reliable disease prognostication in real time, and our work is an important step towards an automatic clinical decision support system. This article is protected by copyright. All rights reserved.

PMID: 33249755 [PubMed – as supplied by publisher]

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Nevin Manimala Statistics

Cost-effectiveness analysis of thoracoscopic versus open esophagectomy for esophageal cancer: a population-based study.

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Cost-effectiveness analysis of thoracoscopic versus open esophagectomy for esophageal cancer: a population-based study.

Dis Esophagus. 2020 Nov 30;:

Authors: Chao YK, Wen YW

Abstract
The question as to whether the clinical benefits of video-assisted thoracoscopic esophagectomy (VATE) do outweigh its increased costs remains unanswered. Here, we analyzed the cost-effectiveness of VATE versus open esophagectomy (OE) in a real-world setting. Using 2008-2015 Taiwanese Health Insurance claim data, we identified 3271 patients with esophageal cancer who underwent transthoracic esophagectomy. By taking into account nine confounding variables, we constructed a 1:1 propensity score-matched sample of patients who underwent VATE or OE (n = 629 each). Direct costs incurred within three years after surgery and survival were analyzed. There were no significant intergroup differences in terms of R0 resection rates, length of stay, as well as 30- and 90-day mortality and unplanned readmission rates. However, the number of dissected nodes was higher in the VATE group (median: 24 vs. 18, P < 0.001). While VATE had higher index hospitalization costs (median, 12331 USD vs. 10730 USD, P < 0.001), cost differences were reduced over time. The average accumulated cost person-month of VATE declined below that of OE at 14 months after hospital discharge. Overall survival (OS) figures were more favorable for patients treated with VATE (3-year OS: 47% vs. 41%; life expectancy: 4.04 life-years [LY] vs. 3.30 LY). The cost-effectiveness plane showed that only 0.3% of all VATE procedures were more costly and less effective than OE. The probabilities for VATE to be cost-effective at the willingness-to-pay (WTP) thresholds of 10000 and 50000 USD/LY were 63.5% and 92.4%, respectively. Using commonly accepted WTP thresholds, VATE was more cost-effective than OE for patients with esophageal cancer.

PMID: 33249485 [PubMed – as supplied by publisher]

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Nevin Manimala Statistics

A new approach in analyzing the accident severity of pedestrian crashes using structural equation modeling.

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A new approach in analyzing the accident severity of pedestrian crashes using structural equation modeling.

J Inj Violence Res. 2020 Nov 22;13(1):

Authors: Tavakoli Kashani A, Jafari M, Azizi Bondarabadi M

Abstract
BACKGROUND: According to official statistics in Iran, there were 17000 fatalities in road traffic crashes in 2018 that 25% of all crash fatalities belong to pedestrians. In most of the researches related to pedestrians’ safety, one aspect of the traffic crash (e.g. the injury or crash severity) is almost considered for the investigation. In order to perform a complete study of the crash, accident size can be utilized which involves different aspects of the crash. Accident size is described in terms of the number of fatalities and injured individuals and the number of damaged and involved vehicles in a crash.
METHODS: According to the fact that accident size has multiple indicators and it is not measured directly, traditional methodologies cannot be applied. So, in the present study the effective factors on the accident size of pedestrian crashes are investigated through structural equation modeling. For the purpose of this study, 3718 pedestrian-involved crash data occurred in Isfahan province is used for the modeling. The independent variables are weather conditions, road surface conditions, time, horizontal and vertical alignments, road type and location, driver’s gender and age, vehicle type, pedestrian’s age, gender and clothing color.
RESULTS: The results indicated that highways, the pedestrians’ invisibility, female and old-aged pedestrians, heavy vehicles, old-aged and female drivers are related to the increase of the accident size in pedestrian crashes. These results denote that the mentioned variables are associated with the higher number of injuries, fatalities, the higher number of involved and damaged vehicles in a crash.
CONCLUSIONS: Present study shows the importance of considering safety improvement measures in highways, educating the people in the society about the traffic safety, the separation of pedestrian and motor vehicle traffic flow and considering the old people in policies and programs for mitigating the accident size.

PMID: 33249418 [PubMed – as supplied by publisher]