Solving Patient Allocation Problem during an Epidemic Dengue Fever Outbreak by Mathematical Modelling

Dengue fever is a mosquito-borne disease that has rapidly spread throughout the last few decades. Most preventive mechanisms to deal with the disease focus on the eradication of the vector mosquito and vaccination campaigns. However, appropriate mechanisms of response are indispensable to face the consequent events when an outbreak takes place. This study applied single and multiple objective linear programming models to optimize the allocation of patients and additional resources during an epidemic dengue fever outbreak, minimizing the summation of the distance travelled by all patients. An empirical study was set in Ciudad del Este, Paraguay. Data provided by a privately run health insurance cooperative was used to verify the applicability of the models in this study. The results can be used by analysts and decision makers to solve patient allocation problems for providing essential medical care during an epidemic dengue fever outbreak.

Dynamic Programming for Response-Adaptive Dose-Finding Clinical Trials

Identifying the right dose is one of the most important decisions in drug development. Adaptive designs are promoted to conduct dose-finding clinical trials as they are more efficient and ethical compared with static designs. However, current techniques in response-adaptive designs for dose allocation are complex and need significant computational effort, which is a major impediment for implementation in practice. This study proposes a Bayesian nonparametric framework for estimating the dose-response curve, which uses a piecewise linear approximation to the curve by consecutively connecting the expected mean response at each dose. Our extensive numerical results reveal that a first-order Bayesian nonparametric model with a known correlation structure in prior for the expected mean response performs competitively when compared with the standard approach and other more complex models in terms of several relevant metrics and enjoys computational efficiency. Furthermore, structural properties for the optimal learning problem, which seeks to minimize the variance of the target dose, are established under this simple model. Summary of Contribution: In this work, we propose a methodology to derive efficient patient allocation rules in response-adaptive dose-finding clinical trials, where computational issues are the main concern. We show that our methodologies are competitive with the state-of-the-art methodology in terms of solution quality, are significantly more computationally efficient, and are more robust in terms of the shape of the dose-response curve, among other parameter changes. This research fits in “the intersection of computing and operations research” as it adapts operations research techniques to produce computationally attractive solutions to patient allocation problems in dose-finding clinical trials.

Village-to-village screening for hepatitis B and C using quantitative HBsAg and anti-HCV testing with reflex HCV core antigen tests in the remote communities of a resource-rich setting: a population-based prospective cohort study

ObjectivesCommunity-based screening for hepatitis B virus (HBV) and hepatitis C virus (HCV) is essential for hepatitis elimination. This study attempted to increase screening accessibility and efficacy by using alternative tools.DesignPopulation-based prospective cohort study.SettingHepatitis elimination program at Yunlin County, Taiwan.ParticipantsAll 4552 individuals participated in 60 screening sessions of a community-based HBV and HCV screening project in five rural townships with approximately 95 000 inhabitants in central-western Taiwan.InterventionsTo increase accessibility, 60 outreach screening sessions were conducted in 41 disseminative sites. Quantitative HBV surface antigen (qHBsAg) and anti-HCV testing with reflex HCV core antigen (HCV Ag) tests were employed as alternative screening tools.Main outcome measuresCalculate village-specific prevalence of HBsAg, anti-HCV and HCV Ag and establish patient allocation strategies according to levels of qHBsAg HCV Ag and alanine aminotransferase (ALT).ResultsOf 4552 participants, 553, 697 and 290 were positive for HBsAg, anti-HCV and HCV Ag, respectively; 75 of them had both HBsAg and anti-HCV positivity. The average (range) number of participants in each screening session was 98 (31–150). The prevalence rates (range) of HBsAg, anti-HCV and HCV Ag were 12.1% (4.3%–19.4%), 15.3% (2.6%–52.3%) and 6.4% (0%–30.2%), respectively. The HCV Ag positivity rate among anti-HCV-positive participants was 42% (0%–100%). Using cut-off values of >200 IU/mL for qHBsAg, >3 fmol/L for HCV Ag and >40 IU/mL for ALT as criteria for patient referral, we noted an 80.2% reduction in referral burden. Three villages had high anti-HCV prevalences of 52.3%, 53.8% and 63.4% with corresponding viraemic prevalences of 23.2%, 30.1% and 22% and thus constituted newly identified HCV-hyperendemic villages.ConclusionOutreach hepatitis screening increases accessibility for residents in rural communities. Screening HBV and HCV through qHBsAg and HCV Ag tests provides information concerning viral activities, which might be conducive to precise patient allocation in remote communities.

Quality of root canal treatment performed by undergraduate students at the Wits Oral Health Centre

In 2012, the School of Oral Health Sciences at the University of the Witwatersrand modified its undergraduateendodontic curriculum which led to a need to assess the impact of curriculum changes on root canal treatmentoutcomes. This study was an audit of root canal treatment performed by undergraduate BDS students using postoperative radiographs, and compared the results between different undergraduate clinical years of study.Postoperative periapical radiographs of patients treated by undergraduate students were examined to assess length, density and taper. Two independent investigators were first calibrated, and thereafter assessed 299 endodontic cases that were performed by third, fourth and fifth year students during the 2013-2015 period at the Wits Oral Health Centre. 68.9%, 73.6% and 70.9% were found for adequate length, acceptable density and acceptable taper of root filling respectively. The most acceptable length, density and taper results were seen in patients treated by final year students, while the lowest results were observed in the fourth year student cohort. There was a tendency for third year students to overfill due to over-instrumentation of anterior teeth.The change in the curriculum has been justified, though room for improvement exists. The superior result found in the 5th year student cohort was most likely due to their relative experience, and the use of rotary instrumentation and dental operating microscopes. Endodontic teaching should further emphasize the importance of length control during endodontic treatment and more stringent steps may be necessary during patient allocation and clinical supervision of fourth year dental students.

Early Prediction of In-Hospital Death of COVID-19 Patients: A Machine-Learning Model Based on Age, Blood Analyses, and Chest X-Ray Score

Background: To develop and validate an early-warning model to predict in-hospital mortality on admission of COVID-19 patients at an emergency department (ED).<br /> Methods: In total, 2782 patients were enrolled between March 2020 and December 2020, including 2106 patients (first wave) and 676 patients (second wave) in the COVID-19 outbreak in Italy. The first wave patients were divided into two groups with 1474 patients used to train the model, and 632 to validate it. The 676 patients in the second wave were used to test the model. Age, 17 blood analytes and Brescia chest X-ray score were the variables processed using a Random Forests classification algorithm to build and validate the model. ROC analysis was used to assess the model performances. A web-based death-risk calculator was implemented and integrated within the Laboratory Information System of the hospital. Results: The final score was constructed by age (the most powerful predictor), blood analytes (the strongest predictors were lactate dehydrogenase, D-dimer, Neutrophil/Lymphocyte ratio, C-reactive protein, Lymphocyte %, Ferritin std and Monocyte %), and Brescia chest X-ray score. The areas under the receiver operating characteristic curve obtained for the three groups (training, validating and testing) were 0.98, 0.83 and 0.78, respectively.<br />Conclusions: The model predicts in-hospital mortality on the basis of data that can be obtained in a short time, directly at the ED on admission. It functions as a web-based calculator, providing a risk score which is easy to interpret. It can be used in the triage process to support the decision on patient allocation.

Would the Recommended Dose Have Been Different Using Novel Dose-Finding Designs? Comparing Dose-Finding Designs in Published Trials

PURPOSE Simulation studies have shown that novel designs such as the continual reassessment method and the Bayesian optimal interval (BOIN) design outperform the 3 + 3 design by recommending the maximum tolerated dose (MTD) more often, using less patients, and allotting more patients to the MTD. However, it is not clear whether these novel designs would have yielded different results in the context of real-world dose-finding trials. This is a commonly mentioned reason for the continuous use of 3 + 3 designs for oncology trials, with investigators considering simulation studies not sufficiently convincing to warrant the additional design complexity of novel designs. METHODS We randomly sampled 60 published dose-finding trials to obtain 22 that used the 3 + 3 design, identified an MTD, published toxicity data, and had more than two dose levels. We compared the published MTD with the estimated MTD using the continual reassessment method and BOIN using target toxicity rates of 25% and 30% and toxicity data from the trial. Moreover, we compared patient allocation and sample size assuming that these novel designs had been implemented. RESULTS Model-based designs chose dose levels higher than the published MTD in about 40% of the trials, with estimated and observed toxicity rates closer to the target toxicity rates of 25% and 30%. They also assigned less patients to suboptimal doses and permitted faster dose escalation. CONCLUSION This study using published dose-finding trials shows that novel designs would recommend different MTDs and confirms the advantages of these designs compared with the 3 + 3 design, which were demonstrated by simulation studies.

Multi-objective Risk-based Resource Allocation for Urban Pandemic Preparedness: The COVID-19 Case in Bogotá, Colombia

ABSTRACTDetermining how best to allocate resources to be used during a pandemic is a strategic decision that directly affects the success of pandemic response operations. However, government agencies have finite resources, so they can’t monitor everything all of the time: they have to decide how best to allocate their scarce resources (i.e., budget for antivirals and preventive vaccinations, Intensive Care Unit (ICU), ventilators, non-intensive Care Unit (non-ICU), doctors) across a broad range of risk exposures (i.e., geographic spread, routes of transmission, overall poverty, medical preconditions). This paper establishes a comprehensive risk-based emergency management framework that could be used by decision-makers to determine how best to manage medical resources, as well as suggest patient allocation among hospitals and alternative healthcare facilities. A set of risk indexes are proposed by modeling the randomness and uncertainty of allocating resources in a pandemic. The city understudy is modeled as a Euclidean complex network, where depending on the neighborhood influence of allocating a resource in a demand point (i.e., informing citizens, limit social contact, allocate a new hospital) different network configurations are proposed. Finally, a multi-objective risk-based resource allocation (MoRRA) framework is proposed to optimize the allocation of resources in pandemics. The applicability of the framework is shown by the identification of high-risk areas where to prioritize the resource allocation during the current COVID-19 pandemic in Bogotá, Colombia.

Optimal patient protocols in regional acute stroke care

In acute stroke care two proven reperfusion treatments exist: (1) a blood thinner and (2) an interventional procedure. The interventional procedure can only be given in a stroke centre with specialized facilities. Rapid initiation of either is key to improving the functional outcome (often emphasized by the common phrase in acute stroke care “time=brain”). Delays between the moment the ambulance is called and the initiation of one or both reperfusion treatment(s) should therefore be as short as possible. The speed of the process strongly depends on five factors: patient location, regional patient allocation by emergency medical services (EMS), travel times of EMS, treatment locations, and in-hospital delays. Regional patient allocation by EMS and treatment locations are sub-optimally configured in daily practice. Our aim is to construct a mathematical model for the joint decision of treatment locations and allocation of acute stroke patients in a region, such that the time until treatment is minimized. We describe acute stroke care as a multi-flow two-level hierarchical facility location problem and the model is formulated as a mixed integer linear program. The objective of the model is the minimization of the total time until treatment in a region and it incorporates volume-dependent in-hospital delays. The resulting model is used to gain insight in the performance of practically oriented patient allocation protocols, used by EMS. We observe that the protocol of directly driving to the nearest stroke centre with special facilities (i.e., the mothership protocol) performs closest to optimal, with an average total time delay that is 3.9% above optimal. Driving to the nearest regional stroke centre (i.e., the drip-and-ship protocol) is on average 8.6% worse than optimal. However, drip-and-ship performs better than the mothership protocol in rural areas and when a small fraction of the population (at most 30%) requires the second procedure, assuming sufficient patient volumes per stroke centre. In the experiments, the time until treatment using the optimal model is reduced by at most 18.9 minutes per treated patient. In economical terms, assuming 150 interventional procedures per year, the value of medical intervention in acute stroke can be improved upon up to € 1,800,000 per year.

Clinical and Virological Characteristics of Hospitalized COVID-19 Patients in a German Tertiary Care Center during the First Wave of the SARS-CoV-2 Pandemic

BackgroundAdequate patient allocation is pivotal for optimal resource management in strained healthcare systems, and requires detailed knowledge of clinical and virological disease trajectories.MethodsA cohort of 168 hospitalized adult COVID-19 patients enrolled in a prospective observational study at a large European tertiary care center was analyzed.ResultsForty-four percent (71/161) of patients required invasive mechanical ventilation (IMV). Shorter duration of symptoms before admission (aOR 1.22 per day less, 95%CI 1.10-1.37, p<0.01), age 60-69 as compared to 18-59 years (aOR 4.33, 95%CI 1.07-20.10, p=0.04), and history of hypertension (aOR 5.55, 95%CI 2.00-16.82, p<0.01) were associated with need for IMV. Patients on IMV had higher maximal concentrations, slower decline rates, and longer shedding of SARS-CoV-2 than non-IMV patients (33 days, IQR 26-46.75, vs 18 days, IQR 16-46.75, respectively, p<0.01). Median duration of hospitalization was 9 days (IQR 6-15.5) for non-IMV and 49.5 days (IQR 36.8-82.5) for IMV-patients.ConclusionOur results indicate a short duration of symptoms before admission as a risk factor for severe disease and different viral load kinetics in severely affected patients.

Management of acute stroke and urgent neurointerventional procedures during COVID-19 pandemic: recommendations on the Scientific Department on Cerebrovascular Diseases of the Brazilian Academy of Neurology, Brazilian Society of Cerebrovascular Diseases and Brazilian Society of Neuroradiology

ABSTRACT Introduction: Although the 2019 severe acute respiratory syndrome coronavirus 2 infection (SARS-CoV-2, COVID-19) pandemic poses new challenges to the healthcare system to provide support for thousands of patients, there is special concern about common medical emergencies, such as stroke, that will continue to occur and will require adequate treatment. The allocation of both material and human resources to fight the pandemic cannot overshadow the care for acute stroke, a time-sensitive emergency that with an inefficient treatment will further increase mortality and long-term disability. Objective: This paper summarizes the recommendations from the Scientific Department on Cerebrovascular Diseases of the Brazilian Academy of Neurology, the Brazilian Society of Cerebrovascular Diseases and the Brazilian Society of Neuroradiology for management of acute stroke and urgent neuro-interventional procedures during the COVID-19 pandemic, including proper use of screening tools, personal protective equipment (for patients and health professionals), and patient allocation.