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Alon Lanyado

Bio: Alon Lanyado is an academic researcher. The author has contributed to research in topics: Vaccination & Medicine. The author has an hindex of 1, co-authored 2 publications receiving 3 citations.

Papers
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Posted ContentDOI
05 Jun 2020-medRxiv
TL;DR: Two approaches are described that can effectively identify patients at high-risk for complication, thus allowing optimization of resources and more focused follow up and early triage these patients if once symptoms worsen.
Abstract: Background The global pandemic of COVID-19 has challenged healthcare organizations and caused numerous deaths and hospitalizations worldwide. The need for data-based decision support tools for many aspects of controlling and treating the disease is evident but has been hampered by the scarcity of real-world reliable data. Here we describe two approaches: a. the use of an existing EMR-based model for predicting complications due to influenza combined with available epidemiological data to create a model that identifies individuals at high risk to develop complications due to COVID-19 and b. a preliminary model that is trained using existing real world COVID-19 data. Methods We have utilized the computerized data of Maccabi Healthcare Services a 2.3 million member state-mandated health organization in Israel. The age and sex matched matrix used for training the XGBoost ILI-based model included, circa 690,000 rows and 900 features. The available dataset for COVID-based model included a total 2137 SARS-CoV-2 positive individuals who were either not hospitalized (n = 1658), or hospitalized and marked as mild (n = 332), or as having moderate (n = 83) or severe (n = 64) complications. Findings The AUC of our models and the priors on the 2137 COVID-19 patients for predicting moderate and severe complications as cases and all other as controls, the AUC for the ILI-based model was 0.852[0.824–0.879] for the COVID19-based model – 0.872[0.847–0.879]. Interpretation These models can effectively identify patients at high-risk for complication, thus allowing optimization of resources and more focused follow up and early triage these patients if once symptoms worsen. Funding There was no funding for this study Research in context Evidence before this study We have search PubMed for coronavirus[MeSH Major Topic] AND the following MeSH terms: risk score, predictive analytics, algorithm, predictive analytics. Only few studies were found on predictive analytics for developing COVID19 complications using real-world data. Many of the relevant works were based on self-reported information and are therefore difficult to implement at large scale and without patient or physician participation. Added value of this study We have described two models for assessing risk of COVID-19 complications and mortality, based on EMR data. One model was derived by combining a machine-learning model for influenza-complications with epidemiological data for age and sex dependent mortality rates due to COVID-19. The other was directly derived from initial COVID-19 complications data. Implications of all the available evidence The developed models may effectively identify patients at high-risk for developing COVID19 complications. Implementing such models into operational data systems may support COVID-19 care workflows and assist in triaging patients.

2 citations

Posted ContentDOI
23 Feb 2021-medRxiv
TL;DR: In this article, the authors used a machine learning model to identify high risk patients for influenza and their complications and to recommend vaccination to those at high risk for infection and serious complications.
Abstract: For many vaccine-preventable diseases like influenza, vaccination rates are lower than optimal to achieve community protection. Those at high risk for infection and serious complications are especially advised to be vaccinated to protect themselves. Using influenza as a model, we studied one method of increasing vaccine uptake: informing high-risk patients, identified by a machine learning model, about their risk status. Patients (N=39,717) were evenly randomized to (1) a control condition (exposure only to standard direct mail or patient portal vaccine promotion efforts) or to be told via direct mail, patient portal, and/or SMS that they were (2) at high risk for influenza and its complications if not vaccinated; (3) at high risk according to a review of their medical records; or (4) at high risk according to a computer algorithm analysis of their medical records. Patients in the three treatment conditions were 5.7% more likely to get vaccinated during the 112 days post-intervention (p < .001), and did so 1.4 days earlier (p < .001), on average, than those in the control group. There were no significant differences among risk messages, suggesting that patients are neither especially averse to nor uniquely appreciative of learning their records had been reviewed or that computer algorithms were involved. Similar approaches should be considered for COVID-19 vaccination campaigns.

2 citations

Journal ArticleDOI
TL;DR: The Geisinger Flu-Complications Flag can better identify high-risk individuals than existing models based on vaccination guidelines, thus creating a population-based risk stratification for individual risk assessment and deployment in vaccine hesitancy reduction programs in the health system.
Abstract: Influenza vaccinations are recommended for high-risk individuals, but few population-based strategies exist to identify individual risks. Patient-level data from unvaccinated individuals, stratified into retrospective cases (n = 111,022) and controls (n = 2,207,714), informed a machine learning model designed to create an influenza risk score; the model was called the Geisinger Flu-Complications Flag (GFlu-CxFlag). The flag was created and validated on a cohort of 604,389 unique individuals. Risk scores were generated for influenza cases; the complication rate for individuals without influenza was estimated to adjust for unrelated complications. Shapley values were used to examine the model’s correctness and demonstrate its dependence on different features. Bias was assessed for race and sex. Inverse propensity weighting was used in the derivation stage to correct for biases. The GFlu-CxFlag model was compared to the pre-existing Medial EarlySign Flu Algomarker and existing risk guidelines that describe high-risk patients who would benefit from influenza vaccination. The GFlu-CxFlag outperformed other traditional risk-based models; the area under curve (AUC) was 0.786 [0.783–0.789], compared with 0.694 [0.690–0.698] (p-value < 0.00001). The presence of acute and chronic respiratory diseases, age, and previous emergency department visits contributed most to the GFlu-CxFlag model’s prediction. When higher numerical scores were assigned to more severe complications, the GFlu-CxFlag AUC increased to 0.828 [0.823–0.833], with excellent discrimination in the final model used to perform the risk stratification of the population. The GFlu-CxFlag can better identify high-risk individuals than existing models based on vaccination guidelines, thus creating a population-based risk stratification for individual risk assessment and deployment in vaccine hesitancy reduction programs in our health system.

Cited by
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01 Jan 2020
TL;DR: Prolonged viral shedding provides the rationale for a strategy of isolation of infected patients and optimal antiviral interventions in the future.
Abstract: Summary Background Since December, 2019, Wuhan, China, has experienced an outbreak of coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Epidemiological and clinical characteristics of patients with COVID-19 have been reported but risk factors for mortality and a detailed clinical course of illness, including viral shedding, have not been well described. Methods In this retrospective, multicentre cohort study, we included all adult inpatients (≥18 years old) with laboratory-confirmed COVID-19 from Jinyintan Hospital and Wuhan Pulmonary Hospital (Wuhan, China) who had been discharged or had died by Jan 31, 2020. Demographic, clinical, treatment, and laboratory data, including serial samples for viral RNA detection, were extracted from electronic medical records and compared between survivors and non-survivors. We used univariable and multivariable logistic regression methods to explore the risk factors associated with in-hospital death. Findings 191 patients (135 from Jinyintan Hospital and 56 from Wuhan Pulmonary Hospital) were included in this study, of whom 137 were discharged and 54 died in hospital. 91 (48%) patients had a comorbidity, with hypertension being the most common (58 [30%] patients), followed by diabetes (36 [19%] patients) and coronary heart disease (15 [8%] patients). Multivariable regression showed increasing odds of in-hospital death associated with older age (odds ratio 1·10, 95% CI 1·03–1·17, per year increase; p=0·0043), higher Sequential Organ Failure Assessment (SOFA) score (5·65, 2·61–12·23; p Interpretation The potential risk factors of older age, high SOFA score, and d-dimer greater than 1 μg/mL could help clinicians to identify patients with poor prognosis at an early stage. Prolonged viral shedding provides the rationale for a strategy of isolation of infected patients and optimal antiviral interventions in the future. Funding Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences; National Science Grant for Distinguished Young Scholars; National Key Research and Development Program of China; The Beijing Science and Technology Project; and Major Projects of National Science and Technology on New Drug Creation and Development.

4,408 citations

Journal ArticleDOI
TL;DR: Externally and prospectively trained and validated machine learning models for mortality and critical events for patients with COVID-19 at different time horizons were developed and established model interpretability to identify and rank variables that drive model predictions.
Abstract: Background: COVID-19 has infected millions of people worldwide and is responsible for several hundred thousand fatalities. The COVID-19 pandemic has necessitated thoughtful resource allocation and early identification of high-risk patients. However, effective methods to meet these needs are lacking. Objective: The aims of this study were to analyze the electronic health records (EHRs) of patients who tested positive for COVID-19 and were admitted to hospitals in the Mount Sinai Health System in New York City; to develop machine learning models for making predictions about the hospital course of the patients over clinically meaningful time horizons based on patient characteristics at admission; and to assess the performance of these models at multiple hospitals and time points. Methods: We used Extreme Gradient Boosting (XGBoost) and baseline comparator models to predict in-hospital mortality and critical events at time windows of 3, 5, 7, and 10 days from admission. Our study population included harmonized EHR data from five hospitals in New York City for 4098 COVID-19–positive patients admitted from March 15 to May 22, 2020. The models were first trained on patients from a single hospital (n=1514) before or on May 1, externally validated on patients from four other hospitals (n=2201) before or on May 1, and prospectively validated on all patients after May 1 (n=383). Finally, we established model interpretability to identify and rank variables that drive model predictions. Results: Upon cross-validation, the XGBoost classifier outperformed baseline models, with an area under the receiver operating characteristic curve (AUC-ROC) for mortality of 0.89 at 3 days, 0.85 at 5 and 7 days, and 0.84 at 10 days. XGBoost also performed well for critical event prediction, with an AUC-ROC of 0.80 at 3 days, 0.79 at 5 days, 0.80 at 7 days, and 0.81 at 10 days. In external validation, XGBoost achieved an AUC-ROC of 0.88 at 3 days, 0.86 at 5 days, 0.86 at 7 days, and 0.84 at 10 days for mortality prediction. Similarly, the unimputed XGBoost model achieved an AUC-ROC of 0.78 at 3 days, 0.79 at 5 days, 0.80 at 7 days, and 0.81 at 10 days. Trends in performance on prospective validation sets were similar. At 7 days, acute kidney injury on admission, elevated LDH, tachypnea, and hyperglycemia were the strongest drivers of critical event prediction, while higher age, anion gap, and C-reactive protein were the strongest drivers of mortality prediction. Conclusions: We externally and prospectively trained and validated machine learning models for mortality and critical events for patients with COVID-19 at different time horizons. These models identified at-risk patients and uncovered underlying relationships that predicted outcomes.

138 citations

Journal ArticleDOI
02 Feb 2022
TL;DR: In this article , a survey of 1,011 potential participants from a national online panel, 22.3% (N = 225, 51.6% female, age = 40.5) met inclusion criteria of Hispanic adults and not receiving at least one dose of COVID-19 vaccine.
Abstract: The COVID-19 pandemic has disproportionately impacted Hispanics in the USA with increased rates of SARS-CoV-2 infections, hospitalizations, and deaths. The objective of this report was to characterize the demographics and beliefs of unvaccinated Hispanics to help address their concerns that lead to vaccine hesitancy.Of 1,011 potential participants from a national online panel, 22.3% (N = 225, 51.6% female, age = 40.5) met inclusion criteria of Hispanic adults and not receiving at least one dose of the COVID-19 vaccine. The 30-item survey included items about demographics, political affiliations, sources of news (e.g., Fox vs. CNN), reasons for being unvaccinated, and ratings (0 = strongly disagree, 100 = strongly agree) of 10 controversial statements regarding COVID-19.Over three-fifths (62.6%) identified side effects and safety concerns, while almost one-third (30.5%) cited a lack of efficacy as their top reasons for being unvaccinated. Agreement to "The developers of the COVID-19 vaccine rushed the development and cut corners" was rated the highest (63.22) which was significantly (p < .001) higher than the other nine statements (e.g., "The COVID-19 vaccine does not work"). Many vaccine attitudes differed significantly by political party affiliation and some by gender and news source. Republicans (59.9 ± 4.2) scored higher than Democrats (38.5 ± 4.2, p ≤ .001) to "If I've already had COVID-19, I don't need the vaccine."This study identified the heterogeneity in COVID-19 vaccine attitudes among Hispanics. Further research is needed to determine if the subgroups identified are differentially receptive to interventions to facilitate reconsideration of prior vaccination decisions.

4 citations

Posted ContentDOI
23 Sep 2021-medRxiv
TL;DR: In this article, the authors characterize the demographics and beliefs of unvaccinated Hispanics to help address their concerns that lead to vaccine hesitancy, and identify heterogeneity in COVID-19 vaccine attitudes among Hispanics.
Abstract: Background: The COVID-19 pandemic has disproportionately impacted Hispanics in the US with increased rates of SARS-Cov2 infections, hospitalizations, and deaths. The objective of this report was to characterize the demographics and beliefs of unvaccinated Hispanics to help address their concerns that lead to vaccine hesitancy. Methods: Of 1,011 potential participants from a national online panel, 22.3% (N = 225, 51.6% female, age = 40.5) met inclusion criteria of Hispanic adults and not receiving at least one dose of the COVID-19 vaccine. The 30-item survey included items about demographics, political affiliations, sources of news (e.g., Fox vs. CNN), reasons for being unvaccinated, and ratings (0 = strongly disagree, 100 = strongly agree) of 10 controversial statements regarding COVID-19. Results: Over three-fifths (62.6%) identified side effects and safety concerns while almost one-third (30.5%) a lack of efficacy as their top reasons for being unvaccinated. Agreement to statement: The developers of the COVID-19 vaccine rushed the development and cut-corners, was rated highest (63.22) which was significantly (p < .001) higher than the other nine statements (e.g., The COVID-19 vaccine does not work). Many vaccine attitudes differed significantly by political party affiliation and some by gender and news source. Republicans (59.9 + 4.2) scored higher than Democrats (38.5 + 4.2, p < .001) to the statement: If I have already had COVID-19, I do not need the vaccine. Conclusions: This study identified heterogeneity in COVID-19 vaccine attitudes among Hispanics. Further research is needed to determine if the subgroups identified are differentially receptive to interventions to facilitate reconsideration of prior vaccination decisions.