12 research outputs found
Severity Index for Suspected Arbovirus (SISA) : machine learning for accurate prediction of hospitalization in subjects suspected of arboviral infection
Funding: This study was supported, in part, by the Department of Defense Global Emerging Infection Surveillance (https://health.mil/Military-Health-Topics/Combat-Support/Armed-Forces-Health-Surveillance-Branch/Global-Emerging-Infections-Surveillance-and-Response) grant (P0220_13_OT) and the Department of Medicine of SUNY Upstate Medical University (http://www.upstate.edu/medicine/). D.F., M.H. and P.H. were supported by the Ben Kean Fellowship from the American Society for Tropical Medicine and Hygeine (https://www.astmh.org/awards-fellowships-medals/benjamin-h-keen-travel-fellowship-in-tropical-medi). S.J.R and A.M.S-I were supported by NSF DEB EEID 1518681, NSF DEB RAPID 1641145 (https://www.nsf.gov/), A.M.S-I was additionally supported by the Prometeo program of the National Secretary of Higher Education, Science, Technology, and Innovation of Ecuador (http://prometeo.educacionsuperior.gob.ec/).Background: Dengue, chikungunya, and Zika are arboviruses of major global health concern. Decisions regarding the clinical management of suspected arboviral infection are challenging in resource-limited settings, particularly when deciding on patient hospitalization. The objective of this study was to determine if hospitalization of individuals with suspected arboviral infections could be predicted using subject intake data. Methodology/Principal findings: Two prediction models were developed using data from a surveillance study in Machala, a city in southern coastal Ecuador with a high burden of arboviral infections. Data were obtained from subjects who presented at sentinel medical centers with suspected arboviral infection (November 2013 to September 2017). The first prediction model-called the Severity Index for Suspected Arbovirus (SISA)-used only demographic and symptom data. The second prediction model-called the Severity Index for Suspected Arbovirus with Laboratory (SISAL)-incorporated laboratory data. These models were selected by comparing the prediction ability of seven machine learning algorithms; the area under the receiver operating characteristic curve from the prediction of a test dataset was used to select the final algorithm for each model. After eliminating those with missing data, the SISA dataset had 534 subjects, and the SISAL dataset had 98 subjects. For SISA, the best prediction algorithm was the generalized boosting model, with an AUC of 0.91. For SISAL, the best prediction algorithm was the elastic net with an AUC of 0.94. A sensitivity analysis revealed that SISA and SISAL are not directly comparable to one another. Conclusions/Significance: Both SISA and SISAL were able to predict arbovirus hospitalization with a high degree of accuracy in our dataset. These algorithms will need to be tested and validated on new data from future patients. Machine learning is a powerful prediction tool and provides an excellent option for new management tools and clinical assessment of arboviral infection.Publisher PDFPeer reviewe
The unexpected discovery of Brucella abortus Buck 19 vaccine in goats from Ecuador underlines the importance of biosecurity measures
Neoplasias mieloproliferativas cr贸nicas BCR-ABL negativas, manifestaci贸n cl铆nica y evoluci贸n. Experiencia en el Hospital Italiano de Buenos Aires
BCR-ABL negative myeloproliferative neoplasms(MPNs) are a heterogeneous group of clonal hematopoietic disorders characterized by the overproduction of differentiated hematopoietic cells, includingpolycythemia vera (PV), essential thrombocythemia(ET), primary myelofibrosis (PMF) and prefibroticprimary myelofibrosis (prePMF).The WHO classification was revised in 2016 adding聽specific molecular findings with an impact on diagnosis and treatment. This integrates clinical, molecular and pathological criteria and provides a central聽role to morphological examination of the bone marrow (BM). It also discriminates between essential聽thrombocythemia (ET) and the prefibrotic phase of聽primary myelofibrosis (pre PMF).In order to better understand this entity and to have聽its own population data, a retrospective cohort study聽was carried out at the Hospital Italiano de Buenos聽Aires, which included 264 patients with a diagnosis聽of Philadelphia-negative chronic myeloproliferative聽neoplasms between January 2004 and December聽2017 and a pathological review of 64 patients with a聽diagnosis of essential thrombocythemia prior to 2016聽(using the criteria of the new WHO classification).The analysis showed that the characteristics of our聽population are similar to those reported in the literature, with rates of global survival and transformation similar to the global population. Regarding聽the review of bone marrow pathology, the diagnosis聽of ET was confirmed in 56% of the patients. In patients who were reclassified to PMF prefibrotic stage聽(44%), a higher percentage of medullary fibrosis and聽splenomegaly was found at diagnosis.聽Las neoplasias mieloproliferativas BCR-ABL negativas son un grupo heterog茅neo de trastornos hematopoy茅ticos clonales caracterizados por la sobreproducci贸n de c茅lulas hematopoy茅ticas diferenciadas.Incluye policitemia vera (PV), trombocitemia esencial (TE), mielofibrosis primaria (MFP) y mielofibrosis primaria prefibr贸tica (MFPpre).La clasificaci贸n de la OMS 2001-2008 fue revisada聽en el a帽o 2016 agregando hallazgos moleculares espec铆ficos con un impacto en el diagn贸stico y el tratamiento. Integra criterios cl铆nicos, moleculares y聽patol贸gicos y mantiene un papel central para el examen morfol贸gico fino de la m茅dula 贸sea.Con la finalidad de comprender mejor esta entidad y聽de contar con datos poblacionales propios, se realiz贸聽un estudio de cohorte retrospectiva en el Hospital Italiano de Buenos Aires que incluye 264 pacientes聽con diagn贸stico de neoplasia mieloproliferativa cr贸nica Phi negativa entre enero 2004 y diciembre 2017y se realiz贸 la revisi贸n anatomopatol贸gica de 64 pacientes con diagn贸stico de trombocitemia esencialprevio al 2016 (utilizando los criterios de la nueva聽clasificaci贸n de la OMS).El an谩lisis demostr贸 que las caracter铆sticas de nuestra poblaci贸n son similares a las reportadas en la聽bibliograf铆a, con tasas de sobrevida global y de聽transformaci贸n similares en la poblaci贸n global.En cuanto a la revisi贸n de la patolog铆a de la m茅dula聽贸sea, el diagn贸stico de TE fue confirmado en el 56%de los pacientes. En los pacientes que recategorizaron a mielofibrosis primaria en estadio prefibr贸tico(44%) se encontr贸 un porcentaje mayor de fibrosis聽medular y de esplenomegalia al diagn贸stico
Severity Index for Suspected Arbovirus (SISA):machine learning for accurate prediction of hospitalization in subjects suspected of arboviral infection
BACKGROUND:Dengue, chikungunya, and Zika are arboviruses of major global health concern. Decisions regarding the clinical management of suspected arboviral infection are challenging in resource-limited settings, particularly when deciding on patient hospitalization. The objective of this study was to determine if hospitalization of individuals with suspected arboviral infections could be predicted using subject intake data. METHODOLOGY/PRINCIPAL FINDINGS:Two prediction models were developed using data from a surveillance study in Machala, a city in southern coastal Ecuador with a high burden of arboviral infections. Data were obtained from subjects who presented at sentinel medical centers with suspected arboviral infection (November 2013 to September 2017). The first prediction model-called the Severity Index for Suspected Arbovirus (SISA)-used only demographic and symptom data. The second prediction model-called the Severity Index for Suspected Arbovirus with Laboratory (SISAL)-incorporated laboratory data. These models were selected by comparing the prediction ability of seven machine learning algorithms; the area under the receiver operating characteristic curve from the prediction of a test dataset was used to select the final algorithm for each model. After eliminating those with missing data, the SISA dataset had 534 subjects, and the SISAL dataset had 98 subjects. For SISA, the best prediction algorithm was the generalized boosting model, with an AUC of 0.91. For SISAL, the best prediction algorithm was the elastic net with an AUC of 0.94. A sensitivity analysis revealed that SISA and SISAL are not directly comparable to one another. CONCLUSIONS/SIGNIFICANCE:Both SISA and SISAL were able to predict arbovirus hospitalization with a high degree of accuracy in our dataset. These algorithms will need to be tested and validated on new data from future patients. Machine learning is a powerful prediction tool and provides an excellent option for new management tools and clinical assessment of arboviral infection