Early high-titer plasma therapy to prevent severe Covid-19 in older adults

BACKGROUND: Therapies to interrupt the progression of early coronavirus disease 2019 (Covid-19) remain elusive. Among them, convalescent plasma administered to hospitalized patients has been unsuccessful, perhaps because antibodies should be administered earlier in the course of illness. METHODS We conducted a randomized, double-blind, placebo-controlled trial of convalescent plasma with high IgG titers against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in older adult patients within 72 hours after the onset of mild Covid-19 symptoms. The primary end point was severe respiratory disease, defined as a respiratory rate of 30 breaths per minute or more, an oxygen saturation of less than 93% while the patient was breathing ambient air, or both. The trial was stopped early at 76% of its projected sample size because cases of Covid-19 in the trial region decreased considerably and steady enrollment of trial patients became virtually impossible. RESULTS A total of 160 patients underwent randomization. In the intention-to-treat population, severe respiratory disease developed in 13 of 80 patients (16%) who received convalescent plasma and 25 of 80 patients (31%) who received placebo (relative risk, 0.52; 95% confidence interval [CI], 0.29 to 0.94; P = 0.03), with a relative risk reduction of 48%. A modified intention-to-treat analysis that excluded 6 patients who had a primary end-point event before infusion of convalescent plasma or placebo showed a larger effect size (relative risk, 0.40; 95% CI, 0.20 to 0.81). No solicited adverse events were observed. CONCLUSIONS Early administration of high-titer convalescent plasma against SARS-CoV-2 to mildly ill infected older adults reduced the progression of Covid-19. (Funded by the Bill and Melinda Gates Foundation and the Fundación INFANT Pandemic Fund; Dirección de Sangre y Medicina Transfusional del Ministerio de Salud number, PAEPCC19, Plataforma de Registro Informatizado de Investigaciones en Salud number, 1421, and ClinicalTrials.gov number, NCT04479163.).Fil: Libster, Romina Paula. Fundación para la Investigación en Infectología Infantil; ArgentinaFil: Pérez Marc, Gonzalo. Hospital Militar Central, Buenos Aires; ArgentinaFil: Wappner, Diego. Fundación para la Investigación en Infectología Infantil; ArgentinaFil: Coviello, Silvina Andrea. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Fundación para la Investigación en Infectología Infantil; ArgentinaFil: Bianchi, Alejandra. Fundación para la Investigación en Infectología Infantil; ArgentinaFil: Braem, Virginia. Fundación para la Investigación en Infectología Infantil; ArgentinaFil: Esteban, Ignacio. Fundación para la Investigación en Infectología Infantil; ArgentinaFil: Caballero, Mauricio Tomás. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Wood, Cristian. Fundación para la Investigación en Infectología Infantil; ArgentinaFil: Berrueta, Mabel. Hospital Militar Central; ArgentinaFil: Rondan, Aníbal. Fundación para la Investigación en Infectología Infantil; ArgentinaFil: Lescano, Gabriela Mariel. Hospital Dr. Carlos Bocalandro; ArgentinaFil: Cruz, Pablo. Fundación para la Investigación en Infectología Infantil; ArgentinaFil: Ritou, Yvonne. Fundación para la Investigación en Infectología Infantil; ArgentinaFil: Fernández Viña, Valeria Silvina. Hospital Simplemente Evita; ArgentinaFil: Álvarez Paggi, Damián Jorge. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Fundación para la Investigación en Infectología Infantil; ArgentinaFil: Esperante, Sebastian. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Bioquímicas de Buenos Aires. Fundación Instituto Leloir. Instituto de Investigaciones Bioquímicas de Buenos Aires; ArgentinaFil: Ferreti, Adrián. Hospital Dr. Carlos Bocalandro; ArgentinaFil: Ofman, Gaston. University of Oklahoma; Estados UnidosFil: Ciganda, Álvaro. Gobierno de la Provincia de Buenos Aires. Hospital Interzonal Especializado de Agudos y Cronicos San Juan de Dios.; ArgentinaFil: Rodriguez, Rocío. Hospital Simplemente Evita; ArgentinaFil: Lantos, Jorge. Fundación para la Investigación en Infectología Infantil; ArgentinaFil: Valentini, Ricardo. No especifíca;Fil: Itcovici, Nicolás. Fundación para la Investigación en Infectología Infantil; ArgentinaFil: Hintze, Alejandra. No especifíca;Fil: Oyarvide, M. Laura. Fundación para la Investigación en Infectología Infantil; ArgentinaFil: Etchegaray, Candela. Fundación para la Investigación en Infectología Infantil; ArgentinaFil: Neira, Alejandra. Instituto de Efectividad Clínica y Sanitaria; ArgentinaFil: Name, Ivonne. Instituto de Efectividad Clínica y Sanitaria; ArgentinaFil: Alfonso, Julieta. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Swiss Medical Group; ArgentinaFil: López Castelo, Rocío. Centro de Educación Médica e Investigaciones Clínicas "Norberto Quirno"; ArgentinaFil: Caruso, Gisela. Hospital Militar Central; ArgentinaFil: Rapelius, Sofía. Hospital Militar Central; ArgentinaFil: Alvez, Fernando. Hospital Militar Central; ArgentinaFil: Etchenique, Federico. Hospital Militar Central; ArgentinaFil: Dimase, Federico. Hospital Militar Central; ArgentinaFil: Alvarez, Darío. Hospital Militar Central; ArgentinaFil: Aranda, Sofía S.. Hospital Militar Central; ArgentinaFil: Sánchez Yanotti, Clara Inés. Hospital Militar Central; ArgentinaFil: De Luca, Julián. Hospital Militar Central; ArgentinaFil: Jares Baglivo, Sofía. Hospital Militar Central; ArgentinaFil: Laudanno, Sofía. Fundación Hematológica Sarmiento; ArgentinaFil: Nowogrodzki, Florencia. Swiss Medical Group; ArgentinaFil: Larrea, Ramiro. Hospital Municipal San Isidro; ArgentinaFil: Silveyra, María. Hospital Militar Central; ArgentinaFil: Leberzstein, Gabriel. No especifíca;Fil: Debonis, Alejandra. Fundación para la Investigación en Infectología Infantil; ArgentinaFil: Molinos, Juan. Fundación para la Investigación en Infectología Infantil; ArgentinaFil: González, Miguel. Fundación para la Investigación en Infectología Infantil; ArgentinaFil: Perez, Eduardo. Fundación para la Investigación en Infectología Infantil; ArgentinaFil: Kreplak, Nicolás. Fundación para la Investigación en Infectología Infantil; ArgentinaFil: Pastor Argüello, Susana. Fundación para la Investigación en Infectología Infantil; ArgentinaFil: Gibbons, Luz. Hospital Municipal de San Isidro; ArgentinaFil: Althabe, Fernando. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Instituto de Efectividad Clínica y Sanitaria; ArgentinaFil: Bergel, Eduardo. Sanatorio Sagrado Corazón; ArgentinaFil: Polack, Fernando Pedro. Provincia de Buenos Aires. Ministerio de Salud; Argentin

Gestión de contratos en salud

Área de Salud, Economía y Socieda

Automated ICD9-CM Coding employing Bayesian Machine Learning:

Statistical text analysis techniques based on Bayesian machine learning were applied to a set of 2180 free-text discharge diagnoses (including both active and inactive problems) occurring in 767 hospitalizations. All of these diagnoses were previously coded according to ICD9-CM using a computerized version of the 1999 Spanish Edition of the named classification scheme, thus producing a set of codes univocally related to one or more of the section codes occurring for each hospitalization. A bag-of-words was parsed out of each phrase set in order to build a dictionary which would serve as the set of attributes to be measured for each set of discharge diagnoses. The probability weights relating each free-text set of diagnoses to one or more known ICD9 corresponding section codes were computed by means of a Bayesian classifier using naïve Bayes and shrinkage-assisted naïve Bayes. A subset of randomly selected hospitalizations were subsequently classified using the previously derived probability set. 86 % of the hospitalizations were correctly classified using this approach. 4 % erroneous assignations were due to spelling mistakes and wrongly coded diagnoses. The remaining incorrect assignations were due to low prevalence diagnoses. Given a sufficient number of cases for a training set, Bayesian techniques may be useful for the automated ICD9-CM classification of discharge diagnoses. Determination of optimal probability thresholds, the use of more sophisticated parsing methods, and the availability of more numerous training sets may further increase the success rates of this technique and allow for coding at the 3, 4 or 5-digit level. Key words: ICD9-CM, Bayesian machine learning