Vaccination impact: mortality and time shift to COVID-19 maximum severity in hospitalized patients. An Argentine multicenter registry.

INTRODUCTION: The COVID-19 vaccine became an effective instrument to prevent severe SARS-CoV-2 infections. However, 5% of vaccinated patients will have moderate or severe disease. OBJECTIVE: to compare mortality and days between the symptom onset to the peak disease severity, in vaccinated vs. unvaccinated COVID-19 hospitalized patients. METHODS: Retrospective observational study in 36 hospitals in Argentina. COVID-19 adults admitted to general wards between January 1, 2021, and May 31, 2022 were included. Days between symptoms onset to peak of severity were compared between vaccinated vs. unvaccinated patients with Cox regression, adjusted by Propensity Score Matching (PSM). Results in patients with one and two doses were also compared. RESULTS: A total of 3663 patients were included (3001 [81.9%] unvaccinated and 662 [18%] vaccinated). Time from symptom onset to peak severity was 7 days (IQR 4-12) vs. 7 days (IQR 4-11) in unvaccinated and vaccinated. In crude Cox regression analysis and matched population, no significant differences were observed. Regarding mortality, a Risk Ratio (RR) of 1.51 (IC95% 1.29-1.77) was observed in vaccinated patients, but in the PSM cohort, the RR was 0.73 (IC95% 0.60-0.88). RR in patients with one COVID-19 vaccine dose in PSM adjusted population was 0.7 (IC95% 0.45-1.03), and with two doses 0.6 (IC95% 0.46-0.79). DISCUSSION: The time elapsed between the onset of COVID-19 symptoms to the highest severity was similar in vaccinated and unvaccinated patients. However, hospitalized vaccinated patients had a lower risk of mortality than unvaccinated patients.

Introducción: A pesar de la eficacia de la vacuna contra el COVID-19 el 5% de los pacientes vacunados presentaran una enfermedad moderada o grave. El objetivo del presente estudio fue comparar los días entre el inicio de los síntomas y la gravedad máxima de la enfermedad, en pacientes con COVID-19 vacunados vs. no vacunados. Métodos: Estudio observacional retrospectivo en 36 hospitales de Argentina. Se incluyeron adultos con COVID-19 hospitalizados entre el 1/01/2021 y 31/5/2022. Se recolectaron datos demográficos, comorbilidades y progresión clínica de la enfermedad. Se compararon los días entre el inicio de los síntomas y el pico de gravedad entre vacunados y no vacunados mediante regresión de Cox, ajustada por emparejamiento por Propensity Score Matching (PSM). En un análisis de subgrupos, se compararon los resultados en pacientes con una y dos dosis de vacuna. Resultados: Se incluyeron 3663 pacientes (3001 [81.9%] no vacunados y 662 [18%] vacunados). El tiempo transcurrido desde el inicio de los síntomas hasta el pico de gravedad fue de 7 días (IQR 4 - 12) en no vacunados, y de 7 días (IQR 4-11) en vacunados. Tanto en el análisis de regresión de Cox crudo como en el ajustado, no se observaron diferencias significativas entre ambos grupos (HR ajustado 1.08 [IC 95% 0.82-1.4; p = 0.56]). En cuanto a la mortalidad, el Riesgo Relativo (RR) fue 1.51 (IC95% 1.29-1.77) en los pacientes vacunados, pero en la cohorte ajustada por Propensity Score, el RR fue de 0.73 (IC95% 0.60-0.88). El RR en el grupo con una dosis de vacuna COVID-19 en el análisis PSM fue 0.7 (IC95% 0.45-1.03), y con dos dosis 0.6 (IC95% 0.46-0.79). Discusión: El tiempo entre el inicio de los síntomas de COVID-19 y el pico de severidad fue igual en vacunados y no vacunados. Sin embargo, los pacientes vacunados hospitalizados presentaron menor mortalidad tras el ajuste por confundidores.

Papel de la persona navegadora del paciente en el cáncer de pulmón / The Role of the Navigator Person for a Patient with Lung Cancer

Introducción: la medicina basada en el valor ha logrado mejorar la calidad de atención del paciente y/o la satisfacción de las personas, reduciendo costos y obteniendo mejores resultados. Se apoya sobre tres pilares básicos: la medicina basada en la evidencia, la atención centralizada en el paciente, y la sustentabilidad. Sin embargo, existen pocas publicaciones sobre la estrategia de personas navegadoras para pacientes con cáncer de pulmón, que podrían ser una herramienta clave para brindar apoyo, asegurando que tengan acceso al conocimiento y los recursos necesarios a fin de completar la vía de atención clínica recomendada. Estado del arte: se trata de una intervención en salud cuyo objetivo principal es lograr vencer las barreras relacionadas con la atención (p. ej., logísticas, burocrático-administrativas, de comunicación y financieras) para poder mejorar la calidad y el acceso a la salud en el marco de la atención del cáncer. Estas personas cumplen un papel de guía para pacientes durante el proceso de diagnóstico y tratamiento activo. Su labor consiste en vincular al paciente con los proveedores clínicos, brindar un sistema de apoyo, asegurar un acompañamiento individualizado, garantizar que tengan acceso al conocimiento y a los recursos necesarios para completar el seguimiento y/o tratamiento adecuado. Discusión/Conclusión: indudablemente, es un elemento cada vez más reconocido en los programas de cáncer, centrado en el paciente y de alta calidad. Su implementación será de especial interés en la Unidad de Práctica Integrada para personas con cáncer de pulmón. (AU)

Introduction: Value-based medicine has succeeded in improving the quality of patient care and or patient satisfaction, reducing costs, and obtaining better outcomes. It rests on three fundamental pillars: evidence-based medicine, patient-centered care, and sustainability. However, there are few publications on the people navigator strategy for lung cancer patients, which could be a crucial tool for providing support, ensuring that they have access to the knowledge and resources needed to complete the recommended clinical care pathway. State of the art: It is a health intervention whose main objective is to overcome care-related barriers (e.g., logistical, bureaucratic-administrative, communication, and financial) to improve quality and access to health in the context of cancer care. These individuals play a guiding role for patients during the diagnostic and active treatment process. Their job is to link the patient with clinical providers, provide a support system, ensure individualized accompaniment, and guarantee that they get access to the knowledge and resources necessary to complete the appropriate follow-up and, or treatment. Discussion/Conclusion: Undoubtedly, patient navigators represent an increasingly recognized element of high-quality, patient-centered cancer programs. Its implementation will be of specific interest in the Integrated Practice Unit for people with lung cancer. (AU)

COVID-19 vaccines reduce mortality in hospitalized patients with oxygen requirements: Differences between vaccine subtypes. A multicontinental cohort study.

The aim of this study was to analyze whether the coronavirus disease 2019 (COVID-19) vaccine reduces mortality in patients with moderate or severe COVID-19 disease requiring oxygen therapy. A retrospective cohort study, with data from 148 hospitals in both Spain (111 hospitals) and Argentina (37 hospitals), was conducted. We evaluated hospitalized patients for COVID-19 older than 18 years with oxygen requirements. Vaccine protection against death was assessed through a multivariable logistic regression and propensity score matching. We also performed a subgroup analysis according to vaccine type. The adjusted model was used to determine the population attributable risk. Between January 2020 and May 2022, we evaluated 21,479 COVID-19 hospitalized patients with oxygen requirements. Of these, 338 (1.5%) patients received a single dose of the COVID-19 vaccine and 379 (1.8%) were fully vaccinated. In vaccinated patients, mortality was 20.9% (95% confidence interval [CI]: 17.9-24), compared to 19.5% (95% CI: 19-20) in unvaccinated patients, resulting in a crude odds ratio (OR) of 1.07 (95% CI: 0.89-1.29; p = 0.41). However, after considering the multiple comorbidities in the vaccinated group, the adjusted OR was 0.73 (95% CI: 0.56-0.95; p = 0.02) with a population attributable risk reduction of 4.3% (95% CI: 1-5). The higher risk reduction for mortality was with messenger RNA (mRNA) BNT162b2 (Pfizer) (OR 0.37; 95% CI: 0.23-0.59; p < 0.01), ChAdOx1 nCoV-19 (AstraZeneca) (OR 0.42; 95% CI: 0.20-0.86; p = 0.02), and mRNA-1273 (Moderna) (OR 0.68; 95% CI: 0.41-1.12; p = 0.13), and lower with Gam-COVID-Vac (Sputnik) (OR 0.93; 95% CI: 0.6-1.45; p = 0.76). COVID-19 vaccines significantly reduce the probability of death in patients suffering from a moderate or severe disease (oxygen therapy).

Development and evaluation of a machine learning-based in-hospital COVID-19 disease outcome predictor (CODOP): A multicontinental retrospective study.

New SARS-CoV-2 variants, breakthrough infections, waning immunity, and sub-optimal vaccination rates account for surges of hospitalizations and deaths. There is an urgent need for clinically valuable and generalizable triage tools assisting the allocation of hospital resources, particularly in resource-limited countries. We developed and validate CODOP, a machine learning-based tool for predicting the clinical outcome of hospitalized COVID-19 patients. CODOP was trained, tested and validated with six cohorts encompassing 29223 COVID-19 patients from more than 150 hospitals in Spain, the USA and Latin America during 2020-22. CODOP uses 12 clinical parameters commonly measured at hospital admission for reaching high discriminative ability up to 9 days before clinical resolution (AUROC: 0·90-0·96), it is well calibrated, and it enables an effective dynamic risk stratification during hospitalization. Furthermore, CODOP maintains its predictive ability independently of the virus variant and the vaccination status. To reckon with the fluctuating pressure levels in hospitals during the pandemic, we offer two online CODOP calculators, suited for undertriage or overtriage scenarios, validated with a cohort of patients from 42 hospitals in three Latin American countries (78-100% sensitivity and 89-97% specificity). The performance of CODOP in heterogeneous and geographically disperse patient cohorts and the easiness of use strongly suggest its clinical utility, particularly in resource-limited countries.

While COVID-19 vaccines have saved millions of lives, new variants, waxing immunity, unequal rollout and relaxation of mitigation strategies mean that the pandemic will keep on sending shockwaves across healthcare systems. In this context, it is crucial to equip clinicians with tools to triage COVID-19 patients and forecast who will experience the worst forms of the disease. Prediction models based on artificial intelligence could help in this effort, but the task is not straightforward. Indeed, the pandemic is defined by ever-changing factors which artificial intelligence needs to cope with. To be useful in the clinic, a prediction model should make accurate prediction regardless of hospital location, viral variants or vaccination and immunity statuses. It should also be able to adapt its output to the level of resources available in a hospital at any given time. Finally, these tools need to seamlessly integrate into clinical workflows to not burden clinicians. In response, Klén et al. built CODOP, a freely available prediction algorithm that calculates the death risk of patients hospitalized with COVID-19 (https://gomezvarelalab.em.mpg.de/codop/). This model was designed based on biochemical data from routine blood analyses of COVID-19 patients. Crucially, the dataset included 30,000 individuals from 150 hospitals in Spain, the United States, Honduras, Bolivia and Argentina, sampled between March 2020 and February 2022 and carrying most of the main COVID-19 variants (from the original Wuhan version to Omicron). CODOP can predict the death or survival of hospitalized patients with high accuracy up to nine days before the clinical outcome occurs. These forecasting abilities are preserved independently of vaccination status or viral variant. The next step is to tailor the model to the current pandemic situation, which features increasing numbers of infected people as well as accumulating immune protection in the overall population. Further development will refine CODOP so that the algorithm can detect who will need hospitalisation in the next 24 hours, and who will need admission in intensive care in the next two days. Equipping primary care settings and hospitals with these tools will help to restore previous standards of health care during the upcoming waves of infections, particularly in countries with limited resources.

[Effect COVID-19 pandemic in an emergency department in Argentina]. / Concurrencia a una central de emergencias de Argentina durante la pandemia COVID-19.

The objective of this study was to reveal how the COVID-19 pandemic process affected the number of visits to an emergency department of a highly complex hospital located in the Ciudad Autónoma de Buenos Aires, to explore the characteristics and reasons for consultation. The monthly number of visits between January 2019 and December 2020 was analyzed. The data showed a strong decrease in the number of visits (176 370 in 2019 and 95 421 in 2020), with an abrupt drop after the lockdown disposal (In aprilshowed the maximum reduction: 77.1%), and the different stages are reflected in the evolution (a consequence of quarantine), yielding a global annual reduction of 45.9%. The number of patients admitted by ambulances increased (5.1% in 2019 to 10.4% in 2020; p < 0.05), and consequently, the number of patients in the more complex sector (area B 2019: 5.3%, 2020: 11.5%; p < 0.01), as well as unscheduled hospitalizations from 6.8% (95% CI 6.7-6.9) to 12.1% in 2020 (95%CI11.8-12.3), p < 0.01. The five most frequent reasons for consultation in 2020 were: fever (5.1%), odynophagia (4.7%), abdominal pain (2.6%), cough (1.8%) and headache (1.8%), probably all related to COVID-19. In conclusion, the number of emergency department visits decreased by half compared to the previous year.

El objetivo de este estudio fue revelar cómo el COVID-19 afectó el número de visitas a un servicio de urgencias de un hospital de alta complejidad ubicado en la Ciudad Autónoma de Buenos Aires, explorar las características y los motivos de consulta. Se analizó el número mensual entre enero 2019 y diciembre 2020. Los datos mostraron una fuerte disminución en el número de visitas (176 370 en 2019 y 95 421 en 2020), con una caída abrupta luego de disposición aislamiento social, preventivo y obligatorio (el mes de abril arrojó el máximo valor de reducción: 77.1%), y se reflejan las diferentes etapas evolutivas (consecuencia de la cuarentena), arrojando una reducción global anual del 45.9%. Sin embargo, aumentó el número de pacientes que ingresaron en ambulancias (5.1% en 2019 a 10.4% en 2020; p < 0.05), y, en consecuencia, los pacientes del sector de mayor complejidad (área B 2019: 5.3%, 2020: 11.5%; p < 0.01), y las hospitalizaciones no programadas de 6.8% (IC95% 6.7-6.9) a 12.1% en 2020 (IC95% 11.8-12.3), p < 0.01. Los cinco motivos de consulta más frecuentes durante 2020 resultaron: fiebre (5.1%), odinofagia (4.7%), dolor abdominal (2.6%), tos (1.8%) y cefalea (1.8%), probablemente todos relacionados a COVID-19. En conclusión, se redujo a la mitad el número de visitas a urgencias en comparación con el año previo.

[Descriptive analysis of 4776 patients admitted to medical clinic services for COVID-19. Results of the Argentine Multi-Center Registry - REMA-COVID-19]. / Análisis descriptivo de 4776 pacientes internados en servicios de clínica médica por COVID-19. Resultados del Registro Multicéntrico Argentino - REMA-COVID-19.

A multicenter registry that included adults hospitalized for COVID-19 was carried out in various provinces of Argentina, from March to October 2020. The objectives were to describe the epidemiological characteristics, clinical manifestations, treatments, complications and risk factors, need for admission to critical care units and mortality. The registry included information on 4776 patients in 37 health centers in Argentina. Of them, 70.2% came from the city of Buenos Aires and from Buenos Aires Province; 52.3% were men. The mean age was 56 years (SD 20.3). Of them, 13.1% stated that they were health personnel. The median time of symptoms at the time of hospitalization was 3 days (CI 1-6). The most frequent comorbidities were hypertension in 32.4% and diabetes mellitus in 15.8%. The most frequent symptoms were: cough 58%, odynophagia 23.3%, myalgia 20.5% and fever / low-grade fever 19.9%. The hospital stay had a median of 8 days (CI 4-15). A 14.8% of the patients required critical care, while 3.2% who also required it, were not transferred to a closed unit due to adequacy of the therapeutic effort. The most frequent complications in critical care were: cardiovascular events (54.1%), septic shock (33.3%), renal failure (9.7%) and pneumonia associated with mechanical ventilation (12.5%). Overall mortality was 12.3%. Old age, dementia and COPD behaved as independent predictors of mortality (p < 0.001, 0.007 and 0.002 respectively) in the multivariate analysis.

Se realizó un registro multicéntrico que incluyó personas adultas internadas por COVID-19 en varias provincias de la Argentina, desde marzo a octubre de 2020. Los objetivos fueron describir las características epidemiológicas, manifestaciones clínicas, tratamientos, complicaciones y factores de riesgo, necesidad de admisión a unidades de cuidados críticos y mortalidad. El registro incluyó información de 4776 pacientes, en 37 centros de salud de Argentina. El 70.2% provenían de la ciudad de Buenos Aires y la provincia de Buenos Aires. El 52.3% eran hombres. La media de edad fue de 56 años (DE 20.3). Un 13.1% de pacientes manifestó ser personal de salud. La mediana de tiempo de síntomas al momento de la internación fue de 3 días (IC 1-6). Las comorbilidades más frecuentes fueron hipertensión arterial en 32.4% y diabetes mellitus en 15.8%. Los síntomas más frecuentes fueron: tos 58%, odinofagia 23.3%, mialgias 20.5% y fiebre/febrícula 19.9%. La estadía hospitalaria tuvo una mediana de internación de 8 días (IC 4-15). El 14.8% de los pacientes requirió cuidados críticos, en tanto que el 3.2%, que también lo requería, no pasó a unidad cerrada por adecuación del esfuerzo terapéutico. Las complicaciones más frecuentes en cuidados críticos fueron: eventos cardiovasculares (54.1%), shock séptico (33.3%), insuficiencia renal (9.7%) y neumonía asociada a la ventilación mecánica (12.5%). La mortalidad global fue del 12.3%. La edad avanzada, demencia y EPOC se comportaron como predictores independientes de mortalidad (p < 0.001, 0.007 y 0.002 respectivamente) en el análisis multivariado.

Concurrencia a una central de emergencias de Argentina durante la pandemia COVID-19 / Effect COVID-19 pandemic in an emergency department in Argentina

Resumen El objetivo de este estudio fue revelar cómo el COVID-19 afectó el número de visitas a un servicio de urgencias de un hospital de alta complejidad ubicado en la Ciudad Autónoma de Buenos Aires, explorar las características y los motivos de consulta. Se analizó el número mensual entre enero 2019 y diciem bre 2020. Los datos mostraron una fuerte disminución en el número de visitas (176 370 en 2019 y 95 421 en 2020), con una caída abrupta luego de disposición aislamiento social, preventivo y obligatorio (el mes de abril arrojó el máximo valor de reducción: 77.1%), y se reflejan las diferentes etapas evolutivas (consecuencia de la cuarentena), arrojando una reducción global anual del 45.9%. Sin embargo, aumentó el número de pacientes que ingresaron en ambulancias (5.1% en 2019 a 10.4% en 2020; p < 0.05), y, en consecuencia, los pacientes del sector de mayor complejidad (área B 2019: 5.3%, 2020: 11.5%; p < 0.01), y las hospitalizaciones no progra madas de 6.8% (IC95% 6.7-6.9) a 12.1% en 2020 (IC95% 11.8-12.3), p < 0.01. Los cinco motivos de consulta más frecuentes durante 2020 resultaron: fiebre (5.1%), odinofagia (4.7%), dolor abdominal (2.6%), tos (1.8%) y cefalea (1.8%), probablemente todos relacionados a COVID-19. En conclusión, se redujo a la mitad el número de visitas a urgencias en comparación con el año previo.

Abstract The objective of this study was to reveal how the COVID-19 pandemic process affected the number of visits to an emergency department of a highly complex hospital located in the Ciudad Autónoma de Buenos Aires, to explore the char acteristics and reasons for consultation. The monthly number of visits between January 2019 and December 2020 was analyzed. The data showed a strong decrease in the number of visits (176 370 in 2019 and 95 421 in 2020), with an abrupt drop after the lockdown disposal (In aprilshowed the maximum reduction: 77.1%), and the different stages are reflected in the evolution (a consequence of quarantine), yielding a global annual reduc tion of 45.9%. The number of patients admitted by ambulances increased (5.1% in 2019 to 10.4% in 2020; p < 0.05), and consequently, the number of patients in the more complex sector (area B 2019: 5.3%, 2020: 11.5%; p < 0.01), as well as unscheduled hospitalizations from 6.8% (95% CI 6.7-6.9) to 12.1% in 2020 (95%CI11.8- 12.3), p < 0.01. The five most frequent reasons for consultation in 2020 were: fever (5.1%), odynophagia (4.7%), abdominal pain (2.6%), cough (1.8%) and headache (1.8%), probably all related to COVID-19. In conclusion, the number of emergency department visits decreased by half compared to the previous year.

Análisis descriptivo de 4776 pacientes internados en servicios de clínica médica POR COVID-19. Resultados del Registro Multicéntrico Argentino - REMA-COVID-19 / Descriptive analysis of 4776 patients admitted to medical clinic services for COVID-19. Results of the Argentine Multi-Center Registry - REMA-COVID-19

Resumen Se realizó un registro multicéntrico que incluyó personas adultas internadas por COVID-19 en varias provincias de la Argentina, desde marzo a octubre de 2020. Los objetivos fueron describir las características epidemiológicas, manifestaciones clínicas, tratamientos, complicaciones y factores de riesgo, necesidad de admisión a unidades de cuidados críticos y mortalidad. El registro incluyó información de 4776 pacientes, en 37 centros de salud de Argentina. El 70.2% provenían de la ciudad de Buenos Aires y la provincia de Buenos Aires. El 52.3% eran hombres. La media de edad fue de 56 años (DE 20.3). Un 13.1% de pacientes manifestó ser personal de salud. La mediana de tiempo de síntomas al momento de la internación fue de 3 días (IC 1-6). Las comorbilidades más frecuentes fueron hipertensión arterial en 32.4% y diabetes mellitus en 15.8%. Los síntomas más frecuentes fueron: tos 58%, odinofagia 23.3%, mialgias 20.5% y fiebre/febrícula 19.9%. La estadía hospitalaria tuvo una mediana de internación de 8 días (IC 4-15). El 14.8% de los pacientes requirió cuidados críticos, en tanto que el 3.2%, que también lo requería, no pasó a unidad cerrada por adecuación del esfuerzo terapéutico. Las complicaciones más frecuentes en cuidados críticos fueron: eventos cardiovasculares (54.1%), shock séptico (33.3%), insuficiencia renal (9.7%) y neumonía asociada a la ventilación mecánica (12.5%). La mortalidad global fue del 12.3%. La edad avanzada, demencia y EPOC se comportaron como predictores independientes de mortalidad (p < 0.001, 0.007 y 0.002 respectivamente) en el análisis multivariado.

Abstract A multicenter registry that in cluded adults hospitalized for COVID-19 was carried out in various provinces of Argentina, from March to October 2020. The objectives were to describe the epidemiological characteristics, clinical manifestations, treatments, complications and risk factors, need for admission to critical care units and mortality. The registry included information on 4776 patients in 37 health centers in Argentina. Of them, 70.2% came from the city of Buenos Aires and from Buenos Aires Province; 52.3% were men. The mean age was 56 years (SD 20.3). Of them, 13.1% stated that they were health personnel. The median time of symptoms at the time of hospitalization was 3 days (CI 1-6). The most frequent comorbidities were hypertension in 32.4% and diabetes mellitus in 15.8%. The most frequent symptoms were: cough 58%, odynophagia 23.3%, myalgia 20.5% and fever / low-grade fever 19.9%. The hospital stay had a median of 8 days (CI 4-15). A 14.8% of the patients required critical care, while 3.2% who also required it, were not transferred to a closed unit due to adequacy of the thera peutic effort. The most frequent complications in critical care were: cardiovascular events (54.1%), septic shock (33.3%), renal failure (9.7%) and pneumonia associated with mechanical ventilation (12.5%). Overall mortality was 12.3%. Old age, dementia and COPD behaved as independent predictors of mortality (p < 0.001, 0.007 and 0.002 respectively) in the multivariate analysis.