Estudio multicéntrico sobre multimorbilidad en internados en áreas clínicas / Multicenter Study on Multimorbidity in Patients in Clinical Areas

Introducción: El estudio de la comorbilidad requiere de un enfoque multilateral con vistas a mejorar la calidad de la atención de los enfermos por el sistema de atención. Objetivos: Explorar la magnitud de la comorbilidad de enfermedades crónicas en adultos internados en los hospitales. Métodos: Se realizó un estudio prospectivo-observacional-longitudinal-analítico. Se incluyeron pacientes internados en las Salas de Clínica Médica o pacientes clínicos en Salas de Internación Indiscriminada. Se realizó un estudio multicéntrico en 42 centros en un período de 2 años, con un muestreo consecutivo. Para el estudio se tuvo en cuenta la estadística descriptiva, inferencial y de regresión. Resultados: El total de pacientes en el estudio fue de 5925, masculinos con el 50,3 por ciento de edad 60,66 ± 0,25 años. Principal procedencia desde la guardia el 73 por ciento. La estadía hospitalaria de 12,61 ± 0,24 días, mayormente en pacientes quirúrgicos (15,45 ± 0,67 vs 11,76 ± 0,23; p < 0,00001). El 23 por ciento recibió tratamiento quirúrgico. El principal nivel educativo: secundario completo 21,6 por ciento. Dificultades económicas: 20 por ciento, mortalidad 9,26 por ciento; prevalencia de dislipemia, diabetes e hipertensión: 22,53 por ciento; 28,82 por ciento y 51,86 por ciento con 473 nuevos diagnósticos, IMC: 27,88 ± 0,65, Charlson global 2,09 ± 0,02 y en óbitos 3,84 ± 0,11. La media de patologías por paciente fue de 2,14 ± 0,01 y aumentó con la edad (p valor regresión lineal < 0,00001). Conclusiones: La hipertensión, la diabetes y la dislipemia representaron las entidades más prevalentes en Salas de Internación Clínica, Las enfermedades cardiovasculares, respiratorias, infectológicas, oncológicas, neurológicas, metabólicas y nefrológicas fueron predictores independientes de mortalidad(AU)

Introduction: The study of comorbidity requires a multilateral approach with a view to improving the quality of care for these patients by the care system. Objectives: To explore the magnitude of the comorbidity of chronic diseases in adults admitted to hospitals. Methods: Prospective-observational-longitudinal-analytical study. Patients hospitalized in a medical clinic room or clinical patients in indiscriminate hospitalization rooms are included, Multicenter study in 42 centers, with 2 years of recruitment. Consecutive sampling. Descriptive, inferential and regression statistics. Results: 5925 recruited, male gender 50,3percent, age 60,66 ± 0,25 years, main origin from the guard 73percent, stay 12,61 ± 0,24 days, longer in surgical (15,45 ± 0,67 vs 11,76 ± 0,23, p < 0,00001), 23percent received surgical treatment. Main educational level: complete secondary school 21,6%. Economic difficulties: 20percent, mortality 9,26percent, prevalence of dyslipidemia, diabetes and hypertension: 22,53percent, 28,82percent and 51,86percent with 473 new diagnoses in said pathologies, BMI: 27,88 ± 0,65, Global Charlson 2,09 ± 0,02 and in deaths 3,84 ± 0,11. The average number of pathologies per patient was 2,14 ± 0,01 and increased with age (p value for linear regression < 0,00001). Conclusions: Hypertension, diabetes and dyslipidemia represented the most prevalent entities in the clinical hospitalization room, cardiovascular, respiratory, infectious, oncological, neurological, metabolic and nephrological diseases were independent predictors of mortality(AU)

Development and validation of COEWS (COVID-19 Early Warning Score) for hospitalized COVID-19 with laboratory features: A multicontinental retrospective study.

Background: The emergence of new SARS-CoV-2 variants with significant immune-evasiveness, the relaxation of measures for reducing the number of infections, the waning of immune protection (particularly in high-risk population groups), and the low uptake of new vaccine boosters, forecast new waves of hospitalizations and admission to intensive care units. There is an urgent need for easily implementable and clinically effective Early Warning Scores (EWSs) that can predict the risk of complications within the next 24-48 hr. Although EWSs have been used in the evaluation of COVID-19 patients, there are several clinical limitations to their use. Moreover, no models have been tested on geographically distinct populations or population groups with varying levels of immune protection. Methods: We developed and validated COVID-19 Early Warning Score (COEWS), an EWS that is automatically calculated solely from laboratory parameters that are widely available and affordable. We benchmarked COEWS against the widely used NEWS2. We also evaluated the predictive performance of vaccinated and unvaccinated patients. Results: The variables of the COEWS predictive model were selected based on their predictive coefficients and on the wide availability of these laboratory variables. The final model included complete blood count, blood glucose, and oxygen saturation features. To make COEWS more actionable in real clinical situations, we transformed the predictive coefficients of the COEWS model into individual scores for each selected feature. The global score serves as an easy-to-calculate measure indicating the risk of a patient developing the combined outcome of mechanical ventilation or death within the next 48 hr.The discrimination in the external validation cohort was 0.743 (95% confidence interval [CI]: 0.703-0.784) for the COEWS score performed with coefficients and 0.700 (95% CI: 0.654-0.745) for the COEWS performed with scores. The area under the receiver operating characteristic curve (AUROC) was similar in vaccinated and unvaccinated patients. Additionally, we observed that the AUROC of the NEWS2 was 0.677 (95% CI: 0.601-0.752) in vaccinated patients and 0.648 (95% CI: 0.608-0.689) in unvaccinated patients. Conclusions: The COEWS score predicts death or MV within the next 48 hr based on routine and widely available laboratory measurements. The extensive external validation, its high performance, its ease of use, and its positive benchmark in comparison with the widely used NEWS2 position COEWS as a new reference tool for assisting clinical decisions and improving patient care in the upcoming pandemic waves. Funding: University of Vienna.

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.