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.

Coronavirus disease 2019 hospitalization outcomes in persons with and without HIV in Spain.

OBJECTIVE: To compare coronavirus disease 2019 (COVID-19) hospitalization outcomes between persons with and without HIV. DESIGN: Retrospective observational cohort study in 150 hospitals in Spain. METHODS: Patients admitted from 1 March to 8 October 2020 with COVID-19 diagnosis confirmed by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2 positive) PCR test in respiratory tract samples. The primary data source was the COVID-19 Sociedad Española de Medicina Interna's registry (SEMI-COVID-19). Demographics, comorbidities, vital signs, laboratory parameters, and clinical severity as well as treatments received during admission, treatment duration, ICU admission, use of invasive mechanical ventilation, and death were recorded. Factors associated with mortality and the composite of ICU admission, invasive mechanical ventilation, and death, were analyzed. RESULTS: Data from 16 563 admissions were collected, 98 (0.59%) of which were of persons with HIV infection. These patients were younger, the percentage of male patients was higher, and their Charlson comorbidity index was also higher. Rates of mortality and composite outcome of ICU admission, invasive mechanical ventilation or death were lower among patients with HIV infection. In the logistic regression analysis, HIV infection was associated with an adjusted odds ratio of 0.53 [95% confidence interval (CI) 0.29-0.96] for the composite outcome. CONCLUSION: HIV infection was associated with a lower probability of ICU admission, invasive mechanical ventilation, or death.

Inadequate use of antibiotics in the covid-19 era: effectiveness of antibiotic therapy.

BACKGROUND: Since December 2019, the COVID-19 pandemic has changed the concept of medicine. This work aims to analyze the use of antibiotics in patients admitted to the hospital due to SARS-CoV-2 infection. METHODS: This work analyzes the use and effectiveness of antibiotics in hospitalized patients with COVID-19 based on data from the SEMI-COVID-19 registry, an initiative to generate knowledge about this disease using data from electronic medical records. Our primary endpoint was all-cause in-hospital mortality according to antibiotic use. The secondary endpoint was the effect of macrolides on mortality. RESULTS: Of 13,932 patients, antibiotics were used in 12,238. The overall death rate was 20.7% and higher among those taking antibiotics (87.8%). Higher mortality was observed with use of all antibiotics (OR 1.40, 95% CI 1.21-1.62; p < .001) except macrolides, which had a higher survival rate (OR 0.70, 95% CI 0.64-0.76; p < .001). The decision to start antibiotics was influenced by presence of increased inflammatory markers and any kind of infiltrate on an x-ray. Patients receiving antibiotics required respiratory support and were transferred to intensive care units more often. CONCLUSIONS: Bacterial co-infection was uncommon among COVID-19 patients, yet use of antibiotics was high. There is insufficient evidence to support widespread use of empiric antibiotics in these patients. Most may not require empiric treatment and if they do, there is promising evidence regarding azithromycin as a potential COVID-19 treatment.

Evolution of the Use of Corticosteroids for the Treatment of Hospitalised COVID-19 Patients in Spain between March and November 2020: SEMI-COVID National Registry.

OBJECTIVES: Since the results of the RECOVERY trial, WHO recommendations about the use of corticosteroids (CTs) in COVID-19 have changed. The aim of the study is to analyse the evolutive use of CTs in Spain during the pandemic to assess the potential influence of new recommendations. MATERIAL AND METHODS: A retrospective, descriptive, and observational study was conducted on adults hospitalised due to COVID-19 in Spain who were included in the SEMI-COVID-19 Registry from March to November 2020. RESULTS: CTs were used in 6053 (36.21%) of the included patients. The patients were older (mean (SD)) (69.6 (14.6) vs. 66.0 (16.8) years; p < 0.001), with hypertension (57.0% vs. 47.7%; p < 0.001), obesity (26.4% vs. 19.3%; p < 0.0001), and multimorbidity prevalence (20.6% vs. 16.1%; p < 0.001). These patients had higher values (mean (95% CI)) of C-reactive protein (CRP) (86 (32.7-160) vs. 49.3 (16-109) mg/dL; p < 0.001), ferritin (791 (393-1534) vs. 470 (236-996) µg/dL; p < 0.001), D dimer (750 (430-1400) vs. 617 (345-1180) µg/dL; p < 0.001), and lower Sp02/Fi02 (266 (91.1) vs. 301 (101); p < 0.001). Since June 2020, there was an increment in the use of CTs (March vs. September; p < 0.001). Overall, 20% did not receive steroids, and 40% received less than 200 mg accumulated prednisone equivalent dose (APED). Severe patients are treated with higher doses. The mortality benefit was observed in patients with oxygen saturation

Predicting critical illness on initial diagnosis of COVID-19 based on easily obtained clinical variables: development and validation of the PRIORITY model.

OBJECTIVES: We aimed to develop and validate a prediction model, based on clinical history and examination findings on initial diagnosis of coronavirus disease 2019 (COVID-19), to identify patients at risk of critical outcomes. METHODS: We used data from the SEMI-COVID-19 Registry, a cohort of consecutive patients hospitalized for COVID-19 from 132 centres in Spain (23rd March to 21st May 2020). For the development cohort, tertiary referral hospitals were selected, while the validation cohort included smaller hospitals. The primary outcome was a composite of in-hospital death, mechanical ventilation, or admission to intensive care unit. Clinical signs and symptoms, demographics, and medical history ascertained at presentation were screened using least absolute shrinkage and selection operator, and logistic regression was used to construct the predictive model. RESULTS: There were 10 433 patients, 7850 in the development cohort (primary outcome 25.1%, 1967/7850) and 2583 in the validation cohort (outcome 27.0%, 698/2583). The PRIORITY model included: age, dependency, cardiovascular disease, chronic kidney disease, dyspnoea, tachypnoea, confusion, systolic blood pressure, and SpO2 ≤93% or oxygen requirement. The model showed high discrimination for critical illness in both the development (C-statistic 0.823; 95% confidence interval (CI) 0.813, 0.834) and validation (C-statistic 0.794; 95%CI 0.775, 0.813) cohorts. A freely available web-based calculator was developed based on this model (https://www.evidencio.com/models/show/2344). CONCLUSIONS: The PRIORITY model, based on easily obtained clinical information, had good discrimination and generalizability for identifying COVID-19 patients at risk of critical outcomes.

La COVID-19 por SARS-CoV-2 también ha afectado a la educación médica / COVID-19 by SARS-CoV-2 has also affected medical education

El virus SARS-CoV-2, además de provocar una pandemia con centenares de miles de enfermos y con decenas de miles de fallecidos, ha tenido la capacidad ("el poder") de romper las prácticas educativas estándar. Esto ha tenido, o puede tener, un beneficio: reconsiderar lo que es realmente importante y lo que no lo es tanto. Y eso tiene un gran valor para el docente y para el discente. Y también ha servido para reconocer y encontrar formas alternativas con las que se puede transmitir el saber. En definitiva, el profesor ha tenido la oportunidad de contribuir a crear un modelo educativo capaz de impulsar el desarrollo de nuevas formas y métodos de aprendizaje. Ciertas técnicas y recursos educativos han podido ser reconocidos como presentes o ausentes de nuestro sistema educativo. Esto implica que ciertos recursos han de estar presentes o que es preciso optimizar los que ya están disponibles para enseñar, para aprender o para evaluar. La tecnología permite facilitar el contacto directo entre profesor y alumno, entre profesores, y entre alumnos. Pero sobra decir que la tecnología ha de estar disponible.

The SARS-CoV-2 virus, besides causing a pandemic, with hundreds of thousands ill, and tens of thousands dead, has caused a major shift in standard education practices. This has had, or may have, one benefit: to reconsider what is ultimately relevant or not in the classroom. And this is of great value for the teacher and for the student. And it has also served to recognise and find alternative ways to transmit the knowledge. All things considered, the teacher has had the opportunity to contribute in creating an education with the aim of developing new ways and methods of learning. Certain educational techniques and resources have been recognised as present or absent in our education system. It is necessary for certain resources used to teach, learn and evaluate to be available and those that are already present, require optimization. Technology helps to provide direct contact between teacher and student, between teachers and between students. But it goes without saying that this technology has to be available.

The selective peroxisome proliferator-activated receptor alpha modulator (SPPARMα) paradigm: conceptual framework and therapeutic potential : A consensus statement from the International Atherosclerosis Society (IAS) and the Residual Risk Reduction Initiative (R3i) Foundation.

In the era of precision medicine, treatments that target specific modifiable characteristics of high-risk patients have the potential to lower further the residual risk of atherosclerotic cardiovascular events. Correction of atherogenic dyslipidemia, however, remains a major unmet clinical need. Elevated plasma triglycerides, with or without low levels of high-density lipoprotein cholesterol (HDL-C), offer a key modifiable component of this common dyslipidemia, especially in insulin resistant conditions such as type 2 diabetes mellitus. The development of selective peroxisome proliferator-activated receptor alpha modulators (SPPARMα) offers an approach to address this treatment gap. This Joint Consensus Panel appraised evidence for the first SPPARMα agonist and concluded that this agent represents a novel therapeutic class, distinct from fibrates, based on pharmacological activity, and, importantly, a safe hepatic and renal profile. The ongoing PROMINENT cardiovascular outcomes trial is testing in 10,000 patients with type 2 diabetes mellitus, elevated triglycerides, and low levels of HDL-C whether treatment with this SPPARMα agonist safely reduces residual cardiovascular risk.

The application of atherogenic dyslipidaemia consensus recommendations in the primary care setting. DAT-AP study.

BACKGROUND AND AIMS: The "DAT-AP" (from the Spanish, "Dislipemia ATerogénica en Atención Primaria", for Atherogenic Dyslipidaemia in Primary Care) study objective is to determine to what extent published consensus guidelines for the diagnostic and therapeutic management of AD are used in the primary care setting, and to evaluate the approach of the participating physicians towards the detection, diagnosis, and treatment of AD. METHODS: This is descriptive, cross-sectional, multicentre study performed between January and May 2015 in primary care centres throughout Spain. Study data were collected in 2 independent blocks, the first addressing theoretical aspects of AD and the second, practical aspects (clinical cases) RESULTS: The theoretical part is in the process of publication. This manuscript depicts the clinical cases block. Although study participants showed good knowledge of the subject, the high prevalence of this disease requires an additional effort to optimise detection and treatment, with the implementation of appropriate lifestyle interventions and the prescription of the best treatment.

Relationship between lipoprotein (a) and micro/macro complications in type 2 diabetes mellitus: a forgotten target.

OBJECTIVES: Increased lipoprotein (a) serum concentrations seems to be a cardiovascular risk factor; this has not been confirmed in extracoronary atherosclerosis complications. We therefore wished to gain a deeper insight into relationship between the plasma concentrations of lipoprotein (a) and the micro- and macro-vascular complications of type 2 diabetes mellitus and to identify possible differences in this association. METHODS: This is a descriptive observational cross-sectional study. Two-hundred and seventeen elderly patients with type 2 diabetes mellitus were included from the internal medicine outclinic. Anthropometric data, analytical data (insulin reserve, basal and postprandial peptide C, glycosylated hemoglobin, renal parameters, lipid profile and clinical data as hypertension, obesity, micro- and macrovascular complications were collected. RESULTS: Patients were grouped according to the type 2 diabetes mellitus time of evolution. The mean plasma concentration of lipoprotein (a) was 22.2 ± 17.3 mg/dL (22.1 ± 15.9 mg/dL for males, and 22.1 ± 18.4 mg/dL for females). Patients with hypertension, coronary heart disease, cerebrovascular accident, microalbuminuria and proteinuria presented a statistically significant increased level of lipoprotein (a). Similarly, the patients with hyperlipoprotein (a) (≥ 30 mg/dL) presented significantly increased levels of urea and total cholesterol. In the multivariate regression model, the level of lipoprotein (a) is positively correlated with coronary heart disease and diabetic nephropathy (P < 0.01 and P < 0.005, respectively). CONCLUSIONS: The elevation of plasma levels of lipoprotein (a) are associated with the development of coronary heart disease and diabe tic nephropathy. Therefore, we consider that the determination of lipoprotein (a) may be a prognostic marker of vascular complications in patients with type 2 diabetes mellitus.

[Indications for the combination of pravastatin and fenofibrate according to the type of dyslipidemia]. / Indicaciones de la combinación de pravastatina más fenofibrato según el tipo de dislipemia.

The combination with fixed doses of pravastatin (40 mg) and fenofibrate (160 mg) offers a therapeutic alternative, especially in the comprehensive approach to mixed hyperlipidemia in patients with high cardiovascular risk of metabolic origin. It also ensures the efficacy and safety as a result of the evidence that supports the clinical benefit of both pravastatin in primary and secondary prevention and fenofibrate in patients with atherogenic dyslipidemia. This combination also has few adverse effects, which are similar in all cases to those produced by the isolated monotherapy of each of the drugs. Consequently, the possible indications for this combination include patients with mixed hyperlipidemia, patients with atherogenic dyslipidemia (increased triglyceride levels, reduced HDL-c levels and moderately increased LDL-c levels), patients with hypertriglyceridemia who need to reduce their LDL-c levels, patients with low HDL syndrome who also require a reduction in LDL-c levels, patients with moderate hypercholesterolemia who require an additional reduction of triglyceride levels and especially patients with high atherogenic metabolic risk who require an overall intervention for each of the lipid fractions.

Residual macrovascular risk in 2013: what have we learned?

Cardiovascular disease poses a major challenge for the 21st century, exacerbated by the pandemics of obesity, metabolic syndrome and type 2 diabetes. While best standards of care, including high-dose statins, can ameliorate the risk of vascular complications, patients remain at high risk of cardiovascular events. The Residual Risk Reduction Initiative (R3i) has previously highlighted atherogenic dyslipidaemia, defined as the imbalance between proatherogenic triglyceride-rich apolipoprotein B-containing-lipoproteins and antiatherogenic apolipoprotein A-I-lipoproteins (as in high-density lipoprotein, HDL), as an important modifiable contributor to lipid-related residual cardiovascular risk, especially in insulin-resistant conditions. As part of its mission to improve awareness and clinical management of atherogenic dyslipidaemia, the R3i has identified three key priorities for action: i) to improve recognition of atherogenic dyslipidaemia in patients at high cardiometabolic risk with or without diabetes; ii) to improve implementation and adherence to guideline-based therapies; and iii) to improve therapeutic strategies for managing atherogenic dyslipidaemia. The R3i believes that monitoring of non-HDL cholesterol provides a simple, practical tool for treatment decisions regarding the management of lipid-related residual cardiovascular risk. Addition of a fibrate, niacin (North and South America), omega-3 fatty acids or ezetimibe are all options for combination with a statin to further reduce non-HDL cholesterol, although lacking in hard evidence for cardiovascular outcome benefits. Several emerging treatments may offer promise. These include the next generation peroxisome proliferator-activated receptorα agonists, cholesteryl ester transfer protein inhibitors and monoclonal antibody therapy targeting proprotein convertase subtilisin/kexin type 9. However, long-term outcomes and safety data are clearly needed. In conclusion, the R3i believes that ongoing trials with these novel treatments may help to define the optimal management of atherogenic dyslipidaemia to reduce the clinical and socioeconomic burden of residual cardiovascular risk.