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Background Nursing homes are highly vulnerable to the occurrence of COVID-19 outbreaks, which result in high lethality rates. Most of them are not prepared to SARS-CoV-2 pandemic. Methods A coordinated on-site medicalization program (MP) in response to a sizeable COVID-19 outbreak in four nursing homes was organized, with the objectives of improving survival, offering humanistic palliative care to residents in their natural environment, and reducing hospital referrals. Ten key processes and interventions were established (provision of informatics infrastructure, medical equipment, and human resources, universal testing, separation of 'clean' and 'contaminated' areas, epidemiological surveys, and unified protocols stratifying for active or palliative care approach, among others). Main outcomes were a composite endpoint of survival or optimal palliative care (SOPC), survival, and referral to hospital. Results 272 out of 457 (59.5%) residents and 85 out of 320 (26.5%) staff members were affected. The SOPC, survival, and referrals to hospital, occurred in 77%, 72.5%, and 29% of patients diagnosed before MP start, with respect to 97%, 83.7% and 17% of those diagnosed during the program, respectively. The SOPC was independently associated to MP (OR=15 [3-81]); and survival in patients stratified to active approach, to the use of any antiviral treatment (OR=28 [5-160]). All outbreaks were controlled in 39 [37-42] days. Conclusions A coordinated on-site medicalization program of nursing homes with COVID-19 outbreaks achieved a higher survival or optimal palliative care rate, and a reduction in referrals to hospital, thus ensuring rigorous but also humanistic and gentle care to residents.

Background and Objectives: Differentiating between hypovolemic (HH) and euvolemic hyponatremia (EH) is crucial for correct diagnosis and therapy, but can be a challenge. We aim to ascertain whether changes in serum creatinine (SC) can be helpful in distinguishing HH from EH. Materials and Methods: Retrospective analysis of patients followed in a monographic hyponatremia outpatient clinic of a tertiary hospital during 1 January 2014–30 November 2019. SC changes during HH and EH from eunatremia were studied. The diagnostic accuracy of the SC change from eunatremia to hyponatremia (∆SC) was analyzed. Results: A total of 122 hyponatremic patients, median age 79 years (70–85), 46.7% women. In total, 70/122 patients had EH, 52/122 HH. During hyponatremia, median SC levels increased in the HH group: +0.18 mg/dL [0.09–0.39, p < 0.001], but decreased in the EH group: −0.07 mg/dL (−0.15–0.02, p < 0.001), as compared to SC in eunatremia. HH subjects presented a higher rate of a positive ∆SC than EH (90.4% vs. 25.7%, p < 0.001). EH subjects presented a higher rate of a negative/null ∆SC than HH (74.3% vs. 9.6%, p < 0.001). ROC curve analysis found an AUC of 0.908 (95%CI: 0.853 to 0.962, p < 0.001) for ∆SC%. A ∆SC% ≥ 10% had an OR of 29.0 (95%CI: 10.3 to 81.7, p < 0.001) for HH. A ∆SC% ≤ 3% had an OR of 68.3 (95%CI: 13.0 to 262.2, p < 0.001) for EH. Conclusions: The assessment of SC changes from eunatremia to hyponatremia can be useful in distinguishing between HH and EH.

Background Owing to the nature of health data, their sharing and reuse for research are limited by legal, technical, and ethical implications. In this sense, to address that challenge and facilitate and promote the discovery of scientific knowledge, the Findable, Accessible, Interoperable, and Reusable (FAIR) principles help organizations to share research data in a secure, appropriate, and useful way for other researchers. Objective The objective of this study was the FAIRification of existing health research data sets and applying a federated machine learning architecture on top of the FAIRified data sets of different health research performing organizations. The entire FAIR4Health solution was validated through the assessment of a federated model for real-time prediction of 30-day readmission risk in patients with chronic obstructive pulmonary disease (COPD). Methods The application of the FAIR principles on health research data sets in 3 different health care settings enabled a retrospective multicenter study for the development of specific federated machine learning models for the early prediction of 30-day readmission risk in patients with COPD. This predictive model was generated upon the FAIR4Health platform. Finally, an observational prospective study with 30 days follow-up was conducted in 2 health care centers from different countries. The same inclusion and exclusion criteria were used in both retrospective and prospective studies. Results Clinical validation was demonstrated through the implementation of federated machine learning models on top of the FAIRified data sets from different health research performing organizations. The federated model for predicting the 30-day hospital readmission risk was trained using retrospective data from 4.944 patients with COPD. The assessment of the predictive model was performed using the data of 100 recruited (22 from Spain and 78 from Serbia) out of 2070 observed (records viewed) patients during the observational prospective study, which was executed from April 2021 to September 2021. Significant accuracy (0.98) and precision (0.25) of the predictive model generated upon the FAIR4Health platform were observed. Therefore, the generated prediction of 30-day readmission risk was confirmed in 87% (87/100) of cases. Conclusions Implementing a FAIR data policy in health research performing organizations to facilitate data sharing and reuse is relevant and needed, following the discovery, access, integration, and analysis of health research data. The FAIR4Health project proposes a technological solution in the health domain to facilitate alignment with the FAIR principles.

The current availability of electronic health records represents an excellent research opportunity on multimorbidity, one of the most relevant public health problems nowadays. However, it also poses a methodological challenge due to the current lack of tools to access, harmonize and reuse research datasets. In FAIR4Health, a European Horizon 2020 project, a workflow to implement the FAIR (findability, accessibility, interoperability and reusability) principles on health datasets was developed, as well as two tools aimed at facilitating the transformation of raw datasets into FAIR ones and the preservation of data privacy. As part of this project, we conducted a multicentric retrospective observational study to apply the aforementioned FAIR implementation workflow and tools to five European health datasets for research on multimorbidity. We applied a federated frequent pattern growth association algorithm to identify the most frequent combinations of chronic diseases and their association with mortality risk. We identified several multimorbidity patterns clinically plausible and consistent with the bibliography, some of which were strongly associated with mortality. Our results show the usefulness of the solution developed in FAIR4Health to overcome the difficulties in data management and highlight the importance of implementing a FAIR data policy to accelerate responsible health research.

During the last months, the pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has hit cruelly elderly citizens living in nursing homes (NHs) across the world. As a matter of fact, NHs are highly vulnerable to the occurrence of this new coronavirus disease (COVID-19), which results in high lethality rates. In addition, most of the long-term care facilities are not yet prepared to manage with this new epidemiological and clinical scenario. In this article, we will review the impact of COVID-19 in NH, its causes and underlying factors, and the possible solutions of keeping SARS-CoV-2 at bay in NH from a triple perspective: the focus of public health policies and global measures, the operative level at NH institutions themselves, and the daily clinical scenario of clinicians and other health care workers.

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BACKGROUND Due to the nature of health data, its sharing and reuse for research are limited by legal, technical and ethical implications. In this sense, to address that challenge, and facilitate and promote the discovery of scientific knowledge, the FAIR (Findable, Accessible, Interoperable, and Reusable) principles help organizations to share research data in a secure, appropriate and useful way for other researchers. OBJECTIVE The objective of this study was the FAIRification of health research existing datasets and applying a federated machine learning architecture on top of the FAIRified datasets of different health research performing organizations. The whole FAIR4Health solution was validated through the assessment of the generated model for real-time prediction of 30-days readmission risk in patients with Chronic Obstructive Pulmonary Disease (COPD). METHODS The application of the FAIR principles in health research datasets in three different health care settings enabled a retrospective multicenter study for the generation of federated machine learning models, aiming to develop the early prediction model for 30-days readmission risk in COPD patients. This prediction model was implemented upon the FAIR4Health platform and, finally, an observational prospective study with 30-days follow-up was carried out in two health care centers from different countries. The same inclusion and exclusion criteria were used in both retrospective and prospective parts of the study. RESULTS The prediction model for the 30-days hospital readmission risk was trained using the retrospective data of 4.944 COPD patients. The assessment of the prediction model was performed using the data of 100 recruited (22 from Spain and 78 from Serbia) out of 2070 observed (records viewed) patients in total for the observational prospective study from April 2021 to September 2021. The significant accuracy (0.98) and precision (0.25) of the prediction model generated upon the FAIR4Health platform was observed and, as a result, the generated prediction of 30-day readmission risk was confirmed in 87% of the cases. CONCLUSIONS A clinical validation was demonstrated through the implementation of federated machine learning models on top of the FAIRified datasets from different health research performing organizations, providing an assessment for predicting 30-days readmission risk in COPD patients. This demonstration allowed to state the relevance and need of implementing a FAIR data policy to facilitate data sharing and reuse in health research.