A 98-Year-Old Male With Paroxysmal Atrial Fibrillation Treated for COVID-19 at Home.

In the absence of evidenced-based guidelines for early home treatment of COVID-19, some Italian groups of volunteer physicians (both general practitioners (GPs) and hospital doctors) virtually gathered themselves to discuss the best available evidence and develop shared schemes of therapy. We present the case of a 98-year-old unvaccinated male on chronic anticoagulant therapy with dabigatran for paroxysmal atrial fibrillation (AF), who has been successfully treated for COVID-19 at home, according to one of the multidrug treatments proposed, since hospital admission was not feasible. At the very beginning of symptoms, anti-inflammatory drugs, vitamin D, and adjuvant dietary supplements (quercetin, vitamin C, zinc, and vitamin K2) were administered, followed by dexamethasone and antibiotic therapy, according to the evolving clinical conditions. Gastroprotection with omeprazole was added. Eventually, our patient fully recovered, thus suggesting that careful home assistance under strict medical supervision can be successful, even in a very old subject with comorbidities, particularly if early treatment simultaneously addressing inflammation, hypercoagulation, and viral replication is started.

Algorithm for Individual Prediction of COVID-19-Related Hospitalization Based on Symptoms: Development and Implementation Study.

BACKGROUND: The COVID-19 pandemic has placed a huge strain on the health care system globally. The metropolitan area of Milan, Italy, was one of the regions most impacted by the COVID-19 pandemic worldwide. Risk prediction models developed by combining administrative databases and basic clinical data are needed to stratify individual patient risk for public health purposes. OBJECTIVE: This study aims to develop a stratification tool aimed at improving COVID-19 patient management and health care organization. METHODS: A predictive algorithm was developed and applied to 36,834 patients with COVID-19 in Italy between March 8 and the October 9, 2020, in order to foresee their risk of hospitalization. Exposures considered were age, sex, comorbidities, and symptoms associated with COVID-19 (eg, vomiting, cough, fever, diarrhea, myalgia, asthenia, headache, anosmia, ageusia, and dyspnea). The outcome was hospitalizations and emergency department admissions for COVID-19. Discrimination and calibration of the model were also assessed. RESULTS: The predictive model showed a good fit for predicting COVID-19 hospitalization (C-index 0.79) and a good overall prediction accuracy (Brier score 0.14). The model was well calibrated (intercept -0.0028, slope 0.9970). Based on these results, 118,804 patients diagnosed with COVID-19 from October 25 to December 11, 2020, were stratified into low, medium, and high risk for COVID-19 severity. Among the overall study population, 67,030 (56.42%) were classified as low-risk patients; 43,886 (36.94%), as medium-risk patients; and 7888 (6.64%), as high-risk patients. In all, 89.37% (106,179/118,804) of the overall study population was being assisted at home, 9% (10,695/118,804) was hospitalized, and 1.62% (1930/118,804) died. Among those assisted at home, most people (63,983/106,179, 60.26%) were classified as low risk, whereas only 3.63% (3858/106,179) were classified at high risk. According to ordinal logistic regression, the odds ratio (OR) of being hospitalized or dead was 5.0 (95% CI 4.6-5.4) among high-risk patients and 2.7 (95% CI 2.6-2.9) among medium-risk patients, as compared to low-risk patients. CONCLUSIONS: A simple monitoring system, based on primary care data sets linked to COVID-19 testing results, hospital admissions data, and death records may assist in the proper planning and allocation of patients and resources during the ongoing COVID-19 pandemic.