Machine-learning-derived predictive score for early estimation of COVID-19 mortality risk in hospitalized patients.

The clinical course of COVID-19 is highly variable. It is therefore essential to predict as early and accurately as possible the severity level of the disease in a COVID-19 patient who is admitted to the hospital. This means identifying the contributing factors of mortality and developing an easy-to-use score that could enable a fast assessment of the mortality risk using only information recorded at the hospitalization. A large database of adult patients with a confirmed diagnosis of COVID-19 (n = 15,628; with 2,846 deceased) admitted to Spanish hospitals between December 2019 and July 2020 was analyzed. By means of multiple machine learning algorithms, we developed models that could accurately predict their mortality. We used the information about classifiers' performance metrics and about importance and coherence among the predictors to define a mortality score that can be easily calculated using a minimal number of mortality predictors and yielded accurate estimates of the patient severity status. The optimal predictive model encompassed five predictors (age, oxygen saturation, platelets, lactate dehydrogenase, and creatinine) and yielded a satisfactory classification of survived and deceased patients (area under the curve: 0.8454 with validation set). These five predictors were additionally used to define a mortality score for COVID-19 patients at their hospitalization. This score is not only easy to calculate but also to interpret since it ranges from zero to eight, along with a linear increase in the mortality risk from 0% to 80%. A simple risk score based on five commonly available clinical variables of adult COVID-19 patients admitted to hospital is able to accurately discriminate their mortality probability, and its interpretation is straightforward and useful.

Impact of the coronavirus disease 2019 (COVID-19) pandemic on nosocomial Clostridioides difficile infection.

OBJECTIVES: The coronavirus disease 2019 (COVID-19) pandemic has induced a reinforcement of infection control measures in the hospital setting. Here, we assess the impact of the COVID-19 pandemic on the incidence of nosocomial Clostridioides difficile infection (CDI). METHODS: We retrospectively compared the incidence density (cases per 10,000 patient days) of healthcare-facility-associated (HCFA) CDI in a tertiary-care hospital in Madrid, Spain, during the maximum incidence of COVID-19 (March 11 to May 11, 2020) with the same period of the previous year (control period). We also assessed the aggregate in-hospital antibiotic use (ie, defined daily doses [DDD] per 100 occupied bed days [BD]) and incidence density (ie, movements per 1,000 patient days) of patient mobility during both periods. RESULTS: In total, 2,337 patients with reverse transcription-polymerase chain reaction-confirmed COVID-19 were admitted to the hospital during the COVID-19 period. Also, 12 HCFA CDI cases were reported at this time (incidence density, 2.68 per 10,000 patient days), whereas 34 HCFA CDI cases were identified during the control period (incidence density, 8.54 per 10,000 patient days) (P = .000257). Antibiotic consumption was slightly higher during the COVID-19 period (89.73 DDD per 100 BD) than during the control period (79.16 DDD per 100 BD). The incidence density of patient movements was 587.61 per 1,000 patient days during the control period and was significantly lower during the COVID-19 period (300.86 per 1,000 patient days) (P < .0001). CONCLUSIONS: The observed reduction of ~70% in the incidence density of HCFA CDI in a context of no reduction in antibiotic use supports the importance of reducing nosocomial transmission by healthcare workers and asymptomatic colonized patients, reinforcing cleaning procedures and reducing patient mobility in the epidemiological control of CDI.