Dataset Information

Risk Factors for COVID-19 Morbidity and Mortality in Institutionalised Elderly People.

ABSTRACT:

Background

SARS-CoV-2 has caused a high mortality in institutionalised individuals. There are very few studies on the involvement and the real impact of COVID-19 in nursing homes. This study analysed factors related to morbidity and mortality of COVID-19 in institutionalised elderly people.

Methods

This cohort study included 842 individuals from 12 nursing homes in Sant Cugat del Vallès (Spain) from 15 March to 15 May 2020. We evaluated individual factors (demographic, dependence, clinical, and therapeutic) and those related to the nursing homes (size and staff) associated with infection and mortality by SARS-CoV-2. Infection was diagnosed by molecular biology test.

Results

Of the 842 residents included in the analysis, 784 underwent a Polymerase Chain Reaction (PCR) test; 74.2% were women, the mean age was 87.1 years, and 11.1% died. The PCR test was positive in 44%. A total of 33.4% of the residents presented symptoms compatible with COVID-19 and of these, 80.9% were PCR-positive for SARS-CoV-2. Infection by SARS-CoV-2 among residents was associated with the rate of staff infected in the homes. Mortality by SARS-CoV-2 was related to male sex and a greater grade of dependence measured with the Barthel index.

Conclusions

SARS-Cov-2 infection in institutionalised people is associated with the infection rate in nursing home workers and mortality by SARS-Cov-2 with sex and greater dependency according to the Barthel index. Adequate management of nursing home staff and special attention to measures of infection control, especially of individuals with greater dependence, are keys for successful management of future pandemic situations.

SUBMITTER: Burgana Agoues A

PROVIDER: S-EPMC8507792 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:ObjectivesCoronavirus Disease 2019 (COVID-19) has become a pandemic outbreak. Risk stratification at hospital admission is of vital importance for medical decision making and resource allocation. There is no sophisticated tool for this purpose. This study aimed to develop neural network models with predictors selected by genetic algorithms (GA).MethodsThis study was conducted in Wuhan Third Hospital from January 2020 to March 2020. Predictors were collected on day 1 of hospital admission. The primary outcome was the vital status at hospital discharge. Predictors were selected by using GA, and neural network models were built with the cross-validation method. The final neural network models were compared with conventional logistic regression models.ResultsA total of 246 patients with COVID-19 were included for analysis. The mortality rate was 17.1% (42/246). Non-survivors were significantly older (median (IQR): 69 (57, 77) vs. 55 (41, 63) years; p < 0.001), had higher high-sensitive troponin I (0.03 (0, 0.06) vs. 0 (0, 0.01) ng/L; p < 0.001), C-reactive protein (85.75 (57.39, 164.65) vs. 23.49 (10.1, 53.59) mg/L; p < 0.001), D-dimer (0.99 (0.44, 2.96) vs. 0.52 (0.26, 0.96) mg/L; p < 0.001), and ?-hydroxybutyrate dehydrogenase (306.5 (268.75, 377.25) vs. 194.5 (160.75, 247.5); p < 0.001) and a lower level of lymphocyte count (0.74 (0.41, 0.96) vs. 0.98 (0.77, 1.26) × 109/L; p < 0.001) than survivors. The GA identified a 9-variable (NNet1) and a 32-variable model (NNet2). The NNet1 model was parsimonious with a cost on accuracy; the NNet2 model had the maximum accuracy. NNet1 (AUC: 0.806; 95% CI [0.693-0.919]) and NNet2 (AUC: 0.922; 95% CI [0.859-0.985]) outperformed the linear regression models.ConclusionsOur study included a cohort of COVID-19 patients. Several risk factors were identified considering both clinical and statistical significance. We further developed two neural network models, with the variables selected by using GA. The model performs much better than the conventional generalized linear models.