Intensive Home Care for COVID-19 to Reduce Admissions
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ABSTRACT:
SUBMITTER: Aviv R
PROVIDER: S-EPMC8335407 | biostudies-literature | 2021 Nov
REPOSITORIES: biostudies-literature
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Project description:Global healthcare systems are challenged by the COVID-19 pandemic. There is a need to optimize allocation of treatment and resources in intensive care, as clinically established risk assessments such as SOFA and APACHE II scores show only limited performance for predicting the survival of severely ill COVID-19 patients. Comprehensively capturing the host physiology, we speculated that proteomics in combination with new data-driven analysis strategies could produce a new generation of prognostic discriminators. We studied two independent cohorts of patients with severe COVID-19 who required intensive care and invasive mechanical ventilation. SOFA score, Charlson comorbidity index and APACHE II score were poor predictors of survival. Instead, using plasma proteomes quantifying 302 plasma protein groups at 387 timepoints in 57 critically ill patients on invasive mechanical ventilation, we found 14 proteins that showed trajectories different between survivors and non-survivors. A proteomic predictor trained on single samples obtained at the first time point at maximum treatment level (i.e. WHO grade 7) and weeks before the outcome, achieved accurate classification of survivors (AUROC 0.81, n=49). We tested the established predictor on an independent validation cohort (AUROC of 1.0, n=24). The majority of proteins with high relevance in the prediction model belong to the coagulation system and complement cascade. Our study demonstrates that predictors derived from plasma protein levels have the potential to substantially outperform current prognostic markers in intensive care.
2022-03-03 | PXD029009 | Pride
Project description:IntroductionIntensive care has played a pivotal role during the COVID-19 pandemic as many patients developed severe pulmonary complications. The availability of information in pediatric intensive care units (PICUs) remains limited. The purpose of this study is to characterize COVID-19 positive admissions (CPAs) in the United States and to determine factors that may impact those admissions.Materials and methodsThis is a retrospective cohort study using data from the COVID-19 Virtual Pediatric System (VPS) dashboard containing information regarding respiratory support and comorbidities for all CPAs between March and April 2020. The state-level data contained 13 different factors from population density, comorbid conditions, and social distancing score. The absolute CPA count was converted to frequency using the state's population. Univariate and multivariate regression analyses were performed to assess the association between CPA frequency and admission endpoints.ResultsA total of 205 CPAs were reported by 167 PICUs across 48 states. The estimated CPA frequency was 2.8 per million children in a one-month period. A total of 3,235 tests were conducted of which 6.3% were positive. Children above 11 years of age comprised 69.7% of the total cohort and 35.1% had moderated or severe comorbidities. The median duration of a CPA was 4.9 days (1.25-12.00 days). Out of the 1,132 total CPA days, 592 (52.2%) involved mechanical ventilation. The inpatient mortalities were 3 (1.4%). Multivariate analyses demonstrated an association between CPAs with greater population density (beta coefficient 0.01, p < 0.01). Multivariate analyses also demonstrated an association between pediatric type 1 diabetes mellitus with increased CPA duration requiring advanced respiratory support (beta coefficient 5.1, p < 0.01) and intubation (beta coefficient 4.6, p < 0.01).ConclusionsInpatient mortality during PICU CPAs is relatively low at 1.4%. CPA frequency seems to be impacted by population density. Type 1 DM appears to be associated with increased duration of HFNC and intubation. These factors should be included in future studies using patient-level data.
| S-EPMC7704189 | biostudies-literature
Project description:As a public health measure during the COVID-19 pandemic, governments around the world instituted a variety of interventions to 'flatten the curve'. The government of Maryland instituted similar measures. We observed a striking decline in paediatric intensive care unit (PICU) admissions during that period, mostly due to a decease in respiratory infections. We believe this decline is multifactorial: less person-to-person contact, better air quality and perhaps 'fear' of going to a hospital during the pandemic. We report an analysis of our PICU admissions during the lockdown period and compared them with the same time period during the four previous years.
| S-EPMC7640529 | biostudies-literature
Project description:National (and global) vaccination provides an opportunity to control the COVID-19 pandemic, which disease suppression by societal lockdown and individual behavioural changes will not. We modelled how vaccination through the UK's vaccine priority groups impacts deaths, hospital and ICU admissions from COVID-19. We used the UK COVID-19 vaccines delivery plan and publicly available data to estimate UK population by age group and vaccination priority group, including frontline health and social care workers and individuals deemed 'extreme clinical vulnerable' or 'high risk'. Using published data on numbers and distributions of COVID-19-related hospital and ICU admissions and deaths, we modelled the impact of vaccination by age group. We then modified the model to account for hospital and ICU admission, and death among health and social care workers and the population with extreme clinical vulnerability and high risk. Our model closely matches the government's estimates for mortality after vaccination of priority groups 1-4 and groups 1-9. The model shows vaccination will have a much slower impact on hospital and ICU admissions than on deaths. The early prioritisation of healthcare staff and clinically vulnerable patients increases the impact of vaccination on admissions and also protects the healthcare service. An inflection point, when 50% of the adult population has been vaccinated - with deaths reduced by 95% and hospital admissions by 80% - may be a useful point for re-evaluating vaccine prioritisation. Our model suggests substantial reductions in hospital and ICU admissions will not occur until late March and into April 2021.
| S-EPMC8013188 | biostudies-literature
Project description:Coronavirus disease 2019 (COVID-19) can lead to multiorgan damage and fatal outcomes. MicroRNAs (miRNAs) are detectable in blood, reflecting cell activation and tissue injury. We performed small RNA-Seq in healthy controls (N=11), non-severe (N=18) and severe (N=16) COVID-19 patients
2021-11-17 | GSE176498 | GEO
Project description:The aim of the present study is to evaluate if an independent association exists between liver enzyme elevations (LEE) and the risk of mortality or intensive care unit (ICU) admissions in patients with COVID-19. This was a single-center observational study, recruiting all consecutive adults with COVID-19. The elevation of aspartate aminotransferase (AST) or alanine aminotransferase (ALT) to the highest level between COVID-19 diagnosis and hospital discharge was categorized according to a standardized toxicity grade scale. In total, 799 patients were included in this study, 39% of which were female, with a mean age of 69.9 (±16.0) years. Of these patients, 225 (28.1%) developed LEE of grade ≥2 after a median of three days (interquartile range (IQR): 0-8 days) from the diagnosis of COVID-19, and they were estimated to have a higher hazard of death or ICU admission (adjusted hazard ratio (aHR): 1.46, 95% confidence interval (CI): 1.14-1.88). The clinical and laboratory variables associated with the development of LEE were male sex, higher respiratory rate, higher gamma glutamyl transpeptidase (GGT) and lower albumin levels at baseline. Among the analyzed treatments, steroids, tocilizumab and darunavir/ritonavir correlated with LEE. In conclusion, LEE were associated with mortality and ICU admission among COVID-19 patients. While the origin of LEE is probably multifactorial, LEE evaluation could add information to the clinical and laboratory variables that are commonly evaluated during the course of COVID-19.
| S-EPMC7766471 | biostudies-literature
Project description:We assessed the impact of COVID-19 vaccination in Italy, by estimating numbers of averted COVID-19 cases, hospitalisations, ICU admissions and deaths between January and September 2021, by age group and geographical macro areas. Timing and speed of vaccination programme implementation varied slightly between geographical areas, particularly for older adults. We estimated that 445,193 (17% of expected; range: 331,059-616,054) cases, 79,152 (32%; range: 53,209-148,756) hospitalisations, 9,839 ICU admissions (29%; range: 6,434-16,276) and 22,067 (38%; range: 13,571-48,026) deaths were prevented by vaccination.
| S-EPMC8619872 | biostudies-literature
Project description:The experience of the COVID-19 pandemic showed the importance of timely monitoring of admissions to the ICU admissions. The ability to promptly forecast the epidemic impact on the occupancy of beds in the ICU is a key issue for adequate management of the health care system.Despite this, most of the literature on predictive COVID-19 models in Italy has focused on predicting the number of infections, leaving trends in ordinary hospitalizations and ICU occupancies in the background.This work aims to present an ETS approach (Exponential Smoothing Time Series) time series forecasting tool for admissions to the ICU admissions based on ETS models. The results of the forecasting model are presented for the regions most affected by the epidemic, such as Veneto, Lombardy, Emilia-Romagna, and Piedmont.The mean absolute percentage errors (MAPE) between observed and predicted admissions to the ICU admissions remain lower than 11% for all considered geographical areas.In this epidemiological context, the proposed ETS forecasting model could be suitable to monitor, in a timely manner, the impact of COVID-19 disease on the health care system, not only during the early stages of the pandemic but also during the vaccination campaign, to quickly adapt possible preventive interventions.
| S-EPMC10404188 | biostudies-literature
Project description:BackgroundModel projections of coronavirus disease 2019 (COVID-19) incidence help policymakers about decisions to implement or lift control measures. During the pandemic, policymakers in the Netherlands were informed on a weekly basis with short-term projections of COVID-19 intensive care unit (ICU) admissions.AimWe aimed at developing a model on ICU admissions and updating a procedure for informing policymakers.MethodThe projections were produced using an age-structured transmission model. A consistent, incremental update procedure integrating all new surveillance and hospital data was conducted weekly. First, up-to-date estimates for most parameter values were obtained through re-analysis of all data sources. Then, estimates were made for changes in the age-specific contact rates in response to policy changes. Finally, a piecewise constant transmission rate was estimated by fitting the model to reported daily ICU admissions, with a changepoint analysis guided by Akaike's Information Criterion.ResultsThe model and update procedure allowed us to make weekly projections. Most 3-week prediction intervals were accurate in covering the later observed numbers of ICU admissions. When projections were too high in March and August 2020 or too low in November 2020, the estimated effectiveness of the policy changes was adequately adapted in the changepoint analysis based on the natural accumulation of incoming data.ConclusionThe model incorporates basic epidemiological principles and most model parameters were estimated per data source. Therefore, it had potential to be adapted to a more complex epidemiological situation with the rise of new variants and the start of vaccination.
| S-EPMC10986673 | biostudies-literature
Project description:BackgroundIn the face of the current COVID-19 pandemic, the timely prediction of upcoming medical needs for infected individuals enables better and quicker care provision when necessary and management decisions within health care systems.ObjectiveThis work aims to predict the medical needs (hospitalizations, intensive care unit admissions, and respiratory assistance) and survivability of individuals testing positive for SARS-CoV-2 infection in Portugal.MethodsA retrospective cohort of 38,545 infected individuals during 2020 was used. Predictions of medical needs were performed using state-of-the-art machine learning approaches at various stages of a patient's cycle, namely, at testing (prehospitalization), at posthospitalization, and during postintensive care. A thorough optimization of state-of-the-art predictors was undertaken to assess the ability to anticipate medical needs and infection outcomes using demographic and comorbidity variables, as well as dates associated with symptom onset, testing, and hospitalization.ResultsFor the target cohort, 75% of hospitalization needs could be identified at the time of testing for SARS-CoV-2 infection. Over 60% of respiratory needs could be identified at the time of hospitalization. Both predictions had >50% precision.ConclusionsThe conducted study pinpoints the relevance of the proposed predictive models as good candidates to support medical decisions in the Portuguese population, including both monitoring and in-hospital care decisions. A clinical decision support system is further provided to this end.
| S-EPMC8080965 | biostudies-literature