Long-term mortality of elderly patients after intensive care unit admission for COVID-19.
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ABSTRACT:
SUBMITTER: Guillon A
PROVIDER: S-EPMC8040757 | biostudies-literature |
REPOSITORIES: biostudies-literature
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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:BackgroundForecasting models for intensive care occupancy of coronavirus disease 2019 (COVID-19) patients are important in the current pandemic for strategic planning of patient allocation and avoidance of regional overcrowding. They are often trained entirely on retrospective infection and occupancy data, which can cause forecast uncertainty to grow exponentially with the forecast horizon.MethodologyWe propose an alternative modeling approach in which the model is created largely independent of the occupancy data being simulated. The distribution of bed occupancies for patient cohorts is calculated directly from occupancy data from "sentinel clinics". By coupling with infection scenarios, the prediction error is constrained by the error of the infection dynamics scenarios. The model allows systematic simulation of arbitrary infection scenarios, calculation of bed occupancy corridors, and sensitivity analyses with respect to protective measures.ResultsThe model was based on hospital data and by adjusting only two parameters of data in the Aachen city region and Germany as a whole. Using the example of the simulation of the respective bed occupancy rates for Germany as a whole, the loading model for the calculation of occupancy corridors is demonstrated. The occupancy corridors form barriers for bed occupancy in the event that infection rates do not exceed specific thresholds. In addition, lockdown scenarios are simulated based on retrospective events.DiscussionOur model demonstrates that a significant reduction in forecast uncertainty in occupancy forecasts is possible by selectively combining data from different sources. It allows arbitrary combination with infection dynamics models and scenarios, and thus can be used both for load forecasting and for sensitivity analyses for expected novel spreading and lockdown scenarios.
| S-EPMC7856858 | biostudies-literature
Project description:This retrospective, population-based cohort study aims to investigate the long-term risk of newly diagnosed dementia in patients discharged for acute respiratory failure that required mechanical ventilation (MV) and intensive care unit (ICU) admission. From the Taiwan National Health Insurance Research Database, first-time ICU patients using MV between June 1, 1998, and December 31, 2012, were enrolled, and they were followed-up until the earliest onset of one of our two endpoints: a new diagnosis of dementia (primary endpoint), or the end of the study. A total of 18,033 patients were enrolled and thirteen hundred eighty-seven patients had been newly diagnosed with dementia (mean onset: 3.2 years post-discharge). Patients ≥ 85 years old had the highest risk (multivariate analysis). Males had a lower risk than did females in both models (HR: 0.81, 95% CI: 0.72-0.9 in model 1; HR: 0.80, 95% CI: 0.72-0.89 in model 2). ICU stays > 5 days, hospital stays > 14 days, and more ICU readmissions were associated with a higher risk of developing dementia. In conclusion, the long-term risks of a subsequent diagnosis of dementia for acute respiratory failure with MV patients who survive to discharge increase with age and are higher in women than in men. Additionally, the longer the ICU or hospital stay is, and the more ICU readmissions a patient has, are both significantly associated with developing dementia.
| S-EPMC5524355 | biostudies-other
Project description:IntroductionCoronavirus disease 2019 (COVID-19) has affected millions of people worldwide, and several sociodemographic variables, comorbidities and care variables have been associated with complications and mortality.ObjectiveTo identify the factors associated with admission to intensive care units (ICUs) and mortality in patients with COVID-19 from 4 clinics in Colombia.MethodsThis was a follow-up study of a cohort of patients diagnosed with COVID-19 between March and August 2020. Sociodemographic, clinical (Charlson comorbidity index and NEWS 2 score) and pharmacological variables were identified. Multivariate analyses were performed to identify variables associated with the risk of admission to the ICU and death (p<0.05).ResultsA total of 780 patients were analyzed, with a median age of 57.0 years; 61.2% were male. On admission, 54.9% were classified as severely ill, 65.3% were diagnosed with acute respiratory distress syndrome, 32.4% were admitted to the ICU, and 26.0% died. The factors associated with a greater likelihood of ICU admission were severe pneumonia (OR: 9.86; 95%CI:5.99-16.23), each 1-point increase in the NEWS 2 score (OR:1.09; 95%CI:1.002-1.19), history of ischemic heart disease (OR:3.24; 95%CI:1.16-9.00), and chronic obstructive pulmonary disease (OR:2.07; 95%CI:1.09-3.90). The risk of dying increased in those older than 65 years (OR:3.08; 95%CI:1.66-5.71), in patients with acute renal failure (OR:6.96; 95%CI:4.41-11.78), admitted to the ICU (OR:6.31; 95%CI:3.63-10.95), and for each 1-point increase in the Charlson comorbidity index (OR:1.16; 95%CI:1.002-1.35).ConclusionsFactors related to increasing the probability of requiring ICU care or dying in patients with COVID-19 were identified, facilitating the development of anticipatory intervention measures that favor comprehensive care and improve patient prognosis.
| S-EPMC8604321 | biostudies-literature
Project description:Trends in routine laboratory tests, such as high white blood cell and low platelet counts, correlate with COVID-19-related intensive care unit (ICU) admissions. Other related biomarkers include elevated troponin, alanine aminotransferase, and aspartate transaminase levels (liver function tests). To this end, the aim of this study was to investigate the effect of changes in laboratory test parameters on ward-based and ICU COVID-19 patients. A total of 280 COVID-19 patients were included in the study and were divided based on admission status into ICU (37) or ward (243) patients. ICU admission correlated significantly with higher levels of several tested parameters, including lactate dehydrogenase, creatinine, D-dimer, creatine kinase, white blood cell count, and neutrophil count. In conclusion, routine laboratory tests offer an indication of which COVID-19 patients are most likely to be admitted to the ICU. These associations can assist healthcare providers in addressing the needs of patients who are at risk of COVID-19 complications.
| S-EPMC9133894 | biostudies-literature
Project description:During the second surge of COVID-19 in France (fall 2020), we assessed the expression of monocyte CD169 (i.e., Siglec-1, one of the numerous IFN-stimulated genes) upon admission to intensive care units of 45 patients with RT-PCR-confirmed SARS-CoV2 pulmonary infection. Overall, CD169 expression was strongly induced on circulating monocytes of COVID-19 patients compared with healthy donors and patients with bacterial sepsis. Beyond its contribution at the emergency department, CD169 testing may be also helpful for patients' triage at the ICU to rapidly reinforce suspicion of COVID-19 etiology in patients with acute respiratory failure awaiting for PCR results for definitive diagnosis.
| S-EPMC8014094 | biostudies-literature
Project description:BackgroundElderly patients (≥ 80 years of age) surviving an episode of critical illness suffer long-term morbidity and risk of mortality. Identifying high risk groups could assist in informing discussions with patients and families.AimTo determine factors associated with long-term survival following ICU admission.DesignA cohort study of patients aged ≥ 80 years of age admitted to the ICU as an emergency.MethodsPatients admitted from January 2010 to December 2018 were included in the study. Primary outcome was five year survival. Mortality was assessed using a multivariable flexible parametric survival analysis adjusted for demographics, and clinically relevant covariates.ResultsThere were 828 patients. Mean age was 84 years (SD 3.2) and 419 (51%) were male. Patients were categorised into medical (423 (51%)) and surgical (405 (49%)) admissions. Adjusted hazard ratios (aHR) for mortality were highest for serum lactate (>8 mmol/l aHR 2.56 (C.I. 1.79-3.67)), lowest systolic blood pressure (< 70 mmHg aHR 2.04 (C.I. 1.36-3.05)) and pH (< 7.05 aHR 4.70 (C.I 2.67-8.21)). There were no survivors beyond one year with severe abnormalities of pH and lactate (< 7.05 and > 8 mmol/l respectively). Relative survival for medical patients was below that expected for the general population for the duration of the study.ConclusionOverall five-year survival was 27%. For medical and surgical patients it was 19% and 35% respectively. Survival at 30 days and one year was 61% and 46%. The presence of features of circulatory shock predicted poor short and long term survival.
| S-EPMC7595304 | biostudies-literature
Project description:BackgroundCOVID-19 is associated with significant morbidity and mortality. This study aimed to explore the early predictors of intensive care unit (ICU) admission among patients with COVID-19.MethodsThis was a case-control study of adult patients with confirmed COVID-19. Cases were defined as patients admitted to ICU during the period February 29-May 29, 2020. For each case enrolled, one control was matched by age and gender.ResultsA total of 1,560 patients with confirmed COVID-19 were included. Each group included 780 patients with a predominant male gender (89.7%) and a median age of 49 years (interquartile range = 18). Predictors independently associated with ICU admission were cardiovascular disease (adjusted odds ratio (aOR) = 1.64, 95% confidence interval (CI): 1.16-2.32, p = 0.005), diabetes (aOR = 1.52, 95% CI: 1.08-2.13, p = 0.016), obesity (aOR = 1.46, 95% CI: 1.03-2.08, p = 0.034), lymphopenia (aOR = 2.69, 95% CI: 1.80-4.02, p < 0.001), high AST (aOR = 2.59, 95% CI: 1.53-4.36, p < 0.001), high ferritin (aOR = 1.96, 95% CI: 1.40-2.74, p < 0.001), high CRP (aOR = 4.09, 95% CI: 2.81-5.96, p < 0.001), and dyspnea (aOR = 2.50, 95% CI: 1.77-3.54, p < 0.001).ConclusionHaving cardiovascular disease, diabetes, obesity, lymphopenia, dyspnea, and increased AST, ferritin, and CRP were independent predictors for ICU admission in patients with COVID-19.
| S-EPMC10987715 | biostudies-literature
Project description:We retrospectively investigated, in 62 consecutive hospitalised COVID-19 patients (aged 70 ± 14 years, 40 males), the prognostic value of CT-derived subcutaneous adipose tissue and visceral adipose tissue (VAT) metrics, testing them in four predictive models for admission to intensive care unit (ICU), with and without pre-existing comorbidities. Multivariate logistic regression identified VAT score as the best ICU admission predictor (odds ratios 4.307-12.842). A non-relevant contribution of comorbidities at receiver operating characteristic analysis (area under the curve 0.821 for the CT-based model, 0.834 for the one including comorbidities) highlights the potential one-stop-shop prognostic role of CT-derived lung and adipose tissue metrics.
| S-EPMC7836243 | biostudies-literature