Peculiar clinical presentation of COVID-19 and predictors of mortality in the elderly: A multicentre retrospective cohort study.
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ABSTRACT: Background
The spectrum of COVID-19 clinical manifestations is not yet known. In the elderly, mortality and extrapulmonary involvement appears more frequent than expected.Methods
A multicentre-retrospective-case-series study of COVID-19 patients, aged ≥65 years, hospitalised between March 1 and June 15, 2020. Patients were classified at admission into 3 groups based on their Clinical Frailty Scale (CFS) score: 1-3 (group A), 4-6 (group B) and 7-9 (group C).Results
Of the 206 patients in the study, 60 (29%) were assigned to group A, 60 (29%) to B and 86 (42%) to C. Significantly more frequent in group C than in B or A were: mental confusion (respectively 65%, 33%, 7%; P < 0.001), kidney failure (39%, 22%, 20%; P = 0.019), dehydration syndrome (55%, 27%, 13%; P < 0.001), electrolyte imbalance (54%, 32%, 25%; P = 0.001), and diabetic decompensation (22%, 12%, 7%; P = 0.026). Crude mortality was 27%. By multivariate logistic regression model independent predictors of death were male sex (adjusted odds ratio (aOR) = 2.87,95%CI = 1.15-7.18), CFS 7-9 (aOR = 9.97,95%CI = 1.82-52.99), dehydration at admission (aOR = 4.27,95%CI = 1.72-10.57) and non-invasive/invasive ventilation (aOR = 4.88,95%CI = 1.94-12.26).Conclusions
Elderly patients with a high CFS showed frequent extrapulmonary signs at admission, even in the absence of lung involvement. These findings, along with a high CFS, predicted a significant risk of mortality.
SUBMITTER: Bavaro DF
PROVIDER: S-EPMC7967397 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundThe optimal timing of tocilizumab treatment during the disease course of COVID-19 has yet to be adequately defined in the context of randomised controlled trials and the effect of tocilizumab on real-world populations remains unclear. We examined the effect of different timing of tocilizumab, on mortality, in a cohort of adults with COVID-19.MethodsAll adults (≥18 years old) with confirmed COVID-19 admitted to four hospitals in the West of Scotland between 8 January 2021 and 31 March 2021 and who received tocilizumab were included in a retrospective observational cohort study. Patients were assigned to either an early (day of admission or first day after admission) or late (days 2-7 of admission) cohort based on tocilizumab initiation. The primary outcome was 90-day all-cause mortality in early versus late cohorts. Secondary outcomes were 28 and 180-day all-cause mortality.Results203 patients were included in the analysis (138 in the early cohort, 65 in the late cohort). Mortality in 90 days in the early cohort was 22% (n=30) compared with 45% (n=29) in the late cohort (p<0.001). The adjusted mortality was significantly higher in the late cohort compared with the early cohort (adjusted OR: 3.33; 95% CI: 1.29 to 8.54; p=0.012). The secondary outcomes demonstrated the same effect with higher rates of death in 28 days (late cohort adjusted OR: 3.28; 95% CI: 1.23 to 8.75; p=0.018) and 180 days (late cohort adjusted OR: 3.70; 95% CI: 1.45 to 9.45; p=0.006). The effect was seen whether the outcome was adjusted or unadjusted.ConclusionEarly administration of tocilizumab within the first 2 days of hospitalisation was associated with a significant survival benefit compared with late exposure. Late administration was associated with particularly high mortality. The observed association may be a result of residual confounders and further research is needed.
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Project description:ObjectiveThis study aimed to develop and externally validate a COVID-19 mortality risk prediction algorithm.DesignRetrospective cohort study.SettingFive designated tertiary hospitals for COVID-19 in Hubei province, China.ParticipantsWe routinely collected medical data of 1364 confirmed adult patients with COVID-19 between 8 January and 19 March 2020. Among them, 1088 patients from two designated hospitals in Wuhan were used to develop the prognostic model, and 276 patients from three hospitals outside Wuhan were used for external validation. All patients were followed up for a maximal of 60 days after the diagnosis of COVID-19.MethodsThe model discrimination was assessed by the area under the receiver operating characteristic curve (AUC) and Somers' D test, and calibration was examined by the calibration plot. Decision curve analysis was conducted.Main outcome measuresThe primary outcome was all-cause mortality within 60 days after the diagnosis of COVID-19.ResultsThe full model included seven predictors of age, respiratory failure, white cell count, lymphocytes, platelets, D-dimer and lactate dehydrogenase. The simple model contained five indicators of age, respiratory failure, coronary heart disease, renal failure and heart failure. After cross-validation, the AUC statistics based on derivation cohort were 0.96 (95% CI, 0.96 to 0.97) for the full model and 0.92 (95% CI, 0.89 to 0.95) for the simple model. The AUC statistics based on the external validation cohort were 0.97 (95% CI, 0.96 to 0.98) for the full model and 0.88 (95% CI, 0.80 to 0.96) for the simple model. Good calibration accuracy of these two models was found in the derivation and validation cohort.ConclusionThe prediction models showed good model performance in identifying patients with COVID-19 with a high risk of death in 60 days. It may be useful for acute risk classification.Web calculatorWe provided a freely accessible web calculator (https://www.whuyijia.com/).
| S-EPMC7768618 | biostudies-literature
Project description:Background and aimsOlder people have higher rates of comorbidities and may experience more severe inflammatory responses; therefore, are at higher risk of death. Herein, we aimed to systematically review the mortality in coronavirus disease 2019 (COVID-19) patients and its predictors in this age group.MethodsWe searched PubMed, Web of Science, and Science Direct using relevant keywords. Retrieved records underwent a two-step screening process consisting of title/abstract and full-text screenings to identify the eligible studies.ResultsSummarizing findings of 35 studies demonstrated that older patients have higher mortality rates compared to the younger population. A review of articles revealed that increasing age, body mass index, a male gender, dementia, impairment or dependency in daily activities, presence of consolidations on chest X-ray, hypoxemic respiratory failure, and lower oxygen saturation at admission were risk factors for death. High d-dimer levels, 25-hydroxy vitamin D serum deficiencies, high C-reactive protein (≥5 mg/L) levels plus any other abnormalities of lymphocyte, higher blood urea nitrogen or lactate dehydrogenase, and higher platelet count were predictors of poor prognosis and mortality in the elderly. Studies have also shown that previous treatment with renin-angiotensin-aldosterone system inhibitors, pharmacological treatments of respiratory disorders, antibiotics, corticosteroids, vitamin K antagonist, antihistamines, azithromycin, Itolizumab (an anti-CD6 monoclonal antibody) in combination with other antivirals reduces COVID-19 worsening and mortality. Vaccination against seasonal influenza might also reduce COVID-19 mortality.ConclusionOverall, a critical consideration is necessary for the care and management of COVID-19 in the aged population considering the drastic contrasts in manifestation and prognosis compared to other age groups. Mortality from COVID-19 is independently associated with the patient's age. Elderly patients with COVID-19 are more vulnerable to poor outcomes. Thus, strict preventive measures, timely diagnosis, and aggressive therapeutic/nontherapeutic care are of great importance to reduce acute respiratory distress syndrome and severe complications in older people.
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Project description:Introduction: COVID-19 patients with hypotension and hypoxemia had a significantly worse outcome. The purpose of this research was to ascertain the risk factors affecting the prognoses of these patients and to develop appropriate prognostic prediction tools. Methods: From March 1, 2020, to April 16, 2020, a retrospective cohort analysis of COVID-19 patients with hypotension and hypoxemia was performed. The univariate and multivariate analyses were performed to identify the associated risk factors influencing the prognosis of COVID-19 patients with hypotension and hypoxemia, and the selected variables were then utilized to construct and validate the prediction model for these patients. Results: Three hundred and twenty-seven COVID-19 patients with hypotension and hypoxemia who met the inclusion and exclusion criteria were included in this study. Age, temperature, troponin, and blood glucose were related to mortality in COVID-19 patients with hypotension and hypoxemia in both univariate and multivariate analyses. The MFP model (multiple fractional polynomial model), full model, and stepwise model were utilized to build the prediction model, and their AUCs were, respectively, 0.902 (0.868, 0.936), 0.902 (0.868, 0.936), and 0.902 (0.868, 0.936). Because the sample size for this research was limited, we utilized bootstrapping for internal validation. The AUCs of Bootstrap full and Bootstrap stepwise were 0.902 (0.867, 0.936) and 0.902 (0.868, 0.936), respectively. Conclusion: Age, temperature, troponin, and blood glucose levels were associated with mortality in COVID-19 patients with hypotension and hypoxemia. Additionally, the prediction model developed using the variables above showed a high predictive value for predicting the prognosis of these individuals.
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Project description:AimsTo assess the prognostic accuracy of comorbidity-adjusted National Early Warning Score in suspected Coronavirus disease 2019 patients transferred from nursing homes by the Emergency Department.DesignMulticentre retrospective cohort study.MethodsPatients transferred by high-priority ambulances from nursing homes to Emergency Departments with suspected severe acute respiratory syndrome coronavirus 2 infection, from March 12 to July 31 2020, were considered. Included variables were: clinical covariates (respiratory rate, oxygen saturation, systolic blood pressure, heart rate, temperature, level of consciousness and supplemental oxygen use), the presence of comorbidities and confirmatory analytical diagnosis of severe acute respiratory syndrome coronavirus 2 infection. The primary outcome was a 2-day mortality rate. The discriminatory capability of the National Early Warning Score was assessed by the area under the receiver operating characteristic curve in two different cohorts, the validation and the revalidation, which were randomly selected from the main cohort.ResultsA total of 337 nursing homes, 10 advanced life support units, 51 basic life support units and 8 hospitals in Spain entailing 1,324 patients (median age 87 years) was involved in this study. Two-day mortality was 11.5% (152 cases), with a positivity rate of severe acute respiratory syndrome coronavirus 2 of 51.2%, 77.7% of hospitalization from whom 1% was of intensive care unit admission. The National Early Warning Score results for the revalidation cohort presented an AUC of 0.771, and of 0.885, 0.778 and 0.730 for the low-, medium- and high-level groups of comorbidities.ConclusionThe comorbidity-adjusted National Early Warning Score provides a good short-term prognostic criterion, information that can help in the decision-making process to guide the best strategy for each older adult, under the current pandemic.ImpactWhat problem did the study address? Under the current coronavirus disease 2019 pandemic, targeting older adults at high risk of deterioration in nursing homes remains challenging. What were the main findings? Comorbidity-adjusted National Early Warning Score helps to forecast the risk of clinical deterioration more accurately. Where and on whom will the research have impact? A high NEWS, with a low level of comorbidity is associated with optimal predictive performance, making these older adults likely to benefit from continued follow up and potentially hospital referral under the current coronavirus disease 2019 pandemic.
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Project description:OBJECTIVES:Being able to predict which patients with COVID-19 are going to deteriorate is important to help identify patients for clinical and research practice. Clinical prediction models play a critical role in this process, but current models are of limited value because they are typically restricted to baseline predictors and do not always use contemporary statistical methods. We sought to explore the benefits of incorporating dynamic changes in routinely measured biomarkers, non-linear effects and applying 'state-of-the-art' statistical methods in the development of a prognostic model to predict death in hospitalised patients with COVID-19. DESIGN:The data were analysed from admissions with COVID-19 to three hospital sites. Exploratory data analysis included a graphical approach to partial correlations. Dynamic biomarkers were considered up to 5 days following admission rather than depending solely on baseline or single time-point data. Marked departures from linear effects of covariates were identified by employing smoothing splines within a generalised additive modelling framework. SETTING:3 secondary and tertiary level centres in Greater Manchester, the UK. PARTICIPANTS:392 hospitalised patients with a diagnosis of COVID-19. RESULTS:392 patients with a COVID-19 diagnosis were identified. Area under the receiver operating characteristic curve increased from 0.73 using admission data alone to 0.75 when also considering results of baseline blood samples and to 0.83 when considering dynamic values of routinely collected markers. There was clear non-linearity in the association of age with patient outcome. CONCLUSIONS:This study shows that clinical prediction models to predict death in hospitalised patients with COVID-19 can be improved by taking into account both non-linear effects in covariates such as age and dynamic changes in values of biomarkers.
| S-EPMC7513423 | biostudies-literature
Project description:ObjectiveThe reliable risk factors for mortality of COVID-19 has not evaluated in well-characterised cohort. This study aimed to identify risk factors for in-hospital mortality within 56 days in patients with severe infection of COVID-19.DesignRetrospective multicentre cohort study.SettingFive tertiary hospitals of Daegu, South Korea.Participants1005 participants over 19 years old confirmed COVID-19 using real-time PCR from nasopharyngeal and oropharyngeal swabs.MethodsThe clinical and laboratory features of patients with COVID-19 receiving respiratory support were analysed to ascertain the risk factors for mortality using the Cox proportional hazards regression model. The relationship between overall survival and risk factors was analysed using the Kaplan-Meier method.OutcomeIn-hospital mortality for any reason within 56 days.ResultsOf the 1005 patients, 289 (28.8%) received respiratory support, and of these, 70 patients (24.2%) died. In multivariate analysis, high fibrosis-4 index (FIB-4; HR 2.784), low lymphocyte count (HR 0.480), diabetes (HR 1.917) and systemic inflammatory response syndrome (HR 1.714) were found to be independent risk factors for mortality in patients with COVID-19 receiving respiratory support (all p<0.05). Regardless of respiratory support, survival in the high FIB-4 group was significantly lower than in the low FIB-4 group (28.8 days vs 44.0 days, respectively, p<0.001). A number of risk factors were also significantly related to survival in patients with COVID-19 regardless of respiratory support (0-4 risk factors, 50.2 days; 49.7 days; 44.4 days; 32.0 days; 25.0 days, respectively, p<0.001).ConclusionFIB-4 index is a useful predictive marker for mortality in patients with COVID-19 regardless of its severity.
| S-EPMC7662142 | biostudies-literature
Project description:BackgroundEstimating the risk of intubation and mortality among COVID-19 patients can help clinicians triage these patients and allocate resources more efficiently. Thus, here we sought to identify the risk factors associated with intubation and intra-hospital mortality in a cohort of COVID-19 patients hospitalized due to hypoxemic acute respiratory failure (ARF).ResultsWe included retrospectively a total of 187 patients admitted to the subintensive and intensive care units of the University Hospital "Maggiore della Carità" of Novara between March 1st and April 30th, 2020. Based on these patients' demographic characteristics, early clinical and laboratory variables, and quantitative chest computerized tomography (CT) findings, we developed two random forest (RF) models able to predict intubation and intra-hospital mortality. Variables independently associated with intubation were C-reactive protein (p < 0.001), lactate dehydrogenase level (p = 0.018) and white blood cell count (p = 0.026), while variables independently associated with mortality were age (p < 0.001), other cardiovascular diseases (p = 0.029), C-reactive protein (p = 0.002), lactate dehydrogenase level (p = 0.018), and invasive mechanical ventilation (p = 0.001). On quantitative chest CT analysis, ground glass opacity, consolidation, and fibrosis resulted significantly associated with patient intubation and mortality. The major predictors for both models were the ratio between partial pressure of arterial oxygen and fraction of inspired oxygen, age, lactate dehydrogenase, C-reactive protein, glycemia, CT quantitative parameters, lymphocyte count, and symptom onset.ConclusionsAltogether, our findings confirm previously reported demographic, clinical, hemato-chemical, and radiologic predictors of adverse outcome among COVID-19-associated hypoxemic ARF patients. The two newly developed RF models herein described show an overall good level of accuracy in predicting intra-hospital mortality and intubation in our study population. Thus, their future development and implementation may help not only identify patients at higher risk of deterioration more effectively but also rebalance the disproportion between resources and demand.
| S-EPMC8626752 | biostudies-literature
Project description:ObjectiveDevelop and validate models that predict mortality of patients diagnosed with COVID-19 admitted to the hospital.DesignRetrospective cohort study.SettingA multicentre cohort across 10 Dutch hospitals including patients from 27 February to 8 June 2020.ParticipantsSARS-CoV-2 positive patients (age ≥18) admitted to the hospital.Main outcome measures21-day all-cause mortality evaluated by the area under the receiver operator curve (AUC), sensitivity, specificity, positive predictive value and negative predictive value. The predictive value of age was explored by comparison with age-based rules used in practice and by excluding age from the analysis.Results2273 patients were included, of whom 516 had died or discharged to palliative care within 21 days after admission. Five feature sets, including premorbid, clinical presentation and laboratory and radiology values, were derived from 80 features. Additionally, an Analysis of Variance (ANOVA)-based data-driven feature selection selected the 10 features with the highest F values: age, number of home medications, urea nitrogen, lactate dehydrogenase, albumin, oxygen saturation (%), oxygen saturation is measured on room air, oxygen saturation is measured on oxygen therapy, blood gas pH and history of chronic cardiac disease. A linear logistic regression and non-linear tree-based gradient boosting algorithm fitted the data with an AUC of 0.81 (95% CI 0.77 to 0.85) and 0.82 (0.79 to 0.85), respectively, using the 10 selected features. Both models outperformed age-based decision rules used in practice (AUC of 0.69, 0.65 to 0.74 for age >70). Furthermore, performance remained stable when excluding age as predictor (AUC of 0.78, 0.75 to 0.81).ConclusionBoth models showed good performance and had better test characteristics than age-based decision rules, using 10 admission features readily available in Dutch hospitals. The models hold promise to aid decision-making during a hospital bed shortage.
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