Underlying conditions and risk of hospitalisation, ICU admission and mortality among those with COVID-19 in Ireland: A national surveillance study

Bennett, Kathleen; Mullooly, Maeve; O’Loughlin, Marie; Fitzgerald, Margaret; O’Donnell, Joan; O’Connor, Lois; Oza, A.; Cuddihy, John

doi:10.1016/j.lanepe.2021.100097

Cited by 37 publications

(41 citation statements)

References 20 publications

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“… 3 Nevertheless, several underlying diseases, such as cancer, hypertension, and cardiovascular diseases, act as risk factors for severe COVID-19 or COVID-19-related mortality. 2 12 32 In COVID-19 patients with cancer, systemic immunosuppression associated with the cancer itself or anticancer therapy might affect the vulnerability to severe COVID-19 or COVID-19-related mortality, which is consistent with our findings. Diabetes, known to be a hazardous comorbidity in fatal COVID-19 cases, was also associated with COVID-19-related mortality in the present study.…”

Section: Discussionsupporting

confidence: 91%

“…Previous studies on the estimation of severity and mortality of COVID-19 showed diverse results, with the identification of a limited number of predictors, such as age and sex. 6 7 8 9 10 Age and underlying diseases are known factors associated with higher risks of mortality in patients with COVID-19, 2 6 7 11 12 which is partially related with increased severity and susceptibility to COVID-19. 13 14 However, heterogeneity in age and underlying diseases could affect COVID-19-related mortality, and therefore, findings from previous studies were inconclusive.…”

Section: Introductionmentioning

confidence: 99%

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Prediction of COVID-19-related Mortality and 30-Day and 60-Day Survival Probabilities Using a Nomogram

et al. 2021

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Background Prediction of mortality in patients with coronavirus disease 2019 (COVID-19) is a key to improving the clinical outcomes, considering that the COVID-19 pandemic has led to the collapse of healthcare systems in many regions worldwide. This study aimed to identify the factors associated with COVID-19 mortality and to develop a nomogram for predicting mortality using clinical parameters and underlying diseases. Methods This study was performed in 5,626 patients with confirmed COVID-19 between February 1 and April 30, 2020 in South Korea. A Cox proportional hazards model and logistic regression model were used to construct a nomogram for predicting 30-day and 60-day survival probabilities and overall mortality, respectively in the train set. Calibration and discrimination were performed to validate the nomograms in the test set. Results Age ≥ 70 years, male, presence of fever and dyspnea at the time of COVID-19 diagnosis, and diabetes mellitus, cancer, or dementia as underling diseases were significantly related to 30-day and 60-day survival and mortality in COVID-19 patients. The nomogram showed good calibration for survival probabilities and mortality. In the train set, the areas under the curve (AUCs) for 30-day and 60-day survival was 0.914 and 0.954, respectively; the AUC for mortality of 0.959. In the test set, AUCs for 30-day and 60-day survival was 0.876 and 0.660, respectively, and that for mortality was 0.926. The online calculators can be found at https://koreastat.shinyapps.io/RiskofCOVID19/ . Conclusion The prediction model could accurately predict COVID-19-related mortality; thus, it would be helpful for identifying the risk of mortality and establishing medical policies during the pandemic to improve the clinical outcomes.

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Section: Discussionsupporting

confidence: 91%

Section: Introductionmentioning

confidence: 99%

Prediction of COVID-19-related Mortality and 30-Day and 60-Day Survival Probabilities Using a Nomogram

et al. 2021

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“…Assuming that 10% of patients diagnosed with COVID-19 will be admitted to hospital [ 6 ], a 0.05 acceptable difference in apparent and adjusted Cox-Snell R-squared, 0.05 margin of error in estimation of the intercept, a binary outcome based on admission to hospital, and a maximum of 24 predictor parameters [ 5 ], we estimate that the minimum sample size required for new model development is 1317 participants enrolled for the development set [ 7 ].…”

Section: Methodsmentioning

confidence: 99%

An Early Warning Risk Prediction Tool (RECAP-V1) for Patients Diagnosed With COVID-19: Protocol for a Statistical Analysis Plan

Fiorentino¹,

Prociuk²,

Espinosa-González³

et al. 2021

JMIR Res Protoc

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Background Since the start of the COVID-19 pandemic, efforts have been made to develop early warning risk scores to help clinicians decide which patient is likely to deteriorate and require hospitalization. The RECAP (Remote COVID-19 Assessment in Primary Care) study investigates the predictive risk of hospitalization, deterioration, and death of patients with confirmed COVID-19, based on a set of parameters chosen through a Delphi process performed by clinicians. We aim to use rich data collected remotely through the use of electronic data templates integrated in the electronic health systems of several general practices across the United Kingdom to construct accurate predictive models. The models will be based on preexisting conditions and monitoring data of a patient’s clinical parameters (eg, blood oxygen saturation) to make reliable predictions as to the patient’s risk of hospital admission, deterioration, and death. Objective This statistical analysis plan outlines the statistical methods to build the prediction model to be used in the prioritization of patients in the primary care setting. The statistical analysis plan for the RECAP study includes the development and validation of the RECAP-V1 prediction model as a primary outcome. This prediction model will be adapted as a three-category risk score split into red (high risk), amber (medium risk), and green (low risk) for any patient with suspected COVID-19. The model will predict the risk of deterioration and hospitalization. Methods After the data have been collected, we will assess the degree of missingness and use a combination of traditional data imputation using multiple imputation by chained equations, as well as more novel machine-learning approaches to impute the missing data for the final analysis. For predictive model development, we will use multiple logistic regression analyses to construct the model. We aim to recruit a minimum of 1317 patients for model development and validation. We will then externally validate the model on an independent dataset of 1400 patients. The model will also be applied for multiple different datasets to assess both its performance in different patient groups and its applicability for different methods of data collection. Results As of May 10, 2021, we have recruited 3732 patients. A further 2088 patients have been recruited through the National Health Service Clinical Assessment Service, and approximately 5000 patients have been recruited through the DoctalyHealth platform. Conclusions The methodology for the development of the RECAP-V1 prediction model as well as the risk score will provide clinicians with a statistically robust tool to help prioritize COVID-19 patients. Trial Registration ClinicalTrials.gov NCT04435041; https://clinicaltrials.gov/ct2/show/NCT04435041 International Registered Report Identifier (IRRID) DERR1-10.2196/30083

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“…Neurotropic nature of SARS-CoV-2 predetermines psychiatric disorders in some patients with COVID-19 [1][2][3]. However, most publications on the psychological and mental impact of COVID-19 present the results of online and cross-sectional studies of the general population [4][5][6][7] or post-recovery data of patients who have suffered the acuteSARS-CoV-2 infection earlier [8][9][10][11]. Even the clinical findings from previous coronavirus crises are mostly symptom-and dimension-oriented.…”

Section: Introductionmentioning

confidence: 99%

Mental State of Inpatients with COVID-19: a Computational Psychiatry Approach

Sorokin¹,

Kibitov²,

Kasyanov

et al. 2021

Preprint

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The overload of healthcare systems around the world and the danger of infection have limited the ability of researchers to obtain sufficient and reliable data on psychopathology in hospitalized patients with COVID-19. The relationship between severe SARS-CoV-2 infection and specific mental disturbances remains poorly understood. Aim: to reveal the possibility of identifying the typology and frequency of psychiatric syndromes associated with acute COVID-19 using cluster analysis of discrete psychopathological phenomena. Materials and methods. Descriptive data on the mental state of 55 inpatients with COVID-19 were obtained by young-career physicians with psychiatric backgrounds. Classification of observed clinical phenomena was performed with k-means cluster analysis of variables codded from the main psychopathological symptoms. Dispersion analysis with p-level 0.05 was used to reveal the clusters differences in demography, parameters of inflammation and respiration function collected on the basis of the original medical records. Results. Three resulting clusters of patients were identified: persons with anxiety, disorders of fluency and tempo of thinking, mood, attention, motor-volitional sphere, reduced insight, and pessimistic plans for the future (n=11); persons without psychopathology (n=37); persons with disorientation, disorders of memory, attention, fluency, and tempo of thinking, reduced insight (n=7). The development of a certain type of impaired mental state was specifically associated with: age, lung lesions according to computed tomography, saturation, respiratory rate, C-reactive protein level, platelet count. Conclusion. The prevalence and typology of psychiatric disorders in patients with acute COVID-19 were described using the computational psychiatry approach.

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Underlying conditions and risk of hospitalisation, ICU admission and mortality among those with COVID-19 in Ireland: A national surveillance study

Cited by 37 publications

References 20 publications

Prediction of COVID-19-related Mortality and 30-Day and 60-Day Survival Probabilities Using a Nomogram

Prediction of COVID-19-related Mortality and 30-Day and 60-Day Survival Probabilities Using a Nomogram

An Early Warning Risk Prediction Tool (RECAP-V1) for Patients Diagnosed With COVID-19: Protocol for a Statistical Analysis Plan

Mental State of Inpatients with COVID-19: a Computational Psychiatry Approach

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