Competing-risk analysis of coronavirus disease 2019 in-hospital mortality in a Northern Italian centre from SMAtteo COvid19 REgistry (SMACORE)

Joint modeling of longitudinal and competing risks for assessing blood oxygen saturation and its association with survival outcomes in COVID-19 patients

BACKGROUND

The objective of the present study is to evaluate the association between longitudinal and survival outcomes in the presence of competing risk events. To illustrate the application of joint modeling in clinical research, we assessed the blood oxygen saturation (SPO2) and its association with survival outcomes in coronavirus disease (COVID-19).

MATERIALS AND METHODS

In this prospective cohort study, we followed 300 COVID-19 patients, who were diagnosed with severe COVID-19 in the Rohani Hospital in Babol, the north of Iran from October 22, 2020 to March 5, 2021, where death was the event of interest, surviving was the competing risk event and SPO2 was the longitudinal outcome. Joint modeling analyses were compared to separate analyses for these data.

RESULT

The estimation of the association parameter in the joint modeling verified the association between longitudinal outcome SPO2 with survival outcome of death (Hazard Ratio (HR) = 0.33, P = 0.001) and the competing risk outcome of surviving (HR = 4.18, P < 0.001). Based on the joint modeling, longitudinal outcome (SPO2) decreased in hypertension patients (β = -0.28, P = 0.581) and increased in those with a high level of SPO2 on admission (β = 0.75, P = 0.03). Also, in the survival submodel in the joint model, the risk of death survival outcome increased in patients with diabetes comorbidity (HR = 4.38, P = 0.026).

CONCLUSION

The association between longitudinal measurements of SPO2 and survival outcomes of COVID-19 confirms that SPO2 is an important indicator in this disease. Thus, the application of this joint model can provide useful clinical evidence in the different areas of medical sciences.

Effectiveness of regdanvimab on mortality in COVID-19 infected patients on hemodialysis

BACKGROUND

Although several therapeutic agents have been evaluated for the treatment of coronavirus disease 2019 (COVID-19), there are lack of effective and proven treatments for end-stage renal disease (ESRD). The present study aims to evaluate the effectiveness of regdanvimab on mortality in COVID-19-infected patients on hemodialysis (HD).

METHODS

We conducted an observational retrospective study in 230 COVID-19-infected patients on HD, of whom 77 (33.5%) were administered regdanvimab alone or in combination with dexamethasone or remdesivir during hospitalization (regdanvimab group) and 153 patients (66.5%) were not (no regdanvimab group). The primary outcome was in-hospital mortality. We compared mortality rates according to the use of regdanvimab and investigated the factors associated with mortality.

RESULTS

Fifty-nine deaths occurred during hospitalization, 49 in the no regdanvimab group (32.0%) and 10 in the regdanvimab group (13.0%), and the mortality rate was significantly higher in the no regdanvimab group than that in the regdanvimab group (p = 0.001). Multivariate Cox regression analysis showed that malignancy (p = 0.001), SPO2 of <95% at admission (p = 0.003), and administration of antibiotics and regdanvimab (p = 0.007 and p = 0.002, respectively) were significantly associated factors with mortality.

CONCLUSION

Regdanvimab administration is beneficial in improving prognosis in hospitalized COVID-19 patients on HD. Considering the vulnerability to infection and high mortality of ESRD patients, regdanvimab may be considered as a therapeutic option in COVID-19 patients on HD.

Multi-event survival analysis through dynamic multi-modal learning for ICU mortality prediction

BACKGROUND AND OBJECTIVE

Survival analysis is widely applied for assessing the expected duration of patient status towards event occurrences such as mortality in healthcare domain, which is generally considered as a time-to-event problem. Patients with multiple complications have high mortality risks and oftentimes require specific intensive care and clinical treatments. The progression of complications is time-varying according to disease development and intrinsic interactions between complications with respect to mortality are uncertain. Classical methods for mortality prediction and survival analysis in critical care, such as risk scoring systems and cause-specific survival models, were not designed for this multi-event survival analysis problem and able to measure the competing risks of death for mutually exclusive events. In addition, multivariate temporal information of complications is not taken into consideration while estimating differentiated mortality risks in the early stage.

METHODS

In this paper, we propose a novel multi-event survival analysis solution using a tree-based autoregressive survival model of multi-modal electronic health record data. Specifically, we focus on modeling the temporal trajectory of complications and estimating the mortality risk associated with multiple potential complications simultaneously. In dynamic modeling, no assumptions are made for the relationships between time-dependent variables and risk transition over time.

RESULTS

Validated with the eICU database, our model achieves a better prediction performance with C-index ranging in 74-80%, compared to state-of-the-art machine learning methods in the literature, for the complications of acute respiratory distress syndrome and cardiovascular disease cases.

CONCLUSIONS

Our model provides the distinguishable mortality risk curves over time for specific complications and the track of risk development that could potentially support the ICU resource reallocation.

Clinical Impact and Risk Factors of Mortality in Hospitalized Children and Adolescents With Hematologic Diseases and COVID-19: An Observational Retrospective Cohort Study

This study aimed to evaluate the risk factors for COVID-19-related death in a large cohort of hospitalized children with hematological disorders. We performed an analysis of all pediatric patients with COVID-19 registered in a Brazilian nationwide surveillance database between February 2020 and May 2021. The primary outcome was time to death, which was evaluated considering discharge as a competitive risk by using the cumulative incidence function. Among 21,591 hospitalized pediatric patients with COVID-19, 596 cases (2.8%) had hematological diseases. Sixty-one children (27.4%) with malignant hematological diseases had a fatal outcome as compared with 4.2% and 7.4% of nonmalignant hematological and nonhematological cohorts, respectively ( P <0.0001). Children with hematological diseases had a significant increased hazard of death compared with those without these conditions (hazard ratio [HR],=2.40, 95% confidence interval, 1.98 - 2.91). In multivariable analysis, the factors associated with death were the presence of malignant hematological disease (HR, 2.22, 95% CI 1.47 - 3.36), age >10 years (HR 2.19, 95% CI 1.46 - 3.19), male (HR 1.52, 95% CI 1.02 - 2.27), oxygen saturation <95% (HR 2.02, 95% CI 1.38 - 2.96), and abdominal pain at admission (HR 2.75, 95% CI 1.76 - 4.27). Children with malignant hematological diseases had a higher risk of death compared with those without these disorders.

The Dynamics of the Neutrophil-to-Lymphocyte and Platelet-to-Lymphocyte Ratios Predict Progression to Septic Shock and Death in Patients with Prolonged Intensive Care Unit Stay

Background and objectives: The prognoses of patients experiencing a prolonged stay in the intensive care unit (ICU) are often significantly altered by hospital-acquired infections (HAIs), the early detection of which might be cumbersome. The aim of this study was to investigate the roles of the neutrophil-to-lymphocyte (NLR), derived-NRL (d-NLR), platelet-to-lymphocyte (PLR), and lymphocyte-to-C-reactive protein (LCR) ratios in predicting the progression to septic shock and death. Materials and Methods: A retrospective analysis of a consecutive series of ninety COVID-19 patients with prolonged hospitalization (exceeding 15 days) admitted to the ICU was conducted. The prevalence of culture-proven HAIs throughout their hospital stays was documented. NLR, dNLR, PLR, and LCR were recorded on admission, day 7, and day 14 to assess their discriminative prowess for detecting further progression to septic shock or death. Results: The prevalence of HAIs was 76.6%, 50% of patients met the criteria for septic shock, and 50% died. The median time to the first positive culture was 13.5 days and 20.5 days for developing septic shock. Mechanical ventilation was a key contributing factor to HAI, septic shock, and mortality. On admission and day 7 NLR, dNLR, PLR, and LCR values had no prognostic relevance for events occurring late during hospitalization. However, day-14 NLR, dNLR, and PLR were independent predictors for progression to septic shock and mortality and have shown good discriminative capabilities. The AUCs for septic shock were 0.762, 0.764, and 0.716, while the values for predicting in-hospital death were 0.782, 0.778, and 0.758, respectively. Conclusions: NLR, dNLR, and PLR are quick, easy-to-use, cheap, effective biomarkers for the detection of a more severe disease course, of the late development of HAIs, and of the risk of death in critically ill patients requiring a prolonged ICU stay.

The role of electrolyte imbalances in predicting the severity of COVID-19 in the hospitalized patients: a cross-sectional study

Coronavirus disease 2019 (COVID-19) can be fatal in severe cases. Accordingly, predicting the severity and prognosis of the disease is valuable. This study examined the role of electrolyte imbalances in predicting the severity of COVID-19. In this cross-sectional study, 169 hospitalized patients with COVID-19 were included and categorized into three groups based on the severity of the disease (moderate, severe, and critical). Serum levels of electrolytes (calcium [Ca], phosphorus [P], sodium [Na], potassium [k], and magnesium [Mg]), inflammatory markers (D-dimer, C-reactive protein [CRP], ferritin, and lactate dehydrogenase [LDH]), and 25OHVitamin D were measured. The mean age of patients was 53 years, and 54% were male. They had moderate, severe, and critical illnesses in 22%, 47%, and 31%, respectively. CRP, D-dimer, and ferritin increased with the severity of the disease. The lower median values of Mg, Na, 25OHVitamin D, Ca, LDH, and higher median lymphocyte counts were observed in the moderate vs. the severe group (P < 0.05). These parameters have acceptable sensitivity and specificity at the suggested cut-off level to discriminate the moderate and critical cases. Serum parameters introduced in this study are appropriate for differentiating between critical and moderate cases. The electrolyte imbalance can predict critical patients.

COVID-19 Variants in Critically Ill Patients: A Comparison of the Delta and Omicron Variant Profiles

BACKGROUND

Coronavirus disease is a pandemic that has disrupted many human lives, threatening people's physical and mental health. Each pandemic wave struck in different ways, infectiveness-wise and mortality-wise. This investigation focuses on critically ill patients affected by the last two variants, Delta and Omicron, and aims to analyse if any difference exists between the two groups.

METHODS

intensive care unit (ICU) COVID-19 consecutive admissions between 1 October 2021 and 31 March 2022 were recorded daily, and data concerning the patients' demographics, variants, main comorbidities, ICU parameters on admission, and the outcome were analysed by a univariate procedure and by a multivariate analysis.

RESULTS

65 patients were enrolled, 31 (47.69%) belonging to the Omicron versus 34 (52.31%) to the Delta group. The mortality rate was 52.94% for the Omicron group versus 41.9% for the Delta group. A univariate analysis showed that the Omicron variant was associated with total comorbidities number, Charlson Comorbidity Index (CCI), pre-existing pulmonary disease, vaccination status, and acute kidney injury (AKI). In stepwise multivariate analysis, the total number of comorbidities was positively associated with the Omicron group, while pulmonary embolism was negatively correlated with the Omicron group.

CONCLUSION

Omicron appears to have lost some of the hallmarks of the Delta variant, such as endothelialitis and more limited cellular tropism when it comes to the patients in the ICU. Further studies are encouraged to explore different therapeutic approaches to treat critical patients with COVID-19.

The analysis of COVID-19 in-hospital mortality: A competing risk approach or a cure model?

Competing risk analyses have been widely used for the analysis of in-hospital mortality in which hospital discharge is considered as a competing event. The competing risk model assumes that more than one cause of failure is possible, but there is only one outcome of interest and all others serve as competing events. However, hospital discharge and in-hospital death are two outcomes resulting from the same disease process and patients whose disease conditions were stabilized so that inpatient care was no longer needed were discharged. We therefore propose to use cure models, in which hospital discharge is treated as an observed “cure” of the disease. We consider both the mixture cure model and the promotion time cure model and extend the models to allow cure status to be known for those who were discharged from the hospital. An EM algorithm is developed for the mixture cure model. We also show that the competing risk model, which treats hospital discharge as a competing event, is equivalent to a promotion time cure model. Both cure models were examined in simulation studies and were applied to a recent cohort of COVID-19 in-hospital patients with diabetes. The promotion time model shows that statin use improved the overall survival; the mixture cure model shows that while statin use reduced the in-hospital mortality rate among the susceptible, it improved the cure probability only for older but not younger patients. Both cure models show that treatment was more beneficial among older patients.

Handling death as an Intercurrent event in time to recovery analysis in COVID-19 treatment clinical trials

In clinical trials with the objective to evaluate the treatment effect on time to recovery, such as investigational trials on therapies for COVID-19 hospitalized patients, the patients may face a mortality risk that competes with the opportunity to recover (e.g., be discharged from the hospital). Therefore, an appropriate analytical strategy to account for death is particularly important due to its potential impact on the estimation of the treatment effect. To address this challenge, we conducted a thorough evaluation and comparison of nine survival analysis methods with different strategies to account for death, including standard survival analysis methods with different censoring strategies and competing risk analysis methods. We report results of a comprehensive simulation study that employed design parameters commonly seen in COVID-19 trials and case studies using reconstructed data from a published COVID-19 clinical trial. Our research results demonstrate that, when there is a moderate to large proportion of patients who died before observing their recovery, competing risk analyses and survival analyses with the strategy to censor death at the maximum follow-up timepoint would be able to better detect a treatment effect on recovery than the standard survival analysis that treat death as a non-informative censoring event. The aim of this research is to raise awareness of the importance of handling death appropriately in the time-to-recovery analysis when planning current and future COVID-19 treatment trials.

Age is the main determinant of COVID-19 related in-hospital mortality with minimal impact of pre-existing comorbidities, a retrospective cohort study

BACKGROUND

Age and comorbidities increase COVID-19 related in-hospital mortality risk, but the extent by which comorbidities mediate the impact of age remains unknown.

METHODS

In this multicenter retrospective cohort study with data from 45 Dutch hospitals, 4806 proven COVID-19 patients hospitalized in Dutch hospitals (between February and July 2020) from the CAPACITY-COVID registry were included (age 69[58-77]years, 64% men). The primary outcome was defined as a combination of in-hospital mortality or discharge with palliative care. Logistic regression analysis was performed to analyze the associations between sex, age, and comorbidities with the primary outcome. The effect of comorbidities on the relation of age with the primary outcome was evaluated using mediation analysis.

RESULTS

In-hospital COVID-19 related mortality occurred in 1108 (23%) patients, 836 (76%) were aged ≥70 years (70+). Both age 70+ and female sex were univariably associated with outcome (odds ratio [OR]4.68, 95%confidence interval [4.02-5.45], OR0.68[0.59-0.79], respectively;both p<  0.001). All comorbidities were univariably associated with outcome (p<0.001), and all but dyslipidemia remained significant after adjustment for age70+ and sex. The impact of comorbidities was attenuated after age-spline adjustment, only leaving female sex, diabetes mellitus (DM), chronic kidney disease (CKD), and chronic pulmonary obstructive disease (COPD) significantly associated (female OR0.65[0.55-0.75], DM OR1.47[1.26-1.72], CKD OR1.61[1.32-1.97], COPD OR1.30[1.07-1.59]). Pre-existing comorbidities in older patients negligibly (<6% in all comorbidities) mediated the association between higher age and outcome.

CONCLUSIONS

Age is the main determinant of COVID-19 related in-hospital mortality, with negligible mediation effect of pre-existing comorbidities.

TRIAL REGISTRATION

CAPACITY-COVID ( NCT04325412 ).

Frailty in Elderly Patients with Covid-19: A Narrative Review

Introduction

The SARS CoV-2 pandemic still generates a very high number of affected patients and a significant mortality rate. It is essential to establish objective criteria to stratify COVID-19 death risk. Frailty has been identified as a potential determinant of increased vulnerability in older adults affected by COVID-19, because it may suggest alterations of physical performance and functional autonomy.

Methods

We have conducted a narrative review of the literature on the evidences regarding COVID-19 and the frailty condition. Thirteen observational studies were included.

Conclusion

Data emerging from the studies indicate that older COVID-19 patients with a frailty condition have an increased risk of mortality compared with non-frail patients, and this association is independent of other clinical and demographic factors. A frailty evaluation is required to help clinicians to better stratify the overall risk of death for older patients with COVID-19.

Predictive Machine Learning Models and Survival Analysis for COVID-19 Prognosis Based on Hematochemical Parameters

The coronavirus disease 2019 (COVID-19) pandemic has affected hundreds of millions of individuals and caused millions of deaths worldwide. Predicting the clinical course of the disease is of pivotal importance to manage patients. Several studies have found hematochemical alterations in COVID-19 patients, such as inflammatory markers. We retrospectively analyzed the anamnestic data and laboratory parameters of 303 patients diagnosed with COVID-19 who were admitted to the Polyclinic Hospital of Bari during the first phase of the COVID-19 global pandemic. After the pre-processing phase, we performed a survival analysis with Kaplan–Meier curves and Cox Regression, with the aim to discover the most unfavorable predictors. The target outcomes were mortality or admission to the intensive care unit (ICU). Different machine learning models were also compared to realize a robust classifier relying on a low number of strongly significant factors to estimate the risk of death or admission to ICU. From the survival analysis, it emerged that the most significant laboratory parameters for both outcomes was C-reactive protein min; HR=17.963 (95% CI 6.548–49.277, p < 0.001) for death, HR=1.789 (95% CI 1.000–3.200, p = 0.050) for admission to ICU. The second most important parameter was Erythrocytes max; HR=1.765 (95% CI 1.141–2.729, p < 0.05) for death, HR=1.481 (95% CI 0.895–2.452, p = 0.127) for admission to ICU. The best model for predicting the risk of death was the decision tree, which resulted in ROC-AUC of 89.66%, whereas the best model for predicting the admission to ICU was support vector machine, which had ROC-AUC of 95.07%. The hematochemical predictors identified in this study can be utilized as a strong prognostic signature to characterize the severity of the disease in COVID-19 patients.

Predicting COVID-19 progression from diagnosis to recovery or death linking primary care and hospital records in Castilla y León (Spain)

This paper analyses COVID-19 patients' dynamics during the first wave in the region of Castilla y León (Spain) with around 2.4 million inhabitants using multi-state competing risk survival models. From the date registered as the start of the clinical process, it is assumed that a patient can progress through three intermediate states until reaching an absorbing state of recovery or death. Demographic characteristics, epidemiological factors such as the time of infection and previous vaccinations, clinical history, complications during the course of the disease and drug therapy for hospitalised patients are considered as candidate predictors. Regarding risk factors associated with mortality and severity, consistent results with many other studies have been found, such as older age, being male, and chronic diseases. Specifically, the hospitalisation (death) rate for those over 69 is 27.2% (19.8%) versus 5.3% (0.7%) for those under 70, and for males is 14.5%(7%) versus 8.3%(4.6%)for females. Among patients with chronic diseases the highest rates of hospitalisation are 26.1% for diabetes and 26.3% for kidney disease, while the highest death rate is 21.9% for cerebrovascular disease. Moreover, specific predictors for different transitions are given, and estimates of the probability of recovery and death for each patient are provided by the model. Some interesting results obtained are that for patients infected at the end of the period the hazard of transition from hospitalisation to ICU is significatively lower (p < 0.001) and the hazard of transition from hospitalisation to recovery is higher (p < 0.001). For patients previously vaccinated against pneumococcus the hazard of transition to recovery is higher (p < 0.001). Finally, internal validation and calibration of the model are also performed.

Frailty Assessment in the Emergency Department for Risk Stratification of COVID-19 Patients Aged ≥80 Years

OBJECTIVES

To evaluate, in a cohort of adults aged ≥80 years, the overlapping effect of clinical severity, comorbidities, cognitive impairment, and frailty, for the in-hospital death risk stratification of COVID-19 older patients since emergency department (ED) admission.

DESIGN

Single-center prospective observational cohort study.

SETTING AND PARTICIPANTS

The study was conducted in the ED of a teaching hospital that is a referral center for COVID-19 in central Italy. We enrolled all patients with aged ≥80 years old consecutively admitted to the ED between April 2020 and March 2021.

METHODS

Clinical variables assessed in the ED were evaluated for the association with all-cause in-hospital death. Evaluated parameters were severity of disease, frailty, comorbidities, cognitive impairment, delirium, and dependency in daily life activities. Cox regression analysis was used to identify independent risk factors for poor outcomes.

RESULTS

A total of 729 patients aged ≥80 years were enrolled [median age 85 years (interquartile range 82-89); 346 were males (47.3%)]. According to the Clinical Frailty Scale, 61 (8.4%) were classified as fit, 417 (57.2%) as vulnerable, and 251 (34.4%) as frail. Severe disease [hazard ratio (HR) 1.87, 95% confidence interval (CI) 1.31-2.59], ≥3 comorbidities (HR 1.54, 95% CI 1.11-2.13), male sex (HR 1.46, 95% CI 1.14-1.87), and frailty (HR 6.93, 95% CI 1.69-28.27) for vulnerable and an overall HR of 12.55 (95% CI 2.96-53.21) for frail were independent risk factors for in-hospital death.

CONCLUSIONS AND IMPLICATIONS

The ED approach to older patients with COVID-19 should take into account the functional and clinical characteristics of patients being admitted. A sole evaluation based on the clinical severity and the presence of comorbidities does not reflect the complexity of this population. A comprehensive evaluation based on clinical severity, multimorbidity, and frailty could effectively predict the clinical risk of in-hospital death for patients with COVID-19 aged ≥80 years at the time of ED presentation.

Clinical characteristics and risk factors for death among hospitalised children and adolescents with COVID-19 in Brazil: an analysis of a nationwide database

Background

COVID-19 is usually less severe and has lower case fatality in children than in adults. We aimed to characterise the clinical features of children and adolescents hospitalised with laboratory-confirmed SARS-CoV-2 infection and to evaluate the risk factors for COVID-19-related death in this population.

Methods

We did an analysis of all patients younger than 20 years who had quantitative RT-PCR-confirmed COVID-19 and were registered in the Influenza Epidemiological Surveillance Information System (SIVEP-Gripe, a nationwide surveillance database of patients admitted to hospital with severe acute respiratory disease in Brazil), between Feb 16, 2020, and Jan 9, 2021. The primary outcome was time to recovery (discharge) or in-hospital death, evaluated by competing risks analysis using the cumulative incidence function.

Findings

Of the 82 055 patients younger than 20 years reported to SIVEP-Gripe during the study period, 11 613 (14·2%) had available data showing laboratory-confirmed SARS-CoV-2 infection and were included in the sample. Among these patients, 886 (7·6%) died in hospital (at a median 6 days [IQR 3–15] after hospital admission), 10 041 (86·5%) patients were discharged from the hospital, 369 (3·2%) were in hospital at the time of analysis, and 317 (2·7%) were missing information on outcome. The estimated probability of death was 4·8% during the first 10 days after hospital admission, 6·7% during the first 20 days, and 8·1% at the end of follow-up. Probability of discharge was 54·1% during the first 10 days, 78·4% during the first 20 days, and 92·0% at the end of follow-up. Our competing risks multivariate survival analysis showed that risk of death was increased in infants younger than 2 years (hazard ratio 2·36 [95% CI 1·94–2·88]) or adolescents aged 12–19 years (2·23 [1·84–2·71]) relative to children aged 2–11 years; those of Indigenous ethnicity (3·36 [2·15–5·24]) relative to those of White ethnicity; those living in the Northeast region (2·06 [1·68–2·52]) or North region (1·55 [1·22–1·98]) relative to those in the Southeast region; and those with one (2·96 [2·52–3·47]), two (4·96 [3·80–6·48]), or three or more (7·28 [4·56–11·6]) pre-existing medical conditions relative to those with none.

Interpretation

Death from COVID-19 was associated with age, Indigenous ethnicity, poor geopolitical region, and pre-existing medical conditions. Disparities in health care, poverty, and comorbidities can contribute to magnifying the burden of COVID-19 in more vulnerable and socioeconomically disadvantaged children and adolescents in Brazil.

Funding

National Council for Scientific and Technological Development, Research Support Foundation of Minas Gerais.