ABSTRACT
Background: Already at the time of hospital admission, clinicians require simple tools to identify hospitalized COVID-19 patients at high risk of mortality. Such tools can significantly improve resource allocation and patient management within hospitals. From the statistical point of view, extended time-to-event models are required to account for competing risks (discharge from hospital) and censoring so that active cases can also contribute to the analysis. Methods: We used the hospital-based open Khorshid COVID Cohort (KCC) study with 630 COVID-19 patients from Isfahan, Iran. Competing risk methods are used to develop a death risk chart based on following variables which can simply be measured at hospital admission: gender, age, hypertension, oxygen saturation, and Charlson Comorbidity Index. The area under the receiver operator curve was used to assess accuracy concerning discrimination between patients discharged alive and dead. Results: Cause-specific hazard regression models show that these baseline variables are associated with both hazards, the death as well as the discharge hazard. The risk chart reflects the combined results of the two cause-specific hazard regression models. The proposed risk assessment method had a very good accuracy (AUC=0.872 [CI 95%: 0.835-0.910]). Conclusions: This study aims to improve and validate a personalized mortality risk calculator based on hospitalized COVID-19 patients. The risk assessment of patient mortality provides physicians with additional guidance for making tough decisions.
Subject(s)
COVID-19 , HypertensionABSTRACT
Background: Reported mortality of hospitalised COVID-19 patients varies substantially, particularly in critically ill patients. So far COVID-19 in-hospital mortality and modes of death under optimised care conditions have not been systematically studied.MethodsThis retrospective observational monocenter cohort study was performed after implementation of a non-restricted, dynamic tertiary care model at the University Medical Center Freiburg, an experienced ARDS and ECMO referral center. All hospitalised patients with PCR-confirmed SARS-CoV-2 infection were included. The primary endpoint was in-hospital mortality, secondary endpoints included major complications and modes of death. A multistate analysis and a Cox regression analysis for competing risk models were performed. Modes of death were determined by two independent reviewers.ResultsBetween February 25, and May 8, 213 patients were included in the analysis. The median age was 65 years, 129 patients (61%) were male. 70 patients (33%) were admitted to the intensive care unit (ICU), of which 57 patients (81%) received mechanical ventilation and 23 patients (33%) extracorporeal membrane-oxygenation (ECMO) support. According to the multistate model the probability to die within 90 days after COVID-19 onset was 24% in the whole cohort. If the levels of care at time of study entry were accounted for, the probabilities to die were 16% if the patient was initially on a regular ward, 47% if in the ICU and 57% if mechanical ventilation was required at study entry. Age ≥ 65 years and male sex were predictors for in-hospital death. Predominant complications – as judged by two independent reviewers – determining modes of death were multi-organ failure, septic shock and thromboembolic and hemorrhagic complications.ConclusionIn a dynamic care model COVID-19-related in-hospital mortality remained substantial. In the absence of potent antiviral agents, strategies to alleviate or prevent the identified complications should be investigated. In this context, multistate analyses enable comparison of models-of-care and treatment strategies and allow estimation and allocation of health care resources.RegistrationGerman Clinical Trials Register (identifier DRKS00021775), retrospectively registered June 10, 2020.
Subject(s)
COVID-19 , ThromboembolismABSTRACT
BackgroundReported mortality of hospitalised COVID-19 patients varies substantially, particularly in critically ill patients. So far COVID-19 in-hospital mortality and modes of death under optimised care conditions have not been systematically studied. MethodsThis retrospective observational monocenter cohort study was performed after implementation of a non-restricted, dynamic tertiary care model at the University Medical Center Freiburg, an experienced ARDS and ECMO referral center. All hospitalised patients with PCR-confirmed SARS-CoV-2 infection were included. The primary endpoint was in-hospital mortality, secondary endpoints included major complications and modes of death. A multistate analysis and a Cox regression analysis for competing risk models were performed. Modes of death were determined by two independent reviewers. ResultsBetween February 25, and May 8, 213 patients were included in the analysis. The median age was 65 years, 129 patients (61%) were male. 70 patients (33%) were admitted to the intensive care unit (ICU), of which 57 patients (81%) received mechanical ventilation and 23 patients (33%) extracorporeal membrane-oxygenation (ECMO) support. According to the multistate model the probability to die within 90 days after COVID-19 onset was 24% in the whole cohort. If the levels of care at time of study entry were accounted for, the probabilities to die were 16% if the patient was initially on a regular ward, 47% if in the ICU and 57% if mechanical ventilation was required at study entry. Age [≥]65 years and male sex were predictors for in-hospital death. Predominant complications - as judged by two independent reviewers - determining modes of death were multi-organ failure, septic shock and thromboembolic and hemorrhagic complications. ConclusionIn a dynamic care model COVID-19-related in-hospital mortality remained substantial. In the absence of potent antiviral agents, strategies to alleviate or prevent the identified complications should be investigated. In this context, multistate analyses enable comparison of models-of-care and treatment strategies and allow estimation and allocation of health care resources. RegistrationGerman Clinical Trials Register (identifier DRKS00021775), retrospectively registered June 10, 2020.
Subject(s)
COVID-19ABSTRACT
Background The clinical progress of patients hospitalized due to COVID-19 is often associated with severe pneumonia which may require intensive care, invasive ventilation, or extracorporeal membrane oxygenation (ECMO). The length of intensive care and the duration of these supportive therapies are clinically relevant outcomes. From the statistical perspective, these quantities are challenging to estimate due to episodes being time-dependent and potentially multiple, as well as being determined by the competing, terminal events of discharge alive and death. Methods We used multistate models to study COVID-19 patients’ time-dependent progress and provide a statistical framework to estimate hazard rates and transition probabilities. These estimates can then be used to quantify average sojourn times of clinically important states such as intensive care and invasive ventilation. We have made two real data sets of COVID-19 patients (n = 24* and n = 53**) and the corresponding statistical code publically available. Results The expected lengths of intensive care unit (ICU) stay at day 28 for the two cohorts were 15.05* and 19.62** days, while expected durations of mechanical ventilation were 7.97* and 9.85** days. Predicted mortality stood at 51%* and 15%**. Patients mechanically ventilated at the start of the example studies had a longer expected duration of ventilation (12.25*, 14.57** days) compared to patients non-ventilated (4.34*, 1.41** days) after 28 days. Furthermore, initially ventilated patients had a higher risk of death (54%* and 20%** vs. 48%* and 6 %**) after 4 weeks. These results are further illustrated in stacked probability plots for the two groups from time zero, as well as for the entire cohort which depicts the predicted proportions of the patients in each state over follow-up. Conclusions The multistate approach gives important insights into the progress of COVID-19 patients in terms of ventilation duration, length of ICU stay, and mortality. In addition to avoiding frequent pitfalls in survival analysis, the methodology enables active cases to be analyzed by allowing for censoring. The stacked probability plots provide extensive information in a concise manner that can be easily conveyed to decision makers regarding healthcare capacities. Furthermore, clear comparisons can be among different baseline characteristics.
Subject(s)
COVID-19 , Pneumonia , DeathABSTRACT
BackgroundMany trials are now underway to inform decision-makers on potential effects of treatments for COVID-19. To provide sufficient information for all involved decision-makers (clinicians, public health authorities, drug regulatory agencies) a multiplicity of endpoints must be considered. It is a challenge to generate detailed high quality evidence from data while ensuring fast availability and evaluation of the results. MethodsWe reviewed all interventional COVID-19 trials on Remdesivir, Lopinavir/ritonavir and Hydroxychloroquine registered in the National Library of Medicine (NLM) at the National Institutes of Health (NIH) and summarized the endpoints used to assess treatment effects. We propose a multistate model that harmonizes heterogeneous endpoints and differing lengths of follow-up within and between trials. ResultsThere are currently, March 27, 2020, 23 registered interventional trials investigating the potential benefits of Remdesivir, Lopinavir/ritonavir and Hydroxychloroquine. The endpoints are highly heterogeneous. Follow-up for the primary endpoints ranges from four to 168 days. A detailed precisely defined endpoint has been proposed by the global network REMAP-CAP, which is specialized on community-acquired pneumonia. Their seven-category endpoint accounts for major clinical events informative for all decision-makers. Moreover, the Core Outcome Measures in Effectiveness Trials (COMET) Initiative is currently working on a core outcome set. We propose a multistate model that accommodates analysis of these recommended endpoints. The model allows for a detailed investigation of treatment effects for various endpoints over the course of time thereby harmonizing differing endpoints and lengths of follow-up. ConclusionMultistate model analysis is a powerful tool to study clinically heterogeneous endpoints (mortality, discharge) as well as endpoints influencing hospital capacities (duration of hospitalization and ventilation) simultaneously over time. Our proposed model extracts all information available in the data and is - by harmonizing endpoints within and between trials - a step towards faster decision making. All ongoing clinical trials, especially those with severe cases, should accommodate primary analysis with a stacked probability plot of the major events mechanical ventilation, discharge alive and death.