Prediction models for severe manifestations and mortality due to COVID-19: A systematic review

An overview of reviews on digital health interventions during COVID- 19 era: insights and lessons for future pandemics

BACKGROUND

The COVID- 19 pandemic has significantly impacted global health, underscoring the crucial role of digital health solutions. The World Health Organization's Classification of Digital Interventions, Services, and Applications in Health (CDISAH) provides a framework for categorizing these technologies. This study aims to analyze the adoption and trends of digital health interventions during the COVID- 19 pandemic, mapping them to the CDISAH framework to identify the most and least utilized interventions and technologies.

METHODS

This overview-of-reviews study was conducted from 1 st January 2020 to 30 th December 2023, focusing on systematic reviews and meta-analyses retrieved from the Cochrane Database of Systematic Reviews, PubMed, Scopus, Web of Science, IEEE Xplore, and ProQuest. Additionally, gray literature was identified through searches on the Google Scholar platform and reviewing the citations and reference lists of the included studies. The findings were qualitatively mapped to the CDISAH framework.

RESULTS

A total of 64 review articles were analyzed. A content analysis of the included studies identified 292 codes related to healthcare providers, 257 codes related to data services, 88 codes related to individuals, and 43 codes related to health management and support personnel. The results revealed that the most frequent interventions were associated with telemedicine and data management subcategories, while gaps were identified in areas such as individual-based data reporting during the pandemic, highlighting the need for individuals to take a more active role in managing their own health in preparation for future crises.

CONCLUSIONS

This study identifies both the strengths and weaknesses of the current digital health landscape. It emphasizes the transformative impact of digital health technologies during the COVID- 19 pandemic and provides a roadmap for future improvements in digital health interventions. By providing a comprehensive overview of digital health during this period, the study underscores the importance of implementing robust digital health strategies within the healthcare system to address existing gaps, leverage strengths, and enhance preparedness and resilience in future public health crises.

Analysis of six consecutive waves of ICU-admitted COVID-19 patients: key findings and insights from a Portuguese population

Identifying high-risk patients, particularly in intensive care units (ICUs), enhances treatment and reduces severe outcomes. Since the pandemic, numerous studies have examined COVID-19 patient profiles and factors linked to increased mortality. Despite six pandemic waves, to the best of our knowledge, there is no extensive comparative analysis of patients' characteristics across these waves in Portugal. Thus, we aimed to analyze the demographic and clinical features of 1041 COVID-19 patients admitted to an ICU and their relationship with the different SARS-Cov-2 variants in Portugal. Additionally, we conducted an in-depth examination of factors contributing to early and late mortality by analyzing clinical data and laboratory results from the first 72 h of ICU admission. Our findings revealed a notable decline in ICU admissions due to COVID-19, with the highest mortality rates observed during the second and third waves. Furthermore, immunization could have significantly contributed to the reduction in the median age of ICU-admitted patients and the severity of their conditions. The factors contributing to early and late mortality differed. Age, wave number, D-dimers, and procalcitonin were independently associated with the risk of early death. As a measure of discriminative power for the derived multivariable model, an AUC of 0.825 (p < 0.001; 95% CI, 0.719-0.931) was obtained. For late mortality, a model incorporating age, wave number, hematologic cancer, C-reactive protein, lactate dehydrogenase, and platelet counts resulted in an AUC of 0.795 (p < 0.001; 95% CI, 0.759-0.831). These findings underscore the importance of conducting comprehensive analyses across pandemic waves to better understand the dynamics of COVID-19.

Clinically Guided Adaptive Machine Learning Update Strategies for Predicting Severe COVID-19 Outcomes

BACKGROUND

Machine learning algorithms are essential for predicting severe outcomes during public health crises like COVID-19. However, the dynamic nature of diseases requires continual evaluation and updating of these algorithms. This study aims to compare three update strategies for predicting severe COVID-19 outcomes postdiagnosis: "naive" (a single initial model), "frequent" (periodic retraining), and "context-driven" (retraining informed by clinical insights). The goal is to determine the most effective timing and approach for adapting algorithms to evolving disease dynamics and emerging data.

METHODS

A dataset of 1.11 million COVID-19 patients from diverse U.S. regions was used to develop and validate an XGBoost algorithm for predicting severe outcomes upon diagnosis. Data included patient demographics, vital signs, comorbidities, and immunity-related factors (prior infection and vaccination status) from January 2007 to November 2021. The study analyzed the performance of the three update strategies from March 2020 to November 2021.

RESULTS

Predictive features changed over the pandemic, with comorbidities and vitals being significant initially, and geography, demographics, and immunity-related variables gaining importance later. The "naive" strategy had an average area under the curve (AUC) of 0.77, the "frequent" strategy maintained stability with an average AUC of 0.81, and the "context-driven" strategy averaged an AUC of 0.80, outperforming the "naive" strategy and aligning closely with the "frequent" strategy.

CONCLUSIONS

A context-driven approach, guided by clinical insights, can enhance predictive performance and offer cost-effective solutions for dynamic public health challenges. These findings have significant implications for efficiently managing healthcare resources during evolving disease outbreaks.

A Systematic Review of Predictor Composition, Outcomes, Risk of Bias, and Validation of COVID-19 Prognostic Scores

BACKGROUND

Numerous prognostic scores have been published to support risk stratification for patients with coronavirus disease 2019 (COVID-19).

METHODS

We performed a systematic review to identify the scores for confirmed or clinically assumed COVID-19 cases. An in-depth assessment and risk of bias (ROB) analysis (Prediction model Risk Of Bias ASsessment Tool [PROBAST]) was conducted for scores fulfilling predefined criteria ([I] area under the curve [AUC)] ≥ 0.75; [II] a separate validation cohort present; [III] training data from a multicenter setting [≥2 centers]; [IV] point-scale scoring system).

RESULTS

Out of 1522 studies extracted from MEDLINE/Web of Science (20/02/2023), we identified 242 scores for COVID-19 outcome prognosis (mortality 109, severity 116, hospitalization 14, long-term sequelae 3). Most scores were developed using retrospective (75.2%) or single-center (57.1%) cohorts. Predictor analysis revealed the primary use of laboratory data and sociodemographic information in mortality and severity scores. Forty-nine scores were included in the in-depth analysis. The results indicated heterogeneous quality and predictor selection, with only five scores featuring low ROB. Among those, based on the number and heterogeneity of validation studies, only the 4C Mortality Score can be recommended for clinical application so far.

CONCLUSIONS

The application and translation of most existing COVID scores appear unreliable. Guided development and predictor selection would have improved the generalizability of the scores and may enhance pandemic preparedness in the future.

Urinary phenotyping of SARS-CoV-2 infection connects clinical diagnostics with metabolomics and uncovers impaired NAD+ pathway and SIRT1 activation

OBJECTIVES

The stratification of individuals suffering from acute and post-acute SARS-CoV-2 infection remains a critical challenge. Notably, biomarkers able to specifically monitor viral progression, providing details about patient clinical status, are still not available. Herein, quantitative metabolomics is progressively recognized as a useful tool to describe the consequences of virus-host interactions considering also clinical metadata.

METHODS

The present study characterized the urinary metabolic profile of 243 infected individuals by quantitative nuclear magnetic resonance (NMR) spectroscopy and liquid chromatography mass spectrometry (LC-MS). Results were compared with a historical cohort of noninfected subjects. Moreover, we assessed the concentration of recently identified antiviral nucleosides and their association with other metabolites and clinical data.

RESULTS

Urinary metabolomics can stratify patients into classes of disease severity, with a discrimination ability comparable to that of clinical biomarkers. Kynurenines showed the highest fold change in clinically-deteriorated patients and higher-risk subjects. Unique metabolite clusters were also generated based on age, sex, and body mass index (BMI). Changes in the concentration of antiviral nucleosides were associated with either other metabolites or clinical variables. Increased kynurenines and reduced trigonelline excretion indicated a disrupted nicotinamide adenine nucleotide (NAD+) and sirtuin 1 (SIRT1) pathway.

CONCLUSIONS

Our results confirm the potential of urinary metabolomics for noninvasive diagnostic/prognostic screening and show that the antiviral nucleosides could represent novel biomarkers linking viral load, immune response, and metabolism. Moreover, we established for the first time a casual link between kynurenine accumulation and deranged NAD+/SIRT1, offering a novel mechanism through which SARS-CoV-2 manipulates host physiology.

The reporting completeness and transparency of systematic reviews of prognostic prediction models for COVID-19 was poor: a methodological overview of systematic reviews

OBJECTIVES

To conduct a methodological overview of reviews to evaluate the reporting completeness and transparency of systematic reviews (SRs) of prognostic prediction models (PPMs) for COVID-19.

STUDY DESIGN AND SETTING

MEDLINE, Scopus, Cochrane Database of Systematic Reviews, and Epistemonikos (epistemonikos.org) were searched for SRs of PPMs for COVID-19 until December 31, 2022. The risk of bias in systematic reviews tool was used to assess the risk of bias. The protocol for this overview was uploaded in the Open Science Framework (https://osf.io/7y94c).

RESULTS

Ten SRs were retrieved; none of them synthesized the results in a meta-analysis. For most of the studies, there was absence of a predefined protocol and missing information on study selection, data collection process, and reporting of primary studies and models included, while only one SR had its data publicly available. In addition, for the majority of the SRs, the overall risk of bias was judged as being high. The overall corrected covered area was 6.3% showing a small amount of overlapping among the SRs.

CONCLUSION

The reporting completeness and transparency of SRs of PPMs for COVID-19 was poor. Guidance is urgently required, with increased awareness and education of minimum reporting standards and quality criteria. Specific focus is needed in predefined protocol, information on study selection and data collection process, and in the reporting of findings to improve the quality of SRs of PPMs for COVID-19.

Relevance of comorbidities for main outcomes during different periods of the COVID-19 pandemic

Background

Throughout the evolution of the COVID-19 pandemic, the severity of the disease has varied. The aim of this study was to determine how patients' comorbidities affected and were related to, different outcomes during this time.

Methods

Retrospective cohort study of all patients testing positive for SARS-CoV-2 infection between March 1, 2020, and January 9, 2022. We extracted sociodemographic, basal comorbidities, prescribed treatments, COVID-19 vaccination data, and outcomes such as death and admission to hospital and intensive care unit (ICU) during the different periods of the pandemic. We used logistic regression to quantify the effect of each covariate in each outcome variable and a random forest algorithm to select the most relevant comorbidities.

Results

Predictors of death included having dementia, heart failure, kidney disease, or cancer, while arterial hypertension, diabetes, ischemic heart, cerebrovascular, peripheral vascular diseases, and leukemia were also relevant. Heart failure, dementia, kidney disease, diabetes, and cancer were predictors of adverse evolution (death or ICU admission) with arterial hypertension, ischemic heart, cerebrovascular, peripheral vascular diseases, and leukemia also relevant. Arterial hypertension, heart failure, diabetes, kidney, ischemic heart diseases, and cancer were predictors of hospitalization, while dyslipidemia and respiratory, cerebrovascular, and peripheral vascular diseases were also relevant.

Conclusions

Preexisting comorbidities such as dementia, cardiovascular and renal diseases, and cancers were those most related to adverse outcomes. Of particular note were the discrepancies between predictors of adverse outcomes and predictors of hospitalization and the fact that patients with dementia had a lower probability of being admitted in the first wave.

Predictive Admission Risk Factors, Clinical Features and Kidney Outcomes in Covid-19 Hospitalised Patients with Acute Kidney Injury

Introduction: In COVID-19 patients, acute kidney injury (AKI) is recognized as a cause of high mortality. The aim of our study was to assess the rate and the predictors of AKI as well as survival among COVID-19 patients. Methods: We analyzed clinical and laboratory admission data, predictors of AKI and outcomes including the need for renal replacement therapy (RRT) and mortality at 30 days. Results: Out of 115 patients, 62 (53.9%) presented with AKI: 21 (33.9%) at stage 1, 7(11.3%) at stage 2, and 34 (54.8%) at stage 3. RRT was required in 22.6% of patients and was resolved in 76%. Pre-existing CKD was associated with a 13-fold risk of AKI (p= 0.0001). Low albumin (p = 0.017), thrombocytopenia (p = 0.022) and increase of creatine kinase over 350UI (p = 0.024) were independently associated with a higher risk for AKI. Mortality rates were significantly higher among patients who developed AKI compared to those without (59.6% vs 30.2%, p= 0.003). Low oxygen blood saturation at admission and albumin were found as powerful independent predictors of mortality (OR 0.937; 95%CI: 0.917 - 0.958, p = 0.000; OR 0.987; 95%CI: 0.885-0.991, p= 0.024, respectively). Longer survival was observed in patients without AKI compared to patients with AKI (22.01± 1.703 vs 16.69 ± 1.54, log rank p= 0.009). Conclusion: Renal impairment is significant in hospitalized COVID-19 patients. The severity of the disease itself is emphasized as main contributing mechanism in the occurrence of AKI, and lower blood saturation at admission is the strongest mortality predictor, surpassing the significance of the AKI itself.

The importance of predictors for in-hospital COVID-19 mortality changes over one month

BACKGROUND

Risk stratification enables care providers to make the proper clinical decision for the management of patients with COVID-19 infection. We aimed to explore changes in the importance of predictors for inpatient mortality of COVID-19 over one month.

METHODS

This research was a secondary analysis of data from in-hospital patients with COVID-19 infection. Individuals were admitted to four hospitals, New York, USA. Based on the length of hospital stay, 4370 patients were categorized into three mutually exclusive interval groups, day 1, day 2-7, and day 8-28. We measured changes in the importance of twelve confirmed predictors for mortality over one month, using principal component analysis.

RESULTS

On the first day of admission, there was a higher risk for organ dysfunction, particularly in elderly patients. On day 1, serum aspartate aminotransferase and sodium were also associated with an increased risk of mortality, while normal troponin opposes in-hospital death. With time, the importance of high aspartate aminotransferase and sodium concentrations decreases, while the variable quality of high troponin levels increases. Our study suggested the importance of maintaining normal blood pressure early in the management of patients. High serum concentrations of creatinine and C-reactive protein remain poor prognostic factors throughout the 28 days. The association of age with mortality increases with the length of hospital stay.

CONCLUSION

The importance of some patients' characteristics changes with the length of hospital stay. This should be considered in developing and deploying predictive models and the management of patients with COVID-19 infection.

Development and validation of COEWS (COVID-19 Early Warning Score) for hospitalized COVID-19 with laboratory features: A multicontinental retrospective study

Background

The emergence of new SARS-CoV-2 variants with significant immune-evasiveness, the relaxation of measures for reducing the number of infections, the waning of immune protection (particularly in high-risk population groups), and the low uptake of new vaccine boosters, forecast new waves of hospitalizations and admission to intensive care units. There is an urgent need for easily implementable and clinically effective Early Warning Scores (EWSs) that can predict the risk of complications within the next 24-48 hr. Although EWSs have been used in the evaluation of COVID-19 patients, there are several clinical limitations to their use. Moreover, no models have been tested on geographically distinct populations or population groups with varying levels of immune protection.

Methods

We developed and validated COVID-19 Early Warning Score (COEWS), an EWS that is automatically calculated solely from laboratory parameters that are widely available and affordable. We benchmarked COEWS against the widely used NEWS2. We also evaluated the predictive performance of vaccinated and unvaccinated patients.

Results

The variables of the COEWS predictive model were selected based on their predictive coefficients and on the wide availability of these laboratory variables. The final model included complete blood count, blood glucose, and oxygen saturation features. To make COEWS more actionable in real clinical situations, we transformed the predictive coefficients of the COEWS model into individual scores for each selected feature. The global score serves as an easy-to-calculate measure indicating the risk of a patient developing the combined outcome of mechanical ventilation or death within the next 48 hr.The discrimination in the external validation cohort was 0.743 (95% confidence interval [CI]: 0.703-0.784) for the COEWS score performed with coefficients and 0.700 (95% CI: 0.654-0.745) for the COEWS performed with scores. The area under the receiver operating characteristic curve (AUROC) was similar in vaccinated and unvaccinated patients. Additionally, we observed that the AUROC of the NEWS2 was 0.677 (95% CI: 0.601-0.752) in vaccinated patients and 0.648 (95% CI: 0.608-0.689) in unvaccinated patients.

Conclusions

The COEWS score predicts death or MV within the next 48 hr based on routine and widely available laboratory measurements. The extensive external validation, its high performance, its ease of use, and its positive benchmark in comparison with the widely used NEWS2 position COEWS as a new reference tool for assisting clinical decisions and improving patient care in the upcoming pandemic waves.

Funding

University of Vienna.

Developing Prediction Models for COVID-19 Outcomes: A Valuable Tool for Resource-Limited Hospitals

Purpose

Coronavirus disease is a global pandemic with millions of confirmed cases and hundreds of thousands of deaths worldwide that continues to create a significant burden on the healthcare systems. The aim of this study was to determine the patient clinical and paraclinical profiles that associate with COVID-19 unfavourable outcome and generate a prediction model that could separate between high-risk and low-risk groups.

Patients and Methods

The present study is a multivariate observational retrospective study. A total of 483 patients, residents of the municipality of Timișoara, the biggest city in the Western Region of Romania, were included in the study group that was further divided into 3 sub-groups in accordance with the disease severity form.

Results

Increased age (cOR=1.09, 95% CI: 1.06-1.11, p<0.001), cardiovascular diseases (cOR=3.37, 95% CI: 1.96-6.08, p<0.001), renal disease (cOR=4.26, 95% CI: 2.13-8.52, p<0.001), and neurological disorder (cOR=5.46, 95% CI: 2.71-11.01, p<0.001) were all independently significantly correlated with an unfavourable outcome in the study group. The severe form increases the risk of an unfavourable outcome 19.59 times (95% CI: 11.57-34.10, p<0.001), while older age remains an independent risk factor even when disease severity is included in the statistical model. An unfavourable outcome was positively associated with increased values for the following paraclinical parameters: white blood count (WBC; cOR=1.10, 95% CI: 1.05-1.15, p<0.001), absolute neutrophil count (ANC; cOR=1.15, 95% CI: 1.09-1.21, p<0.001) and C-reactive protein (CRP; cOR=1.007, 95% CI: 1.004-1.009, p<0.001). The best prediction model including age, ANC and CRP achieved a receiver operating characteristic (ROC) curve with the area under the curve (AUC) = 0.845 (95% CI: 0.813-0.877, p<0.001); cut-off value = 0.12; sensitivity = 72.3%; specificity = 83.9%.

Conclusion

This model and risk profiling may contribute to a more precise allocation of limited healthcare resources in a clinical setup and can guide the development of strategies for disease management.

Development and validation of a scoring system to predict mortality in patients hospitalized with COVID-19: A retrospective cohort study in two large hospitals in Ecuador

OBJECTIVE

To develop and validate a scoring system to predict mortality among hospitalized patients with COVID-19.

METHODS

Retrospective cohort study. We analyzed 5,062 analyzed hospitalized patients with COVID-19 treated at two hospitals; one each in Quito and Guayaquil, from February to July 2020. We assessed predictors of mortality using survival analyses and Cox models. We randomly divided the database into two sets: (i) the derivation cohort (n = 2497) to identify predictors of mortality, and (ii) the validation cohort (n = 2565) to test the discriminative ability of a scoring system. After multivariate analyses, we used the final model's β-coefficients to build the score. Statistical analyses involved the development of a Cox proportional hazards regression model, assessment of goodness of fit, discrimination, and calibration.

RESULTS

There was a higher mortality risk for these factors: male sex [(hazard ratio (HR) = 1.32, 95% confidence interval (95% CI): 1.03-1.69], per each increase in a quartile of ages (HR = 1.44, 95% CI: 1.24-1.67) considering the younger group (17-44 years old) as the reference, presence of hypoxemia (HR = 1.40, 95% CI: 1.01-1.95), hypoglycemia and hospital hyperglycemia (HR = 1.99, 95% CI: 1.01-3.91, and HR = 1.27, 95% CI: 0.99-1.62, respectively) when compared with normoglycemia, an AST-ALT ratio >1 (HR = 1.55, 95% CI: 1.25-1.92), C-reactive protein level (CRP) of >10 mg/dL (HR = 1.49, 95% CI: 1.07-2.08), arterial pH <7.35 (HR = 1.39, 95% CI: 1.08-1.80) when compared with normal pH (7.35-7.45), and a white blood cell count >10 × 103 per μL (HR = 1.76, 95% CI: 1.35-2.29). We found a strong discriminative ability in the proposed score in the validation cohort [AUC of 0.876 (95% CI: 0.822-0.930)], moreover, a cutoff score ≥39 points demonstrates superior performance with a sensitivity of 93.10%, a specificity of 70.28%, and a correct classification rate of 72.66%. The LR+ (3.1328) and LR- (0.0981) values further support its efficacy in identifying high-risk patients.

CONCLUSION

Male sex, increasing age, hypoxemia, hypoglycemia or hospital hyperglycemia, AST-ALT ratio >1, elevated CRP, altered arterial pH, and leucocytosis were factors significantly associated with higher mortality in hospitalized patients with COVID-19. A statistically significant Cox regression model with strong discriminatory power and good calibration was developed to predict mortality in hospitalized patients with COVID-19, highlighting its potential clinical utility.

An external validation of the QCOVID3 risk prediction algorithm for risk of hospitalisation and death from COVID-19: An observational, prospective cohort study of 1.66m vaccinated adults in Wales, UK

INTRODUCTION

At the start of the COVID-19 pandemic there was an urgent need to identify individuals at highest risk of severe outcomes, such as hospitalisation and death following infection. The QCOVID risk prediction algorithms emerged as key tools in facilitating this which were further developed during the second wave of the COVID-19 pandemic to identify groups of people at highest risk of severe COVID-19 related outcomes following one or two doses of vaccine.

OBJECTIVES

To externally validate the QCOVID3 algorithm based on primary and secondary care records for Wales, UK.

METHODS

We conducted an observational, prospective cohort based on electronic health care records for 1.66m vaccinated adults living in Wales on 8th December 2020, with follow-up until 15th June 2021. Follow-up started from day 14 post vaccination to allow the full effect of the vaccine.

RESULTS

The scores produced by the QCOVID3 risk algorithm showed high levels of discrimination for both COVID-19 related deaths and hospital admissions and good calibration (Harrell C statistic: ≥ 0.828).

CONCLUSION

This validation of the updated QCOVID3 risk algorithms in the adult vaccinated Welsh population has shown that the algorithms are valid for use in the Welsh population, and applicable on a population independent of the original study, which has not been previously reported. This study provides further evidence that the QCOVID algorithms can help inform public health risk management on the ongoing surveillance and intervention to manage COVID-19 related risks.

Generalizable machine learning approach for COVID-19 mortality risk prediction using on-admission clinical and laboratory features

We aimed to propose a mortality risk prediction model using on-admission clinical and laboratory predictors. We used a dataset of confirmed COVID-19 patients admitted to three general hospitals in Tehran. Clinical and laboratory values were gathered on admission. Six different machine learning models and two feature selection methods were used to assess the risk of in-hospital mortality. The proposed model was selected using the area under the receiver operator curve (AUC). Furthermore, a dataset from an additional hospital was used for external validation. 5320 hospitalized COVID-19 patients were enrolled in the study, with a mortality rate of 17.24% (N = 917). Among 82 features, ten laboratories and 27 clinical features were selected by LASSO. All methods showed acceptable performance (AUC > 80%), except for K-nearest neighbor. Our proposed deep neural network on features selected by LASSO showed AUC scores of 83.4% and 82.8% in internal and external validation, respectively. Furthermore, our imputer worked efficiently when two out of ten laboratory parameters were missing (AUC = 81.8%). We worked intimately with healthcare professionals to provide a tool that can solve real-world needs. Our model confirmed the potential of machine learning methods for use in clinical practice as a decision-support system.

Diabetes and the COVID-19 pandemic

Almost immediately after the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus emerged, it was evident that people with chronic diseases, including diabetes, were disproportionately affected, with an increased risk of hospitalisation and mortality. Over the ensuing 2 years, the indirect effects of the pandemic on healthcare delivery in the short term have become prominent, along with the lingering effects of the virus in those directly infected. In the wake of the pandemic and without any evidence from high quality studies, a number of national and international consensus recommendations were published, which were subsequently rapidly updated based on observational studies. There have been unprecedented disruptions from both direct and indirect impacts of coronavirus disease-2019 (COVID-19) in people with diabetes. In this review, we summarise the impact of acute COVID-19 in people with diabetes, discuss how the presentation and epidemiology during the pandemic, including presentation of diabetic ketoacidosis and new-onset diabetes, has changed, and we consider the wider impact of the pandemic on patients and healthcare service delivery, including some of the areas of uncertainty. Finally, we make recommendations on prioritising patients as we move into the recovery phase and also how we protect people with diabetes for the future, as COVID-19 is likely to become endemic.

Prediction of intensive care admission and hospital mortality in COVID-19 patients using demographics and baseline laboratory data

INTRODUCTION

Optimized allocation of medical resources to patients with COVID-19 has been a critical concern since the onset of the pandemic.

METHODS

In this retrospective cohort study, the authors used data from a Brazilian tertiary university hospital to explore predictors of Intensive Care Unit (ICU) admission and hospital mortality in patients admitted for COVID-19. Our primary aim was to create and validate prediction scores for use in hospitals and emergency departments to aid clinical decisions and resource allocation.

RESULTS

The study cohort included 3,022 participants, of whom 2,485 were admitted to the ICU; 1968 survived, and 1054 died in the hospital. From the complete cohort, 1,496 patients were randomly assigned to the derivation sample and 1,526 to the validation sample. The final scores included age, comorbidities, and baseline laboratory data. The areas under the receiver operating characteristic curves were very similar for the derivation and validation samples. Scores for ICU admission had a 75% accuracy in the validation sample, whereas scores for death had a 77% accuracy in the validation sample. The authors found that including baseline flu-like symptoms in the scores added no significant benefit to their accuracy. Furthermore, our scores were more accurate than the previously published NEWS-2 and 4C Mortality Scores.

DISCUSSION AND CONCLUSIONS

The authors developed and validated prognostic scores that use readily available clinical and laboratory information to predict ICU admission and mortality in COVID-19. These scores can become valuable tools to support clinical decisions and improve the allocation of limited health resources.

Temporal Improvements in COVID-19 Outcomes for Hospitalized Adults: A Post Hoc Observational Study of Remdesivir Group Participants in the Adaptive COVID-19 Treatment Trial

BACKGROUND

The COVID-19 standard of care (SOC) evolved rapidly during 2020 and 2021, but its cumulative effect over time is unclear.

OBJECTIVE

To evaluate whether recovery and mortality improved as SOC evolved, using data from ACTT (Adaptive COVID-19 Treatment Trial).

DESIGN

ACTT is a series of phase 3, randomized, double-blind, placebo-controlled trials that evaluated COVID-19 therapeutics from February 2020 through May 2021. ACTT-1 compared remdesivir plus SOC to placebo plus SOC, and in ACTT-2 and ACTT-3, remdesivir plus SOC was the control group. This post hoc analysis compared recovery and mortality between these comparable sequential cohorts of patients who received remdesivir plus SOC, adjusting for baseline characteristics with propensity score weighting. The analysis was repeated for participants in ACTT-3 and ACTT-4 who received remdesivir plus dexamethasone plus SOC. Trends in SOC that could explain outcome improvements were analyzed. (ClinicalTrials.gov: NCT04280705 [ACTT-1], NCT04401579 [ACTT-2], NCT04492475 [ACTT-3], and NCT04640168 [ACTT-4]).

SETTING

94 hospitals in 10 countries (86% U.S. participants).

PARTICIPANTS

Adults hospitalized with COVID-19.

INTERVENTION

SOC.

MEASUREMENTS

28-day mortality and recovery.

RESULTS

Although outcomes were better in ACTT-2 than in ACTT-1, adjusted hazard ratios (HRs) were close to 1 (HR for recovery, 1.04 [95% CI, 0.92 to 1.17]; HR for mortality, 0.90 [CI, 0.56 to 1.40]). Comparable patients were less likely to be intubated in ACTT-2 than in ACTT-1 (odds ratio, 0.75 [CI, 0.53 to 0.97]), and hydroxychloroquine use decreased. Outcomes improved from ACTT-2 to ACTT-3 (HR for recovery, 1.43 [CI, 1.24 to 1.64]; HR for mortality, 0.45 [CI, 0.21 to 0.97]). Potential explanatory factors (SOC trends, case surges, and variant trends) were similar between ACTT-2 and ACTT-3, except for increased dexamethasone use (11% to 77%). Outcomes were similar in ACTT-3 and ACTT-4. Antibiotic use decreased gradually across all stages.

LIMITATION

Unmeasured confounding.

CONCLUSION

Changes in patient composition explained improved outcomes from ACTT-1 to ACTT-2 but not from ACTT-2 to ACTT-3, suggesting improved SOC. These results support excluding nonconcurrent controls from analysis of platform trials in rapidly changing therapeutic areas.

PRIMARY FUNDING SOURCE

National Institute of Allergy and Infectious Diseases.

External Validation of the 4C Mortality Score and PRIEST COVID-19 Clinical Severity Score in patients hospitalized with COVID-19 pneumonia in Greece

Background: Prognostic scores can be used to facilitate better management of patients suffering from life-threatening diseases, provided that they have been tested in the population of interest.

Aim: To perform external validation of the 4C Mortality Score and PRIEST COVID-19 Clinical Severity Score.

Study Design: Prospective Observational Study.

Methods: Patients hospitalized with COVID-19 pneumonia in a tertiary hospital in Greece were enrolled in the study. The prognostic scores were calculated based on hospital admission data and ROC curve analysis was performed. We assessed a composite outcome of either in-hospital death or need for invasive ventilation.

Results: Both 4C and PRIEST scores showed good discriminative ability with an AUC value of 0.826 (CI 95%: 0.765-0.887) and 0.852 (CI 95%: 0.793-0.910) respectively. Based on the Youden Index the optimal cut-off for the 4C score was 11 (Sensitivity 75%, Specificity 75.5%) and 10 for the PRIEST score (Sensitivity 83% and Specificity 69.4%). Calibration was adequate for both scores, except for the low and very high risk groups in the PRIEST score.

Conclusion: The 4C Mortality Score and PRIEST COVID-19 Clinical Severity Score can be used for early identification of patients with poor prognosis in a Greek population cohort hospitalized with COVID-19.

A novel intubation prediction model for patients hospitalized with COVID-19: the OTO-COVID-19 scoring model

OBJECTIVE

The method for predicting the risk of intubation in patients with coronavirus disease 2019 (COVID-19) is yet to be standardized. This study aimed to introduce a new disease prognosis scoring model that may predict the intubation risk based on the symptoms, signs, and laboratory tests of patients hospitalized with the diagnosis of COVID-19.

METHOD

This cross-sectional retrospective study analyzed the intubation status of 733 patients hospitalized with COVID-19 diagnosis between March and December 2020 at Ondokuz Mayıs University Faculty of Medicine, Turkey, based on 33 variables. Binary logistic regression analysis was used to select the variables that significantly affect intubation, which constitute the risk factors. The Chi-square Automatic Interaction Detection algorithm, one of the data mining methods, was used to determine the threshold values of the important variables for intubation classification.

RESULTS

The following variables found were mostly associated with intubation: C-reactive protein, lactate dehydrogenase, neutrophil-to-lymphocyte ratio, age, lymphocyte count, and malignancy. The logistic function based on these variables correctly predicted 81.13% of intubated (sensitivity), 99.52% of nonintubated (specificity), and 96.86% of both intubated and nonintubated (accurate classification rate) patients. The scoring model revealed the following risk statuses for the intubated patients: very high risk, 75.47%; moderate risk, 20.75%; and very low risk, 3.77%.

CONCLUSIONS

On the basis of certain variables measured at admission, the OTO-COVID-19 scoring model may help clinicians identify patients at the risk of intubation and subsequently provide a prompt and effective treatment at the earliest.

Performance Evaluation of a Rapid Antigen Test (RAT) during Omicron Pandemic Wave in Greece, Conducted by Different Personnel, and Comparison with Performance in Previous Wave (Alpha Variant) Period

Due to the prevailing ambiguity regarding the performance of rapid antigen tests (RATs) for B.1.1.529 (Omicron) variant diagnosis, a commercial RAT was evaluated in the emergency ward of a general hospital in Larissa, Central Greece. The sampling and the evaluation were repeated twice by different personnel. Discordance between the two samplings was observed regarding the sensitivity (47.5%, 95% CI: 39.0–56.1 vs. 78.6%, 95% CI: 69.1–86.2) and specificity (93.8%, 95% CI: 86.0–97.9 vs. 100.0%, 95% CI: 93.3–100.0) of the RAT. Furthermore, the test displayed slightly lower sensitivity (78.6% vs. 85.5%, 95% CI: 79.1–90.5) compared to its initial evaluation that was conducted by our team when the B.1.1.7 (Alpha) variant was dominant.