New Scoring System for Predicting Mortality in Patients with COVID-19

Development of a new prognostic model to predict pneumonia outcome using artificial intelligence-based chest radiograph results

This study aimed to develop a new simple and effective prognostic model using artificial intelligence (AI)-based chest radiograph (CXR) results to predict the outcomes of pneumonia. Patients aged > 18 years, admitted the treatment of pneumonia between March 2020 and August 2021 were included. We developed prognostic models, including an AI-based consolidation score in addition to the conventional CURB-65 (confusion, urea, respiratory rate, blood pressure, and age ≥ 65) and pneumonia severity index (PSI) for predicting pneumonia outcomes, defined as 30-day mortality during admission. A total of 489 patients, including 310 and 179 patients in training and test sets, were included. In the training set, the AI-based consolidation score on CXR was a significant variable for predicting the outcome (hazard ratio 1.016, 95% confidence interval [CI] 1.001-1.031). The model that combined CURB-65, initial O2 requirement, intubation, and the AI-based consolidation score showed a significantly high C-index of 0.692 (95% CI 0.628-0.757) compared to other models. In the test set, this model also demonstrated a significantly high C-index of 0.726 (95% CI 0.644-0.809) compared to the conventional CURB-65 and PSI (p < 0.001 and 0.017, respectively). Therefore, a new prognostic model incorporating AI-based CXR results along with traditional pneumonia severity score could be a simple and useful tool for predicting pneumonia outcomes in clinical practice.

A Simple Risk Formula for the Prediction of COVID-19 Hospital Mortality

SARS-CoV-2 respiratory infection is associated with significant morbidity and mortality in hospitalized patients. We aimed to assess the risk factors for hospital mortality in non-vaccinated patients during the 2021 spring wave in the Czech Republic. A total of 991 patients hospitalized between January 2021 and March 2021 with a PCR-confirmed SARS-CoV-2 acute respiratory infection in two university hospitals and five rural hospitals were included in this analysis. After excluding patients with unknown outcomes, 790 patients entered the final analyses. Out of 790 patients included in the analysis, 282/790 (35.7%) patients died in the hospital; 162/790 (20.5) were male and 120/790 (15.2%) were female. There were 141/790 (18%) patients with mild, 461/790 (58.3%) with moderate, and 187/790 (23.7%) with severe courses of the disease based mainly on the oxygenation status. The best-performing multivariate regression model contains only two predictors-age and the patient's state; both predictors were rendered significant (p < 0.0001). Both age and disease state are very significant predictors of hospital mortality. An increase in age by 10 years raises the risk of hospital mortality by a factor of 2.5, and a unit increase in the oxygenation status raises the risk of hospital mortality by a factor of 20.

Development and Internal Validation of a Novel Prognostic Score to Predict Mortality in Acute Respiratory Distress Syndrome - Driving Pressure, Oxygenation and Nutritional Evaluation - "DRONE Score"

Introduction

There are few scores for mortality prediction in acute respiratory distress syndrome (ARDS) incorporating comprehensive ventilatory, acute physiological, organ dysfunction, oxygenation, and nutritional parameters. This study aims to determine the risk factors of ARDS mortality from the above-mentioned parameters at 48 h of invasive mechanical ventilation (IMV), which are feasible across most intensive care unit settings.

Methods

Prospective, observational, single-center study with 150 patients with ARDS defined by Berlin definition, receiving IMV with lung protective strategy.

Results

Our study had a mortality of 41.3% (62/150). We developed a 9-point novel prediction score, the driving pressure oxygenation and nutritional evaluation (DRONE) score comprising of driving pressure (DP), oxygenation accessed by the ratio of partial pressure of arterial oxygen to the fraction of inspired oxygen (PaO2/FiO2) ratio and nutritional evaluation using the modified nutrition risk in the critically ill (mNUTRIC) score. Each component of the DRONE score with the cutoff value to predict mortality was assigned a particular score (the lowest DP within 48 h in a patient being always ≥15 cmH2O a score of 2, the highest achievable PaO2/FiO2 <208 was assigned a score of 4 and the mNUTRIC score ≥4 was assigned a score of (3). We obtained the DRONE score ≥4, area under the curve 0.860 to predict mortality. Cox regression for the DRONE score >4 was highly associated with mortality (P < 0.001, hazard ratio 5.43, 95% confidence interval [2.94-10.047]). Internal validation was done by bootstrap analysis. The clinical utility of the DRONE score ≥4 was assessed by Kaplan-Meier curve which showed significance.

Conclusions

The DRONE score ≥4 could be a reliable predictor of mortality at 48 h in ARDS patients receiving IMV.

SARS-CoV-2 susceptibility and COVID-19 illness course and outcome in people with pre-existing neurodegenerative disorders: systematic review with frequentist and Bayesian meta-analyses

BACKGROUND

People with neurodegenerative disease and mild cognitive impairment (MCI) may have an elevated risk of acquiring severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and may be disproportionally affected by coronavirus disease 2019 (COVID-19) once infected.

AIMS

To review all eligible studies and quantify the strength of associations between various pre-existing neurodegenerative disorders and both SARS-CoV-2 susceptibility and COVID-19 illness course and outcome.

METHOD

Pre-registered systematic review with frequentist and Bayesian meta-analyses. Systematic searches were executed in PubMed, Web of Science and preprint servers. The final search date was 9 January 2023. Odds ratios (ORs) were used as measures of effect.

RESULTS

In total, 136 primary studies (total sample size n = 97 643 494), reporting on 268 effect-size estimates, met the inclusion criteria. The odds for a positive SARS-CoV-2 test result were increased for people with pre-existing dementia (OR = 1.83, 95% CI 1.16-2.87), Alzheimer's disease (OR = 2.86, 95% CI 1.44-5.66) and Parkinson's disease (OR = 1.65, 95% CI 1.34-2.04). People with pre-existing dementia were more likely to experience a relatively severe COVID-19 course, once infected (OR = 1.43, 95% CI 1.00-2.03). People with pre-existing dementia or Alzheimer's disease were at increased risk for COVID-19-related hospital admission (pooled OR range: 1.60-3.72). Intensive care unit admission rates were relatively low for people with dementia (OR = 0.54, 95% CI 0.40-0.74). All neurodegenerative disorders, including MCI, were at higher risk for COVID-19-related mortality (pooled OR range: 1.56-2.27).

CONCLUSIONS

Our findings confirm that, in general, people with neurodegenerative disease and MCI are at a disproportionally high risk of contracting COVID-19 and have a poor outcome once infected.

The risk of venous thromboembolism and blood hyperlactatemia is associated with increased mortality among critically ill patients with Covid-19

INTRODUCTION

Coronavirus disease 2019 (Covid-19) following venous thromboembolism (VTE) and blood hyperlactatemia are associated with higher mortality. However, reliable biomarkers for this association remain to be elucidated. This study investigated the associations of VTE risk and blood hyperlactatemia with mortality among critically ill Covid-19 patients admitted to the intensive care unit (ICU).

METHODS

In this single-centre retrospective study, we included 171 patients aged ≥18 years with confirmed Covid-19 admitted to the ICU at a tertiary healthcare clinic in the Eastern region of Saudi Arabia between 1 March 2020 and 31 January 2021. Patients were divided into two groups: survivor and non-survivor. The survivors have been identified as the patients discharged from the ICU alive. The VTE risk was defined using a Padua prediction score (PPS) >4. The blood lactate concentration (BLC) cut-off value >2 mmol/L was used to determine the blood hyperlactatemia.

RESULTS

Multi-factor Cox analysis showed that PPS >4 and BLC >2 mmol/L were more likely to be significantly associated with higher odds of ICU mortality in critically ill Covid-19 patients (hazard ratio [HR] = 2.80, 95% confidence interval [CI] = 1.00-8.08, p = 0.050; HR = 3.87, 95% CI = 1.12-13.45, p = 0.033, respectively). The Area under the Curve for VTE and blood hyperlactatemia were 0.62 and 0.85, respectively.

CONCLUSION

VTE risk and blood hyperlactatemia have been associated with a higher mortality risk in critically ill Covid-19 patients who are hospitalized in the ICU in Saudi Arabia. According to our findings, these people needed more effective VTE prevention strategies based on a personalized assessment of their risk of bleeding. Moreover, persons without diabetes and other groups with a high risk of dying from COVID-19 may be recognized by measuring glucose as having elevated glucose and lactate jointly.

A novel scoring system for early assessment of the risk of the COVID-19-associated mortality in hospitalized patients: COVID-19 BURDEN

BACKGROUND

Corona Virus Disease 2019 (COVID-19) presentations range from those similar to the common flu to severe pneumonia resulting in hospitalization with significant morbidity and/or mortality. In this study, we made an attempt to develop a predictive scoring model to improve the early detection of high risk COVID-19 patients by analyzing the clinical features and laboratory data available on admission.

METHODS

We retrospectively included 480 consecutive adult patients, aged 21-95, who were admitted to Faghihi Teaching Hospital. Clinical and laboratory features were collected from the medical records and analyzed using multiple logistic regression analysis. The final data analysis was utilized to develop a simple scoring model for the early prediction of mortality in COVID-19 patients. The score given to each associated factor was based on the coefficients of the regression analyses.

RESULTS

A novel mortality risk score (COVID-19 BURDEN) was derived, incorporating risk factors identified in this cohort. CRP (> 73.1 mg/L), O2 saturation variation (greater than 90%, 84-90%, and less than 84%), increased PT (> 16.2 s), diastolic blood pressure (≤ 75 mmHg), BUN (> 23 mg/dL), and raised LDH (> 731 U/L) were the features constituting the scoring system. The patients are triaged to the groups of low- (score < 4) and high-risk (score ≥ 4) groups. The area under the curve, sensitivity, and specificity for predicting mortality in patients with a score of ≥ 4 were 0.831, 78.12%, and 70.95%, respectively.

CONCLUSIONS

Using this scoring system in COVID-19 patients, the patients with a higher risk of mortality can be identified which will help to reduce hospital care costs and improve its quality and outcome.

Comorbid Asthma Increased the Risk for COVID-19 Mortality in Asia: A Meta-Analysis

We aimed to explore the influence of comorbid asthma on the risk for mortality among patients with coronavirus disease 2019 (COVID-19) in Asia by using a meta-analysis. Electronic databases were systematically searched for eligible studies. The pooled odds ratio (OR) with 95% confidence interval (CI) was estimated by using a random-effect model. An inconsistency index (I2) was utilized to assess the statistical heterogeneity. A total of 103 eligible studies with 198,078 COVID-19 patients were enrolled in the meta-analysis; our results demonstrated that comorbid asthma was significantly related to an increased risk for COVID-19 mortality in Asia (pooled OR = 1.42, 95% CI: 1.20−1.68; I2 = 70%, p < 0.01). Subgroup analyses by the proportion of males, setting, and sample sizes generated consistent findings. Meta-regression indicated that male proportion might be the possible sources of heterogeneity. A sensitivity analysis exhibited the reliability and stability of the overall results. Both Begg’s analysis (p = 0.835) and Egger’s analysis (p = 0.847) revealed that publication bias might not exist. In conclusion, COVID-19 patients with comorbid asthma might bear a higher risk for mortality in Asia, at least among non-elderly individuals.

Novel Scoring Systems to Predict the Need for Oxygenation and ICU Care, and Mortality in Hospitalized COVID-19 Patients: A Risk Stratification Tool

Introduction: A rapid surge in cases during the COVID-19 pandemic can overwhelm any healthcare system. It is imperative to triage patients who would require oxygen and ICU care, and predict mortality. Specific parameters at admission may help in identifying them.

Methodology: A prospective observational study was undertaken in a COVID-19 ward of a tertiary care center. All baseline clinical and laboratory data were captured. Patients were followed till death or discharge. Univariable and multivariable logistic regression was used to find predictors of the need for oxygen, need for ICU care, and mortality. Objective scoring systems were developed for the same using the predictors.

Results: The study included 209 patients. Disease severity was mild, moderate, and severe in 98 (46.9%), 74 (35.4%), and 37 (17.7%) patients, respectively. The neutrophil-to-lymphocyte ratio (NLR) >4 was a common independent predictor of the need for oxygen (p<0.001), need for ICU transfer (p=0.04), and mortality (p=0.06). Clinical risk scores were developed (10*c-reactive protein (CRP) + 14.8*NLR + 12*urea), (10*aspartate transaminase (AST) + 15.7*NLR + 14.28*CRP), (10*NLR + 10.1*creatinine) which, if ≥14.8, ≥25.7, ≥10.1 predicted need for oxygenation, need for ICU transfer and mortality with a sensitivity and specificity (81.6%, 70%), (73.3%, 75.7%), (61.1%, 75%), respectively. 

Conclusion: The NLR, CRP, urea, creatinine, and AST are independent predictors in identifying patients with poor outcomes. An objective scoring system can be used at the bedside for appropriate triaging of patients and utilization of resources.