Asthma in the elderly and COVID-19: results of the 90-day post-hospital follow-up.

Background. After the first wave of the new SARS-CoV-2 coronavirus infection, the researchers focused on identifying potential short-and long-term complications of COVID-19, especially in high-risk patients, after prolonged hospitalization and intensive care. Objective. To study the outcomes, adverse effects of severe COVID-19 and their predictors 90 days after hospital discharge in elderly patients with asthma. Material and methods. The study included elderly patients (101 subjects, 42 males and 59 females;median age 74 (67;79) years) with asthma, discharged from the hospital after treatment of severe COVID-19. They were followed up for 90 days after discharge. In the hospital, COVID-19 was confirmed by laboratory tests (polymerase chain reaction method) and/or clinically and radiologically. All patients had a documented history of asthma according to GINA 2020 criteria. Results and discussion. During the 90-day post-hospital follow-up, 86 (85%) patients survived, and 15 (15%) died after discharge. Deaths were reported within 1 to 4 weeks after discharge: 6 subjects died during re-hospitalization, 6 at home, and 3 in a rehabilitation center. The multivariate regression analysis model, adjusted for all statistically significant indicators, and the ROC analysis showed the most significant predictors of 90-day post-hospital mortality and their threshold values. They include the Charlson comorbidity index >=4 points, lung damage according to computed tomography >=30%, the absolute number of eosinophils <=100 cells/muL, and concomitant diabetes mellitus. The analysis showed that 90-day post-hospital mortality depends on combinations of identified risk factors;a combination of two, three, and especially four risk factors statistically significantly is associated with patients' lower average survival time. Conclusion. The key risk factors for 90-day post-hospital mortality in elderly patients with asthma after severe COVID-19 include the Charlson comorbidity index, lung damage >=30% according to computed tomography, the absolute number of eosinophils <=100 cells/muL, and concomitant diabetes mellitus. The 90-day post-hospital survival rate is correlated with the number of risk factors identified in patients. The effect of asthma severity on 90-day post-hospital mortality in elderly patients was not observed.Copyright © 2023, Media Sphera Publishing Group. All rights reserved.

Asthma in the elderly and COVID-19: results of the 90-day post-hospital follow-up.

Background. After the first wave of the new SARS-CoV-2 coronavirus infection, the researchers focused on identifying potential short-and long-term complications of COVID-19, especially in high-risk patients, after prolonged hospitalization and intensive care. Objective. To study the outcomes, adverse effects of severe COVID-19 and their predictors 90 days after hospital discharge in elderly patients with asthma. Material and methods. The study included elderly patients (101 subjects, 42 males and 59 females;median age 74 (67;79) years) with asthma, discharged from the hospital after treatment of severe COVID-19. They were followed up for 90 days after discharge. In the hospital, COVID-19 was confirmed by laboratory tests (polymerase chain reaction method) and/or clinically and radiologically. All patients had a documented history of asthma according to GINA 2020 criteria. Results and discussion. During the 90-day post-hospital follow-up, 86 (85%) patients survived, and 15 (15%) died after discharge. Deaths were reported within 1 to 4 weeks after discharge: 6 subjects died during re-hospitalization, 6 at home, and 3 in a rehabilitation center. The multivariate regression analysis model, adjusted for all statistically significant indicators, and the ROC analysis showed the most significant predictors of 90-day post-hospital mortality and their threshold values. They include the Charlson comorbidity index >=4 points, lung damage according to computed tomography >=30%, the absolute number of eosinophils <=100 cells/muL, and concomitant diabetes mellitus. The analysis showed that 90-day post-hospital mortality depends on combinations of identified risk factors;a combination of two, three, and especially four risk factors statistically significantly is associated with patients' lower average survival time. Conclusion. The key risk factors for 90-day post-hospital mortality in elderly patients with asthma after severe COVID-19 include the Charlson comorbidity index, lung damage >=30% according to computed tomography, the absolute number of eosinophils <=100 cells/muL, and concomitant diabetes mellitus. The 90-day post-hospital survival rate is correlated with the number of risk factors identified in patients. The effect of asthma severity on 90-day post-hospital mortality in elderly patients was not observed.Copyright © 2023, Media Sphera Publishing Group. All rights reserved.

Developing a mortality risk prediction model using data of 3663 hospitalized COVID-19 patients: a retrospective cohort study in an Egyptian University Hospital.

PURPOSE: Since the declaration of COVID-19 as a pandemic, a wide between-country variation was observed regarding in-hospital mortality and its predictors. Given the scarcity of local research and the need to prioritize the provision of care, this study was conducted aiming to measure the incidence of in-hospital COVID-19 mortality and to develop a simple and clinically applicable model for its prediction. METHODS: COVID-19-confirmed patients admitted to the designated isolation areas of Ain-Shams University Hospitals (April 2020-February 2021) were included in this retrospective cohort study (n = 3663). Data were retrieved from patients' records. Kaplan-Meier survival and Cox proportional hazard regression were used. Binary logistic regression was used for creating mortality prediction models. RESULTS: Patients were 53.6% males, 4.6% current smokers, and their median age was 58 (IQR 41-68) years. Admission to intensive care units was 41.1% and mortality was 26.5% (972/3663, 95% CI 25.1-28.0%). Independent mortality predictors-with rapid mortality onset-were age ≥ 75 years, patients' admission in critical condition, and being symptomatic. Current smoking and presence of comorbidities particularly, obesity, malignancy, and chronic haematological disorders predicted mortality too. Some biomarkers were also recognized. Two prediction models exhibited the best performance: a basic model including age, presence/absence of comorbidities, and the severity level of the condition on admission (Area Under Receiver Operating Characteristic Curve (AUC) = 0.832, 95% CI 0.816-0.847) and another model with added International Normalized Ratio (INR) value (AUC = 0.842, 95% CI 0.812-0.873). CONCLUSION: Patients with the identified mortality risk factors are to be prioritized for preventive and rapid treatment measures. With the provided prediction models, clinicians can calculate mortality probability for their patients. Presenting multiple and very generic models can enable clinicians to choose the one containing the parameters available in their specific clinical setting, and also to test the applicability of such models in a non-COVID-19 respiratory infection.

An Overview of the Impact of Bacterial Infections and the Associated Mortality Predictors in Patients with COVID-19 Admitted to a Tertiary Center from Eastern Europe.

1. BACKGROUND: Literature data on bacterial infections and their impact on the mortality rates of COVID-19 patients from Romania are scarce, while worldwide reports are contrasting. 2. MATERIALS AND METHODS: We conducted a unicentric retrospective observational study that included 280 patients with SARS-CoV-2 infection, on whom we performed various microbiological determinations. Based on the administration or not of the antibiotic treatment, we divided the patients into two groups. First, we sought to investigate the rates and predictors of bacterial infections, the causative microbial strains, and the prescribed antibiotic treatment. Secondly, the study aimed to identify the risk factors associated with in-hospital death and evaluate the biomarkers' performance for predicting short-term mortality. 3. RESULTS: Bacterial co-infections or secondary infections were confirmed in 23 (8.2%) patients. Acinetobacter baumannii was the pathogen responsible for most of the confirmed bacterial infections. Almost three quarters of the patients (72.8%) received empiric antibiotic therapy. Multivariate logistic regression has shown leukocytosis and intensive care unit admission as risk factors for bacterial infections and C-reactive protein, together with the length of hospital stay, as mortality predictors. The ROC curves revealed an acceptable performance for the erythrocyte sedimentation rate (AUC: 0.781), and C-reactive protein (AUC: 0.797), but a poor performance for fibrinogen (AUC: 0.664) in predicting fatal events. 4. CONCLUSIONS: This study highlighted the somewhat paradoxical association of a low rate of confirmed infections with a high rate of empiric antibiotic therapy. A thorough assessment of the risk factors for bacterial infections, in addition to the acknowledgment of various mortality predictors, is crucial for identifying high-risk patients, thus allowing a timely therapeutic intervention, with a direct impact on improving patients' prognosis.

Clinical characteristics, treatment outcomes and factors associated with severe illness in 813 COVID-19 patients admitted in a tertiary care hospital of eastern India

Introduction: This retrospective observational study has been designed to identify clinical characteristics, treatment outcomes and factors associated with severe illness in 813 COVID-19 patients hospitalised in an Indian tertiary care hospital. Material and methods: This was a retrospective analysis of patient admitted between 1st July to 15th Aug 2020 with COVID-19 infections. Logistic regression was performed to explore the association of clinical characteristics and laboratory parameters with the risk of severe disease and mortality. The statistical significance level was set at 0.05 (two-tailed). Results: Out of 813 study patients, 630 (77.50%) patients were categorised with mild to moderate while 183 (22.50%) patients as severe Covid infection. Mortality was significantly higher in severe Covid patients as compared to mild moderate cases (66.21% vs. 10.31%. p<0.0001. Patients with severe infection were significantly more likely to have diabetes hypertension, chronic kidney disease (CKD) and had significantly higher Neutrophil count, serum creatinine, C-reactive protein (CRP), ferritin, D-Dimer and decreased haemoglobin, lymphocyte and serum calcium than patients with mild-moderate infection. In Multivariate analysis, age more than 60 years [AOR: 2.114, 95% CI (1.05-4.254), 0.036], NLR more than 3.3 [AOR: 1.082, 95% CI (1.030-1.137), 0.002] and D-Dimer >1 mu g/mL [AOR: 2.999 (1.464-6.146), 0.003] were found significantly associated with severe disease (p < 0.05). Factors associated with mortality were age more than 60 years, presence of breathlessness, severe disease or presence of chronic kidney disease. Conclusions: Factors like elderly age (age > 60 years), elevated NRL, CRP, D-Dimer and serum ferritin were associated with significantly higher risk to develop severe COVID-19 infections. Elderly, and patients with CKD were associated with worse outcome.

Identifying Predictors of COVID-19 Mortality Using Machine Learning.

(1) Background: Coronavirus disease 2019 (COVID-19) is a dominant, rapidly spreading respiratory disease. However, the factors influencing COVID-19 mortality still have not been confirmed. The pathogenesis of COVID-19 is unknown, and relevant mortality predictors are lacking. This study aimed to investigate COVID-19 mortality in patients with pre-existing health conditions and to examine the association between COVID-19 mortality and other morbidities. (2) Methods: De-identified data from 113,882, including 14,877 COVID-19 patients, were collected from the UK Biobank. Different types of data, such as disease history and lifestyle factors, from the COVID-19 patients, were input into the following three machine learning models: Deep Neural Networks (DNN), Random Forest Classifier (RF), eXtreme Gradient Boosting classifier (XGB) and Support Vector Machine (SVM). The Area under the Curve (AUC) was used to measure the experiment result as a performance metric. (3) Results: Data from 14,876 COVID-19 patients were input into the machine learning model for risk-level mortality prediction, with the predicted risk level ranging from 0 to 1. Of the three models used in the experiment, the RF model achieved the best result, with an AUC value of 0.86 (95% CI 0.84-0.88). (4) Conclusions: A risk-level prediction model for COVID-19 mortality was developed. Age, lifestyle, illness, income, and family disease history were identified as important predictors of COVID-19 mortality. The identified factors were related to COVID-19 mortality.

Clinical and Laboratory Factors in Predicting Mortality Among COVID-19 RT-PCR Positive Patients: A Retrospective Observational Study From a Tertiary Care Center.

BACKGROUND: In coronavirus disease 2019 (COVID-19) patients, risk stratification based on clinical presentation, co-morbid illness, and combined laboratory parameters is essential to provide an adequate, timely intervention based on an individual's conditions to prevent mortality among cases. METHODS: A retrospective observational study was carried out from June to October 2020, including all reverse transcription-polymerase chain reaction (RT-PCR) positive COVID-19 non-survivors and control group survivors randomly selected after age and sex matching. Clinical and demographic information was collected from the medical records. Categorical variables were expressed by frequency and percentage. To explore the risk factors associated with mortality, univariable and multivariable logistic regression models were used. RESULTS AND DISCUSSIONS: All non-survivors (n = 100) and 100 survivors (out of 1,018) were analyzed. Male gender (67.4%) was the independent risk factor for COVID-19 infection. Advanced age group, diabetes, cardiovascular, neurological, and hypertensive co-morbidities were statistically associated with mortality. Cardiac arrest and acute kidney injury (AKI) were the most common complications. Mortality is significantly associated with lymphopenia and raised lactate dehydrogenase (LDH), as shown by higher odds. In addition, raised neutrophils, monocytes, aspartate aminotransferase (AST), serum creatinine, interleukin 6 (IL-6), and C-reactive protein (CRP) are also significantly associated with mortality. The most common causes of death were respiratory failure (84%) and acute respiratory distress syndrome (77%). Of the non-survivors, 92% received corticosteroids, 63% were on high-flow nasal cannula oxygen therapy, 29% were mechanically ventilated, and 29% received tocilizumab. CONCLUSION: Serial monitoring of neutrophils, lymphocytes, D-dimer, procalcitonin, AST, LDH, CRP, IL-6, serum creatinine, and albumin might provide a reliable and convenient method for classifying and predicting the severity and outcomes of patients with COVID-19.

Dynamics of Inflammatory Markers in Predicting Mortality in COVID-19.

Introduction A cytokine storm is an important cause of morbidity and mortality in patients with coronavirus disease 2019 (COVID-19). The objective of the study was to determine the prognostic significance of pro-inflammatory cytokines with the overall final outcome of patients with COVID-19. Methods We conducted a retrospective study of 142 patients admitted with COVID-19 in the Department of Medicine at All India Institute of Medical Sciences, New Delhi, from May 2021 to June 2021. We obtained their demographic, clinical, and biochemical characteristics at baseline and 48-72 hours prior to the terminal event (survival/death). The data were analyzed to determine the prognostic significance of these markers on the final outcome. Results Higher levels of inflammatory markers were associated with a worse final outcome (ferritin p-value <0.001, c-reactive protein (CRP) p-value <0.001, interleukin 6 (IL-6) p-value 0.007, procalcitonin p-value 0.005, and lactic acid p-value 0.004). Optimal probability cut-offs for these markers for predicting mortality were: ferritin: 963 ng/mL (sensitivity - 67.35%, specificity - 67.50%), CRP: 66.3 mg/L (sensitivity - 78.43%, specificity - 74.12%), IL-6: 46.2 pg/mL (sensitivity - 59.26%, specificity - 59.57%), procalcitonin: 0.3ng/mL (sensitivity - 65.38 %, specificity - 66.67%), lactic acid: 1.5 mg/dL (sensitivity - 59.26%, specificity - 58.57%). Multivariate logistic regression analysis was done, which showed that pre-terminal event CRP was associated with a statistically significant higher risk of mortality (Unadjusted OR 18.89, Adjusted OR 1.008, p=0.002, 95% CI 6.815 - 47.541). Conclusion Inflammatory markers have a prognostic significance in patients with COVID-19, with higher levels being associated with worse outcomes.

Arterial Blood Gas as a Predictor of Mortality in COVID Pneumonia Patients Initiated on Noninvasive Mechanical Ventilation: A Retrospective Analysis.

Background: The alveolar-arterial oxygen (A-a) gradient measures the difference between the oxygen concentration in alveoli and the arterial system, which has considerable clinical utility. Materials and methods: It was a retrospective, observational cohort study involving the analysis of patients diagnosed with acute COVID pneumonia and required noninvasive mechanical ventilation (NIV) over a period of 3 months. The primary objective was to investigate the utility of the A-a gradient (pre-NIV) as a predictor of 28-day mortality in COVID pneumonia. The secondary objective included the utility of other arterial blood gas (ABG) parameters (pre-NIV) as a predictor of 28-day mortality. The outcome was also compared between survivors and nonsurvivors. The outcome variables were analyzed by receiver-operating characteristic (ROC) curve, Youden index, and regression analysis. Results: The optimal criterion for A-a gradient to predict 28-day mortality was calculated as ≤430.43 at a Youden index of 0.5029, with the highest area under the curve (AUC) of 0.755 (p <0.0001). On regression analysis, the odds ratio for the A-a gradient was 0.99. A significant difference was observed in ABG predictors, including PaO2, PaCO2, A-a gradient, AO2, and arterial-alveolar (a-A) (%) among nonsurvivors vs survivors (p-value <0.001). The vasopressor requirement, need for renal replacement therapy, total parenteral requirement, and blood transfusion were higher among nonsurvivors; however, a significant difference was achieved with the vasopressor need (p <0.001). Conclusion: This study demonstrated that the A-a gradient is a significant predictor of mortality in patients initiated on NIV for worsening respiratory distress in COVID pneumonia. All other ABG parameters also showed a significant AUC for predicting 28-day mortality, although with variable sensitivity and specificity. Key messages: COVID-19 pneumonia shows an initial presentation with type 1 respiratory failure with increased A-a gradient, while a subsequent impending type 2 respiratory failure requires invasive ventilation. A significant difference was observed in ABG predictors, including PaO2, PaCO2, A-a gradient, AO2, and a-A (%) among nonsurvivors vs survivors. (p-value <0.001). The vasopressor requirement, need for renal replacement therapy, total parenteral requirement, and blood transfusion need were higher among nonsurvivors than survivors; however, a significant difference was achieved with the vasopressor need (p <0.001). How to cite this article: Gupta B, Jain G, Chandrakar S, Gupta N, Agarwal A. Arterial Blood Gas as a Predictor of Mortality in COVID Pneumonia Patients Initiated on Noninvasive Mechanical Ventilation: A Retrospective Analysis. Indian J Crit Care Med 2021;25(8):866-871.

Immunological predictors of disease severity in patients with COVID-19.

BACKGROUND: Identifying the immune cells involved in coronavirus disease 2019 (COVID-19) disease progression and the predictors of poor outcomes is important to manage patients adequately. METHODS: This prospective observational cohort study enrolled 48 patients with COVID-19 hospitalized in a tertiary hospital in Oman and 53 non-hospitalized patients with confirmed mild COVID-19. RESULTS: Hospitalized patients were older (58 years vs 36 years, P < 0.001) and had more comorbid conditions such as diabetes (65% vs 21% P < 0.001). Hospitalized patients had significantly higher inflammatory markers (P < 0.001): C-reactive protein (114 vs 4 mg/l), interleukin 6 (IL-6) (33 vs 3.71 pg/ml), lactate dehydrogenase (417 vs 214 U/l), ferritin (760 vs 196 ng/ml), fibrinogen (6 vs 3 g/l), D-dimer (1.0 vs 0.3 µg/ml), disseminated intravascular coagulopathy score (2 vs 0), and neutrophil/lymphocyte ratio (4 vs 1.1) (P < 0.001). On multivariate regression analysis, statistically significant independent early predictors of intensive care unit admission or death were higher levels of IL-6 (odds ratio 1.03, P = 0.03), frequency of large inflammatory monocytes (CD14+CD16+) (odds ratio 1.117, P = 0.010), and frequency of circulating naïve CD4+ T cells (CD27+CD28+CD45RA+CCR7+) (odds ratio 0.476, P = 0.03). CONCLUSION: IL-6, the frequency of large inflammatory monocytes, and the frequency of circulating naïve CD4 T cells can be used as independent immunological predictors of poor outcomes in COVID-19 patients to prioritize critical care and resources.

Common hematological values predict unfavorable outcomes in hospitalized COVID-19 patients.

COVID-19 can range from asymptomatic to life-threatening. Early identification of patients who will develop severe disease is crucial. A number of scores and indexes have been developed to predict severity. However, most rely on measurements not readily available. We evaluated hematological and biochemical markers taken on admission and determined how predictive they were of development of critical illness or death. We observed that higher values of readily available tests, including neutrophil:lymphocyte ratio; derived neutrophil index; and troponin I were associated with a higher risk of death or critical care admission (P < 0.001). We show that common hematological tests can be helpful in determining early in the course of illness which patients are likely to develop severe forms, as well as allocating resources to those patients early, while avoiding overuse of limited resources in patients with reduced risk of progression to severe disease.

A Retrospective Observational Study to Determine the Early Predictors of In-hospital Mortality at Admission with COVID-19.

INTRODUCTION: Coronavirus disease-2019 (COVID-19) systemic illness caused by a novel coronavirus severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) has been spreading across the world. The objective of this study is to identify the clinical and laboratory variables as predictors of in-hospital death at the time of admission in a tertiary care hospital in India. MATERIALS AND METHODS: Demographic profile, clinical, and laboratory variables of 425 patients admitted from April to June 2020 with symptoms and laboratory-confirmed diagnosis through real-time polymerase chain reaction (RT-PCR) were studied. Descriptive statistics, an association of these variables, logistic regression, and CART models were developed to identify early predictors of in-hospital death. RESULTS: Twenty-two patients (5.17%) had expired in course of their hospital stay. The median age [interquartile range (IQR)] of the patients admitted was 49 years (21-77 years). Gender distribution was male - 73.38% (mortality rate 5.83%) and female-26.62% (mortality rate 3.34%). The study shows higher association for age (>47 years) [odds ratio (OR) 4.52], male gender (OR 1.78), shortness of breath (OR 2.02), oxygen saturation <93% (OR 9.32), respiratory rate >24 (OR 5.31), comorbidities like diabetes (OR 2.70), hypertension (OR 2.12), and coronary artery disease (OR 3.18) toward overall mortality. The significant associations in laboratory variables include lymphopenia (<12%) (OR 8.74), C-reactive protein (CRP) (OR 1.99), ferritin (OR 3.18), and lactate dehydrogenase (LDH) (OR 3.37). Using this statistically significant 16 clinical and laboratory variables, the logistic regression model had an area under receiver operating characteristic (ROC) curve of 0.86 (train) and 0.75 (test). CONCLUSION: Age above 47 years, associated with comorbidities like hypertension and diabetes, with oxygen saturation below 93%, tachycardia, and deranged laboratory variables like lymphopenia and raised CRP, LDH, and ferritin are important predictors of in-hospital mortality. HOW TO CITE THIS ARTICLE: Jain AC, Kansal S, Sardana R, Bali RK, Kar S, Chawla R. A Retrospective Observational Study to Determine the Early Predictors of In-hospital Mortality at Admission with COVID-19. Indian J Crit Care Med 2020;24(12):1174-1179.