Predictors of poor outcomes in patients with pandemic viral infections (influenza AH1N1pdm09, COVID-19) on a ventilator.

Considering the commonality of the pathogenetic links of the critical forms of COVID-19 and influenza AH1N1pdm09 (cytokine over-release syndrome), the question arises: will the predictors of an unfavorable outcome in patients on mechanical ventilation and, accordingly, the universal tactics of respiratory support in these diseases be identical? Objective. In a comparative aspect, to characterize patients with influenza AH1N1pdm09 and COVID-19 who were on mechanical ventilation, to identify additional clinical and laboratory risk factors for death, to determine the degree of influence of respiratory support (RP) tactics on an unfavorable outcome in the studied category of patients. Patients and methods. Patients treated on the basis of resuscitation and intensive care departments of the State Budgetary Healthcare Institution "SKIB" in Krasnodar and the State Budgetary Healthcare Institution "IB No 2" in Sochi were studied: group 1 - 31 people with influenza AH1N1pdm09 (21 people died - subgroup 1A;10 people survived - subgroup 1B) and group 2 - 50 people with COVID-19 (29 patients died - subgroup 2A;21 people survived - subgroup 2B). All patients developed hypoxemic ARF. All patients received step-by-step tactics of respiratory support, starting with oxygen therapy and ending with the use of "traditional" mechanical ventilation. Continuous variables were compared in subgroups of deceased and surviving patients for both nosologies at the stages: hospital admission;registration of hypoxemia and the use of various methods of respiratory therapy;development of multiple organ dysfunctions. With regard to the criteria for which a statistically significant difference was found (p < 0.05), we calculated a simple correlation, the relative risk of an event (RR [CI 25-75%]), the cut-off point, which corresponded to the best combination of sensitivity and specificity. Results. Risk factors for death of patients with influenza AH1N1pdm09 on mechanical ventilation: admission to the hospital later than the 8th day of illness;the fact of transfer from another hospital;leukocytosis >=10.0 x 109/l, granulocytosis >=5.5 x 109/l and LDH level >=700.0 U/l at admission;transfer of patients to mechanical ventilation on the 9th day of illness and later;SOFA score >=8;the need for pressor amines and replacement of kidney function. Predictors of poor outcome in ventilated COVID-19 patients: platelet count <=210 x 109/L on admission;the duration of oxygen therapy for more than 4.5 days;the use of HPNO and NIV as the 2nd step of RP for more than 2 days;transfer of patients to mechanical ventilation on the 14th day of illness and later;oxygenation index <=80;the need for pressors;SOFA score >=8. Conclusion. When comparing the identified predictors of death for patients with influenza and COVID-19 who needed mechanical ventilation, there are both some commonality and differences due to the peculiarities of the course of the disease. A step-by-step approach to the application of respiratory support methods is effective both in the case of patients with influenza AH1N1pdm09 and patients with COVID-19, provided that the respiratory support method used is consistent with the current state of the patient and his respiratory system, timely identification of markers of ineffectiveness of the respiratory support stage being carried out and determining the optimal moment escalation of respiratory therapy.Copyright © 2022, Dynasty Publishing House. All rights reserved.

Long COVID syndrome after SARS-CoV-2 survival in patients with pulmonary arterial hypertension and chronic thromboembolic pulmonary hypertension.

Pulmonary arterial hypertension (PAH) and chronic thromboembolic pulmonary hypertension (CTEPH) patients have a more severe COVID-19 course than the general population. Many patients report different persistent symptoms after SARS-CoV-2 infection. The aim of our study is to analyze the prevalence of long COVID-19 symptoms and assess if COVID-19 affects pulmonary hypertension (PH) prognosis. PAH/CTEPH patients who survived COVID-19 for at least 3 months before visiting the PH centers were included in the study. The patients were assessed for symptoms in acute phase of SARS-CoV-2 infection and persisting in follow-up visit, WHO functional class, 6-min walk distance, NT-proBNP concentration. The COMPERA 2.0 model was used to calculate 1-year risk of death due to PH at baseline and at follow-up. Sixty-nine patients-54 (77.3%) with PAH and 15 (21.7%) with CTEPH, 68% women, with a median age of 47.5 years (IQR 37-68)-were enrolled in the study. About 17.1% of patients were hospitalized due to COVID-19 but none in an ICU. At follow-up (median: 155 days after onset of SARS-CoV-2 symptoms), 62% of patients reported at least 1 COVID-19-related symptom and 20% at least 5 symptoms. The most frequently reported symptoms were: fatigue (30%), joint pain (23%), muscle pain (17%), nasal congestion (17%), anosmia (13%), insomnia (13%), and dyspnea (12%). Seventy-two percent of PH patients had a low or intermediate-low risk of 1-year death due to PH at baseline, and 68% after COVID-19 at follow-up. Over 60% of PAH/CTEPH patients who survived COVID-19 suffered from long COVID-19 syndrome, but the calculated 1-year risk of death due to PH did not change significantly after surviving mild or moderate COVID-19.

Predictors of poor outcomes in patients with pandemic viral infections (influenza AH1N1pdm09, COVID-19) on a ventilator.

Considering the commonality of the pathogenetic links of the critical forms of COVID-19 and influenza AH1N1pdm09 (cytokine over-release syndrome), the question arises: will the predictors of an unfavorable outcome in patients on mechanical ventilation and, accordingly, the universal tactics of respiratory support in these diseases be identical? Objective. In a comparative aspect, to characterize patients with influenza AH1N1pdm09 and COVID-19 who were on mechanical ventilation, to identify additional clinical and laboratory risk factors for death, to determine the degree of influence of respiratory support (RP) tactics on an unfavorable outcome in the studied category of patients. Patients and methods. Patients treated on the basis of resuscitation and intensive care departments of the State Budgetary Healthcare Institution "SKIB" in Krasnodar and the State Budgetary Healthcare Institution "IB No 2" in Sochi were studied: group 1 - 31 people with influenza AH1N1pdm09 (21 people died - subgroup 1A;10 people survived - subgroup 1B) and group 2 - 50 people with COVID-19 (29 patients died - subgroup 2A;21 people survived - subgroup 2B). All patients developed hypoxemic ARF. All patients received step-by-step tactics of respiratory support, starting with oxygen therapy and ending with the use of "traditional" mechanical ventilation. Continuous variables were compared in subgroups of deceased and surviving patients for both nosologies at the stages: hospital admission;registration of hypoxemia and the use of various methods of respiratory therapy;development of multiple organ dysfunctions. With regard to the criteria for which a statistically significant difference was found (p < 0.05), we calculated a simple correlation, the relative risk of an event (RR [CI 25-75%]), the cut-off point, which corresponded to the best combination of sensitivity and specificity. Results. Risk factors for death of patients with influenza AH1N1pdm09 on mechanical ventilation: admission to the hospital later than the 8th day of illness;the fact of transfer from another hospital;leukocytosis >=10.0 x 109/l, granulocytosis >=5.5 x 109/l and LDH level >=700.0 U/l at admission;transfer of patients to mechanical ventilation on the 9th day of illness and later;SOFA score >=8;the need for pressor amines and replacement of kidney function. Predictors of poor outcome in ventilated COVID-19 patients: platelet count <=210 x 109/L on admission;the duration of oxygen therapy for more than 4.5 days;the use of HPNO and NIV as the 2nd step of RP for more than 2 days;transfer of patients to mechanical ventilation on the 14th day of illness and later;oxygenation index <=80;the need for pressors;SOFA score >=8. Conclusion. When comparing the identified predictors of death for patients with influenza and COVID-19 who needed mechanical ventilation, there are both some commonality and differences due to the peculiarities of the course of the disease. A step-by-step approach to the application of respiratory support methods is effective both in the case of patients with influenza AH1N1pdm09 and patients with COVID-19, provided that the respiratory support method used is consistent with the current state of the patient and his respiratory system, timely identification of markers of ineffectiveness of the respiratory support stage being carried out and determining the optimal moment escalation of respiratory therapy.Copyright © 2022, Dynasty Publishing House. All rights reserved.

Determining the time of maximum risk of death and associated factors in patients with COVID-19 in the hospital and after discharge

Due to the rather specific course of COVID-19, the question of what day after the start of hospitalization should be expected to be the maximum risk of death in patients both during hospitalization and after discharge is relevant. Aim. The aim of the study was to determine the time of maximum risk of death during hospitalization of patients with COVID-19 as well as after their discharge from the hospital. Methodology and Research Methods. A total of 2, 410 patients hospitalized with a diagnosis of COVID-19 were retrospectively studied. Inhospital 28-day mortality rate was 131 patients, and 28-day mortality rate after discharge from the hospital was 9. The accelerated failure time model (AFT) was used to determine the time of maximum risk of death in patients with COVID-19 after hospitalization as well as after discharge from the hospital during the period up to 28 days. Results. Without taking into account the influence of pathological values of other risk factors, lethal outcomes in patients occurred on days 9-11 after admission to hospital. Age over 60 years and the elevated levels of D-dimer, glucose, urea, creatinine, AST, and C-reactive protein were the risk factors (p < 0.01) that shortened the time to death, except for total protein, which lengthened this period. The maximum risk of death in patients after discharge from the hospital occurred on days 13-25, and an increase in creatinine and a decrease in INR were associated with a shorter time to death. Conclusion. The periods of maximum risk of death as well as the factors affecting these periods in patients with COVID-19 were determined for both hospital stay (days 9-11) and time after discharge from hospital (days 13-25). © 2022 Tomsk State University. All rights reserved.

Characteristics and outcomes of an international cohort of 600 000 hospitalized patients with COVID-19.

BACKGROUND: We describe demographic features, treatments and clinical outcomes in the International Severe Acute Respiratory and emerging Infection Consortium (ISARIC) COVID-19 cohort, one of the world's largest international, standardized data sets concerning hospitalized patients. METHODS: The data set analysed includes COVID-19 patients hospitalized between January 2020 and January 2022 in 52 countries. We investigated how symptoms on admission, co-morbidities, risk factors and treatments varied by age, sex and other characteristics. We used Cox regression models to investigate associations between demographics, symptoms, co-morbidities and other factors with risk of death, admission to an intensive care unit (ICU) and invasive mechanical ventilation (IMV). RESULTS: Data were available for 689 572 patients with laboratory-confirmed (91.1%) or clinically diagnosed (8.9%) SARS-CoV-2 infection from 52 countries. Age [adjusted hazard ratio per 10 years 1.49 (95% CI 1.48, 1.49)] and male sex [1.23 (1.21, 1.24)] were associated with a higher risk of death. Rates of admission to an ICU and use of IMV increased with age up to age 60 years then dropped. Symptoms, co-morbidities and treatments varied by age and had varied associations with clinical outcomes. The case-fatality ratio varied by country partly due to differences in the clinical characteristics of recruited patients and was on average 21.5%. CONCLUSIONS: Age was the strongest determinant of risk of death, with a ∼30-fold difference between the oldest and youngest groups; each of the co-morbidities included was associated with up to an almost 2-fold increase in risk. Smoking and obesity were also associated with a higher risk of death. The size of our international database and the standardized data collection method make this study a comprehensive international description of COVID-19 clinical features. Our findings may inform strategies that involve prioritization of patients hospitalized with COVID-19 who have a higher risk of death.

Risk of death in individuals hospitalized for COVID-19 with and without psychiatric disorders: an observational multicenter study in France.

BACKGROUND: Prior research suggests that psychiatric disorders could be linked to increased mortality among patients with COVID-19. However, whether all or specific psychiatric disorders are intrinsic risk factors of death in COVID-19, or whether these associations reflect the greater prevalence of medical risk factors in people with psychiatric disorders, has yet to be evaluated. METHODS: We performed an observational multicenter retrospective cohort study to examine the association between psychiatric disorders and mortality among patients hospitalized for laboratory-confirmed COVID-19 at 36 Greater Paris University hospitals. RESULTS: Of 15,168 adult patients, 857 (5.7%) had an ICD-10 diagnosis of psychiatric disorder. Over a mean follow-up of 14.6 days (SD=17.9), death occurred in 326/857 (38.0%) patients with a diagnosis of psychiatric disorder versus 1,276/14,311 (8.9%) in patients without such a diagnosis (OR=6.27; 95%CI=5.40-7.28; p<0.01). When adjusting for age, sex, hospital, current smoking status, and medications according to compassionate use or as part of a clinical trial, this association remained significant (AOR=3.27; 95%CI=2.78-3.85; p<0.01). However, additional adjustments for obesity and number of medical conditions resulted in a non-significant association (AOR=1.02; 95%CI=0.84-1.23; p=0.86). Exploratory analyses following the same adjustments suggest that a diagnosis of mood disorders was significantly associated with reduced mortality, which might be explained by the use of antidepressants. CONCLUSIONS: These findings suggest that the increased risk of COVID-19-related mortality in individuals with psychiatric disorders hospitalized for COVID-19 might be explained by the greater number of medical conditions and the higher prevalence of obesity in this population, but not by the underlying psychiatric disease.

Alcohol and health. Is regular drinking of small doses of alcohol really good for your health?

Alcohol has been drunk for centuries and in the past also used as a medicine. Alcohol consumption in Poland and in the entire world has gradually increased, which is also nowadays accelerated by the ongoing COVID-19 pandemic. In 2020, the amount of alcohol consumed in Poland was 11.7 l per capita, which was a the highest level since 1961. It is estimated that global alcohol consumption will increase by 17% by the year 2030. There is also increasing alcohol consumption by children and adolescents, as well as pregnant women. Alcohol consumption as a health damaging factor is not always recognized in the general population. Additionally, numerous scientific societies in their guidelines/recommendations indicate that moderate doses of alcohol are beneficial or at least neutral for health. The question remains whether so-called "moderate doses of alcohol" really are not harmful to health. We analyze this issue in this article.

A modified nutrition risk screening 2002 predicts the risk of death among hospitalized patients with COVID-19.

OBJECTIVES: The aim of this study was to evaluate the ability of a modified Nutrition Risk Screening 2002 (modified NRS) compared with other nutrition screening tools such as NRS 2002, Mini Nutrition Assessment Short Form (MNA-SF), and Malnutrition Universal Screening Tool (MUST) on predicting the risk of death in patients with coronavirus disease 2019 (COVID-19). METHODS: We retrospectively collected data of patients who were admitted to the West campus of Union Hospital, Tongji Medical College, Huazhong University of Science and Technology between January 25th, 2020 to April 24th, 2020. The nutritional status of the patients was assessed by modified NRS, NRS 2002, MNA-SF, and MUST. According to the score of modified NRS, patients were divided into malnutrition risk group (score ≥3) and normal nutrition group (score <3). Clinical characteristics were compared between the two groups. Kaplan meier survival curve was used to analyze the difference of compositing survival rate between the two groups. The predictive efficacy of different nutritional scales on the outcome of death was detected by Receiver operating characteristic (ROC) analysis. RESULTS: The modified NRS, NRS 2002, MNA-SF, and MUST identified malnutrition risk in 71.4%, 57.9%, 73.9%, and 43.4% of the patients, respectively. The patients were divided into malnutrition risk group and normal nutrition group by modified NRS score. Patients in the malnutrition risk group were older (65 y vs. 56 y) and with more severe and critical cases (42.30% vs. 5.20%) and diabetes cases (21.50% vs. 9.80%), worse prognosis (death of 13.80% vs. 0.50%), longer hospital stay (29 days vs. 23 days), lower albumin (31.85 g/L vs. 38.55 g/L) and prealbumin (201.95 mg/L vs. 280.25 mg/L) compared with the normal nutrition group (P were <0.001, respectively). There were more patients with chronic respiratory disease in malnutrition risk group (9.70 vs. 2.10%, P = 0.001). BMI was lower in malnutrition risk group (23.45 kg/m2vs. 24.15 kg/m2, P = 0.017). Kaplan meier survival curve demonstrated that the survival of malnutrition risk group was significantly lower than normal nutrition group (P < 0.001). The area under the ROC curve (AUC) of the modified NRS scale (0.895) outperformed NRS 2002 (0.758), MNA-SF (0.688), and MUST (0.485). The former three scales could predict the risk of death (P were < 0.001), while MUST could not (P = 0.690). CONCLUSIONS: Patients with COVID-19 at risk of malnutrition have a worse prognosis than those with normal nutrition. The modified NRS scale could effectively predict the risk of death among patients with COVID-19.

Risk of Seven-Day Worsening and Death: A New Clinically Derived COVID-19 Score.

This monocentric, retrospective, two-stage observational study aimed to recognize the risk factors for a poor outcome in patients hospitalized with SARS-CoV-2 infection, and to develop and validate a risk score that identifies subjects at risk of worsening, death, or both. The data of patients with SARS-CoV-2 infection during the first wave of the pandemic were collected and analyzed as a derivation cohort. Variables with predictive properties were used to construct a prognostic score, which was tried out on a validation cohort enrolled during the second wave. The derivation cohort included 494 patients; the median age was 62 and the overall fatality rate was 22.3%. In a multivariable analysis, age, oxygen saturation, neutrophil-to-lymphocyte ratio, C-reactive protein and lactate dehydrogenase were independent predictors of death and composed the score. A cutoff value of 3 demonstrated a sensitivity (Se), specificity (Sp), positive predictive value (PPV) and negative predictive value (NPV) of 93.5%, 68.5%, 47.4% and 97.2% for death, and 84.9%, 84.5%, 79.6% and 87.9% for worsening, respectively. The validation cohort included 415 subjects. The score application showed a Se, Sp, PPV and NPV of 93.4%, 61.6%, 29.5% and 98.1% for death, and 81%, 76.3%, 72.1% and 84.1% for worsening, respectively. We propose a new clinical, easy and reliable score to predict the outcome in hospitalized SARS-CoV-2 patients.

Conventional risk prediction models fail to accurately predict mortality risk among patients with coronavirus disease 2019 in intensive care units: a difficult time to assess clinical severity and quality of care.

Since the start of the coronavirus disease 2019 (COVID-19) pandemic, it has remained unknown whether conventional risk prediction tools used in intensive care units are applicable to patients with COVID-19. Therefore, we assessed the performance of established risk prediction models using the Japanese Intensive Care database. Discrimination and calibration of the models were poor. Revised risk prediction models are needed to assess the clinical severity of COVID-19 patients and monitor healthcare quality in ICUs overwhelmed by patients with COVID-19.

Prediction of COVID-19 Hospital Length of Stay and Risk of Death Using Artificial Intelligence-Based Modeling.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a highly infectious virus with overwhelming demand on healthcare systems, which require advanced predictive analytics to strategize COVID-19 management in a more effective and efficient manner. We analyzed clinical data of 2017 COVID-19 cases reported in the Dubai health authority and developed predictive models to predict the patient's length of hospital stay and risk of death. A decision tree (DT) model to predict COVID-19 length of stay was developed based on patient clinical information. The model showed very good performance with a coefficient of determination R 2 of 49.8% and a median absolute deviation of 2.85 days. Furthermore, another DT-based model was constructed to predict COVID-19 risk of death. The model showed excellent performance with sensitivity and specificity of 96.5 and 87.8%, respectively, and overall prediction accuracy of 96%. Further validation using unsupervised learning methods showed similar separation patterns, and a receiver operator characteristic approach suggested stable and robust DT model performance. The results show that a high risk of death of 78.2% is indicated for intubated COVID-19 patients who have not used anticoagulant medications. Fortunately, intubated patients who are using anticoagulant and dexamethasone medications with an international normalized ratio of <1.69 have zero risk of death from COVID-19. In conclusion, we constructed artificial intelligence-based models to accurately predict the length of hospital stay and risk of death in COVID-19 cases. These smart models will arm physicians on the front line to enhance management strategies to save lives.

Nutritional Risk Screening and Body Composition in COVID-19 Patients Hospitalized in an Internal Medicine Ward.

BACKGROUND: Malnutrition in patients hospitalized in internal medicine wards is highly prevalent and represents a prognostic factor of worse outcomes. Previous evidence suggested the prognostic role of the nutritional status in patients affected by the coronavirus disease 2019 (COVID-19). We aim to investigate the nutritional risk in patients with COVID-19 hospitalized in an internal medicine ward and their clinical outcomes using the Nutritional Risk Screening 2002 (NRS-2002) and parameters derived from bioelectrical impedance analysis (BIA). METHODS: Retrospective analysis of patients with COVID-19 aimed at exploring: 1) the prevalence of nutritional risk with NRS-2002 and BIA; 2) the relationship between NRS-2002, BIA parameters and selected outcomes: length of hospital stay (LOS); death and need of intensive care unit (ICU); prolonged LOS; and loss of appetite. RESULTS: Data of 90 patients were analyzed. Patients at nutritional risk were 92% with NRS-2002, with BIA-derived parameters: 88% by phase angle; 86% by body cell mass; 84% by fat-free mass and 84% by fat mass (p-value ≤0.001). In ROC analysis, NRS had the maximum sensitivity in predicting the risk of death and need of ICU and a prolonged hospitalization showing moderate-low specificity; phase angle showed a good predictive power in terms of AUC. NRS-2002 was significantly associated with LOS (ß 12.62, SE 5.79). In a multivariate analysis, blood glucose level and the early warning score are independent predictors of death and need of ICU (OR 2.79, p ≤0.001; 1.59, p-0.029, respectively). CONCLUSION: Present findings confirm the clinical utility of NRS-2002 to assess nutritional risk in patients with COVID-19 at hospital admission and in predicting LOS, and that bioimpedance does not seem to add further predictive value. An early detection of nutritional risk has to be systematically included in the management of COVID-19 patients hospitalized in internal medicine wards.

Simultaneous circulation of COVID-19 and flu in Italy: Potential combined effects on the risk of death?

Based on data updated to 20 May 2020, the total recorded number of patients who died due to COVID-19-related reasons in Italy was 31,851. Demographic and clinical characteristics of patients who have died (including the number of comorbidities) are extremely relevant, especially to define those with a higher risk of mortality. Health authorities recommend influenza (flu) vaccinations in a number of categories at risk of serious medical complications, including: people aged ≥65 years, or patients with diabetes, cardiovascular diseases, chronic obstructive pulmonary disease (COPD), renal failure, cancer, immunodeficiencies, chronic hepatopathies, and chronic inflammatory bowel diseases. The seasonal flu peak certainly preceded that of the pandemic; however, it would seem clear that the two viruses have been simultaneously circulating in Italy for a while. Hence, after the peak of seasonal flu, influenza-like illness-related (ILI) deaths started to grow again. While some of the excess mortality reported in the ILI group may be attributable to COVID-19, a question arises: do we have to consider this observation as a result of a random sequence of events or a potential relationship between the two viruses play a role? A cooperation mechanism intended at establishing an absolute advantage over the host could also be assumed; this system often takes place to boost the reproductive probabilities. A characterization of those who died due to virus-related reasons can be performed by cross-linking data (stored in different warehouses) from the same geographical area and developing electronic health records. It would be of great relevance to identify people at very high risk of mortality as a result of an overlapping or combination of risk factors that were separately reported in patients who died from COVID-19 or influenza. A description of the subgroup of people at higher risk of mortality will be crucial for prioritizing and implementing future public health prevention and treatment programs.

Risk of death by age and gender from CoVID-19 in Peru, March-May, 2020.

Peru implemented strict social distancing measures during the early phase of the epidemic and is now experiencing one of the largest CoVID-19 epidemics in Latin America. Estimates of disease severity are an essential indicator to inform policy decisions about the intensity and duration of interventions needed to mitigate the outbreak. Here we derive delay-adjusted case fatality risks (aCFR) of CoVID-19 in a middle-income country in South America.We utilize government-reported time series of CoVID-19 cases and deaths in Peru stratified by age group and gender.As of May 25, 2020, we estimate the aCFR for men and women at 10.8% (95%CrI: 10.5-11.1%) and 6.5% (95%CrI: 6.2-6.8%), respectively, whereas the overall aCFR was estimated at 9.1% (95%CrI: 8.9-9.3%). Our results show that senior individuals have been the most severely affected by CoVID-19, particularly men, with an aCFR of nearly 60% for those aged 80- years. We also found that men have a significantly higher cumulative morbidity ratio across most age groups (proportion test, p-value< 0.001), with the exception of those aged 0-9 years.The ongoing CoVID-19 pandemic is generating a substantial mortality burden in Peru. Senior individuals, especially those older than 70 years, are being disproportionately affected by the CoVID-19 pandemic.