Randomized Clinical Trial of Antioxidant Therapy Patients with Septic Shock and Organ Dysfunction in the ICU: SOFA Score Reduction by Improvement of the Enzymatic and Non-Enzymatic Antioxidant System.

BACKGROUND AND AIM: Here, we assess the effect of adjuvant antioxidant therapies in septic shock patients with organ dysfunction and their effect on the enzymatic and non-enzymatic antioxidant systems. METHODS: Randomized clinical trial run between 2018 and 2022. One hundred and thirty-one patients with septic shock were included in five groups with 25, 27, 24, 26 and 29 patients each. Group 1 received vitamin C (Vit C), Group 2 vitamin E (Vit E), Group 3 n-acetylcysteine (NAC), Group 4 melatonin (MT) and group 5 no treatment. All antioxidants were administered orally or through a nasogastric tube for 5 days as an adjuvant to standard therapy. RESULTS: All patients had multiple organ failure (MOF) and low Vit C levels. Vit C therapy decreased CRP, PCT and NO3-/NO2- but increased Vit C levels. The SOFA score decreased with MT in 75%, Vit C 63% and NAC 50% vs. controls 33% (p = 0.0001, p = 0.03 and p = 0.001 respectively). MT diminished lipid peroxidation (LPO) (p = 0.01) and improved total antioxidant capacity (TAC) (p = 0.04). Vit E increased thiol levels (p = 0.02) and tended to decrease LPO (p = 0.06). Selenium levels were decreased in the control group (p = 0.04). CONCLUSIONS: Antioxidants used as an adjuvant therapy in the standard treatment of septic shock decrease MOF and oxidative stress markers. They increase the TAC and thiols, and maintain selenium levels.

Performance of point-of-care severity scores to predict prognosis in patients admitted through the emergency department with COVID-19.

BACKGROUND: Identifying COVID-19 patients at the highest risk of poor outcomes is critical in emergency department (ED) presentation. Sepsis risk stratification scores can be calculated quickly for COVID-19 patients but have not been evaluated in a large cohort. OBJECTIVE: To determine whether well-known risk scores can predict poor outcomes among hospitalized COVID-19 patients. DESIGNS, SETTINGS, AND PARTICIPANTS: A retrospective cohort study of adults presenting with COVID-19 to 156 Hospital Corporation of America (HCA) Healthcare EDs, March 2, 2020, to February 11, 2021. INTERVENTION: Quick Sequential Organ Failure Assessment (qSOFA), Shock Index, National Early Warning System-2 (NEWS2), and quick COVID-19 Severity Index (qCSI) at presentation. MAIN OUTCOME AND MEASURES: The primary outcome was in-hospital mortality. Secondary outcomes included intensive care unit (ICU) admission, mechanical ventilation, and vasopressors receipt. Patients scored positive with qSOFA ≥ 2, Shock Index > 0.7, NEWS2 ≥ 5, and qCSI ≥ 4. Test characteristics and area under the receiver operating characteristics curves (AUROCs) were calculated. RESULTS: We identified 90,376 patients with community-acquired COVID-19 (mean age 64.3 years, 46.8% female). 17.2% of patients died in-hospital, 28.6% went to the ICU, 13.7% received mechanical ventilation, and 13.6% received vasopressors. There were 3.8% qSOFA-positive, 45.1% Shock Index-positive, 49.8% NEWS2-positive, and 37.6% qCSI-positive at ED-triage. NEWS2 exhibited the highest AUROC for in-hospital mortality (0.593, confidence interval [CI]: 0.588-0.597), ICU admission (0.602, CI: 0.599-0.606), mechanical ventilation (0.614, CI: 0.610-0.619), and vasopressor receipt (0.600, CI: 0.595-0.604). CONCLUSIONS: Sepsis severity scores at presentation have low discriminative power to predict outcomes in COVID-19 patients and are not reliable for clinical use. Severity scores should be developed using features that accurately predict poor outcomes among COVID-19 patients to develop more effective risk-based triage.

Association of the quantitative oral health status of patients with COVID-19 with their organ status and immunity: A single-centre observational study.

OBJECTIVES: To evaluate the prevalence of oral complications in patients with severe COVID-19; investigate the association between their oral health, organ status, and immunity; and determine whether the resazurin disc test is an effective substitute for the Oral Assessment Guide. RESEARCH METHODOLOGY/DESIGN: A single-centre observational study. SETTING: Intensive care unit with restricted access specialising in extracorporeal membrane oxygenation for COVID-19 treatment. MAIN OUTCOME MEASURES: We investigated the oral health of 13 patients with COVID-19 receiving extracorporeal membrane oxygenation therapy between April and December 2021 using the Oral Assessment Guide and colour reactive resazurin disc test. The Sequential Organ Failure Assessment and Prognostic Nutritional Index were used to assess organ status and immunity, respectively. The correlation of oral health status with organ status and immunity was investigated. RESULTS: High bacterial levels, revealed by the resazurin disc test, were associated with elevated Oral Assessment Guide scores, indicating oral health deterioration, particularly in terms of teeth and dentures. Increased Sequential Organ Failure Assessment scores and decreased Prognostic Nutritional Index were correlated with poor oral health, revealed by the Oral Assessment Guide and resazurin disc test. CONCLUSION: Poor oral health is an important risk factor for severe COVID-19 complications in patients admitted to an intensive care unit. The Oral Assessment Guide and resazurin disc test can evaluate oral conditions; however, the resazurin disc test is quantitative and does not require salivary specimens to be transferred outside the patient ward for evaluation. The resazurin disc test can be a useful substitute for the Oral Assessment Guide in intensive care units with restricted access. IMPLICATIONS FOR CLINICAL PRACTICE: The resazurin disc test can be used for quantitative assessment of patients' oral condition in isolation wards. Multidisciplinary management of patients with COVID-19 should be promoted and involve oral healthcare providers such as dentists and dental hygienists.

Multi-horizon predictive models for guiding extracorporeal resource allocation in critically ill COVID-19 patients.

OBJECTIVE: Extracorporeal membrane oxygenation (ECMO) resource allocation tools are currently lacking. We developed machine learning (ML) models for predicting COVID-19 patients at risk of receiving ECMO to guide patient triage and resource allocation. MATERIAL AND METHODS: We included COVID-19 patients admitted to intensive care units for >24 h from March 2020 to October 2021, divided into training and testing development and testing-only holdout cohorts. We developed ECMO deployment timely prediction model ForecastECMO using Gradient Boosting Tree (GBT), with pre-ECMO prediction horizons from 0 to 48 h, compared to PaO2/FiO2 ratio, Sequential Organ Failure Assessment score, PREdiction of Survival on ECMO Therapy score, logistic regression, and 30 pre-selected clinical variables GBT Clinical GBT models, with area under the receiver operator curve (AUROC) and precision recall curve (AUPRC) metrics. RESULTS: ECMO prevalence was 2.89% and 1.73% in development and holdout cohorts. ForecastECMO had the best performance in both cohorts. At the 18-h prediction horizon, a potentially clinically actionable pre-ECMO window, ForecastECMO, had the highest AUROC (0.94 and 0.95) and AUPRC (0.54 and 0.37) in development and holdout cohorts in identifying ECMO patients without data 18 h prior to ECMO. DISCUSSION AND CONCLUSIONS: We developed a multi-horizon model, ForecastECMO, with high performance in identifying patients receiving ECMO at various prediction horizons. This model has potential to be used as early alert tool to guide ECMO resource allocation for COVID-19 patients. Future prospective multicenter validation would provide evidence for generalizability and real-world application of such models to improve patient outcomes.

An artificial intelligence approach for predicting death or organ failure after hospitalization for COVID-19: development of a novel risk prediction tool and comparisons with ISARIC-4C, CURB-65, qSOFA, and MEWS scoring systems.

BACKGROUND: We applied machine learning (ML) algorithms to generate a risk prediction tool [Collaboration for Risk Evaluation in COVID-19 (CORE-COVID-19)] for predicting the composite of 30-day endotracheal intubation, intravenous administration of vasopressors, or death after COVID-19 hospitalization and compared it with the existing risk scores. METHODS: This is a retrospective study of adults hospitalized with COVID-19 from March 2020 to February 2021. Patients, each with 92 variables, and one composite outcome underwent feature selection process to identify the most predictive variables. Selected variables were modeled to build four ML algorithms (artificial neural network, support vector machine, gradient boosting machine, and Logistic regression) and an ensemble model to generate a CORE-COVID-19 model to predict the composite outcome and compared with existing risk prediction scores. The net benefit for clinical use of each model was assessed by decision curve analysis. RESULTS: Of 1796 patients, 278 (15%) patients reached primary outcome. Six most predictive features were identified. Four ML algorithms achieved comparable discrimination (P > 0.827) with c-statistics ranged 0.849-0.856, calibration slopes 0.911-1.173, and Hosmer-Lemeshow P > 0.141 in validation dataset. These 6-variable fitted CORE-COVID-19 model revealed a c-statistic of 0.880, which was significantly (P < 0.04) higher than ISARIC-4C (0.751), CURB-65 (0.735), qSOFA (0.676), and MEWS (0.674) for outcome prediction. The net benefit of the CORE-COVID-19 model was greater than that of the existing risk scores. CONCLUSION: The CORE-COVID-19 model accurately assigned 88% of patients who potentially progressed to 30-day composite events and revealed improved performance over existing risk scores, indicating its potential utility in clinical practice.

Identification of two early blood biomarkers ACHE and CLEC12A for improved risk stratification of critically ill COVID-19 patients.

In order to identify biomarkers for earlier prediction of COVID-19 outcome, we collected blood samples from patients with fatal outcomes (non-survivors) and with positive clinical outcomes (survivors) at ICU admission and after seven days. COVID-19 survivors and non-survivors showed significantly different transcript levels for 93 genes in whole blood already at ICU admission as revealed by RNA-Seq. These differences became even more pronounced at day 7, resulting in 290 differentially expressed genes. Many identified genes play a role in the differentiation of hematopoietic cells. For validation, we designed an RT-qPCR assay for C-type lectin domain family 12 member A (CLEC12A) and acetylcholinesterase (ACHE), two transcripts that showed highest potential to discriminate between survivors and non-survivors at both time points. Using our combined RT-qPCR assay we examined 33 samples to accurately predict patient survival with an AUROC curve of 0.931 (95% CI = 0.814-1.000) already at ICU admission. CLEC12A and ACHE showed improved prediction of patient outcomes compared to standard clinical biomarkers including CRP and PCT in combination (AUROC = 0.403, 95% CI = 0.108-0.697) or SOFA score (AUROC = 0.701 95% CI = 0.451-0.951) at day 0. Therefore, analyzing CLEC12A and ACHE gene expression from blood may provide a promising approach for early risk stratification of severely ill COVID-19 patients.

Clustering analysis of geriatric and acute characteristics in a cohort of very old patients on admission to ICU.

PURPOSE: The biological and functional heterogeneity in very old patients constitutes a major challenge to prognostication and patient management in intensive care units (ICUs). In addition to the characteristics of acute diseases, geriatric conditions such as frailty, multimorbidity, cognitive impairment and functional disabilities were shown to influence outcome in that population. The goal of this study was to identify new and robust phenotypes based on the combination of these features to facilitate early outcome prediction. METHODS: Patients aged 80 years old or older with and without limitations of life-sustaining treatment and with complete data were recruited from the VIP2 study for phenotyping and from the COVIP study for external validation. The sequential organ failure assessment (SOFA) score and its sub-scores taken on admission to ICU as well as demographic and geriatric patient characteristics were subjected to clustering analysis. Phenotypes were identified after repeated bootstrapping and clustering runs. RESULTS: In patients from the VIP2 study without limitations of life-sustaining treatment (n = 1977), ICU mortality was 12% and 30-day mortality 19%. Seven phenotypes with distinct profiles of acute and geriatric characteristics were identified in that cohort. Phenotype-specific mortality within 30 days ranged from 3 to 57%. Among the patients assigned to a phenotype with pronounced geriatric features and high SOFA scores, 50% died in ICU and 57% within 30 days. Mortality differences between phenotypes were confirmed in the COVIP study cohort (n = 280). CONCLUSIONS: Phenotyping of very old patients on admission to ICU revealed new phenotypes with different mortality and potential need for anticipatory intervention.

Quick COVID-19 Severity Index, CURB-65 and Quick SOFA Scores Comparison in Predicting Mortality and Risk Factors of COVID-19 Patients.

BACKGROUND: This study aimed to investigate CURB-65, quick COVID-19 Severity Index (qCSI) and quick Sepsis Related Organ Failure Assessment (qSOFA) scores in predicting mortality and risk factors for death in patients with COVID-19. METHODS: We retrospectively analyzed a total of 1919 cases for whom the rRT-PCR assay for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was positive. For mortality risk factors, univariate and multivariate logistic regression analyses were used. Receiver operator characteristics (ROC) analysis and Kaplan-Meier survival analysis were performed for CURB-65, qCSI and qSOFA scores. RESULTS: The patients' average age was 45.7 (21.6) years. Male patients accounted for 51.7% (n=992). In univariate analysis, some clinical variables including age over 65 years and comorbid diseases such as hypertension, chronic kidney disease, malignancy, lymphopenia, troponin, lactate dehydrogenase (LDH) and fibrinogen elevation were associated with the mortality rate. In multivariate logistic regression analysis: Neutrophil lymphocyte ratio (NLR) 3.3 and above (OR, 9.1; 95% CI, 1.9-42), C-reactive protein (CRP)30 mg/L and above (OR, 4.1; 95% CI, 1.2-13.6), D-dimer 1000 ng/mL and above (OR, 4; 95% CI, 1.5-10.7) and age (OR, 1.11; 95% CI, 1.04-1.18-year increase) were identified as risk factors for mortality among COVID-19 patients. The CURB-65 and qCSI scores exhibited a high degree of discrimination in mortality prediction (AUC values were 0.928 and 0.865, respectively). Also, the qSOFA score had a moderate discriminant power (AUC value was 0.754). CONCLUSION: CURB-65 and qSCI scores had a high discriminatory power to predict mortality. Also, this study identified CURB-65, qCSI and qSOFA scores, NLR, CRP, D-dimer level, and annual age increase as important mortality risk factors.

Comparison of prognostic scores for inpatients with COVID-19: a retrospective monocentric cohort study.

BACKGROUND: The SARS-CoV-2 pandemic led to a steep increase in hospital and intensive care unit (ICU) admissions for acute respiratory failure worldwide. Early identification of patients at risk of clinical deterioration is crucial in terms of appropriate care delivery and resource allocation. We aimed to evaluate and compare the prognostic performance of Sequential Organ Failure Assessment (SOFA), Quick Sequential Organ Failure Assessment (qSOFA), Confusion, Uraemia, Respiratory Rate, Blood Pressure and Age ≥65 (CURB-65), Respiratory Rate and Oxygenation (ROX) index and Coronavirus Clinical Characterisation Consortium (4C) score to predict death and ICU admission among patients admitted to the hospital for acute COVID-19 infection. METHODS AND ANALYSIS: Consecutive adult patients admitted to the Geneva University Hospitals during two successive COVID-19 flares in spring and autumn 2020 were included. Discriminative performance of these prediction rules, obtained during the first 24 hours of hospital admission, were computed to predict death or ICU admission. We further exluded patients with therapeutic limitations and reported areas under the curve (AUCs) for 30-day mortality and ICU admission in sensitivity analyses. RESULTS: A total of 2122 patients were included. 216 patients (10.2%) required ICU admission and 303 (14.3%) died within 30 days post admission. 4C score had the best discriminatory performance to predict 30-day mortality (AUC 0.82, 95% CI 0.80 to 0.85), compared with SOFA (AUC 0.75, 95% CI 0.72 to 0.78), qSOFA (AUC 0.59, 95% CI 0.56 to 0.62), CURB-65 (AUC 0.75, 95% CI 0.72 to 0.78) and ROX index (AUC 0.68, 95% CI 0.65 to 0.72). ROX index had the greatest discriminatory performance (AUC 0.79, 95% CI 0.76 to 0.83) to predict ICU admission compared with 4C score (AUC 0.62, 95% CI 0.59 to 0.66), CURB-65 (AUC 0.60, 95% CI 0.56 to 0.64), SOFA (AUC 0.74, 95% CI 0.71 to 0.77) and qSOFA (AUC 0.59, 95% CI 0.55 to 0.62). CONCLUSION: Scores including age and/or comorbidities (4C and CURB-65) have the best discriminatory performance to predict mortality among inpatients with COVID-19, while scores including quantitative assessment of hypoxaemia (SOFA and ROX index) perform best to predict ICU admission. Exclusion of patients with therapeutic limitations improved the discriminatory performance of prognostic scores relying on age and/or comorbidities to predict ICU admission.

Clinical features and mortality of COVID-19 patients admitted to ICU according to SOFA score.

The Sequential Organ Failure Assessment (SOFA) could function as an effective risk stratification tool in the admission of critically ill patients with COVID-19 and would allow stratification based on a risk assessment. We aimed to examine whether the SOFA score is useful to define 2 severity profiles in COVID-19 patients admitted to ICU: mild with SOFA < 5, and severe with SOFA ≥ 5. A retrospective cohort, multicenter study was conducted from February 11 to May 11, 2020. We analyzed patients admitted to all ICUs of the 14 public hospitals of the Castilla-La Mancha Health Service at the beginning of the pandemic and with SARS-CoV-2 infection. Patients were divided in 2 groups according to the level of severity by SOFA at admission to the ICU. Cox regression was used to evaluate factors associated with survival and Kaplan-Meier test to examine survival probability. In total, 405 patients with a complete SOFA panel were recruited in the 14 participating ICUs. SOFA <5 group showed that age above 60 years and D-dimer above 1000 ng/mL were risk factors associated with lower survival. In SOFA ≥ 5 it was found that high blood pressure was a risk factor associated with shorter survival. Kaplan-Meier showed lower survival in SOFA ≥ 5 in combination with high blood pressure, time since viral symptom onset to admission in ICU < 7 days, D-dimer ≥1000 ng/mL and respiratory pathology. However, SOFA < 5 showed only higher age (≥60 years) associated with lower survival. Age over 60 years and D-dimer over 1000 ng/mL were risk factors reflecting lower survival in patients with SOFA < 5. Moreover, SOFA ≥ 5 patients within a week after COVID-19 onset and comorbidities such as high blood pressure and previous respiratory pathology showed lower survival.

Poor prognosis indicators of type-2 diabetic COVID-19 patients.

Diabetes is associated with a worse prognosis and a high risk of morbidity and mortality in COVID-19 patients. We aimed to evaluate the main factors involved in the poor prognosis in diabetic patients. A total of 984 patients diagnosed with COVID-19 admitted to the hospital were included in this study. Patients were first divided into type-2 diabetic (DM+) and non-diabetic (DM-) groups. The participants were analyzed based on the National Early Warning Score (NEWS) and on the Quick-Sequential Organ Failure Assessment (qSOFA) to find the best prognostic risk score for our study. The DM+ and DM- groups were divided into non-severe and severe groups. Comparative and correlative analyses were used to identify the physiological parameters that could be employed for creating a potential risk indicator for DM+ COVID-19 patients. We found a poorer prognosis for the DM+ COVID-19 patients with a higher ICU admission rate, mechanical ventilation rate, vasopressor use, dialysis, and longer treatment times compared with the DM- group. DM+ COVID-19 patients had increased plasma glucose, lactate, age, urea, NEWS, and D-dimer levels, herein referred to as the GLAUND set, and worse prognosis and outcomes when compared with infected DM- patients. The NEWS score was a better indicator for assessing COVID-19 severity in diabetic patients than the q-SOFA score. In conclusion, diabetic COVID-19 patients should be assessed with the NEWS score and GLAUND set for determining their prognosis COVID-19 prognosis.

Development of COVID-19 severity assessment score in adults presenting with COVID-19 to the emergency department.

BACKGROUND: Critically-ill Covid-19 patients require extensive resources which can overburden a healthcare system already under strain due to a pandemic. A good disease severity prediction score can help allocate resources to where they are needed most. OBJECTIVES: We developed a Covid-19 Severity Assessment Score (CoSAS) to predict those patients likely to suffer from mortalities within 28 days of hospital admission. We also compared this score to Quick Sequential Organ Failure Assessment (qSOFA) in adults. METHODS: CoSAS includes the following 10 components: Age, gender, Clinical Frailty Score, number of comorbidities, Ferritin level, D-dimer level, neutrophil/lymphocyte ratio, C-reactive Protein levels, systolic blood pressure and oxygen saturation. Our study was a single center study with data collected via chart review and phone calls. 309 patients were included in the study. RESULTS: CoSAS proved to be a good score to predict Covid-19 mortality with an Area under the Curve (AUC) of 0.78. It also proved better than qSOFA (AUC of 0.70). More studies are needed to externally validate CoSAS. CONCLUSION: CoSAS is an accurate score to predict Covid-19 mortality in the Pakistani population.

Point of Care Lung Ultrasound Injury Score-A simple and reliable assessment tool in COVID-19 patients (PLIS I): A retrospective study.

BACKGROUND: In COVID-19 patients, lung ultrasound is superior to chest radiograph and has good agreement with computerized tomography to diagnose lung pathologies. Most lung ultrasound protocols published to date are complex and time-consuming. We describe a new illustrative Point-of-care ultrasound Lung Injury Score (PLIS) to help guide the care of patients with COVID-19 and assess if the PLIS would be able to predict COVID-19 patients' clinical course. METHODS: This retrospective study describing the novel PLIS was conducted in a large tertiary-level hospital. COVID-19 patients were included if they required any form of respiratory support and had at least one PLIS study during hospitalization. Data collected included PLIS on admission, demographics, Sequential Organ Failure Assessment (SOFA) scores, and patient outcomes. The primary outcome was the need for intensive care unit (ICU) admission. RESULTS: A total of 109 patients and 293 PLIS studies were included in our analysis. The mean age was 60.9, and overall mortality was 18.3%. Median PLIS score was 5.0 (3.0-6.0) vs. 2.0 (1.0-3.0) in ICU and non-ICU patients respectively (p<0.001). Total PLIS scores were directly associated with SOFA scores (inter-class correlation 0.63, p<0.001), and multivariate analysis showed that every increase in one PLIS point was associated with a higher risk for ICU admission (O.R 2.09, 95% C.I 1.59-2.75) and in-hospital mortality (O.R 1.54, 95% C.I 1.10-2.16). CONCLUSIONS: The PLIS for COVID-19 patients is simple and associated with SOFA score, ICU admission, and in-hospital mortality. Further studies are needed to demonstrate whether the PLIS can improve outcomes and become an integral part of the management of COVID-19 patients.

Mortality estimation using APACHE and CT scores with stepwise linear regression method in COVID-19 intensive care unit: A retrospective study.

BACKGROUND: COVID-19 is a disease with high mortality worldwide, and which parameters that affect mortality in intensive care are still being investigated. This study aimed to show the factors affecting mortality in COVID-19 intensive care patients and write a model that can predict mortality. METHODS: The data of 229 patients in the COVID-19 intensive care unit were scanned. Laboratory tests, APACHE, SOFA, and GCS values were recorded. CT scores were calculated with chest CTs. The effects of these data on mortality were examined. The effects of the variables were modeled using the stepwise regression method. RESULTS: While the mean age of female (30.14%) patients was 69.1 ± 12.2, the mean age of male (69.86%) patients was 66.9 ± 11.5. The mortality rate was 69.86%. Age, CRP, D-dimer, creatinine, procalcitonin, APACHE, SOFA, GCS, and CT score were significantly different in the deceased patients than the survival group. When we attempted to create a model using stepwise linear regression analysis, the appropriate model was achieved at the fourth step. Age, CRP, APACHE, and CT score were included in the model, which has the power to predict mortality with 89.9% accuracy. CONCLUSION: Although, when viewed individually, there is a significant difference in parameters such as creatinine, procalcitonin, D-dimer, GCS, and SOFA score, the probability of mortality can be estimated by knowing only the age, CRP, APACHE, and CT scores. These four simple parameters will help clinicians effectively use resources in treatment.

Assessing mortality differences across acute respiratory failure management strategies in Covid-19.

PURPOSE: Prolonged observation could avoid invasive mechanical ventilation (IMV) and related risks in patients with Covid-19 acute respiratory failure (ARF) compared to initiating early IMV. We aimed to determine the association between ARF management strategy and in-hospital mortality. MATERIALS AND METHODS: Patients in the Weill Cornell Covid-19 registry who developed ARF between March 5 - March 25, 2020 were exposed to an early IMV strategy; between March 26 - April 1, 2020 to an intermediate strategy; and after April 2 to prolonged observation. Cox proportional hazards regression was used to model in-hospital mortality and test an interaction between ARF management strategy and modified sequential organ failure assessment (mSOFA). RESULTS: Among 632 patients with ARF, 24% of patients in the early IMV strategy died versus 28% in prolonged observation. At lower mSOFA, prolonged observation was associated with lower mortality compared to early IMV (at mSOFA = 0, HR 0.16 [95% CI 0.04-0.57]). Mortality risk increased in the prolonged observation strategy group with each point increase in mSOFA score (HR 1.29 [95% CI 1.10-1.51], p = 0.002). CONCLUSION: In Covid-19 ARF, prolonged observation was associated with a mortality benefit at lower mSOFA scores, and increased mortality at higher mSOFA scores compared to early IMV.

Comparison of Quick Sequential Organ Failure Assessment (Qsofa) and National Early Warning Score (News) in Covid-19 Patients and its Correlation with the Outcome.

The recent outbreak of COVID 19 is a great threat to public health. Because of limitation of resources, the number of patients that can be monitored and treated in Intensive Care Units is restricted. Hence identifying medical patients at risk of deterioration at the initial stage by means of simple protocols based on physiological parameters is crucial. The qSOFA score was introduced as a rapid bedside clinical score to identify patients with a suspected infection that are at greater risk for a poor outcome. The National Early Warning Score (NEWS) was developed to improve the detection of and response to clinical deterioration in patients with acute illness. There is paucity of literature regarding the use of these scores in patients with COVID 19 infection. This study aims at comparing the scoring systems qSOFA and NEWS in the setting of COVID-19 infection and its correlation with the final outcome of the illness. MATERIAL: It is a retrospective study in which patients presenting with COVID 19 infection(diagnosed by RT-PCR testing of nasopharyngeal and oral swab) between April 2021 to June 2021 were included. Scoring was done using both the scores at admission and the patients were followed up till the outcome. Outcome was defined as 5-day, 10-day and 15-day mortality after presentation. Predictive performance was expressed as discrimination (AUC). Subsequently, sensitivity and specificity were calculated. OBSERVATION: A total of 100 patients were included in the study, of whom 17 died within 5 days and 37 died within 10 days and 30 died within 15 days after presentation. q SOFA had the best performance, compared to NEWS (5 day auc : .668, .621, 10-day auc: .580, .569, 15-day auc: .625, .511) with q SOFA having sensitivity of 90.2% while that of news being 95.1% where as specificity of q SOFA is 40.7% and that of NEWS is 47.5%. CONCLUSION: qSOFA score is more accurate in predicting 5, 10 and 15-day mortality than NEWS score in COVID 19 patients. In resource limited settings, it is an inexpensive and simple tool for early identification of high risk COVID 19 patients.

Mortality Predictive Value of APACHE II and SOFA Scores in COVID-19 Patients in the Intensive Care Unit.

Background: COVID-19 pandemic has become a global dilemma since December 2019. Are the standard scores, such as acute physiology and chronic health evaluation (APACHE II) and sequential organ failure assessment (SOFA) score, accurate for predicting the mortality rate of COVID-19 or the need for new scores? We aimed to evaluate the mortality predictive value of APACHE II and SOFA scores in critically ill COVID-19 patients. Methods: In a cohort study, we enrolled 204 confirmed COVID-19 patients admitted to the intensive care units at the Imam Khomeini hospital complex. APACHE II on the first day and daily SOFA scoring were performed. The primary outcome was the mortality rate in the nonsurvived and survived groups, and the secondary outcome was organ dysfunction. Two groups of survived and nonsurvived patients were compared by the chi-square test for categorical variables and an independent sample t-test for continuous variables. We used logistic regression models to estimate the mortality risk of high APACHE II and SOFA scores. Result: Among 204 severe COVID-19 patients, 114 patients (55.9%) expired and 169 patients (82.8%) had at least one comorbidity that 103 (60.9%) of them did not survive (P=0.002). Invasive mechanical ventilation and its duration were significantly different between survived and nonsurvived groups (P ≤ 0.001 and P=0.002, respectively). Mean APACHE II and mean SOFA scores were significantly higher in the nonsurvived than in the survived group (14.4 ± 5.7 vs. 9.5 ± 5.1, P ≤ 0.001, 7.3 ± 3.1 vs. 3.1 ± 1.1, P ≤ 0.001, respectively). The area under the curve was 89.5% for SOFA and 73% for the APACHE II score. Respiratory diseases and malignancy were risk factors for the mortality rate (P=0.004 and P=0.007, respectively) against diabetes and hypertension. Conclusion: The daily SOFA was a better mortality predictor than the APACHE II in critically ill COVID-19 patients. But they could not predict death with high accuracy. We need new scoring with consideration of the prognostic factors and daily evaluation of changes in clinical conditions.

Evolving Risk of Acute Kidney Injury in COVID-19 Hospitalized Patients: A Single Center Retrospective Study.

Background and Objectives: Within a year, COVID-19 has advanced from an outbreak to a pandemic, spreading rapidly and globally with devastating impact. The pathophysiological link between COVID-19 and acute kidney injury (AKI) is currently being debated among scientists. While some studies have concluded that the mechanisms of AKI in COVID-19 patients are complex and not fully understood, others have claimed that AKI is a rare complication of COVID-19-related disorders. Considering this information gap and its possible influence on COVID-19-associated AKI management, our study aimed to explore the prevalence of AKI and to identify possible risk factors associated with AKI development among COVID-19 hospitalized patients. Materials and Methods: A retrospective cohort study included 83 laboratory-confirmed COVID-19 patients hospitalized at the isolation department in a tertiary hospital in Zagazig City, Egypt between June and August 2020. Patients younger than 18 years of age, those diagnosed with end-stage kidney disease, or those on nephrotoxic medications were excluded. All study participants had a complete blood count, liver and renal function tests, hemostasis parameters examined, inflammatory markers, serum electrolytes, routine urinalysis, arterial blood gas, and non-enhanced chest and abdominal computer tomography (CT) scans. Results: Of the 83 patients, AKI developed in 24 (28.9%) of them, of which 70.8% were in stage 1, 8.3% in stage 2, and 20.8% in stage 3. Patients with AKI were older than patients without AKI, with hypertension and diabetes being the most common comorbidities. Risk factors for AKI include increased age, hypertension, diabetes mellitus, and a higher sequential organ failure assessment (SOFA) score. Conclusions: AKI occurs in a considerable percentage of patients with COVID-19, especially in elderly males, those with hypertension, diabetes, and a higher sequential organ failure assessment (SOFA) score. Hence, the presence of AKI should be taken into account as an important index within the risk spectrum of disease severity for COVID-19 patients.

Retrospective evaluation of the use of quick Sepsis-related Organ Failure Assessment (qSOFA) as predictor of mortality and length of hospitalization in dogs with pyometra (2013-2019): 52 cases.

OBJECTIVE: To evaluate the prognostic utility of quick Sepsis-related Organ Failure Assessment (qSOFA) for prediction of in-hospital mortality and length of hospitalization in dogs with pyometra. DESIGN: Retrospective cohort study from February 2013 to April 2019 SETTING: Tertiary referral hospital ANIMALS: Fifty-two dogs referred with confirmed diagnosis of pyometra INTERVENTIONS: None MEASUREMENTS AND PRINCIPAL OUTCOMES: Sixty-five percent of dogs survived to discharge. A cut-off score of ≥2 for qSOFA was associated with in-hospital mortality (odds ratio 6.51 [95% CI: 1.35 - 31.3]) P = 0.019. The area under the receiver operator characteristic curve for a qSOFA score ≥ 2 for mortality was 0.72 (95% CI: 0.59-0.85), with a sensitivity of 77.8% and a specificity of 66.7%. The mean ± SD number of organs with dysfunction was significantly higher in dogs with a qSOFA score ≥2 1.76 ± 0.83 compared to dogs with a qSOFA score < 2 1.08 ± 1.09, P = 0.015. The presence of a qSOFA score ≥ 2 was associated with a longer time of hospitalization in survivors with a median (interquartile range) length of stay in qSOFA < 2 (48 [33]) hours versus qSOFA score ≥ 2 (78 [52]) hours, P = 0.027. CONCLUSIONS: In dogs with pyometra, the qSOFA score was associated with mortality and length of hospitalization. This score might be useful to improve the risk stratification in dogs with pyometra. Further studies are necessary to evaluate the predictive capacity of qSOFA in other septic patient populations.

Preintubation Sequential Organ Failure Assessment Score for Predicting COVID-19 Mortality: External Validation Using Electronic Health Record From 86 U.S. Healthcare Systems to Appraise Current Ventilator Triage Algorithms.

OBJECTIVES: Prior research has hypothesized the Sequential Organ Failure Assessment (SOFA) score to be a poor predictor of mortality in mechanically ventilated patients with COVID-19. Yet, several U.S. states have proposed SOFA-based algorithms for ventilator triage during crisis standards of care. Using a large cohort of mechanically ventilated patients with COVID-19, we externally validated the predictive capacity of the preintubation SOFA score for mortality prediction with and without other commonly used algorithm elements. DESIGN: Multicenter, retrospective cohort study using electronic health record data. SETTING: Eighty-six U.S. health systems. PATIENTS: Patients with COVID-19 hospitalized between January 1, 2020, and February 14, 2021, and subsequently initiated on mechanical ventilation. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Among 15,122 mechanically ventilated patients with COVID-19, SOFA score alone demonstrated poor discriminant accuracy for inhospital mortality in mechanically ventilated patients using the validation cohort (area under the receiver operating characteristic curve [AUC], 0.66; 95% CI, 0.65-0.67). Discriminant accuracy was even poorer using SOFA score categories (AUC, 0.54; 95% CI, 0.54-0.55). Age alone demonstrated greater discriminant accuracy for inhospital mortality than SOFA score (AUC, 0.71; 95% CI, 0.69-0.72). Discriminant accuracy for mortality improved upon addition of age to the continuous SOFA score (AUC, 0.74; 95% CI, 0.73-0.76) and categorized SOFA score (AUC, 0.72; 95% CI, 0.71-0.73) models, respectively. The addition of comorbidities did not substantially increase model discrimination. Of 36 U.S. states with crisis standards of care guidelines containing ventilator triage algorithms, 31 (86%) feature the SOFA score. Of these, 25 (81%) rely heavily on the SOFA score (12 exclusively propose SOFA; 13 place highest weight on SOFA or propose SOFA with one other variable). CONCLUSIONS: In a U.S. cohort of over 15,000 ventilated patients with COVID-19, the SOFA score displayed poor predictive accuracy for short-term mortality. Our findings warrant reappraisal of the SOFA score's implementation and weightage in existing ventilator triage pathways in current U.S. crisis standards of care guidelines.