Outcome of COVID-19-Associated Acute Stroke: A Study from South India.

Background and Objective: Coronavirus 2019 (COVID-19) infection is prevalent worldwide. COVID-19 infection can lead to various neurological disorders including acute stroke. We investigated the functional outcome and its determinants among our patients with acute stroke associated with COVID-19 infection in the present setup. Materials and Methods: This study is a prospective study in which we recruited acute stroke patients with COVID-19 positivity. Data on duration of COVID-19 symptoms and type of acute stroke were recorded. All patients underwent stroke subtype workup and measurement of D-dimer, C-reactive protein (CRP), lactate-dehydrogenase (LDH), procalcitonin, interleukin-6, and ferritin levels. Poor functional outcome was defined by modified Rankin score (mRS) ≥3 at 90 days. Results: During the study period, 610 patients were admitted for acute stroke, of whom 110 (18%) tested positive for COVID-19 infection. Majority (72.7%) were men with a mean age of 56.5 years and mean duration of COVID-19 symptoms for 6.9 days. Acute ischemic and hemorrhagic strokes were observed in 85.5% and 14.5% patients, respectively. Poor outcome was observed in 52.7%, including in-hospital mortality in 24.5% patients. COVID-19 symptoms ≤5 days (odds ratio [OR]: 1.41, 95% confidence interval [CI]: 1.20-2.99), CRP positivity (OR: 1.97, 95% CI: 1.41-4.87), elevated levels of D-dimer (OR: 2.11, 95% CI: 1.51-5.61), interleukin-6 (OR: 1.92, 95% CI: 1.04-4.74), and serum ferritin (OR: 2.4, 95% CI: 1.02-6.07), and cycle threshold (Ct) value ≤25 (OR: 8.8, 95% CI: 6.52-12.21) were independent predictors of poor outcome. Conclusion: Poor outcomes were relatively higher among acute stroke patients with concomitant COVID-19 infection. In the present study, we established the independent predictors of poor outcome to be onset of COVID-19 symptoms (<5 days) and elevated levels of CRP, D-dimer, interleukin-6, ferritin, and Ct value ≤25 in acute stroke.

Clinical characteristics and outcomes among patients with COVID-19: A single-center retrospective observational study from Marj, Libya.

OBJECTIVES: To describe the clinical characteristics and the contributing factors potentially associated with the poorer outcome in Libyan COVID-19 ICU patients. METHODS: The present work is a retrospective, single-center study, which included 94 COVID-19 patients admitted to the Isolation Department at Marj Hospital from August 21st, 2020 till April 30th, 2021. The patients' data, including their medical history, clinical manifestations, radiological imaging, and laboratory findings, were obtained from the hospital records. RESULTS: A higher proportion of the admitted patients were males. The patients' mean age was 68.29 ± 13.64. The patients came with varying symptoms, but most commonly they were affected by dyspnea, fever, cough, and fatigue. Diabetes was the most common underlying comorbidity; nonetheless, other chronic diseases like hypertension, cardiovascular disease, renal disease, and lung diseases individually affected a significant proportion of patients. Although there was no effect of gender on patients' outcomes, age had a significant influence on the disease consequences. CONCLUSION: There was a strong effect of age on ICU admission and patients' surviving the illness. Diabetes was the most common underlying comorbid disease in COVID-19 patients. On admission time, inflammatory markers such as CRP, D-dimer, serum ferritin, and LDH, in common, were the most important indicators of poorer prognosis. Male gender, comorbidity, and symptomology adversely affected the rate of admission but not the patient survival.

The return visit, outcome and predicting factors of return visit among suspected COVID-19 outpatients.

Rate of return visit, predicting factors of return visit and occurrence of adverse events in suspected to be or likely cases of COVID-19 patients who received outpatient treatment. This is a retrospective observational cohort study on patients (> 16 years), suspected to be or likely cases of COVID-19 who were visited in a respiratory emergency department and subsequently discharged home. Patients' baseline characteristics were extracted from medical charts. All patients were followed-up for 7 days after their first visit. Patients' outcomes during the7-day follow-up, as well as the severity of pulmonary involvement based on imaging were recorded. A total number of 601 patients (350 men and 251 women) were recruited. The rate of return visit was 27.74% (144 patients) with 6.74% (34 patients) experiencing a poor outcome. Six factors with a significant odds ratio were predictors of poor outcome in patients who received outpatient treatment, namely, older age [odds ratio = 3.278, 95% confidence interval: 1.115-9.632], days from onset of symptoms [1.068, 1.003-1.137], and history of diabetes [6.373, 2.271-17.883]). Predictors of favorable outcome were female gender [0.376, 0.158-0.894], oxygen saturation > 93% [0.862, 0.733-1.014], smoking habit [0.204, 0.045-0.934]. The findings of this study demonstrate that the rate of return visit with poor outcome in patients who received outpatient treatment was reasonably low. Age, male sex, diabetes mellitus and pulmonary disease are predicting factors of poor outcome in these COVID-19 patients who received outpatient management.

Relevant SARS-CoV-2 viremia is associated with COVID-19 severity: Prospective cohort study and validation cohort.

Early kinetics of SARS-CoV-2 viral load (VL) in plasma determined by quantitative reverse-transcription polymerase chain reaction (RT-PCR) was evaluated as a predictor of poor clinical outcome in a prospective study and assessed in a retrospective validation cohort. Prospective observational single-center study including consecutive adult patients hospitalized with COVID-19 between November 2020 and January 2021. Serial plasma samples were obtained until discharge. Quantitative RT-PCR was performed to assess SARS-CoV-2 VL. The main outcomes were in-hospital mortality, admission to the Intensive Care Unit (ICU), and their combination (Poor Outcome). Relevant viremia (RV), established in the prospective study, was assessed in a retrospective cohort including hospitalized COVID-19 patients from April 2021 to May 2022, in which plasma samples were collected according to clinical criteria. Prospective cohort: 57 patients were included. RV was defined as at least a twofold increase in VL within ≤2 days or a VL > 300 copies/ml, in the first week. Patients with RV (N = 14; 24.6%) were more likely to die than those without RV (35.7% vs. 0%), needed ICU admission (57% vs. 0%) or had Poor Outcome (71.4% vs. 0%), (p < 0.001 for the three variables). Retrospective cohort: 326 patients were included, 18.7% presented RV. Patients with RV compared with patients without RV had higher rates of ICU-admission (odds ratio [OR]: 5.6 [95% confidence interval [CI]: 2.1-15.1); p = 0.001), mortality (OR: 13.5 [95% CI: 6.3-28.7]; p < 0.0001) and Poor Outcome (OR: 11.2 [95% CI: 5.8-22]; p < 0.0001). Relevant SARS-CoV-2 viremia in the first week of hospitalization was associated with higher in-hospital mortality, ICU admission, and Poor Outcome. Findings observed in the prospective cohort were confirmed in a larger validation cohort.

CRP, SAA, LDH, and DD predict poor prognosis of coronavirus disease (COVID-19): a meta-analysis from 7739 patients.

Understanding factors associated with disease severity and mortality from coronavirus disease (COVID-19) was critical for effective risk stratification. We aimed to investigate the association between biomarkers of clinical laboratory tests, including serum C-reactive protein (CRP), serum amyloid protein (SAA), lactate dehydrogenase (LDH), and D-dimer (DD) and poor prognosis of COVID-19. We have searched many studies on COVID-19 on PubMed (Medline), Web of Science and Cochrane until 1 March 2021. The interest of this study was original articles reporting on laboratory testing projects and outcome of patients with COVID-19 that comprises mortality, acute respiratory distress syndrome (ARDS), need for care in an intensive care unit (ICU), and severe COVID-19. After synthesizing all data, we performed meta-analysis of random effects, and determined mean difference (MD) and standard mean difference at the biomarker level for different disease severity. A total of 7,739 patients with COVID-19 were pooled from 32 studies. CRP was significantly associated with poor prognosis of COVID-19 (SMD = 0.98, 95% CI = (0.85, 1.11), p < .001). Elevated SAA was associated with an increased composite poor outcome in COVID-19 (SMD = 1.06, 95% CI = (0.39, 1.72), p = .002). An elevated LDH was associated with a composite poor outcome (SMD = 1.18, 95% CI = (1.00, 1.36), p < .001). Patients with a composite poor outcome had a higher DD level (SMD = 0.91, 95% CI = (0.79, 1.02), p < .001). This meta-analysis showed that elevated serum CRP, SAA, LDH, and DD were associated with a poor outcome in COVID-19.

Assessing COVID-19 pneumonia-Clinical extension and risk with point-of-care ultrasound: A multicenter, prospective, observational study.

BACKGROUND: Assessing the extent of lung involvement is important for the triage and care of COVID-19 pneumonia. We sought to determine the utility of point-of-care ultrasound (POCUS) for characterizing lung involvement and, thereby, clinical risk determination in COVID-19 pneumonia. METHODS: This multicenter, prospective, observational study included patients with COVID-19 who received 12-zone lung ultrasound and chest computed tomography (CT) scanning in the emergency department (ED). We defined lung disease severity using the lung ultrasound score (LUS) and chest CT severity score (CTSS). We assessed the association between the LUS and poor outcome (ICU admission or 30-day all-cause mortality). We also assessed the association between the LUS and hospital length of stay. We examined the ability of the LUS to differentiate between disease severity groups. Lastly, we estimated the correlation between the LUS and CTSS and the interrater agreement for the LUS. We handled missing data by multiple imputation with chained equations and predictive mean matching. RESULTS: We included 114 patients treated between March 19, 2020, and May 4, 2020. An LUS ≥12 was associated with a poor outcome within 30 days (hazard ratio [HR], 5.59; 95% confidence interval [CI], 1.26-24.80; P = 0.02). Admission duration was shorter in patients with an LUS <12 (adjusted HR, 2.24; 95% CI, 1.47-3.40; P < 0.001). Mean LUS differed between disease severity groups: no admission, 6.3 (standard deviation [SD], 4.4); hospital/ward, 13.1 (SD, 6.4); and ICU, 18.0 (SD, 5.0). The LUS was able to discriminate between ED discharge and hospital admission excellently, with an area under the curve of 0.83 (95% CI, 0.75-0.91). Interrater agreement for the LUS was strong: κ = 0.88 (95% CI, 0.77-0.95). Correlation between the LUS and CTSS was strong: κ = 0.60 (95% CI, 0.48-0.71). CONCLUSIONS: We showed that baseline lung ultrasound - is associated with poor outcomes, admission duration, and disease severity. The LUS also correlates well with CTSS. Point-of-care lung ultrasound may aid the risk stratification and triage of patients with COVID-19 at the ED.

Risk factors associated with intensive care unit (ICU) admission and in-hospital death among adults hospitalized with COVID-19: a two-center retrospective observational study in tertiary care hospitals.

BACKGROUND: The COVID-19 pandemic is straining the health care systems worldwide. Therefore, health systems should make strategic shifts to ensure that limited resources provide the highest benefit for COVID-19 patients. OBJECTIVE: This study aimed to describe the risk factors associated with poor in-hospital outcomes to help clinicians make better patient care decisions. MATERIAL AND METHODS: This retrospective observational study enrolled 176 laboratory-confirmed COVID-19 patients. Demographic characteristics, clinical data, lymphocyte count, CT imaging findings on admission, and clinical outcomes were collected and compared. Two radiologists evaluated the distribution and CT features of the lesions and also scored the extent of lung involvement. The receiver operating characteristic (ROC) curve was used to determine the optimum cutoff point for possible effective variables on patients' outcomes. Multivariable logistic regression models were used to determine the risk factors associated with ICU admission and in-hospital death. RESULT: Thirty-eight (21.5%) patients were either died or admitted to ICU from a total of 176 enrolled ones. The mean age of the patients was 57.5 ± 16.1 years (males: 61%). The best cutoff point for predicting poor outcomes based on age, CT score, and O2 saturation was 60 years (sensitivity: 71%, specificity: 62%), 10.5 (sensitivity: 73%, specificity: 58%), and 90.5% (sensitivity: 73%, specificity: 59%), respectively. CT score cutoff point was rounded to 11 since this score contains only integer numbers. Multivariable-adjusted regression models revealed that ages of ≥ 60 years, CT score of ≥ 11, and O2 saturation of ≤ 90.5% were associated with higher worse outcomes among study population (odds ratio (OR): 3.62, 95%CI: 1.35-9.67, P = 0.019; OR: 4.38, 95%CI: 1.69-11.35, P = 0.002; and OR: 2.78, 95%CI: 1.03-7.47, P = 0.042, respectively). CONCLUSION: The findings indicate that older age, higher CT score, and lower O2 saturation could be categorized as predictors of poor outcome among COVID-19-infected patients. Other studies are required to prove these associations.

Effect of increased BMI and obesity on the outcome of COVID-19 adult patients: A systematic review and meta-analysis.

BACKGROUND AND AIMS: Corona virus diseases 2019 (COVID-19) pandemic spread rapidly. Growing evidences that overweight and obesity which extent nearly a third of the world population were associated with severe COVID-19. This study aimed to explore the association and risk of increased BMI and obesity with composite poor outcome in COVID-19 adult patients. METHODS: We conducted a systematic literature search from PubMed and Embase database. We included all original research articles in COVID-19 adult patients and obesity based on classification of Body Mass Index (BMI) and composite poor outcome which consist of ICU admission, ARDS, severe COVID-19, use of mechanical ventilation, hospital admission, and mortality. RESULTS: Sixteen studies were included in meta-analysis with 9 studies presented BMI as continuous outcome and 10 studies presented BMI as dichotomous outcome (cut-off ≥30 kg/m2). COVID-19 patients with composite poor outcome had higher BMI with mean difference 1.12 (95% CI, 0.67-1.57, P < 0.001). Meanwhile, obesity was associated with composite poor outcome with odds ratio (OR) = 1.78 (95% CI, 1.25-2.54, P < 0.001) Multivariate meta-regression showed the association between BMI and obesity on composite poor outcome were affected by age, gender, DM type 2, and hypertension. CONCLUSION: Obesity is a risk factor of composite poor outcome of COVID-19. On the other hand, COVID-19 patients with composite poor outcome have higher BMI. BMI is an important routine procedure that should always be assessed in the management of COVID-19 patients and special attention should be given to patients with obesity.

Clinical Utility of a Nomogram for Predicting 30-Days Poor Outcome in Hospitalized Patients With COVID-19: Multicenter External Validation and Decision Curve Analysis.

Aim: Early detection of coronavirus disease 2019 (COVID-19) patients who are likely to develop worse outcomes is of great importance, which may help select patients at risk of rapid deterioration who should require high-level monitoring and more aggressive treatment. We aimed to develop and validate a nomogram for predicting 30-days poor outcome of patients with COVID-19. Methods: The prediction model was developed in a primary cohort consisting of 233 patients with laboratory-confirmed COVID-19, and data were collected from January 3 to March 20, 2020. We identified and integrated significant prognostic factors for 30-days poor outcome to construct a nomogram. The model was subjected to internal validation and to external validation with two separate cohorts of 110 and 118 cases, respectively. The performance of the nomogram was assessed with respect to its predictive accuracy, discriminative ability, and clinical usefulness. Results: In the primary cohort, the mean age of patients was 55.4 years and 129 (55.4%) were male. Prognostic factors contained in the clinical nomogram were age, lactic dehydrogenase, aspartate aminotransferase, prothrombin time, serum creatinine, serum sodium, fasting blood glucose, and D-dimer. The model was externally validated in two cohorts achieving an AUC of 0.946 and 0.878, sensitivity of 100 and 79%, and specificity of 76.5 and 83.8%, respectively. Although adding CT score to the clinical nomogram (clinical-CT nomogram) did not yield better predictive performance, decision curve analysis showed that the clinical-CT nomogram provided better clinical utility than the clinical nomogram. Conclusions: We established and validated a nomogram that can provide an individual prediction of 30-days poor outcome for COVID-19 patients. This practical prognostic model may help clinicians in decision making and reduce mortality.

Radiomics Analysis of Computed Tomography helps predict poor prognostic outcome in COVID-19.

Rationale: Given the rapid spread of COVID-19, an updated risk-stratify prognostic tool could help clinicians identify the high-risk patients with worse prognoses. We aimed to develop a non-invasive and easy-to-use prognostic signature by chest CT to individually predict poor outcome (death, need for mechanical ventilation, or intensive care unit admission) in patients with COVID-19. Methods: From November 29, 2019 to February 19, 2020, a total of 492 patients with COVID-19 from four centers were retrospectively collected. Since different durations from symptom onsets to the first CT scanning might affect the prognostic model, we designated the 492 patients into two groups: 1) the early-phase group: CT scans were performed within one week after symptom onset (0-6 days, n = 317); and 2) the late-phase group: CT scans were performed one week later after symptom onset (≥7 days, n = 175). In each group, we divided patients into the primary cohort (n = 212 in the early-phase group, n = 139 in the late-phase group) and the external independent validation cohort (n = 105 in the early-phase group, n = 36 in the late-phase group) according to the centers. We built two separate radiomics models in the two patient groups. Firstly, we proposed an automatic segmentation method to extract lung volume for radiomics feature extraction. Secondly, we applied several image preprocessing procedures to increase the reproducibility of the radiomics features: 1) applied a low-pass Gaussian filter before voxel resampling to prevent aliasing; 2) conducted ComBat to harmonize radiomics features per scanner; 3) tested the stability of the features in the radiomics signature by several image transformations, such as rotating, translating, and growing/shrinking. Thirdly, we used least absolute shrinkage and selection operator (LASSO) to build the radiomics signature (RadScore). Afterward, we conducted a Fine-Gray competing risk regression to build the clinical model and the clinic-radiomics signature (CrrScore). Finally, performances of the three prognostic signatures (clinical model, RadScore, and CrrScore) were estimated from the two aspects: 1) cumulative poor outcome probability prediction; 2) 28-day poor outcome prediction. We also did stratified analyses to explore the potential association between the CrrScore and the poor outcomes regarding different age, type, and comorbidity subgroups. Results: In the early-phase group, the CrrScore showed the best performance in estimating poor outcome (C-index = 0.850), and predicting the probability of 28-day poor outcome (AUC = 0.862). In the late-phase group, the RadScore alone achieved similar performance to the CrrScore in predicting poor outcome (C-index = 0.885), and 28-day poor outcome probability (AUC = 0.976). Moreover, the RadScore in both groups successfully stratified patients with COVID-19 into low- or high-RadScore groups with significantly different survival time in the training and validation cohorts (all P < 0.05). The CrrScore in both groups can also significantly stratify patients with different prognoses regarding different age, type, and comorbidities subgroups in the combined cohorts (all P < 0.05). Conclusions: This research proposed a non-invasive and quantitative prognostic tool for predicting poor outcome in patients with COVID-19 based on CT imaging. Taking the insufficient medical recourse into account, our study might suggest that the chest CT radiomics signature of COVID-19 is more effective and ideal to predict poor outcome in the late-phase COVID-19 patients. For the early-phase patients, integrating radiomics signature with clinical risk factors can achieve a more accurate prediction of individual poor prognostic outcome, which enables appropriate management and surveillance of COVID-19.