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1.
PLoS One ; 16(3): e0248438, 2021.
Article in English | MEDLINE | ID: covidwho-1574763

ABSTRACT

OBJECTIVES: Accurate and reliable criteria to rapidly estimate the probability of infection with the novel coronavirus-2 that causes the severe acute respiratory syndrome (SARS-CoV-2) and associated disease (COVID-19) remain an urgent unmet need, especially in emergency care. The objective was to derive and validate a clinical prediction score for SARS-CoV-2 infection that uses simple criteria widely available at the point of care. METHODS: Data came from the registry data from the national REgistry of suspected COVID-19 in EmeRgency care (RECOVER network) comprising 116 hospitals from 25 states in the US. Clinical variables and 30-day outcomes were abstracted from medical records of 19,850 emergency department (ED) patients tested for SARS-CoV-2. The criterion standard for diagnosis of SARS-CoV-2 required a positive molecular test from a swabbed sample or positive antibody testing within 30 days. The prediction score was derived from a 50% random sample (n = 9,925) using unadjusted analysis of 107 candidate variables as a screening step, followed by stepwise forward logistic regression on 72 variables. RESULTS: Multivariable regression yielded a 13-variable score, which was simplified to a 13-point score: +1 point each for age>50 years, measured temperature>37.5°C, oxygen saturation<95%, Black race, Hispanic or Latino ethnicity, household contact with known or suspected COVID-19, patient reported history of dry cough, anosmia/dysgeusia, myalgias or fever; and -1 point each for White race, no direct contact with infected person, or smoking. In the validation sample (n = 9,975), the probability from logistic regression score produced an area under the receiver operating characteristic curve of 0.80 (95% CI: 0.79-0.81), and this level of accuracy was retained across patients enrolled from the early spring to summer of 2020. In the simplified score, a score of zero produced a sensitivity of 95.6% (94.8-96.3%), specificity of 20.0% (19.0-21.0%), negative likelihood ratio of 0.22 (0.19-0.26). Increasing points on the simplified score predicted higher probability of infection (e.g., >75% probability with +5 or more points). CONCLUSION: Criteria that are available at the point of care can accurately predict the probability of SARS-CoV-2 infection. These criteria could assist with decisions about isolation and testing at high throughput checkpoints.


Subject(s)
COVID-19/diagnosis , COVID-19/epidemiology , Emergency Service, Hospital/trends , Adult , Aged , Clinical Decision Rules , Coronavirus Infections/diagnosis , Cough , Databases, Factual , Decision Trees , Emergency Service, Hospital/statistics & numerical data , Female , Fever , Humans , Male , Mass Screening , Middle Aged , Registries , SARS-CoV-2/pathogenicity , United States/epidemiology
2.
Ann Med ; 53(1): 257-266, 2021 12.
Article in English | MEDLINE | ID: covidwho-1574445

ABSTRACT

OBJECTIVES: To appraise effective predictors for COVID-19 mortality in a retrospective cohort study. METHODS: A total of 1270 COVID-19 patients, including 984 admitted in Sino French New City Branch (training and internal validation sets randomly split at 7:3 ratio) and 286 admitted in Optical Valley Branch (external validation set) of Wuhan Tongji hospital, were included in this study. Forty-eight clinical and laboratory features were screened with LASSO method. Further multi-tree extreme gradient boosting (XGBoost) machine learning-based model was used to rank importance of features selected from LASSO and subsequently constructed death risk prediction model with simple-tree XGBoost model. Performances of models were evaluated by AUC, prediction accuracy, precision, and F1 scores. RESULTS: Six features, including disease severity, age, levels of high-sensitivity C-reactive protein (hs-CRP), lactate dehydrogenase (LDH), ferritin, and interleukin-10 (IL-10), were selected as predictors for COVID-19 mortality. Simple-tree XGBoost model conducted by these features can predict death risk accurately with >90% precision and >85% sensitivity, as well as F1 scores >0.90 in training and validation sets. CONCLUSION: We proposed the disease severity, age, serum levels of hs-CRP, LDH, ferritin, and IL-10 as significant predictors for death risk of COVID-19, which may help to identify the high-risk COVID-19 cases. KEY MESSAGES A machine learning method is used to build death risk model for COVID-19 patients. Disease severity, age, hs-CRP, LDH, ferritin, and IL-10 are death risk factors. These findings may help to identify the high-risk COVID-19 cases.


Subject(s)
COVID-19/mortality , Clinical Decision Rules , Hospitalization , Machine Learning , Adult , Aged , Aged, 80 and over , C-Reactive Protein/metabolism , COVID-19/epidemiology , COVID-19/metabolism , COVID-19/physiopathology , Cardiovascular Diseases/epidemiology , China/epidemiology , Cohort Studies , Comorbidity , Diabetes Mellitus/epidemiology , Female , Ferritins/metabolism , Humans , Hypertension/epidemiology , Interleukin-10/metabolism , L-Lactate Dehydrogenase/metabolism , Male , Middle Aged , Prognosis , Reproducibility of Results , Retrospective Studies , SARS-CoV-2 , Severity of Illness Index
3.
J Med Internet Res ; 23(2): e24246, 2021 02 10.
Article in English | MEDLINE | ID: covidwho-1573886

ABSTRACT

BACKGROUND: Predicting early respiratory failure due to COVID-19 can help triage patients to higher levels of care, allocate scarce resources, and reduce morbidity and mortality by appropriately monitoring and treating the patients at greatest risk for deterioration. Given the complexity of COVID-19, machine learning approaches may support clinical decision making for patients with this disease. OBJECTIVE: Our objective is to derive a machine learning model that predicts respiratory failure within 48 hours of admission based on data from the emergency department. METHODS: Data were collected from patients with COVID-19 who were admitted to Northwell Health acute care hospitals and were discharged, died, or spent a minimum of 48 hours in the hospital between March 1 and May 11, 2020. Of 11,525 patients, 933 (8.1%) were placed on invasive mechanical ventilation within 48 hours of admission. Variables used by the models included clinical and laboratory data commonly collected in the emergency department. We trained and validated three predictive models (two based on XGBoost and one that used logistic regression) using cross-hospital validation. We compared model performance among all three models as well as an established early warning score (Modified Early Warning Score) using receiver operating characteristic curves, precision-recall curves, and other metrics. RESULTS: The XGBoost model had the highest mean accuracy (0.919; area under the curve=0.77), outperforming the other two models as well as the Modified Early Warning Score. Important predictor variables included the type of oxygen delivery used in the emergency department, patient age, Emergency Severity Index level, respiratory rate, serum lactate, and demographic characteristics. CONCLUSIONS: The XGBoost model had high predictive accuracy, outperforming other early warning scores. The clinical plausibility and predictive ability of XGBoost suggest that the model could be used to predict 48-hour respiratory failure in admitted patients with COVID-19.


Subject(s)
COVID-19/physiopathology , Hospitalization , Intubation, Intratracheal/statistics & numerical data , Machine Learning , Respiration, Artificial/statistics & numerical data , Respiratory Insufficiency/epidemiology , Aged , COVID-19/complications , Clinical Decision Rules , Early Warning Score , Emergency Service, Hospital , Female , Hospitals , Humans , Logistic Models , Male , Middle Aged , Patient Admission , ROC Curve , Respiratory Insufficiency/etiology , Retrospective Studies , SARS-CoV-2 , Triage
4.
mSphere ; 6(5): e0075221, 2021 10 27.
Article in English | MEDLINE | ID: covidwho-1526451

ABSTRACT

During the progression of coronavirus disease 2019 (COVID-19), immune response and inflammation reactions are dynamic events that develop rapidly and are associated with the severity of disease. Here, we aimed to develop a predictive model based on the immune and inflammatory response to discriminate patients with severe COVID-19. COVID-19 patients were enrolled, and their demographic and immune inflammatory reaction indicators were collected and analyzed. Logistic regression analysis was performed to identify the independent predictors, which were further used to construct a predictive model. The predictive performance of the model was evaluated by receiver operating characteristic curve, and optimal diagnostic threshold was calculated; these were further validated by 5-fold cross-validation and external validation. We screened three key indicators, including neutrophils, eosinophils, and IgA, for predicting severe COVID-19 and obtained a combined neutrophil, eosinophil, and IgA ratio (NEAR) model (NEU [109/liter] - 150×EOS [109/liter] + 3×IgA [g/liter]). NEAR achieved an area under the curve (AUC) of 0.961, and when a threshold of 9 was applied, the sensitivity and specificity of the predicting model were 100% and 88.89%, respectively. Thus, NEAR is an effective index for predicting the severity of COVID-19 and can be used as a powerful tool for clinicians to make better clinical decisions. IMPORTANCE The immune inflammatory response changes rapidly with the progression of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and is responsible for clearance of the virus and further recovery from the infection. However, the intensified immune and inflammatory response in the development of the disease may lead to more serious and fatal consequences, which indicates that immune indicators have the potential to predict serious cases. Here, we identified both eosinophils and serum IgA as prognostic markers of COVID-19, which sheds light on new research directions and is worthy of further research in the scientific research field as well as clinical application. In this study, the combination of NEU count, EOS count, and IgA level was included in a new predictive model of the severity of COVID-19, which can be used as a powerful tool for better clinical decision-making.


Subject(s)
COVID-19 Testing/methods , COVID-19/diagnosis , COVID-19/immunology , Clinical Decision Rules , Severity of Illness Index , Adult , Aged , Biomarkers/blood , COVID-19/blood , Clinical Decision-Making/methods , Disease Progression , Eosinophils/metabolism , Female , Humans , Immunoglobulin A/blood , Inflammation/blood , Inflammation/diagnosis , Inflammation/virology , Logistic Models , Male , Middle Aged , Neutrophils/metabolism , Predictive Value of Tests , Prognosis , Sensitivity and Specificity
5.
Clin Infect Dis ; 73(10): 1822-1830, 2021 11 16.
Article in English | MEDLINE | ID: covidwho-1522141

ABSTRACT

BACKGROUND: Prompt identification of infections is critical for slowing the spread of infectious diseases. However, diagnostic testing shortages are common in emerging diseases, low resource settings, and during outbreaks. This forces difficult decisions regarding who receives a test, often without knowing the implications of those decisions on population-level transmission dynamics. Clinical prediction rules (CPRs) are commonly used tools to guide clinical decisions. METHODS: Using early severe acute respiratory syndrome coronavirus disease 2 (SARS-CoV-2) as an example, we used data from electronic health records to develop a parsimonious 5-variable CPR to identify those who are most likely to test positive. To consider the implications of gains in daily case detection at the population level, we incorporated testing using the CPR into a compartmentalized model of SARS-CoV-2. RESULTS: We found that applying this CPR (area under the curve, 0.69; 95% confidence interval, .68-.70) to prioritize testing increased the proportion of those testing positive in settings of limited testing capacity. We found that prioritized testing led to a delayed and lowered infection peak (ie, "flattens the curve"), with the greatest impact at lower values of the effective reproductive number (such as with concurrent community mitigation efforts), and when higher proportions of infectious persons seek testing. In addition, prioritized testing resulted in reductions in overall infections as well as hospital and intensive care unit burden. CONCLUSION: We highlight the population-level benefits of evidence-based allocation of limited diagnostic capacity.SummaryWhen the demand for diagnostic tests exceeds capacity, the use of a clinical prediction rule to prioritize diagnostic testing can have meaningful impact on population-level outcomes, including delaying and lowering the infection peak, and reducing healthcare burden.


Subject(s)
COVID-19 , SARS-CoV-2 , Clinical Decision Rules , Diagnostic Techniques and Procedures , Diagnostic Tests, Routine , Hospitals , Humans
7.
JAMA Netw Open ; 4(10): e2124946, 2021 10 01.
Article in English | MEDLINE | ID: covidwho-1460117

ABSTRACT

Importance: Machine learning could be used to predict the likelihood of diagnosis and severity of illness. Lack of COVID-19 patient data has hindered the data science community in developing models to aid in the response to the pandemic. Objectives: To describe the rapid development and evaluation of clinical algorithms to predict COVID-19 diagnosis and hospitalization using patient data by citizen scientists, provide an unbiased assessment of model performance, and benchmark model performance on subgroups. Design, Setting, and Participants: This diagnostic and prognostic study operated a continuous, crowdsourced challenge using a model-to-data approach to securely enable the use of regularly updated COVID-19 patient data from the University of Washington by participants from May 6 to December 23, 2020. A postchallenge analysis was conducted from December 24, 2020, to April 7, 2021, to assess the generalizability of models on the cumulative data set as well as subgroups stratified by age, sex, race, and time of COVID-19 test. By December 23, 2020, this challenge engaged 482 participants from 90 teams and 7 countries. Main Outcomes and Measures: Machine learning algorithms used patient data and output a score that represented the probability of patients receiving a positive COVID-19 test result or being hospitalized within 21 days after receiving a positive COVID-19 test result. Algorithms were evaluated using area under the receiver operating characteristic curve (AUROC) and area under the precision recall curve (AUPRC) scores. Ensemble models aggregating models from the top challenge teams were developed and evaluated. Results: In the analysis using the cumulative data set, the best performance for COVID-19 diagnosis prediction was an AUROC of 0.776 (95% CI, 0.775-0.777) and an AUPRC of 0.297, and for hospitalization prediction, an AUROC of 0.796 (95% CI, 0.794-0.798) and an AUPRC of 0.188. Analysis on top models submitting to the challenge showed consistently better model performance on the female group than the male group. Among all age groups, the best performance was obtained for the 25- to 49-year age group, and the worst performance was obtained for the group aged 17 years or younger. Conclusions and Relevance: In this diagnostic and prognostic study, models submitted by citizen scientists achieved high performance for the prediction of COVID-19 testing and hospitalization outcomes. Evaluation of challenge models on demographic subgroups and prospective data revealed performance discrepancies, providing insights into the potential bias and limitations in the models.


Subject(s)
Algorithms , Benchmarking , COVID-19/diagnosis , Clinical Decision Rules , Crowdsourcing , Hospitalization/statistics & numerical data , Machine Learning , Adolescent , Adult , Aged , Aged, 80 and over , Area Under Curve , COVID-19/epidemiology , COVID-19/therapy , COVID-19 Testing , Child , Child, Preschool , Female , Humans , Infant , Infant, Newborn , Male , Middle Aged , Models, Statistical , Prognosis , ROC Curve , Severity of Illness Index , Washington/epidemiology , Young Adult
8.
Sci Rep ; 11(1): 19450, 2021 09 30.
Article in English | MEDLINE | ID: covidwho-1447321

ABSTRACT

Recent reports linked acute COVID-19 infection in hospitalized patients to cardiac abnormalities. Studies have not evaluated presence of abnormal cardiac structure and function before scanning in setting of COVD-19 infection. We sought to examine cardiac abnormalities in consecutive group of patients with acute COVID-19 infection according to the presence or absence of cardiac disease based on review of health records and cardiovascular imaging studies. We looked at independent contribution of imaging findings to clinical outcomes. After excluding patients with previous left ventricular (LV) systolic dysfunction (global and/or segmental), 724 patients were included. Machine learning identified predictors of in-hospital mortality and in-hospital mortality + ECMO. In patients without previous cardiovascular disease, LV EF < 50% occurred in 3.4%, abnormal LV global longitudinal strain (< 16%) in 24%, and diastolic dysfunction in 20%. Right ventricular systolic dysfunction (RV free wall strain < 20%) was noted in 18%. Moderate and large pericardial effusion were uncommon with an incidence of 0.4% for each category. Forty patients received ECMO support, and 79 died (10.9%). A stepwise increase in AUC was observed with addition of vital signs and laboratory measurements to baseline clinical characteristics, and a further significant increase (AUC 0.91) was observed when echocardiographic measurements were added. The performance of an optimized prediction model was similar to the model including baseline characteristics + vital signs and laboratory results + echocardiographic measurements.


Subject(s)
COVID-19/complications , Heart Diseases/etiology , Heart Diseases/mortality , Hospitalization/statistics & numerical data , Adolescent , Adult , Aged , COVID-19/diagnosis , COVID-19/mortality , COVID-19/therapy , Clinical Decision Rules , Echocardiography , Extracorporeal Membrane Oxygenation , Female , Heart Diseases/diagnostic imaging , Hospital Mortality/trends , Humans , Machine Learning , Male , Middle Aged , Models, Theoretical , Prognosis , ROC Curve , Retrospective Studies , Young Adult
9.
J Thromb Thrombolysis ; 52(1): 76-84, 2021 Jul.
Article in English | MEDLINE | ID: covidwho-1310591

ABSTRACT

Subpleural consolidations have been found in lung ultrasound in patients with COVID-19, possibly deriving from pulmonary embolism (PE). The diagnostic utility of impact of lung ultrasound in critical-ill patients with COVID-19 for PE diagnostics however is unclear. We retrospectively evaluated all SARS-CoV2-associated ARDS patients admitted to our ICU between March 8th and May 31th 2020. They were enrolled in this study, when a lung ultrasound and a computed tomography pulmonary angiography (CTPA) were documented. In addition, wells score was calculated to estimate the probability of PE. The CTPA was used as the gold standard for the detection of PE. Twenty out of 25 patients met the inclusion criteria. In 12/20 patients (60%) (sub-) segmental PE were detected by CT-angiography. Lung ultrasound found subpleural consolidations in 90% of patients. PE-typical large supleural consolidations with a size ≥ 1 cm were detectable in 65% of patients and were significant more frequent in patients with PE compared to those without (p = 0.035). Large consolidations predicted PE with a sensitivity of 77% and a specificity of 71%. The Wells score was significantly higher in patients with PE compared to those without (2.7 ± 0.8 and 1.7 ± 0.5, respectively, p = 0.042) and predicted PE with an AUC of 0.81. When combining the two modalities, comparing patients with considered/probable PE using LUS plus a Wells score ≥ 2 to patients with possible/unlikely PE in LUS plus a Wells score < 2, PE could be predicted with a sensitivity of 100% and a specificity of 80%. Large consolidations detected in lung ultrasound were found frequently in COVID-19 ARDS patients with pulmonary embolism. In combination with a Wells score > 2, this might indicate a high-risk for PE in COVID-19.


Subject(s)
COVID-19/complications , Clinical Decision Rules , Computed Tomography Angiography , Lung/diagnostic imaging , Pulmonary Artery/diagnostic imaging , Pulmonary Embolism/diagnostic imaging , Ultrasonography , Aged , COVID-19/diagnosis , Critical Illness , Female , Humans , Male , Middle Aged , Multimodal Imaging , Predictive Value of Tests , Pulmonary Embolism/etiology , Registries , Reproducibility of Results , Retrospective Studies , Risk Assessment , Risk Factors
10.
Acad Emerg Med ; 28(7): 761-767, 2021 07.
Article in English | MEDLINE | ID: covidwho-1270815

ABSTRACT

OBJECTIVES: Accurate estimation of the risk of SARS-CoV-2 infection based on bedside data alone has importance to emergency department (ED) operations and throughput. The 13-item CORC (COVID [or coronavirus] Rule-out Criteria) rule had good overall diagnostic accuracy in retrospective derivation and validation. The objective of this study was to prospectively test the inter-rater reliability and diagnostic accuracy of the CORC score and rule (score ≤ 0 negative, > 0 positive) and compare the CORC rule performance with physician gestalt. METHODS: This noninterventional study was conducted at an urban academic ED from February 2021 to March 2021. Two practitioners were approached by research coordinators and asked to independently complete a form capturing the CORC criteria for their shared patient and their gestalt binary prediction of the SARS-CoV-2 test result and confidence (0%-100%). The criterion standard for SARS-CoV-2 was from reverse transcriptase polymerase chain reaction performed on a nasopharyngeal swab. The primary analysis was from weighted Cohen's kappa and likelihood ratios (LRs). RESULTS: For 928 patients, agreement between observers was good for the total CORC score, κ = 0.613 (95% confidence interval [CI] = 0.579-0.646), and for the CORC rule, κ = 0.644 (95% CI = 0.591-0.697). The agreement for clinician gestalt binary determination of SARs-CoV-2 status was κ = 0.534 (95% CI = 0.437-0.632) with median confidence of 76% (first-third quartile = 66-88.5). For 425 patients who had the criterion standard, a negative CORC rule (both observers scored CORC < 0), the sensitivity was 88%, and specificity was 51%, with a negative LR (LR-) of 0.24 (95% CI = 0.10-0.50). Among patients with a mean CORC score of >4, the prevalence of a positive SARS-CoV-2 test was 58% (95% CI = 28%-85%) and positive LR was 13.1 (95% CI = 4.5-37.2). Clinician gestalt demonstrated a sensitivity of 51% and specificity of 86% with a LR- of 0.57 (95% CI = 0.39-0.74). CONCLUSION: In this prospective study, the CORC score and rule demonstrated good inter-rater reliability and reproducible diagnostic accuracy for estimating the pretest probability of SARs-CoV-2 infection.


Subject(s)
COVID-19 , SARS-CoV-2 , Clinical Decision Rules , Humans , Prospective Studies , Reproducibility of Results , Retrospective Studies
11.
J Am Heart Assoc ; 9(21): e017847, 2020 11 03.
Article in English | MEDLINE | ID: covidwho-1255740

ABSTRACT

Background Across the globe, elective surgeries have been postponed to limit infectious exposure and preserve hospital capacity for coronavirus disease 2019 (COVID-19). However, the ramp down in cardiac surgery volumes may result in unintended harm to patients who are at high risk of mortality if their conditions are left untreated. To help optimize triage decisions, we derived and ambispectively validated a clinical score to predict intensive care unit length of stay after cardiac surgery. Methods and Results Following ethics approval, we derived and performed multicenter valida tion of clinical models to predict the likelihood of short (≤2 days) and prolonged intensive care unit length of stay (≥7 days) in patients aged ≥18 years, who underwent coronary artery bypass grafting and/or aortic, mitral, and tricuspid value surgery in Ontario, Canada. Multivariable logistic regression with backward variable selection was used, along with clinical judgment, in the modeling process. For the model that predicted short intensive care unit stay, the c-statistic was 0.78 in the derivation cohort and 0.71 in the validation cohort. For the model that predicted prolonged stay, c-statistic was 0.85 in the derivation and 0.78 in the validation cohort. The models, together termed the CardiOttawa LOS Score, demonstrated a high degree of accuracy during prospective testing. Conclusions Clinical judgment alone has been shown to be inaccurate in predicting postoperative intensive care unit length of stay. The CardiOttawa LOS Score performed well in prospective validation and will complement the clinician's gestalt in making more efficient resource allocation during the COVID-19 period and beyond.


Subject(s)
Cardiac Surgical Procedures , Clinical Decision Rules , Intensive Care Units , Length of Stay , Adult , Aged , Aged, 80 and over , Cardiac Surgical Procedures/adverse effects , Clinical Decision-Making , Female , Humans , Male , Middle Aged , Ontario , Predictive Value of Tests , Risk Assessment , Risk Factors , Time Factors , Treatment Outcome , Triage
12.
PLoS One ; 16(5): e0250569, 2021.
Article in English | MEDLINE | ID: covidwho-1234583

ABSTRACT

OBJECTIVES: Although some prognostic factors for COVID-19 were consistently identified across the studies, differences were found for other factors that could be due to the characteristics of the study populations and the variables incorporated into the statistical model. We aimed to a priori identify specific patient profiles and then assess their association with the outcomes in COVID-19 patients with respiratory symptoms admitted specifically to hospital wards. METHODS: We conducted a retrospective single-center study from February 2020 to April 2020. A non-supervised cluster analysis was first used to detect patient profiles based on characteristics at admission of 220 consecutive patients admitted to our institution. Then, we assessed the prognostic value using Cox regression analyses to predict survival. RESULTS: Three clusters were identified, with 47 patients in cluster 1, 87 in cluster 2, and 86 in cluster 3; the presentation of the patients differed among the clusters. Cluster 1 mostly included sexagenarian patients with active malignancies who were admitted early after the onset of COVID-19. Cluster 2 included the oldest patients, who were generally overweight and had hypertension and renal insufficiency, while cluster 3 included the youngest patients, who had gastrointestinal symptoms and delayed admission. Sixty-day survival rates were 74.3%, 50.6% and 96.5% in clusters 1, 2, and 3, respectively. This was confirmed by the multivariable Cox analyses that showed the prognostic value of these patterns. CONCLUSION: The cluster approach seems appropriate and pragmatic for the early identification of patient profiles that could help physicians segregate patients according to their prognosis.


Subject(s)
COVID-19/mortality , Aged , COVID-19/epidemiology , COVID-19/therapy , Clinical Decision Rules , Cluster Analysis , Female , France/epidemiology , Hospitalization/statistics & numerical data , Hospitals/statistics & numerical data , Humans , Male , Middle Aged , Prognosis , Retrospective Studies , SARS-CoV-2/isolation & purification
13.
Dis Markers ; 2021: 8863053, 2021.
Article in English | MEDLINE | ID: covidwho-1231192

ABSTRACT

Introduction: The clinical course of Coronavirus Disease 2019 (COVID-19) is highly heterogenous, ranging from asymptomatic to fatal forms. The identification of clinical and laboratory predictors of poor prognosis may assist clinicians in monitoring strategies and therapeutic decisions. Materials and Methods: In this study, we retrospectively assessed the prognostic value of a simple tool, the complete blood count, on a cohort of 664 patients (F 260; 39%, median age 70 (56-81) years) hospitalized for COVID-19 in Northern Italy. We collected demographic data along with complete blood cell count; moreover, the outcome of the hospital in-stay was recorded. Results: At data cut-off, 221/664 patients (33.3%) had died and 453/664 (66.7%) had been discharged. Red cell distribution width (RDW) (χ 2 10.4; p < 0.001), neutrophil-to-lymphocyte (NL) ratio (χ 2 7.6; p = 0.006), and platelet count (χ 2 5.39; p = 0.02), along with age (χ 2 87.6; p < 0.001) and gender (χ 2 17.3; p < 0.001), accurately predicted in-hospital mortality. Hemoglobin levels were not associated with mortality. We also identified the best cut-off for mortality prediction: a NL ratio > 4.68 was characterized by an odds ratio for in-hospital mortality (OR) = 3.40 (2.40-4.82), while the OR for a RDW > 13.7% was 4.09 (2.87-5.83); a platelet count > 166,000/µL was, conversely, protective (OR: 0.45 (0.32-0.63)). Conclusion: Our findings arise the opportunity of stratifying COVID-19 severity according to simple lab parameters, which may drive clinical decisions about monitoring and treatment.


Subject(s)
Blood Cell Count , COVID-19/blood , COVID-19/mortality , Clinical Decision Rules , Hospital Mortality , Severity of Illness Index , Adult , Aged , Aged, 80 and over , COVID-19/diagnosis , Female , Humans , Italy/epidemiology , Male , Middle Aged , Multivariate Analysis , Prognosis , Retrospective Studies
14.
J Med Virol ; 93(2): 973-982, 2021 02.
Article in English | MEDLINE | ID: covidwho-1196423

ABSTRACT

Coronavirus disease 2019 (COVID-19) is an infection caused by the virus SARS-CoV-2, and has caused the most widespread global pandemic in over 100 years. Given the novelty of the disease, risk factors of mortality and adverse outcomes in hospitalized patients remain to be elucidated. We present the results of a retrospective cohort study including patients admitted to a large tertiary-care, academic university hospital with COVID-19. Patients were admitted with confirmed diagnosis of COVID-19 between 1 March and 15 April 2020. Baseline clinical characteristics and admission laboratory variables were retrospectively collected. Patients were grouped based on mortality, need for ICU care, and mechanical ventilation. Prevalence of clinical co-morbidities and laboratory abnormalities were compared between groups using descriptive statistics. Univariate analysis was performed to identify predictors of mortality, ICU care and mechanical ventilation. Predictors significant at P ≤ .10 were included in multivariate analysis. Five hundred and sixty patients were included in the analysis. Age and myocardial injury were only independent predictors of mortality, in patients with/without baseline co-morbidities. Body mass index, elevated ferritin, elevated d-dimer, and elevated procalcitonin predicted need for ICU care, and these along with vascular disease at baseline predicted need for mechanical ventilation. Hence, inflammatory markers (ferritin and d-dimer) predicted severe disease, but not death.


Subject(s)
COVID-19/complications , COVID-19/mortality , Heart Injuries/mortality , Heart Injuries/virology , Myocardium/pathology , Adult , Age Factors , Aged , Aged, 80 and over , Clinical Decision Rules , Comorbidity , Critical Care , Female , Hospitalization , Humans , Male , Middle Aged , Retrospective Studies , Risk Factors , United States
15.
BMC Pulm Med ; 21(1): 120, 2021 Apr 14.
Article in English | MEDLINE | ID: covidwho-1183526

ABSTRACT

BACKGROUND: During outbreak of Coronavirus Disease 2019 (COVID-19), healthcare providers are facing critical clinical decisions based on the prognosis of patients. Decision support tools of risk stratification are needed to predict outcomes in patients with different clinical types of COVID-19. METHODS: This retrospective cohort study recruited 2425 patients with moderate or severe COVID-19. A logistic regression model was used to select and estimate the factors independently associated with outcomes. Simplified risk stratification score systems were constructed to predict outcomes in moderate and severe patients with COVID-19, and their performances were evaluated by discrimination and calibration. RESULTS: We constructed two risk stratification score systems, named as STPCAL (including significant factors in the prediction model: number of clinical symptoms, the maximum body temperature during hospitalization, platelet count, C-reactive protein, albumin and lactate dehydrogenase) and TRPNCLP (including maximum body temperature during hospitalization, history of respiratory diseases, platelet count, neutrophil-to-lymphocyte ratio, creatinine, lactate dehydrogenase, and prothrombin time), to predict hospitalization duration for moderate patients and disease progression for severe patients, respectively. According to STPCAL score, moderate patients were classified into three risk categories for a longer hospital duration: low (Score 0-1, median = 8 days, with less than 20.0% probabilities), intermediate (Score 2-6, median = 13 days, with 30.0-78.9% probabilities), high (Score 7-9, median = 19 days, with more than 86.5% probabilities). Severe patients were stratified into three risk categories for disease progression: low risk (Score 0-5, with less than 12.7% probabilities), intermediate risk (Score 6-11, with 18.6-69.1% probabilities), and high risk (Score 12-16, with more than 77.9% probabilities) by TRPNCLP score. The two risk scores performed well with good discrimination and calibration. CONCLUSIONS: Two easy-to-use risk stratification score systems were built to predict the outcomes in COVID-19 patients with different clinical types. Identifying high risk patients with longer stay or poor prognosis could assist healthcare providers in triaging patients when allocating limited healthcare during COVID-19 outbreak.


Subject(s)
COVID-19 Testing/methods , COVID-19/diagnosis , COVID-19/therapy , Clinical Decision Rules , Disease Progression , Hospitalization/statistics & numerical data , Severity of Illness Index , Adolescent , Adult , Aged , Aged, 80 and over , Clinical Decision-Making/methods , Female , Humans , Logistic Models , Male , Middle Aged , Prognosis , Retrospective Studies , Risk Assessment , Risk Factors , Sensitivity and Specificity , Triage/methods , Young Adult
16.
Lancet Respir Med ; 9(4): 349-359, 2021 04.
Article in English | MEDLINE | ID: covidwho-1180127

ABSTRACT

BACKGROUND: Prognostic models to predict the risk of clinical deterioration in acute COVID-19 cases are urgently required to inform clinical management decisions. METHODS: We developed and validated a multivariable logistic regression model for in-hospital clinical deterioration (defined as any requirement of ventilatory support or critical care, or death) among consecutively hospitalised adults with highly suspected or confirmed COVID-19 who were prospectively recruited to the International Severe Acute Respiratory and Emerging Infections Consortium Coronavirus Clinical Characterisation Consortium (ISARIC4C) study across 260 hospitals in England, Scotland, and Wales. Candidate predictors that were specified a priori were considered for inclusion in the model on the basis of previous prognostic scores and emerging literature describing routinely measured biomarkers associated with COVID-19 prognosis. We used internal-external cross-validation to evaluate discrimination, calibration, and clinical utility across eight National Health Service (NHS) regions in the development cohort. We further validated the final model in held-out data from an additional NHS region (London). FINDINGS: 74 944 participants (recruited between Feb 6 and Aug 26, 2020) were included, of whom 31 924 (43·2%) of 73 948 with available outcomes met the composite clinical deterioration outcome. In internal-external cross-validation in the development cohort of 66 705 participants, the selected model (comprising 11 predictors routinely measured at the point of hospital admission) showed consistent discrimination, calibration, and clinical utility across all eight NHS regions. In held-out data from London (n=8239), the model showed a similarly consistent performance (C-statistic 0·77 [95% CI 0·76 to 0·78]; calibration-in-the-large 0·00 [-0·05 to 0·05]); calibration slope 0·96 [0·91 to 1·01]), and greater net benefit than any other reproducible prognostic model. INTERPRETATION: The 4C Deterioration model has strong potential for clinical utility and generalisability to predict clinical deterioration and inform decision making among adults hospitalised with COVID-19. FUNDING: National Institute for Health Research (NIHR), UK Medical Research Council, Wellcome Trust, Department for International Development, Bill & Melinda Gates Foundation, EU Platform for European Preparedness Against (Re-)emerging Epidemics, NIHR Health Protection Research Unit (HPRU) in Emerging and Zoonotic Infections at University of Liverpool, NIHR HPRU in Respiratory Infections at Imperial College London.


Subject(s)
COVID-19/diagnosis , Clinical Decision Rules , Clinical Decision-Making/methods , Clinical Deterioration , Aged , Aged, 80 and over , COVID-19/mortality , COVID-19/therapy , Critical Care/statistics & numerical data , Female , Hospital Mortality , Humans , Intensive Care Units/statistics & numerical data , Logistic Models , Male , Middle Aged , Patient Admission/statistics & numerical data , Prognosis , Prospective Studies , Reproducibility of Results , Respiration, Artificial/statistics & numerical data , SARS-CoV-2/isolation & purification , Severity of Illness Index , United Kingdom/epidemiology
17.
Epidemiol Infect ; 149: e92, 2021 04 05.
Article in English | MEDLINE | ID: covidwho-1169347

ABSTRACT

Case identification is an ongoing issue for the COVID-19 epidemic, in particular for outpatient care where physicians must decide which patients to prioritise for further testing. This paper reports tools to classify patients based on symptom profiles based on 236 severe acute respiratory syndrome coronavirus 2 positive cases and 564 controls, accounting for the time course of illness using generalised multivariate logistic regression. Significant symptoms included abdominal pain, cough, diarrhoea, fever, headache, muscle ache, runny nose, sore throat, temperature between 37.5 and 37.9 °C and temperature above 38 °C, but their importance varied by day of illness at assessment. With a high percentile threshold for specificity at 0.95, the baseline model had reasonable sensitivity at 0.67. To further evaluate accuracy of model predictions, leave-one-out cross-validation confirmed high classification accuracy with an area under the receiver operating characteristic curve of 0.92. For the baseline model, sensitivity decreased to 0.56. External validation datasets reported similar result. Our study provides a tool to discern COVID-19 patients from controls using symptoms and day from illness onset with good predictive performance. It could be considered as a framework to complement laboratory testing in order to differentiate COVID-19 from other patients presenting with acute symptoms in outpatient care.


Subject(s)
Ambulatory Care , COVID-19 Testing/methods , COVID-19/diagnosis , Abdominal Pain/physiopathology , Adolescent , Adult , COVID-19/physiopathology , Case-Control Studies , Clinical Decision Rules , Cough/physiopathology , Diarrhea/physiopathology , Disease Progression , Dyspnea/physiopathology , Female , Fever/physiopathology , Headache/physiopathology , Humans , Logistic Models , Male , Middle Aged , Multivariate Analysis , Myalgia/physiopathology , Odds Ratio , Patient Selection , Pharyngitis/physiopathology , Rhinorrhea/physiopathology , SARS-CoV-2 , Sensitivity and Specificity , Severity of Illness Index , Young Adult
18.
Nutr Clin Pract ; 36(2): 268-274, 2021 Apr.
Article in English | MEDLINE | ID: covidwho-1151955

ABSTRACT

Clinicians have widely recognized that indirect calorimetry (IC) is the "gold standard" for measuring energy expenditure (EE) and thus would intuitively anticipate that its use would be needed to provide optimal nutrition support in critical illness. Recent studies in the literature as well as dramatic changes in clinical practice over the past decade, though, would suggest that such a precise measure by IC to set energy goals is not required to maximize clinical benefit from early feeding in the intensive care unit (ICU). Results from randomized controlled trials evaluating permissive underfeeding, use of supplemental parenteral nutrition to achieve tight calorie control, and caloric density of formulas to increase energy delivery have provided an important perspective on 3 pertinent issues. First, a simple weight-based predictive equation (25 kcal/kg/day) provides a clinically useful approximation of EE. Second, a precise measure of EE by IC does not appear to improve outcomes compared with use of this less accurate estimation of energy requirements. And third, providing some percentage of requirements (50%-80%), achieves similar clinical benefit to full feeding (100%) in the early phases of critical illness. The value from IC use lies in the determination of caloric requirements in conditions for which weight-based equations are rendered inaccurate (anasarca, amputation, severe obesity) or the clinical state is markedly altered (such as the prolonged hyperinflammatory state of coronavirus disease 2019 [COVID-19]). In most other circumstances, routine use of IC would not be expected to change clinical outcomes from early nutrition therapy in the ICU.


Subject(s)
COVID-19/therapy , Clinical Decision Rules , Nutrition Assessment , Nutritional Support/methods , Body Weight , Calorimetry, Indirect , Critical Illness/therapy , Energy Metabolism , Humans , Intensive Care Units , Nutritional Requirements , Nutritional Status , SARS-CoV-2
19.
BMC Pulm Med ; 21(1): 103, 2021 Mar 24.
Article in English | MEDLINE | ID: covidwho-1150397

ABSTRACT

BACKGROUND: Lung auscultation is fundamental to the clinical diagnosis of respiratory disease. However, auscultation is a subjective practice and interpretations vary widely between users. The digitization of auscultation acquisition and interpretation is a particularly promising strategy for diagnosing and monitoring infectious diseases such as Coronavirus-19 disease (COVID-19) where automated analyses could help decentralise care and better inform decision-making in telemedicine. This protocol describes the standardised collection of lung auscultations in COVID-19 triage sites and a deep learning approach to diagnostic and prognostic modelling for future incorporation into an intelligent autonomous stethoscope benchmarked against human expert interpretation. METHODS: A total of 1000 consecutive, patients aged ≥ 16 years and meeting COVID-19 testing criteria will be recruited at screening sites and amongst inpatients of the internal medicine department at the Geneva University Hospitals, starting from October 2020. COVID-19 is diagnosed by RT-PCR on a nasopharyngeal swab and COVID-positive patients are followed up until outcome (i.e., discharge, hospitalisation, intubation and/or death). At inclusion, demographic and clinical data are collected, such as age, sex, medical history, and signs and symptoms of the current episode. Additionally, lung auscultation will be recorded with a digital stethoscope at 6 thoracic sites in each patient. A deep learning algorithm (DeepBreath) using a Convolutional Neural Network (CNN) and Support Vector Machine classifier will be trained on these audio recordings to derive an automated prediction of diagnostic (COVID positive vs negative) and risk stratification categories (mild to severe). The performance of this model will be compared to a human prediction baseline on a random subset of lung sounds, where blinded physicians are asked to classify the audios into the same categories. DISCUSSION: This approach has broad potential to standardise the evaluation of lung auscultation in COVID-19 at various levels of healthcare, especially in the context of decentralised triage and monitoring. TRIAL REGISTRATION: PB_2016-00500, SwissEthics. Registered on 6 April 2020.


Subject(s)
Auscultation/methods , COVID-19 Testing/methods , COVID-19/diagnosis , Deep Learning , Adolescent , Adult , Aged , Aged, 80 and over , Algorithms , Case-Control Studies , Clinical Decision Rules , Clinical Protocols , Female , Humans , Male , Middle Aged , Prognosis , Prospective Studies , Risk Assessment , Triage , Young Adult
20.
Mil Med Res ; 8(1): 21, 2021 03 17.
Article in English | MEDLINE | ID: covidwho-1140518

ABSTRACT

BACKGROUND: To develop an effective model of predicting fatal outcomes in the severe coronavirus disease 2019 (COVID-19) patients. METHODS: Between February 20, 2020 and April 4, 2020, consecutive confirmed 2541 COVID-19 patients from three designated hospitals were enrolled in this study. All patients received chest computed tomography (CT) and serological examinations at admission. Laboratory tests included routine blood tests, liver function, renal function, coagulation profile, C-reactive protein (CRP), procalcitonin (PCT), interleukin-6 (IL-6), and arterial blood gas. The SaO2 was measured using pulse oxygen saturation in room air at resting status. Independent high-risk factors associated with death were analyzed using Cox proportional hazard model. A prognostic nomogram was constructed to predict the survival of severe COVID-19 patients. RESULTS: There were 124 severe patients in the training cohort, and there were 71 and 76 severe patients in the two independent validation cohorts, respectively. Multivariate Cox analysis indicated that age ≥ 70 years (HR = 1.184, 95% CI 1.061-1.321), panting (breathing rate ≥ 30/min) (HR = 3.300, 95% CI 2.509-6.286), lymphocyte count < 1.0 × 109/L (HR = 2.283, 95% CI 1.779-3.267), and interleukin-6 (IL-6) >  10 pg/ml (HR = 3.029, 95% CI 1.567-7.116) were independent high-risk factors associated with fatal outcome. We developed the nomogram for identifying survival of severe COVID-19 patients in the training cohort (AUC = 0.900, 95% CI 0.841-0.960, sensitivity 95.5%, specificity 77.5%); in validation cohort 1 (AUC = 0.811, 95% CI 0.763-0.961, sensitivity 77.3%, specificity 73.5%); in validation cohort 2 (AUC = 0.862, 95% CI 0.698-0.924, sensitivity 92.9%, specificity 64.5%). The calibration curve for probability of death indicated a good consistence between prediction by the nomogram and the actual observation. The prognosis of severe COVID-19 patients with high levels of IL-6 receiving tocilizumab were better than that of those patients without tocilizumab both in the training and validation cohorts, but without difference (P = 0.105 for training cohort, P = 0.133 for validation cohort 1, and P = 0.210 for validation cohort 2). CONCLUSIONS: This nomogram could help clinicians to identify severe patients who have high risk of death, and to develop more appropriate treatment strategies to reduce the mortality of severe patients. Tocilizumab may improve the prognosis of severe COVID-19 patients with high levels of IL-6.


Subject(s)
COVID-19/mortality , Clinical Decision Rules , Nomograms , Acute Disease , Adult , Age Factors , Aged , Aged, 80 and over , COVID-19/pathology , China/epidemiology , Female , Humans , Male , Middle Aged , Proportional Hazards Models , Retrospective Studies , Risk Factors , Sex Factors , Survival Analysis , Young Adult
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