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1.
Sci Rep ; 12(1): 13810, 2022 08 15.
Article in English | MEDLINE | ID: covidwho-2050439

ABSTRACT

Symptoms have been used to diagnose conditions such as frailty and mental illnesses. However, the diagnostic accuracy of the numbers of symptoms has not been well studied. This study aims to use equations and simulations to demonstrate how the factors that determine symptom incidence influence symptoms' diagnostic accuracy for disease diagnosis. Assuming a disease causing symptoms and correlated with the other disease in 10,000 simulated subjects, 40 symptoms occurred based on 3 epidemiological measures: proportions diseased, baseline symptom incidence (among those not diseased), and risk ratios. Symptoms occurred with similar correlation coefficients. The sensitivities and specificities of single symptoms for disease diagnosis were exhibited as equations using the three epidemiological measures and approximated using linear regression in simulated populations. The areas under curves (AUCs) of the receiver operating characteristic (ROC) curves was the measure to determine the diagnostic accuracy of multiple symptoms, derived by using 2 to 40 symptoms for disease diagnosis. With respect to each AUC, the best set of sensitivity and specificity, whose difference with 1 in the absolute value was maximal, was chosen. The results showed sensitivities and specificities of single symptoms for disease diagnosis were fully explained with the three epidemiological measures in simulated subjects. The AUCs increased or decreased with more symptoms used for disease diagnosis, when the risk ratios were greater or less than 1, respectively. Based on the AUCs, with risk ratios were similar to 1, symptoms did not provide diagnostic values. When risk ratios were greater or less than 1, maximal or minimal AUCs usually could be reached with less than 30 symptoms. The maximal AUCs and their best sets of sensitivities and specificities could be well approximated with the three epidemiological and interaction terms, adjusted R-squared ≥ 0.69. However, the observed overall symptom correlations, overall symptom incidence, and numbers of symptoms explained a small fraction of the AUC variances, adjusted R-squared ≤ 0.03. In conclusion, the sensitivities and specificities of single symptoms for disease diagnosis can be explained fully by the at-risk incidence and the 1 minus baseline incidence, respectively. The epidemiological measures and baseline symptom correlations can explain large fractions of the variances of the maximal AUCs and the best sets of sensitivities and specificities. These findings are important for researchers who want to assess the diagnostic accuracy of composite diagnostic criteria.


Subject(s)
Sensitivity and Specificity , Area Under Curve , Humans , ROC Curve
2.
Antimicrob Agents Chemother ; 66(10): e0063222, 2022 10 18.
Article in English | MEDLINE | ID: covidwho-2019711

ABSTRACT

Ensitrelvir is a novel selective inhibitor of the 3C-like protease of SARS-CoV-2, which is essential for viral replication. This phase 1 study of ensitrelvir assessed its safety, tolerability, and pharmacokinetics of single (part 1, n = 50) and multiple (part 2, n = 33) ascending oral doses. Effect of food on the pharmacokinetics of ensitrelvir, differences in pharmacokinetics of ensitrelvir between Japanese and white participants, and effect of ensitrelvir on the pharmacokinetics of midazolam (a cytochrome P450 3A [CYP3A] substrate) were also assessed. In part 1, Japanese participants were randomized to placebo or ensitrelvir at doses of 20, 70, 250, 500, 1,000, or 2,000 mg. In part 2, Japanese and white participants were randomized to placebo or once-daily ensitrelvir at loading/maintenance dose 375/125 mg or 750/250 mg for 5 days. Most treatment-related adverse events observed were mild in severity and were resolved without treatment. Plasma exposures showed almost dose proportionality, and geometric mean half-life of ensitrelvir following the single dose was 42.2 to 48.1 h. Food intake reduced Cmax and delayed Tmax of ensitrelvir but did not impact the area under the curve (AUC), suggesting suitability for administration without food restriction. Compared with Japanese participants, plasma exposures were slightly lower for white participants. Ensitrelvir affected the pharmacokinetics of CYP3A substrates because of increase in AUC of midazolam coadministered with ensitrelvir 750/250 mg on day 6. In conclusion, ensitrelvir was well-tolerated and demonstrated favorable pharmacokinetics, including a long half-life, supporting once-daily oral dosing. These results validate further assessments of ensitrelvir in participants with SARS-CoV-2 infection.


Subject(s)
Antiviral Agents , COVID-19 , Indazoles , Triazines , Adult , Humans , Administration, Oral , Antiviral Agents/pharmacokinetics , Antiviral Agents/therapeutic use , Area Under Curve , COVID-19/drug therapy , Cytochrome P-450 CYP3A , Dose-Response Relationship, Drug , Double-Blind Method , Enzyme Inhibitors , Healthy Volunteers , Midazolam/therapeutic use , Peptide Hydrolases , Protease Inhibitors , SARS-CoV-2 , Indazoles/pharmacokinetics , Indazoles/therapeutic use , Triazines/pharmacokinetics , Triazines/therapeutic use , Triazoles/pharmacokinetics , Triazoles/therapeutic use
3.
BMC Bioinformatics ; 23(1): 264, 2022 Jul 06.
Article in English | MEDLINE | ID: covidwho-1974113

ABSTRACT

BACKGROUND: Here propose a computer-aided diagnosis (CAD) system to differentiate COVID-19 (the coronavirus disease of 2019) patients from normal cases, as well as to perform infection region segmentation along with infection severity estimation using computed tomography (CT) images. The developed system facilitates timely administration of appropriate treatment by identifying the disease stage without reliance on medical professionals. So far, this developed model gives the most accurate, fully automatic COVID-19 real-time CAD framework. RESULTS: The CT image dataset of COVID-19 and non-COVID-19 individuals were subjected to conventional ML stages to perform binary classification. In the feature extraction stage, SIFT, SURF, ORB image descriptors and bag of features technique were implemented for the appropriate differentiation of chest CT regions affected with COVID-19 from normal cases. This is the first work introducing this concept for COVID-19 diagnosis application. The preferred diverse database and selected features that are invariant to scale, rotation, distortion, noise etc. make this framework real-time applicable. Also, this fully automatic approach which is faster compared to existing models helps to incorporate it into CAD systems. The severity score was measured based on the infected regions along the lung field. Infected regions were segmented through a three-class semantic segmentation of the lung CT image. Using severity score, the disease stages were classified as mild if the lesion area covers less than 25% of the lung area; moderate if 25-50% and severe if greater than 50%. Our proposed model resulted in classification accuracy of 99.7% with a PNN classifier, along with area under the curve (AUC) of 0.9988, 99.6% sensitivity, 99.9% specificity and a misclassification rate of 0.0027. The developed infected region segmentation model gave 99.47% global accuracy, 94.04% mean accuracy, 0.8968 mean IoU (intersection over union), 0.9899 weighted IoU, and a mean Boundary F1 (BF) contour matching score of 0.9453, using Deepabv3+ with its weights initialized using ResNet-50. CONCLUSIONS: The developed CAD system model is able to perform fully automatic and accurate diagnosis of COVID-19 along with infected region extraction and disease stage identification. The ORB image descriptor with bag of features technique and PNN classifier achieved the superior classification performance.


Subject(s)
COVID-19 Testing , COVID-19 , Area Under Curve , COVID-19/diagnostic imaging , Diagnosis, Computer-Assisted , Humans , Tomography, X-Ray Computed
4.
Sci Rep ; 12(1): 4132, 2022 03 08.
Article in English | MEDLINE | ID: covidwho-1908246

ABSTRACT

This paper presents a deep learning-driven portable, accurate, low-cost, and easy-to-use device to perform Reverse-Transcription Loop-Mediated Isothermal Amplification (RT-LAMP) to facilitate rapid detection of COVID-19. The 3D-printed device-powered using only a 5 Volt AC-DC adapter-can perform 16 simultaneous RT-LAMP reactions and can be used multiple times. Moreover, the experimental protocol is devised to obviate the need for separate, expensive equipment for RNA extraction in addition to eliminating sample evaporation. The entire process from sample preparation to the qualitative assessment of the LAMP amplification takes only 45 min (10 min for pre-heating and 35 min for RT-LAMP reactions). The completion of the amplification reaction yields a fuchsia color for the negative samples and either a yellow or orange color for the positive samples, based on a pH indicator dye. The device is coupled with a novel deep learning system that automatically analyzes the amplification results and pays attention to the pH indicator dye to screen the COVID-19 subjects. The proposed device has been rigorously tested on 250 RT-LAMP clinical samples, where it achieved an overall specificity and sensitivity of 0.9666 and 0.9722, respectively with a recall of 0.9892 for Ct < 30. Also, the proposed system can be widely used as an accurate, sensitive, rapid, and portable tool to detect COVID-19 in settings where access to a lab is difficult, or the results are urgently required.


Subject(s)
COVID-19/diagnosis , Deep Learning , Molecular Diagnostic Techniques/methods , Nucleic Acid Amplification Techniques/methods , SARS-CoV-2/genetics , Area Under Curve , COVID-19 Testing , Coloring Agents/chemistry , Humans , Molecular Diagnostic Techniques/instrumentation , Nasopharynx/virology , Nucleic Acid Amplification Techniques/instrumentation , Point-of-Care Systems , Printing, Three-Dimensional , RNA, Viral/analysis , RNA, Viral/metabolism , ROC Curve , SARS-CoV-2/isolation & purification , Sensitivity and Specificity
5.
Sci Rep ; 12(1): 3797, 2022 03 08.
Article in English | MEDLINE | ID: covidwho-1908239

ABSTRACT

Infectious threats, like the COVID-19 pandemic, hinder maintenance of a productive and healthy workforce. If subtle physiological changes precede overt illness, then proactive isolation and testing can reduce labor force impacts. This study hypothesized that an early infection warning service based on wearable physiological monitoring and predictive models created with machine learning could be developed and deployed. We developed a prototype tool, first deployed June 23, 2020, that delivered continuously updated scores of infection risk for SARS-CoV-2 through April 8, 2021. Data were acquired from 9381 United States Department of Defense (US DoD) personnel wearing Garmin and Oura devices, totaling 599,174 user-days of service and 201 million hours of data. There were 491 COVID-19 positive cases. A predictive algorithm identified infection before diagnostic testing with an AUC of 0.82. Barriers to implementation included adequate data capture (at least 48% data was needed) and delays in data transmission. We observe increased risk scores as early as 6 days prior to diagnostic testing (2.3 days average). This study showed feasibility of a real-time risk prediction score to minimize workforce impacts of infection.


Subject(s)
Algorithms , COVID-19/diagnosis , Monitoring, Physiologic/methods , Area Under Curve , COVID-19/virology , Humans , Military Personnel , Monitoring, Physiologic/instrumentation , ROC Curve , SARS-CoV-2/isolation & purification , User-Computer Interface , Wearable Electronic Devices
6.
Clin Transl Sci ; 15(9): 2159-2171, 2022 Sep.
Article in English | MEDLINE | ID: covidwho-1891527

ABSTRACT

Ribavirin is an inosine monophosphate dehydrogenase inhibitor. Studies suggest ribavirin aerosol could be a safe and efficacious treatment option in the fight against coronaviruses. However, current treatment is long (12-18 h per day, 3-7 days), limiting clinical utility. A reduction in treatment time would reduce treatment burden. We aimed to evaluate safety and pharmacokinetics (PK) of four, single-dose regimens of ribavirin aerosol in healthy volunteers. Thirty-two subjects were randomized, to four cohorts of aerosolized ribavirin (active) or placebo. Cohort 1 received 50 mg/ml ribavirin/placebo (10 ml total volume); cohort 2, 50 mg/ml ribavirin/placebo (20 ml total volume); cohort 3, 100 mg/ml ribavirin/placebo (10 ml total volume); and cohort 4, 100 mg/ml ribavirin/placebo (20 ml total volume). Intense safety monitoring and PK sampling took place on days 1, 2, 3, and 40. Subjects were (mean ± SD, active vs. placebo) aged 57 ± 4.5 vs. 60 ± 2.5 years; 83% vs. 88% were female; and 75% vs. 50% were Caucasian. Some 12.5% (3/24) and 25% (2/8) experienced at least one treatment-emergent adverse event (TEAE) (two moderate; five mild) in the active and placebo groups, respectively. No clinically significant safety concerns were reported. Mean maximum observed concentration (Cmax ) and area under the curve (AUC) values were higher in cohort 4, whereas cohorts 2 and 3 showed similar PK values. Ribavirin absorption reached Cmax within 2 h across cohorts. Four single-dose regimens of ribavirin aerosol demonstrated systemic exposure with minimal systemic effects. Results support continued clinical development of ribavirin aerosol as a treatment option in patients with coronaviruses.


Subject(s)
Ribavirin , Area Under Curve , Cohort Studies , Double-Blind Method , Female , Healthy Volunteers , Humans , Male , Ribavirin/adverse effects
7.
Sci Rep ; 12(1): 5723, 2022 04 06.
Article in English | MEDLINE | ID: covidwho-1778627

ABSTRACT

Patients affected by SARS-COV-2 have collapsed healthcare systems around the world. Consequently, different challenges arise regarding the prediction of hospital needs, optimization of resources, diagnostic triage tools and patient evolution, as well as tools that allow us to analyze which are the factors that determine the severity of patients. Currently, it is widely accepted that one of the problems since the pandemic appeared was to detect (i) who patients were about to need Intensive Care Unit (ICU) and (ii) who ones were about not overcome the disease. These critical patients collapsed Hospitals to the point that many surgeries around the world had to be cancelled. Therefore, the aim of this paper is to provide a Machine Learning (ML) model that helps us to prevent when a patient is about to be critical. Although we are in the era of data, regarding the SARS-COV-2 patients, there are currently few tools and solutions that help medical professionals to predict the evolution of patients in order to improve their treatment and the needs of critical resources at hospitals. Moreover, most of these tools have been created from small populations and/or Chinese populations, which carries a high risk of bias. In this paper, we present a model, based on ML techniques, based on 5378 Spanish patients' data from which a quality cohort of 1201 was extracted to train the model. Our model is capable of predicting the probability of death of patients with SARS-COV-2 based on age, sex and comorbidities of the patient. It also allows what-if analysis, with the inclusion of comorbidities that the patient may develop during the SARS-COV-2 infection. For the training of the model, we have followed an agnostic approach. We explored all the active comorbidities during the SARS-COV-2 infection of the patients with the objective that the model weights the effect of each comorbidity on the patient's evolution according to the data available. The model has been validated by using stratified cross-validation with k = 5 to prevent class imbalance. We obtained robust results, presenting a high hit rate, with 84.16% accuracy, 83.33% sensitivity, and an Area Under the Curve (AUC) of 0.871. The main advantage of our model, in addition to its high success rate, is that it can be used with medical records in order to predict their diagnosis, allowing the critical population to be identified in advance. Furthermore, it uses the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD 9-CM) standard. In this sense, we should also emphasize that those hospitals using other encodings can add an intermediate layer business to business (B2B) with the aim of making transformations to the same international format.


Subject(s)
COVID-19 , SARS-CoV-2 , Area Under Curve , COVID-19/epidemiology , Humans , Machine Learning , Pandemics
8.
BMC Med Inform Decis Mak ; 22(1): 78, 2022 03 26.
Article in English | MEDLINE | ID: covidwho-1765450

ABSTRACT

BACKGROUND: The coronavirus (COVID-19) is a novel pandemic and recently we do not have enough knowledge about the virus behaviour and key performance indicators (KPIs) to assess the mortality risk forecast. However, using a lot of complex and expensive biomarkers could be impossible for many low budget hospitals. Timely identification of the risk of mortality of COVID-19 patients (RMCPs) is essential to improve hospitals' management systems and resource allocation standards. METHODS: For the mortality risk prediction, this research work proposes a COVID-19 mortality risk calculator based on a deep learning (DL) model and based on a dataset provided by the HM Hospitals Madrid, Spain. A pre-processing strategy for unbalanced classes and feature selection is proposed. To evaluate the proposed methods, an over-sampling Synthetic Minority TEchnique (SMOTE) and data imputation approaches are introduced which is based on the K-nearest neighbour. RESULTS: A total of 1,503 seriously ill COVID-19 patients having a median age of 70 years old are comprised in the research work, with 927 (61.7%) males and 576 (38.3%) females. A total of 48 features are considered to evaluate the proposed method, and the following results are achieved. It includes the following values i.e., area under the curve (AUC) 0.93, F2 score 0.93, recall 1.00, accuracy, 0.95, precision 0.91, specificity 0.9279 and maximum probability of correct decision (MPCD) 0.93. CONCLUSION: The results show that the proposed method is significantly best for the mortality risk prediction of patients with COVID-19 infection. The MPCD score shows that the proposed DL outperforms on every dataset when evaluating even with an over-sampling technique. The benefits of the data imputation algorithm for unavailable biomarker data are also evaluated. Based on the results, the proposed scheme could be an appropriate tool for critically ill Covid-19 patients to assess the risk of mortality and prognosis.


Subject(s)
COVID-19 , Deep Learning , Aged , Algorithms , Area Under Curve , Female , Humans , Male , Prognosis
9.
Eur J Gastroenterol Hepatol ; 33(1S Suppl 1): e368-e374, 2021 12 01.
Article in English | MEDLINE | ID: covidwho-1764695

ABSTRACT

BACKGROUND/AIMS: In this meta-analysis, we aimed to evaluate the prognostic value of fibrosis-4 index (FIB-4) in COVID-19. METHODS: We performed a comprehensive literature search of PubMed, Embase, and Scopus databases on 26 November 2020. FIB-4 was calculated by [age (years) × AST (IU/L)]/[platelet count (109/L) × âˆšALT (U/L)]. A value above cutoff point was considered high and a value below cutoff point was considered low. The main outcome was mortality, the association between high FIB-4 and mortality was reported in odds ratio (OR). Sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic OR (DOR), area under the curve (AUC) were generated. RESULTS: There were 963 patients from five studies included in this systematic review and meta-analysis. Meta-analysis showed that high FIB-4 was associated with increased mortality [OR 3.96 (2.16-7.27), P < 0.001; I2: 41.3%]. High FIB-4 was associated mortality with a sensitivity of 0.56 (0.40-0.70), specificity of 0.80 (0.72-0.86), PLR 2.8 (1.8-4.2), NLR 0.55 (0.39-0.78), DOR 5 (2-10), and AUC of 0.77 (0.73-0.81). Fagan's nomogram indicated that for a pre-test probability (mortality) of 30%, a high FIB-4 was associated with 54% post-test probability and a low FIB-4 was associated with 19%, respectively. The funnel-plot analysis was asymmetrical, trim-and-fill analysis by imputation of a study on the left side using linear estimator resulted in an OR of 3.48 (1.97-6.14). Egger's test showed no indication of small-study effects (P = 0.881). CONCLUSION: High FIB-4 was associated with mortality in patients with COVID-19.


Subject(s)
COVID-19 , Area Under Curve , Fibrosis , Humans , Platelet Count , SARS-CoV-2
10.
Dis Markers ; 2022: 3528312, 2022.
Article in English | MEDLINE | ID: covidwho-1723960

ABSTRACT

BACKGROUND: SARS-CoV-2 is responsible for COVID-19, a clinically heterogeneous disease, ranging from being completely asymptomatic to life-threating manifestations. An unmet clinical need is the identification at disease onset or during its course of reliable biomarkers allowing patients' stratification according to disease severity. In this observational prospective cohort study, patients' immunologic and laboratory signatures were analyzed to identify independent predictors of unfavorable (either death or intensive care unit admission need) or favorable (discharge and/or clinical resolution within the first 14 days of hospitalization) outcome. METHODS: Between January and May 2021 (third wave of the pandemic), we enrolled 139 consecutive SARS-CoV-2 positive patients hospitalized in Northern Italy to study their immunological and laboratory signatures. Multiplex cytokine, chemokine, and growth factor analysis, along with routine laboratory tests, were performed at baseline and after 7 days of hospital stay. RESULTS: According to their baseline characteristics, the majority of our patients experienced a moderate to severe illness. At multivariate analysis, the only independent predictors of disease evolution were the serum concentrations of IP-10 (at baseline) and of C-reactive protein (CRP) after 7 days of hospitalization. Receiver-operating characteristic (ROC) curve analysis confirmed that baseline IP - 10 > 4271 pg/mL and CRP > 2.3 mg/dL at 7 days predict a worsening in clinical conditions (87% sensitivity, 66% specificity, area under the curve (AUC) 0.772, p < 0.001 and 83% sensitivity, 73% specificity, AUC 0.826, p < 0.001, respectively). CONCLUSIONS: According to our results, baseline IP-10 and CRP after 7 days of hospitalization could be useful in driving clinical decisions tailored to the expected disease trajectory in hospitalized COVID-19 patients.


Subject(s)
Biomarkers/blood , COVID-19/immunology , Chemokine CXCL10/blood , Nerve Tissue Proteins/blood , Aged , Area Under Curve , C-Reactive Protein , COVID-19/blood , COVID-19/mortality , Female , Hospitalization , Humans , Italy , Male , Middle Aged , Patient Acuity , Prognosis , Prospective Studies
11.
BMJ ; 376: e068576, 2022 02 17.
Article in English | MEDLINE | ID: covidwho-1691357

ABSTRACT

OBJECTIVE: To create and validate a simple and transferable machine learning model from electronic health record data to accurately predict clinical deterioration in patients with covid-19 across institutions, through use of a novel paradigm for model development and code sharing. DESIGN: Retrospective cohort study. SETTING: One US hospital during 2015-21 was used for model training and internal validation. External validation was conducted on patients admitted to hospital with covid-19 at 12 other US medical centers during 2020-21. PARTICIPANTS: 33 119 adults (≥18 years) admitted to hospital with respiratory distress or covid-19. MAIN OUTCOME MEASURES: An ensemble of linear models was trained on the development cohort to predict a composite outcome of clinical deterioration within the first five days of hospital admission, defined as in-hospital mortality or any of three treatments indicating severe illness: mechanical ventilation, heated high flow nasal cannula, or intravenous vasopressors. The model was based on nine clinical and personal characteristic variables selected from 2686 variables available in the electronic health record. Internal and external validation performance was measured using the area under the receiver operating characteristic curve (AUROC) and the expected calibration error-the difference between predicted risk and actual risk. Potential bed day savings were estimated by calculating how many bed days hospitals could save per patient if low risk patients identified by the model were discharged early. RESULTS: 9291 covid-19 related hospital admissions at 13 medical centers were used for model validation, of which 1510 (16.3%) were related to the primary outcome. When the model was applied to the internal validation cohort, it achieved an AUROC of 0.80 (95% confidence interval 0.77 to 0.84) and an expected calibration error of 0.01 (95% confidence interval 0.00 to 0.02). Performance was consistent when validated in the 12 external medical centers (AUROC range 0.77-0.84), across subgroups of sex, age, race, and ethnicity (AUROC range 0.78-0.84), and across quarters (AUROC range 0.73-0.83). Using the model to triage low risk patients could potentially save up to 7.8 bed days per patient resulting from early discharge. CONCLUSION: A model to predict clinical deterioration was developed rapidly in response to the covid-19 pandemic at a single hospital, was applied externally without the sharing of data, and performed well across multiple medical centers, patient subgroups, and time periods, showing its potential as a tool for use in optimizing healthcare resources.


Subject(s)
COVID-19/diagnosis , Clinical Decision Rules , Hospitalization/statistics & numerical data , Machine Learning , Risk Assessment/methods , Adolescent , Adult , Aged , Aged, 80 and over , Area Under Curve , Clinical Deterioration , Electronic Health Records , Female , Hospitals , Humans , Linear Models , Male , Middle Aged , Predictive Value of Tests , Prognosis , ROC Curve , Retrospective Studies , SARS-CoV-2 , Young Adult
12.
Int J Environ Res Public Health ; 19(4)2022 Feb 15.
Article in English | MEDLINE | ID: covidwho-1686793

ABSTRACT

BACKGROUND: Krebs von den Lungen-6 (KL-6) has been proposed as a disease severity marker of COVID-19. All research articles reported the KL-6 assay detected through Fujirebio reagents by Lumipulse G600/G1200 instrument. In the present study, KL-6 assay was analysed through Tosoh AIA-360 and compared with analytical results by Lumipulse G600 in a population of COVID-19 patients. MATERIALS AND METHODS: Sixty-four patients (median age, IQR 67 (58-76) years), all hospitalized for COVID-19 interstitial pneumonia at Siena COVID Unit. KL-6 was measured by two methods, chemiluminescence enzyme immunoassay (CLEIA) and fluorescent enzyme immunoassay (FEIA) method by Lumipulse G600 II and AIA 360 systems, respectively. RESULTS: KL-6 concentrations evaluated by Lumipulse G600II were significantly higher in severe than those in non-severe patients (p < 0.0001) as well as evaluating by AIA360 (p < 0.0001). Receiver operating curve (ROC) curve analysis showed that KL-6 concentrations, by Lumipuse G600II, distinguished severe from non-severe COVID-19 patients with an area under the curve (AUC) of 99.8% and the best cut-off value was 448 U/mL. AUROC between severe and non-severe COVID-19 patients using T0 KL-6 concentrations by AIA360 was 97.4% and the best cut-off value was 398 U/mL. According to T0 KL-6 concentrations in COVID-19 patients, Bland-Altman difference analysis revealed a mean bias of 78 ± 174.8; while using T1 KL-6 concentrations in COVID-19 patients, Bland-Altman difference analysis revealed a mean bias of 48 ± 126 (95% limits of agreement -199-295) between the Lumipulse G600 II and the AIA360 systems. CONCLUSIONS: In conclusion, our study demonstrated that CLEIA and FEIA methods for serum KL-6 detection are comparable and reliable. KL-6 was confirmed as an easily detectable and effective biomarker to identify severe COVID-19 patients.


Subject(s)
COVID-19 , Aged , Area Under Curve , Biomarkers , COVID-19/diagnosis , Humans , SARS-CoV-2 , Severity of Illness Index
14.
BMC Nephrol ; 23(1): 50, 2022 02 01.
Article in English | MEDLINE | ID: covidwho-1666634

ABSTRACT

BACKGROUND: Acute kidney injury (AKI) is a common complication in patients hospitalized with COVID-19 and may require renal replacement therapy (RRT). Dipstick urinalysis is frequently obtained, but data regarding the prognostic value of hematuria and proteinuria for kidney outcomes is scarce. METHODS: Patients with positive severe acute respiratory syndrome-coronavirus 2 (SARS-CoV2) PCR, who had a urinalysis obtained on admission to one of 20 hospitals, were included. Nested models with degree of hematuria and proteinuria were used to predict AKI and RRT during admission. Presence of Chronic Kidney Disease (CKD) and baseline serum creatinine were added to test improvement in model fit. RESULTS: Of 5,980 individuals, 829 (13.9%) developed an AKI during admission, and 149 (18.0%) of those with AKI received RRT. Proteinuria and hematuria degrees significantly increased with AKI severity (P < 0.001 for both). Any degree of proteinuria and hematuria was associated with an increased risk of AKI and RRT. In predictive models for AKI, presence of CKD improved the area under the curve (AUC) (95% confidence interval) to 0.73 (0.71, 0.75), P < 0.001, and adding baseline creatinine improved the AUC to 0.85 (0.83, 0.86), P < 0.001, when compared to the base model AUC using only proteinuria and hematuria, AUC = 0.64 (0.62, 0.67). In RRT models, CKD status improved the AUC to 0.78 (0.75, 0.82), P < 0.001, and baseline creatinine improved the AUC to 0.84 (0.80, 0.88), P < 0.001, compared to the base model, AUC = 0.72 (0.68, 0.76). There was no significant improvement in model discrimination when both CKD and baseline serum creatinine were included. CONCLUSIONS: Proteinuria and hematuria values on dipstick urinalysis can be utilized to predict AKI and RRT in hospitalized patients with COVID-19. We derived formulas using these two readily available values to help prognosticate kidney outcomes in these patients. Furthermore, the incorporation of CKD or baseline creatinine increases the accuracy of these formulas.


Subject(s)
Acute Kidney Injury/etiology , COVID-19/complications , Hematuria/diagnosis , Proteinuria/diagnosis , Urinalysis/methods , Acute Kidney Injury/ethnology , Acute Kidney Injury/therapy , Aged , Area Under Curve , COVID-19/ethnology , Confidence Intervals , Creatinine/blood , Female , Hospitalization , Humans , Longitudinal Studies , Male , Middle Aged , Predictive Value of Tests , Renal Insufficiency, Chronic/diagnosis , Renal Replacement Therapy/statistics & numerical data
15.
Proc Natl Acad Sci U S A ; 119(4)2022 01 25.
Article in English | MEDLINE | ID: covidwho-1621333

ABSTRACT

The emergence of new variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a major concern given their potential impact on the transmissibility and pathogenicity of the virus as well as the efficacy of therapeutic interventions. Here, we predict the mutability of all positions in SARS-CoV-2 protein domains to forecast the appearance of unseen variants. Using sequence data from other coronaviruses, preexisting to SARS-CoV-2, we build statistical models that not only capture amino acid conservation but also more complex patterns resulting from epistasis. We show that these models are notably superior to conservation profiles in estimating the already observable SARS-CoV-2 variability. In the receptor binding domain of the spike protein, we observe that the predicted mutability correlates well with experimental measures of protein stability and that both are reliable mutability predictors (receiver operating characteristic areas under the curve ∼0.8). Most interestingly, we observe an increasing agreement between our model and the observed variability as more data become available over time, proving the anticipatory capacity of our model. When combined with data concerning the immune response, our approach identifies positions where current variants of concern are highly overrepresented. These results could assist studies on viral evolution and future viral outbreaks and, in particular, guide the exploration and anticipation of potentially harmful future SARS-CoV-2 variants.


Subject(s)
COVID-19/virology , Epistasis, Genetic , Epitopes , Mutation , SARS-CoV-2/genetics , Spike Glycoprotein, Coronavirus/chemistry , Spike Glycoprotein, Coronavirus/genetics , Viral Proteins/chemistry , Algorithms , Area Under Curve , Computational Biology/methods , DNA Mutational Analysis , Databases, Protein , Deep Learning , Epitopes/chemistry , Genome, Viral , Humans , Models, Statistical , Mutagenesis , Probability , Protein Domains , ROC Curve
16.
PLoS One ; 16(12): e0261307, 2021.
Article in English | MEDLINE | ID: covidwho-1598199

ABSTRACT

Medical images commonly exhibit multiple abnormalities. Predicting them requires multi-class classifiers whose training and desired reliable performance can be affected by a combination of factors, such as, dataset size, data source, distribution, and the loss function used to train deep neural networks. Currently, the cross-entropy loss remains the de-facto loss function for training deep learning classifiers. This loss function, however, asserts equal learning from all classes, leading to a bias toward the majority class. Although the choice of the loss function impacts model performance, to the best of our knowledge, we observed that no literature exists that performs a comprehensive analysis and selection of an appropriate loss function toward the classification task under study. In this work, we benchmark various state-of-the-art loss functions, critically analyze model performance, and propose improved loss functions for a multi-class classification task. We select a pediatric chest X-ray (CXR) dataset that includes images with no abnormality (normal), and those exhibiting manifestations consistent with bacterial and viral pneumonia. We construct prediction-level and model-level ensembles to improve classification performance. Our results show that compared to the individual models and the state-of-the-art literature, the weighted averaging of the predictions for top-3 and top-5 model-level ensembles delivered significantly superior classification performance (p < 0.05) in terms of MCC (0.9068, 95% confidence interval (0.8839, 0.9297)) metric. Finally, we performed localization studies to interpret model behavior and confirm that the individual models and ensembles learned task-specific features and highlighted disease-specific regions of interest. The code is available at https://github.com/sivaramakrishnan-rajaraman/multiloss_ensemble_models.


Subject(s)
Algorithms , Diagnostic Imaging , Image Processing, Computer-Assisted/classification , Area Under Curve , Entropy , Humans , Lung/diagnostic imaging , ROC Curve , Thorax/diagnostic imaging , X-Rays
17.
Cytokine ; 150: 155790, 2022 02.
Article in English | MEDLINE | ID: covidwho-1587975

ABSTRACT

BACKGROUND: Several immune mediators (IM) including cytokines, chemokines, and their receptors have been suggested to play a role in COVID-19 pathophysiology and severity. AIM: To determine if early IM profiles are predictive of clinical outcome and which of the IMs tested possess the most clinical utility. METHODS: A custom bead-based multiplex assay was used to measure IM concentrations in a cohort of SARS-CoV-2 PCR positive patients (n = 326) with varying disease severities as determined by hospitalization status, length of hospital stay, and survival. Patient groups were compared, and clinical utility was assessed. Correlation plots were constructed to determine if significant relationships exist between the IMs in the setting of COVID-19. RESULTS: In PCR positive SARS-CoV-2 patients, IL-6 was the best predictor of the need for hospitalization and length of stay. Additionally, MCP-1 and sIL-2Rα were moderate predictors of the need for hospitalization. Hospitalized PCR positive SARS-CoV-2 patients displayed a notable correlation between sIL-2Rα and IL-18 (Spearman's ρ = 0.48, P=<0.0001). CONCLUSIONS: IM profiles between non-hospitalized and hospitalized patients were distinct. IL-6 was the best predictor of COVID-19 severity among all the IMs tested.


Subject(s)
COVID-19/immunology , Cytokines/physiology , Hospitalization , Receptors, Cytokine/physiology , SARS-CoV-2 , Adult , Area Under Curve , Biomarkers , C-Reactive Protein/analysis , COVID-19/physiopathology , COVID-19/therapy , Chemokines/blood , Chemokines/physiology , Cytokines/blood , Female , Ferritins/blood , Fibrin Fibrinogen Degradation Products/analysis , Hospital Mortality , Humans , Interleukin-6/blood , Length of Stay/statistics & numerical data , Male , Middle Aged , Prognosis , ROC Curve , Receptors, Chemokine/physiology , Respiration, Artificial/statistics & numerical data , Severity of Illness Index , Treatment Outcome
18.
Sci Rep ; 11(1): 24439, 2021 12 24.
Article in English | MEDLINE | ID: covidwho-1585782

ABSTRACT

Acute kidney injury (AKI) is frequently associated with COVID-19 and it is considered an indicator of disease severity. This study aimed to develop a prognostic score for predicting in-hospital mortality in COVID-19 patients with AKI (AKI-COV score). This was a cross-sectional multicentre prospective cohort study in the Latin America AKI COVID-19 Registry. A total of 870 COVID-19 patients with AKI defined according to the KDIGO were included between 1 May 2020 and 31 December 2020. We evaluated four categories of predictor variables that were available at the time of the diagnosis of AKI: (1) demographic data; (2) comorbidities and conditions at admission; (3) laboratory exams within 24 h; and (4) characteristics and causes of AKI. We used a machine learning approach to fit models in the training set using tenfold cross-validation and validated the accuracy using the area under the receiver operating characteristic curve (AUC-ROC). The coefficients of the best model (Elastic Net) were used to build the predictive AKI-COV score. The AKI-COV score had an AUC-ROC of 0.823 (95% CI 0.761-0.885) in the validation cohort. The use of the AKI-COV score may assist healthcare workers in identifying hospitalized COVID-19 patients with AKI that may require more intensive monitoring and can be used for resource allocation.


Subject(s)
Acute Kidney Injury/complications , COVID-19/pathology , Hospital Mortality , Machine Learning , Aged , Area Under Curve , COVID-19/complications , COVID-19/mortality , COVID-19/virology , Comorbidity , Female , Humans , Male , Middle Aged , Prospective Studies , ROC Curve , Registries , Risk Factors , SARS-CoV-2/isolation & purification
19.
J Med Internet Res ; 23(2): e23026, 2021 02 22.
Article in English | MEDLINE | ID: covidwho-1575588

ABSTRACT

BACKGROUND: For the clinical care of patients with well-established diseases, randomized trials, literature, and research are supplemented with clinical judgment to understand disease prognosis and inform treatment choices. In the void created by a lack of clinical experience with COVID-19, artificial intelligence (AI) may be an important tool to bolster clinical judgment and decision making. However, a lack of clinical data restricts the design and development of such AI tools, particularly in preparation for an impending crisis or pandemic. OBJECTIVE: This study aimed to develop and test the feasibility of a "patients-like-me" framework to predict the deterioration of patients with COVID-19 using a retrospective cohort of patients with similar respiratory diseases. METHODS: Our framework used COVID-19-like cohorts to design and train AI models that were then validated on the COVID-19 population. The COVID-19-like cohorts included patients diagnosed with bacterial pneumonia, viral pneumonia, unspecified pneumonia, influenza, and acute respiratory distress syndrome (ARDS) at an academic medical center from 2008 to 2019. In total, 15 training cohorts were created using different combinations of the COVID-19-like cohorts with the ARDS cohort for exploratory purposes. In this study, two machine learning models were developed: one to predict invasive mechanical ventilation (IMV) within 48 hours for each hospitalized day, and one to predict all-cause mortality at the time of admission. Model performance was assessed using the area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, positive predictive value, and negative predictive value. We established model interpretability by calculating SHapley Additive exPlanations (SHAP) scores to identify important features. RESULTS: Compared to the COVID-19-like cohorts (n=16,509), the patients hospitalized with COVID-19 (n=159) were significantly younger, with a higher proportion of patients of Hispanic ethnicity, a lower proportion of patients with smoking history, and fewer patients with comorbidities (P<.001). Patients with COVID-19 had a lower IMV rate (15.1 versus 23.2, P=.02) and shorter time to IMV (2.9 versus 4.1 days, P<.001) compared to the COVID-19-like patients. In the COVID-19-like training data, the top models achieved excellent performance (AUROC>0.90). Validating in the COVID-19 cohort, the top-performing model for predicting IMV was the XGBoost model (AUROC=0.826) trained on the viral pneumonia cohort. Similarly, the XGBoost model trained on all 4 COVID-19-like cohorts without ARDS achieved the best performance (AUROC=0.928) in predicting mortality. Important predictors included demographic information (age), vital signs (oxygen saturation), and laboratory values (white blood cell count, cardiac troponin, albumin, etc). Our models had class imbalance, which resulted in high negative predictive values and low positive predictive values. CONCLUSIONS: We provided a feasible framework for modeling patient deterioration using existing data and AI technology to address data limitations during the onset of a novel, rapidly changing pandemic.


Subject(s)
COVID-19/diagnosis , COVID-19/mortality , Machine Learning , Pneumonia, Viral/diagnosis , Aged , Area Under Curve , Cohort Studies , Comorbidity , Female , Hospitalization/statistics & numerical data , Humans , Male , Middle Aged , Pandemics , Pneumonia, Viral/mortality , Predictive Value of Tests , Prognosis , ROC Curve , Respiration, Artificial/statistics & numerical data , Retrospective Studies , SARS-CoV-2 , Treatment Outcome
20.
J Med Internet Res ; 23(2): e23390, 2021 02 22.
Article in English | MEDLINE | ID: covidwho-1574113

ABSTRACT

BACKGROUND: The initial symptoms of patients with COVID-19 are very much like those of patients with community-acquired pneumonia (CAP); it is difficult to distinguish COVID-19 from CAP with clinical symptoms and imaging examination. OBJECTIVE: The objective of our study was to construct an effective model for the early identification of COVID-19 that would also distinguish it from CAP. METHODS: The clinical laboratory indicators (CLIs) of 61 COVID-19 patients and 60 CAP patients were analyzed retrospectively. Random combinations of various CLIs (ie, CLI combinations) were utilized to establish COVID-19 versus CAP classifiers with machine learning algorithms, including random forest classifier (RFC), logistic regression classifier, and gradient boosting classifier (GBC). The performance of the classifiers was assessed by calculating the area under the receiver operating characteristic curve (AUROC) and recall rate in COVID-19 prediction using the test data set. RESULTS: The classifiers that were constructed with three algorithms from 43 CLI combinations showed high performance (recall rate >0.9 and AUROC >0.85) in COVID-19 prediction for the test data set. Among the high-performance classifiers, several CLIs showed a high usage rate; these included procalcitonin (PCT), mean corpuscular hemoglobin concentration (MCHC), uric acid, albumin, albumin to globulin ratio (AGR), neutrophil count, red blood cell (RBC) count, monocyte count, basophil count, and white blood cell (WBC) count. They also had high feature importance except for basophil count. The feature combination (FC) of PCT, AGR, uric acid, WBC count, neutrophil count, basophil count, RBC count, and MCHC was the representative one among the nine FCs used to construct the classifiers with an AUROC equal to 1.0 when using the RFC or GBC algorithms. Replacing any CLI in these FCs would lead to a significant reduction in the performance of the classifiers that were built with them. CONCLUSIONS: The classifiers constructed with only a few specific CLIs could efficiently distinguish COVID-19 from CAP, which could help clinicians perform early isolation and centralized management of COVID-19 patients.


Subject(s)
COVID-19/diagnosis , Community-Acquired Infections/diagnosis , Machine Learning , Pneumonia/diagnosis , SARS-CoV-2/pathogenicity , Area Under Curve , COVID-19/blood , COVID-19/virology , Community-Acquired Infections/blood , Female , Humans , Laboratories , Leukocyte Count , Logistic Models , Male , Middle Aged , Pneumonia/blood , Procalcitonin/blood , ROC Curve , Retrospective Studies
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