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1.
BMC Geriatr ; 22(1): 552, 2022 07 01.
Article in English | MEDLINE | ID: covidwho-1913453

ABSTRACT

BACKGROUND: Infection is more frequent, and serious in people aged > 65 as they experience non-specific signs and symptoms delaying diagnosis and prompt treatment. Monitoring signs and symptoms using decision support tools (DST) is one approach that could help improve early detection ensuring timely treatment and effective care. OBJECTIVE: To identify and analyse decision support tools available to support detection of infection in older people (> 65 years). METHODS: A scoping review of the literature 2010-2021 following Arksey and O'Malley (2005) framework and PRISMA-ScR guidelines. A search of MEDLINE, Cochrane, EMBASE, PubMed, CINAHL, Scopus and PsycINFO using terms to identify decision support tools for detection of infection in people > 65 years was conducted, supplemented with manual searches. RESULTS: Seventeen papers, reporting varying stages of development of different DSTs were analysed. DSTs largely focussed on specific types of infection i.e. urine, respiratory, sepsis and were frequently hospital based (n = 9) for use by physicians. Four DSTs had been developed in nursing homes and one a care home, two of which explored detection of non- specific infection. CONCLUSIONS: DSTs provide an opportunity to ensure a consistent approach to early detection of infection supporting prompt action and treatment, thus avoiding emergency hospital admissions. A lack of consideration regarding their implementation in practice means that any attempt to create an optimal validated and tested DST for infection detection will be impeded. This absence may ultimately affect the ability of the workforce to provide more effective and timely care, particularly during the current covid-19 pandemic.


Subject(s)
COVID-19 , Sepsis , Aged , COVID-19/diagnosis , COVID-19/epidemiology , Dietary Supplements , Early Diagnosis , Humans , Pandemics
2.
Comput Methods Programs Biomed ; 226: 107109, 2022 Nov.
Article in English | MEDLINE | ID: covidwho-2117158

ABSTRACT

BACKGROUND AND OBJECTIVE: COVID-19 outbreak has become one of the most challenging problems for human being. It is a communicable disease caused by a new coronavirus strain, which infected over 375 million people already and caused almost 6 million deaths. This paper aims to develop and design a framework for early diagnosis and fast classification of COVID-19 symptoms using multimodal Deep Learning techniques. METHODS: we collected chest X-ray and cough sample data from open source datasets, Cohen and datasets and local hospitals. The features are extracted from the chest X-ray images are extracted from chest X-ray datasets. We also used cough audio datasets from Coswara project and local hospitals. The publicly available Coughvid DetectNow and Virufy datasets are used to evaluate COVID-19 detection based on speech sounds, respiratory, and cough. The collected audio data comprises slow and fast breathing, shallow and deep coughing, spoken digits, and phonation of sustained vowels. Gender, geographical location, age, preexisting medical conditions, and current health status (COVID-19 and Non-COVID-19) are recorded. RESULTS: The proposed framework uses the selection algorithm of the pre-trained network to determine the best fusion model characterized by the pre-trained chest X-ray and cough models. Third, deep chest X-ray fusion by discriminant correlation analysis is used to fuse discriminatory features from the two models. The proposed framework achieved recognition accuracy, specificity, and sensitivity of 98.91%, 96.25%, and 97.69%, respectively. With the fusion method we obtained 94.99% accuracy. CONCLUSION: This paper examines the effectiveness of well-known ML architectures on a joint collection of chest-X-rays and cough samples for early classification of COVID-19. It shows that existing methods can effectively used for diagnosis and suggesting that the fusion learning paradigm could be a crucial asset in diagnosing future unknown illnesses. The proposed framework supports health informatics basis on early diagnosis, clinical decision support, and accurate prediction.


Subject(s)
COVID-19 , Deep Learning , Humans , COVID-19/diagnostic imaging , X-Rays , SARS-CoV-2 , Speech , Cough/diagnostic imaging , Early Diagnosis
3.
Niger J Clin Pract ; 25(10): 1769-1770, 2022 Oct.
Article in English | MEDLINE | ID: covidwho-2100046
4.
Infect Control Hosp Epidemiol ; 41(7): 820-825, 2020 Jul.
Article in English | MEDLINE | ID: covidwho-2096308

ABSTRACT

OBJECTIVES: Patients with COVID-19 may present with respiratory syndromes indistinguishable from those caused by common viruses. Early isolation and containment is challenging. Although screening all patients with respiratory symptoms for COVID-19 has been recommended, the practicality of such an effort has yet to be assessed. METHODS: Over a 6-week period during a SARS-CoV-2 outbreak, our institution introduced a "respiratory surveillance ward" (RSW) to segregate all patients with respiratory symptoms in designated areas, where appropriate personal protective equipment (PPE) could be utilized until SARS-CoV-2 testing was done. Patients could be transferred when SARS-CoV-2 tests were negative on 2 consecutive occasions, 24 hours apart. RESULTS: Over the study period, 1,178 patients were admitted to the RSWs. The mean length-of-stay (LOS) was 1.89 days (SD, 1.23). Among confirmed cases of pneumonia admitted to the RSW, 5 of 310 patients (1.61%) tested positive for SARS-CoV-2. This finding was comparable to the pickup rate from our isolation ward. In total, 126 HCWs were potentially exposed to these cases; however, only 3 (2.38%) required quarantine because most used appropriate PPE. In addition, 13 inpatients overlapped with the index cases during their stay in the RSW; of these 13 exposed inpatients, 1 patient subsequently developed COVID-19 after exposure. No patient-HCW transmission was detected despite intensive surveillance. CONCLUSIONS: Our institution successfully utilized the strategy of an RSW over a 6-week period to contain a cluster of COVID-19 cases and to prevent patient-HCW transmission. However, this method was resource-intensive in terms of testing and bed capacity.


Subject(s)
Coronavirus Infections/transmission , Cross Infection/transmission , Infection Control/methods , Infectious Disease Transmission, Patient-to-Professional/prevention & control , Occupational Diseases/prevention & control , Patient Isolation , Pneumonia, Viral/transmission , Population Surveillance/methods , Adult , Aged , Aged, 80 and over , Betacoronavirus , COVID-19 , Coronavirus Infections/diagnosis , Coronavirus Infections/prevention & control , Cross Infection/diagnosis , Cross Infection/prevention & control , Early Diagnosis , Female , Humans , Length of Stay , Male , Middle Aged , Pandemics/prevention & control , Patients' Rooms/organization & administration , Personal Protective Equipment , Pneumonia/virology , Pneumonia, Viral/diagnosis , Pneumonia, Viral/prevention & control , SARS-CoV-2 , Singapore , Symptom Assessment , Tertiary Care Centers
5.
Cell Mol Biol (Noisy-le-grand) ; 68(5): 177-185, 2022 May 31.
Article in English | MEDLINE | ID: covidwho-2072247

ABSTRACT

Venous thrombosis is a semi-solid formation of blood components that coalesce in the venous system, and the pathological process of its formation is called venous thrombosis. The deep veins of the lower extremities are a common site of prevalence, and the clinical diagnosis of lower extremity deep vein thrombosis can occur independently or as a complication of other diseases. There is a clear link between inflammation and coagulation/anticoagulation, with inflammatory mechanisms upregulating pro-inflammatory factors, downregulating natural anticoagulant substances, and inhibiting fibrinolytic activity; systemic inflammation is a strong pro-thrombotic stimulus; and in vivo, natural anticoagulant substances not only prevent thrombosis, but also deter inflammatory processes. The interconnection between inflammation and coagulation plays an important role in venous thrombosis. In this study, we analyzed the relationship between inflammatory markers CRP and Fg, FVIII:C and FIX:C by measuring plasma CRP concentration, Fg level, FVIII:C and FIX:C levels in patients with DVT diagnosed by ultrasound, and explored the role and mechanism of inflammatory response and coagulation factor abnormalities and the interaction between them in the development of DVT. In this paper, human blood DNA was extracted by phenol-chloroform-isoamyl alcohol extraction, and CRP 1059G/C gene polymorphism was detected by polymerase chain reaction-restriction enzyme segment length polymorphism (PCR-RFLP) nucleotide typing technique, and the genotypes of each subject were distinguished according to the bands seen by gel electrophoresis, and the frequency of each genotype was counted. Plasma CRP concentrations were measured by immunoturbidimetric assay, FVIII:C and FIX:C levels were measured by phase I assay, and plasma Fg levels were measured by coagulation assay in 59 cases (38 males and 21 females, aged 21-82 years, mean 49.67±11.12 years) and 26 controls (17 males and 9 females, aged 32-67 years, mean 50.13±8.96 years). The above indexes were compared between the two groups, and the correlation between CRP and FVIII:C, FIX:C and Fg was analyzed. Polymerase chain reaction-restriction enzyme segment length polymorphism nucleotide typing technique was used to detect the relationship between CRP 1059G/C gene polymorphism and DVT, to further search for risk factors of venous thrombosis, thus providing new ideas for the future prevention and treatment of this disease in clinical practice.


Subject(s)
Pulmonary Embolism , Thrombosis , Venous Thrombosis , Anticoagulants , Biomarkers , Blood Cells , Early Diagnosis , Female , Humans , Inflammation , Male , Nucleotides , Risk Factors
6.
Virol J ; 19(1): 152, 2022 09 22.
Article in English | MEDLINE | ID: covidwho-2038809

ABSTRACT

The coronavirus pandemic is a worldwide hazard that poses a threat to millions of individuals throughout the world. This pandemic is caused by the severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2), which was initially identified in Wuhan, China's Hubei provincial capital, and has since spread throughout the world. According to the World Health Organization's Weekly Epidemiological Update, there were more than 250 million documented cases of coronavirus infections globally, with five million fatalities. Early detection of coronavirus does not only reduce the spread of the virus, but it also increases the chance of curing the infection. Spectroscopic techniques have been widely used in the early detection and diagnosis of COVID-19 using Raman, Infrared, mass spectrometry and fluorescence spectroscopy. In this review, the reported spectroscopic methods for COVID-19 detection were discussed with emphasis on the practical aspects, limitations and applications.


Subject(s)
COVID-19 , COVID-19/diagnosis , COVID-19 Testing , Early Diagnosis , Global Health , Humans , Pandemics , SARS-CoV-2
7.
Annu Int Conf IEEE Eng Med Biol Soc ; 2022: 933-936, 2022 07.
Article in English | MEDLINE | ID: covidwho-2018757

ABSTRACT

A sensorized face mask could be a useful tool in the case of a viral pandemic event, as well as the Covid-19 emergency. In the context of the proposed project "RESPIRE", we have developed a "Smart-Mask" able to collect the signal patterns of body temperature, respiration, and symptoms such as cough, through a set of textile sensors. The signals have been analyzed by Artificial Intelligence algorithms in order to compare them with gold standard measurements, and to recognize the physiological changes associated with a viral infection. This low-cost prototype of a smart face mask is a reliable tool for the estimation of the individual physiological parameters. Moreover, it enables both personal protection and the early and rapid identification and tracking of potentially infected individuals.


Subject(s)
COVID-19 , Masks , Artificial Intelligence , COVID-19/diagnosis , Early Diagnosis , Humans , Textiles
8.
Medicine (Baltimore) ; 101(30): e29888, 2022 Jul 29.
Article in English | MEDLINE | ID: covidwho-1967940

ABSTRACT

This study aimed to assess the clinical characteristics of patients who registered at the Siriraj Favipiravir Clinic and to share our experiences in this comparatively unique clinical setting. This retrospective study included patients who registered at the Siriraj Favipiravir Clinic during August 11, 2021 to September 14, 2021. Included adult patients were those with severe acute respiratory syndrome coronavirus 2 (coronavirus disease 2019 [COVID-19]) infection confirmed by antigen test kit (ATK) or real-time reverse transcription-polymerase chain reaction, no favipiravir contraindication, no prior COVID-19 treatment, and not receiving care from another medical facility. Demographic data and outcomes were collected and analyzed. Of the 1168 patients (mean age: 44.8 ± 16.4 years, 55.7% female) who registered at the clinic, 117 (10%) did not meet the treatment criteria, and 141 (12%) patients did not pick up their medication. One-third of patients had at least 1 symptom that indicated severe disease. Higher proportion of unvaccinated status (56.7% vs 47.5%, P = .005), higher proportion of persons with risk factors for disease progression (37.7% vs 31.3%, P = .028), and longer duration between the date of clinic registration and the date of positive diagnostic test (3 vs 2 days, P = .004) were significantly more commonly observed in the severe disease group compared to the nonsevere disease group. The duration between symptom onset and the date of clinic registration was significantly longer in the real-time reverse transcription-polymerase chain reaction group than in the ATK group (6 vs 4 days, P < .001). Most patients (90.0%) had completed favipiravir treatment regimen. The improvement and mortality rates were 86.7% and 1.2%, respectively. COVID-19 severity is associated with vaccination status, baseline risk factors, and timing between disease detection and treatment. The use of ATK influences patients to seek treatment significantly earlier in ambulatory setting. Our early diagnosis and antiviral treatment strategy yielded favorable results in an outpatient setting during a COVID-19 outbreak in Thailand.


Subject(s)
COVID-19 , Adult , Antiviral Agents , COVID-19/diagnosis , COVID-19/drug therapy , COVID-19 Testing , Early Diagnosis , Female , Humans , Male , Middle Aged , Retrospective Studies , Thailand/epidemiology , Treatment Outcome
9.
Prev Med ; 162: 107170, 2022 09.
Article in English | MEDLINE | ID: covidwho-1956377

ABSTRACT

Wearable technology is an emerging method for the early detection of coronavirus disease 2019 (COVID-19) infection. This scoping review explored the types, mechanisms, and accuracy of wearable technology for the early detection of COVID-19. This review was conducted according to the five-step framework of Arksey and O'Malley. Studies published between December 31, 2019 and December 15, 2021 were obtained from 10 electronic databases, namely, PubMed, Embase, Cochrane, CINAHL, PsycINFO, ProQuest, Scopus, Web of Science, IEEE Xplore, and Taylor & Francis Online. Grey literature, reference lists, and key journals were also searched. All types of articles describing wearable technology for the detection of COVID-19 infection were included. Two reviewers independently screened the articles against the eligibility criteria and extracted the data using a data charting form. A total of 40 articles were included in this review. There are 22 different types of wearable technology used to detect COVID-19 infections early in the existing literature and are categorized as smartwatches or fitness trackers (67%), medical devices (27%), or others (6%). Based on deviations in physiological characteristics, anomaly detection models that can detect COVID-19 infection early were built using artificial intelligence or statistical analysis techniques. Reported area-under-the-curve values ranged from 75% to 94.4%, and sensitivity and specificity values ranged from 36.5% to 100% and 73% to 95.3%, respectively. Further research is necessary to validate the effectiveness and clinical dependability of wearable technology before healthcare policymakers can mandate its use for remote surveillance.


Subject(s)
COVID-19 , Wearable Electronic Devices , Artificial Intelligence , COVID-19/diagnosis , Early Diagnosis , Humans , Research Design
10.
Curr Med Imaging ; 18(14): 1510-1516, 2022.
Article in English | MEDLINE | ID: covidwho-1879363

ABSTRACT

BACKGROUND: Diagnosis of coronavirus disease 2019 (COVID-19) is mainly based on molecular testing. General population studies have shown that chest Computed Tomography (CT) can also be useful. OBJECTIVE: The study aims to examine the usefulness of high-resolution chest CT for early diagnosis of patients with suspected COVID-19. DESIGN AND SETTING: This is a cross-sectional study from May 1, 2020, to August 31, 2021, at the COVID Hospital, Mexico City. METHODS: This study examined the clinical, high-resolution chest CT imaging, and laboratory data of 160 patients who were suspected to have COVID-19. Patients with positive Reverse Transcription- Polymerase Chain Reaction (RT-PCR) testing and those with negative RT-PCR testing but clinical data compatible with COVID-19 and positive antibody testing were considered to have COVID-19 (positive). Sensitivity and specificity of CT for diagnosis of COVID-19 were calculated. p < 0.05 was considered significant. RESULTS: Median age of 160 study patients was 58 years. The proportion of patients with groundglass pattern was significantly higher in patients with COVID-19 than in those without COVID (65.1% versus 0%; P = 0.005). COVID-19 was ruled out in sixteen (11.1%). Only four of the 132 patients diagnosed with COVID-19 (3.0%) did not show CT alterations (p < 0.001). Sensitivity and specificity of CT for COVID-19 diagnosis were 96.7% and 42.8%, respectively. CONCLUSIONS: Chest CT can identify patients with COVID-19, as characteristic disease patterns are observed on CT in the early disease stage.


Subject(s)
COVID-19 , Humans , Middle Aged , COVID-19/diagnostic imaging , COVID-19 Testing , SARS-CoV-2 , Cross-Sectional Studies , Tomography, X-Ray Computed/methods , Early Diagnosis
11.
Semin Liver Dis ; 42(3): 293-312, 2022 08.
Article in English | MEDLINE | ID: covidwho-1878572

ABSTRACT

Strategies to prevent infection and improve outcomes in patients with cirrhosis. HAV, hepatitis A virus; HBV, hepatitis B virus; COVID-19, novel coronavirus disease 2019; NSBB, nonselective ß-blocker; PPI, proton pump inhibitors.Cirrhosis is a risk factor for infections. Majority of hospital admissions in patients with cirrhosis are due to infections. Sepsis is an immunological response to an infectious process that leads to end-organ dysfunction and death. Preventing infections may avoid the downstream complications, and early diagnosis of infections may improve the outcomes. In this review, we discuss the pathogenesis, diagnosis, and biomarkers of infection; the incremental preventive strategies for infections and sepsi; and the consequent organ failures in cirrhosis. Strategies for primary prevention include reducing gut translocation by selective intestinal decontamination, avoiding unnecessary proton pump inhibitors' use, appropriate use of ß-blockers, and vaccinations for viral diseases including novel coronavirus disease 2019. Secondary prevention includes early diagnosis and a timely and judicious use of antibiotics to prevent organ dysfunction. Organ failure support constitutes tertiary intervention in cirrhosis. In conclusion, infections in cirrhosis are potentially preventable with appropriate care strategies to then enable improved outcomes.


Subject(s)
COVID-19 , Proton Pump Inhibitors , Adrenergic beta-Antagonists/adverse effects , COVID-19 Testing , Early Diagnosis , Humans , Liver Cirrhosis/chemically induced , Liver Cirrhosis/complications , Liver Cirrhosis/diagnosis , Multiple Organ Failure
12.
Comput Biol Med ; 146: 105615, 2022 07.
Article in English | MEDLINE | ID: covidwho-1850902

ABSTRACT

The novel coronavirus disease 2019 (COVID-19) pandemic has severely impacted the world. The early diagnosis of COVID-19 and self-isolation can help curb the spread of the virus. Besides, a simple and accurate diagnostic method can help in making rapid decisions for the treatment and isolation of patients. The analysis of patient characteristics, case trajectory, comorbidities, symptoms, diagnosis, and outcomes will be performed in the model. In this paper, a symptom-based machine learning (ML) model with a new learning mechanism called Intensive Symptom Weight Learning Mechanism (ISW-LM) is proposed. The proposed model designs three new symptoms' weight functions to identify the most relevant symptoms used to diagnose and classify COVID-19. To verify the efficiency of the proposed model, multiple laboratory and clinical datasets containing epidemiological symptoms and blood tests are used. Experiments indicate that the importance of COVID-19 infection symptoms varies between countries and regions. In most datasets, the most frequent and significant predictive symptoms for diagnosing COVID-19 are fever, sore throat, and cough. The experiment also compares the state-of-the-art methods with the proposed method, which shows that the proposed model has a high accuracy rate of up to 97.1711%. The positive results indicate that the proposed learning mechanism can help clinicians quickly diagnose and screen patients for COVID-19 at an early stage.


Subject(s)
COVID-19 , COVID-19/diagnosis , COVID-19 Testing , Early Diagnosis , Humans , Pandemics , SARS-CoV-2
13.
J Ayub Med Coll Abbottabad ; 34(2): 360-363, 2022.
Article in English | MEDLINE | ID: covidwho-1848216

ABSTRACT

The workup of corona virus disease (COVID-19) involves analyzing samples for acute or past presence of SARS-CoV-2 (virus). A detection of 2019 novel Corona virus (2019-nCov) by real-time reverse transcriptase polymerase chain reaction (RT-PCR) indicates current infection and positive IgG antibody level implies a prior infection. Imaging techniques like high resolution computed tomography (HRCT) chest and Xray chest helps in diagnosing and monitoring the disease. Most cases of 2019-nCov are mild and range from asymptomatic carriers to critical illness leading to acute respiratory distress, septic shock and multiorgan failure. We report two cases of COVID-19 who manifested with high grade fever, myalgias, cough and shortness of breath on minimal exertion. All baseline laboratory findings were normal. Initial RT-PCR was negative for oropharyngeal and nasopharyngeal swabs. CT Chest showing typical peripheral patchy and ground glass opacities bilaterally, other markers of infectivity followed by antibody titer confirms the disease.


Subject(s)
COVID-19 , COVID-19/diagnosis , Early Diagnosis , Humans , Reverse Transcriptase Polymerase Chain Reaction , SARS-CoV-2 , Tomography, X-Ray Computed/methods
14.
Front Public Health ; 10: 850191, 2022.
Article in English | MEDLINE | ID: covidwho-1785450

ABSTRACT

Objective: To investigate whether first-trimester fasting plasma glucose (FPG), blood coagulation function and lipid metabolism could predict gestational diabetes mellitus (GDM) risk. Methods: From October 2020 to May 2021, a total of 584 pregnant women who took prenatal care in Shanghai Jiaotong University Affiliated Sixth People's Hospital were chosen as the observation subjects. The clinical information and serum samples of all pregnant women were collected at 10-13 weeks of gestation and the blood coagulation function, fasting blood glucose and lipid profiles of the pregnant women were detected. A 75 g oral glucose tolerance test was performed up to 24-28 weeks of gestation. One hundred forty-two pregnant women with GDM and 442 pregnant women without GDM were detected. Data were expressed by x ± s or median (interquartile range) and were analyzed using student's t-test, Wilcoxon rank sum test and Logistic regression analysis. The area under the curve (AUC) was calculated by receiver operating characteristic curve (ROC) to analyze the predictive values. Results: Compared with non-GDM group, age, pre-pregnancy BMI, FPG, FIB, D-Dimer, FDP, FPG, TC, TG, LDL-C, sdLDL-C, APOB and APOE in GDM group were significantly higher than those in non-GDM group, while PT, INR, APTT and TT were significantly lower than those in non-GDM group. Univariate logistic regression analysis was used to explore the risk factors of GDM. Gestational age, pre-pregnancy BMI, FPG, PT, INR, APTT, FIB, TT, D-Dimer, TC, TG, LDL-C, sdLDL-C, APOB and APOE were all independent predictors of GDM. Multivariatelogistic regression showed that pre-pregnancy BMI, FPG, APTT, TT, TG, LDL-C, sdLDL-C and APOB were risk factors for GDM. The AUC of the established GDM risk prediction model was 0.892 (0.858-0.927), and the sensitivity and specificity were 80.71 and 86.85%, respectively; which were greater than that of pre-pregnancy BMI, FPG, APTT, TT,TG, LDL-C, sdLDL-C, APOB alone, and the difffference was statistically signifificant (P < 0.05). Conclusions: FPG, APTT, TT, TG, LDL-C, sdLDL-C, APOB and pre-pregnancy BMI in early pregnancy has important clinical value for the prediction of GDM, We combined these laboratory indicators and established a GDM risk prediction model, which is conducive to the early identification, intervention and treatment of GDM, so as to reduce the morbidity of maternal and infant complications.


Subject(s)
Diabetes, Gestational , Apolipoproteins B/metabolism , Apolipoproteins E/metabolism , Blood Coagulation , Blood Glucose/analysis , Blood Glucose/metabolism , Body Mass Index , Cholesterol, LDL/metabolism , Diabetes, Gestational/diagnosis , Early Diagnosis , Female , Glycolipids , Humans , Lipid Metabolism , Pregnancy
15.
Theranostics ; 12(6): 2963-2986, 2022.
Article in English | MEDLINE | ID: covidwho-1780235

ABSTRACT

Many factors such as trauma and COVID-19 cause acute kidney injury (AKI). Late AKI have a very high incidence and mortality rate. Early diagnosis of AKI provides a critical therapeutic time window for AKI treatment to prevent progression to chronic renal failure. However, the current clinical detection based on creatinine and urine output isn't effective in diagnosing early AKI. In recent years, the early diagnosis of AKI has made great progress with the advancement of information technology, nanotechnology, and biomedicine. These emerging methods are mainly divided into two aspects: First, predicting AKI through models construct by machine learning; Second, early diagnosis of AKI through detection of newly-discovered early biomarkers. Currently, these methods have shown great potential and become an attractive tool for the early diagnosis of AKI. Therefore, it is very important to discuss and summarize these methods for the early diagnosis of AKI. In this review, we first systematically summarize the application of machine learning in AKI prediction algorithms and specific scenarios. In addition, we introduce the key role of early biomarkers in the progress of AKI, and then comprehensively summarize the application of emerging detection technologies for early AKI. Finally, we discuss current challenges and prospects of machine learning and biomarker detection. The review is expected to provide new insights for early diagnosis of AKI, and provided important inspiration for the design of early diagnosis of other major diseases.


Subject(s)
Acute Kidney Injury , COVID-19 , Acute Kidney Injury/diagnosis , Biomarkers/urine , COVID-19/diagnosis , Creatinine , Early Diagnosis , Humans , Lipocalin-2
16.
Rev Inst Med Trop Sao Paulo ; 64: e28, 2022.
Article in English | MEDLINE | ID: covidwho-1779820

ABSTRACT

In the present study, the importance of laboratory parameters and CT findings in the early diagnosis of COVID-19 was investigated. To this end, 245 patients admitted between April 1st, and May 30th, 2020 with suspected COVID-19 were enrolled. The patients were divided into three groups according to chest CT findings and RT-PCR results. The non-COVID-19 group consisted of 71 patients with negative RT-PCR results and no chest CT findings. Ninety-five patients with positive RT-PCR results and negativechest CT findings were included in the COVID-19 group; 79 patients with positive RT-PCR results and chest CT findings consistent with COVID-19 manifestations were included in COVID-19 pneumonia group. Chest CT findings were positive in 45% of all COVID-19 patients. Patients with positive chest CT findings had mild (n=30), moderate (n=21) andor severe (n=28) lung involvement. In the COVID-19 group, CRP levels and the percentage of monocytes increased significantly. As disease progressed from mild to severe, CRP, LDH and ferritin levels gradually increased. In the ROC analysis, the area under the curve corresponding to the percentage value of monocytes (AUC=0.887) had a very good accuracy in predicting COVID-19 cases. The multinomial logistic regression analysis showed that CRP, LYM and % MONO were independent factors for COVID-19. Furthermore, the chest CT evaluation is a relevant tool in patients with clinical suspicion of COVID-19 pneumonia and negative RT-PCR results. In addition to decreased lymphocyte count, the increased percentage of monocytes may also guide the diagnosis.


Subject(s)
COVID-19 , COVID-19/diagnostic imaging , Early Diagnosis , Humans , Retrospective Studies , SARS-CoV-2 , Tomography, X-Ray Computed/methods
17.
J Cachexia Sarcopenia Muscle ; 13(3): 1883-1895, 2022 06.
Article in English | MEDLINE | ID: covidwho-1772719

ABSTRACT

BACKGROUND: The COVID-19 pandemic has greatly increased the incidence and clinical importance of critical illness myopathy (CIM), because it is one of the most common complications of modern intensive care medicine. Current diagnostic criteria only allow diagnosis of CIM at an advanced stage, so that patients are at risk of being overlooked, especially in early stages. To determine the frequency of CIM and to assess a recently proposed tool for early diagnosis, we have followed a cohort of COVID-19 patients with acute respiratory distress syndrome and compared the time course of muscle excitability measurements with the definite diagnosis of CIM. METHODS: Adult COVID-19 patients admitted to the Intensive Care Unit of the University Hospital Bern, Switzerland requiring mechanical ventilation were recruited and examined on Days 1, 2, 5, and 10 post-intubation. Clinical examination, muscle excitability measurements, medication record, and laboratory analyses were performed on all study visits, and additionally nerve conduction studies, electromyography and muscle biopsy on Day 10. Muscle excitability data were compared with a cohort of 31 age-matched healthy subjects. Diagnosis of definite CIM was made according to the current guidelines and was based on patient history, results of clinical and electrophysiological examinations as well as muscle biopsy. RESULTS: Complete data were available in 31 out of 44 recruited patients (mean [SD] age, 62.4 [9.8] years). Of these, 17 (55%) developed CIM. Muscle excitability measurements on Day 10 discriminated between patients who developed CIM and those who did not, with a diagnostic precision of 90% (AUC 0.908; 95% CI 0.799-1.000; sensitivity 1.000; specificity 0.714). On Days 1 and 2, muscle excitability parameters also discriminated between the two groups with 73% (AUC 0.734; 95% CI 0.550-0.919; sensitivity 0.562; specificity 0.857) and 82% (AUC 0.820; CI 0.652-0.903; sensitivity 0.750; specificity 0.923) diagnostic precision, respectively. All critically ill COVID-19 patients showed signs of muscle membrane depolarization compared with healthy subjects, but in patients who developed CIM muscle membrane depolarization on Days 1, 2 and 10 was more pronounced than in patients who did not develop CIM. CONCLUSIONS: This study reports a 55% prevalence of definite CIM in critically ill COVID-19 patients. Furthermore, the results confirm that muscle excitability measurements may serve as an alternative method for CIM diagnosis and support its use as a tool for early diagnosis and monitoring the development of CIM.


Subject(s)
COVID-19 , Muscular Diseases , Polyneuropathies , Respiratory Distress Syndrome , Adult , COVID-19/complications , COVID-19/diagnosis , Critical Illness/epidemiology , Early Diagnosis , Humans , Middle Aged , Muscular Diseases/diagnosis , Muscular Diseases/epidemiology , Muscular Diseases/etiology , Pandemics , Polyneuropathies/diagnosis , Polyneuropathies/epidemiology , Polyneuropathies/etiology
18.
Semin Respir Crit Care Med ; 42(6): 747-758, 2021 12.
Article in English | MEDLINE | ID: covidwho-1768957

ABSTRACT

Respiratory tract infection is one of the most common diseases in human worldwide. Many viruses are implicated in these infections, including emerging viruses, such as the novel coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Identification of the causative viral pathogens of respiratory tract infections is important to select a correct management of patients, choose an appropriate treatment, and avoid unnecessary antibiotics use. Different diagnostic approaches present variable performance in terms of accuracy, sensitivity, specificity, and time-to-result, that have to be acknowledged to be able to choose the right diagnostic test at the right time, in the right patient. This review describes currently available rapid diagnostic strategies and syndromic approaches for the detection of viruses commonly responsible for respiratory diseases.


Subject(s)
Early Diagnosis , Respiratory Tract Infections/diagnosis , Respiratory Tract Infections/virology , COVID-19/diagnosis , COVID-19/virology , Humans , SARS-CoV-2/isolation & purification , Sensitivity and Specificity , Time Factors
19.
G Ital Cardiol (Rome) ; 23(3): 190-199, 2022 Mar.
Article in Italian | MEDLINE | ID: covidwho-1765603

ABSTRACT

Post-infarction mechanical complications include left ventricular free-wall rupture, ventricular septal rupture, and papillary muscle rupture. With the advent of early reperfusion strategies, including thrombolysis and percutaneous coronary intervention, these events now occur in fewer than 0.3% of patients following acute myocardial infarction. However, unfortunately, there has been no parallel decrease in associated mortality rates over the past two decades. Moreover, during the ongoing COVID-19 pandemic the incidence of mechanical complications resulting from ST-elevation myocardial infarction has possibly risen. Early diagnosis and prompt management are crucial to improving outcomes. Although some percutaneous device repair approaches are available, surgical treatment remains the gold standard for these catastrophic post-infarction complications. The timing of surgery, also related to the type of complication and patient's clinical conditions, and the possible role of mechanical circulatory supports before and after surgery, represent main topics of debate that still need to be fully addressed.


Subject(s)
COVID-19 , Myocardial Infarction , ST Elevation Myocardial Infarction , COVID-19/complications , Early Diagnosis , Humans , Myocardial Infarction/complications , Myocardial Infarction/diagnosis , Myocardial Infarction/therapy , Pandemics , ST Elevation Myocardial Infarction/complications , ST Elevation Myocardial Infarction/diagnosis , ST Elevation Myocardial Infarction/therapy
20.
IEEE J Biomed Health Inform ; 26(3): 1080-1090, 2022 03.
Article in English | MEDLINE | ID: covidwho-1759116

ABSTRACT

Pneumonia is one of the most common treatable causes of death, and early diagnosis allows for early intervention. Automated diagnosis of pneumonia can therefore improve outcomes. However, it is challenging to develop high-performance deep learning models due to the lack of well-annotated data for training. This paper proposes a novel method, called Deep Supervised Domain Adaptation (DSDA), to automatically diagnose pneumonia from chest X-ray images. Specifically, we propose to transfer the knowledge from a publicly available large-scale source dataset (ChestX-ray14) to a well-annotated but small-scale target dataset (the TTSH dataset). DSDA aligns the distributions of the source domain and the target domain according to the underlying semantics of the training samples. It includes two task-specific sub-networks for the source domain and the target domain, respectively. These two sub-networks share the feature extraction layers and are trained in an end-to-end manner. Unlike most existing domain adaptation approaches that perform the same tasks in the source domain and the target domain, we attempt to transfer the knowledge from a multi-label classification task in the source domain to a binary classification task in the target domain. To evaluate the effectiveness of our method, we compare it with several existing peer methods. The experimental results show that our method can achieve promising performance for automated pneumonia diagnosis.


Subject(s)
Deep Learning , Pneumonia , Early Diagnosis , Humans , Pneumonia/diagnostic imaging , Tomography, X-Ray Computed/methods , X-Rays
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