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1.
Cureus ; 14(8): e27814, 2022 Aug.
Article in English | MEDLINE | ID: covidwho-2030310

ABSTRACT

Introduction The COVID-19 pandemic has been a major public health threat for the past three years. The RNA virus has been constantly evolving, changing the manifestations and progression of the disease. Some factors which impact the progression to severe COVID-19 or mortality include comorbidities such as diabetes mellitus, hypertension, and obesity. In this study, we followed a cohort of patients to evaluate the risk factors leading to severe manifestations and mortality from COVID-19. Methodology We conducted a prospective observational study of 589 COVID-19 patients to assess the risk factors associated with the severity and mortality of the disease. Results In our cohort, 83.5% were male, with a median age (p25, p75) of 39.71 (30-48) years. The most common comorbidities included diabetes mellitus (7.8%) and hypertension (7.9%). About 41.7% had an asymptomatic disease, and of the symptomatic, 45% were mild, 6% moderate, and 7% severe. The mortality rate was 4.1%. Risk factors for severity included breathlessness (p=0.02), leukocytosis (p=0.02), and deranged renal function (p=0.04). Risk factors for mortality included older age (p=0.04), anemia (p=0.02), and leukocytosis (p=0.02). Conclusions COVID-19 commonly leads to asymptomatic or mild illness. The major factors we found that were associated with severity include breathlessness at presentation, leukocytosis, and deranged renal functions. The factors associated with mortality include older age, anemia, and leukocytosis.

2.
Pacing Clin Electrophysiol ; 45(4): 574-577, 2022 04.
Article in English | MEDLINE | ID: covidwho-1794593

ABSTRACT

A middle-aged woman presented with symptomatic complete heart block and underwent an uneventful dual chamber pacemaker implantation. Three weeks post procedure, she developed left arm pain and weakness, with neurological localization to the lower trunk of left brachial plexus. Possibilities of traumatic compression by the device/leads or postoperative idiopathic brachial plexopathy were considered. After ruling out traumatic causes, she was started on oral steroids, to which she responded remarkably. This case highlights the importance of recognizing this rare cause of brachial plexopathy following pacemaker implantation, because not only does an expedited diagnosis and medical treatment lead to prompt recovery with minimal neurological deficits, but it also circumvents an unnecessary surgical re-exploration.


Subject(s)
Brachial Plexus Neuropathies , Brachial Plexus , Pacemaker, Artificial , Brachial Plexus Neuropathies/diagnosis , Brachial Plexus Neuropathies/etiology , Female , Humans , Middle Aged , Pacemaker, Artificial/adverse effects
3.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-321185

ABSTRACT

Background: and Aim There is a paucity of data on the clinical presentations and outcomes of Coronavirus disease 2019(COVID-19) in patients with underlying liver disease. We aimed to summarize the presentations and outcomes of COVID-19 positive patients and compare with historical controls. Methods: Patients with known chronic liver disease who presented with superimposed COVID- 19(n=28) between 22nd April and 22nd June 2020 were studied. Seventy-eight cirrhotic patients from historical controls were taken as comparison group. Results: A total of 28 COVID patients- two without cirrhosis, one with compensated cirrhosis, sixteen with acute decompensation (AD), and nine with acute-on-chronic liver failure(ACLF) were included. The etiology of cirrhosis was alcohol(n=9), non-alcoholic fatty liver disease(n=2), viral(n=5), autoimmune hepatitis(n=4), and cryptogenic cirrhosis(n=6). The clinical presentations included complications of cirrhosis in 12(46.2%), respiratory symptoms in 3(11.5%) and combined complications of cirrhosis and respiratory symptoms in 11(42.3%) patients. The median hospital stay was 8(7-12) days. The mortality rate in COVID-19 patients was 42.3%(11/26), as compared to 23.1%(18/78) in the historical controls(p=0.077). All COVID-19 patients with ACLF(9/9) died compared to 53.3%(16/30) in ACLF of historical controls(p=0.015). Mortality rate was higher in COVID patients with compensated cirrhosis and AD as compared to historical controls 2/17(11.8%) vs 2/48(4.2%), though not statistically significant (p=0.278). Requirement of mechanical ventilation independently predicted mortality (hazard ratio, 13.68). Both non-cirrhotic patients presented with respiratory symptoms and recovered uneventfully. Conclusion: COVID-19 is associated with poor outcomes in patients with cirrhosis, with worst survival rates in ACLF. Mechanical ventilation is associated with a poor outcome.

4.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-316351

ABSTRACT

The exponential increase in COVID-19 patients is overwhelming healthcare systems across the world. With limited testing kits, it is impossible for every patient with respiratory illness to be tested using conventional techniques (RT-PCR). The tests also have long turn-around time, and limited sensitivity. Detecting possible COVID-19 infections on Chest X-Ray may help quarantine high risk patients while test results are awaited. X-Ray machines are already available in most healthcare systems, and with most modern X-Ray systems already digitized, there is no transportation time involved for the samples either. In this work we propose the use of chest X-Ray to prioritize the selection of patients for further RT-PCR testing. This may be useful in an inpatient setting where the present systems are struggling to decide whether to keep the patient in the ward along with other patients or isolate them in COVID-19 areas. It would also help in identifying patients with high likelihood of COVID with a false negative RT-PCR who would need repeat testing. Further, we propose the use of modern AI techniques to detect the COVID-19 patients using X-Ray images in an automated manner, particularly in settings where radiologists are not available, and help make the proposed testing technology scalable. We present CovidAID: COVID-19 AI Detector, a novel deep neural network based model to triage patients for appropriate testing. On the publicly available covid-chestxray-dataset [2], our model gives 90.5% accuracy with 100% sensitivity (recall) for the COVID-19 infection. We significantly improve upon the results of Covid-Net [10] on the same dataset.

5.
Eur Radiol ; 31(8): 6039-6048, 2021 Aug.
Article in English | MEDLINE | ID: covidwho-1037943

ABSTRACT

OBJECTIVES: To study whether a trained convolutional neural network (CNN) can be of assistance to radiologists in differentiating Coronavirus disease (COVID)-positive from COVID-negative patients using chest X-ray (CXR) through an ambispective clinical study. To identify subgroups of patients where artificial intelligence (AI) can be of particular value and analyse what imaging features may have contributed to the performance of AI by means of visualisation techniques. METHODS: CXR of 487 patients were classified into [4] categories-normal, classical COVID, indeterminate, and non-COVID by consensus opinion of 2 radiologists. CXR which were classified as "normal" and "indeterminate" were then subjected to analysis by AI, and final categorisation provided as guided by prediction of the network. Precision and recall of the radiologist alone and radiologist assisted by AI were calculated in comparison to reverse transcriptase-polymerase chain reaction (RT-PCR) as the gold standard. Attention maps of the CNN were analysed to understand regions in the CXR important to the AI algorithm in making a prediction. RESULTS: The precision of radiologists improved from 65.9 to 81.9% and recall improved from 17.5 to 71.75 when assistance with AI was provided. AI showed 92% accuracy in classifying "normal" CXR into COVID or non-COVID. Analysis of attention maps revealed attention on the cardiac shadow in these "normal" radiographs. CONCLUSION: This study shows how deployment of an AI algorithm can complement a human expert in the determination of COVID status. Analysis of the detected features suggests possible subtle cardiac changes, laying ground for further investigative studies into possible cardiac changes. KEY POINTS: • Through an ambispective clinical study, we show how assistance with an AI algorithm can improve recall (sensitivity) and precision (positive predictive value) of radiologists in assessing CXR for possible COVID in comparison to RT-PCR. • We show that AI achieves the best results in images classified as "normal" by radiologists. We conjecture that possible subtle cardiac in the CXR, imperceptible to the human eye, may have contributed to this prediction. • The reported results may pave the way for a human computer collaboration whereby the expert with some help from the AI algorithm achieves higher accuracy in predicting COVID status on CXR than previously thought possible when considering either alone.


Subject(s)
Artificial Intelligence , COVID-19 , Humans , Radiography, Thoracic , SARS-CoV-2 , Tomography, X-Ray Computed , X-Rays
6.
Indian J Gastroenterol ; 39(3): 285-291, 2020 06.
Article in English | MEDLINE | ID: covidwho-725536

ABSTRACT

BACKGROUND AND AIM: There is a paucity of data on the clinical presentations and outcomes of Corona Virus Disease-19 (COVID-19) in patients with underlying liver disease. We aimed to summarize the presentations and outcomes of COVID-19-positive patients and compare with historical controls. METHODS: Patients with known chronic liver disease who presented with superimposed COVID-19 (n = 28) between 22 April 2020 and 22 June 2020 were studied. Seventy-eight cirrhotic patients without COVID-19 were included as historical controls for comparison. RESULTS: A total of 28 COVID-19 patients (two without cirrhosis, one with compensated cirrhosis, sixteen with acute decompensation [AD], and nine with acute-on-chronic liver failure [ACLF]) were included. The etiology of cirrhosis was alcohol (n = 9), non-alcoholic fatty liver disease (n = 2), viral (n = 5), autoimmune hepatitis (n = 4), and cryptogenic cirrhosis (n = 6). The clinical presentations included complications of cirrhosis in 12 (46.2%), respiratory symptoms in 3 (11.5%), and combined complications of cirrhosis and respiratory symptoms in 11 (42.3%) patients. The median hospital stay was 8 (7-12) days. The mortality rate in COVID-19 patients was 42.3% (11/26), as compared with 23.1% (18/78) in the historical controls (p = 0.077). All COVID-19 patients with ACLF (9/9) died compared with 53.3% (16/30) in ACLF of historical controls (p = 0.015). Mortality rate was higher in COVID-19 patients with compensated cirrhosis and AD as compared with historical controls 2/17 (11.8%) vs. 2/48 (4.2%), though not statistically significant (p = 0.278). Requirement of mechanical ventilation independently predicted mortality (hazard ratio 13.68). Both non-cirrhotic patients presented with respiratory symptoms and recovered uneventfully. CONCLUSION: COVID-19 is associated with poor outcomes in patients with cirrhosis, with worst survival rates in ACLF. Mechanical ventilation is associated with a poor outcome.


Subject(s)
Acute-On-Chronic Liver Failure , Betacoronavirus/isolation & purification , Coronavirus Infections , Liver Cirrhosis , Pandemics , Pneumonia, Viral , Acute-On-Chronic Liver Failure/diagnosis , Acute-On-Chronic Liver Failure/mortality , Acute-On-Chronic Liver Failure/virology , COVID-19 , Cohort Studies , Comorbidity , Coronavirus Infections/diagnosis , Coronavirus Infections/mortality , Coronavirus Infections/physiopathology , Disease Progression , Female , Humans , India/epidemiology , Length of Stay/statistics & numerical data , Liver Cirrhosis/diagnosis , Liver Cirrhosis/epidemiology , Liver Cirrhosis/etiology , Liver Cirrhosis/virology , Male , Middle Aged , Mortality , Outcome Assessment, Health Care , Pneumonia, Viral/diagnosis , Pneumonia, Viral/mortality , Pneumonia, Viral/physiopathology , Prognosis , Risk Factors , SARS-CoV-2
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