scholarly journals Machine Learning Highlights Downtrending of COVID-19 Patients with a Distinct Laboratory Profile

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
He S. Yang ◽  
Yu Hou ◽  
Hao Zhang ◽  
Amy Chadburn ◽  
Lars F. Westblade ◽  
...  

Background. New York City (NYC) experienced an initial surge and gradual decline in the number of SARS-CoV-2-confirmed cases in 2020. A change in the pattern of laboratory test results in COVID-19 patients over this time has not been reported or correlated with patient outcome. Methods. We performed a retrospective study of routine laboratory and SARS-CoV-2 RT-PCR test results from 5,785 patients evaluated in a NYC hospital emergency department from March to June employing machine learning analysis. Results. A COVID-19 high-risk laboratory test result profile (COVID19-HRP), consisting of 21 routine blood tests, was identified to characterize the SARS-CoV-2 patients. Approximately half of the SARS-CoV-2 positive patients had the distinct COVID19-HRP that separated them from SARS-CoV-2 negative patients. SARS-CoV-2 patients with the COVID19-HRP had higher SARS-CoV-2 viral loads, determined by cycle threshold values from the RT-PCR, and poorer clinical outcome compared to other positive patients without the COVID12-HRP. Furthermore, the percentage of SARS-CoV-2 patients with the COVID19-HRP has significantly decreased from March/April to May/June. Notably, viral load in the SARS-CoV-2 patients declined, and their laboratory profile became less distinguishable from SARS-CoV-2 negative patients in the later phase. Conclusions. Our longitudinal analysis illustrates the temporal change of laboratory test result profile in SARS-CoV-2 patients and the COVID-19 evolvement in a US epicenter. This analysis could become an important tool in COVID-19 population disease severity tracking and prediction. In addition, this analysis may play an important role in prioritizing high-risk patients, assisting in patient triaging and optimizing the usage of resources.


2020 ◽  
Author(s):  
He S. Yang ◽  
Yu Hou ◽  
Hao Zhang ◽  
Amy Chadburn ◽  
Lars F. Westblade ◽  
...  

AbstractBackgroundNew York City (NYC) experienced an initial surge and gradual decline in the number of SARS-CoV-2 confirmed cases in 2020. A change in the pattern of laboratory test results in COVID-19 patients over this time has not been reported or correlated with patient outcome.MethodsWe performed a retrospective study of routine laboratory and SARS-CoV-2 RT-PCR test results from 5,785 patients evaluated in a NYC hospital emergency department from March to June employing machine learning analysis.ResultsA COVID-19 high-risk laboratory test result profile (COVID19-HRP), consisting of 21 routine blood tests, was identified to characterize the SARS-CoV-2 patients. Approximately half of the SARS-CoV-2 positive patients had the distinct COVID19-HRP that separated them from SARS-CoV-2 negative patients. SARS-CoV-2 patients with the COVID19-HRP had higher SARS-CoV-2 viral loads, determined by cycle-threshold values from the RT-PCR, and poorer clinical outcome compared to other positive patients without COVID19-HRP. Furthermore, the percentage of SARS-CoV-2 patients with the COVID19-HRP has significantly decreased from March/April to May/June. Notably, viral load in the SARS-CoV-2 patients declined and their laboratory profile became less distinguishable from SARS-CoV-2 negative patients in the later phase.ConclusionsOur study visualized the down-trending of the proportion of SARS-CoV-2 patients with the distinct COVID19-HRP. This analysis could become an important tool in COVID-19 population disease severity tracking and prediction. In addition, this analysis may play an important role in prioritizing high-risk patients, assisting in patient triaging and optimizing the usage of resources.



Author(s):  
He Sarina Yang ◽  
Ljiljana V. Vasovic ◽  
Peter Steel ◽  
Amy Chadburn ◽  
Yu Hou ◽  
...  

AbstractBackgroundAccurate diagnostic strategies to rapidly identify SARS-CoV-2 positive individuals for management of patient care and protection of health care personnel are urgently needed. The predominant diagnostic test is viral RNA detection by RT-PCR from nasopharyngeal swabs specimens, however the results of this test are not promptly obtainable in all patient care locations. Routine laboratory testing, in contrast, is readily available with a turn-around time (TAT) usually within 1-2 hours.MethodWe developed a machine learning model incorporating patient demographic features (age, sex, race) with 27 routine laboratory tests to predict an individual’s SARS-CoV-2 infection status. Laboratory test results obtained within two days before the release of SARS-CoV-2-RT-PCR result were used to train a gradient boosted decision tree (GBDT) model from 3,346 SARS-CoV-2 RT-PCR tested patients (1,394 positive and 1,952 negative) evaluated at a large metropolitan hospital.ResultsThe model achieved an area under the receiver operating characteristic curve (AUC) of 0.853 (95% CI: 0.829-0.878). Application of this model to an independent patient dataset from a separate hospital resulted in a comparable AUC (0.838), validating the generalization of its use. Moreover, our model predicted initial SARS-CoV-2 RT-PCR positivity in 66% individuals whose RT-PCR result changed from negative to positive within two days.ConclusionThis model employing routine laboratory test results offers opportunities for early and rapid identification of high-risk SARS-COV-2 infected patients before their RT-PCR results are available. This may facilitate patient care and quarantine, indicate who requires retesting, and direct personal protective equipment use while awaiting definitive RT-PCR results.



2016 ◽  
Vol 145 (6) ◽  
pp. 778-788 ◽  
Author(s):  
Yuan Luo ◽  
Peter Szolovits ◽  
Anand S. Dighe ◽  
Jason M. Baron


2021 ◽  
Author(s):  
Babak Afshin-Pour ◽  
Michael Qiu ◽  
Shahrzad Hosseini ◽  
Molly Stewart ◽  
Jan Horsky ◽  
...  

ABSTRACTDespite the high morbidity and mortality associated with Acute Respiratory Distress Syndrome (ARDS), discrimination of ARDS from other causes of acute respiratory failure remains challenging, particularly in the first 24 hours of mechanical ventilation. Delay in ARDS identification prevents lung protective strategies from being initiated and delays clinical trial enrolment and quality improvement interventions. Medical records from 1,263 ICU-admitted, mechanically ventilated patients at Northwell Health were retrospectively examined by a clinical team who assigned each patient a diagnosis of “ARDS” or “non-ARDS” (e.g., pulmonary edema). We then applied an iterative pre-processing and machine learning framework to construct a model that would discriminate ARDS versus non-ARDS, and examined features informative in the patient classification process. Data made available to the model included patient demographics, laboratory test results from before the initiation of mechanical ventilation, and features extracted by natural language processing of radiology reports. The resulting model discriminated well between ARDS and non-ARDS causes of respiratory failure (AUC=0.85, 89% precision at 20% recall), and highlighted features unique among ARDS patients, and among and the subset of ARDS patients who would not recover. Importantly, models built using both clinical notes and laboratory test results out-performed models built using either data source alone, akin to the retrospective clinician-based diagnostic process. This work demonstrates the feasibility of using readily available EHR data to discriminate ARDS patients prospectively in a real-world setting at a critical time in their care and highlights novel patient characteristics indicative of ARDS.



2020 ◽  
Author(s):  
Weiwei Zhang ◽  
Meifen Zhu ◽  
Min Zhang

Abstract ObjectivesThe pneumonia caused by the 2019 novel coronavirus recently break out in Wuhan, China, and was named as COVID-19. With the spread of the disease, it bring numbers of casualties,so now we need a way could fast and accuracy diagnose the disease.This paper aims to compare two way for diagnose COVID-19 in outpatient :Chest CT and RT-PCR.Materials and methodsThe study picked 248 patients who treated in fever clinical of GanZhou people's hospital,their complete clinical and imaging data were analysed retrospectively.Epidemiological data,symoptoms,laboratory test results include RT-PCR and the CT results include CT features,lesion location,lesion distribution of suspected COVID-19 infected patients were gathered.ResultsAll of 248 patients,at last 20 patients confirmed COVID-19,15 patients were confirmed in outpatient.More than 200 cases has laboratory test results disnormal.Only 15/248 patients had initial positive RT-PCR for COVID-19,5 patients had COVID-19 confirmed by two or more RT-PCR.50 cases(20.2%) had Ground glass opacity,42 cases(16.9%) had Consolidation,39 cases(15.7%) had Spider web pattern,38 cases(15.3%) had Interlobular septal thickening.For lesion location,22 cases(8.9%) involved Single lobe of one lung,13 cases(5.2%) involved Multiple lobes of one lung,174 cases(70.2%) involved Multiple lobes of both lungs,9 cases(3.6%) involved Bilateral lower lungs,25 cases(10.1%) involved Bilateral middle and lower lungs.Regarding the distribution of the lesions in the lung lobes,119 cases(47.98%) involved Subpleural distribution,19 cases(7.7%) involved Diffuse distribution,7 cases(2.8%) involved Peribronchial distribution,81 cases(32.7%) involved Mixed distribution.ConclusionChest CT can be applied in outpatient to make early diagnosis with sensitivity and accuracy better than that of nucleic acid detection.Trial registrationChiCTR2000032574. Registered 3 May 2020. retrospectively registered



10.2196/23948 ◽  
2021 ◽  
Vol 23 (4) ◽  
pp. e23948
Author(s):  
Yuanfang Chen ◽  
Liu Ouyang ◽  
Forrest S Bao ◽  
Qian Li ◽  
Lei Han ◽  
...  

Background Effectively and efficiently diagnosing patients who have COVID-19 with the accurate clinical type of the disease is essential to achieve optimal outcomes for the patients as well as to reduce the risk of overloading the health care system. Currently, severe and nonsevere COVID-19 types are differentiated by only a few features, which do not comprehensively characterize the complicated pathological, physiological, and immunological responses to SARS-CoV-2 infection in the different disease types. In addition, these type-defining features may not be readily testable at the time of diagnosis. Objective In this study, we aimed to use a machine learning approach to understand COVID-19 more comprehensively, accurately differentiate severe and nonsevere COVID-19 clinical types based on multiple medical features, and provide reliable predictions of the clinical type of the disease. Methods For this study, we recruited 214 confirmed patients with nonsevere COVID-19 and 148 patients with severe COVID-19. The clinical characteristics (26 features) and laboratory test results (26 features) upon admission were acquired as two input modalities. Exploratory analyses demonstrated that these features differed substantially between two clinical types. Machine learning random forest models based on all the features in each modality as well as on the top 5 features in each modality combined were developed and validated to differentiate COVID-19 clinical types. Results Using clinical and laboratory results independently as input, the random forest models achieved >90% and >95% predictive accuracy, respectively. The importance scores of the input features were further evaluated, and the top 5 features from each modality were identified (age, hypertension, cardiovascular disease, gender, and diabetes for the clinical features modality, and dimerized plasmin fragment D, high sensitivity troponin I, absolute neutrophil count, interleukin 6, and lactate dehydrogenase for the laboratory testing modality, in descending order). Using these top 10 multimodal features as the only input instead of all 52 features combined, the random forest model was able to achieve 97% predictive accuracy. Conclusions Our findings shed light on how the human body reacts to SARS-CoV-2 infection as a unit and provide insights on effectively evaluating the disease severity of patients with COVID-19 based on more common medical features when gold standard features are not available. We suggest that clinical information can be used as an initial screening tool for self-evaluation and triage, while laboratory test results should be applied when accuracy is the priority.



2020 ◽  
Vol 14 (12) ◽  
pp. e0008960
Author(s):  
Sheng-Wen Huang ◽  
Huey-Pin Tsai ◽  
Su-Jhen Hung ◽  
Wen-Chien Ko ◽  
Jen-Ren Wang

Background Dengue virus causes a wide spectrum of disease, which ranges from subclinical disease to severe dengue shock syndrome. However, estimating the risk of severe outcomes using clinical presentation or laboratory test results for rapid patient triage remains a challenge. Here, we aimed to develop prognostic models for severe dengue using machine learning, according to demographic information and clinical laboratory data of patients with dengue. Methodology/Principal findings Out of 1,581 patients in the National Cheng Kung University Hospital with suspected dengue infections and subjected to NS1 antigen, IgM and IgG, and qRT-PCR tests, 798 patients including 138 severe cases were enrolled in the study. The primary target outcome was severe dengue. Machine learning models were trained and tested using the patient dataset that included demographic information and qualitative laboratory test results collected on day 1 when they sought medical advice. To develop prognostic models, we applied various machine learning methods, including logistic regression, random forest, gradient boosting machine, support vector classifier, and artificial neural network, and compared the performance of the methods. The artificial neural network showed the highest average discrimination area under the receiver operating characteristic curve (0.8324 ± 0.0268) and balance accuracy (0.7523 ± 0.0273). According to the model explainer that analyzed the contributions/co-contributions of the different factors, patient age and dengue NS1 antigenemia were the two most important risk factors associated with severe dengue. Additionally, co-existence of anti-dengue IgM and IgG in patients with dengue increased the probability of severe dengue. Conclusions/Significance We developed prognostic models for the prediction of dengue severity in patients, using machine learning. The discriminative ability of the artificial neural network exhibited good performance for severe dengue prognosis. This model could help clinicians obtain a rapid prognosis during dengue outbreaks. However, the model requires further validation using external cohorts in future studies.



2020 ◽  
Author(s):  
Samuel G. Urwin ◽  
B. Clare Lendrem ◽  
Jana Suklan ◽  
Kile Green ◽  
Sara Graziadio ◽  
...  

AbstractBackgroundPoint-of-care (POC) tests for COVID-19 could relieve pressure on isolation resource, support infection prevention and control, and help commence more timely and appropriate treatment. We aimed to undertake a systematic review and pooled diagnostic test accuracy study of available individual patient data (IPD) to evaluate the diagnostic accuracy of a commercial POC test (FebriDx) in patients with suspected COVID-19.MethodsA literature search was performed on the 1st of October 2020 to identify studies reporting diagnostic accuracy statistics of the FebriDx POC test versus real time reverse transcriptase polymerase chain reaction (RT-PCR) testing for SARS-CoV-2. Studies were screened for risk of bias. IPD were sought from studies meeting the inclusion and exclusion criteria. Logistic regression was performed to investigate the study effect on the outcome of the RT-PCR test result in order to determine whether it was appropriate to pool results. Diagnostic accuracy statistics were calculated with 95% confidence intervals (CIs).Results15 studies were screened, and we included two published studies with 527 hospitalised patients. 523 patients had valid FebriDx results for Myxovirus resistance protein A (MxA), an antiviral host response protein. The FebriDx test produced a pooled sensitivity of 0.920 (95% CI: 0.875-0.950) and specificity of 0.862 (0.819-0.896) compared with RT-PCR, where there was an estimated true COVID-19 prevalence of 0.405 (0.364-0.448) and overall FebriDx test yield was 99.2%. Patients were tested at a median of 4 days [interquartile range: 2:9] after symptom onset. No differences were found in a sub-group analysis of time tested since the onset of symptoms.ConclusionsBased on a large sample of patients from two studies during the first wave of the SARS-CoV-2 pandemic, the FebriDx POC test had reasonable diagnostic accuracy in a hospital setting with high COVID-19 prevalence, out of influenza season. More research is required to determine how FebriDx would perform in other healthcare settings with higher or lower COVID-19 prevalence, different patient populations, or when other respiratory infections are in circulation.Trial registrationThis work was based on a pooled analysis of anonymised data from two previous studies; the CoV-19POC study, described by Clark et al. (9), the “Southampton study” [ISRCTN:14966673, date registered: 18/03/2020]; and a study described by Karim et al. (13) the “Kettering study”.Lay summaryTests to diagnose COVID-19 are crucial to help control the spread of the disease and to guide treatment. Over the last few months, tests have been developed that can detect the SARS-CoV-2 virus which causes COVID-19. These tests use complex machines in pathology laboratories accepting samples from large geographical areas. Sometimes it takes days for test results to come back. So, to reduce the wait for results, new portable tests are being developed. These point-of-care (POC) tests are designed to work close to where patients require assessment and care such as hospital emergency departments, GP surgeries or care homes. For these new POC tests to be useful, they should ideally be as good as standard laboratory tests so patients get their result quickly and can benefit from the best, safest care.In this study we looked at published research into a new test, FebriDx, which can detect the presence of any viral infection, including infections due to the SARS-CoV-2 virus, as well as bacterial infections which can have similar symptoms. The FebriDx result was compared with that obtained on the same patient’s throat and nose swab and using the standard COVID-19 viral laboratory test. We were able to analyse data from two studies with a total of 523 adult patients who were receiving emergency hospital care with symptoms of COVID-19 during the early stage of the UK pandemic. Almost half of the patients were diagnosed as positive for SARS-CoV-2 virus using standard laboratory COVID-19 viral tests.Our analysis demonstrated that the FebriDx POC test agreed 94 out of 100 times with the standard laboratory test results when FebriDx diagnosed the patient as free from COVID-19. However, FebriDx agreed only 82 out of 100 times with the standard laboratory test when FebriDx indicated that the patient had a COVID-19 infection. These differences have important implications for how these tests could be used. As there were far fewer FebriDx false results when the results of the FebriDx test were negative (6 out of 100) than when the results of the FebriDx test were positive (18 out of 100), we can have more confidence in a negative test result using FebriDx at the POC than a positive FebriDx result.Overall, we have shown that the FebriDx POC test performed quite well during the first wave of the COVID-19 pandemic when compared with laboratory tests, especially when the POC test returned a negative test. For the future, this means that the FebriDx POC test might be helpful in making a rapid clinical decision whether to isolate a patient with COVID-19-like symptoms arriving in a busy emergency department. However, our results indicate it would not completely replace the need to conduct a confirmatory laboratory test in certain cases.There are limitations to our findings. For example, we do not know if FebriDx will work in a similar way with patients in different settings such as in the community or care homes. Similarly, we do not know whether other viral and bacterial infections which cause similar COVID-19 symptoms, and are more common in the autumn and winter months, could influence the FebriDx test accuracy.



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