scholarly journals Symptom clusters in COVID-19: A potential clinical prediction tool from the COVID Symptom Study app

2021 ◽  
Vol 7 (12) ◽  
pp. eabd4177
Author(s):  
Carole H. Sudre ◽  
Karla A. Lee ◽  
Mary Ni Lochlainn ◽  
Thomas Varsavsky ◽  
Benjamin Murray ◽  
...  
Keyword(s):  
Time Series ◽  
Area Under The Curve ◽  
Personal Characteristics ◽  
Smartphone Application ◽  
Training Dataset ◽  
Symptom Presentation ◽  
Resource Requirements ◽  
Risk Patients ◽  
Independent Replication ◽  
Characteristic Area

As no one symptom can predict disease severity or the need for dedicated medical support in coronavirus disease 2019 (COVID-19), we asked whether documenting symptom time series over the first few days informs outcome. Unsupervised time series clustering over symptom presentation was performed on data collected from a training dataset of completed cases enlisted early from the COVID Symptom Study Smartphone application, yielding six distinct symptom presentations. Clustering was validated on an independent replication dataset between 1 and 28 May 2020. Using the first 5 days of symptom logging, the ROC-AUC (receiver operating characteristic – area under the curve) of need for respiratory support was 78.8%, substantially outperforming personal characteristics alone (ROC-AUC 69.5%). Such an approach could be used to monitor at-risk patients and predict medical resource requirements days before they are required.

scholarly journals Symptom clusters in Covid19: A potential clinical prediction tool from the COVID Symptom study app

2020 ◽  
Author(s):  
Carole H. Sudre ◽  
Karla A. Lee ◽  
Mary Ni Lochlainn ◽  
Thomas Varsavsky ◽  
Benjamin Murray ◽  
...  
Keyword(s):  
Time Series ◽  
Personal Characteristics ◽  
Smartphone Application ◽  
Respiratory Support ◽  
Training Dataset ◽  
Symptom Presentation ◽  
Resource Requirements ◽  
Risk Patients ◽  
Independent Replication ◽  
Clinical Prediction Tool

AbstractAs no one symptom can predict disease severity or the need for dedicated medical support in COVID-19, we asked if documenting symptom time series over the first few days informs outcome. Unsupervised time series clustering over symptom presentation was performed on data collected from a training dataset of completed cases enlisted early from the COVID Symptom Study Smartphone application, yielding six distinct symptom presentations. Clustering was validated on an independent replication dataset between May 1-May 28th, 2020. Using the first 5 days of symptom logging, the ROC-AUC of need for respiratory support was 78.8%, substantially outperforming personal characteristics alone (ROC-AUC 69.5%). Such an approach could be used to monitor at-risk patients and predict medical resource requirements days before they are required.One sentence summaryLongitudinal clustering of symptoms can predict the need for respiratory support in severe COVID-19.

scholarly journals Machine Learning Predictors of Extreme Events Occurring in Complex Dynamical Systems

Entropy ◽  
2019 ◽  
Vol 21 (10) ◽  
pp. 925 ◽  
Author(s):  
Stephen Guth ◽  
Themistoklis P. Sapsis
Keyword(s):  
Time Series ◽  
Extreme Events ◽  
Extreme Event ◽  
Binary Classification ◽  
Selection Criterion ◽  
Area Under The Curve ◽  
Wave Model ◽  
Training Dataset ◽  
Ocean Engineering ◽  
Selection Of

The ability to characterize and predict extreme events is a vital topic in fields ranging from finance to ocean engineering. Typically, the most-extreme events are also the most-rare, and it is this property that makes data collection and direct simulation challenging. We consider the problem of deriving optimal predictors of extremes directly from data characterizing a complex system, by formulating the problem in the context of binary classification. Specifically, we assume that a training dataset consists of: (i) indicator time series specifying on whether or not an extreme event occurs; and (ii) observables time series, which are employed to formulate efficient predictors. We employ and assess standard binary classification criteria for the selection of optimal predictors, such as total and balanced error and area under the curve, in the context of extreme event prediction. For physical systems for which there is sufficient separation between the extreme and regular events, i.e., extremes are distinguishably larger compared with regular events, we prove the existence of optimal extreme event thresholds that lead to efficient predictors. Moreover, motivated by the special character of extreme events, i.e., the very low rate of occurrence, we formulate a new objective function for the selection of predictors. This objective is constructed from the same principles as receiver operating characteristic curves, and exhibits a geometric connection to the regime separation property. We demonstrate the application of the new selection criterion to the advance prediction of intermittent extreme events in two challenging complex systems: the Majda–McLaughlin–Tabak model, a 1D nonlinear, dispersive wave model, and the 2D Kolmogorov flow model, which exhibits extreme dissipation events.

The F2RaD Score: A Novel Prediction Score and Calculator Tool to Identify Patients at Risk of Postoperative C5 Palsy

2020 ◽  
Vol 19 (5) ◽  
pp. 582-588 ◽  
Author(s):  
Daniel Lubelski ◽  
Zach Pennington ◽  
James Feghali ◽  
Andrew Schilling ◽  
Jeff Ehresman ◽  
...  
Keyword(s):  
Scoring System ◽  
Operating Characteristic ◽  
Multivariable Analysis ◽  
Area Under The Curve ◽  
Training Dataset ◽  
Web Based ◽  
C5 Palsy ◽  
Predicted Probability ◽  
Patients At Risk ◽  
Risk Patients

Abstract BACKGROUND Postoperative C5 palsy is a debilitating complication following posterior cervical decompression. OBJECTIVE To create a simple clinical risk score predicting the occurrence of C5 palsy METHODS We retrospectively reviewed all patients who underwent posterior cervical decompressions between 2007 and 2017. Data was randomly split into training and validation datasets. Multivariable analysis was performed to construct the model from the training dataset. A scoring system was developed based on the model coefficients and a web-based calculator was deployed. RESULTS The cohort consisted of 415 patients, of which 65 (16%) developed C5 palsy. The optimal model consisted of: mean C4/5 foraminal diameter (odds ratio [OR] = 9.1 for lowest quartile compared to highest quartile), preoperative C5 radiculopathy (OR = 3.5), and dexterity loss (OR = 2.9). The receiver operating characteristic yielded an area under the curve of 0.757 and 0.706 in the training and validation datasets, respectively. Every characteristic was worth 1 point except the lowest quartile of mean C4/5 foraminal diameter, which was worth 2 points, and the factors were summarized by the acronym F2RaD. The median predicted probability of C5 palsy increased from 2% in patients with a score of 0 to 70% in patients with a score of 4. The calculator can be accessed on https://jhuspine2.shinyapps.io/FRADscore/. CONCLUSION This study yielded a simplified scoring system and clinical calculator that predicts the occurrence of C5 palsy. Individualized risk prediction for patients may facilitate better understanding of the risks and benefits for an operation, and better prepare them for this possible adverse outcome. Furthermore, modifying the surgical plan in high-risk patients may possibly improve outcomes.

scholarly journals A MYC-Driven Plasma Polyamine Signature for Early Detection of Ovarian Cancer

Cancers ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 913
Author(s):  
Johannes Fahrmann ◽  
Ehsan Irajizad ◽  
Makoto Kobayashi ◽  
Jody Vykoukal ◽  
Jennifer Dennison ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Early Stage ◽  
Area Under The Curve ◽  
Polyamine Metabolism ◽  
Healthy Controls ◽  
Test Set ◽  
Exact Test ◽  
Oncogenic Driver ◽  
Characteristic Area ◽  
Validation Set

MYC is an oncogenic driver in the pathogenesis of ovarian cancer. We previously demonstrated that MYC regulates polyamine metabolism in triple-negative breast cancer (TNBC) and that a plasma polyamine signature is associated with TNBC development and progression. We hypothesized that a similar plasma polyamine signature may associate with ovarian cancer (OvCa) development. Using mass spectrometry, four polyamines were quantified in plasma from 116 OvCa cases and 143 controls (71 healthy controls + 72 subjects with benign pelvic masses) (Test Set). Findings were validated in an independent plasma set from 61 early-stage OvCa cases and 71 healthy controls (Validation Set). Complementarity of polyamines with CA125 was also evaluated. Receiver operating characteristic area under the curve (AUC) of individual polyamines for distinguishing cases from healthy controls ranged from 0.74–0.88. A polyamine signature consisting of diacetylspermine + N-(3-acetamidopropyl)pyrrolidin-2-one in combination with CA125 developed in the Test Set yielded improvement in sensitivity at >99% specificity relative to CA125 alone (73.7% vs 62.2%; McNemar exact test 2-sided P: 0.019) in the validation set and captured 30.4% of cases that were missed with CA125 alone. Our findings reveal a MYC-driven plasma polyamine signature associated with OvCa that complemented CA125 in detecting early-stage ovarian cancer.

scholarly journals Machine learning applied on chest x-ray can aid in the diagnosis of COVID-19: a first experience from Lombardy, Italy

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Isabella Castiglioni ◽  
Davide Ippolito ◽  
Matteo Interlenghi ◽  
Caterina Beatrice Monti ◽  
Christian Salvatore ◽  
...  
Keyword(s):  
Deep Learning ◽  
Area Under The Curve ◽  
Clinical Information ◽  
Training Dataset ◽  
Independent Dataset ◽  
X Ray ◽  
Learning Classifier ◽  
Chest X Ray ◽  
Polymerase Chain ◽  
Deep Learning Model

Abstract Background We aimed to train and test a deep learning classifier to support the diagnosis of coronavirus disease 2019 (COVID-19) using chest x-ray (CXR) on a cohort of subjects from two hospitals in Lombardy, Italy. Methods We used for training and validation an ensemble of ten convolutional neural networks (CNNs) with mainly bedside CXRs of 250 COVID-19 and 250 non-COVID-19 subjects from two hospitals (Centres 1 and 2). We then tested such system on bedside CXRs of an independent group of 110 patients (74 COVID-19, 36 non-COVID-19) from one of the two hospitals. A retrospective reading was performed by two radiologists in the absence of any clinical information, with the aim to differentiate COVID-19 from non-COVID-19 patients. Real-time polymerase chain reaction served as the reference standard. Results At 10-fold cross-validation, our deep learning model classified COVID-19 and non-COVID-19 patients with 0.78 sensitivity (95% confidence interval [CI] 0.74–0.81), 0.82 specificity (95% CI 0.78–0.85), and 0.89 area under the curve (AUC) (95% CI 0.86–0.91). For the independent dataset, deep learning showed 0.80 sensitivity (95% CI 0.72–0.86) (59/74), 0.81 specificity (29/36) (95% CI 0.73–0.87), and 0.81 AUC (95% CI 0.73–0.87). Radiologists’ reading obtained 0.63 sensitivity (95% CI 0.52–0.74) and 0.78 specificity (95% CI 0.61–0.90) in Centre 1 and 0.64 sensitivity (95% CI 0.52–0.74) and 0.86 specificity (95% CI 0.71–0.95) in Centre 2. Conclusions This preliminary experience based on ten CNNs trained on a limited training dataset shows an interesting potential of deep learning for COVID-19 diagnosis. Such tool is in training with new CXRs to further increase its performance.

scholarly journals Skin autofluorescence as a novel predictor of acute kidney injury after liver resection

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Maciej Krasnodębski ◽  
Karolina Grąt ◽  
Marcin Morawski ◽  
Jan Borkowski ◽  
Piotr Krawczyk ◽  
...  
Keyword(s):  
Acute Kidney Injury ◽  
Liver Resection ◽  
Operative Time ◽  
Area Under The Curve ◽  
Kidney Injury ◽  
Extent Of Resection ◽  
Primary Outcome Measure ◽  
Skin Autofluorescence ◽  
Predictive Values ◽  
Risk Patients

Abstract Background Skin autofluorescence (SAF) reflects accumulation of advanced glycation end-products (AGEs). The aim of this study was to evaluate predictive usefulness of SAF measurement in prediction of acute kidney injury (AKI) after liver resection. Methods This prospective observational study included 130 patients undergoing liver resection. The primary outcome measure was AKI. SAF was measured preoperatively and expressed in arbitrary units (AU). Results AKI was observed in 32 of 130 patients (24.6%). SAF independently predicted AKI (p = 0.047), along with extent of resection (p = 0.019) and operative time (p = 0.046). Optimal cut-off for SAF in prediction of AKI was 2.7 AU (area under the curve [AUC] 0.611), with AKI rates of 38.7% and 20.2% in patients with high and low SAF, respectively (p = 0.037). Score based on 3 independent predictors (SAF, extent of resection, and operative time) well stratified the risk of AKI (AUC 0.756), with positive and negative predictive values of 59.3% and 84.0%, respectively. In particular, SAF predicted AKI in patients undergoing major and prolonged resections (p = 0.010, AUC 0.733) with positive and negative predictive values of 81.8%, and 62.5%, respectively. Conclusions AGEs accumulation negatively affects renal function in patients undergoing liver resection. SAF measurement may be used to predict AKI after liver resection, particularly in high-risk patients.

scholarly journals Application of artificial intelligence in a real-world research for predicting the risk of liver metastasis in T1 colorectal cancer

2022 ◽  
Vol 22 (1) ◽  
Author(s):  
Tenghui Han ◽  
Jun Zhu ◽  
Xiaoping Chen ◽  
Rujie Chen ◽  
Yu Jiang ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Colorectal Cancer ◽  
Liver Metastasis ◽  
Predictive Model ◽  
Area Under The Curve ◽  
Predictive Ability ◽  
Subsequent Treatment ◽  
Training Dataset ◽  
Discriminative Ability ◽  
Fundamental Parameters

Abstract Background Liver is the most common metastatic site of colorectal cancer (CRC) and liver metastasis (LM) determines subsequent treatment as well as prognosis of patients, especially in T1 patients. T1 CRC patients with LM are recommended to adopt surgery and systematic treatments rather than endoscopic therapy alone. Nevertheless, there is still no effective model to predict the risk of LM in T1 CRC patients. Hence, we aim to construct an accurate predictive model and an easy-to-use tool clinically. Methods We integrated two independent CRC cohorts from Surveillance Epidemiology and End Results database (SEER, training dataset) and Xijing hospital (testing dataset). Artificial intelligence (AI) and machine learning (ML) methods were adopted to establish the predictive model. Results A total of 16,785 and 326 T1 CRC patients from SEER database and Xijing hospital were incorporated respectively into the study. Every single ML model demonstrated great predictive capability, with an area under the curve (AUC) close to 0.95 and a stacking bagging model displaying the best performance (AUC = 0.9631). Expectedly, the stacking model exhibited a favorable discriminative ability and precisely screened out all eight LM cases from 326 T1 patients in the outer validation cohort. In the subgroup analysis, the stacking model also demonstrated a splendid predictive ability for patients with tumor size ranging from one to50mm (AUC = 0.956). Conclusion We successfully established an innovative and convenient AI model for predicting LM in T1 CRC patients, which was further verified in the external dataset. Ultimately, we designed a novel and easy-to-use decision tree, which only incorporated four fundamental parameters and could be successfully applied in clinical practice.

Imaging-based prediction of histological clot composition from admission CT imaging

2021 ◽  
pp. neurintsurg-2020-016774
Author(s):  
Uta Hanning ◽  
Peter B Sporns ◽  
Marios N Psychogios ◽  
Astrid Jeibmann ◽  
Jens Minnerup ◽  
...  
Keyword(s):  
Machine Learning ◽  
Vascular Tissue ◽  
Large Diameter ◽  
Small Diameter ◽  
Area Under The Curve ◽  
Quantitative Information ◽  
Youden Index ◽  
Quantitative Image ◽  
Characteristic Area ◽  
Contrast Ct

BackgroundThrombus composition has been shown to be a major determinant of recanalization success and occurrence of complications in mechanical thrombectomy. The most important parameters of thrombus behavior during interventional procedures are relative fractions of fibrin and red blood cells (RBCs). We hypothesized that quantitative information from admission non-contrast CT (NCCT) and CT angiography (CTA) can be used for machine learning based prediction of thrombus composition.MethodsThe analysis included 112 patients with occlusion of the carotid-T or middle cerebral artery who underwent thrombectomy. Thrombi samples were histologically analyzed and fractions of fibrin and RBCs were determined. Thrombi were semi-automatically delineated in CTA scans and NCCT scans were registered to the same space. Two regions of interest (ROIs) were defined for each thrombus: small-diameter ROIs capture vessel walls and thrombi, large-diameter ROIs reflect peri-vascular tissue responses. 4844 quantitative image markers were extracted and evaluated for their ability to predict thrombus composition using random forest algorithms in a nested fivefold cross validation.ResultsTest set receiver operating characteristic area under the curve was 0.83 (95% CI 0.80 to 0.87) for differentiating RBC-rich thrombi and 0.84 (95% CI 0.80 to 0.87) for differentiating fibrin-rich thrombi. At maximum Youden-Index, RBC-rich thrombi were identified at 77% sensitivity and 74% specificity; for fibrin-rich thrombi the classifier reached 81% sensitivity at 73% specificity.ConclusionsMachine learning based analysis of admission imaging allows for prediction of clot composition. Perspectively, such an approach could allow selection of clot-specific devices and retrieval procedures for personalized thrombectomy strategies.

13C-caffeine breath test results show high dependence of aryl-hydrocarbon receptor SNPs

2020 ◽  
Author(s):  
Mchiko Ishii ◽  
Yukimoto Ishii ◽  
Tomohisa Nakayama ◽  
Yasuo Takahashi ◽  
Satoshi Asai
Keyword(s):  
Aryl Hydrocarbon Receptor ◽  
Breath Test ◽  
Smoking Status ◽  
Area Under The Curve ◽  
Black Tea ◽  
Nucleotide Polymorphisms ◽  
Aryl Hydrocarbon ◽  
Significant Difference ◽  
Caffeine Metabolism ◽  
Characteristic Area

Abstract Aim: We investigated the relationship between trimethyl-13C-caffeine breath test (triCBT) and single nucleotide polymorphisms (SNPs) that are related to caffeine metabolism and consumption.Methods: Subjects were 132 young healthy adults (median 21 years: 101 male, 31 female). Subjects completed a questionnaire that enquired about their smoking status, consumption of caffeinated drinks (including coffee, black tea, green tea), height, weight, and body mass index (BMI). DNA was extracted from saliva, and genotyping was performed using TaqMan® SNP Genotyping for cytochrome P4501A2 rs762551, rs2472297, and aryl-hydrocarbon receptor rs4410790. Trimethyl 13C-caffeine (100 mg) was dissolved in distilled water and administered orally. Subsequently, breath samples were collected every 10 mins for 90 mins. Infrared spectroscopy was used to analyze the amount of 13CO2 in the expired breath, and the sum (Δ13CO2) over 90 min (S90m) was calculated.Results: All subjects had genotype CC for rs2472297. S90m was not significantly different among rs762551 genotypes; however, there was a significant difference in S90m among rs4410790 genotypes. Δ13CO2 was significantly affected by rs4410790 SNPs and smoking. The receiver operating characteristic area under the curve was 0.758 when rs4410790 phenotype C was considered positive. When the cutoff value was set to S90m (23.4 ‰), the sensitivity and specificity were 71.4% and 72.1%, respectively.Conclusions: Our results suggest that caffeine demethylation is affected by rs4410790 SNPs and smoking, and that triCBT can be used to identify SNPs in rs4410790.

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