scholarly journals Development of a Drum Tower Severity Scoring (DTSS) system for pyrrolizidine alkaloid-induced hepatic sinusoidal obstruction syndrome

Author(s):  
Xuan Wang ◽  
Wei Zhang ◽  
Ming Zhang ◽  
Feng Zhang ◽  
Jiangqiang Xiao ◽  
...  

Abstract Background and aims There has been no reliable severity system based on the prognosis to guide therapeutic strategies for patients with pyrrolizidine alkaloid (PA)-induced hepatic sinusoidal obstruction syndrome (HSOS). We aimed to create a novel Drum Tower Severity Scoring (DTSS) system for these patients to guide therapy. Methods 172 Patients with PA-HSOS who received supportive care and anticoagulation therapy in Nanjing Drum Tower Hospital from January 2008 to December 2020 were enrolled and analyzed retrospectively. These patients were randomized into a training or validation set in a 3:1 ratio. Next, we established and validated the newly developed DTSS system. Results Analysis identified a predictive formula: logit (P) = 0.004 × aspartate aminotransferase (AST, U/L) + 0.019 × total bilirubin (TB, μmol/L) − 0.571 × fibrinogen (FIB, g/L) − 0.093 × peak portal vein velocity (PVV, cm/s) + 1.122. Next, we quantified the above variables to establish the DTSS system. For the training set, the area under the ROC curve (AUC) (n = 127) was 0.787 [95% confidence interval (CI) 0.706–0.868; p < 0.001]. With a lower cut-off value of 6.5, the sensitivity and negative predictive value for predicting no response to supportive care and anticoagulation therapy were 94.7% and 88.0%, respectively. When applying a high cut-off value of 10.5, the specificity was 92.9% and the positive predictive value was 78.3%. For the validation set, the system performed stable with an AUC of 0.808. Conclusions The DTSS system can predict the outcome of supportive care and anticoagulation in PA-HSOS patients with satisfactory accuracy by evaluating severity, and may have potential significance for guiding therapy.

2021 ◽  
Vol 49 (4) ◽  
pp. 030006052098064
Author(s):  
Panpan Cen ◽  
Jiexia Ding ◽  
Jie Jin

Hepatic sinusoidal obstruction syndrome (HSOS) is a rare hepatic vascular disorder characterized by intrahepatic congestion, liver injury, and post-sinusoidal portal hypertension, and it is frequently associated with hematopoietic stem cell transplantation. In this study, we observed a case of HSOS associated with the ingestion of Gynura segetum, a pyrrolizidine alkaloid (PA)-containing Chinese herb, in a patient with alcoholic cirrhosis. The patient was a 43-year-old man with chief complaints of physical asthenia and a loss of appetite for more than a month. The diagnosis of HSOS combined with alcoholic cirrhosis was confirmed via the histopathological examination of liver tissues. With proper supportive and symptomatic care and anticoagulation therapy using low-molecular-weight heparin, the patient’s condition was stabilized. Because of its nonspecific symptoms in the early stage and a lack of information about PA consumption, PA-induced HSOS (PA-HSOS) has been long neglected, especially in patients with underlying liver diseases. Early identification and intervention are critical for optimizing outcomes. Further efforts are needed to supervise the use of PA-containing herbal medicines and identify accurate biomarkers for PA-HSOS.


2015 ◽  
Vol 28 (9) ◽  
pp. 1715-1727 ◽  
Author(s):  
Yan-Hong Li ◽  
William Chi-Shing Tai ◽  
Jun-Yi Xue ◽  
Wing-Yan Wong ◽  
Cheng Lu ◽  
...  

Stroke ◽  
2013 ◽  
Vol 44 (suppl_1) ◽  
Author(s):  
Kohkichi Hosoda ◽  
Nobuyuki Akutsu ◽  
Atsushi Fujita ◽  
Eiji Kohmura

[Objective] Recently, we reported a preliminary prediction model with carotid plaque MRI to estimate risk for new ischaemic brain lesions after CEA or CAS. The objective of this study was to validate this model in new set of patients with carotid stenosis. [Methods] One hundred four patients with carotid stenosis undergoing treatment (63 CEA, 41 CAS) were used as a training set for construction of a preliminary prediction model to estimate risk for new ischemic brain lesions after CEA or CAS. T1 and T2 signal intensity of carotid plaque were measured on black-blood MRI. Associations among MRI findings, treatment, clinical factors, and occurrence of new ischemic lesions on DWI 1 day after treatment were studied by logistic regression. The validity of the prediction model was examined using a new set of patients with carotid stenosis (n = 43) as a validation set. [Results] In the training set, new DWI lesions after treatment were observed in 25 patients (24%). The model demonstrated that T1-signal intensity and CAS were positively associated with new lesions on post-treatment DWI scans, and T2 signal intensity was negatively associated (Fig. 1). The C-index was 0.79, which indicated some predictive value. In the validation set, new DWI lesions after treatment were observed in 10 patients (23%). However, C-index was 0.6 and positive predictive value was 33% (Fig. 2), which suggested overfitting of our model and/or differences in case-mix between the training set and validation set. [Conclusions] Our preliminary prediction model may provide some useful information for decision-making regarding treatment strategy, but needs further collection of patients to improve its predictive value.


2018 ◽  
Vol 10 (3) ◽  
Author(s):  
Pokpong Piriyakhuntorn ◽  
Adisak Tantiworawit ◽  
Thanawat Rattanathammethee ◽  
Chatree Chai-Adisaksopha ◽  
Ekarat Rattarittamrong ◽  
...  

This study aims to find the cut-off value and diagnostic accuracy of the use of RDW as initial investigation in enabling the differentiation between IDA and NTDT patients. Patients with microcytic anemia were enrolled in the training set and used to plot a receiving operating characteristics (ROC) curve to obtain the cut-off value of RDW. A second set of patients were included in the validation set and used to analyze the diagnostic accuracy. We recruited 94 IDA and 64 NTDT patients into the training set. The area under the curve of the ROC in the training set was 0.803. The best cut-off value of RDW in the diagnosis of NTDT was 21.0% with a sensitivity and specificity of 81.3% and 55.3% respectively. In the validation set, there were 34 IDA and 58 NTDT patients using the cut-off value of >21.0% to validate. The sensitivity, specificity, positive predictive value and negative predictive value were 84.5%, 70.6%, 83.1% and 72.7% respectively. We can therefore conclude that RDW >21.0% is useful in differentiating between IDA and NTDT patients with high diagnostic accuracy


2017 ◽  
Vol 91 (12) ◽  
pp. 3913-3925 ◽  
Author(s):  
Mengbi Yang ◽  
Jianqing Ruan ◽  
Hong Gao ◽  
Na Li ◽  
Jiang Ma ◽  
...  

2021 ◽  
Vol 11 ◽  
Author(s):  
Yinghao Meng ◽  
Hao Zhang ◽  
Qi Li ◽  
Fang Liu ◽  
Xu Fang ◽  
...  

PurposeTo develop and validate a machine learning classifier based on multidetector computed tomography (MDCT), for the preoperative prediction of tumor–stroma ratio (TSR) expression in patients with pancreatic ductal adenocarcinoma (PDAC).Materials and MethodsIn this retrospective study, 227 patients with PDAC underwent an MDCT scan and surgical resection. We quantified the TSR by using hematoxylin and eosin staining and extracted 1409 arterial and portal venous phase radiomics features for each patient, respectively. Moreover, we used the least absolute shrinkage and selection operator logistic regression algorithm to reduce the features. The extreme gradient boosting (XGBoost) was developed using a training set consisting of 167 consecutive patients, admitted between December 2016 and December 2017. The model was validated in 60 consecutive patients, admitted between January 2018 and April 2018. We determined the XGBoost classifier performance based on its discriminative ability, calibration, and clinical utility.ResultsWe observed low and high TSR in 91 (40.09%) and 136 (59.91%) patients, respectively. A log-rank test revealed significantly longer survival for patients in the TSR-low group than those in the TSR-high group. The prediction model revealed good discrimination in the training (area under the curve [AUC]= 0.93) and moderate discrimination in the validation set (AUC= 0.63). While the sensitivity, specificity, accuracy, positive predictive value, and negative predictive value for the training set were 94.06%, 81.82%, 0.89, 0.89, and 0.90, respectively, those for the validation set were 85.71%, 48.00%, 0.70, 0.70, and 0.71, respectively.ConclusionsThe CT radiomics-based XGBoost classifier provides a potentially valuable noninvasive tool to predict TSR in patients with PDAC and optimize risk stratification.


2020 ◽  
Vol 46 (Supplement_1) ◽  
pp. S309-S309
Author(s):  
Deanna Kelly ◽  
Ann Marie Kearns ◽  
Matthew Glassman ◽  
Matthew Atkins ◽  
Philip McQuire

Abstract Background Clozapine is one of the most underused medications in psychiatry for many reasons including mandatory blood testing, fear of serious side-effects, lack of patient adherence. A critical barrier to adoption could be addressed with the ability to measure clozapine at point-of-care (POC) from a fingerstick. Current practice of clozapine measurements, however, was developed based on serum levels. Therefore, meaningful POC results must be reported as the serum equivalent. We evaluated a new immunoassay method to measure clozapine in whole blood to establish standardization to serum, and to assess the ability of the POCT to detect differences in patients’ clozapine levels compared to an existing laboratory method. Methods A whole blood POCT (MyCare® Insite Clozapine Test on the MyCare Insite)* immunoassay was compared to liquid chromatography tandem mass-spectrometry (LC-MS/MS) in serum with 95 matched patient samples. Passing-Bablok regression was used to compare results and establish calibrator values to standardize the POCT to report whole blood results as equivalent to serum. The standardization was validated by a method comparison to LC-MS/MS with 304 samples collected from patients with schizophrenia who were being treated with clozapine. Serial blood levels were taken for 13 patients to compare deviation from baseline for POCT and LC-MS/MS results. To detect a discordant difference in clozapine levels, the difference to the preceding value was calculated for 73 sequential samples of the 86 total results. Because of high intra-patient variability changes of &gt; ±50% were considered significant. Results There was good correlation (R = 0.9) between the POCT and LC-MS/MS in the training set (N=95). Passing-Bablok statistics were: slope = 1.02, intercept = -2.3, R = 0.9, average bias -17.7 (-3.8%). The average values (± SD) were 479.7 (± 181.5) ng/mL for LC-MS/MS and 462.0 (± 199.1) ng/mL for POCT. The Passing-Bablok regression of the validation set (N=304), using the reassigned calibrator values as the training set, gave a slope = 0.971, intercept = -21.2, R = 0.9, mean values (± SD) of 445.6 (± 242.4) for LC-MS/MS and, 412.6 (± 245.7) for the POCT, average bias was -33.0 (-7.7%). Bias between POCT and LC-MS/MS for 12 individuals ranged from -22% to 22%. One patient with five sequential measurements had a total bias of -34% with 4 of 5 results, agreeing with assignment in or out of the presumptive target range of 350 – 600 ng/mL. The frequency of &gt;±50% change in clozapine levels was &lt;5%. Ninety percent (66 of 73) of results agreed, selectivity = 50%, specificity = 94%, positive predictive value (PPV) = 42.9%, negative predictive value (NPV) = 95.5%. Seven samples had a 50% change by one method and not the other. There was only one discrepant sample that was 66% lower with POCT. Discussion Differences in measurement methods are expected. The good correlation and similarity of results between the calibrator assignment training set and the validation set demonstrates the accuracy of the calibrator value assignment. The POCT was highly selective in detecting important changes in clozapine levels of more than 50% which would occur secondary to non-adherence, change in life-style habits or drug-drug interactions. The collection conditions gave consistent levels for most patients, with few large shifts in concentration, thus underestimating the PPV. These data suggest that clozapine levels can be accurately measured from a small volume of capillary blood collected via a fingerstick sample. This method makes blood sampling easier for both patients and clinical staff, and provides a result in a few minutes, at point of care. Its clinical implementation may facilitate the safe and effective use of clozapine in schizophrenia. *CE mark/US RUO


2021 ◽  
Author(s):  
Jiejun Lin ◽  
Huang Su ◽  
Yaqi Guan ◽  
Qingjie Zhou ◽  
Jie Pan ◽  
...  

Abstract Background and Aim. It is of importance to predict the risk of gastric cancer (GC) for endoscopists because early detection of GC determines the determines the selection of best treatment strategy and the prognosis of patients. The aim of the study was to evaluate the utility of a predictive nomogram based on Kyoto classification of gastritis for GC. Methods. It was a retrospective study that included 2639 patients who received esophagogastroduodenoscopy and serum pepsinogen (PG) assay from January 2020 to November 2020 at the Endoscopy Center of the Department of Gastroenterology, Wenzhou Central Hospital. Routine biopsy was conducted to determine the benign and malignant lesions pathologically. All cases were randomly divided into the training set (70%) and the validation set (30%) by using bootstrap method. A nomogram was formulated according to multivariate analysis of training set. The predictive accuracy and discriminative ability of the nomogram were assessed by concordance index (C-index), area under the curve (AUC) of receiver operating characteristic curve (ROC) as well as calibration curve and were validated by validation set.Results. Multivariate analysis indicated that age, sex, PG I/II ratio and Kyoto classification scores were independent predictive variables for GC. The C-index of the nomogram of the training set was 0.79 (95% CI: 0.74 to 0.84) and the AUC of ROC is 0.79. The calibration curve of the nomogram demonstrated an optimal agreement between predicted probability and observed probability of the risk of GC. In the validation set, the C-index was 0.86 (95% CI: 0.79 to 0.94) with a calibration curve of better concurrence.Conclusion. The nomogram formulated was proven to be of high predictive value for GC.


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