scholarly journals Investigation into the predictive performance of colorimetric sensor strips using RGB, CMYK, HSV, and CIELAB coupled with various data preprocessing methods: a case study on an analysis of water quality parameters

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Nutthatida Phuangsaijai ◽  
Jaroon Jakmunee ◽  
Sila Kittiwachana
Keyword(s):  
Research Study ◽  
Chemical Sensor ◽  
External Validation ◽  
Analytical Data ◽  
Predictive Performance ◽  
Data Preprocessing ◽  
Colorimetric Sensor ◽  
Color Models ◽  
Calibration Models ◽  
Colorimetric Sensors

AbstractThe potential use of colorimetric sensors has received significant attention due to its feasibility for use in various applications. After reacting with a sample, the image of the colorimetric sensor can be captured and converted into digital data using several different color models. The analytical data can then be processed with various chemometric methods. This research study investigated the predictive performance of calibration models established using color models commonly used in analytical chemistry including RGB, CMYK, HSV and CIELAB. A total of eight commercially available colorimetric sensors were used to determine the presence of manganese (Mn2+), copper (Cu2+), iron (Fe2+/Fe3+), nitrate (NO3–), phosphate (PO43–), sulfate (SO42–), as well as total hardness and pH values. As external validation tests, real water samples collected in Chiang Mai, Thailand were used. Based on the resulting data obtained using the synthetic test samples, the color that was most similar to the appearing color of the chemical sensor could offer satisfactory results. However, it was not always the case especially when the strips composed of multiple colorimetric sensors or sensor array were used. When tested with external validation, the predictive performance could be improved using appropriate data preprocessing and, in this research study, a normalization method was recommended to guarantee the accuracy of the calibration models.

scholarly journals Dynamic nomograms combining N classification with ratio-based nodal classifications to predict long-term survival for patients with lung adenocarcinoma after surgery: a SEER population-based study

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Suyu Wang ◽  
Yue Yu ◽  
Wenting Xu ◽  
Xin Lv ◽  
Yufeng Zhang ◽  
...  
Keyword(s):  
Overall Survival ◽  
Lung Adenocarcinoma ◽  
External Validation ◽  
Predictive Performance ◽  
Information Criterion ◽  
Curve Analysis ◽  
Tnm Stage ◽  
Concordance Index ◽  
Cancer Specific Survival ◽  
Decision Curve Analysis

Abstract Background The prognostic roles of three lymph node classifications, number of positive lymph nodes (NPLN), log odds of positive lymph nodes (LODDS), and lymph node ratio (LNR) in lung adenocarcinoma are unclear. We aim to find the classification with the strongest predictive power and combine it with the American Joint Committee on Cancer (AJCC) 8th TNM stage to establish an optimal prognostic nomogram. Methods 25,005 patients with T1-4N0–2M0 lung adenocarcinoma after surgery between 2004 to 2016 from the Surveillance, Epidemiology, and End Results database were included. The study cohort was divided into training cohort (13,551 patients) and external validation cohort (11,454 patients) according to different geographic region. Univariate and multivariate Cox regression analyses were performed on the training cohort to evaluate the predictive performance of NPLN (Model 1), LODDS (Model 2), LNR (Model 3) or LODDS+LNR (Model 4) respectively for cancer-specific survival and overall survival. Likelihood-ratio χ2 test, Akaike Information Criterion, Harrell concordance index, integrated discrimination improvement (IDI) and net reclassification improvement (NRI) were used to evaluate the predictive performance of the models. Nomograms were established according to the optimal models. They’re put into internal validation using bootstrapping technique and external validation using calibration curves. Nomograms were compared with AJCC 8th TNM stage using decision curve analysis. Results NPLN, LODDS and LNR were independent prognostic factors for cancer-specific survival and overall survival. LODDS+LNR (Model 4) demonstrated the highest Likelihood-ratio χ2 test, highest Harrell concordance index, and lowest Akaike Information Criterion, and IDI and NRI values suggested Model 4 had better prediction accuracy than other models. Internal and external validations showed that the nomograms combining TNM stage with LODDS+LNR were convincingly precise. Decision curve analysis suggested the nomograms performed better than AJCC 8th TNM stage in clinical practicability. Conclusions We constructed online nomograms for cancer-specific survival and overall survival of lung adenocarcinoma patients after surgery, which may facilitate doctors to provide highly individualized therapy.

scholarly journals Developing and validating COVID-19 adverse outcome risk prediction models from a bi-national European cohort of 5594 patients

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Espen Jimenez-Solem ◽  
Tonny S. Petersen ◽  
Casper Hansen ◽  
Christian Hansen ◽  
Christina Lioma ◽  
...  
Keyword(s):  
Prediction Models ◽  
Severe Disease ◽  
External Validation ◽  
Area Under The Curve ◽  
Predictive Performance ◽  
Mortality Prediction ◽  
Icu Admission ◽  
Risk Of Death ◽  
Online Risk ◽  
The United Kingdom

AbstractPatients with severe COVID-19 have overwhelmed healthcare systems worldwide. We hypothesized that machine learning (ML) models could be used to predict risks at different stages of management and thereby provide insights into drivers and prognostic markers of disease progression and death. From a cohort of approx. 2.6 million citizens in Denmark, SARS-CoV-2 PCR tests were performed on subjects suspected for COVID-19 disease; 3944 cases had at least one positive test and were subjected to further analysis. SARS-CoV-2 positive cases from the United Kingdom Biobank was used for external validation. The ML models predicted the risk of death (Receiver Operation Characteristics—Area Under the Curve, ROC-AUC) of 0.906 at diagnosis, 0.818, at hospital admission and 0.721 at Intensive Care Unit (ICU) admission. Similar metrics were achieved for predicted risks of hospital and ICU admission and use of mechanical ventilation. Common risk factors, included age, body mass index and hypertension, although the top risk features shifted towards markers of shock and organ dysfunction in ICU patients. The external validation indicated fair predictive performance for mortality prediction, but suboptimal performance for predicting ICU admission. ML may be used to identify drivers of progression to more severe disease and for prognostication patients in patients with COVID-19. We provide access to an online risk calculator based on these findings.

POPULATION PHARMACOKINETICS AND DOSING OPTIMIZATION OF TEICOPLANIN IN CHILDREN WITH MALIGNANT HAEMATOLOGICAL DISEASE

2015 ◽  
Vol 101 (1) ◽  
pp. e1.41-e1
Author(s):  
Wei Zhao ◽  
Daolun Zhang ◽  
Thomas Storme ◽  
André Baruchel ◽  
Xavier Declèves ◽  
...  
Keyword(s):  
Population Pharmacokinetics ◽  
Haematological Malignancy ◽  
External Validation ◽  
Predictive Performance ◽  
Paediatric Population ◽  
Pharmacokinetic Parameters ◽  
Therapeutic Drug ◽  
Population Pharmacokinetic ◽  
High Risk Population ◽  
Dosing Regimen

BackgroundChildren with haematological malignancy represent an identified subgroup of the paediatric population with specific pharmacokinetic parameters. In these patients, inadequate empirical antibacterial therapy may result in infection-related morbidity and increased mortality, making optimization of the dosing regimen essential. As paediatric data are limited, our aim was to evaluate the population pharmacokinetics of teicoplanin in order to define the appropriate dosing regimen in this high-risk population.MethodsThe current dose of teicoplanin was evaluated in children with haematological malignancy. Population pharmacokinetics of teicoplanin was analysed using NONMEM software. The dosing regimen was optimised based on the final model.ResultsEighty-five children (age range: 0.5 to 16.9 years) were included. Therapeutic drug monitoring and opportunistic samples (n=143) were available for analysis. With the current recommended dose of 10 mg/kg/day, 41 children (48%) had sub-therapeutic steady-state trough concentrations (Css,min<10 mg/liter). A two-compartment pharmacokinetic model with first-order elimination was developed. Systematic covariate analysis identified that bodyweight (size) and creatinine clearance significantly influenced teicoplanin clearance. The model was validated internally. Its predictive performance was further confirmed in an external validation. In order to reach the target AUC of 750 mg·h/L, 18 mg/kg was required for infants, 14 mg/kg for children and 12 mg/kg for adolescents. A patient-tailored dose regimen was further developed and reduced variability in AUC and Css,min values compared to the mg/kg-basis dose, making the modelling approach an important tool for dosing individualization.ConclusionsThis first population pharmacokinetic study of teicoplanin in children with haematological malignancy provided evidence-based support to individualize teicoplanin therapy in this vulnerable population.

External Validation and Comparison of the Predictive Performance of Ten Different Tooth‐Level Prognostic Systems.

2021 ◽  
Author(s):  
Muhammad H. A. Saleh ◽  
Himabindu Dukka ◽  
Giuseppe Troiano ◽  
Andrea Ravidà ◽  
Matthew Galli ◽  
...  
Keyword(s):  
External Validation ◽  
Predictive Performance

scholarly journals Development of Calibration Models to Predict Mean Fibre Diameter in Llama (Lama glama) Fleeces with Near Infrared Spectroscopy

Animals ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 1998
Author(s):  
José Ignacio Amorena ◽  
Dolores María Eugenia Álvarez ◽  
Elvira Fernández-Ahumada
Keyword(s):  
Textile Industry ◽  
Near Infrared ◽  
Prediction Models ◽  
External Validation ◽  
Fibre Diameter ◽  
Near Infrared Reflectance ◽  
Screening Programs ◽  
Spectral Signatures ◽  
Calibration Models ◽  
Cross Validation Error

Llama fibre has the potential to become the most valuable textile resource in the Puna region of Argentina. In this study near infrared reflectance spectroscopy was evaluated to predict the mean fibre diameter in llama fleeces. Analyses between sets of carded and non-carded samples in combination with spectral preprocessing techniques were carried out and a total of 169 spectral signatures of llama samples in Vis and NIR ranges (400–2500 nm) were obtained. Spectral preprocessing consisted in wavelength selection (Vis–NIR, NIR and discrete ranges) and multiplicative and derivative pretreatments; spectra without pretreatments were also included, while modified partial least squares (M-PLS) regression was used to develop prediction models. Predictability was evaluated through R2: standard cross validation error (SECV), external validation error (SEV) and residual predictive value (RPD). A total of 54 calibration models were developed in which the best model (R2 = 0.67; SECV = 1.965; SEV = 2.235 and RPD = 1.91) was obtained in the Vis–NIR range applying the first derivative pretreatment. ANOVA analysis showed differences between carded and non-carded sets and the models obtained could be used in screening programs and contribute to valorisation of llama fibre and sustainable development of textile industry in the Puna territory of Catamarca. The data presented in this paper are a contribution to enhance the scarce information on this subject.

scholarly journals External validation of the predictive performance of population pharmacokinetic models for phenobarbital in pediatric patients

2020 ◽  
Author(s):  
Sunae Ryu ◽  
Woo Jin Jung ◽  
Zheng Jiao ◽  
Jung Woo Chae ◽  
Hwi-yeol Yun
Keyword(s):  
External Validation ◽  
Predictive Performance ◽  
Validation Dataset ◽  
Therapeutic Drug ◽  
Population Pharmacokinetic ◽  
Pharmacokinetic Models ◽  
Bayesian Forecasting ◽  
Predictive Capability ◽  
Young Infants ◽  
Simulation Based

Aim: Several studies have reported population pharmacokinetic models for phenobarbital (PB), but the predictive performance of these models has not been well documented. This study aims to do external validation of the predictive performance in published pharmacokinetic models. Methods: Therapeutic drug monitoring data collected in neonates and young infants treated with PB for seizure control, was used for external validation. A literature review was conducted through PubMed to identify population pharmacokinetic models. Prediction- and simulation-based diagnostics, and Bayesian forecasting were performed for external validation. The incorporation of size or maturity functions into the published models was also tested for prediction improvement. Results: A total of 79 serum concentrations from 28 subjects were included in the external validation dataset. Seven population pharmacokinetic studies of PB were selected for evaluation. The model by Voller et al. [27] showed the best performance concerning prediction-based evaluation. In simulation-based analyses, the normalized prediction distribution error of two models (those of Shellhaas et al. [24] and Marsot et al. [25]) obeyed a normal distribution. Bayesian forecasting with more than one observation improved predictive capability. Incorporation of both allometric size scaling and maturation function generally enhanced the predictive performance, but with marked improvement for the adult pharmacokinetic model. Conclusion: The predictive performance of published pharmacokinetic models of PB was diverse, and validation may be necessary to extrapolate to different clinical settings. Our findings suggest that Bayesian forecasting improves the predictive capability of individual concentrations for pediatrics.

scholarly journals Different spectrophotometric and TLC-densitometric methods for determination of olmesartan medoxomil and hydrochlorothiazide and their degradation products

2018 ◽  
Vol 9 (4) ◽  
pp. 400-407 ◽  
Author(s):  
Selvia Maged Adly ◽  
Maha Mohamed Abdelrahman ◽  
Nada Sayed Abdelwahab ◽  
Nourudin Wageh Ali
Keyword(s):  
Multivariate Calibration ◽  
Degradation Products ◽  
External Validation ◽  
Olmesartan Medoxomil ◽  
Partial Least Square ◽  
Least Square ◽  
Pharmaceutical Dosage Form ◽  
Calibration Models ◽  
Significant Difference

In this work, multivariate calibration models and TLC-densitometric methods have been developed and validated for quantitative determination of olmesartan medoxomil (OLM) and hydrochlorothiazide (HCZ) in presence of their degradation products, olmesartan (OL) and salamide (SAL), respectively. In the first method, multivariate calibration models including principal component regression (PCR) and partial least square (PLS) were applied. The wavelength range 210-343 nm was used and data was auto-scaled and mean centered as pre-processing steps for PCR and PLS models, respectively. These models were tested by application to external validation set with mean percentage recoveries 99.78, 100.01, 100.41 and 100.46% for OLM, HCZ, OL and SAL, respectively, for PLS model and also, 100.22, 100.40, 102.25 and 100.13% for them, respectively, for PCR model. The second method is TLC-densitometry at which the chromatographic separation was carried out using silica gel 60F254 TLC plates and the developing system consisted of a mixture of ethyl acetate:chloroform:methanol: formic acid:tri-ethylamine (60:40:4:4:1, by volume) with UV-scanning at 254 nm. The developed methods were successfully applied for determination of OLM and HCZ in their pharmaceutical dosage form. Also, statistical comparison was made between the developed methods and the reported method using student’s-t test and F-test and results showed that there was no significant difference between them concerning both accuracy and precision.

P–419 On prognosis after unexplained recurrent pregnancy losses (RPL); a systematic review and external validation of clinical prediction models

2021 ◽  
Vol 36 (Supplement_1) ◽  
Author(s):  
A Youssef
Keyword(s):  
Systematic Review ◽  
Supportive Care ◽  
Prediction Model ◽  
Pregnancy Outcome ◽  
Prediction Models ◽  
External Validation ◽  
Model Development ◽  
Predictive Performance ◽  
Pregnancy Rates ◽  
Cochrane Library

Abstract Study question Which models that predict pregnancy outcome in couples with unexplained RPL exist and what is the performance of the most used model? Summary answer We identified seven prediction models; none followed the recommended prediction model development steps. Moreover, the most used model showed poor predictive performance. What is known already RPL remains unexplained in 50–75% of couples For these couples, there is no effective treatment option and clinical management rests on supportive care. Essential part of supportive care consists of counselling on the prognosis of subsequent pregnancies. Indeed, multiple prediction models exist, however the quality and validity of these models varies. In addition, the prediction model developed by Brigham et al is the most widely used model, but has never been externally validated. Study design, size, duration We performed a systematic review to identify prediction models for pregnancy outcome after unexplained RPL. In addition we performed an external validation of the Brigham model in a retrospective cohort, consisting of 668 couples with unexplained RPL that visited our RPL clinic between 2004 and 2019. Participants/materials, setting, methods A systematic search was performed in December 2020 in Pubmed, Embase, Web of Science and Cochrane library to identify relevant studies. Eligible studies were selected and assessed according to the TRIPOD) guidelines, covering topics on model performance and validation statement. The performance of predicting live birth in the Brigham model was evaluated through calibration and discrimination, in which the observed pregnancy rates were compared to the predicted pregnancy rates. Main results and the role of chance Seven models were compared and assessed according to the TRIPOD statement. This resulted in two studies of low, three of moderate and two of above average reporting quality. These studies did not follow the recommended steps for model development and did not calculate a sample size. Furthermore, the predictive performance of neither of these models was internally- or externally validated. We performed an external validation of Brigham model. Calibration showed overestimation of the model and too extreme predictions, with a negative calibration intercept of –0.52 (CI 95% –0.68 – –0.36), with a calibration slope of 0.39 (CI 95% 0.07 – 0.71). The discriminative ability of the model was very low with a concordance statistic of 0.55 (CI 95% 0.50 – 0.59). Limitations, reasons for caution None of the studies are specifically named prediction models, therefore models may have been missed in the selection process. The external validation cohort used a retrospective design, in which only the first pregnancy after intake was registered. Follow-up time was not limited, which is important in counselling unexplained RPL couples. Wider implications of the findings: Currently, there are no suitable models that predict on pregnancy outcome after RPL. Moreover, we are in need of a model with several variables such that prognosis is individualized, and factors from both the female as the male to enable a couple specific prognosis. Trial registration number Not applicable

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