Matching by OS Prognostic Score to Construct External Controls in Lung Cancer Clinical Trials.

External controls (eControls) leverage historical data to create non-randomized control arms. The lack of randomization can result in confounding between the experimental and eControl cohorts. To balance potentially confounding variables between the cohorts, one of the proposed methods is to match on prognostic scores. Still, the performance of prognostic scores to construct eControls in oncology has not been analyzed yet. Using an electronic health record-derived de-identified database, we constructed eControls using one of three methods: ROPRO, a state-of-the-art prognostic score, or either a propensity score composed of five (5Vars) or 27 covariates (ROPROvars). We compared the performance of these methods in estimating the overall survival (OS) hazard ratio (HR) of 11 recent advanced non-small cell lung cancer. The ROPRO eControls had a lower OS HR error (median absolute deviation (MAD), 0.072, confidence interval (CI): 0.036-0.185), than the 5Vars (MAD 0.081, CI: 0.025-0.283) and ROPROvars eControls (MAD 0.087, CI: 0.054-0.383). Notably, the OS HR errors for all methods were even lower in the phase III studies. Moreover, the ROPRO eControl cohorts included, on average, more patients than the 5Vars (6.54%) and ROPROvars cohorts (11.7%). The eControls matched with the prognostic score reproduced the controls more reliably than propensity scores composed of the underlying variables. Additionally, prognostic scores could allow eControls to be built on many prognostic variables without a significant increase in the variability of the propensity score, which would decrease the number of matched patients.

Correlation Between Early Trends of a Prognostic Biomarker and Overall Survival in Non-Small-Cell Lung Cancer Clinical Trials.

PURPOSE: Overall survival (OS) is the primary end point in phase III oncology trials. Given low success rates, surrogate end points, such as progression-free survival or objective response rate, are used in early go/no-go decision making. Here, we investigate whether early trends of OS prognostic biomarkers, such as the ROPRO and DeepROPRO, can also be used for this purpose. METHODS: Using real-world data, we emulated a series of 12 advanced non-small-cell lung cancer (aNSCLC) clinical trials, originally conducted by six different sponsors and evaluated four different mechanisms, in a total of 19,920 individuals. We evaluated early trends (until 6 months) of the OS biomarker alongside early OS within the joint model (JM) framework. Study-level estimates of early OS and ROPRO trends were correlated against the actual final OS hazard ratios (HRs). RESULTS: We observed a strong correlation between the JM estimates and final OS HR at 3 months (adjusted R2 = 0.88) and at 6 months (adjusted R2 = 0.85). In the leave-one-out analysis, there was a low overall prediction error of the OS HR at both 3 months (root-mean-square error [RMSE] = 0.11) and 6 months (RMSE = 0.12). In addition, at 3 months, the absolute prediction error of the OS HR was lower than 0.05 for three trials. CONCLUSION: We describe a pipeline to predict trial OS HRs using emulated aNSCLC studies and their early OS and OS biomarker trends. The method has the potential to accelerate and improve decision making in drug development.

Influence of filament aging and conductive additive in 3D printed sensors.

3D printing technology combined with electrochemical techniques have allowed the development of versatile and low-cost devices. However, some aspects need to be considered for the good quality and useful life of the sensors. In this work, we have demonstrated herein that the filament aging, the conductive material, and the activation processes (post-treatments) can influence the surface characteristics and the electrochemical performance of the 3D printed sensors. Commercial filaments and 3D printed sensors were morphologically, thermally, and electrochemically analyzed. The activated graphene-based (Black Magic®) sensor showed the best electrochemical response, compared to the carbon black-filament (Proto-Pasta®). In addition, we have proven that filament aging harms the performance of the sensors since the electrodes produced with three years old filament had a considerably lower intra-days reproducibility. Finally, the activated graphene-based sensor has shown the best performance for the electrochemical detection of bisphenol A, demonstrating the importance of evaluating and control the characteristics and quality of filaments to improve the mechanical, conductive, and electrochemical performance of 3D printed sensors.

Artificial Intelligence for Prognostic Scores in Oncology: a Benchmarking Study.

Introduction: Prognostic scores are important tools in oncology to facilitate clinical decision-making based on patient characteristics. To date, classic survival analysis using Cox proportional hazards regression has been employed in the development of these prognostic scores. With the advance of analytical models, this study aimed to determine if more complex machine-learning algorithms could outperform classical survival analysis methods. Methods: In this benchmarking study, two datasets were used to develop and compare different prognostic models for overall survival in pan-cancer populations: a nationwide EHR-derived de-identified database for training and in-sample testing and the OAK (phase III clinical trial) dataset for out-of-sample testing. A real-world database comprised 136K first-line treated cancer patients across multiple cancer types and was split into a 90% training and 10% testing dataset, respectively. The OAK dataset comprised 1,187 patients diagnosed with non-small cell lung cancer. To assess the effect of the covariate number on prognostic performance, we formed three feature sets with 27, 44 and 88 covariates. In terms of methods, we benchmarked ROPRO, a prognostic score based on the Cox model, against eight complex machine-learning models: regularized Cox, Random Survival Forests (RSF), Gradient Boosting (GB), DeepSurv (DS), Autoencoder (AE) and Super Learner (SL). The C-index was used as the performance metric to compare different models. Results: For in-sample testing on the real-world database the resulting C-index [95% CI] values for RSF 0.720 [0.716, 0.725], GB 0.722 [0.718, 0.727], DS 0.721 [0.717, 0.726] and lastly, SL 0.723 [0.718, 0.728] showed significantly better performance as compared to ROPRO 0.701 [0.696, 0.706]. Similar results were derived across all feature sets. However, for the out-of-sample validation on OAK, the stronger performance of the more complex models was not apparent anymore. Consistently, the increase in the number of prognostic covariates did not lead to an increase in model performance. Discussion: The stronger performance of the more complex models did not generalize when applied to an out-of-sample dataset. We hypothesize that future research may benefit by adding multimodal data to exploit advantages of more complex models.

Inflammatory Response Assessment in Patients with COVID-19 Under Extracorporeal Membrane Oxygenation Support.

BACKGROUND: Extracorporeal membrane oxygenation (ECMO), could be extremely helpful in the management of COVID-19 patients with refractory hypoxemic respiratory failure; however, to date, evidence on the true effecttiveness of ECMO in the COVID19 setting still hangs in the balance. METHODS: This was a prospective cohort study of 39 COVID-19 patients admitted to the intensive care unit (ICU) in an experienced ECMO center at a tertiary hospital during March/April 2020. Among the recruited participants, 10 (25.6%) required ECMO (ICU-ECMO group) and 29 (74.4%) did not have ECMO support (ICU group). Immunological parameters were assessed both at ICU admission and on a daily basis for 7 consecutive days. RESULTS: The absolute lymphocyte count increased significantly in the ICU-ECMO group compared to the ICU group in which it remained relatively stable: ß for the time variable was 127.1 [95% CI 68.9 - 185.3], p < 0.001 and for the interaction term -141.36 [-208.95 - -73.77], p < 0.001. On the other hand, globally, no significant differences were observed over time for the lymphocyte percentage, although it was higher in the ICU patients. Neutrophil counts were overall higher in the ICU-ECMO group (ß -4,275.38 [-6,845.21 - 1,705.55], p = 0.001). In regard to neutrophil percentage, a significant decrease over time was reported (ß -1.76 [-3.16 - -0.36], p = 0.014), namely in the ICU-ECMO group (ß for the interaction 2.09 [0.45 - 3.73], p = 0.013). CONCLUSIONS: Herein, we found ECMO support seems to provide a less aggressive immune response in COVID-19 patients with severe and refractory respiratory dysfunction.

The Enantiomeric Discrimination of 5-Hexyl-2-methyl-3,4-dihydro-2H-pyrrole by Sulfobutyl ether-ß-cyclodextrin: A Case Study.

1-Pyrrolines are important intermediates of active natural products, such as the 2,5-dialkyl-1-pyrroline derivatives found in fire ant venoms. Here, 5-hexyl-2-methyl-3,4-dihydro-2H-pyrrole was synthesized by the enzymatic transamination/cyclization of 2,5-undecadione, and enantiodifferenciation was successfully achieved by capillary electrophoresis with sulfobutyl ether-ß-cyclodextrin as the chiral selector. The rationale of the enantiomeric discrimination was based on the results of a docking simulation that revealed the higher affinity of (S)-5-hexyl-2-methyl-3,4-dihydro-2H-pyrrole for the sulfobutyl ether-ß-cyclodextrin.

ChemPager: Now Expanded for Evev Greener Chemistry.

ChemPager is a freely available data analysis tool for analyzing, comparing and improving synthetic routes. Here, we present an expansion of this application that makes use of the functionality of the PMI Predictor, which the ACS Green Chemistry Institute Pharmaceutical Roundtable has recently published as a web application. This addition enables ChemPager to predict the cumulative process mass intensity of chemical routes, irrespective of their development status, by comparison with a set of reactions executed on large scale. The prediction of this core green chemistry metric aims to improve existing routes and help the decision-making process among route alternatives without the need for experimental data.

3D Printed Graphene Electrodes Modified with Prussian Blue: Emerging Electrochemical Sensing Platform for Peroxide Detection.

3D printing technologies have been considered an important technology due to the ease manufacturing of objects, freedom of design, waste minimization, and fast prototyping. In chemistry, this technology potentializes the fabrication of conductive electrodes in large scale for sensing applications. Herein, we reported the modification of a 3D printed graphene electrode with Prussian blue. The modified electrode (3DGrE/PB) was characterized by microscopy (SEM and AFM) and spectroscopic techniques, and its electrochemical properties were compared to the traditional electrodes: glassy carbon, gold, and platinum. The 3DGrE/PB was used in the sensing of hydrogen peroxide in real-world samples of milk and mouthwash, and the results obtained according to the technique of batch-injection analysis were satisfactory for the concentration range typically found in such samples. Thus, 3DGrE/PB can be used as a new platform for sensing of molecular targets.

Modelling cancer outcomes of bone metastatic patients: combining survival data with N-Telopeptide of type I collagen (NTX) dynamics through joint models.

BACKGROUND: Joint models (JM) have emerged as a promising statistical framework to concurrently analyse survival data and multiple longitudinal responses. This is particularly relevant in clinical studies where the goal is to estimate the association between time-to-event data and the biomarkers evolution. In the context of oncological data, JM can indeed provide interesting prognostic markers for the event under study and thus support clinical decisions and treatment choices. However, several problems arise when dealing with this type of data, such as the high-dimensionality of the covariates space, the lack of knowledge about the function structure of the time series and the presence of missing data, facts that may hamper the accurate estimation of the JM. METHODS: We propose to apply JM for the analysis of bone metastatic patients and infer the association of their survival with several covariates, in particular the N-Telopeptide of Type I Collagen (NTX) dynamics. This biomarker has been identified as a relevant prognostic factor in patients with metastatic cancer, but only using static information in some specific time points. RESULTS: We extended this analysis using the full NTX time series for a larger cohort of patients with bone metastasis, and compared the results obtained by the JM and the extended Cox regression model. Imputation based on fuzzy clustering was used to deal with missing values and several functions for NTX evolution were compared, such as rational, exponential and cubic splines. CONCLUSIONS: The JM obtained confirm the association between NTX values and patients' response, attesting the importance of this time series, and additionally provide a deep understanding of the key survival covariates.

Discontinuing noninvasive ventilation in severe chronic obstructive pulmonary disease exacerbations: a randomised controlled trial.

We assessed whether prolongation of nocturnal noninvasive ventilation (NIV) after recovery from acute hypercapnic respiratory failure (AHRF) in chronic obstructive pulmonary disease (COPD) patients with NIV could prevent subsequent relapse of AHRF.A randomised controlled trial was performed in 120 COPD patients without previous domiciliary ventilation, admitted for AHRF and treated with NIV. When the episode was resolved and patients tolerated unassisted breathing for 4 h, they were randomly allocated to receive three additional nights of NIV (n=61) or direct NIV discontinuation (n=59). The primary outcome was relapse of AHRF within 8 days after NIV discontinuation.Except for a shorter median (interquartile range) intermediate respiratory care unit (IRCU) stay in the direct discontinuation group (4 (2-6) versus 5 (4-7) days, p=0.036), no differences were observed in relapse of AHRF after NIV discontinuation (10 (17%) versus 8 (13%) for the direct discontinuation and nocturnal NIV groups, respectively, p=0.56), long-term ventilator dependence, hospital stay, and 6-month hospital readmission or survival.Prolongation of nocturnal NIV after recovery from an AHRF episode does not prevent subsequent relapse of AHRF in COPD patients without previous domiciliary ventilation, and results in longer IRCU stay. Consequently, NIV can be directly discontinued when the episode is resolved and patients tolerate unassisted breathing.

Electromembrane extraction and preconcentration of carbendazim and thiabendazole in water samples before capillary electrophoresis analysis.

Electromembrane extraction using a polypropylene hollow fiber impregnated with 1-ethyl-2-nitrobenzene was evaluated for the extraction and preconcentration of the fungicides thiabendazole and carbendazim from water samples before capillary electrophoresis analysis. The composition of the supported liquid membrane, the HCl concentration in the acceptor solution, and the stirring rate (of the donor solution) were optimized using the one-variable-at-a-time method. In contrast, a face-centered central composition design was used for optimization of voltage, extraction time, and concentration of HCl in the donor solution. After optimization, electromembrane extraction provided enrichment factors of 50 and 26 for thiabendazole and carbendazim that allowed us to achieve limits of detection of 1.1 and 2.3 µg/L, respectively. Repeatability (intraday precision) expressed as the relative standard deviation varied from 2.5 to 2.8%, while the interday precision ranged from 3.1 to 3.3%. The proposed method was applied for analysis of samples of tap and river water, and acceptable precision and accuracy were attained.

Predictors of prolonged weaning and survival during ventilator weaning in a respiratory ICU.

PURPOSE: An International Consensus Conference proposed classifying weaning into simple, difficult, and prolonged weaning. However, the usefulness of this classification in a respiratory intensive care unit (ICU) is unknown. The aims of the study were: (1) to compare the clinical characteristics and outcomes of patients from the three weaning groups in a respiratory ICU; and (2) to assess predictors for prolonged weaning and survival. METHODS: We prospectively studied 181 mechanically ventilated patients (131, 72% with chronic respiratory disorders) in whom weaning had been initiated, divided into simple (78, 43%), difficult (70, 39%), and prolonged (33, 18%) weaning. We compared the characteristics and outcomes among the three groups and determined the factors associated with prolonged weaning and survival in multivariate analysis. RESULTS: Patients with simple and difficult weaning had similar characteristics and outcomes. A higher proportion of patients with prolonged weaning had chronic obstructive pulmonary disease, and these patients also had more complications, a longer stay and lower survival. Increased heart rate (≥105 min(-1), p < 0.001) and PaCO(2) (≥ 54 mmHg, p = 0.001) during the spontaneous breathing trial independently predicted prolonged weaning. In addition, the need for reintubation (p < 0.001) and hypercapnia during the spontaneous breathing trial (p = 0.003) independently predicted a decreased 90-day survival. CONCLUSION: Because of the similar characteristics and outcomes, the differentiation between simple and difficult weaning had no relevant clinical consequences in a respiratory ICU. Patients with prolonged weaning had the worst outcomes. For the overall population, hypercapnia at the end of spontaneous breathing predicts prolonged weaning and a worse survival, and clinicians should implement measures aimed at improving weaning outcome.

[Tiredness and sleepiness in bus drivers and road accidents in Peru: a quantitative study]. / Cansancio y somnolencia en conductores de ómnibus y accidentes de carretera en el Perú: estudio cuantitativo.

OBJECTIVE: To evaluate the relationship that tiredness and sleepiness in bus drivers have to road accidents in Peru. Information from various countries indicates that driver sleepiness plays an important role in road accidents. However, there is only limited information on this subject in Peru. METHODS: Using a supervised, pretested survey, a cross-sectional observational and comparative study was carried out with 238 bus drivers who drive on the Northern Pan American Highway of Peru. To determine the relationship between variables the chi-square test was used, along with the Pearson correlation coefficient. The level of significance was set at P < 0.05. The variables analyzed were: tiredness, sleepiness, hours of driving per day, daily hours of sleep, body mass index, snoring, sleep apnea, and either having had or almost having had an accident while driving. RESULTS: Of the 238 drivers, all of them were men, 45% said they had had or nearly had had an accident while driving, 55% slept less than 6 hours per day, 31% had slept less than 6 hours in the 24 hours before answering the survey, and 80% were in the habit of driving more than 5 hours without stopping. Of the drivers, 56% of them reported being tired at least some of the time while driving; of this group, 65% of them reported being tired during the early morning. Seventy-six drivers (32%) said that while they were driving their eyes had fallen shut. In terms of where they slept, 194 of the drivers (81%) said they always slept in the lower luggage compartment of the bus while another driver was driving the bus or when the bus was parked in the bus terminal. The steps that drivers took to avoid falling asleep while driving included: wetting the face with water, eating fruit, opening the window of the driver's compartment, drinking coffee, listening to music, smoking, chewing coca leaves, and drinking alcohol mixed with coca leaves. In the opinion of 55% of the drivers, the leading cause of road accidents is tiredness. Accidents and near-accidents while driving occurred mainly between midnight and 6 a. m. Having an accident or a near-accident was strongly associated with tiredness and with having the eyes drop shut while driving (P < 0.0005). CONCLUSIONS: Tiredness and sleepiness while driving were common among the bus drivers, with various possible causes: acute and chronic sleep deprivation, irregular schedule changes, and sleep disorders due to the drivers' working conditions. Our results support the hypothesis that fatigue and sleepiness among bus drivers are related to road accidents.

Cansancio y somnolencia en conductores de ómnibus y accidentes de carretera en el Perú: estudio cuantitativo / Tiredness and sleepiness in bus drivers and road accidents in Peru: a quantitative study

OBJETIVO: Evaluar la relación del cansancio y la somnolencia en los conductores de ómnibus con los accidentes de carretera. La información obtenida de diversos países atribuye a la somnolencia del conductor una parte importante en la génesis de los accidentes de carretera. No obstante, la información al respecto es escasa en el caso del Perú. MÉTODOS: Se realizó un estudio transversal, de índole observacional y comparativa, basado en una encuesta prevalidada y de aplicación supervisada, entre 238 conductores de ómnibus que circulan por la carretera Panamericana Norte del Perú. Para determinar la relación entre variables se emplearon la prueba de ji al cuadrado y el índice de Pearson, con un valor de significación de P < 0,05. Variables de análisis:cansancio, somnolencia, horas de conducción por día, horas de sueño por día, índice de masa corporal, ronquido, pausas respiratorias y el antecedente de haber tenido o estar a punto de tener un accidente durante la conducción. RESULTADOS: De los 238 conductores, todos ellos varones, 45 por ciento refirieron haber estado a punto de tener un accidente o haberlo sufrido durante la conducción, 55 por ciento dormían menos de 6 horas al día, 31 por ciento habían dormido menos de 6 horas en las últimas 24, y 80 por ciento acostumbraban conducir más de 5 horas sin descanso. Señalaron cansancio durante la conducción 56 por ciento y, de estos, 65 por ciento lo experimentaban durante la madrugada. Setenta y seis (32 por ciento) conductores reconocieron que pestañeaban durante la conducción. Ciento noventa y cuatro (81 por ciento) siempre dormían en el maletero, estuviera el ómnibus en ruta o en la terminal. Maniobras para evitar dormirse: mojarse la cara con agua, comer fruta, abrir ventana de la cabina, beber café, escuchar música, fumar, masticar (chacchar) coca y beber alcohol con hoja de coca. En opinión de 55 por ciento de los conductores, la primera causa de accidentes de carretera es el cansancio. Los accidentes durante la conducción, virtuales o consumados, ocurrieron predominantemente entre las 00:00 y las 06:00 horas. Este antecedente mostró firme asociación (P< 0,0005) con el pestañeo y el cansancio. CONCLUSIONES: La somnolencia y el cansancio durante la conducción fueron frecuentes. Su origen puede ser...