Effects of the ABCDE bundle on the prevention of post-intensive care syndrome: A retrospective study.

AIM: To identify the effects of each domain of the early and modified ABCDE bundle on post-intensive care syndrome (PICS). DESIGN: This is a retrospective study. METHODS: We analysed the data from electronic medical records of 91 intensive care patients who received therapeutic interventions in stages, based on the early ABCDE bundle (admitted to the intensive care unit [ICU] from June - August 2013) and 94 patients who received interventions using a modified ABCDE bundle developed through continuous quality improvement activities (admitted to the ICU from June to August 2014). RESULTS: In the ABC domain, the percentage of patients showing sedation levels of alertness and calmness increased significantly from 58.2% using the early ABCDE bundle to 72.4% using the modified ABCDE bundle. Coma prevalence decreased significantly from 45.1% using the early ABCDE bundle to 28.7% using the modified ABCDE bundle. In the E domain, the percentage of patients receiving early mobility interventions increased significantly from 11% using the early ABCDE bundle to 54.3% using the modified ABCDE bundle. CONCLUSION: The ABCDE bundle in the ICU helped prevent PICS by reducing deep sedation and immobilization among intensive care patients. To effectively use the ABCDE bundle, it is necessary for institutions to develop suitable protocols for each constituent element and to test their effectiveness. IMPACT: The ABCDE bundle was a suitable tool to support evidence-based practice in intensive care patients, including oversedation and immobilization, which is related to the prevention of PICS. Individual institutions will need to actively use the ABCDE bundle in the ICU, by developing protocols and testing their effectiveness.

Posttraumatic growth in breast cancer survivors and their husbands based on the actor-partner interdependence model.

OBJECTIVE: This study aimed to verify actor and partner effects, by examining the effects of self-esteem, relationship quality, and subjective distress on posttraumatic growth in breast cancer survivors and their spouses, and involved a structural analysis of descriptive cause-and-effect relationships to verify the suitability of the actor-partner interdependence model. METHODS: A structured questionnaire was used to collect data from breast cancer survivors and their spouses at outpatient centers, wards, and patient meetings in 4 general hospitals in Seoul between April 13 and September 20, 2015. Data for 336 individuals (168 couples) were analyzed. The suitability of the hypothetical model was assessed via SPSS Win 21.0 and AMOS 21.0. Actor and partner effects on posttraumatic growth in breast cancer survivors and their spouses were examined. RESULTS: Self-esteem, relationship quality, and subjective distress exerted significant actor effects, and subjective distress exerted a significant partner effect on posttraumatic growth in breast cancer survivors. Relationship quality and support exerted significant actor effects, and self-esteem, relationship quality, and subjective distress exerted significant partner effects on posttraumatic growth in spouses. CONCLUSIONS: Posttraumatic growth in breast cancer survivors was influenced by not only relationship quality and spouses' self- esteem but also subjective distress; therefore, solidarity between breast cancer survivors and their spouses was important and should be maintained to provide healthy relationship support and enhance posttraumatic growth. Further, health care providers should include spouses in health-related education and involve them in interventions and family support programs for couples.

Robust ridge regression estimators for nonlinear models with applications to high throughput screening assay data.

Nonlinear regression is often used to evaluate the toxicity of a chemical or a drug by fitting data from a dose-response study. Toxicologists and pharmacologists may draw a conclusion about whether a chemical is toxic by testing the significance of the estimated parameters. However, sometimes the null hypothesis cannot be rejected even though the fit is quite good. One possible reason for such cases is that the estimated standard errors of the parameter estimates are extremely large. In this paper, we propose robust ridge regression estimation procedures for nonlinear models to solve this problem. The asymptotic properties of the proposed estimators are investigated; in particular, their mean squared errors are derived. The performances of the proposed estimators are compared with several standard estimators using simulation studies. The proposed methodology is also illustrated using high throughput screening assay data obtained from the National Toxicology Program.

Accounting for Uncertainty in Heteroscedasticity in Nonlinear Regression.

Toxicologists and pharmacologists often describe toxicity of a chemical using parameters of a nonlinear regression model. Thus estimation of parameters of a nonlinear regression model is an important problem. The estimates of the parameters and their uncertainty estimates depend upon the underlying error variance structure in the model. Typically, a priori the researcher would know if the error variances are homoscedastic (i.e., constant across dose) or if they are heteroscedastic (i.e., the variance is a function of dose). Motivated by this concern, in this article we introduce an estimation procedure based on preliminary test which selects an appropriate estimation procedure accounting for the underlying error variance structure. Since outliers and influential observations are common in toxicological data, the proposed methodology uses M-estimators. The asymptotic properties of the preliminary test estimator are investigated; in particular its asymptotic covariance matrix is derived. The performance of the proposed estimator is compared with several standard estimators using simulation studies. The proposed methodology is also illustrated using a data set obtained from the National Toxicology Program.

In silico prediction models for thyroid peroxidase inhibitors and their application to synthetic flavors.

Systematic toxicity tests are often waived for the synthetic flavors as they are added in a very small amount in foods. However, their safety for some endpoints such as endocrine disruption should be concerned as they are likely to be active in low levels. In this case, structure-activity-relationship (SAR) models are good alternatives. In this study, therefore, binary, ternary, and quaternary prediction models were designed using simple or complex machine-learning methods. Overall, hard-voting classifiers outperformed other methods. The test scores for the best binary, ternary, and quaternary models were 0.6635, 0.5083, and 0.5217, respectively. Along with model development, some substructures including primary aromatic amine, (enol)ether, phenol, heterocyclic sulfur, and heterocyclic nitrogen, dominantly occurred in the most highly active compounds. The best predicting models were applied to synthetic flavors, and 22 agents appeared to have a strong inhibitory potential towards TPO activities. Supplementary Information: The online version contains supplementary material available at 10.1007/s10068-022-01041-y.

An accurate deep learning model for wheezing in children using real world data.

Auscultation is an important diagnostic method for lung diseases. However, it is a subjective modality and requires a high degree of expertise. To overcome this constraint, artificial intelligence models are being developed. However, these models require performance improvements and do not reflect the actual clinical situation. We aimed to develop an improved deep-learning model learning to detect wheezing in children, based on data from real clinical practice. In this prospective study, pediatric pulmonologists recorded and verified respiratory sounds in 76 pediatric patients who visited a university hospital in South Korea. In addition, structured data, such as sex, age, and auscultation location, were collected. Using our dataset, we implemented an optimal model by transforming it based on the convolutional neural network model. Finally, we proposed a model using a 34-layer residual network with the convolutional block attention module for audio data and multilayer perceptron layers for tabular data. The proposed model had an accuracy of 91.2%, area under the curve of 89.1%, precision of 94.4%, recall of 81%, and F1-score of 87.2%. The deep-learning model proposed had a high accuracy for detecting wheeze sounds. This high-performance model will be helpful for the accurate diagnosis of respiratory diseases in actual clinical practice.

Hepatobiliary disposition of troglitazone and metabolites in rat and human sandwich-cultured hepatocytes: use of Monte Carlo simulations to assess the impact of changes in biliary excretion on troglitazone sulfate accumulation.

This study examined the hepatobiliary disposition of troglitazone (TGZ) and metabolites [TGZ sulfate (TS), TGZ glucuronide (TG), and TGZ quinone (TQ)] over time in rat and human sandwich-cultured hepatocytes (SCH). Cells were incubated with TGZ; samples were analyzed for TGZ and metabolites by liquid chromatography-tandem mass spectrometry. SCH mimicked the disposition of TGZ/metabolites in vivo in rats and humans; TGZ was metabolized primarily to TS and to a lesser extent to TG and TQ. In human SCH, the biliary excretion index (BEI) was negligible for TGZ and TQ, approximately 16% for TS, and approximately 43% for TG over the incubation period; in rat SCH, the BEI for TS and TG was approximately 13 and approximately 41%, respectively. Hepatocyte accumulation of TS was extensive, with intracellular concentrations ranging from 132 to 222 microM in rat SCH; intracellular TGZ concentrations ranged from 7.22 to 47.7 microM. In human SCH, intracellular TS and TGZ concentrations ranged from 136 to 160 microM and from 49.4 to 84.7 microM, respectively. Pharmacokinetic modeling and Monte Carlo simulations were used to evaluate the impact of modulating the biliary excretion rate constant (K(bile)) for TS on TS accumulation in hepatocytes and medium. Simulations demonstrated that intracellular concentrations of TS may increase up to 3.1- and 5.7-fold when biliary excretion of TS was decreased 2- and 10-fold, respectively. It is important to note that altered hepatobiliary transport and the extent of hepatocyte exposure may not always be evident based on medium concentrations (analogous to systemic exposure in vivo). Pharmacokinetic modeling/simulation with data from SCH is a useful approach to examine the impact of altered hepatobiliary transport on hepatocyte accumulation of drug/metabolites.

Radiation dose reduction and improvement of image quality in digital chest radiography by new spatial noise reduction algorithm.

PURPOSE: To evaluate the image quality of low-dose chest digital radiographic images obtained with a new spatial noise reduction algorithm, compared to a conventional de-noising technique. MATERIALS AND METHODS: In 69 patients, the dose reduction protocol was divided into A, B, and C test groups- 60% (n = 22), 50% (n = 23), and 40% (n = 24) of the baseline dose. In each patient, baseline dose radiographs were obtained with conventional image processing while low-dose images were acquired with new image processing. A set of baseline and low-dose radiographic images per patient was evaluated and scored on a 5-point scale over seven anatomical landmarks (radiolucency of unobscured lung, pulmonary vascularity, trachea, edge of rib, heart border, intervertebral disc space, and pulmonary vessels in the retrocardiac area) and three representative abnormal findings (nodule, consolidation, and interstitial marking) by two thoracic radiologists. A comparison of paired baseline and low-dose images was statistically analyzed using a non-inferiority test based on the paired t-test or the Wilcoxon signed-rank test. RESULTS: In A, B, and C test groups, the mean dose reduction rate of the baseline radiation dose was 63.4%, 53.9%, and 47.8%, respectively. In all test groups, the upper limit of the 95% confidence interval was less than the non-inferiority margin of 0.5 every seven anatomical landmarks and three representative abnormal findings, which suggested that the image quality of the low-dose image was not inferior to that of the baseline dose image even if the maximum average dose reduction rate was reduced to 47.8% of the baseline dose. CONCLUSION: In our study, an image processing technique integrating a new noise reduction algorithm achieved dose reductions of approximately half without compromising image quality for abnormal lung findings and anatomical landmarks seen on chest radiographs. This feature-preserving, noise reduction algorithm adopted in the proposed engine enables a lower radiation dose boundary for the sake of patient's and radiography technologist's radiation safety in routine clinical practice, in compliance with regulatory guidelines.

Assessment of spatial tumor heterogeneity using CT growth patterns estimated by tumor tracking on 3D CT volumetry of multiple pulmonary metastatic nodules.

PURPOSE: Our purpose was to assess the differences in growth rates of multiple pulmonary metastatic nodules using three-dimensional (3D) computed tomography (CT) volumetry and propose a concept of CT spatial tumor heterogeneity. MATERIALS AND METHODS: We manually measured the largest diameter of metastatic pulmonary nodules on chest CT scans, and calculated the 3D maximum diameter and the volume using a semi-automated 3D CT volumetry of each nodule. The tumor response was assessed according to the revised RECIST 1.1. We defined a nodule as an outlier based on 1.5 times growth during follow-up. The CT spatial tumor heterogeneity was statistically analyzed by the "minimum combination t-test method" devised in our study. RESULTS: On manual measurement, the tumor response category was stable disease (SD) in all 10 patients. Of them, total 155 metastatic nodules (4-52 nodules per patient) were segmented using the 3D CT volumetry. In the 3D maximum diameter, 9 patients had SD except for one patient with partial response in the two selected nodules; for the volume, all 10 patients were SD. For the 3D maximum diameter, six patients had at least one outlier; whereas five patients had the outlier on the volume measurement. Six patients were proven to have overall CT spatial tumor heterogeneity. CONCLUSIONS: The spatial tumor heterogeneity determined in a CT parametric approach could be statistically assessed. In patients with CT spatial heterogeneity, tumors with different growth rates may be neglected when the nodules are assessed according to the current guideline.

Symptom Clusters and Quality of Life According to the Survivorship Stage in Ovarian Cancer Survivors.

This cross-sectional study evaluated a convenience sample comprising 182 ovarian cancer survivors to identify symptom clusters according to the cancer survivorship stage and to determine their effects on quality of life using the European Organization for Research and Treatment of Cancer Quality of Life-C30 and -OV28 questionnaires. Factor and multiple regression analyses were performed to identify symptom clusters according to the cancer survivorship stage and the symptom clusters that affected the quality of life in each cancer survivorship stage, respectively. Participants in the acute, extended, and permanent survival stages accounted for 33%, 36.3%, and 30.7% of subjects, respectively. Overall, the most common symptom cluster was fatigue-diarrhea, and the symptom clusters affecting the quality of life differed according to the cancer survivorship stage. Thus, to improve the quality of life of ovarian cancer survivors, the main symptom clusters of each cancer survivorship stage must be identified, and management strategies for the related symptoms must be designed.

Natural course of the nodular bronchiectatic form of Mycobacterium Avium complex lung disease: Long-term radiologic change without treatment.

BACKGROUND/PURPOSE: Although the incidence of Mycobacterium avium complex (MAC) lung disease is increasing, the long-term natural course of the nodular bronchiectatic form of MAC lung disease is not well described. The objective of our study is to evaluate long-term radiologic changes in untreated MAC lung disease by analyzing serial chest computed tomography (CT) scan findings. METHODS: Of 104 patients with MAC lung disease, we selected 40 untreated nodular bronchiectatic MAC patients who underwent serial chest CTs without treatment for at least four years (mean = 6.23 years). Majority of patients have minimal symptoms. Two chest radiologists retrospectively reviewed initial and final chest CT scans. Each chest CT scan was scored for presence and extent of bronchiectasis, cellular bronchiolitis, consolidation, cavity, and nodule (maximum score: 30). RESULTS: Of 40 patients, 39 (97.5%) experienced a significant increase in overall CT score (overall difference = 4.89, p<0.001). On repeated measure analysis of variance analysis, cavity yielded the largest increase compared with cellular bronchiolitis (p = 0.013), nodule (p<0.001), and consolidation (p = 0.004). However, there was no significant difference in mean score change between cavity and bronchiectasis (p = 0.073). In analysis between radiologic parameters and the absolute number of involved segments, bronchiectasis showed most significant change compared with nodule (p<0.001) and consolidation (p<0.001). CONCLUSIONS: Most untreated nodular bronchiectatic MAC lung disease cases showed radiologic deterioration over long-term observation periods when we compared serial chest CT scans. Careful monitoring of MAC lung disease with serial chest CT scan can be beneficial in these untreated patients.

Development and validation of a nursing professionalism evaluation model in a career ladder system.

BACKGROUND: The clinical ladder system categorizes the degree of nursing professionalism and rewards and is an important human resource tool for managing nursing. AIM: We developed a model to evaluate nursing professionalism, which determines the clinical ladder system levels, and verified its validity. METHODS: Data were collected using a clinical competence tool developed in this study, and existing methods such as the nursing professionalism evaluation tool, peer reviews, and face-to-face interviews to evaluate promotions and verify the presented content in a medical institution. Reliability and convergent and discriminant validity of the clinical competence evaluation tool were verified using SmartPLS software. The validity of the model for evaluating overall nursing professionalism was also analyzed. RESULTS: Clinical competence was determined by five dimensions of nursing practice: scientific, technical, ethical, aesthetic, and existential. The structural model explained 66% of the variance. Clinical competence scales, peer reviews, and face-to-face interviews directly determined nursing professionalism levels. CONCLUSIONS: The evaluation system can be used for evaluating nurses' professionalism in actual medical institutions from a nursing practice perspective. IMPLICATIONS FOR NURSING MANAGEMENT: A conceptual framework for establishing a human resources management system for nurses and a tool for evaluating nursing professionalism at medical institutions is provided.

Robust Analysis of High Throughput Screening (HTS) Assay Data.

Quantitative high throughput screening (qHTS) assays use cells or tissues to screen thousands of compounds in a short period of time. Data generated from qHTS assays are then evaluated using nonlinear regression models, such as the Hill model, and decisions regarding toxicity are made using the estimates of the parameters of the model. For any given compound, the variability in the observed response may either be constant across dose groups (homoscedasticity) or vary with dose (heteroscedasticity). Since thousands of compounds are simultaneously evaluated in a qHTS assay, it is not practically feasible for an investigator to perform residual analysis to determine the variance structure before performing statistical inferences on each compound. Since it is well-known that the variance structure plays an important role in the analysis of linear and nonlinear regression models it is therefore important to have practically useful and easy to interpret methodology which is robust to the variance structure. Furthermore, given the number of chemicals that are investigated in the qHTS assay, outliers and influential observations are not uncommon. In this article we describe preliminary test estimation (PTE) based methodology which is robust to the variance structure as well as any potential outliers and influential observations. Performance of the proposed methodology is evaluated in terms of false discovery rate (FDR) and power using a simulation study mimicking a real qHTS data. Of the two methods currently in use, our simulations studies suggest that one is extremely conservative with very small power in comparison to the proposed PTE based method whereas the other method is very liberal. In contrast, the proposed PTE based methodology achieves a better control of FDR while maintaining good power. The proposed methodology is illustrated using a data set obtained from the National Toxicology Program (NTP). Additional information, simulation results, data and computer code are available online as supplementary materials.

Robust nonlinear regression in applications.

Robust statistical methods, such as M-estimators, are needed for nonlinear regression models because of the presence of outliers/influential observations and heteroscedasticity. Outliers and influential observations are commonly observed in many applications, especially in toxicology and agricultural experiments. For example, dose response studies, which are routinely conducted in toxicology and agriculture, sometimes result in potential outliers, especially in the high dose groups. This is because response to high doses often varies among experimental units (e.g., animals). Consequently, this may result in outliers (i.e., very low values) in that group. Unlike the linear models, in nonlinear models the outliers not only impact the point estimates of the model parameters but can also severely impact the estimate of the information matrix. Note that, the information matrix in a nonlinear model is a function of the model parameters. This is not the case in linear models. In addition to outliers, heteroscedasticity is a major concern when dealing with nonlinear models. Ignoring heteroscedasticity may lead to inaccurate coverage probabilities and Type I error rates. Robustness to outliers/influential observations and to heteroscedasticity is even more important when dealing with thousands of nonlinear regression models in quantitative high throughput screening assays. Recently, these issues have been studied very extensively in the literature (references are provided in this paper), where the proposed estimator is robust to outliers/influential observations as well as to heteroscedasticity. The focus of this paper is to provide the theoretical underpinnings of robust procedures developed recently.