Characteristics and Outcomes of Patients Requiring Repeat Intensive Care Unit Consults.

Objective: To better understand the mortality and notable characteristics of patients initially denied intensive care unit (ICU) admission that are later admitted on reconsultation. Patients and Methods: We collected data regarding all adult inpatients (n=3725) who received one or more ICU consults at an academic tertiary care hospital medical center between January 1, 2018 and October 1, 2021. We compared patients who were initially denied ICU admission and later admitted on reconsultation (C2A1, n=144) with those who were admitted after the first consultation (C1A1, n=2286) and those denied at first consult and never later admitted (C1A0, n=1295). Results: Ten percent of patients initially rejected by the ICU were later admitted on reconsultation. There was no significant difference in the adjusted hospital death odds ratios between C1A1 and C2A1 (0.67; 95% CI 0.43-1.01; P=.11). Assessing subgroups of the C2A1 population, we found that 8.2% (n=100) of full code patients were later admitted to the ICU on reconsultation vs 23.2% (n=40) of do not attempt resuscitation patients (P<.001); 7.6% (n=77) of patients initially consulted from the emergency department were later admitted to the ICU on reconsultation vs 15.1% (n=52) of patients initially consulted from an inpatient setting (P<.001). Conclusion: In this cohort, we demonstrated that patients admitted on repeat ICU consultation have no significant difference in mortality compared with equivalent patients admitted after the first consultation. Understanding and further exploring the consequences of these ICU reconsultations is vital to developing optimal critical care triaging practices.

Patients Who Decompensate and Trigger Rapid Response Immediately Upon Hospital Admission Have Higher Mortality Than Equivalent Patients Without Rapid Responses.

BACKGROUND: Rapid response teams (RRTs) have impacted the management of decompensating patients, potentially improving mortality. Few studies address the significance of RRT timing relative to hospital admission. We aimed to identify outcomes of adult patients who trigger immediate RRT activation, defined as within 4 hours of admission and compare with RRT later in admission or do not require RRT activation, and identify risk factors that predispose toward immediate RRT activation. METHODS: A retrospective case-control study was performed using an RRT activation database, comprising 201,783 adult inpatients at an urban, academic, tertiary care hospital. This group was subdivided by timing of RRT activation regarding admission: within the first 4 hours (immediate RRT), between 4 and 24 hours (early RRT), and after 24 hours (late RRT). The primary outcome was 28-day all-cause mortality. Individuals triggering an immediate RRT were compared with demographically matched controls. Mortality was adjusted for age, Quick Systemic Organ Failure Assessment score, intensive care unit admission, and Elixhauser Comorbidity Index. RESULTS: Patients with immediate RRT had adjusted 28-day all-cause mortality of 7.1% (95% confidence interval [CI], 5.6%-8.5%) and death odds ratio of 3.27 (95% CI, 2.5-4.3) compared with those who did not (mortality, 2.9%; 95%CI, 2.8%-2.9%; P < 0.0001). Patients triggering an immediate RRT were more likely to be Black, be older, and have higher Quick Systemic Organ Failure Assessment scores than those who did not trigger RRT activation. CONCLUSIONS: In this cohort, patients who require immediate RRT experienced higher 28-day all-cause mortality, potentially because of evolving or unrecognized critical illness. Further exploring this phenomenon may create opportunities for improved patient safety.

JOURNAL CLUB: Computer-Aided Detection of Lung Nodules on CT With a Computerized Pulmonary Vessel Suppressed Function.

OBJECTIVE: The purpose of this study is to evaluate radiologists' performance in detecting actionable nodules on chest CT when aided by a pulmonary vessel image-suppressed function and a computer-aided detection (CADe) system. MATERIALS AND METHODS: A novel computerized pulmonary vessel image-suppressed function with a built-in CADe (VIS/CADe) system was developed to assist radiologists in interpreting thoracic CT images. Twelve radiologists participated in a comparative study without and with the VIS/CADe using 324 cases (involving 95 cancers and 83 benign nodules). The ratio of nodule-free cases to cases with nodules was 2:1 in the study. Localization ROC (LROC) methods were used for analysis. RESULTS: In a stand-alone test, the VIS/CADe system detected 89.5% and 82.0% of malignant nodules and all nodules no smaller than 5 mm, respectively. The false-positive rate per CT study was 0.58. For the reader study, the mean area under the LROC curve (LROCAUC) for the detection of lung cancer significantly increased from 0.633 when unaided by VIS/CADe to 0.773 when aided by VIS/CADe (p < 0.01). For the detection of all clinically actionable nodules, the mean LROC-AUC significantly increased from 0.584 when unaided by VIS/CADe to 0.692 when detection was aided by VIS/CADe (p < 0.01). Radiologists detected 80.0% of cancers with VIS/CADe versus 64.45% of cancers unaided (p < 0.01); specificity decreased from 89.9% to 84.4% (p < 0.01). Radiologist interpretation time significantly decreased by 26%. CONCLUSION: The VIS/CADe system significantly increased radiologists' detection of cancers and actionable nodules with somewhat lower specificity. With use of the VIS/CADe system, radiologists increased their interpretation speed by a factor of approximately one-fourth. Our study suggests that the technique has the potential to assist radiologists in the detection of additional actionable nodules on thoracic CT.

Factors Associated With Small Aggressive Non-Small Cell Lung Cancers in the National Lung Screening Trial: A Validation Study.

BACKGROUND: A small proportion of non-small cell lung cancers (NSCLCs) have been observed to spread to distant lymph nodes (N3) or metastasize (M1) or both, while the primary tumor is small (≤3 cm, T1). These small aggressive NSCLCs (SA-NSLSC) are important as they are clinically significant, may identify unique biologic pathways, and warrant aggressive follow-up and treatment. This study identifies factors associated with SA-NSCLC and attempts to validate a previous finding that women with a family history of lung cancer are at particularly elevated risk of SA-NSCLC. METHODS: This study used a case-case design within the National Cancer Institute's National Lung Screening Trial (NLST) cohort. Case patients and "control" patients were selected based on TNM staging parameters. Case patients (n = 64) had T1 NSCLCs that were N3 or M1 or both, while "control" patients (n = 206) had T2 or T3, N0 to N2, and M0 NSCLCs. Univariate and multivariable logistic regression were used to identify factors associated with SA-NSCLC. RESULTS: In bootstrap bias-corrected multivariable logistic regression models, small aggressive adenocarcinomas were associated with a positive history of emphysema (odds ratio [OR] = 5.15, 95% confidence interval [CI] = 1.63 to 23.00) and the interaction of female sex and a positive family history of lung cancer (OR = 6.55, 95% CI = 1.06 to 50.80). CONCLUSIONS: Emphysema may play a role in early lung cancer progression. Females with a family history of lung cancer are at increased risk of having small aggressive lung adenocarcinomas. These results validate previous findings and encourage research on the role of female hormones interacting with family history and genetic factors in lung carcinogenesis and progression.

Synthesis and Incorporation of Unnatural Amino Acids To Probe and Optimize Protein Bioconjugations.

The utilization of unnatural amino acids (UAAs) in bioconjugations is ideal due to their ability to confer a degree of bioorthogonality and specificity. In order to elucidate optimal conditions for the preparation of bioconjugates with UAAs, we synthesized 9 UAAs with variable methylene tethers (2-4) and either an azide, alkyne, or halide functional group. All 9 UAAs were then incorporated into green fluorescent protein (GFP) using a promiscuous aminoacyl-tRNA synthetase. The different bioconjugations were then analyzed for optimal tether length via reaction with either a fluorophore or a derivatized resin. Interestingly, the optimal tether length was found to be dependent on the type of reaction. Overall, these findings provide a better understanding of various parameters that can be optimized for the efficient preparation of bioconjugates.

Evaluation of computer-aided detection and diagnosis systems.

Computer-aided detection and diagnosis (CAD) systems are increasingly being used as an aid by clinicians for detection and interpretation of diseases. Computer-aided detection systems mark regions of an image that may reveal specific abnormalities and are used to alert clinicians to these regions during image interpretation. Computer-aided diagnosis systems provide an assessment of a disease using image-based information alone or in combination with other relevant diagnostic data and are used by clinicians as a decision support in developing their diagnoses. While CAD systems are commercially available, standardized approaches for evaluating and reporting their performance have not yet been fully formalized in the literature or in a standardization effort. This deficiency has led to difficulty in the comparison of CAD devices and in understanding how the reported performance might translate into clinical practice. To address these important issues, the American Association of Physicists in Medicine (AAPM) formed the Computer Aided Detection in Diagnostic Imaging Subcommittee (CADSC), in part, to develop recommendations on approaches for assessing CAD system performance. The purpose of this paper is to convey the opinions of the AAPM CADSC members and to stimulate the development of consensus approaches and "best practices" for evaluating CAD systems. Both the assessment of a standalone CAD system and the evaluation of the impact of CAD on end-users are discussed. It is hoped that awareness of these important evaluation elements and the CADSC recommendations will lead to further development of structured guidelines for CAD performance assessment. Proper assessment of CAD system performance is expected to increase the understanding of a CAD system's effectiveness and limitations, which is expected to stimulate further research and development efforts on CAD technologies, reduce problems due to improper use, and eventually improve the utility and efficacy of CAD in clinical practice.

Quality assurance and training procedures for computer-aided detection and diagnosis systems in clinical use.

Computer-aided detection/diagnosis (CAD) is increasingly used for decision support by clinicians for detection and interpretation of diseases. However, there are no quality assurance (QA) requirements for CAD in clinical use at present. QA of CAD is important so that end users can be made aware of changes in CAD performance both due to intentional or unintentional causes. In addition, end-user training is critical to prevent improper use of CAD, which could potentially result in lower overall clinical performance. Research on QA of CAD and user training are limited to date. The purpose of this paper is to bring attention to these issues, inform the readers of the opinions of the members of the American Association of Physicists in Medicine (AAPM) CAD subcommittee, and thus stimulate further discussion in the CAD community on these topics. The recommendations in this paper are intended to be work items for AAPM task groups that will be formed to address QA and user training issues on CAD in the future. The work items may serve as a framework for the discussion and eventual design of detailed QA and training procedures for physicists and users of CAD. Some of the recommendations are considered by the subcommittee to be reasonably easy and practical and can be implemented immediately by the end users; others are considered to be "best practice" approaches, which may require significant effort, additional tools, and proper training to implement. The eventual standardization of the requirements of QA procedures for CAD will have to be determined through consensus from members of the CAD community, and user training may require support of professional societies. It is expected that high-quality CAD and proper use of CAD could allow these systems to achieve their true potential, thus benefiting both the patients and the clinicians, and may bring about more widespread clinical use of CAD for many other diseases and applications. It is hoped that the awareness of the need for appropriate CAD QA and user training will stimulate new ideas and approaches for implementing such procedures efficiently and effectively as well as funding opportunities to fulfill such critical efforts.

Multilevel learning-based segmentation of ill-defined and spiculated masses in mammograms.

PURPOSE: A learning-based approach integrating the use of pixel-level statistical modeling and spiculation detection is presented for the segmentation of mammographic masses with ill-defined margins and spiculations. METHODS: The algorithm involves a multiphase pixel-level classification, using a comprehensive group of features computed from regional intensity, shape, and textures, to generate a mass-conditional probability map (PM). Then, the mass candidate, along with the background clutters consisting of breast fibroglandular and other nonmass tissues, is extracted from the PM by integrating the prior knowledge of shape and location of masses. A multiscale steerable ridge detection algorithm is employed to detect spiculations. Finally, all the object-level findings, including mass candidate, detected spiculations, and clutters, along with the PM, are integrated by graph cuts to generate the final segmentation mask. RESULTS: The method was tested on 54 masses (51 malignant and 3 benign), all with ill-defined margins and irregular shape or spiculations. The ground truth delineations were provided by five experienced radiologists. Area overlapping ratio of 0.689 (+/- 0.160) and 0.540 (+/- 0.164) were obtained for segmenting entire mass and margin portion only, respectively. Williams index of area and contour based measurements indicated that the segmentation results of the algorithm agreed well with the radiologists' delineation. CONCLUSIONS: The proposed approach could closely delineate the mass body. Most importantly, it is capable of including mass margin and its spicule extensions which are considered as key features for breast lesion analyses.

PE-CMOS based C-scan ultrasound for foreign object detection in soft tissue.

In this paper, we introduce a C-scan ultrasound prototype and three imaging modalities for the detection of foreign objects inserted in porcine soft tissue. The object materials include bamboo, plastics, glass and aluminum alloys. The images of foreign objects were acquired using the C-scan ultrasound, a portable B-scan ultrasound, film-based radiography, and computerized radiography. The C-scan ultrasound consists of a plane wave transducer, a compound acoustic lens system, and a newly developed ultrasound sensor array based on the complementary metal-oxide semiconductor coated with piezoelectric material (PE-CMOS). The contrast-to-noise ratio (CNR) of the images were analyzed to quantitatively evaluate the detectability using different imaging modalities. The experimental results indicate that the C-scan prototype has better CNR values in 4 out of 7 objects than other modalities. Specifically, the C-scan prototype provides more detail information of the soft tissues without the speckle artifacts that are commonly seen with conventional B-scan ultrasound, and has the same orientation as the standard radiographs but without ionizing radiation.

Prediction of true positive lung cancers in individuals with abnormal suspicious chest radiographs: a prostate, lung, colorectal, and ovarian cancer screening trial study.

INTRODUCTION: Chest radiographs are routinely employed in clinical practice. Radiographic findings that are abnormal suspicious (AS) for lung cancer occur commonly. The majority of AS radiographic abnormalities are not cancer. This study identifies predictors of true positive (TP) AS and presents models for estimating the probability of lung cancer. METHODS: This is a prospective cohort study nested in the randomized National Cancer Institute's Prostate Lung Colorectal Ovarian Cancer Screening Trial (PLCO). First-time AS screens in the screening arm of the PLCO were studied. Associations between nonradiographic and radiographic factors, and TP AS were evaluated by multiple logistic regression. RESULTS: The PLCO intervention arm had 77,465 individuals, of whom 12,314 were AS and of these 232 (1.9%) had lung cancer (were TP). Important independent predictors of TP were older age, lower education, greater pack years and duration smoking history, body mass index <30, family history of lung cancer, lung nodule, lung mass, unilateral mediastinal or hilar lymphadenopathy, lung infiltrate, and upper/middle chest AS location. The model including these variables had a receiver operator characteristic area under the curve (ROC AUC) of 86.4%. This model excluding the smoking variables had an ROC AUC of 77.1% and excluding all nonradiographic variables had an ROC AUC of 73.3% (p < 0.0001 for all these model differences). Smoking and nonsmoking nonradiographic variables significantly added to prediction. CONCLUSION: This study identifies important nonradiographic and radiographic predictors of lung cancer, and presents an accurate model for estimating the probability of lung cancer in individuals with suspicious radiographs. These findings may be of value for screening, research, and patient and clinician decision-making.

Heat maps: an aid for data analysis and understanding of ROC CAD experiments.

RATIONALE AND OBJECTIVES: To demonstrate the value of a new data visualization and exploration method for mutlireader-multicase receiver operating characteristic (MRMC-ROC) experiments of computer-aided detection (CAD) algorithms that uses three-dimensional (3D) heat maps tool adapted from gene expression array analysis. MATERIALS AND METHODS: We are using data from a clinical trial of a commercial CAD system for lung cancer detection (RapidScreen RS-2000, Riverain Medical Group, Miamisburg, OH, and Rockville, MD). 3D heat maps, originally developed for displaying changes in gene expression after cancer chemotherapy in MATLAB, were modified to display the radiologists confidence levels as they interpreted chest radiographs and used to visualize the radiologists confidence levels before and after the provision of a CAD system. RESULTS: Heat maps demonstrated the variation among radiologists in their interpretation, and the degree of variation in interpretation when a single radiologist reinterpreted the same case without and with CAD modality. They demonstrated the variability in the identification of each cancer/cancer-free case and the variability of change seen when CAD prompts were provided. CONCLUSIONS: CAD increases the consistency of interpretation of a single radiologist and of a group of radiologists. Heat maps provide a method for data visualization that clarifies the effects of reader variability in ROC CAD experiments. We demonstrated how heat maps can be used to document the complexity of reader variability and suggested how clustering can reveal both nonintuitive and intuitive groupings of cases, readers, and the interaction of both with CAD.

Factors associated with human small aggressive non small cell lung cancer.

BACKGROUND: Some non-small cell lung cancers (NSCLC) progress to distant lymph nodes or metastasize while relatively small. Such small aggressive NSCLCs (SA-NSCLC) are no longer resectable with curative intent, carry a grave prognosis, and may involve unique biological pathways. This is a study of factors associated with SA-NSCLC. METHODS: A nested case-case study was embedded in the National Cancer Institute's Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial. SA-NSCLC cases had stage T1, N3, and/or M1 NSCLC (n = 48) and non-SA-NSCLC cases had T2 to T3, N0 to N2, and M0 NSCLC (n = 329). Associations were assessed by multiple logistic regression. RESULTS: SA-NSCLCs were associated with younger age at diagnosis [odds ratio (OR)(>or=65 versus <65), 0.44; 95% confidence interval (95% CI), 0.22-0.88], female gender, family history of lung cancer, and the interaction gender*family history of lung cancer and were inversely associated with ibuprofen use (OR(yes versus no), 0.29; 95% CI, 0.11-0.76). The ORs for associating gender (women versus men) with SA-NSCLC in those with and without a family history of lung cancer were 11.76 (95% CI, 2.00-69.22) and 1.86 (95% CI, 0.88-3.96), respectively. These associations held adjusted for histology and time from screening to diagnosis and when alternative controls were assessed. CONCLUSION: SA-NSCLC was associated with female gender, especially in those with a family history of lung cancer. If these exploratory findings, which are subject to bias, are validated as causal, elucidation of the genetic and female factors involved may improve understanding of cancer progression and lead to preventions and therapies. Ibuprofen may inhibit lung cancer progression.

Nonrigid medical image registration by finite-element deformable sheet-curve models.

Image-based change quantitation has been recognized as a promising tool for accurate assessment of tumor's early response to chemoprevention in cancer research. For example, various changes on breast density and vascularity in glandular tissue are the indicators of early response to treatment. Accurate extraction of glandular tissue from pre- and postcontrast magnetic resonance (MR) images requires a nonrigid registration of sequential MR images embedded with local deformations. This paper reports a newly developed registration method that aligns MR breast images using finite-element deformable sheet-curve models. Specifically, deformable curves are constructed to match the boundaries dynamically, while a deformable sheet of thin-plate splines is designed to model complex local deformations. The experimental results on both digital phantoms and real MR breast images using the new method have been compared to point-based thin-plate-spline (TPS) approach, and have demonstrated a significant and robust improvement in both boundary alignment and local deformation recovery.

Steepest changes of a probability-based cost function for delineation of mammographic masses: a validation study.

Our purpose in this work was to develop an automatic boundary detection method for mammographic masses and to rigorously test this method via statistical analysis. The segmentation method utilized a steepest change analysis technique for determining the mass boundaries based on a composed probability density cost function. Previous investigators have shown that this function can be utilized to determine the border of the mass body. We have further analyzed this method and have discovered that the steepest changes in this function can produce mass delineations that include extended projections. The method was tested on 124 digitized mammograms selected from the University of South Florida's Digital Database for Screening Mammography (DDSM). The segmentation results were validated using overlap, accuracy, sensitivity, and specificity statistics, where the gold standards were manual traces provided by two expert radiologists. We have concluded that the best intensity threshold corresponds to a particular steepest change location within the composed probability density function. We also found that our results are more closely correlated with one expert than with the second expert. These findings were verified via Analysis of Variance (ANOVA) testing. The ANOVA tests obtained p-values ranging from 1.03 x 10(-2)-7.51 x 10(-17) for the single observer studies and 2.03 x 10(-2)-9.43 x 10(-4) for the two observer studies. Results were categorized using three significance levels, i.e., p<0.001 (extremely significant), p <0.01 (very significant), and p <0.05 (significant), respectively.

Optimization of wavelet decomposition for image compression and feature preservation.

A neural-network-based framework has been developed to search for an optimal wavelet kernel that can be used for a specific image processing task. In this paper, a linear convolution neural network was employed to seek a wavelet that minimizes errors and maximizes compression efficiency for an image or a defined image pattern such as microcalcifications in mammograms and bone in computed tomography (CT) head images. We have used this method to evaluate the performance of tap-4 wavelets on mammograms, CTs, magnetic resonance images, and Lena images. We found that the Daubechies wavelet or those wavelets with similar filtering characteristics can produce the highest compression efficiency with the smallest mean-square-error for many image patterns including general image textures as well as microcalcifications in digital mammograms. However, the Haar wavelet produces the best results on sharp edges and low-noise smooth areas. We also found that a special wavelet whose low-pass filter coefficients are 0.32252136, 0.85258927, 1.38458542, and -0.14548269) produces the best preservation outcomes in all tested microcalcification features including the peak signal-to-noise ratio, the contrast and the figure of merit in the wavelet lossy compression scheme. Having analyzed the spectrum of the wavelet filters, we can find the compression outcomes and feature preservation characteristics as a function of wavelets. This newly developed optimization approach can be generalized to other image analysis applications where a wavelet decomposition is employed.

A multiple circular path convolution neural network system for detection of mammographic masses.

A multiple circular path convolution neural network (MCPCNN) architecture specifically designed for the analysis of tumor and tumor-like structures has been constructed. We first divided each suspected tumor area into sectors and computed the defined mass features for each sector independently. These sector features were used on the input layer and were coordinated by convolution kernels of different sizes that propagated signals to the second layer in the neural network system. The convolution kernels were trained, as required, by presenting the training cases to the neural network. In this study, randomly selected mammograms were processed by a dual morphological enhancement technique. Radiodense areas were isolated and were delineated using a region growing algorithm. The boundary of each region of interest was then divided into 36 sectors using 36 equi-angular dividers radiated from the center of the region. A total of 144 Breast Imaging-Reporting and Data System-based features (i.e., four features per sector for 36 sectors) were computed as input values for the evaluation of this newly invented neural network system. The overall performance was 0.78-0.80 for the areas (Az) under the receiver operating characteristic curves using the conventional feed-forward neural network in the detection of mammographic masses. The performance was markedly improved with Az values ranging from 0.84 to 0.89 using the MCPCNN. This paper does not intend to claim the best mass detection system. Instead it reports a potentially better neural network structure for analyzing a set of the mass features defined by an investigator.