Nevin Manimala

Acad Radiol. 2022 Mar 30:S1076-6332(22)00202-1. doi: 10.1016/j.acra.2022.03.025. Online ahead of print.

ABSTRACT

RATIONALE AND OBJECTIVES: Hard data labels for automated algorithm training are binary and cannot incorporate uncertainty between labels. We proposed and evaluated a soft labeling methodology to quantify opacification and percent well-aerated lung (%WAL) on chest CT, that considers uncertainty in segmenting pulmonary opacifications and reduces labeling burden.

MATERIALS AND METHODS: We retrospectively sourced 760 COVID-19 chest CT scans from five international centers between January and June 2020. We created pixel-wise labels for >27,000 axial slices that classify three pulmonary opacification patterns: pure ground-glass, crazy-paving, consolidation. We also quantified %WAL as the total area of lung without opacifications. Inter-user hard label variability was quantified using Shannon entropy (range=0-1.39, low-high entropy/variability). We incorporated a soft labeling and modeling cycle following an initial model with hard labels and compared performance using point-wise accuracy and intersection-over-union of opacity labels with ground-truth, and correlation with ground-truth %WAL.

RESULTS: Hard labels annotated by 12 radiologists demonstrated large inter-user variability (3.37% of pixels achieved complete agreement). Our soft labeling approach increased point-wise accuracy from 60.0% to 84.3% (p=0.01) compared to hard labeling at predicting opacification type and area involvement. The soft label model accurately predicted %WAL (R=0.900) compared to the hard label model (R=0.856), but the improvement was not statistically significant (p=0.349).

CONCLUSION: Our soft labeling approach increased accuracy for automated quantification and classification of pulmonary opacification on chest CT. Although we developed the model on COVID-19, our intent is broad application for pulmonary opacification contexts and to provide a foundation for future development using soft labeling methods.

PMID:35490114 | DOI:10.1016/j.acra.2022.03.025

Aust Crit Care. 2022 Apr 27:S1036-7314(22)00034-0. doi: 10.1016/j.aucc.2022.03.002. Online ahead of print.

ABSTRACT

BACKGROUND: There is a paucity of literature in Australia on patient-focused tracheostomy outcomes and process outcomes. Exploration of processes of care enables teams to identify and address existing barriers that may prevent earlier therapeutic interventions that could improve patient outcomes following critical care survival.

OBJECTIVES: The objectives of this study were to examine and provide baseline data and associations between tracheostomy clinical practices and patient outcomes across three large metropolitan hospitals.

METHODS: We performed a retrospective multisite observational study in three tertiary metropolitan Australian health services who are members of the Global Tracheostomy Collaborative. Deidentified data were entered into the Global Tracheostomy Collaborative database from Jan 2016 to Dec 2019. Descriptive statistics were used for the reported outcomes of length of stay, mortality, tracheostomy-related adverse events and complications, tracheostomy insertion, airway, mechanical ventilation, communication, swallowing, nutrition, length of cannulation, and decannulation. Pearson’s correlation coefficient and one-way analyses of variance were performed to examine associations between variables.

RESULTS: The total cohort was 380 patients. The in-hospital mortality of the study cohort was 13%. Overall median hospital length of stay was 46 days (interquartile range: 31-74). Length of cannulation was shorter in patients who did not experience any tracheostomy-related adverse events (p= 0.036) and who utilised nonverbal communication methods (p = 0.041). Few patients (8%) utilised verbal communication methods while mechanically ventilated, compared with 80% who utilised a one-way speaking valve while off the ventilator. Oral intake was commenced in 20% of patients prior to decannulation. Patient nutritional intake varied prior to and at the time of decannulation. Decannulation occurred in 83% of patients.

CONCLUSIONS: This study provides baseline data for tracheostomy outcomes across three large metropolitan Australian hospitals. Most outcomes were comparable with previous international and local studies. Future research is warranted to explore the impact of earlier nonverbal communication and interventions targeting the reduction in tracheostomy-related adverse events.

PMID:35490111 | DOI:10.1016/j.aucc.2022.03.002

Rev Esp Cir Ortop Traumatol. 2022 Apr 27:S1888-4415(22)00011-X. doi: 10.1016/j.recot.2022.01.005. Online ahead of print.

ABSTRACT

INTRODUCTION AND OBJECTIVE: Dual modularity stems seek to more precisely restore anatomy by allowing intraoperative adjustments thanks to modular necks. Our aim is to compare the radiographic length correction with the H MAX-M® Stem versus its monoblock counterpart H MAX-S®.

MATERIAL AND METHODS: A prospective cohort study was carried out through consecutive sampling on patients who underwent primary total hip arthroplasty with coxarthrosis diagnosis between 2011 and 2015. One arm of the cohort included patients who were operated with a modular stem and the other with a monobloc stem. Length was measured on the anteroposterior pelvic-bearing radiograph at six months. The mean of the measurements obtained for each arm of the cohort were compared with each other.

RESULTS: No statistically significant differences were observed in the correction of asymmetry between both groups, determined as the difference in length between the operated hip and the contralateral hip (P=.106). Nor were differences observed in postoperative length values (P=.053). It should be noted that for both the modular stem and the monobloc stem, the majority group is the one with restored length (84.1% and 80.4%, respectively; P=.001).

CONCLUSION: Despite the theoretical advantage of modularity and that having interchangeable parts could be of great interest, in our study, we have not been able to demonstrate a superiority of modular designs compared to monoblock for control of postoperative leg length discrepancy.

PMID:35490100 | DOI:10.1016/j.recot.2022.01.005

Can J Diabetes. 2021 Dec 30:S1499-2671(21)00468-8. doi: 10.1016/j.jcjd.2021.12.001. Online ahead of print.

ABSTRACT

OBJECTIVES: The National Diabetes Surveillance System (NDSS) case definition, which identifies a case of diabetes using administrative health records as “two physician claims or one hospital discharge abstract record, within a 2-year period for a diagnosis bearing International Classification of Disease codes for diabetes”, was compared with expanded case definitions, including pharmacy (PHARM) and laboratory (LAB) data. The PHARM definition included any therapeutic anti-hyperglycemic agents, and the LAB definition included thresholds of ≥1 glycated hemoglobin measurement of ≥6.5%, or 2 instances of random glucose ≥11.1 mmol/L or fasting glucose ≥7.0 mmol/L.

METHODS: In this retrospective study we used administrative data from the Diabetes Infrastructure for Surveillance, Evaluation, and Research project. Descriptive statistics were used to characterize participants by several subgroups.

RESULTS: The NDSS identified 291,242 diabetes cases, indicating a provincial prevalence of 6.83%. Using LAB plus PHARM identified 52,040 additional cases, so the combination of NDSS or LAB or PHARM identified the largest number of cases (n=343,282), increasing the diabetes prevalence estimate to 8.06%. These 3 sources resulted in 7 unique subsets: NDSS only (n=42,606), PHARM only (n=16,310), LAB only (n=32,202), NDSS+LAB (n=32,582), NDSS+PHARM (n=22,503), LAB+PHARM (n=3,528) and NDSS+LAB+PHARM (n=193,551). Refinement using demographic and clinical characteristics allowed presumptive cases of polycystic ovarian syndrome to be excluded.

CONCLUSIONS: The widely used NDSS case definition can be enhanced by the addition of LAB and PHARM data. Including PHARM and LAB data identified subsets of the diabetes population, which can maximize the yield for detection of diabetes cases in Alberta and provide a richer understanding of this population to target interventions to improve health outcomes.

PMID:35490092 | DOI:10.1016/j.jcjd.2021.12.001

Radiography (Lond). 2022 Apr 27:S1078-8174(22)00053-0. doi: 10.1016/j.radi.2022.04.004. Online ahead of print.

ABSTRACT

INTRODUCTION: Apparent diffusion coefficient (ADC) values are effective in the diagnosis of different gynecological lesions.

METHODS: A retrospective evaluation was made of 12 patients with uterine cervix carcinoma and 151 patients with uterine lesions, comprising endometrial cancer, endometrial polyps, carcinosarcoma, submucous myoma, adenomyosis, endometrial hyperplasia, gestational trophoblastic neoplasm (GTN), and leiomyomas. As a control group, 20 healthy volunteers with normal endometrium and normal cervix were also evaluated. In three series, one-shot, spin echo, echo planar, b = 1000 s/mm² value and diffusion-weighted imaging (DWI) were applied to all subjects and ADC values were obtained.

RESULTS: The mean ADC values of Group 1 (Endometrial carcinoma) were lower than those of all the other groups (P < 0.001) and the mean ADC value of group 6 (GTN) was higher than that all other groups (P < 0.001). A statistically significant difference was found between the groups in terms of the lesion-myometrium ADC ratios (P < 0.001).

CONCLUSION: There are few studies in literature related to ADC measurements in GTN. The ADC values of GTN were found to be significantly higher than the other uterine lesions. These results will aid in the design of future studies and might be used to guide management of patients with GTN.

IMPLICATIONS FOR PRACTICE: Diffusion-weighted MRI seems to be a promising imaging technique in differentiating different uterine lesions.

PMID:35490049 | DOI:10.1016/j.radi.2022.04.004

Trends Pharmacol Sci. 2022 Apr 27:S0165-6147(22)00059-1. doi: 10.1016/j.tips.2022.03.009. Online ahead of print.

ABSTRACT

Researchers, regulatory agencies, and the pharmaceutical industry are moving towards precision pharmacovigilance as a comprehensive framework for drug safety assessment, at the service of the individual patient, by clustering specific risk groups in different databases. This article explores its implementation by focusing on: (i) designing a new data collection infrastructure, (ii) exploring new computational methods suitable for drug safety data, and (iii) providing a computer-aided framework for distributed clinical decisions with the aim of compiling a personalized information leaflet with specific reference to a drug’s risks and adverse drug reactions. These goals can be achieved by using ‘smart hospitals’ as the principal data sources and by employing methods of precision medicine and medical statistics to supplement current public health decisions.

PMID:35490032 | DOI:10.1016/j.tips.2022.03.009

J Chem Phys. 2022 Apr 28;156(16):164105. doi: 10.1063/5.0090055.

ABSTRACT

Feature selection (FS) methods often are used to develop data-driven descriptors (i.e., features) for rapidly predicting the functional properties of a physical or chemical system based on its composition and structure. FS algorithms identify descriptors from a candidate pool (i.e., feature space) built by feature engineering (FE) steps that construct complex features from the system’s fundamental physical properties. Recursive FE, which involves repeated FE operations on the feature space, is necessary to build features with sufficient complexity to capture the physical behavior of a system. However, this approach creates a highly correlated feature space that contains millions or billions of candidate features. Such feature spaces are computationally demanding to process using traditional FS approaches that often struggle with strong collinearity. Herein, we address this shortcoming by developing a new method that interleaves the FE and FS steps to progressively build and select powerful descriptors with reduced computational demand. We call this method iterative Bayesian additive regression trees (iBART), as it iterates between FE with unary/binary operators and FS with Bayesian additive regression trees (BART). The capabilities of iBART are illustrated by extracting descriptors for predicting metal-support interactions in catalysis, which we compare to those predicted in our previous work using other state-of-the-art FS methods (i.e., least absolute shrinkage and selection operator + l₀, sure independence screening and sparsifying operator, and Bayesian FS). iBART matches the performance of these methods yet uses a fraction of the computational resources because it generates a maximum feature space of size O(10²), as opposed to O(10⁶) generated by one-shot FE/FS methods.

PMID:35490030 | DOI:10.1063/5.0090055

J Chem Phys. 2022 Apr 28;156(16):161101. doi: 10.1063/5.0087586.

ABSTRACT

In this Communication, we assess a panel of 18 double-hybrid density functionals for the modeling of the thermochemical and kinetic properties of an extended dataset of 449 organic chemistry reactions belonging to the BH9 database. We show that most of DHs provide a statistically robust performance to model barrier height and reaction energies in reaching the “chemical accuracy.” In particular, we show that nonempirical DHs, such as PBE0-DH and PBE-QIDH, or minimally parameterized alternatives, such as ωB2PLYP and B2K-PLYP, succeed to accurately model both properties in a balanced fashion. We demonstrate, however, that parameterized approaches, such as ωB97X-2 or DSD-like DHs, are more biased to only one of both properties.

PMID:35490016 | DOI:10.1063/5.0087586

J Chem Phys. 2022 Apr 28;156(16):164104. doi: 10.1063/5.0086640.

ABSTRACT

We present our blind predictions for the Statistical Assessment of the Modeling of Proteins and Ligands (SAMPL), ninth challenge, focusing on the binding of WP6 (carboxy-pillar[6]arene) with ammonium/diammonium cationic guests. Host-guest binding free energies have been calculated using the recently developed virtual double system single box approach, based on the enhanced sampling of the bound and unbound end-states followed by fast switching nonequilibrium alchemical simulations [M. Macchiagodena et al., J. Chem. Theory Comput. 16, 7160 (2020)]. As far as Pearson and Kendall coefficients are concerned, performances were acceptable and, in general, better than those we submitted for calixarenes, cucurbituril-like open cavitand, and beta-cyclodextrines in previous SAMPL host-guest challenges, confirming the reliability of nonequilibrium approaches for absolute binding free energy calculations. In comparison with previous submissions, we found a rather large mean signed error that we attribute to the way the finite charge correction was addressed through the assumption of a neutralizing background plasma.

PMID:35490003 | DOI:10.1063/5.0086640

J Fr Ophtalmol. 2022 Apr 27:S0181-5512(21)00473-3. doi: 10.1016/j.jfo.2021.06.007. Online ahead of print.

ABSTRACT

PURPOSE: To investigate the effect of systemic anti-androgen drugs on tear function tests and the ocular surface.

METHODS: Sixty-four male subjects were included in this study. Subjects who were on anti-androgen treatment for prostate cancer (Group A, n: 31) and those who had received only surgical treatment for prostate cancer (Group B, n: 17) were recruited from the department of urology. Age-matched subjects who had never received anti-androgen treatment (Group C, n: 16) constituted the control group. Group A was divided into two subgroups according to the number of anti-androgen drugs used (Group A1: one drug, Group A2: two drugs). All cases underwent a complete ocular examination, including tear film break up time (TBUT), corneal and conjunctival staining, Schirmer 1 test, conjunctival impression cytology, and ocular surface disease index (OSDI) questionnaire.

RESULTS: The mean Schirmer’s values were 6.87mm, 11.41mm, and 13.03mm in Groups A, B, and C, respectively (P=0.001). TBUT was 5.45±2.01, 9.85±2.52 and 9.81±1.96seconds in Groups A, B, and C, respectively (P=0.001). Schirmer and TBUT were significantly lower, and corneal staining and OSDI questionnaire scores were higher in Group A compared to groups B and C (P<0.01). Conjunctival impression cytology results according to the Nelson grading system revealed no statistically significant difference between the groups (P=0.422).

CONCLUSION: Anti-androgen drugs alter tear function tests, cause increased corneal and conjunctival staining scores and worsen complaints of dry eye in patients with prostate cancer.

PMID:35489988 | DOI:10.1016/j.jfo.2021.06.007