Tag: statistics

J Stroke Cerebrovasc Dis. 2021 Mar 5;30(5):105661. doi: 10.1016/j.jstrokecerebrovasdis.2021.105661. Online ahead of print.

ABSTRACT

BACKGROUND AND PURPOSE: Stroke outcome data in Uganda is lacking. The objective of this study was to capture 30-day mortality outcomes in patients presenting with acute and subacute stroke to Mbarara Regional Referral Hospital (MRRH) in Uganda.

METHODS: A prospective study enrolling consecutive adults presenting to MRRH with abrupt onset of focal neurologic deficits suspicious for stroke, from August 2014 to March 2015. All patients had head computed tomography (CT) confirmation of ischemic or hemorrhagic stroke. Data was collected on mortality, morbidity, risk factors, and imaging characteristics.

RESULTS: Investigators screened 134 potential subjects and enrolled 108 patients. Sixty-two percent had ischemic and 38% hemorrhagic stroke. The mean age of all patients was 62.5 (SD 17.4), and 52% were female. More patients had hypertension in the hemorrhagic stroke group than in the ischemic stroke group (53% vs. 32%, p = 0.0376). Thirty-day mortality was 38.1% (p = 0.0472), and significant risk factors were National Institutes of Health Stroke Scale (NIHSS) score, female sex, anemia, and HIV infection. A one unit increase of the NIHSS on admission increased the risk of death at 30 days by 6%. Patients with hemorrhagic stroke had statistically higher NIHSS scores (p = 0.0408) on admission compared to patients with ischemic stroke, and also had statistically higher Modified Rankin Scale (mRS) scores at discharge (p = 0.0063), and mRS score change from baseline (p = 0.04).

CONCLUSIONS: Our study highlights an overall 30-day stroke mortality of 38.1% in southwestern Uganda, and identifies NIHSS at admission, female sex, anemia, and HIV infection as predictors of mortality.

PMID:33684710 | DOI:10.1016/j.jstrokecerebrovasdis.2021.105661

Food Chem. 2021 Feb 23;352:129329. doi: 10.1016/j.foodchem.2021.129329. Online ahead of print.

ABSTRACT

A simple, novel, rapid, and non-destructive spectroscopic method that employs the deep spectral network for beef-freshness classification was developed. The deep-learning-based model classified beef freshness by learning myoglobin information and reflectance spectra over different freshness states. The reflectance spectra (480-920 nm) were measured from 78 beef samples for 17 days, and the datasets were sorted into three freshness classes based on their pH values. Myoglobin information showed statistically significant differences depending on the freshness; consequently, it was utilized as a crucial parameter for classification. The model exhibited improved performance when the reflectance spectra were combined with the myoglobin information. The accuracy of the proposed model improved to 91.9%, whereas that of the single-spectra model was 83.6%. Further, a high value for the area under the receiver operating characteristic curve (0.958) was recorded. This study provides a basis for future studies on the investigation of myoglobin information associated with meat freshness.

PMID:33684719 | DOI:10.1016/j.foodchem.2021.129329

Comput Med Imaging Graph. 2021 Feb 24;89:101885. doi: 10.1016/j.compmedimag.2021.101885. Online ahead of print.

ABSTRACT

Automatic liver and tumor segmentation play a significant role in clinical interpretation and treatment planning of hepatic diseases. To segment liver and tumor manually from the hundreds of computed tomography (CT) images is tedious and labor-intensive; thus, segmentation becomes expert dependent. In this paper, we proposed the multi-scale approach to improve the receptive field of Convolutional Neural Network (CNN) by representing multi-scale features that extract global and local features at a more granular level. We also recalibrate channel-wise responses of the aggregated multi-scale features that enhance the high-level feature description ability of the network. The experimental results demonstrated the efficacy of a proposed model on a publicly available 3Dircadb dataset. The proposed approach achieved a dice similarity score of 97.13 % for liver and 84.15 % for tumor. The statistical significance analysis by a statistical test with a p-value demonstrated that the proposed model is statistically significant for a significance level of 0.05 (p-value < 0.05). The multi-scale approach improves the segmentation performance of the network and reduces the computational complexity and network parameters. The experimental results show that the performance of the proposed method outperforms compared with state-of-the-art methods.

PMID:33684731 | DOI:10.1016/j.compmedimag.2021.101885

Eur J Obstet Gynecol Reprod Biol. 2021 Mar 2;259:178-184. doi: 10.1016/j.ejogrb.2021.02.029. Online ahead of print.

ABSTRACT

OBJECTIVE: Electrohysterography is a non-invasive technique to monitor uterine activity and has a significantly higher sensitivity compared to conventional external tocodynamometry. Whether this technique could lead to improved obstetrical outcomes is still unknown. In this propensity score matched study, clinical results of the first pilot implementing electrohysterography during labor were evaluated. The hypothesis tested is that electrohysterography will help to optimize uterine activity and thereby lead to fewer obstetric interventions. Secondary outcomes were Apgar score, arterial umbilical pH values, first stage labor duration, episiotomy rate and postpartum vaginal blood loss.

STUDY DESIGN: From November 2017 until October 2018, electrohysterography was introduced as a standard alternative for monitoring uterine activity in high-risk deliveries. It could be applied in case of induced labor, previous cesarean delivery, body mass index ≥30 kg/m² or an inadequate external tocodynamometry monitoring. Outcomes were compared to a matched group of women in which external tocodynamometry was applied for uterine activity monitoring during labor. These women were identified using propensity scores.

RESULTS: A total of 348 women received electrohysterography as standard method of uterine monitoring during labor. A match (1:1 ratio) was found for 317 women, resulting in a total population of 634 women. No significant differences were seen in obstetric interventions (i.e. cesarean deliveries and assisted vaginal deliveries) between the electrohysterography and tocodynamometry group (P = 0.80). No statistically significant differences were seen regarding the secondary outcomes.

CONCLUSIONS: This first pilot study implementing electrohysterography as monitoring method during labor in a high-risk population did not result in statistically significant differences regarding obstetric interventions, low Apgar scores or low umbilical artery pH values. Therefore, we suggest that electrohysterography causes no harm and we recommend further implementation and evaluation in clinical practice.

PMID:33684672 | DOI:10.1016/j.ejogrb.2021.02.029

Clin Neurol Neurosurg. 2021 Mar 3;203:106583. doi: 10.1016/j.clineuro.2021.106583. Online ahead of print.

ABSTRACT

INTRODUCTION: The LACE+ (Length of Stay, Acuity of Admission, Charlson Comorbidity Index (CCI) Score, Emergency Department (ED) visits within the previous 6 months) index has never been tested in a purely spine surgery population. This study assesses the ability of LACE + to predict adverse patient outcomes following discectomy for far lateral disc herniation (FLDH).

PATIENTS AND METHODS: Data were obtained for patients (n = 144) who underwent far lateral lumbar discectomy at a single, multi-hospital academic medical center (2013-2020). LACE + scores were calculated for all patients with complete information (n = 100). The influence of confounding variables was assessed and controlled with stepwise regression. Logistic regression was used to test the ability of LACE + to predict risk of unplanned hospital readmission, ED visits, outpatient office visits, and reoperation after surgery.

RESULTS: Mean age of the population was 61.72 ± 11.55 years, 69 (47.9 %) were female, and 126 (87.5 %) were non-Hispanic white. Patients underwent either open (n = 92) or endoscopic (n = 52) surgery. Each point increase in LACE + score significantly predicted, in the 30-day (30D) and 30-90-day (30-90D) post-discharge window, higher risk of readmission (p = 0.005, p = 0.009; respectively) and ED visits (p = 0.045). Increasing LACE + also predicted, in the 30D and 90-day (90D) post-discharge window, risk of reoperation (p = 0.022, p = 0.016; respectively), and repeat neurosurgical intervention (p = 0.026, p = 0.026; respectively). Increasing LACE + score also predicted risk of reoperation (p = 0.011) within 30 days of initial surgery.

CONCLUSIONS: LACE + may be suitable for characterizing risk of adverse perioperative events for patients undergoing far lateral discectomy.

PMID:33684675 | DOI:10.1016/j.clineuro.2021.106583

Clin Neurol Neurosurg. 2021 Mar 2;203:106584. doi: 10.1016/j.clineuro.2021.106584. Online ahead of print.

ABSTRACT

OBJECTIVE: This study evaluated overlapping surgery on long-term outcomes following elective, single-level lumbar fusion on exact matched patients undergoing surgery with or without overlap.

PATIENTS AND METHODS: 3799 consecutive adult patients undergoing single-level, posterior-only lumbar fusion over a six-year period at a multi-hospital university health system were retrospectively followed. Reported outcomes included reoperation, emergency department (ED) visit, readmission, overall morbidity and mortality in the 90 days following surgery. Coarsened Exact Matching was used to match patients with and without overlap on key demographic factors. Patients were subsequently matched by both demographic data and by the attending surgeon performing the operation. Univariate analysis was carried out on the whole population, the demographic matched cohort, and demographic and surgeon matched cohort, with significance set at a p-value < 0.05.

RESULTS: Patients with overlap had a longer duration of surgery and were less likely to have an ED visit within 90 days of surgery (p < 0.03) but had no other significant differences. Within the demographic matched cohort and demographic/surgeon matched cohort, there was no significant difference in age, gender, history of prior surgery, ASA score, or CCI score, but patients with overlap had a longer duration of surgery (p < 0.01). Patients did not have significant differences with respect to any morbidity or mortality outcome in either the demographic or surgeon matched cohort.

CONCLUSIONS: Patients undergoing overlapping, single-level lumbar fusion were not at greater risk of long-term morbidity or mortality, despite having a significantly longer duration of surgery.

PMID:33684676 | DOI:10.1016/j.clineuro.2021.106584

Eur J Radiol. 2021 Feb 25;138:109618. doi: 10.1016/j.ejrad.2021.109618. Online ahead of print.

ABSTRACT

PURPOSE: To compare tomographic (TUS) with radiographic (RUS) union scores in nailed shaft fractures during normal healing and in non-unions.

METHODS: Two radiologists blinded to fracture age separately determined RUS and TUS in nailed femoral or tibial shaft fractures by analyzing the radiographic and CT examinations obtained in 47 patients during normal healing (early fracture group; 24 study participants, 17 men,19 tibias, mean fracture-CT delay 109 ± 57 days [42-204 days]) and in surgically proven non-united fractures (late fracture group, 23 patients, 14 men, 12 tibias, mean fracture-CT delay 565 ± 519 days[180-1983 days]). In both study groups, we determined the inter- and intra-observer agreement of RUS and TUS and compared TUS with RUS.

RESULTS: Intra- and inter-observer agreement of RUS and TUS was very good in the early fracture group and good in the late fracture group for both readers. TUS correlated with RUS substantially in the early fracture group and only weakly in the late fracture group. TUS was statistically significantly lower than RUS in study participants with RUS ≥ 8 or 9 for R2 only and ≥ 10 for both readers in the early fracture group and in patients with RUS ≥ 8, 9 or 10 in the late fracture group for both readers.

CONCLUSION: RUS and TUS of nailed shaft fractures during normal healing or in non-unions are both feasible and reproducible. They yield similar values in fractures with no or limited callus. TUS yields lower values than RUS in fractures with callus.

PMID:33684696 | DOI:10.1016/j.ejrad.2021.109618

Neuroimage. 2021 Mar 5:117914. doi: 10.1016/j.neuroimage.2021.117914. Online ahead of print.

ABSTRACT

Performing a BOLD functional MRI (fMRI) acquisition during breath-hold (BH) tasks is a non-invasive, robust method to estimate cerebrovascular reactivity (CVR). However, movement and breathing-related artefacts caused by the BH can substantially hinder CVR estimates due to their high temporal collinearity with the effect of interest, and attention has to be paid when choosing which analysis model should be applied to the data. In this study, we evaluate the performance of multiple analysis strategies based on lagged general linear models applied on multi-echo BOLD fMRI data, acquired in ten subjects performing a BH task during ten sessions, to obtain subject-specific CVR and haemodynamic lag estimates. The evaluated approaches range from conventional regression models including drifts and motion timecourses as nuisance regressors applied on single-echo or optimally-combined data, to more complex models including regressors obtained from multi-echo independent component analysis with different grades of orthogonalization in order to preserve the effect of interest, i.e. the CVR. We compare these models in terms of their ability to make signal intensity changes independent from motion, as well as the reliability as measured by voxelwise intraclass correlation coefficients of both CVR and lag maps over time. Our results reveal that a conservative independent component analysis model applied on the optimally-combined multi-echo fMRI signal offers the largest reduction of motion-related effects in the signal, while yielding reliable CVR amplitude and lag estimates, although a conventional regression model applied on the optimally-combined data results in similar estimates. This work demonstrate the usefulness of multi-echo based fMRI acquisitions and independent component analysis denoising for precision mapping of CVR in single subjects based on BH paradigms, fostering its potential as a clinically-viable neuroimaging tool for individual patients. It also proves that the way in which data-driven regressors should be incorporated in the analysis model is not straight-forward due to their complex interaction with the BH-induced BOLD response.

PMID:33684602 | DOI:10.1016/j.neuroimage.2021.117914

Neural Netw. 2021 Feb 24;139:105-117. doi: 10.1016/j.neunet.2021.02.008. Online ahead of print.

ABSTRACT

Recently, we have witnessed Deep Learning methodologies gaining significant attention for severity-based classification of dysarthric speech. Detecting dysarthria, quantifying its severity, are of paramount importance in various real-life applications, such as the assessment of patients’ progression in treatments, which includes an adequate planning of their therapy and the improvement of speech-based interactive systems in order to handle pathologically-affected voices automatically. Notably, current speech-powered tools often deal with short-duration speech segments and, consequently, are less efficient in dealing with impaired speech, even by using Convolutional Neural Networks (CNNs). Thus, detecting dysarthria severity-level based on short speech segments might help in improving the performance and applicability of those systems. To achieve this goal, we propose a novel Residual Network (ResNet)-based technique which receives short-duration speech segments as input. Statistically meaningful objective analysis of our experiments, reported over standard Universal Access corpus, exhibits average values of 21.35% and 22.48% improvement, compared to the baseline CNN, in terms of classification accuracy and F1-score, respectively. For additional comparisons, tests with Gaussian Mixture Models and Light CNNs were also performed. Overall, the values of 98.90% and 98.00% for classification accuracy and F1-score, respectively, were obtained with the proposed ResNet approach, confirming its efficacy and reassuring its practical applicability.

PMID:33684609 | DOI:10.1016/j.neunet.2021.02.008

Midwifery. 2021 Feb 14;97:102950. doi: 10.1016/j.midw.2021.102950. Online ahead of print.

ABSTRACT

Trauma to the labia occurs in up to 49% of vaginal births¹. Trauma to the perineal body resulting from childbirth is well defined using widely used categories, and recommended management of perineal body trauma is based on high level evidence. Currently no similar evidence exists to inform the classification or management of labial trauma. This is reflected in variation in clinical practice with some practitioners favouring suturing of labial trauma, whilst others favour healing by secondary intention. A survey of practice was undertaken in three NHS organisations, over a five-week period in 2019 with data collected on 332 vaginal births. Overall, 47.3% (n=157) of women sustained labial trauma, of whom 29.3% (n=46) sustained trauma described as involving skin and underlying tissues. Of the labial trauma which involved skin and underlying tissues 76.0% (n=35) was sutured and the remainder unsutured. The survey confirmed a lack of consistency in practice and the need for further research to inform care for women.

PMID:33684613 | DOI:10.1016/j.midw.2021.102950