Category: Statistics

J Pediatr Orthop. 2022 Jan 20. doi: 10.1097/BPO.0000000000002053. Online ahead of print.

ABSTRACT

BACKGROUND: Growth modulation using tension band plates (TBPs) is increasingly important for lower limb deformity correction in patients with skeletal dysplasia (SKD). Development of rebound deformity is a concern after TBP removal. Data regarding this complication are rare; therefore, we evaluated the prevalence and risk factors for rebound deformity in children with SKD undergoing correction of genu valgum using TBP.

METHODS: All patients with SKD with genu valgum treated by TBP at the distal femur or/and proximal tibia at a single center were reviewed. Inclusion criteria were: (1) minimum 2-year follow-up after TBP removal or having revision surgery for rebound deformity and (2) implant removal age for girls 14 years and below and boys 16 years and below. Exclusion criteria were any femoral/tibial osteotomies during TBP treatment or follow-up. A change of ≥3 degrees of mechanical lateral distal femoral and/or medial proximal tibial angle was accepted as rebound deformity and analyzed statistically.

RESULTS: Thirty-three patients (59 limbs; 52 femur and 29 tibia physes) met our criteria. Mean follow-up after implant removal was 43.7 months. Rebound deformities were seen in 43 limbs (39 femurs and 13 tibias). Boys had more rebound than girls; however, this was not influenced by body mass index. Femurs had more rebound than tibias. Patients in the rebound group were younger than the nonrebound group. Time from application to removal of TBP was shorter in the rebound versus nonrebound group. Overcorrected limbs had more rebound deformity than not overcorrected. The difference in growth velocity of lower limbs in the rebound versus nonrebound group was statistically significant. Patients with epiphyseal dysplasia had more rebound than metaphyseal dysplasia, but this was not statistically significant.

CONCLUSION: Risk factors for developing a rebound deformity after correction of genu valgum using TBP in SKD included male sex, TBP surgery at a young age, short duration of TBP implantation, overcorrected extremity (mechanical axis deviation ≤1), and high percent growth velocity after TBP removal.

LEVEL OF EVIDENCE: Level IV-retrospective study.

PMID:35051956 | DOI:10.1097/BPO.0000000000002053

Urol Int. 2022 Jan 20:1-8. doi: 10.1159/000520531. Online ahead of print.

ABSTRACT

INTRODUCTION: This study aimed to investigate the possible effect of gastrodin in renal ischemia-reperfusion injury (IRI) and the mechanisms.

METHODS: Forty-eight male Sprague Dawley rats were randomly divided into 3 groups: sham-operated group, saline-treated IRI group, and gastrodin-treated IRI group. Gastrodin or 0.9% saline (300 mg/kg/day) was intragastrically administrated for 8 days before operation. We analyzed renal function and histological change. The malondialdehyde level, antioxidant enzymes’ activities, and markers of inflammation and apoptosis were measured. Statistical analysis was performed using 1-way analysis of variance (ANOVA) or Kruskal-Wallis ANOVA on ranks.

RESULTS: Gastrodin pretreatment improved IRI-induced renal dysfunction and histologic injury. Mechanistically, gastrodin reversed the elevation of malondialdehyde level and the reduction of antioxidant enzymes’ activities. Gastrodin also reduced the elevated myeloperoxidase activity, TNF-α and IL-1β levels, and the activation of p38 MAPK. Moreover, gastrodin-treated rats exhibited a dramatic reduction in renal tubular apoptosis, along with a decrease in caspase-3 activation and an increase in the Bcl-2/Bax ratio.

CONCLUSION: Gastrodin pretreatment may alleviate renal IRI via the amelioration of oxidative injury, inflammatory response, and renal tubular apoptosis.

PMID:35051947 | DOI:10.1159/000520531

Urol Int. 2022 Jan 20:1-7. doi: 10.1159/000521394. Online ahead of print.

ABSTRACT

BACKGROUND: Inflammation is one of the major risk factors for SN complications because the dense and fibrotic tissue leads to significant challenges to dissection.

OBJECTIVES: We aimed to evaluate the predictive factors preoperatively, especially inflammation markers and radiologic findings, which can pose challenges to surgery in simple nephrectomy.

METHODS: We retrospectively evaluated the data of 156 patients who underwent simple open nephrectomy. There were 87 patients in group 1 (peroperative nonadherent perinephric fat) and 69 patients in group 2 (peroperative adherent perinephric fat). The preoperative computed tomography findings (renal volume, perinephric stranding, posterior perinephric fat thickness, lateral perinephric fat thickness, Hounsfield unit [HU] of perinephric fat, HU of subcutaneous fat, HU of renal parenchyma, HU of renal pelvis), side of the kidney affected, prior surgery at the same kidney, complication rates, and operative time were analyzed. Preoperative inflammation markers, neutrophil-lymphocyte ratio, systemic immune-inflammation index, monocyte-HDL ratio, and platelet-lymphocyte ratio levels were recorded.

RESULTS: Preoperative NLR and SII were statistically higher, and HDL was statistically lower in group 2; there was no difference in PLR and monocyte-HDL ratio between the 2 groups. According to the preoperative imaging, the perinephric stranding, HU of perinephric fat, and HU of renal parenchyma were higher in group 2, 54 (78.3), -36.93 (-91.46, -21.69), and 38.60 (32.11, 41.94), respectively. DM, history of nonsterile urine culture, HU of perinephric fat >61.78, and SII >689.36 were the factors that could be identified as independent significant predictors of presence of adherent perinephric fat.

CONCLUSION: The radiological findings and inflammation markers can be used as the predictive factor for peroperative adherent perinephric tissue and surgical difficulties.

PMID:35051943 | DOI:10.1159/000521394

Phys Med Biol. 2022 Jan 20. doi: 10.1088/1361-6560/ac4d85. Online ahead of print.

ABSTRACT

OBJECTIVE: This work investigates the use of deep convolutional neural networks (CNN) to automatically perform measurements of fetal body parts, including head circumference, biparietal diameter, abdominal circumference and femur length, and to estimate gestational age and fetal weight using fetal ultrasound videos.

APPROACH: We developed a novel multi-task CNN-based spatio-temporal fetal US feature extraction and standard plane detection algorithm (called FUVAI) and evaluated the method on 50 freehand fetal US video scans. We compared FUVAI fetal biometric measurements with measurements made by five experienced sonographers at two time points separated by at least two weeks. Intra- and inter-observer variabilities were estimated.

MAIN RESULTS: We found that automated fetal biometric measurements obtained by FUVAI were comparable to the measurements performed by experienced sonographers The observed differences in measurement values were within the range of inter- and intra-observer variability. Moreover, analysis has shown that these differences were not statistically significant when comparing any individual medical expert to our model.

SIGNIFICANCE: We argue that FUVAI has the potential to assist sonographers who perform fetal biometric measurements in clinical settings by providing them with suggestions regarding the best measuring frames, along with automated measurements. Moreover, FUVAI is able perform these tasks in just a few seconds, which is a huge difference compared to the average of six minutes taken by sonographers. This is significant, given the shortage of medical experts capable of interpreting fetal ultrasound images in numerous countries.

PMID:35051921 | DOI:10.1088/1361-6560/ac4d85

Folia Phoniatr Logop. 2022 Jan 20. doi: 10.1159/000521222. Online ahead of print.

ABSTRACT

Hypothesis/Objectives This study’s objective was to develop a method to the evaluate the chaotic characteristic of alaryngeal speech. The proposed method will be capable of distinguishing between normal and alaryngeal voices, including esophageal (SE) and tracheoesophageal (TE) voices. It has been previously shown that alaryngeal voices exhibit chaotic characteristics due to the aperiodicity of their signals. The proposed method will be applied for future use to quantify both chaos behavior and the difference between SE and TE voices. Study Design A total of 74 voice recordings including 34 normal and 40 alaryngeal (26 esophageal (SE) and 14 tracheoesophageal (TE)) were used in the study. Voice samples were analyzed to distinguish alaryngeal voices from normal voices and investigate different chaotic characteristics of SE and TE speech. Methods A chaotic distribution detection-based method was used to investigate the chaos behavior of alaryngeal voices. This chaos behavior was used to detect the difference between SE and TE voice types. Quantification of the chaos behavior (CB) parameter was performed. Statistical analyses were used to compare the results of the CB analysis for both the SE and TE voices. Results Statistical analysis revealed that CB effectively differentiated between all normal and alaryngeal voice types (P<0.01). Subsequent multiclass receiver operating characteristic (ROC) analysis demonstrated that CB (area under the curve) possessed the greatest classification accuracy relative to Correlation dimension (D2). Conclusions The CB metric shows strong promise as an accurate, useful metric for objective differentiation between all normal and alaryngaeal, SE and TE voice types. The CB calculations showed expected results, as SE voices have significantly more chaos behavior than TE voices, constituting substantial improvement over previous methods and becoming the first SE and TE classification method. This metric can help clinicians obtain additional acoustical information when monitoring the efficacy of treatment for patients undergoing total laryngectomies.

PMID:35051938 | DOI:10.1159/000521222

Nurse Educ Today. 2022 Jan 14;110:105266. doi: 10.1016/j.nedt.2022.105266. Online ahead of print.

ABSTRACT

BACKGROUND: Obstetric violence is a problem that has grown worldwide, and a particularly worrying one in Spain. Such violence has repercussions for women, and for the professionals who cause them. Preventing this problem seems fundamental.

OBJECTIVE: This study evaluated how health sciences students perceived obstetric violence.

DESIGN: A cross-sectional study conducted between October 2019 and November 2020.

PARTICIPANTS: A sample of Spanish health sciences students studying degrees of nursing, medicine, midwifery, and psychology.

METHODS: A validated questionnaire was used: Perception of Obstetric Violence in Students (PercOV-S). Socio-demographic and control variables were included. A descriptive and comparative multivariate analysis was performed with the obtained data.

RESULTS: 540 questionnaires were completed with an overall mean score of 3.83 points (SD ± 0.63), with 2.83 points (SD ± 0.91) on the protocolised-visible dimension and 4.15 points (SD ± 0.67) on the non-protocolised-invisible obstetric violence dimension. Statistically significant differences were obtained for degree studied (p < 0.001), gender (p < 0.001), experience (p < 0.001), ethnic group (p < 0.001), the obstetric violence concept (p < 0.001) and academic year (p < 0.005). There were three significant multivariate models for the questionnaire’s overall score and dimensions.

CONCLUSIONS: Health sciences students perceived obstetric violence mainly as non-protocolised aspects while attending women. Degree studied and academic year might be related to perceived obstetric violence.

PMID:35051872 | DOI:10.1016/j.nedt.2022.105266

Biomed Phys Eng Express. 2022 Jan 20. doi: 10.1088/2057-1976/ac4d43. Online ahead of print.

ABSTRACT

In this study, we investigated the possibility of predicting expression levels of programmed death-ligand 1 (PD-L1) using radiomic features of intratumoral and peritumoral tumors on computed tomography (CT) images. We retrospectively analyzed 161 patients with non-small cell lung cancer. We extracted radiomics features for intratumoral and peritumoral regions on CT images. The null importance, least absolute shrinkage, and selection operator model were used to select the optimized feature subset to build the prediction models for the PD-L1 expression level. LightGBM with five-fold cross-validation was used to construct the prediction model and evaluate the receiver operating characteristics. The corresponding area under the curve (AUC) was calculated for the training and testing cohorts. The proportion of ambiguously clustered pairs was calculated based on consensus clustering to evaluate the validity of the selected features. In addition, Radscore was calculated for the training and test cohorts. For expression level of PD-L1 above 1%, prediction models that included radiomic features from the intratumoral region and a combination of radiomic features from intratumoral and peritumoral regions yielded an AUC of 0.83 and 0.87 and 0.64 and 0.74 in the training and test cohorts, respectively. In contrast, the models above 50% prediction yielded an AUC of 0.80, 0.97, and 0.74, 0.83, respectively. The selected features were divided into two subgroups based on PD-L1 expression levels ≥ 50% or ≥ 1%. Radscore was statistically higher for subgroup one than subgroup two when radiomic features for intratumoral and peritumoral regions were combined. We constructed a predictive model for PD-L1 expression level using CT images. The model using a combination of intratumoral and peritumoral radiomic features had a higher accuracy than the model with only intratumoral radiomic features.

PMID:35051908 | DOI:10.1088/2057-1976/ac4d43

Comput Biol Med. 2022 Jan 11;142:105230. doi: 10.1016/j.compbiomed.2022.105230. Online ahead of print.

ABSTRACT

OBJECTIVE: To investigate the impact of harmonization on the performance of CT, PET, and fused PET/CT radiomic features toward the prediction of mutations status, for epidermal growth factor receptor (EGFR) and Kirsten rat sarcoma viral oncogene (KRAS) genes in non-small cell lung cancer (NSCLC) patients.

METHODS: Radiomic features were extracted from tumors delineated on CT, PET, and wavelet fused PET/CT images obtained from 136 histologically proven NSCLC patients. Univariate and multivariate predictive models were developed using radiomic features before and after ComBat harmonization to predict EGFR and KRAS mutation statuses. Multivariate models were built using minimum redundancy maximum relevance feature selection and random forest classifier. We utilized 70/30% splitting patient datasets for training/testing, respectively, and repeated the procedure 10 times. The area under the receiver operator characteristic curve (AUC), accuracy, sensitivity, and specificity were used to assess model performance. The performance of the models (univariate and multivariate), before and after ComBat harmonization was compared using statistical analyses.

RESULTS: While the performance of most features in univariate modeling was significantly improved for EGFR prediction, most features did not show any significant difference in performance after harmonization in KRAS prediction. Average AUCs of all multivariate predictive models for both EGFR and KRAS were significantly improved (q-value < 0.05) following ComBat harmonization. The mean ranges of AUCs increased following harmonization from 0.87-0.90 to 0.92-0.94 for EGFR, and from 0.85-0.90 to 0.91-0.94 for KRAS. The highest performance was achieved by harmonized F_R0.66_W0.75 model with AUC of 0.94, and 0.93 for EGFR and KRAS, respectively.

CONCLUSION: Our results demonstrated that regarding univariate modelling, while ComBat harmonization had generally a better impact on features for EGFR compared to KRAS status prediction, its effect is feature-dependent. Hence, no systematic effect was observed. Regarding the multivariate models, ComBat harmonization significantly improved the performance of all radiomics models toward more successful prediction of EGFR and KRAS mutation statuses in lung cancer patients. Thus, by eliminating the batch effect in multi-centric radiomic feature sets, harmonization is a promising tool for developing robust and reproducible radiomics using vast and variant datasets.

PMID:35051856 | DOI:10.1016/j.compbiomed.2022.105230

Epilepsy Behav. 2022 Jan 17;127:108538. doi: 10.1016/j.yebeh.2021.108538. Online ahead of print.

ABSTRACT

AIM: The aim of this study was to evaluate the potential impact of cannabidiol (CBD) on healthcare resource utilization and determine the effect of CBD on seizure-related emergency departments (ED) and hospital admissions in patients with epilepsy.

METHODS: This single-center, retrospective longitudinal cohort study included patients ≥1 year on CBD, excluding participants in CBD clinical trials or on <6 months of CBD therapy. Demographics, antiseizure medications (ASM), ED and hospital admissions were collected from the electronic medical record. Co-primary outcomes included change in seizure-related ED and hospital admissions. Secondary outcomes included change in ASMs and total ED or hospital admissions. Co-primary outcomes were assessed using generalized linear modeling. Descriptive statistics were used to analyze all other variables.

RESULTS: In the one-hundred total patients, there was no difference in either seizure-related ED visits (0.012 vs 0.011, p = 0.85) or hospital admissions per month (0.019 vs 0.021, p = 0.7). However, given the low percentage of the total cohort (n = 100) with either a seizure-related ED visits and hospital admissions (9% and 18%, respectively), a subgroup analysis was conducted. Those with seizure-related hospital admissions prior to CBD (n = 18) had significantly less seizure-related hospital admissions after initiation of CBD (23 admissions [0.104 per month] vs 15 admissions [0.055 per month], p = 0.007).

CONCLUSION: Despite the lack of statistically significant difference in primary outcomes for the total cohort, CBD may have a potential for a clinically beneficial impact in real-world settings on those patients with prior seizure-related admissions, who are the highest utilizers of healthcare resources.

PMID:35051868 | DOI:10.1016/j.yebeh.2021.108538

Biosens Bioelectron. 2022 Jan 2;202:113955. doi: 10.1016/j.bios.2021.113955. Online ahead of print.

ABSTRACT

Proteins secreted by cells play significant roles in mediating many physiological, developmental, and pathological processes due to their functions in intra/intercellular communication and signaling. Conventional end-point methods are insufficient for understanding the temporal response in cell secretion process, which is often highly dynamic. Furthermore, cellular heterogeneity makes it essential to analyze secretory proteins from single cells. To uncover individual cellular activities and the underlying kinetics, new technologies are needed for real-time analysis of the secretomes of many cells at single-cell resolution. This study reports a high-throughput biosensing microarray platform, which is capable of label-free and real-time secretome monitoring from a large number of living single cells using a biochip integrating ultrasensitive nanoplasmonic substrate and microwell compartments having volumes of ∼0.4 nL. Precise synchronization of image acquisition and microscope stage movement of the developed optical platform enables spectroscopic analysis with high temporal and spectral resolution. In addition, our system allows simultaneous optical imaging of cells to track morphology changes for a comprehensive understanding of cellular behavior. We demonstrated the platform performance by detecting interleukin-2 secretion from hundreds of single lymphoma cells in real-time over many hours. Significantly, the analysis of the secretion kinetics allows us to study cellular response to the stimulations in a statistical way. The new platform is a promising tool for the characterization of single-cell functionalities given its versatility, throughput and label-free configuration.

PMID:35051850 | DOI:10.1016/j.bios.2021.113955