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Background: Breast cancer neoadjuvant chemotherapy (NACT) allows for assessing tumor sensitivity to systemic treatment, planning adjuvant treatment and follow-up. However, a sufficiently large number of patients fail to achieve the desired level of pathological tumor response while optimal early response assessment methods have not been established now. In our study, we simultaneously assessed the early chemotherapy-induced changes in the tumor volume by ultrasound (US), the tumor oxygenation by diffuse optical spectroscopy imaging (DOSI), and the state of the tumor vascular bed by Doppler US to elaborate the predictive criteria of breast tumor response to treatment. Methods: A total of 133 patients with a confirmed diagnosis of invasive breast cancer stage II to III admitted to NACT following definitive breast surgery were enrolled, of those 103 were included in the final analysis. Tumor oxygenation by DOSI, tumor volume by US, and tumor vascularization by Doppler US were determined before the first and second cycle of NACT. After NACT completion, patients underwent surgery followed by pathological examination and assessment of the pathological tumor response. On the basis of these, data regression predictive models were created. Results: We observed changes in all three parameters 3 weeks after the start of the treatment. However, a high predictive potential for early assessment of tumor sensitivity to NACT demonstrated only the level of oxygenation, ΔStO2, (ρ = 0.802, p ≤ 0.01). The regression model predicts the tumor response with a high probability of a correct conclusion (89.3%). The "Tumor volume" model and the "Vascularization index" model did not accurately predict the absence of a pathological tumor response to treatment (60.9% and 58.7%, respectively), while predicting a positive response to treatment was relatively better (78.9% and 75.4%, respectively). Conclusions: Diffuse optical spectroscopy imaging appeared to be a robust tool for early predicting breast cancer response to chemotherapy. It may help identify patients who need additional molecular genetic study of the tumor in order to find the source of resistance to treatment, as well as to correct the treatment regimen. [ABSTRACT FROM AUTHOR]

Background: The aim of the study was to predict the probability of intensive care unit (ICU) care for inpatient COVID-19 cases using clinical and artificial intelligence segmentation-based volumetric and CT-radiomics parameters on admission. Methods: Twenty-eight clinical/laboratory features, 21 volumetric parameters, and 74 radiomics parameters obtained by deep learning (DL)-based segmentations from CT examinations of 191 severe COVID-19 inpatients admitted between March 2020 and March 2021 were collected. Patients were divided into Group 1 (117 patients discharged from the inpatient service) and Group 2 (74 patients transferred to the ICU), and the differences between the groups were evaluated with the T-test and Mann–Whitney test. The sensitivities and specificities of significantly different parameters were evaluated by ROC analysis. Subsequently, 152 (79.5%) patients were assigned to the training/cross-validation set, and 39 (20.5%) patients were assigned to the test set. Clinical, radiological, and combined logit-fit models were generated by using the Bayesian information criterion from the training set and optimized via tenfold cross-validation. To simultaneously use all of the clinical, volumetric, and radiomics parameters, a random forest model was produced, and this model was trained by using a balanced training set created by adding synthetic data to the existing training/cross-validation set. The results of the models in predicting ICU patients were evaluated with the test set. Results: No parameter individually created a reliable classifier. When the test set was evaluated with the final models, the AUC values were 0.736, 0.708, and 0.794, the specificity values were 79.17%, 79.17%, and 87.50%, the sensitivity values were 66.67%, 60%, and 73.33%, and the F1 values were 0.67, 0.62, and 0.76 for the clinical, radiological, and combined logit-fit models, respectively. The random forest model that was trained with the balanced training/cross-validation set was the most successful model, achieving an AUC of 0.837, specificity of 87.50%, sensitivity of 80%, and F1 value of 0.80 in the test set. Conclusion: By using a machine learning algorithm that was composed of clinical and DL-segmentation-based radiological parameters and that was trained with a balanced data set, COVID-19 patients who may require intensive care could be successfully predicted. [ABSTRACT FROM AUTHOR]

Background: Negative Pressure Wound Therapy (NPWT) is being increasingly used to treat postoperative infections after osteosynthetic fracture fixation. The aim of the present study was to analyze the influence of epidemiological and microbiological parameters on outcome.Methods: Infections following operative fracture fixation were registered in a comprehensive Critical Incidence Reporting System and subsequently analyzed retrospectively for characteristics of patients including comorbidity, bacteria, and clinical factors. The influence of the investigated parameters was analyzed using logistic regression models based on data from 106 patients.Results: Staged wound lavage in combination with NPWT allowed implant preservation in 44% and led to successful healing in 73% of patients. Fermentation characteristics, load and behavior after gram staining revealed no statistically significant correlation with either healing or implant preservation. Infecting bacteria were successfully isolated in 87% of patients. 20% of all infections were caused by bacterial combinations. We observed a change in the infecting bacterial species under therapy in 23%. Age, gender, metabolic diseases or comorbidities did not influence the probability of implant preservation or healing. The delayed manifestation of infection (>4 weeks) correlated with a higher risk for implant loss (OR 5.1 [95% CI 1.41-17.92]) as did the presence of bacterial mixture (OR 5.0 [95% CI 1.41-17.92]) and open soft-tissue damage ≥ grade 3 (OR 10.2 [CI 1.88-55.28]). Wounds were less likely to heal in conjunction with high CRP blood levels (>20 mg/l) at the time of discharge (OR 3.6 [95% CI 1.31-10.08]) or following a change of the infecting bacterial species under therapy (OR 3.2 [95% CI, 1.13-8.99]).Conclusions: These results indicate that the delayed manifestation of infection, high CRP blood levels at discharge, and alterations in the infecting bacterial species under therapy raise the risk of NPWT failure. [ABSTRACT FROM AUTHOR]

Background Comparison of outcomes between populations or centres may be confounded by any casemix differences and standardisation is carried out to avoid this. However, when the casemix adjustment models are large and complex, direct standardisation has been described as "practically impossible", and indirect standardisation may lead to unfair comparisons. We propose a new method of directly standardising for risk rather than standardising for casemix which overcomes these problems. Methods Using a casemix model which is the same model as would be used in indirect standardisation, the risk in individuals is estimated. Risk categories are defined, and event rates in each category for each centre to be compared are calculated. A weighted sum of the risk category specific event rates is then calculated. We have illustrated this method using data on 6 million admissions to 146 hospitals in England in 2007/8 and an existing model with over 5000 casemix combinations, and a second dataset of 18,668 adult emergency admissions to 9 centres in the UK and overseas and a published model with over 20,000 casemix combinations and a continuous covariate. Results Substantial differences between conventional directly casemix standardised rates and rates from direct risk standardisation (DRS) were found. Results based on DRS were very similar to Standardised Mortality Ratios (SMRs) obtained from indirect standardisation, with similar standard errors. Conclusions Direct risk standardisation using our proposed method is as straightforward as using conventional direct or indirect standardisation, always enables fair comparisons of performance to be made, can use continuous casemix covariates, and was found in our examples to have similar standard errors to the SMR. It should be preferred when there is a risk that conventional direct or indirect standardisation will lead to unfair comparisons. [ABSTRACT FROM AUTHOR]

Background: To understand the influence of the number of years spent at an Australian rural clinical school (RCS) on graduate current, preferred current and intended location for rural workforce practice. Methods: Retrospective online survey of medical graduates who spent 1-3 years of their undergraduate training in the University of New South Wales (UNSW) Rural Clinical School. Associations with factors (gender, rural versus nonrural entry, conscription versus non-conscript and number of years of RCS attendance) influencing current, preferred current and intended locations were assessed using ?2 test. Factors that were considered significant at P < 0.1 were entered into a logistic regression model for further analysis. Results: 214 graduates responded to the online survey. Graduates with three years of previous RCS training were more likely to indicate rural areas as their preferred current work location, than their colleagues who spent one year at an RCS campus (OR = 3.0, 95% CI = 1.2-7.4, P = 0.015). Also RCS graduates that spent three years at an RCS were more likely to intend to take up rural medical practice after completion of training compared to the graduates with one year of rural placement (OR = 5.1, 95% CI = 1.8-14.2, P = 0.002). Non-rural medicine entry graduates who spent three years at rural campuses were more likely to take up rural practice compared to those who spent just one year at a rural campus (OR = 8.4, 95% CI = 2.1-33.5, P = 0.002). Conclusions: Increasing the length of time beyond a year at an Australian RCS campus for undergraduate medical students is associated with current work location, preferred current work location and intended work location in a rural area. Spending three years in a RCS significantly increases the likelihood of rural career intentions of non-rural students. [ABSTRACT FROM AUTHOR]