The effects of physical training without equipment on pain perception and balance in the elderly: a randomized controlled trials

Background: Research supports a link between exercise and falls prevention in the older population. Objectives: Our aims were to evaluate pain perception and balance skills in a group of elderly subjects and to examine the consequences of a standardized equipment-free exercise program intervention on these variables. The study utilized a randomized controlled trial method. Methods: 92 subjects were recruited from a rural Sicilian village (Resuttano, Sicily, Italy). Subjects were randomly split into two groups, an experimental group (EG; n= 49) and a control group (CG; n =43). Qualified fitness instructors delivered the standardized physical exercise program for the EG whilst the CG did not receive this exercise intervention. The Berg Balance Scale and the Oswestry Disability Index were administered in both groups before (T0) and after the intervention (T1). Results: At T1, the EG group significantly improvement in balance (p<0.0001) and pain perception (p<0.0001). No significant differences were found within the CG both in BBS and ODI, respectively. Conclusions: Our findings suggest that a 13-weeks standardized exercise equipment-free program is effective in improving balance and perception of pain in the elderly. This type of intervention can consequently provide a low cost strategy to counteract the rate of disability in elderly

Sex difference and intra-operative tidal volume: Insights from the LAS VEGAS study

BACKGROUND: One key element of lung-protective ventilation is the use of a low tidal volume (VT). A sex difference in use of low tidal volume ventilation (LTVV) has been described in critically ill ICU patients.OBJECTIVES: The aim of this study was to determine whether a sex difference in use of LTVV also exists in operating room patients, and if present what factors drive this difference.DESIGN, PATIENTS AND SETTING: This is a posthoc analysis of LAS VEGAS, a 1-week worldwide observational study in adults requiring intra-operative ventilation during general anaesthesia for surgery in 146 hospitals in 29 countries.MAIN OUTCOME MEASURES: Women and men were compared with respect to use of LTVV, defined as VT of 8 ml kg-1 or less predicted bodyweight (PBW). A VT was deemed 'default' if the set VT was a round number. A mediation analysis assessed which factors may explain the sex difference in use of LTVV during intra-operative ventilation.RESULTS: This analysis includes 9864 patients, of whom 5425 (55%) were women. A default VT was often set, both in women and men; mode VT was 500 ml. Median [IQR] VT was higher in women than in men (8.6 [7.7 to 9.6] vs. 7.6 [6.8 to 8.4] ml kg-1 PBW, P &lt; 0.001). Compared with men, women were twice as likely not to receive LTVV [68.8 vs. 36.0%; relative risk ratio 2.1 (95% CI 1.9 to 2.1), P &lt; 0.001]. In the mediation analysis, patients' height and actual body weight (ABW) explained 81 and 18% of the sex difference in use of LTVV, respectively; it was not explained by the use of a default VT.CONCLUSION: In this worldwide cohort of patients receiving intra-operative ventilation during general anaesthesia for surgery, women received a higher VT than men during intra-operative ventilation. The risk for a female not to receive LTVV during surgery was double that of males. Height and ABW were the two mediators of the sex difference in use of LTVV.TRIAL REGISTRATION: The study was registered at Clinicaltrials.gov, NCT01601223

Connection between IL-6, IL-8 and Tumor Necrosis Factor in amniotic fluid and the occurrence of spontaneous abortion, preterm birth and preeclampsia

GesamtdissertationZiel dieser prospektiven Studie war IL-6, IL-8 und TNF-α im Fruchtwasser in der 15.-20. SSW zu bestimmen und mit dem Outcome der Schwangerschaften zu vergleichen. Dabei wurde besonderes Augenmerk auf Aborte, Frühgeburten sowie Präeklampsien bzw. die Eklampsie gelegt. Insgesamt wurden 4 Aborte, 17 Frühgeburten und 3 Präeklampsien bzw. 1 Eklampsie aus dem Gesamtkollektiv gezählt. Die Messergebnisse der Zytokine ergaben keine signifikanten Unterschiede der drei Fallgruppen verglichen mit einer für jede Fallgruppe erstellten Kontrollgruppe.Aim of thisprospective study was to measure IL-6, IL-8 and TNF-alpha inmidtrimester amniotic fluid and compare the results with the outcome ofthe pregnancies. Special attention was put on spontaneousabortion, preterm birth as well as preeclampsia. Altogether, 4 abortion,17 premature births and 3 preeclamptic pregnancies and 1 eclampsia werefound in the complete collective. The concentrations of the cytokinesdid not show any significant difference of the three case groupscompared with a matched control group

Calcium sensing receptor signalling in physiology and cancer

The calcium sensing receptor (CaSR) is a class C G-protein-coupled receptor that is crucial for the feedback regulation of extracellular free ionised calcium homeostasis. While extracellular calcium (Ca2 +o) is considered the primary physiological ligand, the CaSR is activated physiologically by a plethora of molecules including polyamines and l-amino acids. Activation of the CaSR by different ligands has the ability to stabilise unique conformations of the receptor, which may lead to preferential coupling of different G proteins; a phenomenon termed ‘ligand-biased signalling’. While mutations of the CaSR are currently not linked with any malignancies, altered CaSR expression and function are associated with cancer progression. Interestingly, the CaSR appears to act both as a tumour suppressor and an oncogene, depending on the pathophysiology involved. Reduced expression of the CaSR occurs in both parathyroid and colon cancers, leading to loss of the growth suppressing effect of high Ca2 +o. On the other hand, activation of the CaSR might facilitate metastasis to bone in breast and prostate cancer. A deeper understanding of the mechanisms driving CaSR signalling in different tissues, aided by a systems biology approach, will be instrumental in developing novel drugs that target the CaSR or its ligands in cancer

Predicting hospital admissions from individual patient data (IPD): an applied example to explore key elements driving external validity

Meid AD, Gonzalez-Gonzalez AI, Dinh TS, et al. Predicting hospital admissions from individual patient data (IPD): an applied example to explore key elements driving external validity. BMJ OPEN. 2021;11(8): e045572.Objective To explore factors that potentially impact external validation performance while developing and validating a prognostic model for hospital admissions (HAs) in complex older general practice patients. Study design and setting Using individual participant data from four cluster-randomised trials conducted in the Netherlands and Germany, we used logistic regression to develop a prognostic model to predict all-cause HAs within a 6-month follow-up period. A stratified intercept was used to account for heterogeneity in baseline risk between the studies. The model was validated both internally and by using internal-external cross-validation (IECV). Results Prior HAs, physical components of the health-related quality of life comorbidity index, and medication-related variables were used in the final model. While achieving moderate discriminatory performance, internal bootstrap validation revealed a pronounced risk of overfitting. The results of the IECV, in which calibration was highly variable even after accounting for between-study heterogeneity, agreed with this finding. Heterogeneity was equally reflected in differing baseline risk, predictor effects and absolute risk predictions. Conclusions Predictor effect heterogeneity and differing baseline risk can explain the limited external performance of HA prediction models. With such drivers known, model adjustments in external validation settings (eg, intercept recalibration, complete updating) can be applied more purposefully

A prognostic model predicted deterioration in health-related quality of life in older patients with multimorbidity and polypharmacy

Gonzalez-Gonzalez AI, Meid AD, Dinh TS, et al. A prognostic model predicted deterioration in health-related quality of life in older patients with multimorbidity and polypharmacy. Journal of Clinical Epidemiology. 2021;130:1-12.Objectives: To develop and validate a prognostic model to predict deterioration in health-related quality of life (dHRQoL) in older general practice patients with at least one chronic condition and one chronic prescription. Study Design and Setting: We used individual participant data from five cluster-randomized trials conducted in the Netherlands and Germany to predict dHRQoL, defined as a decrease in EQ-5D-3 L index score of > 5% after 6-month follow-up in logistic regression models with stratified intercepts to account for between-study heterogeneity. The model was validated internally and by using internal -external cross-validation (IECV). Results: In 3,582 patients with complete data, of whom 1,046 (29.2%) showed deterioration in HRQoL, and 12/87 variables were selected that were related to single (chronic) conditions, inappropriate medication, medication underuse, functional status, well-being, and HRQoL. Bootstrap internal validation showed a C-statistic of 0.71 (0.69 to 0.72) and a calibration slope of 0.88 (0.78 to 0.98). In the IECV loop, the model provided a pooled C-statistic of 0.68 (0.65 to 0.70) and calibration-in-the-large of 0 (-0.13 to 0.13). HRQoL/functionality had the strongest prognostic value. Conclusion: The model performed well in terms of discrimination, calibration, and generalizability and might help clinicians identify older patients at high risk of dHRQoL. Registration: PROSPERO ID: CRD42018088129. (c) 2020 Elsevier Inc. All rights reserved

Predicting negative health outcomes in older general practice patients with chronic illness: Rationale and development of the PROPERmed harmonized individual participant data database

Gonzalez-Gonzalez AI, Dinh TS, Meid AD, et al. Predicting negative health outcomes in older general practice patients with chronic illness: Rationale and development of the PROPERmed harmonized individual participant data database. Mechanisms of Ageing and Development. 2021;194: 111436.The prevalence of multimorbidity and polypharmacy increases significantly with age and are associated with negative health consequences. However, most current interventions to optimize medication have failed to show significant effects on patient-relevant outcomes. This may be due to ineffectiveness of interventions themselves but may also reflect other factors: insufficient sample sizes, heterogeneity of population. To address this issue, the international PROPERmed collaboration was set up to obtain/synthesize individual participant data (IPD) from five cluster-randomized trials. The trials took place in Germany and The Netherlands and aimed to optimize medication in older general practice patients with chronic illness. PROPERmed is the first database of IPD to be drawn from multiple trials in this patient population and setting. It offers the opportunity to derive prognostic models with increased statistical power for prediction of patient-relevant outcomes resulting from the interplay of multimorbidity and polypharmacy. This may help patients from this heterogeneous group to be stratified according to risk and enable clinicians to identify patients that are likely to benefit most from resource/timeintensive interventions. The aim of this manuscript is to describe the rationale behind PROPERmed collaboration, characteristics of the included studies/participants, development of the harmonized IPD database and challenges faced during this process

A prognostic model predicted deterioration in health-related quality of life in older patients with multimorbidity and polypharmacy

Objectives: To develop and validate a prognostic model to predict deterioration in health-related quality of life (dHRQoL) in older general practice patients with at least one chronic condition and one chronic prescription. Study Design and Setting: We used individual participant data from five cluster-randomized trials conducted in the Netherlands and Germany to predict dHRQoL, defined as a decrease in EQ-5D-3 L index score of ≥5% after 6-month follow-up in logistic regression models with stratified intercepts to account for between-study heterogeneity. The model was validated internally and by using internal–external cross-validation (IECV). Results: In 3,582 patients with complete data, of whom 1,046 (29.2%) showed deterioration in HRQoL, and 12/87 variables were selected that were related to single (chronic) conditions, inappropriate medication, medication underuse, functional status, well-being, and HRQoL. Bootstrap internal validation showed a C-statistic of 0.71 (0.69 to 0.72) and a calibration slope of 0.88 (0.78 to 0.98). In the IECV loop, the model provided a pooled C-statistic of 0.68 (0.65 to 0.70) and calibration-in-the-large of 0 (−0.13 to 0.13). HRQoL/functionality had the strongest prognostic value. Conclusion: The model performed well in terms of discrimination, calibration, and generalizability and might help clinicians identify older patients at high risk of dHRQoL. Registration: PROSPERO ID: CRD42018088129

Systematic outperformance of 112 dermatologists in multiclass skin cancer image classification by convolutional neural networks

Background: Recently, convolutional neural networks (CNNs) systematically outperformed dermatologists in distinguishing dermoscopic melanoma and nevi images. However, such a binary classification does not reflect the clinical reality of skin cancer screenings in which multiple diagnoses need to be taken into account. Methods: Using 11,444 dermoscopic images, which covered dermatologic diagnoses comprising the majority of commonly pigmented skin lesions commonly faced in skin cancer screenings, a CNN was trained through novel deep learning techniques. A test set of 300 biopsy-verified images was used to compare the classifier's performance with that of 112 dermatologists from 13 German university hospitals. The primary end-point was the correct classification of the different lesions into benign and malignant. The secondary end-point was the correct classification of the images into one of the five diagnostic categories. Findings: Sensitivity and specificity of dermatologists for the primary end-point were 74.4% (95% confidence interval [CI]: 67.0-81.8%) and 59.8% (95% CI: 49.8-69.8%), respectively. At equal sensitivity, the algorithm achieved a specificity of 91.3% (95% CI: 85.5-97.1%). For the secondary end-point, the mean sensitivity and specificity of the dermatologists were at 56.5% (95% CI: 42.8-70.2%) and 89.2% (95% CI: 85.0-93.3%), respectively. At equal sensitivity, the algorithm achieved a specificity of 98.8%. Two-sided McNemar tests revealed significance for the primary end-point (p < 0.001). For the secondary end-point, outperformance (p < 0.001) was achieved except for basal cell carcinoma (on-par performance). Interpretation: Our findings show that automated classification of dermoscopic melanoma and nevi images is extendable to a multiclass classification problem, thus better reflecting clinical differential diagnoses, while still outperforming dermatologists at a significant level (p < 0.001). (C) 2019 The Author(s). Published by Elsevier Ltd