Purpose: The early detection of Mild Cognitive Impairment (MCI) is essential in aging societies where dementia is becoming a common manifestation among the elderly. Thus our aim is to develop a decision tree to discriminate individuals at risk of MCI among non-institutionalized elderly users of community pharmacy. A more clinically and patient-oriented role of the community pharmacist in primary care makes the dispensation of medication an adequate situation for an effective, rapid, easy, and reproducible screening of MCI.Methods: A cross-sectional study was conducted with 728 non-institutionalized participants older than 65. A total of 167 variables were collected such as age, gender, educational attainment, daily sleep duration, reading frequency, subjective memory complaint, and medication. Two screening tests were used to detect possible MCI: Short Portable Mental State Questionnaire (SPMSQ) and the Mini-Mental State Examination (MMSE). Participants classified as positive were referred to clinical diagnosis. A decision tree and predictive models are presented as a result of applying techniques of machine learning for a more efficient enrollment.Results: One hundred and twenty-eight participants (17.4%) scored positive on MCI tests. A recursive partitioning algorithm with the most significant variables determined that the most relevant for the decision tree are: female sex, sleeping more than 9 h daily, age higher than 79 years as risk factors, and reading frequency. Moreover, psychoanaleptics, nootropics, and antidepressants, and anti-inflammatory drugs achieve a high score of importance according to the predictive algorithms. Furthermore, results obtained from these algorithms agree with the current research on MCI.Conclusion: Lifestyle-related factors such as sleep duration and the lack of reading habits are associated with the presence of positive in MCI test. Moreover, we have depicted how machine learning provides a sound methodology to produce tools for early detection of MCI in community pharmacy.Impact of findings on practice: The community of pharmacists provided with adequate tools could develop a crucial task in the early detection of MCI to redirect them immediately to the specialists in neurology or psychiatry. Pharmacists are one of the most accessible and regularly visited health care professionals and they can play a vital role in early detection of MCI.

Community pharmacists and general practitioners have daily contact with patients with Alzheimer’s disease (AD) but the number of positive cases constantly increases every day. Thus, the aim of this research is to describe the level of AD knowledge among community pharmacists and general practitioners in Spain, in order to see where the biggest gaps in the knowledge are. Therefore, a cross-sectional study has been carried out, using the Alzheimer’s disease knowledge survey (ADKS), among members of the Spanish Society of Primary Care Physicians and the Spanish Society of Family and Community Pharmacy to report the differences in AD knowledge in both professional collectives. The ADKS has been responded by 578 community pharmacists and 104 general practitioners and consists of a battery of 30 questions, whose possible answers are true or false. It assesses the AD knowledge in seven areas (impact on the disease, risk factors, course of the disease, diagnosis, care, treatment and symptoms). Results indicate that Spanish pharmacists and general practitioners have a high personal knowledge of AD, nevertheless, it is not associated with greater awareness. Both scored above 80% at the categories: diagnostic, treatment and symptoms. However, lower knowledge level (60% of correct answers) was found in those related to risk factors, such as the ignorance about hypercholesterolemia or hypertension as risk factors for the disease. Community pharmacists are already acting to control cardiovascular risk factors, but a wider knowledge of the relationship of these factors to AD is needed to act against these silent risk factors. Thus, pharmacists may also be involved in the management of AD that includes recognizing early symptoms for early detection of cognitive impairment. Hence, knowledge about risk factors is very important in developing this expanding role.

The standard treatment for advanced ovarian cancer (AOC) is cytoreduction surgery and adjuvant chemotherapy. Tumor volume after surgery is a major prognostic factor for these patients. The ability to perform complete cytoreduction depends on the extent of disease and the skills of the surgical team. Several predictive models have been proposed to evaluate the possibility of performing complete cytoreductive surgery (CCS). External validation of the prognostic value of three predictive models (Fagotti index and the R3 and R4 models) for predicting suboptimal cytoreductive surgery (SCS) in AOC was performed in this study. The scores of the 3 models were evaluated in one hundred and three consecutive patients diagnosed with AOC treated in a tertiary hospital were evaluated. Clinicopathological features were collected prospectively and analyzed retrospectively. The performance of the three models was evaluated, and calibration and discrimination were analyzed. The calibration of the Fagotti, R3 and R4 models showed odds ratios of obtaining SCSs of 1.5, 2.4 and 2.4, respectively, indicating good calibration. The discrimination of the Fagotti, R3 and R4 models showed an area under the ROC curve of 83%, 70% and 81%, respectively. The negative predictive values of the three models were higher than the positive predictive values for SCS. The three models were able to predict suboptimal cytoreductive surgery for advanced ovarian cancer, but they were more reliable for predicting CCS. The R4 model discriminated better because it includes the laparotomic evaluation of the peritoneal carcinomatosis index.

HDlive (high-definition live or real-time US), a new ultrasound software, combines a movable virtual adjustable light source in a software that calculates the proportion of light reflecting through surface structures, depending on light direction. The light source can be manually positioned to illuminate the desired area of interest. The ultrasound technician can control light intensity to create shadows that enhance image quality. HDlive is an innovation that will render even more realistic images of fetal anatomy and of gynecologic lesions. The full potential of this new technology is yet to be determined and deserves scientific evaluation.

Introduction Advanced ovarian cancer surgery (AOCS) frequently results in serious postoperative complications. Because managing AOCS is difficult, some standards need to be established that allow surgeons to assess the quality of treatment provided and consider what aspects should improve. This study aimed to identify quality indicators (QIs) of clinical relevance and to establish their acceptable quality limits (i.e., standard) in AOCS. Materials and methods We performed a systematic search on clinical practice guidelines, consensus conferences, and reviews on the outcome and quality of AOCS to identify which QIs have clinical relevance in AOCS. We then searched the literature (from January 2006 to December 2018) for each QI in combination with the keywords of advanced ovarian cancer, surgery, outcome, and oncology. Standards for each QI were determined by statistical process control techniques. The acceptable quality limits for each QI were defined as being within the limits of the 99.8% interval, which indicated a favorable outcome. Results A total of 38 studies were included. The QIs selected for AOCS were complete removal of the tumor upon visual inspection (complete cytoreductive surgery), a residual tumor of < 1 cm (optimal cytoreductive surgery), a residual tumor of > 1 cm (suboptimal cytoreductive surgery), major morbidity, and 5-year survival. The rates of complete cytoreductive surgery, optimal cytoreductive surgery, suboptimal cytoreductive surgery, morbidity, and 5-year survival had quality limits of < 27%, < 23%, > 39%, > 33%, and < 27%, respectively. Conclusion Our results provide a general view of clinical indicators for AOCS. Acceptable quality limits that can be considered as standards were established.