Objective: When performing endovascular interventions for supra-aortic vessels, the tortuous vascular anatomy observed in patients with atherosclerotic lesions often limits the ability to maintain a stable guiding catheter position. Here, we report a case of right vertebral artery (VA) stenosis treated with transfemoral stenting and discuss the utility of balloon-hooking technique using partially inflated balloon for stabilizing the balloon guiding catheter (BCG) in the supra-aortic vessel. Case presentation: A 74-year-old man who underwent right carotid artery stenting, coronary artery bypass grafting, and bilateral iliac artery stenting was admitted to our emergency department because of dizziness related to head movement. Computed tomography angiography revealed right VA origin stenosis and left subclavian artery (SA) occlusion. The patient underwent stenting of the right VA. After several unsuccessful cannulation attempts into the right VA through transradial access, transfemoral access was obtained through the left iliac stent. A 9-Fr BGC was navigated into the right SA. The balloon was partially inflated just distal to the first curve of the right SA and used as hook by pulling back until the proximal edge of the balloon was pushed distally by the lesser curvature of the SA, resulting in stabilization of the BGC and successful angioplasty and stent deployment at the VA stenosis. The patient's symptoms resolved completely, without any neurological deficits. Conclusions: Balloon-hooking technique using a partially inflated BGC is feasible for stabilizing the guiding catheter in tortuous supra-aortic vessel. Steno-occlusive disease of subclavian artery (SA) or vertebral artery (VA) origin can be treated with either extra-thoracic bypass surgery or endovascular treatment, although endovascular therapy is performed more frequently Here, we report a case treated with transfemoral stenting of right VA origin stenosis and discuss the utility of balloon-hooking technique using partially inflated balloon for stabilizing the BCG in the supra-aortic vessel.A 74-year-old man with a history of right carotid artery stenting, coronary artery bypass grafting, and bilateral iliac artery stenting presented to our hospital with a 3-month history of feeling faint when tilting his head to the right side. Neurological examination was normal except for feeling faint when he tilted his head to the right; the symptoms resolved soon after his head returned to a neutral position. Because the symptoms occurred when sleeping in the right lateral position, the patient developed excessive fear, and he became sleep-deprived. CTA revealed bilateral iliac artery stents A and B aBased on the clinical and radiological findings, we concluded that the stenotic right VA, which was the dominant artery of the posterior circulation, was more constricted by head tilting. Thus, reduced blood flow to the posterior circulation caused the light-headed feeling in this patient. The decision was made to recanalize the right VA.He was preloaded with aspirin (100 mg/daily) and clopidogrel (75 mg/daily) for seven days prior to the procedure. Our first attempt to navigate the guidewire into the right VA through transradial access failed because of acute angulation of the VA origin stenosis from the SA. Therefore, a 9-Fr sheath was placed in the left femoral artery and navigated through the left iliac stent, and a 9-Fr BGC  was inserted into the right SA over a wire and a 6-Fr intermediate catheter. Because the short landing area of the guiding catheter in the SA restricted stabilization of the guiding catheter in the proper position, the balloon was partially inflated just distal to the first curve of the right SA and pulled back until the proximal edge of the balloon was pushed distally by the lesser curvature of the SA, resulting in stable positioning of the BGC A. DuringWe demonstrated the utility of balloon-hooking technique using the partially inflated BGC for stabilizing the BGC at the proximal SA in a case of transfemoral stenting of right VA origin stenosis.Although stable placement of guiding catheters is mandatory for endovascular treatment, the short and tortuous landing area of the guiding catheter can lead to unstable positioning of the guiding catheter when performing transfemoral endovascular treatment for the proximal VA, subclavian artery, or brachiocephalic artery. In this setting, the so-called pull-through technique has been used for stabilizing a guiding catheter When performing balloon-hooking technique, the BGC has to be navigated beyond the lesser curvature of the target vessel. The partially inflated BGC was then pulled back until the proximal edge of the balloon was pushed distally by the lesser curvature of the target vessel. The present technique is different from balloon-inflation anchoring technique enabling stabilization of the BGC in the target vessel with fully inflated balloon We successfully treated a stenotic lesion of right VA origin through a partially inflated BGC. The use of a partially inflated BGC like a hook was useful for stabilizing the guiding catheter at the proximal SA in patients with VA origin stenosis. Further cases are needed to establish its utility.All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation  and with the Helsinki Declaration of 1964 and later versions. Informed consent was obtained from the patient for publication of this case report and any accompanying images.Informed consent was obtained from the patient for the publication of this case report.


The 2021 American College of Rheumatology (ACR) rheumatoid arthritis (RA) guideline considers the specific context of the United States which differs from that of Saudi Arabia in many aspects that may impact recommendations. The objective of this project was to adapt a set of prioritized recommendations from the 2021 ACR guideline for the treatment of rheumatoid arthritis RA for the context of Saudi Arabia, by the Saudi Society for Rheumatology (SSR).The process followed the GRADE-ADOLOPMENT methodology, and the reporting adhered to the RIGHT-Ad@pt checklist. Working groups included a coordination group and a 19-member panel representing different stakeholder groups. The Evidence to Decision (EtD) tables included evidence on health effects from the source guideline and contextual information from the Saudi setting.The panel prioritized and adapted five recommendations from the source guideline. The process led to modifying two out of the five prioritized recommendations, all listed here. In naive patients with low disease activity, methotrexate (MTX) is conditionally recommended over sulfasalazine (SSZ) (modified direction); hydroxychloroquine (HCQ) is conditionally recommended over SSZ (unmodified). Initiation of csDMARDs with short-term glucocorticoids is conditionally recommended over csDMARDs alone in naive patients with moderate to high disease activity (modified direction). Switch to subcutaneous MTX is conditionally recommended over addition/switch to alternative DMARD(s) in patients taking oral MTX who are not at target (unmodified). Discontinuation of MTX is conditionally recommended over gradual discontinuation of the bDMARD or tsDMARD for patients taking MTX plus a bDMARD or tsDMARD who wish to discontinue a DMARD (unmodified).Rheumatologists practicing in Saudi Arabia can use the adoloped recommendations generated by this project while adopting the rest of the recommendations from the 2021 ACR guidelines.The online version contains supplementary material available at 10.1186/s41927-022-00301-y. The global prevalence of rheumatoid arthritis (RA) is estimated to be 460 per 100,000 persons  and globTreatment guidelines for RA have been developed by well-established societies such as the American College of Rheumatology (ACR) and the European League Against Rheumatism (EULAR) , 6. ReceThe 2021 ACR RA recommendations consider the specific context of the United States (U.S.) which differs from that of Saudi Arabia in many aspects that may impact recommendations, e.g., costs, values, and preferences. Notably, the Saudi health system currently consists of several sectors that report to different authorities and have individualized health resources such as infusion units and medication formulary. Healthcare is free for National civilians through the Ministry of Health (MoH) and university institutions. In contrast, the military staff is followed in their respective military hospitals. On the other hand, expatriates can only access hospitals of the private sector and business centers of governmental institutions through their insurance plans.Developing new guidelines is very time- and resource-intensive. A more reasonable alternative is to adapt the recommendations for the local context to ensure applicability and subsequent uptake . The objSimilar to the source guidelines, the target population are RA patients fulfilling the 2010 ACR/EULAR classification criteria . In SaudThis project was a collaborative effort between the Saudi Society for Rheumatology (SSR), ACR, and the American University of Beirut (AUB) GRADE Center. The SSR was established in 2010 to improve education, research, and patient care in rheumatology. Working groups included a coordination group and a guideline panel.The coordination group included two content experts  and three methodologists . Its primary responsibilities included oversight of the work, designing the process and methodology, coordinating the prioritization of questions, summarizing findings for the panel through the preparation of Evidence to Decision (EtD) tables, and coordinating panel meetings.The panel comprised 19 members representing the following stakeholder groups: 15 rheumatologists, one pharmacist, one patient representative, and two policymakers. The panel was balanced in gender, Saudi regions, and type of practice . It also included two international experts: the chair of the source guidelines (LF) and the chair of the 2015 ACR guideline for treating rheumatoid arthritis (JAS). Additional file The panel was involved in prioritizing questions and outcomes, providing relevant newly published studies, providing relevant contextual information, and participating in the final meeting in formulating the final recommendations.For developing these guidelines, we followed the Grading of Recommendations Assessment, Development and Evaluation (GRADE)-ADOLOPMENT methodology  and the The SSR selected the 2021 ACR guideline for the treatment of RA as the source guideline. The ACR guidelines follow rigorous methods for guideline development, including generation of population, intervention, comparator, and outcomes (PICO) questions, selection of critical outcomes, systematic literature review, and conflict of interest (COI) management. Although the ACR guidelines did not address all contextual factors included in GRADE EtD tables, they addressed patient values and preferences, cost-effectiveness studies, and cost information .We used a structured and standardized process to prioritize five recommendations from the ACR guidelines. First, the coordination group asked the panelists to rate the priority of each of the original ACR recommendations based on whether the recommended intervention differed in the Saudi Arabian context (from the U.S. context) in terms of feasibility, acceptability, resource use, impact on equity and current practice. Additional file For the evidence on health effects, and as the adaptation project was launched shortly following the publication of the source guideline, we opted to use the evidence reports as published by the source guideline  and did not formally update the systematic literature review for the adaptation process. However, we solicited panelists to share any newly published relevant studies. Two methodologist members of the coordination group (SY and JK) assessed the eligibility of these studies in duplicate and independently. For eligible studies, the two methodologists abstracted data and assessed the risk of bias in duplicate and independently and updated the evidence reports accordingly.We adopted the source guideline's ratings for the importance of outcomes . For patient values and preferences, we relied on the systematic review on the topic used by the source guidelines  and the The panel relied on cost data collected from the National Unified Procurement Company (NUPCO) for the governmental sector  and the www.gradepro.org) [In the event of no update of the evidence report, we adopted the source guideline's assessments about the certainty of the evidence. For the updated evidence reports, the methodologist members of the coordination group  assessed the certainty of the evidence according to the GRADE methodology and using the GRADEpro GDT software (pro.org) . In addipro.org) , 18.www.gradepro.org), which included both information on health effects from the evidence reports and contextual information. Then, in preparation for the panel meetings, we used the PANELVoice function in GRADEpro GDT to invite panel members to review the EtDs, comment on the included information, and vote on the EtD criteria and recommendations.For each question, the coordination group developed GRADE EtD tables using the GRADEpro GDT software  disclosure of interests form  and sentBelow are the five adapted recommendations, associated remarks, and justifications. Figure The Saudi panel suggests using methotrexate (MTX) over sulfasalazine (SSZ) in DMARD-naive patients with low disease activity .Recommendation modified ; certainty of evidence unmodified.This recommendation applies to patients with low disease activity for which medication treatment is judged to be necessary.The choice should account for the patient's views on the expected benefits and harms of the respective medications.The choice should consider the availability of the medications.It is important to monitor adverse events and adjust medications accordingly.Rationale: The panel favored MTX over SSZ because of the dosing convenience of MTX (once weekly) and its lower cost compared with SSZ.The Saudi panel suggests using hydroxychloroquine (HCQ) over SSZ in DMARD-naive patients with low disease activity .Recommendation unmodified; certainty of evidence unmodified.This recommendation applies to patients with low disease activity for which medication treatment is judged to be necessary.The choice should account for the patient's views on the expected benefits and harms of the respective medications.The choice should consider the availability of the medications.It is important to monitor adverse events and adjust medications accordingly.Rationale: The panel favored HCQ over SSZ because of the dosing convenience (once daily) and the safety profile of HCQ compared to SSZ.The Saudi panel suggests initiating a csDMARD with short-term (< 3 months) glucocorticoids over initiating a csDMARD without short-term glucocorticoids in DMARD-naive patients with moderate-to-high disease activity .Recommendation modified ; certainty of evidence unmodified.The physician should clearly communicate to the patient the potential benefits and harms of glucocorticoids.The choice should account for the patient's views on the expected benefits and harms of glucocorticoids. It should also account for the ability to taper/discontinue glucocorticoids.The glucocorticoid treatment regimen should use the lowest dose for the shortest period possible to reduce harm, with caution in the elderly.It is important to monitor for the adverse events of glucocorticoids.Rationale: The use of short-term (< 3 months) glucocorticoids when initiating a csDMARD was judged by the panel to be the standard of care. The panel judged the benefit of rapid alleviation of symptoms at the time of diagnosis and during flares to outweigh the risks of glucocorticoids. The panel unanimously agreed that the long-term use of glucocorticoids is associated with harm and damage that outweigh benefit. The panel recommended a shared decision making approach in decisions related to initiating and discontinuing glucocorticoids.The Saudi panel suggests a switch to subcutaneous MTX over addition/switch to alternative DMARD(s) in patients taking oral MTX who are not at target .Recommendation unmodified; certainty of evidence unmodified.This recommendation typically applies to patients with moderate or high disease activity but may apply to patients with low disease activity.Target may differ based on initial disease activity status.The choice should consider the availability of the medications.It is important to monitor adverse events and adjust the regimen accordingly.Rationale: The panel favored switching to subcutaneous MTX over addition/switch to alternative DMARD(s) because of the convenience of once-weekly dosing of subcutaneous MTX compared to daily dosing of alternative DMARD(s), and the higher effectiveness of subcutaneous MTX.The Saudi panel suggests gradual discontinuation of MTX over gradual discontinuation of the bDMARD or tsDMARD for patients taking MTX plus a bDMARD or tsDMARD who wish to discontinue a DMARD .Recommendation unmodified; certainty of evidence modified (from very low to moderate).Patient’s health coverage in the private sector should be considered when applying this recommendation.The patient and the physician should closely monitor the progression of symptoms during and following discontinuation.Rationale: The recommendation was based on newly identified evidence suggesting decreased disease worsening with gradual discontinuation of MTX compared to gradual discontinuation of the bDMARD or tsDMARD.We describe the adaptation of five prioritized recommendations from the 2021 ACR guideline for the treatment of RA for the context of Saudi Arabia, following GRADE-ADOLOPMENT methodology. For each recommendation, an EtD table was produced using evidence as published in the source guideline, newly published relevant studies solicited by the panel, and contextual information from Saudi Arabia.Out of five adoloped recommendations, the panel modified two (#1 and #3). For recommendation 1, the panel judged that a generally low rate of adherence to medications in Saudi Arabia favored MTX (administered weekly) over SSZ (administered twice daily). In addition, the cost of a 12-week treatment course of SSZ is substantially higher than that of MTX. Moreover, the KSA panel judged that alcohol-related undesirable effects of MTX were less relevant to the KSA setting given restrictions on alcohol use. These factors tilted the balance of desirable and undesirable effects in favor of MTX.As for recommendation 3, the KSA panel judged that the balance of effects “probably favors” short-term (< 3 months) glucocorticoids when initiating a csDMARD. Although valuation information by the ACR panel for this specific recommendation is not available, the KSA panel highly valued rapid alleviation of symptoms at the time of diagnosis and during flares. Still, the KSA panel acknowledged challenges with glucocorticoid treatment and detailed them in the rationale as a note of caution to users.An additional contextual factor considered by the KSA panel was the increased risk of hemolysis with the use of HCQ in Saudi Arabia due to the relatively high prevalence of glucose-6-phosphatedehydrogenase (G6PD) deficiency in this population . HoweverThe implementation of these five adoloped recommendations should help with standardizing the care of patients with RA in Saudi Arabia. In fact, these recommendations were based not only on a synthesis of the best available evidence, but also on a careful consideration of the Saudi Arabian context. The SSR will lead efforts to disseminate these five adoloped recommendations, as well as the remaining recommendations adopted from the 2021 ACR guidelines.There are two important factors that would enhance the implementation of the recommendations. First, the specific management choice should account for the patient's views on the expected benefits and harms of the respective alternatives. Second, the choice should consider the availability of the medications, given the variability across regions and health systems. Indeed, in a national survey of Saudi rheumatologists conducted in 2014, 24% of respondents reported not using parenteral MTX because of unavailability in the hospital formulary . It is bFor recommendation 5, the decision of tapering csDMARDs is associated with an increased cost in the short term compared to tapering bDMARDs or tsDMARDs. In patients who pay from pocket or have a co-pay insurance system, tapering bDMARDs/tsDMARDs would be a reasonable option. Of note, non-profit organizations such as the Charitable Association for Rheumatic Diseases provide free-of-charge biologics that can help clinicians provide the best standard of care regarding drug tapering.Previously, members of this group contributed to an adolopment exercise of the 2015 ACR RA guidelines for the Middle East and North Africa region , 23. UnlThere is a need for research on how the target population values the outcomes of interest. It is also important to define 'decision thresholds' for judging the extent of benefits and harms  based on the reported effect measures . The SSR will aim to update these recommendations in parallel to future updates by the ACR. Future efforts should address questions related to the use of biosimilar , 25, an Rheumatologists practicing in Saudi Arabia can use the five adoloped recommendations generated by this project while adopting the rest of the recommendations from the 2021 ACR guidelines.Additional file 1. List of KSA panel members.Additional file 2. Prioritized questions for the adolopment of the 2021 American College of Rheumatology (ACR) Guideline for the Treatment of Rheumatoid Arthritis (RA) in Saudi Arabia.Additional file 3. Evidence to Decision tables.


Climacteric syndrome is a set of signs and symptoms resulting from the interaction between sociocultural, psychological and endocrine factors occurring in aging women. Its diagnosis is clinical in women with the expected age group for ovarian hypofunction. The term “menopause” refers to the date of the woman's last menstrual bleeding episode and is defined retrospectively.Women in menopausal transition have disease prevention and health promotion needs. Hypoestrogenism associated with aging and metabolic syndrome can lead to lower quality of life and increased occurrence of cardiovascular disease.The medical consultation of climacteric women is an excellent opportunity of screening for chronic diseases and neoplasms.Complementary evaluation for climacteric women must be carried out judiciously. The benefit and risk of each test must be considered. Complementary tests without a specific definition of a diagnostic and therapeutic plan should be avoided.Menopausal hormone therapy (HT) may be indicated to treat climacteric symptoms. Detailed clinical history and rational complementary exams are essential to define the therapeutic plan and follow-up of women undergoing HT.For women over 45 years of age with symptoms suggestive of hypoestrogenism such as typical hot flashes, the diagnosis of climacteric syndrome is clinical. The date of menopause is defined retrospectively, after 12 months of amenorrhea in a woman over 45 years of age. When in doubt, two doses of follicle-stimulating hormone (FSH) 4-6 weeks apart are recommended. Values above 25 mIU/mL indicate the beginning of menopausal transition. For women under 45 years of age who complain of irregular uterine bleeding pattern and infrequent menstrual cycles, the performance of complementary tests for investigation is recommended.The diagnosis of genitourinary syndrome of menopause should be made proactively, with questioning directed to urogenital symptoms associated with a thorough gynecological examination.In women at usual risk, breast cancer screening should begin at age 40. Mammography is the recommended exam yearly . In women with dense breasts, ultrasound should be considered as a supplement to mammography. Breast cancer screening can be interrupted when life expectancy is less than seven years or if there are no clinical conditions for diagnosis or treatment.Cervical cancer screening is performed using periodic oncotic cytology. After performing two consecutive negative cytology tests at annual intervals, it is recommended to do the exam every three years. Screening can be stopped after age 64 if the patient has two consecutive negative tests in the previous five years. The gynecological physical examination should be part of the periodic gynecological evaluation regardless of the woman no longer having an indication for cervical cancer screening.Requesting pelvic and transvaginal ultrasound for women  without signs or symptoms suggestive of disease is not cost-effective. For symptomatic women, such as those with abnormal uterine bleeding, postmenopausal vaginal bleeding, or abdominal discomfort, transvaginal pelvic ultrasound is cost-effective and is the initial complementary test of choice to evaluate uterine and ovarian diseases.For women at normal risk and over 50 years of age, colorectal cancer screening is recommended.Screening for risk factors for cardiovascular disease, including hypertension, diabetes mellitus, obesity, smoking and dyslipidemia, should always be considered in the consultation of climacteric women. Researching all criteria for the definition of metabolic syndrome is essential. Menopausal transition is a window of vulnerability for the development of mood swings, such as depressive disorder. Identifying women with depressive symptoms is important to institute the appropriate therapy.Bone densitometry is recommended for screening for osteoporosis in all women aged 65 years and older. Densitometry is also indicated for climacteric women under 65 years of age who have at least one risk factor for osteoporosis. The FRAX-Brasil, analyzed according to the recommendation of the National Osteoporosis Guideline Group (NOGG), can also be used to assess the need for densitometry.Sexually transmitted infections (STIs) during menopause and postmenopause cannot be underestimated. Behavioral counseling and treatment of genitourinary syndrome of menopause are important tools to decrease the risk of STIs. Screening for STIs should be performed based on each patient's clinical history data.Thyroid diseases are prevalent in aging women and can increase morbidity and mortality. Clinical evaluation of the thyroid should be performed routinely during physical examination of climacteric women. Women over 60 years of age or with symptoms of thyroid dysfunction should be evaluated initially with a thyroid-stimulating hormone (TSH) measurement.The transition between reproductive and non-reproductive stages in a woman's life is called climacteric.Aging leads to progressive ovarian failure, determining the interruption of ovulatory cycles and cessation of menstrual bleeding. With the objective of standardizing the definition of the different stages of reproductive aging, the STRAW system, Stages of Reproductive Aging Workshop, was developed.The date of the woman's last menstrual bleeding episode is defined as menopause.The term “climacteric syndrome” refers to the set of symptoms and signs resulting from the interaction between sociocultural, psychological and endocrine factors that occur in aging women.Vasomotor symptoms, also known as hot flashes, are the most frequently associated with menopausal transition. They consist of sudden sensations of heat in the central region of the body, most notably in the region of the face, chest and neck, and last an average of three to four minutes.Women often seek care due to changes in the menstrual cycle in the menopausal transition. As a consequence of decreased ovarian production of inhibin B at the end of the fourth decade of life, an increase in serum concentrations of FSH and estradiol may occur at the beginning of the cycle, causing a shortening of the follicular phase. Progesterone level in the luteal phase also decreases given the deterioration of the quality of the corpus luteum. One of the first signs of reduced ovarian reserve is the shortening of the interval between menstruations.Over the years, the process of follicular depletion continues and anovulation becomes more and more frequent. Due to the lack of progestational opposition, the interval between menstrual cycles is longer, passing to 40 to 50 days. The increase in the interval between menstrual cycles occurs at 47 years of age, on average.For women over 45 years of age with complaints suggestive of hypoestrogenism, such as vasomotor symptoms and typical changes in menstrual pattern (infrequent uterine bleeding), the diagnosis of climacteric syndrome is clinical and does not require confirmation by other complementary tests.During the climacteric, an individual evaluation of each woman is essential to meet her needs for disease prevention and health promotion.Breast cancer is the second most frequent neoplasm among Brazilian women, with an estimated incidence of 66,280 new cases for each year of the 2020-2022 triennium, corresponding to an estimated risk of 61.61 new cases per 100,000 women.Breast cancer screening can present risks such as overdiagnosis, overtreatment, and false-positive results.Cervical cancer is the fourth most frequent neoplasm among Brazilian women, with an estimated 16,590 new cases for each year of the 2020-2022 triennium and an estimated risk of 15.43 cases per 100,000 women.To date, there is no scientific evidence to justify screening for ovarian cancer and endometrial cancer in women at normal risk for these neoplasms.Colorectal cancer is the third most frequent neoplasm among Brazilian women, with an estimated 20,470 new cases in the 2020-2022 period and an estimated risk of 19.03 cases per 100,000 women.After menopause, the beneficial effect of endogenous estrogen on the cardiovascular system is mitigated, and the number of cardiovascular events increases.The joint consideration of the therapeutic history and the different individual risk factors is useful for a better determination of the cardiovascular prognosis. Risk calculation tools for the occurrence of events such as myocardial infarction and stroke are available for use. The Brazilian Guideline for Cardiovascular Prevention of the Brazilian Society of Cardiology recommends using the Framingham Global Risk Score as an assessment tool.Osteoporosis is often asymptomatic. Its diagnosis through bone densitometry or the documentation of an asymptomatic bone fragility fracture is essential to adopt the appropriate treatment.https://abrasso.org.br/calculadora/calculadora/.In cases of doubt regarding the indication of bone densitometry, we recommend the analysis of the FRAX-Brasil using recommendations of the National Osteoporosis Guideline Group (NOGG).Bone fragility fractures occur in the absence of trauma or in the presence of “minor” trauma, often in the thoracolumbar spine, wrist, and hip.The menopausal transition is a window of vulnerability for the development of mood changes and depressive disorders. The risk of presenting symptoms is high during perimenopause, even in women without a personal history of depressive disorder.In recent years, there has been an increase in the occurrence of STIs in climacteric and postmenopausal women. The high prevalence of menopausal genitourinary syndrome, which predisposes to bleeding during sexual intercourse, associated with greater accessibility to treatment for male erectile dysfunction, contributes to a greater chance of infection.Thyroid diseases are prevalent in aging women and can increase morbidity and mortality. The Latin American Thyroid Society (LATS) recommends the initial screening of women over 60 years of age for thyroid disease with a thyroid-stimulating hormone (TSH) measurement.Menopausal hormone therapy may be indicated to treat vasomotor symptoms associated with hypoestrogenism and the genitourinary syndrome of menopause, in addition to preventing bone loss and reducing the risk of bone fragility fractures.Some complementary exams help in choosing the best route of hormonal administration. Observational studies have shown that transdermal estrogen offers a lower risk of thromboembolic events.Ensuring patient safety during medication use is critical, therefore, routine clinical evaluation should be maintained. Cardiovascular risk factors should be reassessed periodically.Women using systemic continuous combined HT with estrogen and progestagen are expected to have amenorrhea, even though irregular bleeding episodes may occur in the first few months of use.Population aging is an established phenomenon in several countries. Aspects related to senility should be a constant reason for consultation with health professionals. The climacteric deserves to be highlighted, as it is accompanied by a series of physiological changes that imply disease prevention and health promotion needs. The medical consultation of climacteric women is an opportunity to screen for chronic diseases and neoplasms. The appropriate workup at this stage of life is one of the initial steps for physicians contributing to the quality of life of their patients.National Specialized Commission on Climateric of the Brazilian Federation of Gynecology and Obstetrics Associations (FEBRASGO)President:Luciano de Melo PompeiVice-President:Lúcia Helena Simões da Costa PaivaSecretary:Elizabeth Jeha NasserMembers:Ana Lúcia Ribeiro ValadaresCélia Regina da SilvaEliana Aguiar Petri NahasJaime Kulak JuniorLuiz Francisco Cintra BaccaroMárcio Alexandre Hipólito RodriguesMarco Aurélio AlbernazMaria Celeste Osório WenderMaria Célia MendesRita de Cassia de Maio DardisRodolfo StrufaldiRogerio Cesar Bocardo


Early detection of Pancreatic Ductal Adenocarcinoma (PDAC) is complicated as PDAC remains asymptomatic until cancer advances to late stages when treatment is mostly ineffective. Stratifying the risk of developing PDAC can improve early detection as subsequent screening of high-risk individuals through specialized surveillance systems reduces the chance of misdiagnosis at the initial stage of cancer. Risk stratification is however challenging as PDAC lacks specific predictive biomarkers. Studies reported that the pancreas undergoes local morphological changes in response to underlying biological evolution associated with PDAC development. Accurate identification of these changes can help stratify the risk of PDAC. In this retrospective study, an extensive radiomic analysis of the precancerous pancreatic subregions was performed using abdominal Computed Tomography (CT) scans. The analysis was performed using 324 pancreatic subregions identified in 108 contrast-enhanced abdominal CT scans with equal proportion from healthy control, pre-diagnostic, and diagnostic groups. In a pairwise feature analysis, several textural features were found potentially predictive of PDAC. A machine learning classifier was then trained to perform risk prediction of PDAC by automatically classifying the CT scans into healthy control (low-risk) and pre-diagnostic (high-risk) classes and specifying the subregion(s) likely to develop a tumor. The proposed model was trained on CT scans from multiple phases. Whereas using 42 CT scans from the venous phase, model validation was performed which resulted in ~89.3% classification accuracy on average, with sensitivity and specificity reaching 86% and 93%, respectively, for predicting the development of PDAC . To our knowledge, this is the first model that unveiled microlevel precancerous changes across pancreatic subregions and quantified the risk of developing PDAC. The model demonstrated improved prediction by 3.3% in comparison to the state-of-the-art method that considers the global (whole pancreas) features for PDAC prediction. At prd deaths , 4, 5, w females , 7. The  females . The PDA females . The neg females , 13, 14.Risk prediction of the PDAC assists in improving the chances of diagnosis at an early stage as follow-up surveillance of high-risk individuals on a regular basis would allow early intervention reducing the chance of missing the initial stages of the disease –17. Howepancreatic subregion and subregion are used interchangeably). For instance, tumor histology differs across pancreatic subregions   and globally , during the development of PDAC , 2. Empind tail) , 25 whicAI is the primary choice to perform image-based extensive analysis of such minute alterations and identify potential risk predictors for disease , 26, 27.In our recently published work , risk prIn this extended study, we thoroughly examined the precursory changes taking place across pancreatic subregions during cancer development and characterized the pancreas that is likely to develop PDAC. A rigorous radiomic analysis of morphological and textural features of three pancreatic subregions  in the pre-diagnostic abdominal CT scans was performed to identify the features potentially predictive of cancer. Subsequently, a machine learning model was developed that performs risk prediction by automatically classifying the abdominal CT scans into the pre-diagnostic (pancreas at high-risk for cancer) and healthy control (pancreas at low risk for cancer) groups and specifying the subregion of the pancreas that is expected to develop most part of the tumor than its neighboring subregions. To our knowledge, it is the first proposed model to perform the prediction of PDAC based on the subregional analysis of the pancreas. The model remained stable throughout the analysis and outperformed our previous model. The results are promising and encouraging and further validation with a much larger dataset is warranted.Of many imaging modalities, CT plays an important role in the screening for early detection of PDAC. During the initial evaluation of subjects with suspected PDAC, the abdominal CT examination is the common choice to seek primary and secondary signs of cancer. Two institutes, the Cedars-Sinai Medical Center (CSMC) and the Kaiser Permanente Southern California (KPSC) in Los Angeles, collaborated in the proposed study and provided eligible CT scans for analysis. All CT scans were anonymized before transferring to the host institute CSMS. No informed consent was required as the study design is retrospective.The data obtained for the study consisted of contrast-enhanced abdominal CT scans from Diagnostic, Pre diagnostic, and Healthy controls groups. The diagnostic scan belongs to the subject with biopsy confirmed PDAC and observable tumor on the CT scan. These patients do not have any history of pancreatic tumor resection. The pre-diagnostic scan was acquired for the same subject, as in the diagnostic class, 6 months to 3 years before their PDAC was diagnosed. No primary or secondary signs of PDAC were present at the time the pre-diagnostic scan was acquired. The healthy control scan was obtained for a different subject having healthy  pancreas with no history of any pancreatic disorders. The gender and age of each subject in the healthy control class and the year their scan was acquired match those of exactly one unique subject in the pre-diagnostic class to reduce instrumental and morphologic differences, respectively. No subject in the healthy control class developed PDAC within the next 36 months of their scan. The data design of the study is shown in The two institutes obtained 108 CT scans from 72 subjects and were divided into Internal and External datasets. The former consists of 66 scans (22 from each of the three groups) and the latter consists of 42 scans (14 from each of the three groups) from 44 and 28 subjects at CSMC and KPSC respectively. Also, 58 scans  in the internal dataset and all 42 scans in the external dataset were venous phase images, whereas the rest of 8 scans in the internal dataset belong to multiple phases such as arterial, venous, and connecting phases. The external dataset was used for external validation of the proposed prediction model. For precise measurements of pancreatic features, accurate delineation of the pancreas and the subregions is a prerequisite. The anatomy of the pancreas is complex and requires considerable attention and skills during outlining the pancreas and its subregions. The general shape of the pancreas resembles a hockey stick (J-shaped) structure. On the axial view of an abdominal CT, the pancreas lies across the posterior abdomen. Anatomical subregions of the pancreas consist of the head, body, and tail that appear in the left-to-right order on the axial view of the CT. The head is the expanded medial part lying at the duodenum curve and is attached to the body subregion that connects to a tapered tail subregion. The anteroposterior diameter and the length of the pancreas usually lie between 1 to 3 and 12 to 15 centimeters  with theTwo experienced radiologists at CSMC manually outlined the boundary of the pancreas and three subregions in all 108 scans using the commercial software ITK-Snap . To avoiIn each diagnostic scan, the readers also specified the subregion that contained the greatest amount of pancreatic tumor. This helped grade the subregions in the corresponding pre-diagnostic scans into high-risk and low-risk classes. For instance, if most parts of the tumor were observed in the ‘head’ subregion of the pancreas in a diagnostic scan, then the ‘head’ subregion in the corresponding pre-diagnostic scan was graded as a high-risk subregion, whereas the rest of the neighboring subregions in the same pre-diagnostic scan were graded as low-risk subregions, as given in Each of the 108 scans has 16-bit depth and a slice resolution of 512 by 512  and variable z-axis. No preprocessing was performed on any of the scans except the signal intensities in each scan were scaled between 0 and 1.Risk prediction modeling was carried out by thoroughly examining the morphology and the texture of the precancerous subregions to seek predictive features, followed by utilizing these features in a machine learning classifier to automatically characterize the pancreas and subregions into high-risk and low-risk classes for PDAC. The methodology is explained below.A large amount of radiomic features were obtained from each of 194 subregions in 66 CT scans  of the internal dataset, i.e., three sets of features – one for each of the three groups, whereas each set consists of three subsets: one for each of three subregions. Each feature in the set expressed a unique quantifiable property of a subregion that provided information about the spatial relationship of neighboring voxels in predefined proximity . To calcKernel size, the Angle, and the Bin size  any different. Each radiomic feature represented one of the major characteristics of a subregion that includes shape, size, texture, and signal intensity using a unique mathematical expression. Common types of radiomic features considered include first-order statistics  and higher-order statistics . With different combinations of three parameters, around 4000 radiomic features from each of 194 subregions were extracted by considering the whole subregion as a single ‘region of interest’.An important aspect of radiomic analysis is to consider the variations in a radiomic feature determined by the three parameters that include the Bin size . DiffereUsing the 132 subregions in 44 CT scans  in the internal dataset, a pairwise feature comparison between the corresponding subregions  was performed to identify the features that were significantly different between high-risk and low-risk subregions. For example, the extracted features from all low-risk head subregions in the internal dataset were compared with the same set of features extracted from all high-risk head subregions in the internal dataset. About 3.5% of the extracted features showed significance  at a p-value of 0.05 in the statistical t-tests—supporting the core hypothesis about the presence of precancerous changes occurring locally within the subregions undergoing tumor development. Note that the only purpose of considering the features extracted from the 66 subregions in 22 CT diagnostic scans in the internal dataset during the analysis was to help sub-selecting the predictive features that are highly stable and do not become insignificant when pre-diagnostic and diagnostic scans are mixed.The significant features (predictors) identified through the subregional analysis were used to perform automated risk prediction of PDAC by classifying the pancreas into either low-risk or high-risk categories. The criteria set to perform binary classification was to mark the pancreas as low-risk if none of its subregions was classified as high-risk, whereas the pancreas was marked as high-risk, if at least one of its subregions was classified as high-risk. A misclassification is counted if a) the classifier marks one or more subregions as high-risk in a healthy control scan, or b) the classifier identifies a high-risk subregion as low-risk in the pre-diagnostic scan or vice versa.The Naïve Bayes (NB) model was trained for binary classification in conjunction with the Recursive Feature Elimination (RFE) , 35 methModel performance was evaluated in terms of classification accuracy, sensitivity, and specificity. The classification accuracy was calculated as the total number of correctly classified scans  to the total number of scans input to the NB classifier. The sensitivity is the true positive rate which refers to the total number of correctly classified pre-diagnostic scans (high-risk pancreas) to the total number of pre-diagnostic scans input to the NB classifier. Whereas the specificity is the true negative rate which refers to the total number of correctly classified healthy control scans (low-risk pancreas) to the total number of healthy control scans input to the NB classifier.The mean classification accuracy achieved on the training data  was 93% (41/44), i.e., the number of correctly classified scans to the total number of scans observed. The external validation of the classifier was performed using the 56 subregions in 28 scans  in the external dataset. The validation achieved the mean classification accuracy of 89.3% (25/28), with the sensitivity and specificity reaching 86% and 93% respectively, as given in the confusion matrix Compared to the performance of our previous prediction system  which prMoreover, the radiomic analysis infers that it is essentially the texture of the pancreas that changes locally and appears abnormal on a CT scan during cancer development. These textural changes are the possible indication of the stage the underlying healthy cells are transitioning into tumor cells . Furthermore, the shape of the whole pancreas  and subregions (belong to high-risk and low-risk classes) was observed indifferent, partly because the shape of the pancreas is highly irregular in general. However, the size of the high-risk subregions was observed slightly higher than their corresponding low-risk subregions, though not significantly different to be considered a stable predictor.The Centers for Disease Control and Prevention reports that 7 million patients with abdominal pain visit to ER in the US each year. These patients undergo CT examinations as per the standard care protocol. The initial evaluation of these scans assists clinicians to identify the underlying cause of abdominal pain. Though the scans of majority of these patients do not present any signs of cancer at this stage, some ultimately develop PDAC in coming years. These pre-diagnostic scans, even with no prominent signs of cancer, are clinically useful as these might contain significant morphological signatures of early biological adaptations associated with cancer. AI techniques can efficiently assist in identifying these signs and forecasting cancer incidence for the future. However, AI-based exploration of precancerous signs is challenged by data scarcity as the PDAC has a low prevalence. In this retrospective study, we examined the quantitative difference of the CT-based features between pre-diagnostic and healthy control scans. The study allowed quantitative analysis of the subregional changes that occurred in the precancerous or pre-symptomatic pancreas and helped reduce limitations of low prevalence and low cancer yield in prospective studies as half of the subjects have cancer.The unique data structure designed for this study is the foundation of the proposed prediction model as it allowed examining precancerous changes retrospectively. Although the overall prevalence of PDAC is significantly low, the percentage of enrolled subjects who were at the preclinical stage was set to 50% to reduce the risk of class imbalance during model development. Also, most of the literature considers that the duration of 6-36 months between the pre-diagnostic and diagnostic scan is a reasonable window to seek early signs.Also, most of the scans used for mode training and testing are portal venous phase. It is because tumors slowly uptake contrast whereas the venous phase provides the optimal view of the tumor edges and is thus considered the most valuable phase for PDAC diagnosis. Also, viewing of the vasculature passing across or alongside the pancreas is optimized in this phase. Changes occurring to the vasculature during PDAC development can be quantified and used as potential predictors. Nevertheless, other phases also provide valuable information during PDAC screening and treatment. For example, the arterial phase provides a unique value when seeking lesions or during surgical treatment of PDAC when the arteries are encased or distorted by the pancreatic tumor. Thus, including multiphase scans in the model training helped identify highly stable predictors to ensure the model is sufficiently robust.In accordance with the evidence provided, the proposed research work assures the appropriate blend of imaging type, feature analysis, and modeling techniques to address the challenges of prediction and elevate the chances of cancer diagnosis in the earliest stage. To our knowledge, it is the first automated system developed that predicts the PDAC by identifying early signs through analyzing the precancerous irregularities occurring within pancreatic subregions using CT scans. The proposed model not only demonstrated improved prediction accuracy to existing models but also enabled the system to identify subregions that are at higher risk of developing tumors.H , Body: B, Tail:T) in terms of histology, presentation, and symptoms , metastasis , %), 2-year survival , and resection   , 36–39.This study examined the subregional changes in the precancerous pancreas and enabled automated identification of subregions undergoing tumor development. Knowledge of the location of likely tumor will not only alert clinicians/radiologists to pay attention to certain regions of the pancreas to avoid misdetection of PDAC at an early stage but also enhance the overall management of PDAC by helping determine more appropriate and effective treatment, improving forecasting of the treatment outcome, planning better resection, and ultimately increasing the overall survival rate.In our previous study , we propDue to the low prevalence of PDAC, the eligible pre-diagnostic CT scans were found rare in the data archives of the CSMC and KPSC. Limited training data may have increased the chance of overfitting during model development. Another limitation is the insufficiency of pre-diagnostic CT scans of non-venous CT phases. Also, since the incidence of PDAC in the general population is fairly low, the model specificity of 93% still requires further enhancement to avoid too many false-positive cases. The aim of the study was to present the proof of the concept which encourages the collection of larger datasets including the information on non-imaging factors associated with risk of PDAC from the repositories of different institutes for substantial training and validation of the proposed prediction model. With sufficient data, the biological interpretation of predictive image features and their correlation with genetics would be achievable. A model trained on large data will improve model specificity and will efficiently assist in future prospective research on detecting PDAC at the initial stages.The current study presented the findings of the AI analysis of precancerous changes that occurred across three subregions of the pancreas using pre-diagnostic abdominal CT scans. The study concluded that the pancreas adopts textural changes during PDAC development, predominantly within the subregion undergoing tumor development, potentially regarded as a ‘high-risk’ subregion. A first model was built that performed risk quantification of PDAC using the identified textural changes as potential predictors and characterized the pancreas into ‘high risk’ and ‘low risk’ for PDAC classes. The model also specified the subregion that is likely to develop the tumor, which can potentially assist in improving early diagnosis, treatment planning, forecasting treatment outcome, and overall disease management. The proposed model demonstrates a 3.3% improved prediction when compared with the existing prediction model that considers the global changes occurring in the whole pancreas during PDAC development. The results of this preliminary study are promising and encouraging to further validate the model on a large dataset.The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding authors.DL, SP, SG, BW, CJ, and TQ were responsible for the conception and design. LW, LA, AW, WC, and VA performed data extraction and data labeling. SJ and TQ conducted basic investigation, performed statistical analysis, developed prediction model, and wrote original manuscript. DL performed quality control of this study. All authors contributed to the article and approved the submitted version.NIH R01 CA260955.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


The choice of antibiotic regimens for use in patients presenting with diabetic foot osteomyelitis and their duration differs according to the situation. Antibiotics play a more important role in the medical option where no infected bone has been resected, while their role is reduced but not negligible in the case of surgical options. Some studies have reported the presence of biofilm structures in bone samples taken from patients with diabetic foot osteomyelitis, which raises the question of the place of anti-biofilm antibiotic regimens in this setting. During the last two decades, clinical studies have suggested a potential benefit for anti-biofilm antibiotics, mainly rifampicin against staphylococci and fluoroquinolones against gram-negative bacilli. However, no data from randomized controlled studies have been reported so far. The present work provides a summary of the available data on the question of the place of anti-biofilm antibiotics for the treatment of diabetic foot osteomyelitis, but also the potential limitations of such treatments. About 20% of mild diabetic foot infections (DFIs) and up to 60% of severe cases present concomitant bone involvement ,2. The oThis present work aims to summarize the current data on the structural particularities of DFO, including biofilms, and discusses potential consequences regarding antibiotic use.Staphylococcus aureus is the most common pathogen cultured from bone samples, while in warm climate countries gram-negative bacilli dominate, especially Pseudomonas aeruginosa [Staphylococcus epidermidis and other coagulase-negative staphylococci, beta-haemolytic streptococci, and diphtheroids. Among the Enterobacterales, Escherichia coli, Klebsiella pneumonia, and Proteus spp. are the most common pathogens. Obligate anaerobes  are generally less frequently cultured from bone samples, but this depends on the method by which the bone fragments are taken and transported to the laboratory. A recent prospective study has reported that molecular techniques (16S rRNA sequencing) applied to the assessment of bone biopsies could identify more anaerobes and gram-positive bacilli compared to conventional techniques  [The microbiology of DFO is usually polymicrobial ,6,7,8. Iruginosa . Other gctively) . Accordictively) ,11, the ctively) .The poorer outcomes of DFOs compared to ST-DFIs seem to be related to the reduced activity of most antibiotics in the setting of a chronic bone infection. The main reasons are (i) the low diffusion of most antibiotics into the infected bone tissues, especially if chronically infected and necrotic; (ii) the usual empirical antibiotic therapy of DFOs due to the difficulty encountered in organizing bone biopsy, despite the low correlation between the culture results of bone versus non-bone samples reported in numerous studies; and (iii) the very specific environment of the bacteria involved in chronic osteomyelitis.Bone and joint infections (BJIs) complicating a diabetic foot ulcer are the results of the extension to the infection that involves the skin and soft tissues overlying a bony prominence. There are almost no cases where the pathogens reach bone or joints from the bloodstream. While medullar bone or joint may be directly inoculated in the case of puncture wounds, the first osseous structure generally involved during DFO is the periosteum and cortical bone with secondary extension to the bone marrow . According to Hofmann et al. , this kiAvailable data on the histological features of DFO are scarce. The usual histomorphological aspects reported in patients with DFO diagnosed on a clinical and imaging basis are varied, including a typical aspect of osteomyelitis, bone necrosis, myelofibrosis, and normal bone as well . Of noteS. aureus. On the contrary, sessile bacteria do not express many targets to the antibacterial activity of most antibiotics, which is the cause of adaptive resistance and present as “small colony variants” (SCVs) with significant phenotypic alterations in culture [Beyond the identification of the causative pathogens from bone samples, it is also important to characterize their phenotypic state, which significantly influences the antibacterial effect of most antibiotics. In most cases of DFO, the infection presents as chronic because of the frequent association with the loss of sensation allowing the indolent progression of the infectious process. It is common to oppose planktonic to sessile microorganisms, the former being responsible for acute infections and the latter for chronic infections. Indeed, there are not only two sorts of these microorganisms, but rather a continuum in the metabolic status leading to numerous different types of bacterial cells. Planktonic exhibit a high metabolic and dividing activity exposing them to the activity of most antibiotics. These microorganisms may be involved in rare cases of acute DFO, generally due to highly virulent bacteria with a high tropism for bone tissues, such as  culture . Some of culture . These b culture . The chr culture . Eight y culture . The aut culture . Infecte culture . Still, Sessile bacterial cells present in biofilm structures are characterized by a higher tolerance toward antimicrobial agents compared to bacteria involved in other types of infection. In addition to their decreased penetration through the biofilm matrix, most antibiotics exhibit diminished antibacterial activity due to a lack of intracellular accumulation and the metabolically inactive status of the bacteria . In biofThe term “biofilm” does not refer to a single entity as its structure may significantly differ according to the pathogens involved in its constitution and the level of its maturity. Older (mature) biofilms have more complex structures and are more resistant to antibiotic activity . The cutAnti-biofilm antibiotics should (i) achieve concentrations above the minimal inhibitory concentration of the targeted pathogens; (ii) exhibit maintained antimicrobial effects in the biofilm environment characterized by high protein concentrations, a low local oxygen pressure, a reduced pH, and high bacterial inoculum with an intense exchange between bacterial cells of nucleic material, including antimicrobial resistance genes; (iii) maintain their activity against bacteria in the stationary phase with reduced metabolism; and (iv) act on adhering bacteria. A bactericidal effect is likely to be beneficial given the local immunosuppression within the biofilm due to the difficulty of some components of innate immunity to penetrate the biofilm structures . All theWhile the role of biofilms in the pathogenicity of chronicisation of DFUs has been extensively studied, this is not the case for DFOs. Systemic or topical administrations of antibiotics have not shown efficacy for the management of non-healing DFUs . The reaWhen examining the efficiency of antibiotic regimens for treating DFO, it is of utmost importance to consider whether the patients were treated with or without resection of the infected bone tissues. The main issue for the medical and surgical management of DFOs is the persistence of chronically infected bone tissues and residual non-necrotic infected bone tissues, respectively. The clinical implication of positive bone margins is a matter of debate, but the International Working Group on the Diabetic Foot recommends obtaining a specimen of bone for culture at the stump of the resected bone . The tasThe question of the need for using anti-biofilm antibiotics for the treatment following bone resection or amputation for DFO is also a matter of debate. The identification of bacterial cells aggregated in biofilm structures advocates for using anti-biofilm antibiotics once the culture results are available. On the other hand, these bacterial aggregates seem to be associated with planktonic bacterial cells and located on the bone surface, which suggests the immature rather than mature status of the biofilm structures visualized on the corresponding proximal bone margins .No data support any beneficial effect of anti-biofilm antibiotic regimens for possibly reducing the duration of DFO antibiotic treatment. Of note, most of the patients—who were enrolled in a randomized multicenter study of patients with DFO treated medically, suggesting the equivalence of between a 6- and 12-week duration—were given rifampicin and/or fluoroquinolones (either levofloxacin or ciprofloxacin) . ExampleIn the end, in clinical studies it is thought that some antibiotic associations are better than others because of their proven anti-biofilm efficacy in experimental studies. No studies have compared the outcome of treatments of DFOs according to the presence or absence of biofilm. Cohort studies suggest a better outcome when rifampicin or fluoroquinolones are used, but no clear causal relationship has been established with the anti-biofilm effect of these molecules, which have other interesting characteristics in the treatment of bone infections. Nothing indicates that their effect is not only additive to the associated antibiotic and that the therapeutic success comes from the use of two molecules rather than one with interesting properties in the treatment of bone infections. Rifampicin is the only antibiotic for which significant activity against staphylococcal biofilms has been established in vitro, in animal models, and in human infections. Rifampicin demonstrated potent activity against persister cells in biofilms that exceed that of any other currently available antibiotic . As suchAmong the fluoroquinolones, levofloxacin is the most frequently used antibiotic against susceptible strains, in combination with rifampicin, for the treatment of staphylococcal osteomyelitis, with biofilms potentially involved due to low hepatic metabolism, one daily administration, and high tissue concentrations ,47. Datap = 0.02). The difference between the event rates remained significant after controlling for potentially confounding parameters  [The potential benefit of rifampicin combinations in the medical management of patients with DFO remains a matter of debate. Anti-biofilm antibiotic treatments using rifampicin in combination with a fluoroquinolone (ofloxacin) have already been assessed in patients treated medically for DFOs more than 20 years ago . Remissi = 0.04) .Given the retrospective nature of Wilson’s study, a prospective, randomized, double-blind U.S. multicenter study is currently recruiting with the intention to confirm the results of the previous report . In the Mycobacterium tuberculosis rifampicin-resistant mutants. The high oral bioavailability of rifampicin and fluoroquinolones allow starting the treatment orally, as suggested in a recent RCT [Rifampicin or fluoroquinolone treatment may lead to the emergence of antimicrobial resistance due to the selection of pre-existing resistant mutants in the bacterial population present in the infected site. The most critical period is therefore the initiation of the treatment when the bacterial inoculum is still high. It seems prudent to consider the initiation of rifampicin treatment after surgery, if required, since surgery can result in a rapid and drastic reduction in the bacterial load in the infected tissues. In patients with inflammatory signs of the foot without indication for surgery, rifampicin is best started after an initial phase of bactericidal and low risk for selection of antimicrobial resistance antibiotic therapy, such as beta-lactams and/or glyco(lipo) peptides. For the same reasons, the choice of the rifampicin companion should be based on bone culture results given the low correlation between bone and non-bone culture results ,7. Routicent RCT . It is, cent RCT .New alternatives to antibiotic strategies against biofilms have emerged, especially bacteriophages, antimicrobial peptides, and nanotechnologies ,57,58. WS. aureus dominate in Western countries while gram-negative, especially Pseudomonas spp., are more prevalent in warm-climate countries. The optimal antibiotic regimens to be used in patients presenting with a DFO have not yet been established in well-designed studies. The majority of patients presenting with a DFO have chronic bone and joint infection and most of the infected bone tissues contain biofilm structures. The choice of antibiotics depends on the therapeutic option, e.g., medical versus surgical. The use of antibiotic regimens with anti-biofilm activity, especially rifampicin for staphylococcal infections and fluoroquinolones from gram-negative in patients treated medically, is supported by a solid background issued from in vitro, animal models, and clinical studies; however, the use of these regimens in patients with DFOs has not yet been established, except through a couple of retrospective studies. The VA Intrepid study should provide useful data about the place of rifampicin combinations in these settings.DFO is a frequent complication associated with diabetic foot infections. Gram-positive cocci, especially


Sepsis is associated with high mortality—particularly in low–middle income countries (LMICs). Critical care management of sepsis is challenging in LMICs due to the lack of care providers and the high cost of bedside monitors. Recent advances in wearable sensor technology and machine learning (ML) models in healthcare promise to deliver new ways of digital monitoring integrated with automated decision systems to reduce the mortality risk in sepsis. In this study, firstly, we aim to assess the feasibility of using wearable sensors instead of traditional bedside monitors in the sepsis care management of hospital admitted patients, and secondly, to introduce automated prediction models for the mortality prediction of sepsis patients. To this end, we continuously monitored 50 sepsis patients for nearly 24 h after their admission to the Hospital for Tropical Diseases in Vietnam. We then compared the performance and interpretability of state-of-the-art ML models for the task of mortality prediction of sepsis using the heart rate variability (HRV) signal from wearable sensors and vital signs from bedside monitors. Our results show that all ML models trained on wearable data outperformed ML models trained on data gathered from the bedside monitors for the task of mortality prediction with the highest performance  achieved using time-varying features of HRV and recurrent neural networks. Our results demonstrate that the integration of automated ML prediction models with wearable technology is well suited for helping clinicians who manage sepsis patients in LMICs to reduce the mortality risk of sepsis. Sepsis is a life-threatening condition involving organ dysfunction in response to infection, and is a major global health concern . The mosDespite the seriousness of sepsis as a public health problem in LMICs, the majority of studies target the sepsis population in clinical settings from high-income countries (HICs), further augmenting the skewed generation of medical knowledge, providing evidence and insights that cannot necessarily be generalised to under-represented populations .Early recognition and treatment of sepsis are the two essential elements of improving outcome from the condition . This reThe application of novel digital health technologies may provide a solution to this ,12. WearThe most available wearable sensors are pulse oximetry through pulse plethysmography (PPG) and those measuring the electrocardiogram (ECG). These may be of particular use for sepsis management as, in addition to oxygen saturation and heart rate, through detection of alterations in beat-to-beat PPG or ECG recordings, the heart rate variability (HRV) series can also be calculated. Changes in HRV have been demonstrated to be of prognostic value in sepsis and have been correlated with the outcome and the severity of the disease ,20. HithTraditionally, conventional statistical techniques have been used to build prognostic prediction models in sepsis; however, biomarkers obtained from HRV can be embedded into automatic machine-learning (ML)-based prediction models, providing accurate predictions of a patient’s physiological state. ML models have been shown to outperform both internal decisions made by clinicians and clinical risk scores in predicting mortality in patients with sepsis ,23.However, the majority of these models i) use clinical, laboratory, vital sign observations, and physical data collected from traditional bedside monitoring systems, and ii) are trained and validated on the data gathered from HICs. To the best of our knowledge, no other study has integrated ML models with wearable monitoring systems in critically ill sepsis patients in LMICs. Therefore, in this study, we propose an innovative interpretable automated prediction algorithm integrated into wearable sensing devices for predicting the mortality rate of critically ill sepsis patients in LMICs. We aim to investigate the potential of HRV measures obtained from wearable sensors for the automatic prediction of in-hospital mortality at early admission stage. To achieve this, in our prospective study, we monitored the physiological status of adult sepsis patients admitted to the Hospital for Tropical Diseases (HTD) in Vietnam by acquiring the ECG signal using wearable sensors. The patients’ primary vital signs  were also monitored through bedside monitoring systems. We designed ML models to automatically predict the hospital discharge outcome of patients after nearly two weeks from their hospital admission using data collected during their first day of admission. We compared the performance of the state-of-the-art ML models in predicting the hospital discharge outcome using the primary vital signs and HRV measures extracted from the wearable sensor data. We also provide interpretability analysis of the ML models to identify the most informative inputs, further obtaining a better understanding of the behaviour of each model which is helpful for the clinicians.We gathered a rich dataset of simultaneous long-term ECG recordings from wearable sensors and vital signs from bedside monitoring systems from adult patients with sepsis admitted to the hospital within a resource-limited healthcare system.We demonstrate the high potential of physiological data gathered from wearable devices compared to traditional bedside monitoring systems for sepsis management.We propose an interpretable automatic ML-based solution for a long-term prediction of hospital discharge outcome in critically ill sepsis patients using the data collected during the first day of admission to be potentially implemented in practice.The three major contributions of this work are listed as follows:The main novelty of our study is the design of an automatic mortality prediction ML-based pipeline using wearable data acquired from sepsis patients in LMICs. Our models are trained on a novel dataset comprising simultaneous recording of bedside monitor variables and wearable data collected from critically ill sepsis patients in LMIC settings in contrast to prior studies which have been mostly conducted in HICs. Moreover, our prediction pipeline offers an explanation of the predictions made by all the ML models, highlighting the importance of input variables for clinicians which is not usually studied in previous works.Monitoring physiological status of patients is crucial in modern critical care. Currently, the waveform displays from bedside monitors such as the ECG signal, respiration activity, and the time-averaged data extracted from these waveforms (such as heart rate) have been the basis for clinical decision-making . HoweverA study undertaken in Rwanda has shown the feasibility and high accuracy of wearable biosensor devices in monitoring vital signs of acutely ill paediatric and adult emergency department (ED) patients with sepsis in LMIC settings . The autML models have been trained on various types of data in sepsis population in different clinical settings for mortality prediction. These models have outperformed traditional risk stratification tools based on clinical scoring ,28,29,30In addition to clinical and laboratory data, HRV has been considered as a predictor of sepsis mortality in many studies, as reported by a systematic review . StudiesML models trained on HRV extracted from 5 min ECG tracings performed at triage have been shown to improve the prediction of mortality in the suspected sepsis patients in ED compared to traditional risk stratification tools . HRV meaMost of the studies ,33,34 thAll adult patients with a clinical sepsis diagnosis who were admitted to HTD, Ho Chi Minh City in Vietnam, were screened for inclusion in this study. The hospital is a tertiary referral centre for infectious diseases serving Southern Vietnam. Sepsis was defined according to the HTD guidelines which are applicable to the local clinical situation at HTD. Septic shock was defined based on Sepsis-3 guidelines . These iExclusion criteria included history of allergy to electrodes, failure to give informed consent, and contraindications to the use of wearable sensors. As a result, 50 patients met the inclusion criteria and were recruited for this study. All patients gave informed consent and the study was evaluated to impose no risk to the patients. Our study was approved by the Scientific and Ethical Committee of the HTD, Ho Chi Minh City in Vietnam with the protocol number 1009/BVBND-HDDD and Oxford Tropical Research Ethics Committee with the protocol number 522-20.® ECG patch monitor  as a wearable biosensor. The ePatch® is a CE-marked, three-lead sternal ECG recording device capable of recording two channels of ECG data continuously for 24 h. It is lightweight and adheres to the patient’s chest. This device records single-channel ECG output with a sampling frequency of 256 Hz which is stored in the device and exported at the end of the recording period. The ePatch®’s position was determined according to its ideal position on the patient left chest—the highest edge of the sensor on the mid-line, about 4 cm from the left collarbone. The area was cleaned before attaching the patch by soap or alcohol pad. The sensor was tightly attached to the patch. The device was attached to the patient’s chest and continuous ECG data recording took place over a 24 h period. Meanwhile, the patients underwent routine clinical measurements every 1–6 h during their hospital stay, depending on clinical need, as part of the routine medical care. These include the collection of vital signs from bedside monitors using the GE CARESCAPE B450 patient monitor.The patients were recruited for this study within 24 h after their admission to the HTD. On enrolment, patient baseline clinical information was recorded. We used the ePatch®, it was slowly removed from the patient’s skin and the sensor was detached from the patch. The ePatch® sensor was connected to the computer with its accompanying cable and the ECG data were stored for further analysis. At the hospital discharge, clinical outcome data together with hospital length of stay were reported. This is a prospective study which started on June 2020. The training procedure is performed offline after the training data are stored for all the patients.Once the data collection was completed with the ePatchOut of 50 patients, the hospital discharge outcome for 9 patients was missing and therefore they were excluded for the analysis. Bedside monitoring measurements were missing for one patient. Therefore, the final dataset consisted of 40 patients. The demographic information of patients used for further analysis is reported in A complete illustration of the processing pipeline is depicted in ®. Each signal was normalized and filtered within ECG frequency bands ([0–4 Hz]) using Butterworth band-pass filter algorithm. Each ECG signal was segmented to hourly bins for further processing. The choice of hourly bins is to match wearable recordings with the vital signs. Within each window, the HRV signal was derived by extracting the R-peaks from the ECG signals using using the standard Pan–Tompkins algorithm [®, the detected R peaks, and the corresponding RR interval from 1 min acquisition of a random sepsis patient. We used the open-source packages NeuroKit2 and pyHRV packages for the calculation of standard HRV parameters from time domain, frequency domain, and nonlinear domain [Bedside monitor: Vital signs comprising body temperature, pulse rate, systolic blood pressure, respiratory rate, and oxygen saturation were extracted from bedside monitors depending on their frequency of collection for each patient. These measurements were collected on an hourly basis for the majority of patients. For those patients who did not have regularly hourly vital signs, we interpolated their vital signs on hourly basis based on nearest past value. This choice was to synchronize the collection of vital signs with the monitored ECG signals on hourly basis for implementation purposes. Wearable monitor: We applied standard preprocessing methods to the single lead ECG signals acquired from the ePatchlgorithm . Figure r domain ,39.The list of all features considered in this study along with their definition are described in In order to train the ML models for the task of predicting in-hospital mortality using multivariate time series data from sepsis patients, we considered different modalities of information according to the following feature subsets.Then, we constructed two different datasets from these feature subsets for a binary prediction task. For both datasets, the prediction output is hospital discharge outcome recorded at the discharge (on average two weeks after the patient’s commencement of monitoring); this is coded as positive in the case of a patient’s death, or negative in the case of a patient’s survival.Taking into account the time-varying dynamics of the feature sets, we define a dataset as:P represents the number of sepsis patients, L is the total number of the time series features and T is the time duration of each time series. The T value is 24 since our observations are collected on hourly time bins along the first 24 h of hospital admission. Therefore, each p and We also considered another dataset consisting of averaged dynamics of each multivariate feature along the time.p and L are defined as above.Based on each dataset’s architecture, we chose appropriate state-of-the-art ML models based on their performance and interpretability of the predictions . Using tSupport vector machines with recursive feature elimination (SVM-RFE): SVM models are powerful classification tools aiming to find a hyperplane that maximizes the distance between binary labelled observation samples . We applGaussian process classification model: Gaussian process classification (GPC) models are a class of machine learning models which are based on non-parametric Bayesian formulation. In GPC settings, a latent variable ess (GP) ,44. We uess (GP) . We chosGradient Boosting Decision Tree: Gradient boosting decision tree (GBDT) is an ensemble model of decision trees in which each decision tree is sequentially built on the gradient descent direction of a loss function. In each iteration, GBDT learns the decision trees by fitting the negative gradients known as residual errors .In this paper, we used the software library, eXtreme Gradient Boosting (XGBoost), which is an implementation of GBDT in Python designed for speed and performance . Tuning the data . These nthe data .We used LSTMs for binary classification using the Keras package in Python . Our deeFor each trained ML model, we applied interpretable analysis within the context of the model. The aim was to quantify the ranking and contribution of each single feature in the final prediction performance. To achieve this, we applied the following methods for each classifier.m that has the least effect on the SVM weight-vector norm at each iteration [m times while the last ranked features are removed. Finally, a subset of features with size r that optimises the performance of the SVM classifier are selected.RFE for SVM classifier: RFE is an embedded feature selection method based on a backward sequential selection that eliminates a feature in a feature set of size teration . This waGP interpretability framework: We applied a recently developed interpretability analysis of GPC models, based on an explicit form of the GP inference equations to quantify the importance of each feature contributing to the GPC model prediction . Within x that includes only the indices of L is the total number of features. For any test sample data l, a norm Let us define l of x belongs to class 1. Detailed mathematical formulation of this framework can be found in [Then, we define the interpretability metric Equation  which refound in ,52.Feature importance in GBDT: Decision trees bring the benefit of interpretability by means of decision analysis on the structure of the trees. One of the main features of GBDT algorithms is that they identify attributes that contribute the most towards the performance. We quantified the importance of each feature based on the number of times a feature is used to split the data across all trees.Within XGBoost library, a feature importance score can be obtained based on the relative contribution of the corresponding feature to the model calculated by taking each feature’s contribution for each tree in the model . A higheInterpretable model-agnostic explanation for LSTM: In recent years, among the deep-learning methods, local interpretable model-agnostic explanations (LIME)  has emerFor tabular data, variations of the data are produced by perturbing each feature individually. In particular, we applied the TabularLIME algorithm from the LIME package in Python to quantify the importance of each feature at each timestamp for the trained LSTM model.We report the results of ML models trained on We report the performance of each classifier within the two selected cross-validation schemes by calculating the performance metrics which are known to be suitable for imbalanced prediction problems.Precision (PPV): The percentage of truly positive predictions out of the positive predicted.F1-score: Harmonic mean of precision and recall where recall is the percentage of predicted positive out of the total positive. This metric takes both false positive and false negatives into account.AUCROC: The area under the curve of receiver operating characteristic curve.AUCPRC: The area under the curve of precision recall curve.These results are reported in With GP models, although the results are lower compared to the SVM models, the highest performances were obtained using only the HRV features, 88.46% of PPV, 80.50% of F-score, 0.78 of AUCROC, and 0.67 of AUCPRC. This trend is also true for the XGBoost model except for PPV of 92.3% with x axis in these figures represents the feature index in each feature set and the y axis represents the patient index. The feature contribution values are normalized between  and illustrated in colours according to the colour bar. Interestingly, in the heatmaps obtained from GP and XGBoost models, the contribution values are consistent within different patients. However, the SVM classifier seems to offer a more personalised feature ranking for each patient. The features which contributed most during GP training for the majority of patients are HR (mean) and SD2  and As we can see from the exemplary LIME analysis of a non-survived sepsis patient in The presence of HRV features in the first rankings, considering the We reported our results within different cross-validation schemes suitable for each ML model to avoid the overfitting of the models due to the limitations of having a larger small sample size in real life LMIC settings. Although the generalizability of the results can definitely be improved by increasing the sample size of the patient cohort in our study, to the best of our knowledge, this is the first prospective study in an LMIC setting to collect a relatively large dataset of high-resolution ECG signals for long time duration (24 h) from sepsis patients, and from this data propose ML-based prediction algorithms for a better care management of these patients. Another study  with a sAnother limitation of our study is the imbalanced nature of our dataset (35% for the positive class), which limits the reliance on many performance metrics in supervised learning. However, the task of mortality prediction in clinical practice is often performed with imbalanced datasets and usually with a 5–10% mortality rate. We tried to overcome this limitation by relying on those performance metrics  that are less affected by the low proportion of positive cases in the dataset.Unlike many other studies, we have not applied extensive preprocessing methods on the ECG signals collected from the ePatch. This choice was firstly to reduce the processing computational costs as much as possible, and secondly to make our prediction pipeline suitable for real-life scenarios, rather than controlled lab conditions. Due to the very low computational cost of the training models used in this study, we believe there is a great potential to directly deploy these models in clinical practice. In fact, in our future studies we aim to implement the proposed decision systems in clinical practice for a more efficient method of monitoring critically ill sepsis patients in the HTD hospital. Moreover, to overcome the limitations caused by sample size, we aim to take advantage of similar datasets with a much larger sample size collected in high-income settings and use transfer learning techniques to generalise the results to our smaller dataset.In this study, we presented an interpretable automatic ML-based pipeline for the long-term prediction of mortality in sepsis patients in the Vietnamese hospital. We demonstrated the practicality of wearable sensor monitors in monitoring patients with sepsis in LMIC settings. Our results show that the information extracted from the ECG signal acquired from low-cost wearable sensors results in higher performances compared to the information collected from expensive bedside monitors, for mortality prediction of sepsis patients. The highest performed prediction model was the LSTM model, using the time varying dynamics of HRV indices. With LSTM models, an AUCPRC of 0.83 was achieved using the HRV features extracted from the 24 h ECG signals of sepsis patients after hospital admission.We provided interpretability analysis for all of the ML prediction models at a single patient level, showing the high contribution of HRV-based features leading to mortality prediction. The interpretability analysis of the SVM and Gaussian process models show that the feature patients . MoreoveOur study is among the few studies that have conducted wearable monitoring and proposed an automatic mortality prediction pipeline within an LMIC setting in critically ill patients. The main challenges of our study are the relatively small sample size of the patients and the lack of external validation in the clinical setting. However, the findings of this study are helpful to expand research in utilizing wearable technology integrated with ML-based prediction models in managing infectious diseases in hospitals in resource limited settings.