A Comparative Analysis of Local and Systemic Immunological Biomarkers in Females With Breast Implants and Capsular Contracture.

Background: The etiology of capsular contracture (CC), the most common complication following breast augmentation, remains unclear. Chronic, fibrotic inflammation resulting in excessive fibrosis has been proposed as a potential mechanism. Objectives: In this study, we aimed to investigate the relation between biomarkers that are associated with inflammation and fibrosis and the severity of CC. Methods: Fifty healthy females were categorized into 3 groups: females with no-to-mild CC (Baker 1-2; n = 15), females with severe CC (Baker 3-4; n = 20), and a control group awaiting breast augmentation (n = 15). We assessed 5 biomarkers (galectin-1 [Gal-1], interferon-ß [INF-ß], interferon-γ [INF-γ], interleukin-6 [IL-6], and tumor necrosis factor-α [TNF-α]) in breast implant capsules and serum samples. Results: No significant differences in intracapsular cytokine levels were observed between the Baker 1-2 and the Baker 3-4 groups, as the levels were generally low and, in some cases, almost undetectable. In the blood samples, no significant differences in Gal-1, INF-γ, IL-6, or TNF-α levels were found within the 3 groups. We identified significantly increased levels of INF-ß (P = .009) in the blood samples of females with severe CC, driven mainly by 3 extremely high values. Conclusions: The cytokines assessed in this study did not reflect the degree of CC among females with silicone breast implants. However, 3 females with severe CC, who all had prolonged silicone exposure, showed extremely elevated levels of INF-ß in their serum samples. This possible association between prolonged silicone exposure and systemic inflammation in some females should be further investigated.

Fatal Adverse Events in Femoral Neck Fracture Patients Undergoing Hemiarthroplasty or Total Hip Arthroplasty-A Retrospective Record Review Study in a Nationwide Sample of Deceased Patients.

OBJECTIVES: Patient safety is a core component of quality of hospital care and measurable through adverse event (AE) rates. A high-risk group are femoral neck fracture patients. The Dutch clinical guideline states that the treatment of choice is cemented total hip arthroplasty (THA) or hemiarthroplasty (HA). We aimed to identify the prevalence of AEs related to THA/HA in a sample of patients who died in the hospital. METHODS: We used data of a nationwide retrospective record review study. Records were systematically reviewed for AEs, preventability and contribution to the patient's death. We drew a subsample of THA/HA AEs and analyzed these cases. RESULTS: Of the 2998 reviewed records, 38 patients underwent THA/HA, of whom 24 patients suffered 25 AEs (prevalence = 68.1%; 95% confidence interval, 51.4-81.2), and 24 contributed to death. Patients with a THA/HA AE were of high age (median = 82.5 y) and had severe comorbidity (Charlson score ≥5). The majority of THA/HA AEs had a patient-related cause and was considered partly preventable. Examples of suggested actions that might have prevented the AEs: refraining from surgery, adhering to medication guidelines, uncemented procedures, comprehensive presurgical geriatric assessment, and better postsurgical monitoring. DISCUSSION: Our study shows a high prevalence of (fatal) adverse events in patients undergoing THA/HA. This seems particularly valid for cemented implants in frail old patients, indicating room for improvement of patient safety in this group. Therefore, we recommend physicians to engage in comprehensive shared decision making with these patients and decide on a treatment fitting to a patient's preexisting health status, preferences, and values.

Silicone Migration from Intact Saline Breast Implants.

Breast augmentation is a widely performed surgical procedure worldwide, predominantly using silicone gel-filled implants. Concerns have primarily revolved around ruptures and the potential health risks associated with leaked silicone from silicone gel-filled implants. Cases of silicone migration from the shell of saline breast implants remain scarce. This case report introduces a unique case of a 66-year-old patient with silicone migration from intact saline breast implants. The patient presented with a range of symptoms consistent with breast implant illness. Radiological findings suggested the presence of silicone in the axillary lymph nodes, despite the integrity of the implants, thereby confirming silicone migration. Histopathological evaluation revealed a foreign body reaction and the presence of silicone in the axillary lymph nodes. Given the saline filling, the source is likely the polydimethylsiloxane shell. The rarity of documented silicone migration from intact saline breast implants, especially in patients with breast implant illness, underscores the need for more research into the health implications of leaked silicone particles from breast implants.

Can acutely ill patients predict their outcomes? A scoping review.

INTRODUCTION: The full impact of an acute illness on subsequent health is seldom explicitly discussed with patients. Patients' estimates of their likely prognosis have been explored in chronic care settings and can contribute to the improvement of clinical outcomes and patient satisfaction. This scoping review aimed to identify studies of acutely ill patients' estimates of their outcomes and potential benefits for their care. METHODS: A search was conducted in PubMed, Embase, Web of Science and Google Scholar, using terms related to prognostication and acute care. After removal of duplicates, all articles were assessed for relevance by six investigator pairs; disagreements were resolved by a third investigator. Risk of bias was assessed according to the Cochrane Handbook for Systematic Reviews of Interventions. RESULTS: Our search identified 3265 articles, of which 10 were included. The methods of assessing self-prognostication were very heterogeneous. Patients seem to be able to predict their need for hospital admission in certain settings, but not their length of stay. The severity of their symptoms and the burden of their disease are often overestimated or underestimated by patients. Patients with severe health conditions and their relatives tend to be overoptimistic about the likely outcome. CONCLUSION: The understanding of acutely ill patients of their likely outcomes and benefits of treatment has not been adequately studied and is a major knowledge gap. Limited published literature suggests patients may be able to predict their need for hospital admission. Illness perception may influence help-seeking behaviour, speed of recovery and subsequent quality of life. Knowledge of patients' self-prognosis may enhance communication between patients and their physicians, which improves patient-centred care.

Detecting changes in the performance of a clinical machine learning tool over time.

BACKGROUND: Excessive use of blood cultures (BCs) in Emergency Departments (EDs) results in low yields and high contamination rates, associated with increased antibiotic use and unnecessary diagnostics. Our team previously developed and validated a machine learning model to predict BC outcomes and enhance diagnostic stewardship. While the model showed promising initial results, concerns over performance drift due to evolving patient demographics, clinical practices, and outcome rates warrant continual monitoring and evaluation of such models. METHODS: A real-time evaluation of the model's performance was conducted between October 2021 and September 2022. The model was integrated into Amsterdam UMC's Electronic Health Record system, predicting BC outcomes for all adult patients with BC draws in real time. The model's performance was assessed monthly using metrics including the Area Under the Curve (AUC), Area Under the Precision-Recall Curve (AUPRC), and Brier scores. Statistical Process Control (SPC) charts were used to monitor variation over time. FINDINGS: Across 3.035 unique adult patient visits, the model achieved an average AUC of 0.78, AUPRC of 0.41, and a Brier score of 0.10 for predicting the outcome of BCs drawn in the ED. While specific population characteristics changed over time, no statistical points outside the statistical control range were detected in the AUC, AUPRC, and Brier scores, indicating stable model performance. The average BC positivity rate during the study period was 13.4%. INTERPRETATION: Despite significant changes in clinical practice, our BC stewardship tool exhibited stable performance, suggesting its robustness to changing environments. Using SPC charts for various metrics enables simple and effective monitoring of potential performance drift. The assessment of the variation of outcome rates and population changes may guide the specific interventions, such as intercept correction or recalibration, that may be needed to maintain a stable model performance over time. This study suggested no need to recalibrate or correct our BC stewardship tool. FUNDING: No funding to disclose.

[Take a seat when talking with a patient]. / Ga zitten als je met een patiënt praat.

The quality of communication influences the patient-doctor relationship. Patient satisfaction and compliance improve when a healthcare professional shows empathy and compassion. A substantial part of communication is non-verbal, especially in more complex conversations. A physician's physical posture during interaction with the patient is therefore crucial. Although sitting at the bedside is considered as best practice, we increasingly tend to stand during bedside interactions. This might be caused by time constraints and the idea that sitting down may be more time consuming. In this article we discuss the importance of posture. The psychological background of certain body language is reviewed. In addition, we give a concise review of the literature which shows that patient outcomes such as satisfaction are better in a sitting interactions, and that this interaction is not more time consuming.

Embracing cohort heterogeneity in clinical machine learning development: a step toward generalizable models.

This study is a simple illustration of the benefit of averaging over cohorts, rather than developing a prediction model from a single cohort. We show that models trained on data from multiple cohorts can perform significantly better in new settings than models based on the same amount of training data but from just a single cohort. Although this concept seems simple and obvious, no current prediction model development guidelines recommend such an approach.

Monitoring of pulmonary involvement in critically ill COVID-19 patients - should lung ultrasound be preferred over CT?

BACKGROUND: It is unclear if relevant changes in pulmonary involvement in critically ill COVID-19 patients can be reliably detected by the CT severity score (CTSS) and lung ultrasound score (LUSS), or if these changes have prognostic implications. In addition, it has been argued that adding pleural abnormalities to the LUSS could improve its prognostic value. The objective of this study was to compare LUSS and CTSS for the monitoring of COVID-19 pulmonary involvement through: first, establishing the correlation of LUSS (± pleural abnormalities) and CTSS throughout admission; second, assessing agreement and measurement error between raters for LUSS, pleural abnormalities, and CTSS; third, evaluating the association of the LUSS (± pleural abnormalities) and CTSS with mortality at different timepoints. METHODS: This is a prospective, observational study, conducted during the second COVID-19 wave at the AmsterdamUMC, location VUmc. Adult COVID-19 ICU patients were prospectively included when a CT or a 12-zone LUS was performed at admission or at weekly intervals according to local protocol. Patients were followed 90 days or until death. We calculated the: (1) Correlation of the LUSS (± pleural abnormalities) and CTSS throughout admission with mixed models; (2) Intra-class correlation coefficients (ICCs) and smallest detectable changes (SDCs) between raters; (3) Association between the LUSS (± pleural abnormalities) and CTSS with mixed models. RESULTS: 82 consecutive patients were included. Correlation between LUSS and CTSS was 0.45 (95% CI 0.31-0.59). ICCs for LUSS, pleural abnormalities, and CTSS were 0.88 (95% CI 0.73-0.95), 0.94 (95% CI 0.90-0.96), and 0.84 (95% CI 0.65-0.93), with SDCs of 4.8, 1.4, and 3.9. The LUSS was associated with mortality in week 2, with a score difference between patients who survived or died greater than its SDC. Addition of pleural abnormalities was not beneficial. The CTSS was associated with mortality only in week 1, but with a score difference less than its SDC. CONCLUSIONS: LUSS correlated with CTSS throughout ICU admission but performed similar or better at agreement between raters and mortality prognostication. Given the benefits of LUS over CT, it should be preferred as initial monitoring tool.

The usefulness of implementing minimum retest intervals in reducing inappropriate laboratory test requests in a Dutch hospital.

OBJECTIVES: Inappropriate use of laboratory testing remains a challenging problem worldwide. Minimum retest intervals (MRI) are used to reduce inappropriate laboratory testing. However, their effectiveness and the usefulness in reducing inappropriate laboratory testing is still a matter of debate. The aim of this study was to evaluate the effectiveness of broadly implemented MRIs as a means of reducing inappropriate laboratory test requests. METHODS: We performed a retrospective study in a general care and teaching hospital in the Netherlands, where MRI alerts have been implemented as standard care since June 7th 2017. Clinical chemistry test orders in adult internal medicine patients placed between July 13th 2017 and December 31st 2019 were included. The primary outcome was the effectiveness of MRIs, expressed as percentages of tests ordered and barred as a result of MRIs. RESULTS: Of a total of 218,511 test requests, 4,159 (1.90%) got an MRI alert. These MRIs were overruled by physicians in 21.76% of the cases. As a result of implementing MRIs, 3,254 (1.49%) tests were barred. The financial savings for the department of internal medicine directly related to the included barred laboratory tests during this period were 11,880 euros on a total amount of 636,598 euros for all performed tests. CONCLUSIONS: Only a small proportion of laboratory tests are barred after implementation of MRIs, with a limited impact on the annual costs. However, MRIs provide a continuous reminder to focus on appropriate testing and the effectiveness of MRIs is potentially higher than described in this study.

The impact of a sepsis performance improvement program in the emergency department: a before-after intervention study.

PURPOSE: The latest Surviving Sepsis Campaign guidelines advocate that all hospitals use sepsis performance improvement programs. However, there is a limited evidence about how to structure such programs and what their potential impact is on sepsis management and outcomes in the emergency department (ED). In this study, we evaluated the implementation of a sepsis performance improvement program in the ED including a dedicated sepsis response team and analyzed the management and outcomes of sepsis patients before and after. METHODS: We conducted a before-after interventional study in the ED of the Amsterdam University Medical Centers, the Netherlands. The sepsis performance improvement program included regular educational meetings, daily audits and weekly feedback, a screening tool, and a dedicated multidisciplinary sepsis response team. We studied all adult patients who presented to the ED with a suspected infection and a Modified Early Warning Score (MEWS) ≥ 3 during their stay. In the postintervention phase, these patients were seen by the sepsis team. Process-related and patient-related outcomes were measured between November 2019 and February 2020 (preintervention) and December 2021-May 2022 (postintervention). RESULTS: A total of 265 patients were included in the primary study, 132 patients preintervention and 133 patients postintervention. The postintervention phase was associated with improvements in nearly all process-related outcomes, such as a shorter time to antibiotics (66 vs. 143 min; p < 0.001), increased number of lactate measurements (72.9 vs. 46.2%; p < 0.001), and improved completeness of documented MEWS scores (85.0 vs. 62.9%; p < 0.001). Except for an improvement in the number of immediate versus delayed ICU admissions (100% immediate vs. 64.3% immediate; p = 0.012), there was no improvement in the other patient-related outcomes such as 28 days mortality (14.3 vs. 9.1%; p = 0.261), during the postintervention phase. CONCLUSION: Our program stimulated physicians to make timely decisions regarding diagnostics and treatment of sepsis in the ED. Implementing the sepsis performance improvement program was associated with significant improvements in most process-related outcomes but with minimal improvements in patient-related outcomes in our cohort.

[Unplanned Emergency Department return visits by older patients]. / Ongeplande herhaalbezoeken van ouderen op de SEH.

The majority of older patients' Emergency Department (ED) repeat visits are disease-related or caused by new complaints. Whether these repeat visits should and can be avoided is debated, given the vulnerability of this patient category. A proportion of patients still returns despite careful discharge planning. However, this number represents only a small fraction of the total of ED visits. In conclusion, this situation is likely to persist because the number of patients is growing, the problem is unpredictable and the care capacity is limited. For the small part that we can prevent, there is a shared responsibility for both the ED, but also for the entire care network due to the chronic nature of a large proportion of the complaints in the ED. Advance care planning is crucial in shifting to appropriate, timely and patient-centered care.

Sleep in hospitalized pediatric and adult patients - A systematic review and meta-analysis.

Background: Sleep is essential for recovery from illness. As a result, researchers have shown a growing interest in the sleep of hospitalized patients. Although many studies have been conducted over the past years, an up to date systematic review of the results is missing. Objective: The objective of this systematic review was to assess sleep quality and quantity of hospitalized patients and sleep disturbing factors. Methods: A systematic literature search was conducted within four scientific databases. The search focused on synonyms of 'sleep' and 'hospitalization'. Papers written in English or Dutch from inception to April 25th,2022 were included for hospitalized patients >1 year of age. Papers exclusively reporting about patients receiving palliative, obstetric or psychiatric care were excluded, as well as patients in rehabilitation and intensive care settings, and long-term hospitalized geriatric patients. This review was performed in accordance with the PRISMA guidelines. Results: Out of 542 full text studies assessed for eligibility, 203 were included, describing sleep quality and/or quantity of 17,964 patients. The median sample size of the studies was 51 patients (IQR 67, range 6-1472). An exploratory meta-analysis of the Total Sleep Time showed an average of 7.2 h (95%-CI 4.3, 10.2) in hospitalized children, 5.7 h (95%-CI 4.8, 6.7) in adults and 5.8 h (95%-CI 5.3, 6.4) in older patients (>60y). In addition, a meta-analysis of the Wake After Sleep Onset (WASO) showed a combined high average of 1.8 h (95%-CI 0.7, 2.9). Overall sleep quality was poor, also due to nocturnal awakenings. The most frequently cited external factors for poor sleep were noise and number of patients in the room. Among the variety of internal/disease-related factors, pain and anxiety were most frequently mentioned to be associated with poor sleep. Conclusion: Of all studies, 76% reported poor sleep quality and insufficient sleep duration in hospitalized patients. Children sleep on average 0.7-3.8 h less in the hospital than recommended. Hospitalized adults sleep 1.3-3.2 h less than recommended for healthy people. This underscores the need for interventions to improve sleep during hospitalization to support recovery.

Artificial Intelligence for the Prediction of In-Hospital Clinical Deterioration: A Systematic Review.

To analyze the available literature on the performance of artificial intelligence-generated clinical models for the prediction of serious life-threatening events in non-ICU adult patients and evaluate their potential clinical usage. DATA SOURCES: The PubMed database was searched for relevant articles in English literature from January 1, 2000, to January 23, 2022. Search terms, including artificial intelligence, machine learning, deep learning, and deterioration, were both controlled terms and free-text terms. STUDY SELECTION: We performed a systematic search reporting studies that showed performance of artificial intelligence-based models with outcome mortality and clinical deterioration. DATA EXTRACTION: Two review authors independently performed study selection and data extraction. Studies with the same outcome were grouped, namely mortality and various forms of deterioration (including ICU admission, adverse events, and cardiac arrests). Meta-analysis was planned in case sufficient data would be extracted from each study and no considerable heterogeneity between studies was present. DATA SYNTHESIS: In total, 45 articles were included for analysis, in which multiple methods of artificial intelligence were used. Twenty-four articles described models for the prediction of mortality and 21 for clinical deterioration. Due to heterogeneity of study characteristics (patient cohort, outcomes, and prediction models), meta-analysis could not be performed. The main reported measure of performance was the area under the receiver operating characteristic (AUROC) (n = 38), of which 33 (87%) had an AUROC greater than 0.8. The highest reported performance in a model predicting mortality had an AUROC of 0.935 and an area under the precision-recall curve of 0.96. CONCLUSIONS: Currently, a growing number of studies develop and analyzes artificial intelligence-based prediction models to predict critical illness and deterioration. We show that artificial intelligence-based prediction models have an overall good performance in predicting deterioration of patients. However, external validation of existing models and its performance in a clinical setting is highly recommended.

Evaluation of Nonpharmacologic Interventions and Sleep Outcomes in Hospitalized Medical and Surgical Patients: A Nonrandomized Controlled Trial.

Importance: Inadequate sleep negatively affects patients' physical health, mental well-being, and recovery. Nonpharmacologic interventions are recommended as first-choice treatment. However, studies evaluating the interventions are often of poor quality and show equivocal results. Objective: To assess whether the implementation of nonpharmacologic interventions is associated with improved inpatient night sleep. Design, Setting, and Participants: In a nonrandomized controlled trial, patients were recruited on the acute medical unit and medical and surgical wards of a Dutch academic hospital. All adults who spent exactly 1 full night in the hospital were recruited between September 1, 2019, and May 31, 2020 (control group), received usual care. Patients recruited between September 1, 2020, and May 31, 2021, served as the intervention group. The intervention group received earplugs, an eye mask, and aromatherapy. Nurses received sleep-hygiene training, and in the acute medical unit, the morning medication and vital sign measurement rounds were postponed from the night shift to the day shift. All interventions were developed in collaboration with patients, nurses, and physicians. Main Outcomes and Measures: Sleep was measured using actigraphy and the Dutch-Flemish Patient-Reported Outcomes Measurement Information System sleep disturbance item bank. Other outcomes included patient-reported sleep disturbing factors and the use of sleep-enhancing tools. Results: A total of 374 patients were included (222 control, 152 intervention; median age, 65 [IQR, 52-74] years). Of these, 331 were included in the analysis (195 [59%] men). Most patients (138 [77%] control, 127 [84%] intervention) were in the acute medical unit. The total sleep time was 40 minutes longer in the intervention group (control: median, 6 hours and 5 minutes [IQR, 4 hours and 55 minutes to 7 hours and 4 minutes]; intervention: 6 hours and 45 minutes [IQR, 5 hours and 47 minutes to 7 hours and 39 minutes]; P < .001). This was mainly due to a 30-minute delay in final wake time (median clock-time: control, 6:30 am [IQR, 6:00 am to 7:22 am]; intervention, 7:00 am [IQR, 6:30-7:30 am]; P < .001). Sleep quality did not differ significantly between groups. For both groups, the main sleep-disturbing factors were noises, pain, toilet visits, and being awakened by hospital staff. Sleep masks (23 of 147 [16%]) and earplugs (17 of 147 [12%]) were used most. Nightly vital sign checks decreased significantly (control: 54%; intervention: 11%; P < .001). Conclusions and Relevance: The findings of this study suggest that sleep of hospitalized patients may be significantly improved with nonpharmacologic interventions. Postponement of morning vital sign checks and medication administration rounds from the night to the day shift may be a useful way to achieve this. Trial Registration: Netherlands Trial Registry Identifier: NL7995.

Diagnostic stewardship for blood cultures in the emergency department: A multicenter validation and prospective evaluation of a machine learning prediction tool.

BACKGROUND: Overuse of blood cultures (BCs) in emergency departments (EDs) leads to low yields and high numbers of contaminated cultures, accompanied by increased diagnostics, antibiotic usage, prolonged hospitalization, and mortality. We aimed to simplify and validate a recently developed machine learning model to help safely withhold BC testing in low-risk patients. METHODS: We extracted data from the electronic health records (EHR) for 44.123 unique ED visits with BC sampling in the Amsterdam UMC (locations VUMC and AMC; the Netherlands), Zaans Medical Center (ZMC; the Netherlands), and Beth Israel Deaconess Medical Center (BIDMC; United States) in periods between 2011 and 2021. We trained a machine learning model on the VUMC data to predict blood culture outcomes and validated it in the AMC, ZMC, and BIDMC with subsequent real-time prospective evaluation in the VUMC. FINDINGS: The model had an Area Under the Receiver Operating Characteristics curve (AUROC) of 0.81 (95%-CI = 0.78-0.83) in the VUMC test set. The most important predictors were temperature, creatinine, and C-reactive protein. The AUROCs in the validation cohorts were 0.80 (AMC; 0.78-0.82), 0.76 (ZMC; 0.74-0.78), and 0.75 (BIDMC; 0.74-0.76). During real-time prospective evaluation in the EHR of the VUMC, it reached an AUROC of 0.76 (0.71-0.81) among 590 patients with BC draws in the ED. The prospective evaluation showed that the model can be used to safely withhold blood culture analyses in at least 30% of patients in the ED. INTERPRETATION: We developed a machine learning model to predict blood culture outcomes in the ED, which retained its performance during external validation and real-time prospective evaluation. Our model can identify patients at low risk of having a positive blood culture. Using the model in practice can significantly reduce the number of blood culture analyses and thus avoid the hidden costs of false-positive culture results. FUNDING: This research project was funded by the Amsterdam Public Health - Quality of Care program and the Dutch "Doen of Laten" project (project number: 839205002).

Polypharmacy, comorbidity and frailty: a complex interplay in older patients at the emergency department.

PURPOSE: Older adults at the emergency department (ED) with polypharmacy, comorbidity, and frailty are at risk of adverse health outcomes. We investigated the association of polypharmacy with adverse health outcomes, in relation to comorbidity and frailty. METHODS: This is a prospective cohort study in ED patients ≥ 70 years. Non-polypharmacy was defined as 0-4 medications, polypharmacy 5-9 and excessive polypharmacy ≥ 10. Comorbidity was classified by the Charlson comorbidity index (CCI). Frailty was defined by the Identification of Seniors At Risk-Hospitalized Patients (ISAR-HP) score. The primary outcome was 3-month mortality. Secondary outcomes were readmission to an ED/hospital ward and a self-reported fall < 3 months. The association between polypharmacy, comorbidity and frailty was analyzed by logistic regression. RESULTS: 881 patients were included. 43% had polypharmacy and 18% had excessive polypharmacy. After 3 months, 9% died, 30% were readmitted, and 21% reported a fall. Compared with non-polypharmacy, the odds ratio (OR) for mortality ranged from 2.62 (95% CI 1.39-4.93) in patients with polypharmacy to 3.92 (95% CI 1.95-7.90) in excessive polypharmacy. The OR weakened after adjustment for comorbidity: 1.80 (95% CI 0.92-3.52) and 2.32 (95% CI 1.10-4.90). After adjusting for frailty, the OR weakened to 2.10 (95% CI 1.10-4.00) and OR 2.40 (95% CI 1.15-5.02). No significant association was found for readmission or self-reported fall. CONCLUSIONS: Polypharmacy is common in older patients at the ED. Polypharmacy, and especially excessive polypharmacy, is associated with an increased risk of mortality. The observed association is complex given the confounding effect of comorbidity and frailty.

Feasibility of a new multifactorial fall prevention assessment and personalized intervention among older people recently discharged from the emergency department.

BACKGROUND AND IMPORTANCE: Falls among older people occur frequently and are a leading cause of Emergency department (ED) admissions, disability, death and rising health care costs. Multifactorial fall prevention programs that are aimed to target the population at risk have shown to effectively reduce the rate of falling and fall-related injuries in community-dwelling older people. However, the participation of and adherence to these programs in real life situation is generally low. OBJECTIVE: To test the feasibility of a transitionally organized fall prevention assessment with accompanying personalized intervention initiated at the ED. DESIGN, SETTINGS AND PARTICIPANTS: A process evaluation, of a non-randomized controlled pilot trial for implementing a transitionally organized multifactorial fall prevention intervention, was performed using the Reach, Effectiveness, Adoption, Implementation, Maintenance (RE-AIM) framework to gain insight into the barriers and facilitators of implementation. Older fallers (>70yrs) presenting at the ED were selected based on ZIP-code and after obtaining informed consent, data for the evaluation was collected through questionnaires and interviews. Furthermore, feedback was collected from the healthcare providers. MAIN RESULTS: The consent was obtained by 24 (70%) of the patients approached directly at the ED and 17 (26%) of the patients approached later by phone. Adherence to the protocol by the participants, clinical assessors and family practice were all more than 90%. After three months, nine (26%) of the participants had at least one recurrent fall: three (20%) patients in the intervention group and six (32%) in the control group. CONCLUSION: ED presentation due to a fall in older persons provides a window of opportunity for optimizing adherence to a multifactorial fall prevention program as willingness to participate was higher when the patients were approached at the ED during their stay. Implementing a transitionally organized multidisciplinary fall prevention program was successful with a high protocol adherence. THE NETHERLANDS TRIAL REGISTER: NTR NL8142, November 8, 2019.

Patient-reported systemic symptoms in women with silicone breast implants: a descriptive cohort study.

OBJECTIVE: An unknown portion of women with silicone breast implants (SBI) report development of systemic symptoms, recently named as 'breast implant illness (BII)'. We aim to describe the symptoms and characteristics of women with SBI reporting these systemic symptoms and compare the clinical course of women who chose to keep their implants, to women who had their implants removed. DESIGN: Observational cohort study. SETTING: Specialised BII out-patient clinic at Amsterdam UMC, the Netherlands, from 2011 to 2020. PARTICIPANTS: All women presenting to the BII clinic with SBI and systemic symptoms. RESULTS: 467 women were included for baseline analyses and 398 women for follow-up. Most frequently reported systemic symptoms at baseline included fatigue (88%), arthralgia (71%), morning stiffness (59%), myalgia (48%), cognitive impairment (33%), peripheral neurological symptoms (30%) and lymphadenopathy (22%). Furthermore, 56% reported pre-existing allergies at baseline and positive antinuclear antibodies were observed in 23%. At follow-up with a median of 3.3 years (IQR 2-4), 152 women had their implants removed on clinical grounds. Symptoms improved significantly in 65 women (43%), improved moderately in 37 women (24%), did not change in 37 women (24%) and deteriorated in 13 women (9%). Women who underwent explantation showed more improvement of their systemic symptoms compared with women who did not (OR 2.9, 95% CI 1.3 to 6.2). Additionally, women who underwent explantation within 10 years after implantation improved significantly better than women who got the implants removed after 10 years (p=0.007). Lastly, local symptoms decreased from 75% to 34% after implant removal (p<0.0001). CONCLUSION: Most women with SBI who developed systemic symptoms experienced improvement after explantation, especially when removed within 10 years after implantation. Early recognition of the pattern of systemic symptoms in women with SBI is important and implant removal should be considered.

The host response in different aetiologies of community-acquired pneumonia.

BACKGROUND: Community-acquired pneumonia (CAP) can be caused by a variety of pathogens, of which Streptococcus pneumoniae, Influenza and currently SARS-CoV-2 are the most common. We sought to identify shared and pathogen-specific host response features by directly comparing different aetiologies of CAP. METHODS: We measured 72 plasma biomarkers in a cohort of 265 patients hospitalized for CAP, all sampled within 48 hours of admission, and 28 age-and sex matched non-infectious controls. We stratified the biomarkers into several pathophysiological domains- antiviral response, vascular response and function, coagulation, systemic inflammation, and immune checkpoint markers. We directly compared CAP caused by SARS-CoV-2 (COVID-19, n=39), Streptococcus pneumoniae (CAP-strep, n=27), Influenza (CAP-flu, n=22) and other or unknown pathogens (CAP-other, n=177). We adjusted the comparisons for age, sex and disease severity scores. FINDINGS: Biomarkers reflective of a stronger cell-mediated antiviral response clearly separated COVID-19 from other CAPs (most notably granzyme B). Biomarkers reflecting activation and function of the vasculature showed endothelial barrier integrity was least affected in COVID-19, while glycocalyx degradation and angiogenesis were enhanced relative to other CAPs. Notably, markers of coagulation activation, including D-dimer, were not different between the CAP groups. Ferritin was most increased in COVID-19, while other systemic inflammation biomarkers such as IL-6 and procalcitonin were highest in CAP-strep. Immune checkpoint markers showed distinctive patterns in viral and non-viral CAP, with highly elevated levels of Galectin-9 in COVID-19. INTERPRETATION: Our investigation provides insight into shared and distinct pathophysiological mechanisms in different aetiologies of CAP, which may help guide new pathogen-specific therapeutic strategies. FUNDING: This study was financially supported by the Dutch Research Council, the European Commission and the Netherlands Organization for Health Research and Development.