OBJECTIVE: To identify trajectories of cognitive-affective depressive symptoms among acutely hospitalized older patients and whether trajectories are related to prognostic baseline factors and three-month outcomes such as functional decline, falls, unplanned readmissions, and mortality.METHODS: Prospective multicenter cohort of acutely hospitalized patients aged ≥ 70. Depressive trajectories were based on Group Based Trajectory Modeling, using the Geriatric Depression Scale-15. Outcomes were functional decline, falls, unplanned readmission, and mortality within three months post-discharge.RESULTS: The analytic sample included 398 patients (mean age = 79.6 years; SD = 6.6). Three distinct depressive symptoms trajectories were identified: minimal (63.6%), mild persistent (25.4%), and severe persistent (11.0%). Unadjusted results showed that, compared to the minimal symptoms group, the mild and severe persistent groups showed a significantly higher risk of functional decline (mild: OR = 3.9, p < .001; severe: OR = 3.0, p = .04), falls (mild: OR = 2.0, p = .02; severe: OR = 6.0, p < .001), and mortality (mild: OR = 2.2, p = .05; severe: OR = 3.4, p = .009). Patients with mild or severe persistent symptoms were more malnourished, anxious, and functionally limited and had more medical comorbidities at admission.CONCLUSION: Nearly 40% of the acutely hospitalized older adults exhibited mild to severe levels of cognitive-affective depressive symptoms. In light of the substantially elevated risk of serious complications and the fact that elevated depressive symptoms was not a transient phenomenon identification of these patients is needed. This further emphasizes the need for acute care hospitals, as a point of engagement with older adults, to develop discharge or screening procedures for managing cognitive-affective depressive symptoms.
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In deze studie onderzoeken we de ontwikkelingstrajecten van hackers, op basis van zelfgerapporteerde web defacements. Tijdens een web defacement wordt ongewenst de inhoud van een website aangepast. In totaal hebben we 50.330 defacements van websites met een Nederlandse extensie (.nl websites) geanalyseerd, die door 3640 verschillende defacers zijn uitgevoerd tussen januari 2010 en maart 2017. Met behulp van trajectory-modellen kunnen er zes groepen defacers worden onderscheiden in de analyses: twee groepen chronische daders en vier groepen daders die slechts gedurende een korte periode defacements uitvoerden. Deze groepen verschillen ook van elkaar in hun motivaties en modus operandi. De groep hoogfrequente chronische daders bestaat uit minder dan 2% van de daders, maar is verantwoordelijk voor meer dan de helft van alle defacements. Het zou dan ook het meest efficiënt zijn wanneer toekomstige interventies zich met name richten op deze kleine groep chronische daders. Voor vervolgonderzoek zou het interessant zijn om de inhoudelijke boodschap van de web defacements te onderzoeken.
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Longitudinal criminological studies greatly improved our understanding of the longitudinal patterns of criminality. These studies, however, focused almost exclusively on traditional types of offending and it is therefore unclear whether results are generalizable to online types of offending. This study attempted to identify the developmental trajectories of active hackers who perform web defacements. The data for this study consisted of 2,745,311 attacks performed by 66,553 hackers and reported to Zone-H between January 2010 and March 2017. Semi-parametric group-based trajectory models were used to distinguish six different groups of hackers based on the timing and frequency of their defacements. The results demonstrated some common relationships to traditional types of crime, as a small population of defacers accounted for the majority of defacements against websites. Additionally, the methods and targeting practices of defacers differed based on the frequency with which they performed defacements generally.
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Just what and how eight experienced teachers in four coaching dyads learned during a 1-year reciprocal peer coaching trajectory was examined in the present study. The learning processes were mapped by providing a detailed description of reported learning activities, reported learning outcomes, and the relations between these two. The sequences of learning activities associated with a particular type of learning outcome were next selected, coded, and analyzed using a variety of quantitative methods. The different activity sequences undertaken by the teachers during a reciprocal peer coaching trajectory were found to trigger different aspects of their professional development.
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Introduction: The implementation of oncology care pathways that standardize organizational procedures has improved cancer care in recent years. However, the involvement of “authentic” patients and caregivers in quality improvement of these predetermined pathways is in its infancy, especially the scholarly reflection on this process. We, therefore, aim to explore the multidisciplinary challenges both in practice, when cancer patients, their caregivers, and a multidisciplinary team of professionals work together on quality improvement, as well as in our research team, in which a social scientist, health care professionals, health care researchers, and experience experts design a research project together. Methods and design: Experience-based co-design will be used to involve cancer patients and their caregivers in a qualitative research design. In-depth open discovery interviews with 12 colorectal cancer patients, 12 breast cancer patients, and seven patients with cancer-associated thrombosis and their caregivers, and focus group discussions with professionals from various disciplines will be conducted. During the subsequent prioritization events and various co-design quality improvement meetings, observational field notes will be made on the multidisciplinary challenges these participants face in the process of co-design, and evaluation interviews will be done afterwards. Similar data will be collected during the monthly meetings of our multidisciplinary research team. The data will be analyzed according to the constant comparative method. Discussion: This study may facilitate quality improvement programs in oncologic care pathways, by increasing our real-world knowledge about the challenges of involving “experience experts” together with a team of multidisciplinary professionals in the implementation process of quality improvement. Such co-creation might be challenging due to the traditional paternalistic relationship, actual disease-/treatment-related constraints, and a lack of shared language and culture between patients, caregivers, and professionals and between professionals from various disciplines. These challenges have to be met in order to establish equality, respect, team spirit, and eventual meaningful participation.
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Purpose: To gain a rich understanding of the experiences and opinions of patients, healthcare professionals, and policymakers regarding the design of OGR with structure, process, environment, and outcome components. Methods: Qualitative research based on the constructive grounded theory approach is performed. Semi-structured interviews were conducted with patients who received OGR (n=13), two focus groups with healthcare professionals (n=13), and one focus group with policymakers (n=4). The Post-acute Care Rehabilitation quality framework was used as a theoretical background in all research steps. Results: The data analysis of all perspectives resulted in seven themes: the outcome of OGR focuses on the patient’s independence and regaining control over their functioning at home. Essential process elements are a patient-oriented network, a well-coordinated dedicated team at home, and blended eHealth applications. Additionally, closer cooperation in integrated care and refinement regarding financial, time-management, and technological challenges is needed with implementation into a permanent structure. All steps should be influenced by the stimulating aspect of the physical and social rehabilitation environment. Conclusion: The three perspectives generally complement each other to regain patients’ quality of life and autonomy. This study demonstrates an overview of the building blocks that can be used in developing and designing an OGR trajectory.
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Background and purpose The aim of this study is to investigate changes in movement behaviors, sedentary behavior and physical activity, and to identify potential movement behavior trajectory subgroups within the first two months after discharge from the hospital to the home setting in first-time stroke patients. Methods A total of 140 participants were included. Within three weeks after discharge, participants received an accelerometer, which they wore continuously for five weeks to objectively measure movement behavior outcomes. The movement behavior outcomes of interest were the mean time spent in sedentary behavior (SB), light physical activity (LPA) and moderate to vigorous physical activity (MVPA); the mean time spent in MVPA bouts ≥ 10 minutes; and the weighted median sedentary bout. Generalized estimation equation analyses were performed to investigate overall changes in movement behavior outcomes. Latent class growth analyses were performed to identify patient subgroups of movement behavior outcome trajectories. Results In the first week, the participants spent an average, of 9.22 hours (67.03%) per day in SB, 3.87 hours (27.95%) per day in LPA and 0.70 hours (5.02%) per day in MVPA. Within the entire sample, a small but significant decrease in SB and increase in LPA were found in the first weeks in the home setting. For each movement behavior outcome variable, two or three distinctive subgroup trajectories were found. Although subgroup trajectories for each movement behavior outcome were identified, no relevant changes over time were found. Conclusion Overall, the majority of stroke survivors are highly sedentary and a substantial part is inactive in the period immediately after discharge from hospital care. Movement behavior outcomes remain fairly stable during this period, although distinctive subgroup trajectories were found for each movement behavior outcome. Future research should investigate whether movement behavior outcomes cluster in patterns.
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INTRODUCTION: Patients with COVID-19-related acute respiratory distress syndrome (ARDS) have been postulated to present with distinct respiratory subphenotypes. However, most phenotyping schema have been limited by sample size, disregard for temporal dynamics, and insufficient validation. We aimed to identify respiratory subphenotypes of COVID-19-related ARDS using unbiased data-driven approaches.METHODS: PRoVENT-COVID was an investigator-initiated, national, multicentre, prospective, observational cohort study at 22 intensive care units (ICUs) in the Netherlands. Consecutive patients who had received invasive mechanical ventilation for COVID-19 (aged 18 years or older) served as the derivation cohort, and similar patients from two ICUs in the USA served as the replication cohorts. COVID-19 was confirmed by positive RT-PCR. We used latent class analysis to identify subphenotypes using clinically available respiratory data cross-sectionally at baseline, and longitudinally using 8-hourly data from the first 4 days of invasive ventilation. We used group-based trajectory modelling to evaluate trajectories of individual variables and to facilitate potential clinical translation. The PRoVENT-COVID study is registered with ClinicalTrials.gov, NCT04346342.FINDINGS: Between March 1, 2020, and May 15, 2020, 1007 patients were admitted to participating ICUs in the Netherlands, and included in the derivation cohort. Data for 288 patients were included in replication cohort 1 and 326 in replication cohort 2. Cross-sectional latent class analysis did not identify any underlying subphenotypes. Longitudinal latent class analysis identified two distinct subphenotypes. Subphenotype 2 was characterised by higher mechanical power, minute ventilation, and ventilatory ratio over the first 4 days of invasive mechanical ventilation than subphenotype 1, but PaO2/FiO2, pH, and compliance of the respiratory system did not differ between the two subphenotypes. 185 (28%) of 671 patients with subphenotype 1 and 109 (32%) of 336 patients with subphenotype 2 had died at day 28 (p=0·10). However, patients with subphenotype 2 had fewer ventilator-free days at day 28 (median 0, IQR 0-15 vs 5, 0-17; p=0·016) and more frequent venous thrombotic events (109 [32%] of 336 patients vs 176 [26%] of 671 patients; p=0·048) compared with subphenotype 1. Group-based trajectory modelling revealed trajectories of ventilatory ratio and mechanical power with similar dynamics to those observed in latent class analysis-derived trajectory subphenotypes. The two trajectories were: a stable value for ventilatory ratio or mechanical power over the first 4 days of invasive mechanical ventilation (trajectory A) or an upward trajectory (trajectory B). However, upward trajectories were better independent prognosticators for 28-day mortality (OR 1·64, 95% CI 1·17-2·29 for ventilatory ratio; 1·82, 1·24-2·66 for mechanical power). The association between upward ventilatory ratio trajectories (trajectory B) and 28-day mortality was confirmed in the replication cohorts (OR 4·65, 95% CI 1·87-11·6 for ventilatory ratio in replication cohort 1; 1·89, 1·05-3·37 for ventilatory ratio in replication cohort 2).INTERPRETATION: At baseline, COVID-19-related ARDS has no consistent respiratory subphenotype. Patients diverged from a fairly homogenous to a more heterogeneous population, with trajectories of ventilatory ratio and mechanical power being the most discriminatory. Modelling these parameters alone provided prognostic value for duration of mechanical ventilation and mortality.
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Background: Follow‑up of curatively treated primary breast cancer patients consists of surveillance and aftercare and is currently mostly the same for all patients. A more personalized approach, based on patients’ individual risk of recurrence and personal needs and preferences, may reduce patient burden and reduce (healthcare) costs. The NABOR study will examine the (cost‑)effectiveness of personalized surveillance (PSP) and personalized aftercare plans (PAP) on patient‑reported cancer worry, self‑rated and overall quality of life and (cost‑)effectiveness. Methods: A prospective multicenter multiple interrupted time series (MITs) design is being used. In this design, 10 participating hospitals will be observed for a period of eighteen months, while they ‑stepwise‑ will transit from care as usual to PSPs and PAPs. The PSP contains decisions on the surveillance trajectory based on individual risks and needs, assessed with the ‘Breast Cancer Surveillance Decision Aid’ including the INFLUENCE prediction tool. The PAP contains decisions on the aftercare trajectory based on individual needs and preferences and available care resources, which decision‑making is supported by a patient decision aid. Patients are non‑metastasized female primary breast cancer patients (N= 1040) who are curatively treated and start follow‑up care. Patient reported outcomes will be measured at five points in time during two years of follow‑up care (starting about one year after treatment and every six months thereafter). In addition, data on diagnostics and hospital visits from patients’ Electronical Health Records (EHR) will be gathered. Primary outcomes are patient‑reported cancer worry (Cancer Worry Scale) and over‑all quality of life (as assessed with EQ‑VAS score). Secondary outcomes include health care costs and resource use, health‑related quality of life (as measured with EQ5D‑5L/SF‑12/EORTC‑QLQ‑C30), risk perception, shared decision‑making, patient satisfaction, societal participation, and cost‑effectiveness. Next, the uptake and appreciation of personalized plans and patients’ experiences of their decision‑making process will be evaluated. Discussion: This study will contribute to insight in the (cost‑)effectiveness of personalized follow‑up care and contributes to development of uniform evidence‑based guidelines, stimulating sustainable implementation of personalized surveillance and aftercare plans. Trial registration: Study sponsor: ZonMw. Retrospectively registered at ClinicalTrials.gov (2023), ID: NCT05975437.
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