Local, collaborative, stepped, and personalized care management for older people with chronic diseases - results from the randomized controlled LoChro-trial.

BACKGROUND: In the aging population of Western societies, an increasing number of older adults have multiple chronic diseases. As multifaceted health problems imply the involvement of several healthcare professionals, multimorbid older people frequently face a fragmentation of health care. Addressing these challenges, we developed a local, collaborative, stepped, and personalized care management approach (LoChro-Care) and evaluated its effectiveness. METHODS: A two-group, parallel randomized controlled trial was conducted comparing LoChro-Care recipients (IG) to participants with usual care (CG). Patients aged 65 + with chronic conditions were recruited at inpatient and outpatient departments of the Medical Center, University of Freiburg. Participants were allocated using block randomization (nIG = 261, nCG = 263). LoChro-Care comprised individualized care provided by chronic care managers with 7 to 13 contacts over 12 months. Questionnaires were given at 3 time points (T0: baseline, T1: after 12 months, T2: after 18 months). The primary outcome was the physical, psychological, and social health status represented by a composite score of functional health and depressive symptoms. Secondary outcomes were the participants' evaluation of their health care situation, health-related quality of life (HRQL), and life-satisfaction (LS). The data were analyzed using linear mixed modelling. RESULTS: We analyzed N = 491 participants (nIG = 244, nCG = 247), aged M = 76.78 years (SD = 6.35). For the composite endpoint, neither a significant difference between IG and CG (p = .88) nor a group-time interaction (p = .52; p = .88) could be observed. Participants in both groups showed a significant decline on the primary outcome between T0 and T2 (p < .001). Post hoc analyses revealed a decline in both functional health (p < .001) and depressive symptoms (p = .02). Both groups did not differ in their evaluation of their health care situation (p = .93), HRQL (p = .44) or LS (p = .32). Relevant confounding variables were female gender and multimorbidity. CONCLUSION: Supporting patients' self-management in coordinating their individual care network through LoChro-Care did not result in any significant effect on the primary and secondary outcomes. A decline of functional health and depressive symptoms was observed among all participants. Potential future intervention adaptations are discussed, such as a more active case management through direct referral to (in-)formal support, an earlier treatment initiation, and the consideration of specific sociodemographic factors in care management planning. TRIAL REGISTRATION: German Clinical Trials Register (DRKS): DRKS00013904 (02.02.2018), https://drks.de/search/de/trial/DRKS00013904.

Co-infection and ICU-acquired infection in COIVD-19 ICU patients: a secondary analysis of the UNITE-COVID data set.

BACKGROUND: The COVID-19 pandemic presented major challenges for critical care facilities worldwide. Infections which develop alongside or subsequent to viral pneumonitis are a challenge under sporadic and pandemic conditions; however, data have suggested that patterns of these differ between COVID-19 and other viral pneumonitides. This secondary analysis aimed to explore patterns of co-infection and intensive care unit-acquired infections (ICU-AI) and the relationship to use of corticosteroids in a large, international cohort of critically ill COVID-19 patients. METHODS: This is a multicenter, international, observational study, including adult patients with PCR-confirmed COVID-19 diagnosis admitted to ICUs at the peak of wave one of COVID-19 (February 15th to May 15th, 2020). Data collected included investigator-assessed co-infection at ICU admission, infection acquired in ICU, infection with multi-drug resistant organisms (MDRO) and antibiotic use. Frequencies were compared by Pearson's Chi-squared and continuous variables by Mann-Whitney U test. Propensity score matching for variables associated with ICU-acquired infection was undertaken using R library MatchIT using the "full" matching method. RESULTS: Data were available from 4994 patients. Bacterial co-infection at admission was detected in 716 patients (14%), whilst 85% of patients received antibiotics at that stage. ICU-AI developed in 2715 (54%). The most common ICU-AI was bacterial pneumonia (44% of infections), whilst 9% of patients developed fungal pneumonia; 25% of infections involved MDRO. Patients developing infections in ICU had greater antimicrobial exposure than those without such infections. Incident density (ICU-AI per 1000 ICU days) was in considerable excess of reports from pre-pandemic surveillance. Corticosteroid use was heterogenous between ICUs. In univariate analysis, 58% of patients receiving corticosteroids and 43% of those not receiving steroids developed ICU-AI. Adjusting for potential confounders in the propensity-matched cohort, 71% of patients receiving corticosteroids developed ICU-AI vs 52% of those not receiving corticosteroids. Duration of corticosteroid therapy was also associated with development of ICU-AI and infection with an MDRO. CONCLUSIONS: In patients with severe COVID-19 in the first wave, co-infection at admission to ICU was relatively rare but antibiotic use was in substantial excess to that indication. ICU-AI were common and were significantly associated with use of corticosteroids. Trial registration ClinicalTrials.gov: NCT04836065 (retrospectively registered April 8th 2021).

Using network analysis to model the effects of the SARS Cov2 pandemic on acute patient care within a healthcare system.

Consolidation of healthcare in the US has resulted in integrated organizations, encompassing large geographic areas, with varying services and complex patient flows. Profound changes in patient volumes and behavior have occurred during the SARS Cov2 pandemic, but understanding these across organizations is challenging. Network analysis provides a novel approach to address this. We retrospectively evaluated hospital-based encounters with an index emergency department visit in a healthcare system comprising 18 hospitals, using patient transfer as a marker of unmet clinical need. We developed quantitative models of transfers using network analysis incorporating the level of care provided (ward, progressive care, intensive care) during pre-pandemic (May 25, 2018 to March 16, 2020) and mid-pandemic (March 17, 2020 to March 8, 2021) time periods. 829,455 encounters were evaluated. The system functioned as a non-small-world, non-scale-free, dissociative network. Our models reflected transfer destination diversification and variations in volume between the two time points - results of intentional efforts during the pandemic. Known hub-spoke architecture correlated with quantitative analysis. Applying network analysis in an integrated US healthcare organization demonstrates changing patterns of care and the emergence of bottlenecks in response to the SARS Cov2 pandemic, consistent with clinical experience, providing a degree of face validity. The modelling of multiple influences can identify susceptibility to stress and opportunities to strengthen the system where patient movement is common and voluminous. The technique provides a mechanism to analyze the effects of intentional and contextual changes on system behavior.

Systems approach to improving traumatic brain injury care in Myanmar: a mixed-methods study from lived experience to discrete event simulation.

OBJECTIVES: Traumatic brain injury (TBI) is a global health problem, whose management in low-resource settings is hampered by fragile health systems and lack of access to specialist services. Improvement is complex, given the interaction of multiple people, processes and institutions. We aimed to develop a mixed-method approach to understand the TBI pathway based on the lived experience of local people, supported by quantitative methodologies and to determine potential improvement targets. DESIGN: We describe a systems approach based on narrative exploration, participatory diagramming, data collection and discrete event simulation (DES), conducted by an international research collaborative. SETTING: The study is set in the tertiary neurotrauma centre in Yangon General Hospital, Myanmar, in 2019-2020 (prior to the SARS-CoV2 pandemic). PARTICIPANTS: The qualitative work involved 40 workshop participants and 64 interviewees to explore the views of a wide range of stakeholders including staff, patients and relatives. The 1-month retrospective admission snapshot covered 85 surgical neurotrauma admissions. RESULTS: The TBI pathway was outlined, with system boundaries defined around the management of TBI once admitted to the neurosurgical unit. Retrospective data showed 18% mortality, 71% discharge to home and an 11% referral rate. DES was used to investigate the system, showing its vulnerability to small surges in patient numbers, with critical points being CT scanning and observation ward beds. This explorative model indicated that a modest expansion of observation ward beds to 30 would remove the flow-limitations and indicated possible consequences of changes. CONCLUSIONS: A systems approach to improving TBI care in resource-poor settings may be supported by simulation and informed by qualitative work to ground it in the direct experience of those involved. Narrative interviews, participatory diagramming and DES represent one possible suite of methods deliverable within an international partnership. Findings can support targeted improvement investments despite coexisting resource limitations while indicating concomitant risks.