A Managed Care System with Telemedicine Support for Neurological Emergencies.

OBJECTIVES: Telemedicine is frequently used to provide remote neurological expertise for acute stroke workup and was associated with better functional outcomes when combined with a stroke unit system-of-care. We investigated whether such system-of-care yields additional benefits when implemented on top of neurological competence already available onsite. METHODS: Quality improvement measures were implemented within a "hub-and-spoke" teleneurology network in 11 hospitals already provided with onsite or telestroke expertise. Measures included dedicated units for neurological emergencies, standardization of procedures, multiprofessional training, and quality-of-care monitoring. Intervention effects were investigated in a controlled study enrolling patients insured at 3 participating statutory health insurances diagnosed with acute stroke or other neurological emergencies. Outcomes during the intervention period between November 2017 and February 2020 were compared with those pre-intervention between October 2014 and March 2017. To control for temporal trends, we compared outcomes of patients with respective diagnoses in 11 hospitals of the same region. Primary outcome was the composite of up-to-90-day death, new disability with the need of ambulatory or nursing home care, expressed by adjusted hazard ratio (aHR). RESULTS: We included 1,418 patients post-implementation (55% female, mean age 76.7 ± 12.8 year) and 2,306 patients pre-implementation (56%, 75.8 ± 13.0 year, respectively). The primary outcome occurred in 479/1,418 (33.8%) patients post-implementation and in 829/2,306 (35.9%) pre-implementation. The aHR for the primary outcome was 0.89 (95% confidence interval [CI]: 0.79-0.99, p = 0.04) with no improvement seen in non-participating hospitals between post- versus pre-implementation periods (aHR 1.04; 95% CI: 0.95-1.15). INTERPRETATION: Implementation of a multicomponent system-of-care was associated with a lower risk of poor outcomes. ANN NEUROL 2023;93:511-521.

Improving care coordination for patients with cardiac disease: Study protocol of the randomised controlled new healthcare programme (Cardiolotse).

INTRODUCTION: A lack of effective coordination and communication between ambulatory care physicians and hospitals, including the lack of follow-up care, poses a challenge to the recovery process of patients suffering from cardiac disease, often resulting in rehospitalisation and adverse outcomes. This innovative care programme aims to bridge the gap between ambulatory and hospital care. A key element of this programme is specifically trained care managers (Cardiolotse) who provide post-discharge support, access to additional resources and help the patient to navigate successfully through the healthcare system. MATERIAL AND METHODS: The study is set up as a prospective, randomised, controlled trial. Allocation to intervention group (support of care managers) and control group (usual care) follows an allocation ratio of 1:1 using block randomisation. Sample size calculations resulted in 1454patients per group after adjusting for potential non-compliance. All participants are surveyed at discharge, after 3 and 12 months. The primary outcome of the study is the 12-month rehospitalisation rate. Secondary outcomes include differences in length of hospital stay, mortality, quality-adjusted life years, costs and patient satisfaction. Statistical analysis and economic evaluation will be complemented by a process evaluation. DISCUSSION: The new healthcare programme is designed to support patients when leaving hospital with cardiac conditions by easing the transition between sectors through access to Cardiolotses and individualised care plans. We hypothesise that the programme reduces rehospitalisation and improves clinically relevant patient outcomes. TRIAL REGISTRATION: German Clinical Trial Register, DRKS00020424. Registered 2020-06-18, http://www.drks.de/DRKS00020424.

Impact of morbidity on care need increase and mortality in nursing homes: a retrospective longitudinal study using administrative claims data.

BACKGROUND: A growing number of older people are care dependent and live in nursing homes, which accounts for the majority of long-term-care spending. Specific medical conditions and resident characteristics may serve as risk factors predicting negative health outcomes. We investigated the association between the risk of increasing care need and chronic medical conditions among nursing home residents, allowing for the competing risk of mortality. METHODS: In this retrospective longitudinal study based on health insurance claims data, we investigated 20,485 older adults (≥65 years) admitted to German nursing homes between April 2007 and March 2014 with care need level 1 or 2 (according to the three level classification of the German long-term care insurance). This classification is based on required daily time needed for assistance. The outcome was care level change. Medical conditions were determined according to 31 Charlson and Elixhauser conditions. Competing risks analyses were applied to identify chronic medical conditions associated with risk of care level change and mortality. RESULTS: The probability for care level change and mortality acted in opposite directions. Dementia was associated with increased probability of care level change compared to other conditions. Patients who had cancer, myocardial infarction, congestive heart failure, cardiac arrhythmias, renal failure, chronic pulmonary disease, weight loss, or recent hospitalization were more likely to die, as well as residents with paralysis and obesity when admitted with care level 2. CONCLUSION: This paper identified risk groups of nursing home residents which are particularly prone to increasing care need or mortality. This enables focusing on these risk group to offer prevention or special treatment. Moreover, residents seemed to follow specific trajectories depending on their medical conditions. Some were more prone to increased care need while others had a high risk of mortality instead. Several conditions were neither related to increased care need nor mortality, e.g., valvular, cerebrovascular or liver disease, peripheral vascular disorder, blood loss anemia, depression, drug abuse and psychosis. Knowledge of functional status trajectories of residents over time after nursing home admission can help decision-makers when planning and preparing future care provision strategies (e.g., planning of staffing, physical equipment and financial resources).

Osteoporotic hip fracture prediction from risk factors available in administrative claims data - A machine learning approach.

OBJECTIVE: Hip fractures are among the most frequently occurring fragility fractures in older adults, associated with a loss of quality of life, high mortality, and high use of healthcare resources. The aim was to apply the superlearner method to predict osteoporotic hip fractures using administrative claims data and to compare its performance to established methods. METHODS: We devided claims data of 288,086 individuals aged 65 years and older without care level into a training (80%) and a validation set (20%). Subsequently, we trained a superlearner algorithm that considered both regression and machine learning algorithms (e.g., support vector machines, RUSBoost) on a large set of clinical risk factors. Mean squared error and measures of discrimination and calibration were employed to assess prediction performance. RESULTS: All algorithms used in the analysis showed similar performance with an AUC ranging from 0.66 to 0.72 in the training and 0.65 to 0.70 in the validation set. Superlearner showed good discrimination in the training set but poorer discrimination and calibration in the validation set. CONCLUSIONS: The superlearner achieved similar predictive performance compared to the individual algorithms included. Nevertheless, in the presence of non-linearity and complex interactions, this method might be a flexible alternative to be considered for risk prediction in large datasets.

Multifactorial intervention for hip and pelvic fracture patients with mild to moderate cognitive impairment: study protocol of a dual-centre randomised controlled trial (OF-CARE).

BACKGROUND: A hip or pelvic fracture is a major fall-related injury which often causes a decline in mobility performance and physical activity. Over 40% of patients with hip fracture have cognitive impairment or dementia and poorer rehabilitation outcomes than those without cognitive impairment. In this subgroup, there is a lack of evidence on the best practices supporting recovery. The main aim of this study is to investigate the effects of a transitional care intervention after inpatient rehabilitation on physical activity and functional performance in this group of cognitively impaired patients. METHODS/DESIGN: This dual-centre, randomised controlled trial compares a multifactorial intervention with usual care as control condition. Two hundred and forty community-dwellers (≥ 65 years) with a hip or pelvic fracture and mild to moderate cognitive impairment (MMSE 17-26) are recruited at the end of inpatient rehabilitation. The four-month intervention consists of (a) an individually tailored, progressive home exercise program and physical activity promotion delivered by professional instructors and lay instructors (two home visits per week) and (b) a long-term care counselling approach addressing unmet care needs, pleasurable activities, and caregiver issues if needed. Primary outcome parameters are physical activity, measured as daily walking duration with an accelerometer-based activity monitor (activPAL™) over 72 h, and functional performance, assessed with Short Physical Performance Battery sum scores. Secondary outcome parameters are fear of falling, fall related self-efficacy, falls, quality of life, depression and activity of daily living. Data are collected at the end of rehabilitation, before the intervention at the patient's home (baseline), after four months (post-intervention), and seven months (follow-up). In addition to completer and intent-to-treat analyses of outcomes, economic data and incremental cost-effectiveness are analysed. DISCUSSION: Existing service models of volunteer services and legal counselling provided by care counsellors were considered when developing the intervention protocol. Therefore, it should be feasible to translate and deliver the intervention into real-world practice if it has been demonstrated to be effective. TRIAL REGISTRATION: German Clinical Trials Register, DRKS00008863 (Accessed 17 Apr 2019), ISRCTN registry, ISRCTN69957256 (Accessed 17 Apr 2019).

Development of a risk assessment tool for osteoporotic fracture prevention: A claims data approach.

BACKGROUND: In aging societies osteoporotic fractures are a major health problem with high economic costs. Targeting prevention at individuals at high risk is important to reduce the future burden of fractures. Available risk assessment tools (e.g., FRAX®, QFracture, the algorithm provided by the German Osteology Society (DVO-Tool)) rely on self-reported patient information to predict fracture risk. Time and resource constraints, limited access to clinical data, and (un)willingness to participate may hamper the use of these tools. To overcome such obstacles, the aim is to develop a fracture risk assessment tool based on claims data that may be directly used on an institutional level. METHODS: Administrative claims data of an elderly (≥65years) population (N=298,530) for the period from 2006 through 2014 was used. Major osteoporotic fractures (MOF) were identified based on hospital diagnoses. We applied Cox proportional hazard regression to determine the association of individual risk factors and fracture risk. Hazard ratios were used to construct a risk score. The discriminative ability of the score was evaluated using C-statistics. RESULTS: We identified 7864 MOF during follow-up. The median time to first fracture during follow-up was 371.5days. Individuals with a MOF during follow-up had a higher mean and median risk score (mean: 4.53; median: 4) than individuals without MOF (mean: 3.07; median: 3). Adding drug-related risk factors slightly improved discrimination compared to a simple model with age, gender, and prior fracture. CONCLUSION: We developed a fracture risk score model based on in-hospital treated subjects to predict MOF that can be used on an institutional level. The score included age, sex and prior fracture as risk factors. Adding other risk factors involved very small improvement in discrimination.