A patient-centered composite endpoint weighting technique for orthopaedic trauma research

Background

This study aimed to address the current limitations of the use of composite endpoints in orthopaedic trauma research by quantifying the relative importance of clinical outcomes common to orthopaedic trauma patients and use those values to develop a patient-centered composite endpoint weighting technique.

Methods

A Best-Worst Scaling choice experiment was administered to 396 adult surgically-treated fracture patients. Respondents were presented with ten choice sets, each consisting of three out of ten plausible clinical outcomes. Hierarchical Bayesian modeling was used to determine the utilities associated with the outcomes.

Results

Death was the outcome of greatest importance (mean utility = − 8.91), followed by above knee amputation (− 7.66), below knee amputation (− 6.97), severe pain (− 5.90), deep surgical site infection (SSI) (− 5.69), bone healing complications (− 5.20), and moderate pain (− 4.59). Mild pain (− 3.30) and superficial SSI (− 3.29), on the other hand, were the outcomes of least importance to respondents.

Conclusion

This study revealed that patients’ relative importance towards clinical outcomes followed a logical gradient, with distinct and quantifiable preferences for each possible component outcome. These findings were incorporated into a novel composite endpoint weighting technique.

Which orthopaedic trauma patients are likely to refuse to participate in a clinical trial? A latent class analysis

OBJECTIVE

The study aimed to assess systematic differences in the characteristics of patients that consented for the trial compared with the broader pool of eligible patients in a large, pragmatic orthopaedic trauma trial.

DESIGN

A retrospective observational study performed from April 2017 to March 2018.

SETTING

Academic trauma centre in Baltimore, USA.

PARTICIPANTS

There were 642 eligible adult trial participants with an operative fracture to the appendicular skeleton and were indicated for blood clot prophylaxis. The median age of the sample was 50 years (IQR: 31-63), and 60% were male.

PRIMARY OUTCOME MEASURE

The primary outcome was the refusal to enrol in the trial. Demographic and injury covariates were included in iterations of latent class models. The final model was selected based on a minimum Bayesian information criterion.

RESULTS

The final model identified three clusters with five covariates predictive of cluster membership (age, neighbourhood-based socioeconomic status, alcohol use, multiple fractures, multiple surgeries). The three clusters were associated with 22% (Cluster 1), 38% (Cluster 2) and 62% (Cluster 3) refusal rates, respectively. Members of Cluster 3 (n=84) were most commonly between 66 and 80 years of age (49% vs 6% (Cluster 1) and 21% (Cluster 2)), of high neighbourhood-based socioeconomic status (85% vs 63% (Cluster 1) and 8% (Cluster 2)), with isolated fractures (100% vs 80% (Cluster 1) and 92% (Cluster 2)), and were less likely to have multiple surgeries compared with the other clusters (28% vs 47% (Cluster 1) and 35% (Cluster 2)).

CONCLUSION

In this study, the likelihood of refusing to participate in the trial ranged from 22% to 62% in the three identified clusters. Elderly age, high socioeconomic status, and less severe injuries defined the cluster that was most likely to refuse trial participation.

TRIAL REGISTRATION NUMBER

NCT02984384.

Association Between 6-Week Postdischarge Risk Classification and 12-Month Outcomes After Orthopedic Trauma

Importance

Numerous studies have demonstrated that long-term outcomes after orthopedic trauma are associated with psychosocial and behavioral health factors evident early in the patient's recovery. Little is known about how to identify clinically actionable subgroups within this population.

Objectives

To examine whether risk and protective factors measured at 6 weeks after injury could classify individuals into risk clusters and evaluate whether these clusters explain variations in 12-month outcomes.

Design, Setting, and Participants

A prospective observational study was conducted between July 16, 2013, and January 15, 2016, among 352 patients with severe orthopedic injuries at 6 US level I trauma centers. Statistical analysis was conducted from October 9, 2017, to July 13, 2018.

Main Outcomes and Measures

At 6 weeks after discharge, patients completed standardized measures for 5 risk factors (pain intensity, depression, posttraumatic stress disorder, alcohol abuse, and tobacco use) and 4 protective factors (resilience, social support, self-efficacy for return to usual activity, and self-efficacy for managing the financial demands of recovery). Latent class analysis was used to classify participants into clusters, which were evaluated against measures of function, depression, posttraumatic stress disorder, and self-rated health collected at 12 months.

Results

Among the 352 patients (121 women and 231 men; mean [SD] age, 37.6 [12.5] years), latent class analysis identified 6 distinct patient clusters as the optimal solution. For clinical use, these clusters can be collapsed into 4 groups, sorted from low risk and high protection (best) to high risk and low protection (worst). All outcomes worsened across the 4 clinical groupings. Bayesian analysis shows that the mean Short Musculoskeletal Function Assessment dysfunction scores at 12 months differed by 7.8 points (95% CI, 3.0-12.6) between the best and second groups, by 10.3 points (95% CI, 1.6-20.2) between the second and third groups, and by 18.4 points (95% CI, 7.7-28.0) between the third and worst groups.

Conclusions and Relevance

This study demonstrates that during early recovery, patients with orthopedic trauma can be classified into risk and protective clusters that account for a substantial amount of the variance in 12-month functional and health outcomes. Early screening and classification may allow a personalized approach to postsurgical care that conserves resources and targets appropriate levels of care to more patients.

Comparing the biological washout of β+-activity induced in mice brain after 12C-ion and proton irradiation

In clinical ion beam therapy, protons as well as heavier ions such as carbon are used for treatment. For protons, β(+)-emitters are only induced by fragmentation reactions in the target (target fragmentation), whereas for heavy ions, they are additionally induced by fragmentations of the projectile (further referred to as autoactivation). An approach utilizing these processes for treatment verification, by comparing measured Positron Emission Tomography (PET) data to predictions from Monte Carlo simulations, has already been clinically implemented. For an accurate simulation, it is important to consider the biological washout of β(+)-emitters due to vital functions. To date, mathematical expressions for washout have mainly been determined by using radioactive beams of (10)C- and (11)C-ions, both β(+)-emitters, to enhance the counting statistics in the irradiated area. Still, the question of how the choice of projectile (autoactivating or non-autoactivating) influences the washout coefficients, has not been addressed. In this context, an experiment was carried out at the Heidelberg Ion Beam Therapy Center with the purpose of directly comparing irradiation-induced biological washout coefficients in mice for protons and (12)C-ions. To this aim, mice were irradiated in the brain region with protons and (12)C-ions and measured after irradiation with a PET/CT scanner (Siemens Biograph mCT). After an appropriate waiting time, the mice were sacrificed, then irradiated and measured again under similar conditions. The resulting data were processed and fitted numerically to deduce the main washout parameters. Despite the very low PET counting statistics, a consistent difference could be identified between (12)C-ion and proton irradiated mice, with the (12)C data being described best by a two component fit with a combined medium and slow washout fraction of 0.50 ± 0.05 and the proton mice data being described best by a one component fit with only one (slow) washout fraction of 0.73 ± 0.06.

Automation and uncertainty analysis of a method for in-vivo range verification in particle therapy

We introduce the automation of the range difference calculation deduced from particle-irradiation induced β(+)-activity distributions with the so-called most-likely-shift approach, and evaluate its reliability via the monitoring of algorithm- and patient-specific uncertainty factors. The calculation of the range deviation is based on the minimization of the absolute profile differences in the distal part of two activity depth profiles shifted against each other. Depending on the workflow of positron emission tomography (PET)-based range verification, the two profiles under evaluation can correspond to measured and simulated distributions, or only measured data from different treatment sessions. In comparison to previous work, the proposed approach includes an automated identification of the distal region of interest for each pair of PET depth profiles and under consideration of the planned dose distribution, resulting in the optimal shift distance. Moreover, it introduces an estimate of uncertainty associated to the identified shift, which is then used as weighting factor to 'red flag' problematic large range differences. Furthermore, additional patient-specific uncertainty factors are calculated using available computed tomography (CT) data to support the range analysis. The performance of the new method for in-vivo treatment verification in the clinical routine is investigated with in-room PET images for proton therapy as well as with offline PET images for proton and carbon ion therapy. The comparison between measured PET activity distributions and predictions obtained by Monte Carlo simulations or measurements from previous treatment fractions is performed. For this purpose, a total of 15 patient datasets were analyzed, which were acquired at Massachusetts General Hospital and Heidelberg Ion-Beam Therapy Center with in-room PET and offline PET/CT scanners, respectively. Calculated range differences between the compared activity distributions are reported in a 2D map in beam-eye-view. In comparison to previously proposed approaches, the new most-likely-shift method shows more robust results for assessing in-vivo the range from strongly varying PET distributions caused by differing patient geometry, ion beam species, beam delivery techniques, PET imaging concepts and counting statistics. The additional visualization of the uncertainties and the dedicated weighting strategy contribute to the understanding of the reliability of observed range differences and the complexity in the prediction of activity distributions. The proposed method promises to offer a feasible technique for clinical routine of PET-based range verification.

Schwingungsspektren von komplexen Boraten

The aim of the investigations was the characterization of the polyborates, as far as IR-spectroscopy is concerned. The investigations covered the absorption spectra of different pentaborates, including those substituted by 10B-, 11B-, and H-D-isotopes, as well as the reflection spectra of potassium-pentaborate single crystals with polarized radiation. The concept of group frequency analysis was successfully applied to the interpretation of the pentaborate-spectra; almost all bands were attributed to vibrations of the BO3, BO4 and OH units.

A data-driven approach to patient blood management

BACKGROUND

Patient blood management (PBM) has become a topic of intense interest; however, implementing a robust PBM system in a large academic hospital can be a challenge. In a joint effort between transfusion medicine and information technology, we have developed three overlapping databases that allow for a comprehensive, semiautomated approach to monitoring up-to-date red blood cell (RBC) usage in our hospital. Data derived from this work have allowed us to target our PBM efforts.

STUDY DESIGN AND METHODS

Information on transfusions is collected using three databases: daily report, discharge database, and denominator database. The daily report collects data on all transfusions in the past 24 hours. The discharge database integrates transfusion data and diagnostic billing codes. The denominator database allows for rate calculations by tracking all patients with a hemoglobin test ordered. A set of algorithms is applied to automatically audit RBC transfusions. The transfusions that do not fit the algorithms' rules are manually reviewed. Data from audits are compiled into reports and distributed to medical directors. Data are also used to target education efforts.

RESULTS

Since our PBM program began, the percentage of appropriate RBC orders increased from an initial 70%-80% to 90%-95%, and the overall RBC transfusions/1000 patient-days has decreased by 67% in targeted areas of the hospital. Our PBM program has shaved approximately 3% from our hospital's blood budget.

CONCLUSION

Our semiautomated auditing system allows us to quickly and comprehensively analyze and track blood usage throughout our hospital. Using this technology, we have seen improvements in our hospital's PBM.

[Significantly shorter anesthesia time for surgery of the lumbar spine : process analytical comparison of spinal anesthesia and intubation narcosis]

INTRODUCTION

Most surgery of the lumbar spine is performed with the patient under general anesthesia (GA); however, qualitative benefits of spinal anesthesia (SA) have been reported. The goal of this study was to compare time efficiency between these two anesthesia methods in lumbar spine surgery. To test the hypothesis that the use of SA leads to significant time saving compared to GA for lumbar spine surgery, key points in the preoperative, intraoperative and postoperative anesthesiology care times were analyzed. The focus was on anesthesia time excluding surgery time.

MATERIALS AND METHODS

Electronically based data of 473 anesthesia procedures (368 SA, 105 GA) for lumbar spine interventions performed in the prone position (i. e. decompression, discectomy and transpedicular instrumentation) were analyzed retrospectively. Patient population data including gender, age, American Society of Anesthesiologists (ASA) classification and body mass index (BMI) were analyzed. The focus was on the documented perioperative key time points which are defined as follows: (1) induction, (2) positioning (turning into prone position), (3) scrubbing and covering, (4) surgery time (knife to skin closure), (5) closing (end of surgery until leaving operating room) and (6) handing over to recovery. Differences in the amount of time for each perioperative period were calculated for SA and GA.

RESULTS

In 7 out of the 368 SA patients SA failed and had to be converted to GA. There were no significant differences in BMI, ASA prevalence and gender between SA and GA patients but SA patients were significantly older (median 61.7 ± 15.4 years) than GA patients (median 56.1 ± 14.6 years). However, SA required significantly less time for induction (SA: 17.7 ± 7.0 min, GA: 21.6 ± 7.2 min), preoperative preparation (SA: 9.7 ± 3.6 min, GA: 13.3 ± 5.4 min) and closing period (SA: 4.9 ± 1.1 min, GA: 15.3 ± 5.7 min) compared to GA. Total anesthesia time with exclusion of the surgery time revealed a significant time reduction using SA of 19 min (95 % confidence interval: range 13.6-24.4 min, median in SA: 56.7 min, median in GA: 75.7 min, p < 0.0001).

CONCLUSIONS

This study showed that in lumbar spine surgery 19 min of anesthesia time can be saved using SA compared to GA which could have an impact on economic aspects. Gender, BMI and ASA had no statistically detectable influence on the choice between the two anesthesia methods. The fact that time-intensive complex instrumentation is mainly performed in younger patients may explain why GA patients were younger than SA patients.

A matched-pair cluster-randomized trial of guided care for high-risk older patients

BACKGROUND

Patients at risk for generating high health care expenditures often receive fragmented, low-quality, inefficient health care. Guided Care is designed to provide proactive, coordinated, comprehensive care for such patients.

OBJECTIVE

We hypothesized that Guided Care, compared to usual care, produces better functional health and quality of care, while reducing the use of expensive health services.

DESIGN

32-month, single-blind, matched-pair, cluster-randomized controlled trial of Guided Care, conducted in eight community-based primary care practices.

PATIENTS

The "Hierarchical Condition Category" (HCC) predictive model was used to identify high-risk older patients who were insured by fee-for-service Medicare, a Medicare Advantage plan or Tricare. Patients with HCC scores in the highest quartile (at risk for generating high health care expenditures during the coming year) were eligible to participate.

INTERVENTION

A registered nurse collaborated with two to five primary care physicians in providing eight services to participants: comprehensive assessment, evidence-based care planning, proactive monitoring, care coordination, transitional care, coaching for self-management, caregiver support, and access to community-based services.

MAIN MEASURES

Functional health was measured using the Short Form-36. Quality of care and health services utilization were measured using the Patient Assessment of Chronic Illness Care and health insurance claims, respectively.

KEY RESULTS

Of the eligible patients, 904 (37.8 %) gave written consent to participate; of these, 477 (52.8 %) completed the final interview, and 848 (93.8 %) provided complete claims data. In intention-to-treat analyses, Guided Care did not significantly improve participants' functional health, but it was associated with significantly higher participant ratings of the quality of care (difference = 0.27, 95 % CI = 0.08-0.45) and 29 % lower use of home care (95 % CI = 3-48 %).

CONCLUSIONS

Guided Care improves high-risk older patients' ratings of the quality of their care, and it reduces their use of home care, but it does not appear to improve their functional health.

Who participates in chronic disease self-management (CDSM) programs? Differences between participants and nonparticipants in a population of multimorbid older adults

BACKGROUND

Self-care management is recognized as a key component of care for multimorbid older adults; however, the characteristics of those most likely to participate in Chronic Disease Self-Management (CDSM) programs and strategies to maximize participation in such programs are unknown.

OBJECTIVES

To identify individual factors associated with attending CDSM programs in a sample of multimorbid older adults.

RESEARCH DESIGN

Participants in the intervention arm of a matched-pair cluster-randomized controlled trial of the Guided Care model were invited to attend a 6-session CDSM course. Logistic regression was used to identify factors independently associated with attendance.

SUBJECTS

All subjects (N = 241) were aged 65 years or older, were at high risk for health care utilization, and were not homebound.

MEASURES

Baseline information on demographics, health status, health activities, and quality of care was available for CDSM participants and nonparticipants. Participation was defined as attendance at 5 or more CDSM sessions.

RESULTS

A total of 22.8% of multimorbid older adults who were invited to CDSM courses participated in 5 or more sessions. Having better physical health (odds ratio [95% confidence interval] = 2.3 [1.1-4.8]) and rating one's physician poorly on support for patient activation (odds ratio [95% confidence interval] = 2.8 [1.3-6.0]) were independently associated with attendance.

CONCLUSIONS

Multimorbid older adults who are in better physical health and who are dissatisfied with their physicians' support for patient activation are more likely to participate in CDSM courses.

Formation of nanoparticles by ion beam irradiation of thin films

The possibility of fabricating nanoparticles by ion bombardment was investigated by the ion bombardment of indium films on oxide covered Si and Cr surfaces. The different masses of implanting specimen ensured the different energy transfer while the same Si substrate ensured the same thermal conductivity for the In and Cr layers. Chromium served as a reference for the effect of ion bombardment and as a substrate as well. The SRIM program was used to simulate the ion surface interaction process. The nanoparticles were detected by scanning electron microscopy (SEM). We found that the melting of the In layer results in the formation of nanoparticles of 50-300 nm diameter and 5-10 nm height. This method can be promising for nanoparticle formation of materials with low melting point.

Sensitivity of intravenous and oral alfentanil and pupillary miosis as minimal and noninvasive probes for hepatic and first-pass CYP3A induction

Systemic and oral clearances of alfentanil (ALF) are in vivo probes for hepatic and first-pass cytochrome P450 (CYP) 3A. Both ALF single-point plasma concentrations and miosis are surrogates for area under the concentration-time curve (AUC) and clearance and are minimal and noninvasive CYP3A probes. This investigation determined ALF sensitivity for detecting graded CYP3A induction and compared it with that of midazolam (MDZ). Twelve volunteers (sequential crossover) received 0, 5, 10, 25, or 75 mg oral rifampin for 5 days. MDZ and ALF were given intravenously and orally on sequential days. Dark-adapted pupil diameter was measured with blood sampling. Graded rifampin decreased plasma MDZ AUCs to 83, 76, 62, and 59% (intravenous (i.v.)) and 78, 66, 39, and 24% (oral) of control. Hepatic and first-pass CYP3A induction were detected comparably by plasma MDZ and ALF AUCs. Single ALF concentrations detected all CYP3A induction, whereas MDZ was less sensitive. ALF miosis detected induction of first-pass but not hepatic CYP3A.

The effect of guided care teams on the use of health services: results from a cluster-randomized controlled trial

BACKGROUND

The effect of interdisciplinary primary care teams on the use of health services by patients with multiple chronic conditions is uncertain. This study aimed to measure the effect of guided care teams on multimorbid older patients' use of health services.

METHODS

Eligible patients from 3 health care systems in the Baltimore, Maryland-Washington, DC, area were cluster-randomized to receive guided care or usual care for 20 months between November 1, 2006, and June 30, 2008. Eight services of a guided care nurse working in partnership with patients' primary care physicians were provided: comprehensive assessment, evidence-based care planning, monthly monitoring of symptoms and adherence, transitional care, coordination of health care professionals, support for self-management, support for family caregivers, and enhanced access to community services. Outcome measures were frequency of use of emergency departments, hospitals, skilled nursing facilities, home health agencies, primary care physician services, and specialty physician services.

RESULTS

The study included 850 older patients at high risk for using health care heavily in the future. The only statistically significant overall effect of guided care in the whole sample was a reduction in episodes of home health care (odds ratio, 0.70; 95% confidence interval, 0.53-0.93). In a preplanned analysis, guided care also reduced skilled nursing facility admissions (odds ratio, 0.53; 95% confidence interval, 0.31-0.89) and days (0.48; 0.28-0.84) among Kaiser-Permanente patients.

CONCLUSIONS

Guided care reduces the use of home health care but has little effect on the use of other health services in the short run. Its positive effect on Kaiser-Permanente patients' use of skilled nursing facilities and other health services is intriguing. Trial Registration clinicaltrials.gov Identifier: NCT00121940.

Physician satisfaction with chronic care processes: a cluster-randomized trial of guided care

PURPOSE

Chronically ill older patients with multiple conditions are challenging to care for, and new models of care for this population are needed. This study evaluates the effect of the Guided Care model on primary care physicians' impressions of processes of care for chronically ill older patients.

METHODS

In Guided Care a specially educated registered nurse works at the practice with 2 to 5 primary care physicians, performing 8 clinical activities for 50 to 60 chronically ill older patients. The care model was tested in a cluster-randomized controlled trial between 2006 and 2009. All eligible primary care physicians in 14 pods (teams of physicians and their chronically ill older patients) agreed to participate (n = 49). Pods were randomly assigned to provide either Guided Care or usual care. Physicians were surveyed at baseline and 1 year later. We assessed the effects of Guided Care using responses from 38 physicians who completed both survey questionnaires. We measured physicians' satisfaction with chronic care processes, time spent on chronic care, knowledge of their chronically ill older patients, and care coordination provided by physicians and office staff.

RESULTS

Compared with the physicians in the control group, those in the Guided Care group rated their satisfaction with patient/family communication and their knowledge of the clinical characteristics of their chronically ill older patients significantly higher (rho<0.05 in linear regression models). Other differences did not reach statistical significance.

CONCLUSIONS

Based on physician report, Guided Care provides important benefits to physicians by improving communication with chronically ill older patients and their families and in physicians' knowledge of their patients' clinical conditions.

The effects of guided care on the perceived quality of health care for multi-morbid older persons: 18-month outcomes from a cluster-randomized controlled trial

BACKGROUND

The quality of health care for older Americans with chronic conditions is suboptimal.

OBJECTIVE

To evaluate the effects of "Guided Care" on patient-reported quality of chronic illness care.

DESIGN

Cluster-randomized controlled trial of Guided Care in 14 primary care teams.

PARTICIPANTS

Older patients of these teams were eligible to participate if, based on analysis of their recent insurance claims, they were at risk for incurring high health-care costs during the coming year. Small teams of physicians and their at-risk older patients were randomized to receive either Guided Care (GC) or usual care (UC).

INTERVENTION

"Guided Care" is designed to enhance the quality of health care by integrating a registered nurse, trained in chronic care, into a primary care practice to work with 2-5 physicians in providing comprehensive chronic care to 50-60 multi-morbid older patients.

MEASUREMENTS

Eighteen months after baseline, interviewers blinded to group assignment administered the Patient Assessment of Chronic Illness Care (PACIC) survey by telephone. Logistic and linear regression was used to evaluate the effect of the intervention on patient-reported quality of chronic illness care.

RESULTS

Of the 13,534 older patients screened, 2,391 (17.7%) were eligible to participate in the study, of which 904 (37.8%) gave informed consent and were cluster-randomized. After 18 months, 95.3% and 92.2% of the GC and UC recipients who remained alive and eligible completed interviews. Compared to UC recipients, GC recipients had twice greater odds of rating their chronic care highly (aOR = 2.13, 95% CI = 1.30-3.50, p = 0.003).

CONCLUSION

Guided Care improves self-reported quality of chronic health care for multi-morbid older persons.

Effects of guided care on family caregivers

PURPOSE

Guided Care (GC) is a model of health care for multimorbid older adults that is provided by a registered nurse who works with the patients' primary care physician (PCP). The purpose of this study was to determine whether GC improves patients' primary caregivers' depressive symptoms, strain, productivity, and perceptions of the quality of care recipients' chronic illness care.

DESIGN AND METHODS

A cluster-randomized controlled trial of GC was conducted within 14 PCP teams. The study sample included 196 primary caregivers who completed baseline and 18-month surveys and whose care recipients remained alive and enrolled in the GC study for 18 months. Caregiver outcomes included the following: depressive symptoms (Center for Epidemiological Studies-Depression scale), strain (Modified Caregiver Strain Index), the quality of care recipients' chronic illness care [Patient Assessment of Chronic Illness Care (PACIC)], and personal productivity (Work Productivity and Activity Impairment questionnaire, adapted for caregiving).

RESULTS

In multivariate regression models, between-group differences in depression, strain, work productivity, and regular activity productivity were not statistically significant after 18 months, but GC caregivers reported the overall quality of their recipients' chronic illness care to be significantly higher (adjusted beta = 0.40, 95% confidence interval : 0.14-0.67). Quality was significantly higher in 4 of 5 PACIC subscales, reflecting the dimensions of goal setting, coordination of care, decision support, and patient activation.

IMPLICATIONS

GC improved the quality of chronic illness care received by multimorbid care recipients but did not improve caregivers' depressive symptoms, affect, or productivity.

Guided care and the cost of complex healthcare: a preliminary report

OBJECTIVE

Guided Care (GC) is a model of proactive, evidence-based comprehensive healthcare provided by physician-nurse teams for people with several chronic health conditions. Our objective was to evaluate the preliminary effects of GC on health service utilization and costs.

STUDY DESIGN

Cluster-randomized controlled trial of GC involving 14 primary care teams (49 physicians) and 904 of their chronically ill patients age 65 years or older.

METHODS

Using insurance claims, we compared the health services used by patients who received GC with the health services used by patients who received usual care during the first 8 months of the study.

RESULTS

After adjustment for baseline characteristics, GC patients experienced, on average, 24% fewer hospital days (95% confidence interval [CI]: 49% fewer, 13% more), 37% fewer skilled nursing facility days (95% CI: 65% fewer, 5% more), 15% fewer emergency department visits (95% CI: 38% fewer, 18% more), and 29% fewer home healthcare episodes (95% CI: 53% fewer, 8% more), as well as 9% more specialist visits (95% CI: 8% fewer, 29% more). Based on current Medicare payment rates and GC costs, these differences in utilization represent an annual net savings of $75,000 (95% CI: -$244,000, $150,900) per nurse, or $1364 per patient.

CONCLUSIONS

Initial introduction of GC into primary care practices may be associated with less use of expensive health services and a net savings in healthcare costs among older patients with several chronic health conditions. Final results from the remaining 2 years of this ongoing study will be published in 2011.

Caregiving and chronic care: the guided care program for families and friends

BACKGROUND

The Guided Care Program for Families and Friends (GCPFF) is one component of "Guided Care" (GC), a model of primary care for chronically ill older adults that is facilitated by a registered nurse who has completed a supplemental educational curriculum.

METHODS

The GCPFF melds support for family caregivers with the delivery of coordinated and comprehensive chronic care and seeks to improve the health and well-being of both patients and their family caregivers. The GCPFF encompasses (a) an initial meeting between the nurse and the patient's primary caregiver, (b) education and referral to community resources, (c) ongoing "coaching," (d) a six-session group Caregiver Workshop, and (e) monthly Support Group meetings, all facilitated by the patient's GC nurse.

RESULTS

A cluster-randomized controlled trial of GC is underway in 14 primary care physician teams. Of 904 consented patients, 450 (49.8%) identified a primary caregiver; 308 caregivers met eligibility criteria, consented to participate, and completed a baseline interview. At 6-month follow-up, intervention group caregivers' mean Center for Epidemiological Studies Depression (CESD) and Caregiver Strain Index (CSI) scores were respectively 0.97 points (p = .14) and 1.14 points (p = .06) lower than control group caregivers'. Among caregivers who provided more than 14 hours of weekly assistance at baseline, intervention group caregivers' mean CESD and CSI scores were respectively 1.23 points (p = .20) and 1.83 points (p = .04) lower than control group caregivers'.

CONCLUSIONS

The GCPFF may benefit family caregivers of chronically ill older adults. Outcomes will continue to be monitored at 18-months follow-up.