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Journal of Neuroscience Nursing:
doi: 10.1097/JNN.0000000000000065
Article

Steps Against Recurrent Stroke Plus: Patient Transition Program

Bretz, Miranda N.; Graves, Alex; West, Angie; Kiesz, Karen C.; Toth, Lynn; Welch, Marie

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Author Information

Questions or comments about this article may be directed to Miranda N. Bretz, MA, at rbretz@stroke.org. She is a Research Analyst at National Stroke Association, Centennial, CO.

Alex Graves, MS ANP, is the Clinical Director of Stroke Service at University of Colorado Hospital, Aurora, CO.

Angie West, RN MSN CCRN CNRN, is the Program Director Neuroscience/Stroke at Memorial Stroke Center, Long Beach Memorial Hospital, Long Beach, CA.

Karen C. Kiesz, RN MN CNRN, is the Stroke Program Manager at MultiCare Health System Caroline Administration, Tacoma, WA.

Lynn Toth, RN MSN NP-C, is a Cardiovascular Medical Specialist at Beebe Medical Center, Lewes, DE.

Marie Welch, RN MSN MS CRRN, is a Stroke Coordinator and Clinical Nurse Specialist at Vidant Health, Greenville, NC.

The Steps Against Recurrent Stroke Plus Program was sponsored by Charitable Contributions from the Bristol–Myers Squibb/Sanofi Pharmaceuticals Partnership and Allergan, Inc. The authors declare no conflicts of interest.

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Abstract

ABSTRACT: Stroke is a devastating health event that affects 800,000 people annually in the United States. Nearly 20% of strokes are recurrent strokes. Research shows that support after discharge from the hospital poststroke is frequently inadequate. The purpose of “Steps Against Recurrent Stroke (STARS) Plus: Patient Transition Program” was to design and deliver a program to facilitate optimal recovery for stroke survivors and prevent recurrent stroke. The program began at discharge from the hospital and continued through the first year of rehabilitation and recovery. Twelve hospitals participated; 261 patients enrolled, and contact was established with 193. Outcomes were gathered based on patient self-report of health status using the Short-Form Health Survey at 30, 90, 180, and 360 days. A dependent sample t test was completed comparing participants’ 30- and 360-day follow-up scores. Results demonstrated an overall increase in subjective pain. A repeated multivariate analysis of variance was conducted to compare 30- and 360-day Short-Form Health Survey scores across age and subscales. Results revealed that those in the younger and older age groups reported poorer health outcomes. Findings demonstrate a reduction in rehospitalization after stroke, increased medication adherence, strong patient satisfaction, and significant differences in health-related outcome measures across age groups, suggesting that middle-aged stroke survivors experience better health outcomes than younger or older age groups. Future programs should consider targeting pain management in all ages and education targeted at younger and older age groups, because they reported poorer health outcomes. The findings from this program should contribute to the guidance and insight for others developing transitional interventions for stroke survivors.

Stroke is a devastating health event that affects 800,000 people annually in the United States; furthermore, each year, nearly 200,000 stroke survivors have a second stroke within 90 days of their first stroke (Bushnell et al., 2011). In fact, one in six strokes is recurrent (Levine, 2004). Furthermore, a first time stroke survivor’s risk for a subsequent stroke is estimated to be between 30–40% within 5 years of the initial event (Lawrence, Fraser, Woods, & McCall, 2011). “Steps Against Recurrent Stroke (STARS) Plus: Patient Transition Program” was designed facilitate optimal recovery for stroke survivors and prevent recurrent hospitalizations.

A prior stroke places a patient at considerable risk for subsequent cardiovascular events (Touze et al., 2005). Studies show that recurrent strokes are more fatal and are more likely to result in major disability than first-time strokes (Rothwell, 2007). Whether the stroke is primary or secondary, compelling evidence identifies the following risk factors: high blood pressure, diabetes, high cholesterol, cigarette smoking, alcohol consumption, obesity, physical inactivity, metabolic syndrome, and atrial fibrillation (Furie et al., 2011). The American Heart Association, American Stroke Association, and American College of Cardiology have established research-based guidelines to help prevent and reduce the number of secondary strokes (Smith et al., 2011). The guidelines include aggressive medical and surgical therapies as well as lifestyle modifications. In addition, the organizations advised that the general population be educated on the signs and symptoms of stroke and the necessity for a rapid response (Sacco et al., 2006).

Well-documented clinical studies have identified successful recurrent stroke prevention strategies. Hackam and Spence (2007) examined 12 meta-analyses that included 106 studies published since 2002, which included more than 200,000 patients; the study team concluded that up to 80% of recurrent strokes are avoidable with appropriate risk factor management. American Heart Association, in a 2008 article on the need for healthcare reform, addressed the issue of unmanaged risk factors, healthcare services, and costs of care in cardiovascular disease and stroke (Gibbons et al., 2008). As stated in the article, many people at risk for stroke cannot afford medical care or prescription medications. It was recommended that a patient-centered approach to healthcare reform and preventive patient care be adopted. In 2006, the National Stroke Association (NSA) surveyed stroke survivors and caregivers for barriers that prevented effective rehabilitation after a stroke. A disconnection between the stroke survivors/caregivers and clinicians was explored (NSA, 2006). In addition, the deficiency in education and information for a stroke survivor’s self-care were identified. In the study, 38% of the respondents mentioned insufficient information on rehabilitation and recovery after stroke. In a recent Cochrane Database review, Smith et al. (2008) formulated that stroke information for the stroke survivor and caregiver improved aspects of patient satisfaction and reduced patient depression scores.

In addition, evidence-based medical therapy is an important aspect of recurrent stroke prevention. Yet, there is significant evidence that patients with stroke do not comply with their prescribed medications. Levine and colleagues (2007) studied long-term use of secondary prevention therapies among stroke survivors within the Veterans Health Administration. The study team found that, although use of recurrent stroke prevention therapies increased immediately after a stroke, the use quickly tapered off; at 365 days after stroke, one third of stroke survivors were not using any of the key medications. Bushnell and colleagues (2010) reported early results from the Adherence Evaluation After Ischemic Stroke-Longitudinal Registry measuring secondary prevention medication regimen persistence from hospital discharge. Their findings demonstrated that 25% of patients with stroke reported discontinuing one or more of their prescribed medications for secondary prevention within the first 3 months of discharge. These findings validate the fear that patients are not properly or consistently adhering to their medications.

Recurrent stroke prevention is a common medical problem treated by the healthcare provider (Dickerson & Carek, 2007). The healthcare provider’s office creates a perfect environment for necessary medication reinforcement and education on lifestyle changes and medical therapies for prevention. With recovery from stroke being truly a lifetime process, a healthcare provider’s office could provide the necessary support to encourage patients to make long-term commitments to lifestyle changes.

The NSA’s STARS Plus Program was a pilot program designed to promote the quality of life after stroke while providing preventive strategies. The interventions were based on the review of evidence-based literature. Intervention strategies included (a) supportive telephone calls to assess patient well-being and identify educational needs; (b) scheduled delivery of educational materials based on patient information needs; and (c) contact with healthcare providers of enrolled stroke survivors, encouraging them to use the educational materials to discuss recurrent stroke prevention with their patients.

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Methods

Certified primary stroke centers were recruited from NSA’s Stroke Center Network membership. Twelve hospitals participated in the STARS Plus Program enrolling patients with ischemic stroke at hospital discharge. In September 2009, patient enrollment began and concluded in October 2010. This pilot program enrolled 261 patients; contact was established with 193 of the enrolled patients. Reasons for not being able to contact patients were evenly split between patients withdrawing from the study and incorrect contact information. The NSA conducted training for each hospital to provide an overview of the program objectives, methodologies, and processes. Participating hospitals were provided with materials to use in recruiting patients with stroke at discharge.

Patients were able to opt-in to the STARS Plus Program based on perceived benefits: support with medication management and ongoing support in terms of timed, educational mailings about stroke recovery and recurrent stroke prevention. The mailings were designed to address stages of information needed during the first year of stroke recovery and beyond (Appendix A). Outcomes were gathered based on patient’s health status self-report at 30, 90, 180, and 365 days.

The Short-Form Health Survey (SF-12) was utilized as a standardized questionnaire of health-related outcome. This survey was selected for its brevity and standardization with other stroke survivor populations (Fleishman, Cohen, Manning, & Kosinski, 2006; Myant et al., 2007; Pickard, Johnson, Penn, Lau, & Noseworthy, 1999). Additional questions were added to the SF-12 to help measure STARS Plus specific outcomes. The NSA worked with Drs. Richard Zorowitz and Sharon Ostwald, recognized experts in stroke recovery and rehabilitation, and Judy Johnson RN, a stroke survivor, to develop the additional questions. In addition, NSA assumed responsibility for establishing and training call center personnel to administer the questionnaire with enrolled stroke survivors (survey example in Appendix B).

As part of the enrollment process, stroke survivors were encouraged to identify their healthcare provider. A goal of the STARS Plus Program was to promote communication linkages between the patient and their healthcare provider using standard hospital procedures (or a fax from the program if no standard procedures are in place). In addition, an information packet about the program and strategies for preventing a recurrent stroke was mailed to the healthcare provider, who was informed that their specific patient had just been diagnosed with a stroke and discharged from the hospital.

The NSA designed the educational packets based on feedback from stroke survivors. Feedback was derived from surveys, focus groups, discussion, and evaluations from the Hope conference series conducted by NSA in 2008. The feedback indicated that educational materials coupled with follow-up and opportunities to discuss materials with healthcare providers can positively impact behavior change strategies for recurrent stroke prevention. Educational packets were mailed and emphasized STARS Plus messaging, self-advocacy, caregiver well-being, and community reintegration.

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Results

Twenty-seven stroke center hospitals were sought out to enroll patients. Six of these hospitals dropped out without enrolling any patients. Nine hospitals expressed interest in the program but, however, were not able to complete their respective internal review board requirements in time to participate. Twelve hospitals enrolled one or more patients. Total enrollment reached 261 patients within the enrollment period. Of the 261 patients with stroke who enrolled in the STARS Plus Program, 193 agreed to participate after being informed and attaining consent. The remaining 68 chose not to participate and did not give consent. At each follow-up interval of contact, patients were lost. Seventy-two patients completed each follow-up in the full 12 months of the program. The 72 patients who completed the program represent 27.6% of the enrolled patients and 37.3% of the patients who agreed to participate. The most common reason for not participating included the following: wrong telephone number provided, disconnected telephone number, message left with no call back, or the participant passed away.

Table 1 demonstrates the number of patients enrolled by each participating hospital. Hospital enrollment varied considerably, from 2 to 111 patients per hospital. All hospitals had at least one patient remain in the program for the full 12-month follow-up period.

Table 1
Table 1
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Patient Demographics

Of the 193 patients who agreed to participate, the mean age was 63.18 (SD = 13.20) years, with the range being 24–92 years. Roughly 16% of participants were under 50 years old, 36% under 60 years old, 65% under 70 years old, and 90% under 80 years old. Fifty-eight percent (111) were men, and 42% (82) were women.

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Hospital Readmission After Stroke

Of the 72 participants who completed each follow-up assessment, 66.7% of the participants reported that they had not returned to a hospital since their stroke, at the 365-day follow-up. Of those who had returned, 25.0% returned once in the last year, and 8.4% had returned more than once in the last year.

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SF-12 Questionnaire

A dependent sample t test was utilized to compare the 30-day SF-12 scores (M = 37.80, SD = 8.55) with the 365-day SF-12 scores (M = 38.64, SD = 9.42) within the sample of 70 participants who completed each follow-up assessment and reported their age. The t test analysis did not reveal a significant difference, t(69) = −1.01, p = ns. Despite the lack of significant change from the first to final follow-up assessment, a multivariate analysis of variance (ANOVA) comparing each follow-up total score by age group within the sample did result in significant effects. For this analysis, age groups were created in which age was categorized into three groups based on sample age frequency: less than 63 years (n = 24), ages 63–73 years (n = 22), and 74 years and older (n = 24). The Wilks’ statistic revealed a significant effect for age within the follow-up assessments, Λ = .66, F(8, 128) = 3.66, p = .001. Univariate follow-up analysis revealed a significant effect for age group at the 30-, 90-, and 365-day follow-up periods, but not for the 180-day follow-up (refer to Table 2 for means, standard deviations, and statistics) Tukey HSD post hoc analysis demonstrated that those in the youngest age group reported significantly poorer health scores compared with the medium-age bracket on the 30-day (p < .05), 90-day (p = .001), and 365-day (p = .001) follow-up and significantly poorer health scores compared with the eldest age bracket on the 30-day follow-up. In addition, those in the oldest age group reported significantly poorer health than the medium-age group on the 90-day follow-up (p < .05). Essentially, participants did not significantly improve their self-reported health across follow-ups; however, those who are 63–73 years old, on average, report significantly better health across time compared with those who are younger and those who are older.

Table 2
Table 2
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SF-12 Questionnaire: Subscale Survey

The SF-12 is composed of eight subscales across four follow-up time periods (30, 90, 180, and 365 days), the eight subscales included physical functioning, role limitation physical, pain, general health, vitality, role limitation emotional, social functioning, and mental health. Table 3 demonstrates the participants’ means and standard deviations across follow-up periods for each subscale. Because of patient dropout at each follow-up period, only those who completed the entire 12-month program were included in this comparison (n = 72).

Table 3
Table 3
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A dependent sample t test was completed comparing participants’ 30- and 365-day follow-up self-reported score for each subscale. Participants’ SF-12 pain subscale scores significantly worsened from the initial 30-day survey to the 365-day follow-up assessment, indicating an overall increase in subjective pain reported by the participants. For the pain subscale, a lower score indicates more pain, and a higher score indicates less pain; ideally, a participant’s pain score would get higher, suggesting lessening of pain. The remaining subscales did not significantly differ, although the mental health subscale approached a significant increase in score (see Table 4). This indicates that perceived pain did not improve across assessment periods.

Table 4
Table 4
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SF-12 Questionnaire: Subscale Survey by Age

A repeated measures multivariate analysis of variance (MANOVA) was conducted to compare 30- and 365-day SF-12 scores across age and subscales using the same age groups described above within the sample of 70 participants who completed each follow-up assessment. The Wilks’ statistic revealed a significant effect for age group across the SF-12 subscale scores from 30- to 365-day follow-ups, Λ = .45, F(16, 120) = 3.63, p < .001. Univariate ANOVA found several significant differences between age groups across the SF-12 subscales (see Table 5 for means and statistics). The Tukey HSD post hoc test revealed that those in the youngest age group reported significantly more physical role limitation at both 30 and 365 days compared with the middle age group, p = .001. Similarly, the youngest age group reported significantly more emotional role limitation than both the middle and eldest age groups at both 30 and 365 days, p < .01 and p < .001, respectively. Likewise, the youngest age group reported significantly more pain at both 30 and 365 days compared with both the middle and eldest age group, p = .01 and p < .01, respectively. Following suit, the middle age group reported significantly better general health compared with the youngest age group for both follow-up surveys, p < .05. Along the same lines, the youngest age group reported significantly more mental health distress at both the 30- and 365-day follow-ups compared with the middle and eldest age groups, p = .001 and p = .001, respectfully. In all subscales, those who are younger reported poorer health-related outcomes than the older groups, indicating that those who are younger result in more negative self-proclaimed health afflictions than the older participants.

Table 5
Table 5
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In addition, a within-subject effect was found between the 30- and 365-day assessments (Λ =.78, F(8, 60) = 2.16, p < .05); however, an interaction effect for age group and assessment date was not found within subjects (Λ = .74, F(16, 120) = 1.21, p = ns). Univariate ANOVA demonstrated that, overall, the pain subscale scores significantly worsened from 30-day (M = 4.23, SD = 1.15) to 365-day (M = 3.06, SD = 1.09) follow-ups, regardless of age group, F(1, 67) = 5.87, p < .05), demonstrating a worsening in self-reported pain. Inversely, the mental health subscale significantly increased from 30-day (M = 3.73, SD = .87) to 365-day (M = 3.75, SD = .81) follow-ups, regardless of age group, F(1, 67) = 4.05, p < .05, demonstrating an improvement in mental health. The within-subject finding very closely mirrors the dependent sample t test finding reported above.

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Medication Adherence

Figure 1 demonstrates the observed medication adherence across each follow-up period. Overall, the 72 participants who responded at each follow-up period were found to adhere to their medication guidelines on consistent bases. Although not statistically significant, it was noted that those who reported they “never miss their medication” also reported slightly improved scores on the general health subscale of the SF-12 (refer to Figure 2).

Figure 1
Figure 1
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Figure 2
Figure 2
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STARS Plus Program Resources Utilized

As illustrated in Figure 3, the most commonly utilized STARS Plus Program resource across all follow-up periods included telephone calls that answered participant questions, followed by educational packets sent by NSA to the participant.

Figure 3
Figure 3
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Satisfaction With STARS Plus Program

Participants were asked how they would rate the STARS Plus Program in helpfulness poststroke at the 365-day follow-up. Figure 4 highlights the percent breakdown, in which a nearly 100% of participants reported that the program was either very beneficial or somewhat beneficial.

Figure 4
Figure 4
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Discussion

Because stroke is the leading cause of adult disability and the fourth leading cause of death in the United States, stroke prevention strategies are imperative (Minino, Murphy, Xu, & Kochanek, 2011). Arguably, equal attention should be given toward both primary and secondary prevention of stroke (Rothwell, 2007). Much is known about the medical therapy and lifestyle changes that patients with stroke should adhere to for preventing a subsequent stroke; but, there is significant evidence to suggest that patients have trouble adhering to the recommendations provided to them (Bushnell et al., 2010). Additional support from healthcare providers and organizations to improve adherence is crucial. Designing practical procedures to reduce the number of recurrent emergency department visits and strokes, all while improving satisfaction, are desired to improve the quality of life of the stroke survivor.

Results from this study found that there was a trend toward fewer hospital readmissions for participating patients. The cumulative percentage of stroke survivors who returned to the hospital at 1 year was 33.4%, which is slightly lower than the expected rates of rehospitalizations reported in other studies. Lakshminarayan and colleagues (2011) found that, of Medicare patients (aged ≥ 65 years), the rehospitalization rate for stroke survivors was 48.7% at 1 year, based on rehospitalization rates at 30 days, 1 year, and 5 years. In another study by Bravata, Ho, Meehan, Brass, and Concato (2007), 40% of patients had at least one readmission during the first year after their stroke.

Surprisingly, results suggest that patients may have higher than expected medication adherence. One goal of this study was to improve medication adherence through educational packets and telephone contacts. Participants were asked whether they never miss, sometimes miss, or often forget their medications. They were not asked about adherence to each specific medication, because individual lists of medications were not known to the study members. Although not statistically significant, patients who reported that they did not miss their medication also reported slightly improved scores on the SF-12 subscale survey. From the Adherence Evaluation After Ischemic Stroke-Longitudinal Registry, one quarter of stroke survivors reported stopping a prescribed medication within 3 months of hospitalization of acute stroke (Bushnell et al., 2010).

A significant difference was not found when comparing participants’ 30- to 365-day SF-12 scores, although, when comparing each follow-up total score by age group, significant effects were found. Participants in the 63- to 73-year age group, on average, reported significantly better health across time compared with those who are younger and older. In addition, when divided into two groups, those who originally reported better general health appeared to decline over time, whereas those who initially reported poorer general health appeared to slightly increase over the 365 days.

The SF-12 subscale survey scores did reveal an overall worsening in subjective pain reported by participants, regardless of age, which could indicate a need for healthcare professionals to focus and attune to patient pain levels across time. These findings suggest that patients may experience pain during the recovery period and are vulnerable to continuing pain up to a year after stroke and likely beyond. The detection of pain could lead to opportunities to address the issue more rapidly and appropriately. Pain often does not occur at the time of ischemic strokes and therefore may not be discussed in the acute and subacute stages. This observation should raise awareness, and providers, particularly in the outpatient clinics, should acknowledge this finding and consider that early stroke education includes pain management; after all, pain can interfere with all aspects of daily living (Endler, Corace, Summerfedlt, Johnson, & Rothbart, 2003).

Data demonstrated that nearly 100% of participants felt the program was either very beneficial or somewhat beneficial. In general, participants reported that the telephone calls were most beneficial during the first 6 months of the program. The youngest group of participants uniquely found the greater value in printed educational materials at the end of the program.

This study is unable to conclude that the outreach to physicians improved communication about stroke and recurrent stroke prevention, but the percent of STARS Plus patients who reported conversations with their healthcare providers about stroke is encouraging. It is of concern, however, that, despite the outreach and educational materials provided to physicians, 30% of STARS Plus participants who visited their physicians reported that the physicians had not discussed recurrent stroke prevention with them.

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Limitations

The study has several limitations with the primary limitation being small sample size. In addition, there was a lack of baseline knowledge of education and support provided by the participating hospitals. It is unclear what programs and support existed in the institutions at the time of this study, making additional variables hard to account for. In addition, all patients were recruited from certified primary stroke centers; therefore, although there is a reason to believe that this program would be successful in an outpatient setting, those findings have not been established. In addition, this study is correlative in nature. Therefore, finding certain interventions was associated with higher perceived pain, for example, it does not necessarily mean that these strategies are ineffective.

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Implications for Practice

Many healthcare providers caring for patients with stroke are aware of the secondary stroke prevention guidelines, but successful tools and interventions are needed. “STARS Plus: Patient Transition Program” offers practical tools and recommendations for future development in this area. This study found that there was a trend toward fewer hospital readmissions. This is potentially because of a patient’s involvement in the program and the program’s follow-up schedule. This is an ever-striving goal for healthcare professionals, and by implementing similar programs in other healthcare settings, similar results could be established. In addition, medication adherence was higher than expected. Again, this might be because of the frequency of calls and patient’s awareness that medication management would be discussed. On average, patients in the 63- to 73-year age group reported better health across time compared with those who are younger and older. Subjective pain increased during the follow-up period, whereas mental health improved. Efforts should be made to inform patients about pain during the recovery stages and ask patients routinely about what they are experiencing.

Finally, almost all participants found this program to be helpful. Establishing contact with stroke survivors for up to four times a year may pose a challenge to healthcare providers but was found to be beneficial. The STARS Plus Program encourages consistent stroke survivor and nurse interaction. This contact throughout recovery allows more opportunities to catch warning signs throughout the recovery process, limiting hospital readmissions. Although more research is needed in this area, healthcare providers should consider, at minimum, supportive telephone calls to their stroke survivors. Creative and novel approaches, such as a stroke nurse navigator, to facilitate optimal recovery for stroke survivors and prevent recurrent hospitalizations should be considered.

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References

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Keywords:

recurrent stroke; secondary stroke; stroke education; stroke prevention

Appendix A STARS Plu...
Appendix A STARS Plu...
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APPENDIX B. STARS Pl...
APPENDIX B. STARS Pl...
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