Food Protein-Induced Enterocolitis Syndrome: Healthcare Utilization and Referral Patterns Among a Pediatric Cohort

BACKGROUND: Food protein–induced enterocolitis syndrome (FPIES) is a non-IgE mediated food allergy characterized by delayed, repetitive vomiting. FPIES has gained attention over the last few years and is becoming better recognized; however, there remains a lag in diagnosis. This study aimed to further explore this lag in diagnosis, as well as referral patterns and healthcare utilization, to determine areas for earlier recognition. METHODS: Data was obtained through retrospective chart reviews of pediatric FPIES patients at two hospital systems in New York. Charts were reviewed for age of symptom onset, age of diagnosis, FPIES episodes and healthcare visits prior to diagnosis, and reason/source of referral to allergy. A cohort of patients with IgE-mediated food allergy was reviewed for comparison. RESULTS: In total, 110 patients with FPIES were identified. The median length to diagnosis was 3 months, compared to 2 months in IgE-mediated food allergy (p < 0.05). Most referrals were from the pediatrician (68%) or gastroenterology (28%), none were from the ED. The most common reason for referral was concern of IgE-mediated allergy (51%), followed by FPIES (35%). CONCLUSION: This study demonstrates a lag in the diagnosis of FPIES and a lack of recognition of FPIES outside of the allergy community, as only one-third of the patients were considered to have FPIES prior to an allergy evaluation. Patients with profuse, repetitive vomiting 1- 4 hours after food ingestion, especially of common FPIES trigger foods, should be considered for diagnosis of FPIES, as these encounters provide an opportunity for early recognition.

Care and referral patterns in a large, dedicated nurse-led atrial fibrillation outpatient clinic

Introduction Atrial fibrillation (AF) is the most common arrhythmia and imposes a high burden on the healthcare system. A nurse-led AF outpatient clinic may alleviate the burden on the cardiology outpatient clinic by triaging patients who need care by a cardiologist or general practitioner (GP). However, care and referral patterns after initial assessment in a nurse-led AF outpatient clinic are unknown. We examined the proportion of AF patients assessed in a nurse-led clinic without outpatient follow-up by a cardiologist. Methods All patients with AF referred to our tertiary medical centre underwent cardiac work-up in the nurse-led AF outpatient clinic and were prospectively followed. Data on patient characteristics, rhythm monitoring and echocardiography were collected and described. Odds ratio (OR) for continuing care in the nurse-led AF outpatient clinic was calculated. Results From 2014 to 2018, 478 consecutive individual patients were referred to the nurse-led AF outpatient clinic. After the initial cardiac work-up, 139 patients (29.1%) remained under nurse-led care and 121 (25.3%) were referred to a cardiologist and 218 (45.6%) to a GP. Patients who remained under nurse-led care were significantly younger, were more symptomatic, more often had paroxysmal AF and had less comorbidities than the other two groups. After multivariable testing, CHA2DS2-VASc score ≥ 2 was associated with discontinued nurse-led care (OR 0.57, 95% confidence interval 0.34–0.95). Conclusion After initial cardiac assessment in the nurse-led outpatient clinic, about half of the newly referred AF patients were referred back to their GP. This strategy may reduce the burden of AF patients on secondary or tertiary cardiology outpatient clinics.

Adrenal incidentaloma: prevalence and referral patterns from routine practice in a large UK university teaching hospital

Context Adrenal incidentalomas are increasingly being identified during unrelated imaging. Unlike AI clinical management, data on referral patterns in routine practice are lacking. Objective To identify factors associated with AI referral Design We linked data from imaging reports and outpatient bookings from a large UK teaching hospital. We examined; (i) AI prevalence and (ii) pattern of referral to endocrinology, stratified by age, imaging modality, scan anatomical site, requesting clinical specialty and temporal trends. Patients Utilising key radiology phrases to identify scans reporting potential AI, we identified 4,097 individuals from 479,945 scan reports (2015-19). Main Outcome Measures Prevalence of AI and referral rates Results Overall, AI lesions were identified in 1.2% of scans. They were more prevalent in abdomen CT and MRI scans (3.0% and 0.6%, respectively). Scans performed increased 7.7% year-on-year from 2015-19, with a more pronounced rise in the number with AI lesions (14.7% pa). Only 394/4097 patients (9.6%) had a documented endocrinology referral code within 90 days, with medical (11.8%) more likely to refer than surgical (7.2%) specialties (p&lt;0.001). Despite prevalence increasing with age, older patients were less likely to be referred (p&lt;0.001). Conclusions While overall AI prevalence appeared low, scan numbers are large and rising; the number with identified AI are increasing still further. The poor AI referral rates, even in centres such as ours where dedicated AI multi-disciplinary team meetings and digital management systems are used, highlights the need for new streamlined, clinically-effective systems and processes to appropriately manage the AI workload.

Referral Processes and Dispositions in a Multidisciplinary Team for Older Adults With Cognitive Vulnerability

Multidisciplinary team care for community-dwelling older adults with multiple chronic conditions has proven value. Older adults receiving team care experience better outcomes than by solo practitioners alone, and teams are being established as outgrowths of primary care and other clinical settings. Yet little is known about the inner workings of multidisciplinary teams, both in terms of how referral patterns among team members are established and the extent to which older adults and their families accept referrals from team leaders to other clinical disciplines within teams. In this presentation, we provide details about referral patterns and rates of acceptance by study participants in an ongoing clinical trial testing a multidisciplinary team designed to provide care management to older adults (age &gt;65) with cognitive vulnerability due to dementia, depression, and/or delirium (3D Team). Nurse practitioners lead the 3D Team, conduct in-home clinical assessments and make referrals to other team members based on study protocols specifying participants’ eligibility for each 3D Team member. Results are based on the first 209 older adults randomized to the 3D Team. Pharmacist: all 209 members accepted having their medications reviewed and reconciled. Registered Dietician: of 134 referrals, 52 (38.8%) accepted. Occupational Therapist, of 117 referrals, 65 (55.6%) accepted. Physical Therapist: of 109 referrals, 92 (84.4%) accepted. Community Health Educator: of 106 referrals, 101 (95%) accepted. LCSW for depression-related problem solving therapy: of 76 referrals, 55 (72.4%) accepted. Criteria for referrals and interpretations of variations in referral acceptance rates by older adults and their families will be discussed.

Referral patterns to outpatient child and adolescent mental health services and factors associated with referrals being rejected. A cross-sectional observational study

Background Outpatient child and adolescent mental health services (CAMHS) are faced with the challenge of balancing increasing demands with limited resources. An additional challenge is high rejection rates of referrals which causes frustration for referring agents and families. In order to effectively plan and allocate available resources within CAMHS there is a need for up-to-date knowledge on referral patterns and factors associated with rejection of referrals. Methods In this cross-sectional observational study we did a retrospective review of all referrals (n = 1825) for children (0–18) referred for assessment at the outpatient CAMHS of the North Denmark Region in 2018. Results The most common referral reasons to CAMHS were attention deficit disorder (ADHD/ADD) (27.9%), autism spectrum disorder (22.4%), affective disorders (14.0%) and anxiety disorders (11.6%). The majority of referrals came from general practitioners, but for neurodevelopmental disorders educational psychologists were the primary referral source. Re-referrals constituted more than a third of all referrals (35.9%). Children in care were overrepresented in this clinical sample and had an increased risk (Adj. OR 2.54) of having their referrals rejected by CAMHS. Referrals from general practitioners were also associated with an increased risk of rejection (Adj. OR 3.29). Conclusions A high proportion of children with mental disorders have a repeated need for assessment by CAMHS. There is a need for future research on predictors of re-referral to outpatient services to identify potential targets for reducing re-referral rates as well as research on how to optimize service provision for children with a repeated need for assessment. General practitioners are the main gatekeepers to CAMHS and research on interventions to improve the referral process should be aimed towards general practitioners.