Referrals to secondary care in an outpatient primary care walk-in clinic for refugees in Germany: results from a secondary data analysis based on electronic medical records

OBJECTIVES

The aims of our study were to describe the disease spectrum of refugees, to analyse to what extent their healthcare needs could be met in an outpatient primary care walk-in clinic and which cases required additional services from secondary care (ie, outpatient specialists or hospitals).

DESIGN

Retrospective longitudinal observational study.

SETTING

The study was based on routine data from a walk-in clinic in the largest central first reception centre in Hamburg, Germany between 4 November 2015 and 21 July 2016.

PARTICIPANTS

1467 asylum seekers with 4006 episodes of care (ie, distinctive health problems) resulting in 5545 consultations. The patients were 60% men and had a mean age of 23.2 years. About 90% of the patients were from Central Asia or from the Middle East and North Africa.

PRIMARY AND SECONDARY OUTCOME MEASURES

The endpoint of our analyses was referral to secondary care. Time to event was defined as days under treatment until the first referral. Predictor variables were the patients' diagnoses grouped in 46 categories. The data set was analysed by Cox regression allowing for multiple failure times per patient. This analysis was adjusted for age, sex and country of origin.

RESULTS

Referrals to secondary care occurred in 15.5% of the episodes. The diagnosis groups with the highest referral rates were 'eye' (HR 4.9; 95% CI 3.12 to 7.8; p≤0.001), 'teeth/gum symptom/complaint or disease' (3.51; 2.52 to 4.9; p≤0.001) and 'urological system/female or male genital' (2.50; 1.66 to 3.77; p≤0.001). Age, sex and country of origin had no significant effect on time until referral.

CONCLUSIONS

In most cases, the walk-in clinic physicians could provide first-line medical care for the health problems of patients not integrated in the German healthcare system. Additional resources were needed particularly not only for visual impairment and dental problems but also for psychological disorders, antenatal care and certain infections and injuries.

Episodes of care in a primary care walk-in clinic at a refugee camp in Germany - a retrospective data analysis

BACKGROUND

From 2015 to 2016 Germany faced an influx of 1.16 million asylum seekers. In the state of Hamburg Primary Care walk-in clinics (PCWC) were commissioned at refugee camps because the high number of residents (57,000 individuals) could not be provided with access to regular healthcare services. Our study aims were (1) to describe the utilization of a PCWC by camp residents, (2) to compare episodes of continuous care with shorter care episodes and (3) to analyse which diagnoses predict episodes of continuous care in this setting.

METHODS

A retrospective longitudinal observational study was conducted by reviewing all anonymized electronic medical records of a PCWC that operated from 4th November 2015 to 22nd July 2016 at a refugee camp in Hamburg. Episodes of care (EOC) were extracted based on the international classification of primary care-2nd edition (ICPC-2). Outcome parameters were episode duration, principal diagnoses, and medical procedures.

RESULTS

We analysed 5547 consultations of 1467 patients and extracted 4006 EOC. Mean patient age was 22.7 ± 14.8 years, 37.3% were female. Most common diagnoses were infections (44.7%), non-communicable diseases (22.2%), non-definitive diagnoses describing symptoms (22.0%), and injuries (5.7%). Most patients (52.4%) had only single encounters, whereas 19.8% had at least one EOC with a duration of ≥ 28 days (defined as continuous care). Several procedures were more prevalent in EOC with continuous care: Blood tests (5.2 times higher), administrative procedures (4.3), imaging (3.1) and referrals to secondary care providers (3.0). Twenty prevalent ICPC-2-diagnosis groups were associated with continuous care. The strongest associations were endocrine/metabolic system and nutritional disorders (hazard ratio 5.538, p < 0.001), dermatitis/atopic eczema (4.279, p < 0.001) and psychological disorders (4.056, p < 0.001).

CONCLUSION

A wide spectrum of acute and chronic health conditions could be treated at a GP-led PCWC with few referrals or use of medical resources. But we also observed episodes of continuous care with more use of medical resources and referrals. Therefore, we conclude that principles of primary care like continuity of care, coordination of care and management of symptomatic complaints could complement future healthcare concepts for refugee camps.

FRI0123 SAFETY PROFILE OF BARICITINIB FOR THE TREATMENT OF RHEUMATOID ARTHRITIS UP TO 8.4 YEARS: AN UPDATED INTEGRATED SAFETY ANALYSIS

Background:

Baricitinib (bari) is an oral selective inhibitor of Janus kinase (JAK) 1 and 2, approved for the treatment of moderately to severely active rheumatoid arthritis (RA) in adults.

Objectives:

Here we update the drug’s safety profile with data up to 8.4 years of treatment.

Methods:

Long-term safety of bari was assessed from 9 completed randomized trials (5 Ph3, 3 Ph2, 1 Ph 1b) and 1 ongoing long-term extension (LTE) study. Incidence rates (IR) per 100 patient-years (PY) were calculated for all patients with RA treated with ≥1 dose of bari through 1-Sep-2019 (All-Bari-RA analysis set). IRs for deep vein thrombosis (DVT), pulmonary embolism (PE), and DVT and/or PE (DVT/PE) were also calculated for groups of patients while receiving bari 2mg or bari 4mg within All-Bari-RA. Major adverse cardiovascular events (MACE) were adjudicated in 5 phase 3 studies and the LTE.

Results:

3770 pts received bari for 13,148 PY, with a median and maximum exposure of 4.2 and 8.4 years, respectively. Overall IRs per 100 PY were: for any treatment-emergent adverse event (AE)(25.8); serious AE (including death)(7.2); temporary interruption due to AE (9.5); permanent discontinuation due to AE (4.8); death (0.52); serious infection (2.7); opportunistic infection (0.44) (excluding tuberculosis [TB], including multidermatomal herpes zoster [HZ]); TB (0.15); HZ (3.0); MACE (0.50); DVT (0.31); PE (0.24); DVT/PE (0.45); malignancies excluding non-melanoma skin cancer (NMSC) (0.90); NMSC (0.33); lymphoma (0.06); and gastrointestinal perforation (0.04). Incidence rates (IR)[95% confidence intervals] for patients while receiving bari 2mg (N=1077) and bari 4mg (N=3400) were DVT 2mg (0.38) [0.18, 0.73] and 4mg (0.30) [0.21, 0.43]; PE 2mg (0.26) [0.09, 0.56] and 4mg (0.25) [0.16, 0.36]; and DVT/PE 2mg (0.47) [0.23, 0.84] and 4mg (0.46) [0.34, 0.61]. IRs for death tended to increase in later time intervals (beyond 192 weeks). No particular cause of death contributed to this increase. For all other safety topics of interest, across 48-week treatment intervals, IRs remained stable over time. Across safety topics, IRs were consistent with previous analyses1,2.

Conclusion:

In this update with 3,021 additional PY of exposure, bari maintained a safety profile similar to that previously reported,1,2with no increase of IRs across safety topics through exposures up to 8.4 years.

References:

[1]Smolen JS et al. J Rheumatol. 2019 Jan;46(1):7-18

[2]Genovese MC et al. Ann Rheum Dis. 2019 78(supp. 2):A308

Table.n/NARIRTreatment emergent AE3391/377025.8Serious AE (including death)940/37707.2Temporary d/c due to AE1241/36479.5Permanent d/c due to AE644/37704.8Death69/37700.52Serious infection344/37702.7Opportunistic infection (excluding tuberculosis, including multidermatomal herpes zoster)59/37700.44Herpes zoster384/37703.0Tuberculosis20/37700.15Major adverse cardiovascular events*63/32510.50DVT41/37700.31PE32/37700.24DVT and/or PE60/37700.45Malignancies excluding NMSC120/37700.90NMSC44/37700.33Lymphoma8/37700.06Gastrointestinal perforation6/37700.04

*studies with positive adjudication. AE=adverse event; D/C= discontinuation; DVT=deep vein thrombosis; IR=incidence rate; NAR=number of patients at risk; NMSC=non-melanoma skin cancer; PE=pulmonary embolism

Disclosure of Interests:

Mark C. Genovese Grant/research support from: Abbvie, Eli Lilly and Company, EMD Merck Serono, Galapagos, Genentech/Roche, Gilead Sciences, Inc., GSK, Novartis, Pfizer Inc., RPharm, Sanofi Genzyme, Consultant of: Abbvie, Eli Lilly and Company, EMD Merck Serono, Genentech/Roche, Gilead Sciences, Inc., GSK, Novartis, RPharm, Sanofi Genzyme, Josef S. Smolen Grant/research support from: AbbVie, AstraZeneca, Celgene, Celltrion, Chugai, Eli Lilly, Gilead, ILTOO, Janssen, Novartis-Sandoz, Pfizer Inc, Samsung, Sanofi, Consultant of: AbbVie, AstraZeneca, Celgene, Celltrion, Chugai, Eli Lilly, Gilead, ILTOO, Janssen, Novartis-Sandoz, Pfizer Inc, Samsung, Sanofi, Tsutomu Takeuchi Grant/research support from: Eisai Co., Ltd, Astellas Pharma Inc., AbbVie GK, Asahi Kasei Pharma Corporation, Nippon Kayaku Co., Ltd, Takeda Pharmaceutical Company Ltd, UCB Pharma, Shionogi & Co., Ltd., Mitsubishi-Tanabe Pharma Corp., Daiichi Sankyo Co., Ltd., Chugai Pharmaceutical Co. Ltd., Consultant of: Chugai Pharmaceutical Co Ltd, Astellas Pharma Inc., Eli Lilly Japan KK, Speakers bureau: AbbVie GK, Eisai Co., Ltd, Mitsubishi-Tanabe Pharma Corporation, Chugai Pharmaceutical Co Ltd, Bristol-Myers Squibb Company, AYUMI Pharmaceutical Corp., Eisai Co., Ltd, Daiichi Sankyo Co., Ltd., Gilead Sciences, Inc., Novartis Pharma K.K., Pfizer Japan Inc., Sanofi K.K., Dainippon Sumitomo Co., Ltd., Gerd Rüdiger Burmester Consultant of: AbbVie Inc, Eli Lilly, Gilead, Janssen, Merck, Roche, Pfizer, and UCB Pharma, Speakers bureau: AbbVie Inc, Eli Lilly, Gilead, Janssen, Merck, Roche, Pfizer, and UCB Pharma, Walter Deberdt Shareholder of: Eli Lilly and Company, Employee of: Eli Lilly and Company, Douglas Schlichting Shareholder of: Eli Lilly and Company, Employee of: Eli Lilly and Company, Hongsuk Song Employee of: Syneos Health under contract to Eli Lilly and Company, Daojun Mo Shareholder of: Eli Lilly and Company, Employee of: Eli Lilly and Company, Chad Walls Shareholder of: Eli Lilly and Company, Employee of: Eli Lilly and Company, Kevin Winthrop Grant/research support from: Bristol-Myers Squibb, Consultant of: AbbVie, Bristol-Myers Squibb, Eli Lilly, Galapagos, Gilead, GSK, Pfizer Inc, Roche, UCB

THU0519 OPTIMAL USE OF CONTRAST ENHANCED MRI FOR CLINICAL TRIALS OF INFLAMMATORY DISEASES: RETROSPECTIVE ANALYSIS OF DATA FROM A PHASE IIB STUDY OF BARICITINIB IN PATIENTS WITH ACTIVE RHEUMATOID ARTHRITIS

Background:

Magnetic resonance imaging (MRI) was used to confirm dose selection in a phase IIb clinical trial of baricitinib in patients with active rheumatoid arthritis (RA) on background methotrexate therapy (NCT01185353).[1] MRI data were retrospectively assessed for consistency, timing of post-contrast sequences following intravenous (IV) Gadolinium (Gd), and readability. Data were re-analyzed using a novel quantitative computer-aided methodology to extract the continuous volume of inflammatory changes.[2]

Objectives:

The objective was to examine how image quality and timing of the post-contrast MRI sequence can impact MRI-based exploratory endpoints in RA clinical trials when using novel computer-aided analysis tools.

Methods:

A total of 154 patients with definitive radiographic erosion had an MRI of the hand and wrist at baseline and at weeks 12 and 24. Three-dimensional T1-w fat-suppressed MRI sequences before and after IV Gd contrast were performed with dedicated coils. Due to the limited field of view, the coils were re-positioned during the image acquisition between the metacarpophalangeal (MCP) and finger joints and the wrist, following IV Gd injection, which introduced a time delay of the post-contrast sequences in the two anatomies in all patients.

Digital Imaging and Communications in Medicine (DICOM) headers of the MRIs were automatically assessed; the distribution of the time delay in minutes from Gd injection to post-contrast scan acquisition was calculated and the image quality and suitability for reading were evaluated (Figure 1). The time delays across MRI acquisitions at baseline and weeks 12 and 24 were also compared. Quality scores were assigned for each image using visual image quality assessment by an experienced reader blinded to treatment regimen, patient visits, and time after Gd. The images were categorized by quality based on total score. The reader used a proprietary software, to pre-define regions of interest (ROI) around the wrist and MCP joints (MCP-2 to MCP-5) in all three timepoints as a batch, avoiding adjacent blood vessels and possible artifacts. From these ROIs, the normalized volume of inflammation (NormI) was calculated in each joint relative to a standardized ROI in the thenar muscle. Quantitative Total Volume of Inflammation (QVI) was extracted automatically from all ROIs by counting the pixels that were enhanced two standard deviations above the intensity level of the normal muscle, allowing differentiation of areas with low-to-high inflammation.

Results:

The timing of post-contrast images from Gd injection was closely linked to image quality. In up to 10% of MRI data, the delay from Gd injection to scan acquisition caused significant variation in signal intensities. This led to a perceived increase in enhanced synovial volume due to the known effusion effects of the contrast media over time, which did not correspond to real size of the underlying synovial volume and pathology (Figure 2).

Conclusion:

The acquisition of MRIs in RA trials should be done in a methodical and systematic manner, where the quality of MRI scans and the correct timing of post-contrast sequences are optimized. The incorporation of unacceptable quality data will impact the interpretation of RA clinical trial data, especially when novel computer-assisted quantitative analysis methods for post-processing are used. Incorrect timing and inconsistency in image quality can be prevented by using coils covering the whole hand and/or a dynamic contrast-enhance (DCE)-MRI sequence immediately following IV Gd injection to ensure correct timing of the post-contrast MRI sequence.

References:

[1]Peterfy C, et al. J Rheumatol. 2019 46: 887–895.

[2]Tripathi D, et al. IJRCI. 2014 2(S1):SR2. DOI: 10.15305/ijrci/v2iS1/89.

Disclosure of Interests:

Mikael Boesen Consultant of: AbbVie, AstraZeneca, Eli Lilly, Esaote, Glenmark, Novartis, Pfizer, UCB, Paid instructor for: IAG, Image Analysis Group, AbbVie, Eli Lilly, AstraZeneca, esaote, Glenmark, Novartis, Pfizer, UCB (scientific advisor)., Speakers bureau: Eli Lilly, Esaote, Novartis, Pfizer, UCB, Mark Hinton: None declared, Javier Gonzalez-Zabaleta: None declared, Scott Beattie Shareholder of: Eli Lilly and Company, Employee of: Eli Lilly and Company, Douglas Schlichting Shareholder of: Eli Lilly and Company, Employee of: Eli Lilly and Company, Terence Rooney Shareholder of: Eli Lilly and Company, Employee of: Eli Lilly and Company, Olga Kubassova Shareholder of: IAG, Image Analysis Group, Consultant of: Novartis, Takeda, Lilly, Employee of: IAG, Image Analysis Group

FRI0044 ROBUST ANALYSES FOR RADIOGRAPHIC PROGRESSION IN RHEUMATOID ARTHRITIS

Background:

Reducing structural damage is an important treatment goal for rheumatoid arthritis (RA). Demonstrating a clinically meaningful, statistically significant difference in radiographic progression (assessed by van der Heijde modified total Sharp score, mTSS) is a common objective in trials for RA treatments.

Complete collection of radiographic data is challenging, especially in long term follow-up and pediatric studies. Therefore, scores for individual joints or entire patients are regularly missing. A frequently used analysis method for mTSS is the analysis of covariance model, in which missing data are imputed using linear extrapolation (ANCOVA+LE). However, other ways to deal with missing information have also been proposed.

Objectives:

To evaluate robust analysis methods for mTSS data.

Methods:

Simulated data were used to compare a random coefficient model (RC) without imputation, ANCOVA+LE and ANCOVA with last observation carry forward imputation (LOCF).

A log-normal distribution was used to generate baseline patient level data to simulate a 2-arm clinical trial using baseline mTSS and rate of change in mTSS from recently completed trials. Changes in mTSS (12, 28 and 44 week timepoints) were generated under linear, concave quadratic (fast progression then slow progression), and convex quadratic (slow progression then fast progression) assumptions, with the proportion of change forced to be 0 (a proportion of simulated patients do not have progression). A monotone missing pattern was assumed to generate a data set with missing data (the ‘observed’ dataset).

ANCOVA analyses were performed using baseline and treatment as predictors. The RC model was applied using baseline, treatment, time, and time-by-treatment interactions as fixed effect and time as a random effect. Bias (difference between average of simulation sample mean and true value, the smaller the better), root mean square error (RMSE, a measure of variation among simulation samples, the smaller the better), power and type I error rate were compared between methods.

Results:

The random coefficient model provided better or at least similar results in bias, RMSE, power and type I error rate as ANCOVA+LE under evaluated scenarios (Table 1).

Progression assumptionSimulation parameters(Number of simulations = 500; common sample size=300, baseline mTSS=~11.7)ModelBiasPowerRMSELinearppbo= 0.6, rpbo= 0.065ptrt= 0.68 rpbo= 0.046Δwk44= −0.49ANCOVA + Full0.0020.9240.140ANCOVA + LE0.0030.8660.155ANCOVA+LOCF0.1540.8440.190RC + FULL0.0010.920.139RC + OBS−0.0020.8720.156Concaveppbo= 0.6, rpbo= −0.0009, qpbo= 0.11ptrt= 0.68, rtrt= −0.0011, qtrt= 0.093Δwk44= −0.611ANCOVA + Full0.0020.9820.141ANCOVA + LE−0.0020.9260.180ANCOVA+LOCF0.1880.940.222RC + FULL0.0020.9780.141RC + OBS−0.0050.9240.174Convexppbo=0.6, rpbo= 0.0037, qpbo=−0.09ptrt= 0.68, rtrt= 0.003, qtrt= −0.1Δwk44= −0.83ANCOVA + Full0.00310.139ANCOVA + LE0.3430.9480.368ANCOVA+LOCF0.3910.9740.405RC + FULL−0.00410.140RC + OBS0.1990.9880.249

Abbreviations: FULL = complete dataset with no missing values trt = active treatment, OBS = the ‘observed’ dataset, pbo = placebo, p= proportion of patients with no progression, r = linear progression rate (mTSS units per week), q = quadratic term coefficient. Δ = active treatment progression – placebo r

Conclusion:

RC is a robust analysis method for mTSS. We recommend its use in primary analyses, especially for long-term extension and pediatric studies with a higher likelihood of missing data. This method can also provide reference for time points when no data are collected via estimated slope. ANCOVA+LE can be used for sensitivity analysis.

References:

None.

Disclosure of Interests:

Robert B.M. Landewé Consultant of: AbbVie; AstraZeneca; Bristol-Myers Squibb; Eli Lilly & Co.; Galapagos NV; Novartis; Pfizer; UCB Pharma, Luna Sun Shareholder of: Eli Lilly and Company, Employee of: Eli Lilly and Company, Yun-Fei Chen Shareholder of: Own shares in Eli Lilly and Company., Employee of: Employee of Eli Lilly and Company, Douglas Schlichting Shareholder of: Eli Lilly and Company, Employee of: Eli Lilly and Company, Désirée van der Heijde Consultant of: AbbVie, Amgen, Astellas, AstraZeneca, BMS, Boehringer Ingelheim, Celgene, Cyxone, Daiichi, Eisai, Eli-Lilly, Galapagos, Gilead Sciences, Inc., Glaxo-Smith-Kline, Janssen, Merck, Novartis, Pfizer, Regeneron, Roche, Sanofi, Takeda, UCB Pharma; Director of Imaging Rheumatology BV

Unique solutions in pediatric critical care

This pediatric intensive care unit (PICU) functions as a part of a multidisciplinary adult ICU and has evolved into a unique health care setting. The Special Care Unit (SCU), which is the name for our ICU, serves a population of adult and pediatric patients, including trauma, open-heart surgery, medicine, surgery, and neurosurgery. All staff are expected to provide care for adults and children without regard to admitting diagnosis. In addition to meeting the critical care needs of this diverse pediatric population in the SCU, we provide a pediatric critical care ground transport program for critically ill children from outlying hospitals. Competency-based orientation tools were developed and are used to guide the orientation process. The tools identify the required knowledge base necessary for a staff member to adequately care for the adult and pediatric populations and track each staff member's progress through orientation. The model used to deliver care to adults and children in this setting is innovative. This model represents one solution to providing critical care to both the adult and pediatric population in today's era of health care redesign.