Point Of Triage Research Articles

Community Gynaecology Service Oxfordshire: How a local women's health hub improved access to menopause care.

The Women's Health Strategy 2022 aims to improve access to Women's Healthcare needs, with menopause care being a key component to optimise. This strategy encourages the 'expansion of women's health hubs around the country and other models of "one-stop clinics," bringing essential women's services together to support women'.1 A service evaluation was completed at a local women's health hub in Oxfordshire to review referrals for specialist menopause care. The hub managed 75.1% (970/1291) of referrals in-house at the point of triage. Of these 970 menopause consultations, 10.7% (104/970) were referred onwards for secondary care management, with the remainder 89.3% (866/970) managed within the women's health hub. The service evaluation shows successful application of a local women's health hub in improving access to menopause care.

Exploring the impact of missingness on racial disparities in predictive performance of a machine learning model for emergency department triage.

To investigate how missing data in the patient problem list may impact racial disparities in the predictive performance of a machine learning (ML) model for emergency department (ED) triage. Racial disparities may exist in the missingness of EHR data (eg, systematic differences in access, testing, and/or treatment) that can impact model predictions across racialized patient groups. We use an ML model that predicts patients' risk for adverse events to produce triage-level recommendations, patterned after a clinical decision support tool deployed at multiple EDs. We compared the model's predictive performance on sets of observed (problem list data at the point of triage) versus manipulated (updated to the more complete problem list at the end of the encounter) test data. These differences were compared between Black and non-Hispanic White patient groups using multiple performance measures relevant to health equity. There were modest, but significant, changes in predictive performance comparing the observed to manipulated models across both Black and non-Hispanic White patient groups; c-statistic improvement ranged between 0.027 and 0.058. The manipulation produced no between-group differences in c-statistic by race. However, there were small between-group differences in other performance measures, with greater change for non-Hispanic White patients. Problem list missingness impacted model performance for both patient groups, with marginal differences detected by race. Further exploration is needed to examine how missingness may contribute to racial disparities in clinical model predictions across settings. The novel manipulation method demonstrated may aid future research.

Guidelines for Triage of COVID-19 Patients Presenting With Multisystemic Symptoms.

This article describes how multisystemic symptoms, both respiratory and nonrespiratory, can be used to differentiate coronavirus disease-2019 (COVID-19) from other diseases at the point of patient triage in the community. The article also shows how combinations of symptoms could be used to predict the probability of a patient having COVID-19. We first used a scoping literature review to identify symptoms of COVID-19 reported during the first year of the global pandemic. We then surveyed individuals with reported symptoms and recent reverse transcription polymerase chain reaction (RT-PCR) test results to assess the accuracy of diagnosing COVID-19 from reported symptoms. The scoping literature review, which included 81 scientific articles published by February 2021, identified 7 respiratory, 9 neurological, 4 gastrointestinal, 4 inflammatory, and 5 general symptoms associated with COVID-19 diagnosis. The likelihood ratio associated with each symptom was estimated from sensitivity and specificity of symptoms reported in the literature. A total of 483 individuals were then surveyed to validate the accuracy of predicting COVID-19 diagnosis based on patient symptoms using the likelihood ratios calculated from the literature review. Survey results were weighted to reflect age, gender, and race of the US population. The accuracy of predicting COVID-19 diagnosis from patient-reported symptoms was assessed using area under the receiver operating curve (AROC). In the community, cough, sore throat, runny nose, dyspnea, and hypoxia, by themselves, were not good predictors of COVID-19 diagnosis. A combination of cough and fever was also a poor predictor of COVID-19 diagnosis (AROC = 0.56). The accuracy of diagnosing COVID-19 based on symptoms was highest when individuals presented with symptoms from different body systems (AROC of 0.74-0.81); the lowest accuracy was when individuals presented with only respiratory symptoms (AROC = 0.48). There are no simple rules that clinicians can use to diagnose COVID-19 in the community when diagnostic tests are unavailable or untimely. However, triage of patients to appropriate care and treatment can be improved by reviewing the combinations of certain types of symptoms across body systems.

Development of Ebola virus disease prediction scores: Screening tools for Ebola suspects at the triage-point during an outbreak.

The control of Ebola virus disease (EVD) outbreaks relies on rapid diagnosis and prompt action, a daunting task in limited-resource contexts. This study develops prediction scores that can help healthcare workers improve their decision-making at the triage-point of EVD suspect-cases during EVD outbreaks. We computed accuracy measurements of EVD predictors to assess their diagnosing ability compared with the reference standard GeneXpert® results, during the eastern DRC EVD outbreak. We developed predictive scores using the Spiegelhalter-Knill-Jones approach and constructed a clinical prediction score (CPS) and an extended clinical prediction score (ECPS). We plotted the receiver operating characteristic curves (ROCs), estimated the area under the ROC (AUROC) to assess the performance of scores, and computed net benefits (NB) to assess the clinical utility (decision-making ability) of the scores at a given cut-off. We performed decision curve analysis (DCA) to compare, at a range of threshold probabilities, prediction scores' decision-making ability and to quantify the number of unnecessary isolation. The analysis was done on data from 10432 subjects, including 651 EVD cases. The EVD prevalence was 6.2% in the whole dataset, 14.8% in the subgroup of suspects who fitted the WHO Ebola case definition, and 3.2% for the set of suspects who did not fit this case definition. The WHO clinical definition yielded 61.6% sensitivity and 76.4% specificity. Fatigue, difficulty in swallowing, red eyes, gingival bleeding, hematemesis, confusion, hemoptysis, and a history of contact with an EVD case were predictors of EVD. The AUROC for ECPS was 0.88 (95%CI: 0.86-0.89), significantly greater than this for CPS, 0.71 (95%CI: 0.69-0.73) (p < 0.0001). At -1 point of score, the CPS yielded a sensitivity of 85.4% and specificity of 42.3%, and the ECPS yielded sensitivity of 78.8% and specificity of 81.4%. The diagnostic performance of the scores varied in the three disease contexts (the whole, fitting or not fitting the WHO case definition data sets). At 10% of threshold probability, e.g. in disease-adverse context, ECPS gave an NB of 0.033 and a net reduction of unnecessary isolation of 67.1%. Using ECPS as a joint approach to isolate EVD suspects reduces the number of unnecessary isolations by 65.7%. The scores developed in our study showed a good performance as EVD case predictors since their use improved the net benefit, i.e., their clinical utility. These rapid and low-cost tools can help in decision-making to isolate EVD-suspicious cases at the triage point during an outbreak. However, these tools still require external validation and cost-effectiveness evaluation before being used on a large scale.

Improving triaging from primary care into secondary care using heterogeneous data-driven hybrid machine learning

Effective and rapid triaging from primary care into secondary care plays a pivotal role in providing patients with timely treatment and managing increasing demands for healthcare resources. Existing triaging methods from primary care to secondary care are labor-intensive processes that involve manually reviewing referral data from multiple sources and can cause long referral to treatment time. There has been no research using machine learning methods that automatically analyzes heterogeneous data including referral letters to recognize regularities to support the primary to secondary care triage. In this paper, we propose a heterogeneous data-driven hybrid machine learning model including Natural Language Processing (NLP) to improve hospital triage efficiency at the point of triage. The proposed model achieved a precision of 0.83, recall of 0.82, F1-Score of 0.83, accuracy of 0.82, AUC of 0.90 in identifying patients with non-inflammatory conditions (NIC) and inflammatory arthritis (IA) at the point of triage with explainable risk stratifications. Our model is piloted in a real-world trial in a large secondary care hospital in the UK to compare referral accuracy and time saved between our model and clinicians, and evaluate its acceptability by users. Our model achieved precision and recall of 0.83 and 0.81, compared with the precision and recall of 0.80 and 0.78 by clinicians. The research also shows that our model enabled decision support can save clinicians 8 h per week in assessing the referral assessment. This paper is the first study to streamline hospital triage from primary care to secondary care using machine learning.

Evaluating diagnostic and management agreement between audiology and ENT: a prospective inter-rater agreement study in a paediatric primary contact clinic

BackgroundEar, Nose and Throat (ENT) primary contact models of care use audiologists as the first triage point for children referred to ENT for middle ear and hearing concerns; and have shown reduced waiting time, improved ENT surgical conversion rates and increased service capacity. This study aimed to investigate ‘safety and quality’ of the model by looking at agreement between audiologists’ and an ENT’s clinical decisions. MethodsWe performed an inter-rater agreement study on diagnosis and management decisions made by audiologists and an ENT for 50 children seen in an Australian hospital’s ENT primary contact service, and examined the nature and patterns of disagreements.ResultsProfessionals agreed on at least one site-of-lesion diagnosis for all children (100%) and on the primary management for 74% (Gwet’s AC1 = 0.67). Management disagreements clustered around i) providing ‘watchful waiting’ versus sooner medical opinion (18%), and ii) providing monitoring versus discharge for children with no current symptoms (8%). There were no cases where the audiologist recommended discharge when the ENT recommended further medical opinion.ConclusionsOur novel research provides further evidence that Audiologist-led primary contact models for children with middle ear and hearing concerns are safe as well as efficient.

GP.6 Improving Triaging of EEG Referrals for Rule out Infantile Spasms (ITERIS)

Background: Infantile spasms (IS) is a devastating pediatric seizure disorder for which EEG referrals are prioritized at the Hospital for Sick Children, representing a resource challenge. The goal of this study was to improve the triaging system for these referrals. Methods: Part 1: descriptive analysis was performed retrospectively on EEG referrals. Part 2: prospective questionnaires were used to determine relative risk of various predictive factors. Part 3: electronic referral form was amended to include 5 positive predictive factors. A triage point system was tested by assigning EEGs as high risk (3 days), standard risk (1 week), or low risk (2 weeks). A machine learning model was developed. Results: Most EEG referrals were from community pediatricians with a low yield of IS diagnoses. Using the 5 predictive factors, the proposed triage system accurately diagnosed all IS within 3 days. No abnormal EEGs were missed in the low-risk category. The machine learning model had over 90% predictive accuracy and will be prospectively tested. Conclusions: Improving EEG triaging for IS may be possible to prioritize higher risk patients. Machine Learning techniques can potentially be applied to help with predictions. We hope that our findings will ultimately improve resource utilization and patient care.

Comparison of severity of immunized versus non-immunized COVID-19 patients admitted to ICU: A prospective observational study

BackgroundVaccines against COVID-19 show high efficacy, yet, infection is still being detected among immunized patients, although with blunted severity. The purpose of this study was to assess the severity of COVID-19 infection among immunized versus non-immunized COVID-19 patients admitted to ICU. MethodA prospective observational cohort study, including all COVID-19 patients admitted to intensive care unit between January 1st, 2021 and June 30th, 2021 were eligible for inclusion. A comparison of severity upon hospitalization of immunized versus non-immunized patients on a 7-level ordinal scale was conducted, using ordinal logistic regression. Results592 patients were enrolled, 524 (88.5%) non-immunized, 63 (10.6%) partially immunized, and 5 (0.9%) fully immunized, partially and fully immunized patients were grouped together. Majority of immunized patients (86.7%) were symptomatic before 21 days of immunization. Non-immunized group had fewer patients in the lower severity categories, while more patients in the higher severity categories compared to immunized group. At least one dose of immunization was associated with reduction of odds of moving up severity scale (OR = 0.2 [95% CI: 0.15–0.4]; p < 0.001) in a well fitted ordinal logistic regression model. At least one dose of immunization was associated with lower adjusted odds of 30 day all-cause mortality (OR = 0.45 [95% CI: 0.23–0.89]; p = 0.02). Non-immunized group had higher mortality rate (43.9% versus 29.4% [95% CI: 1.5 to 25.8]; p = 0.02). ConclusionMost COVID-19 patients admitted to ICU were non-immunized, most of the partially immunized patients got infected before immunity could develop, and fully immunized patients were likely non-responders. At least one dose of immunization significantly decreases severity of the disease across all ordinal severity categories, and is significantly associated with lower 30 day all-cause mortality. Accordingly, immunization status may have to be considered when deciding on disposition of COVID-19 patients at the point of triage.

Implementing a Novel Nursing Site Manager Role in the Pediatric Emergency Department for Patient and Staff Safety During the COVID-19 Pandemic

Implementing a Novel Nursing Site Manager Role in the Pediatric Emergency Department for Patient and Staff Safety During the COVID-19 Pandemic

Common data elements for predictors of pediatric sepsis: A framework to standardize data collection.

Standardized collection of predictors of pediatric sepsis has enormous potential to increase data compatibility across research studies. The Pediatric Sepsis Predictor Standardization Working Group collaborated to define common data elements for pediatric sepsis predictors at the point of triage to serve as a standardized framework for data collection in resource-limited settings. A preliminary list of pediatric sepsis predictor variables was compiled through a systematic literature review and examination of global guideline documents. A 5-round modified Delphi that involved independent voting and active group discussions was conducted to select, standardize, and prioritize predictors. Considerations included the perceived predictive value of the candidate predictor at the point of triage, intra- and inter-rater measurement reliability, and the amount of time and material resources required to reliably collect the predictor in resource-limited settings. We generated 116 common data elements for implementation in future studies. Each common data element includes a standardized prompt, suggested response values, and prioritization as tier 1 (essential), tier 2 (important), or tier 3 (exploratory). Branching logic was added to the predictors list to facilitate the design of efficient data collection methods, such as low-cost electronic case report forms on a mobile application. The set of common data elements are freely available on the Pediatric Sepsis CoLab Dataverse and a web-based feedback survey is available through the Pediatric Sepsis CoLab. Updated iterations will continuously be released based on feedback from the pediatric sepsis research community and emergence of new information. Routine use of the common data elements in future studies can allow data sharing between studies and contribute to development of powerful risk prediction algorithms. These algorithms may then be used to support clinical decision making at triage in resource-limited settings. Continued collaboration, engagement, and feedback from the pediatric sepsis research community will be important to ensure the common data elements remain applicable across a broad range of geographical and sociocultural settings.

Physical characteristics of Gayo arabica coffee with semi-washed processing

Gayo, Aceh, Sumatera Island that are famous for producing coffee with distinctive characteristics due to the unique geographical condition and climate. The objective of this study was to determine physical quality of Gayo Green Coffee Bean using Indonesia Semi-Washed Processing Method compare to Full-Washed Processing Method (traditional method). Result showed that green bean had lower moisture content due to over exposure while drying. Green coffee bean was produced in small batch (10kg of coffee cherries) that it made it easier to control over production condition which resulting in lower triage percentage and defect point. Colour and aroma of green coffee bean also indicated good result of processing which is bluish-green colour and pleasant aroma of fresh grass and sweet sugar cane aroma. Compare to full washed, semi-washed had more triage and defect point due to some processing steps which could be improved by more controlled processing steps or sortation on ready to stored green bean.

Hospital admission as a deprescribing triage point for patients discharged to Residential Aged Care Facilities.

Deprescribing may benefit older frail patients experiencing polypharmacy. We investigated the scope for deprescribing in acutely hospitalised patients and the long-term implications of continuation of medications that could potentially be deprescribed. Acutely hospitalised patients (n = 170) discharged to Residential Aged Care Facilities, ≥75years and receiving ≥5 regular medications were assessed during admission to determine eligibility for deprescribing of key drug classes, along with the actual incidence of deprescribing. The impact of continuation of nominated drug classes (anticoagulants, antidiabetics, antiplatelets, antipsychotics, benzodiazepines, proton pump inhibitors (PPIs), statins) on a combined endpoint (death/readmission) was determined. Hyperpolypharmacy (>10 regular medications) was common (49.4%) at admission. Varying rates of deprescribing occurred during hospitalisation for the nominated drug classes (8-53%), with considerable potential for further deprescribing (34-90%). PPI use was prevalent (56%) and 89.5% of these had no clear indication. Of the drug classes studied, only continued PPI use at discharge was associated with increased mortality/readmission at 1year (hazard ratio 1.54, 95% confidence interval (1.06-2.26), P = 0.025), driven largely by readmission. There is considerable scope for acute hospitalisation to act as a triage point for deprescribing in older patients. PPIs in particular appeared overprescribed in this susceptible patient group, and this was associated with earlier readmission. Polypharmacy in older hospitalised patients should be targeted for possible deprescribing during hospitalisation, especially PPIs.

Contact tracing of staff in the emergency department during the COVID-19 pandemic.

Contact tracing of staff in the emergency department during the COVID-19 pandemic.

An Automated Deep Learning Method for Tile AO/OTA Pelvic Fracture Severity Grading from Trauma whole-Body CT.

Admission trauma whole-body CT is routinely employed as a first-line diagnostic tool for characterizing pelvic fracture severity. Tile AO/OTA grade based on the presence or absence of rotational and translational instability corresponds with need for interventions including massive transfusion and angioembolization. An automated method could be highly beneficial for point of care triage in this critical time-sensitive setting. A dataset of 373 trauma whole-body CTs collected from two busy level 1 trauma centers with consensus Tile AO/OTA grading by three trauma radiologists was used to train and test a triplanar parallel concatenated network incorporating orthogonal full-thickness multiplanar reformat (MPR) views as input with a ResNeXt-50 backbone. Input pelvic images were first derived using an automated registration and cropping technique. Performance of the network for classification of rotational and translational instability was compared with that of (1) an analogous triplanar architecture incorporating an LSTM RNN network, (2) a previously described 3D autoencoder-based method, and (3) grading by a fourth independent blinded radiologist with trauma expertise. Confusion matrix results were derived, anchored to peak Matthews correlation coefficient (MCC). Associations with clinical outcomes were determined using Fisher's exact test. The triplanar parallel concatenated method had the highest accuracies for discriminating translational and rotational instability (85% and 74%, respectively), with specificity, recall, and F1 score of 93.4%, 56.5%, and 0.63 for translational instability and 71.7%, 75.7%, and 0.77 for rotational instability. Accuracy of this method was equivalent to the single radiologist read for rotational instability (74.0% versus 76.7%, p=0.40), but significantly higher for translational instability (85.0% versus 75.1, p=0.0007). Mean inference time was <0.1s per test image. Translational instability determined with this method was associated with need for angioembolization and massive transfusion (p=0.002-0.008). Saliency maps demonstrated that the network focused on the sacroiliac complex and pubic symphysis, in keeping with the AO/OTA grading paradigm. A multiview concatenated deep network leveraging 3D information from orthogonal thick-MPR images predicted rotationally and translationally unstable pelvic fractures with accuracy comparable to an independent reader with trauma radiology expertise. Model output demonstrated significant association with key clinical outcomes.

Point-of-care C-reactive protein testing to support the management of respiratory tract infections in community pharmacy: A feasibility study.

Point-of-care (POC) C-reactive protein (CRP) testing is employed in European primary care settings to differentiate viral from bacterial respiratory tract infections (RTIs) the latter, requiring referral for antibiotics. This service has yet to be trialled in Australian community pharmacy to support over-the-counter RTI management. To evaluate the feasibility, based on clinical and operational outcomes, of POC CRP testing to support Western Australian community pharmacists' management of RTIs. Patients with RTI signs and symptoms were recruited from June-August 2019at 5 community pharmacies. Trained pharmacists made recommendations based on participants' POC CRP levels and routine clinical assessment. Participants completed questionnaires and telephone follow-ups on Days 3 (by pharmacists) and 5 (by researchers) post-testing. Service provision and uptake were assessed in 3 separate weekly tally sheets. Clinical outcomes: CRP levels among the 131 participants recruited were: < 5mg/L (bacterial infection unlikely; n=60; 45.8%); 5-19mg/L (bacterial infection possible if suggestive routine assessment; n=52; 39.7%) and 20-100mg/L (bacterial infection likely if suggestive routine assessment; n=19; 14.5%). Pharmacists' management included over-the-counter medicines (131, 100%), self-care advice (125, 95.4%) and immediate general practitioner (GP) referral (15, 11.5%). Sixty-five percent (76/117) of participants had recovered by Day 5. Operational outcomes: The services was provided in 21.2% of eligible RTI presentations, representing a service uptake rate of 28.1%. Post-CRP testing, 50.9% (58/114) of participants had changed perceptions regarding their need for antibiotics, with 14.3% (16/112) subsequently intending to seek a GP prescription. Consumer satisfaction was high (100%) and most participants (93.4%, 123/131) would utilise the service again. POC CRP testing was a feasible and well-accepted strategy to facilitate community pharmacy as a triage point for RTI. Consumer confidence in the pharmacists' advice, supported by CRP testing, potentially reduced unnecessary GP visits and antibiotic prescribing.

A prospective study of placental growth factor in twin pregnancy and development of a dichorionic twin pregnancy specific reference range.

To develop a dichorionic twin pregnancy specific reference range for placental growth factor (PlGF), and to compare gestation-specific placental growth factor levels in twin pregnancies later complicated by pre-eclampsia, hypertensive disorder of pregnancy or fetal growth restriction with control pregnancies. Prospective observational study. Single large tertiary maternity unit in Ireland. Women with a twin pregnancy. Consenting pregnant women, across a variety of gestations, had a single blood sample taken at one time-point only during their pregnancy. The plasma was initially biobanked and PlGF was measured later in batches using the point of care Triage® PlGF test. Development of pre-eclampsia, hypertensive disorder of pregnancy or fetal growth restriction. Placental growth factor levels in uncomplicated dichorionic twin pregnancies were significantly lower in the women who later developed pre-eclampsia than in the controls at all gestational intervals. In those that later developed any hypertensive disorder of pregnancy, median PlGF was lower only in those recruited before 24weeks of gestation, whereas in infants with a customised birthweight below the third centile, PlGF was lower only in those sampled after 24weeks of gestation. Placental growth factor levels in twin pregnancy differ significantly between those women with a pregnancy that will later be complicated by pre-eclampsia and those that will not. This difference is present many weeks before clinical signs or symptoms of disease are present. Using cross-sectional values from uncomplicated twin pregnancies, we have developed a dichorionic twin pregnancy specific reference range for PlGF. Placental growth factor levels in twin pregnancy differ significantly between women that will later develop pre-eclampsia and those that will not.

What to Expect When Switching to a Second Antidepressant Medication Following an Ineffective Initial SSRI

An antidepressant medication switch often follows a failed initial trial with selective serotonin reuptake inhibitors (SSRIs). When, for whom, and how often second-step response and remission occur are unclear, as is preferred second-step trial duration. As more treatments are approved for use following 2 failed "adequate" trials, researchers and clinicians require an evidence-based definition of "adequate." Following citalopram in the randomized Sequenced Treatment Alternatives to Relieve Depression (STAR*D) clinical trial (which ran July 2001-September 2006), participants with score ≥ 11 on the 16-item Quick Inventory of Depressive Symptomatology-Self-Rated (QIDS-SR₁₆) were randomized to bupropion sustained release, sertraline, or venlafaxine extended release (up to 14 weeks). The QIDS-SR₁₆ defined response, remission, and no clinically meaningful benefit based on the modified intent-to-treat sample. About 80% of 438 participants completed ≥ 6 weeks of treatment with the switch medication. All treatments had comparable outcomes. Overall, 21% (91/438) remitted, 9% (40/438) responded without remission, and 58% (255/438) had no meaningful benefit. Half of the responses and two-thirds of remissions occurred after 6 weeks of treatment. Overall, 33% of responses (43/131) occurred after ≥ 9 weeks of treatment. No baseline features differentiated early from later responders or remitters. No early triage point was found, but those with at least 20% reduction from baseline in QIDS-SR₁₆ score around week 2 were 6 times more likely to respond or remit than those without this reduction. Following nonefficacy with an initial SSRI, only about 20% remit and more than half achieve no meaningful benefit with a second-step switch to another monoaminergic antidepressant. A 12-week trial duration seems necessary to capture as many second-step switch responders as possible. ClinicalTrials.gov identifier: NCT00021528.

Envisioning the Post-COVID-19, Pre-Vaccine Emergency Department.

Envisioning the Post-COVID-19, Pre-Vaccine Emergency Department.

P93 Outcomes of enhanced triage of rheumatology referrals at a single point of access

Abstract Background With increasing pressure on rheumatology services there is a need to identify alternative pathways for patients which are better aligned to their needs. East Surrey CCG is formed of 17 GP practices serving an adult population of 137,000. First Community Health and Care acts as a single point of referral for patients with musculoskeletal conditions. In 2018 a triage service was established, run by a GP with an extended role (GPweR), for patients referred to rheumatology. Analysis of triaged patients was undertaken to explore the feasibility of diverting a subgroup of patients with non-inflammatory conditions into community-based care. Methods Referrals from February 2018 to March 2019 were analysed from the point of triage to confirmation of the diagnosis in secondary care. Triage was undertaken with additional reference to the primary care record in EMIS. For each referral it was recorded whether, according to the available information, this was more likely to be an inflammatory or non-inflammatory condition and whether they could have been seen first in a community GPwER rheumatology clinic. The final diagnosis after their hospital appointment was recorded. A pilot GPweR-led community clinic was established in April 2019 to see those patients selected by the process as suitable for community-based care. Results 407 patients were triaged. 96% were triaged to secondary care. Of these, records from the subsequent rheumatology appointment(s) were available for 312. The final diagnosis was inflammatory in 120 (38%) and non-inflammatory in 192 (62%). 152 patients (49%) were thought to have a non-inflammatory condition at triage which was confirmed in 83% of cases after a consultant appointment. 160 patients (51%) were thought likely to have an inflammatory condition which was confirmed in 59%. Positive predictive value of the triage process for non-inflammatory conditions was 83%, negative predictive value 59%, sensitivity 66%, specificity 79%. 57 patients selected from triage were seen in the pilot community clinic. Final diagnoses were: 53% fibromyalgia, 19% osteoarthritis, 12% soft tissue rheumatism, 14% gout/inflammatory arthritis. 54% of patients needed blood tests and 39% imaging. 31% required a face to face follow up appointment and 47% were referred to physiotherapy. 7% were referred on to rheumatology. Conclusion Referrals to secondary care rheumatology are rising and putting increasing pressure on services and referral to treatment times. There is a need to develop alternative pathways for patients with non-inflammatory conditions who could be managed in community settings. Our work shows that a majority of these can be identified at the point of triage with good specificity. We have also shown that of those seen in a pilot community clinic few needed onward referral. Our work suggests that there is a substantial opportunity to develop alternative and potentially more cost-effective community-based pathways for patients. Disclosures B. Forgie None. T. Garrood None.

Estimation of Pregnant Women Possibly Left Behind in Tsunami Predicted Areas of Nagoya City: A Simulation for Rescue Planning.

In case of an outbreak of Nankai Trough Mega-earthquake, it is predicted that a tsunami would invade Nagoya City within 100 minutes, hitting about one third of the City of Nagoya. If the administrative plan of the city and midwives' expertise are coordinated, pregnant women's chances of survival will increase. The authors carried out this simulation study in an attempt to improve consistency of the two efforts. We estimated the number of pregnant women using a machine learning model. The evacuation distance of pregnant women was estimated on the basis of the data of road center line. Through this simulation study, it became clear that preparation for approximately 2600 pregnant women escaping from tsunami predicted area and for about 1200 pregnant women possibly left in the area is needed. Our study suggests that triage point planning is needed in areas where pregnant women are evacuated. The triage makes it possible to transport women to appropriate hospitals.