Only one-third of referrals for fatty liver disease are on time: real-world study reveals opportunities to avoid unnecessary and delayed referrals

BACKGROUND AND AIMS

Fatty liver disease is a global health concern, but in the absence of specific guidelines, current referral patterns differ according to the preferences of the general practitioners. Outpatient Gastroenterology clinics spend futile resources on liver-healthy patients while diagnosing decompensated patients delayed. We aimed to describe referral patterns to a regional outpatient Gastroenterology clinic.

METHODS

We reviewed 9684 referrals from primary care for suspected liver disease in the years 2016-2017, during two years. Data were extracted from the patients' hospital records to assess the clinical workup and patient outcomes until a mean of 43 months after the time of referral. Referrals were categorized as unnecessary (no signs of liver disease), timely (significant fibrosis/compensated cirrhosis), or delayed (decompensated cirrhosis).

RESULTS

We included 375 patient referrals from primary care. The main reason for referral was elevated transaminases. More than half (54%) of patients had no signs of liver disease, being unnecessarily referred for evaluation, while 17% had decompensated liver disease and were thus referred too late.

CONCLUSIONS

Only one-third of patients referred on suspicion of liver disease were referred on time, either before presenting with decompensated liver cirrhosis or with some evidence of significant liver disease, e.g., liver fibrosis. There is a huge unmet need for clinical referral pathways in primary care. Strengths and Limitations of this StudyA strength of this study is the complete mapping of all potential referrals to the outpatient clinic in the two-year period. Instead of retrieving the historic data by ICD-10 diagnosis codes, and reflecting only those patients where the GP clearly suspects liver disease, we have a strong reliance on our methods. We screened all potentially relevant referrals, e.g., referrals due to weight loss or fatigue, which may reflect symptoms of cirrhosis. Thereby we are confident that we have not missed any patients that originally were referred with unspecific symptoms, but after evaluation are diagnosed with liver disease.Another strength of our study is the long follow-up period, which allows us to fully evaluate the course for the individual patient, and the potential later coming diagnoses.Finally, it is a strength of the study that we were not exclusive to one liver disease etiology, both ALD and NAFLD etiology were included in the study.A limitation of this study is the use of historic data, and the fact that it is a single-center study, showing only the referral patterns in one outpatient Gastroenterology clinic.

Deep learning detects and visualizes bleeding events in electronic health records

BACKGROUND

Bleeding is associated with a significantly increased morbidity and mortality. Bleeding events are often described in the unstructured text of electronic health records, which makes them difficult to identify by manual inspection.

OBJECTIVES

To develop a deep learning model that detects and visualizes bleeding events in electronic health records.

PATIENTS/METHODS

Three hundred electronic health records with International Classification of Diseases, Tenth Revision diagnosis codes for bleeding or leukemia were extracted. Each sentence in the electronic health record was annotated as positive or negative for bleeding. The annotated sentences were used to develop a deep learning model that detects bleeding at sentence and note level.

RESULTS

On a balanced test set of 1178   sentences, the best-performing deep learning model achieved a sensitivity of 0.90, specificity of 0.90, and negative predictive value of 0.90. On a test set consisting of 700 notes, of which 49 were positive for bleeding, the model achieved a note-level sensitivity of 1.00, specificity of 0.52, and negative predictive value of 1.00. By using a sentence-level model on a note level, the model can explain its predictions by visualizing the exact sentence in a note that contains information regarding bleeding. Moreover, we found that the model performed consistently well across different types of bleedings.

CONCLUSIONS

A deep learning model can be used to detect and visualize bleeding events in the free text of electronic health records. The deep learning model can thus facilitate systematic assessment of bleeding risk, and thereby optimize patient care and safety.

Paediatric reference intervals are heterogeneous and differ considerably in the classification of healthy paediatric blood samples

The aim was to elude differences in published paediatric reference intervals (RIs) and the implementations hereof in terms of classification of samples. Predicaments associated with transferring RIs published elsewhere are addressed. A local paediatric (aged 0 days to < 18 years) population of platelet count, haemoglobin level and white blood cell count, based on first draw samples from general practitioners was established. PubMed was used to identify studies with transferable RIs. The classification of local samples by the individual RIs was evaluated. Transference was done in accordance with the Clinical and Laboratory Standards Institute EP28-A3C guideline. Validation of transference was done using a quality demand based on biological variance. Twelve studies with a combined 28 RIs were transferred onto the local population, which was derived from 20,597 children. Studies varied considerably in methodology and results. In terms of classification, up to 63% of the samples would change classification from normal to diseased, depending on which RI was applied. When validating the transferred RIs, one RI was implementable in the local population. Conclusion: Published paediatric RIs are heterogeneous, making assessment of transferability problematic and resulting in marked differences in classification of paediatric samples, thereby potentially affecting diagnosis and treatment of children. What is Known: • Reference intervals (RIs) are fundamental for the interpretation of paediatric samples and thus correct diagnosis and treatment of the individual child. • Guidelines for the establishment of adult RIs exist, but there are no specific recommendations for establishing paediatric RIs, which is problematic, and laboratories often implement RIs published elsewhere as a consequence. What is New: • Paediatric RIs published in peer-reviewed scientific journals differ considerably in methodology applied for the establishment of the RI. • The RIs show marked divergence in the classification of local samples from healthy children.