Detection of primary Sjögren's syndrome in primary care: developing a classification model with the use of routine healthcare data and machine learning

Background: Primary Sjögren's Syndrome (pSS) is a rare autoimmune disease that is difficult to diagnose due to a variety of clinical presentations, resulting in misdiagnosis and late referral to specialists. To improve early-stage disease recognition, this study aimed to develop an algorithm to identify possible pSS patients in primary care. We built a machine learning algorithm which was based on combined healthcare data as a first step towards a clinical decision support system. Method: Routine healthcare data, consisting of primary care electronic health records (EHRs) data and hospital claims data (HCD), were linked on patient level and consisted of 1411 pSS and 929,179 non-pSS patients. Logistic regression (LR) and random forest (RF) models were used to classify patients using age, gender, diseases and symptoms, prescriptions and GP visits. Results: The LR and RF models had an AUC of 0.82 and 0.84, respectively. Many actual pSS patients were found (sensitivity LR = 72.3%, RF = 70.1%), specificity was 74.0% (LR) and 77.9% (RF) and the negative predictive value was 99.9% for both models. However, most patients classified as pSS patients did not have a diagnosis of pSS in secondary care (positive predictive value LR = 0.4%, RF = 0.5%). Conclusion: This is the first study to use machine learning to classify patients with pSS in primary care using GP EHR data. Our algorithm has the potential to support the early recognition of pSS in primary care and should be validated and optimized in clinical practice. To further enhance the algorithm in detecting pSS in primary care, we suggest it is improved by working with experienced clinicians

QUALITY OF INTERHOSPITAL TRANSPORT OF THE CRITICALLY ILL: IMPACT OF THE MOBILE INTENSIVE CARE UNIT (MICU)

Interhospital transfer of critically ill patients is associated with hemodynamic and pulmonary deterioration. In order to minimize additional risks of transport, a mobile intensive care unit with a specialized retrieval team (MICU) service was established at our tertiary referral center in 2009. In order to see the effects of this new transporting mode, we performed a prospective audit to investigate the quality of interhospital transfers to our university affiliated ICU. We evaluated transfers performed by MICU from March 2009 until December 2009. Data on fourteen vital variables were collected at the moment of departure, arrival and 24h after admission. Variables before and after transfer were compared using the Paired-Sample Test. Major deterioration was expressed as a variable beyond a predefined critical threshold. Results were compared to the data of our previous study concerning interhospital transfer performed by ambulance (1). 74 transfers over a 10-month period were evaluated: 84 percent of all patients were mechanically ventilated and 53 percent were on vasoactive agents. At arrival, systolic blood pressure, glucose and haemoglobin were significantly different compared to departure, although major deterioration never achieved significant values. 38 percent showed an increase of total number of variables beyond threshold at arrival, 32 percent exhibited a decrease of one or more variables beyond threshold and thirty percent had an even number. There was no correlation with the duration of transfer or severity of disease with patient status at arrival. ICU mortality was 28%. Compared to the transfers performed in 2005, there were far less incidents in the current situation: 12.5% vs. 34%. In the current study, all incidents were due to technical problems. Although mean APACHE II score was significantly higher, patients transferred by MICU showed less deterioration in pulmonary parameters compared to the patients transferred by ambulance. Conclusion Transfer by MICU appears to be well prepared and imposes minimal risk to the critically ill patient when compared to transfer performed by ambulance. The implementation of a transport protocol with a mobile Intensive Care Unit and a specialized retrieval team has therefore led to an improvement in quality of (critical) care.

Validation of the Dutch STOPBANG screening questionnaire for OSAS in healthy workers

Introduction and aims: OSAS is a serious health problem and is often underdiagnosed. The STOPBANG questionnaire has shown to be a useful screening tool for OSAS, especially in the preoperative setting. In developing a two-step screening tool for OSAS in Dutch healthy workers population (Eijsvogel et al. J Clin Sleep Med. 2015) we aimed to validate the Dutch translation and assess the diagnostic properties of the STOP-Bang Questionnaire Methods: 240 employees were included. They completed the Dutch version of the STOPBANG questionnaire and performed a home polysomnography. The questionnaire was translated into Dutch with forward and backward translation and compared with factor analysis to the English version. A score of ≥2 on the STOP questionnaire and ≥3 on the STOPBANG questionnaire indicated a high risk for OSAS. Results: 186 respondents underwent home polysomnography; 37.1% were diagnosed with OSAS (AHI ≥5 with symptoms). Significant differences between risk groups were found for AHI and all STOPBANG related characteristics. Factor analysis revealed that the Dutch version of the STOPBANG questionnaire was valid and resulted in a one-factor model. Sensitivity of the STOP questionnaire was 65.2% and of the STOPBANG 79.7%, while specificity was 66.7% and 56.4% respectively. A female-specific cut-off point of the neck circumference of 32.5cm (instead of 40 cm) improved the sensitivity for women from 54.6% to 81.8%. Conclusions: The Dutch version of the STOPBANG questionnaire is a valid tool for screening for OSAS in a healthy workers population. With a female-specific cut-off point of the neck circumference, a substantial increase in sensitivity can be accomplished in women