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.

Improving Web-Based Treatment Intake for Multiple Mental and Substance Use Disorders by Text Mining and Machine Learning: Algorithm Development and Validation

Background: Text mining and machine learning are increasingly used in mental health care practice and research, potentially saving time and effort in the diagnosis and monitoring of patients. Previous studies showed that mental disorders can be detected based on text, but they focused on screening for a single predefined disorder instead of multiple disorders simultaneously. Objective: The aim of this study is to develop a Dutch multi-class text-classification model to screen for a range of mental disorders to refer new patients to the most suitable treatment. Methods: On the basis of textual responses of patients (N=5863) to a questionnaire currently used for intake and referral, a 7-class classifier was developed to distinguish among anxiety, panic, posttraumatic stress, mood, eating, substance use, and somatic symptom disorders. A linear support vector machine was fitted using nested cross-validation grid search. Results: The highest classification rate was found for eating disorders (82%). The scores for panic (55%), posttraumatic stress (52%), mood (50%), somatic symptom (50%), anxiety (35%), and substance use disorders (33%) were lower, likely because of overlapping symptoms. The overall classification accuracy (49%) was reasonable for a 7-class classifier. Conclusions: A classification model was developed that could screen text for multiple mental health disorders. The screener resulted in an additional outcome score that may serve as input for a formal diagnostic interview and referral. This may lead to a more efficient and standardized intake process