Evaluation of 6 years of eHealth data in the alcohol use disorder field indicates improved efficacy of care.

Background: Predictive eHealth tools will change the field of medicine, however long-term data is scarce. Here, we report findings on data collected over 6 years with an AI-based eHealth system for supporting the treatment of alcohol use disorder. Methods: Since the deployment of Previct Alcohol, structured data has been archived in a data warehouse, currently comprising 505,641 patient days. The frequencies of relapse and caregiver-patient messaging over time was studied. The effects of both introducing an AI-driven relapse prediction tool and the COVID-19 pandemic were analyzed. Results: The relapse frequency per patient day among Previct Alcohol users was 0.28 in 2016, 0.22 in 2020 and 0.25 in 2022 with no drastic change during COVID-19. When a relapse was predicted, the actual occurrence of relapse in the days immediately after was found to be above average. Additionally, there was a noticeable increase in caregiver interactions following these predictions. When caregivers were not informed of these predictions, the risk of relapse was found to be higher compared to when the prediction tool was actively being used. The prediction tool decreased the relapse risk by 9% for relapses that were of short duration and by 18% for relapses that lasted more than 3 days. Conclusions: The eHealth system Previct Alcohol allows for high resolution measurements, enabling precise identifications of relapse patterns and follow up on individual and population-based alcohol use disorder treatment. eHealth relapse prediction aids the caregiver to act timely, which reduces, delays, and shortens relapses.

Processing incomplete questionnaire data into continuous digital biomarkers for addiction monitoring.

PURPOSE: eHealth systems allow efficient daily smartphone-based collection of self-reported data on mood, wellbeing, routines, and motivation; however, missing data is frequent. Within addictive disorders, missing data may reflect lack of motivation to stay sober. We hypothesize that qualitative questionnaire data contains valuable information, which after proper handling of missing data becomes more useful for practitioners. METHODS: Anonymized data from daily questionnaires containing 11 questions was collected with an eHealth system for 751 patients with alcohol use disorder (AUD). Two digital continuous biomarkers were composed from 9 wellbeing questions (WeBe-i) and from two questions representing motivation/self-confidence to remain sober (MotSC-i). To investigate possible loss of information in the process of composing the digital biomarkers, performance of neural networks to predict exacerbation events (relapse) in alcohol use disorder was compared. RESULTS: Long short-term memory (LSTM) neural networks predicted a coming exacerbation event 1-3 days (AUC 0.68-0.70) and 5-7 days (AUC 0.65-0.68) in advance on unseen patients. The predictive capability of digital biomarkers and raw questionnaire data was equal, indicating no loss of information. The transformation into digital biomarkers enable a continuous graphical display of each patient's clinical course and a combined interpretation of qualitative and quantitative aspects of recovery on a time scale. CONCLUSION: By transforming questionnaire data with large proportion of missing data into continuous digital biomarkers, the information captured by questionnaires can be more easily used in clinical practice. Information, assessed by the capability to predict exacerbation events of AUD, is preserved when processing raw questionnaire data into digital biomarkers.

Implant periapical lesion. A case series report.

One complication in implant dentistry is the implant periapical lesion-IPL--which is a lesion around the apex of a stable implant diagnosed radiographically as a radioluscency in the bone at the apical part of an implant.The IPL can perform with or without clinical symptoms such as tenderness, swelling, suppuration and fistulation. This report describes 4 cases of IPL which were treated surgically with sectioning and removal of the affected portion of a stable implant and thorough debridement of the granulomatous tissue around it. This treatment was, up to 4 years after treatment, successful in all 4 cases. It can also from this report be concluded that IPL is a rather rare condition and that it can occur at any stage of implant treatment, in these cases from 4 months up to 11 years after implant installation. Finally there is a discussion about the aetiology of IPL and a comparison to findings in other reports on IPL and it is concluded that it is difficult to claim that there is a single cause to IPL. Rather it is evident that the condition might be a sequel of the summation of many possible causes.This summation exceeds the local biological threshold for the individual patient.

Incidence of neurosensory disturbance after sagittal split osteotomy alone or combined with genioplasty.

We mailed questionnaires to all patients who had had sagittal split osteotomies alone (n= 84) or in combination with genioplasty (n = 37) between 1995 and 2000, to find out the incidence of postoperative sensory disturbances. The patients with sagittal split osteotomies alone reported sensory disturbance in 48/131 (37%) operated sides. When combined with genioplasty patients experienced them in 20/54 (37%) operated sides. The incidences were 36/101 (36%) for mandibular advancement and 12/30 (40%) for mandibular setback. Out of the patients with sagittal split osteotomies alone, 59/66 (89%) were satisfied with the result of the operation, and when combined with the genioplasty the corresponding figure was 23/27 (85%). We conclude that differences in the incidence of sensory disturbance after sagittal split osteotomy for mandibular advancement and setback were not significant. The combination with genioplasty did not increase the incidence of sensory disturbance. Sensory changes after the osteotomies do not serve to be the main determinant of the patients' satisfaction.