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Background and objective : The diagnosis of rare diseases (RDs) is often challenging due to their rarity, variability and the high number of individual RDs, resulting in a delay in diagnosis with adverse effects for patients and healthcare systems. The development of computer assisted diagnostic decision support systems (DDSS) could help to improve these problems by supporting differential diagnosis and by prompting physicians to initiate the right diagnostic tests. Towards this end, we developed, trained and tested a machine learning model implemented as part of the software called Pain2D to classify four rare diseases (EDS, GBS, FSHD and PROMM), as well as a control group of unspecific chronic pain, from pen-and-paper pain drawings filled in by patients. Methods: Pain drawings (PDs) were collected from patients suffering from one of the four RDs, or from unspecific chronic pain. The latter PDs were used as an outgroup in order to test how Pain2D handles more common pain causes. A total of 261 (59 EDS, 29 GBS, 35 FSHD, 89 PROMM, 50 unspecific chronic pain) PDs were collected and used to generate disease specific pain profiles (PP). PDs were then classified by Pain2D in a leave-one-out-cross-validation approach. Results: Pain2D was able to classify the four rare diseases with an accuracy of 61-77% with its binary classifier. EDS, GS and FSHD were classified correctly by the Pain2D k-disease classifier with sensitivities between 63-83% and specificities between 83-90%. For PROMM, the k-disease classifier achieved a sensitivity of 51% and specificity of 90%. Conclusions: Pain2D is a scalable, open-source tool that could potentially be trained for all diseases presenting with pain.

Background Insufficient pain control after lower limb arthroplasty results in delayed recovery and increased risk for pain chronicization. The ideal kind of analgesia is still discussed controversially. We conducted a retrospective analysis of single-center routine data from a German university hospital, including patients receiving either total hip (THA) or knee arthroplasty (TKA). Methods All patients received general anesthesia. Patients undergoing THA received either continuous epidural ropivacaine infusion (0.133%, Epi) or patient-controlled analgesia (PCA) with the Wurzburg Pain Drip (tramadol, metamizole and droperidol, WPD) or with piritramide (Pir). After TKA, patients received either continuous femoral nerve block (ropivacaine 0.2%, PNB) or Pir. Results The analyzed cohort comprised 769 cases. Use of WPD after THA (n = 333) resulted in significantly reduced Numeric Rating Scale (NRS) values at rest, compared to Epi (n = 48) and Pir (n = 72) (.75 [IQR 1.14] vs. 1.17 [1.5], p = .02 vs. 1.47 [1.33], p < .0001) as well as maximum NRS scores (2.4 [1.7] vs. 3.29 [1.94], p < .001 vs. 3.32 [1.76], p < .0001). Positive feedback during follow-up visits was significantly increased in patients with a WPD PCA (p < .0001), while negative feedback (senso-motoric weakness/technical problems/nausea/dizziness/constipation) was particularly increased in Epi patients and lowest in those with WPD (p < .0001). After TKA, Pir (n = 131) resulted in significantly reduced NRS values at rest, compared to PNB (n = 185) (1.4 [1.4] vs. 1.6 [1.68], p = .02). Positive feedback was increased in patients with a Pir PCA in comparison with PNB (p = .04), while negative feedback was increased in PNB patients (p = .04). Overall, WPD presented with the lowest rate of any complications (8.7%), followed by Pir (20.2%), PNB (27.6%) and Epi (31.3%) (p < .001). Conclusions In the assessed population, the use of a WPD PCA after THA offered better pain control and patient comfort in comparison with continuous epidural or piritramide-based analgesia. After TKA, the use of a Pir PCA provided superior analgesia and a lower complication rate compared to continuous PNB.

Objective To assess whether the implementation of patient-controlled analgesia (PCA) with piritramide using an automatic pump system under routine conditions is effective to reduce pain in late abortion inductions Design Prospective observational cohort study Setting Patients requiring medically indicated abortion induction from 14 weeks of pregnancy onwards between July 2019 and July 2020 at the department of Obstetrics and Prenatal Medicine of the Bonn University Hospital in Germany. Methods Evaluation of pain management after implementation of a PCA system compared with previous nurse-controlled tramadol-based standard under routine conditions. Patients answered a validated pain questionnaire and requirement of rescue analgesics was assessed. Pain intensity and satisfaction were measured on a ten-point numeric rating scale. Main Outcome Measure Maximal pain intensity Results Forty patients were included. Patients using Piritramide-PCA complained of higher pain sores than those in the standard group (6.90 (± 2.34) vs. 4.83 (± 2.87), (p < 0.05)). In both groups the level of satisfaction with the analgesia received was comparable (8.00 (± 2.45) vs 7.67 (± 2.62), (p = 0.7)). Patients in the PCA group suffered more nausea (63.2% vs 30% respectively, OR 4.0, 95% CI 1.05-15.20, p<0.05) and expressed more the desire for more analgesic support compared to the control group (OR 5.7 (1-33.25), p = 0.05). Conclusion Women with abortion induction after 14 weeks of gestation suffer from relevant severe pain, which requires adequate therapy. However, addition of PCA does not seem to bring any advantage in patients undergoing this procedure.