Pain and nociception are different phenomena. Nociception is the result of complex activity in sensory pathways. On the other hand, pain is the effect of interactions between nociceptive processes, and cognition, emotions, as well as the social context of the individual. Alterations in the nociceptive route can have different genesis and affect the entire sensorial process. Genetic problems in nociception, clinically characterized by reduced or absent pain sensitivity, compose an important chapter within pain medicine. This chapter encompasses a wide range of very rare diseases. Several genes have been identified. These genes encode the Nav channels 1.7 and 1.9 (SCN9A, and SCN11A genes, respectively), NGFβ and its receptor tyrosine receptor kinase A, as well as the transcription factor PRDM12, and autophagy controllers (TECPR2). Monogenic disorders provoke hereditary sensory and autonomic neuropathies. Their clinical pictures are extremely variable, and a precise classification has yet to be established. Additionally, pain insensitivity is described in diverse numerical and structural chromosomal abnormalities, such as Angelman syndrome, Prader Willy syndrome, Chromosome 15q duplication syndrome, and Chromosome 4 interstitial deletion. Studying these conditions could be a practical strategy to better understand the mechanisms of nociception and investigate potential therapeutic targets against pain.

Appropriate pain care should be regarded as a right and effectively guaranteed to people with chronic pain (CP). Law 38, enacted in Italy in 2010, establishes the citizen’s right not to suffer. Twelve years later, such right appears still disregarded in Italy and the current access to adequate pain care reveals significant shortcomings. In addition, a mismatch between CP-associated burden and the available healthcare resources in the framework of our national health system has been observed. This article gathers the perspectives of a Board of Italian anesthesiologists on the state of the art of CP management in Italy and aims at strengthening the scientific rationale and clinical relevance of pursuing the enforceability of the right not to suffer and at promoting widespread multidisciplinary care of patients with CP.

In recent years, the field of anesthesiology has seen remarkable advancements in patient safety, comfort, and outcomes

Pain therapy for low back pain in pregnancy is a very topical issue. In fact, it is necessary to balance the patient’s needs to control pain with the need to manage a pregnancy without negative effects on the fetus. We report a case of a 37-year-old woman with low back pain treated with neurostimulation before pregnancy. She described severe chronic low back pain unresponsive to pharmacologic treatments. We first implanted a subcutaneous stimulator into the patient, and then a definitive stimulator resulting in excellent pain control. The improvement in her quality of life allowed the woman to become pregnant. We decided to stop neurostimulation with the patient during pregnancy. The patient completed her pregnancy without complications and the baby was born healthy. During the pregnancy, the woman took only paracetamol when needed. However, this painful symptomatology, completely anecdotal, is not attributable solely to the previous spine problem but probably also to the changes occurring during pregnancy. At the end of pregnancy, the neurostimulator was reactivated without any discomfort for the patient, who is now pain free. This case report provides a first line of evidence of a possible treatment of low back pain in women intending to become pregnant, with risk-free management for both the patient and the child.

This case report describes a modified approach for a thoracic paravertebral block by performing a bilateral ultrasound-assisted injection of 12 mL of 0.5% levobupivacaine near the thoracic intervertebral foramen, combined with general anesthesia, in a patient who underwent emergent laparotomy for small intestinal volvulus. Two continuous catheter sets were used for a bilateral continuous block with levobupivacaine 0.25% at a rate of 5–8 mL/h. No complications during the execution of the block were recorded. No supplemental opioids were administered and the patient was hemodynamically stable, requiring no pharmacological cardiovascular support during surgery. At the end of the surgical procedure, the patient received a continuous flow of 0.2% levobupivacaine as postoperative analgesia, at a basal flow of 4 mL/h per each side, a bolus of 4 mL, and a lockout time of 60 min was used. The postoperative pain on the Numeric Rating Scale was 2 at rest and it was 4 in motion, without neurological or respiratory sequelae due to block in the first 72 h after surgery.

Background. Although telehealth strategies can be effectively used for managing cancer pain, identifying the best care pathway for tailoring interventions and allocating resources remains difficult. Artificial intelligence and machine learning (ML) may help clinicians develop more accurate strategies for predicting whether patients need remote consultations or in-person evaluations. Methods. Data from two cohorts of cancer pain patients were analyzed. Variables included sociodemographic and clinical data, including age, sex, ECOG performance status, metastases, bone metastases, pain type, breakthrough cancer pain (BTCP), and rapid onset opioids (ROOs) therapy. The main outcome was the number of televisits (one versus multiple). After harmonizing the dataset, categorical variables were one-hot encoded, and age was standardized. Six models were tested: logistic regression, random forest (RF), gradient boosting machine (GBM), support vector machine (SVM), k-nearest neighbors (KNN), and multilayer perceptron (MLP). Training and tuning used a 7-repeated 5-fold cross-validation approach. Performance was evaluated on a hold-out test set using F1-score, accuracy, and AUC-ROC. A sensitivity analysis with two scenarios was performed to verify the effects of class weighting and excluding the cohort variable. Results. The final dataset included 270 patients. No variable was significantly linked to the number of televisits. F1-scores across models ranged from 0.33 (RF) to 0.65 (MLP), accuracy from 0.45 (RF) to 0.55 (SVM), and AUC-ROC from 0.43 (RF) to 0.65 (LR). DeLong tests showed no significant differences between algorithms (p > 0.05). While the MLP achieved the highest F1-score, it showed instability with 91% null F1-scores. Incorporating class weights slightly improved SVM (F1 = 0.58; AUC = 0.62) and LR (F1 = 0.53; AUC = 0.63), though not significantly. Removing the cohort variable reduced training time by about one hour and yielded similar results. Conclusion. Although no model demonstrated strong predictive power, this ML-based framework shows the potential of using structured telemedicine data to model clinical workload and optimize follow-up strategies in cancer pain care. Further studies with feature-rich datasets are needed to improve clinical usefulness.