Sepsis is one of the most common causes of morbidity and mortality worldwide. Despite the remarkable advances in modern medicine throughout the last century, the mortality rates associated with sepsis have remained significantly elevated, both in high- and low-income countries. The main difficulty in the diagnosis and treatment of septic patients is the tremendous heterogeneity of this condition. The vast heterogeneity that characterizes sepsis ranges from the clinical presentation to the biological aspects of the disease. Evidence-based medicine approaches sepsis as a homogenous syndrome and does not consider the individual discrepancies between septic patients. This approach may contribute to the poor outcomes of septic patients. In recent years, personalized medicine has gained significant interest. This novel form of medicine underlines the importance of understanding the genetic, epigenetic, and molecular basis of a disease in order to provide a more tailored approach for the patient. The study of “omics”, such as cytomics, genomics, epigenomics, transcriptomics, proteomics, and metabolomics, provides a deeper comprehension of the complex interactions between the host, the disease, and the environment. The aim of this review is to summarize the potential role of a personalized approach in sepsis management, considering the interactions between various “omics”.

Despite medical advances, sepsis and septic shock remain some of the leading causes of mortality worldwide, with a high inter-individual variability in prognosis, clinical manifestations and response to treatment. Evidence suggests that pulmonary sepsis is one of the most severe forms of sepsis, while liver dysfunction, left ventricular dysfunction, and coagulopathy impact the prognostic. Sepsis-related hypothermia and a hypoinflammatory state are related to a poor outcome. Given the heterogeneity of sepsis and recent technological progress amongst machine learning analysis techniques, a new, personalized approach to sepsis is being intensively studied. Despite the difficulties when tailoring a targeted approach, with the use of artificial intelligence-based pattern recognition, more and more publications are becoming available, highlighting novel factors that may intervene in the high heterogenicity of sepsis. This has led to the devise of a phenotypical approach in sepsis, further dividing patients based on host and trigger-related factors, clinical manifestations and progression towards organ deficiencies, dynamic prognosis algorithms, and patient trajectory in the Intensive Care Unit (ICU). Host and trigger-related factors refer to patients’ comorbidities, body mass index, age, temperature, immune response, type of bacteria and infection site. The progression to organ deficiencies refers to the individual particularities of sepsis-related multi-organ failure. Finally, the patient’s trajectory in the ICU points out the need for a better understanding of interindividual responses to various supportive therapies. This review aims to identify the main sources of variability in clustering septic patients in various clinical phenotypes as a useful clinical tool for a precision-based approach in sepsis and septic shock.

Introduction: Methadone, a synthetic opioid used for opioid substitution therapy (OST), is typically associated with arrhythmias rather than direct myocardial depression. Neurological complications, especially with concurrent antipsychotic use, have also been reported. Acute left ventricular failure in young adults is uncommon and often linked to genetic or infectious causes. We present a rare case of reversible cardiogenic shock and cerebellar insult due to methadone toxicity. Case Presentation: A 37-year-old man with a history of drug abuse on OST with methadone (130 mg/day) was admitted to the ICU with hemodynamic instability, seizures, and focal neurological deficits. Diagnostic workup revealed low cardiac output syndrome and a right cerebellar insult, attributed to methadone toxicity. The patient received individualized catecholamine support. After 10 days in the ICU, he was transferred to a general ward for ongoing cardiac and neurological rehabilitation and discharged in stable condition seven days later. Conclusions: Methadone-induced reversible left ventricular failure, particularly when accompanied by cerebellar insult, is rare but potentially life-threatening. Early recognition and multidisciplinary management are essential for full recovery in such complex toxicological presentations.

Despite medical advances, sepsis and septic shock remain one of the leading causes of mortality worldwide, with a high inter-individual variability in prognosis, clinical manifestations and response to treatment. Given the heterogeneity of sepsis and recent technological progress amongst machine learning analysis technique, a new, personalized approach in sepsis is being intensively studied. Despite the difficulties when tailoring a targeted approach, with the use of artificial intelligence-based pattern recognition, more and more publications are becoming available, highlighting novel factors that may intervene in the high heterogenicity of sepsis. This has led to the devise of a phenotypical approach in sepsis, further dividing patients based on host and trigger-related factors, clinical manifestations and progression towards organ deficiencies, dynamic prognosis algorithms and patient trajectory in the Intensive Care Unit (ICU). Host and trigger-related factors refer to patients’ comorbidities, body-mass index, age, temperature, immune response, type of bacteria and infection site. The progression to organ deficiencies refers to individual particularities of sepsis-related multiorgan failure. Finally, the patient’s trajectory in the ICU points out the need of a better understanding of interindividual response to various supportive therapies. This review aims to identify the main sources of variability in clustering septic patients in various clinical phenotypes, as a useful clinical tool for precision-based approach in sepsis and septic shock.

Sepsis and septic shock remain major causes of mortality worldwide, affecting approximately 49 million people annually and accounting for an estimated 11 million deaths, representing nearly 20% of all global deaths. Despite sustained efforts in early diagnosis and treatment, sepsis continues to impose a substantial global health burden. Sepsis is defined as a life-threatening organ dysfunction secondary to a dysregulation of the host response to infection. Despite guidelines-based approaches to its diagnosis and therapy, the mortality rates remain higher even in developed countries, reaching up to 30% in intensive care units. High mortality rates in sepsis are largely driven by clinical heterogeneity, highlighting the critical role of epigenetic modulation mechanisms in shaping diverse clinical phenotypes. The main epigenetic mechanisms that modulate the immune response and outcome in sepsis are: DNA methylation, microRNA synthesis, histone modulation, and RNA methylation. This narrative review examines the role of epigenetic mechanisms in sepsis heterogeneity, phenotyping, management, and outcomes, with particular emphasis on the potential contribution of artificial intelligence to the integration of molecular and clinical data for precision medicine. Consequently, the study of AI-integrated strategies for better tailoring of the diversity of epigenetic changes should be prioritized. Thus, machine learning algorithms, deep learning frameworks, and large language models offer helpful insights in redefining the role of AI in sepsis management from this epigenetic perspective. Despite the support of AI in integrating the epigenetic mechanisms into clinical practice, some disadvantages should be also considered like: black box configuration for deep learning frameworks, inequalities from the past data used for training the models, data privacy, ethical concerns, cohort size, number of parameters considered etc. In conclusion, AI tools represent important means in tailoring the personalized decision tree, especially considering the epigenetic influence on the disease course.