Precision medicine for the treatment of sepsis: recent advances and future prospects

The potential of 4D's approach in curbing antimicrobial resistance among bacterial pathogens

INTRODUCTION

Antibiotics are life-saving drugs but irrational/inappropriate use leads to the emergence of antibiotic-resistant bacterial superbugs, making their treatment extremely challenging. Increasing antimicrobial resistance (AMR) among bacterial pathogens is becoming a serious public health concern globally. If ignorance persists, there would not be any antibiotics available to treat even a common bacterial infection in future.

AREA COVERED

This article intends to collate and discuss the potential of 4D's (right Drug, Dose, Duration, and De-escalation of therapy) approach to tackle the emerging problem of AMR. For this, we searched PubMed, Google Scholar, Medline, and clinicaltrials.gov databases primarily using keywords 'optimal antibiotic therapy,' 'antimicrobial resistance,' 'higher versus lower dose antibiotic treatment,' 'shorter versus longer duration antibiotic treatment,' 'de-escalation study', and 'antimicrobial stewardship measures' and based on the findings, form and expressed our opinion.

EXPERT OPINION

More efforts are needed for developing diagnostics for rapid, accurate, point-of-care, and cost-effective pathogen identification and antimicrobial susceptibility testing (AST) to facilitate rational use of antibiotics. Current dosing and duration of therapies also need to be redefined to maximize their impact. Furthermore, de-escalation approaches should be developed and encouraged in the clinic. This altogether will minimize selection pressure on the pathogens and reduce emergence of AMR.

Survival prediction of patients with sepsis from age, sex, and septic episode number alone

Sepsis is a life-threatening condition caused by an exaggerated reaction of the body to an infection, that leads to organ failure or even death. Since sepsis can kill a patient even in just one hour, survival prediction is an urgent priority among the medical community: even if laboratory tests and hospital analyses can provide insightful information about the patient, in fact, they might not come in time to allow medical doctors to recognize an immediate death risk and treat it properly. In this context, machine learning can be useful to predict survival of patients within minutes, especially when applied to few medical features easily retrievable. In this study, we show that it is possible to achieve this goal by applying computational intelligence algorithms to three features of patients with sepsis, recorded at hospital admission: sex, age, and septic episode number. We applied several data mining methods to a cohort of 110,204 admissions of patients, and obtained high prediction scores both on this complete dataset (top precision-recall area under the curve PR AUC = 0.966) and on its subset related to the recent Sepsis-3 definition (top PR AUC = 0.860). Additionally, we tested our models on an external validation cohort of 137 patients, and achieved good results in this case too (top PR AUC = 0.863), confirming the generalizability of our approach. Our results can have a huge impact on clinical settings, allowing physicians to forecast the survival of patients by sex, age, and septic episode number alone.