Reducing infection-related morbidity and mortality in patients with myeloma

Development of a nomogram prognostic model for early Grade ≥ 3 infection in newly diagnosed multiple myeloma based on immunoparesis

BACKGROUND

Infection, a significant cause of death in multiple myeloma (MM) patients, is a common complication and is closely associated with immunoparesis. There exists no clear definition of early infection, so early infection is defined in this paper as the occurrence within 3 months after diagnosis, considering the high incidence of infections within 3 months after diagnosis. This study established a new nomogram model based on immunoparesis to identify MM patients with high-risk early infection.

METHODS

A retrospective collection of 430 NDMM patients from June 2013 to June 2022 was conducted, and the patients were further divided into a training cohort and a validation cohort. In the training cohort, the least absolute shrinkage and selection operator (LASSO) was used to select the best variables that can be used to establish a new nomogram prediction model. Validation was performed in the validation and entire cohorts.

RESULTS

After diagnosis, 67.7 % of the patients suffered from severe infection within 1 year, and 59.5 % experienced the first severe infection within 3 months. Variables associated with an increased risk of severe infection in the first 3 months included: BMPC, D-dimer, serum β2 microglobulin, immunoparesis, albumin, and eGFR. The nomogram based on the above six factors achieved a good C-index of 0.754, 0.73, and 0.731 in predicting early infection in the training cohort, validation cohort, and entire cohort, respectively. Finally, the time-dependent receiver operating characteristic (ROC) curve and decision curve analysis (DCA) of the nomogram showed that the model provided superior diagnostic capacity and clinical net benefit.

CONCLUSION

In this study, we established a nomogram model to predict early grade ≥ 3 infection in NDMM patients. This model can assist clinicians in identifying NDMM patients with high-risk infections and improve their prognosis through early intervention.

A simple score to predict early severe infections in patients with newly diagnosed multiple myeloma

Infections remain a common complication in patients with multiple myeloma (MM) and are associated with morbidity and mortality. A risk score to predict the probability of early severe infection could help to identify the patients that would benefit from preventive measures. We undertook a post hoc analysis of infections in four clinical trials from the Spanish Myeloma Group, involving a total of 1347 patients (847 transplant candidates). Regarding the GEM2010 > 65 trial, antibiotic prophylaxis was mandatory, so we excluded it from the final analysis. The incidence of severe infection episodes within the first 6 months was 13.8%, and majority of the patients experiencing the first episode before 4 months (11.1%). 1.2% of patients died because of infections within the first 6 months (1% before 4 months). Variables associated with increased risk of severe infection in the first 4 months included serum albumin ≤30 g/L, ECOG > 1, male sex, and non-IgA type MM. A simple risk score with these variables facilitated the identification of three risk groups with different probabilities of severe infection within the first 4 months: low-risk (score 0-2) 8.2%; intermediate-risk (score 3) 19.2%; and high-risk (score 4) 28.3%. Patients with intermediate/high risk could be candidates for prophylactic antibiotic therapies.

Antibiotic resistance in the patient with cancer: Escalating challenges and paths forward

Infection is the second leading cause of death in patients with cancer. Loss of efficacy in antibiotics due to antibiotic resistance in bacteria is an urgent threat against the continuing success of cancer therapy. In this review, the authors focus on recent updates on the impact of antibiotic resistance in the cancer setting, particularly on the ESKAPE pathogens (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter spp.). This review highlights the health and financial impact of antibiotic resistance in patients with cancer. Furthermore, the authors recommend measures to control the emergence of antibiotic resistance, highlighting the risk factors associated with cancer care. A lack of data in the etiology of infections, specifically in oncology patients in United States, is identified as a concern, and the authors advocate for a centralized and specialized surveillance system for patients with cancer to predict and prevent the emergence of antibiotic resistance. Finding better ways to predict, prevent, and treat antibiotic-resistant infections will have a major positive impact on the care of those with cancer.