RESUMEN
BACKGROUND: Pain management is fundamental in the treatment of a trauma casualty. Adequate pain management is associated with decreased long-term morbidity and chronic pain. Nonetheless, pain is frequently not documented nor adequately treated in the prehospital setting, a phenomenon described as oligoanalgesia. Gender bias has been suggested as a risk factor for oligoanalgesia. OBJECTIVES: To examine the association between casualty gender and pain management in the prehospital trauma setting. METHODS: We conducted a retrospective cohort study of the Israel Defense Forces Trauma Registry between 2015 and 2020. Univariable analysis followed by multivariable logistic regression was used to assess the association between casualty gender and pain management. For adult patients for whom gender was known, pain scores were documented. RESULTS: A total of 1044 casualties were included in the study; 894 (85.6%) were male. Females and males differed in several demographic and injury characteristics, including age in years (mean 36 vs. 27.6, P value < 0.001) and injury mechanism (16%% vs. 34.5% penetrating injury, P value < 0.001). Female casualties were less likely to be treated for pain (odds ratio [OR] 0.708, 95% confidence interval [95%CI] 0.5-1, P = 0.05). However, after adjustment for various factors, including pain severity, this association was insignificant (OR 0.748, 95%CI 0.46-1.23, P = 0.25). CONCLUSIONS: In this prehospital study, gender bias in pain management was not apparent. As women's role on the battlefield continues to increase, further studies regarding the role of gender bias in casualty care are warranted.
Asunto(s)
Servicios Médicos de Urgencia , Personal Militar , Heridas y Lesiones , Adulto , Femenino , Humanos , Masculino , Dolor , Sistema de Registros , Estudios Retrospectivos , Factores de Riesgo , Sexismo , Heridas y Lesiones/epidemiología , Heridas y Lesiones/terapiaRESUMEN
BACKGROUND: Immune checkpoint inhibitors (ICIs) have revolutionized the prognosis of cancer. Diabetes mellitus (DM) has been shown to have a negative effect on patients treated with ICIs. Sodium-glucose cotransporter 2 inhibitors (SGLT2i) are effective antidiabetic therapies associated with reduced all-cause mortality and cardiovascular (CV) outcomes. OBJECTIVE: To evaluate the prognostic value of SGLT2i on all-cause mortality and cardiotoxicity among patients treated with ICIs. METHODS: We performed a retrospective analysis of patients diagnosed with cancer and type 2 DM (DM2) and treated with ICIs at our center. Patients were divided into two groups according to baseline treatment with or without SGLT2i. The primary endpoint was all-cause mortality and the secondary endpoint was MACE, including myocarditis, acute coronary syndrome, heart failure, and arrhythmia. RESULTS: The cohort included 119 patients, with 24 (20%) patients assigned to the SGLT2i group. Both groups exhibited a comparable prevalence of cardiac risk factors, although the SGLT2i group displayed a higher incidence of ischemic heart disease. Over a median follow-up of 28 months, 61 (51%) patients died, with a significantly lower all-cause mortality rate in the SGLT2i group (21% vs. 59%, p = 0.002). While there were no significant differences in MACE, we observed zero cases of myocarditis and atrial fibrillation in the SGLT2i, compared to 2 and 6 cases in the non-SGLT2i group. CONCLUSIONS: SGLT2i therapy was associated with a lower all-cause mortality rate in patients diagnosed with cancer and DM2 and treated with ICIs. Further studies are needed to understand the mechanism and evaluate its benefit on cardiotoxicity.
RESUMEN
Leptomeningeal involvement among non-small cell lung cancer (NSCLC) patients is an aggressive form of disease that requires quick and efficient treatment. In this case report, we describe a woman in her 40s with a presenting symptom of headache that ultimately was diagnosed as leptomeningeal spread from NSCLC adenocarcinoma. We identified EGFR mutation in less than 48 hours from the biopsy using imagene-artificial intelligence's real-time algorithmic solution on the pathological diagnostic slide.