Identifying Predictors Associated with Risk of Death or Admission to Intensive Care Unit in Internal Medicine Patients with Sepsis: A Comparison of Statistical Models and Machine Learning Algorithms.

Background: Sepsis is a time-dependent disease: the early recognition of patients at risk for poor outcome is mandatory. Aim: To identify prognostic predictors of the risk of death or admission to intensive care units in a consecutive sample of septic patients, comparing different statistical models and machine learning algorithms. Methods: Retrospective study including 148 patients discharged from an Italian internal medicine unit with a diagnosis of sepsis/septic shock and microbiological identification. Results: Of the total, 37 (25.0%) patients reached the composite outcome. The sequential organ failure assessment (SOFA) score at admission (odds ratio (OR): 1.83; 95% confidence interval (CI): 1.41-2.39; p < 0.001), delta SOFA (OR: 1.64; 95% CI: 1.28-2.10; p < 0.001), and the alert, verbal, pain, unresponsive (AVPU) status (OR: 5.96; 95% CI: 2.13-16.67; p < 0.001) were identified through the multivariable logistic model as independent predictors of the composite outcome. The area under the receiver operating characteristic curve (AUC) was 0.894; 95% CI: 0.840-0.948. In addition, different statistical models and machine learning algorithms identified further predictive variables: delta quick-SOFA, delta-procalcitonin, mortality in emergency department sepsis, mean arterial pressure, and the Glasgow Coma Scale. The cross-validated multivariable logistic model with the least absolute shrinkage and selection operator (LASSO) penalty identified 5 predictors; and recursive partitioning and regression tree (RPART) identified 4 predictors with higher AUC (0.915 and 0.917, respectively); the random forest (RF) approach, including all evaluated variables, obtained the highest AUC (0.978). All models' results were well calibrated. Conclusions: Although structurally different, each model identified similar predictive covariates. The classical multivariable logistic regression model was the most parsimonious and calibrated one, while RPART was the easiest to interpret clinically. Finally, LASSO and RF were the costliest in terms of number of variables identified.

Abdominal Lymphadenopathies: Lymphoma, Brucellosis or Tuberculosis? Multidisciplinary Approach-Case Report and Review of the Literature.

Abdominal pain represents a frequent symptom for referral to emergency departments and/or internal medicine outpatient setting. Similarly, fever, fatigue and weight loss are non-specific manifestations of disease. The present case describes the diagnostic process in a patient with abdominal pain and a palpable abdominal mass. Abdominal ultrasonography confirmed the presence of a mass in the mesogastrium. Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) scans oriented toward calcific lymphadenopathies with increased metabolism in the positron emission tomography-computed tomography (PET-CT) scan. Laboratory examinations were inconclusive, although serology for Brucella and the Quantiferon test were positive. After multidisciplinary discussion, the patient underwent surgical excision of the abdominal mass. Histological examination excluded malignancies and oriented toward brucellosis in a patient with latent tuberculosis. The patient was treated with rifampin 600 mg qd and doxycycline 100 mg bid for 6 weeks with resolution of the symptoms. In addition, rifampin was continued for a total of 6 months in order to treat latent tuberculosis. This case underlines the need for a multidisciplinary approach in the diagnostic approach to abdominal lymphadenopathies.