Cost of implementing CAR-T activity and managing CAR-T patients: an exploratory study.

BACKGROUND: Chimeric antigen receptor T cells (CAR-T) represent an innovation but raise issues for healthcare payers because of the uncertainty on impact at market launch, high cost and important organisational impact. The literature has focused on their assessment, appraisal and market access solutions. No evidence on the costs sustained to implement CAR-T is available and a few studies reported the cost of the CAR-T clinical pathway, including the activities that are remunerated through inpatient or outpatient fee-for-service/episode. This paper aims at filling the information gap, assessing the cost of implementing CAR-T activity and the full cost of managing the CAR-T clinical pathway. METHODS: Cost analysis relied on the Activity Based Costing approach, which was applied to two Italian healthcare organisations, both CAR-T Centres authorized by the regional governments with a minimum of 20 patients treated with the first two CAR-T therapies launched on the market. RESULTS: The cost of implementing CAR-T was estimated at €1.31 million (calculated for one of the organizations with complete data). Most of these costs (77%) were generated by quality assurance activity. The mean cost per patient entering the CAR-T pathway (59 and 27) and surviving at follow-up (21 and 5) ranges from €48K to €57K and from €96K to €106K, respectively. Fees for hospitalization and infusion of gene therapy accounts for more than 70% of these costs. The actual hospitalisation cost varies greatly across patients and is in general lower than the fee-for-episode paid by the region to the hospital. CONCLUSIONS: Despite its limitations (exploratory nature; the time spent by staff on activities which are not remunerated through fees was estimated through interviews with the CAR-T coordinators; cost items are not fully comparable), this research highlighted the relevant organisational and economic impact of CAR-T and provided important insights for policy makers and healthcare managers: the necessity to invest resources in CAR-T implementation; the need for assessing activities which are not remunerated through fees for service / episode; the opportunity to shift from fee-for-episode / service to bundled payments for CAR-T clinical pathway.

Infective endocarditis in octogenarians. A retrospective study in a single, high-volume surgical centre.

BACKGROUND: Infective endocarditis (IE) is a severe disease associated with high morbidity and mortality. Little is known about the best management of elderly patients with IE. In these patients, surgery may be challenging. Our study aimed to describe IE's features in octogenarians and to identify the independent predictors of mortality, focusing on the prognostic impact of disability. METHODS: We retrospectively analyzed 551 consecutive patients admitted to a single surgical centre with a definite diagnosis of non-device-related infective endocarditis; of these, 97 (17.6%) were older than 80 years. RESULTS: In patients under eighty, males were mostly involved with a sex ratio exceeding 2:1. This ratio was inverted in older people, where the female gender represented 53.6% of the total. Enterococci (29.8 vs. 17.4%, p = 0.005) were significantly more frequent than in younger people. Comorbidities were more frequent in elderly patients; consequently, EuroSCORE II was higher (median ± IQR 16.4 ± 21.1 vs. 5.0 ± 10.3, p = 0.001). In octogenarians, IE was more frequently left-sided (97.9 vs. 89.8%, p = 0.011). Octogenarians were more often excluded from surgery despite indication (23.7 vs. 8.1%, p = 0.001) and had higher three-year mortality (45.3 vs. 30.6%, p = 0.005) than younger patients. In elderly patients, age did not independently predict mortality, while exclusion from surgery and a high grade of disability did. CONCLUSIONS: Octogenarians with IE have specific clinical and microbiological characteristics. Older patients are more often excluded from surgery, and the overall prognosis is poor. Age per se should not be a reason to deny surgery, while disability predicts futility.

A Computational Approach in the Diagnostic Process of COVID-19: The Missing Link between the Laboratory and Emergency Department.

BACKGROUND: The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for the COVID-19 pandemic and so it is crucial the right evaluation of viral infection. According to the Centers for Disease Control and Prevention (CDC), the Real-Time Reverse Transcription PCR (RT-PCR) in respiratory samples is the gold standard for confirming the disease. However, it has practical limitations as time-consuming procedures and a high rate of false-negative results. We aim to assess the accuracy of COVID-19 classifiers based on Arificial Intelligence (AI) and statistical classification methods adapted on blood tests and other information routinely collected at the Emergency Departments (EDs). METHODS: Patients admitted to the ED of Careggi Hospital from April 7th-30th 2020 with pre-specified features of suspected COVID-19 were enrolled. Physicians prospectively dichotomized them as COVID-19 likely/unlikely case, based on clinical features and bedside imaging support. Considering the limits of each method to identify a case of COVID-19, further evaluation was performed after an independent clinical review of 30-day follow-up data. Using this as a gold standard, several classifiers were implemented: Logistic Regression (LR), Quadratic Discriminant Analysis (QDA), Random Forest (RF), Support Vector Machine (SVM), Neural Networks (NN), K-nearest neighbor (K-NN), Naive Bayes (NB). RESULTS: Most of the classifiers show a ROC >0.80 on both internal and external validation samples but the best results are obtained applying RF, LR and NN. The performance from the external validation sustains the proof of concept to use such mathematical models fast, robust and efficient for a first identification of COVID-19 positive patients. These tools may constitute both a bedside support while waiting for RT-PCR results, and a tool to point to a deeper investigation, by identifying which patients are more likely to develop into positive cases within 7 days. CONCLUSIONS: Considering the obtained results and with a rapidly changing virus, we believe that data processing automated procedures may provide a valid support to the physicians facing the decision to classify a patient as a COVID-19 case or not.

Impact of septic cerebral embolism on prognosis and therapeutic strategies of infective endocarditis: a retrospective study in a surgical centre.

BACKGROUND: Infective endocarditis still has high mortality and invalidating complications, such as cerebral embolism. The best strategies to prevent and manage neurologic complications remain uncertain. This study aimed to identify predictors of cerebral septic embolism and evaluate the role of surgery in these patients in a real-world surgical centre. METHODS: We retrospectively analyzed 551 consecutive patients admitted to our department with a definite diagnosis of non-device-related infective endocarditis; of these, 126 (23%) presented a neurologic complication. RESULTS: Cerebral embolism was significantly more frequent in patients with large vegetations (p = 0.004), mitral valve infection (p = 0.001), and Staphylococcus aureus infection (p = 0.025). At multivariable analysis, only vegetation length was an independent predictor of cerebral embolism (HR per unit 1.057, 95% CI 1.025-1.091, p 0.001), with a best predictive threshold of 10 mm at ROC curve analysis (AUC 0.54, p = 0.001). Patients with neurologic complications were more often excluded from surgery despite an indication to it (16% vs 8%, p = 0.001). If eligible, they were treated within two weeks from diagnosis in similar proportions as patients without cerebral embolism with a similar survival rate. Predictors of mortality were hemorrhagic lesions (p = 0.018), a GCS < 14 (p = 0.001) or a severe degree of disability (p = 0.001) at presentation. The latter was the only independent predictor of mortality at multivariable analysis (HR 2.3, 95% CI 1.43-3.80, p = 0.001). CONCLUSIONS: The present study highlights the prognostic value of functional presentation and the safety of cardiac surgery, when feasible, in patients with cerebral septic embolism.

The presentations/physician ratio predicts door-to-physician time but not global length of stay in the emergency department: an Italian multicenter study during the SARS-CoV-2 pandemic.

To investigate the effects of the dramatic reduction in presentations to Italian Emergency Departments (EDs) on the main indicators of ED performance during the SARS-CoV-2 pandemic. From February to June 2020 we retrospectively measured the number of daily presentations normalized for the number of emergency physicians on duty (presentations/physician ratio), door-to-physician and door-to-final disposition (length-of-stay) times of seven EDs in the central area of Tuscany. Using the multivariate regression analysis we investigated the relationship between the aforesaid variables and patient-level (triage codes, age, admissions) or hospital-level factors (number of physician on duty, working surface area, academic vs. community hospital). We analyzed data from 105,271 patients. Over ten consecutive 14-day periods, the number of presentations dropped from 18,239 to 6132 (- 67%) and the proportion of patients visited in less than 60 min rose from 56 to 86%. The proportion of patients with a length-of-stay under 4 h decreased from 59 to 52%. The presentations/physician ratio was inversely related to the proportion of patients with a door-to-physician time under 60 min (slope - 2.91, 95% CI - 4.23 to - 1.59, R2 = 0.39). The proportion of patients with high-priority codes but not the presentations/physician ratio, was inversely related to the proportion of patients with a length-of-stay under 4 h (slope - 0.40, 95% CI - 0.24 to - 0.27, R2 = 0.36). The variability of door-to-physician time and global length-of-stay are predicted by different factors. For appropriate benchmarking among EDs, the use of performance indicators should consider specific, hospital-level and patient-level factors.