INTRODUCTION: Recent studies have highlighted the prognostic value of easily accessible inflammatory markers, neutrophil-to-lymphocyte ratio (NLR), and platelet-to-lymphocyte ratio (PLR) for predicting severe outcomes in patients affected by Coronavirus disease 2019 (COVID-19). Our study validates NLR and PLR cut-off values from a prior cohort at IRCCS Policlinico San Matteo (OSM) of Pavia, Italy, across two new cohorts from different hospitals. This aims to enhance the generalizability of these prognostic indicators. METHODS: In this retrospective cohort study, conducted at Milan's Ospedale Luigi Sacco (OLS) and IRCCS Ospedale Maggiore Policlinico (OMP) hospitals, we assess the predictive capacity of NLR and PLR for three main outcomes-non-invasive ventilation (NIV) or continuous positive airway pressure (CPAP) usage, invasive ventilation (IV), and death-in patients with COVID-19 at admission. For each outcome, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were computed separately for male and female cohorts. Distinct NLR and PLR cut-off values were used for men (7.00, 7.29, 7.00 for NLR; 239.22, 248.00, 250.39 for PLR) and women (6.36, 7.00, 6.28 for NLR; 233.00, 246.45, 241.54 for PLR), retrieved from the first cohort at OSM. RESULTS: A total of 3599 patients were included in our study, 1842 from OLS and 1757 from OMP. OLS and OMP sensitivity values for both NLR and PLR (NLR: 24-67%, PLR: 40-64%) were inferior to specificity values (NLR: 64-76%, PLR: 55-72%). Additionally, PPVs generally remained lower (< 63%), while NPVs consistently surpassed 68% for PLR and 72% for NLR. Finally, both PLR and NLR exhibited consistently higher NPVs for more severe outcomes (> 82%) compared to NPVs for CPAP/NIV. CONCLUSIONS: Consistent findings across diverse patient populations validate the reliability and applicability of NLR and PLR cut-off values. High NPVs emphasize their role in identifying individuals less likely to experience severe outcomes. These markers not only aid in risk stratification but also guide resource allocation in emergencies or limited-resource situations.
Several criteria exist to diagnose pulmonary aspergillosis with varying degrees of certainty in specific populations, including oncohaematological patients (EORTC/MSG), ICU patients (mAspICU) and COVID-19 patients (ECMM). At the beginning of the pandemic, however, the diagnosis of COVID-19-Associated Pulmonary Aspergillosis (CAPA) could not be performed easily, and the decision to treat (DTT) was empirical. In this cross-sectional retrospective study including patients with SARS-CoV-2 infection and suspicion of CAPA, we studied the concordance between the DTT and the three diagnostic criteria using Cohen's coefficient, and then we identified the factors associated with the DTT and corrected them by treatment to study the influence of the diagnostic criteria on survival. We showed good concordance of the DTT and mAspICU and ECMM criteria, with "compatible signs", "positive culture" and "positive galactomannan" influencing the DTT. Treatment also showed a positive effect on survival once corrected for a putative, possible or probable diagnosis of CAPA using mAspICU and ECMM criteria. We conclude that EORTC/MSGERC are not considered applicable in clinical practice due to the lack of inclusion of signs and symptoms and do not lead to improved survival. mAspICU and ECMM criteria showed a good degree of agreement with the DTT and a positive correlation with patient recovery.
BACKGROUND AND OBJECTIVE: The standard non-invasive imaging technique used to assess the severity and extent of Coronary Artery Disease (CAD) is Coronary Computed Tomography Angiography (CCTA). However, manual grading of each patient's CCTA according to the CAD-Reporting and Data System (CAD-RADS) scoring is time-consuming and operator-dependent, especially in borderline cases. This work proposes a fully automated, and visually explainable, deep learning pipeline to be used as a decision support system for the CAD screening procedure. The pipeline performs two classification tasks: firstly, identifying patients who require further clinical investigations and secondly, classifying patients into subgroups based on the degree of stenosis, according to commonly used CAD-RADS thresholds. METHODS: The pipeline pre-processes multiplanar projections of the coronary arteries, extracted from the original CCTAs, and classifies them using a fine-tuned Multi-Axis Vision Transformer architecture. With the aim of emulating the current clinical practice, the model is trained to assign a per-patient score by stacking the bi-dimensional longitudinal cross-sections of the three main coronary arteries along channel dimension. Furthermore, it generates visually interpretable maps to assess the reliability of the predictions. RESULTS: When run on a database of 1873 three-channel images of 253 patients collected at the Monzino Cardiology Center in Milan, the pipeline obtained an AUC of 0.87 and 0.93 for the two classification tasks, respectively. CONCLUSION: According to our knowledge, this is the first model trained to assign CAD-RADS scores learning solely from patient scores and not requiring finer imaging annotation steps that are not part of the clinical routine.
Coronary Artery Disease , Coronary Stenosis , Deep Learning , Humans , Coronary Artery Disease/diagnostic imaging , Computed Tomography Angiography/methods , Reproducibility of Results , Coronary Angiography/methods , Predictive Value of Tests
Fungal osteomyelitis is considered a rare disease, and the published literature mainly comprises case reports, case series and narrative reviews. A systematic review was undertaken to provide a practice-based global perspective on this disease, focusing on epidemiology and treatment strategies. We searched MEDLINE, EMBASE and Cochrane Library between the 3rd and 8th of March 2023 using a predefined search string. We included studies with at least one patient with a diagnosis of fungal osteomyelitis published before the 1st of January 2023. We included all study designs except for reviews, and we excluded non-English languages and grey literature. After exclusion, 678 studies, mostly case reports, were included. Descriptive analysis was performed on 1072 patients. The most common aetiological agent was Aspergillus (26.5%), followed by Candida (20.7%) and Mucor (16.8%), and the bones most frequently involved were the vertebrae. We described the characteristics of patients divided by site of infection, and we found that diabetes mellitus, disseminated fungal infection, surgery and local lesion were major risk factors. We also successfully associated duration of treatment with outcome. We provided a general overview of this rare disease, and we highlighted the need for high-quality investigations on the subject.
INTRODUCTION: The hyperinflammation phase of severe SARS-CoV-2 is characterised by complete blood count alterations. In this context, the neutrophil-to-lymphocyte ratio (NLR) and the platelet-to-lymphocyte ratio (PLR) can be used as prognostic factors. We studied NLR and PLR trends at different timepoints and computed optimal cutoffs to predict four outcomes: use of continuous positive airways pressure (CPAP), intensive care unit (ICU) admission, invasive ventilation and death. METHODS: We retrospectively included all adult patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pneumonia admitted from 23 January 2020 to 18 May 2021. Analyses included non-parametric tests to study the ability of NLR and PLR to distinguish the patients' outcomes at each timepoint. Receiver operating characteristic (ROC) curves were built for NLR and PLR at each timepoint (minus discharge) to identify cutoffs to distinguish severe and non-severe disease. Their statistical significance was assessed with the chi-square test. Collection of data under the SMACORE database was approved with protocol number 20200046877. RESULTS: We included 2169 patients. NLR and PLR were higher in severe coronavirus disease 2019 (COVID-19). Both ratios were able to distinguish the outcomes at each timepoint. For NLR, the areas under the receiver operating characteristic curve (AUROC) ranged between 0.59 and 0.81, and for PLR between 0.53 and 0.67. From each ROC curve we computed an optimal cutoff value. CONCLUSION: NLR and PLR cutoffs are able to distinguish severity grades and mortality at different timepoints during the course of disease, and, as such, they allow a tailored approach. Future prospects include validating our cutoffs in a prospective cohort and comparing their performance against other COVID-19 scores.
The incidence of long COVID in a cohort of patients with cancer with or without previous treatment with early therapies anti-SARS-CoV-2 in an out-of-hospital setting have to be elucidated. We prospectively enrolled all patients treated for a solid tumor at the department of Medical Oncology of the Fondazione IRCCS Policlinico San Matteo with a positive SARS-CoV-2 antigen or polymerase chain reaction test from January to September 2022 (Omicron surge). Ninety-seven patients answered the survey questions by telephone at least 12 weeks after COVID-19 diagnosis in order to evaluate the incidence of long COVID symptoms. Only twelve patients (12.4%) reported long COVID. No significant difference between early therapies anti-SARS-CoV-2 31 and long COVID (p = 0.443) was seen. The female sex (p = 0.024) and diabetes mellitus (p = 0.014) are significantly associated with long COVID. No statistically significant difference between the two groups (Long COVID vs. No Long COVID) according to the time to nasal swab viral clearance (p = 0.078). The overlap between the symptoms related to the oncological disease/oncological treatment and the symptoms of long COVID is one of the main future challenges that oncologists will have to manage.
Introduction: Few data about the safety of immune checkpoint inhibitors (ICIs) in the patients with solid tumor with Occult Hepatitis B Virus (OBI) are available. According to the Taormina Workshop on Occult HBV Infection Faculty Members we defined as potential-OBI (pOBI) the HBV DNA negativity with anti-hepatitis B core antibody (anti-HBc) positivity (pOBI seropositive), and the patients with HBsAg-negative and anti-HBc-negative and Hepatitis B surface antibody (anti-HBs)-negative are defined pOBI seronegative. The aim of this study is to investigate the prevalence of OBI in patients with solid tumors undergoing ICIs with or without chemotherapy and the incidence of reactivation (HBVr). Methods: We retrospectively enrolled all HBsAg negative subjects who had received ICIs for at least three months. HBsAg and HBV DNA levels were repeated every 3 months until the end of the study and/or in case of ALT alterations. A univariate analysis was conducted in order to study for each variable available its ability to distinguish a potential OBI seropositive patient from a seronegative one. Results: 150 patients in our Oncology Unit were eligible. One hundred and seventeen patients (78%) received ICI as monotherapy, whereas 33 patients (22%) were treated with chemo-immunotherapy. The mainly used drugs for the ICI monotherapy were Pembrolizumab (47%), Nivolumab (33%) and Atezolizumab (11%). The prevalence of pOBI seropositive patients was 25.3%. We did not observe alterations of liver biochemistry nor HBVr. Discussion: This study highlights that about a quarter of our population had a potential occult hepatitis B. Immunotherapy might be considered as low risk of reactivation, regardless of the potential presence of episomal covalently closed circular DNA (cccDNA) in the liver, but the correct management still represents a challenge for oncologists and hepatologists.
Some vaccinated individuals still develop severe COVID-19, and the underlying causes are not entirely understood. We aimed at identifying demographic, clinical, and coinfection characteristics of vaccinated patients who were hospitalized. We also hypothesized that coinfections might play a role in disease severity and mortality. We retrospectively collected data from our COVID-19 registry for whom vaccination data were available. Patients were split into groups based on the number of administered doses (zero, one, two, or three). Data were assessed with Chi-square and Kruskal−Wallis tests and multiple logistic regression analysis. We collected data from 1686 patients and found that intra-hospital mortality was not associated to the vaccination status (e.g., p = 0.2 with three doses), while older age, sepsis, and non-viral pneumonia were (p < 0.001). Unvaccinated patients needed mechanical ventilation more often (8.5%) than vaccinated patients, in whom the probability of mechanical ventilation decreased with increasing doses (8.7%, 2.8%, 0%). We did not find more coinfections in vaccinated people. We concluded that there is a lack of real-life data to adequately characterize the pathophysiology and risk factors of patients who develop severe COVID-19, but coinfections do not appear to play a role in disease severity.
