Imatinib, the first tyrosine kinase inhibitor (TKI) for the treatment of chronic myeloid leukemia (CML), improves overall survival (OS), but the introduction of newer TKIs requires the definition of ...the optimal first-line TKI for newly diagnosed Philadelphia chromosome–positive (Ph+) chronic-phase (CP) CML. This systematic review of randomized controlled trials (RCTs) compares the efficacy and safety of imatinib vs second-generation (dasatinib, nilotinib, bosutinib) and third-generation TKIs (ponatinib) in adults with newly diagnosed Ph+ CP CML, concentrating on OS, progression-free survival (PFS), and hematological and nonhematological adverse events. The quality of the evidence was assessed using the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) method. Seven RCTs published between 1990 and 2019 (involving 3262 participants) satisfied the eligibility criteria. Two RCTs (imatinib vs nilotinib and imatinib vs dasatinib) found no difference in 5-year OS or PFS. Second- and third-generation TKIs improved 3-month major molecular responses (relative risk RR, 4.28; 95% confidence interval CI, 2.20-8.32) and other efficacy outcomes, decreased accelerated/blastic-phase transformations (RR, 0.44; 95% CI, 0.26-0.74), but were associated with more cases of thrombocytopenia (RR, 1.57; 95% CI, 1.20-2.05), cardiovascular events (RR, 2.54; 95% CI, 1.49-4.33), and pancreatic (RR, 2.29; 95% CI, 1.32-3.96) and hepatic effects (RR, 3.51; 95% CI 1.55-7.92). GRADE showed that the certainty of the evidence ranged from high to moderate. This study shows that, in comparison with imatinib, second- and third-generation TKIs improve clinical responses, but the safer toxicity profile of imatinib may make it a better option for patients with comorbidities.
Presepsin (soluble CD14 subtype) has been shown to be beneficial as a sepsis marker in adults. Nevertheless, very few data are available in neonates. The aim of the present study was to determine ...reference ranges of presepsin in term and preterm neonates.
Healthy term neonates and preterm neonates without clinical signs of infection admitted to the Neonatal Unit were consecutively enrolled. Presepsin concentrations in whole blood were measured using a point-of-care assay system located in the Unit. Demographic data, antenatal and perinatal variables commonly affecting C-reactive protein and procalcitonin values were considered.
Of the 684 neonates enrolled in the study, 484 (70.8%) were born at term and 200 (29.2%) were preterm (24-36 weeks' gestation). In term infants, presepsin median value was 603.5 pg/mL (interquartile range: 466.5-791 pg/mL; 5th and 95th centiles: 315 and 1178 pg/mL respectively). In preterm infants, presepsin median value was slightly higher, equal to 620 pg/mL (interquartile range: 503-864 pg/mL; 5th and 95th centiles: 352 and 1370 pg/mL respectively). The reference ranges of presepsin we determined were much higher than those seen in healthy adults. No correlation between presepsin levels and postnatal age was observed, as well as no significant difference was demonstrated in preterm neonates at different gestational ages. None of the variables analyzed affected presepsin levels at a clinical significant extent.
For the first time, this study provides reference ranges of presepsin in term and preterm neonates. Having reliable reference values is crucial for obtaining an adequate diagnostic accuracy. Based on our results, most variables commonly affecting C-reactive protein and procalcitonin values do not affect presepsin levels, which suggests that presepsin could be an effective sepsis marker. Further investigations in large groups of neonates with sepsis are needed to determine the diagnostic and prognostic value of this biomarker.
Analyzing gene expression profiles (GEP) through artificial intelligence provides meaningful insight into cancer disease. This study introduces DeepSHAP Autoencoder Filter for Genes Selection ...(DSAF-GS), a novel deep learning and explainable artificial intelligence-based approach for feature selection in genomics-scale data. DSAF-GS exploits the autoencoder’s reconstruction capabilities without changing the original feature space, enhancing the interpretation of the results. Explainable artificial intelligence is then used to select the informative genes for chronic lymphocytic leukemia prognosis of 217 cases from a GEP database comprising roughly 20,000 genes. The model for prognosis prediction achieved an accuracy of 86.4%, a sensitivity of 85.0%, and a specificity of 87.5%. According to the proposed approach, predictions were strongly influenced by CEACAM19 and PIGP, moderately influenced by MKL1 and GNE, and poorly influenced by other genes. The 10 most influential genes were selected for further analysis. Among them, FADD, FIBP, FIBP, GNE, IGF1R, MKL1, PIGP, and SLC39A6 were identified in the Reactome pathway database as involved in signal transduction, transcription, protein metabolism, immune system, cell cycle, and apoptosis. Moreover, according to the network model of the 3D protein-protein interaction (PPI) explored using the NetworkAnalyst tool, FADD, FIBP, IGF1R, QTRT1, GNE, SLC39A6, and MKL1 appear coupled into a complex network. Finally, all 10 selected genes showed a predictive power on time to first treatment (TTFT) in univariate analyses on a basic prognostic model including IGHV mutational status, del(11q) and del(17p), NOTCH1 mutations, β2-microglobulin, Rai stage, and B-lymphocytosis known to predict TTFT in CLL. However, only IGF1R hazard ratio (HR) 1.41, 95% CI 1.08-1.84, P=0.013), COL28A1 (HR 0.32, 95% CI 0.10-0.97, P=0.045), and QTRT1 (HR 7.73, 95% CI 2.48-24.04, P<0.001) genes were significantly associated with TTFT in multivariable analyses when combined with the prognostic factors of the basic model, ultimately increasing the Harrell’s c-index and the explained variation to 78.6% (versus 76.5% of the basic prognostic model) and 52.6% (versus 42.2% of the basic prognostic model), respectively. Also, the goodness of model fit was enhanced (χ2 = 20.1, P=0.002), indicating its improved performance above the basic prognostic model. In conclusion, DSAF-GS identified a group of significant genes for CLL prognosis, suggesting future directions for bio-molecular research.
Hematological malignancies (HMs) represent a heterogeneous group of diseases with diverse etiology, pathogenesis, and prognosis. HMs' accurate registration by Cancer Registries (CRs) is hampered by ...the progressive de-hospitalization of patients and the transition to molecular rather than microscopic diagnosis.
A dedicated software capable of automatically identifying suspected HMs cases by combining several databases was adopted by Reggio Emilia Province CR (RE-CR). Besides pathological reports, hospital discharge archives, and mortality records, RE-CR retrieved information from general and biomolecular laboratories. Incidence, mortality, and 5-year relative survival (RS) reported according to age, sex, and 4 HMs' main categories, were noted.
Overall, 7,578 HM cases were diagnosed from 1996 to 2020 by RE-CR. HMs were more common in males and older patients, except for Hodgkin Lymphoma and Follicular Lymphoma (FL). Incidence showed a significant increase for FL (annual percent change (APC)=3.0), Myeloproliferative Neoplasms (MPN) in the first period (APC=6.0) followed by a significant decrease (APC=-7.4), and Myelodysplastic Syndromes (APC=16.4) only in the first period. Over the years, a significant increase was observed in 5-year RS for Hodgkin -, Marginal Zone -, Follicular - and Diffuse Large B-cell-Lymphomas, MPN, and Acute Myeloid Leukemia. The availability of dedicated software made it possible to recover 80% of cases automatically: the remaining 20% required direct consultation of medical records.
The study emphasizes that HM registration needs to collect information from multiple sources. The digitalization of CRs is necessary to increase their efficiency.
In the context of suspected neonatal sepsis, early diagnosis and stratification of patients according to clinical severity is not yet effectively achieved. In this diagnostic trial, we aimed to ...assess the accuracy of presepsin (PSEP) for the diagnosis and early stratification of supposedly septic neonates. PSEP, C-reactive protein (CRP), and procalcitonin (PCT) were assessed at the onset of sepsis suspicion (T0), every 12–24 h for the first 48 h (T1–T4), and at the end of antibiotic therapy (T5). Enrolled neonates were stratified into three groups (infection, sepsis, septic shock) according to Wynn and Wong’s definitions. Sensitivity, specificity, and area under the ROC curve (AUC) according to the severity of clinical conditions were assessed. We enrolled 58 neonates with infection, 77 with sepsis, and 24 with septic shock. PSEP levels were higher in neonates with septic shock (median 1557.5 pg/mL) and sepsis (median 1361 pg/mL) compared to those with infection (median 977.5 pg/mL) at T0 (p < 0.01). Neither CRP nor PCT could distinguish the three groups at T0. PSEP’s AUC was 0.90 (95% CI: 0.854–0.943) for sepsis and 0.94 (95% CI: 0.885–0.988) for septic shock. Maximum Youden index was 1013 pg/mL (84.4% sensitivity, 88% specificity) for sepsis, and 971.5 pg/mL for septic shock (92% sensitivity, 86% specificity). However, differences in PSEP between neonates with positive and negative blood culture were limited. Thus, PSEP was an early biomarker of neonatal sepsis severity, but did not support the early identification of neonates with positive blood culture.
The 2016 WHO classification recognized pre-fibrotic primary myelofibrosis (pre-PMF) as a distinct entity. Nevertheless, a prognostic model specific for pre-PMF is still lacking. Our aim was to ...identify the most relevant clinical, histological, and driver mutation information at diagnosis to evaluate outcomes in pre-PMF patients in the real-world setting. We firstly assessed the association between IPSS or DIPSS at diagnosis and response variables in 378 pre-PMF patients. A strict association was observed between IPSS and DIPSS and occurrence of death. Other analyzed endpoints were not associated with IPSS or DIPSS as thrombo-hemorrhagic events at diagnosis or during follow-up, or did not show a clinical plausibility, as transformation into acute leukemia or overt PMF. The only covariates which were significantly associated with death were diabetes and second neoplasia, and were therefore included in two different prognostic settings: the first based on IPSS at diagnosis class 1 vs. 0, OR (95%CIs): 3.34 (1.85-6.04); class 2 vs. 0, OR (95%CIs): 12.55 (5.04-31.24), diabetes OR (95%CIs): 2.95 (1.41-6.18), and second neoplasia OR (95%CIs): 2.88 (1.63-5.07); the second with DIPSS at diagnosis class 1 vs. 0, OR (95%CIs): 3.40 (1.89-6.10); class 2 vs. 0, OR (95%CIs): 25.65 (7.62-86.42), diabetes OR (95%CIs): 2.89 (1.37-6.09), and second neoplasia OR (95%CIs): 2.97 (1.69-5.24). In conclusion, our study underlines the importance of other additional risk factors, such as diabetes and second neoplasia, to be evaluated, together with IPSS and DIPSS, to better define prognosis in pre-PMF patients.
Increased success in the treatment of hematological cancers contributed to the increase of 5-year survival for most adolescent and young adults (AYAs) with these tumours. However, as 5-year survival ...increased, it became clear that AYA long-term survivors were at increased risk for severe late effects. Moreover, limited information on long-term cancer impact is available for AYAs, since most studies focused on children and adolescents. We aimed to assess various long-term outcomes on AYA survivors of hematological cancers.
We selected patients diagnosed with a first primary hematological cancer between 1997 and 2006, in the Italian nationwide population-based cohort of AYA cancer survivors (i.e. alive at least 5 years after cancer diagnosis). Long-term outcomes of interest were: second malignant neoplasms (SMNs), hospitalizations and overall mortality. We calculated standardized incidence ratios (SIRs), standardized hospitalization rate ratios (SHRs) and standardized mortality rate ratios (SMRs). To study morbidity patterns over time, we modeled observed incidence rates by fitting flexible parametric models for nonlinear patterns and we used linear regression for linear patterns.
The study cohort included 5,042 AYA hematological cancer survivors of which 1,237 and 3,805 had a leukaemia and lymphoma diagnosis, respectively. AYA survivors were at substantially increased risk for SMN (SIR=2.1; 95%CI=1.7; 2.6), hospitalisation (SHR=1.5; 95%CI=1.5; 1.6), and mortality (SMR=1.4; 95%CI=1.2; 1.6) with differences between leukaemia and lymphoma survivors. The highest excess risks of hospitalisations were for infectious diseases, respiratory diseases, and diseases of blood and blood-forming organs. The morbidity pattern differs over time by morbidity type.
Our results support the need for strict follow-up plans for survivors, and call for further study to better personalised follow-up plans for AYA cancer survivors.
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