During recent decades, understanding of the molecular mechanisms of acute lymphoblastic leukemia (ALL) has improved considerably, resulting in better risk stratification of patients and increased survival rates. Age, white blood cell count (WBC), and specific genetic abnormalities are the most important factors that define risk groups for ALL. State-of-the-art diagnosis of ALL requires cytological and cytogenetical analyses, as well as flow cytometry and high-throughput sequencing assays. An important aspect in the diagnostic characterization of patients with ALL is the identification of the Philadelphia (Ph) chromosome, which warrants the addition of tyrosine kinase inhibitors (TKI) to the chemotherapy backbone. Data that support the benefit of hematopoietic stem cell transplantation (HSCT) in high risk patient subsets or in late relapse patients are still questioned and have yet to be determined conclusive. This article presents the newly published data in ALL workup and treatment, putting it into perspective for the attending physician in hematology and oncology.

Introduction: The examination of vital signs and their changes during illness can alert physicians to possible impending deterioration and organ dysfunction. The Modified Early Warning Score (MEWS) is used worldwide as a track and trigger system that can help to identify patients at risk of critical illness. Thus, the current study aimed to assess the ability of MEWS to predict the mortality of hematologic patients at the point of transfer from the ward to the intensive care unit (ICU). Materials and Methods: The present study was retrospective, longitudinal, and observational, conducted at an oncology hospital in the city of Cluj-Napoca, Romania. We included 174 patients with hematological disorders transferred from the ward to the ICU between the 1st of January 2018 and the 1st of May 2020. We assessed the MEWS at the moment of admission in these patients in the ICU. The accuracy of MEWS in predicting mortality was assessed via the area under the receiver operating characteristic curves (AUC), and sensitivity, specificity, and hazard ratio (HR) were calculated for different MEWS cutoffs. MEWS values considering the status at discharge and frequency of death by MEWS were also analyzed. Results: We calculated MEWS values considering the status at discharge (p < 0.0001), and we assessed the frequency of death by MEWS. We also calculated the hazard ratio (HR) of death depending on the selected MEWS cutoff. The best cutoff point was found to be ≥6, with an accuracy of 0.667, sensitivity of 0.675, specificity of 0.646, and AUC of 0.731. Patients with higher MEWS had a higher probability of mortality. Conclusion: The MEWS and cutoff points were determined on a sample of hematologic patients at the moment of admission to the ICU. The final aim is to encourage physicians to use these scores to improve awareness of organ failure to admit patients to the ICU sooner and limit overall morbidity and mortality. The presence of an ICU physician on ward rounds might help in reducing the timeframe of access to a high-dependency unit (HDU) or ICU. An extension of these scores outside hematologic patients or considering hematologic patients outside ICU must be further studied.

The initial management of the hematology patient in a critical state is crucial and poses a great challenge both for the hematologist and the intensive care unit (ICU) physician. After years of clinical practice, there is still a delay in the proper recognition and treatment of critical situations, which leads to late admission to the ICU. There is a much-needed systematic ABC (Airway, Breathing, Circulation) approach for the patients being treated on the wards as well as in the high dependency units because the underlying hematological disorder, as well as disease-related complications, have an increasing frequency. Focusing on score-based decision-making on the wards (Modified Early Warning Score (MEWS), together with Quick Sofa score), active sepsis screening with inflammation markers (C-reactive protein, procalcitonin, and presepsin), and assessment of microcirculation, organ perfusion, and oxygen supply by using paraclinical parameters from the ICU setting (lactate, central venous oxygen saturation (ScVO₂), and venous-to-arterial carbon dioxide difference), hematologists can manage the immediate critical patient and improve the overall outcome.

Objective: Our primary objective was to describe the baseline characteristics, main reasons for intensive care unit (ICU) admission, and interventions required in the ICU across patients who received CAR-T cell immunotherapy. The secondary objectives were to evaluate different outcomes (ICU mortality) across patients admitted to the ICU after having received CAR-T cell therapy. Materials and Methods. We performed a medical literature review, which included MEDLINE, Embase, and Cochrane Library, of studies published from the inception of the databases until 2022. We conducted a systematic review with meta-analyses of proportions of several studies, including CAR-T cell-treated patients who required ICU admission. Outcomes in the meta-analysis were evaluated using the random-effects model. Results: We included four studies and analyzed several outcomes, including baseline characteristics and ICU-related findings. CAR-T cell recipients admitted to the ICU are predominantly males (62% CI-95% (57–66)). Of the total CAR-T cell recipients, 4% CI-95% (3–5) die in the hospital, and 6% CI-95% (4–9) of those admitted to the ICU subsequently die. One of the main reasons for ICU admission is acute kidney injury (AKI) in 15% CI-95% (10–19) of cases and acute respiratory failure in 10% CI-95% (6–13) of cases. Regarding the interventions initiated in the ICU, 18% CI-95% (13–22) of the CAR-T recipients required invasive mechanical ventilation during their ICU stay, 23% CI-95% (16–30) required infusion of vasoactive drugs, and 1% CI-95% (0.1–3) required renal replacement therapy (RRT). 18% CI-95% (13–22) of the initially discharged patients were readmitted to the ICU within 30 days, and the mean length of hospital stay is 22 days CI-95% (19–25). The results paint a current state of matter in CAR-T cell recipients admitted to the ICU. Conclusions: To better understand immunotherapy-related complications from an ICU standpoint, acknowledge the deteriorating patient on the ward, reduce the ICU admission rate, advance ICU care, and improve the outcomes of these patients, a standard of care and research regarding CAR-T cell-based immunotherapies should be created. Studies that are looking from the perspective of intensive care are highly warranted because the available literature regarding this area is scarce.

Background: Due to a lack of randomised controlled trials and guidelines, and only case reports being available in the literature, there is no consensus on how to approach anaesthetic management in patients with giant intraabdominal tumours. Methods: This study aimed to evaluate the literature and explore the current status of evidence, by undertaking an observational research design with a descriptive account of characteristics observed in a case series referring to patients with giant intraabdominal tumours who underwent anaesthesia. Results: Twenty patients diagnosed with giant intraabdominal tumours were included in the study, most of them women, with the overall pathology being ovarian-related and sarcomas. Most of the patients were unable to lie supine and assumed a lateral decubitus position. Pulmonary function tests, chest X-rays, and thoracoabdominal CT were the most often performed preoperative evaluation methods, with the overall findings that there was no atelectasis or pleural effusion present, but there was bilateral diaphragm elevation. The removal of the intraabdominal tumour was performed under general anaesthesia in all cases. Awake fiberoptic intubation or awake videolaryngoscopy was performed in five cases, while the rest were performed with general anaesthesia with rapid sequence induction. Only one patient was ventilated with pressure support ventilation while maintaining spontaneous ventilation, while the rest were ventilated with controlled ventilation. Hypoxemia was the most reported respiratory complication during surgery. In more than 50% of cases, there was hypotension present during surgery, especially after the induction of anaesthesia and after tumour removal, which required vasopressor support. Most cases involved blood loss with subsequent transfusion requirements. The removal of the tumor requires prolonged surgical and anaesthesia times. Fluid drainage from cystic tumour ranged from 15.7 L to 107 L, with a fluid extraction rate of 0.5–2.5 L/min, and there was no re-expansion pulmonary oedema reported. Following surgery, all the patients required intensive care unit admission. One patient died during hospitalization. Conclusions: This study contributes to the creation of a certain standard of care when dealing with patients presenting with giant intraabdominal tumour. More research is needed to define the proper way to administer anaesthesia and create practice guidelines.

Acute myeloid leukemia (AML) is a hematologic malignancy characterized by abnormal proliferation and a lack of differentiation of myeloid blasts. Considering the dismal prognosis this disease presents, several efforts have been made to better classify it and offer a tailored treatment to each subtype. This has been formally done by the World Health Organization (WHO) with the AML classification schemes from 2008 and 2016. Nonetheless, there are still mutations that are not currently included in the WHO AML classification, as in the case of some mutations that influence methylation. In this regard, the present study aimed to determine if some of the mutations that influence DNA methylation can be clustered together regarding methylation, expression, and clinical profile. Data from the TCGA LAML cohort were downloaded via cBioPortal. The analysis was performed using R 3.5.2, and the necessary packages for classical statistics, dimensionality reduction, and machine learning. We included only patients that presented mutations in DNMT3A, TET2, IDH1/2, ASXL1, WT1, and KMT2A. Afterwards, mutations that were present in too few patients were removed from the analysis, thus including a total of 57 AML patients. We observed that regarding expression, methylation, and clinical profile, patients with mutated TET2, IDH1/2, and WT1 presented a high degree of similarity, indicating the equivalence that these mutations present between themselves. Nonetheless, we did not observe this similarity between DNMT3A- and KMT2A-mutated AML. Moreover, when comparing the hypermethylating group with the hypomethylating one, we also observed important differences regarding expression, methylation, and clinical profile. In the current manuscript we offer additional arguments for the similarity of the studied hypermethylating mutations and suggest that those should be clustered together in further classifications. The hypermethylating and hypomethylating groups formed above were shown to be different from each other considering overall survival, methylation profile, expression profile, and clinical characteristics. In this manuscript, we present additional arguments for the similarity of the effect generated by TET2, IDH1/2, and WT1 mutations in AML patients. Thus, we hypothesize that hypermethylating mutations skew the AML cells to a similar phenotype with a possible sensitivity to hypermethylating agents.