Dissecting bloodstream infections in febrile neutropenic patients with hematological malignancies, a decade-long single center retrospective observational study (2009-2019).

BACKGROUND: The use of ill-suited antibiotics is a significant risk factor behind the increase in the mortality, morbidity, and economic burden for patients who are under treatment for hematological malignancy (HM) and bloodstream infections (BSI). Such unfitting treatment choices intensify the evolution of resistant variants which is a public health concern due to possible healthcare-associated infection spread to the general population. Hence, this study aims to evaluate antibiograms of patients with BSI and risk factors associated with septicemia. METHODS: A total of 1166 febrile neutropenia episodes (FNE) among 513 patients with HM from the National Center for Cancer Care and Research (NCCCR), Qatar, during 2009-2019 were used for this study. The socio-demographic, clinical, microbial, and anti-microbial data retrieved from the patient's health records were used. RESULTS: We analyzed the sensitivity of gram-negative and gram-positive bacilli reported in HM-FN-BSI patients. Out of the total 512 microorganisms isolated, 416 (81%) were gram-negative bacteria (GNB), 76 (15%) were gram-positive bacteria (GPB) and 20 (4%) were fungi. Furthermore, in 416 GNB, 298 (71.6%) were Enterobacteriaceae sp. among which 121 (41%) were ESBL (Extended Spectrum Beta-Lactamase) resistant to Cephalosporine third generation and Piperacillin-Tazobactam, 54 (18%) were Carbapenem-resistant or multidrug-resistant organism (MDRO). It's noteworthy that the predominant infectious agents in our hospital include E. coli, Klebsiella species, and P. aeruginosa. Throughout the study period, the mortality rate due to BSI was 23%. Risk factors that show a significant correlation with death are age, disease status, mono or polymicrobial BSI and septic shock. CONCLUSION: Decision pertaining to the usage of antimicrobials for HM-FN-BSI patients is a critical task that relies on the latest pattern of prevalence, treatment resistance, and clinical outcomes. Analysis of the antibiogram of HM-FN-BSI patients in Qatar calls for a reconsideration of currently followed empirical antibiotic therapy towards better infection control and antimicrobial stewardship.

An Artificial Intelligence-Based Diagnostic System for Acute Lymphoblastic Leukemia Detection.

This study suggests a novel Acute Lymphoblastic Leukemia (ALL) diagnostic model, built solely on complete blood count (CBC) records. Using a dataset comprised of CBC records of 86 ALL and 86 control patients respectively, we identified the most ALL-specific parameters using a feature selection approach. Next, Grid Search-based hyperparameter tuning with a five-fold cross-validation scheme was adopted to build classifiers using Random Forest, XGBoost, and Decision Tree algorithms. A comparison between the performances of the three models demonstrates that Decision Tree classifier outperformed XGBoost and Random Forest algorithms in ALL detection using CBC-based records.

Machine Learning for Diagnosis and Screening of Chronic Lymphocytic Leukemia Using Routine Complete Blood Count (CBC) Results.

The comprehensive epidemiology and global disease burdens reported recently suggest that chronic lymphocytic leukemia (CLL) constitutes 25-30% of leukemias thus being the most common leukemia subtype. However, there is an insufficient presence of artificial intelligence (AI)-based techniques for CLL diagnosis. The novelty of this study is in the investigation of data-driven techniques to leverage the intricate CLL-related immune dysfunctions reflected in routine complete blood count (CBC) alone. We used statistical inferences, four feature selection methods, and multistage hyperparameter tuning to build robust classifiers. With respective accuracies of 97.05%, 97.63%, and 98.62% for Quadratic Discriminant Analysis (QDA), Logistic Regression (LR), and XGboost (XGb)-based models, CBC-driven AI methods promise timely medical care and improved patient outcome with lesser resource usage and related cost.

A review of Soil and Water Assessment Tool (SWAT) studies of Mediterranean catchments: Applications, feasibility, and future directions.

The Soil and Water Assessment Tool (SWAT) is a well-established eco-hydrological model that has been extensively applied to watersheds across the globe. This work reviews over two decades (2002-2022) of SWAT studies conducted on Mediterranean watersheds. A total of 260 articles have been identified since the earliest documented use of the model in a Mediterranean catchment back in 2002; of which 62% were carried out in Greece, Italy, or Spain. SWAT applications increased significantly in recent years since 86% of the reviewed papers were published in the past decade. A major objective for most of the reviewed works was to check the applicability of SWAT to specific watersheds. A great number of publications included procedures of calibration and validation and reported performance results. SWAT applications in the Mediterranean region mainly cover water resources quantity and quality assessment and hydrologic and environmental impacts evaluation of land use and climate changes. Nevertheless, a tendency towards a multi-purpose use of SWAT is revealed. The numerous examples of SWAT combined with other tools and techniques outline the model's flexibility. Several studies performed constructive comparisons between Mediterranean watersheds' responses or compared SWAT to other models or methods. The effects of inputs on SWAT outputs and innovative model modifications and improvements were also the focus of some of the surveyed articles. However, a significant number of studies reported difficulties regarding data availability, as these are either scarce, have poor resolution or are not freely available. Therefore, it is highly recommended to identify and develop accurate model inputs and testing data to optimize the SWAT performance.

Clustering and Stochastic Simulation Optimization for Outpatient Chemotherapy Appointment Planning and Scheduling.

Outpatient Chemotherapy Appointment (OCA) planning and scheduling is a process of distributing appointments to available days and times to be handled by various resources through a multi-stage process. Proper OCAs planning and scheduling results in minimizing the length of stay of patients and staff overtime. The integrated consideration of the available capacity, resources planning, scheduling policy, drug preparation requirements, and resources-to-patients assignment can improve the Outpatient Chemotherapy Process's (OCP's) overall performance due to interdependencies. However, developing a comprehensive and stochastic decision support system in the OCP environment is complex. Thus, the multi-stages of OCP, stochastic durations, probability of uncertain events occurrence, patterns of patient arrivals, acuity levels of nurses, demand variety, and complex patient pathways are rarely addressed together. Therefore, this paper proposes a clustering and stochastic optimization methodology to handle the various challenges of OCA planning and scheduling. A Stochastic Discrete Simulation-Based Multi-Objective Optimization (SDSMO) model is developed and linked to clustering algorithms using an iterative sequential approach. The experimental results indicate the positive effect of clustering similar appointments on the performance measures and the computational time. The developed cluster-based stochastic optimization approaches showed superior performance compared with baseline and sequencing heuristics using data from a real Outpatient Chemotherapy Center (OCC).

A Bibliometric Analysis and Visualization of Decision Support Systems for Healthcare Referral Strategies.

BACKGROUND: The referral process is an important research focus because of the potential consequences of delays, especially for patients with serious medical conditions that need immediate care, such as those with metastatic cancer. Thus, a systematic literature review of recent and influential manuscripts is critical to understanding the current methods and future directions in order to improve the referral process. METHODS: A hybrid bibliometric-structured review was conducted using both quantitative and qualitative methodologies. Searches were conducted of three databases, Web of Science, Scopus, and PubMed, in addition to the references from the eligible papers. The papers were considered to be eligible if they were relevant English articles or reviews that were published from January 2010 to June 2021. The searches were conducted using three groups of keywords, and bibliometric analysis was performed, followed by content analysis. RESULTS: A total of 163 papers that were published in impactful journals between January 2010 and June 2021 were selected. These papers were then reviewed, analyzed, and categorized as follows: descriptive analysis (n = 77), cause and effect (n = 12), interventions (n = 50), and quality management (n = 24). Six future research directions were identified. CONCLUSIONS: Minimal attention was given to the study of the primary referral of blood cancer cases versus those with solid cancer types, which is a gap that future studies should address. More research is needed in order to optimize the referral process, specifically for suspected hematological cancer patients.

Home Cancer Care Research: A Bibliometric and Visualization Analysis (1990-2021).

Home cancer care research (HCCR) has accelerated, as considerable attention has been placed on reducing cancer-related health costs and enhancing cancer patients' quality of life. Understanding the current status of HCCR can help guide future research and support informed decision-making about new home cancer care (HCC) programs. However, most current studies mainly detail the research status of certain components, while failing to explore the knowledge domain of this research field as a whole, thereby limiting the overall understanding of home cancer care. We carried out bibliometric and visualization analyses of Scopus-indexed papers related to home cancer care published between 1990-2021, and used VOSviewer scientometric software to investigate the status and provide a structural overview of the knowledge domain of HCCR (social, intellectual, and conceptual structures). Our findings demonstrate that over the last three decades, the research on home cancer care has been increasing, with a constantly expanding stream of new papers built on a solid knowledge base and applied to a wide range of research themes.

A Review of Artificial Intelligence Applications in Hematology Management: Current Practices and Future Prospects.

BACKGROUND: Machine learning (ML) and deep learning (DL) methods have recently garnered a great deal of attention in the field of cancer research by making a noticeable contribution to the growth of predictive medicine and modern oncological practices. Considerable focus has been particularly directed toward hematologic malignancies because of the complexity in detecting early symptoms. Many patients with blood cancer do not get properly diagnosed until their cancer has reached an advanced stage with limited treatment prospects. Hence, the state-of-the-art revolves around the latest artificial intelligence (AI) applications in hematology management. OBJECTIVE: This comprehensive review provides an in-depth analysis of the current AI practices in the field of hematology. Our objective is to explore the ML and DL applications in blood cancer research, with a special focus on the type of hematologic malignancies and the patient's cancer stage to determine future research directions in blood cancer. METHODS: We searched a set of recognized databases (Scopus, Springer, and Web of Science) using a selected number of keywords. We included studies written in English and published between 2015 and 2021. For each study, we identified the ML and DL techniques used and highlighted the performance of each model. RESULTS: Using the aforementioned inclusion criteria, the search resulted in 567 papers, of which 144 were selected for review. CONCLUSIONS: The current literature suggests that the application of AI in the field of hematology has generated impressive results in the screening, diagnosis, and treatment stages. Nevertheless, optimizing the patient's pathway to treatment requires a prior prediction of the malignancy based on the patient's symptoms or blood records, which is an area that has still not been properly investigated.

Bibliometric analysis of cancer care operations management: current status, developments, and future directions.

Around the world, cancer care services are facing many operational challenges. Operations management research can provide important solutions to these challenges, from screening and diagnosis to treatment. In recent years, the growth in the number of papers published on cancer care operations management (CCOM) indicates that development has been fast. Within this context, the objective of this research was to understand the evolution of CCOM through a comprehensive study and an up-to-date bibliometric analysis of the literature. To achieve this aim, the Web of Science Core Collection database was used as the source of bibliographic records. The data-mining and quantitative tools in the software Biblioshiny were used to analyze CCOM articles published from 2010 to 2021. First, a historical analysis described CCOM research, the sources, and the subfields. Second, an analysis of keywords highlighted the significant developments in this field. Third, an analysis of research themes identified three main directions for future research in CCOM, which has 11 evolutionary paths. Finally, this paper discussed the gaps in CCOM research and the areas that require further investigation and development.

Prediction of Multiple Clinical Complications in Cancer Patients to Ensure Hospital Preparedness and Improved Cancer Care.

Reliable and rapid medical diagnosis is the cornerstone for improving the survival rate and quality of life of cancer patients. The problem of clinical decision-making pertaining to the management of patients with hematologic cancer is multifaceted and intricate due to the risk of therapy-induced myelosuppression, multiple infections, and febrile neutropenia (FN). Myelosuppression due to treatment increases the risk of sepsis and mortality in hematological cancer patients with febrile neutropenia. A high prevalence of multidrug-resistant organisms is also noted in such patients, which implies that these patients are left with limited or no-treatment options amidst severe health complications. Hence, early screening of patients for such organisms in their bodies is vital to enable hospital preparedness, curtail the spread to other weak patients in hospitals, and limit community outbreaks. Even though predictive models for sepsis and mortality exist, no model has been suggested for the prediction of multidrug-resistant organisms in hematological cancer patients with febrile neutropenia. Hence, for predicting three critical clinical complications, such as sepsis, the presence of multidrug-resistant organisms, and mortality, from the data available from medical records, we used 1166 febrile neutropenia episodes reported in 513 patients. The XGboost algorithm is suggested from 10-fold cross-validation on 6 candidate models. Other highlights are (1) a novel set of easily available features for the prediction of the aforementioned clinical complications and (2) the use of data augmentation methods and model-scoring-based hyperparameter tuning to address the problem of class disproportionality, a common challenge in medical datasets and often the reason behind poor event prediction rate of various predictive models reported so far. The proposed model depicts improved recall and AUC (area under the curve) for sepsis (recall = 98%, AUC = 0.85), multidrug-resistant organism (recall = 96%, AUC = 0.91), and mortality (recall = 86%, AUC = 0.88) prediction. Our results encourage the need to popularize artificial intelligence-based devices to support clinical decision-making.

Robust global reverse logistics network redesign for high-grade plastic wastes recycling.

The circular economy represents an alternative closed-loop production and consumption patterns instead of traditional linear take-make-waste approaches. It presents a new vision with global sustainability where plastic waste is viewed as a material that can be reused, to avoid depleting natural resources. In this context, it is essential to redesign the global reverse logistics network (GRLN) by incorporating the existing facilities across national boundaries into the integrated global recycling system. A mean-variance robust model with quadratic functions is developed to address the time-ambiguous currency exchange rate, the ocean freight rate, and the carbon prices. To the best of our knowledge, this is one of the first efforts to evaluate the effects of the multiplicative relationships between the uncertain elements, e.g. currency exchange rate and carbon trading prices. In the proposed model, the economic and the environmental performances of a GRLN are evaluated by the robustness coefficient. The application of the model is demonstrated in a sample case of the PWR between China and Belgium. The analysis shows that a lower robustness coefficient leads to a higher cost of the GRLN, but lower emissions. It is worthy to note that considering the maritime emission is not definite to guarantee global net sustainability. Moreover, a social GRLN network leads to a more cost-efficient system compared to developing two recycling networks individually in the importing and the exporting countries.

Acute Myeloid Leukemia in Qatar (2010-2016): Clinical, Biological, and Prognostic Factors and Treatment Outcomes.

The current study retrospectively evaluated cytogenetic profiles, various prognostic factors, and survival outcomes in 128 acute myeloid leukemia (AML) patients (14 ≤ age ≤ 70 years) admitted to the National Center for Cancer Care and Research (NCCCR), Hamad Medical Corporation, Doha, Qatar, between January 2010 and December 2016. The median age at diagnosis was 43 years, and 80% were less than 60 years old; 75% of patients were male. Cytogenetic analysis was integrated into the World Health Organization 2008 classification and showed that the percentages of normal and abnormal karyotypes were similar, accounting for 48.4% of each group of patients. The AML risk stratification based on cytogenetic analysis resulted in the following distribution: 18% in the favorable risk group, 57% in the intermediate-risk group, 24% in the unfavorable risk group, and 1% unknown. Only 88 patients received therapy with curative intent; 67% achieved complete remission, increasing to 81% after inductions 1 and 2. The median overall survival (OS) and disease-free survival (DFS) in AML patients were 26.6 and 19.5 months, respectively. The 3-year OS and DFS were 40 and 36%, respectively. Prognostic factors including age, gender, white blood cell count, and risk stratification were not significantly associated with treatment outcomes, whereas response to treatment vs. failure was significantly associated with the outcome (p = 0.01). The current study supports the importance of cytogenetics as a useful tool in diagnosis, prognosis, and risk assessment in AML treatment.

Data of the design of solar assisted district cooling systems.

The collected datasets are relevant and related to Optimization of Design and Operation of Solar Assisted District Cooling Systems [1] paper. Part of the data is collected on the main and common components of the system. That includes solar collectors unit price ($/m2), type, and efficiency; absorption chiller capacity (kW), type, initial cost ($), and COP; the hot/chilled water thermal energy storage tank type, initial cost ($) and capacity (kWh); and auxiliary boiler initial cost ($), capacity (kW), type and efficiency. The other part of the data is collected on hourly cooling demand over the year for the state of Qatar (kW), hourly global solar irradiance over the year for the state of Qatar (W/m2) and variable cost of producing and storing chilled and hot water ($/kWh, $/kW). The data are collected from different resources such as government websites, commercial websites, government sectors, journals and real-life case studies. The value of this data comes from that most of the data required to conduct such research in this area are available in one resource. Also, some of the data such as the annual hourly cooling demand and global solar radiation are not available online. Moreover, the collected data are already filtered and the units are consistent and ready to be used. Finally, the data considered to be crucial and the core of such research are available in this paper.

Global reverse supply chain design for solid waste recycling under uncertainties and carbon emission constraint.

The emergence of concerns over environmental protection, resource conservation as well as the development of logistics operations and manufacturing technology has led several countries to implement formal collection and recycling systems of solid waste. Such recycling system has the benefits of reducing environmental pollution, boosting the economy by creating new jobs, and generating income from trading the recyclable materials. This leads to the formation of a global reverse supply chain (GRSC) of solid waste. In this paper, we investigate the design of such a GRSC with a special emphasis on three aspects; (1) uncertainty of waste collection levels, (2) associated carbon emissions, and (3) challenges posed by the supply chain's global aspect, particularly the maritime transportation costs and currency exchange rates. To the best of our knowledge, this paper is the first attempt to integrate the three above-mentioned important aspects in the design of a GRSC. We have used mixed integer-linear programming method along with robust optimization to develop the model which is validated using a sample case study of e-waste management. Our results show that using a robust model by taking the complex interactions characterizing global reverse supply chain networks into account, we can create a better GRSC. The effect of uncertainties and carbon constraints on decisions to reduce costs and emissions are also shown.