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Blood transfusion is a common therapeutic intervention in hospitalized patients. There are numerous indications for transfusion, including anemia and coagulopathy with deficiency of single or multiple coagulation components such as platelets or coagulation factors. Nevertheless, the practice of transfusion in critically ill patients has been controversial mainly due to a lack of evidence and the need to consider the appropriate clinical context for transfusion. Further, transfusion carries many risk factors that must be balanced with benefits. Therefore, transfusion practice in ICU patients has constantly evolved, and we endeavor to present a contemporary review of transfusion practices in this population guided by clinical trials and expert guidelines. [ABSTRACT FROM AUTHOR]

We herein present a case of pylephlebitis, which is an infective suppurative thrombosis of the portal vein. Pylephlebitis is an uncommon complication of intra-abdominal infections and carries with it significant morbidity and mortality. [West J Emerg Med. 2011;12(4):575–576.]

Introduction: Down syndrome (DS) is associated with multiple comorbid conditions and chronic immune dysfunction. Persons with DS who contract COVID‐19 are at high risk for complications and have a poor prognosis. We aimed to study the clinical symptoms, laboratory and biochemical profiles, radiologic findings, treatment, and outcomes of patients with DS and COVID‐19. Method: We systematically searched PubMed, MEDLINE, Web of Science, Scopus, and the Cochrane Library using the keywords COVID‐19 or coronavirus or SARS‐CoV‐2 and DS or trisomy 21. Seventeen articles were identified: eight case reports and nine case series published from December 2019 through March 2022, with a total of 55 cases. Results: Patients averaged 24.8 years (26 days to 60 years); 29 of the patients were male. The most common symptoms were fever, dyspnea, and cough. Gastrointestinal and upper respiratory tract symptoms were commonly reported for pediatric patients. The most common comorbidities present in patients with DS were obesity (49.0%), hypothyroidism (21.6%) and obstructive sleep apnea (15.6%). The patients were hospitalized for a mean of 14.8 days. When the patients were compared with the general COVID‐19 population, the mean number of hospitalized days was higher. Most patients had leukopenia, lymphopenia, and elevated inflammatory markers (d‐dimer and C‐reactive protein). Bilateral infiltrations and bilateral ground‐glass opacifications were frequently seen in chest radiographs and chest computed tomographic imaging. Most of the patients were treated with methylprednisolone, macrolides, and hydroxychloroquine. Of the 55 patients, 22 died. The mean age of the patients who died was 42.8 years. Mortality rate was higher in individuals with DS over 40 years of age. Conclusion: More studies are needed to better understand COVID‐19 infections among persons with DS. In addition, the study was limited by a lack of statistical analyses and a specific comparison group. [ABSTRACT FROM AUTHOR]

Therapeutic plasma exchange (TPE) is a treatment paradigm used to remove harmful molecules from the body. In short, it is a technique that employs a process that functions partially outside the body and involves the replacement of the patient's plasma. It has been used in the ICU for a number of different disease states, for some as a first-line treatment modality and for others as a type of salvage therapy. This paper provides a brief review of the principles, current applications, and potential future directions of TPE in critical care settings. [ABSTRACT FROM AUTHOR]

Diabetic kidney disease (DKD) causes a progressive decline in renal function, leading to end-stage kidney disease (ESKD), and increases the likelihood of cardiovascular events and mortality. The recent introduction of the sodium-glucose co-transporter 2 (SGLT-2) inhibitor has been a game changer in managing chronic kidney disease (CKD) and congestive heart failure (CHF). These agents not only slow down the progression of kidney disease but also have cardioprotective benefits, including for patients with congestive heart failure and atherosclerotic cardiovascular disease. Some evidence suggests that they can decrease the risk of stroke as well. This review aims to provide a comprehensive overview of the role of SGLT-2 inhibitors in CKD and CHF and their efficacy in stroke prevention. This review includes a comparison with glucagon-like peptide-1 (GLP-1) agonist and finerenone; focuses on safety data, the potential benefits beyond glycemic control, and a review of significant trials; and provides guidance in clinical practice. [ABSTRACT FROM AUTHOR]

Obstructive sleep apnea (OSA) is a prevalent sleep disorder that affects approximately 3–7% of males and 2–5% of females. In the United States alone, 50–70 million adults suffer from various sleep disorders. OSA is characterized by recurrent episodes of breathing cessation during sleep, thereby leading to adverse effects such as daytime sleepiness, cognitive impairment, and reduced concentration. It also contributes to an increased risk of cardiovascular conditions and adversely impacts patient overall quality of life. As a result, numerous researchers have focused on developing automated detection models to identify OSA and address these limitations effectively and accurately. This study explored the potential benefits of utilizing machine learning methods based on demographic information for diagnosing the OSA syndrome. We gathered a comprehensive dataset from the Torr Sleep Center in Corpus Christi, Texas, USA. The dataset comprises 31 features, including demographic characteristics such as race, age, sex, BMI, Epworth score, M. Friedman tongue position, snoring, and more. We devised a novel process encompassing pre-processing, data grouping, feature selection, and machine learning classification methods to achieve the research objectives. The classification methods employed in this study encompass decision tree (DT), naive Bayes (NB), k-nearest neighbor (kNN), support vector machine (SVM), linear discriminant analysis (LDA), logistic regression (LR), and subspace discriminant (Ensemble) classifiers. Through rigorous experimentation, the results indicated the superior performance of the optimized kNN and SVM classifiers for accurately classifying sleep apnea. Moreover, significant enhancements in model accuracy were observed when utilizing the selected demographic variables and employing data grouping techniques. For instance, the accuracy percentage demonstrated an approximate improvement of 4.5%, 5%, and 10% with the feature selection approach when applied to the grouped data of Caucasians, females, and individuals aged 50 or below, respectively. Furthermore, a comparison with prior studies confirmed that effective data grouping and proper feature selection yielded superior performance in OSA detection when combined with an appropriate classification method. Overall, the findings of this research highlight the importance of leveraging demographic information, employing proper feature selection techniques, and utilizing optimized classification models for accurate and efficient OSA diagnosis. [ABSTRACT FROM AUTHOR]

Almost one billion people worldwide are affected by Obstructive Sleep Apnea (OSA). Affected individuals experience disordered breathing patterns during sleep, which results in fatigue, daytime drowsiness, and/or sleep deprivation. Working under the influence of these symptoms significantly impairs work productivity and leads to occupational accidents and errors. This impact is seen in healthcare workers (HCWs) who are not immune to these conditions. However, poorly controlled OSA in this subset of individuals takes a heavy toll on patient care due to the increased risk of medical errors and can also alter the mental and physical well-being of the affected HCW in various ways. OSA and safety issues have been recognized and mitigated among the airline and transport industries; however, the healthcare industry lags in addressing these concerns. This article reviews hypersomnolence and sleep disorder as key clinical features of OSA and their effect on HCW safety. [ABSTRACT FROM AUTHOR]

Background: Hypotension with endotracheal intubation (ETI) is common and associated with adverse outcomes. We sought to evaluate whether a previously described hypotension prediction score (HYPS) for ETI is associated with worse patient outcomes and/or clinical conditions. Methods: This study is a post hoc analysis of a prospective observational multicenter study involving adult (age =18 years) intensive care unit (ICU) patients undergoing ETI in which the HYPS was derived and validated on the entire cohort and a stable subset (ie, patients in stable condition). We evaluated the association between increasing HYPSs in both subsets and several patient-centered outcomes and clinical conditions. Results: Complete data for HYPS calculations were available for 783 of 934 patients (84%). Logistic regression analysis showed increasing odds ratios (ORs) for the highest risk category for new-onset acute kidney injury (OR, 7.37; 95% CI, 2.58-21.08); new dialysis need (OR, 8.13; 95% CI, 1.74-37.91); ICU mortality (OR, 16.39; 95% CI, 5.99-44.87); and hospital mortality (OR, 18.65; 95% CI, 6.81-51.11). Although not increasing progressively, the OR for the highest risk group was significantly associated with new-onset hypovolemic shock (OR, 6.06; 95% CI, 1.47-25.00). With increasing HYPSs, median values (interquartile ranges) decreased progressively (lowest risk vs. highest risk) for ventilator-free days (23 [18-26] vs. 1 [0-21], P < .001) and ICU-free days (20 [11-24] vs. 0 [0-13], P < .001). Of the 729 patients in the stable subset, 598 (82%) had complete data for HYPS calculations. Logistic regression analysis showed significantly increasing ORs for the highest risk category for new-onset hypovolemic shock (OR, 7.41; 95% CI, 2.06-26.62); ICU mortality (OR, 5.08; 95% CI, 1.87-13.85); and hospital mortality (OR, 7.08; 95% CI, 2.63-19.07). Conclusions: As the risk for peri-intubation hypotension increases, according to a validated hypotension prediction tool, so does the risk for adverse clinical events and certain clinical conditions. [ABSTRACT FROM AUTHOR]

Background. Illness predictive scoring systems are significant and meaningful adjuncts of patient management in the Intensive Care Unit (ICU). They assist in predicting patient outcomes, improve clinical decision making and provide insight into the effectiveness of care and management of patients while optimizing the use of hospital resources. We evaluated mortality predictive performance of Simplified Acute Physiology Score (SAPS 3) and Mortality Probability Models (MPM0-III) and compared their performance in predicting outcome as well as identifying disease pattern and factors associated with increased mortality. Methods. This was a retrospective cohort study of adult patients admitted to the ICU of the Aga Khan Hospital, Dar- es- Salaam, Tanzania between August 2018 and April 2020. Demographics, clinical characteristics, outcomes, source of admission, primary admission category, length of stay and the support provided with the worst physiological data within the first hour of ICU admission were extracted. SAPS 3 and MPM0-III scores were calculated using an online web-based calculator. The performance of each model was assessed by discrimination and calibration. Discrimination between survivors and non–survivors was assessed by the area under the receiver operator characteristic curve (ROC) and calibration was estimated using the Hosmer-Lemeshow goodness-of-fit test. Results. A total of 331 patients were enrolled in the study with a median age of 58 years (IQR 43-71), most of whom were male (n = 208, 62.8%), of African origin (n = 178, 53.8%) and admitted from the emergency department (n = 306, 92.4%). In- hospital mortality of critically ill patients was 16.1%. Discrimination was very good for all models, the area under the receiver-operating characteristic (ROC) curve for SAPS 3 and MPM0-III was 0.89 (95% CI [0.844–0.935]) and 0.90 (95% CI [0.864– 0.944]) respectively. Calibration as calculated by Hosmer-Lemeshow goodness-of-fit test showed good calibration for SAPS 3 and MPM0-III with Chi- square values of 4.61 and 5.08 respectively and P–Value > 0.05. Conclusion. Both SAPS 3 and MPM0-III performed well in predicting mortality and outcome in our cohort of patients admitted to the intensive care unit of a private tertiary hospital. The in-hospital mortality of critically ill patients was lower compared to studies done in other intensive care units in tertiary referral hospitals within Tanzania. [ABSTRACT FROM AUTHOR]

Obstructive sleep apnea (OSA) is a well-known sleep ailment. OSA mostly occurs due to the shortage of oxygen for the human body, which causes several symptoms (i.e., low concentration, daytime sleepiness, and irritability). Discovering the existence of OSA at an early stage can save lives and reduce the cost of treatment. The computer-aided diagnosis (CAD) system can quickly detect OSA by examining the electrocardiogram (ECG) signals. Over-serving ECG using a visual procedure is challenging for physicians, time-consuming, expensive, and subjective. In general, automated detection of the ECG signal's arrhythmia is a complex task due to the complexity of the data quantity and clinical content. Moreover, ECG signals are usually affected by noise (i.e., patient movement and disturbances generated by electric devices or infrastructure), which reduces the quality of the collected data. Machine learning (ML) and Deep Learning (DL) gain a higher interest in health care systems due to its ability of achieving an excellent performance compared to traditional classifiers. We propose a CAD system to diagnose apnea events based on ECG in an automated way in this work. The proposed system follows the following steps: (1) remove noise from the ECG signal using a Notch filter. (2) extract nine features from the ECG signal (3) use thirteen ML and four types of DL models for the diagnosis of sleep apnea. The experimental results show that our proposed approach offers a good performance of DL classifiers to detect OSA. The proposed model achieves an accuracy of 86.25% in the validation stage. [ABSTRACT FROM AUTHOR]

Introduction: Transfusion-related acute lung injury (TRALI), an adverse event occurring during or within 6 hours of transfusion, is a leading cause of transfusion-associated fatalities reported to the US Food and Drug Administration. There is limited information on the validity of diagnosis codes for TRALI recorded in inpatient electronic medical records (EMRs).Study Designs and Methods: We conducted a validation study to establish the positive predictive value (PPV) of TRALI International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) diagnosis codes recorded within a large hospital system between 2013 and 2015. A physician with critical care expertise confirmed the TRALI diagnosis. As TRALI is likely underdiagnosed, we used the specific code (518.7), and codes for respiratory failure (518.82) in combination with transfusion reaction (999.80, 999.89, E934.7).Results: Among almost four million inpatient stays, we identified 208 potential TRALI cases with ICD-9-CM codes and reviewed 195 medical records; 68 (35%) met clinical definitions for TRALI (26 [38%] definitive, 15 [22%] possible, 27 [40%] delayed). Overall, the PPV for all inpatient TRALI diagnoses was 35% (95% confidence interval (CI), 28-42). The PPV for the TRALI-specific code was 44% (95% CI, 35-54).Conclusion: We observed low PPVs (<50%) for TRALI ICD-9-CM diagnosis codes as validated by medical charts, which may relate to inconsistent code use, incomplete medical records, or other factors. Future studies using TRALI diagnosis codes in EMR databases may consider confirming diagnoses with medical records, assessing TRALI ICD, Tenth Revision, Clinical Modification codes, or exploring alternative ways for of accurately identifying TRALI in EMR databases.Key Points: In 169 hospitals, we identified 208 potential TRALI cases, reviewed 195 charts, and confirmed 68 (35%) cases met TRALI clinical definitions. As many potential TRALI cases identified with diagnosis codes did not meet clinical definitions, medical record confirmation may be prudent. [ABSTRACT FROM AUTHOR]

Sarcoidosis is a systemic granulomatous disease of unknown etiology characterized by presence of noncaseating granulomas in the involved organs. The pulmonary interstitium is most commonly affected but extrapulmonary involvement can occur in almost any other organ system. Such an involvement can occur with or without the presence of pulmonary involvement, but isolated extrapulmonary involvement has been noted only in around 10% of cases. Isolated splenomegaly is very rare and an uncommon presentation of sarcoidosis. It is clinically challenging because of the extensive differential diagnosis. Among the many considerations are hematologic malignancies, primary splenic or metastatic tumors, infiltrative disorders, inflammatory disorders, and infections. We hereby discuss an interesting case of a 40-year-old female with isolated splenic sarcoidosis. [ABSTRACT FROM AUTHOR]

34-year-old African American female with history of pemphigoid presented with hemoptysis. Patient was found to have mucous membrane pemphigoid involving the oropharynx and extending to trachea, till just above main stem carina. Four other cases described mucosal pemphigoid involving the trachea. We hereby present a brief review of current consensus on management of mucous membrane pemphigoid with airway involvement. [ABSTRACT FROM AUTHOR]

Simple Summary: With the advancement of artificial intelligence, including machine learning, the field of oncology has seen promising results in cancer detection and classification, epigenetics, drug discovery, and prognostication. In this review, we describe what artificial intelligence is and its function, as well as comprehensively summarize its evolution and role in breast, colorectal, and central nervous system cancers. Understanding the origin and current accomplishments might be essential to improve the quality, accuracy, generalizability, cost-effectiveness, and reliability of artificial intelligence models that can be used in worldwide clinical practice. Students and researchers in the medical field will benefit from a deeper understanding of how to use integrative AI in oncology for innovation and research. Well-trained machine learning (ML) and artificial intelligence (AI) systems can provide clinicians with therapeutic assistance, potentially increasing efficiency and improving efficacy. ML has demonstrated high accuracy in oncology-related diagnostic imaging, including screening mammography interpretation, colon polyp detection, glioma classification, and grading. By utilizing ML techniques, the manual steps of detecting and segmenting lesions are greatly reduced. ML-based tumor imaging analysis is independent of the experience level of evaluating physicians, and the results are expected to be more standardized and accurate. One of the biggest challenges is its generalizability worldwide. The current detection and screening methods for colon polyps and breast cancer have a vast amount of data, so they are ideal areas for studying the global standardization of artificial intelligence. Central nervous system cancers are rare and have poor prognoses based on current management standards. ML offers the prospect of unraveling undiscovered features from routinely acquired neuroimaging for improving treatment planning, prognostication, monitoring, and response assessment of CNS tumors such as gliomas. By studying AI in such rare cancer types, standard management methods may be improved by augmenting personalized/precision medicine. This review aims to provide clinicians and medical researchers with a basic understanding of how ML works and its role in oncology, especially in breast cancer, colorectal cancer, and primary and metastatic brain cancer. Understanding AI basics, current achievements, and future challenges are crucial in advancing the use of AI in oncology. [ABSTRACT FROM AUTHOR]

The article presents a case study of a 68-year-old man with past medical history of type II diabetes mellitus, hypertension, hyperlipidemia and a "cardiac murmur" is presented to the emergency department with severe substernal chest pain. Topics include shows that vital signs are unstable as tachycardic and hypotensive despite receives intravenous fluids.

Purulent pericarditis is one of the most common causes of cardiac tamponade and if left untreated has a mortality of 100%. Staphylococcus aureus and Streptococcus pneumonia have been implicated as the main etiology of purulent pericardial effusion followed by fungi and anaerobic sources. Actinomyces odontolyticus pericardial involvement has been reported in the literature only once. To our knowledge, this is the first fatal case of A. odontolyticus purulent pericarditis in the absence of periodontal disease. [ABSTRACT FROM AUTHOR]

Metastatic calcification has been associated with multiple-myeloma-induced hypercalcemia. Despite of a relatively high prevalence of metastatic pulmonary calcification in patients with multiple myeloma, only a few cases have been clinically and radiologically detected. A 45-year-old Hispanic male presented to the Emergency Department with complaint of worsening weakness and myalgia. Laboratory findings revealed renal insufficiency and hypercalcemia. CT scan of chest revealed calcified pleural and pulmonary nodule. Technetium (Tc) 99 bone scan revealed diffuse activity in the pulmonary parenchyma consistent with metastatic pulmonary calcification. Metastatic pulmonary calcification, despite its high prevalence, remains undetected. This is, in part, due to its radiographic characteristic properties that evade detection by routine imaging studies. We present a case of a metastatic pulmonary calcification in a patient diagnosed with multiple myeloma and chronic kidney disease, as well as a brief literature review including clinical findings and treatment options. [ABSTRACT FROM AUTHOR]

Introduction: To date, only corticosteroids and interleukin-6 (IL-6) inhibitors have been shown to reduce mortality of hospitalized patients with COVID-19. In this literature review, we aimed to summarize infection risk of IL inhibitors, with or without the use of corticosteroids, used to treat hospitalized patients with COVID-19., Methods: A literature search was conducted using the following evidence-based medicine reviews: Cochrane Central Register of Controlled Trials; Cochrane Database of Systematic Reviews; Embase; Ovid Medline; and Epub Ahead of Print, In-Process, In-Data-Review & Other Non-Indexed Citations, Daily and Versions 1946 to April 28, 2021. All relevant articles were identified using the search terms COVID-19 or SARS-coronavirus-2 , infections , interleukins , inpatients , adults , and i ncidence ., Results: We identified 36 studies of which 2 were meta-analyses, 5 were randomized controlled trials, 9 were prospective studies, and 20 were retrospective studies. When anakinra was compared with control, 2 studies reported an increased risk of infection, and 3 studies reported a similar or decreased incidence of infection. Canakinumab had a lower associated incidence of infection compared with placebo in one study. When sarilumab was compared with placebo, one study reported an increased risk of infection. Nine studies comparing tocilizumab with placebo reported decreased or no difference in infection risk (odds ratio [OR] for the studies ranged from 0.39-1.21). Fourteen studies comparing tocilizumab with placebo reported an increased risk of infection, ranging from 9.1% to 63.0% (OR for the studies ranged from 1.85-5.04). Infection most commonly presented as bacteremia. Of the 6 studies comparing tocilizumab and corticosteroid use with placebo, 4 reported a nonsignificant increase toward corticosteroids being associated with bacterial infections (OR ranged from 2.76-3.8), and 2 studies reported no increased association with a higher infection risk., Conclusions: Our literature review showed mixed results with variable significance for the association of IL-6 inhibitors with risk of infections in patients with COVID-19., Competing Interests: Conflict of interest None., (Copyright © 2016 - 2021 InfezMed.)

Coronavirus disease 2019 is a pandemic, was first recognized at Wuhan province, China in December 2019. The disease spread quickly across the globe, spreading stealthily from human to human through both symptomatic and asymptomatic individuals. A multisystem disease which appears to primarily spread via bio aerosols, it has exhibited a wide clinical spectrum involving multiple organ systems with the respiratory system pathology being the prime cause of morbidity and mortality. Initially unleashing a huge destructive trail at Wuhan China, Lombardy Italy and New York City, it has now spread to all parts of the globe and has actively thrived and mutated into new forms. Health care systems and Governments responded initially with panic, with containment measures giving way to mitigation strategies. The global medical and scientific community has come together and responded to this huge challenge. Professional medical societies quickly laid out "expert" guidelines which were conservative in their approach. Many drugs were re formulated and tested quickly with the help of national and international collaborative groups, helping carve out effective treatment strategies and help build a good scientific foundation for evidence-based medicine. Out of the darkness of chaos, we now have an orderly approach to manage this disease both from a public health preventive and therapeutic standpoint. With preventive measures such as masking and social distancing to the development of highly effective and potent vaccines, the public health success of such measures has been tempered by behavioral responses and resource mobilization. From a therapy standpoint, we now have drugs that were promising but now proven ineffective, and those that are effective when given early during viral pathogenesis or later when immune dysregulation has established, and the goal is to help reign in the destructive cascade. It has been a fascinating journey for mankind and our work here recapitulates the evolution of various aspects of critical care and other inpatient practices which continue to evolve., Competing Interests: Conflict-of-interest statement: There are nothing to disclose., (©The Author(s) 2021. Published by Baishideng Publishing Group Inc. All rights reserved.)