An Ailment with Which I Will Contend: A Narrative Review of 5000 Years of Esophagogastric Cancers and Their Treatments, with Special Emphasis on Recent Advances in Immunotherapeutics

Esophagogastric cancers are among the most common and deadly cancers worldwide. This review traces their chronology from 3000 BCE to the present. The first several thousand years were devoted to palliation, before advances in operative technique and technology led to the first curative surgery in 1913. Systemic therapies were introduced in 1910, and radiotherapy shortly thereafter. Operative technique improved massively over the 20th century, with operative mortality rates reducing from over 50% in 1933 to less than 5% by 1981. In addition to important roles in palliation, endoscopy became a key nonsurgical curative option for patients with limited-stage disease by the 1990s. The first nonrandomized studies on combination therapies (chemotherapy ± radiation ± surgery) were reported in the early 1980s, with survival benefit only for subsets of patients. Randomized trials over the next decades had similar overall results, with increasing nuance. Disparate conclusions led to regional variation in global practice. Starting with the first FDA approval in 2017, multiple immunotherapies now encompass more indications and earlier lines of therapy. As standards of care incorporate these effective yet expensive therapies, care must be given to disparities and methods for increasing access.

Personalized Prediction Model to Risk Stratify Patients With Myelodysplastic Syndromes

PURPOSE

Patients with myelodysplastic syndromes (MDS) have a survival that can range from months to decades. Prognostic systems that incorporate advanced analytics of clinical, pathologic, and molecular data have the potential to more accurately and dynamically predict survival in patients receiving various therapies.

METHODS

A total of 1,471 MDS patients with comprehensively annotated clinical and molecular data were included in a training cohort and analyzed using machine learning techniques. A random survival algorithm was used to build a prognostic model, which was then validated in external cohorts. The accuracy of the proposed model, compared with other established models, was assessed using a concordance (c)index.

RESULTS

The median age for the training cohort was 71 years. Commonly mutated genes included SF3B1, TET2, and ASXL1. The algorithm identified chromosomal karyotype, platelet, hemoglobin levels, bone marrow blast percentage, age, other clinical variables, seven discrete gene mutations, and mutation number as having prognostic impact on overall and leukemia-free survivals. The model was validated in an independent external cohort of 465 patients, a cohort of patients with MDS treated in a prospective clinical trial, a cohort of patients with paired samples at different time points during the disease course, and a cohort of patients who underwent hematopoietic stem-cell transplantation.

CONCLUSION

A personalized prediction model on the basis of clinical and genomic data outperformed established prognostic models in MDS. The new model was dynamic, predicting survival and leukemia transformation probabilities at different time points that are unique for a given patient, and can upstage and downstage patients into more appropriate risk categories.

Personalized predictions of patient outcomes during and after hospitalization using artificial intelligence

Hospital systems, payers, and regulators have focused on reducing length of stay (LOS) and early readmission, with uncertain benefit. Interpretable machine learning (ML) may assist in transparently identifying the risk of important outcomes. We conducted a retrospective cohort study of hospitalizations at a tertiary academic medical center and its branches from January 2011 to May 2018. A consecutive sample of all hospitalizations in the study period were included. Algorithms were trained on medical, sociodemographic, and institutional variables to predict readmission, length of stay (LOS), and death within 48-72 h. Prediction performance was measured by area under the receiver operator characteristic curve (AUC), Brier score loss (BSL), which measures how well predicted probability matches observed probability, and other metrics. Interpretations were generated using multiple feature extraction algorithms. The study cohort included 1,485,880 hospitalizations for 708,089 unique patients (median age of 59 years, first and third quartiles (QI) [39, 73]; 55.6% female; 71% white). There were 211,022 30-day readmissions for an overall readmission rate of 14% (for patients ≥65 years: 16%). Median LOS, including observation and labor and delivery patients, was 2.94 days (QI [1.67, 5.34]), or, if these patients are excluded, 3.71 days (QI [2.15, 6.51]). Predictive performance was as follows: 30-day readmission (AUC 0.76/BSL 0.11); LOS > 5 days (AUC 0.84/BSL 0.15); death within 48-72 h (AUC 0.91/BSL 0.001). Explanatory diagrams showed factors that impacted each prediction.

Policy Implications of Artificial Intelligence and Machine Learning in Diabetes Management

PURPOSE OF REVIEW

Machine learning (ML) is increasingly being studied for the screening, diagnosis, and management of diabetes and its complications. Although various models of ML have been developed, most have not led to practical solutions for real-world problems. There has been a disconnect between ML developers, regulatory bodies, health services researchers, clinicians, and patients in their efforts. Our aim is to review the current status of ML in various aspects of diabetes care and identify key challenges that must be overcome to leverage ML to its full potential.

RECENT FINDINGS

ML has led to impressive progress in development of automated insulin delivery systems and diabetic retinopathy screening tools. Compared with these, use of ML in other aspects of diabetes is still at an early stage. The Food & Drug Administration (FDA) is adopting some innovative models to help bring technologies to the market in an expeditious and safe manner. ML has great potential in managing diabetes and the future is in furthering the partnership of regulatory bodies with health service researchers, clinicians, developers, and patients to improve the outcomes of populations and individual patients with diabetes.

Equivalent circuit modeling and vibrometry measurements of the Nigerian-origin Udu Utar drum

The Udu drum, sometimes called the water pot drum, is a traditional Nigerian instrument. Musicians who play the Udu exploit its aerophone and idiophone resonances. This paper will discuss an electrical equivalent circuit model for the Udu Utar, a modern innovation of the traditional Udu, to predict the low frequency aerophone resonances and will also present scanning laser vibrometer measurements to determine the mode shapes of the dominant idiophone resonances. These analyses not only provide an understanding of the unique sound of the Udu instrument but may also be used by instrument designers to create instruments with resonance frequencies at traditional musical intervals for the various tones produced and to create musical harmonic ratios. The information, specifically the laser vibrometry measurements, may also be useful to musicians in knowing the best places to strike the Udu to excite musical tones.

Antibodies to proteases and exotoxin A of Pseudomonas aeruginosa in patients with cystic fibrosis: Demonstration by radioimmunoassay

Sera from 33 patients with cystic fibrosis and two pediatric patients being treated for chronic pulmonary infections not related to cystic fibrosis and six sera or serum pools from uninfected individuals were tested with a microtiter radioimmunoassay for reactivity against exotoxin A and two proteases from Pseudomonas aeruginosa. Exotoxin A was purified from a low-protease strain of P. aeruginosa and shown to have adenosine diphosphate-ribose transferase activity and mouse lethality. Proteases were purified from an isolate of P. aeruginosa from a patient with cystic fibrosis and had proteolytic activity against elastin and collagen in an assay employing dimethylated protein substrates. The antibody responses of the patients detected using 125I-labeled antibody to human immunoglobulin were correlated with clinical evaluations expressed as a composite score based on pulmonary findings, case histories, growth and nutrition, and chest X rays. Values in the radioimmunoassay for patients' sera were compared with those of a control serum pool and expressed as the ratio of counts per minute (cpm) in patient serum to the cpm in the control pool. Inverse correlations were found between these ratios for each of the pseudomonas exoproducts and clinical scores; highest ratios occurred in patients showing the lowest clinical scores. These results confirm that proteases and exotoxin A of P. aeruginosa are produced in cystic fibrosis pulmonary infections due to P. aeruginosa and suggest that they may serve as significant virulence factors in these chronic infectious states.