Why implementing machine learning algorithms in the clinic is not a plug-and-play solution: a simulation study of a machine learning algorithm for acute leukaemia subtype diagnosis.

Background: Artificial intelligence (AI) and machine learning (ML) algorithms have shown great promise in clinical medicine. Despite the increasing number of published algorithms, most remain unvalidated in real-world clinical settings. This study aims to simulate the practical implementation challenges of a recently developed ML algorithm, AI-PAL, designed for the diagnosis of acute leukaemia and report on its performance.

Vital lessons from struggling partnerships and potential partnerships: an international study with leaders across the health sector.

Background: Inter-organizational partnerships and collaborations, used here interchangeably, have growing prominence across the health sector. Successful partnerships have received extensive study. However, especially for partnerships including nonprofit partners, limited attention has been given to negative factors that contribute to struggling partnerships, including failed partnerships, and/or impede potential partnerships, including unexplored and undeveloped potential partnerships.

Leveraging machine learning to identify subgroups of misclassified patients in the emergency department: a multi-center proof-of-concept study.

Background: Hospitals use triage systems to prioritize the needs of patients within available resources. Misclassification of a patient can lead to either adverse outcomes in a patient who did not receive appropriate care in the case of undertriage or waste of hospital resources in the case of overtriage. Recent advances in machine learning algorithms allow for the quantification of variables important to under- and overtriage.

Establishing Medical Intelligence-Leveraging Fast Healthcare Interoperability Resources to Improve Clinical Management: Retrospective Cohort and Clinical Implementation Study.

Background: FHIR (Fast Healthcare Interoperability Resources) has been proposed to enable health data interoperability. So far, its applicability has been demonstrated for selected research projects with limited data.

Objective: This study aimed to design and implement a conceptual medical intelligence framework to leverage real-world care data for clinical decision-making.

Evaluating equitable care in the ICU: Creating a causal inference framework to assess the impact of life-sustaining interventions across racial and ethnic groups.

Background: Variability in the provision of intensive care unit (ICU)-interventions may lead to disparities between socially defined racial-ethnic groups.

Research Question: We used causal inference to examine the use of invasive mechanical ventilation (IMV), renal replacement therapy (RRT), and vasopressor agents (VP) to identify disparities in outcomes across race-ethnicity in patients with sepsis.

Study Design And Methods: Single-center, academic referral hospital in Boston, Massachusetts, USA.

Generalizable calibrated machine learning models for real-time atrial fibrillation risk prediction in ICU patients.

Background: Atrial Fibrillation (AF) is the most common arrhythmia in the intensive care unit (ICU) and is associated with increased morbidity and mortality. Identification of patients at risk for AF is not routinely performed as AF prediction models are almost solely developed for the general population or for particular ICU populations. However, early AF risk identification could help to take targeted preemptive actions and possibly reduce morbidity and mortality.

Leveraging electronic health records for data science: common pitfalls and how to avoid them.

Analysis of electronic health records (EHRs) is an increasingly common approach for studying real-world patient data. Use of routinely collected data offers several advantages compared with other study designs, including reduced administrative costs, the ability to update analysis as practice patterns evolve, and larger sample sizes. Methodologically, EHR analysis is subject to distinct challenges because data are not collected for research purposes.

Systematic Review and Comparison of Publicly Available ICU Data Sets-A Decision Guide for Clinicians and Data Scientists.

Objective: As data science and artificial intelligence continue to rapidly gain traction, the publication of freely available ICU datasets has become invaluable to propel data-driven clinical research. In this guide for clinicians and researchers, we aim to: 1) systematically search and identify all publicly available adult clinical ICU datasets, 2) compare their characteristics, data quality, and richness and critically appraise their strengths and weaknesses, and 3) provide researchers with suggestions, which datasets are appropriate for answering their clinical question.

Data Sources: A systematic search was performed in Pubmed, ArXiv, MedRxiv, and BioRxiv.

Understanding critically ill sepsis patients with normal serum lactate levels: results from U.S. and European ICU cohorts.

While serum lactate level is a predictor of poor clinical outcomes among critically ill patients with sepsis, many have normal serum lactate. A better understanding of this discordance may help differentiate sepsis phenotypes and offer clues to sepsis pathophysiology. Three intensive care unit datasets were utilized.

Machine learning can accurately predict pre-admission baseline hemoglobin and creatinine in intensive care patients.

Patients admitted to the intensive care unit frequently have anemia and impaired renal function, but often lack historical blood results to contextualize the acuteness of these findings. Using data available within two hours of ICU admission, we developed machine learning models that accurately (AUC 0.86-0.

Feature selection and prediction of treatment failure in tuberculosis.

Background: Tuberculosis is a major cause of morbidity and mortality in the developing world. Drug resistance, which is predicted to rise in many countries worldwide, threatens tuberculosis treatment and control.

Objective: To identify features associated with treatment failure and to predict which patients are at highest risk of treatment failure.

Neuroendocrine Key Regulator Gene Expression in Merkel Cell Carcinoma.

Merkel cell carcinoma (MCC) is a highly aggressive non-melanoma skin cancer of the elderly which is associated with the Merkel cell polyomavirus (MCPyV). MCC reveals a trilinear differentiation characterized by neuroendocrine, epithelial and pre/pro B-cell lymphocytic gene expression disguising the cellular origin of MCC. Here we investigated the expression of the neuroendocrine key regulators RE1 silencing transcription factor (REST), neurogenic differentiation 1 (NeuroD1) and the Achaete-scute homolog 1 (ASCL1) in MCC.

Improved Survival of Cancer Patients Admitted to the Intensive Care Unit between 2002 and 2011 at a U.S. Teaching Hospital.

Purpose: Cancer patients are at increased risk of treatment- or disease-related admission to the intensive care unit. Over the past decades, both critical care and cancer care have improved substantially. Due to increased cancer-specific survival, we hypothesized that the number of cancer patients admitted to the intensive care unit (ICU) and survival have increased.

Extracorporeal membrane oxygenation use has increased by 433% in adults in the United States from 2006 to 2011.

Recent studies have shown the benefits of extracorporeal membrane oxygenation (ECMO) in supporting adults with severe respiratory or cardiac failure refractory to conventional treatments. The purpose of this investigation was to analyze the usage of ECMO in adults to identify recent trends within the United States. The usage of ECMO, the survival rates, and the hospitalization costs from 2006 to 2011 were analyzed using the Nationwide Inpatient Sample from the Healthcare Cost and Utilization Project.

National trends in the utilization of short-term mechanical circulatory support: incidence, outcomes, and cost analysis.

Background: The number of alternatives to intra-aortic balloon counterpulsation in the treatment of anticipated and established acute circulatory failure is growing. Despite the clinical importance and significant cost of short-term mechanical circulatory support (MCS) devices, the state of their present use has not been analyzed on a national scale.

Objectives: The purpose of this study was to characterize the demographics, treatment practices, survival rates, and cost of short-term MCS.

Endometrial adenocarcinoma presenting as a hematotrachelos.

Background: Hematotrachelos, distension of the uterine cervix with accumulated blood, is an extremely rare condition resulting from a congenital anomaly or an acquired condition. We present a case in which an acquired hematotrachelos was the presenting sign of endometrial cancer.

Case: An asymptomatic 66-year-old woman was found to have a bulging cervix during a well-woman visit.

Intraovarian regulation of gonadotropin-dependent folliculogenesis depends on notch receptor signaling pathways not involving Delta-like ligand 4 (Dll4).

Background: In-situ hybridisation studies demonstrate that Notch receptors and ligands are expressed in granulosa cells (GCs) and in the theca layer vasculature of growing follicles. Notch signaling involves cell-to-cell interaction mediated by transmembrane receptors and ligands. This signaling pathway may represent a novel intraovarian regulator of gonadotropin-dependent follicular development to the preovulatory stage.

Vascular endothelial growth factor receptor 2 (VEGFR-2) functions to promote uterine decidual angiogenesis during early pregnancy in the mouse.

Implantation of an embryo induces rapid proliferation and differentiation of uterine stromal cells, forming a new structure, the decidua. One salient feature of decidua formation is a marked increase in maternal angiogenesis. Vascular endothelial growth factor (VEGF)-dependent pathways are active in the ovary, uterus, and embryo, and inactivation of VEGF function in any of these structures might prevent normal pregnancy development.