Early Warning Scores With and Without Artificial Intelligence.

Importance: Early warning decision support tools to identify clinical deterioration in the hospital are widely used, but there is little information on their comparative performance.

Objective: To compare 3 proprietary artificial intelligence (AI) early warning scores and 3 publicly available simple aggregated weighted scores.

Design, Setting, And Participants: This retrospective cohort study was performed at 7 hospitals in the Yale New Haven Health System.

Development and Validation of a Machine Learning Model for Early Detection of Untreated Infection.

Background: Early diagnostic uncertainty for infection causes delays in antibiotic administration in infected patients and unnecessary antibiotic administration in noninfected patients.

Objective: To develop a machine learning model for the early detection of untreated infection (eDENTIFI), with the presence of infection determined by clinician chart review.

Derivation Cohort: Three thousand three hundred fifty-seven adult patients hospitalized between 2006 and 2018 at two health systems in Illinois, United States.

Causes, Diagnostic Testing, and Treatments Related to Clinical Deterioration Events Among High-Risk Ward Patients.

Importance: Timely intervention for clinically deteriorating ward patients requires that care teams accurately diagnose and treat their underlying medical conditions. However, the most common diagnoses leading to deterioration and the relevant therapies provided are poorly characterized.

Objectives: We aimed to determine the diagnoses responsible for clinical deterioration, the relevant diagnostic tests ordered, and the treatments administered among high-risk ward patients using manual chart review.

Robotic-assisted laparoscopic excision of a rare primary seminal vesicle leiomyoma.

A primary seminal vesicle leiomyoma (PSVL) is a rare male genitourinary tract tumor. No previous reports have utilized a robotic-assisted laparoscopic posterior approach (RALPA) for surgical management. A 76-year-old man was incidentally found to have a 5cm lobulated mass posterior to the bladder in an abdominal/pelvic computed tomography scan.

A Common Longitudinal Intensive Care Unit data Format (CLIF) to enable multi-institutional federated critical illness research.

Background: Critical illness, or acute organ failure requiring life support, threatens over five million American lives annually. Electronic health record (EHR) data are a source of granular information that could generate crucial insights into the nature and optimal treatment of critical illness. However, data management, security, and standardization are barriers to large-scale critical illness EHR studies.

Development and Validation of a Machine Learning COVID-19 Veteran (COVet) Deterioration Risk Score.

Background And Objective: To develop the COVid Veteran (COVet) score for clinical deterioration in Veterans hospitalized with COVID-19 and further validate this model in both Veteran and non-Veteran samples. No such score has been derived and validated while incorporating a Veteran sample.

Derivation Cohort: Adults (age ≥ 18 yr) hospitalized outside the ICU with a diagnosis of COVID-19 for model development to the Veterans Health Administration (VHA) (n = 80 hospitals).

Sildenafil Versus Placebo for Early Pulmonary Vascular Disease in Scleroderma (SEPVADIS): protocol for a randomized controlled trial.

Background: Pulmonary hypertension (PH) is a leading cause of death in patients with systemic sclerosis (SSc). An important component of SSc patient management is early detection and treatment of PH. Recently the threshold for the diagnosis of PH has been lowered to a mean pulmonary artery pressure (mPAP) threshold of > 20 mmHg on right heart catheterization (RHC).

Development and external validation of deep learning clinical prediction models using variable-length time series data.

Objectives: To compare and externally validate popular deep learning model architectures and data transformation methods for variable-length time series data in 3 clinical tasks (clinical deterioration, severe acute kidney injury [AKI], and suspected infection).

Materials And Methods: This multicenter retrospective study included admissions at 2 medical centers that spanned 2007-2022. Distinct datasets were created for each clinical task, with 1 site used for training and the other for testing.

Automated stratification of trauma injury severity across multiple body regions using multi-modal, multi-class machine learning models.

Objective: The timely stratification of trauma injury severity can enhance the quality of trauma care but it requires intense manual annotation from certified trauma coders. The objective of this study is to develop machine learning models for the stratification of trauma injury severity across various body regions using clinical text and structured electronic health records (EHRs) data.

Materials And Methods: Our study utilized clinical documents and structured EHR variables linked with the trauma registry data to create 2 machine learning models with different approaches to representing text.

Multicenter Development and Prospective Validation of eCARTv5: A Gradient Boosted Machine Learning Early Warning Score.

Objective: Early detection of clinical deterioration using machine learning early warning scores may improve outcomes. However, most implemented scores were developed using logistic regression, only underwent retrospective validation, and were not tested in important subgroups. Our objective was to develop and prospectively validate a gradient boosted machine model (eCARTv5) for identifying clinical deterioration on the wards.

Alcohol Withdrawal Severity Measures for Identifying Patients Requiring High-Intensity Care.

Objectives: Alcohol withdrawal syndrome (AWS) may progress to require high-intensity care. Approaches to identify hospitalized patients with AWS who received higher level of care have not been previously examined. This study aimed to examine the utility of Clinical Institute Withdrawal Assessment Alcohol Revised (CIWA-Ar) for alcohol scale scores and medication doses for alcohol withdrawal management in identifying patients who received high-intensity care.

Causes, Diagnostic Testing, and Treatments Related to Clinical Deterioration Events among High-Risk Ward Patients.

Objective: Timely intervention for clinically deteriorating ward patients requires that care teams accurately diagnose and treat their underlying medical conditions. However, the most common diagnoses leading to deterioration and the relevant therapies provided are poorly characterized. Therefore, we aimed to determine the diagnoses responsible for clinical deterioration, the relevant diagnostic tests ordered, and the treatments administered among high-risk ward patients using manual chart review.

Predicting transfers to intensive care in children using CEWT and other early warning systems.

Background And Objective: The Children's Early Warning Tool (CEWT), developed in Australia, is widely used in many countries to monitor the risk of deterioration in hospitalized children. Our objective was to compare CEWT prediction performance against a version of the Bedside Pediatric Early Warning Score (Bedside PEWS), Between the Flags (BTF), and the pediatric Calculated Assessment of Risk and Triage (pCART).

Methods: We conducted a retrospective observational study of all patient admissions to the Comer Children's Hospital at the University of Chicago between 2009-2019.

Association between mortality and critical events within 48 hours of transfer to the pediatric intensive care unit.

Introduction: Critical deterioration in hospitalized children, defined as ward to pediatric intensive care unit (PICU) transfer followed by mechanical ventilation (MV) or vasoactive infusion (VI) within 12 h, has been used as a primary metric to evaluate the effectiveness of clinical interventions or quality improvement initiatives. We explore the association between critical events (CEs), i.e.

Development and external validation of multimodal postoperative acute kidney injury risk machine learning models.

Objectives: To develop and externally validate machine learning models using structured and unstructured electronic health record data to predict postoperative acute kidney injury (AKI) across inpatient settings.

Materials And Methods: Data for adult postoperative admissions to the Loyola University Medical Center (2009-2017) were used for model development and admissions to the University of Wisconsin-Madison (2009-2020) were used for validation. Structured features included demographics, vital signs, laboratory results, and nurse-documented scores.

Internet search analytics for shoulder arthroplasty: what questions are patients asking?

Background: Common questions about shoulder arthroplasty (SA) searched online by patients and the quality of this content are unknown. The purpose of this study is to uncover questions SA patients search online and determine types and quality of webpages encountered.

Methods: The "People also ask" section of Google Search was queried to return 900 questions and associated webpages for general, anatomic, and reverse SA.

Rotator cuff repair: what questions are patients asking online and where are they getting their answers?

Background: This study analyzed questions searched by rotator cuff patients and determined types and quality of websites providing information.

Methods: Three strings related to rotator cuff repair were explored by Google Search. Result pages were collected under the "People also ask" function for frequent questions and associated webpages.

Two Cases of Heterotopic Pancreas of the Small Bowel Incidentally Found During Laparoscopic Bariatric Surgery.

Background: We present two cases of incidentally found heterotopic pancreas during laparoscopic bariatric surgery. Heterotopic pancreas is a rare congenital anomaly where pancreatic tissue is located outside of the pancreas. These lesions may be encountered incidentally during surgery, which raise unexpected management questions.

Temperature Trajectory Subphenotypes in Oncology Patients with Neutropenia and Suspected Infection.

Despite etiologic and severity heterogeneity in neutropenic sepsis, management is often uniform. Understanding host response clinical subphenotypes might inform treatment strategies for neutropenic sepsis. In this retrospective two-hospital study, we analyzed whether temperature trajectory modeling could identify distinct, clinically relevant subphenotypes among oncology patients with neutropenia and suspected infection.

Using Machine Learning to Predict Likelihood and Cause of Readmission After Hospitalization for Chronic Obstructive Pulmonary Disease Exacerbation.

Background: Chronic obstructive pulmonary disease (COPD) is a leading cause of hospital readmissions. Few existing tools use electronic health record (EHR) data to forecast patients' readmission risk during index hospitalizations.

Objective: We used machine learning and in-hospital data to model 90-day risk for and cause of readmission among inpatients with acute exacerbations of COPD (AE-COPD).

Association Between Critical Events in the PICU and Outcomes in Neighboring Patients.

Unlabelled: PICU patients who experience critical illness events, such as intubation, are at high risk for morbidity and mortality. Little is known about the impact of these events, which require significant resources, on outcomes in other patients. Therefore, we aimed to assess the association between critical events in PICU patients and the risk of similar events in neighboring patients over the next 6 hours.

Identifying infected patients using semi-supervised and transfer learning.

Objectives: Early identification of infection improves outcomes, but developing models for early identification requires determining infection status with manual chart review, limiting sample size. Therefore, we aimed to compare semi-supervised and transfer learning algorithms with algorithms based solely on manual chart review for identifying infection in hospitalized patients.

Materials And Methods: This multicenter retrospective study of admissions to 6 hospitals included "gold-standard" labels of infection from manual chart review and "silver-standard" labels from nonchart-reviewed patients using the Sepsis-3 infection criteria based on antibiotic and culture orders.

Predicting neurological outcomes after in-hospital cardiac arrests for patients with Coronavirus Disease 2019.

Background: Machine learning models are more accurate than standard tools for predicting neurological outcomes in patients resuscitated after cardiac arrest. However, their accuracy in patients with Coronavirus Disease 2019 (COVID-19) is unknown. Therefore, we compared their performance in a cohort of cardiac arrest patients with COVID-19.

Development and External Validation of a Machine Learning Model for Prediction of Potential Transfer to the PICU.

Objectives: Unrecognized clinical deterioration during illness requiring hospitalization is associated with high risk of mortality and long-term morbidity among children. Our objective was to develop and externally validate machine learning algorithms using electronic health records for identifying ICU transfer within 12 hours indicative of a child's condition.

Design: Observational cohort study.