Association of COVID-19 Vaccination With Risk of Medically Attended Postacute Sequelae of COVID-19 During the Ancestral, Alpha, Delta, and Omicron Variant Eras.

Background: Uncertainty exists regarding the effectiveness of COVID-19 vaccine to prevent postacute sequelae of COVID-19 (PASC) following a breakthrough infection. While most studies based on symptom surveys found an association between preinfection vaccination status and PASC symptoms, studies of medically attended PASC are less common and have reported conflicting findings.

Methods: In this retrospective cohort of patients with an initial SARS-CoV-2 infection who were continually empaneled for primary care in a large US health system, the electronic health record was queried for preinfection vaccination status, demographics, comorbidity index, and diagnosed conditions.

Influenza infection is not associated with phenotypical frailty in older patients, a prospective cohort study.

Background And Aims: Influenza is a challenging infectious illness for older adults. It is not completely clear whether influenza is associated with frailty or functional decline. We sought to determine the association between incident influenza infection and frailty and prefrailty in community patients over 50 years of age.

Outcomes of a Nursing Home-to-Community Care Transition Program.

Objectives: Most transitional care initiatives to reduce rehospitalization have focused on the transition that occurs between a patient's hospital discharge and return home. However, many patients are discharged from a skilled nursing facility (SNF) to their homes. The goal was to evaluate the effectiveness of the Mayo Clinic Care Transitions (MCCT) program (hereafter called program) among patients discharged from SNFs to their homes.

Using machine learning to improve the accuracy of patient deterioration predictions: Mayo Clinic Early Warning Score (MC-EWS).

Objective: We aimed to develop a model for accurate prediction of general care inpatient deterioration.

Materials And Methods: Training and internal validation datasets were built using 2-year data from a quaternary hospital in the Midwest. Model training used gradient boosting and feature engineering (clinically relevant interactions, time-series information) to predict general care inpatient deterioration (resuscitation call, intensive care unit transfer, or rapid response team call) in 24 hours.

Impact of a Program to Improve Venous Thromboembolism Prophylaxis on Incidence of Thromboembolism and Bleeding Rates in Hospitalized Patients During Implementation of Programs to Improve Venous Thromboembolism Prophylaxis.

Objective: To study the impact of multiphase quality improvement efforts to enhance appropriate use of chemical and mechanical venous thromboembolism (VTE) prophylaxis (VTEP) on the rate of hospital-acquired VTE and determine whether efforts have been associated with increased bleeding complications.

Patients And Methods: All adult inpatients discharged between January 1, 2005, and December 31, 2015, were included in the study. Retrospective interrupted time series analysis compared VTEP performance, VTE outcomes, and unintended consequences (derived from linked administrative and clinical data) across 5 improvement phases: baseline (January 1, 2005-December 31, 2006), paper order set phase (January 1, 2007-February 9, 2009), electronic order set phase (February 10, 2009-December 16, 2009), active reminder phase (December 17, 2009-May 31, 2012), and maintenance phase (June 1, 2012-September 30, 2015).

The fifth vital sign? Nurse worry predicts inpatient deterioration within 24 hours.

Introduction: Identification of hospitalized patients with suddenly unfavorable clinical course remains challenging. Models using objective data elements from the electronic health record may miss important sources of information available to nurses.

Methods: We recorded nurses' perception of patient potential for deterioration in 2 medical and 2 surgical adult hospital units using a 5-point score at the start of the shift (the Worry Factor [WF]), and any time a change or an increase was noted by the nurse.

Evaluating Automated Rules for Rapid Response System Alarm Triggers in Medical and Surgical Patients.

Background: The use of rapid response systems (RRS), which were designed to bring clinicians with critical care expertise to the bedside to prevent unnecessary deaths, has increased. RRS rely on accurate detection of acute deterioration events. Early warning scores (EWS) have been used for this purpose but were developed using heterogeneous populations.

Individualizing and optimizing the use of early warning scores in acute medical care for deteriorating hospitalized patients.

Aim: While early warning scores (EWS) have the potential to identify physiological deterioration in an acute care setting, the implementation of EWS in clinical practice has yet to be fully realized. The primary aim of this study is to identify optimal patient-centered rapid response team (RRT) activation rules using electronic medical records (EMR)-derived Markovian models.

Methods: The setting for the observational cohort study included 38,356 adult general floor patients hospitalized in 2011.

Widely used track and trigger scores: are they ready for automation in practice?

Introduction: Early Warning Scores (EWS) are widely used for early recognition of patient deterioration. Automated alarm/alerts have been recommended as a desirable characteristic for detection systems of patient deterioration. We undertook a comparative analysis of performance characteristics of common EWS methods to assess how they would function if automated.