Hospital Variation in Utilization of Life‐Sustaining Treatments among Patients with Do Not Resuscitate Orders

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/144225/1/hesr12651_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/144225/2/hesr12651.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/144225/3/hesr12651-sup-0001-AuthorMatrix.pd

Atrial Fibrillation Prediction from Critically Ill Sepsis Patients

Sepsis is defined by life-threatening organ dysfunction during infection and is the leading cause of death in hospitals. During sepsis, there is a high risk that new onset of atrial fibrillation (AF) can occur, which is associated with significant morbidity and mortality. Consequently, early prediction of AF during sepsis would allow testing of interventions in the intensive care unit (ICU) to prevent AF and its severe complications. In this paper, we present a novel automated AF prediction algorithm for critically ill sepsis patients using electrocardiogram (ECG) signals. From the heart rate signal collected from 5-min ECG, feature extraction is performed using the traditional time, frequency, and nonlinear domain methods. Moreover, variable frequency complex demodulation and tunable Q-factor wavelet-transform-based time-frequency methods are applied to extract novel features from the heart rate signal. Using a selected feature subset, several machine learning classifiers, including support vector machine (SVM) and random forest (RF), were trained using only the 2001 Computers in Cardiology data set. For testing the proposed method, 50 critically ill ICU subjects from the Medical Information Mart for Intensive Care (MIMIC) III database were used in this study. Using distinct and independent testing data from MIMIC III, the SVM achieved 80% sensitivity, 100% specificity, 90% accuracy, 100% positive predictive value, and 83.33% negative predictive value for predicting AF immediately prior to the onset of AF, while the RF achieved 88% AF prediction accuracy. When we analyzed how much in advance we can predict AF events in critically ill sepsis patients, the algorithm achieved 80% accuracy for predicting AF events 10 min early. Our algorithm outperformed a state-of-the-art method for predicting AF in ICU patients, further demonstrating the efficacy of our proposed method. The annotations of patients\u27 AF transition information will be made publicly available for other investigators. Our algorithm to predict AF onset is applicable for any ECG modality including patch electrodes and wearables, including Holter, loop recorder, and implantable devices

Epidemiology of ventilator-associated pneumonia in a long-term acute care hospital

objective. To characterize the epidemiology and microbiology of ventilator-associated pneumonia (VAP) in a long-term acute care hospital (LTACH). design. Retrospective study of prospectively identified cases of VAP. setting. Single-center, 207-bed LTACH with the capacity to house 42 patients requiring mechanical ventilation, evaluated from April 1, 2006, through January 31, 2008 methods. Data on the occurrence of VAP were collected prospectively as part of routine infection surveillance at Radius Specialty Hospital. After March 2006, Radius Specialty Hospital implemented a bundle of interventions for the prevention of VAP (hereafter referred to as the VAP-bundle approach). A case of VAP was defined as a patient who required mechanical ventilation at Radius Specialty Hospital for at least 48 hours before any symptoms of pneumonia appeared and who met the Centers for Disease Control and Prevention criteria for VAP. Sputum samples were collected from a tracheal aspirate if there was clinical suspicion of VAP, and these samples were semiquantitatively cultured. results. During the 22-month study period, 23 cases of VAP involving 19 patients were associated with 157 LTACH admissions (infection rate, 14.6%), corresponding to a rate of 1.67 cases per 1,000 ventilator-days, which is a 56% reduction from the VAP rate of 3.8 cases per 1,000 ventilator-days reported before the implementation of the VAP-bundle approach ( ). Microbiological data were available for P ! .001 21 (91%) of 23 cases of VAP. Cases of VAP in the LTACH were frequently polymicrobial (mean number ‫ע‬ SD, pathogens per 1.78 ‫ע‬ 1.0 case of VAP), and 20 (95%) of 21 cases of VAP had at least 1 pathogen (Pseudomonas species, Acinetobacter species, gram-negative bacilli resistant to more than 3 antibiotics, or methicillin-resistant Staphylococcus aureus) cultured from a sputum sample. LTACH patients with VAP were more likely to have a neurological reason for ventilator dependence, compared with LTACH patients without VAP (69.6% of cases of VAP vs 39% of cases of respiratory failure; ). In addition, patients with VAP had a longer length of LTACH stay, compared P p .014 with patients without VAP (median length of stay, 131 days vs 39 days; ). In 6 (26%) of 23 cases of VAP, the patient was eventually P p .002 weaned from use of mechanical ventilation. Of the 19 patients with VAP, 1 (5%) did not survive the LTACH stay. conclusions. The VAP rate in the LTACH is lower than the VAP rate reported in acute care hospitals. Cases of VAP in the LTACH were frequently polymicrobial and were associated with multidrug-resistant pathogens and increased length of stay. The guidelines from the Centers for Disease Control and Prevention that are aimed at reducing cases of VAP appear to be effective if applied in the LTACH setting. Ventilator-associated pneumonia (VAP) is the second most common nosocomial infection in the critical care setting. Infect Control Hosp Epidemiol 1 It is associated with increased morbidity and increased use of healthcare resources. 2 The epidemiology of VAP in intensive care units (ICUs) of acute care hospitals has been widely characterized

Epidemiology of ventilator-associated pneumonia in a long-term acute care hospital

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Development and assessment of a new framework for disease surveillance, prediction, and risk adjustment: the diagnostic items classification system

IMPORTANCE: Current disease risk-adjustment formulas in the US rely on diagnostic classification frameworks that predate the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM). OBJECTIVE: To develop an ICD-10-CM-based classification framework for predicting diverse health care payment, quality, and performance outcomes. DESIGN SETTING AND PARTICIPANTS: Physician teams mapped all ICD-10-CM diagnoses into 3 types of diagnostic items (DXIs): main effect DXIs that specify diseases; modifiers, such as laterality, timing, and acuity; and scaled variables, such as body mass index, gestational age, and birth weight. Every diagnosis was mapped to at least 1 DXI. Stepwise and weighted least-squares estimation predicted cost and utilization outcomes, and their performance was compared with models built on (1) the Agency for Healthcare Research and Quality Clinical Classifications Software Refined (CCSR) categories, and (2) the Health and Human Services Hierarchical Condition Categories (HHS-HCC) used in the Affordable Care Act Marketplace. Each model's performance was validated using R 2, mean absolute error, the Cumming prediction measure, and comparisons of actual to predicted outcomes by spending percentiles and by diagnostic frequency. The IBM MarketScan Commercial Claims and Encounters Database, 2016 to 2018, was used, which included privately insured, full- or partial-year eligible enrollees aged 0 to 64 years in plans with medical, drug, and mental health/substance use coverage. MAIN OUTCOMES AND MEASURES: Fourteen concurrent outcomes were predicted: overall and plan-paid health care spending (top-coded and not top-coded); enrollee out-of-pocket spending; hospital days and admissions; emergency department visits; and spending for 6 types of services. The primary outcome was annual health care spending top-coded at $250 000. RESULTS: A total of 65 901 460 person-years were split into 90$5821 [$17 653]) selected 76% of the main effect DXIs with no evidence of overfitting. Validated R 2 was 0.589 in the DXI model, 0.539 for CCSR, and 0.428 for HHS-HCC. Use of DXIs reduced underpayment for enrollees with rare (1-in-a-million) diagnoses by 83% relative to HHS-HCCs. CONCLUSIONS: In this diagnostic modeling study, the new DXI classification system showed improved predictions over existing diagnostic classification systems for all spending and utilization outcomes considered.Published versio

Future research directions in pneumonia

Copyright © 2018 by the American Thoracic Society. Pneumonia is a complex pulmonary disease in need of new clinical approaches. Although triggered by a pathogen, pneumonia often results from dysregulations of host defense that likely precede infection. The coordinated activities of immune resistance and tissue resilience then dictate whether and how pneumonia progresses or resolves. Inadequate or inappropriate host responses lead to more severe outcomes such as acute respiratory distress syndrome and to organ dysfunction beyond the lungs and over extended time frames after pathogen clearance, some of which increase the risk for subsequent pneumonia. Improved understanding of such host responses will guide the development of novel approaches for preventing and curing pneumonia and for mitigating the subsequent pulmonary and extrapulmonary complications of pneumonia. The NHLBI assembled a working group of extramural investigators to prioritize avenues of host-directed pneumonia research that should yield novel approaches for interrupting the cycle of unhealthy decline caused by pneumonia. This report summarizes the working group’s specific recommendations in the areas of pneumonia susceptibility, host response, and consequences. Overarching goals include the development of more host-focused clinical approaches for preventing and treating pneumonia, the generation of predictive tools (for pneumonia occurrence, severity, and outcome), and the elucidation of mechanisms mediating immune resistance and tissue resilience in the lung. Specific areas of research are highlighted as especially promising for making advances against pneumonia

Implementation of a Virtual Interprofessional ICU Learning Collaborative: Successes, Challenges, and Initial Reactions From the Structured Team- Based Optimal Patient-Centered Care for Virus COVID-19 Collaborators

IMPORTANCE: Initial Society of Critical Care Medicine Discovery Viral Infection and Respiratory illness Universal Study (VIRUS) Registry analysis suggested that improvements in critical care processes offered the greatest modifiable opportunity to improve critically ill COVID-19 patient outcomes. OBJECTIVES: The Structured Team-based Optimal Patient-Centered Care for Virus COVID-19 ICU Collaborative was created to identify and speed implementation of best evidence based COVID-19 practices. DESIGN, SETTING, AND PARTICIPANTS: This 6-month project included volunteer interprofessional teams from VIRUS Registry sites, who received online training on the Checklist for Early Recognition and Treatment of Acute Illness and iNjury approach, a structured and systematic method for delivering evidence based critical care. Collaborators participated in weekly 1-hour videoconference sessions on high impact topics, monthly quality improvement (QI) coaching sessions, and received extensive additional resources for asynchronous learning. MAIN OUTCOMES AND MEASURES: Outcomes included learner engagement, satisfaction, and number of QI projects initiated by participating teams. RESULTS: Eleven of 13 initial sites participated in the Collaborative from March 2, 2021, to September 29, 2021. A total of 67 learners participated in the Collaborative, including 23 nurses, 22 physicians, 10 pharmacists, nine respiratory therapists, and three nonclinicians. Site attendance among the 11 sites in the 25 videoconference sessions ranged between 82% and 100%, with three sites providing at least one team member for 100% of sessions. The majority reported that topics matched their scope of practice (69%) and would highly recommend the program to colleagues (77%). A total of nine QI projects were initiated across three clinical domains and focused on improving adherence to established critical care practice bundles, reducing nosocomial complications, and strengthening patient- and family-centered care in the ICU. Major factors impacting successful Collaborative engagement included an engaged interprofessional team; an established culture of engagement; opportunities to benchmark performance and accelerate institutional innovation, networking, and acclaim; and ready access to data that could be leveraged for QI purposes. CONCLUSIONS AND RELEVANCE: Use of a virtual platform to establish a learning collaborative to accelerate the identification, dissemination, and implementation of critical care best practices for COVID-19 is feasible. Our experience offers important lessons for future collaborative efforts focused on improving ICU processes of care

Validation of Automated Data Abstraction for SCCM Discovery VIRUS COVID-19 Registry: Practical EHR Export Pathways (VIRUS-PEEP)

BACKGROUND: The gold standard for gathering data from electronic health records (EHR) has been manual data extraction; however, this requires vast resources and personnel. Automation of this process reduces resource burdens and expands research opportunities. OBJECTIVE: This study aimed to determine the feasibility and reliability of automated data extraction in a large registry of adult COVID-19 patients. MATERIALS AND METHODS: This observational study included data from sites participating in the SCCM Discovery VIRUS COVID-19 registry. Important demographic, comorbidity, and outcome variables were chosen for manual and automated extraction for the feasibility dataset. We quantified the degree of agreement with Cohen\u27s kappa statistics for categorical variables. The sensitivity and specificity were also assessed. Correlations for continuous variables were assessed with Pearson\u27s correlation coefficient and Bland-Altman plots. The strength of agreement was defined as almost perfect (0.81-1.00), substantial (0.61-0.80), and moderate (0.41-0.60) based on kappa statistics. Pearson correlations were classified as trivial (0.00-0.30), low (0.30-0.50), moderate (0.50-0.70), high (0.70-0.90), and extremely high (0.90-1.00). MEASUREMENTS AND MAIN RESULTS: The cohort included 652 patients from 11 sites. The agreement between manual and automated extraction for categorical variables was almost perfect in 13 (72.2%) variables (Race, Ethnicity, Sex, Coronary Artery Disease, Hypertension, Congestive Heart Failure, Asthma, Diabetes Mellitus, ICU admission rate, IMV rate, HFNC rate, ICU and Hospital Discharge Status), and substantial in five (27.8%) (COPD, CKD, Dyslipidemia/Hyperlipidemia, NIMV, and ECMO rate). The correlations were extremely high in three (42.9%) variables (age, weight, and hospital LOS) and high in four (57.1%) of the continuous variables (Height, Days to ICU admission, ICU LOS, and IMV days). The average sensitivity and specificity for the categorical data were 90.7 and 96.9%. CONCLUSION AND RELEVANCE: Our study confirms the feasibility and validity of an automated process to gather data from the EHR

Validation of automated data abstraction for SCCM discovery VIRUS COVID-19 registry: practical EHR export pathways (VIRUS-PEEP)

BackgroundThe gold standard for gathering data from electronic health records (EHR) has been manual data extraction; however, this requires vast resources and personnel. Automation of this process reduces resource burdens and expands research opportunities.ObjectiveThis study aimed to determine the feasibility and reliability of automated data extraction in a large registry of adult COVID-19 patients.Materials and methodsThis observational study included data from sites participating in the SCCM Discovery VIRUS COVID-19 registry. Important demographic, comorbidity, and outcome variables were chosen for manual and automated extraction for the feasibility dataset. We quantified the degree of agreement with Cohen’s kappa statistics for categorical variables. The sensitivity and specificity were also assessed. Correlations for continuous variables were assessed with Pearson’s correlation coefficient and Bland–Altman plots. The strength of agreement was defined as almost perfect (0.81–1.00), substantial (0.61–0.80), and moderate (0.41–0.60) based on kappa statistics. Pearson correlations were classified as trivial (0.00–0.30), low (0.30–0.50), moderate (0.50–0.70), high (0.70–0.90), and extremely high (0.90–1.00).Measurements and main resultsThe cohort included 652 patients from 11 sites. The agreement between manual and automated extraction for categorical variables was almost perfect in 13 (72.2%) variables (Race, Ethnicity, Sex, Coronary Artery Disease, Hypertension, Congestive Heart Failure, Asthma, Diabetes Mellitus, ICU admission rate, IMV rate, HFNC rate, ICU and Hospital Discharge Status), and substantial in five (27.8%) (COPD, CKD, Dyslipidemia/Hyperlipidemia, NIMV, and ECMO rate). The correlations were extremely high in three (42.9%) variables (age, weight, and hospital LOS) and high in four (57.1%) of the continuous variables (Height, Days to ICU admission, ICU LOS, and IMV days). The average sensitivity and specificity for the categorical data were 90.7 and 96.9%.Conclusion and relevanceOur study confirms the feasibility and validity of an automated process to gather data from the EHR

Metabolic Syndrome and Acute Respiratory Distress Syndrome in Hospitalized Patients With COVID-19

Importance: Obesity, diabetes, and hypertension are common comorbidities in patients with severe COVID-19, yet little is known about the risk of acute respiratory distress syndrome (ARDS) or death in patients with COVID-19 and metabolic syndrome. Objective: To determine whether metabolic syndrome is associated with an increased risk of ARDS and death from COVID-19. Design, setting, and participants: This multicenter cohort study used data from the Society of Critical Care Medicine Discovery Viral Respiratory Illness Universal Study collected from 181 hospitals across 26 countries from February 15, 2020, to February 18, 2021. Outcomes were compared between patients with metabolic syndrome (defined as ≥3 of the following criteria: obesity, prediabetes or diabetes, hypertension, and dyslipidemia) and a control population without metabolic syndrome. Participants included adult patients hospitalized for COVID-19 during the study period who had a completed discharge status. Data were analyzed from February 22 to October 5, 2021. Exposures: Exposures were SARS-CoV-2 infection, metabolic syndrome, obesity, prediabetes or diabetes, hypertension, and/or dyslipidemia. Main outcomes and measures: The primary outcome was in-hospital mortality. Secondary outcomes included ARDS, intensive care unit (ICU) admission, need for invasive mechanical ventilation, and length of stay (LOS). Results: Among 46 441 patients hospitalized with COVID-19, 29 040 patients (mean [SD] age, 61.2 [17.8] years; 13 059 [45.0%] women and 15713 [54.1%] men; 6797 Black patients [23.4%], 5325 Hispanic patients [18.3%], and 16 507 White patients [57.8%]) met inclusion criteria. A total of 5069 patients (17.5%) with metabolic syndrome were compared with 23 971 control patients (82.5%) without metabolic syndrome. In adjusted analyses, metabolic syndrome was associated with increased risk of ICU admission (adjusted odds ratio [aOR], 1.32 [95% CI, 1.14-1.53]), invasive mechanical ventilation (aOR, 1.45 [95% CI, 1.28-1.65]), ARDS (aOR, 1.36 [95% CI, 1.12-1.66]), and mortality (aOR, 1.19 [95% CI, 1.08-1.31]) and prolonged hospital LOS (median [IQR], 8.0 [4.2-15.8] days vs 6.8 [3.4-13.0] days; P \u3c .001) and ICU LOS (median [IQR], 7.0 [2.8-15.0] days vs 6.4 [2.7-13.0] days; P \u3c .001). Each additional metabolic syndrome criterion was associated with increased risk of ARDS in an additive fashion (1 criterion: 1147 patients with ARDS [10.4%]; P = .83; 2 criteria: 1191 patients with ARDS [15.3%]; P \u3c .001; 3 criteria: 817 patients with ARDS [19.3%]; P \u3c .001; 4 criteria: 203 patients with ARDS [24.3%]; P \u3c .001). Conclusions and relevance: These findings suggest that metabolic syndrome was associated with increased risks of ARDS and death in patients hospitalized with COVID-19. The association with ARDS was cumulative for each metabolic syndrome criteria present