Accessibility, Relevance, and Impact of a Symptom Monitoring Tool for Home Hospice Care: Theory Elaboration and Qualitative Assessment

BACKGROUND

Early users found Engagement and Visualization to Improve Symptoms in Oncology Care (ENVISION), a web-based application designed to improve home management of hospice patients' symptoms and support patients' and family caregivers' well-being, to be generally useful and easy to use. However, they also raised concerns about potential challenges users with limited technological proficiency might experience.

OBJECTIVE

We sought to concurrently accomplish two interrelated study aims: (1) to develop a conceptual framework of digital inclusivity for health information systems and (2) to apply the framework in evaluating the digital inclusivity of the ENVISION application.

METHODS

We engaged ENVISION users (N=34) in a qualitative study in which data were collected via direct observation, think-aloud techniques, and responses to open-ended queries. Data were analyzed via theory elaboration and basic qualitative description.

RESULTS

Accessibility, relevance, and impact were identified as 3 essential considerations in evaluating a health system's digital inclusivity. Study findings generally supported ENVISION's digital inclusivity, particularly concerning its perceived relevance to the work of family caregivers and hospice clinicians and its potentially positive impact on symptom management and quality of life. Limitations to ENVISION's digital inclusivity centered around issues of accessibility, particularly operability among individuals with limited technological knowledge and skills.

CONCLUSIONS

The Accessibility, Relevance, and Impact conceptual framework of digital inclusivity for health information systems can help identify opportunities to strengthen the digital inclusivity of tools, such as ENVISION, intended for use by a broad and diverse range of users.

Use of artificial intelligence in critical care: opportunities and obstacles

BACKGROUND

Perhaps nowhere else in the healthcare system than in the intensive care unit environment are the challenges to create useful models with direct time-critical clinical applications more relevant and the obstacles to achieving those goals more massive. Machine learning-based artificial intelligence (AI) techniques to define states and predict future events are commonplace activities of modern life. However, their penetration into acute care medicine has been slow, stuttering and uneven. Major obstacles to widespread effective application of AI approaches to the real-time care of the critically ill patient exist and need to be addressed.

MAIN BODY

Clinical decision support systems (CDSSs) in acute and critical care environments support clinicians, not replace them at the bedside. As will be discussed in this review, the reasons are many and include the immaturity of AI-based systems to have situational awareness, the fundamental bias in many large databases that do not reflect the target population of patient being treated making fairness an important issue to address and technical barriers to the timely access to valid data and its display in a fashion useful for clinical workflow. The inherent "black-box" nature of many predictive algorithms and CDSS makes trustworthiness and acceptance by the medical community difficult. Logistically, collating and curating in real-time multidimensional data streams of various sources needed to inform the algorithms and ultimately display relevant clinical decisions support format that adapt to individual patient responses and signatures represent the efferent limb of these systems and is often ignored during initial validation efforts. Similarly, legal and commercial barriers to the access to many existing clinical databases limit studies to address fairness and generalizability of predictive models and management tools.

CONCLUSIONS

AI-based CDSS are evolving and are here to stay. It is our obligation to be good shepherds of their use and further development.

A novel structured debriefing program for consensus determinations of in-hospital cardiac arrest predictability and preventability

AIM

Hospital rapid response systems aim to stop preventable cardiac arrests, but defining preventability is a challenge. We developed a multidisciplinary consensus-based process to determine in-hospital cardiac arrest (IHCA) preventability based on objective measures.

METHODS

We developed an interdisciplinary ward IHCA debriefing program at an urban quaternary-care academic hospital. This group systematically reviewed all IHCAs weekly, reaching consensus determinations of the IHCA's cause and preventability across three mutually exclusive categories: 1) unpredictable (no evidence of physiologic instability < 1 h prior to and within 24 h of the arrest), 2) predictable but unpreventable (meeting physiologic instability criteria in the setting of either a poor baseline prognosis or a documented goals of care conversation) or 3) potentially preventable (remaining cases).

RESULTS

Of 544 arrests between 09/2015 and 11/2023, 339 (61%) were deemed predictable by consensus, with 235 (42% of all IHCAs) considered potentially preventable. Potentially preventable arrests disproportionately occurred on nights and weekends (70% vs 55%, p = 0.002) and were more frequently respiratory than cardiac in etiology (33% vs 15%, p < 0.001). Despite similar rates of ROSC across groups (67-70%), survival to discharge was highest in arrests deemed unpredictable (31%), followed by potentially preventable (21%), and then those deemed predictable but unpreventable which had the lowest survival rate (16%, p = 0.007).

CONCLUSIONS

Our IHCA debriefing procedures are a feasible and sustainable means of determining the predictability and potential preventability of ward cardiac arrests. This approach may be useful for improving quality benchmarks and care processes around pre-arrest clinical activities.

Hospital Policy Variation in Addressing Decisions to Withhold and Withdraw Life-Sustaining Treatment

BACKGROUND

Sociodemographic disparities in physician decisions to withhold and withdraw life-sustaining treatment exist. Little is known about the content of hospital policies that guide physicians involved in these decisions.

RESEARCH QUESTION

What is the prevalence of US hospitals with policies that address withholding and withdrawing life-sustaining treatment; how do these policies approach ethically controversial scenarios; and how do these policies address sociodemographic disparities in decisions to withhold and withdraw life-sustaining treatment?

STUDY DESIGN AND METHODS

This national cross-sectional survey assessed the content of hospital policies addressing decisions to withhold or withdraw life-sustaining treatment. We distributed the survey electronically to American Society for Bioethics and Humanities members between July and August 2023 and descriptively analyzed responses.

RESULTS

Among 93 respondents from hospitals or hospital systems representing all 50 US states, Puerto Rico, and Washington, DC, 92% had policies addressing decisions to withhold or withdraw life-sustaining treatment. Hospitals varied in their stated guidance, permitting life-sustaining treatment to be withheld or withdrawn in cases of patient or surrogate request (82%), physiologic futility (81%), and potentially inappropriate treatment (64%). Of the 8% of hospitals with policies that addressed patient sociodemographic disparities in decisions to withhold or withdraw life-sustaining treatment, these policies provided opposing recommendations to either exclude sociodemographic factors in decision-making or actively acknowledge and incorporate these factors in decision-making. Only 3% of hospitals had policies that recommended collecting and maintaining information about patients for whom life-sustaining treatment was withheld or withdrawn that could be used to identify disparities in decision-making.

INTERPRETATION

Although most surveyed US hospital policies addressed withholding or withdrawing life-sustaining treatment, these policies varied widely in criteria and processes. Surveyed policies also rarely addressed sociodemographic disparities in these decisions.

Post-hospitalization remote monitoring for patients with heart failure or chronic obstructive pulmonary disease in an accountable care organization

BACKGROUND

Post-hospitalization remote patient monitoring (RPM) has potential to improve health outcomes for high-risk patients with chronic medical conditions. The purpose of this study is to determine the extent to which RPM for patients with congestive heart failure (CHF) and chronic obstructive pulmonary disease (COPD) is associated with reductions in post-hospitalization mortality, hospital readmission, and ED visits within an Accountable Care Organization (ACO).

METHODS

Nonrandomized prospective study of patients in an ACO offered enrollment in RPM upon hospital discharge between February 2021 and December 2021. RPM comprised of vital sign monitoring equipment (blood pressure monitor, scale, pulse oximeter), tablet device with symptom tracking software and educational material, and nurse-provided oversight and triage. Expected enrollment was for at least 30-days of monitoring, and outcomes were followed for 6 months following enrollment. The co-primary outcomes were (a) the composite of death, hospital admission, or emergency care visit within 180 days of eligibility, and (b) time to occurrence of this composite. Secondary outcomes were each component individually, the composite of death or hospital admission, and outpatient office visits. Adjusted analyses involved doubly robust estimation to address confounding by indication.

RESULTS

Of 361 patients offered remote monitoring (251 with CHF and 110 with COPD), 140 elected to enroll (106 with CHF and 34 with COPD). The median duration of RPM-enrollment was 54 days (IQR 34-85). Neither the 6-month frequency of the co-primary composite outcome (59% vs 66%, FDR p-value = 0.47) nor the time to this composite (median 29 vs 38 days, FDR p-value = 0.60) differed between the groups, but 6-month mortality was lower in the RPM group (6.4% vs 17%, FDR p-value = 0.02). After adjustment for confounders, RPM enrollment was associated with nonsignificantly decreased odds for the composite outcome (adjusted OR [aOR] 0.68, 99% CI 0.25-1.34, FDR p-value 0.30) and lower 6-month mortality (aOR 0.41, 99% CI 0.00-0.86, FDR p-value 0.20).

CONCLUSIONS

RPM enrollment may be associated with improved health outcomes, including 6-month mortality, for selected patient populations.

Use of Inhaled Epoprostenol in Patients With COVID-19 Receiving Humidified, High-Flow Nasal Oxygen Is Associated With Progressive Respiratory Failure

BACKGROUND

The clinical benefit of using inhaled epoprostenol (iEpo) through a humidified high-flow nasal cannula (HHFNC) remains unknown for patients with COVID-19.

RESEARCH QUESTION

Can iEpo prevent respiratory deterioration for patients with positive SARS-CoV-2 findings receiving HHFNC?

STUDY DESIGN AND METHODS

This multicenter retrospective cohort analysis included patients aged 18 years or older with COVID-19 pneumonia who required HHFNC treatment. Patients who received iEpo were propensity score matched to patients who did not receive iEpo. The primary outcome was time to mechanical ventilation or death without mechanical ventilation and was assessed using Kaplan-Meier curves and Cox proportional hazard ratios. The effects of residual confounding were assessed using a multilevel analysis, and a secondary analysis adjusted for outcome propensity also was performed in a multivariable model that included the entire (unmatched) patient cohort.

RESULTS

Among 954 patients with positive SARS-CoV-2 findings receiving HHFNC therapy, 133 patients (13.9%) received iEpo. After propensity score matching, the median number of days until the composite outcome was similar between treatment groups (iEpo: 5.0 days [interquartile range, 2.0-10.0 days] vs no-iEpo: 6.5 days [interquartile range, 2.0-11.0 days]; P = .26), but patients who received iEpo were more likely to meet the composite outcome in the propensity score-matched, multilevel, and multivariable unmatched analyses (hazard ratio, 2.08 [95% CI, 1.73-2.50]; OR, 4.72 [95% CI, 3.01-7.41]; and OR, 1.35 [95% CI, 1.23-1.49]; respectively).

INTERPRETATION

In patients with COVID-19 receiving HHFNC therapy, use of iEpo was associated with the need for invasive mechanical ventilation.

Risk factors for hospital-onset Clostridioides difficile infections before and during the severe acute respiratory syndrome coronavirus 2 pandemic

In this retrospective cohort from 3 Missouri hospitals from January 2017 to August 2020, hospital-onset Clostridioides difficile infections were more common during the severe acute respiratory syndrome coronavirus 2 pandemic at the tertiary care hospital. Risk factors associated with hospital-onset C difficile infection included the year of hospitalization, age, high-risk antibiotic use, acid-reducing medications, chronic comorbidities, and severe acute respiratory syndrome coronavirus 2 infection.

Scaling up a diagnostic pause at the ICU-to-ward transition: an exploration of barriers and facilitators to implementation of the ICU-PAUSE handoff tool

OBJECTIVES

The transition from the intensive care unit (ICU) to the medical ward is a high-risk period due to medical complexity, reduced patient monitoring, and diagnostic uncertainty. Standardized handoff practices reduce errors associated with transitions of care, but little work has been done to standardize the ICU to ward handoff. Further, tools that exist do not focus on preventing diagnostic error. Using Human-Centered Design methods we previously created a novel EHR-based ICU-ward handoff tool (ICU-PAUSE) that embeds a diagnostic pause at the time of transfer. This study aims to explore barriers and facilitators to implementing a diagnostic pause at the ICU-to-ward transition.

METHODS

This is a multi-center qualitative study of semi-structured interviews with intensivists from ten academic medical centers. Interviews were analyzed iteratively through a grounded theory approach. The Sittig-Singh sociotechnical model was used as a unifying conceptual framework.

RESULTS

Across the eight domains of the model, we identified major benefits and barriers to implementation. The embedded pause to address diagnostic uncertainty was recognized as a key benefit. Participants agreed that standardization of verbal and written handoff would decrease variation in communication. The main barriers fell within the domains of workflow, institutional culture, people, and assessment.

CONCLUSIONS

This study represents a novel application of the Sittig-Singh model in the assessment of a handoff tool. A unique feature of ICU-PAUSE is the explicit acknowledgement of diagnostic uncertainty, a practice that has been shown to reduce medical error and prevent premature closure. Results will be used to inform future multi-site implementation efforts.

Sepsis and acute respiratory failure in patients with cancer: how can we improve care and outcomes even further?

PURPOSE OF REVIEW

Care and outcomes of critically ill patients with cancer have improved over the past decade. This selective review will discuss recent updates in sepsis and acute respiratory failure among patients with cancer, with particular focus on important opportunities to improve outcomes further through attention to phenotyping, predictive analytics, and improved outcome measures.

RECENT FINDINGS

The prevalence of cancer diagnoses in intensive care units (ICUs) is nontrivial and increasing. Sepsis and acute respiratory failure remain the most common critical illness syndromes affecting these patients, although other complications are also frequent. Recent research in oncologic sepsis has described outcome variation - including ICU, hospital, and 28-day mortality - across different types of cancer (e.g., solid vs. hematologic malignancies) and different sepsis definitions (e.g., Sepsis-3 vs. prior definitions). Research in acute respiratory failure in oncology patients has highlighted continued uncertainty in the value of diagnostic bronchoscopy for some patients and in the optimal respiratory support strategy. For both of these syndromes, specific challenges include multifactorial heterogeneity (e.g. in etiology and/or underlying cancer), delayed recognition of clinical deterioration, and complex outcomes measurement.

SUMMARY

Improving outcomes in oncologic critical care requires attention to the heterogeneity of cancer diagnoses, timely recognition and management of critical illness, and defining appropriate ICU outcomes.

Factors Associated With Variability in the Performance of a Proprietary Sepsis Prediction Model Across 9 Networked Hospitals in the US

This cohort study uses data from electronic health records to assess variability in a sepsis prediction model across 9 hospitals.

Temperature Trajectory Subphenotypes in Oncology Patients with Neutropenia and Suspected Infection

Rationale: Despite etiologic and severity heterogeneity in neutropenic sepsis, management is often uniform. Understanding host response clinical subphenotypes might inform treatment strategies for neutropenic sepsis. Objectives: 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. Methods: Among adult oncologic admissions with neutropenia and blood cultures within 24 hours, a previously validated model classified patients' initial 72-hour temperature trajectories into one of four subphenotypes. We analyzed subphenotypes' independent relationships with hospital mortality and bloodstream infection using multivariable models. Measurements and Main Results: Patients (primary cohort n = 1,145, validation cohort n = 6,564) fit into one of four temperature subphenotypes. "Hyperthermic slow resolvers" (pooled n = 1,140 [14.8%], mortality n = 104 [9.1%]) and "hypothermic" encounters (n = 1,612 [20.9%], mortality n = 138 [8.6%]) had higher mortality than "hyperthermic fast resolvers" (n = 1,314 [17.0%], mortality n = 47 [3.6%]) and "normothermic" (n = 3,643 [47.3%], mortality n = 196 [5.4%]) encounters (P < 0.001). Bloodstream infections were more common among hyperthermic slow resolvers (n = 248 [21.8%]) and hyperthermic fast resolvers (n = 240 [18.3%]) than among hypothermic (n = 188 [11.7%]) or normothermic (n = 418 [11.5%]) encounters (P < 0.001). Adjusted for confounders, hyperthermic slow resolvers had increased adjusted odds for mortality (primary cohort odds ratio, 1.91 [P = 0.03]; validation cohort odds ratio, 2.19 [P < 0.001]) and bloodstream infection (primary odds ratio, 1.54 [P = 0.04]; validation cohort odds ratio, 2.15 [P < 0.001]). Conclusions: Temperature trajectory subphenotypes were independently associated with important outcomes among hospitalized patients with neutropenia in two independent cohorts.

Risk factors for hospital-acquired infection during the SARS-CoV-2 pandemic

OBJECTIVE

To evaluate risk factors for hospital-acquired infection (HAI) in patients during the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) pandemic, including historical and concurrent cohorts.

DESIGN

Retrospective cohort.

SETTING

Three Missouri hospitals, data from 1^st January 2017 to 30^th September 2020.

PARTICIPANTS

Patients aged ≥18 years and admitted for ≥48 h.

METHODS

Univariate and multi-variate Cox proportional hazards models incorporating the competing risk of death were used to determine risk factors for HAI. A-priori sensitivity analyses were performed to assess the robustness of the urine-, blood- and respiratory-culture-based HAI definition.

RESULTS

The cohort included 254,792 admissions, with 7147 (2.8%) HAIs (1661 blood, 3407 urine, 2626 respiratory). Patients with SARS-CoV-2 had increased risk of HAI (adjusted hazards ratio 1.65, 95% confidence interval 1.38-1.96), and SARS-CoV-2 infection was one of the strongest risk factors for development of HAI. Other risk factors for HAI included certain admitting services, chronic comorbidities, intensive care unit stay during index admission, extremes of body mass index, hospital, and selected medications. Factors associated with lower risk of HAI included year of admission (declined over the course of the study), admitting service and medications. Risk factors for HAI were similar in sensitivity analyses restricted to patients with diagnostic codes for pneumonia/upper respiratory infection and urinary tract infection.

CONCLUSIONS

SARS-CoV-2 was associated with significantly increased risk of HAI.

Improving Communication in Intensive Care Unit to Ward Transitions: Protocol for Multisite National Implementation of the ICU-PAUSE Handoff Tool

BACKGROUND

The intensive care unit (ICU)-ward transfer poses a particularly high-risk period for patients. The period after transfer has been associated with adverse events and additional work for care teams related to miscommunication or omission of information. Standardized handoff processes have been found to reduce communication errors and adverse patient events in other clinical environments but are understudied at the ICU-ward interface. We previously developed an electronic ICU-ward transfer tool, ICU-PAUSE, which embeds the key elements and diagnostic reasoning to facilitate a safe transfer of care at ICU discharge.

OBJECTIVE

The aim of this study is to evaluate the implementation process of the ICU-PAUSE handoff tool across 10 academic medical centers, including the rate of adoption and acceptability, as perceived by clinical care teams.

METHODS

ICU-PAUSE will be implemented in the medical ICU across 10 academic hospitals, with each site customizing the tool to their institution's needs. Our mixed methods study will include a combination of a chart review, quantitative surveys, and qualitative interviews. After a 90-day implementation period, we will conduct a retrospective chart review to evaluate the rate of uptake of ICU-PAUSE. We will also conduct postimplementation surveys of providers to assess perceptions of the tool and its impact on the frequency of communication errors and adverse events during ICU-ward transfers. Lastly, we will conduct semistructured interviews of faculty stakeholders with subsequent thematic analysis with the goal of identifying benefits and barriers in implementing and using ICU-PAUSE.

RESULTS

ICU-PAUSE was piloted in the medical ICU at Barnes-Jewish Hospital, the teaching hospital of Washington University School of Medicine in St. Louis, in 2019. As of July 2022, implementation of ICU-PAUSE is ongoing at 6 of 10 participating sites. Our results will be published in 2023.

CONCLUSIONS

Our process of ICU-PAUSE implementation embeds each step of template design, uptake, and customization in the needs of users and key stakeholders. Here, we introduce our approach to evaluate its acceptability, usability, and impact on communication errors according to the tenets of sociotechnical theory. We anticipate that ICU-PAUSE will offer an effective handoff tool for the ICU-ward transition that can be generalized to other institutions.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/40918.

Clinician Perspectives on Barriers and Enablers to Implementing an Inpatient Oncology Early Warning System: A Mixed-Methods Study

PURPOSE

To elicit end-user and stakeholder perceptions regarding design and implementation of an inpatient clinical deterioration early warning system (EWS) for oncology patients to better fit routine clinical practices and enhance clinical impact.

METHODS

In an explanatory-sequential mixed-methods study, we evaluated a stakeholder-informed oncology early warning system (OncEWS) using surveys and semistructured interviews. Stakeholders were physicians, advanced practice providers (APPs), and nurses. For qualitative data, we used grounded theory and thematic content analysis via the constant comparative method to identify determinants of OncEWS implementation.

RESULTS

Survey respondents generally agreed that an oncology-focused EWS could add value beyond clinical judgment, with nurses endorsing this notion significantly more strongly than other clinicians (nurse: median 5 on a 6-point scale [6 = strongly agree], interquartile range 4-5; doctors/advanced practice providers: 4 [4-5]; P = .005). However, some respondents would not trust an EWS to identify risk accurately (n = 36 [42%] somewhat or very concerned), while others were concerned that institutional culture would not embrace such an EWS (n = 17 [28%]).Interviews highlighted important aspects of the EWS and the local context that might facilitate implementation, including (1) a model tailored to the subtleties of oncology patients, (2) transparent model information, and (3) nursing-centric workflows. Interviewees raised the importance of sepsis as a common and high-risk deterioration syndrome.

CONCLUSION

Stakeholders prioritized maximizing the degree to which the OncEWS is understandable, informative, actionable, and workflow-complementary, and perceived these factors to be key for translation into clinical benefit.

Hospital trajectories and early predictors of clinical outcomes differ between SARS-CoV-2 and influenza pneumonia

BACKGROUND

A comparison of pneumonias due to SARS-CoV-2 and influenza, in terms of clinical course and predictors of outcomes, might inform prognosis and resource management. We aimed to compare clinical course and outcome predictors in SARS-CoV-2 and influenza pneumonia using multi-state modelling and supervised machine learning on clinical data among hospitalised patients.

METHODS

This multicenter retrospective cohort study of patients hospitalised with SARS-CoV-2 (March-December 2020) or influenza (Jan 2015-March 2020) pneumonia had the composite of hospital mortality and hospice discharge as the primary outcome. Multi-state models compared differences in oxygenation/ventilatory utilisation between pneumonias longitudinally throughout hospitalisation. Differences in predictors of outcome were modelled using supervised machine learning classifiers.

FINDINGS

Among 2,529 hospitalisations with SARS-CoV-2 and 2,256 with influenza pneumonia, the primary outcome occurred in 21% and 9%, respectively. Multi-state models differentiated oxygen requirement progression between viruses, with SARS-CoV-2 manifesting rapidly-escalating early hypoxemia. Highly contributory classifier variables for the primary outcome differed substantially between viruses.

INTERPRETATION

SARS-CoV-2 and influenza pneumonia differ in presentation, hospital course, and outcome predictors. These pathogen-specific differential responses in viral pneumonias suggest distinct management approaches should be investigated.

FUNDING

This project was supported by NIH/NCATS UL1 TR002345, NIH/NCATS KL2 TR002346 (PGL), the Doris Duke Charitable Foundation grant 2015215 (PGL), NIH/NHLBI R35 HL140026 (CSC), and a Big Ideas Award from the BJC HealthCare and Washington University School of Medicine Healthcare Innovation Lab and NIH/NIGMS R35 GM142992 (PS).

SWEAT ICU-An Observational Study of Physician Workload and the Association of Physician Outcomes in Academic ICUs

The optimal staffing model for physicians in the ICU is unknown. Patient-to-intensivist ratios may offer a simple measure of workload and be associated with patient mortality and physician burnout. To evaluate the association of physician workload, as measured by the patient-to-intensivist ratio, with physician burnout and patient mortality.

DESIGN

Cross-sectional observational study.

SETTING

Fourteen academic centers in the United States from August 2020 to July 2021.

SUBJECTS

We enrolled ICU physicians and collected data on adult ICU patients under the physician's care on the single physician-selected study day for each physician.

MEASUREMENTS and MAIN RESULTS

The primary exposure was workload (self-reported number of patients' physician was responsible for) modeled as high (>14 patients) and low (≤14 patients). The primary outcome was burnout, measured by the Well-Being Index. The secondary outcome measure was 28-day patient mortality. We calculated odds ratio for burnout and patient outcomes using a multivariable logistic regression model and a binomial mixed effects model, respectively. We enrolled 122 physicians from 62 ICUs. The median patient-to-intensivist ratio was 12 (interquartile range, 10-14), and the overall prevalence of burnout was 26.4% (n = 32). Intensivist workload was not independently associated with burnout (adjusted odds ratio, 0.74; 95% CI, 0.24-2.23). Of 1,322 patients, 679 (52%) were discharged alive from the hospital, 257 (19%) remained hospitalized, and 347 (26%) were deceased by day 28; 28-day outcomes were unknown for 39 of patients (3%). Intensivist workload was not independently associated with 28-day patient mortality (adjusted odds ratio, 1.33; 95% CI, 0.92-1.91).

CONCLUSIONS

In our cohort, approximately one in four physicians experienced burnout on the study day. There was no relationship be- tween workload as measured by patient-to-intensivist ratio and burnout. Factors other than the number of patients may be important drivers of burnout among ICU physicians.

Lessons in machine learning model deployment learned from sepsis

In three recent and related publications, researchers from Johns Hopkins University and Bayesian Health report results from implementing and prospectively evaluating the Targeted Real-time Early Warning System (TREWS) for sepsis at five hospitals.^1-3.

Clinician Ethical Perspectives on Extracorporeal Membrane Oxygenation in Practice

PURPOSE

Extracorporeal membrane oxygenation (ECMO) is an expensive and scarce life sustaining treatment provided to certain critically ill patients. Little is known about the informed consent process for ECMO or clinician viewpoints on ethical complexities related to ECMO in practice.

METHODS

We sent a cross-sectional survey to all departments providing ECMO within 7 United States hospitals in January 2021. One clinician from each department completed the 42-item survey representing their department.

RESULTS

Fourteen departments within 7 hospitals responded (response rate 78%, N = 14/18). The mean time spent consenting patients or surrogate decision-makers for ECMO varied, from 7.5 minutes (95% CI 5-10) for unstable patients to 20 minutes (95% CI 15-30) for stable patients (p = 0.0001). Few clinician respondents (29%) report patients or surrogate decision-makers always possess informed consent for ECMO. Most departments (92%) have absolute exclusion criteria for ECMO such as older age (43%, cutoffs ranging from 60-75 years), active malignancy (36%), and elevated body mass index (29%). A significant minority of departments (29%) do not always offer the option to withdraw ECMO to patients or surrogate decision-makers. For patients who cannot be liberated from ECMO and are ineligible for heart or lung transplant, 36% of departments would recommend the patient be removed from ECMO and 64% would continue ECMO support.

CONCLUSION

Adequate informed consent for ECMO is a major ethical challenge, and the content of these discussions varies. Use of categorical exclusion criteria and withdrawal of ECMO if a patient cannot be liberated from it differ among departments and institutions.

Machine learning to predict vasopressin responsiveness in patients with septic shock

STUDY OBJECTIVES

The objective of this study was to develop and externally validate a model to predict adjunctive vasopressin response in patients with septic shock being treated with norepinephrine for bedside use in the intensive care unit.

DESIGN

This was a retrospective analysis of two adult tertiary intensive care unit septic shock populations.

SETTING

Barnes-Jewish Hospital (BJH) from 2010 to 2017 and Beth Israel Deaconess Medical Center (BIDMC) from 2001 to 2012.

PATIENTS

Two septic shock populations (548 BJH patients and 464 BIDMC patients) that received vasopressin as second-line vasopressor.

INTERVENTION

Patients who were vasopressin responsive were compared with those who were nonresponsive. Vasopressin response was defined as survival with at least a 20% decrease in maximum daily norepinephrine requirements by one calendar day after vasopressin initiation, without a third-line vasopressor.

MEASUREMENTS

Two supervised machine learning models (gradient-boosting machine [XGBoost] and elastic net penalized logistic regression [EN]) were trained in 1000 bootstrap replications of the BJH data and externally validated in the BIDMC data to predict vasopressin responsiveness.

MAIN RESULTS

Vasopressin responsiveness was similar among each cohort (BJH 45% and BIDMC 39%). Mortality was lower for vasopressin responders compared with nonresponders in the BJH (51% vs. 73%) and BIDMC (45% vs. 83%) cohorts, respectively. Both models demonstrated modest discrimination in the training (XGBoost area under receiver operator curve [AUROC] 0.61 [95% confidence interval (CI) 0.61-0.61], EN 0.59 [95% CI 0.58-0.59]) and external validation (XGBoost 0.68 [95% CI 0.63-0.73], EN 0.64 [95% CI 0.59-0.69]) datasets.

CONCLUSION

Vasopressin nonresponsiveness is common and associated with increased mortality. The models' modest performances highlight the complexity of septic shock and indicate that more research will be required before clinical decision support tools can aid in anticipating patient-specific responsiveness to vasopressin.

Sepsis Prediction for the General Ward Setting

Objective

To develop and evaluate a sepsis prediction model for the general ward setting and extend the evaluation through a novel pseudo-prospective trial design.

Design

Retrospective analysis of data extracted from electronic health records (EHR).

Setting

Single, tertiary-care academic medical center in St. Louis, MO, USA.

Patients

Adult, non-surgical inpatients admitted between January 1, 2012 and June 1, 2019.

Interventions

None.

Measurements and Main Results

Of the 70,034 included patient encounters, 3.1% were septic based on the Sepsis-3 criteria. Features were generated from the EHR data and were used to develop a machine learning model to predict sepsis 6-h ahead of onset. The best performing model had an Area Under the Receiver Operating Characteristic curve (AUROC or c-statistic) of 0.862 ± 0.011 and Area Under the Precision-Recall Curve (AUPRC) of 0.294 ± 0.021 compared to that of Logistic Regression (0.857 ± 0.008 and 0.256 ± 0.024) and NEWS 2 (0.699 ± 0.012 and 0.092 ± 0.009). In the pseudo-prospective trial, 388 (69.7%) septic patients were alerted on with a specificity of 81.4%. Within 24 h of crossing the alert threshold, 20.9% had a sepsis-related event occur.

Conclusions

A machine learning model capable of predicting sepsis in the general ward setting was developed using the EHR data. The pseudo-prospective trial provided a more realistic estimation of implemented performance and demonstrated a 29.1% Positive Predictive Value (PPV) for sepsis-related intervention or outcome within 48 h.

Longitudinal respiratory subphenotypes in patients with COVID-19-related acute respiratory distress syndrome: results from three observational cohorts

BACKGROUND

Patients with COVID-19-related acute respiratory distress syndrome (ARDS) have been postulated to present with distinct respiratory subphenotypes. However, most phenotyping schema have been limited by sample size, disregard for temporal dynamics, and insufficient validation. We aimed to identify respiratory subphenotypes of COVID-19-related ARDS using unbiased data-driven approaches.

METHODS

PRoVENT-COVID was an investigator-initiated, national, multicentre, prospective, observational cohort study at 22 intensive care units (ICUs) in the Netherlands. Consecutive patients who had received invasive mechanical ventilation for COVID-19 (aged 18 years or older) served as the derivation cohort, and similar patients from two ICUs in the USA served as the replication cohorts. COVID-19 was confirmed by positive RT-PCR. We used latent class analysis to identify subphenotypes using clinically available respiratory data cross-sectionally at baseline, and longitudinally using 8-hourly data from the first 4 days of invasive ventilation. We used group-based trajectory modelling to evaluate trajectories of individual variables and to facilitate potential clinical translation. The PRoVENT-COVID study is registered with ClinicalTrials.gov, NCT04346342.

FINDINGS

Between March 1, 2020, and May 15, 2020, 1007 patients were admitted to participating ICUs in the Netherlands, and included in the derivation cohort. Data for 288 patients were included in replication cohort 1 and 326 in replication cohort 2. Cross-sectional latent class analysis did not identify any underlying subphenotypes. Longitudinal latent class analysis identified two distinct subphenotypes. Subphenotype 2 was characterised by higher mechanical power, minute ventilation, and ventilatory ratio over the first 4 days of invasive mechanical ventilation than subphenotype 1, but PaO₂/FiO₂, pH, and compliance of the respiratory system did not differ between the two subphenotypes. 185 (28%) of 671 patients with subphenotype 1 and 109 (32%) of 336 patients with subphenotype 2 had died at day 28 (p=0·10). However, patients with subphenotype 2 had fewer ventilator-free days at day 28 (median 0, IQR 0-15 vs 5, 0-17; p=0·016) and more frequent venous thrombotic events (109 [32%] of 336 patients vs 176 [26%] of 671 patients; p=0·048) compared with subphenotype 1. Group-based trajectory modelling revealed trajectories of ventilatory ratio and mechanical power with similar dynamics to those observed in latent class analysis-derived trajectory subphenotypes. The two trajectories were: a stable value for ventilatory ratio or mechanical power over the first 4 days of invasive mechanical ventilation (trajectory A) or an upward trajectory (trajectory B). However, upward trajectories were better independent prognosticators for 28-day mortality (OR 1·64, 95% CI 1·17-2·29 for ventilatory ratio; 1·82, 1·24-2·66 for mechanical power). The association between upward ventilatory ratio trajectories (trajectory B) and 28-day mortality was confirmed in the replication cohorts (OR 4·65, 95% CI 1·87-11·6 for ventilatory ratio in replication cohort 1; 1·89, 1·05-3·37 for ventilatory ratio in replication cohort 2).

INTERPRETATION

At baseline, COVID-19-related ARDS has no consistent respiratory subphenotype. Patients diverged from a fairly homogenous to a more heterogeneous population, with trajectories of ventilatory ratio and mechanical power being the most discriminatory. Modelling these parameters alone provided prognostic value for duration of mechanical ventilation and mortality.

FUNDING

Amsterdam UMC.

Quantification of Sepsis Model Alerts in 24 US Hospitals Before and During the COVID-19 Pandemic

This descriptive study evaluates the association between nursing reports of sepsis overalerting and alert volume by quantifying the number of alerts generated by the Epic Sepsis Model at 24 US hospitals before and during the COVID-19 pandemic.

Equitable Implementation of Artificial Intelligence in Medical Imaging: What Can be Learned from Implementation Science?

Artificial intelligence (AI) has been rapidly adopted in various health care domains. Molecular imaging, accordingly, has demonstrated growing academic and commercial interest in AI. Unprepared and inequitable implementation and scale-up of AI in health care may pose challenges. Implementation of AI, as a complex intervention, may face various barriers, at individual, interindividual, organizational, health system, and community levels. To address these barriers, recommendations have been developed to consider health equity as a critical lens to sensitize implementation, engage stakeholders in implementation and evaluation, recognize and incorporate the iterative nature of implementation, and integrate equity and implementation in early-stage AI research.

Implementation of a non-emergent medical transportation programme at an integrated health system

OBJECTIVES

To implement a unified non-emergency medical transportation (NEMT) service across a large integrated healthcare delivery network.

METHODS

We assessed needs among key organisational stakeholders, then reviewed proposals. We selected a single NEMT vendor best aligned with organisational priorities and implemented this solution system-wide.

RESULTS

Our vendor's hybrid approach combined rideshares with contracted vehicles able to serve patients with equipment and other needs. After 6195 rides in the first year, we observed shorter wait times and lower costs compared with our prior state.

DISCUSSION

Essential lessons included (1) understanding user and patient needs, (2) obtaining complete, accurate and comprehensive baseline data and (3) adapting existing workflows-rather than designing de novo-whenever possible.

CONCLUSIONS

Our implementation of a single-vendor NEMT solution validates the need for NEMT at large healthcare organisations, geographical challenges to establishing NEMT organisation-wide, and the importance of baseline data and stakeholder engagement.

The Clinical Course of Coronavirus Disease 2019 in a US Hospital System: A Multistate Analysis

There are limited data on longitudinal outcomes for coronavirus disease 2019 (COVID-19) hospitalizations that account for transitions between clinical states over time. Using electronic health record data from a hospital network in the St. Louis, Missouri, region, we performed multistate analyses to examine longitudinal transitions and outcomes among hospitalized adults with laboratory-confirmed COVID-19 with respect to 15 mutually exclusive clinical states. Between March 15 and July 25, 2020, a total of 1,577 patients in the network were hospitalized with COVID-19 (49.9% male; median age, 63 years (interquartile range, 50–75); 58.8% Black). Overall, 34.1% (95% confidence interval (CI): 26.4, 41.8) had an intensive care unit admission and 12.3% (95% CI: 8.5, 16.1) received invasive mechanical ventilation (IMV). The risk of decompensation peaked immediately after admission; discharges peaked around days 3–5, and deaths plateaued between days 7 and 16. At 28 days, 12.6% (95% CI: 9.6, 15.6) of patients had died (4.2% (95% CI: 3.2, 5.2) had received IMV) and 80.8% (95% CI: 75.4, 86.1) had been discharged. Among those receiving IMV, 35.1% (95% CI: 28.2, 42.0) remained intubated after 14 days; after 28 days, 37.6% (95% CI: 30.4, 44.7) had died and only 37.7% (95% CI: 30.6, 44.7) had been discharged. Multistate methods offer granular characterizations of the clinical course of COVID-19 and provide essential information for guiding both clinical decision-making and public health planning.

Communication Training in Adult and Pediatric Critical Care Medicine. A Systematic Review

Background: Interpersonal and communication skills are essential for physicians practicing in critical care settings. Accordingly, demonstration of these skills has been a core competency of the Accreditation Council for Graduate Medical Education since 2014. However, current practices regarding communication skills training in adult and pediatric critical care fellowships are not well described. Objective: To describe the current state of communication curricula and training methods in adult and pediatric critical care training programs as demonstrated by the published literature. Methods: We performed a systematic review of the published literature using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses checklist. Three authors reviewed a comprehensive set of databases and independently selected articles on the basis of a predefined set of inclusion and exclusion criteria. Data were independently extracted from the selected articles. Results: The 23 publications meeting inclusion criteria fell into the following study classifications: intervention (n = 15), cross-sectional survey (n = 5), and instrument validation (n = 3). Most interventional studies assessed short-term and self-reported outcomes (e.g., learner attitudes and perspectives) only. Fifteen of 22 publications represented pediatric subspecialty programs. Conclusion: Opportunities exist to evaluate the influence of communication training programs on important outcomes, including measured learner behavior and patient and family outcomes, and the durability of skill retention.

Bloodstream Infections and Delayed Antibiotic Coverage Are Associated With Negative Hospital Outcomes in Hematopoietic Stem Cell Transplant Recipients

BACKGROUND

Bloodstream infections (BSIs) are common after hematopoietic stem cell transplantation (HSCT) and are associated with increased long-term morbidity and mortality. However, short-term outcomes related to BSI in this population remain unknown. More specifically, it is unclear whether choices related to empiric antimicrobials for potentially infected patients are associated with patient outcomes.

RESEARCH QUESTION

Are potential delays in appropriate antibiotics associated with hospital outcomes among HSCT recipients with BSI?

STUDY DESIGN AND METHODS

We conducted a retrospective cohort study at a large comprehensive inpatient academic cancer center between January 2014 and June 2017. We identified all admissions for HSCT and prior recipients of HSCT. We defined potential delay in appropriate antibiotics as > 24 h between positive blood culture results and the initial dose of an antimicrobial with activity against the pathogen.

RESULTS

We evaluated 2,751 hospital admissions from 1,086 patients. Of these admissions, 395 (14.4%) involved one or more BSIs. Of these 395 hospitalizations, 44 (11.1%) involved potential delays in appropriate antibiotics. The incidence of mortality was higher in BSI hospitalizations than in those without BSI (23% vs 4.5%; P < .001). In multivariable analysis, BSI was an independent predictor of mortality (OR, 8.14; 95% CI, 5.06-13.1; P < .001). Mortality was higher for admissions with potentially delayed appropriate antibiotics than for those with appropriate antibiotics (48% vs 20%; P < .001). Potential delay in antibiotics was also an independent predictor of mortality in multivariable analysis (OR, 13.8; 95% CI, 5.27-35.9; P < .001).

INTERPRETATION

BSIs were common and independently associated with increased morbidity and mortality. Delays in administration of appropriate antimicrobials were identified as an important factor in hospital morbidity and mortality. These findings may have important implications for our current practice of empiric antibiotic treatment in HSCT patients.

Factors Associated With Clinical Deterioration Among Patients Hospitalized on the Wards at a Tertiary Cancer Hospital

PURPOSE

Patients hospitalized outside the intensive care unit (ICU) frequently experience clinical deterioration. Little has been done to describe the landscape of clinical deterioration among inpatients with cancer. We aimed to describe the frequency of clinical deterioration among patients with cancer hospitalized on the wards at a major academic hospital and to identify independent risk factors for clinical deterioration among these patients.

METHODS

This was a retrospective cohort study at a 1,300-bed urban academic hospital with a 138-bed inpatient cancer center. We included consecutive admissions to the oncology wards between January 1, 2014, and June 30, 2017. We defined clinical deterioration as the composite of ward death and transfer to the ICU.

RESULTS

We evaluated 21,219 admissions from 9,058 patients. The composite outcome occurred during 1,945 admissions (9.2%): 1,365 (6.4%) had at least one ICU transfer, and 580 (2.7%) involved ward death. Logistic regression identified several independent risk factors for clinical deterioration, including the following: age (odds ratio [OR], 1.33 per decade; 95% CI, 1.07 to 1.67), male sex (OR, 1.15; 95% CI, 1.05 to 1.33), comorbidities, illness severity (OR, 1.11; 95% CI, 1.10 to 1.13), emergency admission (OR, 1.45; 95% CI, 1.26 to 1.67), hospitalization on particular wards (OR, 1.525; 95% CI, 1.326 to 1.67), bacteremia (OR, 1.24; 95% CI, 1.01 to 1.52), fungemia (OR, 3.76; 95% CI, 1.90 to 7.41), tumor lysis syndrome (OR, 3.01; 95% CI, 2.41 to 3.76), and receipt of antimicrobials (OR, 2.04; 95% CI, 1.72 to 2.42) and transfusions (OR, 1.65; 95% CI, 1.42 to 1.92).

CONCLUSION

Clinical deterioration was common; it occurred in more than 9% of admissions. Factors independently associated with deterioration included comorbidities, admission source, infections, and blood product transfusion.

Characterising ICU-ward handoffs at three academic medical centres: process and perceptions

BACKGROUND

There is limited literature about physician handoffs between the intensive care unit (ICU) and the ward, and best practices have not been described. These patients are uniquely vulnerable given their medical complexity, diagnostic uncertainty and reduced monitoring intensity. We aimed to characterise the structure, perceptions and processes of ICU-ward handoffs across three teaching hospitals using multimodal methods: by identifying the handoff components involved in communication failures and describing common processes of patient transfer.

METHODS

We conducted a study at three academic medical centres using two methods to characterise the structure, perceptions and processes of ICU-ward transfers: (1) an anonymous resident survey characterising handoff communication during ICU-ward transfer, and (2) comparison of process maps to identify similarities and differences between ICU-ward transfer processes across the three hospitals.

RESULTS

Of the 295 internal medicine residents approached, 175 (59%) completed the survey. 87% of the respondents recalled at least one adverse event related to communication failure during ICU-ward transfer. 95% agreed that a well-structured handoff template would improve ICU-ward transfer. Rehabilitation needs, intravenous access/hardware and risk assessments for readmission to the ICU were the most frequently omitted or incorrectly communicated components of handoff notes. More than 60% of the respondents reported that notes omitted or miscommunicated pending results, active subspecialty consultants, nutrition and intravenous fluids, antibiotics, and healthcare decision-maker information at least twice per month. Despite variable process across the three sites, all process maps demonstrated flaws and potential for harm in critical steps of the ICU-ward transition.

CONCLUSION

In this multisite study, despite significant process variation across sites, almost all resident physicians recalled an adverse event related to the ICU-ward handoff. Future work is needed to determine best practices for ICU-ward handoffs at academic medical centres.

Rapid response systems

INTRODUCTION

Rapid response systems are commonly employed by hospitals to identify and respond to deteriorating patients outside of the intensive care unit. Controversy exists about the benefits of rapid response systems.

AIMS

We aimed to review the current state of the rapid response literature, including evolving aspects of afferent (risk detection) and efferent (intervention) arms, outcome measurement, process improvement, and implementation.

DATA SOURCES

Articles written in English and published in PubMed.

RESULTS

Rapid response systems are heterogeneous, with important differences among afferent and efferent arms. Clinically meaningful outcomes may include unexpected mortality, in-hospital cardiac arrest, length of stay, cost, and processes of care at end of life. Both positive and negative interventional studies have been published, although the two largest randomized trials involving rapid response systems - the Medical Early Response and Intervention Trial (MERIT) and the Effect of a Pediatric Early Warning System on All-Cause Mortality in Hospitalized Pediatric Patients (EPOCH) trial - did not find a mortality benefit with these systems, albeit with important limitations. Advances in monitoring technologies, risk assessment strategies, and behavioral ergonomics may offer opportunities for improvement.

CONCLUSIONS

Rapid responses may improve some meaningful outcomes, although these findings remain controversial. These systems may also improve care for patients at the end of life. Rapid response systems are expected to continue evolving with novel developments in monitoring technologies, risk prediction informatics, and work in human factors.

Association Between Opioid and Benzodiazepine Use and Clinical Deterioration in Ward Patients

BACKGROUND

Opioids and benzodiazepines are frequently used in hospitals, but little is known about outcomes among ward patients receiving these medications.

OBJECTIVE

To determine the association between opioid and benzodiazepine administration and clinical deterioration.

DESIGN

Observational cohort study.

SETTING

500-bed academic urban tertiary-care hospital.

PATIENTS

All adults hospitalized on the wards from November 2008 to January 2016 were included. Patients who were "comfort care" status, had tracheostomies, sickle-cell disease, and patients at risk for alcohol withdrawal or seizures were excluded.

MEASUREMENTS

The primary outcome was the composite of intensive care unit transfer or ward cardiac arrest. Discrete-time survival analysis was used to calculate the odds of this outcome during exposed time periods compared to unexposed time periods with respect to the medications of interest, with adjustment for patient demographics, comorbidities, severity of illness, and pain score.

RESULTS

In total, 120,518 admissions from 67,097 patients were included, with 67% of admissions involving opioids, and 21% involving benzodiazepines. After adjustment, each equivalent of 15 mg oral morphine was associated with a 1.9% increase in the odds of the primary outcome within 6 hours (odds ratio [OR], 1.019; 95% confidence interval [CI], 1.013-1.026; P < 0.001), and each 1 mg oral lorazepam equivalent was associated with a 29% increase in the odds of the composite outcome within 6 hours (OR, 1.29; CI, 1.16- 1.45; P < 0.001).

CONCLUSION

Among ward patients, opioids were associated with increased risk for clinical deterioration in the 6 hours after administration. Benzodiazepines were associated with even higher risk. These results have implications for ward-monitoring strategies. Journal of Hospital Medicine 2017;12:428-434.

Obstructive sleep apnea and adverse outcomes in surgical and nonsurgical patients on the wards

BACKGROUND

Obstructive sleep apnea (OSA) has been associated with clinical deterioration in postoperative patients and patients hospitalized with pneumonia. Paradoxically, OSA has also been associated with decreased risk of inpatient mortality in these same populations.

OBJECTIVES

To investigate the association between OSA and in-hospital mortality in a large cohort of surgical and nonsurgical ward patients.

DESIGN

Observational cohort study.

SETTING

A 500-bed academic tertiary care hospital in the United States.

PATIENTS

A total of 93,676 ward admissions from 53,150 unique adult patients between November 1, 2008 and October 1, 2013.

INTERVENTION

None.

MEASUREMENTS

OSA diagnoses and comorbidities were identified by International Classification of Diseases, Ninth Revision, Clinical Modification codes. Logistic regression was used to control for patient characteristics, location prior to ward admission, and admission severity of illness. The primary outcome was in-hospital death. Secondary outcomes included rapid response team (RRT) activation, intensive care unit (ICU) transfer, intubation, and cardiac arrest on the wards.

MAIN RESULTS

OSA was identified in 5,625 (10.6%) patients. Patients with OSA were more likely to be older, male, and obese, and had higher rates of comorbidities. OSA patients had more frequent RRT activations (1.5% vs 1.1%) and ICU transfers (8% vs 7%) than controls (P < 0.001 for both comparisons), but a lower inpatient mortality rate (1.1% vs 1.4%, P < 0.05). OSA was associated with decreased adjusted odds for ICU transfer (odds ratio [OR]: 0.91 [0.84-0.99]), cardiac arrest (OR: 0.72 [0.55-0.95]), and in-hospital mortality (OR: 0.70 [0.58-0.85]).

CONCLUSIONS

After adjustment for important confounders, OSA was not associated with clinical deterioration on the wards and was associated with significantly decreased in-hospital mortality.

Diagnosis and management of obstructive sleep apnea in the perioperative setting

Obstructive sleep apnea (OSA) is a chronic prevalent condition which may be under-recognized in surgical populations. Patients with OSA may be at increased perioperative risk, in part due to the effects sedatives and anesthetics have on upper airway tone and respiratory drive. A growing amount of data suggests that OSA patients have increased odds for adverse postoperative outcomes including intensive care unit transfer, respiratory failure, arrhythmias, and cardiac ischemia. Several screening tools have been developed to identify patients at risk for OSA preoperatively, but it remains to be seen whether routine implementation of these tools improves outcomes. In this review, we discuss the perioperative complications of OSA, the tools with which physicians can screen surgical patients, and the perioperative management of these patients.