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.

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.

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.

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.

Characterization and Outcomes of Hospitalized Children With Coronavirus Disease 2019: A Report From a Multicenter, Viral Infection and Respiratory Illness Universal Study (Coronavirus Disease 2019) Registry.

OBJECTIVES: Multicenter data on the characteristics and outcomes of children hospitalized with coronavirus disease 2019 are limited. Our objective was to describe the characteristics, ICU admissions, and outcomes among children hospitalized with coronavirus disease 2019 using Society of Critical Care Medicine Discovery Viral Infection and Respiratory Illness Universal Study: Coronavirus Disease 2019 registry. DESIGN: Retrospective study. SETTING: Society of Critical Care Medicine Viral Infection and Respiratory Illness Universal Study (Coronavirus Disease 2019) registry. PATIENTS: Children (< 18 yr) hospitalized with coronavirus disease 2019 at participating hospitals from February 2020 to January 2021. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The primary outcome was ICU admission. Secondary outcomes included hospital and ICU duration of stay and ICU, hospital, and 28-day mortality. A total of 874 children with coronavirus disease 2019 were reported to Viral Infection and Respiratory Illness Universal Study registry from 51 participating centers, majority in the United States. Median age was 8 years (interquartile range, 1.25-14 yr) with a male:female ratio of 1:2. A majority were non-Hispanic (492/874; 62.9%). Median body mass index (n = 817) was 19.4 kg/m2 (16-25.8 kg/m2), with 110 (13.4%) overweight and 300 (36.6%) obese. A majority (67%) presented with fever, and 43.2% had comorbidities. A total of 238 of 838 (28.2%) met the Centers for Disease Control and Prevention criteria for multisystem inflammatory syndrome in children, and 404 of 874 (46.2%) were admitted to the ICU. In multivariate logistic regression, age, fever, multisystem inflammatory syndrome in children, and pre-existing seizure disorder were independently associated with a greater odds of ICU admission. Hospital mortality was 16 of 874 (1.8%). Median (interquartile range) duration of ICU (n = 379) and hospital (n = 857) stay were 3.9 days (2-7.7 d) and 4 days (1.9-7.5 d), respectively. For patients with 28-day data, survival was 679 of 787, 86.3% with 13.4% lost to follow-up, and 0.3% deceased. CONCLUSIONS: In this observational, multicenter registry of children with coronavirus disease 2019, ICU admission was common. Older age, fever, multisystem inflammatory syndrome in children, and seizure disorder were independently associated with ICU admission, and mortality was lower among children than mortality reported in adults.

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.

Racial Disparities in Readmissions Following Initial Hospitalization for Sepsis.

OBJECTIVES: To assess whether Black race is associated with a higher rate of all-cause readmission compared with White race following community-onset sepsis. DESIGN: Retrospective cohort study. SETTING: One-thousand three-hundred bed urban academic medical centers. PATIENTS: Three-thousand three-hundred ninety patients hospitalized with community-onset sepsis between January 1, 2010, and December 31, 2017. INTERVENTIONS: Community-onset sepsis was defined as patients admitted through the emergency department with an International Classification of Disease, ninth revision, Clinical Modification code for either severe sepsis (995.92) or septic shock (785.52). Beginning in 2015, we used International Classification of Disease, Tenth Revision, Clinical Modification codes R65.20 (severe sepsis) and R65.21 (septic shock). We excluded those individuals hospitalized at another acute care facility that were transferred to our facility. Race was abstracted electronically, and patients who expired or self-identified as a race other than Black or White race were excluded. Patients who experienced a subsequent hospitalization at our facility were considered to be readmitted. MEASUREMENTS AND MAIN RESULTS: Compared with White race, Black race demonstrated a significantly higher rate of all-cause readmission (60.8% vs 71.1%; p < 0.001), including a higher rate of readmission for sepsis (14.0% vs 19.8%; p < 0.001). Black patients also resided in zip codes with a lower median household income and were more likely to use public insurance compared with White race. Similar rates of comorbid diseases and disease burden were observed between the two groups, but vasopressors were less likely to be administered to Black patients. Multivariable analysis showed that Black race was associated with a 50% increased odds (odds ratio, 1.52, 99% CI, 1.25-1.84) in all-cause readmission risk compared with White race. CONCLUSIONS: Black race was associated with a higher rate of all-cause and sepsis readmission, possibly as a result of unaddressed health disparities, compared with White race. Programs addressing healthcare disparities should use readmission as another marker of equity.

Comparison of Sepsis Definitions as Automated Criteria.

OBJECTIVES: Assess the impact of heterogeneity among established sepsis criteria (Sepsis-1, Sepsis-3, Centers for Disease Control and Prevention Adult Sepsis Event, and Centers for Medicare and Medicaid severe sepsis core measure 1) through the comparison of corresponding sepsis cohorts. DESIGN: Retrospective analysis of data extracted from electronic health record. SETTING: Single, tertiary-care center in St. Louis, MO. PATIENTS: Adult, nonsurgical inpatients admitted between January 1, 2012, and January 6, 2018. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: In the electronic health record data, 286,759 encounters met inclusion criteria across the study period. Application of established sepsis criteria yielded cohorts varying in prevalence: Centers for Disease Control and Prevention Adult Sepsis Event (4.4%), Centers for Medicare and Medicaid severe sepsis core measure 1 (4.8%), International Classification of Disease code (7.2%), Sepsis-3 (7.5%), and Sepsis-1 (11.3%). Between the two modern established criteria, Sepsis-3 (n = 21,550) and Centers for Disease Control and Prevention Adult Sepsis Event (n = 12,494), the size of the overlap was 7,763. The sepsis cohorts also varied in time from admission to sepsis onset (hr): Sepsis-1 (2.9), Sepsis-3 (4.1), Centers for Disease Control and Prevention Adult Sepsis Event (4.6), and Centers for Medicare and Medicaid severe sepsis core measure 1 (7.6); sepsis discharge International Classification of Disease code rate: Sepsis-1 (37.4%), Sepsis-3 (40.1%), Centers for Medicare and Medicaid severe sepsis core measure 1 (48.5%), and Centers for Disease Control and Prevention Adult Sepsis Event (54.5%); and inhospital mortality rate: Sepsis-1 (13.6%), Sepsis-3 (18.8%), International Classification of Disease code (20.4%), Centers for Medicare and Medicaid severe sepsis core measure 1 (22.5%), and Centers for Disease Control and Prevention Adult Sepsis Event (24.1%). CONCLUSIONS: The application of commonly used sepsis definitions on a single population produced sepsis cohorts with low agreement, significantly different baseline demographics, and clinical outcomes.

Characteristics of Rapid Response Calls in the United States: An Analysis of the First 402,023 Adult Cases From the Get With the Guidelines Resuscitation-Medical Emergency Team Registry.

OBJECTIVES: To characterize the rapid response team activations, and the patients receiving them, in the American Heart Association-sponsored Get With The Guidelines Resuscitation-Medical Emergency Team cohort between 2005 and 2015. DESIGN: Retrospective multicenter cohort study. SETTING: Three hundred sixty U.S. hospitals. PATIENTS: Consecutive adult patients experiencing rapid response team activation. INTERVENTIONS: Rapid response team activation. MEASUREMENTS AND MAIN RESULTS: The cohort included 402,023 rapid response team activations from 347,401 unique healthcare encounters. Respiratory triggers (38.0%) and cardiac triggers (37.4%) were most common. The most frequent interventions-pulse oximetry (66.5%), other monitoring (59.6%), and supplemental oxygen (62.0%)-were noninvasive. Fluids were the most common medication ordered (19.3%), but new antibiotic orders were rare (1.2%). More than 10% of rapid response teams resulted in code status changes. Hospital mortality was over 14% and increased with subsequent rapid response activations. CONCLUSIONS: Although patients requiring rapid response team activation have high inpatient mortality, most rapid response team activations involve relatively few interventions, which may limit these teams' ability to improve patient outcomes.

Sepsis-Associated Coagulopathy Severity Predicts Hospital Mortality.

OBJECTIVES: To assess whether sepsis-associated coagulopathy predicts hospital mortality. DESIGN: Retrospective cohort study. SETTING: One-thousand three-hundred beds urban academic medical center. PATIENTS: Six-thousand one-hundred forty-eight consecutive patients hospitalized between January 1, 2010, and December 31, 2015. INTERVENTIONS: Mild sepsis-associated coagulopathy was defined as an international normalized ratio greater than or equal to 1.2 and less than 1.4 plus platelet count less than or equal to 150,000/µL but greater than 100,000/µL; moderate sepsis-associated coagulopathy was defined with either an international normalized ratio greater than or equal to 1.4 but less than 1.6 or platelets less than or equal to 100,000/µL but greater than 80,000/µL; severe sepsis-associated coagulopathy was defined as an international normalized ratio greater than or equal to 1.6 and platelets less than or equal to 80,000/µL. MEASUREMENTS AND MAIN RESULTS: Hospital mortality increased progressively from 25.4% in patients without sepsis-associated coagulopathy to 56.1% in patients with severe sepsis-associated coagulopathy. Similarly, duration of hospitalization and ICU care increased progressively as sepsis-associated coagulopathy severity increased. Multivariable analyses showed that the presence of sepsis-associated coagulopathy, as well as sepsis-associated coagulopathy severity, was independently associated with hospital mortality regardless of adjustments made for baseline patient characteristics, hospitalization variables, and the sepsis-associated coagulopathy-cancer interaction. Odds ratios ranged from 1.33 to 2.14 for the presence of sepsis-associated coagulopathy and from 1.18 to 1.51 for sepsis-associated coagulopathy severity for predicting hospital mortality (p < 0.001 for all comparisons). CONCLUSIONS: The presence of sepsis-associated coagulopathy identifies a group of patients with sepsis at higher risk for mortality. Furthermore, there is an incremental risk of mortality as the severity of sepsis-associated coagulopathy increases.

Prevention of hospital-acquired pneumonia.

PURPOSE OF REVIEW: Hospital-acquired pneumonia (HAP) is the leading cause of death from hospital-acquired infection. Little work has been done on strategies for prevention of HAP. This review aims to describe potential HAP prevention strategies and the evidence supporting them. Oral care and aspiration precautions may attenuate some risk for HAP. Oral and digestive decontamination with antibiotics may be effective but could increase risk for resistant organisms. Other preventive measures, including isolation practices, remain theoretical or experimental. RECENT FINDINGS: Hospital-acquired pneumonia occurs because of pharyngeal colonization with pathogenic organisms and subsequent aspiration of these pathogens. SUMMARY: Most potential HAP prevention strategies remain unproven.

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.

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.