Percutaneous thrombectomy for acute limb ischemia is associated with equivalent limb and mortality outcomes compared with open thrombectomy.

BACKGROUND: Acute limb ischemia (ALI) carries a 15% to 20% risk of combined death or amputation at 30 days and 50% to 60% at 1 year. Percutaneous mechanical thrombectomy (PT) is an emerging minimally invasive alternative to open thrombectomy (OT). However, ALI thrombectomy cases are omitted from most quality databases, limiting comparisons of limb and survival outcomes between PT and OT. Therefore, our aim was to compare in-hospital outcomes between PT and OT using the National Inpatient Sample. METHODS: We analyzed survey-weighted National Inpatient Sample data (2015-2020) to include emergent admissions of aged adults (50+ years) with a primary diagnosis of lower extremity ALI undergoing index procedures within 2 days of hospitalization. We excluded hospitalizations with concurrent trauma or dissection diagnoses and index procedures using catheter-directed thrombolysis. Our primary outcome was composite in-hospital major amputation or death. Secondary outcomes included in-hospital major amputation, death, in-hospital reintervention (including angioplasty/stent, thrombolysis, PT, OT, or bypass), and extended length of stay (eLOS; defined as LOS >75th percentile). Adjusted odds ratios (aORs) with 95% confidence intervals (95% CIs) were generated by multivariable logistic regression, adjusting for demographics, frailty (Risk Analysis Index), secondary diagnoses including atrial fibrillation and peripheral artery disease, hospital characteristics, and index procedure data including the anatomic thrombectomy level and fasciotomy. A priori subgroup analyses were performed using interaction terms. RESULTS: We included 23,795 survey-weighted ALI hospitalizations (mean age: 72.2 years, 50.4% female, 79.2% White, and 22.3% frail), with 7335 (30.8%) undergoing PT. Hospitalization characteristics for PT vs OT differed by atrial fibrillation (28.7% vs 36.5%, P < .0001), frequency of intervention at the femoropopliteal level (86.2% vs 88.8%, P = .009), and fasciotomy (4.8% vs 6.9%, P = .006). In total, 2530 (10.6%) underwent major amputation or died. Unadjusted (10.1% vs 10.9%, P = .43) and adjusted (aOR = 0.96 [95% CI, 0.77-1.20], P = .74) risk did not differ between the groups. PT was associated with increased odds of reintervention (aOR = 2.10 [95% CI, 1.72-2.56], P < .0001) when compared with OT, but this was not seen in the tibial subgroup (aOR = 1.31 [95% CI, 0.86-2.01], P = .21, Pinteraction < .0001). Further, 79.1% of PT hospitalizations undergoing reintervention were salvaged with endovascular therapy. Lastly, PT was associated with significantly decreased odds of eLOS (aOR = 0.80 [95% CI, 0.69-0.94], P = .005). CONCLUSIONS: PT was associated with comparable in-hospital limb salvage and mortality rates compared with OT. Despite an increased risk of reintervention, most PT reinterventions avoided open surgery, and PT was associated with a decreased risk of eLOS. Thus, PT may be an appropriate alternative to OT in appropriately selected patients.

The association between frailty and outcomes following ruptured abdominal aortic aneurysm repair.

OBJECTIVE: Endovascular aortic repair (EVAR) is a less invasive method than the more physiologically stressful open surgical repair (OSR) for patients with anatomically appropriate abdominal aortic aneurysms (AAAs). Early postoperative outcomes are associated with both patients; physiologic reserve and the physiologic stresses of the surgical intervention. Among frail patients with reduced physiologic reserve, the stress of an aortic rupture in combination with the stress of an operative repair are less well tolerated, raising the risk of complications and mortality. This study aims to evaluate the difference in association between frailty and outcomes among patients undergoing minimally invasive EVAR and the physiologically more stressful OSR for ruptured AAAs (rAAAs). METHODS: Our retrospective cohort study included adults undergoing rAAA repair in the Vascular Quality Initiative from 2010 to 2022. The validated Risk Analysis Index (RAI) (robust, ≤20; normal, 21-29; frail, 30-39; very frail, ≥40) quantified frailty. The association between the primary outcome of 1-year mortality and frailty status as well as repair type were compared using multivariable Cox models generating adjusted hazard ratios (aHRs) with 95% confidence intervals (CIs). Interaction terms evaluated the association's moderation. RESULTS: We identified 5806 patients (age, 72 ± 9 years; 77% male; EVAR, 65%; robust, 6%; normal, 48%; frail, 36%; very, frail 10%) with a 53% observed 1-year mortality rate following rAAA repair. OSR (aHR, 1.43; 95% CI, 1.19-1.73) was associated with increased 1-year mortality when compared with EVAR. Increasing frailty status (frail aHR, 1.26; 95% CI, 1.00-1.59; very frail aHR, 1.64; 95% CI, 1.26-2.13) was associated with increased 1-year mortality, which was moderated by repair type (P-interaction < .05). OSR was associated with increased 1-year mortality in normal (aHR, 1.49; 95% CI, 1.20-1.87) and frail (aHR, 1.51; 95% CI, 1.20-1.89), but not among robust (aHR, 0.88; 95% CI, 0.59-1.32) and very frail (aHR, 1.29; 95% CI, 0.97-1.72) patients. CONCLUSIONS: Frailty and OSR were associated with increased adjusted risk of 1-year mortality following rAAA repair. Among normal and frail patients, OSR was associated with an increased adjusted risk of 1-year mortality when compared with EVAR. However, there was no difference between OSR and EVAR among robust patients who can well tolerate the stress of OSR and among very frail patients who are unable to withstand the surgical stress from rAAA regardless of repair type.

Contemporary National Incidence and Outcomes of Acute Limb Ischemia.

INTRODUCTION: Acute Limb Ischemia (ALI) is a morbid and deadly diagnosis. However, existing epidemiologic studies describing ALI predate the introduction of the Affordable Care Act in 2010 and direct oral anticoagulants in 2011. Thus, we synergized the National Inpatient Sample (NIS) and United States (U.S.) Census to define contemporary trends in the incidence, treatment, and outcomes of ALI in the US. METHODS: We included emergent admissions of adults with primary diagnosis of lower extremity ALI in survey-weighted NIS data (2005-2020). Mann-Kendal trend test evaluated ALI incidence (primary outcome), anticoagulation usage, insurance coverage, revascularization type, and in-hospital amputation/death. Multivariable logistic regression quantified covariate associations with in-hospital amputation/death. RESULTS: Of 582,322,862 estimated hospitalizations in the NIS, 227,440 met inclusion criteria (mean age 68.80 years, 49.94% women, 76.66% White). ALI incidence peaked in 2006 (7.16/100,000 person-years) but has declined since 2015 to 4.16/100,000 person-years in 2020 (ptrend=.008). Endovascular revascularization, anticoagulation, and Medicaid coverage increased, while self-pay insurance decreased (ptrend<.05). Amputation rates significantly decreased from 8.04% to 6.54% (ptrend=.01) while death rate remained at 5.59% (ptrend=.16) over the study period. Pre-hospitalization anticoagulation was associated with decreased amputation (aOR=0.74 [95%CI 0.65-0.84]) and death (aOR=0.50 [95%CI 0.43-0.57]). When controlling for covariates, women had a higher risk of death (aOR=1.17 [95%CI 1.07-1.27], p<.0001), while Black patients had a higher risk of amputation (aOR=1.24 [95%CI 1.10-1.41], p<.0001). CONCLUSIONS: Our U.S. population based epidemiological study demonstrates that ALI incidence and in-hospital amputation rates are decreasing, while mortality remains unchanged. We further highlight the ongoing need for ALI investigation specifically as it relates to access to care, antithrombotic therapy use, treatment strategy, and strategies to combat gender and racial disparities.

Cardiac Function Before Sepsis and Clinical Outcomes.

This cohort study characterizes heterogeneity in cardiac function prior to sepsis and describes associations with hospitalization outcomes and mortality.

Data-derived subtypes of delirium during critical illness.

BACKGROUND: To understand delirium heterogeneity, prior work relied on psychomotor symptoms or risk factors to identify subtypes. Data-driven approaches have used machine learning to identify biologically plausible, treatment-responsive subtypes of other acute illnesses but have not been used to examine delirium. METHODS: We conducted a secondary analysis of a large, multicenter prospective cohort study involving adults in medical or surgical ICUs with respiratory failure or shock who experienced delirium per the Confusion Assessment Method for the ICU. We used data collected before delirium diagnosis in an unsupervised latent class model to identify delirium subtypes and then compared demographics, clinical characteristics, and outcomes between subtypes in the final model. FINDINGS: The 731 patients who developed delirium during critical illness had a median age of 63 [IQR, 54-72] years, a median Sequential Organ Failure Assessment score of 8.0 [6.0-11.0] and 613 [83.4%] were mechanically ventilated at delirium identification. A four-class model best fit the data with 50% of patients in subtype (ST) 1, 18% in subtype 2, 17% in subtype 3, and 14% in subtype 4. Subtype 2-which had more shock and kidney impairment-had the highest mortality (33% [ST2] vs. 17% [ST1], 25% [ST3], and 17% [ST4], p = 0.003). Subtype 4-which received more benzodiazepines and opioids-had the longest duration of delirium (6 days [ST4] vs. 3 [ST1], 4 [ST2], and 3 days [ST3], p < 0.001) and coma (4 days [ST4] vs. 2 [ST1], 1 [ST2], and 2 days [ST3], p < 0.001). Each of the four data-derived delirium subtypes was observed within previously identified psychomotor and risk factor-based delirium subtypes. Clinically significant cognitive impairment affected all subtypes at follow-up, but its severity did not differ by subtype (3-month, p = 0.26; 12-month, p = 0.80). INTERPRETATION: The four data-derived delirium subtypes identified in this study should now be validated in independent cohorts, examined for differential treatment effects in trials, and inform mechanistic work evaluating treatment targets. FUNDING: National Institutes of Health (T32HL007820, R01AG027472).

Distribution of Acute and Chronic Kidney Disease Across Clinical Phenotypes for Sepsis.

BACKGROUND: Sepsis is the most common cause of acute kidney injury (AKI) in critically ill patients. Four phenotypes (α, ß, γ, δ) for sepsis, which have different outcomes and responses to treatment, were described using routine clinical data in the electronic health record. RESEARCH QUESTION: Do the frequencies of AKI, acute kidney disease (AKD), chronic kidney disease (CKD), and AKI on CKD differ by sepsis phenotype? STUDY DESIGN AND METHODS: This was a secondary analysis of a randomized clinical trial of early resuscitation, including patients with septic shock at 31 sites. After excluding patients with end-stage kidney disease and missing data, we determined frequencies of the following clinical outcomes: AKI (defined within 24 h as Kidney Disease: Improving Global Outcomes stages 2 or 3 or stage 1 with tissue inhibitor of metalloproteinases-2 × insulin-like growth factor binding protein 7 value of > 2.0), CKD, and AKD (persistence of AKI at any stage on day 7 after enrollment) across four phenotypes. We performed multivariable logistic regression to assess the risk-adjusted association between development of AKI and AKD and phenotype. RESULTS: Among 1,090 eligible patients, 543 patients (50%) had AKI. Across phenotypes, the frequencies of AKI varied, being highest in the δ and ß phenotypes (78% and 71%, respectively) and the lowest in the α phenotype (26%; P < .001). AKD occurred most often in the δ phenotype (41%) and least often in the α phenotype (8%; P < .001). The highest frequencies of CKD and of AKI on CKD were found in the ß phenotype (53% and 38% respectively; P < .001 for both). In the multivariable logistic regression models (α phenotype as reference), δ phenotype showed the strongest association with AKI (OR, 12.33; 95% CI, 7.81-19.47; P < .001) and AKD (OR, 9.18; 95% CI, 5.44-15.51; P < .001). INTERPRETATION: The rates of AKI and AKD differed across clinical sepsis phenotypes and are more common among patients with phenotypes ß and δ. Phenotype ß showed a higher level of underlying CKD that predisposed patients to new AKI. The α and γ phenotypes showed lower frequencies of AKI and less progression to AKD.

Adaptation of the Risk Analysis Index for Frailty Assessment Using Diagnostic Codes.

Importance: Frailty is associated with adverse outcomes after even minor physiologic stressors. The validated Risk Analysis Index (RAI) quantifies frailty; however, existing methods limit application to in-person interview (clinical RAI) and quality improvement datasets (administrative RAI). Objective: To expand the utility of the RAI utility to available International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) administrative data, using the National Inpatient Sample (NIS). Design, Setting, and Participants: RAI parameters were systematically adapted to ICD-10-CM codes (RAI-ICD) and were derived (NIS 2019) and validated (NIS 2020). The primary analysis included survey-weighed discharge data among adults undergoing major surgical procedures. Additional external validation occurred by including all operative and nonoperative hospitalizations in the NIS (2020) and in a multihospital health care system (UPMC, 2021-2022). Data analysis was conducted from January to May 2023. Exposures: RAI parameters and in-hospital mortality. Main Outcomes and Measures: The association of RAI parameters with in-hospital mortality was calculated and weighted using logistic regression, generating an integerized RAI-ICD score. After initial validation, thresholds defining categories of frailty were selected by a full complement of test statistics. Rates of elective admission, length of stay, hospital charges, and in-hospital mortality were compared across frailty categories. C statistics estimated model discrimination. Results: RAI-ICD parameters were weighted in the 9 548 206 patients who were hospitalized (mean [SE] age, 55.4 (0.1) years; 3 742 330 male [weighted percentage, 39.2%] and 5 804 431 female [weighted percentage, 60.8%]), modeling in-hospital mortality (2.1%; 95% CI, 2.1%-2.2%) with excellent derivation discrimination (C statistic, 0.810; 95% CI, 0.808-0.813). The 11 RAI-ICD parameters were adapted to 323 ICD-10-CM codes. The operative validation population of 8 113 950 patients (mean [SE] age, 54.4 (0.1) years; 3 148 273 male [weighted percentage, 38.8%] and 4 965 737 female [weighted percentage, 61.2%]; in-hospital mortality, 2.5% [95% CI, 2.4%-2.5%]) mirrored the derivation population. In validation, the weighted and integerized RAI-ICD yielded good to excellent discrimination in the NIS operative sample (C statistic, 0.784; 95% CI, 0.782-0.786), NIS operative and nonoperative sample (C statistic, 0.778; 95% CI, 0.777-0.779), and the UPMC operative and nonoperative sample (C statistic, 0.860; 95% CI, 0.857-0.862). Thresholds defining robust (RAI-ICD <27), normal (RAI-ICD, 27-35), frail (RAI-ICD, 36-45), and very frail (RAI-ICD >45) strata of frailty maximized precision (F1 = 0.33) and sensitivity and specificity (Matthews correlation coefficient = 0.26). Adverse outcomes increased with increasing frailty. Conclusion and Relevance: In this cohort study of hospitalized adults, the RAI-ICD was rigorously adapted, derived, and validated. These findings suggest that the RAI-ICD can extend the quantification of frailty to inpatient adult ICD-10-CM-coded patient care datasets.

Identification of a hyperinflammatory sepsis phenotype using protein biomarker and clinical data in the ProCESS randomized trial.

Sepsis is a heterogeneous syndrome and phenotypes have been proposed using clinical data. Less is known about the contribution of protein biomarkers to clinical sepsis phenotypes and their importance for treatment effects in randomized trials of resuscitation. The objective is to use both clinical and biomarker data in the Protocol-Based Care for Early Septic Shock (ProCESS) randomized trial to determine sepsis phenotypes and to test for heterogeneity of treatment effect by phenotype comparing usual care to protocolized early, goal-directed therapy(EGDT). In this secondary analysis of a subset of patients with biomarker sampling in the ProCESS trial (n = 543), we identified sepsis phenotypes prior to randomization using latent class analysis of 20 clinical and biomarker variables. Logistic regression was used to test for interaction between phenotype and treatment arm for 60-day inpatient mortality. Among 543 patients with severe sepsis or septic shock in the ProCESS trial, a 2-class model best fit the data (p = 0.01). Phenotype 1 (n = 66, 12%) had increased IL-6, ICAM, and total bilirubin and decreased platelets compared to phenotype 2 (n = 477, 88%, p < 0.01 for all). Phenotype 1 had greater 60-day inpatient mortality compared to Phenotype 2 (41% vs 16%; p < 0.01). Treatment with EGDT was associated with worse 60-day inpatient mortality compared to usual care (58% vs. 23%) in Phenotype 1 only (p-value for interaction = 0.05). The 60-day inpatient mortality was similar comparing EGDT to usual care in Phenotype 2 (16% vs. 17%). We identified 2 sepsis phenotypes using latent class analysis of clinical and protein biomarker data at randomization in the ProCESS trial. Phenotype 1 had increased inflammation, organ dysfunction and worse clinical outcomes compared to phenotype 2. Response to EGDT versus usual care differed by phenotype.

A Prospective Cohort Protocol for the Remnant Investigation in Sepsis Study.

BACKGROUND: Sepsis is a common and deadly syndrome, accounting for more than 11 million deaths annually. To mature a deeper understanding of the host and pathogen mechanisms contributing to poor outcomes in sepsis, and thereby possibly inform new therapeutic targets, sophisticated, and expensive biorepositories are typically required. We propose that remnant biospecimens are an alternative for mechanistic sepsis research, although the viability and scientific value of such remnants are unknown. METHODS AND RESULTS: The Remnant Biospecimen Investigation in Sepsis study is a prospective cohort study of 225 adults (age ≥ 18 yr) presenting to the emergency department with community sepsis, defined as sepsis-3 criteria within 6 hours of arrival. The primary objective was to determine the scientific value of a remnant biospecimen repository in sepsis linked to clinical phenotyping in the electronic health record. We will study candidate multiomic readouts of sepsis biology, governed by a conceptual model, and determine the precision, accuracy, integrity, and comparability of proteins, small molecules, lipids, and pathogen sequencing in remnant biospecimens compared with paired biospecimens obtained according to research protocols. Paired biospecimens will include plasma from sodium-heparin, EDTA, sodium fluoride, and citrate tubes. CONCLUSIONS: The study has received approval from the University of Pittsburgh Human Research Protection Office (Study 21120013). Recruitment began on October 25, 2022, with planned release of primary results anticipated in 2024. Results will be made available to the public, the funders, critical care societies, laboratory medicine scientists, and other researchers.

Machine Learning Approaches for Phenotyping in Cardiogenic Shock and Critical Illness: Part 2 of 2.

Progress in improving cardiogenic shock (CS) outcomes may have been limited by failure to embrace the heterogeneity of pathophysiologic processes driving the underlying syndrome. To better understand the variability inherent to CS populations, recent algorithms for describing underlying CS disease subphenotypes have been described and validated. These strategies hope to identify specific patient subgroups with more favorable responses to standard therapies, as well as those who require novel treatment approaches. This paper is part 2 of a 2-part state-of-the-art review. In this second article, we present machine learning-based statistical approaches to identifying subphenotypes and discuss their strengths and limitations, as well as evidence from other critical illness syndromes and emerging applications in CS. We then discuss how staging and stratification may be considered in CS clinical trials and finally consider future directions for this emerging area of research.

Advances in the Staging and Phenotyping of Cardiogenic Shock: Part 1 of 2.

Cardiogenic shock (CS) is a heterogeneous syndrome reflecting a broad spectrum of shock severity, diverse etiologies, variable cardiac function, different hemodynamic trajectories, and concomitant organ dysfunction. These factors influence the clinical presentation, management, response to therapy, and outcomes of CS patients, necessitating a tailored approach to care. To better understand the variability inherent to CS populations, recent algorithms for staging the severity of CS have been described and validated. This paper is part 1 of a 2-part state-of-the-art review. In this first article, we consider the context for clinical staging and stratification in CS with a focus on established severity staging systems for CS and their use for risk stratification and clinical care. We describe the use of staging for predicting outcomes in populations with or at risk for CS, including risk modifiers that provide more nuanced risk stratification, and highlight how these approaches may allow individualized care.