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.

Time to treatment and mortality for clinical sepsis subtypes.

BACKGROUND: Sepsis is common, deadly, and heterogenous. Prior analyses of patients with sepsis and septic shock in New York State showed a risk-adjusted association between more rapid antibiotic administration and bundled care completion, but not an intravenous fluid bolus, with reduced in-hospital mortality. However, it is unknown if clinically identifiable sepsis subtypes modify these associations. METHODS: Secondary analysis of patients with sepsis and septic shock enrolled in the New York State Department of Health cohort from January 1, 2015 to December 31, 2016. Patients were classified as clinical sepsis subtypes (α, ß, γ, δ-types) using the Sepsis ENdotyping in Emergency CAre (SENECA) approach. Exposure variables included time to 3-h sepsis bundle completion, antibiotic administration, and intravenous fluid bolus completion. Then logistic regression models evaluated the interaction between exposures, clinical sepsis subtypes, and in-hospital mortality. RESULTS: 55,169 hospitalizations from 155 hospitals were included (34% α, 30% ß, 19% γ, 17% δ). The α-subtype had the lowest (N = 1,905, 10%) and δ-subtype had the highest (N = 3,776, 41%) in-hospital mortality. Each hour to completion of the 3-h bundle (aOR, 1.04 [95%CI, 1.02-1.05]) and antibiotic initiation (aOR, 1.03 [95%CI, 1.02-1.04]) was associated with increased risk-adjusted in-hospital mortality. The association differed across subtypes (p-interactions < 0.05). For example, the outcome association for the time to completion of the 3-h bundle was greater in the δ-subtype (aOR, 1.07 [95%CI, 1.05-1.10]) compared to α-subtype (aOR, 1.02 [95%CI, 0.99-1.04]). Time to intravenous fluid bolus completion was not associated with risk-adjusted in-hospital mortality (aOR, 0.99 [95%CI, 0.97-1.01]) and did not differ among subtypes (p-interaction = 0.41). CONCLUSION: Timely completion of a 3-h sepsis bundle and antibiotic initiation was associated with reduced risk-adjusted in-hospital mortality, an association modified by clinically identifiable sepsis subtype.

Validation of cardiogenic shock phenotypes in a mixed cardiac intensive care unit population.

BACKGROUND: Proposed phenotypes have recently been identified in cardiogenic shock (CS) populations using unsupervised machine learning clustering methods. We sought to validate these phenotypes in a mixed cardiac intensive care unit (CICU) population of patients with CS. METHODS: We included Mayo Clinic CICU patients admitted from 2007 to 2018 with CS. Agnostic K means clustering was used to assign patients to three clusters based on admission values of estimated glomerular filtration rate, bicarbonate, alanine aminotransferase, lactate, platelets, and white blood cell count. In-hospital mortality and 1-year mortality were analyzed using logistic regression and Cox proportional-hazards models, respectively. RESULTS: We included 1498 CS patients with a mean age of 67.8 ± 13.9 years, and 37.1% were females. The acute coronary syndrome was present in 57.3%, and cardiac arrest was present in 34.0%. Patients were assigned to clusters as follows: Cluster 1 (noncongested), 603 (40.2%); Cluster 2 (cardiorenal), 452 (30.2%); and Cluster 3 (hemometabolic), 443 (29.6%). Clinical, laboratory, and echocardiographic characteristics differed across clusters, with the greatest illness severity in Cluster 3. Cluster assignment was associated with in-hospital mortality across subgroups. In-hospital mortality was higher in Cluster 3 (adjusted odds ratio [OR]: 2.6 vs. Cluster 1 and adjusted OR: 2.0 vs. Cluster 2, both p < 0.001). Adjusted 1-year mortality was incrementally higher in Cluster 3 versus Cluster 2 versus Cluster 1 (all p < 0.01). CONCLUSIONS: We observed similar phenotypes in CICU patients with CS as previously reported, identifying a gradient in both in-hospital and 1-year mortality by cluster. Identifying these clinical phenotypes can improve mortality risk stratification for CS patients beyond standard measures.

Clinical sepsis phenotypes in critically ill COVID-19 patients.

BACKGROUND: A greater understanding of disease heterogeneity may facilitate precision medicine for coronavirus disease 2019 (COVID-19). Previous work identified four distinct clinical phenotypes associated with outcome and treatment responses in non-COVID-19 sepsis patients, but it is unknown if and how these phenotypes are recapitulated in COVID-19 sepsis patients. METHODS: We applied the four non-COVID-19 sepsis phenotypes to a total of 52,274 critically ill patients, comprising two cohorts of COVID-19 sepsis patients (admitted before and after the introduction of dexamethasone as standard treatment) and three non-COVID-19 sepsis cohorts (non-COVID-19 viral pneumonia sepsis, bacterial pneumonia sepsis, and bacterial sepsis of non-pulmonary origin). Differences in proportions of phenotypes and their associated mortality were determined across these cohorts. RESULTS: Phenotype distribution was highly similar between COVID-19 and non-COVID-19 viral pneumonia sepsis cohorts, whereas the proportion of patients with the δ-phenotype was greater in both bacterial sepsis cohorts compared to the viral sepsis cohorts. The introduction of dexamethasone treatment was associated with an increased proportion of patients with the δ-phenotype (6% vs. 11% in the pre- and post-dexamethasone COVID-19 cohorts, respectively, p < 0.001). Across the cohorts, the α-phenotype was associated with the most favorable outcome, while the δ-phenotype was associated with the highest mortality. Survival of the δ-phenotype was markedly higher following the introduction of dexamethasone (60% vs 41%, p < 0.001), whereas no relevant differences in survival were observed for the other phenotypes among COVID-19 patients. CONCLUSIONS: Classification of critically ill COVID-19 patients into clinical phenotypes may aid prognostication, prediction of treatment efficacy, and facilitation of personalized medicine.

Identifying clinical subtypes in sepsis-survivors with different one-year outcomes: a secondary latent class analysis of the FROG-ICU cohort.

BACKGROUND: Late mortality risk in sepsis-survivors persists for years with high readmission rates and low quality of life. The present study seeks to link the clinical sepsis-survivors heterogeneity with distinct biological profiles at ICU discharge and late adverse events using an unsupervised analysis. METHODS: In the original FROG-ICU prospective, observational, multicenter study, intensive care unit (ICU) patients with sepsis on admission (Sepsis-3) were identified (N = 655). Among them, 467 were discharged alive from the ICU and included in the current study. Latent class analysis was applied to identify distinct sepsis-survivors clinical classes using readily available data at ICU discharge. The primary endpoint was one-year mortality after ICU discharge. RESULTS: At ICU discharge, two distinct subtypes were identified (A and B) using 15 readily available clinical and biological variables. Patients assigned to subtype B (48% of the studied population) had more impaired cardiovascular and kidney functions, hematological disorders and inflammation at ICU discharge than subtype A. Sepsis-survivors in subtype B had significantly higher one-year mortality compared to subtype A (respectively, 34% vs 16%, p < 0.001). When adjusted for standard long-term risk factors (e.g., age, comorbidities, severity of illness, renal function and duration of ICU stay), subtype B was independently associated with increased one-year mortality (adjusted hazard ratio (HR) = 1.74 (95% CI 1.16-2.60); p = 0.006). CONCLUSIONS: A subtype with sustained organ failure and inflammation at ICU discharge can be identified from routine clinical and laboratory data and is independently associated with poor long-term outcome in sepsis-survivors. Trial registration NCT01367093; https://clinicaltrials.gov/ct2/show/NCT01367093 .

Patterns of Opioid Administration Among Opioid-Naive Inpatients and Associations With Postdischarge Opioid Use: A Cohort Study.

Background: Patterns of inpatient opioid use and their associations with postdischarge opioid use are poorly understood. Objective: To measure patterns in timing, duration, and setting of opioid administration in opioid-naive hospitalized patients and to examine associations with postdischarge use. Design: Retrospective cohort study using electronic health record data from 2010 to 2014. Setting: 12 community and academic hospitals in Pennsylvania. Patients: 148 068 opioid-naive patients (191 249 admissions) with at least 1 outpatient encounter within 12 months before and after admission. Measurements: Number of days and patterns of inpatient opioid use; any outpatient use (self-report and/or prescription orders) 90 and 365 days after discharge. Results: Opioids were administered in 48% of admissions. Patients were given opioids for a mean of 67.9% (SD, 25.0%) of their stay. Location of administration of first opioid on admission, timing of last opioid before discharge, and receipt of nonopioid analgesics varied substantially. After adjustment for potential confounders, 5.9% of inpatients receiving opioids had outpatient use at 90 days compared with 3.0% of those without inpatient use (difference, 3.0 percentage points [95% CI, 2.8 to 3.2 percentage points]). Opioid use at 90 days was higher in inpatients receiving opioids less than 12 hours before discharge than in those with at least 24 opioid-free hours before discharge (7.5% vs. 3.9%; difference, 3.6 percentage points [CI, 3.3 to 3.9 percentage points]). Differences based on proportion of the stay with opioid use were modest (opioid use at 90 days was 6.4% and 5.4%, respectively, for patients with opioid use for ≥75% vs. ≤25% of their stay; difference, 1.0 percentage point [CI, 0.4 to 1.5 percentage points]). Associations were similar for opioid use 365 days after discharge. Limitation: Potential unmeasured confounders related to opioid use. Conclusion: This study found high rates of opioid administration to opioid-naive inpatients and associations between specific patterns of inpatient use and risk for long-term use after discharge. Primary Funding Source: UPMC Health System and University of Pittsburgh.

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.

Derivation, Validation, and Potential Treatment Implications of Novel Clinical Phenotypes for Sepsis.

Importance: Sepsis is a heterogeneous syndrome. Identification of distinct clinical phenotypes may allow more precise therapy and improve care. Objective: To derive sepsis phenotypes from clinical data, determine their reproducibility and correlation with host-response biomarkers and clinical outcomes, and assess the potential causal relationship with results from randomized clinical trials (RCTs). Design, Settings, and Participants: Retrospective analysis of data sets using statistical, machine learning, and simulation tools. Phenotypes were derived among 20 189 total patients (16 552 unique patients) who met Sepsis-3 criteria within 6 hours of hospital presentation at 12 Pennsylvania hospitals (2010-2012) using consensus k means clustering applied to 29 variables. Reproducibility and correlation with biological parameters and clinical outcomes were assessed in a second database (2013-2014; n = 43 086 total patients and n = 31 160 unique patients), in a prospective cohort study of sepsis due to pneumonia (n = 583), and in 3 sepsis RCTs (n = 4737). Exposures: All clinical and laboratory variables in the electronic health record. Main Outcomes and Measures: Derived phenotype (α, ß, γ, and δ) frequency, host-response biomarkers, 28-day and 365-day mortality, and RCT simulation outputs. Results: The derivation cohort included 20 189 patients with sepsis (mean age, 64 [SD, 17] years; 10 022 [50%] male; mean maximum 24-hour Sequential Organ Failure Assessment [SOFA] score, 3.9 [SD, 2.4]). The validation cohort included 43 086 patients (mean age, 67 [SD, 17] years; 21 993 [51%] male; mean maximum 24-hour SOFA score, 3.6 [SD, 2.0]). Of the 4 derived phenotypes, the α phenotype was the most common (n = 6625; 33%) and included patients with the lowest administration of a vasopressor; in the ß phenotype (n = 5512; 27%), patients were older and had more chronic illness and renal dysfunction; in the γ phenotype (n = 5385; 27%), patients had more inflammation and pulmonary dysfunction; and in the δ phenotype (n = 2667; 13%), patients had more liver dysfunction and septic shock. Phenotype distributions were similar in the validation cohort. There were consistent differences in biomarker patterns by phenotype. In the derivation cohort, cumulative 28-day mortality was 287 deaths of 5691 unique patients (5%) for the α phenotype; 561 of 4420 (13%) for the ß phenotype; 1031 of 4318 (24%) for the γ phenotype; and 897 of 2223 (40%) for the δ phenotype. Across all cohorts and trials, 28-day and 365-day mortality were highest among the δ phenotype vs the other 3 phenotypes (P < .001). In simulation models, the proportion of RCTs reporting benefit, harm, or no effect changed considerably (eg, varying the phenotype frequencies within an RCT of early goal-directed therapy changed the results from >33% chance of benefit to >60% chance of harm). Conclusions and Relevance: In this retrospective analysis of data sets from patients with sepsis, 4 clinical phenotypes were identified that correlated with host-response patterns and clinical outcomes, and simulations suggested these phenotypes may help in understanding heterogeneity of treatment effects. Further research is needed to determine the utility of these phenotypes in clinical care and for informing trial design and interpretation.

Association of the Quick Sequential (Sepsis-Related) Organ Failure Assessment (qSOFA) Score With Excess Hospital Mortality in Adults With Suspected Infection in Low- and Middle-Income Countries.

Importance: The quick Sequential (Sepsis-Related) Organ Failure Assessment (qSOFA) score has not been well-evaluated in low- and middle-income countries (LMICs). Objective: To assess the association of qSOFA with excess hospital death among patients with suspected infection in LMICs and to compare qSOFA with the systemic inflammatory response syndrome (SIRS) criteria. Design, Settings, and Participants: Retrospective secondary analysis of 8 cohort studies and 1 randomized clinical trial from 2003 to 2017. This study included 6569 hospitalized adults with suspected infection in emergency departments, inpatient wards, and intensive care units of 17 hospitals in 10 LMICs across sub-Saharan Africa, Asia, and the Americas. Exposures: Low (0), moderate (1), or high (≥2) qSOFA score (range, 0 [best] to 3 [worst]) or SIRS criteria (range, 0 [best] to 4 [worst]) within 24 hours of presentation to study hospital. Main Outcomes and Measures: Predictive validity (measured as incremental hospital mortality beyond that predicted by baseline risk factors, as a marker of sepsis or analogous severe infectious course) of the qSOFA score (primary) and SIRS criteria (secondary). Results: The cohorts were diverse in enrollment criteria, demographics (median ages, 29-54 years; males range, 36%-76%), HIV prevalence (range, 2%-43%), cause of infection, and hospital mortality (range, 1%-39%). Among 6218 patients with nonmissing outcome status in the combined cohort, 643 (10%) died. Compared with a low or moderate score, a high qSOFA score was associated with increased risk of death overall (19% vs 6%; difference, 13% [95% CI, 11%-14%]; odds ratio, 3.6 [95% CI, 3.0-4.2]) and across cohorts (P < .05 for 8 of 9 cohorts). Compared with a low qSOFA score, a moderate qSOFA score was also associated with increased risk of death overall (8% vs 3%; difference, 5% [95% CI, 4%-6%]; odds ratio, 2.8 [95% CI, 2.0-3.9]), but not in every cohort (P < .05 in 2 of 7 cohorts). High, vs low or moderate, SIRS criteria were associated with a smaller increase in risk of death overall (13% vs 8%; difference, 5% [95% CI, 3%-6%]; odds ratio, 1.7 [95% CI, 1.4-2.0]) and across cohorts (P < .05 for 4 of 9 cohorts). qSOFA discrimination (area under the receiver operating characteristic curve [AUROC], 0.70 [95% CI, 0.68-0.72]) was superior to that of both the baseline model (AUROC, 0.56 [95% CI, 0.53-0.58; P < .001) and SIRS (AUROC, 0.59 [95% CI, 0.57-0.62]; P < .001). Conclusions and Relevance: When assessed among hospitalized adults with suspected infection in 9 LMIC cohorts, the qSOFA score identified infected patients at risk of death beyond that explained by baseline factors. However, the predictive validity varied among cohorts and settings, and further research is needed to better understand potential generalizability.

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).

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.

Uncovering heterogeneity in sepsis: a comparative analysis of subphenotypes.

PURPOSE: The heterogeneity in sepsis is held responsible, in part, for the lack of precision treatment. Many attempts to identify subtypes of sepsis patients identify those with shared underlying biology or outcomes. To date, though, there has been limited effort to determine overlap across these previously identified subtypes. We aimed to determine the concordance of critically ill patients with sepsis classified by four previously described subtype strategies. METHODS: This secondary analysis of a multicenter prospective observational study included 522 critically ill patients with sepsis assigned to four previously established subtype strategies, primarily based on: (i) clinical data in the electronic health record (α, ß, γ, and δ), (ii) biomarker data (hyper- and hypoinflammatory), and (iii-iv) transcriptomic data (Mars1-Mars4 and SRS1-SRS2). Concordance was studied between different subtype labels, clinical characteristics, biological host response aberrations, as well as combinations of subtypes by sepsis ensembles. RESULTS: All four subtype labels could be adjudicated in this cohort, with the distribution of the clinical subtype varying most from the original cohort. The most common subtypes in each of the four strategies were γ (61%), which is higher compared to the original classification, hypoinflammatory (60%), Mars2 (35%), and SRS2 (54%). There was no clear relationship between any of the subtyping approaches (Cramer's V = 0.086-0.456). Mars2 and SRS1 were most alike in terms of host response biomarkers (p = 0.079-0.424), while other subtype strategies showed no clear relationship. Patients enriched for multiple subtypes revealed that characteristics and outcomes differ dependent on the combination of subtypes made. CONCLUSION: Among critically ill patients with sepsis, subtype strategies using clinical, biomarker, and transcriptomic data do not identify comparable patient populations and are likely to reflect disparate clinical characteristics and underlying biology.

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.

A sepsis early warning system is associated with improved patient outcomes.

In a real-world implementation of a machine-learning (ML)-based sepsis early warning system (EWS), Adams et al.1,2 found that timely provider response to an alert was associated with improved mortality, highlighting the potential utility of these systems in patient care.

Association Between Preexisting Heart Failure With Reduced Ejection Fraction and Fluid Administration Among Patients With Sepsis.

Importance: Intravenous fluid administration is recommended to improve outcomes for patients with septic shock. However, there are few data on fluid administration for patients with preexisting heart failure with reduced ejection fraction (HFrEF). Objective: To evaluate the association between preexisting HFrEF, guideline-recommended intravenous fluid resuscitation, and mortality among patients with community-acquired sepsis and septic shock. Design, Setting, and Participants: A cohort study was conducted of adult patients hospitalized in an integrated health care system from January 1, 2013, to December 31, 2015, with community-acquired sepsis and preexisting assessment of cardiac function. Follow-up occurred through July 1, 2016. Data analyses were performed from November 1, 2020, to August 8, 2022. Exposures: Preexisting heart failure with reduced ejection fraction (≤40%) measured by transthoracic echocardiogram within 1 year prior to hospitalization for sepsis. Main Outcomes and Measures: Multivariable models were adjusted for patient factors and sepsis severity and clustered at the hospital level to generate adjusted odds ratios (aORs) and 95% CIs. The primary outcome was the administration of 30 mL/kg of intravenous fluid within 6 hours of sepsis onset. Secondary outcomes included in-hospital mortality, intensive care unit admission, rate of invasive mechanical ventilation, and administration of vasoactive medications. Results: Of 5278 patients with sepsis (2673 men [51%]; median age, 70 years [IQR, 60-81 years]; 4349 White patients [82%]; median Sequential Organ Failure Assessment score, 4 [IQR, 3-5]), 884 (17%) had preexisting HFrEF, and 2291 (43%) met criteria for septic shock. Patients with septic shock and HFrEF were less likely to receive guideline-recommended intravenous fluid than those with septic shock without HFrEF (96 of 380 [25%] vs 699 of 1911 [37%]; P < .001), but in-hospital mortality was similar (47 of 380 [12%] vs 244 of 1911 [13%]; P = .83). In multivariable models, HFrEF was associated with a decreased risk-adjusted odds of receiving 30 mL/kg of intravenous fluid within the first 6 hours of sepsis onset (aOR, 0.63; 95% CI, 0.47-0.85; P = .002). The risk-adjusted mortality was not significantly different among patients with HFrEF (aOR, 0.92; 95% CI, 0.69-1.24; P = .59) compared with those without, and there was no interaction with intravenous fluid volume (aOR, 1.00; 95% CI, 0.98-1.03; P = .72). Conclusions and Relevance: The results of this cohort study of patients with community-acquired septic shock suggest that preexisting HFrEF was common and was associated with reduced odds of receiving guideline-recommended intravenous fluids.

Developing a shared sepsis data infrastructure: a systematic review and concept map to FHIR.

The development of a shared data infrastructure across health systems could improve research, clinical care, and health policy across a spectrum of diseases, including sepsis. Awareness of the potential value of such infrastructure has been heightened by COVID-19, as the lack of a real-time, interoperable data network impaired disease identification, mitigation, and eradication. The Sepsis on FHIR collaboration establishes a dynamic, federated, and interoperable system of sepsis data from 55 hospitals using 2 distinct inpatient electronic health record systems. Here we report on phase 1, a systematic review to identify clinical variables required to define sepsis and its subtypes to produce a concept mapping of elements onto Fast Healthcare Interoperability Resources (FHIR). Relevant papers described consensus sepsis definitions, provided criteria for sepsis, severe sepsis, septic shock, or detailed sepsis subtypes. Studies not written in English, published prior to 1970, or "grey" literature were prospectively excluded. We analyzed 55 manuscripts yielding 151 unique clinical variables. We then mapped variables to their corresponding US Core FHIR resources and specific code values. This work establishes the framework to develop a flexible infrastructure for sharing sepsis data, highlighting how FHIR could enable the extension of this approach to other important conditions relevant to public health.

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.

The comparative effectiveness of COVID-19 monoclonal antibodies: A learning health system randomized clinical trial.

BACKGROUND: Monoclonal antibodies (mAb) that neutralize SARS-CoV-2 decrease hospitalization and death compared to placebo in patients with mild to moderate COVID-19; however, comparative effectiveness is unknown. We report the comparative effectiveness of bamlanivimab, bamlanivimab-etesevimab, and casirivimab-imdevimab. METHODS: A learning health system platform trial in a U.S. health system enrolled patients meeting mAb Emergency Use Authorization criteria. An electronic health record-embedded application linked local mAb inventory to patient encounters and provided random mAb allocation. Primary outcome was hospital-free days to day 28. Primary analysis was a Bayesian model adjusting for treatment location, age, sex, and time. Inferiority was defined as 99% posterior probability of an odds ratio < 1. Equivalence was defined as 95% posterior probability the odds ratio is within a given bound. FINDINGS: Between March 10 and June 25, 2021, 1935 patients received treatment. Median hospital-free days were 28 (IQR 28, 28) for each mAb. Mortality was 0.8% (1/128), 0.8% (7/885), and 0.7% (6/922) for bamlanivimab, bamlanivimab-etesevimab, and casirivimab-imdevimab, respectively. Relative to casirivimab-imdevimab (n = 922), median adjusted odds ratios were 0.58 (95% credible interval [CI] 0.30-1.16) and 0.94 (95% CI 0.72-1.24) for bamlanivimab (n = 128) and bamlanivimab-etesevimab (n = 885), respectively. These odds ratios yielded 91% and 94% probabilities of inferiority of bamlanivimab versus bamlanivimab-etesevimab and casirivimab-imdevimab, and an 86% probability of equivalence between bamlanivimab-etesevimab and casirivimab-imdevimab. INTERPRETATION: Among patients with mild to moderate COVID-19, bamlanivimab-etesevimab or casirivimab-imdevimab treatment resulted in 86% probability of equivalence. No treatment met prespecified criteria for statistical equivalence. Median hospital-free days to day 28 were 28 (IQR 28, 28) for each mAb. FUNDING AND REGISTRATION: This work received no external funding. The U.S. government provided the reported mAb. This trial is registered at ClinicalTrials.gov, NCT04790786.