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, β, 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 β 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 β 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.
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.
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, β, 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.
55,169 hospitalizations from 155 hospitals were included (34% α, 30% β, 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).
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.
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.
Cardiac Function Before Sepsis and Clinical Outcomes Iyer, Stuthi; Kennedy, Jason N; Jentzer, Jacob C ...
JAMA : the journal of the American Medical Association,
05/2024, Letnik:
331, Številka:
17
Journal Article
Recenzirano
This cohort study characterizes heterogeneity in cardiac function prior to sepsis and describes associations with hospitalization outcomes and mortality.
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.
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.
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).
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 inpatient opioid use and their associations with postdischarge opioid use are poorly understood.
To measure patterns in timing, duration, and setting of opioid administration in ...opioid-naive hospitalized patients and to examine associations with postdischarge use.
Retrospective cohort study using electronic health record data from 2010 to 2014.
12 community and academic hospitals in Pennsylvania.
148 068 opioid-naive patients (191 249 admissions) with at least 1 outpatient encounter within 12 months before and after admission.
Number of days and patterns of inpatient opioid use; any outpatient use (self-report and/or prescription orders) 90 and 365 days after discharge.
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.
Potential unmeasured confounders related to opioid use.
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.
UPMC Health System and University of Pittsburgh.
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.
Because ischemic end-organ damage and endothelial dysfunction may contribute to differences in sepsis 2, we hypothesize that treatment-response subtypes may be partially explained by differential ...activation of hypoxia-mediated pathways. Multiple studies show that circulating microRNAs (miRNAs) are candidate biomarkers for acute illness, but research in sepsis is limited 3. The data were obtained under a waiver of informed consent and with authorization under the Health Insurance Portability and Accountability Act.
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.
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.
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).
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.
Display omitted
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.
PDF
