Daily Sepsis Research Analysis

OBJECTIVES: Sepsis trials likely include patients who vary in response to therapeutic interventions. The optimal approach to identify such differences in treatment response remains unclear. Estimating individualized absolute risk differences (iARDs) to model treatment response at an individual patient level using supervised effect models applied to randomized trial data may be informative. We explored the relationship between two subgrouping approaches and a recently published iARD model for the effect of early goal-directed therapy (EGDT) resuscitation in sepsis. DESIGN: Secondary analysis of the Protocolized Care for Early Septic Shock (ProCESS) and Australasian Resuscitation in Sepsis Evaluation (ARISE) trials. We applied clinical subtypes (α, β, γ, δ) to 829 ProCESS and 1588 ARISE patients and biologic "hyperinflammatory" and "nonhyperinflammatory" subphenotypes to 363 ProCESS patients with biomarker data using established methods. We predicted iARDs with supervised learning using clinical variables as predictors and 90-day mortality as the primary outcome. We evaluated iARD variability within subgroups. SETTING: Eighty-one sites worldwide. PATIENTS/SUBJECTS: Adults with septic shock. INTERVENTIONS: EGDT or usual care. MEASUREMENTS AND MAIN RESULTS: The average treatment effect of EGDT appeared to vary within both clinical and biologic subphenotypes. EGDT appeared potentially beneficial in the β and nonhyperinflammatory subphenotypes but harmful in the γ and hyperinflammatory subphenotypes. However, the predicted iARDs within each subgroup ranged from considerable harm to considerable benefit. For example, for the β-subtype, the average mortality reduction from EGDT was 8.5% (95% CI, -0.4 to 17.5), but the iARDs ranged from a 29% increase to a 16% reduction in mortality, with 39% of patients predicted to be harmed. CONCLUSIONS: Although both clinical and biologic phenotyping may identify subgroups whose average treatment effect is beneficial or harmful, individual risks and benefits within subgroups still vary dramatically, raising concern that phenotyping may not reliably or safely personalize sepsis care.

BACKGROUND: The M1UK sublineage of Streptococcus pyogenes has driven recent post-pandemic surges in invasive group A streptococcal (iGAS) disease. We assessed case fatality rate (CFR) among patients with emm1 iGAS in England and Wales, and then compared outcomes associated with M1UK and ancestral emm1 lineages. METHODS: We linked emm1 iGAS cases (December 2009-July 2022) with demographic and mortality records. Lineage was determined for isolates collected in 2010, 2013-2016, and 2020 via whole-genome sequencing or allele-specific PCR. Seven- and 30-day all-cause CFRs were estimated. Univariate and multivariate models assessed the association between lineage and risk of death. RESULTS: Among 4952 emm1 iGAS cases, lineage was assigned to 1356. The 30-day CFR was 24.4% for M1UK, 22.3% for M1global, 10.5% for M123SNP, and 10.3% for M113SNP. After adjustment for age and sex, lineage was not a significant predictor of 7- or 30-day mortality. Survival analysis showed rapid progression to death in both M1UK and M1global cases: 63.7% of deaths occurred within 1 day of diagnostic sampling. Among children under 15 years, 56.3% of fatal cases died before sampling, and 95.6% within 1 day of sampling. CONCLUSIONS: Mortality did not differ significantly between M1UK and M1global lineages, but more studies are required. Overall mortality from emm1 S. pyogenes remains strikingly high. The rapid time to death underscores the need for preventive measures and rapid diagnostic tools that act prior to culture-based confirmation, and highlights challenges for clinical trial design in iGAS.

BACKGROUND: The Australian Commission on Safety and Quality in Healthcare developed a list of sixteen potentially preventable Hospital-Acquired Complications (HACs) and an algorithm using International Classification of Disease (ICD) codes to detect them. We evaluated this algorithm's performance for diagnosing hospital-onset bloodstream infections (HO-BSI). METHODS: Administrative records were extracted for episodes of admitted patient care from July 2016 to June 2017 at five Australian principal referral hospitals. We applied the BSI HAC algorithm to each episode, then randomly selected 50 patients deemed positive and negative for HO-BSI at each site. Reviewers blinded to HAC status applied the reference surveillance definition for HO-BSI. The positive predictive value (PPV) and negative predictive value (NPV) for the BSI HAC were calculated. We explored changes to the HAC algorithm to improve these metrics. RESULTS: Overall, 352 917 episodes were included; median (IQR) age was 54 (33-71) years, 49.6 % were female, and 43.8 % were elective admissions. Of these, 2229 (0.6 %) had a HO-BSI according to the HAC algorithm. Among manually reviewed episodes, the PPV for the HAC algorithm was 0.28 (95 % CI, 0.23-0.34) and the NPV was 1.00 (95 % CI, 0.98-1.00). The codes 'Sepsis, unspecified' and 'bacterial sepsis of newborn, unspecified' were both common triggers for HO-BSI HACs (accounting for 35.8 % and 18.4 % of HO-BSIs, respectively) and had poor PPV (0.06 and 0.03, respectively). Removal of these codes from the algorithm increased PPV to 0.53 (0.45-0.62). CONCLUSION: The HAC algorithm had sub-optimal PPV for HO-BSI. This performance was improved by removing the 'unspecified sepsis' ICD codes.