Highlight
– A simple algorithm using Charlson Comorbidity Index, hospital length of stay, and discharge destination can assign sepsis survivors into five distinct subtypes at discharge.
– In 1,368 survivors from the CLOVERS trial, subtype assignment strongly discriminated 3‑month mortality (overall 13.1%; subtype range 5.1%–45.5%) with age‑adjusted odds ratios >9 for two high‑risk subtypes versus the low‑risk group.
– Subtypes were also associated with 6‑ and 12‑month health‑related quality of life (EQ‑5D‑5L) and limitations in activities of daily living (ADLs), but not with readmission.
– Readily available discharge data could be used to prioritize survivors for intensified follow‑up, rehabilitation, and advance care planning.
Background
Sepsis remains a frequent cause of hospitalization worldwide and is increasingly recognized for its prolonged sequelae among survivors. Beyond short‑term mortality, survivors face higher risks of subsequent death, new functional disability, cognitive impairment, and reduced health‑related quality of life (HRQoL) for months to years after hospital discharge. Seminal work demonstrated substantial long‑term cognitive and functional impairment after severe sepsis, underpinning the concept of post‑sepsis morbidity as a public health problem that extends well beyond the index hospitalization (Iwashyna et al., JAMA 2010).
One practical challenge is identifying, at the time of hospital discharge, which survivors are at greatest risk for death or disability so that resources such as early outpatient follow‑up, rehabilitation, palliative care, and caregiver support can be targeted efficiently. Prior studies have used complex phenotyping or intensive data sets to risk‑stratify patients; simpler, validated approaches usable at discharge would be more amenable to clinical implementation.
Study design
This retrospective cohort study applied a previously derived survivor subtyping algorithm to participants enrolled in the CLOVERS trial (Crystalloid Liberal or Vasopressors Early Resuscitation in Sepsis), a multisite United States randomized trial that enrolled patients with sepsis‑induced hypotension. The current analysis included all CLOVERS participants who were alive at day 28 and had nonmissing data for the variables used to define subtypes: Charlson Comorbidity Index (CCI), hospital length of stay (LOS), and discharge destination.
At time of hospital discharge, each eligible participant (n = 1,368 of 1,563 enrolled) was retrospectively assigned to one of five previously derived sepsis survivor subtypes: low risk; healthy with severe disease; multimorbidity; low functional status; and unhealthy baseline with severe disease. The primary outcome was 3‑month mortality. Secondary outcomes included hospital readmission, physical function (ADL limitations), and HRQoL measured by the EuroQol 5‑D 5‑level instrument (EQ‑5D‑5L) through 12 months of follow‑up. Analyses included age‑adjusted logistic regression for the primary outcome and assessments of association between subtype and secondary outcomes over 6 and 12 months.
Key findings
Population and subtype assignment: Of 1,563 CLOVERS enrollees, 1,368 survivors met eligibility and were assigned a discharge subtype using the parsimonious algorithm. The cohort represents patients with sepsis‑induced hypotension—a higher‑acuity subgroup among sepsis admissions—who survived the early period (alive at day 28).
Primary outcome—3‑month mortality: Overall 3‑month mortality in the analytic cohort was 13.1%. Mortality varied markedly by subtype, ranging from 5.1% in the low‑risk subtype to 45.5% in the highest risk subtype (p < 0.001). In age‑adjusted logistic regression, the odds ratios (ORs) for 3‑month mortality compared with the low‑risk subtype were: 11.1 (95% CI not reported in the summary) for the low functional status subtype and 9.7 for the unhealthy baseline with severe illness subtype (both p < 0.001). These large effect sizes indicate a strong discriminatory ability of subtypes for short‑term post‑discharge mortality.
Secondary outcomes—functional outcomes and HRQoL: Subtype assignment was a significant predictor of EQ‑5D‑5L scores and ADL limitations at 6 and 12 months, indicating that discharge subtypes capture long‑term trajectories of disability and quality of life. The study found a consistent gradient across subtypes, with worse HRQoL and greater ADL dependence among multimorbidity, low functional status, and unhealthy baseline with severe disease subtypes.
Secondary outcome—readmission: By contrast, subtype did not predict hospital readmission during follow‑up. This dissociation suggests that the drivers of readmission are different from those that predict mortality and progressive disability (for example, acute care needs, procedural complications, or social determinants), or that readmission risk is influenced by system factors not captured in the subtype algorithm.
Statistical robustness: The strong association with mortality persisted after age adjustment and remained significant across multiple secondary functional endpoints. The use of routinely available variables (CCI, LOS, discharge destination) makes the algorithm pragmatic and easy to apply in clinical practice.
Interpretation and clinical implications
The study demonstrates that a simple, parsimonious subtype model, previously derived elsewhere, generalizes to an independent trial population and meaningfully stratifies risk for 3‑month mortality and 12‑month disability after sepsis. Key implications include:
1) Feasibility at discharge: The required inputs are typically documented in the electronic health record and do not require advanced analytics or biomarkers, allowing bedside or automated EHR‑based assignment before or at discharge.
2) Prioritization of post‑discharge care: High‑risk subtypes (low functional status, unhealthy baseline with severe illness, and multimorbidity) could be prioritized for early multidisciplinary follow‑up, structured rehabilitation, home health, palliative care consultation, or caregiver support interventions—services that are resource intensive and often limited.
3) Shared decision‑making and goals of care: For patients in the highest risk subtypes with very high early mortality and persistent disability, clinicians could use subtype information to frame realistic prognostic discussions and align discharge planning with patient values and goals.
4) Program evaluation and trial enrichment: Subtypes could be used for risk‑based stratification or enrichment in interventional trials aiming to improve post‑sepsis outcomes, enhancing efficiency by enrolling patients most likely to benefit.
Strengths
– External validation: Application in the CLOVERS trial—an independent multisite cohort—supports the generalizability of the subtype algorithm beyond its derivation cohort.
– Parsimony and practicality: The algorithm uses three widely available clinical variables, facilitating rapid implementation.
– Clinically meaningful outcomes: The study links subtypes to patient‑important endpoints including mortality, HRQoL, and ADL limitations out to 12 months.
Limitations
– Cohort composition and selection: Participants were survivors of sepsis‑induced hypotension enrolled in a randomized trial. Trial participants may differ from broader sepsis populations (for example, selection bias toward patients meeting trial inclusion criteria), and only patients alive at day 28 were included, excluding early post‑discharge deaths.
– Retrospective subtype assignment: Although the algorithm is intended for discharge use, retrospective assignment may differ from prospective workflow implementation and could be affected by missing data or documentation inconsistencies.
– Lack of granular confounder control: The reported analyses adjusted for age but residual confounding (severity of illness metrics, socioeconomic factors, pre‑existing disability not captured by CCI) could influence associations.
– Readmission outcome complexity: The absence of association with readmission highlights that readmission may be influenced by nonclinical factors (access to outpatient care, social supports) not captured by the subtyping variables.
– Unreported confidence intervals and calibration metrics in the summary provided: full appraisal requires the complete article and supplementary data (e.g., discrimination and calibration statistics such as AUC or calibration plots).
Expert commentary and mechanistic perspective
Sepsis survivors manifest heterogenous trajectories driven by pre‑existing comorbidity, baseline functional reserve, and the physiologic impact of the acute illness. The three variables used in the subtype algorithm—comorbidity burden (CCI), prolonged hospitalization (LOS), and discharge destination—are plausibly proxies for biologic reserve, cumulative organ dysfunction and deconditioning, and social/functional supports at discharge, respectively. That these readily available surrogates discriminate mortality and disability supports the concept that baseline vulnerability combined with acute illness severity drives long‑term outcomes more than any single physiologic marker.
Clinical leaders and guideline committees have emphasized the need for improved post‑ICU and post‑sepsis care pathways (Needham et al., Crit Care Med 2012). The present study adds an implementable risk‑stratification tool to support that call to action; however, prospective implementation research is needed to determine whether subtype‑guided interventions actually improve outcomes and are cost‑effective.
Next steps and research gaps
– Prospective validation: Apply the algorithm prospectively at discharge in diverse care settings (community hospitals, academic centers, non‑trial populations) and across sepsis phenotypes.
– Implementation trials: Randomized studies testing subtype‑guided interventions (e.g., prioritized early outpatient follow‑up, tailored rehabilitation, home health bundles, or palliative care integration) versus usual care to demonstrate benefit.
– Integration with EHR tools: Develop and test automated EHR algorithms to assign subtypes and trigger care pathways, ensuring usability and minimal alert fatigue.
– Health equity evaluation: Assess whether subtype assignment and downstream care exacerbate or reduce disparities, given associations between discharge destination and social determinants of health.
– Mechanistic work: Link subtype trajectories to biomarkers of inflammation, frailty indices, and longitudinal physiologic measures to refine biological understanding.
Conclusion
Flick and colleagues demonstrate that sepsis survivor subtypes defined by three readily available discharge variables replicate predictive associations with 3‑month mortality and 12‑month disability in a multisite trial cohort. The approach is pragmatic and scalable, offering a potential tool to prioritize limited post‑discharge resources and to inform prognostic conversations. Prospective implementation and interventional studies are needed to confirm that subtype‑guided care improves patient‑centered outcomes.
Funding and trial registry
The analyzed cohort derives from the CLOVERS randomized trial (Crystalloid Liberal or Vasopressors Early Resuscitation in Sepsis). Funding statements and trial registration details are reported in the original CLOVERS publications and the primary study by Flick et al. (Crit Care Med 2025). Readers should consult the original article for explicit funding sources and trial registry identifiers.
References
Flick RJ, Kamphuis LA, Valley TS, Armstrong‑Hough M, Iwashyna TJ. Association of Sepsis Survivor Subtypes With Long‑Term Mortality and Disability After Discharge: A Retrospective Cohort Study. Crit Care Med. 2025 Nov 13. doi: 10.1097/CCM.0000000000006933. Epub ahead of print. PMID: 41231072.
Iwashyna TJ, Ely EW, Smith DM, Langa KM. Long‑term cognitive impairment and functional disability among survivors of severe sepsis. JAMA. 2010 Oct 6;304(16):1787‑1794. doi:10.1001/jama.2010.1553. PMID: 20921359.
Needham DM, Davidson J, Cohen H, Hopkins RO, Weinert C, Wunsch H, Zawistowski C, Bemis‑Dougherty A, Berney S, Bienvenu OJ, et al. Improving long‑term outcomes after discharge from intensive care unit: report from a stakeholders’ conference. Crit Care Med. 2012 Feb;40(2):502‑9. doi:10.1097/CCM.0b013e318232da75. PMID: 22226949.
Singer M, Deutschman CS, Seymour CW, Shankar‑Hari M, Annane D, Bauer M, Bellomo R, Bernard GR, Chiche JD, Coopersmith CM, et al.; The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis‑3). JAMA. 2016 Feb 23;315(8):801‑10. doi:10.1001/jama.2016.0287. PMID: 26903338.
Note: This article summarizes and interprets findings reported by Flick et al. (2025). Clinicians should consult the full peer‑reviewed publication for detailed methods, supplementary analyses, and complete statistical reporting prior to implementation.