medRxiv : the preprint server for health sciences · 2026
Derivation and validation of clinical subphenotypes of adult sepsis in Africa: A latent class analysis of infection site and organ dysfunction.
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Gruccio Phoebe, Edwards Louisa, Kakande Reagan, Zulfiqar Muhammad, Ndyomugabe Michael, Adejayan Victor, Ojuman Philemon, Girard William S, Otoupalova Eva, Banguti Paulin R, Hopkinson Dennis, Syed Aamer, Mvukiyehe Jean Paul, Maro Venance P, Rubach Matthew P, Crump John A, Gwaikolo Cozie, Ssekitoleko Richard, Riviello Elisabeth D, Nuwagira Edwin, Moore Christopher C
Аннотация
OBJECTIVE: Sepsis is a heterogeneous syndrome characterized by substantial variation in infection source, organ dysfunction, and clinical outcomes, and a disproportionate burden in Africa. We aimed to derive and externally validate clinical subphenotypes among adults hospitalized with sepsis in Africa.
DESIGN: We used latent class analysis with infection source and organ dysfunction as model inputs. We labeled classes post hoc according to their predominant clinical features. We evaluated associations between subphenotypes and 30-day mortality using multivariable logistic regression.
SETTING: Hospitals in Uganda, Rwanda, Liberia, Tanzania, and Malawi.
PATIENTS: We pooled data from three prospective cohorts of adults hospitalized with sepsis in Uganda, Rwanda, and Liberia for model derivation and independent cohorts from Tanzania and Malawi for external validation.
INTERVENTIONS: None.
MEASUREMENTS AND MAIN RESULTS: The derivation dataset included 467 adults with sepsis, of whom 128 (27.4%) died within 30 days of admission. Latent class analysis identified four subphenotypes: Class 1 (heterogeneous; n=227; 30.8% mortality), Class 2 (abdominal; n=141; 31.9%), Class 3 (pulmonary; n=68; 16.2%), and Class 4 (soft tissue; n=31; 6.5%). Mortality differed significantly across the four classes (p=0.003). Compared with Class 1, Classes 3 (aOR 0.22, 95% CI 0.10-0.47) and 4 (aOR 0.14, 95% CI 0.02-0.52) had lower odds of 30-day mortality. External validation in 316 adults demonstrated high classification certainty (entropy=0.78; mean maximum posterior probabilities, 0.89-0.92), with similar mortality patterns across corresponding subphenotypes.
CONCLUSIONS: Latent class analysis identified reproducible clinical sepsis subphenotypes defined by infection source and organ dysfunction with distinct mortality risks. These externally validated subphenotypes may improve risk stratification and inform subphenotype-based clinical trials and treatment strategies in Africa.
KEY POINTS: Question: Can routinely available infection-source and organ-dysfunction data identify reproducible clinical sepsis subphenotypes with distinct mortality risks among adults hospitalized in Africa?Findings: In a pooled cohort study using latent class analysis, we identified four clinical sepsis subphenotypes among 467 adults and externally validated them in 316 adults from independent African cohorts. The subphenotypes had significantly different 30-day mortality, ranging from 6.5% to 31.9%, with similar mortality patterns in the validation cohorts.Meaning: Clinically identifiable sepsis subphenotypes based on routinely available data may provide a pragmatic framework for risk stratification and patient stratification in future sepsis trials in Africa.
DESIGN: We used latent class analysis with infection source and organ dysfunction as model inputs. We labeled classes post hoc according to their predominant clinical features. We evaluated associations between subphenotypes and 30-day mortality using multivariable logistic regression.
SETTING: Hospitals in Uganda, Rwanda, Liberia, Tanzania, and Malawi.
PATIENTS: We pooled data from three prospective cohorts of adults hospitalized with sepsis in Uganda, Rwanda, and Liberia for model derivation and independent cohorts from Tanzania and Malawi for external validation.
INTERVENTIONS: None.
MEASUREMENTS AND MAIN RESULTS: The derivation dataset included 467 adults with sepsis, of whom 128 (27.4%) died within 30 days of admission. Latent class analysis identified four subphenotypes: Class 1 (heterogeneous; n=227; 30.8% mortality), Class 2 (abdominal; n=141; 31.9%), Class 3 (pulmonary; n=68; 16.2%), and Class 4 (soft tissue; n=31; 6.5%). Mortality differed significantly across the four classes (p=0.003). Compared with Class 1, Classes 3 (aOR 0.22, 95% CI 0.10-0.47) and 4 (aOR 0.14, 95% CI 0.02-0.52) had lower odds of 30-day mortality. External validation in 316 adults demonstrated high classification certainty (entropy=0.78; mean maximum posterior probabilities, 0.89-0.92), with similar mortality patterns across corresponding subphenotypes.
CONCLUSIONS: Latent class analysis identified reproducible clinical sepsis subphenotypes defined by infection source and organ dysfunction with distinct mortality risks. These externally validated subphenotypes may improve risk stratification and inform subphenotype-based clinical trials and treatment strategies in Africa.
KEY POINTS: Question: Can routinely available infection-source and organ-dysfunction data identify reproducible clinical sepsis subphenotypes with distinct mortality risks among adults hospitalized in Africa?Findings: In a pooled cohort study using latent class analysis, we identified four clinical sepsis subphenotypes among 467 adults and externally validated them in 316 adults from independent African cohorts. The subphenotypes had significantly different 30-day mortality, ranging from 6.5% to 31.9%, with similar mortality patterns in the validation cohorts.Meaning: Clinically identifiable sepsis subphenotypes based on routinely available data may provide a pragmatic framework for risk stratification and patient stratification in future sepsis trials in Africa.