Prognostic Accuracy of Presepsis and Intrasepsis Characteristics for Prediction of Cardiovascular Events after a Sepsis Hospitalization

OBJECTIVES: Sepsis survivors face increased risk for cardiovascular complications; however, the contribution of intrasepsis events to cardiovascular risk profiles is unclear. SETTING: Kaiser Permanente Northern California (KPNC) and Intermountain Healthcare (IH) integrated healthcare delivery systems. SUBJECTS: Sepsis survivors (2011–2017 [KPNC] and 2018–2020 [IH]) greater than or equal to 40 years old without prior cardiovascular disease. DESIGN: Data across KPNC and IH were harmonized and grouped into presepsis (demographics, atherosclerotic cardiovascular disease scores, comorbidities) or intrasepsis factors (e.g., laboratory values, vital signs, organ support, infection source) with random split for training/internal validation datasets (75%/25%) within KPNC and IH. Models were bidirectionally, externally validated between healthcare systems. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Changes to predictive accuracy (C-statistic) of cause-specific proportional hazards models predicting 1-year cardiovascular outcomes (atherosclerotic cardiovascular disease, heart failure, and atrial fibrillation events) were compared between models that did and did not contain intrasepsis factors. Among 39,590 KPNC and 16,388 IH sepsis survivors, 3,503 (8.8%) at Kaiser Permanente (KP) and 600 (3.7%) at IH experienced a cardiovascular event within 1-year after hospital discharge, including 996 (2.5%) at KP and 192 (1.2%) IH with an atherosclerotic event first, 564 (1.4%) at KP and 117 (0.7%) IH with a heart failure event, 2,310 (5.8%) at KP and 371 (2.3%) with an atrial fibrillation event. Death within 1 year after sepsis occurred for 7,948 (20%) KP and 2,085 (12.7%) IH patients. Combined models with presepsis and intrasepsis factors had better discrimination for cardiovascular events (KPNC C-statistic 0.783 [95% CI, 0.766–0.799]; IH 0.763 [0.726–0.801]) as compared with presepsis cardiovascular risk alone (KPNC: 0.666 [0.648–0.683], IH 0.660 [0.619–0.702]) during internal validation. External validation of models across healthcare systems showed similar performance (KPNC model within IH data C-statistic: 0.734 [0.725–0.744]; IH model within KPNC data: 0.787 [0.768–0.805]). CONCLUSIONS: Across two large healthcare systems, intrasepsis factors improved postsepsis cardiovascular risk prediction as compared with presepsis cardiovascular risk profiles. Further exploration of sepsis factors that contribute to postsepsis cardiovascular events is warranted for improved mechanistic and predictive models.