Serial platelet count as a dynamic prediction marker of hospital mortality among septic patients

Qian Ye; Xuan Wang; Xiaoshuang Xu; Jiajin Chen; David C Christiani; Feng Chen; Ruyang Zhang; Yongyue Wei

doi:10.1093/burnst/tkae016

Serial platelet count as a dynamic prediction marker of hospital mortality among septic patients

Burns Trauma. 2024 Jun 15:12:tkae016. doi: 10.1093/burnst/tkae016. eCollection 2024.

Authors

Qian Ye¹, Xuan Wang¹, Xiaoshuang Xu¹, Jiajin Chen¹, David C Christiani^{2

3}, Feng Chen^{1

4

5}, Ruyang Zhang¹, Yongyue Wei^{1

6}

Affiliations

¹ Department of Biostatistics, School of Public Health, Nanjing Medical University, 101 Longmian Avenue, Nanjing, Jiangsu 211166, China.
² Department of Environmental Health, Harvard T.H. Chan School of Public Health, Harvard University, 655 Huntington Avenue, Boston, MA 02115, USA.
³ Pulmonary and Critical Care Division, Massachusetts General Hospital, Department of Medicine, Harvard Medical School, 55 Fruit Street, Boston, MA 02114, USA.
⁴ Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Jiangsu Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, 101 Longmian Avenue, Nanjing, Jiangsu 211166, China.
⁵ China International Cooperation Center of Environment and Human Health, Nanjing Medical University, 101 Longmian Avenue, Nanjing, Jiangsu 211166, China.
⁶ Center for Public Health and Epidemic Preparedness & Response, Peking University, Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, 38 Xueyuan Road, Haidian District, Beijing 100191, China.

Abstract

Background: Platelets play a critical role in hemostasis and inflammatory diseases. Low platelet count and activity have been reported to be associated with unfavorable prognosis. This study aims to explore the relationship between dynamics in platelet count and in-hospital morality among septic patients and to provide real-time updates on mortality risk to achieve dynamic prediction.

Methods: We conducted a multi-cohort, retrospective, observational study that encompasses data on septic patients in the eICU Collaborative Research Database (eICU-CRD) and the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. The joint latent class model (JLCM) was utilized to identify heterogenous platelet count trajectories over time among septic patients. We assessed the association between different trajectory patterns and 28-day in-hospital mortality using a piecewise Cox hazard model within each trajectory. We evaluated the performance of our dynamic prediction model through area under the receiver operating characteristic curve, concordance index (C-index), accuracy, sensitivity, and specificity calculated at predefined time points.

Results: Four subgroups of platelet count trajectories were identified that correspond to distinct in-hospital mortality risk. Including platelet count did not significantly enhance prediction accuracy at early stages (day 1 C-index_Dynamic vs C-index_Weibull: 0.713 vs 0.714). However, our model showed superior performance to the static survival model over time (day 14 C-index_Dynamic vs C-index_Weibull: 0.644 vs 0.617).

Conclusions: For septic patients in an intensive care unit, the rapid decline in platelet counts is a critical prognostic factor, and serial platelet measures are associated with prognosis.

Keywords: Dynamic prediction; Intensive care medicine; Joint latent class model; Platelet count trajectory; Sepsis.