Background: The number of patients with thrombocytopenia (TCP) is relatively high in intensive care units (ICUs). It is therefore necessary to evaluate the prognostic risk of such patients.
Aim: This study investigated the risk factors affecting the survival of patients with TCP in the ICU. Using the findings of this investigation, we developed and validated a risk prediction model.
Methods: We evaluated patients admitted to the ICU who presented with TCP. We used LASSO regression to identify important clinical indicators. Based on these indicators, we developed a prediction model complete with a nomogram for the development cohort set. We then evaluated the mode’s accuracy using a receiver operating characteristic (ROC) curve, calibration curves, and decision curve analysis (DCA) in a validation cohort.
Results: A total of 141 cases of ICU TCP were included in the sample, of which 47 involved death of the patient. Clinical results were as follows: N (HR 0.91, 95% CI 0.86– 0.97, P =0.003); TBIL (HR 1.98, 95% CI 1.02– 1.99, P =0.048); APACHE II (HR 1.94, 95% CI 1.39, 2.48, P =0.045); WPRN (HR 6.22, 95% CI 2.86– 13.53, P < 0.001); WTOST (HR 0.56, 95% CI 0.21– 1.46, P < 0.001); and DMV [HR1.87, 95% CI 1.12– 2.33]. The prediction model yielded an area under the curve (AUC) of 0.918 (95% CI 0.863– 0.974) in the development cohort and 0.926 (95% CI 0.849– 0.994) in the validation cohort. Application of the nomogram in the validation cohort gave good discrimination (C-index 0.853, 95% CI 0.810– 0.922) and good calibration. DCA indicated that the nomogram was clinically useful.
Conclusion: The individualized nomogram developed through our analysis demonstrated effective prognostic prediction for patients with TCP in ICUs. Use of this prediction metric may reduce TCP-related morbidity and mortality in ICUs.
Keywords: thrombocytopenia, intensive care unit, nomogram, prediction model