tetano
Editor, Senior Moderator
Ann Transl Med
. 2022 Feb;10(3):149.
doi: 10.21037/atm-21-4126.
A dynamic model for individualized prognosis prediction in patients with avian influenza A H7N9
Mingzhi Zhang[SUP] #[/SUP][SUP] 1 [/SUP], Ke Xu[SUP] #[/SUP][SUP] 2 [/SUP], Qigang Dai[SUP] #[/SUP][SUP] 2 [/SUP], Dongfang You[SUP] 1 3 [/SUP], Zhaolei Yu[SUP] 1 [/SUP], Changjun Bao[SUP] 2 [/SUP], Yang Zhao[SUP] 1 3 4 5 6 [/SUP]
Affiliations
Abstract
Background: Avian influenza A H7N9 progresses rapidly and has a high case fatality rate. However, few models are available to predict the survival of individual patients with H7N9 infection in real-time. This study set out to construct a dynamic model for individual prognosis prediction based on multiple longitudinal measurements taken during hospitalization.
Methods: The clinical and laboratory characteristics of 96 patients with H7N9 who were admitted to hospitals in Jiangsu between January 2016 and May 2017 were retrospectively investigated. A random forest model was applied to longitudinal data to select the biomarkers associated with prognostic outcome. Finally, a multivariate joint model was used to describe the time-varying effects of the biomarkers and calculate individual survival probabilities.
Results: The random forest selected a set of significant biomarkers that had the lowest classification error rates in the feature selection phase, including C-reactive protein (CRP), blood urea nitrogen (BUN), procalcitonin (PCT), base excess (BE), lymphocyte count (LYMPH), white blood cell count (WBC), and creatine phosphokinase (CPK). The multivariate joint model was used to describe the effects of these biomarkers and characterize the dynamic progression of the prognosis. Combined with the covariates, the joint model displayed a good performance in discriminating survival outcomes in patients within a fixed time window of 3 days. During hospitalization, the areas under the curve were stable at 0.75.
Conclusions: Our study has established a novel model that is able to identify significant indicators associated with the prognostic outcomes of patients with H7N9, characterize the time-to-event process, and predict individual-level daily survival probabilities after admission.
Keywords: H7N9 prognosis; biomarker; dynamic prediction; longitudinal data.
. 2022 Feb;10(3):149.
doi: 10.21037/atm-21-4126.
A dynamic model for individualized prognosis prediction in patients with avian influenza A H7N9
Mingzhi Zhang[SUP] #[/SUP][SUP] 1 [/SUP], Ke Xu[SUP] #[/SUP][SUP] 2 [/SUP], Qigang Dai[SUP] #[/SUP][SUP] 2 [/SUP], Dongfang You[SUP] 1 3 [/SUP], Zhaolei Yu[SUP] 1 [/SUP], Changjun Bao[SUP] 2 [/SUP], Yang Zhao[SUP] 1 3 4 5 6 [/SUP]
Affiliations
- PMID: 35284539
- PMCID: PMC8904989
- DOI: 10.21037/atm-21-4126
Abstract
Background: Avian influenza A H7N9 progresses rapidly and has a high case fatality rate. However, few models are available to predict the survival of individual patients with H7N9 infection in real-time. This study set out to construct a dynamic model for individual prognosis prediction based on multiple longitudinal measurements taken during hospitalization.
Methods: The clinical and laboratory characteristics of 96 patients with H7N9 who were admitted to hospitals in Jiangsu between January 2016 and May 2017 were retrospectively investigated. A random forest model was applied to longitudinal data to select the biomarkers associated with prognostic outcome. Finally, a multivariate joint model was used to describe the time-varying effects of the biomarkers and calculate individual survival probabilities.
Results: The random forest selected a set of significant biomarkers that had the lowest classification error rates in the feature selection phase, including C-reactive protein (CRP), blood urea nitrogen (BUN), procalcitonin (PCT), base excess (BE), lymphocyte count (LYMPH), white blood cell count (WBC), and creatine phosphokinase (CPK). The multivariate joint model was used to describe the effects of these biomarkers and characterize the dynamic progression of the prognosis. Combined with the covariates, the joint model displayed a good performance in discriminating survival outcomes in patients within a fixed time window of 3 days. During hospitalization, the areas under the curve were stable at 0.75.
Conclusions: Our study has established a novel model that is able to identify significant indicators associated with the prognostic outcomes of patients with H7N9, characterize the time-to-event process, and predict individual-level daily survival probabilities after admission.
Keywords: H7N9 prognosis; biomarker; dynamic prediction; longitudinal data.