Nomograms predict prognosis and hospitalization time using non-contrast CT and CT perfusion in patients with ischemic stroke

Abstract

BackgroundStroke is a major disease with high morbidity and mortality worldwide. Currently, there is no quantitative method to evaluate the short-term prognosis and length of hospitalization of patients.PurposeWe aimed to develop nomograms as prognosis predictors based on imaging characteristics from non-contrast computed tomography (NCCT) and CT perfusion (CTP) and clinical characteristics for predicting activity of daily living (ADL) and hospitalization time of patients with ischemic stroke.Materials and methodsA total of 476 patients were enrolled in the study and divided into the training set (n = 381) and testing set (n = 95). Each of them owned NCCT and CTP images. We propose to extract imaging features representing as the Alberta stroke program early CT score (ASPECTS) values from NCCT, ischemic lesion volumes from CBF, and TMAX maps from CTP. Based on imaging features and clinical characteristics, we addressed two main issues: (1) predicting prognosis according to the Barthel index (BI)–binary logistic regression analysis was employed for feature selection, and the resulting nomogram was assessed in terms of discrimination capability, calibration, and clinical utility and (2) predicting the hospitalization time of patients–the Cox proportional hazard model was used for this purpose. After feature selection, another specific nomogram was established with calibration curves and time-dependent ROC curves for evaluation.ResultsIn the task of predicting binary prognosis outcome, a nomogram was constructed with the area under the curve (AUC) value of 0.883 (95% CI: 0.781–0.985), the accuracy of 0.853, and F1-scores of 0.909 in the testing set. We further tried to predict discharge BI into four classes. Similar performance was achieved as an AUC of 0.890 in the testing set. In the task of predicting hospitalization time, the Cox proportional hazard model was used. The concordance index of the model was 0.700 (SE = 0.019), and AUCs for predicting discharge at a specific week were higher than 0.80, which demonstrated the superior performance of the model.ConclusionThe novel non-invasive NCCT- and CTP-based nomograms could predict short-term ADL and hospitalization time of patients with ischemic stroke, thus allowing a personalized clinical outcome prediction and showing great potential in improving clinical efficiency.SummaryCombining NCCT- and CTP-based nomograms could accurately predict short-term outcomes of patients with ischemic stroke, including whose discharge BI and the length of hospital stay.Key ResultsUsing a large dataset of 1,310 patients, we show a novel nomogram with a good performance in predicting discharge BI class of patients (AUCs > 0.850). The second nomogram owns an excellent ability to predict the length of hospital stay (AUCs > 0.800).

From past to future: Bibliometric analysis of global research productivity on nomogram (2000–2021)

“…Over time, nomogram is no longer limited to the study of oncology, but more applied to the study of diagnosis and prediction of non-tumor-related diseases. For example, nomograms were used to analyze the correlation between factors in genomics or proteomics and the development and progression of diseases ( 32 – 34 ); Nomograms were used to predict adverse outcomes in obstetrics or reproductive medicine ( 35 – 38 ); it can also be combined with radiomics to analyze the relationship between different image features or image segmentation factors and disease diagnosis ( 39 , 40 ), predict chronic disease incidence in orthopedics ( 41 – 43 ), or analyze and detect the incidence of chronic diseases combined with metabolomics ( 37 ). In addition, the display modes of nomogram are gradually diversified.…”

Utilizing imaging parameters for functional outcome prediction in acute ischemic stroke: A machine learning study

“…Due to the complexity of the course of stroke patients and the bevy of information that stroke physicians need to account for when acutely determining treatment, the prognostic prediction of stroke patients has always been ripe for machine learning-based models. 14 There has been extensive research on the prediction of functional outcome in AIS patients who underwent mechanical thrombectomy. In a meta-analysis conducted by Zeng et al, conventional machine learning models had a pooled area under the receiver operating characteristics curves (AUROC) of 0.81, with a 95% confidence interval (CI) of 0.77-0.85 in predicting the functional outcome in stroke patients due to LVOs who underwent endovascular thrombectomy.…”

“…Due to the complexity of the course of stroke patients and the bevy of information that stroke physicians need to account for when acutely determining treatment, the prognostic prediction of stroke patients has always been ripe for machine learning‐based models 14 . There has been extensive research on the prediction of functional outcome in AIS patients who underwent mechanical thrombectomy.…”