Development and validation of a nomogram for predicting 28-day mortality in patients with ischemic stroke

Abstract

Background/aim: We aimed to construct a validated nomogram model for predicting short-term (28-day) ischemic stroke mortality among critically ill populations.

Materials and methods: We collected raw data from the Medical Information Mart for Intensive Care IV database, a comprehensive repository renowned for its depth and breadth in critical care information. Subsequently, a rigorous analytical framework was employed, incorporating a 10-fold cross-validation procedure to ensure robustness and reliability. Leveraging advanced statistical methodologies, specifically the least absolute shrinkage and selection operator regression, variables pertinent to 28-day mortality in ischemic stroke were meticulously screened. Next, binary logistic regression was utilized to establish nomogram, then applied concordance index to evaluate discrimination of the prediction models. Predictive performance of the nomogram was assessed by integrated discrimination improvement (IDI) and net reclassification index (NRI). Additionally, we generated calibration curves to assess calibrating ability. Finally, we evaluated the nomogram's net clinical benefit using decision curve analysis (DCA), in comparison with scoring systems clinically applied under common conditions.

Results: A total of 2089 individuals were identified and assigned into training (n = 1443) or validation (n = 646) cohorts. Various identified risk factors, including age, ethnicity, marital status, underlying metastatic solid tumor, Charlson comorbidity index, heart rate, Glasgow coma scale, glucose concentrations, white blood cells, sodium concentrations, potassium concentrations, mechanical ventilation, use of heparin and mannitol, were associated with short-term (28-day) mortality in ischemic stroke individuals. A concordance index of 0.834 was obtained in the training dataset, indicating that our nomogram had good discriminating ability. Results of IDI and NRI in both cohorts proved that our nomogram had positive improvement of predictive performance, compared to other scoring systems. The actual and predicted incidence of mortality showed favorable concordance on calibration curves (P > 0.05). DCA curves revealed that, compared with scoring systems clinically used under common conditions, the constructed nomogram yielded a greater net clinical benefit.

Conclusions: Utilizing a comprehensive array of fourteen readily accessible variables, a prognostic nomogram was meticulously formulated and rigorously validated to provide precise prognostication of short-term mortality within the ischemic stroke cohort.

Fig 1. Selection of clinical features by least absolute shrinkage and selection operator (LASSO) regression and 10-fold cross-validation.

(A) Visual plot of the relationship between coefficients for 117 features and the lambda. As lambda increased, the coefficient of each feature gradually tended to zero; (B) Curve of 10-fold cross-validation in the LASSO regression. The dotted vertical line on the left reflected the number of features and optimal log (lambda) corresponding to the smallest mean squared error (λ = 0.007716015). With one standard error criteria of optimal log (lambda), the dotted vertical line on the right reflected the model constructed with 23 variables was relatively accurate and simple (λ = 0.02838243). λ, lambda.

Fig 2. Nomogram for predicting 28-day mortality in patients with ischemic stroke.

The red dot represented the example of a patient. We present the case of a 48-year-old widowed patient of white ethnicity, with no prior history of metabolic solid tumors, who was admitted to the intensive care unit (ICU) with a Charlson comorbidity index of 2. The patient did not receive invasive mechanical ventilation, heparin, or mannitol on the first day of admission. Upon initial evaluation, the patient’s Glasgow Coma Scale (GCS) score was 14 minutes, and the fastest heart rate recorded was 90 beats/min. The patient’s minimum blood glucose level was 5.94mmol/L, while the highest white blood cell count was 9K/ul. The patient’s highest recorded blood potassium level was 4.1mmol/L, while the highest recorded blood sodium level was 136mmol/L. The sum (666) of these points was located on the total points line, and a solid red line was drawn down to the survival axis to determine the risk probability of 28-day mortality (1.25%). *: the min value of indicators on the firstday of ICU stay; **: the max value of indicators on the firstday of ICU stay.

Fig 3

Calibration curve of constructed nomogram in the training set (A) and validation set (B). The Receiver Operating Characteristic (ROC) curve analysis of the training set yielded an area under the curve (AUC) of 0.834, while the validation set exhibited an AUC of 0.839. The predicted and actual 28-day mortality was no statistical significance in both training and validation set (All P > 0.05; P = 0.902 in the training set and P = 0.467 in the validation set).

Fig 4

The decision curves analysis for constructed nomogram and models based on common clinical scoring systems in the training set (A) and validation set (B). The decision curves analysis demonstrates the performance of the constructed nomogram alongside models derived from conventional clinical scoring systems in both the training set (A) and validation set (B). The red line represents the outcomes of the constructed nomogram, indicating its superior performance. Notably, the utilization of the developed nomogram yields enhanced net benefits across a threshold probability range of 3% to 75%. GCS, Glasgow coma score; SOFA, sequential organ failure assessment; APS III, acute physiology score III; LODS, logistic organ dysfunction system; SAPS II, simplified acute physiology score II; OASIS, oxford acute severity of illness score.