Development of a nomogram to predict the incidence of acute kidney injury among ischemic stroke individuals during ICU hospitalization

Background: Limited clinical prediction models exist to assess the likelihood of acute kidney injury (AKI) occurrence in ischemic stroke individuals. In this retrospective study, our aim was to construct a nomogram that utilizes commonly available clinical features to predict the occurrence of AKI during intensive care unit hospitalization among this patient population. Methods: In this study, the MIMIC-IV database was utilized to investigate potential risk factors associated with the incidence of AKI among ischemic stroke individuals. A predictive nomogram was developed based on these identified risk factors. The discriminative performance of the constructed nomogram was assessed. Calibration analysis was utilized to evaluate the calibration performance of the constructed model, assessing the agreement between predicted probabilities and actual outcomes. Furthermore, decision curve analysis (DCA) was employed to assess the clinical net benefit, taking into account the potential risks and benefits associated with different decision thresholds. Results: A total of 2089 ischemic stroke individuals were included and randomly allocated into developing (n = 1452) and verification cohorts (n = 637). Risk factors for AKI incidence in ischemic stroke individuals, determined through LASSO and logistic regression. The constructed nomogram had good performance in predicting the occurrence of AKI among ischemic stroke patients and provided significant improvement compared to existing scoring systems. DCA demonstrated satisfactory clinical net benefit of the constructed nomogram in both the validation and development cohorts. Conclusions: The developed nomogram exhibits robust predictive performance in forecasting AKI occurrence in ischemic stroke individuals

Comparison of NRI and IDI among models predicting 28-day mortality.

Comparison of NRI and IDI among models predicting 28-day mortality.</p

Baseline clinical features of individuals afflicted with ischemic stroke.

Baseline clinical features of individuals afflicted with ischemic stroke.</p

C-index of nomogram and critical care scoring systems in 28-day mortality prediction in ischemic stroke patients.

C-index of nomogram and critical care scoring systems in 28-day mortality prediction in ischemic stroke patients.</p

The multivariable logistic regression analyses of independent risk factors for 28-day mortality in patients with ischemic stroke in the training cohort.

The multivariable logistic regression analyses of independent risk factors for 28-day mortality in patients with ischemic stroke in the training cohort.</p

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.</p

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).</p

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.</p

Schematic representation illustrating the patient selection process.

Schematic representation illustrating the patient selection process.</p