Prediction of in-hospital hypokalemia using machine learning and first hospitalization day records in patients with traumatic brain injury

Machine learning algorithms identify hypokalaemia risk in people with hypertension in the United States National Health and Nutrition Examination Survey 1999-2018

BACKGROUND

Hypokalaemia is a side-effect of diuretics. We aimed to use machine learning to identify features predicting hypokalaemia risk in hypertensive patients.

METHODS

Participants with hypertension in the United States National Health and Nutrition Examination Survey 1999-2018 were included for analysis. To select the most suitable algorithm, we tested and evaluated five machine learning algorithms commonly employed in epidemiological studies: Logistic Regression, k-Nearest Neighbor, Random Forest, Recursive Partitioning and Regression Trees, and eXtreme Gradient Boosting. These algorithms were accessed using a set of 38 screened features. We then selected the key hypokalaemia-associated features in the hypertension group and their cardiovascular diseases (CVD) subgroup using the SHapley Additive exPlanations (SHAP) values. Using SHAP values, the key features and their impact pattern on hypokalaemia risk were determined.

RESULTS

A total of 25,326 hypertensive participants were included for analysis, of whom 4,511 had known CVD. The Random Forest algorithm had the highest AUROC (hypertension dataset: 0.73 [95%CI, 0.71-0.76]; CVD subgroup: 0.72 [95%CI, 0.66-0.78]). Moreover, the nomogram based on the top twelve key features screened by random forest retained good performance: age, sex, race, poverty income ratio, body mass index, systolic and diastolic blood pressure, non-potassium-sparing diuretics use and duration, renin-angiotensin blockers use and duration, and CVD history in hypertension dataset; while in CVD subgroup, the additional key features were comorbid diabetes, education level, smoking status, and use of bronchodilators.

CONCLUSION

Our predictive model based on the random forest algorithm performed best among the tested and evaluated five algorithms. Hypokalaemia-associated key features have been identified in hypertensive patients and the subgroup with CVD. These findings from machine learning facilitate the development of artificial intelligence to highlight hypokalaemia risk in hypertension patients.

Incidence, Risk Factors and Prognosis of Hypokalaemia in Patients with Normokalaemia at Hospital Admission

Background

Hypokalaemia (K+<3.5 mmol/L) is observed in 20% of hospitalised patients. Previous studies have often dealt with the symptoms, prevalence and risk factors in hospitalised patients. Very few studies have dealt with hospital-induced hypokalaemia. The aim was to determine the incidence, predisposing risk factors and prognosis of patients developing hypokalaemia after admission.

Materials and Methods

A prospective observational study was performed for two months. Patients with at least two potassium values after admission and normal K values at admission were considered for inclusion. Clinical features, diagnoses, laboratory reports and treatment details, including antibiotics, were noted.

Results

A total of 653 patients were studied; 138 (21.1%) developed hypokalaemia. Diabetes, ischaemic heart disease (IHD), heart failure, chronic kidney disease, hypertension, chronic liver disease and chronic obstructive pulmonary disease (COPD) were the most associated comorbidities. Urea, creatinine, transaminases and neutrophilia at admission differed significantly between those with and without hypokalaemia groups. Most patients developed mild hypokalaemia (78.2%). Hypokalaemia developed mostly on the second (22.4%) and third (24.6%) days of hospitalisation. Antibiotics were used in 60% of patients. The potassium values returned to normal within 2.5 ± 1.9 days. Three patients subsequently developed hyperkalaemia.

Conclusion

Patients admitted under general medicine mostly developed mild hypokalaemia, even if they had multiple risk factors for developing hypokalaemia. Inpatient hypokalaemia had an incidence of 21%. An overwhelming majority (~88%) had at least one risk factor. Hypokalaemia was not attributed to causing mortality in any patient.

Machine Learning Approach for the Prediction of In-Hospital Mortality in Traumatic Brain Injury Using Bio-Clinical Markers at Presentation to the Emergency Department

BACKGROUND

Accurate prediction of in-hospital mortality is essential for better management of patients with traumatic brain injury (TBI). Machine learning (ML) algorithms have been shown to be effective in predicting clinical outcomes. This study aimed to identify predictors of in-hospital mortality in TBI patients using ML algorithms.

MATERIALS AND METHOD

A retrospective study was performed using data from both the trauma registry and electronic medical records among TBI patients admitted to the Hamad Trauma Center in Qatar between June 2016 and May 2021. Thirteen features were selected for four ML models including a Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), and Extreme Gradient Boosting (XgBoost), to predict the in-hospital mortality.

RESULTS

A dataset of 922 patients was analyzed, of which 78% survived and 22% died. The AUC scores for SVM, LR, XgBoost, and RF models were 0.86, 0.84, 0.85, and 0.86, respectively. XgBoost and RF had good AUC scores but exhibited significant differences in log loss between the training and testing sets (% difference in logloss of 79.5 and 41.8, respectively), indicating overfitting compared to the other models. The feature importance trend across all models indicates that aPTT, INR, ISS, prothrombin time, and lactic acid are the most important features in prediction. Magnesium also displayed significant importance in the prediction of mortality among serum electrolytes.

CONCLUSIONS

SVM was found to be the best-performing ML model in predicting the mortality of TBI patients. It had the highest AUC score and did not show overfitting, making it a more reliable model compared to LR, XgBoost, and RF.

Prediction of Acid-Base and Potassium Imbalances in Intensive Care Patients Using Machine Learning Techniques

Acid–base disorders occur when the body’s normal pH is out of balance. They can be caused by problems with kidney or respiratory function or by an excess of acids or bases that the body cannot properly eliminate. Acid–base and potassium imbalances are mechanistically linked because acid–base imbalances can alter the transport of potassium. Both acid–base and potassium imbalances are common in critically ill patients. This study investigated machine learning models for predicting the occurrence of acid–base and potassium imbalances in intensive care patients. We used an institutional dataset of 1089 patients with 87 variables, including vital signs, general appearance, and laboratory results. Gradient boosting (GB) was able to predict nine clinical conditions related to acid–base and potassium imbalances: mortality (AUROC = 0.9822), hypocapnia (AUROC = 0.7524), hypercapnia (AUROC = 0.8228), hypokalemia (AUROC = 0.9191), hyperkalemia (AUROC = 0.9565), respiratory acidosis (AUROC = 0.8125), respiratory alkalosis (AUROC = 0.7685), metabolic acidosis (AUROC = 0.8682), and metabolic alkalosis (AUROC = 0.8284). Some predictions remained relatively robust even when the prediction window was increased. Additionally, the decision-making process was made more interpretable and transparent through the use of SHAP analysis. Overall, the results suggest that machine learning could be a useful tool to gain insight into the condition of intensive care patients and assist in the management of acid–base and potassium imbalances.