Prediction of periventricular leukomalacia. Part II: Selection of hemodynamic features using computational intelligence

Monitoring of cerebral blood flow autoregulation: physiologic basis, measurement, and clinical implications

Cerebral blood flow (CBF) autoregulation is the physiologic process whereby blood supply to the brain is kept constant over a range of cerebral perfusion pressures ensuring a constant supply of metabolic substrate. Clinical methods for monitoring CBF autoregulation were first developed for neurocritically ill patients and have been extended to surgical patients. These methods are based on measuring the relationship between cerebral perfusion pressure and surrogates of CBF or cerebral blood volume (CBV) at low frequencies (<0.05 Hz) of autoregulation using time or frequency domain analyses. Initially intracranial pressure monitoring or transcranial Doppler assessment of CBF velocity was utilised relative to changes in cerebral perfusion pressure or mean arterial pressure. A more clinically practical approach utilising filtered signals from near infrared spectroscopy monitors as an estimate of CBF has been validated. In contrast to the traditional teaching that 50 mm Hg is the autoregulation threshold, these investigations have found wide interindividual variability of the lower limit of autoregulation ranging from 40 to 90 mm Hg in adults and 20-55 mm Hg in children. Observational data have linked impaired CBF autoregulation metrics to adverse outcomes in patients with traumatic brain injury, ischaemic stroke, subarachnoid haemorrhage, intracerebral haemorrhage, and in surgical patients. CBF autoregulation monitoring has been described in both cardiac and noncardiac surgery. Data from a single-centre randomised study in adults found that targeting arterial pressure during cardiopulmonary bypass to above the lower limit of autoregulation led to a reduction of postoperative delirium and improved memory 1 month after surgery compared with usual care. Together, the growing body of evidence suggests that monitoring CBF autoregulation provides prognostic information on eventual patient outcomes and offers potential for therapeutic intervention. For surgical patients, personalised blood pressure management based on CBF autoregulation data holds promise as a strategy to improve patient neurocognitive outcomes.

A machine learning approach to predict quality of life changes in patients with Parkinson's Disease

OBJECTIVE

Parkinson disease (PD) is a progressive neurodegenerative disorder with an annual incidence of approximately 0.1%. While primarily considered a motor disorder, increasing emphasis is being placed on its non-motor features. Both manifestations of the disease affect quality of life (QoL), which is captured in part II of the Unified Parkinson's Disease Rating Scale (UPDRS-II). While useful in the management of patients, it remains challenging to predict how QoL will change over time in PD. The goal of this work is to explore the feasibility of a machine learning algorithm to predict QoL changes in PD patients.

METHODS

In this retrospective cohort study, patients with at least 12 months of follow-up were identified from the Parkinson's Progression Markers Initiative database (N = 630) and divided into two groups: those with and without clinically significant worsening in UPDRS-II (n = 404 and n = 226, respectively). We developed an artificial neural network using only UPDRS-II scores, to predict whether a patient would clinically worsen or not at 12 months from follow-up.

RESULTS

Using UPDRS-II at baseline, at 2 months, and at 4 months, the algorithm achieved 90% specificity and 56% sensitivity.

INTERPRETATION

A learning model has the potential to rule in patients who may exhibit clinically significant worsening in QoL at 12 months. These patients may require further testing and increased focus.

A Novel Embedded Feature Selection and Dimensionality Reduction Method for an SVM Type Classifier to Predict Periventricular Leukomalacia (PVL) in Neonates

This paper is concerned with the prediction of the occurrence of periventricular leukomalacia (PVL) in neonates after heart surgery. Our prior work shows that the Support Vector Machine (SVM) classifier can be a powerful tool in predicting clinical outcomes of such complicated and uncommon diseases, even when the number of data samples is low. In the presented work, we first illustrate and discuss the shortcomings of the traditional automatic machine learning (aML) approach. Consequently, we describe our methodology for addressing these shortcomings, while utilizing the designed interactive ML (iML) algorithm. Finally, we conclude with a discussion of the developed method and the results obtained. In sum, by adding an additional (Genetic Algorithm) optimization step in the SVM learning framework, we were able to (a) reduce the dimensionality of an SVM model from 248 to 53 features, (b) increase generalization that was confirmed by a 100% accuracy assessed on an unseen testing set, and (c) improve the overall SVM model's performance from 65% to 100% testing accuracy, utilizing the proposed iML method.

Transposition of the Great Arteries and Common Variants

OBJECTIVES

The objectives of this review are to discuss the physiology, perioperative management, surgical correction, and outcomes of infants with transposition of the great arteries and common variants undergoing the arterial switch operation.

DATA SOURCE

MEDLINE and PubMed.

CONCLUSION

The widespread adoption of the arterial switch operation for transposition of great arteries has been one of the more gratifying advances in pediatric cardiovascular care, and represents the simultaneous improvements in diagnostics, surgical and bypass techniques, anesthesia in the neonate, improvements in intensive care technology, nursing strategies, and system-wide care delivery. Many of the strategies adopted for the neonate with transposition of the great arteries have been translated to neonatal care for other congenital heart lesions. Continued work is necessary to investigate the effects of perioperative care on long-term neurodevelopmental outcomes, as well as collaboration between centers to spread "best practices" for outcome, cost, and morbidity reduction.

Neurodevelopmental Outcomes in Children With Congenital Heart Disease-What Can We Impact?

OBJECTIVES

The objectives of this review are to discuss the scope of neurologic injuries in newborns with congenital heart disease, the mechanisms of injury, including prenatal, pre-, intra-, and postoperative factors, neurodevelopmental outcomes, and therapeutic strategies for the timely intervention and prevention of neurologic injury.

DATA SOURCE

MEDLINE and PubMed.

CONCLUSION

At the current time, important research is underway to 1) better understand the developing brain in the fetus with complex congenital heart disease, 2) to identify modifiable risk factors in the operating room and ICU to maximize long-term neurodevelopmental outcomes, and 3) develop strategies to improve family psychosocial health, childhood development, and health-related quality of life following hospital discharge. Crucial in this effort is the identification of an early postoperative surrogate variable with good predictive validity for long-term outcomes. If an appropriate surrogate variable for long-term outcomes can be identified, and measured relatively early after surgical intervention for complex congenital heart disease, reliable clinical trials can be undertaken to improve upon current outcomes.

Application of Mathematical Modeling for Simulation and Analysis of Hypoplastic Left Heart Syndrome (HLHS) in Pre- and Postsurgery Conditions

This paper is concerned with the mathematical modeling of a severe and common congenital defect called hypoplastic left heart syndrome (HLHS). Surgical approaches are utilized for palliating this heart condition; however, a brain white matter injury called periventricular leukomalacia (PVL) occurs with high prevalence at or around the time of surgery, the exact cause of which is not known presently. Our main goal in this paper is to study the hemodynamic conditions under which HLHS physiology may lead to the occurrence of PVL. A lumped parameter model of the HLHS circulation has been developed integrating diffusion modeling of oxygen and carbon dioxide concentrations in order to study hemodynamic variables such as pressure, flow, and blood gas concentration. Results presented include calculations of blood pressures and flow rates in different parts of the circulation. Simulations also show changes in the ratio of pulmonary to systemic blood flow rates when the sizes of the patent ductus arteriosus and atrial septal defect are varied. These changes lead to unbalanced blood circulations and, when combined with low oxygen and carbon dioxide concentrations in arteries, result in poor oxygen delivery to the brain. We stipulate that PVL occurs as a consequence.

A retrospective analysis to identify the factors affecting infection in patients undergoing chemotherapy

PURPOSE

This study compares the performance of the logistic regression and decision tree analysis methods for assessing the risk factors for infection in cancer patients undergoing chemotherapy.

METHOD

The subjects were 732 cancer patients who were receiving chemotherapy at K university hospital in Seoul, Korea. The data were collected between March 2011 and February 2013 and were processed for descriptive analysis, logistic regression and decision tree analysis using the IBM SPSS Statistics 19 and Modeler 15.1 programs.

RESULTS

The most common risk factors for infection in cancer patients receiving chemotherapy were identified as alkylating agents, vinca alkaloid and underlying diabetes mellitus. The logistic regression explained 66.7% of the variation in the data in terms of sensitivity and 88.9% in terms of specificity. The decision tree analysis accounted for 55.0% of the variation in the data in terms of sensitivity and 89.0% in terms of specificity. As for the overall classification accuracy, the logistic regression explained 88.0% and the decision tree analysis explained 87.2%.

CONCLUSIONS

The logistic regression analysis showed a higher degree of sensitivity and classification accuracy. Therefore, logistic regression analysis is concluded to be the more effective and useful method for establishing an infection prediction model for patients undergoing chemotherapy.

D-transposition of the great arteries: the current era of the arterial switch operation

This paper aims to update clinicians on "hot topics" in the management of patients with D-loop transposition of the great arteries (D-TGA) in the current surgical era. The arterial switch operation (ASO) has replaced atrial switch procedures for D-TGA, and 90% of patients now reach adulthood. The Adult Congenital and Pediatric Cardiology Council of the American College of Cardiology assembled a team of experts to summarize current knowledge on genetics, pre-natal diagnosis, surgical timing, balloon atrial septostomy, prostaglandin E1 therapy, intraoperative techniques, imaging, coronary obstruction, arrhythmias, sudden death, neoaortic regurgitation and dilation, neurodevelopmental (ND) issues, and lifelong care of D-TGA patients. In simple D-TGA: 1) familial recurrence risk is low; 2) children diagnosed pre-natally have improved cognitive skills compared with those diagnosed post-natally; 3) echocardiography helps to identify risk factors; 4) routine use of BAS and prostaglandin E1 may not be indicated in all cases; 5) early ASO improves outcomes and reduces costs with a low mortality; 6) single or intramural coronary arteries remain risk factors; 7) post-ASO arrhythmias and cardiac dysfunction should raise suspicion of coronary insufficiency; 8) coronary insufficiency and arrhythmias are rare but are associated with sudden death; 9) early- and late-onset ND abnormalities are common; 10) aortic regurgitation and aortic root dilation are well tolerated; and 11) the aging ASO patient may benefit from "exercise-prescription" rather than restriction. Significant strides have been made in understanding risk factors for cardiac, ND, and other important clinical outcomes after ASO.

Prediction of periventricular leukomalacia occurrence in neonates after heart surgery

This paper is concerned with predicting the occurrence of periventricular leukomalacia (PVL) using vital and blood gas data which are collected over a period of 12 h after the neonatal cardiac surgery. A data mining approach has been employed to generate a set of rules for classification of subjects as healthy or PVL affected. In view of the fact that blood gas and vital data have different sampling rates, in this study we have divided the data into two categories: 1) high resolution (vital), and 2) low resolution (blood gas), and designed a separate classifier based on each data category. The developed algorithm is composed of several stages; first, a feature pool has been extracted from each data category and the extracted features have been ranked based on the data reliability and their mutual information content with the output. An optimal feature subset with the highest discriminative capability has been formed using simultaneous maximization of the class separability measure and mutual information of a set. Two separate decision trees (DTs) have been developed for the classification purpose and more importantly to discover hidden relationships that exist among the data to help us better understand PVL pathophysiology. The DT result shows that high amplitude 20 min variations and low sample entropy in the vital data and the defined out of range index as well as maximum rate of change in blood gas data are important factors for PVL prediction. Low sample entropy represents lack of variability in hemodynamic measurement, and constant blood pressure with small fluctuations is an important indicator of PVL occurrence. Finally, using the different time frames of data collection, we show that the first 6 h of data contain sufficient information for PVL occurrence prediction.

Model based optimization of the cardiopulmonary resuscitation (CPR) procedure

This paper is concerned with the optimization of the cardiopulmonary resuscitation (CPR) procedure, which plays a critical rule in saving the life of patients suffering from cardiac arrest. In this paper, we define the performance index for optimization using the oxygen delivery. A model developed earlier is used to calculate the oxygen delivery through CPR. The free parameters of this model which depend on the rescuer performance are ventilation time, compression speed, tidal volume, and fraction of oxygen in the inspired air. Two different optimization problems are carried out. First, a global optimization is implemented to discover the best values of the free parameters which maximize the oxygen delivery. In addition to this, a sequential optimization scheme is explored which uses a two step optimization in each CPR sequence to maximize the oxygen delivery. Results show that the sequential optimization procedure will enhance the performance of the CPR significantly.

Application of decision tree in the prediction of periventricular leukomalacia (PVL) occurrence in neonates after heart surgery

This paper is concerned with the prediction of the occurrence of periventricular leukomalacia (PVL) that occurs in neonates after heart surgery. The data which is collected over a period of 12 hours after cardiac surgery contains vital measurements as well as blood gas measurements with different resolutions. Vital data measured using near-inferred spectroscopy (NIRS) at the sampling rate of 0.25 Hz and blood gas measurement up to 12 times with irregular time intervals for 35 patients collected at Children's Hospital of Philadelphia (CHOP) are used for this study. Features derived from the data include statistical moments (mean, variance, skewness and kurtosis), trend and minimum and maximum values of the vital data and rate of change, time weighted mean and a custom defined out of range index (ORI) for the blood gas data. A decision tree is developed for the vital data in order to identify the most important vital measurements. In addition, a decision tree is developed for blood gas data to find important factors for the prediction of PVL occurrence. Results show that in the blood gas data, maximum rate of change of concentration of bicarbonate ions in blood (HCO(3)) and minimum rate of change of partial pressure of dissolved CO(2) in the blood (PaCO(2)) are the two most important factors for prediction of the PVL. Also important are the kurtosis of heart rate and hemoglobin values.

A cycle-averaged model of hypoplastic left heart syndrome (HLHS)

This paper is concerned with computational modeling of a severe congenital defect called Hypoplastic left heart syndrome (HLHS) that is the most common cardiac malformation with the highest likelihood of deaths in newborns. A lumped parameter model of the HLHS circulation has been developed to study the hemodynamic variables in the various sections of the cardio-pulmonary circulation system. We applied a short-term, cycle-averaging operation to the differential equations of the HLHS model to obtain the cycle-averaged model. Study has been carried out to analyze the variation of blood flow rate in different parts due to parameter changes. Results show that the developed model, could bring a good insight into understanding of the HLHS disease.