Integration of early physiological responses predicts later illness severity in preterm infants

Machine learning for predicting the response of breast cancer to neoadjuvant chemotherapy

OBJECTIVE

To employ machine learning methods to predict the eventual therapeutic response of breast cancer patients after a single cycle of neoadjuvant chemotherapy (NAC).

MATERIALS AND METHODS

Quantitative dynamic contrast-enhanced MRI and diffusion-weighted MRI data were acquired on 28 patients before and after one cycle of NAC. A total of 118 semiquantitative and quantitative parameters were derived from these data and combined with 11 clinical variables. We used Bayesian logistic regression in combination with feature selection using a machine learning framework for predictive model building.

RESULTS

The best predictive models using feature selection obtained an area under the curve of 0.86 and an accuracy of 0.86, with a sensitivity of 0.88 and a specificity of 0.82.

DISCUSSION

With the numerous options for NAC available, development of a method to predict response early in the course of therapy is needed. Unfortunately, by the time most patients are found not to be responding, their disease may no longer be surgically resectable, and this situation could be avoided by the development of techniques to assess response earlier in the treatment regimen. The method outlined here is one possible solution to this important clinical problem.

CONCLUSIONS

Predictive modeling approaches based on machine learning using readily available clinical and quantitative MRI data show promise in distinguishing breast cancer responders from non-responders after the first cycle of NAC.

Computational medicine: translating models to clinical care

Because of the inherent complexity of coupled nonlinear biological systems, the development of computational models is necessary for achieving a quantitative understanding of their structure and function in health and disease. Statistical learning is applied to high-dimensional biomolecular data to create models that describe relationships between molecules and networks. Multiscale modeling links networks to cells, organs, and organ systems. Computational approaches are used to characterize anatomic shape and its variations in health and disease. In each case, the purposes of modeling are to capture all that we know about disease and to develop improved therapies tailored to the needs of individuals. We discuss advances in computational medicine, with specific examples in the fields of cancer, diabetes, cardiology, and neurology. Advances in translating these computational methods to the clinic are described, as well as challenges in applying models for improving patient health.

3D Sensing Algorithms Towards Building an Intelligent Intensive Care Unit

Intensive Care Units (ICUs) are chaotic places where hundreds of tasks are carried out by many different people. Timely and coordinated execution of these tasks are directly related to quality of patient outcomes. An improved understanding of the current care process can aid in improving quality. Our goal is to build towards a system that automatically catalogs various tasks being performed by the bedside. We propose a set of techniques using computer vision and machine learning to develop a system that passively senses the environment and identifies seven common actions such as documenting, checking up on a patient, and performing a procedure. Preliminary evaluation of our system on 5.5 hours of data from the Pediatric ICU obtains overall task recognition accuracy of 70%. Furthermore, we show how it can be used to summarize and visualize tasks. Our system provides a significant departure from current approaches used for quality improvement. With further improvement, we think that such a system could realistically be deployed in the ICU.

Monitoring and identification of sepsis development through a composite measure of heart rate variability

Tracking the physiological conditions of a patient developing infection is of utmost importance to provide optimal care at an early stage. This work presents a procedure to integrate multiple measures of heart rate variability into a unique measure for the tracking of sepsis development. An early warning system is used to illustrate its potential clinical value. The study involved 17 adults (age median 51 (interquartile range 46-62)) who experienced a period of neutropenia following chemoradiotherapy and bone marrow transplant; 14 developed sepsis, and 3 did not. A comprehensive panel (N = 92) of variability measures was calculated for 5 min-windows throughout the period of monitoring (12 ± 4 days). Variability measures underwent filtering and two steps of data reduction with the objective of enhancing the information related to the greatest degree of change. The proposed composite measure was capable of tracking the development of sepsis in 12 out of 14 patients. Simulating a real-time monitoring setting, the sum of the energy over the very low frequency range of the composite measure was used to classify the probability of developing sepsis. The composite revealed information about the onset of sepsis about 60 hours (median value) before of sepsis diagnosis. In a real monitoring setting this quicker detection time would be associated to increased efficacy in the treatment of sepsis, therefore highlighting the potential clinical utility of a composite measure of variability.

Weaning infants from mechanical ventilation

Protracted mechanical ventilation is associated with increased morbidity and mortality in preterm infants and thus the earliest possible weaning from mechanical ventilation is desirable. Weaning protocols may be helpful in achieving more rapid reduction in support. There is no clear consensus regarding the level of support at which an infant is ready for extubation. An improved ability to predict when a preterm infant has a high likelihood of successful extubation is highly desirable. In this article, available evidence is reviewed and reasonable evidence-based recommendations for expeditious weaning and extubation are provided.

Neonatal Informatics: Transforming Neonatal Care Through Translational Bioinformatics

The future of neonatal informatics will be driven by the availability of increasingly vast amounts of clinical and genetic data. The field of translational bioinformatics is concerned with linking and learning from these data and applying new findings to clinical care to transform the data into proactive, predictive, preventive, and participatory health. As a result of advances in translational informatics, the care of neonates will become more data driven, evidence based, and personalized.

Early detection of impending physiologic deterioration among patients who are not in intensive care: development of predictive models using data from an automated electronic medical record

BACKGROUND

Ward patients who experience unplanned transfer to intensive care units have excess morbidity and mortality.

OBJECTIVE

To develop a predictive model for prediction of unplanned transfer from the medical-surgical ward to intensive care (or death on the ward in a patient who was "full code") using data from a comprehensive inpatient electronic medical record (EMR).

DESIGN

Retrospective case-control study; unit of analysis was a 12-hour patient shift. Shifts where a patient experienced an unplanned transfer were event shifts; shifts without a transfer were comparison shifts. Hospitalization records were transformed into 12-hour shift records, with 10 randomly selected comparison shifts identified for each event shift. Analysis employed logistic regression and split validation.

SETTING

Integrated healthcare delivery system in Northern California.

PATIENTS

Hospitalized adults at 14 hospitals with comprehensive inpatient EMRs.

MEASUREMENTS

Predictors included vital signs, laboratory test results, severity of illness scores, longitudinal chronic illness burden scores, transpired hospital length of stay, and care directives. Patients were also given a retrospective, electronically (not manually assigned) Modified Early Warning Score, or MEWS(re). Outcomes were transfer to the intensive care unit (ICU) from the ward or transitional care unit, or death outside the ICU among patients who were "full code".

RESULTS

We identified 4,036 events and 39,782 comparison shifts from a cohort of 102,422 patients' hospitalizations. The MEWS(re) had a c-statistic of 0.709 in the derivation and 0.698 in the validation dataset; corresponding values for the EMR-based model were 0.845 and 0.775.

LIMITATIONS

Using these algorithms requires hospitals with comprehensive inpatient EMRs and longitudinal data.

CONCLUSIONS

EMR-based detection of impending deterioration outside the ICU is feasible in integrated healthcare delivery systems.

Use of models in identification and prediction of physiology in critically ill surgical patients

BACKGROUND

With higher-throughput data acquisition and processing, increasing computational power, and advancing computer and mathematical techniques, modelling of clinical and biological data is advancing rapidly. Although exciting, the goal of recreating or surpassing in silico the clinical insight of the experienced clinician remains difficult. Advances toward this goal and a brief overview of various modelling and statistical techniques constitute the purpose of this review.

METHODS

A review of the literature and experience with models and physiological state representation and prediction after injury was undertaken.

RESULTS

A brief overview of models and the thinking behind their use for surgeons new to the field is presented, including an introduction to visualization and modelling work in surgical care, discussion of state identification and prediction, discussion of causal inference statistical approaches, and a brief introduction to new vital signs and waveform analysis.

CONCLUSION

Modelling in surgical critical care can provide a useful adjunct to traditional reductionist biological and clinical analysis. Ultimately the goal is to model computationally the clinical acumen of the experienced clinician.

In situ simulation in continuing education for the health care professions: a systematic review

INTRODUCTION

Education in the health sciences increasingly relies on simulation-based training strategies to provide safe, structured, engaging, and effective practice opportunities. While this frequently occurs within a simulation center, in situ simulations occur within an actual clinical environment. This blending of learning and work environments may provide a powerful method for continuing education. However, as this is a relatively new strategy, best practices for the design and delivery of in situ learning experiences have yet to be established. This article provides a systematic review of the in situ simulation literature and compares the state of the science and practice against principles of effective education and training design, delivery, and evaluation.

METHODS

A total of 3190 articles were identified using academic databases and screened for descriptive accounts or studies of in situ simulation programs. Of these, 29 full articles were retrieved and coded using a standard data extraction protocol (kappa = 0.90).

RESULTS

In situ simulations have been applied to foster individual, team, unit, and organizational learning across several clinical and nonclinical areas. Approaches to design, delivery, and evaluation of the simulations were highly variable across studies. The overall quality of in situ simulation studies is low. A positive impact of in situ simulation on learning and organizational performance has been demonstrated in a small number of studies.

DISCUSSION

The evidence surrounding in situ simulation efficacy is still emerging, but the existing research is promising. Practical program planning strategies are evolving to meet the complexity of a novel learning activity that engages providers in their actual work environment.

Early prediction of the response of breast tumors to neoadjuvant chemotherapy using quantitative MRI and machine learning

The ability to predict early in the course of treatment the response of breast tumors to neoadjuvant chemotherapy can stratify patients based on response for patient-specific treatment strategies. Currently response to neoadjuvant chemotherapy is evaluated based on physical exam or breast imaging (mammogram, ultrasound or conventional breast MRI). There is a poor correlation among these measurements and with the actual tumor size when measured by the pathologist during definitive surgery. We tested the feasibility of using quantitative MRI as a tool for early prediction of tumor response. Between 2007 and 2010 twenty consecutive patients diagnosed with Stage II/III breast cancer and receiving neoadjuvant chemotherapy were enrolled on a prospective imaging study. Our study showed that quantitative MRI parameters along with routine clinical measures can predict responders from non-responders to neoadjuvant chemotherapy. The best predictive model had an accuracy of 0.9, a positive predictive value of 0.91 and an AUC of 0.96.

Statistical process control for validating a classification tree model for predicting mortality--a novel approach towards temporal validation

Prediction models are postulated as useful tools to support tasks such as clinical decision making and benchmarking. In particular, classification tree models have enjoyed much interest in the Biomedical Informatics literature. However, their prospective predictive performance over the course of time has not been investigated. In this paper we suggest and apply statistical process control methods to monitor over more than 5 years the prospective predictive performance of TM80+, one of the few classification-tree models published in the clinical literature. TM80+ is a model for predicting mortality among very elderly patients in the intensive care based on a multi-center dataset. We also inspect the predictive performance at the tree's leaves. This study provides important insights into patterns of (in)stability of the tree's performance and its "shelf life". The study underlies the importance of continuous validation of prognostic models over time using statistical tools and the timely recalibration of tree models.