Intensive care units (ICUs) provide care for critically-ill patients who require constant monitoring and the availability of specialized equipment and personnel. In this environment, a high volume of information and a high degree of uncertainty present a burden to clinicians. In specialized cohorts, such as pediatric patients with congenital heart defects (CHDs), this burden is exacerbated by increased complexity, the inadequacy of existing decision support aids, and the limited and decreasing availability of highly-specialized clinicians. Among CHD patients, infants with single ventricle (SV) physiology are one of the most complex and severely-ill sub-populations. While SV mortality rates have dropped, patient deterioration may happen unexpectedly in the period before patients undergo stage-2 palliative surgery. Even in expert hands, critical and potentially catastrophic events (CEs), such as cardiopulmonary resuscitation (CPR), emergent endotracheal intubation (EEI), or extracorporeal membrane oxygenation (ECMO) are common in SV patients, and may negatively impact morbidity, mortality, and hospital length of stay. There is a clinical need of predictive tools that help intensivists assess and forecast the advent of CEs in SV infants. Although ubiquitous, widely adopted ICU severity-of-illness scores or early warning systems (EWS), e.g., PRISM and PIM, have not met this need. They are often v developed for general ICU use and do not generalize well to specialized populations. Furthermore, most EWS are developed for prediction of patient mortality. Among SV patients, however, death is semi-elective. On the other hand, prediction of CEs may help clinicians improve patient care by anticipating the advent of patient deterioration. In this dissertation, we aimed to develop and validate predictive models that achieve early and accurate prediction of CEs in infants with SV physiology. Such models may provide early and actionable information to clinicians and may be used to perform clinical interventions aimed at preventing CEs, and to reducing morbidity, mortality, and healthcare costs. We assert that our work is significant in that it addresses an unmet clinical need by achieving state-of-the-art, early prediction of patient deterioration in a challenging and vulnerable population. vi TABLE OF CONTENTS
Objectives: Develop and test the performance of electronic version of the Children’s Hospital of Pittsburgh Pediatric Risk of Mortality-IV and electronic version of the Children’s Hospital of Pittsburgh Pediatric Logistic Organ Dysfunction-2 scores. Design: Retrospective, single-center cohort derived from structured electronic health record data. Setting: Large, quaternary PICU at a freestanding, university-affiliated children’s hospital. Patients: All encounters with a PICU admission between January 1, 2009, and December 31, 2017, identified using electronic definitions of inpatient encounter. Interventions: None. Measurements and Main Results: The main outcome was predictive validity of each score for hospital mortality, assessed as model discrimination and calibration. Discrimination was examined with the area under the receiver operating characteristics curve and the area under the precision-recall curve. Calibration was assessed with the Hosmer-Lemeshow goodness of fit test and calculation of a standardized mortality ratio. Models were recalibrated with new regression coefficients in a training subset of 75% of encounters selected randomly from all years of the cohort and the calibrated models were tested in the remaining 25% of the cohort. Content validity was assessed by examining correlation between electronic versions of the scores and prospectively calculated data (electronic version of the Children’s Hospital of Pittsburgh Pediatric Risk of Mortality-IV) and an alternative informatics approach (Children’s Hospital of Pittsburgh Pediatric Logistic Organ Dysfunction-2 score). The cohort included 21,335 encounters. Correlation coefficients indicated strong agreement between different methods of score calculation. Uncalibrated area under the receiver operating characteristics curves were 0.96 (95% CI, 0.95–0.97) for electronic version of the Children’s Hospital of Pittsburgh Pediatric Logistic Organ Dysfunction-2 score and 0.87 (95% CI, 0.85–0.89) for electronic version of the Children’s Hospital of Pittsburgh Pediatric Risk of Mortality-IV for inpatient mortality. The uncalibrated electronic version of the Children’s Hospital of Pittsburgh Pediatric Risk of Mortality-IV standardized mortality ratio was 0.63 (0.59–0.66), demonstrating strong agreement with previous, prospective evaluation at the study center. The uncalibrated electronic version of the Children’s Hospital of Pittsburgh Pediatric Logistic Organ Dysfunction-2 score standardized mortality ratio was 0.20 (0.18–0.21). All models required recalibrating (all Hosmer–Lemeshow goodness-of-fit, p < 0.001) and subsequently demonstrated acceptable goodness-of-fit when examined in a test subset (n = 5,334) of the cohort. Conclusions: Electronically derived intensive care acuity scores demonstrate very good to excellent discrimination and can be calibrated to institutional outcomes. This approach can facilitate both performance improvement and research initiatives and may offer a scalable strategy for comparison of interinstitutional PICU outcomes.
BackgroundAdenocarcinoma (ADC) and squamous cell carcinoma (SCC) are the most prevalent histological types among lung cancers. Distinguishing between these subtypes is critically important because they have different implications for prognosis and treatment. Normally, histopathological analyses are used to distinguish between the two, where the tissue samples are collected based on small endoscopic samples or needle aspirations. However, the lack of cell architecture in these small tissue samples hampers the process of distinguishing between the two subtypes.Molecular profiling can also be used to discriminate between the two lung cancer subtypes, on condition that the biopsy is composed of at least 50 % of tumor cells. However, for some cases, the tissue composition of a biopsy might be a mix of tumor and tumor-adjacent histologically normal tissue (TAHN). When this happens, a new biopsy is required, with associated cost, risks and discomfort to the patient. To avoid this problem, we hypothesize that a computational method can distinguish between lung cancer subtypes given tumor and TAHN tissue.MethodsUsing publicly available datasets for gene expression and DNA methylation, we applied four classification tasks, depending on the possible combinations of tumor and TAHN tissue. First, we used a feature selector (ReliefF/Limma) to select relevant variables, which were then used to build a simple naïve Bayes classification model. Then, we evaluated the classification performance of our models by measuring the area under the receiver operating characteristic curve (AUC). Finally, we analyzed the relevance of the selected genes using hierarchical clustering and IPA® software for gene functional analysis.ResultsAll Bayesian models achieved high classification performance (AUC > 0.94), which were confirmed by hierarchical cluster analysis. From the genes selected, 25 (93 %) were found to be related to cancer (19 were associated with ADC or SCC), confirming the biological relevance of our method.ConclusionsThe results from this study confirm that computational methods using tumor and TAHN tissue can serve as a prognostic tool for lung cancer subtype classification. Our study complements results from other studies where TAHN tissue has been used as prognostic tool for prostate cancer. The clinical implications of this finding could greatly benefit lung cancer patients.Electronic supplementary materialThe online version of this article (doi:10.1186/s12885-016-2223-3) contains supplementary material, which is available to authorized users.
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