A semiparametric bivariate probit model for joint modeling of outcomes in STEMI patients

In this work we analyse the relationship among in-hospital mortality and a treatment effectiveness outcome in patients affected by ST-Elevation myocardial infarction. The main idea is to carry out a joint modeling of the two outcomes applying a Semiparametric Bivariate Probit Model to data arising from a clinical registry called STEMI Archive. A realistic quantification of the relationship between outcomes can be problematic for several reasons. First, latent factors associated with hospitals organization can affect the treatment efficacy and/or interact with patient’s condition at admission time. Moreover, they can also directly influence the mortality outcome. Such factors can be hardly measurable. Thus, the use of classical estimation methods will clearly result in inconsistent or biased parameter estimates. Secondly, covariate-outcomes relationships can exhibit nonlinear patterns. Provided that proper statistical methods for model fitting in such framework are available, it is possible to employ a simultaneous estimation approach to account for unobservable confounders. Such a framework can also provide flexible covariate structures and model the whole conditional distribution of the response

Emerging Challenges and Opportunities in Infectious Disease Epidemiology.

Much of the intellectual tradition of modern epidemiology stems from efforts to understand and combat chronic diseases persisting through the 20th century epidemiologic transition of countries such as the United States and United Kingdom. After decades of relative obscurity, infectious disease epidemiology has undergone an intellectual rebirth in recent years amid increasing recognition of the threat posed by both new and familiar pathogens. Here, we review the emerging coalescence of infectious disease epidemiology around a core set of study designs and statistical methods bearing little resemblance to the chronic disease epidemiology toolkit. We offer our outlook on challenges and opportunities facing the field, including the integration of novel molecular and digital information sources into disease surveillance, the assimilation of such data into models of pathogen spread, and the increasing contribution of models to public health practice. We next consider emerging paradigms in causal inference for infectious diseases, ranging from approaches to evaluating vaccines and antimicrobial therapies to the task of ascribing clinical syndromes to etiologic microorganisms, an age-old problem transformed by our increasing ability to characterize human-associated microbiota. These areas represent an increasingly important component of epidemiology training programs for future generations of researchers and practitioners

Three Empirical Essays on Health Informatics and Analytics

Health Information Technology (HIT) has an important and widely acknowledged role in enhancing healthcare performance in the healthcare industry today. A great amount of literature has focused on the impact of HIT implementation, yet the studies provide mixed and inconclusive results on whether HIT implementation actually helps healthcare providers enhance healthcare performance. Here, we identify three possible research gaps that lead to these mixed and inclusive results. First, prior IS research has exclusively examined HIT complementarity simultaneously, but ignored the temporal perspective. Second, extant HIT research has primarily examined the relationship between HIT implementation and healthcare performance in a static framework, which may neglect the dynamic relationship between HIT and healthcare performance. Third, prior HIT value studies have typically examined HIT’s impact on hospital-level outcomes, but no extant studies consider HIT impact on transition-level outcomes as disease progresses over time. This dissertation addresses these gaps in three essays that draw upon three different lenses to study HIT implementation’s impact on healthcare performance using three analytics methods. The first essay applies econometrics to study how various types of HIT complementarities simultaneously and sequentially impact diverse healthcare outcomes. In so doing, we find evidence of simultaneous and sequential complementarity wherein HIT applications are synergistic—not only within the same time period, but also across periods. The second essay uses advanced latent growth modeling to explore the dynamic, longitudinal relationship between HIT and healthcare outcomes after incorporating the nonlinear trajectory change of different HIT functions and the various dimensions of hospital performance. The third essay applies multi-state and hidden Markov models to examine how HIT functions’ implementation levels impact a finer, more-granular-level healthcare outcome. This approach includes the dynamics of the transitions, including observable transitions (chronic to acute, acute to chronic, chronic to death, and acute to death) and underlying and unobservable transitions (minor to major disease and major disease to death). This essay examines how different types of HIT can improve different transitions types as diseases progress over time

Design of the Subpopulations and Intermediate Outcome Measures in COPD (SPIROMICS) AIR Study.

IntroductionPopulation-based epidemiological evidence suggests that exposure to ambient air pollutants increases hospitalisations and mortality from chronic obstructive pulmonary disease (COPD), but less is known about the impact of exposure to air pollutants on patient-reported outcomes, morbidity and progression of COPD.Methods and analysisThe Subpopulations and Intermediate Outcome Measures in COPD (SPIROMICS) Air Pollution Study (SPIROMICS AIR) was initiated in 2013 to investigate the relation between individual-level estimates of short-term and long-term air pollution exposures, day-to-day symptom variability and disease progression in individuals with COPD. SPIROMICS AIR builds on a multicentre study of smokers with COPD, supplementing it with state-of-the-art air pollution exposure assessments of fine particulate matter, oxides of nitrogen, ozone, sulfur dioxide and black carbon. In the parent study, approximately 3000 smokers with and without airflow obstruction are being followed for up to 3 years for the identification of intermediate biomarkers which predict disease progression. Subcohorts undergo daily symptom monitoring using comprehensive daily diaries. The air monitoring and modelling methods employed in SPIROMICS AIR will provide estimates of individual exposure that incorporate residence-specific infiltration characteristics and participant-specific time-activity patterns. The overarching study aim is to understand the health effects of short-term and long-term exposures to air pollution on COPD morbidity, including exacerbation risk, patient-reported outcomes and disease progression.Ethics and disseminationThe institutional review boards of all the participating institutions approved the study protocols. The results of the trial will be presented at national and international meetings and published in peer-reviewed journals

Predicting diabetes-related hospitalizations based on electronic health records

OBJECTIVE: To derive a predictive model to identify patients likely to be hospitalized during the following year due to complications attributed to Type II diabetes. METHODS: A variety of supervised machine learning classification methods were tested and a new method that discovers hidden patient clusters in the positive class (hospitalized) was developed while, at the same time, sparse linear support vector machine classifiers were derived to separate positive samples from the negative ones (non-hospitalized). The convergence of the new method was established and theoretical guarantees were proved on how the classifiers it produces generalize to a test set not seen during training. RESULTS: The methods were tested on a large set of patients from the Boston Medical Center - the largest safety net hospital in New England. It is found that our new joint clustering/classification method achieves an accuracy of 89% (measured in terms of area under the ROC Curve) and yields informative clusters which can help interpret the classification results, thus increasing the trust of physicians to the algorithmic output and providing some guidance towards preventive measures. While it is possible to increase accuracy to 92% with other methods, this comes with increased computational cost and lack of interpretability. The analysis shows that even a modest probability of preventive actions being effective (more than 19%) suffices to generate significant hospital care savings. CONCLUSIONS: Predictive models are proposed that can help avert hospitalizations, improve health outcomes and drastically reduce hospital expenditures. The scope for savings is significant as it has been estimated that in the USA alone, about $5.8 billion are spent each year on diabetes-related hospitalizations that could be prevented.Accepted manuscrip

Predicting Outcomes in Long COVID Patients with Spatiotemporal Attention

Long COVID is a general term of post-acute sequelae of COVID-19. Patients with long COVID can endure long-lasting symptoms including fatigue, headache, dyspnea and anosmia, etc. Identifying the cohorts with severe long-term complications in COVID-19 could benefit the treatment planning and resource arrangement. However, due to the heterogeneous phenotype presented in long COVID patients, it is difficult to predict their outcomes from their longitudinal data. In this study, we proposed a spatiotemporal attention mechanism to weigh feature importance jointly from the temporal dimension and feature space. Considering that medical examinations can have interchangeable orders in adjacent time points, we restricted the learning of short-term dependency with a Local-LSTM and the learning of long-term dependency with the joint spatiotemporal attention. We also compared the proposed method with several state-of-the-art methods and a method in clinical practice. The methods are evaluated on a hard-to-acquire clinical dataset of patients with long COVID. Experimental results show the Local-LSTM with joint spatiotemporal attention outperformed related methods in outcome prediction. The proposed method provides a clinical tool for the severity assessment of long COVID

General anesthesia soon after dialysis may increase postoperative hypotension - A pilot study.

IntroductionPilot study associating hemodialysis-to-general-anesthesia time interval and post-operative complications in hemodialysis patients to better define a more optimal pre-anesthetic waiting period.MethodsPre-anesthetic and 48-hours post-anesthetic parameters (age, gender, body-mass-index, pre-operative ultrafiltrate, potassium, renal disease etiology, hemodialysis sessions per week, Acute Physiology and Chronic Health Evaluation-II score, Portsmouth-Physiologic and Operative Severity Score for the Enumeration of Mortality and Morbidity, American Society of Anesthesiologists physical status, Johns Hopkins Surgical Classification System Category, surgical urgency, intra-operative fluids, estimated blood loss, post-operative complications) were collected on chronic hemodialysis patients between 11/2009-12/2010. Continuous data were analyzed by Analysis of Variance or t-test. Bivariate data were analyzed by Fisher's Exact Test. Relative Risks/Confidence Intervals were calculated for statistically significant comparisons (p=0.05). Exclusion criteria were incomplete records, peritoneal dialysis, intra-operative hemodialysis, liver transplant, and cardiopulmonary bypass.ResultsPatients were grouped by dialysis to anesthesia time interval: Group 1 >24 hours, Group 2 7-23.9 hours, Group 3 < 7 hours. Among Surgical Category 3-5 patients, hypotension was more common in Group 3 than Group 1 (63.6% vs 9.2%, p<0.0001, relative risk=6.9, confidence interval=3.0-15.7) or Group 2 (63.6% vs 17.3%, p=0.0002, relative risk=3.7, confidence interval=1.9-7.2). Other complications rates were not statistically significant. Disease and surgical severity scores, preoperative ultrafiltrate, and intra-operative fluids were not different.ConclusionsPost-anesthetic hypotension within 48 hours was more common in those with < 7 hours interval between dialysis and anesthesia. Therefore, if surgical urgency permits, a delay of ≥7 hours may limit postoperative hypotension. More precise associations should be obtained through a prospective study