Hospital healthcare flows: A longitudinal clustering approach of acute coronary syndrome in women over 45 years.

Acute coronary syndrome (ACS) in women is a growing public health issue and a death leading cause. We explored whether the hospital healthcare trajectory was characterizable using a longitudinal clustering approach in women with ACS. From the 2009-2014 French nationwide hospital database, we extracted spatio-temporal patterns in ACS patient trajectories, by replacing the spatiality by their hospitalization cause. We used these patterns to characterize hospital healthcare flows in a visualization tool. We clustered these trajectories with kmlShape to identify time gap and tariff profiles. ACS hospital healthcare flows have three key categories: Angina pectoris, Myocardial Infarction or Ischemia. Elderly flows were more complex. Time gap profiles showed that readmissions were closer together as time goes by. Tariff profiles were different according to age and initial event. Our approach might be applied to monitoring other chronic diseases. Further work is needed to integrate these results into a medical decision-making tool.

Sequential Pattern Mining to Predict Medical In-Hospital Mortality from Administrative Data: Application to Acute Coronary Syndrome.

Prediction of a medical outcome based on a trajectory of care has generated a lot of interest in medical research. In sequence prediction modeling, models based on machine learning (ML) techniques have proven their efficiency compared to other models. In addition, reducing model complexity is a challenge. Solutions have been proposed by introducing pattern mining techniques. Based on these results, we developed a new method to extract sets of relevant event sequences for medical events' prediction, applied to predict the risk of in-hospital mortality in acute coronary syndrome (ACS). From the French Hospital Discharge Database, we mined sequential patterns. They were further integrated into several predictive models using a text string distance to measure the similarity between patients' patterns of care. We computed combinations of similarity measurements and ML models commonly used. A Support Vector Machine model coupled with edit-based distance appeared as the most effective model. We obtained good results in terms of discrimination with the receiver operating characteristic curve scores ranging from 0.71 to 0.99 with a good overall accuracy. We demonstrated the interest of sequential patterns for event prediction. This could be a first step to a decision-support tool for the prevention of in-hospital death by ACS.

Prediction of In-Hospital Mortality from Administrative Data: A Sequential Pattern Mining Approach.

Study of trajectory of care is attractive for predicting medical outcome. Models based on machine learning (ML) techniques have proven their efficiency for sequence prediction modeling compared to other models. Introducing pattern mining techniques contributed to reduce model complexity. In this respect, we explored methods for medical events' prediction based on the extraction of sets of relevant event sequences of a national hospital discharge database. It is illustrated to predict the risk of in-hospital mortality in acute coronary syndrome (ACS). We mined sequential patterns from the French Hospital Discharge Database. We compared several predictive models using a text string distance to measure the similarity between patients' patterns of care. We computed combinations of similarity measurements and ML models commonly used. A Support Vector Machine model coupled with edit-based distance appeared as the most effective model. Indeed discrimination ranged from 0.71 to 0.99, together with a good overall accuracy. Thus, sequential patterns mining appear motivating for event prediction in medical settings as described here for ACS.

Hospital burden of coronary artery disease: Trends of myocardial infarction and/or percutaneous coronary interventions in France 2009-2014.

BACKGROUND: Currently, cardiovascular disease (CVD) is widely acknowledged to be the first leading cause of fatality in the world with 31% of all deaths worldwide and is predicted to remain as such in 2030. Furthermore, CVD is also a major cause of morbidity in adults worldwide. Among these diseases, the coronary artery disease (CAD) is the most common cause, accounting for over 40% of CVD deaths. Despite a decline in mortality rates, the consequences of more effective preventive and management programs, the burden of CAD remains significant. Indeed, the rise in the prevalence of modifiable risk factors due to changes in lifestyle and health behaviors has further increased the burden of this epidemic. Our objective was to evaluate the hospital burden of CAD via MI trends and Percutaneous Coronary Intervention (PCI) in the French Prospective Payment System (PPS). METHODS: MI/PCI were identified in the national PPS database from 2009 to 2014 for patients aged 20 to 99, living in metropolitan France. We examined hospitalisation, readmission and mortality trends using standardised rates. RESULTS: Over the six-year period, we identified 678,021 patients, representing 900,121 stays of which, 215,224 had a MI and a PCI. Admission trends increased by nearly 25%. Acute MI cases increased every year, with an alarming increase in women, and more specifically in young women. Men were 3 times more hospitalised than women, who were older. A North-South divide was noted. Twenty seven percent of patients experienced readmission within 1 month. Trajectories of care were significantly different by sex and age. Overall in-hospital death was 3.3%, decreasing by 15% during the period. The highest adjusted mortality rates were observed for inpatients aged <40 or >80. CONCLUSION: We outlined the public health burden of this condition and the importance of improving the trajectories of care as an aid for better care.

PaFloChar: An Innovating Approach to Characterise Patient Flows in Myocardial Infarction.

A better knowledge of patient flows would improve decision making in health planning. In this article, we propose a method to characterise patients flows and also to highlight profiles of care pathways considering times and costs. From medico-administrative data, we extracted spatio-temporal patterns. Then, we clustered time between hospitalisations and cost trajectories in order to identify profiles of change over time. This approach may support renewed management strategies.

Patient healthcare trajectory. An essential monitoring tool: a systematic review.

BACKGROUND: Patient healthcare trajectory is a recent emergent topic in the literature, encompassing broad concepts. However, the rationale for studying patients' trajectories, and how this trajectory concept is defined remains a public health challenge. Our research was focused on patients' trajectories based on disease management and care, while also considering medico-economic aspects of the associated management. We illustrated this concept with an example: a myocardial infarction (MI) occurring in a patient's hospital trajectory of care. The patient follow-up was traced via the prospective payment system. We applied a semi-automatic text mining process to conduct a comprehensive review of patient healthcare trajectory studies. This review investigated how the concept of trajectory is defined, studied and what it achieves. METHODS: We performed a PubMed search to identify reports that had been published in peer-reviewed journals between January 1, 2000 and October 31, 2015. Fourteen search questions were formulated to guide our review. A semi-automatic text mining process based on a semantic approach was performed to conduct a comprehensive review of patient healthcare trajectory studies. Text mining techniques were used to explore the corpus in a semantic perspective in order to answer non-a priori questions. Complementary review methods on a selected subset were used to answer a priori questions. RESULTS: Among the 33,514 publications initially selected for analysis, only 70 relevant articles were semi-automatically extracted and thoroughly analysed. Oncology is particularly prevalent due to its already well-established processes of care. For the trajectory thema, 80% of articles were distributed in 11 clusters. These clusters contain distinct semantic information, for example health outcomes (29%), care process (26%) and administrative and financial aspects (16%). CONCLUSION: This literature review highlights the recent interest in the trajectory concept. The approach is also gradually being used to monitor trajectories of care for chronic diseases such as diabetes, organ failure or coronary artery and MI trajectory of care, to improve care and reduce costs. Patient trajectory is undoubtedly an essential approach to be further explored in order to improve healthcare monitoring.