A Narrative Review of Multiinstitutional Data Registries of Pediatric Congenital Heart Disease in Pediatric Cardiac Anesthesia and Critical Care Medicine.

Congenital heart disease (CHD) is one of the most common birth anomalies. While the care of children with CHD has improved over recent decades, children with CHD who undergo general anesthesia remain at increased risk for morbidity and mortality. Electronic health record systems have enabled institutions to combine data on the management and outcomes of children with CHD in multicenter registries. The application of descriptive analytics methods to these data can improve clinicians' understanding and care of children with CHD. This narrative review covers efforts to leverage multicenter data registries relevant to pediatric cardiac anesthesia and critical care to improve the care of children with CHD.

Preoperative Fluid Fasting Times and Postinduction Low Blood Pressure in Children: A Retrospective Analysis.

BACKGROUND: Children are required to fast before elective general anesthesia. This study hypothesized that prolonged fasting causes volume depletion that manifests as low blood pressure. This study aimed to assess the association between fluid fasting duration and postinduction low blood pressure. METHODS: A retrospective cohort study was performed of 15,543 anesthetized children without preinduction venous access who underwent elective surgery from 2016 to 2017 at Children's Hospital of Philadelphia. Low blood pressure was defined as systolic blood pressure lower than 2 standard deviations below the mean (approximately the 2.5th percentile) for sex- and age-specific reference values. Two epochs were assessed: epoch 1 was from induction to completion of anesthesia preparation, and epoch 2 was during surgical preparation. RESULTS: In epoch 1, the incidence of low systolic blood pressure was 5.2% (697 of 13,497), and no association was observed with the fluid fasting time groups: less than 4 h (4.6%, 141 of 3,081), 4 to 8 h (6.0%, 219 of 3,652), 8 to 12 h (4.9%, 124 of 2,526), and more than 12 h (5.0%, 213 of 4,238). In epoch 2, the incidence of low systolic blood pressure was 6.9% (889 of 12,917) and varied across the fasting groups: less than 4 h (5.6%, 162 of 2,918), 4 to 8 h (8.1%, 285 of 3,531), 8 to 12 h (5.9%, 143 of 2,423), and more than 12 h (7.4%, 299 of 4,045); after adjusting for confounders, fasting 4 to 8 h (adjusted odds ratio, 1.33; 95% CI, 1.07 to 1.64; P = 0.009) and greater than 12 h (adjusted odds ratio, 1.28; 95% CI, 1.04 to 1.57; P = 0.018) were associated with significantly higher odds of low systolic blood pressure compared with the group who fasted less than 4 h, whereas the increased odds of low systolic blood pressure associated with fasting 8 to 12 h (adjusted odds ratio, 1.11; 95% CI, 0.87 to 1.42; P = 0.391) was nonsignificant. CONCLUSIONS: Longer durations of clear fluid fasting in anesthetized children were associated with increased risk of postinduction low blood pressure during surgical preparation, although this association appeared nonlinear.

A Narrative Review of Analytics in Pediatric Cardiac Anesthesia and Critical Care Medicine.

Congenital heart disease (CHD) is one of the most common birth anomalies, and the care of children with CHD has improved over the past 4 decades. However, children with CHD who undergo general anesthesia remain at increased risk for morbidity and mortality. The proliferation of electronic health record systems and sophisticated patient monitors affords the opportunity to capture and analyze large amounts of CHD patient data, and the application of novel, effective analytics methods to these data can enable clinicians to enhance their care of pediatric CHD patients. This narrative review covers recent efforts to leverage analytics in pediatric cardiac anesthesia and critical care to improve the care of children with CHD.

A method for detecting and characterizing outbreaks of infectious disease from clinical reports.

Outbreaks of infectious disease can pose a significant threat to human health. Thus, detecting and characterizing outbreaks quickly and accurately remains an important problem. This paper describes a Bayesian framework that links clinical diagnosis of individuals in a population to epidemiological modeling of disease outbreaks in the population. Computer-based diagnosis of individuals who seek healthcare is used to guide the search for epidemiological models of population disease that explain the pattern of diagnoses well. We applied this framework to develop a system that detects influenza outbreaks from emergency department (ED) reports. The system diagnoses influenza in individuals probabilistically from evidence in ED reports that are extracted using natural language processing. These diagnoses guide the search for epidemiological models of influenza that explain the pattern of diagnoses well. Those epidemiological models with a high posterior probability determine the most likely outbreaks of specific diseases; the models are also used to characterize properties of an outbreak, such as its expected peak day and estimated size. We evaluated the method using both simulated data and data from a real influenza outbreak. The results provide support that the approach can detect and characterize outbreaks early and well enough to be valuable. We describe several extensions to the approach that appear promising.

Association of over-the-counter pharmaceutical sales with influenza-like-illnesses to patient volume in an urgent care setting.

We studied the association between OTC pharmaceutical sales and volume of patients with influenza-like-illnesses (ILI) at an urgent care center over one year. OTC pharmaceutical sales explain 36% of the variance in the patient volume, and each standard deviation increase is associated with 4.7 more patient visits to the urgent care center (p<0.0001). Cross-correlation function analysis demonstrated that OTC pharmaceutical sales are significantly associated with patient volume during non-flu season (p<0.0001), but only the sales of cough and cold (p<0.0001) and thermometer (p<0.0001) categories were significant during flu season with a lag of two and one days, respectively. Our study is the first study to demonstrate and measure the relationship between OTC pharmaceutical sales and urgent care center patient volume, and presents strong evidence that OTC sales predict urgent care center patient volume year round.

A method for estimating from thermometer sales the incidence of diseases that are symptomatically similar to influenza.

Early detection and accurate characterization of disease outbreaks are important tasks of public health. Infectious diseases that present symptomatically like influenza (SLI), including influenza itself, constitute an important class of diseases that are monitored by public-health epidemiologists. Monitoring emergency department (ED) visits for presentations of SLI could provide an early indication of the presence, extent, and dynamics of such disease in the population. We investigated the use of daily over-the-counter thermometer-sales data to estimate daily ED SLI counts in Allegheny County (AC), Pennsylvania. We found that a simple linear model fits the data well in predicting daily ED SLI counts from daily counts of thermometer sales in AC. These results raise the possibility that this model could be applied, perhaps with adaptation, in other regions of the country, where commonly thermometer sales data are available, but daily ED SLI counts are not.

Spatial and Temporal Algorithm Evaluation for Detecting Over-The-Counter Thermometer Sale Increases during 2009 H1N1 Pandemic.

BACKGROUND: Spatial outbreak detection algorithms using routinely collected healthcare data have been developed since the late 90s to identify and locate disease outbreaks. However, current well-received spatial algorithms assume only one outbreak cluster present at the same point of time which may not be valid during a pandemic when several clusters of geographic areas concurrently occur. Based on a retrospective evaluation on time-series and spatial algorithms, this paper suggests that time series analysis in detection of pandemics is still a desirable process, which may achieve more sensitive performance with better timeliness. METHODS: In this paper, we first prove in theory that two existing spatial models, the likelihood ratio and the Bayesian spatial scan statistics, are not useful if multiple clusters occur at the same point of time in different geographic regions. Then we conduct a comparison between a spatial algorithm, the Bayesian Spatial Scan Statistic (BSS), and a time series algorithm, the wavelet anomaly detector (WAD), on the performance of detecting the increase of the over-the-counter (OTC) medicine sales during 2009 H1N1 pandemic. RESULTS: The experiments demonstrated that the Bayesian spatial algorithm responded to the increase of thermometer sales about 3 days later than the time series algorithm. CONCLUSION: Time-series algorithms demonstrated an advantage for early outbreak detection, especially when multiple clusters occur at the same time in different geographic regions. Given spatial-temporal algorithms for outbreak detection are widely used, this paper suggests that epidemiologists or public health officials would benefit by applying time series algorithms as a complement to spatial algorithms for public health surveillance.

Rank-based spatial clustering: an algorithm for rapid outbreak detection.

OBJECTIVE: Public health surveillance requires outbreak detection algorithms with computational efficiency sufficient to handle the increasing volume of disease surveillance data. In response to this need, the authors propose a spatial clustering algorithm, rank-based spatial clustering (RSC), that detects rapidly infectious but non-contagious disease outbreaks. DESIGN: The authors compared the outbreak-detection performance of RSC with that of three well established algorithms-the wavelet anomaly detector (WAD), the spatial scan statistic (KSS), and the Bayesian spatial scan statistic (BSS)-using real disease surveillance data on to which they superimposed simulated disease outbreaks. MEASUREMENTS: The following outbreak-detection performance metrics were measured: receiver operating characteristic curve, activity monitoring operating curve curve, cluster positive predictive value, cluster sensitivity, and algorithm run time. RESULTS: RSC was computationally efficient. It outperformed the other two spatial algorithms in terms of detection timeliness, and outbreak localization. RSC also had overall better timeliness than the time-series algorithm WAD at low false alarm rates. CONCLUSION: RSC is an ideal algorithm for analyzing large datasets when the application of other spatial algorithms is not practical. It also allows timely investigation for public health practitioners by providing early detection and well-localized outbreak clusters.

A multi-level spatial clustering algorithm for detection of disease outbreaks.

In this paper, we proposed a Multi-level Spatial Clustering (MSC) algorithm for rapid detection of emerging disease outbreaks prospectively. We used the semi-synthetic data for algorithm evaluation. We applied BARD algorithm [1] to generate outbreak counts for simulation of aerosol release of Anthrax. We compared MSC with two spatial clustering algorithms: Kulldorff's spatial scan statistic [2] and Bayesian spatial scan statistic [3]. The evaluation results showed that the areas under ROC had no significant difference among the three algorithms, so did the areas under AMOC. MSC demonstrated significant computational efficiency (100 + times faster) and higher PPV. However, MSC showed 2-6 hours delay on average for outbreak detection when the false alarm rate was lower than 1 false alarm per 4 weeks. We concluded that the MSC algorithm is computationally efficient and it is able to provide more precise and compact clusters in a timely manner while keeping high detection accuracy (cluster sensitivity) and low false alarm rates.

Establishing a nationwide emergency department-based syndromic surveillance system for better public health responses in Taiwan.

BACKGROUND: With international concern over emerging infectious diseases (EID) and bioterrorist attacks, public health is being required to have early outbreak detection systems. A disease surveillance team was organized to establish a hospital emergency department-based syndromic surveillance system (ED-SSS) capable of automatically transmitting patient data electronically from the hospitals responsible for emergency care throughout the country to the Centers for Disease Control in Taiwan (Taiwan-CDC) starting March, 2004. This report describes the challenges and steps involved in developing ED-SSS and the timely information it provides to improve in public health decision-making. METHODS: Between June 2003 and March 2004, after comparing various surveillance systems used around the world and consulting with ED physicians, pediatricians and internal medicine physicians involved in infectious disease control, the Syndromic Surveillance Research Team in Taiwan worked with the Real-time Outbreak and Disease Surveillance (RODS) Laboratory at the University of Pittsburgh to create Taiwan's ED-SSS. The system was evaluated by analyzing daily electronic ED data received in real-time from the 189 hospitals participating in this system between April 1, 2004 and March 31, 2005. RESULTS: Taiwan's ED-SSS identified winter and summer spikes in two syndrome groups: influenza-like illnesses and respiratory syndrome illnesses, while total numbers of ED visits were significantly higher on weekends, national holidays and the days of Chinese lunar new year than weekdays (p < 0.001). It also identified increases in the upper, lower, and total gastrointestinal (GI) syndrome groups starting in November 2004 and two clear spikes in enterovirus-like infections coinciding with the two school semesters. Using ED-SSS for surveillance of influenza-like illnesses and enteroviruses-related infections has improved Taiwan's pandemic flu preparedness and disease control capabilities. CONCLUSION: Taiwan's ED-SSS represents the first nationwide real-time syndromic surveillance system ever established in Asia. The experiences reported herein can encourage other countries to develop their own surveillance systems. The system can be adapted to other cultural and language environments for better global surveillance of infectious diseases and international collaboration.

Biosurveillance grid: evaluation of the distribution of biosurveillance data across multiple computers.

Storing public health data in a centralize location for biosurveillance can be expensive and susceptible to server down time. We compared the performance of a distributed data grid against a centralized database to determine if a grid framework can provide fast, reliable, and inexpensive biosurveillance system. The results demonstrated that the distributed data grid had close performance with a high-end centralized system, suggesting that a grid framework can be a solution for low-cost, decentralized system.

An evaluation of biosurveillance grid--dynamic algorithm distribution across multiple computer nodes.

Performing fast data analysis to detect disease outbreaks plays a critical role in real-time biosurveillance. In this paper, we described and evaluated an Algorithm Distribution Manager Service (ADMS) based on grid technologies, which dynamically partition and distribute detection algorithms across multiple computers. We compared the execution time to perform the analysis on a single computer and on a grid network (3 computing nodes) with and without using dynamic algorithm distribution. We found that algorithms with long runtime completed approximately three times earlier in distributed environment than in a single computer while short runtime algorithms performed worse in distributed environment. A dynamic algorithm distribution approach also performed better than static algorithm distribution approach. This pilot study shows a great potential to reduce lengthy analysis time through dynamic algorithm partitioning and parallel processing, and provides the opportunity of distributing algorithms from a client to remote computers in a grid network.

Timeliness study of radiology and microbiology reports in a healthcare system for biosurveillance.

We developed a framework to measure the timeliness of two data types--radiology and microbiology reports--for detection of diseases such as inhalational anthrax (IA) in a healthcare system. We measured the timeliness of a data type as the delay between patient registration in an emergency department (ED) and receipt of data type by a biosurveillance system. We also determined the lower and upper bounds of median delay time (LMDT and UMDT) for the two data types to be available for detection of a single IA case. Based on the data received from the University of Pittsburgh Medical Center (UPMC) Health System, the LMDT time was 1.5 days and UMDT time was 6.4 days. The study provides a range of delay time for detection of a single IA case within a healthcare system, and it may benefit outbreak planning and outbreak model simulation.

Key design elements of a data utility for national biosurveillance: event-driven architecture, caching, and Web service model.

The National Retail Data Monitor (NRDM) has monitored over-the-counter (OTC) medication sales in the United States since December 2002. The NRDM collects data from over 18,600 retail stores and processes over 0.6 million sales records per day. This paper describes key architectural features that we have found necessary for a data utility component in a national biosurveillance system. These elements include event-driven architecture to provide analyses of data in near real time, multiple levels of caching to improve query response time, high availability through the use of clustered servers, scalable data storage through the use of storage area networks and a web-service function for interoperation with affiliated systems. The methods and architectural principles are relevant to the design of any production data utility for public health surveillance-systems that collect data from multiple sources in near real time for use by analytic programs and user interfaces that have substantial requirements for time-series data aggregated in multiple dimensions.

Detection of pediatric respiratory and diarrheal outbreaks from sales of over-the-counter electrolyte products.

OBJECTIVE: To determine whether sales of electrolyte products contain a signal of outbreaks of respiratory and diarrheal disease in children and, if so, how much earlier a signal relative to hospital diagnoses. DESIGN: Retrospective analysis was conducted of sales of electrolyte products and hospital diagnoses for six urban regions in three states for the period 1998 through 2001. MEASUREMENTS: Presence of signal was ascertained by measuring correlation between electrolyte sales and hospital diagnoses and the temporal relationship that maximized correlation. Earliness was the difference between the date that the exponentially weighted moving average (EWMA) method first detected an outbreak from sales and the date it first detected the outbreak from diagnoses. The coefficient of determination (r2) measured how much variance in earliness resulted from differences in sales' and diagnoses' signal strengths. RESULTS: The correlation between electrolyte sales and hospital diagnoses was 0.90 (95% CI, 0.87-0.93) at a time offset of 1.7 weeks (95% CI, 0.50-2.9), meaning that sales preceded diagnoses by 1.7 weeks. EWMA with a nine-sigma threshold detected the 18 outbreaks on average 2.4 weeks (95% CI, 0.1-4.8 weeks) earlier from sales than from diagnoses. Twelve outbreaks were first detected from sales, four were first detected from diagnoses, and two were detected simultaneously. Only 26% of variance in earliness was explained by the relative strength of the sales and diagnoses signals (r2 = 0.26). CONCLUSION: Sales of electrolyte products contain a signal of outbreaks of respiratory and diarrheal diseases in children and usually are an earlier signal than hospital diagnoses.

Automated syndromic surveillance for the 2002 Winter Olympics.

The 2002 Olympic Winter Games were held in Utah from February 8 to March 16, 2002. Following the terrorist attacks on September 11, 2001, and the anthrax release in October 2001, the need for bioterrorism surveillance during the Games was paramount. A team of informaticists and public health specialists from Utah and Pittsburgh implemented the Real-time Outbreak and Disease Surveillance (RODS) system in Utah for the Games in just seven weeks. The strategies and challenges of implementing such a system in such a short time are discussed. The motivation and cooperation inspired by the 2002 Olympic Winter Games were a powerful driver in overcoming the organizational issues. Over 114,000 acute care encounters were monitored between February 8 and March 31, 2002. No outbreaks of public health significance were detected. The system was implemented successfully and operational for the 2002 Olympic Winter Games and remains operational today.

Design of a national retail data monitor for public health surveillance.

The National Retail Data Monitor receives data daily from 10,000 stores, including pharmacies, that sell health care products. These stores belong to national chains that process sales data centrally and utilize Universal Product Codes and scanners to collect sales information at the cash register. The high degree of retail sales data automation enables the monitor to collect information from thousands of store locations in near to real time for use in public health surveillance. The monitor provides user interfaces that display summary sales data on timelines and maps. Algorithms monitor the data automatically on a daily basis to detect unusual patterns of sales. The project provides the resulting data and analyses, free of charge, to health departments nationwide. Future plans include continued enrollment and support of health departments, developing methods to make the service financially self-supporting, and further refinement of the data collection system to reduce the time latency of data receipt and analysis.

Technical description of RODS: a real-time public health surveillance system.

This report describes the design and implementation of the Real-time Outbreak and Disease Surveillance (RODS) system, a computer-based public health surveillance system for early detection of disease outbreaks. Hospitals send RODS data from clinical encounters over virtual private networks and leased lines using the Health Level 7 (HL7) message protocol. The data are sent in real time. RODS automatically classifies the registration chief complaint from the visit into one of seven syndrome categories using Bayesian classifiers. It stores the data in a relational database, aggregates the data for analysis using data warehousing techniques, applies univariate and multivariate statistical detection algorithms to the data, and alerts users of when the algorithms identify anomalous patterns in the syndrome counts. RODS also has a Web-based user interface that supports temporal and spatial analyses. RODS processes sales of over-the-counter health care products in a similar manner but receives such data in batch mode on a daily basis. RODS was used during the 2002 Winter Olympics and currently operates in two states-Pennsylvania and Utah. It has been and continues to be a resource for implementing, evaluating, and applying new methods of public health surveillance.