Surveillance Algorithms Research Articles

Improving Administrative Code-Based Algorithms for Sepsis Surveillance*

Improving Administrative Code-Based Algorithms for Sepsis Surveillance*

The impact of the Centers for Disease Control and Prevention new surveillance definitions of ventilator-associated event on our clinical practice

ABSTRACT Objective Surveillance of ventilator-associated pneumonia is subjective, inaccurate, time-consuming, and does not predict outcomes. The Centers for Disease Control and Prevention (CDC) recommended the use of ventilator-associated event (VAE) definitions instead of ventilator-associated pneumonia (VAP) in 2013. Therefore, we evaluated this novel surveillance algorithm by comparing the results of our VAP and VAE methods. Material Methods We evaluated mechanically ventilated adult medical and surgical patients in our 13-bed intensive care unit (ICU). Nine patients diagnosed with VAP in 2018-2019 and 11 patients diagnosed with VAE in 2022-2024 were retrospectively evaluated. The impact of the new definitions on clinical processes such as days on the mechanical ventilator, duration of antibiotic use, ICU stay in determining infectious status was monitored. Results. Statistical analysis revealed that demographic and numeric data were similar in both VAP and VAE diagnosis groups (p

Integrating testing volume into bandit algorithms for infectious disease surveillance

Abstract Mobile testing services provide opportunities for active surveillance of infectious diseases for hard-to-reach and/or high-risk individuals who do not know their disease status. Identifying as many infected individuals as possible is important for mitigating disease transmission. Recently, multi-armed bandit sampling approaches have been adapted and applied in this setting to maximize the cumulative number of positive tests collected over time. However, these algorithms have not considered the possibility of variability in the number of tests administered across testing sites. What impact this variability has on the ability of these approaches to maximize yield is currently unknown. Therefore, we investigate this question by extending existing sampling frameworks to directly account for variability in testing volume while also maintaining the computational tractability of the previous methods. Through a simulation study based on human immunodeficiency virus infection characteristics in the Republic of the Congo (Congo-Brazzaville) as well as an application to COVID-19 testing data in Connecticut, we find improved long- and short-term performances of the new methods compared to several existing approaches. Based on these findings and the ease of computation, we recommend use of the newly developed methods for active surveillance of infectious diseases when variability in testing volume may be present.

A Robust Star Identification Algorithm for Resident Space Object Surveillance

Star identification algorithms can be applied to resident space object (RSO) surveillance, which includes a large number of stars and false stars. This paper proposes an efficient, robust star identification algorithm for RSO surveillance based on a neural network. First, a feature called equal-frequency binning radial feature (EFB-RF) is proposed for guide stars, and a superficial neural network is constructed for feature classification. Then the training set is generated based on EFB-RF. Finally, the remaining stars are identified using a residual star matching method. The simulation experiment and results show that the identification rate of our algorithm can reach 99.82% under 1 pixel position noise, and it can reach 99.54% under 5% false stars. When the percentage of missing stars is 15%, it can reach 99.40%. The algorithm is verified by RSO surveillance.

Is Ventilator-associated Event Algorithm Ideal for Surveillance? – Experience from a Neurosurgery Intensive Care Unit in Kerala

ABSTRACT Background: The ventilator-associated event (VAE) surveillance algorithm was brought in to replace the clinical ventilator-associated pneumonia (VAP) surveillance by NHSN in January 2013. This study intends to find the correlation of these surveillance criteria with the clinical VAP rates in a neurosurgery intensive care unit (ICU). Materials and Methods: This was a retrospective analysis of records of patients undergoing invasive mechanical ventilation in a neurosurgery ICU during a 3-month period. Results: Thirty-seven patients underwent invasive mechanical ventilation. The mean age was 56 years. Hypertension was the most common comorbidity among 22 (59.4%) patients. The neurological diagnosis was traumatic brain injury in 19 cases, nontraumatic intracranial hemorrhage in 12 cases, and ischemic stroke in 6 cases. Twenty-one patients had respiratory sample culture positivity and 14 were clinical VAP. The total ventilator days assessed were 82. The clinical VAP rate was 170.7, and the probable VAP (PVAP) rate for the same period was 12.19. Among the 14 patients who developed clinical VAP, 13 (88%) were hospital acquired and caused by multidrug-resistant pathogens. Six patients died during their ICU stay. ICU stay of more than 7 days was significantly associated with a clinical VAP. Conclusions: VAE was found to correlate poorly with a clinical diagnosis of VAP. When used as a surveillance tool in an ICU, it was found to miss important infection-related events in ventilated patients.

Forest Fire Detection Based on Spatial Characteristics of Surface Temperature

Amidst the escalating threat of global warming, which manifests in more frequent forest fires, the prompt and accurate detection of forest fires has ascended to paramount importance. The current surveillance algorithms employed for forest fire monitoring—including, but not limited to, fixed threshold algorithms, multi-channel threshold algorithms, and contextual algorithms—rely primarily upon the degree of deviation between the pixel temperature and the background temperature to discern pyric events. Notwithstanding, these algorithms typically fail to account for the spatial heterogeneity of the background temperature, precipitating the consequential oversight of low-temperature fire point pixels, thus impeding the expedited detection of fires in their initial stages. For the amelioration of this deficiency, the present study introduces a spatial feature-based (STF) method for forest fire detection, leveraging Himawari-8/9 imagery as the main data source, complemented by the Shuttle Radar Topography Mission (SRTM) DEM data inputs. Our proposed modality reconstructs the surface temperature information via selecting the optimally designated machine learning model, subsequently identifying the fire point through utilizing the difference between the reconstructed surface temperatures and empirical observations, in tandem with the spatial contextual algorithm. The results confirm that the random forest model demonstrates superior efficacy in the reconstruction of the surface temperature. Benchmarking the STF method against both the fire point datasets disseminated by the China Forest and Grassland Fire Prevention and Suppression Network (CFGFPN) and the Wild Land Fire (WLF) fire point product validation datasets from Himawari-8/9 yielded a zero rate of omission errors and a comprehensive evaluative index, predominantly surpassing 0.74. These findings show that the STF method proposed herein significantly augments the identification of lower-temperature fire point pixels, thereby amplifying the sensitivity of forest surveillance.

Perianal disease in inflammatory bowel disease: Broadening treatment and surveillance strategies for anal cancer.

The perianal disease affects up to one-third of individuals with Crohn's disease (CD), causing disabling symptoms and significant impairment in quality of life, particularly for those with perianal fistulising CD (PFCD). The collaborative effort between gastroenterologists and surgeons is essential for addressing PFCD to achieve fistula closure and promote luminal healing. Limited fistula healing rates with conventional therapies have prompted the emergence of new biological agents, endoscopic procedures and surgical techniques that show promising results. Among these, mesenchymal stem cells injection is a particularly hopeful therapy. In addition to the burden of fistulas, individuals with perianal CD may face an increased risk of developing anal cancer. This underscores the importance of surveillance programmes and timely interventions to prevent late diagnoses and poor outcomes. Currently, there is no established formal anal screening programme. In this review, we provide an overview of the current state of the art in managing PFCD, including novel medical, endoscopic and surgical approaches. The discussion also focuses on the relevance of establishing an anal cancer screening programme in CD, intending to propose a risk-based surveillance algorithm. The validation of this surveillance programme would be a significant step forward in improving patient care and outcomes.

Comparison of Barrier Surveillance Algorithms for Directional Sensors and UAVs.

Border surveillance and the monitoring of critical infrastructure are essential components of regional and industrial security. In this paper, our purpose is to study the intricate nature of surveillance methods used by hybrid monitoring systems utilizing Pan-Tilt-Zoom (PTZ) cameras, modeled as directional sensors, and UAVs. We aim to accomplish three occasionally conflicting goals. Firstly, at any given moment we want to detect as many intruders as possible with special attention to newly arriving trespassers. Secondly, we consider it equally important to observe the temporal movement and behavior of each intruder group as accurately as possible. Furthermore, in addition to these objectives, we also seek to minimize the cost of sensor usage associated with surveillance. During the research, we developed and analyzed several interrelated, increasingly complex algorithms. By leveraging RL methods we also gave the system the chance to find the optimal solution on its own. As a result we have gained valuable insights into how various components of these algorithms are interconnected and coordinate. Building upon these observations, we managed to develop an efficient algorithm that takes into account all three criteria mentioned above.

Genetic diversity of enteric viruses responsible of gastroenteritis in urban and rural Burkina Faso.

Viral gastrointestinal infections remain a major public health concern in developing countries. In Burkina Faso, there are very limited updated data on the circulating viruses and their genetic diversity. This study investigates the detection rates and characteristics of rotavirus A (RVA), norovirus (NoV), sapovirus (SaV) and human astrovirus (HAstV) in patients of all ages with acute gastrointestinal infection in urban and rural areas. From 2018 to 2021, stool samples from 1,295 patients with acute gastroenteritis were collected and screened for RVA, NoV, SaV and HAstV. Genotyping and phylogenetic analyses were performed on a subset of samples. At least one virus was detected in 34.1% of samples. NoV and SaV were predominant with detection rates of respectively 10.5 and 8.8%. We identified rare genotypes of NoV GII, RVA and HAstV, recombinant HAstV strains and a potential zoonotic RVA transmission event. We give an up-to-date epidemiological picture of enteric viruses in Burkina Faso, showing a decrease in prevalence but a high diversity of circulating strains. However, viral gastroenteritis remains a public health burden, particularly in pediatric settings. Our data advocate for the implementation of routine viral surveillance and updated management algorithms for diarrheal disease.

An Improved Algorithm for the Detection of Ventilator Associated Pneumonia

Background: Ventilator associated pneumonia (VAP) is associated with significant rates of morbidity and all-cause mortality. Active VAP surveillance can identify risk factors for which targeted preventive measures can be implemented. However, surveillance efforts are complicated by challenges associated with accurate VAP diagnosis. We aimed to improve the accuracy and automation of existing VAP diagnostic algorithms to better identify patients at risk. Methods: The study was conducted at NYU Langone Health from June 2022 through December 2023. We created a semi-automated VAP surveillance system using the Centers for Disease Control & Prevention (CDC) ventilator associated event (VAE) definition as a base framework (Figure 1). We modified this definition to include additional elements, such as having a sputum culture ordered within 48 hours of worsening oxygen status, regardless of culture result. Using this algorithm—followed by manual clinician reviews—we retrospectively assessed possible VAP cases to determine the ability of our surveillance system to correctly identify VAP. Results: Of the 123 possible VAP cases identified through our automated system, 75 (61%) were correctly diagnosed as VAP after clinical review. This reflects a rate of 1.5 infections per 1000 ventilation days across the system and 1.85 infections per 100 patients ventilated for greater than 2 days. Of the 48 remaining patients without VAP after clinical review, 25% (n=12) were characterized as having hospital-acquired pneumonia, 21% (n=10) as acute respiratory distress syndrome or infection at another site and 10% (n=5) as pulmonary embolism/infarction. Among all patients identified through this automated system (VAP and non-VAP), 53% experienced in-hospital death. Discussion: Our automated VAP surveillance algorithm identified 123 cases of potential VAP, 61% of which were consistent with a clinical diagnosis of VAP upon manual chart review. Our VAP rate of 1.5 infections per 1000 ventilation days was similar to published rates at other North American hospital systems. The high in-hospital mortality rate among these patients highlights the need for improved surveillance systems and earlier interventions to reduce the risk of VAP. There are several limitations to the CDC’s VAE definition, including its requirement of a positive microbiologic culture and focus on sputum quality. This potentially misses cases of culture-negative VAP in patients receiving antibiotics prior to sputum collection. Our goal is to continue to validate and improve our algorithm’s ability to correctly identify patients with clinical VAP, so that targeted prevention efforts can be focused upon the patients with the highest risk for poor outcomes.Disclosure: Madeline DiLorenzo: Stocks - Abbvie, Amgen Inc., Becton Dickinson, Biogen Inc., Bristol Myers and Squibb, CVS Health, Davita Inc., Elevance Health, Gilead, Henry Schein, Hologic Inc., Humana Inc., Jazz Pharmaceuticals, Laboratory Corp, Merck and Co., Quest Diagnostics, ResMed Inc., Teladoc Health, Vertex Pharmaceuticals, West Pharmaceuticals

Defining an Algorithm for Long-Term Duplex Ultrasound Surveillance after Carotid Intervention

Defining an Algorithm for Long-Term Duplex Ultrasound Surveillance after Carotid Intervention

Enhancing Public Health Surveillance: Outbreak Detection Algorithms Deployed for Syndromic Surveillance During Arbaeenia Mass Gatherings in Iraq.

Large gatherings often involve extended and intimate contact among individuals, creating environments conducive to the spread of infectious diseases. Despite this, there is limited research utilizing outbreak detection algorithms to analyze real syndrome data from such events. This study sought to address this gap by examining the implementation and efficacy of outbreak detection algorithms for syndromic surveillance during mass gatherings in Iraq. For the study, 10 data collectors conducted field data collection over 10 days from August 25, 2023, to September 3, 2023. Data were gathered from 10 healthcare clinics situated along Ya Hussein Road, a major route from Najaf to Karbala in Iraq. Various outbreak detection algorithms, such as moving average, cumulative sum, and exponentially weighted moving average, were applied to analyze the reported syndromes. During the 10 days from August 25, 2023, to September 3, 2023, 12202 pilgrims visited 10 health clinics along a route in Iraq. Most pilgrims were between 20 and 59 years old (77.4%, n=9444), with more than half being foreigners (58.1%, n=7092). Among the pilgrims, 40.5% (n=4938) exhibited syndromes, with influenza-like illness (ILI) being the most common (48.8%, n=2411). Other prevalent syndromes included food poisoning (21.2%, n=1048), heatstroke (17.7%, n=875), febrile rash (9.0%, n=446), and gastroenteritis (3.2%, n=158). The cumulative sum (CUSUM) algorithm was more effective than exponentially weighted moving average (EWMA) and moving average (MA) algorithms for detecting small shifts. Effective public health surveillance systems are crucial during mass gatherings to swiftly identify and address emerging health risks. Utilizing advanced algorithms and real-time data analysis can empower authorities to improve their readiness and response capacity, thereby ensuring the protection of public health during these gatherings.

Surveillance of catheter-associated bloodstream infections: development and validation of a fully automated algorithm

BackgroundMost surveillance systems for catheter-related bloodstream infections (CRBSI) and central line-associated bloodstream infections (CLABSI) are based on manual chart review. Our objective was to validate a fully automated algorithm for CRBSI and CLABSI surveillance in intensive care units (ICU).MethodsWe developed a fully automated algorithm to detect CRBSI, CLABSI and ICU-onset bloodstream infections (ICU-BSI) in patients admitted to the ICU of a tertiary care hospital in Switzerland. The parameters included in the algorithm were based on a recently performed systematic review. Structured data on demographics, administrative data, central vascular catheter and microbiological results (blood cultures and other clinical cultures) obtained from the hospital’s data warehouse were processed by the algorithm. Validation for CRBSI was performed by comparing results with prospective manual BSI surveillance data over a 6-year period. CLABSI were retrospectively assessed over a 2-year period.ResultsFrom January 2016 to December 2021, 854 positive blood cultures were identified in 346 ICU patients. The median age was 61.7 years [IQR 50–70]; 205 (24%) positive samples were collected from female patients. The algorithm detected 5 CRBSI, 109 CLABSI and 280 ICU-BSI. The overall CRBSI and CLABSI incidence rates determined by automated surveillance for the period 2016 to 2021 were 0.18/1000 catheter-days (95% CI 0.06–0.41) and 3.86/1000 catheter days (95% CI: 3.17–4.65). The sensitivity, specificity, positive predictive and negative predictive values of the algorithm for CRBSI, were 83% (95% CI 43.7–96.9), 100% (95% CI 99.5–100), 100% (95% CI 56.5–100), and 99.9% (95% CI 99.2–100), respectively. One CRBSI was misclassified as an ICU-BSI by the algorithm because the same bacterium was identified in the blood culture and in a lower respiratory tract specimen. Manual review of CLABSI from January 2020 to December 2021 (n = 51) did not identify any errors in the algorithm.ConclusionsA fully automated algorithm for CRBSI and CLABSI detection in critically-ill patients using only structured data provided valid results. The next step will be to assess the feasibility and external validity of implementing it in several hospitals with different electronic health record systems.

Efficacy of a Modular Imaging Algorithm for Surveillance After Endovascular Aortic Repair

Efficacy of a Modular Imaging Algorithm for Surveillance After Endovascular Aortic Repair

Approach towards surveillance-based diagnosis of acute respiratory illness in India: Expert recommendations

BackgroundEmerging infectious diseases, often zoonotic, demand a collaborative “One-Health” surveillance approach due to human activities. The need for standardized diagnostic and surveillance algorithms is emphasized to address the difficulty in clinical differentiation and curb antimicrobial resistance. ObjectiveThe present recommendations are comprehensive diagnostic and surveillance algorithm for ARIs, developed by the Indian Council of Medical Research (ICMR), which aims to enhance early detection and treatment with improved surveillance. This algorithm shall be serving as a blueprint for respiratory infections landscape in the country and early detection of surge of respiratory infections in the country. ContentThe ICMR has risen up to the threat of emerging and re-emerging infections. Here, we seek to recommend a structured approach for diagnosing respiratory illnesses. The recommendations emphasize the significance of prioritizing respiratory pathogens based on factors such as the frequency of occurrence (seasonal or geographical), disease severity, ease of diagnosis and public health importance. The proposed surveillance-based diagnostic algorithm for ARI relies on a combination of gold-standard conventional methods, innovative serological and molecular techniques, as well as radiological approaches, which collectively contribute to the detection of various causative agents.The diagnostic part of the integrated algorithm can be dealt at the local microbiology laboratory of the healthcare facility with the few positive and negative specimens shipped to linked viral disease research laboratories (VRDLs) and other ICMR designated laboratories for genome characterisation, cluster identification and identification of novel agents.

Harnessing of Artificial Intelligence for the Diagnosis and Prevention of Hospital-Acquired Infections: A Systematic Review.

Healthcare-associated infections (HAIs) are the most common adverse events in healthcare and constitute a major global public health concern. Surveillance represents the foundation for the effective prevention and control of HAIs, yet conventional surveillance is costly and labor intensive. Artificial intelligence (AI) and machine learning (ML) have the potential to support the development of HAI surveillance algorithms for the understanding of HAI risk factors, the improvement of patient risk stratification as well as the prediction and timely detection and prevention of infections. AI-supported systems have so far been explored for clinical laboratory testing and imaging diagnosis, antimicrobial resistance profiling, antibiotic discovery and prediction-based clinical decision support tools in terms of HAIs. This review aims to provide a comprehensive summary of the current literature on AI applications in the field of HAIs and discuss the future potentials of this emerging technology in infection practice. Following the PRISMA guidelines, this study examined the articles in databases including PubMed and Scopus until November 2023, which were screened based on the inclusion and exclusion criteria, resulting in 162 included articles. By elucidating the advancements in the field, we aim to highlight the potential applications of AI in the field, report related issues and shortcomings and discuss the future directions.

Monitoring of Women with Anti-Ro/SSA and Anti-La/SSB Antibodies in Germany-Status Quo and Intensified Monitoring Concepts.

Background: The fetuses of pregnant women affected by anti-Ro/anti-La antibodies are at risk of developing complete atrioventricular heart block (CAVB) and other potentially life-threatening cardiac affections. CAVB can develop in less than 24 h. Treatment with anti-inflammatory drugs and immunoglobulins (IVIG) can restore the normal rhythm if applied in the transition period. Routine weekly echocardiography, as often recommended, will rarely detect emergent AVB. The surveillance of these pregnancies is controversial. Home-monitoring using a hand-held Doppler is a promising new approach. Methods: To obtain an overview of the current practice in Germany, we developed a web-based survey sent by the DEGUM (German Society of Ultrasound in Medicine) to ultrasound specialists. With the intention to evaluate practicability of home-monitoring, we instructed at-risk pregnant women to use a hand-held Doppler in the vulnerable period between 18 and 26 weeks at our university center. Results: There are trends but no clear consensus on surveillance, prophylaxis, and treatment of anti-Ro/La positive pregnant between specialists in Germany. Currently most experts do not offer home-monitoring but have a positive attitude towards its prospective use. Intensified fetal monitoring using a hand-held Doppler is feasible for pregnant women at risk and does not lead to frequent and unnecessary contact with the center. Conclusion: Evidence-based guidelines are needed to optimize the care of anti-Ro/La-positive pregnant women. Individual risk stratification could help pregnancy care of women at risk and is welcmed by most experts. Hand-held doppler monitoring is accepted by patients and prenatal medicine specialists as an option for intensified monitoring and can be included in an algorithm for surveillance.

Advancements in the treatment of age-related macular degeneration: a comprehensive review

Abstract Age-related macular degeneration (AMD) stands as a leading cause of irreversible blindness, particularly affecting central vision and impeding daily tasks. This paper provides a thorough exploration of AMD, distinguishing between its two main subtypes—Wet and Dry AMD—while shedding light on the prevalence and risk factors, including age, genetics, and smoking. The focus shifts to the current and future treatment landscape, examining both Dry and Wet AMD. Regarding Dry AMD, interventions such as antioxidant supplementation and ongoing clinical trials offer hope. Notable among these is Pegcetacoplan which is the only Food and Drug Administration (FDA)-approved medication, displaying promising results in reducing geographic atrophy lesions. For Wet AMD, anti-Vascular Endothelial Growth Factor therapies like Ranibizumab (Lucentis®) have been instrumental, and newer drugs like Faricimab and OPT-302 show comparable efficacy with extended dosing intervals. Additionally, gene therapies such as RGX-314 present a potential paradigm shift, reducing or eliminating the need for frequent injections. Biosimilars offer cost-effective alternatives. The paper also delves into the integration of technology and artificial intelligence in AMD management, highlighting the role of smartphone apps for patient monitoring and artificial intelligence algorithms for diagnosis and surveillance. Furthermore, patient perspectives on artificial intelligence demonstrate a positive correlation between understanding and trust. The narrative concludes with a glimpse into ground-breaking technologies, including retinal implants and bionic chips, offering hope for vision restoration. Overall, this paper underscores the multifaceted approach in addressing AMD, combining traditional and innovative strategies, paving the way for a more promising future in AMD treatment.

The accuracy of fully-automated algorithms for the surveillance of central venous catheter-related bloodstream infection in hospitalised patients.

Continuous surveillance for healthcare-associated infections such as central venous catheter-related bloodstream infections (CVC-BSI) is crucial for prevention. However, traditional surveillance methods are resource-intensive and prone to bias. This study aimed to develop and validate fully-automated surveillance algorithms for CVC-BSI. Two algorithms were developed using electronic health record data from 1000 admissions with a positive blood culture (BCx) at Karolinska University Hospital from 2017: (1) Combining microbiological findings in BCx and CVC cultures with BSI symptoms; (2) Only using microbiological findings. These algorithms were validated in 5170 potential CVC-BSI-episodes from all admissions in 2018-2019, and results extrapolated to all potential CVC-BSI-episodes within this period (n = 181,354). The reference standard was manual record review according to ECDC's definition of microbiologically confirmed CVC-BSI (CRI3-CVC). In the potential CVC-BSI-episodes, 51 fulfilled ECDC's definition and the algorithms identified 47 and 49 episodes as CVC-BSI, respectively. Both algorithms performed well in assessing CVC-BSI. Overall, algorithm 2 performed slightly better with in the total period a sensitivity of 0.880 (95%-CI 0.783-0.959), specificity of 1.000 (95%-CI 0.999-1.000), PPV of 0.918 (95%-CI 0.833-0.981) and NPV of 1.000 (95%-CI 0.999-1.000). Incidence according to the reference and algorithm 2 was 0.33 and 0.31 per 1000 in-patient hospital-days, respectively. Both fully-automated surveillance algorithms for CVC-BSI performed well and could effectively replace manual surveillance. The simpler algorithm, using only microbiology data, is suitable when BCx testing adheres to recommendations, otherwise the algorithm using symptom data might be required. Further validation in other settings is necessary to assess the algorithms' generalisability.

Pedestrian Tracking Algorithm for Video Surveillance Based on Lightweight Convolutional Neural Network

Pedestrian Tracking Algorithm for Video Surveillance Based on Lightweight Convolutional Neural Network