Can routinely collected electronic health data be used to develop novel healthcare associated infection surveillance tools?

Background: Healthcare associated infections (HCAI) pose a significant burden to health systems both within the UK and internationally. Surveillance is an essential component to any infection control programme, however traditional surveillance systems are time consuming and costly. Large amounts of electronic routine data are collected within the English NHS, yet these are not currently exploited for HCAI surveillance. Aim: To investigate whether routinely collected electronic hospital data can be exploited for HCAI surveillance within the NHS. Methods: This thesis made use of local linked electronic health data from Imperial College Healthcare NHS Trust, including information on patient admissions, discharges, diagnoses, procedures, laboratory tests, diagnostic imaging requests and traditional infection surveillance data. To establish the evidence base on surveillance and risks of HCAI, two literature reviews were carried out. Based on these, three types of innovative surveillance tools were generated and assessed for their utility and applicability. Results: The key findings were firstly the emerging importance of automated and syndromic surveillance in infection surveillance, but the lack of investigation and application of these tools within the NHS. Syndromic surveillance of surgical site infections was successful in coronary artery bypass graft patients; however it was an inappropriate methodology for caesarean section patients. Automated case detection of healthcare associated urinary tract infections, based on electronic microbiology data, demonstrated similar rates of infection to those recorded during a point prevalence survey. Routine administrative data demonstrated mixed utility in the creation of simplified risk scores or infection, with poorly performing risk models of surgical site infections but reasonable model fit for HCA UTI. Conclusion: Whilst in principle routine administrative data can be used to generate novel surveillance tools for healthcare associated infections; in reality it is not yet practical within the IT infrastructure of the NHS

Automated methods for surveillance of surgical site infections.

Automated data, especially from pharmacy and administrative claims, are available for much of the U.S. population and might substantially improve both inpatient and postdischarge surveillance for surgical site infections complicating selected procedures, while reducing the resources required. Potential improvements include better sensitivity, less susceptibility to interobserver variation, more uniform availability of data, more precise estimates of infection rates, and better adjustment for patients' coexisting illness

Which comorbid conditions should we be analyzing as risk factors for healthcare-associated infections?

OBJECTIVETo determine which comorbid conditions are considered causally related to central-line associated bloodstream infection (CLABSI) and surgical-site infection (SSI) based on expert consensus.DESIGNUsing the Delphi method, we administered an iterative, 2-round survey to 9 infectious disease and infection control experts from the United States.METHODSBased on our selection of components from the Charlson and Elixhauser comorbidity indices, 35 different comorbid conditions were rated from 1 (not at all related) to 5 (strongly related) by each expert separately for CLABSI and SSI, based on perceived relatedness to the outcome. To assign expert consensus on causal relatedness for each comorbid condition, all 3 of the following criteria had to be met at the end of the second round: (1) a majority (&gt;50%) of experts rating the condition at 3 (somewhat related) or higher, (2) interquartile range (IQR)≤1, and (3) standard deviation (SD)≤1.RESULTSFrom round 1 to round 2, the IQR and SD, respectively, decreased for ratings of 21 of 35 (60%) and 33 of 35 (94%) comorbid conditions for CLABSI, and for 17 of 35 (49%) and 32 of 35 (91%) comorbid conditions for SSI, suggesting improvement in consensus among this group of experts. At the end of round 2, 13 of 35 (37%) and 17 of 35 (49%) comorbid conditions were perceived as causally related to CLABSI and SSI, respectively.CONCLUSIONSOur results have produced a list of comorbid conditions that should be analyzed as risk factors for and further explored for risk adjustment of CLABSI and SSI.Infect Control Hosp Epidemiol 2017;38:449–454</jats:sec

Predicting infections using computational intelligence – A systematic review

Infections encompass a set of medical conditions of very diverse kinds that can pose a significant risk to health, and even death. As with many other diseases, early diagnosis can help to provide patients with proper care to minimize the damage produced by the disease, or to isolate them to avoid the risk of spread. In this context, computational intelligence can be useful to predict the risk of infection in patients, raising early alarms that can aid medical teams to respond as quick as possible. In this paper, we survey the state of the art on infection prediction using computer science by means of a systematic literature review. The objective is to find papers where computational intelligence is used to predict infections in patients using physiological data as features. We have posed one major research question along with nine specific subquestions. The whole review process is thoroughly described, and eight databases are considered which index most of the literature published in different scholarly formats. A total of 101 relevant documents have been found in the period comprised between 2003 and 2019, and a detailed study of these documents is carried out to classify the works and answer the research questions posed, resulting to our best knowledge in the most comprehensive study of its kind. We conclude that the most widely addressed infection is by far sepsis, followed by Clostridium difficile infection and surgical site infections. Most works use machine learning techniques, from which logistic regression, support vector machines, random forest and naive Bayes are the most common. Some machine learning works provide some ideas on the problems of small data and class imbalance, which can be of interest. The current systematic literature review shows that automatic diagnosis of infectious diseases using computational intelligence is well documented in the medical literature.publishedVersio

Visualization, Prediction, and Causal Inference: Applications in Healthcare

The recent wave of data collection in the field of healthcare has opened up an ocean of possibilities to learn and develop new exploratory, diagnostic, and prognostic methods. This thesis explores how three fields of statistics (1) data visualization, (2) prediction, (3) and causal inference, can help us leverage this data in order to answer a wide range of questions in healthcare.Part I of this thesis presents a software package called superheat that can be used by researchers to visualize complex datasets and multi-faceted modeling results. The primary users of this software so far have been those in the medical research industry. In this thesis, we apply superheat in three case studies including (1) using a publicly available global organ donation database curated by the World Health Organization to understand and summarize the global organ donation trends, (2) visualizing groups of topics that appear in text data scraped from Google News, (3) examining model performance for a model designed to predict the brain's response to images using fMRI data. The theme of Part 1 of this thesis is visualization in healthcare.Part II of this thesis introduces an analysis for predicting a patient's risk of developing a Surgical Site Infection (SSI) following surgery. A SSI is an infection that occurs at the site of a surgery within 30 days post surgery, and is responsible for up to 30% of hospital acquired infections. This method was developed in collaboration with healthcare professionals including infectious disease experts and surgeons at UC Davis. The theme of Part 2 of this thesis is prediction in healthcare.Part III of this thesis presents a novel application of instrumental variables in causal inference, asking about the possible effectiveness of a "survival-benefit"-based liver transplant allocation scheme. The conclusion is that while there could be substantial benefit yielded from rethinking how organs are allocated, the feasibility of implementing such a scheme that relies drawing causal inferences from complex observational data is extremely difficult. The theme of Part 3 of this thesis is causal inference in healthcare

Epidemiology of and surveillance for postpartum infections.

We screened automated ambulatory medical records, hospital and emergency room claims, and pharmacy records of 2,826 health maintenance organization (HMO) members who gave birth over a 30-month period. Full-text ambulatory records were reviewed for the 30-day postpartum period to confirm infection status for a weighted sample of cases. The overall postpartum infection rate was 6.0%, with rates of 7.4% following cesarean section and 5.5% following vaginal delivery. Rehospitalization; cesarean delivery; antistaphylococcal antibiotics; diagnosis codes for mastitis, endometritis, and wound infection; and ambulatory blood or wound cultures were important predictors of infection. Use of automated information routinely collected by HMOs and insurers allows efficient identification of postpartum infections not detected by conventional surveillance

Prognostic models for surgical-site infection in gastrointestinal surgery: systematic review

BACKGROUND: Identification of patients at high risk of surgical-site infection may allow clinicians to target interventions and monitoring to minimize associated morbidity. The aim of this systematic review was to identify and evaluate prognostic tools for the prediction of surgical-site infection in gastrointestinal surgery.METHODS: This systematic review sought to identify original studies describing the development and validation of prognostic models for 30-day SSI after gastrointestinal surgery (PROSPERO: CRD42022311019). MEDLINE, Embase, Global Health, and IEEE Xplore were searched from 1 January 2000 to 24 February 2022. Studies were excluded if prognostic models included postoperative parameters or were procedure specific. A narrative synthesis was performed, with sample-size sufficiency, discriminative ability (area under the receiver operating characteristic curve), and prognostic accuracy compared.RESULTS: Of 2249 records reviewed, 23 eligible prognostic models were identified. A total of 13 (57 per cent) reported no internal validation and only 4 (17 per cent) had undergone external validation. Most identified operative contamination (57 per cent, 13 of 23) and duration (52 per cent, 12 of 23) as important predictors; however, there remained substantial heterogeneity in other predictors identified (range 2-28). All models demonstrated a high risk of bias due to the analytic approach, with overall low applicability to an undifferentiated gastrointestinal surgical population. Model discrimination was reported in most studies (83 per cent, 19 of 23); however, calibration (22 per cent, 5 of 23) and prognostic accuracy (17 per cent, 4 of 23) were infrequently assessed. Of externally validated models (of which there were four), none displayed 'good' discrimination (area under the receiver operating characteristic curve greater than or equal to 0.7).CONCLUSION: The risk of surgical-site infection after gastrointestinal surgery is insufficiently described by existing risk-prediction tools, which are not suitable for routine use. Novel risk-stratification tools are required to target perioperative interventions and mitigate modifiable risk factors.</p

Automated Methods for Surveillance of Surgical Site Infections

Automated data, especially from pharmacy and administrative claims, are available for much of the U.S. population and might substantially improve both inpatient and postdischarge surveillance for surgical site infections complicating selected procedures, while reducing the resources required. Potential improvements include better sensitivity, less susceptibility to interobserver variation, more uniform availability of data, more precise estimates of infection rates, and better adjustment for patients' coexisting illness