Dataset Information

The use of automated data extraction tools to develop a solid organ transplant registry: Proof of concept study of bloodstream infections.

ABSTRACT:

Background

We created an electronic health record-based registry using automated data extraction tools to study the epidemiology of bloodstream infections (BSI) in solid organ transplant recipients. The overarching goal was to determine the usefulness of an electronic health record-based registry using data extraction tools for clinical research in solid organ transplantation.

Methods

We performed a retrospective single-center cohort study of adult solid organ transplant recipients from 2010 to 2015. Extraction tools were used to retrieve data from the electronic health record, which was integrated with national data sources. Electronic health records of subjects with positive blood cultures were manually adjudicated using consensus definitions. One-year cumulative incidence, risk factors for BSI acquisition, and 1-year mortality were analyzed by Kaplan-Meier method and Cox modeling, and 30-day mortality with logistic regression.

Results

In 917 solid organ transplant recipients the cumulative incidence of BSI was 8.4% (95% confidence interval 6.8-10.4) with central line-associated BSI as the most common source. The proportion of multidrug-resistant isolates increased from 0% in 2010 to 47% in 2015 (p = 0.03). BSI was the strongest risk factor for 1-year mortality (HR=8.44; 4.99-14.27; p<0.001). In 11 of 14 deaths, BSI was the main cause or contributory in patients with non-rapidly fatal underlying conditions.

Conclusions

Our study illustrates the usefulness of an electronic health record-based registry using automated extraction tools for clinical research in the field of solid organ transplantation. A BSI reduces the 1-year survival of solid organ transplant recipients. The most common sources of BSIs in our studies are preventable.

SUBMITTER: La Hoz RM

PROVIDER: S-EPMC8654599 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundThe timely and accurate diagnosis of bloodstream infection (BSI) is critical for patient management. With longstanding challenges for routine blood culture, metagenomics is a promising approach to rapidly provide sequence-based detection and characterisation of bloodborne bacteria. Long-read sequencing technologies have successfully supported the use of clinical metagenomics for syndromes such as respiratory illness, and modified approaches may address two requisite factors for metagenomics to be used as a BSI diagnostic: depletion of the high level of host DNA to then detect the low abundance of microbes in blood.MethodsBlood samples from healthy donors were spiked with different concentrations of four prevalent causative species of BSI. All samples were then subjected to a modified saponin-based host DNA depletion protocol and optimised DNA extraction, whole genome amplification and debranching steps in preparation for sequencing, followed by bioinformatical analyses. Two related variants of the protocol are presented: 1mL of blood processed without bacterial enrichment, and 5mL of blood processed following a rapid bacterial enrichment protocol-SepsiPURE.ResultsAfter first identifying that a large proportion of host mitochondrial DNA remained, the host depletion process was optimised by increasing saponin concentration to 3% and scaling the reaction to allow more sample volume. Compared to non-depleted controls, the 3% saponin-based depletion protocol reduced the presence of host chromosomal and mitochondrial DNA < 106 and < 103 fold respectively. When the modified depletion method was further combined with a rapid bacterial enrichment method (SepsiPURE; with 5mL blood samples) the depletion of mitochondrial DNA improved by a further > 10X while also increasing detectable bacteria by > 10X. Parameters during DNA extraction, whole genome amplification and long-read sequencing were also adjusted, and subsequently amplicons were detected for each input bacterial species at each of the spiked concentrations, ranging from 50-100 colony forming units (CFU)/mL to 1-5 CFU/mL.ConclusionIn this proof-of-concept study, four prevalent BSI causative species were detected in under 12 h to species level (with antimicrobial resistance determinants) at concentrations relevant to clinical blood samples. The use of a rapid and precise metagenomic protocols has the potential to advance the diagnosis of BSI.

Project description:IntroductionDifficulty swallowing (dysphagia) occurs frequently in patients with neurological disorders and can lead to aspiration, choking, and malnutrition. Dysphagia is typically diagnosed using costly, invasive imaging procedures or subjective, qualitative bedside examinations. Wearable sensors are a promising alternative to noninvasively and objectively measure physiological signals relevant to swallowing. An ongoing challenge with this approach is consolidating these complex signals into sensitive, clinically meaningful metrics of swallowing performance. To address this gap, we propose 2 novel, digital monitoring tools to evaluate swallows using wearable sensor data and machine learning.MethodsBiometric swallowing and respiration signals from wearable, mechano-acoustic sensors were compared between patients with poststroke dysphagia and nondysphagic controls while swallowing foods and liquids of different consistencies, in accordance with the Mann Assessment of Swallowing Ability (MASA). Two machine learning approaches were developed to (1) classify the severity of impairment for each swallow, with model confidence ratings for transparent clinical decision support, and (2) compute a similarity measure of each swallow to nondysphagic performance. Task-specific models were trained using swallow kinematics and respiratory features from 505 swallows (321 from patients and 184 from controls).ResultsThese models provide sensitive metrics to gauge impairment on a per-swallow basis. Both approaches demonstrate intrasubject swallow variability and patient-specific changes which were not captured by the MASA alone. Sensor measures encoding respiratory-swallow coordination were important features relating to dysphagia presence and severity. Puree swallows exhibited greater differences from controls than saliva swallows or liquid sips (p < 0.037).DiscussionDeveloping interpretable tools is critical to optimize the clinical utility of novel, sensor-based measurement techniques. The proof-of-concept models proposed here provide concrete, communicable evidence to track dysphagia recovery over time. With refined training schemes and real-world validation, these tools can be deployed to automatically measure and monitor swallowing in the clinic and community for patients across the impairment spectrum.