Clinical phenotypes and outcomes of SARS-CoV-2, influenza, RSV and seven other respiratory viruses: a retrospective study using complete hospital data.

BACKGROUND: An understanding of differences in clinical phenotypes and outcomes COVID-19 compared with other respiratory viral infections is important to optimise the management of patients and plan healthcare. Herein we sought to investigate such differences in patients positive for SARS-CoV-2 compared with influenza, respiratory syncytial virus (RSV) and other respiratory viruses. METHODS: We performed a retrospective cohort study of hospitalised adults and children (≤15 years) who tested positive for SARS-CoV-2, influenza virus A/B, RSV, rhinovirus, enterovirus, parainfluenza viruses, metapneumovirus, seasonal coronaviruses, adenovirus or bocavirus in a respiratory sample at admission between 2011 and 2020. RESULTS: A total of 6321 adult (1721 SARS-CoV-2) and 6379 paediatric (101 SARS-CoV-2) healthcare episodes were included in the study. In adults, SARS-CoV-2 positivity was independently associated with younger age, male sex, overweight/obesity, diabetes and hypertension, tachypnoea as well as better haemodynamic measurements, white cell count, platelet count and creatinine values. Furthermore, SARS-CoV-2 was associated with higher 30-day mortality as compared with influenza (adjusted HR (aHR) 4.43, 95% CI 3.51 to 5.59), RSV (aHR 3.81, 95% CI 2.72 to 5.34) and other respiratory viruses (aHR 3.46, 95% CI 2.61 to 4.60), as well as higher 90-day mortality, ICU admission, ICU mortality and pulmonary embolism in adults. In children, patients with SARS-CoV-2 were older and had lower prevalence of chronic cardiac and respiratory diseases compared with other viruses. CONCLUSIONS: SARS-CoV-2 is associated with more severe outcomes compared with other respiratory viruses, and although associated with specific patient and clinical characteristics at admission, a substantial overlap precludes discrimination based on these characteristics.

SARS-CoV-2 testing in patients with low COVID-19 suspicion at admission to a tertiary care hospital, Stockholm, Sweden, March to September 2020.

BackgroundUniversal SARS-CoV-2 testing at hospital admission has been proposed to prevent nosocomial transmission.AimTo investigate SARS-CoV-2 positivity in patients tested with low clinical COVID-19 suspicion at hospital admission.MethodsWe characterised a retrospective cohort of patients admitted to Karolinska University Hospital tested for SARS-CoV-2 by PCR from March to September 2020, supplemented with an in-depth chart review (16 March-12 April). We compared positivity rates in patients with and without clinical COVID-19 suspicion with Spearman's rank correlation coefficient. We used multivariable logistic regression to identify factors associated with test positivity.ResultsFrom March to September 2020, 66.9% (24,245/36,249) admitted patient episodes were tested; of those, 61.2% (14,830/24,245) showed no clinical COVID-19 suspicion, and the positivity rate was 3.2% (469/14,830). There was a strong correlation of SARS-CoV-2 positivity in patients with low vs high COVID-19 suspicion (rho = 0.92; p < 0.001).From 16 March to 12 April, the positivity rate was 3.9% (58/1,482) in individuals with low COVID-19 suspicion, and 3.1% (35/1,114) in asymptomatic patients. Rates were higher in women (5.0%; 45/893) vs men (2.0%; 12/589; p = 0.003), but not significantly different if pregnant women were excluded (3.7% (21/566) vs 2.2% (12/589); p = 0.09). Factors associated with SARS-CoV-2 positivity were testing of pregnant women before delivery (odds ratio (OR): 2.6; 95% confidence interval (CI): 1.3-5.4) and isolated symptoms in adults (OR: 3.3; 95% CI: 1.8-6.3).ConclusionsThis study shows a relatively high SARS-CoV-2 positivity rate in patients with low COVID-19 suspicion upon hospital admission. Universal SARS-CoV-2 testing of pregnant women before delivery should be considered.

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

BACKGROUND: 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. METHODS: 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). RESULTS: 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. CONCLUSIONS: 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.

Predicting sepsis onset using a machine learned causal probabilistic network algorithm based on electronic health records data.

Sepsis is a leading cause of mortality and early identification improves survival. With increasing digitalization of health care data automated sepsis prediction models hold promise to aid in prompt recognition. Most previous studies have focused on the intensive care unit (ICU) setting. Yet only a small proportion of sepsis develops in the ICU and there is an apparent clinical benefit to identify patients earlier in the disease trajectory. In this cohort of 82,852 hospital admissions and 8038 sepsis episodes classified according to the Sepsis-3 criteria, we demonstrate that a machine learned score can predict sepsis onset within 48 h using sparse routine electronic health record data outside the ICU. Our score was based on a causal probabilistic network model-SepsisFinder-which has similarities with clinical reasoning. A prediction was generated hourly on all admissions, providing a new variable was registered. Compared to the National Early Warning Score (NEWS2), which is an established method to identify sepsis, the SepsisFinder triggered earlier and had a higher area under receiver operating characteristic curve (AUROC) (0.950 vs. 0.872), as well as area under precision-recall curve (APR) (0.189 vs. 0.149). A machine learning comparator based on a gradient-boosting decision tree model had similar AUROC (0.949) and higher APR (0.239) than SepsisFinder but triggered later than both NEWS2 and SepsisFinder. The precision of SepsisFinder increased if screening was restricted to the earlier admission period and in episodes with bloodstream infection. Furthermore, the SepsisFinder signaled median 5.5 h prior to antibiotic administration. Identifying a high-risk population with this method could be used to tailor clinical interventions and improve patient care.

The accuracy of fully automated algorithms for surveillance of healthcare-onset Clostridioides difficile infections in hospitalized patients.

We developed and validated a set of fully automated surveillance algorithms for healthcare-onset CDI using electronic health records. In a validation data set of 750 manually annotated admissions, the algorithm based on International Classification of Disease, Tenth Revision (ICD-10) code A04.7 had insufficient sensitivity. Algorithms based on microbiological test results with or without addition of symptoms performed well.

Validation of automated sepsis surveillance based on the Sepsis-3 clinical criteria against physician record review in a general hospital population: observational study using electronic health records data.

BACKGROUND: Surveillance of sepsis incidence is important for directing resources and evaluating quality-of-care interventions. The aim was to develop and validate a fully-automated Sepsis-3 based surveillance system in non-intensive care wards using electronic health record (EHR) data, and demonstrate utility by determining the burden of hospital-onset sepsis and variations between wards. METHODS: A rule-based algorithm was developed using EHR data from a cohort of all adult patients admitted at an academic centre between July 2012 and December 2013. Time in intensive care units was censored. To validate algorithm performance, a stratified random sample of 1000 hospital admissions (674 with and 326 without suspected infection) was classified according to the Sepsis-3 clinical criteria (suspected infection defined as having any culture taken and at least two doses of antimicrobials administered, and an increase in Sequential Organ Failure Assessment (SOFA) score by >2 points) and the likelihood of infection by physician medical record review. RESULTS: In total 82 653 hospital admissions were included. The Sepsis-3 clinical criteria determined by physician review were met in 343 of 1000 episodes. Among them, 313 (91%) had possible, probable or definite infection. Based on this reference, the algorithm achieved sensitivity 0.887 (95% CI: 0.799 to 0.964), specificity 0.985 (95% CI: 0.978 to 0.991), positive predictive value 0.881 (95% CI: 0.833 to 0.926) and negative predictive value 0.986 (95% CI: 0.973 to 0.996). When applied to the total cohort taking into account the sampling proportions of those with and without suspected infection, the algorithm identified 8599 (10.4%) sepsis episodes. The burden of hospital-onset sepsis (>48 hour after admission) and related in-hospital mortality varied between wards. CONCLUSIONS: A fully-automated Sepsis-3 based surveillance algorithm using EHR data performed well compared with physician medical record review in non-intensive care wards, and exposed variations in hospital-onset sepsis incidence between wards.

Detecting hospital-acquired infections: A document classification approach using support vector machines and gradient tree boosting.

Hospital-acquired infections pose a significant risk to patient health, while their surveillance is an additional workload for hospital staff. Our overall aim is to build a surveillance system that reliably detects all patient records that potentially include hospital-acquired infections. This is to reduce the burden of having the hospital staff manually check patient records. This study focuses on the application of text classification using support vector machines and gradient tree boosting to the problem. Support vector machines and gradient tree boosting have never been applied to the problem of detecting hospital-acquired infections in Swedish patient records, and according to our experiments, they lead to encouraging results. The best result is yielded by gradient tree boosting, at 93.7 percent recall, 79.7 percent precision and 85.7 percent F1 score when using stemming. We can show that simple preprocessing techniques and parameter tuning can lead to high recall (which we aim for in screening patient records) with appropriate precision for this task.

Detection of healthcare-associated urinary tract infection in Swedish electronic health records.

The prevalence of healthcare-associated infections (HAI) stresses the need for automatic surveillance in order to follow the effect of preventive measures. A number of detection systems have been set up for several languages, but none is known for Swedish hospitals. We plan a series of infection type specific programs for detection of HAI in electronic health records at a Swedish university hospital. Also, we aim at detecting HAI for patients entering hospital with HAI from previous care, a task that is not often addressed. This first study aims at surveillance of healthcare-associated urinary tract infections. The created rule-based system depends on acquiring the essential clinical information, and a combination of data and text mining is used. The wide range of diverse clinics with different traditions of documentation poses difficulties for detection. Results from evaluation on 1,867 care episodes from Oncology and Surgery show high precision (0.98), specificity (0.99) and negative predictive value (0.99), but an intermediate recall (0.60). An error analysis of the evaluation is presented and discussed.