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BackgroundSurveillance 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.MethodsA 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.ResultsIn 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.ConclusionsA 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.

Ambulance requests by general practitioners for primary care patients (GP-requested) are often omitted in studies on increased demand within emergency care but may comprise a substantial patient group. We aimed to assess acute severity, intensive care unit (ICU) admission, and diagnostic pattern, including comorbidity, and mortality among GP-requested ambulance patients, compared to emergency call ambulance patients. Our hypothesis was that emergency call patients had more severe health issues than GP-requested ambulance patients. Historic population-based cohort study of ambulance patients in the North Denmark Region, 2016-2020. Hospital contact data including diagnoses, ambulance data, vital signs and vital status was linked using each patient's unique identification number. Primary outcome measure was mortality within 1, 7, and 30 days. Secondary outcomes were disease severity expressed as modified National Early Warning Score (NEWS2), and ICU admission. Admission status and hospital diagnostic pattern, including comorbidity were described and compared. We included 255,487 patients. GP-requested patients (N = 119,361, 46.7%) were older (median years [IQR] 73 [58-83] versus 61 [37-76]) and more had moderate/severe comorbidity (11.9%, N = 13,806 versus 4.9%, N = 6145) than the emergency call patients. Prehospital mNEWS2 median scores were lower for GP-requested patients. For both groups, mNEWS2 was highest among patients aged 66+. GP-requested patients had higher 30-day mortality (9.0% (95% CI: 8.8-9.2), N = 8996) than emergency call patients (5.2% (95% CI: 5.1-5.4), N = 6727). Circulatory (12.0%, 11,695/97,112) and respiratory diseases (11.6%, 11,219/97,112) were more frequent among GP-requested patients than emergency call patients ((10.7%, 12,640/118,102) and (5.8%, 6858/118,102)). The highest number of deaths was found for health issues 'circulatory diseases' in the emergency call group and 'other factors' followed by \"respiratory diseases\" in the GP-requested group. GP-requested patients constituted nearly half of the EMS volume, they were older, with more comorbidity, had serious conditions with substantial acute severity, and a higher 30-day mortality than emergency call patients.

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.

Early warning scores are used to assess acute patients’ risk of being in a critical situation, allowing for early appropriate treatment, avoiding critical outcomes. The early warning scores use changes in vital signs to provide an assessment, however they tend to identify a considerable number of false positive cases, especially among prehospital patients. We investigated the development and validation of predictive scores based on machine learning models among patients (aged ≥ 18 years) who used ambulances in the North Denmark Region from July 1, 2016, to December 31, 2020. The machine learning models were compared to standard early warning scores (NEWS2 and DEPT), on 7- and 30-day mortality and intensive care admission. The cohort of 219,323 patients was split into development (n = 175,458 (80%)) and validation (n = 43,865 (20%)) datasets to respectively develop and test the machine learning models. These models were logistic regression, random forest, Bayesian networks, and gradient boosting. The machine learning models outperformed NEWS2 and DEPT, with fewer false positives, reducing the number of patients needed to screen by nearly half, for 7 day mortality. This has the potential to reduce both under- and over-triage, improving the precision of the triage among prehospital patients.

Importance Early warning scores (EWSs) are designed for in-hospital use but are widely used in the prehospital field, especially in select groups of patients potentially at high risk. To be useful for paramedics in daily prehospital clinical practice, evaluations are needed of the predictive value of EWSs based on first measured vital signs on scene in large cohorts covering unselected patients using ambulance services. Objective To validate EWSs’ ability to predict mortality and intensive care unit (ICU) stay in an unselected cohort of adult patients who used ambulances. Design, Setting, and Participants This prognostic study conducted a validation based on a cohort of adult patients (aged ≥18 years) who used ambulances in the North Denmark Region from July 1, 2016, to December 31, 2020. EWSs (National Early Warning Score 2 [NEWS2], modified NEWS score without temperature [mNEWS], Quick Sepsis Related Organ Failure Assessment [qSOFA], Rapid Emergency Triage and Treatment System [RETTS], and Danish Emergency Process Triage [DEPT]) were calculated using first vital signs measured by ambulance personnel. Data were analyzed from September 2022 through May 2023. Main Outcomes and Measures The primary outcome was 30-day-mortality. Secondary outcomes were 1-day-mortality and ICU admission. Discrimination was assessed using area under the receiver operating characteristic curve (AUROC) and area under the precision recall curve (AUPRC). Results There were 107 569 unique patients (52 650 females [48.9%]; median [IQR] age, 65 [45-77] years) from the entire cohort of 219 323 patients who used ambulance services, among whom 119 992 patients (54.7%) had called the Danish national emergency number. NEWS2, mNEWS, RETTS, and DEPT performed similarly concerning 30-day mortality (AUROC range, 0.67 [95% CI, 0.66-0.68] for DEPT to 0.68 [95% CI, 0.68-0.69] for mNEWS), while qSOFA had lower performance (AUROC, 0.59 [95% CI, 0.59-0.60];Pvs other scores < .001). All EWSs had low AUPRCs, ranging from 0.09 (95% CI, 0.09-0.09) for qSOFA to 0.14 (95% CI, 0.13-0.14) for mNEWS.. Concerning 1-day mortality and ICU admission NEWS2, mNEWS, RETTS, and DEPT performed similarly, with AUROCs ranging from 0.72 (95% CI, 0.71-0.73) for RETTS to 0.75 (95% CI, 0.74-0.76) for DEPT in 1-day mortality and 0.66 (95% CI, 0.65-0.67) for RETTS to 0.68 (95% CI, 0.67-0.69) for mNEWS in ICU admission, and all EWSs had low AUPRCs. These ranged from 0.02 (95% CI, 0.02-0.03) for qSOFA to 0.04 (95% CI, 0.04-0.04) for DEPT in 1-day mortality and 0.03 (95% CI, 0.03-0.03) for qSOFA to 0.05 (95% CI, 0.04-0.05) for DEPT in ICU admission. Conclusions and Relevance This study found that EWSs in daily clinical use in emergency medical settings performed moderately in the prehospital field among unselected patients who used ambulances when assessed based on initial measurements of vital signs. These findings suggest the need of appropriate triage and early identification of patients at low and high risk with new and better EWSs also suitable for prehospital use.

Background: Healthcare-associated infection (HAI) surveillance is essential for most infection prevention programs and continuous epidemiological data can be used to inform healthcare personal, allocate resources, and evaluate interventions to prevent HAIs. Many HAI surveillance systems today are based on time-consuming and resource-intensive manual reviews of patient records. The objective of HAI-proactive, a Swedish triple-helix innovation project, is to develop and implement a fully automated HAI surveillance system based on electronic health record data. Furthermore, the project aims to develop machine-learning–based screening algorithms for early prediction of HAI at the individual patient level. Methods: The project is performed with support from Sweden’s Innovation Agency in collaboration among academic, health, and industry partners. Development of rule-based and machine-learning algorithms is performed within a research database, which consists of all electronic health record data from patients admitted to the Karolinska University Hospital. Natural language processing is used for processing free-text medical notes. To validate algorithm performance, manual annotation was performed based on international HAI definitions from the European Center for Disease Prevention and Control, Centers for Disease Control and Prevention, and Sepsis-3 criteria. Currently, the project is building a platform for real-time data access to implement the algorithms within Region Stockholm. Results: The project has developed a rule-based surveillance algorithm for sepsis that continuously monitors patients admitted to the hospital, with a sensitivity of 0.89 (95% CI, 0.85–0.93), a specificity of 0.99 (0.98–0.99), a positive predictive value of 0.88 (0.83–0.93), and a negative predictive value of 0.99 (0.98–0.99). The healthcare-associated urinary tract infection surveillance algorithm, which is based on free-text analysis and negations to define symptoms, had a sensitivity of 0.73 (0.66–0.80) and a positive predictive value of 0.68 (0.61–0.75). The sensitivity and positive predictive value of an algorithm based on significant bacterial growth in urine culture only was 0.99 (0.97–1.00) and 0.39 (0.34–0.44), respectively. The surveillance system detected differences in incidences between hospital wards and over time. Development of surveillance algorithms for pneumonia, catheter-related infections and Clostridioides difficile infections, as well as machine-learning–based models for early prediction, is ongoing. We intend to present results from all algorithms. Conclusions: With access to electronic health record data, we have shown that it is feasible to develop a fully automated HAI surveillance system based on algorithms using both structured data and free text for the main healthcare-associated infections. Funding: Sweden’s Innovation Agency and Stockholm County Council Disclosures: None

The lysosomal storage disorder Fabry disease is caused by deficient or absent activity of the GLA gene enzyme α-galactosidase A. In the present study we present the molecular and biochemical data of the Danish Fabry cohort and report 20 years’ (2001–2020) experience in cascade genetic screening at the Danish National Fabry Disease Center. The Danish Fabry cohort consisted of 26 families, 18 index patients (9 males and 9 females, no available data for 8 index-patients) and 97 family members with a pathogenic GLA variant identified by cascade genetic testing (30 males and 67 females). Fourteen patients (5 males and 9 females; mean age of death 47.0 and 64.8 years respectively) died during follow-up. The completeness of the Fabry patient identification in the country has resulted in a cohort of balanced genotypes according to gender (twice number of females compared to males), indicating that the cohort was not biased by referral, and further resulted in earlier diagnosis of the disease by a lower age at diagnosis in family members compared to index-patients (mean age at diagnosis: index-patients 42.2 vs. family members 26.0 years). Six previously unreported disease-causing variants in the GLA gene were discovered. The nationwide screening and registration of Fabry disease families provide a unique possibility to establish a complete cohort of Fabry patients and to advance current knowledge of this inherited rare lysosomal storage disorder.

[This corrects the article DOI: 10.1371/journal.pone.0277767.].

Point-of-care blood lactate is a promising prognostic biomarker of short-term mortality risk. Portable lactate meters need validation in the prehospital setting before widespread implementation and it is unknown whether the mode of sampling (arterial, capillary or venous) matters. This study aims to compare the StatStrip Xpress Lactate Meter’s (SSX) accuracy to a validated blood gas analyser, ABL90 FLEX (ABL90), in arterial samples in the prehospital environment and to determine if lactate levels measured in venous and capillary blood samples are sufficiently accurate compared to arterial lactate levels. Patients with arterial samples drawn by the prehospital anaesthesiologist for any reason were eligible for inclusion. Simultaneously, three blood samples (arterial, capillary and venous) were analysed on SSX and arterial blood on ABL90. Measurements of agreements were evaluated by Lin’s concordance correlations coefficient (CCC) and Bland–Altman Plots. One-hundred-and-eleven patients were included. SSX showed good accuracy compared to ABL90 in arterial samples with a CCC of 0.92 (95% CI 0.90–0.94). Compared to the arterial samples measured on ABL90, venous samples analysed on SSX showed higher agreement than capillary samples analysed on SSX with CCCs of 0.88 (95% CI 0.85–0.91) and 0.79 (95% CI 0.72–0.85), respectively. Bland–Altman plots showed that SSX lactate measurements in arterial, venous and capillary blood samples all had systematically negative biases compared to ABL90. We conclude that the SSX is accurate in our prehospital setting. Venous samples should be preferred over capillary samples, when arterial samples cannot be obtained.

Patients with severe COVID-19 have overwhelmed healthcare systems worldwide. We hypothesized that machine learning (ML) models could be used to predict risks at different stages of management and thereby provide insights into drivers and prognostic markers of disease progression and death. From a cohort of approx. 2.6 million citizens in Denmark, SARS-CoV-2 PCR tests were performed on subjects suspected for COVID-19 disease; 3944 cases had at least one positive test and were subjected to further analysis. SARS-CoV-2 positive cases from the United Kingdom Biobank was used for external validation. The ML models predicted the risk of death (Receiver Operation Characteristics—Area Under the Curve, ROC-AUC) of 0.906 at diagnosis, 0.818, at hospital admission and 0.721 at Intensive Care Unit (ICU) admission. Similar metrics were achieved for predicted risks of hospital and ICU admission and use of mechanical ventilation. Common risk factors, included age, body mass index and hypertension, although the top risk features shifted towards markers of shock and organ dysfunction in ICU patients. The external validation indicated fair predictive performance for mortality prediction, but suboptimal performance for predicting ICU admission. ML may be used to identify drivers of progression to more severe disease and for prognostication patients in patients with COVID-19. We provide access to an online risk calculator based on these findings.