Predicting outcome of patients with severe urinary tract infections admitted via the emergency department
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ABSTRACT:
SUBMITTER: Rothrock S
PROVIDER: S-EPMC7493503 | biostudies-literature | 2020 Jun
REPOSITORIES: biostudies-literature
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Project description:BackgroundUrinary tract infection (UTI) is a common emergency department (ED) diagnosis with reported high diagnostic error rates. Because a urine culture, part of the gold standard for diagnosis of UTI, is usually not available for 24-48 hours after an ED visit, diagnosis and treatment decisions are based on symptoms, physical findings, and other laboratory results, potentially leading to overutilization, antibiotic resistance, and delayed treatment. Previous research has demonstrated inadequate diagnostic performance for both individual laboratory tests and prediction tools.ObjectiveOur aim, was to train, validate, and compare machine-learning based predictive models for UTI in a large diverse set of ED patients.MethodsSingle-center, multi-site, retrospective cohort analysis of 80,387 adult ED visits with urine culture results and UTI symptoms. We developed models for UTI prediction with six machine learning algorithms using demographic information, vitals, laboratory results, medications, past medical history, chief complaint, and structured historical and physical exam findings. Models were developed with both the full set of 211 variables and a reduced set of 10 variables. UTI predictions were compared between models and to proxies of provider judgment (documentation of UTI diagnosis and antibiotic administration).ResultsThe machine learning models had an area under the curve ranging from 0.826-0.904, with extreme gradient boosting (XGBoost) the top performing algorithm for both full and reduced models. The XGBoost full and reduced models demonstrated greatly improved specificity when compared to the provider judgment proxy of UTI diagnosis OR antibiotic administration with specificity differences of 33.3 (31.3-34.3) and 29.6 (28.5-30.6), while also demonstrating superior sensitivity when compared to documentation of UTI diagnosis with sensitivity differences of 38.7 (38.1-39.4) and 33.2 (32.5-33.9). In the admission and discharge cohorts using the full XGboost model, approximately 1 in 4 patients (4109/15855) would be re-categorized from a false positive to a true negative and approximately 1 in 11 patients (1372/15855) would be re-categorized from a false negative to a true positive.ConclusionThe best performing machine learning algorithm, XGBoost, accurately diagnosed positive urine culture results, and outperformed previously developed models in the literature and several proxies for provider judgment. Future prospective validation is warranted.
| S-EPMC5841824 | biostudies-literature
Project description:BackgroundIn the Emergency Department (ED), early and accurate recognition of infection is crucial to prompt antibiotic therapy but the initial presentation of patients is variable and poorly characterized. Lymphopenia is commonly associated with bacteraemia and poor outcome in intensive care unit patients. The objective of this retrospective study was to assess the prevalence of community-acquired infection in a cohort of unselected patients admitted to the ED with undifferentiated symptoms and severe lymphopenia.MethodsThis is a retrospective single-center study conducted over a 1 year-period before the COVID-19 pandemic. Consecutive adult patients admitted to the ED with severe lymphopenia (lymphocyte count < 0.5 G/L) were studied. Patients with hematological or oncological diseases, HIV infection, hepato-cellular deficiency, immunosuppression, or patients over 85 years old were excluded. Diagnoses of infection were validated by an independent adjudication committee. The association between various parameters and infection was assessed using a multivariate logistic regression analysis.ResultsOf 953 patients admitted to the ED with severe lymphopenia, 245 were studied (148 men; mean age: 63 ± 19 years). Infection was confirmed in 159 patients (65%) (bacterial: 60%, viral: 30%, other: 10%). Only 61 patients (25%) were referred to the ED for a suspected infection. In the univariate analysis, SIRS criteria (OR: 5.39; 95%CI: 3.04-9.70; p < 0.001) and temperature ≥ 38.3 °C (OR: 10.95; 95%CI: 5.39-22.26; p < 0.001) were strongly associate with infection. In the multivariate analysis, only SIRS criteria (OR: 2.4; 95%CI: 1.48-3.9; p < 0.01) and fever (OR: 3.35; 95%CI: 1.26-8.93; p = 0.016) were independently associated with infection.ConclusionsThe prevalence of underlying infection is high in patients admitted to the ED with lymphopenia, irrespective of the reason for admission. Whether lymphopenia could constitute a valuable marker of underlying infection in this clinical setting remains to be confirmed prospectively in larger cohorts.Trial registrationNo registration required as this is a retrospective study.
| S-EPMC8960225 | biostudies-literature
Project description:BackgroundCOVID-19 may lead to severe disease, requiring intensive care treatment and challenging the capacity of health care systems. The aim of this study was to compare the ability of commonly used scoring systems for sepsis and pneumonia to predict severe COVID-19 in the emergency department.MethodsProspective, observational, single centre study in a secondary/tertiary care hospital in Oslo, Norway. Patients were assessed upon hospital admission using the following scoring systems; quick Sequential Failure Assessment (qSOFA), Systemic Inflammatory Response Syndrome criteria (SIRS), National Early Warning Score 2 (NEWS2), CURB-65 and Pneumonia Severity index (PSI). The ratio of arterial oxygen tension to inspiratory oxygen fraction (P/F-ratio) was also calculated. The area under the receiver operating characteristics curve (AUROC) for each scoring system was calculated, along with sensitivity and specificity for the most commonly used cut-offs. Severe disease was defined as death or treatment in ICU within 14 days.Results38 of 175 study participants developed severe disease, 13 (7%) died and 29 (17%) had a stay at an intensive care unit (ICU). NEWS2 displayed an AUROC of 0.80 (95% confidence interval 0.72-0.88), CURB-65 0.75 (0.65-0.84), PSI 0.75 (0.65-0.84), SIRS 0.70 (0.61-0.80) and qSOFA 0.70 (0.61-0.79). NEWS2 was significantly better than SIRS and qSOFA in predicating severe disease, and with a cut-off of5 points, had a sensitivity and specificity of 82% and 60%, respectively.ConclusionNEWS2 predicted severe COVID-19 disease more accurately than SIRS and qSOFA, but not significantly better than CURB65 and PSI. NEWS2 may be a useful screening tool in evaluating COVID-19 patients during hospital admission.Trial registration: ClinicalTrials.gov Identifier: NCT04345536. (https://clinicaltrials.gov/ct2/show/NCT04345536).
| S-EPMC7577659 | biostudies-literature
Project description:ObjectiveThere are >1 million emergency department visits and 100,000 admissions with urinary tract infection (UTI) annually in the United States. A fraction of total UTI volume, complicated (cUTI) costs the health care system over $3.5 billion per year. We evaluated the contemporary annual burden of emergency department (ED) visits with cUTI.MethodsWe conducted a cross-sectional multicenter study within the National Emergency Department database, a 20% stratified sample of all US hospital-based EDs, 2016-2018, to explore characteristics of visits with a cUTI. We compared cUTI as the principal (PD) versus secondary diagnosis (non-PD). We applied survey methods to develop national estimates.ResultsAmong 2,379,448 ED cUTI visits (44.8% PD), 40.1% were female (45.1% PD; 36.9% non-PD) and 62.2% were ≥ 65 years (52.5% PD; 70.2% non-PD). Mean Charlson score was 2.3 (3.0 PD; 2.1 non-PD); end-stage renal disease prevalence was 2.3% (1.4% PD; 3.0% non-PD). Whereas pyelonephritis occurred in ∼10% of both groups, severe sepsis (7.2% vs 2.0%) and septic shock (7.1% vs 1.8%) were ∼4 times more prevalent among those with cUTI-non-PD than cUTI-PD. Overall, two thirds of all visits ended in hospitalization (44.9% PD; 85.5% non-PD). Despite similar numbers of visits, the annual national ED bill for cUTI rose from $2.8 billion in 2016 to $3.2 billion in 2018.ConclusionThere were over 2 million ED visits with cUTI in 2016-2018. Although <10% met criteria for severe sepsis/septic shock, ∼two thirds were admitted. The aggregate cost for cUTI visits rose by 15% without a substantial increase in volume.
| S-EPMC8931190 | biostudies-literature
Project description:Rapid and accurate bacterial detection methods are needed for clinical diagnostic, water, and food testing applications. The wide diversity of bacterial nucleases provides a rich source of enzymes that could be exploited as signal amplifying biomarkers to enable rapid, selective detection of bacterial species. With the exception of the use of micrococcal nuclease activity to detect Staphylococcus aureus, rapid methods that detect bacterial pathogens via their nuclease activities have not been developed. Here, we identify endonuclease I as a robust biomarker for E. coli and develop a rapid ultrasensitive assay that detects its activity. Comparison of nuclease activities of wild-type and nuclease-knockout E. coli clones revealed that endonuclease I is the predominant DNase in E. coli lysates. Endonuclease I is detectable by immunoblot and activity assays in uropathogenic E. coli strains. A rapid assay that detects endonuclease I activity in patient urine with an oligonucleotide probe exhibited substantially higher sensitivity for urinary tract infections than that reported for rapid urinalysis methods. The 3 hr turnaround time is much shorter than that of culture-based methods, thereby providing a means for expedited administration of appropriate antimicrobial therapy. We suggest this approach could address various unmet needs for rapid detection of E. coli.
| S-EPMC5474879 | biostudies-other
Project description:BACKGROUND:The scope of this work is to build a Machine Learning model able to predict patients risk to contract a multidrug resistant urinary tract infection (MDR UTI) after hospitalization. To achieve this goal, we used different popular Machine Learning tools. Moreover, we integrated an easy-to-use cloud platform, called DSaaS (Data Science as a Service), well suited for hospital structures, where healthcare operators might not have specific competences in using programming languages but still, they do need to analyze data as a continuous process. Moreover, DSaaS allows the validation of data analysis models based on supervised Machine Learning regression and classification algorithms. RESULTS:We used DSaaS on a real antibiotic stewardship dataset to make predictions about antibiotic resistance in the Clinical Pathology Operative Unit of the Principe di Piemonte Hospital in Senigallia, Marche, Italy. Data related to a total of 1486 hospitalized patients with nosocomial urinary tract infection (UTI). Sex, age, age class, ward and time period, were used to predict the onset of a MDR UTI. Machine Learning methods such as Catboost, Support Vector Machine and Neural Networks were utilized to build predictive models. Among the performance evaluators, already implemented in DSaaS, we used accuracy (ACC), area under receiver operating characteristic curve (AUC-ROC), area under Precision-Recall curve (AUC-PRC), F1 score, sensitivity (SEN), specificity and Matthews correlation coefficient (MCC). Catboost exhibited the best predictive results (MCC 0.909; SEN 0.904; F1 score 0.809; AUC-PRC 0.853, AUC-ROC 0.739; ACC 0.717) with the highest value in every metric. CONCLUSIONS:the predictive model built with DSaaS may serve as a useful support tool for physicians treating hospitalized patients with a high risk to acquire MDR UTIs. We obtained these results using only five easy and fast predictors accessible for each patient hospitalization. In future, DSaaS will be enriched with more features like unsupervised Machine Learning techniques, streaming data analysis, distributed calculation and big data storage and management to allow researchers to perform a complete data analysis pipeline. The DSaaS prototype is available as a demo at the following address: https://dsaas-demo.shinyapps.io/Server/.
| S-EPMC7446147 | biostudies-literature
Project description:The present study investigated the incidence of hyperammonemia in urinary tract infections and explored the utility of urinary obstruction relief and antimicrobial administration to improve hyperammonemia.This was an observational study. Subjects were patients who were diagnosed with urinary tract infection and hospitalized between June 2008 and June 2009. We measured plasma ammonia levels on admission in patients who were clinically diagnosed with urinary tract infection and hospitalized. We assessed each patient's level of consciousness on admission using the Glasgow Coma Scale (GCS) and performed urine and blood cultures. We also assessed hearing prior to hospitalization using the Eastern Cooperative Oncology Group performance status (ECOG-PS). In cases with high ammonia levels on admission, plasma ammonia and GCS were measured 24 hours and 5-7 days later.Sixty-seven candidates were enrolled; of these, 60 cases (89.6%) with bacterial cell counts ≥10(4) CFU/mL were studied. Five cases (8.3%) presented with high plasma ammonia levels. Cases with hyperammonemia were significantly more likely to present with low GCS scores and urinary retention rate. All five cases received antimicrobial therapy with an indwelling bladder catheter to relieve urinary retention. The case 5 patient died shortly after admission due to complicated aspiration pneumonia; in the remaining cases, plasma ammonia levels were rapidly normalized and the level of consciousness improved.The occurrence of hyperammonemia in urinary tract infections is not rare. The cause of hyperammonemia is urinary retention obstruction. Therefore, along with antimicrobial administration, relief of obstruction is important for the treatment of hyperammonemia caused by this mechanism.
| S-EPMC4546155 | biostudies-other
Project description:Symptoms in the urogenital organs are common in multiple system atrophy (MSA), also in the years preceding the MSA diagnosis. It is unknown how MSA is triggered and these observations in prodromal MSA led us to hypothesize that synucleinopathy could be triggered by infection of the genitourinary tract causing ɑ-synuclein (ɑSyn) to aggregate in peripheral nerves innervating these organs. As a first proof that peripheral infections could act as a trigger in MSA, this study focused on lower urinary tract infections (UTIs), given the relevance and high frequency of UTIs in prodromal MSA, although other types of infection might also be important triggers of MSA. We performed an epidemiological nested-case control study in the Danish population showing that UTIs are associated with future diagnosis of MSA several years after infection and that it impacts risk in both men and women. Bacterial infection of the urinary bladder triggers synucleinopathy in mice and we propose a novel role of ɑSyn in the innate immune system response to bacteria. Urinary tract infection with uropathogenic E. coli results in the de novo aggregation of ɑSyn during neutrophil infiltration. During the infection, ɑSyn is released extracellularly from neutrophils as part of their extracellular traps. Injection of MSA aggregates into the urinary bladder leads to motor deficits and propagation of ɑSyn pathology to the central nervous system in mice overexpressing oligodendroglial ɑSyn. Repeated UTIs lead to progressive development of synucleinopathy with oligodendroglial involvement in vivo. Our results link bacterial infections with synucleinopathy and show that a host response to environmental triggers can result in ɑSyn pathology that bears semblance to MSA.
| S-EPMC10119259 | biostudies-literature
Project description:BACKGROUND: Patients with infections account for a significant proportion of Emergency Department (ED) workload, with many hospital patients admitted with severe sepsis initially investigated and resuscitated in the ED. The aim of this registry is to systematically collect quality observational clinical and microbiological data regarding emergency patients admitted with infection, in order to explore in detail the microbiological profile of these patients, and to provide the foundation for a significant programme of prospective observational studies and further clinical research. METHODS/DESIGN: ED patients admitted with infection will be identified through daily review of the computerised database of ED admissions, and clinical information such as site of infection, physiological status in the ED, and components of management abstracted from patients' charts. This information will be supplemented by further data regarding results of investigations, microbiological isolates, and length of stay (LOS) from hospital electronic databases. Outcome measures will be hospital and intensive care unit (ICU) LOS, and mortality endpoints derived from a national death registry. DISCUSSION: This database will provide substantial insights into the characteristics, microbiological profile, and outcomes of emergency patients admitted with infections. It will become the nidus for a programme of research into compliance with evidence-based guidelines, optimisation of empiric antimicrobial regimens, validation of clinical decision rules and identification of outcome determinants. The detailed observational data obtained will provide a solid baseline to inform the design of further controlled trials planned to optimise treatment and outcomes for emergency patients admitted with infections.
| S-EPMC3037882 | biostudies-other
Project description:BackgroundWe aimed to evaluate the clinical usefulness of qSOFA as a risk stratification tool for patients admitted with infection compared to traditional SIRS criteria or our triage system; the Rapid Emergency Triage and Treatment System (RETTS).MethodsThe study was an observational cohort study performed at one Emergency Department (ED) in an urban university teaching hospital in Norway, with approximately 20,000 visits per year. All patients >16 years presenting with symptoms or clinical signs suggesting an infection (n = 1535) were prospectively included in the study from January 1 to December 31, 2012. At arrival in the ED, vital signs were recorded and all patients were triaged according to RETTS vital signs, presenting infection, and sepsis symptoms. These admission data were also used to calculate qSOFA and SIRS. Treatment outcome was later retrieved from the patients' electronic records (EPR) and mortality data from the Norwegian population registry.ResultsOf the 1535 admitted patients, 108 (7.0%) fulfilled the Sepsis2 criteria for severe sepsis. The qSOFA score ≥2 identified only 33 (sensitivity 0.32, specificity 0.98) of the patients with severe sepsis, whilst the RETTS-alert ≥ orange identified 92 patients (sensitivity 0.85, specificity 0.55). Twenty-six patients died within 7 days of admission; four (15.4%) of them had a qSOFA ≥2, and 16 (61.5%) had RETTS ≥ orange alert. Of the 68 patients that died within 30 days, only eight (11.9%) scored ≥2 on the qSOFA, and 45 (66.1%) had a RETTS ≥ orange alert.DiscussionIn order to achieve timely treatment for sepsis, a sensitive screening tool is more important than a specific one. Our study is the fourth study were qSOFA finds few of the sepsis cases in prehospital or at arrival to the ED. We add information on the RETTS triage system, the two highest acuity levels together had a high sensitivity (85%) for identifying sepsis at arrival to the ED - and thus, RETTS should not be replaced by qSOFA as a screening and trigger tool for sepsis at arrival.ConclusionIn this observational cohort study, qSOFA failed to identify two thirds of the patients admitted to an ED with severe sepsis. Further, qSOFA failed to be a risk stratification tool as the sensitivity to predict 7-day and 30-day mortality was low. The sensitivity was poorer than the other warning scores already in use at the study site, RETTS-triage and the SIRS criteria.
| S-EPMC5466747 | biostudies-literature