Identifying Sepsis Subtypes from Routine Clinical Data.
Ontology highlight
ABSTRACT:
SUBMITTER: Prescott HC
PROVIDER: S-EPMC6680309 | biostudies-literature | 2019 Aug
REPOSITORIES: biostudies-literature
Publications
Identifying Sepsis Subtypes from Routine Clinical Data.
American journal of respiratory and critical care medicine 20190801 3
Similar Datasets
Project description:Persistent infection with high-risk human papillomavirus (hrHPV) is a major cause of cervical cancer. The effectiveness of current HPV-DNA testing, which is crucial for early detection, is limited in several aspects, including low sensitivity, accuracy issues, and the inability to perform comprehensive hrHPV typing. To address these limitations, we introduce MTIOT (Multiple subTypes In One Time), a novel detection method that utilizes machine learning with a new multichannel integration scheme to enhance HPV-DNA analysis. This approach may enable more accurate and rapid identification of multiple hrHPV types within a single sample. Compared to traditional methods, MTIOT has the potential to overcome their core limitations and offer a more efficient and cost-effective solution for cervical cancer screening. When tested on both simulated samples (to mimic real-world complexities) and clinical samples, MTIOT achieved F1 scores (the harmonic mean of sensitivity and specificity) of 98 % and 92 % respectively for identifying subtypes with a sample size ≥ 50, suggesting that it may significantly improve the precision of cervical cancer screening programs. This work with MTIOT represents a significant step forward in the molecular diagnosis of hrHPV and may suggest a promising avenue for enhancing early detection strategies and potentially reducing the incidence of cervical cancer. This study also underscores the importance of methodological innovation in tackling public health challenges and sets the stage for future clinical trials to validate MTIOT's efficacy in practice.
| S-EPMC11755069 | biostudies-literature
Project description:IntroductionBecause Alzheimer's disease (AD) has significant heterogeneity in encephalatrophy and clinical manifestations, AD research faces two critical challenges: eliminating the impact of natural aging and extracting valuable clinical data for patients with AD.MethodsThis study attempted to address these challenges by developing a novel machine-learning model called tensorized contrastive principal component analysis (T-cPCA). The objectives of this study were to predict AD progression and identify clinical subtypes while minimizing the influence of natural aging.ResultsWe leveraged a clinical variable space of 872 features, including almost all AD clinical examinations, which is the most comprehensive AD feature description in current research. T-cPCA yielded the highest accuracy in predicting AD progression by effectively minimizing the confounding effects of natural aging.DiscussionThe representative features and pathogenic circuits of the four primary AD clinical subtypes were discovered. Confirmed by clinical doctors in Tangdu Hospital, the plaques (18F-AV45) distribution of typical patients in the four clinical subtypes are consistent with representative brain regions found in four AD subtypes, which further offers novel insights into the underlying mechanisms of AD pathogenesis.
| S-EPMC11351280 | biostudies-literature
Project description:BackgroundSepsis is common, deadly, and heterogenous. Prior analyses of patients with sepsis and septic shock in New York State showed a risk-adjusted association between more rapid antibiotic administration and bundled care completion, but not an intravenous fluid bolus, with reduced in-hospital mortality. However, it is unknown if clinically identifiable sepsis subtypes modify these associations.MethodsSecondary analysis of patients with sepsis and septic shock enrolled in the New York State Department of Health cohort from January 1, 2015 to December 31, 2016. Patients were classified as clinical sepsis subtypes (α, β, γ, δ-types) using the Sepsis ENdotyping in Emergency CAre (SENECA) approach. Exposure variables included time to 3-h sepsis bundle completion, antibiotic administration, and intravenous fluid bolus completion. Then logistic regression models evaluated the interaction between exposures, clinical sepsis subtypes, and in-hospital mortality.Results55,169 hospitalizations from 155 hospitals were included (34% α, 30% β, 19% γ, 17% δ). The α-subtype had the lowest (N = 1,905, 10%) and δ-subtype had the highest (N = 3,776, 41%) in-hospital mortality. Each hour to completion of the 3-h bundle (aOR, 1.04 [95%CI, 1.02-1.05]) and antibiotic initiation (aOR, 1.03 [95%CI, 1.02-1.04]) was associated with increased risk-adjusted in-hospital mortality. The association differed across subtypes (p-interactions < 0.05). For example, the outcome association for the time to completion of the 3-h bundle was greater in the δ-subtype (aOR, 1.07 [95%CI, 1.05-1.10]) compared to α-subtype (aOR, 1.02 [95%CI, 0.99-1.04]). Time to intravenous fluid bolus completion was not associated with risk-adjusted in-hospital mortality (aOR, 0.99 [95%CI, 0.97-1.01]) and did not differ among subtypes (p-interaction = 0.41).ConclusionTimely completion of a 3-h sepsis bundle and antibiotic initiation was associated with reduced risk-adjusted in-hospital mortality, an association modified by clinically identifiable sepsis subtype.
| S-EPMC10268363 | biostudies-literature
Project description:ObjectivesTo define the target population of patients who have suspicion of sepsis (SOS) and to provide a basis for assessing the burden of SOS, and the evaluation of sepsis guidelines and improvement programmes.DesignRetrospective analysis of routinely collected hospital administrative data.SettingSecondary care, eight National Health Service (NHS) Acute Trusts.ParticipantsHospital Episode Statistics data for 2013-2014 was used to identify all admissions with a primary diagnosis listed in the 'suspicion of sepsis' (SOS) coding set. The SOS coding set consists of all bacterial infective diagnoses.ResultsWe identified 47 475 admissions with SOS, equivalent to a rate of 17 admissions per 1000 adults in a given year. The mortality for this group was 7.2% during their acute hospital admission. Urinary tract infection was the most common diagnosis and lobar pneumonia was associated with the most deaths. A short list of 10 diagnoses can account for 85% of the deaths.ConclusionsPatients with SOS can be identified in routine administrative data. It is these patients who should be screened for sepsis and are the target of programmes to improve the detection and treatment of sepsis. The effectiveness of such programmes can be evaluated by examining the outcomes of patients with SOS.
| S-EPMC5734411 | biostudies-literature
Project description:BackgroundLate mortality risk in sepsis-survivors persists for years with high readmission rates and low quality of life. The present study seeks to link the clinical sepsis-survivors heterogeneity with distinct biological profiles at ICU discharge and late adverse events using an unsupervised analysis.MethodsIn the original FROG-ICU prospective, observational, multicenter study, intensive care unit (ICU) patients with sepsis on admission (Sepsis-3) were identified (N = 655). Among them, 467 were discharged alive from the ICU and included in the current study. Latent class analysis was applied to identify distinct sepsis-survivors clinical classes using readily available data at ICU discharge. The primary endpoint was one-year mortality after ICU discharge.ResultsAt ICU discharge, two distinct subtypes were identified (A and B) using 15 readily available clinical and biological variables. Patients assigned to subtype B (48% of the studied population) had more impaired cardiovascular and kidney functions, hematological disorders and inflammation at ICU discharge than subtype A. Sepsis-survivors in subtype B had significantly higher one-year mortality compared to subtype A (respectively, 34% vs 16%, p < 0.001). When adjusted for standard long-term risk factors (e.g., age, comorbidities, severity of illness, renal function and duration of ICU stay), subtype B was independently associated with increased one-year mortality (adjusted hazard ratio (HR) = 1.74 (95% CI 1.16-2.60); p = 0.006).ConclusionsA subtype with sustained organ failure and inflammation at ICU discharge can be identified from routine clinical and laboratory data and is independently associated with poor long-term outcome in sepsis-survivors. Trial registration NCT01367093; https://clinicaltrials.gov/ct2/show/NCT01367093 .
| S-EPMC9022336 | biostudies-literature
Project description:Whole genome expression profiles are widely used to discover molecular subtypes of diseases. A remaining challenge is to identify the correspondence or commonality of subtypes found in multiple, independent data sets generated on various platforms. While model-based supervised learning is often used to make these connections, the models can be biased to the training data set and thus miss inherent, relevant substructure in the test data. Here we describe an unsupervised subclass mapping method (SubMap), which reveals common subtypes between independent data sets. The subtypes within a data set can be determined by unsupervised clustering or given by predetermined phenotypes before applying SubMap. We define a measure of correspondence for subtypes and evaluate its significance building on our previous work on gene set enrichment analysis. The strength of the SubMap method is that it does not impose the structure of one data set upon another, but rather uses a bi-directional approach to highlight the common substructures in both. We show how this method can reveal the correspondence between several cancer-related data sets. Notably, it identifies common subtypes of breast cancer associated with estrogen receptor status, and a subgroup of lymphoma patients who share similar survival patterns, thus improving the accuracy of a clinical outcome predictor.
| S-EPMC2065909 | biostudies-literature
Project description:Variability in hospital-level sepsis mortality rates may be due to differences in case mix, quality of care, or diagnosis and coding practices. Centers for Disease Control and Prevention's Adult Sepsis Event definition could facilitate objective comparisons of sepsis mortality rates between hospitals but requires rigorous risk-adjustment tools. We developed risk-adjustment models for Adult Sepsis Events using administrative and electronic health record data.DesignRetrospective cohort study.SettingOne hundred thirty-six U.S. hospitals in Cerner HealthFacts (derivation dataset) and 137 HCA Healthcare hospitals (validation dataset).PatientsA total of 95,154 hospitalized adult patients (derivation) and 201,997 patients (validation) meeting Centers for Disease Control and Prevention Adult Sepsis Event criteria.InterventionsNone.Measurements and main resultsWe created logistic regression models of increasing complexity using administrative and electronic health record data to predict in-hospital mortality. An administrative model using demographics, comorbidities, and coded markers of severity of illness at admission achieved an area under the receiver operating curve of 0.776 (95% CI, 0.770-0.783) in the Cerner cohort, with diminishing calibration at higher baseline risk deciles. An electronic health record-based model that integrated administrative data with laboratory results, vasopressors, and mechanical ventilation achieved an area under the receiver operating curve of 0.826 (95% CI, 0.820-0.831) in the derivation cohort and 0.827 (95% CI, 0.824-0.829) in the validation cohort, with better calibration than the administrative model. Adding vital signs and Glasgow Coma Score minimally improved performance.ConclusionsModels incorporating electronic health record data accurately predict hospital mortality for patients with Adult Sepsis Events and outperform models using administrative data alone. Utilizing laboratory test results, vasopressors, and mechanical ventilation without vital signs may achieve a good balance between data collection needs and model performance, but electronic health record-based models must be attentive to potential variability in data quality and availability. With ongoing testing and refinement of these risk-adjustment models, Adult Sepsis Event surveillance may enable more meaningful comparisons of hospital sepsis outcomes and provide an important window into quality of care.
| S-EPMC7063887 | biostudies-literature
Project description:Multiple sclerosis (MS) can be divided into four phenotypes based on clinical evolution. The pathophysiological boundaries of these phenotypes are unclear, limiting treatment stratification. Machine learning can identify groups with similar features using multidimensional data. Here, to classify MS subtypes based on pathological features, we apply unsupervised machine learning to brain MRI scans acquired in previously published studies. We use a training dataset from 6322 MS patients to define MRI-based subtypes and an independent cohort of 3068 patients for validation. Based on the earliest abnormalities, we define MS subtypes as cortex-led, normal-appearing white matter-led, and lesion-led. People with the lesion-led subtype have the highest risk of confirmed disability progression (CDP) and the highest relapse rate. People with the lesion-led MS subtype show positive treatment response in selected clinical trials. Our findings suggest that MRI-based subtypes predict MS disability progression and response to treatment and may be used to define groups of patients in interventional trials.
| S-EPMC8024377 | biostudies-literature
Project description:ObjectivesAdministrative claims data are commonly used for sepsis surveillance, research, and quality improvement. However, variations in diagnosis, documentation, and coding practices for sepsis and organ dysfunction may confound efforts to estimate sepsis rates, compare outcomes, and perform risk adjustment. We evaluated hospital variation in the sensitivity of claims data relative to clinical data from electronic health records and its impact on outcome comparisons.Design, setting, and patientsRetrospective cohort study of 4.3 million adult encounters at 193 U.S. hospitals in 2013-2014.InterventionsNone.Measurements and main resultsSepsis was defined using electronic health record-derived clinical indicators of presumed infection (blood culture draws and antibiotic administrations) and concurrent organ dysfunction (vasopressors, mechanical ventilation, doubling in creatinine, doubling in bilirubin to ≥ 2.0 mg/dL, decrease in platelets to < 100 cells/µL, or lactate ≥ 2.0 mmol/L). We compared claims for sepsis prevalence and mortality rates between both methods. All estimates were reliability adjusted to account for random variation using hierarchical logistic regression modeling. The sensitivity of hospitals' claims data was low and variable: median 30% (range, 5-54%) for sepsis, 66% (range, 26-84%) for acute kidney injury, 39% (range, 16-60%) for thrombocytopenia, 36% (range, 29-44%) for hepatic injury, and 66% (range, 29-84%) for shock. Correlation between claims and clinical data was moderate for sepsis prevalence (Pearson coefficient, 0.64) and mortality (0.61). Among hospitals in the lowest sepsis mortality quartile by claims, 46% shifted to higher mortality quartiles using clinical data. Using implicit sepsis criteria based on infection and organ dysfunction codes also yielded major differences versus clinical data.ConclusionsVariation in the accuracy of claims data for identifying sepsis and organ dysfunction limits their use for comparing hospitals' sepsis rates and outcomes. Using objective clinical data may facilitate more meaningful hospital comparisons.
| S-EPMC7970408 | biostudies-literature
Project description:The ability to classify patients with bipolar disorder (BD) is restricted by their heterogeneity, which limits the understanding of their neuropathology. Therefore, we aimed to investigate clinically discernible and neurobiologically distinguishable BD subtypes. T1-weighted and resting-state functional magnetic resonance images of 112 patients with BD were obtained, and patients were segregated according to diagnostic subtype (i.e., types I and II) and clinical patterns, including the number of episodes and hospitalizations and history of suicide and psychosis. For each clinical pattern, fewer and more occurrences subgroups and types I and II were classified through nested cross-validation for robust performance, with minimum redundancy and maximum relevance, in feature selection. To assess the proportion of variance in cognitive performance explained by the neurobiological markers, multiple linear regression between verbal memory and the selected features was conducted. Satisfactory performance (mean accuracy, 73.60%) in classifying patients with a high or low number of episodes was attained through functional connectivity, mostly from default-mode and motor networks. Moreover, these neurobiological markers explained 62% of the variance in verbal memory. The number of episodes is a potentially critical aspect of the neuropathology of BD. Neurobiological markers can help identify BD neuroprogression.
| S-EPMC8385023 | biostudies-literature