Development of a Biomarker-Based Diagnostic Algorithm for Posttraumatic Syndrome after Physical Injury: Design of the BioPTS Study.
Ontology highlight
ABSTRACT:
SUBMITTER: Kim JW
PROVIDER: S-EPMC5561411 | biostudies-literature | 2017 Jul
REPOSITORIES: biostudies-literature
Publications
Development of a Biomarker-Based Diagnostic Algorithm for Posttraumatic Syndrome after Physical Injury: Design of the BioPTS Study.
Kim Ju-Wan JW Kang Hee-Ju HJ Bae Kyung-Yeol KY Kim Sung-Wan SW Oh Hyun-Kyong HK Kim Min-Gon MG Kim Jae-Min JM
Psychiatry investigation 20170711 4
Severe physical injury is a leading cause of posttraumatic syndrome (PTS). This is to develop a biomarker-based diagnostic algorithm for posttraumatic syndrome (BioPTS) study. This is a 2-year longitudinal cohort study assessing patients who were hospitalized beginning in 2015 at Chonnam National University Hospital in Gwangju, Korea, after experiencing severe physical injuries. Baseline evaluations were made during the acute phase (within 1 month) of the physical injury and included extensive i ...[more]
Similar Datasets
Project description:Sepsis is a major cause of mortality among hospitalized patients worldwide. Shorter time to administration of broad-spectrum antibiotics is associated with improved outcomes, but early recognition of sepsis remains a major challenge. In a two-center cohort study with prospective sample collection from 1400 adult patients in emergency departments suspected of sepsis, we sought to determine the diagnostic and prognostic capabilities of a machine-learning algorithm based on clinical data and a set of uncommonly measured biomarkers. Specifically, we demonstrate that a machine-learning model developed using this dataset outputs a score with not only diagnostic capability but also prognostic power with respect to hospital length of stay (LOS), 30-day mortality, and 3-day inpatient re-admission both in our entire testing cohort and various subpopulations. The area under the receiver operating curve (AUROC) for diagnosis of sepsis was 0.83. Predicted risk scores for patients with septic shock were higher compared with patients with sepsis but without shock (p < 0.0001). Scores for patients with infection and organ dysfunction were higher compared with those without either condition (p < 0.0001). Stratification based on predicted scores of the patients into low, medium, and high-risk groups showed significant differences in LOS (p < 0.0001), 30-day mortality (p < 0.0001), and 30-day inpatient readmission (p < 0.0001). In conclusion, a machine-learning algorithm based on electronic medical record (EMR) data and three nonroutinely measured biomarkers demonstrated good diagnostic and prognostic capability at the time of initial blood culture.
| S-EPMC8301583 | biostudies-literature
Project description:We propose a Bayesian two-stage biomarker-based adaptive randomization (AR) design for the development of targeted agents. The design has three main goals: (1) to test the treatment efficacy, (2) to identify prognostic and predictive markers for the targeted agents, and (3) to provide better treatment for patients enrolled in the trial. To treat patients better, both stages are guided by the Bayesian AR based on the individual patient's biomarker profiles. The AR in the first stage is based on a known marker. A Go/No-Go decision can be made in the first stage by testing the overall treatment effects. If a Go decision is made at the end of the first stage, a two-step Bayesian lasso strategy will be implemented to select additional prognostic or predictive biomarkers to refine the AR in the second stage. We use simulations to demonstrate the good operating characteristics of the design, including the control of per-comparison type I and type II errors, high probability in selecting important markers, and treating more patients with more effective treatments. Bayesian adaptive designs allow for continuous learning. The designs are particularly suitable for the development of multiple targeted agents in the quest of personalized medicine. By estimating treatment effects and identifying relevant biomarkers, the information acquired from the interim data can be used to guide the choice of treatment for each individual patient enrolled in the trial in real time to achieve a better outcome. The design is being implemented in the BATTLE-2 trial in lung cancer at the MD Anderson Cancer Center.
| S-EPMC5014437 | biostudies-literature
Project description:There is not currently a well-established, if any, biological test to diagnose myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). The molecular aberrations observed in numerous studies of ME/CFS blood cells offer the opportunity to develop a diagnostic assay from blood samples. Here we developed a nanoelectronics assay designed as an ultrasensitive assay capable of directly measuring biomolecular interactions in real time, at low cost, and in a multiplex format. To pursue the goal of developing a reliable biomarker for ME/CFS and to demonstrate the utility of our platform for point-of-care diagnostics, we validated the array by testing patients with moderate to severe ME/CFS patients and healthy controls. The ME/CFS samples' response to the hyperosmotic stressor observed as a unique characteristic of the impedance pattern and dramatically different from the response observed among the control samples. We believe the observed robust impedance modulation difference of the samples in response to hyperosmotic stress can potentially provide us with a unique indicator of ME/CFS. Moreover, using supervised machine learning algorithms, we developed a classifier for ME/CFS patients capable of identifying new patients, required for a robust diagnostic tool.
| S-EPMC6535016 | biostudies-literature
Project description:The p.Ala204Thr mutation (exon 7) of the CLCNKB gene is a "founder" mutation that causes most of type III Bartter syndrome cases in Spain. We performed genetic analysis of the CLCNKB gene, which encodes for the chloride channel protein ClC-Kb, in a cohort of 26 affected patients from 23 families. The diagnostic algorithm was: first, detection of the p.Ala204Thr mutation; second, detecting large deletions or duplications by Multiplex Ligation-dependent Probe Amplification and Quantitative Multiplex PCR of Short Fluorescent Fragments; and third, sequencing of the coding and flanking regions of the whole CLCNKB gene. In our genetic diagnosis, 20 families presented with the p.Ala204Thr mutation. Of those, 15 patients (15 families) were homozygous (57.7% of overall patients). Another 8 patients (5 families) were compound heterozygous for the founder mutation together with a second one. Thus, 3 patients (2 siblings) presented with the c. -19-?_2053+? del deletion (comprising the entire gene); one patient carried the p.Val170Met mutation (exon 6); and 4 patients (3 siblings) presented with the novel p.Glu442Gly mutation (exon 14). On the other hand, another two patients carried two novel mutations in compound heterozygosis: one presented the p.Ile398_Thr401del mutation (exon 12) associated with the c. -19-?_2053+? del deletion, and the other one carried the c.1756+1G>A splice-site mutation (exon 16) as well as the already described p.Ala210Val change (exon 7). One case turned out to be negative in our genetic screening. In addition, 51 relatives were found to be heterozygous carriers of the described CLCNKB mutations. In conclusion, different mutations cause type III Bartter syndrome in Spain. The high prevalence of the p.Ala204Thr in Spanish families thus justifies an initial screen for this mutation. However, should it not be detected further investigation of the CLCNKB gene is warranted in clinically diagnosed families.
| S-EPMC3776854 | biostudies-literature
Project description:Mast cell activation (MCA) accompanies diverse physiologic and pathologic processes and is one of the more frequently encountered conditions in medicine. MCA-related symptoms are usually mild and often transient. In such cases, histamine receptor blockers and other mediator-targeting drugs can usually control MCA. In severe cases, an MCA syndrome (MCAS) may be diagnosed. However, overt MCAS is an unusual condition, and many patients referred because of suspected MCAS are diagnosed with other diseases (autoimmune, neoplastic, or infectious) unrelated to MCA or suffer from MCA-related (eg, allergic) disorders and/or comorbidities without fulfilling criteria of an overt MCAS. These considerations are important as more and more patients are informed that they may have MCA or even MCAS without completing a thorough medical evaluation. In fact, in several instances, symptoms are misinterpreted as MCA/MCAS, and other clinically relevant conditions are not thoroughly pursued. The number of such referrals is increasing. To avoid such unnecessary referrals and to prevent misdiagnoses, we here propose a diagnostic algorithm through which a clinically relevant (systemic) MCA can be suspected and MCAS can subsequently be documented or excluded. In addition, the algorithm proposed should help guide the investigating care providers to consider the 2 principal diagnoses that may underlie MCAS, namely, severe allergy and systemic mastocytosis accompanied by severe MCA. Although validation is required, we anticipate that this algorithm will facilitate the management of patients with suspected MCAS.
| S-EPMC6643056 | biostudies-literature
Project description:Posttraumatic epilepsy (PTE) is a significant complication following traumatic brain injury (TBI), yet the role of genetic variation in modulating PTE onset is unclear. We hypothesized that TBI-induced inflammation likely contributes to seizure development. We assessed whether genetic variation in the interleukin-1beta (IL-1?) gene, IL-1? levels in cerebrospinal fluid (CSF) and serum, and CSF/serum IL-1? ratios would predict PTE development post-TBI.We investigated PTE development in 256 Caucasian adults with moderate-to-severe TBI. IL-1? tagging and functional single nucleotide polymorphisms (SNPs) were genotyped. Genetic variance and PTE development were assessed. Serum and CSF IL-1? levels were collected from a subset of subjects (n = 59) during the first week postinjury and evaluated for their associations with IL-1? gene variants, and also PTE. Temporally matched CSF/serum IL-1? ratios were also generated to reflect the relative contribution of serum IL-1? to CSF IL-1?.Multivariate analysis showed that higher CSF/serum IL-1? ratios were associated with increased risk for PTE over time (p = 0.008). Multivariate analysis for rs1143634 revealed an association between the CT genotype and increased PTE risk over time (p = 0.005). The CT genotype group also had lower serum IL-1? levels (p = 0.014) and higher IL-1? CSF/serum ratios (p = 0.093).This is the first report implicating IL-1? gene variability in PTE risk and linking (1) IL-1? gene variation with serum IL-1? levels observed after TBI and (2) IL-1? ratios with PTE risk. Given these findings, we propose that genetic and IL-1? ratio associations with PTE may be attributable to biologic variability with blood-brain barrier integrity during TBI recovery. These results provide a rationale for further studies (1) validating the impact of genetic variability on IL-1? production after TBI, (2) assessing genetically mediated signaling mechanisms that contribute to IL-1? CSF/serum associations with PTE, and (3) evaluating targeted IL-1? therapies that reduce PTE.
| S-EPMC4107119 | biostudies-literature
Project description:We introduce an efficient iterative algorithm, intended for various least squares problems, based on a design of experiments perspective. The algorithm, called orthogonalizing EM (OEM), works for ordinary least squares and can be easily extended to penalized least squares. The main idea of the procedure is to orthogonalize a design matrix by adding new rows and then solve the original problem by embedding the augmented design in a missing data framework. We establish several attractive theoretical properties concerning OEM. For the ordinary least squares with a singular regression matrix, an OEM sequence converges to the Moore-Penrose generalized inverse-based least squares estimator. For ordinary and penalized least squares with various penalties, it converges to a point having grouping coherence for fully aliased regression matrices. Convergence and the convergence rate of the algorithm are examined. Finally, we demonstrate that OEM is highly efficient for large-scale least squares and penalized least squares problems, and is considerably faster than competing methods when n is much larger than p. Supplementary materials for this article are available online.
| S-EPMC4974022 | biostudies-literature
Project description:BackgroundParaquat ingestion is frequently fatal. While biomarkers of kidney damage increase during paraquat-induced acute kidney injury (AKI), significant concurrent proteinuria may alter diagnostic thresholds for diagnosis and prognosis to an unknown extent. This study evaluated the effect of albuminuria on biomarker cutoffs for diagnosis and outcome prediction.MethodsThis was a multi-centre prospective clinical study of patients following acute paraquat self-poisoning in 5 Sri Lankan hospitals. Biomarker concentrations were quantified using ELISA and microbead assays and correlated with urinary albumin. Functional-AKI was defined by the Acute Kidney Injury Network serum creatinine definition and alternatively by a ≥50% increase in serum cystatin C. Albuminuria was defined as albumin-creatinine ratio >30 mg/g. The study outcomes were compared with a retrospective analysis of a pre-clinical study of paraquat-induced nephrotoxicity with appropriate controls.ResultsAlbuminuria was detected in 34 of 50 patients, and increased with functional-AKI severity. The concentrations of uNGAL, uCysC, uClusterin, uβ2M, and uKIM-1 were higher in albuminuric compared to non-albuminuric patients (p < 0.001). Albuminuria correlated with biomarker concentration (r > 0.6, p < 0.01) and was associated with death (p = 0.006). Optimal biomarker cutoffs for prediction of death were higher in the albuminuric group. Similar outcomes with more detailed analysis were obtained in experimental paraquat nephrotoxicity.ConclusionAlbuminuria was associated with paraquat-induced nephrotoxicity and increased excretion of low-molecular weight protein biomarkers. AKI biomarker cutoffs for diagnosis, outcome prediction and AKI stratification increased in the presence of albuminuria. This may lead to over-diagnosis of AKI in conditions independently associated with proteinuria.
| S-EPMC5379711 | biostudies-literature
Project description:BackgroundThe relationship between event centrality (i.e., the degree to which a stressful event is integrated into one's identity) and acute posttraumatic outcomes after relatively minor physical injury is unknown. We examined pre-injury and Emergency Department (ED) predictors of event centrality at 6-weeks post-injury, and whether event centrality is uniquely associated with 6-week posttraumatic outcomes.MethodsIn the EDs of two Level I trauma centers, 149 patients completed surveys regarding demographic, psychological and injury-related factors within 24 h post-injury; 84 patients (51% male) completed 6-week surveys of event centrality, posttraumatic stress symptoms (PTSS) and trauma-specific QOL (T-QoL). Data were analyzed using linear regression modeling.ResultsAt least 20% of patients agreed or strongly agreed that the injury changed their life. Hospitalization status and peritraumatic dissociation were significant predictors of event centrality at 6-weeks. After controlling for demographics, ED-related factors and pre-injury PTSS, event centrality was uniquely associated with PTSS (p < .001) and T-QOL (p < .001) at 6 weeks.ConclusionOver and above the effects of the injury itself, event centrality conveyed important information for posttraumatic outcomes at 6 weeks post-injury. The centrality scale is brief and feasible to administer; future work is needed to determine the predictive utility of event centrality on post-injury outcomes.
| S-EPMC7722005 | biostudies-literature
Project description:OBJECTIVE:Hepatorenal Syndrome (HRS) is a devastating form of acute kidney injury (AKI) in advanced liver disease patients with high morbidity and mortality, but phenotyping algorithms have not yet been developed using large electronic health record (EHR) databases. We evaluated and compared multiple phenotyping methods to achieve an accurate algorithm for HRS identification. MATERIALS AND METHODS:A national retrospective cohort of patients with cirrhosis and AKI admitted to 124 Veterans Affairs hospitals was assembled from electronic health record data collected from 2005 to 2013. AKI was defined by the Kidney Disease: Improving Global Outcomes criteria. Five hundred and four hospitalizations were selected for manual chart review and served as the gold standard. Electronic Health Record based predictors were identified using structured and free text clinical data, subjected through NLP from the clinical Text Analysis Knowledge Extraction System. We explored several dimension reduction techniques for the NLP data, including newer high-throughput phenotyping and word embedding methods, and ascertained their effectiveness in identifying the phenotype without structured predictor variables. With the combined structured and NLP variables, we analyzed five phenotyping algorithms: penalized logistic regression, naïve Bayes, support vector machines, random forest, and gradient boosting. Calibration and discrimination metrics were calculated using 100 bootstrap iterations. In the final model, we report odds ratios and 95% confidence intervals. RESULTS:The area under the receiver operating characteristic curve (AUC) for the different models ranged from 0.73 to 0.93; with penalized logistic regression having the best discriminatory performance. Calibration for logistic regression was modest, but gradient boosting and support vector machines were superior. NLP identified 6985 variables; a priori variable selection performed similarly to dimensionality reduction using high-throughput phenotyping and semantic similarity informed clustering (AUC of 0.81 - 0.82). CONCLUSION:This study demonstrated improved phenotyping of a challenging AKI etiology, HRS, over ICD-9 coding. We also compared performance among multiple approaches to EHR-derived phenotyping, and found similar results between methods. Lastly, we showed that automated NLP dimension reduction is viable for acute illness.
| S-EPMC5920557 | biostudies-literature