Clinical Characteristics and Outcomes of Drug-Induced Acute Kidney Injury Cases.

Introduction: Drug-induced acute kidney injury (DI-AKI) is a frequent adverse event. The identification of DI-AKI is challenged by competing etiologies, clinical heterogeneity among patients, and a lack of accurate diagnostic tools. Our research aims to describe the clinical characteristics and predictive variables of DI-AKI. Methods: We analyzed data from the Drug-Induced Renal Injury Consortium (DIRECT) study (NCT02159209), an international, multicenter, observational cohort study of enriched clinically adjudicated DI-AKI cases. Cases met the primary inclusion criteria if the patient was exposed to at least 1 nephrotoxic drug for a minimum of 24 hours prior to AKI onset. Cases were clinically adjudicated, and inter-rater reliability (IRR) was measured using Krippendorff's alpha. Variables associated with DI-AKI were identified using L1 regularized multivariable logistic regression. Model performance was assessed using the area under the receiver operating characteristic curve (ROC AUC). Results: A total of 314 AKI cases met the eligibility criteria for this analysis, and 271 (86%) cases were adjudicated as DI-AKI. The majority of the AKI cases were recruited from the United States (68%). The most frequent causal nephrotoxic drugs were vancomycin (48.7%), nonsteroidal antiinflammatory drugs (18.2%), and piperacillin/tazobactam (17.8%). The IRR for DI-AKI adjudication was 0.309. The multivariable model identified age, vascular capacity, hyperglycemia, infections, pyuria, serum creatinine (SCr) trends, and contrast media as significant predictors of DI-AKI with good performance (ROC AUC 0.86). Conclusion: The identification of DI-AKI is challenging even with comprehensive adjudication by experienced nephrologists. Our analysis identified key clinical characteristics and outcomes of DI-AKI compared to other AKI etiologies.

Digital health and acute kidney injury: consensus report of the 27th Acute Disease Quality Initiative workgroup.

Acute kidney injury (AKI), which is a common complication of acute illnesses, affects the health of individuals in community, acute care and post-acute care settings. Although the recognition, prevention and management of AKI has advanced over the past decades, its incidence and related morbidity, mortality and health care burden remain overwhelming. The rapid growth of digital technologies has provided a new platform to improve patient care, and reports show demonstrable benefits in care processes and, in some instances, in patient outcomes. However, despite great progress, the potential benefits of using digital technology to manage AKI has not yet been fully explored or implemented in clinical practice. Digital health studies in AKI have shown variable evidence of benefits, and the digital divide means that access to digital technologies is not equitable. Upstream research and development costs, limited stakeholder participation and acceptance, and poor scalability of digital health solutions have hindered their widespread implementation and use. Here, we provide recommendations from the Acute Disease Quality Initiative consensus meeting, which involved experts in adult and paediatric nephrology, critical care, pharmacy and data science, at which the use of digital health for risk prediction, prevention, identification and management of AKI and its consequences was discussed.

Simulating complex patient populations with hierarchical learning effects to support methods development for post-market surveillance.

BACKGROUND: Validating new algorithms, such as methods to disentangle intrinsic treatment risk from risk associated with experiential learning of novel treatments, often requires knowing the ground truth for data characteristics under investigation. Since the ground truth is inaccessible in real world data, simulation studies using synthetic datasets that mimic complex clinical environments are essential. We describe and evaluate a generalizable framework for injecting hierarchical learning effects within a robust data generation process that incorporates the magnitude of intrinsic risk and accounts for known critical elements in clinical data relationships. METHODS: We present a multi-step data generating process with customizable options and flexible modules to support a variety of simulation requirements. Synthetic patients with nonlinear and correlated features are assigned to provider and institution case series. The probability of treatment and outcome assignment are associated with patient features based on user definitions. Risk due to experiential learning by providers and/or institutions when novel treatments are introduced is injected at various speeds and magnitudes. To further reflect real-world complexity, users can request missing values and omitted variables. We illustrate an implementation of our method in a case study using MIMIC-III data for reference patient feature distributions. RESULTS: Realized data characteristics in the simulated data reflected specified values. Apparent deviations in treatment effects and feature distributions, though not statistically significant, were most common in small datasets (n < 3000) and attributable to random noise and variability in estimating realized values in small samples. When learning effects were specified, synthetic datasets exhibited changes in the probability of an adverse outcomes as cases accrued for the treatment group impacted by learning and stable probabilities as cases accrued for the treatment group not affected by learning. CONCLUSIONS: Our framework extends clinical data simulation techniques beyond generation of patient features to incorporate hierarchical learning effects. This enables the complex simulation studies required to develop and rigorously test algorithms developed to disentangle treatment safety signals from the effects of experiential learning. By supporting such efforts, this work can help identify training opportunities, avoid unwarranted restriction of access to medical advances, and hasten treatment improvements.

Predicting 30-Day Hospital Readmission Risk in a National Cohort of Patients with Cirrhosis.

BACKGROUND: Early hospital readmission for patients with cirrhosis continues to challenge the healthcare system. Risk stratification may help tailor resources, but existing models were designed using small, single-institution cohorts or had modest performance. AIMS: We leveraged a large clinical database from the Department of Veterans Affairs (VA) to design a readmission risk model for patients hospitalized with cirrhosis. Additionally, we analyzed potentially modifiable or unexplored readmission risk factors. METHODS: A national VA retrospective cohort of patients with a history of cirrhosis hospitalized for any reason from January 1, 2006, to November 30, 2013, was developed from 123 centers. Using 174 candidate variables within demographics, laboratory results, vital signs, medications, diagnoses and procedures, and healthcare utilization, we built a 47-variable penalized logistic regression model with the outcome of all-cause 30-day readmission. We excluded patients who left against medical advice, transferred to a non-VA facility, or if the hospital length of stay was greater than 30 days. We evaluated calibration and discrimination across variable volume and compared the performance to recalibrated preexisting risk models for readmission. RESULTS: We analyzed 67,749 patients and 179,298 index hospitalizations. The 30-day readmission rate was 23%. Ascites was the most common cirrhosis-related cause of index hospitalization and readmission. The AUC of the model was 0.670 compared to existing models (0.649, 0.566, 0.577). The Brier score of 0.165 showed good calibration. CONCLUSION: Our model achieved better discrimination and calibration compared to existing models, even after local recalibration. Assessment of calibration by variable parsimony revealed performance improvements for increasing variable inclusion well beyond those detectable for discrimination.

Rate of Risk Factors for and Interventions to Reduce Hospital Readmission in Patients With Inflammatory Bowel Diseases.

BACKGROUND & AIMS: We investigated 30- and 90-day rates and causes of, risk factors for, and interventions to reduce hospital readmission in patients who received medical treatment for inflammatory bowel diseases (IBD). METHODS: We performed a systematic search of publications through July 1, 2018 for studies of rates of hospital readmission and associated causes and risk factors in patients who received medical treatments for IBD. Our final analysis included 17 cohort studies (6324 patients) of hospitalized adults with IBD who had received medical treatment, along with reported readmission rates with detailed chart review. We performed random effects meta-analysis to estimate 30- and 90-day rates of readmission and identified causes and risk factors associated with readmission. We also performed qualitative analyses of studies that focused on interventions to reduce readmission. RESULTS: Overall, the 30-day rate of readmission was 18.1% (95% CI, 14.4-22.4) and the 90-day rate was 26.0% (95% CI, 22.7-29.6). On meta-regression, studies with higher proportions of patients with ulcerative colitis than Crohn's disease reported higher risks for readmission. Most common reasons for readmission were IBD flare, infection, or complications from unplanned surgeries during hospitalizations. Consistent risk factors for 30-day readmission were admission for pain control (odds ratio [OR], 2.27; 95% CI, 1.69-3.03), need for total parenteral nutrition on discharge (OR, 2.13; 95% CI, 1.36-3.35), and prior or unplanned surgery during admission (OR, 3.11; 95% CI, 2.27-4.25). Only 1 study focused on interventions (specialized inpatient IBD service) to reduce risk of readmission. CONCLUSIONS: Overall 30- and 90-day rates of readmission for patients who received medical treatment for IBD are 18.1% and 26.0%, respectively. IBD flares and infections are common reasons for readmission, and inadequate pain control and need for parenteral nutrition were common risk factors. Interventional studies to reduce risk of readmission are needed.

Development of a national Department of Veterans Affairs mortality risk prediction model among patients with cirrhosis.

OBJECTIVE: Cirrhotic patients are at high hospitalisation risk with subsequent high mortality. Current risk prediction models have varied performances with methodological room for improvement. We used current analytical techniques using automatically extractable variables from the electronic health record (EHR) to develop and validate a posthospitalisation mortality risk score for cirrhotic patients and compared performance with the model for end-stage liver disease (MELD), model for end-stage liver disease with sodium (MELD-Na), and the CLIF Consortium Acute Decompensation (CLIF-C AD) models. DESIGN: We analysed a retrospective cohort of 73 976 patients comprising 247 650 hospitalisations between 2006 and 2013 at any of 123 Department of Veterans Affairs hospitals. Using 45 predictor variables, we built a time-dependent Cox proportional hazards model with all-cause mortality as the outcome. We compared performance to the three extant models and reported discrimination and calibration using bootstrapping. Furthermore, we analysed differential utility using the net reclassification index (NRI). RESULTS: The C-statistic for the final model was 0.863, representing a significant improvement over the MELD, MELD-Na, and the CLIF-C AD, which had C-statistics of 0.655, 0.675, and 0.679, respectively. Multiple risk factors were significant in our model, including variables reflecting disease severity and haemodynamic compromise. The NRI showed a 24% improvement in predicting survival of low-risk patients and a 30% improvement in predicting death of high-risk patients. CONCLUSION: We developed a more accurate mortality risk prediction score using variables automatically extractable from an EHR that may be used to risk stratify patients with cirrhosis for targeted postdischarge management.

Descriptive Usability Study of CirrODS: Clinical Decision and Workflow Support Tool for Management of Patients With Cirrhosis.

BACKGROUND: There are gaps in delivering evidence-based care for patients with chronic liver disease and cirrhosis. OBJECTIVE: Our objective was to use interactive user-centered design methods to develop the Cirrhosis Order Set and Clinical Decision Support (CirrODS) tool in order to improve clinical decision-making and workflow. METHODS: Two work groups were convened with clinicians, user experience designers, human factors and health services researchers, and information technologists to create user interface designs. CirrODS prototypes underwent several rounds of formative design. Physicians (n=20) at three hospitals were provided with clinical scenarios of patients with cirrhosis, and the admission orders made with and without the CirrODS tool were compared. The physicians rated their experience using CirrODS and provided comments, which we coded into categories and themes. We assessed the safety, usability, and quality of CirrODS using qualitative and quantitative methods. RESULTS: We created an interactive CirrODS prototype that displays an alert when existing electronic data indicate a patient is at risk for cirrhosis. The tool consists of two primary frames, presenting relevant patient data and allowing recommended evidence-based tests and treatments to be ordered and categorized. Physicians viewed the tool positively and suggested that it would be most useful at the time of admission. When using the tool, the clinicians placed fewer orders than they placed when not using the tool, but more of the orders placed were considered to be high priority when the tool was used than when it was not used. The physicians' ratings of CirrODS indicated above average usability. CONCLUSIONS: We developed a novel Web-based combined clinical decision-making and workflow support tool to alert and assist clinicians caring for patients with cirrhosis. Further studies are underway to assess the impact on quality of care for patients with cirrhosis in actual practice.

A clinical risk prediction model to identify patients with hepatorenal syndrome at hospital admission.

BACKGROUND: Hepatorenal syndrome (HRS) is a life-threatening complication of cirrhosis and early detection of evolving HRS may provide opportunities for early intervention. We developed a HRS risk model to assist early recognition of inpatient HRS. METHODS: We analysed a retrospective cohort of patients hospitalised from among 122 medical centres in the US Department of Veterans Affairs between 1 January 2005 and 31 December 2013. We included cirrhotic patients who had Kidney Disease Improving Global Outcomes criteria based acute kidney injury on admission. We developed a logistic regression risk prediction model to detect HRS on admission using 10 variables. We calculated 95% confidence intervals on the model building dataset and, subsequently, calculated performance on a 1000 sample holdout test set. We report model performance with area under the curve (AUC) for discrimination and several calibration measures. RESULTS: The cohort included 19 368 patients comprising 32 047 inpatient admissions. The event rate for hospitalised HRS was 2810/31 047 (9.1%) and 79/1000 (7.9%) in the model building and validation datasets, respectively. The variable selection procedure designed a parsimonious model involving ten predictor variables. Final model performance in the validation dataset had an AUC of 0.87, Brier score of 0.05, slope of 1.10 and intercept of 0.04. CONCLUSIONS: We developed a probabilistic risk model to diagnose HRS within 24 hours of hospital admission using routine clinical variables in the largest ever published HRS cohort. The performance was excellent and this model may help identify high-risk patients for HRS and promote early intervention.

Consumer Health Informatics Adoption among Underserved Populations: Thinking beyond the Digital Divide.

OBJECTIVES: Underserved populations can benefit from consumer health informatics (CHI) that promotes self-management at a lower cost. However, prior literature suggested that the digital divide and low motivation constituted barriers to CHI adoption. Despite increased Internet use, underserved populations continue to show slow CHI uptake. The aim of the paper is to revisit barriers and facilitators that may impact CHI adoption among underserved populations. METHODS: We surveyed the past five years of literature. We searched PubMed for articles published between 2012 and 2017 that describe empirical evaluations involving CHI use by underserved populations. We abstracted and summarized data about facilitators and barriers impacting CHI adoption. RESULTS: From 645 search results, after abstract and full-text screening, 13 publications met the inclusion criteria of identifying barriers to and facilitators of underserved populations' CHI adoption. Contrary to earlier literature, the studies suggested that the motivation to improve health literacy and adopt technology was high among studied populations. Beyond the digital divide, barriers included: low health and computer literacy, challenges in accepting the presented information, poor usability, and unclear content. Factors associated with increased use were: user needs for information, user-access mediated by a proxy person, and early user engagement in system design. CONCLUSIONS: While the digital divide remains a barrier, newer studies show that high motivation for CHI use exists. However, simply gaining access to technology is not sufficient to improve adoption unless CHI technology is tailored to address user needs. Future interventions should consider building larger empirical evidence on identifying CHI barriers and facilitators.

Application of contextual design methods to inform targeted clinical decision support interventions in sub-specialty care environments.

BACKGROUND & OBJECTIVES: In healthcare, the routine use of evidence-based specialty care management plans is mixed. Targeted computerized clinical decision support (CCDS) interventions can improve physician adherence, but adoption depends on CCDS' 'fit' within clinical work. We analyzed clinical work in outpatient and inpatient settings as a basis for developing guidelines for optimizing CCDS design. METHODS: The contextual design approach guided data collection, collation and analysis. Forty (40) consenting physicians were observed and interviewed in general internal medicine inpatient units and gastroenterology (GI) outpatient clinics at two academic medical centers. Data were collated using interpretive debriefing, and consolidated using thematic analysis and three work modeling approaches (communication flow, sequence and artifact models). RESULTS: Twenty-six consenting physicians were observed at Site A and 14 at Site B. Observations included attending (33%) and resident physicians. During research team debriefings, 220 of 341 unique topics were categorized into 5 CCDS-relevant themes. Resident physicians relied on patient assessment & planning processes to support their roles as communication and coordination hubs within the medical team. Artifact analysis further elucidated the evolution of assessment and planning over work shifts. CONCLUSIONS: The usefulness of CCDS tools may be enhanced in clinical care if the design: 1) accounts for clinical work that is distributed across people, space, and time; 2) targets communication and coordination hubs (specific roles) that can amplify the usefulness of CCDS interventions; 3) integrates CCDS with early clinical assessment & planning processes; and 4) provides CCDS in both electronic & hardcopy formats. These requirements provide a research agenda for future research in clinician-CCDS integration.

Development of an automated phenotyping algorithm for hepatorenal syndrome.

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.

Motor threshold in transcranial magnetic stimulation: the impact of white matter fiber orientation and skull-to-cortex distance.

The electrophysiology of transcranial magnetic stimulation (TMS) of motor cortex is not well understood. In this study, we investigate several structural parameters of the corticospinal tract and their relation to the TMS motor threshold (MT) in 17 subjects, with and without schizophrenia. We obtained structural and diffusion tensor MRI scans and measured the fractional anisotropy and principal diffusion direction for regions of interest in the corticospinal tract. We also measured the skull-to-cortex distance over the left motor region. The anterior-posterior trajectory of principle diffusion direction of the corticospinal tract and skull-to-cortex distance were both found to be highly correlated with MT, while fractional anisotropy, age and schizophrenia status were not. Two parameters-skull-to-cortex distance and the anterior component of the principle diffusion direction of the corticospinal tract as it passes the internal capsule-are highly predictive of MT in a linear regression model, and account for 82% of the variance observed (R2 = 0.82, F = 20.27, P < 0.0001) in measurements of MT. The corticospinal tract's anterior-posterior direction alone contributes 13% of the variance explained.

Emotion facilitates action: a transcranial magnetic stimulation study of motor cortex excitability during picture viewing.

Emotional stimuli capture attention, receive increased perceptual processing resources, and alter peripheral reflexes. In the present study, we examined whether emotional stimuli would modulate the magnitude of the motor evoked potential (MEP) elicited in the abductor pollicus brevis muscle by transcranial magnetic stimulation (TMS) delivered to the motor cortex. The electromyogram (EMG) was recorded from 16 participants while they viewed six blocks of pleasant, neutral, and unpleasant images; 36 TMS pulses at increasing intensities were delivered during each block. The TMS-induced MEP was reliably larger while participants viewed pleasant and unpleasant compared to neutral images. There were no differences in the pre-TMS EMG activity as a function of emotional stimuli. Thus, viewing arousing stimuli, regardless of valence, increased motor cortex excitability. Implications and directions for future research are discussed.

Serial vagus nerve stimulation functional MRI in treatment-resistant depression.

Vagus nerve stimulation (VNS) therapy has shown antidepressant effects in open acute and long-term studies of treatment-resistant major depression. Mechanisms of action are not fully understood, although clinical data suggest slower onset therapeutic benefit than conventional psychotropic interventions. We set out to map brain systems activated by VNS and to identify serial brain functional correlates of antidepressant treatment and symptomatic response. Nine adults, satisfying DSM-IV criteria for unipolar or bipolar disorder, severe depressed type, were implanted with adjunctive VNS therapy (MRI-compatible technique) and enrolled in a 3-month, double-blind, placebo-controlled, serial-interleaved VNS/functional MRI (fMRI) study and open 20-month follow-up. A multiple regression mixed model with blood oxygenation level dependent (BOLD) signal as the dependent variable revealed that over time, VNS therapy was associated with ventro-medial prefrontal cortex deactivation. Controlling for other variables, acute VNS produced greater right insula activation among the participants with a greater degree of depression. These results suggest that similar to other antidepressant treatments, BOLD deactivation in the ventro-medial prefrontal cortex correlates with the antidepressant response to VNS therapy. The increased acute VNS insula effects among actively depressed participants may also account for the lower dosing observed in VNS clinical trials of depression compared with epilepsy. Future interleaved VNS/fMRI studies to confirm these findings and further clarify the regional neurobiological effects of VNS.

Estimating resting motor thresholds in transcranial magnetic stimulation research and practice: a computer simulation evaluation of best methods.

OBJECTIVES: Resting motor threshold is the basic unit of dosing in transcranial magnetic stimulation (TMS) research and practice. There is little consensus on how best to estimate resting motor threshold with TMS, and only a few tools and resources are readily available to TMS researchers. The current study investigates the accuracy and efficiency of 5 different approaches to motor threshold assessment for TMS research and practice applications. METHODS: Computer simulation models are used to test the efficiency and accuracy of 5 different adaptive parameter estimation by sequential testing (PEST) procedures. For each approach, data are presented with respect to the mean number of TMS trials necessary to reach the motor threshold estimate as well as the mean accuracy of the estimates. RESULTS: A simple nonparametric PEST procedure appears to provide the most accurate motor threshold estimates, but takes slightly longer (on average, 3.48 trials) to complete than a popular parametric alternative (maximum likelihood PEST). Recommendations are made for the best starting values for each of the approaches to maximize both efficiency and accuracy. CONCLUSIONS: In light of the computer simulation data provided in this article, the authors review and suggest which techniques might best fit different TMS research and clinical situations. Lastly, a free user-friendly software package is described and made available on the world wide web that allows users to run all of the motor threshold estimation procedures discussed in this article for clinical and research applications.

Decreased cortical response to verbal working memory following sleep deprivation.

STUDY OBJECTIVE: To investigate the cerebral hemodynamic response to verbal working memory following sleep deprivation. DESIGN: Subjects were scheduled for 3 functional magnetic resonance imaging scanning visits: an initial screening day (screening state), after a normal night of sleep (rested state), and after 30 hours of sleep deprivation (sleep-deprivation state). Subjects performed the Sternberg working memory task alternated with a control task during an approximate 13-minute functional magnetic resonance imaging scan. SETTING: Inpatient General Clinical Research Center and outpatient functional magnetic resonance imaging center. PATIENTS OR PARTICIPANTS: Results from 33 men (mean age, 28.6 +/- 6.6 years) were included in the final analyses. INTERVENTIONS: None. MEASUREMENTS AND RESULTS: Subjects performed the same Sternberg working memory task at the 3 states within the magnetic resonance imaging scanner. Neuroimaging data revealed that, in the screening and rested states, the brain regions activated by the Sternberg working memory task were found in the left dorsolateral prefrontal cortex, Broca's area, supplementary motor area, right ventrolateral prefrontal cortex, and the bilateral posterior parietal cortexes. After 30 hours of sleep deprivation, the activations in these brain regions significantly decreased, especially in the bilateral posterior parietal cortices. Task performance also decreased. A repeated-measures analysis of variance revealed that subjects at the screening and rested states had similar activation patterns, with each having significantly more activation than during the sleep-deprivation state. CONCLUSIONS: These results suggest that human sleep-deprivation deficits are not caused solely or even predominantly by prefrontal cortex dysfunction and that the paretal cortex, in particular, and other brain regions involved in verbal working memory exhibit significant sleep-deprivation vulnerability.

The maximum-likelihood strategy for determining transcranial magnetic stimulation motor threshold, using parameter estimation by sequential testing is faster than conventional methods with similar precision.

BACKGROUND: The resting motor threshold (rMT) is the basic unit of transcranial magnetic stimulation (TMS) dosing. Traditional methods of determining rMT involve finding a threshold of either visible movement or electromyography (EMG) motor-evoked potentials, commonly approached from above and below and then averaged. This time-consuming method typically uses many TMS pulses. Mathematical programs can efficiently determine a threshold by calculating the next intensity needed based on the prior results. Within our group of experienced TMS researchers, we sought to perform an illustrative study to compare one of these programs, the Maximum-Likelihood Strategy using Parameter Estimation by Sequential Testing (MLS-PEST) approach, to a modification of the traditional International Federation of Clinical Neurophysiology (IFCN) method for determining rMT in terms of the time and pulses required and the rMT value. METHODS: One subject participated in the study. Five researchers determined the same subject's rMT on 4 separate days-twice using EMG and twice using visible movement. On each visit, researchers used both the MLS-PEST and the IFCN methods, in alternating order. RESULTS: The MLS-PEST approach was significantly faster and used fewer pulses to estimate rMT. For EMG-determined rMT, MLS-PEST and IFCN derived similar rMT, whereas for visible movement MLS-PEST rMT was higher than for IFCN. CONCLUSIONS: The MLS-PEST algorithm is a promising alternative to traditional, time-consuming methods for determining rMT. Because the EMG-PEST method is totally automated, it may prove useful in studies using rMT as a quickly changing variable, as well as in large-scale clinical trials. Further work with PEST is warranted.