Hospital food management: a multi-objective approach to reduce waste and costs.

Food waste contributes significantly to greenhouse emissions and represents a substantial portion of overall waste within hospital facilities. Furthermore, uneaten food leads to a diminished nutritional intake for patients, that typically are vulnerable and ill. Therefore, this study developed mathematical models for constructing patient meals in a 1000-bed hospital located in Florida. The objective is to minimize food waste and meal-building costs while ensuring that the prepared meals meet the required nutrients and caloric content for patients. To accomplish these objectives, four mixed-integer programming models were employed, incorporating binary and continuous variables. The first model establishes a baseline for how the system currently works. This model generates the meals without minimizing waste or cost. The second model minimizes food waste, reducing waste up to 22.53 % compared to the baseline. The third model focuses on minimizing meal-building costs and achieves a substantial reduction of 37 %. Finally, a multi-objective optimization model was employed to simultaneously reduce both food waste and cost, resulting in reductions of 19.70 % in food waste and 32.66 % in meal-building costs. The results demonstrate the effectiveness of multi-objective optimization in reducing waste and costs within large-scale food service operations.

A Multistate Decomposition Analysis of Cesarean Rate Variations, Associated Health Outcomes, and Financial Implications in the United States.

OBJECTIVES: Cesarean rates vary widely across the U.S. states; however, little is known about the causes and implications associated with these variations. The objectives of this study were to quantify the contribution of the clinical and nonclinical factors in explaining the difference in cesarean rates across states and to investigate the associated health outcome of cesarean variations. STUDY DESIGN: Using the Hospital Cost and Utilization Project State Inpatient Databases, this retrospective study included all nonfederal hospital births from Wisconsin, Florida, and New York. A nonlinear extension of the Oaxaca-Blinder method was used to decompose the contributions of differences in characteristics to cesarean variations between these states. The risk factors for cesarean delivery were identified using separate multivariable logistic regression analysis for each State. RESULTS: The difference in clinical and nonclinical factors explained a substantial (~46.57-65.45%) proportion of cesarean variations between U.S. states. The major contributors of variation were patient demographics, previous cesareans, hospital markup ratios, and social determinants of health. Cesarean delivery was significantly associated with higher postpartum readmissions and unplanned emergency department visits, greater lengths of stay, and hospital costs across all states. CONCLUSION: Although a proportion of variations in cesarean rates can be explained by the differences in risk factors, the remaining unexplained variations suggest differences in practice patterns and imply potential quality concerns. Since nonclinical factors are likely to play an important role in cesarean variation, we recommend targeted initiatives increasing access to maternal care and improving maternal health literacy. KEY POINTS: · Cesarean rates vary widely almost two folds within U.S. states.. · The difference in risk factors explained substantial (~46.57-65.45%) of the cesarean variations.. · Mother race, hospital factors, and social determinants comprised major proportion of explained variation.. · Adverse outcomes and increased expenditures were associated with cesarean than vaginal delivery.. · Significant potential cost savings for Medicaid if the unnecessary cesarean deliveries are reduced..

Discovery and Characterization of Potent, Efficacious, Orally Available Antimalarial Plasmepsin X Inhibitors and Preclinical Safety Assessment of UCB7362.

Plasmepsin X (PMX) is an essential aspartyl protease controlling malaria parasite egress and invasion of erythrocytes, development of functional liver merozoites (prophylactic activity), and blocking transmission to mosquitoes, making it a potential multistage drug target. We report the optimization of an aspartyl protease binding scaffold and the discovery of potent, orally active PMX inhibitors with in vivo antimalarial efficacy. Incorporation of safety evaluation early in the characterization of PMX inhibitors precluded compounds with a long human half-life (t1/2) to be developed. Optimization focused on improving the off-target safety profile led to the identification of UCB7362 that had an improved in vitro and in vivo safety profile but a shorter predicted human t1/2. UCB7362 is estimated to achieve 9 log 10 unit reduction in asexual blood-stage parasites with once-daily dosing of 50 mg for 7 days. This work demonstrates the potential to deliver PMX inhibitors with in vivo efficacy to treat malaria.

Rings in Clinical Trials and Drugs: Present and Future.

We present a comprehensive analysis of all ring systems (both heterocyclic and nonheterocyclic) in clinical trial compounds and FDA-approved drugs. We show 67% of small molecules in clinical trials comprise only ring systems found in marketed drugs, which mirrors previously published findings for newly approved drugs. We also show there are approximately 450 000 unique ring systems derived from 2.24 billion molecules currently available in synthesized chemical space, and molecules in clinical trials utilize only 0.1% of this available pool. Moreover, there are fewer ring systems in drugs compared with those in clinical trials, but this is balanced by the drug ring systems being reused more often. Furthermore, systematic changes of up to two atoms on existing drug and clinical trial ring systems give a set of 3902 future clinical trial ring systems, which are predicted to cover approximately 50% of the novel ring systems entering clinical trials.

The Waiting Game - How Cooperation Between Public and Private Hospitals Can Help Reduce Waiting Lists.

Prolonged waiting to access health care is a primary concern for nations aiming for comprehensive effective care, due to its adverse effects on mortality, quality of life, and government approval. Here, we propose two novel bargaining frameworks to reduce waiting lists in two-tier health care systems with local and regional actors. In particular, we assess the impact of 1) trading patients on waiting lists among hospitals, the 2) introduction of the role of private hospitals in capturing unfulfilled demand, and the 3) hospitals' willingness to share capacity on the system performance. We calibrated our models with 2008-2018 Chilean waiting list data. If hospitals trade unattended patients, our game-theoretic models indicate a potential reduction of waiting lists of up to 37%. However, when private hospitals are introduced into the system, we found a possible reduction of waiting lists of up to 60%. Further analyses revealed a trade-off between diagnosing unserved demand and the additional expense of using private hospitals as a back-up system. In summary, our game-theoretic frameworks of waiting list management in two-tier health systems suggest that public-private cooperation can be an effective mechanism to reduce waiting lists. Further empirical and prospective evaluations are needed.

Characteristics and Outcomes of Pediatric Nonindex Readmission: Evidence From Florida Hospitals.

OBJECTIVES: Increasing pediatric care regionalization may inadvertently fragment care if children are readmitted to a different (nonindex) hospital rather than the discharge (index) hospital. Therefore, this study aimed to assess trends in pediatric nonindex readmission rates, examine the risk factors, and determine if this destination difference affects readmission outcomes. METHODS: In this retrospective cohort study, we use the Healthcare Cost and Utilization Project State Inpatient Database to include pediatric (0 to 18 years) admissions from 2010 to 2017 across Florida hospitals. Risk factors of nonindex readmissions were identified by using logistic regression analyses. The differences in outcomes between index versus nonindex readmissions were compared for in-hospital mortality, morbidity, hospital cost, length of stay, against medical advice discharges, and subsequent hospital visits by using generalized linear regression models. RESULTS: Among 41 107 total identified readmissions, 5585 (13.6%) were readmitted to nonindex hospitals. Adjusted nonindex readmission rate increased from 13.3% in 2010% to 15.4% in 2017. Patients in the nonindex readmissions group were more likely to be adolescents, live in poor neighborhoods, have higher comorbidity scores, travel longer distances, and be discharged at the postacute facility. After risk adjusting, no difference in in-hospital mortality was found, but morbidity was 13% higher, and following unplanned emergency department visits were 28% higher among patients with nonindex readmissions. Length of stay, hospital costs, and against medical advice discharges were also significantly higher for nonindex readmissions. CONCLUSIONS: A substantial proportion of children experienced nonindex readmissions and relatively poorer health outcomes compared with index readmission. Targeted strategies for improving continuity of care are necessary to improve readmission outcomes.

Outcomes of patients with COVID-19 in the intensive care unit in Mexico: A multicenter observational study.

BACKGROUND: As of June 15, 2020, a cumulative total of 7,823,289 confirmed cases of COVID-19 have been reported across 216 countries and territories worldwide. However, there is little information on the clinical characteristics and outcomes of critically ill patients with severe COVID-19 who were admitted to intensive care units (ICUs) in Latin America. The present study evaluated the clinical characteristics and outcomes of critically ill patients with severe COVID-19 who were admitted to ICUs in Mexico. METHODS: This was a multicenter observational study that included 164 critically ill patients with laboratory-confirmed COVID-19 who were admitted to 10 ICUs in Mexico, from April 1 to April 30, 2020. Demographic data, comorbid conditions, clinical presentation, treatment, and outcomes were collected and analyzed. The date of final follow-up was June 4, 2020. RESULTS: A total of 164 patients with severe COVID-19 were included in this study. The mean age of patients was 57.3 years (SD 13.7), 114 (69.5%) were men, and 6.0% were healthcare workers. Comorbid conditions were common in patients with critical COVID-19: 38.4% of patients had hypertension and 32.3% had diabetes. Compared to survivors, nonsurvivors were older and more likely to have diabetes, hypertension or other conditions. Patients presented to the hospital a median of 7 days (IQR 4.5-9) after symptom onset. The most common presenting symptoms were shortness of breath, fever, dry cough, and myalgias. One hundred percent of patients received invasive mechanical ventilation for a median time of 11 days (IQR 6-14). A total of 139 of 164 patients (89.4%) received vasopressors, and 24 patients (14.6%) received renal replacement therapy during hospitalization. Eighty-five (51.8%) patients died at or before 30 days, with a median survival of 25 days. Age (OR, 1.05; 95% CI, 1.02-1.08; p<0.001) and C-reactive protein levels upon ICU admission (1.008; 95% CI, 1.003-1.012; p<0.001) were associated with a higher risk of in-hospital death. ICU length of stay was associated with reduced in-hospital mortality risk (OR, 0.89; 95% CI, 0.84-0.94; p<0.001). CONCLUSIONS: This observational study of critically ill patients with laboratory-confirmed COVID-19 who were admitted to the ICU in Mexico demonstrated that age and C-reactive protein level upon ICU admission were associated with in-hospital mortality, and the overall hospital mortality rate was high. TRIAL REGISTRATION: ClinicalTrials.gov, NCT04336345.

Prediction of Chronic Disease-Related Inpatient Prolonged Length of Stay Using Machine Learning Algorithms.

OBJECTIVES: The study aimed to develop and compare predictive models based on supervised machine learning algorithms for predicting the prolonged length of stay (LOS) of hospitalized patients diagnosed with five different chronic conditions. METHODS: An administrative claim dataset (2008-2012) of a regional network of nine hospitals in the Tampa Bay area, Florida, USA, was used to develop the prediction models. Features were extracted from the dataset using the International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM) codes. Five learning algorithms, namely, decision tree C5.0, linear support vector machine (LSVM), k-nearest neighbors, random forest, and multi-layered artificial neural networks, were used to build the model with semi-supervised anomaly detection and two feature selection methods. Issues with the unbalanced nature of the dataset were resolved using the Synthetic Minority Over-sampling Technique (SMOTE). RESULTS: LSVM with wrapper feature selection performed moderately well for all patient cohorts. Using SMOTE to counter data imbalances triggered a tradeoff between the model's sensitivity and specificity, which can be masked under a similar area under the curve. The proposed aggregate rank selection approach resulted in a balanced performing model compared to other criteria. Finally, factors such as comorbidity conditions, source of admission, and payer types were associated with the increased risk of a prolonged LOS. CONCLUSIONS: Prolonged LOS is mostly associated with pre-intraoperative clinical and patient socioeconomic factors. Accurate patient identification with the risk of prolonged LOS using the selected model can provide hospitals a better tool for planning early discharge and resource allocation, thus reducing avoidable hospitalization costs.

Eight weeks of Paritaprevir/r/Ombitasvir + Dasabuvir in HCV genotype 1b with mild-moderate fibrosis: Results from a real-world cohort.

BACKGROUND & AIMS: The interferon-free regimen paritaprevir/ritonavir, ombitasvir + dasabuvir (PTV/r/OBV/DSV) has shown high efficacy in patients with hepatitis C virus (HCV) genotype 1b infection when administered for 8 or 12 weeks, but data regarding the 8-week treatment are scarce. The aim of our study was to assess the efficacy and safety of the 8-week administration of PTV/r/OBV/DSV in a real-world cohort. METHODS: We performed a multicentre observational study from Spanish Hepa-C database including patients receiving 8 weeks of PTV/r/OBV/DSV (October 2016-November 2017). Those with advanced fibrosis, with non-genotype 1b or who were treatment-experienced were excluded. RESULTS: A total of 211 patients were registered from 23 Spanish centres; eleven were excluded. At baseline, 42.5% (n = 85) were male, median (range) age was 57 (23-86), ALT was 45 (11-494) IU/mL, viral load was 6.1 (3.3-8.2) log10 IU/mL, and 74.5% had mild liver fibrosis (F0-F1) and 25.5% moderate fibrosis (F2). At the end of treatment (EOT), HCV viral load was undetectable in 100% (200/200). Seven patients relapsed after treatment discontinuation. Sustained virological response (SVR12) rates by intention-to-treat analysis were 96% (192/200). Regarding treatment safety, 2 patients developed ALT elevation >5x ULN, but there were no treatment discontinuations. One patient died 7 weeks after EOT. CONCLUSION: Treatment with PTV/r/OBV/DSV in genotype 1b-infected treatment-naive patients with mild-moderate fibrosis shows excellent efficacy and safety in real life, similarly to clinical trials. Clinicaltrials.gov, number: NCT03122132.

Direct sulfonylation of anilines mediated by visible light.

Sulfones feature prominently in biologically active molecules and are key functional groups for organic synthesis. We report a mild, photoredox-catalyzed reaction for sulfonylation of aniline derivatives with sulfinate salts, and demonstrate the utility of the method by the late-stage functionalization of drugs. Key features of the method are the straightforward generation of sulfonyl radicals from bench-stable sulfinate salts and the use of simple aniline derivatives as convenient readily available coupling partners.

A strategic gaming model for health information exchange markets.

Current market conditions create incentives for some providers to exercise control over patient data in ways that unreasonably limit its availability and use. Here we develop a game theoretic model for estimating the willingness of healthcare organizations to join a health information exchange (HIE) network and demonstrate its use in HIE policy design. We formulated the model as a bi-level integer program. A quasi-Newton method is proposed to obtain a strategy Nash equilibrium. We applied our modeling and solution technique to 1,093,177 encounters for exchanging information over a 7.5-year period in 9 hospitals located within a three-county region in Florida. Under a set of assumptions, we found that a proposed federal penalty of up to $2,000,000 has a higher impact on increasing HIE adoption than current federal monetary incentives. Medium-sized hospitals were more reticent to adopt HIE than large-sized hospitals. In the presence of collusion among multiple hospitals to not adopt HIE, neither federal incentives nor proposed penalties increase hospitals' willingness to adopt. Hospitals' apathy toward HIE adoption may threaten the value of inter-connectivity even with federal incentives in place. Competition among hospitals, coupled with volume-based payment systems, creates no incentives for smaller hospitals to exchange data with competitors. Medium-sized hospitals need targeted actions (e.g., outside technological assistance, group purchasing arrangements) to mitigate market incentives to not adopt HIE. Strategic game theoretic models help to clarify HIE adoption decisions under market conditions at play in an extremely complex technology environment.

Comparison of Machine Learning Algorithms for the Prediction of Preventable Hospital Readmissions.

A diverse universe of statistical models in the literature aim to help hospitals understand the risk factors of their preventable readmissions. However, these models are usually not necessarily applicable in other contexts, fail to achieve good discriminatory power, or cannot be compared with other models. We built and compared predictive models based on machine learning algorithms for 30-day preventable hospital readmissions of Medicare patients. This work used the same inclusion/exclusion criteria for diseases used by the Centers for Medicare and Medicaid Services. In addition, risk stratification techniques were implemented to study covariate behavior on each risk strata. The new models resulted in improved performance measured by the area under the receiver operating characteristic curve. Finally, factors such as higher length of stay, disease severity index, being discharged to a hospital, and primary language other than English were associated with increased risk to be readmitted within 30 days. In the future, better predictive models for 30-day preventable hospital readmissions can point to the development of systems that identify patients at high risk and lead to the implementation of interventions (e.g., discharge planning and follow-up) to those patients, providing consistent improvement in the quality and efficiency of the healthcare system.

C-H Cyanation of 6-Ring N-Containing Heteroaromatics.

Heteroaromatic nitriles are important compounds in drug discovery, both for their prevalence in the clinic and due to the diverse range of transformations they can undergo. As such, efficient and reliable methods to access them have the potential for far-reaching impact across synthetic chemistry and the biomedical sciences. Herein, we report an approach to heteroaromatic C-H cyanation through triflic anhydride activation, nucleophilic addition of cyanide, followed by elimination of trifluoromethanesulfinate to regenerate the cyanated heteroaromatic ring. This one-pot protocol is simple to perform, is applicable to a broad range of decorated 6-ring N-containing heterocycles, and has been shown to be suitable for late-stage functionalization of complex drug-like architectures.

Percutaneous vertebroplasty: a new animal model.

BACKGROUND CONTEXT: Percutaneous vertebroplasty (PVP) is a minimally invasive surgical procedure and is frequently performed in humans who need surgical treatment of vertebral fractures. PVP involves cement injection into the vertebral body, thereby providing rapid and significant pain relief. PURPOSE: The testing of novel biomaterials depends on suitable animal models. The aim of this study was to develop a reproducible and safe model of PVP in sheep. STUDY DESIGN: This study used ex vivo and in vivo large animal model study (Merino sheep). METHODS: Ex vivo vertebroplasty was performed through a bilateral modified parapedicular access in 24 ovine lumbar hemivertebrae, divided into four groups (n=6). Cerament (Bone Support, Lund, Sweden) was the control material. In the experimental group, a novel composite was tested-Spine-Ghost-which consisted of an alpha-calcium sulfate matrix enriched with micrometric particles of mesoporous bioactive glass. All vertebrae were assessed by micro-computed tomography (micro-CT) and underwent mechanical testing. For the in vivo study, 16 sheep were randomly allocated into control and experimental groups (n=8), and underwent PVP using the same bone cements. All vertebrae were assessed postmortem by micro-CT, histology, and reverse transcription-polymerase chain reaction (rt-PCR). This work has been supported by the European Commission under the 7th Framework Programme for collaborative projects (600,000-650,000 USD). RESULTS: In the ex vivo model, the average defect volume was 1,275.46±219.29 mm3. Adequate defect filling with cement was observed. No mechanical failure was observed under loads which were higher than physiological. In the in vivo study, cardiorespiratory distress was observed in two animals, and one sheep presented mild neurologic deficits in the hind limbs before recovering. CONCLUSIONS: The model of PVP is considered suitable for preclinical in vivo studies, mimicking clinical application. All sheep recovered and completed a 6-month implantation period. There was no evidence of cement leakage into the vertebral foramen in the postmortem examination.

Activating clinical trials: a process improvement approach.

BACKGROUND: The administrative process associated with clinical trial activation has been criticized as costly, complex, and time-consuming. Prior research has concentrated on identifying administrative barriers and proposing various solutions to reduce activation time, and consequently associated costs. Here, we expand on previous research by incorporating social network analysis and discrete-event simulation to support process improvement decision-making. METHODS: We searched for all operational data associated with the administrative process of activating industry-sponsored clinical trials at the Office of Clinical Research of the University of South Florida in Tampa, Florida. We limited the search to those trials initiated and activated between July 2011 and June 2012. We described the process using value stream mapping, studied the interactions of the various process participants using social network analysis, and modeled potential process modifications using discrete-event simulation. RESULTS: The administrative process comprised 5 sub-processes, 30 activities, 11 decision points, 5 loops, and 8 participants. The mean activation time was 76.6 days. Rate-limiting sub-processes were those of contract and budget development. Key participants during contract and budget development were the Office of Clinical Research, sponsors, and the principal investigator. Simulation results indicate that slight increments on the number of trials, arriving to the Office of Clinical Research, would increase activation time by 11 %. Also, incrementing the efficiency of contract and budget development would reduce the activation time by 28 %. Finally, better synchronization between contract and budget development would reduce time spent on batching documentation; however, no improvements would be attained in total activation time. CONCLUSION: The presented process improvement analytic framework not only identifies administrative barriers, but also helps to devise and evaluate potential improvement scenarios. The strength of our framework lies in its system analysis approach that recognizes the stochastic duration of the activation process and the interdependence between process activities and entities.

Preventable Readmission Risk Factors for Patients With Chronic Conditions.

Evidence indicates that the largest volume of hospital readmissions occurs among patients with preexisting chronic conditions. Identifying these patients can improve the way hospital care is delivered and prioritize the allocation of interventions. In this retrospective study, we identify factors associated with readmission within 30 days based on claims and administrative data of nine hospitals from 2005 to 2012. We present a data inclusion and exclusion criteria to identify potentially preventable readmissions. Multivariate logistic regression models and a Cox proportional hazards extension are used to estimate the readmission risk for 4 chronic conditions (congestive heart failure [CHF], chronic obstructive pulmonary disease [COPD], acute myocardial infarction, and type 2 diabetes) and pneumonia, known to be related to high readmission rates. Accumulated number of admissions and discharge disposition were identified to be significant factors across most disease groups. Larger odds of readmission were associated with higher severity index for CHF and COPD patients. Different chronic conditions are associated with different patient and case severity factors, suggesting that further studies in readmission should consider studying conditions separately.

A user needs assessment to inform health information exchange design and implementation.

BACKGROUND: Important barriers for widespread use of health information exchange (HIE) are usability and interface issues. However, most HIEs are implemented without performing a needs assessment with the end users, healthcare providers. We performed a user needs assessment for the process of obtaining clinical information from other health care organizations about a hospitalized patient and identified the types of information most valued for medical decision-making. METHODS: Quantitative and qualitative analysis were used to evaluate the process to obtain and use outside clinical information (OI) using semi-structured interviews (16 internists), direct observation (750 h), and operational data from the electronic medical records (30,461 hospitalizations) of an internal medicine department in a public, teaching hospital in Tampa, Florida. RESULTS: 13.7 % of hospitalizations generate at least one request for OI. On average, the process comprised 13 steps, 6 decisions points, and 4 different participants. Physicians estimate that the average time to receive OI is 18 h. Physicians perceived that OI received is not useful 33-66 % of the time because information received is irrelevant or not timely. Technical barriers to OI use included poor accessibility and ineffective information visualization. Common problems with the process were receiving extraneous notes and the need to re-request the information. Drivers for OI use were to trend lab or imaging abnormalities, understand medical history of critically ill or hospital-to-hospital transferred patients, and assess previous echocardiograms and bacterial cultures. About 85 % of the physicians believe HIE would have a positive effect on improving healthcare delivery. CONCLUSIONS: Although hospitalists are challenged by a complex process to obtain OI, they recognize the value of specific information for enhancing medical decision-making. HIE systems are likely to have increased utilization and effectiveness if specific patient-level clinical information is delivered at the right time to the right users.

Multimodality functional imaging in radiation therapy planning: relationships between dynamic contrast-enhanced MRI, diffusion-weighted MRI, and 18F-FDG PET.

OBJECTIVES: Biologically guided radiotherapy needs an understanding of how different functional imaging techniques interact and link together. We analyse three functional imaging techniques that can be useful tools for achieving this objective. MATERIALS AND METHODS: The three different imaging modalities from one selected patient are ADC maps, DCE-MRI, and 18F-FDG PET/CT, because they are widely used and give a great amount of complementary information. We show the relationship between these three datasets and evaluate them as markers for tumour response or hypoxia marker. Thus, vascularization measured using DCE-MRI parameters can determine tumour hypoxia, and ADC maps can be used for evaluating tumour response. RESULTS: ADC and DCE-MRI include information from 18F-FDG, as glucose metabolism is associated with hypoxia and tumour cell density, although 18F-FDG includes more information about the malignancy of the tumour. The main disadvantage of ADC maps is the distortion, and we used only low distorted regions, and extracellular volume calculated from DCE-MRI can be considered equivalent to ADC in well-vascularized areas. CONCLUSION: A dataset for achieving the biologically guided radiotherapy must include a tumour density study and a hypoxia marker. This information can be achieved using only MRI data or only PET/CT studies or mixing both datasets.

Unsupervised clustering method to detect microsaccades.

Microsaccades, small involuntary eye movements that occur once or twice per second during attempted visual fixation, are relevant to perception, cognition, and oculomotor control and present distinctive characteristics in visual and oculomotor pathologies. Thus, the development of robust and accurate microsaccade-detection techniques is important for basic and clinical neuroscience research. Due to the diminutive size of microsaccades, however, automatic and reliable detection can be difficult. Current challenges in microsaccade detection include reliance on set, arbitrary thresholds and lack of objective validation. Here we describe a novel microsaccade-detecting method, based on unsupervised clustering techniques, that does not require an arbitrary threshold and provides a detection reliability index. We validated the new clustering method using real and simulated eye-movement data. The clustering method reduced detection errors by 62% for binocular data and 78% for monocular data, when compared to standard contemporary microsaccade-detection techniques. Further, the clustering method's reliability index was correlated with the microsaccade-detection error rate, suggesting that the reliability index may be used to determine the comparative precision of eye-tracking devices.