Individualisation method of biomathematical model of fatigue for predicting individual performance in mild and irregular sleep deprivation.

Individual biomathematical models of fatigue (BMMF) are promising tools for detecting fatigue and possible incidents. Existing individual BMMFs have been validated in laboratory experiments in which subjects experience total sleep deprivation (TSD) and regular chronic sleep deprivation (CSD). However, some shift populations experience mild sleep deprivation (MSD) or irregular sleep deprivation (ISD) in real life. We employed the adaptive momentum estimation algorithm to adjust the classical SAFTE model for an individual. Model individualisation can be performed in real-time when new performance data are collected. The individual SAFTE model was compared with existing BMMFs in TSD, CSD, MSD, and ISD. The validation results show that the individual SAFTE model has advantages in MSD and ISD datasets collected from officers and truck drivers in real life. This study expands previous research results on the real-time individualisation of BMMFs and exposes individual BMMFs to various sleep-deprivation conditions in the field. Practitioner summary: This study proposes an individual biomathematical models of fatigue to predict human performance in mild and irregular sleep deprivation. The validation results in both laboratory and field show the proposed model has advantages over existing models when predicting officers' and truck drivers' performance in real life.

Individual prediction of thrombocytopenia at next chemotherapy cycle: Evaluation of dynamic model performances.

AIMS: Thrombocytopenia is a common major side-effect of cytotoxic cancer therapies. A clinically relevant problem is to predict an individual's thrombotoxicity in the next planned chemotherapy cycle in order to decide on treatment adaptation. To support this task, 2 dynamic mathematical models of thrombopoiesis under chemotherapy were proposed, a simple semimechanistic model and a comprehensive mechanistic model. In this study, we assess the performance of these models with respect to existing thrombocytopenia grading schemes. METHODS: We consider close-meshed individual time series data of 135 non-Hodgkin's lymphoma patients treated with 6 cycles of CHOP/CHOEP chemotherapies. Individual parameter estimates were derived on the basis of these data considering a varying number of cycles per patient. Parsimony assumptions were applied to optimize parameter identifiability. Models' predictability are assessed by determining deviations of predicted and observed degrees of thrombocytopenia in the next cycles. RESULTS: The mechanistic model results in better agreement of model prediction and individual time series data. Prediction accuracy of future cycle toxicities by the mechanistic model is higher even if the semimechanistic model is provided with data of more cycles for calibration. CONCLUSION: We successfully established a quantitative and clinically relevant method for assessing prediction performances of biomathematical models of thrombopoiesis under chemotherapy. We showed that the more comprehensive mechanistic model outperforms the semimechanistic model. We aim at implementing the mechanistic model into clinical practice to assess its utility in real life clinical decision-making.

Fatigue and short-term unplanned absences among police officers.

PURPOSE ­: The purpose of this paper is to assess whether shift work, sleep loss and fatigue are related to short-term unplanned absences in policing. DESIGN/METHODOLOGY/APPROACH ­: N = 367 police officers from the Buffalo Police Department were studied. Day-by-day work and sick leave data were obtained from the payroll. Absenteeism was defined as taking a single sick day on a regularly scheduled workday. Biomathematical models of fatigue (BMMF) predicted officers' sleep-wake behaviors and on-duty fatigue and sleepiness. Prior sleep, fatigue and sleepiness were tested as predictors of absenteeism during the next shift. FINDINGS ­: A total of 513,666 shifts and 4,868 cases of absenteeism were studied. The odds of absenteeism increased as on-duty fatigue and sleepiness increased and prior sleep decreased. This was particularly evident for swing shift officers and night shift officers who were predicted by BMMF to obtain less sleep and have greater fatigue and sleepiness than day shift officers. The odds of absenteeism were higher for female officers than male officers; this finding was not due to a differential response to sleep loss, fatigue or sleepiness. PRACTICAL IMPLICATIONS ­: Absenteeism may represent a self-management strategy for fatigue or compensatory behavior to reduced sleep opportunity. Long and irregular work hours that reduce sleep opportunity may be administratively controllable culprits of absenteeism. ORIGINALITY/VALUE ­: Police fatigue has consequences for police officers, departments and communities. BMMF provide a potential tool for predicting and mitigating police fatigue. BMMF were used to investigate the effects of sleep and fatigue on absenteeism.

Can a mathematical model predict an individual's trait-like response to both total and partial sleep loss?

Humans display a trait-like response to sleep loss. However, it is not known whether this trait-like response can be captured by a mathematical model from only one sleep-loss condition to facilitate neurobehavioural performance prediction of the same individual during a different sleep-loss condition. In this paper, we investigated the extent to which the recently developed unified mathematical model of performance (UMP) captured such trait-like features for different sleep-loss conditions. We used the UMP to develop two sets of individual-specific models for 15 healthy adults who underwent two different sleep-loss challenges (order counterbalanced; separated by 2-4 weeks): (i) 64 h of total sleep deprivation (TSD) and (ii) chronic sleep restriction (CSR) of 7 days of 3 h nightly time in bed. We then quantified the extent to which models developed using psychomotor vigilance task data under TSD predicted performance data under CSR, and vice versa. The results showed that the models customized to an individual under one sleep-loss condition accurately predicted performance of the same individual under the other condition, yielding, on average, up to 50% improvement over non-individualized, group-average model predictions. This finding supports the notion that the UMP captures an individual's trait-like response to different sleep-loss conditions.

Individual hematotoxicity prediction of further chemotherapy cycles by dynamic mathematical models in patients with gastrointestinal tumors.

PURPOSE: Hematotoxicity is a common side-effect of cytotoxic gastrointestinal (GI) cancer therapies. An unsolved problem is to predict the individual risk therefore to decide on treatment adaptions. We applied an established biomathematical prediction model and primarily evaluated its predictive value in patients undergoing chemotherapy for GI cancers in curative intent. METHODS: In a prospective, observational multicenter study on patients with gastro-esophageal or pancreatic cancer (n = 28) receiving myelosuppressive adjuvant or neoadjuvant chemotherapy (FLO(T) or FOLFIRINOX), individual model parameters were learned based on patients' observed laboratory values during the first chemotherapy cycle and further external data resources. Grades of hematotoxicity of subsequent cycles were predicted by model simulation and compared with observed data. RESULTS: The most common high-grade hematological toxicity was neutropenia [19/28 patients (68%)]. For the FLO(T) regimen, individual grades of thrombocytopenia and leukopenia could be well predicted for cycles 2-4, as well as grades of neutropenia for cycle 2. Prediction accuracy for neutropenia in the third and fourth cycle differed by one toxicity grade on average. For the FOLFIRINOX-regimen, thrombocytopenia predictions showed a maximum deviation of one toxicity grade up to the end of therapy (8 cycles). Deviations of predictions were less than one degree on average up to cycle 4 for neutropenia, and up to cycle 6 for leukopenia. CONCLUSION: The biomathematical model showed excellent short-term and decent long-term prediction performance for all relevant hematological side effects associated with FLO(T)/FOLFIRINOX. Clinical utility of this precision-medicine approach needs to be further investigated in a larger cohort.

A mathematical model of dynamics of cell populations in squamous epithelium after irradiation.

PURPOSE: To develop multi-compartment mechanistic models of dynamics of stem and functional cell populations in epithelium after irradiation. Methods and materials: We present two models, with three (3C) and four (4C) compartments respectively. We use delay differential equations, and include accelerated proliferation, loss of division asymmetry, progressive death of abortive stem cells, and turnover of functional cells. The models are used to fit experimental data on the variations of the number of cells in mice mucosa after irradiation with 13 Gy and 20 Gy. Akaike information criteria (AIC) was used to evaluate the performance of each model. RESULTS: Both 3C and 4C models provide good fits to experimental data for 13 Gy. Fits for 20 Gy are slightly poorer and may be affected by larger uncertainties and fluctuations of experimental data. Best fits are obtained by imposing constraints on the fitting parameters, so to have values that are within experimental ranges. There is some degeneration in the fits, as different sets of parameters provide similarly good fits. CONCLUSIONS: The models provide good fits to experimental data. Mechanistic approaches like this can facilitate the development of mucositis response models to nonstandard schedules/treatment combinations not covered by datasets to which phenomenological models have been fitted. Studying the dynamics of cell populations in multifraction treatments, and finding links with induced toxicity, is the next step of this work.

Sleep-wake behaviors exhibited by shift workers in normal operations and predicted by a biomathematical model of fatigue.

STUDY OBJECTIVES: To compare rail workers' actual sleep-wake behaviors in normal operations to those predicted by a biomathematical model of fatigue (BMMF). To determine whether there are group-level residual sources of error in sleep predictions that could be modeled to improve group-level sleep predictions. METHODS: The sleep-wake behaviors of 354 rail workers were examined during 1,722 breaks that were 8-24 h in duration. Sleep-wake patterns were continuously monitored using wrist-actigraphy and predicted from the work-rest schedule using a BMMF. Rail workers' actual and predicted sleep-wake behaviors were defined as split-sleep (i.e. ≥2 sleep periods in a break) and consolidated-sleep (i.e. one sleep period in a break) behaviors. Sleepiness was predicted from the actual and predicted sleep-wake data. RESULTS: Consolidated-sleep behaviors were observed during 1,441 breaks and correctly predicted during 1,359 breaks. Split-sleep behaviors were observed during 280 breaks and correctly predicted during 182 breaks. Predicting the wrong type of sleep-wake behavior resulted in a misestimation of hours of sleep during a break. Relative to sleepiness predictions derived from actual sleep-wake data, predicting the wrong type of sleep-wake behavior resulted in a misestimation of sleepiness predictions during the subsequent shift. CONCLUSIONS: All workers with the same work-rest schedule have the same predicted sleep-wake behaviors; however, these workers do not all exhibit the same sleep-wake behaviors in real-world operations. Future models could account for this group-level residual variance with a new approach to modeling sleep, whereby sub-group(s) may be predicted to exhibit one of a number of sleep-wake behaviors.

Generalizability of a biomathematical model of fatigue's sleep predictions.

Introduction: Biomathematical models of fatigue (BMMF) predict fatigue during a work-rest schedule on the basis of sleep-wake histories. In the absence of actual sleep-wake histories, sleep-wake histories are predicted directly from work-rest schedules. The predicted sleep-wake histories are then used to predict fatigue. It remains to be determined whether workers organize their sleep similarly across operations and thus whether sleep predictions generalize.Methods: Officers (n = 173) enrolled in the Buffalo Cardio-Metabolic Occupational Police Stress study were studied. Officers' sleep-wake behaviors were measured using wrist-actigraphy and predicted using a BMMF (FAID Quantum) parameterized in aviation and rail. Sleepiness (i.e. Karolinska Sleepiness Scale (KSS) ratings) was predicted using actual and predicted sleep-wake data. Data were analyzed using sensitivity analyses.Results: During officers' 16.0 ± 1.9 days of study participation, they worked 8.6 ± 3.1 shifts and primarily worked day shifts and afternoon shifts. Across shifts, 7.0 h ± 1.9 h of actual sleep were obtained in the prior 24 h and associated peak KSS ratings were 5.7 ± 1.3. Across shifts, 7.2 h ± 1.1 h of sleep were predicted in the prior 24 h and associated peak KSS ratings were 5.5 ± 1.2. The minute-by-minute predicted and actual sleep-wake data demonstrated high sensitivity (80.4%). However, sleep was observed at all hours-of-the-day, but sleep was rarely predicted during the daytime hours.Discussion: The sleep-wake behaviors predicted by a BMMF parameterized in aviation and rail demonstrated high sensitivity with police officers' actual sleep-wake behaviors. Additional night shift data are needed to conclude whether BMMF sleep predictions generalize across operations.

Large-scale ensemble simulations of biomathematical brain arteriovenous malformation models using graphics processing unit computation.

BACKGROUND: Theoretical modeling allows investigations of cerebral arteriovenous malformation (AVM) hemodynamics, but current models are too simple and not clinically representative. We developed a more realistic AVM model based on graphics processing unit (GPU) computing, to replicate highly variable and complex nidus angioarchitectures with vessel counts in the thousands-orders of magnitude greater than current models. METHODS: We constructed a theoretical electrical circuit AVM model with a nidus described by a stochastic block model (SBM) of 57 nodes and an average of 1000 plexiform and fistulous vessels. We sampled and individually simulated 10,000 distinct nidus morphologies from this SBM, constituting an ensemble simulation. We assigned appropriate biophysical values to all model vessels, and known values of mean intravascular pressure (Pmean) to extranidal vessels. We then used network analysis to calculate Pmean and volumetric flow rate within each nidus vessel, and mapped these values onto a graphic representation of the nidus network. We derived an expression for nidus rupture risk and conducted a model parameter sensitivity analysis. RESULTS: Simulations revealed a total intranidal volumetric blood flow ranging from 268 mL/min to 535 mL/min, with an average of 463 mL/min. The maximum percentage rupture risk among all vessels in the nidus ranged from 0% to 60%, with an average of 29%. CONCLUSION: This easy to implement biomathematical AVM model, allowed by parallel data processing using advanced GPU computing, will serve as a useful tool for theoretical investigations of AVM therapies and their hemodynamic sequelae.

U.S. police rosters: Fatigue and public complaints.

STUDY OBJECTIVES: Recent experimental research indicates a substantial impact of shift work related fatigue on police officers' encounters with the public. In recent years, biomathematical models of fatigue have provided a new way to identify potential relationships between working time arrangements and job performance. This study focused on public complaints against police officers and determined whether the odds of a public complaint were associated with work schedules and/or a biomathematical model's predictions of fatigue and sleepiness. METHODS: N = 144 police officers from two municipal police departments in the United States reported shift start times, shift hours, court hours, and public complaints each duty day during study participation. A biomathematical model of fatigue (FAID Quantum) predicted sleep duration and sleep timing and inferred fatigue and sleepiness for 15 744 shifts. Fatigue, sleepiness, 24 hr sleep estimates, and work schedule were tested as predictors of public complaints. RESULTS: Greater fatigue, greater sleepiness, and less sleep in the 24 hr prior to a shift increased the odds of a public complaint (F ≥ 9.14, p < 0.01). Working back-to-back night shifts increased the odds of a public complaint (OR = 4.27, p < 0.01), particularly when off-duty court hours were worked between the night shifts (OR = 4.73, p < 0.01). CONCLUSIONS: On-duty fatigue and sleepiness, sleep obtained prior to a shift, and working night shifts were strongly associated with public complaints. Off-duty court appearances reduced sleep between night shifts and further increased the odds of a public complaint. The results suggest that off-duty court hours should be limited between night shifts and duty schedules should be considered when scheduling court appearances.

Biomathematical screening of amyloid radiotracers with clinical usefulness index.

INTRODUCTION: To facilitate radiotracers' development, a screening methodology using a biomathematical model and clinical usefulness index (CUI) was proposed to evaluate radiotracers' diagnostic capabilities. METHODS: A total of 31 amyloid positron emission tomography radiotracers were evaluated. A previously developed biomathematical model was used to simulate 1000 standardized uptake value ratios with population and noise simulations, which were used to determine the integrated receiver operating characteristics curve (Az), effect size (Es), and standardized uptake value ratio (Sr) of conditions-pairs of healthy control-mild cognitive impaired and mild cognitive impaired-Alzheimer's disease. CUI was obtained from the product of averaged [Formula: see text], [Formula: see text], and [Formula: see text]. RESULTS: The relationships of [Formula: see text], [Formula: see text], and [Formula: see text] with CUI were different, suggesting that they assessed different radiotracer properties. The combination of Az, Es, and Sr complemented each other and resulted in CUI of 0.10 to 5.72, with clinically applied amyloid positron emission tomography radiotracers having CUI greater than 3.0. DISCUSSION: The CUI rankings of clinically applied radiotracers were close to their reported clinical results, attesting to the applicability of the screening methodology.

Modeling combined chemo- and immunotherapy of high-grade non-Hodgkin lymphoma.

Moderate, but not massive intensification of CHOP-21 improves outcome in aggressive non-Hodgkin lymphoma. Adding immunotherapy with Rituximab was a break-through, but levels differences in chemotherapy. Ongoing trials attempt to optimize R-CHOP type regimens. We present a mathematical model of chemo-immunotherapy to explain published and aiming at predicting future trials comparing R-CHOP variants. We hypothesize that, for cure, the immune system must dominate residual tumor cells at the end of treatment. Chemotherapy reduces both tumor and immune cells. Rituximab immunotherapy boosts the immune response. We translate this reasoning into a differential equations model. Model parameters are estimated using data of randomized clinical trials in elderly patients. The model explains observed hazard ratios between treatments. It explains why too intense chemotherapy could be detrimental. The model is validated predicting six published independent studies. As an application, we varied treatment schedules and predict that current R-CHOP variants have only limited optimization potential.

2B-Alert Web: An Open-Access Tool for Predicting the Effects of Sleep/Wake Schedules and Caffeine Consumption on Neurobehavioral Performance.

STUDY OBJECTIVES: Computational tools that predict the effects of daily sleep/wake amounts on neurobehavioral performance are critical components of fatigue management systems, allowing for the identification of periods during which individuals are at increased risk for performance errors. However, none of the existing computational tools is publicly available, and the commercially available tools do not account for the beneficial effects of caffeine on performance, limiting their practical utility. Here, we introduce 2B-Alert Web, an open-access tool for predicting neurobehavioral performance, which accounts for the effects of sleep/wake schedules, time of day, and caffeine consumption, while incorporating the latest scientific findings in sleep restriction, sleep extension, and recovery sleep. METHODS: We combined our validated Unified Model of Performance and our validated caffeine model to form a single, integrated modeling framework instantiated as a Web-enabled tool. 2B-Alert Web allows users to input daily sleep/wake schedules and caffeine consumption (dosage and time) to obtain group-average predictions of neurobehavioral performance based on psychomotor vigilance tasks. 2B-Alert Web is accessible at: https://2b-alert-web.bhsai.org. RESULTS: The 2B-Alert Web tool allows users to obtain predictions for mean response time, mean reciprocal response time, and number of lapses. The graphing tool allows for simultaneous display of up to seven different sleep/wake and caffeine schedules. The schedules and corresponding predicted outputs can be saved as a Microsoft Excel file; the corresponding plots can be saved as an image file. The schedules and predictions are erased when the user logs off, thereby maintaining privacy and confidentiality. CONCLUSIONS: The publicly accessible 2B-Alert Web tool is available for operators, schedulers, and neurobehavioral scientists as well as the general public to determine the impact of any given sleep/wake schedule, caffeine consumption, and time of day on performance of a group of individuals. This evidence-based tool can be used as a decision aid to design effective work schedules, guide the design of future sleep restriction and caffeine studies, and increase public awareness of the effects of sleep amounts, time of day, and caffeine on alertness.

A Unified Model of Performance for Predicting the Effects of Sleep and Caffeine.

STUDY OBJECTIVES: Existing mathematical models of neurobehavioral performance cannot predict the beneficial effects of caffeine across the spectrum of sleep loss conditions, limiting their practical utility. Here, we closed this research gap by integrating a model of caffeine effects with the recently validated unified model of performance (UMP) into a single, unified modeling framework. We then assessed the accuracy of this new UMP in predicting performance across multiple studies. METHODS: We hypothesized that the pharmacodynamics of caffeine vary similarly during both wakefulness and sleep, and that caffeine has a multiplicative effect on performance. Accordingly, to represent the effects of caffeine in the UMP, we multiplied a dose-dependent caffeine factor (which accounts for the pharmacokinetics and pharmacodynamics of caffeine) to the performance estimated in the absence of caffeine. We assessed the UMP predictions in 14 distinct laboratory- and field-study conditions, including 7 different sleep-loss schedules (from 5 h of sleep per night to continuous sleep loss for 85 h) and 6 different caffeine doses (from placebo to repeated 200 mg doses to a single dose of 600 mg). RESULTS: The UMP accurately predicted group-average psychomotor vigilance task performance data across the different sleep loss and caffeine conditions (6% < error < 27%), yielding greater accuracy for mild and moderate sleep loss conditions than for more severe cases. Overall, accounting for the effects of caffeine resulted in improved predictions (after caffeine consumption) by up to 70%. CONCLUSIONS: The UMP provides the first comprehensive tool for accurate selection of combinations of sleep schedules and caffeine countermeasure strategies to optimize neurobehavioral performance.

A Unified Model of Performance: Validation of its Predictions across Different Sleep/Wake Schedules.

STUDY OBJECTIVES: Historically, mathematical models of human neurobehavioral performance developed on data from one sleep study were limited to predicting performance in similar studies, restricting their practical utility. We recently developed a unified model of performance (UMP) to predict the effects of the continuum of sleep loss-from chronic sleep restriction (CSR) to total sleep deprivation (TSD) challenges-and validated it using data from two studies of one laboratory. Here, we significantly extended this effort by validating the UMP predictions across a wide range of sleep/wake schedules from different studies and laboratories. METHODS: We developed the UMP on psychomotor vigilance task (PVT) lapse data from one study encompassing four different CSR conditions (7 d of 3, 5, 7, and 9 h of sleep/night), and predicted performance in five other studies (from four laboratories), including different combinations of TSD (40 to 88 h), CSR (2 to 6 h of sleep/night), control (8 to 10 h of sleep/night), and nap (nocturnal and diurnal) schedules. RESULTS: The UMP accurately predicted PVT performance trends across 14 different sleep/wake conditions, yielding average prediction errors between 7% and 36%, with the predictions lying within 2 standard errors of the measured data 87% of the time. In addition, the UMP accurately predicted performance impairment (average error of 15%) for schedules (TSD and naps) not used in model development. CONCLUSIONS: The unified model of performance can be used as a tool to help design sleep/wake schedules to optimize the extent and duration of neurobehavioral performance and to accelerate recovery after sleep loss.

Managing fatigue: It really is about sleep.

Biomathematical models of fatigue can assist organisations to estimate the fatigue consequences of a roster before operations commence. These estimates do not account for the diversity of sleep behaviours exhibited by employees. The purpose of this study was to develop sleep transfer functions describing the likely distributions of sleep around fatigue level estimates produced by a commercial biomathematical model of fatigue. Participants included 347 (18 females, 329 males) train drivers working commercial railway operations in Australia. They provided detailed information about their sleep behaviours using sleep diaries and wrist activity monitors. On average, drivers slept for 7.7 (±1.7)h in the 24h before work and 15.1 (±2.5)h in the 48h before work. The amount of sleep obtained by drivers before shifts differed only marginally across morning, afternoon and night shifts. Shifts were also classified into one of seven ranked categories using estimated fatigue level scores. Higher fatigue score categories were associated with significant reductions in the amount of sleep obtained before shifts, but there was substantial within-category variation. The study findings demonstrate that biomathematical models of fatigue have utility for designing round-the-clock rosters that provide sufficient sleep opportunities for the average employee. Robust variability in the amount of sleep obtained by drivers indicate that models are relatively poor tools for ensuring that all employees obtain sufficient sleep. These findings demonstrate the importance of developing approaches for managing the sleep behaviour of individual employees.