Frequently Identified Gaps in Antibiotic Stewardship Programs in Critical Access Hospitals

Background: Nebraska (NE) Infection Control Assessment and Promotion Program (ICAP) is a CDC funded project. ICAP team works in collaboration with NE Department of Health and Human Services (NEDHHS) to assess and improve infection prevention and control programs (IPCP) in various health care settings including resource limited settings like critical access hospitals (CAH). Little is known about the existing gaps in antimicrobial stewardship programs (ASP) of CAH. Hence, we decided to study the current level of ASP activities and factors associated with these activities in CAH. Methods: NE ICAP conducted on-site surveys in 36 CAH from October 2015 to February 2017. ASP activities related to the 7 CDC recommended core elements (CE) including leadership support (LS), accountability, drug expertise (DE), action, tracking, reporting, and education were assessed using a CDC Infection Control Assessment Tool for acute care hospitals. Descriptive analyses evaluated CAH characteristics and frequency of CE implementation. Fisher’s exact, Mann–Whitney, and Kruskal–Wallis tests were used for statistical analyses examining the association of various factors with level of ASP activities. Results: The 36 surveyed CAH had a median of 20 (range 10–25) beds and employed a median of 0.4 (range 0.1–1.6) infection preventionist (IP) full-time equivalent (FTE)/25-bed. Frequency of CE implementation varied among CAH with action and LS as the most (69%) and least (28%) frequently implemented elements, respectively. Close to half (47%) of surveyed CAH had implemented ≥4 CE but only 14% of facilities had all 7 CE. Median bed size and IP FTE/25-bed were similar among CAH with 0–2, 3-5, or ≥6 CE in place. CAH with LS or accountability for ASP implemented higher median numbers of the remaining CE compared with CAH without LS or accountability (5 vs. 2, P \u3c 0.01 and 4 vs. 2, P \u3c 0.01, respectively). Facilities with The presence of LS, accountability and drug expertise were more likely to have all 4 remaining CE implemented than others (56% vs. 8%, P \u3c 0.01). Conclusion: LS, accountability, and DE are important factors for the implementation of the remaining 4 CE in CAH. Although LS was the least frequently implemented CE, when present was associated with implementation of most of the other CE. Acquiring LS will facilitate implementation of additional ASP efforts in CAH.https://digitalcommons.unmc.edu/asap_pres/1000/thumbnail.jp

Absolute risk and predictors of the growth of acute spontaneous intracerebral haemorrhage: a systematic review and meta-analysis of individual patient data.

Background Intracerebral haemorrhage growth is associated with poor clinical outcome and is a therapeutic target for improving outcome. We aimed to determine the absolute risk and predictors of intracerebral haemorrhage growth, develop and validate prediction models, and evaluate the added value of CT angiography. Methods In a systematic review of OVID MEDLINE—with additional hand-searching of relevant studies' bibliographies— from Jan 1, 1970, to Dec 31, 2015, we identified observational cohorts and randomised trials with repeat scanning protocols that included at least ten patients with acute intracerebral haemorrhage. We sought individual patient-level data from corresponding authors for patients aged 18 years or older with data available from brain imaging initially done 0·5–24 h and repeated fewer than 6 days after symptom onset, who had baseline intracerebral haemorrhage volume of less than 150 mL, and did not undergo acute treatment that might reduce intracerebral haemorrhage volume. We estimated the absolute risk and predictors of the primary outcome of intracerebral haemorrhage growth (defined as >6 mL increase in intracerebral haemorrhage volume on repeat imaging) using multivariable logistic regression models in development and validation cohorts in four subgroups of patients, using a hierarchical approach: patients not taking anticoagulant therapy at intracerebral haemorrhage onset (who constituted the largest subgroup), patients taking anticoagulant therapy at intracerebral haemorrhage onset, patients from cohorts that included at least some patients taking anticoagulant therapy at intracerebral haemorrhage onset, and patients for whom both information about anticoagulant therapy at intracerebral haemorrhage onset and spot sign on acute CT angiography were known. Findings Of 4191 studies identified, 77 were eligible for inclusion. Overall, 36 (47%) cohorts provided data on 5435 eligible patients. 5076 of these patients were not taking anticoagulant therapy at symptom onset (median age 67 years, IQR 56–76), of whom 1009 (20%) had intracerebral haemorrhage growth. Multivariable models of patients with data on antiplatelet therapy use, data on anticoagulant therapy use, and assessment of CT angiography spot sign at symptom onset showed that time from symptom onset to baseline imaging (odds ratio 0·50, 95% CI 0·36–0·70; p<0·0001), intracerebral haemorrhage volume on baseline imaging (7·18, 4·46–11·60; p<0·0001), antiplatelet use (1·68, 1·06–2·66; p=0·026), and anticoagulant use (3·48, 1·96–6·16; p<0·0001) were independent predictors of intracerebral haemorrhage growth (C-index 0·78, 95% CI 0·75–0·82). Addition of CT angiography spot sign (odds ratio 4·46, 95% CI 2·95–6·75; p<0·0001) to the model increased the C-index by 0·05 (95% CI 0·03–0·07). Interpretation In this large patient-level meta-analysis, models using four or five predictors had acceptable to good discrimination. These models could inform the location and frequency of observations on patients in clinical practice, explain treatment effects in prior randomised trials, and guide the design of future trials

Refinement, Validation and Application of a Machine Learning Method For Estimating Physical Activity And Sedentary Behavior in Free-Living People

There is limited knowledge of the dose-response relationship between physical activity (PA), sedentary behavior (SB) and health. Poor measures of free-living PA and SB exposure are major contributing factors to these knowledge gaps. The overall objective of this dissertation was to address these issues by refining, validating and applying a machine-learning methodology for measuring PA and SB for use in free-living people. By combining neural networks and decision tree analyses we developed a method better suited for use in free-living people. Our new method is called the sojourn method and it estimates PA and SB from a single hip mounted accelerometer. Study 1 validated two versions of this method: sojourn-1x (soj-1x) and sojourn-3x (soj-3x). Soj-1x uses data from a vertical accelerometer sensor, while soj-3x uses r data from the vertical, anterior-posterior and medial-lateral accelerometer sensors. Seven participants were directly observed in the free-living environment for ten consecutive hours on three separate occasions. PA and SB estimated from soj-1x, soj-3x and a neural network previously calibrated in the laboratory (lab-nnet) were compared to direct observation. Compared to the lab-nnet, soj-1x and soj-3x improved estimates of MET-hours (lab-nnet: bias (95% CI) = 5.4 (4.6-6.2), rMSE = 5.4 (4.6-6.2), soj-1x: bias = 0.3 (-0.2-0.9), rMSE = 1.0 (0.6-1.3), soj-3x: bias = 0.5 (-0.1-1.1), rMSE = 1.1 (0.7-1.5)) and minutes in different intensity categories (lab-nnet: rMSE range = 10.2 (vigorous) - 55.0 (light), soj-1x: rMSE range = 4.0 (MVPA) - 50.1 (sedentary), soj-3x: rMSE range = 7.8 (MVPA) - 27.8 (light)). Soj-1x and soj-3x also produced accurate estimates of qualifying minutes, qualifying bouts, breaks from sedentary time and break-rate. Study 2 evaluated the sensitivity of soj-1x and soj-3x to detect change in habitual activity. Thirteen participants completed three, seven day conditions: sedentary, moderately active and very active. Soj-1x and soj-3x were sensitive to change in MET-hours (mean (95% CI): soj-1x: sedentary = 19.8 (19.0-20.7), moderately active = 22.7 (22.0-23.4), very active = 27.0 (25.8-28.2), soj-3x: sedentary = 18.2 (17.7-18.8), moderately active = 22.3 (21.6-23.1), very active = 27.6 (26.4-28.7)) and time in different intensity categories. Study 3 applied soj-3x to a free-living intervention to elucidate the effects of increased sedentary behavior on markers of cardiometabolic health. Eleven participants completed seven days of an active condition followed by seven days of an inactive condition. Insulin action significantly decreased 17% (5.4-30.2), while total cholesterol, LDL and HDL did not change from the active to inactive condition. This dissertation used novel methods to improve PA and SB estimation in a free-living environment and to improve our understanding of the physiologic response to increased free-living SB. These methods ultimately have the potential to broaden our understanding of how PA and SB dose are linked to health

Sensors for Human Physical Behaviour Monitoring

The understanding and measurement of physical behaviours that occur in everyday life are essential not only for determining their relationship with health, but also for interventions, physical activity monitoring/surveillance of the population and specific groups, drug development, and developing public health guidelines and messages [...

A Method to Estimate Free-Living Active and Sedentary Behavior from an Accelerometer

Methods to estimate physical activity (PA) and sedentary behavior (SB) from wearable monitors need to be validated in free-living settings. PURPOSE: The purpose of this study was to develop and validate two novel machine-learning methods (soj-1x and soj-3x) in a free-living setting. METHODS: Participants were directly observed in their natural environment for ten consecutive hours on three separate occasions. PA and SB estimated from soj-1x, soj-3x and a neural network previously calibrated in the laboratory (lab-nnet) were compared to direct observation. RESULTS: Compared to the lab-nnet, soj-1x and soj-3x improved estimates of MET-hours (lab-nnet: % bias (95% CI) = 33.1 (25.9, 40.4), rMSE = 5.4 (4.6, 6.2), soj-1x: % bias = 1.9 (−2.0, 5.9), rMSE = 1.0 (0.6, 1.3), soj-3x: % bias = 3.4 (0.0, 6.7), rMSE = 1.0 (0.6, 1.5)) and minutes in different intensity categories (lab-nnet: % bias = −8.2 (sedentary), −8.2 (light) and 72.8 (MVPA), soj-1x: % bias = 8.8 (sedentary), −18.5 (light) and −1.0 (MVPA), soj-3x: % bias = 0.5 (sedentary), −0.8 (light) and −1.0 (MVPA)). Soj-1x and soj-3x also produced accurate estimates of guideline minutes and breaks from sedentary time. CONCLUSION: Compared to the lab-nnet algorithm, soj-1x and soj-3x improved the accuracy and precision in estimating free-living MET-hours, sedentary time, and time spent in light intensity activity and MVPA. Additionally, soj-3x is superior to soj-1x in differentiating sedentary behavior from light intensity activity

“What Is a Step?” Differences in How a Step Is Detected among Three Popular Activity Monitors That Have Impacted Physical Activity Research

(1) Background: This study compared manually-counted treadmill walking steps from the hip-worn DigiwalkerSW200 and OmronHJ720ITC, and hip and wrist-worn ActiGraph GT3X+ and GT9X; determined brand-specific acceleration amplitude (g) and/or frequency (Hz) step-detection thresholds; and quantified key features of the acceleration signal during walking. (2) Methods: Twenty participants (Age: 26.7 ± 4.9 years) performed treadmill walking between 0.89-to-1.79 m/s (2–4 mph) while wearing a hip-worn DigiwalkerSW200, OmronHJ720ITC, GT3X+ and GT9X, and a wrist-worn GT3X+ and GT9X. A DigiwalkerSW200 and OmronHJ720ITC underwent shaker testing to determine device-specific frequency and amplitude step-detection thresholds. Simulated signal testing was used to determine thresholds for the ActiGraph step algorithm. Steps during human testing were compared using bias and confidence intervals. (3) Results: The OmronHJ720ITC was most accurate during treadmill walking. Hip and wrist-worn ActiGraph outputs were significantly different from the criterion. The DigiwalkerSW200 records steps for movements with a total acceleration of ≥1.21 g. The OmronHJ720ITC detects a step when movement has an acceleration ≥0.10 g with a dominant frequency of ≥1 Hz. The step-threshold for the ActiLife algorithm is variable based on signal frequency. Acceleration signals at the hip and wrist have distinctive patterns during treadmill walking. (4) Conclusions: Three common research-grade physical activity monitors employ different step-detection strategies, which causes variability in step output

Evaluation of Artificial Neural Network Algorithms for Predicting Mets and Activity Type from Accelerometer Data: Validation on an Independent Sample

Previous work from our laboratory provided a “proof of concept” for use of artificial neural networks (nnets) to estimate metabolic equivalents (METs) and identify activity type from accelerometer data (Staudenmayer J, Pober D, Crouter S, Bassett D, Freedson P, J Appl Physiol 107: 1330–1307, 2009). The purpose of this study was to develop new nnets based on a larger, more diverse, training data set and apply these nnet prediction models to an independent sample to evaluate the robustness and flexibility of this machine-learning modeling technique. The nnet training data set (University of Massachusetts) included 277 participants who each completed 11 activities. The independent validation sample (n = 65) (University of Tennessee) completed one of three activity routines. Criterion measures were 1) measured METs assessed using open-circuit indirect calorimetry; and 2) observed activity to identify activity type. The nnet input variables included five accelerometer count distribution features and the lag-1 autocorrelation. The bias and root mean square errors for the nnet MET trained on University of Massachusetts and applied to University of Tennessee were +0.32 and 1.90 METs, respectively. Seventy-seven percent of the activities were correctly classified as sedentary/light, moderate, or vigorous intensity. For activity type, household and locomotion activities were correctly classified by the nnet activity type 98.1 and 89.5% of the time, respectively, and sport was correctly classified 23.7% of the time. Use of this machine-learning technique operates reasonably well when applied to an independent sample. We propose the creation of an open-access activity dictionary, including accelerometer data from a broad array of activities, leading to further improvements in prediction accuracy for METs, activity intensity, and activity type

A Comprehensive Evaluation of Commonly Used Accelerometer Energy Expenditure and MET Prediction Equations

Numerous accelerometers and prediction methods are used to estimate energy expenditure (EE). Validation studies have been limited to small sample sizes in which participants complete a narrow range of activities and typically validate only one or two prediction models for one particular accelerometer. Purpose—To evaluate the validity of nine published and two proprietary EE prediction equations for three different accelerometers. Methods—277 participants completed an average of 6 treadmill (TRD) (1.34, 1.56, 2.23 m・sec−1 each at 0% and 3% grade) and 5 self-paced activities of daily living (ADLs). EE estimates were compared to indirect calorimetry. Accelerometers were worn while EE was measured using a portable metabolic unit. To estimate EE, 4 ActiGraph prediction models were used, 5 Actical models, and 2 RT3 proprietary models. Results—Across all activities, each equation underestimated EE (bias −0.1 to −1.4 METs and −0.5 to −1.3 kcals, respectively). For ADLs EE was underestimated by all prediction models (bias −0.2 to −2.0 and −0.2 to −2.8, respectively), while TRD activities were underestimated by seven equations, and overestimated by four equations (bias −0.8 to 0.2 METs and −0.4 to 0.5 kcals, respectively). Misclassification rates ranged from 21.7% (95% CI 20.4%, 24.2%) to 34.3% (95% CI 32.3%, 36.3%), with vigorous intensity activities being most often misclassified. Discussion—The prediction equations did not yield accurate point estimates of EE across a broad range of activities, nor were they accurate at classifying activities across a range of intensities (light \u3c 3 METs, moderate 3–5.99 METs, vigorous ≥ 6 METs). Current prediction techniques have many limitations when translating accelerometer counts to EE

Energy cost of common activities in children and adolescents

Background—The Compendium of Energy Expenditures for Youth assigns MET values to a wide range of activities. However, only 35% of activity MET values were derived from energy cost data measured in youth; the remaining activities were estimated from adult values. Purpose—To determine the energy cost of common activities performed by children and adolescents and compare these data to similar activities reported in the compendium. Methods—Thirty-two children (8–11 years old) and 28 adolescents (12–16 years) completed 4 locomotion activities on a treadmill (TRD) and 5 age-specific activities of daily living (ADL). Oxygen consumption was measured using a portable metabolic analyzer. Results—In children, measured METs were significantly lower than compendium METs for 3 activities [basketball, bike riding, and Wii tennis (1.1–3.5 METs lower)]. In adolescents, measured METs were significantly lower than compendium METs for 4 ADLs [basketball, bike riding, board games, and Wii tennis (0.3–2.5 METs lower)] and 3 TRDs [2.24 m·s−1, 1.56 m·s−1, and 1.34 m·s−1 (0.4–0.8 METs lower)]. Conclusion—The Compendium of Energy Expenditures for Youth is an invaluable resource to applied researchers. Inclusion of empirically derived data would improve the validity of the Compendium of Energy Expenditures for Youth

Energy Cost of Common Activities in Children and Adolescents

Background—The Compendium of Energy Expenditures for Youth assigns MET values to a wide range of activities. However, only 35% of activity MET values were derived from energy cost data measured in youth; the remaining activities were estimated from adult values. Purpose—To determine the energy cost of common activities performed by children and adolescents and compare these data to similar activities reported in the compendium. Methods—Thirty-two children (8–11 years old) and 28 adolescents (12–16 years) completed 4 locomotion activities on a treadmill (TRD) and 5 age-specific activities of daily living (ADL). Oxygen consumption was measured using a portable metabolic analyzer. Results—In children, measured METs were significantly lower than compendium METs for 3 activities [basketball, bike riding, and Wii tennis (1.1–3.5 METs lower)]. In adolescents, measured METs were significantly lower than compendium METs for 4 ADLs [basketball, bike riding, board games, and Wii tennis (0.3–2.5 METs lower)] and 3 TRDs [2.24 m・s−1, 1.56 m・s−1, and 1.34 m・s−1 (0.4–0.8 METs lower)]. Conclusion—The Compendium of Energy Expenditures for Youth is an invaluable resource to applied researchers. Inclusion of empirically derived data would improve the validity of the Compendium of Energy Expenditures for Youth