Artemia franciscana,commonly used for marine ecotoxicity testing, as a proxy modelto assess the effects of video analytics on quantitative behavioural parameters. We evaluated parameters such as data processing speed, tracking precision, capability to perform high-throughput batch processing of video files. Using a model toxicant the software algorithms were also finally benchmarked against one another. Our data indicates that variability in video file parameters; such as resolution, frame rate, file containers types, codecs and compression levels, can be a source of experimental biases in behavioural analysis. Similarly, the variability in data outputs between different tracking algorithms should be taken into account when designing standardized behavioral experiments and conducting chemobehavioural phenotyping.Chemobehavioural phenotypic analysis using small aquatic model organisms is becoming an important toolbox in aquatic ecotoxicology and neuroactive drug discovery. The analysis of the organisms’ behavior is usually performed by combining digital video recording with animal tracking software. This software detects the organisms in the video frames, and reconstructs their movement trajectory using image processing algorithms. In this work we investigated the impact of video file characteristics, video optimization techniques and differences in animal tracking algorithms on the accuracy of quantitative neurobehavioural endpoints. We employed larval stages of a free-swimming euryhaline crustacean  The acquisition of organism-wide high-dimensional phenotypic data (phenomics) has undergone an evolution to existing molecular omics paradigms over the last decade . This evBehavioral parameters are predominately analyzed using video recording and video processing techniques with a dedicated animal tracking software suite . Analog Digital video-based tracking of animal behavior involves deconvolution of pattern analysis in a grid of pixels on individual frames of video file. Software algorithms analyze each frame of the video file to distinguish the tracked animals from the background . This isThe time-stamped x-y coordinates assigned to centroids of each detected object are used for calculations of quantitative behavioural data sets such as; average speed of locomotion, average distance travelled of an animal or the distance between several individually identified animals .Despite the increasing number of reports using digital video analytics in chemobehavioral studies there is a paucity of data on how video file characteristics and various animal-tracking algorithms affect accuracy and reproducibility of neurobehavioural endpoints . For insArtemia franciscana was used as a proxy model for chemobehavioral analysis because of their small size and relatively fast, continuous locomotory activities. Such behavioural characteristics parameters are challenging for the video-based animal tracking algorithms especially when large number of animals are to be tracked simultaneously. Additional reasoning for the selection of this species was that larval stages of brine shrimp are commonly used and broadly accepted models in marine ecotoxicology. A considerable number of behavioural toxicity studies have been performed using Artemia sp. nauplii in recent years. However, so far none of the published studies considered a high-throughput behavioural chemophenotypic screening strategies.The main motivation of this work was to investigate the impact of video file characteristics, video optimization techniques and inherent differences in animal tracking algorithms on the accuracy of quantitative neurobehavioural data. We also wanted to demonstrate a capacity to perform high-throughput automated data analysis of quantitative behavioural data in aquatic toxicology. In this regard we demonstrated rapid and inexpensive methods to improve the process of thresholding during contrast-based animal detection and tracking as well different data processing pipelines. Two existing mainstreams commercial as well as one open source software packages were evaluated with higher-throughput toxicological phenotyping to benchmark their associated algorithms. The secondary goal of this work was to guide the scientists in the variability of parameters that need to be considered when undertaking chemobehavioural studies.Artemia franciscana  were hatched according to a standard operating protocol as described before .Eggs of the marine crustacean d before . BrieflyAll test chambers were designed using CorelDraw X3  CAD package as described before . The iniBehavioral data was acquired using a custom build digital video imaging system. The entire neurobehavioural analysis system was installed in a temperature-stabilized room (25 ± 0.5 °C) and consisted of a cold LED  backlit illumination stage and a digital video camera mounted on a vibration-less photographic column . The digital imaging was realized by a Canon EOS 7D Mk2 system, equipped with a CMOS APS-C type sensor . The camera was paired with a true 1:1 Macro lens with a focal length of 90 mm . The setup of the system enabled simultanous imaging of a large area consisting of up to 28 test chambers, thus eliminating any need for a motorized stage and potentially disruptive manipulations of the test specimens. For simplicity of data analysis a scaled down array of four test chambers was used for the majority of the software comparisons as a multitude of video files could be post-processed to represent further replicates. The video recording specifications were modified as required by the testing for each technical recording.Native videos of 55 seconds in duration were recorded with a resolution of 1,920 × 1,080 pixels (1080 p) and a framerate of 24 fps. The final toxicity tests were filmed in a custom array of chambers (7 by 4 matrix) with three organisms loaded per test chamber. This setup was recorded every 6 h for up to 24 h. All video files were acquired using an external High-Definition Multimedia Interface (HDMI) recorder  equiped with a programmable time-resolved video acquisition functionality. Native files were saved in .mov digital containers and encoded with a ProRes 422 HQ codec that provided no temporal compression artefacts (Inter frame-only encoding) and variable bitrate.All video data processing and animal tracking was performed on a Dell Precision 7810 PC workstation designed for parallel video processing tasks. The latter had two six-core Intel Xeon processors (CPU E5−2620 V3 2.40GHz) with hyper-threading technology providing a total of 24 logic processors, 32 GB random access memory (RAM) and an AMD FirePro W5100 graphics card. The operating system was a 64 bit Windows 10 Pro.Native files were ingested into a DaVinci Resolve 15  video-editing software suite used for transcoding and file optimization. To compare the impact of different compression levels. The native sets of video files were transcoded using Uncompressed RGB 10-bit (low compression), Uncompressed YUV 10-bit (medium compression) and GoProCineForm YUV 10-bit (high compression) codecs to common .avi-type containers to allow for comparison between the different software suites. The selection of codecs was based on their compatibility with all the animal tracking software packages (see below) and the ability to achieve different levels of compression as shown in Native video files were post-processed using a combination of Adobe Photoshop CC 2017  and DaVinci Resolve 15  video editing software. The optimization techniques included digital cropping, sharpening and background subtraction. The latter involved initially creating a digital background mask in Adobe Photoshop across the test wells. The digital mask could be completely white or black for positive or negative background subtraction respectively. Digital masks were automatically exported as a .png file types for every video file.The native video files were imported into DaVinci Resolve 15 video editing suite along with the respective digital background masks. For each video file, a single freeze frame was initially generated and subtracted from the entire time period of the video along with the associate digital mask using the inspector composite mode feature. This technique resulted in a video with perfectly black or white background in direct contrast to the organism, all manipulations describe above were performed using DaVinci Resolve automated batch processing capabilities in a high throughput fashion.® Systems, Viborg, Denmark)  , ToxTracDenmark) . All sofDenmark) .The data generated by analysis of each individual video was varied between software suites. Toxtrac provided averaged endpoint data for combined from all subjects in an arena for the duration of the video. Ethovision and LoliTrack provided averaged endpoint data for individual subjects in an arena for the duration of the video.p < 0.05.Replicate data from different experiments was pooled. The behavioral data was averaged out as an average per unit of time (minute). Cumulative distance travelled by animals was measured by summing the incremental distance moved by each animal between frames of video files. Data was then averaged between individual animals kept in the same chamber. Statistical analysis and data presentation was performed in both Prism 8  and RStudio V1.1.463  as beforFor all tested animal-tracking algorithms the computing time was proportionally smaller with an increased digital compression levels , Table 1The data demonstrated that compression and thus size of video file plays an integral role in the high throughput neurobehavioural pipeline. Transcoding of native video files into a different codec can drastically reduce the processing time while maintaining the precision of the endpoint data. The results highlight also an inherent variability between the tracking algorithms. Of note, the .avi file format used here is at present considered to be an obsolete standard, disadvantaged by very large file sizes and limited selection of modern codecs. However, the selection of .avi codecs was necessitated by their compatibility with the all animal tracking software packages used in this study . In thisThe down sampling of video file resolution resulted in proportionally faster computing times for all tested animal tracking software. The smallest tested resolution of 480 p returned computing time on average four times faster than the native resolution of 1080 p . The accThe resolution of a video file is integral when taking into account the small size of the tracked object. The measured pixel distance  for the movement of each organism in a well is only as accurate as the amount of pixels the organism can be thresholded (or measured) to move across. Thus the higher the resolution, the higher the accuracy of distance travelled and as a derivative also velocity and acceleration. When looking into the future one can expect automated higher throughput data acquisition system capable of simultaneously filming multiple test chambers. In such situations resolution of the camera sensor becomes a key factor requiring implementation of high definition or even ultra-high definition (4K and 8K) sensors. This is because with the larger the overall acquisition area the fewer of the pixels representing each test chamber will have assigned to threshold the organism against. To enable accurate thresholding ToxTrac recommends the subject size of an animal to be at least 50 pixels. Ethovision has stated that thresholding of subjects is possible with arena pixel quantity being 200 times larger than that of the target animal. The balancing of resolution to arena and organism ratio will be a key factor in future ultra-high throughput video recording systems. Unfortunately none of the animal tracking software available at present is capable of handling ultra-high definition video files. The results shown in At first we evaluated impact of digital transcoding of native files recorded at 60 fps to lower frame rates that had a proportional impact on reduction of processing time . AccordiThe analysis of video files at native frame rates (recorded in camera and not transcoded in video post-processing pipeline) depicted that lower fps values resulted in proportionally faster computing times for both tested animal tracking software . The accThe above data demonstrate that frame rate of video file can have a major impact on both performance and accuracy of animal tracking algorithms. The results highlight that if the framerate is artificially downgraded in the post-processing pipeline to obtain a small file size, although no visual distortion can be seen, the tracking algorithms cannot interpolate the data lost during a transcoding process. This phenomenon can be easily visualised with the graphical trajectory reconstruction . The traThe contrast-based detection and tracking of objects on video files heavily relies on accurate distinguishing of them from the background. The potentially complex test chambers as well as uneven lighting conditions during experiments can introduce significant obstacles for reproducible video-based animal tracking. Therefore, efficient video file optimization strategies, such as background subtraction techniques that can be performed in high-throughput, aid in rapid thresholding. We conclude that when analyzing hundreds of samples the manual optimization of threshold levels for every video file introduces unnecessary analytical bottlenecks. We postulate that to improve efficiency of analytical pipelines in behavioral studied the positive and/or negative background subtraction techniques can effectively be performed in freely available software suites that support automatically batch processing for increased throughput.One of the approaches to achieve higher throughput analysis in behavioral phenomics is simultaneous recording of multiple organisms in a single arena. The subsequent analysis of multiple objects can be challenging, as it requires continuous and reliable identification of all individual animals simultaneously. This means that although the software doesn’t necessarily lose track of a specific organism, the animal has the potential to cross paths close enough to another subject causing the two to switch identities in the eyes of the software . This isNext we sought to assess impact of simultaneous recording of multiple test chambers using a single camera. A constant of one organism per arena was tracked with increasing arena quantity to ensure any changes in precision were due to the change in arena quantity. As discussed in regards to resolution, the ToxTrac suite recommends the subject size per arena should be more than 50 pixels to enable for accurate thresholding and consistent results. At 18 arenas, the arena size is less than 60 pixels; beyond the recommended limit for thresholding in ToxTrac. Ethovision’s multiple arena module is advertised to be able to easily threshold and track up to 100 arenas at once, with the arena pixel quantity 200 times larger than the subject pixel area to be tracked. The ease of thresholding of Ethovision in comparison to ToxTrac was qualitatively confirmed at 18 arenas, with more than double the time taken to threshold as the organism area pixel recommendation was approached. The increase in number of arenas of interest from 1 to 18 had a minor impact on computing time denoted by 5% and 20% difference for Ethovision and ToxTrac, respectively . FurtherThe number of organisms per arena and number of arenas per video file are important variables to be considered when designing a higher throughput behavioural experiment. The impact of multiple arenas on accuracy of tracking is closely related to the resolution of the camera sensor. Multiple arenas of interest on a single video file are digitally defined in the software package. This process reduces the effective pixel quantity per area and can become a significant limitation when employing low-resolution cameras. In this work we employed a high definition 1080 p sensor and found that imaging even larger quantity of chambers might require systems equipped with ultra-high definition sensors such as 4 K (2160 p) and 8 K (4320 p) technologies. This higher resolution allows for more subjects to be within the recommended pixel subject size limit of 50 for ToxTrac with a larger amount of arenas. Unfortunately none of the animal tracking software available at present is capable of processing ultra-high definition video files desired for a high-throughput pipeline. This is a key limitation that software tracking systems are yet to overcome.When tracking multiple small aquatic organisms contained in a single chamber it is also important to consider that they move in three-dimensional space of the chamber . When reThe capability to performed animal tracking using batch processing of large sets of video files is another fundamental key enabler for high-throughput behavioral phenomics. In this study only ToxTrac and Ethovision suites had a capability for batch processing of videos, however both packages differed in the implementation of this analytical feature.The major feature of ToxTrac package was that an entire library of multiple video files could be batch imported into the software simply through sequential annotation of file names. The software had also an integrated module enabling automated pixel-to-distance calibration of all videos thus completely removing variations in calibration due to a manual user input. The disadvantages of ToxTrac in regards to batch processing were that arena and subject thresholding definitions can only be defined once. A time consuming manual review of output data was often required to ensure each subject was detected properly during the duration of each recording. Any changes required in thresholding would result in re-acquisition of the video file. Moreover, the significant spreadsheet output file providing a rather complex ensemble of numerical data was difficult to process and highlights a further need to improve analytical tools suitable for automated presentation and rapid mining of data. Because this suite is open source, it can potentially lend itself to a greater potential for customized module developments.Compared to ToxTrac, the proprietary Ethovision XT package provides some distinct advantages. Although the import of multiple videos is not user friendly and requires significant amount of time consuming manual tasks, Ethovision features a very flexible arena definition and dynamic subject thresholding tool that can be customized to individual videos. This dynamic thresholding involves the video playing in real time as values for thresholding are changed; invisible subject tracking time and interpolated (estimated) subject tracking time are updated in real-time. This allows for accurately locking the subject pixel area in comparison to the background prior to acquisition of the entire video tracking data; thus drastically reducing the user time required to correctly identify subjects. Interestingly, this proprietary package does not have an automated pixel-to-distance calibration capability creating a potential for user error especially when dealing with large video libraries. The data analytics and graphical outputs available from Ethovision allow, on the other hand to very easily manipulate and export large data sets in a distinctly usable way. These data sets can be viewed graphically; not only with trajectory diagrams and heat maps but instantaneous movement graphs of organisms in respect to time; integral when manipulating data from sensorimotor response assays. There are additional modules available from Ethovision that allow for organism tracking to be completed in real-time with the data outputs potentially able to integrate into an organism stimulus control system. This module could open up avenues in high throughput cognitive behavioural assays.Artemia sp. nauplii exposed to OP as a function of time and concentration. Behavioural data revealed significant hyperactivity of nauplii upon exposure to sub-lethal concentrations of OP  as a model aquatic toxicant. The data were analyzed in a batch-processing pipeline described above. ns of OP –6B. As dns of OP . The agins of OP  and 7. Ons of OP . Comparens of OP  and 7. Hns of OP  and 7. TNeurobehavioral biometric data are sensitive and multiparameter endpoints of high physiological relevance, applicable to a plethora of toxicological and pharmacological studies. The main reservations against behavioral toxicity studies have origins in historically low reproducibility and significant heterogeneity of behavioral experiments. In this work we demonstrated that by employing careful controls as well as high-throughput behavioural analysis paradigms one can achieve robust biometric data sets. Furthermore, high-throughput behavioural analysis provides a straightforward capability to create time-resolved neurobehavioral assays with simultaneous replicates across multiple concentrations and time points.When designing high-throughput neurobehavioural experiments using video-based tracking as the main analytical technique, it is important to anticipate all key biometric outputs that can be generated as well as any potential biasing factors. All components of the experiment design including chamber geometries and sizes, illumination sources as well as any physical stimuli  need to be taken into account and validated prior to the chemobehavioral assays to establish an unbiased control baseline. Furthermore, due to relatively large heterogeneity of behavioral responses among most species, all experiments run even on the same day should have their own independent sets of negative as well as validated positive controls. A particular consideration should be given to the appropriate video-recording facilities. Ideally, they should isolate the specimens from the surrounding environments to reduce any impact of external illumination, vibrations and sound-waves that can bias the behavioral biometric outputs.In addition the video acquisition settings such as framerate, resolution, codecs and post-processing method need to be identical for any independent experiments to be quantitatively comparable. Our data also indicate that caution should be applied when comparing experiments analyzed using different animal tracking algorithms. Although global trends of biometric outputs appear to be similar, careful statistical analysis revealed significant difference between the identical outputs using different software algorithms employed. As such, the variability between different tracking algorithms should be taken into account when designing standardized behavioral experiments and conducting chemobehavioural phenotyping.The main goal of this work was to demonstrate the impact of digital video file parameters and inherent differences in proprietary and open source animal tracking software algorithms on performance and accuracy of quantitative neurobehavioural data outputs. Due to intrinsic variability of video file parameters, such as resolution, frame rate, file containers, codecs and compression levels we sought to benchmark if any of those parameters can significantly affect performance and accuracy of animal tracking algorithms. The presented data and the specific validation in toxicological chemobehavioural phenotyping demonstrate that some video parameters and techniques, such as carefully optimized video file compression and post-processing techniques can be beneficial for both increasing speed of computing and accuracy of output data. On the other hand, the transcoding of video files to lower frame rates leads to irreparable loss of data leading to biased results.We also demonstrated impact of digital video file optimization techniques that may aid in thresholding during contrast-based animal detection. Post-processing a video file with background subtraction not only increases the accuracy of the outputted data as stated above; it also qualitatively shortens the user time in thresholding the organism against the background by twofold.Interestingly, we found variability in certain quantitative data outputs between different animal tracking algorithms exists and data analysis performed with different software packages can be a source of significant experimental biases. We postulate that caution needs to be taken when choosing a particular animal tracking software and all data pertaining to any projects that have to be directly compared should be performed within the same software package. To the best of our knowledge, this work is so far the most comprehensive side-by-side benchmarking of most popular animal tracking software suites. Although our evaluation was performed on only one proxy biological model manifesting simple kinesis behaviors, the data nevertheless highlights potential bias between different animal tracking algorithms. More studies need to be performed in different chemobehavioural assays and more advanced functional endpoints to fully assess impact of variability in animal tracking algorithms.Lastly, we demonstrated that higher throughput animal tracking in conjunction with automated behavioural data analysis is at present, largely underdeveloped. Although semi-automated batch animal tracking in multiple video files is possible in some software suites there is a general lack of fully integrated all-in-one solutions to perform fully automated video file transcoding, optimization, analysis and deep data mining. In line with a growing number of reports, we suggest that in combination with high-throughput automated data acquisition and analysis systems, quantitative behavioural phenotyping utilizing small model organisms can provide distinctive advantages in translational neuropharmacology, neurotoxicology and behavioral ecotoxicology .in silico models (We postulate, however, that critical to unlock the full potential of behavioral phenomics will be high-throughput, automated data analysis and data mining utilizing potential machine learning algorithms to find trends and build predictive o models . At preso models . However10.7717/peerj.7367/supp-1Data S1Graphpad Prism file including all raw data to all figures.Click here for additional data file.


In non-small cell lung cancer (NSCLC) patients, concomitant idiopathic pulmonary fibrosis (IPF) and emphysema (CPFE) are independently related to poor survival. CPFE is a condition with features of both pulmonary fibrosis and emphysema. Here, we evaluated the effect of CPFE and IPF alone on the outcomes of NSCLC patients.We retrospectively evaluated 283 patients with CPFE or IPF who were diagnosed with NSCLC between November 2003 and February 2018 at two tertiary care hospitals in South Korea. Patients were classified into CPFE and IPF groups according to chest computed tomography findings.p <  0.001), and lower forced expiratory volume in 1 s. Of all patients with NSCLC, 71.7% overall died during the follow-up period; 71.6% died in the CPFE group and 72.0% in the IPF group. Multivariate logistic regression analysis showed that CPFE  was significantly correlated with acute exacerbations (AEs). In a Cox proportional hazards analysis, stage > III NSCLC, higher Eastern Cooperative Oncology Group performance status, and higher gender–age–physiology index score was related to higher mortality. However, CPFE was not related to a higher mortality rate in univariate  or multivariate analysis .One-hundred-and-seven patients  had CPFE. Compared with IPF patients, CPFE patients had a heavier smoking history; lower diffusing capacity of carbon monoxide (78.0% vs 64.8%, AE risk, but not all-cause mortality, was higher in patients with CPFE and NSCLC than in those with IPF and NSCLC. Physicians should be aware of the exaggerated risk of AE in patients with concomitant CPFE and NSCLC. Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive fibrosing interstitial pneumonia, characterized by progressively worsening dyspnea and lung function, and is associated with a poor prognosis . IPF is Emphysema is defined as an enlargement of the air spaces distal to the terminal bronchioles due to destruction of the tissues comprising their walls, and can result in an obstructive pattern. Emphysema is also associated with an increased risk of lung cancer . FurtherEmphysema and IPF, which have different radiological, pathological, functional, and prognostic characteristics, have long been regarded as separate entities. However, the coexistence of emphysema and pulmonary fibrosis in individuals is being increasingly recognized . In 2005As the clinical course and complications of CPFE are not fully understood, physicians may be more reluctant to treat CPFE patients with concomitant NSCLC (CPFE-NSCLC) . Therefon = 59); (2) had non-confirmed pathology (n = 17); (3) had incomplete data available (n = 76); or (4) were lost to follow-up (n = 20). Finally, 283 patients were included in the analysis.The study was conducted retrospectively in two tertiary hospitals in South Korea. We evaluated all chest computed tomography (CT) scans of patients with a diagnosis of IPF and lung cancer that were obtained in the Severance Hospital and Seoul National University Bundang Hospital between November 2003 and February 2018. The inclusion criteria were as follows: (1) fulfilment of diagnostic criteria of IPF and CPFE, (2) availability of CT scan images at the time of diagnosis in the institutional radiology database; and (3) availability of clinical data from medical records. In total, 435 patients were considered after applying the inclusion criteria. Among these, patients were excluded if they (1) were diagnosed with small cell lung cancer  index score , EasternIPF was diagnosed using the criteria for the usual interstitial pneumonia (UIP) pattern as described in an official ATS/ERS/JRS/ALAT statement , as follCPFE was defined according to Cottin et al.’s and Ryerson et al.’s definitions, namely the presence of classic features of centrilobular and/or paraseptal emphysemas (≥ 10%) in the upper lobes and pulmonary fibrosis (mainly IPF/UIP) in the lower lobes radiographically , 13. ClaAE was defined according to the International Working Group Report by Collard et al. : previouCO in the GAP index) were excluded in the multivariate Cox proportional hazards models. An adjusted P-value < 0.05 was considered statistically significant. All statistical analyses were performed with SPSS version 23.0 .Baseline characteristics of CPFE-NSCLC and IPF-NSCLC patients were compared using an unpaired t-test for continuous variables or χ2 test for categorical variables and are presented as mean ± standard deviation and numbers (percentage). Multiple logistic regression models were used to estimate odds ratios (ORs) for AE. Survival times were estimated using the Kaplan–Meier method and compared with the log-rank test. Multivariate Cox proportional hazards models were performed to investigate the relationships between clinical parameters and mortality. Variables that overlapped . The study design was approved by the appropriate ethics review boards. The requirement to obtain informed patient consent was waived.P <  0.001), and lower forced expiratory volume in 1 s (FEV1)/FVC ratio . Body mass index (BMI), FVC, NSCLC histology, FVC predicted %, FEV1 predicted %, NSCLC stage, ECOG status, treatment modality, and GAP score did not differ significantly between IPF and CPFE patients.Baseline characteristics of the study subjects are provided in Table P = 0.975). CPFE patients had an increased tendency to develop AE, although this was not statistically significant in the univariate analysis . The time elapsed between diagnosis of CPFE or IPF and NSCLC was analyzed but did not differ significantly between the two groups .Of the NSCLC patients overall, 71.7% died during the follow-up period; 71.6% died in the CPFE group and 72.0% in the IPF group. The respective follow-up periods were 18.6 months and 24.1 months (P = 0.073). Although the incidence of AE was higher in the CPFE group , no statistically significant difference was found.Additional file n = 14), this group was combined with the GAP stage II group. CPFE  and GAP stage > II  showed a significant correlation with AE. NSCLC histology, smoking history (ever-smoker), and NSCLC stage > III did not show significant correlations with AE.Table P = 0.972), according to analysis of the Kaplan–Meier survival curves , AE (P <  0.001), lower FVC predicted, lower FEV1 predicted, lower DLco predicted, higher FEV1/FVC, %, and histological type other than adenocarcinoma or squamous cell carcinoma were significantly correlated with all-cause mortality. BMI, smoking history, time elapsed between diagnosis of IPF or CPFE and diagnosis of NSCLC, and CPFE did not correlate with all-cause mortality.The relationships between all-cause mortality and clinical parameters, including CPFE, were evaluated using Cox proportional hazards analysis Table . UnivariP = 0.003), advanced stage NSCLC , and higher GAP score  were risk factors for all-cause mortality. CPFE  was not a significant risk factor for all-cause mortality in multivariate analysis.Multivariate Cox proportional hazards analyses were performed to compare the contributions of these indices Table . StepwisIn this study, we have described differences in clinical features and outcomes between NSCLC-IPF and NSCLC-CPFE. Although previous studies have compared CPFE to IPF, few studies have compared CPFE-NSCLC to IPF-NSCLC. We demonstrated that CPFE is related to AE, but is not a significantly greater risk factor of all-cause mortality compared with IPF in NSCLC patients.In this study, the prevalence of CPFE was 37.8% in patients with NSCLC and pulmonary fibrosis. In previously reported studies, the proportion of patients with CPFE detectable on a high-resolution CT scan ranged from 8 to 51% in IPF patients , 23. As P = 0.618). This may suggest that CPFE patients do not necessarily need a shorter follow-up period compared with IPF patients to monitor for the presence of lung cancer. The result did not differ when the patients who were diagnosed concurrently  were excluded .We assessed the median duration from the diagnosis of CPFE/IPF and diagnosis of NSCLC in patients who developed CPFE/IPF prior to developing NSCLC. The duration did not differ between the CPFE and IPF groups (20.01 months vs. 21.06 months, The annual incidence of AE in patients with IPF has been reported as 5–15% . AdditioIn IPF and lung cancer patients, efforts are being made to prevent the development of AE. Iwata et al. showed that perioperative pirfenidone for lung cancer surgery in patients with IPF significantly decreased the incidence of AE after surgery . This maCPFE did not affect the mortality rate of NSCLC patients, although AE has been associated with increased mortality. AEs of IPF are a well-described complication after lung resection, with an incidence of approximately 15% and mortality of 33.3–100% in some studies . It is pOur study had some limitations. First, it included only Korean patients and only two centers participated in the study. Some studies of drug-induced lung injury have suggested ethnic differences in the susceptibility to acute progressive respiratory failure during the course of IPF . Large mIn conclusion, the risk of AE was higher in patients with CPFE and NSCLC, but all-cause mortality was not higher in NSCLC patients with concomitant CPFE than in those with concomitant IPF. Physicians should be aware of the increased risk of AE when treating NSCLC patients with CPFE. A multidisciplinary approach is required for treating these patients.Additional file 1: Table S1. Incidence of acute exacerbation according to treatment modality.


Epidemiologic studies have presented protective effects of alcohol against cardiovascular (CV) events. However, such studies were performed mainly on Westerners. We investigated the effects of alcohol on the subclinical CV morbidity in healthy Koreans.The coronary artery calcium (CAC) score, ankle-brachial pulse wave velocity (abPWV), and carotid intima-media thickness (cIMT) of 1004 subjects  with no CV disease history were assessed. The subjects were divided into three groups based on their drinking patterns: Group 0 (abstainers), Group 1 , and Group 2 . As drinking patterns can be influenced by age/sex, a regression analysis was performed in another four groups .p < 0.05 for all). Problematic drinking  was associated with a high prevalence of CAC deposits among men aged < 65 years and casual drinking with a lower prevalence of CAC deposits  among men aged ≥65 years. Conversely, problematic drinking in older women [OR: 0.117; 95% CI: 0.014–0.943) and casual drinking in younger women  were associated with a lower prevalence of CAC deposits. Casual drinking was associated with a lower abPWV and thinner cIMT in the diabetes mellitus/hypertension-adjusted regression analysis.Group 1 exhibited lower CAC  and abPWV  scores and thinner cIMT  than Groups 0 and 2  contains supplementary material, which is available to authorized users. The effects of alcohol on cardiovascular (CV) diseases are still controversial, although various epidemiologic observational studies have demonstrated cardioprotective effects of alcohol, which has been known as the “French paradox” –5. The cWhether drinking alcohol is beneficial to CV morbidities and whether these beneficial effects are from alcohol itself or from other substances contained in alcoholic beverages, such as antioxidants in wine and beer, remain to be elucidated . To answThe study protocol was approved by the institutional review board (IRB) of Gachon University Gil Hospital (GCIRB2018–098), and the study complied with the Declaration of Helsinki principles (6th revision). We used medical data and self-reported questionnaires of 1004 Korean subjects, which were obtained upon a health check-up between 2016 and 2017 in the attending hospital. The self-reported questionnaire contained quantitative drinking habits , smoking history, and comorbidities, including diabetes mellitus (DM) and hypertension. Blood tests including fasting sugar, HbA1c, blood urea nitrogen/creatinine, liver panel, uric acid, and lipid panel were performed in all participants after a 12-h fast. We excluded patients with morbidities other than DM and hypertension, such as overt CV diseases and cancers. Problematic drinking was defined as > 14 standard drinking/week for men and > 7 standard drinking/week for women; the cut-off value in women has been defined as lower than that in men in other investigations because 1) women tend to weigh less than do men; 2) women’s bodies contain less water and more fats that retain alcohol. As water dilutes alcohol, it remains at higher concentrations for longer periods of time in a woman’s body, exposing her organs to more alcohol; 3) women have lower levels of alcohol dehydrogenase and aldehyde dehydrogenase; and 4) changes in hormone levels during the menstrual cycle may also affect alcohol metabolism in a woman’s body [2; 9) the raw data were reconstructed at the early to the mid-diastolic phase; 10) the reconstruction algorithm was medium-sharp kernel (B35F); and 11) the slice thickness/increment was 3.0/1.5 mm. The image datasets acquired from the calcium scoring using CT were transferred to a dedicated workstation  to determine the CAC score. The presence of CAC deposits was defined as a CAC score > 0 using the modified Agatston method.The CAC score was assessed in all subjects with electrocardiogram (ECG)-gated CT examinations in one session using a 64-channel multi-detector CT scanner . The scanning protocol for calcium scoring using ECG-gated CT was as follows: 1) prospective ECG gating was used; 2) the peak voltage was 120 kVp; 3) the tube current-exposure time product was 40 mAs; 4) the rotation time was 0.36 s; 5) the collimation was 64 × 0.6 mm; 6) the pitch was 0.33; 7) the scan range was from the carina to the cardiac apex; 8) the field of view was 250 mmThe abPWV of 569 subjects was measured at their discretion, which was automatically determined using the oscillometric method with a commercially available volume plethysmography equipment . To measure blood pressure (BP), cuffs were wrapped around both upper arms and ankles of the subjects in the supine position after a 5-min rest in a quiet room. Pulse waves from the brachial and tibial arteries were simultaneously obtained and recorded. The abPWV was calculated as the transmission distance (distance between the cuff position of the arm and the ankle) divided by the transmission time . We used the abPWV  as a marker of both central and peripheral arterial stiffness.The cIMT was measured in 562 subjects using ultrasound after obtaining their consent. The subjects rested in the supine position for 15 min, and their cIMT was measured using an 8-MHz linear array transducer  and automatic IMT measurement software  on a computer. Images from the region 2 cm proximal to the carotid bifurcation were obtained at least 1 cm axially, and the distance between the lumen-intima interface and the media-adventitia interface was determined using an automated edge detection algorithm. The mean values of the right and left cIMTs were used for the analysis.Abdominal ultrasound using a 3.5 MHz transducer was performed in all participants after a 12-h fast to diagnose fatty liver. The studies were conducted by two experienced radiologists who were blinded to the aims of the study and laboratory values; images were captured with the participants in the supine position with their right arm raised above their head. Known ultrasound criteria such as hepatorenal echo contrast, liver brightness, deep attenuation, and vascular blurring were assessed. Those presenting hepatorenal contrast and liver brightness were diagnosed as having fatty liver .p-values of < 0.05 were considered significant.Continuous data were expressed as means±standard deviations (SD) after a normality test. The baseline characteristics were compared using the one-way ANOVA test, and post hoc analyses were performed using the Bonferroni procedure. Categorical variables were compared using the chi-square test and Fisher’s exact test. Binary logistic regression , and linear regression analyses  were performed to assess the independent association of casual or problematic drinking compared to abstinence. The Statistical Package for the Social Sciences v18.0  was used, and The baseline characteristics of the cohort and the intergroup comparison of results are presented in Table Figure As drinking pattern can be influenced by age and sex, we divided our cohort into four groups based on age and sex of the participants, and binary logistic regression analyses were performed to assess the predictive power of casual or problematic drinking for the presence of any CAC deposit. Known risk factors such as DM and hypertension were also considered Table . In men Approximately 56% of the total participants were subjected to abPWV and cIMT measurements. A demographic comparison between the groups in whom abPWV was measured (+) vs. not measured (−) and cIMT was measured (+) vs. not measured (−) are presented in the Additional file In the current cross-sectional study, we found that casual drinkers have less severe CV organ damages in the subclinical stages than abstainers or problematic drinkers, which support the “V- or J-shaped” association between alcohol consumption and atherosclerosis. The favorable effects of casual drinking on CAC deposits were remarkable even in subjects with known risk factors for CV diseases, such as DM, hypertension, smoking, and fatty liver. The results of the current investigation are novel because: 1) it was performed in a Korean population whose drinking pattern and taste for liquors are different from those of Westerners; and 2) subclinical CV organ damages were comprehensively assessed using recent diagnostic tools in subjects with no history of CV diseases. Our results would serve as an additional evidence for the French paradox, an old epidemiologic observation.There is an accumulating evidence of a J-shaped association between the amount of alcohol consumption and prevalence/mortality of CV diseases , 12, 13.The proportion of fatty liver  was lower in casual drinkers than in abstainers and problematic drinkers. However, a J-shaped association was exhibited between the parameters associated with fatty liver and amount of alcohol consumption. The effects of light/moderate alcohol consumption on NAFLD is also an issue among studies that reported a positive –17 or neThe potential benefit of casual drinking is still inconclusive ; some auThe mechanisms for the potential cardioprotective effects of drinking have not been fully elucidated to date. Some authors have claimed that a low prevalence of CV diseases observed in wine or beer drinkers might be associated with the antioxidants contained in the beverages , 7, 8. MBased on this study, there are several hypotheses for the potential mechanism for the cardioprotective effect of alcohol. Drinking raises the HDL level , which wFirst, the samples in this study were people presenting to a hospital for health check-up. This limits the generalizability of the current investigation. Second, this study has an inevitable limitation of cross-sectional analyses. Therefore, the causal relationship of alcohol with CV organ damages cannot be determined. Third, the beverage type , drinking pattern (regular or irregular), and drinking duration were not assessed in detail. Alcohol consumption was semi-quantitatively analyzed. Fourth, the treatment and medications for comorbidities, such as DM and hypertension, were not included in the data collection. Finally, the abPWV and cIMT were assessed in only approximately half of the cohort.We performed cross-sectional analyses to investigate the effects of habitual alcohol drinking on the subclinical CV morbidity in healthy Korean subjects with no apparent CV diseases. Compared with abstinence and problematic drinking, casual drinking was associated with less severe CV organ damages in the subclinical stages.Additional file 1:Table S1. Demographic Comparison between the Groups Subjected to abPWV and cIMT Measurement. Description of data: Demographic comparison between the groups in whom abPWV was measured (+) vs. not measured (−) and cIMP was measured (+) vs. not measured (−) are presented. (DOCX 13 kb)


To develop effective and sustainable simulation training programs in low-resource settings, it is critical that facilitators are thoroughly trained in debriefing, a critical component of simulation learning. However, large knowledge gaps exist regarding the best way to train and evaluate debrief facilitators in low-resource settings.Using a mixed methods approach, this study explored the feasibility of evaluating the debriefing skills of nurse mentors in Bihar, India. Videos of obstetric and neonatal post-simulation debriefs were assessed using two known tools: the Center for Advanced Pediatric and Perinatal Education (CAPE) tool and Debriefing Assessment for Simulation in Healthcare (DASH). Video data was used to evaluate interrater reliability and changes in debriefing performance over time. Additionally, twenty semi-structured interviews with nurse mentors explored perceived barriers and enablers of debriefing in Bihar.p < 0.01, p < 0.01, p = 0.04). Barriers included fear of making mistakes, time constraints, and technical challenges. Enablers included creating a safe learning environment, using contextually appropriate debriefing strategies, and team building. Overall, nurse mentors believed that debriefing was a vital aspect of simulation-based training.A total of 73 debriefing videos, averaging 18 min each, were analyzed by two raters. The CAPE tool demonstrated higher interrater reliability than the DASH; 13 of 16 CAPE indicators and two of six DASH indicators were judged reliable (ICC > 0.6 or kappa > 0.40). All indicators remained stable or improved over time. The number of ‘instructors questions,’ the amount of ‘trainee responses,’ and the ability to ‘organize the debrief’ improved significantly over time (Simulation debriefing and evaluation was feasible among nurse mentors in Bihar. Results demonstrated that the CAPE demonstrated higher interrater reliability than the DASH and that nurse mentors were able to maintain or improve their debriefing skills overtime. Further, debriefing was considered to be critical to the success of the simulation training. However, fear of making mistakes and logistical challenges must be addressed to maximize learning. Teamwork, adaptability, and building a safe learning environment enhanced the quality enhanced the quality of simulation-based training, which could ultimately help to improve maternal and neonatal health outcomes in Bihar. Simulation-based training for health providers is becoming widely recognized as a tool for improving facility-based care of mothers and neonates globally . Post-siDebrief facilitation of simulations is difficult to learn, and achieving fluency and expertise requires time and experience . For simDespite the known importance of effective debriefing and the growing demand for simulation-based training globally, the optimal way to train and evaluate simulation facilitators in low-resource settings is unknown . Recent Given the rapid growth of simulation-based training in low-resource settings, it is critical to have tools to accurately evaluate the debriefing abilities of facilitators. This knowledge will allow simulation programs to provide feedback to help facilitators improve their skills and maximize trainee learning. The Debriefing Assessment for Simulation in Healthcare (DASH) tool, developed at the Harvard Simulation Center, is the most widely used debrief evaluation tool and has been extensively validated in high-resource settings . The DASBihar has a population of over 100 million, with 89% living in rural areas . In 2012This was a mixed methods study, including quantitative and qualitative data.The Mobile Nurse Mentoring Program, AMANAT (meaning “something given in trust”), was a large-scale obstetric and neonatal nurse mentoring program led by CARE India in collaboration with the Government of Bihar. The AMANAT program was implemented at 320 PHCs across Bihar between 2015 and 2017 over four 8-month rounds. Rounds 1–3 were included in this analysis as Round 4 was ongoing. During each round, nurse mentors rotated in pairs between four sites, spending one week per month at each site for a total of six to eight weeks at each PHC. Each PHC had six to eight nurse mentees. Starting in week 3 on their third visit, nurse mentors facilitated a minimum of three simulations per week focused on key maternal and neonatal scenarios. Each simulation was followed by a debrief, which was recorded using a handheld camera.A total of 120 nurse mentors participated in AMANAT rounds 1 through 3, which were conducted from March 2015 to June 2016. Nurses were selected by CARE India and the Government of Bihar to work as on-site mentors and simulation facilitators. The details of this program are described elsewhere . Nurse mNurse mentor training was implemented using the train-the-trainer approach. Nurse mentors underwent four weeks of in-depth training with CARE India. One week was entirely devoted to Basic Emergency Obstetric and Neonatal Care (BEmONC) simulation training with PRONTO International. This training included simulation facilitation, teamwork, communication, and debriefing skills. One half-day focused exclusively on the theory of debriefing and 1.5 days allowed for the practice of debriefing skills. Nurse mentors were taught to facilitate debriefs using the diamond debriefing method, a structure that includes three phases: description, analysis, and application . This ap. video coding software.To evaluate debriefing quality, the research team randomly selected one debriefing video per mentor pair during three time points: early (months 3–4), mid (month 5), and late (months 6–7). The target sample size was 85, based on the suggestion of Bujang and Baharum that 85 items are required when the null hypothesis can be assumed to not equal zero and there are two observations per subject . This sa.. During rater training, the video analysts and one Hindi-speaking simulation expert triple-coded 10 debrief videos. Each watched the videos twice, first completing the modified DASH form and then the CAPE code window. With the guidance of a PRONTO expert trained in DASH evaluation, any resulting discrepancies were discussed and resolved. This process was repeated until the PRONTO expert trained in DASH-evaluation determined that both raters demonstrated proficiency with the DASH and CAPE constructs. Additionally, the video analysts participated in biweekly calls throughout the course of the project to review progress and discuss coding-related questions.Two nurses , both based in Bihar, not involved in program implementation, and fluent in Hindi , were trained in debrief video analysis. This training consisted of a two-hour lesson on debrief theory, a detailed review of the modified DASH and CAPE tools, and coding in Studiocode™All videos selected were of sufficient quality to analyze. Any missing individual responses were excluded from analysis, with the exception of certain CAPE variables that asked about the presence of a certain component . In these cases, a missing response was replaced with a zero. All videos were double-coded by the two video analysts. To mitigate rater bias, the video files presented to the video analysts were given in batches. Intra-class correlation coefficients (ICC)Changes in nurse mentors’ debriefing skills were evaluated over each 8-month round, using unpaired debriefing videos. We hypothesized that mentors’ skills would improve over time secondary to increased practice, strengthened relationships with their learners, and the simulation refresher training conducted after month four. Only indicators that were found to have fair to excellent interrater reliability were included in the analysis to assess change over time, a decision that was made a priori to maximize accuracy. Depending on the timing of the debriefs, videos were categorized into three time-points: early (months 3–4), mid (month 5), and late (months 6–7). Trends over time for all continuous and categorical variables were assessed using linear and logistic regression, respectively, adjusted for rater. Because there was significant variation between which of the two mentor pairs led the debrief at each timepoint, a paired analysis was not possible. In more conservative models, we used generalized estimating equations (GEE) to account for correlations between double coding by raters per video. The GEE and non-GEE models yielded similar results. For simplicity of interpretation, only linear and logistic models are reported, except when differences were observed. Regression assumptions, including normality, homoscedasticity, outlier and influential analysis, were examined to detect any potential violations. All analyses were conducted in R Core Team version 0.99.903  .We explored barriers and enablers of simulation debriefing through semi-structured interviews with current AMANAT nurse mentors. Interviews took place between June and August 2016. The interview guide was designed in English, translated into Hindi, and then translated back to English to ensure accuracy. Two pilot interviews were completed to refine the interview guide. The pilot interviews were excluded from the final analysis. Interviews were conducted by two female interviewers, who had received training on study objectives and qualitative methodology. One interviewer was fluent in Hindi. Interviews were conducted in the language preferred by participants. Interviews were held in private rooms at PHCs. Interview duration ranged from 40 to 60 min.Interviews were transcribed and, where necessary, translated to English by a bilingual Indian simulation specialist. To ensure transcription and translation accuracy, two independent staff double-checked all transcriptions and translations. Interview data were analyzed using the thematic content approach, which included four steps: data familiarization; identifying codes and themes; developing a coding scheme and applying it to the data; and organizing codes and themes , 38. TwoWritten informed consent was obtained from all participants. The study was approved by the UCSF Committee on Human Research  and the Indian Institute of Health Management Research Institutional Review Board.Across three mentoring phases from March 2015 to June 2016, 4066 simulation debrief videos were collected. A total of 73 debrief videos were included in the analysis , while only 3 of 16 (19%) demonstrated poor reliability Table . In compOne of the most important CAPE indicators, ‘Instructor questions to instructor statements ratio,’ was not reliably coded. However, a composite indicator, ‘instructor questions and statements,’ demonstrated high reliability (data not shown).Only two DASH indicators, ‘organize the debrief’ and ‘facilitate the debrief,’ demonstrated fair and good reliability, respectively; four of six (66%) demonstrated poor reliability Table . Cronbacp < 0.01). The number of ‘trainee responses’ increased from 50 to 64 per debrief (p < 0.01). The DASH indicator, ‘organize the debrief,’ increased from 3.3 to 3.5 (p = 0.04) on a 5-point Likert scale.Following training, nurse mentors’ performance increased for several reliable CAPE and DASH indicators that are key to the essence of the debrief quality Table . The avep > 0.05).The majority of indicators did not change over time. For example, ‘trainee response to instructor questions and statements ratio’ changed from 0.75 to 0.78. The ‘number of times the videotape was paused’ during debriefing, ‘debrief length’, and ‘number of behavioral and technical objectives’ mentioned all remained constant .A total of 20 nurse mentors, with a median age of 24 years, were interviewed. On average, they had 14 months of mentoring experience. Only three had previous teaching experience and none had prior simulation debriefing experience. Participants were from states across India, including Delhi , West BeMany participants described that mentees disliked having their mistakes identified, especially when these mistakes were captured on video. Mentees, particularly older nurses, worried that such videos would be used to publicly display mistakes to peers. While mentors acknowledged that the videos sometimes made mentees nervous, they found them helpful in providing feedback."It should be good... continuing with the video, because the person... if they are doing mistake, they can observe, 'Oh yeah, they are doing.' According to me, the video should be there." To mitigate anxiety, mentors tried to reassure mentees that the videos were only learning tools.Participants commonly struggled with time management during debriefing. Several mentioned that it was difficult to keep all of the mentees engaged when debriefs were longer than 20 to 30 min. Challenges included mentees exhibiting disinterest, talking simultaneously, and arguing about clinical management. Further, because mentees were frequently scheduled to work on training days, they sometimes had to leave debrief sessions to care for patients.Several mentors described technical barriers related to video-recording. When the video, camera or laptop was not working, mentors often used mobile phones to record videos.Numerous mentors highlighted the importance of creating a safe learning environment for mentees. To do this, mentors would begin debriefs by discussing what went well. Mentors framed mistakes in a constructive way and encouraged mentees to self-identify how they could improve in the future. Additionally, mentors emphasized the importance of using supportive language."First of all, we used to take the positive points what all they have done. Actually, I didn't used to take the negative points... I used to ask them what [they] could have done at this place." “We say... 'Sister, you tell us what got missed and what should you have added,' so then she herself will tell her mistakes. Through this, what happens is that we are on the safe side. In the beginning, they used to feel very guilty... 'Sister, I made a mistake... the mistake has been recorded in the video.' Then we say, 'Sister, let mistakes happen, only then we can learn from them, but don’t repeat them.'" “Don't call out mistakes. [Instead] say 'missed out.'” Several participants discussed the importance of being efficient and organized during debriefs. Mentors tried to keep debriefs as short as possible, while still covering the key points. At times, this required group management skills; for example, “If you sit, it will be done” . When PHCs were really busy, mentors utilized flash debriefs. These pre-written debrief scripts consisted of 3 questions  and rapidly covered the most important messages for a given simulation scenario.Mentors also discussed strategies to increase the participation of mentees and other PHC providers in debriefing sessions. The majority of mentors recommended including doctors in simulations and debriefs. Additionally, several suggested beginning the debriefs with mentees summarizing the preceding simulation scenario.Nearly all mentors had a positive perception of debriefs, describing them as a critical element of simulation training."If we do not debrief, there is no point of simulation." A majority of mentors believed that debriefs helped clarify clinical weaknesses, so that mistakes that occurred during simulations would not happen while taking care of patients."I think debriefing is like the backbone of simulation... because with debriefing, they used to understand everything they did not understand well with the simulation… if they used to think that. ‘I have done this well,’ then in debriefing they used to realize that, ‘No, I could not do it.’ If they feel that I have done any mistake then... it used to get clear in the debrief." Mentors also felt that debriefing was valuable for improving provider communication, discussing doctor-nurse and nurse-nurse hierarchy, and identifying other health system-related challenges such as human resource shortages and long distances between the delivery room and the pharmacy where necessary medications are kept.To develop effective and sustainable simulation training programs in low-resource settings, it is critical that facilitators are thoroughly trained in debriefing. Through this unique approach using video analysis, we were able to remotely monitor and evaluate simulation debriefing in Bihar. Results suggest that the CAPE tool more reliably assessed debriefs, compared to the DASH tool. Thirteen of the 16 CAPE indicators had fair to excellent reliability (81%). This may partially be related to the fact that the DASH evaluates skills at the composite level, whereas the CAPE, which is scored at the individual item level, does not. Notably, this finding suggests that the CAPE tool’s objectivity may be especially helpful in settings where evaluators have less experience evaluating debriefers. One key indicator, ‘ratio of instructor questions to statements,’ had low reliability. However, a composite indicator of the sum of ‘instructor questions and statements’ was highly reliable, suggesting that the two video analysts were systematically categorizing questions and statements differently. For example, one video analyst was coding rhetorical questions as a statement, while the other was not. The DASH tool demonstrated high internal consistency with a Cronbach α of > 0.95, which is higher than the original high-resource validation study that found a Cronbach α of 0.89 . This coMentors identified fear of making mistakes, time-constraints, and technical challenges as key challenges to successful debriefing. Previous studies have similarly identified lack of protected time for professional development  and lackInterviews revealed several approaches to address identified barriers to enable success in this resource-constrained context. While mentees initially felt shameful about mistakes, mentors increased participation by constructively framing mistakes as learning points. This thoughtful attention to language allowed mentees to feel comfortable discussing mistakes, while still maintaining a respectful learning environment.Findings suggest that contextually appropriate flash debriefs, which may be easily adapted to reflect trainee needs, could help overcome the important barrier of time-constraints, though future studies are required to explore whether these are equally effective from a learning perspective. This is consistent with previous studies that have recommended adaptation of debriefs to fit the environment and skill level of trainees . AcceptaThis study has several limitations. First, the video analysts did not participate in the official DASH training due to time and financial constraints. This may have been an important contributor to the low interrater reliability reported in this study. The number of debrief videos analyzed from round 2 was relatively small as a result of missing data from a third video analyst, who left after a brief period of employment; this may have resulted in an underestimation of interrater reliability or failure to detect changes in debriefing performance. Video coding was both time- and resource-intensive. Finally, interviews were conducted by members of the study team, which may have introduced social desirability bias. All mentors were informed in advance that data resulting from interviews was confidential in nature and would not be used for purposes other than research and programmatic improvement.This study has demonstrated the feasibility of evaluating simulation debriefing in Bihar, India. Multiple CAPE indicators reliably assessed debriefing performance, showing that nurse mentors maintained or improved their facilitation skills over time. Barriers included fear of mistakes and time constraints. Enablers included having a safe learning environment, a flexible approach to debriefing, and leadership buy-in. An in-depth understanding of the barriers and enablers of debriefing is essential to improve the quality of simulation training programs in LMICs. Establishing the feasibility of debriefing and debrief evaluation is a meaningful step toward the development of successful simulation training programs and ultimately improving BEmONC skills among providers in Bihar and related low-resource settings.Additional file 1: Table S1 Interrater reliability of additional CAPE variables in Bihar, India, 2015–2017 (N = 73 simulation debrief videos).


Prediction of six-month and/or one-year mortality by the modified MNA-SF was comparable with predictions by the standard MNA-SF and MNA-LF . The modified MNA-LF showed better prediction of six-month and one-year mortality than the standard MNA-SF and MNA-LF . The modified MNA-LF , but none of the other instruments , predicted six-month and one-year mortality independently of age, sex, frailty, comorbidity and ADL disability burden. The modified MNA-SF and MNA-LF emerged as potentially valuable tools for predicting mortality in patients hospitalised on geriatric wards.Recently we introduced a modified Mini Nutritional Assessment (MNA) Short Form (MNA-SF) and Long Form (MNA-SF) with operationalization of the ‘mobility’ and ‘neuropsychological problems’ items of the MNA using scores on Barthel Index mobility item and Mini Mental State Examination and Geriatric Depression Scale scores. We have now evaluated the abilities of this modified MNA-SF and MNA-LF to predict mortality in comparison with the standard MNA-SF and MNA-LF and the Nutritional Risk Screening 2002 (NRS 2002) and the Malnutrition Universal Screening Tool (MUST). A prospective analysis was performed in 240 hospitalised geriatric patients aged ≥ 65 years. Malnutrition and/or malnutrition risk were assessed using the modified MNA-SF and MNA-LF, the standard MNA-SF and MNA-LF, and the NRS 2002 and MUST. The modified MNA-SF and MNA-LF and the standard MNA-SF and MNA-LF assessments (all  Malnutrition is often found in individuals with impairments to body functions such as dysphagia3, immobility4, depression5, limited perception of hunger and thirst, polypharmacy7, and acute and chronic diseases8. Patients on geriatric wards frequently show a high prevalence of malnutrition or are at high risk of malnutrition11. Malnourished patients have higher risks of sarcopenia13, frailty15, morbidity16 and mortality20. Previous analysis has shown that malnourished geriatric patients experience clinical endpoints, including mortality, more often or, strictly speaking, earlier than well-nourished people of the same age17. There are therefore advantages in detecting malnutrition and the risk of malnutrition early to drive timely dietary interventions in patients on geriatric wards.Malnutrition is an acute, subacute or chronic state of overnutrition or undernutrition which can include an inflammatory component and impacts body composition and function22 and Long Form (full MNA or MNA-LF)23, the Nutritional Risk Screening 2002 (NRS 2002)25, and the Malnutrition Universal Screening Tool (MUST)26, among others. Of these, the MNA23 and in particular the MNA-SF22 may be the instruments of choice in all geriatric healthcare settings, including patients on geriatric wards27. However, one drawback is that some items on the MNA-SF and MNA-LF, particularly ‘mobility’ and ‘neuropsychological problems’, depend on subjective interpretation by the examiner28. This suggests that from the clinical point of view a more standardized and objective implementation of these two MNA items would be advantageous28.Several different screening tools for malnutrition and/or the risk of malnutrition have been introduced. These include the Mini Nutritional Assessment (MNA) Short Form (MNA-SF)29) by the multidisciplinary geriatric team30. These five dimensions are cognition, emotion, mobility, activities of daily living (ADL), and social situation29. The Mini Mental State Examination (MMSE), Geriatric Depression Scale (GDS), the Timed Up and Go Test (TUG), Barthel Index, and a 5-item questionnaire addressing the patient’s social situation are frequently applied as assessment instruments29. As the prevalence or risk of malnutrition in patients hospitalised on geriatric wards is high, ever more geriatricians in Germany prefer to include evaluation of nutritional status in their assessments. With the aim of providing an objective and efficient approach to evaluating the nutritional status of older in-patients on geriatric wards, there may be value in using results from the mobility item of the Barthel Index and MMSE and GDS scores to operationalize the ‘mobility’ and ‘neuropsychological problems’ items in parallel with the other standard items of the MNA-SF and MNA-LF28.In Germany, patients hospitalised for acute care on geriatric wards are routinely evaluated in relation to 5 dimensions of a comprehensive geriatric assessment . Patients were followed up six months and one year after baseline examination. Patients, patients’ relatives or legal guardians, and general practitioners were contacted by telephone six months and one year after baseline examination to obtain pertinent follow-up data relating to medical endpoints such as death due to any cause (including exact date of death). The study was approved by the Ethics Committee of the Friedrich-Alexander University, Erlangen-Nuremberg, Germany and complied with currently applicable laws. Informed written consent by the participant or a legal guardian was obtained before inclusion of patients into the study.The study was a prospective longitudinal analysis in patients hospitalised on the geriatric wards of the Department of Internal Medicine III (Medicine of Ageing), Malteser Waldkrankenhaus St. Marien, Erlangen, Germany. The study population consisted of 240 patients. Patients were enrolled in the study programme between October 2015 and March 2016. Inclusion criteria were age ≥ 65 years, willingness and provision of consent for all relevant medical data to be held after baseline examination, even should death occur, and provision of consent to contact relatives, legal guardians, general practitioners, and the local town authority for residents/inhabitants to obtain information on current place of residence and living status . Patients were evaluated in relation to malnutrition and risk of malnutrition by a single trained investigator (LB) during hospital stays on geriatric wards (baseline examination). Comprehensive geriatric assessment was performed in all 240 study participants by the geriatric team  by applying routine comprehensive geriatric assessment instruments  as a malnutrition screening instrument23. In 1996, Rubenstein et al. developed and validated a short form (MNA-SF) including only six of the 18 items of the MNA-LF21. In 2009, Kaiser et al. showed that the ‘Body Mass Index (BMI)’ item of the MNA-SF and MNA-LF could be replaced by calf circumference when BMI could not be determined22. The six items of the standard MNA-SF were: ‘decline of food intake over the past three months’ (item A), ‘weight loss during the last three months’ (item B), ‘mobility’ (item C), ‘psychological stress or acute disease during the last three months’ (item D), ‘neuropsychological problems’ (item E), and ‘Body Mass Index’ (item F1), replacing BMI with ‘calf circumference (CC) in cm’ when BMI could not be determined (item F2)22. The items in the standard MNA-LF included the six items of the MNA-SF, along with twelve additional items: ‘independent living situation (not living in nursing home)’ (item G), ‘intake of more than 3 prescription drugs per day’ (item H), ‘pressure sores or skin ulcers’ (item I), ‘number of full daily meals’ (item J), ‘consumption markers for protein intake’ (item K), ‘consumption of two or more portions of fruit or vegetables per day’ (item L), ‘fluid intake’ (item M), ‘mode of feeding’ (item N), ‘self-assessment of nutritional status’ (item O), ‘self-assessment of health status in comparison with people of the same age’ (item P), ‘mid- arm circumference (MAC) in cm’ (item Q), and ‘calf circumference (CC) in cm’ (item R)23. Patients’ nutritional status in the standard MNA-SF and MNA-LF was classified as ‘normal nutritional status’, ‘at risk of malnutrition’, and ‘malnutrition’ on the basis of criteria described in detail elsewhere23.In the early 1990s Guigoz 28. Item E, ‘neuropsychological problems’, of the modified MNA-SF and MNA-LF was operationalized on the basis of Mini Mental State Examination (MMSE) and Geriatric Depression Scale (GDS) scores28. Thus, the categories “severe dementia or depression”, “mild dementia”, and “no psychological problems” of item E, ‘neuropsychological problems’, of the standard MNA-SF and MNA-LF were operationalized in the modified MNA-SF and MNA-LF based on an MMSE score of <20 or a GDS score of 10–15 points, an MMSE score of 20–26 points or a GDS score of 6–9 points, and an MMSE score of 27–30 points or a GDS score < 6 points respectively28. In addition, the modified MNA-SF and/or MNA-LF also included A, B, D, and F-R of the standard MNA-SF and/or MNA-LF, using the standard implementation and scoring of these items of the standard MNA-SF and MNA-LF28. Like the standard MNA-SF and MNA-LF, the modified MNA-SF and MNA-LF classified patients’ nutritional status into ‘normal nutritional status, ‘at risk of malnutrition’, and ‘malnutrition’28.In the modified MNA-SF and MNA-LF, item C ‘mobility’ of the standard MNA-SF and MNA-LF was operationalized on the basis of the score on the mobility item of the Barthel Index. In brief, the categories “bed or chair bound”, “able to move in the ward” and “able to leave the ward” of item C, ‘mobility’, of the standard MNA-SF and MNA-LF were operationalized in the modified MNA-SF and MNA-LF using scores of 0, 5 to 10, and 15 points on the mobility item of the Barthel Index respectively25. If the prescreen is positive, i.e., at least one of the four prescreening questions generates a ‘yes’ answer, a final and more comprehensive screening is performed and includes three dimensions: I) ‘nutritional, anthropometric and clinical status’; II) ‘disease severity; and III) ‘age of 70 years or older’25. Patients are identified as ‘no risk for malnutrition’ or ‘at risk of malnutrition’ on the NRS 2002, as described in detail elsewhere25.The NRS 2002 consists of a prescreen and a final screen. The prescreen includes four questions: I) ‘is BMI < 20.5 kg/m²?’, II) ‘has the patient lost weight within the last three months?’; III) ‘has the patient had a reduced dietary intake in the last week?’; and IV) ‘is the patient severely ill ?’, which have yes/no answers44. Patients are identified as at ‘low risk’, ‘medium risk’, or ‘high risk of malnutrition’ on the MUST, as described in detail elsewhere44.The MUST addresses three items: ‘body mass index’, ‘undesired weight loss between the last three to six-months’, and ‘acute disease causing fasting for more than five days’45. Statistically significant differences were identified at p < 0.05.Data were analysed in SPSS version 24 . Results are presented in the text and/or tables as mean ± standard deviation and/or percentages. Groups of patients, i.e., patients who had died/survived to follow-up at six months and one year, were compared using the Mann-Whitney U-test or the chi-squared test as appropriate. The six-month and one-year mortality rates for the different categories defined by the malnutrition screening instruments were analysed by Kaplan Meier analysis. The log-rank test was used to differentiate the predictive power of the different categories defined by the malnutrition screening instruments in relation to six-month and one-year mortality. Cox proportional hazard models were run to analyse the hazard ratios for six-month and one-year mortality of each increment in category on the various malnutrition screening instruments. Hazard ratios (HRs) for six-month and one-year mortality of the various malnutrition screening instruments were adjusted for potential confounding factors , and Barthel-Index score) and were considered both separately and together. Receivers operating characteristic (ROC) curves were used to estimate the area under the curves (AUC) for the various malnutrition screening tools, analysed as categorical variables, in relation to six-month and one-year mortality. The abilities of the various malnutrition screening tools to predict six-month and one-year mortality were compared by comparing the AUC of the malnutrition screening tools for six-month and one-year mortality using the Hanley and McNeil method2, mean weight was 66.6 ± 22.3 kg, and mean height was 158.0 ± 30.7 cm. A total of 51.9% of these patients described weight loss of >4.5 kg in the last year, 76.2% had had falls, 36.0% had heart failure, 10.5% had myocardial infarcts, 21.8% had stroke, 17.6% had cancer, 34.3% had diabetes mellitus, 25.9% had pulmonary disease, 41.8% had kidney disease, 11.3% had constipation, 61.9% had urinary incontinence or bladder catheter, 16.3% had bowel incontinence, and 96.2% received more than 5 medications. Mean MMSE was 25.6 ± 3.9 points, GDS was 4.4 ± 2.7 points, Barthel Index was 68.8 ± 19.6 points, CIRS-G was 18.4 ± 5.4, 77.4% were frail on the Frailty Phenotype, and 65.3% had TUG > 19 sec or were unable to perform the TUG. Follow-up data at one year were not available for four persons, including the individual from whom follow-up data were not available at six months. Of the remaining 236 patients for whom follow-up data were available at one-year, 49 individuals (20.8%) had died. These 236 patients included 67.8% females, had an average age of 82.5 ± 6.1 years, a mean BMI of 24.9 ± 8.0 kg/m², a mean weight of 66.7 ± 22 kg, and a mean height of 158.0 ± 30.8 cm. A total of 51.3% of these 236 patients described weight loss >4.5 kg in the last year, 76.3% had had falls, 35.6% had heart failure, 10.2% had myocardial infarcts, 21.6% had stroke, 17.4% had cancer, 34.7% had diabetes mellitus, 25.8% had pulmonary disease, 41.9% had kidney disease, 11.4% had constipation, 61.4% had urinary incontinence or were catheterised, 15.3% had bowel incontinence, and 96.2% took more than five medications. Mean MMSE was 25.6 ± 4.0 points, GDS was 4.4 ± 2.7 points, Barthel Index was 67.2 ± 19.4 points, CIRS-G was 18.4 ± 5.4, 77.1% were frail on the Frailty Phenotype, and 64.8% of the patients had a TUG > 19 sec or were unable to perform the TUG. A total of 16.1% of these 236 patients were institutionalised.The study cohort consisted of 240 patients hospitalised on geriatric wards . The clinical characteristics of the overall study cohort and individuals stratified by death/survival at six months and one year are shown in Table Assessment on both the modified MNA-SF and standard MNA-SF categorised 98.8% of the 240 study participants as “malnourished or at risk of malnutrition”. The proportions classified as malnourished and at risk for malnutrition differed: the modified MNA-SF identified 51.7% of individuals as “malnourished” and 47.1% as “at risk of malnutrition,” whereas the standard MNA-SF identified 56.7% of study participants as “malnourished” and 42.0% as “at risk of malnutrition.” The modified MNA-LF and standard MNA-LF categorised 97.5% and 97.9% patients as “malnourished or at risk of malnutrition”. The modified MNA-LF identified 37.5% as “malnourished” and 60.0% as “at risk of malnutrition”, while the standard MNA-LF classified 37.1% as “malnourished” and 60.8% as “at risk of malnutrition”.p < 0.05) (see Table p ≥ 0.05), though one-year mortality was higher in patients with adverse malnutrition status than in individuals with better nutritional status . Mortality rates showed no statistically significant differences between patients with different malnutrition status on the MUST for either six-month and/or one-year mortality  32. In contrast, a Taiwanese prospective cohort study of 2872 participants aged ≥ 65 years demonstrated that the MNA-SF was comparable or even marginally superior than the MNA-LF in predicting four-year mortality33. However, and of relevance in the busy clinical setting at geriatric wards, it is of note that administration of the modified MNA-LF is more time-consuming than the modified MNA-SF.A further major finding of this study is that the modified MNA-LF displayed better prediction of six-month and one-year mortality than the standard MNA-LF. This indicates that the modification of the two items - ‘mobility’ and ‘neuropsychological problems’ - in the modified MNA-LF may improve the ability of this malnutrition screening instrument to predict mortality in hospitalised geriatric patients. Moreover, with the exception of the modified MNA-SF , the modified MNA-LF yielded better predictions of six-month and one-year mortality than all other malnutrition screening instruments evaluated here. Similarly, Kiesswetter et al. previously reported that the MNA had greater predictive power than the NRS 2002 and MUST for survival in a prospective longitudinal analysis with a follow-up period of up to one year in 200 nursing-home residents34. Koren-Hakim et al. reported that the MNA-SF, but not the NRS 2002 or MUST, was able to predict mortality during a 36-month follow-up period with measurement of nutritional status in 215 older people undergoing hip fracture surgery, with a mean age of 83.9 ± 6.09 years35. Donini et al. reported that the MNA-LF and MNA-SF had higher predictive value for mortality than the NRS 2002 and MUST in a study cohort of 246 institutionalised participants aged 76.5 ± 11 years32. In contrast, a Brazilian study of a cohort of 705 participants, which included 169 geriatric patients aged ≥ 65 years, found that the abilities of the MNA-SF and NRS 2002 to predict complications, prolonged hospital stay, and mortality in older patients were comparable36. Holst et al. studied a cohort of 233 hospitalised geriatric patients and found that neither the MNA-LF, nor the NRS 2002, nor the MUST was able to predict one-year mortality46.In the present study, all the different MNA versions, i.e., the modified MNA-SF, modified MNA-LF, standard MNA-SF, and standard MNA-LF, were able to predict six-month and one-year mortality. In contrast, in this study the NRS 2002 and the MUST, did not reveal such an ability to predict six-month and or one-year mortality. Diekmann et al. reported studies of a cohort of 437 patients with a median age of 81.0 (74.5–84.0) years in an acute geriatric ward showing that malnutrition as defined by the European Society for Clinical Nutrition and Metabolism (ESPEN) criteria, but not by the MNA, was an independent predictor of three-year mortality47. In a study of 131 patients aged ≥ 60 years, the Geriatric Nutritional Risk Index (GNRI) predicted three- and six-month mortality independent of potential confounding factors, including age, sex, and cancer48. In a retrospective study of 1170 participants aged ≥ 60 years living in a community dwelling setting, malnutrition was found to be an independent risk factor for seven-year mortality49. A prospective cohort study of 164 emergency department patients aged ≥ 75 years showed that malnutrition was a strong independent risk factor for short-term mortality in geriatric patients50. Liu et al. reported a prospective observational study including diabetes patients aged ≥ 65 years with a 2.8-year follow-up which showed that malnutrition was an independent predictor of mortality51. Another study of a cohort of 1306 participants aged ≥ 75 years showed that malnutrition as assessed on the MNA-SF was independently related to six-month mortality52. Correia et al. also noted that malnutrition was an independent risk factor of mortality16. Lilamand et al. reported that the MNA-SF predicted one-year mortality independently of potential confounding factors in a nursing home setting in a study of 773 older people with a mean age of 86.2 ± 7.5 years53. It should be noted that the setting on hospital geriatric wards may not be comparable to the setting in the community/home or nursing homes.Among the different malnutrition screening instruments evaluated here, only the modified MNA-LF displayed independent predictive value for six-month and one-year mortality after adjustment of the analysis for potential confounding factors, including age, sex, frailty status, comorbidity, and ADL disability burden. There might be an interaction between age, sex, frailty status, comorbidity and ADL disability on the one hand and malnutrition and the risk of malnutrition on the other in hospitalised geriatric patients with, along with each of these conditions, a potential impact on the mortality risk. Among the malnutrition screening instruments evaluated here, the modified MNA-LF may be the most objective and complex, and probably allows the predictive value of malnutrition and risk for malnutrition to be discriminated from the contributions of age, sex, frailty status, comorbidity and ADL disability to prediction of mortality. Nevertheless, with the exception of the NRS 2002, all the malnutrition screening instruments evaluated here had predictive power for six-month and one-year mortality after adjustment of the analysis for age and sex only. In line with the findings reported here, several other authors have reported that malnutrition has independent predictive value for mortality in different settings. Jiang 55. Dent et al. found a six-month mortality rate of 16% in 172 hospitalised geriatric patients56. A study by Drame et al. of 1306 acute hospitalised study participants aged ≥ 75 years found that a six-month mortality rate of 24.4%52. One of our own earlier studies yielded a one-year mortality of 20.3% in 304 hospitalised geriatric patients58. A study of 2033 geriatric in-patients recorded a one-year mortality of 24.9%59. Holst et al. reported a one-year mortality rate of 27% in 233 hospitalised geriatric patients46.The six-month and one-year mortality rates in the present study in hospitalised geriatric patients-11.7% and 20.8% - are in line with mortality rates reported from other studies by ourselves and other authors in hospitalised geriatric patients. We have previously reported that 15.4% of 306 study participants died during the first six-month post-baseline examination in a cohort of hospitalised geriatric patients28.The mortality rates of individuals with malnutrition or at risk for malnutrition as evaluated by the modified MNA-SF and modified MNA-LF in the present study are similar to those in our previous studies using the malnutrition screening instruments identified above in hospitalised geriatric patients60.This study has a number of strengths. It is the first prospective longitudinal analysis dissecting the ability of the modified MNA (modified MNA-SF and modified MNA-LF) as compared with the standard MNA-SF, MNA-LF, NRS 2002 and MUST to predict six-month and one-year mortality. Furthermore, we adjusted the analysis for potential confounding factors to analyse whether malnutrition as classified by the different screening instruments has independent predictive value for six-month and/or one-year mortality. Follow-up data were obtained at two time points (six months and one year post-baseline). The rate of loss to follow-up was very low. Only 0.4% of the 240 study participants could not be reached at the six-months and 1.7% at one year. Previous studies showed comparable percentages of loss to follow up at six months or one yearOn average it takes 5–10 minutes to perform the MNA-SF and 10–20 minutes to perform the MNA-LF depending on individual patient characteristics. In the case the MMSE, GDS and Barthel Index are routinely applied in terms of the comprehensive geriatric assessment at the geriatric ward the assessor can save approximately up to five minutes by using the score of the MMSE, GDS and mobility item of the Barthel Index to operationalize the modified MNA-SF and modified MNA-LF. Clearly, in the case the MMSE, GDS and Barthel Index are not routinely applied in terms of the comprehensive geriatric assessment at the geriatric ward it would take longer to operationalize the modified MNA-SF and modified MNA-LF in comparison to the standard MNA-SF and standard MNA-LF as the MMSE, GDS and mobility item of the Barthel Index have to be performed in addition.The study also has some limitations. The caring geriatric teams were not blinded to the results of all these scores of the comprehensive geriatric assessment tools at the time of admission and may have altered their care plans on the basis of these data. This may have altered mortality risk potentially confounding the observed results. It was a single-centre study of hospitalised patients on geriatric wards. It may therefore be misleading to extrapolate the findings to the geriatric wards of any other hospital or other clinical setting. All study participants were Caucasian, so the study results may not be transferable to other ethnic groups.In conclusion, the modified MNA-SF and standard MNA-SF showed comparable abilities to predict six-month and one-year mortality in hospitalised geriatric patients. With the exception of the modified MNA-SF , the modified MNA-LF gave better predictions of six-month and one-year mortality than all the other malnutrition screening instruments evaluated here. The various MNA versions evaluated in this study, i.e., the modified MNA-SF and MNA-LF and the standard MNA-SF and MNA-LF, and the MUST, but not the NRS 2002, were able to predict six-month and one-year mortality after adjustment of the analysis for age and sex. The modified MNA-LF, but none of the other malnutrition screening instruments evaluated here, had independent predictive value for six-month and one-year mortality after adjustment of the analysis for age, sex, frailty status, comorbidity and ADL disability burden. The modified MNA-SF and modified MNA-LF therefore emerge as potentially valuable tools for predicting mortality in hospitalised geriatric patients in whom the Barthel Index, MMSE and GDS are routinely applied in the course of standardised comprehensive geriatric assessment.