The recommended interval between updates for systematic reviews included in The Cochrane Library is 2 years. However, it is unclear whether this interval is always appropriate. Whereas excessive updating wastes time and resources, insufficient updating allows out-of-date or incomplete evidence to guide clinical decision-making. We set out to determine, for Cochrane pregnancy and childbirth reviews, the frequency of updates, factors associated with updating, and whether updating frequency was appropriate.
Cochrane pregnancy and childbirth reviews published in Issue 3, 2007 of the Cochrane Database of Systematic Reviews were retrieved, and data were collected from their original and updated versions. Quantitative changes were determined for one of the primary outcomes (mortality, or the outcome of greatest clinical significance). Potential factors associated with time to update were assessed using the Cox proportional hazard model. Among the 101 reviews in our final sample, the median time before the first update was 3.3 years (95% CI 2.7–3.8). Only 32.7% had been updated within the recommended interval of 2 years. In 75.3% (76/101), a median of 3 new trials with a median of 576 additional participants were included in the updated versions. There were quantitative changes in 71% of the reviews that included new trials (54/76): the median change in effect size was 18.2%, and the median change in 95% CI width was 30.8%. Statistical significance changed in 18.5% (10/54) of these reviews, but conclusions were revised in only 3.7% (2/54). A shorter time to update was associated with the same original review team at updating.
Most reviews were updated less frequently than recommended by Cochrane policy, but few updates had revised conclusions. Prescribed time to update should be reconsidered to support improved decision-making while making efficient use of limited resources.
A problem in biosurveillance is how frequently to update controlled vocabularies
that identify various data elements such as laboratory tests
and over-the-counter healthcare products. More frequent updates improve
completeness of data captured over time, but introduction of new codes
into a surveillance system may cause false alarms when codes are aggregated
into analytic categories. We studied the effect of three policies
for updating UPCs, the controlled vocabulary for over-the-counter
healthcare products used by the National Retail Data Monitor.
To compare different policies for updating, we analyzed historical data
from two cities for the 18 product categories of the National Retail
Data Monitor under annual, quarterly, or monthly UPC update policies. We
measured the effect on data completeness and false alarm rate.
We found that the monthly update policy had the highest data completeness
and led to the fewest number of additional false alarms.
Overall, monthly updating of UPCs was the superior policy.
We compared two attentional executive processes: updating, which involved attending to a perceptually-present stimulus, and refreshing, which involved attending to a mentally active representation of a stimulus no longer perceptually present. In separate blocks, participants either replaced a word being held in working memory with a different word (update), or they thought back to a just previously seen word that was no longer perceptually present (refresh). Bilateral areas of frontal cortex, supplementary motor area, and parietal cortex were similarly active for both updating and refreshing, suggesting a common network of areas are recruited to bring information to the current focus of attention. In a direct comparison of update and refresh, regions more active for update than refresh included regions primarily in right frontal cortex, as well as bilateral posterior visual processing regions. Regions more active for refresh than update included regions primarily in left dorsolateral frontal and left temporal cortex and bilateral inferior frontal cortex. These findings help account for the similarity in areas activated across different cognitive tasks and may help specify the particular executive processes engaged in more complex tasks.
working memory; update; refresh; maintenance
Establishing what information is actively maintained in working memory (WM) and how it is represented and controlled is essential to understanding how such information guides future behavior. WM has traditionally been investigated in terms of the maintenance of stimulus-specific information, such as locations or words. More recently, investigators have emphasized the importance of rules that establish relationships between those stimuli and the pending response. The current study used a mental arithmetic task with fMRI to test whether updating of numbers (i.e. stimuli) and updating of mathematical operations (i.e. rules) in WM relies on the same neural system. Results indicate that while a common network is activated by both types of updating, rule updating preferentially activates prefrontal cortex while number updating preferentially activates parietal cortex. The results suggest that both numbers and rules are maintained in WM, but they are different types of information that are controlled independently.
In this paper, we propose a new blind multichannel adaptive filtering scheme, which incorporates a partial-updating mechanism in the error gradient of the update equation. The proposed blind processing algorithm operates in the time-domain by updating only a selected portion of the adaptive filters. The algorithm steers all computational resources to filter taps having the largest magnitude gradient components on the error surface. Therefore, it requires only a small number of updates at each iteration and can substantially minimize overall computational complexity. Numerical experiments carried out in realistic blind identification scenarios indicate that the performance of the proposed algorithm is comparable to the performance of its full-update counterpart, but with the added benefit of a highly reduced computational complexity.
Systematic Reviews (SRs) are an essential part of evidence-based medicine, providing support for clinical practice and policy on a wide range of medical topics. However, producing SRs is resource-intensive, and progress in the research they review leads to SRs becoming outdated, requiring updates. Although the question of how and when to update SRs has been studied, the best method for determining when to update is still unclear, necessitating further research.
In this work we study the potential impact of a machine learning-based automated system for providing alerts when new publications become available within an SR topic. Some of these new publications are especially important, as they report findings that are more likely to initiate a review update. To this end, we have designed a classification algorithm to identify articles that are likely to be included in an SR update, along with an annotation scheme designed to identify the most important publications in a topic area. Using an SR database containing over 70,000 articles, we annotated articles from 9 topics that had received an update during the study period. The algorithm was then evaluated in terms of the overall correct and incorrect alert rate for publications meeting the topic inclusion criteria, as well as in terms of its ability to identify important, update-motivating publications in a topic area.
Our initial approach, based on our previous work in topic-specific SR publication classification, identifies over 70% of the most important new publications, while maintaining a low overall alert rate.
We performed an initial analysis of the opportunities and challenges in aiding the SR update planning process with an informatics-based machine learning approach. Alerts could be a useful tool in the planning, scheduling, and allocation of resources for SR updates, providing an improvement in timeliness and coverage for the large number of medical topics needing SRs. While the performance of this initial method is not perfect, it could be a useful supplement to current approaches to scheduling an SR update. Approaches specifically targeting the types of important publications identified by this work are likely to improve results.
Multi-target therapeutics has been shown to be effective for treating complex diseases, and currently, it is a common practice to combine multiple drugs to treat such diseases to optimize the therapeutic outcomes. However, considering the huge number of possible ways to mix multiple drugs at different concentrations, it is practically difficult to identify the optimal drug combination through exhaustive testing.
In this paper, we propose a novel stochastic search algorithm, called the adaptive reference update (ARU) algorithm, that can provide an efficient and systematic way for optimizing multi-drug cocktails. The ARU algorithm iteratively updates the drug combination to improve its response, where the update is made by comparing the response of the current combination with that of a reference combination, based on which the beneficial update direction is predicted. The reference combination is continuously updated based on the drug response values observed in the past, thereby adapting to the underlying drug response function. To demonstrate the effectiveness of the proposed algorithm, we evaluated its performance based on various multi-dimensional drug functions and compared it with existing algorithms.
Simulation results show that the ARU algorithm significantly outperforms existing stochastic search algorithms, including the Gur Game algorithm. In fact, the ARU algorithm can more effectively identify potent drug combinations and it typically spends fewer iterations for finding effective combinations. Furthermore, the ARU algorithm is robust to random fluctuations and noise in the measured drug response, which makes the algorithm well-suited for practical drug optimization applications.
The ability to update associative memory is an important aspect of episodic memory and a critical skill for social adaptation. Previous research with younger adults suggests that emotional arousal alters brain mechanisms underlying memory updating; however, it is unclear whether this applies to older adults. Given that the ability to update associative information declines with age, it is important to understand how emotion modulates the brain processes underlying memory updating in older adults. The current study investigated this question using reversal learning tasks, where younger and older participants (age ranges 19–35 and 61–78, respectively) learn a stimulus–outcome association and then update their response when contingencies change. We found that younger and older adults showed similar patterns of activation in the frontopolar OFC and the amygdala during emotional reversal learning. In contrast, when reversal learning did not involve emotion, older adults showed greater parietal cortex activity than did younger adults. Thus, younger and older adults show more similarities in brain activity during memory updating involving emotional stimuli than during memory updating not involving emotional stimuli.
aging; emotion; memory updating; functional MRI; reversal learning; associative memory
An American Society of Clinical Oncology (ASCO) focused update updates a single recommendation (or subset of recommendations) in advance of a regularly scheduled guideline update. This document updates one recommendation of the ASCO Guideline Update on Chemotherapy for Stage IV Non–Small-Cell Lung Cancer (NSCLC) regarding switch maintenance chemotherapy.
Recent results from phase III clinical trials have demonstrated that in patients with stage IV NSCLC who have received four cycles of first-line chemotherapy and whose disease has not progressed, an immediate switch to alternative, single-agent chemotherapy can extend progression-free survival and, in some cases, overall survival. Because of limitations in the data, delayed treatment with a second-line agent after disease progression is also acceptable.
Seven randomized controlled trials of carboxyaminoimidazole, docetaxel, erlotinib, gefitinib, gemcitabine, and pemetrexed have evaluated outcomes in patients who received an immediate, non–cross resistant alternative therapy (switch maintenance) after first-line therapy.
In patients with stage IV NSCLC, first-line cytotoxic chemotherapy should be stopped at disease progression or after four cycles in patients whose disease is stable but not responding to treatment. Two-drug cytotoxic combinations should be administered for no more than six cycles. For those with stable disease or response after four cycles, immediate treatment with an alternative, single-agent chemotherapy such as pemetrexed in patients with nonsquamous histology, docetaxel in unselected patients, or erlotinib in unselected patients may be considered. Limitations of this data are such that a break from cytotoxic chemotherapy after a fixed course is also acceptable, with initiation of second-line chemotherapy at disease progression.
The ability to regulate emotions is a critical component of healthy emotional functioning. Therefore, it is important to determine factors that contribute to the efficacy of emotion regulation. The present article examined whether the ability to update emotional information in working memory is a predictor of the efficacy of rumination and reappraisal on affective experience both at the trait level (Study 1) and in daily life (Study 2). In both studies, results revealed that the relationship between use of reappraisal and high arousal negative emotions was moderated by updating ability. Specifically, use of reappraisal was associated with decreased high arousal negative emotions for participants with high updating ability, while no significant relationship was found for those with low updating ability. In addition, both studies also revealed that the relationship between rumination and high arousal negative emotions was moderated by updating ability. In general, use of rumination was associated with elevated high arousal negative emotions. However, this relationship was blunted for participants with high updating ability. That is, use of rumination was associated with less elevated high arousal negative emotions for participants with high updating ability. These results identify the ability to update emotional information in working memory as a crucial process modulating the efficacy of emotion regulation efforts.
A study of the continuing medical education of practising physicians in Nova Scotia, New Brunswick and Prince Edward Island was conducted in 1979-80 by means of a mailed questionnaire. Most of the responding physicians ranked reading as the method most used to update knowledge (73.3%) and skills (55.7%); courses and informal instruction were in second place for updating knowledge and skills respectively, ranked most used by 9.3% and 17.1%. With unlimited time and funds 38.0% and 20.5% of the physicians would still most prefer to read to update knowledge and skills respectively. However, 35.2% would most prefer to attend courses to update knowledge and 26.9% and 24.8% would most prefer to do clinical traineeships or attend courses to update skills. When asked what method of learning had provided the most impetus to change their ways of managing patients, 42.5% chose reading, 18.8% courses, 14.6% informal discussions and 12.4% formal consultations. Appropriate developments would therefore include improving methods of providing physicians with the best information available when it is needed, removing roadblocks to participation in continuing education programs, matching individual learning styles to programs of learning, training physicians as peer tutors and helping consultants become better instructors through written consultations.
Working memory (WM) is the active maintenance of currently relevant information so that it is available for use. A crucial component of WM is the ability to update the contents when new information becomes more relevant than previously maintained information. New information can come from different sources, including from sensory stimuli (SS) or from long-term memory (LTM). Updating WM may involve a single neural system regardless of source, distinct systems for each source, or a common network with additional regions involved specifically in sensory or LTM processes. The current series of experiments indicates that a single fronto-parietal network (including Supplementary Motor Area, Parietal, Left Inferior Frontal Junction, Middle Frontal Gyrus) is active in updating WM regardless of the source of information. Bilateral Cuneus was more active during updating WM from LTM than updating from SS, but the activity in this region was attributable to recalling information from LTM regardless of whether that information was to be entered into WM for future use or not. No regions were found to be more active during updating from SS than updating from LTM. Functional connectivity analysis revealed that different regions within this common update network were differentially more correlated with visual processing regions when participants updated from SS, and more correlated with LTM processing regions when participants updated from the contents of LTM. These results suggest a single neural mechanism is responsible for controlling the contents of WM regardless of whether that information originates from a sensory stimulus or from LTM. This network of regions involved in updating WM interacts with the rest of the brain differently depending on the source of newly-relevant information.
fMRI; frontal; attention; cognitive control; parietal; working memory; long term memory
Each time the eyes move, the visual system must adjust internal representations to account for the accompanying shift in the retinal image. In the lateral intraparietal cortex (LIP), neurons update the spatial representations of salient stimuli when the eyes move. In previous experiments, we found that split-brain monkeys were impaired on double-step saccade sequences that required updating across visual hemifields, as compared to within hemifield (Berman et al. 2005; Heiser et al. 2005). Here we describe a subsequent experiment to characterize the relationship between behavioral performance and neural activity in LIP in the split-brain monkey. We recorded from single LIP neurons while split-brain and intact monkeys performed two conditions of the double-step saccade task: one required across-hemifield updating and the other within-hemifield updating. We found that, despite extensive experience with the task, the split-brain monkeys were significantly more accurate for within-hemifield as compared to across-hemifield sequences. In parallel, we found that population activity in LIP of the split-brain monkeys was significantly stronger for within-hemifield as compared to across-hemifield conditions of the double-step task. In contrast, in the normal monkey, both the average behavioral performance and population activity showed no bias toward the within-hemifield condition. Finally, we found that the difference between within-hemifield and across-hemifield performance in the split-brain monkeys was reflected at the level of single neuron activity in LIP. These findings indicate that remapping activity in area LIP is present in the split-brain monkey for the double-step task and co-varies with spatial behavior on within-hemifield compared to across-hemifield sequences.
The role of environmental geometry in maintaining spatial orientation was measured in immersive virtual reality using a spatial updating task (requiring maintenance of orientation during locomotion) within rooms varying in rotational symmetry (the number of room orientations providing the same perspective). Spatial updating was equally good in trapezoidal, rectangular and square rooms (1-fold, two-fold and four-fold rotationally symmetric, respectively) but worse in a circular room (∞-fold rotationally symmetric). This contrasts with reorientation performance, which was incrementally impaired by increasing rotational symmetry. Spatial updating performance in a shape-changing room (containing visible corners and flat surfaces, but changing its shape over time) was no better than performance in a circular room, indicating that superior spatial updating performance in angular environments was due to remembered room shape, rather than improved self-motion perception in the presence of visible corners and flat surfaces.
This case study examined the recent withdrawal of valdecoxib to determine the timeliness of updates in commonly used information sources used by healthcare professionals. The method included assembling a purposive sample of 15 drug reference and warning systems that were then systematically monitored for several months after the withdrawal of valdecoxib to determine the time to update this information. These information sources were classified and described qualitatively. A time to diffusion curve was plotted and the average number of days to report the drug withdrawal or update reference databases was calculated. Only 2 of 15 information systems reported the drug withdrawal on the actual date of the FDA announcement. Institutional electronic textbooks took an average of 109.8 days (±14 days) to report the withdrawal. In addition, one pharma-sponsored dissemination source (Peerview Press) had not updated their information as of this publication.
OBJECTIVE: Evaluate the effects of long-term maintenance activities on existing portions of a large internal medicine knowledge base. DESIGN: Five physicians who were not among the original developers of the knowledge base independently updated a total of 15 QMR disease profiles; each updated submission was modified by a review of group serving as the "gold standard, " and the pre- and post-study versions of each updated disease profile were compared. MEASUREMENTS: Numbers and types of changes, defined as any difference between the original version and the final version of a disease profile; reason for each change; and bibliographic references cited by the physicians as supporting evidence. RESULTS: A total of 16% of all entries were modified by the updating process; up to 95% of the entries in a disease profile were affected. The two most common modifications were changes to the frequency of an entry, and creation of a new entry. Laboratory findings were affected much more often than were history, symptom, or physical exam findings. The dominant reason for changes was appearance of new evidence in the medical literature. The literature cited ranged from 1944 to the present. CONCLUSIONS: This study provides an evaluation of the rate of change within the QMR medical knowledge base due to long-term maintenance. The results show that this is a demanding activity that may profoundly affect certain portions of a knowledge base, and that different types of knowledge (e.g., simple laboratory vs expensive or invasive laboratory findings) are affected by the process in different ways.
Critical to low-vision navigation are the abilities to recover scale and update a 3-D representation of space. In order to investigate whether these abilities are present under low-vision conditions, we employed the triangulation task of eyes-closed indirect walking to previously viewed targets on the ground. This task requires that the observer continually update the location of the target without any further visual feedback of his/her movement or the target's location. Normally sighted participants were tested monocularly in a degraded vision condition and a normal vision condition on both indirect and direct walking to previously viewed targets. Surprisingly, we found no difference in walked distances between the degraded and normal vision conditions. Our results provide evidence for intact spatial updating even under severely degraded vision conditions, indicating that participants can recover scale and update a 3-D representation of space under simulated low vision.
Systematic reviews (SRs) should be up to date to maintain their importance in informing healthcare policy and practice. However, little guidance is available about when and how to update SRs. Moreover, the updating policies and practices of organizations that commission or produce SRs are unclear.
The objective was to describe the updating practices and policies of agencies that sponsor or conduct SRs. An Internet-based survey was administered to a purposive non-random sample of 195 healthcare organizations within the international SR community. Survey results were analyzed using descriptive statistics. The completed response rate was 58% (n = 114) from across 26 countries with 70% (75/107) of participants identified as producers of SRs. Among responders, 79% (84/107) characterized the importance of updating as high or very-high and 57% (60/106) of organizations reported to have a formal policy for updating. However, only 29% (35/106) of organizations made reference to a written policy document. Several groups (62/105; 59%) reported updating practices as irregular, and over half (53/103) of organizational respondents estimated that more than 50% of their respective SRs were likely out of date. Authors of the original SR (42/106; 40%) were most often deemed responsible for ensuring SRs were current. Barriers to updating included resource constraints, reviewer motivation, lack of academic credit, and limited publishing formats. Most respondents (70/100; 70%) indicated that they supported centralization of updating efforts across institutions or agencies. Furthermore, 84% (83/99) of respondents indicated they favoured the development of a central registry of SRs, analogous to efforts within the clinical trials community.
Most organizations that sponsor and/or carry out SRs consider updating important. Despite this recognition, updating practices are not regular, and many organizations lack a formal written policy for updating SRs. This research marks the first baseline data available on updating from an organizational perspective.
Incorporating new information into a knowledge base is an important problem which has been widely investigated. In this paper, we study this problem in a formal framework for reasoning about actions and change. In this framework, action domains are described in an action language whose semantics is based on the notion of causality. Unlike the formalisms considered in the related work, this language allows straightforward representation of non-deterministic effects and indirect effects of (possibly concurrent) actions, as well as state constraints; therefore, the updates can be more general than elementary statements. The expressivity of this formalism allows us to study the update of an action domain description with a more general approach compared to related work. First of all, we consider the update of an action description with respect to further criteria, for instance, by ensuring that the updated description entails some observations, assertions, or general domain properties that constitute further constraints that are not expressible in an action description in general. Moreover, our framework allows us to discriminate amongst alternative updates of action domain descriptions and to single out a most preferable one, based on a given preference relation possibly dependent on the specified criteria. We study semantic and computational aspects of the update problem, and establish basic properties of updates as well as a decomposition theorem that gives rise to a divide and conquer approach to updating action descriptions under certain conditions. Furthermore, we study the computational complexity of decision problems around computing solutions, both for the generic setting and for two particular preference relations, viz. set-inclusion and weight-based preference. While deciding the existence of solutions and recognizing solutions are PSPACE-complete problems in general, the problems fall back into the polynomial hierarchy under restrictions on the additional constraints. We finally discuss methods to compute solutions and approximate solutions (which disregard preference). Our results provide a semantic and computational basis for developing systems that incorporate new information into action domain descriptions in an action language, in the presence of additional constraints.
Knowledge representation; Reasoning about actions and change; Theory change; Action languages; Preference-based semantics
As we learn new information about the social and moral behaviors of other people, we form and update character judgments of them, and this can profoundly influence how we regard and act towards others. In the study reported here, we capitalized on two interesting neurological patient populations where this process of complex “moral updating” may go awry: patients with bilateral damage to ventromedial prefrontal cortex (vmPFC) and patients with bilateral damage to hippocampus (HC). We predicted that vmPFC patients, who have impaired emotion processing, would exhibit reduced moral updating, and we also investigated how moral updating might be affected by severe declarative memory impairment in HC patients. The vmPFC, HC, and brain-damaged comparison (BDC) participants made moral judgments about unfamiliar persons before and after exposure to social scenarios depicting the persons engaged in morally good, bad, or neutral behaviors. In line with our prediction, the vmPFC group showed the least amount of change in moral judgments, and interestingly, the HC group showed the most amount of change. These results suggest that the vmPFC and hippocampus play critical but complementary roles in updating moral character judgments about others: the vmPFC may attribute emotional salience to moral information, whereas the hippocampus may provide necessary contextual information from which to make appropriate character judgments.
ventromedial; hippocampus; moral; social cognition; memory
A new generalized optimum strapdown algorithm with coning and sculling compensation is presented, in which the position, velocity and attitude updating operations are carried out based on the single-speed structure in which all computations are executed at a single updating rate that is sufficiently high to accurately account for high frequency angular rate and acceleration rectification effects. Different from existing algorithms, the updating rates of the coning and sculling compensations are unrelated with the number of the gyro incremental angle samples and the number of the accelerometer incremental velocity samples. When the output sampling rate of inertial sensors remains constant, this algorithm allows increasing the updating rate of the coning and sculling compensation, yet with more numbers of gyro incremental angle and accelerometer incremental velocity in order to improve the accuracy of system. Then, in order to implement the new strapdown algorithm in a single FPGA chip, the parallelization of the algorithm is designed and its computational complexity is analyzed. The performance of the proposed parallel strapdown algorithm is tested on the Xilinx ISE 12.3 software platform and the FPGA device XC6VLX550T hardware platform on the basis of some fighter data. It is shown that this parallel strapdown algorithm on the FPGA platform can greatly decrease the execution time of algorithm to meet the real-time and high precision requirements of system on the high dynamic environment, relative to the existing implemented on the DSP platform.
strapdown algorithm; coning and sculling compensation; parallelization design; computation complexity; FPGA
The genome-scale metabolic network reconstruction of Escherichia coli, in use since 2000, is thoroughly updated based on the recent findings in the literature and new experiments. The improved reconstruction accounts for 1366 genes and can be used for constraint-based modeling of metabolic phenotypes.
The initial genome-scale reconstruction of the metabolic network of Escherichia coli K-12 MG1655 was assembled in 2000. It has been updated and periodically released since then based on new and curated genomic and biochemical knowledge. An update has now been built, named iJO1366, which accounts for 1366 genes, 2251 metabolic reactions, and 1136 unique metabolites. iJO1366 was (1) updated in part using a new experimental screen of 1075 gene knockout strains, illuminating cases where alternative pathways and isozymes are yet to be discovered, (2) compared with its predecessor and to experimental data sets to confirm that it continues to make accurate phenotypic predictions of growth on different substrates and for gene knockout strains, and (3) mapped to the genomes of all available sequenced E. coli strains, including pathogens, leading to the identification of hundreds of unannotated genes in these organisms. Like its predecessors, the iJO1366 reconstruction is expected to be widely deployed for studying the systems biology of E. coli and for metabolic engineering applications.
constraint-based modeling; Escherichia coli; metabolic network reconstruction; metabolism; phenotypic screening
Unrealistic optimism is a pervasive human trait influencing domains ranging from personal relationships to politics and finance. How people maintain unrealistic optimism, despite frequently encountering information that challenges those biased beliefs, is unknown. Here, we provide an explanation. Specifically, we show a striking asymmetry, whereby people updated their beliefs more in response to information that was better than expected compared to information that was worse. This selectivity was mediated by a relative failure to code for errors that should reduce optimism. Distinct regions of the prefrontal cortex tracked estimation errors when those called for positive update, both in highly optimistic and low optimistic individuals. However, highly optimistic individuals exhibited reduced tracking of estimation errors that called for negative update within right inferior prefrontal gyrus. These findings show that optimism is tied to a selective update failure, and diminished neural coding, of undesirable information regarding the future.
Background. When planning to use a validated prediction model in new patients, adequate performance is not guaranteed. For example, changes in clinical practice over time or a different case mix than the original validation population may result in inaccurate risk predictions. Objective. To demonstrate how clinical information can direct updating a prediction model and development of a strategy for handling missing predictor values in clinical practice. Methods. A previously derived and validated prediction model for postoperative nausea and vomiting was updated using a data set of 1847 patients. The update consisted of 1) changing the definition of an existing predictor, 2) reestimating the regression coefficient of a predictor, and 3) adding a new predictor to the model. The updated model was then validated in a new series of 3822 patients. Furthermore, several imputation models were considered to handle real-time missing values, so that possible missing predictor values could be anticipated during actual model use. Results. Differences in clinical practice between our local population and the original derivation population guided the update strategy of the prediction model. The predictive accuracy of the updated model was better (c statistic, 0.68; calibration slope, 1.0) than the original model (c statistic, 0.62; calibration slope, 0.57). Inclusion of logistical variables in the imputation models, besides observed patient characteristics, contributed to a strategy to deal with missing predictor values at the time of risk calculation. Conclusions. Extensive knowledge of local, clinical processes provides crucial information to guide the process of adapting a prediction model to new clinical practices.
clinical prediction rules; methodology; decision rules; provider decision making; statistical methods