Acute kidney injury: electronic alerts in primary care - findings from a large population cohort

Background: Electronic reporting of AKI has been used to aid early AKI recognition although its relevance to CA-AKI and primary care has not been described. Aims: We described the characteristics and clinical outcomes of patients with CA-AKI, and AKI identified in primary care (PC-AKI) through AKI e-Alerts. Design: A prospective national cohort study was undertaken to collect data on all e-alerts representing adult CA-AKI. Method: The study utilised the biochemistry based AKI electronic (e)-alert system that is established across the Welsh National Health Service. Results: 28.8% of the 22,723 CA-AKI e-alerts were classified as PC-AKI. Ninety-day mortality was 24.0% and lower for PC-AKI vs. non-primary care (non-PC) CA-AKI. Hospitalisation was 22.3% for PC-AKI and associated with greater disease severity, higher mortality, but better renal outcomes (non-recovery: 18.1% vs. 21.6%; progression of pre-existing CKD: 40.5% vs. 58.3%). 49.1% of PC-AKI had a repeat test within seven days, 42.5% between seven and ninety days, and 8.4% was not repeated within ninety days. There was significantly more non-recovery (24.0% vs. 17.9%) and progression of pre-existing CKD (63.3% vs. 47.0%) in patients with late repeated measurement of renal function compared to those with early repeated measurement of renal function. Conclusion: The data demonstrate the clinical utility of AKI e-alerts in primary care. We recommend that a clinical review, or referral together with a repeat measurement of renal function within seven days should be considered an appropriate response to AKI e-alerts in primary care

Seasonal pattern of incidence and outcome of acute kidney injury: A national study of Welsh AKI electronic alerts

Objectives To identify any seasonal variation in the occurrence of, and outcome following Acute Kidney Injury. Methods The study utilised the biochemistry based AKI electronic (e)-alert system established across the Welsh National Health Service to collect data on all AKI episodes to identify changes in incidence and outcome over one calendar year (1st October 2015 and the 30th September 2016). Results There were total of 48 457 incident AKI alerts. The highest proportion of AKI episodes was seen in the quarter of January to March (26.2%), and the lowest in the quarter of October to December (23.3%, P < .001). The same trend was seen for both community-acquired and hospital-acquired AKI sub-sets. Overall 90 day mortality for all AKI was 27.3%. In contrast with the seasonal trend in AKI occurrence, 90 day mortality after the incident AKI alert was significantly higher in the quarters of January to March and October to December compared with the quarters of April to June and July to September (P < .001) consistent with excess winter mortality reported for likely underlying diseases which precipitate AKI. Conclusions In summary we report for the first time in a large national cohort, a seasonal variation in the incidence and outcomes of AKI. The results demonstrate distinct trends in the incidence and outcome of AKI

Acute kidney injury in the era of the AKI E-Alert

Background and objectivesOur aimwas to use a national electronicAKI alert to define the incidence and outcome of all episodes of community– and hospital–acquired adult AKI. Design, setting, participants, & measurements A prospective national cohort study was undertaken in a population of 3.06 million.Datawere collected betweenMarch of 2015 andAugust of 2015. All patients with adult ($18 years of age) AKI were identified to define the incidence and outcome of all episodes of community- and hospital-acquired AKI in adults. Mortality and renal outcomes were assessed at 90 days. Results There was a total of 31,601 alerts representing 17,689 incident episodes, giving an incidence of AKI of 577 per 100,000 population. Community-acquired AKI accounted for 49.3% of all incident episodes, and 42% occurred in the context of preexisting CKD (Chronic Kidney Disease Epidemiology Collaboration eGFR); 90-day mortality rate was 25.6%, and 23.7% of episodes progressed to a higher AKI stage than the stage associated with the alert. AKI electronic alert stage and peak AKI stage were associated with mortality, and mortality was significantly higher for hospital-acquired AKI compared with alerts generated in a community setting. Among patients who survived to 90 days after the AKI electronic alert, those who were not hospitalized had a lower rate of renal recovery and a greater likelihood of developing an eGFR,60 ml/min per 1.73m2 for the first time,which may be indicative of development of de novo CKD. Conclusions The reported incidence of AKI is far greater than the previously reported incidence in studies reliant on clinical identification of adult AKI or hospital coding data. Although an electronic alert systemis Information Technology driven and therefore, lacks intelligence and clinical context, these data can be used to identify deficiencies in care, guide the development of appropriate intervention strategies, and provide a baseline against which the effectiveness of these interventions may be measured

Confidence Measures for Evaluating Pronunciation Models

In this paper, we investigate the use of confidence measures for the evaluation of pronunciation models and the employment of these evaluations in an automatic baseform learning process. The confidence measures and pronunciation models are obtained from the ABBOT hybrid Hidden Markov Model/Artificial Neural Network (HMM/ANN) Large Vocabulary Continuous Speech Recognition (LVCSR) system [8]. Experiments were carried out for a number of baseform learning schemes using the ARPA North American Business News (NAB) and the Broadcast News (BN) corpora from which it was found that a confidence measure based scheme provided the largest reduction in Word Error Rate (WER)

Confidence measures from local posterior probability estimates

In this paper we introduce a set of related confidence measures for large vocabulary continuous speech recognition (LVCSR) based on local phone posterior probability estimates output by an acceptor HMM acoustic model. In addition to their computational efficiency, these confidence measures are attractive as they may be applied at the state-, phone-, word- or utterance-levels, potentially enabling discrimination between different causes of low confidence recognizer output, such as unclear acoustics or mismatched pronunciation models. We have evaluated these confidence measures for utterance verification using a number of different metrics. Experiments reveal several trends in `profitability of rejection', as measured by the unconditional error rate of a hypothesis test. These trends suggest that crude pronunciation models can mask the relatively subtle reductions in confidence caused by out-of-vocabulary (OOV) words and disfluencies, but not the gross model mismatches elicited by non-speech sounds. The observation that a purely acoustic confidence measure can provide improved performance over a measure based upon both acoustic and language model information for data drawn from the Broadcast News corpus, but not for data drawn from the North American Business News corpus suggests that the quality of model fit offered by a trigram language model is reduced for Broadcast News data. We also argue that acoustic confidence measures may be used to inform the search for improved pronunciation models

Confidence measures for hybrid HMM/ANN speech recognition.

In this paper we introduce four acoustic confidence measures which are derived from the output of a hybrid HMM/ANN large vocabulary continuous speech recognition system. These confidence measures, based on local posterior probability estimates computed by an ANN, are evaluated at both phone and word levels, using the North American Business News corpus

Confidence Measures Derived from an Acceptor HMM

In this paper we define a number of confidence measures derived from an acceptor HMM and evaluate their performance for the task of utterance verification using the North American Business News (NAB) and Broadcast News (BN) corpora. Results are presented for decodings made at both the word and phone level which show the relative profitability of rejection provided by the diverse set of confidence measures. The results indicate that language model dependent confidence measures have reduced performance on BN data relative to that for the more grammatically constrained NAB data. An explanation linking the observations that rejection is more profitable for noisy acoustics, for a reduced vocabulary and at the phone level is also given

Acoustic Confidence Measures for Segmenting Broadcast News

In this paper we define an acoustic confidence measure based on the estimates of local posterior probabilities produced by a HMM/ANN large vocabulary continuous speech recognition system. We use this measure to segment continuous audio into regions where it is and is not appropriate to expend recognition effort. The segmentation is computationally inexpensive and provides reductions in both overall word error rate and decoding time. The technique is evaluated using material from the Broadcast News corpus

Metacognition of intentions in mindfulness and hypnosis

In a famous series of experiments, Libet investigated the subjective timing of awareness of an intention to move, a task that can be considered a metacognitive judgement. The ability to strategically produce inaccurate metacognitions about intentions has been postulated to be central to the changes in judgements of agency common to all hypnotic responding. Therefore, differences in hypnotisability may be reflected in Libet’s measure. Specifically, the ability to sustain inaccurate judgements of agency displayed by highly hypnotisable people may result from their having coarser higher order representations of intentions. They therefore should report a delayed time of intention relative to less hypnotisable individuals. Conversely, mindfulness practice aims at accurate metacognition, including of intentions, and may lead to the development of finer grained higher order representations of intending. Thus, the long-term practice of mindfulness may produce an earlier judgement of the time of an intention. We tested these groups using Libet’s task, and found that, consistent with predictions, highly hypnotisable people reported a later time of intention than less hypnotisable people and meditators an earlier time than non-meditators. In a further two studies we replicated the finding that hypnotisable people report later awareness of a motor intention and additionally found a negative relationship between trait mindfulness and this measure. Based on these findings, we argue that hypnotic response and meditation involve opposite processes