Background: The Japan Coma Scale (JCS) is the most frequently adopted method for evaluating level of consciousness in Japan. However, no validated method for converting the JCS to the Glasgow Coma ...Scale (GCS) exists. The aims of the present study were to develop and validate a method to convert the JCS to GCS.Methods: This is a multicenter retrospective analysis involving three emergency departments (EDs) in Japan. We included all adult patients who visited the ED between 2017 and 2020. The participating facilities were divided into two cohorts—one cohort to develop a table to convert the JCS to GCS (development cohort), and the other cohort to validate the conversion table (validation cohort). The conversion table of the JCS to GCS was developed based on the median values of the GCS. The outcome was the concordance rate between the JCS and GCS.Results: We identified 8,194 eligible patients. The development cohort included 7,373 patients and the validation cohort included 821 patients. In the validation cohort, the absolute and relative concordance rates were 80.3% (95% confidence interval, 77.4–82.9%) and 93.2% (95% confidence interval, 91.2–94.8%), respectively.Conclusion: This study developed and validated a novel method for converting the JCS to GCS. Assuming the offset by a single category between the JCS and GCS is acceptable, the concordance rate was over 90% in the general adult patient population visiting the ED. The conversion method may assist researchers to convert JCS scores into GCS scores, which are more commonly recognized among global audiences.
Full text
Available for:
FFLJ, NUK, ODKLJ, UL, UM, UPUK
To investigate the temporal trend in the national incidence of bronchiolitis hospitalizations, their characteristics, inpatient resource use, and hospital cost from 2000 through 2016.
We performed a ...serial, cross-sectional analysis of nationally representative samples (the 2000, 2003, 2006, 2009, 2012, and 2016 Kids' Inpatient Databases) of children (age <2 years) hospitalized for bronchiolitis. We identified all children hospitalized with bronchiolitis by using
466.1 and
J21. Complex chronic conditions were defined by the pediatric complex chronic conditions classification by using inpatient data. The primary outcomes were the incidence of bronchiolitis hospitalizations, mechanical ventilation use, and hospital direct cost. We examined the trends accounting for sampling weights.
From 2000 to 2016, the incidence of bronchiolitis hospitalization decreased from 17.9 to 13.5 per 1000 person-years in US children (25% decrease;
< .001). In contrast, the proportion of bronchiolitis hospitalizations among overall hospitalizations increased from 16% to 18% (
< .001). There was an increase in the proportion of children with a complex chronic condition (6%-13%; 117% increase), hospitalization to children's hospital (15%-29%; 93% increase), and mechanical ventilation use (2%-5%; 184% increase; all
< .001). Likewise, the hospital cost increased from $449 million to $734 million (63% increase) nationally (with an increase in geometric mean of cost per hospitalization from $3267 to $4086; 25% increase;
< .001 adjusted for inflation) from 2003 to 2016.
From 2000 through 2016, the incidence of bronchiolitis hospitalizations among US children declined. In contrast, mechanical ventilation use and nationwide hospital direct cost substantially increased.
Development of emergency department (ED) triage systems that accurately differentiate and prioritize critically ill from stable patients remains challenging. We used machine learning models to ...predict clinical outcomes, and then compared their performance with that of a conventional approach-the Emergency Severity Index (ESI).
Using National Hospital and Ambulatory Medical Care Survey (NHAMCS) ED data, from 2007 through 2015, we identified all adult patients (aged ≥ 18 years). In the randomly sampled training set (70%), using routinely available triage data as predictors (e.g., demographics, triage vital signs, chief complaints, comorbidities), we developed four machine learning models: Lasso regression, random forest, gradient boosted decision tree, and deep neural network. As the reference model, we constructed a logistic regression model using the five-level ESI data. The clinical outcomes were critical care (admission to intensive care unit or in-hospital death) and hospitalization (direct hospital admission or transfer). In the test set (the remaining 30%), we measured the predictive performance, including area under the receiver-operating-characteristics curve (AUC) and net benefit (decision curves) for each model.
Of 135,470 eligible ED visits, 2.1% had critical care outcome and 16.2% had hospitalization outcome. In the critical care outcome prediction, all four machine learning models outperformed the reference model (e.g., AUC, 0.86 95%CI 0.85-0.87 in the deep neural network vs 0.74 95%CI 0.72-0.75 in the reference model), with less under-triaged patients in ESI triage levels 3 to 5 (urgent to non-urgent). Likewise, in the hospitalization outcome prediction, all machine learning models outperformed the reference model (e.g., AUC, 0.82 95%CI 0.82-0.83 in the deep neural network vs 0.69 95%CI 0.68-0.69 in the reference model) with less over-triages in ESI triage levels 1 to 3 (immediate to urgent). In the decision curve analysis, all machine learning models consistently achieved a greater net benefit-a larger number of appropriate triages considering a trade-off with over-triages-across the range of clinical thresholds.
Compared to the conventional approach, the machine learning models demonstrated a superior performance to predict critical care and hospitalization outcomes. The application of modern machine learning models may enhance clinicians' triage decision making, thereby achieving better clinical care and optimal resource utilization.
Digital advancements can reduce the burden of recording clinical information. This intra-subject experimental study compared the time and error rates for recording vital signs and prescriptions ...between an optical character reader (OCR) and manual typing. This study was conducted at three community hospitals and two fire departments in Japan. Thirty-eight volunteers (15 paramedics, 10 nurses, and 13 physicians) participated in the study. We prepared six sample pictures: three ambulance monitors for vital signs (normal, abnormal, and shock) and three pharmacy notebooks that provided prescriptions (two, four, or six medications). The participants recorded the data for each picture using an OCR or by manually typing on a smartphone. The outcomes were recording time and error rate defined as the number of characters with omissions or misrecognitions/misspellings of the total number of characters. Data were analyzed using paired Wilcoxon signed-rank sum and McNemar's tests. The recording times for vital signs were similar between groups (normal state, 21 s interquartile range (IQR), 17-26 s for OCR vs. 23 s IQR, 18-31 s for manual typing). In contrast, prescription recording was faster with the OCR (e.g., six-medication list, 18 s IQR, 14-21 s for OCR vs. 144 s IQR, 112-187 s for manual typing). The OCR had fewer errors than manual typing for both vital signs and prescriptions (0/1056 0% vs. 14/1056 1.32%; p<0.001 and 30/4814 0.62% vs. 53/4814 1.10%, respectively). In conclusion, the developed OCR reduced the recording time for prescriptions but not vital signs. The OCR showed lower error rates than manual typing for both vital signs and prescription data.
Full text
Available for:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Purpose
We aimed to develop and validate models for predicting new-onset functional impairment after intensive care unit (ICU) admission with predictors routinely collected within 2 days of ...admission.
Methods
In this multi-center retrospective cohort study of acute care hospitals in Japan, we identified adult patients who were admitted to the ICU with independent activities of daily living before hospitalization and survived for at least 2 days from April 2014 to October 2020. The primary outcome was functional impairment defined as Barthel Index ≤ 60 at hospital discharge. In the internal validation dataset (April 2014 to March 2019), using routinely collected 94 candidate predictors within 2 days of ICU admission, we trained and tuned the six conventional and machine-learning models with repeated random sub-sampling cross-validation. We computed the variable importance of each predictor to the models. In the temporal validation dataset (April 2019 to October 2020), we measured the performance of these models.
Results
We identified 19,846 eligible patients. Functional impairment at discharge was developed in 33% of patients (
n
= 6488/19,846). In the temporal validation dataset, all six models showed good discrimination ability with areas under the curve above 0.86, and the differences among the six models were negligible. Variable importance revealed newly detected early predictors, including worsened neurologic conditions and catabolism biomarkers such as decreased serum albumin and increased blood urea nitrogen.
Conclusion
We successfully developed early prediction models of new-onset functional impairment after ICU admission that achieved high performance using only data routinely collected within 2 days of ICU admission.
Full text
Available for:
EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
A recent randomised trial showed that recombinant thrombomodulin did not benefit patients who had sepsis with coagulopathy and organ dysfunction. Several recent studies suggested presence of clinical ...phenotypes in patients with sepsis and heterogenous treatment effects across different sepsis phenotypes. We examined the latent phenotypes of sepsis with coagulopathy and the associations between thrombomodulin treatment and the 28-day and in-hospital mortality for each phenotype.
This was a secondary analysis of multicentre registries containing data on patients (aged ≥ 16 years) who were admitted to intensive care units for severe sepsis or septic shock in Japan. Three multicentre registries were divided into derivation (two registries) and validation (one registry) cohorts. Phenotypes were derived using k-means with coagulation markers, platelet counts, prothrombin time/international normalised ratios, fibrinogen, fibrinogen/fibrin-degradation-products (FDP), D-dimer, and antithrombin activities. Associations between thrombomodulin treatment and survival outcomes (28-day and in-hospital mortality) were assessed in the derived clusters using a generalised estimating equation.
Four sepsis phenotypes were derived from 3694 patients in the derivation cohort. Cluster dA (n = 323) had severe coagulopathy with high FDP and D-dimer levels, severe organ dysfunction, and high mortality. Cluster dB had severe disease with moderate coagulopathy. Clusters dC and dD had moderate and mild disease with and without coagulopathy, respectively. Thrombomodulin was associated with a lower 28-day (adjusted risk difference RD: - 17.8% 95% CI - 28.7 to - 6.9%) and in-hospital (adjusted RD: - 17.7% 95% CI - 27.6 to - 7.8%) mortality only in cluster dA. Sepsis phenotypes were similar in the validation cohort, and thrombomodulin treatment was also associated with lower 28-day (RD: - 24.9% 95% CI - 49.1 to - 0.7%) and in-hospital mortality (RD: - 30.9% 95% CI - 55.3 to - 6.6%).
We identified four coagulation marker-based sepsis phenotypes. The treatment effects of thrombomodulin varied across sepsis phenotypes. This finding will facilitate future trials of thrombomodulin, in which a sepsis phenotype with high FDP and D-dimer can be targeted.
The prediction of emergency department (ED) disposition at triage remains challenging. Machine learning approaches may enhance prediction. We compared the performance of several machine learning ...approaches for predicting two clinical outcomes (critical care and hospitalization) among ED patients with asthma or COPD exacerbation.
Using the 2007–2015 National Hospital and Ambulatory Medical Care Survey (NHAMCS) ED data, we identified adults with asthma or COPD exacerbation. In the training set (70% random sample), using routinely-available triage data as predictors (e.g., demographics, arrival mode, vital signs, chief complaint, comorbidities), we derived four machine learning-based models: Lasso regression, random forest, boosting, and deep neural network. In the test set (the remaining 30% of sample), we compared their prediction ability against traditional logistic regression with Emergency Severity Index (ESI, reference model).
Of 3206 eligible ED visits, corresponding to weighted estimates of 13.9 million visits, 4% had critical care outcome and 26% had hospitalization outcome. For the critical care prediction, the best performing approach– boosting – achieved the highest discriminative ability (C-statistics 0.80 vs. 0.68), reclassification improvement (net reclassification improvement NRI 53%, P = 0.002), and sensitivity (0.79 vs. 0.53) over the reference model. For the hospitalization prediction, random forest provided the highest discriminative ability (C-statistics 0.83 vs. 0.64) reclassification improvement (NRI 92%, P < 0.001), and sensitivity (0.75 vs. 0.33). Results were generally consistent across the asthma and COPD subgroups.
Based on nationally-representative ED data, machine learning approaches improved the ability to predict disposition of patients with asthma or COPD exacerbation.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
10.
Machine learning in gastrointestinal surgery Sakamoto, Takashi; Goto, Tadahiro; Fujiogi, Michimasa ...
Surgery today (Tokyo, Japan),
07/2022, Volume:
52, Issue:
7
Journal Article
Peer reviewed
Machine learning (ML) is a collection of algorithms allowing computers to learn directly from data without predetermined equations. It is used widely to analyze “big data”. In gastrointestinal ...surgery, surgeons deal with various data such as clinical parameters, surgical videos, and pathological images, to stratify surgical risk, perform safe surgery and predict patient prognosis. In the current “big data” era, the accelerating accumulation of a large amount of data drives studies using ML algorithms. Three subfields of ML are supervised learning, unsupervised learning, and reinforcement learning. In this review, we summarize applications of ML to surgical practice in the preoperative, intraoperative, and postoperative phases of care. Prediction and stratification using ML is promising; however, the current overarching concern is the availability of ML models. Information systems that can manage “big data” and integrate ML models into electronic health records are essential to incorporate ML into daily practice. ML is fundamental technology to meaningfully process data that exceeds the capacity of the human mind to comprehend. The accelerating accumulation of a large amount of data is changing the nature of surgical practice fundamentally. Artificial intelligence (AI), represented by ML, is being incorporated into daily surgical practice.
Full text
Available for:
EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
PDF
