Epidemiology and patterns of empiric antimicrobial therapy practice in patients with community-onset sepsis using data from a Japanese nationwide medical claims database-the Japan Sepsis Alliance (JaSA) study group.

Objectives: We aimed to describe empiric antimicrobial options for patients with community-onset sepsis using nationwide real-world data from Japan. Methods: This retrospective cohort study used nationwide Japanese data from a medical reimbursement system database. Patients aged ≥20 years with both presumed infections and acute organ dysfunction who were admitted to hospitals from the outpatient department or emergency department between 2010 and 2017 were enrolled. We described the initial choices of antimicrobials for patients with sepsis stratified by intensive care unit (ICU) or ward. Results: There were 1,195,741 patients with community-onset sepsis; of these, 1,068,719 and 127,022 patients were admitted to the wards and ICU, respectively. Third-generation cephalosporins and carbapenem were most commonly used for patients with community-onset sepsis. We found that 1.7% and 6.0% of patients initially used antimicrobials for methicillin-resistant Staphylococcus aureus coverage in the wards and ICU, respectively. Although half of the patients initially used antipseudomonal agents, only a few patients used a combination of antipseudomonal agents. Moreover, few patients initially used a combination of antimicrobials to treat methicillin-resistant Staphylococcus aureus and Pseudomonas sp. Conclusion: Third-generation cephalosporins and carbapenem were most frequently used for patients with sepsis. A combination therapy of antimicrobials for drug-resistant bacteria coverage was rarely provided to these patients.

Association between hospital spending and in-hospital mortality of patients with sepsis based on a Japanese nationwide medical claims database study.

Background: The effect of hospital spending on the mortality rate of patients with sepsis has not yet been fully elucidated. We hypothesized that hospitals that consume more medical resources would have lower mortality rates among patients with sepsis. Methods: This retrospective study used administrative data from 2010 to 2017. The enrolled hospitals were divided into quartiles based on average daily medical cost per sepsis case. The primary and secondary outcomes were the average in-hospital mortality rate of patients with sepsis and the effective cost per survivor among the enrolled hospitals, respectively. A multiple regression model was used to determine the significance of the differences among hospital categories to adjust for baseline imbalances. Results: Among 997 hospitals enrolled in this study, the crude in-hospital mortality rates were 15.7% and 13.2% in the lowest and highest quartiles of hospital spending, respectively. After adjusting for confounding factors, the highest hospital spending group demonstrated a significantly lower in-hospital mortality rate than the lowest hospital spending group (coefficient = -0.025, 95% confidence interval [CI] -0.034 to -0.015; p < 0.0001). Similarly, the highest hospital spending group was associated with a significantly higher effective cost per survivor than the lowest hospital spending group (coefficient = 77.7, 95% CI 73.1 to 82.3; p < 0.0001). In subgroup analyses, hospitals with a small or medium number of beds demonstrated a consistent pattern with the primary test, whereas those with a large number of beds or academic affiliations displayed no association. Conclusions: Using a nationwide Japanese medical claims database, this study indicated that hospitals with greater expenditures were associated with a superior survival rate and a higher effective cost per survivor in patients with sepsis than those with lower expenditures. In contrast, no correlations between hospital spending and mortality were observed in hospitals with a large number of beds or academic affiliations.

Machine-learning model for predicting oliguria in critically ill patients.

This retrospective cohort study aimed to develop and evaluate a machine-learning algorithm for predicting oliguria, a sign of acute kidney injury (AKI). To this end, electronic health record data from consecutive patients admitted to the intensive care unit (ICU) between 2010 and 2019 were used and oliguria was defined as a urine output of less than 0.5 mL/kg/h. Furthermore, a light-gradient boosting machine was used for model development. Among the 9,241 patients who participated in the study, the proportions of patients with urine output < 0.5 mL/kg/h for 6 h and with AKI during the ICU stay were 27.4% and 30.2%, respectively. The area under the curve (AUC) values provided by the prediction algorithm for the onset of oliguria at 6 h and 72 h using 28 clinically relevant variables were 0.964 (a 95% confidence interval (CI) of 0.963-0.965) and 0.916 (a 95% CI of 0.914-0.918), respectively. The Shapley additive explanation analysis for predicting oliguria at 6 h identified urine values, severity scores, serum creatinine, oxygen partial pressure, fibrinogen/fibrin degradation products, interleukin-6, and peripheral temperature as important variables. Thus, this study demonstrates that a machine-learning algorithm can accurately predict oliguria onset in ICU patients, suggesting the importance of oliguria in the early diagnosis and optimal management of AKI.

Prehospital stroke-scale machine-learning model predicts the need for surgical intervention.

While the development of prehospital diagnosis scales has been reported in various regions, we have also developed a scale to predict stroke type using machine learning. In the present study, we aimed to assess for the first time a scale that predicts the need for surgical intervention across stroke types, including subarachnoid haemorrhage and intracerebral haemorrhage. A multicentre retrospective study was conducted within a secondary medical care area. Twenty-three items, including vitals and neurological symptoms, were analysed in adult patients suspected of having a stroke by paramedics. The primary outcome was a binary classification model for predicting surgical intervention based on eXtreme Gradient Boosting (XGBoost). Of the 1143 patients enrolled, 765 (70%) were used as the training cohort, and 378 (30%) were used as the test cohort. The XGBoost model predicted stroke requiring surgical intervention with high accuracy in the test cohort, with an area under the receiver operating characteristic curve of 0.802 (sensitivity 0.748, specificity 0.853). We found that simple survey items, such as the level of consciousness, vital signs, sudden headache, and speech abnormalities were the most significant variables for accurate prediction. This algorithm can be useful for prehospital stroke management, which is crucial for better patient outcomes.

Machine learning algorithms for predicting days of high incidence for out-of-hospital cardiac arrest.

Predicting out-of-hospital cardiac arrest (OHCA) events might improve outcomes of OHCA patients. We hypothesized that machine learning algorithms using meteorological information would predict OHCA incidences. We used the Japanese population-based repository database of OHCA and weather information. The Tokyo data (2005-2012) was used as the training cohort and datasets of the top six populated prefectures (2013-2015) as the test. Eight various algorithms were evaluated to predict the high-incidence OHCA days, defined as the daily events exceeding 75% tile of our dataset, using meteorological and chronological values: temperature, humidity, air pressure, months, days, national holidays, the day before the holidays, the day after the holidays, and New Year's holidays. Additionally, we evaluated the contribution of each feature by Shapley Additive exPlanations (SHAP) values. The training cohort included 96,597 OHCA patients. The eXtreme Gradient Boosting (XGBoost) had the highest area under the receiver operating curve (AUROC) of 0.906 (95% confidence interval; 0.868-0.944). In the test cohorts, the XGBoost algorithms also had high AUROC (0.862-0.923). The SHAP values indicated that the "mean temperature on the previous day" impacted the most on the model. Algorithms using machine learning with meteorological and chronological information could predict OHCA events accurately.

Protocol to acquire time series data on adverse reactions following vaccination using a smartphone or web-based platform.

Data collection on adverse reactions in recipients after vaccination is vital to evaluate potential health issues, but health observation diaries are onerous for participants. Here, we present a protocol to collect time series information using a smartphone or web-based platform, thus eliminating the need for paperwork and data submission. We describe steps for setting up the platform using the Model-View-Controller web framework, uploading lists of recipients, sending notifications, and managing respondent data. For complete details on the use and execution of this protocol, please refer to Ikeda et al. (2022).1.

Mortality analysis among sepsis patients in and out of intensive care units using the Japanese nationwide medical claims database: a study by the Japan Sepsis Alliance study group.

BACKGROUND: A substantial number of sepsis patients require specialized care, including multidisciplinary care, close monitoring, and artificial organ support in the intensive care unit (ICU). However, the efficacy of ICU management on clinical outcomes remains insufficiently researched. Therefore, we tested the hypothesis that ICU admission would increase the survival rate among sepsis patients. METHODS: We conducted a retrospective study using the nationwide medical claims database of sepsis patients in Japan from 2010 to 2017 with propensity score matching to adjust for baseline imbalances. Patients aged over 20 years, with a combined diagnosis of presumed serious infection and organ failure, were included in this study. The primary outcome studied was the in-hospital mortality among non-ICU and ICU patients. In addition to propensity score matching, we performed a multivariable logistic regression analysis for the primary outcome. As the treatment policy was not extracted from the database, we performed sensitivity analyses to determine mortality differences in adults (20 ≤ age ≤ 64), independent patients, patients without malignant tumors, based on the assumption that treatment intensity is likely to increase in those population. RESULTS: Among 1,167,901 sepsis patients (974,289 in non-ICU and 193,612 in ICU settings), the unadjusted in-hospital mortality was 22.5% among non-ICU patients and 26.2% among ICU patients (3.7% [95% CI 3.5-3.9]). After propensity score matching, the in-hospital mortality was 29.2% among non-ICU patients and 25.8% among ICU patients ([Formula: see text] 3.4% [95% CI [Formula: see text] 3.7 to [Formula: see text] 3.1]). In-hospital mortality with a multivariable regression analysis ([Formula: see text] 5.0% [95% CI [Formula: see text] 5.2 to [Formula: see text] 4.8]) was comparable with the results of the propensity score matching analysis. In the sensitivity analyses, the mortality differences between non-ICU and ICU in adults, independent patients, and patients without malignant tumors were [Formula: see text] 2.7% [95% CI [Formula: see text] 3.3 to [Formula: see text] 2.2], [Formula: see text] 5.8% [95% CI [Formula: see text] 6.4 to [Formula: see text] 5.2], and [Formula: see text] 1.3% [95% CI [Formula: see text] 1.7 to [Formula: see text] 1.0], respectively. CONCLUSIONS: Herein, using the nationwide medical claims database, we demonstrated that ICU admission was potentially associated with decreasing in-hospital mortality among sepsis patients. Further investigations are warranted to validate these results and elucidate the mechanisms favoring ICU management on clinical outcomes.

Short- versus long-course antibiotic therapy for sepsis: a post hoc analysis of the nationwide cohort study.

BACKGROUND: The appropriate duration of antibiotic treatment in patients with bacterial sepsis remains unclear. The purpose of this study was to evaluate the association of a shorter course of antibiotics on 28-day mortality in comparison with a longer course using a national database in Japan. METHODS: We conducted a post hoc analysis from the retrospective observational study of patients with sepsis using a Japanese claims database from 2010 to 2017. The patient dataset was divided into short-course (≤ 7 days) and long-course (≥ 8 days) groups according to the duration of initial antibiotic administration. Subsequently, propensity score matching was performed to adjust the baseline imbalance between the two groups. The primary outcome was 28-day mortality. The secondary outcomes were re-initiated antibiotics at 3 and 7 days, during hospitalization, administration period, antibiotic-free days, and medical cost. RESULTS: After propensity score matching, 448,146 pairs were analyzed. The 28-day mortality was significantly lower in the short-course group (hazard ratio, 0.94; 95% CI, 0.92-0.95; P < 0.001), while the occurrence of re-initiated antibiotics at 3 and 7 days and during hospitalization were significantly higher in the short-course group (P < 0.001). Antibiotic-free days (median [IQR]) were significantly shorter in the long-course group (21 days [17 days, 23 days] vs. 17 days [14 days, 19 days], P < 0.001), and short-course administration contributed to a decrease in medical costs (coefficient $-212, 95% CI; - 223 to - 201, P < 0.001). Subgroup analyses showed a significant decrease in the 28-day mortality of the patients in the short-course group in patients of male sex (hazard ratio: 0.91, 95% CI; 0.89-0.93), community-onset sepsis (hazard ratio; 0.95, 95% CI; 0.93-0.98), abdominal infection (hazard ratio; 0.92, 95% CI; 0.88-0.97) and heart infection (hazard ratio; 0.74, 95% CI; 0.61-0.90), while a significant increase was observed in patients with non-community-onset sepsis (hazard ratio; 1.09, 95% CI; 1.06-1.12). CONCLUSIONS: The 28-day mortality was significantly lower in the short-course group, even though there was a higher rate of re-initiated antibiotics in the short course.

Prehospital diagnostic algorithm for acute coronary syndrome using machine learning: a prospective observational study.

Rapid and precise prehospital recognition of acute coronary syndrome (ACS) is key to improving clinical outcomes. The aim of this study was to investigate a predictive power for predicting ACS using the machine learning-based prehospital algorithm. We conducted a multicenter observational prospective study that included 10 participating facilities in an urban area of Japan. The data from consecutive adult patients, identified by emergency medical service personnel with suspected ACS, were analyzed. In this study, we used nested cross-validation to evaluate the predictive performance of the model. The primary outcomes were binary classification models for ACS prediction based on the nine machine learning algorithms. The voting classifier model for ACS using 43 features had the highest area under the receiver operating curve (AUC) (0.861 [95% CI 0.775-0.832]) in the test score. After validating the accuracy of the model using the external cohort, we repeated the analysis with a limited number of selected features. The performance of the algorithms using 17 features remained high AUC (voting classifier, 0.864 [95% CI 0.830-0.898], support vector machine (radial basis function), 0.864 [95% CI 0.829-0.887]) in the test score. We found that the machine learning-based prehospital algorithms showed a high predictive power for predicting ACS.

Prediction algorithm for ICU mortality and length of stay using machine learning.

Machine learning can predict outcomes and determine variables contributing to precise prediction, and can thus classify patients with different risk factors of outcomes. This study aimed to investigate the predictive accuracy for mortality and length of stay in intensive care unit (ICU) patients using machine learning, and to identify the variables contributing to the precise prediction or classification of patients. Patients (n = 12,747) admitted to the ICU at Chiba University Hospital were randomly assigned to the training and test cohorts. After learning using the variables on admission in the training cohort, the area under the curve (AUC) was analyzed in the test cohort to evaluate the predictive accuracy of the supervised machine learning classifiers, including random forest (RF) for outcomes (primary outcome, mortality; secondary outcome, length of ICU stay). The rank of the variables that contributed to the machine learning prediction was confirmed, and cluster analysis of the patients with risk factors of mortality was performed to identify the important variables associated with patient outcomes. Machine learning using RF revealed a high predictive value for mortality, with an AUC of 0.945 (95% confidence interval [CI] 0.922-0.977). In addition, RF showed high predictive value for short and long ICU stays, with AUCs of 0.881 (95% CI 0.876-0.908) and 0.889 (95% CI 0.849-0.936), respectively. Lactate dehydrogenase (LDH) was identified as a variable contributing to the precise prediction in machine learning for both mortality and length of ICU stay. LDH was also identified as a contributing variable to classify patients into sub-populations based on different risk factors of mortality. The machine learning algorithm could predict mortality and length of stay in ICU patients with high accuracy. LDH was identified as a contributing variable in mortality and length of ICU stay prediction and could be used to classify patients based on mortality risk.

Temporal trends of medical cost and cost-effectiveness in sepsis patients: a Japanese nationwide medical claims database.

BACKGROUND: Sepsis is the leading cause of death worldwide. Although the mortality of sepsis patients has been decreasing over the past decade, the trend of medical costs and cost-effectiveness for sepsis treatment remains insufficiently determined. METHODS: We conducted a retrospective study using the nationwide medical claims database of sepsis patients in Japan between 2010 and 2017. After selecting sepsis patients with a combined diagnosis of presumed serious infection and organ failure, patients over the age of 20 were included in this study. We investigated the annual trend of medical costs during the study period. The primary outcome was the annual trend of the effective cost per survivor, calculated from the gross medical cost and number of survivors per year. Subsequently, we performed subgroup and multiple regression analyses to evaluate the association between the annual trend and medical costs. RESULTS: Among 50,490,128 adult patients with claims, a total of 1,276,678 patients with sepsis were selected from the database. Yearly gross medical costs to treat sepsis gradually increased over the decade from $3.04 billion in 2010 to $4.38 billion in 2017, whereas the total medical cost per hospitalization declined (rate = - $1075/year, p < 0.0001). While the survival rate of sepsis patients improved during the study period, the effective cost per survivor significantly decreased (rate = - $1806/year [95% CI - $2432 to - $1179], p = 0.001). In the subgroup analysis, the trend of decreasing medical cost per hospitalization remained consistent among the subpopulation of age, sex, and site of infection. After adjusting for age, sex (male), number of chronic diseases, site of infection, intensive care unit (ICU) admission, surgery, and length of hospital stay, the admission year was significantly associated with reduced medical costs. CONCLUSIONS: We demonstrated an improvement in annual cost-effectiveness in patients with sepsis between 2010 and 2017. The annual trend of reduced costs was consistent after adjustment with the confounders altering hospital expenses.

A prehospital diagnostic algorithm for strokes using machine learning: a prospective observational study.

High precision is optimal in prehospital diagnostic algorithms for strokes and large vessel occlusions. We hypothesized that prehospital diagnostic algorithms for strokes and their subcategories using machine learning could have high predictive value. Consecutive adult patients with suspected stroke as per emergency medical service personnel were enrolled in a prospective multicenter observational study in 12 hospitals in Japan. Five diagnostic algorithms using machine learning, including logistic regression, random forest, support vector machine, and eXtreme Gradient Boosting, were evaluated for stroke and subcategories including acute ischemic stroke with/without large vessel occlusions, intracranial hemorrhage, and subarachnoid hemorrhage. Of the 1446 patients in the analysis, 1156 (80%) were randomly included in the training (derivation) cohort and cohorts, and 290 (20%) were included in the test (validation) cohort. In the diagnostic algorithms for strokes using eXtreme Gradient Boosting had the highest diagnostic value (test data, area under the receiver operating curve 0.980). In the diagnostic algorithms for the subcategories using eXtreme Gradient Boosting had a high predictive value (test data, area under the receiver operating curve, acute ischemic stroke with/without large vessel occlusions 0.898/0.882, intracranial hemorrhage 0.866, subarachnoid hemorrhage 0.926). Prehospital diagnostic algorithms using machine learning had high predictive value for strokes and their subcategories.

Trends in the incidence and outcome of sepsis using data from a Japanese nationwide medical claims database-the Japan Sepsis Alliance (JaSA) study group.

BACKGROUND: Trends in the incidence and outcomes of sepsis using a Japanese nationwide database were investigated. METHODS: This was a retrospective cohort study. Adult patients, who had both presumed serious infections and acute organ dysfunction, between 2010 and 2017 were extracted using a combined method of administrative and electronic health record data from the Japanese nationwide medical claim database, which covered 71.5% of all acute care hospitals in 2017. Presumed serious infection was defined using blood culture test records and antibiotic administration. Acute organ dysfunction was defined using records of diagnosis according to the international statistical classification of diseases and related health problems, 10th revision, and records of organ support. The primary outcomes were the annual incidence of sepsis and death in sepsis per 1000 inpatients. The secondary outcomes were in-hospital mortality rate and length of hospital stay in patients with sepsis. RESULTS: The analyzed dataset included 50,490,128 adult inpatients admitted between 2010 and 2017. Of these, 2,043,073 (4.0%) patients had sepsis. During the 8-year period, the annual proportion of patients with sepsis across inpatients significantly increased (slope = + 0.30%/year, P < 0.0001), accounting for 4.9% of the total inpatients in 2017. The annual death rate of sepsis per 1000 inpatients significantly increased (slope = + 1.8/1000 inpatients year, P = 0.0001), accounting for 7.8 deaths per 1000 inpatients in 2017. The in-hospital mortality rate and median (interquartile range) length of hospital stay significantly decreased (P < 0.001) over the study period and were 18.3% and 27 (15-50) days in 2017, respectively. CONCLUSIONS: The Japanese nationwide data indicate that the annual incidence of sepsis and death in inpatients with sepsis significantly increased; however, the annual mortality rates and length of hospital stay in patients with sepsis significantly decreased. The increasing incidence of sepsis and death in sepsis appear to be a significant and ongoing issue.