Machine learning algorithm for predicting 30-day mortality in patients receiving rapid response system activation: A retrospective nationwide cohort study.

This study investigated the accuracy of a machine learning algorithm for predicting mortality in patients receiving rapid response system (RRS) activation. This retrospective cohort study used data from the In-Hospital Emergency Registry in Japan, which collects nationwide data on patients receiving RRS activation. The missing values in the dataset were replaced using multiple imputations (mode imputation, BayseRidge sklearn. linear model, and K-nearest neighbor model), and the enrolled patients were randomly assigned to the training and test cohorts. We established prediction models for 30-day mortality using the following four types of machine learning classifiers: Light Gradient Boosting Machine (LightGBM), eXtreme Gradient Boosting, random forest, and neural network. Fifty-two variables (patient characteristics, details of RRS activation, reasons for RRS initiation, and hospital capacity) were used to construct the prediction algorithm. The primary outcome was the accuracy of the prediction model for 30-day mortality. Overall, the data from 4,997 patients across 34 hospitals were analyzed. The machine learning algorithms using LightGBM demonstrated the highest predictive value for 30-day mortality (area under the receiver operating characteristic curve, 0.860 [95 % confidence interval, 0.825-0.895]). The SHapley Additive exPlanations summary plot indicated that hospital capacity, site of incidence, code status, and abnormal vital signs within 24 h were important variables in the prediction model for 30-day mortality.

Performance of a Large Language Model in Screening Citations.

Importance: Large language models (LLMs) are promising as tools for citation screening in systematic reviews. However, their applicability has not yet been determined. Objective: To evaluate the accuracy and efficiency of an LLM in title and abstract literature screening. Design, Setting, and Participants: This prospective diagnostic study used the data from the title and abstract screening process for 5 clinical questions (CQs) in the development of the Japanese Clinical Practice Guidelines for Management of Sepsis and Septic Shock. The LLM decided to include or exclude citations based on the inclusion and exclusion criteria in terms of patient, population, problem; intervention; comparison; and study design of the selected CQ and was compared with the conventional method for title and abstract screening. This study was conducted from January 7 to 15, 2024. Exposures: LLM (GPT-4 Turbo)-assisted citation screening or the conventional method. Main Outcomes and Measures: The sensitivity and specificity of the LLM-assisted screening process was calculated, and the full-text screening result using the conventional method was set as the reference standard in the primary analysis. Pooled sensitivity and specificity were also estimated, and screening times of the 2 methods were compared. Results: In the conventional citation screening process, 8 of 5634 publications in CQ 1, 4 of 3418 in CQ 2, 4 of 1038 in CQ 3, 17 of 4326 in CQ 4, and 8 of 2253 in CQ 5 were selected. In the primary analysis of 5 CQs, LLM-assisted citation screening demonstrated an integrated sensitivity of 0.75 (95% CI, 0.43 to 0.92) and specificity of 0.99 (95% CI, 0.99 to 0.99). Post hoc modifications to the command prompt improved the integrated sensitivity to 0.91 (95% CI, 0.77 to 0.97) without substantially compromising specificity (0.98 [95% CI, 0.96 to 0.99]). Additionally, LLM-assisted screening was associated with reduced time for processing 100 studies (1.3 minutes vs 17.2 minutes for conventional screening methods; mean difference, -15.25 minutes [95% CI, -17.70 to -12.79 minutes]). Conclusions and Relevance: In this prospective diagnostic study investigating the performance of LLM-assisted citation screening, the model demonstrated acceptable sensitivity and reasonably high specificity with reduced processing time. This novel method could potentially enhance efficiency and reduce workload in systematic reviews.

Incidence and mortality of community-acquired and nosocomial infections in Japan: a nationwide medical claims database study.

BACKGROUND: It is important to determine the prevalence and prognosis of community-acquired infection (CAI) and nosocomial infection (NI) to develop treatment strategies and appropriate medical policies in aging society. METHODS: Patients hospitalized between January 2010 and December 2019, for whom culture tests were performed and antibiotics were administered, were selected using a national claims-based database. The annual trends in incidence and in-hospital mortality were calculated and evaluated by dividing the patients into four age groups. RESULTS: Of the 73,962,409 inpatients registered in the database, 9.7% and 4.7% had CAI and NI, respectively. These incidences tended to increase across the years in both the groups. Among the patients hospitalized with infectious diseases, there was a significant increase in patients aged ≥ 85 years (CAI: + 1.04%/year and NI: + 0.94%/year, P < 0.001), while there was a significant decrease in hospitalization of patients aged ≤ 64 years (CAI: -1.63%/year and NI: -0.94%/year, P < 0.001). In-hospital mortality was significantly higher in the NI than in the CAI group (CAI: 8.3%; NI: 14.5%, adjusted mean difference 4.7%). The NI group had higher organ support, medical cost per patient, and longer duration of hospital stay. A decreasing trend in mortality was observed in both the groups (CAI: -0.53%/year and NI: -0.72%/year, P < 0.001). CONCLUSION: The present analysis of a large Japanese claims database showed that NI is a significant burden on hospitalized patients in aging societies, emphasizing the need to address particularly on NI.

Suppression of Type I Interferon Signaling in Myeloid Cells by Autoantibodies in Severe COVID-19 Patients.

PURPOSE: Auto-antibodies (auto-abs) to type I interferons (IFNs) have been identified in patients with life-threatening coronavirus disease 2019 (COVID-19), suggesting that the presence of auto-abs may be a risk factor for disease severity. We therefore investigated the mechanism underlying COVID-19 exacerbation induced by auto-abs to type I IFNs. METHODS: We evaluated plasma from 123 patients with COVID-19 to measure auto-abs to type I IFNs. We performed single-cell RNA sequencing (scRNA-seq) of peripheral blood mononuclear cells from the patients with auto-abs and conducted epitope mapping of the auto-abs. RESULTS: Three of 19 severe and 4 of 42 critical COVID-19 patients had neutralizing auto-abs to type I IFNs. Patients with auto-abs to type I IFNs showed no characteristic clinical features. scRNA-seq from 38 patients with COVID-19 revealed that IFN signaling in conventional dendritic cells and canonical monocytes was attenuated, and SARS-CoV-2-specific BCR repertoires were decreased in patients with auto-abs. Furthermore, auto-abs to IFN-α2 from COVID-19 patients with auto-abs recognized characteristic epitopes of IFN-α2, which binds to the receptor. CONCLUSION: Auto-abs to type I IFN found in COVID-19 patients inhibited IFN signaling in dendritic cells and monocytes by blocking the binding of type I IFN to its receptor. The failure to properly induce production of an antibody to SARS-CoV-2 may be a causative factor of COVID-19 severity.

Usability evaluation of a glove-type wearable device for efficient biometric collection during triage.

To efficiently allocate medical resources at disaster sites, medical workers perform triage to prioritize medical treatments based on the severity of the wounded or sick. In such instances, evaluators often assess the severity status of the wounded or sick quickly, but their measurements are qualitative and rely on experience. Therefore, we developed a wearable device called Medic Hand in this study to extend the functionality of a medical worker's hand so as to measure multiple biometric indicators simultaneously without increasing the number of medical devices to be carried. Medic Hand was developed to quantitatively and efficiently evaluate "perfusion" during triage. Speed is essential during triage at disaster sites, where time and effort are often spared to attach medical devices to patients, so the use of Medic Hand as a biometric measurement device is more efficient for collecting biometric information. For Medic Hand to be handy during disasters, it is essential to understand and improve upon factors that facilitate its public acceptance. To this end, this paper reports on the usability evaluation of Medic Hand through a questionnaire survey of nonmedical workers.

Toward an application of automatic evaluation system for central facial palsy using two simple evaluation indices in emergency medicine.

A stroke is a medical emergency and thus requires immediate treatment. Paramedics should accurately assess suspected stroke patients and promptly transport them to a hospital with stroke care facilities; however, current assessment procedures rely on subjective visual assessment. We aim to develop an automatic evaluation system for central facial palsy (CFP) that uses RGB cameras installed in an ambulance. This paper presents two evaluation indices, namely the symmetry of mouth movement and the difference in mouth shape, respectively, extracted from video frames. These evaluation indices allow us to quantitatively evaluate the degree of facial palsy. A classification model based on these indices can discriminate patients with CFP. The results of experiments using our dataset show that the values of the two evaluation indices are significantly different between healthy subjects and CFP patients. Furthermore, our classification model achieved an area under the curve of 0.847. This study demonstrates that the proposed automatic evaluation system has great potential for quantitatively assessing CFP patients based on two evaluation indices.

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.

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.

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.

COAGULOPATHY PARAMETERS PREDICTIVE OF OUTCOMES IN SEPSIS-INDUCED ACUTE RESPIRATORY DISTRESS SYNDROME: A SUBANALYSIS OF THE TWO PROSPECTIVE MULTICENTER COHORT STUDIES.

ABSTRACT: Background: Although coagulopathy is often observed in acute respiratory distress syndrome (ARDS), its clinical impact remains poorly understood. Objectives: This study aimed to clarify the coagulopathy parameters that are clinically applicable for prognostication and to determine anticoagulant indications in sepsis-induced ARDS. Method: This study enrolled patients with sepsis-derived ARDS from two nationwide multicenter, prospective observational studies. We explored coagulopathy parameters that could predict outcomes in the Focused Outcome Research on Emergency Care for Acute Respiratory Distress Syndrome, Sepsis, and Trauma (FORECAST) cohort, and the defined coagulopathy criteria were validated in the Sepsis Prognostication in Intensive Care Unit and Emergency Room-Intensive Care Unit (SPICE-ICU) cohort. The correlation between anticoagulant use and outcomes was also evaluated. Results: A total of 181 patients with sepsis-derived ARDS in the FORECAST study and 61 patients in the SPICE-ICU study were included. In a preliminary study, we found the set of prothrombin time-international normalized ratio ≥1.4 and platelet count ≤12 × 10 4 /µL, and thrombocytopenia and elongated prothrombin time (TEP) coagulopathy as the best coagulopathy parameters and used it for further analysis; the odds ratio (OR) of TEP coagulopathy for in-hospital mortality adjusted for confounding was 3.84 (95% confidence interval [CI], 1.66-8.87; P = 0.005). In the validation cohort, the adjusted OR for in-hospital mortality was 32.99 (95% CI, 2.60-418.72; P = 0.002). Although patients without TEP coagulopathy showed significant improvements in oxygenation over the first 4 days, patients with TEP coagulopathy showed no significant improvement (ΔPaO 2 /FiO 2 ratio, 24 ± 20 vs. 90 ± 9; P = 0.026). Furthermore, anticoagulant use was significantly correlated with mortality and oxygenation recovery in patients with TEP coagulopathy but not in patients without TEP coagulopathy. Conclusion: Thrombocytopenia and elongated prothrombin time coagulopathy is closely associated with better outcomes and responses to anticoagulant therapy in sepsis-induced ARDS, and our coagulopathy criteria may be clinically useful.

Prediction of the neurological outcomes post-cardiac arrest: A prospective validation of the CAST and rCAST.

INTRODUCTION: The neurologic prognosis of out-of-hospital cardiac arrest (OHCA) patients in whom return of spontaneous circulation (ROSC) is achieved remains poor. The aim of this study was to externally and prospectively validate two scoring systems developed by us: the CAST score, a scoring system to predict the neurological prognosis of OHCA patients undergoing targeted temperature management (TTM), and a simplified version of the same score developed for improved ease of use in clinical settings, the revised CAST (rCAST) score. METHODS: This study was a prospective, multicenter, observational study conducted using the SOS KANTO 2017 registry, an OHCA registry involving hospitals in the Kanto region (including Tokyo) of Japan. The primary outcome was favorable neurological outcome (defined as Cerebral Performance Category score of 1 or 2) at 30 days and the secondary outcomes were favorable neurological outcome at 90 days and survival at 30 and 90 days. The predictive accuracies of the original CAST (oCAST) and rCAST scores were evaluated by using area under the receiver operating characteristic curve (AUC). RESULTS: Of 9909 OHCA patients, 565 showed ROSC and received TTM. Of these, we analyzed the data of 259 patients in this study. The areas under the receiver operating characteristic curve (AUCs) of the oCAST and rCAST scores for predicting a favorable neurological outcome at 30 days were 0.86 and 0.87, respectively, and those for predicting a favorable neurological outcome at 90 days were 0.87 and 0.88, respectively. The rCAST showed a higher predictive accuracy for the neurological outcome as compared with the NULL-PLEASE score. The patients with a favorable neurological outcome who had been classified into the high severity group based on the rCAST tended to have hypothermia at hospital arrival and to not show any signs of loss of gray-white matter differentiation on brain CT. Neurological function at 90 days was correlated with the rCAST (r = 0.63, p < 0.001). CONCLUSIONS: rCAST showed high predictive accuracy for the neurological prognosis of OHCA patients managed by TTM, comparable to that of the oCAST score. The scores on the rCAST were strongly correlated with the neurological functions at 90 days, implying that the rCAST is a useful scale for assessing the severity of brain injury after cardiac arrest.

Efficiency and workload reduction of semi-automated citation screening software for creating clinical practice guidelines: a prospective observational study.

BACKGROUND: We evaluated the applicability of automated citation screening in developing clinical practice guidelines. METHODS: We prospectively compared the efficiency of citation screening between the conventional (Rayyan) and semi-automated (ASReview software) methods. We searched the literature for five clinical questions (CQs) in the development of the Japanese Clinical Practice Guidelines for the Management of Sepsis and Septic Shock. Objective measurements of the time required to complete citation screening were recorded. Following the first screening round, in the primary analysis, the sensitivity, specificity, positive predictive value, and overall screening time were calculated for both procedures using the semi-automated tool as index and the results of the conventional method as standard reference. In the secondary analysis, the same parameters were compared between the two procedures using the final list of included studies after the second screening session as standard reference. RESULTS: Among the five CQs after the first screening session, the highest and lowest sensitivity, specificity, and positive predictive values were 0.241 and 0.795; 0.991 and 1.000; and 0.482 and 0.929, respectively. In the secondary analysis, the highest sensitivity and specificity in the semi-automated citation screening were 1.000 and 0.997, respectively. The overall screening time per 100 studies was significantly shorter with semi-automated than with conventional citation screening. CONCLUSIONS: The potential advantages of the semi-automated method (shorter screening time and higher discriminatory rate for the final list of studies) warrant further validation. TRIAL REGISTRATION: This study was submitted to the University Hospital Medical Information Network Clinical Trial Registry (UMIN-CTR [UMIN000049366]).

Efficacy of therapeutic drug monitoring-based antibiotic regimen in critically ill patients: a systematic review and meta-analysis of randomized controlled trials.

BACKGROUND: The efficacy of therapeutic drug monitoring (TDM)-based antimicrobial dosing optimization strategies on pharmacokinetics/pharmacodynamics and specific drug properties for critically ill patients is unclear. Here, we conducted a systematic review and meta-analysis of randomized controlled trials to evaluate the effectiveness of TDM-based regimen in these patients. METHODS: Articles from three databases were systematically retrieved to identify relevant randomized control studies. Version two of the Cochrane tool for assessing risk of bias in randomized trials was used to assess the risk of bias in studies included in the analysis, and quality assessment of evidence was graded using the Grading of Recommendations Assessment, Development, and Evaluation approach. Primary outcome was the 28-day mortality and secondary outcome were in-hospital mortality, clinical cure, length of stay in the intensive care unit (ICU) and target attainment at day 1 and 3. RESULTS: In total, 5 studies involving 1011 patients were included for meta-analysis of the primary outcome, of which no significant difference was observed between TDM-based regimen and control groups (risk ratio [RR] 0.94, 95% confidence interval [CI]: 0.77-1.14; I2 = 0%). In-hospital mortality (RR 0.96, 95% CI: 0.76-1.20), clinical cure (RR 1.23, 95% CI: 0.91-1.67), length of stay in the ICU (mean difference 0, 95% CI: - 2.18-2.19), and target attainment at day 1 (RR 1.14, 95% CI: 0.88-1.48) and day 3 (RR 1.35, 95% CI: 0.90-2.03) were not significantly different between the two groups, and all evidence for the secondary outcomes had a low or very low level of certainty because the included studies had serious risk of bias, variation of definition for outcomes, and small sample sizes. CONCLUSION: TDM-based regimens had no significant efficacy for clinical or pharmacological outcomes. Further studies with other achievable targets and well-defined outcomes are required. TRIAL REGISTRATION: Clinical trial registration; PROSPERO ( https://www.crd.york.ac.uk/prospero/ ), registry number: CRD 42022371959. Registered 24 November 2022.

Epidemiology of sepsis in a Japanese administrative database.

Sepsis is the leading cause of death worldwide. Considering regional variations in the characteristics of patients with sepsis, a better understanding of the epidemiology in Japan will lead to further development of strategies for the prevention and treatment of sepsis. To investigate the epidemiology of sepsis, we conducted a systematic literature review of PubMed between 2003 and January 2023. Among the 78 studies using a Japanese administrative database, we included 20 that defined patients with sepsis as those with an infection and organ dysfunction. The mortality rate in patients with sepsis has decreased since 2010, reaching 18% in 2017. However, the proportion of inpatients with sepsis is increasing. A study comparing short-course (≤7 days) and long-course (≥8 days) antibiotic administration showed lower 28-day mortality in the short-course group. Six studies on the treatment of patients with septic shock reported that low-dose corticosteroids or polymyxin B hemoperfusion reduced mortality, whereas intravenous immunoglobulins had no such effect. Four studies investigating the effects of treatment in patients with sepsis-associated disseminated intravascular coagulation demonstrated that antithrombin may reduce mortality, whereas recombinant human soluble thrombomodulin does not. A descriptive study of medical costs for patients with sepsis showed that the effective cost per survivor decreased over an 8-year period from 2010 to 2017. Sepsis has a significant impact on public health, and is attracting attention as an ongoing issue. Further research to determine more appropriate prevention methods and treatment for sepsis should be a matter of priority.

Associations between fluid overload and outcomes in critically ill patients with acute kidney injury: a retrospective observational study.

Increased fluid overload (FO) is associated with poor outcomes in critically ill patients, especially in acute kidney injury (AKI). However, the exact timing from when FO influences outcomes remains unclear. We retrospectively screened intensive care unit (ICU) admitted patients with AKI between January 2011 and December 2015. Logistic or linear regression analyses were performed to determine when hourly %FO was significant on 90-day in-hospital mortality (primary outcome) or ventilator-free days (VFDs). In total, 1120 patients were enrolled in this study. Univariate analysis showed that a higher %FO was significantly associated with higher mortality from the first hour of ICU admission (odds ratio 1.34, 95% confidence interval 1.15-1.56, P < 0.001), whereas multivariate analysis adjusted with age, sex, APACHE II score, and sepsis etiology showed the association was significant from the 27th hour. Both univariate and multivariate analyses showed that a higher %FO was significantly associated with shorter VFDs from the 1st hour. The significant associations were retained during all following observation periods after they showed significance. In patients with AKI, a higher %FO was associated with higher mortality and shorter VFDs from the early phase after ICU admission. FO should be administered with a physiological target or goal in place from the initial phase of critical illness.

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.

Cut-Off Value of Capillary Refill Time for Peripheral Circulatory Failure Diagnosis.

INTRODUCTION: Capillary refill time (CRT) is an indicator of peripheral circulation and is recommended in the 2021 guidelines for treating and managing sepsis. STUDY OBJECTIVE: This study developed a portable device to realize objective CRT measurement. Assuming that peripheral blood flow obstruction by the artery occlusion test (AOT) or venous occlusion test (VOT) increases the CRT, the cut-off value for peripheral circulatory failure was studied by performing a comparative analysis with CRT with no occlusion test (No OT). METHODS: Fourteen (14) healthy adults (age: 20-26 years) participated in the study. For the vascular occlusion test, a sphygmomanometer was placed on the left upper arm of the participant in the supine position, and a pressure of 30mmHg higher than the systolic pressure was applied for AOT, a pressure of 60mmHg was applied for VOT, respectively, and no pressure was applied for No OT. The CRT was measured from the index finger of the participant's left hand. RESULTS: Experimental results revealed that CRT was significantly longer in the AOT and did not differ significantly in the VOT. The cut-off value for peripheral circulatory failure was found to be 2.88 seconds based on Youden's index by using receiver operating characteristic (ROC) analysis with AOT as positive and No OT as negative. CONCLUSION: Significant results were obtained in a previous study on the evaluation of septic shock patients when CRT > three seconds was considered abnormal, and the cut-off value for peripheral circulatory failure in the current study validated this.

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.

A Comparison of Clinical Presentations in Coronavirus Disease 2019 Caused by Different Omicron Variants in Japan: A Retrospective Study.

Objective We evaluated the clinical differences in coronavirus disease 2019 (COVID-19) patients between the sixth wave with the Omicron BA.1/BA.2 dominant variant (from January to April 2022) and seventh wave with the Omicron BA.5 dominant variant (from July to August 2022). Methods This retrospective, single-center, observational study included COVID-19 patients admitted to our institution in the sixth wave (sixth-wave group) and the seventh wave (seventh-wave group). Inter-group comparisons of clinical presentations, the prognosis, and proportion of nosocomial infections were performed. Results A total of 190 patients were included (93 and 97 patients in the sixth- and seventh-wave groups, respectively). While there were no significant differences in severity, significantly more patients developed pneumonia caused by COVID-19 in the sixth-wave group than in the seventh-wave group. Although there was no marked difference in in-hospital deaths, more patients died from COVID-19 in the sixth-wave group than in the seventh-wave group. There were significantly more COVID-19 inpatients with nosocomial infections in the seventh-wave group than in the sixth-wave group. Pneumonia from COVID-19 was significantly more severe in the sixth-wave group than in the seventh-wave group. Conclusion COVID-19 patients in the seventh wave are at a lower risk of pneumonia than those in the sixth wave. However, even in the seventh wave, patients with underlying diseases have a risk of death because of the exacerbation of underlying diseases triggered by COVID-19.

Urinary neutrophil gelatinase-associated lipocalin and plasma IL-6 in discontinuation of continuous venovenous hemodiafiltration for severe acute kidney injury: a multicenter prospective observational study.

BACKGROUND: Patients with severe acute kidney injury (AKI) who require continuous venovenous hemodiafiltration (CVVHDF) in intensive care unit (ICU) are at high mortality risk. Little is known about clinical biomarkers for risk prediction, optimal initiation, and optimal discontinuation of CVVHDF. METHODS: This prospective observational study was conducted in seven university-affiliated ICUs. For urinary neutrophil gelatinase-associated lipocalin (NGAL) and plasma IL-6 measurements, samples were collected at initiation, 24 h, 48 h after, and CVVHDF discontinuation in adult patients with severe AKI. The outcomes were deaths during CVVHDF and CVVHDF dependence. RESULTS: A total number of 133 patients were included. Twenty-eight patients died without CVVHDF discontinuation (CVVHDF nonsurvivors). Urinary NGAL and plasma IL-6 at the CVVHDF initiation were significantly higher in CVVHDF nonsurvivors than in survivors. Among 105 CVVHDF survivors, 70 patients were free from renal replacement therapy (RRT) or death in the next 7 days after discontinuation (success group), whereas 35 patients died or needed RRT again (failure group). Urinary NGAL at CVVHDF discontinuation was significantly lower in the success group (93.8 ng/ml vs. 999 ng/ml, p < 0.01), whereas no significant difference was observed in plasma IL-6 between the groups. Temporal elevations of urinary NGAL levels during the first 48 h since CVVHDF initiation were observed in CVVHDF nonsurvivors and those who failed in CVVHDF discontinuation. CONCLUSIONS: Urinary NGAL at CVVHDF initiation and discontinuation was associated with mortality and RRT dependence, respectively. The serial changes of urinary NGAL might also help predict the prognosis of patients with AKI on CVVHDF.