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Prolonged hospital stays can lead to inefficiencies in health care delivery and unnecessary consumption of medical resources. This study aimed to identify key clinical variances associated with prolonged length of stay (PLOS) in clinical pathways using a machine learning model trained on real-world data from the ePath system. We analyzed data from 480 patients with lung cancer (age: mean 68.3, SD 11.2 years; n=263, 54.8% men) who underwent video-assisted thoracoscopic surgery at a university hospital between 2019 and 2023. PLOS was defined as a hospital stay exceeding 9 days after video-assisted thoracoscopic surgery. The variables collected between admission and 4 days after surgery were examined, and those that showed a significant association with PLOS in univariate analyses (P<.01) were selected as predictors. Predictive models were developed using sparse linear regression methods (Lasso, ridge, and elastic net) and decision tree ensembles (random forest and extreme gradient boosting). The data were divided into derivation (earlier study period) and testing (later period) cohorts for temporal validation. The model performance was assessed using the area under the receiver operating characteristic curve, Brier score, and calibration plots. Counterfactual analysis was used to identify key clinical factors influencing PLOS. A 3D heatmap illustrated the temporal relationships between clinical factors and PLOS based on patient demographics, comorbidities, functional status, surgical details, care processes, medications, and variances recorded from admission to 4 days after surgery. Among the 5 algorithms evaluated, the ridge regression model demonstrated the best performance in terms of both discrimination and calibration. Specifically, it achieved area under the receiver operating characteristic curve values of 0.84 and 0.82 and Brier scores of 0.16 and 0.17 in the derivation and test cohorts, respectively. In the final model, a range of variables, including blood tests, care, patient background, procedures, and clinical variances, were associated with PLOS. Among these, particular emphasis was placed on clinical variances. Counterfactual analysis using the ridge regression model identified 6 key variables strongly linked to PLOS. In order of impact, these were abnormal respiratory sounds, postoperative fever, arrhythmia, impaired ambulation, complications after drain removal, and pulmonary air leaks. A machine learning-based model using ePath data effectively identified critical variances in the clinical pathways associated with PLOS. This automated tool may enhance clinical decision-making and improve patient management.

No studies have evaluated whether administering intravenous lactated Ringer's (LR) solution to patients with out-of-hospital cardiac arrest (OHCA) improves their outcomes, to our knowledge. Therefore, we examined the association between prehospital use of LR solution and patients' return of spontaneous circulation (ROSC), 1-month survival, and neurological or physical outcomes at 1 month after the event. We conducted a prospective, non-randomized, observational study using national data of all patients with OHCA from 2005 through 2009 in Japan. We performed a propensity analysis and examined the association between prehospital use of LR solution and short- and long-term survival. The study patients were ≥18 years of age, had an OHCA before arrival of EMS personnel, were treated by EMS personnel, and were then transported to hospitals. A total of 531,854 patients with OHCA met the inclusion criteria. Among propensity-matched patients, compared with those who did not receive pre-hospital intravenous fluids, prehospital use of LR solution was associated with an increased likelihood of ROSC before hospital arrival (odds ratio [OR] adjusted for all covariates [95% CI] = 1.239 [1.146-1.339] [p<0.001], but with a reduced likelihood of 1-month survival with minimal neurological or physical impairment (cerebral performance category 1 or 2, OR adjusted for all covariates [95% CI] = 0.764 [0.589-0.992] [p = 0.04]; and overall performance category 1 or 2, OR adjusted for all covariates [95% CI] = 0.746 [0.573-0.971] [p = 0.03]). There was no association between prehospital use of LR solution and 1-month survival (OR adjusted for all covariates [95% CI] = 0.960 [0.854-1.078]). In Japanese patients experiencing OHCA, the prehospital use of LR solution was independently associated with a decreased likelihood of a good functional outcome 1 month after the event, but with an increased likelihood of ROSC before hospital arrival. Prehospital use of LR solution was not associated with 1-month survival. Further study is necessary to verify these findings. Please see later in the article for the Editors' Summary.

An accurate evaluation of the nutritional status of malnourished hospitalized patients at a higher risk of complications, such as frailty or disability, is crucial. Visual methods of estimating food intake are popular for evaluating the nutritional status in clinical environments. However, from the perspective of accurate measurement, such methods are unreliable. The accuracy of estimating leftover liquid food in hospitals using an artificial intelligence (AI)-based model was compared to that of visual estimation. The accuracy of the AI-based model (AI estimation) was compared to that of the visual estimation method for thin rice gruel as staple food and fermented milk and peach juice as side dishes. A total of 576 images of liquid food (432 images of thin rice gruel, 72 of fermented milk, and 72 of peach juice) were used. The mean absolute error, root mean squared error, and coefficient of determination (R ) were used as metrics for determining the accuracy of the evaluation process. Welch t test and the confusion matrix were used to examine the difference of mean absolute error between AI and visual estimation. The mean absolute errors obtained through the AI estimation approach were 0.63 for fermented milk, 0.25 for peach juice, and 0.85 for the total. These were significantly smaller than those obtained using the visual estimation approach, which were 1.40 (P<.001) for fermented milk, 0.90 (P<.001) for peach juice, and 1.03 (P=.009) for the total. By contrast, the mean absolute error for thin rice gruel obtained using the AI estimation method (0.99) did not differ significantly from that obtained using visual estimation (0.99). The confusion matrix for thin rice gruel showed variation in the distribution of errors, indicating that the errors in the AI estimation were biased toward the case of many leftovers. The mean squared error for all liquid foods tended to be smaller for the AI estimation than for the visual estimation. Additionally, the coefficient of determination (R ) for fermented milk and peach juice tended to be larger for the AI estimation than for the visual estimation, and the R value for the total was equal in terms of accuracy between the AI and visual estimations. The AI estimation approach achieved a smaller mean absolute error and root mean squared error and a larger coefficient of determination (R ) than the visual estimation approach for the side dishes. Additionally, the AI estimation approach achieved a smaller mean absolute error and root mean squared error compared to the visual estimation method, and the coefficient of determination (R ) was similar to that of the visual estimation method for the total. AI estimation measures liquid food intake in hospitals more precisely than visual estimation, but its accuracy in estimating staple food leftovers requires improvement.

Osteoporosis has become a worldwide public health problem, in part due to the fact that it increases the risk of fragility hip fractures (FHFs). The epidemiological assessment of FHFs is critical for their prevention; however, datasets for FHFs in Japan remain scarce. This was a multicenter, prospective, observational study in the northern district of Kyushu Island. Inclusion criteria were age > 60 years with a diagnosis of FHF and acquisition of clinical data by an electronic data capture system. Of 1294 registered patients, 1146 enrolled in the study. Nearly one third of patients (31.8%) had a history of previous fragility fractures. The percentage of patients receiving osteoporosis treatment on admission was 21.5%. Almost all patients underwent surgical treatment (99.1%), though fewer than 30% had surgery within 48 h after hospitalization. Bone mineral density (BMD) was evaluated during hospitalization in only 50.4% of patients. The rate of osteoporosis treatment increased from 21.5% on admission to 39.3% during hospitalization. The main reasons that prescribers did not administer osteoporosis treatment during hospitalization were forgetfulness (28.4%) and clinical judgment (13.6%). Age and female ratio were significantly higher in patients with previous FHFs than in those without. There was a significant difference in the rate of osteoporosis treatment or L-spine BMD values in patients with or without previous FHFs on admission. In conclusion, this study confirmed that the evaluation and treatment of osteoporosis and FHFs is still suboptimal in Japan, even in urban districts.

Background Pure dysarthria (PD) and dysarthria-facial paresis syndrome (DFP) mainly result from lenticulostriate artery territory infarction. PD and DFP are rare clinical entities, often grouped without distinction. The purpose of this study was to examine clinical and radiographic differences between PD and DFP due to unilateral internal capsule and/or corona radiata infarction. Methods Using a database that included consecutive patients with ischemic stroke admitted to the neurological stroke units of three hospitals within 7 days from onset between September 2011 and April 2014, we retrospectively extracted first-ever stroke patient data, who presented with PD or DFP with a single ischemic lesion localized in the internal capsule and/or corona radiata. Patients with weakness, ataxia, sensory deficit, or cortical symptoms were excluded. Ischemic lesion volume was calculated by the ABC/2 method on diffusion-weighted imaging (DWI). DWI images were normalized and superimposed to the template for PD and DFP. We compared patients' characteristics between PD and DFP. Results A total of 2126 patients, including 65 patients (3.1 %) with PD or DFP, were registered. Of these, 13 PD patients and 18 patients with DFP due to unilateral internal capsule and/or corona radiata infarction were included for analysis. Compared with DFP patients, PD patients had longer onset-to-door time (median 37.5 vs. 10.8 h, p  = 0.031), shorter vertical length (C component) of ischemic lesions (median 12.0 vs. 18.8 mm, p  = 0.007), and smaller ischemic lesion volume (median 285 vs. 828 mm 3 , p  = 0.023). Ischemic lesions causing PD were located more frequently in the left hemisphere than DFP (92 % vs. 56 %, p  = 0.045). The superimposed lesion pattern indicated that DFP had lesions more medial and involving posterior portions of the putamen and the caudate body, as well as more of the genu and posterior limb of the internal capsule, than PD. Ninety days after onset, symptoms disappeared in 21 (72 %) out of 29 patients. Conclusions In cerebral infarction limited to the internal capsule and/or corona radiata, PD is derived from smaller and left-sided lesions with delay in diagnosis compared with DFP. The clinical course of those with PD and DFP might be benign.

Introduction The reliability of data‐driven predictions in real‐world scenarios remains uncertain. This study aimed to develop and validate a machine‐learning‐based model for predicting clinical outcomes using real‐world data from an electronic clinical pathway (ePath) system. Methods All available data were collected from patients with lung cancer who underwent video‐assisted thoracoscopic surgery at two independent hospitals utilizing the ePath system. The primary clinical outcome of interest was prolonged air leak (PAL), defined as drainage removal more than 2 days post‐surgery. Data‐driven prediction models were developed in a cohort of 314 patients from a university hospital applying sparse linear regression models (least absolute shrinkage and selection operator, ridge, and elastic net) and decision tree ensemble models (random forest and extreme gradient boosting). Model performance was then validated in a cohort of 154 patients from a tertiary hospital using the area under the receiver operating characteristic curve (AUROC) and calibration plots. Results To mitigate bias, variables with missing data related to PAL or those with high rates of missing data were excluded from the dataset. Fivefold cross‐validation indicated improved AUROCs when utilizing key variables, even post‐imputation of missing data. Dichotomizing continuous variables enhanced performance, particularly when fewer variables were employed in the decision tree ensemble models. Consequently, regression models incorporating seven key variables in complete case analysis demonstrated superior discriminatory ability for both internal (AUROCs: 0.77–0.84) and external cohorts (AUROCs: 0.75–0.84). These models exhibited satisfactory calibration in both cohorts. Conclusions The data‐driven prediction model implementing the ePath system exhibited adequate performance in predicting PAL post‐video‐assisted thoracoscopic surgery, optimizing variables and considering population characteristics in a real‐world setting.

Introduction Patients with stroke often experience pneumonia during the acute stage after stroke onset. Oral care may be effective in reducing the risk of stroke‐associated pneumonia (SAP). We aimed to determine the changes in oral care, as well as the incidence of SAP, in patients with intracerebral hemorrhage, following implementation of a learning health system in our hospital. Methods We retrospectively analyzed the data of 1716 patients with intracerebral hemorrhage who were hospitalized at a single stroke center in Japan between January 2012 and December 2018. Data were stratified on the basis of three periods of evolving oral care: period A, during which conventional, empirically driven oral care was provided (n = 725); period B, during which standardized oral care was introduced, with SAP prophylaxis based on known risk factors (n = 469); and period C, during which oral care was risk‐appropriate based on learning health system data (n = 522). Logistic regression analysis was performed to evaluate associations between each of the three treatment approaches and the risk of SAP. Results Among the included patients, the mean age was 71.3 ± 13.6 years; 52.6% of patients were men. During the course of each period, the frequency of oral care within 24 hours of admission increased (P < .001), as did the adherence rate to oral care ≥3 times per day (P < .001). After adjustment for confounding factors, a change in the risk of SAP was not observed in period B; however, the risk significantly decreased in period C (odds ratio 0.61; 95% confidence interval 0.43‐0.87) compared with period A. These associations were maintained for SAP diagnosed using strict clinical criteria or after exclusion of 174 patients who underwent neurosurgical treatment. Conclusions Risk‐appropriate care informed by the use of learning health system data could improve care and potentially reduce the risk of SAP in patients with intracerebral hemorrhage in the acute stage.