Methodologies and key considerations for implementing the International Classification of Diseases-11th revision morbidity coding: insights from a national pilot study in China.

OBJECTIVE: The aim of this study was to disseminate insights from a nationwide pilot of the International Classification of Diseases-11th revision (ICD-11). MATERIALS AND METHODS: The strategies and methodologies employed to implement the ICD-11 morbidity coding in 59 hospitals in China are described. The key considerations for the ICD-11 implementation were summarized based on feedback obtained from the pilot hospitals. Coding accuracy and Krippendorff's alpha reliability were computed based on the coding results in the ICD-11 exam. RESULTS: Among the 59 pilot hospitals, 58 integrated ICD-11 Coding Software into their health information management systems and 56 implemented the ICD-11 in morbidity coding, resulting in 3 723 959 diagnoses for 873 425 patients being coded over a 2-month pilot coding phase. The key considerations in the transition to the ICD-11 in morbidity coding encompassed the enrichment of ICD-11 content, refinement of tools, provision of systematic and tailored training, improvement of clinical documentation, promotion of downstream data utilization, and the establishment of a national process and mechanism for implementation. The overall coding accuracy was 82.9% when considering the entire coding field (including postcoordination) and 92.2% when only one stem code was considered. Krippendorff's alpha was 0.792 (95% CI, 0.788-0.796) and 0.799 (95% CI, 0.795-0.803) with and without consideration of the code sequence, respectively. CONCLUSION: This nationwide pilot study has enhanced national technical readiness for the ICD-11 implementation in morbidity, elucidating key factors warranting careful consideration in future endeavors. The good accuracy and intercoder reliability of the ICD-11 coding achieved following a brief training program underscore the potential for the ICD-11 to reduce training costs and provide high-quality health data. Experiences and lessons learned from this study have contributed to WHO's work on the ICD-11 and can inform other countries when formulating their transition plan.

Predictive model for early functional outcomes following acute care after traumatic brain injuries: A machine learning-based development and validation study.

INTRODUCTION: Few studies on early functional outcomes following acute care after traumatic brain injury (TBI) are available. The aim of this study was to develop and validate a predictive model for functional outcomes at discharge for TBI patients using machine learning methods. PATIENTS AND METHODS: In this retrospective study, data from 5281 TBI patients admitted for acute care who were identified in the Beijing hospital discharge abstract database were analysed. Data from 4181 patients in 52 tertiary hospitals were used for model derivation and internal validation. Data from 1100 patients in 21 secondary hospitals were used for external validation. A poor outcome was defined as a Barthel Index (BI) score ≤ 60 at discharge. Logistic regression, XGBoost, random forest, decision tree, and back propagation neural network models were used to fit classification models. Performance was evaluated by the area under the receiver operating characteristic curve (AUC), the area under the precision-recall curve (AP), calibration plots, sensitivity/recall, specificity, positive predictive value (PPV)/precision, negative predictive value (NPV) and F1-score. RESULTS: Compared to the other models, the random forest model demonstrated superior performance in internal validation (AUC of 0.856, AP of 0.786, and F1-score of 0.724) and external validation (AUC of 0.779, AP of 0.630, and F1-score of 0.604). The sensitivity/recall, specificity, PPV/precision, and NPV of the model were 71.8%, 69.2%, 52.2%, and 84.0%, respectively, in external validation. The BI score at admission, age, use of nonsurgical treatment, neurosurgery status, and modified Charlson Comorbidity Index were identified as the top 5 predictors for functional outcome at discharge. CONCLUSIONS: We established a random forest model that performed well in predicting early functional outcomes following acute care after TBI. The model has utility for informing decision-making regarding patient management and discharge planning and for facilitating health care quality assessment and resource allocation for TBI treatment.

Effects of Extreme Weather Events on Nitrous Oxide Emissions from Rice-Wheat Rotation Croplands.

Cropland ecosystems are significant emission sources of N2O, but a limited number of studies have focused on the impact of extreme weather events on N2O fluxes from cropland. This present study integrated field observations and model simulations to explore the responses of N2O fluxes to extreme weather events in typical rice and wheat rotation croplands in the middle and lower reaches of the Yangtze River (MLRYR) in China. The findings revealed that the studied rice-wheat rotation cropland exhibited a net source of N2O over the three-year monitoring period, with annual cumulative N2O emissions ranging from 190.4 to 261.8 mg N m-2. N2O emissions during the rice and wheat growing seasons accounted for 29% and 71% of the total yearly emissions, respectively. Extreme heat events led to a 23% to 32% increase in observed N2O emissions from cropland. Observed N2O emissions from irrigated rice fields during extreme precipitation events were 45% lower than those during extreme drought events. In contrast, extreme precipitation events raised observed N2O emissions from rain-fed wheat fields by 36% compared to the multi-year average, while extreme drought events reduced N2O emissions from wheat fields by 20%. Regional simulations indicated that annual cumulative N2O emissions from croplands in the MLRYR are projected to increase from 207.8 mg N m-2 under current climate to 303.4 mg N m-2 in the future. Given the episodic nature and uncertainties associated with N2O emissions from cropland, further validation is necessary for utilizing the model to explore the effects of extreme weather events on N2O in cropland ecosystems.

Estimation of variability in soil water content in a forested critical-zone experimental catchment in Eastern China.

Knowledge of soil water content (SWC) dynamics within soil profiles is crucial for the effective management of water and soil resources. This study aims to clarify the temporal variability and stability of SWC in a forested critical-zone experimental catchment, and further to improve the understanding of the temporal and spatial distribution of soil water in a typical hilly catchment in eastern China. The selected Nandadish (NDD) catchment covering 0.79 ha was instrumented with 34 SWC monitoring sites using Frequency Domain Reflectometry. The consecutive high-resolution monitoring data of soil water at different depths of the sites were collected from January 2017 to December 2019. The results showed that the SWC of the shallow layer (0-30 cm) had the strongest variability over time during the three hydrologic years. The interannual variability of SWC showed the opposite regularity with that of the seasonal variability. Specifically, the spatial variability of SWC in the dry years was greater than that in wet years; whilst the temporal stability of SWC in dry seasons was greater than that in rainy seasons. Precipitation and temperature were the two dominant factors influencing the temporal variation of SWC. Precipitation controlled the interannual variation of SWC, while temperature controlled the seasonal variation of SWC. Additionally, soil water had high temporal stability throughout the observation period in NDD catchment, and the most representative point was located at a relatively flat and central place, which can be used to simulate the variability of SWC under different rainfall conditions in the study area. The temporal stability of SWC patterns was controlled by topography, geographic location, throughfall, and the groundwater level in the study area, which was characterized by sloping terrain and forested cover. This research provides scientific bases for the optimum design of ground sampling, and the temporal and spatial prediction for soil moisture in a typical eastern hilly area with forest land uses.