Purpose. To investigate the demographic characteristics, etiology, and comorbidities of Cushing’s syndrome (CS) patients at a large medical center in China.Methods. Records on CS patients discharged from 2008 to 2017 were retrieved from the hospital discharge abstract database (DAD) using ICD-10 codes. Demographic characteristics, etiology, and comorbidity data were analyzed.Results. Cushing’s disease (CD) accounted for 63.0% of CS patients, followed by adrenocortical adenoma (ACA) (20.9%), primary bilateral macronodular adrenal hyperplasia (BMAH) (6.2%), ectopic ACTH syndrome (EAS) (5.9%), primary pigmented nodular adrenocortical disease (PPNAD) (1.8%), and adrenocortical carcinoma (ACC) (1.0%). CD, ACA, ACC, and PPNAD presented marked preponderances in women (4.1 : 1, 10.5 : 1, 4.3 : 1, and 2.3 : 1, respectively), while BMAH (59.8%) and EAS (51.0%) showed slightly higher preponderances in men. CD patients were younger than ACA and EAS patients (36.1±12.9years vs.39.4±12.7years and36.1±12.9years vs.41.0±15.8,P<0.001); PPNAD patients were the youngest (24.2±10.8years,P<0.001), and BMAH patients were the oldest (51.3±9.9years,P<0.001). Hypertension, diabetes mellitus, osteoporosis without fractures, osteoporotic fractures, dyslipidemia, and fatty liver occurred more frequently in CD patients than in ACA patients (P<0.001for all). Osteoporotic fractures were observed more frequently in PPAND than in ACA (26.7% vs. 9.0%,P<0.001) and BMAH (26.7% vs. 4.9%,P<0.001) patients. EAS patients had more severe and diverse comorbidities, with higher prevalences of hypokalemia (52.0%), diabetes mellitus (61.2%), and osteoporotic fractures (28.6%). When adjusted for age, male CD patients were associated with hypertension (OR = 2.266, 95% CI: 1.524–3.371, andP<0.001), osteoporotic fractures (OR = 2.274, 95% CI: 1.568–3.298, andP<0.001), fatty liver (OR = 1.435, 95% CI: 1.028–2.003, andP=0.034), and hypokalemia (OR = 1.944, 95% CI: 1.280–2.951, andP=0.002).Conclusions. The proposed method efficiently evaluates CS patients’ epidemiological profiles using hospital DADs with ICD-10 codes and thus may enrich the limited epidemiological data and contribute to clinical practice for CS.

Hyponatremia (serum sodium concentration < 135 mmol/L) is the most common electrolyte abnormality and is a predictor of the mortality of hospitalized patients in Western countries. However, hyponatremia data are lacking in Asian countries. Here we evaluate the epidemiology and mortality of hyponatremia in general medical hospitalized patients in China. This is a cohort study of 154,378 adults who were hospitalized between 2008 and 2012 at a teaching hospital in Beijing. We identified hospital patients with hyponatremia and calculated the prevalence and in-hospital mortality of hyponatremia. We also conducted a comprehensive retrospective review of the medical records of patients who had severe hyponatremia (serum sodium <120 mmol/L) during hospitalization in 2012. The overall prevalence of hyponatremia at some point during hospitalization was 17.5% (26,990 patients), but only 0.26% (394 patients) of cases were identified with the diagnostic code of hyponatremia. Hyponatremia was more common in patients with infectious disease, cancer, or cardiovascular disease as the primary reason for hospitalization based on discharge diagnosis, with prevalences of 33.0, 25.9 and 24.9%, respectively. The in-hospital mortality was 0.48% amongst patients without hyponatremia compared to 3.57 and 20.23% in patients with serum sodium levels of 130–134 and <120 mmol/L, resulting in multivariable adjusted odds ratios (ORs) of 4.8 (95% CI 4.3–5.4) and 32.9 (95% CI 25.2–42.3), respectively. The mortality risk increased with increasing severity of hyponatremia in all diagnostic groups. After the multivariate adjustment, only the Charlson Comorbidity Index and age were independently associated with death risk (OR 1.36, 95% CI 1.14–1.64 and OR 1.04, 95% CI 1.00–1.09, respectively) in the patients with severe hyponatremia. Hyponatremia is highly prevalent among Chinese hospitalized patients and is associated with increased in-hospital mortality risk. Physicians should raise awareness to improve the prognosis of hyponatremia.

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.

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. 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. 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. 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.

Unintentional injuries (UIs) impose a significant burden on low- and middle-income countries (LMICs). However, available UI epidemiological data are limited for LMICs, including China. This article aimed to provide an overview of the UI hospitalization profile, identify risk factors for in-hospital mortality and provide diagnosis-specific survival risk ratios (SRRs) for reference by LMICs using hospital discharge abstract data (DAD) from Beijing, China. A cross-sectional study was conducted for patients sustaining UIs requiring admission. Information was retrieved from 138 hospitals in Beijing to describe the demographics, injury nature, mechanisms, severity and hospital outcomes. Multivariate logistic regression was performed to identify and evaluate risk factors for in-hospital mortality for UIs. Falls (57.1%), transport accidents (19.9%) and exposure to inanimate mechanical forces (16.4%) were the leading causes of UI hospitalization. Falls and transport accidents were responsible for 94.2% of the in-hospital deaths caused by UIs. Injury mechanisms differed among sex (χ2 = 5322.1, P < 0.001) and age (χ2 = 24,143.3, P < 0.001) groups. Male sex (OR: 1.50, 95% confidence interval (CI): 1.23-1.79), age ≥ 85 years (OR: 16.39, 95% CI: 7.46-36.00), Barthel Index at admission ≤ 60 (OR: 25.78, 95% CI: 13.30-49.95), modified Charlson comorbidity index ≥ 6 (OR: 2.60, 95% CI: 1.91-3.55), International Classification of Diseases-based injury severity score (ICISS) < 0.85 (OR: 15.17, 95% CI: 12.57-18.30), sustaining injuries to the head/neck (OR: 23.20, 95% CI: 7.31-73.64), injuries caused by foreign body entering through natural orifice (OR: 34.00, 95%CI: 6.37-181.54) and injuries resulting from transport accidents (OR: 1.71, 95% CI: 1.41-2.07) were important risk factors for in-hospital mortality for UIs. Hospital DAD are an objective and cost-effective data source that allows for a hospital-based perspective of UI epidemiology. Sex, age, functional status at admission, comorbidities, injury nature, severity and mechanism are significantly associated with the in-hospital mortality of UIs in China. This study generates a reference dataset of diagnosis-specific SRRs from a large trauma population in China, which may be more applicable in injury severity estimation using ICISS in LMICs.

Objective: To investigate the validity of discharge ICD-10 codes in detecting the etiology of endogenous Cushing’s syndrome (CS) in hospitalized patients. Methods: We evaluated the sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of CS etiology-related ICD-10 codes or code combinations by comparing hospital discharge administrative data (DAD) with established diagnoses from medical records. Results: Coding for patients with adrenocortical adenoma (ACA) and those with bilateral macronodular adrenal hyperplasia (BMAH) demonstrated disappointingly low sensitivity at 78.8% (95% CI: 70.1% - 85.6%) and 83.9% (95% CI: 65.5% - 93.9%), respectively. BMAH had the lowest PPV of 74.3% (95% CI: 56.4% - 86.9%). In confirmed ACA patients, the sensitivity for ACA code combinations was higher in patients initially admitted to the Department of Endocrinology before surgery than that in patients directly admitted to the Department of Urology (90.0% vs 73.1%, P = 0.033). The same phenomenon was observed in the PPV for the BMAH code (100.0% vs 60.9%, P = 0.012). Misinterpreted or confusing situations caused by coders (68.1%) and by the omission or denormalized documentation of symptomatic diagnosis by clinicians (26.1%) accounted for the main source of coding errors. Conclusions: Hospital DAD is an effective data source for evaluating the etiology of CS but not ACA and BMAH. Improving surgeons’ documentation, especially in the delineation of symptomatic and locative diagnoses in discharge abstracts; department- or disease-specific training for coders; and more multidisciplinary collaboration are ways to enhance the applicability of administrative data for CS etiologies.