Model based on the automated AI-driven CT quantification is effective for the diagnosis of refractory Mycoplasma pneumoniae pneumonia.

The prediction of refractory Mycoplasma pneumoniae pneumonia (RMPP) remains a clinically significant challenge. This study aimed to develop an early predictive model utilizing artificial intelligence (AI)-derived quantitative assessment of lung lesion extent on initial computed tomography (CT) scans and clinical indicators for RMPP in pediatric inpatients. A retrospective cohort study was conducted on patients with M. pneumoniae pneumonia (MP) admitted to the Children's Hospital of Nanjing Medical University, China from January 2019 to December 2020. An early prediction model was developed by stratifying the patients with Mycoplasma pneumoniae pneumonia (MPP) into two cohorts according to the presence or absence of refractory pneumonia. A retrospective cohort of 126 children diagnosed with Mycoplasma pneumoniae pneumonia (MPP) was utilized as a training set, with 85 cases classified as RMPP. Subsequently, a prospective cohort comprising 54 MPP cases, including 37 instances of RMPP, was assembled as a validation set to assess the performance of the predictive model for RMPP from January to December 2021. We defined a constant Φ which can combine the volume and CT value of pulmonary lesions and be further used to calculate the logarithm of Φ to the base of 2 (Log2Φ). A clinical-imaging prediction model was then constructed utilizing Log2Φ and clinical characteristics. Performance was evaluated by the area under the receiver operating characteristic curve (AUC). The clinical model demonstrated AUC values of 0.810 and 0.782, while the imaging model showed AUC values of 0.764 and 0.769 in the training and test sets, respectively. The clinical-imaging model, incorporating Log2Φ, temperature(T), aspartate aminotransferase (AST), preadmission fever duration (PFD), and preadmission macrolides therapy duration (PMTD), achieved the highest AUC values of 0.897 and 0.895 in the training and test sets, respectively. A prognostic model developed through automated quantification of lung disease on CT scans, in conjunction with clinical data in MPP may be utilized for the early identification of RMPP.

Association between service scope of primary care facilities and prevalence of high-cost population: a retrospective study in rural Guizhou, China.

BACKGROUND: High-cost (HC) patients, defined as the small percentage of the population that accounts for a high proportion of health care expenditures, are a concern worldwide. Previous studies have found that the occurrence of HC population is partially preventable by providing a greater scope of primary health care services. However, no study has examined the association between the service scope of primary care facilities and the prevalence of HC populations. Therefore, this study aimed to investigate the association between the service scope of primary care facilities (PCFs) and the prevalence of HC populations within the same communities. METHODS: A multistage, stratified, clustered sampling method was used to identify the service scope of PCFs as of 2017 in rural Guizhou, China. The claims data of 299,633 patients were obtained from the local information system of the New Rural Cooperation Medical Scheme. Patients were sorted by per capita inpatient medical expenditures in descending order, and the top 1%, top 5% and top 10% of patients who had incurred the highest costs were defined as the HC population. Logistic regression models were used to assess the association between the service scope of PCFs and the prevalence of the HC population. RESULTS: Compared with those in the 95% of the sample deemed as the general population, those in the top 5% of the sample deemed as the HC population were more likely to be over the age of 30 (P <  0.001), to be female (P = 0.014) and to be referred to high-level hospitals (P <  0.001). After controlling for other covariates, patients who lived in the communities serviced by the PCFs with the smallest service scope were more likely to be in the top 1%, top 5% and top 10% of the HC population. CONCLUSION: A greater PCF service scope was associated with a reduction in the prevalence of the HC population, which would mean that providing a broader PCF service scope could reduce some preventable costs, thus reducing the prevalence of the HC population. Future policy efforts should focus on expanding the service scope of primary care providers to achieve better patient outcomes.