BACKGROUND: There is little literature describing the artificial intelligence (AI)-aided diagnosis of severe pneumonia (SP) subphenotypes and the association of the subphenotypes with the ventilatory treatment efficacy. The aim of our study is to illustrate whether clinical and biological heterogeneity, such as ventilation and gas-exchange, exists among patients with SP using chest computed tomography (CT)-based AI-aided latent class analysis (LCA). METHODS: This retrospective study included 413 patients hospitalized at Xinhua Hospital diagnosed with SP from June 1, 2015 to May 30, 2020. AI quantification results of chest CT and their combination with additional clinical variables were used to develop LCA models in an SP population. The optimal subphenotypes were determined though evaluating statistical indicators of all the LCA models, and clinical implications of them such as guiding ventilation strategies were further explored by statistical methods. RESULTS: The two-class LCA model based on AI quantification results of chest CT can describe the biological characteristics of the SP population well and hence yielded the two clinical subphenotypes. Patients with subphenotype-1 had milder infections ( P <0.001) than patients with subphenotype-2 and had lower 30-day ( P <0.001) and 90-day ( P <0.001) mortality, and lower in-hospital ( P = 0.001) and 2-year ( P <0.001) mortality. Patients with subphenotype-1 showed a better match between the percentage of non-infected lung volume (used to quantify ventilation) and oxygen saturation (used to reflect gas exchange), compared with patients with subphenotype-2. There were significant differences in the matching degree of lung ventilation and gas exchange between the two subphenotypes ( P <0.001). Compared with patients with subphenotype-2, those with subphenotype-1 showed a relatively better match between CT-based AI metrics of the non-infected region and oxygenation, and their clinical outcomes were effectively improved after receiving invasive ventilation treatment. CONCLUSIONS A two-class LCA model based on AI quantification results of chest CT in the SP population particularly revealed clinical heterogeneity of lung function. Identifying the degree of match between ventilation and gas-exchange may help guide decisions about assisted ventilation.
Humans ; Tomography, X-Ray Computed/methods* ; Male ; Female ; Retrospective Studies ; Middle Aged ; Artificial Intelligence ; Aged ; Pneumonia/diagnosis* ; Latent Class Analysis ; Adult

Objective　To study the predictability, stability and safety of laser in situ keratomileusis (LASIK) for myopia.Methods　 Five hundred and Sixty-five eyes ( 293 patients ) were treated with the SCMD corneal shaper and laser sight Compak - 200 excimer laser and followed up for more than 6 months. These eyes were divided into 4 groups according to the degrees of myopia: Group Ⅰ:-1.50～-5.75D,120 eyes; Group Ⅱ:-6.00～-9.75D,230 eyes; Group Ⅲ:-10.00～-14.75D,148 eyes and croup Ⅳ:-15.00～-26.00D,67 eyes.Results　Six months post-operatively, mean uncorrected visual acuity of each group was 1.15±0.20, 0.92±0.22, 0.69±0.24 and 0.44±0.22 respectively; The uncorrected visual acuity of 0.5 or better was 100%, 94.6%, 85.8% and 47.7% respectively; And 1.0 or better was 91.7%, 56.7%, 18.0% , 0% respectively. Mean spherical equivalent of each group was -0.14D±0.61D, -0.43D±1.20D, -0.57D±1.47D and -1.79D±2.57D respectively. There were no serious complications. Conclusion　 LASIK is an effective and safe method for reducing myopia. But the potential complications should be noticed.