Obtaining patient phenotypes in SARS-CoV-2 pneumonia, and their association with clinical severity and mortality.

BACKGROUND: There exists consistent empirical evidence in the literature pointing out ample heterogeneity in terms of the clinical evolution of patients with COVID-19. The identification of specific phenotypes underlying in the population might contribute towards a better understanding and characterization of the different courses of the disease. The aim of this study was to identify distinct clinical phenotypes among hospitalized patients with SARS-CoV-2 pneumonia using machine learning clustering, and to study their association with subsequent clinical outcomes as severity and mortality. METHODS: Multicentric observational, prospective, longitudinal, cohort study conducted in four hospitals in Spain. We included adult patients admitted for in-hospital stay due to SARS-CoV-2 pneumonia. We collected a broad spectrum of variables to describe exhaustively each case: patient demographics, comorbidities, symptoms, physiological status, baseline examinations (blood analytics, arterial gas test), etc. For the development and internal validation of the clustering/phenotype models, the dataset was split into training and test sets (50% each). We proposed a sequence of machine learning stages: feature scaling, missing data imputation, reduction of data dimensionality via Kernel Principal Component Analysis (KPCA), and clustering with the k-means algorithm. The optimal cluster model parameters -including k, the number of phenotypes- were chosen automatically, by maximizing the average Silhouette score across the training set. RESULTS: We enrolled 1548 patients, each of them characterized by 92 clinical attributes (d=109 features after variable encoding). Our clustering algorithm identified k=3 distinct phenotypes and 18 strongly informative variables: Phenotype A (788 cases [50.9% prevalence] - age ∼ 57, Charlson comorbidity ∼ 1, pneumonia CURB-65 score ∼ 0 to 1, respiratory rate at admission ∼ 18 min-1, FiO2 ∼ 21%, C-reactive protein CRP ∼ 49.5 mg/dL [median within cluster]); phenotype B (620 cases [40.0%] - age ∼ 75, Charlson ∼ 5, CURB-65 ∼ 1 to 2, respiration ∼ 20 min-1, FiO2 ∼ 21%, CRP ∼ 101.5 mg/dL); and phenotype C (140 cases [9.0%] - age ∼ 71, Charlson ∼ 4, CURB-65 ∼ 0 to 2, respiration ∼ 30 min-1, FiO2 ∼ 38%, CRP ∼ 152.3 mg/dL). Hypothesis testing provided solid statistical evidence supporting an interaction between phenotype and each clinical outcome: severity and mortality. By computing their corresponding odds ratios, a clear trend was found for higher frequencies of unfavourable evolution in phenotype C with respect to B, as well as more unfavourable in phenotype B than in A. CONCLUSION: A compound unsupervised clustering technique (including a fully-automated optimization of its internal parameters) revealed the existence of three distinct groups of patients - phenotypes. In turn, these showed strong associations with the clinical severity in the progression of pneumonia, and with mortality.

Complications and hospital admission in the following 90 days after lung cryobiopsy performed in interstitial lung disease.

Transbronchial lung cryobiopsy (TBLC) is an emerging technique for the diagnosis of interstitial lung disease (ILD), but its risk benefit ratio has been questioned. The objectives of this research were to describe any adverse events that occur within 90 days following TBLC and to identify clinical predictors that could help to detect the population at risk. METHODS: We conducted an ambispective study including all patients with suspected ILD who underwent TBLC. Data were collected concerning the safety profile of this procedure and compared to various clinical variables. RESULTS: Overall, 257 TBLCs were analysed. Complications were observed in 15.2% of patients; nonetheless, only 5.4% of all patients required hospital admission on the day of the procedure. In the 30 and 90 days following the TBLC, rates of readmission were 1.3% and 3.5% and of mortality were 0.38%, and 0.78% respectively. Two models were built to predict early admission (AUC 0.72; 95% CI 0.59-0.84) and overall admission (AUC 0.76; 95% CI 0.67-0.85). CONCLUSIONS: Within 90 days after TBLC, 8.9% of patients suffered a complication serious enough to warrant hospital admission. Modified MRC dyspnoea score ≥2, FVC<50%, and a Charlson Comorbidity Index score ≥2 were factors that predicted early and overall admission.