Latent Class Analysis: An innovative approach for identification of clinical and laboratory markers of disease severity among COVID-19 patients admitted to the Intensive Care Unit

Objective

To identify clinical and laboratory phenotype distribution patterns and their usefulness as prognostic markers in COVID-19 patients admitted to the intensive care unit (ICU) in Tygerberg Hospital, Cape Town.

Methods and Results

We used a latent class analysis (LCA) model in a prospective, observational cohort study. Data from 343 COVID-19 patients was analysed. Two distinct phenotypes 1 and 2, comprising 68.46 % and 31.54% patients respectively, were identified. The phenotype 2 patients were characterised by increased coagulopathy markers (D-dimer, median value 1.73 ng/L vs 0.94 ng/L, p <0.001), end-organ dysfunction (creatinine, median 79 µmol/L vs 69.5 µmol/L, p <0.003), under-perfusion marker (lactate, median value 1.60 mmol/L, vs 1.20 mmol/L, p <0.001), abnormal cardiac function markers (median N-terminal pro-brain natriuretic peptide (NT-proBNP) 314 pg/ml vs 63.5 pg/ml, p <0.001 and median high-sensitivity cardiac troponin (Hs-TropT) 39 ng/l vs 12 ng/ l, p<0.001) and acute inflammatory syndrome (median neutrophil-to-lymphocyte ratio 15.08 vs value 8.68, p <0.001 and monocyte, median value 0.68 × 10⁹/L vs 0.45 × 10⁹/L, p <0.001).

Conclusion

The identification of COVID-19 phenotypes and sub-phenotypes in ICU patients could help as prognostic markers in day-to-day management of COVID-19 patients admitted to the ICU.

Genetics of endometriosis

Endometriosis is a heritable complex disorder that is influenced by multiple genetic and environmental factors. Identification of these genetic factors will aid a better understanding of the underlying biology of the disease. In this article, we describe different methods of studying genetic variation of endometriosis, summarize results from genetic studies performed to date and provide recommendations for future studies to uncover additional factors contributing to the heritable component of endometriosis.

Risk prediction models for oral clefts allowing for phenotypic heterogeneity

Oral clefts are common birth defects that have a major impact on the affected individual, their family and society. World-wide, the incidence of oral clefts is 1/700 live births, making them the most common craniofacial birth defects. The successful prediction of oral clefts may help identify sub-population at high risk, and promote new diagnostic and therapeutic strategies. Nevertheless, developing a clinically useful oral clefts risk prediction model remains a great challenge. Compelling evidences suggest the etiologies of oral clefts are highly heterogeneous, and the development of a risk prediction model with consideration of phenotypic heterogeneity may potentially improve the accuracy of a risk prediction model. In this study, we applied a previously developed statistical method to investigate the risk prediction on sub-phenotypes of oral clefts. Our results suggested subtypes of cleft lip (CL) and palate have similar genetic etiologies (AUC = 0.572) with subtypes of CL only (AUC = 0.589), while the subtypes of cleft palate only (CPO) have heterogeneous underlying mechanisms (AUCs for soft CPO and hard CPO are 0.617 and 0.623, respectively). This highlighted the potential that the hard and soft forms of CPO have their own mechanisms despite sharing some of the genetic risk factors. Comparing with conventional methods for risk prediction modeling, our method considers phenotypic heterogeneity of a disease, which potentially improves the accuracy for predicting each sub-phenotype of oral clefts.