Hyperlipidaemia and mortality among patients hospitalised with pneumonia: retrospective cohort and propensity score matched study

Abstract

ObjectiveTo characterise the potential association of hyperlipidaemia (HLP) versus no HLP with all-cause mortality among patients hospitalised for pneumonia.DesignPropensity score matched retrospective study.ParticipantsThe study cohort consisted of consecutive 8553 adults hospitalised at a large academic centre with a discharge diagnosis of pneumonia from 1996 through 2015, followed until death or end of the study period, 17 August 2017.OutcomesThe outcome was HR for mortality at 28 days and in the long term in patients with pneumonia with concurrent HLP compared with those with no HLP. We first constructed multivariable Cox proportional regression models to estimate the association between concurrent HLP versus no HLP and mortality after pneumonia hospitalisation for the entire cohort. We then identified 1879 patients with pneumonia with concurrent HLP and propensity score matched in a 1:1 ratio to 1879 patients with no HLP to minimise the imbalance from measured covariates for further analysis.ResultsAmong 8553 unmatched patients with pneumonia, concurrent HLP versus no HLP was independently associated with lower mortality at 28 days (HR 0.52, 95% CI 0.41 to 0.66) and at a median follow-up of 3.9 years (HR 0.75, 95% CI 0.70 to 0.80). The risk difference in mortality was consistent between 1879 propensity score matched pairs both at 28 days (HR 0.65, 95% CI 0.49 to 0.86) and at a median follow-up of 4 years (HR 0.88, 95% CI 0.81 to 0.96). In the subgroup of patients with clinically measured low-density lipoprotein cholesterol (LDL-C), graded inverse associations between LDL-C levels and mortality were found in both unmatched and matched cohorts.ConclusionsAmong hospitalised patients with pneumonia, a diagnosis of HLP is protective against both short-term and long-term risk of death after adjustment for other major contributors to mortality in both unmatched and propensity score matched cohorts. These findings should be further investigated.

Development and internal validation of machine learning–based models and external validation of existing risk scores for outcome prediction in patients with ischaemic stroke

“…Potential predictors were selected from multiple domains for model training based on a review of the literature, previous work, and expert opinion. 22 , 25–28 Potential predictors were selected from multiple domains (see Supplementary material online , Table S1 ). The STROBE flow diagram shows that 14 patients (1.4%) had one or more missing data points.…”