Longitudinal Analysis of the Utility of Liver Biochemistry as Prognostic Markers in Hospitalized Patients With Corona Virus Disease 2019.

The association of liver biochemistry with clinical outcomes of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is currently unclear, and the utility of longitudinally measured liver biochemistry as prognostic markers for mortality is unknown. We aimed to determine whether abnormal liver biochemistry, assessed at baseline and at repeat measures over time, was associated with death in hospitalized patients with COVID-19 compared to those without COVID-19, in a United Kingdom population. We extracted routinely collected clinical data from a large teaching hospital in the United Kingdom, matching 585 hospitalized patients who were SARS-CoV-2 real-time reverse transcription-polymerase chain reaction (RT-PCR) positive to 1,165 hospitalized patients who were RT-PCR negative for age, sex, ethnicity, and preexisting comorbidities. A total of 26.8% (157/585) of patients with COVID-19 died compared to 11.9% (139/1,165) in the group without COVID-19 (P < 0.001). At presentation, a significantly higher proportion of the group with COVID-19 had elevated alanine aminotransferase (20.7% vs. 14.6%, P = 0.004) and hypoalbuminemia (58.7% vs. 35.0%, P < 0.001) compared to the group without COVID-19. Within the group with COVID-19, those with hypoalbuminemia at presentation had 1.83-fold increased hazards of death compared to those with normal albumin (adjusted hazard ratio [HR], 1.83; 95% confidence interval [CI], 1.25-2.67), while the hazard of death was ~4-fold higher in those aged ≥75 years (adjusted HR, 3.96; 95% CI, 2.59-6.04) and ~3-fold higher in those with preexisting liver disease (adjusted HR, 3.37; 95% CI, 1.58-7.16). In the group with COVID-19, alkaline phosphatase (ALP) increased (R = 0.192, P < 0.0001) and albumin declined (R = -0.123, P = 0.0004) over time in patients who died. Conclusion: In this United Kingdom population, liver biochemistry is commonly deranged in patients with COVID-19. Baseline hypoalbuminemia and rising ALP over time could be prognostic markers for death, but investigation of larger cohorts is required to develop a better understanding of the relationship between liver biochemistry and disease outcome.

Inpatient hypoglycaemia in older people is associated with a doubling in the increased length of stay compared with the younger population.

BACKGROUND: Hypoglycaemia during hospital admission is associated with poor outcomes including increased length of stay. In this study, we compared the incidence of inpatient hypoglycaemia and length of stays among people of three age groups: ≤65 years, 65-80 years and >80 years old. METHODS: The study was conducted using a 4-year electronic patient record dataset from Oxford University Hospitals NHS Foundation Trust. The dataset contains hospital admission data for people with diabetes. We analysed the blood glucose (BG) measurements and identified all level 1 (BG <4 mmol/l) and level 2 (BG <3 mmol/l) hypoglycaemic episodes. We compared the length of stays between different age groups and with different levels of hypoglycaemia. RESULTS: We analysed data obtained from 17,658 inpatients with diabetes who underwent 32,758 hospital admissions. The length of stays for admissions with no hypoglycaemia were 3[1,6], 3[1,8] and 4[2,11] (median[interquartile range]) days for age groups ≤65 years, 65-80 years and >80 years, respectively. These were statistically significantly lower (P < 0.01 for all pairwise comparisons) than the length of stays for admissions with level 1 hypoglycaemia, which were 6[3,13], 10[5,20] and 12[6,22] days, and level 2 hypoglycaemia, which were 7[3,14], 11[5,24] and 13[6,24] days. CONCLUSIONS: In all age groups, admissions with either level 1 or level 2 hypoglycaemia were associated with an increased length of stay. However, in both the older groups, the length of stay increments were much higher (double) than the younger counterparts. The clinical consequences of hypoglycaemia were more severe in older people compared with the younger population.

Predicting the Risk of Inpatient Hypoglycemia With Machine Learning Using Electronic Health Records.

OBJECTIVE: We analyzed data from inpatients with diabetes admitted to a large university hospital to predict the risk of hypoglycemia through the use of machine learning algorithms. RESEARCH DESIGN AND METHODS: Four years of data were extracted from a hospital electronic health record system. This included laboratory and point-of-care blood glucose (BG) values to identify biochemical and clinically significant hypoglycemic episodes (BG ≤3.9 and ≤2.9 mmol/L, respectively). We used patient demographics, administered medications, vital signs, laboratory results, and procedures performed during the hospital stays to inform the model. Two iterations of the data set included the doses of insulin administered and the past history of inpatient hypoglycemia. Eighteen different prediction models were compared using the area under the receiver operating characteristic curve (AUROC) through a 10-fold cross validation. RESULTS: We analyzed data obtained from 17,658 inpatients with diabetes who underwent 32,758 admissions between July 2014 and August 2018. The predictive factors from the logistic regression model included people undergoing procedures, weight, type of diabetes, oxygen saturation level, use of medications (insulin, sulfonylurea, and metformin), and albumin levels. The machine learning model with the best performance was the XGBoost model (AUROC 0.96). This outperformed the logistic regression model, which had an AUROC of 0.75 for the estimation of the risk of clinically significant hypoglycemia. CONCLUSIONS: Advanced machine learning models are superior to logistic regression models in predicting the risk of hypoglycemia in inpatients with diabetes. Trials of such models should be conducted in real time to evaluate their utility to reduce inpatient hypoglycemia.

Inpatient hypoglycaemia: understanding who is at risk.

AIMS/HYPOTHESIS: We analysed data obtained from the electronic patient records of inpatients with diabetes admitted to a large university hospital to understand the prevalence and distribution of inpatient hypoglycaemia. METHODS: The study was conducted using electronic patient record data from Oxford University Hospitals NHS Foundation Trust. The dataset contains hospital admission data for patients coded for diabetes. We used the recently agreed definition for a level 1 hypoglycaemia episode as any blood glucose measurement <4 mmol/l and a level 2 hypoglycaemia episode as any blood glucose measurement <3 mmol/l. Any two or more consecutive low blood glucose measurements within a 2 h time window were considered as one single hypoglycaemic episode. RESULTS: We analysed data obtained from 17,658 inpatients with diabetes (1696 with type 1 diabetes, 14,006 with type 2 diabetes, and 1956 with other forms of diabetes; 9277 men; mean ± SD age, 66 ± 18 years) who underwent 32,758 hospital admissions between July 2014 and August 2018. The incidence of level 1 hypoglycaemia was 21.5% and the incidence of level 2 hypoglycaemia was 9.6%. Recurrent level 1 and level 2 hypoglycaemia occurred, respectively, in 51% and 39% of hospital admissions in people with type 2 diabetes with at least one hypoglycaemic episode, and in 55% and 45% in those with type 1 diabetes. The incidence of level 2 hypoglycaemia in people with type 2 diabetes, when corrected for the number of people who remained in hospital, remained constant for the first 100 h at approximately 0.15 events per h per admission. With regards to the hypoglycaemia distribution during the day, after correcting for the number of blood glucose tests per h, there were two clear spikes in the rate of hypoglycaemia approximately 3 h after lunch and after dinner. The highest rate of hypoglycaemia per glucose test was seen between 01:00 hours and 05:00 hours. Medication had a significant impact on the incidence of level 2 hypoglycaemia, ranging from 1.5% in people with type 2 diabetes on metformin alone to 33% in people treated with a combination of rapid-acting insulin analogue, long-acting insulin analogue and i.v.-administered insulin. CONCLUSIONS/INTERPRETATION: Retrospective analysis of data from electronic patient records enables clinicians to gain a greater understanding of the incidence and distribution of inpatient hypoglycaemia. This information should be used to drive evidence-based improvements in the glycaemic control of inpatients through targeted medication adjustment for specific populations at high risk of hypoglycaemia.