The Impact of a Machine Learning Early Warning Score on Hospital Mortality: A Multicenter Clinical Intervention Trial.

OBJECTIVES: To determine the impact of a machine learning early warning risk score, electronic Cardiac Arrest Risk Triage (eCART), on mortality for elevated-risk adult inpatients. DESIGN: A pragmatic pre- and post-intervention study conducted over the same 10-month period in 2 consecutive years. SETTING: Four-hospital community-academic health system. PATIENTS: All adult patients admitted to a medical-surgical ward. INTERVENTIONS: During the baseline period, clinicians were blinded to eCART scores. During the intervention period, scores were presented to providers. Scores greater than or equal to 95th percentile were designated high risk prompting a physician assessment for ICU admission. Scores between the 89th and 95th percentiles were designated intermediate risk, triggering a nurse-directed workflow that included measuring vital signs every 2 hours and contacting a physician to review the treatment plan. MEASUREMENTS AND MAIN RESULTS: The primary outcome was all-cause inhospital mortality. Secondary measures included vital sign assessment within 2 hours, ICU transfer rate, and time to ICU transfer. A total of 60,261 patients were admitted during the study period, of which 6,681 (11.1%) met inclusion criteria (baseline period n = 3,191, intervention period n = 3,490). The intervention period was associated with a significant decrease in hospital mortality for the main cohort (8.8% vs 13.9%; p < 0.0001; adjusted odds ratio [OR], 0.60 [95% CI, 0.52-0.71]). A significant decrease in mortality was also seen for the average-risk cohort not subject to the intervention (0.49% vs 0.26%; p < 0.05; adjusted OR, 0.53 [95% CI, 0.41-0.74]). In subgroup analysis, the benefit was seen in both high- (17.9% vs 23.9%; p = 0.001) and intermediate-risk (2.0% vs 4.0 %; p = 0.005) patients. The intervention period was also associated with a significant increase in ICU transfers, decrease in time to ICU transfer, and increase in vital sign reassessment within 2 hours. CONCLUSIONS: Implementation of a machine learning early warning score-driven protocol was associated with reduced inhospital mortality, likely driven by earlier and more frequent ICU transfer.

An effective chromatographic separation of chicken red blood cell coproporphyrinogen oxidase and uroporphyrinogen decarboxylase, two enzymes in heme biosynthesis.

Of the heme biosynthetic pathway enzymes, coproporphyrinogen oxidase is one of the least understood. Substrate recognition studies [Prepr. Biochem. Biotech.1997, 27, 47, J. Org. Chem.1999, 64, 464] have been done using chicken blood hemolysates (CBH) as the source of this enzyme. However, the enzyme uroporphyrinogen decarboxylase is also present in these preparations and separation of these two enzymes from CBH had not yet been achieved. Thus, a substrate ligand column was developed by covalently linking coproporphyrin-III to a sepharose resin following a similar procedure previously used for the purification of uroporphyrinogen decarboxylase [Int. J. Biochem.1992, 24, 105]. The ligand-resin chromatography step rapidly separates coproporphyrinogen oxidase from uroporphyrinogen decarboxylase as well as the majority of the hemoglobin.