Low ALT values amongst hospitalized patients are associated with increased risk of hypoglycemia and overall mortality: a retrospective, big-data analysis of 51 831 patients.

BACKGROUND: Sarcopenia and frailty influence clinical patients' outcomes. Low alanine aminotransferase (ALT) serum activity is a surrogate marker for sarcopenia and frailty. In-hospital hypoglycemia is associated, also with worse clinical outcomes. AIM: We evaluated the association between low ALT, risk of in-hospital hypoglycemia and subsequent mortality. DESIGN: This was a retrospective cohort analysis. METHODS: We included patients hospitalized in a tertiary hospital between 2007 and 2019. Patients' data were retrieved from their electronic medical records. RESULTS: The cohort included 51 831 patients (average age 70.88). The rate of hypoglycemia was 10.8% (amongst diabetics 19.4% whereas in non-diabetics 8.3%). The rate of hypoglycemia was higher amongst patients with ALT < 10 IU/l in the whole cohort (14.3% vs. 10.4%, P < 0.001) as well as amongst diabetics (24.6% vs. 18.8%, P < 0.001). Both the overall and in-hospital mortality were higher in the low ALT group (57.7% vs. 39.1% P < 0.001 and 4.3% vs. 3.2%, P < 0.001). A propensity score matching, after which a regression model was performed, showed that patients with ALT levels < 10 IU/l had higher risk of overall mortality (HR = 1.21, CI 1.13-1.29, P < 0.001). CONCLUSIONS: Low ALT values amongst hospitalized patients are associated with increased risk of in-hospital hypoglycemia and overall mortality.

Reducing drug prescription errors and adverse drug events by application of a probabilistic, machine-learning based clinical decision support system in an inpatient setting.

BACKGROUND: Drug prescription errors are made, worldwide, on a daily basis, resulting in a high burden of morbidity and mortality. Existing rule-based systems for prevention of such errors are unsuccessful and associated with substantial burden of false alerts. OBJECTIVE: In this prospective study, we evaluated the accuracy, validity, and clinical usefulness of medication error alerts generated by a novel system using outlier detection screening algorithms, used on top of a legacy standard system, in a real-life inpatient setting. MATERIALS AND METHODS: We integrated a novel outlier system into an existing electronic medical record system, in a single medical ward in a tertiary medical center. The system monitored all drug prescriptions written during 16 months. The department's staff assessed all alerts for accuracy, clinical validity, and usefulness. We recorded all physician's real-time responses to alerts generated. RESULTS: The alert burden generated by the system was low, with alerts generated for 0.4% of all medication orders. Sixty percent of the alerts were flagged after the medication was already dispensed following changes in patients' status which necessitated medication changes (eg, changes in vital signs). Eighty-five percent of the alerts were confirmed clinically valid, and 80% were considered clinically useful. Forty-three percent of the alerts caused changes in subsequent medical orders. CONCLUSION: A clinical decision support system that used a probabilistic, machine-learning approach based on statistically derived outliers to detect medication errors generated clinically useful alerts. The system had high accuracy, low alert burden and low false-positive rate, and led to changes in subsequent orders.

Corrected QT interval anomalies are associated with worse prognosis among patients suffering from sepsis.

BACKGROUND: Patients suffering from sepsis experience organ failure and metabolic derangements, with a negative impact on their prognosis and survival. Objective markers for dismal prognosis in this group of patients are sought. AIMS: To assess the potential role of corrected QT interval anomalies as surrogates for metabolic derangements leading to increased short and medium-term mortality in patients suffering from sepsis. METHODS: This study utilised a historic-cohort analysis of 257 septic patients admitted to internal medicine departments. Personal data, vital signs, laboratory results and electrocardiograms were collected. Patients were grouped according to QTc duration, weather mid-range (395-490 ms) or non-mid-range, and further defined as shorter (<395 ms) or longer (>490 ms). RESULTS: Mortality rates differed significantly between the mid-range QTc group and the non-mid-range groups at 14 days (23.7 vs 38.2%, respectively; P = 0.014) and at 3 months (38.5 vs 59.6%, respectively; P = 0.001). In a three-group analysis, the 14-day mortality was the highest in the longer QTc group and the lowest in the mid-range group compared with the shorter QTc group (44.4, 23.7 and 35.5%, respectively; P = 0.034), and this difference also remained at 3 months (74.1, 38.5 and 53.2%, respectively; P = 0.001). All differences remained statistically significant in a multivariate Cox regression analysis. CONCLUSIONS: QTc duration anomalies are associated with worse short- and medium-term prognosis and may act as a marker for more severe clinical sequelae.