Warming Prior to Heel Stick: Blood Sample Quality and Infant Comfort-A Randomized Controlled Trial.

BACKGROUND: Capillary blood sampling (heel stick) in infants is commonly performed in neonatal care units. Before the procedure, warming the infant's heel is often a customary practice, but no consensus exists on the most effective heel-warming method. PURPOSE: To compare the effects of routinely used warming methods (glove, gel pack, or blanket) applied prior to heel stick on blood sample quality and infant's comfort. METHODS: This prospective, double-blind, randomized controlled trial conducted in the neonatal intensive care unit included infants (postmenstrual age of ≥28 + 0 weeks and ≤43 + 6 weeks) who were computer-randomized to 1 of 3 warming methods.The primary outcome was blood flow velocity at sampling. Secondary outcomes were hemolysis index, infant COMFORTneo score, and frequency of postprocedure skin injuries. In addition, irrespective of the warming method used, the correlation between heel skin temperature and postprocedure heel skin injury was analyzed. RESULTS: A total of 176 heel warmings were successfully randomized, and 173 were analyzed. Despite a significant difference in obtained heel skin temperature after warming between the 3 warming methods ( P = .001), no difference in blood flow velocity ( P = .91), hemolysis index ( P = .99), or COMFORTneo score ( P = .76) was found. Baseline skin temperatures above 37.0°C were associated with higher incidences of skin injury, and skin temperatures after warming were significantly higher in skin-injured heels ( P = .038). IMPLICATIONS FOR PRACTICE AND RESEARCH: All 3 warming methods had similar effects on blood sample quality and infant's comfort. However, excessive warming of the heel should be avoided to prevent skin injuries.

Using artificial intelligence in a primary care setting to identify patients at risk for cancer: a risk prediction model based on routine laboratory tests.

OBJECTIVES: To evaluate the ability of an artificial intelligence (AI) model to predict the risk of cancer in patients referred from primary care based on routine blood tests. Results obtained with the AI model are compared to results based on logistic regression (LR). METHODS: An analytical profile consisting of 25 predefined routine laboratory blood tests was introduced to general practitioners (GPs) to be used for patients with non-specific symptoms, as an additional tool to identify individuals at increased risk of cancer. Consecutive analytical profiles ordered by GPs from November 29th 2011 until March 1st 2020 were included. AI and LR analysis were performed on data from 6,592 analytical profiles for their ability to detect cancer. Cohort I for model development included 5,224 analytical profiles ordered by GP's from November 29th 2011 until the December 31st 2018, while 1,368 analytical profiles included from January 1st 2019 until March 1st 2020 constituted the "out of time" validation test Cohort II. The main outcome measure was a cancer diagnosis within 90 days. RESULTS: The AI model based on routine laboratory blood tests can provide an easy-to use risk score to predict cancer within 90 days. Results obtained with the AI model were comparable to results from the LR model. In the internal validation Cohort IB, the AI model provided slightly better results than the LR analysis both in terms of the area under the receiver operating characteristics curve (AUC) and PPV, sensitivity/specificity while in the "out of time" validation test Cohort II, the obtained results were comparable. CONCLUSIONS: The AI risk score may be a valuable tool in the clinical decision-making. The score should be further validated to determine its applicability in other populations.