Informatics in Medicine Unlocked

Physiologic alarms are an important modality in the care of critically ill patients. Yet the many electronic devices used in patient care and the combination of alarms can cause sensory overload in caregivers. This sensory overload can lead to monitor fatigue, and caregivers may miss critical alarms, which can be fatal for patients. Many factors not related to a change in patients’ condition can be directly linked to desensitization and alarm fatigue, leading to a failure to recognize or attend to true instability in spite of the alarm. Research demonstrates that the majority of alarms are non-actionable, and staff can develop alarm fatigue trying to determine which alarms are valid and which are not (Hravnak et al., 2018). We postulate that more experience detecting false alarms among professionals in a long-term acute care unit will lead to improved clinical changes and better survival rates among patients. Our proportional hazards model relates missing clinical changes in patients’ condition as time passes, after reduced attention to false alarms, to professional experience. In our proportional hazards model, the unique effect of a unit increase in a covariate is multiplicative with respect to the hazard rate. Therefore, reduced attention to false alarms by experienced professionals decreases the hazard rate for missing a clinical change. We use survival analyses, the hazard function, the receiver-operating characteristic curve, and the Hosmer-Lemeshow test to support our conclusions. Our results show that monitoring equipment is instrumental in alerting staff in a long-term care unit to serious changes in patients’ condition and in preventing false positives and false negatives.

This set consists of a longitudinal collection of 150 subjects aged 60 to 96. Each subject was scanned on two or more visits, separated by at least one year for a total of 373 imaging sessions. For each subject, 3 or 4 individual T1-weighted MRI scans obtained in single scan sessions are included. The subjects are all right-handed and include both men and women. 72 of the subjects were characterized as nondemented throughout the study. 64 of the included subjects were characterized as demented at the time of their initial visits and remained so for subsequent scans, including 51 individuals with mild to moderate Alzheimer’s disease. Another 14 subjects were characterized as nondemented at the time of their initial visit and were subsequently characterized as demented at a later visit.

We collected 250 cancer-related records that had already been coded using a custom terminology at Roche Inc (ROCHE). The purpose was to annotate substances of pharmacological interest, taking both anatomy (Roche.Anatomie) and histology (Roche.Histology) into account. 150 records were given to either of two medical students (CoderA and CoderB). Coders were asked to identify representational units that expressed the same meaning in four target terminologies (SNOMED CT, NCIt, ICD-10 + ICD-O, and MedDRA) with as few codes as possible. Fifty of the cases were overlapping (double-coded), in order to enable the computation of inter-rater agreement. For evaluation, we used the following definitions: Hit=At least one code was provided for that terminology. Agreement=Both coders provided exactly the same codes. Therefore, an absence of codes by both counted as an agreement, and a same primary code but different secondary code counted as a disagreement.

These files give the asymmetry metrics between the right and left sides for 20 subjects with combinations of an asymmetric leg length and distal mass. The rows represent each of the 10 trials and the columns represent the 21 features in the order shown in Table 1. The last two columns identify whether the individual was wearing a distal mass or leg length during that trial with column 22 indicating distal mass and column 23 indicating leg length. For both distal mass and leg length "0" indicates no perturbation, "1" indicates a distal mass or leg length of a small size, and "2" indicates a distal mass or leg length of a large size. All leg length perturbations were performed to the left leg. Distal mass perturbations for subjects 1-10 were performed on the right side and distal mass perturbations for subjects 11-20 were performed on the left side.

Data description “Signals.mat” is a Matlab file, that contains the signals of the clinical study. The file contains a Matlab structure called “Signals”, that has 10 fields, one for each subject. The fields are entitled S1-S10. Each subject repeats the movement (Functional Reach) three times, thus for each subject, there are three sub-fields, called “first”, ”second” and “third”, that refer to the repetition. Each field contains a matrix with 6 rows, that provides the simultaneously acquired signals. Specifically, from the first to the sixth row, they are: left clavicle, sternocleidomastoideus, erectores spinae at L4 level, rectus abdominis, rectus femoris and tibialis anterior. Sampling frequency is 2000Hz and the length of the signals is 30s. For any further information, please contact Laura Burattini, PhD (l.burattini@univpm.it). Details can be found in the corresponding paper: “Sbrollini A., Strazza A., Candelaresi S., Marcantoni I., Morettini M., Fioretti S., Di Nardo F., Burattini L. Surface electromyography low-frequency content: assessment in isometric conditions after electrocardiogram cancellation by the segmented-beat modulation method”.

Fortran code that solves two time delay model of glucose-insulin system

The patient is the 67-year-old male with the long history of atherosclerosis. Nebival (5 mg) and Lozap (50 mg) have been daily drugs. The HBPM was done on semi-automatic tonometer Microlife BP 3AG1. The data sets were the systolic (SBP) and diastolic (DBP) blood pressures as well as the heart rate (HR). The guaranteed accuracy of the tonometer is +-3 mmHg for the blood pressure and +-5% for the heart rate. The heart rate was measured in “beats per minute” (bpm). The monitoring was organized according to the protocol. The patient was properly positioned, the cuffs were on the naked arm, and the measurements were conducted after the bathroom. Three trials with interval about one minute were performed each time. The mean of them was taken as the result. The used device allows checking the pulse irregularities automatically. A few such cases had happened and trials were repeated. The two-day break was set between two serial trials. HBPM performed during over a year (more exact during 384 days). So, each of three data sets consisted of N=128 samples.

A simulation environment for COVID-19 spread