Financial Abuse of Older Adults: Screening, Prevention, and Interventions by Primary Care Providers.

The Psychiatric Consultation Service at Massachusetts General Hospital sees medical and surgical inpatients with comorbid psychiatric symptoms and conditions. During their twice-weekly rounds, Dr Stern and other members of the Consultation Service discuss diagnosis and management of hospitalized patients with complex medical or surgical problems who also demonstrate psychiatric symptoms or conditions. These discussions have given rise to rounds reports that will prove useful for clinicians practicing at the interface of medicine and psychiatry.Prim Care Companion. 2023;25(3):22f03434. Author affiliations are listed at the end of this article.

Fitting and validation of an agent-based model for COVID-19 case forecasting in workplaces and universities.

COVID-19 forecasting models have been critical in guiding decision-making on surveillance testing, social distancing, and vaccination requirements. Beyond influencing public health policies, an accurate COVID-19 forecasting model can impact community spread by enabling employers and university leaders to adapt worksite policies and practices to contain or mitigate outbreaks. While many such models have been developed for COVID-19 forecasting at the national, state, county, or city level, only a few models have been developed for workplaces and universities. Furthermore, COVID-19 forecasting models have rarely been validated against real COVID-19 case data. Here we present the systematic parameter fitting and validation of an agent-based compartment model for the forecasting of daily COVID-19 cases in single-site workplaces and universities with real-world data. Our approaches include manual fitting, where initial model parameters are chosen based on historical data, and automated fitting, where parameters are chosen based on candidate case trajectory simulations that result in best fit to prevalence estimation data. We use a 14-day fitting window and validate our approaches on 7- and 14-day testing windows with real COVID-19 case data from one employer. Our manual and automated fitting approaches accurately predicted COVID-19 case trends and outperformed the baseline model (no parameter fitting) across multiple scenarios, including a rising case trajectory (RMSLE values: 2.627 for baseline, 0.562 for manual fitting, 0.399 for automated fitting) and a decreasing case trajectory (RMSLE values: 1.155 for baseline, 0.537 for manual fitting, 0.778 for automated fitting). Our COVID-19 case forecasting model allows decision-makers at workplaces and universities to proactively respond to case trend forecasts, mitigate outbreaks, and promote safety.

ERS/ESTS statement on the management of pleural infection in adults.

Pleural infection is a common condition encountered by respiratory physicians and thoracic surgeons alike. The European Respiratory Society (ERS) and European Society of Thoracic Surgeons (ESTS) established a multidisciplinary collaboration of clinicians with expertise in managing pleural infection with the aim of producing a comprehensive review of the scientific literature. Six areas of interest were identified: 1) epidemiology of pleural infection, 2) optimal antibiotic strategy, 3) diagnostic parameters for chest tube drainage, 4) status of intrapleural therapies, 5) role of surgery and 6) current place of outcome prediction in management. The literature revealed that recently updated epidemiological data continue to show an overall upwards trend in incidence, but there is an urgent need for a more comprehensive characterisation of the burden of pleural infection in specific populations such as immunocompromised hosts. There is a sparsity of regular analyses and documentation of microbiological patterns at a local level to inform geographical variation, and ongoing research efforts are needed to improve antibiotic stewardship. The evidence remains in favour of a small-bore chest tube optimally placed under image guidance as an appropriate initial intervention for most cases of pleural infection. With a growing body of data suggesting delays to treatment are key contributors to poor outcomes, this suggests that earlier consideration of combination intrapleural enzyme therapy (IET) with concurrent surgical consultation should remain a priority. Since publication of the MIST-2 study, there has been considerable data supporting safety and efficacy of IET, but further studies are needed to optimise dosing using individualised biomarkers of treatment failure. Pending further prospective evaluation, the MIST-2 regimen remains the most evidence based. Several studies have externally validated the RAPID score, but it requires incorporating into prospective intervention studies prior to adopting into clinical practice.

Longitudinal changes in sputum and blood inflammatory mediators during FeNO suppression testing.

To explore whether fractional exhaled nitric oxide (FeNO) non-suppression identifies corticosteroid resistance, we analysed inflammatory mediator changes during a FeNO suppression test with monitored high-intensity corticosteroid therapy. In linear mixed-effects models analysed over time, the 15 clinically distinct 'suppressors' (ie, ≥42% FeNO suppression) normalised Asthma Control Questionnaire scores (mean±SD, start to end of test: 2.8±1.4 to 1.4±0.9, p<0.0001) and sputum eosinophil counts (median (IQR), start to end of test: 29% (6%-41%) to 1% (1%-5%), p=0.0003) while significantly decreasing sputum prostaglandin D2 (254 (89-894) to 93 (49-209) pg/mL, p=0.004) and numerically decreasing other type-2 cytokine, chemokine and alarmin levels. In comparison, the 19 non-suppressors had persistent sputum eosinophilia (10% (1%-67%) despite high-intensity therapy) with raised end-test inflammatory mediator levels (1.9 (0.9-2.8)-fold greater than suppressors). FeNO non-suppression during monitored treatment implies biological corticosteroid resistance.

Diagnosis of cystic fibrosis in adulthood and eligibility for novel CFTR modulator therapy.

BACKGROUND: Cystic fibrosis (CF) is an autosomal recessive condition that primarily manifests as a chronic respiratory disease. CF is usually diagnosed in early childhood or through newborn screening although in a small but important group, diagnosis is not made until adulthood. Highly effective cystic fibrosis transmembrane conductance regulator (CFTR) modulator therapies are now available for most genetic causes of CF highlighting the importance of identifying people with late presentations of CF. AIM: We aimed to identify the clinical characteristics of people diagnosed with CF in adulthood and their resulting eligibility for novel CFTR modulator therapies. DESIGN: Retrospective single-centre cohort study. METHODS: Patients diagnosed with CF at age 18 years or older were identified from a patient database. Paper and electronic medical records were reviewed and clinical, microbiological and radiological data at diagnosis were recorded. RESULTS: Nineteen patients were identified. Median age at diagnosis was 38 years (range: 19-71) and 9 (47%) were female. All patients had a history of chronic respiratory symptoms and 18/19 (94%) had radiological evidence of bronchiectasis. All patients had two pathogenic CFTR mutations identified with 16/19 (84%) compound heterozygotes for the F508del mutation. The majority of patients had a CFTR genotype considered eligible for CFTR modulator therapy (84% and 89% according to European and US licences, respectively). CONCLUSIONS: Adult patients with unexplained chronic bronchiectasis should be thoroughly investigated for CF. A low index of suspicion will help to identify adults with undiagnosed CF who are likely to benefit from CFTR modulator therapy.

Using a learning health system to understand the mismatch between medicines supply and actual medicines use among adults with cystic fibrosis.

BACKGROUND: Studies in separate cohorts suggest possible discrepancies between inhaled medicines supplied (median 50-60%) and medicines used (median 30-40%). We performed the first study that directly compares CF medicine supply against use to identify the cost of excess medicines supply. METHODS: This cross-sectional study included participants from 12 UK adult centres with ≥1 year of continuous adherence data from data-logging nebulisers. Medicine supply was measured as medication possession ratio (MPR) for a 1-year period from the first suitable supply date. Medicine use was measured as electronic data capture (EDC) adherence over the same period. The cost of excess medicines was calculated as whole excess box(es) supplied after accounting for the discrepancy between EDC adherence and MPR with 20% contingency. RESULTS: Among 275 participants, 133 (48.4%) were females and mean age was 30 years (95% CI 29-31 years). Median EDC adherence was 57% (IQR 23-86%), median MPR was 74% (IQR 46-96%) and the discrepancy between measures was median 14% (IQR 2-29%). Even with 20% contingency, mean potential cost of excess medicines was £1,124 (95% CI £855-1,394), ranging from £183 (95% CI £29-338) for EDC adherence ≥80% to £2,017 (95% CI £1,507-2,526) for EDC adherence <50%. CONCLUSIONS: This study provides a conservative estimate of excess inhaled medicines supply cost among adults with CF in the UK. The excess supply cost was highest among those with lowest EDC adherence, highlighting the importance of adherence support and supplying medicine according to actual use. MPR provides information about medicine supply but over-estimates actual medicine use.

Toward a Mobile Platform for Real-world Digital Measurement of Depression: User-Centered Design, Data Quality, and Behavioral and Clinical Modeling.

BACKGROUND: Although effective mental health treatments exist, the ability to match individuals to optimal treatments is poor, and timely assessment of response is difficult. One reason for these challenges is the lack of objective measurement of psychiatric symptoms. Sensors and active tasks recorded by smartphones provide a low-burden, low-cost, and scalable way to capture real-world data from patients that could augment clinical decision-making and move the field of mental health closer to measurement-based care. OBJECTIVE: This study tests the feasibility of a fully remote study on individuals with self-reported depression using an Android-based smartphone app to collect subjective and objective measures associated with depression severity. The goals of this pilot study are to develop an engaging user interface for high task adherence through user-centered design; test the quality of collected data from passive sensors; start building clinically relevant behavioral measures (features) from passive sensors and active inputs; and preliminarily explore connections between these features and depression severity. METHODS: A total of 600 participants were asked to download the study app to join this fully remote, observational 12-week study. The app passively collected 20 sensor data streams (eg, ambient audio level, location, and inertial measurement units), and participants were asked to complete daily survey tasks, weekly voice diaries, and the clinically validated Patient Health Questionnaire (PHQ-9) self-survey. Pairwise correlations between derived behavioral features (eg, weekly minutes spent at home) and PHQ-9 were computed. Using these behavioral features, we also constructed an elastic net penalized multivariate logistic regression model predicting depressed versus nondepressed PHQ-9 scores (ie, dichotomized PHQ-9). RESULTS: A total of 415 individuals logged into the app. Over the course of the 12-week study, these participants completed 83.35% (4151/4980) of the PHQ-9s. Applying data sufficiency rules for minimally necessary daily and weekly data resulted in 3779 participant-weeks of data across 384 participants. Using a subset of 34 behavioral features, we found that 11 features showed a significant (P<.001 Benjamini-Hochberg adjusted) Spearman correlation with weekly PHQ-9, including voice diary-derived word sentiment and ambient audio levels. Restricting the data to those cases in which all 34 behavioral features were present, we had available 1013 participant-weeks from 186 participants. The logistic regression model predicting depression status resulted in a 10-fold cross-validated mean area under the curve of 0.656 (SD 0.079). CONCLUSIONS: This study finds a strong proof of concept for the use of a smartphone-based assessment of depression outcomes. Behavioral features derived from passive sensors and active tasks show promising correlations with a validated clinical measure of depression (PHQ-9). Future work is needed to increase scale that may permit the construction of more complex (eg, nonlinear) predictive models and better handle data missingness.

A holistic approach for suppression of COVID-19 spread in workplaces and universities.

As society has moved past the initial phase of the COVID-19 crisis that relied on broad-spectrum shutdowns as a stopgap method, industries and institutions have faced the daunting question of how to return to a stabilized state of activities and more fully reopen the economy. A core problem is how to return people to their workplaces and educational institutions in a manner that is safe, ethical, grounded in science, and takes into account the unique factors and needs of each organization and community. In this paper, we introduce an epidemiological model (the "Community-Workplace" model) that accounts for SARS-CoV-2 transmission within the workplace, within the surrounding community, and between them. We use this multi-group deterministic compartmental model to consider various testing strategies that, together with symptom screening, exposure tracking, and nonpharmaceutical interventions (NPI) such as mask wearing and physical distancing, aim to reduce disease spread in the workplace. Our framework is designed to be adaptable to a variety of specific workplace environments to support planning efforts as reopenings continue. Using this model, we consider a number of case studies, including an office workplace, a factory floor, and a university campus. Analysis of these cases illustrates that continuous testing can help a workplace avoid an outbreak by reducing undetected infectiousness even in high-contact environments. We find that a university setting, where individuals spend more time on campus and have a higher contact load, requires more testing to remain safe, compared to a factory or office setting. Under the modeling assumptions, we find that maintaining a prevalence below 3% can be achieved in an office setting by testing its workforce every two weeks, whereas achieving this same goal for a university could require as much as fourfold more testing (i.e., testing the entire campus population twice a week). Our model also simulates the dynamics of reduced spread that result from the introduction of mitigation measures when test results reveal the early stages of a workplace outbreak. We use this to show that a vigilant university that has the ability to quickly react to outbreaks can be justified in implementing testing at the same rate as a lower-risk office workplace. Finally, we quantify the devastating impact that an outbreak in a small-town college could have on the surrounding community, which supports the notion that communities can be better protected by supporting their local places of business in preventing onsite spread of disease.

Electronic health record-based clinical decision support alert for severe sepsis: a randomised evaluation.

BACKGROUND: Sepsis remains the top cause of morbidity and mortality of hospitalised patients despite concerted efforts. Clinical decision support for sepsis has shown mixed results reflecting heterogeneous populations, methodologies and interventions. OBJECTIVES: To determine whether the addition of a real-time electronic health record (EHR)-based clinical decision support alert improves adherence to treatment guidelines and clinical outcomes in hospitalised patients with suspected severe sepsis. DESIGN: Patient-level randomisation, single blinded. SETTING: Medical and surgical inpatient units of an academic, tertiary care medical centre. PATIENTS: 1123 adults over the age of 18 admitted to inpatient wards (intensive care units (ICU) excluded) at an academic teaching hospital between November 2014 and March 2015. INTERVENTIONS: Patients were randomised to either usual care or the addition of an EHR-generated alert in response to a set of modified severe sepsis criteria that included vital signs, laboratory values and physician orders. MEASUREMENTS AND MAIN RESULTS: There was no significant difference between the intervention and control groups in primary outcome of the percentage of patients with new antibiotic orders at 3 hours after the alert (35% vs 37%, p=0.53). There was no difference in secondary outcomes of in-hospital mortality at 30 days, length of stay greater than 72 hours, rate of transfer to ICU within 48 hours of alert, or proportion of patients receiving at least 30 mL/kg of intravenous fluids. CONCLUSIONS: An EHR-based severe sepsis alert did not result in a statistically significant improvement in several sepsis treatment performance measures.

Incorporating Observed Physiological Data to Personalize Pediatric Vital Sign Alarm Thresholds.

Bedside monitors are intended as a safety net in patient care, but their management in the inpatient setting is a significant patient safety concern. The low precision of vital sign alarm systems leads to clinical staff becoming desensitized to the sound of the alarm, a phenomenon known as alarm fatigue. Alarm fatigue has been shown to increase response time to alarms or result in alarms being ignored altogether and has negative consequences for patient safety. We present methods to establish personalized thresholds for heart rate and respiratory rate alarms. These thresholds are first chosen based on patient characteristics available at the time of admission and are then adapted to incorporate vital signs observed in the first 2 hours of monitoring. We demonstrate that the adapted thresholds are similar to those chosen by clinicians for individual patients and would result in fewer alarms than the currently used age-based thresholds. Personalized vital sign alarm thresholds can help to alleviate the problem of alarm fatigue in an inpatient setting while ensuring that all critical vital signs are detected.

Coupling of Rigor Mortis and Intestinal Necrosis during C. elegans Organismal Death.

Organismal death is a process of systemic collapse whose mechanisms are less well understood than those of cell death. We previously reported that death in C. elegans is accompanied by a calcium-propagated wave of intestinal necrosis, marked by a wave of blue autofluorescence (death fluorescence). Here, we describe another feature of organismal death, a wave of body wall muscle contraction, or death contraction (DC). This phenomenon is accompanied by a wave of intramuscular Ca2+ release and, subsequently, of intestinal necrosis. Correlation of directions of the DC and intestinal necrosis waves implies coupling of these death processes. Long-lived insulin/IGF-1-signaling mutants show reduced DC and delayed intestinal necrosis, suggesting possible resistance to organismal death. DC resembles mammalian rigor mortis, a postmortem necrosis-related process in which Ca2+ influx promotes muscle hyper-contraction. In contrast to mammals, DC is an early rather than a late event in C. elegans organismal death. VIDEO ABSTRACT.

Addressing vital sign alarm fatigue using personalized alarm thresholds.

Alarm fatigue, a condition in which clinical staff become desensitized to alarms due to the high frequency of unnecessary alarms, is a major patient safety concern. Alarm fatigue is particularly prevalent in the pediatric setting, due to the high level of variation in vital signs with patient age. Existing studies have shown that the current default pediatric vital sign alarm thresholds are inappropriate, and lead to a larger than necessary alarm load. This study leverages a large database containing over 190 patient-years of heart rate data to accurately identify the 1st and 99th percentiles of an individual's heart rate on their first day of vital sign monitoring. These percentiles are then used as personalized vital sign thresholds, which are evaluated by comparing to non-default alarm thresholds used in practice, and by using the presence of major clinical events to infer alarm labels. Using the proposed personalized thresholds would decrease low and high heart rate alarms by up to 50% and 44% respectively, while maintaining sensitivity of 62% and increasing specificity to 49%. The proposed personalized vital sign alarm thresholds will reduce alarm fatigue, thus contributing to improved patient outcomes, shorter hospital stays, and reduced hospital costs.

Assessing Screening Guidelines for Cardiovascular Disease Risk Factors using Routinely Collected Data.

This study investigates if laboratory data can be used to assess whether physician-retesting patterns are in line with established guidelines, and if these guidelines identify deteriorating patients in a timely manner. A total of 7594 patients with high cholesterol were studied, along with 2764 patients with diabetes. More than 90% of borderline high cholesterol patients are retested within the 3 year recommended period, however less than 75% of pre-diabetic patients have repeated tests within the suggested 1-year time frame. Patients with borderline high cholesterol typically progress to full high cholesterol in 2-3 years, and pre-diabetic patients progress to full diabetes in 1-2 years. Data from routinely ordered laboratory tests can be used to monitor adherence to clinical guidelines. These data may also be useful in the design of adaptive testing strategies that reduce unnecessary testing, while ensuring that patient deterioration is identified in a timely manner. Established guidelines for testing of total serum cholesterol for hypercholesterolemia are appropriate and are well-adhered to, whereas guidelines for glycated hemoglobin A1c testing for type 2 diabetes mellitus could be improved to bring them in line with current practice and avoid unnecessary testing.

Inpatient-Derived Vital Sign Parameters Implementation: An Initiative to Decrease Alarm Burden.

OBJECTIVES: To implement data-driven vital sign parameters to reduce bedside monitor alarm burden. METHODS: Single-center, quality-improvement initiative with historical controls assessing the impact of age-based, inpatient-derived heart rate (HR) and respiratory rate (RR) parameters on a 20-bed acute care ward that serves primarily pediatric cardiology patients. The primary outcome was the number of alarms per monitored bed day (MBD) with the aim to decrease the alarms per MBD. Balancing measures included the frequency of missed rapid response team activations, acute respiratory code events, and cardiorespiratory arrest events in the unit with the new vital sign parameters. RESULTS: The median number of all cardiorespiratory monitor alarms per MBD decreased by 21% from 52 (baseline period) to 41 (postintervention period) (P < .001). This included a 17% decrease in the median HR alarms (9-7.5 per MBD) and a 53% drop in RR alarms (16.8-8.0 per MBD). There were 57 rapid response team activations, 8 acute respiratory code events, and no cardiorespiratory arrest events after the implementation of the new parameters. An evaluation of HRs and RRs recorded at the time of the event revealed that all patients with HRs and/or RRs out of range per former default parameters would also be out of range with the new parameters. CONCLUSIONS: Implementation of data-driven HR and iteratively derived RR parameters safely decreased the total alarm frequency by 21% in a pediatric acute care unit.

Predicting Emergency Department Visits.

High utilizers of emergency departments account for a disproportionate number of visits, often for nonemergency conditions. This study aims to identify these high users prospectively. Routinely recorded registration data from the Indiana Public Health Emergency Surveillance System was used to predict whether patients would revisit the Emergency Department within one month, three months, and six months of an index visit. Separate models were trained for each outcome period, and several predictive models were tested. Random Forest models had good performance and calibration for all outcome periods, with area under the receiver operating characteristic curve of at least 0.96. This high performance was found to be due to non-linear interactions among variables in the data. The ability to predict repeat emergency visits may provide an opportunity to establish, prioritize, and target interventions to ensure that patients have access to the care they require outside an emergency department setting.

Safety analysis of proposed data-driven physiologic alarm parameters for hospitalized children.

INTRODUCTION: Modification of alarm limits is one approach to mitigating alarm fatigue. We aimed to create and validate heart rate (HR) and respiratory rate (RR) percentiles for hospitalized children, and analyze the safety of replacing current vital sign reference ranges with proposed data-driven, age-stratified 5th and 95th percentile values. METHODS: In this retrospective cross-sectional study, nurse-charted HR and RR data from a training set of 7202 hospitalized children were used to develop percentile tables. We compared 5th and 95th percentile values with currently accepted reference ranges in a validation set of 2287 patients. We analyzed 148 rapid response team (RRT) and cardiorespiratory arrest (CRA) events over a 12-month period, using HR and RR values in the 12 hours prior to the event, to determine the proportion of patients with out-of-range vitals based upon reference versus data-driven limits. RESULTS: There were 24,045 (55.6%) fewer out-of-range measurements using data-driven vital sign limits. Overall, 144/148 RRT and CRA patients had out-of-range HR or RR values preceding the event using current limits, and 138/148 were abnormal using data-driven limits. Chart review of RRT and CRA patients with abnormal HR and RR per current limits considered normal by data-driven limits revealed that clinical status change was identified by other vital sign abnormalities or clinical context. CONCLUSIONS: A large proportion of vital signs in hospitalized children are outside presently used norms. Safety evaluation of data-driven limits suggests they are as safe as those currently used. Implementation of these parameters in physiologic monitors may mitigate alarm fatigue. Journal of Hospital Medicine 2015;11:817-823. © 2015 Society of Hospital Medicine.

Pharmacokinetic profile and quantitation of protection against soman poisoning by the antinicotinic compound MB327 in the guinea-pig.

Current organophosphorus nerve agent medical countermeasures do not directly address the nicotinic effects of poisoning. A series of antinicotinic bispyridinium compounds has been synthesized in our laboratory and screened in vitro. Their actions can include open-channel block at the nicotinic receptor which may contribute to their efficacy. The current lead compound from these studies, MB327 1,1'-(propane-1,3-diyl)bis(4-tert-butylpyridinium) as either the diiodide (I2) or dimethanesulfonate (DMS) has been examined in vivo for efficacy against nerve agent poisoning. MB327 I2 (0-113mgkg(-1)) or the oxime HI-6 DMS (0-100mgkg(- 1)), in combination with atropine and avizafone (each at 3mgkg(-1)) was administered to guinea-pigs 1min following soman poisoning. Treatment increased the LD50 of soman in a dose-dependent manner. The increase was statistically significant (p<0.01) at the 33.9mgkg(-1) (MB327) or 30mgkg(-1) (HI-6) dose with a comparable degree of protection obtained for both compounds. Following administration of 10mgkg(-1) (i.m.), MB327 DMS reached plasma Cmax of 22µM at 12min with an elimination t1/2 of 22min. In an adverse effect study, in the absence of nerve agent poisoning, a dose of 100mgkg(-1) or higher of MB327 DMS was lethal to the guinea-pigs. A lower dose of MB327 DMS (30mgkg(-1)) caused flaccid paralysis accompanied by respiratory impairment. Respiration normalised by 30min, although the animals remained incapacitated to 4h. MB327 or related compounds may be of utility in treatment of nerve agent poisoning as a component of therapy with atropine, anticonvulsant and oxime, or alternatively as an infusion under medical supervision.

An unsupervised learning method to identify reference intervals from a clinical database.

Reference intervals are critical for the interpretation of laboratory results. The development of reference intervals using traditional methods is time consuming and costly. An alternative approach, known as an a posteriori method, requires an expert to enumerate diagnoses and procedures that can affect the measurement of interest. We develop a method, LIMIT, to use laboratory test results from a clinical database to identify ICD9 codes that are associated with extreme laboratory results, thus automating the a posteriori method. LIMIT was developed using sodium serum levels, and validated using potassium serum levels, both tests for which harmonized reference intervals already exist. To test LIMIT, reference intervals for total hemoglobin in whole blood were learned, and were compared with the hemoglobin reference intervals found using an existing a posteriori approach. In addition, prescription of iron supplements were used to identify individuals whose hemoglobin levels were low enough for a clinician to choose to take action. This prescription data indicating clinical action was then used to estimate the validity of the hemoglobin reference interval sets. Results show that LIMIT produces usable reference intervals for sodium, potassium and hemoglobin laboratory tests. The hemoglobin intervals produced using the data driven approaches consistently had higher positive predictive value and specificity in predicting an iron supplement prescription than the existing intervals. LIMIT represents a fast and inexpensive solution for calculating reference intervals, and shows that it is possible to use laboratory results and coded diagnoses to learn laboratory test reference intervals from clinical data warehouses.

Implementation of Data Drive Heart Rate and Respiratory Rate parameters on a Pediatric Acute Care Unit.

The majority of hospital physiologic monitor alarms are not clinically actionable and contribute to alarm fatigue. In 2014, The Joint Commission declared alarm safety as a National Patient Safety Goal and urged prompt action by hospitals to mitigate the issue [1]. It has been demonstrated that vital signs in hospitalized children are quite different from currently accepted reference ranges [2]. Implementation of data-driven, age stratified vital sign parameters (Table 1) for alarms in this patient population could reduce alarm frequency.