Predicting patient decompensation from continuous physiologic monitoring in the emergency department.
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ABSTRACT:
SUBMITTER: Sundrani S
PROVIDER: S-EPMC10073111 | biostudies-literature | 2023 Apr
REPOSITORIES: biostudies-literature
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Predicting patient decompensation from continuous physiologic monitoring in the emergency department.
Sundrani Sameer S Chen Julie J Jin Boyang Tom BT Abad Zahra Shakeri Hossein ZSH Rajpurkar Pranav P Kim David D
NPJ digital medicine 20230404 1
Anticipation of clinical decompensation is essential for effective emergency and critical care. In this study, we develop a multimodal machine learning approach to predict the onset of new vital sign abnormalities (tachycardia, hypotension, hypoxia) in ED patients with normal initial vital signs. Our method combines standard triage data (vital signs, demographics, chief complaint) with features derived from a brief period of continuous physiologic monitoring, extracted via both conventional sign ...[more]
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Project description:ImportanceAccurate and timely documentation of vital signs affects all aspects of triage, diagnosis, and management. The adequacy of current patient monitoring practices and the potential to improve on them are poorly understood.ObjectiveTo develop measures of fit between documented and actual patient vital signs throughout the visit, as determined from continuous physiologic monitoring, and to compare the performance of actual practice with alternative patient monitoring strategies.Design, setting, and participantsThis cross-sectional study evaluated 25 751 adult visits to continuously monitored emergency department (ED) beds between August 1, 2020, and December 31, 2021. A series of monitoring strategies for the documentation of vital signs (heart rate [HR], respiratory rate [RR], oxygen saturation by pulse oximetry [Spo2], mean arterial pressure [MAP]) was developed and the strategies' ability to capture physiologic trends and vital sign abnormalities simulated. Strategies included equal spacing of charting events, charting at variable intervals depending on the last observed values, and discrete optimization of charting events.Main outcomes and measuresCoverage was defined as the proportion of monitor-derived vital sign measurements (at 1-minute resolution) that fall within the bounds of nursing-charted values over the course of an ED visit (HR ± 5 beats/min, RR ± 3 breaths/min, Spo2 ± 2%, and MAP ± 6 mm Hg). Capture was defined as the documentation of a vital sign abnormality detected by bedside monitor (tachycardia [HR >100 beats/min], bradycardia [HR <60 beats/min], hypotension [MAP <65 mm Hg], and hypoxia [Spo2 <95%]).ResultsMedian patient age was 60 years (IQR, 43-75 years), and 13 329 visits (51.8%) were by women. Monitored visits had a median of 4 (IQR, 2-5) vital sign charting events per visit. Compared with actual practice, a simple rule, which observes vital signs more frequently if the last observation fell outside the bounds of the previous values, and using the same number of observations as actual practice, produced relative coverage improvements of 31.5% (95% CI, 30.5%-32.5%) for HR, 31.0% (95% CI, 30.0%-32.0%) for MAP, 16.8% (95% CI, 16.0%-17.6%) for RR, and 7.8% (95% CI, 7.3%-8.3%) for Spo2. The same strategy improved capture of abnormalities by 38.9% (95% CI, 26.8%-52.2%) for tachycardia, 38.1% (95% CI, 29.0%-47.9%) for bradycardia, 39.0% (95% CI, 24.2%-55.7%) for hypotension, and 123.1% (95% CI, 110.7%-136.3%) for hypoxia. Analysis of optimal coverage suggested an additional scope for improvement through more sophisticated strategies.Conclusions and relevanceIn this cross-sectional study, actual documentation of ED vital signs was variable and incomplete, missing important trends and abnormalities. Alternative monitoring strategies may improve on current practice without increasing the overall frequency of patient monitoring.
| S-EPMC9520367 | biostudies-literature
Project description:BackgroundMany studies have shown the tendency for people without a regular care provider or primary physician to make greater use of emergency departments. We sought to determine the effects of three aspects of care provided by primary physicians (physician specialty, continuity of care and comprehensiveness of care) on their patients' use of the emergency department.MethodsUsing provincial administrative databases, we created a cohort of 367,315 adults aged 18 years and older. Participants were residents of urban areas of Quebec. Affiliation with a primary physician, the specialty of this physician (i.e., family physician v. specialist), continuity of care (as measured using the Usual Provider Continuity index) and comprehensiveness of care (i.e., number of complete annual examinations) were measured among participants (n = 311,701) who had visited a physician three or more times during a two-year baseline period. We used multivariable negative binomial regression to investigate the relationships between measures of care and the number of visits to emergency departments during a 12-month follow-up period.ResultsAmong participants under 65 years of age, emergency department use was higher for those not affiliated than for those affiliated with a family physician (incidence rate ratio [IRR] 1.11, 95% confidence interval [CI] 1.05-1.16) or a specialist (IRR 1.10, 95% CI 1.04-1.17). Among patients aged 65 years and older, having a specialist primary physician, as opposed to a family physician, predicted increased use of the emergency department (IRR 1.13, 95% CI 1.09-1.17). Greater continuity of care with a family physician predicted less use of the emergency department only among participants who made 25 or more visits to a physician during the baseline period. Greater continuity of care with a specialist predicted less use of the emergency department overall, particularly among participants with intermediate numbers of multimorbidities and admissions to hospital. Greater comprehensiveness of care by family physicians predicted less use of the emergency department.InterpretationEfforts to increase the proportion of adults affiliated with a family physician should target older adults, people who visit physicians more frequently and people with multiple comorbidities and admissions to hospital.
| S-EPMC3314059 | biostudies-literature
Project description:PURPOSE:Sepsis is a common acute life-threatening condition that emergency physicians routinely face. Diagnostic options within the Emergency Department (ED) are limited due to lack of infrastructure, consequently limiting the use of invasive hemodynamic monitoring or imaging tests. The mortality rate due to sepsis can be assessed via multiple scoring systems, for example, mortality in emergency department sepsis (MEDS) score and sepsis patient evaluation in the emergency department (SPEED) score, both of which quantify the variation of mortality rates according to clinical findings, laboratory data, or therapeutic interventions. This study aims to improve the management processes of sepsis patients by comparing SPEED score and MEDS score for predicting the 28-day mortality in cases of emergency sepsis. METHODS:The study is a cross-sectional, prospective study including 61 sepsis patients in ED in Suez Canal University Hospital, Egypt, from August 2017 to June 2018. Patients were selected by two steps: (1) suspected septic patients presenting with at least one of the following abnormal clinical findings: (a) body temperature higher than 38 °C or lower than 36 °C, (b) heart rate higher than 90 beats/min, (c) hyperventilation evidenced by respiratory rate higher than 20 breaths/min or PaCO2 lower than 32 mmHg, and (d) white blood cell count higher than 12,000/μL or lower than 4000/μL; (2) confirmed septic patients with at least a 2-point increase from the baseline total sequential organ failure assessment (SOFA) score following infection. Other inclusion criteria included adult patients with an age ≥18 years regardless of gender and those who had either systemic inflammatory response syndrome or suspected/confirmed infection. Patients were shortly follow-up for the 28-day mortality. Each patient was subject to SPEED score and MEDS score and then the results were compared to detect which of them was more effective in predicting outcome. The receiver operating characteristic curves were also done for MEDS and SPEED scores. RESULTS:Among the 61 patients, 41 died with the mortality rate of 67.2%. The mortality rate increased with a higher SPEED and MEDS scores. Both SPEED and MEDS scores revealed significant difference between the survivors and nonsurvivors (p = 0.004 and p < 0.001, respectively), indicating that both the two systems are effective in predicting the 28-day mortality of sepsis patients. Thereafter, the receiver operating characteristic curves were plotted, which showed that SPEED was better than the MEDS score when applied to the complete study population with an area under the curve being 0.87 (0.788-0.963) as compared with 0.75 (0.634-0.876) for MEDS. Logistic regression analysis revealed that the best fitting predictor of 28-day mortality for sepsis patients was the SPEED scoring system. For every one unit increase in SPEED score, the odds of 28-day mortality increased by 37%. CONCLUSION:SPEED score is more useful and accurate than MEDS score in predicting the 28-day mortality among sepsis patients. Therefore SPEED rather than MEDS should be more widely used in the ED for sepsis patients.
| S-EPMC6921192 | biostudies-literature
Project description:ObjectivesEmergency department (ED) crowding is an increasing problem associated with adverse patient outcomes. ED expansion is one method advocated to reduce ED crowding. The objective of this analysis was to determine the effect of ED expansion on measures of ED crowding.MethodsThis was a retrospective study using administrative data from two 11-month periods before and after the expansion of an ED from 33 to 53 adult beds in an academic medical center. ED volume, staffing, and hospital admission and occupancy data were obtained either from the electronic health record (EHR) or from administrative records. The primary outcome was the rate of patients who left without being treated (LWBT), and the secondary outcome was total ED boarding time for admitted patients. A multivariable robust linear regression model was used to determine whether ED expansion was associated with the outcome measures.ResultsThe mean (±SD) daily adult volume was 128 (±14) patients before expansion and 145 (±17) patients after. The percentage of patients who LWBT was unchanged: 9.0% before expansion versus 8.3% after expansion (difference = 0.6%, 95% confidence interval [CI] = -0.16% to 1.4%). Total ED boarding time increased from 160 to 180 hours/day (difference = 20 hours, 95% CI = 8 to 32 hours). After daily ED volume, low-acuity area volume, daily wait time, daily boarding hours, and nurse staffing were adjusted for, the percentage of patients who LWBT was not independently associated with ED expansion (p = 0.053). After ED admissions, ED intensive care unit (ICU) admissions, elective surgical admissions, hospital occupancy rate, ICU occupancy rate, and number of operational ICU beds were adjusted for, the increase in ED boarding hours was independently associated with the ED expansion (p = 0.005).ConclusionsAn increase in ED bed capacity was associated with no significant change in the percentage of patients who LWBT, but had an unintended consequence of an increase in ED boarding hours. ED expansion alone does not appear to be an adequate solution to ED crowding.
| S-EPMC4046120 | biostudies-literature
Project description:IntroductionClinical alarms promote patient safety by alerting clinicians when there is an indication or change in a condition requiring a response. An excessive volume of alarm fires, however, contributes to sensory overload and desensitization, referred to as alarm fatigue, which has significant implications when alarms are missed. This evidence-based, practice project aimed to implement and evaluate a program that reduces the number of clinically nonactionable, physiologic alarms in an emergency department. Although alarm fatigue is an important negative consequence, the focus of this project is not on alarm fatigue but on measures to reduce the volume of clinically nonactionable alarms that lead to alarm fatigue. The Iowa Model was used as a conceptual framework.MethodsThis project involved adjusting default alarm settings and implementing an education plan on the safe use of alarms. The sample population included all patients on physiologic monitors at an emergency department. Retrospective data were collected, and regression discontinuity design was applied to compare the rate of alarm fires triggered by the physiologic monitor between pre- and postimplementation of an alarm protocol.ResultsA significant change in the rate of alarm fires occurred with an estimated reduction of 14.96 (P = 0.003). There were no reports of adverse outcomes such as a delay in responding to a change in patient condition or delay leading to cardiopulmonary arrest.DiscussionA reduction in nonactionable, physiologic alarms was attained after implementing multimodal strategies inclusive of adjusting default settings, staff education on managing alarms, and emphasis on staff accountability.
| S-EPMC7071970 | biostudies-literature
Project description:The COVID-19 pandemic has accelerated the adoption of innovative healthcare methods, including remote patient monitoring. In the setting of limited healthcare resources, outpatient management of individuals newly diagnosed with COVID-19 was commonly implemented, some taking advantage of various personal health technologies, but only rarely using a multi-parameter chest-patch for continuous monitoring. Here we describe the development and validation of a COVID-19 decompensation index (CDI) model based on chest patch-derived continuous sensor data to predict COVID-19 hospitalizations in outpatient-managed COVID-19 positive individuals, achieving an overall AUC of the ROC Curve of 0.84 on 308 event negative participants, and 22 event positive participants, out of an overall study cohort of 400 participants. We retrospectively compare the performance of CDI to standard of care modalities, finding that the machine learning model outperforms the standard of care modalities in terms of both numbers of events identified and with a lower false alarm rate. While only a pilot phase study, the CDI represents a promising application of machine learning within a continuous remote patient monitoring system.
| S-EPMC8576003 | biostudies-literature
Project description:Background: Good-quality data is required for valid and reliable key performance indicators. Little is known of the facilitators and barriers of capturing the required data for emergency department key performance indicators. This study aimed to explore and understand how current emergency department data collection systems relevant to emergency department key performance indicators are integrated into routine service delivery, and to identify the resources required to capture these data elements. Methods: Following pilot testing, we conducted two focus groups with a multi-disciplinary panel of 14 emergency department stakeholders drawn from urban and rural emergency departments, respectively. Focus groups were analyzed using Attride-Stirling's framework for thematic network analysis. Results: The global theme "Understanding facilitators and barriers for emergency department data collection systems" emerged from three organizing themes: "understanding current emergency department data collection systems"; "achieving the ideal emergency department data capture system for the implementation of emergency department key performance indicators"; and "emergency department data capture systems for performance monitoring purposes within the wider context". Conclusion: The pathways to improving emergency department data capture systems for emergency department key performance indicators include upgrading emergency department information systems and investment in hardware technology and data managers. Educating stakeholders outside the emergency department regarding the importance of emergency department key performance indicators as hospital-wide performance indicators underpins the successful implementation of valid and reliable emergency department key performance indicators.
| S-EPMC7490568 | biostudies-literature
Project description:IntroductionThe short-term return visit rate among patients discharged from emergency departments (ED) is a quality metric and target for interventions. The ability to accurately identify which patients are more likely to revisit the ED could allow EDs and health systems to develop more focused interventions, but efforts to reduce revisits have not yet found success. Whether patients with a high number of ED visits are at increased risk of a return visit remains underexplored.MethodsThis was a population-based, retrospective, cohort study using administrative data from a large physician partnership. We included patients discharged from EDs from 80 hospitals in seven states from July 2014 - June 2016. We performed multivariable logistic regression of short-term return visits on patient, visit, hospital, and community characteristics. The primary outcome was the proportion of patients who had a return visit within 14 days of an index ED visit.ResultsAmong 6,699,717 index visits, the overall risk of 14-day revisit was 12.6%. Frequent visitors accounted for 18.7% of all visits and 40.2% of all 14-day revisits. Frequent visitor status was associated with the highest odds of a revisit (odds ratio [OR] 3.06; 95% confidence interval [CI], 3.041 - 3.073). Other predictors of revisits were cellulitis (OR 2.131; 95% CI, 2.106 - 2.156), alcohol-related disorders (OR 1.579; 95%CI, 1.548 - 1.610), congestive heart failure (OR 1.175; 95% CI, 1.126 - 1.226), and public insurance (Medicaid OR 1.514; 95% CI, 1.501 - 1.528; Medicare OR 1.601; 95% CI, 1.583 - 1.620).ConclusionPrevious ED use - even a single previous visit - was a stronger predictor of a return visit than any other patient, hospital, or community characteristic. Clinicians should consider previous ED use when considering treatment decisions and risk of return visit, as should stakeholders targeting patients at risk of a return visit.
| S-EPMC6860392 | biostudies-literature
Project description:BackgroundCOVID-19 may lead to severe disease, requiring intensive care treatment and challenging the capacity of health care systems. The aim of this study was to compare the ability of commonly used scoring systems for sepsis and pneumonia to predict severe COVID-19 in the emergency department.MethodsProspective, observational, single centre study in a secondary/tertiary care hospital in Oslo, Norway. Patients were assessed upon hospital admission using the following scoring systems; quick Sequential Failure Assessment (qSOFA), Systemic Inflammatory Response Syndrome criteria (SIRS), National Early Warning Score 2 (NEWS2), CURB-65 and Pneumonia Severity index (PSI). The ratio of arterial oxygen tension to inspiratory oxygen fraction (P/F-ratio) was also calculated. The area under the receiver operating characteristics curve (AUROC) for each scoring system was calculated, along with sensitivity and specificity for the most commonly used cut-offs. Severe disease was defined as death or treatment in ICU within 14 days.Results38 of 175 study participants developed severe disease, 13 (7%) died and 29 (17%) had a stay at an intensive care unit (ICU). NEWS2 displayed an AUROC of 0.80 (95% confidence interval 0.72-0.88), CURB-65 0.75 (0.65-0.84), PSI 0.75 (0.65-0.84), SIRS 0.70 (0.61-0.80) and qSOFA 0.70 (0.61-0.79). NEWS2 was significantly better than SIRS and qSOFA in predicating severe disease, and with a cut-off of5 points, had a sensitivity and specificity of 82% and 60%, respectively.ConclusionNEWS2 predicted severe COVID-19 disease more accurately than SIRS and qSOFA, but not significantly better than CURB65 and PSI. NEWS2 may be a useful screening tool in evaluating COVID-19 patients during hospital admission.Trial registration: ClinicalTrials.gov Identifier: NCT04345536. (https://clinicaltrials.gov/ct2/show/NCT04345536).
| S-EPMC7577659 | biostudies-literature
Project description:Around 42,000 children suffer from severe sepsis each year in the US alone, resulting in significant morbidity, mortality and billion dollar expenditures in the US healthcare system. Sepsis recognition is a clinical challenge in children. Biomarkers are needed to tailor appropriate antimicrobial therapies and improve risk stratification. The goal of this study was to determine if gene expression profiles from peripheral blood were associated with pathogen type and sepsis severity in children treated for suspected sepsis.
2020-01-01 | GSE119217 | GEO