Table 1.
Study characteristics.
| Authors, year | Setting(s) | Data collection | Cohort description | Event rate | Study purpose | Predictors | Measurement frequency | Outcome |
| Badriyah et al, 2014 [45] | Medical assessment unit for 24 hours | Personal digital assistants running VitalPAC software | 35,585 admissions | 199 (0.56%), cardiac arrest; 1161 (3.26%) unanticipated ICUa admissions; 1789 (5.02%) deaths; 3149 (8.85%) any outcome |
Compare the performance of a decision tree analysis with NEWSb | HRc, RRd, SBPe, temperature, SpO2, AVPUf level, % breathing air at the time of SpO2 measurement | Not specified | Cardiac arrest, unanticipated ICU admission, or death, each within 24 hours of a given vital sign observation |
| Chen at al, 2017 [44] | Step-down unit | Bedside monitors | 1880 patients (1971 admissions) | 997 patients (53%) or 1056 admissions (53.6%) who experienced CRIg events | Describe the dynamic and personal character of CRI risk evolution observed through continuous vital sign monitoring of individual patients | HR, RR, SPO2 (at 1/20 Hz), SBP, DBPh | Every 2 hours | CRI |
| Churpek et al, 2016 [24] | All wards at the University of Chicago and 4 North Shore University Health System hospitals | Data collected manually, documented electronically | 269,999 admissions | 16,452 outcomes (6.09%) | Whether adding trends improves accuracy of early detection of clinical deterioration and which methods are optimal for modelling trends | Temperature, HR, RR, SpO2, DBP, SBP | Every 4 hours | Development of critical illness on the wards: deaths, cardiac arrest, ICU transfers |
| Chiew et al, 2019 [23] | EDi at Singapore general hospital | Measurements at triage; hospital EHRj | 214 patients | 40 patients (18.7%) met outcome | Compare the performance of HR variability–based machine learning models vs conventional risk stratification tools to predict 30-day mortality | Age, gender, ethnicity, temperature, HR, RR, SBP, DBP, GCSk, HR variability | At triage | 30-day mortality due to sepsis |
| Chiu et al, 2019 [42] | Postoperative surgical wards at 4 UK adult cardiac surgical centers | VitalPac to electronically capture patients’ vital signs | Adults undergoing risk-stratified major cardiac surgery, n=13,631 | 578 patients (4.2%) with an outcome; 499 patients (3.66%) with unplanned ICU readmissions | Using logistic regression to model the association of NEWS variables with a serious patient event in the subsequent 24 hours; secondary objectives: comparing the discriminatory power of each model for events in the next 6 hours or 12 hours | RR, SpO2, SBP, HR, temperature, consciousness level | Not specified | Death, cardiac arrest, unplanned ICU readmissions |
| Clifton et al, 2014 [25] | Postoperative ward of the cancer center, Oxford University Hospitals NHSl Trust, United Kingdom | Continuous vitals monitored by wearable devices; intermittent vitals monitored manually by ward staff | 200 patients in the postoperative ward following upper gastrointestinal cancer surgery | Not specified | Using continuous vitals monitoring to provide early warning of physiological deterioration, such that preventative clinical action may be taken | SpO2, HR (256 Hz), BP, RR | Continuously (SpO2, HR), intermittently (BP, RR) | Physiological deterioration |
| Desautels et al, 2016 [37] | Beth Israel Deaconess Medical Center ICU | ICU bedside monitors and medical records (MIMICm-III) | 22,853 ICU stays | 2577 (11.28%) stays with confirmed sepsis | Validate a sepsis prediction method, InSight, for the new Sepsis-3 definitions and make predictions using a minimal set of variables | GCS, HR, RR, SpO2, temperature, invasive and noninvasive SBP and DBP | At least 1 measurement per hour | Onset of sepsis |
| Forkan et al, 2017 [28] | Beth Israel Deaconess Medical Center ICU | ICU bedside monitors and medical records (MIMIC-II) | 1023 patients | Not specified | Develop a probabilistic model for predicting the future clinical episodes of a patient using observed vital sign values prior to the clinical event | HR, SBP, DBP, mean BP, RR, SpO2 | All samples converted to per-minute sampling | Abnormal clinical events |
| Forkan et al, 2017 [27] | Beth Israel Deaconess Medical Center ICU | ICU bedside monitors and medical records (MIMIC & MIMIC-II) | 85 patients | Not specified | Develop an intelligent method for personalized monitoring and clinical decision support through early estimation of patient-specific vital sign values | HR, SBP, DBP, mean BP, RR, SpO2 | Per-minute sampling | Patient-specific anomalies, disease symptoms, and emergencies |
| Forkan et al, 2017 [29] | Beth Israel Deaconess Medical Center ICU | ICU bedside monitors and medical records (MIMIC-II) | 4893 patients | Not specified | Build a prognostic model, ViSiBiD, that can accurately identify dangerous clinical events of a home-monitored patient in advance | HR, SBP, DBP, mean BP, RR, SpO2 | Per-minute sampling | Dangerous clinical events |
| Guillame-Bert et al, 2017 [43] | Step-down unit | Bedside monitor measurements over 8 weeks | 297 admissions | 127 patients (43%) exhibited at least 1 real event during their stay | Forecast CRI utilizing data from continuous monitoring of physiologic vital sign measurements | HR, RR, SPO2, SBP, DBP, mean BP | Every 20 seconds (HR, RR, SPO2), every 2 hours (SBP, DBP, and mean BP) | At least 1 event threshold limit criteria exceeded for >80% of last 3 minutes |
| Ho et al, 2017 [38] | Beth Israel Deaconess Medical Center ICU | ICU bedside monitors and medical records (MIMIC-II) | 763 patients | 197 patients (25.8%) experienced a cardiac arrest event | Build a cardiac arrest risk prediction model capable of early notification at time z (z ≥5 hours prior to the event) | Temperature, SpO2, HR, RR, DBP, SBP, pulse pressure index | 1 reading per hour | Cardiac arrest |
| Jang et al, 2019 [35] | ED visits to a tertiary academic hospital | EHR data from ED visits | Nontraumatic ED visits | 374,605 eligible ED visits of 233,763 patients; 1097 (0.3%) patients with cardiac arrest | Develop and test artificial neural network classifiers for early detection of patients at risk of cardiac arrest in EDs | Age, sex, chief complaint, SBP, DBP, HR, RR, temperature, AVPU | Not specified | Development of cardiac arrest within 24 hours after prediction |
| Kwon et al, 2018 [26] | Cardiovascular teaching hospital and community general hospital | Data collected manually by staff on general wards, by bedside monitors in ICUs | 52,131 patients | 419 patients (0.8%) with cardiac arrest; 814 (1.56%) deaths without attempted resuscitation | Predict whether an input vector belonged within the prediction time window (0.5-24 hours before the outcome) | SBP, HR, RR, temperature | 3 times a day on general wards, every 10 minutes in ICUs | Primary outcome: first cardiac arrest; secondary outcome: death without attempted resuscitation |
| Kwon et al, 2018 [11] | 151 EDs in Korea | Korean National Emergency Department Information System (NEDIS) | 10,967,518 ED visits | 153,217 (1.4%) in-hospital deaths; 625,117 (5.7%) critical care admissions; 2,964,367 (27.0%) hospitalizations | Validate that a DTASn identifies high-risk patients more accurately than existing triage and acuity scores | Age, sex, chief complaint, time from symptom onset to ED visit, arrival mode, trauma, initial vital signs (SBP, DBP, HR, RR, temperature), mental status | At ED admission | Primary outcome: in-hospital mortality; secondary outcome: critical care; tertiary outcome: hospitalization |
| Larburu et al, 2018 [22] | OSI Bilbao-Basurto (Osakidetza) Hospital and ED admissions, ambulatory | Collected manually by clinicians and patients | 242 patients | 202 predictable decompensations | Prevent mobile heart failure patients’ decompensation using predictive models | SBP, DBP, HR, SaO2, weight | At diagnosis and 3-7 times per week in ambulatory patients | Heart failure decompensation |
| Li et al, 2016 [39] | Beth Israel Deaconess Medical Center ICU | ICU bedside monitors and medical records (MIMIC-II) | 12 patients | Not specified | Adaptive online monitoring of patients in ICUs | HR, SBP, DBP, MAPo, RR | At least 1 measurement per hour | Signs of deterioration |
| Liu et al, 2014 [36] | ED of a tertiary hospital in Singapore | Manual vital measurements by nurses or physicians | 702 patients with undifferentiated, nontraumatic chest pain | 29 (4.13%) patients met primary outcome | Discover the most relevant variables for risk prediction of major adverse cardiac events using clinical signs and HR variability | SBP, RR, HR | Not specified | Composite of events such as death and cardiac arrest within 72 hours of arrival at the ED |
| Mao et al, 2018 [34] | ICU, inpatient wards, outpatient visits | UCSFp dataset:inpatient and outpatient visits; MIMIC-III: ICU bedside monitors | UCSF: 90,353 patients; MIMIC-III: 21,604 patients |
UCSF: 1179 (1.3%) sepsis, 349 (0.39%) severe sepsis, 614 (0.68%) septic shock; MIMIC-III: sepsis (1.91%), severe sepsis (2.82%), septic shock (4.36%) | Sepsis prediction | SBP, DBP, HR, RR, SpO2, temperature | Hourly | Sepsis, severe sepsis, septic shock |
| Olsen et al, 2018 [46] | PACUq, Rigshospitalet, University of Copenhagen, Denmark | IntelliVue MP5, BMEYE Nexfin bedside monitors during admission to post anesthetic care unit | 178 patients | 160 (89.9%) had ≥1 microevent occurring during admission; 116 patients (65.2%) had ≥1 microevent with a duration >15 minutes | Develop a predictive algorithm detecting early signs of deterioration in the PACU using continuously collected cardiopulmonary vital signs | SpO2, SBP, HR, MAP | Every minute (SpO2, SBP, HR), every 15 minutes (MAP) | Signs of deterioration |
| Shashikumar et al, 2017 [40] | Adult ICU units | ICU bedside monitors, Bedmaster system; up to 24 hours of monitoring | Patients with unselected mixed surgical procedures | 242 sepsis cases | Predict onset of sepsis 4 hours ahead of time, using commonly measured vital signs | MAP, HR, SpO2, SBP, DBP, RR, GCS, temperature, comorbidity, clinical context, admission unit, surgical specialty, wound type, age, gender, weight, race | ≥1 measurement per hour | Onset of sepsis |
| Tarassenko et al, 2006 [32] | General wards at John Radcliffe Hospital in Oxford, United Kingdom | Bedside monitors for at least 24 hours per patient | 150 general-ward patients | Not specified | A real-time automated system, BioSign, which tracks patient status by combining information from vital signs | HR, RR, SpO2, skin temperature, average SBP -average DBP | Every 30 minutes (BP), every 5 seconds (other vitals) | Signs of deterioration |
| Van Wyk et al, 2017 [33] | Methodist LeBonheur Hospital, Memphis, TN | Bedside monitors: Cerner CareAware iBus system | 2995 patients | 343 patients (11.5%) diagnosed with sepsis | Classify patients into sepsis and nonsepsis groups using data collected at various frequencies from the first 12 hours after admission | HR, MAP, DBP, SBP, SpO2, age, race, gender, fraction of inspired oxygen | Every minute | Sepsis detection |
| Yoon et al, 2019 [41] | Beth Israel Deaconess Medical Center ICU | ICU bedside monitors and medical records (MIMIC-II) | 2809 subjects | 787 tachycardia episodes | Predicting tachycardia as a surrogate for instability | Arterial DBP, arterial SBP, HR, RR, SpO2, MAP | 1/60 Hz or 1 Hz | Tachycardia episode |
aICU: intensive care unit.
bNEWS: National Early Warning Score.
cHR: heart rate.
dRR: respiratory rate.
eSBP: systolic blood pressure.
fAVPU: alert, verbal, pain, unresponsive.
gCRI: cardiorespiratory instability.
hDBP: diastolic blood pressure.
iED: emergency department.
jEHR: electronic health record.
kGCS: Glasgow Coma Score.
lNHS: National Health Service.
mMIMIC: Medical Information Mart for Intensive Care.
nDTAS: Deep learning–based Triage and Acuity Score.
oMAP: mean arterial pressure.
pUCSF: University of California, San Francisco.
qPACU: postanesthesia care unit.