Short-term mortality in older medical emergency patients can be predicted using clinical intuition: A prospective study

The Prognostic Value of the Modified Surprise Question in Critically Ill Emergency Department Patients

Objective: The initiation of palliative care (PC) in the emergency department (ED) is effective in improving the quality of life for seriously ill patients. This study aimed to evaluate the prognostic value of the modified surprise question (mSQ), "Would you be surprised if this patient died in the next 30 days?" as a trigger for initiating PC in critically ill ED patients. Methods: We conducted a prospective cohort study over a 6-month period in an ED, during which 22 emergency residents answered the mSQ for critically ill ED patients (Korean Triage and Acuity Scale 1 or 2). The primary outcome was the accuracy of the positive mSQ (negative response to the mSQ) in predicting 30-day mortality, and logistic regression analysis was performed to identify the prognostic factors. Results: A total of 300 patients were enrolled, and the positive mSQ group included 118 (39.3%) patients. The 30-day mortality rate of the cohort was 10.0%. The sensitivity, specificity, positive predictive value, and negative predictive value of the positive mSQ were 83.3%, 65.6%, 21.2%, and 97.3%, respectively, with a c-statistic of 0.74 and a positive likelihood ratio of 2.42. In a multivariable analysis controlling for clinically relevant variables, the odds ratio for 30-day mortality of the positive mSQ was 4.76 (95% confidence interval, 1.61-14.09; P = .005). Conclusions: The mSQ may be valuable for identifying critically ill ED patients with an increased risk of 30-day mortality. Therefore, it may be utilized as a trigger for PC consultation in the ED.

Accuracy of the "Surprise Question" in Predicting Long-Term Mortality Among Older Patients Admitted to the Emergency Department: Comparison Between Emergency Physicians and Nurses in a Multicenter Longitudinal Study

Background

The "surprise question" (SQ) ("Would you be surprised if this patient died in the next 12 months?") is the most frequently used screening tool in emergency departments (EDs) to identify patients with poor prognosis and potential unmet palliative needs.

Objective

To test and compare the accuracy of the SQ between emergency nurses (ENs) and emergency physicians (EPs) in predicting long-term mortality among older patients (OP) in the ED.

Design and Setting/Subjects

A prospective cohort study of OPs (≥75 years) conducted in two Belgian EDs. EPs and ENs answered the SQ for the patients they cared for. Positive SQ (SQ+) was defined as a "no" answer. One-year mortality was assessed by phone call.

Results

EPs and ENs both answered the SQ for 291 OPs (mean age 83.2 ± 5.4, males 42.6%). The SQ was positive in 43% and 40.6%, respectively. Predictive values were similar in both groups: sensitivity, specificity, c-statistics, negative predictive value, and positive predictive value were 0.79 (0.66-0.88), 0.68 (0.62-0.76), 0.69 (0.63-0.75), 0.92 (0.86-0.96), and 0.4 (0.31-0.50), respectively, for EPs and 0.71 (0.57-0.82), 0.69 (0.62-0.75), 0.69 (0.63-0.75), 0.89 (0.83-0.93), and 0.41 (0.31-0.51), respectively, for ENs. SQ + was associated with a higher mortality risk in both group (EPs hazard ratio: 3.2 [1.6-6.7], p = 0.002; ENs hazard ratio: 2.5 [1.3-4.8], p = 0.006). The survival probability was lower when both EPs and ENs agreed on the SQ+ (p < 0.001).

Conclusion

The SQ is a simple tool to identify older ED patients at high mortality risk. Concordant responses from EPs and ENs are more predictive than either alone.

Acute internal medicine physicians' clinical intuition based on acute care telephone referral: A prospective study

INTRODUCTION

In the Netherlands, most emergency department (ED) patients are referred by a general practitioner (GP) or a hospital specialist. Early risk stratification during telephone referral could allow the physician to assess the severity of the patients' illness in the prehospital setting. We aim to assess the discriminatory value of the acute internal medicine (AIM) physicians' clinical intuition based on telephone referral of ED patients to predict short-term adverse outcomes, and to investigate on which information their predictions are based.

METHODS

In this prospective study, we included adult ED patients who were referred for internal medicine by a GP or a hospital specialist. Primary outcomes were hospital admission and triage category according to the Manchester Triage System (MTS). Secondary outcome was 31-day mortality. The discriminatory performance of the clinical intuition was assessed using an area under the receiver operating characteristics curve (AUC). To identify which information is important to predict adverse outcomes, we performed univariate regression analysis. Agreement between predicted and observed MTS triage category was assessed using intraclass and Spearman's correlation.

RESULTS

We included 333 patients, of whom 172 (51.7%) were referred by a GP, 146 (43.8%) by a hospital specialist, and 12 (3.6%) by another health professional. The AIM physician's clinical intuition showed good discriminatory performance regarding hospital admission (AUC 0.72, 95% CI: 0.66-0.78) and 31-day mortality (AUC 0.73, 95% CI: 0.64-0.81). Univariate regression analysis showed that age ≥65 years and a sense of alarm were significant predictors. The predicted and observed triage category were similar in 45.2%, but in 92.5% the prediction did not deviate by more than one category. Intraclass and Spearman's correlation showed fair agreement between predicted and observed triage category (ICC 0.48, Spearman's 0.29).

CONCLUSION

Clinical intuition based on relevant information during a telephone referral can be used to accurately predict short-term outcomes, allowing for early risk stratification in the prehospital setting and managing ED patient flow more effectively.

Exploiting the Features of Clinical Judgment to Improve Assessment of Disease Severity in the Emergency Department: An Acutelines Study

BACKGROUND

Clinical judgment, also known as gestalt or gut feeling, can predict deterioration and can be easily and rapidly obtained. To date, it is unknown what clinical judgement precisely entails. The aim of this study was to elucidate which features define the clinical impression of health care professionals in the ED.

METHOD

A nominal group technique (NGT) was used to develop a consensus-based instrument to measure the clinical impression score (CIS, scale 1-10) and to identify features associated with either a more severe or less severe estimated disease severity. This single-center observational cohort study included 517 medical patients visiting the ED. The instrument was prospectively validated.. The predictive value of each feature for the clinical impression was assessed using multivariate linear regression analyses to adjust for potential confounders and validated in the infection group.

RESULTS

The CIS at the ED was associated with ICU admission (OR 1.67 [1.37-2.03], p < 0.001), in-hospital mortality (OR 2.25 [1.33-3.81], p < 0.001), and 28-day mortality (OR 1.33 [1.07-1.65], <0.001). Dry mucous membranes, eye glance, red flags during physical examination, results of arterial blood gas analysis, heart and respiratory rate, oxygen modality, triage urgency, and increased age were associated with a higher estimated disease severity (CIS). On the other hand, behavior of family, self-estimation of the patient, systolic blood pressure, and Glascow Coma Scale were associated with a lower estimated disease severity (CIS).

CONCLUSION

We identified several features that were associated with the clinical impression of health care professionals in the ED. Translating the subjective features and objective measurements into quantifiable parameters may aid the development of a novel triage tool to identify patients at risk of deterioration in the ED.

The Physician Surprise Question in the Emergency Department: prospective cohort study

OBJECTIVES

This study aims to test the ability of the surprise question (SQ), when asked to emergency physicians (EPs), to predict in-hospital mortality among adults admitted to an emergency room (ER).

METHODS

This prospective cohort study at an academic medical centre included consecutive patients 18 years or older who received care in the ER and were subsequently admitted to the hospital from 20 April 2018 to 20 October 2018. EPs were required to answer the SQ for all patients who were being admitted to hospital. The primary outcome was in-hospital mortality.

RESULTS

The cohort included 725 adults (mean (SD) age, 60 (17) years, 51% men) from 58 128 emergency department (ED) visits. The mortality rates were 20.6% for 30-day all-cause in-hospital mortality and 23.6% for in-hospital mortality. The diagnostic test characteristics of the SQ have a sensitivity of 53.7% and specificity of 87.1%, and a relative risk of 4.02 (95% CI 3.15 to 5.13), p<0.01). The positive and negative predictive values were 57% and 86%, respectively; the positive likelihood ratio was 4.1 and negative likelihood ratio was 0.53; and the accuracy was 79.2%.

CONCLUSIONS

We found that asking the SQ to EPs may be a useful tool to identify patients in the ED with a high risk of in-hospital mortality.

Machine learning for risk stratification in the emergency department (MARS-ED) study protocol for a randomized controlled pilot trial on the implementation of a prediction model based on machine learning technology predicting 31-day mortality in the emergency department

BACKGROUND

Many prediction models have been developed to help identify emergency department (ED) patients at high risk of poor outcome. However, these models often underperform in clinical practice and their actual clinical impact has hardly ever been evaluated. We aim to perform a clinical trial to investigate the clinical impact of a prediction model based on machine learning (ML) technology.

METHODS

The study is a prospective, randomized, open-label, non-inferiority pilot clinical trial. We will investigate the clinical impact of a prediction model based on ML technology, the RISKINDEX, which has been developed to predict the risk of 31-day mortality based on the results of laboratory tests and demographic characteristics. In previous studies, the RISKINDEX was shown to outperform internal medicine specialists and to have high discriminatory performance. Adults patients (18 years or older) will be recruited in the ED. All participants will be randomly assigned to the control group or the intervention group in a 1:1 ratio. Participants in the control group will receive care as usual in which the study team asks the attending physicians questions about their clinical intuition. Participants in the intervention group will also receive care as usual, but in addition to asking the clinical impression questions, the study team presents the RISKINDEX to the attending physician in order to assess the extent to which clinical treatment is influenced by the results.

DISCUSSION

This pilot clinical trial investigates the clinical impact and implementation of an ML based prediction model in the ED. By assessing the clinical impact and prognostic accuracy of the RISKINDEX, this study aims to contribute valuable insights to optimize patient care and inform future research in the field of ML based clinical prediction models.

TRIAL REGISTRATION

ClinicalTrials.gov NCT05497830. Machine Learning for Risk Stratification in the Emergency Department (MARS-ED). Registered on August 11, 2022. URL: https://clinicaltrials.gov/study/NCT05497830 .

Using emergency department triage for machine learning-based admission and mortality prediction

AIMS

Patient admission is a decision relying on sparsely available data. This study aims to provide prediction models for discharge versus admission for ward observation or intensive care, and 30 day-mortality for patients triaged with the Manchester Triage System.

METHODS

This is a single-centre, observational, retrospective cohort study from data within ten minutes of patient presentation at the interdisciplinary emergency department of the Kepler University Hospital, Linz, Austria. We trained machine learning models including Random Forests and Neural Networks individually to predict discharge versus ward observation or intensive care admission, and 30 day-mortality. For analysis of the features' relevance, we used permutation feature importance.

RESULTS

A total of 58323 adult patients between 1 December 2015 and 31 August 2020 were included. Neural Networks and Random Forests predicted admission to ward observation with an AUC-ROC of 0.842 ± 0.00 with the most important features being age and chief complaint. For admission to intensive care, the models had an AUC-ROC of 0.819 ± 0.002 with the most important features being the Manchester Triage category and heart rate, and for the outcome 30 day-mortality an AUC-ROC of 0.925 ± 0.001. The most important features for the prediction of 30 day-mortality were age and general ward admission.

CONCLUSION

Machine learning can provide prediction on discharge versus admission to general wards and intensive care and inform about risk on 30 day-mortality for patients in the emergency department.

Clinician Trust in Artificial Intelligence: What is Known and How Trust Can Be Facilitated

Predictive analytics based on artificial intelligence (AI) offer clinicians the opportunity to leverage big data available in electronic health records (EHR) to improve clinical decision-making, and thus patient outcomes. Despite this, many barriers exist to facilitating trust between clinicians and AI-based tools, limiting its current impact. Potential solutions are available at both the local and national level. It will take a broad and diverse coalition of stakeholders, from health-care systems, EHR vendors, and clinical educators to regulators, researchers and the patient community, to help facilitate this trust so that the promise of AI in health care can be realized.

Older patients with nonspecific complaints at the Emergency Department are at risk of adverse health outcomes

OBJECTIVE

Older adults at the Emergency Department (ED) often present with nonspecific complaints (NSC) such as 'weakness' or 'feeling unwell'. Health care workers may underestimate illness in patients with NSC, leading to adverse health outcomes. This study compares characteristics and outcomes of NSC-patients versus specific complaints (SC) patients.

METHODS

Cohort study in patients ≥ 70 years in two Dutch EDs. NSC was classified according to the BANC-study-framework based on the medical history in the ED letter, before additional diagnostics took place. A second classification was performed at the end of the ED visit/hospital admission. Primary outcomes were functional decline, institutionalization, and mortality at 30 days.

RESULTS

26% (n = 228) of a total of 888 included patients presented with NSC. Compared with SC-patients, NSC-patients were older, more frail, and more frequently female. NSC-patients had a higher risk of functional decline and institutionalization at 30 days (adjusted ORs 1.84, 95% CI 1.27 - 2.72, and 2.46, 95% CI 1.51-4.00, respectively), but not mortality (adjusted OR 1.26, 95% CI 0.58 - 2.73). Reclassification to a specific complaint after the ED visit or hospital admission occurred in 54% of NSC-patients.

CONCLUSION

NSC occur especially in older, frail female patients and are associated with an increased risk of functional decline and institutionalization, even after adjustment for worse baseline status. In half of the patients, a specific complaint revealed during ED or hospital stay. Physicians at the ED should consider NSC as a red flag needing appropriate observation and evaluation of underlying serious conditions and needs of this vulnerable patient group.

The Value of the First Clinical Impression as Assessed by 18 Observations in Patients Presenting to the Emergency Department

The first clinical impression of emergency patients conveys a myriad of information that has been incompletely elucidated. In this prospective, observational study, the value of the first clinical impression, assessed by 18 observations, to predict the need for timely medical attention, the need for hospital admission, and in-hospital mortality in 1506 adult patients presenting to the triage desk of an emergency department was determined. Machine learning models were used for statistical analysis. The first clinical impression could predict the need for timely medical attention [area under the receiver operating characteristic curve (AUC ROC), 0.73; p = 0.01] and hospital admission (AUC ROC, 0.8; p = 0.004), but not in-hospital mortality (AUC ROC, 0.72; p = 0.13). The five most important features informing the prediction models were age, ability to walk, admission by emergency medical services, lying on a stretcher, breathing pattern, and bringing a suitcase. The inability to walk at triage presentation was highly predictive of both the need for timely medical attention (p < 0.001) and the need for hospital admission (p < 0.001). In conclusion, the first clinical impression of emergency patients presenting to the triage desk can predict the need for timely medical attention and hospital admission. Important components of the first clinical impression were identified.

Mortality Prediction in Hip Fracture Patients: Physician Assessment Versus Prognostic Models

OBJECTIVES

To evaluate 2 prognostic models for mortality after a fracture of the hip, the Nottingham Hip Fracture Score and Hip Fracture Estimator of Mortality Amsterdam and to compare their predictive performance to physician assessment of mortality risk in hip fracture patients.

DESIGN

Prospective cohort study.

SETTING

Two level-2 trauma centers located in the Netherlands.

PATIENTS

Two hundred forty-four patients admitted to the Emergency Departments of both hospitals with a fractured hip.

INTERVENTION

Data used in both prediction models were collected at the time of admission for each individual patient, as well as predictions of mortality by treating physicians.

MAIN OUTCOME MEASURES

Predictive performances were evaluated for 30-day, 1-year, and 5-year mortality. Discrimination was assessed with the area under the curve (AUC); calibration with the Hosmer-Lemeshow goodness-of-fit test and calibration plots; clinical usefulness in terms of accuracy, sensitivity, and specificity.

RESULTS

Mortality was 7.4% after 30 days, 22.1% after 1 year, and 59.4% after 5 years. There were no statistically significant differences in discrimination between the prediction methods (AUC 0.73-0.80). The Nottingham Hip Fracture Score demonstrated underfitting for 30-day mortality and failed to identify the majority of high-risk patients (sensitivity 33%). The Hip fracture Estimator of Mortality Amsterdam showed systematic overestimation and overfitting. Physicians were able to identify most high-risk patients for 30-day mortality (sensitivity 78%) but with some overestimation. Both risk models demonstrated a lack of fit when used for 1-year and 5-year mortality predictions.

CONCLUSIONS

In this study, prognostic models and physicians demonstrated similar discriminating abilities when predicting mortality in hip fracture patients. Although physicians overestimated mortality, they were better at identifying high-risk patients and at predicting long-term mortality.

LEVEL OF EVIDENCE

Prognostic Level II. See Instructions for Authors for a complete description of levels of evidence.

The Prognostic Accuracy of Clinical Judgment Versus a Validated Frailty Screening Instrument in Older Patients at the Emergency Department: Findings of the AmsterGEM Study

STUDY OBJECTIVE

To compare the prognostic accuracy of clinical judgment for frailty in older patients at the emergency department with a validated screening instrument and patient-perceived frailty.

METHODS

A prospective cohort study in patients 70 years of age and older in 2 Dutch EDs with a follow-up of 3 months. A dichotomous question was asked to the physician and patient: "Do you consider the patient / yourself to be frail?" The Identification of Seniors At Risk-Hospitalized Patients (ISAR-HP) was used as a validated screening instrument. The primary composite outcome consisted of either functional decline, institutionalization, or mortality.

RESULTS

A total of 736 patients were included. The physician identified 59% as frail, compared with 49% by ISAR-HP and 43% by patients themselves. The level of agreement was fair (Fleiss Kappa, 0.31). After 3 months, 31% of the patients experienced at least 1 adverse health outcome. The sensitivity was 79% for the physician, 72% for ISAR-HP, 61% for the patient, and 48% for all 3 combined. The specificity was 50% for the physician, 63% for ISAR-HP, 66% for the patient, and 85% for all 3 positive. The highest positive likelihood ratio was 3.03 (physician, ISAR-HP, patient combined), and the lowest negative likelihood ratio was 0.42 (physician). The areas under the receiver operating curves were all poor: 0.68 at best for ISAR-HP.

CONCLUSION

Clinical judgment for frailty showed fair agreement with a validated screening instrument and patient-perceived frailty. All 3 instruments have poor prognostic accuracy, which does not improve when combined. These findings illustrate the limited prognostic value of clinical judgment as a frailty screener in older patients at the ED.

Quality of dying in hospital general wards: a cross-sectional study about the end-of-life care

Background

In the last decade, access to national palliative care programs have improved, however a large proportion of patients continued to die in hospital, particularly within internal medicine wards.

Objectives

To describe treatments, symptoms and clinical management of adult patients at the end of their life and explore whether these differ according to expectation of death.

Methods

Single-centre cross-sectional study performed in the medical and surgical wards of a large tertiary-level university teaching hospital in the north of Italy. Data on nursing interventions and diagnostic procedure in proximity of death were collected after interviewing the nurse and the physician responsible for the patient. Relationship between nursing treatments delivered and patients’ characteristics, quality of dying and nurses’ expectation about death was summarized by means of multiple correspondence analysis (MCA).

Results

Few treatments were found statistically associated with expectation of death in the 187 patients included. In the last 48 h, routine (70.6%) and biomarkers (41.7%) blood tests were performed, at higher extent on patients whose death was not expected. Many symptoms classified as severe were reported when death was highly expected, except for agitation and respiratory fatigue which were reported when death was moderately expected. A high Norton score and absence of anti-bedsore mattress were associated with unexpected death and poor quality of dying, as summarized by MCA. Quality of dying was perceived as good by nurses when death was moderately and highly expected. Physicians rated more frequently than nurses the quality of dying as good or very good, respectively 78.6 and 57.8%, denoting a fair agreement between the two professionals (k = 0.24, P <  0.001). The palliative care consultant was requested for only two patients.

Conclusion

Staff in medical and surgical wards still deal inadequately with the needs of dying people. Presence of hospital-based specialist palliative care could lead to improvements in the patients’ quality of life.

Predicting In-Hospital Mortality at Admission to the Medical Ward: A Big-Data Machine Learning Model

BACKGROUND

General medical wards admit high-risk patients. Artificial intelligence algorithms can use big data for developing models to assess patients' risk stratification. The aim of this study was to develop a mortality prediction machine learning model using data available at the time of admission to the medical ward.

METHODS

We included consecutive patients (ages 18-100) admitted to medical wards at a single medical center (January 1, 2013-December 31, 2018). We constructed a machine learning model using patient characteristics, comorbidities, laboratory tests, and patients' emergency department (ED) management. The model was trained on data from the years 2013 to 2017 and validated on data from the year 2018. The area under the curve (AUC) for mortality prediction was used as an outcome metric. Youden index was used to find an optimal sensitivity-specificity cutoff point.

RESULTS

Of the 118,262 patients admitted to the medical ward, 6311 died (5.3%). The single variables with the highest AUCs were medications administered in the ED (AUC = 0.74), ED diagnosis (AUC = 0.74), and albumin (AUC = 0.73). The machine learning model yielded an AUC of 0.924 (95% confidence interval [CI]: 0.917-0.930). For Youden index, a sensitivity of 0.88 (95% CI: 0.86-0.89) and specificity of 0.83 (95% CI: 0.83-0.83) were observed. This corresponds to a false-positive rate of 1:5.9 and negative predictive value of 0.99.

CONCLUSION

A machine learning model outperforms single variables predictions of in-hospital mortality at the time of admission to the medical ward. Such a decision support tool has the potential to augment clinical decision-making regarding level of care needed for admitted patients.

A comparison of machine learning models versus clinical evaluation for mortality prediction in patients with sepsis

Introduction

Patients with sepsis who present to an emergency department (ED) have highly variable underlying disease severity, and can be categorized from low to high risk. Development of a risk stratification tool for these patients is important for appropriate triage and early treatment. The aim of this study was to develop machine learning models predicting 31-day mortality in patients presenting to the ED with sepsis and to compare these to internal medicine physicians and clinical risk scores.

Methods

A single-center, retrospective cohort study was conducted amongst 1,344 emergency department patients fulfilling sepsis criteria. Laboratory and clinical data that was available in the first two hours of presentation from these patients were randomly partitioned into a development (n = 1,244) and validation dataset (n = 100). Machine learning models were trained and evaluated on the development dataset and compared to internal medicine physicians and risk scores in the independent validation dataset. The primary outcome was 31-day mortality.

Results

A number of 1,344 patients were included of whom 174 (13.0%) died. Machine learning models trained with laboratory or a combination of laboratory + clinical data achieved an area-under-the ROC curve of 0.82 (95% CI: 0.80–0.84) and 0.84 (95% CI: 0.81–0.87) for predicting 31-day mortality, respectively. In the validation set, models outperformed internal medicine physicians and clinical risk scores in sensitivity (92% vs. 72% vs. 78%;p<0.001,all comparisons) while retaining comparable specificity (78% vs. 74% vs. 72%;p>0.02). The model had higher diagnostic accuracy with an area-under-the-ROC curve of 0.85 (95%CI: 0.78–0.92) compared to abbMEDS (0.63,0.54–0.73), mREMS (0.63,0.54–0.72) and internal medicine physicians (0.74,0.65–0.82).

Conclusion

Machine learning models outperformed internal medicine physicians and clinical risk scores in predicting 31-day mortality. These models are a promising tool to aid in risk stratification of patients presenting to the ED with sepsis.

Concerns of older patients and their caregivers in the emergency department

Background

Older emergency department (ED) patients often have complex problems and severe illnesses with a high risk of adverse outcomes. It is likely that these older patients are troubled with concerns, which might reflect their preferences and needs concerning medical care. However, data regarding this topic are lacking.

Methods

This study is a sub study of a prospective, multicenter, observational cohort study among older medical ED patients (≥65 years). Patients or their caregivers were asked about their illness-related concerns during the first stage of the ED visit using a questionnaire. All concerns were categorized into 10 categories, and differences between patients and caregivers, and between age groups were analyzed. Odds Ratios were calculated to determine the association of the concerns for different adverse outcomes.

Results

Most of the 594 included patients (or their caregivers) were concerned (88%) about some aspects of their illness or their need for medical care. The most often reported concerns were about the severity of disease (43.6%), functional decline (9.4%) and dying (5.6%). Caregivers were more frequently concerned than patients (p<0.001) especially regarding the severity of disease (50.5 vs 39.6%, p = 0.016) and cognitive decline (10.8 vs. 0.3%, p <0.001). We found no difference between age groups. The concern about dying was associated with 30-day mortality (OR 2.89; 95%CI: 1.24–6.70) and the composite endpoint (intensive- or medium care admission, length of hospital stay >7 days, loss of independent living and unplanned readmission within 30 days) (OR 2.32; 95%CI: 1.12–4.82). In addition, unspecified concerns were associated with mortality (OR 1.88; 95%CI: 1.09–3.22).

Conclusion

The majority of older patients and especially their caregivers are concerned about their medical condition or need for medical care when they visit the ED. These concerns are associated with adverse outcomes and most likely reflect their needs regarding medical care. More attention should be paid to these concerns because they may offer opportunities to reduce anxiety and provide care that is adjusted to their needs.

Trial registration

This study was registered on clinicalTriagls.gov (NCT02946398).