Measuring adherence among nurses one year after training in applying the Modified Early Warning Score and Situation-Background-Assessment-Recommendation instruments

Patients with a cardiac arrest or unplanned intensive care admission show gradual decline in clinical condition preceding the event. This can be objectified by measuring the vital parameters and subsequently determining the Modified Early Warning Score (MEWS). Contact with the physician by nurses may be structured using the Situation-Background-Assessment-Recommendation (SBAR) communication instrument. The aim of our study was to evaluate whether nurses trained in the use of MEWS and SBAR tools were more likely to recognize a deteriorating patient. This prospective quasi-experimental trial in the Academic Medical Center in Amsterdam, the Netherlands included three medical and three surgical wards. A group of 47 trained and 48 non-trained nurses were presented with a case of a deteriorating patient, and subsequent assessment and actions regarding the patient case were measured. Of the trained nurses, 77% versus 58% of the non-trained group assessed the patient immediately. On subsequent assessment of the patient, respiratory rate was measured twice as frequently (53% trained versus 25% non-trained, p=0.025). No differences were found in the measurement of other vital parameters. The MEWS was determined by 11% of trained nurses. Subsequent notification of the physician was performed by 67% of the trained versus 43% of the non-trained nurses. The SBAR communication tool was used by only one nurse. Trained nurses are able to identify a deteriorating patient and react more appropriately. However, despite rigorously implementing MEWS/SBAR methodology, these tools were rarely use

Rapid response systems in The Netherlands

Sixty-three (approximately 80%) of the 81 hospitals that responded to a survey sent to all hospitals in The Netherlands with nonpediatric intensive care units had a rapid response system (RRS) in place or were in the final process of starting one. Among many other findings regarding RRS infrastructure and implementation, only 38% of the hospitals allowed nurses to activate the rapid response team without physician consen

Artificial Intelligence for the Prediction of In-Hospital Clinical Deterioration: A Systematic Review

OBJECTIVES: To analyze the available literature on the performance of artificial intelligence-generated clinical models for the prediction of serious life-threatening events in non-ICU adult patients and evaluate their potential clinical usage. DATA SOURCES: The PubMed database was searched for relevant articles in English literature from January 1, 2000, to January 23, 2022. Search terms, including artificial intelligence, machine learning, deep learning, and deterioration, were both controlled terms and free-text terms. STUDY SELECTION: We performed a systematic search reporting studies that showed performance of artificial intelligence-based models with outcome mortality and clinical deterioration. DATA EXTRACTION: Two review authors independently performed study selection and data extraction. Studies with the same outcome were grouped, namely mortality and various forms of deterioration (including ICU admission, adverse events, and cardiac arrests). Meta-analysis was planned in case sufficient data would be extracted from each study and no considerable heterogeneity between studies was present. DATA SYNTHESIS: In total, 45 articles were included for analysis, in which multiple methods of artificial intelligence were used. Twenty-four articles described models for the prediction of mortality and 21 for clinical deterioration. Due to heterogeneity of study characteristics (patient cohort, outcomes, and prediction models), meta-analysis could not be performed. The main reported measure of performance was the area under the receiver operating characteristic (AUROC) (n = 38), of which 33 (87%) had an AUROC greater than 0.8. The highest reported performance in a model predicting mortality had an AUROC of 0.935 and an area under the precision-recall curve of 0.96. CONCLUSIONS: Currently, a growing number of studies develop and analyzes artificial intelligence-based prediction models to predict critical illness and deterioration. We show that artificial intelligence-based prediction models have an overall good performance in predicting deterioration of patients. However, external validation of existing models and its performance in a clinical setting is highly recommended

Characterizing Predictive Models of Mortality for Older Adults and Their Validation for Use in Clinical Practice

OBJECTIVES: To systematically identify and characterize prognostic models of mortality for older adults, their reported potential use, and the actual level of their (external) validity. DESIGN: The Scopus database until January 2010 was searched for articles that developed and validated new models or validated existing prognostic models of mortality or survival in older adults. SETTING: All domains of health care. PARTICIPANTS: Adults aged 50 and older. MEASUREMENTS: Study and model characteristics were summarized, including the model's development method and degree of validation, data types used, and outcomes. RESULTS: One hundred three articles describing 193 models in 10 domains and mostly originating from the United States were included. These domains were mostly secondary or tertiary care settings (54%) such as intensive care (7%) or geriatric units (8%). Half of the studies (50%) were not disease specific. Heart failure-related diseases (9%) and pneumonia (9%) constituted the major disease-specific subgroups. Most studies (67%) reported support of clinical individual (treatment) decisions as use of prognostic models, but only 34% were externally validated, and only four models (2%) were validated in more than two studies. Most studies (68%) developed at least one new model, but they did not often go beyond addressing their apparent validation (49%). CONCLUSION: Although prognostic models are regularly developed to support clinical individual decisions and could be useful for this purpose, their use is premature. Because clinical credibility and evidence of external validity build trust in prognostic models, both require much more consideration to enhance model acceptance in the future. J Am Geriatr Soc 59:1110-1115, 201

Prognostic models for predicting mortality in elderly ICU patients: a systematic review

To systematically review prognostic research literature on development and/or validation of mortality predictive models in elderly patients. We searched the Scopus database until June 2010 for articles aimed at validating prognostic models for survival or mortality in elderly intensive care unit (ICU) patients. We assessed the models' fitness for their intended purpose on the basis of barriers for use reported in the literature, using the following categories: (1) clinical credibility, (2) methodological quality (based on an existing quality assessment framework), (3) external validity, (4) model performance, and (5) clinical effectiveness. Seven studies were identified which met our inclusion criteria, one of which was an external validation study. In total, 17 models were found of which six were developed for the general adult ICU population and eleven specifically for elderly patients. Cohorts ranged from 148 to 12,993 patients and only smaller ones were obtained prospectively. The area under the receiver operating characteristic curve (AUC) was most commonly used to measure performance (range 0.71-0.88). The median number of criteria met for clinical credibility was 4.5 out of 7 (range 2.5-5.5) and 17 out of 20 for methodological quality (range 15-20). Although the models scored relatively well on methodological quality, none of them can be currently considered sufficiently credible or valid to be applicable in clinical practice for elderly patients. Future research should focus on external validation, addressing performance measures relevant for their intended use, and on clinical credibility including the incorporation of factors specific for the elderly populatio

How nurses and physicians judge their own quality of care for deteriorating patients on medical wards: Self-assessment of quality of care is suboptimal

Objective: To describe how nurses and physicians judge their own quality of care for deteriorating patients on medical wards compared with the judgment of independent experts. Design: Cross-sectional study using interviews of care-providers regarding their perceived quality of care for clinically deteriorating patients compared with retrospective judgment by independent experts. Setting: Academic Medical Center of Amsterdam, the Netherlands. Patients: Between April and July 2009, all patients with cardiopulmonary arrests and unplanned intensive care unit admissions from six medical nursing wards were included. The care-providers (nurses and physicians) taking care of these patients in the previous 12 hrs were included. Measurements and Main Results: Forty-seven events and 198 interviews were analyzed. Skill and knowledge level regarding the recognition of a deteriorating patient were rated on a scale of 1-10 with means (SD) of 7.9 (0.8) and 7.7 (0.9), respectively. Nurses and residents attributed coordination of care largely to themselves (74% and 76%, respectively). Communication, cooperation, and coordination were graded in a positive manner (medians between 7.3 and 8), whereas the medical staff graded these factors higher compared to the grading by nurses and residents. Negative predictive values regarding the presence of a delay compared with an expert panel was 37% for nurses and 38% for residents and specialists. Conclusions: Care-providers mostly rate their care provided to patients in the hours preceding a life-threatening adverse event as good. In contrast, independent experts had a more critical appraisal of the provided care in regards to timely recognition. These findings may partly explain the reluctance of care-providers to implement patient safety initiatives. (Crit Care Med 2012; 40: 2982-2986

A Protocolised Once a Day Modified Early Warning Score (MEWS) Measurement Is an Appropriate Screening Tool for Major Adverse Events in a General Hospital Population

The Modified Early Warning Score (MEWS) was developed to timely recognise clinically deteriorating hospitalised patients. However, the ability of the MEWS in predicting serious adverse events (SAEs) in a general hospital population has not been examined prospectively. The aims were to (1) analyse protocol adherence to a MEWS protocol in a real-life setting and (2) to determine the predictive value of protocolised daily MEWS measurement on SAEs: death, cardiac arrests, ICU-admissions and readmissions. All adult patients admitted to 6 hospital wards in October and November 2015 were included. MEWS were checked each morning by the research team. For each critical score (MEWS ≥ 3), the clinical staff was inquired about the actions performed. 30-day follow-up for SAEs was performed to compare between patients with and without a critical score. 1053 patients with 3673 vital parameter measurements were included, 200 (19.0%) had a critical score. The protocol adherence was 89.0%. 18.2% of MEWS were calculated wrongly. Patients with critical scores had significant higher rates of unplanned ICU admissions [7.0% vs 1.3%, p < 0.001], in-hospital mortality [6.0% vs 0.8%, p < 0.001], 30-day readmission rates [18.6% vs 10.8%, p < 0.05], and a longer length of stay [15.65 (SD: 15.7 days) vs 6.09 (SD: 6.9), p < 0.001]. Specificity of MEWS related to composite adverse events was 83% with a negative predicting value of 98.1%. Protocol adherence was high, even though one-third of the critical scores were calculated wrongly. Patients with a MEWS ≥ 3 experienced significantly more adverse events. The negative predictive value of early morning MEWS < 3 was 98.1%, indicating the reliability of this score as a screening too

Unexpected versus all-cause mortality as the endpoint for investigating the effects of a Rapid Response System in hospitalized patients

The purpose of this study was to assess the effect of replacing all-cause mortality by death without limitation of medical treatments (LOMT) as the endpoint in a study of rapid response teams (RRTs) in hospitalized patients. We also described the time course of LOMT orders in patients dying on a general ward and the influence of RRTs on such orders. This study is a secondary analysis of the COMET trial, a pragmatic prospective Dutch multicenter before-after study. We repeated the original analysis of the influence of RRTs on death before hospital discharge by replacing all-cause mortality by death without an LOMT order. In a subgroup of all patients dying before hospital discharge, we documented patient demographics, admission characteristics and LOMT orders of each patient. Patients age 18 ears or above were included. In total, 166,569 patients were included in the study. The unadjusted ORs were 0.865 (95 % CI 0.77-0.98) in the original analysis using all-cause mortality and 0.557 (95 % CI 0.40-0.78) when choosing death without LOMT as the endpoint. In total, 3408 patients died before discharge. At time of death, 2910 (85 %) had an LOMT order. Median time from last change in LOMT status and death was 2 days (IQR 1-5) in the before-phase and median time after introduction of the RRT was 1 day (IQR 1-4) (p value not significant). The improvement in survival of hospitalized patients after introduction of a rapid response team in the COMET study was more pronounced when choosing death without limitation of medical treatment, rather than all deaths as the endpoint. Most patients who died during hospitalization had limitation of medical treatments ordered, often shortly before death. Rapid response teams did not influence the institution of limitation of medical treatment

Clinical Relevance of Routinely Measured Vital Signs in Hospitalized Patients: A Systematic Review

BACKGROUND: Conflicting evidence exists on the effectiveness of routinely measured vital signs on the early detection of increased probability of adverse events. PURPOSE: To assess the clinical relevance of routinely measured vital signs in medically and surgically hospitalized patients through a systematic review. DATA SOURCES: MEDLINE, Embase, Cochrane Central Register of Controlled Trials (CENTRAL), Cumulative Index to Nursing and Allied Health Literature, and Meta-analysen van diagnostisch onderzoek (in Dutch; MEDION) were searched to January 2013. STUDY SELECTION: Prospective studies evaluating routine vital sign measurements of hospitalized patients, in relation to mortality, septic or circulatory shock, intensive care unit admission, bleeding, reoperation, or infection. DATA EXTRACTION: Two reviewers independently assessed potential bias and extracted data to calculate likelihood ratios (LRs) and predictive values. DATA SYNTHESIS: Fifteen studies were performed in medical (n = 7), surgical (n = 4), or combined patient populations (n = 4; totaling 42,565 participants). Only three studies were relatively free from potential bias. For temperature, the positive LR (LR+) ranged from 0 to 9.88 (median 1.78; n = 9 studies); heart rate 0.82 to 6.79 (median 1.51; n = 5 studies); blood pressure 0.72 to 4.7 (median 2.97; n = 4 studies); oxygen saturation 0.65 to 6.35 (median 1.74; n = 2 studies); and respiratory rate 1.27 to 1.89 (n = 3 studies). Overall, three studies reported area under the Receiver Operator Characteristic (ROC) curve (AUC) data, ranging from 0.59 to 0.76. Two studies reported on combined vital signs, in which one study found an LR+ of 47.0, but in the other the AUC was not influenced. CONCLUSIONS: Some discriminative LR+ were found, suggesting the clinical relevance of routine vital sign measurements. However, the subject is poorly studied, and many studies have methodological flaws. Further rigorous research is needed specifically intended to investigate the clinical relevance of routinely measured vital signs. CLINICAL RELEVANCE: The results of this research are important for clinical nurses to underpin daily routine practices and clinical decision making