Background
Early recognition of and response to deteriorating patients in general wards are core competencies for nurses. Etymologically, clinical deterioration refers to a condition or process that worsens at the bedside [1]. Jones et al. [2] defined a deteriorating patient as a patient who moves from one clinical state to a worse one, which increases their individual risk of morbidity, including organ dysfunction, prolonged hospital stay, disability, or death. Accordingly, early detection and intervention for deteriorating patients can reduce not only intensive care unit (ICU) admissions, length of hospital stay, morbidity such as cardiac arrest, and mortality, but also unnecessary medical costs, by preventing adverse patient safety incidents [2, 3].
The last 20 years have witnessed the development and application of the track and trigger, early warning score, and rapid response team systems. These systems detect signs of deterioration in patients, allowing for timely intervention by appropriately skilled personnel in clinical settings [4]. They consist of predetermined criteria that are generally based on measured vital signs, such as respiratory rate, oxygen saturation, heart rate, blood pressure, and body temperature [3, 5, 6]. For example, the National Early Warning Score published by the United Kingdom Royal College of Physicians comprises parameters such as respiratory rate, oxygen saturation, temperature, systolic blood pressure, pulse rate, and level of consciousness [7].
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Such objective data on the patients’ condition alone, however, provide insufficient explanatory power for recognizing patient deterioration, with the average area under the receiver operating characteristic curve ranging from 0.7 to 0.85 [8]. Against this backdrop, recent research suggests that subjective data—such as nurses’ worries or concerns about patients—can serve as important indicators for detecting or predicting patient deterioration. Douw et al. [3] identified underlying signs and symptoms of nurses’ worry criterion through a systematic literature review and developed the Dutch-Early-Nurse-Worry-Indicator-Score [9, 10]. This scale consists of indicators captured by nurses, such as “changes in breathing,” “changes in circulation,” “rigors,” “changes in mentation,” “agitation,” “pain,” “no clinical progress,” “patient indicating feeling unwell,” and “subjective nurse observations” [9]. Subjective nurse observations include “changes in behavior” and/or “doesn’t look good” and/or “doesn’t look in the eyes.” Nurses often intuitively feel that something is wrong with a patient, even when objective data, such as vital signs, do not indicate a decline in the patient’s condition [3, 10]. A systematic review by Odell et al. [11] found that nurses tend to rely on intuition, known as a “gut feeling,” when detecting clinical deterioration among inpatients. They use their gut feelings along with vital signs and laboratory test data to recognize clinical deterioration. A scoping review [12] that identified signs and symptoms beyond vital signs that triggered nurses’ concerns about the deterioration of hospitalized pediatric patients also highlighted that both objective observations and nurses’ intuitive feelings were important indicators. Additionally, because changes in objective and subjective data may affect nurses’ activities or interventions, alterations in nurses’ specific activities or interventions before deterioration can also be used as indicators to detect deterioration in patients. Since nurses’ concern for patient deterioration and the change in nurses’ specific intervention are reflected in the nursing notes, studies [13‐15] have attempted to identify the signals or indicators of patient deterioration from nursing notes. Nevertheless, from nurses’ perspectives, comprehensive knowledge regarding the indicators of deterioration in adult patients admitted to general wards is limited.
In this context, this mixed-methods systematic review aims to comprehensively identify and classify the indicators used to detect clinical deterioration in patients hospitalized in general wards, from nurses’ perspectives. The findings may provide insights for future research to uncover key indicators of deterioration among inpatients from nursing records.
Methods
Design
This mixed-methods systematic review employed a convergent integrated approach to identify indicators of patient deterioration from nurses’ perspectives, following the Joanna Briggs Institute (JBI) Methodology for mixed-methods systematic reviews [16]. In this approach, data from quantitative and qualitative studies are integrated by “qualitizing” quantitative data into textual descriptions. This process enables the integration of quantitative results with qualitative data through narrative interpretation. The research questions are framed based on the PICo (population of interest, phenomenon of interest, and context) criteria and the studies are selected by two or more reviewers according to the eligibility criteria. The methodological quality of the selected studies is then assessed using appropriate tools. Quantitative data are extracted for descriptive or analytical information, and qualitative data for themes or subthemes. Finally, categories are created by grouping similar concepts, resulting in integrated findings that combine both quantitative and qualitative data [16]. This review was reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) [17]. This study was prospectively registered with PROSPERO under registration number CRD42024552344.
Search methods
Data screening was conducted using Covidence systematic review software, a web-based collaboration tool designed to simplify the systematic review procedure. We utilized four electronic databases: PubMed, CINAHL, Embase, and Scopus. Search terms encompassing relevant MeSH terms and keywords were developed, considering several terms and phrases corresponding to various operational definitions of deterioration. The search terms applied to each database are listed in Table S1 of Additional file 1. The data search was conducted between January 2 and 31, 2024. All screening steps were independently performed by the two authors, and disagreements were resolved through face-to-face or virtual meetings.
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Eligibility criteria
Population of interest
Registered nurses were the population of interest in this study. Studies directly involving nurses and capturing their perspectives through interviews were included. Those that retrospectively analyzed nursing records and utilized nurse-driven tools to screen for patient deterioration were also included. Studies were excluded if nurses’ perspectives were mixed with those of other healthcare professionals in a way that made it impossible to distinguish the nurses’ viewpoints. Studies that focused solely on physician records or involved nursing students were also excluded.
Phenomenon of interest
The phenomenon of interest in this study was patients’ clinical deterioration. We therefore included studies that investigated phenomena such as rapid response team calls, the use of early warning score systems, cardiopulmonary resuscitation, transfers to the ICU, or patient death [1‐3]. We referred to previous studies that investigated clinical deterioration, identified their operational definitions of clinical deterioration, and searched MeSH terms and relevant keywords to specify the concept of deterioration.
Context
This study focused on adult care in acute hospital settings, specifically within general wards, such as medical and surgical wards. Studies were excluded if they pertained to specialized departments such as the ICU or emergency department, pediatric care, and non-acute care settings such as nursing homes, community care, outpatient care, or any other setting where the acute care context was not clearly distinguished.
Type of studies
This systematic review included original studies published in English in peer-reviewed journals between January 2014 and December 2023. Quantitative, qualitative, and mixed-methods studies were considered. Reviews, conference papers, letters, editorials, and other forms of nonoriginal research were excluded.
Search outcome
Figure 1 illustrates the literature search and screening process using the PRISMA flow diagram [17]. The database search yielded 5,866 studies, which were imported into Covidence. After duplicates were removed, the titles and abstracts of the remaining 2,663 studies were screened according to the eligibility criteria. Next, 110 studies underwent full-text screening, during which the reasons for exclusion were documented. Data were extracted from the final 21 studies that were included.
Fig. 1
PRISMA flow diagram for this systematic review
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Quality appraisal
The methodological quality of the included studies was independently assessed by the two authors using the JBI Critical Appraisal Checklist for each study design, including cohort, case-control, cross-sectional, and qualitative studies [18]. Each tool comprises eight to eleven questions. For mixed-methods studies, we used the Mixed-Methods Appraisal Tool, Version 2018 [19], which consists of 17 questions including screening, qualitative component, quantitative component, and mixed-methods component questions. Disagreements were resolved after reaching a consensus via face-to-face or virtual meetings. Regardless of the methodological quality assessment results, all studies were included in this review to ensure a comprehensive analysis of the screened data.
Data extraction and synthesis
Data extraction and synthesis were performed using the JBI Methodology for the mixed-methods systematic review convergent integrated approach [16]. Data were extracted by the two authors, independently. The extracted data were synthesized into a tabular format. Studies were categorized and pooled based on our research question; the categories included study characteristics (author, year, country, and study design); purpose, population, and phenomenon of interest (operational definition of deterioration); context (setting); and outcomes relevant to the aim of this study (identifying indicators of patient deterioration from nurses’ perspectives).
Indicators were classified into objective data, subjective data, and nursing interventions. We further classified objective data into vital signs, intake and output (I&O), laboratory data, and observational data. Subjective data were categorized into patients’ complaints and nurses’ intuitions. Unclassified indicators, including other situations that patients faced, were included in an “others” category. Features that were not addressed were left blank. Disagreements were resolved after reaching a consensus via face-to-face or virtual meetings.
Results
Study characteristics
Table 1 summarizes the characteristics of the included studies. Most of the studies were conducted in the United States (n = 9) [14, 15, 20‐26], with others conducted in the Netherlands (n = 3) [9, 10, 27] and Canada (n = 2) [28, 29]. One study each was conducted in Australia [30], Brazil [31], Finland [32], Norway [33], the United Kingdom [34], and Singapore [35]. Among the 21 included studies, eleven were quantitative [9, 10, 14, 15, 20, 22, 23, 27, 29, 31, 33], seven were qualitative [21, 24, 25, 30, 34‐36], and three were mixed-methods [26, 28, 32] studies. Retrospective chart review studies that analyzed nursing records primarily focused on patient populations [10, 14, 15, 20, 22, 23, 29, 31, 33], whereas qualitative studies targeted nurses [21, 24, 25, 30, 34‐36]. Mixed-methods studies included both nurses and patients as participants [26, 28, 32]. These studies were conducted in general wards, including medical and surgical units, within hospitals.
Table 1
Characteristics of the included studies
Author | Year | Country | Design | Time period | Purpose | Population | Setting |
|---|---|---|---|---|---|---|---|
Capan et al. | 2017 | United States | Retrospective observational study | From Jan to Apr 2015 | To examine the impact of the electronic nursing assessment categories on the discriminative performance of EWS-based quantitative risk prediction for selected outcomes within 24 h | 2,405 adult inpatients | Four medical, surgical, or step-down units of a single institution at Christiana Care Health System, Newark |
Chua et al. | 2019 | Singapore | Descriptive qualitative study | From Oct 2016 to Feb 2017 | To explore the experiences of nurses in recognizing clinically deteriorating patients in general wards | 22 nurses | General wards of a 1,000-bed acute general hospital |
Dalton et al. | 2018 | United Kingdom | Generic qualitative study | From Mar to Apr 2016 | To discover the factors that influence how nurses assess patient acuity and their response to acute deterioration | 10 nurses | Inpatient medical and surgical wards within an acute National Health Service trust |
Douw et al. | 2016 | Netherlands | Prospective cohort observational study | From Mar 2013 to Apr 2014 | To determine the significance of nurses’ worry and/or indicators of underlying worry to predict unplanned ICU admission or unexpected mortality among surgical ward patients | 3,522 adult surgical patients, 96 nurses | Three surgical wards of a 500-bed tertiary university-affiliated teaching hospital |
Douw et al. | 2017 | Netherlands | Prospective cohort observational study | From Mar 2013 to Apr 2014 | To determine the predictive value of individual and combined DENWIS indicators at various EWS levels | 3,522 adult surgical patients | Three surgical wards of a 500-bed tertiary university-affiliated teaching hospital |
Douw et al. | 2018 | Netherlands | Prospective cohort observational study | From Mar 2013 to Apr 2014 | To explore the occurrence of nurses’ worry in real time, determine whether acting on worry leads to unnecessary action, and determine the indicators present at different levels of deterioration | 3,742 adult surgical patients, 96 nurses | Three surgical wards of a 500-bed tertiary university-affiliated teaching hospital |
Dresser et al. | 2023 | United States | Descriptive qualitative study | From Mar to Aug 2018 | To describe medical-surgical nurses’ perception of factors that influence their clinical judgement in situations of acute physiologic deterioration in adult patients | 20 nurses | A medical-surgical unit of an academic medical center hospital |
Fasolino and Verdin | 2015 | United States | Retrospective chart review | During 2009 | To investigate trends and documentation of physiological measurements, mental status, and urinary output 24 h prior to the initiation of RRTs for patients hospitalized in medical-surgical units | 79 patients | Medical-surgical units of a 245-bed nonprofit acute care hospital in northeastern South Carolina |
Gyang et al. | 2015 | United States | Prospectively observational study | During Oct 2010 | To examine the performance of a nurse-driven, simple sepsis screening tool in a mixed medical and surgical non-ICU setting | 245 patients | Medical-surgical units of a 613-bed university tertiary referral hospital |
Hart et al. | 2016 | United States | Descriptive qualitative study | From Oct 2014 to Feb 2015 | To explore and understand the experiences of medical-surgical nurses as first responders during clinical deterioration events | 28 nurses | Medical-surgical units at five hospitals |
Horwood et al. | 2018 | United States | Qualitative study (ethnography) | From Jul to Aug 2017 | To assess how nurses perceive early warning signs that predict clinical decompensation, escalation of care, and clinical acuity in surgical patients | 13 nurses | Surgical units of the Ohio State University Wexner Medical Center |
Kalliokoski et al. | 2019 | Finland | Mixed-methods study | From Apr 2016 to Apr 2017 | To understand the concerns of nurses when making MET calls that did not fulfil the vital sign criteria, and MET nurses’ subsequent responses to theses calls | 37 nurses, 39 patients | A university hospital |
Katadzic and Jelsness-Jørgensen | 2017 | Norway | Observational descriptive design | From Jun to Dec 2015 | To map the number of requests for MICNs, and to assess the reasons for the requests, measures initiated by MICNs, and the number of admissions to an ICU | Adult patients (163 call-outs) | Wards in the division of medicine at Oslo University Hospital, Ulleval |
Korach et al. | 2019 | United States | Retrospective observational study | From 2015 to 2018 | To discover rapid response event risk/protective factors from unstructured nursing notes by unsupervised machine learning | 45,299 patients | Partner’s healthcare, a healthcare delivery network in Boston |
Korach et al. | 2020 | United States | Retrospective observational study | From 2015 to 2018 | To develop a data-driven, unsupervised method to discover potential risk factors of rapid response events from nursing notes | 45,817 patients | Partner’s healthcare, a healthcare delivery network in Boston |
Lavoie et al. | 2020 | Canada | Mixed-methods study | From Sep to Dec 2017 | To explore how change-of-shift handoffs relate to nurses’ clinical judgements regarding patients’ risk of deterioration | 62 nurses, 158 patients | One surgical and two medical units of a single tertiary acute care university-affiliated hospital in Montreal |
Marshall and Finlayson | 2022 | No report | Exploratory qualitative study | From Jan 2013 to Jun 2015 | To identify the cognitive skills surgical nurses require to rescue deteriorating patients | 6 nurses | Acute general surgical wards of a large metropolitan hospital and a smaller private surgical hospital |
Mohammmed Iddrisu et al. | 2018 | Australia | Exploratory descriptive qualitative study | From Sep to Oct 2014 | To explore nurses’ role in recognizing and responding to deteriorating post-operative patients | 14 nurses | Surgical units of a metropolitan teaching hospital in Melbourne |
Schnock et al. | 2021 | United States | Multi-method study | From 2015 to 2018 | To quantify variations in nursing documentation patterns, confirm those patterns and variations with clinicians, and identify which patterns indicate patient deterioration and recovery from clinical deterioration events in critical and acute care settings | 29 nurses and physicians, 8,552 adult patients | ICU and acute care units in one of five Mass General Brigham hospitals |
Vieira et al. | 2020 | Brazil | Retrospective observational study | From Jul 2015 to May 2017 | To evaluate the accuracy and predictive ability of the defining characteristics of respiratory nursing diagnoses in patients with respiratory deterioration | 391 adult patients | The clinical and surgical units of a 795-bed federal referral hospital |
Wong et al. | 2017 | Canada | Retrospective observational study | From Jan 2012 to Aug 2014 | To determine (1) how many had critical messages prior to transfer and the quality of these messages; (2) the quality of the messages and the quality of the response or the timeliness of RRT activation related to death. | 236 patients | Inpatient general internal medicine beds at Toronto general hospital |
Operational definition of clinical deterioration in the included studies
Table 2 presents the operational definition of clinical deterioration and the relevant studies included. Of the 21 studies, 13 defined clinical deterioration as the “activation of a warning system”—including rapid response systems, track and trigger systems, medical emergency team calls, and code blue systems—to identify at-risk patients [14, 15, 20‐22, 24, 26, 28, 30‐33, 35]. Unplanned transfers or admissions to ICUs for escalation of care were also considered representative events of clinical deterioration in nine studies [9, 10, 20, 25‐29, 33]. Additionally, unexpected in-hospital mortality (n = 5) [9, 10, 20, 27, 28], cardiac arrest (n = 2) [26, 28], respiratory arrest or respiratory nursing diagnoses such as impaired spontaneous ventilation (n = 2) [26, 31], sepsis (n = 2) [23, 26], and serious postoperative complications such as hemorrhage [36] were defined as clinical deterioration.
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Table 2
Definitions of clinical deterioration and relevant studies
Definitions of clinical deterioration | Studies |
|---|---|
- RRT activation | |
- Code Blue activation | |
- Track and trigger system call | - Chua et al. (2019) [35] |
- Calling out MICNs | - Katadzic and Jelsness-Jørgensen (2017) [33] |
- MET calls | - Kalliokoski et al. (2019) [32] |
- Transfer to higher level care such as ICU | |
- Suddenly became unwell | - Dalton et al. (2018) [34] |
- Clinical decompensation | - Horwood et al. (2018) [25], |
- Cardiac arrests | |
- Respiratory arrest | - Schnock et al. (2021) [26] |
- Mortality | |
- Incidence of sepsis | |
- Serious postoperative complication | - Marshall and Finlayson (2022) [36] |
- Respiratory nursing diagnoses: impaired gas exchange, impaired spontaneous ventilation, ineffective breathing pattern | - Vieira et al. (2020) [31] |
Indicators of clinical deterioration
Objective data
Objective data were categorized into four domains: vital signs, I&O, laboratory data, and observational data (Table 3). Of the 21 studies, 20 considered vital signs and changes to them as indicators for nurses to detect clinical deterioration [9, 10, 15, 20‐36]. The volume or patterns of I&O have also been reported as important indicators of deterioration. Specifically, decreased urine output due to urinary retention [15, 32, 33] and fluid imbalance [15] were used as indicators. Abnormal levels of arterial blood gas analysis [29, 31], unstable blood glucose levels, such as hyperglycemia or hypoglycemia [21, 32, 33], white blood cell count [23, 25], serum lactate [23, 25], and electrolyte shifts [25, 30] were considered representative laboratory data for nurses to recognize deterioration in patients.
Table 3
Indicators of clinical deterioration from nurses’ perspectives
Categories | Indicators of clinical deterioration and relevant studies | |
|---|---|---|
Objective data | ||
Vital signs | - Vital signs (changes) | |
- BP | ||
- SBP | ||
- MAP | - Gyang et al. (2015) [23] | |
- Hypotension | - Mohammmed Iddrisu et al. (2018) [30] | |
- HR | - Capan et al. (2017) [20], Dalton et al. (2018) [34], Douw et al. (2016) [9], Douw et al. (2017) [10], Douw et al. (2018) [27], Fasolino and Verdin (2015) [22], Gyang et al. (2015) [23], Horwood et al. (2018) [25], Kalliokoski et al. (2019) [32], Katadzic and Jelsness-Jørgensen (2017) [33], Lavoie et al. (2020) [28], Schnock et al. (2021) [26], Wong et al. (2017) [29], | |
- Tachycardia | - Vieira et al. (2020) [31] | |
- BT | ||
- RR | - Dalton et al. (2018) [34], Douw et al. (2016) [9], Douw et al. (2017) [10], Douw et al. (2018) [27], Fasolino and Verdin (2015) [22], Gyang et al. (2015) [23], Horwood et al. (2018) [25], Katadzic and Jelsness-Jørgensen (2017) [33], Korach et al. (2019) [15], Schnock et al. (2021) [26], Wong et al. (2017) [29] | |
- Bradypnea | - Vieira et al. (2020) [31] | |
- SpO2 | ||
- Hypoxemia | - Katadzic and Jelsness-Jørgensen (2017) [33] | |
- Hemodynamic instability | - Mohammmed Iddrisu et al. (2018) [30] | |
- Hypovolemia | - Mohammmed Iddrisu et al. (2018) [30] | |
Intake/output | - Intake and output | - Horwood et al. (2018) [25] |
- Nutritional intake | - Capan et al. (2017) [20] | |
- Urine output | - Gyang et al. (2015) [23] | |
- Urinary retention | ||
- Fluid balance | - Korach et al. (2019) [15] | |
- Urine accident | - Hart et al. (2016) [24] | |
- Diuresis | - Kalliokoski et al. (2019) [32] | |
- Not eating or drinking very well | - Dalton et al. (2018) [34] | |
Laboratory data | - ABGA | |
- PaCO2 | ||
- PaO2 | - Vieira et al. (2020) [31] | |
- SaO2 | ||
- Hypercapnia | - Vieira et al. (2020) [31] | |
- Hypoxemia | - Vieira et al. (2020) [31] | |
- WBC count | ||
- Hemoglobin | - Lavoie et al. (2020) [28] | |
- Platelet count | - Gyang et al. (2015) [23] | |
- INR | - Gyang et al. (2015) [23] | |
- Partial thromboplastin time | - Gyang et al. (2015) [23] | |
- High risk of bleeding | - Korach et al. (2019) [15] | |
- Serum lactate | ||
- Serum creatinine | ||
- Elevated troponin levels | - Lavoie et al. (2020) [28] | |
- Electrolytes | ||
- Total bilirubin | - Gyang et al. (2015) [23] | |
- Blood sugar | ||
- Hyperglycemia | - Katadzic and Jelsness-Jørgensen (2017) [33] | |
- Increase in metabolic rate | - Vieira et al. (2020) [31] | |
- Infection | - Katadzic and Jelsness-Jørgensen (2017) [33] | |
- Auxiliary tests | - Korach et al. (2020) [14] | |
Observational data | - Change in mental status/consciousness | - Chua et al. (2019) [35], Dalton et al. (2018) [34], Douw et al. (2016) [9], Douw et al. (2017) [10], Douw et al. (2018) [27], Gyang et al. (2015) [23], Hart et al. (2016) [24], Horwood et al. (2018) [25], Kalliokoski et al. (2019) [32], Katadzic and Jelsness-Jørgensen (2017) [33], Lavoie et al. (2020) [28], Wong et al. (2017) [29] |
- Changes in the patient’s physical, behavioral, or emotional status | - Marshall and Finlayson (2022) [36] | |
- Change in behavior | ||
- Agitation (restless, anxious) | ||
- Decrease in cooperation | - Vieira et al. (2020) [31] | |
- Resisting treatment | - Kalliokoski et al. (2019) [32] | |
- Aggression | - Hart et al. (2016) [24] | |
- Somnolence | - Vieira et al. (2020) [31] | |
- Decreased awareness and arousal | - Hart et al. (2016) [24] | |
- Confusion | - Hart et al. (2016) [24] | |
- Neurological symptoms | - Lavoie et al. (2020) [28] | |
- Collapse or dizziness | - Kalliokoski et al. (2019) [32] | |
- Change in breathing | ||
- Dyspnea | ||
- Airway threatened | - Korach et al. (2020) [14] | |
- Aspiration | ||
- Secretion stagnation | - Katadzic and Jelsness-Jørgensen (2017) [33] | |
- Abnormal breathing pattern | ||
- Shortness of breath | - Hart et al. (2016) [24] | |
- Gasping for air | ||
- Abnormal lung (breath) sounds | ||
- Increase in accessory muscle use | - Vieira et al. (2020) [31] | |
- Obvious signs of respiratory distress and patient’s breathing patterns | - Chua et al. (2019) [35] | |
- Cough | - Korach et al. (2020) [14] | |
- Spasms | - Kalliokoski et al. (2019) [32] | |
- Change in circulation | ||
- Change in capillary refill | - Gyang et al. (2015) [23] | |
- Abnormal skin color | ||
- Cold periphery | - Kalliokoski et al. (2019) [32] | |
- Skin temperature and peripheral warmth | - Marshall and Finlayson (2022) [36] | |
- Sweating | - Kalliokoski et al. (2019) [32] | |
- Skin barrier | - Korach et al. (2020) [14] | |
- Arrhythmia | ||
- Chest pain | - Lavoie et al. (2020) [28] | |
- Strength and regularity of pulse | - Marshall and Finlayson (2022) [36] | |
- Syncope | - Kalliokoski et al. (2019) [32] | |
- Rigors | ||
- Bleeding | ||
- Discomfort | - Korach et al. (2019) [15] | |
- Change in bowel sound | ||
- Bowel pattern | - Capan et al. (2017) [20] | |
- Abdominal distress | - Kalliokoski et al. (2019) [32] | |
- Nausea or vomiting | - Capan et al. (2017) [20] | |
- Indigestion | - Korach et al. (2019) [15] | |
- Difficulty with chewing or swallowing | - Capan et al. (2017) [20] | |
- Abdominal exam | - Horwood et al. (2018) | |
- Urine color | - Capan et al. (2017) [20] | |
- Voids with difficulty | - Capan et al. (2017) [20] | |
- Urinary catheter obstructed | - Capan et al. (2017) [20] | |
- Anaphylaxis | - Katadzic and Jelsness-Jørgensen (2017) [33] | |
- Unable to move all extremities and perform functional activities independently | - Capan et al. (2017) [20] | |
- No progress | ||
- Not as chatty as the day before | - Dalton et al. (2018) [34] | |
- Change from a baseline, not an absolute threshold | - Horwood et al. (2018) [25] | |
- Generalized weakness | - Hart et al. (2016) [24] | |
- General health deterioration | - Korach et al. (2019) [15] | |
- Evaluation of response to administration of fluids and electrolyte | - Korach et al. (2019) [15] | |
- Abnormal physical assessment findings | ||
- Surgical incisions | - Horwood et al. (2018) [25] | |
- Arteriovenous fistula | - Korach et al. (2019) [15] | |
- Hemodialysis observable | - Korach et al. (2019) [15] | |
Subjective data | ||
Patients’ complaints | - Pain | |
- Dizziness | ||
- Not feeling well | ||
- Feeling what they say | - Marshall and Finlayson (2022) [36] | |
- Tiredness | - Kalliokoski et al. (2019) [32] | |
- Feeling of impending doom | ||
- Patient verbal communication | - Korach et al. (2020) [14] | |
- Patient’s own subjective sensation | - Kalliokoski et al. (2019) [32] | |
- Dissatisfaction with their own state | - Kalliokoski et al. (2019) [32] | |
- Concern for their own health | - Kalliokoski et al. (2019) [32] | |
- Patient decision | - Korach et al. (2020) [14] | |
Nurses’ intuitions | - Does not look good in the eyes | |
- Worried about patient | - Wong et al. (2017) [29] | |
- Gut feeling or intuition | ||
- Difficulties getting an overall picture of the patient’s different problems | - Kalliokoski et al. (2019) [32] | |
Nursing intervention | ||
- Oxygen supply | ||
- Increased frequencies of notifications | - Horwood et al. (2018) [25] | |
- Monitoring and physical assessment | - Horwood et al. (2018) [25] | |
- Physical examination | - Korach et al. (2019) [15] | |
- Examination of limb | - Korach et al. (2019) [15] | |
- Procedure aiding diagnosis | - Korach et al. (2019) [15] | |
- Abdominal examination | - Korach et al. (2020) [14] | |
- Nausea care management | - Korach et al. (2019) [15] | |
- Nasogastric tube maintenance | - Korach et al. (2019) [15] | |
- Increased time spent with patient | - Horwood et al. (2018) [25] | |
- Emotional support | - Korach et al. (2020) [14] | |
- PRN medications given | - Schnock et al. (2021) [26] | |
- Intravenous medications | - Korach et al. (2020) [14] | |
- Withheld medication administrations | - Schnock et al. (2021) [26] | |
- Pressure ulcer care | - Korach et al. (2019) [15] | |
- Dressing | - Korach et al. (2020) [14] | |
- Seizure precautions | - Korach et al. (2019) [15] | |
- Falls education | - Korach et al. (2019) [15] | |
- Handoff communication | - Korach et al. (2019) [15] | |
Others | ||
- Medical doctor awareness | - Korach et al. (2020) [14] | |
- Nurse’s shift transfer | - Korach et al. (2020) [14] | |
- Fasting | - Korach et al. (2020) [14] | |
- Patient alarm | - Korach et al. (2020) [14] | |
- Pre/post operation | - Korach et al. (2020) [14] | |
- Transferred from step-down unit | - Dresser et al. (2023) [21] | |
- Inadequate fluid management | - Mohammmed Iddrisu et al. (2018) [30] | |
- Anesthetic type (spinal/regional/general) | - Mohammmed Iddrisu et al. (2018) [30] | |
- Early return of patients to the ward from the post anesthetic care unit | - Mohammmed Iddrisu et al. (2018) [30] | |
- Medical staff preference to minimize intravenous fluid peri-operatively to prevent fluid overload | - Mohammmed Iddrisu et al. (2018) [30] | |
Observational data collected by nurses or other medical personnel included change in level of consciousness or mental status [9, 10, 23‐25, 27‐29, 32‐35], dyspnea including abnormal breathing pattern and increased use of accessory muscles [9, 10, 27‐29, 31, 33, 35], changes in lung sounds [9, 10, 27, 28, 32] or bowel sounds [20, 23], changes in skin color or temperature [9, 10, 27, 31, 32, 35, 36], behavioral changes including restlessness, agitation, aggression, and decreased cooperation [9, 10, 24, 27, 31, 32, 34‐36], and neurological symptoms such as seizures, collapse, and rigidity [28, 32].
Subjective data
Subjective data were obtained based on patients’ complaints or nurses’ intuitions. Eight studies identified increased or new pain in the chest or abdomen, patients’ descriptions of their feelings, subjective sensations, or concerns for their health as indicators of clinical deterioration, in terms of patients’ complaints [9, 10, 14, 15, 24, 27, 32, 36]. Meanwhile, seven studies included nurses’ gut feelings or intuitions—such as patients look unwell or difficulties in obtaining an overall picture of patients’ various problems—as important data on which nurses relied to recognize deterioration [9, 10, 21, 24, 27, 29, 32].
Nursing interventions
Eight studies identified nursing interventions—such as oxygen supply, seizure precautions, physical examinations, and informing doctors—as key activities for detecting clinical deterioration in patients [9, 10, 14, 15, 25, 26, 27, 33]. In two qualitative studies involving interviews with nurses, an increased frequency of notifications and patient monitoring, as well as increased time spent with patients, were included as factors predicting patient deterioration [14, 25]. One study also considered administering or withholding pro re nata (also known as “as needed”) medications such as analgesics, as an indicator of nursing intervention [26].
Quality appraisal
Table S2 of Additional file 1 presents the results of the methodological quality assessment of the included studies and reveals the total scores for each item. On average, the studies demonstrated approximately 82% attainment, indicating that the methodological quality was generally adequate. Follow-up descriptions were not applicable for cohort studies that used retrospectively reviewed charts and nursing records. Additionally, none of the cross-sectional studies adequately considered confounding factors. Several qualitative studies lacked statements locating the researcher culturally or theoretically.
Discussion
In this mixed-methods systematic review, we identified indicators of patient deterioration from nurses’ perspectives, including objective data, subjective data, and nursing interventions. We further classified objective data into vital signs, I&O, laboratory data, and observational data. Subjective data were categorized into patients’ complaints and nurses’ subjective intuitions.
In most of the studies, patient deterioration was operationally defined as the activation of a warning system, such as the rapid response team, track and trigger, and code blue systems. Hospitals usually predetermine criteria to define when a patient’s deterioration has reached the point where healthcare professionals’ assistance is required [3]. Thus, when these criteria are met, the patient’s condition is objectively judged to have worsened. The second most common event representing deterioration is unplanned transfer to ICUs. ICU transfer or admission is a consequence of patient deterioration, along with cardiac arrest or death [6]. Patients whose condition worsens often require a higher level of treatment in the ICU, such as ventilator care and close monitoring. The definitions of deterioration derived from this review are consistent with the results of Lavoie et al.’s [6] study, which defined deterioration as a “disordered physiology” and “a precursor to adverse events such as ICU admission, cardiac arrest, or death.”
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Among objective data, vital signs and changes to them are found to be the data nurses most relied on to detect deterioration in patients. This is because warning score systems generally comprise vital signs, including systolic blood pressure, respiratory rate, oxygen saturation, and heart rate. Although differences exist, depending on the type of warning score system, certain parameters are critical indicators of patient deterioration. These include respiratory rates outside the range of 8–30 breaths per minute, oxygen saturation under 90% despite high-flow oxygen, acute drop in systolic blood pressure below 90 mmHg, and heart rates outside the range of 40–140 beats per minute [4]. I&O and laboratory data are also used as key indicators underlying deterioration, along with vital signs. Nevertheless, observational data contain the most indicators of clinical deterioration. Observational data refers to data collected objectively through nurses’ observations and assessments—such as the level of consciousness and dyspnea—excluding vital signs and I&O. Deterioration in patients’ condition can be detected early through observation and assessment by nurses who spend most of their time closest to patients; such observational data may precede physiological data such as vital signs and I&O [10]. Therefore, nursing records that reflect the majority of observational data should be included as a data source to recognize clinical deterioration in patients.
A significant portion of the subjective data that prompted nurses to recognize the risk of deterioration involves patients’ complaints of increased or new pain, as well as their descriptions of feelings, subjective sensations, or their own concerns for their health. Another important subjective data point is nurses’ intuitive feelings. Studies have highlighted that nurses’ concerns or gut feelings were significant predictors of patient deterioration. These intuitions can arise from objective signs and symptoms [3] or past experiences [20]. A scoping review [12] identified non-vital sign indicators and symptoms that triggered nurses’ concerns about the deteriorating condition of hospitalized pediatric patients, emphasizing the importance of nurses’ intuitions or gut feelings. Douw et al. [3] identified the signs and symptoms underlying nurses’ worries or concerns, noting that intuition played a crucial role in their decision-making, before deterioration in a patient’s vital signs. This indicates the potential of nurses’ worries or concerns to predict patient deterioration early. The studies included in this review developed the Dutch-Early-Nurse-Worry-Indicator-Score tool, which aims to objectify worry by identifying the signs and symptoms that influence nurses’ concerns [9, 10]. This tool helps detect deterioration early, before vital signs reach critical thresholds, allowing for timely interventions. Further research is needed to develop objective measures to assess nurses’ worry, which can be integrated into nursing records.
The frequency and pattern of specific nursing interventions have also been used as indicators of patient deterioration. Nurses’ intuitions or gut feelings influence nursing interventions, such as increased frequency of notifications, monitoring, physical assessment, and increased time spent with the patient [25]. As these changes in nursing interventions are documented in nursing records, researchers can quantitatively or objectively identify changes in the frequency or pattern of interventions by analyzing nursing records. Thus, when objectifying a nurse’s worry/concern or developing a prediction model for patient deterioration, the nurse’s behavioral (interventional) factors must be considered, along with other factors (e.g., vital signs and patient symptoms).
Besides, the indicators for detecting patient deterioration were not differentiated or presented separately based on unit type, such as medical or surgical units. Meanwhile, in the studies conducted solely in surgical units, several indicators were found to be related to patients’ recovery and complications after surgery such as “surgical incision” [25], “peripheral warmth” [9, 10, 27, 36], and “bleeding” [9, 10, 27, 30]. This finding is consistent with the results of Kang et al. [13], who identified concepts related to nurses’ concern about patient deterioration across settings and unit types.
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The findings of this study have important implications for nursing practice and research. Not only vital signs and lab data but also nurses’ observations, intuition, and interventions play an important role in the early detection of patient deterioration and warrant greater attention in clinical settings. The use of intuition leads to an increase in specific nursing interventions. However, because intuition is often vague, nurses may find it difficult to articulate what triggers their concerns [3, 12]. Therefore, formalizing and objectifying these intuitive decisions is essential to improve clarity and communication in clinical practice.
Primarily, nurses should faithfully document the problems they observed and situations they experienced as well as the nursing interventions they provided to patients in the nursing notes. Thorough nursing documentation facilitates effective communication within multidisciplinary teams, enabling the prompt sharing of patient condition, and contributes significantly to improving the quality of patient care. Nursing documentation is not only evidence in legal matters but also an information source to demonstrate nursing’s contributions to patient care outcomes; hence, it must be recorded clearly, accurately, and accessibly [37]. To allow adequate access to nursing documentation, standardization is needed to improve the quality of nursing data and promote its secondary use (e.g., in research). As the American Nurses Association recommends using standardized terminologies to represent nursing practices in electronic health records [38, 39], unstructured nursing documentation (notes), which includes a variety of terms and abbreviations, must be standardized.
In nursing education, it is essential to include both objective and subjective indicators of patient deterioration to enable nurses to identify early warning signs. While intuition is based on experience and cannot be directly taught, organizing the objective and subjective indicators related to intuition and ensuring thorough documentation can help nurses articulate their intuitive judgments more clearly. This process can also support junior nurses in decision-making, expand the knowledge of senior nurses, and ultimately improve patient outcomes [12].
Furthermore, nurses’ intuitive judgments should be systematically evaluated, and protocols that incorporate these indicators into structured systems need to be developed. In addition to promoting consistent early warning systems, integrating such protocols into nursing practice is a critical policy initiative. This approach would help nurses to recognize their intuition more clearly, detect patient deterioration earlier, and respond rapidly, ultimately building a framework that improves patient care and safety.
To the best of our knowledge, this study is the first mixed-methods systematic review to comprehensively identify indicators of patient deterioration from nurses’ perspectives. One notable strength of this study is its rigorous methodology, following the guidelines established by the JBI Methodology for mixed-methods systematic reviews, which ensured a thorough and systematic approach. Additionally, including both qualitative and quantitative studies from various countries, encompassing patient charts and nurses’ narratives, enhances the generalizability of our findings.
However, a possible limitation of this study is that the operational definitions of deterioration, based on previous studies incorporated into our literature search, may not encompass the full breadth of the concept. Different studies may have varied operational definitions that could not be fully captured, potentially leading to the exclusion of relevant studies during our search process. Further, our review was limited to studies published in English, which may have resulted in the exclusion of pertinent studies published in other languages. This may affect the generalizability of our findings and introduce potential selection bias [40].
Conclusions
This study identified critical indicators from nurses’ perspectives for predicting patient deterioration. Nurses, who spend the most time closest to patients, play a crucial role in the recognition and prediction of patient deterioration, based on objective and subjective data. They intuitively develop concerns based on objective signs and symptoms, patients’ subjective complaints, and nurses’ own experiences. This has led to increased frequency and patterns of specific nursing interventions. Therefore, in predicting patient deterioration, not only objective data but also nurses’ intuitions and intervention patterns should be considered to facilitate early recognition of deterioration and implement timely interventions. Further research is also needed to objectify the subjective elements that can be incorporated into nursing records as significant indicators.
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