Application of national early warning score in assessing postoperative illness severity in elderly patients with gastrointestinal illnesses

Abstract

BACKGROUND:

Population aging is a social problem that is being faced in most countries.

OBJECTIVE:

To apply the National Early Warning Score (NEWS) for an early warning on the vital signs and consciousness of elderly patients who are hospitalized in the gastrointestinal surgical department and to provide a reference for early detection of changes in illness severity in elderly patients by studying the correlation between NEWS value and changes in illness severity.

METHODS:

We enrolled 528 elderly patients who were hospitalized in the gastrointestinal surgical department of a tertiary grade A hospital in Guizhou Province between June 2020 and May 2021, to analyze how NEWS max value correlates with illness severity and obtain the optimal NEWS cutoff value for both potentially critically ill and critically ill elderly patients using the receiver operating characteristic (ROC) curve.

RESULTS:

There were statistically significant differences in NEWS values between elderly patients with various illness severities ( $P<$ 0.05). NEWS values correlated positively with illness severity ( $r=$ 0.605, $P<$ 0.001). Based on the ROC curve, early warning trigger values for NEWS to identify potentially critically ill, critically ill and terminally ill elderly patients were 6, 7 and 8, respectively. The area under the curve (AUC) for potentially critically ill, critically ill and terminally ill elderly patients was 0.907, 0.921 and 0.939, respectively. NEWS performed better in detecting patient illness severity than Modified Early Warning Score (MEWS) in AUC, sensitivity, specificity, and Youden’s index, with statistically significant differences ( $P<$ 0.05).

CONCLUSION:

An early warning on the vital signs and consciousness of hospitalized elderly patients using NEWS can facilitate advanced detection of changes in illness severity of elderly patients by medical staff and enable timely treatment, thus significantly lowering the risks of illness deterioration.

Keywords

Gastrointestinal illness geriatric illness severity assessment National Early Warning Score

1. Introduction

Population aging is a social problem that is being faced in most countries. The number of elderly people is rising annually, as is the number of elderly people who develop illnesses or undergo surgeries. In America, gastrointestinal diseases consume more medical resources than other common diseases, and the expense will continue to increase [1]. In China, gastrointestinal diseases are among the most prevalent diseases among the elderly population (24.53%) [2]. Aging causes the body to gradually lose its physiological integrity, which is then followed by physiological dysfunction or even death. For instance, aging can negatively impact peristalsis, digestion, and absorption functions of the stomach and intestine. Elderly patients with gastrointestinal disorders should be closely observed during their treatment in case there are changes in the severity of their illness.

Abdominal surgery, the primary treatment approach for gastrointestinal diseases, is one of the prevalent procedures among elderly patients [3]. Elderly patients have a poor stress response and more underlying conditions that are concealed and atypical in symptoms. Thus, elderly patients are more likely to experience changes in illness severity following surgeries. Subtle changes in their vital signs are potential predictors of the risk of their illness worsening [4]. Anesthesia and trauma brought on by abdominal surgery place an extra load on their hearts and impair the protection function of their lungs. Elderly patients have worse prognoses than young and middle-aged adults. This may show up as a higher risk of pulmonary infection, pulmonary embolism, arrhythmia, stress ulcer bleeding, or other complications. They may even suffer from dangerous outcomes such as multiple organ failures.

According to the results of previous research, early warning signs often show up several hours before a patient’s condition gets worse, as manifested by physiological indicator changes like changes in respiration, heart rate, blood pressure, consciousness, and oxygen saturation [5, 6]. Timely detection of these early warning signs and appropriate interventions can improve patient safety [7].

The UK Emergency Response Team (ERT) developed the Early Warning Score (EWS) based on abundant clinical research, for the timely detection of potentially critically ill patients. Later, Subbe et al. modified and developed EWS to establish the Modified Early Warning Score (MEWS), which is mainly employed to predict the illness trends and prognoses of patients who visited emergency departments. Medical staff can select an appropriate treatment based on MEWS values. For better clinical applicability, the UK Royal College of Physicians further modified and developed MEWS to establish the National Early Warning Score (NEWS) in 2012. In addition to the items in MEWS – patient temperature, respiratory rate, heart rate, systolic blood pressure, and state of consciousness, the 20-point NEWS additionally included two indicators – oxygen saturation and oxygen inhalation intervention, and made minor adjustments to the values assigned to each vital sign indicator. After NEWS was published, it was popularized and applied by a great number of medical institutions in the UK [8].

In first-line clinical practice, the timely application of appropriate disease assessment tools to predict risk [9, 10] and identify the condition of potentially critically ill or critically ill patients and to objectively and scientifically assess the severity of their illness, is important. These tools help medical professionals make prompt assessments of patient conditions and decide on interventions [11].

In this paper, MEWS and NEWS scoring systems were used to objectively quantify the relevant physiological indicators, compare the correlation between the two and different disease degrees of patients and the sensitivity of clinical identification of patients’ disease, and determine the trigger value of early warning of disease changes, so as to provide a basis for clinical nurses to rationally select the disease assessment system to observe the condition of patients after surgery.

2. Study participants and methods

2.1 Study participants

This prospective, observational study was conducted over a year, between June 2020 and May 2021. A total of 594 patients were collected, 66 patients who were directly transferred to the intensive care unit (ICU) were excluded, and 528 elderly patients were finally included in this study. There are 350 men and 178 women, aged 68.35 $\pm$ 6.23 years, who underwent surgeries in the gastrointestinal surgical department of a tertiary grade A hospital in Guizhou Province, were included in this study.

Inclusion criteria: (1) Patients who were aged 60 or above. (2) Patients who underwent gastrointestinal surgeries. Exclusion criteria: (1) Patients with multiple organ failures and critical conditions on admission. (2) Patients who were transferred directly to ICU (intensive care unit) following surgery. (3) Patients who were transferred to other departments during the observation period and failed to have their complete set of relevant data collected.

This study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of the Affiliated Hospital of Zunyi Medical University. Written informed consent was obtained from all participants.

2.2 Research tools

(1) MEWS [12]: a 14-point scale consisting of 5 physiological indicators: consciousness, body temperature, heart rate, respiration, and systolic blood pressure.

(2) The United Kingdom (UK) NEWS [8]: a 20-point scale consisting of 6 physiological indicators: body temperature, heart rate, respiration, oxygen saturation, systolic blood pressure, and consciousness.

2.3 Research methods

2.3.1 Grading of illness severity

Grading of illness severity in this study was based on the Nursing Classification issued by the National Health Commission of China and the actual conditions of patients [13]. Nursing Classification segregates nursing into four grades: Special Care, Primary Care, Secondary Care, and Tertiary Care. As this study is centered on illness severity, we excluded the section on self-care ability in Nursing Classification. Then, we referred to the description of illness severity in Nursing Classification and subdivided the Primary Care section into two parts to form a five-grade classification (Table 1) of the severity of patient illness.

Table 1
Illness severity grading

Illness severity	Illness condition feature
I	Terminally ill patients who are subject to changes in their condition at any time and need intensive care
	and rescue (terminally ill)
II	Critically ill patients with a stable condition (critically ill)
III	Potentially critically ill patients whose condition is unstable and subject to changes at any time
	(potentially critically ill)
IV	General patients whose condition is stable but requiring observation and treatment
V	Patients with stable disease or in recovery

2.3.2 Training

Nursing staff in the gastrointestinal department were imparted instruction about NEWS and MEWS theory and our research team members conducted simulation practice training for the nurses. The nurses were also trained for standardized operations in collecting and recording physiological indicators and for developing a stronger awareness on the need for objectively quantifying the severity of illness in patients.

2.3.3 Items studied

Based on the data from the medical record department and information department, two members of our research team took the time point of the patients’ return to wards following their surgeries as the observation start point to obtain relevant indicators of the patients according to NEWS, MEWS; the time point when patients began to exhibit need for intensive care, rescue, change of status to critically or terminally ill as determined by doctors, or other terminally or critically ill indicators, or when patients died, as the observation endpoint. Based on these observations, the following research was carried out: how NEWS and MEWS values correlate with the severity of illness; to obtain early warning trigger values of patients with various illness severities like potentially critically ill, critically ill, and terminally ill through the receiver operating characteristic (ROC) curve; to compare the accuracy of NEWS and MEWS in predicting the illness severity of patients.

2.4 Quality control

We adopted on-site questioning and awarded respondents for successfully answering the questions. We evaluated the outcomes of the previously mentioned training of medical staff and also clarified the doubts of the medical staff and provided retraining.

We designed data collection forms that could record all pertinent data in detail and completely.

Two members of our research team were engaged in data collection – one was responsible for data collection and recording, while the other was responsible for checking and verifying the data. This division of labor ensured data accuracy and reliability.

2.5 Statistical analysis

SPSS Statistics 18.0 and Spearman’s rank correlation coefficient were applied for correlation analysis. MedCalc statistical software (version 12.7) was used to plot the ROC curve to determine the trigger value, sensitivity, specificity, and Youden’s index for illnesses of different severities. $P<$ 0.05 indicated there was a statistically significant difference.

3. Results

3.1 Analysis of how NEWS and MEWS correlate to the illness severity of patients

According to our findings, NEWS and MEWS values correlated to the illness severity of patients: the more severe the illness, the higher the NEWS and MEWS values. The NEWS and MEWS values correlated positively with illness severity. There were statistically significant differences between different illness severities ( $P<$ 0.05). Differences in pairwise comparisons among all the five-grade classifications of illness severity were statistically significant ( $P<$ 0.05, Table 2).

Table 2
Correlation between illness severity and scores

Illness severity	Case number (N)	$\bar{x}\pm s$
		MEWS	NEWS
I	51	5.76 $\pm$ 2.01^abcd	10.04 $\pm$ 2.51^abcd
II	31	4.10 $\pm$ 1.78^abc	7.71 $\pm$ 2.09^abc
III	122	3.25 $\pm$ 1.51^ab	5.81 $\pm$ 2.10^ab
IV	169	1.85 $\pm$ 1.04^a	3.88 $\pm$ 1.64^a
V	155	1.70 $\pm$ 0.94	3.59 $\pm$ 1.61

Note: MEWS, Modified Early Warning Score; NEWS, National Early Warning Score. Comparison between a and grade V, $P<$ 0.05; comparison between b and grade IV, $P<$ 0.05; comparison between c and grade III, $P<$ 0.05; comparison between d and grade II, $P<$ 0.05.

3.2 Accuracy of NEWS and MEWS in predicting illness severity

We plotted an ROC curve where the illness severity of the patient was used as the assessment indicator and applied it to assess illness sensitivity and specificity of NEWS and MEWS, with statistically significant differences in the results ( $P<$ 0.05) (Table 3).

3.2.1 Accuracy of NEWS and MEWS in detecting potentially critically ill patients

The AUC of MEWS was 0.875 (95% CI: 0.844, 0.902), the optimal cut-off value was 3 points, the sensitivity was 78.9%, the specificity was 78.4%, and the Yodon index was 0.5732. The AUC of NEWS was 0.907 (95% CI: 0.878, 0.930), the best cut-off value was 5 points, the sensitivity was 82.4%, the specificity was 83.6%, and the Yoden index was 0.6599 (Fig. 1).

3.2.2 Accuracy of NEWS and MEWS in detecting critically ill patients

The AUC of MEWS was 0.872 (95% CL: 0.840, 0.899), the best cut-off value was 4 points, the sensitivity was 79.3%, the specificity was 83.2%, and the Yoden index was 0.6245. The AUC of NEWS was 0.921 (95% CL: 0.891, 0.940), the best cut-off value was 7 points, the sensitivity was 89.0%, the specificity was 79.6%, and the Yoden index was 0.6862 (Fig. 2).

3.2.3 Accuracy of NEWS and MEWS in detecting terminally ill patients

The AUC of MEWS was 0.906 (95% CI: 0.877, 0.929), the best cut-off value was 5 points, the sensitivity was 78.4%, the specificity was 87.0%, and the Jordon index was 0.6543. The AUC of NEWS was 0.939 (95% CI: 0.915, 0.948), the best truncation value was 8 points, the sensitivity was 90.2%, the specificity was 86.0%, and the Jordon index was 0.7615 (Fig. 3).

Table 3
Comparison and analysis of illness severity detection between MEWS and NEWS

Illness severity	MEWS					NEWS					$Z$ value	$P$ value
	Cutoff value	AUC	Sensitivity	Specificity	Youden’s index	Cutoff value	AUC	Sensitivity	Specificity	Youden’s index
Potentially critically ill	3	0.875	78.9%	78.4%	0.5732	6	0.907	82.4%	83.6%	0.6599	2.408	0.02^*
Critically ill	4	0.872	79.3%	83.2%	0.6245	7	0.921	89.0%	79.6%	0.6862	2.840	0.01^*
Terminally ill	5	0.906	78.4%	87.0%	0.6543	8	0.939	90.2%	86.0%	0.7615	1.961	0.04^*

Note: MEWS, Modified Early Warning Score; NEWS, National Early Warning Score. ^*P< 0.05.

4. Discussion

In China, the evaluation criteria for tertiary general hospitals propose that timely detection and treatment of “potentially critically ill” patients in general wards is the key inspection content of ward quality management [13]. However, due to the shortage of human resources and insufficient monitoring equipment in general inpatient wards, the judgment of the nurses on the severity of the patient’s condition is mainly based on the doctor’s order and clinical experience, and the objective quantification of the condition cannot be achieved, and often cannot be timely Discover the risk of the patient’s condition worsening and delay the timing of effective treatment for the patient [14, 15]. In recent years, medical institutions at home and abroad often use some scales to judge the severity of patients’ conditions, such as the acute physiology and chronic health status (APACHE II) evaluation system [16, 17], which is widely used in clinical practice. Many indicators are needed, which can comprehensively evaluate the patient’s condition, but it takes a long time, and some indicators cannot be obtained timely and accurately in general inpatient wards and primary hospitals; secondly, Glasgow Score (GCS) and Trauma Index Score (TI) are also used in Condition assessment of specific populations [18], but these assessment tools cannot meet the needs of elderly patients in general inpatient wards whose condition changes rapidly and rapidly. Elderly patients with declining physiological function are often accompanied by weakened heart and lung function, and patients with gastrointestinal diseases often need oxygen therapy after surgery. We speculate that NEWS is more effective in predicting postoperative condition changes in elderly patients with gastrointestinal diseases specific. Some scholars used the NEWS score to conduct relevant research and analysis on the pre-examination evaluation, death prediction and condition assessment of elderly patients in the emergency department [19, 20, 21, 22, 23]. However, there are few reports on the application of the NEWS scoring system in general hospitalized elderly patients undergoing gastrointestinal surgery.

In this study, we used MEWS and NEWS to comprehensively assess patients with various severities of illness and found that the more severe the illness, the higher the values of MEWS and NEWS, indicating that both MEWS and NEWS positively correlate to illness severity. Spearman’s rank correlation coefficient $r_{s}$ between the MEWS value and disease severity was 0.679, and between the NEWS value and illness severity was 0.720. There were statistically significant differences between different illness severities ( $P<$ 0.05). Differences in pairwise comparisons among the five groups were statistically significant ( $P<$ 0.05, Table 2). It shows that both tools can pre-check the condition of elderly gastrointestinal patients, but the correlation between NEWS score and condition is more significant, that is, NEWS score can more accurately assess the condition change of elderly patients in gastrointestinal surgery ward, reminding clinical medical staff to patients, take timely and effective observation and intervention. This is consistent with the domestic research results of Ma Zhiqun and Wang Bo et al. who used the two scores in the assessment of elderly patients [24, 25]. However, a multicenter study in two hospitals in northwestern Iran showed that MEWS is more valuable than NEWS in predicting ICU readmission and mortality, which may be due to the different characteristics of ICU patients and general ward patients [26].

The area under the ROC curve (AUC) assesses the accuracy of illness severity assessment tools based on AUC values: the larger the AUC value, the more accurate the prediction ability of the illness severity assessment tool [27]. In this study, we applied MEWS and NEWS for the assessment of postoperative illness severity in elderly gastrointestinal patients. NEWS performed better in detecting patient illness severity than MEWS in AUC, sensitivity, specificity, and Youden’s index, with statistically significant differences ( $P<$ 0.05, Table 3). NEWS is more accurate in predicting potentially postoperative worsening conditions in elderly gastrointestinal patients. This indicates that after the optimization of NEWS based on MEWS, namely, adjustments of values assigned to each indicator, and the addition of two indicators: oxygen saturation and oxygen inhalation, NEWS caters better to postoperative illness severity changes in elderly patients with gastrointestinal disorders and is more sensitive to illness severity. The NEWS optimal cutoff value for detecting potentially critically ill, critically ill, and terminally ill elderly gastrointestinal patients are 6, 7, and 8 points, respectively. Clinical front-line nurses can treat patients accordingly based on these scores. For instance, when the NEWS value is 6 in a patient following surgery, medical staff should be more alert and keep a close eye on changes in physiological indicators in the patient so that they can provide timely intervention through oxygen inhalation, venous access establishment, and so on, as the patient is exposed to potentially condition worsening risks. When NEWS value is 7, the patient is critically or potentially critically ill. Nursing staff should promptly convey the condition to the responsible team leader and doctor, followed by appropriate treatment to prevent more severe outcomes. When the NEWS value is 8 or above, the patient is terminally ill. Nursing staff should immediately cooperate with doctors for rescue or more comprehensive treatment measures, to monitor and actively save the life of the patient.

5. Conclusion

Abnormal vital signs and changes in consciousness appear early in elderly patients who are subject to deteriorating conditions, and their conditions change quickly. To prevent the condition of the patient from getting worse, medical staff should dynamically observe changes in their conditions for timely and appropriate intervention. Therefore, an appropriate tool is needed to enable medical staff to detect patient deterioration trends earlier and cooperate with doctors to carry out prompt treatment, thereby ensuring a better outcome for patients. Due to their simplicity in acquiring parameters and swiftness in assessment, MEWS and NEWS facilitate fast and efficient early warning of elderly patients’ conditions, even in general wards. Notably, with respect to condition specificity in elderly patients, NEWS caters better to actual changes in patient condition than MEWS.

Footnotes

Conflict of interest

The authors have no conflict of interest to declare.

Funding

This study was supported by the Zunyi City Science and Technology Plan Project (HZ (2022) No. 216).

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Application of national early warning score in assessing postoperative illness severity in elderly patients with gastrointestinal illnesses

Abstract

BACKGROUND:

OBJECTIVE:

METHODS:

RESULTS:

CONCLUSION:

Keywords

1. Introduction

2. Study participants and methods

2.1 Study participants

2.2 Research tools

2.3 Research methods

2.3.1 Grading of illness severity

Table 1 Illness severity grading

2.3.3 Items studied

2.4 Quality control

2.5 Statistical analysis

3. Results

3.1 Analysis of how NEWS and MEWS correlate to the illness severity of patients

Table 2 Correlation between illness severity and scores

3.2.1 Accuracy of NEWS and MEWS in detecting potentially critically ill patients

3.2.3 Accuracy of NEWS and MEWS in detecting terminally ill patients

Table 3 Comparison and analysis of illness severity detection between MEWS and NEWS

5. Conclusion

Footnotes

Conflict of interest

Funding

References

Table 1
Illness severity grading

Table 2
Correlation between illness severity and scores

Table 3
Comparison and analysis of illness severity detection between MEWS and NEWS