Content validation of the NANDA-I nursing diagnosis risk for perioperative hypothermia (00254)

Abstract

Purpose

The objectives of this study were to refine and validate the NANDA-I nursing diagnosis risk for perioperative hypothermia (RPH) (00254).

Methods

A quantitative, descriptive study was carried out according to the adapted diagnostic content validation model by Fehring. Data from a previously conducted literature study were triangulated with expert validation data to examine the nursing diagnosis RPH as well as potentially suggested new factors resulted from the literature review. In addition, the Wisdom of Crowds model was also considered. A nonprobability sampling technique, including purposive and snowball sampling methods, was used to recruit a panel of nurse experts. An anonymous and standardized questionnaire was developed in three languages for data collection. For validation, descriptive statistics, weighted ratios, and a one-sample T-test were used.

Results

Ninety-two nurse experts from seven countries and three continents participated in this study. Fifty-eight nurse experts (63%) were female, and 33 (36%) were male, with a mean age of 42.26 years and 19.22 years of working experience. The diagnosis label, definition, 4 out of 5 risk factors (RF), 6 out of 14 at-risk populations (ARPs), and 5 out of 9 associated conditions (ACs) were classified as major. One RF, eight ARP, and four ACs were considered minor. In addition, the experts validated 1 RF, 5 ARP, and 12 ACs from a previous literature study at least minor.

Conclusions

The nursing diagnosis RPH (00254) could be confirmed by specialized experts. No RF, ARP, or AC of the current nursing diagnosis needed to be rejected, and the added diagnostic indicators increased the robustness of the diagnosis.

Implications for nursing practice

A precise concept of the nursing diagnosis RPH improves nurses' clinical reasoning and strengthens an individualized, evidence-based care plan.

Ziel

Das Ziel dieser Studie war die Verfeinerung und Validierung der NANDA-I Pflegediagnose Risiko einer perioperativen Hypothermie (00254).

Methode

Es wurde eine quantitative, deskriptive Studie nach dem adaptierten diagnostischen Inhaltsvalidierungsmodell nach Fehring durchgeführt. Daten aus einer zuvor durchgeführten Literaturstudie wurden mit Validierungsdaten von Expert*innen trianguliert, um die Pflegediagnose Risiko einer perioperativen Hypothermie sowie potenziell vorgeschlagene neue Faktoren zu untersuchen, die sich aus der Literaturübersicht ergaben. Darüber hinaus wurde auch das Wisdom-of-Crowds-Modell berücksichtigt. Für die Rekrutierung von Pflegeexpert*innen wurde eine Nicht-Wahrscheinlichkeitsstichprobe verwendet und für die Datenerhebung wurde ein anonymer, standardisierter Fragebogen in drei Sprachen entwickelt. Zur Validierung wurden deskriptive Statistiken, gewichtete Verhältnisse und ein T-Test bei einer Stichprobe angewendet.

Ergebnisse

92 Pflegeexpert*innen aus sieben Ländern und drei Kontinenten nahmen an dieser Studie teil. 58 Pflegeexpert*innen (63 %) waren weiblich und 33 (36 %) waren männlich, mit einem Durchschnittsalter von 42.26 Jahren und 19.22 Jahren Berufserfahrung. Der Diagnosetitel, die Diagnosedefinition, vier von fünf Risikofaktoren, sechs von 14 Risikopopulationen und fünf von neun assoziierten Bedingungen wurden als wichtig (Major) eingestuft. Ein Risikofaktor, acht Risikopopulationen und vier assoziierte Bedingungen wurden als geringfügig (Minor) eingestuft. Darüber hinaus stuften die Expert*innen einen Risikofaktor, fünf Risikopopulationen und 12 assoziierte Bedingungen aus einer zuvor durchgeführten Literaturstudie als zumindest geringfügig (Minor) ein.

Schlussfolgerungen

Die Pflegediagnose Risiko einer perioperativen Hypothermie konnte von spezialisierten Fachexpert*innen bestätigt werden. Keiner der Risikofaktoren, Risikopopulationen und assoziierten Bedingungen der aktuellen Pflegediagnose mussten abgelehnt werden und die berechneten zusätzlichen diagnostischen Indikatoren erhöhen die Robustheit der Diagnose.

Implikationen für die pflegepraxis

Ein präzises Konzept der Pflegediagnose Risiko einer perioperativen Hypothermie verbessert die klinische Entscheidungsfindung der Pflegenden und stärkt einen individualisierten evidenzbasierten Pflegeplan.

Keywords

body temperature content validation study NANDA-I nursing diagnosis perioperative hypothermia perioperative nursing

CONTENT VALIDATION OF THE NANDA-I NURSING DIAGNOSIS RISK FOR PERIOPERATIVE HYPOTHERMIA (00254)

Perioperative hypothermia (PH) is a common problem in patients undergoing surgery, encompassing the preoperative, intraoperative, and postoperative phases. The preoperative phase is defined as 1 h before induction of anesthesia, the intraoperative phase as the entire anesthesia time, and the postoperative phase as the 24 h after the patient has entered the recovery area. It occurs when the patient's core body temperature decreases below the normal range during the surgical procedure. Its reported incidence is still high, ranging from 4% to 90% (National Institute for Health and Clinical Excellence [NICE], 2008, 2016; Oden et al., 2022; Torossian et al., 2014, 2015). Research also indicates that body temperature-related nursing diagnoses (NDs) are evident in the perioperative period (Dalri et al., 2006; Rossi et al., 2000). The effects of PH can be severe, ranging from increased infection rates to increased recovery time. Caring for patients at risk for PH (RPH) requires comprehensive body temperature assessment and monitoring and an effective thermal management strategy (Rauch et al., 2021). Thus, nurses must identify patients at increased RPH and take appropriate preventive and treatment measures to prevent or minimize the occurrence of this condition because nurses need to understand the importance of risk mechanisms and how they are investigated to prevent harm and reduce risks during the caring process (International Council of Nurses, 2023).

Background

In this context, the NANDA-I ND RPH (00254) (Herdman et al., 2021) supports identifying patients at increased risk and then taking appropriate interventions to protect them (Alves Mendes et al., 2021). RPH was approved and accepted first in 2013 for “NANDA International Nursing Diagnoses: Definitions and Classification.” It was revised twice, in 2017 and 2020, and is defined as follows: Susceptible to an inadvertent drop in core body temperature below 36°C/96.8°F occurring one hour before to 24 h after surgery, which may compromise health (Herdman et al., 2021, p. 545). Risk factors (RF) contributing to this ND include anxiety, body mass index below normal range for age and gender, environmental temperature <21°C/69.8°F, inadequate availability of appropriate warming equipment, and wound area uncovered. Those populations at risk include, for example, individuals aged 60 years and older, in an environment with laminar air flow, receiving anesthesia for a period >2 h, undergoing long induction time, open surgery, or surgical procedure >2 h. Patients with associated conditions (ACs) like hepatic failure, anemia, burns, cardiovascular complications, chronic renal impairment, or neurological disorders are also vulnerable (Herdman et al., 2021, p. 545).

Further, the Nursing Outcome Classification (NOC) provides a set of nursing outcomes relevant to caring for patients at RPH. These include, for example, thermoregulation (0800), vital signs (0802), anxiety level (1211), and fear level (1210) (Moorhead et al., 2018). By measuring and documenting these outcomes, nurses can monitor the success of their nursing interventions and adapt treatment to the patient's individual needs. In addition, the Nursing Intervention Classification (NIC) provides a variety of effective nursing interventions appropriate for the prevention and treatment of PH, for example, temperature regulation (3900), temperature regulation: perioperative (3902), vital signs monitoring (6680), anxiety reduction (5820), and calming technique (5880) (Butcher et al., 2018). Caring for patients at RPH is a complex concern requiring comprehensive assessment, effective nursing interventions, and outcome monitoring. By using the NANDA-I ND RPH, appropriate nursing outcomes from NOC, and nursing interventions from NIC, nurses can minimize the RPH and promote successful patient recovery (Schwanda & Müller Staub, 2018).

Nurses have the responsibility to implement an evidence-based care plan, including nursing diagnosis, nursing interventions, and nursing outcomes (Monteiro Mantovani et al., 2020) from a Standardized Nursing Language (SNL), which is also known as Advanced Nursing Process (Müller Staub et al., 2015). In this regard, NANDA-I is the most widely used system by nurses worldwide (Rodríguez-Suárez et al., 2023; Tastan et al., 2014), and it is the only association that addresses evidence criteria for the diagnostic validity of ND (Herdman et al., 2021; Müller Staub & Rappold et al., 2017; Odenbreit et al., 2011). The hierarchy of evidence for the validity of NANDA-I diagnoses is based on criteria related to the types of studies from which they emerged. The level of evidence (LOE) ranges from concept generation and theoretical support up to clinical support. It allows a variety of research projects through, for example, concept analyses, content validations, and clinical validation studies. Developing an SNL is an ongoing process that must be continually revised, reviewed, refined, and researched to keep diagnoses, interventions, and outcomes current, valid, and evidence-based (Herdman et al., 2021). Although the ND RPH (00254) underwent three literature studies (Alves Mendes et al., 2021; Schwanda et al., 2015, 2021), further validations, for example, content and clinical validation studies, are missing.

Objectives

Therefore, the objectives of this research project were to refine and validate the NANDA-I ND RPH (00254) and to increase its LOE.

Research questions

What are the major and minor RF, at-risk populations (ARPs), and ACs of the NANDA-I ND RPH (00254) according to content validity testing?

Design

A quantitative, descriptive diagnostic content validation (DCV) study, according to the adapted model by Fehring (1987), triangulates data from a previously conducted literature study with expert validation data (Bhandari, 2023; Heale & Forbes, 2013).

METHODS

The authors examined the current RF, ARP, and AC of the NANDA-I ND RPH (00254) and potentially suggested new ones from a previously conducted literature study (Schwanda et al., 2021). This current content validation study also considered the Wisdom of Crowds model (Surowiecki, 2004). In this model, the assumption is that pooling the estimates or decisions of multiple individuals can result in group responses that outperform most or all individuals (Thomas et al., 2021). In other words, the collective opinion of the experts represents a better estimate than the opinion of an individual expert (Mendes et al., 2021).

Sample

Consequently, a panel of nurse experts was recruited using a nonprobability sampling technique. The recruitment process involved purposive and snowball sampling methods in Austria, Brazil, and Switzerland from November 2021 to September 2022. The panel members were selected from research or SNL groups, participants of SNL congresses, and association members of specialist organizations, such as NANDA-I (e.g., suggestions from Diagnosis Development Committee [DDC]) and/or board members from the Association for Common European Nursing Diagnoses Interventions and Outcomes (ACENDIO). To determine the sample size for the proposed DCV study, the following parameters were used and based on the formula

n = \frac{Z^{2} \times P \times (1 - P)}{e^{2}}

(Maia Pascoal et al., 2021): A minimum agreement (P) of 80% was assumed as the cut-off value for weighted ratios representing “major” RF, ARP, and AC is ≥0.80; a margin of error (e) of 10% was chosen (Koller, 2022); and a confidence level of 95% was set (Z-value of 1.96). Therefore, a sample size of 62 nurse experts was necessary.

The specified inclusion criteria for the experts encompassed registered nurses specializing in surgical units, anesthesia or intensive care nursing, and operating room nursing, with a minimum clinical experience of 4 years. According to the criteria established by Quatrini Carvalho Passos Guimarães et al. (2016), experts were defined based on the following requirements: A minimum of 4 years of clinical experience in the study-related nursing specialty (4 points), at least 1 year of experience in clinical teaching of the specialty and teaching of nursing classifications (1 point), research experience with published articles in nursing classifications in reputable journals (1 point), involvement in a research group in the specific research topic and specialty for at least 2 years (1 point), a Ph.D. in nursing in the specific specialty (2 points), a Master's degree in nursing in the specific specialty (1 point), and completion of a nursing residency in the specific specialty (1 point). Additionally, an extra point was granted for each additional year of clinical or teaching experience. Following the calculation of the total score, the experts were categorized as junior experts (minimum score of 5 points), master experts (scores ranging from 6 to 20 points), or senior experts (scores exceeding 20 points).

Data collection instrument

To assess the content validity of the NANDA-I ND RPH (00254), an anonymous and standardized questionnaire was developed in Brazilian Portuguese, English, and German languages. The latest versions of the Brazilian Portuguese, English, and German-language book “NANDA International Nursing Diagnoses: Definitions and Classification” were used to ensure that all languages have the same meaning. The questionnaire consisted of two parts. In the first part, information was collected about the experts’ profiles, including their education, field of study, age, gender, years of clinical experience and teaching experience, research experience, number of published articles, participation in research groups, as well as a nursing residency in the specific specialty according to the inclusion criteria mentioned above. The second part comprised statements and questions concerning the relevance of the current (and potentially new) RF, ARP, and AC regarding the ND RPH (00254). For each statement or question, the experts were asked to indicate the clarity and comprehensibility on a nominal scale using response options of “yes” or “no.” Additionally, the experts were requested to rank the relevance of the current (and potentially new) RF, ARP, and AC using a 5-point Likert-type scale. The response options on this scale were as follows: 1 = not all characteristic of the diagnosis; 2 = very little characteristic of the diagnosis; 3 = somewhat characteristic of the diagnosis; 4 = considerably characteristic of the diagnosis; and 5 = very characteristic of the diagnosis. A free-text field was also provided to allow experts to provide further suggestions and recommendations.

Data analysis

Descriptive statistics were employed to analyze the data from part one of the questionnaire. For the variables of professional education, academic degree, gender, and participation in research groups, absolute and relative frequencies were calculated. The modal value was determined for these variables as well. In addition, for the variables age, work experience, expert score, and teaching experience, the modal value, minimum value, maximum value, median, arithmetic mean, and standard deviation were calculated. In analyzing the data regarding the relevance of the current (and potentially new) RF, ARP, and AC, weights were assigned to the Likert scale response options. These weights were then summed and divided by the number of responding experts. The following weight scale was used: 1 = 0, 2 = 0.25, 3 = 0.5, 4 = 0.75, 5 = 1. RF, ARP, and AC with weighted ratios equal to or greater than 0.80 were classified as “major,” whereas those with ratios between 0.79 and 0.51 were considered “minor.” Any RF, ARP, and AC with ratios equal to or lower than 0.50 were excluded from consideration, following the suggestion provided by Fehring (1987). In addition, to assess whether the RF, ARP, and AC deviated significantly from the weighted ratio of ≥0.80, the mean values of the Likert scale responses provided by the experts were analyzed using a one-sample T-test (McAleer et al., 2021). Moreover, Cohen's d values were calculated to determine the effect size. A significance level of p < 0.05 was used to define statistical significance. Interpretation of Cohen's d values was as follows: no effect (<0.2), small effect (0.2–0.5), medium effect (0.5–0.8), and large effect (>0.8). The comments provided in the free-text fields were qualitatively analyzed and categorized into the following groups: “reason for agreement,” “critical comments,” and “reason for rejection.”

Data were analyzed using Microsoft Excel version 16.71 and with IBM SPSS Statistics version 28.0.0.0.

RESULTS

Ninety-two nurse experts (2 junior, 41 masters, and 49 senior experts) from Austria, Brazil, Germany, Italy, Portugal, Switzerland, and the United States validated the current NANDA-I-ND RPH (00254). Most of the nurse experts (n = 58, 63%) were female, and 33 (36%) were male, with a mean age of 42.26 years and 19.22 years of working experience (see Table 1).

Table 1

Sample characteristics of nurse experts (n = 92).

	n	%
Sex
Female	58	63
Male	33	36
Other (specify)	1	1
Nationality
Austria	40	44
Brazil	25	27
Switzerland	8	9
Germany	10	11
Portugal	3	3
Italy	3	3
USA	2	2
No answer	1	1
Professional education
Registered nurse with a clinical experience of at least 4 years on surgical units	15	16
Registered anesthesia care trained nurse with clinical experience of at least 4 years	30	33
Registered intensive care trained nurse with clinical experience of at least 4 years	24	26
Operating room nurse with clinical experience of at least 4 years	19	21
Other	4	4
Highest academic degree
PhD	10	11
DNP	1	1
MSc	39	42
BSc	14	15
Other (no academic degree)	28	31
Age in years
Mean	42.26
SD	±8.86
Min	27
Max	69
Median	40
Modal value	36
Work experience in years
Mean	19.22
SD	±9.73
Min	4.5
Max	47
Median	19
Modal value	9
Teaching experience in years
Mean	4.07
SD	±6.38
Min	1
Max	30
Median	0.5
Modal value	0
Expert score
Mean	21.68
SD	±9.71
Min	4
Max	52
Median	21
Modal value	17
Expert classification
Junior expert	2	2
Master expert	41	45
Senior expert	49	53

The study provided the following key findings: The label, the definition, 4 out of 5 RF, 6 out of 14 ARP, and 5 out of 9 AC were classified as major. One RF, eight ARP, and four AC were considered minor. Additionally, regarding Fehring's interpretation, no RF, ARP, or AC of the current ND needed to be rejected.

The diagnosis label RPH achieved a weighted ratio of 0.87 (mean 4.47; SD ± 0.78) and the diagnosis definition 0.81 (mean 4.24; SD ± 0.79). Thus, the label statistically significant (p < 0.05) overruns the T-test value of 80%, but not the definition.

Major RF, on the one hand, were body mass index below normal range for age and gender (mean 4.37; SD ± 0.83; weighted ratio 0.84), environmental temperature <21°C/69.8°F (mean 4.67; SD ± 0.67; weighted ratio 0.92), inadequate availability of appropriate warming equipment (mean 4.66; SD ± 0.77; weighted ratio 0.91), and wound area uncovered (mean 4.32; SD ± 0.92; weighted ratio 0.83). Except for body mass index below normal range for age and gender and wound area uncovered, all statistically significant (p < 0.05) overrun the T-test value of 80%.

A minor RF was anxiety with a weighted ratio of 0.53 (mean 3.12; SD ± 1.23) and did not statistically significant (p < 0.05) overrun the T-test value of 80%.

Major ARPs on the other hand were individuals receiving anesthesia for a period >2 h (mean 4.53; SD ± 0.76; weighted ratio 0.88), individuals undergoing long induction time (mean 4.31; SD ± 0.90; weighted ratio 0.83), individuals undergoing open surgery (mean 4.58; SD ± 0.72; weighted ratio 0.90), individuals undergoing surgical procedure >2 h (mean 4.52; SD ± 0.74; weighted ratio 0.88), individuals with increased intraoperative blood loss (mean 4.63; SD ± 0.65; weighted ratio 0.91), and neonates <37 weeks gestational age (mean 4.80; SD ± 0.48; weighted ratio 0.95). Except for individuals undergoing long induction time, all statistically significant (p < 0.05) overrun the T-test value of 80%.

Minor ARPs were women (mean 3.39; SD ± 1.01; weighted ratio 0.60), individuals aged >60 years (mean 4.11; SD ± 1.11; weighted ratio 0.78), individuals in environment with laminar air flow (mean 4.13; SD ± 0.97; weighted ratio 0.78), individuals with an American Society of Anesthesiologist (ASA) physical status classification score >1 (mean 3.51; SD ± 1.08; weighted ratio 0.63), individuals with a high Model for End-Stage Liver Disease (MELD) score (mean 3.72; SD ± 1.00; weighted ratio 0.68), individuals with intraoperative systolic blood pressure <140 mmHg (mean 3.46; SD ± 1.06; weighted ratio 0.62), individuals with low body surface area (mean 3.84; SD ± 1.13; weighted ratio 0.71), and individuals with intraoperative diastolic arterial blood pressure <60 mmHg (mean 4.04; SD ± 1.07; weighted ratio 0.76). None of them statistically significant (p < 0.05) overruns the T-test value of 80%.

Major ACs were anemia (mean 4.36; SD ± 0.82; weighted ratio 0.84), burns (mean 4.75; SD ± 0.55; weighted ratio 0.94), cardiovascular complications (mean 4.23; SD ± 0.93; weighted ratio 0.81), combined regional and general anesthesia (mean 4.27; SD ± 0.97; weighted ratio 0.82), and trauma (mean 4.58; SD ± 0.67; weighted ratio 0.89). Only burns and trauma statistically significant (p < 0.05) overrun the T-test value of 80%.

Minor AC were chronic renal impairment (mean 3.74; SD ± 0.99; weighted ratio 0.68), neurological disorders (mean 4.05; SD ± 0.97; weighted ratio 0.76), pharmaceutical preparations (mean 3.90; SD ± 1.03; weighted ratio 0.72), and acute hepatic failure (mean 3.67; SD ± 1.03; weighted ratio 0.67). The T-test value for all of them was significantly under 0.8 (see Table 2).

Table 2

NANDA-I ND RPH (00254): Content validation of risk factors, at-risk populations, and patient-related associated conditions.

	Descriptive statistics				One-sample T-test (test value 0.8)
	Mean	SD±	Weighted ratio	Interpretation	T	p-value	Cohen's d	Interpretation
Label Risk for perioperative hypothermia (00254) (n = 92 rater)	4.47	0.78	0.87	Major	3.302	0.001**	0.344	Small positive effect
Definition (n = 92 rater) Susceptible to an inadvertent drop in core body temperature below 36°C/96.8°F occurring one hour before to 24 h after surgery, which may compromise health	4.24	0.79	0.81	Major	0.475	0.636	0.05	No effect
Risk factors (n = 90 rater)
Body mass index below normal range for age and gender	4.37	0.83	0.84	Major	−8.344	0.059	0.201	Small positive effect
Environmental temperature <21°C/69.8°F	4.67	0.67	0.92	Major	6.604	<0.001**	0.696	Medium positive effect
Inadequate availability of appropriate warming equipment	4.66	0.77	0.91	Major	5.637	<0.001**	0.594	Medium positive effect
Wound area uncovered	4.32	0.92	0.83	Major	1.258	0.212	0.133	No effect
Anxiety	3.12	1.23	0.53	Minor	−8.344	<0.001*	−0.879	Large negative effect
Scale ratio for risk factors			0.81
At-risk population (n = 89 rater)
Individuals receiving anesthesia for a period >2 h	4.53	0.76	0.88	Major	4.099	<0.001**	0.434	Small positive effect
Individuals undergoing long induction time	4.31	0.90	0.83	Major	1.202	0.233	0.127	No effect
Individuals undergoing open surgery	4.58	0.72	0.90	Major	5.035	<0.001**	0.534	Medium positive effect
Individuals undergoing surgical procedure >2 h	4.52	0.74	0.88	Major	4.038	<0.001**	0.428	Small positive effect
Individuals with increased intraoperative blood loss	4.63	0.65	0.91	Major	6.265	<0.001**	0.664	Medium positive effect
Neonates <37 weeks gestational age	4.80	0.48	0.95	Major	11.724	<0.001**	1.243	Large positive effect
Women	3.39	1.01	0.60	Minor	−7.558	<0.001*	−0.801	Large negative effect
Individuals aged >60 years	4.11	1.11	0.78	Minor	−0.743	0.459	−0.079	No effect
Individuals in environment with laminar airflow	4.13	0.97	0.78	Minor	−0.635	0.527	−0.067	No effect
Individuals with American Society of Anesthesiologist (ASA) physical status classification score >1	3.51	1.08	0.63	Minor	−6.077	<0.001*	−0.644	Medium negative effect
Individuals with high Model for End-Stage Liver Disease (MELD) score	3.72	1.00	0.68	Minor	−4.537	<0.001*	−0.481	Small negative effect
Individuals with intraoperative systolic blood pressure <140 mmHg	3.46	1.06	0.62	Minor	−6.606	<0.001*	−0.700	Medium negative effect
Individuals with low body surface area	3.84	1.13	0.71	Minor	−2.991	0.004*	−0.317	Small negative effect
Individuals with intraoperative diastolic arterial blood pressure <60 mmHg	4.04	1.07	0.76	Minor	−1.373	0.173	−0.146	No effect
Scale ratio for at-risk population			0.78
Associated conditions (n = 88 rater)
Anemia	4.36	0.82	0.84	Major	1.874	0.064	0.200	Small positive effect
Burns	4.75	0.55	0.94	Major	9.348	<0.001**	0.997	Large positive effect
Cardiovascular complications	4.23	0.93	0.81	Major	0.275	0.784	0.029	No effect
Combined regional and general anesthesia	4.27	0.97	0.82	Major	0.705	0.483	0.075	No effect
Trauma	4.58	0.67	0.89	Major	5.288	<0.001**	0.564	Medium positive effect
Chronic renal impairment	3.74	0.99	0.68	Minor	−4.379	<0.001*	−0.467	Small negative effect
Neurological disorder	4.05	0.97	0.76	Minor	−1.495	0.139	−0.159	No effect
Pharmaceutical preparations	3.90	1.03	0.72	Minor	−2.756	0.007*	−0.294	Small negative effect
Acute hepatic failure	3.67	1.03	0.67	Minor	−4.845	<0.001*	−0.517	Medium negative effect
Scale ratio for associated conditions			0.79

0.8 is significantly underrun.

**0.8 is significantly overrun.

In addition, RF, ARP, and AC from a previous literature study were also validated. All RF and ARP statistically significant (p < 0.05) overrun the test value of 80%, except for significant fluid shifts and hypotension. The weighted ratios ranged from 0.94 to 0.80 and, therefore, could be classified as major. One AC (general anesthesia) was classified as major, and 11 AC (e.g., diabetic neuropathy, patient body positioning (e.g., supine position), or decreased preoperative heart rate) were classified as minor. The weighted ratios ranged from 0.89 to 0.55, and except for general anesthesia, none of them statistically significant (p < 0.05) overrun the T-test value of 80% (see Table 3).

Table 3

Content validation of additional risk factors, at-risk populations, and procedure-related associated conditions.

	Descriptive statistics				One-sample T-test (test value 0.8)
	Mean	SD ±	Weighted ratio	Interpretation	T	p-value	Cohen's d	Interpretation
Risk factors (n = 88 rater)
Body surface area uncovered	4.67	0.60	0.92	Major	7.34	<0.001**	0.783	Medium positive effect
At-risk population (n = 88 rater)
Low body weight^#	4.49	0.71	0.87	Major	3.81	<0.001**	0.406	Small positive effect
Low pre- and intraoperative core body temperature (<36°C/96.8°F) ^#	4.75	0.51	0.94	Major	10.145	<0.001**	1.082	Large positive effect
Increased fluid loss	4.58	0.62	0.89	Major	5.743	<0.001**	0.612	Medium positive effect
Significant fluid shifts	4.18	0.78	0.80	Major	−0.218	0.828	−0.023	No effect
Hypotension	4.25	0.87	0.81	Major	0.536	0.593	0.057	No effect
Associated conditions (n = 88 rater)
General anesthesia^#	4.55	0.77	0.89	Major	4.199	<0.001**	0.448	Small positive effect
Diabetic neuropathy^#	3.90	0.88	0.72	Minor	−3.205	0.002*	−0.342	Small negative effect
Patient body positioning (e.g., supine position)	3.48	1.10	0.62	Minor	−6.143	<0.001*	−0.655	Medium negative effect
Decreased preoperative heart rate^#	3.74	1.04	0.68	Minor	−4.142	<0.001*	−0.442	Small negative effect
The volume of insufflated CO₂ during laparoscopic surgery	3.80	0.94	0.70	Minor	−4	<0.001*	−0.426	Small negative effect
Redistribution of body heat	4.16	0.77	0.79	Minor	−0.497	0.62	−0.053	No effect
Use of a pneumatic tourniquet	3.53	0.92	0.63	Minor	−6.778	<0.001*	−0.723	Medium negative effect
Use of intermittent pneumatic compression device	3.20	1.13	0.55	Minor	−8.219	<0.001*	−0.876	Large negative effect
Use of wet skin preps	4.17	0.88	0.79	Minor	−0.312	0.755	−0.03	No effect
Preexisting illness^#	4.02	0.94	0.76	Minor	−1.757	0.082	−0.187	No effect
Regional anesthesia^#	3.89	1.03	0.72	Minor	−2.848	0.005*	−0.304	Small negative effect
Higher spinal blockade^#	3.94	0.69	0.74	Minor	−2.501	0.014*	−0.267	Small negative effect

*0.8 is significantly underrun.

**0.8 is significantly overrun.

Significant RF from the literature.

DISCUSSION

The study aims to refine and validate the NANDA-I ND RPH (00254) and increase its LOE, which could be achieved. Most RF, several ARP, and AC could be classified as major. No RF, ARP, or AC had to be rejected, which strengthens this ND and means that this considerable number of factors can be further researched in a clinical validation study.

In interpreting the inferential statistics, the factors from the current ND that statistically significantly exceed the test value of 80% are substantial, highly relevant, and supported by current publications, including empirical studies and clinical guidelines. These are as follows: environmental temperature <21°C/69.8°F (Aksu et al., 2014; Calvo Vecino et al., 2018; Collins et al., 2019; Putnam, 2015; Torossian et al., 2014; Yi et al., 2015, 2017), inadequate availability of appropriate warming equipment (Burlingame, 2020; Vural et al., 2018; Yi et al., 2015, 2017), individuals receiving anesthesia for a period >2 h (Collins et al., 2019; Putnam, 2015; Torossian et al., 2014; Yi et al., 2015, 2017), individuals undergoing open surgery (Akers et al., 2019; Aksu et al., 2014; Belayneh et al., 2014; Burlingame, 2020; Calvo Vecino et al., 2018; Collins et al., 2019; Kleimeyer et al., 2018; Putnam, 2015; Torossian et al., 2014; Yi et al., 2015, 2017), individuals undergoing surgical procedure >2 h (Aksu et al., 2014; Chalari et al., 2019a, 2019b; Collins et al., 2019; Frisch et al., 2017; Putnam, 2015; Scholten et al., 2018; Torossian et al., 2014; Ziolkowski et al., 2017), individuals with increased intraoperative blood loss (Collins et al., 2019; Tsukamoto et al., 2016), neonates <37 weeks gestational age, burns, and trauma (Burlingame, 2020; Calvo Vecino et al., 2018; Collins et al., 2019; Putnam, 2015; Torossian et al., 2014). In addition, body surface area uncovered (Calvo Vecino et al., 2018; Collins et al., 2019; Putnam, 2015), low body weight (Burlingame, 2020; Kim & Yoon, 2014; Putnam, 2015; Torossian et al., 2014; Yi et al., 2015), low pre- and intraoperative core body temperature (<36°C/96.8°F) (Calvo Vecino et al., 2018; Chalari et al., 2019a; Collins et al., 2019; Kim & Yoon, 2014; Kleimeyer et al., 2018; Tsukamoto et al., 2016; Vural et al., 2018; Yi et al., 2015, 2017), increased fluid loss (Collins et al., 2019; Tsukamoto et al., 2016), and general anesthesia (Belayneh et al., 2014; Burlingame, 2020; Calvo Vecino et al., 2018; Collins et al., 2019; Frisch et al., 2017; Putnam, 2015; Scholten et al., 2018; Torossian et al., 2014) were another statistically significant factors that are currently not included in the ND but also supported by current publications. We highly suggest integrating those in an upcoming edition of “NANDA International Nursing Diagnoses: Definitions and Classification.”

The results of this study indicate that PH may also trigger additional NDs, such as an increased risk for delayed surgical recovery (00246), as reported in a recent publication by Rembold et al. (2018), and clinical nurses should be mindful of this fact. Considering the consequences of PH and the RF of the ND RPH (00254) validated in the present study, nurses in the healthcare team are responsible for protecting their patients from this condition. Nurses perform evidence-based nursing interventions following legal and ethical accountability and observe/assess patients throughout the perioperative phase. As a result of this validation study, the following measures are suggested: The ambient room temperature on the ward or operating room should be at least 21°C (Hooper et al., 2010; NICE, 2008, 2016; Torossian et al., 2014, 2015), which was also a major and statistically significant RF in our study. In addition, it may protect patients from postoperative complications if they stay warm wearing their own clothes before surgery and inform the nurses or healthcare providers if they feel cold at any time. As stated above, another study result is that low pre- and intraoperative core body temperature (<36°C/96.8°F) and individuals undergoing surgical procedure >2 h are major and statistically significant factors. Combined regional and general anesthesia and cardiovascular complications were major factors, and ASA physical status classification score >1 was minor. Current guidelines and scientific publications suggest that if two of those appear, a surgical patient should be classified as higher risk, and nurses should consider screening every surgical patient for RF associated with PH (Bashaw, 2016; Butcher et al., 2018; Hooper et al., 2010; NICE, 2008, 2016; Ribeiro et al., 2021; Torossian et al., 2014, 2015). From our point of view, this can also be done with the NANDA-I ND RPH (00254) (Herdman et al., 2021) because this diagnosis has all the factors mentioned above, except the preoperative temperature <36°C, which is currently part of the ND. This should be taken into account because the target core temperature of ≥36°C is crucial, especially in the pre-, but also in the intra- and postoperative phase (Hooper et al., 2010; NICE, 2008, 2016; Torossian et al., 2014, 2015), and the patient's core temperature should be measured 1 h preoperative on the ward and then continuously intra- and postoperative. Inadequate availability of appropriate warming equipment was one more major and statistically significant RF among experts' opinions. Therefore, healthcare institutions should be aware and ensure that active and passive warming measures are available not only in the operating room but also in the surgical ward because, compared to intraoperative warming, prewarming is not sufficiently implemented in clinical practice in many hospitals (Grote et al., 2018). It is suggested that if the patient's core temperature is ≥36°C, active warming (forced-air warming) can be started 30 min before anesthesia. If the patient's temperature is below this target, active warming may be started on the ward and needs to be continued as soon as possible upon transfer to the operating room (Hooper et al., 2010; NICE, 2008, 2016; Torossian et al., 2014, 2015). In addition, intravenous fluids, blood products, irrigation fluids, and humidified anesthesia gases are suggested to be warmed/heated intraoperatively, and the patient should not be transferred to the ward if the core temperature is below 36°C postoperatively. Finally, evidence suggests that implementing those temperature measures may result in fewer perioperative complications and optimal patient-sensitive outcomes (Bashaw, 2016; Butcher et al., 2018; Hooper et al., 2010; NICE, 2008, 2016; Torossian et al., 2014, 2015) as well as in a reduced PH incidence, especially if nurses anesthetists with more autonomy and expanded scope of practice to anesthetic care are involved (Yin et al., 2021).

Strengths and limitations

A major strength of this study was that the sample size exceeded the calculated necessary sample size by one third and consisted of mainly master or senior nurse experts from seven countries on three continents. In contrast, other content validation studies achieved sample sizes ranging from 16 to 74 nurse experts (Appoloni et al., 2013; Barreiro & De Oliveira Lopes, 2023; Cardoso et al., 2023; Da Silva et al., 2023; Garbuio et al., 2015; Maia Pascoal et al., 2021; Šerková & Marečková, 2019; Zeleníková et al., 2014) except Fernández-Donaire et al. (2019) sample size of 202. Another strength of this investigation was the applied recruitment strategy of winning intercontinental experts of SNL specialist associations. The approach to statistical analysis may be considered a limitation, as it does not account for agreements occurring by chance. It may, therefore, be criticized due to its percentage/proportionate agreement calculation instead of multi-rater kappa statistics, which is not foreseen in the chosen validation model. To account for this, a one-sample T-test was additionally used (McAleer et al., 2021).

CONCLUSIONS

The content-validated ND RPH (00254) allows nurses to detect patients at risk accurately. Specialized experts could confirm the current ND, and no RF, ARP, or AC needed to be rejected. In addition, the experts validated one RF, 5 ARP, and 12 AC from a previous literature study, which will be submitted to the NANDA-I Diagnosis Development Committee for inclusion and may help improve the accuracy of the nursing diagnosis. Moreover, it is necessary to educate health professionals about the concept of the newly validated ND on PH (NICE, 2008, 2016) and the Advanced Nursing Process (Müller Staub et al., 2015). For nursing research, the study results help to increase the LOE (Herdman et al., 2021) of the current NANDA-I ND RPH (00254). In addition, this study can provide a basis for further clinical validation studies LOE 2.3.1–2.3.10 (Herdman et al., 2021) with a multicenter approach and large samples.

IMPLICATIONS FOR NURSING PRACTICE

Validated NDs lower complications and support nurses when implemented into practice and electronic health records. They contribute to patients' safety and assure good patient-sensitive outcomes (Brunner et al., 2021; Leoni-Scheiber et al., 2019, 2020; Moorhead et al., 2018; Müller Staub et al., 2008). Moreover, a precise concept of the ND RPH (00254) improves nurses' clinical reasoning and strengthens an individualized, evidence-based care plan (Da Silva et al., 2023).

AUTHOR CONTRIBUTIONS

Manuel Schwanda, Silvia Brunner, Miriam de Abreu Almeida, Martina Koller, Maria Müller Staub, and Andre Ewers substantially contributed to the conception and design of the work, drafting the manuscript, final approval of the version to be published, and agree to be accountable for all aspects of the work.

CONFLICT OF INTEREST STATEMENT

The authors declare no conflicts of interest.

ETHICS STATEMENT

The planned DCV study, which encompassed three countries, has received ethical approval from the following institutions: The ethics committee of the provincial government of Lower Austria (GS1-EK-12/476-2019), the ethics committee at Hospital de Clínicas de Porto Alegre of the Federal University of Rio Grande do Sul (CAAE: 59899822.1.0000.5327), and the cantonal ethics committee of Zurich (BASEC-Nr. 2021-01099).

Footnotes

ACKNOWLEDGMENTS

The authors thank Rita Gruber, BScN, MSc and FH-Prof. Mag. Petra Ganaus, MSc for their kind support and all nursing experts who participated in this study. The study was funded as part of the RTI strategy Lower Austria 2027 by the Society for Research Promotion Lower Austria and was supported by a grant from the NANDA International Foundation and by an ACENDIO research grant and received a Marjory Gordon Award as well as an ACENDIO Research Network Award.

ORCID

Manuel Schwanda

Miriam de Abreu Almeida

Maria Müller Staub

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