Application of intelligent nursing based on cloud computing of internet of things in children with pneumonia and sepsis treated with human gamma globulin

Abstract

Purpose: To explore the application of intelligent nursing (IN) based on the Internet of Things (IoT) in children with pneumonia and sepsis treated with human gamma globulin (HGG).

Methods: A total of 200 children with pneumonia combined with sepsis who attended the First People’s Hospital of Shangqiu from January 1, 2020 to February 13, 2022 were consecutively collected. Children were randomly divided into IN group and routine nursing (RN) group, with 100 children in each group. All children received standard anti-infection treatment along with intravenous HGG. In IN group, IN measures based on the IoT cloud computing platform monitored the treatment process of children with HGG throughout the whole process, while children in the RN group only received RN measures. Information on both groups was collected from the medical records, such as gender, age, duration of hospitalization, fever, antibiotic use, serological indicators, pulmonary function indicators, immune function indicators and adverse effects of HGG. Multi-factorial logistic regression was performed to access the correlation between IN and the duration of hospitalization and a range of other factors studied above.

Results: After adjusting for numerous confounding factors, multifactorial logistic regression revealed that the application of IN was associated with a shorter duration of hospitalization (p = .030) and lower white blood cell (WBC) and creatinine (Cr) levels in post-treatment children (p = .003, p = .010). It was also associated with higher levels of forced expiratory volume in one second (FEV1), forced vital capacity (FVC) and peak expiratory flow (PEV) after treatment (p = .014, p = .001, p = .002) and higher levels of immune CD4⁺/CD8⁺ ratio after treatment (p = .001) and reduced symptoms of vomiting among the adverse effects (p = .047).

Conclusion: The IoT cloud-based IN model significantly improved the efficacy of HGG in the treatment of pneumonia sepsis in children and reduce occurrence of some adverse reactions.

Keywords

pneumonia sepsis internet of things cloud computing gamma globulin intelligent nursing

Introduction

Pneumonia is not uncommon in children with infectious diseases. If it leads to sepsis, the disease has reached a serious stage and the mortality rate becomes significantly higher.¹ Precise and robust anti-infective therapy is the mainstay of treatment for sepsis. However, if the initial empirical antibiotics do not successfully cover the causative organism or if the causative organism is a drug-resistant strain, the effectiveness of anti-infective therapy is greatly reduced and the patient’s prognosis is greatly associated.² In such severe cases, clinicians will use adjuvant therapies in addition to escalating antibiotics to treat the disease together with antibiotics.³ Human gamma globulin (HGG) is a drug that passively boosts the immunity of the child. It is enriched with various antibodies in the serum of healthy people, and a direct intravenous infusion of HGG boost the immune system of the child in the short term, and in combination with antibiotics can enhance the combined effect of anti-infective treatment.^4,5 However, HGG administration may cause a variety of adverse events and there is little experience with this drug in children and febrile patients.⁶ Therefore, the use of this adjunctive therapy in pediatric patients with pneumonia sepsis requires close supervision. In practice, clinical nursing staff have been tasked with caring for patients, observing the effects of treatment and preventing adverse reactions. However, nurses primarily perform these tasks by manual in RN model, which is burdensome and easily distracted by other tasks, thus missing subtle changes in the child’s condition and leading to misunderstandings between doctors, nurses and family members due to insufficient communication.

The IoT cloud computing platform is an Internet technology that has emerged in recent years. The IoT connect people and devices with a variety of infinite communication technologies to share and exchange real-time data with each other.⁷ Cloud computing, on the other hand, provides support for the establishment of the IoT in terms of application software, data storage and data processing.⁸ The combination of IoT and cloud computing has been taken seriously by the Chinese medical community. It has become a consensus in the Chinese medical community to apply the IoT cloud computing platform to various medical fields to achieve the innovation of medical, research and teaching models, so as to optimize the allocation of resources and improve the service to patients.⁹ Similarly, the IoT cloud computing platform has been integrated with several areas of nursing, resulting in a more efficient, more intelligent and more user-friendly IN model.^10,11 However, the application of IoT cloud-based IN to the monitoring and nursing of pediatric pneumonia sepsis treated with HGG has not been reported in China or abroad.

In summary, this study included 200 pediatric patients with pneumonia sepsis and applied a clinical randomized trial to investigate the value of this new IN model in the treatment of children pneumonia sepsis with HGG, and to provide theoretical and practical basis for the improvement and promotion of this nursing model.

Materials and methods

Clinical sample

A clinical randomized trial was conducted, and the trial was registered at chictr. org.cn (No. ChiCTR2100047685). According to the pre-defined criteria, 200 children with pneumonia and sepsis who were admitted to the First People’s Hospital of Shangqiu from January 1, 2020 to February 13, 2022 were continuously included. The inclusion criteria were as follows: (1) Age ≤8 years, both male and female. (2) Diagnosis of pneumonia and sepsis was confirmed according to the diagnostic criteria for childhood pneumonia issued by the Chinese Medical Association.¹² Briefly, the patient has clear evidence of pulmonary infection; clear signs of infection toxicity, such as abnormal body temperature (manifested by fever or failure to increase body temperature) and lethargy; signs such as altered respiratory status, skin blebs, jaundice, and hepatosplenomegaly. Laboratory tests suggest an increased or decreased white blood cell count, increased neutrophils with left shift, etc. The etiology test was positive. (3) Patient should be given standard anti-infective and other comprehensive treatment measures. (4) Adjunctive treatment with HGG at 200 to 300 mg/kg once daily for 7 days by intravenous drip. (5) No history of other serious congenital or acquired diseases. (6) Family members agree to participate in this study and informed consent forms were obtained from the legally authorized representative of the minor subject. Children who do not meet the above inclusion criteria will not be included. The study was obtained the approval from the ethics committee of Shangqiu first people’s hospital (Protocol Number 2020012).

All enrolled children were allocated to the RN group and IN group according to the randomization principle, with 100 children in each group. Children in RN group received only routine clinical care, while children in IN group received IN care based on the IoT cloud computing platform when receiving HGG adjuvant therapy, as shown in Figure 1.

Figure 1.

Flowchart of this study.

Nursing model

The RN model is the currently prevailing clinical model of nursing. In this model, the head nurse is the main leader and is in overall charge of all nursing work in her ward, under which there are quality control nurses, lead nurses who are responsible for specific areas of work, finally, the nurses on duty who carry out the specific work and manage the patients. The whole organization is “branched”. Instructions from the head nurse is issued step by step, and the feedback from the nurse on duty or the patient is also uploaded step by step. The nurses in each post are reactive in carrying out their duties, mainly based on the needs of the patient or treatment at the time. Before a child receives HGG treatment, the verification of drug information and patient information is done manually. During the course of treatment, the children’s condition is monitored through “manual rounds” and communication with the family is mainly through “face-to-face” information exchange. After treatment, the nurse on duty completes the nursing records manually.

IN model is a new nursing model based on the IoT cloud computing platform (Supplemental Figure 1). Wireless temperature monitoring systems (WTMS) and vital signs monitoring systems (VSMS) play an important role in this. Firstly, WTMS detect the patient’s temperature in real time by adhering radio frequency identification (RFID) sensor to the patient’s armpit or groin aorta. Collected data is reported to the IoT data collection engine via the platform and then transmitted to the VSMS via application programming interface (API), which then automatically generates an electronic temperature form and continuously monitors the patient’s temperature, reducing the number of manual temperature measurements, increasing patient rest time and improving the efficiency of nurses. Secondly, VSMS allows real-time monitoring of patient data such as heart rate, breathing rate, body movements, turning conditions and body temperature through the intelligent mattress and temperature monitoring system. It measures all aspects of the patient’s vital signs data, making it easy for nurses to keep track of the patient’s physical condition, upload abnormal data in a timely and effective manner, reduce the workload of medical staff and improve the efficiency of hospital bed nursing. In this model, the head nurse, the nurses in each position and all patients and families are able to exchange information in both directions through a wireless network of terminal devices, with a reticulated organizational structure. The head nurse is no longer just the person who gives orders and listens to reports, but has an overview of the work for all nurses and the treatment of all patients on the IN platform, and easily participate in all works. The nurses in each position are no longer “single-player”, they always are guided by their supervisors and always respond to them. Coordination between nurses is also extremely efficient. Children and their families are kept informed of all information about their treatment, costs. Before the HGG treatment is administered, the platform automatically checks the child’s information and medication, and educates the child and family about the need for the treatment, the precautions and the risks through the ward multimedia system. During treatment, the platform monitors the child’s vital signs and other conditions at all times and transmits real-time monitoring data to the nurse on duty. The head nurse, nurse on duty and the family also communicated information in real time to address any queries that arise. After treatment, the platform completed the nursing record according to a pre-defined template based on the data collected and submits it to the duty nurse for revision and confirmation, before sending it to the cloud for storage.

Data collection

Data were collected by a dedicated researcher according to a pre-written survey catalogue, which included the child’s gender, age, number of days in hospital, fever, antibiotic use, various serological findings, pulmonary function tests, immune function tests and adverse reactions to HGG. Fever includes temperature and number of days in hospital. Antibiotic use includes type, whether antibiotics were escalated, and duration of treatment. Serological tests include WBC, ALT and blood Cr. Pulmonary function tests include maximum FEV1, FVC and PEF. Immune function tests include percentage of CD3⁺ cells, CD4⁺ cells to CD8⁺ cells ratio. Adverse reactions to HGG include rash, pruritus, dry mouth and vomiting. Serology, pulmonary function and immune function tests include initial results within 24 h of admission (defined as pre-treatment) and repeat results before discharge (defined as post-treatment).

Data analysis

SPSS 23.0 statistical software was conducted for the data analysis. Normally distributed continuous variables were expressed as mean ± standard deviation and comparisons between two continuous variables were completed with independent samples t-tests. Categorical variables were expressed as frequencies, and differences between two categorical variables were assessed by chi-square tests. Multi-factor logistic regression analysis was applied to exclude potential confounders and further assess the correlation between IN and days in hospital, fever status, serological findings, pulmonary function test results, immune function test results and HGG adverse reactions to infer the effect of IN on HGG efficacy and safety. Potential confounders that were adjusted for in this process included gender, age, days in hospital, fever, antibiotics, serological markers, lung function, immunological markers, and adverse drug reactions. Independent sample t-tests report statistics as t-values, chi-square tests report statistics as χ² values, and multifactorial logistic regressions report statistics as ratio (OR) as well as 95% confidence intervals (95% CI). p-values are reported for all of the above statistical methods, and a p-value of less than .05 is defined as a significant difference or association.

Results

Basic information and clinical manifestations of children in two groups

There was no statistical difference between the IN group and the RN group in terms of gender ratio, age, duration of fever, duration of high fever, course of antibiotics, whether to change antibiotics and the use of penicillin, cephalosporins and carbapenems antibiotics (p > .05). However, the mean duration of hospitalization in IN group was significantly less than that in RN group (p = .014), as shown in Table 1.

Table 1.

Comparison of basic characteristics and clinical presentation of children in two groups.

Groups	Total (case)	Male/female (case)	Age (year)	Duration of hospitalization (day)	Duration of fever (day)	Duration of high fever (day)
IN group	100	56/44	5.32 ± 1.47	9.67 ± 2.89*	4.99 ± 1.71	1.69 ± 0.79
RN group	100	52/48	5.57 ± 1.62	10.68 ± 2.87	5.42 ± 1.55	1.83 ± 0.73
t/χ²	—	0.322	1.145	2.482	1.863	1.308
p	—	.570	.254	.014	.064	.193

分组	Course of antibiotics (day)	Change antibiotics (case)	Penicillin (case)	Cephalosporins (case)	Carbapenems (case)	Other antibiotics (case)
IN group	11.14 ± 2.97	30	38	58	24	10
RN group	11.92 ± 2.73	24	48	54	16	6
t/χ²	1.936	0.913	2.040	0.325	2.000	1.087
p	.054	.339	.153	.569	.157	.297

Note: *compared to RN group, p < .05.

Serological indicators between two groups of children

Before HGG treatment, the levels of WBC, ALT and Cr were not statistically different between the IN group and the RN group (p > .05). After treatment, all the above three indicators improved to different degrees. Moreover, WBC and Cr levels were significantly lower in the IN group than in RN group after treatment (p = .013, p = .001), while ALT levels remained not significantly different between the two groups (p > .05), as shown in Table 2.

Table 2.

Comparison of serological indicators between the two groups of children.

	Pre-treatment			Post-treatment
	WBC (10⁹/l)	ALT (U/l)	Cr (μmol/l)	WBC (10⁹/l)	ALT (U/l)	Cr (μmol/l)
IN group	16.12 ± 4.67	41.47 ± 17.99	77.04 ± 14.68	7.16 ± 2.60*	33.69 ± 18.02	59.08 ± 18.91*
RN group	16.77 ± 4.79	39.51 ± 17.46	75.39 ± 15.51	8.10 ± 2.68	36.94 ± 17.63	67.07 ± 15.57
t value	0.973	0.780	0.771	2.507	1.290	3.265
p value	.332	.436	.441	.013	.199	.001

Note: *compared to RN group, p < .05.

Pulmonary function between two groups of children

Before HGG treatment, there was no statistical difference in the levels of the three lung function indicators, FEV1, FVC and PEF, between the IN group and the RN group (p > .05). After treatment, all three of these indicators improved to varying degrees. Moreover, the levels of FEV1, FVC and PEF were significantly higher in the IN group than in RN group after treatment (p < .001, p < .001, p < .001), as shown in Table 3.

Table 3.

Comparison of pulmonary function indexes between the two groups.

	Pre-treatment			Post-treatment
	FEV1 (l)	FVC (l)	PEF (l/s)	FEV1 (l)	FVC (l)	PEF (l/s)
IN group	1.11 ± 0.07	1.37 ± 0.10	1.73 ± 0.18	1.74 ± 0.18*	2.31 ± 0.13*	3.36 ± 0.22*
RN group	1.12 ± 0.07	1.35 ± 0.11	1.69 ± 0.19	1.64 ± 0.19	2.23 ± 0.11	3.23 ± 0.20
t value	0.377	0.992	1.304	3.657	4.267	4.477
p value	.707	.323	.194	<.001	<.001	<.001

Note: *compared to RN group, p < .05.

Immune indicators between two groups of children

The levels of two immune indicators, CD3⁺ and CD4⁺/CD8⁺ ratio, were not statistically different between the IN group and the RN group before HGG treatment (p > .05). After treatment, both of these indicators improved to varying degrees. Moreover, the levels of CD3⁺ and CD4⁺/CD8⁺ ratios were significantly higher in the IN group than in the RN group after treatment (p = .026, p < .001), as shown in Table 4.

Table 4.

Comparison of immune indexes between the two groups of children.

	Pre-treatment		Post-treatment
	CD3⁺ (%)	CD4⁺/CD8⁺	CD3⁺ (%)	CD4⁺/CD8⁺
IN group	50.25 ± 2.80	0.85 ± 0.14	62.87 ± 4.52*	1.28 ± 0.15*
RN group	49.70 ± 2.79	0.86 ± 0.15	61.56 ± 3.68	1.21 ± 0.15
t value	1.386	0.782	2.237	3.439
p value	.167	.435	.026	.001

Note: *compared to RN group, p < .05.

Adverse reactions to HGG in two groups of children

The three adverse reactions associated with HGG treatment, pruritus, dry mouth and vomiting, were more common in the RN group than in the IN group (p = .030, p = .016, p = .010). In contrast, the occurrence of rash was not statistically different in the two groups mentioned above (p > .05), as shown in Table 5.

Table 5.

Comparison of adverse reactions of gamma globulin in two groups of children.

Groups	Total (case)	Rash (case)	Pruritus (case)	Dry mouth (case)	Vomiting (case)
IN group	100	1	2*	3*	2*
RN group	100	4	9	12	11
t/χ² value	—	1.846	4.714	5.838	6.664
p value	—	.174	.030	.016	.010

Note: *compared to RN group, p < .05.

Multi factor logistic regression of the correlation between IN and various clinical indicators in children with pneumonia

After excluding confounding factors such as gender, age, duration of hospitalization, fever, antibiotics, serological indicators, pulmonary function indicators, immune function indicators and adverse drug reactions, multifactorial logistic regression demonstrated that the implementation of IN was associated with short-term duration of hospitalization for children (OR = 0.700, 95% CI = 0.507 ∼ 0.965, p = .030), associated with lower post-treatment WBC and Cr levels (OR = 0.802, 95% CI = 0.695 ∼ 0.926, p = .003; OR = 0.766, 95% CI = 0.624 ∼ 0.939, p = .010) and associated with higher post-treatment pulmonary function FEV1, FVC and PEF levels (OR = 1.297, 95% CI = 1.054 ∼ 1.596, p = .014; OR = 1.649, 95% CI = 1.215 ∼ 2.237, p = .001; OR = 1.332, 95% CI = 1.108 ∼ 1.600, p = .002), associated with higher post-treatment levels of immune CD4⁺/CD8⁺ ratio (OR = 1.514, 95% CI = 1.175 ∼ 1.950, p = .001), and was related to reduced symptoms of vomiting in adverse reactions (OR = 0.156, 95% CI = 0.025 ∼ 0.974, p = .047), as shown in Table 6.

Table 6.

Multivariate logistic regression of the correlation between smart nursing care and various clinical indicators in children with pneumonia.

	B	SE	Wald χ²	p value	OR	95%CI
Duration of hospitalization	−0.357	0.164	4.737	.030	0.700	0.507 ∼ 0.965
Clinical symptoms
Duration of fever	0.451	0.287	2.476	.116	1.570	0.895 ∼ 2.754
Duration of high fever	−0.038	0.374	0.010	.919	0.963	0.462 ∼ 2.004
Serological indicators
Post-treatment WBC	−0.221	0.073	9.100	.003	0.802	0.695 ∼ 0.926
Post-treatment ALT	0.001	0.104	0.000	.991	1.001	0.817 ∼ 1.227
Post-treatment Cr	−0.267	0.104	6.571	.010	0.766	0.624 ∼ 0.939
Pulmonary function
Post-treatment FEV1	0.260	0.106	6.019	.014	1.297	1.054 ∼ 1.596
Post-treatment FVC	0.500	0.156	10.321	.001	1.649	1.215 ∼ 2.237
Post-treatment PEF	0.286	0.094	9.312	.002	1.332	1.108 ∼ 1.600
Immune function indicators
CD3⁺	0.040	0.046	0.743	.389	1.040	0.951 ∼ 1.138
CD4⁺/CD8⁺	0.415	0.129	10.310	.001	1.514	1.175 ∼ 1.950
Adverse reactions
Rash	−1.440	1.267	1.293	.255	0.237	0.020 ∼ 2.835
Pruritus	−1.770	0.983	3.242	.072	0.170	0.025 ∼ 1.170
Dry mouth	−0.721	0.790	0.831	.362	0.486	0.103 ∼ 2.290
Vomiting	−1.856	0.933	3.955	.047	0.156	0.025 ∼ 0.974

Note: Multivariate logistic regression was adjusted according to gender, age, length of hospitalization, fever, antibiotics, serological indicators, pulmonary function indicators, immune indicators and adverse drug reactions.

Discussion

It is generally considered that nursing is always in a supportive and subordinate position compared to medical work. The nurse’s primary role is to carry out medical orders accurately, to care for the patient and to assist the doctor in treating the disease.¹³ However, with the advancement of technology and the renewal of medical philosophy, the biomedical model, which is solely focused on the treatment of diseases, is being replaced by the “biopsychosocial” model, which focuses more on the patient’s treatment experience, the quality of life (QOL) and the psychological health of the patient.¹⁴ Nurses have more access to patients than doctors. In the new medical model, nursing naturally plays a greater and more individual role. The IN model based on the IoT cloud computing platform has developed in line with this trend. The application of new technologies allows nursing to be supported by vast amounts of network resources and break the boundaries of space and time to achieve real-time, all-round and full-process care and attention to patients, thus strengthening the psychological and social functions of nursing.

At present, China’s healthcare system suffers from a number of problems, the most prominent of which is the inadequate and uneven distribution of total healthcare resources. This has led directly to an influx of patients into large medical institutions, overwhelming the latter.¹⁵ Another important review affirmed IoT as a new technology, which help us improve health care services during the COVID-19 pandemic, and looked forward to its application in chronic disease management.¹⁶ As mentioned earlier, nurses are the most overburdened positions as they directly face patients. The IN model based on the IoT cloud computing platform facilitates the maximum deployment of nursing resources, and through real-time data exchange and sharing, enables each nurse on duty to have all the information related to his or her work as quickly as possible and to do his or her job with the highest efficiency and best results, thus alleviating the pressure faced by the healthcare system to a certain extent,¹⁷ which is another important contribution of the IN model.

Although this technological platform is promising, its use and dissemination still requires gradual experience. Although the cloud computing environment is considered as a potential Internet-based computing platform, the security concerns encountered are notable. Identifying these challenges is the first step to tackle them.¹⁸ In present study, we investigated the value of IN model in the treatment of childhood pneumonia sepsis with HGG. The results indicated that the application of the IN model could significantly improve the therapeutic effect of HGG. Specifically, after balancing the confounding factors between the two groups, children receiving IN had a shorter duration of hospitalization and improved serology, lung function and immune function. The IN model also reduced some of the adverse reaction of HGG, improving safety and patient experience. Future studies could gradually apply the IN model to more treatments and even directly to patients throughout their hospitalization and even at follow-up.

A series of similar studies have been conducted by other research teams in China. One study explored the application of an IoT-based ward-based IN system in the management and care of patients in respiratory critical care, and showed that this model of care could significantly improve the efficiency and safety of care and treatment, increase patient satisfaction, and give optimized recommendations for clinical treatment.¹⁹ Wang’s research applied a similar IN infusion system to manage drug infusions for all patients on the ward, resulting in a significant reduction in infusion disputes, while got an increase in nursing satisfaction.²⁰ Liu Jingwen applied the Internet-hospital-community-home IN model to the nursing of diabetic foot patients at home, and demonstrated that the IN model effectively improved the ability of diabetic foot patients to manage themselves at home, facilitated blood glucose control, prevented new foot ulcers and effectively improved QOL of patients.²¹ Above studies confirm the positive effects achieved by the integration of Internet technology and nursing care from different perspectives and are consistent with the results of this study. Methodologically, a randomization method based on a random number table was conducted to assign the subject children into two study groups, which helped to balance the distribution of characteristics between the two groups and reduce the influence of confounding factors on the results. Multi-factor logistic regression was then applied to remove all known confounding factors and produce more reliable results.

In this study, the researcher made every effort to enlarge the sample size and therefore did not make any pre-calculation or validation of the sample size. Due to various objective conditions such as financial constraints, the number of subjects selected was still small, which is also a limitation of this study. This may reduce the representativeness of the sample and the validity of the statistical analysis to varying degrees. Hence, we look forward to conducting similar studies with larger sample sizes in the future. We also look forward to conducting studies on the application of IoT cloud computing platform-based IN in other clinical work, so as to provide more clinical reality for the promotion of this model of nursing in the future.

Conclusion

In conclusion, the IN model based on the IoT cloud computing platform significantly improve the efficacy of HGG in the treatment of childhood pneumonia sepsis and reduce the incidence of some adverse effects. The promotion of IN model to a wider nursing field will surely make nursing work more efficient and humanized, and ultimately benefit patients.

Supplemental Material

Supplemental Material - Application of intelligent nursing based on cloud computing of internet of things in children with pneumonia and sepsis treated with human gamma globulin

Supplemental Material for Application of intelligent nursing based on cloud computing of internet of things in children with pneumonia and sepsis treated with human gamma globulin by Aihua Qin, Yuling Liu, Changming Shao, and Hongyan Dong in European Journal of Inflammation.

Footnotes

Author contributions

We declare that this work was done by the authors named in this article and all liabilities pertaining to claims relating to the content of this article will be borne by the authors. Aihua Qin and Yuling Li conceived and designed the study; Changming Shao, Hongyan Dong and Aihua Qin collected and analyzed the data, while Aihua Qin wrote the manuscript which was approved by all authors.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported by the Henan Province Higher Vocational School Young Key Teacher Training Program Project (Grant No. 2020GZGG120) and Henan Province Higher Education Teaching Reform Research and Practice Project (Grant No. 2021SJGLX813).

Ethical approval

Ethical approval for this study was obtained from the ethics committee of Shangqiu first people’s hospital (Approval Number/2020012).

Informed consent

Written informed consent were obtained from the legally authorized representative of the minor subject before the study.

Trial registration

The trial was registered at chictr.org.cn (No. ChiCTR2100047685).

ORCID iD

Aihua Qin

Supplemental Material

Supplemental material for this article is available online.

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