Which Factors Predict Hospital Length-of-Stay for Children Admitted to the Neonatal Intensive Care Unit and Pediatric Ward? A Hospital-Based Prospective Study

Global Pediatric Health, Volume 7, Year 2020

Interpretation

Background. The ability to accurately predict hospital length of stay (LOS) or time to discharge could aid in resource planning, stimulate quality improvement activities, and provide evidence for future research and medical practice. This study aimed to determine the predictive factors of time to discharge among patients admitted to the neonatal intensive care unit (NICU) and pediatric ward in Goba referral hospital, Ethiopia. Methods. A facility-based prospective follow up study was conducted for 8 months among 438 patients. Survival analyses were carried out using the Kaplan Meier statistics and Cox regression model. Results. The median length of hospital stay was 7 days (95% confidence interval (CI): 6.45-7.54) and 6 days (95% CI: 5.21-6.78) for patients admitted to NICU and pediatric ward, respectively. In the multivariable Cox regression, the hazard of neonatal patients with less than 37 weeks of gestational age, low birth weight, and those who develop hospital-acquired infection (HAI) after admission had prolonged time to discharge by 54% [adjusted hazard ratio (AHR): 0.46, (95% CI: 0.31-0.66)], 40% [AHR: 0.60, (95% CI: 0.40-0.90)], and 56% [AHR: 0.44, (95% CI: 0.26-0.74)], respectively. The rate of time to discharge among patients who were admitted to the pediatric ward and had HAI delayed discharge time by 49% [AHR: 0.51, (95% CI: 0.30-0.85)] compared to their counterparts. Conclusion. Hospital-acquired infections prolonged hospital stay among neonates and children admitted to the pediatric ward. On a similar note, low gestational age and low birth weight were found to be the independent predictor of longer hospital stay among neonates. The study was conducted at Madda Walabu University Goba Referral Hospital in southeast Ethiopia. It is the only referral hospital in the Bale zone serving over 1.5 million people. It has 20 inpatient units with a total capacity of 127 beds and is also the referral center for advance diagnostic procedures and management of pediatrics. The annual average admission of the hospital is over 8000 patients, of which 869 were in the pediatrics and NICU wards. And the average annual outpatient patient flow is over 110 661 patients. The average bed occupancy rate of the hospital was 66.2% and the average length of stay (ALOS) was 3.6 days. The present study was conducted at the department of pediatric taking care of pediatric and neonatal patients. A prospective follow up study was conducted among pediatric patients who were admitted to the NICU and pediatric ward from November 2018, to June 2019. Accordingly, all children admitted to the NICU and pediatric ward during the specified period were eligible for the study and followed from the time of admission until discharge. All patients who are admitted to the NICU and pediatric ward in Goba referral hospital were the source and study population. A total of 438 admitted neonates and pediatrics were included in the current study after consent was obtained. All patients (age less than 18 years) admitted to the pediatric ward, neonatal intensive care unit (NICU) and those transferred from outside hospitals were enrolled. All patients whose parent/guardian consented for the study were eligible. Patients were excluded if they: (1) died prior to NICU and pediatric ward admission; (2) had a major congenital anomaly. Data were collected prospectively after consent was sought from all pediatric parents or legal guardians. Socio-demographic and clinical data were collected by the structured questioner. First, all admitted patients were followed for the first 48 hours and patients who have developed any form of hospital-acquired infection (HAIs) after 48 hours of admission were recorded following the Center for Disease Prevention and Control (CDC) guideline.21 Afterwards, all pediatric patients were followed until for outcomes such as hospital discharge, improvement at the time of discharge, death, referral, longer duration of follow up and discharge without medical advice was recorded. The event of interest was time to discharge or hospital length of stay of children admitted to the NICU and pediatric ward. The length of stay was measured using days from the time of admission until the time of death, transfer, left against medical advice or the end of the study period. Survival times of children who died during their hospital stay, those transferred to other healthcare facilities, or left against medical advice were considered censored times. Days after the transfer to a pediatric ward or another center were not included. The independent variables were divided into 2 categories. The first category consisted of socio-demographic characteristics; age (months), gestational age, birth weight, gender, place of residence, history of the previous hospitalization. The second category consisted of clinically related factors: patients put on mechanical ventilation, presence of peripheral intravenous (IV) catheter, presence of central venous catheter (CVC), McCabe score, surgery after admission (surgery while in hospital), severe anemia status, presence of underlying diseases, HIV status, and presence of HAIs. Healthcare acquired infections (HAIs): HAIs can be defined as those occurring within 48 hours of hospital admission or 30 days of an operation. Newborns who had an infection in the first 48 hrs of life should be considered to have Early-Onset Sepsis (EOS) and not HAIs; because we just enrolled neonates presenting with no new signs or symptoms of infection after the first 48 hours of admission. In addition, EOS reflects transplacental or, more frequently, ascending infections from the maternal genital tract, whereas Late-Onset Sepsis (LOS) is associated with the postnatal nosocomial or community environment. Low birth weight: any neonate weighting less than 2500 g at birth irrespective of gestational age. Presence of underling disease: indicates patients with the following underlying conditions severe acute malnutrition (SAM), diabetes mellitus, chronic renal failure, and cardiac disorder. Presence of invasive device: references to intubation, presence of urinary catheter, peripheral vascular catheter or central vascular catheter. Central venous catheter (CVC): is a catheter placed into a large vein/ inserted in a central vein/. Peripheral intravenous (IV) catheter: A peripheral intravenous (IV) catheter is inserted into small peripheral veins to provide access to administer IV fluids and medications. The collected data were checked for completeness and then entered into EpiData version 3.1 and exported to SPSS version 20. The survival data can summarize through life tables, Kaplan-Meier Survival functions, and median time. Accordingly, data for NICU and the pediatrics were analyzed separately. Descriptive analyses were carried out to present the given data. Kaplan-Meier survival curves were generated and the log-rank analysis was used to compare hospital length of stays between subcategories. Cox regression analysis was performed to assess the predictive factors for hospital discharge status. Crude hazard ratios (CHR) and adjusted hazard ratios (AHR), with 95% confidence intervals (CIs) were used to assess the strength of association. To select the potential variables for the multivariable Cox regression model, variables associated with P-value ≤ .25 at bivariate regression were considered. Backward stepwise procedures were employed (these procedures deleting one variable at a time as the regression model progresses). The Log-likelihood (LL) value was used to remove factors from the model in a backward fashion. The model with the highest value of LL was considered to be the best fit model. Finally, model adequacy was assessed for the variables remained in the multivariate model using Schoenfeld residuals plots and tests of proportional hazard. Statistically significant variables (P-values < .05) in the multivariate analysis of Cox-regression were considered predictors of LOS among the study participants. Ethical clearance was obtained from the Ethical Review Committee of Madda Walabu University (Ref. No.: RMW 14/66/64) and a formal letter from Madda Walabu University Research Community Engagement and Technology Transfer Vice President Office was obtained. Written informed consent from the parents/legal guardians was obtained after explaining the objectives of the study. Throughout the data collection period, confidentiality and privacy of the patients were observed, and a unique identification code rather than their names were substituted.

AI Digest

Study Justification:

The ability to accurately predict hospital length of stay (LOS) for children admitted to the neonatal intensive care unit (NICU) and pediatric ward is important for resource planning, quality improvement, and future research. This study aimed to identify the factors that predict time to discharge among patients in Goba referral hospital, Ethiopia.

Highlights:

– The study was conducted at Madda Walabu University Goba Referral Hospital, the only referral hospital in the Bale zone serving over 1.5 million people.
– The study included 438 patients admitted to the NICU and pediatric ward over an 8-month period.
– The median length of hospital stay was 7 days for NICU patients and 6 days for pediatric ward patients.
– Factors such as gestational age, birth weight, and hospital-acquired infections were found to prolong the time to discharge.
– Low gestational age and low birth weight were identified as independent predictors of longer hospital stay among neonates.

Recommendations:

– Implement measures to prevent hospital-acquired infections among neonates and children in the pediatric ward.
– Provide specialized care and support for neonates with low gestational age and low birth weight to optimize their health outcomes and reduce hospital stay.

Key Role Players:

– Hospital administrators and management: Responsible for implementing infection prevention measures and allocating resources for specialized care.
– Healthcare providers: Involved in implementing infection control protocols, providing specialized care, and monitoring the health of neonates and children.
– Public health officials: Provide guidance and support in implementing infection prevention strategies and monitoring the overall health of the population.

Cost Items for Planning Recommendations:

– Infection prevention measures: Budget for training healthcare staff, providing necessary equipment and supplies, and implementing protocols for infection control.
– Specialized care for neonates: Allocate resources for specialized equipment, medications, and healthcare professionals with expertise in neonatal care.
– Monitoring and surveillance: Budget for regular monitoring of infection rates, health outcomes, and length of stay to assess the effectiveness of interventions and make necessary adjustments.
Please note that the provided cost items are general suggestions and may vary depending on the specific context and resources available in Goba referral hospital.

Strength of Evidence

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is rated 7 because the study provides specific details about the methodology, sample size, and statistical analysis. However, there are some areas where the evidence could be improved. Firstly, the abstract does not mention the specific results of the study, such as the hazard ratios and confidence intervals for each predictive factor. Including this information would provide more concrete evidence. Secondly, the abstract does not mention any limitations of the study, which is important for evaluating the strength of the evidence. Adding a brief discussion of the study’s limitations would improve the overall rating of the evidence.

Abstract

The study was conducted at Madda Walabu University Goba Referral Hospital in southeast Ethiopia. It is the only referral hospital in the Bale zone serving over 1.5 million people. It has 20 inpatient units with a total capacity of 127 beds and is also the referral center for advance diagnostic procedures and management of pediatrics. The annual average admission of the hospital is over 8000 patients, of which 869 were in the pediatrics and NICU wards. And the average annual outpatient patient flow is over 110 661 patients. The average bed occupancy rate of the hospital was 66.2% and the average length of stay (ALOS) was 3.6 days. The present study was conducted at the department of pediatric taking care of pediatric and neonatal patients. A prospective follow up study was conducted among pediatric patients who were admitted to the NICU and pediatric ward from November 2018, to June 2019. Accordingly, all children admitted to the NICU and pediatric ward during the specified period were eligible for the study and followed from the time of admission until discharge. All patients who are admitted to the NICU and pediatric ward in Goba referral hospital were the source and study population. A total of 438 admitted neonates and pediatrics were included in the current study after consent was obtained. All patients (age less than 18 years) admitted to the pediatric ward, neonatal intensive care unit (NICU) and those transferred from outside hospitals were enrolled. All patients whose parent/guardian consented for the study were eligible. Patients were excluded if they: (1) died prior to NICU and pediatric ward admission; (2) had a major congenital anomaly. Data were collected prospectively after consent was sought from all pediatric parents or legal guardians. Socio-demographic and clinical data were collected by the structured questioner. First, all admitted patients were followed for the first 48 hours and patients who have developed any form of hospital-acquired infection (HAIs) after 48 hours of admission were recorded following the Center for Disease Prevention and Control (CDC) guideline.21 Afterwards, all pediatric patients were followed until for outcomes such as hospital discharge, improvement at the time of discharge, death, referral, longer duration of follow up and discharge without medical advice was recorded. The event of interest was time to discharge or hospital length of stay of children admitted to the NICU and pediatric ward. The length of stay was measured using days from the time of admission until the time of death, transfer, left against medical advice or the end of the study period. Survival times of children who died during their hospital stay, those transferred to other healthcare facilities, or left against medical advice were considered censored times. Days after the transfer to a pediatric ward or another center were not included. The independent variables were divided into 2 categories. The first category consisted of socio-demographic characteristics; age (months), gestational age, birth weight, gender, place of residence, history of the previous hospitalization. The second category consisted of clinically related factors: patients put on mechanical ventilation, presence of peripheral intravenous (IV) catheter, presence of central venous catheter (CVC), McCabe score, surgery after admission (surgery while in hospital), severe anemia status, presence of underlying diseases, HIV status, and presence of HAIs. Healthcare acquired infections (HAIs): HAIs can be defined as those occurring within 48 hours of hospital admission or 30 days of an operation. Newborns who had an infection in the first 48 hrs of life should be considered to have Early-Onset Sepsis (EOS) and not HAIs; because we just enrolled neonates presenting with no new signs or symptoms of infection after the first 48 hours of admission. In addition, EOS reflects transplacental or, more frequently, ascending infections from the maternal genital tract, whereas Late-Onset Sepsis (LOS) is associated with the postnatal nosocomial or community environment. Low birth weight: any neonate weighting less than 2500 g at birth irrespective of gestational age. Presence of underling disease: indicates patients with the following underlying conditions severe acute malnutrition (SAM), diabetes mellitus, chronic renal failure, and cardiac disorder. Presence of invasive device: references to intubation, presence of urinary catheter, peripheral vascular catheter or central vascular catheter. Central venous catheter (CVC): is a catheter placed into a large vein/ inserted in a central vein/. Peripheral intravenous (IV) catheter: A peripheral intravenous (IV) catheter is inserted into small peripheral veins to provide access to administer IV fluids and medications. The collected data were checked for completeness and then entered into EpiData version 3.1 and exported to SPSS version 20. The survival data can summarize through life tables, Kaplan-Meier Survival functions, and median time. Accordingly, data for NICU and the pediatrics were analyzed separately. Descriptive analyses were carried out to present the given data. Kaplan-Meier survival curves were generated and the log-rank analysis was used to compare hospital length of stays between subcategories. Cox regression analysis was performed to assess the predictive factors for hospital discharge status. Crude hazard ratios (CHR) and adjusted hazard ratios (AHR), with 95% confidence intervals (CIs) were used to assess the strength of association. To select the potential variables for the multivariable Cox regression model, variables associated with P-value ≤ .25 at bivariate regression were considered. Backward stepwise procedures were employed (these procedures deleting one variable at a time as the regression model progresses). The Log-likelihood (LL) value was used to remove factors from the model in a backward fashion. The model with the highest value of LL was considered to be the best fit model. Finally, model adequacy was assessed for the variables remained in the multivariate model using Schoenfeld residuals plots and tests of proportional hazard. Statistically significant variables (P-values < .05) in the multivariate analysis of Cox-regression were considered predictors of LOS among the study participants. Ethical clearance was obtained from the Ethical Review Committee of Madda Walabu University (Ref. No.: RMW 14/66/64) and a formal letter from Madda Walabu University Research Community Engagement and Technology Transfer Vice President Office was obtained. Written informed consent from the parents/legal guardians was obtained after explaining the objectives of the study. Throughout the data collection period, confidentiality and privacy of the patients were observed, and a unique identification code rather than their names were substituted.

Materials

N/A

Innovations

Based on the provided information, here are some potential innovations that could improve access to maternal health:

1. Telemedicine: Implementing telemedicine services can allow pregnant women in remote or underserved areas to access prenatal care and consultations with healthcare providers through video calls or online platforms. This can help overcome geographical barriers and improve access to maternal health services.

2. Mobile health (mHealth) applications: Developing mobile applications that provide information and resources related to maternal health can empower pregnant women to take control of their own health. These apps can provide educational content, appointment reminders, medication reminders, and access to teleconsultations with healthcare providers.

3. Community health workers: Training and deploying community health workers who can provide basic maternal health services, such as antenatal care, postnatal care, and health education, can improve access to care in rural or underserved areas. These workers can also serve as a bridge between the community and healthcare facilities, ensuring that pregnant women receive the necessary care and referrals.

4. Transportation services: Establishing transportation services, such as ambulances or mobile clinics, can help overcome transportation barriers and ensure that pregnant women can reach healthcare facilities in a timely manner. This is particularly important in areas with limited transportation infrastructure.

5. Maternal health clinics: Setting up dedicated maternal health clinics within healthcare facilities can streamline care and ensure that pregnant women receive specialized services. These clinics can provide comprehensive antenatal care, postnatal care, family planning services, and counseling.

6. Maternal health awareness campaigns: Conducting targeted awareness campaigns to educate communities about the importance of maternal health and the available services can help increase utilization of maternal health services. These campaigns can include community meetings, radio broadcasts, and distribution of informational materials.

It is important to note that the specific context and needs of the community should be taken into consideration when implementing these innovations.

AI Innovations Description

Based on the description provided, the study aimed to determine the predictive factors of time to discharge among patients admitted to the neonatal intensive care unit (NICU) and pediatric ward in Goba referral hospital, Ethiopia. The study found that factors such as gestational age, birth weight, and hospital-acquired infections (HAIs) were associated with prolonged time to discharge. Neonatal patients with less than 37 weeks of gestational age, low birth weight, and those who developed HAIs after admission had a longer hospital stay. Additionally, pediatric patients who were admitted to the pediatric ward and had HAIs also experienced delayed discharge compared to their counterparts.

Based on these findings, a recommendation to improve access to maternal health could be to implement strategies to prevent and manage HAIs in neonates and children. This could include improving infection control practices, providing adequate training to healthcare providers, and ensuring the availability of necessary resources and equipment for infection prevention and management. Additionally, efforts should be made to identify and address risk factors such as low gestational age and low birth weight, which contribute to longer hospital stays. This could involve implementing interventions to improve prenatal care, promote healthy pregnancies, and provide appropriate medical interventions for high-risk pregnancies.

By addressing these factors, healthcare facilities can potentially reduce the length of hospital stays for neonates and children, improving access to maternal health services and freeing up resources for other patients in need.

AI Innovations Methodology

Based on the provided information, here are some potential recommendations to improve access to maternal health:

1. Strengthening healthcare infrastructure: Investing in the expansion and improvement of healthcare facilities, particularly in rural areas, can help increase access to maternal health services. This includes building more hospitals, clinics, and maternity centers, as well as ensuring they are adequately equipped and staffed.

2. Mobile health (mHealth) interventions: Utilizing mobile technology to deliver maternal health information and services can help overcome barriers to access, especially in remote areas. This can include mobile apps for prenatal care, SMS reminders for antenatal appointments, and telemedicine consultations for remote monitoring.

3. Community-based interventions: Implementing community-based programs that focus on maternal health education, awareness, and support can help improve access. This can involve training community health workers to provide basic maternal health services, conducting outreach programs, and organizing support groups for pregnant women.

4. Transportation support: Lack of transportation is a significant barrier to accessing maternal health services, especially in rural areas. Providing transportation support, such as ambulances or vouchers for transportation, can help ensure that pregnant women can reach healthcare facilities in a timely manner.

To simulate the impact of these recommendations on improving access to maternal health, a methodology could include the following steps:

1. Define the target population: Determine the specific population group that will be the focus of the simulation, such as pregnant women in a particular region or healthcare facility.

2. Collect baseline data: Gather relevant data on the current state of maternal health access, including factors such as distance to healthcare facilities, availability of services, and utilization rates.

3. Develop a simulation model: Create a mathematical or computational model that represents the current system of maternal health access. This model should incorporate various factors that affect access, such as distance, transportation, healthcare infrastructure, and socio-economic factors.

4. Introduce the recommendations: Modify the simulation model to incorporate the proposed recommendations, such as increasing healthcare infrastructure, implementing mHealth interventions, or providing transportation support. Adjust the relevant parameters in the model to reflect the expected impact of these interventions.

5. Run the simulation: Use the modified model to simulate the impact of the recommendations on access to maternal health. This can involve running multiple scenarios with different combinations of interventions and comparing the outcomes.

6. Analyze the results: Evaluate the simulation results to assess the potential impact of the recommendations on improving access to maternal health. This can include measuring changes in utilization rates, reduction in travel time, increase in service availability, and other relevant indicators.

7. Refine and iterate: Based on the simulation results, refine the recommendations and the simulation model as needed. Repeat the simulation process to further optimize the interventions and assess their potential long-term impact.

It’s important to note that the accuracy and reliability of the simulation results depend on the quality of the data used and the assumptions made in the model. Therefore, it is crucial to ensure that the data used is accurate and representative of the target population, and that the model is validated and verified against real-world data where possible.

Author

Dima Health

Statistics:

Citations: 7

Identifiers:

DOI: 10.1177/2333794X20968715

Research Areas:

Community Interventions, Environmental, Food Security, Health System and Policy, Infectious Diseases, Maternal Access, Maternal and Child Health, Noncommunicable Diseases, Quality of Care, Sexual and Reproductive Health, Social Determinants, Technology and Innovations

Study Countries:

Ethiopia

Study Design:

Cohort Study, Cross Sectional Study, Grounded Theory

Study Approach:

Quantitative