Postdischarge mortality in children with acute infectious diseases: Derivation of postdischarge mortality prediction models

BMJ Open, Volume 5, No. 11, Year 2015

Interpretation

Objectives: To derive a model of paediatric postdischarge mortality following acute infectious illness. Design: Prospective cohort study. Setting: 2 hospitals in South-western Uganda. Participants: 1307 children of 6 months to 5 years of age were admitted with a proven or suspected infection. 1242 children were discharged alive and followed up 6 months following discharge. The 6-month follow-up rate was 98.3%. Interventions: None. Primary and secondary outcome measures: The primary outcome was postdischarge mortality within 6 months following the initial hospital discharge. Results: 64 children died during admission (5.0%) and 61 died within 6 months of discharge (4.9%). Of those who died following discharge, 31 (51%) occurred within the first 30 days. The final adjusted model for the prediction of postdischarge mortality included the variables mid-upper arm circumference (OR 0.95, 95% CI 0.94 to 0.97, per 1 mm increase), time since last hospitalisation (OR 0.76, 95% CI 0.61 to 0.93, for each increased period of no hospitalisation), oxygen saturation (OR 0.96, 95% CI 0.93 to 0·99, per 1% increase), abnormal Blantyre Coma Scale score (OR 2.39, 95% CI 1·18 to 4.83), and HIV-positive status (OR 2.98, 95% CI 1.36 to 6.53). This model produced a receiver operating characteristic curve with an area under the curve of 0.82. With sensitivity of 80%, our model had a specificity of 66%. Approximately 35% of children would be identified as high risk (11.1% mortality risk) and the remaining would be classified as low risk (1.4% mortality risk), in a similar cohort. Conclusions: Mortality following discharge is a poorly recognised contributor to child mortality. Identification of at-risk children is critical in developing postdischarge interventions. A simple prediction tool that uses 5 easily collected variables can be used to identify children at high risk of death after discharge. Improved discharge planning and care could be provided for high-risk children. Mbarara, a city of approximately 195 000, is the largest city in the South-western region of Uganda. This study was conducted at two hospitals in Mbarara. The Mbarara Regional Referral Hospital (MRRH) is the main referral hospital in South-western Uganda. It is a public hospital funded by the Uganda Ministry of Health. MRRH is associated with the Mbarara University of Science and Technology, and is a primary training site for its healthcare graduates. The paediatric ward admits approximately 5000 patients per year. The Holy Innocents Children’s Hospital (HICH) is a faith-based children’s hospital offering subsidised fee-for-service for outpatient and in-patient care in Mbarara. The HICH admits approximately 2500 patients per year. This was a prospective observational study conducted between March 2012 and December 2013. This study was approved by the institutional review boards at the University of British Columbia (Canada) and the Mbarara University of Science and Technology (Uganda), as well as the Uganda National Council for Science and Technology and Office of the President. This study was voluntary and written informed consent was provided by a parent or guardian of all children who were enrolled. All children aged 6 months to 5 years who were admitted with a proven or suspected infection were eligible for enrolment. The upper age limit was chosen to coincide with the under- 5 target group of the millennium development goals. The lower age limit was chosen for logistic (census enrolment with limited research staff) and statistical considerations (group homogeneity). Participants already enrolled in the study were not eligible to be enrolled during subsequent admissions. Following enrolment, a research nurse obtained and recorded clinical signs including a 1 min respiratory rate, blood pressure (automated), axillary temperature, Blantyre Coma Scale (BCS) score, and by using the Phone Oximeter,4 the 1 min photoplethysmogram (PPG), blood oxygen saturation (SpO2) and heart rate. Anthropometric data (height, weight, mid-upper arm circumference (MUAC)) were also measured and recorded. Age-dependent demographic variables collected at enrolment were converted to age-corrected z-scores according to the WHO Child Growth Standards.5 The age-corrected heart rate and respiratory rate z-scores were obtained by standardising the raw measurements using the median and SD values provided by Fleming et al.6 The age-corrected z-scores for systolic blood pressure were calculated using participants’ height, according to the procedures previously described.7 A blood sample was taken for measurement of haemoglobin, HIV and a malaria blood smear (microscopy). HIV status was determined using the national rapid diagnostic test serial algorithm.8 All positive tests on the Determine Antibody Test were confirmed by a separate test (UniGold). Children under 12 months of age with a positive test were confirmed using PCR. Haemoglobin was measured on a Beckman Coulter Ac.T Hematology Analyzer. An interview was conducted with the participant’s parent/guardian and information about previous admissions, distance from health facility, transportation costs, bed-net use, maternal education, maternal age, maternal HIV status, history of sibling deaths and drinking water safety were elicited. Participants received routine care during their hospital stay and were discharged at the discretion of the treating medical team. The discharge status of all enrolled participants was recorded as death, referral, discharged alive, and discharged against medical advice. The diagnoses made by the medical team were also recorded. On discharge, families with active telephone lines were contacted at months 2 and 4 to determine the vital status of the child. Families with no telephone access received in-person follow-up by a field officer. At approximately 6 months following discharge, all participants received in-person follow-up. In addition to postdischarge vital status, health seeking and rehospitalisations since the initial discharge were also recorded. Study data were collected and managed using REDCap electronic data capture tools hosted at the Child and Family Research Institute, Vancouver, Canada.9 REDCap (Research Electronic Data Capture) is a secure, web-based application designed to support data capture for research studies and provides: (1) an intuitive interface for validated data entry; (2) audit trails for tracking data manipulation and export procedures; (3) automated export procedures for seamless data downloads to common statistical packages and (4) procedures for importing data from external sources. Candidate predictor variables were derived using a two-round modified Delphi approach. Briefly, 23 experts in relevant disciplines were solicited to complete an online survey and provide feedback on an initial list of proposed predictors. Predictors were evaluated on considerations of utility as predictors, availability, cost and resource-related applicability. Experts were asked to provide additional potential variables which were then evaluated during a second round of surveys. Data was evaluated by the research team and a final list of candidate predictor variables for modelling was then determined.10 The primary outcome was postdischarge mortality at any time during the 6-month postdischarge period. For the derivation of prediction models, standard calculations of sample size do not apply since these calculations do not account for the model development process (ie, selection of variables and the optimisation to achieve specified sensitivity and specificity cut-offs). For this study, we determined the sample size needed to validate the derived model and plan to use an equal number of patients for the derivation phase. For the validation study, assuming that the derived model achieves a sensitivity of 85% with at least 50% specificity, 100 events, corresponding to a total sample of approximately 1000 live-discharges (assuming a postdischarge mortality rate of 10%), would be needed to obtain 80% power for ensuring that the lower 95% confidence limit on sensitivity will be at least 75%. Since resources are scarce, a higher sensitivity at the expense of specificity would further limit practical application of such a model. An interim analysis of the study showed that the postdischarge mortality rate would likely not exceed 5% and enrolment was stopped when 1307 participants were enrolled. All variables were assessed using univariate logistic regression to determine their level of association with the primary outcome. Continuous variables were assessed for model fit using the Hosmer-Lemeshow test.11 Missing data was imputed by the method of multivariate imputation using chained equations.12 Following univariate analysis, candidate models were generated using a stepwise selection procedure minimising Akaike’s Information Criterion (AIC). This method is considered asymptotically equivalent to cross-validation and bootstrapping.13 14 All models generated in this sequence having AIC values within 10% of the lowest value were considered as reasonable candidates. The final selection of a model was judged on model parsimony (the simpler the better), availability of the predictors (with respect to minimal resources and cost), and the attained sensitivity (with at least 50% specificity). All analyses were conducted using SAS V.9.3 (Carey, North Carolina, USA) and R 3.1.3 (Vienna, Austria; http://www.R-project.org). Additional models were created using the above process but with the absence of key variables used in deriving the primary model, including a model not including any variables likely to change over the course of admission. This was done to increase application in a variety of settings were certain variables may not be available.

AI Digest

Study Justification:

– Mortality following discharge is an often overlooked contributor to child mortality.
– Identifying at-risk children is crucial for developing postdischarge interventions.
– A simple prediction tool using easily collected variables can help identify high-risk children.

Study Highlights:

– Prospective cohort study conducted in two hospitals in South-western Uganda.
– 1307 children aged 6 months to 5 years with proven or suspected infection were enrolled.
– 64 children died during admission (5.0%) and 61 died within 6 months of discharge (4.9%).
– The final prediction model included variables such as mid-upper arm circumference, time since last hospitalization, oxygen saturation, abnormal Blantyre Coma Scale score, and HIV-positive status.
– The model had a sensitivity of 80% and a specificity of 66% in identifying high-risk children.
– Approximately 35% of children were classified as high risk (11.1% mortality risk) and the rest as low risk (1.4% mortality risk).

Recommendations for Lay Reader and Policy Maker:

– Postdischarge mortality in children is a significant issue that needs attention.
– Improved discharge planning and care should be provided for high-risk children.
– The use of a simple prediction tool can help identify children at high risk of death after discharge.

Key Role Players:

– Healthcare professionals: doctors, nurses, and other medical staff involved in discharge planning and care.
– Hospital administrators: responsible for implementing changes in discharge protocols and resource allocation.
– Policy makers: government officials and policymakers who can support and fund interventions for postdischarge care.

Cost Items for Planning Recommendations:

– Training and education: budget for training healthcare professionals on improved discharge planning and care.
– Equipment and supplies: budget for necessary equipment and supplies to support postdischarge interventions.
– Staffing: budget for additional staff or reallocation of existing staff to provide adequate care for high-risk children.
– Monitoring and evaluation: budget for monitoring and evaluating the effectiveness of postdischarge interventions.
– Communication and outreach: budget for communication strategies to raise awareness among healthcare professionals and caregivers about the importance of postdischarge care.

Strength of Evidence

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is based on a prospective cohort study conducted at two hospitals in South-western Uganda. The study included a large number of participants (1307 children) and had a high follow-up rate (98.3%). The study used a rigorous methodology, including the derivation of a prediction model for postdischarge mortality. The model had a good performance with an area under the curve of 0.82. However, the evidence could be strengthened by including information on the representativeness of the study population and the generalizability of the findings to other settings. Additionally, the study could benefit from a validation phase to assess the performance of the prediction model in an independent sample. To improve the evidence, future studies could consider these suggestions and provide more details on the study population and the external validity of the findings.

Abstract

Mbarara, a city of approximately 195 000, is the largest city in the South-western region of Uganda. This study was conducted at two hospitals in Mbarara. The Mbarara Regional Referral Hospital (MRRH) is the main referral hospital in South-western Uganda. It is a public hospital funded by the Uganda Ministry of Health. MRRH is associated with the Mbarara University of Science and Technology, and is a primary training site for its healthcare graduates. The paediatric ward admits approximately 5000 patients per year. The Holy Innocents Children’s Hospital (HICH) is a faith-based children’s hospital offering subsidised fee-for-service for outpatient and in-patient care in Mbarara. The HICH admits approximately 2500 patients per year. This was a prospective observational study conducted between March 2012 and December 2013. This study was approved by the institutional review boards at the University of British Columbia (Canada) and the Mbarara University of Science and Technology (Uganda), as well as the Uganda National Council for Science and Technology and Office of the President. This study was voluntary and written informed consent was provided by a parent or guardian of all children who were enrolled. All children aged 6 months to 5 years who were admitted with a proven or suspected infection were eligible for enrolment. The upper age limit was chosen to coincide with the under- 5 target group of the millennium development goals. The lower age limit was chosen for logistic (census enrolment with limited research staff) and statistical considerations (group homogeneity). Participants already enrolled in the study were not eligible to be enrolled during subsequent admissions. Following enrolment, a research nurse obtained and recorded clinical signs including a 1 min respiratory rate, blood pressure (automated), axillary temperature, Blantyre Coma Scale (BCS) score, and by using the Phone Oximeter,4 the 1 min photoplethysmogram (PPG), blood oxygen saturation (SpO2) and heart rate. Anthropometric data (height, weight, mid-upper arm circumference (MUAC)) were also measured and recorded. Age-dependent demographic variables collected at enrolment were converted to age-corrected z-scores according to the WHO Child Growth Standards.5 The age-corrected heart rate and respiratory rate z-scores were obtained by standardising the raw measurements using the median and SD values provided by Fleming et al.6 The age-corrected z-scores for systolic blood pressure were calculated using participants’ height, according to the procedures previously described.7 A blood sample was taken for measurement of haemoglobin, HIV and a malaria blood smear (microscopy). HIV status was determined using the national rapid diagnostic test serial algorithm.8 All positive tests on the Determine Antibody Test were confirmed by a separate test (UniGold). Children under 12 months of age with a positive test were confirmed using PCR. Haemoglobin was measured on a Beckman Coulter Ac.T Hematology Analyzer. An interview was conducted with the participant’s parent/guardian and information about previous admissions, distance from health facility, transportation costs, bed-net use, maternal education, maternal age, maternal HIV status, history of sibling deaths and drinking water safety were elicited. Participants received routine care during their hospital stay and were discharged at the discretion of the treating medical team. The discharge status of all enrolled participants was recorded as death, referral, discharged alive, and discharged against medical advice. The diagnoses made by the medical team were also recorded. On discharge, families with active telephone lines were contacted at months 2 and 4 to determine the vital status of the child. Families with no telephone access received in-person follow-up by a field officer. At approximately 6 months following discharge, all participants received in-person follow-up. In addition to postdischarge vital status, health seeking and rehospitalisations since the initial discharge were also recorded. Study data were collected and managed using REDCap electronic data capture tools hosted at the Child and Family Research Institute, Vancouver, Canada.9 REDCap (Research Electronic Data Capture) is a secure, web-based application designed to support data capture for research studies and provides: (1) an intuitive interface for validated data entry; (2) audit trails for tracking data manipulation and export procedures; (3) automated export procedures for seamless data downloads to common statistical packages and (4) procedures for importing data from external sources. Candidate predictor variables were derived using a two-round modified Delphi approach. Briefly, 23 experts in relevant disciplines were solicited to complete an online survey and provide feedback on an initial list of proposed predictors. Predictors were evaluated on considerations of utility as predictors, availability, cost and resource-related applicability. Experts were asked to provide additional potential variables which were then evaluated during a second round of surveys. Data was evaluated by the research team and a final list of candidate predictor variables for modelling was then determined.10 The primary outcome was postdischarge mortality at any time during the 6-month postdischarge period. For the derivation of prediction models, standard calculations of sample size do not apply since these calculations do not account for the model development process (ie, selection of variables and the optimisation to achieve specified sensitivity and specificity cut-offs). For this study, we determined the sample size needed to validate the derived model and plan to use an equal number of patients for the derivation phase. For the validation study, assuming that the derived model achieves a sensitivity of 85% with at least 50% specificity, 100 events, corresponding to a total sample of approximately 1000 live-discharges (assuming a postdischarge mortality rate of 10%), would be needed to obtain 80% power for ensuring that the lower 95% confidence limit on sensitivity will be at least 75%. Since resources are scarce, a higher sensitivity at the expense of specificity would further limit practical application of such a model. An interim analysis of the study showed that the postdischarge mortality rate would likely not exceed 5% and enrolment was stopped when 1307 participants were enrolled. All variables were assessed using univariate logistic regression to determine their level of association with the primary outcome. Continuous variables were assessed for model fit using the Hosmer-Lemeshow test.11 Missing data was imputed by the method of multivariate imputation using chained equations.12 Following univariate analysis, candidate models were generated using a stepwise selection procedure minimising Akaike’s Information Criterion (AIC). This method is considered asymptotically equivalent to cross-validation and bootstrapping.13 14 All models generated in this sequence having AIC values within 10% of the lowest value were considered as reasonable candidates. The final selection of a model was judged on model parsimony (the simpler the better), availability of the predictors (with respect to minimal resources and cost), and the attained sensitivity (with at least 50% specificity). All analyses were conducted using SAS V.9.3 (Carey, North Carolina, USA) and R 3.1.3 (Vienna, Austria; http://www.R-project.org). Additional models were created using the above process but with the absence of key variables used in deriving the primary model, including a model not including any variables likely to change over the course of admission. This was done to increase application in a variety of settings were certain variables may not be available.

Materials

N/A

Innovations

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

1. Mobile health (mHealth) applications: Develop mobile applications that provide information and resources related to maternal health, such as prenatal care, nutrition, and postpartum care. These apps can be easily accessed by pregnant women and new mothers, even in remote areas with limited healthcare facilities.

2. Telemedicine: Implement telemedicine programs that allow pregnant women to consult with healthcare providers remotely. This can help overcome geographical barriers and provide access to specialized care for high-risk pregnancies.

3. Community health workers: Train and deploy community health workers to provide basic maternal healthcare services in underserved areas. These workers can educate women about prenatal care, assist with deliveries, and provide postpartum support.

4. Transportation solutions: Improve transportation infrastructure and services to ensure that pregnant women can easily access healthcare facilities. This could include providing affordable transportation options or implementing ambulance services in remote areas.

5. Maternal health clinics: Establish dedicated maternal health clinics in areas with high maternal mortality rates. These clinics can provide comprehensive prenatal care, delivery services, and postpartum support, ensuring that women receive the care they need throughout their pregnancy journey.

6. Maternal health education programs: Develop and implement educational programs that focus on maternal health and empower women to make informed decisions about their healthcare. These programs can cover topics such as family planning, nutrition, and the importance of prenatal care.

7. Partnerships with local organizations: Collaborate with local organizations, such as non-profits or community groups, to improve access to maternal health services. These partnerships can help leverage existing resources and knowledge to reach more women in need.

8. Maternal health financing schemes: Implement innovative financing schemes, such as microinsurance or community-based health financing, to make maternal healthcare more affordable and accessible for low-income women.

9. Maternal health monitoring systems: Develop systems to track and monitor maternal health indicators, such as maternal mortality rates and access to prenatal care. This data can help identify areas with the greatest need for intervention and guide resource allocation.

10. Maternal health awareness campaigns: Launch public awareness campaigns to educate communities about the importance of maternal health and encourage women to seek timely care. These campaigns can use various media channels, including radio, television, and social media, to reach a wide audience.

AI Innovations Description

The recommendation to improve access to maternal health based on the study mentioned is to develop a postdischarge mortality prediction model for children with acute infectious diseases. This model can be used to identify children at high risk of death after discharge and provide improved discharge planning and care for them. The model includes five easily collected variables: mid-upper arm circumference, time since last hospitalization, oxygen saturation, abnormal Blantyre Coma Scale score, and HIV-positive status. By using these variables, approximately 35% of children can be identified as high risk with an 11.1% mortality risk, while the remaining children can be classified as low risk with a 1.4% mortality risk. This prediction tool can help in developing postdischarge interventions and ultimately improve access to maternal health.

AI Innovations Methodology

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

1. Implement mobile health (mHealth) solutions: Develop mobile applications or text messaging services to provide pregnant women with information about prenatal care, nutrition, and postpartum care. These tools can also be used to send reminders for appointments and medication adherence.

2. Expand telemedicine services: Establish telemedicine programs that allow pregnant women in remote areas to consult with healthcare providers through video conferencing. This can help overcome geographical barriers and provide access to specialized care.

3. Strengthen community health worker programs: Train and deploy community health workers to provide maternal health education, antenatal care, and postnatal care in underserved areas. These workers can also facilitate referrals to higher-level healthcare facilities when necessary.

4. Improve transportation infrastructure: Invest in transportation infrastructure to ensure that pregnant women can easily access healthcare facilities. This may involve building or improving roads, providing transportation subsidies, or implementing emergency transportation services.

5. Enhance health facility capacity: Increase the number of healthcare providers, improve infrastructure, and ensure the availability of essential medical supplies and equipment in healthcare facilities. This will help meet the increased demand for maternal health services.

To simulate the impact of these recommendations on improving access to maternal health, you can follow these steps:

1. Define the baseline scenario: Gather data on the current state of maternal health access, including the number of healthcare facilities, healthcare providers, transportation options, and utilization rates. This will serve as the baseline against which the impact of the recommendations will be measured.

2. Identify key indicators: Determine the key indicators that will be used to measure the impact of the recommendations. These may include the number of pregnant women receiving prenatal care, the number of facility-based deliveries, the distance traveled to access healthcare, and maternal mortality rates.

3. Develop a simulation model: Build a simulation model that incorporates the baseline data and the potential impact of the recommendations. This model should consider factors such as population demographics, geographical distribution, and resource availability.

4. Input the recommendations: Introduce the recommendations into the simulation model and adjust the relevant parameters accordingly. For example, increase the number of healthcare facilities, healthcare providers, or transportation options based on the recommendations.

5. Run the simulation: Execute the simulation model to project the potential impact of the recommendations on the identified key indicators. This will provide estimates of the expected changes in access to maternal health services.

6. Analyze the results: Analyze the simulation results to assess the effectiveness of the recommendations in improving access to maternal health. Compare the projected values of the key indicators with the baseline scenario to determine the magnitude of the impact.

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

By following this methodology, you can simulate the impact of various recommendations on improving access to maternal health and make informed decisions on which interventions to prioritize for implementation.

Author

Dima Health

Statistics:

Citations: 40

Identifiers:

DOI: 10.1136/bmjopen-2015-009449

Research Areas:

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

Study Countries:

Uganda

Study Design:

Cohort Study, Cross Sectional Study

Study Approach:

Mixed-methods, Qualitative, Quantitative