Selecting candidate predictor variables for the modelling of post-discharge mortality from sepsis: a protocol development project

Derivation and Internal Validation of a Novel Risk Assessment Tool to Identify Infants and Young Children at Risk for Post-Discharge Mortality in Dar es Salaam, Tanzania and Monrovia, Liberia

OBJECTIVE

To derive and validate internally a novel risk assessment tool to identify young children at risk for all-cause mortality ≤60 days of discharge from hospitals in sub-Saharan Africa.

STUDY DESIGN

We performed a prospective observational cohort study of children aged 1-59 months discharged from Muhimbili National Hospital in Dar es Salaam, Tanzania and John F. Kennedy Medical Center in Monrovia, Liberia (2019-2022). Caregivers received telephone calls up to 60 days after discharge to ascertain participant vital status. We collected socioeconomic, demographic, clinical, and anthropometric data during hospitalization. Candidate variables with P < .20 in bivariate analyses were included in a multivariable logistic regression model with best subset selection to identify risk factors for the outcome. We internally validated our tool using bootstrapping with 500 repetitions.

RESULTS

There were 1933 young children enrolled in the study. The median (IQR) age was 11 (4, 23) months and 58.7% were males. In total, 67 (3.5%) died during follow-up. Ten variables contributed to our tool (total possible score 82). Cancer (aOR 10.6, 95% CI 2.58, 34.6), pedal edema (aOR 6.94, 95% CI 1.69, 22.6), and leaving against medical advice (aOR 6.46, 95% CI 2.46, 15.3) were most predictive of post-discharge mortality. Our risk assessment tool demonstrated good discriminatory value (optimism corrected area under the receiver operating characteristic curve 0.77), high precision, and sufficient calibration.

CONCLUSIONS

After validation, this tool may be used to identify young children at risk for post-discharge mortality to direct resources for follow-up of high-risk children.

Prediction models for post-discharge mortality among under-five children with suspected sepsis in Uganda: A multicohort analysis

In many low-income countries, over five percent of hospitalized children die following hospital discharge. The lack of available tools to identify those at risk of post-discharge mortality has limited the ability to make progress towards improving outcomes. We aimed to develop algorithms designed to predict post-discharge mortality among children admitted with suspected sepsis. Four prospective cohort studies of children in two age groups (0-6 and 6-60 months) were conducted between 2012-2021 in six Ugandan hospitals. Prediction models were derived for six-months post-discharge mortality, based on candidate predictors collected at admission, each with a maximum of eight variables, and internally validated using 10-fold cross-validation. 8,810 children were enrolled: 470 (5.3%) died in hospital; 257 (7.7%) and 233 (4.8%) post-discharge deaths occurred in the 0-6-month and 6-60-month age groups, respectively. The primary models had an area under the receiver operating characteristic curve (AUROC) of 0.77 (95%CI 0.74-0.80) for 0-6-month-olds and 0.75 (95%CI 0.72-0.79) for 6-60-month-olds; mean AUROCs among the 10 cross-validation folds were 0.75 and 0.73, respectively. Calibration across risk strata was good: Brier scores were 0.07 and 0.04, respectively. The most important variables included anthropometry and oxygen saturation. Additional variables included: illness duration, jaundice-age interaction, and a bulging fontanelle among 0-6-month-olds; and prior admissions, coma score, temperature, age-respiratory rate interaction, and HIV status among 6-60-month-olds. Simple prediction models at admission with suspected sepsis can identify children at risk of post-discharge mortality. Further external validation is recommended for different contexts. Models can be digitally integrated into existing processes to improve peri-discharge care as children transition from the hospital to the community.

Identifying neonates at risk for post-discharge mortality in Dar es Salaam, Tanzania, and Monrovia, Liberia: Derivation and internal validation of a novel risk assessment tool

INTRODUCTION

The immediate period after hospital discharge carries a large burden of childhood mortality in sub-Saharan Africa. Our objective was to derive and internally validate a risk assessment tool to identify neonates discharged from the neonatal ward at risk for 60-day post-discharge mortality.

METHODS

We conducted a prospective observational cohort study of neonates discharged from Muhimbili National Hospital in Dar es Salaam, Tanzania, and John F Kennedy Medical Centre in Monrovia, Liberia. Research staff called caregivers to ascertain vital status up to 60 days after discharge. We conducted multivariable logistic regression analyses with best subset selection to identify socioeconomic, demographic, clinical, and anthropometric factors associated with post-discharge mortality. We used adjusted log coefficients to assign points to each variable and internally validated our tool with bootstrap validation with 500 repetitions.

RESULTS

There were 2344 neonates discharged and 2310 (98.5%) had post-discharge outcomes available. The median (IQR) age at discharge was 8 (4, 15) days; 1238 (53.6%) were male. In total, 71 (3.1%) died during follow-up (26.8% within 7 days of discharge). Leaving against medical advice (adjusted OR [aOR] 5.62, 95% CI 2.40 to 12.10) and diagnosis of meconium aspiration (aOR 6.98, 95% CI 1.69 to 21.70) conferred the greatest risk for post-discharge mortality. The risk assessment tool included nine variables (total possible score=63) and had an optimism corrected area under the receiver operating characteristic curve of 0.77 (95% CI 0.75 to 0.80). A score of ≥6 was most optimal (sensitivity 68.3% [95% CI 64.8% to 71.5%], specificity 72.1% [95% CI 71.5% to 72.7%]).

CONCLUSIONS

A small number of factors predicted all-cause, 60-day mortality after discharge from neonatal wards in Tanzania and Liberia. After external validation, this risk assessment tool may facilitate clinical decision making for eligibility for discharge and the direction of resources to follow-up high risk neonates.

A Prospective, observational cohort study to identify neonates and children at risk of postdischarge mortality in Dar es Salaam, Tanzania and Monrovia, Liberia: the PPDM study protocol

INTRODUCTION

Over half of the 5 million annual deaths among children aged 0-59 months occur in sub-Saharan Africa. The period immediately after hospitalisation is a vulnerable time in the life of a child in sub-Saharan Africa as postdischarge mortality rates are as high as 1%-18%. Identification of neonates and children who are at highest risk for postdischarge mortality may allow for the direction of interventions to target patients at highest risk.

METHODS AND ANALYSIS

The Predicting Post-Discharge Mortality study is a prospective, observational study being conducted at Muhimbili National Hospital (Dar es Salaam, Tanzania) and John F. Kennedy Medical Center (Monrovia, Liberia). The aim is to derive and validate two, age population specific, clinical prediction rules for the identification of neonates (n=2000) and children aged 1-59 months (n=2000) at risk for all-cause mortality within 60 days of discharge from the neonatal intensive care unit or paediatric ward. Caregivers of participants will receive phone calls 7, 14, 30, 45 and 60 days after discharge to assess vital status. Candidate predictor variables will include demographic, anthropometric and clinical factors. Elastic net regression will be used to derive the clinical prediction rules. Bootstrapped selection with repetitions will be used for internal validation. Planned secondary analyses include the external validation of existing clinical prediction models, determination of clinicians' ability to identify neonates and children at risk of postdischarge mortality at discharge, analysis of factors associated with hospital readmission and unplanned clinic visits and description of health-seeking behaviours in the postdischarge period.

ETHICS AND DISSEMINATION

This study received ethical clearance from the Tanzania National Institute of Medical Research, Muhimbili University of Health and Allied Sciences, the John F. Kennedy Medical Center Institutional Review Board, and the Boston Children's Hospital Institutional Review Board. Findings will be disseminated at scientific conferences and as peer-reviewed publications.

Common data elements for predictors of pediatric sepsis: A framework to standardize data collection

Background

Standardized collection of predictors of pediatric sepsis has enormous potential to increase data compatibility across research studies. The Pediatric Sepsis Predictor Standardization Working Group collaborated to define common data elements for pediatric sepsis predictors at the point of triage to serve as a standardized framework for data collection in resource-limited settings.

Methods

A preliminary list of pediatric sepsis predictor variables was compiled through a systematic literature review and examination of global guideline documents. A 5-round modified Delphi that involved independent voting and active group discussions was conducted to select, standardize, and prioritize predictors. Considerations included the perceived predictive value of the candidate predictor at the point of triage, intra- and inter-rater measurement reliability, and the amount of time and material resources required to reliably collect the predictor in resource-limited settings.

Results

We generated 116 common data elements for implementation in future studies. Each common data element includes a standardized prompt, suggested response values, and prioritization as tier 1 (essential), tier 2 (important), or tier 3 (exploratory). Branching logic was added to the predictors list to facilitate the design of efficient data collection methods, such as low-cost electronic case report forms on a mobile application. The set of common data elements are freely available on the Pediatric Sepsis CoLab Dataverse and a web-based feedback survey is available through the Pediatric Sepsis CoLab. Updated iterations will continuously be released based on feedback from the pediatric sepsis research community and emergence of new information.

Conclusion

Routine use of the common data elements in future studies can allow data sharing between studies and contribute to development of powerful risk prediction algorithms. These algorithms may then be used to support clinical decision making at triage in resource-limited settings. Continued collaboration, engagement, and feedback from the pediatric sepsis research community will be important to ensure the common data elements remain applicable across a broad range of geographical and sociocultural settings.

The PRECISE (PREgnancy Care Integrating translational Science, Everywhere) Network's first protocol: deep phenotyping in three sub-Saharan African countries

BACKGROUND

The PRECISE (PREgnancy Care Integrating translational Science, Everywhere) Network is a new and broadly-based group of research scientists and health advocates based in the UK, Africa and North America.

METHODS

This paper describes the protocol that underpins the clinical research activity of the Network, so that the investigators, and broader global health community, can have access to 'deep phenotyping' (social determinants of health, demographic and clinical parameters, placental biology and agnostic discovery biology) of women as they advance through pregnancy to the end of the puerperium, whether those pregnancies have normal outcomes or are complicated by one/more of the placental disorders of pregnancy (pregnancy hypertension, fetal growth restriction and stillbirth). Our clinical sites are in The Gambia (Farafenni), Kenya (Kilifi County), and Mozambique (Maputo Province). In each country, 50 non-pregnant women of reproductive age will be recruited each month for 1 year, to provide a final national sample size of 600; these women will provide culturally-, ethnically-, seasonally- and spatially-relevant control data with which to compare women with normal and complicated pregnancies. Between the three countries we will recruit ≈10,000 unselected pregnant women over 2 years. An estimated 1500 women will experience one/more placental complications over the same epoch. Importantly, as we will have accurate gestational age dating using the TraCer device, we will be able to discriminate between fetal growth restriction and preterm birth. Recruitment and follow-up will be primarily facility-based and will include women booking for antenatal care, subsequent visits in the third trimester, at time-of-disease, when relevant, during/immediately after birth and 6 weeks after birth.

CONCLUSIONS

To accelerate progress towards the women's and children's health-relevant Sustainable Development Goals, we need to understand how a variety of social, chronic disease, biomarker and pregnancy-specific determinants health interact to result in either a resilient or a compromised pregnancy for either mother or fetus/newborn, or both. This protocol has been designed to create such a depth of understanding. We are seeking funding to maintain the cohort to better understand the implications of pregnancy complications for both maternal and child health.

Development and validation of models to predict respiratory function in persons with long-term spinal cord injury

STUDY DESIGN

Multicenter, cross-sectional study.

OBJECTIVES

To validate previously developed respiratory function prediction models for persons with long-term spinal cord injury (SCI) and if necessary develop and validate new models.

SETTING

Ten SCI rehabilitation centers.

METHODS

Five respiratory function parameters were measured in adults with chronic, traumatic, motor complete SCI (C4-T12). First, the models published in 2012 were validated using Bland-Altman plots. Then, new models were calculated using 80% of the dataset by multiple regression analysis with the candidate predictors gender, age, height, weight, time post injury (TPI), lesion level, and smoking. In a third step, the new models were validated using the other 20% of the dataset by Bland-Altman plots.

RESULTS

In total 613 participants were included. For persons with long-term SCI, the 2012 models were poorly predictive, especially for respiratory muscle strength (R2 = 0.4). Significant predictors for all respiratory function parameters in the new models (R2 = 0.7-0.8) were lesion level, gender and weight. Small effects on single outcome parameters were observed for TPI and age whereas smoking had no effect. For the new models the mean differences between measured and predicted values for respiratory muscle strength were 4.0 ± 36.0 cm H₂O and for lung function parameters -0.5 ± 1.2 L (FVC), -0.3 ± 0.9 L (FEV1) and -0.5 ± 2.0 L/s (PEF).

CONCLUSION

We did not find better models for lung function in long-term SCI but those for respiratory muscle strength showed better accuracy.

SPONSORSHIP

The content of this publication was developed under grant from Wings for Life, grant number WFL-CH-017/14.

Determining predictors of sepsis at triage among children under 5 years of age in resource-limited settings: A modified Delphi process

Sepsis is a life-threatening dysfunction of the immune system leading to multiorgan failure that is precipitated by infectious diseases and is a leading cause of death in children under 5 years of age. It is necessary to be able to identify a sick child at risk of developing sepsis at the earliest point of presentation to a healthcare facility so that appropriate care can be provided as soon as possible. Our study objective was to generate a list of consensus-driven predictor variables for the derivation of a prediction model that will be incorporated into a mobile device and operated by low-skilled healthcare workers at triage. By conducting a systematic literature review and examination of global guideline documents, a list of 72 initial candidate predictor variables was generated. A two-round modified Delphi process involving 26 experts from both resource-rich and resource-limited settings, who were also encouraged to suggest new variables, yielded a final list of 45 predictor variables after evaluating each variable based on three domains: predictive potential, measurement reliability, and level of training and resources required. The final list of predictor variables will be used to collect data and contribute to the derivation of a prediction model.

Predictor variables for post-discharge mortality modelling in infants: a protocol development project

BACKGROUND

Over two-thirds of the five million annual deaths in children under five occur in infants, mostly in developing countries and many after hospital discharge. However, there is a lack of understanding of which children are at higher risk based on early clinical predictors. Early identification of vulnerable infants at high-risk for death post-discharge is important in order to craft interventional programs.

OBJECTIVES

To determine potential predictor variables for post-discharge mortality in infants less than one year of age who are likely to die after discharge from health facilities in the developing world.

METHODS

A two-round modified Delphi process was conducted, wherein a panel of experts evaluated variables selected from a systematic literature review. Variables were evaluated based on (1) predictive value, (2) measurement reliability, (3) availability, and (4) applicability in low-resource settings.

RESULTS

In the first round, 18 experts evaluated 37 candidate variables and suggested 26 additional variables. Twenty-seven variables derived from those suggested in the first round were evaluated by 17 experts during the second round. A final total of 55 candidate variables were retained.

CONCLUSION

A systematic approach yielded 55 candidate predictor variables to use in devising predictive models for post-discharge mortality in infants in a low-resource setting.

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

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.