External validation of the RISC, RISC-Malawi, and PERCH clinical prediction rules to identify risk of death in children hospitalized with pneumonia

Predicting paediatric pneumonia severity in the emergency department: a multinational prospective cohort study of the Pediatric Emergency Research Network

BACKGROUND

Risk stratification tools for paediatric community-acquired pneumonia (CAP) in well-resourced settings are scarce. We prospectively developed models to predict CAP severity within a multinational cohort of paediatric emergency departments (EDs). Our primary objective was to develop a risk prediction model to discriminate between mild CAP and moderate or severe CAP to assist clinicians in determining the need for hospitalisation.

METHODS

This prospective cohort study was conducted from Feb 6, 2019, to June 30, 2021, at 73 EDs in 14 countries. Children aged 3 months to <14 years with clinical diagnoses of CAP were included. Children were excluded if they were recently hospitalised or had a chronic complex condition (eg, immunodeficiency). The primary outcome was severity, defined as mild (CAP treated in the outpatient setting or hospitalisation <24 h with no use of oxygen or intravenous fluids during that time), moderate (hospitalisation <24 h with oxygen or fluids, or hospitalisation ≥24 h regardless of interventions but without an outcome qualifying as severe CAP), or severe (chest drainage, intensive care unit admission >24 h, positive-pressure ventilation, septic shock, vasoactive infusions, extracorporeal membrane oxygenation, or death) occurring within 7 days of the ED visit. Models were developed using logistic regression with bootstrap validation.

FINDINGS

Of 2222 children in the overall study population (1103 [49·7%] female, 1119 [50·3%] male; median age 3 years [IQR 1-5]), 1290 (58·1%) had mild CAP, 812 (36·5%) moderate, and 120 (5·4%) severe. Primary analyses were performed in 1901 patients with complete data: 1011 (53·2%) mild, 772 (40·6%) moderate, and 118 (6·2%) severe CAP. Congestion or rhinorrhoea was negatively associated with moderate or severe CAP (adjusted odds ratio 0·59 [95% CI 0·46-0·76]), while abdominal pain (1·52 [1·17-1·97]), refusal to drink (1·57 [1·24-2·00]), antibiotics before ED visit (1·64 [1·29-2·10]), chest retractions (2·86 [2·24-3·65]), respiratory rate above the 95th percentile for age (1·63 [1·29-2·06]), heart rate above the 95th percentile for age (1·64 [1·27-2·12]), and hypoxaemia (oxygen saturation 90-92%, 3·24 [2·46-4·27]; <90%, 13·39 [8·64-20·73]) were positively associated. The model accurately discriminated between mild CAP and moderate or severe CAP (c-statistic 0·82 [95% CI 0·80-0·84]). Similar results were found in those with radiographic CAP, with decreased breath sounds and multifocal opacities on radiography as additional predictors (c-statistic 0·82 [0·80-0·85]).

INTERPRETATION

We developed accurate, pragmatic severity risk prediction models among children with CAP. After future external validation, these models have the potential to provide individualised risk assessments that can be incorporated into clinical judgement in well-resourced health systems to improve management.

FUNDING

Division of Emergency Medicine at Cincinnati Children's Hospital Medical Center, Division of Emergency Medicine at Ann & Robert H. Lurie Children's Hospital of Chicago, and Department of Emergency Medicine at University of California, Davis.

Prediction of inpatient mortality in hospitalised children in low- and middle-income countries: An external validation of paediatric mortality risk scores

Background

Risk prediction tools for acutely ill children have been developed in high- and low-income settings, but few are validated or incorporated into clinical guidelines. We aimed to assess the performance of existing paediatric early warning scores for use in low- and middle-income countries using clinical data from a recent large multi-country study in Africa and South-Asia.

Methods

We used data (children across three nutritional strata) from the Childhood Acute Illness and Nutrition (CHAIN) Network cohort study (n = 3101). We assessed 10 scores where similar predictor variables were available in the CHAIN cohort. We evaluated performance using the area under the receiver operating curve (AUC) (primary outcome), sensitivity, specificity, positive and negative predictive value, and positive and negative likelihood ratio (secondary outcomes).

Results

Most scores showed poor discrimination, and all scores had low sensitivity. The paediatric early death index for Africa (AUC = 0.80; 95% confidence interval (CI) = 0.77-0.83), respiratory index of severity in children (AUC = 0.77; 95% CI = 0.74-0.81), and respiratory index of severity in children in Malawi (AUC = 0.78; 95% CI = 0.75-0.82) showed acceptable/good overall discrimination. Among children without wasting, most scores had acceptable/good performance, some even excellent. Poor discrimination was found for most scores among children with moderate and severe wasting or kwashiorkor.

Conclusions

All scores demonstrated lower validation performance than originally reported. Among children without wasting, most risk prediction scores performed acceptably whilst in malnourished children they performed poorly. There is a need for a malnutrition specific score. Further research is needed on specific actions in responding to scores. Integration into future guidelines will require acknowledging staffing, resources and workflows.

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.

Community-Acquired Pneumonia in Children

ABSTRACT

Community-acquired pneumonia (CAP) is the most common cause of childhood mortality globally. In the United States, CAP is a leading cause of pediatric hospitalization and antibiotic use and is associated with substantial morbidity. There has been a dramatic shift in microbiological etiologies for CAP in children over time as pneumococcal pneumonia has become less common and viral etiologies have become predominant. There is no commonly agreed on approach to the diagnosis of CAP in children. When indicated, antimicrobial treatment should consist of narrow-spectrum antibiotics. In this article, we will describe the current understanding of the microbiological etiologies, clinical presentation, diagnostic approach, risk factors, treatment, and future directions in the diagnosis and management of pediatric CAP.

Pediatric prognostic models predicting inhospital child mortality in resource-limited settings: An external validation study

Background and Aims

Prognostic models provide evidence-based predictions and estimates of future outcomes, facilitating decision-making, patient care, and research. A few of these models have been externally validated, leading to uncertain reliability and generalizability. This study aims to externally validate four models to assess their transferability and usefulness in clinical practice. The models include the respiratory index of severity in children (RISC)-Malawi model and three other models by Lowlavaar et al.

Methods

The study used data from the Clinical Information Network (CIN) to validate the four models where the primary outcome was in-hospital mortality. 163,329 patients met eligibility criteria. Missing data were imputed, and the logistic function was used to compute predicted risk of in-hospital mortality. Models' discriminatory ability and calibration were determined using area under the curve (AUC), calibration slope, and intercept.

Results

The RISC-Malawi model had 50,669 pneumonia patients who met the eligibility criteria, of which the case-fatality ratio was 4406 (8.7%). Its AUC was 0.77 (95% CI: 0.77-0.78), whereas the calibration slope was 1.04 (95% CI: 1.00 -1.06), and calibration intercept was 0.81 (95% CI: 0.77-0.84). Regarding the external validation of Lowlavaar et al. models, 10,782 eligible patients  were included, with an in-hospital mortality rate of 5.3%. The primary model's AUC was 0.75 (95% CI: 0.72-0.77), the calibration slope was 0.78 (95% CI: 0.71-0.84), and the calibration intercept was 0.37 (95% CI: 0.28-0.46). All models markedly underestimated the risk of mortality.

Conclusion

All externally validated models exhibited either underestimation or overestimation of the risk as judged from calibration statistics. Hence, applying these models with confidence in settings other than their original development context may not be advisable. Our findings strongly suggest the need for recalibrating these model to enhance their generalizability.

Prognostic accuracy of biomarkers of immune and endothelial activation in Mozambican children hospitalized with pneumonia

Pneumonia is a leading cause of child mortality. However, currently we lack simple, objective, and accurate risk-stratification tools for pediatric pneumonia. Here we test the hypothesis that measuring biomarkers of immune and endothelial activation in children with pneumonia may facilitate the identification of those at risk of death. We recruited children <10 years old fulfilling WHO criteria for pneumonia and admitted to the Manhiça District Hospital (Mozambique) from 2010 to 2014. We measured plasma levels of IL-6, IL-8, Angpt-2, sTREM-1, sFlt-1, sTNFR1, PCT, and CRP at admission, and assessed their prognostic accuracy for in-hospital, 28-day, and 90-day mortality. Healthy community controls, within same age strata and location, were also assessed. All biomarkers were significantly elevated in 472 pneumonia cases versus 80 controls (p<0.001). IL-8, sFlt-1, and sTREM-1 were associated with in-hospital mortality (p<0.001) and showed the best discrimination with AUROCs of 0.877 (95% CI: 0.782 to 0.972), 0.832 (95% CI: 0.729 to 0.935) and 0.822 (95% CI: 0.735 to 0.908), respectively. Their performance was superior to CRP, PCT, oxygen saturation, and clinical severity scores. IL-8, sFlt-1, and sTREM-1 remained good predictors of 28-day and 90-day mortality. These findings suggest that measuring IL-8, sFlt-1, or sTREM-1 at hospital presentation can guide risk-stratification of children with pneumonia, which could enable prioritized care to improve survival and resource allocation.

Assembling a global database of child pneumonia studies to inform WHO pneumonia management algorithm: Methodology and applications

BACKGROUND

The existing World Health Organization (WHO) pneumonia case management guidelines rely on clinical symptoms and signs for identifying, classifying, and treating pneumonia in children up to 5 years old. We aimed to collate an individual patient-level data set from large, high-quality pre-existing studies on pneumonia in children to identify a set of signs and symptoms with greater validity in the diagnosis, prognosis, and possible treatment of childhood pneumonia for the improvement of current pneumonia case management guidelines.

METHODS

Using data from a published systematic review and expert knowledge, we identified studies meeting our eligibility criteria and invited investigators to share individual-level patient data. We collected data on demographic information, general medical history, and current illness episode, including history, clinical presentation, chest radiograph findings when available, treatment, and outcome. Data were gathered separately from hospital-based and community-based cases. We performed a narrative synthesis to describe the final data set.

RESULTS

Forty-one separate data sets were included in the Pneumonia Research Partnership to Assess WHO Recommendations (PREPARE) database, 26 of which were hospital-based and 15 were community-based. The PREPARE database includes 285 839 children with pneumonia (244 323 in the hospital and 41 516 in the community), with detailed descriptions of clinical presentation, clinical progression, and outcome. Of 9185 pneumonia-related deaths, 6836 (74%) occurred in children <1 year of age and 1317 (14%) in children aged 1-2 years. Of the 285 839 episodes, 280 998 occurred in children 0-59 months old, of which 129 584 (46%) were 2-11 months of age and 152 730 (54%) were males.

CONCLUSIONS

This data set could identify an improved specific, sensitive set of criteria for diagnosing clinical pneumonia and help identify sick children in need of referral to a higher level of care or a change of therapy. Field studies could be designed based on insights from PREPARE analyses to validate a potential revised pneumonia algorithm. The PREPARE methodology can also act as a model for disease database assembly.

Factors associated with severe childhood community-acquired pneumonia: a retrospective study from two hospitals

Background

Community-acquired pneumonia (CAP) is the leading cause of death in children globally. Indonesia is ranked 1st in South East Asia with the highest burden of pneumonia. Identification of risk factors is necessary for early intervention and better management. This study intended to describe CAP’s clinical signs and laboratory findings and explore the risk factors of severe CAP among children in Indonesia.

Methods

This was a retrospective study of childhood hospitalizations in Siloam General Hospitals and Siloam Hospitals Lippo Village from December 2015 to December 2019. Demographic data, clinical signs, and laboratory findings were collected and processed using IBM SPSS 26.0.

Results

This study included 217 participants with 66 (30.4%) severe pneumonia cases. Multivariate analysis shows that fever that lasts more than 7 days (ORadj = 4.95; 95%CI 1.61–15.21, Padj = 0.005) and increase in respiratory rate (ORadj = 1.05, 95%CI 1.01–1.08, Padj = 0.009) are two predictors of severe pneumonia. Meanwhile, a normal hematocrit level (ORadj = 0.9; 95%CI 0.83–0.98, Padj = 0.011) and children with normal BMI (ORadj = 0.7; 95%CI 0.57–0.84, Padj < 0.001) are significant independent predictors of severe pneumonia. The Hosmer-Lemeshow test shows that this model is a good fit with a P-value of 0.281. The AUC for this model is 0.819 (95%CI = 0.746–0.891, P-value < 0.001) which shows that this model has good discrimination.

Conclusion

Pediatric CAP hospitalizations with fever lasting > 7 days and tachypnea were at higher risk for progressing to severe pneumonia. A normal hematocrit level and a normal BMI are protective factors for severe pneumonia.

Derivation and validation of a novel risk assessment tool to identify children aged 2–59 months at risk of hospitalised pneumonia-related mortality in 20 countries

Introduction

Existing risk assessment tools to identify children at risk of hospitalised pneumonia-related mortality have shown suboptimal discriminatory value during external validation. Our objective was to derive and validate a novel risk assessment tool to identify children aged 2–59 months at risk of hospitalised pneumonia-related mortality across various settings.

Methods

We used primary, baseline, patient-level data from 11 studies, including children evaluated for pneumonia in 20 low-income and middle-income countries. Patients with complete data were included in a logistic regression model to assess the association of candidate variables with the outcome hospitalised pneumonia-related mortality. Adjusted log coefficients were calculated for each candidate variable and assigned weighted points to derive the Pneumonia Research Partnership to Assess WHO Recommendations (PREPARE) risk assessment tool. We used bootstrapped selection with 200 repetitions to internally validate the PREPARE risk assessment tool.

Results

A total of 27 388 children were included in the analysis (mean age 14.0 months, pneumonia-related case fatality ratio 3.1%). The PREPARE risk assessment tool included patient age, sex, weight-for-age z-score, body temperature, respiratory rate, unconsciousness or decreased level of consciousness, convulsions, cyanosis and hypoxaemia at baseline. The PREPARE risk assessment tool had good discriminatory value when internally validated (area under the curve 0.83, 95% CI 0.81 to 0.84).

Conclusions

The PREPARE risk assessment tool had good discriminatory ability for identifying children at risk of hospitalised pneumonia-related mortality in a large, geographically diverse dataset. After external validation, this tool may be implemented in various settings to identify children at risk of hospitalised pneumonia-related mortality.