In-hospital mortality risk stratification in children aged under 5 years with pneumonia with or without pulse oximetry: A secondary analysis of the Pneumonia REsearch Partnership to Assess WHO REcommendations (PREPARE) dataset

Objectives We determined the pulse oximetry benefit in pediatric pneumonia mortality risk stratification and chest-indrawing pneumonia in-hospital mortality risk factors. Methods We report the characteristics and in-hospital pneumonia-related mortality of children aged 2-59 months who were included in the Pneumonia Research Partnership to Assess WHO Recommendations dataset. We developed multivariable logistic regression models of chest-indrawing pneumonia to identify mortality risk factors. Results Among 285,839 children, 164,244 (57.5%) from hospital-based studies were included. Pneumonia case fatality risk (CFR) without pulse oximetry measurement was higher than with measurement (5.8%, 95% confidence interval [CI] 5.6-5.9% vs 2.1%, 95% CI 1.9-2.4%). One in five children with chest-indrawing pneumonia was hypoxemic (19.7%, 95% CI 19.0-20.4%), and the hypoxemic CFR was 10.3% (95% CI 9.1-11.5%). Other mortality risk factors were younger age (either 2-5 months [adjusted odds ratio (aOR) 9.94, 95% CI 6.67-14.84] or 6-11 months [aOR 2.67, 95% CI 1.71-4.16]), moderate malnutrition (aOR 2.41, 95% CI 1.87-3.09), and female sex (aOR 1.82, 95% CI 1.43-2.32). Conclusion Children with a pulse oximetry measurement had a lower CFR. Many children hospitalized with chest-indrawing pneumonia were hypoxemic and one in 10 died. Young age and moderate malnutrition were risk factors for in-hospital chest-indrawing pneumonia-related mortality. Pulse oximetry should be integrated in pneumonia hospital care for children under 5 years

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

Implementing logical connectives in constraint programming

Combining constraints using logical connectives such as disjunction is ubiquitous in constraint programming, because it adds considerable expressive power to a constraint language. We explore the solver architecture needed to propagate such combinations of constraints efficiently. In particular we describe two new features named satisfying sets and constraint trees. We also make use of movable triggers [1], and with these three complementary features we are able to make considerable efficiency gains. A key reason for the success of Boolean Satisfiability (SAT) solvers is their ability to propagate OR constraints efficiently, making use of movable triggers. We successfully generalise this approach to an OR of an arbitrary set of constraints, maintaining the crucial property that at most two constraints are active at any time, and no computation at all is done on the others. We also give an AND propagator within our framework, which may be embedded within the OR. Using this approach, we demonstrate speedups of over 10,000 times in some cases, compared to traditional constraint programming approaches. We also prove that the OR algorithm enforces generalised arc consistency (GAC) when all its child constraints have a GAC propagator, and no variables are shared between children. By extending the OR propagator, we present a propagator for ATLEASTK, which expresses that at least k of its child constraints are satisfied in any solution. Some logical expressions (e.g. exclusive-or) cannot be compactly expressed using AND, OR and ATLEASTK. Therefore we investigate reification of constraints. We present a fast generic algorithm for reification using satisfying sets and movable triggers.