https://orcid.org/0009-0009-2792-9681
Figures
Abstract
Background
In low- and middle-income countries (LMICs), child mortality rates remain substantially higher compared to high-income countries, with many deaths preventable through early recognition of deterioration. This systematic review and meta-analysis investigated predictive values of vital signs and anthropometric measurements for paediatric in-hospital mortality in LMICs.
Methods
A search of publicly available data in PubMed and OVID Embase was conducted in November 2021 and last updated in March 2025. Studies that reported on oxygen saturation; respiratory rate; heart rate; blood pressure; temperature; mid-upper arm circumference (MUAC); and/or weight-for-height z-score (WHZ), and paediatric in-hospital mortality were included. Neonatal and paediatric intensive care unit (PICU) studies were excluded. Data was extracted by two independent authors. Forest plots presented odds ratios (OR) using random effect models. Newcastle Ottawa Scale assessed risk of bias.
Findings
104 out of 21,494 yielded studies were included in descriptive analysis and 75 in meta-analysis, encompassing 255,546 children. Associations with in-hospital mortality were observed in hypoxaemia (OR 5.53, 95% CI 4.18–7.30), tachypnoea (OR 1.65, 95% CI 1.16–2.34), tachycardia (OR 1.80, 95% CI 1.22–2.66), bradycardia (OR 3.29, 95% CI 1.38–7.83), hypotension (OR 4.42, 95% CI 2.54–7.70), hyperthermia (OR 1.31, 95% CI 1.04–1.66), hypothermia (OR 3.92, 95% CI 2.76–5.58), low MUAC (OR 3.22, 95% CI 2.12–4.91), and low WHZ (OR 3.19, 95% CI 2.47–4.11).
Interpretation
Several vital signs and anthropometric measurements are strongly associated with in-hospital mortality in children. Hypoxaemia demonstrated the highest odds of mortality, followed by hypotension, hypothermia, bradycardia and severe malnutrition. These findings highlight the need for early recognition and targeted interventions for children presenting with these high-risk signs, to improve outcomes in resource-limited settings and stress the need to monitor vital signs.
Citation: Smits LCA, Datema M, Voskuijl WP, Ngari MM, Kumwenda M, Calis JCJ (2025) Predicting in-hospital mortality in children in low- and middle-income countries: A systematic review and meta-analysis of vital signs and anthropometric measurements. PLoS One 20(11): e0336233. https://doi.org/10.1371/journal.pone.0336233
Editor: Chris A. Rees, Emory University School of Medicine, UNITED STATES OF AMERICA
Received: July 1, 2025; Accepted: October 21, 2025; Published: November 10, 2025
Copyright: © 2025 Smits et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data are within the paper and its Supporting information files.
Funding: The author(s) received no specific funding for this work.
Competing interests: The authors have declared that no competing interests exist.
Abbreviations: LMIC, low- and middle-income country; MUAC, mid-upper arm circumference; WHZ, weight-for-height z-score; OR, odds ratio; SSA, sub-Saharan Africa; HIC, high-income country; HR, heart rate; RR, respiratory rate; BP, blood pressure; HCW, healthcare worker; PRISMA, Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines; PICU, paediatric intensive care unit; PMID, pubMed identifier; ED, emergency department; NOS, Newcastle Ottawa Scale; ABCDE, Airway, Breathing, Circulation, Disability, Environment; GRADE, Grading of Recommendations Assessment, Development and Evaluations; PEWS, Paediatric Early Warning Score; PEWS-RL, Paediatric Early Warning Score for resource-limited settings; WHO, World Health Organization
Introduction
Since 2000, child mortality rate has decreased by approximately 52%. Nevertheless, an estimated 4.8 million children under the age of five died in 2023 [1]. With 80%, the vast majority of these deaths occur in sub-Saharan Africa (SSA) and South Asia, where child mortality rates remain substantially higher than in high-income countries (HICs) [2–4]. Most paediatric in-hospital deaths in LMICs happen within 24 hours after admission and have preventable and treatable causes [5–8]. Therefore, early recognition of deterioration may reduce mortality in these countries.
Deranging vital signs are often the first to indicate clinical deterioration [6,9–12]. In HICs, monitoring non-invasive bedside vital signs such as oxygen saturation, respiratory rate (RR), heart rate (HR), blood pressure (BP) and temperature, is commonly used to discriminate between children at high and low mortality risk [13–16]. These are routinely collected both at admission and during hospital stay, and help healthcare workers (HCWs) to stratify the risk and determine the response to treatment. However, the predictive role of these parameters is less clear in LMICs and may be different than in HICs, due to other etiologies, comorbidities, pathophysiology and late presentation [2,17,18]. Malnutrition is especially prevalent in LMICs and contributes to nearly half of deaths under five years old [19]. Therefore, this might be an important predictor of outcome in these settings and may also affect vital signs [20].
Collecting vital signs requires equipment, which is often scarce in the low-resource setting, and valuable time and effort of the often overstrained HCWs. To use these resources as efficiently as possible, it is important to identify which vital signs are most useful for identifying serious illness and impending deterioration.
Therefore, this systematic review investigated the predictive value of vital signs and anthropometric measurements in determining the risk of paediatric in-hospital mortality in LMICs. Moreover, our data can provide a framework for enhancing monitoring strategies or systems for use in LMICs.
Methods
This systematic review and meta-analysis, without PROSPERO registration, was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines (PRISMA, S1 Table) [21].
Search strategy
After creating a search strategy in collaboration with a multidisciplinary team including a clinical librarian, a systematic search was conducted in both Medline and OVID Embase. It was first performed on November 18th 2021 and updated on December 25th 2023 and March 24th 2025. Terms regarding low- and middle-income countries, mortality, children and the parameters of our interest were searched. Full search strategies are available in S1 File.
Study selection
Studies that reported data on at least one of the vital signs hypoxaemia, tachypnoea, bradypnoea, tachycardia, bradycardia, hypertension, hypotension, hyperthermia, hypothermia, or anthropometric measurements mid-upper arm circumference (MUAC) and weight-for-height z-score (WHZ) regarding the outcome all-cause in-hospital mortality were included. The following definitions were used: Hypoxaemia was a decreased blood oxygen content (SpO₂) below the defined cut-off. Tachypnoea and bradypnoea were defined as respiratory rates above and below age-specific cut-offs, respectively. Tachycardia and bradycardia were defined as heart rates above and below age-specific cut-offs, hypertension and hypotension as blood pressures above and below the defined cut-offs, and hyperthermia and hypothermia as body temperatures above and below the respective cut-offs.
Studies were eligible if they included children aged 1 month up to 18 years, presenting or admitted to a hospital in a LMIC, as defined by the World Bank [22]. Studies were excluded if they only included neonates (age < 1 month) or if the setting was limited to the paediatric intensive care unit (PICU). Studies including both neonates and children were not excluded. There was no exclusion based on language, study design or year of publication. Although we extracted the timing of the specific vital sign measurement, no restriction was used concerning the vital sign studied and outcome measured.
After removing duplicates, all yielded studies were uploaded in ‘Rayyan’ for screening on title and abstract, which was done by two independent reviewers (LS, MD). Full texts of the remaining articles were separately screened by the same two reviewers for final inclusion. Discrepancies were resolved through discussion, or if necessary with a third reviewer (JC).
Data extraction
Data extraction was performed by one author and checked by a second author. For all included articles, the following study characteristics were extracted: author, title, year of publication, study country, PubMed identifier (PMID), study design, study duration, hospital setting (emergency department (ED); paediatric ward), study population characteristics, sample size, number of events, measured parameters and outcome (in-hospital mortality). For each parameter, odds ratios (OR) on in-hospital mortality were extracted, preferably including raw data for calculating the OR as well as cut-off values. If ORs were not reported, other ratio measures were extracted. The primary outcome was in-hospital mortality.
Data analysis
For each individual vital sign or anthropometric measurement, forest plots were created using Review Manager 5.4 (RevMan). Forest plots per subgroups, based on their admission diagnosis (e.g., pneumonia), as well as overview plots were provided, and meta-analyses were performed. Additional subgroup analyses comparing different cut-off values were performed regarding oxygen saturation. Heterogeneity was quantified using I². Data was analysed using random effect models using the DerSimonian and Laird method. Raw data was used to calculate odds ratios [23]. Studies not presenting raw data for calculating odds ratios were included in descriptive analysis. Results of vital signs and anthropometric measurements of interest were discussed following the Airway, Breathing, Circulation, Disability, Environment (ABCDE) approach. Grading of Recommendations Assessment, Development and Evaluations (GRADE) assessment per parameter was performed by two independent authors (LS, MD) to assess the quality of evidence.
Risk of bias assessment
The Newcastle Ottawa Scale (NOS) was used to assess the risk of bias of all included studies on three domains: selection, comparability, and (cohort) outcome or (cross sectional) exposure. Each domain contained one to four questions, all having different answer options with the rating of either “star” or “slash”. Total number of stars per domain per study was assessed, giving an overview of the likelihood of bias per domain. The maximum score was ten. Scores were classified as high (7–9), indicating low risk of bias; moderate (4–6), indicating moderate risk of bias; or low (0–3), indicating high risk of bias. Funnel plots were created to assess publication bias.
Results
Study selection
After removing duplicates, 21,494 articles were screened on title and abstract. Subsequently, 198 full-text articles were assessed for eligibility. A final 104 articles were included in this study (Fig 1), representing 255,546 children. Seventy-five studies presented extractable data and contributed to the meta-analysis.
From: Moher D, Liberati A, Tetzlaff J, Altman DG, The PRISMA Group (2009). Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement. PLoS Med 6(6): e1000097. https://doi.org/10.1371/journal.pmed1000097.
Study characteristics
Included studies took place in a range of geographic regions: South Asia (n = 24, 23.1%), Southeast Asia (n = 4, 3.8%), sub-Saharan Africa (n = 67, 64.4%), North Africa (n = 3, 2.9%) and other areas (n = 6, 5.8%). The majority of the studies (n = 94, 90.4%) were conducted in the paediatric ward, while ten studies (9.6%) were conducted in the ED. A detailed description of included studies is presented in Table 1.
Data analysis
Hypoxaemia, tachypnoea, tachycardia, bradycardia, hypotension, hyperthermia, hypothermia, MUAC and WHZ showed associations with paediatric in-hospital mortality, whereas bradypnoea and hypertension showed no association. A summary of the statistics is provided in Fig 2. Included studies used various cut-off values for each parameter. All extracted data are presented in S2 Table, including cut-off values and raw data for odds calculation.
OR, Odds Ratio; CI, Confidence Interval.
Breathing.
Respiratory parameters on in-hospital mortality were hypoxaemia (Fig 3), and tachy- and bradypnoea (S1 Fig). Of studies examining hypoxaemia, 86.0% (37 out of 43) found an association. The pooled results from 36 (83.7%) studies demonstrated an almost six times higher odds of in-hospital mortality (OR 5.53, 95% CI 4.18–7.30, I² 78%) if hypoxaemia was present (Fig 3). The prevalence of hypoxaemia was 17.7%. Cut-offs ranged from 70% to 95% (S2 Table). Subgroup analysis consistently showed associations between hypoxaemia and mortality, particularly pronounced in diarrhoea and other populations. Lower cut-off values were associated with higher odds of in-hospital mortality in all compared subgroups (pneumonia, malnutrition and other diseases) (S1b Fig).
OR, Odds Ratio; CI, Confidence Interval.
Thirty-four studies reported on tachypnoea and outcome of which 18 (52.9%) reported an association with mortality (S1c Fig). Meta-analysis of 24 studies showed an OR of 1.65 for mortality in children with tachypnoea (95% CI 1.16–2.34, I² 93%). The prevalence of tachypnoea was 83.9%.
Four out of eight studies (50%) that reported on bradypnoea found an association with mortality (S1d Fig). The pooled results of five studies showed no association (OR 3.17, 95% CI 0.91–11.07, I² 74%, prevalence 6.5%). For tachypnoea and bradypnoea, used cut-offs were age-specific and ranged from >16–80/min and <8–40/min, respectively (S2 Table).
Circulation.
Circulatory parameters are displayed in S2 Fig. Twenty-one studies reported on tachycardia and outcome. An association was found in 42.9% (n = 9) and the pooled results of 14 studies reported an OR of 1.80 in tachycardic children (95% CI 1.22–2.66, I² 72%) (S2a Fig). Prevalence of tachycardia was 30.0%.
An association between bradycardia and in-hospital mortality was found in eight out of twelve (66.7%) studies. Pooled results of six studies showed an increased odds ratio of 3.29 in bradycardic children (95% CI 1.38–7.83, I² 84%) (S2b Fig). The prevalence of bradycardia was 14.2%. Age-specific cut-offs were used to define tachycardia, ranging from >101 to >220/min, and bradycardia, ranging from <50 to <100/min (S2 Table).
In the meta-analysis of four studies, no association was found between hypertension and in-hospital mortality (OR 0.64, 95% CI 0.29–1.41, I² 3%, prevalence 19.1%) (S2c Fig).
Six out of 13 (46.2%) studies reporting on hypotension found an association with in-hospital mortality. Pooled results of ten studies showed an OR of 4.42 (95% CI 2.54–7.70, I² 52%) (S2d Fig). The prevalence of hypotension was 14.6%. Cut-offs for hypotension using systolic blood pressures were <100 mmHg for infants and <117 mmHg for children above 12 years old (S2 Table).
Environment.
Temperature forest plots are presented in S3 Fig. Thirteen out of 49 studies (26.5%) reporting on hyperthermia and outcome, found an association. Besides, 4 out of 49 studies (8.2%) were inversely associated with mortality. Pooled results of 40 studies showed an OR of 1.31 with in-hospital mortality in hyperthermic children (95% CI 1.04–1.66, I² 87%) (S3a Fig). Cut-offs ranged from temperatures >37.5 to >40.0 degrees Celsius (S2 Table). The prevalence of hyperthermia was 34.3%.
An association between hypothermia and in-hospital mortality was found in 70.8% of the studies (17 out of 24). Pooled results of 17 studies showed an OR of 3.92 (95% CI 2.76–5.58 I² 46%) (S3b Fig). The prevalence of hypothermia was 6.2%. Hypothermia cut-offs ranged from <35.0 to <37.0 degrees Celsius (S2 Table).
Anthropometric measurements.
Forest plots of anthropometric measurements are presented in S4 Fig. Fourteen out of 22 studies (68.2%) reporting on MUAC, and 17 out of 23 studies (73.9%) reporting on WHZ, found an association with in-hospital mortality. Pooled results showed ORs of 3.22 for MUAC (95% CI 2.12–4.91, I² 88%, 15 studies, prevalence 21.1%) (S4a Fig) and 3.19 for WHZ (95% CI 2.47–4.11, I² 57%, 18 studies, prevalence 17.1%) (S4b Fig). The most common cut-off for low MUAC was < 11.5 cm and cut-offs for WHZ ranged from −2 to −3 SD (S2 Table).
Risk of bias
Results of the risk of bias assessment are shown in S3 Table. Ninety studies (86.5%) had low risk of bias, while 14 studies (13.5%) had moderate risk of bias. No high risk of bias was observed. Funnel plots are available in S5 Fig and show no major signs of publication bias. According to the GRADE assessment, the found evidence was of low-moderate quality (S4 Table).
Discussion
This systematic review and meta-analysis is the largest assessment of the predictive value of vital signs and anthropometric measurements on in-hospital mortality in children in LMICs. We found evidence that hypoxaemia is the strongest predictor of in-hospital mortality, followed by hypotension, hypothermia, and bradycardia. Hyperthermia, tachypnoea and tachycardia, were more common, yet less strongly associated. Low values of static anthropometric measurements MUAC and WHZ also proved to be good predictors on admission. No association was found between bradypnoea or hypertension and in-hospital mortality.
In our analysis, hypoxaemia was highly prevalent (17.7%) among all included children and was found to be a major predictor of in-hospital mortality, independent of admission diagnosis with increased odds of almost six. In sensitivity analysis we found that in children with pneumonia, hypoxaemia was associated with mortality (OR 4.22), which is in line with a recent systematic review by Lazzerini et al, showing an increased odds of death (OR 5.47) in hypoxaemic children with acute lower respiratory infections [127]. Despite considerable improvement in child mortality in LMICs, pneumonia is still the leading cause of under-five mortality, with more than 700,000 under-five deaths annually. The vast majority is occurring in resource-limited settings and is preventable [128,129]. A systematic review by Rahman et al. showed high prevalence of hypoxaemia (31–47%) in children with pneumonia [130]. Both the high OR and prevalence emphasize the importance of measuring oxygen levels in children with respiratory infections. Surprisingly, an even stronger association between hypoxaemia and outcome was found in children with non-respiratory conditions such as diarrhoea (OR 13.47). Several factors may explain this finding. Hypoxaemia may identify the sickest children, in whom other organ systems are also compromised, or may select cases with multiple etiologies. Moreover, misdiagnosis at admission and undertreatment may lead to hypoxaemia. Irrespective of these various explanations, consistent findings across studies underscore the powerful predictive value of hypoxaemia for in-hospital mortality in all children. Lower cut-off values were associated with higher odds of mortality, regardless of the subpopulation. The majority of the studies used a cut-off of <90%. This could be used as a boundary for intervention in LMICs. This all together, highlights the importance of oxygen saturation monitoring in all children, regardless of their initial diagnosis and indicates that measurement of oxygen saturation should be prioritised in LMICs over other vital signs with lower odds ratios.
With pooled odds ratios of 3.29 or higher, bradycardia, hypotension and hypothermia, were identified as reliable predictors. This finding may appear surprising, given that healthcare professionals typically emphasize heightened vital signs such as fever and tachycardia during clinical assessment. These decreased vital signs appear to be stronger predictors. However, they were less common than the increased vital signs (6.2–14.6% vs 31.2–83.9%). The importance of decreased vital signs as recognized predictors is well established as they are components of the widely utilized Paediatric Early Warning Score (PEWS) and PEWS for resource-limited settings (PEWS-RL) to identify children at risk for deterioration. However, they are not included in the WHO ETAT flowcharts [8,131,132]. A comprehensive study by Chapman et al. conducted in the United Kingdom compared 18 different PEWS and found that bradycardia was included in 100% of the PEWS and hypotension in 61% of the investigated PEWS [16].
The more prevalent increased vital signs tachypnoea, tachycardia and hyperthermia had only moderate predictive value, with odds ratios ranging from 1.31 to 1.80. According to the World Health Organization (WHO) guidelines, respiratory distress and tachycardia are being reported as emergency signs that require immediate treatment to avert death. Hyperthermia is flagged as a priority sign indicating the need for prompt assessment of a child [133,134]. All increased vital signs are incorporated in the PEWS and PEWS-RL [131,132]. Chapman et al. found that hyperthermia was included in 39%, tachycardia and tachypnoea in 100% and hypertension in 61% of the 18 evaluated PEWS [16]. Together, these findings stress that both abnormalities should alert HCWs of a poor outcome, acknowledging that low values are more strongly associated with outcome, as they may indicate later stages of deterioration.
In our analysis, abnormally low anthropometric measurements (MUAC and WHZ) were highly prevalent and good predictors of in-hospital mortality (OR 3.22 and 3.19, respectively). Worldwide, nearly half of under-five mortality is linked to any form of undernutrition [125]. In 2022, an estimated 45 million children were wasted (low weight-for-height), subsequently leading to increased risk of death [135]. The WHO indicates severe visible wasting as a priority sign that indicates the need for prompt assessment of a child [133,134]. Our findings are in line with previous studies that repeatedly find associations with mortality, and stress the importance of assessing and improving nutritional status [44,104,136–139].
This systematic review has several strengths. We employed a comprehensive search strategy, identifying 104 studies comprising a total of 255,546 children. Of these, 75 studies were included in the meta-analysis. This study is the first to provide forest plots for vital signs and anthropometric measurements with subgroup analysis. Although looking at isolated vital signs and not taking into account possible confounding factors, this provides clinicians in LMICs an immediate hands-on insight into the best performing predictive parameter. Risk of bias was low in the vast majority of included studies. Lack of adjustment for confounding factors was the most frequent risk of bias. Main limitations were due to the lack of available information and the heterogeneity of the studies, which makes it difficult to generalize the results. Due to the diverse aspect of studies included in this review some elements were not or inconsistently described that would ideally be used in sensitivity analyses: e.g., more detailed analyses of age groups, timing of death, location and resource levels of hospitals. Few studies investigated the predictive value of hypertension. Heterogeneity was caused by differences in populations, variations in clinical setting (e.g., emergency department versus ward, rural versus urban hospitals), diversity in study designs, differences across countries and variability in the timing of parameter measurement (at admission versus within 24–48 hours). Subgroup analyses based on parameter cut-offs, apart from hypoxaemia, as well as age-specific subgroup analyses, were not conducted. Due to the wide variability in cut-offs and age groups within the available data, the resulting subgroups were too small to allow for meaningful analyses. Performing such analyses could however enhance the clinical applicability of the findings in LMICs, where standardized protocols are often lacking.
These shortcomings limit the interpretation of our findings in terms of generalizability and in the sense that we cannot identify ideal cut offs, nor report on specific populations or settings. However, the data do underline which parameters may be most useful to collect in general populations and further highlights the complex context of vital sign measurement in paediatrics both for clinical work and research.
Using GRADE, we assessed the certainty of the evidence to be low to moderate due to high inconsistency because of substantial heterogeneity, indirectness and imprecise results because of lack of data on some of the included parameters.
In LMICs there is an increasing focus on measuring vital signs of sick children to prioritize care and allow timely delivery of critical care interventions. Advanced equipment, electronic health records and continuous monitoring are becoming increasingly available [140,141]. Vital signs and anthropometric measurements are useful to help distinguish severity in children in LMICs in both ED and ward settings. Especially in LMICs, diagnostic facilities (e.g., laboratory testing or imaging) are limited [142–144]. Decisions are often based on just clinical signs, making monitoring bedside parameters even more important. In the absence of clear guidelines on which vital signs to measure for assessing a child’s mortality risk, this review provides guidance on what to determine in a setting with limited resources and a lack of trained personnel. Although our data supports the collection of vital signs and anthropometric measurements on admission, and preferably also on regular intervals during admission, none of the included studies reported on the impact of vital sign monitoring to reduce outcome. If the aim of monitoring is to allow timely interventions, one could argue if intermittent vital sign monitoring is sufficient enough to allow prompt action. Besides the selection of the most prominent vital signs to monitor, these practical issues need to be addressed to improve cost-effective monitoring strategies for these vulnerable critically ill populations. Future research should use a prospective cohort or randomised trial design in order to investigate to what extent monitoring these vital signs and anthropometric parameters can reduce mortality, thereby addressing an important knowledge gap. Moreover, we suggest using standard operating procedures and multivariate analyses as well as subgroup analyses (e.g., cut-off values, age groups, or based on diagnoses), to address the issue of confounding factors.
According to the WHO, pulse oximetry should be used in children who have fast breathing or chest indrawing [145]. Pulse oximetry is a feasible, cheap and non-invasive procedure that can effectively identify children at high risk of in-hospital mortality, regardless of their diagnosis. Clinicians should be aware of the high predictive value of hypoxaemia and therefore, oxygen saturation should be measured on a regular basis in all admitted children and could be considered more important than temperature measurement. Referral or oxygen therapy to manage hypoxaemic children can reduce in-hospital mortality and should be implemented in standard routines in hospitals in LMICs. Ideally, in addition to oxygen saturation, measurements of respiratory rate, heart rate, blood pressure, and temperature should be obtained upon presentation in all patients and results below normal limits should alarm health care workers more than higher readings. This finding should be incorporated in WHO and other guidelines and prediction tools. MUAC and WHZ are good yet relatively static predictors of mortality in all children. Therefore, children with low MUAC and/or WHZ should be monitored closely. Equipment for measuring MUAC and WHZ should be procured to improve triage of all children in LMICs. Given that measuring MUAC only requires a measuring tape and WHZ requires both tape and a scale, and considering their similar predictive values, MUAC may be preferable in low-resource settings.
In conclusion, this systematic review shows that hypoxaemia is the strongest predictor of paediatric in-hospital mortality in LMICS, with an odds ratio of 5.53 in the analysis and a substantial prevalence of approximately one in six cases. Decreased vital signs (bradycardia, hypotension, and hypothermia) exhibit greater odds of mortality than increased vital signs (tachypnoea, tachycardia, and hyperthermia). Therefore, despite their lower prevalence, decreased vital signs warrant serious clinical attention. Although anthropometric measurements represent relatively static parameters, they are nonetheless relevant in predicting in-hospital mortality. Although none of the included studies assessed the impact of vital sign monitoring, this can help to timely start life saving interventions such as administering oxygen. Policymakers should prioritize enhancing access to equipment for measuring vital signs such as pulse oximetry and anthropometric measures in LMIC healthcare settings. Most of these measurements can be obtained at a low cost and do not require advanced equipment. Enhancing access to such tools can facilitate more accurate assessments of mortality risk and are expected to ultimately contribute to the reduction of child mortality. Routine assessment is important to identify those at elevated risk of mortality in LMICs.
Supporting information
S2 Table. Extracted data on in-hospital mortality per vital sign or anthropometric measurement.
https://doi.org/10.1371/journal.pone.0336233.s003
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S1 Fig. Forest plots of abnormal respiratory parameters compared to control on in-hospital mortality.
https://doi.org/10.1371/journal.pone.0336233.s004
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S2 Fig. Forest plots of abnormal circulatory parameters compared to control on in-hospital mortality.
https://doi.org/10.1371/journal.pone.0336233.s005
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S3 Fig. Forest plots of abnormal temperature compared to control on in-hospital mortality.
https://doi.org/10.1371/journal.pone.0336233.s006
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S4 Fig. Forest plot of abnormal anthropometric measurements compared to control on in-hospital mortality.
https://doi.org/10.1371/journal.pone.0336233.s007
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S3 Table. Risk of bias assessment (Newcastle Ottawa Scale).
https://doi.org/10.1371/journal.pone.0336233.s008
(PDF)
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