Simplified age-weight mortality risk classification for very low birth weight infants in low-resource settings

Artificial intelligence in early detection and prediction of pediatric/neonatal acute kidney injury: current status and future directions

Acute kidney injury (AKI) has a significant impact on the short-term and long-term clinical outcomes of pediatric and neonatal patients, and it is imperative in these populations to mitigate the pathways leading to AKI and be prepared for early diagnosis and treatment intervention of established AKI. Recently, artificial intelligence (AI) has provided more advent predictive models for early detection/prediction of AKI utilizing machine learning (ML). By providing strong detail and evidence from risk scores and electronic alerts, this review outlines a comprehensive and holistic insight into the current state of AI in AKI in pediatric/neonatal patients. In the pediatric population, AI models including XGBoost, logistic regression, support vector machines, decision trees, naïve Bayes, and risk stratification scores (Renal Angina Index (RAI), Nephrotoxic Injury Negated by Just-in-time Action (NINJA)) have shown success in predicting AKI using variables like serum creatinine, urine output, and electronic health record (EHR) alerts. Similarly, in the neonatal population, using the "Baby NINJA" model showed a decrease in nephrotoxic medication exposure by 42%, the rate of AKI by 78%, and the number of days with AKI by 68%. Furthermore, the "STARZ" risk stratification AI model showed a predictive ability of AKI within 7 days of NICU admission of AUC 0.93 and AUC of 0.96 in the validation and derivation cohorts, respectively. Many studies have reported the superiority of using biomarkers to predict AKI in pediatric patients and neonates as well. Future directions include the application of AI along with biomarkers (NGAL, CysC, OPN, IL-18, B2M, etc.) in a Labelbox configuration to create a more robust and accurate model for predicting and detecting pediatric/neonatal AKI.

Patterns of antibiotic use, pathogens, and prediction of mortality in hospitalized neonates and young infants with sepsis: A global neonatal sepsis observational cohort study (NeoOBS)

BACKGROUND

There is limited data on antibiotic treatment in hospitalized neonates in low- and middle-income countries (LMICs). We aimed to describe patterns of antibiotic use, pathogens, and clinical outcomes, and to develop a severity score predicting mortality in neonatal sepsis to inform future clinical trial design.

METHODS AND FINDINGS

Hospitalized infants <60 days with clinical sepsis were enrolled during 2018 to 2020 by 19 sites in 11 countries (mainly Asia and Africa). Prospective daily observational data was collected on clinical signs, supportive care, antibiotic treatment, microbiology, and 28-day mortality. Two prediction models were developed for (1) 28-day mortality from baseline variables (baseline NeoSep Severity Score); and (2) daily risk of death on IV antibiotics from daily updated assessments (NeoSep Recovery Score). Multivariable Cox regression models included a randomly selected 85% of infants, with 15% for validation. A total of 3,204 infants were enrolled, with median birth weight of 2,500 g (IQR 1,400 to 3,000) and postnatal age of 5 days (IQR 1 to 15). 206 different empiric antibiotic combinations were started in 3,141 infants, which were structured into 5 groups based on the World Health Organization (WHO) AWaRe classification. Approximately 25.9% (n = 814) of infants started WHO first line regimens (Group 1-Access) and 13.8% (n = 432) started WHO second-line cephalosporins (cefotaxime/ceftriaxone) (Group 2-"Low" Watch). The largest group (34.0%, n = 1,068) started a regimen providing partial extended-spectrum beta-lactamase (ESBL)/pseudomonal coverage (piperacillin-tazobactam, ceftazidime, or fluoroquinolone-based) (Group 3-"Medium" Watch), 18.0% (n = 566) started a carbapenem (Group 4-"High" Watch), and 1.8% (n = 57) a Reserve antibiotic (Group 5, largely colistin-based), and 728/2,880 (25.3%) of initial regimens in Groups 1 to 4 were escalated, mainly to carbapenems, usually for clinical deterioration (n = 480; 65.9%). A total of 564/3,195 infants (17.7%) were blood culture pathogen positive, of whom 62.9% (n = 355) had a gram-negative organism, predominantly Klebsiella pneumoniae (n = 132) or Acinetobacter spp. (n = 72). Both were commonly resistant to WHO-recommended regimens and to carbapenems in 43 (32.6%) and 50 (71.4%) of cases, respectively. MRSA accounted for 33 (61.1%) of 54 Staphylococcus aureus isolates. Overall, 350/3,204 infants died (11.3%; 95% CI 10.2% to 12.5%), 17.7% if blood cultures were positive for pathogens (95% CI 14.7% to 21.1%, n = 99/564). A baseline NeoSep Severity Score had a C-index of 0.76 (0.69 to 0.82) in the validation sample, with mortality of 1.6% (3/189; 95% CI: 0.5% to 4.6%), 11.0% (27/245; 7.7% to 15.6%), and 27.3% (12/44; 16.3% to 41.8%) in low (score 0 to 4), medium (5 to 8), and high (9 to 16) risk groups, respectively, with similar performance across subgroups. A related NeoSep Recovery Score had an area under the receiver operating curve for predicting death the next day between 0.8 and 0.9 over the first week. There was significant variation in outcomes between sites and external validation would strengthen score applicability.

CONCLUSION

Antibiotic regimens used in neonatal sepsis commonly diverge from WHO guidelines, and trials of novel empiric regimens are urgently needed in the context of increasing antimicrobial resistance (AMR). The baseline NeoSep Severity Score identifies high mortality risk criteria for trial entry, while the NeoSep Recovery Score can help guide decisions on regimen change. NeoOBS data informed the NeoSep1 antibiotic trial (ISRCTN48721236), which aims to identify novel first- and second-line empiric antibiotic regimens for neonatal sepsis.

TRIAL REGISTRATION

ClinicalTrials.gov, (NCT03721302).

External Validation of the Neonatal Mortality Risk-2000 Score to Predict In-Hospital Mortality in Neonates Weighing 2000 g or Less

The authors aimed to externally validate the Neonatal Mortality Risk-2000 (NMR-2000) score, a simplified tool to predict in-hospital mortality in the setting of a tertiary care hospital. They conducted a single-center prospective cohort study on neonates weighing ≤ 2000 g who were admitted to a neonatal intensive care unit within 6 h of age. The predictors included in the NMR-2000 score were birth weight, SpO₂ at admission, and the highest level of respiratory support during the first 24 h of life. The outcome was in-hospital mortality. Among 243 neonates ≤ 2000 g, there were 94 (38.7%) deaths. The area under the receiver operating characteristic curve value for the NMR score was 0.84 (95% CI 0.79-0.89) in the present sample. The calibration slope was 1, and the intercept was 0. The NMR-2000 score had good discriminating ability and calibration to predict in-hospital mortality.

Validation of the STARZ neonatal acute kidney injury risk stratification score

BACKGROUND

Neonatal acute kidney injury (AKI) is common in neonatal intensive care units (NICU) and leads to worse outcomes. Stratifying neonates into an "at risk" category allows health care providers to objectively recognize opportunities for improvements in quality of care.

METHODS

The "Neonatal AKI Risk Prediction Scoring" was devised as the "STARZ [Sethi, Tibrewal, Agrawal, Raina, waZir]" Score. The STARZ score was derived from our prior multicentre study analysing risk factors for AKI in neonates admitted to the NICU. This tool includes 10 variables with a total score ranging from 0 to 100 and a cut-off score of 31.5. In the present study, the scoring model has been validated in our multicentre cohort of 744 neonates.

RESULTS

In the validation cohort, this scoring model had sensitivity of 82.1%, specificity 91.7%, positive predictive value 81.2%, negative predictive value 92.2% and accuracy 88.8%. Based on the STARZ cut-off score of ≥ 31.5, an area under the receiver operating characteristic (ROC) curve was observed to be 0.932 (95% CI, 0.910-0.954; p < 0.001) signifying that the discriminative power was high. In the validation cohort, the probability of AKI was less than 20% for scores up to 32, 20-40% for scores between 33 and 36, 40-60% for scores between 37 and 43, 60-80% for scores between 44 and 49, and ≥ 80% for scores ≥ 50.

CONCLUSIONS

To promote the survival of susceptible neonates, early detection and prompt interventional measures based on highly evidenced research is vital. The risk of AKI in admitted neonates can be quantitatively determined by the rapid STARZ scoring system. A higher resolution version of the Graphical abstract is available as Supplementary information.

Neonatal acute kidney injury risk stratification score: STARZ study

BACKGROUND

Neonates admitted in the neonatal intensive care unit are vulnerable to acute kidney injury leading to worse outcomes. It is important to identify "at-risk" neonates for early preventive measures.

METHODS

The study was a multicenter, national, prospective cohort study done in 11 centers in India. A multivariable logistic regression technique with step-wise backward elimination method was used, and a "Risk Prediction Scoring" was devised [the STARZ score].

RESULTS

The neonates with admission in the NICU within <25.5 h of birth, requirement of positive pressure ventilation in the delivery room, <28 weeks gestational age, sepsis, significant cardiac disease, urine output <1.32 ml/kg/h or serum creatinine ≥0.98 mg/dl during the first 12 h post admission, use of nephrotoxic drugs, use of furosemide, or use of inotrope had a significantly higher risk of AKI at 7 days post admission in the multivariate logistic regression model. This scoring model had a sensitivity of 92.8%, specificity of 87.4% positive predictive value of 80.5%, negative predictive value of 95.6%, and accuracy of 89.4%.

CONCLUSIONS

The STARZ neonatal score serves to rapidly and quantitatively determine the risk of AKI in neonates admitted to the neonatal intensive care unit.

IMPACT

The STARZ neonatal score serves to rapidly and quantitatively determine the risk of AKI in neonates admitted to the neonatal intensive care unit. These neonates with a higher risk stratification score need intense monitoring and daily kidney function assessment. With this intensification of research in the field of AKI risk stratification prediction, there is hope that we will be able to decrease morbidity and mortality associated with AKI in this population.

Prediction modelling of inpatient neonatal mortality in high-mortality settings

OBJECTIVE

Prognostic models aid clinical decision making and evaluation of hospital performance. Existing neonatal prognostic models typically use physiological measures that are often not available, such as pulse oximetry values, in routine practice in low-resource settings. We aimed to develop and validate two novel models to predict all cause in-hospital mortality following neonatal unit admission in a low-resource, high-mortality setting.

STUDY DESIGN AND SETTING

We used basic, routine clinical data recorded by duty clinicians at the time of admission to derive (n=5427) and validate (n=1627) two novel models to predict in-hospital mortality. The Neonatal Essential Treatment Score (NETS) included treatments prescribed at the time of admission while the Score for Essential Neonatal Symptoms and Signs (SENSS) used basic clinical signs. Logistic regression was used, and performance was evaluated using discrimination and calibration.

RESULTS

At derivation, c-statistic (discrimination) for NETS was 0.92 (95% CI 0.90 to 0.93) and that for SENSS was 0.91 (95% CI 0.89 to 0.93). At external (temporal) validation, NETS had a c-statistic of 0.89 (95% CI 0.86 to 0.92) and SENSS 0.89 (95% CI 0.84 to 0.93). The calibration intercept for NETS was -0.72 (95% CI -0.96 to -0.49) and that for SENSS was -0.33 (95% CI -0.56 to -0.11).

CONCLUSION

Using routine neonatal data in a low-resource setting, we found that it is possible to predict in-hospital mortality using either treatments or signs and symptoms. Further validation of these models may support their use in treatment decisions and for case-mix adjustment to help understand performance variation across hospitals.

Prediction of mortality in very low birth weight neonates in Spain

Objective

Predictive models for preterm infant mortality have been developed internationally, albeit not valid for all populations. This study aimed to develop and validate different mortality predictive models, using Spanish data, to be applicable to centers with similar morbidity and mortality.

Methods

Infants born alive, admitted to NICU (BW<1500 g or GA<30 w), and registered in the SEN1500 database, were included. There were two time periods; development of the predictive models (2009–2012) and validation (2013–2015). Three models were produced; prenatal (1), first 24 hours of life (2), and whilst admitted (3). For the statistical analysis, hospital mortality was the dependent variable. Significant variables were used in multivariable regression models. Specificity, sensitivity, accuracy, and area under the curve (AUC), for all models, were calculated.

Results

Out of 14953 included newborns, 2015 died; 373 (18.5%) in their first 24 hours, 1315 (65.3%) during the first month, and 327 (16.2%) thereafter, before discharge. In the development stage, mortality prediction AUC was 0.834 (95% CI: 0.822–0.846) (p<0.001) in model 1 and 0.872 (95% CI: 0.860–0.884) (p<0.001) in model 2. Model 3’s AUC was 0.989 (95% CI: 0.983–0.996) (p<0.001) and 0.942 (95% CI: 0.929–0.956) (p<0.001) during the 0–30 and >30 days of life, respectively. During validation, models 1 and 2 showed moderate concordance, whilst that of model 3 was good.

Conclusion

Using dynamic models to predict individual mortality can improve outcome estimations. Development of models in the prenatal period, first 24 hours, and during hospital admission, cover key stages of mortality prediction in preterm infants.

Development and validation of a simplified score to predict neonatal mortality risk among neonates weighing 2000 g or less (NMR-2000): an analysis using data from the UK and The Gambia

Background

78% of neonatal deaths occur in sub-Saharan Africa and southern Asia, among which, more than 80% are in low birthweight babies. Existing neonatal mortality risk scores have primarily been developed for high-resource settings. The aim of this study was to develop and validate a score that is practicable for low-income and middle-income countries to predict in-hospital mortality among neonates born weighing 2000 g or less using datasets from the UK and The Gambia.

Methods

This analysis used retrospective data held in the UK National Neonatal Research Database from 187 neonatal units, and data from the Edward Francis Small Teaching Hospital (EFSTH), Banjul, The Gambia. In the UK dataset, neonates were excluded if birthweight was more than 2000 g; if the neonate was admitted aged more than 6 h or following discharge; if the neonate was stillborn; if the neonate died in delivery room; or if they were moribund on admission. The Gambian dataset included all neonates weighing less than 2000 g who were admitted between May 1, 2018, and Sept 30, 2019, who were screened for but not enrolled in the Early Kangaroo Mother Care Trial. 18 studies were reviewed to generate a list of 84 potential parameters. We derived a model to score in-hospital neonatal mortality risk using data from 55 029 admissions to a random sample of neonatal units in England and Wales from Jan 1, 2010, to Dec 31, 2016. All candidate variables were included in a complete multivariable model, which was progressively simplified using reverse stepwise selection. We validated the new score (NMR-2000) on 40 329 admissions to the remaining units between the same dates and 14 818 admissions to all units from Jan 1, to Dec 31, 2017. We also validated the score on 550 neonates admitted to the EFSTH in The Gambia.

Findings

18 candidate variables were selected for inclusion in the modelling process. The final model included three parameters: birthweight, admission oxygen saturation, and highest level of respiratory support within 24 h of birth. NMR-2000 had very good discrimination and goodness-of-fit across the UK samples, with a c-index of 0·8859–0·8930 and a Brier score of 0·0232–0·0271. Among Gambian neonates, the model had a c-index of 0·8170 and a Brier score of 0·1688. Predictive ability of the simplified integer score was similar to the model using regression coefficients, with c-indices of 0·8903 in the UK full validation sample and 0·8082 in the Gambian validation sample.

Interpretation

NMR-2000 is a validated mortality risk score for hospitalised neonates weighing 2000 g or less in settings where pulse oximetry is available. The score is accurate and simplified for bedside use. NMR-2000 requires further validation using a larger dataset from low-income and middle-income countries but has the potential to improve individual and population-level neonatal care resource allocation.

Funding

Bill & Melinda Gates Foundation; Eunice Kennedy Shriver National Institute of Child Health & Human Development; Wellcome Trust; and Joint Global Health Trials scheme of Department of Health and Social Care, Department for International Development, Medical Research Council, and Wellcome Trust.

Admissions and mortality over a 5-year period in a limited-resource neonatal unit in Ghana

Objective

To review admissions and deaths at the neonatal intensive care unit (NICU) of the Korle Bu Teaching Hospital (KBTH), Ghana from 2011 to 2015, for the purposes of documentation of outcomes and identification of areas for improvement.

Design

A retrospective descriptive study of NICU Admissions & Discharges from 2011 to 2015. All data in the NICU Admissions & Discharge books were transferred into a spreadsheet and analysed.

Setting

The NICU of KBTH provides secondary and tertiary care for premature and critically ill term babies in the southern half of Ghana.

Results

Over the 5-year period, 9213 babies were admitted to the NICU. Admission weights ranged from 300 to 6700g with median of 2400g. Overall mortality rate was 19.2%. Mortality rates were progressively and significantly higher in babies with lower admission weights and earlier gestations.

Conclusions

We report a high NICU mortality rate of 19.2%, compared to the worldwide range of 3.1% to 29%. This wide range of outcomes is attributable to differences in the severity of illness of patients and to the organisation of resources devoted to obstetric and neonatal care. To substantially improve perinatal and neonatal outcomes, there is a need for wider coverage and better quality of health care; and to consider rationing of care. Complex interventions are necessary to improve outcomes, not just an increase in the allocation of particular resources.

Funding

None declared.

[Prediction of mortality in premature neonates. An updated systematic review]

INTRODUCTION

Extreme prematurity is associated with high mortality rates. The probability of death at different points in time is a priority for professionals and parents, and needs to be established on an individual basis. The aim of this study is to carry out a systematic review of predictive models of mortality in premature infants that have been published recently.

METHODS

A double search was performed for article published in PubMed on models predicting mortality in premature neonates. The population studied were premature neonates with a gestational age of ≤30 weeks and / or a weight at birth of ≤1500g. Works published with new models from June 2010 to July 2019 after a systematic review by Medlock (2011) were included. An assessment was made of the population, characteristics of the model, variables used, measurements of functioning, and validation.

RESULTS

Of the 7744 references (1st search) and 1435 (2nd search) found, 31 works were selected, with 8 new models finally being included. Five models (62.5%) were developed in North America and 2 (25%) in Europe. A sequential model (Ambalavanan) enables predictions of mortality to be made at birth, 7, 28 days of life, and 36 weeks post-menstrual. A multiple logistic regression analysis was performed on 87.5% of the models. The population discrimination was measured using Odds Ratio (75%) and the area under the curve (50%). "Internal Validation" had been carried out on 5 models. Three models can be accessed on-line. There are no predictive models validated in Spain.

CONCLUSIONS

The making of decisions based on predictive models can lead to the care given to the premature infant being more individualised and with a better use of resources. Predictive models of mortality in premature neonates in Spain need to be developed.

A systematic review of neonatal treatment intensity scores and their potential application in low-resource setting hospitals for predicting mortality, morbidity and estimating resource use

BACKGROUND

Treatment intensity scores can predict mortality and estimate resource use. They may therefore be of interest for essential neonatal care in low resource settings where neonatal mortality remains high. We sought to systematically review neonatal treatment intensity scores to (1) assess the level of evidence on predictive performance in predicting clinical outcomes and estimating resource utilisation and (2) assess the applicability of the identified models to decision making for neonatal care in low resource settings.

METHODS

We conducted a systematic search of PubMed, EMBASE (OVID), CINAHL, Global Health Library (Global index, WHO) and Google Scholar to identify studies published up until 21 December 2016. Included were all articles that used treatments as predictors in neonatal models. Individual studies were appraised using the CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS). In addition, Grading of Recommendations Assessment, Development, and Evaluation (GRADE) was used as a guiding framework to assess certainty in the evidence for predicting outcomes across studies.

RESULTS

Three thousand two hundred forty-nine articles were screened, of which ten articles were included in the review. All of the studies were conducted in neonatal intensive care units with sample sizes ranging from 22 to 9978, with a median of 163. Two articles reported model development, while eight reported external application of existing models to new populations. Meta-analysis was not possible due heterogeneity in the conduct and reporting of the identified studies. Discrimination as assessed by area under receiver operating characteristic curve was reported for in-hospital mortality, median 0.84 (range 0.75-0.96, three studies), early adverse outcome and late adverse outcome (0.78 and 0.59, respectively, one study).

CONCLUSION

Existing neonatal treatment intensity models show promise in predicting mortality and morbidity. There is however low certainty in the evidence on their performance in essential neonatal care in low resource settings as all studies had methodological limitations and were conducted in intensive care. The approach may however be developed further for low resource settings like Kenya because treatment data may be easier to obtain compared to measures of physiological status.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO CRD42016034205.

Auditing Neonatal Intensive Care: Is PREM a Good Alternative to CRIB for Mortality Risk Adjustment in Premature Infants?

BACKGROUND

Comparing outcomes at different neonatal intensive care units (NICUs) requires adjustment for intrinsic risk. The Clinical Risk Index for Babies (CRIB) is a widely used risk model, but it has been criticized for being affected by therapeutic decisions. The Prematurity Risk Evaluation Measure (PREM) is not supposed to be prone to treatment bias, but has not yet been validated.

OBJECTIVES

We aimed to validate the PREM, compare its accuracy to that of the original and modified versions of the CRIB and CRIB-II, and examine the congruence of risk categorization.

METHODS

Very-low-birth-weight (VLBW) infants with a gestational age (GA) <33 weeks, who were admitted to NICUs in Baden-Württemberg from 2003 to 2008, were identified from the German neonatal quality assurance program. CRIB, CRIB-II and PREM scores were calculated and modified. Omitting variables that directly reflected therapeutic decisions [the applied fraction of inspired oxygen (FiO2)] or that may have been prone to early-treatment bias (base excess and temperature), non-NICU-therapy-influenced scores were obtained. Score performance was assessed by the area under their ROC curve (AUC).

RESULTS

The CRIB showed the largest AUC (0.89), which dropped significantly (to 0.85) after omitting the FiO2. The PREM birth condition model, PREM(bcm) (AUC 0.86), and the PREM birth model, PREM(bm) (AUC 0.82), also demonstrated good discrimination. PREM(bm) was superior to other non-therapy-affected scores and to GA, particularly in infants with <750 g birth weight. Congruence of risk categorization was low, especially among higher-risk cases.

CONCLUSIONS

The CRIB score had the largest AUC, resulting from its inclusion of FiO2. PREM(bm), as the most accurate score among those unaffected by early treatment, seems to be a good alternative for strict risk adjustment in NICU auditing. It could be useful to combine scores.

Prediction of mortality in very premature infants: a systematic review of prediction models

Context

Being born very preterm is associated with elevated risk for neonatal mortality. The aim of this review is to give an overview of prediction models for mortality in very premature infants, assess their quality, identify important predictor variables, and provide recommendations for development of future models.

Methods

Studies were included which reported the predictive performance of a model for mortality in a very preterm or very low birth weight population, and classified as development, validation, or impact studies. For each development study, we recorded the population, variables, aim, predictive performance of the model, and the number of times each model had been validated. Reporting quality criteria and minimum methodological criteria were established and assessed for development studies.

Results

We identified 41 development studies and 18 validation studies. In addition to gestational age and birth weight, eight variables frequently predicted survival: being of average size for gestational age, female gender, non-white ethnicity, absence of serious congenital malformations, use of antenatal steroids, higher 5-minute Apgar score, normal temperature on admission, and better respiratory status. Twelve studies met our methodological criteria, three of which have been externally validated. Low reporting scores were seen in reporting of performance measures, internal and external validation, and handling of missing data.

Conclusions

Multivariate models can predict mortality better than birth weight or gestational age alone in very preterm infants. There are validated prediction models for classification and case-mix adjustment. Additional research is needed in validation and impact studies of existing models, and in prediction of mortality in the clinically important subgroup of infants where age and weight alone give only an equivocal prognosis.

High mortality rates for very low birth weight infants in developing countries despite training

OBJECTIVE

The goal was to determine the effect of training in newborn care and resuscitation on 7-day (early) neonatal mortality rates for very low birth weight (VLBW) infants. The study was designed to test the hypothesis that these training programs would reduce neonatal mortality rates for VLBW infants.

METHODS

Local instructors trained birth attendants from 96 rural communities in 6 developing countries in protocol and data collection, the World Health Organization Essential Newborn Care (ENC) course, and a modified version of the American Academy of Pediatrics Neonatal Resuscitation Program (NRP), by using a train-the-trainer model. To test the impact of ENC training, data on infants of 500 to 1499 g were collected by using a before/after, active baseline, controlled study design. A cluster-randomized, controlled trial design was used to test the impact of the NRP.

RESULTS

A total of 1096 VLBW (500-1499 g) infants were enrolled, and 98.5% of live-born infants were monitored to 7 days. All-cause, 7-day neonatal mortality, stillbirth, and perinatal mortality rates were not affected by ENC or NRP training.

CONCLUSIONS

Neither ENC nor NRP training of birth attendants decreased 7-day neonatal, stillbirth, or perinatal mortality rates for VLBW infants born at home or at first-level facilities. Encouragement of delivery in a facility where a higher level of care is available may be preferable when delivery of a VLBW infant is expected.

Risk factors for sclerema neonatorum in preterm neonates in Bangladesh

BACKGROUND

This study presents a retrospective analysis of risk factors for sclerema neonatorum in preterm neonates in Bangladesh.

METHODS

Preterm neonates admitted to Dhaka Shishu Hospital in Bangladesh were enrolled in a clinical trial to evaluate the effects of topical treatment with skin barrier-enhancing emollients on prevention of sepsis and mortality. Four hundred ninety-seven neonates were enrolled in the study and 51 (10.3%) developed sclerema neonatorum. We explored risk factors for sclerema neonatorum by comparing patients with and without sclerema neonatorum. Diagnosis of sclerema neonatorum was based on the presence of uniform hardening of skin and subcutaneous tissues to the extent that the skin could not be pitted nor picked up and pinched into a fold. Cultures of blood and cerebrospinal fluid were obtained in all neonates with clinical suspicion of sepsis.

RESULTS

In multivariate analysis, lower maternal education (OR: 1.94; 95% CI: 1.02-3.69; P = 0.043), and signs of jaundice (OR: 2.82; 95% CI: 1.19-6.69; P = 0.018) and poor feeding (OR: 4.71; 95% CI: 1.02-21.74; P = 0.047) on admission were risk factors for developing sclerema neonatorum. The incidence rate ratio of sepsis in neonates who developed sclerema neonatorum was 1.81 (95% CI: 1.16-2.73; P = 0.004), primarily due to Gram-negative pathogens, and risk of death in infants with sclerema neonatorum was 46.5-fold higher (P < 0.001, 95% CI: 6.37-339.81) than for those without sclerema neonatorum.

CONCLUSIONS

Sclerema neonatorum was a relatively common, grave condition in this setting, heralded by poor feeding, jaundice, and bacteremia, and signaling the need for prompt antibiotic treatment.