Hospital-Based Prospective Cohort of Malnutrition Under 6-Month Study (HOSPIC-6-Study)

Rationale and Scientific Background Acute malnutrition in infants younger than 6 months has historically received less attention than malnutrition in older children, both in global nutrition programs and in national health monitoring systems. The World Health Organization (WHO) 2013 guideline on the management of severe acute malnutrition addressed infants under 6 months only briefly, largely extrapolating recommendations developed for older children. The WHO 2023 guideline expanded this guidance substantially, introducing the broader concept of infants "at risk of poor growth and development" alongside the traditional anthropometric thresholds (weight-for-age, weight-for-length, and mid-upper arm circumference).

National data support the clinical relevance of this population. The 2023 Indonesian Health Survey / Survey Kesehatan Indonesia (SKI) reported a 2.6% prevalence of acute malnutrition among infants aged 0-5 months, and the 2024 Indonesian Nutritional Status Survey / Survei Status Gizi Indonesia (SSGI) reported 1.6% in the same age group - both higher than prevalence figures reported for children 24 months and older. Institutional data from Cipto Mangunkusumo Hospital / Rumah Sakit Ciptomangunkusumo (RSCM) identified 282 infants under 6 months admitted with severe malnutrition in 2024 alone.

Despite this burden, the current WHO 2023 guideline does not specify detailed, concrete caloric targets for nutritional therapy in this age group, and there are no Indonesian studies linking specific nutrition therapy approaches, patient risk factors, and treatment outcomes in this population. This creates uncertainty in day-to-day clinical decision-making and a gap in the evidence needed to develop a standardized, locally relevant treatment protocol. This study (HOSPIC-6) was designed to generate multicenter, prospective, hospital-based evidence to help close this gap.

Research Questions and Objectives

This study is designed to address three research questions:

• What is the prevalence of acute malnutrition among infants under 6 months admitted to participating hospitals?
• What proportion of infants experience each type of nutrition therapy outcome (successful therapy, death, or improvement in nutritional status)?
• What factors are associated with successful nutrition therapy in this population?

The general objective is to characterize the clinical management challenges of acute malnutrition in infants under 6 months. The specific objectives are to determine:

• The prevalence of acute malnutrition in this age group,
• The proportion of nutrition therapy outcomes among affected infants, and
• The factors associated with successful nutrition therapy outcomes.

Hypothesis The investigators hypothesize that the outcome of nutrition therapy in infants under 6 months with acute malnutrition - including growth improvement, treatment success, and survival - is influenced by measurable and identifiable clinical factors (such as gestational history, comorbidities, type of nutritional support received, and achieved daily caloric intake), and that characterizing these factors will help inform more effective, evidence-based treatment strategies.

Study Procedures and Data Collection All infants under 6 months admitted to participating pediatric or perinatology wards will first undergo baseline screening data collection (age, sex, gestational age, weight, length, and mid-upper arm circumference if organomegaly is present). Infants who meet WHO 2023 acute malnutrition criteria and do not meet any exclusion criteria will be enrolled as study subjects after informed consent is obtained; infants who are screened but not enrolled will still have their baseline screening data entered into the study's REDCap database for descriptive purposes.

For enrolled subjects, baseline data collected include anthropometric measurements (weight, length, mid-upper arm circumference, and head circumference obtained using standardized equipment and techniques according to Indonesian Ministry of Health regulation No. HK.01.07/MENKES/51/2022), demographic information, comorbidities, birth history, and nutritional history. Details of the nutrition therapy provided (e.g., breast milk, therapeutic milk such as F-75 or diluted F-100, or infant formula) and available laboratory results are also recorded. Follow-up data collection, including repeat anthropometric measurements, ongoing nutrition therapy details, and available laboratory results, occurs on Day 3, Day 7, and then weekly until the infant is discharged. At each time point, the percentage of estimated daily caloric needs achieved is also recorded.

Registry Infrastructure and Data Management All study data are captured directly into REDCap (Research Electronic Data Capture), a secure, access-controlled electronic data capture platform, using a structured electronic case report form built specifically for this protocol. Use of a single shared REDCap database across all participating sites is intended to standardize data structure and definitions across the multicentre network, rather than relying on site-specific paper records that would later require harmonization.

Data Validation and Quality Assurance The REDCap case report form incorporates field-level validation rules (for example, restricting anthropometric values to clinically plausible ranges and enforcing required fields for key variables such as enrollment eligibility criteria) to reduce data entry error at the point of capture. The principal investigator's team performs centralized data verification and cleaning of records entered into REDCap on an ongoing basis, cross-checking entries for internal consistency (for example, agreement between recorded eligibility criteria and enrollment status) and following up directly with local investigators when discrepancies, implausible values, or missing critical fields are identified.

Roles and Responsibilities The principal investigator team is responsible for developing and maintaining the study protocol, the REDCap database structure, and the ethics/regulatory documentation, as well as for centralized data verification and cleaning. Local investigators at each participating site are responsible for obtaining local research permits, ensuring anthropometric equipment at their site is available, calibrated, and used according to the standard operating procedure specified in the protocol, ensuring eligible subjects are identified and enrolled consecutively according to protocol, obtaining informed consent, and entering (or supervising the entry of) subject data into REDCap in a timely manner. This division of responsibilities functions as the study's core standard operating procedure for recruitment, data collection, and data management across sites; coordination between the principal investigator team and local investigators occurs as needed throughout enrollment and follow-up to resolve site-level questions or data queries.

Source Data Verification. Data entered into REDCap are expected to be traceable to source documentation at each site (hospital medical records and/or paper case report forms completed at the bedside at the time of measurement), allowing source data verification and consistency checks against the electronic record as part of routine data cleaning.

Data Dictionary A structured data dictionary underlies the REDCap database, defining each variable collected (for example, anthropometric measures, nutrition therapy type, and outcome classification), its permitted values or coding scheme, and its collection time point, to ensure consistent interpretation and entry of each field across all participating sites.

Handling of Missing Data Because this is an observational, hospital-based cohort in which not every laboratory test or measurement is routinely available for every infant at every time point (for example, laboratory results are recorded only when clinically obtained as part of standard care), some degree of missing data is anticipated, particularly for laboratory variables, rather than for core anthropometric measurements, which are collected prospectively as part of the study procedure at each scheduled time point. Data queries generated during centralized cleaning (for missing, out-of-range, or internally inconsistent entries) are routed back to the responsible local investigator for resolution against source documentation where possible. Where a data point remains genuinely unavailable (for example, a laboratory test not clinically indicated or not performed), it is recorded as missing rather than imputed at the point of data entry. The approach to handling such missingness in the final analytic dataset (for example, complete-case analysis for the specific variable, or descriptive reporting of missingness) will be determined at the analysis stage according to the extent and pattern of missingness observed for each variable, following standard descriptive and analytic conventions for observational cohort data.

Statistical Analysis Plan Descriptive statistics will be used to characterize the screened and enrolled populations and to estimate the prevalence of acute malnutrition among infants under 6 months admitted to participating hospitals with corresponding proportions reported for each nutrition therapy outcome category (treatment success, death, and improvement in nutritional status). Continuous variables such as, anthropometric measures and estimated caloric intake will be summarized as means with standard deviations or medians with interquartile ranges, depending on their distribution, and categorical variables will be summarized as frequencies and percentages. Bivariate comparisons between infants with successful versus unsuccessful nutrition therapy outcomes will be conducted using chi-square or Fisher's exact tests for categorical variables and t-tests or non-parametric equivalents for continuous variables, as appropriate to the data distribution. Factors independently associated with successful nutrition therapy outcomes will be assessed using multivariable regression analysis (logistic regression), adjusting for clinically relevant covariates such as gestational history, comorbidities, and achieved caloric intake. A two-sided p-value of less than 0.05 will generally be considered statistically significant, and results will be reported with corresponding effect estimates and 95% confidence intervals.

Significance As a purely observational, hospital-based registry-style cohort with no experimental intervention, HOSPIC-6 is designed to generate real-world, multicenter evidence on the epidemiology and treatment outcomes of acute malnutrition in infants under 6 months in Indonesia. Its centralized REDCap infrastructure, standardized measurement procedures, and structured data verification process are intended to support data quality across a multicenter, resource-varied hospital network. Findings are expected to inform the development of a more detailed, locally relevant clinical pathway - including specific caloric targets - for the management of acute malnutrition in this age group, both at network hospital and more broadly within the Indonesian health system.

Inclusion criteria

• Infants under 6 months of age admitted to inpatient care who meet the WHO 2023 criteria for acute malnutrition.
• For infants aged 0-1 month, criteria include a documented weekly weight gain of less than 200 grams, or failure to return to birth weight by 2 weeks of chronological age.
• For infants aged 1 month to under 6 months, at least one of the following is required:

• Weight-for-age less than -2 SD
• Weight-for-length less than -2 SD, or
• Mid-upper arm circumference under 110 mm (for infants older than 6 weeks), with or without nutritional oedema, and currently receiving treatment for acute malnutrition.
• Parents or legal guardians must provide written informed consent.

Exclusion criteria

• Infants with inborn error of metabolism
• Infant with syndromic condition (e.g., Down syndrome, Silver-Russell syndrome, Turner syndrome, Klinefelter syndrome, or other diagnosed syndromic conditions),
• Infant with anatomical abnormality affecting body weight, such as hydrocephalus.

Drop Outs Criteria:

Participants will be considered "drop-outs" if they are discharged against medical advice or transferred to a non-participating hospital.