Time to diagnosis of acute kidney injury and its predictors among outborn neonates in India: a competing-risk analysis of tele-ICU network data (2022–2024)

Background There is limited data on neonatal acute kidney injury (AKI) from resource-constrained settings. In this study, we describe time to AKI diagnosis and its predictors among neonates born outside the treating hospital (outborn) in India, accounting for competing events like death and discharge against medical advice (DAMA). Methods This multicentre retrospective study leveraged data from a tele-NICU network across 27 sites in India from May 2022–June 2024. It included neonates 28 days or younger at admission with two or more serum creatinine values measured within a seven-day span, beginning after 48 h of life. AKI was determined using neonatal modified Kidney Disease: Improving Global Outcomes (KDIGO) criteria. Primary outcome was time-to-AKI diagnosis. Cumulative incidence function was used to estimate AKI risk over time and Cox proportional hazards model was used to identify predictors amid competing events. Secondary outcomes like death, DAMA and length of hospitalisation were described. Findings Among 570 neonates, sex was recorded for 506; of these, 411 (81.2%) were male and 95 (18.8%) were female. The mean gestational age was 35.4 weeks (SD 3.1; range 24.3–42.7 weeks). Median post-natal age at admission was 2 days (IQR 1–5). AKI occurred in 170 (29.8%) neonates. Median time-to-AKI diagnosis was 2 days (IQR 1–5) post-admission; five-day cumulative incidence was 0.23 (95% CI: 0.20–0.27). Significant predictors of early AKI included dehydration (HR 3.63, 95% CI: 1.67–7.92), mechanical ventilation (HR 1.84, 95% CI: 1.32–2.56) and vasopressor use (HR 1.33, 95% CI: 1.05–1.69). AKI was associated with higher DAMA (34.7% vs. 25.2%, p=0.02) and Death (13.5% vs. 5.3%, p < 0.01). Interpretation Outborn neonates have rapid time-to-AKI diagnosis post-admission, highlighting a critical high-risk window for kidney-protective interventions. Policy changes to strengthen medical transport and referral systems may additionally reduce AKI risk among outborn neonates. Funding Fred Lovejoy Resident Research and Education Award (Boston Children's Hospital).

Authors

Institutions

Publication Details

Journal
The Lancet Regional Health - Southeast Asia
Published
2026-09-18
DOI
https://doi.org/10.1016/j.lansea.2026.100869
Primary Topic
Acute Kidney Injury Research
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
article

Time to diagnosis of acute kidney injury and its predictors among outborn neonates in India: a competing-risk analysis of tele-ICU network data (2022–2024)

Ryan Brewster, Sitarah Mathias, Lakshmi Ganapathi, Ankana Daga et al.
The Lancet Regional Health - Southeast Asia
Acute Kidney Injury Research
article

Time to diagnosis of acute kidney injury and its predictors among outborn neonates in India: a competing-risk analysis of tele-ICU network data (2022–2024)

Ryan Brewster, Sitarah Mathias, Lakshmi Ganapathi, Ankana Daga, Geetanjali Srivastava, Dileep Unnikrishnan, Eva Robinson, Michael Monuteaux, Carl Britto, Prashantha YN
article en

Abstract

Background There is limited data on neonatal acute kidney injury (AKI) from resource-constrained settings. In this study, we describe time to AKI diagnosis and its predictors among neonates born outside the treating hospital (outborn) in India, accounting for competing events like death and discharge against medical advice (DAMA). Methods This multicentre retrospective study leveraged data from a tele-NICU network across 27 sites in India from May 2022–June 2024. It included neonates 28 days or younger at admission with two or more serum creatinine values measured within a seven-day span, beginning after 48 h of life. AKI was determined using neonatal modified Kidney Disease: Improving Global Outcomes (KDIGO) criteria. Primary outcome was time-to-AKI diagnosis. Cumulative incidence function was used to estimate AKI risk over time and Cox proportional hazards model was used to identify predictors amid competing events. Secondary outcomes like death, DAMA and length of hospitalisation were described. Findings Among 570 neonates, sex was recorded for 506; of these, 411 (81.2%) were male and 95 (18.8%) were female. The mean gestational age was 35.4 weeks (SD 3.1; range 24.3–42.7 weeks). Median post-natal age at admission was 2 days (IQR 1–5). AKI occurred in 170 (29.8%) neonates. Median time-to-AKI diagnosis was 2 days (IQR 1–5) post-admission; five-day cumulative incidence was 0.23 (95% CI: 0.20–0.27). Significant predictors of early AKI included dehydration (HR 3.63, 95% CI: 1.67–7.92), mechanical ventilation (HR 1.84, 95% CI: 1.32–2.56) and vasopressor use (HR 1.33, 95% CI: 1.05–1.69). AKI was associated with higher DAMA (34.7% vs. 25.2%, p=0.02) and Death (13.5% vs. 5.3%, p < 0.01). Interpretation Outborn neonates have rapid time-to-AKI diagnosis post-admission, highlighting a critical high-risk window for kidney-protective interventions. Policy changes to strengthen medical transport and referral systems may additionally reduce AKI risk among outborn neonates. Funding Fred Lovejoy Resident Research and Education Award (Boston Children's Hospital).

The Lancet Regional Health - Southeast AsiaVol. 54
Boston Children's Hospital (US), Lucile Packard Children's Hospital (US), Harvard University (US), Massachusetts General Hospital (US), M.V. Hospital and Research Centre (IN), Bangalore Baptist Hospital (IN), Stanford Medicine (US), Boston Children's Museum (US)
Boston Children's Hospital
Openalex Percentile: Top 11%
Acute Kidney Injury Research
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.