Application of the FULLPIERS Risk Scoring
Model to Predict Maternal Complications in
Hypertensive Pregnancies: A Snapshot
Study in a Tertiary Care Hospital
• Authors :
• Noorullah Ahmed Shaikh
• Mouzma Shafi
• Muneeza Memon
• Institution involved : Gynae Unit-III, LUMHS
Hospital, Hyderabad
• Presenting : Liaquat University of Medical and
Health Sciences Jamshoro
Background
Hypertensive
Disorders in
Pregnancy (HDP):
Global Prevalence:
The Importance of
Early Identification:
Gap in Research:
A leading cause of maternal and perinatal morbidity and mortality worldwide.
Includes conditions like gestational hypertension, preeclampsia, and eclampsia.
The burden is especially high in low and middle-income countries, including Pakistan.
Increased prevalence of HDP has become a significant challenge in maternal health.
Identifying high-risk cases early can improve maternal outcomes significantly.
FULLPIERS: A clinically effective, low-cost scoring model that can predict complications like preeclampsia by assessing key factors
results.
Although the FULLPIERS model has global interest, its application in high-burden, low-resource settings like Pakistan is underevaluated.
Objective of the Study
Main Aim:
• To assess the predictive accuracy of the FULLPIERS risk scoring model in forecasting maternal
complications in women with hypertensive disorders of pregnancy (HDP).
Hypothesis:
• A higher FULLPIERS score would correlate with adverse maternal outcomes such as ICU
admission, eclampsia, HELLP syndrome, and maternal death.
Specific Research Questions:
• Does the FULLPIERS model predict complications effectively in a low-resource, high-burden setting
like Pakistan?
• Can it assist clinicians in decision-making and prioritizing care for high-risk pregnant women?
Methodology
Study
Design:
Prospective
Observational
Study conducted
at Liaquat
University
Hospital,
Hyderabad,
Pakistan, between
June and July
2025.
Participants:
196 pregnant
women aged 20–
45 years with
gestational
hypertension,
preeclampsia, and
eclampsia, beyond
20 weeks of
gestation.
Inclusion
Criteria:
Exclusion
Criteria:
Data
Collection:
Risk
Stratification:
Data
Analysis:
Hypertensive
pregnant women
without preexisting liver or
kidney diseases,
aged 20–45 years.
Women with
documented
complications
such as eclampsia
and HELLP
syndrome.
Multiple
pregnancies and
incomplete
medical records.
Demographic
Data: Age,
gestational age,
prenatal care
status.
Clinical
Parameters:
Blood pressure,
oxygen saturation,
platelet count,
creatinine levels,
AST levels, etc.
Maternal
Outcomes: ICU
admission,
eclampsia, HELLP
syndrome,
maternal mortality.
Participants were
categorized into
two risk groups
based on their
FULLPIERS
score:
SPSS was used
for statistical
analysis, with chisquare tests to
assess
associations.
Significance
Level: p<0.05.
• Low-Moderate
Risk (Score 3–4)
• High Risk (Score ≥5)
•
Participant Demographics:
• Mean Age: 28.88 ± 3.83 years
• Mean Gestational Age: 34.04
± 2.80 weeks
• Prenatal Care: 51% were
booked (received prenatal
care), 49% were unbooked.
•
Risk Classification:
• 88.8% of participants were
classified as high risk
(FULLPIERS score ≥5).
•
Key Findings:
• Neurological Symptoms:
81.1% of high-risk cases
showed neurological symptoms
(p<0.001).
• Maternal Adverse Outcomes:
70% of participants in the highrisk group had significant
complications (p=0.0005).
• Eclampsia: 33.2% developed
eclampsia, while 20.4% had
pre-eclampsia (p=0.032).
• ICU Admission: 28.6% of the
participants required ICU
admission due to severe
complications.
•
Outcome:
• 55.6% were discharged healthy
with no significant morbidity.
• The FULLPIERS model
showed a strong ability to
predict adverse maternal
outcomes.
Results
Discussion
Interpretation of
Clinical
Limitations:
FULLPIERS Model’s Predictive
Early risk stratification using
FULLPIERS could improve
decision-making in clinical
practice.
The study is based on a single
hospital, limiting the ability to
generalize findings to other
settings.
This scoring model could help in
triaging high-risk pregnancies for
closer monitoring and timely
interventions, thus reducing
maternal morbidity and mortality.
Further multi-center studies are
needed to validate these results
across different demographics.
• The model demonstrated robust
predictive accuracy in
identifying high-risk pregnancies
that require intensive monitoring
and care.
• A higher score (≥5) strongly
correlated with adverse
maternal outcomes such as ICU
admission, HELLP syndrome,
and preeclampsia.
Conclusion
Key Conclusions:
Impact on Maternal Health:
• FULLPIERS is an effective, low-cost tool for
high-risk hypertensive pregnancies in lowresource settings.
• The model has strong predictive capabilities for
maternal complications, such as ICU admission,
HELLP syndrome, and maternal mortality.
• The study supports the integration of the
FULLPIERS risk score into routine obstetric
care in high-burden, low-resource settings like
Pakistan.
• Timely identification and management of highrisk pregnancies can significantly improve
maternal outcomes and reduce preventable
complications.
Recommendations
Integrating FULLPIERS in
Policy Implications:
Future Research:
It is recommended that the FULLPIERS
model be incorporated into the regular
screening protocols in hospitals,
especially in areas with high maternal
morbidity.
National Health Policies: Adoption of
models at the national level could help
prioritize care for high-risk pregnancies
and reduce maternal mortality.
Larger-scale studies and multi-center
trials are essential to evaluate the
model’s performance across diverse
populations and healthcare systems.
Training healthcare professionals on the
use of this model could improve its
adoption in clinical practice.
Limitations & Future Directions
Study Limitations:
Future Research:
• Single-Center Study: Results may not
applicable to all regions.
• Potential Bias: Limited diversity in the
affect the generalizability of the
findings.
• Multi-Center Studies: To assess the
FULLPIERS across different regions
and settings.
• Longitudinal Studies: To track the
and impact on maternal health
outcomes.
• Technological Integration: Explore
integrating the FULLPIERS model into
digital platforms for real-time
monitoring.
Acknowledgments &
References
• Acknowledgments:
• Special thanks to our supervisor,
hospital staff, participants and my
research fellows.