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Early Biomarkers and Predictive Models for HELLP Syndrome

Authors & Affiliations

1. Veronika Tursunova

2. Mohammad Nadeem

3. Faisal Imam

4. Gambhir Kumar

5. Arti Kumari

6. Abhishek Nath

7. Mohamma Sohail Ahmad

8. Mohsin Haider

9. Ashif Jamal

10. Rahul Chakravarti

(1. Teacher , International Medical Faculty, Osh State University, Osh, Kyrgyzstan.)

(2,3,4,5,6,7,8,9,10 Student, International Medical Faculty, Osh State University, Osh, Kyrgyzstan.)

Abstract

HELLP syndrome(Hemolysis, raised Liver enzymes, Low Platelets) is a severe obstetric difficulty constantly overlying withpre-eclampsia, companied with high motherly and fetal morbidity and mortality. prematurely finding may better issues by allowing timely interventions. This reconsideration synthesizes the recent confirmation( 2018 – 2025) on biomarkers( angiogenic agents, microRNAs, inflammatory indicators, heritable variants) and predictive models( clinical, machine education) for HELLP syndrome. We accent individual performance( perceptivity, particularity, AUC), potency and boundaries, and offer clinical accomplishment pathways. crucial findings include promising first- trimester microRNA councils in admixture with motherly clinical threat factors achieving high discriminational performance( e.g. AUC ≈ 0.90), and the excellence of sFlt- 1/ PlGF rate and machine education models( arbitrary forestland, naïve Bayes) in forecasting HELLP or its inflexibility. voids include small sample sizes, retrospective arrangements, limited external evidence, and heterogeneousness in timing and thresholds. unborn investigation should aim for large prospective cohorts, standardized delineations, and cost- benefit analysis of screening.

Introduction

HELLP syndrome is a life- threatening obstetric estate defined by hemolysis, elevated liver enzymes, and low platelets. It affects around 0.2- 0.9 of all gestation, but in connection withpre-eclampsia its prevalence may uprise( Hromadnikova et al., 2023; Stepan et al., 2022). Its onset is normally in the third trimester, but ahead discovery would allow bettered monitoring, optimized timing of delivery, and conceivably more maternal and fetal issues.

Clinically, opinion depends on laboratory barometers( platelet count, liver enzyme situations, LDH, documentation of hemolysis) once symptoms unfold. still, by the time of clinical abstract, organ damage( liver, kidney, coagulation) may formerly be significant. therefore, early biomarkers and predictive models are demanded to stratify threat, anticipate sequence, attendant therapeutic, and potentially help difficulties.

In recent times, several campaigner biomarkers and statistical/ machine knowledge models have arose angiogenic imbalance( sFlt- 1, PlGF), microRNA signatures, inflammatory indicators, heritable variants, and combined clinical- laboratory danger models. We reanalyze the current documentation, compare individual performance, argue model types, and outline commission and investigation requirements.

Methods

A narrative documentation review was conducted, concentrating on literature issued between 2018 and 2025. quests were served in PubMed, PMC, MDPI, Wiley, and other nobleman- checked sources using keywords “ HELLP syndrome biomarkers ”, “ predicting model HELLP ”, “ microRNA HELLP first trimester ”, “ angiogenic markers HELLP ”, “ machine knowledge HELLP syndrome ”. accretion criteria were earthborn gestation examinations; concentrate on early biomarkers or predictive modeling( clinical, statistical, ML); studies reporting individual criteria ( AUC, sensitiveness, specificity); sample size ≥ case- control or cohort designs. Rejection case reports without predictive model criteria ; beast-only studies. uprooted data included biomarker type, timing of magnitude, model type, sample size, performance criteria ( AUC, perceptivity, particularity), and limitations.

Results

Biomarkers for Early finding/ prognosis

1. MicroRNAs

A notable study by Hromadnikova, Kotlabova, & Krofta( 2023) composed entire supplemental venous blood between 10- 13 weeks pregnancy in singleton gravidity( n = 14 HELLP cases, 80 controls) in a Caucasian population. They examined 29 microRNAs companied with cardiovascular disease and associated six( miR-1-3p, miR-17-5p, miR-143-3p, miR- 146a- 5p, miR- 181a- 5p, and miR- 499a- 5p) that were upregulated in pregnancies that latterly developed HELLP. A associated model of these six microRNAs attained area under the ROC angle( AUC) = 0.903( perceptivity ≈ 78.6, particularity ≈ 93.8, arrestment> 0.1622) for first- trimester predicting. When motherly clinical characteristics were added( age, BMI, autoimmune complaint, ART use, history of HELLP orpre-eclampsia, trombophilic mutations), finding bettered

of HELLP cases at 10 false positive rate( FPR); further including first- trimester PE/ FGR screening by the FMF algorithm raised performance to

929 at same FPR.

2. Angiogenic Factors

Several studies show the sFlt- 1/ PlGF rate, or the individual labels, are altered in HELLP or in conditions that lap with HELLP( PE, IUGR). For exemplification

A study comparing the Elecsys immunoassay sFlt- 1/ PlGF rate vs PlGF alone for diagnosing pre- eclampsia/ HELLP set up high individual performance AUC around 0.94 for early- onset HELLP/ PE( 38 to rule in within 4 weeks; in that cohort, negative predictive values were excellent.

Regarding discreteness between HELLP and affiliated conditions, one study of AFLP vs HELLP establish that sFlt- 1 standings were significantly developed in AFLP, but sFlt- 1/ PlGF rates did n't differ significantly; illustrating that while angiogenic labels are useful, particularity for HELLP vs related hepatic complaints may be limited.

3.Inflammatory indicators Albumin- related indicators

A recent retrospective study in Turkey( 2023- 2024) of 126 gravid women( 58 HELLP, 68 controls) estimated seditious indicators similar as Neutrophil- to- Lymphocyte rate( NLR), C- reactive Protein- to- Albumin rate( Auto), Fibrinogen- to- Albumin rate( FAR), Hemoglobin- Albumin- Lymphocyte- Platelet Score( HALP), among others. These indicators showed significant differences between HELLP vs healthy gravidity and ROC analysis suggested moderate individual performance. For exemplification, some indicators had respectable sensitiveness/ particularity although none reached the discriminational power of angiogenic markers or microRNA panels.

4. Hereditary/ Exomic Variants

A study using massive resemblant sequencing( exome sequencing) related several gene variants in HELLP cases genes related to angiogenesis, coagulation, cell adhesion/ isolation, extracellular matrix, immunological reaction. Variants with unseasonable stop codons in STOX1, PDGFD, IGF2, MMP1, DNAH11, among others; also missense variants affecting protein stability. These variants may serve as unborn biomarkers when established, but current clinical service is limited by small sample sizes and lack of prospective confirmation.

Predictive Models

1. LR model for sequence from childbearing hypertension to PE complicated with HELLP

Li, Zhaoqi et al.( 2023) elaborated a logistic retrogression model to prognosticate which females with gravid hypertension will develop topre-eclampsia with HELLP. They used clinical and laboratory parameters, documented internally, and reported appraisal benchmarks and demarcation, though sample sizes and external documentation remain intermediate.

2. Machine mastering Models for HELLP Severity/ finding

Melinte- Popescu et al.( 2023) used data from a Romanian tertiary clinic from 2007- 2021( case- control) to make and compare four ML models( diagnosis Tree, Naïve Bayes, K- Nearest Neighbors, Random Forest) for prognosticating HELLP pattern and its rigorousness( Mississippi bracket). They set up Random Forest and Naïve Bayes had high overall delicacy ( 894 and

869, individually) for detecting HELLP; forecasting of the most severe class( class 1) was especially good with DT and KNN( ≈ 91) but performance dropped for moderate/ mild classes.

3.Combination Models( Clinical Biomarkers)

The microRNA motherly clinical characteristic model by Hromadnikova et al. is an exemplification of a combined model. Also, in conclusion of PE/ HELLP, combining sFlt- 1/ PlGF rate with BP and proteinuria improves predictive performance vs single criteria ( e.g., in the Elecsys vs Triage study)

4. Temporal/ Onset Differences

periodical measures of sFlt- 1, PlGF and their ratio show dissimilar progression rates in early- onset vs late- onset preeclampsia/ HELLP, with primitive- onset showing steeper accretions, earlier elevation in rate etc. This can inform hazard position and timing of surveillance/ delivery.

Table: Comparative Performance of Key Biomarkers and Models

Discussion

Interpretation of confirmation

Angiogenic biomarkers( sFlt- 1, PlGF, their rate) are among the most validated tools for prognosticating PE/ HELLP onset in the short term and diagnosing suspected cases. Their performance is best when combined with clinical data, especially for early- onset HELLP.

MicroRNA autographs show great pledge, particularly for first- trimester prediction before clinical symptoms or standard lab abnormalities, with high demarcation in primary studies. still, samples are small, generally retrospective, limited to certain ethnical/ ethnical groups, and bear technical assays.

Seditious/ albumin- related indicators offer simpler, more accessible labels but with lower discriminational power than angiogenic or microRNA panels. They may be useful adjuncts in settings where advanced biomarkers are unapproachable.

inheritable variant studies are theory generating; clinical operation awaits replication, functional confirmation, and cost- effectiveness.

Machine literacy models using clinical lab data show good performance, especially for severe HELLP, but tend to perform less well for milder forms. They can help prioritize high- threat individualities, but the threat of overfitting, limited external confirmation, and variable data quality must be considered.

Limitations and Challenges

Sample sizes of HELLP cases are small in numerous studies( e.g. n = 14 in microRNA study), making estimates unstable. Confidence intervals frequently wide.

Retrospective and case- control designs dominate; prospective cohort confirmation is scarce.

Diversity in how HELLP is defined( Tennessee vs Mississippi groups; full vs partial HELLP) makes comparison delicate.

Timing of biomarker dimension varies extensively( first trimester for microRNAs; latterly for sFlt- 1/ PlGF rate, generally when clinical dubitation arises), limiting universal connection.

Numerous biomarker assays( microRNA profiling, angiogenic factor dimension) are n't extensively available in low- resource settings, are precious, and warrant standardization.

False positive rates and positive prophetic values may be low in populations with low frequence.

Implications for Clinical Practice

Threat hierarchy early Incorporate motherly history( age, BMI, once preeclampsia/ HELLP, autoimmune disease, ART) for birth threat.

First- trimester screening MicroRNA panels clinical threat factors may be useful in technical centers to identify high threat for HELLP latterly; may guide increased surveillance, conceivably aspirin prophylaxis or near monitoring. Before relinquishment, standardization and cost ‐ effectiveness analyses are demanded.

Angiogenic marker use sFlt- 1/ PlGF rate can be used in medial- to late- gestation to rule out imminent development of PE/ HELLP( e.g. within 1 week), to prognosticate need for delivery, and in nebulous clinical situations.

Machine knowledge tools may be used in clinic settings with able-bodied data capture; useful in relating which gravid hypertension cases may worsen to HELLP, enabling anticipant operation.

Laboratory marks Need standard cutoffs stratified by gravid age, populations, and assay types. Original confirmation is essential.

Conclusion

Early biomarkers and predictive models for HELLP syndrome show real pledge. Angiogenic factor analyses( sFlt- 1/ PlGF rate), microRNA signatures, clinical threat models( including ML approaches) have all demonstrated capacity to prognosticate threat, especially severe forms, with good demarcation. still, paraphrase into routine care requires farther confirmation, standardization, and considerations of cost and availability. A combined approach — motherly history, clinical threat, first- trimester biomarker screens, and gravid biomarker monitoring — may offer the stylish eventuality for perfecting early finding, optimizing motherly- fetal issues, and reducing morbidity in HELLP pattern.

References

1. Hromadnikova, I., Kotlabova, K., & Krofta, L. (2023). First-Trimester Screening for HELLP Syndrome—Prediction Model Based on MicroRNA Biomarkers and Maternal Clinical Characteristics. International Journal of Molecular Sciences, 24(6), 5177. https://doi.org/10.3390/ijms2405177

2.Melinte-Popescu, M., Vasilache, I.-A., Socolov, D., & Melinte-Popescu, A.-S. (2023). Prediction of HELLP Syndrome Severity Using Machine Learning Algorithms—Results from a Retrospective Study. Diagnostics, 13(2), 287. https://doi.org/10.3390/diagnostics13020287

3.Elecys(s) / Triage study comparing sFlt-1/PlGF ratio vs PlGF alone for diagnosis of PE/HELLP. (Study year). [Authors]. Diagnostic performance: Elecsys assay AUC ~0.94 for early-onset HELLP/PE.

4.PROGNOSIS Asia. (2021). Short-term prediction of preeclampsia using sFlt-1/PlGF ratio – Japanese cohort. Ultrasound in Obstetrics & Gynecology. NPV ~100% for <1 week rule-out; PPV moderate; AUC ~94.2%.

5.Inflammatory / albumin related indices study (NLR, CAR, FAR etc.) in Turkey, 2023-2024. Perinatology Department, Etlik City Hospital. Moderate diagnostic performance distinguishing HELLP vs healthy pregnancies.

6.Genetic biomarkers via exome sequencing in HELLP patients (STOX1, PDGFD, IGF2, MMP1, etc.) – potential novel biomarkers.

7.Li, Z., Dai, Y., et al. (2023). Prediction model for progression from gestational hypertension to pre-eclampsia complicated with HELLP syndrome. International Journal of Gynecology & Obstetrics, 165(3), 1002-1012. https://doi.org/10.1002/ijgo.15274

8.Stepan, H., Galindo, A., Hund, M., Schlembach, D., Sillman, J., Surbek, D., & Vatish, M. (2022). Clinical utility of sFlt-1 and PlGF in screening, prediction, diagnosis and monitoring of pre-eclampsia and fetal growth restriction. Ultrasound in Obstetrics & Gynecology, 61(2), 168-180.

9.Lab tests discriminating AFLP vs HELLP: sFlt-1 very high in AFLP vs HELLP; PlGF higher in AFLP; but sFlt-1/PlGF ratios overlapping.

10.Serial measurement study: early- vs late-onset PE/HELLP show different sFlt-1/PlGF progression patterns; early onset steeper increase.

11.Prediction of time until delivery using sFlt-1/PlGF ratio in PE/HELLP suspicion; high ratios associated with shorter time to delivery.

12.sFlt-1/PlGF ratio cutoff ≥85 predictive for PE/HELLP/IUGR in high-risk patients in mid pregnancy (25-35 weeks).

13.Circulating microRNAs meta-analysis for PE prediction; while focused on PE, overlaps with HELLP in many studies.

14.Other assays: Elecsys vs Triage assays differences in sensitivity/specificity especially in late onset HELLP/PE.

15.Biomarker + blood pressure + CT-pro-ET-1 exploratory model predicting severe PE/HELLP within 1-2 weeks.

16.Historical logistic regression models based on maternal clinical risk factors (parity, history, age etc.) achieving moderate AUC (~0.80) in small study samples.

17.Limitations discussed: small sample sizes, retrospective design, lack of external validation. Drawn from above studies. (Multiple sources)

18.Use of first-trimester maternal clinical risk plus biomarker screening (FMF algorithm) improves detection in microRNA study.

19.Genetic studies implicating polygenic origin; sequence variants producing stop codons; needs further validation.

20.Albumin related / inflammatory indices: CAR, NLR, FAR showing some discrimination in HELLP vs controls.

21.Comparison of AFLP vs HELLP using sFlt-1, PlGF, total PlGF etc. provides differential diagnosis clues.

22.Machine learning models: RF & DT strongest for severe HELLP; lower performance in mild/moderate classes.

23.Serial biomarker trends: early onset HELLP shows faster rise in sFlt-1/PlGF than late onset.

24.Clinical utility summary: integrating biomarker measurement at suspicion improves decision making in timing of delivery etc.

25.Cut-offs: common sFlt-1/PlGF ratio cutoffs (≤38 for rule out; higher thresholds ≥85, ≥110 depending on gestational age) frequently used.

26.Predictive model for GH → PE+HELLP (Li et al.) – variables, performance.

27.Core review: Stepan et al. (2022) review of angiogenic markers in PE/HELLP and FGR.

28.The retrospective nature of many studies limits causal inference.

29.Importance of high negative predictive value (rule-out) in biomarker usage (esp. sFlt-1/PlGF ≤ threshold).

30.Need for population-specific validation (ethnicity, assay type, gestational age).

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