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A Risk Prediction Model for the Assessment and Triage of Women with Hypertensive Disorders of Pregnancy in Low-Resourced Settings: The miniPIERS (Pre-eclampsia Integrated Estimate of RiSk) Multi-country Prospective Cohort Study


Background:
Pre-eclampsia/eclampsia are leading causes of maternal mortality and morbidity, particularly in low- and middle- income countries (LMICs). We developed the miniPIERS risk prediction model to provide a simple, evidence-based tool to identify pregnant women in LMICs at increased risk of death or major hypertensive-related complications.

Methods and Findings:
From 1 July 2008 to 31 March 2012, in five LMICs, data were collected prospectively on 2,081 women with any hypertensive disorder of pregnancy admitted to a participating centre. Candidate predictors collected within 24 hours of admission were entered into a step-wise backward elimination logistic regression model to predict a composite adverse maternal outcome within 48 hours of admission. Model internal validation was accomplished by bootstrapping and external validation was completed using data from 1,300 women in the Pre-eclampsia Integrated Estimate of RiSk (fullPIERS) dataset. Predictive performance was assessed for calibration, discrimination, and stratification capacity. The final miniPIERS model included: parity (nulliparous versus multiparous); gestational age on admission; headache/visual disturbances; chest pain/dyspnoea; vaginal bleeding with abdominal pain; systolic blood pressure; and dipstick proteinuria. The miniPIERS model was well-calibrated and had an area under the receiver operating characteristic curve (AUC ROC) of 0.768 (95% CI 0.735–0.801) with an average optimism of 0.037. External validation AUC ROC was 0.713 (95% CI 0.658–0.768). A predicted probability ≥25% to define a positive test classified women with 85.5% accuracy. Limitations of this study include the composite outcome and the broad inclusion criteria of any hypertensive disorder of pregnancy. This broad approach was used to optimize model generalizability.

Conclusions:
The miniPIERS model shows reasonable ability to identify women at increased risk of adverse maternal outcomes associated with the hypertensive disorders of pregnancy. It could be used in LMICs to identify women who would benefit most from interventions such as magnesium sulphate, antihypertensives, or transportation to a higher level of care.

Please see later in the article for the Editors' Summary


Vyšlo v časopise: A Risk Prediction Model for the Assessment and Triage of Women with Hypertensive Disorders of Pregnancy in Low-Resourced Settings: The miniPIERS (Pre-eclampsia Integrated Estimate of RiSk) Multi-country Prospective Cohort Study. PLoS Med 11(1): e32767. doi:10.1371/journal.pmed.1001589
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pmed.1001589

Souhrn

Background:
Pre-eclampsia/eclampsia are leading causes of maternal mortality and morbidity, particularly in low- and middle- income countries (LMICs). We developed the miniPIERS risk prediction model to provide a simple, evidence-based tool to identify pregnant women in LMICs at increased risk of death or major hypertensive-related complications.

Methods and Findings:
From 1 July 2008 to 31 March 2012, in five LMICs, data were collected prospectively on 2,081 women with any hypertensive disorder of pregnancy admitted to a participating centre. Candidate predictors collected within 24 hours of admission were entered into a step-wise backward elimination logistic regression model to predict a composite adverse maternal outcome within 48 hours of admission. Model internal validation was accomplished by bootstrapping and external validation was completed using data from 1,300 women in the Pre-eclampsia Integrated Estimate of RiSk (fullPIERS) dataset. Predictive performance was assessed for calibration, discrimination, and stratification capacity. The final miniPIERS model included: parity (nulliparous versus multiparous); gestational age on admission; headache/visual disturbances; chest pain/dyspnoea; vaginal bleeding with abdominal pain; systolic blood pressure; and dipstick proteinuria. The miniPIERS model was well-calibrated and had an area under the receiver operating characteristic curve (AUC ROC) of 0.768 (95% CI 0.735–0.801) with an average optimism of 0.037. External validation AUC ROC was 0.713 (95% CI 0.658–0.768). A predicted probability ≥25% to define a positive test classified women with 85.5% accuracy. Limitations of this study include the composite outcome and the broad inclusion criteria of any hypertensive disorder of pregnancy. This broad approach was used to optimize model generalizability.

Conclusions:
The miniPIERS model shows reasonable ability to identify women at increased risk of adverse maternal outcomes associated with the hypertensive disorders of pregnancy. It could be used in LMICs to identify women who would benefit most from interventions such as magnesium sulphate, antihypertensives, or transportation to a higher level of care.

Please see later in the article for the Editors' Summary


Zdroje

1. SteegersEA, von DadelszenP, DuvekotJJ, PijnenborgR (2010) Pre-eclampsia. Lancet 376: 631–644.

2. LozanoR, NaghaviM, ForemanK, LimS, ShibuyaK, et al. (2013) Global and regional mortality from 235 causes of death for 20 age groups in 1990 and 2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet 380: 2095–2128.

3. KhanKS, WojdylaD, SayL, GulmezogluAM, Van LookPF (2006) WHO analysis of causes of maternal death: a systematic review. Lancet 367: 1066–1074.

4. HutcheonJA, LisonkovaS, JosephKS (2011) Epidemiology of pre-eclampsia and the other hypertensive disorders of pregnancy. Best Pract Res Clin Obstet Gynaecol 25: 391–403.

5. FirozT, SanghviH, MerialdiM, von DadelszenP (2011) Pre-eclampsia in low and middle income countries. Best Pract Res Clin Obstet Gynaecol 25: 537–548.

6. Joint Learning Initiative (2004) Human Resources for Health: overcoming the crisis. Available: http://www.who.int/hrh/documents/JLi_hrh_report.pdf. Accessed 3 August 2012.

7. GanzevoortW, SibaiBM (2011) Temporising versus interventionist management (preterm and at term). Best Pract Res Clin Obstet Gynaecol 25: 463–476.

8. GabryschS, CampbellOM (2009) Still too far to walk: literature review of the determinants of delivery service use. BMC Pregnancy Childbirth 9: 34.

9. ThaddeusS, MaineD (1994) Too far to walk: maternal mortality in context. Soc Sci Med 38: 1091–1110.

10. FultonBD, SchefflerRM, SparkesSP, AuhEY, VujicicM, et al. (2011) Health workforce skill mix and task shifting in low income countries: a review of recent evidence. Hum Resour Health 9: 1.

11. von DadelszenP, PayneB, LiJ, AnserminoJM, PipkinFB, et al. (2011) Prediction of adverse maternal outcomes in pre-eclampsia: development and validation of the fullPIERS model. Lancet 377: 219–227.

12. PayneB, MageeLA, von DadelszenP (2011) Assessment, surveillance and prognosis in pre-eclampsia. Best Pract Res Clin Obstet Gynaecol 25: 449–462.

13. BrownB, CochranSW, HelmerO (1967) An evaluation of methodology of Delphi Technique. Biometrics 23: 600–606.

14. HarrellFE, LeeKL, MarkDB (1996) Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat Med 15: 361–387.

15. SteyerbergEW, BorsboomGJ, van HouwelingenHC, EijkemansMJ, HabbemaJD (2004) Validation and updating of predictive logistic regression models: a study on sample size and shrinkage. Stat Med 23: 2567–2586.

16. VergouweY, SteyerbergEW, EijkemansMJ, HabbemaJD (2005) Substantial effective sample sizes were required for external validation studies of predictive logistic regression models. J Clin Epidemiol 58: 475–483.

17. SteyerbergEW, VickersAJ, CookNR, GerdsT, GonenM, et al. (2010) Assessing the performance of prediction models: a framework for traditional and novel measures. Epidemiology 21: 128–138.

18. HanleyJA, McNeilBJ (1982) The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143: 29–36.

19. DeeksJ, AltmanD (2004) Statistics notes - Diagnostic tests 4: likelihood ratios. Br Med J 329: 168–169.

20. JanesH, PepeMS, GuW (2008) Assessing the value of risk predictions by using risk stratification tables. Ann Intern Med 149: 751–760.

21. Efron B, Tibsherani R (1993) An introduction to the bootstrap. New York: Chapman and Hal.

22. MartinJNJr, ThigpenBD, MooreRC, RoseCH, CushmanJ, MayW (2005) Stroke and severe preeclampsia and eclampsia: a paradigm shift focusing on systolic blood pressure. Obstet Gynecol 105: 246–254.

23. DissanayakeVH, MorganL, Broughton PipkinF, VathananV, PremaratneS, et al. (2004) The urine protein heat coagulation test–a useful screening test for proteinuria in pregnancy in developing countries: a method validation study. BJOG 111: 491–494.

24. RichardosnDK, CorcoranJD, EscobarGJ, LeeSK (2001) Canadian NICU Network, Kaiser Permanente Neonatal Data Se, (2001) et al. SNAP-II and SNAPPE-II: Simplified newborn illness severity and mortality risk scores. J Pediatr 138: 92–100.

25. D'AgostinoRBSr, GrundyS, SullivanLM, WilsonP (2001) CHD Risk Prediction G. Validation of the Framingham coronary heart disease prediction scores: results of a multiple ethnic groups investigation. JAMA 286: 180–187.

26. AustinPC, TuJV (2004) Automated variable selection methods for logistic regression produced unstable models for predicting acute myocardial infarction mortality. J Clin Epidemiol 57: 1138–1146.

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Interné lekárstvo

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