Poronienie – Rokowania, prognozy i postęp choroby

Poronienie
Rokowania, prognozy i postęp choroby

Poronienie stanowi jedno z najczęstszych powikłań ciąży, a najnowsze badania koncentrują się na rozwoju zaawansowanych modeli predykcyjnych opartych na uczeniu maszynowym (ML), które umożliwiają przewidywanie ryzyka poronienia oraz skuteczności różnych metod leczenia, takich jak postępowanie wyczekujące i farmakoterapia. Modele regresji logistycznej z zastosowaniem metod LASSO i RFE wykazały wartości AUC odpowiednio 0,63-0,72 dla postępowania wyczekującego oraz 0,62-0,71 dla leczenia farmakologicznego, potwierdzając ich uogólnialność i kliniczną użyteczność. Dodatkowo opracowano skalę ryzyka opartą na przedkoncepcyjnych czynnikach matczynych, z AUC 0,74, pozwalającą na klasyfikację pacjentek na niskie (<10%), pośrednie (10-40%) i wysokie (≥40%) ryzyko poronienia. W modelu predykcyjnym uwzględniono m.in. wiek matki, historię zatrzymania rozwoju zarodka, dysfunkcję tarczycy, zespół policystycznych jajników, rozród wspomagany, ekspozycję na zanieczyszczenia oraz czynniki psychospołeczne, co podkreśla wielowymiarowy charakter ryzyka poronienia.

Poronienie – Prognozy poronień (predykcja wyników)

Modele uczenia maszynowego w przewidywaniu skuteczności leczenia poronień
Skala ryzyka poronienia przed koncepcją
Model predykcyjny oparty na czynnikach klinicznych i psychospołecznych
MikroRNA jako biomarkery ryzyka poronienia
Model dynamiczny do przewidywania wyniku w pierwszym trymestrze ciąży
Pojedynczy pomiar progesteronu jako predyktor wyniku ciąży
Wpływ stresu psychologicznego na ryzyko poronienia
Podsumowanie modeli predykcyjnych w poronieniu
Kolejne rozdziały

Poronienie – Prognozy poronień (predykcja wyników)

Poronienie to jedno z najczęstszych powikłań ciąży. Badania pokazują, że rozwijane są zaawansowane metody pozwalające na przewidywanie ryzyka poronienia oraz powodzenia różnych metod postępowania w przypadku jego wystąpienia. Możliwość dokładnego prognozowania wyników ma kluczowe znaczenie dla personalizacji opieki nad pacjentkami.¹²

Modele uczenia maszynowego w przewidywaniu skuteczności leczenia poronień

Najnowsze badania wykorzystują metody uczenia maszynowego (ML) do tworzenia modeli predykcyjnych, które mogą przewidywać powodzenie postępowania wyczekującego oraz leczenia farmakologicznego poronień. Modele te wykorzystują łatwo dostępne dane dotyczące pacjentki, wyniki badań ultrasonograficznych oraz dane z wcześniejszych wyników leczenia.³⁴

Dla postępowania wyczekującego najlepszym modelem predykcyjnym okazała się regresja logistyczna w połączeniu z metodą redukcji cech LASSO (Least Absolute Shrinkage and Selection Operator). Model ten wykazał dobrą skuteczność predykcyjną z wartościami AUC (obszar pod krzywą ROC) wynoszącymi 0,72 (95% CI 0,67-0,77) dla zbioru treningowego, 0,63 (95% CI 0,53-0,73) dla zbioru walidacyjnego oraz 0,70 (95% CI 0,60-0,79) dla zewnętrznego zbioru testowego.⁵

W przypadku leczenia farmakologicznego poronień, najlepszy okazał się model regresji logistycznej z metodą eliminacji rekurencyjnej cech (RFE). Wykazał on podobną skuteczność z wartościami AUC wynoszącymi 0,64 (95% CI 0,56-0,72) dla zbioru treningowego, 0,62 (95% CI 0,45-0,77) dla zbioru walidacyjnego oraz 0,71 (95% CI 0,58-0,83) dla zewnętrznego zbioru testowego.⁶

Co istotne, modele te wykazały stałą skuteczność zarówno w zbiorach walidacyjnych, jak i zewnętrznych zbiorach testowych, co świadczy o ich uogólnialności – kluczowej cesze z perspektywy użyteczności klinicznej. Modele te są niewątpliwie lepsze niż obecna praktyka stosowania uogólnionych prognoz dla wszystkich kobiet, ponieważ mogą oferować spersonalizowane podejście do opieki poprzez dostarczanie indywidualnych prognoz i tym samym wspomagać proces podejmowania decyzji dotyczących postępowania w przypadku poronienia.⁷⁸⁹

Skala ryzyka poronienia przed koncepcją

Opracowano również skalę ryzyka opartą na przedkoncepcyjnych czynnikach ryzyka ze strony matki, która może być wykorzystana do identyfikacji poziomów ryzyka poronienia wśród kobiet planujących zajście w ciążę. W badaniu kohortowym S-PRESTO zaobserwowano wyższe wskaźniki poronień przy wyższych wynikach w skali ryzyka:¹⁰¹¹

5,3% przy wyniku 3
17,0% przy wyniku 4-6
40,0% przy wyniku 7-8
46,2% przy wyniku 9

¹²

Na podstawie tych wyników kobiety zostały podzielone na trzy poziomy ryzyka:¹³

Niskie ryzyko (wynik ≤3): <10% ryzyka poronienia
Pośrednie ryzyko (wynik 4-6): 10% do 40% ryzyka poronienia
Wysokie ryzyko (wynik 7-9): ≥40% ryzyka poronienia

¹⁴

Skala ryzyka wykazała zadowalającą zdolność dyskryminacyjną w przewidywaniu poronienia, z wartością AUC wynoszącą 0,74 (95% przedział ufności 0,67-0,81; p < 0,001).¹⁵¹⁶

Model predykcyjny oparty na czynnikach klinicznych i psychospołecznych

Opracowano i wewnętrznie zwalidowano model predykcyjny do szacowania ryzyka samoistnego poronienia we wczesnej ciąży. Ostateczny model predykcyjny obejmował dziewięć zmiennych:¹⁷¹⁸

Wiek matki
Historia zatrzymania rozwoju zarodka
Dysfunkcja tarczycy
Zespół policystycznych jajników
Rozród wspomagany
Ekspozycja na zanieczyszczenia
Niedawny remont domu
Wynik w skali depresji
Wynik w skali stresu

¹⁹

Model ten wykazał dobrą skuteczność w szacowaniu ryzyka samoistnego poronienia we wczesnej ciąży na podstawie czynników demograficznych, klinicznych i psychospołecznych. Badanie ujawniło korelację między różnymi czynnikami a prawdopodobieństwem niepłodności. Zidentyfikowano kilka silnych predyktorów, w tym zaawansowany wiek matki, historię położniczą, przewlekłe schorzenia (zaburzenia tarczycy i zespół policystycznych jajników), rozród wspomagany, toksyczne narażenia środowiskowe oraz zły stan zdrowia psychicznego.²⁰

MikroRNA jako biomarkery ryzyka poronienia

Najnowsze badania oceniły potencjał mikroRNA związanych z chorobami układu sercowo-naczyniowego w przewidywaniu wystąpienia poronienia lub martwego urodzenia we wczesnych etapach ciąży (od 10 do 13 tygodnia ciąży).²¹

Model predykcyjny dla samych poronień opierał się na zmienionych ekspresji genów ośmiu biomarkerów mikroRNA. Kombinacja tych ośmiu biomarkerów mikroRNA o zmienionych ekspresji we wczesnych etapach ciąży była w stanie poprawnie zidentyfikować 80,52% ciąż, niezależnie od momentu wystąpienia poronienia (wczesne i późne poronienia), przy 10,0% współczynniku fałszywie dodatnich wyników (FPR).²²²³

Dla martwych urodzeń kombinacja jedenastu biomarkerów mikroRNA o zmienionych ekspresji we wczesnych etapach ciąży pozwalała poprawnie zidentyfikować 95,83% ciąż z martwymi urodzeniami przy 10,0% FPR. Co ciekawe, skuteczne badanie przesiewowe w kierunku martwego urodzenia było możliwe również przy użyciu kombinacji tylko dwóch biomarkerów mikroRNA. Kombinacja miR-1-3p i miR-181a-5p wykrywała we wczesnych etapach ciąży 91,67% przypadków przy 10,0% FPR.²⁴

Modele oparte na kombinacji wybranych mikroRNA związanych z chorobami układu sercowo-naczyniowego miały bardzo wysoki potencjał predykcyjny dla poronień lub martwych urodzeń i mogą być wdrożone w rutynowych programach badań przesiewowych w pierwszym trymestrze. Kombinacja dziewięciu biomarkerów mikroRNA o zmienionych ekspresji we wczesnych etapach ciąży identyfikowała ciąże z późniejszymi poronieniami lub martwymi urodzeniami z doskonałą dokładnością – wykryto 99,01% przypadków przy 10,0% FPR.²⁵

Model dynamiczny do przewidywania wyniku w pierwszym trymestrze ciąży

Opracowano również dynamiczny model do przewidywania wyniku w pierwszym trymestrze ciąży, wykorzystujący podstawowe dane demograficzne oraz seryjnie pobierane próbki krwi i badania ultrasonograficzne przezpochwowe.²⁶

Wyniki wykazały, że 18% kobiet doświadczyło poronień. Wykrycie tętna płodu przed 8. tygodniem ciąży wskazywało na 90% (95% CI 85-95%) szansę na późniejszy poród. Wiek matki (≥35 lat), niewystarczający rozwój długości ciemieniowo-siedzeniowej (CRL) i średniej średnicy pęcherzyka ciążowego (MSD) oraz obecność krwawienia zwiększały ryzyko poronienia.²⁷

Najlepszym modelem do przewidywania poronienia była kombinacja wieku matki, krwawienia z pochwy, CRL i hCG. Drugim najlepszym modelem był model bez ultrasonografii, obejmujący wiek matki, krwawienie, hCG i estradiol. Kobiety, które poroniły, były średnio o 2 lata starsze niż kobiety z trwającymi ciążami (31,5 vs. 29,4 lat, p=0,021).²⁸²⁹

Szanse na poronienie były znacząco zwiększone w skorygowanym modelu dla otyłych (≥30 kg/m²) kobiet (aOR 3,4, 95% CI [1,1; 10], p=0,03) i zmniejszone dla kobiet z dwoma lub więcej wcześniejszymi porodami (aOR 0,1, 95% CI [0,01; 0,6], p=0,02). Szanse na urodzenie żywego dziecka zazwyczaj wzrastały porównując niższe (referencyjne) do wyższych kwantyli zarówno przed, jak i po 7. tygodniu ciąży. Co ciekawe, estradiol okazał się lepszym predyktorem poronienia niż progesteron.³⁰

Pojedynczy pomiar progesteronu jako predyktor wyniku ciąży

Meta-analiza wykazała, że pojedynczy pomiar progesteronu jest przydatny w przewidywaniu nieżywotnych ciąż u kobiet z bólem lub krwawieniem, gdy badanie ultrasonograficzne okazuje się niejednoznaczne. Niskie stężenie progesteronu (mniej niż 3,2 do 6 ng/ml) u tych kobiet wykluczało żywotną ciążę w 99,2% przypadków.³¹

Wpływ stresu psychologicznego na ryzyko poronienia

Systematyczny przegląd i meta-analiza wykazały, że ryzyko poronienia było znacząco wyższe u kobiet z historią narażenia na stres psychologiczny (OR 1,42, 95% CI 1,19-1,70). Wyniki tej meta-analizy wspierają przekonanie, że stres psychologiczny przed ciążą i w jej trakcie jest związany z poronieniem. Obecne wyniki pokazują, że te czynniki psychologiczne mogą zwiększyć ryzyko o około 42%.³²

W podsumowaniu, badania te dostarczają najsilniejszych jak dotąd dowodów na to, że wcześniejszy stres psychologiczny jest szkodliwy dla kobiet we wczesnej ciąży, wskazując na krytyczną potrzebę dalszych wysokiej jakości badań nad relacją między poronieniem a stresem doświadczanym przed ciążą i we wczesnym okresie ciąży.³³

Podsumowanie modeli predykcyjnych w poronieniu

Modele predykcyjne oparte na uczeniu maszynowym, biomarkerach mikroRNA, parametrach hormonalnych i ultrasonograficznych, a także czynnikach psychospołecznych i demograficznych oferują nowe możliwości w personalizacji opieki nad kobietami zagrożonymi poronieniem lub przechodzącymi przez ten trudny proces. Mają one potencjał, aby znacząco poprawić podejmowanie decyzji klinicznych i zapewnić kobietom bardziej spersonalizowane informacje na temat ich indywidualnego ryzyka i potencjalnych wyników różnych strategii postępowania.³⁴³⁵

W miarę postępu badań i dalszej walidacji tych modeli, możemy spodziewać się ich coraz szerszego wdrażania w praktyce klinicznej, co może przyczynić się do optymalizacji wyników i zmniejszenia obciążenia związanego z tym powszechnym powikłaniem ciąży.³⁶

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Materiały źródłowe

#1 Risk score to stratify miscarriage risk levels in preconception women | Scientific Reports
https://www.nature.com/articles/s41598-021-91567-8
Spontaneous miscarriage is one of the most common complications of pregnancy. […] In the S-PRESTO cohort study, Asian women attempting to conceive, aged 18-45 years, were recruited. […] Higher rates of miscarriage were observed at higher risk scores (5.3% at score 3, 17.0% at score 4-6, 40.0% at score 7-8 and 46.2% at score 9). […] Women with scores 3 were defined as low-risk level (10% miscarriage); scores 4-6 as intermediate-risk level (10% to 40% miscarriage); scores 7-9 as high-risk level (40% miscarriage). […] The risk score yielded an area under the receiver-operating-characteristic curve of 0.74 (95% confidence interval 0.67, 0.81; p < 0.001). [...] This novel scoring tool allows women to self-evaluate their miscarriage risk level, which facilitates lifestyle changes to optimize modifiable risk factors in the preconception period and reduces risk of spontaneous miscarriage.
#2 The association between psychological stress and miscarriage: A systematic review and meta-analysis | Scientific Reports
https://www.nature.com/articles/s41598-017-01792-3
This systematic review and meta-analysis was designed to investigate whether maternal psychological stress and recent life events are associated with an increased risk of miscarriage. […] The risk of miscarriage was significantly higher in women with a history of exposure to psychological stress (OR 1.42, 95% CI 1.191.70). […] Our finding provides the most robust evidence to date, that prior psychological stress is harmful to women in early pregnancy. […] The results of this meta-analysis support the belief that psychological stress before and during pregnancy is associated with miscarriage. […] The present results show that these psychological factors can increase the risk by approximately 42%. […] In summary, the result of this systematic review and meta-analysis support the belief that psychological stress, including life events and occupational stress, in pregnancy is associated with an increased risk of miscarriage and indicates a critical need for further high quality research into the relationship between miscarriage and stress experienced prior to pregnancy and in the early gestational period.
#3 Predicting outcomes of expectant and medical management in early pregnancy miscarriage using machine learning to develop and validate multivariable clinical prediction models – PubMed
https://pubmed.ncbi.nlm.nih.gov/40021998/
Objective: To determine whether readily available patient, ultrasound and treatment outcome data can be used to develop, validate and externally test two machine learning (ML) models for predicting the success of expectant and medical management of miscarriage respectively. […] Performance of our expectant and medical miscarriage management ML models demonstrate consistency across validation and external test sets. These ML methods, validated and externally tested, have the potential to offer personalised prediction outcome of expectant and medical management of miscarriage.
#4 Predicting outcomes of expectant and medical management in early pregnancy miscarriage using machine learning to develop and validate multivariable clinical prediction models | BMC Pregnancy and Childbirth | Full Text
https://bmcpregnancychildbirth.biomedcentral.com/articles/10.1186/s12884-025-07283-y
To determine whether readily available patient, ultrasound and treatment outcome data can be used to develop, validate and externally test two machine learning (ML) models for predicting the success of expectant and medical management of miscarriage respectively. […] Performance of our expectant and medical miscarriage management ML models demonstrate consistency across validation and external test sets. These ML methods, validated and externally tested, have the potential to offer personalised prediction outcome of expectant and medical management of miscarriage. […] A machine learning based model could provide each individual a personalised chance of success with expectant and medical management respectively. […] The final prediction model chosen for expectant management was Logistic Regression in combination with Least Absolute Shrinkage and Selection Operator (LASSO) feature reduction. Results of the final models on training, validation and external test sets are shown in Fig. 2 (ROC curves). The training set AUC=0.72 (95% CI 0.67,0.77), validation set AUC=0.63 (95% CI 0.53,0.73) and external test set AUC=0.70 (95% CI 0.60,0.79). Performance of our models were consistent across training, validation and external test sets.
#5 Predicting outcomes of expectant and medical management in early pregnancy miscarriage using machine learning to develop and validate multivariable clinical prediction models | BMC Pregnancy and Childbirth | Full Text
https://bmcpregnancychildbirth.biomedcentral.com/articles/10.1186/s12884-025-07283-y
To determine whether readily available patient, ultrasound and treatment outcome data can be used to develop, validate and externally test two machine learning (ML) models for predicting the success of expectant and medical management of miscarriage respectively. […] Performance of our expectant and medical miscarriage management ML models demonstrate consistency across validation and external test sets. These ML methods, validated and externally tested, have the potential to offer personalised prediction outcome of expectant and medical management of miscarriage. […] A machine learning based model could provide each individual a personalised chance of success with expectant and medical management respectively. […] The final prediction model chosen for expectant management was Logistic Regression in combination with Least Absolute Shrinkage and Selection Operator (LASSO) feature reduction. Results of the final models on training, validation and external test sets are shown in Fig. 2 (ROC curves). The training set AUC=0.72 (95% CI 0.67,0.77), validation set AUC=0.63 (95% CI 0.53,0.73) and external test set AUC=0.70 (95% CI 0.60,0.79). Performance of our models were consistent across training, validation and external test sets.
#6 Predicting outcomes of expectant and medical management in early pregnancy miscarriage using machine learning to develop and validate multivariable clinical prediction models | BMC Pregnancy and Childbirth | Full Text
https://bmcpregnancychildbirth.biomedcentral.com/articles/10.1186/s12884-025-07283-y
The final prediction model chosen for medical management was Logistic Regression in combination with Recursive Feature Elimination (RFE) feature reduction. Results of the final models on training, validation and external test sets are shown in Fig. 5 (ROC curves). The training set AUC=0.64 (95% CI 0.56,0.72), validation set AUC=0.62 (95% CI 0.45,0.77) and external test set AUC=0.71 (95% CI 0.58,0.83). Performance of our models were consistent across training, validation and external test sets. […] We have developed two machine learning models that can predict the outcomes from expectant and medical management of miscarriage respectively. This is the first multi-site UK study, with over 1000 patients, to systematically apply multiple ML techniques and feature reduction techniques in the development of a high performing, robust ML-based model to predict the outcome of expectant and medical management of miscarriage.
#7 Predicting outcomes of expectant and medical management in early pregnancy miscarriage using machine learning to develop and validate multivariable clinical prediction models | BMC Pregnancy and Childbirth | Full Text
https://bmcpregnancychildbirth.biomedcentral.com/articles/10.1186/s12884-025-07283-y
The models had a consistent performance between the validation and external test sets, demonstrating generalisability which is important from a clinical utility perspective. These models are undoubtedly better than the current practice of using generalised outcome prediction for all women. These models could offer a personalised approach to care, through providing individualised outcome prediction and thus support the decision making process for the management of miscarriage.
#8 Predicting outcomes of expectant and medical management in early pregnancy miscarriage using machine learning to develop and validate multivariable clinical prediction models
https://pmc.ncbi.nlm.nih.gov/articles/PMC11869538/
To determine whether readily available patient, ultrasound and treatment outcome data can be used to develop, validate and externally test two machine learning (ML) models for predicting the success of expectant and medical management of miscarriage respectively. […] Performance of our expectant and medical miscarriage management ML models demonstrate consistency across validation and external test sets. These ML methods, validated and externally tested, have the potential to offer personalised prediction outcome of expectant and medical management of miscarriage. […] A clinical decision support tool which generates personalised outcome predictions for each miscarriage management option, may alleviate some of this burden. A machine learning based model could provide each individual a personalised chance of success with expectant and medical management respectively.
#9 Predicting outcomes of expectant and medical management in early pregnancy miscarriage using machine learning to develop and validate multivariable clinical prediction models
https://pmc.ncbi.nlm.nih.gov/articles/PMC11869538/
The aim of this study is to harness the use of multiple machine learning methods applied to a range of readily available patient demographic, symptom and ultrasound features. […] The best performing model was the Logistic Regression model, with the Least Absolute Shrinkage and Selection Operator (LASSO) feature reduction method. […] The best performing medical management outcome prediction model was the Logistic Regression model, with the Recursive Feature Elimination (RFE) feature reduction, using eight features. […] These models could offer a personalised approach to care, through providing individualised outcome prediction and thus support the decision making process for the management of miscarriage.
#10 Risk score to stratify miscarriage risk levels in preconception women | Scientific Reports
https://www.nature.com/articles/s41598-021-91567-8
Spontaneous miscarriage is one of the most common complications of pregnancy. […] In the S-PRESTO cohort study, Asian women attempting to conceive, aged 18-45 years, were recruited. […] Higher rates of miscarriage were observed at higher risk scores (5.3% at score 3, 17.0% at score 4-6, 40.0% at score 7-8 and 46.2% at score 9). […] Women with scores 3 were defined as low-risk level (10% miscarriage); scores 4-6 as intermediate-risk level (10% to 40% miscarriage); scores 7-9 as high-risk level (40% miscarriage). […] The risk score yielded an area under the receiver-operating-characteristic curve of 0.74 (95% confidence interval 0.67, 0.81; p < 0.001). [...] This novel scoring tool allows women to self-evaluate their miscarriage risk level, which facilitates lifestyle changes to optimize modifiable risk factors in the preconception period and reduces risk of spontaneous miscarriage.
#11 Risk score to stratify miscarriage risk levels in preconception women | Scientific Reports
https://www.nature.com/articles/s41598-021-91567-8
We aimed to develop a risk score based on a set of preconception maternal risk factors, which could be used to identify the risk levels for miscarriage among women planning to conceive. […] We classified the risk score into 4 categories, i.e. scores 0-3, 4-6, 7-8 and 9-17 based on the distribution of miscarriage rates in each category. […] We further classified them into low-, intermediate- and high-risk levels, based on the percentage of miscarriage at each category. […] The ROC analysis revealed a fair discriminatory ability of the risk score in predicting miscarriage, as indicated by an AUC of 0.74 (95% confidence interval 0.67, 0.81; p < 0.001). [...] We developed a simple risk score based on a group of preconception maternal risk factors, to identify the risk levels for spontaneous miscarriage among women planning to conceive. [...] Overall, the risk model showed a fair discriminatory ability to predict miscarriage before 16 weeks gestation, with an AUC of 0.74.
#12 Risk score to stratify miscarriage risk levels in preconception women | Scientific Reports
https://www.nature.com/articles/s41598-021-91567-8
Spontaneous miscarriage is one of the most common complications of pregnancy. […] In the S-PRESTO cohort study, Asian women attempting to conceive, aged 18-45 years, were recruited. […] Higher rates of miscarriage were observed at higher risk scores (5.3% at score 3, 17.0% at score 4-6, 40.0% at score 7-8 and 46.2% at score 9). […] Women with scores 3 were defined as low-risk level (10% miscarriage); scores 4-6 as intermediate-risk level (10% to 40% miscarriage); scores 7-9 as high-risk level (40% miscarriage). […] The risk score yielded an area under the receiver-operating-characteristic curve of 0.74 (95% confidence interval 0.67, 0.81; p < 0.001). [...] This novel scoring tool allows women to self-evaluate their miscarriage risk level, which facilitates lifestyle changes to optimize modifiable risk factors in the preconception period and reduces risk of spontaneous miscarriage.
#13 Risk score to stratify miscarriage risk levels in preconception women | Scientific Reports
https://www.nature.com/articles/s41598-021-91567-8
Spontaneous miscarriage is one of the most common complications of pregnancy. […] In the S-PRESTO cohort study, Asian women attempting to conceive, aged 18-45 years, were recruited. […] Higher rates of miscarriage were observed at higher risk scores (5.3% at score 3, 17.0% at score 4-6, 40.0% at score 7-8 and 46.2% at score 9). […] Women with scores 3 were defined as low-risk level (10% miscarriage); scores 4-6 as intermediate-risk level (10% to 40% miscarriage); scores 7-9 as high-risk level (40% miscarriage). […] The risk score yielded an area under the receiver-operating-characteristic curve of 0.74 (95% confidence interval 0.67, 0.81; p < 0.001). [...] This novel scoring tool allows women to self-evaluate their miscarriage risk level, which facilitates lifestyle changes to optimize modifiable risk factors in the preconception period and reduces risk of spontaneous miscarriage.
#14 Risk score to stratify miscarriage risk levels in preconception women | Scientific Reports
https://www.nature.com/articles/s41598-021-91567-8
We aimed to develop a risk score based on a set of preconception maternal risk factors, which could be used to identify the risk levels for miscarriage among women planning to conceive. […] We classified the risk score into 4 categories, i.e. scores 0-3, 4-6, 7-8 and 9-17 based on the distribution of miscarriage rates in each category. […] We further classified them into low-, intermediate- and high-risk levels, based on the percentage of miscarriage at each category. […] The ROC analysis revealed a fair discriminatory ability of the risk score in predicting miscarriage, as indicated by an AUC of 0.74 (95% confidence interval 0.67, 0.81; p < 0.001). [...] We developed a simple risk score based on a group of preconception maternal risk factors, to identify the risk levels for spontaneous miscarriage among women planning to conceive. [...] Overall, the risk model showed a fair discriminatory ability to predict miscarriage before 16 weeks gestation, with an AUC of 0.74.
#15 Risk score to stratify miscarriage risk levels in preconception women | Scientific Reports
https://www.nature.com/articles/s41598-021-91567-8
Spontaneous miscarriage is one of the most common complications of pregnancy. […] In the S-PRESTO cohort study, Asian women attempting to conceive, aged 18-45 years, were recruited. […] Higher rates of miscarriage were observed at higher risk scores (5.3% at score 3, 17.0% at score 4-6, 40.0% at score 7-8 and 46.2% at score 9). […] Women with scores 3 were defined as low-risk level (10% miscarriage); scores 4-6 as intermediate-risk level (10% to 40% miscarriage); scores 7-9 as high-risk level (40% miscarriage). […] The risk score yielded an area under the receiver-operating-characteristic curve of 0.74 (95% confidence interval 0.67, 0.81; p < 0.001). [...] This novel scoring tool allows women to self-evaluate their miscarriage risk level, which facilitates lifestyle changes to optimize modifiable risk factors in the preconception period and reduces risk of spontaneous miscarriage.
#16 Risk score to stratify miscarriage risk levels in preconception women | Scientific Reports
https://www.nature.com/articles/s41598-021-91567-8
We aimed to develop a risk score based on a set of preconception maternal risk factors, which could be used to identify the risk levels for miscarriage among women planning to conceive. […] We classified the risk score into 4 categories, i.e. scores 0-3, 4-6, 7-8 and 9-17 based on the distribution of miscarriage rates in each category. […] We further classified them into low-, intermediate- and high-risk levels, based on the percentage of miscarriage at each category. […] The ROC analysis revealed a fair discriminatory ability of the risk score in predicting miscarriage, as indicated by an AUC of 0.74 (95% confidence interval 0.67, 0.81; p < 0.001). [...] We developed a simple risk score based on a group of preconception maternal risk factors, to identify the risk levels for spontaneous miscarriage among women planning to conceive. [...] Overall, the risk model showed a fair discriminatory ability to predict miscarriage before 16 weeks gestation, with an AUC of 0.74.
#17 Development and internal validation of a clinical prediction model for spontaneous abortion risk in early pregnancy | Clinics
https://www.elsevier.es/en-revista-clinics-22-articulo-development-internal-validation-clinical-prediction-S1807593223001540
This study aimed to develop and internally validate a prediction model for estimating the risk of spontaneous abortion in early pregnancy. […] The final prediction model included nine variables: maternal age, history of embryonic arrest, thyroid dysfunction, polycystic ovary syndrome, assisted reproduction, exposure to pollution, recent home renovation, depression score, and stress score. […] The prediction model demonstrated good performance in estimating spontaneous abortion risk in early pregnancy based on demographic, clinical, and psychosocial factors. […] There remains a need for robust and well-validated prognostic models that can estimate the risk of spontaneous abortion in early pregnancy based on multiple demographics, clinical, and lifestyle predictors. […] The model enables individualized risk assessment based on a multitude of demographic, clinical, lifestyle and mental health predictors. […] With ongoing validation and refinement, it has significant potential to optimize outcomes and reduce the burden of this common pregnancy complication.
#18 Development and internal validation of a clinical prediction model for spontaneous abortion risk in early pregnancy | Clinics
https://www.elsevier.es/es-revista-clinics-22-articulo-development-internal-validation-clinical-prediction-S1807593223001540
This study aimed to develop and internally validate a prediction model for estimating the risk of spontaneous abortion in early pregnancy. […] The spontaneous abortion rate was 5.95% (589/9,895). […] The final prediction model included nine variables: maternal age, history of embryonic arrest, thyroid dysfunction, polycystic ovary syndrome, assisted reproduction, exposure to pollution, recent home renovation, depression score, and stress score. […] The prediction model demonstrated good performance in estimating spontaneous abortion risk in early pregnancy based on demographic, clinical, and psychosocial factors. […] There remains a need for robust and well-validated prognostic models that can estimate the risk of spontaneous abortion in early pregnancy based on multiple demographics, clinical, and lifestyle predictors.
#19 Development and internal validation of a clinical prediction model for spontaneous abortion risk in early pregnancy | Clinics
https://www.elsevier.es/es-revista-clinics-22-articulo-development-internal-validation-clinical-prediction-S1807593223001540
This study aimed to develop and internally validate a prediction model for estimating the risk of spontaneous abortion in early pregnancy. […] The spontaneous abortion rate was 5.95% (589/9,895). […] The final prediction model included nine variables: maternal age, history of embryonic arrest, thyroid dysfunction, polycystic ovary syndrome, assisted reproduction, exposure to pollution, recent home renovation, depression score, and stress score. […] The prediction model demonstrated good performance in estimating spontaneous abortion risk in early pregnancy based on demographic, clinical, and psychosocial factors. […] There remains a need for robust and well-validated prognostic models that can estimate the risk of spontaneous abortion in early pregnancy based on multiple demographics, clinical, and lifestyle predictors.
#20 Development and internal validation of a clinical prediction model for spontaneous abortion risk in early pregnancy | Clinics
https://www.elsevier.es/es-revista-clinics-22-articulo-development-internal-validation-clinical-prediction-S1807593223001540
The study revealed a correlation between various factors and the likelihood of infertility. […] Several robust predictors emerged, including advanced maternal age, obstetric history, chronic conditions like thyroid disorders and polycystic ovary syndrome, assisted reproduction, toxic environmental exposures, and poor mental health. […] The prediction model enables individualized risk quantification to guide the management of high-risk women.
#21 First-Trimester Screening for Miscarriage or StillbirthâPrediction Model Based on MicroRNA Biomarkers
https://www.mdpi.com/1422-0067/24/12/10137
We evaluated the potential of cardiovascular-disease-associated microRNAs to predict in the early stages of gestation (from 10 to 13 gestational weeks) the occurrence of a miscarriage or stillbirth. […] The predictive model for miscarriage only was based on the altered gene expressions of eight microRNA biomarkers. […] The models based on the combination of selected cardiovascular-disease-associated microRNAs had very high predictive potential for miscarriages or stillbirths and may be implemented in routine first-trimester screening programs. […] The combination of nine microRNA biomarkers with altered expressions in early stages of gestation identified pregnancies with subsequent miscarriages or stillbirths with an excellent accuracy. A total of 99.01% of cases were revealed at a 10.0% FPR.
#22 First-Trimester Screening for Miscarriage or StillbirthâPrediction Model Based on MicroRNA Biomarkers
https://www.mdpi.com/1422-0067/24/12/10137
We evaluated the potential of cardiovascular-disease-associated microRNAs to predict in the early stages of gestation (from 10 to 13 gestational weeks) the occurrence of a miscarriage or stillbirth. […] The predictive model for miscarriage only was based on the altered gene expressions of eight microRNA biomarkers. […] The models based on the combination of selected cardiovascular-disease-associated microRNAs had very high predictive potential for miscarriages or stillbirths and may be implemented in routine first-trimester screening programs. […] The combination of nine microRNA biomarkers with altered expressions in early stages of gestation identified pregnancies with subsequent miscarriages or stillbirths with an excellent accuracy. A total of 99.01% of cases were revealed at a 10.0% FPR.
#23 First-Trimester Screening for Miscarriage or StillbirthâPrediction Model Based on MicroRNA Biomarkers
https://www.mdpi.com/1422-0067/24/12/10137
The combination of these eight microRNA biomarkers with altered expressions in early stages of gestation was able to correctly identify 80.52% of pregnancies, regardless of the onset of miscarriage (early and late miscarriages), at a 10.0% FPR. […] The combination of eleven microRNA biomarkers with altered expressions in early stages of gestation was able to correctly identify 95.83% of pregnancies with stillbirths at a 10.0% FPR. […] Effective screening for stillbirth was also possible using the combination of two microRNA biomarkers only. The combination of miR-1-3p and miR-181a-5p revealed at early stages of gestation 91.67% of cases at a 10.0% FPR.
#24 First-Trimester Screening for Miscarriage or StillbirthâPrediction Model Based on MicroRNA Biomarkers
https://www.mdpi.com/1422-0067/24/12/10137
The combination of these eight microRNA biomarkers with altered expressions in early stages of gestation was able to correctly identify 80.52% of pregnancies, regardless of the onset of miscarriage (early and late miscarriages), at a 10.0% FPR. […] The combination of eleven microRNA biomarkers with altered expressions in early stages of gestation was able to correctly identify 95.83% of pregnancies with stillbirths at a 10.0% FPR. […] Effective screening for stillbirth was also possible using the combination of two microRNA biomarkers only. The combination of miR-1-3p and miR-181a-5p revealed at early stages of gestation 91.67% of cases at a 10.0% FPR.
#25 First-Trimester Screening for Miscarriage or StillbirthâPrediction Model Based on MicroRNA Biomarkers
https://www.mdpi.com/1422-0067/24/12/10137
We evaluated the potential of cardiovascular-disease-associated microRNAs to predict in the early stages of gestation (from 10 to 13 gestational weeks) the occurrence of a miscarriage or stillbirth. […] The predictive model for miscarriage only was based on the altered gene expressions of eight microRNA biomarkers. […] The models based on the combination of selected cardiovascular-disease-associated microRNAs had very high predictive potential for miscarriages or stillbirths and may be implemented in routine first-trimester screening programs. […] The combination of nine microRNA biomarkers with altered expressions in early stages of gestation identified pregnancies with subsequent miscarriages or stillbirths with an excellent accuracy. A total of 99.01% of cases were revealed at a 10.0% FPR.
#26
https://link.springer.com/article/10.1007/s43032-023-01323-8
This study aimed to develop a dynamic model for predicting outcome during the first trimester of pregnancy using baseline demographic data and serially collected blood samples and transvaginal sonographies. […] The results showed that 18% of the women experienced miscarriages. A fetal heart rate detected before 8 weeks gestation indicated a 90% (95% CI 85-95%) chance of subsequent delivery. Maternal age (35 years), insufficient crown-rump-length (CRL) and mean gestational sac diameter (MSD) development, and presence of bleeding increased the risk of miscarriage. […] The best model to predict miscarriage was a combination of maternal age, vaginal bleeding, CRL, and hCG. The second-best model was the sonography-absent model of maternal age, bleeding, hCG, and estradiol. […] This study suggests that combining maternal age, and evolving data from hCG, estradiol, CRL, and bleeding could be used to predict fetal outcome during the first trimester of pregnancy.
#27
https://link.springer.com/article/10.1007/s43032-023-01323-8
This study aimed to develop a dynamic model for predicting outcome during the first trimester of pregnancy using baseline demographic data and serially collected blood samples and transvaginal sonographies. […] The results showed that 18% of the women experienced miscarriages. A fetal heart rate detected before 8 weeks gestation indicated a 90% (95% CI 85-95%) chance of subsequent delivery. Maternal age (35 years), insufficient crown-rump-length (CRL) and mean gestational sac diameter (MSD) development, and presence of bleeding increased the risk of miscarriage. […] The best model to predict miscarriage was a combination of maternal age, vaginal bleeding, CRL, and hCG. The second-best model was the sonography-absent model of maternal age, bleeding, hCG, and estradiol. […] This study suggests that combining maternal age, and evolving data from hCG, estradiol, CRL, and bleeding could be used to predict fetal outcome during the first trimester of pregnancy.
#28
https://link.springer.com/article/10.1007/s43032-023-01323-8
This study aimed to develop a dynamic model for predicting outcome during the first trimester of pregnancy using baseline demographic data and serially collected blood samples and transvaginal sonographies. […] The results showed that 18% of the women experienced miscarriages. A fetal heart rate detected before 8 weeks gestation indicated a 90% (95% CI 85-95%) chance of subsequent delivery. Maternal age (35 years), insufficient crown-rump-length (CRL) and mean gestational sac diameter (MSD) development, and presence of bleeding increased the risk of miscarriage. […] The best model to predict miscarriage was a combination of maternal age, vaginal bleeding, CRL, and hCG. The second-best model was the sonography-absent model of maternal age, bleeding, hCG, and estradiol. […] This study suggests that combining maternal age, and evolving data from hCG, estradiol, CRL, and bleeding could be used to predict fetal outcome during the first trimester of pregnancy.
#29
https://link.springer.com/article/10.1007/s43032-023-01323-8
Women who miscarried were on average 2 years older than women with ongoing pregnancies (31.5 vs. 29.4 years, p=0.021). […] Odds of miscarriage were significantly increased in the adjusted model for obese (30 kg/m2) women (aOR 3.4, 95% CI [1.1; 10], p=0.03) and reduced for women with two or more previous deliveries (aOR 0.1, 95% CI [0.01; 0.6], p=0.02). […] The odds of a live birth typically increased comparing lower (reference) to higher quantiles both before and after 7 weeks gestation. […] The best combination of variables for prediction of miscarriage was age, hCG, CRL and bleeding. The second-best model was the sonography-absent model of maternal age, bleeding, hCG, and estradiol. […] Surprisingly, estradiol was a better predictor of miscarriage than progesterone.
#30
https://link.springer.com/article/10.1007/s43032-023-01323-8
Women who miscarried were on average 2 years older than women with ongoing pregnancies (31.5 vs. 29.4 years, p=0.021). […] Odds of miscarriage were significantly increased in the adjusted model for obese (30 kg/m2) women (aOR 3.4, 95% CI [1.1; 10], p=0.03) and reduced for women with two or more previous deliveries (aOR 0.1, 95% CI [0.01; 0.6], p=0.02). […] The odds of a live birth typically increased comparing lower (reference) to higher quantiles both before and after 7 weeks gestation. […] The best combination of variables for prediction of miscarriage was age, hCG, CRL and bleeding. The second-best model was the sonography-absent model of maternal age, bleeding, hCG, and estradiol. […] Surprisingly, estradiol was a better predictor of miscarriage than progesterone.
#31 Accuracy of single progesterone test to predict early pregnancy outcome in women with pain or bleeding: meta-analysis of cohort studies | The BMJ
https://www.bmj.com/content/345/bmj.e6077
Objective To determine the accuracy with which a single progesterone measurement in early pregnancy discriminates between viable and non-viable pregnancy. […] A single progesterone measurement for women in early pregnancy presenting with bleeding or pain and inconclusive ultrasound assessments can rule out a viable pregnancy. […] The meta-analysis shows that a single progesterone measurement is useful in predicting non-viable pregnancies in women with pain or bleeding when an ultrasound investigation proves to be inconclusive. A low concentration of progesterone (less than 3.2 to 6 ng/mL) in these women ruled out a viable pregnancy in 99.2% of women. […] A single low progesterone measurement for women in early pregnancy presenting with bleeding or pain and inconclusive ultrasound results can rule out a viable pregnancy.
#32 The association between psychological stress and miscarriage: A systematic review and meta-analysis | Scientific Reports
https://www.nature.com/articles/s41598-017-01792-3
This systematic review and meta-analysis was designed to investigate whether maternal psychological stress and recent life events are associated with an increased risk of miscarriage. […] The risk of miscarriage was significantly higher in women with a history of exposure to psychological stress (OR 1.42, 95% CI 1.191.70). […] Our finding provides the most robust evidence to date, that prior psychological stress is harmful to women in early pregnancy. […] The results of this meta-analysis support the belief that psychological stress before and during pregnancy is associated with miscarriage. […] The present results show that these psychological factors can increase the risk by approximately 42%. […] In summary, the result of this systematic review and meta-analysis support the belief that psychological stress, including life events and occupational stress, in pregnancy is associated with an increased risk of miscarriage and indicates a critical need for further high quality research into the relationship between miscarriage and stress experienced prior to pregnancy and in the early gestational period.
#33 The association between psychological stress and miscarriage: A systematic review and meta-analysis | Scientific Reports
https://www.nature.com/articles/s41598-017-01792-3
This systematic review and meta-analysis was designed to investigate whether maternal psychological stress and recent life events are associated with an increased risk of miscarriage. […] The risk of miscarriage was significantly higher in women with a history of exposure to psychological stress (OR 1.42, 95% CI 1.191.70). […] Our finding provides the most robust evidence to date, that prior psychological stress is harmful to women in early pregnancy. […] The results of this meta-analysis support the belief that psychological stress before and during pregnancy is associated with miscarriage. […] The present results show that these psychological factors can increase the risk by approximately 42%. […] In summary, the result of this systematic review and meta-analysis support the belief that psychological stress, including life events and occupational stress, in pregnancy is associated with an increased risk of miscarriage and indicates a critical need for further high quality research into the relationship between miscarriage and stress experienced prior to pregnancy and in the early gestational period.
#34 Predicting outcomes of expectant and medical management in early pregnancy miscarriage using machine learning to develop and validate multivariable clinical prediction models | BMC Pregnancy and Childbirth | Full Text
https://bmcpregnancychildbirth.biomedcentral.com/articles/10.1186/s12884-025-07283-y
The models had a consistent performance between the validation and external test sets, demonstrating generalisability which is important from a clinical utility perspective. These models are undoubtedly better than the current practice of using generalised outcome prediction for all women. These models could offer a personalised approach to care, through providing individualised outcome prediction and thus support the decision making process for the management of miscarriage.
#35 Development and internal validation of a clinical prediction model for spontaneous abortion risk in early pregnancy | Clinics
https://www.elsevier.es/en-revista-clinics-22-articulo-development-internal-validation-clinical-prediction-S1807593223001540
This study aimed to develop and internally validate a prediction model for estimating the risk of spontaneous abortion in early pregnancy. […] The final prediction model included nine variables: maternal age, history of embryonic arrest, thyroid dysfunction, polycystic ovary syndrome, assisted reproduction, exposure to pollution, recent home renovation, depression score, and stress score. […] The prediction model demonstrated good performance in estimating spontaneous abortion risk in early pregnancy based on demographic, clinical, and psychosocial factors. […] There remains a need for robust and well-validated prognostic models that can estimate the risk of spontaneous abortion in early pregnancy based on multiple demographics, clinical, and lifestyle predictors. […] The model enables individualized risk assessment based on a multitude of demographic, clinical, lifestyle and mental health predictors. […] With ongoing validation and refinement, it has significant potential to optimize outcomes and reduce the burden of this common pregnancy complication.
#36 Development and internal validation of a clinical prediction model for spontaneous abortion risk in early pregnancy | Clinics
https://www.elsevier.es/en-revista-clinics-22-articulo-development-internal-validation-clinical-prediction-S1807593223001540
This study aimed to develop and internally validate a prediction model for estimating the risk of spontaneous abortion in early pregnancy. […] The final prediction model included nine variables: maternal age, history of embryonic arrest, thyroid dysfunction, polycystic ovary syndrome, assisted reproduction, exposure to pollution, recent home renovation, depression score, and stress score. […] The prediction model demonstrated good performance in estimating spontaneous abortion risk in early pregnancy based on demographic, clinical, and psychosocial factors. […] There remains a need for robust and well-validated prognostic models that can estimate the risk of spontaneous abortion in early pregnancy based on multiple demographics, clinical, and lifestyle predictors. […] The model enables individualized risk assessment based on a multitude of demographic, clinical, lifestyle and mental health predictors. […] With ongoing validation and refinement, it has significant potential to optimize outcomes and reduce the burden of this common pregnancy complication.