External validation of a frequently used prediction model for ongoing pregnancy in couples with unexplained recurrent pregnancy loss

2021 ◽  
Author(s):  
A Youssef ◽  
M L P van der Hoorn ◽  
M Dongen ◽  
J Visser ◽  
K Bloemenkamp ◽  
...  
Keyword(s):  
Prediction Model ◽  
Success Rate ◽  
Maternal Age ◽  
Pregnancy Loss ◽  
Recurrent Pregnancy Loss ◽  
External Validation ◽  
Predictive Performance ◽  
Pregnancy Rates ◽  
Successful Pregnancy ◽  
Discriminative Ability

Abstract STUDY QUESTION What is the predictive performance of a currently recommended prediction model in an external Dutch cohort of couples with unexplained recurrent pregnancy loss (RPL)? SUMMARY ANSWER The model shows poor predictive performance on a new population; it overestimates, predicts too extremely and has a poor discriminative ability. WHAT IS KNOWN ALREADY In 50–75% of couples with RPL, no risk factor or cause can be determined and RPL remains unexplained. Clinical management in RPL is primarily focused on providing supportive care, in which counselling on prognosis is a main pillar. A frequently used prediction model for unexplained RPL, developed by Brigham et al. in 1999, estimates the chance of a successful pregnancy based on number of previous pregnancy losses and maternal age. This prediction model has never been externally validated. STUDY DESIGN, SIZE, DURATION This retrospective cohort study consisted of 739 couples with unexplained RPL who visited the RPL clinic of the Leiden University Medical Centre between 2004 and 2019. PARTICIPANTS/MATERIALS, SETTING, METHODS Unexplained RPL was defined as the loss of two or more pregnancies before 24 weeks, without the presence of an identifiable cause for the pregnancy losses, according to the ESHRE guideline. Obstetrical history and maternal age were noted at intake at the RPL clinic. The outcome of the first pregnancy after intake was documented. The performance of Brigham’s model was evaluated through calibration and discrimination, in which the predicted pregnancy rates were compared to the observed pregnancy rates. MAIN RESULTS AND THE ROLE OF CHANCE The cohort included 739 women with a mean age of 33.1 years (±4.7 years) and with a median of three pregnancy losses at intake (range 2–10). The mean predicted pregnancy success rate was 9.8 percentage points higher in the Brigham model than the observed pregnancy success rate in the dataset (73.9% vs 64.0% (95% CI for the 9.8% difference 6.3–13.3%)). Calibration showed overestimation of the model and too extreme predictions, with a negative calibration intercept of −0.46 (95% CI −0.62 to −0.31) and a calibration slope of 0.42 (95% CI 0.11–0.73). The discriminative ability of the model was very low with a concordance statistic of 0.55 (95% CI 0.51–0.59). Recalibration of the Brigham model hardly improved the c-statistic (0.57; 95% CI 0.53–0.62) LIMITATIONS, REASONS FOR CAUTION This is a retrospective study in which only the first pregnancy after intake was registered. There was no time frame as inclusion criterium, which is of importance in the counselling of couples with unexplained RPL. Only cases with a known pregnancy outcome were included. WIDER IMPLICATIONS OF THE FINDINGS This is the first study externally validating the Brigham prognostic model that estimates the chance of a successful pregnancy in couples with unexplained RPL. The results show that the frequently used model overestimates the chances of a successful pregnancy, that predictions are too extreme on both the high and low ends and that they are not much more discriminative than random luck. There is a need for revising the prediction model to estimate the chance of a successful pregnancy in couples with unexplained RPL more accurately. STUDY FUNDING/COMPETING INTEREST(S) No external funding was used and no competing interests were declared. TRIAL REGISTRATION NUMBER N/A.

P–419 On prognosis after unexplained recurrent pregnancy losses (RPL); a systematic review and external validation of clinical prediction models

2021 ◽  
Vol 36 (Supplement_1) ◽  
Author(s):  
A Youssef
Keyword(s):  
Systematic Review ◽  
Supportive Care ◽  
Prediction Model ◽  
Pregnancy Outcome ◽  
Prediction Models ◽  
External Validation ◽  
Model Development ◽  
Predictive Performance ◽  
Pregnancy Rates ◽  
Cochrane Library

Abstract Study question Which models that predict pregnancy outcome in couples with unexplained RPL exist and what is the performance of the most used model? Summary answer We identified seven prediction models; none followed the recommended prediction model development steps. Moreover, the most used model showed poor predictive performance. What is known already RPL remains unexplained in 50–75% of couples For these couples, there is no effective treatment option and clinical management rests on supportive care. Essential part of supportive care consists of counselling on the prognosis of subsequent pregnancies. Indeed, multiple prediction models exist, however the quality and validity of these models varies. In addition, the prediction model developed by Brigham et al is the most widely used model, but has never been externally validated. Study design, size, duration We performed a systematic review to identify prediction models for pregnancy outcome after unexplained RPL. In addition we performed an external validation of the Brigham model in a retrospective cohort, consisting of 668 couples with unexplained RPL that visited our RPL clinic between 2004 and 2019. Participants/materials, setting, methods A systematic search was performed in December 2020 in Pubmed, Embase, Web of Science and Cochrane library to identify relevant studies. Eligible studies were selected and assessed according to the TRIPOD) guidelines, covering topics on model performance and validation statement. The performance of predicting live birth in the Brigham model was evaluated through calibration and discrimination, in which the observed pregnancy rates were compared to the predicted pregnancy rates. Main results and the role of chance Seven models were compared and assessed according to the TRIPOD statement. This resulted in two studies of low, three of moderate and two of above average reporting quality. These studies did not follow the recommended steps for model development and did not calculate a sample size. Furthermore, the predictive performance of neither of these models was internally- or externally validated. We performed an external validation of Brigham model. Calibration showed overestimation of the model and too extreme predictions, with a negative calibration intercept of –0.52 (CI 95% –0.68 – –0.36), with a calibration slope of 0.39 (CI 95% 0.07 – 0.71). The discriminative ability of the model was very low with a concordance statistic of 0.55 (CI 95% 0.50 – 0.59). Limitations, reasons for caution None of the studies are specifically named prediction models, therefore models may have been missed in the selection process. The external validation cohort used a retrospective design, in which only the first pregnancy after intake was registered. Follow-up time was not limited, which is important in counselling unexplained RPL couples. Wider implications of the findings: Currently, there are no suitable models that predict on pregnancy outcome after RPL. Moreover, we are in need of a model with several variables such that prognosis is individualized, and factors from both the female as the male to enable a couple specific prognosis. Trial registration number Not applicable

scholarly journals Puzzle Out the Reason behind Habitual Miscarriage

2020 ◽  
pp. 92-98
Author(s):  
Waqas Ahmad ◽  
Shahid Bilal ◽  
Sarah Azhar ◽  
Muhammad Aitmaud Uddolah Khan ◽  
Nasima Iqbal ◽  
...  
Keyword(s):  
Risk Factors ◽  
Maternal Age ◽  
Pregnancy Loss ◽  
Recurrent Pregnancy Loss ◽  
Multinomial Logistic Regression ◽  
Strong Association ◽  
Multinomial Logistic Regression Model ◽  
Multiple Risk Factors ◽  
Bivariate Correlation ◽  
History Of

Aims: As no data is available in Pakistan so the aim of current study is to find out the link of multiple risk factors with recurrent pregnancy loss (RPL) in Pakistan. Study Design: Case control study. Place and Duration of Study: Study conducted in Obstetrics and Gynecology Clinic of Benazir Bhutto Hospital, Holy Family Hospital Rawalpindi and Polyclinic Hospital Islamabad from November 2018 to April 2019. Methodology: Subjects were investigated on the basis of an in depth Performa. For data analysis Statistical package for social sciences version-20 was used. Beside this, height in cm, weight in kg and blood pressure in mmHg were recorded. All the statistical calculations were performed by using SPSS 20. For association analysis of qualitative variables Spearman bivariate correlation was calculated while for numerical variables ANOVA was applied. Multinomial logistic regression model was used and the odd ratio and relative risk were calculated. Results: Among cases 91.34% were having spontaneous miscarriage and majority (64.86%) were during first trimester. Spearman bivariate correlation reported a strong association of recurrent pregnancy loss with the risk factors including family history, smoking, obesity, history of hypertension and history of diabetes, having highly significant p-values, on the hand, significant association of maternal age with the frequency of recurrent pregnancy loss was found but not with the paternal age and parity. The multinomial logistic regression model showed that smokers were19.012 times more prone to develop recurrent pregnancy loss. Conclusion: The multiple risk factors including maternal age, obesity, smoking, family history, body mass index, hypertension and diabetes have a strong association with the recurrent pregnancy loss. So keeping these risk factors in mind a careful evaluation of each pregnancy is necessary to reduce the risk of recurrent pregnancy loss.

P–355 Cancer diagnosis among patients with recurrent pregnancy loss: a cohort study

2021 ◽  
Vol 36 (Supplement_1) ◽  
Author(s):  
A Cahe. Peretz ◽  
J Haas ◽  
E Hadi ◽  
H Carp ◽  
A Hershk. Klement
Keyword(s):  
Cohort Study ◽  
Survival Analysis ◽  
Cancer Cells ◽  
Maternal Age ◽  
Pregnancy Loss ◽  
Recurrent Pregnancy Loss ◽  
Comparison Group ◽  
Cancer Type ◽  
Gynecological Cancers ◽  
Cancer Morbidity

Abstract Study question Is unexplained recurrent pregnancy loss (RPL) related to long term cancer morbidity? Summary answer Recurrent unexplained pregnancy loss patients showed lower cancer morbidity. This trend was significant in the secondary aborters and in a sub-analysis for gynecological cancers. What is known already The association between infertility and cancer was studied, but has scarcely been studied in RPL; One study reported a higher incidence of breast and uterine cancers, while another found no association. Immune dysfunction is a possible cause of ‘unexplained RPL’; RPL patients have an increased number of toxic natural killer cells (NKs) in both peripheral blood and decidua. The immune system is also involved in the recognition of cancer cells, potentially leading to effective killing. It is possible that the NK populations in RPL are capable of a better response towards cancer cells in the uterine environment and related organs. Study design, size, duration A retrospective cohort study comparing RPL patients and patients with normal deliveries presenting between 1990 –2010 and followed up until 2018. Participants/materials, setting, methods The RPL (exposed) group consisted of patients with 3 or more losses between 5–24 weeks. The comparison (unexposed) group included women who gave birth, and were not listed in the registry of RPL patients. Matching was based on maternal age and year of delivery, which was matched to the date of admission to the RPL clinic. Patients’ data were cross-linked to the national cancer registry. Kaplan-Meier survival curves were used to compare cancer incidence. Main results and the role of chance The RPL group comprised of 937 RPL patients, compared to 4685 patients with a live birth. The mean follow up time was 16.3 ±5.3 years for RPL cases and 15.9 ± 4.9 for the comparison group. Groups were compared in terms of lifetime risk, post-admission risk and according to cancer type. In a Univariate analysis, the life time risk for cancer was 5.3% (49/937) among RPL patients and 6.8% (317/4685) in the comparison group (p = 0.08). Survival analysis showed the same trend - a lower cancer morbidity in RPL patients (p = 0.06). The low cancer morbidity was more prominent, reaching statistical significance in secondary RPL patients (p = 0.05) , but not in primary RPL (p = 0.4). Breast cancer was the most common tumor, but was neither more nor less common in RPL than in the comparison group. Gynecological cancers, however, were significantly less common in RPL patients: 0.3% (3/937) compared to 1.3% (60/4685) in the comparison group (p = 0.01). After adjustment for maternal age the odds ratio for gynecological cancer was 0.247 (p = 0.018, 95% CI 0.077–0.791) and significantly represented in the survival analysis (p = 0.01). Limitations, reasons for caution There was no access to BMI and smoking status. Patients were followed for a mean period of 16 years; cancer may present later than 16 years. Wider implications of the findings: Unexplained RPL is assumed to have an immunological basis. Our study may provide an indirect support for hyper-responsive immunological mechanisms in RPL patients. Further research is needed to deepen our understanding of the underlying mechanisms and possibly to facilitate treatment options. Trial registration number Not applicable

scholarly journals Clinical Utility of a Nomogram for Predicting 30-Days Poor Outcome in Hospitalized Patients With COVID-19: Multicenter External Validation and Decision Curve Analysis

2020 ◽  
Vol 7 ◽  
Author(s):  
Bin Zhang ◽  
Qin Liu ◽  
Xiao Zhang ◽  
Shuyi Liu ◽  
Weiqi Chen ◽  
...  
Keyword(s):  
Prognostic Factors ◽  
Clinical Utility ◽  
Predictive Accuracy ◽  
Fasting Blood Glucose ◽  
External Validation ◽  
Predictive Performance ◽  
Curve Analysis ◽  
Discriminative Ability ◽  
Decision Curve Analysis ◽  
Poor Outcome

Aim: Early detection of coronavirus disease 2019 (COVID-19) patients who are likely to develop worse outcomes is of great importance, which may help select patients at risk of rapid deterioration who should require high-level monitoring and more aggressive treatment. We aimed to develop and validate a nomogram for predicting 30-days poor outcome of patients with COVID-19.Methods: The prediction model was developed in a primary cohort consisting of 233 patients with laboratory-confirmed COVID-19, and data were collected from January 3 to March 20, 2020. We identified and integrated significant prognostic factors for 30-days poor outcome to construct a nomogram. The model was subjected to internal validation and to external validation with two separate cohorts of 110 and 118 cases, respectively. The performance of the nomogram was assessed with respect to its predictive accuracy, discriminative ability, and clinical usefulness.Results: In the primary cohort, the mean age of patients was 55.4 years and 129 (55.4%) were male. Prognostic factors contained in the clinical nomogram were age, lactic dehydrogenase, aspartate aminotransferase, prothrombin time, serum creatinine, serum sodium, fasting blood glucose, and D-dimer. The model was externally validated in two cohorts achieving an AUC of 0.946 and 0.878, sensitivity of 100 and 79%, and specificity of 76.5 and 83.8%, respectively. Although adding CT score to the clinical nomogram (clinical-CT nomogram) did not yield better predictive performance, decision curve analysis showed that the clinical-CT nomogram provided better clinical utility than the clinical nomogram.Conclusions: We established and validated a nomogram that can provide an individual prediction of 30-days poor outcome for COVID-19 patients. This practical prognostic model may help clinicians in decision making and reduce mortality.

scholarly journals Development and validation of a prediction model for fat mass in children and adolescents: meta-analysis using individual participant data

BMJ ◽  
2019 ◽  
pp. l4293 ◽  
Author(s):  
Mohammed T Hudda ◽  
Mary S Fewtrell ◽  
Dalia Haroun ◽  
Sooky Lum ◽  
Jane E Williams ◽  
...  
Keyword(s):  
Confidence Interval ◽  
Prediction Model ◽  
Fat Mass ◽  
Meta Analysis ◽  
External Validation ◽  
Predictive Performance ◽  
Individual Participant Data ◽  
Internal Validation ◽  
Individual Participant ◽  
Complex Forms

Abstract Objectives To develop and validate a prediction model for fat mass in children aged 4-15 years using routinely available risk factors of height, weight, and demographic information without the need for more complex forms of assessment. Design Individual participant data meta-analysis. Setting Four population based cross sectional studies and a fifth study for external validation, United Kingdom. Participants A pooled derivation dataset (four studies) of 2375 children and an external validation dataset of 176 children with complete data on anthropometric measurements and deuterium dilution assessments of fat mass. Main outcome measure Multivariable linear regression analysis, using backwards selection for inclusion of predictor variables and allowing non-linear relations, was used to develop a prediction model for fat-free mass (and subsequently fat mass by subtracting resulting estimates from weight) based on the four studies. Internal validation and then internal-external cross validation were used to examine overfitting and generalisability of the model’s predictive performance within the four development studies; external validation followed using the fifth dataset. Results Model derivation was based on a multi-ethnic population of 2375 children (47.8% boys, n=1136) aged 4-15 years. The final model containing predictor variables of height, weight, age, sex, and ethnicity had extremely high predictive ability (optimism adjusted R 2 : 94.8%, 95% confidence interval 94.4% to 95.2%) with excellent calibration of observed and predicted values. The internal validation showed minimal overfitting and good model generalisability, with excellent calibration and predictive performance. External validation in 176 children aged 11-12 years showed promising generalisability of the model (R 2 : 90.0%, 95% confidence interval 87.2% to 92.8%) with good calibration of observed and predicted fat mass (slope: 1.02, 95% confidence interval 0.97 to 1.07). The mean difference between observed and predicted fat mass was −1.29 kg (95% confidence interval −1.62 to −0.96 kg). Conclusion The developed model accurately predicted levels of fat mass in children aged 4-15 years. The prediction model is based on simple anthropometric measures without the need for more complex forms of assessment and could improve the accuracy of assessments for body fatness in children (compared with those provided by body mass index) for effective surveillance, prevention, and management of clinical and public health obesity.

scholarly journals Systematic review of risk prediction scores for surgical site infection or periprosthetic joint infection following joint arthroplasty

2017 ◽  
Vol 145 (9) ◽  
pp. 1738-1749 ◽  
Author(s):  
S. K. KUNUTSOR ◽  
M. R. WHITEHOUSE ◽  
A. W. BLOM ◽  
A. D. BESWICK
Keyword(s):  
Systematic Review ◽  
Risk Prediction ◽  
Prediction Models ◽  
Joint Infection ◽  
External Validation ◽  
Surgical Site Infections ◽  
Predictive Performance ◽  
Risk Scores ◽  
Accurate Identification ◽  
Discriminative Ability

SUMMARYAccurate identification of individuals at high risk of surgical site infections (SSIs) or periprosthetic joint infections (PJIs) influences clinical decisions and development of preventive strategies. We aimed to determine progress in the development and validation of risk prediction models for SSI or PJI using a systematic review. We searched for studies that have developed or validated a risk prediction tool for SSI or PJI following joint replacement in MEDLINE, EMBASE, Web of Science and Cochrane databases; trial registers and reference lists of studies up to September 2016. Nine studies describing 16 risk scores for SSI or PJI were identified. The number of component variables in a risk score ranged from 4 to 45. The C-index ranged from 0·56 to 0·74, with only three risk scores reporting a discriminative ability of >0·70. Five risk scores were validated internally. The National Healthcare Safety Network SSIs risk models for hip and knee arthroplasties (HPRO and KPRO) were the only scores to be externally validated. Except for HPRO which shows some promise for use in a clinical setting (based on predictive performance and external validation), none of the identified risk scores can be considered ready for use. Further research is urgently warranted within the field.

212: Luteal phase hCG improves outcomes but not pregnancy rates in unexplained recurrent pregnancy loss

2012 ◽  
Vol 206 (1) ◽  
pp. S106
Author(s):  
Linda Swart ◽  
Kristin Holoch ◽  
Katharine Amalfitano ◽  
David Forstein ◽  
Bruce Lessey
Keyword(s):  
Luteal Phase ◽  
Pregnancy Loss ◽  
Recurrent Pregnancy Loss ◽  
Pregnancy Rates
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