Diuretics in pregnancy: Data from the ESC Registry of Pregnancy and Cardiac disease (ROPAC).
Data on diuretic use in pregnancy are limited and inconsistent, and consequently it remains unclear whether they can be used safely. Our study aims to evaluate the perinatal outcomes after in-utero diuretic exposure.
The Registry Of Pregnancy And Cardiac disease (ROPAC) is a prospective, global registry of pregnancies in women with heart disease. Outcomes were compared between women who used diuretics during pregnancy versus those who did not. Multivariable regression analysis was used to assess the impact of diuretic use on the occurrence of congenital anomalies and foetal growth. Diuretics were used in 382 (6.7%) of the 5739 ROPAC pregnancies, most often furosemide (86%). Age >35 years (odds ratio [OR] 1.5, 95% confidence interval [CI] 1.2-2.0), other cardiac medication use (OR 5.4, 95% CI 4.2-6.9), signs of heart failure (OR 1.7, 95% CI 1.2-2.2), estimated left ventricular ejection fraction <40% (OR 2.9, 95% CI 2.0-4.2), New York Heart Association class >II (OR 3.4, 95% CI 2.3-5.1), valvular heart disease (OR 6.3, 95% CI 4.7-8.3) and cardiomyopathy (OR 3.9, 95% CI 2.6-5.7) were associated with diuretic use during pregnancy. In multivariable analysis, diuretic use during the first trimester was not significantly associated with foetal or neonatal congenital anomalies (OR 1.3, 95% CI 0.7-2.6), and diuretic use during pregnancy was also not significantly associated with small for gestational age (OR 1.4, 95% CI 1.0-1.9).
Our study does not conclusively establish an association between diuretic use during pregnancy and adverse foetal outcomes. Given these findings, it is essential to assess the risk-benefit ratio on an individual basis to guide clinical decisions.
van der Zande JA
，Greutmann M
，Tobler D
，Ramlakhan KP
，Cornette JMJ
，Ladouceur M
，Collins N
，Adamson D
，Paruchuri VP
，Hall R
，Johnson MR
，Roos-Hesselink JW
， on behalf of the ROPAC Investigators Group
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Comparison of Two Modern Survival Prediction Tools, SORG-MLA and METSSS, in Patients With Symptomatic Long-bone Metastases Who Underwent Local Treatment With Surgery Followed by Radiotherapy and With Radiotherapy Alone.
Survival estimation for patients with symptomatic skeletal metastases ideally should be made before a type of local treatment has already been determined. Currently available survival prediction tools, however, were generated using data from patients treated either operatively or with local radiation alone, raising concerns about whether they would generalize well to all patients presenting for assessment. The Skeletal Oncology Research Group machine-learning algorithm (SORG-MLA), trained with institution-based data of surgically treated patients, and the Metastases location, Elderly, Tumor primary, Sex, Sickness/comorbidity, and Site of radiotherapy model (METSSS), trained with registry-based data of patients treated with radiotherapy alone, are two of the most recently developed survival prediction models, but they have not been tested on patients whose local treatment strategy is not yet decided.
(1) Which of these two survival prediction models performed better in a mixed cohort made up both of patients who received local treatment with surgery followed by radiotherapy and who had radiation alone for symptomatic bone metastases? (2) Which model performed better among patients whose local treatment consisted of only palliative radiotherapy? (3) Are laboratory values used by SORG-MLA, which are not included in METSSS, independently associated with survival after controlling for predictions made by METSSS?
Between 2010 and 2018, we provided local treatment for 2113 adult patients with skeletal metastases in the extremities at an urban tertiary referral academic medical center using one of two strategies: (1) surgery followed by postoperative radiotherapy or (2) palliative radiotherapy alone. Every patient's survivorship status was ascertained either by their medical records or the national death registry from the Taiwanese National Health Insurance Administration. After applying a priori designated exclusion criteria, 91% (1920) were analyzed here. Among them, 48% (920) of the patients were female, and the median (IQR) age was 62 years (53 to 70 years). Lung was the most common primary tumor site (41% [782]), and 59% (1128) of patients had other skeletal metastases in addition to the treated lesion(s). In general, the indications for surgery were the presence of a complete pathologic fracture or an impending pathologic fracture, defined as having a Mirels score of ≥ 9, in patients with an American Society of Anesthesiologists (ASA) classification of less than or equal to IV and who were considered fit for surgery. The indications for radiotherapy were relief of pain, local tumor control, prevention of skeletal-related events, and any combination of the above. In all, 84% (1610) of the patients received palliative radiotherapy alone as local treatment for the target lesion(s), and 16% (310) underwent surgery followed by postoperative radiotherapy. Neither METSSS nor SORG-MLA was used at the point of care to aid clinical decision-making during the treatment period. Survival was retrospectively estimated by these two models to test their potential for providing survival probabilities. We first compared SORG to METSSS in the entire population. Then, we repeated the comparison in patients who received local treatment with palliative radiation alone. We assessed model performance by area under the receiver operating characteristic curve (AUROC), calibration analysis, Brier score, and decision curve analysis (DCA). The AUROC measures discrimination, which is the ability to distinguish patients with the event of interest (such as death at a particular time point) from those without. AUROC typically ranges from 0.5 to 1.0, with 0.5 indicating random guessing and 1.0 a perfect prediction, and in general, an AUROC of ≥ 0.7 indicates adequate discrimination for clinical use. Calibration refers to the agreement between the predicted outcomes (in this case, survival probabilities) and the actual outcomes, with a perfect calibration curve having an intercept of 0 and a slope of 1. A positive intercept indicates that the actual survival is generally underestimated by the prediction model, and a negative intercept suggests the opposite (overestimation). When comparing models, an intercept closer to 0 typically indicates better calibration. Calibration can also be summarized as log(O:E), the logarithm scale of the ratio of observed (O) to expected (E) survivors. A log(O:E) > 0 signals an underestimation (the observed survival is greater than the predicted survival); and a log(O:E) < 0 indicates the opposite (the observed survival is lower than the predicted survival). A model with a log(O:E) closer to 0 is generally considered better calibrated. The Brier score is the mean squared difference between the model predictions and the observed outcomes, and it ranges from 0 (best prediction) to 1 (worst prediction). The Brier score captures both discrimination and calibration, and it is considered a measure of overall model performance. In Brier score analysis, the "null model" assigns a predicted probability equal to the prevalence of the outcome and represents a model that adds no new information. A prediction model should achieve a Brier score at least lower than the null-model Brier score to be considered as useful. The DCA was developed as a method to determine whether using a model to inform treatment decisions would do more good than harm. It plots the net benefit of making decisions based on the model's predictions across all possible risk thresholds (or cost-to-benefit ratios) in relation to the two default strategies of treating all or no patients. The care provider can decide on an acceptable risk threshold for the proposed treatment in an individual and assess the corresponding net benefit to determine whether consulting with the model is superior to adopting the default strategies. Finally, we examined whether laboratory data, which were not included in the METSSS model, would have been independently associated with survival after controlling for the METSSS model's predictions by using the multivariable logistic and Cox proportional hazards regression analyses.
Between the two models, only SORG-MLA achieved adequate discrimination (an AUROC of > 0.7) in the entire cohort (of patients treated operatively or with radiation alone) and in the subgroup of patients treated with palliative radiotherapy alone. SORG-MLA outperformed METSSS by a wide margin on discrimination, calibration, and Brier score analyses in not only the entire cohort but also the subgroup of patients whose local treatment consisted of radiotherapy alone. In both the entire cohort and the subgroup, DCA demonstrated that SORG-MLA provided more net benefit compared with the two default strategies (of treating all or no patients) and compared with METSSS when risk thresholds ranged from 0.2 to 0.9 at both 90 days and 1 year, indicating that using SORG-MLA as a decision-making aid was beneficial when a patient's individualized risk threshold for opting for treatment was 0.2 to 0.9. Higher albumin, lower alkaline phosphatase, lower calcium, higher hemoglobin, lower international normalized ratio, higher lymphocytes, lower neutrophils, lower neutrophil-to-lymphocyte ratio, lower platelet-to-lymphocyte ratio, higher sodium, and lower white blood cells were independently associated with better 1-year and overall survival after adjusting for the predictions made by METSSS.
Based on these discoveries, clinicians might choose to consult SORG-MLA instead of METSSS for survival estimation in patients with long-bone metastases presenting for evaluation of local treatment. Basing a treatment decision on the predictions of SORG-MLA could be beneficial when a patient's individualized risk threshold for opting to undergo a particular treatment strategy ranged from 0.2 to 0.9. Future studies might investigate relevant laboratory items when constructing or refining a survival estimation model because these data demonstrated prognostic value independent of the predictions of the METSSS model, and future studies might also seek to keep these models up to date using data from diverse, contemporary patients undergoing both modern operative and nonoperative treatments.
Level III, diagnostic study.
Lee CC
，Chen CW
，Yen HK
，Lin YP
，Lai CY
，Wang JL
，Groot OQ
，Janssen SJ
，Schwab JH
，Hsu FM
，Lin WH
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The effectiveness of abstinence-based and harm reduction-based interventions in reducing problematic substance use in adults who are experiencing homelessness in high income countries: A systematic review and meta-analysis: A systematic review.
Homelessness is a traumatic experience, and can have a devastating effect on those experiencing it. People who are homeless often face significant barriers when accessing public services, and have often experienced adverse childhood events, extreme social disadvantage, physical, emotional and sexual abuse, neglect, low self-esteem, poor physical and mental health, and much lower life expectancy compared to the general population. Rates of problematic substance use are disproportionately high, with many using drugs and alcohol to deal with the stress of living on the street, to keep warm, or to block out memories of previous abuse or trauma. Substance dependency can also create barriers to successful transition to stable housing.
To understand the effectiveness of different substance use interventions for adults experiencing homelessness.
The primary source of studies for was the 4th edition of the Homelessness Effectiveness Studies Evidence and Gaps Maps (EGM). Searches for the EGM were completed in September 2021. Other potential studies were identified through a call for grey evidence, hand-searching key journals, and unpacking relevant systematic reviews.
Eligible studies were impact evaluations that involved some comparison group. We included studies that tested the effectiveness of substance use interventions, and measured substance use outcomes, for adults experiencing homelessness in high income countries.
Descriptive characteristics and statistical information in included studies were coded and checked by at least two members of the review team. Studies selected for the review were assessed for confidence in the findings. Standardised effect sizes were calculated and, if a study did not provide sufficient raw data for the calculation of an effect size, author(s) were contacted to obtain these data. We used random-effects meta-analysis and robust-variance estimation procedures to synthesise effect sizes. If a study included multiple effects, we carried out a critical assessment to determine (even if only theoretically) whether the effects are likely to be dependent. Where dependent effects were identified, we used robust variance estimation to determine whether we can account for these. Where effect sizes were converted from a binary to continuous measure (or vice versa), we undertook a sensitivity analysis by running an additional analysis with these studies omitted. We also assessed the sensitivity of results to inclusion of non-randomised studies and studies classified as low confidence in findings. All included an assessment of statistical heterogeneity. Finally, we undertook analysis to assess whether publication bias was likely to be a factor in our findings. For those studies that we were unable to include in meta-analysis, we have provided a narrative synthesis of the study and its findings.
We included 48 individual papers covering 34 unique studies. The studies covered 15, 255 participants, with all but one of the studies being from the United States and Canada. Most papers were rated as low confidence (n = 25, or 52%). By far the most common reason for studies being rated as low confidence was high rates of attrition and/or differential attrition of study participants, that fell below the What Works Clearinghouse liberal attrition standard. Eleven of the included studies were rated as medium confidence and 12 studies as high confidence. The interventions included in our analysis were more effective in reducing substance use than treatment as usual, with an overall effect size of -0.11 SD (95% confidence interval [CI], -0.27, 0.05). There was substantial heterogeneity across studies, and the results were sensitive to the removal of low confidence studies (-0.21 SD, 95% CI [-0.59, 0.17] - 6 studies, 17 effect sizes), the removal of quasi-experimental studies (-0.14 SD, 95% CI [-0.30, 0.02] - 14 studies, 41 effect sizes) and the removal of studies where an effect size had been converted from a binary to a continuous outcome (-0.08 SD, 95% CI [-0.31, 0.15] - 10 studies, 31 effect sizes). This suggests that the findings are sensitive to the inclusion of lower quality studies, although unusually the average effect increases when we removed low confidence studies. The average effect for abstinence-based interventions compared to treatment-as-usual (TAU) service provision was -0.28 SD (95% CI, -0.65, 0.09) (6 studies, 15 effect sizes), and for harm reduction interventions compared to a TAU service provision is close to 0 at 0.03 SD (95% CI, -0.08, 0.14) (9 studies, 30 effect sizes). The confidence intervals for both estimates are wide and crossing zero. For both, the comparison groups are primarily abstinence-based, with the exception of two studies where the comparison group condition was unclear. We found that both Assertative Community Treatment and Intensive Case Management were no better than treatment as usual, with average effect on substance use of 0.03 SD, 95% CI [-0.07, 0.13] and -0.47 SD, 95% CI [-0.72, -0.21] 0.05 SD, 95% CI [-0.28, 0.39] respectively. These findings are consistent with wider research, and it is important to note that we only examined the effect on substance use outcomes (these interventions can be effective in terms of other outcomes). We found that CM interventions can be effective in reducing substance use compared to treatment as usual, with an average effect of -0.47 SD, 95% CI (-0.72, -0.21). All of these results need to be considered in light of the quality of the underlying evidence. There were six further interventions where we undertook narrative synthesis. These syntheses suggest that Group Work, Harm Reduction Psychotherapy, and Therapeutic Communities are effective in reducing substance use, with mixed results found for Motivational Interviewing and Talking Therapies (including Cognitive Behavioural Therapy). The narrative synthesis suggested that Residential Rehabilitation was no better than treatment as usual in terms of reducing substance use for our population of interest.
Although our analysis of harm reduction versus treatment as usual, abstinence versus treatment as usual, and harm reduction versus abstinence suggests that these different approaches make little real difference to the outcomes achieved in comparison to treatment as usual. The findings suggest that some individual interventions are more effective than others. The overall low quality of the primary studies suggests that further primary impact research could be beneficial.
O'Leary C
，Ralphs R
，Stevenson J
，Smith A
，Harrison J
，Kiss Z
，Armitage H
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ResearchGate
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