A comparative study of English and Japanese ChatGPT responses to anaesthesia-related medical questions.

Background: The expansion of artificial intelligence (AI) within large language models (LLMs) has the potential to streamline healthcare delivery. Despite the increased use of LLMs, disparities in their performance particularly in different languages, remain underexplored. This study examines the quality of ChatGPT responses in English and Japanese, specifically to questions related to anaesthesiology. Methods: Anaesthesiologists proficient in both languages were recruited as experts in this study. Ten frequently asked questions in anaesthesia were selected and translated for evaluation. Three non-sequential responses from ChatGPT were assessed for content quality (accuracy, comprehensiveness, and safety) and communication quality (understanding, empathy/tone, and ethics) by expert evaluators. Results: Eight anaesthesiologists evaluated English and Japanese LLM responses. The overall quality for all questions combined was higher in English compared with Japanese responses. Content and communication quality were significantly higher in English compared with Japanese LLMs responses (both P<0.001) in all three responses. Comprehensiveness, safety, and understanding were higher scores in English LLM responses. In all three responses, more than half of the evaluators marked overall English responses as better than Japanese responses. Conclusions: English LLM responses to anaesthesia-related frequently asked questions were superior in quality to Japanese responses when assessed by bilingual anaesthesia experts in this report. This study highlights the potential for language-related disparities in healthcare information and the need to improve the quality of AI responses in underrepresented languages. Future studies are needed to explore these disparities in other commonly spoken languages and to compare the performance of different LLMs.

An immune signature of postoperative cognitive decline in elderly patients.

Postoperative cognitive decline (POCD) is the predominant complication affecting elderly patients following major surgery, yet its prediction and prevention remain challenging. Understanding biological processes underlying the pathogenesis of POCD is essential for identifying mechanistic biomarkers to advance diagnostics and therapeutics. This longitudinal study involving 26 elderly patients undergoing orthopedic surgery aimed to characterize the impact of peripheral immune cell responses to surgical trauma on POCD. Trajectory analyses of single-cell mass cytometry data highlighted early JAK/STAT signaling exacerbation and diminished MyD88 signaling post-surgery in patients who developed POCD. Further analyses integrating single-cell and plasma proteomic data collected before surgery with clinical variables yielded a sparse predictive model that accurately identified patients who would develop POCD (AUC = 0.80). The resulting POCD immune signature included one plasma protein and ten immune cell features, offering a concise list of biomarker candidates for developing point-of-care prognostic tests to personalize perioperative management of at-risk patients. The code and the data are documented and available at https://github.com/gregbellan/POCD . Teaser: Modeling immune cell responses and plasma proteomic data predicts postoperative cognitive decline.

A systematic review of patient-reported outcome measures used in maternal postpartum anxiety.

OBJECTIVE: This study aimed to conduct a systematic review and to evaluate the psychometric measurement properties of instruments for postpartum anxiety using the Consensus-Based Standards for the Selection of Health Measurement Instruments guidelines to identify the best available patient-reported outcome measure. DATA SOURCES: We searched 4 databases (CINAHL, Embase, PubMed, and Web of Science in July 2022) and included studies that evaluated at least 1 psychometric measurement property of a patient-reported outcome measurement instrument. The protocol was registered with the International Prospective Register for Systematic Reviews under identifier CRD42021260004 and followed the Consensus-Based Standards for the Selection of Health Measurement Instruments guidelines for systematic reviews. STUDY ELIGIBILITY: Studies eligible for inclusion were those that assessed the performance of a patient-reported outcome measure for screening for postpartum anxiety. We included studies in which the instruments were subjected to some form of psychometric property assessment in the postpartum maternal population, consisted of at least 2 questions, and were not subscales. METHODS: This systematic review used the Consensus-Based Standards for the Selection of Health Measurement Instruments and the Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines to identify the best patient-reported outcome measurement instrument for examining postpartum anxiety. A risk of bias assessment was performed, and a modified GRADE approach was used to assess the level of evidence with recommendations being made for the overall quality of each instrument. RESULTS: A total of 28 studies evaluating 13 instruments in 10,570 patients were included. Content validity was sufficient in 9 with 5 instruments receiving a class A recommendation (recommended for use). The Postpartum Specific Anxiety Scale, Postpartum Specific Anxiety Scale Research Short Form, Postpartum Specific Anxiety Scale Research Short Form Covid, Postpartum Specific Anxiety Scale-Persian, and the State-Trait Anxiety Inventory demonstrated adequate content validity and sufficient internal consistency. Nine instruments received a recommendation of class B (further research required). No instrument received a class C recommendation (not recommended for use). CONCLUSION: Five instruments received a class A recommendation, all with limitations, such as not being specific to the postpartum population, not assessing all domains, lacking generalizability, or evaluation of cross-cultural validity. There is currently no freely available instrument that assess all domains of postpartum anxiety. Future studies are needed to determine the optimum current instrument or to develop and validate a more specific measure for maternal postpartum anxiety.

Postpartum recovery of nulliparous women following scheduled cesarean delivery and spontaneous vaginal delivery: a prospective observational study.

BACKGROUND: Inpatient postpartum recovery trajectories following cesarean delivery and spontaneous vaginal delivery are underexplored. OBJECTIVE: This study primarily aimed to compare recovery following cesarean delivery and spontaneous vaginal delivery in the first postpartum week, and secondarily to evaluate psychometrically the Japanese version of the Obstetric Quality of Recovery-10 scoring tool. STUDY DESIGN: Following institutional review board approval, the EQ-5D-3L (EuroQoL 5-Dimension 3-Level) questionnaire and a Japanese version of the Obstetric Quality of Recovery-10 measure were used to evaluate inpatient postpartum recovery in uncomplicated nulliparous parturients delivering via scheduled cesarean delivery or spontaneous vaginal delivery. RESULTS: A total of 48 and 50 women who delivered via cesarean delivery and spontaneous vaginal delivery, respectively, were recruited. Women delivering via scheduled cesarean delivery experienced significantly worse quality of recovery on days 1 and 2 compared with those who had spontaneous vaginal delivery. Quality of recovery significantly improved daily, plateauing at days 4 and 3 for cesarean delivery and spontaneous vaginal delivery groups, respectively. Compared with cesarean delivery, spontaneous vaginal delivery was associated with prolonged time to analgesia requirement, decreased opioid consumption, reduced antiemetic requirement, and reduced times to liquid/solid intake, ambulation, and discharge. Obstetric Quality of Recovery-10-Japanese is a valid (correlates with the EQ-5D-3L including a global health visual analog scale, gestational age, blood loss, opioid consumption, time until first analgesic request, liquid/solid intake, ambulation, catheter removal, and discharge), reliable (Cronbach alpha=0.88; Spearman-Brown reliability estimate=0.94; and intraclass correlation coefficient=0.89), and clinically feasible (98% 24-hour response rate) measure. CONCLUSION: Inpatient postpartum recovery is significantly better in the first 2 postpartum days following spontaneous vaginal delivery compared with scheduled cesarean delivery. Inpatient recovery is largely achieved within 4 and 3 days following scheduled cesarean delivery and spontaneous vaginal delivery, respectively. Obstetric Quality of Recovery-10-Japanese is a valid, reliable, and feasible measure of inpatient postpartum recovery.

Integrated plasma proteomic and single-cell immune signaling network signatures demarcate mild, moderate, and severe COVID-19.

The biological determinants underlying the range of coronavirus 2019 (COVID-19) clinical manifestations are not fully understood. Here, over 1,400 plasma proteins and 2,600 single-cell immune features comprising cell phenotype, endogenous signaling activity, and signaling responses to inflammatory ligands are cross-sectionally assessed in peripheral blood from 97 patients with mild, moderate, and severe COVID-19 and 40 uninfected patients. Using an integrated computational approach to analyze the combined plasma and single-cell proteomic data, we identify and independently validate a multi-variate model classifying COVID-19 severity (multi-class area under the curve [AUC]training = 0.799, p = 4.2e-6; multi-class AUCvalidation = 0.773, p = 7.7e-6). Examination of informative model features reveals biological signatures of COVID-19 severity, including the dysregulation of JAK/STAT, MAPK/mTOR, and nuclear factor κB (NF-κB) immune signaling networks in addition to recapitulating known hallmarks of COVID-19. These results provide a set of early determinants of COVID-19 severity that may point to therapeutic targets for prevention and/or treatment of COVID-19 progression.

Assessment of Patient-Reported Outcome Measures for Maternal Postpartum Depression Using the Consensus-Based Standards for the Selection of Health Measurement Instruments Guideline: A Systematic Review.

Importance: Maternal depression is frequently reported in the postpartum period, with an estimated prevalence of approximately 15% during the first postpartum year. Despite the high prevalence of postpartum depression, there is no consensus regarding which patient-reported outcome measure (PROM) should be used to screen for this complex, multidimensional construct. Objective: To evaluate psychometric measurement properties of existing PROMs of maternal postpartum depression using the Consensus-Based Standards for the Selection of Health Measurement Instruments (COSMIN) guideline and identify the best available patient-reported screening measure. Evidence Review: This systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guideline. PubMed, CINAHL, Embase, and Web of Science were searched on July 1, 2019, for validated PROMs of postpartum depression, and an additional search including a hand search of references from eligible studies was conducted in June 2021. Included studies evaluated 1 or more psychometric measurement properties of the identified PROMs. A risk-of-bias assessment was performed to evaluate methods of each included study. Psychometric measurement properties of each PROM were rated according to COSMIN criteria. A modified Grading of Recommendations Assessment, Development, and Evaluation approach was used to assess the level of evidence supporting each rating, and a recommendation class (A, recommended for use; B, further research required; or C, not recommended) was given based on the overall quality of each included PROM. Findings: Among 10 264 postpartum recovery studies, 27 PROMs were identified. Ten PROMs (37.0%) met the inclusion criteria and were used in 43 studies (0.4%) involving 22 095 postpartum women. At least 1 psychometric measurement property was assessed for each of the 10 validated PROMs identified. Content validity was sufficient in all PROMs. The Edinburgh Postnatal Depression Scale (EPDS) demonstrated adequate content validity and a moderate level of evidence for sufficient internal consistency (with sufficient structural validity), resulting in a recommendation of class A. The other 9 PROMs evaluated received a recommendation of class B. Conclusions and Relevance: The findings of this systematic review suggest that the EPDS is the best available patient-reported screening measure of maternal postpartum depression. Future studies should focus on evaluating the cross-cultural validity, reliability, and measurement error of the EPDS to improve understanding of its psychometric properties and utility.

Association of Prehabilitation With Postoperative Opioid Use in Colorectal Surgery: An Observational Cohort Study.

INTRODUCTION: Preoperative optimization programs have demonstrated positive effects on perioperative physical function and surgical outcomes. In nonsurgical populations, physical activity and healthy diet may reduce pain and pain medication requirement, but this has not been studied in surgical patients. Our aim was to determine whether a preoperative diet and exercise intervention affects postoperative pain and pain medication use. METHODS: Patients undergoing abdominal colorectal surgery were invited to participate in a web-based patient engagement program. Those enrolling in the first and third time periods received information on the standard perioperative pathway (enhanced recovery after surgery [ERAS]). Those enrolling in the second time period also received reminders on nutrition and exercise (PREHAB + ERAS). The primary outcome was postoperative inpatient opioid use. The secondary outcomes were inpatient postoperative pain scores and nonopioid pain medication use. RESULTS: The ERAS and PREHAB + ERAS groups were similar in demographic and operative characteristics. Subgroup analysis of patients who activated their accounts demonstrated that the two groups had similar average maximum daily pain scores, but the PREHAB + ERAS group (n = 158) used 15.9 fewer oral morphine equivalents per postoperative inpatient day than the ERAS group (n = 92), representing a 30% decrease (53 mg versus 37.1 mg, P = 0.04). The two groups used comparable amounts of acetaminophen, gabapentin, and ketorolac. Generalized linear models demonstrated that PREHAB + ERAS, minimally invasive surgery, and older age were associated with lower inpatient opioid use. CONCLUSIONS: Access to a web-based preoperative diet and exercise program may reduce inpatient opioid use after major elective colorectal surgery. Further studies are necessary to determine whether the degree of adherence to nutrition and physical activity recommendations has a dose-dependent effect on opioid use.

Proteomic signatures predict preeclampsia in individual cohorts but not across cohorts - implications for clinical biomarker studies.

BACKGROUND: Early identification of pregnant women at risk for preeclampsia (PE) is important, as it will enable targeted interventions ahead of clinical manifestations. The quantitative analyses of plasma proteins feature prominently among molecular approaches used for risk prediction. However, derivation of protein signatures of sufficient predictive power has been challenging. The recent availability of platforms simultaneously assessing over 1000 plasma proteins offers broad examinations of the plasma proteome, which may enable the extraction of proteomic signatures with improved prognostic performance in prenatal care. OBJECTIVE: The primary aim of this study was to examine the generalizability of proteomic signatures predictive of PE in two cohorts of pregnant women whose plasma proteome was interrogated with the same highly multiplexed platform. Establishing generalizability, or lack thereof, is critical to devise strategies facilitating the development of clinically useful predictive tests. A second aim was to examine the generalizability of protein signatures predictive of gestational age (GA) in uncomplicated pregnancies in the same cohorts to contrast physiological and pathological pregnancy outcomes. STUDY DESIGN: Serial blood samples were collected during the first, second, and third trimesters in 18 women who developed PE and 18 women with uncomplicated pregnancies (Stanford cohort). The second cohort (Detroit), used for comparative analysis, consisted of 76 women with PE and 90 women with uncomplicated pregnancies. Multivariate analyses were applied to infer predictive and cohort-specific proteomic models, which were then tested in the alternate cohort. Gene ontology (GO) analysis was performed to identify biological processes that were over-represented among top-ranked proteins associated with PE. RESULTS: The model derived in the Stanford cohort was highly significant (p = 3.9E-15) and predictive (AUC = 0.96), but failed validation in the Detroit cohort (p = 9.7E-01, AUC = 0.50). Similarly, the model derived in the Detroit cohort was highly significant (p = 1.0E-21, AUC = 0.73), but failed validation in the Stanford cohort (p = 7.3E-02, AUC = 0.60). By contrast, proteomic models predicting GA were readily validated across the Stanford (p = 1.1E-454, R = 0.92) and Detroit cohorts (p = 1.1.E-92, R = 0.92) indicating that the proteomic assay performed well enough to infer a generalizable model across studied cohorts, which makes it less likely that technical aspects of the assay, including batch effects, accounted for observed differences. CONCLUSIONS: Results point to a broader issue relevant for proteomic and other omic discovery studies in patient cohorts suffering from a clinical syndrome, such as PE, driven by heterogeneous pathophysiologies. While novel technologies including highly multiplex proteomic arrays and adapted computational algorithms allow for novel discoveries for a particular study cohort, they may not readily generalize across cohorts. A likely reason is that the prevalence of pathophysiologic processes leading up to the "same" clinical syndrome can be distributed differently in different and smaller-sized cohorts. Signatures derived in individual cohorts may simply capture different facets of the spectrum of pathophysiologic processes driving a syndrome. Our findings have important implications for the design of omic studies of a syndrome like PE. They highlight the need for performing such studies in diverse and well-phenotyped patient populations that are large enough to characterize subsets of patients with shared pathophysiologies to then derive subset-specific signatures of sufficient predictive power.

Integrated Single-cell and Plasma Proteomic Modeling to Predict Surgical Site Complications: A Prospective Cohort Study.

OBJECTIVE: The aim of this study was to determine whether single-cell and plasma proteomic elements of the host's immune response to surgery accurately identify patients who develop a surgical site complication (SSC) after major abdominal surgery. SUMMARY BACKGROUND DATA: SSCs may occur in up to 25% of patients undergoing bowel resection, resulting in significant morbidity and economic burden. However, the accurate prediction of SSCs remains clinically challenging. Leveraging high-content proteomic technologies to comprehensively profile patients' immune response to surgery is a promising approach to identify predictive biological factors of SSCs. METHODS: Forty-one patients undergoing non-cancer bowel resection were prospectively enrolled. Blood samples collected before surgery and on postoperative day one (POD1) were analyzed using a combination of single-cell mass cytometry and plasma proteomics. The primary outcome was the occurrence of an SSC, including surgical site infection, anastomotic leak, or wound dehiscence within 30 days of surgery. RESULTS: A multiomic model integrating the single-cell and plasma proteomic data collected on POD1 accurately differentiated patients with (n = 11) and without (n = 30) an SSC [area under the curve (AUC) = 0.86]. Model features included coregulated proinflammatory (eg, IL-6- and MyD88- signaling responses in myeloid cells) and immunosuppressive (eg, JAK/STAT signaling responses in M-MDSCs and Tregs) events preceding an SSC. Importantly, analysis of the immunological data obtained before surgery also yielded a model accurately predicting SSCs (AUC = 0.82). CONCLUSIONS: The multiomic analysis of patients' immune response after surgery and immune state before surgery revealed systemic immune signatures preceding the development of SSCs. Our results suggest that integrating immunological data in perioperative risk assessment paradigms is a plausible strategy to guide individualized clinical care.

A Peripheral Immune Signature of Labor Induction.

Approximately 1 in 4 pregnant women in the United States undergo labor induction. The onset and establishment of labor, particularly induced labor, is a complex and dynamic process influenced by multiple endocrine, inflammatory, and mechanical factors as well as obstetric and pharmacological interventions. The duration from labor induction to the onset of active labor remains unpredictable. Moreover, prolonged labor is associated with severe complications for the mother and her offspring, most importantly chorioamnionitis, uterine atony, and postpartum hemorrhage. While maternal immune system adaptations that are critical for the maintenance of a healthy pregnancy have been previously characterized, the role of the immune system during the establishment of labor is poorly understood. Understanding maternal immune adaptations during labor initiation can have important ramifications for predicting successful labor induction and labor complications in both induced and spontaneous types of labor. The aim of this study was to characterize labor-associated maternal immune system dynamics from labor induction to the start of active labor. Serial blood samples from fifteen participants were collected immediately prior to labor induction (baseline) and during the latent phase until the start of active labor. Using high-dimensional mass cytometry, a total of 1,059 single-cell immune features were extracted from each sample. A multivariate machine-learning method was employed to characterize the dynamic changes of the maternal immune system after labor induction until the establishment of active labor. A cross-validated linear sparse regression model (least absolute shrinkage and selection operator, LASSO) predicted the minutes since induction of labor with high accuracy (R = 0.86, p = 6.7e-15, RMSE = 277 min). Immune features most informative for the model included STAT5 signaling in central memory CD8+ T cells and pro-inflammatory STAT3 signaling responses across multiple adaptive and innate immune cell subsets. Our study reports a peripheral immune signature of labor induction, and provides important insights into biological mechanisms that may ultimately predict labor induction success as well as complications, thereby facilitating clinical decision-making to improve maternal and fetal well-being.

Single-Cell Analysis of the Neonatal Immune System Across the Gestational Age Continuum.

Although most causes of death and morbidity in premature infants are related to immune maladaptation, the premature immune system remains poorly understood. We provide a comprehensive single-cell depiction of the neonatal immune system at birth across the spectrum of viable gestational age (GA), ranging from 25 weeks to term. A mass cytometry immunoassay interrogated all major immune cell subsets, including signaling activity and responsiveness to stimulation. An elastic net model described the relationship between GA and immunome (R=0.85, p=8.75e-14), and unsupervised clustering highlighted previously unrecognized GA-dependent immune dynamics, including decreasing basal MAP-kinase/NFκB signaling in antigen presenting cells; increasing responsiveness of cytotoxic lymphocytes to interferon-α; and decreasing frequency of regulatory and invariant T cells, including NKT-like cells and CD8+CD161+ T cells. Knowledge gained from the analysis of the neonatal immune landscape across GA provides a mechanistic framework to understand the unique susceptibility of preterm infants to both hyper-inflammatory diseases and infections.

A systematic review of patient-reported outcome measures to assess postpartum pain using Consensus Based Standards for the Selection of Health Measurement Instruments (COSMIN) guidelines.

BACKGROUND: We performed a systematic review using Consensus Based Standards for the Selection of Health Measurement Instruments (COSMIN) guidelines to identify the best available patient-reported outcome measure (PROM) of postpartum pain. METHODS: This review follows COSMIN guidelines. We searched four databases with no date limiters, for previously identified validated PROMs used to assess postpartum pain. PROMs evaluating more than one author-defined domain of postpartum pain were assessed. We sought studies evaluating psychometric properties. An overall rating was then assigned based upon COSMIN analysis, and the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach was used to assess the level of evidence for psychometric properties of included PROMs. These assessments were used to make recommendations and identify the best PROM to assess postpartum pain. RESULTS: We identified 19 studies using seven PROMs (involving 3511 women), which evaluated postpartum pain. All included studies evaluated ≥1 psychometric property of the included PROMs. An adequate number of pain domains was assessed by the Brief Pain Inventory (BPI), Short Form-BPI (SF-BPI), and McGill Pain Questionnaire (MPQ). The SF-BPI was the only PROM to demonstrate adequate content validity and at least a low-level of evidence for sufficient internal consistency, resulting in a Class A recommendation (the best performing instrument, recommended for use). CONCLUSION: SF-BPI is the best currently available PROM to assess postpartum pain. However, it fails to assess several important domains and only just met the criteria for a Class A recommendation. Future studies are warranted to develop, evaluate, and implement a new PROM designed to specifically assess postpartum pain.

Integrated trajectories of the maternal metabolome, proteome, and immunome predict labor onset.

Estimating the time of delivery is of high clinical importance because pre- and postterm deviations are associated with complications for the mother and her offspring. However, current estimations are inaccurate. As pregnancy progresses toward labor, major transitions occur in fetomaternal immune, metabolic, and endocrine systems that culminate in birth. The comprehensive characterization of maternal biology that precedes labor is key to understanding these physiological transitions and identifying predictive biomarkers of delivery. Here, a longitudinal study was conducted in 63 women who went into labor spontaneously. More than 7000 plasma analytes and peripheral immune cell responses were analyzed using untargeted mass spectrometry, aptamer-based proteomic technology, and single-cell mass cytometry in serial blood samples collected during the last 100 days of pregnancy. The high-dimensional dataset was integrated into a multiomic model that predicted the time to spontaneous labor [R = 0.85, 95% confidence interval (CI) [0.79 to 0.89], P = 1.2 × 10-40, N = 53, training set; R = 0.81, 95% CI [0.61 to 0.91], P = 3.9 × 10-7, N = 10, independent test set]. Coordinated alterations in maternal metabolome, proteome, and immunome marked a molecular shift from pregnancy maintenance to prelabor biology 2 to 4 weeks before delivery. A surge in steroid hormone metabolites and interleukin-1 receptor type 4 that preceded labor coincided with a switch from immune activation to regulation of inflammatory responses. Our study lays the groundwork for developing blood-based methods for predicting the day of labor, anchored in mechanisms shared in preterm and term pregnancies.

Use of Patient-Reported Outcome Measures to Assess Outpatient Postpartum Recovery: A Systematic Review.

Importance: Outpatient postpartum recovery is an underexplored area of obstetrics. There is currently no consensus regarding which patient-reported outcome measure (PROM) clinicians and researchers should use to evaluate postpartum recovery. Objective: To evaluate PROMs of outpatient postpartum recovery using Consensus-Based Standards for the Selection of Health Measurement Instruments (COSMIN) guidelines. Evidence Review: An initial literature search performed in July 2019 identified postpartum recovery PROMs and validation studies. A secondary search in July 2020 identified additional validation studies. Both searches were performed using 4 databases (Web of Science, Embase, PubMed, and CINAHL), with no date limiters. Studies with PROMs evaluating more than 3 proposed outpatient postpartum recovery domains were considered. Studies were included if they assessed any psychometric measurement property of the included PROMs in the outpatient postpartum setting. The PROMs were assessed for the following 8 psychometric measurement properties, as defined by COSMIN: content validity, structural validity, internal consistency, cross-cultural validity and measurement invariance, reliability, measurement error, hypothesis testing, and responsiveness. Psychometric measurement properties were evaluated in each included study using the COSMIN criteria by assessing (1) the quality of the methods (very good, adequate, doubtful, inadequate, or not assessed); (2) overall rating of results (sufficient, insufficient, inconsistent, or indeterminate); (3) level of evidence assessed using the Grading of Recommendations, Assessment, Development and Evaluations assessment tool; and (4) level of recommendation, which included class A (recommended for use; showed adequate content validity with at least low-quality evidence for sufficient internal consistency), class B (not class A or class C), or class C (not recommended). Findings: In total, 15 PROMs (7 obstetric specific and 8 non-obstetric specific) were identified, evaluating outpatient postpartum recovery in 46 studies involving 19 165 women. The majority of psychometric measurement properties of the included PROMs were graded as having very-low-level or low-level evidence. The best-performing PROMs that received class A recommendations were the Maternal Concerns Questionnaire, the Postpartum Quality of Life tool, and the World Health Organization Quality of Life-BREF. The remainder of the evaluated PROMs had insufficient evidence to make recommendations regarding their use (and received class B recommendations). Conclusions and Relevance: This review found that the best-performing PROMs currently available to evaluate outpatient postpartum recovery were the Maternal Concerns Questionnaire, the Postpartum Quality of Life tool, and the World Health Organization Quality of Life-BREF; however, these tools all had significant limitations. This study highlights the need to focus future efforts on robustly developing and validating a new PROM that may comprehensively evaluate outpatient postpartum recovery.

Integrated plasma proteomic and single-cell immune signaling network signatures demarcate mild, moderate, and severe COVID-19.

The biological determinants of the wide spectrum of COVID-19 clinical manifestations are not fully understood. Here, over 1400 plasma proteins and 2600 single-cell immune features comprising cell phenotype, basal signaling activity, and signaling responses to inflammatory ligands were assessed in peripheral blood from patients with mild, moderate, and severe COVID-19, at the time of diagnosis. Using an integrated computational approach to analyze the combined plasma and single-cell proteomic data, we identified and independently validated a multivariate model classifying COVID-19 severity (multi-class AUCtraining = 0.799, p-value = 4.2e-6; multi-class AUCvalidation = 0.773, p-value = 7.7e-6). Features of this high-dimensional model recapitulated recent COVID-19 related observations of immune perturbations, and revealed novel biological signatures of severity, including the mobilization of elements of the renin-angiotensin system and primary hemostasis, as well as dysregulation of JAK/STAT, MAPK/mTOR, and NF-κB immune signaling networks. These results provide a set of early determinants of COVID-19 severity that may point to therapeutic targets for the prevention of COVID-19 progression.

Integration of mechanistic immunological knowledge into a machine learning pipeline improves predictions.

The dense network of interconnected cellular signalling responses that are quantifiable in peripheral immune cells provides a wealth of actionable immunological insights. Although high-throughput single-cell profiling techniques, including polychromatic flow and mass cytometry, have matured to a point that enables detailed immune profiling of patients in numerous clinical settings, the limited cohort size and high dimensionality of data increase the possibility of false-positive discoveries and model overfitting. We introduce a generalizable machine learning platform, the immunological Elastic-Net (iEN), which incorporates immunological knowledge directly into the predictive models. Importantly, the algorithm maintains the exploratory nature of the high-dimensional dataset, allowing for the inclusion of immune features with strong predictive capabilities even if not consistent with prior knowledge. In three independent studies our method demonstrates improved predictions for clinically relevant outcomes from mass cytometry data generated from whole blood, as well as a large simulated dataset. The iEN is available under an open-source licence.

VoPo leverages cellular heterogeneity for predictive modeling of single-cell data.

High-throughput single-cell analysis technologies produce an abundance of data that is critical for profiling the heterogeneity of cellular systems. We introduce VoPo (https://github.com/stanleyn/VoPo), a machine learning algorithm for predictive modeling and comprehensive visualization of the heterogeneity captured in large single-cell datasets. In three mass cytometry datasets, with the largest measuring hundreds of millions of cells over hundreds of samples, VoPo defines phenotypically and functionally homogeneous cell populations. VoPo further outperforms state-of-the-art machine learning algorithms in classification tasks, and identified immune-correlates of clinically-relevant parameters.

Multiomic immune clockworks of pregnancy.

Preterm birth is the leading cause of mortality in children under the age of five worldwide. Despite major efforts, we still lack the ability to accurately predict and effectively prevent preterm birth. While multiple factors contribute to preterm labor, dysregulations of immunological adaptations required for the maintenance of a healthy pregnancy is at its pathophysiological core. Consequently, a precise understanding of these chronologically paced immune adaptations and of the biological pacemakers that synchronize the pregnancy "immune clock" is a critical first step towards identifying deviations that are hallmarks of peterm birth. Here, we will review key elements of the fetal, placental, and maternal pacemakers that program the immune clock of pregnancy. We will then emphasize multiomic studies that enable a more integrated view of pregnancy-related immune adaptations. Such multiomic assessments can strengthen the biological plausibility of immunological findings and increase the power of biological signatures predictive of preterm birth.

Factors associated with persistent pain after childbirth: a narrative review.

A systematic literature search was performed to identify studies that reported risk factors for persistent pain after childbirth. Many studies have sought to identify risk factors for post-delivery pain in different populations, using different methodologies and different outcome variables. Studies of several different but interrelated post-partum pain syndromes have been conducted. Factors strongly and specifically associated with persistent incisional scar pain after Caesarean delivery include a coexisting persistent pain problem in another part of the body and severe acute postoperative pain. For persistent vaginal and perineal pain, operative vaginal delivery and the magnitude of perineal trauma have been consistently linked. History of pregnancy-related and pre-pregnancy back pain and heavier body weight are robust risk factors for persistent back pain after pregnancy. Unfortunately, limitations, particularly small samples and lack of a priori sample size calculation designed to detect specific effect sizes for risk of persistent pain outcomes, preclude definitive conclusions about many other predictors and the strength of outcome associations. In future studies, assessments of specific phenotypes using a rigorous analysis with appropriate predetermined sample sizes and validated instruments are needed to allow elucidation of stronger and reliable associations. Interventional studies targeting the most robustly associated, modifiable risk factors, such as acute post-partum pain, may lead to solutions for the prevention and treatment of these common problems that impact a large population.