Datasheet3_Prognostic algorithms for post-discharge readmission and mortality among mother-infant dyads: an observational study protocol.pdf

IntroductionIn low-income country settings, the first six weeks after birth remain a critical period of vulnerability for both mother and newborn. Despite recommendations for routine follow-up after delivery and facility discharge, few mothers and newborns receive guideline recommended care during this period. Prediction modelling of post-delivery outcomes has the potential to improve outcomes for both mother and newborn by identifying high-risk dyads, improving risk communication, and informing a patient-centered approach to postnatal care interventions. This study aims to derive post-discharge risk prediction algorithms that identify mother-newborn dyads who are at risk of re-admission or death in the first six weeks after delivery at a health facility.
MethodsThis prospective observational study will enroll 7,000 mother-newborn dyads from two regional referral hospitals in southwestern and eastern Uganda. Women and adolescent girls aged 12 and above delivering singletons and twins at the study hospitals will be eligible to participate. Candidate predictor variables will be collected prospectively by research nurses. Outcomes will be captured six weeks following delivery through a follow-up phone call, or an in-person visit if not reachable by phone. Two separate sets of prediction models will be built, one set of models for newborn outcomes and one set for maternal outcomes. Derivation of models will be based on optimization of the area under the receiver operator curve (AUROC) and specificity using an elastic net regression modelling approach. Internal validation will be conducted using 10-fold cross-validation. Our focus will be on the development of parsimonious models (5–10 predictor variables) with high sensitivity (>80%). AUROC, sensitivity, and specificity will be reported for each model, along with positive and negative predictive values.
DiscussionThe current recommendations for routine postnatal care are largely absent of benefit to most mothers and newborns due to poor adherence. Data-driven improvements to postnatal care can facilitate a more patient-centered approach to such care. Increasing digitization of facility care across low-income settings can further facilitate the integration of prediction algorithms as decision support tools for routine care, leading to improved quality and efficiency. Such strategies are urgently required to improve newborn and maternal postnatal outcomes.

Clinical trial registrationhttps://clinicaltrials.gov/, identifier (NCT05730387).

引言：在低收入国家的医疗场景中，产后最初六周仍是母亲与新生儿共同的关键脆弱期。尽管已有分娩及出院后常规随访的相关推荐，但在此期间，仅有极少数母亲与新生儿能够获得指南推荐的照护服务。产后结局预测建模可通过识别高风险母婴对、优化风险沟通，以及为产后照护干预提供以患者为中心的决策依据，从而改善母婴双方的产后结局。本研究旨在推导出院后风险预测算法，以识别在医疗机构分娩后六周内存在再入院或死亡风险的母婴对。

方法：本项前瞻性观察研究将从乌干达西南部与东部的两家区域转诊医院招募7000对母婴。年龄≥12岁、在研究医院分娩单胎或双胎妊娠的女性及青春期少女，均符合本研究的入组标准。研究护士将前瞻性采集候选预测变量数据。将在分娩后六周通过随访电话采集结局数据；若无法通过电话联系，则采用上门随访的方式。将构建两套独立的预测模型，一套针对新生儿结局，另一套针对母亲结局。模型推导将基于弹性网回归建模方法，以受试者工作特征曲线下面积（area under receiver operator curve, AUROC）与特异度为优化目标。将采用10折交叉验证法开展内部验证。本研究将重点开发兼具高灵敏度（>80%）与简洁性的预测模型（仅包含5~10个预测变量）。将报告各模型的AUROC、灵敏度、特异度，以及阳性预测值与阴性预测值。

讨论：由于依从性不佳，当前的常规产后照护推荐对大多数母婴而言基本无法获益。基于数据的产后照护优化，可助力构建更贴合以患者为中心的照护模式。低收入地区医疗机构照护的数字化程度不断提升，可进一步推动预测算法作为常规照护的决策支持工具得以整合，从而提升照护质量与服务效率。亟需此类策略以改善母婴的产后结局。

临床试验注册：https://clinicaltrials.gov/，注册号（NCT05730387）。