Informative missingness: What can we learn from patterns in missing laboratory data in the electronic health record?
Обработка статьи · этапы, ошибки и управление
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Tan Amelia L M, Getzen Emily J, Hutch Meghan R, Strasser Zachary H, Gutiérrez-Sacristán Alba, Le Trang T, Dagliati Arianna, Morris Michele, Hanauer David A, Moal Bertrand, Bonzel Clara-Lea, Yuan William, Chiudinelli Lorenzo, Das Priam, Zhang Harrison G, Aronow Bruce J, Avillach Paul, Brat Gabriel A, Cai Tianxi, Hong Chuan, La Cava William G, Hooi Will Loh He, Luo Yuan, Murphy Shawn N, Yuan Hgiam Kee, Omenn Gilbert S, Patel Lav P, Jebathilagam Samayamuthu Malarkodi, Shriver Emily R, Shakeri Hossein Abad Zahra, Tan Byorn W L, Visweswaran Shyam, Wang Xuan, Weber Griffin M, Xia Zongqi, Verdy Bertrand, COVID-19 by EHR (4CE), Long Qi, Mowery Danielle L, Holmes John H
Аннотация
METHODS: We collected and analyzed demographic, diagnosis, and laboratory data for 69,939 patients with positive COVID-19 PCR tests across three countries from 1 January 2020 through 30 September 2021. We analyzed missing laboratory measurements across sites, missingness stratification by demographic variables, temporal trends of missingness, correlations between labs based on missingness indicators over time, and clustering of groups of labs based on their missingness/ordering pattern.
RESULTS: With these analyses, we identified mapping issues faced in seven out of 15 sites. We also identified nuances in data collection and variable definition for the various sites. Temporal trend analyses may support the use of laboratory test result missingness patterns in identifying severe COVID-19 patients. Lastly, using missingness patterns, we determined relationships between various labs that reflect clinical behaviors.
CONCLUSION: In this work, we use computational approaches to relate missingness patterns to hospital treatment capacity and highlight the heterogeneity of looking at COVID-19 over time and at multiple sites, where there might be different phases, policies, etc. Changes in missingness could suggest a change in a patient's condition, and patterns of missingness among laboratory measurements could potentially identify clinical outcomes. This allows sites to consider missing data as informative to analyses and help researchers identify which sites are better poised to study particular questions.