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Informative missingness: What can we learn from patterns in missing laboratory data in the electronic health record?
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; Long, Qi; Mowery, Danielle L; Holmes, John H.
  • Tan ALM; Harvard Medical School, Cambridge, MA, USA.
  • Getzen EJ; University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
  • Hutch MR; Northwestern University, Chicago, IL, USA.
  • Strasser ZH; Massachusetts General Hospital, Boston, MA, USA.
  • Gutiérrez-Sacristán A; Harvard Medical School, Cambridge, MA, USA.
  • Le TT; University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
  • Dagliati A; University of Pavia, Pavia, Italy.
  • Morris M; University of Pittsburgh, Pittsburgh, PA, USA.
  • Hanauer DA; University of Michigan, Ann Arbor, MI, USA.
  • Moal B; Bordeaux University Hospital, Talence, France.
  • Bonzel CL; Harvard Medical School, Cambridge, MA, USA.
  • Yuan W; Harvard Medical School, Cambridge, MA, USA.
  • Chiudinelli L; ASST Papa Giovanni XXIII, Bergamo, Italy.
  • Das P; Harvard Medical School, Cambridge, MA, USA.
  • Zhang HG; Harvard Medical School, Cambridge, MA, USA.
  • Aronow BJ; Cincinnati Children's Hospital Medical Center, University of Cincinnati, Cincinnati, OH, USA.
  • Avillach P; Harvard Medical School, Cambridge, MA, USA.
  • Brat GA; Harvard Medical School, Cambridge, MA, USA.
  • Cai T; Harvard Medical School, Cambridge, MA, USA.
  • Hong C; Harvard Medical School, Cambridge, MA, USA; Duke University, Durham, NC, USA.
  • La Cava WG; Harvard Medical School, Cambridge, MA, USA; Boston Children's Hospital, Boston, MA, USA.
  • Hooi Will Loh H; National University Health Systems, Singapore.
  • Luo Y; Northwestern University, Chicago, IL, USA.
  • Murphy SN; Massachusetts General Hospital, Boston, MA, USA.
  • Yuan Hgiam K; National University Health Systems, Singapore.
  • Omenn GS; University of Pittsburgh, Pittsburgh, PA, USA.
  • Patel LP; University of Kansas Medical Center, United States.
  • Jebathilagam Samayamuthu M; University of Pittsburgh, Pittsburgh, PA, USA.
  • Shriver ER; University of Pennsylvania Health System, Philadelphia, PA, USA.
  • Shakeri Hossein Abad Z; Harvard Medical School, Cambridge, MA, USA.
  • Tan BWL; National University Health Systems, Singapore.
  • Visweswaran S; University of Pittsburgh, Pittsburgh, PA, USA.
  • Wang X; Harvard Medical School, Cambridge, MA, USA.
  • Weber GM; Harvard Medical School, Cambridge, MA, USA.
  • Xia Z; University of Pittsburgh, Pittsburgh, PA, USA.
  • Verdy B; Bordeaux University Hospital, Talence, France.
  • Long Q; University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
  • Mowery DL; University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
  • Holmes JH; University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA.
J Biomed Inform ; 139: 104306, 2023 03.
Article in English | MEDLINE | ID: covidwho-2220929
ABSTRACT

BACKGROUND:

In electronic health records, patterns of missing laboratory test results could capture patients' course of disease as well as ​​reflect clinician's concerns or worries for possible conditions. These patterns are often understudied and overlooked. This study aims to identify informative patterns of missingness among laboratory data collected across 15 healthcare system sites in three countries for COVID-19 inpatients.

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.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Electronic Health Records / COVID-19 Type of study: Experimental Studies / Prognostic study / Randomized controlled trials Limits: Humans Language: English Journal: J Biomed Inform Journal subject: Medical Informatics Year: 2023 Document Type: Article Affiliation country: J.jbi.2023.104306

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Electronic Health Records / COVID-19 Type of study: Experimental Studies / Prognostic study / Randomized controlled trials Limits: Humans Language: English Journal: J Biomed Inform Journal subject: Medical Informatics Year: 2023 Document Type: Article Affiliation country: J.jbi.2023.104306