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\begin{document}

\title{Validation of a major and clinically relevant non-major bleeding
phenotyping algorithm on electronic health records}



\author[1]{Aaron Jun Yi Yap}%
\author[1]{Desmond Teo}%
\author[1]{Pei San ANG}%
\author[2]{Eng Soo Yap}%
\author[1]{Siew Har Tan}%
\author[1]{Celine Wei Ping Loke}%
\author[1]{Sreemanee Dorajoo}%
\affil[1]{Health Sciences Authority}%
\affil[2]{National University Hospital}%


\vspace{-1em}



  
  \date{May 16, 2024}


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\begin{abstract}
\textbf{Background:} Bleeding is an important health outcome of interest
in epidemiological studies. We aimed to develop and validate rule-based
algorithms to identify major bleeding and all bleeding within real-world
electronic healthcare data. \textbf{Methods:} We took a random sample
(n=1630) of patient admissions to Singapore public hospitals in 2019 and
2020, stratifying by hospital and year of admission. We adopted the
International Society on Thrombosis and Haemostasis definition for major
bleeding. Presence of major bleeding and all bleeding was ascertained by
two annotators through chart review. A total of 630 and 1,000 records
were used for algorithm development and validation, respectively. We
formulated two algorithms: sensitivity- and positive predictive value
(PPV)-optimized algorithms. A combination of hemoglobin test patterns
and diagnosis codes were used in the final algorithms. \textbf{Results:}
During validation, diagnosis codes alone yielded low sensitivities for
major bleeding (0.14) and all bleeding (0.24), although specificities
and PPV were high (\textgreater{}0.97). For major bleeding, the
sensitivity-optimized algorithm had much higher sensitivity and negative
predictive values (NPV) (sensitivity=0.94, NPV=1.00), however false
positive rates were also relatively high (specificity=0.90, PPV=0.34).
PPV-optimized algorithm had improved specificity and PPV
(specificity=0.96, PPV=0.52), with little reduction in sensitivity and
NPV (sensitivity=0.88, NPV=0.99). For all bleeding events, our
algorithms had less optimal performances, with lower sensitivities (0.53
to 0.61). \textbf{Conclusions:} The use of diagnosis codes alone misses
many genuine major bleeding events. We have developed major bleeding
algorithms with high sensitivities which can be used in conjunction with
chart reviews to ascertain events within populations of interest.%
\end{abstract}%



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