Validity of an ICD-10 Coding Algorithm for Acute Heat Illness in the Emergency Department: A Retrospective Cohort Study
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Design and Setting
2.2. Population and Data Sources
2.3. Administrative Coding Algorithm
2.4. “Gold-Standard” Definition of AHI
2.5. Data Abstraction
2.6. Outcomes
2.7. Statistical Analysis
3. Results
3.1. PPV
3.2. NPV, Sn, and Sp
4. Discussion
4.1. Main Findings
4.2. Results in Relation to Other Studies
4.3. Strengths and Limitations
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
AHI | acute heat illness |
ED | emergency department |
ICD | International Classification of Disease |
STARD | Standards for Reporting Diagnostic accuracy studies |
LHSC | London Health Sciences Centre |
NACRS | National Ambulatory Care Reporting System |
PPV | positive predictive value |
NPV | negative predictive value |
Sn | sensitivity |
Sp | specificity |
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ICD-10 Code | Description |
---|---|
X30 | Exposure to excessive natural heat |
W92 * | Excessive heat due to human-made conditions |
X32 | Exposure to sunlight |
T670 | Heat stroke and sunstroke |
T671 | Heat syncope |
T672 | Heat cramp |
T673 | Heat exhaustion, anhidrotic |
T674 | Heat exhaustion due to salt depletion |
T675 | Heat exhaustion, unspecified |
T676 | Heat fatigue, transient |
T677 | Heat edema |
T678 | Other effects of heat and light |
T679 | Effect of heat and light, unspecified |
E860, E868 | Volume depletion |
N17 | Acute kidney injury |
R508 | Other specified fever (hyperthermia) |
R509 | Fever, unspecified |
M628, T296, G210 | Rhabdomyolysis |
E87 | Sodium disorders |
E875 | Potassium disorders, hyperkalemia |
R55 | Syncope and collapse |
Sample Size Calculation for Positive Predictive Value | Sample Size Calculation for Sensitivity and Specificity |
---|---|
Estimated positive predictive value = 0.80 Desired precision = 0.10 Confidence level = 0.95 Total sample size = 62 | Estimated sensitivity = 0.40 Estimated specificity = 0.90 Expected prevalence = 0.10 Desired precision = 0.10 Confidence level = 0.95 Total sample size = 931 * |
Year | Maximum Temperature (°C) | Proportion of Days by Temperature | |||
---|---|---|---|---|---|
Median | IQR | Cool | Moderate | Hot | |
2014 | 24.2 | 6.1 | 36% | 45% | 19% |
2015 | 24.5 | 4.5 | 19% | 60% | 20% |
2016 | 26.1 | 5.7 | 20% | 47% * | 33% * |
2017 | 24.3 | 6.6 | 28% | 47% | 25% |
2018 | 25.7 | 6.0 | 21% | 43% * | 36% * |
Algorithm-Positive Medical Charts (n = 62) | Non-Algorithm-Positive Medical Charts (n = 964) | |
---|---|---|
Mean age at time of encounter (SD) | 38.8 (23.8) | 41.7 (26.5) |
Age range | 0–92 | 0–102 |
65 years and above | 5 (8.1) | 223 (23.1) |
Male | 39 (62.9) | 469 (48.7) |
Female | 23 (37.1) | 493 (51.1) |
Canadian Triage and Acuity Scale | ||
Resuscitation | 2 (3.2) | 38 (3.9) |
Emergent | 19 (30.6) | 241 (25.0) |
Urgent | 31 (50.0) | 444 (46.1) |
Less Urgent | 9 (14.5) | 224 (23.2) |
Non-urgent | 1 (1.6) | 11 (1.1) |
Not recorded | 0 (0) | 6 (0.6) |
Presenting Complaint | ||
Heat Stroke 9 (14.5) | Abdominal pain 87 (9.0) | |
Heat Exhaustion 7 (11.3) | Chest pain 53 (5.5) | |
Headache 6 (9.7) | Lower extremity pain 30 (320) | |
Disposition | ||
Discharged home | 49 (79.0) | 729 (75.8) |
Admitted to hospital | 3 (4.8) | 167 (17.3) |
Left against medical advice | 10 (16.1) | 64 (6.6) |
“True AHI Encounters” | Not “True AHI Encounters” | ||
---|---|---|---|
ICD-10 code-positive | TP = 1 | FP = 0 | |
ICD-10 code-negative | FN = 3 | TN = 960 | NPV = 960/(960 + 3) = 99.7% |
Sn = 1/(3 + 1) = 25% | Sp = 960/(960 + 0) = 100% |
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© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Baassiri, H.; Varghese, T.; Clemens, K.K.; Ouédraogo, A.M.; Van Aarsen, K.; Vujčić, B.; Yan, J.W. Validity of an ICD-10 Coding Algorithm for Acute Heat Illness in the Emergency Department: A Retrospective Cohort Study. Int. J. Environ. Res. Public Health 2024, 21, 1132. https://doi.org/10.3390/ijerph21091132
Baassiri H, Varghese T, Clemens KK, Ouédraogo AM, Van Aarsen K, Vujčić B, Yan JW. Validity of an ICD-10 Coding Algorithm for Acute Heat Illness in the Emergency Department: A Retrospective Cohort Study. International Journal of Environmental Research and Public Health. 2024; 21(9):1132. https://doi.org/10.3390/ijerph21091132
Chicago/Turabian StyleBaassiri, Hasan, Timothy Varghese, Kristin K. Clemens, Alexandra M. Ouédraogo, Kristine Van Aarsen, Branka Vujčić, and Justin W. Yan. 2024. "Validity of an ICD-10 Coding Algorithm for Acute Heat Illness in the Emergency Department: A Retrospective Cohort Study" International Journal of Environmental Research and Public Health 21, no. 9: 1132. https://doi.org/10.3390/ijerph21091132