Table 1.
List of Potential Risk Factors
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April
2019, 15(2): 947-962.
doi: 10.3934/jimo.2018079
Predicting 72-hour reattendance in emergency departments using discriminant analysis via mixed integer programming with electronic medical records
| 1. | Department of Health Services and Outcomes Research, National Healthcare Group, 138543, Singapore |
| 2. | Department of Emergency Medicine, Tan Tock Seng Hospital, 308433, Singapore |
The proportion of patients who reattended emergency department (ED) within 72 hours is an important indicator of quality of care. This study develops a practical framework to predict patients who will reattend ED in 72 hours from clinical perspectives. We analyze 328,733 ED patients from 1 January 2011 to 31 December 2013, with an average of 4.6% reattendances. We feature over 100 factors including demographics, diagnosis, patient acuity, chief complaints, selected laboratory tests, summarized vital signs. Using univariate analysis, a pool of risk variables is selected for subsequent factor selection. We then apply filter methods to derive a set of candidate factors. With these factors in combination with suggestions from ED clinicians, a mixed integer programming model based on discriminant analysis is proposed to determine a classification rule for 72-hour reattendance. In numerical experiments, various small subsets of risk factors are used for classification and prediction. The results show that favorable predicting performances can be achieved in both training and test sets.
Keywords:
Mixed integer programming,
discriminant analysis,
filter methods,
emergency department reattendance,
predict.
Mathematics Subject Classification:
Primary: 90C11, 90C90; Secondary: 62P10.
Citation:
Fanwen Meng, Kiok Liang Teow, Kelvin Wee Sheng Teo, Chee Kheong Ooi, Seow Yian Tay. Predicting 72-hour reattendance in emergency departments using discriminant analysis via mixed integer programming with electronic medical records. Journal of Industrial & Management Optimization,
2019, 15
(2)
: 947-962.
doi: 10.3934/jimo.2018079
![]()
References:
| [1] |
E. A. Alessandrini, J. M. Lavelle, S. M. Grenfell, C. R. Jacobstein and K. N. Shaw, Return visits to a pediatric emergency department, Pediatr. Emerg. Care, 20 (2004), 166-171. Google Scholar |
| [2] |
J. A. Anderson,
Constrained discrimination between k populations, J. R. Stat. Soc. Series B, 31 (1969), 123-139.
|
| [3] |
D. Aujesky, M. K. Mor, M. Geng, R. A. Stone, M. J. Fine and S. A. Ibrahim,
Predictors of early hospital readmission after acute pulmonary embolism, Arch. Intern. Med., 169 (2009), 287-293.
doi: 10.1001/archinternmed.2008.546. |
| [4] |
K. P. Bennett and O. L. Mangasarian,
Robust linear programming discrimination of two linearly inseparable sets, Optim. Method Softw., 1 (1992), 23-34.
doi: 10.1080/10556789208805504. |
| [5] |
R. T. Boonyasai, O. M. Ijagbemi and J. C. Pham et al, Improving the Emergency Department Discharge Process, Agency for Healthcare Research and Quality, Rockville, Maryland, United States. 2014.Google Scholar |
| [6] |
G. Brown, A. Pocock, M.-J. Zhao and M. Luján,
Conditional likelihood maximisation: A unifying framework for information theoretic feature selection, J. Mach. Learn. Res., 13 (2012), 27-66.
|
| [7] |
CDC, ICD-9-CM official guidelines for coding and reporting, http://www.cdc.gov/nchs/data/icd/icd9cm＄_{-}＄guidelines＄_{-}＄2011.pdf (accessed on 20 March 2016).Google Scholar |
| [8] |
R. J. Gallagher, E. K. Lee and D. A. Patterson, Constrained discriminant analysis via 0/1 mixed integer programming, Ann. Oper. Res., 74 (1997), 65-88. Google Scholar |
| [9] |
E. Goksu, C. Oktay, M. Kartal, A. Oskay and A. V. Sayrac,
Factors affecting revisit of COPD exacerbated patients presenting to emergency department, Eur. J. Emerg. Med., 17 (2010), 283-285.
doi: 10.1097/MEJ.0b013e3283314795. |
| [10] |
C. Y. Han, L. C. Chen, A. Barnard, C. C. Lin, Y. C. Hsiao and H. E. Liu et al, Early revisit
to the emergency department: An integrative review, J. Emerg. Nurs., 41 (2015), 285-295
doi: 10.1016/j.jen.2014.11.013. |
| [11] |
S. Hao, B. Jin and A. Y. Shin et al, Risk prediction of emergency department revisit 30 days
post discharge: A prospective study, PLoS One, 10 (2015), e0117633.
doi: 10.1371/journal.pone.0112944. |
| [12] |
S. C. Hu, Analysis of patient revisits to the emergency department, Am. J. Emerg. Med., 10 (1992), 366-370. Google Scholar |
| [13] |
I. Imsuwan, Characteristics of unscheduled emergency department return visit patients within 48 hours in Thammasat university hospital, J. Med. Assoc. Thai., 94 (2011), S73-S80. Google Scholar |
| [14] |
Y. Jenab, S. Haghani, A. Jalali and F. Darabi,
Unscheduled return visits and leaving the chest pain unit against medical advice, Iran Red Crescent Med. J., 17 (2015), e18320.
doi: 10.5812/ircmj.17(5)2015.18320. |
| [15] |
K. D. Keith, J. J. Bocka, M. S. Kobernick, R. L. Krome and M. A. Ross,
Emergency department revisits, Ann. Emerg. Med., 18 (1989), 964-968.
doi: 10.1016/S0196-0644(89)80461-5. |
| [16] |
M. Ko, Y. Lee, C. Chen, P. Chou and D. Chu,
Incidence of and predictors for early return visits to the emergency department: a population-based survey, Medicine, 94 (2015), e1770.
doi: 10.1097/MD.0000000000001770. |
| [17] |
E. K. Lee, Y. Wang, M. S. Hagen, X. Wei, R. A. Davis and B. M. Egan, Machine learning:
Multi-site evidence-based best practice discovery, in Machine Learning, Optimization, and
Big Data. MOD 2016. Lecture Notes in Computer Science (eds. P. Pardalos, P. Conca, G.
Giuffrida and G. Nicosia), Springer, Cham, (2016), 1-15.
doi: 10.1007/978-3-319-51469-7_1. |
| [18] |
E. K. Lee,
Large-scale optimization-based classification models in medicine and biology, Ann. Biomed. Eng., 35 (2007), 1095-1109.
doi: 10.1007/s10439-007-9317-7. |
| [19] |
E. K. Lee, F. Yuan, D. Hirsh, M. Mallory and H. K. Simon, A clinical decision tool for predicting patient care characteristics: patients returning within 72 hours in the emergency department, AMIA Annu. Symp. Proc., (2012), 495-504. Google Scholar |
| [20] |
E. K. Lee, R. J. Gallagher and D. A. patterson,
A linear programming approach to discriminant analysis with a reserved-judgment region, INFORMS J. Comput., 15 (2003), 23-41.
doi: 10.1287/ijoc.15.1.23.15158. |
| [21] |
S. J. Liaw, M. J. Bullard, P. M. Hu, J. C. Chen and H. C. Liao, Rates and causes of emergency department revisits within 72 hours, J. Formos Med. Assoc., 98 (1999), 422-425. Google Scholar |
| [22] |
J. A. Lowthian, A. J. Curtis and P. A. Cameron et al,
Systematic review of trends in emergency department attendances: an Australian perspective, Emerg. Med. J., 28 (2011), 373-377.
doi: 10.1136/emj.2010.099226. |
| [23] |
F. Meng, C. K. Ooi, C. K. K. Soh, K. L. Teow and P. Kannapiran,
Quantifying patient flow and utilization with patient flow pathway and diagnosis of an emergency in Singapore, Health Syst., 5 (2016), 140-148.
doi: 10.1057/hs.2015.15. |
| [24] |
F. Meng, J. Qi, M. Zhang, J. Ang, S. Chu and M. Siim,
A robust optimization model for managing elective admission in a public hospital, Oper. Res., 63 (2015), 1452-1467.
doi: 10.1287/opre.2015.1423. |
| [25] |
A. S. Newton, S. Ali, D. W. Johnson, C. Haines, R. J. Rosychuk, R. A. Keaschuk, P. Jacobs, M. Cappelli and T. P. Klassen,
Who comes back? Characteristics and predictors of return to emergency department services for pediatric mental health care, Acad. Emerg. Med., 17 (2010), 177-186.
doi: 10.1111/j.1553-2712.2009.00633.x. |
| [26] |
Y. Y. Ng, Optimal use of emegency services, SFP, 40 (2014), supplement 8-13. Google Scholar |
| [27] |
S. Nunëz, A. Hexdall and A. Aguirre-Jaime, Unscheduled returns to the emergency department: An outcome of medical errors?, Qual. Saf. Health Care., 15 (2006), 102-108. Google Scholar |
| [28] |
K. O'Loughlin, K. A. Hacking, N. Simmons, W. Christian, R. Syahanee, A. Shamekh and N. J. Prince, Paediatric unplanned reattendance rate: A&E clinical quality indicators, Arch. Dis. Child., 98 (2013), 211-213. Google Scholar |
| [29] |
L. Pereira, C. Choquet and A. Perozziello et al, Unscheduled return visits after an emergency
department (ED) attendance and clinical link between both visits in patients aged 75 years
and over: a prospective observational study, PloS One, 10 (2015), e0123803.
doi: 10.1371/journal.pone.0123803. |
| [30] |
H. Qiu, X. Chen, W. Liu, G. Zhou, Y. Wang and J. Lai,
A fast11-solver and its applications to robust face recognition, J. Ind. Manag. Optim., 8 (2012), 163-178.
|
| [31] |
J. Ryan, J. Hendler and K. P. Bennett,
Understanding emergency department 72-hour revisits among medicaid patients using electronic healthcare records, Big Data, 3 (2015), 238-248.
doi: 10.1089/big.2015.0038. |
| [32] |
Y. Saeys, I. Inza and P. Larrañaga, A review of feature selection techniques in bioinformatics, 23 (2007), 2507-2517.
doi: 10.1093/bioinformatics/btm344. |
| [33] |
G. Sauvin, Y. Freund, K. Saidi, B. Riou and P. Hausfater,
Unscheduled return visits to the emergency department: Consequences for triage, Acad. Emerg. Med., 20 (2013), 33-39.
doi: 10.1111/acem.12052. |
| [34] |
Y. Sun, B. H. Heng, S. Y. Tay and K. B. Tan,
Unplanned 3-day re-attendance rate at Emergency Department and hospital's bed occupancy rate, Int. J. Emerg. Med., 8 (2015), 324-329.
doi: 10.1186/s12245-015-0082-3. |
| [35] |
C. R. Trivedy and M. W. Cooke,
Unscheduled-return-visits (URV) in adults to the emergency department (ED): A rapid evidence assessment policy review, Emerg. Med. J., 32 (2015), 324-329.
doi: 10.1136/emermed-2013-202719. |
| [36] |
S. J. J. Verelst, S. Pierloot, D. Desruelles, J.-B. Gillet and J. Bergs,
Short-term unscheduled return visits of adult patients to the emergency department, J. Emerg. Med., 47 (2014), 131-199.
doi: 10.1016/j.jemermed.2014.01.016. |
| [37] |
A. K. C. Wai, C. M. Chor, A. T. C. Lee, Y. Sittambunka, C. A. Graham and T. H. Rainer,
Analysis of trends in emergency department attendances, hospital admissions and medical staffing in a Hong Kong university hospital: 5-year study, Int J. Emerg. Med., 2 (2009), 141-148.
doi: 10.1007/s12245-009-0098-7. |
| [38] |
Y. Wang, G. Zhou, L. Caccetta and W. Liu,
An alternative Lagrange-dual based algorithm for sparse signal reconstruction, IEEE Trans. Sig. Process., 59 (2011), 1895-1901.
doi: 10.1109/TSP.2010.2103066. |
| [39] |
C. Wei and X. Tang,
A fuzzy group decision making approach based on entropy, similarity measure, Int. J. Inf. Technol. Decis. Mak., 10 (2011), 1111-1130.
doi: 10.1142/S0219622011004737. |
| [40] |
C. L. Wu, F. T. Wang, Y. C. Chiang, Y. F. Chiu, T. G. Lin and L. F. Fu et al,
Unplanned emergency department revisits within 72 hours to a secondary teaching referral hospital in Taiwan, J. Emerg. Med., 38 (2010), 512-517.
doi: 10.1016/j.jemermed.2008.03.039. |
show all references
References:
| [1] |
E. A. Alessandrini, J. M. Lavelle, S. M. Grenfell, C. R. Jacobstein and K. N. Shaw, Return visits to a pediatric emergency department, Pediatr. Emerg. Care, 20 (2004), 166-171. Google Scholar |
| [2] |
J. A. Anderson,
Constrained discrimination between k populations, J. R. Stat. Soc. Series B, 31 (1969), 123-139.
|
| [3] |
D. Aujesky, M. K. Mor, M. Geng, R. A. Stone, M. J. Fine and S. A. Ibrahim,
Predictors of early hospital readmission after acute pulmonary embolism, Arch. Intern. Med., 169 (2009), 287-293.
doi: 10.1001/archinternmed.2008.546. |
| [4] |
K. P. Bennett and O. L. Mangasarian,
Robust linear programming discrimination of two linearly inseparable sets, Optim. Method Softw., 1 (1992), 23-34.
doi: 10.1080/10556789208805504. |
| [5] |
R. T. Boonyasai, O. M. Ijagbemi and J. C. Pham et al, Improving the Emergency Department Discharge Process, Agency for Healthcare Research and Quality, Rockville, Maryland, United States. 2014.Google Scholar |
| [6] |
G. Brown, A. Pocock, M.-J. Zhao and M. Luján,
Conditional likelihood maximisation: A unifying framework for information theoretic feature selection, J. Mach. Learn. Res., 13 (2012), 27-66.
|
| [7] |
CDC, ICD-9-CM official guidelines for coding and reporting, http://www.cdc.gov/nchs/data/icd/icd9cm＄_{-}＄guidelines＄_{-}＄2011.pdf (accessed on 20 March 2016).Google Scholar |
| [8] |
R. J. Gallagher, E. K. Lee and D. A. Patterson, Constrained discriminant analysis via 0/1 mixed integer programming, Ann. Oper. Res., 74 (1997), 65-88. Google Scholar |
| [9] |
E. Goksu, C. Oktay, M. Kartal, A. Oskay and A. V. Sayrac,
Factors affecting revisit of COPD exacerbated patients presenting to emergency department, Eur. J. Emerg. Med., 17 (2010), 283-285.
doi: 10.1097/MEJ.0b013e3283314795. |
| [10] |
C. Y. Han, L. C. Chen, A. Barnard, C. C. Lin, Y. C. Hsiao and H. E. Liu et al, Early revisit
to the emergency department: An integrative review, J. Emerg. Nurs., 41 (2015), 285-295
doi: 10.1016/j.jen.2014.11.013. |
| [11] |
S. Hao, B. Jin and A. Y. Shin et al, Risk prediction of emergency department revisit 30 days
post discharge: A prospective study, PLoS One, 10 (2015), e0117633.
doi: 10.1371/journal.pone.0112944. |
| [12] |
S. C. Hu, Analysis of patient revisits to the emergency department, Am. J. Emerg. Med., 10 (1992), 366-370. Google Scholar |
| [13] |
I. Imsuwan, Characteristics of unscheduled emergency department return visit patients within 48 hours in Thammasat university hospital, J. Med. Assoc. Thai., 94 (2011), S73-S80. Google Scholar |
| [14] |
Y. Jenab, S. Haghani, A. Jalali and F. Darabi,
Unscheduled return visits and leaving the chest pain unit against medical advice, Iran Red Crescent Med. J., 17 (2015), e18320.
doi: 10.5812/ircmj.17(5)2015.18320. |
| [15] |
K. D. Keith, J. J. Bocka, M. S. Kobernick, R. L. Krome and M. A. Ross,
Emergency department revisits, Ann. Emerg. Med., 18 (1989), 964-968.
doi: 10.1016/S0196-0644(89)80461-5. |
| [16] |
M. Ko, Y. Lee, C. Chen, P. Chou and D. Chu,
Incidence of and predictors for early return visits to the emergency department: a population-based survey, Medicine, 94 (2015), e1770.
doi: 10.1097/MD.0000000000001770. |
| [17] |
E. K. Lee, Y. Wang, M. S. Hagen, X. Wei, R. A. Davis and B. M. Egan, Machine learning:
Multi-site evidence-based best practice discovery, in Machine Learning, Optimization, and
Big Data. MOD 2016. Lecture Notes in Computer Science (eds. P. Pardalos, P. Conca, G.
Giuffrida and G. Nicosia), Springer, Cham, (2016), 1-15.
doi: 10.1007/978-3-319-51469-7_1. |
| [18] |
E. K. Lee,
Large-scale optimization-based classification models in medicine and biology, Ann. Biomed. Eng., 35 (2007), 1095-1109.
doi: 10.1007/s10439-007-9317-7. |
| [19] |
E. K. Lee, F. Yuan, D. Hirsh, M. Mallory and H. K. Simon, A clinical decision tool for predicting patient care characteristics: patients returning within 72 hours in the emergency department, AMIA Annu. Symp. Proc., (2012), 495-504. Google Scholar |
| [20] |
E. K. Lee, R. J. Gallagher and D. A. patterson,
A linear programming approach to discriminant analysis with a reserved-judgment region, INFORMS J. Comput., 15 (2003), 23-41.
doi: 10.1287/ijoc.15.1.23.15158. |
| [21] |
S. J. Liaw, M. J. Bullard, P. M. Hu, J. C. Chen and H. C. Liao, Rates and causes of emergency department revisits within 72 hours, J. Formos Med. Assoc., 98 (1999), 422-425. Google Scholar |
| [22] |
J. A. Lowthian, A. J. Curtis and P. A. Cameron et al,
Systematic review of trends in emergency department attendances: an Australian perspective, Emerg. Med. J., 28 (2011), 373-377.
doi: 10.1136/emj.2010.099226. |
| [23] |
F. Meng, C. K. Ooi, C. K. K. Soh, K. L. Teow and P. Kannapiran,
Quantifying patient flow and utilization with patient flow pathway and diagnosis of an emergency in Singapore, Health Syst., 5 (2016), 140-148.
doi: 10.1057/hs.2015.15. |
| [24] |
F. Meng, J. Qi, M. Zhang, J. Ang, S. Chu and M. Siim,
A robust optimization model for managing elective admission in a public hospital, Oper. Res., 63 (2015), 1452-1467.
doi: 10.1287/opre.2015.1423. |
| [25] |
A. S. Newton, S. Ali, D. W. Johnson, C. Haines, R. J. Rosychuk, R. A. Keaschuk, P. Jacobs, M. Cappelli and T. P. Klassen,
Who comes back? Characteristics and predictors of return to emergency department services for pediatric mental health care, Acad. Emerg. Med., 17 (2010), 177-186.
doi: 10.1111/j.1553-2712.2009.00633.x. |
| [26] |
Y. Y. Ng, Optimal use of emegency services, SFP, 40 (2014), supplement 8-13. Google Scholar |
| [27] |
S. Nunëz, A. Hexdall and A. Aguirre-Jaime, Unscheduled returns to the emergency department: An outcome of medical errors?, Qual. Saf. Health Care., 15 (2006), 102-108. Google Scholar |
| [28] |
K. O'Loughlin, K. A. Hacking, N. Simmons, W. Christian, R. Syahanee, A. Shamekh and N. J. Prince, Paediatric unplanned reattendance rate: A&E clinical quality indicators, Arch. Dis. Child., 98 (2013), 211-213. Google Scholar |
| [29] |
L. Pereira, C. Choquet and A. Perozziello et al, Unscheduled return visits after an emergency
department (ED) attendance and clinical link between both visits in patients aged 75 years
and over: a prospective observational study, PloS One, 10 (2015), e0123803.
doi: 10.1371/journal.pone.0123803. |
| [30] |
H. Qiu, X. Chen, W. Liu, G. Zhou, Y. Wang and J. Lai,
A fast11-solver and its applications to robust face recognition, J. Ind. Manag. Optim., 8 (2012), 163-178.
|
| [31] |
J. Ryan, J. Hendler and K. P. Bennett,
Understanding emergency department 72-hour revisits among medicaid patients using electronic healthcare records, Big Data, 3 (2015), 238-248.
doi: 10.1089/big.2015.0038. |
| [32] |
Y. Saeys, I. Inza and P. Larrañaga, A review of feature selection techniques in bioinformatics, 23 (2007), 2507-2517.
doi: 10.1093/bioinformatics/btm344. |
| [33] |
G. Sauvin, Y. Freund, K. Saidi, B. Riou and P. Hausfater,
Unscheduled return visits to the emergency department: Consequences for triage, Acad. Emerg. Med., 20 (2013), 33-39.
doi: 10.1111/acem.12052. |
| [34] |
Y. Sun, B. H. Heng, S. Y. Tay and K. B. Tan,
Unplanned 3-day re-attendance rate at Emergency Department and hospital's bed occupancy rate, Int. J. Emerg. Med., 8 (2015), 324-329.
doi: 10.1186/s12245-015-0082-3. |
| [35] |
C. R. Trivedy and M. W. Cooke,
Unscheduled-return-visits (URV) in adults to the emergency department (ED): A rapid evidence assessment policy review, Emerg. Med. J., 32 (2015), 324-329.
doi: 10.1136/emermed-2013-202719. |
| [36] |
S. J. J. Verelst, S. Pierloot, D. Desruelles, J.-B. Gillet and J. Bergs,
Short-term unscheduled return visits of adult patients to the emergency department, J. Emerg. Med., 47 (2014), 131-199.
doi: 10.1016/j.jemermed.2014.01.016. |
| [37] |
A. K. C. Wai, C. M. Chor, A. T. C. Lee, Y. Sittambunka, C. A. Graham and T. H. Rainer,
Analysis of trends in emergency department attendances, hospital admissions and medical staffing in a Hong Kong university hospital: 5-year study, Int J. Emerg. Med., 2 (2009), 141-148.
doi: 10.1007/s12245-009-0098-7. |
| [38] |
Y. Wang, G. Zhou, L. Caccetta and W. Liu,
An alternative Lagrange-dual based algorithm for sparse signal reconstruction, IEEE Trans. Sig. Process., 59 (2011), 1895-1901.
doi: 10.1109/TSP.2010.2103066. |
| [39] |
C. Wei and X. Tang,
A fuzzy group decision making approach based on entropy, similarity measure, Int. J. Inf. Technol. Decis. Mak., 10 (2011), 1111-1130.
doi: 10.1142/S0219622011004737. |
| [40] |
C. L. Wu, F. T. Wang, Y. C. Chiang, Y. F. Chiu, T. G. Lin and L. F. Fu et al,
Unplanned emergency department revisits within 72 hours to a secondary teaching referral hospital in Taiwan, J. Emerg. Med., 38 (2010), 512-517.
doi: 10.1016/j.jemermed.2008.03.039. |
| Index | Factor Name |
| 1 | Age |
| 2 | Gender |
| 3 | Race |
| 4 | Registration day of week (DOW) |
| 5 | Registration public holiday and Sunday (HAS) |
| 6 | CC1 orthopaedic (ORTHO) |
| 7 | CC1 gastrointestinal (GI) |
| 8 | CC1 respiratory (RES) |
| 9 | CC1 general & minor |
| 10 | CC1 external causes of morbidity |
| 11 | CC1 neurologic |
| 12 | CC1 cardiovascular (CV) |
| 13 | CC1 skin |
| 14 | CC1 eye complaints (EC) |
| 15 | CC1 ear, neck, throat |
| 16 | CC1 substance misuse (SM) |
| 17 | CC1 others |
| 18 | EDTC2 category |
| 19 | Diagnosis: general symptoms (GM) |
| 20 | Diagnosis: acute respiratory infection (ARI) |
| 21 | Diagnosis: sprains and strains (SS) |
| 22 | Diagnosis: minor head injury (MHI) |
| 23 | Diagnosis: diseases of oesophagus, stomach, duodenum |
| 24 | Diagnosis: COPD3 |
| 25 | Diagnosis: fracture upper limb (FUL) |
| 26 | Diagnosis: fracture lower limb (FLL) |
| 27 | Diagnosis: open wound upper limb (OWUL) |
| 28 | Diagnosis: superficial injuries (SIN) |
| 29 | Diagnosis: intestinal diseases (ID) |
| 30 | Diagnosis: rheumatism (RHE) |
| 31 | Diagnosis: diseases of ear and mastoid (DEM) |
| 32 | Diagnosis: open wound of head, neck, and trunk (OW of HNT) |
| 33 | Diagnosis: neurotic disorders (ND) |
| 34 | Diagnosis: viral diseases (VD) |
| 35 | Diagnosis: complications of care (COMC) |
| 36 | Diagnosis: open wound lower limb (OWLL) |
| 37 | Diagnosis: others in ICD-9-CM4 groups |
| 38 | PAC5 status |
| 39 | Social issues (SI) |
| 40 | National service full-time (NSTF) |
| 41 | Handover |
| 42 | Medical history diabetes |
| 43 | Medical history asthma |
| 44 | Medical history cardiovascular disease |
| 45 | Average temperature (ave temp.) |
| 46 | Average pulse |
| 47 | Diastolic blood pressure (DBP) |
| 1 CC stands for chief complaint 2 EDTC stands for emergency diagnostic and therapeutic centre. Certain emergency patients are observed at EDTC for up to 24 hours before being admitted or discharged 3 COPD stands for chronic obstructive pulmonary disease 4 ICD-9-CM stands for international classification of diseases, 9th revision, clinical modification 5 PAC stands for patient acuity category |
|
| Index | Factor Name |
| 1 | Age |
| 2 | Gender |
| 3 | Race |
| 4 | Registration day of week (DOW) |
| 5 | Registration public holiday and Sunday (HAS) |
| 6 | CC1 orthopaedic (ORTHO) |
| 7 | CC1 gastrointestinal (GI) |
| 8 | CC1 respiratory (RES) |
| 9 | CC1 general & minor |
| 10 | CC1 external causes of morbidity |
| 11 | CC1 neurologic |
| 12 | CC1 cardiovascular (CV) |
| 13 | CC1 skin |
| 14 | CC1 eye complaints (EC) |
| 15 | CC1 ear, neck, throat |
| 16 | CC1 substance misuse (SM) |
| 17 | CC1 others |
| 18 | EDTC2 category |
| 19 | Diagnosis: general symptoms (GM) |
| 20 | Diagnosis: acute respiratory infection (ARI) |
| 21 | Diagnosis: sprains and strains (SS) |
| 22 | Diagnosis: minor head injury (MHI) |
| 23 | Diagnosis: diseases of oesophagus, stomach, duodenum |
| 24 | Diagnosis: COPD3 |
| 25 | Diagnosis: fracture upper limb (FUL) |
| 26 | Diagnosis: fracture lower limb (FLL) |
| 27 | Diagnosis: open wound upper limb (OWUL) |
| 28 | Diagnosis: superficial injuries (SIN) |
| 29 | Diagnosis: intestinal diseases (ID) |
| 30 | Diagnosis: rheumatism (RHE) |
| 31 | Diagnosis: diseases of ear and mastoid (DEM) |
| 32 | Diagnosis: open wound of head, neck, and trunk (OW of HNT) |
| 33 | Diagnosis: neurotic disorders (ND) |
| 34 | Diagnosis: viral diseases (VD) |
| 35 | Diagnosis: complications of care (COMC) |
| 36 | Diagnosis: open wound lower limb (OWLL) |
| 37 | Diagnosis: others in ICD-9-CM4 groups |
| 38 | PAC5 status |
| 39 | Social issues (SI) |
| 40 | National service full-time (NSTF) |
| 41 | Handover |
| 42 | Medical history diabetes |
| 43 | Medical history asthma |
| 44 | Medical history cardiovascular disease |
| 45 | Average temperature (ave temp.) |
| 46 | Average pulse |
| 47 | Diastolic blood pressure (DBP) |
| 1 CC stands for chief complaint 2 EDTC stands for emergency diagnostic and therapeutic centre. Certain emergency patients are observed at EDTC for up to 24 hours before being admitted or discharged 3 COPD stands for chronic obstructive pulmonary disease 4 ICD-9-CM stands for international classification of diseases, 9th revision, clinical modification 5 PAC stands for patient acuity category |
|
Table 2.
Top 15 Risk Factors Selected Using Filter Methods
| JMI | MIFS | CMIM | MRMR | ICAP | CIFE | DISR | CMI | ConDred |
| SI | SI | SI | SI | SI | SI | SI | SI | SI |
| PAC | PAC | PAC | PAC | PAC | PAC | diag: COPD | PAC | PAC |
| DBP | diag:ID | DBP | diag:ID | DBP | ave pulse | CC stance | ave SM | DBP |
| ave pulse | reg HAS | ave pulse | reg HAS | ave pulse | DBP | diag:ND | DBP | EDTC |
| diag:COPD | diag:COMC | diag:COPD | diag:OWLL | diag:COPD | age | diag:OWLL | reg DOW | CC others |
| age | diag:OWLL | age | diag:OW of HNT | age | reg DOW | handover | age | diag:MHI |
| CC RES | diag:OW of HNT | handover | diag:COMC | gender | race | CC RES | race | age |
| gender | diag:VD | gender | diag:ND | handover | diag:COPD | diag:OWUL | ave temp. | NSFT |
| ave temp. | diag:DEM | ave temp. | diag:DEM | ave temp. | ave temp. | diag:FLL | gender | ave pulse |
| handover | CC SM | CC RES | ave temp. | CC GI | gender | diag:OW of HNT | CC ORTHO | CC ORTHO |
| race | diag:RHE | CC EC | diag:FLL | race | CC RES | PAC | diag:GM | diag:GM |
| EDTC | diag:SIN | CC ORTHO | diag:RHE | NSFT | handover | EDTC | diag:others in ICD9 | CC CV |
| NSFT | diag:FLL | race | diag:OWUL | reg DOW | NSFT | diag:DEM | CC RES | diag:ARI |
| CC ORTHO | diag:FUL | NSFT | diag:SIN | diag:ND | diag:GM | diag:ID | CC GI | CC RES |
| reg DOW | diag:OWUL | diag:ND | gender | diag:GM | medical history asthma | CC EC | NSFT | diag:SS |
| JMI | MIFS | CMIM | MRMR | ICAP | CIFE | DISR | CMI | ConDred |
| SI | SI | SI | SI | SI | SI | SI | SI | SI |
| PAC | PAC | PAC | PAC | PAC | PAC | diag: COPD | PAC | PAC |
| DBP | diag:ID | DBP | diag:ID | DBP | ave pulse | CC stance | ave SM | DBP |
| ave pulse | reg HAS | ave pulse | reg HAS | ave pulse | DBP | diag:ND | DBP | EDTC |
| diag:COPD | diag:COMC | diag:COPD | diag:OWLL | diag:COPD | age | diag:OWLL | reg DOW | CC others |
| age | diag:OWLL | age | diag:OW of HNT | age | reg DOW | handover | age | diag:MHI |
| CC RES | diag:OW of HNT | handover | diag:COMC | gender | race | CC RES | race | age |
| gender | diag:VD | gender | diag:ND | handover | diag:COPD | diag:OWUL | ave temp. | NSFT |
| ave temp. | diag:DEM | ave temp. | diag:DEM | ave temp. | ave temp. | diag:FLL | gender | ave pulse |
| handover | CC SM | CC RES | ave temp. | CC GI | gender | diag:OW of HNT | CC ORTHO | CC ORTHO |
| race | diag:RHE | CC EC | diag:FLL | race | CC RES | PAC | diag:GM | diag:GM |
| EDTC | diag:SIN | CC ORTHO | diag:RHE | NSFT | handover | EDTC | diag:others in ICD9 | CC CV |
| NSFT | diag:FLL | race | diag:OWUL | reg DOW | NSFT | diag:DEM | CC RES | diag:ARI |
| CC ORTHO | diag:FUL | NSFT | diag:SIN | diag:ND | diag:GM | diag:ID | CC GI | CC RES |
| reg DOW | diag:OWUL | diag:ND | gender | diag:GM | medical history asthma | CC EC | NSFT | diag:SS |
Table 3.
Prediction Results Including Factor of Social Issues
| Set of risk factors | Training set | Test set | ||||
| sensitivity | specificity | overall | sensitivity | specificity | overall | |
| S0 | 29.4% | 86.2% | 83.5% | 29.3% | 85.8% | 83.3% |
| Set of risk factors | Training set | Test set | ||||
| sensitivity | specificity | overall | sensitivity | specificity | overall | |
| S0 | 29.4% | 86.2% | 83.5% | 29.3% | 85.8% | 83.3% |
Table 4.
Prediction Results Excluding Factor of Social Issues
| Set of risk factors | Training set | Test set | ||||
| sensitivity | specificity | overall | sensitivity | specificity | overall | |
| S1 | 41.1% | 74.2% | 72.5% | 40.3% | 74.1% | 72.4% |
| S2 | 40.6% | 74.5% | 72.8% | 40.1% | 74.1% | 72.6% |
| S3 | 40.6% | 74.4% | 72.7% | 40.1% | 74.2% | 72.7% |
| S4 | 40.3% | 74.4% | 72.8% | 39.9% | 74.4% | 72.8% |
| Set of risk factors | Training set | Test set | ||||
| sensitivity | specificity | overall | sensitivity | specificity | overall | |
| S1 | 41.1% | 74.2% | 72.5% | 40.3% | 74.1% | 72.4% |
| S2 | 40.6% | 74.5% | 72.8% | 40.1% | 74.1% | 72.6% |
| S3 | 40.6% | 74.4% | 72.7% | 40.1% | 74.2% | 72.7% |
| S4 | 40.3% | 74.4% | 72.8% | 39.9% | 74.4% | 72.8% |
Table 5.
Prediction Results Using Logistic Regression
| Training set: 211,160 | Test set: 117,573 | |||||
| Hosmer-Lemeshow test | p < 0.001 | p < 0.001 | ||||
| C-statistic of ROC curve | 0.67 | 0.66 | ||||
| Predictive accuracy | sensitivity | specificity | overall | sensitivity | specificity | overall |
| 70% cutoff | 1.7% | 99.9% | 95.4% | 1.0% | 99.9% | 95.5% |
| 50% cutoff | 4.6% | 99.9% | 95.5% | 2.4% | 99.9% | 95.5% |
| 20% cutoff | 11.9% | 99.3% | 95.2% | 7.2% | 99.4% | 95.2% |
| 10% cutoff | 18.5% | 96.3% | 92.7% | 14.1% | 96.1% | 92.4% |
| 5% cutoff | 51.6% | 72.2% | 71.2% | 51.0% | 70.8% | 69.9% |
| Training set: 211,160 | Test set: 117,573 | |||||
| Hosmer-Lemeshow test | p < 0.001 | p < 0.001 | ||||
| C-statistic of ROC curve | 0.67 | 0.66 | ||||
| Predictive accuracy | sensitivity | specificity | overall | sensitivity | specificity | overall |
| 70% cutoff | 1.7% | 99.9% | 95.4% | 1.0% | 99.9% | 95.5% |
| 50% cutoff | 4.6% | 99.9% | 95.5% | 2.4% | 99.9% | 95.5% |
| 20% cutoff | 11.9% | 99.3% | 95.2% | 7.2% | 99.4% | 95.2% |
| 10% cutoff | 18.5% | 96.3% | 92.7% | 14.1% | 96.1% | 92.4% |
| 5% cutoff | 51.6% | 72.2% | 71.2% | 51.0% | 70.8% | 69.9% |
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