| Literature DB >> 35476697 |
Naomi Akiyama1, Ryuji Uozumi2, Tomoya Akiyama3, Keisuke Koeda1, Takeru Shiroiwa4, Kuniaki Ogasawara1.
Abstract
Choking can lead to mortality and residual impairments. This study aimed to determine the factors associated with choking among acute hospital patients and examine error-producing conditions to suggest choking-prevention policies. Among 36,364 cases reported by hospital staff at an acute university hospital from 2012 to 2018 were examined using a retrospective study, 35,440 were analysis as the number of cases analysed for the study. We used descriptive statistics to present patient characteristics and conducted univariable and multivariable logistic regression analyses to identify factors associated with choking. Additionally, we conducted content analysis (root cause analysis) to examine error-producing conditions and prevention policies. Sixty-eight cases were related to choking injuries; of these, 43 patients (63.2%) were male, and 38 (55.9%) were aged 65 years and older. Choking cases had a high percent of adverse outcomes involving residual impairment or death (n = 23, 33.8%). Mental illness (adjusted odds ratio [95% confidence interval]: 3.14 [1.39-7.08]), and hospitalisation in the general wards (adjusted odds ratio [95% confidence interval]: 3.13 [1.70-5.76]) were associated with an increased probability of choking. Error production was caused by food (n = 25, 36.8%) and medical devices or supplies (n = 13, 19.1%). Almost all contributory factors were associated with inadequate checking (n = 66, 97.1%) and misperception of risk (n = 65, 95.6%). Choking poses a highly significant burden on patients, and hospital administrators should minimise the risk of choking to prevent related injuries. Hospital administrators should provide training and education to their staff and develop adequate protocols and procedures to prevent choking.Entities:
Mesh:
Year: 2022 PMID: 35476697 PMCID: PMC9045662 DOI: 10.1371/journal.pone.0267430
Source DB: PubMed Journal: PLoS One ISSN: 1932-6203 Impact factor: 3.240
Fig 1Flow diagram demonstrating the study process.
Characteristics of choking cases.
| Choking n = 68 | Others n = 35372 | |||
|---|---|---|---|---|
| n(%) | n(%) | |||
| Patient age | ||||
| 5 years ≦ age < 65 | 20(29.4) | 13911(39.3) | ||
| < 5 years | 10(14.7) | 3953(11.2) | ||
| ≧ 65 years | 38(55.9) | 17508(49.5) | ||
| Patient gender | ||||
| Female | 25(36.8) | 15078(42.6) | ||
| Male | 43(63.2) | 20294(57.4) | ||
| Patient Status | ||||
| Cognitive impairment | Have | 5(7.4) | 1223(3.5) | |
| None | 63(92.6) | 34149(96.5) | ||
| Mental illness | Have | 7(10.3) | 1261(3.6) | |
| None | 61(89.7) | 34111(96.4) | ||
| Occurring situation | ||||
| Intensive care unit | 2(3.0) | 4270(12.1) | ||
| Hospitalised in the general wards | 53 (77.9) | 16985(48.0) | ||
| Others | 13(19.1) | 14117(39.9) | ||
| Occurrence year | ||||
| Fiscal year 2012 | 5(7.4) | 3226(9.1) | ||
| Fiscal year 2013 | 9(13.2) | 3425(9.7) | ||
| Fiscal year 2014 | 6(8.8) | 4276(12.1) | ||
| Fiscal year 2015 | 14(20.6) | 5334(15.1) | ||
| Fiscal year 2016 | 2(2.9) | 6072(17.2) | ||
| Fiscal year 2017 | 8(11.8) | 6124(17.3) | ||
| Fiscal year 2018 | 24(35.3) | 6915(19.5) | ||
| Adverse outcome | ||||
| Light residual impairment or light treatment | 45(66.2) | 35084(99.2) | ||
| High residual impairment or death | 23(33.8) | 288(0.8) | ||
Contributory factors for choking in logistic regression analysis.
| Univariable model | Multivariable model | ||||||||
|---|---|---|---|---|---|---|---|---|---|
| n | Choking (%) | Crude OR | 95% CI | p value | Adjusted OR | 95% CI | p value | ||
| Patient age | |||||||||
| 5 years ≦ age < 65 | 13931 | 20(0.14) | ref. | ref. | |||||
| < 5 years | 3963 | 10(0.25) | 1.76 | [0.82–3.76] | 0.145 | 2.73 | [1.24–6.02] | 0.013 | |
| ≧ 65 years | 17546 | 38(0.22) | 1.51 | [0.88–2.60] | 0.139 | 1.48 | [0.84–2.60] | 0.171 | |
| Patient Status | |||||||||
| Cognitive impairment (ref. = none) | 1228 | 5(0.41) | 2.22 | [0.89–5.52] | 0.087 | 1.42 | [0.55–3.68] | 0.465 | |
| Mental illness (ref. = none) | 1268 | 7(0.55) | 3.10 | [1.42–6.80] | 0.005 | 3.14 | [1.39–7.08] | 0.006 | |
| Occurring situation | |||||||||
| Others | ref. | ref. | |||||||
| Intensive care unit | 17038 | 53(0.31) | 0.51 | [0.11–2.25] | 0.374 | 0.39 | [0.08–1.74] | 0.213 | |
| Hospitalised in the general wards | 4272 | 2(0.05) | 3.39 | [1.85–6.22] | 0.033 | 3.13 | [1.70–5.76] | <0.001 | |
ref. = reference; OR = odds ratio; 95% CI = 95% confidence interval.
Physical contributory factors for choking.
| Code | Example sub-codes |
|---|---|
| Food (n = 25, 36.8%) | Chunks of meat, milk, textural diet |
| Medical device/supplies (n = 13, 19.1%) | Thermometer, tube, medical tape, oral care sponge |
| Fluid (n = 12, 17.6%) | Vomit, sputum |
| Daily necessities (n = 11, 16.2%) | Shampoo, mouthwash, diapers |
| Medicine (n = 7, 10.3%) | Anaesthetic, herbal medicine, press-through package |
aRCA, Root cause analysis.
bNumber (%).
Human or systemic contributory factors and solutions related to choking.
| Code | Example sub-codes of solutions |
|---|---|
| Sudden turn in underlying disease (n = 10, 14.7%) |
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| Inadequate checking (n = 66, 97.1%) |
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| Misperception of risk (n = 65, 95.6%) |
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| Poor instructions or procedures (n = 51, 75.0%) |
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| Inadequate coordination (n = 47, 69.1%) |
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| Inadequate environment (n = 29, 42.6%) |
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| Lack of communication among staff (n = 19, 27.9%) |
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| Non-compliance with rules |
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| (n = 10, 14.7%) |
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| Staff/time shortage/busyness (n = 8, 11.8%) |
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| Inadequate medication management (n = 7, 10.3%) | |
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| Unfamiliarity with the task |
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| (n = 7, 10.3%) | |
| Educational mismatch of person and task (n = 7, 10.3%) |
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aRCA, Root cause analysis.
bNumber (%).
cError-producing conditions were multiple choices.