Incidence, Etiology, and Clinical Outcomes of Acute Coma

doi:10.21203/rs.3.rs-4697186/v1

Objectives

To investigate the clinical nature of acute coma, which will serve as a reference for subsequent clinical decision-making

Methods and analysis

This observational study utilized Taiwan National Health Insurance Database to identify cases of acute coma from 2000 to 2017 based on ED discharge diagnoses. Clinical Classification Software (CCS) was employed to categorize the causes of acute coma. We examined the characteristics of acute coma cases, age-specific incidence rates, underlying causes, and clinical outcomes such as reversible coma, hospitalization, and 30-day mortality. Additionally, we assessed functional outcomes at a one-year follow-up. Long-term factors influencing mortality were ascertained using Cox regression.

Results

Among 99,217,322 ED visits between 2000 and 2017, 419,480 acute coma events were identified, with an event rate of 4.23 per 1,000 ED visits and an incidence rate of 0.93 per 1,000 person-years. We analyzed 205,747 first-ever acute coma cases, predominantly male (58.90%), aged 58.27 years (SD 23.04). Infection and CNS causes were predominant. CNS and drug-related causes contributed to increased 30-day mortality, while psychiatric, alcohol, women's health and perinatal care, and seizure are causes linked to reversible coma. Patients needed intensive care (26.54%), life-sustaining treatments (41.09%), or disability (6.57%). Generalized estimating equations revealed that CNS (aOR, 0.68; 95% CI, 0.62 to 0.74; p < .0001) and drug-related causes (aOR, 0.72; 95% CI, 0.65 to 0.81; p < .0001) were less likely to result in reversible coma, suggesting higher 30-day mortality risk factors. Cox regression showed drugs (aHR, 1.30, 95% CI 1.20 to 1.41, p < .001), neoplasm (aHR, 1.18, 95% CI 1.11 to 1.25, p < .001), and symptoms (aHR, 1.44, 95% CI 1.24 to 1.67, p < .001) elevated the long-term death risk.

Conclusion

Our study demonstrates the use of ICD codes aggregation to CCS in acute coma clinical study, providing insights into its clinical nature.

Coma

Clinical Classifications Software

Incidence

Risk factors

Natural history studies

Prognosis

Acute coma with diverse causes and significant impacts on clinical course, mortality, and disability.
Timely diagnosis and appropriate treatment are essential because acute coma often reflects life-threatening, systemic or intracranial processes.
Heterogeneity results of acute coma studies may be due to research design and the underlying mechanism or cause are classified.
Adopting secondary data for studying acute coma can provide valuable insight into preventing and treating acute coma.

Using the AHRQ Clinical Classification Software (CCS) method, we propose a new clinical research model for studying acute coma, addressing the research gap
The incidence of acute coma is approximately 0.93 per 1,000 person-years, with older adults experiencing it at rates approximately 54 and 12 times higher than pediatric and adult groups, respectively.
In the clinical course of acute coma, 45.49% of cases are reversible, 41.66% necessitate hospitalization, and within the 30-day mortality group, older adults constitute two-thirds.
Infections and CNS-related diseases were the most common causes.
The one-year follow-up after acute coma index date showed 26.54% of cases needed ICU treatment, 6.57% were complicated with a disability, and 1.88% were confined in nursing homes.

Acute coma is a critical time-sensitive condition with heterogeneous causes that requires urgent attention and has significant impacts on patients and healthcare professionals ¹. It is characterized by profound failure of the neurological system responsible for maintaining arousal and awareness, leading to either a reflex response or no response to external stimuli at all ². Prior studies estimate that 1–5% of patients presenting to the emergency department (ED) have a disturbance in consciousness ^{3 4}. Emergency care researchers often categorize acute coma into three etiological factors: primary CNS disease, severe medical conditions that affect the CNS secondarily, or functional such as psychogenic disorder ^{5 6}. The clinical course of acute coma has been classified into three main categories: reversible coma, where patients recover quickly after ED management and can be discharged without any functional deficits; mortality group consisting of patients who do not survive their coma event despite medical interventions; and hospitalization group, which includes patients requiring hospitalization that may need intensive care or life-sustaining treatments (LSTs), or complicated with long-term disabilities ^{7 8}. Major challenge in studying acute coma is its heterogeneous nature, with multiple contributing factors often present in a single patient. Variations in acute coma study results may arise due to differences in definitions, cause classifications, and follow-up periods ⁹. These factors can affect outcomes and complicate direct comparisons between studies, underscoring the need for standardized methodologies ¹⁰. Despite the urgent need for a better understanding clinical nature of acute coma, there is a lack of large-scale longitudinal studies that can comprehensively address the incidence, causes, clinical course, and outcomes of acute coma.

The Agency for Healthcare Research and Quality (AHRQ) has developed the Clinical Classification Software (CCS) to provide a standardized method for classifying diagnosis codes into CCS categories based on clinical characteristics ^{11 12}. The CCS categories employ the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) and Tenth Revision, Clinical Modification (ICD-10-CM) classification systems to aggregate large numbers of ICD diagnostic codes into 285 clinically meaningful categories, thereby making clinical research more feasible. Our study aims to (1) estimate acute coma incidence, (2) use the CCS to identify acute coma causes, and (3) investigate the clinical course and outcomes.

Study design and setting

In this observational study, we utilized Taiwan National Health Insurance Research Database (NHIRD) to examine ED visits between January 1, 2000, and December 31, 2017. The NHIRD, managed by the Ministry of Health and Welfare, offered a comprehensive dataset with information on demographics, comorbidities, hospitalization, functional status, and mortality. This study was conducted with the approval of the local ethics board and involved no direct patient interaction. We carried out a retrospective analysis of claims data, ensuring all personal identifiers were encrypted to uphold patient confidentiality.

Acute coma participants definition

Given the nature of this study, we utilized the NHIRD dataset to investigate acute coma incidences. However, it should be noted that the NHIRD dataset lacks specific indicators like the Glasgow Coma Scale (GCS) to represent coma status. Consequently, we relied on the judgment of emergency physicians in diagnosing acute coma instances, especially in cases where there was no explicit diagnosis but an indication of coma in the ED's diagnoses. We employed International Classification of Diseases (ICD) codes to define acute coma objectively. These codes encompass a range of acute coma conditions, including "780.01" for comatose, "780.09" for other alterations of consciousness, "R40.0" for somnolence, "R40.1" for stupor, "R40.2" for unspecified coma, and "S06.7" for intracranial injury-related coma. Therefore, our study population consisted of cases that included any of these codes within the three diagnoses upon ED discharge records and remained as the final research cohort (Fig. 1). The present study implemented several exclusion criteria to ensure precise estimation of the cause, disease progression, and clinical outcomes associated with acute coma. First, we omitted cases lacking comprehensive sociodemographic data. Second, we excluded those who were undergoing life-sustaining treatments or were disabled or residing in a nursing home prior to the acute coma event. Additionally, cases diagnosed with acute coma in the ED that CCS could not further classify were removed from the study. To rule out hospitalizations potentially unrelated to the acute coma events, we excluded samples hospitalized more than 14 days following the acute coma.

Incidence estimates

We estimated the annual acute coma event rate from 2000 to 2017, with acute coma events as the unit of analysis. The event rate of acute coma is calculated by dividing the number of events by ED visits. In addition, we determined crude age group-stratified incidence rates were determined per 1000 person-years, with denominators based on the number of insured individuals during the year, taking into account their survival status and the person-years they contributed within that year. Considering insured individuals' survival status and person-years contributed and reported age-specific incidence rates in pediatric (1–18), adult (19–64), and senior adult (65+) groups with corresponding summary statistics.

Clinical course, causes and outcomes assessment

The study explored the clinical course of acute coma using each patient's first-ever event as the unit of analysis. Index date was set as the date of first diagnosis acute coma. ED visits were categorized into reversible coma, hospitalization, and 30-day mortality ¹³. Individuals who died within 30 days of the acute coma ED index date were classified as the 30-day mortality group. Those requiring hospitalization within seven days post-episode but not dying within 30 days constituted the hospitalization group. Patients diagnosed with acute coma in the ED without needing hospitalization or facing death were categorized as the reversible coma group.

Using CCS methodology ^{12 14 15}, we categorized ICD codes from death or hospitalization into 23 acute coma causes (Supplementary Table 1) and a statistical analysis plan is available in the (Supplementary Program). The diagnosis sequence began with death, hospitalization, and ED diagnosis if no death or hospitalization occured. These causes were further classified into three etiological mechanisms: (1) primary CNS diseases (neurological etiology), (2) medical conditions affecting the CNS secondarily (medical etiology), and (3) functional etiology⁵. Neurological etiology included acute CNS insult, chronic neurodegenerative encephalopathy, paroxysmal seizure disorders, and brain trauma. Medical etiology included alcohol-related coma, drugs, and organ system dysfunction. Functional factors included psychogenic disorders, symptoms, syncope, and other related causes. Patients were followed for one year to evaluate short-term outcome (30-day mortality or reversible course) and long-term outcomes (ICU admission, LSTs ¹⁶, rehabilitation, disability status, or nursing home residency).

Statistical analysis

We used χ² tests to analyze baseline categorical characteristics and compared continuous variables' mean among coma groups with One-Way ANOVA. Generalized estimating equations (GEE ) were used to estimate the adjusted odds ratio (aOR) of acute coma, accounting for multiple causes and covariates like sex, age, CCI, occupation, urbanization, and income. Survival analysis was conducted for reversible and hospitalization groups, tracking survival probability and calculating time to event (death) or censoring. Cox regression investigated potential death event causes, with hazard ratios identifying factors affecting long-term outcomes. Analyses were performed with SAS software, version 9.4, and a significance level of p < 0.05.

Cohort characteristics and clinical course estimate

Among 99,217,322 ED visits between 2000 and 2017, 419,480 acute coma events were identified. Of these, 365,675 patients were discharged or hospitalized within seven days. After excluding 4,385 ED visits with only acute coma diagnosis code, lacking further information, and participants lacking sociodemographic data or with prior nursing home or disabled status, 205,747 cases remained in the final research cohort (Fig. 1). The cohort clinical course classified 93,598 (45.49%) as reversible acute coma group, 85,712 (41.66%) as hospitalization group, and 26,437 (12.85%) as 30-day mortality group. The study population was 54.39% male, with an average age of 58.27 (SD 23.04) years (Table 1).

Table 1

Characteristics of acute coma cohort
	Total acute coma n (%)		Reversible coma n (%)		Hospitalization n (%)		30-day mortality n (%)		p-value
Total	205,747	(100·00)	93,598	(45·49)	85,712	(41·66)	26,437	(12·85)	< 0·001
Sex
Male	111,897	(54·39)	49,738	(53·14)	46,910	(54·73)	15,249	(57·68)	< 0·001
Female	93,850	(45·61)	43,860	(46·86)	38,802	(45·27)	11,188	(42·32)
Age	58·27	± 23·04	39·81	± 19·99	52·93	± 22·93	70·99	± 16·90	< 0·001
Age group
< 18	61,756	(30·02)	38,509	(13·56)	19,661	(22·94)	3,586	(13·56)	< 0·001
18–64	49,039	(23·83)	22,955	(23·88)	19,771	(23·07)	6,313	(23·88)
≧ 65	94,952	(46·15)	32,134	(62·56)	46,280	(53·99)	16,538	(62·56)
CCI index
CCI ≦ 1	133,867	(65·06)	73,552	(78·58)	46,694	(54·48)	13,621	(51·52)	< 0·001
CCI > 1	71,880	(34·94)	20,046	(21·42)	39,018	(45·52)	12,816	(48·48)
Income
Low	58,488	(28·43)	26,255	(28·05)	25,054	(29·23)	7,179	(27·15)	< 0·001
Middle	73,869	(35·90)	35,899	(38·35)	29,228	(34·10)	8,742	(33·07)
High	73,390	(35·67)	31,444	(33·60)	31,430	(36·67)	10,516	(39·78)
Occupation
Dependents of the insured individuals	62,271	(30·27)	27,616	(29·50)	26,700	(31·15)	7,955	(30·09)	< 0·001
Civil servants, teachers, military, veterans	2,915	(1·42)	1,429	(1·53)	1,151	(1·34)	335	(1·27)
Nonmanual workers and professionals	20,121	(9·78)	11,891	(12·70)	6,401	(7·47)	1,829	(6·92)
Manual workers	72,036	(35·01)	29,707	(31·74)	31,824	(37·13)	10,505	(39·73)
Other	48,404	(23·53)	22,955	(24·53)	19,636	(22·91)	5,813	(21·99)
Urbanization
Urban	83,476	(40·57)	41,892	(44·76)	31,882	(37·20)	9,702	(36·70)	< 0·001
Suburban	76,632	(37·25)	33,150	(35·42)	33,456	(39·03)	10,026	(37·92)
Rural	45,639	(22·18)	18,556	(19·82)	20,374	(23·77)	6,709	(25·38)
Causes of acute coma									< 0·001
Neurological cause group	56,790	(27·60)	22,153	(23·67)	24,430	(28·50)	10,207	(38·61)	< 0·001
CNS	30,065	(14·61)	8,183	(8·74)	14,639	(17·08)	7,243	(27·40)
Encephalopathy	6,700	(3·26)	2,616	(2·79)	3,573	(4·17)	511	(1·93)
Seizure	2,225	(1·08)	1,157	(1·24)	1,020	(1·19)	48	(0·18)
Trauma	17,800	(8·65)	10,197	(10·89)	5,198	(6·06)	2,405	(9·10)
Medical cause group	137,330	(66·75)	65,158	(69·61)	57,007	(66·51)	15,165	(57·36)
Alcohol	2,533	(1·23)	2,255	(2·41)	257	(0·30)	21	(0·08)
Cardiovascular	19,367	(9·41)	7,938	(8·48)	8,148	(9·51)	3,281	(12·41)
Diabetes and insulin	11,155	(5·42)	4,178	(4·46)	5,529	(6·45)	1,448	(5·48)
Digestive	19,904	(9·67)	9,364	(10·00)	7,968	(9·30)	2,572	(9·73)
Drugs	5,036	(2·45)	2,002	(2·14)	1,941	(2·26)	1,093	(4·13)
Electrolyte	456	(0·22)	249	(0·27)	152	(0·18)	55	(0·21)
Endocrine	2,427	(1·18)	1,086	(1·16)	904	(1·05)	437	(1·65)
Genitourinary	3,463	(1·68)	1,836	(1·96)	1,327	(1·55)	300	(1·13)
Hematology	587	(0·29)	292	(0·31)	228	(0·27)	67	(0·25)
Infection	31,063	(15·10)	14,714	(15·72)	14,005	(16·34)	2,344	(8·87)
Musculoskeletal and integumentary	6,144	(2·99)	3,659	(3·91)	2,208	(2·58)	277	(1·05)
Neoplasm	10,062	(4·89)	3,938	(4·21)	4,459	(5·20)	1,665	(6·30)
Renal	3,564	(1·73)	1,149	(1·23)	1,884	(2·20)	531	(2·01)
Respiratory	9,419	(4·58)	3,550	(3·79)	5,007	(5·84)	862	(3·26)
Women's health and perinatal care	12,150	(5·91)	8,948	(9·56)	2,990	(3·49)	212	(0·80)
Functional cause group	11,627	(5·65)	6,287	(6·72)	4,275	(4·99)	1,065	(4·03)
Psychiatry	4,765	(2·32)	2,923	(3·12)	1,827	(2·13)	15	(0·06)
Symptoms	1,379	(0·67)	881	(0·94)	342	(0·40)	156	(0·59)
Syncope	521	(0·25)	352	(0·38)	169	(0·20)	0	(0·00)
Others	4,962	(2·41)	2,131	(2·28)	1,937	(2·26)	894	(3·38)
Outcome
ICU	54,614	(26·54)	0	(0·00)	39,144	(45·67)	15,470	(58·52)	< 0·001
LSTs	84,538	(41·09)	10,578	(11·30)	50,056	(58·40)	23,904	(90·42)	< 0·001
Rehab	29,273	(14·23)	4,816	(5·15)	23,728	(27·68)	729	(2·76)	< 0·001
Nursing home	3,861	(1·88)	492	(0·53)	3,261	(3·80)	108	(0·41)	< 0·001
Disable	13,514	(6·57)	2,856	(3·05)	10,629	(12·40)	29	(0·11)	< 0·001
CCI: Charlson Comorbidity Index; CI: confidence interval; CNS: central nervous system; ED: emergency department; ICU: intensive care units; LST: life-sustaining treatment;
Chi-Square Test analyzed category variables distribution among groups; continue variable by One-way ANOVA·

Incidence of acute coma

Table 2 analyzes ICD diagnosis codes for acute coma events, revealing: (1) a crude event rate of 4.23 per 1,000 ED visits, (2) an average incidence rate of 0.93 per 1,000 person-years, and (3) age-specific incidence rates: 0.13 for pediatric, 0.57 for middle-aged, and 7.13 for senior adult groups. A significant mean decrease in incidence rate in 2016 suggests that age and temporal factors may influence acute coma incidence.

Table 2

Acute coma event rate and incidence by year and age group
Year	ED visits	Coma events	Coma rate (‰)	Incidence (‰) (95% CI)		Age 1–18 Incidence (‰) (95% CI)		Age 19–64 Incidence (‰) (95% CI)		Age ≧ 65 Incidence (‰) (95% CI)
2000	4,519,482	10,330	2·29	0·45	(0·44 − 0·46)	0·08	(0·08 − 0·09)	0·30	(0·30 − 0·31)	7·74	(7·53 − 7·95)
2001	4,707,002	11,480	2·44	0·49	(0·48 − 0·50)	0·09	(0·08 − 0·10)	0·32	(0·32 − 0·33)	7·77	(7·57 − 7·97)
2002	5,028,446	12,567	2·50	0·53	(0·52 − 0·54)	0·10	(0·09 − 0·11)	0·34	(0·33 − 0·34)	7·78	(7·59 − 7·97)
2003	4,776,136	13,246	2·77	0·56	(0·55 − 0·57)	0·10	(0·09 − 0·10)	0·36	(0·35 − 0·36)	7·21	(7·04–7·38)
2004	5,354,185	16,072	3·00	0·67	(0·66 − 0·68)	0·11	(0·10 − 0·11)	0·44	(0·43 − 0·45)	7·58	(7·41 − 7·74)
2005	5,416,581	20,535	3·79	0·85	(0·83 − 0·86)	0·12	(0·11 − 0·13)	0·56	(0·54 − 0·57)	8·80	(8·63 − 8·97)
2006	5,171,689	21,769	4·21	0·89	(0·88 − 0·90)	0·13	(0·12 − 0·13)	0·57	(0·56 − 0·58)	8·59	(8·43 − 8·75)
2007	5,282,870	23,591	4·47	0·96	(0·94 − 0·97)	0·13	(0·12 − 0·14)	0·58	(0·57 − 0·60)	8·74	(8·59 − 8·89)
2008	5,191,529	25,548	4·92	1·02	(1·01–1·04)	0·14	(0·13 − 0·15)	0·63	(0·62 − 0·64)	8·53	(8·39 − 8·67)
2009	5,770,750	27,062	4·69	1·08	(1·07 − 1·09)	0·15	(0·14 − 0·16)	0·65	(0·64 − 0·67)	8·43	(8·30 − 8·57)
2010	5,878,033	31,184	5·31	1·23	(1·22 − 1·25)	0·17	(0·16 − 0·18)	0·73	(0·71 − 0·74)	9·27	(9·13 − 9·41)
2011	6,060,366	33,944	5·60	1·33	(1·32 − 1·35)	0·19	(0·18 − 0·20)	0·80	(0·78 − 0·81)	9·24	(9·11 − 9·37)
2012	6,098,194	34,259	5·62	1·33	(1·32 − 1·34)	0·19	(0·18 − 0·20)	0·79	(0·78 − 0·80)	8·60	(8·47 − 8·72)
2013	5,753,114	33,531	5·83	1·29	(1·28 − 1·31)	0·20	(0·19 − 0·21)	0·76	(0·75 − 0·77)	7·80	(7·69 − 7·91)
2014	5,904,262	34,917	5·91	1·34	(1·32 − 1·35)	0·19	(0·18 − 0·21)	0·78	(0·77 − 0·79)	7·48	(7·38 − 7·59)
2015	6,055,577	33,366	5·51	1·27	(1·25 − 1·28)	0·21	(0·19 − 0·22)	0·73	(0·72 − 0·74)	6·57	(6·47 − 6·66)
2016	6,303,662	19,355	3·07	0·73	(0·72 − 0·74)	0·09	(0·09 − 0·10)	0·39	(0·38 − 0·40)	3·70	(3·63 − 3·76)
2017	5,945,444	16,724	2·81	0·63	(0·62 − 0·64)	0·07	(0·07 − 0·08)	0·33	(0·32 − 0·34)	2·96	(2·90 − 3·02)
Total Average	99,217,322	419,480	4·23	0·93	(0·93 − 0·94)	0·13	(0·13 − 0·13)	0·57	(0·57 − 0·57)	7·13	(7·10 − 7·16)
CI: confidence interval; ED: emergency department;
Coma rate(‰) = acute coma events/1,000ED visits
Incidence of acute coma per 1,000 person-year

Causes and outcomes of acute coma

Table 1 presents leading acute coma causes, including infection (15.10%), CNS (14.61%), digestive (9.67%), cardiovascular (9.41%), and trauma-related (8.65%). Common reversible causes included infection (15.72%), trauma (10.89%), digestive (10.00%), women's health and perinatal care (9.56%), and CNS (8.74%). Hospitalization for acute coma frequently resulted from CNS (17.08%), infection (16.34%), cardiovascular (9.51%), digestive (9.30%), and diabetes and insulin (6.45%). Leading causes of death were CNS (27.40%), cardiovascular (12.41%), digestive (9.73%), trauma (9.10%), and infection (8.87%). Medical etiologies were the primary factor (66.75%), with neurological (27.60%) and functional (5.65%) etiologies also contributing. Short-term outcomes indicated 45.49% of cases left the ED without sequelae, 12.85% experienced 30-day mortality, and 41.66% necessitated hospitalization within seven days. Elderly patients had a significantly higher mortality rate of 62.56% compared to 11.56% for younger patients. The one-year follow-up showed ICU treatment (26.54%), LSTs (41.09%), rehabilitation (14.23%), disability (6.57%), and nursing care (1.88%).

Multivariate analysis of acute coma

The GEE analysis identified covariates significantly associated with increased acute coma mortality, including females, older age, higher Charlson Comorbidity Index (CCI) scores, low income, and rural residence (Table 3). Compared to other causes, CNS (adjusted odds ratio [aOR], 0.68; 95% CI: 0.62 to 0.74; p < .0001) and drug-related causes (aOR, 0.72; 95% CI: 0.65 to 0.81; p < .0001) had lower odds of reversible coma compared to 30-day mortality, while psychiatric (aOR, 57.02; 95% CI: 34.11 to 95.33; p < .0001), alcohol (aOR, 33.8; 95% CI: 21.81 to 52.38; p < .0001), women's health and perinatal care (aOR, 11.86; 95% CI: 10.11 to 13.92; p < .0001), seizures (aOR, 8.32; 95% CI: 6.15 to 11.24; p < .0001), and musculoskeletal/integumentary causes (aOR, 8.16; 95% CI: 7.04 to 9.47; p < .0001) had higher odds. Drug causes had lower odds of hospitalization compared to mortality (aOR, 0.82; 95% CI: 0.73 to 0.91; p = .0003), while psychiatry (aOR, 48.29; 95% CI: 28.88 to 80.77; p < .0001), seizure (aOR, 9.01; 95% CI: 6.67 to 12.17; p < .0001), women's health and perinatal care (aOR, 5.44; 95% CI: 4.63 to 6.40; p < .0001), and alcohol (aOR, 5.20; 95% CI: 3.31 to 8.17; p < .0001) causes increased the odds. Compared to functional etiology, neurological etiology had lower odds of reversible coma (aOR, 0.55; 95% CI, 0.51 to 0.59, p < .0001) and hospitalization (aOR, 0.70; 95% CI 0.65 to 0.75, p < .0001), while medical etiology had higher odds of reversible coma (aOR, 1.39; 95% CI: 1.30 to 1.49, p < .0001) and hospitalization (aOR, 1.16; 95% CI: 1.09 to 1.25, p < .0001).

Table 3

Generalized linear model analysis of acute coma patients
		Reversible coma v.s. 30-day mortality				Hospitalization v.s. 30-day mortality
		OR (95%CI)	p-value	aOR (95%CI)	p-value	OR (95%CI)	p-value	aOR (95%CI)	p-value
Sex	Male vs Female	1·20 (1·17 − 1·23)	< 0·0001	1·29 (1·25 − 1·33)	< 0·0001	1·13 (1·10 − 1·16)	< 0·0001	1·14 (1·10 − 1·17)	< 0·0001
Age	19–64 vs ≤ 18 years old	0·34 (0·32 − 0·35)	< 0·0001	0·44 (0·42 − 0·46)	< 0·0001	0·57 (0·55 − 0·60)	< 0·0001	0·59 (0·56 − 0·62)	< 0·0001
	≥ 65 vs ≤ 18 years old	0·18 (0·17 − 0·19)	< 0·0001	0·26 (0·25 − 0·27)	< 0·0001	0·51 (0·49 − 0·53)	< 0·0001	0·44 (0·42 − 0·46)	< 0·0001
CCI	> 1 vs ≤ 1	0·29 (0·28 − 0·30)	< 0·0001	0·44 (0·42 − 0·45)	< 0·0001	0·89 (0·87 − 0·91)	< 0·0001	1·04 (1·01–1·07)	0·0219
Income	Middle vs low group	1·22 (1·18 − 1·26)	< 0·0001	1·58 (1·52 − 1·64)	< 0·0001	1·17 (1·13 − 1·21)	< 0·0001	1·30 (1·25 − 1·35)	< 0·0001
	High vs low group	1·37 (1·33 − 1·42)	< 0·0001	1·33 (1·28 − 1·37)	< 0·0001	1·12 (1·08 − 1·16)	< 0·0001	1·12 (1·09 − 1·17)	< 0·0001
Occupation	Dependents of the insured individuals vs others	0·82 (0·72 − 0·92)	0·0012	0·83 (0·73 − 0·95)	0·0055	0·98 (0·87 − 1·11)	0·7416	0·94 (0·83 − 1·07)	0·3469
	Civil servants, teachers, military, veterans vs others	1·53 (1·34 − 1·74)	< 0·0001	0·93 (0·81 − 1·07)	0·3002	1·02 (0·89 − 1·16)	0·774	0·84 (0·74 − 0·97)	0·0136
	Nonmanual workers and professionals vs others	0·67 (0·59 − 0·75)	< 0·0001	0·84 (0·74 − 0·96)	0·0084	0·88 (0·78 − 1·00)	0·0503	0·89 (0·78 − 1·01)	0·0799
	Manual workers vs others	0·93 (0·82 − 1·05)	0·2501	1·04 (0·91 − 1·18)	0·6073	0·99 (0·87 − 1·12)	0·8258	1·01 (0·89 − 1·15)	0·8776
Urbanization	Urban	0·77 (0·74 − 0·79)	< 0·0001	0·83 (0·80 − 0·86)	< 0·0001	1·02 (0·98 − 1·05)	0·3561	1·05 (1·01–1·08)	0·0064
	Urbanization	0·64 (0·62 − 0·66)	< 0·0001	0·77 (0·74 − 0·80)	< 0·0001	0·92 (0·89 − 0·96)	< 0·0001	0·98 (0·94 − 1·02)	0·2124
Causes of coma	Neurological group	0·37 (0·34 − 0·39)	< 0·0001	0·55 (0·51 − 0·59)	< 0·0001	0·60 (0·56 − 0·64)	< 0·0001	0·70 (0·65 − 0·75)	< 0·0001
	CNS	0·47 (0·44 − 0·52)	< 0·0001	0·68 (0·62 − 0·74)	< 0·0001	0·93 (0·86 − 1·02)	0·1051	1·09 (1·00–1·19)	0·0517
	Encephalopathy	2·15 (1·90 − 2·43)	< 0·0001	4·08 (3·59 − 4·63)	< 0·0001	3·23 (2·86 − 3·65)	< 0·0001	4·24 (3·75 − 4·80)	< 0·0001
	Seizure	10·11 (7·50 − 13·64)	< 0·0001	8·32 (6·15 − 11·24)	< 0·0001	9·81 (7·27 − 13·24)	< 0·0001	9·01 (6·67 − 12·17)	< 0·0001
	Trauma	1·78 (1·63 − 1·95)	< 0·0001	1·75 (1·59 − 1·92)	< 0·0001	1·00 (0·91 − 1·10)	0·9585	1·02 (0·92 − 1·11)	0·7595
	Medical group	0·73 (0·68 − 0·78)	< 0·0001	1·39 (1·30 − 1·49)	< 0·0001	0·94 (0·87 − 1·00)	0·0641	1·16 (1·09 − 1·25)	< 0·0001
	Alcohol	45·05 (29·11–69·72)	< 0·0001	33·8 (21·81 − 52·38)	< 0·0001	5·65 (3·60 − 8·88)	< 0·0001	5·20 (3·31 − 8·17)	< 0·0001
	Cardiovascular	1·02 (0·93 − 1·11)	0·7406	2·04 (1·86 − 2·24)	< 0·0001	1·15 (1·05 − 1·25)	0·0027	1·50 (1·37 − 1·65)	< 0·0001
	Diabetes and insulin	1·21 (1·10 − 1·34)	0·0001	3·13 (2·82 − 3·47)	< 0·0001	1·76 (1·60 − 1·94)	< 0·0001	2·31 (2·09 − 2·55)	< 0·0001
	Digestive	1·53 (1·40 − 1·67)	< 0·0001	2·53 (2·31 − 2·78)	< 0·0001	1·43 (1·31 − 1·57)	< 0·0001	1·69 (1·54 − 1·85)	< 0·0001
	Drugs	0·77 (0·69 − 0·86)	< 0·0001	0·72 (0·65 − 0·81)	< 0·0001	0·82 (0·74 − 0·91)	0·0003	0·82 (0·73 − 0·91)	0·0003
	Electrolyte	1·90 (1·40 − 2·57)	< 0·0001	2·96 (2·17 − 4·04)	< 0·0001	1·28 (0·93 − 1·75)	0·1342	1·53 (1·11 − 2·11)	0·0091
	Endocrine	1·04 (0·91 − 1·19)	0·5473	1·49 (1·29 − 1·72)	< 0·0001	0·96 (0·83 − 1·10)	0·5139	1·15 (1·00–1·32)	0·0521
	Genitourinary	2·57 (2·22 − 2·97)	< 0·0001	4·41 (3·80 − 5·12)	< 0·0001	2·04 (1·76 − 2·37)	< 0·0001	2·59 (2·23 − 3·01)	< 0·0001
	Hematology	1·83 (1·39 − 2·41)	< 0·0001	2·53 (1·90 − 3·36)	< 0·0001	1·57 (1·18 − 2·09)	0·0018	1·83 (1·38 − 2·44)	< 0·0001
	Infection	2·63 (2·41 − 2·88)	< 0·0001	4·26 (3·88 − 4·67)	< 0·0001	2·76 (2·52 − 3·02)	< 0·0001	3·36 (3·07 − 3·69)	< 0·0001
	Musculoskeletal and integumentary	5·54 (4·79 − 6·41)	< 0·0001	8·16 (7·04–9·47)	< 0·0001	3·68 (3·17 − 4·27)	< 0·0001	4·35 (3·75 − 5·05)	< 0·0001
	Neoplasm	0·99 (0·90 − 1·09)	0·8747	2·06 (1·86 − 2·28)	< 0·0001	1·24 (1·12 − 1·36)	< 0·0001	1·57 (1·42 − 1·73)	< 0·0001
	Renal	0·91 (0·80 − 1·03)	0·142	2·30 (2·01–2·62)	< 0·0001	1·64 (1·45 − 1·86)	< 0·0001	2·21 (1·95 − 2·51)	< 0·0001
	Respiratory	1·73 (1·55 − 1·93)	< 0·0001	3·54 (3·16 − 3·96)	< 0·0001	2·68 (2·41 − 2·98)	< 0·0001	3·57 (3·20 − 3·98)	< 0·0001
	Women's health and perinatal care	17·71 (15·13–20·72)	< 0·0001	11·86 (10·11–13·92)	< 0·0001	6·51 (5·55 − 7·64)	< 0·0001	5·44 (4·63 − 6·40)	< 0·0001
	Functional group	(ref· of coma group)
	Psychiatry	81·68 (48·90–136·46)	< 0·0001	57·02 (34·11–95·33)	< 0·0001	56·17 (33·59–93·92)	< 0·0001	48·29 (28·88 − 80·77)	< 0·0001
	Symptoms	2·37 (1·97 − 2·86)	< 0·0001	2·78 (2·30 − 3·38)	< 0·0001	1·01 (0·82 − 1·24)	0·9106	1·11 (0·90 − 1·37)	0·3158
	Syncope	NC		NC		NC		NC
	Others	(ref of causes of coma)
aOR: adjusted odds ratio; CCI: Charlson Comorbidity Index; CI: confidence interval; CNS: central nervous system; ED: emergency department; ICU: intensive care units; LST: life-sustaining treatment;
Syncope: no convergence

The Kaplan-Meier estimation (Supplementary Fig. 1) and Cox proportional hazards regression (Table 4) revealed increased mortality risk associated with higher CCI score (adjusted hazard ratios [aHR], 1.08, 95% CI 1.07 to 1.09, p < .001), older age (aHR, 2.17, 95% CI 2.13 to 2.22, p < .001), manual labor (aHR, 1.03, 95% CI 1.02 to 1.04, p < .001), drug (aHR, 1.30, 95% CI 1.20 to 1.41, p < .001), neoplasm (aHR, 1.18, 95% CI 1.11 to 1.25, p < .001), and symptoms cause (aHR, 1.44, 95% CI 1.24 to 1.67, p < .001). In addition, the average mortality post-acute coma for the reversible group was observed at 7.10 years, while for the hospitalization group, it occurred at 6.41 years.

Table 4

Multivariate Cox regression analysis of factors contributing to all-cause mortality in acute coma patients
	Cox proportional hazards aHR	p-value
Sex (male)	0·82 (0·80 − 0·84)	< 0·001
CCI (CCI > 1)	1·08 (1·07 − 1·09)	< 0·001
Age (old age)	2·17 (2·13–2·22)	< 0·001
Income (high)	0·98 (0·97 − 1·00)	0·05
Occupation (manual)	1·03 (1·02 − 1·04)	< 0·001
Area (urban)	1·02 (1·01–1·04)	0·01
Neurological group
CNS	0·83 (0·79 − 0·88)	< 0·001
Encephalopathy	0·93 (0·87 − 0·99)	0·04
Seizure	0·32 (0·26 − 0·39)	< 0·001
Trauma	0·48 (0·45 − 0·52)	< 0·001
Medical group
Alcohol	0·39 (0·30–0·51)	< 0·001
Cardiovascular	0·94 (0·89 − 0·99)	0·02
Digestive	0·91 (0·86 − 0·96)	< 0·001
Drugs	1·30 (1·20 − 1·41)	< 0·001
Electrolyte	0·99 (0·78 − 1·25)	0·93
Endocrine	0·76 (0·67 − 0·86)	< 0·001
Genitourinary	0·43 (0·38–0·49)	< 0·001
Hematology	0·63 (0·49 − 0·80)	< 0·001
Infection	0·66 (0·63 − 0·69)	< 0·001
Musculoskeletal and integumentary	0·31 (0·28 − 0·35)	< 0·001
Neoplasm	1·18 (1·11 − 1·25)	< 0·001
Renal	1·05 (0·97 − 1·13)	0·21
Respiratory	0·80 (0·75 − 0·85)	< 0·001
Women's health and perinatal care	0·15 (0·13 − 0·18)	< 0·001
Functional group
Psychiatry	0·05 (0·03 − 0·05)	< 0·001
Symptoms	1·44 (1·24–1·67)	< 0·001
Syncope	0·00	·
Others	0·47 (0·42 − 0·53)	< 0·001
Age (old age group), CCI (CCI > 1 group), Income (high-income group), Area (urban), Occupation (manual), Sex (male)
Syncope: no convergence

Sensitivity test of acute coma

To assess the robustness of our findings, we focused on the definition of an acute coma cohort, explicitly examining the first-ever episode that led to hospitalization within either a 7-day or 14-day period. Our analysis revealed no significant differences between these two cohort definitions in terms of clinical course subgroup distribution and cause classification for acute coma (see Supplementary Table 2). This suggests that our findings are consistent and reliable across different definitions.

Acute coma frequently represents a common pathway of organ dysfunction from diverse causes, significantly impacting patients' survival and quality of life and straining healthcare resources. This study aims to explore the incidence density, causes, clinical courses, and outcomes of acute coma. Several methodological and result issues warrant discussion.

Methodology discussion

In our 18-year longitudinal retrospective cohort study, we utilize the ICD coding system alongside the CCS method to navigate the complex etiology of acute coma. This complexity, stemming from an array of reversible and time-sensitive factors, creates challenges in synthesizing diverse clinical causes into a unified cohort for claims-based research. Previous studies have often relied on medical record reviews ¹⁷ or rigorously designed cohort studies¹⁸, lacking a comprehensive and longitudinal perspective. To bridge this research gap, we have devised an innovative clinical research model that integrates big data analytics with clinical investigation. This approach offers a novel framework for examining the multifaceted clinical scenarios related to acute coma through claims-based data, thereby opening new avenues for neuroscientific research and enhancing emergency medical decision-making systems.

Study design, population and cohort definition

The Taiwan NHIRD, encompassing the entire population and offering comprehensive medical services, facilitated a thorough analysis of acute coma's clinical nature. Besides, The large cohort of over 200,000 patients offered a robust population representation. Moreover, we defined the cohort based on one impaired consciousness in the ED study, where the average hospitalization duration was 6.4 days. Thereforee we included acute coma onset and hospitalization within seven days as our study cohort ¹⁹. By excluding patients with prior nursing home residence or disability status, provids a better understanding of the true incidence and outcomes of first-ever acute coma. Meanwhile, the lack of clinical coma scale data raises concerns about the methodology accuracy, which relied on ICD coding and the CCS method. Our study adopted a broader definition of acute coma, using ICD codes, covering various alterations of consciousness such as somnolence, stupor, unspecified coma, and intracranial injury-related coma. Our study adopted a broad range of acute coma diagnosis codes to capture various clinical scenarios ²⁰. We used ICD coding methodology covering the qualitative spectrum of 'decreased consciousness,' including somnolence, stupor, coma, and quantitative GCS score ranges ²⁰. We also included the current quantitative approach to coma assessment, coding GCS scores of 13–15 as R40.0, 9–12 as R40.1, and ≤ 8 as R40.2. This approach ensured a thorough representation of acute coma in our research sample.

Defined of acute coma cause

Integrating CCS with the ICD coding system in clinical research potentially offers a holistic and nuanced methodology for categorizing complex clinical data into clinically meaningful classes ¹⁴. While established frameworks for transforming a myriad of ICD codes into clinically relevant categories that can guide clinical decision-making, inform policy interventions, or enable regular monitoring are not yet widespread ¹², In our study, we utilized CCS to condense 285 CCS categories into 23 clinically relevant causes of acute coma, rendering the study practically feasible and enabling the in-depth analysis of acute coma's multifaceted clinical manifestations. This approach facilitates large-scale, longitudinal population-based studies in EDs, optimizing approaches to address acute coma's clinical nature.

Results discussion

Understanding the clinical characteristics of acute coma makes it crucial for intensivist clinicians to identify the cause to prevent disability ²¹ and emergency medical policy applications.

Causes, clinical courses, and outcomes

Infections, CNS disorders, digestive issues, cardiovascular events, and trauma are leading causes of acute coma. Our research results are consistent with international findings, with infection being the most common cause ^{22 23}. Acute coma causes differ based on geography ²⁴ or age ²². For instance, poisoning contributes to approximately one-third of unconsciousness cases in Nordic countries ²⁴. In children, common causes are intoxication, epilepsy, infection, and traumatic brain injury ¹⁷. In adults and older adults, CNS and infectious disorders are more common ^{17 20}. The prominence of digestive causes for acute coma in our cohort may be due to the prevalence of hepatitis and hepatocellular carcinoma in Taiwan ²⁵. To facilitate a broader understanding of public health implications related to the potential etiologies and mechanisms underlying acute coma, and to enable meaningful comparisons with existing literature, we have classified the etiologies of acute coma into three major categories: neurological, medical, and functional factors^{5 6 26}. This categorization approach aids in the development of targeted intervention strategies and informs policy-making. Neurological causes account for about one-third of cases, while non-neurological causes comprise the remaining two-thirds ²⁷. Schmidt (2017) reported that neurological and medical etiologies each contributed to about 50% of acute coma cases ⁵. Functional or psychogenic coma constituted around 5% of cases. It is worth further exploring the causes of coma resulting from functional factors.

The clinical course of acute coma varies due to differing underlying causes or etiologies ^{9 22}. Over half of first-ever acute coma patients required hospitalization or faced mortality, whereas the other near half demonstrated reversible outcomes. Short-term in-hospital mortality rate for patients with acute coma is about 5–11% ^{3 19 28} with longer follow-up reaching 25% ²⁸. Our study found that 27.60% of acute coma cases were attributed to neurological etiology, and within the mortality group, 38.16% of cases had a neurological cause. This supports prior research indicating that clinical course is highly dependent on etiology ²². Syncope and seizures are generally believed to be the most common causes of reversible coma. However, in our study, these two common causes accounted for only 1.33% of cases of overall acute coma. This may support researchers' definition of coma as a state of prolonged sustained unconsciousness lasting at least one hour ²⁹. Our emergency physicians may better understand syncope and seizure, improving diagnostic accuracy ³⁰. Study showed that twenty percent of patients with acute coma may have already been reversible on admission ²⁸. If these patients are monitored for two months after hospitalization, one-third of them may fully recover consciousness ³¹. Our study found that approximately 45.49% of patients had reversible coma. The higher proportion of reversible coma in our study may reflect a more lenient coding of coma or the higher quality of emergency medical care by emergency physicians in our study. These results suggest that the outcome of acute coma is highly dependent on the underlying cause and severity of the condition ³². Regarding long-term outcomes, one-quarter of patients with first-ever acute coma necessitated ICU admission, and forty percent required LSTs within one year. The high percentage of patients in the LSTs group, who require long-term care and have a high mortality rate, emphasizes the need for improved management strategies for patients with acute coma ⁷.

Incidence

Our study found an acute coma event rate of 4.23 visits per 1,000 ED visits, consistent with the Schmidt et al. (2019) ED cohort study ²⁸. However, our results differ from another study that reported 0.29–0.40 cases of coma per 1,000 ED visits ³³. Based on the ICD code approach, studies suggested that acute coma is about 0.93-5% of all ED visits ^{27 34}. Pediatric non-trauma coma studies also have reported incidences ranging from 0.3 to 1.6 per 1,000 person-years ²². This disparity in results may be attributed to differences in research questions, study design, study population, or definitions ³⁵.

We investigated the incidence rates of acute coma in different age groups and temporal trends. The highest incidence rate of acute coma was observed in the elderly age group, emphasizing the significance of this public health concern in the aging population. However, there is also some variability in the incidence rates over time. We found that the incidence rate stabilized at around 1 per 1,000 person-years from 2007 to 2015 and observed a significant mean decrease in the incidence rate in 2016 compared to previous years. Specifically, there was a significant mean decrease from 0.73 per 1,000 person-years in 2016 to 0.63 per 1,000 person-years in 2017. One possible explanation for the decrease in acute coma incidence during 2016–2017 is the transition from the ICD-9 to ICD-10 coding system in 2015. We also found that there was no significant difference in the number of ED visits between 2014 and 2017 (5,904,262 vs 5,945,444, respectively). Thus, the significant change in acute coma incidence could be an artifact of the ICD coding transition effect ³⁶.

Strengths and Limitations

This study has several strengths and limitations. Strengths include using nationwide longitudinal data to observe first-ever acute coma patterns, enabling tracking of clinical progression. The average post-acute coma mortality occurring seven years highlights its importance as a risk factor and common pathway for mortality. Additionally, the study employed AHRQ CCS methodology, facilitating regular monitoring of acute coma clinical information and enabling tailored intervention plans.

The present study has several limitations that need to be acknowledged. Firstly, the absence of a coma scale to accurately define the first-ever acute coma cohort represents a significant limitation. Instead, the study relied on acute coma-related diagnoses coded by emergency physicians in the ED, potentially leading to an underestimation of acute coma incidence and compromising the accuracy of identifying the causes of coma. Additionally, the conversion between ICD-9 and ICD-10 coding systems may introduce inaccuracies in estimating coma-related diagnoses due to potential discrepancies and inconsistencies in classification. Consequently, the reliability of the results may be affected. Furthermore, it is important to recognize that the acute coma diagnosis employed in this study may not fully capture the underlying causes or medical utilization, as multiple contributing pathologies could be involved due to potential multiple underlying pathologies ²⁸. The complexity of coma etiology, along with the potential presence of various underlying factors, may limit the accuracy of attributing the diagnosis to a single cause. Moreover, a small proportion (about 2%) of acute coma patients presented in the ED lacked further diagnostic information, which reflects the challenge in diagnosing cases of coma with unknown origins and introduces potential uncertainty and incomplete data in the analysis. Another limitation is the reliance on data limited to the year 2017, preventing the examination of the potential effects of the COVID-19 pandemic. Incorporating the impact of the pandemic would have enhanced the understanding of the significance of infections and central nervous system-related causes in estimating acute coma incidence. Finally, it should be noted that this study did not utilize the World Health Organization's (WHO) World Standard Population for age-specific rates adjustment, which may limit the generalizability and comparability of the findings with other studies that utilize standardized rates based on the WHO standard populations. These limitations should be considered when interpreting the study's results, and future research should address these limitations to enhance the robustness and applicability of the findings.

Acute coma often represents a common pathway of organ dysfunction with diverse causes or etiologies, significantly impacting mortality and disability. Our study demonstrates the innovative use of ICD code aggregation to CCS in acute coma clinical study, providing valuable insights into its clinical nature. This research model has the potential to facilitate international comparative studies of acute coma characteristics using health databases.

Ethics approval and consent to participate

All authors have completed the ICMJE conflict of interest form and declare no conflicts of interest in relation to this manuscript. The study involved a retrospective analysis of encrypted unique personal identification data without direct patient involvement. Therefore, no patient consent was necessary for the completion of this study. As the corresponding author, I confirm that we complied with all applicable laws regarding data protection and privacy. No patients were involved. This study was a retrospective claim data analysis that included all encrypted unique personal identification. Ethics approval: IRB of Taipei City Hospital, number- TCHIRB-10807003-E.

Consent for publication

Not applicable.

Patients or the public involvement:

Patients or the public were not involved in the design, or conduct, or reporting, or dissemination plans of our research

Availability of data and materials

The data that support the findings of this study are available from Taiwan National Health Insurance Research Database but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the academic request and with permission of Taiwan National Health Insurance Administration.

Competing interests

The authors declare that they have no competing interests.

Funding

The authors declare that they have no funding.

Confirm the use of reporting checklist (see below), if appropriate

The authors have confirmed the use of reporting checklists. We adhered to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement when reporting observational studies and the Standards of Reporting of Neurological Disorders (STROND) for reporting incidence studies in neuroepidemiology.

Authors' contributors

CY Lin took a lead role in conceptualizing the study and writing the original draft, and was responsible for formal data analysis. CY Lin and CC Liu verified the underlying data in the manuscript. MC Tsai contributed to study design, data curation, and formal data analysis, and was in charge of data collection. JF Liang and MC Tsai ensured accurate data analysis and interpretation, and verified the manuscript's underlying data. YC Lee supervised the study, validated the results, and significantly contributed to reviewing and editing the manuscript. All authors participated in developing the study concept and design, analysing and interpreting data, and preparing the manuscript. We have all approved the final manuscript and agree to be accountable for all aspects of the work, promising to appropriately investigate and resolve any question related to the work's accuracy or integrity.

Acknowledgments

We acknowledge the Taiwan National Health Insurance Research Database, which was provided by the National Health Insurance Administration, and is managed by National Health Research Institutes. We also thank the Center for Public Health, Department of Education and Research, Taipei City Hospital, for providing administrative and research grant support.

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All authors have completed the ICMJE conflict of interest form and declare no conflicts of interest in relation to this manuscript. The study involved a retrospective analysis of encrypted unique personal identification data without direct patient involvement. Therefore, no patient consent was necessary for the completion of this study. As the corresponding author, I confirm that we complied with all applicable laws regarding data protection and privacy. No patients were involved. This study was a retrospective claim data analysis that included all encrypted unique personal identification. Ethics approval: IRB of Taipei City Hospital, number- TCHIRB-10807003-E.

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6) Confirm the use of reporting checklist (see below), if appropriate

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7) Source of funding for the study

The research grant supported by Taipei City Hospital

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No competing interests reported.

SupplementarySAPComaCCSprogramfinal.zip
STROBEchecklistv4combined.docx
STRONDChecklistReportingofNeurologicalDisorders.doc.docx
SupplementaryFigure1Survivalanalysisofacutecomapatients.doc
Supplementary Figure 1 Survival analysis of acute coma patients
SupplementaryTable1Clinicalclassificationsoftware.doc
Supplementary Table 1 Clinical classification software for grouping the causes of acute coma
SupplementaryTable2acutecomahospitalization14days.doc
Supplementary Table 2 Characteristics of acute coma hospitalization within 14 days cohort Supplementary statistical analysis plan SAS program using clinical classification software for grouping the cause of acute coma

Incidence, Etiology, and Clinical Outcomes of Acute Coma

Status:

Version 1

Abstract

Figures

WHAT IS ALREADY KNOWN ON THIS TOPIC

WHAT THIS STUDY ADDS

INTRODUCTION

MATERIALS AND METHODS

Study design and setting

Acute coma participants definition

Incidence estimates

Clinical course, causes and outcomes assessment

Statistical analysis

RESULTS

Cohort characteristics and clinical course estimate

Incidence of acute coma

Causes and outcomes of acute coma

Multivariate analysis of acute coma

Sensitivity test of acute coma

DISCUSSION

Methodology discussion

Results discussion

Strengths and Limitations

CONCLUSION

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1