Abstract
Background
COVID-19 is associated with a range of neuropsychiatric manifestations. While case reports and case series have reported catatonia in the setting of COVID-19 infection, its rate has been poorly characterized.
Objective
This study reports the co-occurrence of catatonia and COVID-19 diagnoses among acute care hospital discharges in the United States in 2020.
Methods
The National Inpatient Sample, an all-payors database of acute care hospital discharges, was queried for patients of any age discharged with a diagnosis of catatonia and COVID-19 in 2020.
Results
Among 32,355,827 hospitalizations in the 2020 National Inpatient Sample, an estimated 15,965 (95% confidence interval: 14,992–16,938) involved a diagnosis of catatonia without COVID-19 infection, 1,678,385 (95% confidence interval: 1,644,738–1,712,022) involved a diagnosis of COVID-19 without a co-occurring catatonia diagnosis, and 610 (95% confidence interval: 578–642) involved both catatonia and COVID-19 infection. In an adjusted model, a diagnosis of COVID-19, but not a diagnosis of catatonia or the combination of catatonia and COVID-19, was associated with increased mortality. Patients with catatonia and COVID-19 were frequently diagnosed with encephalopathy and delirium codes.
Conclusions
Catatonia and COVID-19 were rarely co-diagnosed in 2020, and catatonia diagnosis was not associated with increased mortality in patients with COVID-19. Further research is needed to better characterize the phenomenology of catatonia in the setting of COVID-19 infection and its optimal treatment.
Key words: catatonia, COVID-19, consult liaison psychiatry, cohort studies, demography
Introduction
COVID-19 is associated with a range of neuropsychiatric manifestations in the acute1 and postinfectious phases.2 , 3 One such manifestation is catatonia, a neuropsychiatric condition characterized by motor, behavioral, and affective disturbances.4, 5, 6 Catatonia has been recognized in the setting of COVID-19 since the earliest days of the pandemic,7 and since that time dozens of case reports and small case series have been published on co-occurring catatonia and COVID-19 infection.8 While mechanisms of catatonia in the setting of an acute viral illness such as COVID-19 are uncertain, pathologic studies have indicated a range of structural abnormalities and inflammatory infiltrates, including microglial activation, lymphoid inflammation acute hypoxic-ischemic changes, and microthrombi.9
Prospective studies have tracked neuropsychiatric manifestations of COVID-19,10 , 11 but there has been little large-scale research into catatonia co-occurring with COVID-19. Data from a large sample of acute care hospitalizations would allow for more accurate characterization of catatonia in the setting of COVID-19 infection and may help clarify the burden of catatonic illness among patients. This study characterizes the co-occurrence of catatonia and COVID-19 diagnosis among hospital discharges in the United States in 2020 using a nationally representative all-payor database of nonfederal acute care hospitalizations.
Methods
Data Source
This analysis used the 2020 edition of the National Inpatient Sample (NIS) from the Healthcare Cost and Utilization Project, Agency for Healthcare Research and Quality. The NIS is an all-payor database, which samples nonfederal hospitals in the United States. In total, 4580 acute care hospitals in 49 states, covering 98% of the US population, are included in the NIS. Freestanding psychiatric or rehabilitation hospitals are not included. Under the sampling methodology of the NIS, hospitals are stratified based on geographic region, urban vs. rural location, teaching status, bed size, and ownership, and then 20% of discharges are sampled from within strata without replacement to allow weighting of the sampled discharges to produce nationally representative estimates. This produces a final sample size of 32,355,827 hospitalizations in 2020. The NIS provides demographic information, information about hospital length of stay and overall hospital charges, and up to 40 discharge diagnoses. As this is a deidentified, publicly available database, this study was determined to be Not Human Subjects Research by the Mass General Brigham Institutional Review Board.
Data Selection and Analysis
Encounters with catatonic patients were defined as those that include the International Statistical Classification of Diseases, Tenth Revision, Clinical Modification, discharge diagnosis codes F06.1 (catatonic disorder due to a known physiological condition) or F20.2 (catatonic schizophrenia). Encounters involving COVID-19 infection were defined as those with a discharge diagnosis of U07.1 (COVID-19). This code has been validated as having high sensitivity and positive predictive value for identifying COVID-19 cases.12 , 13 Co-occurring catatonia and COVID-19 hospitalizations are those that included both a catatonia and COVID-19 code among the discharge diagnosis list. Patients of any age were included in this analysis.
Statistical Analysis
All analyses were conducted on data weighted according to the appropriate NIS discharge weight to obtain nationwide estimates. As the NIS is a survey, all values come with an associated variance derived from the sampling methodology. This variance is used to present sample uncertainty for the overall number of discharges, whereas weighted point estimates are reported for all subsequent analyses. Due to the nonnormal distribution of age, length of stay, and total hospital charges, these values are reported as medians with interquartile range. Associations between categorical variables are assessed using the χ2 test.
For the primary statistical analysis, a logistic regression incorporating the sampling methodology of the NIS was conducted on the binary outcome of in-hospital death (yes/no) with catatonia diagnosis (yes/no), COVID diagnosis (yes/no), the interaction between catatonia and COVID diagnoses, age, sex, admission type (elective vs. nonelective), primary service line, injury status, and whether the hospitalization involved a major surgical procedure (yes/no) as descriptor variables. Analyses were conducted using SPSS (version 29; IBM Software, Inc., Armonk, NY).
Results
Among 32,355,827 hospitalizations in the 2020 NIS, an estimated 15,965 (95% confidence interval: 14,992–16,938) involved a diagnosis of catatonia without COVID-19 infection, and 610 (95% confidence interval: 578–642) involved both catatonia and COVID-19 infection. A further 1,678,385 (95% confidence interval: 1,644,738–1,712,022) hospitalizations involved a diagnosis of COVID-19 without a co-occurring catatonia diagnosis. Thus, a total of 3.7% of overall catatonia cases diagnosed in 2020 involved co-occurring COVID-19 infection. There was a significant association between the absence of COVID-19 diagnosis and the presence of catatonia diagnosis (χ2 (1, N = 32,355,827) = 76.7, P < 0.001), with 0.036% of hospitalizations with a COVID-19 diagnosis also involving a catatonia diagnosis compared with 0.052% of hospitalizations without a COVID-19 diagnosis. Demographically, individuals with catatonia and COVID-19 had a median age of 62 years, compared with a median of 47 for those with catatonia without COVID-19 and 65 for those with COVID-19 without catatonia. Full demographic information for catatonia patients with and without co-occurring COVID-19 diagnosis, as well as COVID-19 patients without catatonia are given in Table 1 . While catatonia hospitalizations without COVID-19 diagnosis occurred at a relatively similar rate throughout the year, catatonia hospitalizations with co-occurring COVID-19 diagnosis displayed greater temporal variability, which tracked closely with overall COVID-19 hospitalizations (Figure S1).
Table 1.
Demographics of Patients With a Discharge Diagnosis of Catatonia With Co-Occurring COVID-19, Catatonia Without Co-Occurring COVID-19, and COVID-19 Without Co-Occurring Catatonia in the 2020 NIS
| Catatonia and COVID-19 |
Catatonia without COVID-19 |
COVID-19 without catatonia |
||||
|---|---|---|---|---|---|---|
| n | % | n | % | n | % | |
| N | 610 (578–642) | 15,965 (14,992–16,938) | 1,678,385 (1,644,738–1,712,022) | |||
| Age (y) | 62 (41–71) | 47 (28–63) | 65 (51–77) | |||
| <19 | <11 | <1.8 | 875 | 5.5 | 22,640 | 1.2 |
| 19–39 | 130 | 21.3 | 5600 | 35.1 | 191,575 | 11.5 |
| 40–59 | 135 | 22.1 | 4465 | 28 | 433,875 | 25.7 |
| 60–79 | 285 | 46.7 | 4390 | 27.5 | 701,780 | 41.9 |
| 80+ | 50 | 8.2 | 635 | 4 | 328,510 | 19.7 |
| Sex | ||||||
| Male | 290 | 47.5 | 7160 | 44.8 | 873,115 | 52 |
| Female | 320 | 52.5 | 8805 | 55.2 | 805,195 | 48 |
| Race | ||||||
| White | 330 | 54.1 | 8170 | 51.2 | 825,395 | 49.2 |
| Black | 155 | 25.4 | 4165 | 26.1 | 310,625 | 18.5 |
| Hispanic | 70 | 11.5 | 1650 | 10.3 | 353,375 | 21.1 |
| Asian or Pacific Islander | 20 | 3.3 | 695 | 4.4 | 52,815 | 3.1 |
| Native American | 0 | 0.0 | 90 | 0.6 | 16,765 | 1 |
| Other | <11 | <1.8 | 635 | 4.0 | 70,230 | 4.2 |
| Missing | 25 | 4.1 | 560 | 3.5 | 49,180 | 2.9 |
| Census division of hospital | ||||||
| New England | 65 | 10.7 | 1055 | 6.6 | 63,400 | 3.8 |
| Middle Atlantic | 80 | 13.1 | 2340 | 14.7 | 245,020 | 14.6 |
| East North Central | 125 | 20.5 | 2490 | 15.6 | 260,090 | 15.5 |
| West North Central | 45 | 7.4 | 1135 | 7.1 | 113,435 | 6.8 |
| South Atlantic | 125 | 20.5 | 3400 | 21.3 | 336,905 | 20.1 |
| East South Central | 15 | 2.5 | 1155 | 7.2 | 112,335 | 6.7 |
| West South Central | 45 | 7.4 | 1390 | 8.7 | 240,751 | 14.3 |
| Mountain | 30 | 4.9 | 865 | 5.4 | 116,244 | 6.9 |
| Pacific | 80 | 13.1 | 2135 | 13.4 | 190,205 | 11.3 |
| Population of county of residence | ||||||
| Central metro county >1 million | 235 | 38.5 | 5970 | 37.4 | 564,250 | 33.6 |
| Fringe metro county >1 million | 140 | 23.0 | 3475 | 21.8 | 397,130 | 23.7 |
| Metro area 250,000–999,999 | 120 | 19.7 | 3300 | 20.7 | 309,770 | 18.5 |
| Metro area 50,000–249,000 | 50 | 8.2 | 1220 | 7.6 | 140,320 | 8.4 |
| Micropolitan | 40 | 6.6 | 1110 | 7.0 | 144,220 | 8.6 |
| Noncore county | 15 | 2.5 | 635 | 4.0 | 114,690 | 6.8 |
| Household income quartile for Pt ZIP code | ||||||
| 1 | 160 | 26.2 | 4950 | 31.0 | 564,365 | 33.6 |
| 2 | 160 | 26.2 | 4150 | 26.0 | 448,840 | 26.7 |
| 3 | 120 | 19.7 | 3360 | 21.0 | 365,740 | 21.8 |
| 4 | 145 | 23.8 | 3100 | 19.4 | 272,510 | 16.2 |
| Primary service line | ||||||
| Maternal and neonatal | <11 | <1.8 | 125 | 0.8 | 57,615 | 3.4 |
| Mental health/substance use | 175 | 28.7 | 10,160 | 63.6 | 14,750 | 0.9 |
| Injury | 15 | 2.5 | 375 | 2.3 | 22,905 | 1.4 |
| Surgical | 15 | 2.5 | 465 | 2.9 | 68,620 | 4.1 |
| Medical | 400 | 65.6 | 4840 | 30.3 | 1,514,495 | 90.2 |
| Admission type | ||||||
| Nonelective | 595 | 97.5 | 14,630 | 91.6 | 1,599,780 | 95.3 |
| Elective | 15 | 2.5 | 1320 | 8.3 | 77,165 | 4.6 |
| Primary payor | ||||||
| Medicare | 330 | 54.1 | 6190 | 38.8 | 840,115 | 50.1 |
| Medicaid | 150 | 24.6 | 4785 | 30.0 | 252,615 | 15.1 |
| Private insurance | 100 | 16.4 | 3505 | 22.0 | 441,315 | 26.3 |
| Self-pay | 15 | 2.5 | 905 | 5.7 | 63,025 | 3.8 |
| Other | 15 | 2.5 | 560 | 3.5 | 78,325 | 4.7 |
| Admission status | ||||||
| Not transferred in | 405 | 66.4 | 11,125 | 69.7 | 1,431,645 | 85.3 |
| Transferred from acute care hospital | 65 | 10.7 | 2200 | 13.8 | 123,575 | 7.4 |
| Transferred from another facility | 140 | 23.0 | 2505 | 15.7 | 114,195 | 6.8 |
| Disposition of patient | ||||||
| Discharged home | 150 | 24.6 | 8415 | 52.7 | 863,390 | 51.4 |
| Transfer to short-term hospital | 20 | 3.3 | 965 | 6.0 | 50,485 | 3 |
| Transfer to other facility type | 330 | 54.1 | 5085 | 31.9 | 307,545 | 18.3 |
| Home health care | 50 | 8.2 | 1195 | 7.5 | 213,510 | 12.7 |
| Against medical advice | <11 | <1.8 | 85 | 0.5 | 18,720 | 1.1 |
| Died during hospitalization | 55 | 9.0 | 205 | 1.3 | 222,560 | 13.3 |
| Hospital length of stay (median, IQR) | 11 (6–23) | 10 (5–19) | 5 (3–10) | |||
| Total charges (median, IQR) | $71,550 ($33,808–$150,102) | $47,781 ($25,520–$99,714) | $44,835 ($23,958–$90,099) | |||
COVID-19 = coronavirus disease 2019; IQR = interquartile range; NIS = National Inpatient Sample.
Death occurred in 9.0% of hospitalizations for COVID-19 and catatonia, compared with 1.3% of those for catatonia alone and 13.3% of hospitalizations for COVID-19 not involving catatonia. In a logistic regression on the binary outcome of in-hospital death (yes/no), COVID-19 diagnosis, older age, male sex, nonelective admission, admission to a medical service, lack of major surgical procedures, an injury diagnosis, and non-White race were all independently associated with higher in-hospital mortality (Table 2 ). Neither catatonia diagnosis nor the interaction between catatonia and COVID-19 was associated with increased in-hospital mortality in this adjusted model. Cumulatively, hospitalizations for catatonia involved an aggregate length of hospital stay of 269,000 days and total hospital charges of $1.53 billion.
Table 2.
Logistic Regression Incorporating the Sampling Methodology of the NIS Conducted on the Binary Outcome of In-Hospital Death (Yes/No)
| Explanatory Variiable | 95% confidence interval |
||
|---|---|---|---|
| aOR | Lower | Upper | |
| Catatonia diagnosis | |||
| Yes | 1.206 | 0.903 | 1.611 |
| No | 1 | ||
| COVID-19 diagnosis | |||
| Yes | 4.273 | 4.195 | 4.352 |
| No | 1 | ||
| Catatonia × COVID | 0.836 | 0.436 | 1.605 |
| Age | 1.031 | 1.03 | 1.031 |
| Sex | |||
| Male | 1.296 | 1.284 | 1.308 |
| Female | 1 | ||
| Admission type | |||
| Nonelective | 1.682 | 1.531 | 1.848 |
| Elective | 1 | ||
| Primary service line | |||
| Maternal and neonatal | 0.232 | 0.218 | 0.246 |
| Mental health/substance use | 0.053 | 0.047 | 0.059 |
| Injury | 0.920 | 0.820 | 1.031 |
| Surgical | 0.963 | 0.928 | 0.999 |
| Medical | 1 | ||
| Operating room | |||
| No major surgical procedure | 1.413 | 1.370 | 1.458 |
| Major surgical procedure | 1 | ||
| Injury status | |||
| Injury diagnosis is primary | 0.975 | 0.873 | 1.087 |
| Injury diagnosis, nonprimary | 1.602 | 1.567 | 1.638 |
| No injury diagnosis | 1 | ||
| Race | |||
| White | 1 | ||
| Black | 1.126 | 1.099 | 1.154 |
| Hispanic | 1.125 | 1.097 | 1.154 |
| Asian or Pacific Islander | 1.26 | 1.219 | 1.303 |
| Native American | 1.427 | 1.320 | 1.544 |
| Other | 1.325 | 1.257 | 1.397 |
aOR = adjusted odds ratio.
Among the primary discharge diagnoses for hospitalizations with both catatonia and COVID-19, infectious diagnoses predominated, whereas for catatonia hospitalizations without COVID-19, most primary discharge diagnoses were psychiatric in nature (Table 3 ; Table S1). Expanding diagnoses to all primary and secondary discharge diagnoses (up to 40 per patient) demonstrates further differences in diagnoses between catatonia patients with and without COVID-19 infection (Table 4 ; Tables S2 and S3). Medical complications such as acute respiratory failure, acute kidney failure, hyperosmolarity and hypernatremia, metabolic encephalopathy, and acidosis were each diagnosed in twice as many COVID-19 catatonia patients as those without COVID-19. Among psychiatric diagnoses, F20.2 (“catatonic schizophrenia”) was diagnosed in a higher percentage of COVID-19 patients (69.7% vs. 62.2%), whereas F06.1 (“catatonic disorder due to known physiological condition”) was less frequently diagnosed (33.6% vs. 39.2%).
Table 3.
Primary Discharge Diagnoses for Catatonia Hospitalizations With and Without Co-Occurring COVID-19 Discharge Diagnosis
| COVID-19 diagnosis |
No COVID-19 diagnosis |
||||||
|---|---|---|---|---|---|---|---|
| Code | Description | n | % | Code | Description | n | % |
| U071 | Coronavirus disease 2019 (COVID-19) | 245 | 25.4 | F202 | Catatonic schizophrenia | 4360 | 25.4 |
| F202 | Catatonic schizophrenia | 80 | 3.7 | A419 | Sepsis, unspecified organism | 490 | 3.7 |
| A4189 | Other specified sepsis | 70 | 3.4 | F29 | Unspecified psychosis not due to a substance or known physiological condition | 475 | 3.4 |
| A419 | Sepsis, unspecified organism | 15 | 3.0 | F333 | Major depressive disorder, recurrent, severe with psychotic symptoms | 465 | 3.0 |
| T83511A | Infection and inflammatory reaction due to indwelling urethral catheter, initial encounter | 15 | 2.8 | F061 | Catatonic disorder due to known physiological condition | 460 | 2.8 |
| F29 | Unspecified psychosis not due to a substance or known physiological condition | 15 | 2.3 | F250 | Schizoaffective disorder, bipolar type | 405 | 2.3 |
| F061 | Catatonic disorder due to known physiological condition | <11 | <1.8 | F200 | Paranoid schizophrenia | 345 | 2.3 |
| F250 | Schizoaffective disorder, bipolar type | <11 | <1.8 | F332 | Major depressive disorder, recurrent severe without psychotic features | 295 | 2.1 |
| F23 | Brief psychotic disorder | <11 | <1.8 | F319 | Bipolar disorder, unspecified | 290 | 1.7 |
| F209 | Schizophrenia, unspecified | 260 | 1.7 | ||||
Table 4.
Discharge Diagnoses for Hospitalizations Involving Catatonia With and Without Co-Occurring COVID-19 Diagnosis
| Code | Description | COVID diagnosis |
No COVID diagnosis |
||||
|---|---|---|---|---|---|---|---|
| Rank | n | % | Rank | n | % | ||
| U071 | Coronavirus disease 2019 (COVID-19) | 1 | 610 | 100.0% | n/a | n/a | n/a |
| F202 | Catatonic schizophrenia | 2 | 425 | 69.7% | 1 | 9925 | 62.2% |
| J1289 | Other viral pneumonia | 3 | 255 | 41.8% | 1927 | <11 | <0.07% |
| I10 | Essential (primary) hypertension | 4 | 220 | 36.1% | 3 | 4825 | 30.2% |
| F061 | Catatonic disorder due to known physiological condition | 5 | 205 | 33.6% | 2 | 6265 | 39.2% |
| E785 | Hyperlipidemia, unspecified | 6 | 195 | 32.0% | 7 | 3090 | 19.4% |
| J9601 | Acute respiratory failure with hypoxia | 7 | 190 | 31.1% | 56 | 775 | 4.9% |
| N179 | Acute kidney failure, unspecified | 8 | 170 | 27.9% | 13 | 2055 | 12.9% |
| E870 | Hyperosmolality and hypernatremia | 9 | 165 | 27.0% | 22 | 1365 | 8.5% |
| F419 | Anxiety disorder, unspecified | 10 | 145 | 23.8% | 5 | 3475 | 21.8% |
| E876 | Hypokalemia | 11 | 140 | 23.0% | 11 | 2455 | 15.4% |
| E039 | Hypothyroidism, unspecified | 12 | 125 | 20.5% | 14 | 1990 | 12.5% |
| G9341 | Metabolic encephalopathy | 12 | 125 | 20.5% | 25 | 1255 | 7.9% |
| N390 | Urinary tract infection, site not specified | 12 | 125 | 20.5% | 16 | 1880 | 11.8% |
| E860 | Dehydration | 15 | 120 | 19.7% | 9 | 2525 | 15.8% |
| Z66 | Do not resuscitate | 16 | 110 | 18.0% | 46 | 925 | 5.8% |
| Z79899 | Other long term (current) drug therapy | 17 | 105 | 17.2% | 6 | 3130 | 19.6% |
| E872 | Acidosis | 18 | 100 | 16.4% | 23 | 1330 | 8.3% |
| F319 | Bipolar disorder, unspecified | 18 | 100 | 16.4% | 15 | 1985 | 12.4% |
| Z9114 | Patients other noncompliance with medication regimen | 20 | 90 | 14.8% | 8 | 2870 | 18.0% |
Included are the top 20 diagnoses for patient with COVID-19 and catatonia. “Rank” indicates the order of the diagnostic category in frequency of diagnoses, with the most common diagnosis ranked 1.
Discussion
Consistent with data from the 2019 NIS,14 , 15 catatonia was rarely diagnosed in 2020, with a catatonia code included in a total of 0.05% of the 32,355,827 hospitalizations in the 2020 NIS. Among the total 16,575 catatonia cases, 610 (3.7%) involved a co-occurring diagnosis of COVID-19. Catatonia was diagnosed less frequently in the setting of COVID-19 infection than among hospitalizations without COVID-19 diagnosis.
Although the pathogenesis of catatonia remains unknown, it is hypothesized to involve immune dysregulation resulting in the neurovegetative symptoms of the disorder, including immobility and decreased oral intake.16, 17, 18 Catatonia and related syndromes have been described in the setting of other infectious illnesses, including malaria, syphilis, human immunodeficiency virus, and prion diseases.16 Of cases of catatonia with a general medical cause, infectious and immune causes accounted for 29% in 1 study, and a recent systematic review identified 124 infective cases of catatonia.16 , 19 Numerous reports of catatonia associated with COVID-19 infection have been published since the earliest days of the pandemic, with a recent scoping review identifying 27 studies involving 42 patients.8 While the underlying studies in this review were heterogeneous, among the 22 individuals treated with lorazepam, 18 showed improvement and another 4 responded to electroconvulsive therapy, which suggests that catatonia co-occurring with COVID may be treated similarly to other cases of catatonia. The finding that catatonia is diagnosed less frequently in the setting of COVID-19 infection in the NIS may highlight the sampling bias of case report literature, leading to overestimates of associations. Conversely, it is possible that the results highlight the limitations of claims data, as physicians may be less likely to identify catatonia in patients with severe medical illness.
Diagnosis of catatonia is complicated in the setting of COVID-19 infection, as multiple neuropsychiatric manifestations of the illness including delirium20 , 21 and akinetic mutism may also present,22 and may be challenging to differentiate from catatonia. Although definitionally under the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, Text Revision (DSM-5-TR) catatonia and delirium may not be diagnosed at the same time, evidence suggests a high comorbidity of the 2 conditions in patients without COVID-19, with up to 30% of patients with delirium exhibiting features of catatonia and vice versa.23, 24, 25 Among COVID-19 catatonia patients in this sample, diagnoses of acute brain dysfunction were common, with 20.0% also having a diagnosis of metabolic encephalopathy, 13.1% of unspecified encephalopathy, 12.3% of delirium, and 11.5% of toxic encephalopathy, all codes that are used for delirium in prior coding studies.26, 27, 28 This has potential treatment implications, as benzodiazepines can worsen delirium, and antipsychotics, which are often used to manage sequelae of delirium,29 may worsen catatonia or risk conversion to malignant catatonia. Moreover, delirium itself is associated with increased mortality in hospitalized patients,30 and so the high rate of mortality in COVID-19 patients may be in part driven by high delirium incidence.
The rates of catatonia diagnosis in patients with or without COVID-19 diagnosis were low in this sample, occurring more than 100× less frequently than the 9% pooled catatonia prevalence in mixed samples in a 2018 systematic review.31 Whether this represents underdiagnosis in the NIS (perhaps caused by lack of systematic catatonia screening) or differences in the clinical population of the NIS relative to that of prior studies (which mostly focused on psychiatric inpatients) cannot be determined from this administrative claims data.
Catatonia hospitalizations with co-occurring COVID-19 diagnosis displayed markedly higher crude mortality relative to those without COVID-19 (9% vs. 1.3%) but lower mortality than COVID-19 hospitalizations without catatonia (13.3%). In an adjusted model, while demographic factors such as age, sex, and race were associated with higher mortality, as was COVID-19 diagnosis, neither catatonia diagnosis nor the interaction between catatonia and COVID-19 was associated with differential in-hospital mortality. Thus, while catatonia is associated with potential medical complications including aspiration and deep vein thrombosis, such effects were not associated with a change in overall mortality.
The strengths of this study derive from its utilization of a large, nationally representative sample not restricted to a particular region or payor. This minimizes sources of bias in findings. The limitations of the study likewise derive from the data source, which involves a retrospective analysis of billing records. It is unclear how the diagnosis of catatonia was made for individual cases, nor the particular catatonic symptoms present, and there is no mechanism for grading catatonia severity, as there would be if validated catatonia rating scales were used consistently.4 , 32 Higher rates of catatonic schizophrenia (F20.2) compared with catatonia due to physiologic dysfunction (F06.1) in the COVID-19 sample points to likely errors in coding among catatonia diagnoses. Moreover, for a catatonia case to be included in the study, it requires the treating team to correctly diagnose and code for the condition, and any failure in diagnosis or coding will mean that a case is erroneously classified as noncatatonic. This is also true for COVID-19 diagnoses. Particularly, in the early phase of the pandemic when testing supplies were limited, other studies demonstrate variability in COVID-19 diagnoses by region, and likewise an under-counting of excess mortality caused by the pandemic.33 Thus, some catatonic individuals with COVID-19 infection may be erroneously counted as noninfected. In addition, we are unable to assess for the temporal relationship between COVID-19 infection and development of catatonic symptoms, so no conclusions can be drawn about the causality of COVID-19 and catatonia.34 Finally, as the NIS samples discharges and not individual patients, if an individual was treated at more than 1 hospital or multiple times in the study year, they may be counted more than once among NIS hospitalizations.
Conclusions
COVID-19 infection was diagnosed in 3.7% of hospitalizations for catatonia in the 2020 NIS, for a total of 610 cases. Delirium and encephalopathy were frequently diagnosed along with catatonia and COVID-19. In an adjusted model, catatonia diagnosis was not associated with differential in-hospital mortality. Further research is needed to better characterize the phenomenology of catatonia in the setting of COVID-19 infection and its optimal treatment.
Footnotes
Conflicts of Interest: J.L. holds equity in Revival Therapeutics, Inc. M.K. is employed by and has equity in Watershed Informatics. He also holds equity in Revival Therapeutics, Inc. T.H.M. receives research funding from the Stanley Center at the Broad Institute, the Brain and Behavior Research Foundation, National Institute of Mental Health, National Human Genome Research Institute Home, and Telefonica Alfa. G.F. holds equity in Revival Therapeutics, Inc. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Ethical Approval: As this is a deidentified, publicly available database, this study was determined to be Not Human Subjects Research by the Mass General Brigham Institutional Review Board.
Funding: This work was supported by the National Institute of Mental Health (T32MH112485, J.L.; R01MH120991, T.H.M.) and the Avery D. Weisman Fund of the Massachusetts General Hospital Department of Psychiatry. The sponsors had no role in study design, writing of the report, or data collection, analysis, or interpretation.
Supplementary data related to this article can be found at https://doi.org/10.1016/j.jaclp.2022.12.010.
Supplementary Data
References
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