key: cord-0683106-g2aqtolz authors: Mendes, Aline; Serratrice, Christine; Herrmann, François R.; Genton, Laurence; Périvier, Samuel; Scheffler, Max; Fassier, Thomas; Huber, Philippe; Jacques, Marie-Claire; Prendki, Virginie; Roux, Xavier; Di Silvestro, Katharine; Trombert, Véronique; Harbarth, Stephan; Gold, Gabriel; Graf, Christophe E.; Zekry, Dina title: Predictors of in-hospital mortality in older patients with COVID-19: The COVIDAge Study date: 2020-09-15 journal: J Am Med Dir Assoc DOI: 10.1016/j.jamda.2020.09.014 sha: 2bcbbd8957e36dea9215b758646217941e818bdb doc_id: 683106 cord_uid: g2aqtolz Objective To determine predictors of in-hospital mortality related to COVID-19 in older patients. Design Retrospective cohort study. Setting and Participants Patients aged 65 years and older hospitalized for a diagnosis of COVID-19. Methods Data from hospital admission was collected from the electronic medical records. Logistic regression and Cox proportional-hazard models were used to predict mortality, our primary outcome. Variables at hospital admission were categorized according to the following domains: demographics, clinical history, comorbidities, previous treatment, clinical status, vital signs, clinical scales and scores, routine laboratory analysis and imaging results. Results Of a total of 235 Caucasian patients, 43% were male, with a mean age of 86 ± 6.5 years. Seventy-six patients (32%) died. Non-survivors had a shorter number of days from initial symptoms to hospitalization (p=0.007) and the length of stay in acute wards than survivors (p<0.001). Similarly, they had a higher prevalence of heart failure (p=0.044), peripheral artery disease (p=0.009), crackles at clinical status (p<0.001), respiratory rate (p=0.005), oxygen support needs (p<0.001), C-reactive protein (p<0.001), bilateral and peripheral infiltrates on chest radiographs (p=0.001) and a lower prevalence of headache (p=0.009). Furthermore, non-survivors were more often frail (p<0.001), with worse functional status (p<0.001), higher comorbidity burden (p<0.001) and delirium at admission (p=0.007). A multivariable Cox model showed that male sex (HR 4.00, 95% CI 2.08-7.71, p=0.0001), increased fraction of inspired oxygen (HR 1.06, 95% CI 1.03-1.09, p<0.0001) and crackles (HR 2.42, 95% CI 1.15-6.06, p=0.0190) were the best predictors of mortality, while better functional status was protective (HR 0.98, 95% CI 0.97-0.99, p=0.0013). Conclusions and implications In older patients hospitalized for COVID-19 male sex, crackles, a higher fraction of inspired oxygen and functionality were independent risk factors of mortality. These routine parameters, and not differences in age, should be used to evaluate prognosis in older patients. Methods: Data from hospital admission was collected from the electronic medical records. Logistic regression and Cox proportional-hazard models were used to predict mortality, our 8 primary outcome. Variables at hospital admission were categorized according to the 9 following domains: demographics, clinical history, comorbidities, previous treatment, clinical 10 status, vital signs, clinical scales and scores, routine laboratory analysis and imaging results. Results: Of a total of 235 Caucasian patients, 43% were male, with a mean age of 86 ± 6.5 12 years. Seventy-six patients (32%) died. Non-survivors had a shorter number of days from 13 initial symptoms to hospitalization (p=0.007) and the length of stay in acute wards than 14 survivors (p<0.001). Similarly, they had a higher prevalence of heart failure (p=0.044), 15 peripheral artery disease (p=0.009), crackles at clinical status (p<0.001), respiratory rate 16 (p=0.005), oxygen support needs (p<0.001), C-reactive protein (p<0.001), bilateral and 17 peripheral infiltrates on chest radiographs (p=0.001) and a lower prevalence of headache 18 (p=0.009). Furthermore, non-survivors were more often frail (p<0.001), with worse functional 19 status (p<0.001), higher comorbidity burden (p<0.001) and delirium at admission (p=0.007). A multivariable Cox model showed that male sex (HR 4.00, 95% CI 2.08-7.71, p=0.0001), 21 increased fraction of inspired oxygen (HR 1.06, 95% CI 1.03-1.09, p<0.0001) and crackles 22 (HR 2.42, 95% CI 1.15-6.06, p=0.0190) were the best predictors of mortality, while better 23 functional status was protective (HR 0.98, 95% CI 0.97-0.99, p=0.0013). CoV-2, the fatality rate was 18.8% for patients older than 80 years 7 while the overall fatality 36 rate is estimated at up to 5% 2,8 . Observational Chinese cohort studies reported that the 37 presence of comorbidities, old age and male sex were associated with a higher rate of 38 severe disease course and mortality. The three most commonly found comorbidities were 39 hypertension, cardiovascular disease, and diabetes [9] [10] [11] . Although most of the studies were 40 not stratified by age groups, the authors found that patients older than 65 years had a higher 41 prevalence of comorbidities, more severe symptoms, more laboratory abnormalities and 42 were more likely to develop multi-organ failure and die 12 . A recent Chinese study including 43 244 adults with a median age of 67 years, and no patient older than 72 years, showed that 44 age and lower lymphocyte count were associated with in-hospital death 13 . Moreover, the 45 median number of days from the occurrence of the first symptom to death tended to be 46 shorter among people aged over 70 as compared to younger people 14 .This study aims to 47 determine the risk factors for in-hospital mortality related to COVID-19 in the older patients. We hypothesized that functionality, comorbidities and frailty at hospital admission are better 49 predictors of mortality in older patients than age per se. Furthermore, COVID-19 pneumonia was defined as the presence of cough and at least one 80 associated respiratory sign and/or symptom, with fever for more than 4 days, chest imaging 81 consistent with COVID-19. The data were collected and analyzed once all included cases either died or were 83 discharged alive from acute geriatric care. The data of this retrospective study was retrieved from the electronic patient record system. For each included patient, collected data were categorized according to the following The following scores and scales were computed and documented in medical records: documented in medical records before data collection for this study, as a 9-point scale based 106 on clinical judgment varying from 1 "Very fit" to 9 "Terminally ill". The CIRS-G measures the 107 chronic medical illness ("morbidity") burden in 14 individual body systems and grades each 108 from 0 (no disease) to 4 (very severe). The total score ranges from 0 to 56 points. The CAM 109 is the standard screening tool to detect delirium in medical and surgical settings. It integrates 110 information from clinical assessment and diagnostic criteria to determine whether delirium is 111 present or not. The FIM takes into account physical, psychological and social functions, such 112 as activities of self-care, sphincter control, locomotion, mobility/transfer, and social cognition. For each evaluated activity, the score ranges from 1 -totally dependent, to 7 -totally 114 independent. The scoring system ranges from 18 points (extreme disability) to 126 points 115 (complete independence). Finally, the PSI is a prognostic score for community-acquired The proportional-hazards assumption was tested with Schoenfeld's residuals (eg, Methods2 144 in the Appendices). Kaplan-Meier curves were drawn for the best predictors and compared 145 using log rank tests. For this analysis, we considered functional status according to FIM in 146 J o u r n a l P r e -p r o o f three categories: 0-49; 50-99 and 100-126 25 . Stepwise forward competing-risks regression 147 were also performed using Stata's "stcrreg" command, the two competing risk being death 148 and discharged alive. Multiple logistic regression models were used to predict mortality with the same data 150 reduction process. Results are presented as odds ratio (OR) along with their 95% CI. Then, we studied the association with the best predictors by calculating the area under the receiver 152 operating characteristic (AUC) curves, using Stata's roccomp command. We calculated 153 sensitivity, specificity, positive predictive value and negative predictive value according to the 154 optimum criterion value determined by the Youden's index (sensitivity + specificity-1). Data Arthralgia, anosmia, ageusia, sore throat, conjunctivitis and abdominal or chest pain 184 occurred in less than 5% of the cases and were not different in survivors and non survivors. Initial laboratory values showed a mean C-reactive protein level of 66.3±69.9 (0-10 mg/L), 186 albumin of 3.66±1.17 (3.5-5.0 g/dL), and creatinine of 110.3±81.4 (50-110 µmol/L). Lymphocytes were under 1.0 10 9 /l in 42.2% of the patients. The most common radiological 188 features at admission were the presence of an interstitial infiltrate in 58.2% of cases, bilateral 189 in 33.8% and peripheral in 47.6% of cases (Table 1) . Of the total of 159 patients alive by the 190 end of this study, 32 were transferred to rehabilitation wards, 30 to nursing homes, 62 were 191 discharged home directly from acute wards and 30 were still hospitalized in acute wards. shorter in the group of non-survivors (p=0.007). Asthenia (p=0.026), dyspnea (p=0.04), a 205 higher heart rate (p=0.011), a higher respiratory rate (p=0.005), need for a higher FiO 2 206 (p<0.001), and the presence of signs of heart failure (p=0.011) and crackles (p<0.001) were 207 associated with increased risk of in-hospital mortality. Other co-morbidities and clinical 208 findings were not statistically different between the two groups (Table A1) . Patients who died during their hospital stay took more medications prior to admission than 210 patients who were discharged alive (p=0.038). However, there were no differences between 211 the two groups regarding intake of nonsteroidal anti-inflammatory drugs, corticosteroids, 212 immunosuppressants, angiotensin-converting enzyme inhibitors (ACE2) or angiotensin 213 receptor-II antagonists and anticoagulants (Table A1 ). The group of non-survivors had higher CRP and creatinine values with lower eGFR and Table 2 ) and after adjustment for sex (Model 2, We performed univariate and multivariable logistic regression models to evaluate whether 243 mortality could be predicted on an individual basis. We obtained the same best predictors as (Table A2 ). We report an elevated mortality rate of 32% which is consistent with many previous studies 259 of COVID-19 patients that underline the marked impact of age on mortality and fatality ratios However, the fact that worse functionality according to FIM is associated with mortality does 282 not mean that functional decline is the main pathophysiological mechanism underlying the 283 cause of death. Frailty, delirium, severity of respiratory symptoms are all associated with this 284 measure. We believe that there are two different questions to be answered (i) which 285 mechanism is associated with the increased mortality observed in older patients with The two other predictive items in the multivariable Cox model were related to the presence 305 and severity of pneumonia, the most common pathology leading to complications and death 306 in SARS-CoV-2 infection. Every 2% increase of FiO 2 added 7% of risk of dying. This was in 307 accordance with a previous study showing that symptoms related to hypoxemia were more 308 common in patients who died 9 . Frail patients are at increased risk of worse outcomes in the acute setting such as disability, A striking result was that only 10% of patients with delirium on admission survived to 331 discharge from acute care. This underlines the importance of screening older hospitalized 332 patients for delirium with simple instruments such as the CAM and is in agreement with other 333 studies that recognized delirium as an independent marker of disease severity 40,41 . As in previous studies in older patients 42 lymphopenia was frequent in patients with COVID-335 19 and associated with poor outcome 3,13 independently of sex. Lymphocyte counts at 336 admission whether treated as a transformed continuous variable or a binary variable were 337 significantly associated with in-hospital mortality but in contrast to a recent study by Sun 13 338 was not retained in our best predictive model. This is perhaps related to the high frequency 339 of lymphopenia in Sun's study. The main limitation of COVIDAge is its retrospective monocentric design, with the utilization 342 of information extracted from medical records. Consequently, missing data could not be 343 retrieved. The primary endpoint was in hospital mortality without a fixed follow-up period. However, we were careful to collect data once all inpatients had either died or been 345 discharged from acute geriatric care. We can equally not exclude some variability in clinical 346 scale ratings taking into consideration subjective judgment even though our clinical teams 347 are well trained for this assessment. 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