K. Yamada1,2, K. Iwata1,2, Y. Yoshimura3, H. Ota4, Y. Oki2, Y. Mitani2, Y. Oki2, Y. Yamada2, A. Yamamoto5, K. Ono2, A. Honda1, T. Kitai1, R. Tachikawa6, N. Kohara1, K. Tomii6, A. Ishikawa2
1. Department of Rehabilitation, Kobe City Medical Center General Hospital, Kobe, Japan; 2. Department of Public Health, Kobe University Graduate School of Health Sciences, Kobe, Japan; 3. Center for Sarcopenia and Malnutrition Research, Kumamoto Rehabilitation Hospital, Kumamoto, Japan; 4. Department of Rehabilitation, Shinshu University Hospital, Nagano, Japan; 5. Faculty of Nursing, Osaka Medical College, Osaka, Japan; 6. Department of Respiratory Medicine, Kobe City Medical Center General Hospital, Kobe, Japan
Corresponding Author: Kentaro Iwata, PT, MSc, Department of Rehabilitation, Kobe City Medical Center General Hospital, 2-1-1, Minatojimaminami, Chuo, Kobe 650-0047 Hyogo, Japan. Tel.: +81 78 302, email@example.com
J Frailty Aging 2022;in press
Published online April 28, 2022, http://dx.doi.org/10.14283/jfa.2022.36
Background: In older people, frailty has been recognized as an important prognostic factor. However, only a few studies have focused on multidimensional frailty as a predictor of mortality and readmission among inpatients with pneumonia.
Objective: The present study aimed to assess the association between preadmission frailty and clinical outcomes after the hospitalization of older patients with pneumonia.
Design: Single-center, retrospective case-control study.
Setting: Acute phase hospital at Kobe, Japan.
Participants: The present study included 654 consecutive older inpatients with pneumonia.
Measurements: Frailty status before admission was assessed using total Kihon Checklist (KCL) score, which has been used as a self-administered questionnaire to assess comprehensive frailty, including physical, social, and cognitive status. The primary outcome was a composited 6-month mortality and readmission after discharge.
Results: In total, 330 patients were analyzed (median age: 79 years, male: 70.4%, median total KCL score: 10 points), of which 68 were readmitted and 10 died within 6 months. After multivariate analysis, total KCL score was associated with a composited 6-month mortality and readmission (adjusted hazard ratio, 1.07; 95% confidence interval, 1.02–1.12; p = 0.006). The cutoff value for total KCL score determined by receiver operating characteristic curve analysis was 15 points (area under the curve = 0.610). The group with a total KCL score ≥ 15 points had significantly higher readmission or mortality rates than the groups with a total KCL score < 15 points (p < 0.001).
Conclusions: Preadmission frailty status in older patients with pneumonia was an independent risk factor for readmission and survival after hospitalization.
Key words: Activities of daily living, elderly frail, mortality, patient readmission, pneumonia.
Hospitalization for older patients with acute pneumonia is frequent in Japan. The combination of pneumonia, including aspiration pneumonia, ranks fourth among the leading causes of mortality in the Japanese population (1). Moreover, most patients with pneumonia are at least 65 years old. Some studies have reported that rehospitalization and mortality rates range from 10% to 20% in older adults with pneumonia (2–4).
Frailty is a syndrome resulting from an age-related decline of multiple physiological systems characterized by a decreased function in homeostatic systems (5). Furthermore, frailty is associated with adverse health outcomes, such as disability (6), hospitalizations (7), quality of life (8), and mortality (9), with an overall prevalence ranging from 27% to 80% among inpatients (10–12). Severe frailty has been reported to be a predictor of mortality and hospital readmission in the acute care setting (13). The Kihon checklist (KCL) is used as a self-administered questionnaire to assess multidimensional frailty, including physical functions, nutritional state, cognitive function, depressive mood, and the number of frailty phenotypes (14). While the total KCL score was highly correlated with Fried’s frailty phenotype and predicted incidence of long-term care insurance certification (15), various cutoffs and scoring methods have been reported for KCL as a frailty assessment (16). It is unclear whether the reported cutoffs are applicable to older patients with pneumonia.
Previous studies have shown that higher muscle mass, daily walking time for ≥ 1 hour/day, and earlier rehabilitation were associated with mortality decline in pneumonia patients (4, 17, 18). Some studies have reported that predictors of aspiration pneumonia in nursing home residents include weight loss, malnutrition, swallowing ability, and activities of daily living (ADL) (19–22). Those factors are included in an aspect of frailty, respectively. Moreover, previous studies have reported frailty was associated with 30-day mortality and readmission (23), and physical frailty predicted 1-year mortality in patients with pneumonia (24). However, few studies have investigated the impact of multidimensional frailty on hospital readmission and mortality in older patients with pneumonia.
Furthermore, the value of multidimensional frailty as a predictor of adverse events is currently unclear in older patients with acute pneumonia. We hypothesized that frailty in older patients with pneumonia contributes to poor prognosis after hospitalization. During the shortening hospital stay, assessing pre-hospital frailty status immediately after admission could help estimate prognosis and adjust clinical outcomes in high-risk patients. Therefore, the present study aimed to assess whether preadmission frailty is an independent predictor of mortality and readmission within 6 months after hospitalization for pneumonia in older patients.
Materials and Methods
Study design and participants
This retrospective single-center cohort study was performed at the Kobe City Medical Center General Hospital, Hyogo Prefecture, Japan. This study was conducted in accordance with the TRIPOD statement for transparent reporting of a multivariable prediction study (25).
This study included 654 consecutive patients who had been admitted as emergency to the department of respiratory medicine, aged ≥65 years, and diagnosed with pneumonia from October 2018 to September 2020. Exclusion criteria were patients with communication disorders, those who did not receive rehabilitation during hospitalization, death during hospitalization, and missing data. In the present study, communication disorders was defined as who are unable to communicate and answer simple question, such as comatose, severe dementia, or aphasic.
The present study was approved by the ethics committee of Kobe City Medical Center General Hospital (approval no. ZN200714) in accordance with the principles of the Declaration of Helsinki regarding investigations in humans. Patients were not required to give informed consent to the study because the analysis used anonymous clinical data that were obtained after each patient agreed to treatment by written consent. Further, we applied Opt-out method to obtain consent on this study.
Outcomes and data collection
The primary outcome was a composited 6-month mortality and readmission for any reason within 6 months of discharge. The secondary outcome was an all-cause mortality within 6 months of discharge. Readmission was defined as admission for all causes within 6 months after discharge, except scheduled hospitalizations. Data on adverse events were collected by review of index hospital medical records up to 6 months after initial discharge.
Data were retrospectively collected by reviewing the subjects’ computerized medical records, including age; body mass index (BMI); sex; severity and type of pneumonia; laboratory values of C-reactive protein (CRP), blood urea nitrogen (BUN), albumin, and white blood cell (WBC) at admission; the Charlson Comorbidity Index (CCI) (26); total KCL score (14) length of stay during hospitalization; discharge destination; and a composited 6-month mortality and readmission.
The A-DROP score was used to assess pneumonia. The A-DROP score, a scoring system proposed by the Japanese Respiratory Society, was used to assess severity for patients with community-acquired pneumonia (CAP) or nursing and healthcare-associated pneumonia (NHCAP) (27). A-DROP scores consist of the following items: age (male age ≥70 years; female age ≥75 years), dehydration status (BUN ≥ 21 mg/mL), respiratory failure status (pulse oximetry saturation SpO2 ≤ 90%), consciousness disturbance, and low blood pressure (systolic blood pressure ≤ 90 mmHg). The minimum total A-DROP score was 0 points, and the maximum total A-DROP score was 5 points. A higher A-DROP score indicated more severe pneumonia.
In Japan, NHCAP has been reported as a type of pneumonia. The guidelines for NHCAP were reported to adapt with the healthcare and insurance system in Japan (28). Patients with NHCAP were defined based on the following criteria: 1) a resident of an extended care facility or nursing home, 2) a person who has been discharged from a hospital within the preceding 90 days, 3) an older person receiving nursing care, or 4) a person receiving regular endovascular treatment as an outpatient. In the present study, participants were assessed for NHCAP or CAP.
CCI was used to assess the prognostic burden of comorbid disease. The index assigns weights for specific diseases. Comorbid conditions with a weight of 1 include myocardial infarction, congestive heart failure, peripheral vascular disease, cerebrovascular disease, dementia, chronic pulmonary disease, ulcer disease, mild liver disease, and diabetes. Diabetes with end-organ damage, any tumor, leukemia, and lymphoma have a weight of 2. Moderate or severe liver disease has a weight of 3, and metastatic solid tumor and AIDS have a weight of 6. The total score was calculated by adding the weights (26).
Three discharge outcomes were documented, and patients were discharged to (i) home, (ii) a post-acute hospital, or (iii) nursing home.
Preadmission frailty (Kihon Checklist)
Preadmission frailty in older patients with acute pneumonia was assessed using total KCL score, which was developed by the Japanese Ministry of Health, Labour and Welfare to identify older individuals at risk of requiring care/support (14). KCL is a self-administered questionnaire consisting of 25 yes/no items, and the sum of the 25 answers ranges from 0 (no frailty) to 25 (severe frailty). Frailty status before admission was assessed during hospitalization by either a physical therapist during an interview or was self-reported by the patient. In this study, preadmission frailty status was defined as not the condition immediately before hospitalization but clinically stable before the onset of pneumonia.
Sample size was calculated 298 patients using the following index; event free rate in high risk: 0.82, event free rate in low risk: 0.73, α error: 0.05, 1-β: 0.8, two-sided test, observation period: 2 years, follow-up period: 6 months (23).
Quantitative variables are expressed as median (interquartile range); qualitative variables are expressed as number (percentage). The Mann-Whitney U test and chi-square test were used to compare patient characteristics and clinical parameters between the non-readmission & death group (non-event group) and readmission or death group (event group). All statistical analysis was performed as complete case analysis.
The relationship between the composited 6-month mortality and readmission rates and frailty was assessed using the Cox proportion hazard model according to age, sex, pneumonia severity, pneumonia type, CCI, discharge destination, and KCL score. These variables were selected based on biological plausibility and preexisting knowledge. Independent variables for collinearity were checked using the variance inflation factor.
Detailed items between the two groups that were significant in these analyses were examined. To determine the cutoff value of the most influential factor, a receiver operating characteristic (ROC) curve was constructed by plotting the sensitivity against the false positive rate. Patients were classified into two groups according to these cutoff values, a Kaplan-Meier curve was constructed, and a log-rank test was used. Patients who were lost to follow-up were censored at the date of the last follow-up.
All statistical analyses were performed with EZR (Saitama Medical Center, Jichi Medical University, Saitama, Japan)(29), a graphical user interface for R (The R Foundation for Statistical Computing, Vienna, Austria). More precisely, EZR is a modified version of R commander designed to add statistical functions frequently used in biostatistics. A p-value <0.05 was considered statistically significant.
A flow chart describing the inclusion of patients in this study is shown in Figure 1. A total of 654 consecutive patients were considered eligible for this study, but patients were later excluded (Figure 1) if they had a communication disorder (n = 104), including severe dementia (n = 78), died during hospitalization (n = 77), did not participate in a rehabilitation program (n = 23), or had missing data (n = 120). Of the 330 patients enrolled, after discharge from the initial hospitalization, 68 were readmitted and 10 died within 6 months during the median follow-up of 117 days (interquartile range: 17–180). Therefore, 330 patients were ultimately included and divided into the non-event group (n = 252) or event group (n = 78).
The demographic data are shown in Table 1. The median age was 79 years, and 228 (69.1%) were men. The median of total KCL score was 10 points. In the non-event group, BMI (22.2 vs. 20.7; p = 0.026), CRP (9.07 vs. 4.61; p = 0.001), and WBC (9.65 vs. 6.80; p = 0.002) were significantly higher than in the event group, and CCI (3 vs. 5; p < 0.001) and total KCL score (10 vs. 12; p = 0.003) were significantly lower than in the event group.
Data are expressed as median (interquartile range) or number (percentage); Mann-Whitney U and chi-square tests were used for quantitative and qualitative variables, respectively. BMI: body mass index; Alb: albumin; BUN: blood urea nitrogen; CRP: C-reactive protein; WBC: white blood cell; CCI: Charlson Comorbidity Index; LOS: Length of hospital stay; KCL: Kihon checklist; Clinical parameters were assessed on admission.
The results of the Cox proportion hazard model are shown in Table 2. In the univariable analysis, the severity of pneumonia (hazard ratio [HR], 1.48; 95% CI [confidence interval], 1.12–1.95; p = 0.006), NHCAP (HR, 3.33; 95% CI, 1.71–6.46; p < 0.001), CCI (HR, 1.08; 95% CI, 1.03–1.13; p = 0.004), and total KCL score (HR, 1.10; 95% CI, 1.05–1.14; p < 0.001) were significantly associated with a 6-month event. After multivariate analysis, the severity of pneumonia (adjusted HR, 1.55; 95% CI, 1.14–2.12; p = 0.006), NHCAP (adjusted HR, 2.52; 95% CI, 1.17–5.45; p = 0.018), and total KCL score (adjusted HR, 1.07; 95% CI, 1.02–1.12; p = 0.006) remained significantly associated with a composited 6-month mortality and readmission.
The cutoff value of total KCL score that predicted readmission in the ROC curve was 15 points (area under the curve: 0.610 [95% CI: 0.538–0.682], sensitivity: 0.385, specificity: 0.790) (Figure 2).
The cutoff value of the KCL score that predicted the occurrence of readmission in the ROC curve was 15 points (area under the curve: 0.610 [95% CI: 0.538–0.682]).
In the Kaplan-Meier analysis for primary outcome, we divided patients into two groups based on the cutoff values of total KCL score. The group with a total KCL score ≥ 15 points had significantly higher composited 6-month mortality and readmission rates than the groups with a total KCL score < 15 points (log-rank test, p < 0.001) (Figure 3). Moreover, the group with a KCL score ≥ 15 points had significantly higher mortality (log-rank test, p = 0.003) and readmission rates (log-rank test, p = 0.004) than the groups with a KCL score < 15 points, respectively (Supplemental figure).
The present study reported the association between preadmission frailty and prognostic factors in older hospitalized patients with pneumonia. The main ﬁnding was that preadmission frailty may predict a composited 6-month mortality and readmission after initial admission in this population.
The cutoff value of the KCL score that predicted the occurrence of readmission in the ROC curve was 15 points (area under the curve: 0.610 [95% CI: 0.538–0.682]).
Comparison with previous studies and possible explanations and implications
Of the patients with pneumonia in the present study, 21% were readmitted and 7% died within 6 months of initial discharge. Previous studies have reported that rehospitalization and mortality rates range from 10% to 20% in patients with pneumonia (2–4). Compared with these previous studies, the readmission rate was similar, but the mortality rate was lower in because this study excluded communication disorders, including severe dementia and death.
In the Cox proportion hazard model, total KCL score was significantly associated with the 6-month mortality and readmission rates after initial hospitalization. A prospective cohort study previously reported that 88 of 220 older inpatients (40%) were assessed as frail, and frailty was associated with 6-month mortality (10). In previous studies, frailty was associated with death and readmission of older patients to acute care wards (13, 30). Another study reported that instability vital sign at discharge predicted rehospitalization within 30 days (31). Frailty likely reﬂects the general poor health status of inpatients, and frailty may also be the strongest prognostic factor in pneumonia patients. Previous studies might support the results of the present study, and frail older adults with unstable physiological status may have been more susceptible to the effects of the development of pneumonia and treatment during hospitalization. As a result, their physiological instability may have affected readmissions and death. It is important to investigate the effective intervention for the frailty patient with pneumonia after admission.
In this study, the cutoff value of total KCL score to predict post-discharge events was 14/15. A previous study has reported that the total KCL cutoffs for identifying frailty and prefrailty were 7/8 and 3/4, respectively (14). Moreover, the classification of frailty status by KCL score predicted the incidences of dependency and mortality in older adults (32). Previous studies have reported that independent predictors of aspiration pneumonia include weight loss, malnutrition, swallowing ability, and ADL function (19, 20). These factors might reflect just one aspect of frailty, which was observed in multiple physiological systems in previous studies. Furthermore, frail older individuals might have a higher risk of hospitalization for pneumonia. As a result, the total KCL score was higher and the KCL cutoff point was lower in patients with pneumonia than in other study populations (14, 32).
This study demonstrated the usefulness of assessing preadmission frailty to predict the risk of mortality and hospital readmission in older patients with pneumonia. The KCL used in this study is a non-invasive, simple tool for the rapid assessment (in minutes) of frailty, and it is an easy indicator to use in clinical practice. Assessments of frailty in patients with communication disorders, which were excluded in the present study, might be substituted by assessments from their families or guardians. Future studies are required to determine effective interventions to prevent hospital readmissions and disability in frail patients with pneumonia.
Our study has some limitations. First, there was a risk of sampling bias and problems with generalizability because this retrospective study was conducted at a single institution with a small patient sample. Based on the inclusion and exclusion criteria, only 50% of hospitalized older patients with acute pneumonia were included as study subjects. As a result, the number of total events was low and only one event occurred in the robust group within 6 months after discharge. Moreover, the excluded subjects, such as more severity ill or communication disorder, may have been and more frailty older adults. Second, the total KCL score was assessed for clinical indicators and not as part of a study protocol. As a result, there were numerous patients with missing data. The prevalence of frailty in this study may have been overestimated due to recall bias. And there was no information on the proportion of the way of assessment of KCL; 1) assessed by the physical therapist or 2) self-reported KCL by a patient. Third, additional factors, such as muscle weakness, ADL function, or swallowing function, were challenging to investigate using a retrospective study design. Finally, we were unable to follow up some patients, such as those admitted to another hospital after initial discharge.
In conclusion, the preadmission frailty status in older patients with pneumonia was an independent factor of a composited 6-month mortality and readmission. Future studies are needed to define effective interventions to prevent death and hospital readmission in frail patients with pneumonia.
Conflicts of interest: The authors declare no conflict of interest.
Funding: The authors declare no funding.
Ethical approval: The present study was approved by the ethics committee of Kobe City Medical Center General Hospital (approval no. ZN200714).
Informed consents: Not applicable.
Acknowledgments: We are indebted to the large number of patients who agreed participate and cooperate in this study. We thank all of the staff and participants at the Kobe City Medical Center General Hospital for their support in the present study. We would like to thank Editage (www.editage.com) for English language editing.
Authorship statement: KY prepared the manuscript and reviewed the literature. KY, KI, YO (Yutaro Oki), YM, YO (Yukari Oki), YY, AY, AH, and AI conceived and designed the study. KY and HO collected the data. KY analyzed the data. KY, YY (Yoshihiro Yoshimura), YO (Yutaro Oki), YM, YO (Yukari Oki), YY (Yoji Yamada), AY, RT, KT, NK, and AI drafted or critically revised the manuscript for important intellectual content. All authors read and approved the final manuscript.
1. Ministry of Health, Labour and Welfare. Annual Statistical Report of National Health Conditions and Health Promotion 2020. Tokyo: Health, Labour and Welfare Statistical Association (In Japanese).
2. Neupane B, Walter SD, Krueger P, et al. Predictors of inhospital mortality and re-hospitalization in older adults with community-acquired pneumonia: a prospective cohort study. BMC Geriatr 2010;10: 22. https://doi.org/10.1186/1471-2318-10-22
3. Mortensen EM, Coley CM, Singer DE, et al. Causes of death for patients with community-acquired pneumonia. Arch Intern Med 2002; 162: 1059-64. https://doi.org/10.1001/archinte.162.9.1059
4. Maeda K, Akagi J. Muscle mass loss is a potential predictor of 90-day mortality in older adults with aspiration pneumonia. J Am Geriatr Soc 2017; 65: e18–22. https://doi.org/10.1111/jgs.14543
5. Fried LP, Tangen CM, Walston J, et al. Frailty in older adults: Evidence for a phenotype. J Gerontol A Biol Sci Medical Sci 2001; 56: M146–57. https://doi.org/10.1093/gerona/56.3.m146
6. Kojima G. Frailty as a predictor of disabilities among community-dwelling older people: a systematic review and meta-analysis. Disabil Rehabil 2016; 39: 1897–1908. https://doi.org/10.1080/09638288.2016.1212282
7. Chang S-F, Lin H-C, Cheng C-L. The relationship of frailty and hospitalization among older people: Evidence from a meta-analysis. J Nurs Scholarsh 2018; 50: 383–91. https://doi.org/10.1111/jnu.12397
8. Kojima G, Iliffe S, Jivraj S, et al. Association between frailty and quality of life among community-dwelling older people: a systematic review and meta-analysis. J Epidemiol Community Health 2016; 70: 716–21. https://doi.org/10.1136/jech-2015-206717
9. Kojima G, Iliffe S, Walters K. Frailty index as a predictor of mortality: a systematic review and meta-analysis. Age Ageing 2017; 47: 193–200. https://doi.org/10.1093/ageing/afx162
10. Joosten E, Demuynck M, Detroyer E, et al. Prevalence of frailty and its ability to predict in hospital delirium, falls, and 6-month mortality in hospitalized older patients. BMC Geriatr 2014; 14: 1. https://doi.org/10.1186/1471-2318-14-1
11. Hilmer SN, Perera V, Mitchell S, et al. The assessment of frailty in older people in acute care. Australas J Ageing 2009; 28: 182–8. https://doi.org/10.1111/j.1741-6612.2009.00367.x
12. Wou F, Gladman JRF, Bradshaw L, et al. The predictive properties of frailty-rating scales in the acute medical unit. Age Ageing 2013; 42: 776–81. https://doi.org/10.1093/ageing/aft055
13. Kahlon S, Pederson J, Majumdar SR, et al. Association between frailty and 30-day outcomes after discharge from hospital. Can Med Assoc J 2015; 187: 799–804. https://doi.org/10.1503/cmaj.150100
14. Satake S, Senda K, Hong Y, et al. Validity of the Kihon Checklist for assessing frailty status. Geriatr Gerontol Int 2016; 16: 709–15. https://doi.org/10.1111/ggi.12543
15. Ito K, Kawai H, Tsuruta H, et al. Predicting incidence of long-term care insurance certification in Japan with the Kihon Checklist for frailty screening tool: analysis of local government survey data. BMC Geriatr 2021; 21: 22. https://doi.org/10.1186/s12877-020-01968-z
16. Sampaio PYS, Sampaio RAC, Yamada M, et al. Systematic review of the Kihon Checklist: Is it a reliable assessment of frailty? Geriatr Gerontol Int 2016; 16: 893–902. https://doi.org/10.1111/ggi.12833
17. Ukawa S, Zhao W, Yatsuya H, et al. Associations of daily walking time with pneumonia mortality among elderly individuals with or without a medical history of myocardial infarction or stroke: Findings from the Japan Collaborative Cohort Study. J Epidemiol 2019; 29: 233–237. https://doi.org/10.2188/jea.je20170341
18. Momosaki R, Yasunaga H, Matsui H, et al. Effect of early rehabilitation by physical therapists on in-hospital mortality after aspiration pneumonia in the elderly. Arch Phys Med Rehab 2015; 96: 205–9. https://doi.org/10.1016/j.apmr.2014.09.014
19. Langmore SE, Skarupski KA, Park PS, et al. Predictors of aspiration pneumonia in nursing home residents. Dysphagia 2002; 17: 298–307. https://doi.org/10.1007/s00455-002-0072-5
20. Manabe T, Teramoto S, Tamiya N, et al. Risk factors for aspiration pneumonia in older adults. Plos One 2015; 10: e0140060. https://doi.org/10.1371/journal.pone.0140060
21. Mitani Y, Oki Y, Fujimoto Y, et al. Relationship between functional independence measure and geriatric nutritional risk index in pneumonia patients in long-term nursing care facilities. Geriatr Gerontol Int 2017; 17: 1617–22. https://doi.org/10.1111/ggi.12942
22. Mitani Y, Oki Y, Fujimoto Y, et al. Relationship between the Functional Independence Measure and Mann Assessment of Swallowing Ability in hospitalized patients with pneumonia. Geriatr Gerontol Int 2018; 18: 1620–4. https://doi.org/10.1111/ggi.13543
23. Kundi H, Wadhera RK, Strom JB, et al. Association of Frailty With 30-Day Outcomes for Acute Myocardial Infarction, Heart Failure, and Pneumonia Among Elderly Adults. JAMA Cardiol 2019; 4: 1084–1091. https://doi.org/10.1001/jamacardio.2019.3511
24. Luo J, Tang W, Sun Y, et al. Impact of frailty on 30-day and 1-year mortality in hospitalised elderly patients with community-acquired pneumonia: a prospective observational study. BMJ Open 2020; 10: e038370. http://dx.doi.org/10.1136/bmjopen-2020-038370
25. Collins GS, Reitsma JB, Altman DG, et al. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD Statement. BMC Med 2015; 13: 1. https://doi.org/10.1186/s12916-014-0241-z
26. Charlson ME, Pompei P, Ales KL, et al. A new method of classifying prognostic comorbidity in longitudinal studies: Development and validation. J Chron Dis 1987; 40: 373–83. https://doi.org/10.1016/0021-9681(87)90171-8
27. Koizumi T, Tsukada H, Ito K, et al. A-DROP system for prognostication of NHCAP inpatients. J Infect Chemother 2017; 23: 523–30. https://doi.org/10.1016/j.jiac.2017.04.013
28. Kohno S, Imamura Y, Shindo Y, et al. Clinical practice guidelines for nursing- and healthcare-associated pneumonia (NHCAP) [Complete translation]. Respir Investigation 2013; 51:103–26. https://doi.org/10.1016/j.resinv.2012.11.001
29. Kanda Y. Investigation of the freely available easy-to-use software “EZR” for medical statistics. Bone Marrow Transplant 2012; 48: 452–8. https://doi.org/10.1038/bmt.2012.244
30. Hao Q, Zhou L, Dong B, et al. The role of frailty in predicting mortality and readmission in older adults in acute care wards: a prospective study. Sci Rep 2019; 9: 1207. https://doi.org/10.1038/s41598-018-38072-7
31. Capelastegui A, Yandiola PPE, Quintana JM, et al. Predictors of short-term rehospitalization following discharge of patients hospitalized with community-acquired pneumonia. Chest 2009; 136: 1079–85. https://doi.org/10.1378/chest.08-2950
32. Satake S, Shimokata H, Senda K, et al. Validity of total Kihon Checklist score for predicting the incidence of 3-year dependency and mortality in a community-dwelling older population. J Am Med Dir Assoc 2017; 18: 552.e1–e6. https://doi.org/10.1016/j.jamda.2017.03.013