Original Research

A 2-year review of stroke admissions and short term out-come predictors in a teaching hospital, Southeast, Nigeria.

Ernest Nwazor^1,2,*Ikechukwu Chukwuocha¹, Benneth Ajuonuma, Patrick Obi, Onyedika Madueke.

¹Department of Internal Medicine Federal University Teaching Hospital Owerri, Nigeria, ²Department of Internal Medicine, Rivers State University Teaching Hospital, Port Harcourt, Nigeria.

Background: Stroke is a common neurological disorder with a huge global burden in terms of mortality and morbidity. Epidemiological evidence has shown that modifiable risk factors are responsible for more than 90% of all strokes. Stroke outcome in hospitalized patients is influenced by several variables, such as socio-demographic factors, stroke subtype, and admission severity. The interaction between stroke outcomes and these parameters is often complex. The study is aimed to profile hospitalized stroke patients and determine outcome predictors.

Methodology: A descriptive retrospective study of 100 patients hospitalized for acute stroke. Their medical records were reviewed for demographic and clinical variables and relevant data were retrieved and analysed using appropriate statistical methods.

Results: Of the 100 acute stroke patients studied, 36% were men and 64% were women. The mean age was 65.16±15.72. About 78%had ischemic stroke while 21% had haemorrhagic strokes. The commonest risk factor was hypertension (71.2%). On multivariate analysis, stroke subtype and admission duration were significantly linked to stroke outcome.

Conclusion: Ischemic stroke comprises more than two-thirds of stroke admissions, with hypertension being the most common risk factor and stroke case fatality of 23%. Stroke subtype and admission duration significantly predicted stroke outcomes. The need to step up measures aimed at improving acute stroke care in hospitalized patients is imperative as this will hopefully improve overall outcomes in resource constraint settings such as Nigeria.

Key words: Stroke, Neurology, Risk Factors, Outcome.

*Correspondence: Dr Ikechukwu Chukwuocha, Department of Internal Medicine Federal University Teaching Hospital Owerri, Nigeria.

Email: [email protected]

How to cite: Nwazor E, Chukwuocha I, Ajuonuma B, Obi P, Maduake O. A 2-year review of stroke admissions and short term out-come predicators in a teaching hospital, Southeast, Nigeria. Niger Med Journal 2024;65(2):185-194.

This is an open access journal, and articles are distributed under the terms of the Creative Commons Attribution-Non-Commercial-Share Alike 4.0 License, which allows others to remix, tweak, and build upon the work non-commercially, as long as

appropriate credit is given, and the new creations are licensed under identical terms.

Introduction

Stroke is the second most common cause of death and foremost leading cause of disability globally¹ In-hospital stroke mortality rates for general ward and stroke unit admissions are about 14.7% and 6.9% respectively.² Stroke-related mortality and disability-adjusted life years in low- and middle-income countries continue to surge.³ Age, stroke type, side and site of the lesion, level of consciousness, degree of neurological impairment and disability at baseline, medical risk factors (hypertension, diabetes), premorbid state, fever, blood pressure at baseline, and prior stroke have all been shown to predict outcome, including independence after stroke,⁴ while a protracted hospital stay and severe stroke account for the highest mean direct medical cost with the significant economic impact in developing countries⁵

Compared to high-income countries, stroke-related disability and mortality are higher in low- and middle-income countries.⁶ Individuals of African descent are more vulnerable to having a stroke than those who are of Caucasian origin.⁷ While stroke hospitalization rates vary between 0.9 and 4.0%, stroke admissions make up and make up 0.5 to 45% of neurological admissions and one of the foremost leading causes of death in Nigeria.⁸ A plausible reason for these could be the lack of clarity surrounding the correlation between stroke predictor variables and stroke outcome.⁹This slow pace of acute stroke management in low- and middle-income countries makes it challenging to stem the burden of acute stroke in hospitals. The underlying risk factors and cause of the stroke must be known in order to properly manage patients who have experienced an acute stroke.^.10

Acute stroke management in Nigeria is still an unmet need, and the overall implication in terms of outcome prediction, identification of high-risk patients and providing them with optimal care should be the focus of every stroke clinician.

In our cohort of patients, there is need to identify those factors that predict outcome, addressing them hopefully translates to improved care for stroke patients in our environment.

Materials and methods

This retrospective cross-sectional study was conducted at the Federal University Hospital in Owerri, Nigeria, among hospitalized acute stroke patients between October 2017 and October 2019.

Participants in the study were adult patients (18 years of age and older) who had received a clinical diagnosis of either an ischemic stroke or a haemorrhagic stroke with neuroimaging confirmation. The study excluded patients who could not do brain imaging as well those with incomplete medical records. The primary outcome was in-hospital mortality or discharge following a stroke. Age, gender, admission duration, stroke type, dyslipidaemia, alcohol use, and comorbid conditions like hypertension, diabetes mellitus, previous stroke, seizures and atrial fibrillation were evaluated to determine their relationship with the primary outcome.

Sample size

A total of one hundred and sixty-eight (168) adult patients were hospitalized for stroke in the Federal University Hospital Owerri during the study period, one hundred (100) of these individuals were included in this study after meeting the inclusion criteria.

Data collection

Data on sociodemographic factors like age and sex, admission duration, stroke subtype, length of hospital stay, comorbidity status and in-hospital treatment outcomes were retrieved from patients’ records as mentioned above and the influence of the variables on stroke outcome was assessed.

Statistical analysis

Data analysis was carried out using SPSS version 21and both descriptive and inferential methods. Continuous variables were presented as mean (±standard deviation (SD)) while categorical variables were represented in frequencies and proportions. The relationship between various sociodemographic and clinical factors was further explored using student t-test and Chi square. A multivariate logistic regression analysis was conducted, after a univariate regression analysis, to identify the independent predictors of stroke mortality. With the corresponding 95% CI, adjusted odds ratios (AOR) were developed. Statistical significance was set at P of 0.05.

Results

We included 100 patients with acute ischemic stroke in this study, as shown in Table 1. The patients were mostly females (64%). The mean age of the study population was 65.16±15.72. The mean age of the male subject was 63.41±15.41 while the mean age of the females subjects66.34±16.72. About 78 (78%) had ischemic stroke and 22 (22%) had haemorrhagic stroke. History of hypertension was present in most of the patients 89 (89%) of the participants and about 29% had a history of DM. Only 2% of the participants in the study were smokers while 6% consumed alcohol. Among the study population, 11% had repeat stroke, 39(3%) had a coexisting atrial fibrillation and 10% of the participants had seizures during the course of their admission. Additional variables observed in this study included bedsores of 2 (2%) of the admitted patients and seizures in 8 (10.1%) and 2 (8.7%) of the discharged and deceased patients, respectively. Although 23 (23%) of the study participants died while receiving medical care, this was not statistically significant. On further analysis of the distribution of study variables by outcome, only stroke subtype, out of all the study variables examined, revealed a statistically significant difference (p=0.003) between the patient groups that survived and those who passed away during the study period.

Table 1: Sociodemographic and clinical characteristics of stroke patients by outcome status

A total of 100 stroke patients were admitted during the study period, 77 (77%) were discharged from the hospital while 23 (23%) died during the admission.

Figure 1: Case fatality rate in the study period

Comparison of clinical and sociodemographic variables between stroke patients that died and those that were discharged.

There was a statistically significant difference in the gender distribution between individuals that died and those that were discharged (male: 27%; vs. female: 50%; p = 0.000). Patients who died while hospitalized had much shorter admission durations at a statistically significant level(days) (8 vs. 14, P = 0.003) when compared to those who survived and were discharged from the hospital.

Table 2: comparison of clinical and sociodemographic variables between stroke patients that died and those that were discharged.

On further comparison of the prevalence of the clinical, sociodemographic, and co-morbid characteristics of stroke patients who died while hospitalized versus those who survived and were discharged. The study revealed that males were more likely to die while receiving medical care and the difference was found to be statistically significant (p value= 0.000); other variables (hypertension, diabetes, atrial fibrillation, TIA, repeat stroke, alcohol, smoking, retroviral disease status, seizures, bedsores, patients who underwent physiotherapy, age more than 50 years) examined did not demonstrate a statistically significant difference between the two categories in the study period.

On multivariate logistic regression analysis, only admission duration was noted to significantly predict stroke outcome (AOR = 5.9; 95% CI: 1.87-8.56; P0.003)

Table 3: logistic Regression to determine predictors of outcome.

Discussion

In this 2-year review of acute ischemic stroke in a tertiary care centre in Eastern Nigeria, t the study showed that more female patients were affected by strokes than male patients and this is similar to the findings from other studies in different settings¹¹^.12. Although the difference was not statistically significant, the higher frequency of female stroke patients might be attributed to the fact that stroke incidence increases with age and women generally enjoy longer longevity than men. Another perspective to this finding could be related to a better health seeking behaviour by women who and the less likelihood to have a history of dyslipidaemia, diabetes, coronary artery disease, or myocardial infarction or to be current smokers as shown in a study by Eileen et al.¹³ There appears to be a rise in the prevalence of stroke among young females.¹⁴ Apart from certain genetic predispositions to stroke in the young females,¹⁵ certain lifestyle factors such as cigarette smoking and use of various substances of abuse may also be contributory.

On the other hand, some other studies found equal frequency of stroke between males and females.¹⁶^,17However, other studies found that the prevalence in male to be higher, this could due to risk factors such as cigarette smoking, and alcohol consumption, which are more common among men when compared to women¹⁸^,19. The mean age of 65.16 ± 15.72 years in this study, was slightly higher than the mean age of 62.9 years reported by Lisk et al. in Sierra Leone²⁰ but in accordance with other underdeveloped countries stroke studies reporting an average age range of 50–65 years⁸^{,21, 22}. The most common stroke subtype reported in this study was a ischaemic stroke similar to the findings from other local studies.²³

In terms of stroke risk, this study demonstrated a high prevalence of hypertension occurring in more than half of participants. This was consistent with previous findings in Nigeria and elsewhere.²⁴^,25.Hypertension continues to remain a major driver for stroke as demonstrated in other studies.²⁶^,27 Uncontrolled hypertension is a risk factor for stroke and also a strong predictor stroke outcome.²⁸ A multicentre study across Ghana and Nigeria by Sarfo et al among stroke patients noted that each 10 mm Hg in systolic blood pressure increased was linked to a 2% (1–4%) increased risk of inpatient death, indicating that higher systolic blood pressure at presentation increased fatality.²⁹ While blood pressure elevation following a stroke may be necessary to sustain cerebral perfusion pressure, excessive blood pressure spikes are harmful and stimulate expansion of cerebral oedema.³⁰ Indeed, a meta-analysis of several studies has found a continuous and graded relationship between blood pressure and stroke risk in many populations, with higher levels of blood pressure conferring greater risks of stroke in hypertensive and normotensive subjects. DM was discovered in 29% of stroke patients, which is in close proximity to the findings of other hospital-based studies in Africa, where the prevalence of DM was 26% and 33.7%, respectively, in Ethiopia and Kenya.^{19, 20} This alludes to the synergistic impact of DM and hypertension on stroke and shows that the importance of diagnosing and treating hypertension and diabetes for the prevention of stroke cannot be overemphasized

In this study, 3% of the patients who underwent electrocardiography (ECG) had atrial fibrillation, the frequency of atrial fibrillation was quite low in our cohort. This could be related to our diagnostic evaluation for atrial fibrillation as most of our patients were tested using a single resting ECG, which is limited by its inability to detect paroxysmal episodes of atrial fibrillation. It is markedly lower than what was found in the Copenhagen Stroke Study, which found that atrial fibrillation affected 18% of stroke patients .These findings were not found to have an impact on the survival of stroke patients who were admitted.³¹^{, 32,33}However, the fact that the focus of our study was only short-term outcome may have not accounted for this

Between 2004 and 2021, hospital-based studies in Nigeria revealed 30-day case fatality rates ranging from 21.2% to 40%.³⁴^,35,36In our study the in-hospital case fatality rate was 23%, which was within the range of other hospital based studies in Nigeria ; however, lower figures have even emerged from other similar studies from Addis Abeba and Southern parts of Ethiopia, which reported rates ranging from 12 to 21% ³⁷^,38 However, this was lower than the 30% 30 day case fatality rate published in a systematic evaluation of hospital-based prospective studies in Sub-Saharan Africa³⁹^{, 33} but was higher than data from some other hospital based study in India.⁴⁰ These discrepancies could be due to variations in the diagnostic methods, treatment modalities, and stroke care provided in these centres. In contrast to studies⁴¹^.42from other regions where haemorrhagic stroke is primarily responsible for in-hospital mortality, the ischaemic stroke and haemorrhagic stroke case fatality rates were 56% and 43.6%, respectively, among the 22 patients who passed away during the study period. The case fatality reported in these investigations may also have been influenced by other comorbid factors particularly, hypertension, atrial fibrillation, structural heart diseases, diabetes mellitus, obesity, dyslipidaemia and kidney disease as elaborated in these studies.

Similar to some studies⁴³^{, 44} stroke subtype, gender (male), admission duration, .have a significant relationship with in-hospital death (p-values of 0.003 (CI=1.865-8.559), <0.001, 0.002 and respectively). In addition to focusing Interventions to reduce stroke mortality in the acute phase, studies have shown that addressing other parameters such as admission duration as shown in this study, would help to reduce the morbidity and mortality associated with stroke outcome. ⁴⁵^,46 While increase risk of in-hospital death rate was not linked to patients who have atrial fibrillation or dyslipidaemia among the listed risk variables. This result is contrary with research from the Copenhagen Stroke Centre and the European Community Stroke Project⁴⁷. Similar to what was seen in an earlier study, a previous history of stroke and transient ischemic attack was linked to a higher rate of in-hospital death respectively⁴⁷ though not at a statistically significant level.

Conclusion

This study has provided a peep into the in-hospital mortality of admitted stroke patients in the Eastern region of the country. revealed that indices such as male gender, length of hospitalization, and stroke subtype were related to in- hospital stroke mortality. To lower the rates of in-hospital fatality in stroke patients, additional attention should be given to these predictors of in-hospital stroke outcome especially reducing hospitalization time.

Reference



1. Feigin VL, Stark BA, Johnson CO, Roth GA, Bisignano C, Abady GG, et al. Global, regional, and national burden of stroke and its risk factors, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. The Lancet Neurology. 2021 Oct 1;20(10):795–820.

2. Shah B, Bartaula B, Adhikari J, Neupane HS, Shah BP, Poudel G. Predictors of In-hospital Mortality of Acute Ischemic Stroke in Adult Population. J Neurosci Rural Pract. 2017;8(4):591–4.

3. Owolabi MO, Akarolo-Anthony S, Akinyemi R, Arnett D, Gebregziabher M, Jenkins C, Tiwari H, Arulogun O, Akpalu A, Sarfo FS, Obiako R, Owolabi L, Sagoe K, Melikam S, Adeoye AM, Lackland D, Ovbiagele B; Members of the H3Africa Consortium. The burden of stroke in Africa: a glance at the present and a glimpse into the future. Cardiovasc J Afr. 2015 Mar-Apr;26(2 Suppl 1):S27-38. doi: 10.5830/CVJA-2015-038.

4. Nakibuuka J, Sajatovic M, Nankabirwa J, Ssendikadiwa C, Furlan AJ, Katabira E, et al. Early mortality and functional outcome after acute stroke in Uganda: prospective study with 30 day follow-up. Springerplus. 2015 Aug 25;4:450.

5. Kaur P, Kwatra G, Kaur R, Pandian JD. Cost of stroke in low and middle income countries: a systematic review. Int J Stroke. 2014 Aug;9(6):678–82.

6. Lanas F, Seron P. Facing the stroke burden worldwide. The Lancet Global Health 2021;9(3):E235 - E236.

7. Prapiadou S, Demel SL, Hyacinth HI. Genetic and Genomic Epidemiology of Stroke in People of African Ancestry. Genes (Basel). 2021 Nov 19;12(11):1825.

8. Desalu OO, Wahab KW, Fawale B, Olarenwaju TO, Busari OA, Adekoya AO, et al. A review of stroke admissions at a tertiary hospital in rural Southwestern Nigeria. Ann Afr Med. 2011;10(2):80–5.

9. Krishnamurthi RV, Feigin VL, Forouzanfar MH, Mensah GA, Connor M, Bennett DA, et al. Global and regional burden of first-ever ischaemic and haemorrhagic stroke during 1990-2010: findings from the Global Burden of Disease Study 2010. Lancet Glob Health. 2013 Nov;1(5):e259-281.

10. Berkowitz AL. Managing acute stroke in low-resource settings. Bull World Health Organ. 2016 Jul 1;94(7):554–6.

11. Kapral MK, Fang J, Hill MD, Silver F, Richards J, Jaigobin C, et al. Sex differences in stroke care and outcomes: results from the Registry of the Canadian Stroke Network. Stroke. 2005 Apr;36(4):809–14.

12. Akpalu A, Gebregziabher M, Ovbiagele B, Sarfo F, Iheonye H, Akinyemi R, et al. Differential Impact of Risk Factors on Stroke Occurrence Among Men Versus Women in West Africa. Stroke. 2019 Apr;50(4):820-827.

13. Stuart-Shor EM, Wellenius GA, DelloIacono DM, Mittleman MA. Gender Differences in Presenting and Prodromal Stroke Symptoms. Stroke. 2009 Apr;40(4):1121–6.

14. Eze CO, Kalu UA, Isiguzo GC. Stroke in Young Adults: Experience at Abakaliki South East Nigeria. World Journal of Neuroscience. 2019 Sep 12;9(4):217–23.

15. Terni E, Giannini N, Brondi M, Montano V, Bonuccelli U, Mancuso M. Genetics of ischaemic stroke in young adults. BBA Clin. 2014 Dec 29;3:96–106.

16. Medlin F, Amiguet M, Eskandari A, Michel P. Sex differences in acute ischaemic stroke patients: clinical presentation, causes and outcomes. Eur J Neurol. 2020 Aug;27(8):1680-1688.

17. Vyas MV, Silver FL, Austin PC, Yu AYX, Pequeno P, Fang J, Laupacis A, Kapral MK. Stroke Incidence by Sex Across the Lifespan. Stroke. 2021 Jan;52(2):447-451

18. Greffie ES, Mitiku T, Getahun S. Risk Factors, Clinical Pattern and Outcome of Stroke in a Referral Hospital, Northwest Ethiopia. Clinical Medicine Research. 2015 Oct 30;4(6):182.

19. Russell JBW, Charles E, Conteh V, Lisk DR. Risk factors, clinical outcomes and predictors of stroke mortality in Sierra Leoneans: A retrospective hospital cohort study. Annals of Medicine and Surgery. 2020 Dec 1;60:293–300.

20. Lisk DR, Ngobeh F, Kumar B, Moses F, Russell JB. Stroke in Sierra Leonean Africans:Perspectives from a Private Health Facility. West Afr J Med. 2020 Sep;37(4):418–22.

21. Tshituta J, Lepira F, Kajingulu F, Makulo J, Sumaili E, Akilimali P, et al. Prognostic Signification of Admission Hyperglycemia among Acute Stroke Patients in Intensive Care Units in Kinshasa, the Democratic Republic of the Congo. World Journal of Cardiovascular Diseases 2019;9(9):665-680.

22. Gedefa B, Menna T, Berhe T, Abera H (2017) Assessment of Risk Factors and Treatment Outcome of Stroke Admissions at St. Paul’s Teaching Hospital, Addis Ababa, Ethiopia. J Neurol Neurophysiol 8: 431. doi:10.4172/2155-9562.1000431

23. Alkali NH, Bwala SA, Akano AO, Osi-Ogbu O, Alabi P, Ayeni OA. Stroke risk factors, subtypes, and 30-day case fatality in Abuja, Nigeria. Nigerian Medical Journal. 2013 Jan 3;54(2):129.

24. Walker R, Ogungbo B. The profile of stroke in Nigeria’s federal capital territory, Imam and Olorunfemi. Trop Doct. 2003 Apr;33(2):123.

25. Delbari A, Salman Roghani R, Tabatabaei SS, Rahgozar M, Lokk J. Stroke epidemiology and one-month fatality among an urban population in Iran. Int J Stroke. 2011 Jun;6(3):195–200.

26. Akpa OM, Okekunle AP, Ovbiagele B, Sarfo FS, Akinyemi RO, Akpalu A, et al. SIREN Study as part of the H3Africa Consortium. Factors associated with hypertension among stroke-free indigenous Africans: Findings from the SIREN study. J Clin Hypertens (Greenwich). 2021 Apr;23(4):773-784. doi: 10.1111/jch.14183

27. Zeng X, Deng A, Ding Y. The INTERSTROKE study on risk factors for stroke - The Lancet 2017;389(10064):35.

28. Upoyo AS, Setyopranoto I, Pangastuti HS. The Modifiable Risk Factors of Uncontrolled Hypertension in Stroke: A Systematic Review and Meta-Analysis. Stroke Res Treat. 2021 Feb 24;2021:6683256.

29. Sarfo FS, Akpa OM, Ovbiagele B, Akpalu A, Wahab K, Obiako R, et al. Patient-level and system-level determinants of stroke fatality across 16 large hospitals in Ghana and Nigeria: a prospective cohort study. The Lancet Global Health. 2023 Apr 1;11(4):e575–85.

30. Leonardi-Bee J, Bath PM, Phillips SJ, Sandercock PA; IST Collaborative Group. Blood pressure and clinical outcomes in the International Stroke Trial. Stroke. 2002 May;33(5):1315-20. doi: 10.1161/01.str.0000014509.11540.66.

31. Jorgensen HS, Nakayama H, Reith J, Raaschou HO, Olsen TS. Acute Stroke with Atrial Fibrillation: The Copenhagen Stroke Study. Stroke 1996;27(10):1765 - 1769.

32. Abebe M, Haimanot RT. Cerebrovascular accidents in Ethiopia. Ethiop Med J. 1990 Apr 1;28(2):53–61.

33. Gebremariam SA, Yang HS. Types, risk profiles, and outcomes of stroke patients in a tertiary teaching hospital in northern Ethiopia. eNeurologicalSci. 2016 Jun 1;3:41–7.

34. Njoku CH, Aduloju AB. Stroke in Sokoto, Nigeria: A five-year retrospective study. Annals of African Medicine 2004;3(2):73-76.

35. Ekeh B, Ogunniyi A, Isamade E, Ekrikpo U. Stroke mortality and its predictors in a Nigerian teaching hospital. Afr Health Sci. 2015 Mar;15(1):74-81

36. Arabambi B, Oshinaike O.Akilo OO, Yusuf Y, Ogun AS. Arabambi: Pattern, Risk Factors, and Outcome of Acute Stroke in a Nigerian University Teaching Hospital: A 1‑Year Review. Nigerian Journal of Medicine 2021;30(3);252 - 256.

37. Deresse B, Shaweno D. Epidemiology, and in-hospital outcome of stroke in South Ethiopia. J Neurol Sci. 2015 Aug 15;355(1-2):138-42.

38. Asefa G, Meseret S. CT and clinical correlation of stroke diagnosis, pattern and clinical outcome among stroke patients visting Tikur Anbessa Hospital. Ethiop Med J. 2010 Apr;48(2):117-22.

39. Achmirowicz J. Burden of Stroke in Black Populations in Sub-Saharan Africa, The Lancet Neurology 2007;6(3):269-278.

40. Das S, Chandra Ghosh K, Malhotra M, Yadav U, Sankar Kundu S, Kumar Gangopadhyay P. Short term mortality predictors in acute stroke. Ann Neurosci. 2012 Apr;19(2):61–7.

41. Deljavan R, Farhoudi M, Sadeghi-Bazargani H. Stroke in-hospital survival and its predictors: the first results from Tabriz Stroke Registry of Iran. Int J Gen Med. 2018;11:233–40.

42. Ranasinghe VS, Pathirage M, Gawarammana IB. Predictors of in-hospital mortality in stroke patients. PLOS Glob Public Health. 2023 Feb 8;3(2):e0001278.

43. Greffie ES, Mitiku T, Getahun S. Risk Factors, Clinical Pattern and Outcome of Stroke in a Referral Hospital, Northwest Ethiopia. Clinical Medicine Research 2015;4(6):182 - 188.

44. Balami JS, Chen RL, Grunwald IQ, Buchan AM. Neurological complications of acute ischaemic stroke. Lancet Neurol. 2011;10(4):357-71.

45. Osaigbovo GO, Amusa GA, Salaam AJ, Imoh LC, Okeke EN, Zoakah AI, et al. Predictors and Prognosis of Stroke in Jos, North-Central Nigeria. West Afr J Med. 2021 May 29;38(5):478–85.

46. Norrving B, Barrick J, Davalos A, Dichgans M, Cordonnier C, Guekht A, et al. Action Plan for Stroke in Europe 2018–2030. Eur Stroke J. 2018 Dec;3(4):309–36.