Predicting the risk of mortality during hospitalization in sick severely malnourished children using daily evaluation of key clinical warning signs

Bijun Wen, Daniella Brals, Celine Bourdon, Lauren Erdman, Moses Ngari, Emmanuel Chimwezi, Isabel Potani, Johnstone Thitiri, Laura Mwalekwa, James A Berkley, Robert H J Bandsma, Wieger Voskuijl, Bijun Wen, Daniella Brals, Celine Bourdon, Lauren Erdman, Moses Ngari, Emmanuel Chimwezi, Isabel Potani, Johnstone Thitiri, Laura Mwalekwa, James A Berkley, Robert H J Bandsma, Wieger Voskuijl

Abstract

Background: Despite adherence to WHO guidelines, inpatient mortality among sick children admitted to hospital with complicated severe acute malnutrition (SAM) remains unacceptably high. Several studies have examined risk factors present at admission for mortality. However, risks may evolve during admission with medical and nutritional treatment or deterioration. Currently, no specific guidance exists for assessing daily treatment response. This study aimed to determine the prognostic value of monitoring clinical signs on a daily basis for assessing mortality risk during hospitalization in children with SAM.

Methods: This is a secondary analysis of data from a randomized trial (NCT02246296) among 843 hospitalized children with SAM. Daily clinical signs were prospectively collected during ward rounds. Multivariable extended Cox regression using backward feature selection was performed to identify daily clinical warning signs (CWS) associated with time to death within the first 21 days of hospitalization. Predictive models were subsequently developed, and their prognostic performance evaluated using Harrell's concordance index (C-index) and time-dependent area under the curve (tAUC).

Results: Inpatient case fatality ratio was 16.3% (n=127). The presence of the following CWS during daily assessment were found to be independent predictors of inpatient mortality: symptomatic hypoglycemia, reduced consciousness, chest indrawing, not able to complete feeds, nutritional edema, diarrhea, and fever. Daily risk scores computed using these 7 CWS together with MUAC<10.5cm at admission as additional CWS predict survival outcome of children with SAM with a C-index of 0.81 (95% CI 0.77-0.86). Moreover, counting signs among the top 5 CWS (reduced consciousness, symptomatic hypoglycemia, chest indrawing, not able to complete foods, and MUAC<10.5cm) provided a simpler tool with similar prognostic performance (C-index of 0.79; 95% CI 0.74-0.84). Having 1 or 2 of these CWS on any day during hospitalization was associated with a 3 or 11-fold increased mortality risk compared with no signs, respectively.

Conclusions: This study provides evidence for structured monitoring of daily CWS as recommended clinical practice as it improves prediction of inpatient mortality among sick children with complicated SAM. We propose a simple counting-tool to guide healthcare workers to assess treatment response for these children.

Trial registration: NCT02246296.

Keywords: Danger signs; Mortality prediction; SAM; Severe malnutrition; Sub-Saharan Africa.

Conflict of interest statement

All authors have completed the Unified Competing Interest form (available on request from the corresponding author) and declare no support from any organization for the submitted work, no financial relationships with any organizations that might have an interest in the submitted work in the previous three years, and no other relationships or activities that could appear to have influenced the submitted work.

© 2021. The Author(s).

Figures

Fig. 1
Fig. 1
Dynamics in number of daily clinical warning signs (CWS) and survival outcome. Conditional density plot of the number of CWS and outcome (discharged versus died) among 780 SAM patients. The number of observed CWS were counted from a all 8 identified CWS (reduced consciousness, symptomatic hypoglycemia, chest indrawing, not able to complete feeds, MUAC<10.5cm, diarrhea, nutritional edema, and fever) and b the top 5 CWS (reduced consciousness, symptomatic hypoglycemia, chest indrawing, not able to complete feeds, and MUAC<10.5cm). The hatch area within each CWS count category indicates the proportion of patients who eventually died during hospitalization
Fig. 2
Fig. 2
Performance of counting scores evaluated on selected landmarking days over time. a Time-dependent AUC of using the number of CWS (counted among reduced consciousness, symptomatic hypoglycemia, chest indrawing, not able to complete feeds, MUAC<10.5cm, diarrhea, nutritional edema, and fever) as risk scores assessed on a specific day (admission, days 2, 5, 7, 10) to predict survival outcome for the subsequent days (including the score day) up to 15 days since admission. b Time-dependent AUC of using the number of the top 5 CWS (counted among reduced consciousness, symptomatic hypoglycemia, chest indrawing, not able to complete feeds, and MUAC<10.5cm) as risk scores assessed a specific day (admission, days 2, 5, 7, and 10) to predict survival outcome for the subsequent days (including the score day) up to 15 days since admission. AUC=0.5 implies performance is no better than random chance

References

    1. Black RE, Victora CG, Walker SP, Bhutta ZA, Christian P, de Onis M, et al. Maternal and child undernutrition and overweight in low-income and middle-income countries. Lancet (London, England) 2013;382(9890):427–451. doi: 10.1016/S0140-6736(13)60937-X.
    1. WHO Guidelines Approved by the Guidelines Review Committee . WHO child growth standards and the identification of severe acute malnutrition in infants and children: a joint statement by the World Health Organization and the United Nations Children’s Fund. Geneva: World Health Organization; 2009.
    1. Bhutta ZA, Berkley JA, Bandsma RHJ, Kerac M, Trehan I, Briend A. Severe childhood malnutrition. Nat Rev Dis Primers. 2017;3(1):17067. doi: 10.1038/nrdp.2017.67.
    1. Ashworth AKS, Jackson A, Schofield C. Guidelines for the inpatient treatment of severely malnourished children. Geneva: World Health Organization; 2003.
    1. WHO W, SCN and UNICEF . WHO/WFP/SCN and UNICEF joint statement on community-based management of severe acute malnutrition. Geneva: UNICEF; 2007.
    1. Schofield C, Ashworth A. Why have mortality rates for severe malnutrition remained so high? Bull World Health Organ. 1996;74(2):223–229.
    1. Hossain M, Chisti MJ, Hossain MI, Mahfuz M, Islam MM, Ahmed T. Efficacy of World Health Organization guideline in facility-based reduction of mortality in severely malnourished children from low and middle income countries: a systematic review and meta-analysis. J Paediatr Child Health. 2017;53(5):474–479. doi: 10.1111/jpc.13443.
    1. Talbert A, Thuo N, Karisa J, Chesaro C, Ohuma E, Ignas J, Berkley JA, Toromo C, Atkinson S, Maitland K. Diarrhoea complicating severe acute malnutrition in Kenyan children: a prospective descriptive study of risk factors and outcome. PLoS One. 2012;7(6):e38321. doi: 10.1371/journal.pone.0038321.
    1. Irena AH, Mwambazi M, Mulenga V. Diarrhea is a major killer of children with severe acute malnutrition admitted to inpatient set-up in Lusaka, Zambia. Nutr J. 2011;10(1):110. doi: 10.1186/1475-2891-10-110.
    1. World Health Organization . Pocket book of hospital care for children: second edition. Guidelines for the management of common childhood illnesses. 2013.
    1. Guideline: Updates on Paediatric Emergency Triage, Assessment and Treatment: Care of Critically-Ill Children. Geneva: World Health Organization; 2016. Available from: .
    1. Maitland K, Berkley JA, Shebbe M, Peshu N, English M, Newton CRJC. Children with severe malnutrition: can those at highest risk of death be identified with the WHO protocol? Plos Med. 2006;3(12):2431–2439. doi: 10.1371/journal.pmed.0030500.
    1. Girum T, Kote M, Tariku B, Bekele H. Survival status and predictors of mortality among severely acute malnourished children < 5 years of age admitted to stabilization centers in Gedeo Zone: a retrospective cohort study. Ther Clin Risk Manag. 2017;13:101–110. doi: 10.2147/TCRM.S119826.
    1. De Maayer T, Saloojee H. Clinical outcomes of severe malnutrition in a high tuberculosis and HIV setting. Arch Dis Childhood. 2011;96(6):560–564. doi: 10.1136/adc.2010.205039.
    1. Bachou H, Tumwine JK, Mwadime RKN, Tylleskar T. Risk factors in hospital deaths in severely malnourished children in Kampala, Uganda. BMC Pediatr. 2006;6:7. doi: 10.1186/1471-2431-6-7.
    1. Bandsma RHJ, Voskuijl W, Chimwezi E, Fegan G, Briend A, Thitiri J, et al. A reduced-carbohydrate and lactose-free formulation for stabilization among hospitalized children with severe acute malnutrition: a double-blind, randomized controlled trial. Plos Med. 2019;16(2):e1002747. doi: 10.1371/journal.pmed.1002747.
    1. Who . Management of severe malnutrition: a manual for physicians and other senior health workers. Management of severe malnutrition: a manual for physicians and other senior health workers. 1999.
    1. Harrell FE., Jr . Regression Modeling Strategies R package rms. Comprehensive R Archive Network. 2018.
    1. Berkley JA, Ngari M, Thitiri J, Mwalekwa L, Timbwa M, Hamid F, Ali R, Shangala J, Mturi N, Jones KDJ, Alphan H, Mutai B, Bandika V, Hemed T, Awuondo K, Morpeth S, Kariuki S, Fegan G. Daily co-trimoxazole prophylaxis to prevent mortality in children with complicated severe acute malnutrition: a multicentre, double-blind, randomised placebo-controlled trial. Lancet Global Health. 2016;4(7):e464–e473. doi: 10.1016/S2214-109X(16)30096-1.
    1. Mwangome MK, Fegan G, Prentice AM, Berkley JA. Are diagnostic criteria for acute malnutrition affected by hydration status in hospitalized children? A repeated measures study. Nutr J. 2011;10(1). 10.1186/1475-2891-10-92.
    1. Therneau TM, Grambsch PM. Modeling Survival Data: Extending the Cox Model. New York: Springer; 2000.
    1. Thomas L, Reyes EM. Tutorial survival estimation for Cox regression models with time-varying coefficients. J Stat Software. 2014;61(CS1):1–23.
    1. Harrell F. Regression modeling strategies, with applications to linear models, logistic regression and survival analysis. New York: Springer-Verlag; 2001.
    1. Austin PC, Latouche A, Fine JP. A review of the use of time-varying covariates in the Fine-Gray subdistribution hazard competing risk regression model. Stat Med. 2020;39(2):103-113. 10.1002/sim.8399.
    1. Poguntke I, Schumacher M, Beyersmann J, Wolkewitz M, Consortium CM. Simulation shows undesirable results for competing risks analysis with time-dependent covariates for clinical outcomes. BMC Med Res Methodol. 2018;18(1):79. doi: 10.1186/s12874-018-0535-5.
    1. Singer JD, Willett JB. Its about time - using discrete-time survival analysis to study duration and the timing of events. J Educ Stat. 1993;18(2):155–195.
    1. Cox DR. Regression models and life-tables. J Royal Stat Society Series B (Methodological). 1972;34(2):187–220. doi: 10.1111/j.2517-6161.1972.tb00899.x.
    1. Greene T, Li L. From static to dynamic risk prediction: time is everything. Am J Kidney Dis. 2017;69(4):492–494. doi: 10.1053/j.ajkd.2017.01.004.
    1. Blanche P, Dartigues JF, Jacqmin-Gadda H. Estimating and comparing time-dependent areas under receiver operating characteristic curves for censored event times with competing risks. Stat Med. 2013;32(30):5381–5397. doi: 10.1002/sim.5958.
    1. Girum T. Incidence and predictors of mortality among severe acute malnourished under five children admitted to dilla university referal hospital: a retrospective longitudinal study. J Biol Agric Healthc. 2016;6:114–127.
    1. Maitland K, Kiguli S, Opoka RO, Engoru C, Olupot-Olupot P, Akech SO, Nyeko R, Mtove G, Reyburn H, Lang T, Brent B, Evans JA, Tibenderana JK, Crawley J, Russell EC, Levin M, Babiker AG, Gibb DM. Mortality after fluid bolus in african children with severe infection. N Engl J Med. 2011;364(26):2483–2495. doi: 10.1056/NEJMoa1101549.
    1. Berkley JA, Ross A, Mwangi I, Osier FHA, Mohammed M, Shebbe M, Lowe BS, Marsh K, Newton CR. Prognostic indicators of early and late death in children admitted to district hospital in Kenya: cohort study. BMJ. 2003;326(7385):361–364. doi: 10.1136/bmj.326.7385.361.
    1. O'Reilly CE, Jaron P, Ochieng B, Nyaguara A, Tate JE, Parsons MB, et al. Risk factors for death among children less than 5 years old hospitalized with diarrhea in rural Western Kenya, 2005–2007: a cohort study. Plos Med. 2012;9(7):e1001256. doi: 10.1371/journal.pmed.1001256.
    1. Dembele BPP, Kamigaki T, Dapat C, Tamaki R, Saito M, Okamoto M, et al. Aetiology and risks factors associated with the fatal outcomes of childhood pneumonia among hospitalised children in the Philippines from 2008 to 2016: a case series study. BMJ Open. 2019;9(3):e026895. doi: 10.1136/bmjopen-2018-026895.
    1. George CM, Oldja L, Biswas S, Perin J, Lee GO, Kosek M, et al. Geophagy is associated with environmental enteropathy and impaired growth in children in rural Bangladesh. Am J Trop Med Hygiene. 2015;93(4):174.
    1. Aluvaala J, Collins G, Maina B, Mutinda C, Waiyego M, Berkley JA, et al. Prediction modelling of inpatient neonatal mortality in high-mortality settings. Arch Dis Child. 2020;106(5):449–454. doi: 10.1136/archdischild-2020-319217.
    1. Ogero M, Sarguta RJ, Malla L, Aluvaala J, Agweyu A, English M, et al. Prognostic models for predicting in-hospital paediatric mortality in resource-limited countries: a systematic review. BMJ Open. 2020;10(10):e035045. doi: 10.1136/bmjopen-2019-035045.
    1. Khan MR, Maheshwari PK, Masood K, Qamar FN, Anwar-ul H. Epidemiology and outcome of sepsis in a tertiary care PICU of Pakistan. Indian Journal of Pediatrics. 2012;79(11):1454–1458. doi: 10.1007/s12098-012-0706-z.
    1. Menif K, Khaldi A, Bouziri A, Kechaou W, Belhadj S, Hamdi A, Kazdaghli K, Benjaballah N. Mortality rates in pediatric septic shock subordinate to community infection: about 70 cases. Medecine Et Maladies Infect. 2009;39(12):896–900. doi: 10.1016/j.medmal.2008.11.002.
    1. Sachdeva S, Dewan P, Shah D, Malhotra RK, Gupta P. Mid-upper arm circumference v. weight-for-height Z-score for predicting mortality in hospitalized children under 5 years of age. Public Health Nutr. 2016;19(14):2513–2520. doi: 10.1017/S1368980016000719.
    1. Taneja S, Rongsen-Chandola T, Mohan SB, Mazumder S, Bhandari N, Kaur J, et al. Mid upper arm circumference as a predictor of risk of mortality in children in a low resource setting in India. Plos One. 2018;13(6):e0197832. doi: 10.1371/journal.pone.0197832.
    1. Myatt M, Khara T, Collins S. A review of methods to detect cases of severely malnourished children in the community for their admission into community-based therapeutic care programs. Food Nutr Bull. 2006;27(3):S7–S23. doi: 10.1177/15648265060273S302.
    1. Briend A, Maire B, Fontaine O, Garenne M. Mid-upper arm circumference and weight-for-height to identify high-risk malnourished under-five children. Matern Child Nutr. 2012;8(1):130–133. doi: 10.1111/j.1740-8709.2011.00340.x.
    1. Children, HIV and AIDS: global and regional snapshots. [Internet]. November 2019. Available from: . Accessed 12 Nov 2020.
    1. Njunge JM, Gwela A, Kibinge NK, Ngari M, Nyamako L, Nyatichi E, et al. Biomarkers of post-discharge mortality among children with complicated severe acute malnutrition. Sci Rep. 2019;9(1):5981. doi: 10.1038/s41598-019-42436-y.
    1. Wiens MO, Pawluk S, Kissoon N, Kumbakumba E, Ansermino JM, Singer J, et al. Pediatric post-discharge mortality in resource poor countries: a systematic review. Plos One. 2013;8(6):e66698. doi: 10.1371/journal.pone.0066698.
    1. Nemetchek B, English L, Kissoon N, Ansermino JM, Moschovis PP, Kabakyenga J, et al. Paediatric postdischarge mortality in developing countries: a systematic review. BMJ Open. 2018;8(12):e023445. doi: 10.1136/bmjopen-2018-023445.
    1. Monaghan A. Detecting and managing deterioration in children. Paediatric nursing. 2005;17(1):32–35. doi: 10.7748/paed.17.1.32.s27.
    1. Akre M, Finkelstein M, Erickson M, Liu M, Vanderbilt L, Billman G. Sensitivity of the pediatric early warning score to identify patient deterioration. Pediatrics. 2010;125(4):E763–E7E9. doi: 10.1542/peds.2009-0338.

Source: PubMed

Sponsorzy i współpracownicy

Warunki medyczne

Interwencje lekowe

3
Subskrybuj